code/TCGA/maf_scatter.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "476f3f9a-dd38-4986-ae88-428d40de8b91",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "27c02011-e07d-4819-a821-9020bfcb4453",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"merged_groups_maf.csv\", index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f88014e2-ea99-4246-9198-05d65ba71377",
   "metadata": {
    "tags": []
   },
   "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>paper_IDH.specific.DNA.Methylation.Cluster</th>\n",
       "      <th>paper_IDH.status</th>\n",
       "      <th>paper_IDH.specific.RNA.Expression.Cluster</th>\n",
       "      <th>paper_IDH.codel.subtype</th>\n",
       "      <th>Project ID</th>\n",
       "      <th>Case ID</th>\n",
       "      <th>Case UUID</th>\n",
       "      <th>ID</th>\n",
       "      <th>File Name</th>\n",
       "      <th>Data Category</th>\n",
       "      <th>...</th>\n",
       "      <th>EGFR</th>\n",
       "      <th>RB1</th>\n",
       "      <th>TP53</th>\n",
       "      <th>NF1</th>\n",
       "      <th>PIK3R1</th>\n",
       "      <th>ATRX</th>\n",
       "      <th>PIK3CA</th>\n",
       "      <th>TERT</th>\n",
       "      <th>CIC</th>\n",
       "      <th>FUBP1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TCGA-06-6698-01A</th>\n",
       "      <td>IDHwt-K3</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>TCGA-06-6698</td>\n",
       "      <td>c129c34a-8d40-4d98-b9ad-a10d3f7b3ee0</td>\n",
       "      <td>c30759a4-c89f-4bcb-9a64-edbfe336a320</td>\n",
       "      <td>jhu-usc.edu_GBM.HumanMethylation450.8.lvl-3.TC...</td>\n",
       "      <td>DNA Methylation</td>\n",
       "      <td>...</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Multiple</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>Nonsense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-5418-01A</th>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>IDHwt-R2</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>TCGA-06-5418</td>\n",
       "      <td>51c3409d-5df7-4720-83db-0e6de065f82c</td>\n",
       "      <td>5c3e021e-cb23-439f-b917-8d284b96db76</td>\n",
       "      <td>jhu-usc.edu_GBM.HumanMethylation450.6.lvl-3.TC...</td>\n",
       "      <td>DNA Methylation</td>\n",
       "      <td>...</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-19-5953-01B</th>\n",
       "      <td>IDHwt-K1</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>TCGA-19-5953</td>\n",
       "      <td>aa703529-7153-4f74-8577-2601876e3374</td>\n",
       "      <td>8c34c33b-505a-493f-99f9-011f21dfbe5a</td>\n",
       "      <td>jhu-usc.edu_GBM.HumanMethylation450.8.lvl-3.TC...</td>\n",
       "      <td>DNA Methylation</td>\n",
       "      <td>...</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Nonsense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-28-6450-01A</th>\n",
       "      <td>IDHwt-K1</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>TCGA-28-6450</td>\n",
       "      <td>82ecf2ce-0782-4238-be0a-668a2b22c57c</td>\n",
       "      <td>2814b9db-21ab-4a77-9f95-931aacc7e056</td>\n",
       "      <td>jhu-usc.edu_GBM.HumanMethylation450.7.lvl-3.TC...</td>\n",
       "      <td>DNA Methylation</td>\n",
       "      <td>...</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Multiple</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-RR-A6KA-01A</th>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>TCGA-RR-A6KA</td>\n",
       "      <td>225f3689-221d-4296-8472-d8c21eedaf8d</td>\n",
       "      <td>6add0994-928b-4685-896a-9f6c979d2ddd</td>\n",
       "      <td>jhu-usc.edu_GBM.HumanMethylation450.10.lvl-3.T...</td>\n",
       "      <td>DNA Methylation</td>\n",
       "      <td>...</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-76-6282-01A</th>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>TCGA-76-6282</td>\n",
       "      <td>c472482e-225d-4ab0-8ba5-b659e7ff3e89</td>\n",
       "      <td>533a10c5-918b-4a2c-87de-8bfe1739917f</td>\n",
       "      <td>jhu-usc.edu_GBM.HumanMethylation450.7.lvl-3.TC...</td>\n",
       "      <td>DNA Methylation</td>\n",
       "      <td>...</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-6694-01A</th>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>TCGA-06-6694</td>\n",
       "      <td>863d58e7-91c2-4602-98ed-c6c6e0859566</td>\n",
       "      <td>3f122900-b127-4e38-a35b-8aeaf9a57231</td>\n",
       "      <td>jhu-usc.edu_GBM.HumanMethylation450.8.lvl-3.TC...</td>\n",
       "      <td>DNA Methylation</td>\n",
       "      <td>...</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-0125-01A</th>\n",
       "      <td>IDHwt-K1</td>\n",
       "      <td>WT</td>\n",
       "      <td>IDHwt-R1</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>TCGA-06-0125</td>\n",
       "      <td>8da3103e-3e6c-4176-a583-d5fe5e60601e</td>\n",
       "      <td>4a06ffb9-1f87-4cc1-a611-537316a2dc4a</td>\n",
       "      <td>jhu-usc.edu_GBM.HumanMethylation450.1.lvl-3.TC...</td>\n",
       "      <td>DNA Methylation</td>\n",
       "      <td>...</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-76-6283-01A</th>\n",
       "      <td>IDHwt-K1</td>\n",
       "      <td>WT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>TCGA-76-6283</td>\n",
       "      <td>e5c9b14e-3bc7-4892-bd51-4cd1599819ec</td>\n",
       "      <td>8c892034-0ae6-480d-b1c3-a72fa4f606b6</td>\n",
       "      <td>jhu-usc.edu_GBM.HumanMethylation450.8.lvl-3.TC...</td>\n",
       "      <td>DNA Methylation</td>\n",
       "      <td>...</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Frame_Shift_Del</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-28-5215-01A</th>\n",
       "      <td>IDHwt-K2</td>\n",
       "      <td>WT</td>\n",
       "      <td>IDHwt-R3</td>\n",
       "      <td>IDHwt</td>\n",
       "      <td>TCGA-GBM</td>\n",
       "      <td>TCGA-28-5215</td>\n",
       "      <td>c9e4f0bf-48fc-4600-9e03-314bc575273f</td>\n",
       "      <td>1cd0e23f-cb94-4871-a421-57885260706e</td>\n",
       "      <td>jhu-usc.edu_GBM.HumanMethylation450.6.lvl-3.TC...</td>\n",
       "      <td>DNA Methylation</td>\n",
       "      <td>...</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Splice_Region</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>121 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 paper_IDH.specific.DNA.Methylation.Cluster paper_IDH.status  \\\n",
       "Sample ID                                                                      \n",
       "TCGA-06-6698-01A                                   IDHwt-K3               WT   \n",
       "TCGA-06-5418-01A                                   IDHwt-K2               WT   \n",
       "TCGA-19-5953-01B                                   IDHwt-K1               WT   \n",
       "TCGA-28-6450-01A                                   IDHwt-K1               WT   \n",
       "TCGA-RR-A6KA-01A                                   IDHwt-K2               WT   \n",
       "...                                                     ...              ...   \n",
       "TCGA-76-6282-01A                                   IDHwt-K2               WT   \n",
       "TCGA-06-6694-01A                                   IDHwt-K2               WT   \n",
       "TCGA-06-0125-01A                                   IDHwt-K1               WT   \n",
       "TCGA-76-6283-01A                                   IDHwt-K1               WT   \n",
       "TCGA-28-5215-01A                                   IDHwt-K2               WT   \n",
       "\n",
       "                 paper_IDH.specific.RNA.Expression.Cluster  \\\n",
       "Sample ID                                                    \n",
       "TCGA-06-6698-01A                                       NaN   \n",
       "TCGA-06-5418-01A                                  IDHwt-R2   \n",
       "TCGA-19-5953-01B                                       NaN   \n",
       "TCGA-28-6450-01A                                       NaN   \n",
       "TCGA-RR-A6KA-01A                                       NaN   \n",
       "...                                                    ...   \n",
       "TCGA-76-6282-01A                                       NaN   \n",
       "TCGA-06-6694-01A                                       NaN   \n",
       "TCGA-06-0125-01A                                  IDHwt-R1   \n",
       "TCGA-76-6283-01A                                       NaN   \n",
       "TCGA-28-5215-01A                                  IDHwt-R3   \n",
       "\n",
       "                 paper_IDH.codel.subtype Project ID       Case ID  \\\n",
       "Sample ID                                                           \n",
       "TCGA-06-6698-01A                   IDHwt   TCGA-GBM  TCGA-06-6698   \n",
       "TCGA-06-5418-01A                   IDHwt   TCGA-GBM  TCGA-06-5418   \n",
       "TCGA-19-5953-01B                   IDHwt   TCGA-GBM  TCGA-19-5953   \n",
       "TCGA-28-6450-01A                   IDHwt   TCGA-GBM  TCGA-28-6450   \n",
       "TCGA-RR-A6KA-01A                   IDHwt   TCGA-GBM  TCGA-RR-A6KA   \n",
       "...                                  ...        ...           ...   \n",
       "TCGA-76-6282-01A                   IDHwt   TCGA-GBM  TCGA-76-6282   \n",
       "TCGA-06-6694-01A                   IDHwt   TCGA-GBM  TCGA-06-6694   \n",
       "TCGA-06-0125-01A                   IDHwt   TCGA-GBM  TCGA-06-0125   \n",
       "TCGA-76-6283-01A                   IDHwt   TCGA-GBM  TCGA-76-6283   \n",
       "TCGA-28-5215-01A                   IDHwt   TCGA-GBM  TCGA-28-5215   \n",
       "\n",
       "                                             Case UUID  \\\n",
       "Sample ID                                                \n",
       "TCGA-06-6698-01A  c129c34a-8d40-4d98-b9ad-a10d3f7b3ee0   \n",
       "TCGA-06-5418-01A  51c3409d-5df7-4720-83db-0e6de065f82c   \n",
       "TCGA-19-5953-01B  aa703529-7153-4f74-8577-2601876e3374   \n",
       "TCGA-28-6450-01A  82ecf2ce-0782-4238-be0a-668a2b22c57c   \n",
       "TCGA-RR-A6KA-01A  225f3689-221d-4296-8472-d8c21eedaf8d   \n",
       "...                                                ...   \n",
       "TCGA-76-6282-01A  c472482e-225d-4ab0-8ba5-b659e7ff3e89   \n",
       "TCGA-06-6694-01A  863d58e7-91c2-4602-98ed-c6c6e0859566   \n",
       "TCGA-06-0125-01A  8da3103e-3e6c-4176-a583-d5fe5e60601e   \n",
       "TCGA-76-6283-01A  e5c9b14e-3bc7-4892-bd51-4cd1599819ec   \n",
       "TCGA-28-5215-01A  c9e4f0bf-48fc-4600-9e03-314bc575273f   \n",
       "\n",
       "                                                    ID  \\\n",
       "Sample ID                                                \n",
       "TCGA-06-6698-01A  c30759a4-c89f-4bcb-9a64-edbfe336a320   \n",
       "TCGA-06-5418-01A  5c3e021e-cb23-439f-b917-8d284b96db76   \n",
       "TCGA-19-5953-01B  8c34c33b-505a-493f-99f9-011f21dfbe5a   \n",
       "TCGA-28-6450-01A  2814b9db-21ab-4a77-9f95-931aacc7e056   \n",
       "TCGA-RR-A6KA-01A  6add0994-928b-4685-896a-9f6c979d2ddd   \n",
       "...                                                ...   \n",
       "TCGA-76-6282-01A  533a10c5-918b-4a2c-87de-8bfe1739917f   \n",
       "TCGA-06-6694-01A  3f122900-b127-4e38-a35b-8aeaf9a57231   \n",
       "TCGA-06-0125-01A  4a06ffb9-1f87-4cc1-a611-537316a2dc4a   \n",
       "TCGA-76-6283-01A  8c892034-0ae6-480d-b1c3-a72fa4f606b6   \n",
       "TCGA-28-5215-01A  1cd0e23f-cb94-4871-a421-57885260706e   \n",
       "\n",
       "                                                          File Name  \\\n",
       "Sample ID                                                             \n",
       "TCGA-06-6698-01A  jhu-usc.edu_GBM.HumanMethylation450.8.lvl-3.TC...   \n",
       "TCGA-06-5418-01A  jhu-usc.edu_GBM.HumanMethylation450.6.lvl-3.TC...   \n",
       "TCGA-19-5953-01B  jhu-usc.edu_GBM.HumanMethylation450.8.lvl-3.TC...   \n",
       "TCGA-28-6450-01A  jhu-usc.edu_GBM.HumanMethylation450.7.lvl-3.TC...   \n",
       "TCGA-RR-A6KA-01A  jhu-usc.edu_GBM.HumanMethylation450.10.lvl-3.T...   \n",
       "...                                                             ...   \n",
       "TCGA-76-6282-01A  jhu-usc.edu_GBM.HumanMethylation450.7.lvl-3.TC...   \n",
       "TCGA-06-6694-01A  jhu-usc.edu_GBM.HumanMethylation450.8.lvl-3.TC...   \n",
       "TCGA-06-0125-01A  jhu-usc.edu_GBM.HumanMethylation450.1.lvl-3.TC...   \n",
       "TCGA-76-6283-01A  jhu-usc.edu_GBM.HumanMethylation450.8.lvl-3.TC...   \n",
       "TCGA-28-5215-01A  jhu-usc.edu_GBM.HumanMethylation450.6.lvl-3.TC...   \n",
       "\n",
       "                    Data Category  ...               EGFR              RB1  \\\n",
       "Sample ID                          ...                                       \n",
       "TCGA-06-6698-01A  DNA Methylation  ...        No_mutation         Multiple   \n",
       "TCGA-06-5418-01A  DNA Methylation  ...        No_mutation      No_mutation   \n",
       "TCGA-19-5953-01B  DNA Methylation  ...        No_mutation      No_mutation   \n",
       "TCGA-28-6450-01A  DNA Methylation  ...        No_mutation      No_mutation   \n",
       "TCGA-RR-A6KA-01A  DNA Methylation  ...        No_mutation      No_mutation   \n",
       "...                           ...  ...                ...              ...   \n",
       "TCGA-76-6282-01A  DNA Methylation  ...  Missense_Mutation      No_mutation   \n",
       "TCGA-06-6694-01A  DNA Methylation  ...        No_mutation      No_mutation   \n",
       "TCGA-06-0125-01A  DNA Methylation  ...  Missense_Mutation      No_mutation   \n",
       "TCGA-76-6283-01A  DNA Methylation  ...        No_mutation  Frame_Shift_Del   \n",
       "TCGA-28-5215-01A  DNA Methylation  ...        No_mutation      No_mutation   \n",
       "\n",
       "                               TP53                NF1       PIK3R1  \\\n",
       "Sample ID                                                             \n",
       "TCGA-06-6698-01A  Missense_Mutation  Nonsense_Mutation  No_mutation   \n",
       "TCGA-06-5418-01A        No_mutation        No_mutation  No_mutation   \n",
       "TCGA-19-5953-01B        No_mutation  Nonsense_Mutation  No_mutation   \n",
       "TCGA-28-6450-01A        No_mutation           Multiple  No_mutation   \n",
       "TCGA-RR-A6KA-01A        No_mutation        No_mutation  No_mutation   \n",
       "...                             ...                ...          ...   \n",
       "TCGA-76-6282-01A        No_mutation        No_mutation  No_mutation   \n",
       "TCGA-06-6694-01A  Missense_Mutation        No_mutation  No_mutation   \n",
       "TCGA-06-0125-01A        No_mutation        No_mutation  No_mutation   \n",
       "TCGA-76-6283-01A  Missense_Mutation        No_mutation  No_mutation   \n",
       "TCGA-28-5215-01A      Splice_Region        No_mutation  No_mutation   \n",
       "\n",
       "                         ATRX             PIK3CA               TERT  \\\n",
       "Sample ID                                                             \n",
       "TCGA-06-6698-01A  No_mutation        No_mutation        No_mutation   \n",
       "TCGA-06-5418-01A  No_mutation        No_mutation  Missense_Mutation   \n",
       "TCGA-19-5953-01B  No_mutation        No_mutation        No_mutation   \n",
       "TCGA-28-6450-01A  No_mutation  Missense_Mutation        No_mutation   \n",
       "TCGA-RR-A6KA-01A  No_mutation  Missense_Mutation        No_mutation   \n",
       "...                       ...                ...                ...   \n",
       "TCGA-76-6282-01A  No_mutation        No_mutation        No_mutation   \n",
       "TCGA-06-6694-01A  No_mutation        No_mutation        No_mutation   \n",
       "TCGA-06-0125-01A  No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-6283-01A  No_mutation        No_mutation        No_mutation   \n",
       "TCGA-28-5215-01A  No_mutation        No_mutation        No_mutation   \n",
       "\n",
       "                          CIC        FUBP1  \n",
       "Sample ID                                   \n",
       "TCGA-06-6698-01A  No_mutation  No_mutation  \n",
       "TCGA-06-5418-01A  No_mutation  No_mutation  \n",
       "TCGA-19-5953-01B  No_mutation  No_mutation  \n",
       "TCGA-28-6450-01A  No_mutation  No_mutation  \n",
       "TCGA-RR-A6KA-01A  No_mutation  No_mutation  \n",
       "...                       ...          ...  \n",
       "TCGA-76-6282-01A  No_mutation  No_mutation  \n",
       "TCGA-06-6694-01A  No_mutation  No_mutation  \n",
       "TCGA-06-0125-01A  No_mutation  No_mutation  \n",
       "TCGA-76-6283-01A  No_mutation  No_mutation  \n",
       "TCGA-28-5215-01A  No_mutation  No_mutation  \n",
       "\n",
       "[121 rows x 30 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.iloc[:,5:]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "856dc679-781b-42e6-8c29-9c202c592a31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['paper_IDH.specific.DNA.Methylation.Cluster', 'paper_IDH.status',\n",
       "       'paper_IDH.specific.RNA.Expression.Cluster', 'paper_IDH.codel.subtype',\n",
       "       'Project ID', 'Case ID', 'Case UUID', 'ID', 'File Name',\n",
       "       'Data Category', 'Data Type', 'Sample Type', 'year of diagnosis',\n",
       "       'age at index', 'days to death', 'gender',\n",
       "       'vital status (0=alive, 1=dead)', 'Neural signature score',\n",
       "       'Neural subgroup (0=low, 1=high)', 'PTEN', 'EGFR', 'RB1', 'TP53', 'NF1',\n",
       "       'PIK3R1', 'ATRX', 'PIK3CA', 'TERT', 'CIC', 'FUBP1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7e72c76b-9c11-47d5-aadf-1dce08195854",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['No_mutation', 'Missense_Mutation', 'Unavailable',\n",
       "       'Frame_Shift_Del', 'Splice_Site', 'Multiple', 'Nonsense_Mutation',\n",
       "       'Frame_Shift_Ins', 'In_Frame_Del', 'In_Frame_Ins', 'Silent',\n",
       "       'Splice_Region', 'Nonstop_Mutation'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['PTEN', 'EGFR', 'RB1', 'TP53', 'NF1',\n",
    "       'PIK3R1', 'ATRX', 'PIK3CA', 'TERT', 'CIC', 'FUBP1']].melt()[\"value\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f90874f5-8ee6-4b5f-aaca-06b20be63689",
   "metadata": {},
   "outputs": [],
   "source": [
    "grs = ['Missense_Mutation', \n",
    "       'Frame_Shift_Del', 'Splice_Site', 'Multiple', 'Nonsense_Mutation',\n",
    "       'Frame_Shift_Ins', 'In_Frame_Del', 'In_Frame_Ins', 'Silent',\n",
    "       'Splice_Region', 'Nonstop_Mutation']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "63a64927-dabb-4f12-aca7-3fe5ed36c21c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from IPython.display import display\n",
    "#for col in df.columns[1:]:\n",
    "    #display(pd.DataFrame(df[col].replace({\"no\":np.nan}).value_counts()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "344ff2fa-9442-40f7-903f-7762e3937063",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1b0d973d-6df2-48c4-8fdc-09a5414b1b60",
   "metadata": {},
   "outputs": [],
   "source": [
    "gr = \"Missense_Mutation\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cf94600c-557f-4657-b65d-aac65b20d165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Missense_Mutation',\n",
       " 'Frame_Shift_Del',\n",
       " 'Splice_Site',\n",
       " 'Multiple',\n",
       " 'Nonsense_Mutation',\n",
       " 'Frame_Shift_Ins',\n",
       " 'In_Frame_Del',\n",
       " 'In_Frame_Ins',\n",
       " 'Silent',\n",
       " 'Splice_Region',\n",
       " 'Nonstop_Mutation']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dd2d770c-70a3-420f-bf04-6250777801aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"ticks\", font_scale=1.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "20e3d0cc-b2ac-4204-92d6-6c1e3be3521b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(grs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dff41147-62f5-4d1e-99d9-362cc5caf2bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['paper_IDH.specific.DNA.Methylation.Cluster', 'paper_IDH.status',\n",
       "       'paper_IDH.specific.RNA.Expression.Cluster', 'paper_IDH.codel.subtype',\n",
       "       'Project ID', 'Case ID', 'Case UUID', 'ID', 'File Name',\n",
       "       'Data Category', 'Data Type', 'Sample Type', 'year of diagnosis',\n",
       "       'age at index', 'days to death', 'gender',\n",
       "       'vital status (0=alive, 1=dead)', 'Neural signature score',\n",
       "       'Neural subgroup (0=low, 1=high)', 'PTEN', 'EGFR', 'RB1', 'TP53', 'NF1',\n",
       "       'PIK3R1', 'ATRX', 'PIK3CA', 'TERT', 'CIC', 'FUBP1'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "585bd847-92c6-4ebc-a201-5fe70d73f98a",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = ['PTEN', 'EGFR', 'RB1', 'TP53', 'NF1',\n",
    "       'PIK3R1', 'ATRX', 'PIK3CA', 'TERT', 'CIC', 'FUBP1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "915db2c6-ac1f-4d6e-9442-38688e7487b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 72/72 [00:01<00:00, 44.62it/s]\n",
      "100%|██████████| 49/49 [00:00<00:00, 49.22it/s]\n",
      "100%|██████████| 72/72 [00:01<00:00, 44.45it/s]\n",
      "100%|██████████| 49/49 [00:01<00:00, 45.72it/s]\n",
      "100%|██████████| 72/72 [00:01<00:00, 48.98it/s]\n",
      "100%|██████████| 49/49 [00:01<00:00, 42.01it/s]\n",
      "100%|██████████| 72/72 [00:01<00:00, 48.95it/s]\n",
      "100%|██████████| 49/49 [00:01<00:00, 38.04it/s]\n",
      "100%|██████████| 72/72 [00:01<00:00, 48.95it/s]\n",
      "100%|██████████| 49/49 [00:01<00:00, 34.84it/s]\n",
      "100%|██████████| 72/72 [00:01<00:00, 48.61it/s]\n",
      "100%|██████████| 49/49 [00:00<00:00, 54.67it/s]\n",
      "100%|██████████| 72/72 [00:01<00:00, 48.85it/s]\n",
      "100%|██████████| 49/49 [00:01<00:00, 30.92it/s]\n",
      "100%|██████████| 72/72 [00:01<00:00, 48.83it/s]\n",
      "100%|██████████| 49/49 [00:00<00:00, 54.65it/s]\n",
      "100%|██████████| 72/72 [00:01<00:00, 48.63it/s]\n",
      "100%|██████████| 49/49 [00:01<00:00, 27.10it/s]\n",
      "100%|██████████| 72/72 [00:01<00:00, 48.81it/s]\n",
      "100%|██████████| 49/49 [00:00<00:00, 54.52it/s]\n",
      "100%|██████████| 72/72 [00:01<00:00, 49.31it/s]\n",
      "100%|██████████| 49/49 [00:00<00:00, 55.15it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLoAAAvPCAYAAADwP+z7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzdf3zN9f//8fsZGxubX43ROJMy5ueEokIt77cQTdFXa807GkWUJWNCsVaTiHknmVKmvLGUiTBSKj/68ZZKqXdlWwz5NQxjXt8/fJx3573NduacnR+7XS+Xc7k4z9fz9Xw9Xq/X2Xk9PM7rPI/JMAxDAAAAAAAAgJvzcnYAAAAAAAAAgD1Q6AIAAAAAAIBHoNAFAAAAAAAAj0ChCwAAAAAAAB6BQhcAAAAAAAA8QlVnB+Cpzp49q++++06BgYGqUqWKs8MBAAB2VlhYqMOHD6t169aqXr26s8PB/yEHAwDAs5WWg1HocpDvvvtOUVFRzg4DAAA4WFpamjp27OjsMPB/yMEAAKgcSsrBKHQ5SGBgoKRLBz4oKMjJ0QAAAHvLzc1VVFSU5ZoP10AOBgCAZystB6PQ5SCXb5UPCgpScHCwk6MBAACOwtfjXAs5GAAAlUNJORiT0QMAAAAAAMAjUOgCAAAAAACAR3CpQld0dLRCQ0OLPJYsWaL09HT17du32PXmzp2r4cOHlzhOhw4d9MQTT+jo0aOWPunp6VZ9Wrdurfvvv19ffvml1di5ubmKiYlRaGio1foAAACegPwLAAB4EpcqdEnSyJEjtXXrVqvHvffee1XjLF26VEeOHNGECROs+jRt2tTSZ+3aterevbuGDRumnJwcSdK6det09913y9fX1y77BgAA4IrIvwAAgKdwuUJXjRo1FBgYaPUoT6Lz13FatGihp59+Wlu2bNHZs2ctfapWrWrp07hxYz322GNq06aNVq1aJelSovXCCy9o0qRJ9to9AAAAl0P+BVROaWlpCgkJkZeXl0JCQpSWlubskADgqlWaX100DEOSdPHixSv2a9q0qfbv3y9JmjFjhry9vS2fMAIAAKDsyL8A15WWlqbY2Fjl5+dLkvbt26fY2FhJUlRUlDNDA4CrUikKXYcPH9aMGTPUt29f+fn5XbHvH3/8odatW0uSvL29yzR+Xl6e8vLyrNpyc3PLFywAAIAHcHT+JZGDAVcjISHBUuS6LD8/XwkJCRS6ALg1lyt0zZo1SykpKZbnvr6++vzzz69qnDNnzqhFixaaMWNGif0LCwu1evVqbd++XU8//bRN21q8eLFVzAAAAO7EHfMviRwMuBpZWVk2tQOAu3C5QteQIUM0cOBAy3Mvr/JNI/bXcU6fPq0333xT9913nz744APVrVtXkvTzzz8rPDxcklRQUKBatWrphRdeUGhoqE3biomJUWRkpFVbbm4un4QAAAC34I75l0QOBlyNJk2aaN++fcW2A4A7c7lCV506dWQ2m4u0+/j46Ny5c8Wuc+7cOfn4+FxxnBdeeEG9e/fW2rVrLclP48aNlZqaahm/QYMG5UrsAgICFBAQYPN6AAAArsAd8y+JHAy4GomJiVZzdEmSn5+fEhMTnRgVAFw9l/vVxZK0bt1a2dnZ+uWXX6zaCwsLtWXLFsu8DiXx8vJS3bp1dfr0aUtb9erVZTabZTab1bBhw3InWQAAAJ6I/AvwXFFRUVqwYIHMZrNMJpPMZrMWLFjAHZEA3J7L3dFVkpCQEA0cOFDDhw/XhAkT1KpVKx0+fFivvfaaTp8+rcGDB1v1P336tA4fPixJOnv2rDZu3Khdu3Zp8uTJzggfAADA7ZB/AZ4tKiqKwhYAj+M2hS5Jmjp1qhYtWqTk5GTl5OTI399ft99+u959990it63PmzdP8+bNk3Tp13tuuOEGvfLKK2rRooUzQgcAAHBL5F8AAMCdmAzDMJwdhCfKyclRRESEMjMzFRwc7OxwAACAnXGtd02cFwAAPFtp13omRQAAAAAAAIBHoNAFAAAAAAAAj0ChCwAAAAAAAB6BQhcAAAAAAAA8AoUuAAAAAAAAeAQKXQAAAAAAAPAIFLoAAAAAAADgESh0AQAAAAAAwCNQ6AIAAAAAAIBH8PhCV35+vpKTk9WtWzeFhYXpjjvu0Jw5c3Tu3DlJUnp6uvr27Wu1zoULF/Taa6+pZ8+eCgsLU7du3TR9+nSdPHnSGbsAAADgVsi/AACAs1R1dgCOdO7cOQ0ZMkReXl568cUX1bRpU+3du1cvvfSSdu3apQULFhRZxzAMPfHEE/r11181ceJEtWzZUllZWZo7d64eeOABLVu2TH5+fk7YGwAAANdH/gUAAJzJo+/oeuONN3Tq1Cm9+eab6tKli4KCgtStWzctXrxYu3fv1kcffVRknYyMDH399dd6++23dfvttysoKEidO3fWwoULVVBQoCVLljhhTwAAANwD+RdQVFpamkJCQuTl5aWQkBClpaU5OyS4EV4/gG08+o6ulStX6tFHH1X16tWt2uvUqaPk5GTVqFFD2dnZRda5//77Va9ePav2atWqKTExUadOnXJ43AAAAO6K/AuwlpaWptjYWOXn50uS9u3bp9jYWElSVFSUM0ODG+D1A9jOYwtdJ0+eVFZWlsLDw4td3qNHD0kqkmj98MMPGjZsWLHrdOzYsdj2vLw85eXlWbXl5ubaGDEAAIB7q8j8SyIHg3tISEiwFCkuy8/PV0JCAoUKlIrXD2A7jy50SVJAQIBN6+Xl5dm8zuLFi5WSkmLTOgAAAJ6mIvMviRwM7iErK8umduCveP0AtvPYQpe/v7+kS4nT/94GX9p6//vJYGliYmIUGRlp1Zabm0uFHQAAVCoVmX9J5GBwD02aNNG+ffuKbQdKw+sHsJ3HTkbv7++vxo0b65tvvil2+aZNm7R9+/Yi7WFhYfrqq6+KXWfHjh3KzMws0h4QEKDg4GCrR1BQ0NXtAAAAgJupyPxLIgeDe0hMTCzyq6F+fn5KTEx0UkRwJ7x+ANt5bKFLkgYMGKCFCxfq7NmzVu1HjhzR+PHjdeTIkSLr3HvvvVq2bFmRZWfPntWkSZP066+/OjRmAAAAd0b+BViLiorSggULZDabZTKZZDabtWDBAu48RJnw+gFs59GFrqFDh8rX11dDhgzRtm3blJubq08++URDhgxR27Zt1atXryLr9O3bV+3atVN0dLQ2b96s3Nxc7dixQ0OHDlW1atV4QwEAALgC8i+gqKioKP3++++6ePGifv/9d17TsAmvH8A2HjtHl3TpJ6nfeustpaSkaNy4cTpy5IiCgoLUv39/DR8+XF5eRet8Xl5emjNnjhYuXKjExETt379f9erVU8+ePTV69Ogit40CAADgv8i/AACAM5kMwzCcHYQnysnJUUREhDIzMxUcHOzscAAAgJ1xrXdNnBcAADxbadd6j/7qIgAAAAAAACoPCl0AAAAAAADwCBS6AAAAAAAA4BEodAEAAAAAAMAjUOgCAAAAAACAR6DQBQAAAAAAAI9AoQsAAAAAAAAegUIXAAAAAAAAPAKFLgAAAAAAAHiEqs4OoKy2b9+uhx56qMTlo0aNkiSlpKQUWXbrrbcqNTVVkhQaGmq1LCgoSAMHDtRjjz0mLy8vpaena8KECSVuJykpSQMGDCjPLgAAALgdcjAAAOBOylXoOnXqlE6ePCnDMIosa9So0VUHVZzw8HBt3brV8rxnz55KTk5WeHi4JMnPz0+LFi1S165dlZycbLWuj4+P1fOFCxeqRYsWunjxon788UdNmjRJ3t7eGj58uHr37q3bbrtNknTixAn16dNHy5cvV8OGDSVJ/v7+Dtk/AAAAV0QOBgAA3IlNha4DBw7o6aef1pdffllkmWEYMplM2rNnj92C+ysfHx8FBgZanptMJtWqVcuqrbh+xaldu7alT4MGDTR69GjNnz9fw4cPV/Xq1VW9enVJUpUqVSRJdevWLXVMAAAAT0QOdklaWpoSEhKUlZWlJk2aKDExUVFRUc4OC26C1w8AVBybCl0TJ07Uvn37NGPGDDVr1kwmk8lRcVWo66+/XgcOHHB2GAAAAJWKu+RgaWlpio2NVX5+viRp3759io2NlSSKFSgVrx8AqFg2Fbq+/PJLJSUlqW/fvo6KxykOHTqkOnXqlHv9vLw85eXlWbXl5uZebVgAAAAezV1ysISEBEuR4rL8/HwlJCRQqECpeP0AQMWyqdAVEBBwVclIRfj0008tc0ZcNnnyZEVGRhbbPycnR/Pnz9ddd91V7m0uXry42AlYAQAAKgtPzsGysrJsagf+itcPAFQsmwpdjz32mN5++23ddNNNqlrVNX+w8cYbb9T06dOt2urVq2f1PCoqSlWqVJFhGDpz5ox69+6tuLi4cm8zJiamSBKXm5vLJzQAAKDS8OQcrEmTJtq3b1+x7UBpeP0AQMWyqVr173//W3v37tUdd9yhsLCwYotdzr6zyc/PT2az+Yp9XnrpJYWGhurkyZOKjo5Wly5d5OvrW+5tBgQEKCAgoNzrAwAAuDtPzsESExOt5liSLu1vYmKiw7cN98frBwAqlk2FrqpVq+qmm25yVCwVpmHDhpZE7LHHHtPs2bN111138bPVAAAADuSuOdjlO8T41TyUB68fAKhYNhW6kpKSHBWH3RQUFOjw4cNWbVWrVi1xbrGYmBj961//0quvvqqnn366IkIEAADwOJ6eg0VFRVGYQLnx+gGAilOuibYKCgr0ww8/6NixY6pbt65atmwpHx8fe8dWLp9//rluvfVWq7ZWrVopPT292P4+Pj6Kj4/XmDFjdP/995d6yz0AAACKIgcDAACuwGQYhmHLCmlpaZozZ45OnDhhaatVq5ZGjx7NpxR/kZOTo4iICGVmZio4ONjZ4QAAADvjWu+aOC8AAHi20q71Nt3RtXz5ciUlJWn06NG6++67FRQUpIMHD+qDDz7QCy+8oGrVqum+++6zW/AAAAAAAABAWdlU6EpNTdXo0aMVGxtraQsKClJsbKwMw1BqaiqFLgAAAAAAADiFly2ds7Oz1b59+2KXtW/fXtnZ2faICQAAAAAAALCZTYWuhg0bas+ePcUu27Nnjxo2bGiXoAAAAAAAAABb2VToevDBBzV79mz961//0unTpyVJ+fn5WrFihV555RU9+OCDDgkSAAAAAAAAKI1Nc3QNGTJEp0+fVmJioqZMmaIaNWro9OnTqlatmh555BHFxMQ4Kk4AAAAAAADgimwqdEnSyJEjFRMTo6+++krHjh1TnTp11KFDB/n7+zsiPgAAAAAAAKBMbC50SVLNmjXVvXt3e8cCAAAAAAAAlFupha4+ffro9ddfV6NGjTRq1KhSB0xJSbFLYAAAAAAAAIAtSp2MPigoSFWrXqqH+fv7l/q4kujoaIWGhloenTp1Unx8vPLy8izLU1NTLf1DQ0O1e/duqzEWLVqkNm3a6Msvv7S0rV+/Xv369VNYWJh69Oih5cuXF9n2hQsX9Nprr6lnz54KCwtTt27dNH36dJ08ebLYWGfNmqXQ0FD9/vvvpR0iAAAAl0YOBgAAKotS7+j6a9KTlJR01RscOXKkBg8eLMMwdPDgQSUnJysuLk6vv/56qetu3LhRM2fO1MyZM9WxY0dJ0meffaa4uDhNnDhRt912m3bv3q0JEyaofv36lq9XGoahJ554Qr/++qsmTpyoli1bKisrS3PnztUDDzygZcuWyc/Pz7IdwzC0Zs0aeXt7KyMjo0x3sgEAALgycjAAAFAZlHpH11+lpKQoNze32GUHDhzQrFmzSh2jRo0aCgwMVP369dWmTRslJSXpk08+0f79+6+43vfff69x48Zp3Lhx6tWrl6V9w4YNGjx4sAYPHqzg4GDddddd6tevnz744ANLn4yMDH399dd6++23dfvttysoKEidO3fWwoULVVBQoCVLllhta9euXTpw4IAee+wxrVmzptR9cldpaWkKCQmRl5eXQkJClJaW5uyQ4AEqy+vK3vtpz/EqyzmwN46b/XFMXQs5mOOV9TXP3wYAifeCv+JYOE6lPLaGDVq0aGF8/fXXxS7btGmT0bp16yuu/+CDDxoLFy4s0t6qVStj586dRZY3b97c+Pbbb40DBw4Yt956q5GYmFimOKdNm2aMHTvW8jwmJsaYPXt2sX137txpbN68ucj6w4YNM/7880+jZcuWxvfff1+m7f5Vdna20bx5cyM7O9vmdSvCkiVLDD8/P0OS5eHn52csWbLE2aHBjVWW15W999Oe41WWc2BvHDf7qwzH1NWv9X9FDuZ4ZX3NV4a/DQCl473gvzgWjuOpx7a0a32Z7ugaNWqURo0aJcMwNHPmTMvzy4/HHntMEyZMUI8ePWwutB0+fFjnz5/XNddcU+zy/Px8jRgxQuHh4YqPjy91vO3bt2vVqlXq16+fpe2HH37QjTfeWGz/jh07WsVdWFiodevWqU+fPqpXr546d+6sjIyMK24zLy9POTk5Vo+S7nxzFQkJCcrPz7dqy8/PV0JCgpMigieoLK8re++nPcerLOfA3jhu9scxdX3kYPZV1tc8fxsAJN4L/opj4TiV9diWOkeXJMsk8yaTSb6+vsVOOh8dHa0hQ4bYtPHjx49r2rRpatu2rUJCQort88wzzygrK0vNmjWTl1fJdbl3331XiYmJKigoUGxsrGVuCOlSEhQQEFCmmLZv364TJ07ozjvvlHTpVydTUlL01FNPlbj9xYsXu92vTWZlZdnUDpRFZXld2Xs/7TleZTkH9sZxsz+OqWsjB7O/sr7m+dsAIPFe8FccC8eprMe2TIWuy5PQf/rppxo9erTatGlT7g3OmjXLkpDk5+erQ4cOmj17don9z549q6VLl2rYsGFKT0/XgAEDiu3Xp08fde7cWb/++qtmzJihixcvaty4cZIuFeou/6pQaVavXq3u3burZs2akqSePXtq6tSp+uqrr9SpU6di14mJiVFkZKRVW25urqKiosq0TWdo0qSJ9u3bV2w7UF6V5XVl7/2053iV5RzYG8fN/jimrocczLHK+prnbwOAxHvBX3EsHKeyHlubJqPfunXrVRW5pEsJyapVq7Rq1Spt27ZN77zzjq699toS+0+bNk0dOnTQlClTNG3aNP3222/F9vP399d1112nO++8U4mJiVq8eLEKCgokSWFhYfrqq6+KXW/Hjh3KzMyUJBUUFGjDhg3atGmT2rRpozZt2ui2227ThQsXrnjrfEBAgIKDg60eQUFBZT0kTpGYmGj1K0eS5Ofnp8TERCdFBE9QWV5X9t5Pe45XWc6BvXHc7I9j6nrIwRyrrK95/jYASLwX/BXHwnEq7bG1ddKv/fv3GytXrjQWL15c7ONKSpoItaTllydCvezJJ5807rnnHuPcuXOWtgkTJhgbNmywGmfXrl1GixYtjPz8fMMwDOP99983unTpYvz5559W/c6cOWP07NnTWLBggWEYhvHRRx8Z7dq1M77//nvjl19+sTxSU1ONzp07G+fPny/l6PyXO0xQu2TJEsNsNhsmk8kwm81uPyEdXENleV3Zez/tOV5lOQf2xnGzP08/pu5wrb+MHKxilPU17+l/GwDKhveC/+JYOI4nHtvSrvUmwzCMshbFtmzZolGjRql69eo6d+6cfH19VaNGDR08eFDVqlVTnTp1LJ/MFSc6Olo9evTQ0KFDy7Q8NDRUK1assNxFduLECfXr10933XWXZVLU5ORkvf/++3rmmWfUtm1bZWVl6fnnn1doaKhmzJghSbp48aJGjhypffv2ady4cWrZsqWysrL0yiuvKC8vT8uWLZOfn5/GjBkjLy8vzZo1yyquU6dO6eabb9a8efOs5p24kpycHEVERCgzM1PBwcFlWgcAALgPd7rWk4MBAABPUdq1vkxzdF328ssvKzIyUlOnTtXSpUu1c+dOvfLKK8rNzdXw4cP1xBNP2CvuYtWqVUsvvvii/vGPf6hLly7q3r274uLiVLNmTSUnJ+vAgQOqV6+eIiMj9fjjj1vW8/Ly0pw5c7Rw4UIlJiZq//79qlevnnr27KnRo0fLz89Pp06d0scff6yZM2cW2W7NmjXVvXt3ZWRklDnJAgAA8BTkYAAAwF3YdEdX69atlZqaqptuuklffPGFpk2bpg8//FDSpQlE582bp3Xr1jksWHfCp4kAAHg2rvWuifMCAIBnK+1ab9Nk9P7+/jp8+LAk6brrrlNWVpZlstHAwEDl5OTYIWQAAAAAAADAdjYVurp3767Zs2crOztbDRo0UN26dfX+++9LktasWaOQkBBHxAgAAAAAAACUyqZC19NPP62AgADt3btXkvTII4/omWeeUbt27bRq1SrFxcU5JEgAAAAAAACgNDZNRl+3bl2tXLlSl6f1io6OVps2bZSdna327durcePGDgkSAAAAAAAAKI1Nd3Tt379f58+fl5fXf1dr37697r77bl177bX6z3/+Y/cAAQAAAAAAgLKwqdAVERGh7777rthlO3fuVP/+/e0SFAAAAAAAAGCrMn11MTMzU5JkGIa+/PJLHTt2zGq5YRhauXKlmjRpYv8IAQAAAAAAgDIoU6Fr5MiRkiSTyaSXX3652D7BwcF64YUX7BcZAAAAAAAAYIMyFbp+/PFHSVKLFi20dOlSdejQwaFBAQAAAAAAALayaY6u119/XTfccIOjYimTU6dOqW3btoqLi7Nqj4+PV2hoaImPnJwcSdIdd9xh1d61a1dNnz5d586dkyRt3LhRLVu2tBT3Ljtx4oRuvvlmzZ07t2J2FAAAwEWQfwEAAHdhU6ErPDxcJpNJp06dKvHhaBs3btTFixe1adMmnTlzxtKekJCgrVu3auvWrZo+fbqCgoIsz7du3aqGDRta+k6fPl1bt27Vp59+qldeeUWff/65EhMTJUl33nmnunfvbnl+2dy5c+Xn56dHHnnE4fsIAADgSsi/AACAu7Cp0NWxY0d16tTpig9HW716tQYPHqwaNWpo8+bNlnZ/f38FBgYqMDBQ/v7+qlKliuV5YGCgqlSpUqRv/fr11alTJz3zzDNatWqVCgoKJF1K2r799lutXbtWkrR3714tXbpUEyZMUPXq1R2+j4AnSktLU0hIiLy8vBQSEqK0tDRnhwQAKCPyr8qBazUAW1SW94zKsp+epExzdF321ltvFWkrLCzUtm3btH79eo0bN85ugRXnyJEj+uKLLzRmzBgZhqHVq1erd+/eVz1us2bNdO7cOR09elRBQUFq3LixRowYoeTkZPXo0UOJiYnq0qWLevbsaYe9ACqftLQ0xcbGKj8/X5K0b98+xcbGSpKioqKcGRoAoBTkX5UD12oAtqgs7xmVZT89jU2Frs6dOxfb3qVLF3l7eysjI0N33HGHXQIrzrp169SoUSO1adNGBQUFGjJkiE6cOKFatWpd1biHDh2Sl5eXAgICLG1Dhw7VqlWrNGzYMO3atUurV68ucf28vDzl5eVZteXm5l5VTIAnSUhIsFwcLsvPz1dCQgIXCABwca6af0nkYPbEtRqALSrLe0Zl2U9PY1Oh60o6duyo119/3V7DFSsjI0O9e/eWyWTSjTfeqHr16umjjz7SoEGDyj3mkSNHNHv2bHXv3l1+fn6Wdh8fH0VHR2vatGnq2bOnmjZtWuIYixcvVkpKSrljADxdVlaWTe0AANfhqvmXRA5mT1yrAdiisrxnVJb99DQ2zdF1JRkZGapRo4a9hivijz/+0Ndff225Vd5kMumuu+5SRkaGzWM99dRTCg8PV/v27dW1a1cVFBTo+eeft+pTUFCgt99+W82bN9fHH3+s3377rcTxYmJilJmZafXge7vAfzVp0sSmdgCAa3Dl/EsiB7MnrtUAbFFZ3jMqy356Gpvu6Bo1alSRNsMw9Ntvv+nXX39VfHy83QL7X2vWrJEkDRw40NJWWFgowzB08OBBNWjQoMxjjRs3Tj169NC5c+c0fPhwtW/fXnXr1rXqk5qaqlOnTunDDz9UbGyspk+frtTU1GLHCwgIsLrtHoC1xMREq++2S5Kfn1+RX9cCALgWV86/JHIwe+JaDcAWleU9o7Lsp6ex6Y4uf3//Io+AgAB169ZNixcv1pAhQxwU5qVf+4mOjtaqVassj9WrV6tRo0aWX+cpqwYNGshsNqt58+YaN26c3nzzTWVnZ1uWZ2dna/78+Ro3bpxq1aqlyZMn6/PPP9eGDRvsvVtApRAVFaUFCxbIbDbLZDLJbDZrwYIFfK8dAFwc+VflwbUagC0qy3tGZdlPT2PTHV1JSUmOiuOKfvnlF+3du1ezZ89Ws2bNrJb17t1bGRkZ5S6y9e7dW2lpaZoxY4bmzJkj6VLVNiwsTP3795cktWrVSvfff7+SkpJ022238RPXQDlERUVxQQAAN0L+VflwrQZgi8rynlFZ9tOT2G2OLkfKyMhQixYtiiRZktSnTx/t3r1b+/btK/f4kyZN0oYNG7Rz505t3LhRW7Zs0ZQpU2QymSx9nnzySeXn5zt8wn0AAABXQP4FAADckckwDONKHR566CGbBnzrrbeuKiBPkZOTo4iICGVmZio4ONjZ4QAAADvjWu+aOC8AAHi20q71pX51cefOnTIMQ1WrVtXf/vY3XXPNNQ4JFAAAAAAAALgapRa6Nm/erJUrV+q9997TRx99pO7du2vgwIHq3r27vLzc4puPAAAAAAAAqARKrVQFBQVp5MiR2rBhg15//XX5+vpqzJgx6tatm2bNmmX1azkAAAAAAACAs5T5VxdNJpO6du2qrl276sSJE/rggw+0cuVKLViwQB07dtSgQYP0t7/9TdWqVXNkvAAAAAAAAECxyvXdw1q1aik6OlqrVq3S8uXL1aRJEz399NPq1q2bveMDAAAAAAAAyuSqJtnKysrS+vXrtWXLFlWtWlW33nqrveICAAAAAAAAbFLmry5edu7cOa1bt04rVqzQl19+qeuuu07Dhg1T//79VadOHUfECAAAAAAAAJSqzIWu77//XsuXL1dGRoYuXLigu+66S08++aQ6dOjgyPgAAAAAAACAMim10JWWlqYVK1boxx9/VGhoqJ588kn1799fNWvWrIj4AAAAAAAAgDIpdY6uadOmac+ePTIMQ3/88YdeeeUV3XHHHercuXOxD1tFR0crNDTU8ujUqZPi4+OVl5dnWZ6ammrpHxoaqt27d1uNsWjRIrVp00ZffvmlpW39+vXq16+fwsLC1KNHDy1fvtxqnfj4eKvthoeH69FHH9X+/fut+v3yyy8aMGCAwsPDbd43AAAAV0T+BQAAPFWpd3QlJSU5PIiRI0dq8ODBMgxDBw8eVHJysuLi4vT666+Xuu7GjRs1c+ZMzZw5Ux07dpQkffbZZ4qLi9PEiRN12223affu3ZowYYLq16+v7t27W9YdMGCAxo4dK0k6evSo5s6dq2HDhmn16tWqUqWK0tLSNGPGDHXu3Fm//fabY3YeAADACci/AACAJyr1jq7IyMgyP+655x7t3LlT+fn5NgVRo0YNBQYGqn79+mrTpo2SkpL0ySefFPl07399//33GjdunMaNG6devXpZ2jds2KDBgwdr8ODBCg4O1l133aV+/frpgw8+sFrf19dXgYGBCgwMVGhoqJKTk5Wdna1vvvlGkvTRRx8pNTVVQ4cOtWl/UHmlpaUpJCREXl5eCgkJUVpamrNDAgCXw3ulayD/giPxdw7AEcr63mLP9yDez9yPzb+6eCXnz5/XQw89pBUrVqhVq1blHic4OFje3t5XTLRyc3M1YsQIDRw4UEOGDLFaNnXq1CL9fXx8dPr06Stu18/PT40aNdIff/yhjh07KjU1Vd7e3tq+fXt5dgOVTFpammJjYy2F3n379ik2NlaSFBUV5czQAMBl8F7pusi/YC/8nQNwhLK+t9jzPYj3M/dU6h1dtjIM46rHOHz4sM6fP69rrrmm2OX5+fkaMWKEwsPDFR8fX+p427dv16pVq9SvX78r9issLNSRI0dUp04dSZK3t3eZ4s3Ly1NOTo7VIzc3t0zrwnMkJCQUuZsxPz9fCQkJTooIAFwP75Wuy93yL4kczFXxdw7AEcr63mLP9yDez9yTXe/osofjx49r2rRpatu2rUJCQort88wzzygrK0vNmjWTl1fJtbp3331XiYmJKigoUGxsrNX8EP/r7NmzevXVV1WtWjV16tTJppgXL16slJQUm9aB58nKyrKpHQAqI94rXZM75l8SOZir4u8cgCOU9b3Fnu9BvJ+5J5codM2aNcuSpOTn56tDhw6aPXt2if3Pnj2rpUuXatiwYUpPT9eAAQOK7denTx917txZv/76q2bMmKGLFy9q3LhxluXvvPOO3nvvPcuYjRo10quvvipfX1+b4o+JiVFkZKRVW25uLrcyVjJNmjTRvn37im0HAFzCe6XrcPf8SyIHc1X8nQNwhLK+t9jzPYj3M/fkEoWumJgYDRo0SJIUEBBguXW9JNOmTVOHDh00ZcoUTZ06VeHh4WratGmRfv7+/vL399d1112n2rVra8iQIRozZox8fHwkSb1799bo0aMlXfp1yePHj6tNmzY2xx8QEKCAgACb14NnSUxMtPr+tnRp3pHExEQnRgUAroX3Stfh7vnX5bjJwVwPf+cAHKGs7y32fA/i/cw92X2OrvKoW7euzGazzGZzqUnW5f6S1L9/f91+++0aO3asCgoKLMsnTpyojRs3Wq3j4+OjwsJCFRYWWtpq1apl2e4zzzyjH374QR9++KGd9gqVTVRUlBYsWCCz2SyTySSz2awFCxbwqTIA/AXvla6D/AuOwt85AEco63uLPd+DeD9zT3a/o6tRo0Y2TSJ6taZMmaJ+/frp5ZdftkyMWrt2bU2ZMkUXLlxQ27ZtlZWVpeeff159+/Yt8bb4a6+9VkOHDtWMGTN0xx13lOv2eSAqKoo3PQAoBe+V7o/8C6Xh7xyAI5T1vcWe70G8n7mfchW6jh49qt27d+vYsWPq0aOH/Pz8VFhYKF9fX23atMneMV5RrVq19OKLL+of//iHunTpou7duysuLk41a9ZUcnKyDhw4oHr16ikyMlKPP/74FceKjY1Venq6UlNTNWrUqAraAwAAAPdC/gUAAFyVyTAMo6ydL1y4oOeff17/+te/ZDKZdOHCBa1YsUIHDhxQYmKi0tLS1KhRI0fG6zZycnIUERGhzMxMBQcHOzscAABgZ1zrXRPnBQAAz1batd6mObpSUlK0bt06zZ8/X1999ZUu18i6dOmiRo0a6cUXX7RP1AAAAAAAAICNbCp0rVy5UnFxcbr11ltVpUoVS3uNGjX0j3/8Q5999pndAwQAAAAAAADKwqZC17Fjx3TttdcWu8zX19fql3cAAAAAAACAimRToatFixZFfjb6svXr1yssLMwuQQEAAAAAAAC2sulXF5944gmNGDFCFy9eVK9evWQymbRr1y69++67ll/LAQAAAAAAAJzBpju6br31Vr3xxhvas2ePYmJiZBiGnnvuOe3du1eLFi3SzTff7Kg4AQAAAAAAgCuy6Y4uSerUqZPeeecdnTlzRsePH1etWrXk5+fniNgAAAAAAACAMrPpji5JOnXqlAoKCuTr66uGDRtq//79WrNmjQ4ePOiI+AAAAAAAAIAysanQtXv3bvXs2VNbtmyRJG3evFn9+vXThAkT1KtXL+3atcshQQIAAAAAAAClsanQ9dxzz6lz58664447JElz587ViBEj9M0336h79+566aWXHBKkraKjoy1ziBW3bO7cuZKk0NDQYh979+619P/qq68UERGhvn37Vlj8AAAA7ogcDAAAOJtNc3T98MMPGjt2rKpUqaLjx4/rhx9+0Ny5c1WlShXdd999euyxxxwVp822bdumpUuXKioq6or9Fi5cqBYtWli11alTR5L08ssv66233lKHDh106NAhh8UKAADgKcjBAACAM9l0R5e/v78OHz4sSdq6dasaNmyoa6+9VpJUUFCgqlVtntveYRo3bqwZM2YoKyvriv1q166twMBAq8fl/di6datWrFjBJ4kAnCYtLU0hISHy8vJSSEiI0tLSnB0SAFwRORgAAHAmmwpdf//73/XSSy9p/vz5mjlzpvr06SNJysnJ0T//+U/ddNNNDgmyPPr166fw8HDFx8fr4sWL5Rpj2bJluv766+0cGQCUTVpammJjY7Vv3z4ZhqF9+/YpNjaWYhcAl0YOBgAAnMmmQld8fLxuueUWvffee7r55pv1+OOPS5LWr1+vM2fOaNKkSQ4JsjxMJpMSExP1448/6q233irXGN7e3mXql5eXp5ycHKtHbm5uubYJAJclJCQoPz/fqi0/P18JCQlOiggASkcOBgAAnMmm7xr6+voqKSmpSPuDDz6of/zjHzKZTHYLzB4aNWqk+Ph4TZ8+XbfddpuaNWtWpM+DDz4oL6//1vtuu+02zZkzx6btLF68WCkpKVcdLwD8VUlf+ynt60AA4GzkYAAAwFnsMqmWj4+PPYZxiIEDB2rjxo2aMGGC3nnnnSLLX3rpJTVv3tzy3NfX1+ZtxMTEKDIy0qotNze31ElYAeBKmjRpon379hXbDgCujhwMAAA4g82Frvfee0+bN2/WiRMniv3p6PLeou4oJpNJ06ZNU58+fbRw4cIiy4OCgmQ2m69qGwEBAQoICLiqMQDgfyUmJio2Ntbq64t+fn5KTEx0YlQAUDbkYAAAwBlsKnTNmjVLr732mm655RY1b97c5b6qWJIGDRpo4sSJmjx5sq655hp17tzZ2SEBQKku35GQkJCgrKwsNWnSRImJidypAMBtkIMBAICKZlOh691339Xw4cP15JNPOioehxkwYIDWr1+vzZs3OzsUACizqKgoClsA3Bo5GAAAqEg2/erimTNndMsttzgqFoebNm2aateu7ewwAAAAKhVyMAAAUFFsuqOrR48e+ve//+3yt52//fbbxbYHBgZq+/btluc//fRTmcYbMGCABgwYYJfYAAAAPBU5GAAAcDabCl19+/bVyy+/rBMnTqhdu3aqUqVKkT4RERF2Cw4AAAAAAAAoK5sKXaNHj5YkpaamFrvcZDJpz549Vx8VAAAAAAAAYCObCl0//vijo+IAAAAAAAAAropNk9EDAAAAAAAArsrmQteuXbv02GOPqUuXLgoLC9P333+v77//XklJSbp48aIjYgQAAAAAAABKZVOha+vWrXrggQfk6+ur8ePHWxW2Vq1apQULFtg9QAAAAAAAAKAsbCp0zZo1S4MHD9bMmTN19913W9pbtWql0aNH61//+pfdAwQAAAAAAADKwqZC108//aSePXsWu+yGG25Qbm6uXYICAAAAAAAAbGVToatOnTrKzs4udtnPP/+soKAguwQFAAAAAAAA2KqqLZ3vu+8+zZ49W6GhoQoLC5MkmUwmff3110pJSdGDDz7okCAlafv27XrooYdKXD5q1ChJUkpKiqWtWrVqCg8P16RJk3TDDTdY2tu2batz585ZrT9mzBg99thjlufvvPOOFi1apOzsbF133XV66qmndMcdd9hrdwAAANwCORgAAHAnNhW6Ro4cqSNHjmjgwIFq3LixJOnhhx/WiRMnFBkZqREjRjgkSEkKDw/X1q1bLc979uyp5ORkhYeHS5L8/Py0aNEide3aVcnJyZKkkydPKi0tTUOGDNFHH32kmjVr6ujRozp37pzS09NVv359y3h+fn6Wfy9fvlwzZ87Us88+q3bt2umTTz7RmDFjtHz5crVo0cJh+wgAAOBqyMEAAIA7semri1WrVtVzzz2ntWvX6uGHH9bjjz+uxx9/XGvWrNHzzz+vKlWqOCpO+fj4KDAw0PIwmUyqVauW5XmNGjWK9Lvuuus0adIkVatWTRs3bpQkHTx4UCaTSc2bN7ca7/L6krRhwwaNHDlSffr0UXBwsB544AHddNNN+vDDDx22f2WVlpamkJAQeXl5KSQkRGlpaZVq+1fiyrHB/irL+S7PfrrrsXHXuJ2N48YxcDRyMKBiuPJ7mSvHVtHKeiwq+pg54xzxunAP9nzNus05N9xU+/btjW3btlm1zZkzx4iNjS3S9+GHHzZSUlIMwzCMzMxMo0uXLjZvb8SIEcbLL79c5v7Z2dlG8+bNjezsbJu3VZIlS5YYfn5+hiTLw8/Pz1iyZIndtuHK23fX2GB/leV8l2c/3fXYuGvczsZxc+4xcMS13h1UxhwMcDRXfj935dgqWlmPRUUfM2ecI14X7sGer1lXOuelXettKnTl5+cbzzzzjPHdd98ZhmEYubm5xqBBg4zWrVsbI0aMME6ePHn1EZeRLUnW3//+d2PZsmWGYVw6Obfccovx8MMPGx07djR69+5trFy5ssTtFBYWGmvXrjXatWtn7Nmzp9g+J06cMLKzs60eO3futHuSZTabrV5Ulx9ms9lu23Dl7btrbLC/ynK+y7Of7nps3DVuZ+O4OfcYVNaCSmXMwQBHc+X3c1eOraKV9VhU9DFzxjnideEe7PmadaVzXloOZtMcXc8995x27NihmJgYSdKLL76oKlWqKDExUXPmzNGMGTP07LPP2jKkQxUUFOjNN9/UwYMHFRERIUlq3LixunXrpoiICE2YMEHbt2/XlClT5O3trbvvvttq/VmzZik1NVWFhYWaNGlSiXNDLF682GoCVkfJysqyqd3Ttn8lrhwb7K+ynO/y7Ke7Hht3jdvZOG4cA1flaTkY4Giu/F7myrFVtLIei4o+Zs44R7wu3IM9X7PudM5tKnRt2LBB06dPV7NmzXT+/Hlt2rRJb7zxhmUy0qSkJKcXuj755BNLPOfOnVNQUJD++c9/ql69epKkbt26qVu3bpb+119/vY4fP65FixYVSbJiYmLUr18/7d69Wy+99JIkKSoqqsg2Y2JiFBkZadWWm5tbbN+r0aRJE+3bt6/Y9org7O1fiSvHBvurLOe7PPvprsfGXeN2No4bx8CVeHIOBjiaK7+XuXJsFa2sx6Kij5kzzhGvC/dgz9esO51zmyajP3funAICAiRJu3fvlslkUtu2bSVJ9evXV15env0jtFHHjh21atUqrVq1Sps3b1ZmZqa6dOlyxXXCw8OVnZ1dpL1u3bpq1qyZ7rnnHo0bN04LFy4sdv2AgAAFBwdbPYKCguyyP3+VmJho9ctE0qVfKkpMTLT7tlxx+1fiyrHB/irL+S7PfrrrsXHXuJ2N48YxcCWenIMBjubK72WuHFtFK+uxqOhj5oxzxOvCPdjzNetW59yW70E++OCDxqOPPmocPHjQGDlypDFy5EjLsunTpxt9+/a9ui9a2sCW+SH+6rXXXjNef/11q7ZFixZZYr9w4YIxYsQI45tvvrHq8+GHHxq33HJLmeNz1LwdS5YsMcxms2EymQyz2VzhE785e/tX4sqxwf4qy/kuz36667Fx17idjePmvGPAHF3/VRlyMMDRXPn93JVjq2hlPRYVfcyccY54XbgHe75mXeWc23Uy+t27dxudOnUyWrRoYXTu3NkyMegbb7xhtGjRwvjwww+vPuIyKm+StXHjRqN169bGypUrjf379xtr1qwxwsPDjRUrVlj6jB492vjb3/5mfPrpp8b+/fuNTZs2Gbfffrsxe/bsMsdHkgUAgGerrNd6cjAAAOBMdp2MvnXr1tq0aZN+/vlnXX/99fL395ck3XLLLXr33XfVrl07h9x1Zk8RERGaOnWqXnvtNU2cOFHBwcEaP3687r33XkufF154QXPmzFF8fLz+/PNPNWrUSIMHD9bQoUOdGDkAAID7IgcDAAAVwWQYhuHsIDxRTk6OIiIilJmZqeDgYGeHAwAA7IxrvWvivAAA4NlKu9bbNBl9YWGhXnvtNf3nP/+RJJ08eVJjx45Vr1699Pzzz+v8+fP2iRoAAAAAAACwkU2FrpkzZ2rRokU6ffq0JGnGjBn68ccf1b9/f61evVpz5851SJAAAAAAAABAaWyaoys9PV3jx49X27ZtdfHiRa1du1Zz5sxRly5dVK9ePaWkpGjs2LGOihUAAAAAAAAokU13dJ08eVINGzaUJP300086e/asbrzxRklSSEiIjh49av8IAQAAAAAAgDKwqdDVokULZWRkyDAMpaWlqWPHjvLx8ZEkff3115YiGAAAAAAAAFDRbPrq4pNPPqkRI0Zo9erVMgxDixYtkiStWLFCc+bMUUJCgkOCBAAAAAAAAEpjU6Hr1ltv1Ycffqhvv/1Wbdq0kdlsliQ1a9ZML730knr37u2QIAEAAAAAAIDS2FTokqQmTZqoSZMmVm3h4eEKDw+3W1AAAAAAAACArUotdC1YsEDR0dHy9fVVZmZmqQNGRETYJbCrER0drR07dlieBwQE6Pbbb9fEiRNVu3ZtxcfH67333rMsr1atmjp37qwpU6aocePGlvYTJ07oueeeU0ZGhlasWKE2bdpU6H4AAAC4E3IwAADgbKUWul577TX16NFDzZs318iRI6/Y12Qyac+ePXYL7mqMHDlSgwcPlmEYOnjwoF544QXFxcUpNTVVkjRgwACNHTtWkpSXl6e5c+dqxIgRysjIkMlk0s6dOxUXF6ewsDBn7gYAAIBbIQcDAADOVGqh67333rN8VfHHH390eED2UqNGDQUGBkqS6tevr+eff15/+9vftH//fkmSr6+vZXlgYKAmT56sLl266LffftN1112ntWvXatSoURo0aJBCQ0Odth8AAADuhBwMAAA4U6mFrg8++KDMg5lMplLv+nIWs9ksb29vS5JVEsMwJEkTJkyQt7d3RYQGAADgscjBACktLU0JCQnKyspSkyZNlJiYqKioKGeHBQAeqdRCV0pKikwmk6pUqaIbbrjBkoQUx5ULXceOHdP58+dVp06dIstOnTql5557Tp06ddJ1110nSSRYAAAAdkAOhsouLS1NsbGxys/PlyTt27dPsbGxkkSxCwAcoNRC14wZM7RixQrt2LFDFy5c0L333qv+/fsXm6y4qpMnTyo5OVktWrSwJFHLli2zTIZ65swZNWrUSK+++qpMJpPN4+fl5SkvL8+qLTc39+oDBwAAcGPkYICUkJBgKXJdlp+fr4SEBApdAOAApRa67r77bt19993Kzs7WypUr9cYbb+jll1/WHXfcoYEDB+qWW26piDhtNmvWLKWkpEi6lES1bNlSc+bMsSRRvXv31qhRoyRJZ8+e1apVqzR48GCtWLHCkoiV1eLFiy3bAgAAqMzIwQBrWVlZNrUDAK5OqYWuyxo3bqwnnnhCo0eP1qeffqqVK1dq+PDhql+/vgYMGKB7771XDRs2dGSsNomJidGgQYNkGIaeeuopmc1mmc1my3J/f3+r5+PHj9dPP/2k5cuXa/z48TZvKzIy0qotNzeXT2gAAEClQw4GWGvSpIn27dtXbDsAwP68bF7By0vdu3fXnDlz9MknnygqKkoffvihIiIiNGzYMEfEWC5169aV2WxWSEiInnnmGa1Zs0bffPPNFde55pprdPr0aZu3FRAQoODgYKtHUFBQeUMHAABwW+RggLXExET5+flZtfn5+SkxMdFJEQGAZ7O50PVXdevWVWhoqJo1a6aLFy/ql19+sVdcdtWuXTv169dPzz//vGUy/TNnzujw4cM6fPiw/vjjD6Wnp2vt2rW6/fbbnRwtAACAZyAHAy5NOL9gwQKZzWaZTCaZzWYtWLCAOw8BwEHK/NXFv9q/f7/S09OVnp6uQ4cOqVu3bnr11VfVvXt3e8dnN3FxcerVq5fef/99SbLEL0lVq1ZVSEiIJk+eTJIFAABgR+RgwKViF4UtAKgYJuPyx2ulOH/+vDZs2KCVK1fq888/17XXXqv77rtPAwYMUP369R0dp9vJyclRRESEMjMzFRwc7OxwAACAnXGtd02cFwAAPFtp1/pS7+j6+eeftWLFCr3//vs6deqUIiIilJqaqi5dupTrZ6ABAAAAAAAARyi10HX33XfLZDLJMAy1b99ehYWFWrp0qZYuXVpsf37iGQAAAAAAAM5QaqHrnnvu4c4tAAAAAAAAuLxSC10vvPBCRcQBAAAAAAAAXBUvZwcAAAAAAAAA2AOFLgAAAAAAAHgECl0AAAAAAADwCBS6AAAAAAAA4BEodAEAAAAAAMAjlPqri64iOjpaO3bsKNIeFxen2NhYSdLnn3+uefPm6dtvv5W3t7duvvlmjR07Vtdff72lf3x8vN57770i4zz88MMaP368tm/froceesjSXqVKFTVr1kwjR45Ur169HLBnAAAAroscDAAAuBO3KXRJ0siRIzV48GCrtho1akiS1qxZowkTJmjUqFFKSkrS+fPnlZaWpkGDBmnJkiUKCwuzrDNgwACNHTvWahxfX1+rf2/YsEGSVFBQoC1btuipp55SYGCgbrzxRkftHgAAgEsiBwMAAO7CrQpdNWrUUGBgYJH2U6dOaerUqXr22WcVGRlpaZ88ebJOnTqlyZMna8WKFZZ2X1/fYse5zGQyWS1/4IEH9PXXX2vFihUkWQAAoNIhBwMAAO7CI+bo2rhxo/z9/XXPPfcUWRYbG6uaNWvqxIkTV7WNG264QQcOHLiqMZwlLS1NISEh8vLyUkhIiNLS0pwdkgWxlY8rx1YSd4z5Sjxtf+zNlY+PK8dWEnvHXJ7xnH3c3DHmyoAcDJ7Mnu8hrjqWPblqXLBWWc6Tq+5nWeNy1fjdhuEmHnzwQWPhwoXFLps+fboxduzYMo0zfvx449lnny1x+bZt24z27dsXaX/uueeMJ554omzBGoaRnZ1tNG/e3MjOzi7zOo6wZMkSw8/Pz5Bkefj5+RlLlixxalzE5pmxlcQdY74ST9sfe3Pl4+PKsZXE3jGXZzxnHzdXjdlVrvWORg6Gysie7yGuOpY9uWpcsFZZzpOr7mdZ43LV+F1Jadd6typ0tWrVymjfvr3VIysry4iPjzemTJlSpnHGjx9vhIWFFRnn6NGjhmEUTbIuXrxo7Ny507jxxhuN9evXFzvmiRMnjOzsbKvHzp07XSLJMpvNVn8glx9ms9mpcRGbZ8ZWEneM+Uo8bX/szZWPjyvHVhJ7x1ye8Zx93Fw15spSUCEHQ2Vkz/cQVx3Lnlw1LlirLOfJVfezrHG5avyupLQczK3m6BoyZIgGDhxo1RYUFCR/f3/9+eefVu2jR4/Wp59+ann++uuvq2PHjpKk3r17a9SoUVb9AwICLP/Oz89XeHi4JOnChQsyDEOPP/64evbsWWxcixcvVkpKSvl3zIGysrJsaq9IxFY+rhxbSdwx5ivxtP2xN1c+Pq4cW0nsHXN5xnP2cXPHmD0NORgqG3u+h7jqWPbkqnHBWmU5T666n2WNy1XjdyduVeiqU6eOzGZzkfaWLVtqzpw5unjxory8Lk07NmnSJJ05c0aFhYXq3bu3qlb97676+/sXO85l1apV06pVqyRJ33zzjRISEhQREVFi/5iYGKsJWCUpNzdXUVFRtuyeQzRp0kT79u0rtt3ZiK18XDm2krhjzFfiaftjb658fFw5tpLYO+byjOfs4+aOMXsacjBUNvZ8D3HVsezJVeOCtcpynlx1P8sal6vG7048YjL6iIgInTx5UitXrrS01a9fX2azWXv37lX16tV1ww03lHm8KlWqyGw2y2w265577lHXrl2VlJRUYv+AgAAFBwdbPYKCgq5qn+wlMTFRfn5+Vm1+fn5KTEx0UkT/RWzl48qxlcQdY74ST9sfe3Pl4+PKsZXE3jGXZzxnHzd3jLmyIAeDp7Lne4irjmVPrhoXrFWW8+Sq+1nWuFw1frdSoV+kvAoPPvig8corrxiHDh2yepw8edIwDMP44IMPjNatWxspKSnGr7/+amRlZRlvv/220b59e+ONN96wjFOeiVD/85//GGFhYcbHH39c5nhdad6OJUuWGGaz2TCZTIbZbHapSeyIrXxcObaSuGPMV+Jp+2Nvrnx8XDm2ktg75vKM5+zj5ooxu9K13pHIwVBZ2fM9xFXHsidXjQvWKst5ctX9LGtcrhq/qyjtWm8yDMNwdrGtLKKjo7Vjx44i7Q8//LDGjx8vSfr888/1z3/+U99++60Mw1CLFi30j3/8Q71797b0j4+Pl5+fnyZPnlzsdrZv364RI0bom2++sWpPSkrSxx9/rIyMDHl7e5cab05OjiIiIpSZmang4GBbdhUAALiBynKtJwcDAACupLRrvdsUutwNSRYAAJ6Na71r4rwAAODZSrvWe8QcXQAAAAAAAACFLgAAAAAAAHgECl0AAAAAAADwCBS6AAAAAAAA4BEodAEAAAAAAMAjUOgCAAAAAACAR6DQBQAAAAAAAI9AoQsAAAAAAAAegUIXAAAAAAAAPIJbFLqio6MVGhpqeXTq1Enx8fHKy8vT9u3brZb972Pu3LmSpLlz5xa7fOjQoZbthIaGKiUlpdgYQkNDtX379grZXwAAAFdADgYAANxNVWcHUFYjR47U4MGDZRiGDh48qOTkZMXFxWnevHnaunWrpV/Pnj2VnJys8PBwSZKfn59lWdeuXZWcnGw1ro+Pj9XzV199VREREWrZsqUD9wYAAMA9kIMBAAB34jaFrho1aigwMFCSVL9+fSUlJSkiIkJ//vmnGjVqZOlnMplUq1YtS9+/8vHxKbb9rxo2bKinn35aK1euLJKAAQAAVDbkYAAAwJ24xVcXixMcHCxvb2/t37/fruOOGzdOR44c0bx58+w6rqOlpaUpJCREXl5eCgkJUVpamrNDcnuufEwrKjZXPgaVhbueA3eN29lc+bi5cmyoWJU1B+NvwDYcL9uV9ZhxbN1DRZ8nXheexZ7ns1K+Ngw38OCDDxoLFy60ajt06JDRvHlz47fffrNqb9++vbFt27YiY8yZM8eIjY294naaN29ubNu2zdiwYYPRsmVL49tvvy2yrKyys7ON5s2bG9nZ2WVep7yWLFli+Pn5GZIsDz8/P2PJkiUO37ancuVjWlGxufIxqCzc9Ry4a9zO5srHzZVjc6aKvNY7CznYJfwN2IbjZbuyHjOOrXuo6PPE68Kz2PN8eupro7RrvVsWuo4dO2Y8/vjjxn333Vek75WSrJYtWxrt27e3eqSnp1v6/DWRGjt2rNGrVy/j7NmzRZb9rxMnThjZ2dlWj507d1ZY8ms2m61euJcfZrPZ4dv2VK58TCsqNlc+BpWFu54Dd43b2Vz5uLlybM5UGQtdlTUH42/ANhwv25X1mHFs3UNFnydeF57FnufTU18bpeVgbjNH16xZsyy/xpOfn68OHTpo9uzZNo1x4403avr06VZt9erVK7bv5MmT1bdvX82ePVvjx4+/4riLFy8u8ZeCKkJWVpZN7SidKx/TiorNlY9BZeGu58Bd43Y2Vz5urhwbHI8cjL8BW3G8bFfWY8axdQ8VfZ54XXgWe57PyvracJtCV0xMjAYNGiRJCggIUJ06dWwew8/PT2azuUx9a9WqpWnTpunRRx9VREREqbFFRkZateXm5ioqKsrmGMujSZMm2rdvX7HtKB9XPqYVFZsrH4PKwl3PgbvG7WyufNxcOTY4HjkYfwO24njZrqzHjGPrHir6PPG68Cz2PJ+V9bXhNpPR161bV2azWWazuVwJVnn06NFD/fv318SJE6/YLyAgQMHBwVaPoKCgColRkhITE61+wlu6lFAmJiZWWAyexpWPaUXF5srHoLJw13PgrnE7mysfN1eODY5HDsbfgK04XrYr6zHj2LqHij5PvC48iz3PZ6V9bVTwVynLpbiJUEtypfkhhgwZYhw6dMjqcfToUUuf4uaAOHHihNG9e3eXmAj1SpYsWWKYzWbDZDIZZrPZ7SeXcwWufEwrKjZXPgaVhbueA3eN29lc+bi5cmzOUhnn6LoST8/B+BuwDcfLdmU9Zhxb91DR54nXhWex5/n0xNdGadd6k2EYhrOLbaWJjo5Wjx49NHTo0FL7hoeHa/78+brpppus2ufOnVvsHA6tWrVSenq6JCk0NFRvvfVWkXW3bt2qoUOHFrusJDk5OYqIiFBmZqaCg4PLtA4AAHAfleFaTw4GAABcTWnXercodLkjkiwAADwb13rXxHkBAMCzlXatd5s5ugAAAAAAAIArodAFAAAAAAAAj0ChCwAAAAAAAB6hqrMD8FSFhYWSpNzcXCdHAgAAHOHyNf7yNR+ugRwMAADPVloORqHLQQ4fPixJioqKcnIkAADAkQ4fPiyz2ezsMPB/yMEAAKgcSsrB+NVFBzl79qy+++47BQYGqkqVKs4OBwAA2FlhYaEOHz6s1q1bq3r16s4OB/+HHAwAAM9WWg5GoQsAAAAAAAAegcnoAQAAAAAA4BEodAFAMeLj4/XYY485OwwAAIBK4Uq51549exQaGqqcnBzNnz9fHTp00L59+8o8dk5OjkJDQ7Vnzx57hQvAhTEZPQAAAADALTz44IPq0aMHPwICoETc0QUAAAAAcAs1a9ZUixYtnB0GABdGoQsAyuH8+fOaPXu2br31VoWFhal///7auHGjZfm6desUFhamkydPWtrmz5+v0NBQ/fTTT5a2VatWqV27djp79myFxg8AAOCO0tPT1bFjR6u2devW6a677lJYWJj69u2r77//Xt26dVN6erpVv0OHDunhhx9W69atdfPNN2v+/PkVGTqACkKhCwDKYfLkyXrnnXc0evRopaamqkuXLnr88cctxa5bbrlFXl5e2r59u2Wdzz//XJL0xRdfWNo+++wz3XzzzcX+LC4AAEBlYhiGLly4UORRWFhY4jqffPKJnnjiCd1yyy1KTU3V4MGD9fTTT+v06dNF+s6YMUPdu3fXwoUL1adPH82aNUsbNmxw5C4BcALm6AIAG/36669KT0/X66+/rm7dukmSunTpolOnTunll1/WnXfeKX9/f4WHh+uLL77QnXfeqTNnzuibb75R37599cUXX2jIkCEyDEOff/65Hn30USfvEQAAgPNt2rRJrVq1smmduXPnqnfv3po0aZKkSzmZr6+vJkyYUKTvoEGD9NBDD0mSbr75Zu3atUubNm1Sz549rz54AC6DO7oAwEZffPGFateurdtuu82qvX///vrPf/6jgwcPSpK6d+9uuYvrq6++Ur169RQTE6OdO3fqwoUL2rt3r/7880917969wvcBAADA1XTu3FkrVqwo8njppZeK7Z+fn6/du3erb9++Vu1/+9vfiu0fHh5u9bxZs2bKy8uzT/AAXAZ3dAGAjY4dO6b69evLZDJZtQcFBVmWN2jQQN26ddOMGTOUm5urzz77TF27dlWrVq1UtWpVffvtt9q1a5eaNm2qxo0bO2M3AAAAXIq/v7/atGlTpL1q1eL/23rixAkZhqEGDRpYtdesWVM1atQodRyTySTDMK4iYgCuiDu6AMBGderU0eHDh4skRrm5uZblktS8eXM1bNhQn332mbZt26ZbbrlFVapU0U033aRt27bp888/524uAACAcrpczDpy5IhV+7lz53TmzBlnhATABVDoAgAb3XzzzTp27Jjla4mXZWRk6LrrrrP6VLFbt25as2aNfvrpJ3Xp0kXSpbkjPv74Y+3cuZNCFwAAQDkFBASoefPm+uijj6zaMzMzdfHiRSdFBcDZ+OoiAJTgyJEj+uSTT6zaqlSpoltuuUUDBgzQU089pSeffFJNmjTRli1b9K9//Utz58616t+tWzeNHDlSLVu2VN26dSVJt956q5599ln5+fkV+XlsAAAAlN3IkSM1ZswYBQQEqHv37vrtt9+0dOlS+fj4ODs0AE5CoQsASvDvf/9bjzzyiFWbn5+fvvnmGz333HOaN2+e5syZo6NHj+qGG27Q3Llzdeedd1r179Kli7y9vS13c0lSkyZNFBwcrNDQUJIwAACAq9CrVy8lJyfrn//8p9544w01a9ZMSUlJevjhh50dGgAnMRnMvgcAAAAAcEOGYej06dOqWbOmpe3UqVPq3Lmz5syZU+RDSACejzu6AAAAAABuad68efrXv/6lESNGqFmzZjp27Jjeeust1atXTzfffLOzwwPgBBS6AAAAAABuKTY2VpK0cOFCHThwQDVr1tRNN92k559/3uouLwCVB19dBAAAAAAAgEfwcnYAAAAAAAAAgD1Q6AIAAAAAAIBHoNAFAAAAAAAAj0ChCwAAAAAAAB6BQhcAAAAAAAA8AoUuAAAAAAAAeAQKXQAAAAAAAPAIFLoAAAAAAADgESh0AQAAAAAAwCNQ6AIAAAAAAIBHoNAFAAAAAAAAj0ChCwAAAAAAAB6BQhcAAAAAAAA8AoUuAAAAAAAAeAQKXQAAAAAAAPAIFLoAAAAAAADgESh0AQAAAAAAwCNQ6AIAAAAAAIBHoNAFAAAAAAAAj0ChCwAAAAAAAB6BQhcAAAAAAAA8AoUuAAAAAAAAeAQKXQAAAAAAAPAIFLoAAAAAAADgESh0AQAAAAAAwCNUdXYAnurs2bP67rvvFBgYqCpVqjg7HAAAYGeFhYU6fPiwWrdurerVqzs7HPwfcjAAADxbaTkYhS4H+e677xQVFeXsMAAAgIOlpaWpY8eOzg4D/4ccDACAyqGkHIxCl4MEBgZKunTgg4KCnBwNAACwt9zcXEVFRVmu+XAN5GAAAHi20nIwCl0OcvlW+aCgIAUHBzs5GgAA4Ch8Pc61kIMBAFA5lJSDMRk9AAAAAAAAPAKFLgAAAAAAAHgElyp0RUdHKzQ0tMhjyZIlSk9PV9++fYtdb+7cuRo+fHiJ43To0EFPPPGEjh49aumTnp5u1ad169a6//779eWXX1qNnZubq5iYGIWGhlqtDwAA4AnIvwAAgCdxqUKXJI0cOVJbt261etx7771XNc7SpUt15MgRTZgwwapP06ZNLX3Wrl2r7t27a9iwYcrJyZEkrVu3Tnfffbd8fX3tsm8AAACuiPwLAAB4CpebjL5GjRp2+fWiv44TGBiop59+WgMHDtTZs2dVvXp1SVLVqlWttvXYY4/piy++0KpVqzRq1CitW7dOL7zwgkJDQ7V58+arjgkAAMAVkX8BAABP4XKFLkcxDEOSdPHixSv2a9q0qfbv3y9JmjFjhry9vS2fMAIAAKDsyL8AAEBFqxSFrsOHD2vGjBnq27ev/Pz8rtj3jz/+UOvWrSVJ3t7eZRo/Ly9PeXl5Vm25ubnlCxYAAMADODr/ksjBAABAUS5X6Jo1a5ZSUlIsz319ffX5559f1ThnzpxRixYtNGPGjBL7FxYWavXq1dq+fbuefvppm7a1ePFiq5gBAADciTvmXxI5GAAAKMrlCl1DhgzRwIEDLc+9vMo3X/5fxzl9+rTefPNN3Xffffrggw9Ut25dSdLPP/+s8PBwSVJBQYFq1aplmRPCFjExMYqMjLRqy83NVVRUVLliBwAAqEjumH9J5GAAAKAolyt01alTR2azuUi7j4+Pzp07V+w6586dk4+PzxXHeeGFF9S7d2+tXbvWkvw0btxYqamplvEbNGhQrsQuICBAAQEBNq8HAADgCtwx/5LIwQAAQFHlyyqcoHXr1srOztYvv/xi1V5YWKgtW7ZY5nUoiZeXl+rWravTp09b2qpXry6z2Syz2ayGDRuWO8kCAADwRORfAADA3bhNZhESEqKBAwdq+PDh2rhxow4cOKBvv/1Wo0eP1unTpzV48GCr/qdPn9bhw4d1+PBhZWdn64033tCuXbvUrVs3J+0BAACAeyH/AgAA7sblvrp4JVOnTtWiRYuUnJysnJwc+fv76/bbb9e7775b5Lb1efPmad68eZIu/XrPDTfcoFdeeUUtWrRwRugAAABuifwLAAC4E5NhGIazg/BEOTk5ioiIUGZmpoKDg50dDgAAsDOu9a6J8wIAgGcr7VrvNl9dBAAAAAAAAK6EQhcAAAAAAAA8AoUuAAAAAAAAeAQKXQAAAAAAAPAIFLoAAAAAAADgESh0AQAAAAAAwCNQ6AIAAAAAAIBHoNAFAAAAAAAAj0ChCwAAAAAAAB7B4wtd+fn5Sk5OVrdu3RQWFqY77rhDc+bM0blz5yRJ6enp6tu3r9U6Fy5c0GuvvaaePXsqLCxM3bp10/Tp03Xy5Eln7AIAAIBbIf8CAADOUtXZATjSuXPnNGTIEHl5eenFF19U06ZNtXfvXr300kvatWuXFixYUGQdwzD0xBNP6Ndff9XEiRPVsmVLZWVlae7cuXrggQe0bNky+fn5OWFvAAAAXB/5FwAAcCaPLnS98cYbOnXqlNLT01W9enVJUlBQkNq0aaO///3v+uijj4qsk5GRoa+//lqrV69WvXr1LOssXLhQ/fr105IlSxQbG1uh+wEAAOAuyL8AAIAzeXSha+XKlXr00UctSdZlderUUXJysmrUqKHs7Owi69x///2WJOuyatWqKTExUadOnXJ43AAAAO6K/AsAADiTxxa6Tp48qaysLIWHhxe7vEePHpJUJNH64YcfNGzYsGLX6dixY7HteXl5ysvLs2rLzc21MWIAAAD3VpH5l0QOBgAAivLoQpckBQQE2LReXl6ezessXrxYKSkpNq0DAADgaSoy/5LIwQAAQFEeW+jy9/eXdClx+t/b4Etb738/GSxNTEyMIiMjrdpyc3MVFRVl0zgAAADurCLzL4kcDAAAFOXRha7GjRvrm2++UdOmTYss37Rpk2rUqFGkPSwsTF999ZVuvfXWIst27NihkydPKiIiwqo9ICCgXJ9CAgAAeJKKzL8kcjAAAFCUl7MDcKQBAwZo4cKFOnv2rFX7kSNHNH78eB05cqTIOvfee6+WLVtWZNnZs2c1adIk/frrrw6NGQAAwJ2RfwEAAGfy6ELX0KFD5evrqyFDhmjbtm3Kzc3VJ598oiFDhqht27bq1atXkXX69u2rdu3aKTo6Wps3b1Zubq527NihoUOHqlq1atwKDwAAcAXkXwAAwJk89quL0qWfpH7rrbeUkpKicePG6ciRIwoKClL//v01fPhweXkVrfN5eXlpzpw5WrhwoRITE7V//37Vq1dPPXv21OjRo+Xn5+eEPQEAAHAP5F8AAMCZTIZhGM4OwhPl5OQoIiJCmZmZCg4OdnY4AADAzrjWuybOCwAAnq20a71Hf3URAAAAAAAAlQeFLgAAAAAAAHgECl0AAAAAAADwCBS6AAAAAAAA4BEodAEAAAAAAMAjUOgCAAAAAACAR6DQBQAAAAAAAI9AoQsAAAAAAAAegUIXAAAAAAAAPEJVZwdQVtu3b9dDDz1U4vJRo0ZJklJSUoosu/XWW5WamipJCg0NtVoWFBSkgQMH6rHHHpOXl5fS09M1YcKEEreTlJSkAQMGlGcXAAAA3A45GAAAcCduU+gKDw/X1q1bLc979uyp5ORkhYeHS5L8/Py0aNEide3aVcnJyVbr+vj4WD1fuHChWrRooYsXL+rHH3/UpEmT5O3treHDh6t379667bbbJEknTpxQnz59tHz5cjVs2FCS5O/v78jdBAAAcCnkYAAAwJ24TaHLx8dHgYGBlucmk0m1atWyaiuuX3Fq165t6dOgQQONHj1a8+fP1/Dhw1W9enVVr15dklSlShVJUt26dUsdEwAAwBORgwEAAHfCHF2Srr/+eh04cMDZYQAAAFQq5GAAAMDe3OaOLkc6dOiQ6tSpU+718/LylJeXZ9WWm5t7tWEBAAB4NHIwAABgbx5X6Pr0008tc0ZcNnnyZEVGRhbbPycnR/Pnz9ddd91V7m0uXry42AlYAQAAKgtyMAAA4Ao8rtB14403avr06VZt9erVs3oeFRWlKlWqyDAMnTlzRr1791ZcXFy5txkTE1MkicvNzVVUVFS5xwQAAHAn5GAAAMAVeFyhy8/PT2az+Yp9XnrpJYWGhurkyZOKjo5Wly5d5OvrW+5tBgQEKCAgoNzrAwAAuDtyMAAA4Aoq5WT0DRs2lNlsVuvWrfXYY49p9uzZOnnypLPDAgAA8GjkYAAAwNE8rtBVUFCgw4cPWz2OHTtWYv+YmBjVqFFDr776agVGCQAA4FnIwQAAgCvwuK8ufv7557r11lut2lq1aqX09PRi+/v4+Cg+Pl5jxozR/fffX+ot9wAAACiKHAwAALgCk2EYhrOD8EQ5OTmKiIhQZmamgoODnR0OAACwM671ronzAgCAZyvtWl/qHV1vvfWWTRt86KGHbOoPAAAAAAAA2EOpha4333yzzIOZTCYKXQAAAAAAAHCKUgtdmzZtqog4AAAAAAAAgKtS7l9dPHPmjA4cOKDz58/bMx4AAAAAAACgXGwudK1bt079+vVTeHi4IiIitHfvXu3cuVOPP/64zp0754gYAQAAAAAAgFLZVOhavXq14uLi1KNHD7399tu6ePGiJOmaa67Rd999p9mzZzsiRgAAAAAAAKBUNhW65s+fr0cffVRjx45Vhw4dLO1NmzbV6NGjtXr1arsHCAAAAAAAAJSFTYWu33//XZ07dy52WaNGjXT8+HF7xAQAAAAAAADYzKZCV8OGDbVnz55il+3atUvBwcF2CQoAAAAAAACwlU2FrujoaL3yyitat26dCgsLJUkXLlzQ6tWr9eqrr+qhhx4qdf3Q0FDLo1OnToqPj1deXp5leWpqqqV/aGiodu/ebTXGokWL1KZNG3355ZeWtvXr16tfv34KCwtTjx49tHz58iLbvnDhgl577TX17NlTYWFh6tatm6ZPn66TJ08WG+usWbMUGhqq33//vUzHBgAAwFWRgwEAgMrCpkJXTEyMYmNjFR8frxtvvFGS9P/+3//TM888o0ceeUQPPPBAqWOMHDlSW7du1aeffqpFixbpjz/+UFxcXJm2v3HjRs2cOVMzZsxQx44dJUmfffaZ4uLiNHjwYK1fv17jx49XYmKitmzZYlnPMAw98cQTev/99zVx4kRt2rRJL730kn766Sc98MADys/Pt9qOYRhas2aNvL29lZGRUdbDAwAA4LLIwQAAQGVQ1dYVRowYoQcffFBfffWVjh07pjp16ig8PFwBAQFlWr9GjRoKDAyUJNWvX19JSUmKiIjQ/v37r7je999/r3HjxmncuHHq1auXpX3Dhg0aPHiwBg8eLEkKDg7WF198oQ8++EDdu3eXJGVkZOjrr7/W6tWrVa9ePUlSUFCQFi5cqH79+mnJkiWKjY21jLlr1y4dOHBAI0eO1OrVqzVq1KiyHyAAAAAXRA4GAAAqA5sLXZJUs2ZNSwJztYKDg+Xt7X3FJCs3N1cjRozQwIEDNWTIEKtlU6dOLdLfx8dHp0+ftjxfuXKl7r//fkuCdVm1atWUmJioU6dOWbVnZGSoa9euuv/++5WSkqIffvhBYWFhtu8cAACAiyIHAwAAnqjMha5///vfWrp0qb766isdPHhQJpNJDRo0UKdOnRQVFaXWrVuXK4DDhw/r/Pnzuuaaa4pdnp+frxEjRig8PFzx8fGljrd9+3atWrVKM2fOtLT98MMPGjZsWLH9L99+f1lhYaHWrVunp556SvXq1VPnzp2VkZFxxSQrLy/PMsfFZbm5uaXGCgAA4CzkYAAAwBOVqdA1b948zZ07Vx07dtQDDzygRo0aSZJycnK0adMmDRo0SE8++aQeeeQRmzZ+/PhxTZs2TW3btlVISEixfZ555hllZWWpWbNm8vIqeUqxd999V4mJiSooKFBsbKzVHWd5eXll/mrl9u3bdeLECd15552SpD59+iglJUVPPfVUidtfvHixUlJSyjQ+AACAs5GDAQAAT1Vqoevjjz/WvHnz9OKLL6p///5Flj/yyCNavny5Jk+erFatWqlr165XHG/WrFmWhCQ/P18dOnTQ7NmzS+x/9uxZLV26VMOGDVN6eroGDBhQbL8+ffqoc+fO+vXXXzVjxgxdvHhR48aNkyT5+/sX+bSvJKtXr1b37t1Vs2ZNSVLPnj01depUffXVV+rUqVOx68TExCgyMtKqLTc3V1FRUWXaJgAAgKORgwEAgMqg1ELXG2+8of79+xdb5Lps4MCB+uKLL/TGG2+UWuiKiYnRoEGDJEkBAQGqU6fOFftPmzZNHTp00JQpUzR16lSFh4eradOmRfr5+/vL399f1113nWrXrq0hQ4ZozJgx8vHxUVhYmL766ivdeuutRdbbsWOHTp48qYiICBUUFGjDhg3Kz89XmzZtLH0uXLigjIyMEpOsgICAMn9aCQAA4AzkYAAAoDIo+T70//Pdd99ZbiG/kr/97W/69ttvS+1Xt25dmc1mmc3mUhOsy/0lqX///rr99ts1duxYFRQUWJZPnDhRGzdutFrHx8dHhYWFKiwslCTde++9WrZsmY4cOWLV7+zZs5o0aZJ+/fVXSZfuXrtw4YJWrFihVatWWR7jx4/XunXrdOHChVLjBQAAcEXkYAAAoDIotdB1+vRp1a5du9SBrrnmmjLfml5eU6ZM0dGjR/Xyyy9b2mrXrq0pU6Zo3bp12r9/v7Zt26ZJkyapb9++8vX1lST17dtX7dq1U3R0tDZv3qzc3Fzt2LFDQ4cOVbVq1Sy3t69Zs0a33367wsLC1KxZM8tj0KBBOn36tD777DOH7h8AAIArIgcDAADuokyT0ZtMJrv0uVq1atXSiy++qH/84x/q0qWLunfvrri4ONWsWVPJyck6cOCA6tWrp8jISD3++OOW9by8vDRnzhwtXLhQiYmJ2r9/v+rVq6eePXtq9OjR8vPz06lTp/Txxx9b/VLQZTVr1lT37t2VkZFhNcEqAABAZUAOBgAA3IXJMAzjSh1atGih6OhoNW7c+IoDZWVlKS0tTXv27LFrgO4qJydHERERyszMVHBwsLPDAQAAdsa13jVxXgAA8GylXetLvaOrUaNGyszMLNPGGjZsaHuEAAAAAAAAgB2UWujatGlTRcQBAAAAAAAAXJVSJ6O3xfnz5/XQQw/p999/t+ewAAAAAAAAQKnsWugyDEM7duzQ6dOn7TksAAAAAAAAUCq7FroAAAAAAAAAZ6HQBQAAAAAAAI9AoQsAAAAAAAAegUIXAAAAAAAAPAKFLgAAAAAAAHgEuxa6vL29lZSUpGuvvdaew1o5deqU2rZtq7i4OKv2+Ph4hYaGlvjIycmRJN1xxx1W7V27dtX06dN17tw5SdLGjRvVsmVL/fjjj1bjnzhxQjfffLPmzp3rsH0DAABwReRfAADAXdhU6IqIiNB3331X7LLvvvtO3bt3V2RkpGrXrm2P2Iq1ceNGXbx4UZs2bdKZM2cs7QkJCdq6dau2bt2q6dOnKygoyPJ869atatiwoaXv9OnTtXXrVn366ad65ZVX9PnnnysxMVGSdOedd6p79+6W55fNnTtXfn5+euSRRxy2bwAAAK6I/AsAALiLMhW6Tp06pZMnT+qPP/7Q8ePHderUKatHXl6eMjMzdfHiRUfHq9WrV2vw4MGqUaOGNm/ebGn39/dXYGCgAgMD5e/vrypVqlieBwYGqkqVKkX61q9fX506ddIzzzyjVatWqaCgQNKlpO3bb7/V2rVrJUl79+7V0qVLNWHCBFWvXt3h+wgAAOBKyL8A2EtaWppCQkLk5eWlkJAQpaWlOTskAB6malk6dezYUSaTSSaTqcRP1EwmkyZNmmTX4P7XkSNH9MUXX2jMmDEyDEOrV69W7969r3rcZs2a6dy5czp69KiCgoLUuHFjjRgxQsnJyerRo4cSExPVpUsX9ezZ0w57AQAA4D7IvwDYS1pammJjY5Wfny9J2rdvn2JjYyVJUVFRzgwNgAcpU6HrrbfekmEYiomJ0aRJk9S8efMifa699lqHzs0lSevWrVOjRo3Upk0bFRQUaMiQITpx4oRq1ap1VeMeOnRIXl5eCggIsLQNHTpUq1at0rBhw7Rr1y6tXr26xPXz8vKUl5dn1Zabm3tVMQEAALgCV82/JHIwwN0kJCRYilyX5efnKyEhgUIXALspU6Grc+fOkqRHHnlEd9xxhxo1auTQoEqSkZGh3r17y2Qy6cYbb1S9evX00UcfadCgQeUe88iRI5o9e7a6d+8uPz8/S7uPj4+io6M1bdo09ezZU02bNi1xjMWLFyslJaXcMQAAALgqV82/JHIwwN1kZWXZ1A4A5VHqHF3Tp0+3/GJOy5Yt5evr6/CgivPHH3/o66+/ttwqbzKZdNdddykjI8PmsZ566imFh4erffv26tq1qwoKCvT8889b9SkoKNDbb7+t5s2b6+OPP9Zvv/1W4ngxMTHKzMy0evBdcwAA4O5cOf+SyMEAd9OkSROb2gGgPEq9o+vdd9/VnXfeqeDgYMXFxSktLU116tSpiNisrFmzRpI0cOBAS1thYaEMw9DBgwfVoEGDMo81btw49ejRQ+fOndPw4cPVvn171a1b16pPamqqTp06pQ8//FCxsbGaPn26UlNTix0vICDA6rZ7AAAAT+DK+ZdEDga4m8TERKs5uiTJz8+vyC+uAsDVKLXQFRYWppkzZ6p3794yDENr167Vd999V2L/hx56yK4BXrZ69WpFR0dr8ODBVu2xsbFau3athgwZUuaxGjRoILPZLOlS0hUfH6+BAweqcePGkqTs7GzNnz9fzz77rGrVqqXJkyfrvvvu04YNG5gQFQAAVBrkXwDs6fI8XAkJCcrKylKTJk2UmJjI/FwA7KrUQldSUpKmTp2qt99+WyaTSWvXrpWPj0+xfU0mk0MKXb/88ov27t2r2bNnq1mzZlbLevfurYyMDJsSrf9dPy0tTTNmzNCcOXMkXfqkISwsTP3795cktWrVSvfff7+SkpJ022238RPXAADA45F/AXCEqKgoClsAHKrUQlezZs309ttvS5JatGihOXPmqEOHDg4P7K8yMjLUokWLIkmWJPXp00cLFizQvn37LJ8S2mrSpEkaMGCAdu7cqRMnTmjLli167733ZDKZLH2efPJJrVu3Tq+//roef/zxcu8LAACAOyD/AgAA7shkGIZR1s47duxQWFiYatas6ciYPEJOTo4iIiKUmZmp4OBgZ4cDAADsjGu9a+K8AADg2Uq71pd6R1dGRoZ69eqlqlWrKiAgwPILjCVp0aJF+aMFAAAAAAAAyqnUQtekSZPUrFkztWzZUvfcc49MJpP+9yawy20mk0l79uxxWLAAAAAAAABASUotdL3++uuWu7QyMzMdHhAAAAAAAABQHqUWujp16mT597XXXuvQYAAAAAAAAIDyKrXQ9b/27NmjzZs3Ky8vr8hXGCVpwoQJdgkMAAAAAAAAsIVNha4VK1Zo8uTJqlevnkJCQoos/+vPQQMAAAAAAAAVyaZC17x589SrVy/NmDFDVapUcVRMAAAAAAAAgM28bOl8+PBhDRw4kCIXAAAAAAAAXI5Nha5WrVrpwIEDjooFAAAAAAAAKLdSC12nTp2yPBISEvSvf/1LH3/8sY4fP2617PLDVtHR0QoNDbU8OnXqpPj4eOXl5VmWp6amWvqHhoZq9+7dVmMsWrRIbdq00ZdffmlpW79+vfr166ewsDD16NFDy5cvt1onPj7earvh4eF69NFHtX//fqt+v/zyiwYMGKDw8HCb9w0AAMAVkX8BAABPVWqhq2PHjurUqZM6deqk+++/X//+9781YsQIdenSxdLeqVMnS7/yGDlypLZu3apPP/1UixYt0h9//KG4uLgyrbtx40bNnDlTM2bMUMeOHSVJn332meLi4jR48GCtX79e48ePV2JiorZs2WK17oABA7R161Zt3bpV7777rqpUqaJhw4apsLBQkpSWlqb77rtP11xzTbn2CwAAwFWRfwEAAE9U6mT0b731lsODqFGjhgIDAyVJ9evXV1JSkiIiIop8uve/vv/+e40bN07jxo1Tr169LO0bNmzQ4MGDNXjwYElScHCwvvjiC33wwQfq3r27pZ+vr69lu4GBgUpOTtZNN92kb775Rh07dtRHH32k1NRUXbhwQTt37rT3bgMVKi0tTQkJCcrKylKTJk2UmJioqKgoZ4cFAHAS8i8AAOCJSi10de7cuSLisBIcHCxvb+8rJlq5ubkaMWKEBg4cqCFDhlgtmzp1apH+Pj4+On369BW36+fnp0aNGumPP/5Qx44dlZqaKm9vb23fvr08uwG4jLS0NMXGxio/P1+StG/fPsXGxkoSxS4AgCTyLwAA4BlKLXRJUn5+vtavX6/evXvLx8fH0r5y5Urt2rVLDRo00P3332+3W8wPHz6s8+fPlzhefn6+RowYofDwcMXHx5c63vbt27Vq1SrNnDnziv0KCwt15MgR1alTR5Lk7e1dpnjz8vIsc1pclpubW6Z1gYqQkJBgKXJdlp+fr4SEBApdAABJ7pd/SeRgAACgqFILXYcOHdIDDzygI0eOKCIiwlLoSkhI0AcffKDOnTvr448/1tKlS7Vs2TIFBwdfVUDHjx/XtGnT1LZtW4WEhBTb55lnnlFWVpaaNWsmL6+Spxl79913lZiYqIKCAsXGxlrdNv+/zp49q1dffVXVqlWzea6xxYsXKyUlxaZ1gIqUlZVlUzsAoHJxx/xLIgcDAABFlVromjVrlnx9fbV582b5+/tLkvbs2aP09HTNnTtXd955p86cOaMHHnhAc+bMUXJyss1BzJo1y5Kk5Ofnq0OHDpo9e3aJ/c+ePaulS5dq2LBhSk9P14ABA4rt16dPH3Xu3Fm//vqrZsyYoYsXL2rcuHGW5e+8847ee+89y5iNGjXSq6++Kl9fX5vij4mJUWRkpFVbbm4ud8rAZTRp0kT79u0rth0AUDm5e/4lkYMBAICiSi10bdmyRePHj1ft2rUtbR988IEaN26sO++8U9KlSUVjYmLKVeSSLiUpgwYNkiQFBARYbl0vybRp09ShQwdNmTJFU6dOVXh4uJo2bVqkn7+/v/z9/XXdddepdu3aGjJkiMaMGWO5K613794aPXq0JCkpKUnHjx9XmzZtbI4/ICBAAQEBNq8HVJTExESrObqkS3OiJCYmOjEqAIAzuXv+dTlucjAAAPBXJd93/n9OnDihhg0bWrVt3bpVERERVm2NGjXS8ePHyxVE3bp1ZTabZTabS02yLveXpP79++v222/X2LFjVVBQYFk+ceJEbdy40WodHx8fFRYWWn66WpJq1apl2e4zzzyjH374QR9++GG59gFwZVFRUVqwYIHMZrNMJpPMZrMWLFjAJ94AUImRfwEAAE9UaqGrUaNG+s9//mN5/ttvv+nnn3/WHXfcYdUvKyvLkgBVpClTpujo0aN6+eWXLW21a9fWlClTtG7dOu3fv1/btm3TpEmT1Ldv3xJvi7/22ms1dOhQzZgxQ2fOnKmo8IEKExUVpd9//10XL17U77//TpELAFBu5F8AAMBVlVroioyM1Pz58/Xvf/9bOTk5mjx5spo3b241YeixY8eUmpqq2267zaHBFqdWrVp68cUXtXjxYm3ZskWSFBcXp6ioKCUnJysiIkJPPfWUunfvXurXtGJjY2UYhlJTUysidAAAALdE/gUAAFyVyTAM40odzp8/r3HjxmndunUymUyqV6+eFi1apObNm0uSfv/9d91zzz2qW7eu3n33XdWvX79CAnd1OTk5ioiIUGZm5lX/EiUAAHA9XOtdE+cFAADPVtq1vtTJ6L29vTV79mxlZWXp2LFjatGihapVq2ZZXrNmTT311FPq37+/5VcZAQAAAAAAgIpW6lcXL2vSpInatWtnVeSSpGuuuUYPPvig/P39VVhYqJSUFB0+fNjugQIAAAAAAABXUuZCV1kUFhZq3rx5OnTokD2HBQAAAAAAAEpl10KXJJUy5RcAAAAAAADgEHYvdAEAAAAAAADOQKELAAAAAAAAHoFCFwAAAAAAADwChS4AAAAAAAB4BLsWury8vBQZGanatWvbc1gAAAAAAACgVFVtXaGwsFCbNm3Sv//9bx07dkwjR46Ur6+vDhw4oFatWikpKckRcdokOjpaXl5eevPNN2UymYos69y5sx5//HGFhoYWu/7q1avVvHlzSdJXX32lp59+Wr6+vsrIyHB47AAAAO6KHAwAADibTYWuI0eO6OGHH1Z2drbatWunbdu2KSoqSj/++KPGjBmjN954Q506dXJUrDbZtm2bli5dqqioqCv2W7hwoVq0aGHVVqdOHUnSyy+/rLfeeksdOnTQoUOHHBYrAACApyAHAwAAzmTTVxeff/55ValSRZmZmXr99ddlGIYkKSIiQvfcc49efvllhwRZHo0bN9aMGTOUlZV1xX61a9dWYGCg1aNq1Uv1v61bt2rFihXq27dvRYQMAADg9sjBAACAM9lU6NqyZYtGjhypOnXqFLkdvXfv3vr+++/tGtzV6Nevn8LDwxUfH6+LFy+Wa4xly5bp+uuvt3NkAAAAnoscDAAAOJNNha4LFy7I29u72GX5+fny8fGxS1D2YDKZlJiYqB9//FFvvfVWucYoaV//V15ennJycqweubm55domAACAOyMHAwAAzmTTHF0333yzFi9erFtvvdXSZjKZdOHCBb399tvq2rWr3QO8Go0aNVJ8fLymT5+u2267Tc2aNSvS58EHH5SX13/rfbfddpvmzJlj03YWL16slJSUq44XAADAE5CDAQAAZ7Gp0DVx4kRFRUUpMjJSPXv2lCQtWbJE3377rY4fP6533nnHIUFejYEDB2rjxo2aMGFCsfG99NJLll/3kSRfX1+btxETE6PIyEirttzc3FInYQUAAPBU5GAAAMAZbCp0NWnSRB988IHefPNNffLJJwoODtbevXt1++236x//+Ifq1q3rqDjLzWQyadq0aerTp48WLlxYZHlQUJDMZvNVbSMgIEABAQFXNQYAAIAnIQcDAADOYFOhS7r0s89PPvmknnzySUfE4xANGjTQxIkTNXnyZF1zzTXq3Lmzs0MCAADweORgAACgotlc6DIMQzk5OTp9+nSxy1u0aHHVQTnCgAEDtH79em3evNnZoQAAAFQa5GAAAKAi2VTo+vrrr/Xkk0/q0KFDMgyjyHKTyaQ9e/bYLTh7mzZtmvr27evsMAAAACoVcjAAAFBRTEZxFasS9OnTR/7+/po0aZLq1KlTbJ9rr73WbsG5s5ycHEVERCgzM1PBwcHODgcAANgZ13rXxHkBAMCzlXatt+mOrn379umf//ynWrdubbcAAQAAAAAAAHvwsqVz8+bNlZeX56hYAAAAAAAAgHKz6Y6u5557TpMnT9b58+dLvBW8U6dOdgkMAAAAAAAAsIVNha6zZ8/q0KFDmjBhQrHLXX0yegAAAAAAAHgumwpdkyZN0vXXX6933nlHtWvXdlBIAAAAAAAAgO1sKnTl5OTomWeeUePGjR0VDwAAAAAAAFAuNk1Gf/PNN+uXX35xVCwAAAAAAABAudl0R1dkZKTmzp2rvLw8tWzZUiaTqUifiIgIuwUHAAAAAAAAlJVNha64uDhJ0rx584pd7sjJ6Ldv366HHnqoxOWjRo2SJKWkpFjaqlWrpvDwcE2aNEk33HCDpb1t27Y6d+6c1fpjxozRY489Znn+zjvvaNGiRcrOztZ1112np556SnfccYe9dgcAAMAtkIMBAAB3YlOh68cff3RUHKUKDw/X1q1bLc979uyp5ORkhYeHS5L8/Py0aNEide3aVcnJyZKkkydPKi0tTUOGDNFHH32kmjVr6ujRozp37pzS09NVv359y3h+fn6Wfy9fvlwzZ87Us88+q3bt2umTTz7RmDFjtHz5crVo0aKC9hgAAMD5yMEAAIA7sanQ5Uw+Pj4KDAy0PDeZTKpVq5ZV2//2CwwM1KRJk7R582Zt3LhR99xzjw4ePCiTyaTmzZvL29u72G1t2LBBI0eOVJ8+fSRJDzzwgDZt2qQPP/yQJAsAAFQq5GAAAMCd2Fzo2rVrlz755BOdOnWq2OUTJky46qDsyWQyqWnTpvrjjz8kSQcOHFDdunVLTLAkacGCBUXavL29i52TDAAAAEWRgwEAAGewqdCVnp6uiRMn6oYbbtCRI0dUvXp1XXvttfr5559lGIZCQ0MdFedV+eOPP/T3v/9d0qUky8vLS0OHDtW3336r+vXra+jQoRowYECx6168eFHr16/XF198oTFjxlRk2AAAAG6NHAwAAFQ0mwpdr732mh599FHLXAmbN2/WP//5T50+fVpDhw5V//79HRVnuRQUFOjNN9/UwYMHLb8G2bhxY3Xr1k0RERGaMGGCtm/frilTpsjb21t333231fqzZs1SamqqCgsLNWnSpBJvmc/Ly1NeXp5VW25urmN2CgAAwMWRgwEAAGexqdCVk5Ojrl27SpLMZrN+/vlnSVKNGjU0dOhQPf/887r33nvtH6UNPvnkE8vkqOfOnVNQUJD++c9/ql69epKkbt26qVu3bpb+119/vY4fP65FixYVSbJiYmLUr18/7d69Wy+99JIkKSoqqsg2Fy9ebPVLQwAAAJUNORgAAHAFNhW66tatq99//12dOnXS9ddfr/379+vMmTPy9fWVn5+fDh8+7Kg4y6xjx46aPn26JKl69eqqX79+qfM6hIeH64033ijSXrduXdWtW1fNmjWTyWTS7Nmzi02yYmJiFBkZadWWm5tbbF8AAABPRA4GAABcgU2Frr///e96+eWX1bp1a7Vs2VLBwcFauHCh/t//+39aunSpS/wajp+fn8xmc4nLFyxYIC8vLw0bNszS9tNPP6lhw4aSpMLCQo0aNUrDhw9X+/btLX18fHx0/vz5YscMCAhQQECAfXYAAADADZGDAQAAV+BlS+exY8fqxhtv1PHjxyVJTzzxhObPn69u3brpyy+/VEJCgiNitKtmzZrplVdeUXp6ug4cOKAPP/xQc+fO1ZAhQyRJVapUkY+Pj8aPH6+tW7fqwIED2rx5s2bMmKGBAwc6N3gAAAA3RQ4GAAAqgk13dPn5+VnNg3DXXXcpPDxc+/fvV2hoqGrUqGH3AO0tIiJCU6dO1WuvvaaJEycqODhY48ePt5pb7IUXXtCcOXMUHx+vP//8U40aNdLgwYM1dOhQJ0YOAADgvsjBAABARTAZhmGUtfOECRP08MMP64YbbnBkTB4hJydHERERyszMVHBwsLPDAQAAdsa13jVxXgAA8GylXett+uriRx99pEOHDtktOAAAAAAAAMBebCp0PfDAA3r33Xd18eJFR8UDAAAAAAAAlItNc3Tl5uZq+/btuuuuu3T99dcX+5PRf53DCwAAAAAAAKgoNhW6vL29FRER4ahYAAAAAAAAgHIrtdB18OBB1a1bV97e3kpKSqqImAAAAAAAAACblTpHV48ePbR7927Lc8MwNHXqVB04cMChgQEAAAAAAAC2KLXQZRiG1fOLFy9q2bJlOnr0qMOCAgAAAAAAAGxl068uXva/xS8AAAAAAADA2cpV6AIAAAAAAABcTZkKXSaTqUxtriI6OlqhoaGWR6dOnfT000/r+PHjkqT4+Hir5W3bttWwYcOUnZ1tNc6JEycUFxen0NBQq3nKAAAAUBQ5GAAAcLZSf3VRkmbOnKnatWtL+u/XFl944QUFBAQU6ZuSkmK/6K7CyJEjNXjwYBmGoYMHD+qFF15QXFycUlNTJUkDBgzQ2LFjJUl5eXmaO3euRowYoYyMDJlMJu3cuVNxcXEKCwtz5m4AAAC4FXIwAADgTKUWuiIjI8vU5mpq1KihwMBASVL9+vX1/PPP629/+5v2798vSfL19bUsDwwM1OTJk9WlSxf99ttvuu6667R27VqNGjVKgwYNUmhoqNP2AwAAwJ2QgwEAAGcqtdCVlJRUEXE4nNlslre3tyXJKsnlO9YmTJggb2/viggNQDmkpaUpISFBWVlZatKkiRITExUVFeXssAAA/4McDAAAz+Au/wcr01cXPcGxY8d0/vx51alTp8iyU6dO6bnnnlOnTp103XXXSRIJFuDC0tLSFBsbq/z8fEnSvn37FBsbK0ku+UYLAJUZORgAAO7Pnf4PVikKXSdPnlRycrJatGhhSaKWLVum9957T5J05swZNWrUSK+++mq5JtnPy8tTXl6eVVtubu7VBw6gWAkJCZY32Mvy8/OVkJDgcm+yAFCZkYMBAOAZ3On/YB5b6Jo1a5ZlYvwzZ86oZcuWmjNnjiWJ6t27t0aNGiVJOnv2rFatWqXBgwdrxYoVlkSsrBYvXuwyk/ADlUFWVpZN7QCAikMOBgCA53Gn/4N5bKErJiZGgwYNkmEYeuqpp2Q2m2U2my3L/f39rZ6PHz9eP/30k5YvX67x48fbvK3/naA/NzfX5aqagKdo0qSJ9u3bV2w7AMC5yMEAAPA87vR/MC9nB+AodevWldlsVkhIiJ555hmtWbNG33zzzRXXueaaa3T69GmbtxUQEKDg4GCrR1BQUHlDB1CKxMRE+fn5WbX5+fkpMTHRSREBAC4jBwMAwPO40//BPLbQ9Vft2rVTv3799Pzzz1t+0efMmTM6fPiwDh8+rD/++EPp6elau3atbr/9didHC6A0UVFRWrBggcxms0wmk8xmsxYsWMAn+ADgYsjBAADwDO70fzCP/eri/4qLi1OvXr30/vvvS5LS09OVnp4uSapatapCQkI0efJkkizATURFRbnkmyoAwBo5GAAAnsFd/g9mMi5/vAa7ysnJUUREhDIzMxUcHOzscAAAgJ1xrXdNnBcAADxbadf6SvHVRQAAAAAAAHg+Cl0AAAAAAADwCBS6AAAAAAAA4BEodAEAAAAAAMAjUOgCAAAAAACAR6DQBQAAAAAAAI9AoQsAAAAAAAAegUIXAAAAAAAAPAKFLgAAAAAAAHiEqs4OoKyio6O1Y8eOIu1xcXGKjY2VJH3++eeaN2+evv32W3l7e+vmm2/W2LFjdf3111v6x8fH67333isyzsMPP6zx48dr+/bteuihhyztVapUUbNmzTRy5Ej16tXLAXsGAADgusjBAACAO3GbQpckjRw5UoMHD7Zqq1GjhiRpzZo1mjBhgkaNGqWkpCSdP39eaWlpGjRokJYsWaKwsDDLOgMGDNDYsWOtxvH19bX694YNGyRJBQUF2rJli5566ikFBgbqxhtvdNTuAQAAuCRyMAAA4C7cqtBVo0YNBQYGFmk/deqUpk6dqmeffVaRkZGW9smTJ+vUqVOaPHmyVqxYYWn39fUtdpzLTCaT1fIHHnhAX3/9tVasWEGSBQAAKh1yMAAA4C48Yo6ujRs3yt/fX/fcc0+RZbGxsapZs6ZOnDhxVdu44YYbdODAgasaAwAAwJOQgwEAAFfjVnd0leT7779XeHi4TCZTkWXXX3+93nzzzavexqFDh1SnTp2rHgcAAMBTkIMBAABX41aFrlmzZiklJcWq7YMPPtCpU6fk7+9f5nGWLVtWZDLUTZs2FZtEGYahr776Su+//76SkpKKHS8vL095eXlWbbm5uWWOBwAAwJWRgwEAAHfhVoWuIUOGaODAgVZtQUFB8vf3159//mnVPnr0aH366aeW56+//ro6duwoSerdu7dGjRpl1T8gIMDy7/z8fIWHh0uSLly4IMMw9Pjjj6tnz57FxrV48eIiyR8AAICnIAcDAADuwq0KXXXq1JHZbC7S3rJlS82ZM0cXL16Ul9elaccmTZqkM2fOqLCwUL1791bVqv/dVX9//2LHuaxatWpatWqVJOmbb75RQkKCIiIiSuwfExNjNQGrdOnTxKioKFt2DwAAwCWRgwEAAHfhEZPRR0RE6OTJk1q5cqWlrX79+jKbzdq7d6+qV6+uG264oczjValSRWazWWazWffcc4+6du1a4i3z0qVPIoODg60eQUFBV7VPAAAAro4cDAAAuBq3uqPr9OnTOnz4sFWbr6+vAgICNGXKFP1/9u49Lso6////c0BQUVAhFF1yMCrMtBZPqZlYyKaImrbaj4hg00XLQ22e8JBWShZYmuJq5iErrFZFSy3zmEqeOm3ntW0tFHWKTEVERXF+f/Rxvk2ccWBmLh73221uN+d9va/39bpmgOvla97XeyZPnqyff/7Z9unhzp079fzzz+uxxx5TgwYNqnzcSZMmqV+/ftq5c6ciIiKu9jQAAADcCjkYAABwF25V6FqwYIEWLFhg1/bQQw9p4sSJ6tevnwICAvTPf/5TL730kqxWq1q3bq2UlBRFR0df1XGvu+46PfDAA3rmmWfUrVs3eXl5XdV4AAAA7oQcDAAAuAuT1Wq1OjsII8rJyVFkZKS2bdum4OBgZ4cDAAAcjGu9a+J9AQDA2Mq71htijS4AAAAAAACAQhcAAAAAAAAMgUIXAAAAAAAADIFCFwAAAAAAAAyBQhcAAAAAAAAMgUIXAAAAAAAADIFCFwAAAAAAAAyBQhcAAAAAAAAMgUIXAAAAAAAADMEtCl3x8fEKCwuzPTp16qTk5GTl5eVp//79dtv++Jg/f74kaf78+SVuHzp0qO04YWFhSk9PLzGGsLAw7d+/v0bOFwAAwBWQgwEAAHdTx9kBVNTIkSMVGxsrq9Wqn376SampqRo7dqwWLFigrKwsW7+oqCilpqYqPDxckuTj42Pb1q1bN6WmptqN6+3tbfd84cKFioyM1E033VSNZwMAAOAeyMEAAIA7cZtCV4MGDRQYGChJatq0qWbNmqXIyEj98ssvatGiha2fyWRSo0aNbH1/z9vbu8T232vevLkmTJigNWvWFEvAAAAAahtyMAAA4E7c4tbFkgQHB8vLy0vHjh1z6Ljjx4/XiRMntGDBAoeOCxhJRkaGQkJC5OHhoZCQEGVkZDg7JLfA61ZzeK0h8XNQXcjBUFvwN6R24f2Gu3H0z6yRfgfcZkbXH+Xm5urixYu65pprHDpu48aN9fTTT2vMmDHq1auX2rVr59DxAXeXkZGhpKQkFRQUSJKys7OVlJQkSYqLi3NmaC6N163m8FpD4uegOpGDoTbgb0jtwvsNd+Pon1mj/Q645YyuU6dOacaMGbrlllsUEhJS4f12796t8PBwu8fatWuL9evVq5f69OmjCRMm6MKFC+WOm5eXp5ycHLuHxWKpzCkBbmPKlCm2P4BXFBQUaMqUKU6KyD3wutUcXmtI/BxUF3Iw1Bb8DaldeL/hbhz9M2u03wG3mdE1Z84c27fxFBQUqH379po7d26lxujQoYNmzpxp1xYQEFBi32nTpikmJkZz587VxIkTyxx3xYoVpX5TEGA0hw8frlQ7fsPrVnN4rSHxc+BI5GCojfgbUrvwfsPdOPpn1mi/A25T6EpISNCQIUMkSX5+fmrSpEmlx/Dx8ZHZbK5Q30aNGmnGjBl6+OGHFRkZWW5sAwcOtGuzWCxuOcUPKE/Lli2VnZ1dYjtKx+tWc3itIfFz4EjkYKiN+BtSu/B+w904+mfWaL8DbnPror+/v8xms8xmc5USrKro2bOnBgwYoMmTJ5fZz8/PT8HBwXaPoKCgGokRqGkpKSl2Xxkv/fYfmJSUFCdF5B543WoOrzUkfg4ciRwMtRF/Q2oX3m+4G0f/zBrtd8BtCl2OUFhYqNzcXLvHyZMny9xn8uTJKiwsrKEIAdcXFxenxYsXy2w2y2QyyWw2a/HixXx6Xg5et5rDaw2JnwNXQw4Gd8PfkNqF9xvuxtE/s0b7HXCbWxcdYc+ePerevbtd280336zMzMxS9/Hz89PMmTM1dOjQ6g4PcBtxcXFu+0fPmXjdag6vNSR+DlwJORjcEX9Dahfeb7gbR//MGul3wGS1Wq3ODsKIcnJyFBkZqW3btik4ONjZ4QAAAAfjWu+aeF8AADC28q71terWRQAAAAAAABgXhS4AAAAAAAAYAoUuAAAAAAAAGEKtWoy+JhUVFUmSLBaLkyMBAADV4co1/so1H66BHAwAAGMrLwej0FVNcnNzJckw31oAAABKlpubK7PZ7Oww8H/IwQAAqB1Ky8H41sVqcv78eX311VcKDAyUp6ens8MBAAAOVlRUpNzcXLVt21b16tVzdjj4P+RgAAAYW3k5GIUuAAAAAAAAGAKL0QMAAAAAAMAQKHQBQAmSk5P1yCOPODsMAACAWqGs3Ovbb79VWFiYcnJytGjRIrVv317Z2dkVHjsnJ0dhYWH69ttvHRUuABfGYvQAAAAAALfwwAMPqGfPnnwJCIBSMaMLAAAAAOAWGjZsqNatWzs7DAAujEIXAFTBxYsXNXfuXHXv3l1t2rTRgAEDtHXrVtv2TZs2qU2bNjpz5oytbdGiRQoLC9PBgwdtbevWrdOtt96q8+fP12j8AAAA7igzM1MdO3a0a9u0aZP69OmjNm3aKCYmRl9//bV69OihzMxMu34///yzHnroIbVt21ZdunTRokWLajJ0ADWEQhcAVMG0adP0xhtvaMyYMVq6dKm6du2q0aNH24pdt99+uzw8PLR//37bPnv27JEk7d2719b24YcfqkuXLiV+LS4AAEBtYrVadenSpWKPoqKiUvfZtWuXHnvsMd1+++1aunSpYmNjNWHCBJ09e7ZY37S0NEVERGjJkiXq27ev5syZoy1btlTnKQFwAtboAoBKOnTokDIzM/Xyyy+rR48ekqSuXbsqPz9fL7zwgnr16iVfX1+Fh4dr79696tWrl86dO6fPPvtMMTEx2rt3rxITE2W1WrVnzx49/PDDTj4jAAAA59u+fbtuvvnmSu0zf/58RUdHa+rUqZJ+y8nq16+vSZMmFes7ZMgQPfjgg5KkLl266PPPP9f27dsVFRV19cEDcBnM6AKAStq7d68aN26sO+64w659wIAB+t///qeffvpJkhQREWGbxfXJJ58oICBACQkJ+uijj3Tp0iV99913+uWXXxQREVHj5wAAAOBqOnfurNWrVxd7zJ49u8T+BQUF+vLLLxUTE2PX/pe//KXE/uHh4XbPQ0NDlZeX55jgAbgMZnQBQCWdPHlSTZs2lclksmsPCgqybW/WrJl69OihtLQ0WSwWffjhh+rWrZtuvvlm1alTR1988YU+//xztWrVStdee60zTgMAAMCl+Pr6ql27dsXa69Qp+b+tp0+fltVqVbNmzezaGzZsqAYNGpQ7jslkktVqvYqIAbgiZnQBQCU1adJEubm5xRIji8Vi2y5JN954o5o3b64PP/xQ+/bt0+233y5PT0/ddttt2rdvn/bs2cNsLgAAgCq6Usw6ceKEXfuFCxd07tw5Z4QEwAVQ6AKASurSpYtOnjxpuy3xig0bNui6666z+1SxR48e2rhxow4ePKiuXbtK+m3tiA8++EAfffQRhS4AAIAq8vPz04033qj333/frn3btm26fPmyk6IC4GzcuggApThx4oR27dpl1+bp6anbb79dgwYN0rhx4/SPf/xDLVu21M6dO/Wvf/1L8+fPt+vfo0cPjRw5UjfddJP8/f0lSd27d9dTTz0lHx+fYl+PDQAAgIobOXKkHn30Ufn5+SkiIkI//PCDVq5cKW9vb2eHBsBJKHQBQCn+/e9/6+9//7tdm4+Pjz777DM9/fTTWrBggebNm6dff/1VN9xwg+bPn69evXrZ9e/atau8vLxss7kkqWXLlgoODlZYWBhJGAAAwFXo3bu3UlNT9c9//lPLly9XaGioZs2apYceesjZoQFwEpOV1fcAAAAAAG7IarXq7Nmzatiwoa0tPz9fnTt31rx584p9CAnA+JjRBQAAAABwSwsWLNC//vUvjRgxQqGhoTp58qReffVVBQQEqEuXLs4OD4ATUOgCAAAAALilpKQkSdKSJUt0/PhxNWzYULfddpueeeYZu1leAGoPbl0EAAAAAACAIXg4OwAAAAAAAADAESh0AQAAAAAAwBAodAEAAAAAAMAQKHQBAAAAAADAECh0AQAAAAAAwBAodAEAAAAAAMAQKHQBAAAAAADAECh0AQAAAAAAwBAodAEAAAAAAMAQKHQBAAAAAADAECh0AQAAAAAAwBAodAEAAAAAAMAQKHQBAAAAAADAECh0AQAAAAAAwBAodAEAAAAAAMAQKHQBAAAAAADAECh0AQAAAAAAwBAodAEAAAAAAMAQKHQBAAAAAADAECh0AQAAAAAAwBAodAEAAAAAAMAQKHQBAAAAAADAECh0AQAAAAAAwBAodAEAAAAAAMAQKHQBAAAAAADAEOo4OwCjOn/+vL766isFBgbK09PT2eEAAAAHKyoqUm5urtq2bat69eo5Oxz8H3IwAACMrbwcjEJXNfnqq68UFxfn7DAAAEA1y8jIUMeOHZ0dBv4PORgAALVDaTkYha5qEhgYKOm3Fz4oKMjJ0QAAAEezWCyKi4uzXfPhGsjBAAAwtvJyMApd1eTKVPmgoCAFBwc7ORoAAFBduD3OtZCDAQBQO5SWg7EYPQAAAAAAAAyBQhcAAAAAAAAMwaUKXfHx8QoLCyv2eP3115WZmamYmJgS95s/f76GDx9e6jjt27fXY489pl9//dXWJzMz065P27Ztdd999+njjz+2G9tisSghIUFhYWF2+wMAABgB+RcAADASlyp0SdLIkSOVlZVl97j33nuvapyVK1fqxIkTmjRpkl2fVq1a2fq89957ioiI0LBhw5STkyNJ2rRpk/r166f69es75NwAAABcEfkXAAAwCpdbjL5BgwYO+fai348TGBioCRMmaPDgwTp//rzq1asnSapTp47dsR555BHt3btX69at06hRo7Rp0yY9++yzCgsL044dO646JgAAAFdE/gUAAIzC5Qpd1cVqtUqSLl++XGa/Vq1a6dixY5KktLQ0eXl52T5hBAAAQMWRfwEAgJpWKwpdubm5SktLU0xMjHx8fMrse/ToUbVt21aS5OXlVaHx8/LylJeXZ9dmsViqFiwAAIABVHf+JZGDAQCA4lyu0DVnzhylp6fbntevX1979uy5qnHOnTun1q1bKy0trdT+RUVFWr9+vfbv368JEyZU6lgrVqywixkAAMCduGP+JZGDAQCA4lyu0JWYmKjBgwfbnnt4VG29/N+Pc/bsWb3yyiv661//qnfeeUf+/v6SpP/+978KDw+XJBUWFqpRo0a2NSEqIyEhQQMHDrRrs1gsiouLq1LsAAAANckd8y+JHAwAABTncoWuJk2ayGw2F2v39vbWhQsXStznwoUL8vb2LnOcZ599VtHR0Xrvvfdsyc+1116rpUuX2sZv1qxZlRI7Pz8/+fn5VXo/AAAAV+CO+ZdEDgYAAIqrWlbhBG3bttWRI0f0/fff27UXFRVp586dtnUdSuPh4SF/f3+dPXvW1lavXj2ZzWaZzWY1b968ykkWAACAEZF/AQAAd+M2mUVISIgGDx6s4cOHa+vWrTp+/Li++OILjRkzRmfPnlVsbKxd/7Nnzyo3N1e5ubk6cuSIli9frs8//1w9evRw0hkAAAC4F/IvAADgblzu1sWyPPnkk1q2bJlSU1OVk5MjX19f3XnnnXrzzTeLTVtfsGCBFixYIOm3b++54YYb9OKLL6p169bOCB0AAMAtkX8BAAB3YrJarVZnB2FEOTk5ioyM1LZt2xQcHOzscAAAgINxrXdNvC8AABhbedd6t7l1EQAAAAAAACgLhS4AAAAAAAAYAoUuAAAAAAAAGAKFLgAAAAAAABgChS4AAAAAAAAYAoUuAAAAAAAAGAKFLgAAAAAAABgChS4AAAAAAAAYAoUuAAAAAAAAGILhC10FBQVKTU1Vjx491KZNG911112aN2+eLly4IEnKzMxUTEyM3T6XLl3SSy+9pKioKLVp00Y9evTQzJkzdebMGWecAgAAgFsh/wIAAM5Sx9kBVKcLFy4oMTFRHh4eeu6559SqVSt99913mj17tj7//HMtXry42D5Wq1WPPfaYDh06pMmTJ+umm27S4cOHNX/+fN1///1666235OPj44SzAQAAcH3kXwAAwJkMXehavny58vPzlZmZqXr16kmSgoKC1K5dO9199916//33i+2zYcMGffrpp1q/fr0CAgJs+yxZskT9+/fX66+/rqSkpBo9DwAAAHdB/gUAAJzJ0IWuNWvW6OGHH7YlWVc0adJEqampatCggY4cOVJsn/vuu8+WZF1Rt25dpaSkKD8/v9rjBgAAcFfkXwAAwJkMW+g6c+aMDh8+rPDw8BK39+zZU5KKJVrffPONhg0bVuI+HTt2LLE9Ly9PeXl5dm0Wi6WSEQMAALi3msy/JHIwAABQnKELXZLk5+dXqf3y8vIqvc+KFSuUnp5eqX0AAACMpibzL4kcDAAAFGfYQpevr6+k3xKnP06DL2+/P34yWJ6EhAQNHDjQrs1isSguLq5S4wAAALizmsy/JHIwAABQnKELXddee60+++wztWrVqtj27du3q0GDBsXa27Rpo08++UTdu3cvtu3AgQM6c+aMIiMj7dr9/Pyq9CkkAACAkdRk/iWRgwEAgOI8nB1AdRo0aJCWLFmi8+fP27WfOHFCEydO1IkTJ4rtc++99+qtt94qtu38+fOaOnWqDh06VK0xAwAAuDPyLwAA4EyGLnQNHTpU9evXV2Jiovbt2yeLxaJdu3YpMTFRt9xyi3r37l1sn5iYGN16662Kj4/Xjh07ZLFYdODAAQ0dOlR169ZlKjwAAEAZyL8AAIAzGfbWRem3r6R+9dVXlZ6ervHjx+vEiRMKCgrSgAEDNHz4cHl4FK/zeXh4aN68eVqyZIlSUlJ07NgxBQQEKCoqSmPGjJGPj48TzgQAAMA9kH8BAABnMlmtVquzgzCinJwcRUZGatu2bQoODnZ2OAAAwMG41rsm3hcAAIytvGu9oW9dBAAAAAAAQO1BoQsAAAAAAACGQKELAAAAAAAAhkChCwAAAAAAAIZAoQsAAAAAAACGQKELAAAAAAAAhkChCwAAAAAAAIZAoQsAAAAAAACGQKELAAAAAAAAhlDH2QFU1P79+/Xggw+Wun3UqFGSpPT09GLbunfvrqVLl0qSwsLC7LYFBQVp8ODBeuSRR+Th4aHMzExNmjSp1OPMmjVLgwYNqsopAAAAuB1yMAAA4E7cptAVHh6urKws2/OoqCilpqYqPDxckuTj46Nly5apW7duSk1NtdvX29vb7vmSJUvUunVrXb58Wf/5z380depUeXl5afjw4YqOjtYdd9whSTp9+rT69u2rVatWqXnz5pIkX1/f6jxNAAAAl0IOBgAA3InbFLq8vb0VGBhoe24ymdSoUSO7tpL6laRx48a2Ps2aNdOYMWO0aNEiDR8+XPXq1VO9evUkSZ6enpIkf3//cscEAAAwInIwAADgTlijS9L111+v48ePOzsMAACAWoUcDAAAOJrbzOiqTj///LOaNGlS5f3z8vKUl5dn12axWK42LAAAAEMjBwMAAI5muELX7t27bWtGXDFt2jQNHDiwxP45OTlatGiR+vTpU+VjrlixosQFWAEAAGoLcjAAAOAKDFfo6tChg2bOnGnXFhAQYPc8Li5Onp6eslqtOnfunKKjozV27NgqHzMhIaFYEmexWBQXF1flMQEAANwJORgAAHAFhit0+fj4yGw2l9ln9uzZCgsL05kzZxQfH6+uXbuqfv36VT6mn5+f/Pz8qrw/AACAuyMHAwAArqBWLkbfvHlzmc1mtW3bVo888ojmzp2rM2fOODssAAAAQyMHAwAA1c1wha7CwkLl5ubaPU6ePFlq/4SEBDVo0EALFy6swSgBAACMhRwMAAC4AsPdurhnzx51797dru3mm29WZmZmif29vb2VnJysRx99VPfdd1+5U+4BAABQHDkYAABwBSar1Wp1dhBGlJOTo8jISG3btk3BwcHODgcAADgY13rXxPsCAICxlXetN9ytiwAAAAAAAKidKHQBAAAAAADAECh0AQAAAAAAwBAodAEAAAAAAMAQKHQBAAAAAADAECh0AQAAAAAAwBAodAEAAAAAAMAQKHQBAAAAAADAECh0AQAAAAAAwBBqtNAVHx+vsLAw26NTp05KTk5WXl6ebfvSpUtt/cPCwvTll1/ajbFs2TK1a9dOH3/8sa1t8+bN6t+/v9q0aaOePXtq1apVxY596dIlvfTSS4qKilKbNm3Uo0cPzZw5U2fOnCkx1jlz5igsLEw//vijA84cAADAecjBAABAbVHjM7pGjhyprKws7d69W8uWLdPRo0c1duzYCu27detWPf/880pLS1PHjh0lSR9++KHGjh2r2NhYbd68WRMnTlRKSop27txp289qteqxxx7T22+/rcmTJ2v79u2aPXu2Dh48qPvvv18FBQV2x7Fardq4caO8vLy0YcMGx508AACAk5CDAQCA2qDGC10NGjRQYGCgmjZtqnbt2mnWrFnatWuXjh07VuZ+X3/9tcaPH6/x48erd+/etvYtW7YoNjZWsbGxCg4OVp8+fdS/f3+98847tj4bNmzQp59+qtdee0133nmngoKC1LlzZy1ZskSFhYV6/fXX7Y71+eef6/jx43rkkUe0ceNGx74AAAAATkAOBgAAaoM6zg4gODhYXl5eZSZZFotFI0aM0ODBg5WYmGi37cknnyzW39vbW2fPnrU9X7Nmje677z4FBATY9atbt65SUlKUn59v175hwwZ169ZN9913n9LT0/XNN9+oTZs2lT85AAAAF0UOBgAAjMjpi9Hn5ubq4sWLuuaaa0rcXlBQoBEjRig8PFzJycnljrd//36tW7dO/fv3t7V988036tChQ4n9O3bsqJ49e9qeFxUVadOmTerbt68CAgLUuXPncqfO5+XlKScnx+5hsVjKjRUAAMBZyMEAAIAROXVG16lTpzRjxgzdcsstCgkJKbHPE088ocOHDys0NFQeHqXX5d58802lpKSosLBQSUlJioiIsG3Ly8uTn59fhWLav3+/Tp8+rV69ekmS+vbtq/T0dI0bN67U469YsULp6ekVGh8AAMDZyMEAAIBR1fiMrjlz5ig8PFzh4eG67bbblJubq7lz55ba//z581q5cqV27NihzMzMUvv17dtXb7/9thYsWKDNmzcrLS3Nts3X19f2rULlWb9+vSIiItSwYUNJUlRUlH755Rd98sknpe6TkJCgbdu22T0yMjIqdDwAAICaQA4GAABqgxqf0ZWQkKAhQ4ZIkvz8/NSkSZMy+8+YMUPt27fX9OnT9eSTTyo8PFytWrUq1s/X11e+vr667rrr1LhxYyUmJurRRx+Vt7e32rRpo08++UTdu3cvtt+BAwd05swZRUZGqrCwUFu2bFFBQYHatWtn63Pp0iVt2LBBnTp1KjFGPz+/Cn9aCQAA4AzkYAAAoDao8Rld/v7+MpvNMpvN5SZYV/pL0oABA3TnnXfq8ccfV2FhoW375MmTtXXrVrt9vL29VVRUpKKiIknSvffeq7feeksnTpyw63f+/HlNnTpVhw4dkiR98MEHunTpklavXq1169bZHhMnTtSmTZt06dKlqzp3AAAAZyEHAwAAtUGVCl3nzp3T8ePHdfHiRUfHU6bp06fr119/1QsvvGBra9y4saZPn65Nmzbp2LFj2rdvn6ZOnaqYmBjVr19fkhQTE6Nbb71V8fHx2rFjhywWiw4cOKChQ4eqbt26iouLkyRt3LhRd955p9q0aaPQ0FDbY8iQITp79qw+/PDDGj1fAAAAV0AOBgAA3EWlbl3ctGmT/vnPf+q7776Th4eHVq1apYKCAr366quaPXu26tatW11xSpIaNWqk5557Tn/729/UtWtXRUREaOzYsWrYsKFSU1N1/PhxBQQEaODAgRo9erRtPw8PD82bN09LlixRSkqKjh07poCAAEVFRWnMmDHy8fFRfn6+PvjgAz3//PPFjtuwYUNFRERow4YNdgusAgAA1AbkYAAAwF2YrFartSId169fr+TkZA0dOlR33HGH4uPjtWbNGvn4+Oihhx5S7969NXHixOqO123k5OQoMjJS27ZtU3BwsLPDAQAADsa13jXxvgAAYGzlXesrfOviokWL9PDDD+vxxx9X+/btbe2tWrXSmDFjtH79esdEDAAAAAAAAFRBhQtdP/74ozp37lzithYtWujUqVOOigkAAAAAAACotAoXupo3b65vv/22xG2ff/45U8MBAAAAAADgVBUudMXHx+vFF1/Upk2bbF8ZfenSJa1fv14LFy7Ugw8+WG1BAgAAAAAAAOWp8LcuJiQk6Ny5c0pOTrYVuv6//+//U926dZWUlKT777+/2oIEAAAAAAAAylPhQpckjRgxQg888IA+/fRT/frrr2rSpInCw8Pl5+dXXfEBAAAAAAAAFVLhQtexY8d0zTXXqGHDhurRo4fdtsuXL+uHH35QaGiowwMEAAAAAAAAKqLCa3RFRkbqq6++KnHbRx99pAEDBjgsKAAAAAAAAKCyyp3RtW3bNkmS1WrVxx9/rJMnT9ptt1qtWrNmjVq2bFk9EQIAAAAAAAAVUG6ha+TIkZIkk8mkF154ocQ+wcHBevbZZx0bWSny8/PVrVs3RUVF6fnnn7e1Jycna+3ataXut23bNgUHB+uuu+7S0aNHbe0BAQGKjo7W+PHjVbduXW3dulWjR4/W2rVr1bp1a1u/06dP6+6771ZcXJxGjx5dPScHAADggsi/AACAuyi30PWf//xHktS6dWutXLlS7du3r/agyrJ161ZdvnxZ27dv17lz51S/fn1J0pQpUzR27FhJ0gcffKD09HStXr3atp+/v7/t3zNnzlTPnj1ltVqVnZ2t6dOnKyUlRU8//bR69eqliIgIpaSk6LXXXrPtM3/+fPn4+Ojvf/97DZ0pAACAayD/AgAA7qLCa3S9/PLLuuGGG6ozlgpZv369YmNj1aBBA+3YscPW7uvrq8DAQAUGBsrX11eenp6254GBgfL09CzWt2nTpurUqZOeeOIJrVu3ToWFhZJ+S9q++OILvffee5Kk7777TitXrtSkSZNUr169mj1hAAAAJyP/AgAA7qLMQtfHH39s+3d4eLhMJpPy8/NLfVS3EydOaO/everXr5969+6t9evXO2Tc0NBQXbhwQb/++qsk6dprr9WIESOUmpqqc+fOKSUlRV27dlVUVJRDjgcAAOAuyL8AAIA7KfPWxWHDhumNN97QTTfdpI4dO8pkMpXYz2q1ymQy6dtvv62WIK/YtGmTWrRooXbt2qmwsFCJiYk6ffq0GjVqdFXj/vzzz/Lw8JCfn5+tbejQoVq3bp2GDRumzz//vMykLi8vT3l5eXZtFovlqmICAABwBa6af0nkYAAAoLgyC10zZsyw3a746quv1khAZdmwYYOio6NlMpnUoUMHBQQE6P3339eQIUOqPOaJEyc0d+5cRUREyMfHx9bu7e2t+Ph4zZgxQ1FRUWrVqlWpY6xYsULp6elVjgEAAMBVuWr+JZGDAQCA4sosdPXr18/2786dO1d7MGU5evSoPv30U02fPl3Sb98C2adPH23YsKHSida4ceM0adIkWa1WnTt3TrfddptSU1Pt+hQWFuq1117TjTfeqA8++EA//PBDqclWQkKCBg4caNdmsVgUFxdXqbgAAABciSvnXxI5GAAAKK7cb120WCxas2aNHnroIds37BQWFuqll17Sv//9bzVt2lQPPfRQtS9Uv3HjRknS4MGDbW1FRUWyWq366aef1KxZswqPNX78ePXs2VMXLlzQ8OHD9ec//9nuW4EkaenSpcrPz9e7776rpKQkzZw5U0uXLi1xPD8/P7tp9wAAAEbgyvmXRA4GAACKK3Mx+h9++EEDBgzQmjVrVFRUZGsfPXq0Xn/9df3pT3/S999/ryFDhug///lPtQa6fv16xcfHa926dbbH+vXr1aJFC9u381RUs2bNZDabdeONN2r8+PF65ZVXdOTIEdv2I0eOaNGiRRo/frwaNWqkadOmac+ePdqyZYujTwsAAMBlkX8BAAB3U2aha86cObrhhhu0adMmNWzYUJL00UcfadeuXVqwYIGefvppvfnmm2rbtq3mz59fbUF+//33+u677xQbG6vQ0FC7R3R0tDZs2FDlsaOjo9WuXTulpaXZ2lJSUtSmTRsNGDBAknTzzTfrvvvu06xZs3T+/PmrPh8AAABXR/4FAADcUZmFrr179yo+Pl7e3t62tvfee0833HCDOnbsKEny9PRUbGysPv7442oLcsOGDWrdurVCQ0OLbevbt6++/PJLZWdnV3n8qVOnasuWLfroo4+0detW7dy5U9OnT7f7lsl//OMfKigo0Msvv1zl4wAAALgL8i8AAOCOTFar1VraxptvvllLly5Vly5dbG1RUVHq27evHnvsMVvb/v379dBDD+nrr7+u1mDdSU5OjiIjI7Vt2zYFBwc7OxwAAOBgXOtdE+8LAADGVt61vswZXa1atdJXX31le/7VV18pJydHUVFRdv3++9//KjAw0EEhAwAAAAAAAJVX5rcuxsbG6sUXX1Tz5s11zTXXKCUlRR06dNDNN99s6/O///1PixcvVp8+fao9WAAAAAAAAKA0ZRa64uLidOzYMSUnJ+vixYtq3bq1Zs+ebdv+/fffKyYmRu3atdPo0aOrPVgAAAAAAACgNGUWuiRp/PjxGj16tPLz83XNNdfYbbvmmmu0cOFC3XHHHapTp9yhAAAAAAAAgGpT5hpdV9SrV69YkUuSGjdurDvvvFN16tTRpUuXNGnSJB09etThQQIAAAAAAADlqVChqyIuX76sdevW6dSpU44aEgAAAAAAAKgwhxW6JMlqtTpyOAAAAAAAAKDCHFroAgAAAAAAAJyFQhcAAAAAAAAMwemFrvj4eIWFhdkenTp1UnJysvLy8mzbly5dausfFhamL7/80m6MZcuWqV27dvr4449tbZs3b1b//v3Vpk0b9ezZU6tWrbLbJzk52e644eHhevjhh3Xs2DG7ft9//70GDRqk8PBwR586AACAU5B/AQAAo3J6oUuSRo4cqaysLO3evVvLli3T0aNHNXbs2Artu3XrVj3//PNKS0tTx44dJUkffvihxo4dq9jYWG3evFkTJ05USkqKdu7cabfvoEGDlJWVpaysLL355pvy9PTUsGHDVFRUJEnKyMjQX//61xK/cRIAAMCdkX8BAAAjclihy9PTU6NGjVLTpk0rvW+DBg0UGBiopk2bql27dpo1a5Z27dpV7NO9P/r66681fvx4jR8/Xr1797a1b9myRbGxsYqNjVVwcLD69Omj/v3765133rHbv379+goMDFRgYKDCwsKUmpqqI0eO6LPPPpMkvf/++1q6dKmGDh1a6XNyJRkZGQoJCZGHh4dCQkKUkZHh7JAAoFrxdw8oH/kXAAAwojqV6Zyfn6/Vq1fr3//+t06ePKmnnnpK3t7e+vbbbxUZGalRo0Y5JKjg4GB5eXmVmWhZLBaNGDFCgwcPVmJiot22J598slh/b29vnT17tszj+vj4qEWLFjp69Kg6duyopUuXysvLS/v376/KabiEjIwMJSUlqaCgQJKUnZ2tpKQkSVJcXJwzQwOAasHfPaBqyL8AAIARVHhG1+HDhxUdHa1XX31VDRs21IEDB3T27FkdOnRIo0eP1ubNmx0WVG5uri5evFjqlPWCggKNGDFC4eHhSk5OLne8/fv3a926derfv3+Z/YqKinTixAk1adJEkuTl5VWhePPy8pSTk2P3sFgsFdq3uk2ZMsX2n70rCgoKNGXKFCdFBADVi797QNW4W/4luXYOBgAAnKPCM7pSUlJ0/fXX66WXXpLJZNLq1aslSd27d9fQoUO1cOFC/eUvf7nqgE6dOqUZM2bolltuUUhISIl9nnjiCR0+fFihoaHy8Ci9Vvfmm28qJSVFhYWFSkpKUkRERKl9z58/r4ULF6pu3brq1KlTpWJesWKF0tPTK7VPTTl8+HCl2gHA3fF3D6g8d8y/JNfOwQAAgHNUuNB14MABzZs3T15eXrbFQq/o1q2bli9fXuUg5syZY0tSCgoK1L59e82dO7fU/ufPn9fKlSs1bNgwZWZmatCgQSX269u3rzp37qxDhw4pLS1Nly9f1vjx423b33jjDa1du9Y2ZosWLbRw4ULVr1+/UvEnJCRo4MCBdm0Wi8UlbpFp2bKlsrOzS2wHACPi7x5QMe6ef0munYMBAADnqHChy9PTUxcuXChx28mTJ9WgQYMqB5GQkKAhQ4ZIkvz8/GxT10szY8YMtW/fXtOnT9eTTz6p8PBwtWrVqlg/X19f+fr66rrrrlPjxo2VmJioRx99VN7e3pKk6OhojRkzRpI0a9YsnTp1Su3atat0/H5+fvLz86v0fjUhJSXFbq0a6be1MFJSUpwYFQBUH/7uARXj7vnXlbhdNQcDAADOUeE1uiIiIrRo0SK7/ziYTCadOXNGL7/8su66664qB+Hv7y+z2Syz2VxuknWlvyQNGDBAd955px5//HEVFhbatk+ePFlbt26128fb21tFRUV2s9EaNWpkO+4TTzyhb775Ru+++26Vz8MVxcXFafHixTKbzTKZTDKbzVq8eDGfdAIwLP7uARVD/gUAAIyowoWuKVOm6MKFC/rLX/6iJ554QpL0/PPP6+6779bFixftpqTXpOnTp+vXX3/VCy+8YGtr3Lixpk+frk2bNunYsWPat2+fpk6dqpiYmFKnxf/pT3/S0KFDlZaWpnPnztVU+DUiLi5OP/74oy5fvqwff/yR/+wBMDz+7gHVi/wLAAC4qgoXuvz9/bVmzRqNGTNG586d02233aaGDRvq0Ucf1Zo1a2yf8tW0Ro0a6bnnntOKFSu0c+dOSdLYsWMVFxen1NRURUZGaty4cYqIiCj3tpWkpCRZrVYtXbq0JkIHAABwS+RfAADAVZmsVqvV2UEYUU5OjiIjI7Vt2zYFBwc7OxwAAOBgXOtdE+8LAADGVt61vsIzuiTpnXfe0XPPPWfXlpqaqg0bNlxdlAAAAAAAAMBVqnCha/Xq1Zo0aZICAgLs2hs1aqTk5GStWrXK4cEBAAAAAAAAFVWnoh2XLFmiUaNGadiwYXbtw4cP18WLF/Xyyy9r8ODBDg8QAAAAAAAAqIgKz+g6fPiwOnToUOK2Tp06KScnx2FBAQAAAAAAAJVV4UJXixYt9OWXX5a47YsvvlCLFi0cFhQAAAAAAABQWRUudD3wwAOaP3++Vq1apYKCAklSQUGB3nzzTS1YsEAPPPBAtQUJAAAAAAAAlKfCa3QlJibq7NmzmjlzpqZPny4fHx+dPXtW3t7eSkpKUmJiYjWGCQAAAAAAAJStwoUuSRo5cqQSEhL0ySef6OTJk2rSpIk6dOighg0bVld8AAAAAAAAQIVUqtAlSQ0bNlRERER1xAIAAAAAAABUWZmFrr59++rll19WixYtNGrUqHIHS09Pd1hgVyM+Pl4eHh565ZVXZDKZim3r3LmzRo8erbCwsBL3X79+vW688UZJ0ieffKIJEyaofv362rBhQ7XHDgAA4K7IwQAAgLOVWegKCgpSnTq/dfH19a2RgBxl3759WrlypeLi4srst2TJErVu3dqurUmTJpKkF154Qa+++qrat2+vn3/+udpiBQAAMApyMAAA4ExlFrqWLl1q+/esWbOqPRhHuvbaa5WWlqY77rhDLVu2LLVf48aNFRgYWOK2rKwsrV69Wl988YWWLVtWXaECAAAYBjkYAABwJo+yNubn51fq4Ur69++v8PBwJScn6/Lly1Ua46233tL111/v4MgAAACMixwMAAA4U5kzujp27FhsfYWSWK1WmUwmffvttw4L7GqZTCalpKQoJiZGr776qhITEys9hpeXV4X65eXlKS8vz67NYrFU+ngAAADujhwMAAA4U5mFrldffbWm4qgWLVq0UHJysmbOnKk77rhDoaGhxfo88MAD8vD4fxPb7rjjDs2bN69Sx1mxYoXLLMQPAADgbORgAADAWcosdHXu3Lmm4qg2gwcP1tatWzVp0iS98cYbxbbPnj3b9u0+klS/fv1KHyMhIUEDBw60a7NYLOUuwgoAAGBU5GAAAMAZyix0/VFBQYEyMzP18ccf69SpU2rcuLFuu+02DRw4UPXq1auuGK+KyWTSjBkz1LdvXy1ZsqTY9qCgIJnN5qs6hp+fn/z8/K5qDAAAACMhBwMAAM5Q5mL0v5eTk6OYmBjNmzdPHh4euv7663X58mWlpaVpwIABLr0eQrNmzTR58mTNnz9fR44ccXY4AAAAtQI5GAAAqGkVntE1c+ZMXXPNNVq3bp3dJ2e//vqrhg0bpmeeeabS6yrUpEGDBmnz5s3asWOHs0MBAACoNcjBAABATarwjK59+/Zp+PDhxaaH+/v76+GHH9bu3bsdHpyjzZgxQ40bN3Z2GAAAALUKORgAAKgpFZ7RVadOHXl7e5e4zdvbW56eng4L6mq99tprJbYHBgZq//79tucHDx6s0HiDBg3SoEGDHBIbAACAUZGDAQAAZ6vwjK4HH3xQy5cv18WLF+3aCwsLtXz5cj344IMODw4AAAAAAACoqArP6GrYsKFyc3N1991366677pK/v79++eUX7dixQ5cuXdKNN96oWbNm2fpPmjSpWgIGAAAAAAAASlLhQteOHTvUuHFjNW7c2G66eXBwsCTp22+/tbWZTCYHhggAAAAAAACUr8KFrj+uuWC1WpWbm6vGjRuXunYXAAAAAAAAUFPKXaMrIyNDvXr10unTp21tX3zxhSIjIxUREaHOnTtr/vz51RokAAAAAAAAUJ4yC13vvPOOUlJS1Lt3b/n4+EiSzp07p0ceeUShoaF644039I9//EMvvfSSVq1aVSMBAwAAAAAAACUp89bFV155RQkJCRo3bpytbePGjcrLy1NqaqqaNGmiP//5z7JYLFq5cqUGDx5c7QEDAAAAAAAAJSlzRtd///tf3XHHHXZt27dvV7du3dSkSRNbW/fu3fW///2veiIEAAAAAAAAKqDMGV1eXl4qLCy0PS8sLNS+ffs0ceJEu36XLl2qnuh+Z//+/XrwwQdL3T5q1ChJUnp6uq2tbt26Cg8P19SpU3XDDTfY2m+55RZduHDBbv9HH31UjzzyiO35G2+8oWXLlunIkSO67rrrNG7cON11112OOh0AAAC3QA4GAADcSZmFro4dO+rdd99Vz549JUnr1q3TxYsXFRUVZddv165ddklMdQgPD1dWVpbteVRUlFJTUxUeHi5J8vHx0bJly9StWzelpqZKks6cOaOMjAwlJibq/fffV8OGDfXrr7/qwoULyszMVNOmTW3jXVmDTJJWrVql559/Xk899ZRuvfVW7dq1S48++qhWrVql1q1bV+t5AgAAuBJyMAAA4E7KLHSNGTNGcXFxOnHihAIDA7Vx40YNHTpU/v7+kn5LYhYtWqQ33nhDzz77bLUG6u3trcDAQNtzk8mkRo0a2bX9sV9gYKCmTp2qHTt2aOvWrbrnnnv0008/yWQy6cYbb5SXl1eJx9qyZYtGjhypvn37SpLuv/9+bd++Xe+++y5JFgAAqFXIwQAAgDspc42utm3bauXKlfL399cvv/yicePG6dFHH7Vtt1gsWrNmjSZMmKD+/ftXe7BVYTKZ1KpVKx09elSSdPz4cfn7+5eaYEnS4sWL9be//c2uzcvLSyaTqVpjBQAAMApyMDhSRkaGQkJC5OHhoZCQEGVkZDg7JAAOxO84HKnMGV2SdPPNNystLa3Ebddff7327dvn8KAc7ejRo7r77rsl/ZZkeXh4aOjQofriiy/UtGlTDR06VIMGDSpx38uXL2vz5s3au3evXZEPAAAAZSMHgyNkZGQoKSlJBQUFkqTs7GwlJSVJkuLi4pwZGgAH4HccjlZuoassrv7pWmFhoV555RX99NNPioyMlCRde+216tGjhyIjIzVp0iTt379f06dPl5eXl/r162e3/5w5c7R06VIVFRVp6tSppU6Zz8vLU15enl2bxWKpnpMCAABwceRgcKQpU6bY/gN8RUFBgaZMmcJ/ggED4HccjnZVhS5XtGvXLtviqBcuXFBQUJD++c9/KiAgQJLUo0cP9ejRw9b/+uuv16lTp7Rs2bJiSVZCQoL69++vL7/8UrNnz5ZUckV5xYoVdt80BAAAUNuQg6G6HD58uFLtANwLv+NwNMMVujp27KiZM2dKkurVq6emTZuWO/MsPDxcy5cvL9bu7+8vf39/hYaGymQyae7cuSUmWQkJCRo4cKBdm8ViofoMAABqDXIwVJeWLVsqOzu7xHYA7o/fcThamYvRuyMfHx+ZzWaZzWY1a9asWIK1ePFiLVmyxK7t4MGDat68uSSpqKhIDz/8sP7973/b9fH29tbFixdLPKafn5+Cg4PtHkFBQY47KQAAABdHDobqkpKSIh8fH7s2Hx8fpaSkOCkiAI7E7zgczXCFrvKEhobqxRdfVGZmpo4fP653331X8+fPV2JioiTJ09NT3t7emjhxorKysnT8+HHt2LFDaWlpGjx4sHODBwAAcFPkYKiquLg4LV68WGazWSaTSWazWYsXL2bmHmAQ/I7D0Qx362J5IiMj9eSTT+qll17S5MmTFRwcrIkTJ+ree++19Xn22Wc1b948JScn65dfflGLFi0UGxuroUOHOjFyAAAA90UOhqsRFxfHf3oBA+N3HI5kslqtVmcHYUQ5OTmKjIzUtm3bFBwc7OxwAACAg3Gtd028LwAAGFt51/pad+siAAAAAAAAjIlCFwAAAAAAAAyBQhcAAAAAAAAMgUIXAAAAAAAADIFCFwAAAAAAAAyBQhcAAAAAAAAMgUIXAAAAAAAADIFCFwAAAAAAAAyBQhcAAAAAAAAMwZCFrvj4eIWFhdkenTp10oQJE3Tq1ClJUnJyst32W265RcOGDdORI0fsxjl9+rTGjh2rsLAwffnll044EwAAAPdBDgYAAJzNkIUuSRo5cqSysrK0e/duLVu2TEePHtXYsWNt2wcNGqSsrCxlZWVp7dq1atiwoUaMGCGr1SpJ+uijj9SvXz+dPXvWWacAAADgdsjBAACAMxm20NWgQQMFBgaqadOmateunZ555hllZWXp2LFjkqT69esrMDBQgYGBCg0N1bRp0/T999/rhx9+kCS99957GjVqlBYtWuTM0wAAAHAr5GAAAMCZDFvo+iOz2SwvLy9bklWaK58mTpo0SUOGDKmJ0AAAAAyLHAwAjCkjI0MhISHy8PBQSEiIMjIynB0SIEmq4+wAasrJkyd18eJFNWnSpNi2/Px8Pf300+rUqZOuu+46SZKXl1dNhwgAAGA45GAAYDwZGRlKSkpSQUGBJCk7O1tJSUmSpLi4OGeGBtSOQteZM2eUmpqq1q1b25Kot956S2vXrpUknTt3Ti1atNDChQtlMpkqPX5eXp7y8vLs2iwWy9UHDgAA4MbIwQDAmKZMmWIrcl1RUFCgKVOmUOiC0xm20DVnzhylp6dL+i2JuummmzRv3jxbEhUdHa1Ro0ZJks6fP69169YpNjZWq1evtiViFbVixQrbsQAAAGozcjAAML7Dhw9Xqh2oSYYtdCUkJGjIkCGyWq0aN26czGazzGazbbuvr6/d84kTJ+rgwYNatWqVJk6cWOljDRw40K7NYrFQyQYAALUOORgAGF/Lli2VnZ1dYjvgbIZdjN7f319ms1khISF64okntHHjRn322Wdl7nPNNddU6aus/fz8FBwcbPcICgqqaugAAABuixwMAIwvJSVFPj4+dm0+Pj5KSUlxUkTA/2PYQtfv3Xrrrerfv7+eeeYZ2zf6nDt3Trm5ucrNzdXRo0eVmZmp9957T3feeaeTowUAADAGcjAAMKa4uDgtXrxYZrNZJpNJZrNZixcvZkYtXIJhb138o7Fjx6p37956++23JUmZmZnKzMyUJNWpU0chISGaNm0aSRYAAIADkYMBgDHFxcVR2IJLMlmvfLwGh8rJyVFkZKS2bdum4OBgZ4cDAAAcjGu9a+J9AQDA2Mq71teKWxcBAAAAAABgfBS6AAAAAAAAYAgUugAAAAAAAGAIFLoAAAAAAABgCBS6AAAAAAAAYAgUugAAAAAAAGAIFLoAAAAAAABgCBS6AAAAAAAAYAgUugAAAAAAAGAIdZwdQEXFx8frwIEDxdrHjh2rpKQkSdKePXu0YMECffHFF/Ly8lKXLl30+OOP6/rrr7f1T05O1tq1a4uN89BDD2nixInav3+/HnzwQVu7p6enQkNDNXLkSPXu3bsazgwAAMB1kYMBAAB34jaFLkkaOXKkYmNj7doaNGggSdq4caMmTZqkUaNGadasWbp48aIyMjI0ZMgQvf7662rTpo1tn0GDBunxxx+3G6d+/fp2/96yZYskqbCwUDt37tS4ceMUGBioDh06VNfpAQAAuCRyMAAA4C7cqtDVoEEDBQYGFmvPz8/Xk08+qaeeekoDBw60tU+bNk35+fmaNm2aVq9ebWuvX79+ieNcYTKZ7Lbff//9+vTTT7V69WqSLAAAUOuQgwEAAHdhiDW6tm7dKl9fX91zzz3FtiUlJalhw4Y6ffr0VR3jhhtu0PHjx69qDAAAACMhBwMAAK7GrWZ0lebrr79WeHi4TCZTsW3XX3+9Xnnllas+xs8//6wmTZpc9TgAAABGQQ4GAABcjVsVuubMmaP09HS7tnfeeUf5+fny9fWt8DhvvfVWscVQt2/fXmISZbVa9cknn+jtt9/WrFmzShwvLy9PeXl5dm0Wi6XC8QAAALgycjAAAOAu3KrQlZiYqMGDB9u1BQUFydfXV7/88otd+5gxY7R7927b85dfflkdO3aUJEVHR2vUqFF2/f38/Gz/LigoUHh4uCTp0qVLslqtGj16tKKiokqMa8WKFcWSPwAAAKMgBwMAAO7CrQpdTZo0kdlsLtZ+0003ad68ebp8+bI8PH5bdmzq1Kk6d+6cioqKFB0drTp1/t+p+vr6ljjOFXXr1tW6deskSZ999pmmTJmiyMjIUvsnJCTYLcAq/fZpYlxcXGVODwAAwCWRgwEAAHdhiMXoIyMjdebMGa1Zs8bW1rRpU5nNZn333XeqV6+ebrjhhgqP5+npKbPZLLPZrHvuuUfdunUrdcq89NsnkcHBwXaPoKCgqzonAAAAV0cOBgAAXI1bzeg6e/ascnNz7drq168vPz8/TZ8+XZMnT9bPP/9s+/Rw586dev755/XYY4+pQYMGVT7upEmT1K9fP+3cuVMRERFXexoAAABuhRwMAAC4C7cqdC1YsEALFiywa3vooYc0ceJE9evXTwEBAfrnP/+pl156SVarVa1bt1ZKSoqio6Ov6rjXXXedHnjgAT3zzDPq1q2bvLy8rmo8AAAAd0IOBgAA3IXJarVanR2EEeXk5CgyMlLbtm1TcHCws8MBAAAOxrXeNfG+AABgbOVd6w2xRhcAAAAAAABAoQsAAAAAAACGQKELAAAAAAAAhkChCwAAAAAAAIZAoQsAAAAAAACGQKELAAAAAAAAhkChCwAAAAAAAIZAoQsAAAAAAACGQKELAAAAAAAAhuAWha74+HiFhYXZHp06dVJycrLy8vK0f/9+u21/fMyfP1+SNH/+/BK3Dx061HacsLAwpaenlxhDWFiY9u/fXyPnCwAA4ArIwQAAgLup4+wAKmrkyJGKjY2V1WrVTz/9pNTUVI0dO1YLFixQVlaWrV9UVJRSU1MVHh4uSfLx8bFt69atm1JTU+3G9fb2tnu+cOFCRUZG6qabbqrGswEAAHAP5GAAAMCduMWMLklq0KCBAgMD1bRpU7Vr106zZs3Srl279MsvvygwMND2MJlMatSoke15gwYNbGN4e3vb9Q0MDFSjRo3sjtO8eXNNmDBBhYWFNX2KuEoZGRkKCQmRh4eHQkJClJGR4eyQADiAu/5uu2vcwB+RgwHGxbUKgBG5TaHrj4KDg+Xl5aVjx445dNzx48frxIkTWrBggUPHRfXKyMhQUlKSsrOzZbValZ2draSkJC7WgJtz199td40bqAhyMMAYuFYBMCq3LXTl5ubq4sWLuuaaaxw6buPGjfX000/r5Zdf1pdfflmhffLy8pSTk2P3sFgsDo0LZZsyZYoKCgrs2goKCjRlyhQnRQTAEdz1d9td4wYqghwMMAauVQCMym3W6Pq9U6dOacaMGbrlllsUEhJS4f12795tWzfiimnTpmngwIF2bb169VKfPn00YcIErVu3TnXr1i1z3BUrVpS6gCpqxuHDhyvVDsA9uOvvtrvGDZSHHAwwDq5VAIzKbQpdc+bMsSUyBQUFat++vebOnVupMTp06KCZM2fatQUEBJTYd9q0aYqJidHcuXM1ceLEMsdNSEgolqhZLBbFxcVVKj5UXcuWLZWdnV1iOwD35a6/2+4aN1AScjDAmLhWATAqtyl0JSQkaMiQIZIkPz8/NWnSpNJj+Pj4yGw2V6hvo0aNNGPGDD388MOKjIwss6+fn5/8/PwqHQ8cJyUlRUlJSXbTr318fJSSkuLEqABcLXf93XbXuIGSkIMBxsS1CoBRuc0aXf7+/jKbzTKbzVVKsKqiZ8+eGjBggCZPnlwjx0PVxcXFafHixTKbzTKZTDKbzVq8eDGf6AJuzl1/t901bqAk5GCAMXGtAmBUbjOjyxEKCwuVm5tr11anTp0yk7bJkyerf//+1R0aHCAuLo4LM2BA7vq77a5xA9WBHAxwTVyrABhRrSp07dmzR927d7dru/nmm5WZmVnqPn5+fpo5c6aGDh1a3eEBAAAYEjkYAACoKSar1Wp1dhBGlJOTo8jISG3btk3BwcHODgcAADgY13rXxPsCAICxlXetd5s1ugAAAAAAAICyUOgCAAAAAACAIVDoAgAAAAAAgCHUqsXoa1JRUZEkyWKxODkSAABQHa5c469c8+EayMEAADC28nIwCl3V5MpXaPN1vQAAGFtubq7MZrOzw8D/IQcDAKB2KC0H41sXq8n58+f11VdfKTAwUJ6ens4OBwAAOFhRUZFyc3PVtm1b1atXz9nh4P+QgwEAYGzl5WAUugAAAAAAAGAILEYPAAAAAAAAQ6DQBQAlSE5O1iOPPOLsMAAAAGqFsnKvb7/9VmFhYcrJydGiRYvUvn17ZWdnV3jsnJwchYWF6dtvv3VUuABcGIvRAwAAAADcwgMPPKCePXvyJSAASsWMLgAAAACAW2jYsKFat27t7DAAuDAKXQBQBRcvXtTcuXPVvXt3tWnTRgMGDNDWrVtt2zdt2qQ2bdrozJkztrZFixYpLCxMBw8etLWtW7dOt956q86fP1+j8QMAALijzMxMdezY0a5t06ZN6tOnj9q0aaOYmBh9/fXX6tGjhzIzM+36/fzzz3rooYfUtm1bdenSRYsWLarJ0AHUEApdAFAF06ZN0xtvvKExY8Zo6dKl6tq1q0aPHm0rdt1+++3y8PDQ/v37bfvs2bNHkrR3715b24cffqguXbqU+LW4AAAAtYnVatWlS5eKPYqKikrdZ9euXXrsscd0++23a+nSpYqNjdWECRN09uzZYn3T0tIUERGhJUuWqG/fvpozZ462bNlSnacEwAlYowsAKunQoUPKzMzUyy+/rB49ekiSunbtqvz8fL3wwgvq1auXfH19FR4err1796pXr146d+6cPvvsM8XExGjv3r1KTEyU1WrVnj179PDDDzv5jAAAAJxv+/btuvnmmyu1z/z58xUdHa2pU6dK+i0nq1+/viZNmlSs75AhQ/Tggw9Kkrp06aLPP/9c27dvV1RU1NUHD8BlMKMLACpp7969aty4se644w679gEDBuh///uffvrpJ0lSRESEbRbXJ598ooCAACUkJOijjz7SpUuX9N133+mXX35RREREjZ8DAACAq+ncubNWr15d7DF79uwS+xcUFOjLL79UTEyMXftf/vKXEvuHh4fbPQ8NDVVeXp5jggfgMpjRBQCVdPLkSTVt2lQmk8muPSgoyLa9WbNm6tGjh9LS0mSxWPThhx+qW7duuvnmm1WnTh198cUX+vzzz9WqVStde+21zjgNAAAAl+Lr66t27doVa69Tp+T/tp4+fVpWq1XNmjWza2/YsKEaNGhQ7jgmk0lWq/UqIgbgipjRBQCV1KRJE+Xm5hZLjCwWi227JN14441q3ry5PvzwQ+3bt0+33367PD09ddttt2nfvn3as2cPs7kAAACq6Eox68SJE3btFy5c0Llz55wREgAXQKELACqpS5cuOnnypO22xCs2bNig6667zu5TxR49emjjxo06ePCgunbtKum3tSM++OADffTRRxS6AAAAqsjPz0833nij3n//fbv2bdu26fLly06KCoCzcesiAJTixIkT2rVrl12bp6enbr/9dg0aNEjjxo3TP/7xD7Vs2VI7d+7Uv/71L82fP9+uf48ePTRy5EjddNNN8vf3lyR1795dTz31lHx8fIp9PTYAAAAqbuTIkXr00Ufl5+eniIgI/fDDD1q5cqW8vb2dHRoAJ6HQBQCl+Pe//62///3vdm0+Pj767LPP9PTTT2vBggWaN2+efv31V91www2aP3++evXqZde/a9eu8vLyss3mkqSWLVsqODhYYWFhJGEAAABXoXfv3kpNTdU///lPLV++XKGhoZo1a5YeeughZ4cGwElMVlbfAwAAAAC4IavVqrNnz6phw4a2tvz8fHXu3Fnz5s0r9iEkAONjRhcAAAAAwC0tWLBA//rXvzRixAiFhobq5MmTevXVVxUQEKAuXbo4OzwATkChCwAAAADglpKSkiRJS5Ys0fHjx9WwYUPddttteuaZZ+xmeQGoPbh1EQAAAAAAAIbg4ewAAAAAAAAAAEeg0AUAAAAAAABDoNAFAAAAAAAAQ6DQBQAAAAAAAEOg0AUAAAAAAABDoNAFAAAAAAAAQ6DQBQAAAAAAAEOg0AUAAAAAAABDoNAFAAAAAAAAQ6DQBQAAAAAAAEOg0AUAAAAAAABDoNAFAAAAAAAAQ6DQBQAAAAAAAEOg0AUAAAAAAABDoNAFAAAAAAAAQ6DQBQAAAAAAAEOg0AUAAAAAAABDoNAFAAAAAAAAQ6DQBQAAAAAAAEOg0AUAAAAAAABDoNAFAAAAAAAAQ6DQBQAAAAAAAEOg0AUAAAAAAABDoNAFAAAAAAAAQ6DQBQAAAAAAAEOo4+wAjOr8+fP66quvFBgYKE9PT2eHAwAAHKyoqEi5ublq27at6tWr5+xw8H/IwQAAMLbycjAKXdXkq6++UlxcnLPDAAAA1SwjI0MdO3Z0dhj4P+RgAADUDqXlYBS6qklgYKCk3174oKAgJ0cDAAAczWKxKC4uznbNh2sgBwMAwNjKy8EodFWTK1Plg4KCFBwc7ORoAABAdeH2ONdCDgYAQO1QWg7GYvQAAAAAAAAwBApdAAAAAAAAMASXKnTFx8crLCys2OP1119XZmamYmJiStxv/vz5Gj58eKnjtG/fXo899ph+/fVXW5/MzEy7Pm3bttV9992njz/+2G5si8WihIQEhYWF2e0PAABgBORfAADASFyq0CVJI0eOVFZWlt3j3nvvvapxVq5cqRMnTmjSpEl2fVq1amXr89577ykiIkLDhg1TTk6OJGnTpk3q16+f6tev75BzAwAAcEXkXwAAwChcbjH6Bg0aOOTbi34/TmBgoCZMmKDBgwfr/PnzqlevniSpTp06dsd65JFHtHfvXq1bt06jRo3Spk2b9OyzzyosLEw7duy46pgAAABcEfkXAAAwCpcrdFUXq9UqSbp8+XKZ/Vq1aqVjx45JktLS0uTl5WX7hBEAAAAVR/4FAABqWq0odOXm5iotLU0xMTHy8fEps+/Ro0fVtm1bSZKXl1eFxs/Ly1NeXp5dm8ViqVqwAAAABlDd+ZdEDgYAAIpzuULXnDlzlJ6ebntev3597dmz56rGOXfunFq3bq20tLRS+xcVFWn9+vXav3+/JkyYUKljrVixwi5mAAAAd+KO+ZdEDgYAAIpzuUJXYmKiBg8ebHvu4VG19fJ/P87Zs2f1yiuv6K9//aveeecd+fv7S5L++9//Kjw8XJJUWFioRo0a2daEqIyEhAQNHDjQrs1isSguLq5KsQMAANQkd8y/JHIwAABQnMsVupo0aSKz2Vys3dvbWxcuXChxnwsXLsjb27vMcZ599llFR0frvffesyU/1157rZYuXWobv1mzZlVK7Pz8/OTn51fp/QAAAFyBO+ZfEjkYAAAormpZhRO0bdtWR44c0ffff2/XXlRUpJ07d9rWdSiNh4eH/P39dfbsWVtbvXr1ZDabZTab1bx58yonWQAAAEZE/gUAANyN22QWISEhGjx4sIYPH66tW7fq+PHj+uKLLzRmzBidPXtWsbGxdv3Pnj2r3Nxc5ebm6siRI1q+fLk+//xz9ejRw0lnAAAA4F7IvwAAgLtxuVsXy/Lkk09q2bJlSk1NVU5Ojnx9fXXnnXfqzTffLDZtfcGCBVqwYIGk376954YbbtCLL76o1q1bOyN0AAAAt0T+BQAA3InJarVanR2EEeXk5CgyMlLbtm1TcHCws8MBAAAOxrXeNfG+AABgbOVd693m1kUAAAAAAACgLBS6AAAAAAAAYAgUugAAAAAAAGAIFLoAAAAAAABgCBS6AAAAAAAAYAgUugAAAAAAAGAIFLoAAAAAAABgCBS6AAAAAAAAYAgUugAAAAAAAGAIhi90FRQUKDU1VT169FCbNm101113ad68ebpw4YIkKTMzUzExMXb7XLp0SS+99JKioqLUpk0b9ejRQzNnztSZM2eccQoAAABuhfwLAAA4Sx1nB1CdLly4oMTERHl4eOi5555Tq1at9N1332n27Nn6/PPPtXjx4mL7WK1WPfbYYzp06JAmT56sm266SYcPH9b8+fN1//3366233pKPj48TzgYAAMD1kX8BAABnMnSha/ny5crPz1dmZqbq1asnSQoKClK7du1099136/333y+2z4YNG/Tpp59q/fr1CggIsO2zZMkS9e/fX6+//rqSkpJq9DwAAADcBfkXAABwJkMXutasWaOHH37YlmRd0aRJE6WmpqpBgwY6cuRIsX3uu+8+W5J1Rd26dZWSkqL8/PxqjxsAAMBdkX8BAABnMmyh68yZMzp8+LDCw8NL3N6zZ09JKpZoffPNNxo2bFiJ+3Ts2LHE9ry8POXl5dm1WSyWSkYMAADg3moy/5LIwQAAQHGGLnRJkp+fX6X2y8vLq/Q+K1asUHp6eqX2AQAAMJqazL8kcjAAAFCcYQtdvr6+kn5LnP44Db68/f74yWB5EhISNHDgQLs2i8WiuLi4So0DAADgzmoy/5LIwQAAQHGGLnRde+21+uyzz9SqVati27dv364GDRoUa2/Tpo0++eQTde/evdi2AwcO6MyZM4qMjLRr9/Pzq9KnkAAAAEZSk/mXRA4GAACK83B2ANVp0KBBWrJkic6fP2/XfuLECU2cOFEnTpwots+9996rt956q9i28+fPa+rUqTp06FC1xgwAAODOyL8AAIAzGbrQNXToUNWvX1+JiYnat2+fLBaLdu3apcTERN1yyy3q3bt3sX1iYmJ06623Kj4+Xjt27JDFYtGBAwc0dOhQ1a1bl6nwAAAAZSD/AgAAzmTYWxel376S+tVXX1V6errGjx+vEydOKCgoSAMGDNDw4cPl4VG8zufh4aF58+ZpyZIlSklJ0bFjxxQQEKCoqCiNGTNGPj4+TjgTAAAA90D+BQAAnMlktVqtzg7CiHJychQZGalt27YpODjY2eEAAAAH41rvmnhfAAAwtvKu9Ya+dREAAAAAAAC1B4UuAAAAAAAAGAKFLgAAAAAAABgChS4AAAAAAAAYAoUuAAAAAAAAGAKFLgAAAAAAABgChS4AAAAAAAAYAoUuAAAAAAAAGAKFLgAAAAAAABhCHWcHUFH79+/Xgw8+WOr2UaNGSZLS09OLbevevbuWLl0qSQoLC7PbFhQUpMGDB+uRRx6Rh4eHMjMzNWnSpFKPM2vWLA0aNKgqpwAAAOB2yMEAAIA7cZtCV3h4uLKysmzPo6KilJqaqvDwcEmSj4+Pli1bpm7duik1NdVuX29vb7vnS5YsUevWrXX58mX95z//0dSpU+Xl5aXhw4crOjpad9xxhyTp9OnT6tu3r1atWqXmzZtLknx9favzNAEAAFwKORgAAHAnblPo8vb2VmBgoO25yWRSo0aN7NpK6leSxo0b2/o0a9ZMY8aM0aJFizR8+HDVq1dP9erVkyR5enpKkvz9/csdEwAAwIjIwQAAgDthjS5J119/vY4fP+7sMAAAAGoVcjAAAOBobjOjqzr9/PPPatKkSZX3z8vLU15enl2bxWK52rAAAAAMjRwMAAA4muEKXbt377atGXHFtGnTNHDgwBL75+TkaNGiRerTp0+Vj7lixYoSF2AFAACoLcjBAACAKzBcoatDhw6aOXOmXVtAQIDd87i4OHl6espqtercuXOKjo7W2LFjq3zMhISEYkmcxWJRXFxclccEAABwJ+RgAADAFRiu0OXj4yOz2Vxmn9mzZyssLExnzpxRfHy8unbtqvr161f5mH5+fvLz86vy/gAAAO6OHAwAALiCWrkYffPmzWU2m9W2bVs98sgjmjt3rs6cOePssAAAAAyNHAwAAFQ3wxW6CgsLlZuba/c4efJkqf0TEhLUoEEDLVy4sAajBAAAMBZyMAAA4AoMd+vinj171L17d7u2m2++WZmZmSX29/b2VnJysh599FHdd9995U65BwAAQHHkYAAAwBWYrFar1dlBGFFOTo4iIyO1bds2BQcHOzscAADgYFzrXRPvCwAAxlbetd5wty4CAAAAAACgdqLQBQAAAAAAAEOg0AUAAAAAAABDoNAFAAAAAAAAQ6DQBQAAAAAAAEOg0AUAAAAAAABDoNAFAAAAAAAAQ6DQBQAAAAAAAEOg0AUAAAAAAABDqNFCV3x8vMLCwmyPTp06KTk5WXl5ebbtS5cutfUPCwvTl19+aTfGsmXL1K5dO3388ce2ts2bN6t///5q06aNevbsqVWrVhU79qVLl/TSSy8pKipKbdq0UY8ePTRz5kydOXOmxFjnzJmjsLAw/fjjjw44cwAAAOchBwMAALVFjc/oGjlypLKysrR7924tW7ZMR48e1dixYyu079atW/X8888rLS1NHTt2lCR9+OGHGjt2rGJjY7V582ZNnDhRKSkp2rlzp20/q9Wqxx57TG+//bYmT56s7du3a/bs2Tp48KDuv/9+FRQU2B3HarVq48aN8vLy0oYNGxx38gAAAE5CDgYAAGqDGi90NWjQQIGBgWratKnatWunWbNmadeuXTp27FiZ+3399dcaP368xo8fr969e9vat2zZotjYWMXGxio4OFh9+vRR//799c4779j6bNiwQZ9++qlee+013XnnnQoKClLnzp21ZMkSFRYW6vXXX7c71ueff67jx4/rkUce0caNGx37AgAAarWMjAyFhITIw8NDISEhysjIcHZIqCXIwQAAQG1Qx9kBBAcHy8vLq8wky2KxaMSIERo8eLASExPttj355JPF+nt7e+vs2bO252vWrNF9992ngIAAu35169ZVSkqK8vPz7do3bNigbt266b777lN6erq++eYbtWnTpvInBwDA72RkZCgpKck2iyU7O1tJSUmSpLi4OGeGhlqIHAwAABiR0xejz83N1cWLF3XNNdeUuL2goEAjRoxQeHi4kpOTyx1v//79Wrdunfr3729r++abb9ShQ4cS+3fs2FE9e/a0PS8qKtKmTZvUt29fBQQEqHPnzuVOnc/Ly1NOTo7dw2KxlBsrAKB2mTJlSrFbtQoKCjRlyhQnRYTajBwMAAAYkVNndJ06dUozZszQLbfcopCQkBL7PPHEEzp8+LBCQ0Pl4VF6Xe7NN99USkqKCgsLlZSUpIiICNu2vLw8+fn5VSim/fv36/Tp0+rVq5ckqW/fvkpPT9e4ceNKPf6KFSuUnp5eofEBALXX4cOHK9UOVBdyMAAAYFQ1PqNrzpw5Cg8PV3h4uG677Tbl5uZq7ty5pfY/f/68Vq5cqR07digzM7PUfn379tXbb7+tBQsWaPPmzUpLS7Nt8/X1tX2rUHnWr1+viIgINWzYUJIUFRWlX375RZ988kmp+yQkJGjbtm12D9ZcAQD8UcuWLSvVDjgSORgAAKgNanxGV0JCgoYMGSJJ8vPzU5MmTcrsP2PGDLVv317Tp0/Xk08+qfDwcLVq1apYP19fX/n6+uq6665T48aNlZiYqEcffVTe3t5q06aNPvnkE3Xv3r3YfgcOHNCZM2cUGRmpwsJCbdmyRQUFBWrXrp2tz6VLl7RhwwZ16tSpxBj9/Pwq/GklAKD2SklJsVujS5J8fHyUkpLixKhQW5CDAQCA2qDGZ3T5+/vLbDbLbDaXm2Bd6S9JAwYM0J133qnHH39chYWFtu2TJ0/W1q1b7fbx9vZWUVGRioqKJEn33nuv3nrrLZ04ccKu3/nz5zV16lQdOnRIkvTBBx/o0qVLWr16tdatW2d7TJw4UZs2bdKlS5eu6twBALVbXFycFi9eLLPZLJPJJLPZrMWLF7MQPWoEORgAAKgNnL4YfWVMnz5dv/76q1544QVbW+PGjTV9+nRt2rRJx44d0759+zR16lTFxMSofv36kqSYmBjdeuutio+P144dO2SxWHTgwAENHTpUdevWtf0HY+PGjbrzzjvVpk0bhYaG2h5DhgzR2bNn9eGHHzrlvAEAxhEXF6cff/xRly9f1o8//kiRC26BHAwAALgLpy5GX1mNGjXSc889p7/97W/q2rWrIiIiNHbsWDVs2FCpqak6fvy4AgICNHDgQI0ePdq2n4eHh+bNm6clS5YoJSVFx44dU0BAgKKiojRmzBj5+PgoPz9fH3zwgZ5//vlix23YsKEiIiK0YcMGuwVWAQAAagNyMAAA4C5MVqvV6uwgjCgnJ0eRkZHatm2bgoODnR0OAABwMK71ron3BQAAYyvvWu9Wty4CAAAAAAAApaHQBQAAAAAAAEOg0AUAAAAAAABDoNAFAAAAAAAAQ6DQBQAAAAAAAEOg0AUAAAAAAABDoNAFAAAAAAAAQ6DQBQAAAAAAAEOg0AUAAAAAAABDqFKh69tvv9XKlSu1YMEC5ebmKj8/Xz/99JOjYytRfn6+brnlFo0dO9auPTk5WWFhYaU+cnJyJEl33XWXXXu3bt00c+ZMXbhwQZK0detW3XTTTfrPf/5jN/7p06fVpUsXzZ8/v0bOEwAAwFWQfwEAAHdRqUJXQUGBRowYoUGDBmnFihVKT0/Xzz//rD179ig6OrpYclIdtm7dqsuXL2v79u06d+6crX3KlCnKyspSVlaWZs6cqaCgINvzrKwsNW/e3NZ35syZysrK0u7du/Xiiy9qz549SklJkST16tVLERERtudXzJ8/Xz4+Pvr73/9e7ecIAADgSsi/AACAu6hUoSs1NVX/+9//tGHDBm3cuFFWq1XSb8lJ165dNXv27GoJ8vfWr1+v2NhYNWjQQDt27LC1+/r6KjAwUIGBgfL19ZWnp6fteWBgoDw9PYv1bdq0qTp16qQnnnhC69atU2FhoaTfkrYvvvhC7733niTpu+++08qVKzVp0iTVq1ev2s8RAADAlZB/AVcnIyNDISEh8vDwUEhIiDIyMpwdEgAYVqUKXe+//77+8Y9/KDQ0VCaT6f8N4uGhv/71r/r4448dHuDvnThxQnv37lW/fv3Uu3dvrV+/3iHjhoaG6sKFC/r1118lSddee61GjBih1NRUnTt3TikpKeratauioqIccjwAAAB3Qf4FXJ2MjAwlJSUpOztbVqtV2dnZSkpKotgFANWkTmU65+fnq3HjxiVu8/Co/nXtN23apBYtWqhdu3YqLCxUYmKiTp8+rUaNGl3VuD///LM8PDzk5+dnaxs6dKjWrVunYcOG6fPPPy8zqcvLy1NeXp5dm8ViuaqYAAAAXIGr5l8SORjcw5QpU1RQUGDXVlBQoClTpiguLs5JUQGAcVWq0PXnP/9ZmZmZ6tatW7Ftq1evVocOHRwWWEk2bNig6OhomUwmdejQQQEBAXr//fc1ZMiQKo954sQJzZ07VxEREfLx8bG1e3t7Kz4+XjNmzFBUVJRatWpV6hhX1isDAAAwGlfNvyRyMLiHw4cPV6odAHB1KjUNa+LEidq+fbuGDx+u9evXy2QyafPmzRo6dKh2796tcePGVVecOnr0qD799FNFR0dLkkwmk/r06aMNGzZUeqxx48YpPDxcf/7zn9WtWzcVFhbqmWeesetTWFio1157TTfeeKM++OAD/fDDD6WOl5CQoG3bttk9mIoMAADcnSvnXxI5GNxDy5YtK9UOALg6lZrR1bZtW61Zs0bp6elKS0uTJP3rX/9St27dtHr1aoWGhlZLkJK0ceNGSdLgwYNtbUVFRbJarfrpp5/UrFmzCo81fvx49ezZUxcuXNDw4cP15z//Wf7+/nZ9li5dqvz8fL377rtKSkrSzJkztXTp0hLH8/Pzs5t2DwAAYASunH9J5GBwDykpKUpKSrK7fdHHx6fYt4wCAByjUoUuSWrVqpWef/756oilTOvXr1d8fLxiY2Pt2pOSkvTee+8pMTGxwmM1a9ZMZrNZ0m9JV3JysgYPHqxrr71WknTkyBEtWrRITz31lBo1aqRp06bpr3/9q7Zs2cKCqAAAoNYg/wKu3pV1uKZMmaLDhw+rZcuWSklJYX0uAKgm5d66mJ+fX6lHdfj+++/13XffKTY2VqGhoXaP6OjoKk2fvyI6Olrt2rWzzVCTfvvUpU2bNhowYIAk6eabb9Z9992nWbNm6fz581d9PgAAAK6O/AtwnLi4OP3444+6fPmyfvzxR4pcAFCNyp3R1bFjR5lMpnIHslqtMplM+vbbbx0S2O9t2LBBrVu3LvHWyL59+2rx4sXKzs62fUpYWVOnTtWgQYP00Ucf6fTp09q5c6fWrl1rd97/+Mc/tGnTJr388ssaPXp0lc8FAADAHZB/AQAAd2SyWq3WsjocOHCgUgN27tz5qgIyipycHEVGRmrbtm0KDg52djgAAMDBuNa7Jt4XAACMrbxrfbkzuihcAQAAAAAAwB1UejH6goICZWZm6uOPP9apU6fUuHFj3XbbbRo4cKDq1atXHTECAAAAAAAA5Sp3Mfrfy8nJUUxMjObNmycPDw9df/31unz5stLS0jRgwABZLJbqihMAAAAAAAAoU6VmdM2cOVPXXHON1q1bJz8/P1v7r7/+qmHDhumZZ57RvHnzHB4kAAAAAAAAUJ5Kzejat2+fhg8fblfkkiR/f389/PDD2r17t0ODAwAAAAAAACqqUoWuOnXqyNvbu8Rt3t7e8vT0dEhQAAAAAAAAQGVVqtD14IMPavny5bp48aJde2FhoZYvX64HH3zQocEBAAAAAAAAFVWpNboaNmyo3Nxc3X333brrrrvk7++vX375RTt27NClS5d04403atasWbb+kyZNcnjAAAAAAAAAQEkqVejasWOHGjdurMaNG+vgwYO29uDgYEnSt99+a2szmUwOChEAAAAAAAAoX6UKXa+99prDA4iPj9eBAwdsz/38/BQZGanJkyfLz89P8fHx6tmzp4YOHSpJCgsL0+rVq9WuXTvbPsuWLdOcOXO0fPlydezYUZK0efNmpaen6/vvv1fTpk01cuRIDR482LZPcnKy1q5da3vu4+OjLl266IknnlCLFi1s7d9//70mTJigH374QZ999pnDzx8AAKCmkX8BAACjKneNroKCgmoPYuTIkcrKytLu3bu1bNkyHT16VGPHjq3Qvlu3btXzzz+vtLQ0W5L14YcfauzYsYqNjdXmzZs1ceJEpaSkaOfOnXb7Dho0SFlZWcrKytKbb74pT09PDRs2TEVFRZKkjIwM/fWvf9U111zj2BMGAABwMvIvAABgROXO6OrVq5cyMjLUqlUr3XPPPeXekvj7T+kqqkGDBgoMDJQkNW3aVLNmzVJkZKSOHTtW5n5ff/21xo8fr/Hjx6t379629i1btig2NlaxsbGSfru1cu/evXrnnXcUERFh61e/fn3bcQMDA5WamqrbbrtNn332mTp27Kj3339fS5cu1aVLl/TRRx9V+rwAAABcFfkX/igjI0NTpkzR4cOH1bJlS6WkpCguLs7ZYRkCry0A1JxyC11RUVEKCAiQJEVGRtbI2lvBwcHy8vIqM9GyWCwaMWKEBg8erMTERLttTz75ZLH+3t7eOnv2bJnH9fHxUYsWLXT06FF17NhRS5culZeXl/bv31+V0wAAAHAb5F+1W0ZGhpKSkmx3c2RnZyspKUmSKMhcJV5bAKhZ5Ra6nnrqKdu/R48eXa3BXJGbm6uLFy+WOmW9oKBAI0aMUHh4uJKTk8sdb//+/Vq3bp2ef/75MvsVFRXpxIkTatKkiSTJy8urQvHm5eUpLy/Prs1isVRoXwAAAFfgbvmXRA7mSFOmTCm2ZElBQYGmTJlCMeYq8doCQM2q1GL0kyZN0ogRI2Q2m4tty87O1rx588pNZspz6tQpzZgxQ7fccotCQkJK7PPEE0/o8OHDCg0NlYdH6cuMvfnmm0pJSVFhYaGSkpLsps3/0fnz57Vw4ULVrVtXnTp1qlTMK1asUHp6eqX2AQAAcBXumH9J5GCOdPjw4Uq1o+J4bQGgZlWq0LV27VoNGTKkxELX119/re3bt1cpiDlz5tiSlIKCArVv315z584ttf/58+e1cuVKDRs2TJmZmRo0aFCJ/fr27avOnTvr0KFDSktL0+XLlzV+/Hjb9jfeeMO2ptj58+fVokULLVy4UPXr169U/AkJCRo4cKBdm8Vi4RMaAADgstw9/5LIwRypZcuWys7OLrEdV4fXFgBqVoUKXb9fhD45OVk+Pj522y9fvqxDhw4VSzQqKiEhQUOGDJH029dbX5m6XpoZM2aoffv2mj59up588kmFh4erVatWxfr5+vrK19dX1113nRo3bqzExEQ9+uij8vb2liRFR0drzJgxkqRZs2bp1KlTdl+bXVF+fn7y8/Or9H4AAADO4u7515W4ycEcIyUlxW4dKem39dNSUlKcGJUx8NoCQM2qUKGrV69ekqRvv/1WHTp0UIsWLYr1CQ4OVr9+/aoUhL+/f4mzxMrqL0kDBgzQzp079fjjj+utt96yJVCTJ0/WXXfdZYtb+m0x1KKiIttXV0tSo0aNbMd94okn1KdPH7377rvq27dvlc4DAADAXZB/4feuzILjmwEdj9cWAGpWhQpdo0aNkiTt3r1bCQkJCgsLq9agKmP69Onq37+/XnjhBdvCqI0bN9b06dN16dIl3XLLLTp8+LCeeeYZxcTElDot/k9/+pOGDh2qtLQ03XXXXVWaPg8AAFAbkH8ZU1xcHMWXasJrCwA1p/SVREvw1ltvuVSRS/rtU8HnnntOK1as0M6dOyVJY8eOVVxcnFJTUxUZGalx48YpIiKi3OnBSUlJslqtWrp0aU2EDgAA4JbIvwAAgKsyWa1Wa1kdZs2aVakBJ02adFUBGUVOTo4iIyO1bds2BQcHOzscAADgYFzrXRPvCwAAxlbetb7cWxe/+eabCh/syoL1AAAAAAAAQE0rt9D12muv1UQcAAAAAAAAwFWp1BpdAAAAAAAAgKuq0LcuXnHPPfeUe3vi2rVrryogAAAAAAAAoCoqVejq1atXsbZLly7pwIEDys3N1YABAxwWGAAAAAAAAFAZlSp0jRo1qtRt48aNu+pgAAAAAAAAgKpy2BpdgwYNYuF6AAAAAAAAOI3DCl0HDx5UYWGho4YDAAAAAAAAKqVSty6++uqrxdqsVqsOHTqkNWvWqF+/fg4LDAAAAAAAAKiMShW6XnnllWJtJpNJjRo10vDhwzV8+HBHxXVV4uPj5eHhoVdeeaXYt0TGx8erc+fOGj16tMLCwkrcf/369brxxhslSZ988okmTJig+vXra8OGDdUeOwAAgLsiBwMAAM5WqULX9u3bqysOh9u3b59WrlypuLi4MvstWbJErVu3tmtr0qSJJOmFF17Qq6++qvbt2+vnn3+utlgBAACMghwMAAA4U7lrdKWnp1fq4SquvfZapaWl6fDhw2X2a9y4sQIDA+0eder8Vv/LysrS6tWrFRMTUxMho4IyMjIUEhIiDw8PhYSEKCMjw9khARXCzy6A2qA252Cu/HfelWOrDXj9AaDmlDujKz09XSaTSZ6enrrhhhtktVpL7WsymTRq1CiHBlhV/fv312effabk5GS9/vrr8vCo/Lr7b731lry8vPTFF19UQ4SoioyMDCUlJamgoECSlJ2draSkJEkq95NjwJn42QVQW9TWHMyV/867cmy1Aa8/ANSscjOPtLQ0de7cWUVFRbp06ZLuueceLV++XOvWrSv2WLt2bU3EXCEmk0kpKSn6z3/+U+Ii+hXh5eVVoX55eXnKycmxe1gsliodE2WbMmWKLUm4oqCgQFOmTHFSREDF8LMLoLaorTmYK/+dd+XYagNefwCoWeXO6OrXr5/69eunI0eOaM2aNVq+fLleeOEF3XXXXRo8eLBuv/32moizSlq0aKHk5GTNnDlTd9xxh0JDQ4v1eeCBB+w+abzjjjs0b968Sh1nxYoVLnXbppGVdhtEebdHAM7Gzy6A2qQ25mCu/HfelWOrDXj9AaBmVXgx+muvvVaPPfaYxowZo927d2vNmjUaPny4mjZtqkGDBunee+9V8+bNqzPWKhk8eLC2bt2qSZMm6Y033ii2ffbs2bZv95Gk+vXrV/oYCQkJGjhwoF2bxWJhKnI1aNmypbKzs0tsB1wZP7sAapvaloO58t95V46tNuD1B4CaVelFEzw8PBQREaF58+Zp165diouL07vvvqvIyEgNGzasOmK8KiaTSTNmzNChQ4e0ZMmSYtuDgoJkNpttj6ZNm1b6GH5+fgoODrZ7BAUFOSJ8/EFKSop8fHzs2nx8fJSSkuKkiICK4WcXQG1T23IwV/4778qx1Qa8/gBQsyq/Oujv+Pv7KywsTKGhobp8+bK+//57R8XlUM2aNdPkyZM1f/58HTlyxNnh4CrExcVp8eLFMpvNMplMMpvNWrx4MbPn4PL42QVQG9WmHMyV/867cmy1Aa8/ANSsCt+6+HvHjh1TZmamMjMz9fPPP6tHjx5auHChIiIiHB2fwwwaNEibN2/Wjh07nB0KrlJcXByJAdwSP7sAaqPalIO58t95V46tNuD1B4CaU+FC18WLF7VlyxatWbNGe/bs0Z/+9CcNGTJEgwYNqtJUc2eYMWOGYmJinB0GAABArUIOBgAAakq5ha7//ve/Wr16td5++23l5+crMjJSS5cuVdeuXWUymWoixkp77bXXSmwPDAzU/v37bc8PHjxYofEGDRqkQYMGOSQ2AAAAoyIHAwAAzlZuoatfv34ymUyyWq3685//rKKiIq1cuVIrV64ssb+rfMUzAAAAAAAAapdyC1333HOPy87cAgAAAAAAAK4ot9D17LPP1kQcAAAAAAAAwFXxcHYAAAAAAAAAgCNQ6AIAAAAAAIAhUOgCAAAAAACAIVDoAgAAAAAAgCFQ6AIAAAAAAIAhlPuti65i//79evDBB0vdPmrUKElSenq6ra1u3boKDw/X1KlTdcMNN9jab7nlFl24cMFu/0cffVSPPPKI7fkbb7yhZcuW6ciRI7ruuus0btw43XXXXY46HQAAALdADgYAANyJ2xS6wsPDlZWVZXseFRWl1NRUhYeHS5J8fHy0bNkydevWTampqZKkM2fOKCMjQ4mJiXr//ffVsGFD/frrr7pw4YIyMzPVtGlT23g+Pj62f69atUrPP/+8nnrqKd16663atWuXHn30Ua1atUqtW7euoTMGAABwPnIwAADgTtym0OXt7a3AwEDbc5PJpEaNGtm1/bFfYGCgpk6dqh07dmjr1q2655579NNPP8lkMunGG2+Ul5dXicfasmWLRo4cqb59+0qS7r//fm3fvl3vvvsuSRYAAKhVyMEAAIA7MfwaXSaTSa1atdLRo0clScePH5e/v3+pCZYkLV68WH/729/s2ry8vGQymao1VqA6ZWRkKCQkRB4eHgoJCVFGRoazQwIAGBg5GICSkJMCqG6GL3RJ0tGjR22fMB4/flweHh4aOnSoOnXqpL59+yozM7PUfS9fvqxNmzZp79696tOnT02FDDhURkaGkpKSlJ2dLavVquzsbCUlJZFYAACqFTkYgN8jJwVQE9zm1sWqKCws1CuvvKKffvpJkZGRkqRrr71WPXr0UGRkpCZNmqT9+/dr+vTp8vLyUr9+/ez2nzNnjpYuXaqioiJNnTq11CnzeXl5ysvLs2uzWCzVc1JAFUyZMkUFBQV2bQUFBZoyZYri4uKcFBUAwKjIwQCUhJwUQE0wXKFr165dtsVRL1y4oKCgIP3zn/9UQECAJKlHjx7q0aOHrf/111+vU6dOadmyZcWSrISEBPXv319ffvmlZs+eLUkl/gFesWKF3TcNAa7m8OHDlWoHAKCyyMEAlIecFEBNMFyhq2PHjpo5c6YkqV69emratGm56zqEh4dr+fLlxdr9/f3l7++v0NBQmUwmzZ07t8QkKyEhQQMHDrRrs1gsfCoBl9GyZUtlZ2eX2A4AgCOQgwEoDzkpgJpguDW6fHx8ZDabZTab1axZs2IJ1uLFi7VkyRK7toMHD6p58+aSpKKiIj388MP697//bdfH29tbFy9eLPGYfn5+Cg4OtnsEBQU57qSAq5SSkmL39e3Sb78rKSkpTooIAGA05GAAykNOCqAmGK7QVZ7Q0FC9+OKLyszM1PHjx/Xuu+9q/vz5SkxMlCR5enrK29tbEydOVFZWlo4fP64dO3YoLS1NgwcPdm7wQBXFxcVp8eLFMpvNMplMMpvNWrx4MZ94AwBqDDkYAHJSADXBcLculicyMlJPPvmkXnrpJU2ePFnBwcGaOHGi7r33XlufZ599VvPmzVNycrJ++eUXtWjRQrGxsRo6dKgTIweuTlxcHEkEAMBpyMEASOSkAKqfyWq1Wp0dhBHl5OQoMjJS27ZtU3BwsLPDAQAADsa13jXxvgAAYGzlXetr3a2LAAAAAAAAMCYKXQAAAAAAADAECl0AAAAAAAAwBApdAAAAAAAAMAQKXQAAAAAAADAECl0AAAAAAAAwBApdAAAAAAAAMAQKXQAAAAAAADAECl0AAAAAAAAwBEMWuuLj4xUWFmZ7dOrUSRMmTNCpU6ckScnJyXbbb7nlFg0bNkxHjhyxG+f06dMaO3aswsLC9OWXXzrhTAAAANwHORgAAHA2Qxa6JGnkyJHKysrS7t27tWzZMh09elRjx461bR80aJCysrKUlZWltWvXqmHDhhoxYoSsVqsk6aOPPlK/fv109uxZZ50CAACA2yEHAwAAzmTYQleDBg0UGBiopk2bql27dnrmmWeUlZWlY8eOSZLq16+vwMBABQYGKjQ0VNOmTdP333+vH374QZL03nvvadSoUVq0aJEzTwMAAMCtkIMBAABnMmyh64/MZrO8vLxsSVZprnyaOGnSJA0ZMqQmQquUjIwMhYSEyMPDQyEhIcrIyHB2SAAAAKUySg4GAADcQx1nB1BTTp48qYsXL6pJkybFtuXn5+vpp59Wp06ddN1110mSvLy8ajrEcmVkZCgpKUkFBQWSpOzsbCUlJUmS4uLinBkaAABAiYyQgwEAAPdRKwpdZ86cUWpqqlq3bm1Lot566y2tXbtWknTu3Dm1aNFCCxculMlkqvT4eXl5ysvLs2uzWCxXH/gfTJkyxVbkuqKgoEBTpkyh0AUAAFyOUXIwAADgPgxb6JozZ47S09Ml/ZZE3XTTTZo3b54tiYqOjtaoUaMkSefPn9e6desUGxur1atX2xKxilqxYoXtWNXp8OHDlWoHAACoaUbMwQAAgPswbKErISFBQ4YMkdVq1bhx42Q2m2U2m23bfX197Z5PnDhRBw8e1KpVqzRx4sRKH2vgwIF2bRaLxeGzrFq2bKns7OwS2wEAAFyBEXMwAADgPgy7GL2/v7/MZrNCQkL0xBNPaOPGjfrss8/K3Oeaa66p0ldZ+/n5KTg42O4RFBRU1dBLlZKSIh8fH7s2Hx8fpaSkOPxYAAAAVWHEHAwAALgPwxa6fu/WW29V//799cwzz9i+0efcuXPKzc1Vbm6ujh49qszMTL333nu68847nRxt6eLi4rR48WKZzWaZTCaZzWYtXryYTy0BAIBLMkoOBgAA3Idhb138o7Fjx6p37956++23JUmZmZnKzMyUJNWpU0chISGaNm2ayydZcXFxFLYAAIDbMEoOBgAA3IPJeuXjNThUTk6OIiMjtW3bNgUHBzs7HAAA4GBc610T7wsAAMZW3rW+Vty6CAAAAAAAAOOj0AUAAAAAAABDoNAFAAAAAAAAQ6DQBQAAAAAAAEOg0AUAAAAAAABDoNAFAAAAAAAAQ6DQBQAAAAAAAEOg0AUAAAAAAABDoNAFAAAAAAAAQ6jj7AAqKj4+XgcOHCjWPnbsWCUlJUmS9uzZowULFuiLL76Ql5eXunTposcff1zXX3+9rX9ycrLWrl1bbJyHHnpIEydO1P79+/Xggw/a2j09PRUaGqqRI0eqd+/e1XBmAAAAroscDAAAuBO3KXRJ0siRIxUbG2vX1qBBA0nSxo0bNWnSJI0aNUqzZs3SxYsXlZGRoSFDhuj1119XmzZtbPsMGjRIjz/+uN049evXt/v3li1bJEmFhYXauXOnxo0bp8DAQHXo0KG6Tg8AAMAlkYMBAAB34VaFrgYNGigwMLBYe35+vp588kk99dRTGjhwoK192rRpys/P17Rp07R69Wpbe/369Usc5wqTyWS3/f7779enn36q1atXk2QBAIBahxwMAAC4C0Os0bV161b5+vrqnnvuKbYtKSlJDRs21OnTp6/qGDfccIOOHz9+VWMAAAAYCTkYAABwNW41o6s0X3/9tcLDw2UymYptu/766/XKK69c9TF+/vlnNWnS5KrHAQAAMApyMAAA4GrcqtA1Z84cpaen27W98847ys/Pl6+vb4XHeeutt4othrp9+/YSkyir1apPPvlEb7/9tmbNmlXieHl5ecrLy7Nrs1gsFY4HAADAlZGDAQAAd+FWha7ExEQNHjzYri0oKEi+vr765Zdf7NrHjBmj3bt3256//PLL6tixoyQpOjpao0aNsuvv5+dn+3dBQYHCw8MlSZcuXZLVatXo0aMVFRVVYlwrVqwolvwBAAAYBTkYAABwF25V6GrSpInMZnOx9ptuuknz5s3T5cuX5eHx27JjU6dO1blz51RUVKTo6GjVqfP/TtXX17fEcf5/9u49LKpy/f/4Z0BQQFAhPIWiUaJ4KEzNMxa6t2fFUr9EhqWhqZnlCdDUUrIwD1/BMk+lptXOyK9aWYqmebYys8ys7d4a6ih5QkRFcX5/tJ3fngBhcGAOvl/Xta7LedaznnWvGS/X7T3PPOum8uXLa/Xq1ZKkffv2acKECYqMjCy0f2xsrMUCrNKf3ybGxMRYc3kAAAAOiRwMAAA4C5dYjD4yMlIXL17Uxx9/bG6rWrWqgoODdfjwYVWoUEH33Xdfscdzd3dXcHCwgoOD1bt3b7Vu3brQKfPSn99EBgUFWWzVq1e/rWsCAABwdORgAADA0TjVjK5Lly4pMzPTos3Ly0t+fn6aPHmyEhMTdfr0afO3h1u2bNHMmTM1atQo+fj4lPi8CQkJ6tGjh7Zs2aKIiIjbvQwAAACnQg4GAACchVMVuubNm6d58+ZZtD399NMaP368evTooYCAAL355pt6++23ZTKZVL9+fSUlJalr1663dd577rlHTzzxhF599VW1bt1aHh4etzUeAACAMyEHAwAAzsJgMplM9g7CFWVkZCgyMlLp6ekKCgqydzgAAMDGuNc7Jj4XAABcW1H3epdYowsAAAAAAACg0AUAAAAAAACXQKELAAAAAAAALoFCFwAAAAAAAFwChS4AAAAAAAC4BApdAAAAAAAAcAkUugAAAAAAAOASKHQBAAAAAADAJVDoAgAAAAAAgEtwikLXgAEDFBoaat6aN2+u+Ph4ZWVlaffu3Rb7/rqlpKRIklJSUgrcP2jQIPN5QkNDlZqaWmAMoaGh2r17d5lcLwAAgCMgBwMAAM6mnL0DKK7hw4crOjpaJpNJp06dUnJyskaPHq158+Zp27Zt5n6dOnVScnKywsPDJUne3t7mfa1bt1ZycrLFuJ6enhav33rrLUVGRqpBgwaleDUAAADOgRwMAAA4E6eY0SVJPj4+CgwMVNWqVdW4cWNNnz5dW7du1R9//KHAwEDzZjAYVKlSJfNrHx8f8xienp4WfQMDA1WpUiWL89SoUUPjxo1Tbm5uWV8iAACAwyEHA27fihUrVKdOHbm5ualOnTpasWKFvUMCAJflNIWuvwoKCpKHh4dOnDhh03HHjh2rM2fOaN68eTYdFwAAwBWQgwHWWbFiheLi4nT06FGZTCYdPXpUcXFxFLsAoJQ4baErMzNT165d01133WXTcStXrqxXXnlFCxcu1IEDB4p1TFZWljIyMiw2o9Fo07gAAAAcATkYYJ0JEyYoJyfHoi0nJ0cTJkywU0QA4NqcZo2u/3b+/HlNnTpVTZo0UZ06dYp93Ndff21eN+KmSZMmKSoqyqKtY8eO6tKli8aNG6fVq1erfPnytxx36dKlhS6gCgAA4CrIwQDrHTt2zKp2AMDtcZpC1+zZs82JTE5Ojpo2bao5c+ZYNcaDDz6oadOmWbQFBAQU2HfSpEnq3r275syZo/Hjx99y3NjY2HyJmtFoVExMjFXxAQAAOBpyMOD21K5dW0ePHi2wHQBge05T6IqNjVW/fv0kSX5+fqpSpYrVY3h7eys4OLhYfStVqqSpU6fq2WefVWRk5C37+vn5yc/Pz+p4AAAAHB05GHB7kpKSFBcXZ/HzRW9vbyUlJdkxKgBwXU5T6PL39y92gmQrHTp0UK9evZSYmFim5wUAAHAU5GDA7bk5w3DChAk6duyYateuraSkJGYeAkApcZpCly3k5uYqMzPToq1cuXK3/GYyMTFRPXv2LO3QAAAAXBY5GO50MTExFLYAoIzcUYWuHTt2qG3bthZtDRs2VFpaWqHH+Pn5adq0aRo0aFBphwcAAOCSyMEAAEBZMZhMJpO9g3BFGRkZioyMVHp6uoKCguwdDgAAsDHu9Y6JzwUAANdW1L3ezQ4xAQAAAAAAADZHoQsAAAAAAAAugUIXAAAAAAAAXMIdtRh9WcrLy5MkGY1GO0cCAABKw817/M17PhwDORgAAK6tqByMQlcpufkIbR4jDACAa8vMzFRwcLC9w8B/kIMBAHBnKCwH46mLpeTKlSv68ccfFRgYKHd3d3uHAwAAbCwvL0+ZmZlq1KiRKlSoYO9w8B/kYAAAuLaicjAKXQAAAAAAAHAJLEYPAAAAAAAAl0ChCwAKEB8fr2HDhtk7DAAAgDvCrXKvn3/+WaGhocrIyND8+fPVtGlTHT16tNhjZ2RkKDQ0VD///LOtwgXgwFiMHgAAAADgFJ544gl16NCBh4AAKBQzugAAAAAATqFixYqqX7++vcMA4MAodAFACVy7dk1z5sxR27ZtFRYWpl69emnjxo3m/evXr1dYWJguXrxobps/f75CQ0P1yy+/mNtWr16t+++/X1euXCnT+AEAAJxRWlqamjVrZtG2fv16denSRWFhYerevbt++ukntW/fXmlpaRb9Tp8+raefflqNGjVSy5YtNX/+/LIMHUAZodAFACUwadIkvf/++xo5cqQWL16sVq1a6bnnnjMXu9q0aSM3Nzft3r3bfMyOHTskSTt37jS3bd++XS1btizwsbgAAAB3EpPJpOvXr+fb8vLyCj1m69atGjVqlNq0aaPFixcrOjpa48aN06VLl/L1nTFjhiIiIrRo0SJ169ZNs2fP1oYNG0rzkgDYAWt0AYCVjhw5orS0NC1cuFDt27eXJLVq1UrZ2dmaNWuWOnbsKF9fX4WHh2vnzp3q2LGjLl++rH379ql79+7auXOnBg4cKJPJpB07dujZZ5+18xUBAADY36ZNm9SwYUOrjklJSVHXrl01ceJESX/mZF5eXkpISMjXt1+/fnrybnwUjQABAABJREFUySclSS1bttT+/fu1adMmderU6faDB+AwmNEFAFbauXOnKleurHbt2lm09+rVS//85z916tQpSVJERIR5Fte3336rgIAAxcbGau/evbp+/boOHz6sP/74QxEREWV+DQAAAI6mRYsWWrVqVb7tjTfeKLB/Tk6ODhw4oO7du1u0/+1vfyuwf3h4uMXrkJAQZWVl2SZ4AA6DGV0AYKVz586patWqMhgMFu3Vq1c3769WrZrat2+vGTNmyGg0avv27WrdurUaNmyocuXK6YcfftD+/ftVt25d1apVyx6XAQAA4FB8fX3VuHHjfO3lyhX839YLFy7IZDKpWrVqFu0VK1aUj49PkeMYDAaZTKbbiBiAI2JGFwBYqUqVKsrMzMyXGBmNRvN+SapXr55q1Kih7du3a9euXWrTpo3c3d310EMPadeuXdqxYwezuQAAAEroZjHrzJkzFu1Xr17V5cuX7RESAAdAoQsArNSyZUudO3fO/LPEm9atW6d77rnH4lvF9u3b69NPP9Uvv/yiVq1aSfpz7YivvvpKe/fupdAFAABQQn5+fqpXr56++OILi/b09HTduHHDTlEBsDd+uggAhThz5oy2bt1q0ebu7q42bdqoT58+GjNmjF544QXVrl1bW7Zs0T/+8Q+lpKRY9G/fvr2GDx+uBg0ayN/fX5LUtm1bvfzyy/L29s73eGwAAAAU3/Dhw/X888/Lz89PERER+te//qWVK1fK09PT3qEBsBMKXQBQiO+//17PPPOMRZu3t7f27dunV155RfPmzdPcuXN19uxZ3XfffUpJSVHHjh0t+rdq1UoeHh7m2VySVLt2bQUFBSk0NJQkDAAA4DZ07txZycnJevPNN/XOO+8oJCRE06dP19NPP23v0ADYicHE6nsAAAAAACdkMpl06dIlVaxY0dyWnZ2tFi1aaO7cufm+hATg+pjRBQAAAABwSvPmzdM//vEPDR06VCEhITp37pyWLVumgIAAtWzZ0t7hAbADCl0AAAAAAKcUFxcnSVq0aJFOnjypihUr6qGHHtKrr75qMcsLwJ2Dny4CAAAAAADAJbjZOwAAAAAAAADAFih0AQAAAAAAwCVQ6AIAAAAAAIBLoNAFAAAAAAAAl0ChCwAAAAAAAC6BQhcAAAAAAABcAoUuAAAAAAAAuAQKXQAAAAAAAHAJFLoAAAAAAADgEih0AQAAAAAAwCVQ6AIAAAAAAIBLoNAFAAAAAAAAl0ChCwAAAAAAAC6BQhcAAAAAAABcAoUuAAAAAAAAuAQKXQAAAAAAAHAJFLoAAAAAAADgEih0AQAAAAAAwCVQ6AIAAAAAAIBLoNAFAAAAAAAAl0ChCwAAAAAAAC6BQhcAAAAAAABcAoUuAAAAAAAAuAQKXQAAAAAAAHAJFLoAAAAAAADgEsrZOwBXdeXKFf34448KDAyUu7u7vcMBAAA2lpeXp8zMTDVq1EgVKlSwdzj4D3IwAABcW1E5GIWuUvLjjz8qJibG3mEAAIBStmLFCjVr1szeYeA/yMEAALgzFJaDUegqJYGBgZL+fOOrV69u52gAAICtGY1GxcTEmO/5cAzkYAAAuLaicjAKXaXk5lT56tWrKygoyM7RAACA0sLP4xwLORgAAHeGwnIwFqMHAAAAAACAS6DQBQAAAAAAAJfgUIWuAQMGKDQ0NN/23nvvKS0tTd27dy/wuJSUFA0ZMqTQcZo2bapRo0bp7Nmz5j5paWkWfRo1aqT+/fvrm2++sRjbaDQqNjZWoaGhFscDAAC4AvIvAADgShyq0CVJw4cP17Zt2yy2Rx999LbGWblypc6cOaOEhASLPnXr1jX3+fzzzxUREaHBgwcrIyNDkrR+/Xr16NFDXl5eNrk2AAAAR0T+BQAAXIXDLUbv4+Njk6cX/fc4gYGBGjdunPr27asrV66oQoUKkqRy5cpZnGvYsGHauXOnVq9erREjRmj9+vV67bXXFBoaqs2bN992TAAAAI6I/AsAALgKhyt0lRaTySRJunHjxi371a1bVydOnJAkzZgxQx4eHuZvGAEAAFB85F8AAKCs3RGFrszMTM2YMUPdu3eXt7f3LfseP35cjRo1kiR5eHgUa/ysrCxlZWVZtBmNxpIFCwAA4AJKO/+SyMEAAEB+Dlfomj17tlJTU82vvby8tGPHjtsa5/Lly6pfv75mzJhRaP+8vDytXbtWu3fv1rhx46w619KlSy1iBgAAcCbOmH9J5GAAACA/hyt0DRw4UH379jW/dnMr2Xr5/z3OpUuX9O677+qxxx7TmjVr5O/vL0n69ddfFR4eLknKzc1VpUqVzGtCWCM2NlZRUVEWbUajUTExMSWKHQAAoCw5Y/4lkYMBAID8HK7QVaVKFQUHB+dr9/T01NWrVws85urVq/L09LzlOK+99pq6du2qzz//3Jz81KpVS4sXLzaPX61atRIldn5+fvLz87P6OAAAAEfgjPmXRA4GAADyK1lWYQeNGjXS77//rt9++82iPS8vT1u2bDGv61AYNzc3+fv769KlS+a2ChUqKDg4WMHBwapRo0aJkywAAABXRP4FAACcjdNkFnXq1FHfvn01ZMgQbdy4USdPntQPP/ygkSNH6tKlS4qOjrbof+nSJWVmZiozM1O///673nnnHe3fv1/t27e30xUAAAA4F/IvAADgbBzup4u3MmXKFC1ZskTJycnKyMiQr6+vHn74YX3wwQf5pq3PmzdP8+bNk/Tn03vuu+8+/e///q/q169vj9ABAACcEvkXAABwJgaTyWSydxCuKCMjQ5GRkUpPT1dQUJC9wwEAADbGvd4x8bkAAODairrXO81PFwEAAAAAAIBbodAFAAAAAAAAl0ChCwAAAAAAAC6BQhcAAAAAAABcAoUuAAAAAAAAuAQKXQAAAAAAAHAJFLoAAAAAAADgEih0AQAAAAAAwCVQ6AIAAAAAAIBLcPlCV05OjpKTk9W+fXuFhYXpkUce0dy5c3X16lVJUlpamrp3725xzPXr1/X222+rU6dOCgsLU/v27TVt2jRdvHjRHpcAAADgVMi/AACAvZSzdwCl6erVqxo4cKDc3Nz0+uuvq27dujp8+LDeeOMN7d+/XwsWLMh3jMlk0qhRo3TkyBElJiaqQYMGOnbsmFJSUvT444/rww8/lLe3tx2uBgAAwPGRfwEAAHty6ULXO++8o+zsbKWlpalChQqSpOrVq6tx48b6+9//ri+++CLfMevWrdN3332ntWvXKiAgwHzMokWL1LNnT7333nuKi4sr0+sAAABwFuRfAADAnly60PXxxx/r2WefNSdZN1WpUkXJycny8fHR77//nu+Y/v37m5Osm8qXL6+kpCRlZ2eXetwAAADOivwLAADYk8sWui5evKhjx44pPDy8wP0dOnSQpHyJ1sGDBzV48OACj2nWrFmB7VlZWcrKyrJoMxqNVkYMAADg3Moy/5LIwQAAQH4uXeiSJD8/P6uOy8rKsvqYpUuXKjU11apjAAAAXE1Z5l8SORgAAMjPZQtdvr6+kv5MnP46Db6o4/76zWBRYmNjFRUVZdFmNBoVExNj1TgAAADOrCzzL4kcDAAA5OfSha5atWpp3759qlu3br79mzZtko+PT772sLAwffvtt2rbtm2+fXv27NHFixcVGRlp0e7n51eibyEBAABcSVnmXxI5GAAAyM/N3gGUpj59+mjRokW6cuWKRfuZM2c0fvx4nTlzJt8xjz76qD788MN8+65cuaKJEyfqyJEjpRozAACAMyP/AgAA9uTSha5BgwbJy8tLAwcO1K5du2Q0GrV161YNHDhQTZo0UefOnfMd0717d91///0aMGCANm/eLKPRqD179mjQoEEqX748U+EBAABugfwLAADYk8v+dFH685HUy5YtU2pqqsaOHaszZ86oevXq6tWrl4YMGSI3t/x1Pjc3N82dO1eLFi1SUlKSTpw4oYCAAHXq1EkjR46Ut7e3Ha4EAADAOZB/AQAAezKYTCaTvYNwRRkZGYqMjFR6erqCgoLsHQ4AALAx7vWOic8FAADXVtS93qV/uggAAAAAAIA7B4UuAAAAAAAAuAQKXQAAAAAAAHAJFLoAAAAAAADgEih0AQAAAAAAwCVQ6AIAAAAAAIBLoNAFAAAAAAAAl0ChCwAAAAAAAC6hnLUHfPLJJ9q8ebMuXLggk8mUb/+yZctsEhgAAAAAAABgDasKXbNnz9bbb7+tNm3aqF69ejIYDKUVVz67d+/Wk08+Wej+ESNGSJJSU1Pz7Wvbtq0WL14sSQoNDbXYV716dfXt21fDhg2Tm5ub0tLSlJCQUOh5pk+frj59+pTkEgAAAJwOORgAAHAmVhW6PvjgAw0ZMkQvvPBCacVTqPDwcG3bts38ulOnTkpOTlZ4eLgkydvbW0uWLFHr1q2VnJxscaynp6fF60WLFql+/fq6ceOGDh06pIkTJ8rDw0NDhgxR165d1a5dO0nShQsX1K1bN3300UeqUaOGJMnX17c0LxMAAMChkIMBAABnYlWh6/Lly2rTpk1pxXJLnp6eCgwMNL82GAyqVKmSRVtB/QpSuXJlc59q1app5MiRmj9/voYMGaIKFSqoQoUKkiR3d3dJkr+/f5FjAgAAuCJyMAAA4EysWoy+Q4cO+v7770spFPu59957dfLkSXuHAQAAcEchBwMAALZm1Yyu7t27a9asWbpw4YLuv/9+87dt/y0yMtJmwZWV06dPq0qVKiU+PisrS1lZWRZtRqPxdsMCAABwaeRgAADA1qwqdI0cOVKSzIuK/pXBYNDPP/98+1Hdhq+//tq8ZsRNkyZNUlRUVIH9MzIyNH/+fHXp0qXE51y6dGmBC7ACAADcKcjBAACAI7Cq0HXo0KHSisNmHnzwQU2bNs2iLSAgwOJ1TEyM3N3dZTKZdPnyZXXt2lWjR48u8TljY2PzJXFGo1ExMTElHhMAAMCZkIMBAABHYFWhyxl4e3srODj4ln3eeOMNhYaG6uLFixowYIBatWolLy+vEp/Tz89Pfn5+JT4eAADA2ZGDAQAAR2B1oevkyZNauHChduzYoXPnzqlKlSpq3bq1nnnmGfPjnx1djRo1zInYsGHDNGfOHHXp0oXHVgMAAJQicjAAAFDarHrq4q+//qrevXtr9+7d6tGjh8aNG6cePXpo79696t27t/75z3+WVpzFlpubq8zMTIvt3LlzhfaPjY2Vj4+P3nrrrTKMEgAAwLWQgwEAAEdg1Yyu1157TQ888IDmzZuncuX+/6FDhw7VsGHD9Nprr2nhwoU2D9IaO3bsUNu2bS3aGjZsqLS0tAL7e3p6Kj4+Xs8//7z69+9f5JR7AAAA5EcOBgAAHIHBZDKZitu5SZMmmjdvntq1a5dv39dff60RI0Zo//79Ng3QWWVkZCgyMlLp6ekKCgqydzgAAMDGuNc7Jj4XAABcW1H3eqt+uujp6amrV68WuO/KlSvy9PQsWZQAAAAAAADAbbKq0BUREaE333xT58+ft2g/f/683nrrLXXo0MGGoQEAAAAAAADFZ9UaXYmJiXryyScVGRmpNm3aqFq1ajp9+rR27NihatWqKSEhobTiBAAAAAAAAG7JqhldAQEBSktL07hx4+Tm5qbDhw/LYDBozJgx+vjjj+Xv719acQIAAAAAAAC3ZNWMLkkqX768+vfvr/79+5dGPAAAAAAAAECJFFnoWrBggQYMGCAvLy+lp6cXOWBkZKRNAgMAAAAAAACsUWSh6+2331aHDh1Ur149DR8+/JZ9DQaDfv75Z5sFBwAAAAAAABRXkYWuTz75RLVr15YkHTp0qNQDAgAAAAAAAEqiyMXobxa5JOnEiRPKzc0tsN+NGzf0z3/+03aRAQAAAAAAAFaw6qmLkZGR+vHHHwvct3fvXvXq1euWxw8YMEChoaHmrXnz5oqPj1dWVpZ5/+LFi839Q0NDdeDAAYsxlixZosaNG+ubb74xt3355Zfq2bOnwsLC1KFDB3300Uf5zn39+nW9/fbb6tSpk8LCwtS+fXtNmzZNFy9eLDDW2bNnKzQ0VP/+979veU0AAACOjhwMAADcKYr11MWbi9CbTCZ98803OnfunMV+k8mkjz/+2GL2V2GGDx+u6OhomUwmnTp1SsnJyRo9erQWLlxY5LEbN27UzJkzNXPmTDVr1kyStH37do0ePVqJiYlq166dDhw4oISEBFWtWlURERHm+EaNGqUjR44oMTFRDRo00LFjx5SSkqLHH39cH374oby9vS2u59NPP5WHh4fWrVunESNGFOdtAgAAcFjkYAAA4E5QrELXzUXoDQaDZs2aVWCfoKAgvfbaa0WO5ePjo8DAQElS1apVNX36dEVGRurEiRO3PO6nn37S2LFjNXbsWHXu3NncvmHDBkVHRys6Otocx86dO7VmzRpzkrVu3Tp99913Wrt2rQICAiRJ1atX16JFi9SzZ0+99957iouLM4+5f/9+nTx5UsOHD9fatWtJsgAAgNMjBwMAAHeCYhW6bi5CX79+fa1cuVJNmza1WQBBQUHy8PC4ZZJlNBo1dOhQ9e3bVwMHDrTYN2XKlHz9PT09denSJfPrjz/+WP379zcnWDeVL19eSUlJys7Otmhft26dWrdurf79+ys1NVUHDx5UWFiY9RcHAADgoMjBAACAK7Jqja6FCxfqvvvus2kAmZmZunbtmu66664C9+fk5Gjo0KEKDw9XfHx8kePt3r1bq1evVs+ePc1tBw8e1IMPPlhg/2bNmqlDhw7m13l5eVq/fr26deumgIAAtWjRQuvWrbvlObOyspSRkWGxGY3GImMFAACwF3IwAADgioo1o+umdu3aSfozEbl8+XKBfSpWrFjs8c6fP6+pU6eqSZMmqlOnToF9XnrpJR07dkwhISFycyu8LvfBBx8oKSlJubm5iouLM0+Zl/5Mgvz8/IoV0+7du3XhwgV17NhRktStWzelpqZqzJgxhZ5/6dKlSk1NLdb4AAAA9kYOBgAAXJVVha7MzEwlJiZqx44dunHjRoF9fv7551uOMXv2bHNCkpOTo6ZNm2rOnDmF9r9y5YpWrlypwYMHKy0tTX369CmwX7du3dSiRQsdOXJEM2bM0I0bNzR27FhJkq+vr/mpQkVZu3atIiIizAW7Tp06acqUKfr222/VvHnzAo+JjY1VVFSURZvRaFRMTEyxzgkAAFDayMEAAMCdwKpC19SpU2U0GjV79mylp6frwoULeuqpp7R//34tX75cCQkJRY4RGxurfv36SZL8/PxUpUqVIs/ZtGlTTZ48WVOmTFF4eLjq1q2br5+vr698fX11zz33qHLlyho4cKCef/55eXp6KiwsTN9++63atm2b77g9e/bo4sWLioyMVG5urjZs2KCcnBw1btzY3Of69etat25doUmWn59fsb+tBAAAsAdyMAAAcCewao2u7du3a9SoUfrb3/6mTp066dy5c3rooYcUFxen6OhorVmzpsgx/P39FRwcrODg4CITrJv9JalXr156+OGH9eKLLyo3N9e8PzExURs3brQ4xtPTU3l5ecrLy5MkPfroo/rwww915swZi35XrlzRxIkTdeTIEUnSV199pevXr2vVqlVavXq1eRs/frzWr1+v69evFxkvAACAIyIHAwAAdwKrCl3Xrl1ThQoVJEm1a9c2JyeS9OCDD2rHjh22je4vJk+erLNnz2rWrFnmtsqVK2vy5Mlav369Tpw4oV27dmnixInq3r27vLy8JEndu3fX/fffrwEDBmjz5s0yGo3as2ePBg0apPLly5unt3/66ad6+OGHFRYWppCQEPPWr18/Xbp0Sdu3by/V6wMAAHBE5GAAAMBZWPXTxbCwMK1atUpt2rTRPffco6tXr+rQoUOqX7++jh49Km9v79KKU5JUqVIlvf7663rqqafUqlUrRUREaPTo0apYsaKSk5N18uRJBQQEKCoqSs8995z5ODc3N82dO1eLFi1SUlKSTpw4oYCAAHXq1EkjR46Ut7e3srOz9dVXX2nmzJn5zluxYkVFRERo3bp1FgusAgAA3AnIwQAAgLMwmEwmU3E779ixQ3FxcZo2bZp69+6tF198UQcOHFCrVq302WefqU+fPkpMTCzNeJ1GRkaGIiMjlZ6erqCgIHuHAwAAbIx7vWPicwEAwLUVda+36qeLrVu31gcffKD27dtLkqZMmaJWrVopIyNDgwcP1rhx42wTNQAAAAAAAGAlq366KEmNGjUy/9nPz0+vvPKKTQMCAAAAAAAASsKqGV1PPvmkfv311wL3HT58WNHR0TYJCgAAAAAAALCWVYWuPXv2KDs7u8B9e/fu1W+//WaToAAAAAAAAABrFeuni82bN5fBYJDBYNCgQYNUrpzlYTdu3NClS5c0dOjQUgkSAAAAAAAAKEqxCl2JiYkymUxKTEzUgAEDVKdOnXx9atWqpWbNmtk6PgAAAAAAAKBYilXoioqKkiRt27ZNUVFRBRa6AAAAAAAAAHuy6qmLs2bNKq04AAAAAAAAgNtiVaFrxIgRRfZJTU0tcTAAAAAAAABASVn11EVfX998m5eXlw4dOqRffvlFvr6+pRWnWXZ2tpo0aaLRo0dbtMfHxys0NLTQLSMjQ5L0yCOPWLS3bt1a06ZN09WrVyVJGzduVIMGDXTo0CGL8S9cuKCWLVsqJSWl1K8RAADAkZB/AQAAZ2HVjK7p06cX2J6bm6vhw4fr/vvvt0lQt7Jx40bduHFDmzZt0uXLl+Xl5SVJmjBhgjn5+uqrr5SamqpVq1aZj/P39zf/edq0aerQoYNMJpOOHj2qyZMnKykpSa+88oo6duyoiIgIJSUlafny5eZjUlJS5O3trWeeeabUrxEAAMCRkH8BAABnYdWMrsJ4enpqwIABmjdvni2Gu6W1a9cqOjpaPj4+2rx5s7nd19dXgYGBCgwMlK+vr9zd3c2vAwMD5e7unq9v1apV1bx5c7300ktavXq1cnNzJf2ZtP3www/6/PPPJUmHDx/WypUrlZCQoAoVKpT6NQIAADgS8i8AAOAsbFLokqRTp07p/PnzthquQGfOnNHOnTvVo0cPde7cWWvXrrXJuCEhIbp69arOnj0rSapVq5aGDh2q5ORkXb58WUlJSWrVqpU6depkk/MBAAA4C/IvAADgTKz66WJ6enq+NpPJpCNHjmj+/Plq27atzQIryPr161WzZk01btxYubm5GjhwoC5cuKBKlSrd1rinT5+Wm5ub/Pz8zG2DBg3S6tWrNXjwYO3fv/+WSV1WVpaysrIs2oxG423FBAAA4AgcNf+SyMEAAEB+VhW6hg8fXmB7pUqV9Le//U0JCQk2Caow69atU9euXWUwGPTggw8qICBAX3zxhfr161fiMc+cOaM5c+YoIiJC3t7e5vabP8ecOnWqOnXqpLp16xY6xtKlS3naJAAAcEmOmn9J5GAAACA/qwpdf30STlk6fvy4vvvuO02ePFmSZDAY1KVLF61bt87qRGvMmDFKSEiQyWTS5cuX9dBDDyk5OdmiT25urpYvX6569erpq6++0r/+9a9Ck63Y2FhFRUVZtBmNRsXExFgVFwAAgCNx5PxLIgcDAAD5WVXosqdPP/1UktS3b19zW15enkwmk06dOqVq1aoVe6yxY8eqQ4cOunr1qoYMGaIHHnjA4qlAkrR48WJlZ2frs88+U1xcnKZNm6bFixcXOJ6fn5/FtHsAAABX4Mj5l0QOBgAA8iuy0LV69WqrBuzdu3cJQ7m1tWvXasCAAYqOjrZoj4uL0+eff66BAwcWe6xq1aopODhY0p9JV3x8vPr27atatWpJkn7//XfNnz9fL7/8sipVqqRJkybpscce04YNG1gQFQAA3DHIvwAAgLMpstAVHx8vg8Egg8GgihUrymQyFdrXYDCUSqHrt99+0+HDhzVnzhyFhIRY7OvatavWrVtnVaL11+NXrFihGTNmaO7cuZKkpKQkhYWFqVevXpKkhg0bqn///po+fbratWvHI64BAIDLI/8CAADOyK2oDqNGjVLt2rVlMpnUtGlTvfrqq9q1a5f27t2bb9uzZ0+pBLlu3TrVr18/X5IlSd26ddOBAwd09OjREo8/ceJEbdiwQXv37tXGjRu1ZcsWTZ48WQaDwdznhRdeUE5OjhYuXFji8wAAADgL8i8AAOCMDKZbTdH6L3v27NFHH32kDRs2yMfHR1FRUXrsscdUp06dUg7ROWVkZCgyMlLp6ekKCgqydzgAAMDGuNc7Jj4XAABcW1H3+mIvRt+iRQu1aNFCFy9e1Jo1a/Txxx9r8eLFatq0qfr27avOnTszpRwAAAAAAAB2U+RPF//K19dXMTExSktLU1pamkJDQ/Xqq6+qbdu25kdPAwAAAAAAAGXN6kLXf2vQoIEGDBigHj16KDs7W2vWrLFVXAAAAAAAAIBViv3Txf92+fJlrV+/XqtWrdJ3332n0NBQTZw40fyUHAAAAAAAAKCsWVXo+uGHH7Rq1Sp99tlnysvLU/fu3TV+/Hg1adKktOIDAAAAAAAAiqXIQtf58+f1f//3f1q1apV+++03NWrUSOPHj1fXrl3l4+NTFjECAAAAAAAARSqy0NWuXTtdv35dktSsWTOFhYXpt99+09y5cwvsn5CQYNsIAQAAAAAAgGIostD1wAMPWLw+ePBgoX0NBsNtBwQAAAAAAACURJGFruXLl5dFHAAAAAAAAMBtcbPlYLm5uYqMjNThw4eLfcyAAQMUGhpq3po3b674+HhlZWWZ9y9evNjcPzQ0VAcOHLAYY8mSJWrcuLG++eYbc9uXX36pnj17KiwsTB06dNBHH31kcUx8fLzFecPDw/Xss8/qxIkTFv1+++039enTR+Hh4cW+JgAAAEdG/gUAAFyVTQtdknT8+HFdu3bNqmOGDx+ubdu26euvv9aSJUt0/PhxjR49uljHbty4UTNnztSMGTPUrFkzSdL27ds1evRoRUdH68svv9T48eOVlJSkLVu2WBzbp08fbdu2Tdu2bdMHH3wgd3d3DR48WHl5eZKkFStW6LHHHtNdd91l1fUAAAA4OvIvAADgior86WJZ8PHxUWBgoCSpatWqmj59uiIjI/N9u/dXP/30k8aOHauxY8eqc+fO5vYNGzYoOjpa0dHRkqSgoCDt3LlTa9asUUREhLmfl5eX+byBgYFKTk7WQw89pH379qlZs2b64osvtHjxYl2/fl179+619WUDAADYDfkXAABwRQ5R6PqroKAgeXh43DLRMhqNGjp0qPr27auBAwda7JsyZUq+/p6enrp06dItz+vt7a2aNWvq+PHjatasmRYvXiwPDw/t3r27JJcBAADgNMi/AACAK3DIQldmZqauXbtW6JT1nJwcDR06VOHh4YqPjy9yvN27d2v16tWaOXPmLfvl5eXpzJkzqlKliiTJw8OjWPFmZWWZ17S4yWg0FutYAAAAR+Bs+ZdEDgYAAPJzuELX+fPnNXXqVDVp0kR16tQpsM9LL72kY8eOKSQkRG5uhS8z9sEHHygpKUm5ubmKi4uzmDb/V1euXNFbb72l8uXLq3nz5lbFvHTpUqWmplp1DAAAgKNwxvxLIgcDAAD5OUSha/bs2eYkJScnR02bNtWcOXMK7X/lyhWtXLlSgwcPVlpamvr06VNgv27duqlFixY6cuSIZsyYoRs3bmjs2LHm/e+//74++eQT85g1a9bUW2+9JS8vL6vij42NVVRUlEWb0WhUTEyMVeMAAACUFWfPvyRyMAAAkJ9NC10eHh5atmyZ6tata9VxsbGx6tevnyTJz8/PPHW9MFOnTlXTpk01efJkTZkyReHh4QWe09fXV76+vrrnnntUuXJlDRw4UM8//7w8PT0lSV27dtXIkSMlSdOnT9f58+fVuHFjq2K/GbOfn5/VxwEAANiLs+dfN+MmBwMAAP+t8Hnnhdi/f7+GDRum1q1bKywsTD/99JN++uknTZ8+XSaTSS1atJC3t7dVY/r7+ys4OFjBwcFFJlk3+0tSr1699PDDD+vFF19Ubm6ueX9iYqI2btxocYynp6fy8vLMj66WpEqVKpnP+9JLL+ngwYP67LPPrIodAADAGZF/AQAAV2RVoWvbtm16/PHH5eXlpXHjxunGjRvmfatXr9aCBQtsHmBRJk+erLNnz2rWrFnmtsqVK2vy5Mlav369Tpw4oV27dmnixInq3r17odPi7777bg0aNEgzZszQ5cuXyyp8AAAAp0P+BQAAHJVVha7Zs2crOjpaM2fOVI8ePcztDRs21MiRI/WPf/zD5gEWpVKlSnr99de1dOlSbdmyRZI0evRoxcTEKDk5WZGRkRozZowiIiKUlJR0y7Hi4uJkMpm0ePHisggdAADAKZF/AQAAR2UwmUym4nZu1KiRFi9erIceekh5eXlq2LChPv74YzVs2FB79uzRwIEDdfDgwdKM12lkZGQoMjJS6enpCgoKsnc4AADAxrjXOyY+FwAAXFtR93qrZnRVqVJFv//+e4H7fv31V1WvXr1kUQIAAAAAAAC3yapC12OPPaY5c+bowIED5jaDwaDvvvtOqampevTRR20eIAAAAAAAAFAc5azpPHz4cJ05c0Z9+/ZVrVq1JElPP/20Lly4oKioKA0dOrRUggQAAAAAAACKYlWhq1y5cnrllVf01FNPadeuXTp79qwqV66sVq1a6Z577imtGAEAAAAAAIAiWVXouqlu3bqqW7eurWMBAAAAAAAASsyqNbouX76sSZMm6aeffpIknTp1Sv3791fjxo317LPPKjs7u1SCBAAAAAAAAIpiVaHrlVde0fbt21WhQgVJ0uuvvy53d3clJSXp119/1YwZM0olSAAAAAAAAKAoVhW6NmzYoLFjxyokJETXrl3Tpk2bNHbsWPXs2VMjR47Ul19+WVpxAgAAAAAAALdkVaHr6tWr8vPzkyQdOHBABoNBTZo0kSRVrVpVWVlZto8QAAAAAAAAKAarCl0PPPCA3nvvPZ0+fVpLlixRmzZt5O7uLklKT093mCcvDhgwQLGxsTKZTAXuS0lJkSSFhoYWuB0+fNjc/9tvv1VkZKS6d+9eZvEDAAA4I3IwAABgb1Y9dXH8+PF6+umnFRERIT8/Py1dulSS9O677+q9997TrFmzSiXIkti1a5dWrlypmJiYW/ZbtGiR6tevb9FWpUoVSdKsWbO0bNkyNW3aVKdPny61WAEAAFwFORgAALAnq2Z0NWrUSJs2bdLKlSu1ceNGc3LSpk0bffDBB+rSpUupBFkStWrV0owZM3Ts2LFb9qtcubICAwMttnLl/qz/bdu2TatWrXKobxJXrFihOnXqyM3NTXXq1NGKFStu2e6sXO16UHb4uwMA9uWqOVhZ434GAEDJWDWjS5IqVqyo8PBwi7b77rvPZgHZSs+ePbVv3z7Fx8frvffek5ubVTU9SdKHH34oDw8P/fDDD6UQofVWrFihuLg45eTkSJKOHj2quLg4bd++XUuXLs3XLqnIb1MdUWHXKTnn9aDs8HcHAOzPFXOwssb9DACAkrM688jOztbmzZu1evXqAjdHYTAYlJSUpEOHDmnZsmUlGsPDw6NY/bKyspSRkWGxGY3GEp3zViZMmGBOeG7KycnRggULCmyfMGGCzWMoC4Vdp7NeD8oOf3cAwP5cMQcra9zPAAAoOatmdO3atUvPPfecLl68qIoVK8pgMFjsNxgM6t27ty3juy01a9ZUfHy8pk2bpnbt2ikkJCRfnyeeeMLim8Z27dpp7ty5Vp1n6dKlSk1Nve14i1LYTwDy8vKs6u/oCovbWa8HZYe/OwDgGFwtBytr3M8AACg5qwpdSUlJuvfeezVnzhxVq1attGKyqb59+2rjxo1KSEjQ+++/n2//G2+8oXr16plfe3l5WX2O2NhYRUVFWbQZjUabTy2vXbu2jh49mq/d3d29wGJX7dq1bXr+slLYdTrr9aDs8HcHAByHK+VgZY37GQAAJWfVTxePHDmiZ5991mmKXNKfs8ymTp2qI0eOaNGiRfn2V69eXcHBweatatWqVp/Dz89PQUFBFlv16tVtEb6FpKQkeXt7W7R5e3srLi6uwPakpCSbx1AWCrtOZ70elB3+7gCA43ClHKyscT8DAKDkrCp0BQcH6/Lly6UVS6mpVq2aEhMTlZKSot9//93e4ZRYTEyMFixYoODgYBkMBgUHB2vBggV68803C2x31m8zC7tOZ70elB3+7gCAY3GVHKyscT8DAKDkrPrp4ssvv6zZs2crJCRE9957b2nFVCr69OmjL7/8Ups3b7Z3KLclJiamwCSnsHZn5WrXg7LD3x0AcCyukoOVNe5nAACUjFWFrjFjxig7O1s9evSQl5eXypXLf/iePXtsFpytTZ06Vd27d7d3GAAAAHcUcjAAAFBWrCp0jRo1qpTCsK3ly5cX2B4YGKjdu3ebX//yyy/FGq9Pnz7q06ePTWIDAABwVeRgAADA3qwqdP31qTYAAAAAAACAo7Cq0CVJeXl52rRpk77//nudO3dOw4cPl5eXl06ePKmGDRuWRowAAAAAAABAkax66uKZM2fUp08fjR8/XgcPHtQnn3yi8+fPa9++ferfv7/27t1bWnECAAAAAAAAt2RVoevVV1+Vu7u70tPTtXDhQplMJklSZGSkevfurVmzZpVKkAAAAAAAAEBRrCp0bdmyRcOHD1eVKlVkMBgs9nXt2lU//fSTTYMDAAAAAAAAisuqQtf169fl4eFR4L6cnBx5enraJCgAAAAAAADAWlYVulq2bKmlS5fqxo0b5jaDwaDr169r+fLlat26tc0DBAAAAAAAAIrDqqcuJiYmKiYmRlFRUerUqZMk6b333tMPP/yg8+fP6/333y+VIAEAAAAAAICiWFXoql27ttasWaN3331XW7duVVBQkA4fPqyHH35YTz31lPz9/UsrTu3evVtPPvlkoftHjBghSUpNTTW3lS9fXuHh4Zo4caLuu+8+c3uTJk109epVi+Off/55DRs2zPz6/fff15IlS/T777/rnnvu0ZgxY/TII4/Y6nIAAACcAjkYAABwJlYVuiSpSpUqeuGFF/TCCy+URjyFCg8P17Zt28yvO3XqpOTkZIWHh0uSvL29tWTJErVu3VrJycmSpIsXL2rFihUaOHCgvvjiC1WsWFFnz57V1atXlZaWpqpVq5rH8/b2Nv/5o48+0syZM/Xyyy/r/vvv19atW/X888/ro48+Uv369cvoigEAAOyPHAwAADgTqwtd3333nerUqWOevfXBBx/o4MGDat++vTp27GjzAG/y9PRUYGCg+bXBYFClSpUs2v7aLzAwUBMnTtTmzZu1ceNG9e7dW6dOnZLBYFC9evUKXVh/w4YNGj58uLp16yZJevzxx7Vp0yZ99tlnJFkAAOCOQg4GAACciVWL0a9evVoDBgzQjz/+KElatGiRXn/9df3+++96/vnn9cknn5RKkLfDYDCobt26On78uCTp5MmT8vf3LzTBkqQFCxboqaeesmjz8PCQwWAo1VgBAABcBTkY8P+tWLFCderUkZubm+rUqaMVK1bYOyQAcFlWFbrmzZunZ599Vu3bt5ckrVy5Ui+//LLeeecdDRkyRIsXLy6VIG/X8ePHzd8wnjx5Um5ubho0aJCaN2+ubt26KS0trdBjb9y4ofXr12vnzp3q0qVLWYUMAADg9MjBgD+LXHFxcTp69KhMJpOOHj2quLg4il0AUEqs+uliRkaGmjdvbv7zyZMnFRERIUl66KGHtGDBAttHeBtyc3P17rvv6tSpU4qMjJQk1apVS+3bt1dkZKQSEhK0e/duTZ48WR4eHurRo4fF8bNnz9bixYuVl5eniRMnFjplPisrS1lZWRZtRqOxdC4KAADAwZGDAf/fhAkTlJOTY9GWk5OjCRMmKCYmxk5RAYDrsqrQdffdd+vAgQN66KGHtH79eoWGhqpSpUqS/vyWzs/Pr1SCtMbWrVvNi6NevXpV1atX15tvvqmAgABJUvv27c0z0iTp3nvv1fnz57VkyZJ8SVZsbKx69uypAwcO6I033pCkAm9GS5cutXjSEAAAwJ2GHAwo2LFjx6xqBwDcHqsKXU888YTeeOMN7dixQ998840mTpwoSdq5c6eSk5PVtWvXUgnSGs2aNdO0adMkSRUqVFDVqlWLXNchPDxc77zzTr52f39/+fv7KyQkRAaDQXPmzCkwyYqNjVVUVJRFm9Fo5BsaAABwxyAHAwpWu3ZtHT16tMB2AIDtWVXoGjhwoCpXrqzvvvtOPXv2VO/evSVJZ8+eVadOnTR27NjSiNEq3t7eCg4OLnT/ggUL5ObmpsGDB5vbfvnlF9WoUUOSlJeXpxEjRmjIkCF64IEHzH08PT117dq1Asf08/NziNlsAAAA9kIOBhQsKSlJcXFxFj9f9Pb2VlJSkh2jAgDXZVWhS5J69+5tLnDd1K1bN/NjoB1dSEiIRo0aJX9/f7Vq1Ur79u1TSkqKJkyYIElyd3eXp6enxo8fr5deekkhISE6dOiQZsyYob59+9o5egAAAOdEDoY71c0ZhhMmTNCxY8dUu3ZtJSUlMfMQAEqJ1YUuZxcZGakpU6bo7bffVmJiooKCgjR+/Hg9+uij5j6vvfaa5s6dq/j4eP3xxx+qWbOmoqOjNWjQIDtGDgAA4LzIwXAni4mJobAFAGXEYDKZTMXtfPHiRb366qvatGlTvifc3PTzzz/bLDhnlpGRocjISKWnpysoKMje4QAAABvjXu+Y+FwAAHBtRd3rrZrRNWHCBG3fvl1xcXEKCQmxWZAAAAAAAADA7bKq0LVlyxZNnDiRdRIAAAAAAADgcNys6Vy+fHmmgAMAAAAAAMAhWVXoiomJ0Zo1a0orFgAAAAAAAKDErPrp4o0bN7Rnzx717dtX999/v9zd3fP1SUhIsFlwAAAAAAAAQHFZVej67rvvVLNmTUnSL7/8km+/wWCwTVQAAAAAAACAlawqdC1fvry04gAAAAAAAABui1WFrv+WmZmpc+fOqUqVKgoMDLRlTAAAAAAAAIDVrC50bdq0SbNmzdI///lPmUwmGQwG3XPPPRozZowefvjh0ogRAAAAAAAAKJJVT13cuHGjRowYofDwcC1fvlwbNmzQ8uXL1axZM40YMUKbNm0qrTitMmDAAIWGhpq35s2ba9y4cTp//rwkKT4+3mJ/kyZNNHjwYP3+++8W41y4cEGjR49WaGioDhw4YIcrAQAAcB7kYAAAwN6sKnSlpqYqNjZWU6dOVbNmzVSrVi01a9ZML7/8sp588kmlpKSUVpxWGz58uLZt26avv/5aS5Ys0fHjxzV69Gjz/j59+mjbtm3atm2bPvnkE1WsWFFDhw6VyWSSJO3du1c9evTQpUuX7HUJAAAAToccDAAA2JNVha5ff/1VHTp0KHBfhw4d9Ouvv9oiJpvw8fFRYGCgqlatqsaNG+vVV1/Vtm3bdOLECUmSl5eXAgMDFRgYqJCQEE2aNEm//fab/vWvf0mSPv/8c40YMULz58+352UAAAA4FXIwAABgT1YVuvz9/fNNLb/p2LFj8vf3t0lQpSE4OFgeHh7mJKswN79NTEhIUL9+/coiNABwOStWrFCdOnXk5uamOnXqaMWKFfYOCYCdkIOVDP+OAgBs4U68n1i1GP2jjz6qmTNnqkaNGmrTpo25fefOnZozZ4769+9v8wBt5dy5c7p27ZqqVKmSb192drZeeeUVNW/eXPfcc48kycPDo6xDBACXsGLFCsXFxSknJ0eSdPToUcXFxUmSYmJi7BkaADsgB7Me/44CAGzhTr2fWFXoGjFihE6dOqVBgwapSpUqqlq1qjIzM3X27FlFRUVp+PDhpRXnbbl48aKSk5NVv359cxL14Ycf6pNPPpEkXb58WTVr1tRbb70lg8Fg9fhZWVnKysqyaDMajbcfOAA4oQkTJphvpjfl5ORowoQJLn1DBZAfOVjJ8O8oAMAW7tT7iVWFrnLlymn69OmKi4vTzp07de7cOVWpUkUtW7Y0Jy+OYvbs2UpNTZX0ZxLVoEEDzZ0715xEde3aVSNGjJAkXblyRatXr1Z0dLRWrVpl9bUsXbrUfC4AuNMdO3bMqnYAroUc7Pbx7ygAwBbu1PtJkYWu1atXF9ju7e0tb29vSdIPP/ygH374QZLUu3dvmwV3O2JjY9WvXz+ZTCaNGTNGwcHBCg4ONu/39fW1eD1+/Hj98ssv+uijjzR+/HirzxUVFWXRZjQaXbpCCgCFqV27to4ePVpgOwDXRw52+/h3FABgC3fq/aTIQld8fHyRg/z3VHNHKXT5+/ubk6iXXnpJ/fv31xNPPKHw8PBCj7nrrrtK9ChrPz8/+fn5lThWAHAlSUlJFmsBSH9+OZKUlGTHqACUFXKw28e/owAAW7hT7ydFPnXx0KFDt9ymT5+uihUrqkaNGlqyZElZxGy1+++/Xz179tSrr75qfqLP5cuXlZmZqczMTB0/flxpaWn6/PPP9fDDD9s5WgBwbjExMVqwYIGCg4NlMBgUHBysBQsWOP0MCwDWIwcrGf4dBQDYwp16P7Fqja7/dubMGb300kvavHmz+vbtq3HjxqlixYq2jM2mRo8erc6dO+v//u//JElpaWlKS0uT9OfaY3Xq1NGkSZNIsgDABmJiYlz+BgqgeMjBSoZ/RwEAtnAn3k8Mpptfr1nh008/1dSpU+Xl5aVp06apTZs2pRGbU8vIyFBkZKTS09MVFBRk73AAAICNca93THwuAAC4tqLu9VbN6Dp79qymTJmiL7/8Un369FFiYqJDz+ICAAAAAADAnaPYha7169drypQp8vT01Ntvv62IiIjSjAsAAAAAAACwSpGFrvPnz+vll1/W+vXr1bt3b02YMIFZXAAAAAAAAHA4RRa6unXrprNnz6pixYo6fvy4hg0bdsv+y5Yts1lwAAAAAAAAQHEVWehq166dDAZDWcQCAAAAAAAAlFiRha7XXnutLOIAAAAAAAAAboubvQMAAAAAAAAAbIFCFwAAAAAAAFwChS4AAAAAAAC4hCLX6HIUAwYM0J49e/K1jx49WnFxcZKkHTt2aN68efrhhx/k4eGhli1b6sUXX9S9995r7h8fH69PPvkk3zhPP/20xo8fr927d+vJJ580t7u7uyskJETDhw9X586dS+HKAAAAHBc5GAAAcCZOU+iSpOHDhys6OtqizcfHR5L06aefKiEhQSNGjND06dN17do1rVixQv369dN7772nsLAw8zF9+vTRiy++aDGOl5eXxZ83bNggScrNzdWWLVs0ZswYBQYG6sEHHyytywMAAHBI5GAAAMBZOFWhy8fHR4GBgfnas7OzNWXKFL388suKiooyt0+aNEnZ2dmaNGmSVq1aZW738vIqcJybDAaDxf7HH39c3333nVatWkWSBQAA7jjkYAAAwFm4xBpdGzdulK+vr3r37p1vX1xcnCpWrKgLFy7c1jnuu+8+nTx58rbGAAAAcCXkYAAAwNE41Yyuwvz0008KDw+XwWDIt+/ee+/Vu+++e9vnOH36tKpUqXLb4wAAALgKcjAAAOBonKrQNXv2bKWmplq0rVmzRtnZ2fL19S32OB9++GG+xVA3bdpUYBJlMpn07bff6v/+7/80ffr0AsfLyspSVlaWRZvRaCx2PAAAAI6MHAwAADgLpyp0DRw4UH379rVoq169unx9ffXHH39YtI8cOVJff/21+fXChQvVrFkzSVLXrl01YsQIi/5+fn7mP+fk5Cg8PFySdP36dZlMJj333HPq1KlTgXEtXbo0X/IHAADgKsjBAACAs3CqQleVKlUUHBycr71BgwaaO3eubty4ITe3P5cdmzhxoi5fvqy8vDx17dpV5cr9/0v19fUtcJybypcvr9WrV0uS9u3bpwkTJigyMrLQ/rGxsRYLsEp/fpsYExNjzeUBAAA4JHIwAADgLFxiMfrIyEhdvHhRH3/8sbmtatWqCg4O1uHDh1WhQgXdd999xR7P3d1dwcHBCg4OVu/evdW6detCp8xLf34TGRQUZLFVr179tq4JAADA0ZGDAQAAR+NUM7ouXbqkzMxMizYvLy/5+flp8uTJSkxM1OnTp83fHm7ZskUzZ87UqFGj5OPjU+LzJiQkqEePHtqyZYsiIiJu9zIAAACcCjkYAABwFk5V6Jo3b57mzZtn0fb0009r/Pjx6tGjhwICAvTmm2/q7bfflslkUv369ZWUlKSuXbve1nnvuecePfHEE3r11VfVunVreXh43NZ4AAAAzoQcDAAAOAuDyWQy2TsIV5SRkaHIyEilp6crKCjI3uEAAAAb417vmPhcAABwbUXd611ijS4AAAAAAACAQhcAAAAAAABcAoUuAAAAAAAAuAQKXQAAAAAAAHAJFLoAAAAAAADgEih0AQAAAAAAwCVQ6AIAAAAAAIBLoNAFAAAAAAAAl0ChCwAAAAAAAC7BKQpdAwYMUGhoqHlr3ry54uPjlZWVpd27d1vs++uWkpIiSUpJSSlw/6BBg8znCQ0NVWpqaoExhIaGavfu3WVyvQAAAI6AHAwAADibcvYOoLiGDx+u6OhomUwmnTp1SsnJyRo9erTmzZunbdu2mft16tRJycnJCg8PlyR5e3ub97Vu3VrJyckW43p6elq8fuuttxQZGakGDRqU4tUAAAA4B3IwAADgTJxiRpck+fj4KDAwUFWrVlXjxo01ffp0bd26VX/88YcCAwPNm8FgUKVKlcyvfXx8zGN4enpa9A0MDFSlSpUszlOjRg2NGzdOubm5ZX2JcGErVqxQnTp15Obmpjp16mjFihX2DqnM2fo94D11Pc74mTpjzIC1yMEAwHXYMndx9jzI2eNH4Zym0PVXQUFB8vDw0IkTJ2w67tixY3XmzBnNmzfPpuPizrVixQrFxcXp6NGjMplMOnr0qOLi4u6of0ht/R7wnroeZ/xMnTFmwBbIwQDAOdkyd3H2PMjZ48etOW2hKzMzU9euXdNdd91l03ErV66sV155RQsXLtSBAweKdUxWVpYyMjIsNqPRaNO44LwmTJignJwci7acnBxNmDDBThGVPVu/B7ynrscZP1NnjBmwBXIwAHBOtsxdnD0Pcvb4cWtOs0bXfzt//rymTp2qJk2aqE6dOsU+7uuvvzavG3HTpEmTFBUVZdHWsWNHdenSRePGjdPq1atVvnz5W467dOnSQhdQBY4dO2ZVuyuy9XvAe+p6nPEzdcaYgdtFDgYAzsuWuYuz50HOHj9uzWkKXbNnzzYnMjk5OWratKnmzJlj1RgPPvigpk2bZtEWEBBQYN9Jkyape/fumjNnjsaPH3/LcWNjY/MlakajUTExMVbFB9dUu3ZtHT16tMD2O4Wt3wPeU9fjjJ+pM8YMlAQ5GAC4BlvmLs6eBzl7/Lg1pyl0xcbGql+/fpIkPz8/ValSxeoxvL29FRwcXKy+lSpV0tSpU/Xss88qMjLyln39/Pzk5+dndTy4MyQlJSkuLs5iaqy3t7eSkpLsGFXZsvV7wHvqepzxM3XGmIGSIAcDANdgy9zF2fMgZ48ft+Y0a3T5+/srODhYwcHBJUqwSqJDhw7q1auXEhMTy+R8cE0xMTFasGCBgoODZTAYFBwcrAULFtxR3zbb+j3gPXU9zviZOmPMQEmQgwGAa7Bl7uLseZCzx49bc5oZXbaQm5urzMxMi7Zy5crdMmlLTExUz549Szs0uLiYmJg7/h9NW78HvKeuxxk/U2eMGbAHcjAAcAy2zF2cPQ9y9vhRuDuq0LVjxw61bdvWoq1hw4ZKS0sr9Bg/Pz9NmzZNgwYNKu3wAAAAXBI5GAAAKCsGk8lksncQrigjI0ORkZFKT09XUFCQvcMBAAA2xr3eMfG5AADg2oq61zvNGl0AAAAAAADArVDoAgAAAAAAgEug0AUAAAAAAACXcEctRl+W8vLyJElGo9HOkQAAgNJw8x5/854Px0AOBgCAaysqB6PQVUpuPkKbx5UCAODaMjMzFRwcbO8w8B/kYAAA3BkKy8F46mIpuXLlin788UcFBgbK3d3d3uEAAAAby8vLU2Zmpho1aqQKFSrYOxz8BzkYAACuragcjEIXAAAAAAAAXAKL0QMAAAAAAMAlUOgCgALEx8dr2LBh9g4DAADgjnCr3Ovnn39WaGioMjIyNH/+fDVt2lRHjx4t9tgZGRkKDQ3Vzz//bKtwATgwFqMHAAAAADiFJ554Qh06dOAhIAAKxYwuAAAAAIBTqFixourXr2/vMAA4MApdAFAC165d05w5c9S2bVuFhYWpV69e2rhxo3n/+vXrFRYWposXL5rb5s+fr9DQUP3yyy/mttWrV+v+++/XlStXyjR+AAAAZ5SWlqZmzZpZtK1fv15dunRRWFiYunfvrp9++knt27dXWlqaRb/Tp0/r6aefVqNGjdSyZUvNnz+/LEMHUEYodAFACUyaNEnvv/++Ro4cqcWLF6tVq1Z67rnnzMWuNm3ayM3NTbt37zYfs2PHDknSzp07zW3bt29Xy5YtC3wsLgAAwJ3EZDLp+vXr+ba8vLxCj9m6datGjRqlNm3aaPHixYqOjta4ceN06dKlfH1nzJihiIgILVq0SN26ddPs2bO1YcOG0rwkAHbAGl0AYKUjR44oLS1NCxcuVPv27SVJrVq1UnZ2tmbNmqWOHTvK19dX4eHh2rlzpzp27KjLly9r37596t69u3bu3KmBAwfKZDJpx44devbZZ+18RQAAAPa3adMmNWzY0KpjUlJS1LVrV02cOFHSnzmZl5eXEhIS8vXt16+fnnzySUlSy5YttX//fm3atEmdOnW6/eABOAxmdAGAlXbu3KnKlSurXbt2Fu29evXSP//5T506dUqSFBERYZ7F9e233yogIECxsbHau3evrl+/rsOHD+uPP/5QREREmV8DAACAo2nRooVWrVqVb3vjjTcK7J+Tk6MDBw6oe/fuFu1/+9vfCuwfHh5u8TokJERZWVm2CR6Aw2BGFwBY6dy5c6pataoMBoNFe/Xq1c37q1Wrpvbt22vGjBkyGo3avn27WrdurYYNG6pcuXL64YcftH//ftWtW1e1atWyx2UAAAA4FF9fXzVu3Dhfe7lyBf+39cKFCzKZTKpWrZpFe8WKFeXj41PkOAaDQSaT6TYiBuCImNEFAFaqUqWKMjMz8yVGRqPRvF+S6tWrpxo1amj79u3atWuX2rRpI3d3dz300EPatWuXduzYwWwuAACAErpZzDpz5oxF+9WrV3X58mV7hATAAVDoAgArtWzZUufOnTP/LPGmdevW6Z577rH4VrF9+/b69NNP9csvv6hVq1aS/lw74quvvtLevXspdAEAAJSQn5+f6tWrpy+++MKiPT09XTdu3LBTVADsjZ8uAkAhzpw5o61bt1q0ubu7q02bNurTp4/GjBmjF154QbVr19aWLVv0j3/8QykpKRb927dvr+HDh6tBgwby9/eXJLVt21Yvv/yyvL298z0eGwAAAMU3fPhwPf/88/Lz81NERIT+9a9/aeXKlfL09LR3aADshEIXABTi+++/1zPPPGPR5u3trX379umVV17RvHnzNHfuXJ09e1b33XefUlJS1LFjR4v+rVq1koeHh3k2lyTVrl1bQUFBCg0NJQkDAAC4DZ07d1ZycrLefPNNvfPOOwoJCdH06dP19NNP2zs0AHZiMLH6HgAAAADACZlMJl26dEkVK1Y0t2VnZ6tFixaaO3duvi8hAbg+ZnQBAAAAAJzSvHnz9I9//ENDhw5VSEiIzp07p2XLlikgIEAtW7a0d3gA7IBCFwAAAADAKcXFxUmSFi1apJMnT6pixYp66KGH9Oqrr1rM8gJw5+CniwAAAAAAAHAJbvYOAAAAAAAAALAFCl0AAAAAAABwCRS6AAAAAAAA4BIodAEAAAAAAMAlUOgCAAAAAACAS6DQBQAAAAAAAJdAoQsAAAAAAAAugUIXAAAAAAAAXAKFLgAAAAAAALgECl0AAAAAAABwCRS6AAAAAAAA4BIodAEAAAAAAMAlUOgCAAAAAACAS6DQBQAAAAAAAJdAoQsAAAAAAAAugUIXAAAAAAAAXAKFLgAAAAAAALgECl0AAAAAAABwCRS6AAAAAAAA4BIodAEAAAAAAMAlUOgCAAAAAACAS6DQBQAAAAAAAJdAoQsAAAAAAAAugUIXAAAAAAAAXAKFLgAAAAAAALiEcvYOwFVduXJFP/74owIDA+Xu7m7vcAAAgI3l5eUpMzNTjRo1UoUKFewdDv6DHAwAANdWVA5GoauU/Pjjj4qJibF3GAAAoJStWLFCzZo1s3cY+A9yMAAA7gyF5WAUukpJYGCgpD/f+OrVq9s5GgAAYGtGo1ExMTHmez4cAzkYAACuragcjEJXKbk5Vb569eoKCgqyczQAAKC08PM4x0IOBgDAnaGwHIzF6AEAAAAAAOASKHQBAAAAAADAJThUoWvAgAEKDQ3Nt7333ntKS0tT9+7dCzwuJSVFQ4YMKXScpk2batSoUTp79qy5T1pamkWfRo0aqX///vrmm28sxjYajYqNjVVoaKjF8QAAAK6A/AsAALgShyp0SdLw4cO1bds2i+3RRx+9rXFWrlypM2fOKCEhwaJP3bp1zX0+//xzRUREaPDgwcrIyJAkrV+/Xj169JCXl5dNrg0AAMARkX8BAABX4XCL0fv4+Njk6UX/PU5gYKDGjRunvn376sqVK6pQoYIkqVy5chbnGjZsmHbu3KnVq1drxIgRWr9+vV577TWFhoZq8+bNtx0TAACAIyL/AgAArsLhCl2lxWQySZJu3Lhxy35169bViRMnJEkzZsyQh4eH+RtGAAAAFB/5FwAAKGt3RKErMzNTM2bMUPfu3eXt7X3LvsePH1ejRo0kSR4eHsUaPysrS1lZWRZtRqOxZMECAAC4gNLOvyRyMAAAkJ/DFbpmz56t1NRU82svLy/t2LHjtsa5fPmy6tevrxkzZhTaPy8vT2vXrtXu3bs1btw4q861dOlSi5gBAACciTPmXxI5GAAAyM/hCl0DBw5U3759za/d3Eq2Xv5/j3Pp0iW9++67euyxx7RmzRr5+/tLkn799VeFh4dLknJzc1WpUiXzmhDWiI2NVVRUlEWb0WhUTExMiWIHAAAoS86Yf0nkYAAAID+HK3RVqVJFwcHB+do9PT119erVAo+5evWqPD09bznOa6+9pq5du+rzzz83Jz+1atXS4sWLzeNXq1atRImdn5+f/Pz8rD4OAADAEThj/iWRgwEAgPxKllXYQaNGjfT777/rt99+s2jPy8vTli1bzOs6FMbNzU3+/v66dOmSua1ChQoKDg5WcHCwatSoUeIkCwAAwBWRfwEAAGfjNJlFnTp11LdvXw0ZMkQbN27UyZMn9cMPP2jkyJG6dOmSoqOjLfpfunRJmZmZyszM1O+//6533nlH+/fvV/v27e10BQAAAM6F/AsAADgbh/vp4q1MmTJFS5YsUXJysjIyMuTr66uHH35YH3zwQb5p6/PmzdO8efMk/fn0nvvuu0//+7//q/r169sjdAAAAKdE/gUAAJyJwWQymewdhCvKyMhQZGSk0tPTFRQUZO9wAACAjXGvd0x8LgAAuLai7vVO89NFAAAAAAAA4FYodAEAAAAAAMAlUOgCAAAAAACAS6DQBQAAAAAAAJdAoQsAAAAAAAAugUIXAAAAAAAAXAKFLgAAAAAAALgECl0AAAAAAABwCRS6AAAAAAAA4BJcvtCVk5Oj5ORktW/fXmFhYXrkkUc0d+5cXb16VZKUlpam7t27Wxxz/fp1vf322+rUqZPCwsLUvn17TZs2TRcvXrTHJQAAADgV8i8AAGAv5ewdQGm6evWqBg4cKDc3N73++uuqW7euDh8+rDfeeEP79+/XggUL8h1jMpk0atQoHTlyRImJiWrQoIGOHTumlJQUPf744/rwww/l7e1th6sBAABwfORfAADAnly60PXOO+8oOztbaWlpqlChgiSpevXqaty4sf7+97/riy++yHfMunXr9N1332nt2rUKCAgwH7No0SL17NlT7733nuLi4sr0OgAAAJwF+RcAALAnly50ffzxx3r22WfNSdZNVapUUXJysnx8fPT777/nO6Z///7mJOum8uXLKykpSdnZ2aUeNwAAgLMi/wIAAPbksoWuixcv6tixYwoPDy9wf4cOHSQpX6J18OBBDR48uMBjmjVrVmB7VlaWsrKyLNqMRqOVEQMAADi3ssy/JHIwAACQn0sXuiTJz8/PquOysrKsPmbp0qVKTU216hgAAABXU5b5l0QOBgAA8nPZQpevr6+kPxOnv06DL+q4v34zWJTY2FhFRUVZtBmNRsXExFg1DgAAgDMry/xLIgcDAAD5uXShq1atWtq3b5/q1q2bb/+mTZvk4+OTrz0sLEzffvut2rZtm2/fnj17dPHiRUVGRlq0+/n5lehbSAAAAFdSlvmXRA4GAADyc7N3AKWpT58+WrRoka5cuWLRfubMGY0fP15nzpzJd8yjjz6qDz/8MN++K1euaOLEiTpy5EipxgwAAODMyL8AAIA9uXSha9CgQfLy8tLAgQO1a9cuGY1Gbd26VQMHDlSTJk3UuXPnfMd0795d999/vwYMGKDNmzfLaDRqz549GjRokMqXL89UeAAAgFsg/wIAAPbksj9dlP58JPWyZcuUmpqqsWPH6syZM6pevbp69eqlIUOGyM0tf53Pzc1Nc+fO1aJFi5SUlKQTJ04oICBAnTp10siRI+Xt7W2HKwEAAHAO5F8AAMCeDCaTyWTvIFxRRkaGIiMjlZ6erqCgIHuHAwAAbIx7vWPicwEAwLUVda936Z8uAgAAAAAA4M5BoQsAAAAAAAAugUIXAAAAAAAAXAKFLgAAAAAAALgECl0AAAAAAABwCRS6AAAAAAAA4BIodAEAAAAAAMAlUOgCAAAAAACAS6DQBQAAAAAAAJdQzt4BFNfu3bv15JNPFrp/xIgRkqTU1NR8+9q2bavFixdLkkJDQy32Va9eXX379tWwYcPk5uamtLQ0JSQkFHqe6dOnq0+fPiW5BAAAAKdDDgYAAJyJ0xS6wsPDtW3bNvPrTp06KTk5WeHh4ZIkb29vLVmyRK1bt1ZycrLFsZ6enhavFy1apPr16+vGjRs6dOiQJk6cKA8PDw0ZMkRdu3ZVu3btJEkXLlxQt27d9NFHH6lGjRqSJF9f39K8TAAAAIdCDgYAAJyJ0xS6PD09FRgYaH5tMBhUqVIli7aC+hWkcuXK5j7VqlXTyJEjNX/+fA0ZMkQVKlRQhQoVJEnu7u6SJH9//yLHBAAAcEXkYAAAwJmwRpeke++9VydPnrR3GAAAAHcUcjAAAGBrTjOjqzSdPn1aVapUKfHxWVlZysrKsmgzGo23GxYAAIBLIwcDAAC25nKFrq+//tq8ZsRNkyZNUlRUVIH9MzIyNH/+fHXp0qXE51y6dGmBC7ACAADcKcjBAACAI3C5QteDDz6oadOmWbQFBARYvI6JiZG7u7tMJpMuX76srl27avTo0SU+Z2xsbL4kzmg0KiYmpsRjAgAAOBNyMAAA4AhcrtDl7e2t4ODgW/Z54403FBoaqosXL2rAgAFq1aqVvLy8SnxOPz8/+fn5lfh4AAAAZ0cOBgAAHMEduRh9jRo1FBwcrEaNGmnYsGGaM2eOLl68aO+wAAAAXBo5GAAAKG0uV+jKzc1VZmamxXbu3LlC+8fGxsrHx0dvvfVWGUYJAADgWsjBAACAI3C5ny7u2LFDbdu2tWhr2LCh0tLSCuzv6emp+Ph4Pf/88+rfv3+RU+4BAACQHzkYAABwBAaTyWSydxCuKCMjQ5GRkUpPT1dQUJC9wwEAADbGvd4x8bkAAODairrXu9xPFwEAAAAAAHBnsrrQlZ2drdzcXPPr3377TZ9++qlOnTpl08AAAAAAAAAAa1hV6Dpw4IA6deqkLVu2SJI2b96snj17KiEhQZ07d9b+/ftLJUgAAAAAAACgKFYVul555RW1aNFCjzzyiCQpJSVFQ4cO1b59+xQREaE33nijVIIEAAAAAAAAimJVoevgwYP6n//5H7m7u+v8+fM6ePCgHn30Ubm7u+uxxx5jRhcAAAAAAADsxqpCl6+vrzIzMyVJ27ZtU40aNXT33XdLknJzc1WuXDnbRwgAAAAAAAAUg1WVqb///e964403dOLECX344Yfq1q2bpD8f7fjmm2/qoYceKpUgAQAAAAAAgKJYVeiKj49Xbm6uPvnkE7Vs2VLPPfecJOnLL7/U5cuXNXHixFIJEgAAAAAAACiKVYUuLy8vTZ8+PV/7E088oaeeekoGg8FmgQEAAAAAAADWsGqNrsJ4enoWq8g1YMAAhYaGmrfmzZsrPj5eWVlZ5v2LFy829w8NDdWBAwcsxliyZIkaN26sb775xtz25ZdfqmfPngoLC1OHDh300Ucf5Tv39evX9fbbb6tTp04KCwtT+/btNW3aNF28eLHAWGfPnq3Q0FD9+9//Ls5bAAAA4LDIwQAAwJ3C6tXjP/nkE23evFkXLlyQyWTKt3/ZsmW3PH748OGKjo6WyWTSqVOnlJycrNGjR2vhwoVFnnvjxo2aOXOmZs6cqWbNmkmStm/frtGjRysxMVHt2rXTgQMHlJCQoKpVqyoiIkKSZDKZNGrUKB05ckSJiYlq0KCBjh07ppSUFD3++OP68MMP5e3tbT6PyWTSp59+Kg8PD61bt04jRoyw5i0CAABwOORgAADgTmDVjK7Zs2crISFBly5dUr169VS/fv18W1F8fHwUGBioqlWrqnHjxpo+fbq2bt2qEydO3PK4n376SWPHjtXYsWPVuXNnc/uGDRsUHR2t6OhoBQUFqUuXLurZs6fWrFlj7rNu3Tp99913Wr58uR5++GFVr15dLVq00KJFi5Sbm6v33nvP4lz79+/XyZMnNWzYMH366afWvEUAAAAOiRwMAADcCaya0fXBBx9oyJAheuGFF2wWQFBQkDw8PG6ZZBmNRg0dOlR9+/bVwIEDLfZNmTIlX39PT09dunTJ/Prjjz9W//79FRAQYNGvfPnySkpKUnZ2tkX7unXr1Lp1a/Xv31+pqak6ePCgwsLCrL84AAAAB0UOBgAAXJFVM7ouX76sNm3a2DSAzMxMXbt2TXfddVeB+3NycjR06FCFh4crPj6+yPF2796t1atXq2fPnua2gwcP6sEHHyywf7NmzdShQwfz67y8PK1fv17dunVTQECAWrRooXXr1t3ynFlZWcrIyLDYjEZjkbECAADYCzkYAABwRVbN6OrQoYO+//57tWjRwiYnP3/+vKZOnaomTZqoTp06BfZ56aWXdOzYMYWEhMjNrfC63AcffKCkpCTl5uYqLi7OvDaE9GcS5OfnV6yYdu/erQsXLqhjx46SpG7duik1NVVjxowp9PxLly5VampqscYHAACwN3IwAADgqqwqdHXv3l2zZs3ShQsXdP/998vd3T1fn8jIyFuOMXv2bHNCkpOTo6ZNm2rOnDmF9r9y5YpWrlypwYMHKy0tTX369CmwX7du3dSiRQsdOXJEM2bM0I0bNzR27FhJkq+vr/mpQkVZu3atIiIiVLFiRUlSp06dNGXKFH377bdq3rx5gcfExsYqKirKos1oNComJqZY5wQAACht5GAAAOBOYFWha+TIkZJk8fjp/2YwGPTzzz/fcozY2Fj169dPkuTn56cqVarcsv/UqVPVtGlTTZ48WVOmTFF4eLjq1q2br5+vr698fX11zz33qHLlyho4cKCef/55eXp6KiwsTN9++63atm2b77g9e/bo4sWLioyMVG5urjZs2KCcnBw1btzY3Of69etat25doUmWn59fsb+tBAAAsAdyMAAAcCewao2uQ4cO3XIrqsglSf7+/goODlZwcHCRCdbN/pLUq1cvPfzww3rxxReVm5tr3p+YmKiNGzdaHOPp6am8vDzl5eVJkh599FF9+OGHOnPmjEW/K1euaOLEiTpy5Igk6auvvtL169e1atUqrV692ryNHz9e69ev1/Xr14uMFwAAwBGRgwEAgDuBVYUue5s8ebLOnj2rWbNmmdsqV66syZMna/369Tpx4oR27dqliRMnqnv37vLy8pL0508u77//fg0YMECbN2+W0WjUnj17NGjQIJUvX948vf3TTz/Vww8/rLCwMIWEhJi3fv366dKlS9q+fbtdrhsAAMCeyMEAAICzKPKni8uWLbNqwCeffLLEwRSlUqVKev311/XUU0+pVatWioiI0OjRo1WxYkUlJyfr5MmTCggIUFRUlJ577jnzcW5ubpo7d64WLVqkpKQknThxQgEBAerUqZNGjhwpb29vZWdn66uvvtLMmTPznbdixYqKiIjQunXrLBZYBQAAuBOQgwEAAGdhMJlMplt1eOSRR4o/mMGg9PT02w7KFWRkZCgyMlLp6ekKCgqydzgAAMDGuNc7Jj4XAABcW1H3+iJndG3atKlUAgMAAAAAAABsyaZrdF27dk1PPvmk/v3vf9tyWAAAAAAAAKBINi10mUwm7dmzR5cuXbLlsAAAAAAAAECRnOqpiwAAAAAAAEBhKHQBAAAAAADAJVDoAgAAAAAAgEug0AUAAAAAAACXQKELAAAAAAAALsGmhS4PDw9Nnz5dd999ty2HBQAAAAAAAIpkVaErMjJSP/74Y4H7fvzxR0VERCgqKkqVK1e2RWwFys7OVpMmTTR69GiL9vj4eIWGhha6ZWRkSJIeeeQRi/bWrVtr2rRpunr1qiRp48aNatCggQ4dOmQx/oULF9SyZUulpKSU2rUBAAA4IvIvAADgLIpV6MrOztbFixd1/PhxnT9/XtnZ2RZbVlaW0tPTdePGjdKOVxs3btSNGze0adMmXb582dw+YcIEbdu2Tdu2bdO0adNUvXp18+tt27apRo0a5r7Tpk3Ttm3b9PXXX+t///d/tWPHDiUlJUmSOnbsqIiICPPrm1JSUuTt7a1nnnmm1K8RAADAkZB/AQAAZ1GsQlezZs3UokULGQwGPfPMM2revLnF9tBDD+ntt9/WsGHDSjterV27VtHR0fLx8dHmzZvN7b6+vgoMDFRgYKB8fX3l7u5ufh0YGCh3d/d8fatWrarmzZvrpZde0urVq5Wbmyvpz6Tthx9+0Oeffy5JOnz4sFauXKmEhARVqFCh1K8RAADAkZB/AQAAZ1GuOJ2WLVsmk8mk2NhYTZw4UfXq1cvX5+677y71tbnOnDmjnTt36vnnn5fJZNLatWvVtWvX2x43JCREV69e1dmzZ1W9enXVqlVLQ4cOVXJysjp06KCkpCS1atVKnTp1ssFVAAAAOA/yLwAA4EyKVehq0aKFJOmZZ57RI488opo1a5ZqUIVZv369atasqcaNGys3N1cDBw7UhQsXVKlSpdsa9/Tp03Jzc5Ofn5+5bdCgQVq9erUGDx6s/fv3a+3atYUen5WVpaysLIs2o9F4WzEBAAA4AkfNvyRyMAAAkF+Rha5p06Zp4MCBCgoKUoMGDeTl5VUWcRVo3bp16tq1qwwGgx588EEFBAToiy++UL9+/Uo85pkzZzRnzhxFRETI29vb3O7p6akBAwZo6tSp6tSpk+rWrVvoGEuXLlVqamqJYwAAAHBUjpp/SeRgAAAgvyLX6Prggw/MT8wZPXq0/vWvf5V6UAU5fvy4vvvuO/NUeYPBoC5dumjdunVWjzVmzBiFh4frgQceUOvWrZWbm6tXX33Vok9ubq6WL1+uevXq6auvvrrldcfGxio9Pd1iW7FihdVxAQAAOBJHzr8kcjAAAJBfkTO6wsLCNHPmTHXt2lUmk0mff/65fvzxx0L7P/nkkzYN8KZPP/1UktS3b19zW15enkwmk06dOqVq1aoVe6yxY8eqQ4cOunr1qoYMGaIHHnhA/v7+Fn0WL16s7OxsffbZZ4qLi9O0adO0ePHiAsfz8/OzmHYPAADgChw5/5LIwQAAQH5FFrqmT5+uKVOmaPny5TIYDPr888/l6elZYF+DwVBqha61a9dqwIABio6OtmiPi4vT559/roEDBxZ7rGrVqik4OFjSn0lXfHy8+vbtq1q1akmSfv/9d82fP18vv/yyKlWqpEmTJumxxx7Thg0bWBAVAADcMci/AACAsynyp4shISFavny5Nm3aJJPJpLlz52rTpk0Fbunp6aUS5G+//abDhw8rOjpaISEhFlvXrl1LNH3+pq5du6px48aaMWOGuS0pKUlhYWHq1auXJKlhw4bq37+/pk+fritXrtz29QAAADg68i8AAOCMiix0/bdly5apXr16pRVLodatW6f69esrJCQk375u3brpwIEDOnr0aInHnzhxojZs2KC9e/dq48aN2rJliyZPniyDwWDu88ILLygnJ0cLFy4s8XkAAACcBfkXAABwRgaTyWS6VYd169apc+fOKleunA4dOlTkgPXr17dZcM4sIyNDkZGRSk9PV1BQkL3DAQAANsa93jHxuQAA4NqKutcXuUbXxIkTFRISogYNGqh3794yGAz6a23sZpvBYNDPP/9su+gBAAAAAACAYiqy0LVw4ULzLK3SWoMLAAAAAAAAuF1FFrqaN29u/vPdd99dqsEAAAAAAAAAJVVkoeuvfv75Z23evFlZWVn5fsIoSQkJCTYJDAAAAAAAALCGVYWuVatWadKkSQoICFCdOnXy7f/vp+QAAAAAAAAAZcmqQte8efPUuXNnzZgxQ+7u7qUVEwAAAAAAAGA1N2s6Z2Zmqm/fvhS5AAAAAAAA4HCsKnQ1bNhQJ0+eLK1YAAAAAAAAgBIr8qeL2dnZ5j9PmDBBr776qvz9/fXAAw+oXLn8h1esWNG2EQIAAAAAAADFUOSMrmbNmql58+Zq3ry5+vfvr++//15Dhw5Vq1atzO3Nmzc397PWgAEDFBoaat6aN2+u+Ph4ZWVlmfcvXrzY3D80NFQHDhywGGPJkiVq3LixvvnmG3Pbl19+qZ49eyosLEwdOnTQRx99ZHFMfHy8xXnDw8P17LPP6sSJExb9fvvtN/Xp00fh4eFWXxsAAIAjIv8CAACuqsgZXcuWLSv1IIYPH67o6GiZTCadOnVKycnJGj16tBYuXFjksRs3btTMmTM1c+ZMNWvWTJK0fft2jR49WomJiWrXrp0OHDighIQEVa1aVREREeZj+/TpoxdffFGSdPbsWaWkpGjw4MFau3at3N3dtWLFCs2YMUMtWrTQv/71r9K5eAAAADsg/wIAAK6oyEJXixYtSj0IHx8fBQYGSpKqVq2q6dOnKzIyMt+3e3/1008/aezYsRo7dqw6d+5sbt+wYYOio6MVHR0tSQoKCtLOnTu1Zs0ai0TLy8vLfN7AwEAlJyfroYce0r59+9SsWTN98cUXWrx4sa5fv669e/fa+rIB3GFWrFihCRMm6NixY6pdu7aSkpIUExNj77AA3KHIvwAAgCsqstAlSTk5Ofryyy/VtWtXeXp6mts//vhj7d+/X9WqVVP//v1111132SSooKAgeXh43DLRMhqNGjp0qPr27auBAwda7JsyZUq+/p6enrp06dItz+vt7a2aNWvq+PHjatasmRYvXiwPDw/t3r27JJcBAGYrVqxQXFyccnJyJElHjx5VXFycJFHsAuAQyL8AAIArKLLQdfr0aT3++OM6c+aMIiMjzYWuCRMmaM2aNWrRooW++uorrVy5Uh9++KGCgoJuO6jMzExdu3at0MJZTk6Ohg4dqvDwcMXHxxc53u7du7V69WrNnDnzlv3y8vJ05swZValSRZLk4eFRrHizsrLMa1rcZDQai3UsgDvDhAkTzEWum3JycjRhwgQKXQAcgrPlXxI5GAAAyK/IQtfs2bPl5eWlzZs3y9fXV5L0888/Ky0tTSkpKerYsaMuX76sxx9/XHPnzlVycvJtBXT+/HlNnTpVTZo0UZ06dQrs89JLL+nYsWMKCQmRm1vh6+l/8MEHSkpKUm5uruLi4iymzf/VlStX9NZbb6l8+fJWL6q/dOlSpaamWnUMgDvLsWPHrGoHgLLkjPmXRA4GAADyK7LQtWXLFo0fP16VK1c2t61Zs0a1atVSx44dJf251kJsbGyJi1yzZ882Jyk5OTlq2rSp5syZU2j/K1euaOXKlRo8eLDS0tLUp0+fAvt169ZNLVq00JEjRzRjxgzduHFDY8eONe9///339cknn5jHrFmzpt566y15eXlZFX9sbKyioqIs2oxGI7M0AJjVrl1bR48eLbAdAOzB2fMviRwMAADkV2Sh68KFC6pRo4ZF27Zt2xQZGWnRVrNmTZ0/f75EQcTGxqpfv36SJD8/P/PU9cJMnTpVTZs21eTJkzVlyhSFh4erbt26+fr5+vrK19dX99xzjypXrqyBAwfq+eefN//8smvXrho5cqQkafr06Tp//rwaN25sdfx+fn7y8/Oz+jgAd46kpCSLNbqkP9elSUpKsmNUAO5kzp5/3YybHAwAAPy3wued/0fNmjX1z3/+0/z6X//6l3799Vc98sgjFv2OHTsmf3//EgXh7++v4OBgBQcHF5lk3ewvSb169dLDDz+sF198Ubm5ueb9iYmJ2rhxo8Uxnp6eysvLU15enrmtUqVK5vO+9NJLOnjwoD777LMSXQMA3EpMTIwWLFig4OBgGQwGBQcHa8GCBcw6AGA35F8AAMAVFVnoioqK0vz58/X9998rIyNDkyZNUr169SzWUTh37pwWL16sdu3alWqwBZk8ebLOnj2rWbNmmdsqV66syZMna/369Tpx4oR27dqliRMnqnv37oVOi7/77rs1aNAgzZgxQ5cvXy6r8AHcQWJiYvTvf/9bN27c0L///W+KXACcFvkXAABwVEX+dPGZZ57R4cOH9T//8z8yGAwKCAjQkiVLzPv//e9/q3fv3vL399cLL7xQqsEWpFKlSnr99df11FNPqVWrVoqIiNDo0aNVsWJFJScn6+TJkwoICFBUVJSee+65W44VFxentLQ0LV68WCNGjCijKwAAAHAu5F8AAMBRGUwmk6k4HY8dO6Zz586pfv36Kl++vLn9jz/+0Pr169WrVy/zUxkhZWRkKDIyUunp6QoKCrJ3OAAAwMa41zsmPhcAAFxbUff6In+6eFPt2rV1//33WxS5JOmuu+7SE088IV9fX+Xl5Sk1NVWZmZm3HzkAAAAAAABghWIXuoojLy9P8+bN0+nTp205LAAAAAAAAFAkmxa6JKmYv4QEAAAAAAAAbMrmhS4AAAAAAADAHih0AQAAAAAAwCVQ6AIAAAAAAIBLoNAFAAAAAAAAl2DTQpebm5uioqJUuXJlWw4LAAAAAAAAFKmctQfk5eVp06ZN+v7773Xu3DkNHz5cXl5eOnnypBo2bKjp06eXRpwAAAAAAADALVk1o+vMmTPq06ePxo8fr4MHD+qTTz7R+fPntW/fPvXv31979+4trTitMmDAAMXGxspkMhW4LyUlRZIUGhpa4Hb48GFz/2+//VaRkZHq3r17mcUPAADgjMjBAACAvVk1o+vVV1+Vu7u70tPT5evrq0aNGkmSIiMj1bt3b82aNUvvv/9+qQRqrV27dmnlypWKiYm5Zb9Fixapfv36Fm1VqlSRJM2aNUvLli1T06ZNdfr06VKLFQAAwFWQgwEAAHuyakbXli1bNHz4cFWpUkUGg8FiX9euXfXTTz/ZNLjbUatWLc2YMUPHjh27Zb/KlSsrMDDQYitX7s/637Zt27Rq1Sq+SQQAACgmcjDntWLFCtWpU0dubm6qU6eOVqxYYe+QAACwmlWFruvXr8vDw6PAfTk5OfL09LRJULbQs2dPhYeHKz4+Xjdu3CjRGB9++KHuvfdeG0cGAADgusjBnNOKFSsUFxeno0ePymQy6ejRo4qLi6PYBQBwOlYVulq2bKmlS5daJC0Gg0HXr1/X8uXL1bp1a5sHWFIGg0FJSUk6dOiQli1bVqIxCivq/VVWVpYyMjIsNqPRWKJzAgAAODNyMOc0YcIE5eTkWLTl5ORowoQJdooIAICSsWqNrsTERMXExCgqKkqdOnWSJL333nv64YcfdP78eYdZn+ummjVrKj4+XtOmTVO7du0UEhKSr88TTzwhN7f/X+9r166d5s6da9V5li5dqtTU1NuOFwAAwBWQgzmfwn5qWtRPUAEAcDRWFbpq166tNWvW6N1339XWrVsVFBSkw4cP6+GHH9ZTTz0lf3//0oqzxPr27auNGzcqISGhwELcG2+8oXr16plfe3l5WX2O2NhYRUVFWbQZjcYiF2EFAABwVeRgzqV27do6evRoge0AADgTqwpd0p9Pw3nhhRf0wgsvlEY8NmcwGDR16lR169ZNixYtyre/evXqCg4Ovq1z+Pn5yc/P77bGAAAAcCXkYM4lKSlJcXFxFj9f9Pb2VlJSkh2jAgDAelYXukwmkzIyMnTp0qUC9//1MdGOoFq1akpMTNSkSZN01113qUWLFvYOCQAAwOWRgzmPm7PgJkyYoGPHjql27dpKSkpidhwAwOlYVej67rvv9MILL+j06dMymUz59hsMBv388882C86W+vTpoy+//FKbN2+2dygAAAB3DHIw5xETE0NhCwDg9KwqdL300kuqUaOG5s2bpypVqpRWTKVm6tSp6t69u73DAAAAuKOQgwEAgLJiVaHr6NGjevPNN9WoUaPSiscmli9fXmB7YGCgdu/ebX79yy+/FGu8Pn36qE+fPjaJDQAAwFWRgwEAAHtzK7rL/1evXj1lZWWVViwAAAAAAABAiVk1o+uVV17RpEmTdO3aNQUFBRXYp3nz5jYJDAAAAAAAALCGVYWuK1eu6PTp00pISChwvyMvRg8AAAAAAADXZlWha+LEibr33nv1/vvvq3LlyqUUEgAAAAAAAGA9qwpdGRkZeumll1SrVq3SigcAAAAAAAAoEasWo2/ZsqV+++230ooFAAAAAAAAKDGrZnRFRUUpJSVFWVlZatCggQwGQ74+kZGRNgsOAAAAAAAAKC6rCl2jR4+WJM2bN6/A/SxGDwAAAAAAAHuxqtB16NCh0oqjSLt379aTTz5Z6P4RI0ZIklJTU81t5cuXV3h4uCZOnKj77rvP3N6kSRNdvXrV4vjnn39ew4YNM79+//33tWTJEv3++++65557NGbMGD3yyCO2uhwAAACnQA4GAACciVWFLnsKDw/Xtm3bzK87deqk5ORkhYeHS5K8vb21ZMkStW7dWsnJyZKkixcvasWKFRo4cKC++OILVaxYUWfPntXVq1eVlpamqlWrmsfz9vY2//mjjz7SzJkz9fLLL+v+++/X1q1b9fzzz+ujjz5S/fr1y+iKAQAA7I8cDAAAOBOrC1379+/X1q1blZ2dXeD+hISE2w6qIJ6engoMDDS/NhgMqlSpkkXbX/sFBgZq4sSJ2rx5szZu3KjevXvr1KlTMhgMqlevnjw8PAo814YNGzR8+HB169ZNkvT4449r06ZN+uyzz0iyAADAHYUcDAAAOBOrCl1paWlKTEzUfffdpzNnzqhChQq6++679euvv8pkMik0NLS04iwxg8GgunXr6vjx45KkkydPyt/fv9AES5IWLFiQr83Dw6PAxfcBAACQHzkYAACwBzdrOr/99tt69tlntXbtWr3wwguqX7++li9frvT0dNWtW1e9evUqrThvy/Hjx83fMJ48eVJubm4aNGiQmjdvrm7duiktLa3QY2/cuKH169dr586d6tKlS1mFDAAA4PTIwQAAQFmzakZXRkaGWrduLUkKDg7Wr7/+Kkny8fHRoEGD9Oqrr+rRRx+1fZQllJubq3fffVenTp1SZGSkJKlWrVpq3769IiMjlZCQoN27d2vy5Mny8PBQjx49LI6fPXu2Fi9erLy8PE2cOLHQKfNZWVnKysqyaDMajaVzUQAAAA6OHAwAANiLVYUuf39//fvf/1bz5s1177336sSJE7p8+bK8vLzk7e2tzMzM0oqz2LZu3WpeHPXq1auqXr263nzzTQUEBEiS2rdvr/bt25v733vvvTp//ryWLFmSL8mKjY1Vz549deDAAb3xxhuSpJiYmHznXLp0qcWThgAAAO405GAAAMARWFXo+vvf/65Zs2apUaNGatCggYKCgrRo0SL9z//8j1auXOkQi4Q2a9ZM06ZNkyRVqFBBVatWLXJdh/DwcL3zzjv52v39/eXv76+QkBAZDAbNmTOnwCQrNjZWUVFRFm1Go7HAvgAAAK6IHAwAADgCqwpdL774ooxGo86fPy9JGjVqlMaMGaM333xTfn5+mj9/fmnEaBVvb28FBwcXun/BggVyc3PT4MGDzW2//PKLatSoIUnKy8vTiBEjNGTIED3wwAPmPp6enrp27VqBY/r5+cnPz882FwAAAOCEyMEAAIAjsKrQ5e3tbTE9vEuXLgoPD9eJEycUGhoqHx8fmwdoayEhIRo1apT8/f3VqlUr7du3TykpKZowYYIkyd3dXZ6enho/frxeeuklhYSE6NChQ5oxY4b69u1r5+gBAACcEzkYAAAoC1YVuhISEvT000/rvvvuM7dVr15d1atXt3lgpSUyMlJTpkzR22+/rcTERAUFBWn8+PEWi+i/9tprmjt3ruLj4/XHH3+oZs2aio6O1qBBg+wYOQAAgPMiBwMAAGXBYDKZTMXt3LRpU6WkpKhNmzalGZNLyMjIUGRkpNLT0xUUFGTvcAAAgI1xr3dMfC4AALi2ou71btYM9vjjj+uDDz7QjRs3bBYgAAAAAAAAYAtW/XTRaDRq9+7d6tKli+69994Cn6TDI54BAAAAAABgD1YVujw8/h979x4f453///85ISGJDJIG9QlBqiF12CiKqkPDljg1afFLU42WDS2l2zjEoShCN6EssVVFi6aHraZaurQSLeLYg1U9rG4/7SKYNlUVEYSY3x/9mO9OE5KJSWbm8rjfbtftlnlf7/f7el3CbV5e85735a2oqKjKigUAAAAAAACosDILXT/++KMCAwPl7e2t+fPnV0VMAAAAAAAAgMPK3KOrR48eOnTokO211WrVrFmzdPLkyUoNDAAAAAAAAHBEmYWu3z+U8cqVK3rzzTf1yy+/VFpQAAAAAAAAgKMceuriVb8vfgEAAAAAAACuVqFCFwAAAAAAAOBuylXoMplM5WoDAAAAAAAAXKXMpy5K0sKFC1WnTh1J/+9ri88995zMZnOJvunp6c6LroKGDRum/fv3216bzWb17NlTU6dOVZ06dZScnKx33nnHdr5GjRrq2LGjZs6cqUaNGtnaz5w5o9mzZ2vTpk1av369WrduXaX3AQAA4EnIwQAAgKuVuaIrJiZGjRo1UkBAgAICAmQ2mxUTE6P/+Z//sbX99+EuxowZo5ycHO3cuVOrV6/W8ePHlZSUZDsfGxurnJwc5eTk6J133lGtWrU0evRoWyHvk08+0YABA3Tu3DlX3QIAAIDHIQcDAACuVOaKrvnz51dFHE7n7++v4OBgSVK9evU0b948/fGPf9SJEyckSb6+vrbzwcHBmjFjhjp37qwffvhBzZo10+bNmzV27FgNGTJE4eHhLrsPAAAAT0IOBgAAXKlcX100gtDQUHl7e9uSrGu5+mnilClT5O3tXRWhAQAAGBY5GAAAqEo3zVMXT58+rUuXLqlu3bolzhUUFGj27Nnq0KGDmjVrJkkkWAAAAE5ADgYAAKrSTbGi6+zZs0pNTVWLFi1sSdSbb75p2wz1/PnzatiwoV544YUKPU0yPz9f+fn5dm0Wi+XGAwcAAPBg5GAAAKCqGbbQtWjRItsTIM+fP6+WLVtqyZIltiQqOjpaY8eOlSRduHBBGzZsUFxcnNavX29LxMprzZo1bvG0SQAAAFcjBwMAAK5k2EJXQkKChgwZIqvVqgkTJig0NFShoaG28wEBAXavJ0+erMOHD+utt97S5MmTHb5WTEyMXZvFYlF8fPyN3QQAAICHIQcDAACuZNhCV2BgoC2JeuaZZzR06FA9/PDDioyMvOaYW265pUKPsjabzTKbzRWOFQAAwCjIwQAAgCvdFJvRt23bVgMHDtS8efNsT/Q5f/688vLylJeXp+PHjyszM1ObN29Wz549XRwtAACAMZCDAQCAqmbYFV2/l5SUpD59+ujdd9+VJGVmZiozM1OSVL16dTVp0kQzZswgyQIAAHAicjAAAFCVTNarH6/BqXJzcxUVFaXs7GyFhIS4OhwAAOBkvNe7J34vAAAYW1nv9TfFVxcBAAAAAABgfBS6AAAAAAAAYAgUugAAAAAAAGAIFLoAAAAAAABgCBS6AAAAAAAAYAgUugAAAAAAAGAIFLoAAAAAAABgCBS6AAAAAAAAYAgUugAAAAAAAGAI1V0dQHkNGzZM+/fvL9GelJSkxMRESdLu3bu1bNkyffHFF/L29lanTp309NNP67bbbrP1T05O1jvvvFNinscee0yTJ0/Wvn379Mgjj9jaq1WrprCwMI0ZM0Z9+vSphDsDAABwX+RgAADAk3hMoUuSxowZo7i4OLs2f39/SdL777+vKVOmaOzYsZo/f74uXbqkjIwMDRkyRK+++qoiIiJsY2JjY/X000/bzePr62v389atWyVJRUVF2r59uyZMmKDg4GDdeeedlXV7AAAAbokcDAAAeAqPKnT5+/srODi4RHtBQYFmzZqlZ599VjExMbb2GTNmqKCgQDNmzND69ett7b6+vqXOc5XJZLI7/9BDD+nzzz/X+vXrSbIAAMBNhxwMAAB4CkPs0ZWVlaWAgADdf//9Jc4lJiaqVq1aOnPmzA1do3nz5jp58uQNzQEAAGAk5GAAAMDdeNSKrmv56quvFBkZKZPJVOLcbbfdpldeeeWGr/HTTz+pbt26NzwPAACAUZCDAQAAd+NRha5FixYpPT3dru29995TQUGBAgICyj3Pm2++WWIz1G3btpWaRFmtVn322Wd69913NX/+/FLny8/PV35+vl2bxWIpdzwAAADujBwMAAB4Co8qdA0fPlyDBw+2a2vQoIECAgL0888/27WPGzdOO3futL1+6aWX1L59e0lSdHS0xo4da9ffbDbbfi4sLFRkZKQk6fLly7JarXryySfVu3fvUuNas2ZNieQPAADAKMjBAACAp/CoQlfdunUVGhpaor1ly5ZasmSJrly5Ii+v37Ydmz59us6fP6/i4mJFR0erevX/d6sBAQGlznNVjRo1tGHDBknSgQMHNG3aNEVFRV2zf0JCgt0GrNJvnybGx8c7cnsAAABuiRwMAAB4CkNsRh8VFaWzZ8/q7bfftrXVq1dPoaGh+vbbb1WzZk01b9683PNVq1ZNoaGhCg0N1f33368uXbpcc8m89NsnkSEhIXZHgwYNbuieAAAA3B05GAAAcDcetaLr3LlzysvLs2vz9fWV2WzWzJkzNXXqVP3000+2Tw+3b9+uhQsX6qmnnpK/v3+FrztlyhQNGDBA27dvV/fu3W/0NgAAADwKORgAAPAUHlXoWrZsmZYtW2bX9thjj2ny5MkaMGCAgoKC9Le//U0vvviirFarWrRooZSUFEVHR9/QdZs1a6aHH35Y8+bNU5cuXeTt7X1D8wEAAHgScjAAAOApTFar1erqIIwoNzdXUVFRys7OVkhIiKvDAQAATsZ7vXvi9wIAgLGV9V5viD26AAAAAAAAAApdAAAAAAAAMAQKXQAAAAAAADAECl0AAAAAAAAwBApdAAAAAAAAMAQKXQAAAAAAADAECl0AAAAAAAAwBApdAAAAAAAAMAQKXQAAAAAAADAEjyh0DRs2TOHh4bajQ4cOSk5OVn5+vvbt22d37vfH0qVLJUlLly4t9fyIESNs1wkPD1d6enqpMYSHh2vfvn1Vcr8AAADugBwMAAB4muquDqC8xowZo7i4OFmtVv34449KTU1VUlKSli1bppycHFu/3r17KzU1VZGRkZIkPz8/27kuXbooNTXVbl4fHx+71y+88IKioqLUsmXLSrwbAAAAz0AOBgAAPIlHrOiSJH9/fwUHB6tevXpq3bq15s+frx07dujnn39WcHCw7TCZTKpdu7bttb+/v20OHx8fu77BwcGqXbu23XVuvfVWTZo0SUVFRVV9iwAAVImMjAw1adJEXl5eatKkiTIyMlwdEtwYORgA3jcAeBKPKXT9XkhIiLy9vXXixAmnzjtx4kSdOnVKy5Ytc+q8AAC4g4yMDCUmJurIkSOyWq06cuSIEhMT+U8Lyo0cDLi58L4BwNN4bKErLy9Ply5d0i233OLUeevUqaPZs2frpZde0qFDh8o1Jj8/X7m5uXaHxWJxalwAADjDtGnTVFhYaNdWWFioadOmuSgieBpyMODmwvsGAE/jMXt0/bdff/1Vc+bMUZs2bdSkSZNyj9u5c6dt34irZsyYoZiYGLu2Xr16qW/fvpo0aZI2bNigGjVqXHfeNWvWXHMDVQAA3MnRo0cdagf+GzkYcPPhfQOAp/GYQteiRYtsiUxhYaHatWunxYsXOzTHnXfeqblz59q1BQUFldp3xowZ6t+/vxYvXqzJkydfd96EhIQSiZrFYlF8fLxD8QEAUNkaN26sI0eOlNoOlIYcDLi58b4BwNN4TKErISFBQ4YMkSSZzWbVrVvX4Tn8/PwUGhparr61a9fWnDlz9PjjjysqKuq6fc1ms8xms8PxAABQ1VJSUpSYmGj3NRQ/Pz+lpKS4MCq4M3Iw4ObG+wYAT+Mxha7AwMByJ0jO0qNHDw0aNEhTp06t0usCAFBZrq50mTZtmo4eParGjRsrJSWFFTC4JnIw4ObG+wYAT+MxhS5nKCoqUl5enl1b9erVr/vJ5NSpUzVw4MDKDg0AgCoTHx/Pf1BQpcjBAM/G+wYAT3JTFbp2796trl272rXdcccdyszMvOYYs9msuXPnasSIEZUdHgAAgCGRgwEAgKpislqtVlcHYUS5ubmKiopSdna2QkJCXB0OAABwMt7r3RO/FwAAjK2s93ovF8QEAAAAAAAAOB2FLgAAAAAAABgChS4AAAAAAAAYwk21GX1VKi4uliRZLBYXRwIAACrD1ff4q+/5cA/kYAAAGFtZORiFrkpy9RHaPIYXAABjy8vLU2hoqKvDwP8hBwMA4OZwrRyMpy5WkgsXLujLL79UcHCwqlWr5upwAACAkxUXFysvL0+tWrVSzZo1XR0O/g85GAAAxlZWDkahCwAAAAAAAIbAZvQAAAAAAAAwBApdAFCK5ORkPfHEE64OAwAA4KZwvdzrm2++UXh4uHJzc7V8+XK1a9dOR44cKffcubm5Cg8P1zfffOOscAG4MTajBwAAAAB4hIcfflg9evTgISAArokVXQAAAAAAj1CrVi21aNHC1WEAcGMUugCgAi5duqTFixera9euioiI0KBBg5SVlWU7v2XLFkVEROjs2bO2tuXLlys8PFyHDx+2tW3YsEFt27bVhQsXqjR+AAAAT5SZman27dvbtW3ZskV9+/ZVRESE+vfvr6+++krdunVTZmamXb+ffvpJjz32mFq1aqVOnTpp+fLlVRk6gCpCoQsAKmDGjBl6/fXXNW7cOK1atUqdO3fWk08+aSt23X333fLy8tK+fftsY3bv3i1J2rNnj61t165d6tSpU6mPxQUAALiZWK1WXb58ucRRXFx8zTE7duzQU089pbvvvlurVq1SXFycJk2apHPnzpXom5aWpu7du2vlypXq16+fFi1apK1bt1bmLQFwAfboAgAHff/998rMzNRLL72kbt26SZI6d+6sgoICPf/88+rVq5cCAgIUGRmpPXv2qFevXjp//rwOHDig/v37a8+ePRo+fLisVqt2796txx9/3MV3BAAA4Hrbtm3THXfc4dCYpUuXKjo6WtOnT5f0W07m6+urKVOmlOg7ZMgQPfLII5KkTp066eDBg9q2bZt69+5948EDcBus6AIAB+3Zs0d16tTRPffcY9c+aNAg/e///q9+/PFHSVL37t1tq7g+++wzBQUFKSEhQZ988okuX76sb7/9Vj///LO6d+9e5fcAAADgbjp27Kj169eXOBYsWFBq/8LCQh06dEj9+/e3a//jH/9Yav/IyEi712FhYcrPz3dO8ADcBiu6AMBBp0+fVr169WQymezaGzRoYDtfv359devWTWlpabJYLNq1a5e6dOmiO+64Q9WrV9cXX3yhgwcPqmnTpmrUqJErbgMAAMCtBAQEqHXr1iXaq1cv/b+tZ86ckdVqVf369e3aa9WqJX9//zLnMZlMslqtNxAxAHfEii4AcFDdunWVl5dXIjGyWCy285J0++2369Zbb9WuXbu0d+9e3X333apWrZruuusu7d27V7t372Y1FwAAQAVdLWadOnXKrv3ixYs6f/68K0IC4AYodAGAgzp16qTTp0/bvpZ41aZNm9SsWTO7TxW7deum999/X4cPH1bnzp0l/bZ3xMcff6xPPvmEQhcAAEAFmc1m3X777frggw/s2rOzs3XlyhUXRQXA1fjqIgBcw6lTp7Rjxw67tmrVqunuu+9WbGysJkyYoD//+c9q3Lixtm/frr///e9aunSpXf9u3bppzJgxatmypQIDAyVJXbt21bPPPis/P78Sj8cGAABA+Y0ZM0bjx4+X2WxW9+7d9cMPP+i1116Tj4+Pq0MD4CIUugDgGv75z3/qT3/6k12bn5+fDhw4oNmzZ2vZsmVasmSJfvnlFzVv3lxLly5Vr1697Pp37txZ3t7ettVcktS4cWOFhIQoPDycJAwAAOAG9OnTR6mpqfrb3/6ml19+WWFhYZo/f74ee+wxV4cGwEVMVnbfAwAAAAB4IKvVqnPnzqlWrVq2toKCAnXs2FFLliwp8SEkAONjRRcAAAAAwCMtW7ZMf//73zV69GiFhYXp9OnTWrt2rYKCgtSpUydXhwfABSh0AQAAAAA8UmJioiRp5cqVOnnypGrVqqW77rpL8+bNs1vlBeDmwVcXAQAAAAAAYAherg4AAAAAAAAAcAYKXQAAAAAAADAECl0AAAAAAAAwBApdAAAAAAAAMAQKXQAAAAAAADAECl0AAAAAAAAwBApdAAAAAAAAMAQKXQAAAAAAADAECl0AAAAAAAAwBApdAAAAAAAAMAQKXQAAAAAAADAECl0AAAAAAAAwBApdAAAAAAAAMAQKXQAAAAAAADAECl0AAAAAAAAwBApdAAAAAAAAMAQKXQAAAAAAADAECl0AAAAAAAAwBApdAAAAAAAAMAQKXQAAAAAAADAECl0AAAAAAAAwBApdAAAAAAAAMAQKXQAAAAAAADAECl0AAAAAAAAwBApdAAAAAAAAMITqrg7AqC5cuKAvv/xSwcHBqlatmqvDAQAATlZcXKy8vDy1atVKNWvWdHU4+D/kYAAAGFtZORiFrkry5ZdfKj4+3tVhAACASpaRkaH27du7Ogz8H3IwAABuDtfKwSh0VZLg4GBJv/3BN2jQwMXRAAAAZ7NYLIqPj7e958M9kIMBAGBsZeVgFLoqydWl8g0aNFBISIiLowEAAJWFr8e5F3IwAABuDtfKwdiMHgAAAAAAAIZAoQsAAAAAAACG4FaFrmHDhik8PLzE8eqrryozM1P9+/cvddzSpUs1atSoa87Trl07PfXUU/rll19sfTIzM+36tGrVSkOHDtWnn35qN7fFYlFCQoLCw8PtxgMAABgB+RcAADAStyp0SdKYMWOUk5NjdzzwwAM3NM9rr72mU6dOacqUKXZ9mjZtauuzefNmde/eXSNHjlRubq4kacuWLRowYIB8fX2dcm8AAADuiPwLAAAYhdttRu/v7++Upxf99zzBwcGaNGmSBg8erAsXLqhmzZqSpOrVq9td64knntCePXu0YcMGjR07Vlu2bNFzzz2n8PBwffTRRzccEwAAgDsi/wIAAEbhdoWuymK1WiVJV65cuW6/pk2b6sSJE5KktLQ0eXt72z5hBAAAQPmRfwEAgKp2UxS68vLylJaWpv79+8vPz++6fY8fP65WrVpJkry9vcs1f35+vvLz8+3aLBZLxYIFAAAwgMrOvyRyMAAAUJLbFboWLVqk9PR022tfX1/t3r37huY5f/68WrRoobS0tGv2Ly4u1saNG7Vv3z5NmjTJoWutWbPGLmYAAABP4on5l0QOBgAASnK7Qtfw4cM1ePBg22svr4rtl//f85w7d06vvPKKHnzwQb333nsKDAyUJP373/9WZGSkJKmoqEi1a9e27QnhiISEBMXExNi1WSwWxcfHVyh2AACAquSJ+ZdEDgYAAEpyu0JX3bp1FRoaWqLdx8dHFy9eLHXMxYsX5ePjc915nnvuOUVHR2vz5s225KdRo0ZatWqVbf769etXKLEzm80ym80OjwMAAHAHnph/SeRgAACgpIplFS7QqlUrHTt2TN99951de3FxsbZv327b1+FavLy8FBgYqHPnztnaatasqdDQUIWGhurWW2+tcJIFAABgRORfAADA03hMZtGkSRMNHjxYo0aNUlZWlk6ePKkvvvhC48aN07lz5xQXF2fX/9y5c8rLy1NeXp6OHTuml19+WQcPHlS3bt1cdAcAAACehfwLAAB4Grf76uL1zJo1S6tXr1Zqaqpyc3MVEBCgnj176o033iixbH3ZsmVatmyZpN+e3tO8eXP99a9/VYsWLVwROgAAgEci/wIAAJ7EZLVara4Owohyc3MVFRWl7OxshYSEuDocAADgZLzXuyd+LwAAGFtZ7/Ue89VFAAAAAAAA4HoodAEAAAAAAMAQKHQBAAAAAADAECh0AQAAAAAAwBAodAEAAAAAAMAQKHQBAAAAAADAECh0AQAAAAAAwBAodAEAAAAAAMAQKHQBAAAAAADAEAxf6CosLFRqaqq6deumiIgI3XvvvVqyZIkuXrwoScrMzFT//v3txly+fFkvvviievfurYiICHXr1k1z587V2bNnXXELAAAAHoX8CwAAuEp1VwdQmS5evKjhw4fLy8tLf/nLX9S0aVN9++23WrBggQ4ePKgVK1aUGGO1WvXUU0/p+++/19SpU9WyZUsdPXpUS5cu1UMPPaQ333xTfn5+LrgbAAAA90f+BQAAXMnQha6XX35ZBQUFyszMVM2aNSVJDRo0UOvWrXXffffpgw8+KDFm06ZN+vzzz7Vx40YFBQXZxqxcuVIDBw7Uq6++qsTExCq9DwAAAE9B/gUAAFzJ0IWut99+W48//rgtybqqbt26Sk1Nlb+/v44dO1ZizNChQ21J1lU1atRQSkqKCgoKKj1uAAAAT0X+BQAAXMmwha6zZ8/q6NGjioyMLPV8jx49JKlEovX1119r5MiRpY5p3759qe35+fnKz8+3a7NYLA5GDAAA4NmqMv+SyMEAAEBJhi50SZLZbHZoXH5+vsNj1qxZo/T0dIfGAAAAGE1V5l8SORgAACjJsIWugIAASb8lTr9fBl/WuN9/MliWhIQExcTE2LVZLBbFx8c7NA8AAIAnq8r8SyIHAwAAJRm60NWoUSMdOHBATZs2LXF+27Zt8vf3L9EeERGhzz77TF27di1xbv/+/Tp79qyioqLs2s1mc4U+hQQAADCSqsy/JHIwAABQkperA6hMsbGxWrlypS5cuGDXfurUKU2ePFmnTp0qMeaBBx7Qm2++WeLchQsXNH36dH3//feVGjMAAIAnI/8CAACuZOhC14gRI+Tr66vhw4dr7969slgs2rFjh4YPH642bdqoT58+Jcb0799fbdu21bBhw/TRRx/JYrFo//79GjFihGrUqMFSeAAAgOsg/wIAAK5k2K8uSr89knrt2rVKT0/XxIkTderUKTVo0ECDBg3SqFGj5OVVss7n5eWlJUuWaOXKlUpJSdGJEycUFBSk3r17a9y4cfLz83PBnQAAAHgG8i8AAOBKJqvVanV1EEaUm5urqKgoZWdnKyQkxNXhAAAAJ+O93j3xewEAwNjKeq839FcXAQAAAAAAcPOg0AUAAAAAAABDoNAFAAAAAAAAQ6DQBQAAAAAAAEOg0AUAAAAAAABDoNAFAAAAAAAAQ6DQBQAAAAAAAEOg0AUAAAAAAABDoNAFAAAAAAAAQ6ju6gDKa9++fXrkkUeueX7s2LGSpPT09BLnunbtqlWrVkmSwsPD7c41aNBAgwcP1hNPPCEvLy9lZmZqypQp17zO/PnzFRsbW5FbAAAA8DjkYAAAwJN4TKErMjJSOTk5tte9e/dWamqqIiMjJUl+fn5avXq1unTpotTUVLuxPj4+dq9XrlypFi1a6MqVK/rXv/6l6dOny9vbW6NGjVJ0dLTuueceSdKZM2fUr18/vfXWW7r11lslSQEBAZV5mwAAAG6FHAwAAHgSjyl0+fj4KDg42PbaZDKpdu3adm2l9StNnTp1bH3q16+vcePGafny5Ro1apRq1qypmjVrSpKqVasmSQoMDCxzTgAAACMiBwMAAJ6EPbok3XbbbTp58qSrwwAAALipkIMBAABn85gVXZXpp59+Ut26dSs8Pj8/X/n5+XZtFovlRsMCAAAwNHIwAADgbIYrdO3cudO2Z8RVM2bMUExMTKn9c3NztXz5cvXt27fC11yzZk2pG7ACAADcLMjBAACAOzBcoevOO+/U3Llz7dqCgoLsXsfHx6tatWqyWq06f/68oqOjlZSUVOFrJiQklEjiLBaL4uPjKzwnAACAJyEHAwAA7sBwhS4/Pz+FhoZet8+CBQsUHh6us2fPatiwYercubN8fX0rfE2z2Syz2Vzh8QAAAJ6OHAwAALiDm3Iz+ltvvVWhoaFq1aqVnnjiCS1evFhnz551dVgAAACGRg4GAAAqm+EKXUVFRcrLy7M7Tp8+fc3+CQkJ8vf31wsvvFCFUQIAABgLORgAAHAHhvvq4u7du9W1a1e7tjvuuEOZmZml9vfx8VFycrLGjx+voUOHlrnkHgAAACWRgwEAAHdgslqtVlcHYUS5ubmKiopSdna2QkJCXB0OAABwMt7r3RO/FwAAjK2s93rDfXURAAAAAAAANycKXQAAAAAAADAECl0AAAAAAAAwBApdAAAAAAAAMAQKXQAAAAAAADAECl0AAAAAAAAwBApdAAAAAAAAMAQKXQAAAAAAADAECl0AAAAAAAAwhCotdA0bNkzh4eG2o0OHDkpOTlZ+fr7t/KpVq2z9w8PDdejQIbs5Vq9erdatW+vTTz+1tX344YcaOHCgIiIi1KNHD7311lslrn358mW9+OKL6t27tyIiItStWzfNnTtXZ8+eLTXWRYsWKTw8XP/5z3+ccOcAAACuQw4GAABuFlW+omvMmDHKycnRzp07tXr1ah0/flxJSUnlGpuVlaWFCxcqLS1N7du3lyTt2rVLSUlJiouL04cffqjJkycrJSVF27dvt42zWq166qmn9O6772rq1Knatm2bFixYoMOHD+uhhx5SYWGh3XWsVqvef/99eXt7a9OmTc67eQAAABchBwMAADeD6mV1mD9/vkMTTpky5brn/f39FRwcLEmqV6+e5s+fr6ioKJ04ceK647766itNnDhREydOVJ8+fWztW7duVVxcnOLi4iRJISEh2rNnj9577z11795dkrRp0yZ9/vnn2rhxo4KCgiRJDRo00MqVKzVw4EC9+uqrSkxMtM158OBBnTx5UmPGjNHGjRs1duxYh/4MYC8jI0PTpk3T0aNH1bhxY6WkpCg+Pt7VYQEAcFMhBwMAADeDMgtdX3/9dbknM5lMDgcQEhIib2/v6yZZFotFo0eP1uDBgzV8+HC7c7NmzSrR38fHR+fOnbO9fvvttzV06FBbgnVVjRo1lJKSooKCArv2TZs2qUuXLho6dKjS09P19ddfKyIiwuF7w29FrsTERNsntkeOHLEltBS7AABwHXIwAABgRGUWutatW1epAeTl5enSpUu65ZZbSj1fWFio0aNHKzIyUsnJyWXOt2/fPm3YsEELFy60tX399dcaOXJkqf2vLr+/qri4WFu2bNGECRMUFBSkjh07atOmTddNsvLz8217XFxlsVjKjPVmMG3atBJfSygsLNS0adModAEA4ELkYAAAwIjKLHSV5sqVK/rhhx90+vRpRUREqGbNmvLycny7r19//VVz5sxRmzZt1KRJk1L7PPPMMzp69KjCwsKue4033nhDKSkpKioqUmJiom3JvPRbEmQ2m8sV0759+3TmzBn16tVLktSvXz+lp6drwoQJ17z+mjVrlJ6eXq75bzZHjx51qB0AAFQ+cjAAAGBUDlenVq9erc6dO6tfv34aNmyYfvjhB+3evVuxsbE6ffp0meMXLVqkyMhIRUZG6q677lJeXp4WL158zf4XLlzQa6+9po8++kiZmZnX7NevXz+9++67WrZsmT788EOlpaXZzgUEBJT4tO9aNm7cqO7du6tWrVqSpN69e+vnn3/WZ599ds0xCQkJys7OtjsyMjLKdT2ja9y4sUPtAACgcpCDAQCAm4FDK7peeeUVLV26VMnJybrnnnt07733SpKaN2+uy5cva+HChZo7d+5150hISNCQIUMkSWazWXXr1r1u/zlz5qhdu3aaOXOmZs2apcjISDVt2rREv4CAAAUEBKhZs2aqU6eOhg8frvHjx8vHx0cRERH67LPP1LVr1xLj9u/fr7NnzyoqKkpFRUXaunWrCgsL1bp1a1ufy5cva9OmTerQoUOpMZrN5nJ/WnmzSUlJsdujS5L8/PyUkpLiwqgAALj5kIMBAICbgUMrul599VX9+c9/1tChQ1W/fn1be/369TV69Ght3bq1zDkCAwMVGhqq0NDQMhOsq/0ladCgQerZs6eefvppFRUV2c5PnTpVWVlZdmN8fHxUXFys4uJiSdIDDzygN998U6dOnbLrd+HCBU2fPl3ff/+9JOnjjz/W5cuXtX79em3YsMF2TJ48WVu2bNHly5fLjBf24uPjtWLFCoWGhspkMik0NFQrVqxgfy4AAKoYORgAALgZOFToslgsatGiRannAgMD7Z6yUxlmzpypX375Rc8//7ytrU6dOpo5c6a2bNmiEydOaO/evZo+fbr69+8vX19fSVL//v3Vtm1bDRs2TB999JEsFov279+vESNGqEaNGraiy/vvv6+ePXsqIiJCYWFhtmPIkCE6d+6cdu3aVan3Z1Tx8fH6z3/+oytXrug///kPRS4AADwMORgAAPAUDn11MTQ0VPv371fHjh1LnNu9e7duu+02pwVWmtq1a+svf/mLHn30UXXu3Fndu3dXUlKSatWqpdTUVJ08eVJBQUGKiYnRk08+aRvn5eWlJUuWaOXKlUpJSdGJEycUFBSk3r17a9y4cfLz81NBQYE+/vhjuycFXVWrVi11795dmzZtsttgFQAA4GZADgYAADyFyWq1WsvbedOmTUpOTtaf//xn9enTR1FRUVq6dKn+93//V0uXLtWCBQvUt2/fyozXY+Tm5ioqKkrZ2dkKCQlxdTgAAMDJeK93T/xeAAAwtrLe6x1a0dW/f39J0oIFC7RgwQJJ0pNPPql69eopJSWFIhcAAAAAAABcxqFCl/Rbsatfv376/vvvdfr0adWpU0dhYWEymUyVER8AAAAAAABQLg4XuoqLi3Xq1CmZzWY1bdpUXl4O7WcPAAAAAAAAVIpyF7r+8Y9/KCMjQwcPHrQ9MrpatWpq166dHn74Yf3xj3+stCABAAAAAACAspSr0DV16lS9++676t+/v4YNG6aGDRtK+m0DsOzsbD311FMaPHiwnn322UoNFgAAAAAAALiWMgtd77zzjjZt2qTVq1frrrvusjvXpk0bRUdH6/7779fjjz+uu+66S9HR0ZUWLAAAAAAAAHAtZW6w9cYbb+j/+//+vxJFrv92zz33KDY2Vq+//rpTgwMAAAAAAADKq8xC17fffqu77767zIm6deumr7/+2ilBAQAAAAAAAI4qs9B1/vx5BQQElDlRnTp1VFhY6JSgAAAAAAAAAEeVWegqL5PJ5KyprqugoEBt2rRRUlKSXXtycrLCw8OveeTm5kqS7r33Xrv2Ll26aO7cubp48aIkKSsrSy1bttS//vUvu/nPnDmjTp06aenSpVVynwAAAO6C/AsAAHiKcj11cd26dfrggw+u2+fHH390SkBlycrK0pUrV7Rt2zadP39evr6+kqRp06bZkq+PP/5Y6enpWr9+vW1cYGCg7ee5c+eqR48eslqtOnLkiGbOnKmUlBTNnj1bvXr1Uvfu3ZWSkqJ169bZxixdulR+fn7605/+VCX3CQAA4C7IvwAAgKcoc0VXhw4d9PPPP+vrr7++7nHq1Cm1b9++0gPeuHGj4uLi5O/vr48++sjWHhAQoODgYAUHBysgIEDVqlWzvQ4ODla1atVK9K1Xr546dOigZ555Rhs2bFBRUZGk35K2L774Qps3b5b02z5lr732mqZMmaKaNWtW+j0CAAC4E/IvAADgKcpc0fXfn6q52qlTp7Rnzx6NHz9eVqtVGzduVHR09A3PGxYWposXL+qXX35RgwYN1KhRI40ePVqpqanq0aOHUlJS1LlzZ/Xu3dsJdwEAAOA5yL8AAIAnKddXF8urqKhIffv21QsvvKDbb7/dmVNLkrZs2aKGDRuqdevWKioq0vDhw3XmzBnVrl37hub96aef5OXlJbPZbGsbMWKENmzYoJEjR+rgwYPauHHjNcfn5+crPz/frs1isdxQTAAAAO7AXfMviRwMAACU5NRClyQdP35cly5dcva0kqRNmzYpOjpaJpNJd955p4KCgvTBBx9oyJAhFZ7z1KlTWrx4sbp37y4/Pz9bu4+Pj4YNG6Y5c+aod+/eatq06TXnWLNmjdLT0yscAwAAgLty1/xLIgcDAAAlOe2pi5Xt+PHj+vzzz21L5U0mk/r27atNmzY5PNeECRMUGRmpP/zhD+rSpYuKioo0b948uz5FRUVat26dbr/9dn388cf64YcfrjlfQkKCsrOz7Y6MjAyH4wIAAHAn7px/SeRgAACgJKev6Kos77//viRp8ODBtrbi4mJZrVb9+OOPql+/frnnmjhxonr06KGLFy9q1KhR+sMf/mD3VCBJWrVqlQoKCvSPf/xDiYmJmjt3rlatWlXqfGaz2W7ZPQAAgBG4c/4lkYMBAICSPGZF18aNGzVs2DBt2LDBdmzcuFENGza0PZ2nvOrXr6/Q0FDdfvvtmjhxol555RUdO3bMdv7YsWNavny5Jk6cqNq1a2vGjBnavXu3tm7d6uzbAgAAcFvkXwAAwNN4RKHru+++07fffqu4uDiFhYXZHdHR0RVaPn9VdHS0WrdurbS0NFtbSkqKIiIiNGjQIEnSHXfcoaFDh2r+/Pm6cOHCDd8PAACAuyP/AgAAnsgjCl2bNm1SixYtFBYWVuJcv379dOjQIR05cqTC80+fPl1bt27VJ598oqysLG3fvl0zZ86UyWSy9fnzn/+swsJCvfTSSxW+DgAAgKcg/wIAAJ7IZLVarc6azGq16pNPPlGrVq3snqBzM8rNzVVUVJSys7MVEhLi6nAAAICT8V7vnvi9AABgbGW91zu8ouvgwYN64okn1KVLF0VEROirr77SV199pfnz58tqtapjx443fZELAAAAAAAAVc+hQldOTo4eeugh+fr6atKkSbpy5Yrt3IYNG7RixQqnBwgAAAAAAACUh0OFrkWLFikuLk4LFy7UgAEDbO133HGHxo0bp7///e9ODxAAAAAAAAAoD4cKXYcPH1bv3r1LPde8eXNZLBanBAUAAAAAAAA4yqFCV926dXXs2LFSz/373/9WgwYNnBIUAAAAAAAA4CiHCl0PPvigFi9erEOHDtnaTCaTPv/8c6Wnp+uBBx5weoAAAAAAAABAeVR3pPOYMWN06tQpDR48WI0aNZIkPfbYYzpz5oxiYmI0evToSgkSAAAAAAAAKItDha7q1atr9uzZevTRR7V371798ssvqlOnjjp37qxmzZpVVowAAAAAAABAmRwqdF3VtGlTNW3a1NmxAAAAAAAAABXm0B5dkrRz50799a9/1fz580s9HDVs2DCFh4fbjg4dOig5OVn5+fm286tWrbL1Dw8Pt9sjTJJWr16t1q1b69NPP7W1ffjhhxo4cKAiIiLUo0cPvfXWW3ZjkpOT7a4bGRmpxx9/XCdOnLDr99133yk2NlaRkZEO3xsAAIA7Iv8CAABG5VChKz09XYmJicrKytLXX39d4vjmm28qFMSYMWOUk5OjnTt3avXq1Tp+/LiSkpLKNTYrK0sLFy5UWlqa2rdvL0natWuXkpKSFBcXpw8//FCTJ09WSkqKtm/fbjc2NjZWOTk5ysnJ0RtvvKFq1app5MiRKi4uliRlZGTowQcf1C233FKh+wIAAHBX5F8AAMCIHPrq4rp16zRixAhNmDDBqUH4+/srODhYklSvXj3Nnz9fUVFRJT7d+72vvvpKEydO1MSJE9WnTx9b+9atWxUXF6e4uDhJUkhIiPbs2aP33ntP3bt3t/Xz9fW1XTc4OFipqam66667dODAAbVv314ffPCBVq1apcuXL+uTTz5x6j0DgKfLyMjQtGnTdPToUTVu3FgpKSmKj493dVgAyon8CwBQFcgZUdUcKnQVFRXpnnvuqaxYbEJCQuTt7X3dRMtisWj06NEaPHiwhg8fbndu1qxZJfr7+Pjo3Llz172un5+fGjZsqOPHj6t9+/ZatWqVvL29tW/fvorcBgAYVkZGhhITE1VYWChJOnLkiBITEyWJxAXwUORfAABnI2eEKzj01cU+ffpo165dlRWLTV5eni5dunTNJeuFhYUaPXq0IiMjlZycXOZ8+/bt04YNGzRw4MDr9isuLtapU6dUt25dSZK3t3e54s3Pz1dubq7dYbFYyjUWADzRtGnTbAnLVYWFhZo2bZqLIgJwozwt/5LIwQDA3ZEzwhUcWtE1c+ZMxcXF6cCBAwoJCSm1T0U2pP9vv/76q+bMmaM2bdqoSZMmpfZ55plndPToUYWFhcnL69q1ujfeeEMpKSkqKipSYmKi3bL537tw4YJeeOEF1ahRQx06dHAo5jVr1ig9Pd2hMQDgyY4ePepQOwD35on5l0QOBgDujpwRruBQoWv58uX65ptv1L59e9tTeZxh0aJFtiSlsLBQ7dq10+LFi6/Z/8KFC3rttdc0cuRIZWZmKjY2ttR+/fr1U8eOHfX9998rLS1NV65c0cSJE23nX3/9db3zzju2ORs2bKgXXnhBvr6+DsWfkJCgmJgYuzaLxcJSTACG1bhxYx05cqTUdgCewdPzL4kcDADcHTkjXMGhQtfatWs1btw4PfHEE04NIiEhQUOGDJEkmc1m29L1a5kzZ47atWunmTNnatasWYqMjFTTpk1L9AsICFBAQICaNWumOnXqaPjw4Ro/frx8fHwkSdHR0Ro3bpyk31ai/frrr2rdurXD8ZvNZpnNZofHAYCnSklJsdtvQfptn52UlBQXRgXAEZ6ef12NmxwMANwXOSNcwaE9ury9vdWuXTunBxEYGKjQ0FCFhoaWmWRd7S9JgwYNUs+ePfX000+rqKjIdn7q1KnKysqyG+Pj46Pi4mLbo6slqXbt2rbrPvPMM/r666/1j3/8w0l3BQDGFR8frxUrVig0NFQmk0mhoaFasWIFqygAD0L+BQCobOSMcAWHCl2PPfaYXnvtNbtkxdVmzpypX375Rc8//7ytrU6dOpo5c6a2bNmiEydOaO/evZo+fbr69+9/zWXx//M//6MRI0YoLS1N58+fr6rwAcBjxcfH6z//+Y+uXLmi//znPyQswE2E/AsAUF7kjKhqDn118bvvvtOhQ4d03333KTw8XCaTqUSfqt4QtHbt2vrLX/6iRx99VJ07d1b37t2VlJSkWrVqKTU1VSdPnlRQUJBiYmL05JNPXneuxMREZWZmatWqVRo7dmwV3QEAAIBnIf8CAADuymS1Wq3l7TxlypQy+9zoUxeNIjc3V1FRUcrOzr7mEyoBAIDn4r3ePfF7AQDA2Mp6r3doRRdFLAAAAAAAALgrh/boAgAAAAAAANyVQyu6zp07p7S0NO3YsUMFBQWl9tm/f79TAgMAAAAAAAAc4VCha968edq5c6ceffRR7dmzRxcvXtSgQYP0z3/+Ux9++GGZm40CAAAAAAAAlcWhQldWVpZmzpyp6OhoNWzYUGvXrlVsbKxiY2Pl6+urgwcP8qhQAAAAAAAAuIRDe3SdO3dOdevWlSQ1btxY33//ve1cz549tXXrVudGBwAAAAAAAJSTQ4Wu5s2bKysrS5LUrFkz5efn69ixY5KkvLw8eXmxtz0AAAAAAABcw6HK1J/+9CdlZGRo8+bNqlGjhjp37qykpCStWLFCCxYs0B//+MfKihMAAAAAAAC4LocKXdHR0UpPT1fbtm0lSbNnz1ZAQIDefvttde3aVdOmTauUIB01bNgwJSQkyGq1lnpu6dKlkqTw8PBSj2+//dbW/7PPPlNUVJT69+9fZfEDAAB4InIwAADgag5tRi9JvXr1sv3csGFDrVq1yqkBOcvevXv12muvlbk5/sqVK9WiRQu7tqv7kD3//PNau3at2rVrp59++qnSYgUAADAKcjAAAOBKDq3oatmypfbu3VtZsThVo0aNlJaWpqNHj163X506dRQcHGx3VK/+W/0vJydH69ev55NEAACAciIHAwAAruRQoSs0NFR5eXmVFYtTDRw4UJGRkUpOTtaVK1cqNMebb76p2267zcmRAQAAGBc5GAAAcCWHCl3PPPOMVq9erU8//VRnz55VQUFBicNdmEwmpaSk6F//+pfWrl1boTm8vb2dHBUAAICxkYMBAABXcmiPrhEjRkj6bTPR37NarTKZTPrmm2+cE5kTNGzYUMnJyZo7d67uuecehYWFlejz8MMPy8vr/9X77rnnHi1ZssSh6+Tn5ys/P9+uzWKxVCxoAAAAD0cOBgAAXMWhQldFP5VzpcGDBysrK0tTpkzR66+/XuL8ggULdPvtt9te+/r6OnyNNWvWKD09/YbiBAAAMBJyMAAA4AoOFbo6duxYWXFUGpPJpDlz5qhfv35auXJlifMNGjRQaGjoDV0jISFBMTExdm0Wi6XMpw0BAAAYFTkYAABwhTILXcuXL1dMTIzq169fFfFUivr162vq1KmaMWOGbrnlFqcX7Mxms8xms1PnBAAA8HTkYAAAoKqVuRn9X//6Vx0/ftz2uri4WG3btnWrvbjKIzY2Vl27dtXJkyddHQoAAMBNgxwMAABUpTILXVartUTbxYsXK/y4aFeaM2eO6tSp4+owAAAAbirkYAAAoKo4tEeXp1i3bl2p7cHBwdq3b5/t9eHDh8s1X2xsrGJjY50SGwAAgFGRgwEAAFcrc0UXAAAAAAAA4AnKtaLr/PnzKigokCRdunRJklRYWGhr+2+1atVyYngAAAAAAABA+ZSr0DVy5MgSbY888ojda6vVKpPJ5HGb1AMAAAAAAMAYyix0rV27tiriAAAAAAAAAG5ImYWujh07lnuyK1euaMOGDerRowdP1gEAAAAAAECVcupm9JcvX9aUKVN0/PhxZ04LAAAAAAAAlMnpT120Wq3OnhIAAAAAAAAok9MLXQAAAAAAAIArUOgCAAAAAACAIZS5Gb272Ldvnx555JFrnh87dqwkKT093dZWo0YNRUZGavr06WrevLmtvU2bNrp48aLd+PHjx+uJJ56wvX799de1evVqHTt2TM2aNdOECRN07733Out2AAAAPAI5GAAA8CQeU+iKjIxUTk6O7XXv3r2VmpqqyMhISZKfn59Wr16tLl26KDU1VZJ09uxZZWRkaPjw4frggw9Uq1Yt/fLLL7p48aIyMzNVr14923x+fn62n9966y0tXLhQzz77rNq2basdO3Zo/Pjxeuutt9SiRYsqumMAAADXIwcDAACexKmFLpPJpA4dOsjf39+Z00qSfHx8FBwcbHet2rVr27X9vl9wcLCmT5+ujz76SFlZWbr//vv1448/ymQy6fbbb5e3t3ep19q6davGjBmjfv36SZIeeughbdu2Tf/4xz9IsgAAwE2FHAwAAHgSpxa6vL29tW7dOmdOecNMJpOaNm2q48ePS5JOnjypwMDAayZYkrRixYoSbd7e3jKZTJUWJwAAgJGQgwEAAFdwaDP6s2fPasqUKbrrrrvUsmXLUg93dPz4cdsnjCdPnpSXl5dGjBihDh06qF+/fsrMzLzm2CtXrmjLli3as2eP+vbtW1UhAwAAeDxyMAAAUNUcWtE1bdo07dq1S4mJiQoLC6usmJymqKhIr7zyin788UdFRUVJkho1aqRu3bopKipKU6ZM0b59+zRz5kx5e3trwIABduMXLVqkVatWqbi4WNOnT7/mkvn8/Hzl5+fbtVkslsq5KQAAADdHDgYAAFzFoULX9u3bNX36dA0ePLiy4rlhO3bssG2OevHiRTVo0EB/+9vfFBQUJEnq1q2bunXrZut/22236ddff9Xq1atLJFkJCQkaOHCgDh06pAULFkiS4uPjS1xzzZo1dk8aAgAAuNmQgwEAAHfgUKGrRo0aCgkJqaxYnKJ9+/aaO3euJKlmzZqqV69emfs6REZG6uWXXy7RHhgYqMDAQIWFhclkMmnx4sWlJlkJCQmKiYmxa7NYLKX2BQAAMCJyMAAA4A4cKnTFx8frvffeU+fOnSsrnhvm5+en0NDQa55fsWKFvLy8NHLkSFvb4cOHdeutt0qSiouLNXbsWI0aNUp/+MMfbH18fHx06dKlUuc0m80ym83OuQEAAAAPRA4GAADcgUOFritXrmj//v0aPHiw2rZtq2rVqpXoM2XKFKcFVxnCwsL01FNPKTAwUJ07d9aBAwe0dOlSTZs2TZJUrVo1+fj4aPLkyXrmmWcUFhamf/3rX0pLS3Prr2wCAAC4M3IwAABQFRwqdH3++edq2LChpN8+gfNEUVFRmjVrll588UVNnTpVISEhmjx5sh544AFbn+eee05LlixRcnKyfv75ZzVs2FBxcXEaMWKECyMHAADwXORgAACgKpisVqvV1UEYUW5urqKiopSdne32+5oBAADH8V7vnvi9AABgbGW915e5omvs2LHlvpjJZNLSpUsdixAAAAAAAABwgjILXQEBAVURBwAAAAAAAHBDyix0zZ8/vyriAAAAAAAAAG6Il6sDAAAAAAAAAJyBQhcAAAAAAAAMgUIXAAAAAAAADIFCFwAAAAAAAAyBQhcAAAAAAAAMgUIXAAAAAAAADMGQha5hw4YpPDzcdnTo0EGTJk3Sr7/+KklKTk62O9+mTRuNHDlSx44ds5vnzJkzSkpKUnh4uA4dOuSCOwEAAPAc5GAAAMDVDFnokqQxY8YoJydHO3fu1OrVq3X8+HElJSXZzsfGxionJ0c5OTl65513VKtWLY0ePVpWq1WS9Mknn2jAgAE6d+6cq24BAADA45CDAQAAVzJsocvf31/BwcGqV6+eWrdurXnz5iknJ0cnTpyQJPn6+io4OFjBwcEKCwvTjBkz9N133+mHH36QJG3evFljx47V8uXLXXkbAAAAHoUcDAAAuJJhC12/FxoaKm9vb1uSdS1XP02cMmWKhgwZUhWhuaWMjAw1adJEXl5eatKkiTIyMlwdEgAA8EDkYAAAoCpVd3UAVeX06dO6dOmS6tatW+JcQUGBZs+erQ4dOqhZs2aSJG9v76oO0W1kZGQoMTFRhYWFkqQjR44oMTFRkhQfH+/K0AAAgIchBwMAAFXppih0nT17VqmpqWrRooUtiXrzzTf1zjvvSJLOnz+vhg0b6oUXXpDJZHJ4/vz8fOXn59u1WSyWGw/cRaZNm2Yrcl1VWFioadOmUegCAADlRg4GAACqmmELXYsWLVJ6erqk35Koli1basmSJbYkKjo6WmPHjpUkXbhwQRs2bFBcXJzWr19vS8TKa82aNbZrGcHRo0cdagcAALiKHAwAALiSYQtdCQkJGjJkiKxWqyZMmKDQ0FCFhobazgcEBNi9njx5sg4fPqy33npLkydPdvhaMTExdm0Wi8VjVz81btxYR44cKbUdAADgesjBAACAKxl2M/rAwECFhoaqSZMmeuaZZ/T+++/rwIED1x1zyy23VOhR1mazWSEhIXZHgwYNKhq6y6WkpMjPz8+uzc/PTykpKS6KCAAAeApyMAAA4EqGLXT9t7Zt22rgwIGaN2+e7Yk+58+fV15envLy8nT8+HFlZmZq8+bN6tmzp4ujdb34+HitWLFCoaGhMplMCg0N1YoVK/h0FAAAOIQcDAAAVDXDfnXx95KSktSnTx+9++67kqTMzExlZmZKkqpXr64mTZpoxowZJFn/Jz4+nsIWAAC4YeRgAACgKpmsVz9eg1Pl5uYqKipK2dnZCgkJcXU4AADAyXivd0/8XgAAMLay3utviq8uAgAAAAAAwPgodAEAAAAAAMAQKHQBAAAAAADAECh0AQAAAAAAwBAodAEAAAAAAMAQKHQBAAAAAADAECh0AQAAAAAAwBAodAEAAAAAAMAQKHQBAAAAAADAEKq7OoDyGjZsmPbv31+iPSkpSYmJiZKk3bt3a9myZfriiy/k7e2tTp066emnn9Ztt91m65+cnKx33nmnxDyPPfaYJk+erH379umRRx6xtVerVk1hYWEaM2aM+vTpUwl3BgAA4L7IwQAAgCfxmEKXJI0ZM0ZxcXF2bf7+/pKk999/X1OmTNHYsWM1f/58Xbp0SRkZGRoyZIheffVVRURE2MbExsbq6aeftpvH19fX7uetW7dKkoqKirR9+3ZNmDBBwcHBuvPOOyvr9gAAANwSORgAAPAUHlXo8vf3V3BwcIn2goICzZo1S88++6xiYmJs7TNmzFBBQYFmzJih9evX29p9fX1Lnecqk8lkd/6hhx7S559/rvXr15NkAQCAmw45GAAA8BSG2KMrKytLAQEBuv/++0ucS0xMVK1atXTmzJkbukbz5s118uTJG5oDAADASMjBAACAu/GoFV3X8tVXXykyMlImk6nEudtuu02vvPLKDV/jp59+Ut26dW94HgAAAKMgBwMAAO7GowpdixYtUnp6ul3be++9p4KCAgUEBJR7njfffLPEZqjbtm0rNYmyWq367LPP9O6772r+/Pmlzpefn6/8/Hy7NovFUu54AAAA3Bk5GAAA8BQeVegaPny4Bg8ebNfWoEEDBQQE6Oeff7ZrHzdunHbu3Gl7/dJLL6l9+/aSpOjoaI0dO9auv9lstv1cWFioyMhISdLly5dltVr15JNPqnfv3qXGtWbNmhLJHwAAgFGQgwEAAE/hUYWuunXrKjQ0tER7y5YttWTJEl25ckVeXr9tOzZ9+nSdP39excXFio6OVvXq/+9WAwICSp3nqho1amjDhg2SpAMHDmjatGmKioq6Zv+EhAS7DVil3z5NjI+Pd+T2AAAA3BI5GAAA8BSG2Iw+KipKZ8+e1dtvv21rq1evnkJDQ/Xtt9+qZs2aat68ebnnq1atmkJDQxUaGqr7779fXbp0ueaSeem3TyJDQkLsjgYNGtzQPQEAALg7cjAAAOBuPGpF17lz55SXl2fX5uvrK7PZrJkzZ2rq1Kn66aefbJ8ebt++XQsXLtRTTz0lf3//Cl93ypQpGjBggLZv367u3bvf6G0AAAB4FHIwAADgKTyq0LVs2TItW7bMru2xxx7T5MmTNWDAAAUFBelvf/ubXnzxRVmtVrVo0UIpKSmKjo6+oes2a9ZMDz/8sObNm6cuXbrI29v7huYDAADwJORgAADAU5isVqvV1UEYUW5urqKiopSdna2QkBBXhwMAAJyM93r3xO8FAABjK+u93hB7dAEAAAAAAAAUugAAAAAAAGAIFLoAAAAAAABgCBS6AAAAAAAAYAgUugAAAAAAAGAIFLoAAAAAAABgCBS6AAAAAAAAYAgUugAAAAAAAGAIFLoAAAAAAABgCB5R6Bo2bJjCw8NtR4cOHZScnKz8/Hzt27fP7tzvj6VLl0qSli5dWur5ESNG2K4THh6u9PT0UmMIDw/Xvn37quR+AQAA3AE5GAAA8DTVXR1AeY0ZM0ZxcXGyWq368ccflZqaqqSkJC1btkw5OTm2fr1791ZqaqoiIyMlSX5+frZzXbp0UWpqqt28Pj4+dq9feOEFRUVFqWXLlpV4NwAAAJ6BHAwAAHgSj1jRJUn+/v4KDg5WvXr11Lp1a82fP187duzQzz//rODgYNthMplUu3Zt22t/f3/bHD4+PnZ9g4ODVbt2bbvr3HrrrZo0aZKKioqq+hYBAADcDjkYAACQpIyMDDVp0kReXl5q0qSJMjIyXB1SqTym0PV7ISEh8vb21okTJ5w678SJE3Xq1CktW7bMqfMCAAAYATkYAAA3n4yMDCUmJurIkSOyWq06cuSIEhMT3bLY5bGFrry8PF26dEm33HKLU+etU6eOZs+erZdeekmHDh0q15j8/Hzl5ubaHRaLxalxAQAAuANyMAAAbj7Tpk1TYWGhXVthYaGmTZvmooiuzWP26Ppvv/76q+bMmaM2bdqoSZMm5R63c+dO274RV82YMUMxMTF2bb169VLfvn01adIkbdiwQTVq1LjuvGvWrLnmBqoAAABGQQ4GAMDN6ejRow61u5LHFLoWLVpkS2QKCwvVrl07LV682KE57rzzTs2dO9euLSgoqNS+M2bMUP/+/bV48WJNnjz5uvMmJCSUSNQsFovi4+Mdig8AAMDdkIMBAIDGjRvryJEjpba7G48pdCUkJGjIkCGSJLPZrLp16zo8h5+fn0JDQ8vVt3bt2pozZ44ef/xxRUVFXbev2WyW2Wx2OB4AAAB3Rw4GAABSUlKUmJho9/VFPz8/paSkuDCq0nlMoSswMLDcCZKz9OjRQ4MGDdLUqVOr9LoAAADughwMAABcXS09bdo0HT16VI0bN1ZKSopbrqL2mEKXMxQVFSkvL8+urXr16tf9ZHLq1KkaOHBgZYcGAABgWORgAAB4vvj4eLcsbP3eTVXo2r17t7p27WrXdscddygzM/OaY8xms+bOnasRI0ZUdngAAACGRA4GAACqislqtVpdHYQR5ebmKioqStnZ2QoJCXF1OAAAwMl4r3dP/F4AADC2st7rvVwQEwAAAAAAAOB0FLoAAAAAAABgCBS6AAAAAAAAYAg31Wb0Vam4uFiSZLFYXBwJAACoDFff46++58M9kIMBAGBsZeVgFLoqydVHaHvCozcBAEDF5eXlKTQ01NVh4P+QgwEAcHO4Vg7GUxcryYULF/Tll18qODhY1apVc3U4AADAyYqLi5WXl6dWrVqpZs2arg4H/4ccDAAAYysrB6PQBQAAAAAAAENgM3oAAAAAAAAYAoUuAChFcnKynnjiCVeHAQAAcFO4Xu71zTffKDw8XLm5uVq+fLnatWunI0eOlHvu3NxchYeH65tvvnFWuADcGJvRAwAAAAA8wsMPP6wePXrwEBAA18SKLgAAAACAR6hVq5ZatGjh6jAAuDEKXQBQAZcuXdLixYvVtWtXRUREaNCgQcrKyrKd37JliyIiInT27Flb2/LlyxUeHq7Dhw/b2jZs2KC2bdvqwoULVRo/AACAJ8rMzFT79u3t2rZs2aK+ffsqIiJC/fv311dffaVu3bopMzPTrt9PP/2kxx57TK1atVKnTp20fPnyqgwdQBWh0AUAFTBjxgy9/vrrGjdunFatWqXOnTvrySeftBW77r77bnl5eWnfvn22Mbt375Yk7dmzx9a2a9cuderUqdTH4gIAANxMrFarLl++XOIoLi6+5pgdO3boqaee0t13361Vq1YpLi5OkyZN0rlz50r0TUtLU/fu3bVy5Ur169dPixYt0tatWyvzlgC4AHt0AYCDvv/+e2VmZuqll15St27dJEmdO3dWQUGBnn/+efXq1UsBAQGKjIzUnj171KtXL50/f14HDhxQ//79tWfPHg0fPlxWq1W7d+/W448/7uI7AgAAcL1t27bpjjvucGjM0qVLFR0drenTp0v6LSfz9fXVlClTSvQdMmSIHnnkEUlSp06ddPDgQW3btk29e/e+8eABuA1WdAGAg/bs2aM6deronnvusWsfNGiQ/vd//1c//vijJKl79+62VVyfffaZgoKClJCQoE8++USXL1/Wt99+q59//lndu3ev8nsAAABwNx07dtT69etLHAsWLCi1f2FhoQ4dOqT+/fvbtf/xj38stX9kZKTd67CwMOXn5zsneABugxVdAOCg06dPq169ejKZTHbtDRo0sJ2vX7++unXrprS0NFksFu3atUtdunTRHXfcoerVq+uLL77QwYMH1bRpUzVq1MgVtwEAAOBWAgIC1Lp16xLt1auX/t/WM2fOyGq1qn79+nbttWrVkr+/f5nzmEwmWa3WG4gYgDtiRRcAOKhu3brKy8srkRhZLBbbeUm6/fbbdeutt2rXrl3au3ev7r77blWrVk133XWX9u7dq927d7OaCwAAoIKuFrNOnTpl137x4kWdP3/eFSEBcAMUugDAQZ06ddLp06dtX0u8atOmTWrWrJndp4rdunXT+++/r8OHD6tz586Sfts74uOPP9Ynn3xCoQsAAKCCzGazbr/9dn3wwQd27dnZ2bpy5YqLogLganx1EQCu4dSpU9qxY4ddW7Vq1XT33XcrNjZWEyZM0J///Gc1btxY27dv19///nctXbrUrn+3bt00ZswYtWzZUoGBgZKkrl276tlnn5Wfn1+Jx2MDAACg/MaMGaPx48fLbDare/fu+uGHH/Taa6/Jx8fH1aEBcBEKXQBwDf/85z/1pz/9ya7Nz89PBw4c0OzZs7Vs2TItWbJEv/zyi5o3b66lS5eqV69edv07d+4sb29v22ouSWrcuLFCQkIUHh5OEgYAAHAD+vTpo9TUVP3tb3/Tyy+/rLCwMM2fP1+PPfaYq0MD4CImK7vvAQAAAAA8kNVq1blz51SrVi1bW0FBgTp27KglS5aU+BASgPGxogsAAAAA4JGWLVumv//97xo9erTCwsJ0+vRprV27VkFBQerUqZOrwwPgAhS6AAAAAAAeKTExUZK0cuVKnTx5UrVq1dJdd92lefPm2a3yAnDz4KuLAAAAAAAAMAQvVwcAAAAAAAAAOAOFLgAAAAAAABgChS4AAAAAAAAYAoUuAAAAAAAAGAKFLgAAAAAAABgChS4AAAAAAAAYAoUuAAAAAAAAGAKFLgAAAAAAABgChS4AAAAAAAAYAoUuAAAAAAAAGAKFLgAAAAAAABgChS4AAAAAAAAYAoUuAAAAAAAAGAKFLgAAAAAAABgChS4AAAAAAAAYAoUuAAAAAAAAGAKFLgAAAAAAABgChS4AAAAAAAAYAoUuAAAAAAAAGAKFLgAAAAAAABgChS4AAAAAAAAYAoUuAAAAAAAAGAKFLgAAAAAAABgChS4AAAAAAAAYAoUuAAAAAAAAGEJ1VwdgVBcuXNCXX36p4OBgVatWzdXhAAAAJysuLlZeXp5atWqlmjVrujoc/B9yMAAAjK2sHIxCVyX58ssvFR8f7+owAABAJcvIyFD79u1dHQb+DzkYAAA3h2vlYBS6KklwcLCk3/7gGzRo4OJoAACAs1ksFsXHx9ve8+EeyMEAADC2snIwCl2V5OpS+QYNGigkJMTF0QAAgMrC1+PcCzkYAAA3h2vlYGxGDwAAAAAAAEOg0AUAAAAAAABDcKtC17BhwxQeHl7iePXVV5WZman+/fuXOm7p0qUaNWrUNedp166dnnrqKf3yyy+2PpmZmXZ9WrVqpaFDh+rTTz+1m9tisSghIUHh4eF24wEAAIyA/AsAABiJWxW6JGnMmDHKycmxOx544IEbmue1117TqVOnNGXKFLs+TZs2tfXZvHmzunfvrpEjRyo3N1eStGXLFg0YMEC+vr5OuTcAAAB3RP4FAACMwu02o/f393fK04v+e57g4GBNmjRJgwcP1oULF1SzZk1JUvXq1e2u9cQTT2jPnj3asGGDxo4dqy1btui5555TeHi4PvrooxuOCQAAwB2RfwEAAKNwu0JXZbFarZKkK1euXLdf06ZNdeLECUlSWlqavL29bZ8wAgAAoPzIvwAAQFW7KQpdeXl5SktLU//+/eXn53fdvsePH1erVq0kSd7e3uWaPz8/X/n5+XZtFoulYsECAAAYQGXnXxI5GAAAKMntCl2LFi1Senq67bWvr6927959Q/OcP39eLVq0UFpa2jX7FxcXa+PGjdq3b58mTZrk0LXWrFljFzMAAIAn8cT8SyIHAwAAJbldoWv48OEaPHiw7bWXV8X2y//vec6dO6dXXnlFDz74oN577z0FBgZKkv79738rMjJSklRUVKTatWvb9oRwREJCgmJiYuzaLBaL4uPjKxQ7AABAVfLE/EsiBwMAACW5XaGrbt26Cg0NLdHu4+Ojixcvljrm4sWL8vHxue48zz33nKKjo7V582Zb8tOoUSOtWrXKNn/9+vUrlNiZzWaZzWaHxwEAALgDT8y/JHIwAABQUsWyChdo1aqVjh07pu+++86uvbi4WNu3b7ft63AtXl5eCgwM1Llz52xtNWvWVGhoqEJDQ3XrrbdWOMkCAAAwIvIvAADgaTwms2jSpIkGDx6sUaNGKSsrSydPntQXX3yhcePG6dy5c4qLi7Prf+7cOeXl5SkvL0/Hjh3Tyy+/rIMHD6pbt24uugMAAADPQv4FAAA8jdt9dfF6Zs2apdWrVys1NVW5ubkKCAhQz5499cYbb5RYtr5s2TItW7ZM0m9P72nevLn++te/qkWLFq4IHQAAwCORfwEAAE9islqtVlcHYUS5ubmKiopSdna2QkJCXB0OAABwMt7r3RO/FwAAjK2s93qP+eoiAAAAAAAAcD0UugAAAAAAAGAIFLoAAAAAAABgCBS6AAAAAAAAYAgUugAAAAAAAGAIFLoAAAAAAABgCBS6AAAAAAAAYAgUugAAAAAAAGAIFLoAAAAAAABgCIYvdBUWFio1NVXdunVTRESE7r33Xi1ZskQXL16UJGVmZqp///52Yy5fvqwXX3xRvXv3VkREhLp166a5c+fq7NmzrrgFAAAAj0L+BQAAXKW6qwOoTBcvXtTw4cPl5eWlv/zlL2ratKm+/fZbLViwQAcPHtSKFStKjLFarXrqqaf0/fffa+rUqWrZsqWOHj2qpUuX6qGHHtKbb74pPz8/F9wNAACA+yP/AgAArmToQtfLL7+sgoICZWZmqmbNmpKkBg0aqHXr1rrvvvv0wQcflBizadMmff7559q4caOCgoJsY1auXKmBAwfq1VdfVWJiYpXeBwAAgKcg/wIAAK5k6ELX22+/rccff9yWZF1Vt25dpaamyt/fX8eOHSsxZujQobYk66oaNWooJSVFBQUFlR43AACApyL/AgAArmTYQtfZs2d19OhRRUZGlnq+R48eklQi0fr66681cuTIUse0b9++1Pb8/Hzl5+fbtVksFgcjBgAA8GxVmX9J5GAAAKAkQxe6JMlsNjs0Lj8/3+Exa9asUXp6ukNjAAAAjKYq8y+JHAwAAJRk2EJXQECApN8Sp98vgy9r3O8/GSxLQkKCYmJi7NosFovi4+MdmgcAAMCTVWX+JZGDAQCAkgxd6GrUqJEOHDigpk2blji/bds2+fv7l2iPiIjQZ599pq5du5Y4t3//fp09e1ZRUVF27WazuUKfQgIAABhJVeZfEjkYAAAoycvVAVSm2NhYrVy5UhcuXLBrP3XqlCZPnqxTp06VGPPAAw/ozTffLHHuwoULmj59ur7//vtKjRkAAMCTkX8BAABXMnSha8SIEfL19dXw4cO1d+9eWSwW7dixQ8OHD1ebNm3Up0+fEmP69++vtm3batiwYfroo49ksVi0f/9+jRgxQjVq1GApPAAAwHWQfwEAAFcy7FcXpd8eSb127Vqlp6dr4sSJOnXqlBo0aKBBgwZp1KhR8vIqWefz8vLSkiVLtHLlSqWkpOjEiRMKCgpS7969NW7cOPn5+bngTgAAADwD+RcAAHAlk9Vqtbo6CCPKzc1VVFSUsrOzFRIS4upwAACAk/Fe7574vQAAYGxlvdcb+quLAAAAAAAAuHlQ6AIAAAAAAIAhUOgCAAAAAACAIVDoAgAAAAAAgCFQ6AIAAAAAAIAhUOgCAAAAAACAIVDoAgAAAAAAgCFQ6AIAAAAAAIAhUOgCAAAAAACAIVR3dQDltW/fPj3yyCPXPD927FhJUnp6eolzXbt21apVqyRJ4eHhducaNGigwYMH64knnpCXl5cyMzM1ZcqUa15n/vz5io2NrcgtAAAAeBxyMAAA4Ek8ptAVGRmpnJwc2+vevXsrNTVVkZGRkiQ/Pz+tXr1aXbp0UWpqqt1YHx8fu9crV65UixYtdOXKFf3rX//S9OnT5e3trVGjRik6Olr33HOPJOnMmTPq16+f3nrrLd16662SpICAgMq8TQAAALdCDgYAADyJxxS6fHx8FBwcbHttMplUu3Ztu7bS+pWmTp06tj7169fXuHHjtHz5co0aNUo1a9ZUzZo1JUnVqlWTJAUGBpY5JwAAgBGRgwEAAE/CHl2SbrvtNp08edLVYQAAANxUyMEAAICzecyKrsr0008/qW7duhUen5+fr/z8fLs2i8Vyo2EBAAAYGjkYAABwNsMVunbu3GnbM+KqGTNmKCYmptT+ubm5Wr58ufr27Vvha65Zs6bUDVgBAABuFuRgAADAHRiu0HXnnXdq7ty5dm1BQUF2r+Pj41WtWjVZrVadP39e0dHRSkpKqvA1ExISSiRxFotF8fHxFZ4TAADAk5CDAQAAd2C4Qpefn59CQ0Ov22fBggUKDw/X2bNnNWzYMHXu3Fm+vr4VvqbZbJbZbK7weAAAAE9HDgYAANzBTbkZ/a233qrQ0FC1atVKTzzxhBYvXqyzZ8+6OiwAAABDIwcDAACVzXCFrqKiIuXl5dkdp0+fvmb/hIQE+fv764UXXqjCKAEAAIyFHAwAALgDw311cffu3eratatd2x133KHMzMxS+/v4+Cg5OVnjx4/X0KFDy1xyDwAAgJLIwQAAgDswWa1Wq6uDMKLc3FxFRUUpOztbISEhrg4HAAA4Ge/17onfCwAAxlbWe73hvroIAAAAAACAmxOFLgAAAAAAABgChS4AAAAAAAAYAoUuAAAAAAAAGAKFLgAAAAAAABgChS4AAAAAAAAYAoUuAAAAAAAAGAKFLgAAAAAAABgChS4AAAAAAAAYQpUWuoYNG6bw8HDb0aFDByUnJys/P992ftWqVbb+4eHhOnTokN0cq1evVuvWrfXpp5/a2j788EMNHDhQERER6tGjh956660S1758+bJefPFF9e7dWxEREerWrZvmzp2rs2fPlhrrokWLFB4erv/85z9OuHMAAADXIQcDAAA3iypf0TVmzBjl5ORo586dWr16tY4fP66kpKRyjc3KytLChQuVlpam9u3bS5J27dqlpKQkxcXF6cMPP9TkyZOVkpKi7du328ZZrVY99dRTevfddzV16lRt27ZNCxYs0OHDh/XQQw+psLDQ7jpWq1Xvv/++vL29tWnTJufdPAAAgIuQgwEAgJtBlRe6/P39FRwcrHr16ql169aaP3++duzYoRMnTlx33FdffaWJEydq4sSJ6tOnj61969atiouLU1xcnEJCQtS3b18NHDhQ7733nq3Ppk2b9Pnnn2vdunXq2bOnGjRooI4dO2rlypUqKirSq6++anetgwcP6uTJk3riiSf0/vvvO/cPAAAAwAXIwQAAwM2guqMDcnNzVadOHdWqVUuStHPnTn3zzTfq1KmT2rRp43AAISEh8vb2vm6SZbFYNHr0aA0ePFjDhw+3Ozdr1qwS/X18fHTu3Dnb67fffltDhw5VUFCQXb8aNWooJSVFBQUFdu2bNm1Sly5dNHToUKWnp+vrr79WRESEw/cGAADgrsjBAACAETm0omv79u3q16+f9u/fL+m35GXUqFFav3694uLi7Jaql1deXp4uXbqkW265pdTzhYWFGj16tCIjI5WcnFzmfPv27dOGDRs0cOBAW9vXX3+tO++8s9T+7du3V48ePWyvi4uLtWXLFvXr109BQUHq2LFjmUvn8/PzlZuba3dYLJYyYwUAAHAVcjAAAGBEDq3oSktL04MPPqh7771XkrRq1SpNnTpVDz/8sGbPnq0lS5aoe/fu5Z7v119/1Zw5c9SmTRs1adKk1D7PPPOMjh49qrCwMHl5Xbsu98YbbyglJUVFRUVKTEy0iyM/P19ms7lcMe3bt09nzpxRr169JEn9+vVTenq6JkyYcM3rr1mzRunp6eWaHwAAwNXIwQAAgFE5tKLrhx9+0H333SdJ+umnn/TDDz/Y9mro3bu3Dh8+XOYcixYtUmRkpCIjI3XXXXcpLy9Pixcvvmb/Cxcu6LXXXtNHH32kzMzMa/br16+f3n33XS1btkwffvih0tLSbOcCAgJsTxUqy8aNG9W9e3fbVzN79+6tn3/+WZ999tk1xyQkJCg7O9vuyMjIKNf1AAAAqgI5GAAAuBk4tKIrMDBQP/zwgzp27KiPPvpITZo0sS13P336tPz8/MqcIyEhQUOGDJEkmc1m1a1b97r958yZo3bt2mnmzJmaNWuWIiMj1bRp0xL9AgICFBAQoGbNmqlOnToaPny4xo8fLx8fH0VEROizzz5T165dS4zbv3+/zp49q6ioKBUVFWnr1q0qLCxU69atbX0uX76sTZs2qUOHDqXGaDaby/1pJQAAgCuQgwEAgJuBQyu6YmNjlZaWphkzZuj5559XTEyMpN+exrNo0SK7fRauJTAwUKGhoQoNDS0zwbraX5IGDRqknj176umnn1ZRUZHt/NSpU5WVlWU3xsfHR8XFxSouLpYkPfDAA3rzzTd16tQpu34XLlzQ9OnT9f3330uSPv74Y12+fFnr16/Xhg0bbMfkyZO1ZcsWXb58ucx4AQAA3BE5GAAAuBk4VOgaN26chg8fruPHj2vYsGEaOXKkpN8eBd20aVNNnTq1UoK8aubMmfrll1/0/PPP29rq1KmjmTNnasuWLTpx4oT27t2r6dOnq3///vL19ZUk9e/fX23bttWwYcP00UcfyWKxaP/+/RoxYoRq1Kih+Ph4SdL777+vnj17KiIiQmFhYbZjyJAhOnfunHbt2lWp9wcAAOCOyMEAAICncOiri9WqVdPYsWNLtD/00EN66KGHnBbUtdSuXVt/+ctf9Oijj6pz587q3r27kpKSVKtWLaWmpurkyZMKCgpSTEyMnnzySds4Ly8vLVmyRCtXrlRKSopOnDihoKAg9e7dW+PGjZOfn58KCgr08ccfa+HChSWuW6tWLXXv3l2bNm1yaLN9AAAAIyAHAwAAnsJktVqtrg7CiHJzcxUVFaXs7GyFhIS4OhwAAOBkvNe7J34vAAAYW1nv9Q6t6Lp06ZJefPFFbdu2Tfn5+SqtRpadnV3xaAEAAAAAAIAKcqjQNWfOHGVmZio2NlbNmjWTyWSqrLgAAAAAAAAAhzhU6Hr//ff19NNP67HHHquseAAAAAAAAIAKceipi1euXFGrVq0qKxYAAAAAAACgwhwqdA0YMEAff/xxJYUCAAAAAAAAVJxDX11s0aKFXn75ZeXm5ioyMlLVqlUr0eeRRx5xWnAAAAAAAABAeTlU6Fq5cqUk6csvv9SXX35Z4rzJZKLQBQAAAAAAAJdwqNC1bdu2yooDAAAAAAAAuCEO7dEFAAAAAAAAuKsyV3TNnz/foQmnTJlS4WDKo6CgQF26dFHv3r21cOFCW3tycrLeeeeda47Lzs5WSEiI7r33Xh0/ftzWHhQUpOjoaE2cOFE1atRQVlaWnnzySb3zzjtq0aKFrd+ZM2d03333KT4+Xk8++WTl3BwAAIAbIv8CAACeosxC19dff13uyUwm0w0FUx5ZWVm6cuWKtm3bpvPnz8vX11eSNG3aNCUlJUmSPv74Y6Wnp2v9+vW2cYGBgbaf586dqx49eshqterIkSOaOXOmUlJSNHv2bPXq1Uvdu3dXSkqK1q1bZxuzdOlS+fn56U9/+lOl3yMAAIA7If8CAACeosxC138nG+5g48aNiouL0+bNm/XRRx8pOjpakhQQEKCAgADbz9WqVVNwcHCpcwQEBNjO1atXT88884xGjRql6dOny8fHR9OmTVP//v21efNm9e3bV99++61ee+01/fWvf1XNmjWr5kYBAADcBPkXAADwFE7do6uoqEhRUVH69ttvnTmtzalTp7Rnzx4NGDBAffr00caNG50yb1hYmC5evKhffvlFktSoUSONHj1aqampOn/+vFJSUtS5c2f17t3bKdcDAADwFORfAADAkzj01MXyOH78uC5duuTsaSVJW7ZsUcOGDdW6dWsVFRVp+PDhOnPmjGrXrn1D8/7000/y8vKS2Wy2tY0YMUIbNmzQyJEjdfDgwesmdfn5+crPz7drs1gsNxQTAACAO3DX/EsiBwMAACU5vdBVmTZt2qTo6GiZTCbdeeedCgoK0gcffKAhQ4ZUeM5Tp05p8eLF6t69u/z8/GztPj4+GjZsmObMmaPevXuradOm15xjzZo1Sk9Pr3AMAAAA7spd8y+JHAwAAJTk1K8uVqbjx4/r888/t+0JYTKZ1LdvX23atMnhuSZMmKDIyEj94Q9/UJcuXVRUVKR58+bZ9SkqKtK6det0++236+OPP9YPP/xwzfkSEhKUnZ1td2RkZDgcFwAAgDtx5/xLIgcDAAAlecyKrvfff1+SNHjwYFtbcXGxrFarfvzxR9WvX7/cc02cOFE9evTQxYsXNWrUKP3hD3+weyqQJK1atUoFBQX6xz/+ocTERM2dO1erVq0qdT6z2Wy37B4AAMAI3Dn/ksjBAABASR6zomvjxo0aNmyYNmzYYDs2btyohg0bavPmzQ7NVb9+fYWGhur222/XxIkT9corr+jYsWO288eOHdPy5cs1ceJE1a5dWzNmzNDu3bu1detWZ98WAACA2yL/AgAAnsYjCl3fffedvv32W8XFxSksLMzuiI6OrtDy+auio6PVunVrpaWl2dpSUlIUERGhQYMGSZLuuOMODR06VPPnz9eFCxdu+H4AAADcHfkXAADwRE4tdHl7e2vt2rVlbhzqqE2bNqlFixYKCwsrca5fv346dOiQjhw5UuH5p0+frq1bt+qTTz5RVlaWtm/frpkzZ8pkMtn6/PnPf1ZhYaFeeumlCl8HAADAU5B/AQAAT2SyWq1WRwYcPHhQL774ov75z3/q119/1VtvvSVJeu+99zR58mR5eXnEIrFKl5ubq6ioKGVnZyskJMTV4QAAACfjvd498XsBAMDYynqvd6gqlZOTo4ceeki+vr6aNGmSrly5Yju3YcMGrVix4sYjBgAAAAAAACrAoULXokWLFBcXp4ULF2rAgAG29jvuuEPjxo3T3//+d6cHCAAAAAAAAJSHQ4Wuw4cPq3fv3qWea968uSwWi1OCAgAAAAAAABzlUKGrbt26do+B/m///ve/1aBBA6cEBQAAAAAAADjKoULXgw8+qMWLF+vQoUO2NpPJpM8//1zp6el64IEHnB4gAAAAAAAAUB7VHek8ZswYnTp1SoMHD1ajRo0kSY899pjOnDmjmJgYjR49ulKCBAAAAAAAAMriUKGrevXqmj17th599FHt3btXv/zyi+rUqaPOnTurWbNmlRUjAAAAAAAAUCaHCl1XNW3aVE2bNnV2LAAAAAAAAECFOVzo2rlzpz7//HMVFhaWen7KlCk3HBQAAAAAAADgKIc2o09PT1diYqKysrL09ddflzi++eYbhwMYNmyYwsPDbUeHDh2UnJys/Px82/lVq1bZ+oeHh9tthi9Jq1evVuvWrfXpp5/a2j788EMNHDhQERER6tGjh9566y27McnJyXbXjYyM1OOPP64TJ07Y9fvuu+8UGxuryMhIh+8NAADAHZF/AQAAo3Ko0LVu3TqNGDFCGzdu1Lp160oca9eurVAQY8aMUU5Ojnbu3KnVq1fr+PHjSkpKKtfYrKwsLVy4UGlpaWrfvr0kadeuXUpKSlJcXJw+/PBDTZ48WSkpKdq+fbvd2NjYWOXk5CgnJ0dvvPGGqlWrppEjR6q4uFiSlJGRoQcffFC33HJLhe4LAADAXZF/AQAAI3Ko0FVUVKR77rnH6UH4+/srODhY9erVU+vWrTV//nzt2LGjxKd7v/fVV19p4sSJmjhxovr06WNr37p1q+Li4hQXF6eQkBD17dtXAwcO1HvvvWc33tfXV8HBwQoODlZ4eLhSU1N17NgxHThwQJL0wQcfaNWqVRoxYoTT79lTZWRkqEmTJvLy8lKTJk2UkZHh6pBwk+LvInB9/BtBWci/AACAETm0R1efPn20a9cu3XXXXZUVjyQpJCRE3t7e1020LBaLRo8ercGDB2v48OF252bNmlWiv4+Pj86dO3fd6/r5+alhw4Y6fvy42rdvr1WrVsnb21v79u2ryG0YTkZGhhITE237sx05ckSJiYmSpPj4eFeGhpsMfxeB6+PfCCqC/AsAABiBQ4WumTNnKi4uTgcOHFBISEipfebPn3/DQeXl5enSpUvXXLJeWFio0aNHKzIyUsnJyWXOt2/fPm3YsEELFy68br/i4mKdOnVKdevWlSR5e3uXK978/HzbnhZXWSyWco31JNOmTSvxEILCwkJNmzaN/zihSvF3Ebg+/o2gIjwt/5JunhwMAACUn0OFruXLl+ubb75R+/btSyQVzvLrr79qzpw5atOmjZo0aVJqn2eeeUZHjx5VWFiYvLyu/e3LN954QykpKSoqKlJiYqK6d+9+zb4XLlzQCy+8oBo1aqhDhw4OxbxmzRqlp6c7NMYTHT161KF2oLLwdxG4Pv6NwFGemH9JN08OBgAAys+hQtfatWs1btw4PfHEE04NYtGiRbYkpbCwUO3atdPixYuv2f/ChQt67bXXNHLkSGVmZio2NrbUfv369VPHjh31/fffKy0tTVeuXNHEiRNt519//XW98847tjkbNmyoF154Qb6+vg7Fn5CQoJiYGLs2i8ViuE/NGzdurCNHjpTaDlQl/i4C18e/EZSHp+df0s2TgwEAgPJzqNDl7e2tdu3aOT2IhIQEDRkyRJJkNpttS9evZc6cOWrXrp1mzpypWbNmKTIyUk2bNi3RLyAgQAEBAWrWrJnq1Kmj4cOHa/z48fLx8ZEkRUdHa9y4cZJ++8rlr7/+qtatWzscv9lsltlsdnicp0lJSbHb80X6bV+NlJQUF0aFmxF/F4Hr498IysPT86+rcd8MORgAACg/h566+Nhjj+m1116zPf7ZWQIDAxUaGqrQ0NAyk6yr/SVp0KBB6tmzp55++mkVFRXZzk+dOlVZWVl2Y3x8fFRcXGwXe+3atW3XfeaZZ/T111/rH//4h5Puynji4+O1YsUKhYaGymQyKTQ0VCtWrOBTU1Q5/i4C18e/EZQH+RcAADAih1Z0fffddzp06JDuu+8+hYeHy2QylehT1fskzJw5UwMHDtTzzz9v2xi1Tp06mjlzpi5fvqw2bdro6NGjmjdvnvr373/NZfH/8z//oxEjRigtLU333ntvhZbP3wzi4+P5jxLcAn8Xgevj3wgqE/kXAABwVw4VuqpXr65OnTpVViwVUrt2bf3lL3/Ro48+qs6dO6t79+5KSkpSrVq1lJqaqpMnTyooKEgxMTF68sknrztXYmKiMjMztWrVKo0dO7aK7gAAAMCzkH8BAAB3ZbJarVZXB2FEubm5ioqKUnZ2tkJCQlwdDgAAcDLe690TvxcAAIytrPd6h/boAgAAAAAAANyVQ19dPHfunNLS0rRjxw4VFBSU2mf//v1OCQwAAAAAAABwhEOFrnnz5mnnzp169NFHtWfPHl28eFGDBg3SP//5T3344Ydl7sEAAAAAAAAAVBaHCl1ZWVmaOXOmoqOj1bBhQ61du1axsbGKjY2Vr6+vDh48yBOeAAAAAAAA4BIO7dF17tw51a1bV5LUuHFjff/997ZzPXv21NatW50bHQAAAAAAAFBODhW6mjdvrqysLElSs2bNlJ+fr2PHjkmS8vLy5OXF3vYAAAAAAABwDYcqU3/605+UkZGhzZs3q0aNGurcubOSkpK0YsUKLViwQH/84x8rK04AAAAAAADguhwqdEVHRys9PV1t27aVJM2ePVsBAQF6++231bVrV02bNq1SggQAAAAAAADK4tBm9JLUq1cv288NGzbUqlWrnBqQMwwbNkxeXl565ZVXZDKZSpzr2LGjnnzySYWHh5c6fuPGjbr99tslSZ999pkmTZokX19fbdq0qdJjBwAA8FTkYAAAwNUcKnS1bNlSL7/8sjp16lRZ8TjN3r179dprr5X5FMiVK1eqRYsWdm1XN9x//vnntXbtWrVr104//fRTpcUKAABgFORgAADAlRz66mJoaKjy8vIqKxanatSokdLS0nT06NHr9qtTp46Cg4PtjurVf6v/5eTkaP369erfv39VhAwAAODxyMEAAIArOVToeuaZZ7R69Wp9+umnOnv2rAoKCkoc7mLgwIGKjIxUcnKyrly5UqE53nzzTd12221OjgwAAMC4yMEAAIArOfTVxREjRkj6bY+F37NarTKZTPrmm2+cE9kNMplMSklJUf/+/bV27VoNHz7c4Tm8vb2dHxgAAICBkYMBAABXcqjQtXbt2sqKo1I0bNhQycnJmjt3ru655x6FhYWV6PPwww/Ly+v/LWy75557tGTJEoeuk5+fr/z8fLs2i8VSsaABAAA8HDkYAABwFYcKXR07dqysOCrN4MGDlZWVpSlTpuj1118vcX7BggW2p/tIkq+vr8PXWLNmjdLT028oTgAAACMhBwMAAK5QZqFr+fLliomJUf369asiHqczmUyaM2eO+vXrp5UrV5Y436BBA4WGht7QNRISEhQTE2PXZrFYynzaEAAAgFGRgwEAAFcoczP6v/71rzp+/LjtdXFxsdq2bes2e3GVR/369TV16lQtXbpUx44dc/r8ZrNZISEhdkeDBg2cfh0AAABPQg4GAACqWpmFLqvVWqLt4sWLFX6KjqvExsaqa9euOnnypKtDAQAAuGmQgwEAgKpUZqHLSObMmaM6deq4OgwAAICbCjkYAACoKg5tRu8p1q1bV2p7cHCw9u3bZ3t9+PDhcs0XGxur2NhYp8QGAABgVORgAADA1cpV6Dp//rwKCgokSZcuXZIkFRYW2tr+W61atZwYHgAAAAAAAFA+5Sp0jRw5skTbI488YvfaarXKZDJ51Cb1AAAAAAAAMI4yC11r166tijgAAAAAAACAG1Jmoatjx47lnuzKlSvasGGDevTowYajAAAAAAAAqFJOferi5cuXNWXKFB0/ftyZ0wIAAAAAAABlcmqhS/ptry4AAAAAAACgqjm90AUAAAAAAAC4AoUuAAAAAAAAGAKFLgAAAAAAABhCmU9ddBf79u3TI488cs3zY8eOlSSlp6fb2mrUqKHIyEhNnz5dzZs3t7W3adNGFy9etBs/fvx4PfHEE7bXr7/+ulavXq1jx46pWbNmmjBhgu69915n3Q4AAIBHIAcDAACexKmFLpPJpA4dOsjf39+Z00qSIiMjlZOTY3vdu3dvpaamKjIyUpLk5+en1atXq0uXLkpNTZUknT17VhkZGRo+fLg++OAD1apVS7/88osuXryozMxM1atXzzafn5+f7ee33npLCxcu1LPPPqu2bdtqx44dGj9+vN566y21aNHC6fcGAADgrsjBAACAJ3Fqocvb21vr1q1z5pQ2Pj4+Cg4Otr02mUyqXbu2Xdvv+wUHB2v69On66KOPlJWVpfvvv18//vijTCaTbr/9dnl7e5d6ra1bt2rMmDHq16+fJOmhhx7Stm3b9I9//IMkCwAA3FTIwQAAgCdxqNB19uxZzZs3T9u2bVN+fn6pfb755hunBOYsJpNJTZs21fHjxyVJJ0+eVGBg4DUTLElasWJFiTZvb2+ZTKZKixMAAMBIyMEAAIArOFTomjZtmnbt2qXExESFhYVVVkxOd/z4cd13332SfkuyvLy8NGLECH3xxReqV6+eRowYodjY2FLHXrlyRR9++KH27Nmj8ePHV2XYAAAAHo0cDAAAVDWHCl3bt2/X9OnTNXjw4MqKx6mKior0yiuv6Mcff1RUVJQkqVGjRurWrZuioqI0ZcoU7du3TzNnzpS3t7cGDBhgN37RokVatWqViouLNX369Gsumc/Pzy+xws1isVTOTQEAALg5cjAAAOAqDhW6atSooZCQkMqKxSl27Nhh2xz14sWLatCggf72t78pKChIktStWzd169bN1v+2227Tr7/+qtWrV5dIshISEjRw4EAdOnRICxYskCTFx8eXuOaaNWvsnjQEAABwsyEHAwAA7sChQld8fLzee+89de7cubLiuWHt27fX3LlzJUk1a9ZUvXr1ytzXITIyUi+//HKJ9sDAQAUGBiosLEwmk0mLFy8uNclKSEhQTEyMXZvFYim1LwAAgBGRgwEAAHfgUKHrypUr2r9/vwYPHqy2bduqWrVqJfpMmTLFacFVhJ+fn0JDQ695fsWKFfLy8tLIkSNtbYcPH9att94qSSouLtbYsWM1atQo/eEPf7D18fHx0aVLl0qd02w2y2w2O+cGAAAAPBA5GAAAcAcOFbo+//xzNWzYUNJviYknCgsL01NPPaXAwEB17txZBw4c0NKlSzVt2jRJUrVq1eTj46PJkyfrmWeeUVhYmP71r38pLS3NY/YmAwAAcDfkYAAAoCo4VOhat25dZcVRZaKiojRr1iy9+OKLmjp1qkJCQjR58mQ98MADtj7PPfeclixZouTkZP38889q2LCh4uLiNGLECBdGDgAA4LnIwQAAQFUwWa1W6/U6jB07tvyTmUxaunTpDQdlBLm5uYqKilJ2drbbb+APAAAcx3u9e+L3AgCAsZX1Xl/miq6AgIBKCQwAAAAAAABwpjILXfPnz6+KOAAAAAAAAIAb4uXqAAAAAAAAAABnoNAFAAAAAAAAQ6DQBQAAAAAAAEOg0AUAAAAAAABDoNAFAAAAAAAAQ6DQBQAAAAAAAEOg0AUAAAAAAABDMGSha9iwYQoPD7cdHTp00KRJk/Trr79KkpKTk+3Ot2nTRiNHjtSxY8fs5jlz5oySkpIUHh6uQ4cOueBOAAAAPAc5GAAAcDVDFrokacyYMcrJydHOnTu1evVqHT9+XElJSbbzsbGxysnJUU5Ojt555x3VqlVLo0ePltVqlSR98sknGjBggM6dO+eqWwAAAPA45GAAAMCVDFvo8vf3V3BwsOrVq6fWrVtr3rx5ysnJ0YkTJyRJvr6+Cg4OVnBwsMLCwjRjxgx99913+uGHHyRJmzdv1tixY7V8+XJX3gYAAIBHIQcDAACuVN3VAVSV0NBQeXt725Ksa7n6aeKUKVPk7e1dFaEBAAAYFjkYAACoSoZd0fV7p0+f1qVLl1S3bt0S5woKCjR79mx16NBBzZo1kyQSLAAAACcgBwMAAFXppljRdfbsWaWmpqpFixa2JOrNN9/UO++8I0k6f/68GjZsqBdeeEEmk8nh+fPz85Wfn2/XZrFYbjxwAAAAD0YOBgAAqpphC12LFi1Senq6pN+SqJYtW2rJkiW2JCo6Olpjx46VJF24cEEbNmxQXFyc1q9fb0vEymvNmjW2awEAANzMyMEAAIArGbbQlZCQoCFDhuj/Z+/ew6Ks8/+PvwYEBWQUCE+LoWLhuTA1NRMN2TVETVv0IiLYdNE8lyc82ypakOkqlplaVlquSuahNEXTyFNrbedyW1sVdYo8IaKiOL8/+jq/nTgICAxz+3xc11yX87k/9+d+3/HHvHvNfd9jtVo1btw4BQYGKjAw0Lbd29vb7v3EiRP1ww8/aO3atZo4cWKpj9WvXz+7MYvFopiYmFs7CQAAACdDDwYAABzJsEGXr6+vrYmaNm2aBg4cqMcff1whISFF7nPHHXeU6aeszWazzGZzmWsFAAAwCnowAADgSLfFw+jvuece9enTR3PmzLH9os+lS5eUlZWlrKwsnThxQmlpafrggw/UvXt3B1cLAABgDPRgAACgshn2iq7fGzt2rHr27Kn33ntPkpSWlqa0tDRJUrVq1dSoUSNNnz6dJgsAAKAc0YMBAIDKZLLe+HoN5SozM1NhYWFKT09XQECAo8sBAADljM/6qom/CwAAxnazz/rb4tZFAAAAAAAAGB9BFwAAAAAAAAyBoAsAAAAAAACGQNAFAAAAAAAAQyDoAgAAAAAAgCEQdAEAAAAAAMAQCLoAAAAAAABgCARdAAAAAAAAMASCLgAAAAAAABhCNUcXUFKxsbE6ePBggfGxY8cqISFBkrR3714tXrxYX375pdzc3NSxY0c988wzatq0qW1+YmKi3n333QLrPPnkk5o4caIOHDigJ554wjbu6uqqoKAgDR8+XD179qyAMwMAAKi66MEAAIAzcZqgS5KGDx+u6OhouzEvLy9J0pYtWzRp0iSNGDFCc+fO1dWrV7Vq1SoNGDBAb731llq0aGHbp3///nrmmWfs1vHw8LD79/bt2yVJeXl52r17t8aNGyd/f3/dd999FXV6AAAAVRI9GAAAcBZOFXR5eXnJ39+/wHhOTo5mzpypZ599Vv369bONT58+XTk5OZo+fbrWrVtnG/fw8Ch0nRtMJpPd9scee0yfffaZ1q1bR5MFAABuO/RgAICyWrVqlaZMmaJjx47pzjvvVFJSkmJiYhxdFgzMEM/o2rFjh7y9vfXII48U2JaQkKCaNWvq/Pnzt3SMu+66S6dOnbqlNQAAAIyEHgwAUJxVq1YpISFBR48eldVq1dGjR5WQkKBVq1Y5ujQYmFNd0VWUb775RiEhITKZTAW2NW3aVK+//votH+OXX36Rj49Poduys7OVnZ1tN2axWG75mAAAAFUZPRgAoDhTpkxRbm6u3Vhubq6mTJnCVV2oME4VdM2fP1+pqal2Yxs3blROTo68vb1LvM6aNWsKPAx1586dhTZRVqtVhw4d0nvvvae5c+cWut7KlSsL1AUAAGAU9GAAgLI4duxYqcaB8uBUQVd8fLyioqLsxurVqydvb2/9+uuvduOjRo3Sxx9/bHv/6quvql27dpKkiIgIjRgxwm6+2Wy2/Ts3N1chISGSpGvXrslqtWrkyJEKDw8vtK64uDi751JIv32bSEINAACMgB4MAFAWd955p44ePVroOFBRnCro8vHxUWBgYIHx5s2ba+HChbp+/bpcXH577NjUqVN16dIl5efnKyIiQtWq/f9T9fb2LnSdG6pXr64NGzZIkj7//HNNmTJFYWFhRc43m812TRoAAICR0IMBAMoiKSlJCQkJdrcvenp6KikpyYFVwegM8TD6sLAwXbhwQevXr7eN1alTR4GBgTp8+LBq1Kihu+66q8Trubq6KjAwUIGBgXrkkUfUuXPnIi+ZBwAAuF3RgwEAihMTE6OlS5cqMDBQJpNJgYGBWrp0KVfeokI51RVdFy9eVFZWlt2Yh4eHzGazZsyYocmTJ+uXX36xfXu4e/duzZs3T2PGjJGXl1eZjztp0iT17t1bu3fvVmho6K2eBgAAgFOhBwMAlFVMTAzBFiqVUwVdixcv1uLFi+3GnnzySU2cOFG9e/eWn5+fXnrpJb3yyiuyWq1q1qyZkpKSFBERcUvHbdKkiR5//HHNmTNHnTt3lpub2y2tBwAA4EzowQAAgLMwWa1Wq6OLMKLMzEyFhYUpPT1dAQEBji4HAACUMz7rqyb+LgAAGNvNPusN8YwuAAAAAAAAgKALAAAAAAAAhkDQBQAAAAAAAEMg6AIAAAAAAIAhEHQBAAAAAADAEAi6AAAAAAAAYAgEXQAAAAAAADAEgi4AAAAAAAAYAkEXAAAAAAAADMEpgq7Y2FgFBwfbXu3bt1diYqKys7N14MABu22/fy1atEiStGjRokK3Dxo0yHac4OBgpaamFlpDcHCwDhw4UCnnCwAAUBXQgwEAAGdTzdEFlNTw4cMVHR0tq9Wqn3/+WcnJyRo7dqwWL16sjIwM27zw8HAlJycrJCREkuTp6Wnb1rlzZyUnJ9ut6+7ubvf+5ZdfVlhYmJo3b16BZwMAAOAc6MEAAIAzcZqgy8vLS/7+/pKkOnXqaO7cuQoLC9Ovv/6qBg0a2OaZTCbVqlXLNvd/ubu7Fzr+v+rXr68JEyZo/fr1BRowAACA2w09GAAAcCZOcetiYQICAuTm5qaTJ0+W67rjx4/X6dOntXjx4nJdFwAAwAjowQAAQFXmNFd0/V5WVpauXr2qO+64o1zXrV27tv72t79p1KhR6tGjh1q3bn3TfbKzs5WdnW03ZrFYyrUuAACAqoAeDAAAVGVOGXSdO3dOs2bNUps2bdSoUaMS7/fxxx/bnhtxw/Tp09WvXz+7sR49eujhhx/WhAkTtGHDBlWvXr3YdVeuXFnkA1QBAACMgh4MAABUdU4TdM2fP9/WyOTm5qpt27ZasGBBqda47777NHv2bLsxPz+/QudOnz5dkZGRWrBggSZOnFjsunFxcQUaNYvFopiYmFLVBwAAUNXQgwEAAGfiNEFXXFycBgwYIEkym83y8fEp9Rqenp4KDAws0dxatWpp1qxZeuqppxQWFlbsXLPZLLPZXOp6AAAAqjp6MAAA4EycJujy9fUtcYNUXrp166a+fftq8uTJlXpcAACAqoIeDAAAOBOn/dXFssjLy1NWVpbd6+zZs8XuM3nyZOXl5VVShQAAAMZDDwYAACqL01zRVR727t2rLl262I21bNlSaWlpRe5jNps1e/ZsDRo0qKLLAwAAMCR6MAAAUFlMVqvV6ugijCgzM1NhYWFKT09XQECAo8sBAADljM/6qom/CwAAxnazz/rb6tZFAAAAAAAAGBdBFwAAAAAAAAyBoAsAAAAAAACGcFs9jL4y5efnS5IsFouDKwEAABXhxmf8jc98VA30YAAAGNvNejCCrgqSlZUlSYqJiXFwJQAAoCJlZWUpMDDQ0WXg/9CDAQBweyiqB+NXFyvI5cuX9fXXX8vf31+urq6OLgcAAJSz/Px8ZWVlqVWrVqpRo4ajy8H/oQcDAMDYbtaDEXQBAAAAAADAEHgYPQAAAAAAAAyBoAsACpGYmKhhw4Y5ugwAAIDbQnG913fffafg4GBlZmZqyZIlatu2rY4ePVritTMzMxUcHKzvvvuuvMoFUIXxMHoAAAAAgFN4/PHH1a1bN34EBECRuKILAAAAAOAUatasqWbNmjm6DABVGEEXAJTB1atXtWDBAnXp0kUtWrRQ3759tWPHDtv2rVu3qkWLFrpw4YJtbMmSJQoODtYPP/xgG9uwYYPuueceXb58uVLrBwAAcEZpaWlq166d3djWrVv18MMPq0WLFoqMjNQ333yjrl27Ki0tzW7eL7/8oieffFKtWrVSx44dtWTJksosHUAlIegCgDKYPn263n77bY0aNUrLly9Xp06dNHLkSFvY9cADD8jFxUUHDhyw7bN3715J0r59+2xjn3zyiTp27Fjoz+ICAADcTqxWq65du1bglZ+fX+Q+e/bs0ZgxY/TAAw9o+fLlio6O1oQJE3Tx4sUCc1NSUhQaGqply5apV69emj9/vrZv316RpwTAAXhGFwCU0pEjR5SWlqZXX31VXbt2lSR16tRJOTk5evHFF9WjRw95e3srJCRE+/btU48ePXTp0iV9/vnnioyM1L59+xQfHy+r1aq9e/fqqaeecvAZAQAAON7OnTvVsmXLUu2zaNEiRUREaOrUqZJ+68k8PDw0adKkAnMHDBigJ554QpLUsWNHffHFF9q5c6fCw8NvvXgAVQZXdAFAKe3bt0+1a9fWgw8+aDfet29f/ec//9HPP/8sSQoNDbVdxXXo0CH5+fkpLi5On376qa5du6bDhw/r119/VWhoaKWfAwAAQFXToUMHrVu3rsDrhRdeKHR+bm6uvvrqK0VGRtqN//GPfyx0fkhIiN37oKAgZWdnl0/xAKoMrugCgFI6e/as6tSpI5PJZDder1492/a6deuqa9euSklJkcVi0SeffKLOnTurZcuWqlatmr788kt98cUXaty4sRo2bOiI0wAAAKhSvL291bp16wLj1aoV/r+t58+fl9VqVd26de3Ga9asKS8vr5uuYzKZZLVab6FiAFURV3QBQCn5+PgoKyurQGNksVhs2yXp7rvvVv369fXJJ59o//79euCBB+Tq6qr7779f+/fv1969e7maCwAAoIxuhFmnT5+2G79y5YouXbrkiJIAVAEEXQBQSh07dtTZs2dttyXesHnzZjVp0sTuW8WuXbtqy5Yt+uGHH9SpUydJvz074qOPPtKnn35K0AUAAFBGZrNZd999t7Zt22Y3np6eruvXrzuoKgCOxq2LAFCE06dPa8+ePXZjrq6ueuCBB9S/f3+NGzdOTz/9tO68807t3r1b//jHP7Ro0SK7+V27dtXw4cPVvHlz+fr6SpK6dOmiZ599Vp6engV+HhsAAAAlN3z4cI0ePVpms1mhoaH66aeftHr1arm7uzu6NAAOQtAFAEX417/+pb/+9a92Y56envr888/1t7/9TYsXL9bChQt15swZ3XXXXVq0aJF69OhhN79Tp05yc3OzXc0lSXfeeacCAgIUHBxMEwYAAHALevbsqeTkZL300kt67bXXFBQUpLlz5+rJJ590dGkAHMRk5el7AAAAAAAnZLVadfHiRdWsWdM2lpOTow4dOmjhwoUFvoQEYHxc0QUAAAAAcEqLFy/WP/7xDw0dOlRBQUE6e/as3njjDfn5+aljx46OLg+AAxB0AQAAAACcUkJCgiRp2bJlOnXqlGrWrKn7779fc+bMsbvKC8Dtg1sXAQAAAAAAYAguji4AAAAAAAAAKA8EXQAAAAAAADAEgi4AAAAAAAAYAkEXAAAAAAAADIGgCwAAAAAAAIZA0AUAAAAAAABDIOgCAAAAAACAIRB0AQAAAAAAwBAIugAAAAAAAGAIBF0AAAAAAAAwBIIuAAAAAAAAGAJBFwAAAAAAAAyBoAsAAAAAAACGQNAFAAAAAAAAQyDoAgAAAAAAgCEQdAEAAAAAAMAQCLoAAAAAAABgCARdAAAAAAAAMASCLgAAAAAAABgCQRcAAAAAAAAMgaALAAAAAAAAhkDQBQAAAAAAAEMg6AIAAAAAAIAhEHQBAAAAAADAEAi6AAAAAAAAYAjVHF2AUV2+fFlff/21/P395erq6uhyAABAOcvPz1dWVpZatWqlGjVqOLoc/B96MAAAjO1mPRhBVwX5+uuvFRMT4+gyAABABVu1apXatWvn6DLwf+jBAAC4PRTVgxF0VRB/f39Jv/2Hr1evnoOrAQAA5c1isSgmJsb2mY+qgR4MAABju1kPRtBVQW5cKl+vXj0FBAQ4uBoAAFBRuD2uaqEHAwDg9lBUD8bD6AEAAAAAAGAIBF0AAAAAAAAwhCoVdMXGxio4OLjA66233lJaWpoiIyML3W/RokUaMmRIkeu0bdtWY8aM0ZkzZ2xz0tLS7Oa0atVKAwcO1D//+U+7tS0Wi+Li4hQcHGy3PwAAgBHQfwEAACOpUkGXJA0fPlwZGRl2r0cfffSW1lm9erVOnz6tSZMm2c1p3Lixbc4HH3yg0NBQDR48WJmZmZKkrVu3qnfv3vLw8CiXcwMAAKiK6L8AAIBRVLmH0Xt5eZXLrxf97zr+/v6aMGGCoqKidPnyZdWoUUOSVK1aNbtjDRs2TPv27dOGDRs0YsQIbd26Vc8995yCg4O1a9euW64JAACgKqL/AgAARlHlgq6KYrVaJUnXr18vdl7jxo118uRJSVJKSorc3Nxs3zACAACg5Oi/AABAZbstgq6srCylpKQoMjJSnp6exc49ceKEWrVqJUlyc3Mr0frZ2dnKzs62G7NYLGUrFgAAwAAquv+S6MEAAEBBVS7omj9/vlJTU23vPTw8tHfv3lta59KlS2rWrJlSUlKKnJ+fn69NmzbpwIEDmjBhQqmOtXLlSruaAQAAnIkz9l8SPRgAACioygVd8fHxioqKsr13cSnb8/L/d52LFy/q9ddf15///Gdt3LhRvr6+kqR///vfCgkJkSTl5eWpVq1atmdClEZcXJz69etnN2axWBQTE1Om2gEAACqTM/ZfEj0YAAAoqMoFXT4+PgoMDCww7u7uritXrhS6z5UrV+Tu7l7sOs8995wiIiL0wQcf2Jqfhg0bavny5bb169atW6bGzmw2y2w2l3o/AACAqsAZ+y+JHgwAABRUtq7CAVq1aqXjx4/rxx9/tBvPz8/X7t27bc91KIqLi4t8fX118eJF21iNGjUUGBiowMBA1a9fv8xNFgAAgBHRfwEAAGfjNJ1Fo0aNFBUVpSFDhmjHjh06deqUvvzyS40aNUoXL15UdHS03fyLFy8qKytLWVlZOn78uF577TV98cUX6tq1q4POAAAAwLnQfwEAAGdT5W5dLM7MmTO1YsUKJScnKzMzU97e3urevbveeeedApetL168WIsXL5b026/33HXXXfr73/+uZs2aOaJ0AAAAp0T/BQAAnInJarVaHV2EEWVmZiosLEzp6ekKCAhwdDkAAKCc8VlfNfF3AQDA2G72We80ty4CAAAAAAAAxSHoAgAAAAAAgCEQdAEAAAAAAMAQCLoAAAAAAABgCARdAAAAAAAAMASCLgAAAAAAABgCQRcAAAAAAAAMgaALAAAAAAAAhkDQBQAAAAAAAEMwfNCVm5ur5ORkde3aVS1atNBDDz2khQsX6sqVK5KktLQ0RUZG2u1z7do1vfLKKwoPD1eLFi3UtWtXzZ49WxcuXHDEKQAAADgV+i8AAOAo1RxdQEW6cuWK4uPj5eLioueff16NGzfW4cOH9cILL+iLL77Q0qVLC+xjtVo1ZswYHTlyRJMnT1bz5s117NgxLVq0SI899pjWrFkjT09PB5wNAABA1Uf/BQAAHMnQQddrr72mnJwcpaWlqUaNGpKkevXqqXXr1vrTn/6kbdu2Fdhn8+bN+uyzz7Rp0yb5+fnZ9lm2bJn69Omjt956SwkJCZV6HgAAAM6C/gsAADiSoYOu9evX66mnnrI1WTf4+PgoOTlZXl5eOn78eIF9Bg4caGuybqhevbqSkpKUk5NT4XUDAAA4K/ovAADgSIYNui5cuKBjx44pJCSk0O3dunWTpAKN1rfffqvBgwcXuk+7du0KHc/OzlZ2drbdmMViKWXFAAAAzq0y+y+JHgwAABRk6KBLksxmc6n2y87OLvU+K1euVGpqaqn2AQAAMJrK7L8kejAAAFCQYYMub29vSb81Tr+/DP5m+/3+m8GbiYuLU79+/ezGLBaLYmJiSrUOAACAM6vM/kuiBwMAAAUZOuhq2LChPv/8czVu3LjA9p07d8rLy6vAeIsWLXTo0CF16dKlwLaDBw/qwoULCgsLsxs3m81l+hYSAADASCqz/5LowQAAQEEuji6gIvXv31/Lli3T5cuX7cZPnz6tiRMn6vTp0wX2efTRR7VmzZoC2y5fvqypU6fqyJEjFVozAACAM6P/AgAAjmTooGvQoEHy8PBQfHy89u/fL4vFoj179ig+Pl5t2rRRz549C+wTGRmpe+65R7Gxsdq1a5csFosOHjyoQYMGqXr16lwKDwAAUAz6LwAA4EiGvXVR+u0nqd944w2lpqZq/PjxOn36tOrVq6e+fftqyJAhcnEpmPO5uLho4cKFWrZsmZKSknTy5En5+fkpPDxco0aNkqenpwPOBAAAwDnQfxs5QGEAAKo0SURBVAEAAEcyWa1Wq6OLMKLMzEyFhYUpPT1dAQEBji4HAACUMz7rqyb+LgAAGNvNPusNfesiAAAAAAAAbh8EXQAAAAAAADAEgi4AAAAAAAAYAkEXAAAAAAAADIGgCwAAAAAAAIZA0AUAAAAAAABDIOgCAAAAAACAIRB0AQAAAAAAwBAIugAAAAAAAGAI1RxdQEkdOHBATzzxRJHbR4wYIUlKTU0tsK1Lly5avny5JCk4ONhuW7169RQVFaVhw4bJxcVFaWlpmjRpUpHHmTt3rvr371+WUwAAAHA69GAAAMCZOE3QFRISooyMDNv78PBwJScnKyQkRJLk6empFStWqHPnzkpOTrbb193d3e79smXL1KxZM12/fl3ff/+9pk6dKjc3Nw0ZMkQRERF68MEHJUnnz59Xr169tHbtWtWvX1+S5O3tXZGnCQAAUKXQgwEAAGfiNEGXu7u7/P39be9NJpNq1aplN1bYvMLUrl3bNqdu3boaNWqUlixZoiFDhqhGjRqqUaOGJMnV1VWS5Ovre9M1AQAAjIgeDAAAOBOe0SWpadOmOnXqlKPLAAAAuK3QgwEAgPLmNFd0VaRffvlFPj4+Zd4/Oztb2dnZdmMWi+VWywIAADA0ejAAAFDeDBd0ffzxx7ZnRtwwffp09evXr9D5mZmZWrJkiR5++OEyH3PlypWFPoAVAADgdkEPBgAAqgLDBV333XefZs+ebTfm5+dn9z4mJkaurq6yWq26dOmSIiIiNHbs2DIfMy4urkATZ7FYFBMTU+Y1AQAAnAk9GAAAqAoMF3R5enoqMDCw2DkvvPCCgoODdeHCBcXGxqpTp07y8PAo8zHNZrPMZnOZ9wcAAHB29GAAAKAquC0fRl+/fn0FBgaqVatWGjZsmBYsWKALFy44uiwAAABDowcDAAAVzXBBV15enrKysuxeZ8+eLXJ+XFycvLy89PLLL1dilQAAAMZCDwYAAKoCw926uHfvXnXp0sVurGXLlkpLSyt0vru7uxITEzV69GgNHDjwppfcAwAAoCB6MAAAUBWYrFar1dFFGFFmZqbCwsKUnp6ugIAAR5cDAADKGZ/1VRN/FwAAjO1mn/WGu3URAAAAAAAAtyeCLgAAAAAAABgCQRcAAAAAAAAMgaALAAAAAAAAhkDQBQAAAAAAAEMg6AIAAAAAAIAhEHQBAAAAAADAEAi6AAAAAAAAYAgEXQAAAAAAADCESg26YmNjFRwcbHu1b99eiYmJys7Otm1fvny5bX5wcLC++uoruzVWrFih1q1b65///Kdt7MMPP1SfPn3UokULdevWTWvXri1w7GvXrumVV15ReHi4WrRooa5du2r27Nm6cOFCobXOnz9fwcHB+u9//1sOZw4AAOA49GAAAOB2UelXdA0fPlwZGRn6+OOPtWLFCp04cUJjx44t0b47duzQvHnzlJKSonbt2kmSPvnkE40dO1bR0dH68MMPNXHiRCUlJWn37t22/axWq8aMGaP33ntPkydP1s6dO/XCCy/ohx9+0GOPPabc3Fy741itVm3ZskVubm7avHlz+Z08AACAg9CDAQCA20GlB11eXl7y9/dXnTp11Lp1a82dO1d79uzRyZMni93vm2++0fjx4zV+/Hj17NnTNr59+3ZFR0crOjpaAQEBevjhh9WnTx9t3LjRNmfz5s367LPP9Oabb6p79+6qV6+eOnTooGXLlikvL09vvfWW3bG++OILnTp1SsOGDdOWLVvK9z8AAACAA9CDAQCA20E1RxcQEBAgNze3Ypssi8WioUOHKioqSvHx8XbbZs6cWWC+u7u7Ll68aHu/fv16DRw4UH5+fnbzqlevrqSkJOXk5NiNb968WZ07d9bAgQOVmpqqb7/9Vi1atCj9yQEAAFRR9GAAAMCIHP4w+qysLF29elV33HFHodtzc3M1dOhQhYSEKDEx8abrHThwQBs2bFCfPn1sY99++63uu+++Que3a9dO3bp1s73Pz8/X1q1b1atXL/n5+alDhw43vXQ+OztbmZmZdi+LxXLTWgEAAByFHgwAABiRQ6/oOnfunGbNmqU2bdqoUaNGhc6ZNm2ajh07pqCgILm4FJ3LvfPOO0pKSlJeXp4SEhIUGhpq25adnS2z2Vyimg4cOKDz58+rR48ekqRevXopNTVV48aNK/L4K1euVGpqaonWBwAAcDR6MAAAYFSVfkXX/PnzFRISopCQEN1///3KysrSggULipx/+fJlrV69Wrt27VJaWlqR83r16qX33ntPixcv1ocffqiUlBTbNm9vb9uvCt3Mpk2bFBoaqpo1a0qSwsPD9euvv+rQoUNF7hMXF6f09HS716pVq0p0PAAAgMpADwYAAG4HlX5FV1xcnAYMGCBJMpvN8vHxKXb+rFmz1LZtW82YMUMzZ85USEiIGjduXGCet7e3vL291aRJE9WuXVvx8fEaPXq03N3d1aJFCx06dEhdunQpsN/Bgwd14cIFhYWFKS8vT9u3b1dubq5at25tm3Pt2jVt3rxZ7du3L7RGs9lc4m8rAQAAHIEeDAAA3A4q/YouX19fBQYGKjAw8KYN1o35ktS3b191795dzzzzjPLy8mzbJ0+erB07dtjt4+7urvz8fOXn50uSHn30Ua1Zs0anT5+2m3f58mVNnTpVR44ckSR99NFHunbtmtatW6cNGzbYXhMnTtTWrVt17dq1Wzp3AAAAR6EHAwAAtwOHP4y+NGbMmKEzZ87oxRdftI3Vrl1bM2bM0NatW3Xy5Ent379fU6dOVWRkpDw8PCRJkZGRuueeexQbG6tdu3bJYrHo4MGDGjRokKpXr66YmBhJ0pYtW9S9e3e1aNFCQUFBtteAAQN08eJFffLJJw45bwAAAEeiBwMAAM7CoQ+jL61atWrp+eef11/+8hd16tRJoaGhGjt2rGrWrKnk5GSdOnVKfn5+6tevn0aOHGnbz8XFRQsXLtSyZcuUlJSkkydPys/PT+Hh4Ro1apQ8PT2Vk5Ojjz76SPPmzStw3Jo1ayo0NFSbN2+2e8AqAADA7YAeDAAAOAuT1Wq1OroII8rMzFRYWJjS09MVEBDg6HIAAEA547O+auLvAgCAsd3ss96pbl0EAAAAAAAAikLQBQAAAAAAAEMg6AIAAAAAAIAhEHQBAAAAAADAEAi6AAAAAAAAYAgEXQAAAAAAADAEgi4AAAAAAAAYAkEXAAAAAAAADIGgCwAAAAAAAIbgdEFXTk6O2rRpo7Fjx9qNJyYmKjg4uMhXZmamJOmhhx6yG+/cubNmz56tK1euSJJ27Nih5s2b6/vvv7db//z58+rYsaMWLVpUOScKAABQRdB/AQAAZ+F0QdeOHTt0/fp17dy5U5cuXbKNT5kyRRkZGcrIyNDs2bNVr1492/uMjAzVr1/fNnf27NnKyMjQxx9/rL///e/au3evkpKSJEk9evRQaGio7f0NixYtkqenp/76179WzokCAABUEfRfAADAWThd0LVp0yZFR0fLy8tLu3btso17e3vL399f/v7+8vb2lqurq+29v7+/XF1dC8ytU6eO2rdvr2nTpmnDhg3Ky8uT9FvT9uWXX+qDDz6QJB0+fFirV6/WpEmTVKNGjco9YQAAAAej/wIAAM7CqYKu06dPa9++ferdu7d69uypTZs2lcu6QUFBunLlis6cOSNJatiwoYYOHark5GRdunRJSUlJ6tSpk8LDw8vleAAAAM6C/gsAADiTao4uoDS2bt2qBg0aqHXr1srLy1N8fLzOnz+vWrVq3dK6v/zyi1xcXGQ2m21jgwYN0oYNGzR48GB98cUXxTZ12dnZys7OthuzWCy3VBMAAEBVUFX7L4keDAAAFORUQdfmzZsVEREhk8mk++67T35+ftq2bZsGDBhQ5jVPnz6tBQsWKDQ0VJ6enrZxd3d3xcbGatasWQoPD1fjxo2LXGPlypVKTU0tcw0AAABVVVXtvyR6MAAAUJDT3Lp44sQJffbZZ4qIiJAkmUwmPfzww9q8eXOp1xo3bpxCQkJ07733qnPnzsrLy9OcOXPs5uTl5enNN9/U3XffrY8++kg//fRTkevFxcUpPT3d7rVq1apS1wUAAFCVVOX+S6IHAwAABTnNFV1btmyRJEVFRdnG8vPzZbVa9fPPP6tu3bolXmv8+PHq1q2brly5oiFDhujee++Vr6+v3Zzly5crJydH77//vhISEjR79mwtX7680PXMZrPdZfcAAABGUJX7L4keDAAAFFTiK7pSU1OLfObBqVOnNH/+/HIrqjCbNm1SbGysNmzYYHtt2rRJDRo0sP06T0nVrVtXgYGBuvvuuzV+/Hi9/vrrOn78uG378ePHtWTJEo0fP161atXS9OnTtXfvXm3fvr28TwsAAKDKov8CAADOpsRB1+LFi3Xq1KlCt33//fdasWJFuRX1ez/++KMOHz6s6OhoBQUF2b0iIiLKdPn8DREREWrdurVSUlJsY0lJSWrRooX69u0rSWrZsqUGDhyouXPn6vLly7d8PgAAAFUd/RcAAHBGN711ccSIEZIkq9WqefPmqXbt2nbbr1+/rs8++0zdunWriPok/fYQ1GbNmikoKKjAtl69emnp0qU6evSoAgMDy7T+1KlT1b9/f3366ac6f/68du/erXfffVcmk8k25+mnn9bWrVv16quvauTIkWU+FwAAAGdA/wUAAJyRyWq1WoubMGnSJEnShg0b1KVLF91xxx0F5gQEBCg+Pl5eXl4VU6UTyszMVFhYmNLT0xUQEODocgAAQDnjs75q4u8CAICx3eyz/qZXdM2dO1eS9PHHH2vUqFFq3bp1+VcJAAAAAAAA3KIS/+piRkZGRdYBAAAAAAAA3JISB12SlJOTY3uOQmEeeeSR8qgJAAAAAAAAKLUSB1379+/XyJEjdeHCBdWsWdPuQaGSZDKZCLoAAAAAAADgMCUOupKSktS0aVMtWLBAdevWrciaAAAAAAAAgFJzKenEI0eO6KmnniLkAgAAAAAAQJVU4qArMDBQly5dqshaAAAAAAAAgDIrcdD17LPPauXKlfrxxx8rsh4AAAAAAACgTEr8jK5x48YpJydHvXv3loeHh6pVK7jrwYMHy7U4AAAAAAAAoKRKHHSNGTOmQgqIjY21C8jMZrPCwsI0efJkmc1mxcbGqlu3bho0aJAkKTg4WOvWrVPr1q1t+6xYsULz58/Xa6+9pnbt2kmSPvzwQ6WmpurHH39UnTp1NHz4cEVFRdn2SUxM1Lvvvmt77+npqY4dO2ratGlq0KCBbfzHH3/UhAkT9NNPP+nzzz+vkP8GAAAAlYn+CwAAGFWJg65+/fpVWBHDhw9XdHS0rFarfv75ZyUnJ2vs2LF69dVXb7rvjh07NG/ePM2bN8/WZH3yyScaO3asJk+erAcffFBfffWVJk2apDp16ig0NNS2b//+/fXMM89Iks6cOaNFixZp8ODB2rRpk1xdXbVq1SqlpKSoQ4cO+umnnyrm5AEAAByA/gsAABhRiYMuScrPz9fOnTv1r3/9S2fPntXw4cPl4eGhU6dOqWXLlmUuwsvLS/7+/pKkOnXqaO7cuQoLC9PJkyeL3e+bb77R+PHjNX78ePXs2dM2vn37dkVHRys6OlqSFBAQoH379mnjxo12jZaHh4ftuP7+/kpOTtb999+vzz//XO3atdO2bdu0fPlyXbt2TZ9++mmZzw8AAKCqof8CAABGVOKg6/Tp03ryySd1/Phx3XPPPdq/f79iYmL0/fffa/To0XrttdfUvn37cikqICBAbm5uxTZaFotFQ4cOVVRUlOLj4+22zZw5s8B8d3d3Xbx4sdjjenp6qkGDBjpx4oTatWun5cuXy83NTQcOHCjLaQAAADgN+i8AAGAEJQ665syZI1dXV6Wnp8vb21utWrWSJIWFhemRRx7Riy++qLfffrtcisrKytLVq1d1xx13FLo9NzdXQ4cOVUhIiBITE2+63oEDB7RhwwbNmzev2Hn5+fk6ffq0fHx8JElubm4lqjc7O1vZ2dl2YxaLpUT7AgAAVAXO1n9J9GAAAKCgEgddu3fv1vPPPy8fHx/l5+fbbYuIiNDQoUPLpaBz585p1qxZatOmjRo1alTonGnTpunYsWMKCgqSi4tLkWu98847SkpKUl5enhISEuwum/+9y5cv6+WXX1b16tVLfWXaypUrlZqaWqp9AAAAqgpn7L8kejAAAFBQiYOua9euFfkNW25urtzd3ctcxPz5821NSm5urtq2basFCxYUOf/y5ctavXq1Bg8erLS0NPXv37/Qeb169VKHDh105MgRpaSk6Pr16xo/frxt+9tvv2375Z/Lly+rQYMGevnll+Xh4VGq+uPi4go8rN9isSgmJqZU6wAAAFQWZ++/JHowAABQUImDro4dO2rlypXq0qWLbcxkMunatWt688031blz5zIXERcXpwEDBkj67eetb1y6XpRZs2apbdu2mjFjhmbOnKmQkBA1bty4wDxvb295e3urSZMmql27tuLj4zV69GhbKBcREaFRo0ZJkubOnatz587Z/Wx2SZnNZpnN5lLvBwAA4CjO3n/dqJseDAAA/K+irzv/ncmTJ+vw4cPq16+fXn75ZUnSW2+9pUceeUT/+c9/7L6pKy1fX18FBgYqMDDwpk3WjfmS1LdvX3Xv3l3PPPOM8vLy7GrdsWOH3T7u7u7Kz8+3u+2yVq1atuNOmzZN3377rd5///0ynwcAAICzoP8CAABGVOKg684779TGjRvVrVs37dmzRwEBATp8+LC6d++ujRs3qmHDhhVZZ5FmzJihM2fO6MUXX7SN1a5dWzNmzNDWrVt18uRJ7d+/X1OnTlVkZGSRl8X/4Q9/0KBBg5SSkqJLly5VVvkAAABOh/4LAABUVSW+dVGSfHx89PTTT+vpp5+uqHpKrVatWnr++ef1l7/8RZ06dVJoaKjGjh2rmjVrKjk5WadOnZKfn5/69eunkSNHFrtWQkKC0tLStHz5co0YMaKSzgAAAMC50H8BAICqymS1Wq1Fbfz+++9LtVizZs1uuSCjyMzMVFhYmNLT0xUQEODocgAAQDnjs75q4u8CAICx3eyzvtgruh555BGZTCYVk4XZmEwmfffdd2WvFAAAAAAAALgFxQZd6enplVUHAAAAAAAAcEuKDbr+8Ic/VFYdAAAAAAAAwC0pNuiaOXOmnnnmGZnNZr3xxhs3XeyJJ54ot8IAAAAAAACA0ig26Nq2bZueeOIJmc1mvf7668UuZDKZCLoAAAAAAADgMMUGXTt27JCXl5ckaefOnXbbrFarsrKyVLt2bbm7u1dchQAAAAAAAEAJuBS30cvLS6tXr1aPHj10/vx52/iXX36psLAwhYaGqkOHDlq0aFGFFwoAAAAAAAAUp9iga+PGjZo9e7Z69uwpT09PSdKlS5c0bNgwBQUF6e2339bTTz+tV155RWvXrq2UggEAAAAAAIDCFHvr4uuvv664uDiNGzfONrZlyxZlZ2crOTlZPj4+uvfee2WxWLR69WpFRUVVeMElERsbKxcXF73++usymUwFtnXo0EEjR45UcHBwoftv2rRJd999tyTp0KFDmjBhgjw8PLR58+YKrx0AAMBZ0YMBAABHKzbo+ve//20Xckm/Paurc+fO8vHxsY116dJFq1atqpgKy2j//v1avXq1YmJiip23bNkyNWvWzG7sxrm9+OKLeuONN9S2bVv98ssvFVYrAACAUdCDAQAARyr21kU3Nzfl5eXZ3ufl5Wn//v3q3r273bxr165VTHW3oGHDhkpJSdGxY8eKnVe7dm35+/vbvapV+y3/y8jI0Lp16xQZGVkZJQMAADg9ejAAAOBIxQZd7dq10/vvv297v2HDBl29elXh4eF28/bs2aO77rqrYiosoz59+igkJESJiYm6fv16mdZYs2aNmjZtWs6VAQAAGBc9GEpj1apVatSokVxcXNSoUaMqd5cIAMD5FBt0jRo1Stu2bdOgQYOUmJioWbNmadCgQfL19ZUkXbhwQSkpKXr77bcVFxdXKQWXlMlkUlJSkr7//nu98cYbZVrDzc2tnKsCAAAwNnowlNSqVauUkJCgo0ePymq16ujRo0pISCDsAgDckmKf0dWqVSutXr1ar7/+un799VeNGzdOTzzxhG27xWLR+vXrNWHCBPXp06fCiy2tBg0aKDExUbNnz9aDDz6ooKCgAnMef/xxubj8/7zvwQcf1MKFC0t1nOzsbGVnZ9uNWSyWshUNAADg5OjBUBJTpkxRbm6u3Vhubq6mTJly02e8AQBQlGKDLklq2bKlUlJSCt3WtGlT7d+/v9yLKk9RUVHasWOHJk2apLfffrvA9hdeeMH26z6S5OHhUepjrFy5UqmpqbdUJwAAgJHQg+FminqO282e7wYAQHFuGnQV5/c/G10VmUwmzZo1S7169dKyZcsKbK9Xr54CAwNv6RhxcXHq16+f3ZjFYuGbKAAAcNuiB8PN3HnnnTp69Gih4wAAlNUtBV3Oom7dupo8ebKmT5+uO+64Qx06dCjX9c1ms8xmc7muCQAA4OzowVCcpKQkJSQk2N2+6OnpqaSkJAdWBQBwdsU+jN5I+vfvry5duujUqVOOLgUAAOC2QQ+GosTExGjp0qUKDAyUyWRSYGCgli5dyhV5AIBbctsEXZI0a9Ys1a5d29FlAAAA3FbowVCUmJgY/fe//9X169f13//+l5ALAHDLDHnr4ptvvlnouL+/vw4cOGB7/8MPP5Rovf79+6t///7lUhsAAIBR0YMBAABHu62u6AIAAAAAAIBxEXQBAAAAAADAEAi6AAAAAAAAYAgEXQAAAAAAADAEgi4AAAAAAAAYAkEXAAAAAAAADIGgCwAAAAAAAIZA0AUAAAAAAABDIOgCAAAAAACAIVRzdAEldeDAAT3xxBNFbh8xYoQkKTU11TZWvXp1hYSEaOrUqbrrrrts423atNGVK1fs9h89erSGDRtme//2229rxYoVOn78uJo0aaJx48bpoYceKq/TAQAAcAr0YAAAwJk4TdAVEhKijIwM2/vw8HAlJycrJCREkuTp6akVK1aoc+fOSk5OliRduHBBq1atUnx8vLZt26aaNWvqzJkzunLlitLS0lSnTh3bep6enrZ/r127VvPmzdOzzz6re+65R3v27NHo0aO1du1aNWvWrJLOGAAAwPHowQAAgDNxmqDL3d1d/v7+tvcmk0m1atWyG/v9PH9/f02dOlW7du3Sjh079Mgjj+jnn3+WyWTS3XffLTc3t0KPtX37dg0fPly9evWSJD322GPauXOn3n//fZosAABwW6EHAwAAzsRpgq6yMplMaty4sU6cOCFJOnXqlHx9fYtssCRp6dKlBcbc3NxkMpkqrE4AAAAjoQcDAACOcFs8jP7EiRO2bxhPnTolFxcXDRo0SO3bt1evXr2UlpZW5L7Xr1/X1q1btW/fPj388MOVVTIAAIDTowcDAACVzdBXdOXl5en111/Xzz//rLCwMElSw4YN1bVrV4WFhWnSpEk6cOCAZsyYITc3N/Xu3dtu//nz52v58uXKz8/X1KlTi7xkPjs7W9nZ2XZjFoulYk4KAACgiqMHAwAAjmK4oGvPnj22h6NeuXJF9erV00svvSQ/Pz9JUteuXdW1a1fb/KZNm+rcuXNasWJFgSYrLi5Offr00VdffaUXXnhBkhQTE1PgmCtXrrT7pSEAAIDbDT0YAACoCgwXdLVr106zZ8+WJNWoUUN16tS56XMdQkJC9NprrxUY9/X1la+vr4KCgmQymbRgwYJCm6y4uDj169fPbsxisRQ6FwAAwIjowQAAQFVguKDL09NTgYGBRW5funSpXFxcNHjwYNvYDz/8oPr160uS8vPzNWLECA0ZMkT33nuvbY67u7uuXr1a6Jpms1lms7l8TgAAAMAJ0YMBAICq4LZ4GP3/CgoK0t///nelpaXp1KlTev/997Vo0SLFx8dLklxdXeXu7q6JEycqIyNDp06d0q5du5SSkqKoqCjHFg8AAOCk6MEAAEBlMNwVXTcTFhammTNn6pVXXtHkyZMVEBCgiRMn6tFHH7XNee6557Rw4UIlJibq119/VYMGDRQdHa1BgwY5sHIAAADnRQ8GAAAqg8lqtVodXYQRZWZmKiwsTOnp6QoICHB0OQAAoJzxWV818XcBAMDYbvZZf9vduggAAAAAAABjIugCAAAAAACAIRB0AQAAAAAAwBAIugAAAAAAAGAIBF0AAAAAAAAwBIIuAAAAAAAAGAJBFwAAAAAAAAyBoAsAAAAAAACGQNAFAAAAAAAAQzBk0BUbG6vg4GDbq3379powYYLOnTsnSUpMTLTb3qZNGw0ePFjHjx+3W+f8+fMaO3asgoOD9dVXXzngTAAAAJwHPRgAAHA0QwZdkjR8+HBlZGTo448/1ooVK3TixAmNHTvWtr1///7KyMhQRkaG3n33XdWsWVNDhw6V1WqVJH366afq3bu3Ll686KhTAAAAcDr0YAAAwJEMG3R5eXnJ399fderUUevWrTVnzhxlZGTo5MmTkiQPDw/5+/vL399fQUFBmj59un788Uf99NNPkqQPPvhAI0aM0JIlSxx5GgAAAE6FHgwAADiSYYOu3wsMDJSbm5utySrKjW8TJ02apAEDBlRGaQAAlNmqVavUqFEjubi4qFGjRlq1apWjSwLs0IMBAIDKVM3RBVSWs2fP6urVq/Lx8SmwLScnR3/729/Uvn17NWnSRJLk5uZW2SUCAFAqq1atUkJCgnJzcyVJR48eVUJCgiQpJibGkaUBNvRgAACgMt0WQdeFCxeUnJysZs2a2ZqoNWvW6N1335UkXbp0SQ0aNNDLL78sk8lU6vWzs7OVnZ1tN2axWG69cAAAijFlyhRbyHVDbm6upkyZQtCFKoEeDAAAVDbDBl3z589XamqqpN+aqObNm2vhwoW2JioiIkIjRoyQJF2+fFkbNmxQdHS01q1bZ2vESmrlypW2YwEAUFmOHTtWqnGgMtCDAQAARzJs0BUXF6cBAwbIarVq3LhxCgwMVGBgoG27t7e33fuJEyfqhx9+0Nq1azVx4sRSH6tfv352YxaLhW/TAQAV6s4779TRo0cLHQcchR4MAAA4kmEfRu/r66vAwEA1atRI06ZN05YtW/T5558Xu88dd9xRpp+yNpvNCggIsHvVq1evrKUDAFAiSUlJ8vT0tBvz9PRUUlKSgyoC6MEAAIBjGTbo+l/33HOP+vTpozlz5th+0efSpUvKyspSVlaWTpw4obS0NH3wwQfq3r27g6sFAKBkYmJitHTpUgUGBspkMikwMFBLly7lahZUGfRgAACgshn21sXfGzt2rHr27Kn33ntPkpSWlqa0tDRJUrVq1dSoUSNNnz6dJgsA4FRiYmIItlCl0YMBAIDKZLLe+HoN5SozM1NhYWFKT09XQECAo8sBAADljM/6qom/CwAAxnazz/rb4tZFAAAAAAAAGB9BFwAAAAAAAAyBoAsAAAAAAACGQNAFAAAAAAAAQyDoAgAAAAAAgCEQdAEAAAAAAMAQCLoAAAAAAABgCARdAAAAAAAAMASCLgAAAAAAABhCNUcXUFKxsbE6ePBggfGxY8cqISFBkrR3714tXrxYX375pdzc3NSxY0c988wzatq0qW1+YmKi3n333QLrPPnkk5o4caIOHDigJ554wjbu6uqqoKAgDR8+XD179qyAMwMAAKi66MEAAIAzcZqgS5KGDx+u6OhouzEvLy9J0pYtWzRp0iSNGDFCc+fO1dWrV7Vq1SoNGDBAb731llq0aGHbp3///nrmmWfs1vHw8LD79/bt2yVJeXl52r17t8aNGyd/f3/dd999FXV6AAAAVRI9GAAAcBZOFXR5eXnJ39+/wHhOTo5mzpypZ599Vv369bONT58+XTk5OZo+fbrWrVtnG/fw8Ch0nRtMJpPd9scee0yfffaZ1q1bR5MFAABuO/RgAADAWRjiGV07duyQt7e3HnnkkQLbEhISVLNmTZ0/f/6WjnHXXXfp1KlTt7QGAACAkdCDAQCAqsaprugqyjfffKOQkBCZTKYC25o2barXX3/9lo/xyy+/yMfHp9Bt2dnZys7OthuzWCy3fEwAAICqjB4MAABUNU4VdM2fP1+pqal2Yxs3blROTo68vb1LvM6aNWsKPAx1586dhTZRVqtVhw4d0nvvvae5c+cWut7KlSsL1AUAAGAU9GAAAMBZOFXQFR8fr6ioKLuxevXqydvbW7/++qvd+KhRo/Txxx/b3r/66qtq166dJCkiIkIjRoywm282m23/zs3NVUhIiCTp2rVrslqtGjlypMLDwwutKy4uzu65FNJv3ybGxMSU8gwBAACqHnowAADgLJwq6PLx8VFgYGCB8ebNm2vhwoW6fv26XFx+e+zY1KlTdenSJeXn5ysiIkLVqv3/U/X29i50nRuqV6+uDRs2SJI+//xzTZkyRWFhYUXON5vNdk0aAACAkdCDAQAAZ2GIh9GHhYXpwoULWr9+vW2sTp06CgwM1OHDh1WjRg3dddddJV7P1dVVgYGBCgwM1COPPKLOnTsXeck8AADA7YoeDAAAVDVOdUXXxYsXlZWVZTfm4eEhs9msGTNmaPLkyfrll19s3x7u3r1b8+bN05gxY+Tl5VXm406aNEm9e/fW7t27FRoaequnAQAA4FTowQAAgLNwqqBr8eLFWrx4sd3Yk08+qYkTJ6p3797y8/PTSy+9pFdeeUVWq1XNmjVTUlKSIiIibum4TZo00eOPP645c+aoc+fOcnNzu6X1AAAAnAk9GAAAcBYmq9VqdXQRRpSZmamwsDClp6crICDA0eUAAIByxmd91cTfBQAAY7vZZ70hntEFAAAAAAAAEHQBAAAAAADAEAi6AAAAAAAAYAgEXQAAAAAAADAEgi4AAAAAAAAYAkEXAAAAAAAADIGgCwAAAAAAAIZA0AUAAAAAAABDIOgCAAAAAACAIThF0BUbG6vg4GDbq3379kpMTFR2drYOHDhgt+33r0WLFkmSFi1aVOj2QYMG2Y4THBys1NTUQmsIDg7WgQMHKuV8AQAAqgJ6MAAA4GyqObqAkho+fLiio6NltVr1888/Kzk5WWPHjtXixYuVkZFhmxceHq7k5GSFhIRIkjw9PW3bOnfurOTkZLt13d3d7d6//PLLCgsLU/PmzSvwbAAAAJwDPRgAAHAmThN0eXl5yd/fX5JUp04dzZ07V2FhYfr111/VoEED2zyTyaRatWrZ5v4vd3f3Qsf/V/369TVhwgStX7++QAMGAABwu6EHAwAAzsQpbl0sTEBAgNzc3HTy5MlyXXf8+PE6ffq0Fi9eXK7rAgAAGAE9GAAAqMqc5oqu38vKytLVq1d1xx13lOu6tWvX1t/+9jeNGjVKPXr0UOvWrW+6T3Z2trKzs+3GLBZLudYFAABQFdCDAQCAqswpg65z585p1qxZatOmjRo1alTi/T7++GPbcyNumD59uvr162c31qNHDz388MOaMGGCNmzYoOrVqxe77sqVK4t8gCoAAIBR0IMBAICqzmmCrvnz59samdzcXLVt21YLFiwo1Rr33XefZs+ebTfm5+dX6Nzp06crMjJSCxYs0MSJE4tdNy4urkCjZrFYFBMTU6r6AAAAqhp6MAAA4EycJuiKi4vTgAEDJElms1k+Pj6lXsPT01OBgYElmlurVi3NmjVLTz31lMLCwoqdazabZTabS10PAABAVUcPBgAAnInTBF2+vr4lbpDKS7du3dS3b19Nnjy5Uo8LAABQVdCDAQAAZ+K0v7pYFnl5ecrKyrJ7nT17tth9Jk+erLy8vEqqEAAAwHjowQAAQGVxmiu6ysPevXvVpUsXu7GWLVsqLS2tyH3MZrNmz56tQYMGVXR5AAAAhkQPBgAAKovJarVaHV2EEWVmZiosLEzp6ekKCAhwdDkAAKCc8VlfNfF3AQDA2G72WX9b3boIAAAAAAAA4yLoAgAAAAAAgCEQdAEAAAAAAMAQbquH0Vem/Px8SZLFYnFwJQAAoCLc+Iy/8ZmPqoEeDAAAY7tZD0bQVUGysrIkSTExMQ6uBAAAVKSsrCwFBgY6ugz8H3owAABuD0X1YPzqYgW5fPmyvv76a/n7+8vV1dXR5QAAgHKWn5+vrKwstWrVSjVq1HB0Ofg/9GAAABjbzXowgi4AAAAAAAAYAg+jBwAAAAAAgCEQdAFAIRITEzVs2DBHlwEAAHBbKK73+u677xQcHKzMzEwtWbJEbdu21dGjR0u8dmZmpoKDg/Xdd9+VV7kAqjAeRg8AAAAAcAqPP/64unXrxo+AACgSV3QBAAAAAJxCzZo11axZM0eXAaAKI+gCgDK4evWqFixYoC5duqhFixbq27evduzYYdu+detWtWjRQhcuXLCNLVmyRMHBwfrhhx9sYxs2bNA999yjy5cvV2r9AAAAzigtLU3t2rWzG9u6dasefvhhtWjRQpGRkfrmm2/UtWtXpaWl2c375Zdf9OSTT6pVq1bq2LGjlixZUpmlA6gkBF0AUAbTp0/X22+/rVGjRmn58uXq1KmTRo4caQu7HnjgAbm4uOjAgQO2ffbu3StJ2rdvn23sk08+UceOHQv9WVwAAIDbidVq1bVr1wq88vPzi9xnz549GjNmjB544AEtX75c0dHRmjBhgi5evFhgbkpKikJDQ7Vs2TL16tVL8+fP1/bt2yvylAA4AM/oAoBSOnLkiNLS0vTqq6+qa9eukqROnTopJydHL774onr06CFvb2+FhIRo37596tGjhy5duqTPP/9ckZGR2rdvn+Lj42W1WrV371499dRTDj4jAAAAx9u5c6datmxZqn0WLVqkiIgITZ06VdJvPZmHh4cmTZpUYO6AAQP0xBNPSJI6duyoL774Qjt37lR4ePitFw+gyuCKLgAopX379ql27dp68MEH7cb79u2r//znP/r5558lSaGhobaruA4dOiQ/Pz/FxcXp008/1bVr13T48GH9+uuvCg0NrfRzAAAAqGo6dOigdevWFXi98MILhc7Pzc3VV199pcjISLvxP/7xj4XODwkJsXsfFBSk7Ozs8ikeQJXBFV0AUEpnz55VnTp1ZDKZ7Mbr1atn2163bl117dpVKSkpslgs+uSTT9S5c2e1bNlS1apV05dffqkvvvhCjRs3VsOGDR1xGgAAAFWKt7e3WrduXWC8WrXC/7f1/Pnzslqtqlu3rt14zZo15eXlddN1TCaTrFbrLVQMoCriii4AKCUfHx9lZWUVaIwsFottuyTdfffdql+/vj755BPt379fDzzwgFxdXXX//fdr//792rt3L1dzAQAAlNGNMOv06dN241euXNGlS5ccURKAKoCgCwBKqWPHjjp79qzttsQbNm/erCZNmth9q9i1a1dt2bJFP/zwgzp16iTpt2dHfPTRR/r0008JugAAAMrIbDbr7rvv1rZt2+zG09PTdf36dQdVBcDRuHURAIpw+vRp7dmzx27M1dVVDzzwgPr3769x48bp6aef1p133qndu3frH//4hxYtWmQ3v2vXrho+fLiaN28uX19fSVKXLl307LPPytPTs8DPYwMAAKDkhg8frtGjR8tsNis0NFQ//fSTVq9eLXd3d0eXBsBBCLoAoAj/+te/9Ne//tVuzNPTU59//rn+9re/afHixVq4cKHOnDmju+66S4sWLVKPHj3s5nfq1Elubm62q7kk6c4771RAQICCg4NpwgAAAG5Bz549lZycrJdeekmvvfaagoKCNHfuXD355JOOLg2Ag5isPH0PAAAAAOCErFarLl68qJo1a9rGcnJy1KFDBy1cuLDAl5AAjI8rugAAAAAATmnx4sX6xz/+oaFDhyooKEhnz57VG2+8IT8/P3Xs2NHR5QFwAIIuAAAAAIBTSkhIkCQtW7ZMp06dUs2aNXX//fdrzpw5dld5Abh9cOsiAAAAAAAADMHF0QUAAAAAAAAA5YGgCwAAAAAAAIZA0AUAAAAAAABDIOgCAAAAAACAIRB0AQAAAAAAwBAIugAAAAAAAGAIBF0AAAAAAAAwBIIuAAAAAAAAGAJBFwAAAAAAAAyBoAsAAAAAAACGQNAFAAAAAAAAQyDoAgAAAAAAgCEQdAEAAAAAAMAQCLoAAAAAAABgCARdAAAAAAAAMASCLgAAAAAAABgCQRcAAAAAAAAMgaALAAAAAAAAhkDQBQAAAAAAAEMg6AIAAAAAAIAhEHQBAAAAAADAEAi6AAAAAAAAYAgEXQAAAAAAADAEgi4AAAAAAAAYAkEXAAAAAAAADKGaowswqsuXL+vrr7+Wv7+/XF1dHV0OAAAoZ/n5+crKylKrVq1Uo0YNR5eD/0MPBgCAsd2sByPoqiBff/21YmJiHF0GAACoYKtWrVK7du0cXQb+Dz0YAAC3h6J6MIKuCuLv7y/pt//w9erVc3A1AACgvFksFsXExNg+81E10IMBAGBsN+vBCLoqyI1L5evVq6eAgAAHVwMAACoKt8dVLfRgAADcHorqwXgYPQAAAAAAAAyBoAsAAAAAAACGUKWCrtjYWAUHBxd4vfXWW0pLS1NkZGSh+y1atEhDhgwpcp22bdtqzJgxOnPmjG1OWlqa3ZxWrVpp4MCB+uc//2m3tsViUVxcnIKDg+32BwAAMAL6LwAAYCRVKuiSpOHDhysjI8Pu9eijj97SOqtXr9bp06c1adIkuzmNGze2zfnggw8UGhqqwYMHKzMzU5K0detW9e7dWx4eHuVybgAAAFUR/RcAADCKKvcwei8vr3L59aL/Xcff318TJkxQVFSULl++rBo1akiSqlWrZnesYcOGad++fdqwYYNGjBihrVu36rnnnlNwcLB27dp1yzUBAABURfRfAADAKKpc0FVRrFarJOn69evFzmvcuLFOnjwpSUpJSZGbm5vtG0YAAACUHP0XAACobLdF0JWVlaWUlBRFRkbK09Oz2LknTpxQq1atJElubm4lWj87O1vZ2dl2YxaLpWzFAgAAGEBF918SPRgAACioygVd8+fPV2pqqu29h4eH9u7de0vrXLp0Sc2aNVNKSkqR8/Pz87Vp0yYdOHBAEyZMKNWxVq5caVczAACAM3HG/kuiBwMAAAVVuaArPj5eUVFRtvcuLmV7Xv7/rnPx4kW9/vrr+vOf/6yNGzfK19dXkvTvf/9bISEhkqS8vDzVqlXL9kyI0oiLi1O/fv3sxiwWi2JiYspUOwAAQGVyxv5LogcDAAAFVbmgy8fHR4GBgQXG3d3ddeXKlUL3uXLlitzd3Ytd57nnnlNERIQ++OADW/PTsGFDLV++3LZ+3bp1y9TYmc1mmc3mUu8HAABQFThj/yXRgwEAgILK1lU4QKtWrXT8+HH9+OOPduP5+fnavXu37bkORXFxcZGvr68uXrxoG6tRo4YCAwMVGBio+vXrl7nJAgAAMCL6LwAA4GycprNo1KiRoqKiNGTIEO3YsUOnTp3Sl19+qVGjRunixYuKjo62m3/x4kVlZWUpKytLx48f12uvvaYvvvhCXbt2ddAZAAAAOBf6LwAA4Gyq3K2LxZk5c6ZWrFih5ORkZWZmytvbW927d9c777xT4LL1xYsXa/HixZJ++/Weu+66S3//+9/VrFkzR5QOAADglOi/AACAMzFZrVaro4swoszMTIWFhSk9PV0BAQGOLgcAAJQzPuurJv4uAAAY280+653m1kUAAAAAAACgOARdAAAAAAAAMASCLgAAAAAAABgCQRcAAAAAAAAMgaALAAAAAAAAhkDQBQAAAAAAAEMg6AIAAAAAAIAhEHQBAAAAAADAEAi6AAAAAAAAYAiGD7pyc3OVnJysrl27qkWLFnrooYe0cOFCXblyRZKUlpamyMhIu32uXbumV155ReHh4WrRooW6du2q2bNn68KFC444BQAAAKdC/wUAABylmqMLqEhXrlxRfHy8XFxc9Pzzz6tx48Y6fPiwXnjhBX3xxRdaunRpgX2sVqvGjBmjI0eOaPLkyWrevLmOHTumRYsW6bHHHtOaNWvk6enpgLMBAACo+ui/AACAIxk66HrttdeUk5OjtLQ01ahRQ5JUr149tW7dWn/605+0bdu2Avts3rxZn332mTZt2iQ/Pz/bPsuWLVOfPn301ltvKSEhoVLPAwAAwFnQfwEAAEcydNC1fv16PfXUU7Ym6wYfHx8lJyfLy8tLx48fL7DPwIEDbU3WDdWrV1dSUpJycnIqvG4AAABnRf8FAAAcybBB14ULF3Ts2DGFhIQUur1bt26SVKDR+vbbbzV48OBC92nXrl2h49nZ2crOzrYbs1gspawYAADAuVVm/yXRgwEAgIIMHXRJktlsLtV+2dnZpd5n5cqVSk1NLdU+AAAARlOZ/ZdEDwYAAAoybNDl7e0t6bfG6feXwd9sv99/M3gzcXFx6tevn92YxWJRTExMqdYBAABwZpXZf0n0YAAAoCBDB10NGzbU559/rsaNGxfYvnPnTnl5eRUYb9GihQ4dOqQuXboU2Hbw4EFduHBBYWFhduNms7lM30ICAAAYSWX2XxI9GAAAKMjF0QVUpP79+2vZsmW6fPmy3fjp06c1ceJEnT59usA+jz76qNasWVNg2+XLlzV16lQdOXKkQmsGAABwZvRfAADAkQwddA0aNEgeHh6Kj4/X/v37ZbFYtGfPHsXHx6tNmzbq2bNngX0iIyN1zz33KDY2Vrt27ZLFYtHBgwc1aNAgVa9enUvhAQAAikH/BQAAHMmwty5Kv/0k9RtvvKHU1FSNHz9ep0+fVr169dS3b18NGTJELi4Fcz4XFxctXLhQy5YtU1JSkk6ePCk/Pz+Fh4dr1KhR8vT0dMCZAAAAOAf6LwAA4Egmq9VqdXQRRpSZmamwsDClp6crICDA0eUAAIByxmd91cTfBQAAY7vZZ72hb10EAAAAAADA7YOgCwAAAAAAAIZA0AUAAAAAAABDIOgCAAAAAACAIRB0AQAAAAAAwBAIugAAAAAAAGAIBF0AAAAAAAAwBIIuAAAAAAAAGAJBFwAAAAAAAAyhmqMLKKkDBw7oiSeeKHL7iBEjJEmpqakFtnXp0kXLly+XJAUHB9ttq1evnqKiojRs2DC5uLgoLS1NkyZNKvI4c+fOVf/+/ctyCgAAAE6HHgwAADgTpwm6QkJClJGRYXsfHh6u5ORkhYSESJI8PT21YsUKde7cWcnJyXb7uru7271ftmyZmjVrpuvXr+v777/X1KlT5ebmpiFDhigiIkIPPvigJOn8+fPq1auX1q5dq/r160uSvL29K/I0AQAAqhR6MAAA4EycJuhyd3eXv7+/7b3JZFKtWrXsxgqbV5jatWvb5tStW1ejRo3SkiVLNGTIENWoUUM1atSQJLm6ukqSfH19b7omAACAEdGDAQAAZ8IzuiQ1bdpUp06dcnQZAAAAtxV6MAAAUN6c5oquivTLL7/Ix8enzPtnZ2crOzvbbsxisdxqWQAAAIZGDwYAAMqb4YKujz/+2PbMiBumT5+ufv36FTo/MzNTS5Ys0cMPP1zmY65cubLQB7ACAADcLujBAABAVWC4oOu+++7T7Nmz7cb8/Pzs3sfExMjV1VVWq1WXLl1SRESExo4dW+ZjxsXFFWjiLBaLYmJiyrwmAACAM6EHAwAAVYHhgi5PT08FBgYWO+eFF15QcHCwLly4oNjYWHXq1EkeHh5lPqbZbJbZbC7z/gAAAM6OHgwAAFQFt+XD6OvXr6/AwEC1atVKw4YN04IFC3ThwgVHlwUAAGBo9GAAAKCiGS7oysvLU1ZWlt3r7NmzRc6Pi4uTl5eXXn755UqsEgAAwFjowQAAQFVguFsX9+7dqy5dutiNtWzZUmlpaYXOd3d3V2JiokaPHq2BAwfe9JJ7AAAAFEQPBgAAqgKT1Wq1OroII8rMzFRYWJjS09MVEBDg6HIAAEA547O+auLvAgCAsd3ss95wty4CAAAAAADg9kTQBQAAAAAAAEMg6AIAAAAAAIAhEHQBAAAAAADAEAi6AAAAAAAAYAgEXQAAAAAAADAEgi4AAAAAAAAYAkEXAAAAAAAADKFaSSdeuHBBc+bM0c6dO5WdnV3onO+++67cCgMAAAAAAABKo8RXdE2ZMkUffvihnnzySS1atKjQ183ExsYqODjY9mrfvr0SExNtwVlsbKyWL19umx8cHKyvvvrKbo0VK1aodevW+uc//2kb+/DDD9WnTx+1aNFC3bp109q1awsc+9q1a3rllVcUHh6uFi1aqGvXrpo9e7YuXLhQaK3z589XcHCw/vvf/5bkPw8AAECVRQ8GAABuFyW+omv37t2aOnWqoqKibumAw4cPV3R0tKxWq37++WclJydr7NixevXVV2+6744dOzRv3jzNmzdP7dq1kyR98sknGjt2rCZPnqwHH3xQX331lSZNmqQ6deooNDRUkmS1WjVmzBgdOXJEkydPVvPmzXXs2DEtWrRIjz32mNasWSNPT0/bcaxWq7Zs2SI3Nzdt3rxZI0aMuKVzBgAAcDR6MAAAcDso8RVd1atXV0BAwC0f0MvLS/7+/qpTp45at26tuXPnas+ePTp58mSx+33zzTcaP368xo8fr549e9rGt2/frujoaEVHRysgIEAPP/yw+vTpo40bN9rmbN68WZ999pnefPNNde/eXfXq1VOHDh20bNky5eXl6a233rI71hdffKFTp05p2LBh2rJlyy2fMwAAgKPRgwEAgNtBiYOumJgYu8alvAQEBMjNza3YJstisWjo0KGKiopSfHy83baZM2dq8uTJdmPu7u5279evX6+BAwfKz8/Pbrx69epKSkrS3XffbTe+efNmde7cWQMHDtTRo0f17bffluHMAAAAqi56MAAAYEQlvnXx+vXrOnjwoKKionTPPffI1dW1wJxJkyaVuoCsrCxdvXpVd9xxR6Hbc3NzNXToUIWEhCgxMfGm6x04cEAbNmzQvHnzbGPffvutBg8eXOj8G5ff35Cfn6+tW7dq3Lhx8vPzU4cOHbR582a1aNGiyGNmZ2cXeEC/xWK5aa0AAACOQg8GAACMqMRB12effaYGDRpIkn744YcC200mU6kPfu7cOc2aNUtt2rRRo0aNCp0zbdo0HTt2TEFBQXJxKfoCtHfeeUdJSUnKy8tTQkKC7dkQ0m9NkNlsLlFNBw4c0Pnz59WjRw9JUq9evZSamqpx48YVefyVK1cqNTW1ROsDAAA4Gj0YAAAwqhIHXW+++Wa5HHD+/Pm2hiQ3N1dt27bVggULipx/+fJlrV69WoMHD1ZaWpr69+9f6LxevXqpQ4cOOnLkiFJSUnT9+nWNHz9ekuTt7V3g276ibNq0SaGhoapZs6YkKTw8XDNnztShQ4fUvn37QveJi4tTv3797MYsFotiYmJKdEwAAICKRg8GAABuByUOuv5XVlaWzp49Kx8fH/n7+5dq37i4OA0YMECSZDab5ePjU+z8WbNmqW3btpoxY4ZmzpypkJAQNW7cuMA8b29veXt7q0mTJqpdu7bi4+M1evRoubu7q0WLFjp06JC6dOlSYL+DBw/qwoULCgsLU15enrZv367c3Fy1bt3aNufatWvavHlzkU2W2Wwu8beVAAAAjkAPBgAAbgclfhi9JO3cuVORkZHq2rWr+vTpo65du6pXr17atWtXidfw9fVVYGCgAgMDb9pg3ZgvSX379lX37t31zDPPKC8vz7Z98uTJ2rFjh90+7u7uys/PV35+viTp0Ucf1Zo1a3T69Gm7eZcvX9bUqVN15MgRSdJHH32ka9euad26ddqwYYPtNXHiRG3dulXXrl0r8XkCAABUJfRgAADgdlDioGvHjh0aMWKEQkJC9Oabb2r79u1688031a5dO40YMUI7d+6syDolSTNmzNCZM2f04osv2sZq166tGTNmaOvWrTp58qT279+vqVOnKjIyUh4eHpKkyMhI3XPPPYqNjdWuXbtksVh08OBBDRo0SNWrV7dd3r5lyxZ1795dLVq0UFBQkO01YMAAXbx4UZ988kmFnyMAAEBVQw8GAACcRYlvXUxNTVVcXJwmTpxoG2vYsKHatWsnT09PLVq0SA899FCFFHlDrVq19Pzzz+svf/mLOnXqpNDQUI0dO1Y1a9ZUcnKyTp06JT8/P/Xr108jR4607efi4qKFCxdq2bJlSkpK0smTJ+Xn56fw8HCNGjVKnp6eysnJ0UcffWT3S0E31KxZU6Ghodq8ebPdA1YBAABuB/RgAADAWZisVqu1JBNbtmypFStW6P777y+w7cCBAxo0aJC+/vrrci/QWWVmZiosLEzp6ekKCAhwdDkAAKCc8VlfNfF3AQDA2G72WV/iWxd9fX11/PjxQrcdO3bM9hwHAAAAAAAAwBFKHHQ9+uijmjdvXoFnJOzbt08LFizQn//853IvDgAAAAAAACipEj+ja8SIEfr55581aNAg+fj4qE6dOsrKytLZs2f1yCOPaPjw4RVZJwAAAAAAAFCsEgdd1apV09y5c/XXv/5V+/bt07lz5+Tj46OOHTuqSZMmFVkjAAAAAAAAcFMlDrpuaNKkCcEWAAAAAAAAqpxig67Bgwdr7ty58vf319y5c2+62KRJk8qtMAAAAAAAAKA0ig26Tp48qatXr0qSvv3222IXMplM5VcVAAAAAAAAUErFBl3vv/++7d9vvvlmhRcDAAAAAAAAlJWLowsAAAAAAAAAykOJH0b/0EMP3fT2xFq1aqlv376Ki4u75cKKkpOTo86dOys8PFzz5s2zjScmJurdd98tcr/09HQFBATooYce0okTJ2zjfn5+ioiI0Pjx41W9enXt2LFDI0eO1LvvvqtmzZrZ5p0/f15/+tOfFBMTo5EjR1bMyQEAAFRB9F8AAMBZlPiKrqFDh8rDw0PVqlXTH//4R8XGxioyMlKenp6qVq2aYmNj1bFjR82fP19vvfVWhRW8Y8cOXb9+XTt37tSlS5ds41OmTFFGRoYyMjI0e/Zs1atXz/Y+IyND9evXt82dPXu2MjIy9PHHH+vvf/+79u7dq6SkJElSjx49FBoaant/w6JFi+Tp6am//vWvFXZuAAAAVRH9FwAAcBYlDrqOHDmihg0basuWLZo4caLi4+P19NNP67333tNdd92lkydPasKECfrrX/+qd955p8IK3rRpk6Kjo+Xl5aVdu3bZxr29veXv7y9/f395e3vL1dXV9t7f31+urq4F5tapU0ft27fXtGnTtGHDBuXl5Un6rWn78ssv9cEHH0iSDh8+rNWrV2vSpEmqUaNGhZ0bAABAVUT/BQAAnEWJg641a9bo8ccfV7Vq9nc7uri4aODAgVq7dq0k6b777tN///vfci3yhtOnT2vfvn3q3bu3evbsqU2bNpXLukFBQbpy5YrOnDkjSWrYsKGGDh2q5ORkXbp0SUlJSerUqZPCw8PL5XgAAADOgv4LAAA4kxI/o8vFxUXZ2dmFbrt48aLt31euXJG7u/utV1aIrVu3qkGDBmrdurXy8vIUHx+v8+fPq1atWre07i+//CIXFxeZzWbb2KBBg7RhwwYNHjxYX3zxRbFNXXZ2doH/NhaL5ZZqAgAAqAqqav8l0YMBAICCShx0de/eXfPnz1ezZs3UuHFj2/jJkye1cOFCde/eXZK0ZcsWhYSElH+lkjZv3qyIiAiZTCbdd9998vPz07Zt2zRgwIAyr3n69GktWLBAoaGh8vT0tI27u7srNjZWs2bNUnh4uN05/97KlSuVmppa5hoAAACqqqraf0n0YAAAoKASB13Tpk3T0KFD1atXLzVv3lx169bVmTNn9PXXX6tp06aaOnWqcnJytGfPHq1YsaLcCz1x4oQ+++wzzZgxQ5JkMpn08MMPa/PmzaVutMaNG6dJkybJarXq0qVLuv/++5WcnGw3Jy8vT2+++abuvvtuffTRR/rpp5+KbLbi4uLUr18/uzGLxaKYmJhS1QUAAFCVVOX+S6IHAwAABZU46KpVq5ZWr16tPXv26NNPP9XZs2cVEBCgJ598UmFhYbaHjW7btu2WL2UvzJYtWyRJUVFRtrH8/HxZrVb9/PPPqlu3bonXGj9+vLp166YrV65oyJAhuvfee+Xr62s3Z/ny5crJydH777+vhIQEzZ49W8uXLy90PbPZbHfZPQAAgBFU5f5LogcDAAAFlTjokn77Fi80NFShoaFFzqmIkEv67dd+YmNjFR0dbTeekJCgDz74QPHx8SVeq27dugoMDJT0W9OVmJioqKgoNWzYUJJ0/PhxLVmyRM8++6xq1aql6dOn689//rO2b9/OA1EBAMBtg/4LAAA4mxL/6qL0WwMye/ZsRUVFKTw8XIcPH9Z//vMfvfnmmxVVnyTpxx9/1OHDhxUdHa2goCC7V0REhDZv3lzmtSMiItS6dWulpKTYxpKSktSiRQv17dtXktSyZUsNHDhQc+fO1eXLl2/5fAAAAKo6+i8AAOCMShx0ff311+rbt6++/vprde/eXZmZmbp69arOnj2rlJQUrVmzpsKK3Lx5s5o1a6agoKAC23r16qWvvvpKR48eLfP6U6dO1fbt2/Xpp59qx44d2r17t2bMmCGTyWSb8/TTTys3N1evvvpqmY8DAADgLOi/AACAMzJZrVZrSSbGxsaqTp06mjdvnq5du6ZWrVpp/fr1atmypZYuXaq0tDRt3bq1out1GpmZmQoLC1N6eroCAgIcXQ4AAChnfNZXTfxdAAAwtpt91pf4iq5//etf+vOf/yxJdt+0SdK9996rzMzMWywVAAAAAAAAKLsSB11eXl46c+ZModsyMzNVu3bt8qoJAAAAAAAAKLUSB10PP/ywFi5cKIvFYhszmUzKzMzUSy+9pF69elVIgQAAAAAAAEBJlDjoGj9+vAICAvSnP/1JTz75pCRp0qRJevjhh9WwYUONGTOmomoEAAAAAAAAbqpaSSd6enpq+fLl+uSTT7Rv3z794Q9/UO3atTVu3Dg9+OCDFVkjAAAAAAAAcFMlDrpueOCBB/TAAw8UGL9+/bpcXEp8gRgAAAAAAABQrm4adB0/flz/+Mc/dOzYMfn4+KhPnz5q27atbbvVatXmzZuVmpqqbdu2VWixAAAAAAAAQFGKvQTrm2++UZ8+fbRjxw65ubnpxx9/VExMjNavXy9J+uCDD9S7d2+NHz9eQUFBlVIwAAAAAAAAUJhir+iaN2+eOnXqpMWLF8tkMkmSli5dquTkZK1cuVKHDx9Wly5dNGfOHLVp06ZSCgYAAAAAAAAKU+wVXf/6178UFRVlC7kkKSoqSufPn5fZbNaqVau0bNmyWwq5YmNjFRwcbHu1b99eiYmJys7Otm1fvny5bX5wcLC++uoruzVWrFih1q1b65///Kdt7MMPP1SfPn3UokULdevWTWvXrrXbJzEx0e64ISEheuqpp3Ty5Em7eT/++KP69++vkJCQMp8jAABAVUL/BQAAjKrYoCs3N1e1atWyGzObzZKkSZMm6b777iuXIoYPH66MjAx9/PHHWrFihU6cOKGxY8eWaN8dO3Zo3rx5SklJUbt27SRJn3zyicaOHavo6Gh9+OGHmjhxopKSkrR79267ffv376+MjAxlZGTonXfekaurqwYPHqz8/HxJ0qpVq/TnP/9Zd9xxR7mcJwAAQFVB/wUAAIyo1L+6WBG8vLzk7+8vSapTp47mzp2rsLCwAt/u/d4333yj8ePHa/z48erZs6dtfPv27YqOjlZ0dLQkKSAgQPv27dPGjRsVGhpqm+fh4WE7rr+/v5KTk3X//ffr888/V7t27bRt2zYtX75c165d06efflrepw0AAOAw9F8AAMCIbhp0HTp0SGfPnrW9v379ukwmkw4cOCCLxWI3NywsrFyKCggIkJubW7GNlsVi0dChQxUVFaX4+Hi7bTNnziww393dXRcvXiz2uJ6enmrQoIFOnDihdu3aafny5XJzc9OBAwfKchoAAABOg/4LAAAYwU2Drnnz5hU6npycbPfeZDLpu+++K5eisrKydPXq1SIvWc/NzdXQoUMVEhKixMTEm6534MABbdiwochzuSE/P1+nT5+Wj4+PJMnNza1E9WZnZ9ueaXHD70NAAACAqszZ+i+JHgwAABRUbND1/fffV1YdNufOndOsWbPUpk0bNWrUqNA506ZN07FjxxQUFCQXl6IfM/bOO+8oKSlJeXl5SkhIsLts/vcuX76sl19+WdWrV1f79u1LVfPKlSuVmppaqn0AAACqCmfsvyR6MAAAUFC5PaPr2rVrmjZtmkaMGKE//OEPpdp3/vz5tiYlNzdXbdu21YIFC4qcf/nyZa1evVqDBw9WWlqa+vfvX+i8Xr16qUOHDjpy5IhSUlJ0/fp1jR8/3rb97bff1rvvvmtbs0GDBnr55Zfl4eFRqvrj4uLUr18/uzGLxaKYmJhSrQMAAFBZnL3/kujBAABAQeUWdF2/fl0bNmzQ448/XuqgKy4uTgMGDJD026863rh0vSizZs1S27ZtNWPGDM2cOVMhISFq3LhxgXne3t7y9vZWkyZNVLt2bcXHx2v06NFyd3eXJEVERGjUqFGSpLlz5+rcuXNq3bp1qWq/UfONX6MEAABwBs7ef92omx4MAAD8r6KvOy8Dq9Vapv18fX0VGBiowMDAmzZZN+ZLUt++fdW9e3c988wzysvLs22fPHmyduzYYbePu7u78vPzbT9dLUm1atWyHXfatGn69ttv9f7775fpHAAAAJwJ/RcAADCicg26HGHGjBk6c+aMXnzxRdtY7dq1NWPGDG3dulUnT57U/v37NXXqVEVGRhZ5Wfwf/vAHDRo0SCkpKbp06VJllQ8AAOB06L8AAEBV5fRBV61atfT8889r5cqV2r17tyRp7NixiomJUXJyssLCwjRu3DiFhoYqKSmp2LUSEhJktVq1fPnyyigdAADAKdF/AQCAqspkLev9hr+Tl5enNm3aaP369WrZsmV5LOnUMjMzFRYWpvT0dAUEBDi6HAAAUM74rK+a+LsAAGBsN/usd/orugAAAAAAAACpHIMuV1dXjRgxQnXq1CmvJQEAAAAAAIASq1aayTk5OVq3bp3+9a9/6ezZs3r22Wfl7u6u7777TmFhYRoxYkRF1QkAAAAAAAAUq8RXdB07dkwRERF64403VLNmTR08eFAXL17UkSNHNHLkSH344YcVWScAAAAAAABQrBIHXUlJSWratKm2bdummTNn6sYz7Lt06aJBgwbp5ZdfrrAiAQAAAAAAgJspcdB18OBB/eUvf5Gbm5tMJpPdts6dO+vf//53uRcHAAAAAAAAlFSJgy5XV1dduXKl0G1nz56Vl5dXuRUFAAAAAAAAlFaJg67Q0FAtWbJEubm5tjGTyaQLFy7o1Vdf1UMPPVQhBQIAAAAAAAAlUeKga8qUKbpy5Yr++Mc/atq0aZKkefPm6U9/+pOuXr2q8ePHV1iRpRUbG6u4uDjbc8R+v23RokWSpODg4EJfhw8fts0/dOiQwsLCFBkZWWn1AwAAOCN6MAAA4GjVSjrR19dX69ev14YNG7Rv3z7df//9qlmzpkaPHq1HHnlE1atXr8g6S23//v1avXq1YmJiip23bNkyNWvWzG7Mx8dHkvTiiy/qjTfeUNu2bfXLL79UWK0AAABGQQ8GAAAcqcRBlyS5u7trwIABGjBgQEXVU24aNmyolJQUPfjgg7rzzjuLnFe7dm35+/sXui0jI0Pr1q3Tl19+qRUrVlRUqQAAAIZBDwYAABypxLcuStLGjRv1/PPP240lJydr8+bN5VpUeejTp49CQkKUmJio69evl2mNNWvWqGnTpuVcGQAAgHHRgwEAAEcqcdC1bt06TZo0SX5+fnbjtWrVUmJiotauXVvuxd0Kk8mkpKQkff/993rjjTfKtIabm1s5VwUAAGBs9GAAAMCRSnzr4rJlyzRixAgNHjzYbnzIkCG6evWqXn31VUVFRZV7gbeiQYMGSkxM1OzZs/Xggw8qKCiowJzHH39cLi7/P+978MEHtXDhwlIdJzs7W9nZ2XZjFoulbEUDAAA4OXowAADgKCUOuo4dO6b77ruv0G3t27fXSy+9VG5FlaeoqCjt2LFDkyZN0ttvv11g+wsvvKC7777b9t7Dw6PUx1i5cqVSU1NvqU4AAAAjoQcDAACOUOKgq0GDBvrqq6/UoUOHAtu+/PJLNWjQoFwLKy8mk0mzZs1Sr169tGzZsgLb69Wrp8DAwFs6RlxcnPr162c3ZrFYbvprQwAAAEZFDwYAAByhxEHX448/rgULFshsNqtXr17y9PRUbm6uNm7cqMWLF2vMmDEVWOatqVu3riZPnqzp06frjjvuKDSsuxVms1lms7lc1wQAAHB29GAAAKCylTjoio+P18WLFzV79mzNmDFDnp6eunjxotzd3ZWQkKD4+PgKLPPW9e/fXx9++KF27drl6FIAAABuG/RgAACgMpU46JKk4cOHKy4uTocOHdLZs2fl4+Oj++67TzVr1qyo+srVrFmzFBkZ6egyAAAAbiv0YAAAoLKYrFar1dFFGFFmZqbCwsKUnp6ugIAAR5cDAADKGZ/1VRN/FwAAjO1mn/XFXtHVq1cvvfrqq2rQoIFGjBhx04PxqzcAAAAAAABwlGKDrnr16qlatd+meHt7V0pBAAAAAAAAQFkUG3QtX77c9u+5c+dWeDEAAAAAAABAWRUbdOXk5JRqMWd5KD0AAAAAAACMp9igq127djKZTDddxGq1ymQy6bvvviu3wgAAAAAAAIDSKDboeuONNyqrDgAAAAAAAOCWFBt0dejQobLqAAAAAAAAAG5JsUHX7+Xm5iotLU3//Oc/de7cOdWuXVv333+/+vXrpxo1alRUjQAAAAAAAMBNuZR0YmZmpiIjI7Vw4UK5uLioadOmun79ulJSUtS3b19ZLJaKrBMAAAAAAAAoVomv6Jo9e7buuOMObdiwQWaz2TZ+5swZDR48WHPmzNHChQsrpEhJOnDggJ544okit48YMUKSlJqaahurXr26QkJCNHXqVN1111228TZt2ujKlSt2+48ePVrDhg2zvX/77be1YsUKHT9+XE2aNNG4ceP00EMPldfpAAAAOAV6MAAA4ExKHHTt379f8+bNswu5JMnX11dPPfWUJkyYUO7F/a+QkBBlZGTY3oeHhys5OVkhISGSJE9PT61YsUKdO3dWcnKyJOnChQtatWqV4uPjtW3bNtWsWVNnzpzRlStXlJaWpjp16tjW8/T0tP177dq1mjdvnp599lndc8892rNnj0aPHq21a9eqWbNmFXqeAAAAVQk9GAAAcCYlDrqqVasmd3f3Qre5u7vL1dW13Ioq6hj+/v629yaTSbVq1bIb+/08f39/TZ06Vbt27dKOHTv0yCOP6Oeff5bJZNLdd98tNze3Qo+1fft2DR8+XL169ZIkPfbYY9q5c6fef/99miwAAHBboQcDAADOpMTP6HriiSf02muv6erVq3bjeXl5eu2114q9pN2RTCaTGjdurBMnTkiSTp06JV9f3yIbLElaunSp/vKXv9iNubm5yWQyVWitAAAARkEPBgDA7WnVqlVq1KiRXFxc1KhRI61atapSj1/iK7pq1qyprKws/elPf9JDDz0kX19f/frrr9q1a5euXbumu+++W3PnzrXNnzRpUoUUXBYnTpzQn/70J0m/NVkuLi4aNGiQvvzyS9WpU0eDBg1S//79C933+vXr+vDDD7Vv3z6NHj26MssGAABwavRgAADcXlatWqWEhATl5uZKko4ePaqEhARJUkxMTKXUUOKga9euXapdu7Zq166tH374wTYeEBAgSfruu+9sY1XlW7e8vDy9/vrr+vnnnxUWFiZJatiwobp27aqwsDBNmjRJBw4c0IwZM+Tm5qbevXvb7T9//nwtX75c+fn5mjp1apGXzGdnZys7O9tujF+hBAAAtyt6MAAAbk9TpkyxhVw35ObmasqUKVUv6HrzzTft3lutVmVlZal27dpFPrvLEfbs2WN7OOqVK1dUr149vfTSS/Lz85Mkde3aVV27drXNb9q0qc6dO6cVK1YUaLLi4uLUp08fffXVV3rhhRckFZ5Arly50u6XhgAAAG439GAAAODYsWOlGq8INw26Vq1apddee03r169XrVq1JElffvmlxowZo1OnTql69eoaNGiQRo4cWeHFlkS7du00e/ZsSVKNGjVUp06dm15hFhISotdee63AuK+vr3x9fRUUFCSTyaQFCxYU2mTFxcWpX79+dmMWi6XS0koAAABHowcDAAB33nmnjh49Wuh4ZSk26Nq4caOSkpL05JNP2n76+dKlSxo2bJiaN2+uF198UV988YVSUlJUr149RUVFVUrRxfH09FRgYGCR25cuXSoXFxcNHjzYNvbDDz+ofv36kqT8/HyNGDFCQ4YM0b333mub4+7uXuBB/DeYzWaZzebyOQEAAAAnRA8GAACSkpLsntEl/dYjJCUlVVoNxf7q4uuvv664uDiNGzfO9gs5W7ZsUXZ2tpKTk3XvvfcqLi5OsbGxWr16daUUfKuCgoL097//XWlpaTp16pTef/99LVq0SPHx8ZIkV1dXubu7a+LEicrIyNCpU6e0a9cupaSkVIkgDwAAwBnRgwEAYHwxMTFaunSpAgMDZTKZFBgYqKVLl1bq1dbFXtH173//W+PGjbMb27lzpzp37iwfHx/bWJcuXSr95yLLKiwsTDNnztQrr7yiyZMnKyAgQBMnTtSjjz5qm/Pcc89p4cKFSkxM1K+//qoGDRooOjpagwYNcmDlAAAAzoseDACA20NMTIxDHyNQbNDl5uamvLw82/u8vDzt379fEydOtJt37dq1iqmuGJ9//nmBsZI+J+zRRx+1a6p+z8PDQxMnTixwngAAALc7ejAAAFCVFXvrYrt27fT+++/b3m/YsEFXr15VeHi43bw9e/borrvuqpgKAQAAAAAAgBIo9oquUaNGKSYmRqdPn5a/v7+2bNmiQYMGydfXV5J04cIFLVmyRG+//baee+65SikYAAAAAAAAKEyxV3S1atVKq1evlq+vr3799VeNGzdOo0ePtm23WCxav369JkyYoD59+lR4sQAAAAAAAEBRir2iS5JatmyplJSUQrc1bdpU+/fvL/eiAAAAAAAAgNIq9oqumzGZTOVVBwAAAAAAAHBLbinoAgAAAAAAAKoKgi4AAAAAAAAYAkEXAAAAAAAADIGgCwAAAAAAAIZgyKArNjZWwcHBtlf79u01YcIEnTt3TpKUmJhot71NmzYaPHiwjh8/brfO+fPnNXbsWAUHB+urr75ywJkAAAA4D3owAADgaIYMuiRp+PDhysjI0Mcff6wVK1boxIkTGjt2rG17//79lZGRoYyMDL377ruqWbOmhg4dKqvVKkn69NNP1bt3b128eNFRpwAAAOB06MEAAIAjGTbo8vLykr+/v+rUqaPWrVtrzpw5ysjI0MmTJyVJHh4e8vf3l7+/v4KCgjR9+nT9+OOP+umnnyRJH3zwgUaMGKElS5Y48jQAAACcCj0YAABwpGqOLqCyBAYGys3NzdZkFeXGt4mTJk2Sm5tbZZQGAABgWPRgAACgMhn2iq7fO3v2rK5evSofH58C23JycvS3v/1N7du3V5MmTSSJBgsAAKAc0IMBAIDKdFtc0XXhwgUlJyerWbNmtiZqzZo1evfddyVJly5dUoMGDfTyyy/LZDKVev3s7GxlZ2fbjVksllsvHAAAwInRgwEAgMpm2KBr/vz5Sk1NlfRbE9W8eXMtXLjQ1kRFRERoxIgRkqTLly9rw4YNio6O1rp162yNWEmtXLnSdiwAAIDbGT0YAABwJMMGXXFxcRowYICsVqvGjRunwMBABQYG2rZ7e3vbvZ84caJ++OEHrV27VhMnTiz1sfr162c3ZrFYFBMTc2snAQAA4GTowQAAgCMZNujy9fW1NVHTpk3TwIED9fjjjyskJKTIfe64444y/ZS12WyW2Wwuc60AAABGQQ8GAAAc6bZ4GP0999yjPn36aM6cObZf9Ll06ZKysrKUlZWlEydOKC0tTR988IG6d+/u4GoBAACMgR4MAABUNsNe0fV7Y8eOVc+ePfXee+9JktLS0pSWliZJqlatmho1aqTp06fTZAEAAJQjejAAAFCZTNYbX6+hXGVmZiosLEzp6ekKCAhwdDkAAKCc8VlfNfF3AQDA2G72WX9b3LoIAAAAAAAA4yPoAgAAAAAAgCEQdAEAAAAAAMAQCLoAAAAAAABgCARdAAAAAAAAMASCLgAAAAAAABgCQRcAAAAAAAAMgaALAAAAAAAAhkDQBQAAAAAAAEOo5ugCSio2NlYHDx4sMD527FglJCRIkvbu3avFixf/v/buPDzme/3/+GsSCQkZkjS2Ew2CEFSjdppow/kRux5cGsSpHjvV2kIJRWhDy0GKEi1KqyXUUlpLq7WrOnRDe9rTNhhijdhCzO+PHvPtnCSSYWIWz8d1zXWZ9/a5P80fc/ee97w/OnLkiLy8vNSoUSO99NJLqlKlimV8fHy81qxZk2Od5557TqNHj9a+ffvUq1cvS7unp6dCQ0M1aNAgtWrVqhDuDAAAwHmRgwEAAFfiMoUuSRo0aJC6d+9u1Va8eHFJ0saNGzVmzBgNHjxY06ZN082bN7V8+XJ17dpV7777rsLDwy1zOnfurJdeeslqHR8fH6t/b9myRZKUlZWlHTt2aMSIEQoKCtITTzxRWLcHAADglMjBAACAq3CpQlfx4sUVFBSUoz0zM1MTJ07UK6+8ok6dOlnaExISlJmZqYSEBK1atcrS7uPjk+s6dxgMBqv+Z599Vl9//bVWrVpFkgUAAB465GAAAMBVuMUZXVu3bpWfn586duyYo69v374qUaKELl26dF/XqFq1qk6dOnVfawAAALgTcjAAAOBsXGpHV16+++47RUREyGAw5OirUqWK3nnnnfu+xpkzZ+Tv759rX0ZGhjIyMqzaTCbTfV8TAADAmZGDAQAAZ+NSha6ZM2dq7ty5Vm3r1q1TZmam/Pz8CrzOypUrcxyGun379lyTKLPZrIMHD+qjjz7StGnTcl1vyZIlOeICAABwF+RgAADAVbhUoat3797q0qWLVVvZsmXl5+ens2fPWrUPHTpUX375peX9woULVa9ePUlSTEyMBg8ebDXeaDRa/n316lVFRERIkm7duiWz2awhQ4aoZcuWucYVFxdndS6F9Me3ibGxsTbeIQAAgPMhBwMAAK7CpQpd/v7+CgkJydFeo0YNzZ49W7dv35aHxx/Hjo0bN07Xrl1Tdna2YmJiVKTI/92qn59fruvcUbRoUa1du1aSdOjQIb388suKjo7Oc7zRaLRK0gAAANwJORgAAHAVbnEYfXR0tC5fvqzVq1db2kqXLq2QkBAdP35cxYoVU9WqVQu8nqenp0JCQhQSEqKOHTuqSZMmeW6ZBwAAeFiRgwEAAGfjUju6rly5ovT0dKs2Hx8fGY1GTZgwQWPHjtWZM2cs3x7u2LFDr7/+uoYNG6bixYvf83XHjBmjdu3aaceOHYqKirrf2wAAAHAp5GAAAMBVuFShKzk5WcnJyVZtzz33nEaPHq127dopMDBQb775phYsWCCz2azq1asrMTFRMTEx93XdypUrq0ePHpo6daqaNGkiLy+v+1oPAADAlZCDAQAAV2Ewm81mRwfhjtLS0hQdHa1t27YpODjY0eEAAAA747PeOfF3AQDAveX3We8WZ3QBAAAAAAAAFLoAAAAAAADgFih0AQAAAAAAwC1Q6AIAAAAAAIBboNAFAAAAAAAAt0ChCwAAAAAAAG6BQhcAAAAAAADcAoUuAAAAAAAAuAUKXQAAAAAAAHALLlHo6tmzp8LCwiyv+vXrKz4+XhkZGdq3b59V3/++5syZI0maM2dOrv19+vSxXCcsLExz587NNYawsDDt27fvgdwvAACAMyAHAwAArqaIowMoqEGDBql79+4ym806ffq0kpKSNHz4cCUnJ2vnzp2WcS1btlRSUpIiIiIkSb6+vpa+Jk2aKCkpyWpdb29vq/fz5s1TdHS0atSoUYh3AwAA4BrIwQAAgCtxmUJX8eLFFRQUJEkqXbq0pk2bpujoaJ09e1bly5e3jDMYDCpZsqRl7J95e3vn2v5n5cqV06hRo7R69eocCRgAAMDDhhwMAAC4Epf46WJugoOD5eXlpZMnT9p13ZEjR+rcuXNKTk6267oAAADugBwMAAA4M5fZ0fW/0tPTdfPmTT3yyCN2XbdUqVKaNGmShg4dqhYtWqh27dr5zsnIyFBGRoZVm8lksmtcAAAAzoAcDAAAODOXLHRdvHhRkydP1mOPPaaKFSsWeN6XX35pOTfijoSEBHXq1MmqrUWLFmrdurVGjRqltWvXqmjRonddd8mSJXkeoAoAAOAuyMEAAICzc5lC18yZMy2JzNWrV1W3bl3NmjXLpjWeeOIJTZkyxaotMDAw17EJCQlq27atZs2apdGjR9913bi4uByJmslkUmxsrE3xAQAAOBtyMAAA4EpcptAVFxenrl27SpKMRqP8/f1tXsPX11chISEFGluyZElNnjxZAwYMUHR09F3HGo1GGY1Gm+MBAABwduRgAADAlbhMoSsgIKDACZK9NG/eXB06dNDYsWMf6HUBAACcBTkYAABwJS771MV7kZWVpfT0dKvXhQsX7jpn7NixysrKekARAgAAuB9yMAAA8KC4zI4ue9i9e7eaNWtm1VazZk2lpqbmOcdoNGrKlCnq06dPYYcHAADglsjBAADAg2Iwm81mRwfhjtLS0hQdHa1t27YpODjY0eEAAAA747PeOfF3AQDAveX3Wf9Q/XQRAAAAAAAA7otCFwAAAAAAANwChS4AAAAAAAC4hYfqMPoHKTs7W5JkMpkcHAkAACgMdz7j73zmwzmQgwEA4N7yy8EodBWS9PR0SVJsbKyDIwEAAIUpPT1dISEhjg4D/0UOBgDAwyGvHIynLhaS69ev69tvv1VQUJA8PT0dHQ4AALCz7Oxspaenq1atWipWrJijw8F/kYMBAODe8svBKHQBAAAAAADALXAYPQAAAAAAANwChS4AyEV8fLwGDhzo6DAAAAAeCnfLvX744QeFhYUpLS1N8+fPV926dfXrr78WeO20tDSFhYXphx9+sFe4AJwYh9EDAAAAAFxCjx491Lx5cx4CAiBP7OgCAAAAALiEEiVKqHr16o4OA4ATo9AFAPfg5s2bmjVrlpo1a6bw8HB16NBBW7dutfRv3rxZ4eHhunz5sqVt/vz5CgsL07Fjxyxta9euVZ06dXT9+vUHGj8AAIArSk1NVb169azaNm/erNatWys8PFxt27bVd999p8jISKWmplqNO3PmjJ577jnVqlVLjRo10vz58x9k6AAeEApdAHAPEhIS9N5772no0KFKSUlR48aNNWTIEEuxq2nTpvLw8NC+ffssc3bv3i1J2rNnj6Vt165datSoUa6PxQUAAHiYmM1m3bp1K8crOzs7zzlffPGFhg0bpqZNmyolJUXdu3fXqFGjdOXKlRxjp0+frqioKC1atEht2rTRzJkztWXLlsK8JQAOwBldAGCjn3/+WampqVq4cKEiIyMlSY0bN1ZmZqbeeOMNtWjRQn5+foqIiNCePXvUokULXbt2TYcOHVLbtm21Z88e9e7dW2azWbt379aAAQMcfEcAAACOt337dtWsWdOmOXPmzFFMTIzGjRsn6Y+czMfHR2PGjMkxtmvXrurVq5ckqVGjRjp8+LC2b9+uli1b3n/wAJwGO7oAwEZ79uxRqVKl9OSTT1q1d+jQQf/+9791+vRpSVJUVJRlF9fBgwcVGBiouLg4HThwQLdu3dLx48d19uxZRUVFPfB7AAAAcDYNGjTQqlWrcrxmzJiR6/irV6/qm2++Udu2ba3a//rXv+Y6PiIiwup9aGioMjIy7BM8AKfBji4AsNGFCxdUunRpGQwGq/ayZcta+suUKaPIyEhNnz5dJpNJu3btUpMmTVSzZk0VKVJER44c0eHDh1WpUiVVqFDBEbcBAADgVPz8/FS7du0c7UWK5P6/rZcuXZLZbFaZMmWs2kuUKKHixYvnu47BYJDZbL6PiAE4I3Z0AYCN/P39lZ6eniMxMplMln5JqlatmsqVK6ddu3Zp7969atq0qTw9PdWwYUPt3btXu3fvZjcXAADAPbpTzDp37pxV+40bN3Tt2jVHhATACVDoAgAbNWrUSBcuXLD8LPGODRs2qHLlylbfKkZGRmrjxo06duyYGjduLOmPsyM+//xzHThwgEIXAADAPTIajapWrZo++eQTq/Zt27bp9u3bDooKgKPx00UAyMO5c+f0xRdfWLV5enqqadOm6ty5s0aMGKEXX3xRjz76qHbs2KEPPvhAc+bMsRofGRmpQYMGqUaNGgoICJAkNWvWTK+88op8fX1zPB4bAAAABTdo0CC98MILMhqNioqK0i+//KIVK1bI29vb0aEBcBAKXQCQh3/961/6xz/+YdXm6+urQ4cOadKkSUpOTtbs2bN1/vx5Va1aVXPmzFGLFi2sxjdu3FheXl6W3VyS9Oijjyo4OFhhYWEkYQAAAPehVatWSkpK0ptvvqm3335boaGhmjZtmp577jlHhwbAQQxmTt8DAAAAALggs9msK1euqESJEpa2zMxMNWjQQLNnz87xJSQA98eOLgAAAACAS0pOTtYHH3yg/v37KzQ0VBcuXNDSpUsVGBioRo0aOTo8AA5AoQsAAAAA4JL69u0rSVq0aJFOnTqlEiVKqGHDhpo6darVLi8ADw9+uggAAAAAAAC34OHoAAAAAAAAAAB7oNAFAAAAAAAAt0ChCwAAAAAAAG6BQhcAAAAAAADcAoUuAAAAAAAAuAUKXQAAAAAAAHALFLoAAAAAAADgFih0AQAAAAAAwC1Q6AIAAAAAAIBboNAFAAAAAAAAt0ChCwAAAAAAAG6BQhcAAAAAAADcAoUuAAAAAAAAuAUKXQAAAAAAAHALFLoAAAAAAADgFih0AQAAAAAAwC1Q6AIAAAAAAIBboNAFAAAAAAAAt0ChCwAAAAAAAG6BQhcAAAAAAADcAoUuAAAAAAAAuAUKXQAAAAAAAHALFLoAAAAAAADgFih0AQAAAAAAwC1Q6AIAAAAAAIBbKOLoANzV9evX9e233yooKEienp6ODgcAANhZdna20tPTVatWLRUrVszR4eC/yMEAAHBv+eVgFLoKybfffqvY2FhHhwEAAArZ8uXLVa9ePUeHgf8iBwMA4OGQVw5GoauQBAUFSfrjP3zZsmUdHA0AALA3k8mk2NhYy2c+nAM5GAAA7i2/HIxCVyG5s1W+bNmyCg4OdnA0AACgsPDzOOdCDgYAwMMhrxyMw+gBAAAAAADgFih0AQAAAAAAwC04VaGrZ8+eCgsLy/F69913lZqaqrZt2+Y6b86cOerXr1+e69StW1fDhg3T+fPnLWNSU1OtxtSqVUvdunXTV199ZbW2yWRSXFycwsLCrOYDAAC4A/IvAADgTpyq0CVJgwYN0s6dO61ezzzzzH2ts2LFCp07d05jxoyxGlOpUiXLmE2bNikqKkrPP/+80tLSJEmbN29Wu3bt5OPjY5d7AwAAcEbkXwAAwF043WH0xYsXt8vTi/68TlBQkEaNGqUuXbro+vXrKlasmCSpSJEiVtcaOHCg9uzZo7Vr12rw4MHavHmzXn31VYWFhemzzz6775gAAACcEfkXAABwF05X6CosZrNZknT79u27jqtUqZJOnjwpSZo+fbq8vLws3zACAACg4Mi/AADAg/ZQFLrS09M1ffp0tW3bVr6+vncde+LECdWqVUuS5OXlVaD1MzIylJGRYdVmMpnuLVgAAAA3UNj5l0QOBgAAcnK6QtfMmTM1d+5cy3sfHx/t3r37vta5du2aqlevrunTp+c5Pjs7W+vXr9e+ffs0atQom661ZMkSq5gBAABciSvmXxI5GAAAyMnpCl29e/dWly5dLO89PO7tvPw/r3PlyhW98847+tvf/qZ169YpICBAkvTjjz8qIiJCkpSVlaWSJUtazoSwRVxcnDp16mTVZjKZFBsbe0+xAwAAPEiumH9J5GAAACAnpyt0+fv7KyQkJEe7t7e3bty4keucGzduyNvb+67rvPrqq4qJidGmTZssyU+FChWUkpJiWb9MmTL3lNgZjUYZjUab5wEAADgDV8y/JHIwAACQ071lFQ5Qq1Yt/f777/rpp5+s2rOzs7Vjxw7LuQ558fDwUEBAgK5cuWJpK1asmEJCQhQSEqJy5crdc5IFAADgjsi/AACAq3GZzKJixYrq0qWL+vXrp61bt+rUqVM6cuSIhg4dqitXrqh79+5W469cuaL09HSlp6fr999/19tvv63Dhw8rMjLSQXcAAADgWsi/AACAq3G6ny7ezcSJE7V48WIlJSUpLS1Nfn5+euqpp/T+++/n2LaenJys5ORkSX88vadq1ar65z//qerVqzsidAAAAJdE/gUAAFyJwWw2mx0dhDtKS0tTdHS0tm3bpuDgYEeHAwAA7IzPeufE3wUAAPeW32e9y/x0EQAAAAAAALgbCl0AAAAAAABwCxS6AAAAAAAA4BYodAEAAAAAAMAtUOgCAAAAAACAW6DQBQAAAAAAALdAoQsAAAAAAABugUIXAAAAAAAA3AKFLgAAAAAAALgFty90Xb16VUlJSYqMjFR4eLiefvppzZ49Wzdu3JAkpaamqm3btlZzbt26pQULFqhly5YKDw9XZGSkpkyZosuXLzviFgAAAFwK+RcAAHCUIo4OoDDduHFDvXv3loeHh1577TVVqlRJx48f14wZM3T48GG99dZbOeaYzWYNGzZMP//8s8aOHasaNWrot99+05w5c/Tss89q5cqV8vX1dcDdAAAAOD/yLwAA4EhuXeh6++23lZmZqdTUVBUrVkySVLZsWdWuXVv/7//9P33yySc55mzYsEFff/211q9fr8DAQMucRYsWqX379nr33XfVt2/fB3ofAAAAroL8CwAAOJJbF7pWr16tAQMGWJKsO/z9/ZWUlKTixYvr999/zzGnW7duliTrjqJFiyoxMVGZmZmFHjcAAICrIv8CAACO5LaFrsuXL+u3335TRERErv3NmzeXpByJ1vfff6/nn38+1zn16tXLtT0jI0MZGRlWbSaTycaIAQAAXNuDzL8kcjAAAJCTWxe6JMloNNo0LyMjw+Y5S5Ys0dy5c22aAwAA4G4eZP4lkYMBAICc3LbQ5efnJ+mPxOl/t8HnN+9/vxnMT1xcnDp16mTVZjKZFBsba9M6AAAAruxB5l8SORgAAMjJrQtdFSpU0KFDh1SpUqUc/du3b1fx4sVztIeHh+vgwYNq1qxZjr79+/fr8uXLio6Otmo3Go339C0kAACAO3mQ+ZdEDgYAAHLycHQAhalz585atGiRrl+/btV+7tw5jR49WufOncsx55lnntHKlStz9F2/fl3jxo3Tzz//XKgxAwAAuDLyLwAA4EhuXejq06ePfHx81Lt3b+3du1cmk0lffPGFevfurccee0ytWrXKMadt27aqU6eOevbsqc8++0wmk0n79+9Xnz59VLRoUbbCAwAA3AX5FwAAcCS3/emi9McjqZcuXaq5c+dq5MiROnfunMqWLasOHTqoX79+8vDIWefz8PDQ7NmztWjRIiUmJurkyZMKDAxUy5YtNXToUPn6+jrgTgAAAFwD+RcAAHAkg9lsNjs6CHeUlpam6Ohobdu2TcHBwY4OBwAA2Bmf9c6JvwsAAO4tv896t/7pIgAAAAAAAB4eFLoAAAAAAADgFih0AQAAAAAAwC1Q6AIAAAAAAIBboNAFAAAAAAAAt0ChCwAAAAAAAG6BQhcAAAAAAADcAoUuAAAAAAAAuAUKXQAAAAAAAHALRRwdQEHt27dPvXr1yrN/8ODBkqS5c+fm6GvWrJlSUlIkSWFhYVZ9ZcuWVZcuXTRw4EB5eHgoNTVVY8aMyfM606ZNU+fOne/lFgAAAFwOORgAAHAlLlPoioiI0M6dOy3vW7ZsqaSkJEVEREiSfH19tXjxYjVp0kRJSUlWc729va3eL1q0SNWrV9ft27d19OhRjRs3Tl5eXurXr59iYmL05JNPSpIuXbqkNm3a6MMPP1S5cuUkSX5+foV5mwAAAE6FHAwAALgSlyl0eXt7KygoyPLeYDCoZMmSVm25jctNqVKlLGPKlCmjoUOHav78+erXr5+KFSumYsWKSZI8PT0lSQEBAfmuCQAA4I7IwQAAgCvhjC5JVapU0alTpxwdBgAAwEOFHAwAANiby+zoKkxnzpyRv7//Pc/PyMhQRkaGVZvJZLrfsAAAANwaORgAALA3mwtda9as0WeffaZLly7JbDbn6F+6dKldArtXX375peXMiDsSEhLUqVOnXMenpaVp/vz5at269T1fc8mSJbkewAoAAPCwIAcDAADOwKZC18yZM7VgwQI1bdpU1apVk8FgKKy47tkTTzyhKVOmWLUFBgZavY+NjZWnp6fMZrOuXbummJgYDR8+/J6vGRcXlyOJM5lMio2Nvec1AQAAXAk5GAAAcAY2Fbref/999evXTy+++GJhxXPffH19FRISctcxM2bMUFhYmC5fvqyePXuqcePG8vHxuedrGo1GGY3Ge54PAADg6sjBAACAM7DpMPpr166padOmhRXLA1OuXDmFhISoVq1aGjhwoGbNmqXLly87OiwAAAC3Rg4GAAAKm02FrubNm+tf//pXIYViH1lZWUpPT7d6XbhwIc/xcXFxKl68uObNm/cAowQAAHAv5GAAAMAZ2PTTxbZt2+qNN97QpUuXVKdOHXl6euYYEx0dbbfg7sXu3bvVrFkzq7aaNWsqNTU11/He3t6Kj4/XCy+8oG7duuW75R4AAAA5kYMBAABnYDDn9ujEPFSvXv3uixkM+uGHH+47KHeQlpam6Ohobdu2TcHBwY4OBwAA2Bmf9c6JvwsAAO4tv896m3Z0HT161G6BAQAAAAAAAPZk0xldAAAAAAAAgLOyaUeXJJ06dUoLFy7U7t27deHCBfn7+6tJkyb6xz/+oXLlyhVGjAAAAAAAAEC+bCp0/fjjj+rRo4ceeeQRtWvXTmXLlpXJZNLmzZu1ceNGrVixQqGhoYUVKwAAAAAAAJAnmwpdr776qh5//HElJyerSJH/m9q/f38NHDhQr776qhYuXGj3IAEAAAAAAID82HRG14EDB9SjRw+rIpckeXp6qkePHtq/f79dgwMAAAAAAAAKyqZCl7e3t27cuJFr3/Xr1+Xt7W2XoAAAAAAAAABb2VToioqK0ptvvqmLFy9atV+8eFHz5s1T8+bN7RgaAAAAAAAAUHA2ndE1duxY9erVS9HR0WratKnKlCmjM2fOaPfu3SpTpozGjBlTWHECAAAAAAAAd2XTjq7AwEClpqZq1KhR8vDw0PHjx2UwGDRixAitXr1aAQEBd53fs2dPhYWFWV7169dXfHy8MjIyLP0pKSmW8WFhYfrmm2+s1li8eLFq166tr776ytL26aefqn379goPD1fz5s314Ycf5rj2rVu3tGDBArVs2VLh4eGKjIzUlClTdPny5VxjnTlzpsLCwvSf//ynoP95AAAAnBI5GAAAeFjYVOiSpKJFi6pbt26aNWuWlixZolmzZqlbt24qWrRogeYPGjRIO3fu1JdffqnFixfrxIkTGj58eIHmbt26Va+//rqmT5+uevXqSZJ27dql4cOHq3v37vr00081evRoJSYmaseOHZZ5ZrNZw4YN00cffaSxY8dq+/btmjFjho4dO6Znn31WV69etbqO2WzWxo0b5eXlpQ0bNhTwvwwAAIDzIgcDAAAPg3wLXW+99ZauXbsmSdq2bVu+r/wUL15cQUFBKl26tGrXrq1p06bpiy++0MmTJ+8677vvvtPIkSM1cuRItWrVytK+ZcsWde/eXd27d1dwcLBat26t9u3ba926dZYxGzZs0Ndff61ly5bpqaeeUtmyZdWgQQMtWrRIWVlZevfdd62udfjwYZ06dUoDBw7Uxo0b870nAAAK0/Lly1WxYkV5eHioYsWKWr58uaNDggsiBwMAAA+DfM/oWrBggZo3b65q1app0KBBdx1rMBj0ww8/2BRAcHCwvLy87ppkmUwm9e/fX126dFHv3r2t+iZOnJhjvLe3t65cuWJ5v3r1anXr1k2BgYFW44oWLarExERlZmZatW/YsEFNmjRRt27dNHfuXH3//fcKDw+36b4AALCH5cuXq2/fvpadL7/++qv69u0rSYqNjXVkaHBx5GAAAMAd5VvoWrNmjR599FFJ0tGjR+0eQHp6um7evKlHHnkk1/6rV6+qf//+ioiIUHx8fL7r7du3T2vXrtXrr79uafv+++/1/PPP5zr+zvb7O7Kzs7V582aNGDFCgYGBatCggTZs2HDXJCsjI8NyxsUdJpMp31gBAMjPyy+/nOPnXVevXtXLL79MoQv3hRwMAAC4o3wLXXeKXJJ08uRJPfLII/L29s4x7vbt2/rll18UGhpa4ItfvHhRkydP1mOPPaaKFSvmOmb8+PH67bffFBoaKg+PvH9p+f777ysxMVFZWVnq27evoqKiLH0ZGRkyGo0Fimnfvn26dOmSWrRoIUlq06aN5s6dqxEjRuR5/SVLlmju3LkFWh8AAFv89ttvNrUDBUEOBgAA3JVNh9FHR0fr22+/zbXvwIED6tChQ75rzJw5UxEREYqIiFDDhg2Vnp6uWbNm5Tn++vXrWrFihT777DOlpqbmOa5Nmzb66KOPlJycrE8//VTTp0+39Pn5+eX4ti8v69evV1RUlEqUKCFJatmypc6ePauDBw/mOScuLi7HWWWcnwIAsIc/f+FUkHYgL+RgAADgYZDvji5JlkPmzWazvvrqK124cMGq32w2a/Xq1QVKuuPi4tS1a1dJktFolL+//13HT548WXXr1tWECRM0ceJERUREqFKlSjnG+fn5yc/PT5UrV1apUqXUu3dvvfDCC/L29lZ4eLgOHjyoZs2a5Zi3f/9+Xb58WdHR0crKytKWLVt09epV1a5d2zLm1q1b2rBhg+rXr59rjEajscDfVgIAYIvExESrM7okydfXV4mJiQ6MCq6IHAwAADwMClTounMIvcFg0BtvvJHrmODgYL366qv5rhUQEKCQkJACBxgQECBJ6tChg3bs2KGXXnpJK1eutPx8cuzYsXr66act29ylPw5Czc7OVnZ2tiTpmWee0auvvqoePXpYHYZ6/fp1jRs3Tl26dFF0dLQ+//xz3bp1S6tWrVLRokUt43bs2KEFCxZo/PjxKlKkQP/JAACwizvncL388sv67bff9OijjyoxMZHzuWAzcjAAAPAwKNBPF48ePaqjR4/KbDZrxYoVlvd/fm3dujXHoaL2NmHCBJ0/f96q2FaqVClNmDBBmzdv1smTJ7V3716NGzdObdu2lY+PjySpbdu2qlOnjnr27KnPPvtMJpNJ+/fvV58+fVS0aFHL/yxs3LhRTz31lMLDwxUaGmp5de3aVVeuXNGuXbsK9f4AAMhNbGys/vOf/+j27dv6z3/+Q5ELDxw5GAAAcBU2fTW2cOFCVa1atbBiyVfJkiX12muv6e9//7saN26sqKgoDR8+XCVKlFBSUpJOnTqlwMBAderUSUOGDLHM8/Dw0OzZs7Vo0SIlJibq5MmTCgwMVMuWLTV06FD5+voqMzNTn3/+udWTgu4oUaKEoqKitGHDBqsDVgEAAB4G5GAAAMBVGMxms9nWSdnZ2bp27VqufXcOEH3YpaWlKTo6Wtu2bVNwcLCjwwEAAHbGZ71z4u8CAIB7y++z3qYdXenp6Ro7dqx2796t27dv5zrmhx9+uLdIAQAAAAAAgPtgU6Fr8uTJMplMmjlzprZt26ZLly7p73//uw4fPqxly5ZpzJgxhRUnAAAAAAAAcFc2Fbp27dqlpKQkRUdHy8PDQwsXLlTDhg3VsGFD3bp1S+vWrVNMTExhxQoAAAAAAADkqUBPXbzj5s2bKlasmCTp0Ucf1c8//2zpe+KJJ7R79277RgcAAAAAAAAUkE2FrvDwcK1atUqSVLlyZd24cUNHjx6VJP3666/y9fW1f4QAAAAAAABAAdhU6Bo6dKi2bNmitWvXqkiRImrRooWGDBmihIQEJSUlqX379oUVJwAAAAAAAHBXNhW6mjRpovfff1+RkZGSpIkTJ6px48ZKS0vT888/r1GjRhVKkAAAAAAAAEB+bDqMXpJq1apl+bfRaNSkSZPsGhAAAAAAAABwL2za0dWrVy/9+OOPufYdP35c3bt3t0tQAAAAAAAAgK1sKnTt379fmZmZufYdOHBAP/30k12CupvMzEw99thjGj58uFV7fHy8wsLC8nylpaVJkp5++mmr9iZNmmjKlCm6ceOGJGnr1q2qUaOG5ZD9Oy5duqRGjRppzpw5hX6PAAAAzoT8CwAAuIoC/XSxfv36MhgMMhgM6tOnj4oUsZ52+/ZtXblyRf379y+UIP9s69atun37trZv365r167Jx8dHkvTyyy9bkq/PP/9cc+fOtTwhUpICAgIs/54yZYqaN28us9msX3/9VRMmTFBiYqImTZqkFi1aKCoqSomJiVq2bJllzpw5c+Tr66t//OMfhX6PAAAAzoT8CwAAuIoCFbrGjh0rs9mssWPHqmfPnqpYsWKOMRUqVFC9evXsHV8O69evV/fu3bVp0yZ99tlniomJkST5+fnJz8/P8m9PT08FBQXluoafn5+lr3Tp0ho/frz69euncePGydvbWy+//LLatm2rTZs2qXXr1jp+/LhWrFihf/7znypWrFih3yMAAIAzIf8CAACuokCFrk6dOkmSdu7cqU6dOuVa6HoQzp07pz179uiFF16Q2WzW+vXrLYnW/QgNDdWNGzd0/vx5lS1bVhUqVFD//v2VlJSk5s2bKzExUY0bN1bLli3tcBcAAACug/wLAAC4EpueuvjGG28UVhwFsnnzZpUvX161a9dWVlaWevfurUuXLqlkyZL3te6ZM2fk4eEho9FoaevTp4/Wrl2r559/XocPH9b69evznJ+RkaGMjAyrNpPJdF8xAQAAOANnzb8kcjAAAJCTTYWuwYMH5ztm7ty59xxMfjZs2KCYmBgZDAY98cQTCgwM1CeffKKuXbve85rnzp3TrFmzFBUVJV9fX0u7t7e3evbsqcmTJ6tly5aqVKlSnmssWbKkUO8bAADAUZw1/5LIwQAAQE42PXXxzjkMf375+Pjo6NGjOnbsmOWMhsJw4sQJff3115at8gaDQa1bt9aGDRtsXmvEiBGKiIjQ448/riZNmigrK0tTp061GpOVlaVly5apWrVq+vzzz/XLL7/kuV5cXJy2bdtm9Vq+fLnNcQEAADgTZ86/JHIwAACQk007uqZNm5Zre1ZWlgYNGqQ6derYJajcbNy4UZLUpUsXS1t2drbMZrNOnz6tMmXKFHitkSNHqnnz5rpx44b69eunxx9/3OqpQJKUkpKizMxMffzxx+rbt6+mTJmilJSUXNczGo1W2+4BAADcgTPnXxI5GAAAyMmmHV15ubPNPDk52R7L5Wr9+vXq2bOn1q5da3mtX79e5cuX16ZNm2xaq0yZMgoJCVG1atU0cuRIvfPOO/r9998t/b///rvmz5+vkSNHqmTJkkpISNDu3bu1ZcsWe98WAACA0yL/AgAArsYuhS5JOn36tC5evGiv5az89NNPOn78uLp3767Q0FCrV0xMzD1tn78jJiZGtWvX1vTp0y1tiYmJCg8PV4cOHSRJNWvWVLdu3TRt2jRdv379vu8HAADA2ZF/AQAAV2TTTxe3bduWo81sNuvnn3/W/Pnz1axZM7sF9mcbNmxQ9erVFRoamqOvTZs2euutt/Trr78qJCTkntYfN26cOnfurAMHDujSpUvasWOH1qxZI4PBYBnz4osvavPmzVq4cKGGDBlyz/cCAADgCsi/AACAKzKYzWZzQQdXr1491/aSJUvqqaee0pgxY+77UdPuIi0tTdHR0dq2bZuCg4MdHQ4AALAzPuudE38XAADcW36f9Tbt6Dp69KjdAgMAAAAAAADsyW5ndAEAAAAAAACOlO+OrrVr19q0YMeOHe8xFAAAAAAAAODe5Vvoio+Pl8FgkMFgUIkSJXS3I70MBgOFLgAAAAAAADhEvoWuYcOGac2aNfrtt99Ut25dPfPMM3r66afl6en5IOIDAAAAAAAACiTfM7r69++vTz75REuWLJGfn59GjhypyMhIzZgxQ//5z38eQIgAAAAAAABA/gr81MUGDRqoQYMGunz5statW6fVq1crJSVFdevWVZcuXdSqVSsVK1asMGMFAAAAAAAA8mTzUxf9/PwUGxur1NRUpaamKiwsTFOnTlWzZs00YcKEwogRAAAAAAAAyJfNha4/q1Gjhnr27Kl27dopMzNT69ats1dcAAAAAAAAgE3uqdB17do1rVmzRrGxsYqJidHBgwc1btw4ffHFFzav1bNnT4WFhVle9evXV3x8vDIyMiz9KSkplvFhYWH65ptvrNZYvHixateura+++srS9umnn6p9+/YKDw9X8+bN9eGHH1rNiY+Pt7puRESEBgwYoJMnT1qN++mnn9S5c2dFRETYfG8AAADOiPwLAAC4K5sKXUeOHFFCQoKefPJJTZo0SZUrV9bKlSu1du1a9ejRQ35+fvcUxKBBg7Rz5059+eWXWrx4sU6cOKHhw4cXaO7WrVv1+uuva/r06apXr54kadeuXRo+fLi6d++uTz/9VKNHj1ZiYqJ27NhhNbdz587auXOndu7cqffff1+enp56/vnnlZ2dLUlavny5/va3v+mRRx65p/sCAABwVuRfAADAHeV7GP3Fixf10UcfadWqVfrpp59Uq1YtjR49WjExMSpevLhdgihevLiCgoIkSaVLl9a0adMUHR2d49u9//Xdd99p5MiRGjlypFq1amVp37Jli7p3767u3btLkoKDg7Vnzx6tW7dOUVFRlnE+Pj6W6wYFBSkpKUkNGzbUoUOHVK9ePX3yySdKSUnRrVu3dODAAbvcKwAAgDMg/wIAAO4o30LXk08+qVu3bkmS6tWrp/DwcP3000+aPXt2ruPHjBlz30EFBwfLy8vrromWyWRS//791aVLF/Xu3duqb+LEiTnGe3t768qVK3e9rq+vr8qXL68TJ06oXr16SklJkZeXl/bt23cvtwEAAOAyyL8AAIA7yLfQ9fjjj1u9//777/McazAY7jsgSUpPT9fNmzfz3LJ+9epV9e/fXxEREYqPj893vX379mnt2rV6/fXX7zouOztb586dk7+/vyTJy8urQPFmZGRYzrS4w2QyFWguAACAM3C1/EsiBwMAADnlW+hatmzZg4jD4uLFi5o8ebIee+wxVaxYMdcx48eP12+//abQ0FB5eOR9zNj777+vxMREZWVlqW/fvlbb5v/X9evXNW/ePBUtWlT169e3KeYlS5Zo7ty5Ns0BAABwFq6Yf0nkYAAAIKd8C122yMrKUuvWrTVv3jxVq1atwPNmzpxpSVKuXr2qunXratasWXmOv379ulasWKHnn39eqamp6ty5c67j2rRpowYNGujnn3/W9OnTdfv2bY0cOdLS/95772nNmjWWNcuXL6958+bJx8enwLFLUlxcnDp16mTVZjKZFBsba9M6AAAAD4qr518SORgAAMjJroUuSTpx4oRu3rxp05y4uDh17dpVkmQ0Gi1b1/MyefJk1a1bVxMmTNDEiRMVERGhSpUq5Rjn5+cnPz8/Va5cWaVKlVLv3r31wgsvyNvbW5IUExOjoUOHSpKmTZumixcvqnbt2jbFfidmo9Fo8zwAAABHcfX8607c5GAAAODP8t53/gAFBAQoJCREISEh+SZZd8ZLUocOHfTUU0/ppZdeUlZWlqV/7Nix2rp1q9Ucb29vZWdnWx5dLUklS5a0XHf8+PH6/vvv9fHHH9vprgAAAJwX+RcAAHBHTlHouh8TJkzQ+fPn9cYbb1jaSpUqpQkTJmjz5s06efKk9u7dq3Hjxqlt27Z5bov/y1/+oj59+mj69Om6du3agwofAADA5ZB/AQAAZ+Xyha6SJUvqtdde05IlS7Rjxw5J0vDhwxUbG6ukpCRFR0drxIgRioqKUmJi4l3X6tu3r8xms1JSUh5E6AAAAC6J/AsAADgrg9lsNttrsaysLD322GNavXq1atasaa9lXVJaWpqio6O1bds2BQcHOzocAABgZ3zWOyf+LgAAuLf8PutdfkcXAAAAAAAAINm50OXl5aWlS5fm+gQeAAAAAAAAoDAVsXXC999/r/nz5+urr77SxYsXVapUKTVs2FADBgxQtWrV1KBBg8KIEwAAAAAAALgrm3Z07d27V127dtXFixc1dOhQ/fOf/9TAgQN16tQpdenSRQcPHiysOAEAAAAAAIC7smlH1/Tp0xUTE6OkpCSr9h49emjYsGFKSkrSypUr7RogAAAAAAAAUBA27eg6duyYOnXqlGtf165d9d1339klKAAAAAAAAMBWNhW6AgMDdeHChVz7zp8/r8DAQLsEBQAAAAAAANjKpkLX+PHjNW/ePP38889W7T/++KOSk5M1fvx4uwYHAAAAAAAAFJRNZ3StWrVK169fV5s2bVSlShUFBgbq7Nmz+ve//y2j0ajk5GQlJydbxq9Zs8buARdEz5495eHhoXfeeUcGgyFHX4MGDTRkyBCFhYXlOn/9+vWqVq2aJOngwYMaNWqUfHx8tGHDhkKPHQAAwFWRgwEAAEezqdBVq1Yt1apVq7Bisau9e/dqxYoVio2Nveu4RYsWqXr16lZt/v7+kqQ33nhDS5cuVd26dXXmzJlCixUAAMBdkIMBAABHsqnQNXjw4MKKw+4qVKig6dOn68knn9Sjjz6a57hSpUopKCgo176dO3dq1apVOnLkiBYvXlxYoQIAALgNcjAAAOBINp3RdUd6ero2btyod999Vxs3bnTKb9rat2+viIgIxcfH6/bt2/e0xsqVK1WlShU7RwYAAOC+yMEAAIAj2VToMpvNeu2119S8eXONGTNGKSkpGjNmjJ5++mklJSUVVoz3xGAwKDExUUePHtXSpUvvaQ0vLy87RwUAAODeyMEAAIAj2fTTxbfeeksffPCBXn31VbVq1UpeXl66efOmtmzZooSEBJUqVUp9+/YtrFhtVr58ecXHx2vKlCl68sknFRoammNMjx495OHxf/W+J598UrNnz7bpOhkZGcrIyLBqM5lM9xY0AACAiyMHAwAAjmJToev999/XSy+9pHbt2lnavLy8FBMTowsXLiglJcWpCl2S1KVLF23dulVjxozRe++9l6N/xowZlqf7SJKPj4/N11iyZInmzp17X3ECAAC4E3IwAADgCDYVus6cOaOqVavm2lelShWnPKvLYDBo8uTJatOmjRYtWpSjv2zZsgoJCbmva8TFxalTp05WbSaTKd+nDQEAALgrcjAAAOAINhW6KlasqP3796tBgwY5+vbv36+KFSvaKy67KlOmjMaOHauEhAQ98sgjucZ/P4xGo4xGo13XBAAAcHXkYAAA4EGz6TD6/v37680339TMmTP1448/KiMjQz/99JNmzZqlBQsWaMCAAYUV533r3LmzmjVrplOnTjk6FAAAgIcGORgAAHiQbNrR1a5dO2VnZ2vmzJl66623JP3xJMbSpUtrypQpatOmTaEEaS+TJ09W27ZtHR0GAADAQ4UcDAAAPCgGs9lstnWS2WzWzz//rAsXLsjf31+VK1eWwWAojPhcVlpamqKjo7Vt2zYFBwc7OhwAAGBnfNY7J/4uAAC4t/w+62366eKBAweUmZkpg8Gg0NBQ1atXT6GhoTIYDMrMzNTOnTvtFjgAAAAAAABgC5sKXb169dLx48dz7fv222/Vv39/uwQFAAAAAAAA2KpAZ3QtXbpU0h8/Wdy0aZO+/fZbq/7bt29r8+bNqlatmv0jBAAAAAAAAAqgQIWud955R5JkMBi0adMmeXt75xhToUIFJSYm2jU4AAAAAAAAoKAKVOjavn27JKl69eqaPXu26tatW6hBAQAAAAAAALYqUKHrjo8//pin1wAAAAAAAMAp2XQYfeXKlfXtt9/q/Pnzkv44s+v9999XQkKCtm7dWigBAgAAAAAAAAVhU6Fr7dq16tmzp+Uw+pSUFL322mv6/fff9cILL2jNmjWFEiQAAAAAAACQH5sKXcnJyRowYIAiIyMlSStWrNArr7yit99+W/369VNKSkqhBAkAAAAAAADkx6YzutLS0lS/fn3Lv0+dOqWoqChJUsOGDfXWW2/ZP8L/2rdvn3r16pVn/+DBgyVJc+fOtbQVLVpUERERGjdunKpWrWppf+yxx3Tjxg2r+S+88IIGDhxoef/ee+9p8eLF+v3331W5cmWNGDFCTz/9tL1uBwAAwCWQgwEAAFdiU6HrL3/5i7755hs1bNhQmzdvVlhYmEqWLClJOnXqlIxGY6EEKUkRERHauXOn5X3Lli2VlJSkiIgISZKvr68WL16sJk2aKCkpSZJ0+fJlLV++XL1799Ynn3yiEiVK6Pz587px44ZSU1NVunRpy3q+vr6Wf3/44Yd6/fXX9corr6hOnTr64osv9MILL+jDDz9U9erVC+0eAQAAnA05GAAAcCU2Fbp69OihGTNmaPfu3frqq680btw4SdKePXuUlJSkmJiYQglSkry9vRUUFGR5bzAYVLJkSau2/x0XFBSkcePG6bPPPtPWrVvVsWNHnT59WgaDQdWqVZOXl1eu19qyZYsGDRqkNm3aSJKeffZZbd++XR9//DFJFgAAeKiQgwEAAFdiU6Grd+/eKlWqlL7++mu1b99eHTt2lCSdP39eLVu21MiRIwsjxvtiMBhUqVIlnThxQtIfO88CAgLyTLAk5foTTC8vLxkMhkKLEwAAwJ2QgwEAAEew6TB6SerYsaMmTZpkKXJJUps2bfTKK6+oaNGi9ozNbk6cOGH5hvHUqVPy8PBQnz59VL9+fbVp00apqal5zr19+7Y2b96sPXv2qHXr1g8qZAAAAJdHDgYAAB40m3Z0uZqsrCy98847On36tKKjoyVJFSpUUGRkpKKjozVmzBjt27dPEyZMkJeXl9q1a2c1f+bMmUpJSVF2drbGjRuX55b5jIwMZWRkWLWZTKbCuSkAAAAnRw4GAAAcxaZC1+XLlzV16lRt3749R1Jxxw8//GCXwO7VF198YTkc9caNGypbtqzefPNNBQYGSpIiIyMVGRlpGV+lShVdvHhRixcvzpFkxcXFqX379vrmm280Y8YMSVJsbGyOay5ZssTqSUMAAAAPG3IwAADgDGwqdL388svatWuX+vbtq9DQ0MKK6b7Uq1dPU6ZMkSQVK1ZMpUuXzvdch4iICL399ts52gMCAhQQEKDQ0FAZDAbNmjUr1yQrLi5OnTp1smozmUy5jgUAAHBH5GAAAMAZ2FTo2rFjh8aNG6cuXboUVjz3zdfXVyEhIXn2v/XWW/Lw8NDzzz9vaTt27JjKlSsnScrOztbgwYPVr18/Pf7445Yx3t7eunnzZq5rGo1GGY1G+9wAAACACyIHAwAAzsCmw+iLFi2q4ODgworlgQgNDdU///lPpaam6tSpU/r44481Z84c9e7dW5Lk6ekpb29vjR49Wjt37tSpU6f02Wefafr06U5d4AMAAHBm5GAAAOBBsGlHV2xsrNatW6fGjRsXVjyFLjo6WhMnTtSCBQs0duxYBQcHa/To0XrmmWcsY1599VXNnj1b8fHxOnv2rMqXL6/u3burT58+DowcAADAdZGDAQCAB8FgNpvNBR08c+ZMbdiwQQEBAapTp448PT1zjBkzZoxdA3RVaWlpio6O1rZt21x+FxwAAMiJz3rnxN8FAAD3lt9nvU07ur7++muVL19e0h9nKvyv/A4cBQAAAAAAAAqLTYWuZcuWFVYcAAAAAAAAwH2xqdD1Z+np6bpw4YL8/f0VFBRkz5gAAAAAAAAAm9lc6Nq+fbveeOMN/fvf/5bZbJbBYFDlypU1YsQIPfXUU4URIwAAAAAAAJAvD1sGb926VYMHD1ZERISWLVumLVu2aNmyZapXr54GDx6s7du3F1acAAAAAAAAwF3ZtKNr7ty5iouL0+jRoy1tFSpUUL169eTr66s5c+bo6aeftnuQAAAAAAAAQH5s2tH1448/qnnz5rn2NW/eXD/++KM9YgIAAAAAAABsZlOhKyAgQL///nuufb/99psCAgLsEhQAAAAAAABgK5sKXc8884xef/117dq1y6p9z549mjVrlv72t7/ZNTgAAAAAAACgoGw6o2vw4ME6ffq0+vTpI39/f5UuXVrp6ek6f/68OnXqpEGDBhVWnDbp2bOn9u/fb3lvNBr11FNPaezYsSpVqpTi4+O1Zs0aS3/RokXVoEEDTZgwQRUqVLC0X7p0SZMmTdKGDRu0atUq1a5d+4HeBwAAgCshBwMAAI5m046uIkWKaNq0adq0aZOGDBmiv/71rxo8eLA+/vhjTZs2TZ6enoUVp80GDRqknTt36ssvv9TixYt14sQJDR8+3NLfuXNn7dy5Uzt37tSaNWtUokQJ9e/fX2azWZJ04MABtWvXTleuXHHULQAAALgccjAAAOBI+e7oWrt2ba7tvr6+8vX1lSQdOXJER44ckSR17NjRbsHdj+LFiysoKEiSVLp0aU2dOlV//etfdfLkSUmSj4+PpT8oKEgJCQlq3LixfvnlF1WuXFmbNm3S4MGD1bVrV4WFhTnsPgAAAFwJORgAAHCkfAtd8fHx+S5iMBgs/3aWQtf/CgkJkZeXlyXJysudbxPHjBkjLy+vBxEaAACA2yIHAwAAD1K+ha6jR4/etX/NmjWaOnWq/Pz8lJiYaLfA7O3ChQu6efOm/P39c/RlZmZq0qRJql+/vipXrixJJFgAAAB2QA4GAAAeJJsOo/+zc+fOafz48frss8/UpUsXjRo1SiVKlLBnbHZz+fJlJSUlqXr16pYkauXKlZbDUK9du6by5ctr3rx5VrvTCiojI0MZGRlWbSaT6f4DBwAAcGHkYAAA4EG7p0LXxo0bNXnyZPn4+GjRokVq2rSpveO6bzNnztTcuXMl/ZFE1ahRQ7Nnz7YkUTExMRo8eLAk6fr161q7dq26d++uVatWWRKxglqyZInlWgAAAA8zcjAAAOBINhW6zp8/r4kTJ+rTTz9V586dNXbsWKfdxRUXF6euXbvKbDZrxIgRCgkJUUhIiKXfz8/P6v3o0aN17Ngxffjhhxo9erTN1+rUqZNVm8lkUmxs7P3dBAAAgIshBwMAAI5U4ELX5s2bNXHiRHl7e2vBggWKiooqzLjuW0BAgCWJGj9+vLp166YePXooIiIizzmPPPLIPT3K2mg0ymg03nOsAAAA7oIcDAAAOJJHfgMuXryoF198US+++KKeeuopffzxx05f5PpfderUUfv27TV16lTLE32uXbum9PR0paen68SJE0pNTdWmTZv01FNPOThaAAAA90AOBgAAHrR8d3S1adNG58+fV4kSJXTixAkNHDjwruOXLl1qt+Dsafjw4WrVqpU++ugjSVJqaqpSU1MlSUWKFFHFihWVkJBAkgUAAGBH5GAAAOBByrfQ9eSTT97TU3AcadmyZTnaypQpo0OHDkmSOnbsqFdffbXA6x07dsxusQEAALgrcjAAAOBo+Ra6bElGAAAAAAAAAEfJ94wuAAAAAAAAwBVQ6AIAAAAAAIBboNAFAAAAAAAAt0ChCwAAAAAAAG6BQhcAAAAAAADcAoUuAAAAAAAAuAUKXQAAAAAAAHALFLoAAAAAAADgFoo4OoCC6tmzp/bv35+jffjw4erbt68kaffu3UpOTtaRI0fk5eWlRo0a6aWXXlKVKlUs4+Pj47VmzZoc6zz33HMaPXq09u3bp169elnaPT09FRoaqkGDBqlVq1aFcGcAAADOixwMAAC4EpcpdEnSoEGD1L17d6u24sWLS5I2btyoMWPGaPDgwZo2bZpu3ryp5cuXq2vXrnr33XcVHh5umdO5c2e99NJLVuv4+PhY/XvLli2SpKysLO3YsUMjRoxQUFCQnnjiicK6PQAAAKdEDgYAAFyFSxW6ihcvrqCgoBztmZmZmjhxol555RV16tTJ0p6QkKDMzEwlJCRo1apVlnYfH59c17nDYDBY9T/77LP6+uuvtWrVKpIsAADw0CEHAwAArsItzujaunWr/Pz81LFjxxx9ffv2VYkSJXTp0qX7ukbVqlV16tSp+1oDAADAnZCDAQAAZ+NSO7ry8t133ykiIkIGgyFHX5UqVfTOO+/c9zXOnDkjf3//XPsyMjKUkZFh1WYyme77mgAAAM6MHAwAADgblyp0zZw5U3PnzrVqW7dunTIzM+Xn51fgdVauXJnjMNTt27fnmkSZzWYdPHhQH330kaZNm5brekuWLMkRFwAAgLsgBwMAAK7CpQpdvXv3VpcuXazaypYtKz8/P509e9aqfejQofryyy8t7xcuXKh69epJkmJiYjR48GCr8Uaj0fLvq1evKiIiQpJ069Ytmc1mDRkyRC1btsw1rri4OKtzKaQ/vk2MjY218Q4BAACcDzkYAABwFS5V6PL391dISEiO9ho1amj27Nm6ffu2PDz+OHZs3LhxunbtmrKzsxUTE6MiRf7vVv38/HJd546iRYtq7dq1kqRDhw7p5ZdfVnR0dJ7jjUajVZIGAADgTsjBAACAq3CLw+ijo6N1+fJlrV692tJWunRphYSE6Pjx4ypWrJiqVq1a4PU8PT0VEhKikJAQdezYUU2aNMlzyzwAAMDDihwMAAA4G5fa0XXlyhWlp6dbtfn4+MhoNGrChAkaO3aszpw5Y/n2cMeOHXr99dc1bNgwFS9e/J6vO2bMGLVr1047duxQVFTU/d4GAACASyEHAwAArsKlCl3JyclKTk62anvuuec0evRotWvXToGBgXrzzTe1YMECmc1mVa9eXYmJiYqJibmv61auXFk9evTQ1KlT1aRJE3l5ed3XegAAAK6EHAwAALgKg9lsNjs6CHeUlpam6Ohobdu2TcHBwY4OBwAA2Bmf9c6JvwsAAO4tv896tzijCwAAAAAAAKDQBQAAAAAAALdAoQsAAAAAAABugUIXAAAAAAAA3AKFLgAAAAAAALgFCl0AAAAAAABwCxS6AAAAAAAA4BYodAEAAAAAAMAtUOgCAAAAAACAW3CJQlfPnj0VFhZmedWvX1/x8fHKyMjQvn37rPr+9zVnzhxJ0pw5c3Lt79Onj+U6YWFhmjt3bq4xhIWFad++fQ/kfgEAAJwBORgAAHA1RRwdQEENGjRI3bt3l9ls1unTp5WUlKThw4crOTlZO3futIxr2bKlkpKSFBERIUny9fW19DVp0kRJSUlW63p7e1u9nzdvnqKjo1WjRo1CvBsAAADXQA4GAABcicsUuooXL66goCBJUunSpTVt2jRFR0fr7NmzKl++vGWcwWBQyZIlLWP/zNvbO9f2PytXrpxGjRql1atX50jAAAAAHjbkYAAAwJW4xE8XcxMcHCwvLy+dPHnSruuOHDlS586dU3Jysl3XBQAAcAfkYAAAwJm5zI6u/5Wenq6bN2/qkUceseu6pUqV0qRJkzR06FC1aNFCtWvXzndORkaGMjIyrNpMJpNd4wIAAHAG5GAAAMCZuWSh6+LFi5o8ebIee+wxVaxYscDzvvzyS8u5EXckJCSoU6dOVm0tWrRQ69atNWrUKK1du1ZFixa967pLlizJ8wBVAAAAd0EOBgAAnJ3LFLpmzpxpSWSuXr2qunXratasWTat8cQTT2jKlClWbYGBgbmOTUhIUNu2bTVr1iyNHj36ruvGxcXlSNRMJpNiY2Ntig8AAMDZkIMBAABX4jKFrri4OHXt2lWSZDQa5e/vb/Mavr6+CgkJKdDYkiVLavLkyRowYICio6PvOtZoNMpoNNocDwAAgLMjBwMAAK7EZQpdAQEBBU6Q7KV58+bq0KGDxo4d+0CvCwAA4CzIwQAAgCtx2acu3ousrCylp6dbvS5cuHDXOWPHjlVWVtYDihAAAMD9kIMBAIAHxWV2dNnD7t271axZM6u2mjVrKjU1Nc85RqNRU6ZMUZ8+fQo7PAAAALdEDgYAAB4Ug9lsNjs6CHeUlpam6Ohobdu2TcHBwY4OBwAA2Bmf9c6JvwsAAO4tv8/6h+qniwAAAAAAAHBfFLoAAAAAAADgFih0AQAAAAAAwC08VIfRP0jZ2dmSJJPJ5OBIAABAYbjzGX/nMx/OgRwMAAD3ll8ORqGrkKSnp0uSYmNjHRwJAAAoTOnp6QoJCXF0GPgvcjAAAB4OeeVgPHWxkFy/fl3ffvutgoKC5Onp6ehwAACAnWVnZys9PV21atVSsWLFHB0O/oscDAAA95ZfDkahCwAAAAAAAG6Bw+gBAAAAAADgFih0AUAu4uPjNXDgQEeHAQAA8FC4W+71ww8/KCwsTGlpaZo/f77q1q2rX3/9tcBrp6WlKSwsTD/88IO9wgXgxDiMHgAAAADgEnr06KHmzZvzEBAAeWJHFwAAAADAJZQoUULVq1d3dBgAnBiFLgC4Bzdv3tSsWbPUrFkzhYeHq0OHDtq6daulf/PmzQoPD9fly5ctbfPnz1dYWJiOHTtmaVu7dq3q1Kmj69evP9D4AQAAXFFqaqrq1atn1bZ582a1bt1a4eHhatu2rb777jtFRkYqNTXVatyZM2f03HPPqVatWmrUqJHmz5//IEMH8IBQ6AKAe5CQkKD33ntPQ4cOVUpKiho3bqwhQ4ZYil1NmzaVh4eH9u3bZ5mze/duSdKePXssbbt27VKjRo1yfSwuAADAw8RsNuvWrVs5XtnZ2XnO+eKLLzRs2DA1bdpUKSkp6t69u0aNGqUrV67kGDt9+nRFRUVp0aJFatOmjWbOnKktW7YU5i0BcADO6AIAG/38889KTU3VwoULFRkZKUlq3LixMjMz9cYbb6hFixby8/NTRESE9uzZoxYtWujatWs6dOiQ2rZtqz179qh3794ym83avXu3BgwY4OA7AgAAcLzt27erZs2aNs2ZM2eOYmJiNG7cOEl/5GQ+Pj4aM2ZMjrFdu3ZVr169JEmNGjXS4cOHtX37drVs2fL+gwfgNNjRBQA22rNnj0qVKqUnn3zSqr1Dhw7697//rdOnT0uSoqKiLLu4Dh48qMDAQMXFxenAgQO6deuWjh8/rrNnzyoqKuqB3wMAAICzadCggVatWpXjNWPGjFzHX716Vd98843atm1r1f7Xv/411/ERERFW70NDQ5WRkWGf4AE4DXZ0AYCNLly4oNKlS8tgMFi1ly1b1tJfpkwZRUZGavr06TKZTNq1a5eaNGmimjVrqkiRIjpy5IgOHz6sSpUqqUKFCo64DQAAAKfi5+en2rVr52gvUiT3/229dOmSzGazypQpY9VeokQJFS9ePN91DAaDzGbzfUQMwBmxowsAbOTv76/09PQciZHJZLL0S1K1atVUrlw57dq1S3v37lXTpk3l6emphg0bau/evdq9eze7uQAAAO7RnWLWuXPnrNpv3Liha9euOSIkAE6AQhcA2KhRo0a6cOGC5WeJd2zYsEGVK1e2+lYxMjJSGzdu1LFjx9S4cWNJf5wd8fnnn+vAgQMUugAAAO6R0WhUtWrV9Mknn1i1b9u2Tbdv33ZQVAAcjZ8uAkAezp07py+++MKqzdPTU02bNlXnzp01YsQIvfjii3r00Ue1Y8cOffDBB5ozZ47V+MjISA0aNEg1atRQQECAJKlZs2Z65ZVX5Ovrm+Px2AAAACi4QYMG6YUXXpDRaFRUVJR++eUXrVixQt7e3o4ODYCDUOgCgDz861//0j/+8Q+rNl9fXx06dEiTJk1ScnKyZs+erfPnz6tq1aqaM2eOWrRoYTW+cePG8vLysuzmkqRHH31UwcHBCgsLIwkDAAC4D61atVJSUpLefPNNvf322woNDdW0adP03HPPOTo0AA5iMHP6HgAAAADABZnNZl25ckUlSpSwtGVmZqpBgwaaPXt2ji8hAbg/dnQBAAAAAFxScnKyPvjgA/Xv31+hoaG6cOGCli5dqsDAQDVq1MjR4QFwAApdAAAAAACX1LdvX0nSokWLdOrUKZUoUUINGzbU1KlTrXZ5AXh48NNFAAAAAAAAuAUPRwcAAAAAAAAA2AOFLgAAAAAAALgFCl0AAAAAAABwCxS6AAAAAAAA4BYodAEAAAAAAMAtUOgCAAAAAACAW/j/zhaaEUc7nMYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x3960 with 22 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.family'] = \"Inter\"\n",
    "plt.figure(figsize=(20,5*len(grs)))\n",
    "figcount = 1\n",
    "for gr in grs:\n",
    "    \n",
    "    for gr_ in [0,1]:\n",
    "        sub = df.loc[df.loc[:,\"Neural subgroup (0=low, 1=high)\"]==gr_]\n",
    "        # y = sub.columns[1:].tolist()\n",
    "        x = sub.index.tolist()\n",
    "        plt.subplot(len(grs),2,figcount)\n",
    "\n",
    "        for idx in tqdm(x):\n",
    "            for col in y:\n",
    "                color = \"none\"\n",
    "                if sub.loc[idx, col]==gr:\n",
    "                    color=\"black\"\n",
    "                plt.scatter(idx, col, color=color)\n",
    "\n",
    "        plt.xticks([],[])\n",
    "        if gr_==0:\n",
    "            name=\"Low\"\n",
    "        else:\n",
    "            name=\"High\"\n",
    "        \n",
    "        plt.xlabel(name)\n",
    "        # plt.ylabel(gr)\n",
    "        if figcount%2==1:\n",
    "            plt.ylabel(gr)\n",
    "        figcount+=1\n",
    "#plt.suptitle(\"Mutation variants (TCGA)\")\n",
    "plt.savefig(\"TCGA_mut.pdf\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "02bfb2e9-3c2d-40fb-b16d-dd2cc953dae1",
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = sns.color_palette(\"colorblind\", len(grs))\n",
    "colormapping = dict(zip(grs, colors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d023ede9-6bed-4f56-b35a-0d5730ef5fd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['PTEN',\n",
       " 'EGFR',\n",
       " 'RB1',\n",
       " 'TP53',\n",
       " 'NF1',\n",
       " 'PIK3R1',\n",
       " 'ATRX',\n",
       " 'PIK3CA',\n",
       " 'TERT',\n",
       " 'CIC',\n",
       " 'FUBP1']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ab976197-d534-4f8a-84eb-f7da3fa98f51",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 72/72 [00:01<00:00, 49.52it/s]\n",
      "100%|██████████| 72/72 [00:02<00:00, 29.32it/s]\n",
      "100%|██████████| 72/72 [00:03<00:00, 20.86it/s]\n",
      "100%|██████████| 72/72 [00:04<00:00, 15.93it/s]\n",
      "100%|██████████| 72/72 [00:07<00:00,  9.29it/s]\n",
      "100%|██████████| 72/72 [00:06<00:00, 10.98it/s]\n",
      "100%|██████████| 72/72 [00:07<00:00,  9.52it/s]\n",
      "100%|██████████| 72/72 [00:08<00:00,  8.13it/s]\n",
      "100%|██████████| 72/72 [00:09<00:00,  7.38it/s]\n",
      "100%|██████████| 72/72 [00:11<00:00,  6.30it/s]\n",
      "100%|██████████| 72/72 [00:12<00:00,  5.58it/s]\n",
      "100%|██████████| 49/49 [00:00<00:00, 56.11it/s]\n",
      "100%|██████████| 49/49 [00:01<00:00, 36.68it/s]\n",
      "100%|██████████| 49/49 [00:02<00:00, 20.32it/s]\n",
      "100%|██████████| 49/49 [00:02<00:00, 21.79it/s]\n",
      "100%|██████████| 49/49 [00:02<00:00, 17.86it/s]\n",
      "100%|██████████| 49/49 [00:03<00:00, 15.18it/s]\n",
      "100%|██████████| 49/49 [00:03<00:00, 13.29it/s]\n",
      "100%|██████████| 49/49 [00:04<00:00,  9.96it/s]\n",
      "100%|██████████| 49/49 [00:04<00:00, 10.56it/s]\n",
      "100%|██████████| 49/49 [00:05<00:00,  9.45it/s]\n",
      "100%|██████████| 49/49 [00:05<00:00,  8.69it/s]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1728x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['font.family'] = \"Inter\"\n",
    "plt.figure(figsize=(24,7))\n",
    "\n",
    "for gr_ in [0,1]:\n",
    "    ax = plt.subplot(1,2,gr_+1)\n",
    "    for gr in grs:\n",
    "        k=0\n",
    "        sub = df.loc[df.loc[:,\"Neural subgroup (0=low, 1=high)\"]==gr_]\n",
    "        # y = sub.columns[1:].tolist()\n",
    "        x = sub.index.tolist()\n",
    "        for idx in tqdm(x):\n",
    "            for col in y:\n",
    "                color = \"none\"\n",
    "                if sub.loc[idx, col]==gr:\n",
    "                    color=colormapping[gr]\n",
    "                    if k==0:\n",
    "                        ax.scatter(idx, col, color=color, label=gr, marker=\"s\", s=100)\n",
    "                        k+=1\n",
    "                    else:\n",
    "                        ax.scatter(idx, col, color=color, label=\"_gr\", marker=\"s\", s=100)\n",
    "                else:\n",
    "                    ax.scatter(idx, col, color=\"none\", label=\"_gr\", marker=\"s\", s=1)\n",
    "                \n",
    "        ax.set_xticks([],[])\n",
    "        if gr_==0:\n",
    "            name=\"Low\"\n",
    "        else:\n",
    "            name=\"High\"\n",
    "        \n",
    "        ax.set_xlabel(name)\n",
    "\n",
    "        if gr_==0:\n",
    "            if ax.get_legend():\n",
    "                ax.get_legend().remove()\n",
    "        else:\n",
    "            ax.set_yticklabels([])\n",
    "plt.tight_layout()\n",
    "plt.legend(bbox_to_anchor=(1,1))\n",
    "plt.savefig(\"TCGA_mut_joint.pdf\", bbox_inches=\"tight\", frameon=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2a545c5b-2822-46ab-bc14-123d3d482115",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fcbf6b13550>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ax.get_legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3c6fad0-370c-44d7-a456-dcaf87e8143b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f26f10f0-e606-4559-9c8b-f0427ea05b92",
   "metadata": {},
   "source": [
    "-----"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "dbe1c8b3-eb39-487e-a090-17b5c4b0854c",
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = sub[y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bcc3ad77-2d8b-41eb-aa74-7d6da9ce79a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Missense_Mutation',\n",
       " 'Frame_Shift_Del',\n",
       " 'Splice_Site',\n",
       " 'Multiple',\n",
       " 'Nonsense_Mutation',\n",
       " 'Frame_Shift_Ins',\n",
       " 'In_Frame_Del',\n",
       " 'In_Frame_Ins',\n",
       " 'Silent',\n",
       " 'Splice_Region',\n",
       " 'Nonstop_Mutation']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "28104b96-51a0-4b4b-94cc-c8f0135f6168",
   "metadata": {},
   "outputs": [],
   "source": [
    "i=0\n",
    "for gr in grs+[\"No_mutation\", \"Unavailable\"]:\n",
    "    tmp.replace({gr: i}, inplace=True)\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0e2d71b8-b7ba-431f-8883-5ba7743079a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "tmp = sub[y]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cefca4cd-1e95-49cb-8844-543df9c95e67",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "0b186293-3087-4b67-aaa6-60be930e7bf9",
   "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>PTEN</th>\n",
       "      <th>EGFR</th>\n",
       "      <th>RB1</th>\n",
       "      <th>TP53</th>\n",
       "      <th>NF1</th>\n",
       "      <th>PIK3R1</th>\n",
       "      <th>ATRX</th>\n",
       "      <th>PIK3CA</th>\n",
       "      <th>TERT</th>\n",
       "      <th>CIC</th>\n",
       "      <th>FUBP1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TCGA-06-5418-01A</th>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-28-6450-01A</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Multiple</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-RR-A6KA-01A</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-76-6663-01A</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Nonsense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Nonsense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-74-6584-01A</th>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-14-1043-01B</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Multiple</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-74-6575-01A</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Frame_Shift_Ins</td>\n",
       "      <td>Multiple</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-6700-01A</th>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-26-6174-01A</th>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-5408-01A</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Silent</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-28-5219-01A</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Splice_Site</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-28-5207-01A</th>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-26-5134-01A</th>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-14-1395-01B</th>\n",
       "      <td>Frame_Shift_Del</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-12-5299-01A</th>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-19-5956-01A</th>\n",
       "      <td>Multiple</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>Nonsense_Mutation</td>\n",
       "      <td>Multiple</td>\n",
       "      <td>Multiple</td>\n",
       "      <td>Multiple</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-28-5216-01A</th>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-OX-A56R-01A</th>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-19-5960-01A</th>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-19-0957-01C</th>\n",
       "      <td>Splice_Site</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-76-4925-01A</th>\n",
       "      <td>Frame_Shift_Ins</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-76-4926-01B</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-A5U0-01A</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Multiple</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Frame_Shift_Del</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-5415-01A</th>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-26-5135-01A</th>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-74-6581-01A</th>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-19-5959-01A</th>\n",
       "      <td>Frame_Shift_Del</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-14-0740-01B</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-76-6660-01A</th>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-12-5301-01A</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-A7TK-01A</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-76-4934-01A</th>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-76-4931-01A</th>\n",
       "      <td>Nonsense_Mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-76-6285-01A</th>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-5858-01A</th>\n",
       "      <td>Nonsense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Splice_Site</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-6695-01A</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>In_Frame_Ins</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>In_Frame_Del</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-41-5651-01A</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Splice_Site</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-RR-A6KB-01A</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-76-6656-01A</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-5411-01A</th>\n",
       "      <td>Nonsense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-6388-01A</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-26-5132-01A</th>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "      <td>Unavailable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-06-5856-01A</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-76-6191-01A</th>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-76-4935-01A</th>\n",
       "      <td>Frame_Shift_Del</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-76-6286-01A</th>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Nonsense_Mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-4W-AA9R-01A</th>\n",
       "      <td>Frame_Shift_Del</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-76-6283-01A</th>\n",
       "      <td>In_Frame_Del</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Frame_Shift_Del</td>\n",
       "      <td>Missense_Mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TCGA-28-5215-01A</th>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>Splice_Region</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "      <td>No_mutation</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               PTEN               EGFR                RB1  \\\n",
       "Sample ID                                                                   \n",
       "TCGA-06-5418-01A  Missense_Mutation        No_mutation        No_mutation   \n",
       "TCGA-28-6450-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-RR-A6KA-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-6663-01A        No_mutation        No_mutation  Nonsense_Mutation   \n",
       "TCGA-74-6584-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-14-1043-01B        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-74-6575-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-06-6700-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-26-6174-01A  Missense_Mutation        No_mutation        No_mutation   \n",
       "TCGA-06-5408-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-28-5219-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-28-5207-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-26-5134-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-14-1395-01B    Frame_Shift_Del  Missense_Mutation        No_mutation   \n",
       "TCGA-12-5299-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-19-5956-01A           Multiple  Missense_Mutation  Missense_Mutation   \n",
       "TCGA-28-5216-01A  Missense_Mutation        No_mutation        No_mutation   \n",
       "TCGA-OX-A56R-01A  Missense_Mutation  Missense_Mutation        No_mutation   \n",
       "TCGA-19-5960-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-19-0957-01C        Splice_Site        No_mutation        No_mutation   \n",
       "TCGA-76-4925-01A    Frame_Shift_Ins        No_mutation        No_mutation   \n",
       "TCGA-76-4926-01B        No_mutation  Missense_Mutation        No_mutation   \n",
       "TCGA-06-A5U0-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-06-5415-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-26-5135-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-74-6581-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-19-5959-01A    Frame_Shift_Del        No_mutation        No_mutation   \n",
       "TCGA-14-0740-01B        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-6660-01A  Missense_Mutation        No_mutation        No_mutation   \n",
       "TCGA-12-5301-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-06-A7TK-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-4934-01A  Missense_Mutation        No_mutation        No_mutation   \n",
       "TCGA-76-4931-01A  Nonsense_Mutation  Missense_Mutation        No_mutation   \n",
       "TCGA-76-6285-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-06-5858-01A  Nonsense_Mutation        No_mutation        Splice_Site   \n",
       "TCGA-06-6695-01A        No_mutation       In_Frame_Ins        No_mutation   \n",
       "TCGA-41-5651-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-RR-A6KB-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-6656-01A        No_mutation  Missense_Mutation        No_mutation   \n",
       "TCGA-06-5411-01A  Nonsense_Mutation        No_mutation        No_mutation   \n",
       "TCGA-06-6388-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-26-5132-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-06-5856-01A        No_mutation  Missense_Mutation        No_mutation   \n",
       "TCGA-76-6191-01A  Missense_Mutation  Missense_Mutation        No_mutation   \n",
       "TCGA-76-4935-01A    Frame_Shift_Del        No_mutation        No_mutation   \n",
       "TCGA-76-6286-01A  Missense_Mutation        No_mutation  Nonsense_Mutation   \n",
       "TCGA-4W-AA9R-01A    Frame_Shift_Del        No_mutation        No_mutation   \n",
       "TCGA-76-6283-01A       In_Frame_Del        No_mutation    Frame_Shift_Del   \n",
       "TCGA-28-5215-01A        No_mutation        No_mutation        No_mutation   \n",
       "\n",
       "                               TP53                NF1             PIK3R1  \\\n",
       "Sample ID                                                                   \n",
       "TCGA-06-5418-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-28-6450-01A        No_mutation           Multiple        No_mutation   \n",
       "TCGA-RR-A6KA-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-6663-01A        No_mutation  Nonsense_Mutation        No_mutation   \n",
       "TCGA-74-6584-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-14-1043-01B        No_mutation           Multiple        No_mutation   \n",
       "TCGA-74-6575-01A        No_mutation    Frame_Shift_Ins           Multiple   \n",
       "TCGA-06-6700-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-26-6174-01A        No_mutation        No_mutation  Missense_Mutation   \n",
       "TCGA-06-5408-01A        No_mutation             Silent        No_mutation   \n",
       "TCGA-28-5219-01A        Splice_Site        No_mutation        No_mutation   \n",
       "TCGA-28-5207-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-26-5134-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-14-1395-01B        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-12-5299-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-19-5956-01A  Nonsense_Mutation           Multiple           Multiple   \n",
       "TCGA-28-5216-01A  Missense_Mutation        No_mutation        No_mutation   \n",
       "TCGA-OX-A56R-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-19-5960-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-19-0957-01C        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-4925-01A  Missense_Mutation        No_mutation        No_mutation   \n",
       "TCGA-76-4926-01B        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-06-A5U0-01A           Multiple        No_mutation        No_mutation   \n",
       "TCGA-06-5415-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-26-5135-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-74-6581-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-19-5959-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-14-0740-01B  Missense_Mutation        No_mutation        No_mutation   \n",
       "TCGA-76-6660-01A  Missense_Mutation        No_mutation        No_mutation   \n",
       "TCGA-12-5301-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-06-A7TK-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-4934-01A  Missense_Mutation        No_mutation        No_mutation   \n",
       "TCGA-76-4931-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-6285-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-06-5858-01A  Missense_Mutation        No_mutation        No_mutation   \n",
       "TCGA-06-6695-01A        No_mutation        No_mutation       In_Frame_Del   \n",
       "TCGA-41-5651-01A        Splice_Site        No_mutation        No_mutation   \n",
       "TCGA-RR-A6KB-01A        No_mutation        No_mutation  Missense_Mutation   \n",
       "TCGA-76-6656-01A        No_mutation        No_mutation  Missense_Mutation   \n",
       "TCGA-06-5411-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-06-6388-01A        No_mutation        No_mutation  Missense_Mutation   \n",
       "TCGA-26-5132-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-06-5856-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-6191-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-4935-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-6286-01A  Missense_Mutation        No_mutation        No_mutation   \n",
       "TCGA-4W-AA9R-01A  Missense_Mutation        No_mutation        No_mutation   \n",
       "TCGA-76-6283-01A  Missense_Mutation        No_mutation        No_mutation   \n",
       "TCGA-28-5215-01A      Splice_Region        No_mutation        No_mutation   \n",
       "\n",
       "                               ATRX             PIK3CA               TERT  \\\n",
       "Sample ID                                                                   \n",
       "TCGA-06-5418-01A        No_mutation        No_mutation  Missense_Mutation   \n",
       "TCGA-28-6450-01A        No_mutation  Missense_Mutation        No_mutation   \n",
       "TCGA-RR-A6KA-01A        No_mutation  Missense_Mutation        No_mutation   \n",
       "TCGA-76-6663-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-74-6584-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-14-1043-01B        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-74-6575-01A  Missense_Mutation        No_mutation        No_mutation   \n",
       "TCGA-06-6700-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-26-6174-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-06-5408-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-28-5219-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-28-5207-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-26-5134-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-14-1395-01B        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-12-5299-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-19-5956-01A           Multiple        No_mutation  Missense_Mutation   \n",
       "TCGA-28-5216-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-OX-A56R-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-19-5960-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-19-0957-01C        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-4925-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-4926-01B        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-06-A5U0-01A    Frame_Shift_Del        No_mutation        No_mutation   \n",
       "TCGA-06-5415-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-26-5135-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-74-6581-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-19-5959-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-14-0740-01B        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-6660-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-12-5301-01A        No_mutation  Missense_Mutation        No_mutation   \n",
       "TCGA-06-A7TK-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-4934-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-4931-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-6285-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-06-5858-01A  Missense_Mutation        No_mutation        No_mutation   \n",
       "TCGA-06-6695-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-41-5651-01A        No_mutation  Missense_Mutation        No_mutation   \n",
       "TCGA-RR-A6KB-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-6656-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-06-5411-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-06-6388-01A  Missense_Mutation        No_mutation        No_mutation   \n",
       "TCGA-26-5132-01A        Unavailable        Unavailable        Unavailable   \n",
       "TCGA-06-5856-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-6191-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-4935-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-6286-01A        No_mutation  Missense_Mutation        No_mutation   \n",
       "TCGA-4W-AA9R-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-76-6283-01A        No_mutation        No_mutation        No_mutation   \n",
       "TCGA-28-5215-01A        No_mutation        No_mutation        No_mutation   \n",
       "\n",
       "                                CIC              FUBP1  \n",
       "Sample ID                                               \n",
       "TCGA-06-5418-01A        No_mutation        No_mutation  \n",
       "TCGA-28-6450-01A        No_mutation        No_mutation  \n",
       "TCGA-RR-A6KA-01A        No_mutation        No_mutation  \n",
       "TCGA-76-6663-01A        No_mutation        No_mutation  \n",
       "TCGA-74-6584-01A        Unavailable        Unavailable  \n",
       "TCGA-14-1043-01B        No_mutation        No_mutation  \n",
       "TCGA-74-6575-01A        No_mutation        No_mutation  \n",
       "TCGA-06-6700-01A        Unavailable        Unavailable  \n",
       "TCGA-26-6174-01A        No_mutation        No_mutation  \n",
       "TCGA-06-5408-01A        No_mutation        No_mutation  \n",
       "TCGA-28-5219-01A        No_mutation        No_mutation  \n",
       "TCGA-28-5207-01A        Unavailable        Unavailable  \n",
       "TCGA-26-5134-01A        Unavailable        Unavailable  \n",
       "TCGA-14-1395-01B        No_mutation        No_mutation  \n",
       "TCGA-12-5299-01A        Unavailable        Unavailable  \n",
       "TCGA-19-5956-01A  Missense_Mutation        No_mutation  \n",
       "TCGA-28-5216-01A        No_mutation        No_mutation  \n",
       "TCGA-OX-A56R-01A        No_mutation        No_mutation  \n",
       "TCGA-19-5960-01A        Unavailable        Unavailable  \n",
       "TCGA-19-0957-01C        No_mutation  Missense_Mutation  \n",
       "TCGA-76-4925-01A        No_mutation        No_mutation  \n",
       "TCGA-76-4926-01B        No_mutation        No_mutation  \n",
       "TCGA-06-A5U0-01A        No_mutation        No_mutation  \n",
       "TCGA-06-5415-01A        Unavailable        Unavailable  \n",
       "TCGA-26-5135-01A        Unavailable        Unavailable  \n",
       "TCGA-74-6581-01A        Unavailable        Unavailable  \n",
       "TCGA-19-5959-01A        No_mutation        No_mutation  \n",
       "TCGA-14-0740-01B        No_mutation        No_mutation  \n",
       "TCGA-76-6660-01A        No_mutation        No_mutation  \n",
       "TCGA-12-5301-01A        No_mutation        No_mutation  \n",
       "TCGA-06-A7TK-01A  Missense_Mutation        No_mutation  \n",
       "TCGA-76-4934-01A        No_mutation        No_mutation  \n",
       "TCGA-76-4931-01A        No_mutation        No_mutation  \n",
       "TCGA-76-6285-01A        Unavailable        Unavailable  \n",
       "TCGA-06-5858-01A        No_mutation        No_mutation  \n",
       "TCGA-06-6695-01A        No_mutation        No_mutation  \n",
       "TCGA-41-5651-01A        No_mutation        No_mutation  \n",
       "TCGA-RR-A6KB-01A        No_mutation        No_mutation  \n",
       "TCGA-76-6656-01A        No_mutation        No_mutation  \n",
       "TCGA-06-5411-01A        No_mutation        No_mutation  \n",
       "TCGA-06-6388-01A        No_mutation        No_mutation  \n",
       "TCGA-26-5132-01A        Unavailable        Unavailable  \n",
       "TCGA-06-5856-01A        No_mutation        No_mutation  \n",
       "TCGA-76-6191-01A        No_mutation        No_mutation  \n",
       "TCGA-76-4935-01A        No_mutation        No_mutation  \n",
       "TCGA-76-6286-01A        No_mutation        No_mutation  \n",
       "TCGA-4W-AA9R-01A        No_mutation        No_mutation  \n",
       "TCGA-76-6283-01A        No_mutation        No_mutation  \n",
       "TCGA-28-5215-01A        No_mutation        No_mutation  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00831afd-a6e4-44b8-be9d-5e52cbedbf05",
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b51667f4-7cd4-446f-94d0-26168fb7f10d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}