rrd_modeling_explorer.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "import rrdtool\n",
    "import pickle\n",
    "import datetime as dt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.dates import HourLocator, DateFormatter\n",
    "from auserverdata.preparation.rrd import (\n",
    "    parse_rrds_for_all_collectd_servers,\n",
    "    parse_rrds_for_all_snmp_servers,\n",
    "    get_time_series_data_for_collectd_servers,\n",
    "    get_time_series_data_for_snmp_servers,\n",
    "    get_feature_names_for_timestamp_set,\n",
    "    get_feature_names_with_substring_present,\n",
    "    map_feature_names_to_timestamps,\n",
    "    map_timestamp_sets_to_feature_names,\n",
    "    print_timestamp_set_to_feature_names_map_info\n",
    ")\n",
    "from auserverdata.local import get_local_config\n",
    "from auserverdata.analysis import (\n",
    "    plot_time_series_data_on_single_plot,\n",
    "    plot_time_series_data_in_vertical_stack\n",
    ")\n",
    "from joblib import Parallel,delayed\n",
    "import pyarrow.parquet as pq\n",
    "import pyarrow.compute as pc\n",
    "import xgboost as xgb\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "local_config = get_local_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "collectd_ts_df = get_time_series_data_for_collectd_servers(rrd_dir=local_config.rrd_dir, collectd_servers=['gpu1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "timestamps_per_feature_name = map_feature_names_to_timestamps(collectd_ts_df)\n",
    "feature_names_per_timestamp_set = map_timestamp_sets_to_feature_names(timestamps_per_feature_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "************************************************************\n",
      "Info about Timestamp Set 0\n",
      "    Number of timestamps: 1200\n",
      "    Number of features with the these timestamps: 225\n",
      "    Step size between timestamps: 0 days 00:00:10\n",
      "    Start timestamp: 2023-05-20 00:51:28\n",
      "    End timestamp: 2023-05-20 04:11:18\n",
      "    Total duration: 0 days 03:19:50\n",
      "************************************************************\n",
      "Info about Timestamp Set 1\n",
      "    Number of timestamps: 1200\n",
      "    Number of features with the these timestamps: 8\n",
      "    Step size between timestamps: 0 days 00:00:10\n",
      "    Start timestamp: 2023-05-20 00:51:33\n",
      "    End timestamp: 2023-05-20 04:11:23\n",
      "    Total duration: 0 days 03:19:50\n",
      "************************************************************\n",
      "Info about Timestamp Set 2\n",
      "    Number of timestamps: 1200\n",
      "    Number of features with the these timestamps: 1\n",
      "    Step size between timestamps: 0 days 00:00:10\n",
      "    Start timestamp: 2020-07-09 14:49:41\n",
      "    End timestamp: 2020-07-09 18:09:31\n",
      "    Total duration: 0 days 03:19:50\n",
      "************************************************************\n",
      "Info about Timestamp Set 3\n",
      "    Number of timestamps: 1200\n",
      "    Number of features with the these timestamps: 1\n",
      "    Step size between timestamps: 0 days 00:00:10\n",
      "    Start timestamp: 2023-05-17 21:46:18\n",
      "    End timestamp: 2023-05-18 01:06:08\n",
      "    Total duration: 0 days 03:19:50\n",
      "************************************************************\n",
      "Info about Timestamp Set 4\n",
      "    Number of timestamps: 1200\n",
      "    Number of features with the these timestamps: 57\n",
      "    Step size between timestamps: 0 days 00:00:10\n",
      "    Start timestamp: 2023-05-20 00:51:25\n",
      "    End timestamp: 2023-05-20 04:11:15\n",
      "    Total duration: 0 days 03:19:50\n",
      "************************************************************\n",
      "Info about Timestamp Set 5\n",
      "    Number of timestamps: 1200\n",
      "    Number of features with the these timestamps: 6\n",
      "    Step size between timestamps: 0 days 00:00:10\n",
      "    Start timestamp: 2023-05-20 00:51:26\n",
      "    End timestamp: 2023-05-20 04:11:16\n",
      "    Total duration: 0 days 03:19:50\n",
      "************************************************************\n",
      "Info about Timestamp Set 6\n",
      "    Number of timestamps: 1200\n",
      "    Number of features with the these timestamps: 6\n",
      "    Step size between timestamps: 0 days 00:00:10\n",
      "    Start timestamp: 2019-07-24 07:02:56\n",
      "    End timestamp: 2019-07-24 10:22:46\n",
      "    Total duration: 0 days 03:19:50\n",
      "************************************************************\n",
      "Info about Timestamp Set 7\n",
      "    Number of timestamps: 1200\n",
      "    Number of features with the these timestamps: 2\n",
      "    Step size between timestamps: 0 days 00:00:10\n",
      "    Start timestamp: 2019-07-24 07:02:46\n",
      "    End timestamp: 2019-07-24 10:22:36\n",
      "    Total duration: 0 days 03:19:50\n",
      "************************************************************\n"
     ]
    }
   ],
   "source": [
    "print_timestamp_set_to_feature_names_map_info(feature_names_per_timestamp_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = get_feature_names_for_timestamp_set(feature_names_per_timestamp_set,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>server</th>\n",
       "      <th colspan=\"21\" halign=\"left\">gpu1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>component</th>\n",
       "      <th colspan=\"8\" halign=\"left\">cpu-0</th>\n",
       "      <th colspan=\"2\" halign=\"left\">cpu-1</th>\n",
       "      <th>...</th>\n",
       "      <th>interface-lo</th>\n",
       "      <th colspan=\"3\" halign=\"left\">load</th>\n",
       "      <th colspan=\"6\" halign=\"left\">memory</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rrd</th>\n",
       "      <th>cpu-idle</th>\n",
       "      <th>cpu-interrupt</th>\n",
       "      <th>cpu-nice</th>\n",
       "      <th>cpu-softirq</th>\n",
       "      <th>cpu-steal</th>\n",
       "      <th>cpu-system</th>\n",
       "      <th>cpu-user</th>\n",
       "      <th>cpu-wait</th>\n",
       "      <th>cpu-idle</th>\n",
       "      <th>cpu-interrupt</th>\n",
       "      <th>...</th>\n",
       "      <th>if_packets</th>\n",
       "      <th colspan=\"3\" halign=\"left\">load</th>\n",
       "      <th>memory-buffered</th>\n",
       "      <th>memory-cached</th>\n",
       "      <th>memory-free</th>\n",
       "      <th>memory-slab_recl</th>\n",
       "      <th>memory-slab_unrecl</th>\n",
       "      <th>memory-used</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>data_source</th>\n",
       "      <th>value</th>\n",
       "      <th>value</th>\n",
       "      <th>value</th>\n",
       "      <th>value</th>\n",
       "      <th>value</th>\n",
       "      <th>value</th>\n",
       "      <th>value</th>\n",
       "      <th>value</th>\n",
       "      <th>value</th>\n",
       "      <th>value</th>\n",
       "      <th>...</th>\n",
       "      <th>tx</th>\n",
       "      <th>shortterm</th>\n",
       "      <th>midterm</th>\n",
       "      <th>longterm</th>\n",
       "      <th>value</th>\n",
       "      <th>value</th>\n",
       "      <th>value</th>\n",
       "      <th>value</th>\n",
       "      <th>value</th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</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>2023-05-20 00:51:28</th>\n",
       "      <td>99.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.12</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.398</td>\n",
       "      <td>0.470</td>\n",
       "      <td>3100672.0</td>\n",
       "      <td>7.505963e+09</td>\n",
       "      <td>1.221774e+11</td>\n",
       "      <td>508661760.0</td>\n",
       "      <td>165689753.6</td>\n",
       "      <td>4.539599e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-20 00:51:38</th>\n",
       "      <td>99.72</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.18</td>\n",
       "      <td>99.98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.146</td>\n",
       "      <td>0.388</td>\n",
       "      <td>0.468</td>\n",
       "      <td>3100672.0</td>\n",
       "      <td>7.505968e+09</td>\n",
       "      <td>1.221775e+11</td>\n",
       "      <td>508663398.4</td>\n",
       "      <td>165807718.4</td>\n",
       "      <td>4.539404e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-20 00:51:48</th>\n",
       "      <td>99.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>99.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.126</td>\n",
       "      <td>0.376</td>\n",
       "      <td>0.460</td>\n",
       "      <td>3100672.0</td>\n",
       "      <td>7.505965e+09</td>\n",
       "      <td>1.221781e+11</td>\n",
       "      <td>508671590.4</td>\n",
       "      <td>165782323.2</td>\n",
       "      <td>4.538814e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-20 00:51:58</th>\n",
       "      <td>99.68</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.10</td>\n",
       "      <td>100.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.106</td>\n",
       "      <td>0.358</td>\n",
       "      <td>0.458</td>\n",
       "      <td>3100672.0</td>\n",
       "      <td>7.505966e+09</td>\n",
       "      <td>1.221781e+11</td>\n",
       "      <td>508678144.0</td>\n",
       "      <td>165710233.6</td>\n",
       "      <td>4.538816e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-20 00:52:08</th>\n",
       "      <td>99.62</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.12</td>\n",
       "      <td>99.98</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.088</td>\n",
       "      <td>0.348</td>\n",
       "      <td>0.450</td>\n",
       "      <td>3100672.0</td>\n",
       "      <td>7.505969e+09</td>\n",
       "      <td>1.221789e+11</td>\n",
       "      <td>508678144.0</td>\n",
       "      <td>165416960.0</td>\n",
       "      <td>4.538332e+09</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>2023-05-20 04:10:38</th>\n",
       "      <td>99.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.10</td>\n",
       "      <td>99.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.184</td>\n",
       "      <td>0.288</td>\n",
       "      <td>0.430</td>\n",
       "      <td>3100672.0</td>\n",
       "      <td>7.518631e+09</td>\n",
       "      <td>1.221455e+11</td>\n",
       "      <td>515051520.0</td>\n",
       "      <td>166771097.6</td>\n",
       "      <td>4.551388e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-20 04:10:48</th>\n",
       "      <td>99.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.10</td>\n",
       "      <td>99.96</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.278</td>\n",
       "      <td>0.430</td>\n",
       "      <td>3100672.0</td>\n",
       "      <td>7.518633e+09</td>\n",
       "      <td>1.221553e+11</td>\n",
       "      <td>515051520.0</td>\n",
       "      <td>166620364.8</td>\n",
       "      <td>4.541681e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-20 04:10:58</th>\n",
       "      <td>95.64</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.12</td>\n",
       "      <td>0.00</td>\n",
       "      <td>3.98</td>\n",
       "      <td>99.78</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.428</td>\n",
       "      <td>3100672.0</td>\n",
       "      <td>7.518633e+09</td>\n",
       "      <td>1.221500e+11</td>\n",
       "      <td>515051520.0</td>\n",
       "      <td>166568755.2</td>\n",
       "      <td>4.547097e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-20 04:11:08</th>\n",
       "      <td>84.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.00</td>\n",
       "      <td>15.64</td>\n",
       "      <td>99.74</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.262</td>\n",
       "      <td>0.420</td>\n",
       "      <td>3100672.0</td>\n",
       "      <td>7.519425e+09</td>\n",
       "      <td>1.221543e+11</td>\n",
       "      <td>515236659.2</td>\n",
       "      <td>166552371.2</td>\n",
       "      <td>4.541832e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-05-20 04:11:18</th>\n",
       "      <td>99.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.08</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "      <td>99.90</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.274</td>\n",
       "      <td>0.422</td>\n",
       "      <td>3100672.0</td>\n",
       "      <td>7.522635e+09</td>\n",
       "      <td>1.221430e+11</td>\n",
       "      <td>516186931.2</td>\n",
       "      <td>166483558.4</td>\n",
       "      <td>4.549056e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1200 rows × 225 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "server                  gpu1                                               \\\n",
       "component              cpu-0                                                \n",
       "rrd                 cpu-idle cpu-interrupt cpu-nice cpu-softirq cpu-steal   \n",
       "data_source            value         value    value       value     value   \n",
       "time                                                                        \n",
       "2023-05-20 00:51:28    99.70           0.0      0.0        0.00       0.0   \n",
       "2023-05-20 00:51:38    99.72           0.0      0.0        0.00       0.0   \n",
       "2023-05-20 00:51:48    99.78           0.0      0.0        0.00       0.0   \n",
       "2023-05-20 00:51:58    99.68           0.0      0.0        0.00       0.0   \n",
       "2023-05-20 00:52:08    99.62           0.0      0.0        0.00       0.0   \n",
       "...                      ...           ...      ...         ...       ...   \n",
       "2023-05-20 04:10:38    99.70           0.0      0.0        0.08       0.0   \n",
       "2023-05-20 04:10:48    99.66           0.0      0.0        0.00       0.0   \n",
       "2023-05-20 04:10:58    95.64           0.0      0.0        0.00       0.0   \n",
       "2023-05-20 04:11:08    84.08           0.0      0.0        0.00       0.0   \n",
       "2023-05-20 04:11:18    99.60           0.0      0.0        0.00       0.0   \n",
       "\n",
       "server                                                                   ...  \\\n",
       "component                                           cpu-1                ...   \n",
       "rrd                 cpu-system cpu-user cpu-wait cpu-idle cpu-interrupt  ...   \n",
       "data_source              value    value    value    value         value  ...   \n",
       "time                                                                     ...   \n",
       "2023-05-20 00:51:28       0.02     0.02     0.12   100.00           0.0  ...   \n",
       "2023-05-20 00:51:38       0.08     0.08     0.18    99.98           0.0  ...   \n",
       "2023-05-20 00:51:48       0.02     0.00     0.10    99.92           0.0  ...   \n",
       "2023-05-20 00:51:58       0.10     0.02     0.10   100.00           0.0  ...   \n",
       "2023-05-20 00:52:08       0.10     0.08     0.12    99.98           0.0  ...   \n",
       "...                        ...      ...      ...      ...           ...  ...   \n",
       "2023-05-20 04:10:38       0.02     0.02     0.10    99.92           0.0  ...   \n",
       "2023-05-20 04:10:48       0.10     0.08     0.10    99.96           0.0  ...   \n",
       "2023-05-20 04:10:58       0.12     0.00     3.98    99.78           0.0  ...   \n",
       "2023-05-20 04:11:08       0.18     0.00    15.64    99.74           0.0  ...   \n",
       "2023-05-20 04:11:18       0.08     0.00     0.18    99.90           0.0  ...   \n",
       "\n",
       "server                                                                       \\\n",
       "component           interface-lo      load                           memory   \n",
       "rrd                   if_packets      load                  memory-buffered   \n",
       "data_source                   tx shortterm midterm longterm           value   \n",
       "time                                                                          \n",
       "2023-05-20 00:51:28          0.0     0.174   0.398    0.470       3100672.0   \n",
       "2023-05-20 00:51:38          0.0     0.146   0.388    0.468       3100672.0   \n",
       "2023-05-20 00:51:48          0.0     0.126   0.376    0.460       3100672.0   \n",
       "2023-05-20 00:51:58          0.0     0.106   0.358    0.458       3100672.0   \n",
       "2023-05-20 00:52:08          0.0     0.088   0.348    0.450       3100672.0   \n",
       "...                          ...       ...     ...      ...             ...   \n",
       "2023-05-20 04:10:38          0.0     0.184   0.288    0.430       3100672.0   \n",
       "2023-05-20 04:10:48          0.0     0.156   0.278    0.430       3100672.0   \n",
       "2023-05-20 04:10:58          0.0     0.136   0.268    0.428       3100672.0   \n",
       "2023-05-20 04:11:08          0.0     0.132   0.262    0.420       3100672.0   \n",
       "2023-05-20 04:11:18          0.0     0.206   0.274    0.422       3100672.0   \n",
       "\n",
       "server                                                            \\\n",
       "component                                                          \n",
       "rrd                 memory-cached   memory-free memory-slab_recl   \n",
       "data_source                 value         value            value   \n",
       "time                                                               \n",
       "2023-05-20 00:51:28  7.505963e+09  1.221774e+11      508661760.0   \n",
       "2023-05-20 00:51:38  7.505968e+09  1.221775e+11      508663398.4   \n",
       "2023-05-20 00:51:48  7.505965e+09  1.221781e+11      508671590.4   \n",
       "2023-05-20 00:51:58  7.505966e+09  1.221781e+11      508678144.0   \n",
       "2023-05-20 00:52:08  7.505969e+09  1.221789e+11      508678144.0   \n",
       "...                           ...           ...              ...   \n",
       "2023-05-20 04:10:38  7.518631e+09  1.221455e+11      515051520.0   \n",
       "2023-05-20 04:10:48  7.518633e+09  1.221553e+11      515051520.0   \n",
       "2023-05-20 04:10:58  7.518633e+09  1.221500e+11      515051520.0   \n",
       "2023-05-20 04:11:08  7.519425e+09  1.221543e+11      515236659.2   \n",
       "2023-05-20 04:11:18  7.522635e+09  1.221430e+11      516186931.2   \n",
       "\n",
       "server                                                \n",
       "component                                             \n",
       "rrd                 memory-slab_unrecl   memory-used  \n",
       "data_source                      value         value  \n",
       "time                                                  \n",
       "2023-05-20 00:51:28        165689753.6  4.539599e+09  \n",
       "2023-05-20 00:51:38        165807718.4  4.539404e+09  \n",
       "2023-05-20 00:51:48        165782323.2  4.538814e+09  \n",
       "2023-05-20 00:51:58        165710233.6  4.538816e+09  \n",
       "2023-05-20 00:52:08        165416960.0  4.538332e+09  \n",
       "...                                ...           ...  \n",
       "2023-05-20 04:10:38        166771097.6  4.551388e+09  \n",
       "2023-05-20 04:10:48        166620364.8  4.541681e+09  \n",
       "2023-05-20 04:10:58        166568755.2  4.547097e+09  \n",
       "2023-05-20 04:11:08        166552371.2  4.541832e+09  \n",
       "2023-05-20 04:11:18        166483558.4  4.549056e+09  \n",
       "\n",
       "[1200 rows x 225 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "collectd_ts_df[feature_names].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cpu_feature_names = get_feature_names_with_substring_present(feature_names,'cpu')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The CPU features for GPU1 have timestamps 2 seconds after the CPU Temperature feature. The below sets all features to have the timestamps of the CPU Usage features. There are 1200 datapoints in the data, each separated by 10 seconds. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = collectd_ts_df[cpu_feature_names].dropna()\n",
    "df1.columns = ['/'.join(col) for col in df1.columns]\n",
    "df2 = collectd_ts_df['gpu1']['ipmi']['temperature-CPU1 Temp processor (3.1)'].dropna()\n",
    "df1['temperature-CPU1 Temp processor (3.1)'] = df2.values\n",
    "columns_to_drop = [col for col in df1.columns if df1[col].nunique() == 1]\n",
    "df1.drop(columns=columns_to_drop, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df1.iloc[:, :-1]\n",
    "y = df2.iloc[:, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize\n",
    "x=(x-x.mean())/x.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train Test Splits\n",
    "n_training_points = 1170\n",
    "x_train = x.iloc[:n_training_points,:]\n",
    "y_train = y.iloc[:n_training_points]\n",
    "x_test = x.iloc[n_training_points + 1:,:]\n",
    "y_test = y.iloc[n_training_points + 1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6223803816170527\n"
     ]
    }
   ],
   "source": [
    "model = xgb.XGBRegressor(n_estimators = 100)\n",
    "model.fit(x_train,y_train);\n",
    "y_test_pred = model.predict(x_test)\n",
    "print(mean_absolute_error(y_test, y_test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f51085d23d0>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3UAAAGDCAYAAACMWjkNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOydd7wcVd3/39+Z2d1700MSmgkh9JYQei8iIFItKFiwPQLK44Pl+anYERuWBxUVsQIKAoKAIEU6inQkQOgtpBDS2829d3fK+f0xc2Znd2fLvXf3bu7mvF8vXuTuzs6caeecz/k2UUphMBgMBoPBYDAYDIaRidXuBhgMBoPBYDAYDAaDYfAYUWcwGAwGg8FgMBgMIxgj6gwGg8FgMBgMBoNhBGNEncFgMBgMBoPBYDCMYIyoMxgMBoPBYDAYDIYRjBF1BoPBYDAYDAaDwTCCMaLOYDAYDB2FiBwuIgsTfz8jIocPYj+HiMgLzWxbg8ftEZFthvu4BoPBYBi5GFFnMBgMAySadOv/AhHpS/z9wXa3bzCIyDwROXIYj3euiLjRNVstIg+IyAGtOJZSalel1L0NtEmJyHaJ3/1LKbVjK9pUC6XUGKXUqwP9nYicICJvisgmic9OEpFFIjI++ltE5NMi8pSI9Ebb3ysipyZ+c6+I9Ef3Zo2I/FNEZia+H7Z7ZzAYDIbGMKLOYDAYBkg06R6jlBoDzAdOSHx2RbvbV46IOBvoMa6OruEU4H7gOhGRlH3bQ23fxoBS6ibgbuAnACIyAfgV8Cml1JposwuBzwL/C0wC3gJ8DTimbHefju7NJOBe4E9l3+t7Nxm4B7imuWcTC9C2zlPMs2cwGEYKRtQZDAZDkxARS0TOEZFXRGSFiPxFW01EZOvIEvQxEVkgIqtE5JMisk9kNVktIr9I7OujIvJvEfl5ZC15XkTelvh+vIj8XkQWR5aY7+gJaOK3PxGRlcC5IrKtiNwdtWu5iFwRTfoRkT8BWwE3RdaXL5a7MEbbxda8yFpzrYhcLiJrgY/WalMtlFIucBmwOTBJRC4VkV+JyC0ish54q4hsKSJ/FZFlIvKaiJydaFd39JtVIvIssE+Ndtsi8pXoHq0TkcdFZJqI/DPa/MnoGpxSfg1EZOfIirVaQpfOExPfXSoivxSRm6P9Piwi21Z5Tm4TkU+XffakiLw7+ndsMRSR40TkCRFZGz0359a5nGcD7xCRtxOKu/uUUjdG+9oBOAs4VSl1h1KqTynlK6XuV0p9tMq98YCrgF1qfH8F8BYRmRIdp9azaYvI/0XP4GsSWg2VRIsC0fX9roj8G+gFthGRnUTkDhFZKSIviMj7EtftWBF5Nrrmi0Tk/0WfTxaRv0f3aqWI/EsigdjAfSx59upcb4PBYNggMKLOYDAYmsfZwDuBw4AtgVXAL8u22Q/YHjgF+CnwVeBIYFfgfSJyWNm2rxJaQ75JaMnSrnWXAR6wHbAHcDTwiZTfbgp8FxDg+1G7dgamAecCKKVOo9Ti+MMGz/ck4FpgAuHEvl6bUhGRHPBRYKFSann08Qeido8FHgBuAp4ktCy9DfhsJFwgvDbbRv+9HfhIjcN9Hng/cCwwDvg40KuUOjT6fvfoGlxd1sZM1IbbCa/p/wBXiEjSPfP9wLeAicDLUfvT+HO0rd73LsB04OaUbdcDHya8xscBnxKRd1Y7uej6fYbwfhxP+ExqjgAWKKUeq/b7ckQkC3wQeKjG9x8GVhA+71D7OTgdeAcwG9iT8H0p5zTgDMJ7vwy4g/CabUp43S4SkV2jbX8PnKmUGgvsRmiphNASuZDQCrwZ8BVANXgfk8/e/dWvjsFgMGw4GFFnMBgMzeNM4KtKqYVKqTyhaDpZSl0Tv62U6ldK3U44Yb9SKbVUKbUI+BfhJFizFPipUsqNRMYLwHEishnhxPizSqn1SqmlhFaZUxO/fUMp9XOllBdZZF6OrDN5pdQy4AJC8TkUHlRK3aCUCggFUr02lfM+EVkNLAD2onSC/zel1L+jfc8EpiilzlNKFaJ4s98m9v0+4LtKqZVKqQWELobV+ATwNaXUCyrkSaXUigbOdX9gDHB+1Ia7gb+TEGfAdUqpRxLWq9lV9nU9MFtEpkd/fzD6bb58Q6XUvUqpp5VSgVLqKeBK6t+3h4DxwO3RvdZMBt5MbigiCyOLVX+iPQAXRvemB/g0oVhNou9dH6FQO1kp5TXwbL4P+Fn0jqwCzk9p/6VKqWei63gMME8pdUn0LP8H+CtwcrStC+wiIuOUUqui7/XnWwDTo/fnX0opRWP3MX72lFL9Ke0zGAyGDQ4j6gwGg6F5TAeujybJq4HnAJ/QUqBZkvh3X8rfYxJ/L4omoprXCS1t04EMsDhxrF8TWh40C5INE5FNReSqyEVtLXA54SR/KCSP0UibyvmLUmqCUmpTpdQRSqnHa+x7S73faN9foXhdtyzb/vUax5wGvFLrpKqwJaGVKyg7zlsSfycFUy+l9zJGKbWO0Cqnhc6phCKwAhHZT0TuidxO1wCfpP59+w3wR+BYETkw8fkKQqGTbMvUaH85Qmuu5myl1ASgi9Did62IzEp8/5fo+82AuYSiHOo/B+X3quQ5TflsOrBf2b3/IKGrLsB7CK2ur4vIfVJM2PIjQmvp7SLyqoickzx+nfuY1iaDwWDYoDGizmAwGJrHAuAdkVDR/3VFVrjB8BaRksQhWwFvRMfJA5MTxxmnlNo1sW1SDELoeqmAWUqpccCHKJ3El2+/Hhil/4hioqaUbZP8TSNtGgjl+36t7LqOVUodG32/mFCsabaqsd8FhG6aA+UNYJqUJu7YChjsvb0SeH8kQroJk42k8WfgRmCaUmo8cDGl960EEfkvwmtxFqHw/W3kIgmha+JUEdm70UZG1qp/EQqko1O+X05ooT5XRLag/nOwGJia2MU0Kim/9/eV3fsxSqlPRcd/VCl1EqFovAH4S/T5OqXU/yqltgFOAD4vYUxqI/ex/F0wGAyGDR4j6gwGg6F5XAx8V7uxicgUETlpCPvbFDhbRDIi8l7CWLhblFKLCWOC/k9ExkmYoGXbsni8csYSutKtFpG3AF8o+34JkKyN9iLQFSXqyBBmSMxV2/kg29QojwBrReRLEiZFsUVkNxHRCVH+AnxZRCaKyFTCOKlq/A74tohsLyGzRGRS9F35NUjyMKHQ/WJ0Pw4nFAtXDfKcbiG0Qp1HmEkyqLLdWGClUqpfRPYljPdKRUS2JLRQnR65cl5MaJ37KoBS6gVCq9lVInKUvpbAgdX2Ge33AMJEKc+kfa+Ueh74B/DFBp6DvwCfEZG3SJio50u1jk3oGrmDiJwWXfeMhMmFdhaRrIh8UETGqzDZzlpCyzgicryIbBctiujPfZp/Hw0Gg2GDwIg6g8FgaB4/I7Sq3C4i6whjm/Ybwv4eJkyqspwwccPJifivDwNZ4FnCBBXXUuZaV8a3CBNTrCF0/buu7PvvA1+LXNz+nwpT4J9FKIIWEU6EF1KbgbapIZRSPuHEezbwGuH1+B1h3BiE5/Z69N3tVKbfT3IBobC4nXCy/3tCSxmEMZCXRdfgfckfKaUKwImE8WLLgYuAD0eCZjDnlCe8B0cSWuOqcRZwXvQ8fSNqezUuAq6KLGtErrunEyaV0Zay/yaMObwAWEl4T79NmLhnfmJfv5Co9iLh9fyaUurWGsf+EXCGiGxK7efgt4TX/ingCUJx6xGJsXIiV9WjCV1U3yB0cf0BxQWG04B5kUvxJwkt0BC+N3cSLmQ8CFwUxSc29T4aDAbDhoKUhmsYDAaDYUNARD4KfEIpdXC722IwtAoReQdwsVJqet2NDQaDwVAVY6kzGAwGg8EwLEQun8eKiBO5AX+TMBuowWAwGIaAEXUGg8FgMBiGCyF0l11F6H75HKFbqcFgMBiGgHG/NBgMBoPBYDAYDIYRjLHUGQwGg8FgMBgMBsMIxog6g8FgMBgMBoPBYBjBOO1uQCNMnjxZbb311u1uhsFgMBgMBoPBYDC0hccff3y5UmpK2ncjQtRtvfXWPPbYY+1uhsFgMBgMBoPBYDC0BRF5vdp3xv3SYDAYDAaDwWAwGEYwRtQZDAaDwWAwGAwGwwjGiDqDwWAwGAwGg8FgGMGMiJg6g8FgMBgMBoPBMHBc12XhwoX09/e3uymGBunq6mLq1KlkMpmGf2NEncFgMBgMBoPB0KEsXLiQsWPHsvXWWyMi7W6OoQ5KKVasWMHChQuZMWNGw78z7pcGg8FgMBgMBkOH0t/fz6RJk4ygGyGICJMmTRqwZdWIOoPBYDAYDAaDoYMxgm5kMZj7ZUSdwWAwGAwGg8FgGBHce++9HH/88QDceOONnH/++VW3Xb16NRdddNGAj3Huuefy4x//eNBtbAdG1BkMBoPBYDAYDIa24vv+gH9z4okncs4551T9frCibiTSMlEnIl0i8oiIPCkiz4jIt6LPzxWRRSIyJ/rv2Fa1wWAwGAwGg8FgMLSXefPmsdNOO/GRj3yEWbNmcfLJJ9Pb28vWW2/Neeedx8EHH8w111zD7bffzgEHHMCee+7Je9/7Xnp6egC47bbb2GmnnTj44IO57rrr4v1eeumlfPrTnwZgyZIlvOtd72L33Xdn991354EHHuCcc87hlVdeYfbs2XzhC18A4Ec/+hH77LMPs2bN4pvf/Ga8r+9+97vsuOOOHHnkkbzwwgvDeHWaQyuzX+aBI5RSPSKSAe4XkVuj736ilBpZNk2DwWAwGAwGg2EE862bnuHZN9Y2dZ+7bDmOb56wa93tXnjhBX7/+99z0EEH8fGPfzy2oHV1dXH//fezfPly3v3ud3PnnXcyevRofvCDH3DBBRfwxS9+kdNPP527776b7bbbjlNOOSV1/2effTaHHXYY119/Pb7v09PTw/nnn8/cuXOZM2cOALfffjsvvfQSjzzyCEopTjzxRP75z38yevRorrrqKp544gk8z2PPPfdkr732ato1Gg5aJuqUUgroif7MRP+pVh3PYDAYDAbDAFi/At74D4yeAlvObndrDAZDhzNt2jQOOuggAD70oQ9x4YUXAsQi7aGHHuLZZ5+NtykUChxwwAE8//zzzJgxg+233z7+7W9+85uK/d9999388Y9/BMC2bcaPH8+qVatKtrn99tu5/fbb2WOPPQDo6enhpZdeYt26dbzrXe9i1KhRQOjWOdJoaZ06EbGBx4HtgF8qpR4WkXcAnxaRDwOPAf+rlFqV8tszgDMAttpqq1Y202AwGAyGjY41N/w/xr90HUos5IuvQvfEdjfJYDC0mEYsaq2iPKOj/nv06NFAWJ/tqKOO4sorryzZbs6cOQ1lgwyCgIULF7LttttW3UYpxZe//GXOPPPMks9/+tOfjvgMoS1NlKKU8pVSs4GpwL4ishvwK2BbYDawGPi/Kr/9jVJqb6XU3lOmTGllMw0Gg8Fg2Oh4/Y3FAIgK6F/fXHcsg8FgKGf+/Pk8+OCDAFx55ZUcfPDBJd/vv//+/Pvf/+bll18GoLe3lxdffJGddtqJ1157jVdeeSX+bRoHHnggl112GRAmXVm7di1jx45l3bp18TZvf/vb+cMf/hDH6i1atIilS5dy6KGHcv3119PX18e6deu46aabmnvyw8CwZL9USq0G7gWOUUoticReAPwW2Hc42mAwGAwGgyFBUMw0t3Z9bxsbYjAYNgZ23nlnLrvsMmbNmsXKlSv51Kc+VfL9lClTuPTSS3n/+9/PrFmz2H///Xn++efp6uriN7/5DccddxwHH3ww06dPT93/7373O5588klmzpzJXnvtxTPPPMOkSZM46KCD2G233fjCF77A0UcfzQc+8AEOOOAAZs6cycknn8y6devYc889OeWUU5g9ezbvec97OOSQQ4bjkjQVCUPfWrBjkSmAq5RaLSLdwO3AD4DHlVKLo20+B+ynlDq11r723ntv9dhjj7WknQaDwWAwbIw8ff4RzOx/HIA3PngfW24/u70NMhgMLeG5555j5513bmsb5s2bx/HHH8/cuXPb2o6RRNp9E5HHlVJ7p23fypi6LYDLorg6C/iLUurvIvInEZlNmDRlHnBm9V0YDAaDwWBoBZYqWup8L9/GlhgMBsPQWbBgARAmZNkYaWX2y6eAPVI+P61VxzQYDAaDwdAYJaLOLbSxJQaDodPZeuutW26lC4Kgpfvf0Glp9kuDwWAwGAwbJmJEncFg6CCqxdptLAxLohSDwWAwGAwbFhY+eRWu7Xqucb80GAyGkYwRdQaDwWAwbIRYyqefHACBZyx1BoNhZDN//nzmz5/f7ma0DSPqDAaDwWDYCLGUT16yAASe2+bWGAwGg2EoGFFnMBgMBsNGiKiAAlrUGfdLg8HQGlavXs1FF13U8uNstdVWbLXVVi0/zoaKEXUGg8FgMGyE2PgUJHS/9I37pcFgaBHVRJ3v+ylbGwaLEXUGg8FgMGyEWMrHtUJRp4yoMxgMLeKcc87hlVdeYfbs2eyzzz689a1v5QMf+AAzZ85k3rx57LbbbvG2P/7xjzn33HMBeOWVVzjmmGPYa6+9OOSQQ3j++edrHuf111/n9ddfb+WpbNCYkgYGg8FgMGyE2ESizjeizmDYaLj1HHjz6ebuc/OZ8I7zq359/vnnM3fuXObMmcO9997Lcccdx9y5c5kxYwbz5s2r+rszzjiDiy++mO23356HH36Ys846i7vvvrvq9pa1cduqjKgzGAwGg2EjRFSAZ3UBEPgmUYrBYBge9t13X2bMmFFzm56eHh544AHe+973xp/l87Vjf6dNm9aU9o1UjKgzGAwGg2EjxMbHN+6XBsPGRQ2L2nAxevTo+N+O4xAEQfx3f38/AEEQMGHCBObMmTPczRuxbNx2SoPBYDAYNlJsfHynO/zDiDqDwdAixo4dy7p161K/22yzzVi6dCkrVqwgn8/z97//HYBx48YxY8YMrrnmGgCUUjz55JM1jzNv3rya7pydjrHUGQwGg8GwEWLjE9iRpc64XxoMhhYxadIkDjroIHbbbTe6u7vZbLPN4u8ymQzf+MY32G+//ZgxYwY77bRT/N0VV1zBpz71Kb7zne/gui6nnnoqu+++e9XjOM7GLWs27rM3GAwGg2EjxSIgiCx1yjeWOoPB0Dr+/Oc/V/3u7LPP5uyzz674fMaMGdx2220NH2Pq1KmDalunYNwvDQaDwWDYCLFVAHYWXwkExlJnMBgMIxkj6gwGg8Fg2Ahx8MFycHEQ435pMBhGOK+99hqvvfZau5vRNoz7pcFgMBgMGyE2PmLZuDhgRJ3BYBjhZLPZdjehrRhRZzAYDAbDRoYKAmxRYDl44iCBiakzGAwjm7e85S3tbkJbMe6XBoPBYDBsZASBH/7DsvFwTEydwWAwjHCMqDMYDAaDYSPD03XpTEydwWDoEF599VVeffXVdjejbRhRZzAYDAbDRobvRSLOsvHFwTKWOoPBsAEzZsyYutt0dXXR1dUFwIUXXsjOO+/MBz/4wZJt5syZwy233DLg47/xxhucfPLJdbc79thjWb169YD33wxMTJ3BYDAYDBsZvucBIJaDLw4SeG1ukcFgMAyNLbfcMv73RRddxK233sqMGTNKtpkzZw6PPfYYxx57bMXvPc+rWsB8yy235Nprr63bhsEIxmZhLHUGg8FgMGxkBH4k4iwHj4yx1BkMhpZy+eWXs++++zJ79mzOPPNMfD+M6x0zZgxf/epX2X333dl///1ZsmQJEJYnOOCAA9hnn334+te/XnW/F1xwAbvtthu77bYbP/3pTwH45Cc/yauvvsqJJ57IT37yk3jbQqHAN77xDa6++mpmz57N1VdfzbnnnssZZ5zB0UcfzYc//GHmzZvHIYccwp577smee+7JAw88AMC8efPYbbfdALj00kt597vfzTHHHMP222/PF7/4xfgYW2+9NcuXL2fevHnsvPPOnH766ey6664cffTR9PX1AfDoo48ya9YsDjjgAL7whS/E+x0qRtQZDAaDwbCR4UXul6GlzsZSLrx0Jyx5ps0tMxgMrebwww/n0ksvBcB1XQ4//HAuv/xyAHp7ezn88MO5+uqrAVizZg2HH3441113HQDLly/n8MMP56abbgLgzTffrHu85557jquvvpp///vfzJkzB9u2ueKKKwBYv349+++/P08++SSHHnoov/3tbwH4zGc+w6c+9SkeffRRNt9889T9Pv7441xyySU8/PDDPPTQQ/zyl7/kxhtv5OKLL2bLLbfknnvu4XOf+1y8fTab5bzzzuOUU05hzpw5nHLKKfF+/va3v/HnP/+ZTTfdlDvuuIP//Oc/XH311Zx99tmpx54zZw5XX301Tz/9NFdffTULFiyo2Oall17iv//7v3nmmWeYMGECf/3rXwH42Mc+xsUXX8yDDz6Ibdt1r1+jGFFnMBgMBsNGRhC5X2I7BDqm7or3wK8ObG/DDAZDx3HXXXfx+OOPs88++zB79mzuuuuuOKFJNpvl+OOPB2CvvfZi3rx5APz73//m/e9/PwCnnXZa6n7vv/9+3vWudzF69GjGjBnD8ccfz5NPPjng9p144ol0d3cDocg9/fTTmTlzJu9973t59tlnU3/ztre9jfHjx9PV1cUuu+zC66+/XrHNjBkzmD17dsm5rV69mnXr1nHggWFf+4EPfGDA7a2GiakzGAwGg2Ejw4+yXVqWjRIbUUGbW2QwGIaLe++9N/53JpMp+XvUqFElf48fP77k78mTJ5f8Xc2KlkQpxUc+8hG+//3vV3yXyWQQEQBs28bzivG9+vNa+00yZswYxo8fX7c95YwePTr+909+8hM222wznnzySYIgiBOvlJPL5eJ/l7e72jZ9fX0VbW4mxlJnMBgMBsNGhu+F8SzKCi11ovw2t8hgMHQqb3vb27j22mtZunQpACtXrky1bCU56KCDuOqqqwBiV81yDj30UG644QZ6e3tZv349119/PYccckjN/Y4dO5Z169ZV/X7NmjVsscUWWJbFn/70pzj2r1lMnDiRsWPH8tBDDwHE59gMjKgzGAwGg2EjI9CWOtshEJuMKrS5RQaDoVPZZZdd+M53vsPRRx/NrFmzOOqoo1i8eHHN3/zsZz/jl7/8Jfvssw9r1qxJ3WbPPffkox/9KPvuuy/77bcfJ510EmPHjq2537e+9a08++yzcaKUcs466ywuu+wy9t9/f1588cUSK16z+P3vf88ZZ5zBAQccgFJqUNbFNKSVZsBmsffee6vHHnus3c0wGAwGg6EjeP35x5l+1RE8ts8FZJ6+kimFRWwZvBF+eW76BMpgMIxMnnvuOXbeeed2N6Pl6KQtjbiEtpOenp647t7555/P4sWL+dnPflaxXdp9E5HHlVJ7p+3XxNQZDAaDwbCRoUsaWLZNIA5dwfo2t8hgMBiGxoYu5jQ333wz3//+9/E8j+nTp8eZSIeKEXWGkcHK1+CNJ2CHYyA7qt2tMRgMhhFNsvi4EpvR9La5RQaDwbBxcMopp8TlFJqJiakzjAjU9Z+Eaz/GmsevaXdTDAaDYcQTW+qcUNTlMMXHDQbDyOall17ipZdeancz2oYRdYYRwcrloZ/0nFcWtrklBoPBMPLRiVKwMiirecVvDQbDhslIyKExVMaNG8e4cePa3YymMJj7ZUSdYUSwPh+uKnebuYfBYDAMmdhSF7lfxp+r2nWhDAbDyKOrq4sVK1Z0vLDbbLPN2GyzzdrdjCGjlGLFihVVa+RVw8TUGUYUyjcuQgaDwTBUVFR7ybJLRV2eDN3tapTBYGgJU6dOZeHChSxbtqzdTTE0SFdXF1OnTh3Qb4yoM4wsAq/dLTAYDIYRT0lMnVWcCrhG1BkMHUcmk2HGjBntbkbLecc73gHArbfe2uaWtAcj6gwji8BY6gwGg2GoaK8HKbPUFSTTriYZDAbDkDjhhBPa3YS2YkSdYUQgRH7gvrHUGQwGw1AJIq8H23YgkSjFxYg6g8EwMjnrrLPa3YS2YhKlGEYEtgrjP4z7pcFgMAwd5Rfr1CHF9V3PWOoMBoNhRGJEnWFEYKFFnXG/NBgMhqESeAUAnEy2pKSBSGdnxzMYDJ3LkUceyZFHHtnuZrQN435pGBE4KrLQGUudwWAwDBkViTo7kytJlBJ7RRgMBsMI45RTTml3E9qKEXWGEYG21IkRdQaDwTBkdPFxO5ODRKIUGyPqDAbDyOT0009vdxPainG/NIwIHExMncFgMDQLbanLZHNhXF2EpYJ2NclgMBgMQ8CIOsOIwCEK6jeizmAwGIaOnx5TZyx1BoNhpHL44Ydz+OGHt7sZbcO4XxpGBLYKQIyoMxgMhmagEu6XJZY6jKXOYDCMTD760Y+2uwltxYg6w4ggttQpI+oMBoNhqKjIUpfN5oylzmAwdARG1NVARLqA44FDgC2BPmAucLNS6pnWN89gAIIAO0qzbSx1BoPB0AQiS115TJ0RdQaDYaTiulG/ltk4621WFXUici5wAnAv8DCwFOgCdgDOjwTf/yqlnmp9Mw0bMypwkejflrHUGQwGw9DxXQIl2LYDCVHnmJIGBoNhhHLUUUcBcO+997a3IW2ilqXuUaXUuVW+u0BENgW2an6TDIZSPM9Fr7lIYCYcBoPBMGR8FxeHnIiJqTMYDB3BJz7xiXY3oa1UFXVKqZtr/VAptZTQemcwtBSvUBR1xlJnMBgMQ0eCAh42OYBkTJ0oCAKwTHJsg8EwsvjQhz7U7ia0lUH12iLym2Y3xGCohhcF9IMRdQaDwdAUfBdXwnVdsUvXd5WJXTYYDCOQ3t5eent7292MtlErpm6Tal8Bx7amOQZDJX4hKeqM+6XBYDAMFQlcPD0FsEqnAoHvYTvZNrTKYDAYBs+xx4byxMTUVbIMeB3iHBUAKvp701Y2ymBI4kVZ2sBY6gwGg6EZWAlRZ5VZ6nzfw077kcFgMGzAfOpTn2p3E9pKLVH3KvA2pdT88i9EZEHrmmQwlOK7RUudGEudwWAwDBkJXDztfmmVSjjfM4tnBoNh5HHKKae0uwltpVZM3U+BiVW++2Hzm2IwpBP4xQmGbSx1BoPBMGSswMWXMAVVeUyd7xXSfmIwGAwbNGvWrGHNmjXtbkbbqJX98pc1vvt5a5pjMFTiuSamzmAwGJqJBC6+hBY6KY+pM5Y6g8EwAjnppJMAE1M3IERkc6XUm81ujMGQhh/F1BWUjY2ZbBgMBsNQsZSXsNRlSr7zE3HMBoPBMFI4++yz292EtjIoUQf8HjiumQ0xGKoRuOEEI08W21jqDAaDYcgk3S+t8pg63yyeGQyGkce73/3udjehrQyqTp1Sygg6w7ChV43zksXGiDqDwWAYKrbyCLT7ZUVMnRF1BoNh5LF8+XKWL1/e7ma0jQFZ6kRkE6XUylY1xmBIw/ci90tyZEyiFIPBYBgyVuDh2t3hv+3yOnXG/dJgMIw8Tj75ZMDE1FUgIgcBvwMC4OPAd4BtRSQDvE8p9eDwNNGwsRNoUSdZulR/m1tjMBgMIx9bueStcUClpc4kSjEYDCOR//3f/213E9pKLUvdT4D3AWOAm4F3KqXuF5E9gZ8DBw1D+wwGVLRq7ErOxNQZDAZDE3CURxBlvbSs0kgME1NnMBhGIieccEK7m9BWaom6jFLqaQARWaaUuh9AKfUfEekeltYZDBTr1LlWDsc3os5gMBiGioVHECVKIQhKvguMqDMYDCOQN98ME/NvvvnmbW5Je6gl6pJLd18u+y7bgrYYDKkEUSFcz8rhmEQpBoPBMGQc5aKsUNQpVS7qTEydwWAYeZx66qmAialL4+siMkop1auUukF/KCLbAn+st2MR6QL+CeSi41yrlPpm4vv/B/wImKKU2nhT1RjqoleNfTtnsl8aDAZDE3CUF4u6ICjtV5Wx1BkMhhHIOeec0+4mtJWqok4pdWOVz18BftjAvvPAEUqpnii5yv0icqtS6iERmQYcBcwfTKMNpQSB4nN/mcO6fo8/fHSfdjen6eiYOt/qwpEAFQSINahqHAaDwWAAbHwCXXRcqZLvTPFxg8EwEjnmmGPa3YS2UnVmLCK/EZGZVb4bLSIfF5EPVvu9CumJ/sxE/+mR4yfAFxN/GwbButf+w/p//4Zn5y0g+/Sf6XvxntYdbP0KeOS3sPyl1h0jBbXiVcbMvwsILXUA199+V+sOuPS58Dz7VrfuGCnMe/x21t13ITz8a1i3ZFiPPdw8/tA9vPLSc+1uRkt58tH7efnmn0J+XbubYugEggCev7lCfKWyfgXq4d+EffWr98FDv4IFj1RslsGF2P0ytNQVVFi3bsJrt8DaxeHz+8rdYZ/08K/hP3+CFCve0jW9XPDzC1jVk6/erhduDdvy5tMNnHBzWdPTz58u/RX9hRZbIAdynyDc7vlbIDAeKI3w+ktP89JTD7W7GYYNmAULFrBgwYJ2N6Nt1HK/vIjQBXMmMBdYBnQB2wPjgD8AV9TauYjYwOPAdsAvlVIPi8iJwCKl1JMiUuu3ZwBnAGy11VYNn9DGxKorP8FWhVewp8/hRxl9K77YmoPNuQLu+DrsfCKc8qfWHCOFuVd+jZnLb6ZHdfFabif24x+MfujHcMxRrTngzf8PXr8fLBv2/nhrjpGCc+OnGCuhF/LSJW+w6YnfGrZjDzd73fbO8B/nrmlrO1pJ5u//zXYyjzfGjGPLw4bvOTJ0KE/8EW76DJxwIez1kdrbzrkCuePrPDP+MHZ1n4He5TBlZ/jv0smwo3xUlP1STd4ZgEsyp/Ax92o2fe4y3IkTyax4CV64Gfb4EDxxOQDexG1wZpQmv57z1x/x+RUXcNfNDm875eyKJimvgLryA1gEMOMw+EiqI1DLeOTK8zht0S+572aHw951esuO4z/8G+x/fIngnb/Gmn1q/R88+ze45iNw1Hlw0Gda1q5OYcUNX2FM/k2Y9Wi7m2LYQDnttNMAE1NXgVJqDvA+ERkD7A1sAfQBzymlXmhk5ypc/pstIhOA60VkFvBV4OgGfvsb4DcAe++9t7HopWDn14BAz6plrT+YF63AesNbJ67Qv55Xg8154OgbOXrWdF778VVsOrr6YsDQDxgZl93hPc8cLtd4h3KC/SCvLV7OpsN6dEOzmSDrAVi0bCVbtrkthpGPv+YNbMBfswi7zrYr1qxhErBk5Rp2yPaSAXB7K7azKYq6WbvszA0nPcsO3RlmXXoUj+TO4tmXF7O/93y4ce+q+HePvLSIA2eU7mucG3oXdOfTx6J8vp8uwmQsy1atZkqdc2g24/JhRrxcf2vHymeee5ZZwNPPv8Dus+tv37tqMaOAniWvMqalLesMHL+PbGBq1Rqq87Wvfa3dTWgrtSx1AEQulPcO5SBKqdUici9wEjAD0Fa6qcB/RGRfpdSbQznGxogVWTotv9jJKaWoZQEdNEHktjLMbiKW8sDJ8aGDdwTgxex4LDUM8R7B8MaUOBIwbfPJuMscJCgM67ENrSB8B03CCUMzeHLBavYEnlywij3rbPv60lDUjc8JgeuGj2JKjJxNAFZxCvDOPd7C6yvWQ6YbFwe3kC8GaCT6w1F2QCXhhqLSvgPfK/5+8cq1wy7qlEQnUqV9zaI3cu/sbdDN86lF69gfePaN1ezbwnZ1CpZysZXpUw3VOfLII9vdhLbSsmwTIjIlstAR1bU7EnhCKbWpUmprpdTWwEJgTyPoBoeKxJudEAF+0CKjZizqhlfsiPIJpDjxCMTBDlrXqQfRZHzxqp46WzYXGx/EwRUHMUkKRjwqeo7SJtMGw0DJe2G/XnDrL6rpwuE5KyhmC/ZLF4qUUmTEJ4yQKDJ90mge/eqRuDh0Wz5EY8zqnr54G/ErF530QqKqEkuWLJGQoQ2T8mESdXpMlkZj6uLtW9uuTkGUj2UyYBtq8Oqrr/Lqq6+2uxlto5UpBLcA7hGRp4BHgTuUUn9v4fE2OvTE0Q6Kwemu25oBc01vaA1UwzxJtQIPPzHx8K1MaL1rEWv7w30/9NLwrjPYKgDLxiNTFNCGEY8y99LQDGLni/piQS8KOcrFkUgslC3GBVGxcWVVOuuM7cqERckDNz7wmvVF983UMUB7h1QRM75bFIKZdkzKI1FXXo+vBQeK/t/gcWKxaSJMGsFWPrYyos5QnY9//ON8/OMbbxx7TffLKNHJ+UqpLwx0x0qpp4A96myz9UD3a6gkaalzvTzdLagNf8fcNzgZcAv5Ya08bymPICnqJENOtc6nvq/gMQHYbExdz+SmEsa32Hg4qSvhhpGFEgFl3C8NzWIA66/RQkJGFRf7lO+SdMr3vAI2ICmiDsAXGytwwYlc/BOiMPDS+qfIUldFdPpe8T1Id99sMcNmERtY6IMMkwWxU7CUZ2rVGmryrW91bpK5Rqg5c1VK+SKyl4iIquZXYWg7mYSlzsvnYczYph/DkbAj9dw2iLrEY6rEaalPfb4QTli67OF93G0CEAdfnJIJlGFkI+ZeGppKA/1SJOp0QglfCVaZEAu0yLLS0654kil5dpMLhypN1NWx1Gkh6CkLR9rxTtR2D23yYRov1mRE3YCwlI9jRJ2hBocddli7m9BWGjFHPAH8TUSuAdbrD5VS17WsVYaG0OOHo4qDrOfVqBM0BMZmgf5SN5rhQJRPYBVlZGBlWirqLO3aMZwWlii+BcvBEwfp4EBwFQQDXMsemUg0qzOWOkNTqK2ZStGiLrLU9ZFjjOoPfxyJLy9KXCJVRJ0vDhJ49LkB3YDl5+lXGbrEHZz7ZfSbPnJtcZ9T2ttjmCx11SyWFVtrUWdK9jaErXxjqTPU5IUXwuT8O+64Y5tb0h4aEXWbACuAIxKfKcCIujZjRQNUJiHq3EJrRNeYTDhYpbvetA5beRRkVPx3KOpat9IbTziG0cIS+H7oXGXZHW+p83wvTLHe4eh3U0xMnaEZSOMmIP3MdUVu6n3kGBOuyIETLpAFerHBruZ+6WArlyXr8mwN4PXTT5Yu3HRLHbUtTkEkIvvJkm1LohQtOlssCAZwnwCwtFuoESqNYOHhdPCip2HonHnmmYCpU1cVpdTHhqMhhoGjs0BlE6IuaJElLWtFlodhFnWW8lHJ7JeWg9PCSYHoAPdhnIx7vkeWML6l00Wd77kbh6iL3k1lJiCGptC4qU5b+nMqDwJ9KhuVNSjEok5b6qgaU5fBCtw4GVdGFQgd79dXZNKEYvbLepa6vOQYzZq659B0hi0hyUBMqsTZRxvOlrmRE1rqjKuqoTrf+9732t2EtlJX1InIVODnwEGEy0/3A59RSi1scdsMdbCizi1Lwv2yRUWz44yTw5z90i4raYCVaelKnXa/HE4Li67hpCw7nEx1sBBwC3m62t2IYUBPPEx5CkMzkAEEa8WWOim6PAIlYqwYU5c+BQgkg51YXMqoAutldNiCWu6XVdqnLXV56cIJVtY9h2YjG2iWSRHdng2rXRsqtvJauqhrGPkceOCB7W5CW2kkpdYlwI3AlsBbgJuizwxtRvuW5xKWOr9VlrSo6PhwCw4rygoZN8PKtLRTj/31h1PU+Tq+xSGIVsg7lcDr3HNLop8j41ZlaAZqAF595QtSfTq1VeJz3edYVWLqAsuJ+vrwwFlVoCBaHKa9w7VjyfR7X5AcWfGGXVyp2JI4TNkvGy1TF42rJlFKY9j42KJQgelXDenMnTuXuXPntrsZbaORmLopSqmkiLtURD7bovYYBkAs6igOsq1KZKInp62MZ0vDVh5KEg57dmtFXTssdYFbXDUPLIeM11f7ByMYdyMRdZYKIpe3jeN8Da2lmFyocfdLTX+apc6PJsVVYuoCCRNS6aNlKeCLgxdYqJRFJ1/pY9cuPl6wuiAI6zeKPYyO2JGlrtUlDWKDZYMugjqRkik+3hh6zuN5Lpls+oKEYePm05/+NGBi6mqxXEQ+BFwZ/f1+wsQphjbjKB8ELCkOpIHbmuyXWuQMd5CyrbwSS52ysmRa2IbYUjeM5+lF1lWx7Xgy1am0zJK8geEYS52hqTSeJbHCUqciS52f9OgoegekoSwHR7lEKZywUCix8bDT62hGx6xmQdGWOs8Kna99N48znKIuLmmwYdWpU/G9Mu6XjRCLOtclk90YHPkNA+VHP/pRu5vQVhoRdR8HfgH8JPr739FnhjZjpawG+i2yhOiVRHuY/dnDotyJOnV2pqV1avQ1HVZLnV41Fyd0Lx1ma+hw4rude25JYvdLk/3S0AzqJCJJYpUtJPTGlrris6gtZ1LNUmeFsb2BFMvJ+Ni4OOlxovo5r2KZ1nF4nh1OxN1CAWc45+RazLVY1KkB3CcoimBjqWsMvZDtDXNpJcPIYZ999ml3E9pKI9kv5wMnDkNbDAMkLQuU36I6ddqlx8GHIACrkXDMoVMu6rAyZMRHBX7VGktDoS2JUoLiBEtZrS2u3m5ateiwoREnSunge2kYPsqFWi0acb8sxtRVF3UOHq4UlZeSUNSllnvRn1XpN7Wo87Woc/N01z+V5qHb1eJ45YG4yQJFEbyBJXDZUNGLZRvLOGIYOHPmzAFg9uzZbW1Hu6g7MxeRH4rIOBHJiMhdIqLdMQ3tRBesLv+4Re5tknSrGcZEHjY+JLJfih2uHLeqHp8eNIYzIYxfElOXGXZr6HASJFbyVYdOZIJAFUWdsdQZmkEs6uq/M+WLQto6Vpr9snZMHZaDo7y4pAFAIGEdzTRLnR4fJEjvl3VdPN8OpZxXaM3iY1Wi97Dl2Wh1fcoGRXjRXdVY6hrBiWPqjKXOkM5nP/tZPvvZz7a7GW2jEffLo5VSXxSRdwELgfcC9wCXt7RlhprEBavLaJV7W8kg5bvg5FpynHIcVel+CeFKb7ar+Wu9sagbVvfLaNXctiP3y84VAslEPr7n4WQ6r2qd5/tkozjXTi5PYRg+4sWBRurUlcW1BXYX+JRY0bTIqmapU3a2IiGVEit0v6xhqUv9jqKlLoh8LltVeqcqWtS12lKnxa3f4Huv29WhC1zNRAVBvJBtLHWGavz0pz9tdxPaSiOiTs+6jgWuVEqtFBlYMLCh+eiC1eUov0Xul8k4trRA+RZh45fUUtIZ0/wWWerimLphTHDh65g6K3S/7OQ6PEFisuO6hY4UdckJh7HUGZqBtug04ppdvpDg26NCUVeS/bJ2TJ1KWVwKxMGTdFFXFDPVRF24r8AZBYBXGOZJubagtdrLRI8bDS7mxDF1xlJXF9/34gnrxhKbbRg4G6vbpaaRwKibROR5YG/gLhGZAgzzMpuhnGorVUGL3BKsoMxSN0w4+CUuQtr9stCKlV6lcKLBdTgtLMkJVtoKeSeRzH7ptihTa7tJugaZ7JeGZiCxpakBUVeWSCrIRB4NCVGnRZZVzf3SzpCpsNTZBOKk1tHUYqlavxkXLM9Eom6Y330ZJktdMWHMQC11RtTVI5kcJTClYgxVePTRR3n00Ufb3Yy20UiilHNE5AfAWqWULyLrgZNa3zRDLbwqok61qLMrmZwOU0yddrdQVtGaI04o6lqSGj8hXIdzMh540cBuZcJEMB3sspe01Hn5zlwb8r3iOXZy0hvDMDKABE4Vz5zTHe3CjSPk9HtYraQBdjZcUJPSmDqPKqJOabf1KuOS/jwSmK1K6FWNeu1rGnE2y4GJOuOmXZ/knGdjKY1jGDhf+MIXAFOnrioi8l7gtkjQfQ3YE/gO8GarG2eoTlBN1LXKUpccdIbJ/dLzPTKUTTxa6X4ZJCfjw7cSqOKYOicqrt651p3kc+t1qAuNnxCuxlJnaApxTF0j7pdlz1w2Sk7i5uNYijimzqlhqRO/JIRPiY0vmVTBUs8Spi2DZEcDw+8+NxBL55COo69No8eJRZ3pJ+oRJJ6ZoFFLqGGj4xe/+EW7m9BWGnG//LpSap2IHAy8HbgM+FVrm2WoRzVLXatcI5MuPa0SjuXELqYJS50VWepa4r6TGIiHc5DV6cXFssHOkhEf3+9Md5yk28ywJ0sYJpKu0cZSZ2gGAxEltvJwVbHci5UNXR7dRMbJ2OXbSo9p1Z8nn18VZb9Mt9R5Fdsnid09taVuuF2vg+Gx1A1YPEbjjD2MGaVHKm5i3uGbOnWGKuy2227stttu7W5G22hE1OnZ7XHAr5RSf4PUHB2GYUT56aJDtciKlvT5H67Cn/EkJBlT10r3y4SQG05Rp++lRJY66Nx4s1JLXWcOzMZSZ2g6qvESGTY+/Ykh2sqOAcret2g/tl2l1mfUz9rJxTwrtNSluQrqbMFVswZrb4RcKDCH230udr9s9SJLdJ8azp4cDFO7OoDk2GEsdYZqPPDAAzzwwAPtbkbbaCT75SIR+TVwJPADEcnRmBg0tBCvzCLnKjt0l2lRql9L+RSUTVZ83IQbTyvRsUnJDG3aUteSld6gPaIuSCQt0IlgPDcPLSjZ0G6SlrpOXW1NugkZS52hKcQJNRqx1PnkJcdY+sK/czrjZNJSV9m3JtH9UKmlziGwHOygsu/Voqmq23r03tu50dHpdGZMnQzATTa5vekn6pNMQNWqhHCGkc9XvvIVwMTU1eJ9wDHAj5VSq0VkC+ALrW2WoR6BVzoI9EuODL1Iiyx1lvLpJ0eW3mErHBt34nal+2UrBEEykcBwFgAvj6kD8PJ5GDtsTRg2kol8hjtZwnCRtNSZWBlDMyiKkvrPk41Pn3TFdcqdrlBIJV3WtXeA7aQvz1nR51mVyJhphdkv04SbFbtfVkuUEn0fCczhnpRLnfY17zgDrHMa3U9nGGO4RyrGUmdohF//+tftbkJbaST7Za+ILAUOBl4CvOj/hjbil1nq8mQZS28xy1iTsSKXnnH04g6ThUXHJiUTpdiRqGvFpMD33PiFsNtlqYuKum8M7pcda6lLvJvDuThg6FxkAFkSbXzyCVGX69bJSRKWDi2yqiRK0W7uORL9kNgEVibVqqTbVXURIzqek2tXopTG6/wN6Tg6S2mD40csNk0/URcvsZBdLVGcwbDjjju2uwltpa4bpYh8E/gS8OXoowxweSsbZaiPFgK+Cm1Lvjh4ympdohTl06905snhERx6EiIJS11cfLwFos6LVq99JcM6yAZBMaZOu0MNdx2nYSOxgt2paam127CnLONWZWgKOqa5EfdLBw/X6or/znSFMXXJ961enTrdzyZFXex+mRpTF/ZhGaqMP9FiY7ZLu1+2J6ZuuERdwzFysaXO9BP1SD4zwTBl4DaMPO677z7uu+++djejbTQSG/cu4ERgPYBS6g060jGsBTx2Cbz2z9LPnvoLPH8L5Hvgli9CYX3139/3I1jyTOpXWtTpgHgfGxdncOUG/vV/sPipmptYBPQRWpHSBIcq9LLgiv/B71098ONrgoAVf/sqhWWvAUU3Nish6uxs2IagCaKnv7+fO3/yX7yxaD5QdA3sJ9saS11hPdz6JcivK/lYJc7T0pa6YRLOMW/Mgft/0rLdP/PMU/z9Rx9j5lPfiT9rxj3cENHvZkEy2O0sKjz3OnjmhvYdvxNo8XtRjwt/8l1+/dtfxGKuEXdeW/l4CVGX645EnX7f3D52f+rb4f6qZb+MLHVdCZEWul9mUl0FrWgRrKo48cOMnNlc2L+pwbpe59eF42a+Z0A/qxvzNwgefuo5fve1U1m+dj14BVZc+3nGuCvD4wUeBAHc8U1YPb9uuzZhNdx6DjRL7L56Lzx+aXP2tYGQzCpcHn5iMGi++c1v8s1vfrPdzWgbjYi6glJKETlziMjo1japg/j7Z+GyE0o/u+50uOr9rLrjR/DIr1l1X5XqEF4e7vkOwe+PSf1aux8UpCjqCpJF/AEOloEPd50Hv3tbzc1sVcyo5qUMPM/cdCHTXvojz1x97sCOn2DZq/9h0hO/YMklHwAS7kKJ1WQ7CuAPmmCRfOaeqzlyzbUsvOpz4fGifebJlGR9axb5f10ID19M/wOlPt863sSyM7GlrlVF5Kvym8PgznNbt//rzuD49deRc9fGHzXjHm6I6MWIAtn2ulVd+zG45iPtO34n0Or3og5nr/khZy76auw+2EiiDwcfPzc+/rt7dJmlbs4VZKP3sFqdOp0opQTLASu9b9Ris2q/GXh42OSiRbnB9m9Lbvk+PPJr3rxnYHEzuiRPMxfrnFs/xyecW1n0n9t45e4/MGnu79k+Pzc8nvJg8RPw75/CdWdW3YcWdRl8ePhX8OzfmtO4P54EN32mOfvaQEiOF8M+PhpGDH/4wx/4wx/+0O5mtI1GRN1fouyXE0TkdOBO4LetbVbn89LCsHb7s4vXpX7v9oWf91cpsh0kJo4ASiz6yWF7fQNriLYU1gnstvDxJErikbJKtr5nDQA9hcEPmqtXrw73Fe1Dr8xZJdkvtegZ+uAcROduRdlRfDc8rzzZloi6F15+BYAH5veWfK4tdbZTdL/02xUInqw23ES6VeVz2Yx7uCGiJxx5ac1zZGgDLXovGiW21NV7npTCkYDC6M3jj7q7tctj+Nt8vrjwVzVRipUi9sRBWXaqtVB/VrV9kajLZKPxqoGEL2m8sXAeAC+tHtj90O6hda/fAOjyw/HDsR1WLX2j9HjKRxXCft6t0c9VlKiwTGLxapQkSjF1/QxV2Gabbdhmm23a3Yy2UbcHUUr9GLgW+CuwI/ANpdTPW92wTie2qDldqd/390aiTqUPutqq5CYsdXmrC8er4c6Zhttbsx0aW/l4+lgpQe7KrX0+DTUlH078fdHJUKJEKYlVYz0JUY1mF6tF5AIUWNF5Rde0QAanBZPxrBsK30JmYsnnQbL4eOQO1Tb3kha5CwqVk7CgGfdwAySOqZNsS56jhmizCOk02pJtL2GNiEVTHUtT7MGRLfYxo7pzeMqK+7enFq4u7reKpY60WDvLQlkONpV9hB0n/EjvPyQSddnM0PpvJx/2ofaYSQP6XWxJbGL/pguG29ku6F9VdjyP15etBuDF5dVdKssTqhRMCeCqlCRHMZY6QxXuvPNO7rzzznY3o23UzX4pIjOAfyml7oj+7haRrZVS81rduE5GIkFhZXKp3xciUafdK8vRVg5XsqAgEBuPDI4/OEtdYOdqKnyLAN/KQlCZeROAWKSmn08jePlQYHp2KAz9KD4waakTKyyW2wz3C+X2A+G5AwTRNc23KKYuV1gd/iNTJnz1hMPJYEXFgNuWsjnwwKpSkLjJdKoLjUosuAxnFtUkbu/qYaklubHgeS7ZKlatVuH3LEO/iTpRSr0EHJ7nhrIgEYfclcvhYxVr04nE3zlVEqVYaUXJLQfETrU+azfjqs974OFhkdHXcJCiLueuBqA7N7BxRmi+pc6Jyj1Ipgu7XNThx4uUfar6NKtc1D22qJcDd2taEzuKUvfLzlwQNAyd73wnjNs/8sgj29yS9tBInbprgAMTf/vRZ/u0pEWdTGL13AqiAaGKZavQFwaCu1WmZnqAdiUSJGLjSg7H7x9Qk/x8DzawMg+Ta2xn4xdFXYoVSYtUGYKo8wvhIKiD/LW1StemA7BtvdI79MFZB+urWNQVJ+MOQXi/EhOgodIVTUgcKV0tTmai00lh2mbFasJ1TafSctSMe7ghorOZttNSt2zxArZsy5E7k3YssvSuXBxnJCsm+qj9PHlePhR1iQQoXdkMPnZ8DnaiS5MqQlXS3C8tBywbK8UaV4ypq2KpUx4+DrnM0PrvUV5oqZMqx6lG0T20eZY6LepU4JPJl4o6W3m40aKhZ1UfE8tFXTY7dEudn+9leJblhhcTU2dohD/96U/tbkJbacSB21GqWIE0+rfxERgMbtGKZmkrVCb9UuqYOlfSB13dqXmR62AgNq7dTTboTd2+Gn3rw+P0BbVXoUNRFwW5pwgO7U4qVSyPjRBElrqi5SwSvlZaTF0TJllRm1UkRLWI1EJ5sKvJ1ejywuQEOStd1NmZZKKUNlrqWsDooDJbXaeuturJs2fl2hZTt2LporYct1NJiyNuNf2r34z/XSzuXUfURXHBkrDA5RwLL2GpS2aorG6pSxkPLAclTmobnDqJUiSy1Dna3XOQ7/6oIOxDB9p31E3kMggyWtT5XuxanzxeoD1BqmQYTbZL0+UMfRFx0cLq2TZHMsmFlU4dOwxDZ9q0aUybNq3dzWgbjYi6ZSJyov5DRE4ClreuSR2MWxRcVhAKCrvKHXD7I0tdFVEX16mLRJ3CwrW72c57GV5/sOEm9a1fW/M4GpsgHpzSOlQJtKvk4PV+EF2fwCm11NmZYtviyUYTxEecVrsso6YWys2uozbKDwf+CkudLgRsObGraZo1dFhogajzfZ+Jak3F5506MBffzRyOBKhg+MsarFv+Rv2NmkG+B568aniO1UbaUey4sGYJAD2qKyFKEu+MUvDEFeAWvTN0nyUJIZF1LHzseCHQ8orjULVEKZIi9sSywXbSs19Sw1JX6GXm8lsIsIvHS7HULVrVy+e+cg73PvVqapsAxqlwETLuO9YtQd37A3jjiaq/gVaJuvB6Br5Ht1fav9nKj+PM9Rid3q7SPrAZlv0VSxYOeR8bIspY6gwNcNttt3Hbbbe1uxltoxFR90ngKyKyQEQWEBYiP6O1zepQEjXp7EgEVXN38yJR51WLqYsm3wU7zGyWUS6ePSr88pL0Mghp5CNLnVdL1CkVijpbZy5LKT6rLXVDcG9Rkftl4HSH/49j6hJ16qJYj2YkStEuozoOsGhhCc/TdZsr6nJBeH7lok4/A5btYDUxZnAwNCUBTRm9PWvISOVz3opjbQjoe+dHFme/DYLAyq8GwvIcraRw/X/D9WeiFj/Z0uO0m7QyLi0/Zm/o0tdDd9yvJi076rkb4W9n4d/7g/izOImV7cChX4DRm5KxLHys4ljjFschp4qos6vG1Fmha3oZjo6pw0eVJenpe/4OAFYyFqfGotyzD9zCT7K/InvPt1LbFB4nPLaKkpQ8c/vvkXu/h3t79d9A0e3SaWKMayY658B3yfmlHjI2PhItUvo13C/LE1M1Y6FLone/0yhJHtahrvuGoXP++edz/vnnt7sZbaOR7JevKKX2B3YGdlFKHaiUeqX1TetAEpY6R4u6KpP3IB8OvG4VUReXNOjeFIBufx2BM2rATcr3aVFXw8IWdaBF98uUQHntyjiEzlZ54Yqzcspj6hKirsZK70Cx4pp+ocuLX2b99FKyfDaFsrbHljonW4ypa5Ooa4UA0VaOh3b4Apy7hiWfeFx/0fRjbQjomDqdVbUdgkC7twU0LyY0jeUvh/fy5eWdWUheE7Sh/Ea+UHw/tJhLWnJeeuEZAJ56bXH8mX7WLNuBI74GX3gJy5JQ1EX7kKTHSBX3S0lxvxQ7zM5riaro57UFzMHHD0pF3b3/CWu3nZH/HJZO2Z/y7vcuew2ASZkG3pfo+C+9sQKA5WtqFyOvF/M3GLIJ98vymGFbeahY1NWy1Pmspzv+uxlxxu2wKg8Hyi8k/t2Z52gYOldddRVXXdX53iPVqCvqRGS8iFwA3AvcIyL/JyLj6/zMkILKFwcebamrNlkI8nUsdXpFb2xYj2isWkuQ6U7dthZulJDFqzHw6AFYJxRJW03URXGHtNKoJxvxcaKU0QlRpwVeU1Y0dcyePr/YUheJyiZb6jQVFqpoILczxTp17Uoi4rXAJdKLS1OE5xYnu+nQOnWxoIoWJ9xWLQ7UQMV1nFor6rTbWTMSPGzItMPamnd1bTqV6j7YuyaMglitRsefxdaMMjd4Hzvux62EqJMqmW5TSx1EiVKgctyKY+pE4Ze5G0vPMiC01Ill4SoblWIx0+cTdE9IbVMSvai5+Ziwnav7at8fO85+2URRRzTmBV5FHigHH6KF2dSkMxGW8umVxP1rRlbnDnVrLxFyHbogaBg6m2++OZtvvnn9DTuURtwv/wCsA94X/bcWuKSVjepUdEZLKGbOqtY5aUudXyXIWguDzMS3ADCWXtQgLHVef2ipCxoSddmqbbajGMEhdbZRbIhOuR3Eoi6Z/TIaIJvgRhPXCoz2pScKOlGL16LVwAoxoxOl2Jl4MjWs2fYS7lKtqI8XT4ijZ9mKhWtnDszlz1HQRktdq6vVTSSMyW1VfcN2koyF9NswUS54CVEXx6wl2tEXumfKqGJNuhJLXQIfG9Fu3l6pq2AadlpMnVihsKPS+pwUm+WCr9tdySo1Bi9Ktu1jxW1JEvSuBKDfHle3ffE4oxcv6jx/+vplxG9ajKt2KQ98r6IOp4UfL1JWuNsnt1M+fVZR1DVDrHRsYe7kO2gsdYYq3HTTTdx0003tbkbbaKSkwbZKqfck/v6WiMxpUXs6h5Tiv/m+dWjvekclVvnSfq5XUyX9FunVuFGTpsafZRuR6GX4/VGduiFa6uzofIbiFinRZEPXZIoD+xMTDGeIdY6S2LqmX1CMjYDiefotstRRPujqIra2g10jGU3L8Iquc62wSPhlE81YpHeoqIvvXfQctcdSp69tay11MR0Y41Io9MX9dTtc2rQ4siSIM04mi2db/asByOWKZXH82CpeuhgYYMXvm+3Vr2Walv3Syq+Jk2WV9xMOPr4SbFGR4CvGkWX6lrNcFZ17klbDJKODcIExbzewOKmfNy1U6yzyJTN2BkGAbQ1isKxCWl/t4MVurjVFHT55ZzTEp9OMBGCdKXhUctysU6/RsPHyf//3fwCccMIJbW5Je2hE1PWJyMFKqfsBROQgYIAVrjdCUlYOC71FS12mxB+/EomSqthVBgRt7Rk7uSjquhhYjToAP7IIOrXGOH0uugZdygCaCYo1ewaLFZV8ULGo09kvi4IzLmnQhElkPLmJ9qX3qUWd16LBsaLt0fk6jpM4v2EctBLuWK2wSBSTN4QTQtuJXL46VNTFNQbj2NA2TLL0AsUwHS7oQFHX11NchPPb4Cqs+0FRxXpmTsJSlymErq9JwRJnvyy31IldrHXXgKXOSrgMrlajmSDrkb6VyJiw+mGyn1BBgCMBveQYRb7C2t9VWFkq6sRCUibl41Q4Pjbilq37R9ELYnUm+Um3S88rYKe5lw4SFXgVbp22CmJRl6mR0dJSPr32mMS+mlB/tUMtdSVzpQ51MTUMnWuvvbbdTWgrjfRsnwT+mIijWwV8pHVN6hBSOmddew4Soq5KJ64HBFvSp2V6UHO6iwNClxqE1o4yodk1pn+B54Z+uk51S109d9JGcPRko8xSl8zQprOnSRMEQUbX9Cuz1AV2OBlvWRxN+fULvHCV27aLronDOYksFBcb/Ba4tegJoJ5o6vvZqcXH4/cjEz5HrVocqEUcJ9pCS51Sxb23o2xDq+nvXRv/uy2Ji6J+0CKIE3wkM092RfGMyUl8XGDcKbfU2fFiXMZvwFKXKCOzSiYwgfVY+dXIuK3C/SWEm+e5ZIACGUaRr1gYmqhW8wzT479DS13luz9Wlyuo1rcnvF/i/lGLu3qWuoSwavbClQr8ilIJDh62t15vUPW3lvIpWIlEKU2x1HVqv1p8ztMWBQwGgMmTJ7e7CW2lpg+CiNjAh5RSuwOzgFlKqT2UUk8NS+tGMimDjBslPwmUxOmQ02ILACSyIlVbgYwzJkYip2eT3Vi56X4Db2dBuzxW7yT1pNSyHTxlpQo3J3a/HHxnG7tDRoO3PkcrEVNnWYKnrKYIgowuw6AtdHpAdVocC1X+bARemJ2OotvT8FrqipO8VsTUBV5paYpm1hrcENGTD8loS93wx9TF/UMTE0OUk7ReBR14L/MJz4r2iLqwH5RETJ0lKn5Hc34kGhJCoPxd0/hSjKlzgvqZSpMxdc9ndgHguexMiEodJOONdZZgl6i+Z9m1mkAP4ydtzu2fOzT8PiEwk4xW0SJblWudNqnXixf1LHVJ0dXsQvLK81JEXYAVZXOuNbba+HHyGaA5fWLQhhje4UCXcVK2iakzVOW6667juuuua3cz2kZNUafCFFV7Rf9eq5RaW2t7Q4KU1TmvEHa2HlbsrlF18q7daKqtQEaZG51MFr7yBmPOuofl04/nz95bcQdSm8qvcxyKqYQtJ4uPldrm2AVoCIOSFQlD3RYtsjKJVWcRiQLthz74FY+ns19G1tNMGNPRVEtd0pJRthorgYuLzgw5/HXq3EKyeHErYupK43ycJpal2CAJtKgLEyC0I3OifqbtJtblKsctFCePQQda6rxETO2wJi7SKJ2xUcWFriFh+S1zUwfwPW0VLx0DFAmXR+Xzz+wh8OVFVQ+ddL/MTZvNl7e/ibee8tm4qHmppS68ToUoU7MqE00ZXLq7R7HDZmPD32JhpdU6pfYYkrwH8Tnrhb86Rbsd5eGqqG9twvuoStxPK0WdJQrLj0RdjX7OUR6BlWHRhx+q2O+g25a8fimx/SOW6Nr0k6trmTVsvFx44YVceOGF7W5G22jE/fIJEbkRuAaIq5YqpTZeKdwIKR25V4hquWEhSoU5DKqJEy2kqnReOk7HznZBNpw87j51An9jfN0BLomOSagVaO65BXKA5eQi15m04rN6ojH4ztYuz2Sms19mUtJzN6FTj5PV6H1pIRWVhvCbuaKbbG95230XT3QSkeG31PX398fLAK1I0KKtHNr90rJtAiVNEeYbIrGlLtuCxYEGEe2GN4C+YKAUCv1xha1OzGTqu4kEQm0QdTphlBCUWZryZOkuZlxMul9qrwqnrM8UO+7jRfkoKwu5MVQjGXPmZLr4/vtDK9uiSOwln2kdM1uQHKhSK54fKDJ4xczJVO+/HVUq1MrxvDyxTUufS4OWOgeffsmSoa8p7tCeVyj2mYFHRnkVOYnsWNTVttQpK4uMmhB+0IyFLr9M1MkwJUtqNdF1zEu2Y708DEPnb3/7W7ub0FYaEXWbACuAIxKfKcCIulqkDFp6RVORCNyu1jnpFcgq4kXXWMsk6kNtNWkU2202HntFEAqvBjJ8WXWOA8V6beJk8ajifllM31X3mFXbEq8kl16b8qB2v0obBoodTwzKRF08GW+iG0uyvWVtDy11kWuiPtdhHLT6+vsZq5vWClGnU7MnrAfVLL4dgZ5Y58LnqD2ue9G7I6plE7tkdthOjKkref/bIOr0NbUJsAgtTRnxceP6dZXeHnHG4LI+MxC72N+poGp9Oo2diKmTpKeErlOXYqnzrCwEpZY61/PJ4selEMK2pJc0sOuIupJFtqBU1NUav3w/ICcePYwB+prSx3mFoqgj8ErcnPV9cgLtflnDUoeLsjI4TSzzUrIwpwIaq1w1AghcAiV44nTsgqBh6Iwfv3GX0a4r6pRSHxuOhnQage9XdKV65dciwNKrrFU6fF3Mu677pZMr/TwZr1SrTIE+jqpzHMCPUt6LnQnrHaWsijpxjOAQYurKRF3sfpkpPcdkzaWhoK2LcZv1JC6y1DVV4ASVExKN+G64eg040Yr2cCZKKeSL7petiI2K43ycUlHXDGvrhohEMS2WduNtVWmMmm0oe95S6o4NFa9E1HXevUxa6trjflkUdRDQT2hp0u9T3F8mFg3SantCmCjFCpKirvZE30rUR01a/SQSi8m4Of1vT3RMXVLUuXSJQhKWugA7dbzRY0iaayaUPm/FRc8oYVgNi7TrFrCJ3ENVc6yurpuPrdT4BaxEQjN9nzKRqKvVz2WUh7KanPU4YblVykcaWrsfAQQeHja+OMUFYIOhjKuvvhqAU045pc0taQ91l3BEZBsRuUlElonIUhH5m4jMGI7GjWTSBg4VDcZZ8Yu1a6pMhrTYqupKqYuPZ8ri56yBWXoastRFAhI7U1LvKEk915lG0JMUiS11eoJSlsmtSkrsgR+v6I4ExSxyVuTO2tQEF3Usddr9smipG75JcrKQcCsSpcTuiCWWuuYI8w2S2OIbJkoZ1pqDmsQz1ipLoecWFwM60VKXLEXRTlGXEZ8MPv3o+plRVmAq+1x9r8sTpSix4n5OaMBS5xS/TwoyicaX5PXQ7fF1HdNEe+KQg+SCjqQvDGYGZKkrxgdCnfCBOOZPX79mWOqKgl+80sQz+eg4uaA0EVcaDj7KzsYJz5qTKKUyxrIjiERdUKXOocEA8Ktf/Ypf/epX7W5G22hkCefPwC+Bd0V/nwpcBQwi1eLGg+97FelKUpNfVOmc6ootv0BB2WTLV1z1CmuDtWrqikcS7peRpS4tpi7O5lkjfXM9YmEYWTElcrewyiYg1VJiD/h4cZujfekV8NhtrpmWuuoxdRK4+KLT/Q9/nbqgxQkhfL9SnPvSwQNz4OIqO54MtyWmLjmx9lyy2e4aWw8Ot8RS13n3MvA2DEudRosFLVK0dSp5r7UQdSoWwhwkKi/biKUu+ftkTLOOi/VLkrNo90udNTjx7MVjR31LnR33x9VEXUI86aLjQZnATcHLh79zrRwEEDShjluJa65bWiIiH8UW5ohEXY0FyAweWE7s7toUi3fScttJiy1JS10H9jeG5nDLLbe0uwltpRFna1FK/Ukp5UX/Xc7w1bQdsQQp7nOSIuqquSvaWmxVi6lLZEws+dwemKWn3nGguCppZXKphWOV7xXr6Q3FUhcnWym6X3opj6hfZVIwUDJlyV2UXyBQQjbXHf/dNBLXpfyeW8rF1zF1bUj3n7RItsKqoy1VUhZT16kZzCRKfKNFbDti6pLuSa1arQ9KRF3nDQnJ96ItBZ3LRF1saYqEW6plKy4DUy7qiolSLAKU1LPUpbtf6qyYJVbM6N+BVVnSwHOLrvslbUkZnzIplsckyec4LmmgC6rXKN3hRhblouhsQqKUhKUOv9RSV5DQQp+jdrIz3488duwsTpT1uCmxYonjBR3kDSGBhy92ZOntnPMyNJdRo0YxatSodjejbTQi6u4RkXNEZGsRmS4iXwRuFpFNRGSTVjdwpOKlxUSl1Y+pYtmy41i3KoOV7+KlGVq1+2WDE0m94lXLwqZXhu1MNtVtLrliP5TO1tG/1VnfAi92S0zStJg6PcmJ69SFQjmb1ZOn5k2GS6w1ZdfISpxnO9L9+y22SKg0S12TrK0bInrBRU9k2yHqSix1LTp+MuasMy11SeHSjvMrFcp5S2fl1Za6yjjmOCtyWUydKhF1CqT20C+JxCYlMXXRomFSLOi+LXa/TIx9+hmRxD4Cqcx+qZSKRV01K0yJO3xsqYsSAtWw1OnxybNCseU34VktsdSVuV8WouNoqo2JrnZNtTNYImHJhSb0icnF405yi5bAw4/cL01MnaEal19+OZdffnm7m9E2GnG/1NGGZ5Z9/nHCUWebpraoQ0iLo5G0CXM198t4BbK6pS5N8MgALT3a5bFW6nM9MNtONoypK+tQPbcfPWQPZaXRKXfhTBTlTtKUmDqlyEqpu4/yCrjYdHXpyUnzJsNeFKwPKZa6wCXQMXX28Ge/bHXskN5nMiNfQHPiIjdEJAgXXIqWuuE/z+REsrxuWLMoicUcgtv1hkqppa4dcZGl19S1cuCHi03VvCN0O52yWGuFVWKpo46lLpkt1U4kqtL9U2mfEZWGiURdUGKpS3e/LLdeeYEqJjup6n5Z6e2gn3OnxmKiTlTk211R25sg6pILmV5/yXee3UVSY1YXdf10EV4bx5KqmaUHTOJ4fgctnMWiTpyOHTsMQ+d3v/sdAB/60Ifa3JL20Ej2S5MUZRD4DVrqqnVOTh23SCtIt9TFMQ9eUUTUQq9w1qtTB+BkcgRSaSXz8klL3RBi6srcL0V5+CnnGGBXtXA2TNIdMlEXz8Uhl22+qHO9AnpqVH79bOXi61hIyyJQMqyiTrU4Niq21JXVqurURClWtOBi6fNtphtvw21Iul+2xlKXdL9kGLO1Dht+8r1ow/mV9XGxpckr4BbyqQtp2mW83P1SWQ6WV0yUUs9Sl8ROs9SVJEoJjxlEoinZh/iJeGxN0hU0PjcvoFtqW+pK3OF1ZtBo/MyIX7V0h3aVbKao8xJWaqvM/dIrs9RVdb/U74+dwbaE/iYlAJFk9stO6mOVhy8Ovth16xIaNl7uuOOOdjehrXRIAZMNj5Lsl9GKq5USl1FtYmvXs6BVcU3Ug2ejBVbrHofi4GNncqnxbG7SFWUIna1ebS0W1fXiVP8l7amSPW0gJCcI8flEbnNdOV1WoImirlDD/VL5caIUIFqxHb7BOEhOXofL/bIJ93BDRVvqdNKbdmSgS15bfzjcLzswxiVpjWpLBtOyOLGkKHET177kPYrEdSYlpk5IxtQNQNQlEqVYKS7F+tooR1vqkolSonjsROmdZFs0yTGkmmUrOaaVx9SFDUhf6NOukr4TlappQnxkMolOuagLnNKkRNWSkMWWbjuLiERlXppg8U6MHcrvHAu6lbTUdZJYNTSVTCZTmRV+I8KIuhYRpGQ7lFRRV8VSV8eCVs9S5zWYjl+7X9YsaeAVC50HKUHKfiK1ea391CMOlFdaBHt4KW5CyZpLgyVZ8yj2z/cLeNhkYktd8yZypROSSkudSog6f5hTNpe4mbVQ1CWtB6qDE6VYKsxmaunJcIcmSvFL3BM7Z/KoUS1+L+pR7vUQixK/UJpSP6X4uF1W21Mlio9bKGRAoi7pfmlHx0mxBDuR6EzWsIuuYbKAuRKr0lKX1h+XoZJ9aNT/Jy021RJb6cUH5TSvxEhS8FtBqajT4jv+vpqlTicg00K5BZa6ZsQPbiiI8vHFJhC7ZgylYePm0ksv5dJLL213M9qGEXUtIvASHbku+prWwVZZmYvFVpWsXlbglVh34s91GvVCg5Y6ah8HIIgGRSfTlRoLVRJfMFixFQTF2n2JmLogxVIXyNAFQXJSpAddidwvMy3IWui5iUlA2fWzg4T7JeH5DWthbr+17jraHavSUtehoi56N227fTF1yYlkq46flriio0iKhHZMjsvGhsDWiVLcEhFUEj+pY+qc0rGhJFGKClB16tQlyWTTLHUpz5ejF8OK7dYuukkXTiVOhdDxk/1xlWcp6f0iKYuR1bxT4vEpFp1Df1aTY55dZqlTZZa6aiEJxcyg4bXxsJpigbJKnofOWWyxApcAO/X5MRg0G7uoayRRCiLybuBgwsQo9yulrm9pqzqAIC3NdIqlrtqqpLbUVXOLtFQBTypNzBIN5l6DcTyZOseBRO2jbBZPHKQ8gL8J2S+TMYCiQvdLS6UL1zRr4UDxStx9SrNtaotSyyx15TF1eCirNDPkcLqXlFokWmBV8rWoK6tV1YlCAG1hztAd155qr6WuVaJOlSRK6bx7qUrqfbUjpq40+2WQ0aVW3NKYruTzFbXZyZRlv7TsOO2/RTAES53Ofpl8vrRoKrYv/s7TMX6l2S8rRF3SnbGapc6v7LOTljrfdcmklGOMLcoZXX906O+jSrTXKbPUBZnSdOp2NXdSvdAXXZuwfl9zLXUdVdIgClMILMdkvzRU5d577213E9pK3Z5dRC4CPgk8DcwFzhSRX7a6YSOdkolUXEg8xf2ySodfV9QlMiaWfB6tpPpu87JfBtFKZMbJphaODZog6txCMoOYFll+uqUuJXvaQPFTLHUELj5OMYakiZPxpKWufOXWVh5BmagbSmziQFElCSFa537plE3sOtVSZ0ful3YLFgcaJfl+tCxRSoklq3MsAjHJ89sAio9rURJa6pIxdUmvkEqrOITWMSFMJmKLqp/9MnnYpKiLn+nKmDrJpCRK0aIuk7TUpYi6kv6xgeyXsStp0lJX2/2yaKlrgvtl4vztsgRoUibqqsXU6bg8fU09aVJJg5I6dZ3zXlrKIxC7xJXYYKjHxfe9wl8eW9DuZgwbjVjqDgN2UypcNhSRywgFnqEGJS4eZTV1kqRObJUiG4u6Ku6XZck14s+1qCurnVONeuIRirEMmVwXgdjForcRydiaagNYPVy3gI5E0KKnlqVuyKIupc0SuPiSCLJt4kTOTwny1ziq3FJnNacIbaOkTNCaip5oZhIlDZpwDzdUwsmHUxSxbYips4fBUldafLwD72VJFsENQdRFMXWeW5KkpsRqEbj4SrDL3SutaCKsrX9W45Y6J+F+qfeb7CfijJspok4LFzuRKEWJXTFOeInxqtqEPSkkdf+YfM6TC2clv4v6XtFmvKYkSik++44qE5Nl5sKqMXWxFTNyW21SnHFJjGUHvZe6Xw3EMaLOUJXf/va3AJx++ukAnH/r8wC8b+9pbWvTcNJIz/4CsFXi72nAU61pTufgp7hf2o1a6hK/taoVJw/cdFEXTSQbSY6ggoCs6IDzGjF1fjFRikqJZ6s6wRgASctZsaRBGBhd0W4ZevHREveluAi5iy92PBlvpttcMgNh+SDv4KGsZA23JpRsGAgtTt2uJ4BOMgNeisW3U7CixYHYWtIGQWApn4IK352WFT9P7reDJo8xJefXhkQpZQt62gKkAq8km25JfxL4qRmDlUTul9G2A8l+mckUE3/ElrrE/Y7rUGYqE5FoQWUlstEpy66I4fYbSJSij1NQyULq9S3SsQtoVieaGfqzmnQ9zpSJOsk2ZqnT56yvaeh2P/TnrCSetoPeS1F+aKmznEEvHhs6n6uvvpqrr7663c1oG41Y6iYBz4nII9Hf+wAPisiNAEqpE1vVuJFMMlhcBR5CtNKkBCsqGhsoSZ3YKr+ARN87VTovW7kE9ujKzxN16urhewWc6DjVLIJQHMByma7IwlK677hOkZJB16krcSdCx9Slu5iGom5ookcnfwmUlIm6DI7TCktdeI08VSmKnbKYukCG11Kn/Gh1X1RL3Mx0jEfSJayTLXWOcum3xsRxTS2JU6yDjU+eLFn6UC2qIVdSCqMDi49L4Mb9dVssHmWuc1qUKN8tTalfZqnzsCmNqAMsJ/TG0AtmAxB1JZY6u9KlOBZuun1JwecVa5zG26e6X9b39tDjUJ5sSdHxvHLIiVd1zNNeK3ZudEXbB4u2TvpKyKpSrxg7VxR1fo2xNRabOrlZk1zSS5+HznkvbeVRsEahLMfUqTNU5c4772x3E9pKI6LuGy1vRQfiB6XuTzahEOsjy2jCQaCPbOrE1nMLZKLvu0gfqEL3y8pEKQOx1HluKOr6yDJa8uEAkOaW47sUlE02Y0exUKX71oNTtfNphLSU/1a0MldOcxKlhMfrIxtPIizl4Vs5LNvGb3IBcO0alCdTaalTPqq8MPcwCh4JXPqj57IlMXVBuJiRzMjXyaLOwg/dL7V1oi2JUnwKkgX6WlanTnV89kuXfskyinx7sl9SmihFsqEoCTw3FjCuKn2PJPBSvRuIrGOB74XuOQPIfpnNJmPqwndYlbj4affqykQpWvyUul9Wus9pkeqq6vFS+jhJUacXL3J4VS11cZmHnE6U0oySBuF59ZMlU+aBY2eLi639ifGl2j4kU0yU0oysx1Iy9+ic9zKeD4hTdbHbYNjYqSvqlFL3AYjIuOT2SqmVLWzXiCe5Ou55LjZh7FR/QtTlJV0EuYV+MoSD12jJo4IAKRNbTlltM43lNG6pK7h5uvRx9MTFqljjDbOt4ZBzrNR0wnpAzg9B1CXdL4uWOo/ASo+pG6pPfVqbw+Qz4YDs4TR1Mh67Dkm2ovBuBg+s9lmxJHDpI1d8Bpq9f9/DwyJrSfyZSrH4dgqOcgmspMV3+CcgNh55tKWwRYIkOXnvQEudFbjkybVN1JV7PTiRpSnw3diylZdcqdUi8FLrl2pLnR/4oagbiPtlIsFRnPwnrTZeVsesJa142lKXSJRi2RUx3Do+s7/8fBLo4xQSC2Na1EFvdVEXtSGtfYMmaks/uYqFV7uraKnLS65qvLoWdTorsJ9Sv28wJMfGTnK/tKP5QKCtzgZDChdddBEAZ511Vptb0h4ayX55hogsIYyjewx4PPq/oQbJ1UAd02Irj36KK5Z5cqkWGS+Kl8hLuG1ax2zjlmRM1Oh4sKCBjHdevr/0ONUmf34BFwfbklTBoQenvOQGL+oS7kTFRCl+dffLIXbq8SSC4qBrK5dAdHyD1VTrg34G3DLhq5QKRZ1dnv1yGActP7TUQYsC61XlRDPNBatTcKJspmJpi+/wW+oc5eFK1BcMQ0xdJyVk0EjgRtZO2mKJrMiSq8WC78Xu4/3kKi11KcO6EgeHAF/XTx1A9kvLthL/jix1iYUKvWhg53SilEToQUox9LD/Loupi10rc9XdLxMLYyWiTrR3Sm1R50SWuma4I2sLZEGy5MoWpzJdRUtdnmzVGrD6Hupsy6pJZV6S7pedtNhiKT9cyLZM8XFDdW666SZuuummdjejbTTifvkFYFel1PJWN6aTSE5yfF/7/3uskzHx524VEeS6odgqSA5UmKq5PEW1o/xUUaeDrhsSdZFLYPE4LglPmyJRUW4AxKoYpPSxCpKrsEI1SrKYLokaRF6VRCmDjd3T6ElEOChHMSGJaxrW42tiopTIHdaTUvdLz/PIiAK7dsrvViJBaIlttstpcv/lE81Odr+0lRdb0UOLbxsSpRCEok61UHD5le9sJyGBi0sGX0lbsl+WJ0pxcuHYoQIvdqktSK5kgitBesZgidwttYdCuedHo+iY7ZLMoLpkSVYnckl8l2KpC62GpedWFEn1LXVuwtvBUR6uFY5fQRXvFP07LbaacS9VYsxzyu9TV2KMt3KM9ntrtktnv/RTQhsGQ1IUd1JJA22pU1amZmI3w8bNrbfe2u4mtJVGevZXgPReyVCVZGcaROLJwcOVompyrXRR50WuiIUaK5BhGvzKwVu7fDWyOq+Tk7hSOw5PfDeO0whS3C+Tq5aDdYtMBv4X3S/9VPfLMJPb0CaqyitOIqx4guDG19TDbupkXAtf18qWJHmJ6/PZpe6Xw1mYW4u68JxbcNzAx5ONyFJHse5gs5+jgbTBiy11LSo+3uHZL60ow7DfJAvKgCmbuGa7daKPQrH/snIlfa4oDz9lrVbFoi6K4xqApS6JzmJZYu2Knu9MLnJvTH7npydKqXC/TPTH1eKltKXOTVjqHPx4nKy2kKnHp2wTRZ0kxrxycglLXUG66rpfxmN2ExYrobTMQ1tKcbQIC22py8SlmAwGQymNWOq+DDwgIg8D8cxbKXV2y1rVAQTJAqw6pTseBSuH7uNdK0cm6K/4bRwEb3VBEFpzyrHxUi119gBEnS5FUIiO4/vpK50SFIruc1ZlPJtKtLcrWFv3uOltSRYw15Y6PzVuUFmVwnKgJCcRdsIyqLNQpqWXXp8P/x6da+S1KUWvrHuSLYklcwvRJCthqQuLq3tc/tDrTByV5bhZWwz4eANB1+fzVWsESOgSVjqJDJrgQruh4uChdL1IaU6a8oFi44cWDL91MXVJS3YnuXlptKjzsKEN2fbKJ/gZbQHy/WJiEaubbMISVC1RikSLVXFtwQEkSknixJa6xPXQljCd9THp4RBtl8ygiVWZKCW2fFldjAtWpR88KPahXWo9KIUjQficB9DT10/BC8g6ZWvVOntzJIqbkuE38PCUhZ8m6kaNLR7aqrTkaeIkMlEpiLDfb4b7ZTEjqOooS13R/bLaNTUYfvaznwHwmc98ps0taQ+NWOp+DdwNPEQYT6f/M9SgtI5PwlJnFWv+uFZXqmVLiy29bZBSVDVMg185oNh10qj3FjyiOvKxy6MXHafcInjzU4v52Z0vhQOY6NXEyhoxsVuM1TXoQSnpOqPdjmzSrZHNsNQVLWfFlVQ7ORnHiSfj/a7Pmj6Xfb57J3t/Z3DpcvXE2itbWdfWUim31Cmfr90wl//+838aPsbafpd1/QN3GdX1+cI4wtYUH6+onZWyONApJIvJe9hNdeNtvA0+vtVaS50kFoHaYslqMZbywsUOsZpyfsvW5QkCVX9DjSrdtmtU5H7pu3H/5dldle6XKXXq0B4IkWfAQEoaJLFTsl/GlrquypIGWrgka90hVkX/rbRItbtw6mS/dK3Qu0KPO54VWgF/cMtczrqisr/U7qBOIvvl7/71KivXDyFRk18IxX6KG2uuOyHq7OqWOi1k7UjwKrHieDjXH7xosfFw0QltOkf82Pgoy0HZmbDMSKsSQBlGNHfddRd33XVXu5vRNhrp2T2l1OeVUpcopS7T/9X7kYh0icgjIvKkiDwjIt+KPv+2iDwlInNE5HYR2XLIZ9Fm8p7P0nWlFrdkKmHfcyEIwkB1q+iG4ttdFXETAF5kvdHbeimdV5hco5b7ZSJRS6AIAsVlD8xjl2/8g78/tTjcfyQoPDvy6S8LIP/vP/+Hn9z5IoGbL8ZpWJUxddpS50eD7ctLeyraVQ3XD/jLYwtwI5fTfpWJJzNhYHS14uOlbXh5aQ+/vu+Vho+r/OjcraKlLpO01CVqxX3skkfZ/Vu301vw6XOrT+6CQPGPZ95MPX99PwKrNMV1LOpKarhZJav0J/3i/liI1+I9Fz3AAd+/G38gE0ciUWdlQlHXAouLlTLRVGKnPvvl9OS9hs59QyKZ+MZvlUtrHWz8uP9QrRKVgRu+r3SOpe7NNf30R++48goE4hBgVSQuWp/3+PzVc1i8pq+h/T65YDX7fPdOrv3PwnDfDTzT5e9Htjuy1AVesc+1u7BVgOsHKKXozxdSy8DomDpX1wMdrKXOqUwco8VWNrbUJdxB/dIC2xB5WpTHZccCrboI0hZB38phqSBehNSizsHnzueWVP2dFbmHLlrVw3dufo6vXPd07ZNN4eWlPVz1yPw4eVhaIq9kopTAyuFIQBCJtIWrilZVFbumRosv0YLpa8vXs/1Xb+XWpxcPuH0Qjptu1C7VQQtnDj5Ydmx1rpYYx7Bxc+ONN3LjjTe2uxltoxFRd0+UAXMLEdlE/9fA7/LAEUqp3YHZwDEisj/wI6XULKXUbODvjPA6eJ4fcMSP7+PQH96DUoo7nl3CBXe8WJb90o9dR3y7uGLpJ6w2a3rdeKD3EwM2hFae659YyAOvFHPVJK0BSfSg67kFvn7DXBas7OUTf3yMXb55GzdHYk5PRLxYjIXHWbu+OEFJriiv7umN4zTSagzpuAnfzkHgc+QF93HFw6/HX7+5pp+PXfII81dUhmbe+ewSvnjtU1z7yKtAVIMoYalLxppp0lJiv/+3D/H9W5+PLVWuH1DwAvxAUfCKE4h5y9fz18cXxtZP3w6F6HZfuQVLJd3mnDgz6YOvrig51l3PLeGphavjv3sLHv2uz8kXP8CZf3qcb930TGWbo4lFYOdKBKm+5mKX1nHyEpbLJxeuYcna0gK3AN/821x2+cZtACxd289LS3voyXtcdM/LFdvWwgoii0QL4r+CQLG+v7/CJSyInqMXl6wrea6TrO4tsNs3/8EvB3g+aSxa3cdLS9YNeT/1WLymDwc/4cbrDL/7pVJkxQ/fR0BFE8r1CYG8dF0/K3oqn6mBIIGXyA7ZPlF3wR0v8uSC1RWfr+t3a57j4jV9XPbAvBKBtf/37+JDv3uYC+54kXwhz1pXSt6L9XmPNX0uVz26gOueWMTv/vVa3fb5geKCO14E4In5qznrisfZ93t3pT73QaA480+PhX21CnBV8b2J3QcDNxYEY8aOx8HjoVdXcN1/FlEo5OkPUoZ1W1vqhpYoRWe/9DyXnsgdXQIXXwlOlGXL89xYGON7FJQNUixnIlaYifOxecWqSFqkenZ39Rpk0Zjq2+HCmO4jg2j8Svvd+ryH5xZwlR0veK7rDRdg3ygT5P98cRnv/OW/6Sv4VS2qR15wH+dc9zQSeJGoqxTHTi4xxjuhkDzg+3fwv395koN/cE/xWdUePFGiFJ0A7I5n3wTg3heWpV+HGgSBwlJJS10VK6FSrOkbWaLIjvrVOO69gbJNBsPGRiPBQR+I/v/lxGcK2KbWj1Q4UmqTRSb6TymlkkFXo6N9jTjUA7/glRefxrEszuxZhgJef24yj1zxB6bJMuwtih1736rFzL30M+wGBAlRh5NjfLCaG757Kmv7PPaePpFdthzHpDfnAaEAAFh2w5dZt9jjCv8AZr5nGtktdiYnqqS2WbzLbPjZ5Hk3s/3ax3nwSYvD3YAJwbaMy8/gGOcenN5PA9uiIvdLfZwnLvt/TDjsCF4ZfwAr7vs15zmhCNvJms8aJoUHsGxGq178mz6PbQn/mb+KzJtPgYQidAI9nOdcQtc/bF6YO55VvQVeXNLDW4Hbr9yDT3z6q+F+nr+FF/59A8teW8l5Dmy9/k2wwRUHR3m8evWX2Vz1sKYrJR2nOIxWvdz0vfdT8AN22WIc/9O3ChzoeWkMYwuv8a9772LZujwKmDx+LEeefj7uqgXc9+vvIcCYCeGA6dtddFHgG9Yf6CaPRNdUicPePfewevGrnOP8OaxVFbHoiktYBMzcfzpPLlzN3EVryNgW7/QCPjzWoXe+j3/TNCwR5q/OM/6tn4lXopWTY0uWwtLneeFf1/DsnAfZ3oZMIuZkwpguMstf4zznkviz+35yGTMmj2bh6l6mTRzF4tX9bNvncg6w/vrbWL58Pec5q5gwKsOae11WrtiCTbbfD/b4EPQsg2dvgG3eyqp7f8Gzi1Yxa/+3MXbTGfDsDWzmLuRVewYeNmPefJj7f/YRDp61I8z+ADz0q3BCu9mu9E3ZHW/1IsaOnxR+ts1hlfemjLvnvMSR+ft4OSg1yPf5ME6t5Y6ffwwPm+CkL3HwPnuG8S8PXwyz3se6m3/Aec4CuBvWr3kLa/t8tjj8E7Dl7LrHZfV8eO2f8Ja9YNnzfPbyVznBfpDtD5gOdg4O/hyMmVLykyv++CtmrH6YXWfvx/hDP1X/GGW8uXQJN174Oc50gjgG1ReLSavmsOJff2DSIR8vbeK69dz2+2/yto98gykTxw34eADM+3fpvehfy6rbvstEQisBwPRX/sTKV/bk9j/9kMljsvQWfFb3ujw19mB+/KXPDux4vsebN57LosVvsPmqx8NkLGo9qID1T1zLf+67iV22HM+Egz/BV375J/Y78j28+637V9/f8pd56vof8uSbed56+o+ZuvmU6tumNSdQrLn3Fzx53xvs+qFPsGbdWp6470b22Pcwzr7XI9u/gp++fy/u//uf2HmLcax0HSa+9X947oYf0rt+HeIGLMp+jKljgPFT+YB9Fzstmg+LYBt7MWvG7oC/zmaTZY+y6IqzuOu5pUwek2XX0VnOc3qYvWgC3DkVDjsHFj0Ogcf6Ncu457brsASOnbkFryxZx9teXcnbHMg+ZVHwAvYH3rxuEpz+1fAd2/4o1qx4k9XP/4uDXlzGKy+MZvqYUDhkIrGSjcTC5EV30z0qtIpN2WQCmcX9vHZZ+Lzuby9gzCbTKi9UNBF2n7sZKFruBopYNgHC+AV3cdP33s/e0yey9co5+NjY0YLYqFdv47Yfvsrbd92cLZbdX5GNc01eYYniud+dzq4nnog1aVvcx68AG1RmFBk8Vv7lf+jK2DzzxhpmT59M5qD/Zub8PwKgrAwTg9XYd3wtfAaiMfXD9h0cbj1J4cY7ydoWC1b2ct+Ly9hDXsS3nTjefPa6f3KeM5/+3q2BgwHIv3I/8/74U94N3PZDh3X9HtO3mMJhH/xK2B9tdQC9nuI85zJeVFMj98sqoi6R6XPUqDGwFs7u/zX+Uxa7O/Dkry9h7eTRTO0Phb6ON9QZgb3n/8F5zt1svnYmMKvuPXnqnzeSf/p6HMti6bp+DlHrWM54EOh+/T7cJXeSOeSzMK4Ym33D/U+w8h8/5OS93sL4I78IYzere5xU3nwaVs2DnU9o/Df9a+GfP4It94Dd3h1+5nvw0EWw7+kQFbFPsn7eY4xjPcpykGhhIbjjXHjbl+Cpv8B+nwSnMhxlSKxdDA9cCNsfBdseMbDf+h5Lb/oWk98yA2ufRL+/al74vu9yEkw/sPrvC+vhsT/A/mcN2qq+wRP44T3f+78gO6r+9g3y4x//GID/9//+X9P2OZJopPj4jMHuXMIUW48D2wG/VEo9HH3+XeDDwBrgrVV+ewZwBsBWW2012Ca0jHXP38PE1x8G4DgbJsk6XnpgHF/N/DXcILHI1vPsP5i55G8sV+OYP+UwNl8zh+XWJKbPOgz/Xw9ySOF+sMFaJPQusei2hNeDTVm79TEsfvJRNlt6Px+0e5iaXcfYvz9U3HGKFcvOjWVOsC3Te1/nOHseBDDG7uft9qMsWz+d3Zwn+efSXYBDYovgCzKDaWoT3m49xqj7/83LwRGcYt3NCnss3RmbPtfnsa6Z7AK8mt2JHbgT67G/0J21mV4IJxyPBTvwyqjd2W7NgxxnPxQmg5kPk4HtbRhDP2uXPwaEos6/93y2eXMuk+3iy/xKsAX99mimF+Yx5rmLWEs3TN2n4hxf69qJ7bmDA/P/Cj9YEN6DCfSw5olu+l+7gwMDjz7pRpTPhDXr6Xv+CHpevI/T7DtZxRisHmFusDVjdz2a1Q89zHH2Q6xkLEvG7gLAqE23hsWvs/imb/NJ5++sVaNwy1wI1z7+MNMCxXRbCJQCG8aIQw8eqx97kJwtTA/Wcv3qUUwcHU6wV0/eC9bch/fAL9nx6T8y1crR27U5O83aL97vZrsezur7nuE4+yFG55wwQYsKn6ltAZZGKypRc/rnwGbA8Q5MsLOstguMfjaP/+rfsff4EFz3CXj1Xtj5RCY+dyN7qwz+XfcTTN8LXrqTgNG80L0LC/pzHGTNZYuVt8F9N+CtWYwz549hsL9t0+2WWVrPXVNxb8oZ/cYDALyhJrFd4vO+KXvQt+4BjrcfZBPp4cEXdoF99oSHfwV3fAOeuZ5pb/yHE+3R+FjROfbgjrbJnPSzusflkuNgzfz4z/9y9uEo6zHU0+OR/tWwxSzY/dSSn8x8+dfMsl6jcNeNMAhRt/ipezjTuZmVagxP+ltzKLBqyr5st+Q21H3nQpmoe+rGn3Hq6t9y319tDvvEDwZ8PAAuPTb8v74X8x9k4pyLWanGMH/SQey/9lY2WfUUc/5xIafyD1asC+N9xtm9bN3zJvDZAR1uxetPsvmTP2e06sa3MljbvhVe/hson8Kd32XfnvnkVnu4GZ8fZC5n/r1/g7e+WHV/C/55KbMWXc0s4MnnT2Lq5u8eUHs8z+VbmTAaYOHNS6BvNUd688nfdzdXBOshCwvv2JOj+5+k77Uc20gvd123guN7b2GNGsUoO8/Kh9bB8rBf/aaTQQF90s347gyT9zqSxS9PYdKCf8GLN3KcDfSF/21vw+ilASxeD9u/Pb4Xfd0zOKp/AT10U3jKYbOCz4mZ0C3GDRSWI4yyfXLr19N3l0P305fD6/9m7aI32FxW8S47w1jpY0VhEouZhBX4LGUi+9g2K6bsx6Slz8JaeN7alu22fyt9L9zOcYTtt0QYv+MhFddp7djtAcJjCamxYOX07XUmsvYNuso+f23Mnkxd9yJTWQaRQ8YcZyazbZuHg53YThYxrbCU3idgHLBo4t4l7/6WO+/HsqV/5b32fbi3Pkxh6gHsaz3Hc8FWbLrHsay6+wHs524A22Jb1yOzrAeCPFm/l16VY/6oXdmp5yGY+3dWqgk8O+4gNlszh5nWq8zkVeSZx1G2MLbP5R1WuGa8bvMDmJLt5vnMzmxVWMiO9nxG9d4B3nfByTHv7z/kg/Y/Wc0YcCFj+4xb1svCv3lMfe0vBE9cgT9qKh92nsZTFnP8o/HFZv2me7Pi9RdZqKaw/VGnM+rpyxHLobDFPjj5VUzd750sveGfHGM/UnoRo1wwj6vt2SUqiq7ExlYeRyz9Izs5z8H8OyD4et17pR74GXv0/Yc1jGYroJcci8fOZKueu5j69M8BCKbsgLXvfxV/9MJt/JdzKzwJTN8d9vxw3echlYtDUdzIWBAz/8FQLEFR1D11FdzxdehfDW+rdN564e8XsiewZuJMCl2TWabGMWXOH2DpE/DGE6El+MD/Gdw5VOOFW0LR8fq/ByzqXnzmcXaYcyHMAZKi7pnr4eGLWbnwRTY5/fqqv/fv/Db2I79Cjd0SmfmewbV/A2fFI1cz6favsWThq2z2vp80bb8PPvhg0/Y1Eqkr6kRkFPB5YCul1Bkisj2wo1Lq7/V+q0KH7tkiMgG4XkR2U0rNVUp9FfiqiHwZ+DTwzZTf/gb4DcDee++9wVnz/rn3z/n0i08AMGvqeC5ddirr161O3dZyQzePkwvfZM+uvTnha2cyOVCM7cqg3noWM758S+rvbj3gEG6ecjjfufk5/p79ChOyQDJ0L8VSl81keGfh2/Hfx83cgn2f+x4n2A8WUx1rq1HkzrLJ9vtz4P2Hcpp9O9/OXMqUnMeawij2yv+a1849lsXL17NnVID2j73787X8buF+Ep5N0zbp5tzDduW8Ocfyvr2n8aHfPxx/d8j2k/nv3ovZccUd8Were3p5zN+DM93Pl7T/mlE/IEvYvs+5Z3Hu/h+rOMdnJr+Dr7yyK5uOzTGuOxPHsP0j+0XG5/OMDjyuyRzP+875A/95+mn2/9uhvPLmSuw168kzicPcn8dxZ//c961s8vZPsO1Xb0Ep+Mxm4eQnf8R34IpD6FsfGpYfPvgPnPOQw4qy4PqTZm/JBe+bzdlXPsGEURm+fvwufP+GuVz7+EIcPF7u+jBW4EIQKrCVO5/G2pcvRtavZSxw7cTT+chnv1uyT/uIL7PX7eEK7T3/czg/vfNFdthsLP95fRUHbz+ZF5f0cOUj8ylns3E5Hv7SkVx178vk7vwa7+u7l7EA60P30aCwHk85/Nk/gpPdB1i0Yh3Lgxm8q3Ae+2w+kUdXhDONd1gP86vsz1i5ejWbApcU3sYnsrcjFUesT0ZC0X/nVp/h0MTnR532JX5217v59V3P8ELXR4uJffKRi2Rf6Jr1nuB8XnFDK/EDuU9jr+2hoTXl9UtL/nTweFZN50e5b/DH/o+X1ljTbY0STmTFD+M6ZWBnrJM9XDz9J7zv+HcAsNtZl/O38z/MUfnbK4/nh/1CzmueW+jyNeuYDPxi6gV86Njj+N5Pn+ErmSuh0MtSJvLUKQ+z+7QJPP6jw5hUPltvgFVre5kEPLnvjzjwHR8Kr9O3J6JUQF9/P/cF+3KQ9QxqzVo2BSazuub+Fq3sQduVqtUZq0Uyrqavr5/u6DlKJqfp7evlIbUr57uncnPuK4xzwu+umvlbZj/9PTbrK7rhOXhc5J/ELh/8IW/bOXzStjwczr/1eS5Oidndt/Acf8l9m6Wr17Fp9Fk+38/9wb581v103Ed+6ZideGnJOq57YhGfPXJ7DrOeZI9/foLFy1awDeDm+8iIx/X+wTzWdQA/9r6PHeRZyyTeVTgXgHmWMOm/b+f2Z95kybo8791rKk7GxtrlvZx/4zNc8/hCco7FC0e/o6Kdr3Ttyr/83djFilRYSixYOd0n/DD18ymf/gc//scL/PHBoov9LWcegm1bnFIonZDPmjqeq84otdTOPOJUOOJU7vnZ6ey76kZWretlsZrOL7b/A788eE/+tdnb+egljwLQRZ7nuz5Gvm89OeDz7qcYNfldnDN/33h/39p1V454ZVu++67duOD2F1mxpvgc/fA9szhu1hZxxmL5rzvY66f/5HT773w182cKhTxZJ0d/f57XnBkcuf47AFx4qOLERz7I0hXLmQp4/evI23nGAo4E4Lv42Bzw8R9y3IUn8Mwba3luv2PgkLMAyJ4ZJtQaDazd5lheXLqeV5f38ON/vMDafo+fv38PfnDb8yxc1cfLTjg2+JLBwistrB24YKV4qySwlcdL2Z3Z9DP38PLSHvbeehNGPXgL3FFMFvHSGyvZMfGbzcYUFyj7+vqotI21kLQkboVowbA/XRwGXp6FajJHnPwpnlq4mnc//C3+lfscwfoVYQxRYX3Tm7lszTqmAG6+n8qZVm0KhXS37/7+PF3A0/OXU8vP5bnXFrAb8Mzrb7LbzAEefITwyuLlTALmv/FGY2N6g/z1r39t4t5GHo24X15CaG3TtuKFwDWE8XANoZRaLSL3AscAcxNf/Rm4mRRRt6GTTIbx/n23wrvFQdz0oHnLD5WYj8Uxu27OqGzxsosId37+MC5/6HUufWAeB283macWrubwHTdlp83H0htZwjxsLFXaGYpTefu6sqWrej88eRbP/mETnDeDop9rFB8SRP72O2w5kXnnH87D1zwPz8CEjI9fsDhw20mICNtOKRZT/d67ZvLAKyt4bvFabp0b+v7f/b+HMW2TUWRsi7ftvFlFEoDz3zOLBX8ujcXr6SswKpcl0m/87NTZAORuy+D0+fH1esuEyuHmf4/ekVP2mcasqRMA+OU9L/Ojf7yAj43rFrAI2GbT8WQdi2mTQstEf76A1V9gnFj87NTZfPrPoSB3bMGyJE40t9Pm4fbZXOjKoe/pVpPH8uP37sDHLn2UsTmHdVE8yRmHboNtCb/84J5x+3783t356rE7s8e3w4n8lNE2heiabz5xDC42fm8PY4EtNykG1Sf5/FE7cMEdL7LF+C5+duoeFd+fvNdUHnltJT+47fn4s72nh6GuHz9oBn+804oL/Pb7AV1AId9PgB0/S2t7++Oi4I/OK6YR158tWxVOzPNkBh0XppMonHH4DiWf25bwuSO35/jdNg3z60YiSymFAK4bRoWcst8M/vychwL8HpvVa3sbGgCUWCUiNGeDH1g8v6QPuqBQcCl31slKMS7M973YXatRdBztBw/YhumbFjPgbTJ2FNJfGdsiWjQ2McnIGyt7mAy8b78ZzJg8mkw2LMzsBHkUFkfuEl69Jd1diD/wmDrdZ4zpymJZAtFVlsBHAo9JY0fhrbdZtTp8dqSed32ir1CDEXWJ2GVL+XGsrVVSu81n7Kgudh4zEZaDFZ33sbOmsvyFHF7CAm2LYte3TOSInUufss8ftQMFL+D43bfgtWXr2W+bTfjQ7x7GWxlOjles7YlFnaV8RnXl4r7tU4dvy+mHzOCie19BBN6z51RWzX0OgNVrw0WjZWv7yOLjY3PCHlvBo5DDxYqsNEfvUmzP0btuXtK20TmHb79zN655fCEfPmB66nVa3eviYZONBEN47wbHuK4M5520G3tNn8jrK3qZOrGbXbYch1KKk/eayrEzN+fReav41b2vcNnH9i0Z75J0deXIKI++fIFsxuHi0/YC4PAdN+X9+27FlY/M57CdNod58ObK1UwnTCI1PeozbUt45ltvJ+dYnLb/dCxLcCzhS38tJkA5dIcpJSVodtx8LM986+08fOVjMA/cQp7sqPAZUdjsuNlYLEvYZaoDj4Adjd0oyCeyUFt+MU74r586kBeXrKM7m+4mt8X4brYY383B20/mrTtuytp+l123HM+xM7dg2bo8TrRgGkShByUJcvwCOLVFnRX4BGIzaUyOSWOibcuym67rLfWySCbien7RCipHmBaSlrwl7gur9BeBD5ZD1rHYe+tN+On794brYG1vPxMI52Pbpf9y0Ly2dC1TgCVr+5g6wN9mrcR5BH7sQvnSkjXMBLrqzLz1vK+3RmK2kU/0jHZIkq0NhUZE3bZKqVNE5P0ASqk+kfpL2CIyBXAjQdcNHAn8QES2V0q9FG12IvB81Z1swLwUibpvn7Qrp+4zjcW3ONgpNeegODD85ZMHs8X0zSu+327TMXzzhF3436N3YGxXhn7XJ2tbiAizp03gs0duj/VArmJiLSmWupxjc80nD+C9F4cm6NE5B2U5YRB51GHqVWxtqdPB7xL5pDt+Pz42f/hopevj3ltvwt5bb8L3bw0nJRlbmDF5NMlHQkQYnbVZX/D5v/fuzlsmdLPAypQEslvKJ9eVJdtn8c49tuSk2W8B4KnbBEvCdn7i0O1TJx+bjM6yyejilHz6pNCF08XGcwtkxI8zZDlRsVx8F1E+vtgcP2vLElGXZNa0CeF5RS4x+t5ZToaJ0THHdjl89qgd+OvjC9lli/RYqImjs3z7nTPxbo2yaAbhcbacOBoXh0Jv+PyMyqUP2P9zxHZ86vBtydjprjd7TZ/ImJwTi7rrzzqQ7TYNxXdXxmbXaZPIvBHe34Wr+tgOWNOznm5sRo/qJlPwEOXTlctyyqxpbLvpaL53S7iv0w7aDh4F5UUJdQa8TllEFym2UlyFRYQdtpiArwSJVm/nvrGWmUBPbx8TgaN2ewunHT0dy4I3v+NUXQEtp+BD8sqOyYCfDwUtwPNvrKiIVslaQRzh67n5AYs6bW20ymI7xM6kp2mPJ17Nc0RQcfKFLCLCFpuMgxXRO52I/wnEIcPAV7eDqGamJDLvespCqQAbn0wmF2aMjFbe64u6hJAelKWu2CfayosXjpJ9ja08AnH4n6N2gSvBDsJnyLYzBFYGq8xyMGZUpQkz61h844TQNXvPrSYCsKrXZUw0hOq6ceHxfEZ1dUFkgP3SMTsB8JEDt+bg7SczbZNRrImekZwKfxcEARnx2W7zCXSPC9/jrCqQyTj8+5wj2HJ8bbNqV8Zm7rfeTncmXVic/bbtePQRm5xWmk2I09F9tkZE+PF7dwfg8B025cxDt2HCqBpxTlaWrHhhgqqy2LTvvWs3PnXYtozvcuBHoAphX7T5hDHsOyNcvPIDRVd0vnr4ed/e0yj4iq/fEK4db55y3UbnHLq6ojI+OnGMCoXRzWeH7oQLXpwDJEQdpbVfHT9cIIPw2usFxnpM26QYbmBbUtI+38rgKK80CVgD9WYt5eFapYuf5TGTqtwzITGXCKrUp20VnuvWmHim9xeW8uLrDTBlfPiO6Pq2y3ryTRd1+toHg0gCVVIn2C9AdH/68+HzNmbwQ2rHoMuqSJOzW59//vkAnHPOOU3d70ihEVFXiESZAhCRbSlxvKvKFsBlUVydBfxFKfV3EfmriOwIBIQe+Z8cXNPbS8ELePuum3HaAVsD4ImD41cRdZHYSwZQlyMijO0K3/SuxMBsW8Jnj9yBZx/LxnVtil+m3749ImESYznhQKEnUFGHrlOd6wm3nozaQT9K7JJ2lDM2Wv3U4rOc7qzD+oLPZuOiQcuyS0UdHkocXvxuqauQSqwwThjTmFPIVtFA6WMRuOG1VtGgZju6bp+HJAaGTx62LRff9wqjo1XkbaaM5tVl6+PJUzYSW/reWbbDqGgldosJ3fzXwTP4r4Nrh5uetv908reGKe0lEDwsNhvfzZs4+NEkJS0uEsLnIWPXXjtJCts9oolmjOXElrogWhHz3AIeFmNGjcLJ+9jKwxeHH5wcypvv3fI8tiWMG6UFbQFfCTM2Dy0cg0I/YylWZY2LE2+nM+rpZzSXzcYr4Lp+XyMUfEUucfmy4pO3HG749KHwu/R02ElL8mDSZWtLXYUYtJzQDbXcpVM/600c1PRkws6E13uziWNhRfgcJydFyhpcoePyPgMgQBDlh8WBLTtMmhFZw+qu/iVEXbXamrVI3icLn7R0+FY0Ybej/jJum+OEos4rs0KnJL9IY02fy+Y6dXyiHTY+jpPBtoT37V1MWjK+OxMLQl2brLgQqLBVgLKcuM+yRaGQVG+FNMbkqr9jE0ZlmTR2NNlebalrbfIFy5Lagg7iTMN2UCAom4qICFtNGkXBC/CURSEf9pdH7voW9thqAgDv2qNUVOrfnbb/dGZPnYBf473SdUHdSBToZ0RbzXS910xiTE++L06QT02SMhSUlcHGKyl5E3iFuinKLfyKtlTc37J3K1njrWJe0WIWrFhH+cjpBkKGsDxU2jKnBF5Jwp34XR5ivdpaaC+TugtTKZTUBvXdYvKXyNMhDoXZmLGav6gJMGfOnKbub6RRdRQQkduVUkcD5wK3AdNE5ArgIOCj9XaslHoKKq36SqmOiPr87Yf3LnEz9HHIVrHUOdEkwqoiwhohkAxZVVr/TKoIAqfMuqOsDBn8RExd5H6pJ6F2qQDKBIWKFPTl6AlENaPt6JzN8p6E8IgsdUopRCSawKTVoCu2vVFLyaypE/jdh/dGrsmCF92DyFIX7yNwkcCPz+tLx+zIWW/dNnbN+esnD2RdvxefTy5Kz+3Eq/oOM6aM4ewjtuPUfRtP3BMXnw7CKWfOtvBxsDwtFgc/MZg4qsb1scICreEgEp5T4ObxcBg7qhtrtcJWhThDIsDtnzuUiaOyLJ7zDyA8dw+bsaMGH3GhU2rbNZ59Dye21Gn0YO1kEnUdccL4xAYIyqZCtvJCgdily4SkiLpELIvnDmKiEwuq0vuiU3AT+KULMfFKZRMnJrH1PWyDXkgqt9SFpUkGPrEoWuqSok5b6sIyK77YsTWsXi3CpBvY4NwvE2JKlcUj6c+josX6vjiqaKlTVujyVoLVWD+9yxbjyL8ZxUMl2q6vw8tlC1ZJdBp7PTaICnDwwHKwEot/apBFwtNI9rfSZEEyKPQzGuTpl/Q+JmMLBWys6HnqzmXoytg8/JW3MaFG/zdz6viahxZbl/7Rz2mpMNLvTSYxptvloq6hilCNo5/FpKgrFPIViWrKsZVHUOa1I+UeLjUsdWnxxQNmADHIvf2V86TH569mf2DuwtXslfIbUV7Z/SnWJmwZOj53UKIuaakr/lt7WxlRV5w7SpPdL6+66qqm7Oend77IluO7ed8+KdmEN2BqjV5TAJRSt4vI48D+hLPDzyilBrtu31EkBY0nDl0q3YCphYFTw1pRj8DK4ASlnW+aS5vm/HfPLFraLBtLFLaOyYs6FL1ap12p9GQio/Ilq/ppjOmqLbh+/N7d+eFtz7PNlChmzHKwROF5Hk4mEw5cqROL4jUdiAg+cpfNmGPZxZXvaAKjB+fQUuejooFYRBiXOIeJo7Oxe2X4u3DQyARFQW5ZwuePToab18eXsMaVqND1L0f4rHQHugjw4J8JLd7fvmtKlJml3cLceEhygjw+Vnxu2aBAT0JU7rBZGAe2RK+CRhMXGUIb9WpnTUudOBA92/ru68HaTvzOFzs9HiPtuGU2oowqhIXmE89DOclV38G4Amorll2eWtvOxN8n3RbjZ7GJK5V6oUY/91Y0edX3Pt7OSqk32QBFkZ5w5cRCIvdLLJtAigtc9aZ5yYWxChexBgj8hJslfuokz1ZeOGHW7uXRs2bboaWufIIlDS60/PG/9uWhxy24p9TaERdJrjHJ1ZY6LTAtgtCybjux4AsbM/jYt3JKCmUPsk5dM5HEs6mc9NhiEcHFxtZFzKN3KfYAGSzRs+AnLHVeQhg5mXD/mXhMVyViK6PyFKR2rNtACRdfS90vXbe+qLOUX+G+Wu5+Wb5oVtKPDsJCXoHvNlxSIPnOxs2J+oGginXVCry4Vi4UPRHiRZwWpNJTsQAbeD8ZJKz/ys8X+8F4n/Wu+QaXG7DpxLUyN9CYuisfmc+h20/pKFE3XkTS8ksfKiIopa5rVaNGIoE45Pww6L2g7DCDXoRe7atmWWto/5ZDtkw0Sg3RU2JNio6rfy+x+6WeOEful9Egmw36K1b+yhkbRfqWJ0XR7LP1JlzzyUQdlkTRWieTwVJBxUAEpSvTA7Vs+uLEAloLkYxe9Q7cKG6iwX3qa6FX9QcYX6XRljpRQVjEmNAy5QShO9FQrLcAz3/7GJy0pAfRfl0vj55aZ4N+enGQ6FwyKp96D7QbrqMKYZuH8NwSDeB2jefJx66IF3XQ4qQ4cQrEwWowYUtQIeryrJeJJTGW5TjKp4BDVjz8QVjqVDSQZ1LcLyF0f80kajDFq+nNjCmIJr/aKiWZ4sS5kLCGKHFKJqmNolegk31PgAUqwFFhcWAfmy418Ji6wbiBBUm3x8gFNI9DThKxdvjh+cbPfXSNHAcil7cSGuwjJo/JMXPa5LDtCUFqq2Lx+WpogZnVC354oYuulcHOFn+rmmgNUonFGWsICzXNQvdDWZUvaVs5Pna8uJaWHGwwaHGoLXXa/VKj+4lcwlJnEdCvMnSJS1bl6beaV1sLQNnZ0P1SFY/jNRBDrBctkpRbYlW5h0PUj/arTHNEXeBCReqpdFSyDw+CcIEh9lpIn+BbZZa6jNN6S10cn5xi/a/720TRd7dQKF4ZZdwvY6J+tp43x0D59rfD7O9f//rXB72PvOezZG2eqROb+44PBzVFHXA86YutCjCiLoEnGXJRqGGeLFmKmTAzTbDUKcmEQfXJkJxGJ9tRh58jsobEoq40UYq2YuTIsz7Vs73I2BrxG2nopC6eVwBGxS5R5SRFhj1AMaEsB0dbWGL3S4dACfh+HAzfENFKp77m1iBFnRYsArH1M7RklIrPwVIt7lHvNylOsqqfdTK2ROSniVz9PGSCMAav1uJBPeJnLFP9+nlkKt0vlQ8CmcTvfLEbXzWVSkudEpuMkyFQUjqx0MfEp1+yZPEGZanTcYF2pvTd0dfP99ySlDMizV+pjFeX7bKFGpWnR4pZbJXYlW6Hjew/JW4wkDCmLrRQOWESFl9bXmuLOiGMmQrTxA8++2W/ymCLh4NPHzlyiYmYnvRmtHt5JOqsTJgoJVOeVXgAz7uTDe0oyXIMDmVutmm/iyxBOlFKVhX7rUzi+Wmu+2Ui6/IGZKnLqdrxaR52ictsM9ALVzrBTbm1y4nc73OJ9AG28umXHF24dds8GMTO4BCQwaWP8DhuI6KOykQz5TF1FZa6wCNQQoFMU9wvfbeAnU23tpZTEs/nFxCrq65Funzs1otWmRaKOtFeD3WtapUkY+pcN18UdQ3H1GnXxM612MW3vMnn+MILL8T/rmZ0qMcbq8PFnKkTh7XYR1OoNfK8rpT6eI3vDQkC7X4pkJccYxOibqjWHgiDyrvK8tOUZ9mrhp7gZ1UBpCjqiCdoUWB+NJnIqQLr6sXU1cvJW4ayS0VGuHqeYqlLul8ONKW8OPGELc5+aQkuFgRuuBpbx600RoQCTiyEByvIfZwwZkpJHNPkSaYo6oZoqauGikW0G1utciqPz4SSyVTaPdDPQzay1KVlWW0YLTJqJGbwxKko12GLCif7CVc4JTbSoBApt9RllUtg2Th2mLCGFPdLB58eRgO9pYHuDaIFVfmzEi9ouKWTJ9WCkgbJ7JeQWKgpm4QqKzM4S51OHlBuqQv8yNLkEIhNlkYTpSjyZHDIxy64A2pPZKnLSxaHsA2rVI5xFFO4O4SiTi8saAHl2A7Ymbg+YcwAFloy0eQ/ae3Q16EWWjTo66THDuwMdqbocFfuRjwkSkRd+2PqdLblLOn9kCYQOxbezWq3FY11ejwqF3XZSKx3JRZSLQL6yQE9Yd/Z6FjSKLpfpsC6yOnSd+u/E6mW4TLRLuXvVuDjYZUkqRoKbqEfuzFNVxLP57v5eIEjJH0SbisPTxLhEdFirc6U3QriBbJB9JPJmG03X7T26rlXfetf54q5Cpos6i6//HIA5i5aQ/8gS0IsWBmOH8lstSOFWiNPE0eTzscXh5yEL3JBciXvZDYWBkMQdVYmFhiaWsknSn4bDRZd2lKnyi11UfyZUxxU6q1C1sq0lkZFtjGC9LTayUQpAxQ8SVGnV8pFBA8nimvz6yaASeLhxNdsoG3R+GIhgYsFsftlYDlkg1Lx2Wy0tc1zC/GSWBYXX5x4MSBXZTKlXZMyFOiXLFR7bhsIji938U3DF6ekWLTGwyaXyADqi0NOpScjKqc8iUFoqXNwLKEvZSKjlMLGpyBZUJUCrCHK4tliYvfLsnOMrn0zY+r0hEm7j8ULNWXvdFzmZIAUy6Ak9oWFpcW2HVrqctECUr1Jl6gATxwgPzj3y8i9N0+OsaoHhPAeJsjpe58pLlYg0TNppXlANP5O6ue6PMlLfffLTNw2hHjsEMspuoyHHzTclnqUul+2X9TF/VB0f6rhYTM6eu+HsjBacuyoj/O9YkKf5PXRz4q+LzZB1D+EY3sjY+SAsbVHTZ4VEiZ60e6hNX+GX5F0rCKmrtwzIQhrInpV+t6B4rqFurF/mhLXxKi8ge4Dq1mmRPkEifcyY1u42CUW+Wajr0vFok8DJD1B3ORYYhKlxMQ5HZrsfqk5/uf3D/q3C1eFRplOs9SdNmyt6ACSMWh6YqjRncKQBlIrUxKnB8WYhLo/jSZg3VLmfqknoTq1tF49lvoWLW2pa3Q6mnRBg2j1K2UgV0MQdcqyYwGdvNY+Ya04UT5KGrNuQijqRomeTDT+uyS+hJY6UUIgxSKz8TPRIkuddrEME6UUZ6wBdrxCnkmZDEBx4pSlQC9dVduoAq+uC3C8MlnjWfWqZLX0sOlKiEYlDlaDFrRyC0eOQliQXCRMGFI2yfH9gKz4uNGkLRhMnEmcKKUsE51+9sv2GU9gmmqpK41F1G3JlLto6TInAyQoc++E6P3S7pZWhkDsuK+p3+Aw1tRT1qAml/qaupKhO3r3y/vfbimAXRRLWbQ1M4OyM6FlLcFArEGZXDSVLXNhq7dY40S/q+jT7UxsxYPmul+ygSZKyYpf8zwDEv16k/rLoqUu8qIps9TZtl10Cyb0HHDwcKNnKysDcOVvFLvYL8eLSw25X/oVorjcM6Lc/TLMJhlmYq5wzRwEXqGxxTYoEzyFfrqhuChUw1JX4n5pCXlsaKGoixfIBpMlOJEMpuQeRn19o3F6rRI8GwTRdW129stvfOMb0b/2G/Q+lveE92zK2OYmQxoOqvakSqm5w9mQkU5S1HmJNPGesuL/DyWOQaVMnhuOLyhbNbZiS13Y8WhxaCdWiOsNWGNz4W9m1UkdrdEuaDpWyVF+lQK4CffLGnFYaSgpWtaSrkYeDirwKoLh61FSF2co7pfaShgJZV+K59Uq90sttjzPJXlNk5Y6oEqilKheIm4oAqtYHRqyZsWWuhqJEKxMVVFXsqsBJPcoTzARirrIYhbdk5JjRYOwG727g6lTR+CFhdTLJ1Q60155nF4LBjUJXHwlOE5UpiTh2lRhqRtMKYWykgkQCmgrsjwr2yEYiPVZBQTaDWwQsT1axLqJxRo3JSuhEide6NGC07KdeCKdZCDW82wkDipKZNTpm7PZKpMF2ylJDtRM90tlb2iWukzyj6rbeYl+fSjeLkmK9Usj98uycIAw62Zpm7pwKVgJ19imx9QVn8WChMfxGojtdah0vyyfa1QsmDTZUjcgz4bEwlwxUU3tmnBWinAtHx+ajR4j4hqjA0ClnCMUvaQyjQrFQYQBjBSK16i57pcLFixgwYIFQ9qH50fiOy0J3QZO+1NgdQjJhBPJSUU/WcbQj489tIudMvloNOasXDjouli6g9ExQJkBiLrurM21nzyAHTYfO6A2+J72Uw9KJhma5IqtM2BLXSJmJPFbXyyswEsdGGrhixP3N4OdTARRHJhgxdc0OeltlaVOn3+FpU7sOHkGkHo9nLLkJNWes0I+T6arTiCFCgPya00iA7ETK7VF/LJBW1mNJ0opj6mD4iQszVKnJ09a1AXlBakbOqgXFt4u/zwlaQ0kB7UmiLrIFVb5Hh422WgwKs0eOnRLXRxnkkyUgoWVsNQNbLIbFtj2otIfA0Vb6pLxNq6Vq7ikYjuIZeEqm4z4xWcyRXwNKFGKHe6z3J23njDMZNJFnWVnyGRb436ZtNQNpT5ms7BKEsLU7h90P9w8S13pImO5pQ7SRUNywbbZoi7Zz3rRM+w34n6pKmM4y0Vd+aKZKC8SdZlK18xB0EjsX0yQFDy6pET0wlYRT3bKgmz5+NB0Em6igVcoeV7roVLOEUCifTZqqQuacG82VGL3yyZb6i655BIAtj7n5kHvww0UGVtqlqXZUGm/D0aHkFwp8+3iy1+IpJw/1EudMkloNL5AygShVeZ+qffjNDjIavbeepOSWm+NtMF3CxAEWKLSC+AOovi4piSbZpmlDuVVuHDUI2lRG7SlLkrDn0zSknxWWifqSictJe0pWSGvnigFInfNKm303AZcbgIvTExSA1+KtcKSfWh5/GMwgILZaangtaupJ06lqIsGXl+LukFYjcR3UyeCukZfufVPT6aakuFM7ysI26AHIyeTbpUdbEwd1WLqdIyo7RBI4++tqCAUdWQqkzk0gC4j4Sa9I6wUS51OVhOXFYlSqKdZ6gaQYdG2QouOlLtf1tlH1b7NysRJOqDJ7pfJ/nEDKD6e7GdqlzQYusdEOboWYKAtRSnZmN2UBS/PTrd8N4Pks6iPEwzaUleWDbM8w2vg42PhY8fv7lBoJEunptT9Mrr+WnRWFXVexTl6Jde/+W6KSQtmYQDupVDLUhf2uRm8xjIzNqPcxAZK7Cm2ASaF8QOFPQKtdDBIS52I3KqUekezGzOSKXW/DDvkPBm8KIm5P9TBOWXy0WicV7lwiDMIap9xncI+1zrXkuTEVkUp/lMH8iHE1GGlr0QHuqxAldp41ShxvxxkQpMAG0v5CF7CUtd6UVdMBOCVJA1RYpeuOKaIumSh8ECsqhNQt0H3y3orqr5kyPnrw2MnLHEVljpxSr6vRarbWmypq6yLp5OYFEXdIAbTKol4LKsY31iyuV4JbsJKpfLdUEio0utdbaFGrDCWzPcDbLvxvinQK80J0RKIFU/KxM7UnKBXNlzF7pfWIK65Xsn2rWJfmFxU08QlPnSyolqibgBu8tpNT3llk9p677UIBRXWRCz52Haw7GJmv2bWqUu2aUOw1JWMX3Us+fFmzUqUErvNRosUKYm7/JTpUZB4tgb0nDeAJK6H7ocayn5JZShDuWdERX3PaJGxmuv7QGnEohiTsIDp3xWFXg33y7LrneznGq1fOhAkMdYUBpAIBiipC+gXEpa6aO71/9s77zBJjvL+f9/q7pnZvb18OiWUs1ACCRCghIRA5IzJyQSbYGMMBn5gBCYYDLaxCcYyxsIEkwwITBQCISGQhARKIAlJJwnplC7o8u5Md1X9/qiq7uowMz27M3s7d+/nee65ndRdPdPdVd83BqSRJEkuKqaSXdhTlxoih+ype+c732n/OmXW24ilQrQA8o5nQ9e7EhE9vNtLAE4YyWjGGN+K5G78JrwhW0jOiarwy7oW5cL7Um+HskngdhEfDhB+OSjOcq1kDCWNvbyqIEGuUMqgOXW5kt2eyCaziBddCoN0w4VJmtL6s7vAlQghdAKBIBWJeU/dcBYpRVyepEw60J6+URSi4S+mKqzRrrEr4Dx1XURdDess6f6iTomsAIovtmRhbFqE5UbR3bZJVFofuPNDUlnUOY+mTC3kgy90jJes/H2mXtOi92+Ilsok6SBqTAIFb2G+6IbnXXPVUWWMoEIEdcVVzPWFPwiBynJZBzMIGU+dnGX4pVuUS887J0XF8svlmLr7sas8Whl+Odg1mSCASKZzz9Ux1iQI0CiczyKMAKLstaFWv/TuOwvAUyeiuqLOM64N6X7p5jonxk3hrmL4Zfk3lMEoc+r8NUQ9T52SyhRzKXwvohh+WeGpUyRM5MYQKjEO1NezIjTR5TZ2uxeGutyLLzevjELUecIsaQ/qqfOEq/RFnS8U2/1F3S7tqRtN9csNGzaYP1bMfhuJ1AiDXc9T92sAPwcqM7WXjWQ0Y0xO1IW2xwxEOjHMVdRVVbrse0OwlD119sZiCzu4SllRNEJPXZpT10GSdMy3UWnpzE63gSfwLrlqzlNXlTfRCxdGNpd8SEUBAhVDIEkXJ/MRfilEllPnf6da5D11VSJXRH4+YtD1d6iTHE8qSat+dkNRhFDlvcdAxTUzQE5dZfil85Si3O/OeerUHEQdbEhTEeddKOXpDdFSmcQmJoBU3lsYdRF1znOVdDpodivaUYEu9LYEzHcduD5iQdi3nH9+gwqKhPFoz8Jj4PoJ+t45f+GdIvLGNfc/FdtPYPBrMqEQVBB1dXo7xhQBxd6jdt8xhWggGWr4pR9GTbM0Ug2TvKeu+3fuGxhn2y+0iPNgO498oFXJE5RQVDIMqcArcT7kOTLw819Ds59+oi5JYtPYungfL4i6Yti6y6lTFGVtgOaALHqqe1HhqUuNRV0EZgBVuq9IL9dyJKLOm2tqRaX4FHrxpdv02zm0O0CXNmjusPSu7KlzIapDjr4877zzAMwtpy5RataG/J1NrzvkjQBep7W+pfgCEc2ttMwuiH/D0W5hiMDkZely36xBqQoTqp9TV/TUeaLOK+wQNbxjGLqoa9hdJlCJdE9WDDb7nqIBPXV+lc98oRTTVmDQQikuTHIu+ZCmd940BIJUQPmVTEcm6uyCqZgbpijMnzcVi6mcp46CrlVIa4Xc2HOsF8YDZ0VBD0+dElHt4h6V4Zepp65c/dIV3NDWIKNmITCEiktjBrrnN6YT/2yqUAKpp91s2/bbUnEubMwvuuF7PNLeeYOK10JvS7NdgVC7ipKNgbzhpLX11FW3teiHE5nKL2ARVohUl9NbEHVVnvJBe0fGCBHIvCW/TruZquvC3SczL9Ewm4/7952d76nzvchVEQOOnKduWNUvG85TZwulVIQw5kSDxd0fgGqD2FzwqxLXF3VtNFA2IhRTF4JKT11g5gO5Yw6jNsy2UIpyn3PGIl19DwiQlL5v/z43jGIvRXyBmXQGE3W6Srgin14Q98jTE857tQuLuuw7WnhtGxKpx7LyJdBb1L0X3QupvGn4Qxlz/BLfkb0h2/AG6GF46ipEXYWVuXJohRt8Wj5Y5z0LFPT24MwFF66lZCddQFcunjxRN7Dg8cac89RRAKFjCK0GOi49BFGnyeTUCcisgIQ3AQ8aYloXkVa/TCC8lYmmMGcRrsoLyVc2DLqGiNbpoWT6kPXx1InICwn2JvyiYYHqV2zUJCrCL7t76tziwi3aZtMIu5jP5kh/i0J5am0n+FmHP3kLhCTOBKI/hqhRvQhNheaAFmg3EfvVYDUEQrsYE2GIZABRZEJvyBpeZp9Tl8t1CssNY52IcQt1d06KCgE4aEi0pLKoq3Pvir3qug4Ks7YbwHALpfhjWggtDcI+ub0Ov1rwsERdWn1UOlGnSgYuWVHwJ3duDWAgrEPOcxnWC79MnPe/GI1Tqn5ZzqmTCMy9dzaVfgvUKeji79uRtmxwPT67iJigohiMGrGnTuTur4OFX/rVcP2oD6o69qp9u+PZhVsajCqn7q1vfav5I3zcrLeRqF0w/FJr/Y0er317JKMZY3I3HHdDhkgt93PNUavK86hbZr8oCIueuuwF31M33AnLLZRUkmRVACsWf/4iZlCLuT9+8izRxjMjK/MmepGKujn8dq5io0AIZXsP+fmRwYgs5mnIn4xzoTda5PtgVVUgDUOvUEOPQil1eihRDU+dElHa4NW3uKrC50zFxpo5db0KpdiKpD5ucZSKullMpkIllZ661GvaxVM320IFKolTuZz4nrou4Ze5c79LQ/R+UNp3sCjqZuwuop6hdCW0gko9dbNp8mt/Nz8vMKwKv3QGGns/dp66inObBrwmJUKEBVHXr0+d+Vz5Pamnzgm+YebULTBR5xsle93r9SjCL8OsUIrWunJuqLqWEY3QU+eL3Mjdh3pfn6lRplj9snDeFO+bwqYiGIPa3PO2BhF11DP8snosVX1t/d9nGL32iuQ8dQOHX2bHqL3vJu/9624UpYr5cFcjrf485PjL6WkbCl+v21Ylu2qhlLcUntIA1gP4hdb69pGOahzxBUWUiTotQkDO3VMnqjx1s6x+mTZw1jJflZMIMQJEGCz3rNYYUpHRgXRFMaqs2f54BhR1lMupK3ibdGKtsQOIOrfwncNvp4VpmC0gsxLMuSp09X7DQUlFtExypes1hbmCOFXfsRCEDgQaNlw17HKeyZqFUvqGHosoW3R4k54qLqpEiKC2Va97+KXpHZj3+LnQSLJe9n6Lqco96qTyWF0hoqJQdIub2Yq6NJ8GmbgpFqbxm1znPXWzC790OR6+QUmRQKQ87/sAni5TkVZY48csvgeZF+Pm77KnLi3U5Dx15Dx1FTl1A953JIVoqHxOXZ3S+1Xhfe5zWWjZED11/v1xyJUbZ0PUJ7fX4d8HhtV8PGra3112IJVGWOmpqwiljrxza8iizjeUpPehfp66OGslkttW4XFYuLaE7fumRP02Mb0YzFPn3eNTT50NvxzEU+fPy7MNYe9B3lM3e1Hnp0D4RUFkD+9fauDahUWdi/qoW9G6Lp/61KcAAN+bQ07dOLc06DVjLC78WwLgJAA/IKIXzMPYxgt/ce7CLyHSEI65htGIilBLPweuF2VPXWYFKgqWNJdj6BOWzWmRmaeuqvqlW4wrTaVk7/47qc5VU7bCV1BRFrkntkz6XPIhzWJV2iItZt+U89SNKqfOeUY7ueIixlPnnQ9dvBLuPDDNyqvPszoTOXUp85/bjifqhOoh6oL6nrrK27ELv6yo+JbmuzmDzDA9dV7l19wYXXnrWS6qpBc25Sz2xTFE1usK5L3v5M7teFBPXVWfOkIEF34ZDRSWRnA5ddGsPHVpIQFP1IlG2VOXtTTIe+qqQtgHLZufUISGLhY86W+sSSrC+5zITNKiSkNcWHhjEgugCEAuYqDHfdC/Z9ctDtZ330FkrgvZQSIlBOlSCGNV+CU1ssoWwzZ85jyXdj/9RF06lxbu0cW5tVhgyhigAmjRqF1RuBeDFJainODJ51JXeuq0RoNkyZub99QNX/wEOkkrCQ/UsgHIiTE/lF/ULL6Seep23eqXTogXDQ4LgVjqXa9Qitb6fVXPE9EKAD8B8JVRDWos8QWFs7KR8IptzNFTV7FICKsKAlQNrZunrqKHmFnMt4cffuks10kMacv9VobcWCEnSQwspbp66ihAZHPqBllw6kJxhdmgRWA8ddYyCuRF3bD6LhUJwsxTlxMwIswVKOgW9uRyjhQFXRdSdcpYk9d0vftgI0RVOXWlim4hAtKmQEgfwS+qkq89T11UmEjSflWRXUzNylMnK8OsgzT8srDN1FI5S1HnjdF53EgnuTGQEOggRBNxzhOReeoGz6mLdZALHFQUoKFjgMx5p4tGAK2R6ypfeE1DQIoIzWTAvBVUizqq8tSF2W/v/1+ZUzdgiJ+kEA2ZX/T1Ein+50r7dtEBqaAYTU5dnfGNmjr3IaAg6oY07ig014WWcRp6XRRCqqKCae7cGrK3MydywyakplxuVhXOsFP8PUWf8EvSCooCaC/0fS6U+jT2IheamIWNA9X3QiXNbFQ0yOaq+Y4op24GLUxh+8C5x3lR54df+qGnvUSdi6badT11WcjtcD11b37zm80frbNnvY1EKURjmlM38Iyhtd6IoZbk2jXwLWXCWtmMJWw4OXVVnrraLQ0K7wu9QilFL5SzEA/dU+cEkoyzxW2P8MvZCKncb+BX57MhJlUVznpvbwi/HZlJM9c81Rvn6HLqXHhRXPLURTUKFKTnAQVdhWedia6Op077nrqe4ZfW613DeilQMVG46qOi7KlzXsfUyzOLRYLQSXnMyIR7USi6iXu2OS3SE4nOGixUUjpf0751fr6qF547EDopFb4xnjrnwYtKi+PeZblNnzpNswsDS8fvndOiWVUoxd5/vLYWQBdP3YD3PkUhWsgL0jpFrNy50tHlQiDpeTTUlgbefWeI250tYS6nrvt37gyMiRYDNYbvBdlUA5IxpPNWF0RD1bWMIDJiC9VFpuaCf86IIESCELrY27JAWvW2mFPneRnaOkKIcvilJgEdhOm1Oxf6eRRzY9MJ2vacT6tfulzdint77I6x5En1jLgjED8BEnRcE/hBRCsAqCS9rv3fUGiJtnZFqrpv04Wh7g45dQvRU7erhl9WQkRnAnhwBGMZb3zvSyOrfqldmNNcRV2FRblufoHfZ6yDrIJg1YI7rbo29PBLd4OL08VoVYU4Z32bTcVJyuWq+Z66EEJLG345wHENyVMXQCL0euT54bDDquZWJEirjSZ5gVPbU5d5NKIuC9Q6E7lQSQ1PXQMNkoDWudCcUniT6+FVI2Swsp9damAJS9ZBl+cZNOp56rTWuG9zoeKhlpXel3ShXpig3aQ26/BLT5BlnjoJVQjAiFNDTdlTM1DTYKDSu68pQETW6xhGpVCwuEc5cNLaVAn2K6AOQJpsnwu/rMqpy3578785hlwFRvf5QatfigitUvhl/wW/8wTNkCdIQ1ecaRSizvv9w51fKMWvzFos9OGj09DZ4Y45QQhSHSRdqjG738cX3RSGiO31RUOOZgkj//sw+6E+96E0l7bgXfajc2bQMIVGPEzkSAgMKfxykMgG0hIzcC138uGXVQaurLBa/hzJ5Qir4Xp7zFgkYmF7Dg/qqdMKCQWIdZD7bgQU2tTou01n8NuVRR1sfnzdlIq6fPzjH8fHP/7xOW1jnAuldB01EV1PRNcV/t0N4MMAXj9/QxwPfG+GE2AKIg1FmmtOXVWoZVXvuuqxZe9ro5GJOlUOjUvDfoYefuk8R0k2EfXoUzdXT50/qZm2AkllMnzv7dleRnP47UwPNpnz1Pm/x8AN1mviipvoQvVLiChfEbHL4tN9/1oEXdsu1A2/7Pf9uWtEJnHeU1f4rZw1uk7SeqWnjrKFfVFApJ465+XpM5n+289vw8l/fxHu3LA926dKKvNsnKjTSQypNO7ZZItqpDkFs82p88tm2/Hrcl5fZqjxjB5pzuXgOXWSyp66dLtBo1QoJe5Z5c166kQ0K4utE8rkCbnAy3tKx1XwgDlRF1SIukGvSUURGpT/DYMa92Z3r20je6+7bqUYzrzh44tVMeR8sNmQKwzSSwSnom64i6yYQkAl2TVQ9DDb/bpFOGCEX2oIHbLh0zcwiCA00RJ9q186A2n+fPO9zW1qlrxxpsWOWZ9Es/VyeX0yBxJ1KkEbtvqoK1Clu4fixTU8qcUWNcMgQCbqBioEYz4AicAYAIqeOjjvX/U279ywHTNtW014FxZ1czVqjpJE7oItDQA8tfBYA9igtd5e9ebdnVyelLMKQ6TWpb7eij40mhX5czUtCb7A6VATYdofJCl56rQIAYURTFhZsYhM1FUsnmzuzWy+r1z4pWe5dIU4BOnBRJ2zms+x+mWgJQLyCqV4i5mR9alzQkLF+d5uQYAozC9SqnCV+TSFCLssUOsVSlF9vdTuPIg7M7nJuZTXOUDFRlFRJdOVqncVSX3u2bgNDwcQ1vTUXXTjAwCA+7e0ccDKRWafkEioXKTDiXitEnzkhzfhvEvW4NfvenzWHmCW4SdK+gUHskWRiw5wuF6Z/jWdFg4ZtB9fRS8+5QmPIAzLBQ16FhlQ0BAmRHo2FluXl+h5OcJmWdTlql8iu7+EFV7ootej7xCqKsjW8MC7z8XUTKtgpgaAEXvqBu4BOgq8PMtebSRST92QQ0al9YR1q8bsPHVtNLEYO+x7oqxdyrDnSL/wmQiRIMgVytiwrY2f3bwOzz3xIdkYrWAoXnN+mGqHGgihoJVMDankCneJaKDwyx2dBK0wgBBko0AM/cJEHZf8YR3am3ZgmZtT3PhdKB7K9yOVFoMp5tSNLvzSVESV6ASzE3XkIhoIOVEXQKJDDVOBt+K+ePWdG/Gcf/sVLmiY+WsUYaULhvQ3H+4xvuENbzB/LH7yrLcRK43JMS2U0mvU9wN4FoC3ATgHwD0s6Lrjh9S5vzWJVOxVxucPQKtVryhKFX6sfocaufBLXTgFUk/dsPMFvIWt6tXSAK5QyuATpi/kfMulpgCRdv18BsmpG0LorIgQIh/6GXhe12GV6C4Spt6hfEsDiChX+a57+GUWhtstd1PVqAgmKgwH5cGa7Tc+dyYe0sm6pRRzVtxYkxr7rWxSnno/AmMd/O2XgAvegC07pvHU695ohuIEQZ9cs31mbsXXG+9FE9lYuuXUpb+xjHHbby/Gf0d/j5mZmTTEptakdscvgC89v1Aqu9zgNqgYQxbK5+dumXP7gF+8Hbj6fGw8/4XYfM13c59b+9+vwb2Xnp97rsq7708jQVT21CVxG7ec/+e49QefKh0W2SIqrmDD7/73Q7j/Xx4HfP5pfb0UAKClE3WZkKsSdUEq6vKeuipj2Ww8dUVq5Ts7Y4bIxpCKuvT+NTxrsd8CRyyw0KKqPqwpQyo2ViRGCKHirNhIKbzP/j6UN9imc+aQhbEfjkpBiIQikOxg692/x9p/OgOf/ux52Pvbz8c9G7ak73MGrqIRwc/Vjm14ry8i0sJdQQMB6VwlXQBAexvwX08GHrgpfWrrjmlc8f4z8fVvf8Nsz78+64ieH78b4gvPwNnB1eiQ6xPYwdrf/gjHbv8VgOqoBRemWP59su//gM4a4D+fYMb9o3cBvyrfa/qyfb3Zxua7EUuFEBKJFXVIOrj1x+fhps++uv927vwlHrPxmwCycwwA7vrOB7GPvj+73p3Yu+ca4HNPAuIZXPsff44vRh/E8WKNOeYRhJXuTO78/ZW49UOPwr0ffTT23no9AMzeU9yFiYkJTEyUQ/AHQSqFcExz6nrdlT4PIAZwKYAnATgawF/Ox6DGEdVckj1IS6cH0K5E+hytjK3JqVl/1hc7MTURYhOAak9d4m44w67s5YWgucVopbXYfk+zaSPg3/QDL2dEizAVdYPkQbiQrjmFQInAiDqdNbf1c37q9LOaDWlok0rsvtMXcu/rVqBAkjDeAwoqvRkAoOLpyudz29cSut9izFaUExtuwRL/+cJvRWmYZv9JIKiofkleoZRAK+ACE0U+feJb0v1GNcMvX7vtMzhW/AFrNlwHHLS3Gb+ubpnhvj8tE5zb+WfsH9yP+7be5RVK6T9xyy+9AEG8FWrHgxBTq8xz3veQXlMVFThTUVfRp25y083Ad/8SKwDgju8DJ2xO37Pvmq8Ba74GnPqK7HMVhVL8/QVBWLquk6SDw+74MnAHgCe9IfcaIQu/DCDx0Os/Yl54ENCb7watOKjn95L2t/IWxFFVoRR3/3HngL2mWxPl++qgbUaqfvM6eXnKLhjb0TJA3gkgO1eUyxEcovjKeepGdN+ZLT2L0wiXZz1cUScpAFSSVoAtFfixc3osmnA2IiHCbBxDniP981aIABKmn+Yf/+ev8NDtv8Xf4rdAAKzbdg+w0owtrdpbON/879OJujiO4eyJgetF61XBzc1Ft10E3HkZ9EXvA73wfwAAm+6+GY8LrsXd1/8t8OznQSZxWgW3dzEkyy8/gVPssDrOOy0lll7wimzcFffCNJqgcIz+fWeJ3gzcdQVw5y+BX33SPPno/L2mH/Kq8xHcdQWSy/8dyRnnIoSEtNeoVgkO/eU77Ds/23tD3/hTAMAybMU6rEgNcfv95h8AZL+H++22/e+bMLXhOszcfQ1eFf4wt6ldzVO39YK34ZjOTYBnA6g0wM6Bj33sYwCAb8yhT10i9diKul4zxtFa65dorf8dwHMBnDpPYxpL1MSq7G/n5YAArAU50XMUdROLZ/1ZPx8vEU0EkFBKV4bGpZapEfWpIxX39tTZxZaehYW6W66aphANF9YxwILN9QoSxQ7BAw3KeepUuvgLWtlCMhjy95xuNxUScc5TV6qS1q2yZc5T1yU/qLOj7ziqREbpPRU5UEBFTp1bgNTIqauaKFKvZCHUb2YmK3jSaE7UKiUeWiOBDjyvK7rl1GVFgpZhqzkGEaUTdmX+X3Ebsfnc5mnPO1fhqavyFqaeBu/7rCrQs11nx9KZ7hKUocq/p3/MImyWzqneBQFM+CVIlH6ze7fVaDTvQng8Q0nUWlTeT8lTZ/6fWFS+rw4qeKoMI7WqX9q5YVu0wvucHacrnT/EkEPfUxcsgJw6n14i2OXczqVfaOV2KTAtQLq1BZhabd/o3T+D0ITPobtBbLY0CgWsFBlRF7Tzdem0N5+76pFFT52/HnX5mUmhX5oWAcgVJiuEYd973z0AgBs3Z9vdsc0YfGZsiHniG9f6iTqdn0PT31Il2AHPU11xL5Tdwi8rRPXmZPbnyE23G8PK5fdqSKnNOskauWuJVjcu729FopTvl+bL2m2u22Q8r3durjBE7mKiriryYNiibjZcfeeDucJnsVSIdsHwy/Qq13qXO7OGzuJV+6R/uxuWokzU6TnoAgAIW3Pw1Hk3w0Q0EUEiUdosuIt96gKzmOjVN2g2hJFd2KokzQWq9NQJJ+oGv6CCLpZoLUI0ZhN+2TCLw0ZFnH9tRAhB2mzDfqcN77cUI7IGRS78S8W58L7SwqXL7+wW7ppCRN0WuXH/aGyC6tukt+u5XRJ1+YppvajKqXOCXoswJ3Q7O7amf0dRZFoA9JnEnefXbyAttKzMr0oLIKgYS2iHPQaZVv+q8ip2xe9z5IWIupwW07ajGH7pFqFeYYGKRfQWygTOxnVrK3dPWpZbGni/bxiGJcPB9IP3VR9L+nmyeY7576HOnKpdXmIz89Q1KkRdGGa/PZCd31HFuTdoRdqqkNs6IZzazg3S+75c/qoO7bwxRCGTzzleGKKubX09veYbF10xbE+dQgBolV5HxfmouXRPAEBLZREJwoZF9hvzbGiGXlh8GEJBgLTEomRL7n1SlkOwi2MnL18xcRW4vfuFcPdlZyiT+YX15g33AwDunsmuq5lN9wIA2uTyzHxR12dhPrM59zDSMRItoLXEjCfqqvqLuoIipTzViuvuto2zX6rq7RsBADvCpZBaI4LMBPQAYZDb29kYjLc1f0xJKhTNNt1c0mhW5GMPuYfbQqSyp+wceP5LXoGnPP+ltd8vlcZz/u2XeMF5v8o9tyu2NDieiLbYf1sBHOf+JqItPT63W7LvPvumf7uKbBoBqGkWDYGuEXPeiy7ejDr4npZENCFIQ0oJocueBWU9D/0W4oPieqPddt8m/OIPZnKo8tSRC7/s1qy4B/5NP5erJkJTMh+DTcTC/naNufx29hibiNPwy2hy9l7X+ru1VfSSOCcaXNNt5znuVnUuXaiKsOvNTcTD8dSFXbzQpZy61FNXo6VBX09d9no8vS37XNSoKerMGC5fsxHP/bdfmklAy3JxF3jnot9YPYkhXOjgAJbKfMVLb/HgWjK4UuX+Z5xoEL5HrSw6tonsd9hkRd20znucqprJ+6XFg6hR2vb29Xd1PZ7MUxeUyq5XVX7rJIUFgPPUeXl0uVL5bltpnrMVd+6crCgCNGhLgypDUR1PnY6M+JReFEf6OWtQSuZoDMyNyftdohH1xxwUF8DX0zs6orxjaasiZ5Ej+f0sWmHCqid1dp8TYVTp+R4GvhBzVTZJS0zprbn3+cacVNSF3c83aUVEyVNHWVEjVbynThvvYNxYlj7V2bzO/C+M4Tfxi6P0uV+qbetyjyN0jOBRSSoSgT6eumLz8Yq5q2tUSQ2iziazjeYiJEqZENUgHypZhx0dT3RTULqPSZGF4wNZn7aqSsS7WvhlFSEU9Fy9Hh4/um07fnHXTP83WlwF67WbMuNNPMbVL7uKOq11oLVeYv8t1lqH3t9Lun1ud2Xl4mxRMdUwJ0OjEUHYyTms0TC5J+HsEz/9pH0XIx7HbbNAK4ZS2UVOPGSrqFsordu8HZf9wVgBK3NX0nCjuYk633Lpi4NBQmYCK+qqKnLVxXktBOl0HM05hNLWJXIVWGWMiGTa7JVmNgEAOqmFvPr7SM+LHuFfIukv6oIaoq7KWwJUeBVdWfoanrpKUZdWv4xyCfnxTLZoCoIoXWz0wp0TX//1Hbjqzgexo5PYYjhVoi6E0pQrviJl4uXU1bdU5kIu/e/BiTokpV6MblHnVxis8kbtCJamf2/fYLxrWyl/rlJFc3NfRIZhVMoT7TxoBOKMLu/TeHLJtv7Ifw/F3MnpjsTh7/4BPvqjrHiDs6BHffJU05ze1ANtz+sK41E4oBer6jeP6ggRV2nVKw0f2YgGFyUg5fCs2PlCUgsjp84JpG4RA8DwwxwdZsEtu7bYWbqHMdROUbZAFCLIPN8j/A5FEEIhgNASy2lb7jXfsKN75adblPPUedeT8ehnnrq4UL2SrKgTnoFEbzXzdkeUPXXUx6O0ZcM9uceRjo3Hv5aoc6K7INgqzplia5FBaFhRNxkRpFRokIS2ua1xp3/+uMO/pbi8SB/3ezgh7AyEM9vK7Z93B1EnSEOp4d3nlp/+Ciw//RW133/TfWb+P2SPbB2SqF2zT90jiOhJFc8/jYhOHO2wxg/fynboKrPAOGDV4jR/Kpyrp24OJ1joN+IOXCWs2FjJS6LOJlUP1yOeWrNDkln1zZ45dbMIv+zhqUs3P8BEHDTNwiqaZcl5IG9NdPueS9GbuohAoKMDkK14tkksAwCoHSbEpGNDiLotBqTnqevGAxs24uKbH0gf33zfVvzVV69B7C1EBWRfr2+zi+ey2OvLjbVfI1ittelJWCDvqcteTzxP3bKpSSQIcNPajXjyv1zadR/Oe+v2o7QRVFXfVyAIiV3AOPym8IN46pI4vw1HHHfwsR/dbArTFMIfnWU4n1NXtmjPRJmtbttGswjbHuTtd1WGIF/4B1EDB+yxLPey3mo885up6nfW5loXQel7KIr3rTPm8ad+dhukspZdFUNpyvWbCyp6ejqjkk4roHY/rwcNv5xtTp0z+AUqq0roFq5k7z2U9K/0Wpfcbz7k9gCzJQ0N7HGPqDpXh4G24Y1pW4DCvXDZHvuUPiOCKB3zqMSm2U+Y5vwV0bJ8D+h1vil7TvnVKgMbfumOIYkTKJV5S0KbxzfhfSW0w3jbyGaN5fLw+hjBtq7Ph3M30IEkAVIyF8IeQOf635ljtOGXPVoapNuNt5aeq0srMSGiESkkib0XuYJFW3uHkHdDkzBeUc8TlRqW7G8X2aohne2bSp/fHcIvAaTFinYGTtQdtCoL299V+9R9FMArKp6/EcB5AM4cxYB2CexNSQThcEL45oiftxXaxUKSdCC0RFzoq+ViyOM5FnYpQYQEAc4Uv8UB5Dx15cXT0kmz/7Yc3B3f8Ca2fE6dL/DqL9hCWxkvmsNv51ugnUW/tWh+HN0SAUgaC+OOaAXQWY8pZW5gMZy1ufr7SCeeHqLuGcEv8cLPn4/9T5a49pqrsa7TwLGQuO/x/4mp338ZdPdVWKI2YyPt33OcjS6i7sh9l+ceuzYVbjGx9e6bsP6qb+CgZ7479z6lFAIqnz/poi0Ic3mGsu3lBmoFSQFOFjfiuge+A+BUYP0twM//ATj2ucDhTwRgw2kBHEL34GTxeyTy8Sb8suL7IiIkCCHjzOKvkzjNtQhJmaTbqpDjLffirq+/Hfu5Y5Mx1vzwU1i0//FpTgYAXLvmHkxu/ACWBltL4YBuUeeHL1W20rAGnXWbtuLEG/8BIGA6LIo6VRF+md/uxET+nhJuN9f7VlqCPX/xceDBO4BzPgxELQitjAFBhOZ78Ch66hKp8P/CL0FA4Qcf/gIevU+Ax6z9P4Dy3jmXv5s7tDDvXSl6M3PvHTD8sspo0Wr2FyJpaL7y7i/CeeqMF49k/TCifuTCShdIoRTXQme6x9p1kT2f1AAe7TpoMp4wt4AvegvDxatLnxFhiLYYvaeOAlMoRVTkcikZmyJVP/8wqG3GWBUy3NYRmhSnniGZZNsSNvfW3RPO/+rXsGXj/fj7D/4TgMxr1RKe0JveAAB4ZPtXwC0/gQw80dsn52zHg/fnHjd0bHuhSjRRmF+1hO9vcMadYgGmqjWKmt5ceq4ui6TJKtJKZn3p7HqoMZ0ZL7VSpg+gTLDpB3+HJWf8RVqRGMjHGJm8TYlOJ8scdFEg2gq2po4BAmTF2NPG3PddD1z2L8DDXgocfPqsj3Fn000mySSGvuwfgZktoHM+PCcnxvrvfRwAsOopby69duXtG3HRjffjFY89EHsvNc6XB7fbfHRvnZyo8a1+2euutFJrfUfxSa31rUS0cnRDGl/is94Pte0BNA94NPCQRwBnvx/RHabv1lxC+EqsOhw47Am1305E+K48GfvSeoTLDgIeMFa7ynwn58kbYtiPYxotHCvuwLG4A0C1l2jf1aYSXDSL72vJ0kwEhLkG214u0QATcdQyYmMuog4VHsPJiop7oyBBAGEt/Vv2OxPrH5zCAc/+ewBATBGgu38fbWHDie1393X1OByOO9P+OY7/aXwQ+A1wMJDeTe7avBbLL3pb+p7b+7SRmCyI3A41EK44EJOHnZZ73gl1N8knnzsHB6kHMf34N2FiKgsdjOPq4OH0WCnMiT7dNpN5svIIhFN7YhotHCbW4qPiPAAfxZZrv4sl138Nt9+5Bge9xYg6VzznHxufAQA8MPMhtCqKlDgkBMI48whKJfNWWK0qF9prr/kx9rvrgvRxO45xyOX/D7gc2HDW59PnT9v6fRwemnuNLvRNc83I/QInU4uXlfYVWAv8utuuwdE25EwWfruqvoO5QilRA9jzocBex2H79A4s2nwrwvYmAEActICfnGveeOLLgX0eBsCK2YrvTRWKN+jt6/Ha0Jap7gAP3j6VrhKCJXviV/JozCDCaZOL8ePGWdhz5nYkCJAgwEHL9jLjs5ETvaoRDxya6I1dLt0fwR5HIGiUi7UUCVvOU9cBXvgV4LdfTK83VxFWDFHUqak9cYU6ElNLluOhg+YNjggKm0DS23M6tdhc263i4n+OKAoR6DhrC1A0dIRN4KinA8c8G/j6K+x7wix8bsg5dT7CijqqiDhQUkL/4p9Bl/0LDlx2DIBq73IHIZqI0xBC3/Md2Cgdd66/c+uHYCLyjagLEmMM9PcfxF4phS89B+rFl6QP+4VfzmzNhxZGiDGDFqAkmtqc439Ue2B/sc4IRD+6KHYhpvlj3LT6kbjl/p9jn+BBLLJ5j+0Kb1ddGsqMQ/sN6SOz8J/witUopRAIgdt+8TUcctW/4Nb7/ohDX31++joR0hKYyhoOprduyUSd+67sPppkjk/uyIs6BZHNEb//DnD91wHQWIs6dKkkLtvbQZd8FACw9eGvw+K9Dpn1HsIlq7q+9sXL78R3rr0HRIR3POlIAMB0bL7jxHMkJFIh3AWrX/ZK4uo/Y+2GRKf+BZpP+oBJcn/1T4C9jkE04Tx1QxR1f3YZ8MQPDvSRLz7kvbjmCd9A3DQTpIzjrFeNR9pEXQ4v7MexNViWe1xlXVxsk9OLeQR1WLxyr2zbnpDTQVlY1SGasIuuOVRnyiV3u5YG81SkQFKQLgqTxlKs+oufYfIhZhGQpOGX1Yu7TtOWWbeLrWNf/9+IX3URNtqOblt199uDLITN9fKKAECrIHKvXv5kiDf9Gjjs7NzzRU/dcmWT+QsenWJ57mwDzlNXED3WQjrzsh8CQYhNtDT3+s33GAv1ph3ZorJF+X0ksntOHQAkCHMLdC2TXL5EVZI8ANz3oFlMXHrQmwEAt9x1b24bjlVqffahkqfOGWqy90+t2LO0Lxfq1bZV7mJdzgcxBRaKoq7gAVx+APBnl+L2k95jPmPvJb7d856N2+w+lclvqzhHiuGXxabzn0qekf69etkS3Pusr+Oot/4YQRDgUW/+Ki465St4Tud9+JPOe7B6uflNwyXGs6HiHmJp0CJN3ti3v+h7wEu+UcvSHPhFtI54EvCCL3mvGVEXDlHUHbDnSsQv/R4OffP3Bj/GEbGH/V1OOKD7QmzxSjMnrJjFnNALt+Du2WLnT74APPRZ6cMgiLIctSG3WABg2qnA3Ot0RZENwFzHt99+GwBg47StoFsl6mzun66oGpxWye1y/5cuXNDbf7ERtr+9fk2yZzr5a7eBBAomvLSlZ/At+Vh8QZ5d2ieQzSdF0f2EZ74Mzb+8CjcemFU6jOcg6pzgMBW6nafOCGLfm+5CBW+3BvttPWwNri3F9A5PEDuPc/H7nMnGfuUez8HVU6dnIem2JsOaB2bviVwQdBH/M9NZfv7W7XO75y079SVYdupLKl9LbBTd7euze8mME3Ve2G+sds3wy58Q0QcBvFt7AcFE9D4APx35yHYRGrYoxmw8T12ZhYXwq697NADgN992DZzjygWau4kNM5fDsSVcjn1kFlsvqsSN7Q20CINf2M0l2UKVvEWVL6wq99mFxhAKmvjhjcMugd2PBCECtygsTN6JaAAK3Refkytge9QDAI7cy4i5+2zYZtKjkI4qxMf3y6mbKIRfdhNGqaeuIIBUIccu6SKQnFW6tHiznjrXs2xruAL+5er2F/ZYqMu4bdokdPPUkch+C5gFke+pS5IYjYqqjdoKVmEX+XfdcVu2Da/40grKckmKOXVpRVvvmhZROe/MCbh4iwmV2kArSjkdpvploaWBFTVKU87L5f52OWN+ztz2afNdkM2po4pzRBUqzhV/99NOPBa4Lnv87Ic/JP176WSEvzr7cGyejvGMh2WViVvLjEBoxUNcHHnfdxDV94C58O5QlVeFLkzez7cbBqcc1l087QxEZM75XvflqRXl3LZhoEUAIWVabKSOsU0EIaS9nor3uWHQRgOTaEMEAooCBLpcoEPJDrZtMvltW5QVHRV5h64YFlJRVy6U0q3pe1q8xxMexaIdMlcopU9Bj8K1HJE0PdxUgqaeQdCcwuHLlgHr7b3RH0tS7alrRQH2XzmJBxZlYbJVIYyDomUC6XKX7W8deddhGv7/oAnJbCzOB655qYmp4WBmRyYi3PdY6n3nFezSIEBE6f1349YdWAHg5ns2mqiYMSXqYqSKPVE3yvy6ji0nfOeGbH+ZqMt+OLmLhl/+NYDPAriViK6xz50A4NcAXj3aYe06NGxRjOZQRd3sLYSuGqNKOqAKT90ey83i/YClw7dCbhd5D0hlQYJFe8x+B93CpsTshBU15l7QJJezNs8V5yREuigsJvUnZCarThcDa7jITFTasx4Ctlqd7n0+F3Oh+om6UrXCLqFYblIveXAKj13YXgKRK5iS5swUG7C3t6KjQzTs+TjTyIs6t7Dp1U+nE9sm711yNo3AzhYGWia50CbZpWS2m/jJGlvENiO4ZsRkKTwxpXieuQbpvbxTyBZmeptZrGwKV1aKupLn1f6+CQT8paUT4U60+IvCtEy5C7+suDZKv3NB1C3zPPOVx0OE9z3jmNxzU9brs0gOT9T5BqRwAFHXsOHdVUW0IuepG7KoW3C4Ykg98uXE4jnMCT1wOXWyS0uDKoIgSsOZdZ/raTa0EVlRZzx1VYJfS4mwY87fMBBADAQVeaSJvVc7Q63MVb804d7d8gJTkeZ76gr3gtw9q19BjyqPo+0T2NIz2HPVCrSnFgHrTVNz/z7i7oFVbVgAQExl54eemX23LUo9dV5OXRAi1kHuGnVN1+NtpujYTCHvOPSisrStftn2PHXpfbDgqROd7D0EDU2BKb4F4K71W7ACwKrJhZELO1saqmykAIBOJxNZXSNtarL+ux8DAKx62ltLr7kibnds2A6lNIQgzNiqgC78UmttRd14hl92XWVqrbcDeCERHQzgofbp32mt13T7DFOmZb0QpWTgnYS7icskQUOrkldkTxsOc/jKsiV/7jvPWz4qc1fmIuq67dazwPbsh1RkDr0B0/3tRE+dRJguCosLFmXFR9KpXjQ2bVw6TW/KPZ/YXLxJ6r7YLHnqBjxu3WVx5YwAuhSWV/TUmcczaGEK2WThzv2iVyCMt2AHtdKFhGotA7zaKWQ9YkEPa3RnZtrk6XXr+4cg53VRSiIoeOoqcV4E68Vbkpgwy+1icdnS6yh+3zVDql3hFtqxHjt0E51wCs0kX01OaJmWdC/uL0GYF3X2d4y02a9fLMo1FM761FWJuoJxoPAdNZqD36OW2TL1SzDEUL5coZj6lRpdfl9Vzm7UMveeSA1fOCwoXKXSXpEhI5gTANjqkiotzR/WEXVRmFaIVvHwBXfaty8IoCioPDeUTDBpPc3OgxRVVHx192pnaHLXk1LaVL/0CqVk21YQgUh7aPrCo3j/yzUz79ecu6Klk0IAUjEWURuIFqXXUdEo6Bb53arSRn6ETnvuog4qSXsNUxBCQuR+Bxf2nhYdK+Qwt7woI1Pspp3rhZqFX+aPM/Qqd2ptWiC5OcLdL1ti+LUO5pOWrr6fJTPZ83KOnrpwxb5dX3OibiZWeGBrG3stbaU5de612Iq7aBcMvwQAWBG3BgCI6L0A3jvaIe1atCaNFaeqGt/OIO31lXQgUFEoxU0OI8ipK9Y+qrxJt5aWn5vrXr3S+DRA9cu0nPGc9l3OqZsvFAXppF8Uda2JSaANLI6qJ4nJpUbUhbYKmiMuLOhjHSCi3qXoB25k3yUkyJXtLi72ZVxcBNjFSzQJeA3SndWz2HA6jLeapH332EuQ1lpn3jI3wSpdCj7tzNjCAl3OL0lBzuuiZZy2NAC659SlVmobprbUF3XdGuKWRJ09tj6TpfPKhdPr8SAtNQuSQpsBAYmkmFNnfy9ZKJPvjDZuQeQvjNI8JmhookqPQfF3Lnpk63hWirSWmgXgCjE8UeePfZB2CGHT5KVWtUzZY7kp+nTAkvG0FtemjqirUXRmNjhPiAtj7mXwU5ogSCMQEVRYDmceFq7VTCAENAWVaRtKJpi0gsLdU6q8WC7E3lXJTQtM2R5sEGHJsJrIBI2gkV33PTx1g/Spq8q5kxQgTIz1jJqLUsNOMScbsvfv01yWeeyDObQ0gIZZoqgkNUySiCAL4toZ4JxhyL8PayUx4RnwFQWIIDHjibr0u1IJtNbpqsgXdYIIiQhN9Id9LzD+LQ5aXdJqYs9TV7zvD8qyx76w8nmtda7d0rZ2bEZUCL907XJ2xUIpVTx9JKPYhREjmpBmi1sIySQ2VqAuOXU9J9lZ0ggLi76qJr+jSOCfZU7dMMaS89TNU4EUR4IAoa4WdQfsaQqhHLaiehG6/36miP5RSwteMZEXRNtRFr6l8IlB+zl1EUauUIpKYiDOwjhUwQDhPHVUuPbUdhMuUxQQzWQb2sJrgOuFXcSJKnnq4orKsOmk1KPvn5+XoWSS89R1C7/MPHXGc7NcmqIt23qKuvz3t7+tKHvw8t5GBVeUodXegM3B8jQ8Lbfpipy61MJesBG6BaOrFJoTde43MomdledI8fiKHtpiH8NaWK9PMMzFUa4YUn1Rt2SxOT+XVBhWnKduSgwxbH8hMsL5ph9ahKalhvPU9RB1W4QxziZKYdVSE32z59wDOUq4VjOhakNTiGalpy7GEm1EgrunVIX9ulxaF0rpFsuJ12y92MbBFSMKdVnUFT11/vXYN6euwlOnIdBMbMGkxqJ0fizOH2kvvi7X++TyvdO/G3Z7ySyK2GSeOpkKVtMEXuQirdx4lsCKMO97aE8XjEUuxLedPb/XInOcpCTiJLv23XcBAMfvtwymn6rt6ZsaFse3GbnWGhO6+jqXbW8+79OHdrYonXnhAKBtv/us+qX11Nl80nHNqRv0zB/Po9yZuAXiqeX43oE5+fWY60/gFlpKxlkFLJ8DTzH/H/u8Oe2nioPPeWPucdBtAbTiYOCop81qHzuOfyVklF/Mz9aSDsAsOh4x+xRS37roT0q/wAm4RXUPExgGioI05K1okaWT/tT8sffxlZ9t7m0irhed8ue559ce8QoAwA9WvhwAME0VxT2KYqOGp27zkiOyB108mq7/mJYJMJ2VyC71M7MlsO/f+ywAwHeWvxIA8MAis49iwn1D7kBMWfjSLSsel21byjQEyQmBOC4vUJK2EXXdvEcSARra99QleU9dn5w6F365TG2y28s3M89RDKk+0dji9jn5+bnnPxrnHzsB14y3YCZcYjwZJVGnyp7XVNTlnw+cqLPnYCO3MLI5dVpDk6gMTS4XSsk/DsIQGw9+OqYn9kZtXHjkKW/JnjvT9DnceOSLkISDr9TzFW7rGzAmrNdw0VlvK7+4xBYHecwby6/tSjzMVi7c7xG937fHUcBhTxzqrp0n2t2ves0N2p4je+6xBw4+/cUAgMMe+6yu758t/9N4DgAgXrQXtAiq0zZkkoa/u9Dmqr6Tez3hrwAAyw88DkB2PaX3ryAsGbicF8pFNfgeON/As55W5Dxq1K+HYOFe9bnkHCgK0FT2vhlNpGMpRXo4o1qX32fxkqyVUUsaz1/SPwithJ9Tp131yyCCRJAT1050Tjqvk+eF3LHN8xQefg6UsBVWPU/UyjPfhFgH0F7jewBoqOw9jROenwu/dN9frxSAhY6UMm3fUHrNM9J2NXDWZN0FH8G6Cz5Set556pxYc6KuHefDLl1u3biKukHP/BNHMopdnfcOKSn/nL83/+aA76kLkZQq5WH5gcMbb4FFRz8BV+39Ipx075cBAGG3nJi/+O2s9zH5rI8Dz/p4/kk//HKAnBcAwLvv7/+eHuQW+d6kdOL/+2lWNnpESAqxyIbplEpXH/b43r9za2nl649/zmuw8UmvwBMnIvz2Y7dinx03lvdbCPNTNbwXS99yJdrnrjI3/S7eLlfhTcsO4k7b1XYrVcuKbZ7g9hVHAe/dDH3NWhz4lbPxgwOMqCsKr4Zu54r4bFp2NP4h/hP8TfRVxEk7tZI6EVaVhyitqOte/TJE5BU90CpBqBN0dIAGya7J4WQXOGHDhurZRZ7wQoRKnyn+1quPrPwt/3DE6/CtW9biWcFlueMTOjZ5Q5Xhl7YFQX6HAMoVUZ14dgvTpu6kNimVFkpRttJb9r29qfNGfKLxyXQx5ygebxBFWPGyL5S/gH4Uv4vT3gac9jYYf+a/Dby5nKgbxLsfNoH3bq62rDYWjew+vKA4+PR6x/mGy4e+a7JGCxdGGVZUhHUsP/W1wKmvNafvPg8b2W/zs9ZZOG/Lo/DjYAqaqkWd8gSSM5hUCZ6lJz4XOPG50Ff8CEBmbHPeOAoapbBN6XrCVXnqkOCqxY+DhsA+236XXo+xLnv0i5BKcK9egUe3P5k+d2rrXLRs4QwKGyC7yC4ab9Lql11E3dREhMfO/Asua/1lGg0wcLsJrRFSlusmvYqbEgEmvdzszIhornX/PuXyu6885lw88rlvgf7Y0yCgUu/TxtddixV7Hwj5v68H6QTKE4QN1TZhvu990NxHxJcQOnGtyiJ73Ig7bYQALj/ojdjvjq9jX31/Ov+pjpeHOMecusae1fVBz/3O7/C7e7Zg2WSETTviVMw5T50Lu3Qeu102/JKIDieii4joBq21IqLjiOjd8zE4Zvi4hZaWZlFZzC8aOd6Cs6qE+yjwvVS9Ju5R4E+2gZfMPtEIMNUcbY6doszC2K0f3aAQEVYsakAIAoVRrvhFut9OocJVzXPMWUq75aWFYXbuupL4QHkR4CZW930/44R9cd17n4Cj9jYhVEXRE+k2pLfPV596MFz7chnHJStpVcllFz7SrZqcogBNeJ46lSCExIxtSdvVU6clEi1MU28ATRdOC1kKR3R0G0ORT7zwYVgymV2DztoeaGPs0WTC03LbRpWnzjzWhenEhYQ1bM5lg8qeSVf90h/zs08+zL6nkMNTEHmzCr8cAQM3K2cWBFqEZsFtz8X5nhuqOPdpD8V+Kyaw/4pJgILcNePQ3nXRQAdSU6m6sU/mAXOiznmhGmVPnfUcpblcRU8dBdAiMvn4VvjMULNGTl0CTSE++tzjsuOgLHqBwmZqHCl6atLfp4tBNhCENz/hKABIt6cGjWjyc/5UkuVR20IpuXtXMULCe+x/twDSaAfnfYysIVsiAKTM3eMauo0YYWYYEkFWvXkX8NR1OlkqiLLzq5v/ZMdPp5jbMS49+XlYenI50uxLV/wRANJ1Vzsx373LqXNhly63blw9dXWk6H8AeCdskW+t9XUAXjDKQTGjw1m7ZNJBhKT2AnCIA0j/jOZpEvUFjYjmdyEovIWnGNRLOEcUhamldzZFJfqhKUSrwpLsh1IAGMBwYD2X3Tx1kfPUxWj7MfgFkeU8aYH3Wy9peedAIXempdtQXvXFpRMRnnCsCY1Nkk5aCS711FVUvXNCVnQ5VoV8foxOEoSI0SbXUL16IiMZm9xIO2b3fQcqAewCrKMLC7qa13QrCjDZyq7BtFCKTqBECC1E2VOnFVDw1LnFWNHv3EvspNZ323zczzddudR4TYvVPYvFZMQA7QNGybzfQ5mhoEVgUhCku1/sfFF3ymGrcOnfnIlWFHStGuyLiqZu9w01DDxDLpDdHymMSr35nKcuy+XKrv9QSyiKjNjQEsp6ONtolnrYFSGdQFKA551kcrX3XTYBRQItP+c7bVlTFE29PXUAcNqRJgTbGc6odDfqTS66RCWed7BRKiankoI3U/qibiY3Vu1CfO323ZpHkih56ppo56MdRARB2rS/2QUKpaQe4jCCsnNIh8rtQUbR/9EnE3UKWmsvp86cMx3rMS7WgBgX6ox6Umt9ZeG58TUX7Oa4hVYSx4iQQM+ztdtNVIkWCObLve176uZZWOUE5aD5fHNEIcAEWU/dIK0caqJFmG4/t9/2jvwTNRe9zi7WrfVD2tJAJbkQyGLoopugu4nooteyiU4pRNR5C2UcA3bBEtr/ZVX4Zdw7/FKJAC0vn0DrBJFO0Ek9dV0KYiiJBEF67kzYRYu/UHDWzvT4BijW4XvdnIALEKeeumJOnalWWbhu3e9bMGwGPX5353Uziy+RK9gQtUxObLlQSqEE+DxfT12Z56q2zJBw57cLcZ5ng18/ulUN9lt7tCg2+bU9SPPorTCQSSakivdCJ1iKYX+AveeI0PPU2fBLavTs4em243Juf/xXp+G7bzoFGpnRUUSN9B5QvJ+76z7qYcRx85sTiYMWQor94hw6894K66nzSfOB08Iq2XhlnJ97tM2pczl6jYbnqdMJlPIKpegOYr+1i8g8rK5Ayjh76lJjQtBMPXVtm8uu4+F56tZ960NY960PdX19cct8r1++4o+4f0sbLgvGhV125HiLujqz0XoiOgTWEEtEzwVw70hHxYwMd7NJYtNXi+ZZ1GVW/flzbXcLgZwPfG+CmGdLsMpV/RzBArhLk3BV8NQNeo518yq6cD4tE0jPW1YMQ3SLlm6W9+J30aK4LOqCbIGR5dTZ8IwKS6KblLqJZ1X8rqRECImOaAKqe3I4qRgSWZU6JwyFTtLFzgw1scTvxzeA2PEXjm4hFNnwS4JKLfYOI/wKnjqqDr/sZcTQxebj3vcf2sbbRRFX9NTNt4GmGwNV1GUWDtZT5+4fUWPne+pydAmpLIqepMt9ON1M6BWYQlZdkIJG6V6Yhq5rBVA+hytEYsYkQoRI0uuxI1p9PUhCJ6nH6/A9TfXQeykzdImgCQpclc6CgatP83Eg6zHothf0EZlF4riT1nEmlaTnhB8q6HBRFW4+8IVvZlC0YoUC0xMwbctgjXgIQEpBe6KuRTG2wSvyZo8pjtulaJFxxJ1bFDYg7bnQoSagkXp9AeSKx8yGxj5H9nzdeep+/od1eMI//zx9PlYFT92Y5tTVEXVvAHAegCOJaC2A2wG8eKSjYkaGEziyT7W+UaGd1XAeRZ0/cYWN+V0IBv6+51lQ+kIiGEX4ZRdPjJ5l+GWaU9dlu+kiXsW5huPlvnU2L6SL5b1qcaALCyN3XSRJnIYWOet1sdk5AMCGj3S7nopWdy1jhKTMpIaKNhAOlSChAEFBLBoPQwdKU6n5bV3PKICcl8kt4AJI46nTSckC38tTVwx46tXMOc1ZsdvzQzUbtpx/MfyyKPIGrmQ7Ijj8ckwRgTm/ZbbYXEgU70kOWchZLhYoKiIKYY3+4roYIq1kDCgJYfvq+qLO3RdIBAi0Sr1PsWhC9GlJQUpCFo7HD2sUYZR56gq5tKno7uFJLRrTBhV10q9orGTWHzQIjQDR/suuaIndh+c980MM7cCMYcwZAl1PT9jwy2IvTn9J7t6bJNm9eZw9dXHWU9EJ5bTqtBd+qbukItRl6aOe3fP1KS8VY8uM9YAKSgultMc8/LLnbETGBPvnWuvHE9EiAEJrPYfujszORhREHeZ5IsuKYMyfqPMX2vPtqQtynrp5Dr/0JtFBvDe16RZ2FucbjNbddybqqs/JdFKXSS4HohiD77x4A4m6Lp46nXRSS6zzZKmKnDrXN6+bqCt66pzwTUQTkBVtINw4VAKJsGRRD3QCqBgxArNY8hYdA3llPW+A88pFiKGDCJC67KnT5eqX3cNle+TUpSFMChr5nLqu4ZfFQikLJFyOC6WMKSI0hSjceTZA2PK80OW6KhrNiq1EiqR5c/Y+lvaIDKJSiLRMssJQQMFTp00LJAoCUznbGqIS0UQThZD74qF4nrps4969J2qk986iR97df3t55osGHkEaWsmeBWR8/OgL0kkqWEXYQFzMqZMFT513n1JpkS47VldBWHXQQYiGLYJiPHUyl1MHFLyudh6UcSc1LI63p86JukY6h8R2/qPEE3UVPQ2HyVSzfE4sboVp/9ldOqdOay1h2xhorbezoBt/0qRp2zelW2GH0Q2g2qo/SvxF13yH2Pjf73wLSt/SOwqvRrdEfhQWHXULxKQ5dV0WyalgUnEu/LJULc3lhXT5rau8lqpwHYjUU5dkBUTSQillTx0l0123DVSEX9ocvERYT53sMllrCQmBICo09oYEyRgxwlIo80CizhuXq3QZ6gQQke2TlLd4C5QLpWTXNBWe7uGpS63dGij0qYuapn2DLix4dDH8MloYYmokoc3M6HFeFK8n2YKimyDp5AVU0QNWJBVLaU5dJliKHi6VxFmPNlR56kJARCak0G5HBq1SldzSGLQs3QOVb1CKmulYSjlV1gBULOriU5VfO0huVi5SQstcTl0x/NKPMgDy35ETzC7VQluvJmRiKlu6/dlCKVrlV0L+b+kEaZzE6T7CMS6Uks83NHNFImzQq/TDL+fmqXvgf/8OD/zv33V9fVGjfL1MNcOsUIoVd80xFXV1ZsXfEtF3AHwdwHb3pNb6myMbFTMyXPVH1wxzvot37IycOt9DNt8VzvyF53yXzNbepDmSMNtuoi4peOpqGg5cyE/XRTIREi1sHyGvkmQhB8NNrN2+70qRWTiWrJ9jZiV1Xquq6lzO0tjNK1kSwFb4ymCi8hjSYakYEiGikqfOWH8ThKVKb4OEA+qip05rU747iAAtK3Lqunvqild02Ks4j6toBwUQ5QwvqcDr0oTYEc2zkaQb7KkbU0SIkIw4SSAQDtA4fj7oajRLip663ovP1GNuDUcy9Zg0S94vKWPEcQL3rJ8vFsJ56kJEJNPcZRm0SveJIkInJfHpGx1F4Hnqiot6189TdD/OqqJMSdKpPd/7hjpSWfEcEUZp/pdDlXLqvEIpSaFvoDUckOxAestthcAUZCkYrvz3ZJ7LThp22e97XshIz1NH9ruTgRF1wj+nu6Ui1KR1wPE9X19U0Upqqhni/i1mDs9y6hbW/aAudWajFQA2ADjTe04DYFE3hoT25kcuXGyeF0buRjWvhVI8URHNd06d9/3Od7hYLqduBNUvu/WTo4KoG7SVA/UYq+nvk6RhLkBFQY00/LL63K4SG6Xwy9AtMDIraYMkpFSpxdFH2GPutsAvVbKz75dB7+qXpCUkBaVFS2A9dQkFJVE30PftLRwD23tKANBBA1rGlaIOhWOhtE/dIJ46WzIdGkBe1KW/T5+cuvk2SHWjW/gps7Bx5y0lM0gQ1loMzSfUpfolFUVdH0+dXzUY8EryV3jqdBIjidupqHNRClop05w7iNLz3bVxUUGrb1hgoBNIke9Lqwvhl8LdR0qeuqR/iKkQkJoQUHYvTOIY1unfl8TLqTMeNCuiwqjc0qAYflnhqXNi0hVKgYpzoZWSAlPsqnCP8/ul+nndWb7zOOfU5YvFANn8R553uPidDMqSk57R8/VWVD6XJhoBYqmhtR778Mu+9zGt9SvnYyDM/OBivcmGf817sYEuPa1Gu8vsGOc7/NJfjM+/p86zhI4id7LLYlYUPXUD7rtXOFtCAaCSXLWskqeuTzW7qu+i6Ml0bQFUEqetDAATClNVnYtkZoWsQhU9gUm2IDJj7pFTR2HpOg0hQSpGgvJ3NZDnyPNOCCjEsWmQQCKy1QHLhVKK4Zeii6Gml9ih1FNXLpSSVusrhl8Whe8CEVOjMJgw84ATdXKmb6+3nUIXT0HRaFYMDyxvxlW/NNePu3eKqFHOqZMxksQXOOb6j+PYCD0RpJEAbg2hg2bfFgJV4Zd+lEAYZY3Qq5p79y0GIwhtBDnRk3QLaa/Av6cLJdM8SxFGpaq+bnzCVQj1PHWpqHPzgK2wSjbiwqFdTp3M31/z4Zdhus0gbaszvp46lValjlJjgZv/hOo+nw+bKCg7FCYb5vz690vWpM/tsqKOiP4LFWtwrfWrRjIiZqS4wh1uUTnfZfbT/i21WiQOBz/Pab6t6kHD9xLuPFE3kp5eXcSXkLPLqUvf30PUSQRG6HgLD10Ih3Q5Id2+76pFeLFfY+qp88IvAWM1rSqUEkiz0Oram62woHHCV4XGlFwMw0nfp01Lg1L1SyRG1FFYqlA5mKcu39IgjmPT9S6MgCREUCg4EFSEX3atttkrnM0rlAISuZDRMPUsFL6T4mKP5s/b34v5riDMDAl7fwzk9MIUdV3mqqKoq++ps163VHg0S/cVncS5SAS3+HZhhSQic2+Aifbp6MD2reudUxfopFTNs+SpKzRJd5CKSyGQVRhvnle0ZIAwvmJOnRtDEJSbj+tCnzp/ftBp6L8TdaExjMkqT52E1kVR561V7G8jk2SXCL/0Ba/77tz85+ZPYO6euvu/di4AYM/nv6/y9SqxNmnz7D78g5uwz9JW1/eNA3XuZP/n/d0C8CwA94xmOMyocRYkt/Ce95y6nVAoxa9AOd8LwcjvkTff1fpG7KnrlrsVyLzoCYbpqYPx1PlCrmjZda91K4Fd6TEt5qw5q7FMcj2Ykk6n0pIYqqxccxXF9g9uEtOhSxTv5qmTUBSUPXVappUxQ+THM5inLh9+KTuuNUMjV1I7tJZM0qoUSiq6GUpsDmRI5QWf1q7YAKCJcr95eqwVFvuFSK8m68zCxYnxQLb79nrbKXSrKisLnro+gscZNakUfhmV7itK5g1mZEVSnMSYAIAgTK93SqwYtt6onoeiZSlawb+PhLlCKYXql1rWEt3F3MKubWIqyHsnZZbzG4YV7WicwHI5dX74pS3qEtn7us3bFLKTE2yKAlOQpWC4ylWsFo10m5GXAgCtF4xBaxD80FRtV4DaiTrlGWbnWChl8tBH9nw9rMjNXNTIfuN7Npvra5ftU6e1/l//MRH9D4CfjGxEzEhxhTsCa+3rVvZ9VFCXSnmjJJjvCp/+vqOF4qkbwaKlS05dqPKibmBPXQ9Dg0IA0nG+WEmxoIazpHbZb9UivBR+GboJtZPrDSRlXFkoxR1zNwFbtFKnC7PIeuq6TGSuwEBYGHMICaE6SChEpPLjGUREU07UKbRdn6UgSs+fJInTSqICClSYFF07Al2x0JAUmLLxxf266pdQAES+l2Q3UTfisJzZ0q3hPLOwcd7nUM7kQuMWCt1y6sqirp+nLh/O7CIZgqhRIeriXHn/1FPnvHciSqMYAjltxLBtDaG1BnURGwGScl6xPz9FTQTOs1+47kkmUDUie4rvSQYQdb5XT2jp5dQ1ymGjyr935XvHue/W99QBQKBmcqJWwXrqioVSvDnV3VeSpIOWJ5q1kmPZGzMtIhM1ILU161vxmzMEz7GlweKHP6Xn61Wn6ERFRcxxFXWzGfVhAPYf9kCY+cE1wA6U89TNc6EUMf+FUua7P5xPuBPz+XLl6ufTU6fyi45gwOI0vURdQiFIyby3rIunrlvT86qKaMUKnX55bb8IgIw7fURdl7GLbqKuutF2Oi5d7akLICFUAklRuVDKrPvUqVxjYjdmv2WEy4HLb6K7oaZrgQOXl2Jz9HI5dUJAaQKKOXUL1lPH4ZfjiDNohGqBeuq63F+LRrN+nrqikcQPvyyG5ZvwS1/gGOHihB4FWc/MUE6bMv22wqPqEX4jtCxV88xV3m000vu+KubC6aTW71OqUjlITl3BU0c5UdfHU+dFcpTDL53hoJ0Lk1U2/FKpvMFL+eGXLhw1ifNzUMX8Mw7oOKtK7QQxrKcu1N4xjfg+r3T5RJ1slK+hXTb8koi2Ih8tdx+At49sRMxIcVb8SPauEDgqdkZPp2gnVsnzPSF1S/sPC53LU5o/UVf0HA26717Fe5TNqdO5lgZFy66doLt5Eqv6mxXGmOahyDhniY3juNJjFGkXftnlllr0tNn3U+RaGlQvQAKdIKGo1EQ3II1AdawVOT9JBQMYMYRntgwgEbez8Eu3zyRJvPdUVL9Mj60s6rpZ2Mm3dpOA8IpCEBFiCEAnlZ9ZaHD45XjizttQt/vmpe0MuuV/RyVRN6inzoUIRmnFyXRbMoaUea8VgFToiSDywlath9MWVEqUyrxtBUItS9EK/n0kilpZD93CdS50UsuTWjQgDRJ+6R8z6axQShhFpcJQ7nuszKmz2wmbxgOVeoN1OyfYNAkIlD11VcXNlLTFuuztNUliLJBuLgPhitGEjSZiN2c1jFEzd07PNafuK+8CAOz5gg9Wj6PC+rBbiTqt9eL5GAgzT9ibTMN66uY7zyutbDefnrqdWfrc9/XPdxz8iJuPdxPoTuCk+x7QcCB6iF9JIUgnuWplpRy3Ps2Ew4oZsXgsbgwyiXMNuFUSQyflQikNe8xV2wbyLRM6OkwnMdFwjba7tzRQogUQIdYBIsoWAaGaMZ7LwmcG+q09q2VICu1OVsXThQvpXI5Nufplr+JDLs+lgxANr4gBaVdBznj+imOWNncy/+RCDb9kT9044s7bpmpDLsTfsKvRbLCcurSPZMFTF0WtUti5llmhlLaOUg+RdIadIEzFV+TuP1bUdaRGRQswADACpnif8B5HUSPz+JcKpSRQxeJMFcxF1CkrWjsIjZBVmYh1ormtIzQphpIJtNapp87PuXZzT8Ouq9z321BtKLEk2x8FCFQCXfTU+eGXgSuUEueqEMdxB/nmEONB6sX0+tQJG34Z6eHk1CmlMXnkqT3fIytEXbHNQSAIgRi/vEWgRvglEV1U5zlmTLCLw3QROu+eOmt9mkeBM98NxxcMfp7SCMR7VdW/to7QUHMTdb08Ta6/D3qFX6rYhAV1OceiRnn7xfw7v1hHgARtnTUjryqU0tCFprNFvAVMmxrpdySspbLUm8mNQyfpoqJYCCBSM5CiHH45aGEan3hmOwDzfThPRuIdb2A9az5O1FRFX7mFVtb5Cmgj8jx1Jum/WNzFiLpy9cvOAsx9mve2MMxQcOd3Q8+MjacugUjnbkc/T50rWFTKqWs0S153LZO0uXYbUdqqwDUaF0GUXu8NG7aqhWlGnsjuFTBDJGVRZ8cd6wBBkBl2ip46GtBTF9v/u/X+rMIZCdtoQGiZtSkIojRMtE3ZnKC1DR1HMafOeurcnGe/3wY6OcGm0j51hUIpOVHntdVBgjayx+NI+t00miBrTCQ7/7n5s62j1OA3G6TWWHzCOVh8wjld36N02TOnCyGZ4yrogB6ijohaRLQCwCoiWk5EK+y/AwHsM28jZIaO9CaG+c7z6hWqNSqiqnC73QG/B1mXnkdzoSr8sk0NNDDC8Esy/X0gO+joLon1Ku7Z16iqaXox59CFLKkkQQCJNpnXkySuFHVNZEnglXgLmhhh6s0M+og64YUtuVBGt2hp6HZ1+OVAhTvyn03aLte2kfVJsqGhSioI0qWFYFb9srpQCgB0PFHXQSMVdQIKBCp5uxSJXK6KHUDfflU7Aw6/HE+cIaGJTqky40KgGHINADNopgvgWLsCRfUETxq+rFz4ZQMQxdDCGNIKnA5lHhVXyImCKL2/NHQbEmF6n/DDtIsEkKW8YieWYoQgz7BTvBcKWwG4H86b5+41A+XU2X12yIg6uHsPibTAi+3UBy0TSK3TNg6+p86F/rs5zM2RLbRz0RqaQptTVxhjl0IpIVRqGPMbpY8Tbt6MolZqiHTzX2TnzzY15hSRUeWFK6K0xtXvPhsXvOGx6XOxNJ+bsB4714B8HOnlqXsdgKsBHGn/d/8uAPCp0Q+NGRUJhWjBeermO/zS9ambR0/dKBpvjwMjzl+s8tTFiNJFRzqMAQ0HvX4vRWHqqUsQmoVNsQR2oSdQeQd1RJ0Zs1YdhJDpQkEmcRbeaUkQoIV+njrPSguBprv+mi78sntOnVvQOEFj2oO7kJ6oIvxykPO9IOqspy4Is5y6/7j4D2bcKlvo+PQKqU6FKHmijppZ+CU0NAWl6p4SouSpM2J94S2+R1JZlhk5vqjzy80vFKrC22NE6dw9Q+Y+UEeQShKZUEnc4rp8X9YySUMtY89TlxZQCqL0XtmEyUV01W9lj7C5sKJQSpqza+9rQZTP/UvfZysA98M1YXcGuF7jKeIER4wGBCRIxmZuIUpFXSf11EkoT9QFSHD93ZvxvevuTY2NrkKwO8YWOgVRZ3PqCh4i7bd2sVFNcdxBiASd1LA4noVS4MJ+G1FqLHDzX0O77z8qG/MGQCqN+778Dtz35XekzzULuXGrl7Qw0QiwbDL7rmPrZT5g5eSs971Q6CrqtNb/orU+CMBbtdYHa60Psv+O11p/ch7HyAwZiQAT2Fnhl24BOH9Eu6uoG7H1uWrRISkTOI5Bz7FeniZFgREEKkZMYdfFv0SPRVrF99LNUweZINASHbuAkkmnJCJ9URd2CR31vZoSAZpW+IZNO4l0SQ73c1GcaGrbsTRhRF2x8e9AjebtokJps+2kvcPs16t++aMb1mLd1jak+54LHoTeOXXWyu2JuthZw+1RdQu/LBZGMWFYC89Txzl144k7b1uF0LgFQ0V0hYRIowLcfaDoOa/C3CfznrpKg66M09ynDjXT/nNpb7sgTA1XE2ib8G+v9UnXQ4EsF65yn3Oizs0nRU+druupc+KrZcdcX/y4kNNE2HuTkkjIeULdPcwZ+hIoqRGQK5Qi8bRP/gJv+PJvgILhKfPU5UWdCb9UpVY2/ntc1e6400ZAOt2/K1rz2z8+iOnO+DQjz1ppNDNPXXMRAGNYiXUASUEW+joLEqUxdezjMXXs4wEATzpmL7z/mccAAPZe2sL5r3wEnnbc3gCAyGtZ0LaeuYP3WDTrfS8U+ubUaa0/QUTHENHziehl7t98DI4ZDb6oiwYsNz9XsnypeSyUMqb9RuZKVZjhMKlazEoEaFL+plxlEe5FP09doBPjjUNoWxwUPHUqLpW3zr+hfO4VjyVMK48lCCERC3MMV952PzZu2Z57r0SAlj3mrgaEwoQ+YRdmUctMIt3K9fs5dU68ucXchJ6pDL2aTfEjt4CRHSPqAi+nLoTERTfen1nQC566dMFV8b06IZoIT9SJvKcOJEq/uRF1RbG+MEXdQCKaWTCQNdxMoFMrhHG+qbp/m7nbiS4rXmrc53PXk+xAaqr2MKsk9XDFopkVSpGuGXcjvVYnYMK/naiTPcIvQ8hy4RcX3m2Xod1y6kRNT52kvAGp6Km7bd02fOKiW0reMSDLqYupmebUObHpjGpOVEElUF7xLL/xOqlClIjIzjE/QkRTaL7bQqEUf55wnjrVmcntX8kOHtzewbM+/Uv89dev6fZ1LDxc/9igAWF/A2fUDEjDJDqE5VzqAVAFUbffiklEgZmDBBHOOGJ12kvRF3V/fsYheMqxe+Mvzjps1vteKNQplHIugE/Yf48D8A8Anj7icTEjRCJAw1bR21ktDeYz/HJ3ZdQNSqs8alUW1ag5mMiIehRK0RTaPkJG1EkEWViRG0PcHtjyXizmkhofVGxK/duFwnd+cxfuXLcJic5unb7Q6Oa1KXrqhLXyOlHXzVMXeJ46l5gf28VcgxKooFwopdf3V0bnjkF3sqq4bswBFO7cuANSVou60uMKEtHM/S3sxJ32qQsLpdUrWxrI3mJ9JzFYDiOzUAjcdUV6QXrqqvKgFWX3Dhc9UPScV6EQpNcTyTjNYyuiZZx6uBJqlDx1FISYbNlIAUqgKEqjE+7duBUAMBNL3P3gjmzfUiEklavGDCAVeW4tkOamevfCHZ0EM+12uXF55TGa+5C71xRz8/78i1fjHy/8A9Zumi599s51WwAAUjSgrLBN74kuTNQaprQnfAGkIapA9t06XASCIJ2LBtEUINASWhdEnVf5Oe2Vag1tzrAo4xibps3vccWajQCANeu24fSP/qzy2Abljxt24MT3X4hbH9g6523lUDGkJkAEafhlZMMvATMHuQIysyVRGlom6W9PAMieX4X00Vxz8VVTTXzqxQ/HwaumZr3vhUIdF8ZzAZwF4D6t9SsBHA9gNy0nuGuQFpgAQPPc8MRZ+RSLutEz4py6qobmVTlPUWOwAsy9GqVLChF3Oti0bQckRdYCnffUxXGna+PxbgSF6yD1HMkYISkkgXk9gjSeO+84c0Kjy+LQzz/0y3M3Uk9dt5y6TNQ58dYR2fepRWNu1S91XtSp2Im6ZtrDKoBCIrNQoaoCDkDvkGrpeepU0DQ5LFrbwisCYeFclRSkuRVaa7zks1fgnge31qqCN9+wp2488Q0tC1HUVYU1+30fO8Lm49YIs1cQkEliFv0qLzzyb0xST5kUjbSdy5bttoBSEGHF4izvSIkIh+65FADw5SvuAAC84LzLccpHfpZ6xGIXBlm4xsneN91aIEw9ddl1f+bHfg6hk1q5tGmot70/fuh7v8OdG7bjN398EACwvW22e9u6fKTF7eu344a7NgBw4ZcKd63bnN1rKC/qoJJcEZbAa9VSCr/0fpvFk9n3pimo7lPn59TZ+/iOHWa8MnApADE2W1HXkQpaa3zip7fizg078IPr7+3xDdXjS1fciQ3bO7jgmnvmvK0c0qRMAJmBMmxOZqH/FJgcTZXg0lvW4ff3bBl8F0rj/q++G/d/9d3mCcoCSETBiBGF5TXouPam86lzBNPamBMSIloC4AEAB492WMwoiT0vw6jzrorsjOqXuyuj9tQdsGpJ6bkqT12xCEY/enmaNuyQCCDRac9AUoAEAe54YAs+8sOb8Jmf34atM6Y5eC9hWEUxv8SFJv12zX0AAGkXCgFJNJCkFSiBQvW5LoLHn9yV99nGhJ3ou7U0gEyt1C78MvZEHURYEnWzERlpVUnnqQsb6WIxgEQsddq0VZea8br9l69pl3fiV7+caLUgZYz7NlurMlGpwIxCJurWbprGL25djxD1cmvmm145hczCZe8VWQveOsJovsnN05aO95wzNNWqfkkB7tm4DY/98E+hkk73QlIySftSStGAgMIlf1iHT150EwATiZAzUIkQDZvC8bu7NmDtpmlcc9cmAMCmHWY7abXG4neceups+KXzTFpRecsD23DflhmEUJiu4bxxglcG5v4YQuL0j16MZ3/6lwCARU2z/SvWbMh97ub7tqQeyQ1tYeM/VBaB4VrKuGgDla9a6XvqhIpzoaL+HLxkyhN1wuTUlYrC+KLOzoPX3W7moMVT5nyVMsamHTZcVCqcd8kafOu3a83QKkJLB0Frnf5+ExUNuecCqU4qeFNPXdRA4n43z1P30v+8Ek/+10sH3ofUGlPHPxFTxz/R7odSj3RJ1O2iaTl17mRXEdEyAP8BU/1yG4Ar+32IiFoALoHx6oUAvqG1PpeIPgrgaQA6AG4D8Eqt9aZZjZ6ZFbmy4AN6NOaKqwrF4ZejZ9Q5dVUeoaqcJyrGPfShVwuEJYsmEG1bjwgJEopAuo2IJP7t4tsAAFtnYjwcycB5ZfuszAtU93kVTwNhFtJz4kOmEN2T5CzdYdQA2rDNwavPa/JES+LZ0prWU7dx6zSuvH0jHnnQitznQsg0ed5t2S1aAOOpK+1roO/beuooBDSweetms99GEzOS0jEkSmUhLYXtL7VVxCYb5elk2WQD2GarnNmeyVOTLUQbHsTVd6zHU8wGSyGM0rauuGLNBly/dnM2jgWYUzffhjFmOEy1Mu/8tFx459WG6XJZ9Y4SqSneGXfqCFKJAIFNudiweVul50tqsl4oc51vUxFCSLzsc1fiDGE+GwRRqT2Le7xtuo3Hfvin6Wv3bp7B8kWNtIBKsVqySHPqXPilMBUnraj7rfWwhZDYlvRfL7hQxsTeH/1cN6U0ZmLz+qcvvg2POnglHtzewdpN00ikRmTfS1ELgVIIKUkjMFz4ZRptoGQWim7Hlx5TIafOLwA1NZGFGmoKjMGumFPnrcecoatJNv/a5p+pJE4F80xsRJ1j7YP1wi8/8sObsGwiwutOPwQAcOeG7TjvkjXYZ9kErrjdhHQ+sKVd+dmr7tiI39+7BS979IG19pUiY0/U2UIpUcOuGcy9Xdr0itkipcbUQx+XPiYCXMu54hkUdulF93fPeGhJXY9yqwAAOdhJREFUAI4TPe8GZCTu31vR9Rki+iGAJVrr62psuw3gTK31NiKKAPyCiH4A4EIA79RaJ0T0EQDvBPD2OR0FMxB5UTe/YSdZkvL4XjTjAo0616diMRHruS+OevWpO27/lVh7y10IO9JOAEFu8v7Uz27Df0fJwLmixfc7QZxW8ozMQuENpx2Ib30lH37ZahpR18sz6r+2I6F0YdZoTUBqwm0PbMIbz/sVnnLs3njVKQfh4fsvB+CqxuU9dTLwFgdBufrlQLiE9TACYuCuBzYCoWlDsd3134VCnOis+mXBW7Z6yixE9lxSDrNt2RCXPVcsA4w2w9RECyEp/ObOjVbUEURBKCpb/fJPzrs8fS50va4WWgshFnXjife7HbzX8p04kGpOPHAVcFX+Od9oljiPfZ3wSwoQ2gtnemYGccXcIBEAKuvDKaIJBDZkMc2tE2Fuf2u3SJzkPPokAQ286cxD8Ymf3or7tkzj9/duwdYHH8ArgVIUw7Ipcx+TNvwuIEIHIi3ocs1dmTGno/obquJEAgJYtmQJsBXp8QLAreu24Z5N01jSCrFlJsEPb7gX/3PlXQCApxy7N46eEEACPPzgvTD9h5sRqsxT50SzotDkUqskLeahQIVCKUmuPcYhey3LXvNz6oQRLz+7+T4c6x0DeaLOhaS7OUiH5vfeuHUHHghn0vdt2N7BWUeuxk33bcXaTTPoxaYdHWiN1AjqRN27v30DLr1lPQBTJXJ7O8EDW8227tk0jS9efide/7hDMdUM8aefvwqbp2Occfhq7F+jBcB1d29CLBU2bNmehjmnoi4MstB/BCYsdQ45ddOxhIrNuEXUyuXUFXVaVU4pgMHF6gKj591Aa62J6NsATrSP76i7YW1W79vsw8j+01rrH3tvuxwmZ4+ZR/ZcNgVsgSlwUCPJepi0bCWiaBeIXV7oVLUcGCoVImb/PZYA989ts708TUHYwF7qfiwWG7E5OgB7LdJ4SvsmnLXiU7hn0wzWbW3jWHE7ms1ju26jesPFctvmunhqYETFDmUn2Es/hKe07sWMilJx0bB9+Hp5GNv+osQTRVHURAKBZ4nL8OjG7Zi5UeKK9U/BEUcR1t/wU+yns1LYQRAASmJy0RRgc9gf2C5xzGBHWsCGVNqFizveMIzQUeZafU/0BVyybQ8odZoZfun3MduoniTNc81WJkRF0MCh4l6c/ru/tW8Rpc9qEth/x/X4QvQhAMBkI8Dx4R+B5QcA983uSEdGeg9lQ9VY4c19ey1f3OONO4flUxOl55qNCCaFi7BNmms2qtEHdNWSSZyx5ff43qKPYY/pNdiuyp9JYLxkh173MQDA0fvvgcaNEl+IPoRVZPKbFk+2cvfKY/Zbmd7PPh59GiuWLcW+d0/gkdFGTH0jRNROsB8lgDBh1z6rlxpBIK2FSwiChMBh636Mq/7uNDw5lnjxsgb2nV6P6/VBfY/RhfTtvWo5sBb4yIrv4PebfwQAuP+TH8J/BcBhq6bw4PYYG6/t4MmRxqXqWCy/f088S18IAGi1FiHSW/AYcQO2aBvuaEWnFqEJ8VQJpMp6+U2ggy9EH8IV6ijbTic7r1p+xIj3vW1pK6ykrWjc8fPcKrzthde6SBh3T3aiburiv8VRegpf8KasY2eWYi2mseUOifbtH0bzoMcAV38emFwBHPW09H1/+4H3IUKCPw224QxxDdTnz8MDW2fw2vu34bV2e4dNTWEd2rhp7RHA756E6R9+Eo/eNI3/u+tpOG7pDD6ZXAhEwJb/+Bg6pz4XjUe9Guu+8CrI7eux59l/BTriHMS//Qpw/dex/qS/xh//531Yim04WdyNoNmyv5VtB0FBmmMuKTR9aKXES4ILsVavArY8DNef92rc2DwOz3/Th3v+/vfdcDHu++rfovONu7EU2/HZlx2G/W6cxNSaEF+ItuCe9mHATTuAK//dfODoZ+JPg6twhrgG+O/zgOYS4JmfBpr2XvDt1wMrDgJOe1vP/S406pgYLyeiR2itfz3oxslkwl4N4FAAn9JaX1F4y6sAfLXLZ18L4LUAsP/++w+6a6YHy09+MXDjd4E9Hzr/+97nUKw74kWYOvXP5nW/3175GkzucxSeMK97tTz1n4Fg/msL7XHEo3HpZcfgV+qh+JtR7GDlYcBhTwBWHIJrxZFor78DjzxsX0z/BriL9sHWw56J7bdcitNqbm7bi76LmRu+i1W93nTUU3HXrTfiwR0xcOBTceDeDeCWH6OBBFMrAhy6fBJKHY3ouD62orPOBVYdDqz5GbDpLmBFIU2YCPEJL0fzzmtxrz4QR5zzRuBqDcxswpK9D8WSg88Alh0AyDawYyNwy4+BfR7edXfLDn0ULr3yGOx/4jk4aPlSbLnh27g32h9HBAFu3vfZiNb9Difs0UB8zw1Ysv0itH91OyZljKv04bhz2ck4FYA4813AbT/FxAmvxiXfuANNiqEPPhMXRk/G9l+djyc859WIb70Y+9X8vgHgtgNegHW3/gY7jno9XrD5s4jv34j/23o4Tl+yBx53+pm4bc2jcNyWK7Fuy2XQ8lT73RQMQXseC5zwEuCxf1newWPeBPz+Akw95jVQnQ0Qq48E9joO03dcg9Pal+S295nkaZg+8Ez8FYC1+z8Nm277Piaog8XNEAesbCGMDgeOejpwwguByZUDHOWIIQE86s+BY56zs0fCDMIeRwCHng20twKHnb2zR1Nm9VHAoY838/RexwHb1+EQJaFv/A5o9dH48q+WoRXejX1PeFLfTbUe/kK0bvkxJqXCtXevxiXyOLzFvXj23wHLDkDy9dcjaG/GMm09ZCe8AJs33IaJ+zZj9aqViJcfiX0OOtqc74c/CZjeiEMe83xg9dHAQafhuJlpRIGAVjOYoA5kp4OlIaEVBdiy/GQ89NHn5MY0ddip+OUPj8YP1CPxfvvc19TjcOzMGgDTmCDg0GUt/H76QGzev//M/YPmkzDT/ilOPvHlwPQGrJx+EMc0O2lhlL2WtLC6pTBJGtu3t3GQuBf70TrsmF6FJWozcMKLgaOfjvj+m7Fh/XbIQ805sXnf03HF2sux6NinI7nsUtPLz4ZN/nHlKXhw3VocKf6Iw8Ra3KsOzuf97nUscMiZQDxj/rf875aj8URxAZ4sjGC7da8nY93aOzBxyGPS90STi/G/8hQcQA+gtfpQhI98JX595xpQZxsmbEjm0okIB++xCAEliFsKR01fh+t+9lWccNBjgO/+hdnQezen2/xEw7SYvkXti1W0GQ9ujnD3+u2Y8OxRK5oSE5v/iAPaf8CWX2/CPluuxWoRYPm672PxXfdgtWjjDr0X9p2+Aw/8/LOI9j4Fe975f+Y4Lv4CDj3iHNzzo4/jgJkbsWnHFJ4aXI7b1N64S6/GogPPxFIAb2mci8ft+BGeOrkSP26cjQNnfo9bFz8SJ8gbEOgOPhD9FwAg+eOxOHbbZTh222UAeou6O3/1TZweXIcXnXgIHiHuxyHiHmj1EAQywKHiHjyqcxPwuwi481dA2ALa2/CC4F6spM3A1gRYc7GZr/Z7pNngH38FyPFr9E5VPTtybyD6PYAjANwBYDuMOVJrrY+rvROTk/ctAG/SWt9gn3sXgJMAPFv3GcRJJ52kr7rqql5vYRimgsvXbMC2mQSPP3rPnT2UofH96+/F67/0G1z6N4/Dfiv6h38sFBKpEPZJzr7pg49GTA3s07kdP0hOwruTP8VjDlmJL7/m5Nz7tNZoJwrNsOzlGoROonDeJbfh1acejFYUIJYKt63bhiP3ynIMp9+7J36x7Gk49jnvxF7/+XD8+thz8YjnvKXHVvtz+f/+C06+/j3m70P+Eie/9O+wvZ2gGYr0O7r2rk248vaNeM1pXJeLYYqcd8lt+NpVd+Mnbzl9oM+95avXYEdH4jMvPTH3/Ib37o/bF5+Ik7b+FBcd8Gac9cr3QWuNn938AM44fDVElxykKp7wzz/HPssmcN5LT+pZUfDo9/wQyyYi/PKdZwEADnzH99LXbv7AOWiG9SOJtrcTJEpj6UTmwrp9/XY87mMXAwDu+PBT0ue/cPmdWPyDN+FR9DtsDlchCRfhmHf8tLhJAKai4ubpGCsWNbD5vfvidyvPwQHPeg/2/exx+M0x78ZX8AQ89qa/x2Pjy3B34yC0EOPId/2q51gvuvF+HP2VkxGQxmo8iFuffgEOOP60UvEO9318+w2PxQn7LQMA/NOPb8a//vRWLGmFuO69T8x/B+euxg17PxuP+rPPAO81VUl9UeeeW6P2wg36IPxF/KbS2O748FPwy0//GU544Ju4eeJhiLbfhy16EstahGWde3GpPA5rT/8oTvvtX2OPmduhnvM5HPR1I4B/s+QsPPwt38TN7z8JR8hbcPWSx+PELT/BL076BH7dPBl/fsYhaEUB1qzbhu9ffy/eeOZhuPauTfjg92/E+59xDJL/fhZEezOOkn8AANxy6r/isEvL4rSKyz/zepx835fwXXkynhZcjn+OnwN9+tvx0H2X4qb/eQfeFH4b4qHPBO67wRhxt92H29feh2v1IXjmK98B/PfTgVd8HzjwsWaD/3ys+ftZn+m5350BEV2ttT6p6rU6nrr+ZqA+aK03EdHFAM4BcAMRvRzAUwGc1U/QMQwze04+eAF5NIbEk4/dG7d96MkIBlhkLAT6CTog68MXQOKAPZZg8cYQzzvpIaX3ERkL+FxphAJvPDNruBoFIifoACChEEIlWcW3IVSgFH4LCRt+s6iZn46O328ZjrcLGYZh8rz2tEPw2tMOGfhz//QnJ1Q+LxEiSEyhjakJGyZHhDOPHNwg+J03noJGIPoKwd/87dm5ohT7LpvA2k3T+KfnHz+QoAPK9w8A2GNxdYTMS08+AFdftRThugRCJ1A9KogGgrBikW1ojgBQnbRqJQmBf3j28fj955ajcWcCUhKqRrXns47aE/eKEA213bhJhKisxvjiR+2PL13xR+y3PAvFfdrx++Azl6zBIw8qz+0JhSAZl54v0hAKiVcc6Iwj9sDZR++J0w7bAwCggwZCnZh2NhBYNDmBQG5FCAUJgYAIECECLZF4+3PthdKKk9IUW1myqIW/etzh6fsO3mMqnXeO328Zvva6RwMAbqAQgZdTt32mulhLJa4Vx8w0NgcaSRAgIJNRF+vQtFFIOiYnNIhMuyJX5dTlMvqeOZWMZb503xFrre8kolMAHKa1/i8i2gNA3w599n2xFXQTAB4P4CNEdA5MYZTTtdY7em6EYRimgnETdHWRwiysAkiEUQPXv++J/T80YhKEIBVDSxNyNGg10yrIzzWp0bycYZjRklCIQNlF9Bxz7esanIrvu/htZyAUNKfoA5+pZog3Pu5QPOGhZWGqRcO0L9CJKQBTg4RCQMZZ83FXITMw2yKdQFO9vqwJRWjYHneiy/f9/mccg9ecejBWTmXi9LA9F+PGvzsHVVNgjAhQ/UVdAInVSxcBpjYKzn/lI/NvEBEaJCF0gkajgZmggSAxxsYEAU48YDnUdSECZNVSAVMoJrcfez7Vba+kRJQTdSoZIPzR7vt9X7sWH6cdOOfFAiFMK4O0sFkybUVdw/aglYiVJ+r88at41xR1RHQuTJjkEQD+C6bgyRcBPLbPR/cG8HmbVycAfE1r/X9EdCtMm4ML7YV7udZ6fhOsGIZhFiCm+pc0TX8XiNiRFEKoGMpNeEMorpRrh7FAjpNhdmckhQitZ2Vn9V4cRe+wtz7xiOoXghChKaSf9gLth7SeMNeb0xm4SESIYD11tbcVpJUtu4k6IQgHrlpUHnoXo6ZEUMtTJ6Aw2Wzgycfuhccdsbr8BlvUJVRtKAqgRYRAx4hI4aDVS3DKYatwhTBzlfL2JwqC0om6bsdXRIkIoSfqdJf+rZXYVggveOQeOEzEuBECZJuPpxXf42lACOup6yCEtJ46e74XPXXzXB1+GNS5cp8F4GEAfgMAWut7iKhvqSjb9uBhFc8fOuggGYZhdgcUhRA27GWhWAkTiiB0DG0nTRqCCGNRxzALC0khImXKwdf1rIwzWjTQQIJAJ7Wbz0uKQBUGLh00EJFEgARJbVEXQZATh8PxTCbWANePQCtABPj0i0+sfN21XwhVGzNiEppChDqB0AoN2xRdU2hEUeL16dM2/NK1LFBWtNYUR1oYoZ1S41jSMduQ2CcftQjHigh/Fwe2+TjQcVInns6FXy5vEZ52xP7V4ZdyPMMv68ymHZv3pgGAiMpmA4ZhGGbOaBFCQNr+dAtjQpEUIlAxlGu4OwxPXVTOqWMYZuchKUKkBwuXG2uCCCESkJbQPXLqfJQVdWlOnb13ke2t2tCdnvl5Pn4/u2F5RpMBwi97ClkrwhqqDU2B9aDF+c+JEAEyT53SBGHFrrAGwNCKurrnkwmJ9cIvB/LUmffu2DGN9TtMLqAgk98Xl0RdA1Amp27J5IQn6sY//LLObPo1Ivp3AMuI6DUAfgLgP0Y7LIZhmN0PYxGVCGnheOqc99CV8R7GAiTwcuqGlT/DMMzskRSioXdu+OW8IiIEpNHQcW1PnRI2FL1g4CLbS7SpO7VDOf0wzWFEPwDWAKf7i7qwn9HQHk+k21AUQgcNBAVjoxYmp86FSM6gke7biTp3PtX11BXDL+GFdirVp1K/Fdpv//ZaPPdr0yYUlUwdmjT8MpdT1zHbF2H2XewmhVI+RkRnw7SrPhzAe7S2nRoZhmGYoaFFgEjbiWUIHrFhICkyFeKks07PXYSFHH7JMAsKRSGaugPQbuKps/egFtr1RR1FECrrU5fmirltURu65n3b99SJIeUSyrrhl1A95xcXftnUxlMHESKE8WylnxMhAq1Sb1qbmmmRkwDWU6dd+GXN80mEiDxPnfYKl8RKodlrzHbff33qEmBmMy6DafcjiNDRvqfOHA9knAm3Yvil1rt0Th0AXA9gAiYE8/rRDYdhGGb3RYsQERaWqFMiRBBnOXXDsF6GDQ6/ZJiFhBIRGvbes1t46uyCvYV27XuaEhFE7Ld3ceGXTiDG9T11whN1Q/q+Tc5f/5DFAKpnaxphj6eBDkDCnBs6gSANcnmEwoSvOlHXoQaEFVakjeht6MHOJx000ISf15YJ1DhRPdtckBXaTzssADoRLontb0Pwwi9nzHE7T50Tbqmos/tTw5vr5pu+sykRvRrAlQCeDeC5AC4noleNemAMwzC7G5pCNFyy+QKZUBSZMtPFPJK54Iu6YbRIYBhmbigKMQEXLrcw7j2jhFzeGNXPqdMihNBxKurS6pepQOzU35b/viHdA5WoG37ZO7TQHU9Td4wX0xdbaU5dgBAq9aZ1qInQ7tt56hqwj8O632+EJnXx1MW9xarz1N23cRvu26aQaBN+KYiqwy/TYjdhVv3SeTnd/wtkDh6EOiN+G4CHaa03AAARrQTwSwCfG+XAGIZhdjtElJa5XigTiukd1IZyIUfB3D2IfvjlsPJJGIaZPSpomFxeIPXG7MpQ4IWA1y3kEdicr1TUmc8Ju62IZO0IC+l56oIhiTpTwbR/w+6AdG9RFxqjW0S2RUMQmWODZ2wUpnqnTsz+YtFEK9lmXrJRHS6cV4Q1wxiL4Y7SF3VtmIDBLmN2LQ2+YcZw3Its9Ut4nro03NIX1BXhl07w7aLhl3cD2Oo93grgrtEMh2EYZvdFiwBNWlhWQkUmnyLtGTQEERZ5njrNoo5hdjp+1ca6hS3GGfKOUYu6JfdNiKF0RaMK4ZcAavep8/c5LBGtKESgt9V6b6+8yZwIozAvbrzqlwCgOqYNRiKaCLAJQOapc3NZUNfzWzzvPE9dkvT21Dkh+Y5TzNzyXdunDgBieN+v8JqNu+Nwv4ULv5QLaw4ehDojXgvgCiK6ACan7hkAriSitwCA1vqfRjg+hmGY3Qd/oh+CR2wYGE+dhLJ5EsMYV9RsZQ9Y1DHMzid37xm/xeyg+EKs9uI9MP3aYmvgcvdCkdvWThR1tkm4VhL9yln12qd/PFqInAhKP2cFmIqNqJOilVaudKIu+0zd77eRf+wVfUk6fTyQVtSdc6jZ17c6AgRAaSDWvmeuIOqCyP5mtEvk1NUZ8W32n+MC+3/fBuQMwzDMAHgT7ULJqTMJ8THabiFT0xLdiyiqWCQwDLPT0J6XpG4O1DgzG1FHgemjFifuXijsx/1qvvXzx9KPDOkeqJ0BTibou8UexyxCP5Ii76lzgj8tmGJFXRK0TKsEAAIqt70gqvv95j11fp+6JOmdK+g8dXdtNvuWEwEEEZTWWfglkG9h4B4TZcVTgF07p05r/b75GAjDMMxujz95LpAJRVvr9Iw2fYKGsQBpNDJPXWNias7bYxhmbuRExm4QfumHGNb3JJncsk5si1mlnjo/lLOupy7b57By6pynLkk6fUVdr2POhV+KMBeOm37Oert0Mm32HbQQIYHWuuSpC+qeTwVPnfaEnMmp644TdS/9lhnPgS8y4ZcaxfDLsBx+CZi5N/XU7cI5dUR0EoB3ATjAf7/W+rgRjothGGb3w5toF0oIlPHUybQS2bCrXzYnOeiDYXY6vqdugYR+jxIReG1Vai7eKWiggQTtTg9P3QDl+9OPDNFTF+oEso9Xy+y0+zjDqBBO6n9X9nNO0GortnQ4gQgJEqURaAU//rN2NdXC70ByJv27bk7du08zY/1vW9y/0lPXTdSp3SOn7kswFTCvBwo+VYZhGGZ4LEBruRYNREiGWv3SP86JRUvmvj2GYeaEFl5I9AIxKI0SX4jVPl4bftl24Zeu+mXk990cPKcOQxLRyvaOS6TnKdMaIILWOpdn1+uYgzAv4nJeTfc5e76Q89RFE4hIYkci0zDMbHuFXLkuiIKnLpTT6d8y7hTfnsNVv3z8wWZ8n+sEIHvceVEX5MWj+zsXfrlr59St01p/Z+QjYRiG2c3xJ9phWW/njAgRIYF0ltIh5NT5k+rkFIs6htnZ+PeeoFiwYhckJzRqGtBEGCGCRKfjeomae2FQ5fnpOwC/pcGQ7vUiNA3B48xTp1UCCiIopXIhmb3mF+F76ijI5R86rxu5vMtkxj42IfWdThvNkqird3xUaH0Q+J66uF5O3ZoHbU7dZGAKpahC+CUVRJ3XoiELv9y1PXXnEtFnAVwEIA1q1Vp/c2SjYhiG2R0JFmD4ZdBACJlaf4fiqaPMZjy1mEUdw+x0gvLCfVeGIj/8sd7xBmETDSSYsaJO2Fy4wPfU1RVonrAQQ8qp00EDkU4w4xcYiTuIgggyKRRP6fEbh4XjoVz1yzD/v/NuRaaHXNxumz54/q7qhrf6HkIAoSfqZNLbUyeskHzVBca7t/KFXk6drhl+uTvk1AF4JYAjAUTIwi81ABZ1DMMwQ4QWYlnxIEIDSVp9jIa0AHG0OKeOYXY+3kK3dl+xMSaIBvfURY0mQiTY0bYCw3nqIr8FwODl+4d2T7UGOCW9VgBxjKiFUp5dL+GeF3VRZfil894J56lrGFHXmdleHlbN5uPF/oiR8kVdb09dYD117zvDjP3jcC0NqqpfVnjq/PBLJ4p3UU/d8VrrY0c+EoZhmN2dBVj9EkGECAnixEyaw25BQI1FQ90ewzCD4+fwit2gpYGfN1bXgBY1mghIY2bGiA0XtRDMspJm+uewRLQNlfeLiiTWw5XIvCjqmVPXyBdK8XMGnRh03k2hTACfsJ66doWoC2uKOirk3kU6q3ip+og6sj6n0w804/pYO4AQLqeu2Hy8KqeuwlO3UObgAagz4suJ6Git9e9HPhqGYZjdGD+0ccGEQAUNCNJQNlF9GH3qckSTw90ewzAD4y+od4c+dUGuUEo90dFoGnHTaZsQvzSnzmvRUjf8UuTCL4d0T7X36rjtFxgxQkUVqkf2CjkN/e9GBLmiMmlOnR1/II3wInsfb0+XRV3dkH1REHVNz1NXFKVFnKfu5vXmf7nYaz5eKpRS0Szer365i+fUnQLg5UR0O0xOHQHQ3NKAYRhmuNACzGtxY1K2ytlQcup8FkpBGIbZjckv3Mcvl2hQ/LL9dUVd07ZicaJJkPPU5UVQLbw2AcPKqXNipeMJKxe2WAxf7OWpC32RGoR5kWc/57yTzlNH9jPF8MtYB6h7NpVEHep76lxO3ev+zwjBxgsDoFvz8VyhlKrql7t2Tt05Ix8FwzAMkxNytRu2jhg3ibt+RENbgDAMs2AQOU/dwrj3jJKgIqSwH82WES6ynTdwRY1Z9LzLhWwOx7DlxGnS3pE+JxNz31ay4KnrMc6okS+M4lfDLHrqQtVGokXajiApiDoJUV/URXlR19LttN9dP1EXQCKmBj50ljnOv7U5dVoDSan5eJ/ql2OcU9d3dtZa3wlgPwBn2r931PkcwzAMMyB+Tt0C8dQJm3vSnjYLhYUyLoZhhofY3Qql+MKlbh81+z4Zu/BLW/0yzIugOvhRGaDhLKmrRZ0RKElB1PX01EXFPnV+/qH10NkQ3VB1IClIRXLRUydRX7AGheqXLbTRsZJQyT7VL7VCIpp4zH4hHrNfCAlT/XLpRASTcSfS48mFX1bm1I1v+GXfM4mIzgXwdgDvtE9FAL44ykExDMPsjvgWY7FArOWhXfxcc/u9AAAa0gKEYZiFg++d2R08dZEnIGqLWBuqJzu24qNfDt9S1+uWu78TdX/jADjvX17UuZy6+tUvo0K1Sz9U1UWQOCNApDuQEGg2nfGvIOoGyMEuznkNkuhQ0x5HUvWRbFxQSEQLNzwgccMDEgkCEAinHrYKn37xw0HuOxZBofqll1NXDL/cFUUdgGcBeDqA7QCgtb4HANegZhiGGTK+tXyhNB+PIhNy1IRZFOwOVnyG2d0IvRDC3aFQStgYPKfOeXjcvVAEIve82dYsPHVDgmyenmz7OXVGqBTDL3vdx4MgkwYkgpznzoWcOk9dpDtQCDDRMtUvZ3aYfXe0eZ8cILAvjMrfSSbqenvqAkgkQRNv/P4M3vj9GUgdgAggIjz52L0zY2Sp+bifU+fCL+3/CyQFYhDqfNsdrbWG6U0HIuL60wzDMCPALyW+UKzlkbXA/nX0DfMEe+oYZpfDXecAEO4GhpuGVwykdlSEXeS/Nfq6+Vyaj5UZ4OqGXwaNZv83DYjz1MlOVv1SdSmUUrdthQhCtFrZd+UEv8vJc5661oQrlLLN/A8bqjpA+KUIW6XnEmG+pyf/7q3Y8J/Pzb129T8+Ezd94GRorY2oEy189GzzT0JA+A5Q9xv1DL/sADs2At94ZfbeMaPOiL9GRP8OYBkRvQbAqwB8drTDYhiG2f3IlbleINbyqJlvORAMq/rlqy8ay0mTYXZFomZmr18oBqVR4ld4rF3tc2JZ7qGoMnDVFMSN5vBbuTiPoytqBWRhi0rK3HtrH7MIsWjRVGkf7hxpaJNTt3jJKvP2HRsAAG1qYArTWS5bDYJFy0vPxSITvyvvujD32olbfwYASJRGAIU4nMQj9rUewhkBgqfqRARgxsw5/u+YCvMQ0BJ44Mbya2NE3xFrrT9GRGcD2ALgCADv0Vpf2OdjDMMwzID4FuOFUlY8mpjKPR5a8/GHnDSc7TAMM2caE1lWTe1wxHHGy2OrLWIXrc4/rjBw9er/5tOYXFJvnwPgvIQ6yfq7uQIjUtbPqcu/L0JzMpsDwlTUuVDUDnbQIkRL9gQAtKbvBwDEtsDJIJ66qSXLSs8lor9HM1EaIRTicDGuuc+I12RZAF/TIWwAHRiPnW2UDiAv6lQCJJmXcxzDL/v+qkT0Ea312wFcWPEcwzAMMyR8ITc0j9gcaU7kFx9D71PHMMxOpznplUrYzUKsg5rVL7Foj9zDZlReQtfNqWtNDr80hRNqOvH6u1kxp0stDXqPc0ZHaFEMEgGokYk6EQa5zzcRYxsEsMh46hZ11gEAYmoAGlADnEtTzbKIqivqWpCIo8V48w+NoJUvEDlNBxfaWTRK+iG0SgLb15dfGyPqfNtnVzz3pGEPhGEYZnfHX1yECyQEqllYfAzNU8cwzIIhL+qGU41xXKgd6j65MvdwqlUWHHU9da3Jqf5vGhByBjffU+eqXxY8df36oHbIzEUiyPd1c15NV9QkJAUFU3xkMxajMf0AACvqAPNaTYQon3d1RJ1MFEIynrqPn9PCx89p2ZYG3vZctdPi7+OOjQLjqdv2gDeghTEHD0LXs4+I/hzA6wEcTETXeS8tBnDZqAfGMAyzu9H0KrItlJYGxfDLgEUdw+xyBM3hi4yFzg60MImZAapfFpbMnhcqQYAQsranbmJqad1h1iYt0lLhqSvm1AUVXkYfFz5Z/G6ch86fn5RtW7BZLMWe+kHzedEElK2wOAdk0F/UxdYLKRtTOGEvl1MX5D11QRdRlwu/lMD2dd5r4zfX9fpVvwzgBwD+HsA7vOe3aq03jnRUDMMwuyFLp7Lk+X6W1HmjkS94zM3HGWYXpDH8wh0LnZlwKSaTGeOlmQ3eor+DCCFk7Vy15gg8dWn4vteouz3TxuVrNmDxgJ66RDQAVfZihjaaJIiyz7tedPcmi3Ei3WOeE8Np2SBFuSJmkU3bdmAVgCRchF+vtTl1K4O8w7mbp66YU+eLuoUyBw9A1/BLrfVmrfUdWusXaq3v9P6xoGMYhhkBrabfAHiBiKcov9gjqJ00EIZhRka0+3WrWrbnfgCAhyyapT/JE0/Os1XXu0PRKKpfWi+azDx1e/7yPcD5T8G9a/+Ye2+/kFNJ5niK4ZNuXgq9tgDuPev0UoRk5ofEijEa0Fc3rfNisNJT9/vvQP37GenDCz5lSnxQ0MDbLpzB2y6cgQJ1EXX2eCZspU3nba3y1M1W7O9EFsiqgWEYhqFWVpRkIXrqHjj8RVi9fHWPNzMMM5bshh548bzzgSs+A+x5TP0PveB/gHt+Y7w6S/ZNn44pAvQA9+0R5C06oUZeTt1DaD0eQuvxywdvyr036hPeL0UTkMCOfH2VtGm576nTVvzcecCzgbsvBwAkjSXADNDUMxiEZ3Tej1cEP8KLwp8CAOKwXFBm7a+/g33v/W362PVQ7WjCJ59sxORTQRBVOXVOqL3qR8D13wCadvtCmN+0s90UxHnEq4Gov5dwobF7lThiGIZZyEyuSv9cMJ46z/K8+nn/tNsVUWAYZhdl2X7AEz9oFvR1OfLJwJnvBh7/3ty90BUG2Zni2BVpEapTftHLswPy+duV24qqwxVdoZQoyjxoLqfu9X/62vS5eNK0OFiqt9YYecYf9H74x+R56WMdlMd574PV22wrgWNWBzhmdYWHLSh46vY4AjjzXdlv6PrUyRjY61jgjHeUtzEGsKhjGIZZKHiWwYVS/TJHOH6WS4ZhmFGT2HBFvRND9pzgIlkWdSTzoq5fuf69Vy4DADxs/3zFz6DQfBzIRJ1fbVIv3hsA0KSCq68GiR/yWTFOUrL0HAA88uDV+OVdCX55V1IaT+qp013SB1xOnUrGsuqlg0UdwzDMAmShVL/MwV46hmGYEs5T11E77x7pql8GFZ46kRTCIPuIOuepE8gLKJHm1GWfV56QbdvcwnDZvpgN737KUZC+NAlCxIVMMdLVQrFBCv/vojb+30VGwOb71FlRl1R4MQHrqVPGUzeG/ekc4ztyhmGYXZkxTNJmGIbZHZG2n5rrC7czcIIrUO3Sa1R8rt/84qIykurPiUAg1gEikrltbaXFaOqNaK6Ynah79akH45WPWA182D4hQrSpicgTcqS6fMfTD+Lfn2rG/UQUbJDp8XTJ8XPHkMyMdX4pe+oYhmEWImPYI4dhGGZ3JGoa0TAZDB5uOCxcS4OwwlMXlMIv+8wvLpeth4fPhUn6IafbhCn2FTZnX0011xZCRJhBPuy/W/glpjfiiFUBjlhlw0F9X12348l2al9vs6eOYRiGGTIc6sgwDDMW7L96ObAVOHzlcPqzzYYgMMIk0GVPVsl7129+cZ6tYn6eJ3ikFXV++OWOYAkgAbl99t3P/IbnIgihG4uA9obs9YrjMzvfiJ/fYUX1Xl08dRX5hmZHrnH7DOfUMQzDMEPi2f8BHHzGzh5FnlPfCjziNTt7FAzDjJLHvQs44SU7exRjiTjrPcDKQ4H9Hln/Q2e9BzjuBcMbQ9jfU7fpjA8Ce58ANPo0Pz/1r4HlBwGHnm0eP+98YL9H5SqFJlbMaU/orX3ku3CL2hfLDj0ZX0nOwEfj5w9+IOTn1EVY+vxPAwA62nrglMQOyvf5U5N7AKf8Fc69bl+8+ec2H9AXdY96LbD8QOChz67eZ07Uja+/a3xHzjAMsyty3PPNv4XEWX+7s0fAMMyoOf1vdvYIxpd9TgDedPVgnzn1r4c6BFeZslHRGy5S5jl5xNOAM97Yf2OrjwT+8prs8UOfZf55pJ46TwSd/fhzMHP62WhFAU5761fQCGfhO/JcbBSEaBxyKq7c5yU4du3XAABCJ7gzOhRb9zsDj7ztXwEAnTdcjdaipfjcN36Il33uCjyYALlSKSsOBv7y2u77FJxTxzAMwzAMwzDMTsYVSmnosqfOiTq/v9xcSXPqRD7ktBWZ5/dZNoFVU3Pbn7C5cFqECCChtYbQEkoEIE98NRomvPLggw/GopWmSMtAGQxO1KlkrD11LOoYhmEYhmEYZowJrMhpoY1E55f3Teu9C/s0HR8ElRZKGZ0IcsKNKEAIBaWNp05TmBNfIjTH9ZOf/AQb/3CV+cwgOxL54izjCos6hmEYhmEYhhljXEPwpu6Uers1tcmpixrD89RJK+ZUMDoRlBZNESEEaSQygdASWoRpXz7zRiPhPvCBD+C2C79gnxpA1uVE3fhWnmZRxzAMwzAMwzBjjMupa1LcVdSF0fA8dTItlDI6UZe2N7BCSyYJAp0YUVeR+/aFL3wBx7/kXQDm4KkboUgdNSzqGIZhGIZhGGaMCUKv3UAhJLKFNpSmvHdrjrhCKaMMVyQbVuk8dkkSQ0BCUwgKyh61/fbbD5Mr9jSfGUTV+c3YOaeOYRiGYRiGYZidQRhm4qroqWuQNM8Nsf+p60+nR+jZEmn4pdlX4nvqKsTkD3/4Q6y78QoAsyyUAnBOHcMwDMMwDMMwOwffU5dUFC9JaLi5YokTjvMQfklp+GUHASTQJfzywx/+MG77yZfMZzinjmEYhmEYhmGYccIPrZQVbaiTIbemTsMvR+mpc+GX9tikTBBoK+oqwiS/8pWv4OGveK/5zGA7yv7mnDqGYRiGYRiGYXYKRGkrg2JOHQDEGI1Y0cHwiq8USb2P1isn4wQhTC85UZFTt9dee2Fi6UoAg3rqOKeOYRiGYRiGYZgFgPOeJVQWcHLI4ZcEbf4faU5dw/5v9iFljAAKOogq9/vd734X919/mR3fQDvy/mZPHcMwDMMwDMMwOwlpl/WqQsDJCqE3N4yogxi9p87l1LXbHYSQIBFk7Q48/vEf/xG3/fQr5jMDFUrhnDqGYRiGYRiGYRYAklz4ZVnAVRVPmQupZgpHKOqifE7dTCdGCAmIqFLUfeMb38AjXv0BO75ZFkrhnDqGYRiGYRiGYXYWLvxSVoQQVuXZzQUXflklroaFa6juKl3GHeupC0IIVxFTZ+Jt1apVaC1eZj7DfeoYhmEYhmEYhhk3nKhTFQJu2KIuDb8MmkPebkbqqbOirtNpIyIJeDl1ypMy3/zmN3HPNT83n5l1+CV76hiGYRiGYRiG2UkoJ+oqhIkack6dcJ66cISeOrttVyil05mxj8P0Oe2FWf7rv/4r1lz8dQCDhl8G1X+PGSzqGIZhGIZhGGbMUTanrlrUDVl8aVf9coSeOpuv50Itk/a03WeUCj7libcLLrgAj/6zj5j3zNZTxzl1DMMwDMMwDMPsLFz4pfYE3LQ2wqhK6M2FtKXBCAulhFE+py7pZKLO9anzwy+XLl2KxsSUHd8AcJ86hmEYhmEYhmEWAq6VgfYEXJucqBtRoZRwdJ6t1FNnvXKyvcM8DqIs/NJzyX31q1/F3Vf/ZPAdcU4dwzAMwzAMwzALgbRQihdC2IYRRnrofeoMYoSeuigqNB+Ps5y6IC2Ukom6f/u3f8OaS74NIC3jUg/uU8cwDMMwDMMwzEJAu2W9522KyeS8qSE3Cc88daPMqTPH4domKBd+GTYgIifEMlH3/e9/H6e+6R8BpCl/9fCFHOfUMQzDMAzDMAyzs6gKv+zY8Es95H5yTtQF0QhFkPWgBXbs7Rkj6iaaDQjhCqVkUmZychJRswUA0IP46rhPHcMwDMMwDMMwCwHpRF2QeeViYTxpekS5YqP01DmPo8upa09vBwBMtFrQolwo5Ytf/CLuuPyH5sFAnjrOqWMYhmEYhmEYZgHgPHXkiZTEhl/6Qm8YCCgAWYjkSLDCTdixt2dMoZRFE01olHPfPvvZz2LNZd8FwDl1Q4WIWkR0JRFdS0S/I6L32eefZx8rIjppVPtnGIZhGIZhmN0Fl1PnC7jE9pGjoYcV2pYGoxRBtrLl4glzDFu3bQMAtJotrF62KPcaAFx44YU4883/aka3G+bUjTJwtA3gTK31NiKKAPyCiH4A4AYAzwbw7yPcN8MwDMMwDMPsNpD1nsETddulWeo3bK7Z0PZlRZMerCPcrFgyacYe6o7ZtwgB22g9DDJBFkURAltAZaCcOu5T1xtt2GYfRvaf1lrfqLW+eVT7ZRiGYRiGYZjdDWHdU9Ir/LHRdAHAHkunhrovSkXT6EWdE1otGFGHIAK0FbCUSZnzzz8fay77HoBBPXVh9d9jxkhz6ogoIKJrADwA4EKt9RWj3B/DMAzDMAzD7I6sXjoJAFg6tSh9Lra5ZyuWLKr8zGzZRnZ785GDZvfxguBn9nHmqcPE8vRt559/Ptb80oq6gba/a4i6kY5cay0BnEBEywB8i4iO0VrfUOezRPRaAK8FgP333390g2QYhmEYhmGYMWfpOe9CfMMFOOaMP8FHth2ODTdfhmeceRquvWkljj/ueUPd1+RLv4wf/eR8nH3IMUPdLgDgdZcC9/8ue7z8QNx2+J/itzfegqg1hWc85CSgtRR44oeAo56evu3iiy/GS//zClx6y3roQVx1YRM482+B9lZg6X5DPJD5hQY66LnsiOhcANu11h+zjy8G8Fat9VX9PnvSSSfpq67q+zaGYRiGYRiG2e1RSiNRGo1w1yl0/7lf3I4j916Mxxyyqut7Xva5K3HJH9bh/Fc+AmccsXoeRzc/ENHVWuvKQpMj89QR0R4AYq31JiKaAPB4AB8Z1f4YhmEYhmEYhgGEIDTEPOS7zSOvOuWg2u+dH5fVwmKU8n1vAD8jousA/Bomp+7/iOhZRHQ3gEcD+B4R/WiEY2AYhmEYhmEYZjcglbG7oaobmadOa30dgIdVPP8tAN8a1X4ZhmEYhmEYhtn9sK3tBmtpsIuw6wTaMgzDMAzDMAyz2+I8dfNUMmRBwaKOYRiGYRiGYZixh6yrjkUdwzAMwzAMwzDMGJJ66nbqKHYOLOoYhmEYhmEYhhl7AuE8dbufrBvftukMwzAMwzAMwzCWDzzzGOy5pIXHHbnr9ajrB4s6hmEYhmEYhmHGntVLWnj/M4/Z2cPYKXD4JcMwDMMwDMMwzBjDoo5hGIZhGIZhGGaMYVHHMAzDMAzDMAwzxrCoYxiGYRiGYRiGGWNY1DEMwzAMwzAMw4wxLOoYhmEYhmEYhmHGGBZ1DMMwDMMwDMMwYwyLOoZhGIZhGIZhmDGGRR3DMAzDMAzDMMwYw6KOYRiGYRiGYRhmjGFRxzAMwzAMwzAMM8awqGMYhmEYhmEYhhljWNQxDMMwDMMwDMOMMaS13tlj6AsRrQNw584exwJkFYD1O3sQDMOk8DXJMAsHvh4ZZmHB1+TcOUBrvUfVC2Mh6phqiOgqrfVJO3scDMMY+JpkmIUDX48Ms7Dga3K0cPglwzAMwzAMwzDMGMOijmEYhmEYhmEYZoxhUTfenLezB8AwTA6+Jhlm4cDXI8MsLPiaHCGcU8cwDMMwDMMwDDPGsKeOYRiGYRiGYRhmjGFRt4AgonOI6GYiupWI3mGfex4R/Y6IFBF1rRhU9Vn7/AoiupCIbrH/L5+PY2GYcafL9fhRIrqJiK4jom8R0bK6n7XP8/XIMLOkyzX5fns9XkNEPyaifep+1j7P1yTDzIJu15R97a1EpIlo1SCf5etxbrCoWyAQUQDgUwCeBOBoAC8koqMB3ADg2QAumcVnAeAdAC7SWh8G4CL7mGGYHvS4pi4EcIzW+jgAfwDwzgE+C/D1yDCzosd19VGt9XFa6xMA/B+A9wzwWYCvSYYZmF7XFBHtB+BsAH8c9LPg63FOsKhbODwSwK1a6zVa6w6ArwB4htb6Rq31zbP5rH3tGQA+b//+PIBnDn/oDLPL0e16/LHWOrHvuRzAQ+p+1r7G1yPDzI5u1+QW7z2LAFQVCuBrkmGGS69r6p8B/A2qr8V+n+XrcQ6wqFs47AvgLu/x3fa5SohoHyL6fo3P7qm1vhcA7P+rhzZihtl1qXM9vgrADwC+HhlmHuh6XRHRB4noLgAvhvXU8TXJMCOl8poioqcDWKu1vtZ/M1+P8wOLuoUDVTzXtTSp1voerfWTZ/NZhmH60vOaIqJ3AUgAfAng65Fh5oGu15XW+l1a6/1grsc32uf4mmSY0VF1TTUBvAsVIdB8Pc4PLOoWDncD2M97/BAA9wzhs/cT0d4AYP9/YI7jZJjdga7XFBG9HMBTAbxYV/eE4euRYYZPnTnyywCeM+Bn+ZpkmMGpuqb+COAgANcS0R32ud8Q0V41PsvX4xBgUbdw+DWAw4joICJqAHgBgO8M4bPfAfBy+/fLAVwwxDEzzK5K5TVFROcAeDuAp2utdwzyWfsaX48MMzu6XZOHee95OoCb6n7WvsbXJMMMTtU19U2t9Wqt9YFa6wNhxNvDtdb31fgsX49DgEXdAsEWX3gjgB8BuBHA17TWvyOiZxHR3QAeDeB7RPQjIB+f3O2zdtMfBnA2Ed0CU43ow/N5XAwzjvS4pj4JYDGAC20J9c8AfD0yzKjpcV19mIhuIKLrADwBwF8CfE0yzCjpc02V4OtxfqDq6CGGYRiGYRiGYRhmHGBPHcMwDMMwDMMwzBjDoo5hGIZhGIZhGGaMYVHHMAzDMAzDMAwzxrCoYxiGYRiGYRiGGWNY1DEMwzAMwzAMw4wxLOoYhmGY3QoiOpCIbtjZ42AYhmGYYcGijmEYhmHmCBGFO3sMDMMwzO4LizqGYRhmdyQgov8got8R0Y+JaIKITiCiy4noOiL6FhEtBwAiupiITrJ/ryKiO+zfryCirxPRdwH8mIj2JqJLbGP6G4jo1J13eAzDMMzuBIs6hmEYZnfkMACf0lo/FMAmAM8B8N8A3q61Pg7A9QDOrbGdRwN4udb6TAAvAvAjrfUJAI4HcM3wh80wDMMwZThchGEYhtkduV1rfY39+2oAhwBYprX+uX3u8wC+XmM7F2qtN9q/fw3gc0QUAfi2t32GYRiGGSnsqWMYhmF2R9re3xLAsh7vTZDNl63Ca9vdH1rrSwCcBmAtgC8Q0cvmPkyGYRiG6Q+LOoZhGIYBNgN40MuDeykA57W7A8CJ9u/ndtsAER0A4AGt9X8A+E8ADx/NUBmGYRgmD4dfMgzDMIzh5QA+Q0STANYAeKV9/mMAvkZELwXw0x6fPwPA24goBrANAHvqGIZhmHmBtNY7ewwMwzAMwzAMwzDMLOHwS4ZhGIZhGIZhmDGGRR3DMAzDMAzDMMwYw6KOYRiGYRiGYRhmjGFRxzAMwzAMwzAMM8awqGMYhmEYhmEYhhljWNQxDMMwDMMwDMOMMSzqGIZhGIZhGIZhxhgWdQzDMAzDMAzDMGPM/wf/H9CeD9n+1QAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,6))\n",
    "timestamps = [pd.to_datetime(x) for x in list(df1.iloc[:, :-1].index)]\n",
    "plt.plot(timestamps, model.predict(x), label = 'predicted')\n",
    "plt.plot(timestamps, y, label = 'true')\n",
    "plt.title('Temperature Prediction via XGBRegressor')\n",
    "plt.ylabel('temperature-CPU1 Temp processor (3.1)')\n",
    "plt.vlines(x = [timestamps[n_training_points]], ymin = 31, ymax = 35, linestyles='dotted', color = 'black', label='end of training')\n",
    "plt.xlabel('hours')\n",
    "# Set the x-axis locator to show hours\n",
    "ax.xaxis.set_major_locator(HourLocator(interval=1))  # This sets the tick interval to 1 hour\n",
    "\n",
    "# Format the x-axis tick labels as hour:minute\n",
    "ax.xaxis.set_major_formatter(DateFormatter('%H:%M'))\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_weights = model.get_booster().get_score(importance_type='weight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "important_feature_indices = [x.replace('f','') for x in list(feature_weights.keys())]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Neural Forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the LSTM model\n",
    "model = Sequential()\n",
    "\n",
    "# Add an LSTM layer\n",
    "model.add(LSTM(units=64, return_sequences=False))\n",
    "# Add a Dense output layer (you can adjust the number of units as needed)\n",
    "model.add(Dense(units=1))\n",
    "\n",
    "# Compile the model\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "This model has not yet been built. Build the model first by calling `build()` or by calling the model on a batch of data.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/home/bshook/Projects/server-log-api/rrd_modeling_explorer.ipynb Cell 20\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell://ssh-remote%2Bauton/home/bshook/Projects/server-log-api/rrd_modeling_explorer.ipynb#X36sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a>\u001b[0m model\u001b[39m.\u001b[39msummary()\n",
      "File \u001b[0;32m~/miniconda3/envs/ARMOR/lib/python3.11/site-packages/keras/src/engine/training.py:3482\u001b[0m, in \u001b[0;36mModel.summary\u001b[0;34m(self, line_length, positions, print_fn, expand_nested, show_trainable, layer_range)\u001b[0m\n\u001b[1;32m   3451\u001b[0m \u001b[39m\"\"\"Prints a string summary of the network.\u001b[39;00m\n\u001b[1;32m   3452\u001b[0m \n\u001b[1;32m   3453\u001b[0m \u001b[39mArgs:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3479\u001b[0m \u001b[39m    ValueError: if `summary()` is called before the model is built.\u001b[39;00m\n\u001b[1;32m   3480\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   3481\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mbuilt:\n\u001b[0;32m-> 3482\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m   3483\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mThis model has not yet been built. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   3484\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mBuild the model first by calling `build()` or by calling \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   3485\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mthe model on a batch of data.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m   3486\u001b[0m     )\n\u001b[1;32m   3487\u001b[0m layer_utils\u001b[39m.\u001b[39mprint_summary(\n\u001b[1;32m   3488\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m   3489\u001b[0m     line_length\u001b[39m=\u001b[39mline_length,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   3494\u001b[0m     layer_range\u001b[39m=\u001b[39mlayer_range,\n\u001b[1;32m   3495\u001b[0m )\n",
      "\u001b[0;31mValueError\u001b[0m: This model has not yet been built. Build the model first by calling `build()` or by calling the model on a batch of data."
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 2s 33ms/step - loss: 885.3566 - val_loss: 588.0540\n",
      "Epoch 2/20\n",
      "35/35 [==============================] - 1s 28ms/step - loss: 521.4236 - val_loss: 396.7949\n",
      "Epoch 3/20\n",
      "35/35 [==============================] - 1s 27ms/step - loss: 382.3892 - val_loss: 296.3107\n",
      "Epoch 4/20\n",
      "35/35 [==============================] - 1s 27ms/step - loss: 290.6520 - val_loss: 222.0075\n",
      "Epoch 5/20\n",
      "35/35 [==============================] - 1s 28ms/step - loss: 220.9729 - val_loss: 165.0653\n",
      "Epoch 6/20\n",
      "35/35 [==============================] - 1s 28ms/step - loss: 166.7829 - val_loss: 121.0494\n",
      "Epoch 7/20\n",
      "35/35 [==============================] - 1s 28ms/step - loss: 124.5025 - val_loss: 87.2423\n",
      "Epoch 8/20\n",
      "35/35 [==============================] - 1s 28ms/step - loss: 91.7566 - val_loss: 61.5871\n",
      "Epoch 9/20\n",
      "35/35 [==============================] - 1s 29ms/step - loss: 66.6603 - val_loss: 42.4415\n",
      "Epoch 10/20\n",
      "35/35 [==============================] - 1s 28ms/step - loss: 47.6841 - val_loss: 28.4675\n",
      "Epoch 11/20\n",
      "35/35 [==============================] - 1s 28ms/step - loss: 33.5876 - val_loss: 18.4676\n",
      "Epoch 12/20\n",
      "35/35 [==============================] - 1s 27ms/step - loss: 23.2744 - val_loss: 11.5457\n",
      "Epoch 13/20\n",
      "35/35 [==============================] - 1s 28ms/step - loss: 15.8964 - val_loss: 6.8686\n",
      "Epoch 14/20\n",
      "35/35 [==============================] - 1s 27ms/step - loss: 10.6916 - val_loss: 3.8679\n",
      "Epoch 15/20\n",
      "35/35 [==============================] - 1s 27ms/step - loss: 7.1037 - val_loss: 2.0166\n",
      "Epoch 16/20\n",
      "35/35 [==============================] - 1s 25ms/step - loss: 4.6762 - val_loss: 0.9554\n",
      "Epoch 17/20\n",
      "35/35 [==============================] - 1s 27ms/step - loss: 3.0655 - val_loss: 0.4146\n",
      "Epoch 18/20\n",
      "35/35 [==============================] - 1s 28ms/step - loss: 2.0225 - val_loss: 0.1967\n",
      "Epoch 19/20\n",
      "35/35 [==============================] - 1s 28ms/step - loss: 1.3565 - val_loss: 0.1697\n",
      "Epoch 20/20\n",
      "35/35 [==============================] - 1s 28ms/step - loss: 0.9471 - val_loss: 0.2427\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x7f757c537f50>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train, y_train, epochs=20, batch_size=32, validation_data=(x_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38/38 [==============================] - 0s 7ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f757c149790>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,6))\n",
    "timestamps = [pd.to_datetime(x) for x in list(df1.iloc[:, :-1].index)]\n",
    "plt.plot(timestamps, model.predict(x), label = 'predicted')\n",
    "plt.plot(timestamps, y, label = 'true')\n",
    "plt.title('Temperature Prediction via XGBRegressor')\n",
    "plt.ylabel('temperature-CPU1 Temp processor (3.1)')\n",
    "plt.xlabel('hours')\n",
    "# Set the x-axis locator to show hours\n",
    "ax.xaxis.set_major_locator(HourLocator(interval=1))  # This sets the tick interval to 1 hour\n",
    "\n",
    "# Format the x-axis tick labels as hour:minute\n",
    "ax.xaxis.set_major_formatter(DateFormatter('%H:%M'))\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ARMOR",
   "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": "3.11.4"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}