notebooks/kg_distributions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distributions of nodes and edges in MoA-net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "from tqdm import tqdm\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "KG_DIR = '../data/kg'\n",
    "SPLITS_DIR = os.path.join(KG_DIR, 'splits')\n",
    "MoA_net_DIR = os.path.join(SPLITS_DIR, 'MoA-net')\n",
    "MoA_subclassed_DIR = os.path.join(SPLITS_DIR, 'MoA-net-protclass')\n",
    "FIG_DIR = '../figures'\n",
    "os.makedirs(FIG_DIR, exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>source_node_type</th>\n",
       "      <th>target</th>\n",
       "      <th>target_node_type</th>\n",
       "      <th>edge_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ncbigene:5602</td>\n",
       "      <td>Gene</td>\n",
       "      <td>ncbigene:2354</td>\n",
       "      <td>Gene</td>\n",
       "      <td>interacts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ncbigene:8021</td>\n",
       "      <td>Gene</td>\n",
       "      <td>ncbigene:6430</td>\n",
       "      <td>Gene</td>\n",
       "      <td>interacts</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          source source_node_type         target target_node_type  edge_type\n",
       "0  ncbigene:5602             Gene  ncbigene:2354             Gene  interacts\n",
       "1  ncbigene:8021             Gene  ncbigene:6430             Gene  interacts"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kg = pd.read_csv(f'{MoA_net_DIR}/kg_with_train_smpls.tsv', sep='\\t')\n",
    "kg.drop_duplicates(inplace=True)\n",
    "kg.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>source</th>\n",
       "      <th>source_node_type</th>\n",
       "      <th>target</th>\n",
       "      <th>target_node_type</th>\n",
       "      <th>edge_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>pubchem.compound:6167</td>\n",
       "      <td>Compound</td>\n",
       "      <td>ncbigene:3371</td>\n",
       "      <td>Gene</td>\n",
       "      <td>upregulates</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ncbigene:4303</td>\n",
       "      <td>Developmental protein</td>\n",
       "      <td>ncbigene:359</td>\n",
       "      <td>Gene</td>\n",
       "      <td>interacts</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  source       source_node_type         target  \\\n",
       "0  pubchem.compound:6167               Compound  ncbigene:3371   \n",
       "1          ncbigene:4303  Developmental protein   ncbigene:359   \n",
       "\n",
       "  target_node_type    edge_type  \n",
       "0             Gene  upregulates  \n",
       "1             Gene    interacts  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kg_p = pd.read_csv(f'{MoA_subclassed_DIR}/kg_with_train_smpls.tsv', sep='\\t')\n",
    "kg_p.drop_duplicates(inplace=True)\n",
    "kg_p.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get basic stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "edge_type\n",
       "interacts        86786\n",
       "participates      4325\n",
       "downregulates     2205\n",
       "upregulates       1631\n",
       "induces            986\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kg.edge_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "edge_type\n",
       "interacts        78617\n",
       "participates      5027\n",
       "downregulates     2486\n",
       "upregulates       1949\n",
       "induces            986\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kg_p.edge_type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get node counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 95933/95933 [00:00<00:00, 478774.53it/s]\n"
     ]
    }
   ],
   "source": [
    "drugs = set()\n",
    "proteins = set()\n",
    "bps = set()\n",
    "\n",
    "for source, target in tqdm(kg[['source', 'target']].values):\n",
    "    if source.startswith('pubchem.compound:'):\n",
    "        drugs.add(source)\n",
    "    elif source.startswith('ncbigene:'):\n",
    "        proteins.add(source)\n",
    "    else:\n",
    "        raise ValueError(f'Unknown source: {source}')\n",
    "\n",
    "    if target.startswith('pubchem.compound:'):\n",
    "        drugs.add(target)\n",
    "    elif target.startswith('ncbigene:'):\n",
    "        proteins.add(target)\n",
    "    elif target.startswith('GO:'):\n",
    "        bps.add(target)\n",
    "    else:\n",
    "        raise ValueError(f'Unknown target: {target}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 89065/89065 [00:00<00:00, 449990.23it/s]\n"
     ]
    }
   ],
   "source": [
    "drugs_p = set()\n",
    "proteins_p = set()\n",
    "bps_p = set()\n",
    "\n",
    "for source, target in tqdm(kg_p[['source', 'target']].values):\n",
    "    if source.startswith('pubchem.compound:'):\n",
    "        drugs_p.add(source)\n",
    "    elif source.startswith('ncbigene:'):\n",
    "        proteins_p.add(source)\n",
    "    else:\n",
    "        raise ValueError(f'Unknown source: {source}')\n",
    "\n",
    "    if target.startswith('pubchem.compound:'):\n",
    "        drugs_p.add(target)\n",
    "    elif target.startswith('ncbigene:'):\n",
    "        proteins_p.add(target)\n",
    "    elif target.startswith('GO:'):\n",
    "        bps_p.add(target)\n",
    "    else:\n",
    "        raise ValueError(f'Unknown target: {target}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get edges counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'interacts': 86786, 'participates': 4325, 'downregulates': 2205, 'upregulates': 1631, 'induces': 986}\n"
     ]
    }
   ],
   "source": [
    "relations = dict(kg.edge_type.value_counts())\n",
    "print(relations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'interacts': 78617, 'participates': 5027, 'downregulates': 2486, 'upregulates': 1949, 'induces': 986}\n"
     ]
    }
   ],
   "source": [
    "relations_p = dict(kg_p.edge_type.value_counts())\n",
    "print(relations_p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "drug_protein = relations['downregulates'] + relations['upregulates']\n",
    "protein_protein = relations['interacts']\n",
    "protein_bps = relations['participates']\n",
    "drug_bps = relations['induces']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "drug_protein_p = relations_p['downregulates'] + relations_p['upregulates']\n",
    "protein_protein_p = relations_p['interacts']\n",
    "protein_bps_p = relations_p['participates']\n",
    "drug_bps_p = relations_p['induces']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary stats"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MoA-net:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##### Number of nodes in MoA-net #####\n",
      "300: # of unique drugs\n",
      "9301: # of unique proteins\n",
      "86: # of unique biological processes\n",
      "\n",
      "##### Number of edges in MoA-net ######\n",
      "95933: total # of interactions\n",
      "3836: # of drug-protein interactions\n",
      "86786: # of protein-protein interactions\n",
      "4325: # of protein-process interactions\n",
      "986: # of drug-process interactions\n"
     ]
    }
   ],
   "source": [
    "print('##### Number of nodes in MoA-net #####')\n",
    "print(f'{len(drugs)}: # of unique drugs')\n",
    "print(f'{len(proteins)}: # of unique proteins')\n",
    "print(f'{len(bps)}: # of unique biological processes')\n",
    "\n",
    "print('\\n##### Number of edges in MoA-net ######')       \n",
    "print(f'{len(kg)}: total # of interactions')\n",
    "print(f'{drug_protein}: # of drug-protein interactions')\n",
    "print(f'{protein_protein}: # of protein-protein interactions')\n",
    "print(f'{protein_bps}: # of protein-process interactions')\n",
    "print(f'{drug_bps}: # of drug-process interactions')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Protein-classed MoA-net:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##### Number of nodes in MoA-net #####\n",
      "300: # of unique drugs\n",
      "6833: # of unique proteins\n",
      "86: # of unique biological processes\n",
      "\n",
      "##### Number of edges in MoA-net ######\n",
      "89065: total # of interactions\n",
      "4435: # of drug-protein interactions\n",
      "78617: # of protein-protein interactions\n",
      "5027: # of protein-process interactions\n",
      "986: # of drug-process interactions\n"
     ]
    }
   ],
   "source": [
    "print('##### Number of nodes in MoA-net #####')\n",
    "print(f'{len(drugs_p)}: # of unique drugs')\n",
    "print(f'{len(proteins_p)}: # of unique proteins')\n",
    "print(f'{len(bps_p)}: # of unique biological processes')\n",
    "\n",
    "print('\\n##### Number of edges in MoA-net ######')       \n",
    "print(f'{len(kg_p)}: total # of interactions')\n",
    "print(f'{drug_protein_p}: # of drug-protein interactions')\n",
    "print(f'{protein_protein_p}: # of protein-protein interactions')\n",
    "print(f'{protein_bps_p}: # of protein-process interactions')\n",
    "print(f'{drug_bps_p}: # of drug-process interactions')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot distributions of nodes"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MoA-net:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "      <th>Node Types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>300</td>\n",
       "      <td>Drugs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9301</td>\n",
       "      <td>Genes/Proteins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>86</td>\n",
       "      <td>Biological Processes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Count            Node Types\n",
       "0    300                 Drugs\n",
       "1   9301        Genes/Proteins\n",
       "2     86  Biological Processes"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_nodes = {\n",
    "    'Count':[\n",
    "        len(drugs),\n",
    "        len(proteins), \n",
    "        len(bps), \n",
    "    ],\n",
    "    'Node Types':['Drugs','Genes/Proteins','Biological Processes']\n",
    "}\n",
    "\n",
    "node_df = pd.DataFrame(data=data_nodes)\n",
    "node_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicates = {'interacts': 'interacts\\n(Protein, Protein)',\n",
    " 'participates': 'participates\\n(Protein, BP)',\n",
    " 'downregulates': 'downregulates\\n(Drug, Protein)',\n",
    " 'upregulates': 'upregulates\\n(Drug, Protein)',\n",
    " 'induces': 'induces\\n(Drug, BP)'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "relations = {predicates[key]: val for key, val in relations.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Edge Types</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>interacts\\n(Protein, Protein)</td>\n",
       "      <td>86786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>participates\\n(Protein, BP)</td>\n",
       "      <td>4325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>downregulates\\n(Drug, Protein)</td>\n",
       "      <td>2205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>upregulates\\n(Drug, Protein)</td>\n",
       "      <td>1631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>induces\\n(Drug, BP)</td>\n",
       "      <td>986</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                       Edge Types  Count\n",
       "0   interacts\\n(Protein, Protein)  86786\n",
       "1     participates\\n(Protein, BP)   4325\n",
       "2  downregulates\\n(Drug, Protein)   2205\n",
       "3    upregulates\\n(Drug, Protein)   1631\n",
       "4             induces\\n(Drug, BP)    986"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert dictionary items to a list of tuples\n",
    "data_list = [(key, value) for key, value in relations.items()]\n",
    "\n",
    "# Create a DataFrame from the list of tuples\n",
    "edges_df = pd.DataFrame(data_list, columns=['Edge Types', 'Count'])\n",
    "edges_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10,10)})\n",
    "\n",
    "# grid lines\n",
    "sns.set_style(\"darkgrid\")\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "bx1 = sns.barplot(\n",
    "    x=\"Node Types\", y=\"Count\", data=node_df,\n",
    "    palette=['#F6A0D8','#FE9A6B', '#95F3CC']\n",
    ") \n",
    "plt.title(\n",
    "    'Distribution of Node Types', \n",
    "    fontsize=18, \n",
    "    fontweight=\"bold\"\n",
    ")\n",
    "plt.xlabel(\"Node Type\", fontsize=16, fontweight=\"bold\")\n",
    "plt.ylabel(\"Count\", fontsize=16, fontweight=\"bold\")\n",
    "plt.xticks(bx1.get_xticks(), labels=['Drug', 'Protein', 'Biological Process (BP)'], fontsize=16)\n",
    "plt.ylim(10, 15000)\n",
    "plt.yscale(\"log\") #the log transformation\n",
    "\n",
    "for p in bx1.patches:\n",
    "    _x = p.get_x() + p.get_width() / 2\n",
    "    _y = p.get_y() + p.get_height() + 50\n",
    "    value = int(p.get_height())\n",
    "    bx1.text(_x, _y, f'{value:,}', ha=\"center\") \n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "bx2 = sns.barplot(\n",
    "    x=\"Edge Types\", y=\"Count\", data=edges_df, \n",
    "    color='#fe6b86'\n",
    ")\n",
    "plt.title(\n",
    "    'Distribution of Relations', \n",
    "    fontsize=18, \n",
    "    fontweight=\"bold\"\n",
    ")\n",
    "plt.xlabel(\"Edge Type\", fontsize=16, fontweight=\"bold\")\n",
    "plt.ylabel(\"Count\", fontsize=16, fontweight=\"bold\") \n",
    "plt.xticks(bx2.get_xticks(), labels=list(edges_df['Edge Types']), fontsize=16)\n",
    "plt.ylim(10, 150000)\n",
    "\n",
    "plt.yscale(\"log\") #the log transformation\n",
    "\n",
    "# Add counts above bars\n",
    "for p in bx2.patches:\n",
    "    _x = p.get_x() + p.get_width() / 2\n",
    "    _y = p.get_y() + p.get_height() + 50\n",
    "    value = int(p.get_height())\n",
    "    bx2.text(_x, _y, f'{value:,}', ha=\"center\")   \n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\n",
    "    f'{FIG_DIR}/graph-stats.png',\n",
    "    dpi=400,\n",
    "    bbox_inches='tight', \n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9046522051848686"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio_interacts = edges_df.loc[edges_df['Edge Types']=='interacts\\n(Protein, Protein)']['Count'].sum() / edges_df['Count'].sum()\n",
    "ratio_interacts"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Protein-classed MoA-net:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Count</th>\n",
       "      <th>Node Types</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>300</td>\n",
       "      <td>Drugs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6833</td>\n",
       "      <td>Genes/Proteins</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>86</td>\n",
       "      <td>Biological Processes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Count            Node Types\n",
       "0    300                 Drugs\n",
       "1   6833        Genes/Proteins\n",
       "2     86  Biological Processes"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_nodes = {\n",
    "    'Count':[\n",
    "        len(drugs_p),\n",
    "        len(proteins_p), \n",
    "        len(bps_p), \n",
    "    ],\n",
    "    'Node Types':['Drugs','Genes/Proteins','Biological Processes']\n",
    "}\n",
    "\n",
    "node_df = pd.DataFrame(data=data_nodes)\n",
    "node_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "predicates = {'interacts': 'interacts\\n(Protein, Protein)',\n",
    " 'participates': 'participates\\n(Protein, BP)',\n",
    " 'downregulates': 'downregulates\\n(Drug, Protein)',\n",
    " 'upregulates': 'upregulates\\n(Drug, Protein)',\n",
    " 'induces': 'induces\\n(Drug, BP)'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "relations = {predicates[key]: val for key, val in relations_p.items()}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Edge Types</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>interacts\\n(Protein, Protein)</td>\n",
       "      <td>78617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>participates\\n(Protein, BP)</td>\n",
       "      <td>5027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>downregulates\\n(Drug, Protein)</td>\n",
       "      <td>2486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>upregulates\\n(Drug, Protein)</td>\n",
       "      <td>1949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>induces\\n(Drug, BP)</td>\n",
       "      <td>986</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Edge Types  Count\n",
       "0   interacts\\n(Protein, Protein)  78617\n",
       "1     participates\\n(Protein, BP)   5027\n",
       "2  downregulates\\n(Drug, Protein)   2486\n",
       "3    upregulates\\n(Drug, Protein)   1949\n",
       "4             induces\\n(Drug, BP)    986"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert dictionary items to a list of tuples\n",
    "data_list = [(key, value) for key, value in relations.items()]\n",
    "\n",
    "# Create a DataFrame from the list of tuples\n",
    "edges_df = pd.DataFrame(data_list, columns=['Edge Types', 'Count'])\n",
    "edges_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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du3Y9cL+kbXV2Pc7KLRaLrl+/rmLFijn8/rm7uzt8MSQ9eAbvtA51eJTj3rt166YpU6bYvlzYtWuXLly4oGLFimn9+vV2dR/UPVxK/jkpWrSojh07ZttO/Bwb8Tz16tVLa9eu1fnz5yXdmwgycbd/SfL29tZzzz2nN954gzXtATwU7mADwCNmsVgcxlC7uLgku8xRciZPnqwvv/xSfn5+Tpd4unv3rjZt2qQePXoYfpfRWaDILCwWi0NZ4u6+WV3Su9WSHJbMehL079/fbqjDuHHjnPYMMdqjOMf9JO4und5y585tN/bearXq559/1tGjR3Xu3Dlbebly5WxdujOLxM+Tr6+vli9friFDhqhChQpOewuFhYVp4cKFeuGFFxQREfEomwrgMcMdbAB4xFavXq0rV67YlVWvXl25c+dO1XHMZrM6d+6szp07Ky4uThcvXtT58+d18uRJLV261PYBODY2Vt99952hMxCnR6C7ePGiQ5nFYtHly5ftyhJPpJZ0vK8kh8mlYmJinK5znZ6cfQFx4cIFh7H3Fy5ccKiXEXfPkrY3NjZWV69eVaFChezKM0t7pXvh77XXXrNNtBYUFOQwjj6p/Pnz240bTrijmVTS6zSbzbbfu6QT+cXExOjatWsOP1tnv89J25JY6dKlNXPmzPvuIz36L1MCAgLs5gn45ZdfHGZgv9/SXIkl95wkLU/8HCf9mTVs2NBuLoPkJP3SMWfOnHrjjTf0xhtvKDo6WufOndP58+d15MgRLViwwDb2+sqVK1q+fLnDJIQAkFLcwQaARygwMNDph8P+/fun6jgRERF2d2hcXV1VokQJNWnSRH379nVYZzvp8k9J15J+lHfFkrN9+3aHLrybNm1yWMIo8eREuXLlcjhO0mtdsWKFoqKiHnh+I5+TxEscJVi8eHGKypztm96SzkQupay9rq6uqlWrVrq160F69eplF1STTmCWVNLn9sKFCw5dy2/fvq3Vq1fblVWuXNk2vjjpbPPSvTWaE4uOjtbatWvv25akz/mZM2d07do1FS1a1Ol/RYoU0b///uswwWF6K1u2rN049ZCQEM2fP9+27ebm5jDDfHKOHTvmsLzfkSNH7LqHS/bPcdLn6cCBA4qLi0v2eSpUqJCOHDliF7BDQkLseh5ky5ZNFSpU0LPPPquhQ4c6TLKWkuXyACA53MEGgHRy8eJFWa1W3b59W2fOnNHGjRu1YcMGh/Gxbdu2TfWEYfv27dPw4cPVtGlT+fn5qVSpUvLx8ZHZbNb58+c1Y8YMu/o5cuSw20463vvGjRtavHix/Pz8bEHTyJmfUyImJka9evXSO++8o2LFiunvv/92WFLJ1dVVzz//vG3b09NTRYsWtbsDNn36dBUoUECVK1dWYGCgxo4dm6Lz+/j42G3v3r1bO3bsUPHixWUymeTh4ZHiu7WFCxdWs2bN7GYSX716tXLkyKEOHTrIzc1Na9eutbszKN0LE2XKlEnROYxUu3ZtlStXTv/884+tLOFuasuWLXX37l0tWbJEO3bssNuvXbt2GTopVLZs2TRw4EB9/PHHKarfpUsXzZgxwy6IDxkyRMOHD1f16tV19epVTZgwweFLnZdeesn27/r16ytv3rx244SnTp0qs9msZs2a6caNG5o8efID5wp49tln9fXXX9vGClutVvXv31+9e/dW/fr1lSdPHkVFRencuXM6ePCgNm3apEuXLmnevHnJjh1PLwEBAdq9e7dtO/FyXS1atEjV/BH9+/fXiBEjVK5cOZ08eVJffvmlQ50OHTrY/p3wM0v4wisqKkoBAQHq27evatasqdy5cysyMtI22/qWLVsUEhKizZs324ZTzJkzRxs2bFDz5s1Vo0YNFS9eXF5eXoqNjdWRI0ccvgxJujwgAKQGARsA0om/v/8D67Ro0cJhjdeUioyM1Jo1a7RmzZoH1m3evLndtrO1iJOGlKTLNqU3V1dXnT17VgMHDky2Tu/evR0m32rbtq2mT59u275165bDHXyTyfTAsbM1a9a0ey7DwsLUt29f23a9evXs7tw9yMiRI/X333/bjf9esmSJQ6hOkCtXrhQHRaOZTCZ9/vnnCggIsAUZi8Wi6dOn2z23iRUqVEhvv/32o2ymU127dtX3339vNyY4OQULFtTw4cP12Wef2coiIiL00UcfJbtPo0aN7AKfu7u73nrrLY0cOdJWlrAed+I1uV1dXe97tzl79uwaPXq0Bg4caPvSLSIiQpMmTdKkSZMeeC2PUvPmzVWsWDGnQwRS2j1cunfNISEh9/29ad26td0d7AIFCmjEiBF2a8Rfu3bNaTC/n0uXLmnBggVasGDBA+s2bdo0VccGgMToIg4AGcDX11cffvihpk2b9sBxow/r6aef1muvvWZXVq5cObVo0SJdz5taAQEB9+1u7O/vb7cuc4J+/fqpbNmyye7XvXt31a5d+4Hn79ChgwoXLpyitqZEsWLFNG/ePKezfidVpEgRff/99xly9zpBtWrV9N1336VoAruKFSvqhx9+yBSzLbu6ujr9vUhOQECARo0a5XRiwKSef/55TZw40WHc8wsvvKBXXnkl2f1KlChht357cpo3b64pU6Y49J5Ijqenp8Ma9o+C2Wy2rSmdWMGCBdW4ceMUH6dVq1bq1q1bso/XqFHD7suPBD169NDnn3/u0BMnOT4+Pg4z66dU//799fTTT6dpXwCQuIMNAOnGZDLJ1dVV2bJlk5eXlwoUKKDSpUurfv36euaZZ1L0AT85DRo00Ny5c7Vv3z4dOHBAV65cUWhoqCIiImzLBlWsWFFt2rRRq1atnM6aO2HCBM2cOVMbNmzQhQsXHCYHe9Q8PT01f/58LVy4UCtXrtSZM2ckSWXKlFG3bt3UtWtXp9fh6emphQsXatq0adq4caOuXr0qT09PVatWTT179lTTpk0VEBDwwPPnypVLixcv1pQpU7Rz504FBwfbdYVNi3LlymnVqlVat26dNm7cqKCgIIWGhspqtcrb21uVK1eWv7+/2rdvn+5ftKRE3bp19dtvv2n58uXaunWrjh07prCwMLm4uMjX11dVq1ZV69at1apVq0w1c3nr1q1VpUoVBQUFpaj+yy+/rJYtW2rJkiXatWuXzp49q1u3bsnDw0MFCxZUrVq11Llz5/t+4TNixAj5+fnphx9+UFBQkGJjY1WkSBG1atVKr732msMyVslp3ry5Nm3apJUrV2r79u2259xisShXrlx66qmnVLlyZdWvX1+NGzdOc3B8WF26dNGECRPs5jPo1KmTzOaU36sxmUwaPXq0GjZsqB9//FHHjx9XTEyMihcvrg4dOiggICDZ98WuXbuqRYsW+vnnn7Vr1y6dOnVK4eHhttdS8eLFVbVqVTVo0ED169e3m1OhX79+qlmzpvbv36/Dhw8rJCREN27c0J07d5Q9e3YVKVJENWrUUJcuXZyOsQeA1DBZM3q9CQAAAGR6zzzzjG3mdZPJpI0bN953PHhAQIACAwNt2506ddKYMWPSvZ0AkJG4gw0AAIBkWa1WzZ8/325Zs4YNGz7yydYAICsgYAMAAMDB119/rd9++003b95UZGSkrdxkMt13MkIAeJIRsAEAAODgxo0bdnetE/Tr1y9D1z8HgMyMgA0AAID78vb2VpkyZdS7d289++yzGd0cAMi0mOQMAAAAAAADsA42AAAAAAAGoIt4KlmtVlks3PQHAAAAgCeB2WySyWRKUV0CdipZLFaFht7O6GYAAAAAAB4BX19PubikLGDTRRwAAAAAAAMQsAEAAAAAMAABGwAAAAAAAxCwAQAAAAAwAAEbAAAAAAADELABAAAAADAAARsAAAAAAAMQsAEAAAAAMAABGwAAAAAAAxCwAQAAAAAwAAEbAAAAAAADELABAAAAADAAARsAAAAAAAM8UQF75MiRKl++fEY3AwAAAADwGHpiAvauXbsUFxeX0c0AAAAAADymMm3A/vfffzVq1Ch16NBBlSpVUtu2bZ3WO336tF555RXVqFFDDRs21NixYxUTE2NXJyoqSt9++63ee++9R9F0AAAAAMATyDWjG5CckydPatu2bapevbosFousVqtDnfDwcPXu3VslSpTQpEmTFBwcrDFjxig6OlqjRo2y1fvf//6nXr16ycfH51FeAgAAAADgCZJpA3aLFi3UsmVLSdKIESMUFBTkUGfx4sW6ffu2Jk+eLG9vb0lSfHy8PvnkE/Xv318FChTQvn37dP78eY0cOfJRNh8AAAAA8ITJtAHbbH5w7/Xt27erfv36tnAtSW3atNHHH3+snTt3qnPnztq3b5+OHj2qFi1a2Oq0aNFCS5YsUb58+dLUNlfXTNuzHgAAAACQQTJtwE6JM2fOqEuXLnZlXl5eypcvn86cOSNJ6tevn/r162d7vHz58tqyZUuaz2k2m+Tj45nm/QEAAAAAj6csHbAjIiLk5eXlUJ47d26Fh4enyzktFqsiIqLS5dgAAAAAgMzFyyu7XFxS1os5SwfstDhx4sRDHyMuzmJASwAAAAAAj5MsPZjYy8tLkZGRDuXh4eHKnTt3BrQIAAAAAPCkytIBu1SpUrax1gkiIyMVEhKiUqVKZVCrAAAAAABPoiwdsJs0aaJdu3YpIiLCVrZ+/XqZzWY1bNgwA1sGAAAAAHjSZNox2Hfu3NG2bdskSZcuXdKtW7e0fv16SVK9evXk6+ur7t27a/78+Ro4cKD69++v4OBgjR07Vt27d1eBAgUysvkAAAAAgCeMyWq1WjO6Ec5cvHhR/v7+Th+bN2+e/Pz8JEmnT5/W6NGjdeDAAXl6eqpDhw4aOnSo3N3d06Vd8fEWhYbeTpdjAwBgpBMnjmvOnBk6dOhvxcTcVeHCRdS+fWe98EL3++53/PgxzZkzQ8ePH9OdO1EqXLiI2rbtqM6dX5CLi4ut3sSJ43Tw4H5duXJFMTF3VbBgIbVo8Yx69AhQjhw5bPXOnDmtOXNm6sSJ4woNva5s2bKpRIlS6tEjQI0aNUm36wcAwAi+vp4pnkU80wbszIqADQDICgID/9R77w1V2bLl5e//jLJnz6FLly7KarXojTeGJLvf8ePH9Prrr6po0WJq27aDPDyyac+eXfrjj23q2rW73nrrHVvd11/vq/LlK6po0aJyd/fQP/+c0Lp1q1S+fEVNmTJLZvO9DyO7d+/QTz8tUZUqVZU3bz5FR0dr27Yt+vvvAxo+/AN16NA53Z8PAADSioCdjgjYAIDM7vbtW+rRo4uqVKmmzz77yhZ0U+Krrz7X+vVrtHLlenl5/d+KHIMG9dPJkye0YcO2++6/aNECTZkyXtOnf68qVaomWy8+Pl59+wYoJuauFi78JcXtAwDgUUtNwM7Sk5wBAABHGzeuV2joDfXr94bMZrPu3Lkji8WSon2jom7J3d1dOXPmsivPkyePPDyyPXD/QoUKSZJu3XJcRjMxFxcX5c9fQLdu3UpRuwAAyAoI2AAAPGb27g2Up6enQkKuqUePznrmmcZq1aqpvvnmS929e/e++9asWVu3b9/W119/oXPnzurq1StaseJnbdu2VT179nGoHxcXp7CwMF2/HqLAwD81a9Y05cjhqUqVKjvUvXPnjsLCwnTp0kUtWfKj9uzZpdq16xp12QAAZLhMO4s4AABImwsXLig+Pl7vv/+22rbtoAEDBunAgX36+eclioyM1CeffJHsvu3addLZs2e0cuUyrV69QtK9u81Dhw5Xx45dHeofP35MAwa8Ytt+6qniGjNmnF338gSTJ3+rlSuXSZLMZrOaNGmuoUPffcirBQAg8yBgAwDwmLlzJ0rR0dHq2LGL3npruCSpadMWio2N1cqVy/Sf/wxQsWJPOd3XxcVFRYoUVb169dW8ub/c3T20adMGffvt1/L1zasmTZrZ1S9ZsqS+/XaKoqPv6PDhQ9q7N1B37txxeuxu3V5Ss2b+un49RFu3bpLFYlFcXKyh1w4AQEZikrNUYpIzAEBmFxDQTWfPntHkyTNVo0YtW/nBg/s1aFA/ffjhf9WmTVun+86fP1c//7xIixYtt1tqa/Dg/rpw4bx+/nm1XF2T/37+t9/W67PPRmn27AUqW7bcfds5dOhA3boVqZkzf5DJZErlVQIA8GgwyRkAAE+wvHnzSZJ8fX3tyn187m1HRiY/Adny5T+pVq26duFakho1aqLr10N09eqV+567adPmkqTNm397YDubNfPXsWNHdeHCvw+sCwBAVkDABgDgMVO+fEVJUkhIiF359ev3tr29fZLd9+bNUFks8Q7lcXFxku4tr3U/sbGxslgsKZodPGHCNWYSBwA8LgjYAAA8Zlq0aClJWrNmpV356tUr5OLiolq1aie7b7FiT+mvvwIVHh5mK4uPj9eWLZuUI4enihQpKuneXfCE0J30HJJUoUJFW9nNm6EO9eLi4rR+/Vp5eHioRIlSKb42AAAyMyY5AwDgMVOuXAU9/3x7rV27SvHx8apRo5YOHNinrVs3KSDgFVsX8tmzZ+j772dp4sTpqlWrjiSpZ88++vTTj9SvXx+1b99JHh4e2rTpN504cUyvvfa6bfz1gQP7NH7812re3F9Fiz6l2NhYHTp0QNu2bVWFCpXUqtVztvaMHfuFoqJuq3r1msqXL79u3LiujRvX699/z2nQoLccuqMDAJBVEbABAIYym00ym5mwKqO9//6HKlSokNasWaXt27eqYMFCeuutt9W9+8u2OnfvRstkMil//nxydb3Xqe25556Xr6+Pfvjhey1aNF+3b9/WU08V13vvfaBOnf5vma5y5cqqTp262rFju65fvy7JqiJFiurVV19Tz569lT27h63us8+20qpVK7RixS8KDw+Xp2cOlS9fUQMHDlGTJk0f2XOC+7NYrLJYmPsWAB4Gs4inErOIA0DyzGaTfH1yyGRmBFJW0LVrVxUuXFgTJ07M6KYgE7BaLAq9GUXIBoAkUjOLOHewAQCGMZtNMpnNivzjZ8WHhzx4B2SYW9F3dfzoUX3YpqbC1kzL6OYgg7nkzqdcjbvKbDYRsAHgIRCwAQCGiw8PUXzo/ZdzQsbKLmnbiK6S7vKzAgDAIPThAwAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwACuGd2A9GaxWNSjRw9FR0fLYrGoZMmS+uKLL5QzZ86MbhoAAAAA4DHy2N/BNpvNmj17tlauXKnVq1erUKFC+u677zK6WQAAAACAx0ymDdj//vuvRo0apQ4dOqhSpUpq27at03qnT5/WK6+8oho1aqhhw4YaO3asYmJi7Ook3K22WCy6c+eOTCZTurcfAAAAAPBkybRdxE+ePKlt27apevXqslgsslqtDnXCw8PVu3dvlShRQpMmTVJwcLDGjBmj6OhojRo1yq5unz59dOzYMZUrV07vvffeo7oMAAAAAMATItPewW7RooW2bdumiRMnqnLlyk7rLF68WLdv39bkyZPVuHFjde3aVcOHD9fixYsVHBxsV3fu3LnatWuXqlSpooULFz6KSwAAAAAAPEEy7R1ss/nB2X/79u2qX7++vL29bWVt2rTRxx9/rJ07d6pz58529V1cXNSpUycNGzZMr732Wprb5uqaab+XAIAM5eLC+yOQlfEaBoCHk2kDdkqcOXNGXbp0sSvz8vJSvnz5dObMGUlSaGioJMnX11dWq1UbNmxQ2bJl03xOs9kkHx/PtDcaAAAgk/Lyyp7RTQCALC1LB+yIiAh5eXk5lOfOnVvh4eGSpBs3bui9995TbGysJKlMmTIaOXJkms9psVgVERGV5v0B4HHm4mLmAzqQhUVE3FF8vCWjmwEAmYqXV/YU9/DJ0gE7JcqWLatly5YZesy4OP7wAACAx098vIXPOQDwELL0QBsvLy9FRkY6lIeHhyt37twZ0CIAAAAAwJMqSwfsUqVK2cZaJ4iMjFRISIhKlSqVQa0CAAAAADyJsnTAbtKkiXbt2qWIiAhb2fr162U2m9WwYcMMbBkAAAAA4EmTacdg37lzR9u2bZMkXbp0Sbdu3dL69eslSfXq1ZOvr6+6d++u+fPna+DAgerfv7+Cg4M1duxYde/eXQUKFMjI5gMAAAAAnjAmq9VqzehGOHPx4kX5+/s7fWzevHny8/OTJJ0+fVqjR4/WgQMH5OnpqQ4dOmjo0KFyd3dPl3bFx1sUGno7XY4NAFmdq6tZPj6eClszTfGhVzK6OQBSyMW3kLzbvq6bN28zyRkAJOHr65n1ZxEvWrSoTpw48cB6pUuX1ty5c9O/QQAAAAAA3EeWHoMNAAAAAEBmQcAGAAAAAMAABGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAARsAAAAAAAMkOZ1sN9//31JUtWqVfXSSy85rXP06FGFhIRIkpo2bZrWUwEAAAAAkOmlOWAvX75cJpNJUVFRyQbs6dOna+PGjTKZTDp69GiaGwkAAAAAQGaX5oCdUlarNb1PAQAAAABAhkvXMdjXrl1Lz8MDAAAAAJBppOoOdsK468SCgoKcll+7dk0HDx6UJLm5uaWtdQAAAAAAZBGpCtgJ464TWK1WXb58WStWrLjvfoUKFUpT4wAAAAAAyCrSdQx2Qhj39/dPz9MAAAAAAJDhUh2wk05adr9JzMxms5599lkNHjw49S0DAAAAACALSVXAnjdvnqR7obp3794ymUyqU6eOQ4A2mUzy8PBQiRIl5OXlZVxrAQAAAADIpFIVsOvVq2e3bbVa5evr61AOAAAAAMCTJs1jsBPuZvv6+hrWGAAAAAAAsqo0B2zuWgMAAAAA8H8eahbx+Ph4rV+/Xrt27VJwcLBiYmKc1jOZTPrhhx8e5lQAAAAAAGRqaQ7Yt27d0quvvqrDhw/ft57VarVbOxsAAAAAgMeROa07jh8/XocOHbrvMl0AAAAAADwp0nwHe/PmzbY704RsAAAAAMCTLs0BOyQkxPbvQYMGqUWLFsqVK5fM5jTfFAcAAAAAIMtKc8D29fVVSEiIqlevrkGDBhnZJgAAAAAAspw0325u2LAhXcMBAAAAAPj/0hywX3/9dWXLlk1BQUHas2ePkW0CAAAAACDLSXMX8f3796tp06basGGD+vbtq1atWqlq1ary9vZ2Wr9jx45pPRUAAAAAAJmeyZrGft4VKlSwm0X8QWtdHzt2LC2nyXTi4y0KDb2d0c0AgEzJ1dUsHx9Pha2ZpvjQKxndHAAp5OJbSN5tX9fNm7cVF2fJ6OYAQKbi6+spF5eUdf5O8x3sxB60XNeDwjcAAAAAAFndQwVsJjkDAAAAAOCeNAdsluYCAAAAAOD/ELABAAAAADCAIWOwAdxz5sxpzZkzUydOHFdo6HVly5ZNJUqUUo8eAWrUqIld3XPnzmrixP/p8OGDcnV1U4MGDTVo0DD5+PjY1bNYLFq0aL5WrPhFN25cV7FiT6lnzz565pnWj/LSAAAAADwAARswUHDwFUVFRalNm+eVN28+RUdHa9u2LRoxYpiGD/9AHTp0liRduxasQYNek6dnTvXrN1B37kRp0aIFOn36tGbN+kFubm62Y86cOVULFsxVu3adVLFiJe3YsU2ffDJSJpNJLVu2yqhLBQAAAJBEmpfp6tWrV8pPYjLphx9+SMtpMh2W6UJqxcfHq2/fAMXE3NXChb9Ikr75Zox+/XW1fvzxFxUsWFCS9NdfezR06EC7IB4Sck0vvNBe7dt30rBh70m6N7ngoEH9dOXKZf300yq5uLhkzIUBTrBMF5A1sUwXACTvkSzTFRgYmKLlt1KyRjbwOHNxcVH+/AV0/PhRW9m2bVvUoEFjW7iWpLp1/VSs2FPasmWTLWD/8cc2xcXFqVOnF2z1TCaTOnbsok8+GamgoMOqXr3GI7sWAAAAAMlLWQxPA5bwwpPszp07CgsL06VLF7VkyY/as2eXateuK+neXembN0NVoUJFh/0qVaqskydP2LZPnjyh7Nmzq0SJkknqVfn/jx9Px6sAAAAAkBrptg42d63xJJs8+VutXLlMkmQ2m9WkSXMNHfquJOnGjeuSpDx58jrslydPXkVEhCsmJkbu7u66ceO6fHx8HV5PCftev349PS8DAAAAQCqkOWBv3rzZafmNGzf0119/6bvvvlNYWJh69uypV155Jc0NBLKibt1eUrNm/rp+PURbt26SxWJRXFysJOnu3buSJDc3d4f93N09bHXc3d1t/3es5253LAAAAAAZL80Bu0iRIsmWV6tWTXXr1lW3bt20cOFCPffcc8nWBx5HxYuXUPHiJSRJbdq01dChA/Xee0M1c+YP8vC4F6JjY2Mc9ouJuReYE+p4eHgoJsZZvRi7egAAAAAyXrqNwa5WrZoKFCggq9WqmTNnptdpgCyhWTN/HTt2VBcu/Gvr3p3QVTyxGzeuy8srt+0OdZ48eRUaesNhOEbCvnnzOnYzBwAAAJAx0i1g37p1S+Hh4bJarTpw4EB6nQbIEhK6ct+6dUv58uWXt7ePjh8/5lDv6NEjKlu2nG27TJlyio6O1rlzZ+3qHTkSJEkqW7Z8OrYaAAAAQGqkuYv4ihUrnJbHxcUpLCxMa9euVXR0tKR7MyoDT4KbN0Pl4+NrVxYXF6f169fKw8NDJUqUkiQ1a9ZCv/66RsHBV1WgwL2luvbuDdSFC+f14osv2fZt3LipJk36n5Yv/8luHeyVK39Rvnz5VaVKtUd0ZQAAAAAeJM0Be8SIEfedKTyhS6vJZFLx4sXTehogSxk79gtFRd1W9eo1lS9fft24cV0bN67Xv/+e06BBbylHjhySpICAV7R16ya9+eYAvfBCd925c0cLF85X6dJl9Nxz7W3Hy5+/gLp166GFC+crLi5OFStW1h9//K6//z6gUaM+k4uLS8ZcKAAAAAAHD7VMl5T8Ul2Jw/cLL7zwsKcBsgR//2e0Zs1KrVjxi8LDw5Qjh6fKl6+g118frEaNmtrqFShQUJMmzdTkyd9q+vTJcnV1U4MGjTRo0FsOs4YPGDBYuXJ5aeXKZfr11zUqWrSYRo0arWefbf2oLw8AAADAfZis91vM+j4qVKjwwDpms1k9e/bUBx98kJZTZErx8RaFht7O6GYAQKbk6mqWj4+nwtZMU3zolYxuDoAUcvEtJO+2r+vmzduKi7NkdHMAIFPx9fWUi0vKpi9L8x3sQYMGJfuYm5ub8ufPr6efflqFChVK6ykAAAAAAMgy0iVgAwAAAADwpEm3ZboAAAAAAHiSPPQkZ5K0c+dObd26VRcvXpQkFS1aVM2aNVOjRo2MODwAAAAAAJneQwXsW7duaciQIdq1a5fDYz/++KOefvppTZgwQV5eXg9zGgAAAAAAMr00B2yr1aqBAwdqz549kuSwJrbVatWff/6pQYMGad68eQ/XSshsNslsTn7dcQCZl8VilcWSpgUbAAAAkIWkOWBv3LhRe/bssQXrpKt9mUwmWa1W/fXXX9qwYYNatWr1cC19gpnNJvn6eMpEwAayJKvFqtCbtwnZAAAAj7k0B+zVq1fb/p0zZ05169bNtjb2iRMntHTpUkVERNjqErDTzmw2yWQ2KXLPRcVFxmR0cwCkgmsud+XyKyqz2UTABgAAeMylOWAHBQVJkjw8PLRo0SKVKVPG9li7du3UsWNHde3aVXfv3rXVxcOJi4xRfFh0RjcDAAAAAOBEmpfpunHjhkwmk6pWrWoXrhOUKVNGVatWldVqVWho6EM1EgAAAACAzC7NAdvFxUWSFBsbm2ydhMcS6gIAAAAA8LhKc8DOmzevrFargoKCtG/fPofH9+/fr6CgIJlMJuXNm/ehGgkAAAAAQGaX5jHY1atX14ULFxQXF6fevXurVatWKleunEwmk06cOKHffvtNcXFxMplMql69upFtBgAAAAAg00lzwO7SpYvWrFkjk8mkuLg4rVu3TuvWrbM9nnjZrs6dOz9cKwEAAAAAyOTS3EW8fv36atOmjaxWq91a2AnBOqGsVatWatCggQFNBQAAAAAg80pzwJaksWPHqkuXLpLs71gn/LtTp04aO3bsw5wCAAAAAIAsIc1dxCXJzc1Nn3/+uV599VX9/vvvunTpkqxWq4oWLapmzZqpdOnSRrUTAAAAAIBM7aECdoLSpUsTpgEAAAAAT7QUdxG/cuWK/Pz8VK9ePXXo0OG+61/HxMSoffv2qlevnp5++mldvXrVkMYCAAAAAJBZpThgr1mzRuHh4YqMjFSvXr3k5uaWbF13d3cFBAQoIiJC4eHhWr16tSGNBQAAAAAgs0pxwP7zzz8lSR4eHmrXrt0D67dv314eHh52+wIAAAAA8LhKccA+deqUTCaTKlSoIHd39wfW9/DwUMWKFWW1WnX69OmHaiQAAAAAAJldigP2zZs3JUm+vr4pPnhC3dDQ0FQ2CwAAAACArCXV62BHRUWluq7JZErtaQAAAAAAyFJSHLBz584tq9WqoKAgxcTEPLB+TEyMDh8+LEny8vJKewsBAAAAAMgCUhywS5UqJUm6ffu2Zs2a9cD6M2fO1O3bt2UymVSyZMm0txAAAAAAgCwgxQG7bt26kiSr1aopU6Zo+vTpTtfCjo2N1bRp0zR16lRbWb169QxoKgAAAAAAmZdrSit26dJF06dPV3x8vCwWiyZMmKD58+fr6aefVtGiRWW1WnXp0iXt3r1bN2/elNVqlSS5uLioS5cu6XYBAAAAAABkBikO2IUKFVL37t21YMECmUwmWa1W3bhxQ+vWrbOrlxCsEyY269GjhwoVKmRgkwEAAAAAyHxSNYv4u+++q1q1aslqtcpkMtmCduL/EpfXrl1b7777bnq1HQAAAACATCNVAdvd3V1z585Vly5dbCFaki1US7KF7C5dumj27Nlyc3MzvtUAAAAAAGQyKe4insDd3V2ff/65+vTpo3Xr1mn//v26ceOGJClPnjyqVauWnnvuOZUtW9bwxgIAAAAAkFmlOmAnKFu2rIYMGWJkWwAAAAAAyLJS1UUcAAAAAAA4R8AGAAAAAMAAae4iDgAAAACP2oUL5/Xdd9N06NDfiogIV4ECBfXMM63Vo0eAsmXLZqsXGxurRYvma/36tbp69Yo8PXOqQoWKGj78A+XPXyADrwCPMwI2AAAAgCwhOPiqXnutt3LmzKnOnbvJy8tLR44c1uzZM3TixDGNGfM/SVJcXJyGDx+ioKBDateuk0qXLqPIyEgdPRqkW7duEbCRbgjYAAAAALKEDRvW6datSE2d+p1KlSotSerQobMsFovWr1+riIgIeXl5acmSH3Xw4H5NnfqdKlWqksGtxpOEMdgAAAAAsoTbt29Lknx989iV58mTV2azWW5ubrJYLPrpp8Vq0qS5KlWqori4OEVHR2dEc/EEImADAAAAyBJq1qwtSRoz5lOdPHlCwcFXtXnzb1qx4md17fqismfPrnPnzuj69RCVLl1GX331uZ55prFatmyk3r27a//+vRl8BXjc0UUcAAAAQJbw9NMN9J//DND8+d9rx47ttvJevV5Vv35vSJIuXLggSVq6dKFy5cqt4cM/kCTNm/e93n57sGbNmqcyZco++sbjiUDABgAAAJBlFCpUWNWr11KzZi2UO3du7dq1Q/Pnf688efKoS5cXdedOlCQpKipKc+b8qAIFCkqSatWqq+7dO2rhwnkaNWp0Rl4CHmMEbAAAAABZwqZNGzR27OdatGiZbSbwpk1byGq1atq0SWrZspU8PDwkSVWrVreFa0kqWLCgqlWrocOHD2VI2/FkYAw2AAAAgCxh+fKfVa5ceYdltho2bKLo6Gj9888J5c2bT5Lk4+PrsL+Pj48iIyMeSVvxZCJgAwAAAMgSQkNvKD7e4lAeFxcnSYqPj1fp0mXk6uqq69dDHOpdv35d3t4+6d5OPLkI2AAAAACyhGLFiuvkyRM6f/5fu/JNmzbIbDarTJmyypHDU/XrN1RQ0CH9++85W51z584qKOiQ6tb1e8StxpOEMdgAAAAAsoSXXgrQnj27NHDga+rSpZu8vHJr164/9Oefu9SuXUdb9/B+/QZq796/9OabA9S1a3dJ0s8/L1auXF7q1euVjLwEPOYI2AAAAACyhBo1amnatNmaM2emli//SeHh4SpUqLD69XtDL73Uy1avZMlSmjx5pqZNm6h582bLZDKrdu06euONIcqXL38GXgEedwRsAAAAAFlGpUpV9M03Ex9Yr3z5Cho/fuojaBHwfxiDDQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAR77dbBv3ryp4cOH68KFC3J3d1eVKlX0ySefyN3dPaObBgAAAAB4jDz2d7BNJpP69++vDRs2aOXKlbp7964WLFiQ0c0CAAAAADxmMm3A/vfffzVq1Ch16NBBlSpVUtu2bZ3WO336tF555RXVqFFDDRs21NixYxUTE2N73NvbW3Xr1pUkmc1mValSRZcvX34k1wAAAAAAeHJk2oB98uRJbdu2TcWLF1fp0qWd1gkPD1fv3r0VGxurSZMmaejQoVq6dKnGjBnjtP7du3e1fPlyNWnSJD2bDgAAAAB4AmXaMdgtWrRQy5YtJUkjRoxQUFCQQ53Fixfr9u3bmjx5sry9vSVJ8fHx+uSTT9S/f38VKFDAVtdisei9996Tn58fARsAAAAAYLhMG7DN5gffXN++fbvq169vC9eS1KZNG3388cfauXOnOnfubCv/5JNPZDab9cEHHzx021xdH+2NfxeXTNvRAEAKPSmv4yflOoHHFa9hAHg4mTZgp8SZM2fUpUsXuzIvLy/ly5dPZ86csZWNHTtWV69e1eTJk1MU3O/HbDbJx8fzoY4B4Mnj5ZU9o5sAAA/EexUAPJwsHbAjIiLk5eXlUJ47d26Fh4dLujeWe/bs2SpVqpS6du0qSWrQoIHee++9NJ3TYrEqIiIq7Y1OAxcXM3/wgCwuIuKO4uMtGd2MdMf7FZC1PSnvVQCQGl5e2VPcwydLB+yUKFu2rE6cOGHoMePi+MMDIHXi4y28dwDI9HivAoCHk6UH2nh5eSkyMtKhPDw8XLlz586AFgEAAAAAnlRZOmCXKlXKbqy1JEVGRiokJESlSpXKoFYBAAAAAJ5EWTpgN2nSRLt27VJERIStbP369TKbzWrYsGEGtgwAAAAA8KTJtGOw79y5o23btkmSLl26pFu3bmn9+vWSpHr16snX11fdu3fX/PnzNXDgQPXv31/BwcEaO3asunfvbrcGNgAAAAAA6S3TBuwbN25oyJAhdmUJ2/PmzZOfn59y586tH374QaNHj9bAgQPl6emprl27aujQoRnRZAAAAGQRZrNJZrMpo5sBIJUsFqssFmtGNyNZmTZgFy1aNEWzf5cuXVpz585N/wYBAADgsWA2m+Tjm0NmU5YeLQk8kSxWi26GRmXakJ1pAzYAAACQHsxmk8wms/4IO63wuOiMbg6AFMrtmk2NvUvLbDYRsAEAAIDMJDwuWqFxURndDACPEfrFAAAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYAACNgAAAAAABiBgAwAAAABgAAI2AAAAAAAGIGADAAAAAGAAAjYAAAAAAAYgYAMAAAAAYIAnImCPGjVKjRs3Vvny5TO6KQAAAACAx9QTEbDbtWun5cuXZ3QzAAAAAACPsUwbsP/991+NGjVKHTp0UKVKldS2bVun9U6fPq1XXnlFNWrUUMOGDTV27FjFxMTY1albt67y5s37KJoNAAAAAHhCuWZ0A5Jz8uRJbdu2TdWrV5fFYpHVanWoEx4ert69e6tEiRKaNGmSgoODNWbMGEVHR2vUqFEZ0GoAAAAAwJMq0wbsFi1aqGXLlpKkESNGKCgoyKHO4sWLdfv2bU2ePFne3t6SpPj4eH3yySfq37+/ChQo8CibDAAAAAB4gmXaLuJm84Obtn37dtWvX98WriWpTZs2slgs2rlzZzq2DgAAAAAAe5n2DnZKnDlzRl26dLEr8/LyUr58+XTmzJl0OafZbJKvr2e6HDs5JtO9/3s1KCZZHLvKA8jEzPdewLlzZ5eTkS6PnYT3q1zNX5YscRnbGAApZ773kfBJe69q7lNWFqslYxsDIMXMpns3YR/1e5X5/3+eS4ksHbAjIiLk5eXlUJ47d26Fh4fbtkeMGKFdu3ZJkpo0aSI/Pz99/fXXaTqnyWSSi0vKn2AjueRwy5DzAnh4KemV8zhx8XR8bwaQ+T1p71WeLu4Z3QQAaZCZ36uydMBOqTFjxmR0EwAAAAAAj7nMG/1TwMvLS5GRkQ7l4eHhyp07dwa0CAAAAADwpMrSAbtUqVIOY60jIyMVEhKiUqVKZVCrAAAAAABPoiwdsJs0aaJdu3YpIiLCVrZ+/XqZzWY1bNgwA1sGAAAAAHjSZNox2Hfu3NG2bdskSZcuXdKtW7e0fv16SVK9evXk6+ur7t27a/78+Ro4cKD69++v4OBgjR07Vt27d2cNbAAAAADAI2WyWjPnYgwXL16Uv7+/08fmzZsnPz8/SdLp06c1evRoHThwQJ6enurQoYOGDh0qd3dmhQQAAAAAPDqZNmADAAAAAJCVZOkx2AAAAAAAZBYEbAAAAAAADEDABgAAAADAAARsAAAAAAAMQMAGAAAAAMAABGwAAAAAAAzgmtENADK7Fi1a6NKlS7Ztk8mk7NmzK1euXCpevLiqVKmiNm3aqFq1ahnYSgCPm6TvPZLk5uamvHnzqkaNGurZs6fq1KmTQa17eBcvXpS/v7+KFCmiLVu2ZHRz8Jhz9nqSpBw5cqhYsWJq2rSpXn31Vfn4+DjUGTFihJYvX64vv/xSnTt3fui2BAQEKDAwUPPmzZOfn99DHy+rtiMtz+uePXvUq1cvh/KEn2OTJk30yiuvKE+ePEY394kWFRWlNm3aKHfu3FqxYoXM5v+7R1u+fHmH+iaTSZ6enipRooSeffZZ9erVS9mzZ3eol/A7mJibm5u8vb1VpUoVdevWTS1atLB7fOrUqZowYYJmzpyppk2bGnSFxiJgAylUq1YtFS9eXJIUHR2tmzdv6tixYwoMDNScOXNUr149ffHFFypWrFgGtxTA4yTxe09ERISCgoL066+/av369Xrvvff0yiuvpOv5J02apMmTJ2vQoEEaPHhwup4LSG+JX08Wi0XXrl3TgQMHNHPmTK1YsUILFy7k73gW0alTJ0mS1WrV5cuXdfDgQZ04cULLly/XvHnzVLp06Qxu4eNjxowZunr1qj755BO7cJ1Yo0aNlC9fPklSXFycrl69qgMHDigoKEirVq3Sjz/+KG9vb6f7VqhQQRUrVpR0L8wfPXpUW7du1datWxUQEKCRI0fa6vbp00c//vijvvjiCzVo0EBubm7GXqwBCNhACr3wwgsO37BarVZt375dX3zxhQIDA9W9e3ctXryYP84ADJP0vefu3bsaNWqUVqxYoa+//lrNmjVTyZIlM7CFaVOgQAGtW7cuU344wuPL2d/ykJAQ9ezZU+fOndPXX3+tiRMn2j0+bNgwvfbaa8qfP/+jbGq6++qrr3Tnzh0VLlw4o5uSJmPGjLHbPnv2rPr06aOrV69q1KhR+vHHHzOoZY+X4OBgzZkzR1WrVlWzZs2SrdevXz+HnhBnz55Vjx49dOrUKc2YMUPvvfee031btmxp9wWuxWLR+PHjNWPGDM2fP1/+/v6qX7++pHu9Ffr27auvvvpKixYtctqjIaMxBht4CCaTSU2bNtVPP/2kEiVK6Pr163bfsgGA0Tw8PDRq1CjlyJFD8fHx2rhxY0Y3KU3c3NxUunRpPfXUUxndFDzh8uXLp//85z+SpN27dzs8nj9/fpUuXVq5cuV61E1LV4ULF1bp0qWddt3NikqWLKkhQ4ZIkvbu3atr165lcIseDwsXLlRMTIy6du2a6n1LliypF198UZLz11ZyzGazhgwZYrth9euvv9o93rFjR7m5uWn+/PmyWq2pbld6I2ADBvDy8tIHH3wgSfrzzz8VFBRke6xFixYqX768Ll68qE2bNqlXr16qV6+eypcvrz179ki6NwYl8XZSkyZNUvny5TVp0iSHx+Li4jRnzhy1bdtWVatWVf369fXmm2/q1KlTWrZsmcqXL68RI0akw1UDyCienp62u9YXL16UdG8cXMJYuF9++UUvvviiateubXv/SXDo0CENGTJEjRo1UpUqVVS/fn0NGDBAO3fudDhP+fLlNXnyZEnS5MmTbedw9r4SFxenn376SQEBAapXr56qVKmiFi1a6OOPP9aVK1ccjn3x4kWVL1/eYXxd0mvZsGGDevTooVq1aqlGjRrq3r27tm3blpanDUhW3rx5Jd37PU5qxIgRKl++vJYtW+Z037Vr16p379623/vmzZvr/fff19mzZ1Pdjri4OC1atEjdu3dX7dq1VbVqVT377LP67LPPFBwcnOx+//zzjwYPHiw/Pz9Vr15d7dq109y5c2WxWOw+hyT2oM8eu3fv1ptvvqkmTZqoSpUqevrpp9WlSxdNnDhRN2/etNWLjY3VypUr9fbbb6t169aqVauWqlWrplatWj2w3UarXLmy7d+XL1+WJLvPQmFhYfr888/VsmVLValSRQEBAbb6aX3u79y5o7lz56pHjx6qW7eu7XdgwIABWr16tdN91q9fr759++rpp59WlSpV1LhxY73zzjs6deqU0/pBQUF66623bD+LWrVqyd/fX4MHD9amTZvs6losFi1ZskTdu3dXnTp1VLlyZdWvX1/t27fX6NGjHX4P7icmJkZLly6Vu7u7nn/++RTvl1hCt/H4+PhU7efi4mLrNp50/gRfX181adJE58+f1/bt29PUrvREF3HAIE2aNJG3t7fCwsK0a9cuValSxe7x77//XgsWLLC9kV67dk0uLi4PdU6LxaJBgwZp69atcnNzk5+fn7y8vHT48GF17dpVXbp0eajjA8i8bt26JUlyd3e3Kx89erQWLlyomjVrqlmzZrpw4YJMJpMkaenSpfr4449lsVhUqVIl+fn56dKlS7axboMHD9agQYNsx+rUqZOOHTum48eP242Rk6TatWvbteX1119XYGCgcuTIoSpVqsjHx0f//POPFi9erPXr1+v7779XpUqVUnWNEydO1NSpU1WzZk01bdpUZ86c0YEDB9S/f39NmjRJzzzzTKqfN8CZQ4cOSZLKli2b4n2sVqtGjBihFStWyNXVVXXq1FGePHl05MgRLVu2TL/++qsmTpyoJk2apOh4MTEx6t+/v3bt2iUPDw/5+fkpZ86cOnDggObPn681a9Zo9uzZdiFSkgIDA/Xaa68pOjpaTz31lBo2bKiwsDB98803+vvvv1P+JCTy2Wefaf78+ZKkihUrqk6dOoqMjNTZs2c1ZcoU+fn52boD37hxQ++++65y5cql0qVLq3z58rpz546OHTum+fPna+3atVq8eLFt7Ht6SnhflBzfG2/evKkuXbooMjJStWvXVuXKlW1DVNL63F+5ckX/+c9/dOrUKWXPnl21atWSt7e3goODtXfvXv3zzz9q166drX5cXJzeeecd/frrr3J3d1flypVVoEABnTt3TqtXr9bGjRs1adIku9+Z3bt367XXXlNsbKwqVKigGjVqyGKxKDg4WL///rvi4+PVsmVLW/0PP/xQy5Ytk4eHh2rXri1fX1+FhYXp4sWLWrBggerXr6+iRYum6Pncv3+/QkNDVbt27TT34Eh4bZUpUybV+yb3d06SGjZsqM2bN2vz5s2ZbrIzAjZgEJPJpEqVKmnXrl06efKkw+OLFi3S1KlT5e/vb9g5FyxYoK1btypfvnyaN2+eSpUqJenet4RjxozRvHnzDDsXgMzj+PHjunDhgiTZhV5JWrFihRYtWqQaNWrYlZ84cUKffPKJrFarvvrqK3Xs2NH22LZt2zRw4EBNmjRJNWvWVMOGDSXdG+M4adIkHT9+3GGMXGIff/yxAgMD1bx5c33++ed2M/jOnTtXX375pYYOHap169al6ovF+fPna8mSJapevbqtLGHStW+++YaAjYdisVgUEhKijRs36rvvvpOLi4tef/31FO+/ePFirVixQj4+Pvr+++9tr0Wr1arJkydr8uTJevvtt7Vhwwb5+vo+8HgTJ07Url279NRTT+n777+3haDY2Fj997//1c8//6w333zTFs6ke5OuvvPOO4qOjtarr76q4cOH2yahOnXqlHr37q3r16+n6nmZP3++5s+fL29vb02YMEFPP/203eOHDh2y3ZWUpJw5c2rq1Klq3LixXRCKjY3VpEmTNGPGDH3++eeaOXNmqtqRFgl3cz08PGyfiRL8/vvvql+/viZPnqycOXPaPZaW5z7hJsepU6fUqFEjff3113Y/57t37+rPP/+0O8+kSZP066+/qnr16ho3bpzdnD3r16/XsGHD9M4772jTpk3y8vKSJE2bNk2xsbH6+uuv1b59e7vjRUZG6vTp07bty5cva9myZSpYsKB+/vlnu5+TJJ0+fTpVQwIS2p/078mDJExytmLFCq1atUrZs2dX3759U3WMa9eu2b4gSvp3TpJq1qwpKXVdzx8VuogDBkpY3iMsLMzhsY4dOxoariXZAvTgwYPt/pC4uLho+PDhKlCggKHnA5CxIiMjtW3bNg0ePFgWi0X58+dXmzZt7Oq8+uqrTj8MzZs3T3FxcXrmmWfswrUkNW3a1DZObvbs2alq0+nTp7V27Vrlz59f33zzjcPyOH369FHTpk117ty5VHfle/PNN+3CtST1799fuXLl0rlz55x2PQfu5/3337cNQahYsaKaNGmi0aNHq3z58po/f76aN2+e4mPNmTNHkjRw4EC7AGAymTRo0CCVL19eERERWrp06QOPdffuXdukXO+//77dHUY3NzeNHDlSefPm1cWLF7VhwwbbY+vXr1dwcLCKFCmiYcOG2c3wXKZMGb3xxhspvh7pXjCaOnWqpHu9YZKGa0mqVq2aChUqZNvOmTOn/P39He4yurm5adiwYcqfP7/++OMPu7vLRkqYRXzatGm2z0XdunVTtmzZHNozevRoh3Cd1ud+y5YtCgoKUr58+TRx4kSHL1E8PDzs7qyGhYVp7ty58vDw0KRJkxwmxG3durVefPFFhYeHa9WqVbbyGzduSJLTu7S5cuWye79P+DKlUqVKDuFakkqXLp2qSe2OHTtm2+9BevXqZXttVa5cWf7+/po0aZIaNmyoJUuWOPTsTE5UVJT++usvDRgwQLdv31aOHDn0wgsvONRLuCN+/vz5dPvdSisCNmAgi8UiSbbumIm1atXK0HNdvXrVdgerbdu2Do+7u7sbfk4Aj17iQFCnTh3169dP58+f11NPPaVZs2YpR44cdvVbt27t9DgJa40mLG2TVMIENnv37k3VWLlt27bJarWqSZMmDh9cE9SrV0+SdODAgRQfV5LTsOPu7m77YPoox3bi8VCrVi116tTJ9l+zZs1UqFAhHT58WF9++aXOnTuXouNcvXpV58+fl+T8NWUymWyzlSc3xjmxw4cPKyoqSt7e3k7nJciePbuee+45h+P99ddfku59xnA2I3/i7skpceTIEYWGhsrHxyfVPUSOHz+u77//XqNHj9b777+vESNGaMSIEYqPj5fFYrE9X0ZJeF+sUKGCmjdvrvHjxysuLk5t27bVu+++61C/YsWKTld5Setz/8cff0i69xx7eno+sL179uxRdHS0atWqlewNEGfvldWqVZMkvfPOO9q7d6/TeQISlCpVSp6entq+fbumTZtm+5yYVgmBPbnltRJr1KiR7XXVsWNHNWrUSHny5NGOHTs0duzY+/akSDzHR82aNdWzZ08dOXJEefLk0dSpU+2+0Eng7u5u+/uX2l4a6Y0u4oCBEib9yJ07t8NjKR3vklJXr16VdO+ueXJv7EWKFDH0nAAevcTr9rq5ucnX11c1atRQ48aN5erq+Gc8udd9QhhN7r0o4YPn3bt3FRYW5nAnOjkJH+B+/vln/fzzz/etGxoamqJjJkjuTktCkL97926qjgc4W6YrLi5OEydO1IwZM9SzZ0+tX78+2S+LEiS8nry9vZOtmzBDfkq+CEqY8fp+f7edHS/hs0Byr2svLy/lypVLkZGRD2yD9H+TSZUsWdLpzQJnoqKi9O677z5wRQOj7zImfLFhMpmULVs2FS1aVI0bN1a5cuWc1k/uuU3rc58wiVrSrujJSXiv3L17t20Sx+Qkfq8cNmyYTpw4oe3bt2v79u3Kli2bKlWqpHr16ql9+/Z2d5dz5sypL7/8Uu+//77Gjx+v8ePHK1++fLa/GW3btk3RlwEJEn5mD3o9SM6X6YqOjtbHH3+sFStWqG/fvlq2bJnTYUKJ5/hwdXVV7ty5VblyZbVo0cKhJ0JiOXPmVFRUlCIiIlJ8TY8CARswiNVqtXWlcfbm7uHhkeZjJ9wZd+Z+fwBT+scRQOblLBDcz/0+jKSHhPenihUrqkKFCvetm7S794Mk7u4KpBdXV1e99dZbWrp0qUJCQrRy5Uq9/PLLGd0sw6T3Z4H//e9/2rhxo0qVKqW3335bVatWlY+Pj63LePfu3XXgwAHDl1NKug72gzzq98akEt4rixcvrlq1at23buLQni9fPv3yyy8KDAzUrl27tH//fh06dEj79+/XjBkzNGzYMPXr189Wv1WrVmrQoIE2b96sffv2af/+/dq4caM2btyoiRMnas6cOQ8M+AkSJjZL65cj2bJl00cffaSVK1fq+PHj+uOPP5yupX2/OT7uJ6FdCePVMwsCNmCQbdu2KTw8XNK9bjKpkdCt6/bt204fT/iWNLGE7kWhoaGKiopy6CYqOS5rAODJVaBAAZ0/f14XLlxw+iVgwtItHh4eTnvhJCeh616tWrU0atQoYxoLPGJms1lFihTRzZs37SaNSk7C3+CwsDDdunXL6R2+hDuWKZkPJX/+/JLu/3fb2fES/p3cfpGRkam6u5fQa+TcuXOyWq0pCucJaxR/++23Tr9kS2m3+4yS1uc+4b3vzJkzKTpPQv2SJUum+ssBk8lkN3P73bt3tWzZMn366af69ttv1bp1a9tdduleMO7YsaNtvo0rV65o9OjR2rx5s0aPHq0FCxak6LwJPZmczS2UUjlz5pS3t7ftteUsYKdFTEyMoqKiJP3fMnuZBV8NAwaIjIzUl19+KenesgHOZju8n4Q3bGd/1O/cueN0/FahQoVs3ZnWrl3r8HhMTIx+++23VLUDwOMrYWzf8uXLnT6e0L27Tp06dl3PE74ATG7cX8JyMlu2bKHLNrIsi8ViC1jOvrBOqmDBgrZA42x9bKvVanutJe0260zVqlWVI0cOhYWFafPmzQ6PR0dHa926dQ7Hq1u3rqR7k505e40mtw5zchKW2AsNDXVYXzk5CTcXnHWx/uOPP+zWzM6M0vrcJ7z3rVmzxhb07qd+/fpyc3NTYGCgbeKytPLw8FCPHj1Uvnx5WSwWnThx4r71CxUqpDfffFPS/01clhIJSysmtz53SkRGRtoCekpeWyn1zz//SLrXIyAlXdgfJQI28BCsVqu2bdumrl276ty5c8qXL59Gjx6d6uPUr19fkrRw4UK78T1RUVH66KOPkp0pt1evXpLuLS9x9uxZW7nFYtH//vc/ZtgFYNOrVy+5urpq06ZNWrlypd1jO3bs0JIlSyTdm4U8sYIFC0pK/gNWpUqV1KpVK125ckWDBg2y3QlPLCoqSqtWrcp0E9EA0r0vj8aPH28Lgs4munIm4bUydepUHT9+3FZutVo1depUHTt2TF5eXurWrdsDj+Xh4WHrlv7VV1/Z3U2NjY3V559/rpCQEBUtWtRuAtPWrVsrX758unTpkr799lu7IWWnT5+2zQieUq6urhowYIAk6aOPPrJNopbYoUOHbGO/pf/rzpywbnaCM2fO6OOPP07V+TNCWp/7Fi1aqFKlSrp27ZqGDBni8EXC3bt3tW3bNtt23rx5FRAQoKioKA0YMMBpKI6JidHmzZvtbrjMnj3baU/G06dP699//5X0fz0Pjh49qnXr1ik6Otqh/pYtW+zqpkTCFwoHDx5M8T6JRUdH67PPPpPVapWbm1uK14RPiYSJ4JzNdJ/R6CIOpNBPP/1km4U3JiZGN2/e1NGjR23fytWrV09ffPFFmiYWa9OmjX744QcFBQXp+eefV+3atWWxWBQUFCQ3Nzd16dJFv/zyi8N+AQEB2rlzp7Zv36727dvLz89PXl5eOnz4sK5du6aXXnpJCxcudDqzKIAnS/ny5TVq1Cj997//1bvvvqsffvhBJUuW1OXLl23jIwcPHuwwxKVRo0bKkSOHNm3apB49eqhEiRIym82qVauWunTpIkn64osvFBERoe3bt6t169aqUKGCihYtKqvVqkuXLun48eOKjY3VunXrMl1XPjxZEv8tl+51fT1+/LjtC+kBAwY8cHxsgoSxxStXrlSXLl1Ut25d5cmTR0eOHNHZs2eVLVs2ffPNNylaA1u6tyxdUFCQdu/ereeee05+fn7y9PTUwYMHdfnyZdu61ImXw8qePbu+/vpr9e/fX9999502btyoKlWqKDw8XHv27JG/v78OHTqky5cvp/izQO/evXX27FktXrxYPXv2VKVKlVSyZEndunVLZ86c0YULFzRv3jzbl2+DBg3Sm2++qQkTJujXX39V2bJldePGDe3bt0+1a9dW/vz5U72CwKOWlufebDZr8uTJ6tu3r7Zv367mzZurdu3a8vb2VnBwsI4fPy4vLy9bsJWkt99+W9euXdOaNWvUsWNHVahQQcWKFZOLi4uuXr2q48ePKyoqSrNmzbJNXjZt2jSNHTtWpUqVUunSpeXh4aFr165p//79iouLU8eOHVW5cmVJ94YUDh061DYRWqFChRQXF6d//vlHZ8+elZubm4YPH57i56VWrVry9fVVUFCQIiIi7jvWeebMmbZeG1arVTdu3NDRo0d148YNmc1mffTRR4ZOvrtr1y5JMnwJXCMQsIEU2r9/v/bv3y/pXheXnDlzqly5cqpSpYratGljW0YhLdzc3PT9999rwoQJ2rRpk3bu3ClfX18988wzGjJkiBYuXOh0PxcXF02dOlU//PCDli9frj179sjT01N16tTRlClTbDN6JqzPDeDJ9uKLL6pChQqaPXu29u/frxMnTihnzpxq2rSpevXqpYYNGzrskzdvXs2aNUtTpkzRkSNHdPDgQVksFsXHx9sCds6cOTVnzhytW7dOq1at0pEjR3T8+HF5enoqf/78ateunfz9/e3GCAIZIfHfcune39/8+fPrueeeU/fu3VPUnTuByWTS2LFj1aRJEy1ZskRHjhzRnTt3lDdvXnXu3FmvvfZaimeYlu4tO/Tdd99p6dKlWrlypfbu3auYmBgVKlRIAQEBeu2115yO565fv76WLl2qyZMn66+//tKmTZtUrFgxDR06VAEBAapVq5bMZnOKllpKuK5PPvlE/v7+Wrx4sf7++2+dPHlSuXLlUtGiRdWxY0e7SbKeffZZLViwQJMnT9bx48d14cIFFStWTIMGDdKrr76qvn37pvg5yChpfe6LFCmiX375RQsXLtSGDRt04MABxcbGKl++fKpbt67DMmmurq4aN26c2rdvr59//tn23GbPnl358uVT8+bN1aJFC1vXf0kaNWqUdu/eraCgIP3111+KiopSvnz51KBBA7344ot2AbN69ep6++23tXfvXp0+fVrHjh2Ti4uLChYsqJdfflk9e/ZM9e9kt27dNH36dK1Zs0YvvfRSsnV37Nhht+3h4aGCBQuqadOmCggIsHU3N0JoaKj++OMPPfXUU4beFTeKyWr0lH4AMo1evXppz549mjRpkp599tmMbg4AAHiE/vrrL/Xs2VPlypVL9XhsQLq3NFnLli1VtmxZp/MNZIQ5c+boq6++0ocffmgbLpmZMAYbyOKOHTummJgYu7KYmBhNmjRJe/bsUZ48edS0adMMah0AAEhPoaGhtlmuE/vnn380cuRISbL1NgFSq0CBAnr11Vd15MgRbd26NaObo6ioKH333XcqUaKEevTokdHNcYou4kAW98UXX+jYsWOqUKGC8uXLp4iICJ04cUIhISHy8PDQmDFjHmoNbgAAkHmdPHlSvXr1UpkyZVSsWDF5eHjo4sWLOnr0qCwWixo2bKiePXtmdDORhfXv318rVqzQt99+q6ZNm8pszrh7tHPnztWNGzf05ZdfZto5hugiDmRxq1at0urVq3XixAmFhYXJarUqf/788vPz06uvvqoyZcpkdBMBAEA6CQ4O1owZM/TXX38pODhYt2/flqenp8qUKaO2bduqW7dudkvvAUhfBGwAAAAAAAzAGGwAAAAAAAxAwAYAAAAAwAAEbAAAAAAADEDABgAAAADAAEwpCABAKpQvX95u29fXV5s2bZKnp6dd+aRJkzR58mTb9qBBgzR48OBH0kbJvp1FihTRli1b0vV8e/bsUa9evdK0b7169TR//nyDWwQAwKPHHWwAAB5CaGiovv/++4xuBgAAyAS4gw0AwEP6/vvv9fLLL8vHxyejm5JhfH191apVK4fy7du3686dO7btunXrytfX165OmTJl0r19AAA8CgRsAAAe0q1btzRjxgyNGDEio5uSYcqWLauJEyc6lLdo0UKXLl2ybQ8ePFh+fn6PsmkAADwyBGwAAAywcOFC9enTRwULFkzVfocOHdKSJUu0b98+BQcHKzY2Vt7e3qpYsaJat26tdu3ayd3d3em+W7du1Zw5cxQUFCSTyaQKFSqoV69eat26dYrOfe3aNS1evFg7duzQuXPndPv2beXMmVMVK1bU888/r44dO8rNzS1V15Ma+/fvV48ePWzbbdu21bhx4xzqDRgwQFu3brVtr169WuXKldOyZcv0/vvv28oHDRqkbt26aerUqdq2bZuuX7+uPHnyqGXLlho4cKDDnfME58+f16JFi7R7925dvHhR0dHRyp07t6pUqaJOnTqpVatWMplMBl45AOBxRcAGAOAh1KtXT4GBgbp7964mT56szz77LEX7Wa1WjRkzRnPnznV4LCQkRCEhIdq+fbvmzZunadOmqXDhwnZ1ZsyYof/97392Zfv27dO+ffv06quvPvD8Gzdu1IgRI3Tr1i278rCwMO3evVu7d+/W0qVLNW3aNOXNmzdF15RatWrVUs2aNXXgwAFJ0oYNG/T+++/bne/mzZvasWOHbbt27doqV66c0+MdP35cHTp00M2bN21lV69e1YIFC7RlyxbNnz9fRYsWtdvnxx9/1JdffqnY2Fi78uvXr+v333/X77//riZNmmjixInKnj37Q18zAODxxiRnAAA8hGHDhtn+vXz5cp09ezZF+02bNs0hXFeqVEn169e3m5H8+PHjeu211xQTE2Mr27t3r7799lu7fQsVKqRGjRopX758mjNnzn3PvX//fg0dOtQWrk0mk6pUqaLmzZvrqaeestU7dOiQBg0aJKvVmqJrSov//Oc/tn/HxsZq6dKldo+vW7fOLvx279492WNt2rRJ4eHhqlatmurWrWt35//y5csaPny4Xf1ff/1Vn376qe34Li4uqlmzppo1a6YCBQrY6m3fvl0ffPBB2i4QAPBEIWADAPAQatasqRYtWkiS4uLiNH78+AfuEx4erhkzZtiVjRs3TsuXL9fcuXO1du1aFSlSxPbYqVOntGzZMtv2d999Zxd6n332WW3cuFGzZ8/Wxo0b9fTTT9/3/N98840tVLq6umrBggX65ZdfNH36dP3222968cUXbXUPHDig33777YHXlFYtWrRQiRIlbNtLlixRXFycbXv16tW2f/v4+Dyw+/vUqVP1008/acGCBVqyZImyZctme2z//v3666+/JEkWi0Vjx461PZY7d26tXr1aixcv1owZM7RlyxY1a9bM9vi6desUFBSU1ssEADwhCNgAADykoUOHymy+9yd1w4YNOnLkyH3r79q1S9HR0bbt6tWrq23btrbtQoUKqW/fvnb7JIxBjo+P159//ulw/oSx0tmzZ9ebb76Z7LlDQ0O1f/9+23aOHDk0b948vfnmm3rzzTc1ZMgQnTx50um504PZbLbr0n716lVt2rRJknThwgVb93FJ6tSpU7Lj0SWpfv36at68uW27UqVKateunV2dnTt3SpKOHDmiy5cv28qzZcumCRMm2J6HYcOG6dq1a3b7pufzAAB4PDAGGwCAh1SuXDm1a9dOK1eulNVq1f/+9z/VqFEj2fqJZ9VO2D+pChUq2G1fvHhR0r0xyYmXvXJzc1PJkiUfeLzEx0l89zsiIkIbNmxItn7ic6eXjh07asKECbpx44ake+OiW7durVWrVtnqmEym+3YPl6Ty5cs7lJUtW9ZuOyFUJ72m4ODgDH8eAABZH3ewAQAwwJtvvmm7i7xjxw4FBgYmWzfpmObMPkN1VFRUuh7fw8NDPXv2tG0HBgbq5MmTdt3D69evr+LFi6drOx4k8RcbAAA4wx1sAAAMULRoUb344otasGCBJN03YCedyfqff/5xqHPixAmn+/j4+Ch79uy2sBcbG6tz587Z3cVO2sU7sSJFishkMtlCfqlSpfTrr7/e79IeiZdeekmzZs2yhfmPPvrIbsK4B929lpw/j6dOnbLbTpiNPenPoHHjxvruu+9S3W4AABLjDjYAAAZ54403lCNHjgfWq1+/vt3kWwcPHtS6dets28HBwZo9e7bdPgkTbrm4uKhevXp2j3377be2Scuio6M1adKkZM+dJ08eu+7rZ86c0cyZMxUfH29XLy4uTn/++ac++OAD/f333w+8pofl7e2tzp0727YTj73Onz+//P39H3iM3bt3a9u2bbbt48eP290Fl6QGDRpIkipXrmw3U/jOnTu1YsUKh2PevXtX27Zt05AhQ3T16tUUXw8A4MnEHWwAAAySJ08e9e7dW9OmTbtvPW9vb/3nP//R5MmTbWVDhw7Vd999Jy8vLx0+fNhufepSpUqpS5cutu3//Oc/2r59u+0u9IYNG3T48GGVKlVK//zzj8PkXEm9/fbb6tOnj2227nHjxmnevHkqV66c3N3ddf36dZ06dcp2l7xDhw6peyLSqE+fPlq0aJFD2O/atatcXR/8kcVqtWrAgAGqWrWqPDw8dPDgQbvlzWrWrGn7csJsNmv48OF65513JN2bVfy9997TxIkTVapUKZnNZl27dk2nT5+2HSPpMl8AACTFHWwAAAzUt29feXt7P7DeoEGD7MYdS/dmtt69e7dduC5XrpxmzZplN3t2vXr1NHjwYLt9L1++rB07dujatWt2YdyZunXr6ptvvlHOnDltZSEhIdq5c6e2bt2qw4cP2403dnFxeeD1GKFYsWJ69tln7cpcXFzUrVu3FO3frl075c2bV3///bcCAwPtwnXBggX19ddfO9QfOXKkbey8dG8Cuj/++EPbtm3TsWPH7I7xqJ4HAEDWxR1sAAAMlCtXLvXr189ujWVnTCaTPvroI7Vr105Lly7Vvn37dO3aNcXGxip37tyqUKGCWrdurQ4dOjhdmmrgwIGqUKGC5syZo6NHj0q6N2N2jx491KlTJ/3yyy/3PX+bNm1Up04dLV26VDt37tTp06d169Ytubi4KG/evCpZsqRq166tli1b3ndWcqO9+uqrdmPCmzRpokKFCqVo3+LFi+uDDz7Q5MmTtWXLFl2/fl2+vr7y9/fXwIEDlTdvXod9AgIC1KxZMy1ZskR//vmnzp8/r1u3bsnd3V358uVTmTJlVLduXT3zzDMpbgcA4MllsiadyhQAACCD/P777+rfv79te9asWWrSpInTusuWLdP7779v2x40aJDDnX0AAB4l7mADAIAMtX//fh04cEDXr1/XsmXLbOWVKlVS48aNM7BlAACkDgEbAABkqJ07d9pN+CZJ2bJl0+jRozP9GuEAACRGwAYAAJlG3rx5VaNGDQ0ePFgVKlTI6OYAAJAqjMEGAAAAAMAALNMFAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAQjYAAAAAAAYgIANAAAAAIABCNgAAAAAABiAgA0AAAAAgAEI2AAAAAAAGICADQAAAACAAVwzugEAAGPt2bNHvXr1sivbvHmzihYtmkEtSp1JkyZp8uTJtu0iRYpoy5YtdnVatGihS5cu2bYHDRqkwYMHP7I2PoyLFy/K39/frmzevHny8/PLoBZlLlevXtWsWbO0e/duXb58WXfu3LE91qlTJ40ZMyYDW5d2Kfm9zghZ+bUEAJkRARsAMlhAQIACAwPtykwmk9zc3JQtWzblypVLBQsWVKlSpeTn56dWrVrJ3d09g1p7L8Anbm+uXLnUp0+fDGtPZrBs2TK7kFKxYkW1bNkyA1uUNV24cEEvvPCCbt68+dDHGjFihJYvX+70MZPJpOzZs8vb21ulSpVSo0aN1KVLF3l5eT30eTMar08AyFgEbADIhKxWq2JiYhQTE6OIiAhdunRJ+/bt008//aTPP/9c/fv3V58+fWQymR552wIDAx3uxD3pH+CXL19uF2o6depEwE6D6dOnGxKuH8RqtSoqKkpRUVG6fPmyduzYoenTp2vq1KmqXbt2up8/PfH6BICMRcAGgCzm5s2bGjNmjPbs2aOJEyc63M2uUaOGNm/ebFdWsGDBR9nEh9K7d2916tTJtu3q+nj9qSpYsKDDzydfvnwZ1JrM5dChQ3bb1apV06hRo+Tj4yNJypEjR7qdOywsTEOGDNFvv/2WrufJbBYuXKi4uDjb9uNwFx8AMtLj9akFAB4TmzdvltVq1e3bt3XmzBlt3LhRGzZsUHx8vK3O1q1b9f7772vcuHF2+3p4eGSZ8dbOeHl5PdYf8l1dXbP0zyc93b592267cePGqlq1qmHH//HHH1WwYEFFRUXp8OHD+vrrr+3umIeEhGjHjh169tlnDTtnZpeVvnwDgKyAgA0AmVDiAFahQgU999xzCgwM1Ouvv65bt27ZHluzZo3atWunZs2a2cpSOsnZ7t27tWzZMh0+fFjBwcGKiYlRzpw55evrq6eeekpVq1ZV9erV1bhxY0nJj2m9dOmSypcvb1f25ZdfqnPnzpKcT6LUv39/zZ07V+vWrdO///6rqKgo20RfaZ0Mymq16qefftIvv/yiU6dOyWKxqFy5cnrhhRfUpUsXh+70KZ1sLOkY+cQTbSW9tgTLly93eK4SfgapmeQsNjZW69at06ZNmxQUFKTQ0FBZrVb5+PioYsWK8vf3V4cOHZyOyU/u98Dd3V0zZ87U77//ruDgYOXIkUM1a9bU66+/rurVqzscJzXu3Lmj5cuX6/fff9exY8cUFhYmFxcX+fr6qkqVKmrdurVatWolFxcXu/2S/v4kmDJliqZMmWLbftjJ4AoWLGh7HZQrV05RUVH67LPP7Or8+++/ye4fHx+vDRs2aMOGDTp8+LBCQ0NlsViUN29e1axZU507d1bDhg3T3L6///5bu3fv1tGjR3Xu3DmFhYUpLCxMVqtVuXLlUsmSJVWvXj298MILKly4sN2+Rr4+k5vkLDAwUCtWrNCBAwd07do13b17V7ly5VLp0qXVoEEDde/eXb6+vk73ddaG559/XvPmzdPatWttz3v58uX10ksvqX379k6PEx4erkWLFumPP/7Q2bNnFRERYfsdy5MnjypWrKhq1aqpcePGfHkAIEMQsAEgi6hXr54+/vhjDR8+3K58xowZdgE7Jb766ivNmTPHoTzhA/2ZM2f0+++/y93dXYcPH36YZju4deuWunfvriNHjhh2zNjYWPXv31/btm2zKz948KAOHjyo7du3a9y4cXJzczPsnOnt5MmTGjJkiE6fPu3w2NWrV3X16lVt3bpV06dP1/jx41N0p3f37t0aM2aM3Zc0MTEx2rp1q20ccqNGjdLU3r1792rYsGEKDg52eOzSpUu6dOmSNmzYoEqVKmn8+PEqXrx4ms5jpKeeesqhzNPT02ndM2fO6K233tKJEyccHku4vjVr1uiZZ57RmDFjlDNnzlS3Z8aMGQ7DBxLcuHFDN27c0N69ezV37lx9/vnneu6551J9jrQIDw/XiBEjnH7RFRoaqtDQUP3111+aNWuWPv74Y3Xs2PGBx7x8+bI6derk8Pt94MABHThwQP/++69D0D979qwCAgIUEhJiVx4bG6vLly/r8uXLOnz4sJYuXaq3335b/fr1S/3FAsBDYh1sAMhC2rZt63Dn6uDBgwoPD0/xMQ4fPuw0XD8qCxYsMDRcS/fubCYN14lt2LBBEyZMMPSc6enChQvq1auX03Cd1MWLF/XKK6+kqO5HH31kF64Ti42N1ahRo+yGIaTUoUOH1LdvX6fhOqmjR4+qd+/eDiEpI5w9e9ahrGLFig5lly9fVq9evZyG66Q2btyoIUOGpOl5TKmoqCi9++67KfqZP6yYmBgNGDAgRb1IoqKi9N5772nVqlUPrDt58uT7tn/q1KkOP5+vvvoqU/zeAMD9cAcbALIQs9msOnXq2H2AtVgsOn78eIq7zu7du9duu3Llyho2bJiKFCmi+Ph4BQcH6/jx49qzZ4/2799vq/fuu+9q0KBB+uGHHzRv3jxbeYECBbRw4UK7YyZMSuVMXFyccubMqcGDB6tBgwaKi4vT33//rbx586ao/c7cuXNHJUuW1DvvvKNixYrp4MGD+uqrr+zG9H7//fcKCAhQgQIF0nyepBImiBo2bJj+/vtvW3mrVq307rvv2tVNTXfVzz77TKGhoXZlL774otq3by83NzetXbtWP/zwg+2xyMhI/fe//9X8+fPve1yr1aq2bduqT58+io2N1bfffmvX/f3SpUs6cOCA6tSpk+K2Wq1WjRw5UtHR0bYys9msfv36yd/fXzExMVqyZInd7+yVK1c0btw4W1f7hLu2L730kl1I79Wrl3r37m3bftjJ4K5evSrpXhA8dOiQpk2bZvf4008/rZo1azrs99lnn9kFu3z58unNN99UtWrVJEk7d+7U+PHjFRMTI0nasWOHli9frq5du6aqfV5eXmrdurUaNWqkwoULK0+ePMqWLZvCw8N14MABTZkyRREREZLufSHyww8/6NNPP5Vk3Oszqfnz59u9D0hSnTp1NGDAAOXLl0979+7VuHHjFBUVZXt89OjRatas2X3nUrBarapcubLeeecd+fr6aunSpfrxxx9tj1ssFq1bt04DBw60lSVdznDYsGFq0qSJcuTIoYiICJ07d04HDhzQH3/8keLrAwCjEbABIItxFhCThrH7STxjsCTbB/oEZcqUUcOGDdW3b1+7u52+vr7y9fV1+NCclkm7xo0bZ9etvVKlSqnaPyl3d3fNmzdP+fPnl3RvHKePj49dF9O4uDitXbtWr7766kOdK7GE0Ozh4WFXniNHjjRPZHb58mX9/vvvdmXt27e3BSlJql69uqKjo7VkyRJbWWBgoE6fPq3SpUsne+yaNWvqm2++sY1HnzBhgurXr29X58SJE6kK2Pv27XO4s9u/f3+99dZbtu06dero5s2bdsFn9erVGjlypHLmzGl7rpLOGO/l5WXohHAvv/xyso81a9bMYcJA6V4oT9pte+rUqbZwLd2bJyEyMtIusC9cuDDVATvhCwdnqlevLovFoq+++spW9tdff9n+beTrM7FFixbZbRctWlRz5861DbeoUKGCfHx8NGzYMFudiIgIrVmzRi+99FKyx82RI4dmz55tC/ujRo1SYGCgTp48aauT9Pcqca+AnDlz6pVXXrGbf6Bq1apq166dJCXbUwMA0hsBGwCyGKvV6lCWmvWwq1SpYrc9ceJEHThwQOXLl1eJEiVUunRplS9fXu7u7mkaR/oglSpVSvWY8Qdp0qSJLVwnaNmypby9vRUWFmYrO3jwoKHnTQ+JQ1OCF1980aGse/fudgE7Yd/7BeyXXnrJ7nfF19fX4TlKzXADyfGuYnLtffHFF+0CdlxcnA4cOGCbRC8jvfrqq3r33Xedvo6cXd8LL7zwwGMeO3ZMt2/fTnZMd3L27t2r1atX6/Dhw7p06ZJu376t2NhYp3UT7sinl6tXr+rChQt2ZZ07d3aYy6BNmzb65JNP7H539u7de9+A/fzzzzvcSS9VqpRdwE64W5+gcuXK2rdvn6R7Abpdu3aqX7++SpQooZIlS6pChQq2LyDT470LAFKCgA0AWYyzca7JzdzrTP369eXv72+7KxcbG6stW7bYjbH08PBQ48aNNWDAAEOXSZKcj3F9WM7u0JnNZhUuXNguPF6/ft3wcxvt2rVrDmXFihVLUZmzfRMrVaqUQ1m2bNnstlM7djjpOd3c3Jx2h09Lex+VOXPm6N9//9WkSZMcZjhPybhyZywWi27cuJHigG2xWPTBBx84nQk8OYm7ZacHZz8fZxPDJbzWEgfstPwuJu0JkvR38e2339arr75qG45w7tw5nTt3zq5OiRIl1KVLF/Xp08fp7PoAkN6Y5AwAshCLxeIwhtrFxSXZZY6SM3nyZH355Zfy8/Nz+iH07t272rRpk3r06KEDBw48VJuTSnqnOTOxWCwOZYnXSc7qvL29HcqSBsrH2ebNmxUUFKQVK1aoXr16Do8lHZP9sBKPS3+Qn376KVXhOqtLy+9i7dq1tWrVKr300ksqUqSI0zrnzp3TuHHj9OabbxrRTABINe5gA0AWsnr1al25csWurHr16sqdO3eqjmM2m9W5c2d17txZcXFxunjxos6fP6+TJ09q6dKltrtCsbGx+u677+zWIn5Y6RHoLl686FBmsVh0+fJlu7LEE6klHe8r3ZssLbGYmBin61ynJ2dfQFy4cMFh7H3SrrvSw08ClhZJ2xsbG6urV6+qUKFCduWZpb1ubm6qWLGipk+frrZt29r9jsyaNUtdunSxa3vS6zOZTFq+fLly5cr1wHOlZkK9NWvW2G3nzp1bw4YNU40aNWzdnVevXq3x48en+JgPy9nv4vnz5x3KnL3W0utnW7x4cX388ceS7i0r+O+//+rcuXMKDAzUL7/8YhtCs3XrVh0/flwVKlRIl3YAQHK4gw0AWURgYKDdRFcJ+vfvn6rjRERE2N1Zc3V1VYkSJdSkSRP17dvXYZ3tpEvpJB1/mZq7dOll+/btDl1SN23aZNc9XLr3ZUQCZwEp6bWuWLEiRd1wjXxO6tat61C2ePHiFJU52ze9Jb0TLKWsva6urqpVq1a6tetBPD097SZik+793JLexU56fVarVdu3b1fRokWT/e/OnTu6cuVKqtZdT/r72759e3Xv3l0VKlSwHTfxTPXJMfJ3sWDBgg5d+5ctW+YwJvzXX391GLufmonyUippd31vb29Vr15dHTp00Oeff+7Qk+dRLGMGAElxBxsAMqGLFy/KarXq9u3bOnPmjDZu3KgNGzY4jEls27ZtqicM27dvn4YPH66mTZvKz89PpUqVko+Pj8xms86fP68ZM2bY1c+RI4fddtLx3jdu3NDixYvl5+dn+3Bv5MzPKRETE6NevXrZlun6+++/7WZblu4Fuueff9627enpqaJFi9rd/Z4+fboKFCigypUrKzAwUGPHjk3R+ZNO1rR7927t2LFDxYsXl8lkkoeHR4rv6BUuXFjNmjWzm0l89erVypEjhzp06GBbpivpBGf16tVTmTJlUnQOI9WuXVvlypXTP//8YyubOXOmpHsTzd29e1dLlizRjh077PZr165dhk9E1bZtW02aNMnu7vqyZcv0xhtv2MaRFypUSM2bN9fWrVttdcaPH68LFy7oueeeU8GCBRUXF6crV67o8OHD2rp1q4KCgjRo0KBUfeHh4+NjN554/fr1tp/ptWvXtHDhQrs2JMfo12ePHj3sXgcXL15Unz59bMt07du3T998843dPl5eXrbZvI30xhtvSLo3qWHlypVVtGhR5ciRQ7du3dK2bdvsJkiTHN+7AOBRIGADQCbk7+//wDotWrTQF198kabjR0ZGas2aNQ7dUp1p3ry53XaNGjUc6iR02UyQdHmd9Obq6qqzZ8/arZmbVO/evR0m32rbtq2mT59u275165bDHXyTyeR05vbEatasafdchoWFqW/fvrbtevXqPXCN6sRGjhypv//+227895IlSxxCdYJcuXI5/AweFZPJpM8//1wBAQG2u6UWi0XTp0+3e24TK1SokN5+++1H2UynXFxc1LdvX/33v/+1lcXGxmrmzJkaNWqUrWzkyJE6fPiwbZI8i8Win376ST/99JNhbXn22Wft5jsICQmxW2ZOutftOvF63M4Y/foMCAjQxo0b7dq2d+9e/ec//0l2n48++ui+a2CnldVq1ZEjRxQUFPTAup6enhnSowMA6CIOAFmMr6+vPvzwQ02bNs1h1l2jPf3003rttdfsysqVK6cWLVqk63lTKyAg4L7djf39/R26A0tSv379VLZs2WT36969u2rXrv3A83fo0EGFCxdOUVtTolixYpo3b57TmZaTKlKkiL7//vsMuXudoFq1avruu+9SNIFdxYoV9cMPP2TI+GtnunTp4tCWn3/+2a7LdtGiRTV//vwUz4Dv4uKS6uvr2bOn0+72CRo0aKBBgwY98DhGvz7d3d01Y8YMhy/anMmePbu++uortW/f3rDzp0W2bNk0duzYDO8hAeDJxB1sAMiETCaTXF1dlS1bNnl5ealAgQIqXbq06tevr2eeeeahlp9p0KCB5s6dq3379unAgQO6cuWKQkNDFRERIXd3d+XPn18VK1ZUmzZt1KpVK6drA0+YMEEzZ87Uhg0bdOHCBYfJwR41T09PzZ8/XwsXLtTKlSt15swZSVKZMmXUrVs3de3a1el1eHp6auHChZo2bZo2btyoq1evytPTU9WqVVPPnj3VtGlTBQQEPPD8uXLl0uLFizVlyhTt3LlTwcHBya5dnFLlypXTqlWrtG7dOm3cuFFBQUEKDQ2V1WqVt7e3KleuLH9/f7Vv3z7dv2hJibp16+q3337T8uXLtXXrVh07dkxhYWFycXGRr6+vqlatqtatW6tVq1aZauZyd3d39enTR19//bWt7O7du5o1a5Y+/PBDW1mpUqX0yy+/aMuWLVq/fr0OHz6skJAQ3b17V56enipUqJDKly+vevXqqUWLFsqTJ0+q2zF79mz98MMPWrVqlc6dOyc3NzeVLFlSHTt21EsvvaSVK1em6FhGvz5z586t6dOn688//9TKlSt14MABXbt2TXfv3lWuXLlUunRpNWjQQN27d0/1dafG+PHj9ddff2n//v06ceKEbty4odDQUMXHxytnzpwqUaKE/Pz81L17d4dJ9gDgUTFZH9TvDQAAAAAAPBBdxAEAAAAAMAABGwAAAAAAAxCwAQAAAAAwwP9j777Do6j6v49/skkIIZCEQOi9bCgJLVRBkIAgikhTQSmiICqooD8ULChiQW71BiKiKL2jAirSkQ4G6b2rhIAQAumEtHn+4Nm9WbOBlE0j79d1eUnOnJk5s3vOzHx3zjlDgA0AAAAAgAMQYAMAAAAA4AAE2AAAAAAAOAABNgAAAAAADuCS1wUoaAzDUGoqrw4HAAAAgMLAZHKSk5NThvISYGdSaqqha9fi8roYAAAAAIBc4OPjIWfnjAXYdBEHAAAAAMABCLABAAAAAHAAAmwAAAAAAByAABsAAAAAAAcgwAYAAAAAwAEIsAEAAAAAcAACbAAAAAAAHKBQvAc7KChIRYsWlaurqyTp888/V61atfK4VAAAAACAe0mhCLAlafr06apUqVJeFwMAAAAAcI/Kt13E//77b40dO1aPPfaY6tWrp65du9rNd/bsWQ0aNEiNGjVS69atNXHiRCUmJuZyaQEAAAAAhV2+fYJ9+vRpbdmyRQ0bNlRqaqoMw0iTJyoqSgMHDlS1atUUHBysy5cva8KECUpISNDYsWNt8g4fPlypqalq166dXnnlFWt3cQAAAAAAHMHJsBe55gOpqakymW49YB89erSOHDmilStX2uT55ptv9PXXX2vTpk3y9vaWJC1ZskTjxo3Tpk2bVLZsWUnSP//8o3LlyikuLk5vvPGG/P399eKLL2apXCkpqbp2LS7rB1aAffTR+1q9emW6y5cvXyVf3zJKTU3Vzz8v04oVyxQWFqqiRd1lNtfRM888p4CAhhnaV3x8nGbP/k6bNm3U1avh8vLylr9/gN555wMVLVpUknT16lV9//0iHTt2RCdOHNeNG/GaMuVrNWnS1GZbly5d1OOPd0t3X48+2l1vvvlOhsoFAAAAoHDx8fGQs3PGOn/n2yfYluD6TrZu3apWrVpZg2tJ6tKli9577z3t2LFDPXv2lCSVK1dOkuTh4aHevXtryZIlOVLme91jj/VU06bNbdIMw9Bnn32icuUqyNe3jCRp6tTJWrJkgTp37qIePXorNjZGP/20TMOHP69p02aoXj3/O+4nNjZWw4c/r/Dwy+rWracqVqykyMjrOnTogBITE60B9vnzf2nBgjmqVKmKataspSNHDtndnrd3Sb377gdp0kNCdmndutVq1qxlVj4OAAAAALCRbwPsjDh37px69eplk+bp6SlfX1+dO3dOkhQfH6/U1FQVL15cycnJWrdunfz8/LK1XxeXfDt0PUc1atRIjRo1skk7cGC/EhIS9NBDXeTiYlJycrJWrPhRQUEdNW7cR9Z8Dz7YST17PqoNG9aqQYMGd9zP9OlTdfnyJc2Zs1AVKlRMN1/9+vW1du0meXl56bffNuitt96Qs7MpzfdTooSHHnkk7Rj+1atXysOjuNq1a1dov1MAAAAAjlOgA+zo6Gh5enqmSffy8lJUVJQkKSIiwjr+OiUlRY0bN9YLL7yQ5X2aTE4qWdIjy+vfa7Zs2SAnJyc9/nhPlSzpoYSEBN28maDy5cvafE5ubpVkMpnk5VX8jp9fdHS0fv31Z/Xv31/165utE9YVKVIkTd7bt+Ph4SZJKlGiaIa+nytXrmjfvj3q3r27ypXzyfDxAgAAAEB6CnSAnRGVK1fWTz/95LDtpaYaio6Od9j2CrLk5CStWrVaAQEN5eFRUtev3xqbXr++v5YtW6bateuqUaPGiomJ0axZ36lECU899NCj1nz2bN++Qzdv3lTp0uX0wgsvaevWzUpNTZW/fwONGjVaZrP93gdxcTclSTExCXfcvsUPPyxXamqqHnjgwQzlBwAAAFA4eXq6F/wx2Bnh6empmJiYNOlRUVHy8vLKsf0mJ6fm2LYLkh07dioqKlIPPviQzWfy7rvjNXbsGL3//v8mDqtQoaKmTftOZctWuOPn9/fff0uSpk0LVoUKlfT22+MUFxerWbO+1bBhQzVv3lKVLl06zXopKanW/2fk+1mzZrVKlSqtRo0C+T4BAAAAOESBDrBr1KhhHWttERMTo/DwcNWoUSOPSlV4rF+/Ri4uLgoK6miTXqxYMVWvXkP+/gEKDGyua9ciNH/+bI0Z83+aOvU7m0np/u3GjRuSJCcnJ02ePE3FihWTJNWu7acXXhikZcuW6vnnX8pWuc+f/1snTx7Xk08+laHJ9AAAAAAgIwp0dNG2bVvt3LlT0dHR1rQ1a9bIZDKpdevWeViye198fLy2b9+i5s1bycvL25qenJysESNeUvHixfXaa2+qXbv26tGjtyZN+kphYRe0cOHcO263SJFbY6nvu+9+a3AtSf7+ASpfvmK6M4Vnxvr1ayRJDz7YJdvbAgAAAACLfBtg37hxQ2vWrNGaNWsUFham2NhY69/Xrl2TJPXp00ceHh4aNmyYtm/frh9//FETJ05Unz59rO/ARs7Ytm2zEhIS1KnTQzbpBw/u17lzZ9WmTVub9MqVq6hq1eo6fPjgHbdburSvJMnHp1SaZSVLllRMTHSa9Mxav36NqlSpqjp16mZ7WwAAAABgkW+7iEdEROjVV1+1SbP8PXfuXLVo0UJeXl6aM2eOxo8fr2HDhlnfcz1y5Mi8KHKhsm7dGrm7F1ObNu1s0q9di5D0vzHRt0tJSVZKSsodt1unTh1JUnj4lTTLrl4NV9Wq1bJY4luOHj2iCxdCNXhw1meSBwAAAAB78m2AXalSJZ08efKu+WrWrKnZs2fnfIFgdf36de3ZE6KOHTuraNGiNssqV64qSdq4cZ1atrzPmn7y5AmdP/+3unXrccdtV6lSTbVqmbV9+xZFRkZax2vv3v27rly5rN69n8xW2f/XPfyhu+QEAAAAgMzJtwH2vc5kcpLJ5JTXxciSzZvXKyUlRV26PCwXF9tRBv7+9dW8eUutXr1S8fFxatGilSIiwvX990vk5uamvn2ftlmnZcsmatw4UNOmfWtNGznydb3yyksaNmywunfvqdjYWC1atEBVqlRV795P2Kw/c+Z3kqQ//zwrSVq/frV1nPazzw62KVtKSop++229/P0DVLVqFcd+KLksNdVQaqqR18UAAAAAcBsnwzC4S8+ElJRUXbuWvfcmm0xO8vEuJqcMvkstv3nyyScVGhqqbdu2ydnZOc3yhIQEzZgxQ6tWrdKFCxfk6uqqpk2b6tVXX1Xduv8b9xwXF6cmTZrokUce0RdffGGzjZ07d2ry5Mk6fvy43N3d1a5dO40aNUq+vr42+fz87L8XW1KaHhDbtm3T4MGD9c4776h///5ZOfR8w0hJ1bXIeIJsAAAAIIf5+Hhk+D3YBNiZ5IgA28XFpJIlPRTzw1qlhF93UMkKnh1/ntLrPy3U/H4vqlZpJqXLKGffkirRu7OuX4/jHd4AAABADstMgE0X8TyUEn5dKZfC87oYeWbPiWPqWKWWqieZCvXnAAAAAODeQICNPDO8Ee8qBwAAAHDvKJiDgAEAAAAAyGcIsAEAAAAAcAACbAAAAAAAHIAAGwAAAAAAByDABgAAAADAAQiwAQAAAABwAAJsAAAAAAAcgAAbAAAAAAAHIMAGAAAAAMABCLABAAAAAHAAAmwAAAAAAByAABsAAAAAAAcgwAYAAAAAwAEIsAEAAAAAcAACbAAAAAAAHIAAGwAAAAAAByDABgAAAADAAQiwAQAAAABwAAJsAAAAAAAcgAAbAAAAAAAHIMAGAAAAAMABCLABAAAAAHAAAmwAAAAAAByAABsAAAAAAAcgwAYAAAAAwAEIsAEAAAAAcAACbAAAAAAAHIAAGwAAAAAAByDABgAAAADAAQiwAQAAAABwAAJsAAAAAAAcgAAbAAAAAAAHIMAGAAAAAMABCLABAAAAAHAAAmwAAAAAAByAABsAAAAAAAcgwAYAAAAAwAEIsAEAAAAAcAACbAAAAAAAHIAAGwAAAAAAByDABgAAAADAAQiwAQAAAABwAAJsAAAAAAAcgAAbAAAAAAAHIMAGAAAAAMABCLABAAAAAHAAAmwAAAAAAByAABsAAAAAAAcgwAYAAAAAwAEIsAEAAAAAcAACbAAAAAAAHIAAGwAAAAAAByDABgAAAADAAQiwAQAAAABwAAJsAAAAAAAcgAAbAAAAAAAHIMAGAAAAAMABCLABAAAAAHAAAmwAAAAAAByAABsAAAAAAAcgwAYAAAAAwAEIsAEAAAAAcAACbAAAAAAAHIAAGwAAAAAAByDABgAAAADAAQiwAQAAAABwAAJsAAAAAAAcgAAbAAAAAAAHIMAGAAAAAMABCLABAAAAAHCAQhVgv/POO/Lz88vrYgAAAAAA7kGFJsDeuXOnkpOT87oYAAAAAIB7VL4NsP/++2+NHTtWjz32mOrVq6euXbvazXf27FkNGjRIjRo1UuvWrTVx4kQlJiba5ImPj9d///tfvfnmm7lRdAAAAABAIeSS1wVIz+nTp7VlyxY1bNhQqampMgwjTZ6oqCgNHDhQ1apVU3BwsC5fvqwJEyYoISFBY8eOteb74osvNGDAAJUsWTI3DwEAAAAAUIjk2wA7KChIHTt2lCSNHj1aR44cSZNn8eLFiouL05dffilvb29JUkpKisaNG6ehQ4eqbNmy2rt3r86fP6933nknN4sPAAAAAChk8m2AbTLdvff61q1b1apVK2twLUldunTRe++9px07dqhnz57au3evjh07pqCgIGueoKAgLVmyRL6+vlkqm4tL9nrWOzvn2575KECoRwAAAED+km8D7Iw4d+6cevXqZZPm6ekpX19fnTt3TpL0/PPP6/nnn7cu9/Pz02+//ZblfZpMTipZ0iPL6wOO4unpntdFAAAAAHCbAh1gR0dHy9PTM026l5eXoqKicmSfqamGoqPjs7UNZ2cTwRGyLTr6hlJSUvO6GAAAAMA9zdPTPcO9Rwt0gJ0VJ0+ezPY2kpMJapD3UlJSqYsAAABAPlKgB3F6enoqJiYmTXpUVJS8vLzyoEQAAAAAgMKqQAfYNWrUsI61toiJiVF4eLhq1KiRR6UCAAAAABRGBTrAbtu2rXbu3Kno6Ghr2po1a2QymdS6des8LBkAAAAAoLDJt2Owb9y4oS1btkiSwsLCFBsbqzVr1kiSmjdvLh8fH/Xp00fz5s3TsGHDNHToUF2+fFkTJ05Unz59VLZs2bwsPgAAAACgkHEyDMPI60LYc+HCBXXo0MHusrlz56pFixaSpLNnz2r8+PHav3+/PDw89Nhjj2nkyJEqUqRIjpQrJSVV167FZWsbLi4mlSzpochpi5VyKdxBJUNh4VzeV94v9tH163FMcgYAAADkMB8fj4I/i3ilSpUyNON3zZo1NXv27JwvEAAAAAAAd1Cgx2ADAAAAAJBfEGADAAAAAOAABNgAAAAAADgAATYAAAAAAA5AgA0AAAAAgAMQYAMAAAAA4AD59jVdAJCb9u3bo1deecHusq+/niV//4A7rh8TE6Ovvpqibds2KSEhQXXr1tfw4SPl51fHmicqKlK//vqzduzYpr///lPJycmqUqWannzyKXXo0Mlmex999L5Wr16Z7v6WL18lX98ymThCAAAA5DQCbAC4Te/efVS3bj2btEqVKt9xndTUVL3xxqs6c+a0+vbtLy8vby1f/oNefnmoZsyYp8qVq0iSjhw5rOnTv1KrVq01YMBzcnZ21pYtv+m9997SX3/9qeeeG2rd5mOP9VTTps1t9mMYhj777BOVK1eB4BoAACAfIsAGgNs0bNhI7dt3zNQ6mzZt1OHDhzR+/ATrukFBD6pv356aMeMbvf/+R5Kk6tVraPHi5SpXrrx13Z49H9eIES9pwYI5euqpAXJ3d5ck+fs3kL9/A5v9HDx4QAkJCerU6aHsHCIAAAByCGOwAeBf4uPjlJycnOH8mzdvlI9PKbVrF2RNK1mypIKCOmr79i1KTEyUJFWoUNEmuJYkJycn3X9/OyUmJurixbA77mf9+jVycnLSgw8SYAMAAORHBNgAcJuPP/5AnTq1U4cOrfXyy0N14sSxu65z+vRJmc1+MplsT6n16tVXQkKCQkPP33H9iIgISZKXl3e6eZKTk7Vp03r5+zdQ+fIV7n4gAAAAyHV0EQcASa6urnrggSC1bNla3t7e+vPPP7V48Ty99NIQff31DJnNddJdNyLiqho2bJwmvVSp0pKkq1fDVbNmLbvrRkdHaeXKn9SwYWOVLl063X2EhOxSVFSUOnXqkskjAwAAQG4hwAYASQEBDRUQ0ND6d5s27dS+fQcNHNhHX389VV98EZzuujdv3pSra5E06UWKuFmX25Oamqpx495VbGyMRowYdcfyrV+/Ri4uLgoKytz4cAAAAOQeuogDQDoqVaqsNm3aaf/+PUpJSUk3n5ubm5KSEtOkJybetC6357///Y9CQnbqzTffUe3a5nS3Hx8fr+3bt6h581Z37EYOAACAvEWADQB3ULZsWSUlJSkh4Ua6eUqVKq2IiKtp0i1ppUv7plk2c+Z0LV/+vV54YbgeeuiRO5Zh27bNzB4OAABQABBgA8AdXLwYpiJF3OTuXizdPLVqmXXq1EmlpqbapB89elRFixa1vgfb4scfl2rmzOl64om+6tfvmbuWYd26NXJ3L6Y2bdpl6RgAAACQOwiwAUDS9evX06SdPn1K27dvVfPmLdLMEH679u076Nq1CG3Z8ps1LTIyUps2bVDr1verSJH/jc/euHGdJk/+TJ06ddHLL7+WoXLt2ROitm0fUNGiRTN5VAAAAMhNTHIGINtMJieZTE55XYxsef/9MXJzK6qAgAYqWdJHf/11TitWLFPRokU1bNgrcnG5FWB/++3XmjFjuqZOna7AwKaSpI4dH9T33y/SJ598oPPn/5KXl7eWLfteqampev75F63rHj16RB9++J68vLzUvHlzbdiwxqYMDRo0VMWKlWzSNm9er5SUFHXp8rB1O/ea1FRDqalGXhcDAAAg2wiwAWSLyeQkH+9icnIu2MHfQw911i+//KIlSxYqNjZWJUuWVKdOnTR8+HBVrVrVms8wkuXk5KTq1SupZEkPa/rMmTM0ceJEff/9Yt28eVMBAQGaOPFTBQTUt+a5ciVMSUlJun79uj78cFyaMnzyySfy9/ezSdu4cZ1KlSqlTp2C5OzsnANHnveMlFRdi4wnyAYAAAWek2EY3NFkQkpKqq5di8vWNlxcTCpZ0kOR0xYr5VK4g0qGwsK5vK+8X+yj69fjlJycevcVcpilPsf8sFYp4Wm7Wd9rBi2arnKeXvrkkSfzuij3BGffkirRu3O+qc8AAAD/5uPjIecMPkziCTYAh0gJv37P/2AUl5So0+GX9E6Tdvf8sQIAACDzCLABIIM8XItoy+Mv5XUxAAAAkE8V7EGTAAAAAADkEwTYAAAAAAA4AAE2AAAAAAAOQIANAAAAAIADEGADAAAAAOAABNgAAAAAADgAATYAAAAAAA5AgA0AAAAAgAMQYAMAAAAA4AAE2AAAAAAAOAABNgAAAAAADuCS1wUAAACOdfz4Ua1evVL79u3VP/9clKenl+rXD9CQIS+qSpWqmdrWnDkz9O2301S9eg3Nm7fUZllqaqp+/nmZVqxYprCwUBUt6i6zuY6eeeY5BQQ0TLOtkydPaObMb3To0EElJt5UhQoV1a1bTz3+eJ9sHS8AAPkFATYAAPeYBQvm6PDhg2rfvqNq1qytiIirWrbsez33XD99880s1ahRK0PbuXLlsubNmyV3d3e7y6dOnawlSxaoc+cu6tGjt2JjY/TTT8s0fPjzmjZthurV87fm3b37d7355kjVru2nZ555Tu7uxRQWdkHh4ZcdcswAAOQHBNgAANxjnnzyab333kdydXW1pnXo0EkDB/bR/PlzNHbs+AxtZ+rUSapfP0ApKSmKioq0WZacnKwVK37QAw900Lvv/m977dt31BNPPKZ169ZYA+y4uFh9+OF7atWqjT788FOZTIxQAwDcm7jCAQBwjwkIaGgTXEtS5cpVVK1aDf31158Z2saBA/u0efNveuWV1+0uT0lJ1s2bN+Xj42OTXrKkj0wmk9zc3Kxp69ev0bVrEXr++ZdkMpl048YNpaamZvKoAADI/3iCDQBAIWAYhq5fv6bq1WvcNW9KSor++9//qGvXx1Szpv3u5G5uRVWvnr9Wr14pf/8GatCgsWJjYzR79ncqUaKEunXrYc27Z89ueXh4KDz8isaMeV2hoefl7u6uzp0f1ssvv2YTjAMAUJARYAMAUAisW7da4eFX9NxzQ++ad8WKH3X58iVNmvTVHfONHTteY8eO0QcfvGtNq1ChoqZNm6GKFStZ00JDQ5WSkqIxY15X166P6YUXhmv//r364YcliomJ0bhxH2f9wAAAyEcIsAEAuMf9/fdf+uKLT+Xv30BdunS9Y96oqEjNmPGNBg4crJIlS94xb7FixVS9eg35+wcoMLC5rl2L0Pz5szVmzP9p6tTv5O3tLUm6cSNeCQkJ6t69l0aMGCVJatcuSElJSfrpp2UaPPgFVa5cxSHHCgBAXspygD1mzBhJUkBAgJ566im7eY4dO6bw8HBJUrt27bK6KwAAkEUREVc1atSr8vAorg8//FTOzs53zP/tt9Pk6emp3r2fvGO+5ORkjRjxkho3DtTIkW9Y05s2ba7+/Z/QwoVz9dJLr0iStQt4x46dbbbx4IMP6aeflunIkUME2ACAe0KWA+zly5fLyclJ8fHx6QbYX3/9tdavXy8nJycdO3Ysy4UEAACZFxsbq//7v1cUGxurqVO/VenSvnfMHxp6Xj//vFyvvPKarl4Nt6YnJiYqOTlZly5dlIeHhzw9vXTw4H6dO3dWL7880mYblStXUdWq1XX48EFrWunSvvrzz3N2J0STpJiYmOweKgAA+UKOdxE3DCOndwEAAP7l5s2bevPNkQoNPa9Jk77K0ORm4eFXlJqaqkmTPtOkSZ+lWf744930+ON99eqrr+vatQhJUkpK2tnAU1KSlZKSYv3bz6+u/vgjROHh4apSpZo13RLEe3vfuSs6Crf4+HgtWjRPR48e0fHjRxUTE6233npPDz/8aIbWP3HiuKZP/0pHjhySYRjy9w/QSy+9otq1/dJdJyYmRn379lRk5HWNHz9B7dt3zPY2ARQOORpgX7lyJSc3DwAA7EhJSdF7743RkSOHNGHC5/L3b5Ch9WrUqKWPP04bWH/77VeKj4/Xq6/+n3XyssqVq0qSNm5cp5Yt77PmPXnyhM6f/9tmFvGgoI6aP3+2Vq78SYGBzazpv/yyQs7OzmrSJDBLx4nCISoqUrNmfauyZcupVq3a2r9/b4bXPXnyhF56abDKlCmrQYOGyDBStXz5Dxo+/Hl9++0cmx98bjdjxte6eTPBodsEUDhkKsC2jLu+3ZEjR+ymX7lyRQcOHJCkNO/iBAAAOefLLydp+/atat36fkVHR2vt2lU2yzt3fliStGrVL/r443HWp4He3t5q2/aBNNtbunShJNksq1Onrpo1a6HVq1cqLi5OzZu3UEREhH74YYnc3Nz0xBN9rXnN5jp65JFu+vXXn5WSkqJGjZpo//692rRpg/r3H3TXruso3EqVKq2fflqjUqVK68SJYxo8eECG1/3uu2lyc3PTN9/MlJeXtySpU6eH1bdvT33zzVR99NF/0qxz7twZLV/+gwYNGqLvvvvaIdsEUHhkKsC2jLu2MAxDFy9e1IoVK+64Xvny5bNUOAAAcpPJ5CSTyenuGfO5M2dOSZJ27NimHTu2pVn+yCO3ZhK/efOGJKlMGV+5uJjS3Z6Tk5OcnJzS5PnPf/6rhQvnaf36tQoJ2SVXVxc1bNhYQ4e+pBo1bLukjxnztsqXL6+VK3/W1q2bVK5ceY0Y8br69Hk6W8ean6SmGkpNZWicoxUpUkSlSpXO0roHDx5QixatrIGwJJUuXVqNGzfRzp3bFR8fr2LFitmsM3ny52rbtr0aNGjksG0CKDxytIu4JRjv0KFDTu4GAIBsM5mc5ONdTE7O6QeaBcXixQszlO/o0UMKCAhQly4PZnF7Hnr99RF6/fURGdrfqFGvadSo1zKUtyAyUlJ1LTKeIDsfSUpKtM5ifzs3t6JKSkrSuXNn5e8fYE3/7bcNOnz4kBYs+F6XLl10yDYBFC6ZDrD/PWnZnSYxM5lM6tSpk15++eXMlwwAgFxkMjnJydmkmB/WKiX8el4XJ8cZhqGQLdv0/kM9FTltcV4Xp8Bz9i2pEr07y2RyIsDOR6pUqaqjRw8rJSXF+oq6pKQkHTt2RJJ09er/5gu6eTNBU6dO0pNPPqXy5SukG2BnZpsACp9MBdhz586VdOuiPHDgQDk5Oalp06ZpAmgnJye5ubmpWrVq8vT0dFxpAQDIYSnh15VyKfzuGe8Bv3YbJEmF5nhR+PTo0VuffTZBEyaM11NPDZBhpGrOnBmKiLgq6dZs+xbz589RcnKy+vcf5LBtAih8MhVgN2/e3OZvwzDk4+OTJh0AAADIa92799bly5e1aNE8rV69UpJUp049PfXUAM2dO1Pu7rfGSl+6dFELF87Va6+9edfx0xndJoDCKctjsC1Ps318fBxWGAAAAMCRhg4dpr59++vPP8+pePHiqlmzlr75ZqokqXLlKpKk7777Wr6+ZdS4caC1a7jlXe+RkZG6dOmiypYtJ5PJlOFtAiicshxg89QaAAAABYGnp6caNmxk/XvPnhCVKVNWVatWkyRdvvyPLlwI1RNPPJZm3c8/nyBJWr16k0qUKJHhbQIonLI1i3hKSorWrFmjnTt36vLly0pMTLSbz8nJSXPmzMnOrgAAAIBs27hxnY4fP6Zhw0ZYn0gPGfKSoqIibfKdO3dG3333tZ56aoD8/RvI3d09U9sEUDhlOcCOjY3Vs88+q8OHD98xn2EYNu/OBgAAADLjxx+XKCYmRlev3ppIbMeOrbpy5bIkqXfvPipevLhWrfpFH388Tm+99Z4efvhRSdKBA/s0a9Z3at68hby8vHT06BGtWvWLWrS4T48/3se6/dufRFsUL15cklS3bj21bfuANT2j2wRQOGU5wJ40aZIOHTokSQTQAAAA+YzJ5CST6d64R1u0aL7++eeS9e8tWzZpy5ZNkqSHH+4qb29P3bx5Q5JUpoyvXFxuPUUuV66sXFxMWrRonuLj41W+fAUNHfqS+vbtJ1dX1zvu09nZZP2/ZXvZ3WZBkppq8Mo5IAuyHGBv3LjRGljf6V3YAAAAyF0mk5N8vIvJyfne6K68Zcvmu+Y5evSQAgIC1KXLg9a0kiXraO7crA1T7NixnU6ePJkmPTvbLEiMlFRdi4wnyAYyKcsBdnj4/96ZOXz4cAUFBalEiRKMOwEAAMhjJpOTnJxNivlhrVLCr+d1cXKcYRgK2bJN7z/UU5HTFud1cQo8Z9+SKtG7s0wmJwJsIJOyHGD7+PgoPDxcDRs21PDhwx1ZJgAAADhASvh1pVwKv3vGe8Cv3QZJUqE5XgD5U5YfN7du3Zqu4QAAAAAA/H9ZDrBffPFFFS1aVEeOHFFISIgjywQAAAAAQIGT5S7i+/btU7t27bR27Vo999xz6ty5swICAuTt7W03f/fu3bO6KwAAAAAA8r0sB9ijR4+Wk5OTnJyclJycrFWrVmnVqlXp5ifABgAAAADcy7IcYN/ubq/r4j3ZAAAAAIB7XbYCbCY5AwAAAADgliwH2LyaCwAAAACA/yHABgAAAADAAbL8mi4AAAAAAPA/BNgAAAAAADhAlruIDxgwIMN5nZycNGfOnKzuCgAAAADuaSdOHNf06V/pyJFDMgxD/v4BeumlV1S7tp9NvtTUVP388zKtWLFMYWGhKlrUXWZzHT3zzHMKCGiYZrsnT57QzJnf6NChg0pMvKkKFSqqW7eeevzxPrl1aIVKlgPs3bt3Z+j1W4Zh8JouAAAAAEjHyZMn9NJLg1WmTFkNGjREhpGq5ct/0PDhz+vbb+eoSpVq1rxTp07WkiUL1LlzF/Xo0VuxsTH66adlGj78eU2bNkP16vlb8+7e/bvefHOkatf20zPPPCd392IKC7ug8PDLeXCUhYND3oNtD4E1AAAAANzdd99Nk5ubm775Zqa8vLwlSZ06Pay+fXvqm2+m6qOP/iNJSk5O1ooVP+iBBzro3XfHW9dv376jnnjiMa1bt8YaYMfFxerDD99Tq1Zt9OGHn8pkYnRwbsjWp2wYRrr/EVwDAAAAwN0dPHhATZs2twbXklS6dGk1btxEO3duV3x8vCQpJSVZN2/elI+Pj836JUv6yGQyyc3NzZq2fv0aXbsWoeeff0kmk0k3btxQampqrhxPYZblJ9gbN260mx4REaE//vhD3333nSIjI9WvXz8NGjQoywUEAAAAgHtZUlKiTXBs4eZWVElJSTp37qz8/QPk5lZU9er5a/XqlfL3b6AGDRorNjZGs2d/pxIlSqhbtx7Wdffs2S0PDw+Fh1/RmDGvKzT0vNzd3dW588N6+eXX7O4P2ZflALtixYrppjdo0EDNmjXTE088oYULF+rhhx9ONz8AAAAAFGZVqlTV0aOHlZKSImdnZ0lSUlKSjh07Ikm6evWKNe/YseM1duwYffDBu9a0ChUqatq0GapYsZI1LTQ0VCkpKRoz5nV17fqYXnhhuPbv36sffliimJgYjRv3cS4dXeGSYx3xGzRooLJly8owDE2fPj2ndgMAAAAABVqPHr0VGnpeEyaM159/ntO5c2f04YdjFRFxVZJ08+ZNa95ixYqpevUa6tnzcX300X/0+uuj/38g/X+KjIy05rtxI14JCQl66KFHNGLEKLVrF6QRI0bpscd6auPGdQoNPZ/bh1ko5NgkZ7GxsYqKipJhGNq/f39O7eauUlNT1bdvXyUkJCg1NVXVq1fXxx9/rOLFi+dZmQAAAADAonv33rp8+bIWLZqn1atXSpLq1Kmnp54aoLlzZ8rdvZikW5OcjRjxkho3DtTIkW9Y12/atLn6939CCxfO1UsvvSJJ1i7gHTt2ttnXgw8+pJ9+WqYjRw6pcuUquXF4hUqWA+wVK1bYTU9OTlZkZKR+/fVXJSQkSJJu3LiR1d1km8lk0owZM6wB9SeffKLvvvtOI0aMyLMyAQAAAMDthg4dpr59++vPP8+pePHiqlmzlr75ZqokWQPhgwf369y5s3r55ZE261auXEVVq1bX4cMHrWmlS/vqzz/P2Z0QTZJiYmJy8nAKrSwH2KNHj77jTOGGYUiSnJycVLVq1Uxv/++//9aMGTN08OBBnT59WjVq1NDKlSvT5Dt79qw+/PBD7d+/Xx4eHnrsscc0YsQIFSlSxJrHElynpqbqxo0bKlasWKbLAwAAAAA5ydPTUw0bNrL+vWdPiMqUKauqVatJkq5di5AkpaSknQ08JSVZKSkp1r/9/Orqjz9CFB4ebvMe7atXwyVJ3t4lHX8AyP4Y7Du9pssSgD/++OOZ3u7p06e1ZcsWVa1aVTVr1rSbJyoqSgMHDlRSUpKCg4M1cuRILV26VBMmTEiT95lnnlGrVq30559/avDgwZkuDwAAAADklo0b1+n48WN6/PG+1ndYV65c1brsdidPntD583/LbPazpgUFdZQkrVz5k03eX35ZIWdnZzVpEpiTxS+0sjUG2/KUOr1lJpNJ/fr104ABAzK97aCgIHXseKtSjB49WkeOHEmTZ/HixYqLi9OXX34pb29vSVJKSorGjRunoUOHqmzZsta8s2fPVkpKij777DMtXLhQQ4YMyXSZAAAAAMDRDhzYp1mzvlPz5i3k5eWlo0ePaNWqX9SixX16/PE+1nx16tRVs2YttHr1SsXFxal58xaKiIjQDz8skZubm554oq81r9lcR4880k2//vqzUlJS1KhRE+3fv1ebNm1Q//6DVLq0b14c6j0vywH28OHD013m6uqqMmXKqGXLlipfvnyWtm/5leZOtm7dqlatWlmDa0nq0qWL3nvvPe3YsUM9e/a0ye/s7KwePXrotddey1aA7eKSvQf/zs45Nnk7CpH8Uo/ySzlQsOWHepQfyoCCL7/Uo/xSDhRs1KPcU65cWbm4mLRo0TzFx8erfPkKGjr0JfXt20+urq42ef/zn/9q4cJ5Wr9+rUJCdsnV1UUNGzbW0KEvqUaNGjZ5x4x5W+XLl9fKlT9r69ZNKleuvEaMeF19+jydm4dXqORIgJ1bzp07p169etmkeXp6ytfXV+fOnZMkXbt2TZLk4+MjwzC0du1a1a5dO8v7NJmcVLKkR9YLDTiIp6d7XhcBcBjqM+4V1GXcS6jPuadkyTqaO3dOBnN76PXXR+j110dkKPeoUa9p1KjXslw2ZE6OvaYrN0RHR8vT0zNNupeXl6KioiRJERERevPNN5WUlCRJqlWrlt55550s7zM11VB0dHyW15du/RrICQvZFR19w+4EF7mN+gxHyA/1mboMR8gPdVmiPsMx8kN9dnJykpdnUTlloHcrYI+Rmqqo6IQ7Dm++G09P9wz36HBIgL1jxw5t2rRJFy5ckCRVqlRJDzzwgNq0aeOIzWdL7dq1tWzZModuMzk57y+cQEpKKnUR9wzqM+4V1GXcS/JDfXZxMcnJZFLMD2uVEn49T8uCgsfZt6RK9O4swzByrS5nK8COjY3Vq6++qp07d6ZZtmDBArVs2VKTJ0+2+5TZETw9Pe2+vy0qKkpeXl45sk8AAAAAuSsl/LpSLoXndTGAu8pyXwvDMDRs2DDt2LHD7uN2wzD0+++/5+hY7Ro1aljHWlvExMQoPDw8zQB/AAAAAAByUpYD7PXr1yskJMT6vmt778E2DEN//PGH1q5d68gyW7Vt21Y7d+5UdHS0NW3NmjUymUxq3bp1juwTAAAAAAB7stxF/JdffrH+u3jx4nriiSdUp04dSdLJkye1dOlSa+D7yy+/qHPnzpna/o0bN7RlyxZJUlhYmGJjY7VmzRpJUvPmzeXj46M+ffpo3rx5GjZsmIYOHarLly9r4sSJ6tOnj807sAEAAAAAyGlZDrCPHDkiSXJzc9OiRYtUq1Yt67JHH31U3bt3V+/evXXz5k1r3syIiIjQq6++apNm+Xvu3Llq0eLWS9jnzJmj8ePHa9iwYfLw8FDv3r01cuTIrB4WAAAAAABZkuUAOyIiQk5OTgoICLAJri1q1aqlgIAA/fHHH9Z3UWdGpUqVdPLkybvmq1mzpmbPnp3p7QMAAAAA4EhZHoPt7OwsSdb3S9tjWWbJCwAAAADAvSrLAXbp0qVlGIaOHDmivXv3plm+b98+HTlyRE5OTipdunS2CgkAAAAAQH6X5S7iDRs2VGhoqJKTkzVw4EB17txZZrNZTk5OOnnypNatW6fk5GQ5OTmpYcOGjiwzAAAAAAD5TpYD7F69emnlypVycnJScnKyVq1apVWrVlmX3/5u7J49e2avlAAAAAAA5HNZ7iLeqlUrdenSxfrOa0nWd2BLsqZ17txZ9913nwOKCgAAAABA/pXlAFuSJk6cqF69ekmyfWJt+XePHj00ceLE7OwCAAAAAIACIctdxCXJ1dVVH330kZ599llt3rxZYWFhMgxDlSpV0gMPPKCaNWs6qpwAAAAAAORr2QqwLWrWrEkwDQAAAAAo1DLcRfzSpUtq0aKFmjdvrscee+yO779OTExUt27d1Lx5c7Vs2VL//POPQwoLAAAAAEB+leEAe+XKlYqKilJMTIwGDBggV1fXdPMWKVJE/fv3V3R0tKKiovTLL784pLAAAAAAAORXGQ6wf//9d0mSm5ubHn300bvm79atm9zc3GzWBQAAAADgXpXhAPvMmTNycnJSnTp1VKRIkbvmd3NzU926dWUYhs6ePZutQgIAAAAAkN9lOMC+fv26JMnHxyfDG7fkvXbtWiaLBQAAAABAwZLp92DHx8dnOq+Tk1NmdwMAAAAAQIGS4QDby8tLhmHoyJEjSkxMvGv+xMREHT58WJLk6emZ9RICAAAAAFAAZDjArlGjhiQpLi5O33777V3zT58+XXFxcXJyclL16tWzXkIAAAAAAAqADAfYzZo1kyQZhqGpU6fq66+/tvsu7KSkJE2bNk1fffWVNa158+YOKCoAAAAAAPmXS0Yz9urVS19//bVSUlKUmpqqyZMna968eWrZsqUqVaokwzAUFhamXbt26fr16zIMQ5Lk7OysXr165dgBAAAAAACQH2Q4wC5fvrz69Omj+fPny8nJSYZhKCIiQqtWrbLJZwmsLROb9e3bV+XLl3dgkQEAAAAAyH8yNYv4G2+8oSZNmsgwDDk5OVkD7dv/uz09MDBQb7zxRk6VHQAAAACAfCNTAXaRIkU0e/Zs9erVyxpES7IG1ZKsQXavXr00Y8YMubq6Or7UAAAAAADkMxnuIm5RpEgRffTRR3rmmWe0atUq7du3TxEREZKkUqVKqUmTJnr44YdVu3ZthxcWAAAAAID8KtMBtkXt2rX16quvOrIsAAAAAAAUWJnqIg4AAAAAAOwjwAYAAAAAwAEIsAEAAAAAcAACbAAAAAAAHIAAGwAAAAAAByDABgAAAADAAQiwAQAAAABwAAJsAAAAAAAcgAAbAAAAAAAHIMAGAAAAAMABCLABAAAAAHAAAmwAAAAAAByAABsAAAAAAAcgwAYAAAAAwAEIsAEAAAAAcAACbAAAAAAAHIAAGwAAAAAAByDABgAAAADAAQiwAQAAAABwAAJsAAAAAAAcgAAbAAAAAAAHIMAGAAAAAMABCLABAAAAAHAAAmwAAAAAAByAABsAAAAAAAcgwAYAAAAAwAEIsAEAAAAAcAACbAAAAAAAHIAAGwAAAAAAByDABgAAAADAAQiwAQAAAABwAAJsAAAAAAAcgAAbAAAAAAAHIMAGAAAAAMABCLABAAAAAHAAAmwAAAAAAByAABsAAAAAAAcgwAYAAAAAwAEIsAEAAAAAcAACbAAAAAAAHIAAGwAAAAAAByDABgAAAADAAQiwAQAAAABwAAJsAAAAAAAcgAAbAAAAAAAHIMAGAAAAAMABCLABAAAAAHAAAmwAAAAAAByAABsAAAAAAAcgwAYAAAAAwAEIsAEAAAAAcAACbAAAAAAAHMAlrwuQ065fv65Ro0YpNDRURYoUkb+/v8aNG6ciRYrkddEAAAAAAPeQe/4JtpOTk4YOHaq1a9fqp59+0s2bNzV//vy8LhYAAAAA4B6TbwPsv//+W2PHjtVjjz2mevXqqWvXrnbznT17VoMGDVKjRo3UunVrTZw4UYmJidbl3t7eatasmSTJZDLJ399fFy9ezJVjAAAAAAAUHvk2wD59+rS2bNmiqlWrqmbNmnbzREVFaeDAgUpKSlJwcLBGjhyppUuXasKECXbz37x5U8uXL1fbtm1zsugAAAAAgEIo347BDgoKUseOHSVJo0eP1pEjR9LkWbx4seLi4vTll1/K29tbkpSSkqJx48Zp6NChKlu2rDVvamqq3nzzTbVo0YIAGwAAAADgcPk2wDaZ7v5wfevWrWrVqpU1uJakLl266L333tOOHTvUs2dPa/q4ceNkMpn01ltvZbtsLi7Ze/Dv7JxvOw6gAMkv9Si/lAMFW36oR/mhDCj48ks9yi/lQMGWH+pRfigDCr7crEf5NsDOiHPnzqlXr142aZ6envL19dW5c+esaRMnTtQ///yjL7/8MkOB+52YTE4qWdIjW9sAHMHT0z2viwA4DPUZ9wrqMu4l1GfcK3KzLhfoADs6Olqenp5p0r28vBQVFSXp1ljuGTNmqEaNGurdu7ck6b777tObb76ZpX2mphqKjo7PeqF16xcUTljIrujoG0pJSc3rYlCf4RD5oT5Tl+EI+aEuS9RnOEZ+qM/UZThCduuyp6d7hp+CF+gAOyNq166tkydPOnSbycl5f+EEUlJSqYu4Z1Cfca+gLuNeQn3GvSI363KBHtTg6empmJiYNOlRUVHy8vLKgxIBAAAAAAqrAh1g16hRw2astSTFxMQoPDxcNWrUyKNSAQAAAAAKowIdYLdt21Y7d+5UdHS0NW3NmjUymUxq3bp1HpYMAAAAAFDY5Nsx2Ddu3NCWLVskSWFhYYqNjdWaNWskSc2bN5ePj4/69OmjefPmadiwYRo6dKguX76siRMnqk+fPjbvwAYAAAAAIKfl2wA7IiJCr776qk2a5e+5c+eqRYsW8vLy0pw5czR+/HgNGzZMHh4e6t27t0aOHJkXRQYAAAAAFGL5NsCuVKlShmb/rlmzpmbPnp3zBQIAAAAA4A4K9BhsAAAAAADyCwJsAAAAAAAcgAAbAAAAAAAHIMAGAAAAAMABCLABAAAAAHAAAmwAAAAAAByAABsAAAAAAAcgwAYAAAAAwAEIsAEAAAAAcAACbAAAAAAAHIAAGwAAAAAAByDABgAAAADAAQiwAQAAAABwAAJsAAAAAAAcgAAbAAAAAAAHIMAGAAAAAMABCLABAAAAAHAAAmwAAAAAAByAABsAAAAAAAcgwAYAAAAAwAEIsAEAAAAAcAACbAAAAAAAHIAAGwAAAAAAByDABgAAAADAAQiwAQAAAABwAAJsAAAAAAAcgAAbAAAAAAAHIMAGAAAAAMABCLABAAAAAHAAAmwAAAAAAByAABsAAAAAAAcgwAYAAAAAwAEIsAEAAAAAcAACbAAAAAAAHIAAGwAAAAAAByDABgAAAADAAQiwAQAAAABwAAJsAAAAAAAcgAAbAAAAAAAHIMAGAAAAAMABCLABAAAAAHAAAmwAAAAAAByAABsAAAAAAAcgwAYAAAAAwAEIsAEAAAAAcAACbAAAAAAAHIAAGwAAAAAAByDABgAAAADAAQiwAQAAAABwAAJsAAAAAAAcgAAbAAAAAAAHIMAGAAAAAMABCLABAAAAAHAAAmwAAAAAAByAABsAAAAAAAcgwAYAAAAAwAEIsAEAAAAAcAACbAAAAAAAHIAAGwAAAAAAByDABgAAAADAAQiwAQAAAABwAAJsAAAAAAAcgAAbAAAAAAAHIMAGAAAAAMABCLABAAAAAHAAAmwAAAAAAByAABsAAAAAAAcgwAYAAAAAwAEIsAEAAAAAcAACbAAAAAAAHIAAGwAAAAAAByDABgAAAADAAQiwAQAAAABwAAJsAAAAAAAcgAAbAAAAAAAHKBQB9tixY3X//ffLz88vr4sCAAAAALhHFYoA+9FHH9Xy5cvzuhgAAAAAgHtYvg2w//77b40dO1aPPfaY6tWrp65du9rNd/bsWQ0aNEiNGjVS69atNXHiRCUmJtrkadasmUqXLp0bxQYAAAAAFFIueV2A9Jw+fVpbtmxRw4YNlZqaKsMw0uSJiorSwIEDVa1aNQUHB+vy5cuaMGGCEhISNHbs2DwoNQAAAACgsMq3AXZQUJA6duwoSRo9erSOHDmSJs/ixYsVFxenL7/8Ut7e3pKklJQUjRs3TkOHDlXZsmVzs8gAAAAAgEIs3wbYJtPde69v3bpVrVq1sgbXktSlSxe999572rFjh3r27JkD5XKSj49Htrbh5HTr/yWe7iolpzigVChUXJwlSV5e7rLTsSPXUZ+RLfmoPlOXkS35qC5L1GdkUz6qz9RlZIuD6rLJ5JTxXWZ9N3nv3Llz6tWrl02ap6enfH19de7cuRzZp5OTk5ydM/4B34mzZ3GHbAeFU0Z+hMpN1GdkR36qz9RlZEd+qssS9RnZk5/qM3UZ2ZGbdTn/tJosiI6OlqenZ5p0Ly8vRUVFWf8ePXq02rZtK0lq27atRo0alWtlBAAAAAAUDgX6CXZGTZgwIa+LAAAAAAC4xxXoJ9ienp6KiYlJkx4VFSUvL688KBEAAAAAoLAq0AF2jRo10oy1jomJUXh4uGrUqJFHpQIAAAAAFEYFOsBu27atdu7cqejoaGvamjVrZDKZ1Lp16zwsGQAAAACgsMm3Y7Bv3LihLVu2SJLCwsIUGxurNWvWSJKaN28uHx8f9enTR/PmzdOwYcM0dOhQXb58WRMnTlSfPn14BzYAAAAAIFc5GUZev93OvgsXLqhDhw52l82dO1ctWrSQJJ09e1bjx4/X/v375eHhoccee0wjR45UkSJFcrO4AAAAAIBCLt8G2AAAAAAAFCQFegw2AAAAAAD5BQE2AAAAAAAOQIANAAAAAIADEGADAAAAAOAABNgAAAAAADgAAfY9LCgoSH5+frpw4UJeFwUocC5cuCA/Pz8FBQVle1ujR4+Wn5+fli1b5oCSISdx3sx/HNkWAUfw8/OTn59fXhcDuSQ3rwvBwcHy8/NTcHBwju8LOYcAGxlSWBo8F83CozAGUgT6gBQSEiI/Pz/1798/r4sC8AMScA9yyesCIOfMnj1bSUlJKlu2bF4XBShwypYtq1WrVsnV1TXb23rttdc0ZMgQlSlTxgElAwAAuYX7aWQWAfY9rEqVKnldBKDAcnV1Vc2aNR2yrTJlyhBcAwBQAHE/jcwiwL6HBQUFKSwsTBs3blSlSpUkSf3799fu3bs1d+5ceXp6aurUqfrjjz8UFxenKlWqqHfv3ho0aJCcnJys27m9y/SXX36pL7/80vp3jx49NGHCBOvfycnJWr58uX7++WedPHlS8fHxKlOmjO6//3698MILKl++vE0ZQ0JCNGDAADVv3lzTp0/X9OnTtWbNGl28eFGlSpXSb7/9Jkk6dOiQ1qxZo927d+vSpUuKioqSp6enGjRooAEDBui+++5L93P4888/NWfOHO3atUv//POPnJ2dVa5cOTVv3lxPPfWUzGazgoODbY7r393Eb/8MV69erSVLluj48eOKjY1V8eLFVapUKTVp0kT9+vVTnTp1MvwdFRaWz/PkyZNaunSpFi9erHPnzsnFxUWNGzfWsGHD1KhRozTrnTlzRqtWrdLOnTsVFham69evy8PDQ3Xr1tUTTzyhhx9+OM06d6tTw4cP15gxY6z5O3ToYLP+3Llz1aJFC124cEEdOnRQxYoVrfXwdjdu3NCSJUu0du1anTlzRjdu3JCvr6/8/Pz0yCOP6NFHH7XmHT16tJYvX65PPvlEPXv2tKZb6t3w4cPVs2dPTZ48WTt37lRUVJQqVKigrl27asiQISpatKjNvpOSkrRq1Spt3bpVR48e1ZUrV5ScnKzy5cvr/vvv15AhQ2x+abcci8WYMWNsPoPhw4fr5Zdftv6dkJCghQsXas2aNTp37pxu3rypChUqqEOHDhoyZIhKliyZ5vMoSO3izJkzmjJlikJCQnTjxg1VqVJFPXr00DPPPJPuOpGRkZo5c6Y2btyoCxcuyGQyqXr16urSpYv69+9v8x2dOHFCjz32mMxms3755Reb7Xz77bf67LPPJEm//vqratWqZV128eJFtW/fXuXLl9fmzZut6be3n7Vr12r27Nk6efKkUlNTVadOHb344otq165dmjLffg04ceKE5s6dqxMnTigqKspazyUpKipKc+bM0caNG3X+/HmlpqaqSpUq6tKliwYNGiR3d/c0205OTtbcuXO1bNky/f333ypevLiaNWumV155RYcOHdKYMWPSXB9ur++31zeL29vuvHnz0v0ubpfZa4PlGihJu3fvtjnX22vru3bt0oIFC3TgwAFFRkaqRIkSatKkiQYPHqzGjRunKc9ff/2lb775RiEhIbpy5YpcXV3l7e2t2rVrq3PnzurVq1eGjis33O0cJ9m/j7g97fjx45o5c6ZOnjwpwzDk7++vwYMH262Pt99/ODs767vvvrN+rh9//LH13JiV849hGPrxxx+1cOFCnT17VkWLFlVAQIBeeuklJSUl2a1Xy5Yts1tPM/P5/Ftmr1mWa4MkhYWFpbn3OHnypM3fR44c0axZs7R3715dvXpVxYoVU0BAgAYMGGD3M79y5YqmT5+ubdu26eLFizKZTPL29la1atXUtm1bPffccxk6rsLOUffTFgkJCfr222/1yy+/6OLFi/L29lbr1q316quvpluG9O4jLO5WnzNyL3y7zF4XUlNT9f3332v58uXWeyJPT0/5+vqqWbNmGjRokPWzKwwIsAup7du3a9asWapSpYpat26t8PBw7d27V59++qkuXbqkt99+25q3R48eOn78uE6cOKE6deqobt261mWBgYHWf8fGxurFF1/U7t27VaxYMfn7+6tkyZI6deqUFi9erDVr1mjWrFmqV69emvLcvHlT/fv319mzZ9W0aVPVqVNHkZGR1uVffPGFQkJCVKtWLdWvX1/u7u4KDQ3Vpk2btGnTJr311lsaOHBgmu3+8ssveuutt5SYmKgKFSqoXbt2Sk1NVWhoqBYvXqxSpUrJbDarbt266tGjh/VC16NHD5vtFCtWTNKtHxiCg4OtgWHZsmUVExOjS5cu6YcfflCtWrXyVSCR33zyySeaM2eOmjRpog4dOujUqVPaunWrdu7cqUmTJunBBx+0yT9r1iz98MMPqlGjhsxmszw9PXXp0iWFhIRo165dOnjwoE2geLv06pQlmFq7dq3i4+PVuXNn6/crSaVLl77rcVy6dEmDBw/WmTNn5O7uriZNmsjb21uXL1/Wnj17dOrUKZsA+24uXLignj17ysXFRU2bNtXNmzcVEhKiL7/8Ujt37tTs2bPl5uZmzR8REaE33nhDJUqUUM2aNeXn56cbN27o+PHjmjdvnn799VctXrxYVatWlXSr/vbo0UN79+7V+fPn1aRJE+sySTZt+vLlyxo8eLBOnTolb29vBQQEyMPDQ8eOHdOMGTO0Zs0azZs3TxUrVrSuU5DaxZ49ezRkyBDFx8ercuXKat26ta5fv67//ve/OnjwoN11QkNDNXDgQIWFhcnHx0ft2rVTUlKSQkJC9Nlnn2n16tWaNWuWvLy8JN0KiEuVKqXTp08rIiJCpUqVsm5r165d1n/v3LnTJsDeuXOnJKlVq1Z2yzFlyhR99dVXaty4sdq1a6dz585p//79Gjp0qIKDg9O0H4tZs2Zp/vz58vf31/33368rV67I2dlZ0q2AYPDgwbp06ZJ8fX0VGBgoFxcXHT58WJMnT9a6des0b948lShRwrq91NRUDR8+XJs2bZKrq6tatGghT09PHT58WL17987VIDKz14b7779fRYoU0fbt21W6dGndf//91mX/Dtw+/fRTzZw5UyaTSf7+/goMDNSlS5e0ceNGbdq0SePHj7c51lOnTqlv376KjY1V9erV1b59e5lMJl2+fFl//PGHLl++nK8C7OyaN2+eZs+eLX9/f7Vv317nz5/X7t27tXv3br3zzjvpjnFfs2aNFi9erBo1aui+++5TVFSUihQpIilr5x9JGjdunBYtWiSTyaSmTZvK19dXp06dUr9+/ezeG+SUzF6zAgMDFR8fr7Vr16pYsWLq3LlzutueM2eOJkyYoNTUVNWtW1cNGjTQ1atXFRISou3bt+vll1/W8OHDrfnDw8PVq1cvXblyRRUqVND9998vNzc3XblyRSdOnNDRo0cJsB0gM/fT0q0f55955hkdOHBAxYoVU5s2beTm5qbt27dr8+bNeuCBBxxexozeC1tk5brw9ttva9myZXJzc1NgYKB8fHwUGRmpCxcuaP78+WrVqlWhCrBl4J7Vvn17w2w2G6Ghoda0fv36GWaz2TCbzcaiRYts8u/cudPw8/Mz6tata1y6dMlm2ZQpUwyz2WxMmTIl3f299tprhtlsNoYOHWpcvXrVZtmsWbMMs9lsdOrUyUhOTram//7779byPProo8aVK1fsbnvz5s3G5cuX06Tv27fPaNKkiVG/fn3jn3/+sVl2+PBho379+oafn58xd+5cIyUlxWb5hQsXjMOHD9ukWcpiz82bN40GDRoYjRo1Ms6ePZtm+YULF4wzZ87YXbews3yuDRo0MHbu3Gmz7NtvvzXMZrMRGBiYpt6EhIQY58+fT7O9s2fPGm3btjXMZrNx8OBBm2UZrVP22sftQkNDDbPZbLRv394mPSUlxejZs6dhNpuNZ5991oiIiLBZnpCQYGzevNkm7c033zTMZrPx448/2qRb2pXZbDZefPFF48aNG9Zlly5dMjp16mSYzWbjs88+s1kvJibG2LBhg3Hz5k2b9MTEROPzzz83zGazMWTIkDTHlF45LFJTU40+ffoYZrPZeOutt4yYmBjrsqSkJGPChAmG2Ww2+vfvb00vSO0iISHBaNeunWE2m42PPvrI5lx0/Phxo0WLFtbv4/Z68fjjjxtms9l44YUXjLi4OGt6RESE0aNHD8NsNhuvvfaazb4s58Off/7Zmmb5rB555BGjTp06xtChQ++6jmH8r/00bdrUOHDggM0ySx3q1KlTmuO11PG6desaGzZsSLP8xo0bRseOHQ2z2Wz897//talP8fHx1vKMHj3aZr05c+YYZrPZaN26tc13npycbHz44YfW8r755pt2y5redcTSdvv162eTnl5bNIysXRvS28/tlixZYpjNZuPBBx80jh8/brNs9+7dRuPGjY369esbf/75pzV99OjRhtlsNr766qs027tx44axe/fudPeXF+70uVrYO09a0vz8/IyffvrJJv+vv/5q+Pn5GfXq1TNOnjxps+z2+4/58+en2VdWzj+GYRgbNmwwzGaz0ahRI2Pv3r02y2bOnGnd57+/7x9//NFuPc3I55PevUJWrlkZ+R62bt1q+Pn5GS1atEhTj06cOGHddkhIiDU9ODjYMJvNxrvvvmukpqbarJOYmJjmWoz0OfJ+2lKPH3roIZtzU3x8vPHiiy9at/nv8+Tdrt/p1efM3gtn5boQFhZmmM1mo23btnbvuc6cOWOEhYXZLfe9ilnEC6lOnTqpT58+NmmtWrVSmzZtlJKSot9//z1T2zt79qx+/fVXlSlTRp999pnNExtJeuaZZ9SuXTv99ddf2rp1q91tjB07Vr6+vnaXtWvXzu4Y1saNG+vpp59WUlKSNmzYYLNs2rRpSkpKUr9+/dS/f3+ZTLbVvWLFivL398/wMcbGxiohIUGVK1dWjRo10iyvWLGiw8bs3quefPLJNE/nBg8eLH9/f8XExOj777+3Wda8eXNVrlw5zXZq1Kihl156SdKtpyHpuVOdyqrffvtNR44cka+vr6ZMmSIfHx+b5W5ubna76t1J0aJFNW7cOJtuxuXKldPo0aMlSQsXLtTNmzety4oXL64OHTpYn/pYuLq66rXXXlOZMmW0bds2xcbGZqoc27Zt0759+1S3bl2NGzdOxYsXty5zcXHRqFGjZDabFRISolOnTkkqWO1i7dq1unTpksqXL69Ro0ZZn+JKUp06dfTCCy+kWWfPnj06ePCg3N3dNX78eJveDj4+Pvrggw8kSatWrdI///xjXWap55an0pK0d+9eJSQkqHPnzqpfv752796t5ORkSbe6uP7+++9ycnJK9wn2K6+8ooYNG9qkDR06VCVKlNBff/2lS5cu2V2ve/fuaYZCSNLy5ct1/vx5tW/fXiNGjLCpT+7u7vrggw9UqlQp/fzzz4qKirIumzt3riTp5ZdftvnOnZ2dNWrUqFydCCgr14a7SU1Ntb4x44svvkjT+6JZs2bWrsdLliyxpkdERFjL9G9FixZVs2bNMlWO/K5Dhw7q1q2bTdrDDz+sTp06KTk5Od1u/i1bttTTTz+dJj0r5x/pf/Wxf//+atKkic02Bw0apICAgCwfY2Zl95qVnuDgYBmGoXHjxqWpR35+ftZrxfz5863plvp4//33p+mm7Orqmu55BpmTmfvphIQE6zljzJgxNudKd3d3jRs3zqa3miNk9l44K9eFq1evSpLq1atn956rZs2aqlChgkOPK7+ji3gh1b59e7vpNWvW1LZt23TlypVMbW/Lli0yDENt27a1uSjernnz5tqyZYv279+fZv+lSpVS06ZN77iP69eva8uWLTp16pSio6OtN6Z//fWXpFvjSyxSUlKsN7ZPPPFEpo4lPT4+PqpYsaJOnjypCRMmqHfv3jbdO3F3/+56b9G9e3cdOXJEu3fvThPkxMXFaevWrTp+/LiuX7+upKQkSbe6v0m23/vtMlKnsmLbtm2SpEcffVQeHh4O2Wbr1q3tXpTat28vb29vRUZG6ujRo2luHk+cOKFdu3bpwoULio+Pl2EYkm7V/9TUVJ0/f97ukIz0bNmyRdKtGwYXl7SXB0v3y1OnTmn//v0ym80Fql1Yxt526dLF7uzwPXr00CeffGJ3nfvvv9/u8AF/f3/VqVNHJ06c0O7du60Bh2Xs7+1dwi3/vu+++5SUlKTDhw/r4MGDCgwM1MmTJ3X16lWZzeZ0hynYO28XKVJElStX1rFjx3T58uU081xISrfbqeX77tKli93lHh4e8vf315YtW3T48GG1adNG//zzj0JDQyVJXbt2tVuezp07W4Oe3JCZa0NGHDt2TFeuXFGVKlXS/RG2efPmkqT9+/db0xo0aKAtW7bo/fff18svv6zmzZs7/GY5P7nT+Xzt2rXWtvNvd6uPmTn/JCcnW7+D9IbldO3aVYcPH77r8ThKVq9Z6bl27ZoOHTqkokWLpnvvZplPYd++fda0Bg0aaOHChfrss89kGIZat27tsGsW/icz99NHjx5VXFycSpYsqbZt26ZZx9fXV61bt87wmP+7ycq9cFauCzVq1JCHh4e2bt2qadOmqWvXrnZ/aCpMCLALKXs3YZKswfHtT8sywnLD9cMPP+iHH364Y95r166lSfv3eKp/W7p0qT755BPFx8enmycuLs7678jISGve6tWr33HbmTFx4kS98sormjVrlmbNmiVvb281aNBArVu3Vrdu3dI8zYSt9MbfWNJvfwIo3XpaPGbMGJvx+P+W3lPau9WprLp48aIk2X1am1V3GpdUsWJFRUZG2nw28fHxeuONN7R+/fo7bjezT7At7Xjy5MmaPHnyHfPe3o4LSruwfIbpfd5eXl4qUaKEYmJirGmXL1++4zrSrRlmT5w4Yc0rSRUqVFC1atX0119/6dy5c6pRo4Z27typ4sWLq2HDhkpMTNQ333yjXbt2KTAw0Cb4Tk96TwDudt5Or+yW7/uNN97QG2+8ke5+pf9935bPsGTJkunerOdU27Mns9eGjLB8LufPn08z6dS/3d4OnnvuOe3du1c7d+7U4MGD5erqKj8/PzVr1kwPP/ywGjRokKly5HeZPZ9bpFc/snL+uX79urXep7fd3Bz3mZ1rVnouXLggwzCUkJBw16fx169ft/77scce044dO/TLL7/o5ZdflrOzs2rWrKnAwEB17tyZJ9gOkpn7acs14k7nSEfW16zcC2flulC8eHF98sknGjNmjCZNmqRJkybJ19dXjRo10v3336+uXbsWuh93CLALqX93Ecmu1NRUSbcmS7rbZEb/7uIoKc0sybc7cuSIxo4dK2dnZ/3f//2fgoKCVL58ebm7u8vJyUlLlizR2LFjrU/vclLTpk3122+/afPmzfrjjz+0f/9+bd++XVu3btWUKVM0depULlrZcPt3ePnyZY0cOVIJCQkaPHiwHn30UVWqVEnFihWTyWTS9u3b7zhBy53qVEH3xRdfaP369apRo4Zef/11BQQEqGTJktauXH369NH+/fsz3SYs7TgwMPCuryWpXbu29d+0C/tatWqlv/76Szt37lTp0qV19OhRPfDAA3JxcVFgYKCKFi2qHTt2aPjw4danDHcKsLN63k7vKarl+07v6fzt/h3c25sZNyPL7sRSnozKqWuDJb+vr6/atGlzx7y3T4zm7u6uWbNm6dChQ9q2bZv279+v/fv3W2d+fuqpp/Tee+9lqix5LbPfye3S+9zTOzdn9fyTUzJbb7J7zbpbOe42Cdq/mUwmffbZZ3rhhRe0efNm7du3T/v27dOiRYu0aNEitW/fXlOnTrUZKoPMc/T9dFZlp63a205mrwudO3fWfffdp40bN2rv3r3at2+f1q9fr/Xr12vKlCmaOXPmXX+wvJcQYMMhLL/gNWnSRGPHjnXottesWSPDMNSvXz8NGTIkzXJLN8DbeXt7y93dXTdu3NCff/6Z5vUD2VG0aFE99NBDeuihhyTd+gVv0qRJWrJkid566y1t2rTJYfu611y4cMFmxmqLsLAwSbfGHVv89ttvSkhI0IMPPqhRo0alWefvv//OuYLegaWunzt3zmHbvHDhQrrLLJ/N7WO1Vq9eLUn673//a/cHLXttIiMsx9ahQ4dM3wgWhHZh+QzT+7yjo6Ntnl7fvo7lV317LMv+Pfb4vvvu06JFi7Rz506VKVNGqamp1gC6SJEiCgwMVEhIiK5fv649e/bI1dU1V8fpli9fXufOnVPv3r2t39vdWI7x2rVrio+PtxmTbmGps/9m6Zaf3hNlS++QjMrKtSEjLOchb29vu6+7uZsGDRpYn1YnJydrw4YNevPNN7Vw4UJ17txZLVu2zFK5HO1u30dSUpK1W7M9Fy5csHv+sXc+z4isnH+8vb1VpEgRJSYm6uLFi3aHp2S1Pqa3Xnpy6ppl+RydnJz08ccfZzqgq1WrlvVzscz18Prrr2vTpk1asWLFPTWzfX5nmS/iTnXLkefPrNwLZ+W6YFGiRAl1795d3bt3l3TrjSvjx4/Xxo0bNX78eJs5Au51+eNnF+R7loZtGdv2b5axJL/99lumu5ffjWUSBXvdI2/evKl169alSXd2drbeyC5dujTD+7rbcdrj4+NjvZhevHjRZjIg2Prpp5/umG4Z1yjd+Xs3DCPN+4Uzy/Jdp6SkZGo9S11fuXLlHbulZsaOHTusE9LcbsuWLYqMjLSOebKwfDb2uplt27bNppvg7e52zJZjswQu2ZEf24UleF2zZo11XOTtVqxYkSbNUie3bdtmncjldseOHdPx48dlMpnSBMctW7aUyWSyvkZHsn1C3apVKyUnJ2v69OmKj49Xw4YN7QasOcXyfVt+sMmI8uXLW+vdr7/+mmZ5YmKi3XOy9L/g/OzZs3aXW8b+ZVRWrg3S3c/zlh4hZ86c0enTpzNVpn9zcXHRQw89ZH0SfuLEiWxtz5F8fHzk6uqqyMhIu+ef7du33/FamN753NKObj+fZ0RWzj+urq5q1KiRJKV7TbBXT6X/1cf0fix1ZH280zXrbvWxbNmy8vPzU1xcnHUOkKyyTKJomT/h+PHj2doeMsff31/FihXT9evXrdeE2129elU7duywu+6dzp+GYdidQDgr98JZuS6kp3z58nrllVckFb66RoCNDLH8gnrmzBm7y+vVq6fOnTvr0qVLGj58uN0nRPHx8fr555/t3qTeiWUG4hUrVtiMXbp586bef//9dJ9GvfDCC3JxcdGCBQu0YMGCNBfssLAwHTlyxCbNcgKzd5xhYWH6/vvv7Y6fskxI4eXlle4kb5AWLVqkkJAQm7TZs2fr0KFD8vDwUO/eva3plu997dq1NpOEpKSkaPLkyTaTC2WF5bvO7A10UFCQ6tWrpytXrujVV19NE8zevHkz0zdmCQkJev/995WQkGBNu3z5svXpWZ8+fWy6+VrGf/97lt5z587dsQvq3Y65Q4cOCggI0KFDhzRmzBi78yVERUVp0aJF1pvBgtQuHnroIZUtW1YXL17UF198YdOl7tSpU5o2bVqadZo2baqGDRsqISFBY8eO1Y0bN6zLrl27Zu2x8/DDD6cZi+fp6an69esrNjZWK1asULly5WxmVLfc+CxYsEBS+u+/zilPPPGEKlasqDVr1ug///mP3e8wPDw8zY3ZgAEDJN16L/ftEzalpqbqiy++SHc2c8sPDtu3b7eZAMswDM2dO1dr167NVPmzem2wXM/+/vtvuz+0uLq6avjw4TIMQ8OHD9eePXvS5ElJSdGuXbt04MABa9qCBQvsBmvh4eHWa01+mkn39h4TkyZNsmkPJ06c0Pjx4++4/vr169MEr2vWrNG6devk4uKifv36Zao8WTn/SP+rj/PmzbP5PqRb745O7/32DRo0UPHixXXmzJk0P66tXr063VnQ05PVa5blh46rV6+mO3Z7xIgRkm7NPG1vAizDMHTw4EGboG3FihVp7nGkW2PALe0vN+dLwK2eXk8++aQk6ZNPPrGpJ/buA25nuT789NNPNveoSUlJ+s9//pPuRH6ZvRfOynXh2LFjWrVqld2yW+prfjr35Qa6iCND2rRpo2LFimnDhg3q27evqlWrJpPJpCZNmli7F3388ceKjo7W1q1b9dBDD6lOnTqqVKmSDMNQWFiYTpw4oaSkJK1atequ4zpu17NnT82dO1fHjh1Thw4d1LRpUzk7O2vPnj1KSEjQgAED7M5Y26BBA3300Ud655139MEHH2jGjBny9/eXYRgKDQ3ViRMnNGzYMJsng506ddLMmTP1zDPPqGXLltZJGf7v//5P0dHReueddzRu3DjrsUm3btKOHTsmJyenNK/+ga0nn3xSAwcOVNOmTVW2bFmdOnVKp06dkrOzsz7++GObmbTbt2+v+vXr6+jRo+rcubOaN28ud3d3HTp0SFeuXNGQIUP07bffZrksnTt3VkhIiEaNGqU2bdrI09NT0q2Jiu40gZnJZNKXX36p5557Tlu3blX79u0VGBgob29vXb58WSdOnJCnp2emZgHt3r27Nm/erI4dOyowMFA3b95USEiI4uPj1bhxY+svwBbDhw/XK6+8osmTJ2v16tWqXbu2IiIitHfvXgUGBqpMmTJ2b+Y6duyoqVOnat68eTp9+rTKlSsnk8mkoKAgdejQQSaTSVOnTtXQoUO1fPlyrV27Vn5+fqpQoYKSkpIUGhqqU6dOKSUlRT179pSLi0uBahdFixbVZ599pueff14zZ87Uhg0bFBAQoMjISO3evVvt27fX0aNH03TR+/zzzzVw4EBt3LjReg5KTk5WSEiIYmNjVb9+/XSHxtx33306fPiwbt68mWZ8db169ayzxFvy5qZixYrpm2++0dChQ/Xdd99p6dKl8vPzU9myZZWQkKC//vpLZ8+eValSpWxmoO3fv7927NihrVu3qlu3bmrRooU8PT11+PBhXblyRU899ZQWLlyYZqb28uXLq1+/fpo7d66eeeYZa7s5ceKELl26pOeff17Tp0/PcPmzem2oUKGC/P39deTIET366KPy9/eXm5ubSpYsqf/7v/+TJPXr108XL17UjBkz9PTTT6t27dqqUqWKihYtqvDwcJ04cULR0dF6//33rU9Qly5dqg8++ECVKlVS7dq1Vbx4cWv3/4SEBLVs2VJBQUFZ+KZyzogRI/THH39o6dKl2r17t/z8/HTlyhUdOXJEXbt21e7du9PtsjpgwAC99tprmjVrlqpWrarQ0FBrMPvmm2/edT6Wf8vK+UeSHnzwQT355JNasmSJnnrqKes58NSpUzp79qyeeeYZzZ49O019LFq0qF5++WV98sknevPNN7V48WKVKVNG586d05kzZ/Tiiy/qq6++ynD5s3rNcnV1VVBQkNauXavu3btb52eQpI8++kjSrR923377bX366ad68cUXVbVqVVWvXt1ax06cOKGIiAgNGTLE2lti3bp1evPNN1WmTBnVrVtXnp6eio6O1r59+xQTEyOz2azHH388U98Rsu+VV17R3r17dejQIXXu3FktWrSQm5ub9u7dq6SkJHXv3t1ub6rAwEB16NBBGzduVK9evRQYGCg3NzcdO3ZMsbGxDrsXzsp14eLFixo5cqSKFi2qevXqqXz58kpOTtapU6f0559/ytXV1e6wiXsZATYypHTp0vr22281depUHT16VAcOHFBqaqpSUlKsAXbx4sU1c+ZMrVq1Sj///LOOHj2qEydOyMPDQ2XKlNGjjz6qDh063HXykn/z9PTUDz/8oODgYOvESd7e3mrdurWGDx+uvXv3prtu9+7d5e/vr1mzZun333/Xpk2b5ObmprJly+rpp59O8xqCESNGyGQyaf369dqwYYP16caLL76oypUr66233tIff/yh06dPW59SlilTRt27d1f//v0z9V7twuitt95S9erVtWTJEh0+fFguLi66//779dJLL6V5BZWLi4vmzZun6dOna+3atdq1a5eKFy+uxo0ba8qUKYqLi8tWgN23b1/FxcXp559/1pYtW6xDG7p163bXGcIrVqyoH3/8UQsXLtTatWu1f/9+JSUlydfXV82aNUv3dTHpqVSpkn744QdNmjRJv//+u6KiolShQgV17dpVQ4YMSTMpUKdOnTR//nx9+eWXOnHihEJDQ1W5cmUNHz5czz77bLrjF+vUqaPg4GDNmDFDBw8e1K5du2QYhsqVK2d9T3LZsmW1dOlSLVu2TKtWrdLJkyd1+PBheXl5qUyZMurTp4+CgoKsT9QLWrto3ry5li5dquDgYO3evVvr169X5cqV9corr+jZZ59Vp06d0qxTuXJlLVu2zBqUb968WSaTSdWrV1eXLl00YMCAdCduatWqlb755htJaQNoJycntWzZUmvWrFHx4sXzZJbp2rVr6+eff9bixYu1YcMGnTx5UgcOHJC3t7fKlSunZ599Vg8++KDNOs7Ozvrqq680Z84cLV++XCEhIfLw8FDTpk01depU6+z2t08AZvHWW2+pQoUK+v7777V//355eHiocePGmjRpkmJjYzMVYGfn2hAcHKzPP/9cISEhWr16tZKTk1WxYkVrgC3dmkW3Y8eOWrhwofbt26dt27bJ1dVVvr6+at68uR544AGb+jJy5Eht3rxZBw8e1MGDBxUTE6NSpUqpQYMG6tWrlx555BG7r5/KSw0bNtT8+fMVHBysAwcO6J9//lG1atX01ltvqW/fvnbfn24xYMAANW7cWHPmzLH+oNi0aVMNHjw43VcX3U1mzz8W48aNU0BAgBYtWqSDBw/Kzc1NDRo00HvvvWftyWCvPj7zzDPy9va2/lBz+vRp+fv766233lKVKlUyFWBn55r1wQcfyNvbW9u2bdPatWut9x6WAFu69Xm3bNlS8+fPV0hIiHbt2iWTyaTSpUurbt26aerjs88+q0qVKmn//v06duyYIiMj5e3trVq1aqlr167q2bNnrg5JwS3FihXT3LlzNX36dK1cuVLbt2+Xl5eXWrVqpREjRmj58uXprjtp0iR99dVXWrlypXbv3i1PT0+1atVKr776qt2eNhaZvRfO7HWhYcOGev3117Vnzx6dPXtWx48fl7Ozs8qVK6enn35a/fr1c+ibVwoCJyM3pl4GUKhZZo48efJkHpckfwkODtaXX36p4cOH6+WXX87r4gAOMWDAAIWEhCg4ONjuDxYo2IKCghQWFqaNGzfm6iuwsmrMmDFatmyZRo8erUGDBuV1cQAUAozBBgAAmXL8+HElJibapCUmJio4OFghISEqVaqU2rVrl0elQ2Fz+vTpNJNOpqamaunSpVq+fLnc3Nz0yCOP5FHpABQ2+auvEgAAyPc+/vhjHT9+XHXq1JGvr6+io6N18uRJhYeHy83NTRMmTEj3/duAo82YMUOrV69W3bp1VbZsWd24cUNnzpxRWFiYnJ2d9d5771lfkQQAOY0AGwAAZMrjjz+uX375RSdPntShQ4dkGIbKlCmjXr166dlnn7X7PmIgp3Tp0kWxsbHWuV+Sk5NVqlQpPfzwwxo4cKB1IjoAyA2MwQYAAAAAwAEYgw0AAAAAgAMQYAMAAAAA4ACMwc6kY8eOqVevXnrqqaf07rvvWtNDQkI0YMCANPmLFSumypUrq23btho0aJBKlSqVm8V1uPz0WiHLq59u5+bmpjJlyqhZs2YaOHCg6tSpkwclcxxLvWrevLnmzZuX7e3FxMSoY8eOqlKlipYuXSonJycHlPLOaDP5u804OTnJw8ND1apVU6dOnTRgwAC5u7unyde/f3/t3r3bJs3V1VXe3t7y9/fXE088oaCgIJvlX331lSZPnqzp06fnuxmlM1MvXVxc5OHhoZIlS8rPz09NmjTRo48+WuDrZkZZ6vDtTCaTSpQooVq1aqlLly7q06ePXF1d86iEjmGp43PnzlWLFi2yvb3crP/U54yjPmdNfj6fZxbtJePstRfp1vW/dOnSatSokQYMGKAmTZqkyZOV+7y8uE/NCQTYmTR+/HgVLVpUL730Urp5evToIUkyDEMXL17UgQMHdPLkSS1fvlxz585VzZo1c7ycBe09ldnRpk0b+fr6SpIiIiJ0+PBhLVu2TL/88osmTpyohx9+OMfLMHr0aC1fvlyffPKJevbsmeP7y6oSJUro+eef18SJE7VixQprXc1JtJn85/Y2k5ycrH/++Uf79+/XkSNH9PPPP2vBggXy9va2u26dOnVUt25dSVJ8fLyOHTumTZs2adOmTerfv7/eeecda95nnnlGCxYs0Mcff6z77rsvX92wZrZexsbG6tKlS/rtt9+0du1affbZZxo8eLCGDRuWr44rJ5UuXVr333+/JCkpKUl//vmn9u7dq7179+rXX3/VzJkzVaxYsRwtw4ULF9ShQwdVrFhRv/32W47uK7tys/5TnzOP+pw5+fl8nlm0l8y7vb1IUnR0tE6cOKHVq1drzZo1Gjt2rJ566ql018/ofV5e3KfmCAMZtnr1asNsNhuffvppmmW///67YTabDbPZnGbZuXPnjLZt2xpms9l46qmncqOoRvv27Q2z2WyEhoY6dLsRERHGmTNnjIiICIduNyssn/fvv/9ukx4dHW0MGjTIMJvNRpMmTYzIyMgcL8ubb75pmM1m48cff3ToduPj440zZ84YYWFhDttmQkKC0bJlS6N169bGzZs3HbZde2gzBaPNGMatz7xFixaG2Ww2JkyYkGZ5v379DLPZbEyZMsUmPSUlxfj888+t2965c6fN8hkzZhhms9mYM2eOYw8mG7JaLw3DMKKiooypU6ca9evXN8xmszFixAgjNTU1p4ucp6ZMmWKYzWajX79+aZZt3LjRqFu3rmE2m41JkybleFlCQ0MNs9lstG/f3uHbDgsLM86cOWPEx8c7bJu5Uf+pz5lDfc66/Hg+zyzaS+bcqb0kJycbH3/8sWE2m42AgIA09zlZvc/LzfvUnMIY7EyYPXu2JKl3796ZWq969ep69dVXJUl79uzRlStXHF20XOPj46OaNWvKx8cnr4uSrhIlSuiDDz6QJMXGxmr79u15XKKsc3d3V82aNVWhQgWHbdPNzU1du3ZVeHi4Vq1a5bDt2kObKRhtRrr1mT/55JOSpF27dmV4PZPJpFdffVWVK1eWJK1evdpmeffu3eXq6qp58+bJyCcvrchqvZQkT09PvfTSSwoODpaTk5NWrVqln376ycElLDiCgoLUrVs3SWm/+4KmQoUKqlmzpt0hElmVG/Wf+uw41Oc7y4/n88yivTiOs7OzXnvtNTk7O+vmzZvat29fhte9031ebt6n5hQC7Aw6duyY9u/fr0aNGqlGjRqZXr9+/frWf1+8eFGStGzZMvn5+Wn06NGKjIzURx99pI4dO8rf31/9+/e35k9OTtaiRYvUp08fBQYGKiAgQJ06ddKHH36oy5cv2+zHss2wsDBJUocOHeTn52f9LyQkxCb/5cuX9cknn6hLly5q2LChGjdurF69emn+/PlKTk5OcxzBwcHy8/NTcHCw3f2OHj1a8fHx+vzzz/Xggw/K399frVu31ptvvpmmrDmpUqVK1i6uls/iwoUL8vPzU1BQkFJSUjRr1ix1795djRs3TjM2ddu2bRo6dKhatWolf39/tWnTRiNGjNDhw4dt8lm2uXz5cknSmDFjbD7vf39OCQkJmjlzpp544gk1bdpUAQEB6ty5syZOnKjr16+nOY6QkBD5+fnZ1Id/H4thGFqyZIl69uypRo0aKTAwUM8++6z279+f7udj6ca+YMGCDHyaWUObuaWgtBlJ1m7jKSkpmVrP2dnZ2m3c8jla+Pj4qG3btjp//ry2bt3qmIJmQ3brpUX79u3VuXNnSdKMGTNslt3ebm/cuKHJkydb64tlnPrtbTg9QUFB8vPz04ULF9IsCwsL0+jRo9W6dWtr/Z4yZYpu3ryp/v372627OcXSVm//7kePHi0/Pz8tW7ZMp06d0ogRI9SmTRvVrVvXpi1ERkbqiy++0COPPGJtTz179tS3336rhIQEm/2MHj1aHTp0sO7r9nZqb36BI0eO6PXXX9cDDzwgf39/NW/eXM8995y2bNli9zjS+9xuP5bQ0FCNGjVKrVu3lr+/vzp27Kj//ve/SkxMtLvNnK7/1GfHoz7nXX3OabQXx3Nzc1OJEiUkZf7ewd59nkVu3KfmJMZgZ9CGDRskSa1atcrS+rGxsdZ/FylSxGbZ9evX1atXL8XExCgwMFD169e3julITEzU0KFDtXPnTrm5ualFixYqXry49u/fr3nz5mnlypWaMWOGtZJWqVJFPXr00Nq1axUfH6/OnTvbjCEqXbq09d9//PGHhg0bpqioKFWsWFH33XefEhMTdfjwYY0fP16bNm3S119/nanxJTExMerTp48uXbqkwMBA1a5dWwcOHNCKFSv0xx9/6KeffrI2xJyUmpqq+Ph4SWk/b8MwNHz4cG3btk1NmzZVzZo1dfr0aevySZMmadq0aXJyclLjxo1VoUIFnT17VqtXr9a6dev0wQcfWH/5LFasmHr06KG9e/fq/PnzatKkiapWrWrdliXokG4FZoMHD9apU6fk7e2tgIAAeXh46NixY5oxY4bWrFmjefPmqWLFipk61jFjxmjlypUKDAzUAw88oOPHj2vHjh36448/NH/+fDVs2DDNOnXr1pWPj48OHTqkK1euqEyZMpnaZ0bQZjImv7QZSTp06JAkqVatWple1/J9/fu7kqTWrVtr48aN2rhxY55PjpPdenm7bt26ac2aNTp16pTCw8OtP1BYWG52zp49q6ZNm6pOnTqKjIzM9n7PnDmjfv366fr16ypTpow6dOigGzduaNasWfr999+Vmpqa7X1kxp2++/379+u9996Tr6+vmjZtqoSEBHl4eEiSQkNDNXDgQIWFhcnHx0ft2rVTUlKSQkJC9Nlnn2n16tWaNWuWvLy8JEmBgYGKj4/X2rVrVaxYMesNrj1z5szRhAkTlJqaqrp166pBgwa6evWqQkJCtH37dr388ssaPnx4po7z+PHj+uijj+Tl5aVmzZopKipK+/bt09dff60zZ85o6tSpdtfLyfpPfXY86nPe1eecRntxvNDQUOtxZfbe4U73eblxn5qTCLAz6Pfff5ckNW7cOEvrWxq1m5tbml/NNm/erFatWunLL79U8eLFbZZNmTJFO3fuVJUqVTRr1izr5EtJSUl6//339cMPP+iVV17R6tWrVaRIETVt2lRNmzbV7t27FR8frzfeeMPuhE3h4eEaPny4oqOj9d5776lPnz4ymW51aLh+/bpGjBih7du365tvvsnUSXvDhg1q06aNFi5caD2WqKgoDRw4UMePH9fChQs1dOjQjH9wWbR161brL7D/nkn84sWLSk1N1S+//KLq1aunWW/atGlyc3PTtGnT1Lp1a+uy77//Xu+8847ef/99NWzYULVr15aPj48mTJig0aNH6/z583r88cftTnJmGIZGjBihU6dOqXfv3hozZoz180lOTtbnn3+umTNnasyYMZo7d26GjzMsLEy7d++2OZaUlBS9++67+vHHHzVlypQ0v65aNGrUSL/99pt27dqlxx57LMP7zCjaTMaPMy/bjGWSsxUrVujnn3+Wu7u7nnvuuUxt48qVKzp48KAk2x+VLCx1IDNdz3NKduvl7fz9/a3/PnPmTJobrIMHD8rPz0/r1q1Lsyw73njjDV2/fl2PPPKIJkyYYL0xuXz5sgYOHKg///zTYfu6G8MwrJMz2fvuly5dqueff14jR460theL119/XWFhYQoKCtLnn39u/WHr2rVrGjx4sI4ePaoPPvhAn3/+uSTp8ccfV6tWrbR27VqVLFlSEyZMsFumbdu26ZNPPpG3t7eCg4PVrFkz67KTJ0/q+eefV3BwsJo3b67mzZtn+Fjnzp2rF154Qa+88oqcnZ0lSadOndKTTz6pDRs2aP/+/XbrVU7Wf+qzY1Gf87Y+5zTai+PExMToyJEj+vTTTyVJDz/8cKYnpL3TfZ6U8/epOYku4hl0/PhxScpUlxLj/8+UN23aNGvQ9MQTT6ho0aI2+VxdXTV+/Pg0gcLNmzetXSPGjBljc9Pv6uqqd955R6VLl9aFCxe0du3aTB3PnDlzFBkZqaefflpPPfWUzYWiZMmSmjhxolxdXbVgwYJMjbMpVqyYPvnkE5tj8fLy0vPPPy9J2rlzZ6bKmVnXrl3TypUr9dZbb0m6dYG0d8EZOXJkmuBakmbOnClJeuqpp2yCa+nWxbB9+/ZKSkrKVBAs3bpA7tu3T3Xr1tW4ceNsPh8XFxeNGjVKZrNZISEhOnXqVKa2/c4779gci7Ozs0aOHClJ2r17t5KSkuyuV7t2bUm3ukzlBNpMxuRFmxkwYIC1G2L9+vXVoUMHBQcHq3Xr1lqyZInNjcOdxMfH648//tALL7yguLg4FStWTI8//niafJZftc+fP2/zi3VeyEq9TE/JkiWt/7Y3xEOSxo4d69Cbqz179ujo0aMqVqyYxo4da/Orf9myZTV69GiH7etOkpKSdObMGb3++uvWng8DBw5Mk69atWoaMWJEmmBkz549OnjwoNzd3TV+/HibXiM+Pj7WeTRWrVqlf/75J1NlCw4OlmEYGjdunE0wIsk6LEOS5s+fn6nt1q9fXyNGjLAGI5JkNputY3bTa6s5Wf+pz45Bfb4lr+tzTqO9ZN3u3btthjA0bdpUzzzzjEJDQzVmzBh99tlnGdpORu/zpJy/T81JPMHOgPj4eGt349sbVHrsjZ2RpK5du+qNN95Ik163bl3rBEG3O3z4sOLj4+Xt7W13nIa7u7sefvhhzZ07VyEhIXr00UfvWjYLy5idLl262F1etmxZVa1aVWfOnNFff/1lNxi1x9/f3243DsvJLCfGlNp7x5506+IRHByc5kIoyW5XrOTkZOsEDem9FqB3797atGlTpse2WD7vTp06ycUlbbMzmUxq2rSpTp06pf3798tsNmdouy4uLjavTbDw9fWVl5eXoqKiFBkZafcEbxmjfvXq1UwcScbQZvJ3m7n9NV2GYejq1as6fvy4dULATz/91KZr/O2+/PJLu+/ELFWqlD7//HOVL18+zbIiRYqoWLFiio+P19WrV9P8MJJbMlsv7+b2rnj23tVZqlQpNW3aNNv7uZ3lXeT333+/3VepPfDAA/L09FR0dLRD92vZt7226urqqtdff10PPvhgmmUdO3a0uYG/fVvSreOwV9f8/f1Vp04dnThxQrt377be9N/NtWvXdOjQIRUtWlTt27e3m8fyTuDMTMgj3Rp3ae97tjy1Sa+t5lT9pz5nf9/U5/xTn3Ma7SV7/v2aroSEBF24cEGHDx/WtGnTVLx48TtOHJfZ+zwpZ+9TcxoBdgbExMRY/20Za3MnluDMyclJRYsWVaVKlXT//fenGzSlN+bWMqPencbkVqlSRVLmb8JDQ0MlSU8//fRd8167di3DwYK9m2tJ1hNwehNnZMftwUKRIkVUpkwZBQYGqmXLlume9OzNqhkZGambN29KUrrvQbYEdVn9vCdPnqzJkyffMe+1a9cyvF1fX990x/sWL15cUVFR1mOyt1xSjpyIaTP5u808//zz1psyi4SEBL333ntasWKFnnvuOS1btszujeTt78F2cXGRl5eX6tevr6CgILu/QFsUL15c8fHxOVLfMiqz9fJubn9qYe9mJ7PzKWSE5enXnbZdoUKFHL/BcnJyUvHixVWrVi0FBQWl+5QmvXJa2t+d3jlfpUoVnThxIlNt9cKFCzIMQwkJCQoICLhj3vSeOqUnO201J+o/9Tl7qM/5qz7nNNpL9tSoUcPuMIZDhw5p4MCBevvtt1W8eHE99NBDdtfP7H2elLP3qTmNADsDPD09rf+Oi4u766916Y2jSc+dbkpziuWXt39P6GSPvRNHeuw9Lc5p9oKFO8nLzzswMNAa4KXH0iUmI7LzeVsuNrfXb0ehzXhneLt50WbsKVq0qN5991399NNPOnHihLZt26YHHnggTb6OHTvq5ZdfzvT2LV0Jc6K+ZVRm6+XdHD161PpvezcJ2a2nd5qsxt6PhxlZlh3p3WDdSW63VcvwjLtNGpUV2WmrOVH/qc/ZQ33OX/U5p9FeckaDBg305JNPatasWfr222/TDbAz29aknL1PzWkE2Bng7u5u7Q5z/fr1XOsOY+k2+u/X3tzO8lStbNmymdp2+fLl9ddff2nIkCF3/VW0sPD29laRIkWUmJio0NDQNJOjSdn7vKVbr4DK7ARSOcUy62N6XYGzgzZTMBUvXlze3t66fv26zp49azfAzorExERr17ycqG8Z5eh6+fPPP0u69VS/VKlSmVrX0vMkLi7O7vKkpCSFh4enSbfU2zvV8X+/7iQ/shyHpT3ak5W2Wq5cOUm3bjI//vjjfPEDVk7Vf+pz/kF9zv9oLznH0rvz3LlzDt1uTt6n5rS8b6kFRL169SRJZ8+ezbV9BgQEqFixYoqMjNTGjRvTLE9ISLC+gP3fT3AtjTe9d9JZukWtXr3akUUu0FxcXBQYGChJ1vda/9uPP/4oKfOfd9u2bSVJa9asydQEWDnJ8mqy299D6Ei0mYInJibGekG721P6zLBM3Fe1atU8H6/nqHq5efNmrVu3TpI0ePDgTK/v4+MjV1dXRUZGKiIiIs3y7du3232vumWSo23btikqKirN8i1btthNz28sk09u27bN7vi6Y8eO6fjx4zKZTDYTO1naqb3PRrp1A+rn56e4uDht27YtB0qeeTlZ/6nP+QP1uWCgveSM8+fPS3LsfYOU8/epOYkAO4MsN+P79+/PtX26ublZx3t++umnNr9YJSUl6aOPPlJ4eLgqVaqUpuuQ5Veu29/vfLvBgwfL09NTs2fP1syZM+2OtQkNDdVPP/3kqMNJ1+jRo21mwsxLgwYNkiQtWrQozSsoli1bpt9++02urq5pJla72+fdoUMHBQQE6NChQxozZozdcdZRUVFatGhRuhdaR7PU5ZYtW+bI9mkzOScn2kxCQoI+/PBDGYYhV1dX649CjpDTdS0zslsvo6OjNW3aNA0fPlyGYahr167q2rVrprfj6upqvVmaNGmSTXfAEydOaPz48XbXa9asmerUqaO4uDiNHz/eph5evnzZ+soUey5cuGCdAfbChQuZLrMjNW3aVA0bNlRCQoLGjh2rGzduWJddu3ZNY8eOlXTr1S+3jxW13JhevXo13XfKjhgxQtKtNwlYXrl0O8MwdPDgQeukfjktJ+s/9Zn6fC/V55xGe3F8ezl06JCWLl0q6da9riMV5LpGF/EM6tixo6ZOnaqdO3daX4GUG1555RUdOXJEu3bt0sMPP6wWLVrIw8NDBw4c0MWLF+Xt7a3JkyeneUF7586dFRISolGjRqlNmzbW8QvPPfecatSooXLlyumrr77Syy+/rE8//VTfffedateuLV9fX8XGxurs2bM6f/68GjZsmOPvnrOcWOzNrp3b2rVrpxdffFHTpk3ToEGD1KRJE5UvX15//vmnjh49KmdnZ73//vtpxklb6se8efN0+vRplStXTiaTSUFBQerQoYNMJpOmTp2qoUOHavny5Vq7dq38/PxUoUIFJSUlKTQ0VKdOnVJKSop69uyZ45/FsWPHFBkZqQYNGtidwdoRaDM5J7ttZvr06dZeGoZhKCIiQseOHVNERIRMJpPeffddh07QYnndi6MvvlmRmXpp+QHDMAzFx8fr4sWLOnnypJKSkuTq6qphw4bpxRdfzPKYtxEjRuiPP/7Q0qVLrTMaX7lyRUeOHFHXrl21e/fuNF0BnZyc9J///Ef9+/fXL7/8ot27d6tJkyZKSEhQSEiI6tSpo8aNG2v//v1pJkC8vfdMepMj5qbPP/9cAwcO1MaNG9WhQwc1bdpUycnJCgkJUWxsrOrXr28NTCxcXV0VFBSktWvXqnv37goMDLSOdfzoo48kSUFBQXr77bf16aef6sUXX1TVqlVVvXp1FS9eXNevX9eJEycUERGhIUOGqE2bNjl+nDlZ/6nPt1Cf7436nNNoL7dkpb2cO3fO5kf9hIQEhYWFWV9rV6dOHYfe6+XGfWpOyvuIpoCoV6+etdKePXs20y9Tz6oiRYrou+++09KlS/XTTz9pz549SkxMVPny5dW/f38NGTLE7nievn37Ki4uTj///LO2bNlinUm6W7du1tf/NGvWTL/++qvmz5+vLVu26PDhw0pMTFSpUqVUvnx5devWTZ06dcrxY7RMFHGn6f1z04gRI9SkSRPNnz9fBw8e1MGDB1WyZEk99NBDeu6559SgQYM069SpU0fBwcGaMWOGDh48qF27dskwDJUrV856ESpbtqyWLl2qZcuWadWqVTp58qQOHz4sLy8vlSlTRn369FFQUJDc3Nxy/BiXLVsmKWMzYmcVbSbnZLfN/PtJh5ubm8qVK6d27dqpf//+1m50jnDt2jVt27ZNVapUcehT8azKTL20/Ajh7OwsDw8PlSxZUu3bt1dgYKC6desmHx+fbJWlYcOGmj9/voKDg3XgwAH9888/qlatmt566y317ds33RtYs9msH3/8UVOmTNH27du1YcMGlS9fXgMGDNCLL75ofaLy71fRHDlyRNKtNy9kdg6CnFC5cmUtW7ZMM2fO1IYNG7R582aZTCZVr15dXbp00YABA+xOFPTBBx/I29tb27Zt09q1a5WUlCTpfwGJdOv1jS1bttT8+fMVEhKiXbt2yWQyqXTp0qpbt64eeOCBXGmrOV3/qc/U53upPuc02kvW28vVq1dthk86OzurRIkSatq0qTp37qw+ffqkeXCRHblxn5qjDGTY6tWrDbPZbHzyySd5XZR7xj///GOYzWZj2LBheV2UQiMhIcFo0aKF0bp1a+PmzZs5ui/ajOMVtDYzY8YMw2w2G3PmzMnroljdy/Xy/PnzRp06dYzAwEAjJSXFZtnbb79t+Pn5GceOHcuj0hU+uVH/qc/U59ySH8/nmUV7yf/tJTfvU3MKY7Az4aGHHlKTJk20ZMmSAvnS8/xo+/btcnZ2ztUuxIXdvHnzdP36db3++usO/bXRHtqM4xWkNhMfH6/vvvtO1apVU9++ffO6OFYFvV7Gx8fbnSsgLCxMo0aNUmpqqrp3755mxuGdO3eqa9eu1veYI2flVv2nPlOfc0N+PZ9nFu0l/7eX3LxPzSlOhpFPpjQuII4dO6ZevXqpb9++acbSAPldTEyMOnbsqCpVqmjp0qW58r5E2kzh9dVXX2ny5MmaPn262rVrl9fFsVGQ6+WFCxfUoUMHValSRdWqVVPx4sV16dIlHT16VImJiapTp44WLFhQ4Gb4vdfkZv2nPiOn5efzeWbRXvKvvLhPzQkE2AAAFCBxcXH68ssvFRISoosXiMsY/QAAC0tJREFULyomJkZFixZV9erV1alTJ/Xv31/u7u55XUwgQ6jPQMbRXgoGAmwAAAAAAByAMdgAAAAAADgAATYAAAAAAA5AgA0AAAAAgAMQYAMAAAAA4AAE2AAAAAAAOIBLXhcAAICCxs/PL8N5p06dqo4dO2Y4f//+/bV7927r3xs3blSlSpUyVb7cZHkva1ZUrFhRv/32m4NLBABA3uEJNgAAAAAADsATbAAAsqlt27Zyd3e3u6xMmTK5XJrcVaxYMXXu3DlN+u7du3X9+nXr3/7+/qpYsaJNHh8fnxwvHwAAuYkAGwCAbHrvvffydTfunOTj46MpU6akSf93V/enn35aPXv2zM2iAQCQ6wiwAQDIZWfOnFFwcLBCQkIUHx+vypUr67HHHtOgQYMytP6BAwf01Vdfaf/+/UpMTFS1atXUs2dP9evXTw8++KDCwsKseU+ePJlm/ejoaC1dulSbN2/W6dOnFRsbKw8PD9WsWVOdO3fWE088oWLFijnseP/t4sWLevDBB5WcnCxJaty4sRYvXpwm34cffqh58+ZZ//7mm2/0wAMPKCQkRAMGDLCm9+jRQ2+//ba+/vprrVu3Tv/88488PT3Vpk0bDR8+XJUrV7ZbjitXrmjx4sXavn27/vrrL8XFxal48eKqW7euHnnkEXXv3l2urq4OPnoAwL2MABsAgFy0a9cuvfDCC0pISLCmnTlzRp9//rm2bt1qk27PypUr9cYbbyglJcWaduLECX388cfauXOnkpKS7rj+nj17NGLECIWHh9ukR0VFad++fdq3b58WLVqkr7/+WtWrV8/CEd5dhQoV9NBDD2nlypWSpP379+vYsWOqV6+eNU9ycrJWrVpl/btixYpq27at3e1dunRJPXr0UGhoqDXt6tWrWrFihTZt2qRZs2apfv36NuusX79eo0ePVmxsrE16ZGSkdu3apV27dmnp0qWaNm2aSpcune1jBgAUDgTYAABk07hx4+yOwfbx8dH7779v/TsqKkqvvfaaTRDt6ekpf39/Xb58WX/88ccd9xMaGqq3337bJrguWbKk6tevrz///FObN2++4/rnz5/X0KFDbYJKs9msihUr6sKFCzp9+rQk6a+//tKQIUP0yy+/pDu2PLuee+45a4AtSQsWLNBHH31k/XvHjh2KiIiw/v3444/LZLI/N+vvv/8uSapTp468vb116NAhxcfHS7r1mb/66qtatWqVihQpIknat2+fRo4caf0xwsnJSfXr15evr6/Onj2r8+fPS5IOHTqk4cOHa9GiRXJycnLg0QMA7lUE2AAAZNPWrVvtpv97Uq8ff/xR165ds/5dtWpVLViwQL6+vpKkzz//XNOnT093P3PmzLEJzgMCAjRr1iyVKFFCycnJ+r//+z+tXr063fWDg4NtgusvvvhCjzzyiPXv/9fe/YY09YVxAP+qmw51a7Y0WJkZtGkiRH80TdIi0MpaSmEWRdmLjEqhF0IRFpQkYtmbJCOKBBuKECVkGhVI/yRKLKLIocF0Yg0d/qksa78Xw5NXt5m5FPt9PxD03HvP9dzz7tk55zllZWU4f/48AEcybzQakZWV5fJ9k7FkyRLEx8fjyZMnAH7NzM+aNQsAcPv2bfGsXC7Htm3b3L4vPz8fu3btEn3PzMwUs/Rmsxl37tzB1q1bAQDFxcUiuZbJZLh+/TpWrFgBALDb7Th58iQqKysBOGbX6+vrnRZyIyIiGo3HdBEREU2Rx48fS+KsrCyRXAPAoUOHEBAQ4LL9o0ePJPGRI0egVCoBOBLFvLw8l21//vwpOXNaLpejrq4OOTk54t/IomQA8PDhw/E/ahL2798v/v/161dUV1cDAD5//izp67p16yTjNFpYWBh27twp4tDQUJFsDxse++7ubrx8+VJc9/f3R3l5uRiD3NxcMZM/7G+PAxER/Ts4g01ERDRJ9+/f/60q4haLRRLrdDpJrFAosGDBArx9+/a32kdEREhirVYLpVKJvr6+MW1tNptk9vr79++oq6tz29/29na39ycrISEBer1eFGIzGo3Yt28f6uvrxRJvAMjMzHT7Hp1ON2YJ9+LFiyXx8Ni1t7fDbreL6729vdM+DkRE9O/gDDYREdEM5WxfsCf3Co9Mcv+WkbPYZrMZDQ0NqKmpEdcWLlyIVatW/fV+uDMV40BERP8GzmATERFNEa1Wi9bWVhG3tLRg2bJlIh4cHJRUwnbWvq2tTcQmkwkhISEitlgs6O3tddpWrVYjICAAAwMDAIDAwEA8ffpUFP6aLps2bUJJSQk6OzsBABcvXsSbN2/E/YyMjHF/NBi9pBtwjM1IWq0WgGNfvJeXl5jFXrRokdt960RERBPBGWwiIqIpEhcXJ4mvXr0Kq9Uq4tLS0jHHRo2UkJAgiUtLS8Xs6tDQEIqKily29fb2xtq1a0Xc39+PwsJCfPv2TfKc3W5Hc3MzCgoKcO/evfE/apJkMpnkTOtXr16JKul+fn5IT08f9x0fPnyA0WgUcUdHByoqKiTPxMfHAwA0Gg2WLl0qrre2tuLy5cuSyuyAYzyfPXuG48ePo7m5ecLfRURE/09e9pEbkYiIiGhcer1eEq9Zs8blcVYpKSnYuHEjAMc+6JSUFPT09Ij7KpUK0dHR6OrqGjPrCkj3d5vNZqSmpkoqic+ZMwcRERFoa2tDR0fHmPbD+5sBoK2tDenp6ZIlz2q1Gnq9HgEBAejp6YHJZBJ7uM+ePftbCa4zu3fvlhRNc/eu/v5+JCUljdk7bjAYnP5o0NjYKEnKh0VGRopjuoZn6gFg/vz5qK2tFbP1z58/x969ezE0NCSeCQ4Ohk6ng6+vL6xWK0wmE758+QIAKC8vR2xs7AS+noiI/q+4RJyIiGiSXB3TBUiLbanVapw7dw4HDx7E4OAgAEeRreEK11FRUfjx4wfevXvn9F2hoaEoKChAXl6emHG1Wq2iunhycjKamprw8eNHAI5K4SOFh4ejrKwMR48eFUdY2Ww2NDY2Ov17Pj4+4367JwQGBiIjIwNXrlyRXN+xY8dvtU9MTITFYnFaHE6pVOLChQuSpfArV65EcXExTpw4IVYMfPr0SYzJaFM1DkRENPNxiTgREdEUWr16Naqrq5GcnAy1Wg1fX1+Eh4fj8OHDqKiogEqlcts+NTUVN27cQGJiIpRKJRQKBSIjI5Gfn4+ioiLJOdsj92cPi4mJQW1tLY4dO4a4uDhoNBrI5XL4+vpi7ty5iI2NRXZ2NqqqqmAwGDz+/a7s2bMHMtmv3/31er1kf7o7s2fPRmVlJbKzsxEWFga5XA6NRoMtW7bg5s2biI6OHtNmw4YNuHv3LnJycrB8+XKo1WrIZDL4+flh3rx5SEhIQG5uLmpqasQZ2UREROPhEnEiIqIZpKurC0FBQU6Lk5WUlODSpUsi3r59O86cOTOV3ftj79+/x+bNm0V86tQpl8dzjV4inpaWhsLCwr/eRyIiovFwiTgREdEMUlVVhWvXriE2NhZarRYqlQrd3d148eKFpJq2v78/Dhw4MI09HV9LSwsaGhrQ29uLW7duievBwcFIS0ubxp4RERH9GSbYREREM8zAwAAePHjg8n5ISAhKSkoQGho6hb2auNevX48pYubj44PTp09DoVBMU6+IiIj+HBNsIiKiGWT9+vXo6+tDU1MTOjs7YbPZ4O3tjaCgIOh0OiQlJcFgMCAwMHC6uzoharUaUVFRyM7ORkxMzHR3h4iI6I9wDzYRERERERGRB7CKOBEREREREZEHMMEmIiIiIiIi8gAm2EREREREREQewASbiIiIiIiIyAOYYBMRERERERF5ABNsIiIiIiIiIg9ggk1ERERERETkAUywiYiIiIiIiDzgP97BqqqQAqnEAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc={'figure.figsize':(10,10)})\n",
    "\n",
    "# grid lines\n",
    "sns.set_style(\"darkgrid\")\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "bx1 = sns.barplot(\n",
    "    x=\"Node Types\", y=\"Count\", data=node_df,\n",
    "    palette=['#F6A0D8','#FE9A6B', '#95F3CC']\n",
    ") \n",
    "plt.title(\n",
    "    'Distribution of Node Types', \n",
    "    fontsize=18, \n",
    "    fontweight=\"bold\"\n",
    ")\n",
    "plt.xlabel(\"Node Type\", fontsize=16, fontweight=\"bold\")\n",
    "plt.ylabel(\"Count\", fontsize=16, fontweight=\"bold\")\n",
    "plt.xticks(bx1.get_xticks(), labels=['Drug', 'Protein', 'Biological Process (BP)'], fontsize=16)\n",
    "plt.ylim(10, 15000)\n",
    "plt.yscale(\"log\") #the log transformation\n",
    "\n",
    "for p in bx1.patches:\n",
    "    _x = p.get_x() + p.get_width() / 2\n",
    "    _y = p.get_y() + p.get_height() + 50\n",
    "    value = int(p.get_height())\n",
    "    bx1.text(_x, _y, f'{value:,}', ha=\"center\") \n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "bx2 = sns.barplot(\n",
    "    x=\"Edge Types\", y=\"Count\", data=edges_df, \n",
    "    color='#fe6b86'\n",
    ")\n",
    "plt.title(\n",
    "    'Distribution of Relations', \n",
    "    fontsize=18, \n",
    "    fontweight=\"bold\"\n",
    ")\n",
    "plt.xlabel(\"Edge Type\", fontsize=16, fontweight=\"bold\")\n",
    "plt.ylabel(\"Count\", fontsize=16, fontweight=\"bold\") \n",
    "plt.xticks(bx2.get_xticks(), labels=list(edges_df['Edge Types']), fontsize=16)\n",
    "plt.ylim(10, 150000)\n",
    "\n",
    "plt.yscale(\"log\") #the log transformation\n",
    "\n",
    "# Add counts above bars\n",
    "for p in bx2.patches:\n",
    "    _x = p.get_x() + p.get_width() / 2\n",
    "    _y = p.get_y() + p.get_height() + 50\n",
    "    value = int(p.get_height())\n",
    "    bx2.text(_x, _y, f'{value:,}', ha=\"center\")   \n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\n",
    "    f'{FIG_DIR}/graph-stats-protclassed.png',\n",
    "    dpi=400,\n",
    "    bbox_inches='tight', \n",
    ")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8826924156514905"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ratio_interacts = edges_df.loc[edges_df['Edge Types']=='interacts\\n(Protein, Protein)']['Count'].sum() / edges_df['Count'].sum()\n",
    "ratio_interacts"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "moanet_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}