From 4725c4df9843330f401e1c239779a8875f32c443 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Micha=C5=82=20Sar?=
 <101715585+michal-sar@users.noreply.github.com>
Date: Wed, 22 May 2024 13:01:06 +0200
Subject: [PATCH] minor fixes

---
 main.ipynb | 4962 ++++++++++++++--------------------------------------
 1 file changed, 1290 insertions(+), 3672 deletions(-)

diff --git a/main.ipynb b/main.ipynb
index e4cbb667..24454d1a 100644
--- a/main.ipynb
+++ b/main.ipynb
@@ -1,3171 +1,32 @@
 {
  "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "..."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 411,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 412,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.options.display.float_format = '{:.2f}'.format"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 413,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_7800\\3760256257.py:1: DtypeWarning: Columns (25) have mixed types. Specify dtype option on import or set low_memory=False.\n",
-      "  df_dofinansowanie = pd.read_csv(\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_dofinansowanie = pd.read_csv(\n",
-    "    'umowy_pelna_lista_krajowe.csv',\n",
-    "    encoding='ISO-8859-2',\n",
-    "    converters={'TERYT pe?ny': str},\n",
-    "    thousands=',')\n",
-    "\n",
-    "df_dofinansowanie = df_dofinansowanie.loc[df_dofinansowanie['TERYT pe?ny'] != ''].reset_index(drop=True)\n",
-    "\n",
-    "df_dofinansowanie['Dofinansowanie UE (PLN)'] = \\\n",
-    "    df_dofinansowanie['Dofinansowanie UE (PLN)'].apply(pd.to_numeric)\n",
-    "\n",
-    "df_dofinansowanie['Data rozpocz?cia realizacji'] = pd.to_datetime(df_dofinansowanie['Data rozpocz?cia realizacji'])\n",
-    "df_dofinansowanie['Rok rozpocz?cia realizacji'] = df_dofinansowanie['Data rozpocz?cia realizacji'].dt.year\n",
-    "\n",
-    "df_dofinansowanie['Data podpisania umowy pierwotnej'] = pd.to_datetime(df_dofinansowanie['Data podpisania umowy pierwotnej'])\n",
-    "df_dofinansowanie['Rok podpisania umowy pierwotnej'] = df_dofinansowanie['Data podpisania umowy pierwotnej'].dt.year"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 414,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['Program Operacyjny Inteligentny Rozwój'\n",
-      " 'Program Operacyjny Infrastruktura i ?rodowisko 2014-2020'\n",
+      "['Program Operacyjny Infrastruktura i ?rodowisko 2014-2020'\n",
+      " 'Program Operacyjny Inteligentny RozwĂłj'\n",
       " 'Program Operacyjny Polska Cyfrowa'\n",
-      " 'Program Operacyjny Pomoc Techniczna 2014-2020'\n",
-      " 'Program Operacyjny Polska Wschodnia'\n",
-      " 'Program Operacyjny Wiedza Edukacja Rozwój']\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df_dofinansowanie['Program operacyjny'].drop_duplicates().values)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 415,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Wybór programu operacyjnego...\n",
-    "df_dofinansowanie = df_dofinansowanie.loc[df_dofinansowanie['Program operacyjny'] == 'Program Operacyjny Infrastruktura i ?rodowisko 2014-2020'].reset_index(drop=True)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 416,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_dofinansowanie_agg = df_dofinansowanie \\\n",
-    "    .groupby(['TERYT pe?ny', 'Rok rozpocz?cia realizacji'])['Dofinansowanie UE (PLN)'].sum().reset_index()\n",
-    "df_dofinansowanie_agg = df_dofinansowanie_agg \\\n",
-    "    .rename(columns={'TERYT pe?ny': 'Kod', 'Rok rozpocz?cia realizacji': 'Rok', 'Dofinansowanie UE (PLN)': 'Suma'})\n",
-    "df_dofinansowanie_agg = df_dofinansowanie_agg \\\n",
-    "    .loc[df_dofinansowanie_agg['Kod'].str.len() == 7].reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 417,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_podz = pd.read_csv(\n",
-    "    'PODZ_1410_CREL.csv',\n",
-    "    sep=';',\n",
-    "    converters={'Kod': str})\n",
-    "df_podz = df_podz[['Kod', 'Rok', 'Wartosc']]\n",
-    "df_podz = df_podz.loc[df_podz['Kod'].str.endswith(('1', '2', '3'))]\n",
-    "df_podz = df_podz.dropna()\n",
-    "df_podz = df_podz.rename(columns={\n",
-    "    'Wartosc': 'Powierzchnia'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 418,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_wyna = pd.read_csv(\n",
-    "    'WYNA_2497_CREL.csv',\n",
-    "    sep=';',\n",
-    "    converters={'Kod': str},\n",
-    "    decimal=',')\n",
-    "df_wyna = df_wyna[['Kod', 'Wyszczególnienie', 'Rok', 'Wartosc']]\n",
-    "df_wyna = df_wyna.dropna()\n",
-    "df_wyna = df_wyna.pivot_table(index=['Kod', 'Rok'], columns='Wyszczególnienie', values='Wartosc').reset_index()\n",
-    "df_wyna = df_wyna.rename(columns={\n",
-    "    'ogółem': 'Wynagrodzenie_ogolem',\n",
-    "    'przeciętne miesięczne wynagrodzenia brutto w relacji do średniej krajowej (Polska=100)': 'Wynagrodzenie_w_relacji_do_sredniej'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 419,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_7800\\1671418303.py:1: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
-      "  df_fina_1 = pd.read_csv(\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Rodzaje dochodów</th>\n",
-       "      <th>Kod</th>\n",
-       "      <th>Rok</th>\n",
-       "      <th>Dochody_podatek_lesny</th>\n",
-       "      <th>Dochody_podatek_PCC</th>\n",
-       "      <th>Dochody_podatek_od_dzialalnosci_gospodarczej</th>\n",
-       "      <th>Dochody_podatek_od_nieruchomosci</th>\n",
-       "      <th>Dochody_podatek_od_spadkow</th>\n",
-       "      <th>Dochody_podatek_od_srodkow_transportowych</th>\n",
-       "      <th>Dochody_podatek_rolny</th>\n",
-       "      <th>Dochody_podatek_odrebne_ustawy</th>\n",
-       "      <th>Dochody_razem</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2004</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>549608.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>13532989.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>625159.00</td>\n",
-       "      <td>23687.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>41378568.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>609855.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>13667398.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>700134.00</td>\n",
-       "      <td>26634.00</td>\n",
-       "      <td>15438121.00</td>\n",
-       "      <td>43417443.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>844223.65</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>14633962.72</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>747182.64</td>\n",
-       "      <td>11683.60</td>\n",
-       "      <td>16647124.98</td>\n",
-       "      <td>50319253.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1344365.01</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>14944781.74</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>777345.52</td>\n",
-       "      <td>19377.36</td>\n",
-       "      <td>17436387.93</td>\n",
-       "      <td>62025513.24</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>6799.55</td>\n",
-       "      <td>1790135.40</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>16089534.56</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>836441.10</td>\n",
-       "      <td>30823.60</td>\n",
-       "      <td>19149551.45</td>\n",
-       "      <td>80755930.93</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47078</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>154462.39</td>\n",
-       "      <td>5361951.37</td>\n",
-       "      <td>572868.36</td>\n",
-       "      <td>108107448.79</td>\n",
-       "      <td>437144.83</td>\n",
-       "      <td>589658.88</td>\n",
-       "      <td>51297.75</td>\n",
-       "      <td>115274832.37</td>\n",
-       "      <td>261780766.79</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47079</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>150329.31</td>\n",
-       "      <td>6088184.20</td>\n",
-       "      <td>468411.51</td>\n",
-       "      <td>38527846.59</td>\n",
-       "      <td>228886.23</td>\n",
-       "      <td>608637.40</td>\n",
-       "      <td>64855.15</td>\n",
-       "      <td>46137150.39</td>\n",
-       "      <td>167638796.15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47080</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>156556.52</td>\n",
-       "      <td>5125090.74</td>\n",
-       "      <td>329522.12</td>\n",
-       "      <td>78767466.83</td>\n",
-       "      <td>552009.16</td>\n",
-       "      <td>558925.68</td>\n",
-       "      <td>48689.09</td>\n",
-       "      <td>85538260.14</td>\n",
-       "      <td>263006955.07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47081</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2021</td>\n",
-       "      <td>163778.36</td>\n",
-       "      <td>9082482.28</td>\n",
-       "      <td>492045.28</td>\n",
-       "      <td>78491368.35</td>\n",
-       "      <td>947992.83</td>\n",
-       "      <td>602586.14</td>\n",
-       "      <td>59824.46</td>\n",
-       "      <td>89840077.70</td>\n",
-       "      <td>252345800.93</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47082</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2022</td>\n",
-       "      <td>174823.49</td>\n",
-       "      <td>7474079.65</td>\n",
-       "      <td>1019054.56</td>\n",
-       "      <td>84996948.99</td>\n",
-       "      <td>593315.54</td>\n",
-       "      <td>627169.86</td>\n",
-       "      <td>50987.00</td>\n",
-       "      <td>94936379.09</td>\n",
-       "      <td>259310641.60</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>47083 rows × 11 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Rodzaje dochodów      Kod   Rok  Dochody_podatek_lesny  Dochody_podatek_PCC   \n",
-       "0                 0201011  2004                    NaN            549608.00  \\\n",
-       "1                 0201011  2005                    NaN            609855.00   \n",
-       "2                 0201011  2006                    NaN            844223.65   \n",
-       "3                 0201011  2007                    NaN           1344365.01   \n",
-       "4                 0201011  2008                6799.55           1790135.40   \n",
-       "...                   ...   ...                    ...                  ...   \n",
-       "47078             3263011  2018              154462.39           5361951.37   \n",
-       "47079             3263011  2019              150329.31           6088184.20   \n",
-       "47080             3263011  2020              156556.52           5125090.74   \n",
-       "47081             3263011  2021              163778.36           9082482.28   \n",
-       "47082             3263011  2022              174823.49           7474079.65   \n",
-       "\n",
-       "Rodzaje dochodów  Dochody_podatek_od_dzialalnosci_gospodarczej   \n",
-       "0                                                          NaN  \\\n",
-       "1                                                          NaN   \n",
-       "2                                                          NaN   \n",
-       "3                                                          NaN   \n",
-       "4                                                          NaN   \n",
-       "...                                                        ...   \n",
-       "47078                                                572868.36   \n",
-       "47079                                                468411.51   \n",
-       "47080                                                329522.12   \n",
-       "47081                                                492045.28   \n",
-       "47082                                               1019054.56   \n",
-       "\n",
-       "Rodzaje dochodów  Dochody_podatek_od_nieruchomosci   \n",
-       "0                                      13532989.00  \\\n",
-       "1                                      13667398.00   \n",
-       "2                                      14633962.72   \n",
-       "3                                      14944781.74   \n",
-       "4                                      16089534.56   \n",
-       "...                                            ...   \n",
-       "47078                                 108107448.79   \n",
-       "47079                                  38527846.59   \n",
-       "47080                                  78767466.83   \n",
-       "47081                                  78491368.35   \n",
-       "47082                                  84996948.99   \n",
-       "\n",
-       "Rodzaje dochodów  Dochody_podatek_od_spadkow   \n",
-       "0                                        NaN  \\\n",
-       "1                                        NaN   \n",
-       "2                                        NaN   \n",
-       "3                                        NaN   \n",
-       "4                                        NaN   \n",
-       "...                                      ...   \n",
-       "47078                              437144.83   \n",
-       "47079                              228886.23   \n",
-       "47080                              552009.16   \n",
-       "47081                              947992.83   \n",
-       "47082                              593315.54   \n",
-       "\n",
-       "Rodzaje dochodów  Dochody_podatek_od_srodkow_transportowych   \n",
-       "0                                                 625159.00  \\\n",
-       "1                                                 700134.00   \n",
-       "2                                                 747182.64   \n",
-       "3                                                 777345.52   \n",
-       "4                                                 836441.10   \n",
-       "...                                                     ...   \n",
-       "47078                                             589658.88   \n",
-       "47079                                             608637.40   \n",
-       "47080                                             558925.68   \n",
-       "47081                                             602586.14   \n",
-       "47082                                             627169.86   \n",
-       "\n",
-       "Rodzaje dochodów  Dochody_podatek_rolny  Dochody_podatek_odrebne_ustawy   \n",
-       "0                              23687.00                             NaN  \\\n",
-       "1                              26634.00                     15438121.00   \n",
-       "2                              11683.60                     16647124.98   \n",
-       "3                              19377.36                     17436387.93   \n",
-       "4                              30823.60                     19149551.45   \n",
-       "...                                 ...                             ...   \n",
-       "47078                          51297.75                    115274832.37   \n",
-       "47079                          64855.15                     46137150.39   \n",
-       "47080                          48689.09                     85538260.14   \n",
-       "47081                          59824.46                     89840077.70   \n",
-       "47082                          50987.00                     94936379.09   \n",
-       "\n",
-       "Rodzaje dochodów  Dochody_razem  \n",
-       "0                   41378568.00  \n",
-       "1                   43417443.00  \n",
-       "2                   50319253.08  \n",
-       "3                   62025513.24  \n",
-       "4                   80755930.93  \n",
-       "...                         ...  \n",
-       "47078              261780766.79  \n",
-       "47079              167638796.15  \n",
-       "47080              263006955.07  \n",
-       "47081              252345800.93  \n",
-       "47082              259310641.60  \n",
-       "\n",
-       "[47083 rows x 11 columns]"
-      ]
-     },
-     "execution_count": 419,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_fina_1 = pd.read_csv(\n",
-    "    'FINA_2622_CREL_1.csv',\n",
-    "    sep=';',\n",
-    "    converters={'Kod': str},\n",
-    "    decimal=',')\n",
-    "df_fina_1 = df_fina_1[['Kod', 'Rodzaje dochodów', 'Rok', 'Wartosc']]\n",
-    "df_fina_1 = df_fina_1.dropna()\n",
-    "df_fina_1 = df_fina_1.pivot_table(index=['Kod', 'Rok'], columns='Rodzaje dochodów', values='Wartosc').reset_index()\n",
-    "df_fina_1 = df_fina_1.rename(columns={\n",
-    "    'dochody podatkowe - podatek leśny': 'Dochody_podatek_lesny',\n",
-    "    'dochody podatkowe - podatek od czynności cywilnoprawnych': 'Dochody_podatek_PCC',\n",
-    "    'dochody podatkowe - podatek od działalności gospodarczej osób fizycznych, opłacany w formie karty podatkowej': 'Dochody_podatek_od_dzialalnosci_gospodarczej',\n",
-    "    'dochody podatkowe - podatek od nieruchomości': 'Dochody_podatek_od_nieruchomosci',\n",
-    "    'dochody podatkowe - podatek od spadków i darowizn': 'Dochody_podatek_od_spadkow',\n",
-    "    'dochody podatkowe - podatek od środków transportowych': 'Dochody_podatek_od_srodkow_transportowych',\n",
-    "    'dochody podatkowe - podatek rolny': 'Dochody_podatek_rolny',\n",
-    "    'dochody podatkowe - ustalone i pobierane na podstawie odrębnych ustaw': 'Dochody_podatek_odrebne_ustawy',\n",
-    "    'razem': 'Dochody_razem'})\n",
-    "\n",
-    "df_fina_1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 420,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_7800\\2161929356.py:1: DtypeWarning: Columns (7) have mixed types. Specify dtype option on import or set low_memory=False.\n",
-      "  df_fina_2 = pd.read_csv(\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Rodzaje dochodów</th>\n",
-       "      <th>Kod</th>\n",
-       "      <th>Rok</th>\n",
-       "      <th>Dochody_z_majatku</th>\n",
-       "      <th>Dochody_z_najmu_i_dzierzawy</th>\n",
-       "      <th>Dochody_z_uslug</th>\n",
-       "      <th>Dochody_dofinansowanie_inwestycyjne</th>\n",
-       "      <th>Dochody_dofinansowanie_razem</th>\n",
-       "      <th>Udzialy_w_podatkach_dochodowych_od_osob_fizycznych</th>\n",
-       "      <th>Udzialy_w_podatkach_dochodowych_od_osob_prywatnych</th>\n",
-       "      <th>Udzialy_w_podatkach_dochodowych_razem</th>\n",
-       "      <th>Wplywy_z_innych_lokalnych_oplat</th>\n",
-       "      <th>Wplywy_z_oplaty_eksploatacyjnej</th>\n",
-       "      <th>Wplywy_z_oplaty_skarbowej</th>\n",
-       "      <th>Wplywy_z_oplaty_targowej</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2004</td>\n",
-       "      <td>5344205.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>184307.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>519209.00</td>\n",
-       "      <td>13285456.00</td>\n",
-       "      <td>1065169.00</td>\n",
-       "      <td>14350625.00</td>\n",
-       "      <td>44200.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1209998.00</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2005</td>\n",
-       "      <td>4560489.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>96462.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>9024183.00</td>\n",
-       "      <td>15985331.00</td>\n",
-       "      <td>1170863.00</td>\n",
-       "      <td>17156194.00</td>\n",
-       "      <td>42840.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1282943.00</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2006</td>\n",
-       "      <td>8528727.69</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>231470.96</td>\n",
-       "      <td>8752288.98</td>\n",
-       "      <td>8864860.57</td>\n",
-       "      <td>18101668.00</td>\n",
-       "      <td>1048115.83</td>\n",
-       "      <td>19149783.83</td>\n",
-       "      <td>37365.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1203990.73</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2007</td>\n",
-       "      <td>15042480.34</td>\n",
-       "      <td>9219682.12</td>\n",
-       "      <td>339654.15</td>\n",
-       "      <td>18153240.30</td>\n",
-       "      <td>18438743.21</td>\n",
-       "      <td>21785308.00</td>\n",
-       "      <td>1336702.02</td>\n",
-       "      <td>23122010.02</td>\n",
-       "      <td>78798.51</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1228704.53</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2008</td>\n",
-       "      <td>22797881.07</td>\n",
-       "      <td>9546379.31</td>\n",
-       "      <td>787256.69</td>\n",
-       "      <td>5046691.69</td>\n",
-       "      <td>5182137.79</td>\n",
-       "      <td>23974587.00</td>\n",
-       "      <td>1532633.44</td>\n",
-       "      <td>25507220.44</td>\n",
-       "      <td>83882.94</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1364245.93</td>\n",
-       "      <td>NaN</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47078</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>16419859.31</td>\n",
-       "      <td>4261374.83</td>\n",
-       "      <td>1996824.80</td>\n",
-       "      <td>25285.92</td>\n",
-       "      <td>237485.34</td>\n",
-       "      <td>52799183.00</td>\n",
-       "      <td>2690098.17</td>\n",
-       "      <td>55489281.17</td>\n",
-       "      <td>10458871.30</td>\n",
-       "      <td>4684.54</td>\n",
-       "      <td>434077.88</td>\n",
-       "      <td>608625.90</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47079</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>8844350.07</td>\n",
-       "      <td>4324758.68</td>\n",
-       "      <td>2187576.47</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>225831.84</td>\n",
-       "      <td>55319040.00</td>\n",
-       "      <td>2770684.17</td>\n",
-       "      <td>58089724.17</td>\n",
-       "      <td>11369287.11</td>\n",
-       "      <td>3456.95</td>\n",
-       "      <td>415686.53</td>\n",
-       "      <td>610059.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47080</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>13485033.97</td>\n",
-       "      <td>6159923.01</td>\n",
-       "      <td>1917372.55</td>\n",
-       "      <td>21002107.00</td>\n",
-       "      <td>21192313.05</td>\n",
-       "      <td>53739656.00</td>\n",
-       "      <td>3144444.38</td>\n",
-       "      <td>56884100.38</td>\n",
-       "      <td>12281916.71</td>\n",
-       "      <td>5157.50</td>\n",
-       "      <td>355201.29</td>\n",
-       "      <td>507341.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47081</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2021</td>\n",
-       "      <td>16928500.75</td>\n",
-       "      <td>7582499.62</td>\n",
-       "      <td>4110105.72</td>\n",
-       "      <td>888293.63</td>\n",
-       "      <td>1072910.83</td>\n",
-       "      <td>63936763.00</td>\n",
-       "      <td>3975531.95</td>\n",
-       "      <td>67912294.95</td>\n",
-       "      <td>17127683.55</td>\n",
-       "      <td>27746.70</td>\n",
-       "      <td>416473.03</td>\n",
-       "      <td>0.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>47082</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2022</td>\n",
-       "      <td>30415536.99</td>\n",
-       "      <td>8651170.05</td>\n",
-       "      <td>4117086.30</td>\n",
-       "      <td>207597.50</td>\n",
-       "      <td>800347.63</td>\n",
-       "      <td>64657287.40</td>\n",
-       "      <td>4082611.64</td>\n",
-       "      <td>68739899.04</td>\n",
-       "      <td>19150342.25</td>\n",
-       "      <td>5035.87</td>\n",
-       "      <td>421424.91</td>\n",
-       "      <td>1233266.30</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>47083 rows × 14 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Rodzaje dochodów      Kod   Rok  Dochody_z_majatku   \n",
-       "0                 0201011  2004         5344205.00  \\\n",
-       "1                 0201011  2005         4560489.00   \n",
-       "2                 0201011  2006         8528727.69   \n",
-       "3                 0201011  2007        15042480.34   \n",
-       "4                 0201011  2008        22797881.07   \n",
-       "...                   ...   ...                ...   \n",
-       "47078             3263011  2018        16419859.31   \n",
-       "47079             3263011  2019         8844350.07   \n",
-       "47080             3263011  2020        13485033.97   \n",
-       "47081             3263011  2021        16928500.75   \n",
-       "47082             3263011  2022        30415536.99   \n",
-       "\n",
-       "Rodzaje dochodów  Dochody_z_najmu_i_dzierzawy  Dochody_z_uslug   \n",
-       "0                                         NaN        184307.00  \\\n",
-       "1                                         NaN         96462.00   \n",
-       "2                                         NaN        231470.96   \n",
-       "3                                  9219682.12        339654.15   \n",
-       "4                                  9546379.31        787256.69   \n",
-       "...                                       ...              ...   \n",
-       "47078                              4261374.83       1996824.80   \n",
-       "47079                              4324758.68       2187576.47   \n",
-       "47080                              6159923.01       1917372.55   \n",
-       "47081                              7582499.62       4110105.72   \n",
-       "47082                              8651170.05       4117086.30   \n",
-       "\n",
-       "Rodzaje dochodów  Dochody_dofinansowanie_inwestycyjne   \n",
-       "0                                                 NaN  \\\n",
-       "1                                                 NaN   \n",
-       "2                                          8752288.98   \n",
-       "3                                         18153240.30   \n",
-       "4                                          5046691.69   \n",
-       "...                                               ...   \n",
-       "47078                                        25285.92   \n",
-       "47079                                            0.00   \n",
-       "47080                                     21002107.00   \n",
-       "47081                                       888293.63   \n",
-       "47082                                       207597.50   \n",
-       "\n",
-       "Rodzaje dochodów  Dochody_dofinansowanie_razem   \n",
-       "0                                    519209.00  \\\n",
-       "1                                   9024183.00   \n",
-       "2                                   8864860.57   \n",
-       "3                                  18438743.21   \n",
-       "4                                   5182137.79   \n",
-       "...                                        ...   \n",
-       "47078                                237485.34   \n",
-       "47079                                225831.84   \n",
-       "47080                              21192313.05   \n",
-       "47081                               1072910.83   \n",
-       "47082                                800347.63   \n",
-       "\n",
-       "Rodzaje dochodów  Udzialy_w_podatkach_dochodowych_od_osob_fizycznych   \n",
-       "0                                                       13285456.00   \\\n",
-       "1                                                       15985331.00    \n",
-       "2                                                       18101668.00    \n",
-       "3                                                       21785308.00    \n",
-       "4                                                       23974587.00    \n",
-       "...                                                             ...    \n",
-       "47078                                                   52799183.00    \n",
-       "47079                                                   55319040.00    \n",
-       "47080                                                   53739656.00    \n",
-       "47081                                                   63936763.00    \n",
-       "47082                                                   64657287.40    \n",
-       "\n",
-       "Rodzaje dochodów  Udzialy_w_podatkach_dochodowych_od_osob_prywatnych   \n",
-       "0                                                        1065169.00   \\\n",
-       "1                                                        1170863.00    \n",
-       "2                                                        1048115.83    \n",
-       "3                                                        1336702.02    \n",
-       "4                                                        1532633.44    \n",
-       "...                                                             ...    \n",
-       "47078                                                    2690098.17    \n",
-       "47079                                                    2770684.17    \n",
-       "47080                                                    3144444.38    \n",
-       "47081                                                    3975531.95    \n",
-       "47082                                                    4082611.64    \n",
-       "\n",
-       "Rodzaje dochodów  Udzialy_w_podatkach_dochodowych_razem   \n",
-       "0                                           14350625.00  \\\n",
-       "1                                           17156194.00   \n",
-       "2                                           19149783.83   \n",
-       "3                                           23122010.02   \n",
-       "4                                           25507220.44   \n",
-       "...                                                 ...   \n",
-       "47078                                       55489281.17   \n",
-       "47079                                       58089724.17   \n",
-       "47080                                       56884100.38   \n",
-       "47081                                       67912294.95   \n",
-       "47082                                       68739899.04   \n",
-       "\n",
-       "Rodzaje dochodów  Wplywy_z_innych_lokalnych_oplat   \n",
-       "0                                        44200.00  \\\n",
-       "1                                        42840.00   \n",
-       "2                                        37365.00   \n",
-       "3                                        78798.51   \n",
-       "4                                        83882.94   \n",
-       "...                                           ...   \n",
-       "47078                                 10458871.30   \n",
-       "47079                                 11369287.11   \n",
-       "47080                                 12281916.71   \n",
-       "47081                                 17127683.55   \n",
-       "47082                                 19150342.25   \n",
-       "\n",
-       "Rodzaje dochodów  Wplywy_z_oplaty_eksploatacyjnej  Wplywy_z_oplaty_skarbowej   \n",
-       "0                                             NaN                 1209998.00  \\\n",
-       "1                                             NaN                 1282943.00   \n",
-       "2                                             NaN                 1203990.73   \n",
-       "3                                             NaN                 1228704.53   \n",
-       "4                                             NaN                 1364245.93   \n",
-       "...                                           ...                        ...   \n",
-       "47078                                     4684.54                  434077.88   \n",
-       "47079                                     3456.95                  415686.53   \n",
-       "47080                                     5157.50                  355201.29   \n",
-       "47081                                    27746.70                  416473.03   \n",
-       "47082                                     5035.87                  421424.91   \n",
-       "\n",
-       "Rodzaje dochodów  Wplywy_z_oplaty_targowej  \n",
-       "0                                      NaN  \n",
-       "1                                      NaN  \n",
-       "2                                      NaN  \n",
-       "3                                      NaN  \n",
-       "4                                      NaN  \n",
-       "...                                    ...  \n",
-       "47078                            608625.90  \n",
-       "47079                            610059.50  \n",
-       "47080                            507341.00  \n",
-       "47081                                 0.00  \n",
-       "47082                           1233266.30  \n",
-       "\n",
-       "[47083 rows x 14 columns]"
-      ]
-     },
-     "execution_count": 420,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_fina_2 = pd.read_csv(\n",
-    "    'FINA_2622_CREL_2.csv',\n",
-    "    sep=';',\n",
-    "    converters={'Kod': str},\n",
-    "    decimal=',')\n",
-    "df_fina_2 = df_fina_2[['Kod', 'Rodzaje dochodów', 'Rok', 'Wartosc']]\n",
-    "df_fina_2 = df_fina_2.dropna()\n",
-    "df_fina_2 = df_fina_2.pivot_table(index=['Kod', 'Rok'], columns='Rodzaje dochodów', values='Wartosc').reset_index()\n",
-    "df_fina_2 = df_fina_2.rename(columns={\n",
-    "    'dochody z majątku': 'Dochody_z_majatku',\n",
-    "    'dochody z majątku - dochody z najmu i dzierżawy składników majątkowych JST oraz innych umów o podobnym charakterze': 'Dochody_z_najmu_i_dzierzawy',\n",
-    "    'pozostałe dochody - wpływy z usług': 'Dochody_z_uslug',\n",
-    "    'pozostałe dochody - środki na dofinansowanie własnych zadań pozyskane z innych źródeł - inwestycyjne': 'Dochody_dofinansowanie_inwestycyjne',\n",
-    "    'pozostałe dochody - środki na dofinansowanie własnych zadań pozyskane z innych źródeł - razem': 'Dochody_dofinansowanie_razem',\n",
-    "    'udziały w podatkach stanowiących dochody budżetu państwa podatek dochodowy od osób fizycznych': 'Udzialy_w_podatkach_dochodowych_od_osob_fizycznych',\n",
-    "    'udziały w podatkach stanowiących dochody budżetu państwa podatek dochodowy od osób prawnych': 'Udzialy_w_podatkach_dochodowych_od_osob_prywatnych',\n",
-    "    'udziały w podatkach stanowiących dochody budżetu państwa razem': 'Udzialy_w_podatkach_dochodowych_razem',\n",
-    "    'wpływy z innych lokalnych opłat pobieranych przez jednostki samorządu terytorialnego na podstawie odrębnych ustaw': 'Wplywy_z_innych_lokalnych_oplat',\n",
-    "    'wpływy z opłaty eksploatacyjnej': 'Wplywy_z_oplaty_eksploatacyjnej',\n",
-    "    'wpływy z opłaty skarbowej': 'Wplywy_z_oplaty_skarbowej',\n",
-    "    'wpływy z opłaty targowej': 'Wplywy_z_oplaty_targowej'})\n",
-    "\n",
-    "df_fina_2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 421,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Wiek</th>\n",
-       "      <th>Kod</th>\n",
-       "      <th>Rok</th>\n",
-       "      <th>Ludnosc_ogolem</th>\n",
-       "      <th>Ludnosc_w_wieku_poprodukcyjnym</th>\n",
-       "      <th>Ludnosc_w_wieku_produkcyjnym</th>\n",
-       "      <th>Ludnosc_w_wieku_produkcyjnym_mobilnym</th>\n",
-       "      <th>Ludnosc_w_wieku_produkcyjnym_niemobilnym</th>\n",
-       "      <th>Ludnosc_w_wieku_przedprodukcyjnym</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>40309.00</td>\n",
-       "      <td>7683.00</td>\n",
-       "      <td>26085.00</td>\n",
-       "      <td>15183.00</td>\n",
-       "      <td>10902.00</td>\n",
-       "      <td>6541.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>40119.00</td>\n",
-       "      <td>8020.00</td>\n",
-       "      <td>25647.00</td>\n",
-       "      <td>15047.00</td>\n",
-       "      <td>10600.00</td>\n",
-       "      <td>6452.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>39851.00</td>\n",
-       "      <td>8392.00</td>\n",
-       "      <td>25160.00</td>\n",
-       "      <td>14932.00</td>\n",
-       "      <td>10228.00</td>\n",
-       "      <td>6299.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>39603.00</td>\n",
-       "      <td>8678.00</td>\n",
-       "      <td>24720.00</td>\n",
-       "      <td>14784.00</td>\n",
-       "      <td>9936.00</td>\n",
-       "      <td>6205.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>39464.00</td>\n",
-       "      <td>8971.00</td>\n",
-       "      <td>24307.00</td>\n",
-       "      <td>14645.00</td>\n",
-       "      <td>9662.00</td>\n",
-       "      <td>6186.00</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48606</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>40910.00</td>\n",
-       "      <td>10472.00</td>\n",
-       "      <td>24549.00</td>\n",
-       "      <td>14683.00</td>\n",
-       "      <td>9866.00</td>\n",
-       "      <td>5889.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48607</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>40888.00</td>\n",
-       "      <td>10788.00</td>\n",
-       "      <td>24209.00</td>\n",
-       "      <td>14429.00</td>\n",
-       "      <td>9780.00</td>\n",
-       "      <td>5891.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48608</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>40326.00</td>\n",
-       "      <td>10962.00</td>\n",
-       "      <td>23544.00</td>\n",
-       "      <td>13798.00</td>\n",
-       "      <td>9746.00</td>\n",
-       "      <td>5820.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48609</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2021</td>\n",
-       "      <td>39834.00</td>\n",
-       "      <td>11050.00</td>\n",
-       "      <td>22976.00</td>\n",
-       "      <td>13277.00</td>\n",
-       "      <td>9699.00</td>\n",
-       "      <td>5808.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48610</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2022</td>\n",
-       "      <td>39368.00</td>\n",
-       "      <td>11157.00</td>\n",
-       "      <td>22486.00</td>\n",
-       "      <td>12802.00</td>\n",
-       "      <td>9684.00</td>\n",
-       "      <td>5725.00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>48611 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Wiek       Kod   Rok  Ludnosc_ogolem  Ludnosc_w_wieku_poprodukcyjnym   \n",
-       "0      0201011  2010        40309.00                         7683.00  \\\n",
-       "1      0201011  2011        40119.00                         8020.00   \n",
-       "2      0201011  2012        39851.00                         8392.00   \n",
-       "3      0201011  2013        39603.00                         8678.00   \n",
-       "4      0201011  2014        39464.00                         8971.00   \n",
-       "...        ...   ...             ...                             ...   \n",
-       "48606  3263011  2018        40910.00                        10472.00   \n",
-       "48607  3263011  2019        40888.00                        10788.00   \n",
-       "48608  3263011  2020        40326.00                        10962.00   \n",
-       "48609  3263011  2021        39834.00                        11050.00   \n",
-       "48610  3263011  2022        39368.00                        11157.00   \n",
-       "\n",
-       "Wiek   Ludnosc_w_wieku_produkcyjnym  Ludnosc_w_wieku_produkcyjnym_mobilnym   \n",
-       "0                          26085.00                               15183.00  \\\n",
-       "1                          25647.00                               15047.00   \n",
-       "2                          25160.00                               14932.00   \n",
-       "3                          24720.00                               14784.00   \n",
-       "4                          24307.00                               14645.00   \n",
-       "...                             ...                                    ...   \n",
-       "48606                      24549.00                               14683.00   \n",
-       "48607                      24209.00                               14429.00   \n",
-       "48608                      23544.00                               13798.00   \n",
-       "48609                      22976.00                               13277.00   \n",
-       "48610                      22486.00                               12802.00   \n",
-       "\n",
-       "Wiek   Ludnosc_w_wieku_produkcyjnym_niemobilnym   \n",
-       "0                                      10902.00  \\\n",
-       "1                                      10600.00   \n",
-       "2                                      10228.00   \n",
-       "3                                       9936.00   \n",
-       "4                                       9662.00   \n",
-       "...                                         ...   \n",
-       "48606                                   9866.00   \n",
-       "48607                                   9780.00   \n",
-       "48608                                   9746.00   \n",
-       "48609                                   9699.00   \n",
-       "48610                                   9684.00   \n",
-       "\n",
-       "Wiek   Ludnosc_w_wieku_przedprodukcyjnym  \n",
-       "0                                6541.00  \n",
-       "1                                6452.00  \n",
-       "2                                6299.00  \n",
-       "3                                6205.00  \n",
-       "4                                6186.00  \n",
-       "...                                  ...  \n",
-       "48606                            5889.00  \n",
-       "48607                            5891.00  \n",
-       "48608                            5820.00  \n",
-       "48609                            5808.00  \n",
-       "48610                            5725.00  \n",
-       "\n",
-       "[48611 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 421,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_ludn_1 = pd.read_csv(  # ogolem\n",
-    "    'LUDN_1342_CREL_1.csv',\n",
-    "    sep=';',\n",
-    "    converters={'Kod': str},\n",
-    "    decimal=',')\n",
-    "df_ludn_1 = df_ludn_1[['Kod', 'Wiek', 'Rok', 'Wartosc']]\n",
-    "df_ludn_1 = df_ludn_1.dropna()\n",
-    "df_ludn_1 = df_ludn_1.pivot_table(index=['Kod', 'Rok'], columns='Wiek', values='Wartosc').reset_index()\n",
-    "df_ludn_1 = df_ludn_1.rename(columns={\n",
-    "    'ogółem': 'Ludnosc_ogolem',\n",
-    "    'w wieku poprodukcyjnym': 'Ludnosc_w_wieku_poprodukcyjnym',\n",
-    "    'w wieku produkcyjnym': 'Ludnosc_w_wieku_produkcyjnym',\n",
-    "    'w wieku produkcyjnym mobilnym': 'Ludnosc_w_wieku_produkcyjnym_mobilnym',\n",
-    "    'w wieku produkcyjnym niemobilnym': 'Ludnosc_w_wieku_produkcyjnym_niemobilnym',\n",
-    "    'w wieku przedprodukcyjnym': 'Ludnosc_w_wieku_przedprodukcyjnym'})\n",
-    "\n",
-    "df_ludn_1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 422,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Wiek</th>\n",
-       "      <th>Kod</th>\n",
-       "      <th>Rok</th>\n",
-       "      <th>Ludnosc_mezczyzni</th>\n",
-       "      <th>Ludnosc_mezczyzni_w_wieku_poprodukcyjnym</th>\n",
-       "      <th>Ludnosc_mezczyzni_w_wieku_produkcyjnym</th>\n",
-       "      <th>Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym</th>\n",
-       "      <th>Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym</th>\n",
-       "      <th>Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>19085.00</td>\n",
-       "      <td>2153.00</td>\n",
-       "      <td>13535.00</td>\n",
-       "      <td>7720.00</td>\n",
-       "      <td>5815.00</td>\n",
-       "      <td>3397.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>18985.00</td>\n",
-       "      <td>2222.00</td>\n",
-       "      <td>13398.00</td>\n",
-       "      <td>7647.00</td>\n",
-       "      <td>5751.00</td>\n",
-       "      <td>3365.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>18859.00</td>\n",
-       "      <td>2370.00</td>\n",
-       "      <td>13238.00</td>\n",
-       "      <td>7611.00</td>\n",
-       "      <td>5627.00</td>\n",
-       "      <td>3251.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>18737.00</td>\n",
-       "      <td>2477.00</td>\n",
-       "      <td>13028.00</td>\n",
-       "      <td>7501.00</td>\n",
-       "      <td>5527.00</td>\n",
-       "      <td>3232.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>18640.00</td>\n",
-       "      <td>2620.00</td>\n",
-       "      <td>12832.00</td>\n",
-       "      <td>7442.00</td>\n",
-       "      <td>5390.00</td>\n",
-       "      <td>3188.00</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48606</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>19690.00</td>\n",
-       "      <td>3501.00</td>\n",
-       "      <td>13202.00</td>\n",
-       "      <td>7547.00</td>\n",
-       "      <td>5655.00</td>\n",
-       "      <td>2987.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48607</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>19683.00</td>\n",
-       "      <td>3644.00</td>\n",
-       "      <td>13044.00</td>\n",
-       "      <td>7417.00</td>\n",
-       "      <td>5627.00</td>\n",
-       "      <td>2995.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48608</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>19356.00</td>\n",
-       "      <td>3749.00</td>\n",
-       "      <td>12617.00</td>\n",
-       "      <td>6986.00</td>\n",
-       "      <td>5631.00</td>\n",
-       "      <td>2990.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48609</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2021</td>\n",
-       "      <td>19096.00</td>\n",
-       "      <td>3852.00</td>\n",
-       "      <td>12267.00</td>\n",
-       "      <td>6747.00</td>\n",
-       "      <td>5520.00</td>\n",
-       "      <td>2977.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48610</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2022</td>\n",
-       "      <td>18869.00</td>\n",
-       "      <td>3901.00</td>\n",
-       "      <td>12009.00</td>\n",
-       "      <td>6485.00</td>\n",
-       "      <td>5524.00</td>\n",
-       "      <td>2959.00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>48611 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Wiek       Kod   Rok  Ludnosc_mezczyzni   \n",
-       "0      0201011  2010           19085.00  \\\n",
-       "1      0201011  2011           18985.00   \n",
-       "2      0201011  2012           18859.00   \n",
-       "3      0201011  2013           18737.00   \n",
-       "4      0201011  2014           18640.00   \n",
-       "...        ...   ...                ...   \n",
-       "48606  3263011  2018           19690.00   \n",
-       "48607  3263011  2019           19683.00   \n",
-       "48608  3263011  2020           19356.00   \n",
-       "48609  3263011  2021           19096.00   \n",
-       "48610  3263011  2022           18869.00   \n",
-       "\n",
-       "Wiek   Ludnosc_mezczyzni_w_wieku_poprodukcyjnym   \n",
-       "0                                       2153.00  \\\n",
-       "1                                       2222.00   \n",
-       "2                                       2370.00   \n",
-       "3                                       2477.00   \n",
-       "4                                       2620.00   \n",
-       "...                                         ...   \n",
-       "48606                                   3501.00   \n",
-       "48607                                   3644.00   \n",
-       "48608                                   3749.00   \n",
-       "48609                                   3852.00   \n",
-       "48610                                   3901.00   \n",
-       "\n",
-       "Wiek   Ludnosc_mezczyzni_w_wieku_produkcyjnym   \n",
-       "0                                    13535.00  \\\n",
-       "1                                    13398.00   \n",
-       "2                                    13238.00   \n",
-       "3                                    13028.00   \n",
-       "4                                    12832.00   \n",
-       "...                                       ...   \n",
-       "48606                                13202.00   \n",
-       "48607                                13044.00   \n",
-       "48608                                12617.00   \n",
-       "48609                                12267.00   \n",
-       "48610                                12009.00   \n",
-       "\n",
-       "Wiek   Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym   \n",
-       "0                                              7720.00  \\\n",
-       "1                                              7647.00   \n",
-       "2                                              7611.00   \n",
-       "3                                              7501.00   \n",
-       "4                                              7442.00   \n",
-       "...                                                ...   \n",
-       "48606                                          7547.00   \n",
-       "48607                                          7417.00   \n",
-       "48608                                          6986.00   \n",
-       "48609                                          6747.00   \n",
-       "48610                                          6485.00   \n",
-       "\n",
-       "Wiek   Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym   \n",
-       "0                                                5815.00   \\\n",
-       "1                                                5751.00    \n",
-       "2                                                5627.00    \n",
-       "3                                                5527.00    \n",
-       "4                                                5390.00    \n",
-       "...                                                  ...    \n",
-       "48606                                            5655.00    \n",
-       "48607                                            5627.00    \n",
-       "48608                                            5631.00    \n",
-       "48609                                            5520.00    \n",
-       "48610                                            5524.00    \n",
-       "\n",
-       "Wiek   Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym  \n",
-       "0                                          3397.00  \n",
-       "1                                          3365.00  \n",
-       "2                                          3251.00  \n",
-       "3                                          3232.00  \n",
-       "4                                          3188.00  \n",
-       "...                                            ...  \n",
-       "48606                                      2987.00  \n",
-       "48607                                      2995.00  \n",
-       "48608                                      2990.00  \n",
-       "48609                                      2977.00  \n",
-       "48610                                      2959.00  \n",
-       "\n",
-       "[48611 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 422,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_ludn_2 = pd.read_csv(  # mezczyzni\n",
-    "    'LUDN_1342_CREL_2.csv',\n",
-    "    sep=';',\n",
-    "    converters={'Kod': str},\n",
-    "    decimal=',')\n",
-    "df_ludn_2 = df_ludn_2[['Kod', 'Wiek', 'Rok', 'Wartosc']]\n",
-    "df_ludn_2 = df_ludn_2.dropna()\n",
-    "df_ludn_2 = df_ludn_2.pivot_table(index=['Kod', 'Rok'], columns='Wiek', values='Wartosc').reset_index()\n",
-    "df_ludn_2 = df_ludn_2.rename(columns={\n",
-    "    'ogółem': 'Ludnosc_mezczyzni',\n",
-    "    'w wieku poprodukcyjnym': 'Ludnosc_mezczyzni_w_wieku_poprodukcyjnym',\n",
-    "    'w wieku produkcyjnym': 'Ludnosc_mezczyzni_w_wieku_produkcyjnym',\n",
-    "    'w wieku produkcyjnym mobilnym': 'Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym',\n",
-    "    'w wieku produkcyjnym niemobilnym': 'Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym',\n",
-    "    'w wieku przedprodukcyjnym': 'Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym'})\n",
-    "\n",
-    "df_ludn_2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 423,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Wiek</th>\n",
-       "      <th>Kod</th>\n",
-       "      <th>Rok</th>\n",
-       "      <th>Ludnosc_kobiety</th>\n",
-       "      <th>Ludnosc_kobiety_w_wieku_poprodukcyjnym</th>\n",
-       "      <th>Ludnosc_kobiety_w_wieku_produkcyjnym</th>\n",
-       "      <th>Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym</th>\n",
-       "      <th>Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym</th>\n",
-       "      <th>Ludnosc_kobiety_w_wieku_przedprodukcyjnym</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>21224.00</td>\n",
-       "      <td>5530.00</td>\n",
-       "      <td>12550.00</td>\n",
-       "      <td>7463.00</td>\n",
-       "      <td>5087.00</td>\n",
-       "      <td>3144.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>21134.00</td>\n",
-       "      <td>5798.00</td>\n",
-       "      <td>12249.00</td>\n",
-       "      <td>7400.00</td>\n",
-       "      <td>4849.00</td>\n",
-       "      <td>3087.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>20992.00</td>\n",
-       "      <td>6022.00</td>\n",
-       "      <td>11922.00</td>\n",
-       "      <td>7321.00</td>\n",
-       "      <td>4601.00</td>\n",
-       "      <td>3048.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>20866.00</td>\n",
-       "      <td>6201.00</td>\n",
-       "      <td>11692.00</td>\n",
-       "      <td>7283.00</td>\n",
-       "      <td>4409.00</td>\n",
-       "      <td>2973.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>20824.00</td>\n",
-       "      <td>6351.00</td>\n",
-       "      <td>11475.00</td>\n",
-       "      <td>7203.00</td>\n",
-       "      <td>4272.00</td>\n",
-       "      <td>2998.00</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48606</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>21220.00</td>\n",
-       "      <td>6971.00</td>\n",
-       "      <td>11347.00</td>\n",
-       "      <td>7136.00</td>\n",
-       "      <td>4211.00</td>\n",
-       "      <td>2902.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48607</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>21205.00</td>\n",
-       "      <td>7144.00</td>\n",
-       "      <td>11165.00</td>\n",
-       "      <td>7012.00</td>\n",
-       "      <td>4153.00</td>\n",
-       "      <td>2896.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48608</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>20970.00</td>\n",
-       "      <td>7213.00</td>\n",
-       "      <td>10927.00</td>\n",
-       "      <td>6812.00</td>\n",
-       "      <td>4115.00</td>\n",
-       "      <td>2830.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48609</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2021</td>\n",
-       "      <td>20738.00</td>\n",
-       "      <td>7198.00</td>\n",
-       "      <td>10709.00</td>\n",
-       "      <td>6530.00</td>\n",
-       "      <td>4179.00</td>\n",
-       "      <td>2831.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48610</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2022</td>\n",
-       "      <td>20499.00</td>\n",
-       "      <td>7256.00</td>\n",
-       "      <td>10477.00</td>\n",
-       "      <td>6317.00</td>\n",
-       "      <td>4160.00</td>\n",
-       "      <td>2766.00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>48611 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Wiek       Kod   Rok  Ludnosc_kobiety  Ludnosc_kobiety_w_wieku_poprodukcyjnym   \n",
-       "0      0201011  2010         21224.00                                 5530.00  \\\n",
-       "1      0201011  2011         21134.00                                 5798.00   \n",
-       "2      0201011  2012         20992.00                                 6022.00   \n",
-       "3      0201011  2013         20866.00                                 6201.00   \n",
-       "4      0201011  2014         20824.00                                 6351.00   \n",
-       "...        ...   ...              ...                                     ...   \n",
-       "48606  3263011  2018         21220.00                                 6971.00   \n",
-       "48607  3263011  2019         21205.00                                 7144.00   \n",
-       "48608  3263011  2020         20970.00                                 7213.00   \n",
-       "48609  3263011  2021         20738.00                                 7198.00   \n",
-       "48610  3263011  2022         20499.00                                 7256.00   \n",
-       "\n",
-       "Wiek   Ludnosc_kobiety_w_wieku_produkcyjnym   \n",
-       "0                                  12550.00  \\\n",
-       "1                                  12249.00   \n",
-       "2                                  11922.00   \n",
-       "3                                  11692.00   \n",
-       "4                                  11475.00   \n",
-       "...                                     ...   \n",
-       "48606                              11347.00   \n",
-       "48607                              11165.00   \n",
-       "48608                              10927.00   \n",
-       "48609                              10709.00   \n",
-       "48610                              10477.00   \n",
-       "\n",
-       "Wiek   Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym   \n",
-       "0                                            7463.00  \\\n",
-       "1                                            7400.00   \n",
-       "2                                            7321.00   \n",
-       "3                                            7283.00   \n",
-       "4                                            7203.00   \n",
-       "...                                              ...   \n",
-       "48606                                        7136.00   \n",
-       "48607                                        7012.00   \n",
-       "48608                                        6812.00   \n",
-       "48609                                        6530.00   \n",
-       "48610                                        6317.00   \n",
-       "\n",
-       "Wiek   Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym   \n",
-       "0                                               5087.00  \\\n",
-       "1                                               4849.00   \n",
-       "2                                               4601.00   \n",
-       "3                                               4409.00   \n",
-       "4                                               4272.00   \n",
-       "...                                                 ...   \n",
-       "48606                                           4211.00   \n",
-       "48607                                           4153.00   \n",
-       "48608                                           4115.00   \n",
-       "48609                                           4179.00   \n",
-       "48610                                           4160.00   \n",
-       "\n",
-       "Wiek   Ludnosc_kobiety_w_wieku_przedprodukcyjnym  \n",
-       "0                                        3144.00  \n",
-       "1                                        3087.00  \n",
-       "2                                        3048.00  \n",
-       "3                                        2973.00  \n",
-       "4                                        2998.00  \n",
-       "...                                          ...  \n",
-       "48606                                    2902.00  \n",
-       "48607                                    2896.00  \n",
-       "48608                                    2830.00  \n",
-       "48609                                    2831.00  \n",
-       "48610                                    2766.00  \n",
-       "\n",
-       "[48611 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 423,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_ludn_3 = pd.read_csv(  # kobiety\n",
-    "    'LUDN_1342_CREL_3.csv',\n",
-    "    sep=';',\n",
-    "    converters={'Kod': str},\n",
-    "    decimal=',')\n",
-    "df_ludn_3 = df_ludn_3[['Kod', 'Wiek', 'Rok', 'Wartosc']]\n",
-    "df_ludn_3 = df_ludn_3.dropna()\n",
-    "df_ludn_3 = df_ludn_3.pivot_table(index=['Kod', 'Rok'], columns='Wiek', values='Wartosc').reset_index()\n",
-    "df_ludn_3 = df_ludn_3.rename(columns={\n",
-    "    'ogółem': 'Ludnosc_kobiety',\n",
-    "    'w wieku poprodukcyjnym': 'Ludnosc_kobiety_w_wieku_poprodukcyjnym',\n",
-    "    'w wieku produkcyjnym': 'Ludnosc_kobiety_w_wieku_produkcyjnym',\n",
-    "    'w wieku produkcyjnym mobilnym': 'Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym',\n",
-    "    'w wieku produkcyjnym niemobilnym': 'Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym',\n",
-    "    'w wieku przedprodukcyjnym': 'Ludnosc_kobiety_w_wieku_przedprodukcyjnym'})\n",
-    "\n",
-    "df_ludn_3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 424,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_ludn_4 = pd.read_csv(\n",
-    "    'LUDN_2425_CREL.csv',\n",
-    "    sep=';',\n",
-    "    converters={'Kod': str},\n",
-    "    decimal=',')\n",
-    "df_ludn_4 = df_ludn_4[['Kod', 'Wskaźniki', 'Rok', 'Wartosc']]\n",
-    "df_ludn_4 = df_ludn_4.dropna()\n",
-    "df_ludn_4 = df_ludn_4.pivot_table(index=['Kod', 'Rok'], columns='Wskaźniki', values='Wartosc').reset_index()\n",
-    "df_ludn_4 = df_ludn_4.rename(columns={\n",
-    "    'gęstość zaludnienia powierzchni zabudowanej i zurbanizowanej (osoby/km2)': 'Gestosc_zaludnienia',\n",
-    "    'ludność na 1 km2': 'Ludnosc_na_1_km2',\n",
-    "    'ludność w tysiącach': 'Ludnosc',\n",
-    "    'ludność w tysiącach kobiety': 'Ludnosc_kobiety',\n",
-    "    'ludność w tysiącach mężczyźni': 'Ludnosc_mezczyzni',\n",
-    "    'wskaźnik urbanizacji': 'Wskaznik_urbanizacji',\n",
-    "    'zmiana liczby ludności na 1000 mieszkańców': 'Zmiana_liczby_ludnosci'})\n",
-    "\n",
-    "df_ludn_4 = df_ludn_4[[\n",
-    "    'Kod',\n",
-    "    'Rok',\n",
-    "    # 'Gestosc_zaludnienia',\n",
-    "    'Ludnosc_na_1_km2',\n",
-    "    'Ludnosc',\n",
-    "    'Ludnosc_kobiety',\n",
-    "    'Ludnosc_mezczyzni',\n",
-    "    'Wskaznik_urbanizacji',\n",
-    "    'Zmiana_liczby_ludnosci']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 425,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Kierunki migracji</th>\n",
-       "      <th>Kod</th>\n",
-       "      <th>Rok</th>\n",
-       "      <th>Saldo_migracji_na_1000_ludnosci</th>\n",
-       "      <th>Saldo_migracji</th>\n",
-       "      <th>Wymeldowania_do_miast_kobiety</th>\n",
-       "      <th>Wymeldowania_do_miast_mezczyzni</th>\n",
-       "      <th>Wymeldowania_do_miast_ogolem</th>\n",
-       "      <th>Wymeldowania_na_wies_kobiety</th>\n",
-       "      <th>Wymeldowania_na_wies_mezczyzni</th>\n",
-       "      <th>Wymeldowania_na_wies_ogolem</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Wymeldowania_za_granice_ogolem</th>\n",
-       "      <th>Zameldowania_kobiety</th>\n",
-       "      <th>Zameldowania_mezczyzni</th>\n",
-       "      <th>Zameldowania_ogolem</th>\n",
-       "      <th>Zameldowania_z_miast_kobiety</th>\n",
-       "      <th>Zameldowania_z_miast_mezczyzni</th>\n",
-       "      <th>Zameldowania_z_miast_ogolem</th>\n",
-       "      <th>Zameldowania_ze_wsi_kobiety</th>\n",
-       "      <th>Zameldowania_ze_wsi_mezczyzni</th>\n",
-       "      <th>Zameldowania_ze_wsi_ogolem</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>-3.70</td>\n",
-       "      <td>-151.00</td>\n",
-       "      <td>108.00</td>\n",
-       "      <td>96.00</td>\n",
-       "      <td>204.00</td>\n",
-       "      <td>170.00</td>\n",
-       "      <td>177.00</td>\n",
-       "      <td>347.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.00</td>\n",
-       "      <td>223.00</td>\n",
-       "      <td>177.00</td>\n",
-       "      <td>400.00</td>\n",
-       "      <td>70.00</td>\n",
-       "      <td>52.00</td>\n",
-       "      <td>122.00</td>\n",
-       "      <td>147.00</td>\n",
-       "      <td>118.00</td>\n",
-       "      <td>265.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>-4.60</td>\n",
-       "      <td>-186.00</td>\n",
-       "      <td>111.00</td>\n",
-       "      <td>99.00</td>\n",
-       "      <td>210.00</td>\n",
-       "      <td>170.00</td>\n",
-       "      <td>157.00</td>\n",
-       "      <td>327.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.00</td>\n",
-       "      <td>196.00</td>\n",
-       "      <td>156.00</td>\n",
-       "      <td>352.00</td>\n",
-       "      <td>67.00</td>\n",
-       "      <td>59.00</td>\n",
-       "      <td>126.00</td>\n",
-       "      <td>125.00</td>\n",
-       "      <td>94.00</td>\n",
-       "      <td>219.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>-3.70</td>\n",
-       "      <td>-149.00</td>\n",
-       "      <td>100.00</td>\n",
-       "      <td>92.00</td>\n",
-       "      <td>192.00</td>\n",
-       "      <td>147.00</td>\n",
-       "      <td>153.00</td>\n",
-       "      <td>300.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>9.00</td>\n",
-       "      <td>197.00</td>\n",
-       "      <td>155.00</td>\n",
-       "      <td>352.00</td>\n",
-       "      <td>78.00</td>\n",
-       "      <td>61.00</td>\n",
-       "      <td>139.00</td>\n",
-       "      <td>116.00</td>\n",
-       "      <td>92.00</td>\n",
-       "      <td>208.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>-4.80</td>\n",
-       "      <td>-191.00</td>\n",
-       "      <td>115.00</td>\n",
-       "      <td>88.00</td>\n",
-       "      <td>203.00</td>\n",
-       "      <td>182.00</td>\n",
-       "      <td>158.00</td>\n",
-       "      <td>340.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>24.00</td>\n",
-       "      <td>211.00</td>\n",
-       "      <td>165.00</td>\n",
-       "      <td>376.00</td>\n",
-       "      <td>83.00</td>\n",
-       "      <td>58.00</td>\n",
-       "      <td>141.00</td>\n",
-       "      <td>128.00</td>\n",
-       "      <td>101.00</td>\n",
-       "      <td>229.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>-4.20</td>\n",
-       "      <td>-167.00</td>\n",
-       "      <td>100.00</td>\n",
-       "      <td>86.00</td>\n",
-       "      <td>186.00</td>\n",
-       "      <td>168.00</td>\n",
-       "      <td>161.00</td>\n",
-       "      <td>329.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>41.00</td>\n",
-       "      <td>196.00</td>\n",
-       "      <td>193.00</td>\n",
-       "      <td>389.00</td>\n",
-       "      <td>71.00</td>\n",
-       "      <td>71.00</td>\n",
-       "      <td>142.00</td>\n",
-       "      <td>125.00</td>\n",
-       "      <td>121.00</td>\n",
-       "      <td>246.00</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>48606</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>1.70</td>\n",
-       "      <td>71.00</td>\n",
-       "      <td>125.00</td>\n",
-       "      <td>152.00</td>\n",
-       "      <td>277.00</td>\n",
-       "      <td>40.00</td>\n",
-       "      <td>66.00</td>\n",
-       "      <td>106.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>245.00</td>\n",
-       "      <td>240.00</td>\n",
-       "      <td>485.00</td>\n",
-       "      <td>156.00</td>\n",
-       "      <td>138.00</td>\n",
-       "      <td>294.00</td>\n",
-       "      <td>73.00</td>\n",
-       "      <td>79.00</td>\n",
-       "      <td>152.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48607</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>3.40</td>\n",
-       "      <td>141.00</td>\n",
-       "      <td>151.00</td>\n",
-       "      <td>116.00</td>\n",
-       "      <td>267.00</td>\n",
-       "      <td>48.00</td>\n",
-       "      <td>53.00</td>\n",
-       "      <td>101.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>273.00</td>\n",
-       "      <td>259.00</td>\n",
-       "      <td>532.00</td>\n",
-       "      <td>179.00</td>\n",
-       "      <td>149.00</td>\n",
-       "      <td>328.00</td>\n",
-       "      <td>71.00</td>\n",
-       "      <td>90.00</td>\n",
-       "      <td>161.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48608</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>3.20</td>\n",
-       "      <td>129.00</td>\n",
-       "      <td>98.00</td>\n",
-       "      <td>99.00</td>\n",
-       "      <td>197.00</td>\n",
-       "      <td>40.00</td>\n",
-       "      <td>44.00</td>\n",
-       "      <td>84.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>226.00</td>\n",
-       "      <td>203.00</td>\n",
-       "      <td>429.00</td>\n",
-       "      <td>159.00</td>\n",
-       "      <td>131.00</td>\n",
-       "      <td>290.00</td>\n",
-       "      <td>52.00</td>\n",
-       "      <td>53.00</td>\n",
-       "      <td>105.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48609</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2021</td>\n",
-       "      <td>-1.40</td>\n",
-       "      <td>-55.00</td>\n",
-       "      <td>122.00</td>\n",
-       "      <td>126.00</td>\n",
-       "      <td>248.00</td>\n",
-       "      <td>63.00</td>\n",
-       "      <td>50.00</td>\n",
-       "      <td>113.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>171.00</td>\n",
-       "      <td>168.00</td>\n",
-       "      <td>339.00</td>\n",
-       "      <td>109.00</td>\n",
-       "      <td>95.00</td>\n",
-       "      <td>204.00</td>\n",
-       "      <td>49.00</td>\n",
-       "      <td>46.00</td>\n",
-       "      <td>95.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48610</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2022</td>\n",
-       "      <td>-3.50</td>\n",
-       "      <td>-138.00</td>\n",
-       "      <td>116.00</td>\n",
-       "      <td>105.00</td>\n",
-       "      <td>221.00</td>\n",
-       "      <td>73.00</td>\n",
-       "      <td>69.00</td>\n",
-       "      <td>142.00</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>141.00</td>\n",
-       "      <td>138.00</td>\n",
-       "      <td>279.00</td>\n",
-       "      <td>85.00</td>\n",
-       "      <td>71.00</td>\n",
-       "      <td>156.00</td>\n",
-       "      <td>38.00</td>\n",
-       "      <td>39.00</td>\n",
-       "      <td>77.00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>48611 rows × 25 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Kierunki migracji      Kod   Rok  Saldo_migracji_na_1000_ludnosci   \n",
-       "0                  0201011  2010                            -3.70  \\\n",
-       "1                  0201011  2011                            -4.60   \n",
-       "2                  0201011  2012                            -3.70   \n",
-       "3                  0201011  2013                            -4.80   \n",
-       "4                  0201011  2014                            -4.20   \n",
-       "...                    ...   ...                              ...   \n",
-       "48606              3263011  2018                             1.70   \n",
-       "48607              3263011  2019                             3.40   \n",
-       "48608              3263011  2020                             3.20   \n",
-       "48609              3263011  2021                            -1.40   \n",
-       "48610              3263011  2022                            -3.50   \n",
-       "\n",
-       "Kierunki migracji  Saldo_migracji  Wymeldowania_do_miast_kobiety   \n",
-       "0                         -151.00                         108.00  \\\n",
-       "1                         -186.00                         111.00   \n",
-       "2                         -149.00                         100.00   \n",
-       "3                         -191.00                         115.00   \n",
-       "4                         -167.00                         100.00   \n",
-       "...                           ...                            ...   \n",
-       "48606                       71.00                         125.00   \n",
-       "48607                      141.00                         151.00   \n",
-       "48608                      129.00                          98.00   \n",
-       "48609                      -55.00                         122.00   \n",
-       "48610                     -138.00                         116.00   \n",
-       "\n",
-       "Kierunki migracji  Wymeldowania_do_miast_mezczyzni   \n",
-       "0                                            96.00  \\\n",
-       "1                                            99.00   \n",
-       "2                                            92.00   \n",
-       "3                                            88.00   \n",
-       "4                                            86.00   \n",
-       "...                                            ...   \n",
-       "48606                                       152.00   \n",
-       "48607                                       116.00   \n",
-       "48608                                        99.00   \n",
-       "48609                                       126.00   \n",
-       "48610                                       105.00   \n",
-       "\n",
-       "Kierunki migracji  Wymeldowania_do_miast_ogolem  Wymeldowania_na_wies_kobiety   \n",
-       "0                                        204.00                        170.00  \\\n",
-       "1                                        210.00                        170.00   \n",
-       "2                                        192.00                        147.00   \n",
-       "3                                        203.00                        182.00   \n",
-       "4                                        186.00                        168.00   \n",
-       "...                                         ...                           ...   \n",
-       "48606                                    277.00                         40.00   \n",
-       "48607                                    267.00                         48.00   \n",
-       "48608                                    197.00                         40.00   \n",
-       "48609                                    248.00                         63.00   \n",
-       "48610                                    221.00                         73.00   \n",
-       "\n",
-       "Kierunki migracji  Wymeldowania_na_wies_mezczyzni   \n",
-       "0                                          177.00  \\\n",
-       "1                                          157.00   \n",
-       "2                                          153.00   \n",
-       "3                                          158.00   \n",
-       "4                                          161.00   \n",
-       "...                                           ...   \n",
-       "48606                                       66.00   \n",
-       "48607                                       53.00   \n",
-       "48608                                       44.00   \n",
-       "48609                                       50.00   \n",
-       "48610                                       69.00   \n",
-       "\n",
-       "Kierunki migracji  Wymeldowania_na_wies_ogolem  ...   \n",
-       "0                                       347.00  ...  \\\n",
-       "1                                       327.00  ...   \n",
-       "2                                       300.00  ...   \n",
-       "3                                       340.00  ...   \n",
-       "4                                       329.00  ...   \n",
-       "...                                        ...  ...   \n",
-       "48606                                   106.00  ...   \n",
-       "48607                                   101.00  ...   \n",
-       "48608                                    84.00  ...   \n",
-       "48609                                   113.00  ...   \n",
-       "48610                                   142.00  ...   \n",
-       "\n",
-       "Kierunki migracji  Wymeldowania_za_granice_ogolem  Zameldowania_kobiety   \n",
-       "0                                            0.00                223.00  \\\n",
-       "1                                            1.00                196.00   \n",
-       "2                                            9.00                197.00   \n",
-       "3                                           24.00                211.00   \n",
-       "4                                           41.00                196.00   \n",
-       "...                                           ...                   ...   \n",
-       "48606                                         NaN                245.00   \n",
-       "48607                                         NaN                273.00   \n",
-       "48608                                         NaN                226.00   \n",
-       "48609                                         NaN                171.00   \n",
-       "48610                                         NaN                141.00   \n",
-       "\n",
-       "Kierunki migracji  Zameldowania_mezczyzni  Zameldowania_ogolem   \n",
-       "0                                  177.00               400.00  \\\n",
-       "1                                  156.00               352.00   \n",
-       "2                                  155.00               352.00   \n",
-       "3                                  165.00               376.00   \n",
-       "4                                  193.00               389.00   \n",
-       "...                                   ...                  ...   \n",
-       "48606                              240.00               485.00   \n",
-       "48607                              259.00               532.00   \n",
-       "48608                              203.00               429.00   \n",
-       "48609                              168.00               339.00   \n",
-       "48610                              138.00               279.00   \n",
-       "\n",
-       "Kierunki migracji  Zameldowania_z_miast_kobiety   \n",
-       "0                                         70.00  \\\n",
-       "1                                         67.00   \n",
-       "2                                         78.00   \n",
-       "3                                         83.00   \n",
-       "4                                         71.00   \n",
-       "...                                         ...   \n",
-       "48606                                    156.00   \n",
-       "48607                                    179.00   \n",
-       "48608                                    159.00   \n",
-       "48609                                    109.00   \n",
-       "48610                                     85.00   \n",
-       "\n",
-       "Kierunki migracji  Zameldowania_z_miast_mezczyzni   \n",
-       "0                                           52.00  \\\n",
-       "1                                           59.00   \n",
-       "2                                           61.00   \n",
-       "3                                           58.00   \n",
-       "4                                           71.00   \n",
-       "...                                           ...   \n",
-       "48606                                      138.00   \n",
-       "48607                                      149.00   \n",
-       "48608                                      131.00   \n",
-       "48609                                       95.00   \n",
-       "48610                                       71.00   \n",
-       "\n",
-       "Kierunki migracji  Zameldowania_z_miast_ogolem  Zameldowania_ze_wsi_kobiety   \n",
-       "0                                       122.00                       147.00  \\\n",
-       "1                                       126.00                       125.00   \n",
-       "2                                       139.00                       116.00   \n",
-       "3                                       141.00                       128.00   \n",
-       "4                                       142.00                       125.00   \n",
-       "...                                        ...                          ...   \n",
-       "48606                                   294.00                        73.00   \n",
-       "48607                                   328.00                        71.00   \n",
-       "48608                                   290.00                        52.00   \n",
-       "48609                                   204.00                        49.00   \n",
-       "48610                                   156.00                        38.00   \n",
-       "\n",
-       "Kierunki migracji  Zameldowania_ze_wsi_mezczyzni  Zameldowania_ze_wsi_ogolem  \n",
-       "0                                         118.00                      265.00  \n",
-       "1                                          94.00                      219.00  \n",
-       "2                                          92.00                      208.00  \n",
-       "3                                         101.00                      229.00  \n",
-       "4                                         121.00                      246.00  \n",
-       "...                                          ...                         ...  \n",
-       "48606                                      79.00                      152.00  \n",
-       "48607                                      90.00                      161.00  \n",
-       "48608                                      53.00                      105.00  \n",
-       "48609                                      46.00                       95.00  \n",
-       "48610                                      39.00                       77.00  \n",
-       "\n",
-       "[48611 rows x 25 columns]"
-      ]
-     },
-     "execution_count": 425,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_ludn_5 = pd.read_csv(\n",
-    "    'LUDN_1355_CREL.csv',\n",
-    "    sep=';',\n",
-    "    converters={'Kod': str},\n",
-    "    decimal=',')\n",
-    "df_ludn_5['Kierunki migracji'] = df_ludn_5['Kierunki migracji'] + df_ludn_5['Płeć']\n",
-    "df_ludn_5 = df_ludn_5[['Kod', 'Kierunki migracji', 'Rok', 'Wartosc']]\n",
-    "df_ludn_5 = df_ludn_5.dropna()\n",
-    "df_ludn_5 = df_ludn_5.pivot_table(index=['Kod', 'Rok'], columns='Kierunki migracji', values='Wartosc').reset_index()\n",
-    "df_ludn_5 = df_ludn_5.rename(columns={\n",
-    "    'saldo migracji na 1000 ludnościogółem': 'Saldo_migracji_na_1000_ludnosci',\n",
-    "    'saldo migracjiogółem': 'Saldo_migracji',\n",
-    "    'wymeldowania do miastkobiety': 'Wymeldowania_do_miast_kobiety',\n",
-    "    'wymeldowania do miastmężczyźni': 'Wymeldowania_do_miast_mezczyzni',\n",
-    "    'wymeldowania do miastogółem': 'Wymeldowania_do_miast_ogolem',\n",
-    "    'wymeldowania na wieśkobiety': 'Wymeldowania_na_wies_kobiety',\n",
-    "    'wymeldowania na wieśmężczyźni': 'Wymeldowania_na_wies_mezczyzni',\n",
-    "    'wymeldowania na wieśogółem': 'Wymeldowania_na_wies_ogolem',\n",
-    "    'wymeldowania ogółemkobiety': 'Wymeldowania_kobiety',\n",
-    "    'wymeldowania ogółemmężczyźni': 'Wymeldowania_mezczyzni',\n",
-    "    'wymeldowania ogółemogółem': 'Wymeldowania_ogolem',\n",
-    "    'wymeldowania za granicękobiety': 'Wymeldowania_za_granice_kobiety',\n",
-    "    'wymeldowania za granicęmężczyźni': 'Wymeldowania_za_granice_mezczyzni',\n",
-    "    'wymeldowania za granicęogółem': 'Wymeldowania_za_granice_ogolem',\n",
-    "    'zameldowania ogółemkobiety': 'Zameldowania_kobiety',\n",
-    "    'zameldowania ogółemmężczyźni': 'Zameldowania_mezczyzni',\n",
-    "    'zameldowania ogółemogółem': 'Zameldowania_ogolem',\n",
-    "    'zameldowania z miastkobiety': 'Zameldowania_z_miast_kobiety',\n",
-    "    'zameldowania z miastmężczyźni': 'Zameldowania_z_miast_mezczyzni',\n",
-    "    'zameldowania z miastogółem': 'Zameldowania_z_miast_ogolem',\n",
-    "    'zameldowania ze wsikobiety': 'Zameldowania_ze_wsi_kobiety',\n",
-    "    'zameldowania ze wsimężczyźni': 'Zameldowania_ze_wsi_mezczyzni',\n",
-    "    'zameldowania ze wsiogółem': 'Zameldowania_ze_wsi_ogolem'})\n",
-    "\n",
-    "df_ludn_5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 426,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Turystyczne obiekty noclegowe</th>\n",
-       "      <th>Kod</th>\n",
-       "      <th>Rok</th>\n",
-       "      <th>Miejsca_noclegowe_caloroczne</th>\n",
-       "      <th>Miejsca_noclegowe_ogolem</th>\n",
-       "      <th>Obiekty_caloroczne</th>\n",
-       "      <th>Obiekty_ogolem</th>\n",
-       "      <th>Turysci_ogolem</th>\n",
-       "      <th>Turysci_zagraniczni</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2010</td>\n",
-       "      <td>265.00</td>\n",
-       "      <td>265.00</td>\n",
-       "      <td>7.00</td>\n",
-       "      <td>7.00</td>\n",
-       "      <td>16427.00</td>\n",
-       "      <td>5173.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>267.00</td>\n",
-       "      <td>267.00</td>\n",
-       "      <td>7.00</td>\n",
-       "      <td>7.00</td>\n",
-       "      <td>13134.00</td>\n",
-       "      <td>4486.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>295.00</td>\n",
-       "      <td>295.00</td>\n",
-       "      <td>8.00</td>\n",
-       "      <td>8.00</td>\n",
-       "      <td>13159.00</td>\n",
-       "      <td>4856.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>293.00</td>\n",
-       "      <td>293.00</td>\n",
-       "      <td>8.00</td>\n",
-       "      <td>8.00</td>\n",
-       "      <td>11914.00</td>\n",
-       "      <td>4701.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>292.00</td>\n",
-       "      <td>292.00</td>\n",
-       "      <td>8.00</td>\n",
-       "      <td>8.00</td>\n",
-       "      <td>12398.00</td>\n",
-       "      <td>3919.00</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34697</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2018</td>\n",
-       "      <td>9757.00</td>\n",
-       "      <td>11717.00</td>\n",
-       "      <td>76.00</td>\n",
-       "      <td>107.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34698</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>9963.00</td>\n",
-       "      <td>11805.00</td>\n",
-       "      <td>74.00</td>\n",
-       "      <td>103.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34699</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>9673.00</td>\n",
-       "      <td>11557.00</td>\n",
-       "      <td>68.00</td>\n",
-       "      <td>97.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34700</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2021</td>\n",
-       "      <td>8731.00</td>\n",
-       "      <td>10551.00</td>\n",
-       "      <td>66.00</td>\n",
-       "      <td>92.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>34701</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2022</td>\n",
-       "      <td>8893.00</td>\n",
-       "      <td>10738.00</td>\n",
-       "      <td>68.00</td>\n",
-       "      <td>92.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>34702 rows × 8 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Turystyczne obiekty noclegowe      Kod   Rok  Miejsca_noclegowe_caloroczne   \n",
-       "0                              0201011  2010                        265.00  \\\n",
-       "1                              0201011  2011                        267.00   \n",
-       "2                              0201011  2012                        295.00   \n",
-       "3                              0201011  2013                        293.00   \n",
-       "4                              0201011  2014                        292.00   \n",
-       "...                                ...   ...                           ...   \n",
-       "34697                          3263011  2018                       9757.00   \n",
-       "34698                          3263011  2019                       9963.00   \n",
-       "34699                          3263011  2020                       9673.00   \n",
-       "34700                          3263011  2021                       8731.00   \n",
-       "34701                          3263011  2022                       8893.00   \n",
-       "\n",
-       "Turystyczne obiekty noclegowe  Miejsca_noclegowe_ogolem  Obiekty_caloroczne   \n",
-       "0                                                265.00                7.00  \\\n",
-       "1                                                267.00                7.00   \n",
-       "2                                                295.00                8.00   \n",
-       "3                                                293.00                8.00   \n",
-       "4                                                292.00                8.00   \n",
-       "...                                                 ...                 ...   \n",
-       "34697                                          11717.00               76.00   \n",
-       "34698                                          11805.00               74.00   \n",
-       "34699                                          11557.00               68.00   \n",
-       "34700                                          10551.00               66.00   \n",
-       "34701                                          10738.00               68.00   \n",
-       "\n",
-       "Turystyczne obiekty noclegowe  Obiekty_ogolem  Turysci_ogolem   \n",
-       "0                                        7.00        16427.00  \\\n",
-       "1                                        7.00        13134.00   \n",
-       "2                                        8.00        13159.00   \n",
-       "3                                        8.00        11914.00   \n",
-       "4                                        8.00        12398.00   \n",
-       "...                                       ...             ...   \n",
-       "34697                                  107.00             NaN   \n",
-       "34698                                  103.00             NaN   \n",
-       "34699                                   97.00             NaN   \n",
-       "34700                                   92.00             NaN   \n",
-       "34701                                   92.00             NaN   \n",
-       "\n",
-       "Turystyczne obiekty noclegowe  Turysci_zagraniczni  \n",
-       "0                                          5173.00  \n",
-       "1                                          4486.00  \n",
-       "2                                          4856.00  \n",
-       "3                                          4701.00  \n",
-       "4                                          3919.00  \n",
-       "...                                            ...  \n",
-       "34697                                          NaN  \n",
-       "34698                                          NaN  \n",
-       "34699                                          NaN  \n",
-       "34700                                          NaN  \n",
-       "34701                                          NaN  \n",
-       "\n",
-       "[34702 rows x 8 columns]"
-      ]
-     },
-     "execution_count": 426,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_tury = pd.read_csv(\n",
-    "    'TURY_2017_CREL.csv',\n",
-    "    sep=';',\n",
-    "    converters={'Kod': str},\n",
-    "    decimal=',')\n",
-    "df_tury = df_tury[['Kod', 'Turystyczne obiekty noclegowe', 'Rok', 'Wartosc']]\n",
-    "df_tury = df_tury.dropna()\n",
-    "df_tury = df_tury.pivot_table(index=['Kod', 'Rok'], columns='Turystyczne obiekty noclegowe', values='Wartosc').reset_index()\n",
-    "df_tury = df_tury.rename(columns={\n",
-    "    'miejsca noclegowe całoroczne lipiec': 'Miejsca_noclegowe_caloroczne',\n",
-    "    'miejsca noclegowe ogółem lipiec': 'Miejsca_noclegowe_ogolem',\n",
-    "    'obiekty całoroczne lipiec': 'Obiekty_caloroczne',\n",
-    "    'obiekty ogółem lipiec': 'Obiekty_ogolem',\n",
-    "    'turyści (korzystający) ogółem styczeń-grudzień': 'Turysci_ogolem',\n",
-    "    'turyści zagraniczni (korzystający) - nierezydenci styczeń-grudzień': 'Turysci_zagraniczni'})\n",
-    "\n",
-    "df_tury"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 427,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Bezrobotni</th>\n",
-       "      <th>Kod</th>\n",
-       "      <th>Rok</th>\n",
-       "      <th>Bezrobotni_do_25_roku_zycia</th>\n",
-       "      <th>Bezrobotni_do_30_roku_zycia</th>\n",
-       "      <th>Dlugotrwale_bezrobotni</th>\n",
-       "      <th>Bezrobotne_kobiety</th>\n",
-       "      <th>Bezrobotni_mezczyzni</th>\n",
-       "      <th>Bezrobotni_ogolem</th>\n",
-       "      <th>Bezrobotni_powyzej_50_roku_zycia</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2011</td>\n",
-       "      <td>284.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>819.50</td>\n",
-       "      <td>900.50</td>\n",
-       "      <td>818.00</td>\n",
-       "      <td>1718.50</td>\n",
-       "      <td>486.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2012</td>\n",
-       "      <td>293.00</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>756.50</td>\n",
-       "      <td>894.50</td>\n",
-       "      <td>888.00</td>\n",
-       "      <td>1782.50</td>\n",
-       "      <td>498.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2013</td>\n",
-       "      <td>253.50</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>788.00</td>\n",
-       "      <td>869.50</td>\n",
-       "      <td>874.00</td>\n",
-       "      <td>1743.50</td>\n",
-       "      <td>521.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2014</td>\n",
-       "      <td>172.50</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>651.50</td>\n",
-       "      <td>648.50</td>\n",
-       "      <td>667.50</td>\n",
-       "      <td>1316.00</td>\n",
-       "      <td>402.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>0201011</td>\n",
-       "      <td>2015</td>\n",
-       "      <td>107.50</td>\n",
-       "      <td>238.00</td>\n",
-       "      <td>434.50</td>\n",
-       "      <td>504.00</td>\n",
-       "      <td>518.50</td>\n",
-       "      <td>1022.50</td>\n",
-       "      <td>359.00</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",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48530</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2019</td>\n",
-       "      <td>27.50</td>\n",
-       "      <td>66.00</td>\n",
-       "      <td>226.50</td>\n",
-       "      <td>272.50</td>\n",
-       "      <td>221.00</td>\n",
-       "      <td>493.50</td>\n",
-       "      <td>181.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48531</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2020</td>\n",
-       "      <td>56.00</td>\n",
-       "      <td>142.00</td>\n",
-       "      <td>239.50</td>\n",
-       "      <td>390.00</td>\n",
-       "      <td>361.50</td>\n",
-       "      <td>751.50</td>\n",
-       "      <td>250.00</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48532</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2021</td>\n",
-       "      <td>34.50</td>\n",
-       "      <td>88.00</td>\n",
-       "      <td>260.50</td>\n",
-       "      <td>295.00</td>\n",
-       "      <td>341.00</td>\n",
-       "      <td>636.00</td>\n",
-       "      <td>239.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48533</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2022</td>\n",
-       "      <td>31.50</td>\n",
-       "      <td>72.00</td>\n",
-       "      <td>199.00</td>\n",
-       "      <td>211.50</td>\n",
-       "      <td>270.50</td>\n",
-       "      <td>482.00</td>\n",
-       "      <td>182.50</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>48534</th>\n",
-       "      <td>3263011</td>\n",
-       "      <td>2023</td>\n",
-       "      <td>33.50</td>\n",
-       "      <td>81.00</td>\n",
-       "      <td>200.00</td>\n",
-       "      <td>241.00</td>\n",
-       "      <td>287.50</td>\n",
-       "      <td>528.50</td>\n",
-       "      <td>189.00</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>48535 rows × 9 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Bezrobotni      Kod   Rok  Bezrobotni_do_25_roku_zycia   \n",
-       "0           0201011  2011                       284.00  \\\n",
-       "1           0201011  2012                       293.00   \n",
-       "2           0201011  2013                       253.50   \n",
-       "3           0201011  2014                       172.50   \n",
-       "4           0201011  2015                       107.50   \n",
-       "...             ...   ...                          ...   \n",
-       "48530       3263011  2019                        27.50   \n",
-       "48531       3263011  2020                        56.00   \n",
-       "48532       3263011  2021                        34.50   \n",
-       "48533       3263011  2022                        31.50   \n",
-       "48534       3263011  2023                        33.50   \n",
-       "\n",
-       "Bezrobotni  Bezrobotni_do_30_roku_zycia  Dlugotrwale_bezrobotni   \n",
-       "0                                   NaN                  819.50  \\\n",
-       "1                                   NaN                  756.50   \n",
-       "2                                   NaN                  788.00   \n",
-       "3                                   NaN                  651.50   \n",
-       "4                                238.00                  434.50   \n",
-       "...                                 ...                     ...   \n",
-       "48530                             66.00                  226.50   \n",
-       "48531                            142.00                  239.50   \n",
-       "48532                             88.00                  260.50   \n",
-       "48533                             72.00                  199.00   \n",
-       "48534                             81.00                  200.00   \n",
-       "\n",
-       "Bezrobotni  Bezrobotne_kobiety  Bezrobotni_mezczyzni  Bezrobotni_ogolem   \n",
-       "0                       900.50                818.00            1718.50  \\\n",
-       "1                       894.50                888.00            1782.50   \n",
-       "2                       869.50                874.00            1743.50   \n",
-       "3                       648.50                667.50            1316.00   \n",
-       "4                       504.00                518.50            1022.50   \n",
-       "...                        ...                   ...                ...   \n",
-       "48530                   272.50                221.00             493.50   \n",
-       "48531                   390.00                361.50             751.50   \n",
-       "48532                   295.00                341.00             636.00   \n",
-       "48533                   211.50                270.50             482.00   \n",
-       "48534                   241.00                287.50             528.50   \n",
-       "\n",
-       "Bezrobotni  Bezrobotni_powyzej_50_roku_zycia  \n",
-       "0                                     486.50  \n",
-       "1                                     498.50  \n",
-       "2                                     521.00  \n",
-       "3                                     402.00  \n",
-       "4                                     359.00  \n",
-       "...                                      ...  \n",
-       "48530                                 181.00  \n",
-       "48531                                 250.00  \n",
-       "48532                                 239.50  \n",
-       "48533                                 182.50  \n",
-       "48534                                 189.00  \n",
-       "\n",
-       "[48535 rows x 9 columns]"
-      ]
-     },
-     "execution_count": 427,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_ryne = pd.read_csv(\n",
-    "    'RYNE_3733_CREL.csv',\n",
-    "    sep=';',\n",
-    "    converters={'Kod': str},\n",
-    "    decimal=',')\n",
-    "df_ryne = df_ryne[['Kod', 'Bezrobotni', 'Rok', 'Wartosc']]\n",
-    "df_ryne = df_ryne.dropna()\n",
-    "df_ryne = df_ryne.pivot_table(index=['Kod', 'Rok'], columns='Bezrobotni', values='Wartosc').reset_index()\n",
-    "df_ryne = df_ryne.rename(columns={\n",
-    "    'do 25 roku życia': 'Bezrobotni_do_25_roku_zycia',\n",
-    "    'do 30 roku życia': 'Bezrobotni_do_30_roku_zycia',\n",
-    "    'długotrwale bezrobotni': 'Dlugotrwale_bezrobotni',\n",
-    "    'kobiety': 'Bezrobotne_kobiety',\n",
-    "    'mężczyźni': 'Bezrobotni_mezczyzni',\n",
-    "    'ogółem': 'Bezrobotni_ogolem',\n",
-    "    'powyżej 50 roku życia': 'Bezrobotni_powyzej_50_roku_zycia'})\n",
-    "\n",
-    "df_ryne"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 428,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2273\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_data = df_dofinansowanie_agg.copy()\n",
-    "print(len(df_data))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 429,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wojewodztwo_dictionary = {\n",
-    "'02': 'Dolnoslaskie',\n",
-    "'04': 'Kujawsko_Pomorskie',\n",
-    "'06': 'Lubelskie',\n",
-    "'08': 'Lubuskie',\n",
-    "'10': 'Lodzkie',\n",
-    "'12': 'Malopolskie',\n",
-    "'14': 'Mazowieckie',\n",
-    "'16': 'Opolskie',\n",
-    "'18': 'Podkarpackie',\n",
-    "'20': 'Podlaskie',\n",
-    "'22': 'Pomorskie',\n",
-    "'24': 'Slaskie',\n",
-    "'26': 'Swietokrzyskie',\n",
-    "'28': 'Warminsko_Mazurskie',\n",
-    "'30': 'Wielkopolskie',\n",
-    "'32': 'Zachodniopomorskie'}\n",
-    "\n",
-    "df_data = pd.concat([df_data, pd.get_dummies(df_data['Kod'].apply(lambda x: wojewodztwo_dictionary.get(x[:2], None)), prefix='Wojewodztwo').astype(int)], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 430,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rodzaj_gminy_dictionary = {\n",
-    "'1': 'Gmina miejska',\n",
-    "'2': 'Gmina wiejska',\n",
-    "'3': 'Gmina miejsko-wiejska'}\n",
-    "\n",
-    "df_data = pd.concat([df_data, pd.get_dummies(df_data['Kod'].apply(lambda x: rodzaj_gminy_dictionary.get(x[-1], None))).astype(int)], axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 431,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2273\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_data = df_data.merge(df_podz, left_on=[df_data['Kod'].str.slice(stop=-1), 'Rok'], right_on=[df_podz['Kod'].str.slice(stop=-1), 'Rok'], how='left', suffixes=(None, '_podz'))\n",
-    "df_data = df_data.drop(['key_0', 'Kod_podz'], axis=1)\n",
-    "print(len(df_data))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 432,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2273\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_data = df_data.merge(df_wyna, left_on=[df_data['Kod'].str.slice(stop=-3), 'Rok'], right_on=[df_wyna['Kod'].str.slice(stop=-3), 'Rok'], how='left', suffixes=(None, '_wyna'))\n",
-    "df_data = df_data.drop(['key_0', 'Kod_wyna'], axis=1)\n",
-    "print(len(df_data))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 433,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2273\n",
-      "2273\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_data = df_data.merge(df_fina_1, left_on=[df_data['Kod'].str.slice(stop=-1), 'Rok'], right_on=[df_fina_1['Kod'].str.slice(stop=-1), 'Rok'], how='left', suffixes=(None, '_fina_1'))\n",
-    "df_data = df_data.drop(['key_0', 'Kod_fina_1'], axis=1)\n",
-    "print(len(df_data))\n",
-    "\n",
-    "df_data = df_data.merge(df_fina_2, left_on=[df_data['Kod'].str.slice(stop=-1), 'Rok'], right_on=[df_fina_2['Kod'].str.slice(stop=-1), 'Rok'], how='left', suffixes=(None, '_fina_2'))\n",
-    "df_data = df_data.drop(['key_0', 'Kod_fina_2'], axis=1)\n",
-    "print(len(df_data))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 434,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2273\n",
-      "2273\n",
-      "2273\n",
-      "2273\n",
-      "2273\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_data = df_data.merge(df_ludn_1, left_on=['Kod', 'Rok'], right_on=['Kod', 'Rok'], how='left', suffixes=(None, '_ludn_1'))\n",
-    "# df_data = df_data.drop(['key_0', 'Kod_ludn_1'], axis=1)\n",
-    "print(len(df_data))\n",
-    "\n",
-    "df_data = df_data.merge(df_ludn_2, left_on=['Kod', 'Rok'], right_on=['Kod', 'Rok'], how='left', suffixes=(None, '_ludn_2'))\n",
-    "# df_data = df_data.drop(['key_0', 'Kod_ludn_2'], axis=1)\n",
-    "print(len(df_data))\n",
-    "\n",
-    "df_data = df_data.merge(df_ludn_3, left_on=['Kod', 'Rok'], right_on=['Kod', 'Rok'], how='left', suffixes=(None, '_ludn_3'))\n",
-    "# df_data = df_data.drop(['key_0', 'Kod_ludn_3'], axis=1)\n",
-    "print(len(df_data))\n",
-    "\n",
-    "df_data = df_data.merge(df_ludn_4, left_on=['Kod', 'Rok'], right_on=['Kod', 'Rok'], how='left', suffixes=(None, '_ludn_4'))\n",
-    "# df_data = df_data.drop(['key_0', 'Kod_ludn_4'], axis=1)\n",
-    "print(len(df_data))\n",
-    "\n",
-    "df_data = df_data.merge(df_ludn_5, left_on=['Kod', 'Rok'], right_on=['Kod', 'Rok'], how='left', suffixes=(None, '_ludn_5'))\n",
-    "# df_data = df_data.drop(['key_0', 'Kod_ludn_5'], axis=1)\n",
-    "print(len(df_data))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 435,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2273\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_data = df_data.merge(df_tury, left_on=['Kod', 'Rok'], right_on=['Kod', 'Rok'], how='left', suffixes=(None, '_tury'))\n",
-    "# df_data = df_data.drop(['key_0', 'Kod_tury'], axis=1)\n",
-    "print(len(df_data))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 436,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2273\n"
-     ]
-    }
-   ],
-   "source": [
-    "df_data = df_data.merge(df_ryne, left_on=['Kod', 'Rok'], right_on=['Kod', 'Rok'], how='left', suffixes=(None, '_ryne'))\n",
-    "# df_data = df_data.drop(['key_0', 'Kod_ryne'], axis=1)\n",
-    "print(len(df_data))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 437,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6733698870134954"
-      ]
-     },
-     "execution_count": 437,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_data['Gestosc_zaludnienia'] = df_data['Ludnosc'] / df_data['Powierzchnia']\n",
-    "\n",
-    "df_data['Gestosc_zaludnienia'].mean()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 438,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_data['Suma'] = df_data['Suma'] / df_data['Ludnosc']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 439,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Suma</th>\n",
-       "      <th>Ludnosc</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>62917.04</td>\n",
-       "      <td>39.46</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>3520.19</td>\n",
-       "      <td>39.08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>224163.57</td>\n",
-       "      <td>37.66</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>32473.38</td>\n",
-       "      <td>14.09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>25713.81</td>\n",
-       "      <td>5.35</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2268</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2269</th>\n",
-       "      <td>196539.43</td>\n",
-       "      <td>41.28</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2270</th>\n",
-       "      <td>7900.24</td>\n",
-       "      <td>41.15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2271</th>\n",
-       "      <td>11386418.96</td>\n",
-       "      <td>41.03</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2272</th>\n",
-       "      <td>188601.28</td>\n",
-       "      <td>40.33</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2273 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            Suma  Ludnosc\n",
-       "0       62917.04    39.46\n",
-       "1        3520.19    39.08\n",
-       "2      224163.57    37.66\n",
-       "3       32473.38    14.09\n",
-       "4       25713.81     5.35\n",
-       "...          ...      ...\n",
-       "2268         NaN      NaN\n",
-       "2269   196539.43    41.28\n",
-       "2270     7900.24    41.15\n",
-       "2271 11386418.96    41.03\n",
-       "2272   188601.28    40.33\n",
-       "\n",
-       "[2273 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 439,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df_data[['Suma', 'Ludnosc']]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 440,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# df_data[df_data.isna().any(axis=1)]  # ['Rok'].drop_duplicates().reset_index(drop=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 441,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "s = df_data.isna().sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 442,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Wymeldowania_za_granice_kobiety       775\n",
-       "Wymeldowania_za_granice_mezczyzni     775\n",
-       "Wymeldowania_za_granice_ogolem        775\n",
-       "Turysci_ogolem                       1724\n",
-       "Turysci_zagraniczni                  1724\n",
-       "Bezrobotni_do_30_roku_zycia           657\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 442,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "s[s > 330]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 443,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2273\n",
-      "2191\n",
-      "Mean Squared Error: 20582414771111.633\n"
+      " 'Program Operacyjny Wiedza Edukacja RozwĂłj'\n",
+      " 'Program Operacyjny Polska Wschodnia']\n"
      ]
     }
    ],
    "source": [
+    "import pandas as pd\n",
+    "from statistics import median\n",
     "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.tree import DecisionTreeRegressor, plot_tree, export_text\n",
+    "from sklearn.feature_selection import RFE\n",
+    "from sklearn.tree import DecisionTreeRegressor\n",
+    "import numpy as np\n",
     "from sklearn.metrics import mean_squared_error\n",
     "import matplotlib.pyplot as plt\n",
-    "\n",
-    "df_data[[\n",
-    "    'Miejsca_noclegowe_caloroczne',\n",
-    "    'Miejsca_noclegowe_ogolem',\n",
-    "    'Obiekty_caloroczne',\n",
-    "    'Obiekty_ogolem',\n",
-    "    'Turysci_ogolem',\n",
-    "    'Turysci_zagraniczni']] = df_data[[\n",
-    "        'Miejsca_noclegowe_caloroczne',\n",
-    "        'Miejsca_noclegowe_ogolem',\n",
-    "        'Obiekty_caloroczne',\n",
-    "        'Obiekty_ogolem',\n",
-    "        'Turysci_ogolem',\n",
-    "        'Turysci_zagraniczni']].fillna(0)\n",
+    "import seaborn as sns\n",
     "\n",
     "feature_names = [\n",
     "    'Powierzchnia',                                        # 1\n",
@@ -3229,124 +90,713 @@
     "    'Gestosc_zaludnienia',                                 # 59\n",
     "    'Ludnosc_na_1_km2',                                    # 60\n",
     "    'Ludnosc',                                             # 61\n",
-    "    'Ludnosc_kobiety',                                     # 62\n",
-    "    'Ludnosc_mezczyzni',                                   # 63\n",
-    "    'Wskaznik_urbanizacji',                                # 64\n",
-    "    'Zmiana_liczby_ludnosci',                              # 65\n",
-    "    'Saldo_migracji_na_1000_ludnosci',                     # 66\n",
-    "    'Saldo_migracji',                                      # 67\n",
-    "    'Wymeldowania_do_miast_kobiety',                       # 68\n",
-    "    'Wymeldowania_do_miast_mezczyzni',                     # 69\n",
-    "    'Wymeldowania_do_miast_ogolem',                        # 70\n",
-    "    'Wymeldowania_na_wies_kobiety',                        # 71\n",
-    "    'Wymeldowania_na_wies_mezczyzni',                      # 72\n",
-    "    'Wymeldowania_na_wies_ogolem',                         # 73\n",
-    "    'Wymeldowania_kobiety',                                # 74\n",
-    "    'Wymeldowania_mezczyzni',                              # 75\n",
-    "    'Wymeldowania_ogolem',                                 # 76\n",
-    "    'Zameldowania_kobiety',                                # 77\n",
-    "    'Zameldowania_mezczyzni',                              # 78\n",
-    "    'Zameldowania_ogolem',                                 # 79\n",
-    "    'Zameldowania_z_miast_kobiety',                        # 80\n",
-    "    'Zameldowania_z_miast_mezczyzni',                      # 81\n",
-    "    'Zameldowania_z_miast_ogolem',                         # 82\n",
-    "    'Zameldowania_ze_wsi_kobiety',                         # 83\n",
-    "    'Zameldowania_ze_wsi_mezczyzni',                       # 84\n",
-    "    'Zameldowania_ze_wsi_ogolem',                          # 85\n",
-    "    'Miejsca_noclegowe_caloroczne',                        # 86\n",
-    "    'Miejsca_noclegowe_ogolem',                            # 87\n",
-    "    'Obiekty_caloroczne',                                  # 88\n",
-    "    'Obiekty_ogolem',                                      # 89\n",
-    "    'Turysci_ogolem',                                      # 90\n",
-    "    'Turysci_zagraniczni',                                 # 91\n",
-    "    'Bezrobotni_do_25_roku_zycia',                         # 92\n",
-    "    'Dlugotrwale_bezrobotni',                              # 93\n",
-    "    'Bezrobotne_kobiety',                                  # 94\n",
-    "    'Bezrobotni_mezczyzni',                                # 95\n",
-    "    'Bezrobotni_ogolem',                                   # 96\n",
-    "    'Bezrobotni_powyzej_50_roku_zycia']                    # 97\n",
+    "    'Wskaznik_urbanizacji',                                # 62\n",
+    "    'Zmiana_liczby_ludnosci',                              # 63\n",
+    "    'Saldo_migracji_na_1000_ludnosci',                     # 64\n",
+    "    'Saldo_migracji',                                      # 65\n",
+    "    'Wymeldowania_do_miast_kobiety',                       # 66\n",
+    "    'Wymeldowania_do_miast_mezczyzni',                     # 67\n",
+    "    'Wymeldowania_do_miast_ogolem',                        # 68\n",
+    "    'Wymeldowania_na_wies_kobiety',                        # 69\n",
+    "    'Wymeldowania_na_wies_mezczyzni',                      # 70\n",
+    "    'Wymeldowania_na_wies_ogolem',                         # 71\n",
+    "    'Wymeldowania_kobiety',                                # 72\n",
+    "    'Wymeldowania_mezczyzni',                              # 73\n",
+    "    'Wymeldowania_ogolem',                                 # 74\n",
+    "    'Zameldowania_kobiety',                                # 75\n",
+    "    'Zameldowania_mezczyzni',                              # 76\n",
+    "    'Zameldowania_ogolem',                                 # 77\n",
+    "    'Zameldowania_z_miast_kobiety',                        # 78\n",
+    "    'Zameldowania_z_miast_mezczyzni',                      # 79\n",
+    "    'Zameldowania_z_miast_ogolem',                         # 80\n",
+    "    'Zameldowania_ze_wsi_kobiety',                         # 81\n",
+    "    'Zameldowania_ze_wsi_mezczyzni',                       # 82\n",
+    "    'Zameldowania_ze_wsi_ogolem',                          # 83\n",
+    "    'Miejsca_noclegowe_caloroczne',                        # 84\n",
+    "    'Miejsca_noclegowe_ogolem',                            # 85\n",
+    "    'Obiekty_caloroczne',                                  # 86\n",
+    "    'Obiekty_ogolem',                                      # 87\n",
+    "    'Turysci_ogolem',                                      # 88\n",
+    "    'Turysci_zagraniczni',                                 # 89\n",
+    "    'Bezrobotni_do_25_roku_zycia',                         # 90\n",
+    "    'Dlugotrwale_bezrobotni',                              # 91\n",
+    "    'Bezrobotne_kobiety',                                  # 92\n",
+    "    'Bezrobotni_mezczyzni',                                # 93\n",
+    "    'Bezrobotni_ogolem',                                   # 94\n",
+    "    'Bezrobotni_powyzej_50_roku_zycia',                    # 95\n",
+    "    'Gmina_miejska',                                       # 96\n",
+    "    'Gmina_miejsko_wiejska',                               # 97\n",
+    "    'Gmina_wiejska',                                       # 98\n",
+    "    'Odleglosc_Warszawa',                                  # 99\n",
+    "    'Odleglosc_od_centrum_decyzyjnego']                    # 100\n",
     "\n",
-    "df_data.drop(columns=[\n",
-    "    'Wymeldowania_za_granice_kobiety',\n",
-    "    'Wymeldowania_za_granice_mezczyzni',\n",
-    "    'Wymeldowania_za_granice_ogolem',\n",
-    "    'Bezrobotni_do_30_roku_zycia'], inplace=True, errors='ignore')\n",
+    "all_columns = ['Kod', 'Rok'] + feature_names\n",
     "\n",
-    "print(len(df_data))\n",
+    "df_data = pd.read_csv(\n",
+    "    'dane1.csv',\n",
+    "    encoding='ISO-8859-2',\n",
+    "    converters={'Kod': str})\n",
+    "\n",
+    "df_odl = pd.read_csv(\n",
+    "    'gminy_centroid.csv',\n",
+    "    encoding='ISO-8859-2',\n",
+    "    converters={'TERYT': str})\n",
+    "df_odl['TERYT'] = df_odl['TERYT'].astype('str')\n",
+    "df_odl = df_odl[['TERYT', 'odl_Wawa', 'odl_woj']]\n",
+    "df_odl = df_odl.rename(columns={\n",
+    "    'TERYT': 'Kod',\n",
+    "    'odl_Wawa': 'Odleglosc_Warszawa',\n",
+    "    'odl_woj': 'Odleglosc_od_centrum_decyzyjnego'})\n",
+    "\n",
+    "df_data = df_data.merge(df_odl, on=['Kod'], how='left')\n",
+    "\n",
+    "print(df_data['Program_operacyjny'].drop_duplicates().values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Median 549828.735\n",
+      "Mean 893364.8266077801\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_14224\\845662055.py:23: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
+      "  df_data = df_data.groupby(all_columns)['Suma'].sum().reset_index()\n",
+      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_14224\\845662055.py:23: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
+      "  df_data = df_data.groupby(all_columns)['Suma'].sum().reset_index()\n",
+      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_14224\\845662055.py:23: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
+      "  df_data = df_data.groupby(all_columns)['Suma'].sum().reset_index()\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Wybór programu operacyjnego...\n",
+    "df_data = df_data.loc[df_data['Program_operacyjny'] == 'Program Operacyjny Polska Cyfrowa'].reset_index(drop=True)\n",
+    "\n",
+    "# Uzupełnienie brakujących danych...\n",
+    "df_data[[\n",
+    "    'Miejsca_noclegowe_caloroczne',\n",
+    "    'Miejsca_noclegowe_ogolem',\n",
+    "    'Obiekty_caloroczne',\n",
+    "    'Obiekty_ogolem',\n",
+    "    'Turysci_ogolem',\n",
+    "    'Turysci_zagraniczni']] = df_data[[\n",
+    "        'Miejsca_noclegowe_caloroczne',\n",
+    "        'Miejsca_noclegowe_ogolem',\n",
+    "        'Obiekty_caloroczne',\n",
+    "        'Obiekty_ogolem',\n",
+    "        'Turysci_ogolem',\n",
+    "        'Turysci_zagraniczni']].fillna(0)\n",
+    "\n",
+    "# Usunięcie niepotrzebnych rzędów...\n",
     "df_data.dropna(inplace=True)\n",
     "df_data = df_data[df_data['Suma'] > 0]\n",
-    "print(len(df_data))\n",
+    "\n",
+    "df_data = df_data.groupby(all_columns)['Suma'].sum().reset_index()\n",
+    "\n",
+    "# ...\n",
+    "# df_data['Suma'] = df_data['Suma'] / df_data['Ludnosc']\n",
+    "\n",
+    "print('Median', median(df_data['Suma']))\n",
+    "print('Mean', sum(df_data['Suma'])/len(df_data['Suma']))\n",
     "\n",
     "X = df_data[feature_names]\n",
     "y = df_data['Suma']\n",
     "\n",
-    "color_column = df_data['Gestosc_zaludnienia']\n",
-    "color_column = (df_data['Gestosc_zaludnienia'] > 1.5).astype(int)\n",
-    "\n",
-    "X_train, X_test, y_train, y_test, color_column_train, color_column_test = train_test_split(X, y, color_column, test_size=0.2, random_state=1)\n",
-    "\n",
-    "model = DecisionTreeRegressor(random_state=1)\n",
-    "model.fit(X_train, y_train)\n",
-    "\n",
-    "y_pred = model.predict(X_test)\n",
-    "mse = mean_squared_error(y_test, y_pred)\n",
-    "print('Mean Squared Error:', mse)"
+    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 444,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Gmina wiejska                                         0.14\n",
-      "Wojewodztwo_Dolnoslaskie                              0.09\n",
-      "Dochody_podatek_rolny                                 0.07\n",
-      "Saldo_migracji_na_1000_ludnosci                       0.06\n",
-      "Zmiana_liczby_ludnosci                                0.05\n",
-      "Wojewodztwo_Warminsko_Mazurskie                       0.05\n",
-      "Wojewodztwo_Pomorskie                                 0.04\n",
-      "Wojewodztwo_Opolskie                                  0.02\n",
-      "Saldo_migracji                                        0.01\n",
-      "Wojewodztwo_Mazowieckie                               0.01\n",
-      "Turysci_ogolem                                        0.01\n",
-      "Turysci_zagraniczni                                   0.01\n",
-      "Wojewodztwo_Podkarpackie                             -0.00\n",
-      "Wplywy_z_oplaty_eksploatacyjnej                      -0.01\n",
-      "Wojewodztwo_Swietokrzyskie                           -0.01\n",
-      "Wojewodztwo_Zachodniopomorskie                       -0.01\n",
-      "Powierzchnia                                         -0.01\n",
-      "Wojewodztwo_Slaskie                                  -0.01\n",
-      "Wojewodztwo_Lubelskie                                -0.01\n",
-      "Obiekty_ogolem                                       -0.02\n",
-      "Wojewodztwo_Lubuskie                                 -0.02\n",
-      "Miejsca_noclegowe_ogolem                             -0.02\n",
-      "Wojewodztwo_Podlaskie                                -0.02\n",
-      "Dochody_podatek_PCC                                  -0.02\n",
-      "Dochody_podatek_od_spadkow                           -0.02\n",
-      "Udzialy_w_podatkach_dochodowych_od_osob_prywatnych   -0.02\n",
-      "Wynagrodzenie_w_relacji_do_sredniej                  -0.02\n",
-      "Dochody_z_uslug                                      -0.02\n",
-      "Name: Suma, dtype: float64\n"
+      "max_depth:  3, n_features: 20, mse_train:       985605388909.4, mse_test:      1473640288814.4 <-\n",
+      "max_depth:  3, n_features: 19, mse_train:       985605388909.4, mse_test:      1473640288814.4 <-\n",
+      "max_depth:  3, n_features: 18, mse_train:       985605388909.4, mse_test:      1473640288814.4 <-\n",
+      "max_depth:  3, n_features: 17, mse_train:       985605388909.4, mse_test:      1473640288814.4\n",
+      "max_depth:  3, n_features: 16, mse_train:       985605388909.4, mse_test:      1473640288814.4\n",
+      "max_depth:  3, n_features: 15, mse_train:       985605388909.4, mse_test:      1473640288814.4\n",
+      "max_depth:  3, n_features: 14, mse_train:       985605388909.4, mse_test:      1473640288814.4\n",
+      "max_depth:  3, n_features: 13, mse_train:       985605388909.4, mse_test:      1473640288814.4\n",
+      "max_depth:  3, n_features: 12, mse_train:       985605388909.4, mse_test:      1473640288814.4\n",
+      "max_depth:  3, n_features: 11, mse_train:       985605388909.4, mse_test:      1473640288814.4\n",
+      "max_depth:  3, n_features: 10, mse_train:       985605388909.4, mse_test:      1473640288814.4\n",
+      "max_depth:  3, n_features:  9, mse_train:       985605388909.4, mse_test:      1473640288814.4\n",
+      "max_depth:  3, n_features:  8, mse_train:       985605388909.4, mse_test:      1473640288814.4\n",
+      "max_depth:  3, n_features:  7, mse_train:       985605388909.4, mse_test:      1473640288814.4\n",
+      "max_depth:  3, n_features:  6, mse_train:       985605388909.4, mse_test:      1473640288814.4\n",
+      "max_depth:  3, n_features:  5, mse_train:       985605388909.4, mse_test:      1473640288814.4\n",
+      "max_depth:  3, n_features:  4, mse_train:       985605388909.4, mse_test:      1473640288814.4\n",
+      "max_depth:  3, n_features:  3, mse_train:      1016104087301.3, mse_test:      1477893916125.6\n",
+      "max_depth:  3, n_features:  2, mse_train:      1089815493742.4, mse_test:      1594239666432.3\n",
+      "max_depth:  4, n_features: 20, mse_train:       899824809745.5, mse_test:      1499284611541.4\n",
+      "max_depth:  4, n_features: 19, mse_train:       899824809745.5, mse_test:      1499284611541.4\n",
+      "max_depth:  4, n_features: 18, mse_train:       899824809745.5, mse_test:      1499284611541.4\n",
+      "max_depth:  4, n_features: 17, mse_train:       899824809745.5, mse_test:      1499284611541.4\n",
+      "max_depth:  4, n_features: 16, mse_train:       899824809745.5, mse_test:      1499284611541.4\n",
+      "max_depth:  4, n_features: 15, mse_train:       899824809745.5, mse_test:      1499284611541.4\n",
+      "max_depth:  4, n_features: 14, mse_train:       899824809745.5, mse_test:      1499284611541.4\n",
+      "max_depth:  4, n_features: 13, mse_train:       899824809745.5, mse_test:      1499284611541.4\n",
+      "max_depth:  4, n_features: 12, mse_train:       899824809745.5, mse_test:      1499284611541.4\n",
+      "max_depth:  4, n_features: 11, mse_train:       899824809745.5, mse_test:      1499284611541.4\n",
+      "max_depth:  4, n_features: 10, mse_train:       899824809745.5, mse_test:      1499284611541.4\n",
+      "max_depth:  4, n_features:  9, mse_train:       899824809745.5, mse_test:      1499284611541.4\n",
+      "max_depth:  4, n_features:  8, mse_train:       899824809745.5, mse_test:      1499284611541.4\n",
+      "max_depth:  4, n_features:  7, mse_train:       899824809745.5, mse_test:      1499284611541.4\n",
+      "max_depth:  4, n_features:  6, mse_train:       908346033942.9, mse_test:      1490386752565.4\n",
+      "max_depth:  4, n_features:  5, mse_train:       926529787722.3, mse_test:      1402781153280.7 <-\n",
+      "max_depth:  4, n_features:  4, mse_train:       964049275485.7, mse_test:      1489236448304.3\n",
+      "max_depth:  4, n_features:  3, mse_train:      1041479069310.2, mse_test:      1645003773149.2\n",
+      "max_depth:  4, n_features:  2, mse_train:      1048024137736.7, mse_test:      1631458739231.6\n",
+      "max_depth:  5, n_features: 20, mse_train:       777894457863.3, mse_test:      1481628937180.8\n",
+      "max_depth:  5, n_features: 19, mse_train:       777894457863.3, mse_test:      1481628937180.8\n",
+      "max_depth:  5, n_features: 18, mse_train:       777894457863.3, mse_test:      1456513737768.1\n",
+      "max_depth:  5, n_features: 17, mse_train:       777894457863.3, mse_test:      1481628937180.8\n",
+      "max_depth:  5, n_features: 16, mse_train:       777894457863.3, mse_test:      1467015002114.1\n",
+      "max_depth:  5, n_features: 15, mse_train:       777894457863.3, mse_test:      1471127672834.8\n",
+      "max_depth:  5, n_features: 14, mse_train:       777894457863.3, mse_test:      1456513737768.1\n",
+      "max_depth:  5, n_features: 13, mse_train:       777894457863.3, mse_test:      1471079590321.0\n",
+      "max_depth:  5, n_features: 12, mse_train:       777894457863.3, mse_test:      1471127672834.8\n",
+      "max_depth:  5, n_features: 11, mse_train:       777894457863.3, mse_test:      1456513737768.1\n",
+      "max_depth:  5, n_features: 10, mse_train:       778314815085.4, mse_test:      1418071734165.4\n",
+      "max_depth:  5, n_features:  9, mse_train:       778314815085.4, mse_test:      1432733751745.9\n",
+      "max_depth:  5, n_features:  8, mse_train:       787935644695.6, mse_test:      1430421492243.5\n",
+      "max_depth:  5, n_features:  7, mse_train:       798366359029.2, mse_test:      1412228802401.6\n",
+      "max_depth:  5, n_features:  6, mse_train:       849020081720.8, mse_test:      1379810167829.9 <-\n",
+      "max_depth:  5, n_features:  5, mse_train:       855870271330.2, mse_test:      1392618114515.3\n",
+      "max_depth:  5, n_features:  4, mse_train:       829431106712.3, mse_test:      1477225230229.6\n",
+      "max_depth:  5, n_features:  3, mse_train:       843820926828.0, mse_test:      1536212207013.9\n",
+      "max_depth:  5, n_features:  2, mse_train:       990725881946.2, mse_test:      1714327909974.3\n",
+      "max_depth:  6, n_features: 20, mse_train:       702120313181.4, mse_test:      1281312959244.0 <-\n",
+      "max_depth:  6, n_features: 19, mse_train:       702120313181.4, mse_test:      1348026449571.3\n",
+      "max_depth:  6, n_features: 18, mse_train:       702120313181.4, mse_test:      1337477102711.6\n",
+      "max_depth:  6, n_features: 17, mse_train:       702120313181.4, mse_test:      1337477102711.6\n",
+      "max_depth:  6, n_features: 16, mse_train:       702120313181.4, mse_test:      1337525185225.3\n",
+      "max_depth:  6, n_features: 15, mse_train:       702142989310.9, mse_test:      1337358249457.9\n",
+      "max_depth:  6, n_features: 14, mse_train:       704419162738.6, mse_test:      1352770446230.8\n",
+      "max_depth:  6, n_features: 13, mse_train:       703703386581.1, mse_test:      1289272765063.3\n",
+      "max_depth:  6, n_features: 12, mse_train:       703703386581.1, mse_test:      1309887199769.1\n",
+      "max_depth:  6, n_features: 11, mse_train:       708724446143.4, mse_test:      1234642841999.7 <-\n",
+      "max_depth:  6, n_features: 10, mse_train:       711594460766.6, mse_test:      1517285033906.0\n",
+      "max_depth:  6, n_features:  9, mse_train:       716036934079.4, mse_test:      1475542279563.4\n",
+      "max_depth:  6, n_features:  8, mse_train:       722046384072.1, mse_test:      1484380668197.3\n",
+      "max_depth:  6, n_features:  7, mse_train:       734664074932.7, mse_test:      1541936247863.0\n",
+      "max_depth:  6, n_features:  6, mse_train:       740451460882.2, mse_test:      1473087677930.4\n",
+      "max_depth:  6, n_features:  5, mse_train:       781881945028.2, mse_test:      1432693773418.1\n",
+      "max_depth:  6, n_features:  4, mse_train:       769096509833.5, mse_test:      1500627250338.5\n",
+      "max_depth:  6, n_features:  3, mse_train:       726228905814.3, mse_test:      1553623318775.1\n",
+      "max_depth:  6, n_features:  2, mse_train:       954694116124.2, mse_test:      1793011349050.2\n",
+      "max_depth:  7, n_features: 20, mse_train:       640943402298.7, mse_test:      1496067658852.6\n",
+      "max_depth:  7, n_features: 19, mse_train:       640942908841.0, mse_test:      1540205675724.2\n",
+      "max_depth:  7, n_features: 18, mse_train:       642047277467.7, mse_test:      1306670793440.2\n",
+      "max_depth:  7, n_features: 17, mse_train:       642047277467.7, mse_test:      1507534134549.3\n",
+      "max_depth:  7, n_features: 16, mse_train:       643760549982.0, mse_test:      1521231780106.0\n",
+      "max_depth:  7, n_features: 15, mse_train:       644701007059.7, mse_test:      1553343966490.6\n",
+      "max_depth:  7, n_features: 14, mse_train:       646961921791.8, mse_test:      1309275764750.8\n",
+      "max_depth:  7, n_features: 13, mse_train:       649210980784.4, mse_test:      1267172470255.6\n",
+      "max_depth:  7, n_features: 12, mse_train:       644449493626.2, mse_test:      1429604209608.8\n",
+      "max_depth:  7, n_features: 11, mse_train:       645231854299.2, mse_test:      1261650869426.7\n",
+      "max_depth:  7, n_features: 10, mse_train:       661000789616.8, mse_test:      1469653596135.2\n",
+      "max_depth:  7, n_features:  9, mse_train:       671851731055.9, mse_test:      1443317676733.9\n",
+      "max_depth:  7, n_features:  8, mse_train:       668963275155.1, mse_test:      1389604788794.3\n",
+      "max_depth:  7, n_features:  7, mse_train:       632753850957.4, mse_test:      1485243578684.9\n",
+      "max_depth:  7, n_features:  6, mse_train:       637473670864.0, mse_test:      1531783207565.3\n",
+      "max_depth:  7, n_features:  5, mse_train:       644623424816.4, mse_test:      1649506611800.3\n",
+      "max_depth:  7, n_features:  4, mse_train:       603471006353.0, mse_test:      1617678875412.5\n",
+      "max_depth:  7, n_features:  3, mse_train:       641958171995.7, mse_test:      1498648924671.5\n",
+      "max_depth:  7, n_features:  2, mse_train:       912569491717.4, mse_test:      1845041901590.2\n",
+      "max_depth:  8, n_features: 20, mse_train:       597165835865.7, mse_test:      1486064437752.0\n",
+      "max_depth:  8, n_features: 19, mse_train:       597165835865.7, mse_test:      1357765835520.1\n",
+      "max_depth:  8, n_features: 18, mse_train:       597168055646.7, mse_test:      1489452845516.8\n",
+      "max_depth:  8, n_features: 17, mse_train:       595624202073.7, mse_test:      1245652964515.9\n",
+      "max_depth:  8, n_features: 16, mse_train:       597142513301.3, mse_test:      1161784034070.4 <-\n",
+      "max_depth:  8, n_features: 15, mse_train:       590349542179.8, mse_test:      1398505656143.7\n",
+      "max_depth:  8, n_features: 14, mse_train:       590350436688.6, mse_test:      1379732640660.7\n",
+      "max_depth:  8, n_features: 13, mse_train:       597330891191.9, mse_test:      1209803349967.5\n",
+      "max_depth:  8, n_features: 12, mse_train:       597330891191.9, mse_test:      1115005152982.1 <-\n",
+      "max_depth:  8, n_features: 11, mse_train:       557581134904.5, mse_test:      1458540933633.0\n",
+      "max_depth:  8, n_features: 10, mse_train:       570830636069.8, mse_test:      1578735817222.2\n",
+      "max_depth:  8, n_features:  9, mse_train:       569650304338.8, mse_test:      1433881734475.8\n",
+      "max_depth:  8, n_features:  8, mse_train:       573104648489.6, mse_test:      1502189683249.2\n",
+      "max_depth:  8, n_features:  7, mse_train:       563187464782.4, mse_test:      1375553354162.2\n",
+      "max_depth:  8, n_features:  6, mse_train:       549292559957.6, mse_test:      1530005089808.0\n",
+      "max_depth:  8, n_features:  5, mse_train:       508679202857.1, mse_test:      1719509236785.1\n",
+      "max_depth:  8, n_features:  4, mse_train:       518356817030.3, mse_test:      1522726943641.9\n",
+      "max_depth:  8, n_features:  3, mse_train:       530467344219.3, mse_test:      1583900127054.1\n",
+      "max_depth:  8, n_features:  2, mse_train:       840574918161.4, mse_test:      1959195323321.1\n",
+      "max_depth:  9, n_features: 20, mse_train:       535278923105.8, mse_test:      1235800919626.8\n",
+      "max_depth:  9, n_features: 19, mse_train:       535278923105.8, mse_test:      1260992057688.6\n",
+      "max_depth:  9, n_features: 18, mse_train:       556983466445.3, mse_test:      1410233226055.4\n",
+      "max_depth:  9, n_features: 17, mse_train:       556983466445.3, mse_test:      1487549309939.7\n",
+      "max_depth:  9, n_features: 16, mse_train:       556990568094.9, mse_test:      1388042124631.4\n",
+      "max_depth:  9, n_features: 15, mse_train:       554494150822.8, mse_test:      1464222669501.7\n",
+      "max_depth:  9, n_features: 14, mse_train:       554487180480.0, mse_test:      1032355164763.0 <-\n",
+      "max_depth:  9, n_features: 13, mse_train:       558206503594.1, mse_test:      1347599535027.3\n",
+      "max_depth:  9, n_features: 12, mse_train:       558206503594.1, mse_test:      1367310577205.7\n",
+      "max_depth:  9, n_features: 11, mse_train:       552726857484.2, mse_test:      1490722225296.5\n",
+      "max_depth:  9, n_features: 10, mse_train:       553755807995.8, mse_test:      1448803928556.4\n",
+      "max_depth:  9, n_features:  9, mse_train:       554088151006.5, mse_test:      1578725764570.2\n",
+      "max_depth:  9, n_features:  8, mse_train:       484989533193.1, mse_test:      1437051627725.8\n",
+      "max_depth:  9, n_features:  7, mse_train:       486975950965.4, mse_test:      1342285901100.4\n",
+      "max_depth:  9, n_features:  6, mse_train:       478622464423.1, mse_test:      1473164828920.0\n",
+      "max_depth:  9, n_features:  5, mse_train:       432573475473.1, mse_test:      1680321217679.1\n",
+      "max_depth:  9, n_features:  4, mse_train:       468243324296.5, mse_test:      1282206447519.6\n",
+      "max_depth:  9, n_features:  3, mse_train:       632221414282.3, mse_test:      1555661257162.9\n",
+      "max_depth:  9, n_features:  2, mse_train:       779576331336.2, mse_test:      2030846366295.0\n",
+      "max_depth: 10, n_features: 20, mse_train:       477981874413.5, mse_test:      1257412564339.1\n",
+      "max_depth: 10, n_features: 19, mse_train:       478442315188.5, mse_test:      1434519348953.7\n",
+      "max_depth: 10, n_features: 18, mse_train:       483342739666.9, mse_test:      1446577247965.3\n",
+      "max_depth: 10, n_features: 17, mse_train:       490221937003.1, mse_test:      1363984337540.8\n",
+      "max_depth: 10, n_features: 16, mse_train:       489439792580.7, mse_test:      1543380931079.9\n",
+      "max_depth: 10, n_features: 15, mse_train:       496906427050.9, mse_test:      1633841281853.9\n",
+      "max_depth: 10, n_features: 14, mse_train:       497520112488.3, mse_test:      1449600244722.9\n",
+      "max_depth: 10, n_features: 13, mse_train:       501658494242.7, mse_test:      1380728247943.9\n",
+      "max_depth: 10, n_features: 12, mse_train:       501658494242.7, mse_test:      1454363983728.7\n",
+      "max_depth: 10, n_features: 11, mse_train:       489829363264.4, mse_test:      1664372030318.3\n",
+      "max_depth: 10, n_features: 10, mse_train:       488671118005.6, mse_test:      1612139091918.5\n",
+      "max_depth: 10, n_features:  9, mse_train:       457849519912.5, mse_test:      1502657280958.6\n",
+      "max_depth: 10, n_features:  8, mse_train:       459807995358.8, mse_test:      1513864214709.3\n",
+      "max_depth: 10, n_features:  7, mse_train:       404774716895.9, mse_test:      1386087956438.7\n",
+      "max_depth: 10, n_features:  6, mse_train:       409177169353.2, mse_test:      1610529856196.3\n",
+      "max_depth: 10, n_features:  5, mse_train:       430759770781.9, mse_test:      1278180267806.7\n",
+      "max_depth: 10, n_features:  4, mse_train:       418544300813.8, mse_test:      1398710933396.4\n",
+      "max_depth: 10, n_features:  3, mse_train:       409012092288.3, mse_test:      1486487918171.3\n",
+      "max_depth: 10, n_features:  2, mse_train:       743684742050.3, mse_test:      2168019410594.0\n",
+      "max_depth: 11, n_features: 20, mse_train:       416725715554.3, mse_test:      1552418840658.4\n",
+      "max_depth: 11, n_features: 19, mse_train:       417392535898.3, mse_test:      1182974406963.9\n",
+      "max_depth: 11, n_features: 18, mse_train:       463393612745.3, mse_test:      1303845378119.8\n",
+      "max_depth: 11, n_features: 17, mse_train:       444798380141.2, mse_test:      1234422395499.4\n",
+      "max_depth: 11, n_features: 16, mse_train:       436849570085.9, mse_test:      1195193713252.5\n",
+      "max_depth: 11, n_features: 15, mse_train:       436849570085.9, mse_test:      1483902499838.3\n",
+      "max_depth: 11, n_features: 14, mse_train:       418288121657.2, mse_test:      1498839202505.4\n",
+      "max_depth: 11, n_features: 13, mse_train:       418332890292.9, mse_test:      1483700139479.6\n",
+      "max_depth: 11, n_features: 12, mse_train:       399243201699.6, mse_test:      1414872223096.5\n",
+      "max_depth: 11, n_features: 11, mse_train:       399952039592.2, mse_test:      1364836195145.8\n",
+      "max_depth: 11, n_features: 10, mse_train:       398462598840.9, mse_test:      1518513560007.6\n",
+      "max_depth: 11, n_features:  9, mse_train:       405000338035.8, mse_test:      1559991110169.3\n",
+      "max_depth: 11, n_features:  8, mse_train:       415856879256.2, mse_test:      1404528089021.4\n",
+      "max_depth: 11, n_features:  7, mse_train:       323719625767.4, mse_test:      1419726733331.6\n",
+      "max_depth: 11, n_features:  6, mse_train:       409078643742.7, mse_test:      1841584998365.5\n",
+      "max_depth: 11, n_features:  5, mse_train:       347195980618.8, mse_test:      1715057088184.1\n",
+      "max_depth: 11, n_features:  4, mse_train:       384194651833.2, mse_test:      1293316989636.8\n",
+      "max_depth: 11, n_features:  3, mse_train:       485121266494.1, mse_test:      1709543614376.7\n",
+      "max_depth: 11, n_features:  2, mse_train:       714333718125.8, mse_test:      2224215416510.5\n",
+      "max_depth: 12, n_features: 20, mse_train:       424021593216.7, mse_test:      1550307240970.9\n",
+      "max_depth: 12, n_features: 19, mse_train:       423996985355.2, mse_test:      1289134735150.6\n",
+      "max_depth: 12, n_features: 18, mse_train:       421112820764.1, mse_test:      1496960227836.8\n",
+      "max_depth: 12, n_features: 17, mse_train:       421772490901.8, mse_test:      1425767098574.0\n",
+      "max_depth: 12, n_features: 16, mse_train:       421772490901.8, mse_test:      1173261398077.8\n",
+      "max_depth: 12, n_features: 15, mse_train:       364832705279.4, mse_test:      1430131824111.4\n",
+      "max_depth: 12, n_features: 14, mse_train:       355775743062.1, mse_test:      1280514112363.6\n",
+      "max_depth: 12, n_features: 13, mse_train:       352975929795.1, mse_test:      1492779993383.1\n",
+      "max_depth: 12, n_features: 12, mse_train:       352719835141.9, mse_test:      1204499038531.5\n",
+      "max_depth: 12, n_features: 11, mse_train:       354069569608.4, mse_test:      1032375857213.2\n",
+      "max_depth: 12, n_features: 10, mse_train:       361461012142.0, mse_test:      1231373063858.6\n",
+      "max_depth: 12, n_features:  9, mse_train:       352496690921.7, mse_test:      1477650715338.0\n",
+      "max_depth: 12, n_features:  8, mse_train:       293839876759.7, mse_test:      1475843768581.8\n",
+      "max_depth: 12, n_features:  7, mse_train:       300469982263.1, mse_test:      1325868695926.0\n",
+      "max_depth: 12, n_features:  6, mse_train:       259421009073.4, mse_test:      1350045475173.0\n",
+      "max_depth: 12, n_features:  5, mse_train:       333972356440.9, mse_test:      1338205435230.8\n",
+      "max_depth: 12, n_features:  4, mse_train:       363386281411.1, mse_test:      1380334618760.4\n",
+      "max_depth: 12, n_features:  3, mse_train:       337161312627.4, mse_test:      1503970002701.4\n",
+      "max_depth: 12, n_features:  2, mse_train:       697549285729.4, mse_test:      2216505358868.2\n",
+      "max_depth: 13, n_features: 20, mse_train:       326900866212.4, mse_test:      1272709957514.6\n",
+      "max_depth: 13, n_features: 19, mse_train:       370773628675.9, mse_test:      1362867614861.3\n",
+      "max_depth: 13, n_features: 18, mse_train:       413802536184.0, mse_test:      1594041454103.7\n",
+      "max_depth: 13, n_features: 17, mse_train:       409910833394.8, mse_test:      1269269851309.2\n",
+      "max_depth: 13, n_features: 16, mse_train:       408920959510.2, mse_test:      1464664036551.1\n",
+      "max_depth: 13, n_features: 15, mse_train:       408391261322.5, mse_test:      1528116560331.1\n",
+      "max_depth: 13, n_features: 14, mse_train:       289021774282.3, mse_test:      1273491265745.6\n",
+      "max_depth: 13, n_features: 13, mse_train:       288948837436.4, mse_test:      1293400305808.9\n",
+      "max_depth: 13, n_features: 12, mse_train:       288587714980.1, mse_test:      1390093475183.3\n",
+      "max_depth: 13, n_features: 11, mse_train:       217409522268.1, mse_test:      1064187535360.5\n",
+      "max_depth: 13, n_features: 10, mse_train:       219634447071.6, mse_test:      1076690997264.8\n",
+      "max_depth: 13, n_features:  9, mse_train:       226075644337.6, mse_test:      1262646102151.2\n",
+      "max_depth: 13, n_features:  8, mse_train:       233947945428.2, mse_test:      1278212853663.7\n",
+      "max_depth: 13, n_features:  7, mse_train:       222603578244.8, mse_test:      1235392567282.1\n",
+      "max_depth: 13, n_features:  6, mse_train:       206073462712.5, mse_test:      1060915323340.2\n",
+      "max_depth: 13, n_features:  5, mse_train:       277848834374.4, mse_test:      1209382633518.8\n",
+      "max_depth: 13, n_features:  4, mse_train:       337469158806.1, mse_test:      1370262367697.3\n",
+      "max_depth: 13, n_features:  3, mse_train:       396048327549.5, mse_test:      1711114015358.1\n",
+      "max_depth: 13, n_features:  2, mse_train:       680324528339.2, mse_test:      2287852137283.0\n",
+      "max_depth: 14, n_features: 20, mse_train:       334016286802.3, mse_test:      1204076681219.3\n",
+      "max_depth: 14, n_features: 19, mse_train:       334575228513.3, mse_test:      1482593267162.9\n",
+      "max_depth: 14, n_features: 18, mse_train:       334579988171.9, mse_test:      1345345492191.0\n",
+      "max_depth: 14, n_features: 17, mse_train:       288693871319.2, mse_test:      1257775313637.5\n",
+      "max_depth: 14, n_features: 16, mse_train:       288699162744.5, mse_test:      1230281180727.9\n",
+      "max_depth: 14, n_features: 15, mse_train:       246009978122.7, mse_test:      1301656081776.7\n",
+      "max_depth: 14, n_features: 14, mse_train:       243548648577.9, mse_test:      1257384597789.8\n",
+      "max_depth: 14, n_features: 13, mse_train:       240153595680.5, mse_test:      1081139368056.0\n",
+      "max_depth: 14, n_features: 12, mse_train:       242686689810.6, mse_test:      1392243929363.7\n",
+      "max_depth: 14, n_features: 11, mse_train:       242692914710.0, mse_test:      1290996774974.0\n",
+      "max_depth: 14, n_features: 10, mse_train:       242630493459.2, mse_test:      1405134434140.2\n",
+      "max_depth: 14, n_features:  9, mse_train:       199087898232.6, mse_test:      1191089503939.3\n",
+      "max_depth: 14, n_features:  8, mse_train:       203704038887.1, mse_test:      1443698228005.1\n",
+      "max_depth: 14, n_features:  7, mse_train:       201997271890.9, mse_test:      1377709992754.4\n",
+      "max_depth: 14, n_features:  6, mse_train:       201450093050.1, mse_test:      1076764654390.9\n",
+      "max_depth: 14, n_features:  5, mse_train:       235269853854.8, mse_test:      1192401603471.1\n",
+      "max_depth: 14, n_features:  4, mse_train:       307256256523.8, mse_test:      1370887604660.0\n",
+      "max_depth: 14, n_features:  3, mse_train:       305425215366.8, mse_test:      1514296867478.7\n",
+      "max_depth: 14, n_features:  2, mse_train:       677761842740.6, mse_test:      2292210089482.5\n",
+      "max_depth: 15, n_features: 20, mse_train:       296033323492.8, mse_test:      1199283904522.9\n",
+      "max_depth: 15, n_features: 19, mse_train:       242678219821.0, mse_test:      1101522025193.1\n",
+      "max_depth: 15, n_features: 18, mse_train:       242678219821.0, mse_test:      1487956702249.7\n",
+      "max_depth: 15, n_features: 17, mse_train:       242865553929.2, mse_test:      1241352030456.0\n",
+      "max_depth: 15, n_features: 16, mse_train:       243727826670.5, mse_test:      1318839918591.2\n",
+      "max_depth: 15, n_features: 15, mse_train:       243857052518.4, mse_test:      1253681989464.0\n",
+      "max_depth: 15, n_features: 14, mse_train:       245669813380.7, mse_test:      1152020772119.4\n",
+      "max_depth: 15, n_features: 13, mse_train:       251033541531.2, mse_test:      1695617954247.6\n",
+      "max_depth: 15, n_features: 12, mse_train:       251118073572.4, mse_test:      1780299655166.2\n",
+      "max_depth: 15, n_features: 11, mse_train:       237022506027.9, mse_test:      1747775306691.7\n",
+      "max_depth: 15, n_features: 10, mse_train:       228699140524.8, mse_test:      1770080154100.6\n",
+      "max_depth: 15, n_features:  9, mse_train:       230999974547.2, mse_test:      1249927459415.7\n",
+      "max_depth: 15, n_features:  8, mse_train:       146733112412.8, mse_test:      1077109079098.7\n",
+      "max_depth: 15, n_features:  7, mse_train:       150087100870.5, mse_test:      1238353911628.7\n",
+      "max_depth: 15, n_features:  6, mse_train:       202716271510.6, mse_test:      1220972425827.8\n",
+      "max_depth: 15, n_features:  5, mse_train:       209281946196.2, mse_test:      1123618552167.6\n",
+      "max_depth: 15, n_features:  4, mse_train:       244419255770.9, mse_test:      1465545478593.7\n",
+      "max_depth: 15, n_features:  3, mse_train:       284198758490.5, mse_test:      1442028281990.6\n",
+      "max_depth: 15, n_features:  2, mse_train:       675729031674.7, mse_test:      2289119927579.1\n",
+      "max_depth: 16, n_features: 20, mse_train:       288140324352.4, mse_test:      1510365082965.7\n",
+      "max_depth: 16, n_features: 19, mse_train:       211508275060.7, mse_test:      1440666244875.9\n",
+      "max_depth: 16, n_features: 18, mse_train:       213726588121.6, mse_test:      1434562330348.8\n",
+      "max_depth: 16, n_features: 17, mse_train:       213726588121.6, mse_test:      1347888915463.1\n",
+      "max_depth: 16, n_features: 16, mse_train:       213241163989.5, mse_test:      1352106364953.5\n",
+      "max_depth: 16, n_features: 15, mse_train:       208602868794.6, mse_test:      1232196368066.8\n",
+      "max_depth: 16, n_features: 14, mse_train:       208706161927.1, mse_test:      1242990604993.6\n",
+      "max_depth: 16, n_features: 13, mse_train:       174038140706.1, mse_test:      1221750109283.4\n",
+      "max_depth: 16, n_features: 12, mse_train:       174242113490.9, mse_test:      1105134987771.2\n",
+      "max_depth: 16, n_features: 11, mse_train:       174142848270.4, mse_test:      1007478987995.9 <-\n",
+      "max_depth: 16, n_features: 10, mse_train:       174961135964.7, mse_test:      1193898873983.6\n",
+      "max_depth: 16, n_features:  9, mse_train:       169284705489.0, mse_test:      1226803337767.2\n",
+      "max_depth: 16, n_features:  8, mse_train:       111948900290.2, mse_test:      1369097264335.9\n",
+      "max_depth: 16, n_features:  7, mse_train:       292484025380.9, mse_test:      1686383882704.4\n",
+      "max_depth: 16, n_features:  6, mse_train:       223585535630.0, mse_test:      1719149252266.6\n",
+      "max_depth: 16, n_features:  5, mse_train:       154548813658.6, mse_test:      1494087001176.9\n",
+      "max_depth: 16, n_features:  4, mse_train:       214045689563.6, mse_test:      1313968357091.3\n",
+      "max_depth: 16, n_features:  3, mse_train:       252733890830.4, mse_test:      1409855848518.7\n",
+      "max_depth: 16, n_features:  2, mse_train:       673091235192.7, mse_test:      2295462882349.6\n",
+      "max_depth: 17, n_features: 20, mse_train:       201270505979.9, mse_test:      1357840701303.8\n",
+      "max_depth: 17, n_features: 19, mse_train:       201289528365.0, mse_test:      1374166404469.6\n",
+      "max_depth: 17, n_features: 18, mse_train:       201361451033.1, mse_test:      1388733834768.3\n",
+      "max_depth: 17, n_features: 17, mse_train:       201354276447.9, mse_test:      1291104821210.8\n",
+      "max_depth: 17, n_features: 16, mse_train:       201393174045.6, mse_test:      1370455012584.9\n",
+      "max_depth: 17, n_features: 15, mse_train:       171599584298.4, mse_test:      1197094361358.0\n",
+      "max_depth: 17, n_features: 14, mse_train:       171600643024.2, mse_test:      1150016000780.4\n",
+      "max_depth: 17, n_features: 13, mse_train:       172213613910.6, mse_test:      1345922521125.9\n",
+      "max_depth: 17, n_features: 12, mse_train:       147079891814.2, mse_test:      1389399785957.7\n",
+      "max_depth: 17, n_features: 11, mse_train:       108282072242.9, mse_test:      1293247288696.6\n",
+      "max_depth: 17, n_features: 10, mse_train:       103391257737.6, mse_test:      1174658490101.2\n",
+      "max_depth: 17, n_features:  9, mse_train:       115312087684.4, mse_test:      1313368012017.6\n",
+      "max_depth: 17, n_features:  8, mse_train:       107332201181.6, mse_test:      1096206501023.0\n",
+      "max_depth: 17, n_features:  7, mse_train:       100715021906.7, mse_test:      1254087467758.4\n",
+      "max_depth: 17, n_features:  6, mse_train:       106297912517.5, mse_test:      1328729052866.7\n",
+      "max_depth: 17, n_features:  5, mse_train:       121756789096.6, mse_test:      1226402990577.2\n",
+      "max_depth: 17, n_features:  4, mse_train:       188286233698.2, mse_test:      1311822594980.3\n",
+      "max_depth: 17, n_features:  3, mse_train:       222136949866.6, mse_test:      1312277262365.1\n",
+      "max_depth: 17, n_features:  2, mse_train:       671799209176.2, mse_test:      2303478638550.0\n",
+      "max_depth: 18, n_features: 20, mse_train:       152831992492.7, mse_test:      1209461207250.2\n",
+      "max_depth: 18, n_features: 19, mse_train:       139730657588.1, mse_test:      1102399672099.0\n",
+      "max_depth: 18, n_features: 18, mse_train:       157323947880.5, mse_test:      1375218408164.1\n",
+      "max_depth: 18, n_features: 17, mse_train:       157478734571.4, mse_test:      1358769890271.5\n",
+      "max_depth: 18, n_features: 16, mse_train:       157984636263.6, mse_test:      1241705010949.1\n",
+      "max_depth: 18, n_features: 15, mse_train:       157424524072.3, mse_test:      1402681240406.2\n",
+      "max_depth: 18, n_features: 14, mse_train:       120131329224.5, mse_test:      1492069534262.4\n",
+      "max_depth: 18, n_features: 13, mse_train:       119500757995.5, mse_test:      1120319715387.4\n",
+      "max_depth: 18, n_features: 12, mse_train:       121029789076.8, mse_test:      1060243248491.4\n",
+      "max_depth: 18, n_features: 11, mse_train:       108415505352.8, mse_test:      1304228167204.5\n",
+      "max_depth: 18, n_features: 10, mse_train:        81034397031.0, mse_test:      1370722673122.4\n",
+      "max_depth: 18, n_features:  9, mse_train:        80859900170.5, mse_test:      1656330294051.0\n",
+      "max_depth: 18, n_features:  8, mse_train:        94285259081.7, mse_test:      1440950367711.9\n",
+      "max_depth: 18, n_features:  7, mse_train:        81087235240.0, mse_test:      1214216017201.5\n",
+      "max_depth: 18, n_features:  6, mse_train:        66983219816.5, mse_test:      1119848519674.8\n",
+      "max_depth: 18, n_features:  5, mse_train:       138272666271.2, mse_test:       924309503293.0 <-\n",
+      "max_depth: 18, n_features:  4, mse_train:       155481113509.0, mse_test:      1337096016251.0\n",
+      "max_depth: 18, n_features:  3, mse_train:       197239862148.3, mse_test:      1368894405814.7\n",
+      "max_depth: 18, n_features:  2, mse_train:       670285071135.2, mse_test:      2303218298775.9\n",
+      "max_depth: 19, n_features: 20, mse_train:       229914843440.3, mse_test:      1238488815537.5\n",
+      "max_depth: 19, n_features: 19, mse_train:       229727257353.9, mse_test:      1465210025524.1\n",
+      "max_depth: 19, n_features: 18, mse_train:       139494888531.6, mse_test:      1163444865180.7\n",
+      "max_depth: 19, n_features: 17, mse_train:       140696102306.3, mse_test:      1617550712690.5\n",
+      "max_depth: 19, n_features: 16, mse_train:       138313118252.9, mse_test:      1340624456194.5\n",
+      "max_depth: 19, n_features: 15, mse_train:       109466142177.2, mse_test:      1068546497722.5\n",
+      "max_depth: 19, n_features: 14, mse_train:       103979181917.5, mse_test:      1335710346792.0\n",
+      "max_depth: 19, n_features: 13, mse_train:       104626791389.5, mse_test:       986621190686.0\n",
+      "max_depth: 19, n_features: 12, mse_train:       104028504624.2, mse_test:      1167695265629.7\n",
+      "max_depth: 19, n_features: 11, mse_train:       103750532322.5, mse_test:      1117696215118.3\n",
+      "max_depth: 19, n_features: 10, mse_train:        89912027092.1, mse_test:      1292551304758.2\n",
+      "max_depth: 19, n_features:  9, mse_train:        58720228785.7, mse_test:      1171814403090.5\n",
+      "max_depth: 19, n_features:  8, mse_train:        58564437822.0, mse_test:      1264077177824.6\n",
+      "max_depth: 19, n_features:  7, mse_train:        54032197778.9, mse_test:      1391914893899.8\n",
+      "max_depth: 19, n_features:  6, mse_train:        19108020802.8, mse_test:      1292213870022.3\n",
+      "max_depth: 19, n_features:  5, mse_train:        81642224014.8, mse_test:      1184491331827.7\n",
+      "max_depth: 19, n_features:  4, mse_train:       138352235193.8, mse_test:      1169527657863.8\n",
+      "max_depth: 19, n_features:  3, mse_train:       169584878103.6, mse_test:      1340514157746.4\n",
+      "max_depth: 19, n_features:  2, mse_train:       669055030059.1, mse_test:      2305768427003.3\n",
+      "max_depth: 20, n_features: 20, mse_train:        99818647306.9, mse_test:      1232861338365.6\n",
+      "max_depth: 20, n_features: 19, mse_train:        99818636202.7, mse_test:      1409618818334.5\n",
+      "max_depth: 20, n_features: 18, mse_train:        94440380872.8, mse_test:      1369044209909.5\n",
+      "max_depth: 20, n_features: 17, mse_train:       125415025554.3, mse_test:      1543791579848.3\n",
+      "max_depth: 20, n_features: 16, mse_train:       125512001705.3, mse_test:      1412722891501.0\n",
+      "max_depth: 20, n_features: 15, mse_train:        94408017936.3, mse_test:      1256265257689.8\n",
+      "max_depth: 20, n_features: 14, mse_train:        89060147269.1, mse_test:       961285177059.1\n",
+      "max_depth: 20, n_features: 13, mse_train:        89643306022.6, mse_test:       879912454221.0 <-\n",
+      "max_depth: 20, n_features: 12, mse_train:        90295978214.3, mse_test:      1123639446094.5\n",
+      "max_depth: 20, n_features: 11, mse_train:        59437782375.2, mse_test:      1242649913627.1\n",
+      "max_depth: 20, n_features: 10, mse_train:        46604716119.1, mse_test:      1202278080826.4\n",
+      "max_depth: 20, n_features:  9, mse_train:        46246508227.5, mse_test:      1311829935890.1\n",
+      "max_depth: 20, n_features:  8, mse_train:        46777150202.9, mse_test:      1501930355823.5\n",
+      "max_depth: 20, n_features:  7, mse_train:        46315432892.2, mse_test:      1106751860516.1\n",
+      "max_depth: 20, n_features:  6, mse_train:        57300759689.8, mse_test:      1346829091643.2\n",
+      "max_depth: 20, n_features:  5, mse_train:        63292945243.9, mse_test:      1165413114524.4\n",
+      "max_depth: 20, n_features:  4, mse_train:       111031406683.0, mse_test:      1140112215185.6\n",
+      "max_depth: 20, n_features:  3, mse_train:       134454145355.2, mse_test:      1290965931407.2\n",
+      "max_depth: 20, n_features:  2, mse_train:       667901897756.1, mse_test:      2305980504297.8\n",
+      "max_depth: 21, n_features: 20, mse_train:       176071825306.2, mse_test:      1303255156046.1\n",
+      "max_depth: 21, n_features: 19, mse_train:       172092643462.5, mse_test:      1581387413486.2\n",
+      "max_depth: 21, n_features: 18, mse_train:        81379303765.5, mse_test:      1458698766791.3\n",
+      "max_depth: 21, n_features: 17, mse_train:       113642528347.3, mse_test:      1469853541707.2\n",
+      "max_depth: 21, n_features: 16, mse_train:       108756257845.7, mse_test:      1386594881248.7\n",
+      "max_depth: 21, n_features: 15, mse_train:        80234429955.4, mse_test:      1381565692159.9\n",
+      "max_depth: 21, n_features: 14, mse_train:        77531830843.2, mse_test:      1199477427106.2\n",
+      "max_depth: 21, n_features: 13, mse_train:        76941363189.7, mse_test:       913116853373.1\n",
+      "max_depth: 21, n_features: 12, mse_train:        76937857696.7, mse_test:      1083489883486.7\n",
+      "max_depth: 21, n_features: 11, mse_train:        42664001241.9, mse_test:      1206385134705.6\n",
+      "max_depth: 21, n_features: 10, mse_train:        42975098149.2, mse_test:      1344484961821.7\n",
+      "max_depth: 21, n_features:  9, mse_train:        26502867875.2, mse_test:      1267874780351.7\n",
+      "max_depth: 21, n_features:  8, mse_train:        29166057473.1, mse_test:      1354778058778.7\n",
+      "max_depth: 21, n_features:  7, mse_train:        23928844473.3, mse_test:      1291562326052.7\n",
+      "max_depth: 21, n_features:  6, mse_train:         6369250224.5, mse_test:      1250904154555.1\n",
+      "max_depth: 21, n_features:  5, mse_train:        44903116798.1, mse_test:      1107717254082.6\n",
+      "max_depth: 21, n_features:  4, mse_train:        86096850822.3, mse_test:      1126414191599.2\n",
+      "max_depth: 21, n_features:  3, mse_train:       102690661204.1, mse_test:      1230017537500.6\n",
+      "max_depth: 21, n_features:  2, mse_train:       194428901800.2, mse_test:      1066274325803.1\n",
+      "max_depth: 22, n_features: 20, mse_train:       104489417403.5, mse_test:      1561793950317.6\n",
+      "max_depth: 22, n_features: 19, mse_train:       103866801451.6, mse_test:      1366406104592.0\n",
+      "max_depth: 22, n_features: 18, mse_train:       104927002403.9, mse_test:      1231386974035.7\n",
+      "max_depth: 22, n_features: 17, mse_train:       104929684394.0, mse_test:      1452565465353.3\n",
+      "max_depth: 22, n_features: 16, mse_train:        68081719827.6, mse_test:      1208543673748.4\n",
+      "max_depth: 22, n_features: 15, mse_train:        61259240771.7, mse_test:      1260166858299.0\n",
+      "max_depth: 22, n_features: 14, mse_train:        61042629140.1, mse_test:       934177960432.3\n",
+      "max_depth: 22, n_features: 13, mse_train:        61415595373.7, mse_test:       968723647299.6\n",
+      "max_depth: 22, n_features: 12, mse_train:        59534628185.2, mse_test:      1126929566173.8\n",
+      "max_depth: 22, n_features: 11, mse_train:        33182524659.2, mse_test:      1277672529886.9\n",
+      "max_depth: 22, n_features: 10, mse_train:        33527623941.3, mse_test:      1410480647443.2\n",
+      "max_depth: 22, n_features:  9, mse_train:        10626247469.6, mse_test:      1309029635507.7\n",
+      "max_depth: 22, n_features:  8, mse_train:        10688173623.2, mse_test:      1355927090505.1\n",
+      "max_depth: 22, n_features:  7, mse_train:        15687267712.7, mse_test:      1683363669059.5\n",
+      "max_depth: 22, n_features:  6, mse_train:         1587154099.0, mse_test:      1452632595762.0\n",
+      "max_depth: 22, n_features:  5, mse_train:        35471732026.0, mse_test:      1081779221280.1\n",
+      "max_depth: 22, n_features:  4, mse_train:        47807680266.2, mse_test:      1023039716965.3\n",
+      "max_depth: 22, n_features:  3, mse_train:        65757333996.1, mse_test:      1236459583746.5\n",
+      "max_depth: 22, n_features:  2, mse_train:       135268910244.4, mse_test:      1065979937243.9\n",
+      "max_depth: 23, n_features: 20, mse_train:        83956586465.5, mse_test:      1496072927827.8\n",
+      "max_depth: 23, n_features: 19, mse_train:        83992520283.1, mse_test:      1515116942810.8\n",
+      "max_depth: 23, n_features: 18, mse_train:        84011579417.1, mse_test:      1260608301719.3\n",
+      "max_depth: 23, n_features: 17, mse_train:        74421801173.4, mse_test:      1374591557320.4\n",
+      "max_depth: 23, n_features: 16, mse_train:        74282312827.3, mse_test:      1598478038477.0\n",
+      "max_depth: 23, n_features: 15, mse_train:        52267132533.8, mse_test:       919318584073.2\n",
+      "max_depth: 23, n_features: 14, mse_train:        52538552410.6, mse_test:      1239026454455.8\n",
+      "max_depth: 23, n_features: 13, mse_train:        50456013190.3, mse_test:      1069689581221.0\n",
+      "max_depth: 23, n_features: 12, mse_train:        43911206137.5, mse_test:      1051643374311.4\n",
+      "max_depth: 23, n_features: 11, mse_train:        44449723839.3, mse_test:      1089129203341.7\n",
+      "max_depth: 23, n_features: 10, mse_train:        20620286807.9, mse_test:      1159284624313.6\n",
+      "max_depth: 23, n_features:  9, mse_train:        20758968369.9, mse_test:      1282815778915.7\n",
+      "max_depth: 23, n_features:  8, mse_train:        24158069851.9, mse_test:      1372686461680.3\n",
+      "max_depth: 23, n_features:  7, mse_train:        13764214478.8, mse_test:      1191367174285.8\n",
+      "max_depth: 23, n_features:  6, mse_train:         1095743495.4, mse_test:      1250152844697.8\n",
+      "max_depth: 23, n_features:  5, mse_train:        44419633244.9, mse_test:      1443310784349.3\n",
+      "max_depth: 23, n_features:  4, mse_train:        51679711818.2, mse_test:      1113152388953.6\n",
+      "max_depth: 23, n_features:  3, mse_train:        45932745214.8, mse_test:      1280771993094.8\n",
+      "max_depth: 23, n_features:  2, mse_train:       665122219863.5, mse_test:      2316039506992.8\n",
+      "max_depth: 24, n_features: 20, mse_train:        93434908701.4, mse_test:      1492624132722.0\n",
+      "max_depth: 24, n_features: 19, mse_train:        75031262408.6, mse_test:      1690561746636.5\n",
+      "max_depth: 24, n_features: 18, mse_train:        80042434120.4, mse_test:      1404385854408.3\n",
+      "max_depth: 24, n_features: 17, mse_train:        80219694036.2, mse_test:      1352552644970.9\n",
+      "max_depth: 24, n_features: 16, mse_train:        80365479777.5, mse_test:      1314988411444.3\n",
+      "max_depth: 24, n_features: 15, mse_train:        40529113743.5, mse_test:      1216484047884.8\n",
+      "max_depth: 24, n_features: 14, mse_train:        36140047521.2, mse_test:      1059101335194.7\n",
+      "max_depth: 24, n_features: 13, mse_train:        34324239307.8, mse_test:       883544711827.3\n",
+      "max_depth: 24, n_features: 12, mse_train:        34767946437.8, mse_test:       989146098066.4\n",
+      "max_depth: 24, n_features: 11, mse_train:        25211970404.3, mse_test:       931215946860.1\n",
+      "max_depth: 24, n_features: 10, mse_train:         4573532339.0, mse_test:      1140621506935.4\n",
+      "max_depth: 24, n_features:  9, mse_train:         4320874047.0, mse_test:      1240978215172.8\n",
+      "max_depth: 24, n_features:  8, mse_train:         8515000026.9, mse_test:      1341818219250.8\n",
+      "max_depth: 24, n_features:  7, mse_train:        12743847457.5, mse_test:      1220985719094.9\n",
+      "max_depth: 24, n_features:  6, mse_train:         2907903557.1, mse_test:       963583401536.3\n",
+      "max_depth: 24, n_features:  5, mse_train:        15587890946.6, mse_test:      1120417043231.8\n",
+      "max_depth: 24, n_features:  4, mse_train:        42598954160.5, mse_test:      1123769747925.0\n",
+      "max_depth: 24, n_features:  3, mse_train:        25853836524.7, mse_test:      1204449413786.6\n",
+      "max_depth: 24, n_features:  2, mse_train:        94531956262.6, mse_test:      1028748493563.2\n",
+      "max_depth: 25, n_features: 20, mse_train:        65278675204.5, mse_test:      1355784806220.1\n",
+      "max_depth: 25, n_features: 19, mse_train:        63709623835.1, mse_test:      1342343310703.4\n",
+      "max_depth: 25, n_features: 18, mse_train:        68766978492.4, mse_test:      1423375943739.1\n",
+      "max_depth: 25, n_features: 17, mse_train:        42444963094.6, mse_test:      1405036150658.1\n",
+      "max_depth: 25, n_features: 16, mse_train:        42437066410.1, mse_test:      1241971435103.1\n",
+      "max_depth: 25, n_features: 15, mse_train:        29678288006.6, mse_test:       975694605636.3\n",
+      "max_depth: 25, n_features: 14, mse_train:        29576148620.7, mse_test:      1123734178435.9\n",
+      "max_depth: 25, n_features: 13, mse_train:        30732235569.3, mse_test:      1079791526511.7\n",
+      "max_depth: 25, n_features: 12, mse_train:        25038488371.4, mse_test:      1286559673326.0\n",
+      "max_depth: 25, n_features: 11, mse_train:        25558913939.2, mse_test:       926827570852.4\n",
+      "max_depth: 25, n_features: 10, mse_train:         5663963296.9, mse_test:      1487725398939.6\n",
+      "max_depth: 25, n_features:  9, mse_train:         5010489285.1, mse_test:      1392355900608.1\n",
+      "max_depth: 25, n_features:  8, mse_train:         1990142618.8, mse_test:      1220478081159.5\n",
+      "max_depth: 25, n_features:  7, mse_train:          459420252.4, mse_test:      1225244334264.1\n",
+      "max_depth: 25, n_features:  6, mse_train:          445903174.5, mse_test:      1061203201781.0\n",
+      "max_depth: 25, n_features:  5, mse_train:         7789846208.2, mse_test:      1073538713267.4\n",
+      "max_depth: 25, n_features:  4, mse_train:        35222330370.0, mse_test:      1129038949059.7\n",
+      "max_depth: 25, n_features:  3, mse_train:        17685619506.6, mse_test:      1223079813449.3\n",
+      "max_depth: 25, n_features:  2, mse_train:       664759876307.5, mse_test:      2317782985074.5\n",
+      "max_depth: 26, n_features: 20, mse_train:        42993707681.4, mse_test:      1604904942020.1\n",
+      "max_depth: 26, n_features: 19, mse_train:        54780684379.6, mse_test:      1590779779077.3\n",
+      "max_depth: 26, n_features: 18, mse_train:        54920175020.0, mse_test:      1270013909732.2\n",
+      "max_depth: 26, n_features: 17, mse_train:        52199196631.2, mse_test:      1217786560326.2\n",
+      "max_depth: 26, n_features: 16, mse_train:         8035367880.5, mse_test:      1279991806941.3\n",
+      "max_depth: 26, n_features: 15, mse_train:         4181423603.1, mse_test:      1488625419035.4\n",
+      "max_depth: 26, n_features: 14, mse_train:         4358364326.0, mse_test:      1176626013623.0\n",
+      "max_depth: 26, n_features: 13, mse_train:         3967318247.2, mse_test:      1332774096639.1\n",
+      "max_depth: 26, n_features: 12, mse_train:         5302763500.7, mse_test:      1160540897591.6\n",
+      "max_depth: 26, n_features: 11, mse_train:         5413367344.8, mse_test:      1061923109034.3\n",
+      "max_depth: 26, n_features: 10, mse_train:         7867580794.5, mse_test:      1160901704143.8\n",
+      "max_depth: 26, n_features:  9, mse_train:         8360277141.4, mse_test:      1186433178325.8\n",
+      "max_depth: 26, n_features:  8, mse_train:         2288267848.9, mse_test:      1508735098235.1\n",
+      "max_depth: 26, n_features:  7, mse_train:         2448965249.1, mse_test:      1256712606896.6\n",
+      "max_depth: 26, n_features:  6, mse_train:          155631833.6, mse_test:      1036017490193.2\n",
+      "max_depth: 26, n_features:  5, mse_train:         4308924411.2, mse_test:      1054265862113.9\n",
+      "max_depth: 26, n_features:  4, mse_train:        31130594885.1, mse_test:      1175963944751.0\n",
+      "max_depth: 26, n_features:  3, mse_train:        12850611389.1, mse_test:      1201096981875.0\n",
+      "max_depth: 26, n_features:  2, mse_train:        69407859881.2, mse_test:      1048353738665.2\n",
+      "max_depth: 27, n_features: 20, mse_train:        37929075234.1, mse_test:      1495534126323.5\n",
+      "max_depth: 27, n_features: 19, mse_train:        37004116084.5, mse_test:      1503503047226.6\n",
+      "max_depth: 27, n_features: 18, mse_train:        41603694561.8, mse_test:      1408204384970.6\n",
+      "max_depth: 27, n_features: 17, mse_train:        30349013888.5, mse_test:      1492403719402.8\n",
+      "max_depth: 27, n_features: 16, mse_train:        38722569959.6, mse_test:      1651509764428.8\n",
+      "max_depth: 27, n_features: 15, mse_train:        11840723433.7, mse_test:      1038002973924.0\n",
+      "max_depth: 27, n_features: 14, mse_train:         9895380075.4, mse_test:      1281611100933.8\n",
+      "max_depth: 27, n_features: 13, mse_train:         8929197893.3, mse_test:      1131336461767.0\n",
+      "max_depth: 27, n_features: 12, mse_train:         9140695805.2, mse_test:       903726672128.5\n",
+      "max_depth: 27, n_features: 11, mse_train:         4396975783.7, mse_test:      1211979070130.1\n",
+      "max_depth: 27, n_features: 10, mse_train:         4336006852.2, mse_test:      1085573573322.2\n",
+      "max_depth: 27, n_features:  9, mse_train:          182277177.6, mse_test:       923981085402.1\n",
+      "max_depth: 27, n_features:  8, mse_train:          253370526.0, mse_test:       922773302183.0\n",
+      "max_depth: 27, n_features:  7, mse_train:         2863339813.1, mse_test:      1147364680118.7\n",
+      "max_depth: 27, n_features:  6, mse_train:               7272.9, mse_test:      1175304944956.2\n",
+      "max_depth: 27, n_features:  5, mse_train:         3403485178.1, mse_test:      1075097884124.8\n",
+      "max_depth: 27, n_features:  4, mse_train:        23783872433.2, mse_test:      1192393635469.4\n",
+      "max_depth: 27, n_features:  3, mse_train:         8105747511.5, mse_test:      1224672727488.6\n",
+      "max_depth: 27, n_features:  2, mse_train:        66022684853.2, mse_test:      1048931809459.1\n",
+      "max_depth: 28, n_features: 20, mse_train:        46651558596.1, mse_test:      1488067351628.6\n",
+      "max_depth: 28, n_features: 19, mse_train:        46651544698.0, mse_test:      1342267182895.0\n",
+      "max_depth: 28, n_features: 18, mse_train:        46701414853.5, mse_test:      1528952552864.8\n",
+      "max_depth: 28, n_features: 17, mse_train:        43090311447.9, mse_test:      1670489027349.9\n",
+      "max_depth: 28, n_features: 16, mse_train:        43079340624.0, mse_test:      1358118262757.2\n",
+      "max_depth: 28, n_features: 15, mse_train:        26018818502.0, mse_test:      1448782524839.2\n",
+      "max_depth: 28, n_features: 14, mse_train:         6126351404.2, mse_test:      1136330953814.9\n",
+      "max_depth: 28, n_features: 13, mse_train:         5197161310.7, mse_test:       971593005235.8\n",
+      "max_depth: 28, n_features: 12, mse_train:         5197161310.7, mse_test:      1263639378492.3\n",
+      "max_depth: 28, n_features: 11, mse_train:         5673642287.6, mse_test:      1032533277517.4\n",
+      "max_depth: 28, n_features: 10, mse_train:         5048611695.4, mse_test:      1147372164198.8\n",
+      "max_depth: 28, n_features:  9, mse_train:         3102339115.6, mse_test:      1346971955277.1\n",
+      "max_depth: 28, n_features:  8, mse_train:         1449114301.5, mse_test:      1305299937071.3\n",
+      "max_depth: 28, n_features:  7, mse_train:          464765009.2, mse_test:      1286283590615.3\n",
+      "max_depth: 28, n_features:  6, mse_train:                  0.0, mse_test:      1211697559449.3\n",
+      "max_depth: 28, n_features:  5, mse_train:         2470313319.8, mse_test:      1096808443528.2\n",
+      "max_depth: 28, n_features:  4, mse_train:        20728732736.9, mse_test:      1163987068645.2\n",
+      "max_depth: 28, n_features:  3, mse_train:         5002178892.2, mse_test:      1127689867980.4\n",
+      "max_depth: 28, n_features:  2, mse_train:       664649701829.0, mse_test:      2317579435548.2\n"
      ]
     }
    ],
    "source": [
-    "correlation_matrix = df_data.corr()\n",
-    "print(correlation_matrix['Suma'].sort_values(ascending=False)[1:29])"
+    "param_grid = {\n",
+    "    'max_depth': [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28],\n",
+    "    'n_features_to_select': [20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2]\n",
+    "}\n",
+    "\n",
+    "best_mse = np.inf\n",
+    "best_model = None\n",
+    "best_params = None\n",
+    "\n",
+    "results_train = []\n",
+    "results_test = []\n",
+    "\n",
+    "for params in [{'max_depth': max_depth, 'n_features_to_select': n_features} \n",
+    "               for max_depth in param_grid['max_depth'] \n",
+    "               for n_features in param_grid['n_features_to_select']]:\n",
+    "\n",
+    "    selector = RFE(estimator=DecisionTreeRegressor(random_state=0, max_depth=params['max_depth']), n_features_to_select=params['n_features_to_select'])\n",
+    "    selector.fit(X_train, y_train)\n",
+    "    X_train_selected = selector.transform(X_train)\n",
+    "    X_test_selected = selector.transform(X_test)\n",
+    "    \n",
+    "    model = DecisionTreeRegressor(random_state=0, max_depth=params['max_depth'])\n",
+    "    model.fit(X_train_selected, y_train)\n",
+    "    \n",
+    "    y_pred_train = model.predict(X_train_selected)\n",
+    "    y_pred_test = model.predict(X_test_selected)\n",
+    "\n",
+    "    mse_train = mean_squared_error(y_pred_train, y_train)\n",
+    "    mse_test = mean_squared_error(y_pred_test, y_test)\n",
+    "\n",
+    "    results_train.append((params['max_depth'], params['n_features_to_select'], mse_train))\n",
+    "    results_test.append((params['max_depth'], params['n_features_to_select'], mse_test))\n",
+    "\n",
+    "    if mse_test < best_mse:\n",
+    "        best_mse = mse_test\n",
+    "        best_model = (model, selector)\n",
+    "        best_params = (params['max_depth'], params['n_features_to_select'])\n",
+    "\n",
+    "        print(f\"max_depth: {params['max_depth']:>{2}}, \"\n",
+    "              f\"n_features: {params['n_features_to_select']:>{2}}, \"\n",
+    "              f\"mse_train: {mse_train:>{20}.1f}, \"\n",
+    "              f\"mse_test: {mse_test:>{20}.1f} <-\")\n",
+    "    else:\n",
+    "        print(f\"max_depth: {params['max_depth']:>{2}}, \"\n",
+    "              f\"n_features: {params['n_features_to_select']:>{2}}, \"\n",
+    "              f\"mse_train: {mse_train:>{20}.1f}, \"\n",
+    "              f\"mse_test: {mse_test:>{20}.1f}\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 445,
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(20, 13)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(best_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAGHCAYAAABieS8lAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9eZBk6Vnfj37OmvteW1dVd1X1Mt09+0zP2iMJCSQGIQsEP35gi00owMYYg6V7Lz9kwETYF3QJ2UIOIVvh8E8WujYG7CsbsARGmtFIaGY0e+/Ve3fta+7rWd/7R1ZmZ1ZlVVdVV3Vnz5xPhCI01XXOefNUnvd73ud9nu8jCSEEHh4eHh4eu4B8pwfg4eHh4fH2xRMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZDw8PD49dwxMZj9uOEALHcRBC3OmheHh47DLqnR6AxzsL13WxLItKpYIsyyiKgqqqqKqKoihIknSnh+jh4bGDSMJ7nfS4DTRWL7Zt47oupmk2fy6EQJKkpuhomoaiKJ7oeHi8DfBExmPXEUJgWRaO4zR/Zpomsiw3/x3qq5zVotO6yvFEx8Pj7sMTGY9dxXEcLMvCdV1kWUaSpGbIbD3B8ETHw+PtgycyHruCEALbtrFtGyFEU2CAZrhMkqRNiUSr6Liu2zzGEx0Pj+7HExmPHcd1XXK5HKqqomnaGjHZqsisprG6EUKsEZ3Gfo6qqm3C5uHhcWfwsss8dozGpG9ZFqdPn2bfvn0MDg7u+HVaxUlRlDbRqdVqbb+jKAo+nw9FUTzR8fC4A3gi47EjtIbHgNs6ma8nOqVSibfeeovjx48393Q0TWuG1zzR8fDYfTyR8bhlGqsXx3GaE7ckSXes2LJ1FQOgqmrHlc7qGh1PdDw8dh5PZDy2zeral9ZJeiORuZ0TeSMzrdNKx3XdpujIsrwmkcATHQ+PW8cTGY9tsbr2ZfWEfLOVzJ2cvNcTHcdxcByHWq3miY6Hxw7hiYzHlmlkh61evbRyJ8NlrWPY7O819myANaJjGEbbnk4jzLbd7DgPj3cSnsh4bJrGxGtZ1pral9V0g8hsl41Ex7bttj2f1b5rnuh4eLTjiYzHprhZeGw13SIyOzGG9UTHtu2mc0FDdFp91xq/7+HxTsYTGY+b0rp62ezbereIzG7giY6Hx+bxRMZjXTrVvmxln+NOi8ztCl3dTHSApsNBLBbzRMfjHYUnMh4dcV2XbDbL9PQ099xzz5YnxG4QmTtFJ9GZnp6mXC5z+PDh5r+v9l3zRMfj7YgnMh5ttFrDVKtVFhYWOHLkyJbP0y0i0w1jaBUdTdPa7nFjpbNadBrZax4edzueyHg0Wb2536gh2Q7dIDLdNEm33otWN4LGvzVEp9U4tCE6XtdQj7sZT2Q8gM7WMLIs39Uic7ewGdHxuoZ63K14IvMO52bWMK7rbuu8G4mMbdvMzMwQDAaJx+O7vhfRyIq702wlaWIzouP10vG4G/BE5h3MzWpfdmMlUywWOXHiBACWZWHbNrFYjEQiQTKZJBwO75jodNOkeyurulbRaW3gZppmmxuBJzoe3YgnMu9QOrVFXs2thrxajxVCMDMzw/j4OCMjI+zbtw+AarVKNpslk8kwOTkJQDweJ5FIkEgkCIVC3mTZQqvnGnii49H9eCLzDmN17cvNrGG2Gy6TZbm5QrJtm7Nnz5JOp3nkkUdIpVLN4s5QKEQoFGJ4eBghBMVikWw2Szqd5sqVKyiK0hScRCJBIBDY8mR5t4XLtnPOVtFp/M8wDEzTBLxW1R53Dk9k3kE04voN4bhZceVOrGQKhQInTpzA7/dz/Phx/H7/hi0AotEo0WiUkZERXNelUCiQzWZZWFjg4sWL6LreFJxkMonP59v2+G4ntysJYqOuoYZhYBgGjuNQKpXo7+/3HKY9dh1PZN4BtG4cbxQeW01rMeFWJyBJkigWi7zyyiuMjY1x4MCBLZ9DlmXi8TjxeJyxsTEcxyGfz5PNZpuht2Aw2BSdeDyOruttY+gm7sR4OolOtVrl3LlzxGKx5u94XUM9dgtPZN7mbNXYspXG721VZGzbZmFhgWq1yrFjx0ilUlsfeAcURSGZTJJMJoF64kAulyObzXLt2jXK5TLhcLgpOsFgcEeuuxN0Szp3Q3QkSWoWhnpdQz12E09k3sa4rku1WuXll1/mqaeeQtO0LR3fmFQaq5/NkM/nOXnyJAC9vb2bFhhXWAhRQ5b8SNLmxqlpGr29vfT29gJ1f7BsNks2m+XSpUtUq1UArl69SjKZbPqGvdNpfWnYTNdQT3Q8bgVPZN6GrK59KZfL23qTbg2Xbeaak5OTXLx4kf379yOEoFwub+I4m7J9nqpzCVdUUKQQQfUIAeUeJGlrqcy6rtPf309/fz9QT5d+7bXXMAyDc+fOYds20Wi0uZ8TiURuq19Yt0zKG61MNxIdwzC8rqEeW8YTmbcZq8Njqlr/E28nS6w1XLYRlmVx5swZcrkcjz32GIlEgmvXrm1KnMrWGYr2ayhSCEUK4YgyefO7CM0hpN235TG34vf7AThy5AiyLFOtVslkMk3jT9d129Klw+Hwrk2U3RIug62FP1cnh3hdQz22iicybyM2qn3ZziTXGi5bj1wux8mTJwmHwzzzzDNtG+83u6btlqk4F1CkKJpc32dRpBCWu0TFPk9APYgs3Xr2WGNSDQaDBIPBZrp0qVRqhteuXbuGJElt6dLBYPBtOVHeSkq31zXUY6t4IvM2oLX2pVNbZFmWd3wlI4Tg+vXrXL58mYMHDzI6Otp2zc2kPzuihEsFXRpo+7kiRbFEBkeUd0RkOiFJEpFIhEgkwr59+3Bdt1mjs7S0xOXLl1FVdU2Nzq1esxvYybqhrTRwWx1e83hn4InMXY7ruti2vWH22HaLKhsTxGqxME2T06dPUywWefzxx4nH4x2PXX2c7RapWJcxnGmEoyGTRBIaLlUUKXLjM4kqMjqy5N/ymFePYbPIskwsFiMWizE6OorjOM0anbm5OS5cuIDP5yOZTDZFp3XVdjO6pSAUdncsWxEdr2voOwNPZO5SWmtfbtYWeSc9yLLZLCdPniQajfLMM8+sm7G2+jjbzZOufgPDmUKWQthODSFARscWJXySjEwQV5SxRJaw+jCKdOdSkFudBqCelt1Il56YmODs2bOEQqG2Gp2bZe+9E0RmNTcTncbPLMsiHo97ovM2xBOZu5CttkXebriscW7XdRFCcO3aNa5cucKhQ4cYGRnZkltAyRyn5kwTVPcDCio2pp3DlYr4pCEckcMSS8hSgJB6H2HtoW2NtxM7semuqio9PT309PQA9WSHxn7OlStXqFQqRCKRNtHp1nTpO7mq6iQ6mUyGCxcu8PjjjwOdLXA80bl78UTmLqNT35ebcSv2MLIsY5omb7zxBuVymSeeeKJZKb7ZawohqDnX0OQokqTQGIoqxzDcLAHtILrSjysqyFIITU5sa6y3E03T6Ovro6+vDwDDMJqic/78eUzTbEuX3kqt0W7TTaG7xji8rqFvXzyRuUvYqO/LzbiVlYwQghMnTpBMJjl+/PimCzpdqYirXqdkhfDJA0goCNrHUBchgSTJK9llyW2NcT1u50Tk8/kYGBhgYGCgad3SEJ2ZmRls28bn86FpGolEgkgkcscmStd1u2qSbiSrgNc19O2IJzJ3AbdiDQPb2/gXQnD16lVs22Z0dJTDhw9v+pol8zRl6W8hOMNydQJViqAQxXaLaHICiXrGmCWWUOUYurxnS2PrdlrTpYeGhhBCcPbsWSzLIp/PMzExAdzZlgbdNClvtMrbjOh4XUO7G09kuhzXdcnn85w6dYonn3xyWw/PVjf+DcPg1KlTVKtVdF2nr69v09c1nDmyxndBEghrDwHlALabwRRzaHIvhj2LwK2H+6QQEe0JVDm65c+0Fe50IWTjzTsQCHDgwIFda2mwWbopXAZbW1ltVnS8tgbdgycyXUojPNZYweTz+VsqoNvsSiadTnPy5ElSqRSPPPIIL7300pYm6ao1iStKqPQixFLdiFFJYVsFdG0PIfVdWG4GxwGVAXxq37Y+02botoml1S9svZYG8/PzzZYGrenSO9nSoBtFZrv7Va2i4zVw6048kelCVofHWj2kdmslI4Tg8uXLXL9+nSNHjjA8PLxuncxqHLeCwEKRwggMoFOtjgLCJqDtJ8D+ts+30ZiKziI5axZDlAjKCZLaXgLKzRMPuo2N7mGnlgaNdOmpqSnOnTvX1tIgkUhs2ex09Vi6aZJt3ZO5FVo91xrnBU907jSeyHQZjdVLa+1L60OznYfhZiuZWq3GyZMnMU2Tp556ikgk0nbsehOk45bJG69QsS/gCgtd7kFVkvXfF3ZLdpmDwMSnbm3vJW1dZ6Z2GgcbVfJRsBfI2TOM+B8jrG6+fcCdDpdtFUVRSKVSTQfr1S0Nzpw509bSIB6PNz3qNkO3icxuJSJsJDpe19DbhycyXcJGtS+Nt7zthhU2WsksLS1x+vRpenp6OHbs2JrJaj2BEsIhXfsGZessmpREkcIYziyGO4sqR6lK06DUMJ1FbDePXx0hoB7c9Jgtt8aCeRFZ0ogofSvXFOSdORbNy4SU5F03EWx3vDdraVCr1YhEIs3wWjQa3bBGp9tEZqdWMjejVXTW6xrayezTc5i+NTyR6QJWt0Ve/cC1isx26JTC7Louly5dYnJyknvvvZehoaF1j+0kUIYzQ9W6hE8ZRpHqnl6qHKbqXEOVIoSkEdLOy8iSn7jvfsL6/ahyuHn8zR7aqpvHcEvElBurH0mSCMgxKk4GS9TQpY29xLppYtjJ1dTqlgat6dKzs7PYtk0sFmuudFa3NOg2kblTNUStL3KrRafhajE4OOh1Db1FPJG5g2y2LfKtiszq1Ui1WuXkyZPYts3TTz9NOBze8NhOE6Tl5nCxmwLTQCWKLQoEle/HzfvZE3o3nfrCNHrcNLpXCuFSctLU3AKKpIGQkJBxcVFomSCph1YkuqOwsRsIBAIEAgEGBwcRQlCpVJqiMzk5iRCiLV3acZyumiS7pW6nVXQaxc6Nlyyvgdv28UTmDrGV2pfGl/9WVjINoVhcXOT06dP09/dz9OjRm1qfNETGdBYoW5dwRB5NTiGh10VAWMgtnSwdUUVXBpAkFdcVawSmYU9z+fJlhBD4/X7iyShGdBbLnwG5Pk6/HEVCoeQuEZUH6p9f2FSdPH2+Q2jy5rOtumVP5nZMRJIkEQqFCIVC67Y0EEKgaRrT09Nd0dKgm9wQGjiO02Zn43UN3T6eyNwBtmMNc6v+Y47jcP78eaamprjvvvsYHBzc1LGyLGO4V5kvv4HlZpElbWWTvxdFClNzJvErQ0joWG4GgUlYux/bXDtpWJbF6dOnKRQKHDt2DJ/PR6FQ4Fr+BDPZk4iKn6AvQjAUwAgu4/cH8Cthcs4MICEBMW0Pffrm9na66UG/U0InSWtbGly4cIFiscji4iKXL19uuhA0/tdo9na7uF17MlvBdd01L2Drhde8rqEb44nMbeROWcO4rsvU1BS6rnP8+HFCodDmD5ZsqnyPgDAJaYeAemiral/Fpwzjl5OY7gyusFClGHHfewhr95GXim0Taz6f58SJE4TDYY4fP94UvlQqyaLfZVQcwudGKZfLlMtlinMGNWWOAel+YpFBQtEAiXAvUa2/Hk7z2BayLOPz+ZAkiSNHjjRrsBr2N+fPn8fv97eJzlZaGmyHThP6nWYzq6vVxrQbdQ19J4uOJzK3iVu1htmuyCwsLLC4uEgkEuHJJ5/c8sMsqVls0viUe278TJLRlT5skWNP8EdwhYHARJNTqHJs5Xek5kM3NTXFhQsXOHDgAGNjY0iSRMnI4bgOmuzHETYKKpqmNetFhBhkqXqdeK0XJxtgejLLNEUSiWzTdHKzVfHvpHDZZmjd+FcUhWQySTJZ943biZYGW8V13R0/563SiDJshfXaGrzTu4Z6InMbcByH6elpJEnakkVLK1vdk2mERWZmZkgmkzdNa23FcrPYbh5FCm9QJyOBAAkFvzq87nhPnTpFJpPh2LFjJJNJynaGmepZsuY0rusSVfvRJB8FZ6m+D7Nyb0xRIaCHGU0cIjicaFqxZDKZZudKTdOaabvJZLLjG3e3PLzdInSwcXbZ6pYGpmk2Rae1pUHjvsdisVtehdwt4bKtsp7o3Kxr6NtNdDyR2UVaa1/S6TSKojTTTrfKVvzHKpUKJ06cAOD48eNMTk5uSqBcYZCpfYuSeQpHlFGkAK7PjyQiGM4cAXWk+blMZ4Ggdhh1HVv+arWKbdsYhsHx48fx+XwYbpnL5Zco2ksEpBiuJEhbE6iSjopGzp7BJ4dwhIUtTAZ8Rwkq9fO3WrE0Olfm83kymUyzKj4UCjUnv60WKN4OumXi2EoKc8O7rtHSoFarNZMIxsfHMU2zLV06Go1uWTC6Jbusle2sZG7GVkTn7dQ1tLuewrcRnWpfbuVtdrPhsvn5ec6cOcPQ0BCHDx9uhuU2c2y29h1yte+gKb345X04ogT6eSSxD0UKULEuIUkarjDRlX7ivnd3nBxmZ2c5e/YskiTx+OOPN38na05RspdIasO4Lrg4+OQwWXuKfn0URVIpOov45QhJbR9JbWTdsa4O87Q2EWsUKEaj0XoBZz6Pz+e76x/WneJWVg5+v589e/awZ8+eNS0NpqencRynLV16My0NunUls9tj2qzoqKrK888/z9GjRzl06NCujmk38ERmh1mv9kVRlGYTpu1wM5FpZI/Nzc3xwAMPtK2YZFm+6bVtt0TJPIUqJ1d6u4AqxZHcJI6aJxn4CJaziOXm0OVeQtoRNKW9/4vjOIyPj7OwsMCRI0cYHx9vm2CqTgEJZSWtuf5ZJElCkXQENiOBJ7d6W5qsbiLWmPzy+TyXLl3iwoULzckvmUzedmv9uyVcthUkaW1Lg3K53BSd69evI0lS233vlC7dbSnMjWf4dicjrCc6lmXxe7/3e/zqr/6qJzLvdDba3Jdl+aaGkBuxkciUy2VOnDiBLMscP368WeDYYDMrGUeUcEUVVelpP5YggmUUyU/Y/33rHt8I0UmSxPHjx+shNSXP9fLrWMIgrKaQUHBZew9cYaFLW8h42wSNAsWLFy/y6KOPNtv8NmpFWq31k8nkbUnb7ZaQ0G56hYXDYcLhMHv37sV1XUqlEplMhuXlZa5cuYKqqmtaGnRbuGw9543bTavolMvlDYumuxlPZHaIm9W+KIqy7RRkWF9kGqGpvXv3cs8993R8MDqF6kxngaJ5EsOZQ5XjBNWDKHIIxy2gKC1V/HIFXD+qHF93bAsLC5w+fbotRDeRP0kuepbJam5FXGyCchxV8pG35gjKKVzhUHay+OQgCW3vtu/NzWgUKIbD4WatSKFQIJPJMDc3x4ULF/D7/W3W+t2W7bST3C5bGVmW2/bRGr2Rstls8777fD4cxyEYDGIYxo62NNgu3SIyDRorxFbj2rsJT2Rukc3WvtxKxT6sFZnW0NRDDz3UDBNt5tiaPcl85c8wncX65r4wKFkn8MnDWO41TAcUOYrjlhByDsV5FK3DBr/ruly8eJGpqSkeeOABBgYG6ud3SswYp5GQSWr7QAJXOGStKRLaXgQuRWsRx3EJqnGG/Q9syVV5q6wW2FZrfbiRtpvJZJoux7uRQdVN3ImVgyzLTRGH+n3P5/OMj4+TTqeZmZnZ0ZYG26XxrHRT7U6lUtlafVsX4YnMLbCV2pdbXcm0ilSpVOLEiROoqsrx48cJBG5uFHnDdl+Qqb2A5SwRVO9pjtewZzDdJeK+91C2zmI7y8hSAM16HNV+ZM05G+0BLMtaU+BZtBcxRAnFDiEQSEjIkoJfjmK6ZR6KfZiSmcGyLcJaClXafrFfya5iC5ug4keXtzchrU7bNQyDbDZLJpNhfHwcy7KaGVTJZHJTm9md6JaQULcYZKqqSiqVQtd1xsbGiMfja1oaRCKRpuDEYrHbkjHY8HbrhnvUwAuXvQNp9H3ZbOX+Tu3JzMzMcO7cOfbt28ehQ4c2taRvXcnYIo/hTKIr/W1j1pV+avZ1/OoIcd+7sEUBRQpTnZvGFe3iuLy8zKlTp+jt7eXee+9FURSqTgHTLaPLIaDlrV0IaF5HQgCypBJR+7CEhSJt722x6hhcKE2waGaxXZug4mM0OMi+wAByi1/adiYKn8/HwMAAAwMDzQyqxn7O5OQkQNt+zm62St4NukVkGjSyy1a3NGiIfTab5cKFCxiGQTQabUuX3o3VRrclIriu64nMO4nW2pfGw7Hb3mMNZmdnqdVqPPzww80HcTO4UhFXmcZwhlGkIPXJvn0sAkHdH0xGkYMoBJvjboijEIIrV65w7do1jh49yvDwMJZrcLX0MkvGFUy3hi4HSGhD6HIQR1lcOWs9XFZz8+wNPIIiqdjY274PrnA5W7zKjLFIQo2gKQHKTpUzpStossqQf+daOrdmUDUMJ1uLQi9dutTWKnm9otBuCpd1m8isN6m3ij1svaXBrYyn20JlgLcn807AdV1s296WNcythMuKxSLpdBpVVXnmmWc2nQnlCotM7ZsU5RdwI0tMFV8npB5Glwcp2+dQpSiSVDf6M5wZNKUfv9q+Ad90YTZNTp48SbVa5cknnyQajQJwvfIaU9WThJQkcS2O4VaYMy4QlnsQkkvanEBVdFxhE9eG2OM/sq170ErWKrJgpOnV4s0QWVyOsGzmmKwssMfX07aa2ckJ/laKQrtlYr9bRGY1W2lpcCtp6rtRiHkrlMtlAG9P5u1MY3PfNM1mrHarX97trGSEEExPT3P+/HlCoRCpVGpLqba52nfJVL8Bkg9h9qJKEQrmGwTUA/iUvVTsK0grqxpNTtIT+CFkqf38kiRRq9V48cUXicfjHD9+vDlp1pwiS8ZVQkqCgFIXnYASReBiuzXCpQMM+w6CbBPWekjpI+hycM04t4rhWri4a/Zg/LKPilvDEW5TZHZ7Ml2vKDSTybQVhZqmSaVS6YpQTDeKzFbHs5mWBq2JBo106c1cp9tWMuVyGU3TuiLzbjt4InMTGuGxiYkJZmdneeKJJ7b1gG5VZGzb5uzZs6TTaR555BGWlpa29EbuCpOC+RqKHEaVIggyqCs9Wkx3loHgz+KIEpazjCKHCWlH0JX2MJMQglwuRzqd5vDhw4yMjLR9dtOtYIsagVXH6VKQklhGceLs8z96yw9H0a4wXV0gbRUIKj6Csh9ZyJiu1SY0VbdGQouhdGiSdrtYryj06tWrzM7OMjs7Szweb650bndRKHSfjctOVPx3amlQKBTIZrMsLCxw8eJFdF3fVEuDbngRaKVUKt2R78lO4YnMBrTWvjTCXdv9Q29FZAqFAidOnCAQCPDMM8/g8/lIp9NbShxwRAVHlFCkEHZLdpkih6hZM4BDzPfEusdblsWZM2fI5XIkEglGR0epODkWapco2Uv4lAgxdQBNDmC4ZYJKvHms4ZbR5ACK47vlUFXOKvF6/hw5q0hA9rFkZJEkUCSZJTNHXAujSRolp4KLYF+gvy1UBnd2P6QR4llcXKS3t5dYLEYmkyGTyawpTrxdRaFvh5XMzWhNUx8bG9tSS4NuDJetLrC+m/BEpgOdal9UVd2VYsrV123Y4o+NjXHgwIE2x4CNrGFMZ5mieRLTmUWVk4S0e1HlOJaTQSLRTPiy3SKKHFzX2BJuiFwwGGR0dJRisUjBWuRc8RsU7SVUyY8jTDTZT0hJUrQXEULgk4MYbpmqW2Qs8Bh5kbnlCf5qZYa8VWLI19u8F3mrhCEs9vhS5J0yFccgpPg5Ehxlj6/nJme8MzQm9kZFfONte3VxYiAQ2PU6kW4TmdvhXbaZlgbhcJhEItF1K71GZlk3jWkreCKzivVqX3YiBXmj4y3L4uzZs2Sz2aYt/urj1xOpmj3FXPkrGPYMkqQjsMibLxJUj2I4s9gsIjCwnDSmu0TU9wQ+ZajjZ2/sAe3fv5/9+/c3HZwnq29RspdJaSM0WioX7UVqbpEh/wNkrEnKbhZN8rM/+CR7gw9xUXpuXZHZzANjuTZLZpao2u53FVVDzBrLDAX6uV+LYLsOAcWHJq/9Onfzg9mpOHG3i0Khu0Sm4c91u1cOG7U0WFxcxLIsXn/99bYanTu1T1Mul+/aTX/wRKYN13UxTbNj7cutFlM2ju/0gDe6RoZCIZ555pmOKbDriYwQguXq32A4swTUQ0iSvJItNolhT5DyP8uS9RKoSwgpQcL/XlL+H1wzBtu2OXfuHMvLyzz66KOkUqnmdS1Ro2rNEFSSTYEBCCk95Kwpkvowo6FjmG4VXQ6iy/Xi0PV70axPzTFJW3kAImoQGWlNnU4j3VpGIqj4oXv2aDfkZhP7ZotCG6Kz3aLQzYzldtH4Tt/p8bS2NPD7/eRyOfr6+na0pcF2KZVKd22NDHgiA9wIj1mWtW7ty06sZBrXapxbCMHExASXLl1q6xrZifUmbNvNUnOuocv9TQGQJAldGcRwpvCre+nXf5m5xb9j3z0faHaubKXhIKBpGsePH8fv92O6VSpOlhq5letKtBVZAnUnZQlJklfEZa0x51ZEZqq6wOniZYp2BUFdZPySRs6pEFL8qLJaF1UzT0wLkdLXfpZOdEONynbGsLootJGym8lkmJiYAGju5Wwle6qbVjLd5hMGNzp1btTSwHXdNsHfzXBWpVLx9mTuZjZrDbMTBpdwI3PFsixOnz5NoVDgsccea4ZMNjq+cX1X1Fb2VsLU3+s3msAEqhLEtRIdBWZubo4zZ840HQQkSWKycoLJ6lvUnAI1x8TR/exTRlg2r+KTw8grVfoFe4mQmiSqdm7EtpHIrL7HWavAW4ULuMJljy8FSOSsIhm3SlQLsWjmVj6pIKKGuD9yYEMbGVcI8laVojAx3e2/HHQLq1N2Wx2OW7OnblYUCt0lMo3vRzeJTCPRp8HNWhpcu3YNSZLa9tI6tTTYLt5K5i6mdfVys9qXxkpmuw9o4yFyHKe5cohEIhw/fnzdyWDN9V2LdPVvyRkv4ogiihQmqj+NX95L2T6HIkWa4TLTmas3H1NGqEruGoF0XZfz588zOzvbZrA5XzvPxdJ3UGWdqNaPK2VYUiepOTGiaj9ZawoJGReHgBLlQOgpNLlzRtRGIlMul8nlciSTSTRNY76WpmxX2Ru4IVhJPcp0bZFeLc4DkR5KThVNUujVk4TV9f3acmaFU/kZFo0il0lTy17hmDbGSGj3TDg3w05O7Ksdjh3Hae4pNIpCGxvZq4tCu0lkuiVc1srNUpg7tTQoFotks9lma/BOLQ22y91sKQPvUJFptYYBbiowcMOR9VZFZmJigomJCQ4ePMjo6OimzyXLMq7/FRar4yhSCEWK4ogSy9W/IKo/ga70U7UvIUk+hDBRlRg9gR9GkYPIcqVNZCqVCidPnkQIsab/zEztLAARtW5bo8tBfHaSkpPh/uizuMKh4uTQ5QApfXRD9+T1RKbhv6brOmfPniUajbIUKWNpJq5fILfcE01SMYXDoH9zNjqGY/NadoIlo0ivL0JM6NQci9eyE/gUlQH/5kJsO81uh+wURSGVSjX30hob2auLQpPJZPN73w00Mrm6TWQ28+LXQJZlYrEYsVhsw5YGraKzldqxu9mBGd6BItOpLfJmaF2JbGdp30g/npmZ4fHHH2/azG8aqYrwn0WVYuhK/W1fJYzJElX7EoPhX6RiX8Z05tHkBGH9IQJqvX1xY7IXQrC0tMTp06fZs2cPR44cafssjrCp2Dl8q/ZWZFSEcHGEzXDggc0PeZXINFZPM3OzJI4MsKwUKJoyllHDKBrMZxepzpUIh0KEQyGCoRCGsEjp0U1fc8EosGQUGQzEUSQZRZJJ6UEyrslEOXPHROZ207qRDTeKQjOZDIZhcPr06bb9nDtV7NeNrZdvtU5mvZYGravMUCjUtsrcKFW9XC5vyauw23jHiMx6bZE3S2Ml4zjOlmsXstksJ0+eRJIkHn744a0LDODKOYRUQZX3tf1clWMYziwAPYFnOx7beGAuXrzI5OQk9913H4ODg1iuwVz1MgV7HgWdHt8oITVBxpwiSP0BkSQJR1hIkg+/vLUle6vI1Go13nrrLYQQxO7v52xtAmzwKxpLWhFfj8498gFy1SLClFgopckuXyUmhSjlsyykfG0Fc+tRc+pi3qz6X/kbBxSNvFXd0vh3kjsdomr1/frud7/L/v37sW2bdDrdVhTaEJ3bURQK3ec+ADtf8d9oadBYZVqW1VxlXrlyhUqlsmFLg1KpxP79+3dsPLebd4TICCHI5/NYlkU4HN6ywMCNmPFWNv+FEFy9epWrV69yzz33cPny5U1/eR23Qsk6iyMK6HI/ihQAV8URlTZ/scZ/qxsIgGmaACwuLvL0008TDocx3DKn819nybxKw5V5ovoGPdooEhIFa4GAEscUJWpqmqT+JAl9690rhRCk02lOnjxJb28vw4dG+Obyq4SVACEtgCxJJLQo07Ul4kqEfckB5ow0iUSCR9T76HNiWPlaW8Fco6guFotRcAyuV5bJmlXCqo4uqUhI2K6L2nKvq47FcGDj5Ip3EqFQiFgstqYotFEN3ygKTSaTN33TvhW6cSWz295lm21pUCgUMAxjx+pkvvOd7/CZz3yGN954g7m5Of7H//gffOQjH9nwmBdeeIFPfvKTze67v/3bv83HPvaxLV33bS8yjdXL9PQ01WqVBx98cFvnkSQJRVE2ncZsGAanTp2iUqnwxBNPEIvFuHbt2qaOr9pXmS39MYYzAwgkVGQxilMbwnQWkVBRpMiK99giMd9xNLmzvX1jggd4/PHHm2+o05XTLBpXiOtDqFJ9AinbGTLWFPuCj7JkXKXspHFwCBnD3Bv5AIq0ta+LJEnMzs4yNzfHkSNH2Lt3L5OVecpOlSFfL06LYMfUECWnwrvDD/NA5CACcSN7bCVSYJpm05JlfHycJavEZMDE8akkwxGEqqDJMjIyM7UcKT2EKRzmjSLRYJiRULLDKG8f3fLGvnpV1RreaaxwGm/aV69ebbb+3emiUOg+nzC4/bYy67U0+M53vsPnPvc5yuUy169fxzRNvv/7v59HH310W83byuUyDz30EB//+Mf58R//8Zv+/rVr1/jQhz7EL//yL/Nf/st/4bnnnuMXf/EX2bNnD88+2zlq0om3rcistoZRVfWW6lxg8/5j6XSaU6dOkUgkOH78ePMtcDPHu8JivvwnGM40fmUUWVJxRJWSdRbh7iOmP0XZPovpLKBIQaL64/QFf3zNBNa6ijp8+DDnzp1r+50F8wK6HGwKDEBQSZCxJvDJIZ5I/H0qTo5SvsKl0kTTZXmz2LaNaZosLC6w/+F7MIMSs7WllY41Ms6qAktbOCiSjCrJqB0q96G+z9B4GF3X5X/PnIbMHDEDitNL9dTwgEwwGOJAYoC8Y1LBZp8a4NHkCL2+O9ePoxtqdVrZSPA6FYU2mrbtdFFoN4rMnR5TI7T5iU98gl//9V/n/e9/P/fddx+vvvoqf/AHf8Dhw4f53ve+t+XzfvCDH+SDH/zgpn//i1/8ImNjY/ybf/NvADh69Cjf/e53+cM//ENPZDrVvtxqnQtw05VMa1Ovw4cPs3fv3i27BlTty9TsKfzKXuSVlYMiBVDlJLJ/ir7gp3BEDsvNoslxfMreNQ+4aZqcPn2aUqnEE088QTQa5ey5s9ScIrIr0GR/xz0CSZJA1ENnqqwTlftwlOyW71uxWOStt97Cwsbcq/Pd6mmsso2MTL8vSVD1s2hm6FHjyJKC4VoU7QoPRQ+tKzCrqbgWRSz29w4SUn24K8WK+WKByfwyqQWTZDDCA26cI1KSPVtIHni7s9X9IZ/P11aYuFNFodC9ezLdYvUvyzLlcpkf+7Ef40d/9EdxHIf5+fnbcu2XX36Z97///W0/e/bZZ/ln/+yfbek8bzuRWa8t8lZCXeux0UrEMAxOnjxJrVZra+q12eMbuKKGwEaiPQYuSzpIDgIDv7oXP533R3K5HCdOnCAajTZXUUvGNbLJN3g5dw5N8zHgO0xCG+Za5TVCSqIpZlUnjyYHiGs3fM1kWd7SW3ijuHN0dJTvLrxFwbIYCQ0RUHyYrsWMsUSPFiOmhpkzlusrG0lmLDDI4dDIhuc2HItls97AKajoSJKM2yjmkyTCoRB6wIeIBXgydQitYjM+Ps7M9AzXr11vvn0nk0l8K8Vy+m2cTLplMr2VJIRORaGNGpFGUWgjXbchOhsla3Tjnkw3uTA3RL3RFVNRFIaG1voO7gbz8/P097cXWvf391MoFKhWq5uu/XnbiMzq2pdO3mO3KjLrnaPR8z6VSm0YL10tMkLY5M1XKRhv4IoKQe0IIe0oqhzFcjPoyg1HYUfkcMwY2jruyUIIJicnuXjxYlsNTsac4mT+LzH8SyjSAYRwuVp+maQ+QlIbImNNo0garnBQUBkNPdZWwb9ZaxjXdblw4QIzMzM89NBDRFJRlpe/Q0pJElDqNQG6rNGnJyjYJd6beoyKWcXGIaIE6fMlUKT1J/yrpSXeyk2RteqtaJN6CE2SSZtlgqqOsiI4i0aRHl+E/mAMNaxw+fJljhw5gt/vr3evXF7kG5fPksEiFApyINnHo4Oj9EfiN/2Mt0I3hct2MtNtdY1Ia1HoanfjRrJG6/PhrWRujmeQ2QWsrn3pVNy1GysZ13W5cuUK169f5+jRowwNDW3KNQBACJe58n8lazxPY3O/ZJ0moO4nrD5EznwR164iSwFsUUCWfJi5+4C1b1i2bXPmzBmy2ewai5qpyglqThHdSqFLIXTVh08OkbdmeSD6wwxyHxlrGg0/ff4D9Or72z7DZkSmVqtx8uRJbNvm6aefJhQKkbdKOLhoq75iPlnDch1kSWI0sGdTb4yLtSIvZ67iCMFQII5YEROAqOZnqpqj4auW1EI8Et+LKq+1BUHXOCNVkIP97BVQKZV5Y26SsxPXeCzSx2Cqt7kBvp2N1buF3Uyn3qgotDVzqrGi7KZVQ4M7vSezmjtV8T8wMMDCwkLbzxYWFohGo1tyMLirn6St1L7s9EqmMbGapslTTz3VXM5uRKtIVexL5I2/Q5NveIoJ4VC1rxBSj7In9DPkjO9iu3nC6gOElWeYLC+tmSAa+x+BQIDjx4+vqSTO2bP4lAiyVKChFarsw8XBElX2h55ihGPrjlmSpA1DfNlslhMnTuBLBlGHo3wj/xqBko99gQH8aJScKq15bwW7TEgJEFVDdX/NTTBZzVC2TUYbtjASDAbiXCsvc38wSVIPUXZMfLLKYCBOWL1xD1rv1Uwlz1y1yGikXqhJLMl+MczVYhq/HkcY9T21arXaNhFGIpEdmXS64Y29UZR7u8ayUVHo9PR00ydsamrqjhaFttJNwuc4DtVq9Y6IzNNPP83Xv/71tp994xvf4Omnn97See5akdmssWWDnRIZ13VZWlri1KlT9PX1cezYsU2/9bZu/FftKziihk++EV+VJAVFjlC0TjEQ/gfEfe8BHCRJXXEMeK7tLathzzI6OsrBgwcByFmzFKxFNNlHSh/FL0cpO7m2FYkr6vdBk2/+NrLenkyrg/Seg8OMazMs5ScJKn6WzCzXK7MggSMc5mrLhJUAVdfAcC2OxY8QVoPN+p2bUbJraPLa8IUiydjCZX9442roxvhzZg11xQWg+fkkiYCqI4J+Do/WC11bJ8KpqSmAtu6V23HE7ZZwWWMcd2oiby0KbSTKLC8v3/Gi0AaNF9duEZlyub4HuRMiUyqVuHz5cvO/r127xokTJ0gmk+zbt49PfepTzMzM8JWvfAWAX/7lX+aP/uiP+I3f+A0+/vGP8/zzz/Pnf/7nfO1rX9vSde9KkWlti7zZwspbteqH+oM5Pz9PPp/n3nvv3dIGnBACSSlhr7y9S8hAh7dKIZBXMqzqP1eb44f6Z3cch/HxcRYWFnjkkUfo6enBERZnC3/LTO0MpltFRias9tDnO0javI614l/mCIu8NU9E7aVXP7Cpz7z67bc1PPf4448z7k6ylMuyNzDQ9B0r2RXS0jKP+Q7g+GQKdpmYFuGe8D4Ohfet7RrQcp8mKxmulpepOCb9vii6pGK6dtsYXOHiCEFMW3/Cr9oWc04NUUyzR6sLitNhsrdch6B6I9Fi9URYLBbJZDIsLi5y6dIlfD5fc5Wzle6Vd/oNHe68yLQiSRK6rhMKhXjggQfuaFFog2ab8i7Zk9lJkXn99dd53/ve1/zvT37ykwD8/M//PF/+8peZm5tjcnKy+e9jY2N87Wtf4xOf+AT/9t/+W4aHh/mP//E/bil9Ge4ykenUFnmzD8tGTcM2Q+PtVpKkZtX8ZqlYl1is/k/M6AlsWWGy8Bgx39OocgTLXUKT662FXWHgiApR/Yk152iMuVQqMT4+jqIoPPPMM803vcnKW1yvvE5ISRDRehE45Kx5XBxGAo+RkZ8n787gt/xE1F7ui/4gfuXmn6G1940kSU0HaV3Xmw7SkzPzhNVgm7FlWA1i4eCTdN7d/ziWa6PKanMVsboRWYOT+WleyVzHFS66rHK1vExUDRBUdCYqGVJ6CIEgbZYZ8MfYF+xcYJk2Kry4OMEJI0tPTiFo5ohoPiRgoVqi11/fSF02ygRUjcFg5xRnSZI6uh23dq9shNYahYrd8hbciW4SGWjPLutUFNqohG/Yr0Sj0aboRKPRHReD1shIN1Aul/H5fDsiru9973s3XFF/+ctf7njMW2+9dUvXvWtEZqvhsdUoirLtePTi4iKnT59G13V6e3u3JDA1e4ap4h+tVOr76hY35ksYzgwJ/X1kjOeo2leA+uomoj9Ewv++NedpfOnfeOMN9u7dyz333NPWCG2qehJV0vErkZVzqcS1PWStGWLhAYaK38dAb4xUoo+kthdV3pzLbOs15ufnOXPmDAPDe0js6yUrivSIBJqsUnFqbcc1+4SspCj7lJtfL29VOZmfJqjoJPVQ8zzXK8uMBFMMB3zM1QpIwJHIHh6MDRFS157XFYI30jMsVEv0KT6GAhECgRCTlTwxzYcuK0xV6t03Y5qfBxMDTdG5Gas3thuFiplMhpmZGVzXJR6PN1c6jb4iXrisMxtll6mqusZ+pVEUevbsWWzbbktL34nGYd3WRK2RWdYtf6/tcFeIjOM4LC0tEQqF0DRtWze81eBys18g13W5ePEiU1NT3HfffeTz+S1fN2f8Haa7QEA9gEUB4QoCyiBV5zoJ33sZif4/KFmncVyDoDZKRH+kzZusMY4LFy4AcOjQIUZHR9v+XeBiuhVUqX3CrTcXE1huDV1ESMkH6PN1tp9Zj8a9vnjxItPT00Tv6eW0Mk12/hwSEr2+BAktynwtjeGY+BS97vZsZglJfnqUzbseLxlFipbBaPBG+wBJkohrQfJ2lWcH7sVyXSQg2EFcGmTNKgvVEgP+MFPSEgCqLNPnC1J2LN7btx9LOCAg5QsSWAmV5U2DK/k00+UimiwzFk2wP5LYsJZmdaFiqVQim8029xg0TWva699quHYn6ZZJayv7HxsVhV6/fh1ZlpsCv9Wi0AaO4yBJUteITKlUuqu7YkKXi0xr7curr77KM888s6U+D61s1UW50XPFdV2OHz9OKBSiWCxuuRdHzb6GjL+ZVu26AklSkJAw3Bl6tA8S1A6te3y1WuXEiRPN3P1UKkXZzjJdPUXenscvhxn030tcHWLGOENQJJoPlulWUSSdiNqDLM9vy/Gg0aJgeXmZ/ccO80LhLWzLps+XRCBYMNKU7CrDgX7ma8s41EOSETXEYXmYoLTxxm3BqlG0awQVDVmSkaR6r0+JG5ODIwSaJCMjdVy51H/HbW7sO66LI1wUWYaW8yiyjGsLdFmh39e+Gi2YBn83N8FCpURU91GxXF6amyJdrfJU/9DKuTZGkiQikQiRSIR9+/bhOA75fJ5MJoNlWZw5c2aNweftjv1320pmu8WY6xWFtnYK3UpRaINu2vSHG+nL3fL32g5dKzKu67a9/amqeku2MI23k828TTbCQoODgxw+fLg5ESiKctOMqJo9jeHMosoRguo9aHIPLqcbg6g3EBaibt0ixTc8VyOLrb+/n6NHj/Ltb3+bvDXPhdrfkLfmUSUdR1hMVU+wN/AwATlCxpoioMRwhIXhlhjyP0BS34ckLWz5/uVyuWY89tFHH+Wt6iXKTpWR4J7m7wz5+5iszPNg7CAPx+4hY+bRZJWhQB9Xly+tGyayXIeX0lc5X1qg4pj4ZZWhQJyw4mO+VmCPP4YkSViuQ96u8GRyrK32pZWJUo4TmVmWjQqyJNHvC6NKMmmj0vZ7aaNCrz9ERFvbMOpaMctitcRoJN7cW6rZNlcKGcaicQZDW/c9UxSlKShLS0vs378fIUTT4NOyrLbQ2u0Ii3SbyOxUMWZrUejY2NhNi0Lj8XhHgfcKMXeerhOZ1tqX1rbIu1mx36C1JfH999/fdEVtsJFIuaLGbOmPyRvfwxElZEknoB4k4fs+FPN7GM48EEQIG8OZRpXiRH2PdzyXEILLly9z/fr1tiw2SZa4VnuZAvOktH1IK5voRXuRudo490d+iBnjLHlrFl0OMBZ8gv2hJ5ElZUv2MEIIpqamuHDhAgcPHuTChQsoikLOLuJbtZfT+PvUHIN9wQH2BQfa/m29a76ZneTV7DWSepghf5yqY3K+OE+/P4pfkrheWQapvk+1P9TL/dHBjudZqJb49sJVao5Njy+EI1yuljNokoKmyCw6VVSjQhqboKrzQGKgrQVAg/lKeWU1dWPC86sq9soKaTsisxpd10kkEgwMDDTDPY39nGvXrqEoSnMSTCaTW+qeuFm6rd2x67q7kjHWqSh0tZ1+LBZr3u9GLVQ31ciAJzI7zkZtkRVFueW2sRuJTLlcblrir25J3Hr8equBxcr/JFP7JqqcwC+P4lKjbJ0F4dIX+AmWa1/DlWdwZQdVHmUg9FPNzpWtNFoE1Gq1tUWeik3GniTgSzQFBiCk9JCzpkGCx+M/iS1MZElps+a/WVFlA8dxOHv2LMvpZfrv38ucXuCKtsBIZZaEFuVqeWZNKrEQgqi2NhlivYms5liMF2aJqgESev0+R2Q/SFC2DT7Qd5SyY2K6NjEtwN5AEp/S+at6qbhMwTI5ELmRZRZQNSZKOe6P9uOqaUKqzmi8n/2RJP2B+jgt12G+UsZ0bKK6D12WsVZlvAkhQIAq3fqks1psW8M9jT7xjdDazMwM4+PjhEKhpuCs9+a9nXF0i8DA7fMu03Wd/v7+phdXtVptJhFMT0/jum5bSK1b7pMnMjtIa+1Lp4233TS4nJub4+zZswwNDXH48OF1v/TrrWQcUSVnfAdFCqHJ8fp4CaAre6g4V+hXf4oD8f83E5UXyeSzHNj3kY5NxjKZDCdPnqw37HrkEVS13vbYEgaqpK+MS7C2yKTx33VR1qS1b8CbWclUKhXeeustZFXBPRLkhcoJrJLNvL7E15de4kBkmIgSZKa6SNIXQwjBspmjz59gtCWE1mA9Yas6JlXHWrO/ElJ8pI0yqqzwQHhzNUgZo0JIbX8TbqRJB1WNB/0J9veONW3rAdK1Ci8vTDNXKeK4Ar+iEtI0TMehaJlENL3ug1YtE/X56A/ufrX16pa9lmU1N7Vb37xbXQi2Mwl2y+TZ4E55lwUCAYaGhhgaGmpL2Jifn6dWq/Hiiy/e0aLQBnfKUmYnueMis9nal90IlzmOw/nz55mfn+eBBx5Y4zja6fhOk6bjlnHcus9YKzJ+hLCw3Rxh+X6C8iOka1NrBEYIwbVr17hy5Qr33HMP+/btAwTXKq9xrfwKVadAQIlS80eIspescwm/HEGW6mnZJXuZgBInpa/vYnwzB+jFxUVOnTrF0NAQvr0R/tfCd4mqoXq9i1smroS5XpnlscS9LBs5lswsEhIHQ3t5PHEvYXXtyq/xdzQcm8ulRearBXyywp5AnKCsU7INQi0WMEW7RlDViajrP9BLtTJT5TyW65D0BQmrOtOVQtvvuEIgEAQVjdX5gLbr8vLCNNPlIntDETRZoWSZzFdKpHwBSpbJcq2+lxPTfRzrHSSm70zYaiuTqaZpTTsWIUTzzbthr98QpYbobHYS7EaRudPhqdaEDV3XmZ6e5sCBA81V5Z0oCm1QKpU8kbkVtlL7shNNx1pFplQqcfLkSWRZ5vjx45syfGusZExnmYL5Go4o41f2EdLuR1OSmM48KjfCW/W9mfqKpnH86onesixOnz5NoVBodtAEuFR6kTOFv0FCQpdDFKxFcsHzHOBp4togWWsaCRmBi08OcTTy/RsWV663P9K6/3P//fezZ88evrP8JpbrNIVDkiQCsk7RrVBxanx4z3soWCUkSSamrr9ZLUkSVcfkr2ZPcqm4gAS4CAKKTq8WpmKbLNYKRLUAFccka1Z4NLGvGUJbzXhuke8uTVC0DCRAQiLlC6JKMjOVAr2+ILYQzFeL9PvD7A3FyNMeqlqslpmvlBgORppWNWFNJ6b7kGWJ79szStEykCWZ/kCIsLa9bMZO93m7NAw+g8Hgmkyqubk5Lly40DYJbmTw2W0i021W/42N/9WrykYSQcPbLhKJtDlL79ZnqFQqXgrzdnFdF9M0N125v5MrmdnZWc6ePcu+ffs4dOjQpr8giqIg9HEu5/5vLDcL1Demw9oDxPX3sFj9s3pmmRTDFVVsUSDue4aAuh9YKzL5fJ4TJ04QDoeb1fMAplvhSvllFEkjotYL0QJKlCIlltxL/ED8l1k2r5G35vDJEfb4j5LUhzcceyeBM02TkydPUq1WeeLJJwitvDG5QrD6r7GSk4QrXBRJJrGJJmCSJHGpluaClWckmERf2VdZNkpcy8yTLEFRr1Dwl4kFwzyZHOOxROfVWMGs8fLSJELAwcjKZq7jMFHOcjCSwnIdFmplZEliJBznyZ69BFV9bUM318F2XbRVf3OfomI6DjHdx8Auhcd2w15/bGysWRmfyWRuavDZbSLTbVb/nbLLNE27aVFoPB5vis5OphyXy2X27Fkbir6buCMi01jBbMUaZic2/mVZZmpqikqlwkMPPdR0ht0sQipB/G+wXfAr9ewuR9Qomm/hV0bYE/p5lmt/je1kkSUfKf+H6A/+n83P11gJtWZvHThwgLGxsbZ7ULLT1NwiIbndMkUVIUxRxhI1DoS25oS6en8kn8/z1ltvoUcDuIeD/PfMt3DSLmOhIeJaZCVjzMSv1Cdq0zFxJZe9gY1Diquvea2WJRTzNwVGCIGVLTGfW+LhPfezxw0wm1mCpRqBWJ758kzHB3W2WiRv1RgL37gnuqIQ1fyUbYufGLmPrFlDkSSSvhsWNyXH5o3MIqXiEkFFozcQwq9qFC2TaEsYLGfW2BuO4l8nwcCwbeZKJQqGgaYo7AmHid+hOP1qVlfGb2TwqetrhfdO0g3hslY2k13WqSi0ITqNotBWQ9Wt2OKvxtuT2SaNjf2tvFXd6kqmsbGnaVqb59dWMKVzSEoBn3Kkmd2lSH4UOULefInD4S+Q8L8Py02jSGFUuT3ttfEZTp06RSaT4dixYySTSYQQ5KxZLLdKRO1Dk/0oqNjCQOVGuEbINrJQ0KWtf2lbN/6npqY4f/48+w6McFKf4GpuZsV7TOaN3Dh9eoJ9gX4mK/PIkkxOLuNaKvcmDnAw1LkjZ4OaYzFbzQFg4eLionDDWHN6egbXdejr66ent5cHEvt4UJLa0nmvX7+Ooij4YhG0aJjBVM9KB0ypLcUYGqaXLpqsNDPHGmRqVV4tZ3Co0h+Ls+xWuFrMElBUlqomFdvCr6jkzRo+VeNoorfj97FiWbw8Pc10sd7DRghBxKfz2J5BRmKbczS4nbYyGxl85vN5hBCcP39+ywafu0E3hsu2Mp5OWYI7URTaoFKpeCKzXTo1FtuI7a5khBDNlNBgMEhvb+/2M0VkG4HL6sZhkqTiChMhLBQ5iE/pvLyt1WoYhoFhGM3eLyU7zYncX7BkXsPBwi+HGAs8SY++n5naaRRJR5N92K6BKRfo5wgRdWsrsPoYJWzb5vTp0ywtLfHoo4+yoOWZWJhjKNCHtuL8HNfCTFbmORjey5HIGNcrs4j5Gk9EH+ZY330bepCNF+b49uIF0kap/oNijYjsp2AbhCpl5qdnCYWC+FMpaq7NoP/GBN2652DYFt+cOM/JpWkySyWkcYd+f4iaT2HGEeyJJZvikjVrPN27t+N3aTy7RNY2eTDYR2wlBFYwDfKWwQOpfpZrFWqOzWgkwdFED8PhziHAC+k0k4UCe6PRZo3NQrnMiYV5+kIhApts9XAnVhCrDT6Xl5ebBqutBp+3Y3+hE922krnVYsz1ikIbCRubLQptUCqVvBTm24WqqhiGsaVjbNvm3LlzLC8v88gjj7C4uLjpqnfHrZAzXqRsnUWSNKL64/jlMYSj44gCqtRoNCaw3RxR/dia7LJWZmdnOXPmDJIk8dhjj9VDZ8Lmtdyfs1S7QlhLoUo+qk6B8dJzHI38AJY4RNq8jus4yChE3GFGeNe2JquG/1ujuZnf7+fs0gSuEE2BAZAlGb/iY8HI8J6eR7kvup/vXHQ5HNi3ocDMVXP877kzGK7FcLC+YTqen6DkmiSMMG8uX6QnlkCN+HAck8dTo/T5Ohc4vp6Z5XQ1Q0+yh/39g+TNKlO5LJppc31+lssz04SDAWSfzv5EL/fG1vaTEUIwUcoTUtS2+xXVfSzVKiT9AY4P7G3uz6x3T23XZbKQJ+bztRVx9gaDTBcLpCsVhqM335/qFmRZRlVVDh2qWxm1GnyeOXNmXYPP3aLb9mR2uhhzvaLQ1anpq4tCG5TL5U01ROxm7uhKZivcNFwmBDjnkJyLIEUo1u7jrZOX8fl8zUk1nU5vKuRmu0WuF/6AknmKxpZ3pvYcMeUHsIqHsWITOG4JSdJx3CKqnKA38GMdP1NrmvTRo0cZHx9vfomWjKtkzAliWj+qXN8fCKtJCtYCM7UzfH/Pr5K2Jqg4OYJynPl0GV3d+tJ5aWmJubk5fEE/ww+OseDmGHCSdTPLDj1tbNcmqNxY7W2mkPNicYG8VeVg5MYqq18Lc6m4QE9Z4SfveZqC6uBTNA6EexkL9WCv+KK1UrFNzuQWiGt+kr66aKf8YdSkSsWx+HDPPmbyGdLFPP6qTWQ6x/jyCVKpVL1KPhIGWSas6eiywuq/ttvoFyLJyJK0rvllzbbrAqQoCFgTpoMVd4otuCh0A6v/1ps1+GxMgtv1DtxoPN22ktnN8OFGRaFTU1MIIUgkEqTTaeLx+I6uZL7whS/wmc98hvn5eR566CE+//nP88QTa9uKNPjc5z7Hv//3/57JyUl6enr4iZ/4CT796U9vORJ016xkNgyXiSpy5dPI1svASjOzWpD9w/+YwdEfbXMN2Ew3xkztbymZJ9GVAeSVwkbbzZO3n8cu/QADgfdRsP6uvoLxP0oq8EFC2pE156lUKpw4cQJJkjh+/DhAW0+bqpPHFU5TYBrocpCaU8TBot93wzxzSTqzJf+xRufBa9eu4aZUXuMKr85exxWChBbh3uh+IkqQJTNLjx5HQiJvl5BlmXvC+5rn2UwhZ8Fq715p2zaFQgHJFYwd3M97Rx9YM7ZOVGyLqmPT42tfFYZUnaxZpS8Q5tGeG4WajuPUH9DFBZ4//QZTlRKaT2c4lqAvlqDm2FRtm+jKNecrJRI+P0Pr2MRULIszS4tcy+VxhEtPIIguKyxWKkR9vqbYZGs1IrqP5BY2dbvhjX2jfdCNDD4nJyc5d+7cjht8duNK5nYWXnYqCs1kMnz5y1/mK1/5Crqu8/nPf55sNssP/MAPbKlRYit/9md/xic/+Um++MUv8uSTT/K5z32OZ599lgsXLnRMgPqTP/kTfvM3f5MvfelLHD9+nIsXL/Kxj30MSZL47Gc/u6Vr31Uis94qRDb+K7L1HQQhqjUdx7EJB6pEwl/BEe+DldDWZpMHcsb3kCStKTAAqhzDcrOogSkS+i/SH/7whudYWFjg9OnTDA4OcuTIEWRZJludpxS6xuXSy/T7DxBSE8iSiuXW0OQbX2zDLRNV+9FWORjfrKCyFcuyOHXqFKVSiSPH7uO/Tv4NWavIQX0MGYm0mee17DkejB7iUmmS6eoCAggpfp5M3MeB0I2U6E41Ngu1PNfKy9iuy55AnJQviOnauEJg1GrMzMwgSzKa38dQNMV6VGyTi8U005U8PlllOBglqKgULBO/cuONsmgb9ULNVeaWiqIQTSS4VliimIwy2pPCrNW4VshxZWmBsCRzdWmB+VoZvz9AKhjieP/eZv2L5bpULBNdUdFkmZenp7mcy5L0B/ApGjPFIkIIfKrCRD6PX1WxHAdNkXl0YIDwDr/Z7zZbTbZpCArUQz2N0NpOGXx2457MnRpPq8h/9rOf5V/+y3/JY489xtDQEF/4whf4hV/4BX7lV36Fz3/+81s+92c/+1l+6Zd+iV/4hV8A4Itf/CJf+9rX+NKXvsRv/uZvrvn9l156iWeeeYaPfvSjAIyOjvIP/sE/4JVXXtnyte+acNm6xZjCQTL/GlcolKsCWZIIhSLIUhhEGsn+LkL/ELC+yNSbmRWQJB+S5AfhwJpKkZWfSOKmJpuNHjSN4kaAy6UXOZ3/azKpOV7PXcCvhDkYehc92hgL5gVCShJV0qm6BVzhsj/01Eo/mBtsVmQKhQJvvfVWs/7mROkSebdMiig+uT5xD/hTTFTmsITNR/f+EFPVBVzhssffQ0qPtf19VovMq+lrvLB4noJVQ5JAk1QORfro80U5szCBW6iQTCRIW2VSIsjhyMCaMQIULYO/mj7PlVIGTZJxhOAtWSalBylYVSQgovko2yZZs8qTPcPE9LVvmZOlPNeLeUbDsXr4KxJhuLeXy7kMoWyBQ7Eky9UKxlKO4bDAldOkbcGicDmbXiZvGuiyTNznZ65QYjgaxbfyhh7Wda7nc/QFQwxFIixXqwQ0jeFIhIEthDG6NVy2FXRdZ2BgYEcNPrsxXNYt4wmFQqTTaX7jN36Do0ePksvlyOVyWz6PaZq88cYbfOpTn2r+TJZl3v/+9/Pyyy93POb48eP85//8n3n11Vd54oknuHr1Kl//+tf52Z/92S1f/65fyQhhYJt5LMtB1zR0n36jF4kAyS00nb1Wn0MIQa7yn6jW/hhJpJHw49N/kKh+P5XKRVxhIUv1SbmxB+NWR9ad6Gu1GidPnsSyrGYPGoBl4xqnCl9DINDMGDFlAIMC50vP82j8x/EpIRaMixhuBb8c4d7occaCax2aN9OqYGZmhnPnzrF//37279+PJEkUrXI9m2/V7/plnaxVIKqFuE/bv+45W/dk5qt5vrV4Hgk4tLL/UrINxguzjNYCRAsOUl8Pfr+fQ+UA90hx4utU8J/OznO5mGYsnGiG2rJGlaxV5cH4ADPVAstGhYCicrx3H0/2dE6fLpgGQog1+ysRXScnXO4f2sfAwACWZTUnxedPnuCNQpZgIMBANI7h9/F6NothOYzF42vOUzBNPtC7NsFgK3RDWGinijF3yuCzmyZ1qIfLusXq37ZtDMNopjDH43Hiq76bm2F5eRnHcdbYZvX393P+/PmOx3z0ox9leXmZd73rXU3j4l/+5V/mn//zf77l69/VIlNvBHWesUSCZHQOWdFprkBEDVAQygFMe4JC7S8w1NfwJxWKRpaw/gGWSn+IXfsPgLNSzWFQM/4bqvoQQfUeKvYlJBQELpIkk/B9H0u1/R0n+uXlZU6dOkVvby/33ntv2xd1pnYWw62QUIcpMgdASE2QNWdYNq7yVPKnKdlpTLdKWE2hy53j/ButZFzXZXx8nLn5OfruHWbOX2Quc5LhQB+xFYdkp+VYIQQ116BXj9/03rfuyVwrL1O0ak2BAfChkFnO4JNCfPJdP46p1v8Ky1NzFFdqSzpxobBMRPO17eUkfAGWixX6A2HeO7B/xd9MbzPTzBs1Lhcy5A2DmM+HUzdLrjcqa3FMrtoO4ZbiSk3T6O/vp7evjwuqxFA2RAwolUtU0mlMx2HKtpgMBhlKpVBXjq1aNkOR9fde5gslrmWyZKs1Yn4/o8k4g9HubDS1WxX/2zX47LY9mW4SvVKpXgpwJ+pkXnjhBX7/93+ff/fv/h1PPvkkly9f5td//df5V//qX/E7v/M7WzrXXSUyrRv/DUuWUChEpO8fI1ufBrEEhEDYQA2hPkqNCAuFT2C7iziSjBaosVT8NFXfKWq1P0XFQUhBQEJQ7wuDc5bh8C9Sct9DyTyJJOnEfE8S972bK8p32yb61s31o0eP0rMnypXqd8hbM+hSmOHgQxhuud7rXpbqjctWJmxZUqi59Uk4rK6/b9FgPZFpds8ULs6RAH9TfoVaoZ7goMsaR6P7SSkx5lgiaseRJZmMmSeqhrkveuCm120Nl9nCoXVOqFarzM7N4ffp9A8M1gsBV/4tvSrMVrEtpst5QDAYiK7rp9Y4fVDVCK5yWJ4pF/ibycvMV8v1LpjCJa778SsKk8UCQ6EIiiyRrlWRJdjnXxvSMhyHvGnSE4kQ9flI9fQgXJd8scjCxHXOzM6SXVgkFAjg+nxIPp39ezuvoqZyeV6ZmMFwHEK6xmQuz3S+wON7B9mfSjR/7+0QLtsKmzX4FEJgmmbX+HN1U9OycrkM3LrI9PT0oCgKCwsLbT9fWFhY0zOrwe/8zu/wsz/7s/ziL/4iAA888ADlcpl/+A//Ib/1W7+1JSG+a/ZkGisZ13WZnJzk0qVLbZYsjiUj1/6/SO40SD5c7Ydx/R8jV/rXWO4CmjwEroNhVZD9gqLxF/gpgKTRuv8ioYOoYTvj9IU+QV/wI23jaA1ZmabJqVOnqFQqPPnkk0jBGt9Z/qN6bxfqXTCvVr5Lr+/IioDZK9lJoi4IwiKljW76HnQSmXQ6zYkTJ+jv70ffF+E7c88TVPz0+eqiVbIrnM1f4cHAGFQdao6Bi2DY38fx1EPs8fd0uhRQrxE5lZviG7VryAuzPKLkSehBVEmhbBtYpSpLy8skUkkc1eJQtH053ioiZ7MLfHP2Eku1MgJByhciruqUbBPTddBXVjNpo0JI1RkOrq2kd4Xgu3NTLNUqHIomm+e/WsyS0AP0+IPMVUrYriDu8/GewRGciXr/m0ytymypiCsg5Q8QUBSKlkV0Zd9AkmUCoRD3Dw3RHwhRMUyK5RKSadJTrTJ9+jSVlbfwVCqF3+/Hdl3GF5ZxhWBv/EatzFKpzPjiMsOxKLp6Y8Lqhjf2O+Fdtp7BZzqdBuDNN98kGAxuyuBzt+mmlUy5XCYQCNyy6Om6zrFjx3juuef4yEc+AtQ/53PPPcev/uqvdjymUql0bLcCW39humtWMo0v3VtvvUWxWOSxxx5rLs0BhPYeHPVdIAog+UHy4wqDqvUGCkF0MU9AShMK2CDilIWJWPExbr1l9Yp+kFZaI9drIQxkSUeS5Kbdfzab5c3T3yMc8/PkU0/j0/28lv1LstYUMXWwacNfdpZZNi4S1frIWrPYik3FUbFFhZg6wGjosU3fg1aRaW0PcPToUYaHh/nbhe9hujYD/htvPmE1SNYqknNKPM0R7tl3tJ7CrEfbQkurEULw13OneCl9hbxbJGzbPLdwjj2BGCOBJG/MXsapmfT19JBRLEZCKR6Kt7/tN0RgrlLgf02NYzg2o+EEEjBbKZKrVejzh5ks55EBF/ApCu/uG11jEQP1PjBzlSJ7gjd6qUiSxJ5ghIxR5e8NHqqXSwmXHn+QkKbz5uQsF/I5Li3OUzANQBBQNeJ+HxXLJFurEvP5qdk2c6Uih1Mp3r9vjJxRw3JdEj4/QU1rTorz8/NcvHiRQCCAFo4wWygz0PI9BIgH/CyWKhQMg54OLRDuJN1gkNmoig+FQly/fp2nn366mbp7M4PP3aabOmOWy+UdK4b95Cc/yc///M/z2GOP8cQTT/C5z32OcrnczDb7uZ/7OYaGhvj0pz8NwIc//GE++9nP8sgjjzTDZb/zO7/Dhz/84S2L3l0jMsUW36hWx+I2JBlWxAHqVvCSkPAzhyoMkCSQBDIZIhKURQxZ5JAkBYECCGQMkCIEfR9mufYSM+W/ouJMoUoRBoLvB6mHyfmLTFh/jbxvkZqu8HLuW4yG3s9c7WyzzwusbI4qKfL2HIcjH6Bs5zmT/zskJPYHn+JI5H2bCpM1aIjMeu0BLGF3yIkDGQlHchCuIKlvzmtruprljewEKT1MQLUJqAGi4RgX83O4pTyPy71Yo2FcWeJQuI+HE/vWbPA3ROZCfomcWeVwS2X+cCjG+dwCe4MxnukbYa5aQJMV9oeTjITidKLRJ2b1Z5Sofy8USWYg1C5OWcvkreUi0UiEA7E4kiSRN2oslCuMRmMUTIOJQg6frHJPIsnxob34VJX+VUWvDWuWPXv3Mp8vkMvlKRfzLC0ukZ6bJxWNEI5EiEYiuIqCKkttDgHvtHDZZmjcE13Xb2rw2ZoqHQgEdu0zdFO4bCcLMX/qp36KpaUl/sW/+BfMz8/z8MMP8zd/8zfNZIDJyck2cf3t3/5tJEnit3/7t5mZmaG3t5cPf/jD/N7v/d6Wr9314TIhBNevX+fy5csA3HvvvZuuOpYknZC6D8k+j0ADFFzhAC4yLhHtXgrWBXwi01zRCClAOPTPSZvnuFT4Aq4wUOUwhrvMtcJXqIUPMo+D3JNG1+IokkbBnuFM/r9gCwVFWl3kV/+cfjnM0cQHKJ/p4UjfYfqTW7fvlmUZy7J4+eWXUQIa6r0x/jL/ImpB5nBklD2+HpDAdC30lVRly7VxhMOwr29LE910JUvVsRgKJKiSR1Cvf6llCiwHQ/z6kz9205BGQ2QKltGxhbEuK9RciwcTAzyY6BwbtlyHrFFDk2VS/iB9/jCzlSKjkXjzdxaqZfYEw/T4164alkyTsm1xKHjjYY35/GQMg5Cm8wMjYxRMA5+i0nOTyWsym+fN6Tky1SogEfHpjI6Okq9UCEr1N8+5hXmypsOB3hTVXJagkmp+X7thcu8mkWmsylePZyODz0uXLuHz+ZqCs9MGn922ktnJtgG/+qu/um547IUXXmj7b1VV+d3f/V1+93d/95av29UrGdM0OX36NKVSiccff5zXXntty07MfnUPhl3PEEM4SJIAVGQphCJyDMT/nFztz3Hsy+jKANHAx9DVvVxO/z9xhUlAHQRAsh2KlQVE9AyyFCes72tW6mtykKI1i4xD1cnjlyNNl+aqm0WXQ/T6DgKgyhqS2N6bUi6Xo1AoMLR/L2/4rnJleRpNVnGF4HzpOkcj+zkQHOJyeRpd1pGAmmuwPzTMPb59TLrXO55XCMGl0iLjhVmqjslwMInjus0VAtywvwhFI+xJ9K4rMIZj80Z6hpOZWRayGeKG4HD/IWzhtmV/uUJgOA79/vU3Nc9llvjewjTpWhVVkhmLxrkv0UPeqnEpn8avqNQcm7jPz/GBvW0rhwb2OsKqSRJVu74nE91EPUeuWuN7kzOYts1wrJ60kKlUKZkm8VAIw3GQdR/xWJy9ssSoX2Nqaorx8XEikQiu61IoFPD7/Xd0EutGkdnofqw2+Gw1nNxpg08hRNftydzt5phwh0VmvewiuNHvPh6Pc/z4cTRN21Z3TFXuw5YiyPhxMTAtG78/hSRKSFKIgDZCQPt/tR1TcxapOQtochQE1AyDSrmMPxCnRh5Z1DpYwYRwhEVE7SNnz6JIGq6wUSSdeyMfILzSfGwztS6rcV2XCxcuMDMzQyAQoNzjcmV+mj3+3qa5ZdUxOF+8zo/ueQ8Hwns5X7yOQHA4PMJDsXuo5Svrpj9/e+kC31w4R9WxUCWZ1zLXGfBH8csas9UsVq2G4zqk+ntZcqs8HN/X8TyOcPnLyXO8ujxFQFExXZuTtTzV7AxxLcDlQpo+fxhJgoVqiT3BCPfGOvenuZrP8teTl3GES8ofxHZdTqYXKFkmPzJymKuFLBmjStIX4HA8Rf+Ky/K1fI7z6WXS1Sp9wSCOqBfoGraNb0UYHdel6jjsi24udAgwVyiRrxmMJW4UqvaEgpRNi8FohJFknKpl4VdV+sKh5oZ/o0r+3LlzXLp0iQsXLrQVLN7urKpuEpntFGKuNpxsNfg8e/YsjuNs2+CzMRd1S7jME5ldQgjB1atXuXr1arPffav32FYnaE1/L0btv4BwUOR+HKsIPhdw0Xw/1PEYRQoiSSqOMKmV6w3WItEIigpGVUEgr2SK3bh9tjAIq308lvhVJiqvkrau4Zej7A0cY4//vhvnXkkc2Cy1Wo3XT7xB3ikxds8ImallLpem0GS1zT05oPhwhMOikeXZ/qd5KtnuFWZI1Y6CvlAr8O3FC+iyylCgvoFtuw7XKsuM+JJcnZ+m4NYI+P3ImDwYH+bx1GjHsV4vZjmRmWMwECGs+Si4RbSKSbpW4ZHUEAeUJFcKGQAeTg7yVGq4aYK5mlPpBWq2zf7YjU31gKpyvZijbJm8Z3BtB82zy0t8c+IahmMTVDWmS0UKpQJ9sRhTpQJBVUORZPKmwb5olIOJ5JpzNBBCkK5UKZt14ahaFjJrQzu6olC1bYZinb3QGlXy4+PjHDt2DMdxSKfTzdCP3+9vC/3sdlZVN4nMTqwaVht8lstlMplMm8Fnq6hvFGpvbQPfDXgiswsYhsGpU6fq7YBbNrQbbEdkFPUI/sA/olb9DwixiKabIPxovnfj8/9kx2M0OUxUOsZs5etIIk4sngTJpeYsoLoDKCJO2V4gqPYio2G4BVxhsy/4HiJaH/fH/t6649mK/1g6neYvz/4t0+E0UkjhzdJlgrJG0h1Y1/1X7rj1v/51r5eXKdg1DoRubMqrsoLfkbkyM8GP9zzErF3AVSQeHTnKPZF+VLnzm95ctYjl2oRX/MXqNjwSMd3PYrXEJ+5/N0WrXp0f0XxYlrXuSnaxWl7jC6bJCkKwkiXWTs22eWVuBgnahOm15WVkWeZdwyNczmVxheDBvj7uTfUSWWfCMWybVyZmuJrJUrVsfIqKrsoYjoPtuKjKjZBf1bbpC6+/GsmUq8xli1zOVOhdyjE20MPo6Cijo6PYtt0M/Vy+fJlardaxYHEn6TaR2cmxSJJEOBwmHA5vy+BzM+G728nboSsmdFG4LJ1Oc+rUKRKJBI888kjHN7rtNi7zBX4GVXsKy/o20zNn2DvywwTD348krTNZzs0xMz5GcP9RCE5juPOAREAZQKv9CHHZTy70AkV7DiEcNDnI/vD7GQt9/80/syxRtPPkrTBRNd7xIWskO3z7+ve4kpzH7/cTVcNUnAqTygKOVU9gqDoGAaU+oRetMrqssT88vOZ8cONe267LdDWN4dgMBOJrJEkgKOQLpDMZ+uJJ3v3QY1wYP48syxyJDW742XS5bovfnMgkCYTAdB0CK0WVDYPL1eKSrlWZLOYQwFAoQsof5EJuGQI33uRs10WSIKStFYflaoVMrcrgKnfluKpRME1GY3Ee27Px+BucnF3gzMISfaEQA5EIVctiKl/AclwmsjkSwQCyJJGr1ugLBRlJxDueZyqd543rc1QMk8WKwevXZpnJV3jq4DCxoJ9CzWTBcDF9EfYc7CXh1ygV8qTT6WbBYmNC3KwX2M3oJpHZbd+y9Qw+s9nsGoPPRCKBoijNrr3dwNuhKyZ0wUpGCMHly5e5fv06R44cYXh4eN2H4FZaMCvqQRT1IEvpbzG875GOAuO6LufPn2d2dpaHH3yant4PkTNPUbWn0eQYSd8xLqencW2X9/T+C9LGBSxRIaaNEFY77y20smDM8Ib2AqVaDt+0n37fIMeT38+g/0box7Ztzpw5QzaXpTYEuqs3CytlVSLiBKk4VYYCA8zVMjiifj90WeOJxH2MBTtPpLIss+xW+OKV55isZLBdh5ge5MHYXiKKn2WzREoPs7y0RKlSRo+FeHLocL3vyjpW/8u1MufzS1Rsk75AmOFQjKQeYLqSZzgYQwIqro3s2Dy0QTbda4uzfHvmOjmjBkhEdJ194SgyEnPlIil/EMt1mS0X2RuJMhaNrzmHKssokly3zml5O3Wo94JRNjlx1Cyby+kscb+fsK8uZgFNYygaYblc4UBPkly1hhCCo309HO3vIepfO/kbts2Z6UUcx2FfKkZuRmM4FWOhWOHCXJqeSJC3JueomBbyygvAUCLKkweGGRoaaiYKZDIZpqenGR8fb3sLj8fj25oMu0lkbrelzM0MPhtjmZub2zFRvxXeDl0x4Q6LTK1W48SJExiGwVNPPXXTDnDb2fhfTSehEsIlU3qDS9e+jXAiPPX0TxIO1UMuSd+j4Hu07XjbtlEkjT7//Zu+bt7K8rWFP2NJniUkImiSxmT1CtmFZf6PPR9Dkfy8uXSa8YkLRNUwP3Ds+3hj/hpB5caehSRJKKKebP1g7CDv6YkxWZlHRmJ/eJix4CDyOgWWNdfiRWaRSiH2BOLoskraKPFS+hJHIoNcLizw+uIMCjKRZIQjkQGe6TnYvO7qUNvZ7AL/v+tnWDbKyFK9JumeWC/vGdjPdxaucamYplarUXMtnu3dx7FU5z4Y06UCz01fRULiYKz+xpk2qlzKZ3m4p5/ZconZchFVljkcT/K+4TECqka6WuX00gLX83kCmsbhRJI9oTDXC3nGojEUWcZ2XbKWyUPJJIkNeoQslSvMF+s+UQFVxbAs4qv6xPjVuiHb0b4eesNBLMdBluS2iv5WsuUauUqNwXiYhj7LkkQy6GdiOctkOocsy4yk4gDYjstUJk9PJMgDw/3Istw0RNy/f3+zo2I6nebcuXPYtr0mgWAzE3a3icydtNVfbfA5NzfHpUuXtmTwuZuUy+XmKuxu5o6KzPj4OH6/n0cffXRTG563spJZ7xy2W+TCwj+naL2JnBKoisZE9QXG9N8l2KER2XaywwDOl06RtdLESKJICgEliF8OsGTN82Lm25xNLzJXWkD36/h9PpYX/heKJFNyKkSpL5kl6t5hGhpxLcLRyBhHI2Obuv7lyhIZUeOR4L7mnkqfP8pkJU22VOD+ZR8jsX3E+3oYCfXwQHyIsFqfmFeLTNky+cupcxStGvdEe5AkCdOxOZdbYCgY5R8dfpLLhTTL2QxlZ5EfH7l/TWdJy7K4cuUKJ8o5ctUqR1M3DDd7/EHyRgYJiZ8/8iDLtSqaLNMXCCFLEsuVCl+9dIHZUpGormNWSlzKprknkWIgGORaIVd3zJSg3+fn0VRn6xwhBCdm53ljZo6SYYEEflWhYliARKCl/qJQMwhrOmGfztXlLOMLy5QMk4jPx9GBHg71JhFAsWqA1Ln4snEHSoYJSIz13dg7UhWZiN/HVDrPfUN9a+5Xa0fF1g3u5eVlrly5gq7rm6od6SaR6Sabf1mWCQaD6LrOY489tmmDz93E25PZAR566KEtN1HaSZFxXZezk/8fqvKraGocXQ0jsDHsaa7n/xVHUv8JWdLXHL+ZjXtXuFSdErrsR5N10uYCMjKydCP0JEkSCgqvL71GvuJnJDJEMBDCFS5zxiIhJYTjOnUzSy1M1TUpKGUe8O/lQKhzGnErpmvjCBe/rFF2TQQCtTVMKEBULa6UpvjJez7Evn2dz7k61fxqMcNitcRYONn82+mKSlIPcjIzx7ND9/BE716W8HN+odA2YS5USpycm2b80kX6/CFmjSrL+WUmy7Wmv1UgEECTFSq2SUDV2BtunzDfXJxnrlzkUCLRPHfeMJgo5PmxQ4cxHIeSZRLVfRQnJ4nrncMes4USr0zNEtBUDvTUH+ZctcZSqYK78nEjPh8Vy6Jimjy2d5Dr6RwvXZtCVxVCus5yucILl64zny9hGg7pUgVJgnjIjyrLLBcr9EbrIQ/Dtlkq1egJh8jVah2cC6TmdTei0wZ3a+3I2bNn22xZotFo8+/UTSLTTTUp0D6e9Qw+s9ksExMTSJLUJuqBLXRI3SzenswOsFXR2O7Gf6dr1mo1Tp76HqL/ZXx6BE2ph+okNFQ5ieHMUDRfJ+Y73nb8zVYyQgjOl97izfx3KdgZNEnnaPhR/EoIF7fNJ811XfKVAhVbZjh+gKBen4xkSSapxSk7FY7F7+VCeYIlI40kJHrsGB8ZeH+zor8TBavKc4tnOZmbxBYu+0N9DKhRJCQMx8KnaghXsJxeZrma54nBQ+sKTOMzt4qMLZz6W+iqyUqVZVwhcBou06uOe2tpjv82foLri/OEgkEiskosGqDH10tE9WPVaiwuLmDZDhkFHvJHqFQqbbUkQgiu5rLEW1ohA8R8PhYrFQqmyaP9N9wDTs3Mrvu5JnN5jFXpx/GAn5jfT184SFDTKNQMQrrGo0MDjCbj/NWZi4R0nZ6VjLKo38f1dI7/ffYyB1NJBqIhXAFzuRKyBD5VZXx2kXPpMtes68TDQfqiIXRZIVepkQjVJyfHdSlUazy0b2DNfb0Zq2tHarVac6+hYcvSmBBt2960Y8Zu0402/51CYusZfGYyGebm5rhw4cKupKLfTSnMH/vYx/jjP/5j/tE/+kd88YtfbPu3O55dthUURcGyrFu6pizLFAoFLl68SLIX8CtIq1YrEioCB9vNdhzDRiuZ86W3eG75q7jCxa+EMIXBq7nn2Rs4SEAKUiJPQIQwzBoLxTlUVSPgS6Cr7WOQpfrey6Px+3hv71MsGRlkR2J84jQpLb7u9Q3H4isT3+VcfoaYHkKVZN7MXiOuBokJjalKmoQaJJ/JUpZsBlK9vHdo/b0lVwjGyxleS0/zVyeW2ReOcyTWS1T3s2xU6F2x0hdCsFwrc6xnuM2evyEymVqF/3ryNZbyWR7es49YLErZNLiSzxDWfCy6FrFwkEA4RLlcZEzRSBo2r7zyCn6/Hy0aIR5PMNrfj64olC1zzThBdKz6Xw/LcTtO6Ioi0RsO8d79I9TsegqzqsgsFMuUDJP+SPuD7zgOuWqNVNhPQK9/9oAeZSKdZ280xGKhhJBgrC9BPBRgOlvApyk4hkuxZqApCoZtMxAPc6Dv1mPwfr+fwcHBpi1LI4Fgbm6OfD6PpmkIIe7YXkODblvJbNZSpmHwGYvFGBsbw7btZmhtJw0+y+XyTfepu4m9e/fyp3/6p/zhH/5hc2VXq9XufHbZVrjVjf/Gsnd5eZmjR48yNDzA+UwPpjOHIt1Y7rpUkfERUNf2WtloJeMKlzfzf4crXBJ6o+4kiCbpLBhTHEu8l1fnXyJvZ8kW08T9Sd498Cx/lxknZ+bp1VPN0FTOypPSE/T5etBklYgawrZtznNmQxO/s4UZLhbn2RfqQV8p1oxrQa6UFkjiY69/gDfnL6H5/RxJjfC+/nu5N7p+au9zs5f5n0uXMS2TQcvPy4sTnMstcjCa5HxuiYJZw6+qlCyTPn+Y7xu4sUfUWMk4jsPfvvkas/ksx/aNEVz5AvoVlYTPj1/ReKx3kHPZZRzh8v6RgzzWN0jSH2A6n+N/X7rA5akJKpfOk5AUesIR5h2ToKwQCwQBwVypRNIfYCQaXfMZGkI3XyxxeTlDoWbSEwoQ0OqWPKbjNLtqWo6L5bgMRMIoskyo5a3fpyhoikzNttFa7n++auBTlbafyZKEIknM5Yv4NJVD8QAH+upvuKbtMJ8rct9IPyAwLIeecJChZJSgvnM+XFB/kWudEM+ePYvrujiOw/nz57Esi1gsRiqVIplMEgqFbtvqopv2ZGD7oqeq6oYGn0KItiSNzRp83k0rGYBHH32UK1eu8NWvfpWf/umfBuCrX/3q3SUyt7In0/BBq1arDA0NNcND/cG/z1Tx32K5y8hSECFMXAzivu8joB7uOIb1VjIVp0TBzhJQ2r8YPjlAySkQkiI8lH038+Y0h48c5nDvvQSUEIoU5X8tPM+csYguaxiuSVAJ8N6ep9qq+hsPwEYrqYVaHleIpsDASiaN6iMv5XnPjODd9/wgqYFeUnp4TWGl7bqYrk1A0ciZVV6Yv4pfVunRNAaCEfpFmCvFDCXL5KMHHuFUZo6cWeXJniSP9w4zGGyf5F3X5ZVXXqFaM+jp7WkKTPN+SjKSJPHeoVHeNzSKgObqomAY/NW1K8xUqwz29SEhmCsWWXIcUkicuX4NZBmfz0dfJMp7RsaI+TpnkV1azvD85WsUDBNdUTizYJMI+EkFA0xmC4R8GhJQNkz2pxLsTybWnCMe9LMvEWN8fhlNVvBrdSeAmuMQ8fnaEgVcIXBcgWXahHwalZZJRVcVBBI+VeG+ob4119lNGvs5Y2Nja9J4r169iqZpbbU5O2k+uZq7JVy2VXbC4LOR3HG37cl8/OMf5z/9p//UFJkvfelLd1+4bDsik8vlOHHiRN2qfc+etj9qKvAjgMxi5U8x3SVkOUiP7yPsCf/CyqrCYdl4neXaa4BAFfuxnc6bfLrsQ5N0TGHg58Y+giNscOHq+esk7AH2hw7z8MDjzX9/MHaUiBrmrfxZls0MvXqKR+L3MRpsL6xs3K+GyOStCn+3dIGTuQkAHoqPrFT8i7YNXuG6pPNZeoTGww8/TF/f2onNch1emL/CS0vXqdgmA4EIe4MJMrUKfXqAWrXaHEOfP8RspcBIOM4jqc6roAvZZZ6/eJaLuVkO9Qzw+L33cnXiAlmjSmLFSsYVgpxR492DI01haf1GXMikmSkVOJhINv89ovu4lM3w2P5D/HgkwvWlRaqlMoGaQfrcOG/OzDb3Jxpv5abj8L2JaQzH4cBKt0ohBNczeeIBH9+3fx/XMjmEEBwb3MPhvhQBTW3+XtEwkZEI+3WeGBnCclwms3ls10VTZB4dHqBWs5nNFekJ133W5vNlkqF6+Gw2W2j/+620LPCtk/68m7R+Lzql8TYSCCYmJjh79iyRSKS5yolGozu68ni7rGQ2YjMGn5FIpM2FoDGGnVrJfOELX+Azn/kM8/PzPPTQQ3z+85/niSeeWPf3c7kcv/Vbv8VXv/pVMpkMIyMjfO5zn+OHf/iHb3qtn/mZn+FTn/oUExP1OenFF1+8+1YyW9n4F0IwOTnJxYsXOXjwIKOjo1y8eLFNqCRJoif4YVKBD2K5WRQ53AyducLmXO7fMl99AUH9usIFt2cMR7wLZdVeji77OBx+mNfz30ZzKvjkAI6wWa4toJb9HIzfSzQUY35+fs1Yx0J7GQt1bvHbOtaGPUzJrvF/X32Bi8U5giv7Of9r9k32BlOEVT8z1Sx7AnFc2+ba4ixCEoyKaMc3IyEEX508zfNzlwioGkFF41JhmbO5eUzHxVHavyaW66LIMto69jIvzU3yJ2feYCmXRZckLFFjafISo5E4F7LLZGtVdEWlYNQYDEV5sq9zDU26Vq0Xg7a8jEiShF9VWa5WeP/oGPe0pCc3MoDS6TTXr19HURRkWSZtWCyWagwnYm3n6QkHWS5X+cChBI8OrS0WXSiUeGNijvlCCQkYSkQ5NrKHDxzZz2KxTMW0COoafZEQs9kipybnOTO9yHKxgq4phHy99K3s35Qsp+m4sJAvkQwF2BO//fH2jbLLWh0GoN188vTp07iu27bKudWMqrt1T+ZW2IzB57e+9S00TcPv99/ySubP/uzP+OQnP8kXv/hFnnzyST73uc/x7LPPcuHChY4vm6Zp8oEPfIC+vj7++3//7wwNDTExMUE8Ht/U9Xp7e/nQhz7El7/8ZYQQfOhDH7r7RGazK5lm5Xw229ZFU1EUTNNc8/uSpKIrvW0/W6q9xHz1WyiSH1WuH2+KKlbwInOV5xgOfXDNeZ5IfD8FO8v1ynlKTgHbtFEqPn6w7//kvuH7mZ2d3ZJB5moaIvNmdpJLpXn2BlPNyd5yHaarGZ5KHeR6Oc2V3BzFUpG4P8JHRh/Hfy7d8drz1SKvLk+S9AVJ+uorsIQvyLVihrxTZtYqkXTrD5/h2CwZJb6vf3/TIqaVfK3Kfzv5OsVykYf27GV5eYmxeA+X8xn6A2H+/qEHOL3ipvxk7yAPp/rpC4brCQbpZc4sL1I0LUZjUYSoh+9WT4yG43QsrgwEAgwNDaHF4szPznF+fpHiUpqIIjNVKOMW88QiEcKhEH6/vyWVfO19zlaqfGP8KrlKjVQogADOzy+Rr9b44P2HGIi2P/zDySjTmTyyJLGvJ0oiGKRUMzk/v8yeaIgZAVOZAoqi0BMO8NjYECHf7c/y2koK82rzyUbYZ2FhgYsXL+L3+5urnHg8vuWMqrdruGwrdDL4PHnyJH/xF3/B0tIS73vf+/ihH/ohPvCBD/CBD3ygue+zWT772c/yS7/0S80OmF/84hf52te+xpe+9CV+8zd/c83vf+lLXyKTyfDSSy81Iz6jo6NbuubHP/7xZt+aL3zhC3dXuGyzG//FYpG33nqLQCDA8ePH2+whtiJUS7VXETio8o3Qlyr7QAgWay92FBmf7OeH+z7KdPkab156FWFIvPe+HyQeqYvUdoo5LdfmbGGaqcoyl+VF9lWzXC0vokhS22pCkxVkJFwh+HH9KC8XzjA8cj+P7D3MnkCcb45/EyEEebPKmdw8FcdkMBClYluULIMDkfYunSlf3So/aMvMFvNU8svIksTRWB/PDt+zZpymafK3r36PxUqJR0bGVvrRrITYAiGmSnl+8tD9PNpXj1VbltUUvW9NXudbUxP1/SRF5kJ2mYQvUHdeLuQZCkeQgPlK3Tjz3lTnh22hVOYvzl5gqVwh6tMpIVFEJpRMUUPgq9bIZjIIICdk7h/sx9/he3hlKUu6VGV/zw1/ubCucz2TYyKd477B9rfAQtXg8mKWPYkI8WBdAOMhPzPZAobt8FBviPuOjmG5gohfJxne+bqKzbDdOpnVYZ/WjKpLly61mXumUqlNNdvqtnDZnW5Y1tgv+7Vf+zV+5md+htHRUb74xS/y0ksv8a//9b9mYWGBT3ziE5s+n2mavPHGG3zqU59q/kyWZd7//vfz8ssvdzzmL//yL3n66af5J//kn/AXf/EX9Pb28tGPfpT/6//6vzYtwD/0Qz+EaZpIksSzzz779lvJzMzMcO7cOUZHRzl48OCaL/pWJnmX9dOlXWExWZ3kTOEUBTtPv28PD0UfJqknyefzXDkxyb7YYR547IG2N7ytWv2X7RpfnniBc/kpHAQFOc/E1DfpDyY7OjE7wiUzv0jaEPzUEx9oc7KWZZlz+QX+59J5FqpFJElClWQGA1FAwnAd/C2hsZprk/IF+ft7DvP6xCX2jhygxx/i3ngfvlUhtGKxyJtvvomua/T0pJAVFRwHViqDGn1dlJb9gAYL5TIvzU4T0TVSgeDK/RVczmY5EIsDErOlIkJAKhDgvftG2NshiwzgxOw8i+UyB5MJJEnCLvpxFYW8kAj5/dRcFzUYolozGJAEfY7Fiy++2Nx7SKVSRKNRlksV/JrSNk5ZlprGmKvJV2uUagajPfG2n8eDfnKVGjFXMLmcZzJbwLJdwn6d+4f7GO2JMbVcYLlQwa+pDPdE6V+xoilWDYQriIR8m/Zeuxk7VYy5OqOqNYGg1dyzsdLpVJvTbeEy13V3NdFhK5RKdZujD33oQ/zoj/4of/AHf7DlFt7Ly8s4jtNssdygv7+f8+fPdzzm6tWrPP/88/z0T/80X//617l8+TK/8iu/gmVZm+6SqSgK4+Pjzf//thEZx3EYHx9nYWGBRx55hJ6ezlYiW5nkk/pDLFb/DldYyFL9yyeoX7/q9vIn01+h6tZQkBkvjXMi/ybvUd5L5lK2uQfUSeS2IjIvLJ3lVG6CAX8cv6IzX7SxXJvJ6hIImaxZJq7VJ+Z0rUi1WGavf5jjx4+vebANXP565jQ5YTIWSaFIMhXb5Go5gy4pTJVzjITi6IpKyTLImVU+OHSEPj3EUT3G00OHOo5xfn6e06dPMzY2xpOjI5w/+T2mSnmGA6FmyGuxWuKpgb1t2V+NezNTKlKyTA6GbmR0yZJEwu8nbxr8k4cfY6laQQB7QuG6j1gHhBBcz+aJ+/1t9z2oqdRcODY0QFDTKJkWiaCfA6kEEZ8P0zRJp9NkMhlOnTqFEIKsJZOpmPSHg82XBCEEjus205oLVYOlYhllxYBTVxUM28GvtQi1ZaPKMlezVXR5gb5YhKhfIV81eP7sVUKajuQKdFXFdhwuzaY50J/ANGzS+QoCQTIS5PBIH/3JcHMcVaP+AhT0by3ktlsV/6uLFRsW+1NTU20W+6lUqrm5fSfCUxvRTeOpVCqEQqE2Eb4doUXXdenr6+M//If/gKIoHDt2jJmZGT7zmc9sqRVztOUl8K4TGdd117wBlctlTpw4gaIoHD9+fMMNya2EywYC72Ou+i1y5lkkJEBC4CCMXl5zqtjCoVfrbXp7zVZmea76TT7+yC+uK3JbERkhBK9nrxBQdPxKfTKRkUlpYRbcIociA8xW81wrL2PbFla5xvHYAX78kfejdnhYZkWVRaPEgXh/sw1yUNWJaQFA0O+LMF2pp0D7FJUnUvt4dvAI5Uyu45hbHbQffPDB5hvTj+w/wp9fOsPlQpZlu4ZbyDAWTfCBvTfqjuq9WCw06qubek5ce3aZK1x0WcOnqozG4h3vkeO6LJYrCCHoDQXr3mPm2kJNSZJJhYLcP7B2s1PXdXr6+qhpfqREL4pj48vluDx+lVfOnqMvHCIYClEREslohH3JKCcm5jgxOU+xZiJJkAgF0BSZuVyRwUQEn6pSNkxylRpjqRjnDZuH4xFCK47NfZrK5FKOqXKe99+/v1ljM58t8o03rzCSijLYU3dpWMqVKdWmOX7/SL3V9uQSS7kyAAPJMEf29RELr28AuvpvttuTlSzLJBIJEokEBw4caFrsr+5eaVkWkUika6xu7nS4rJVSqbSlrp6d6OnpQVEUFhYW2n6+sLDAwMBAx2Ma2betYnv06FHm5+cxTXNdt4gvf/nL647jrtuTgfYvw/z8PGfOnGFoaIjDhw/f9EuyFZFR5QAPJn6HM7mvsGR8Dwno9x/n9CWFfP8UPSvFk45jUipl0YWKHbfQYusvuSVJYk6k+dr83+EIh/2hYY5GxlAkBSEE1yqLpI0iCT3EWLAP07Xb/cYkCUTd5+rh+Aj/x9AAL149xWJukXcdeBePj9677n21JIErRDNk1aDeB0bwK4efYbqSp2gZ9AfCjIVTyJJERVrbJtu2bU6fPk2hUFjjoH000cuvPPAEb81N81rxJN9/6AEeSPUT1n04rsurczN8b3aabKVCwufnob4BEj4/s6Vife9lJe04Zxh8YGRo3Sr+6XyBb125zkyhiBCCvlCIZDDAdL5A2TQJ6ToCwWKlyp5UDyMt2WWtLJcqPD9+jbl8EVcINEXmYF+KjzzzOCcn55nNZFkolNFci5Co8fpJkxNLJZLRKKM9cVwhmM/Xwxs94RCL+TK2K/BpCvcO9ZIK+LBd0XQDAHBdgWk5SLJE260VkC1VOLK3B//K7+/piTK1kOPydJp0oUKmWCURqb8YXJnJUCgbPPPAyKZWNXdiQl9tsV8ul0mn00xNTVEoFEin081Vzu3oDroe3RS+24n0ZV3XOXbsGM899xwf+chHgPpnfO6555ob86t55pln+JM/+ZO2e3Hx4kX27NmzbTuiO76SWW2+uBENdXUcB0VRmn3v77///nWVudM5Np2h5lp8ffGvuFiawRGD9dqGyjyqr/HHFwj7IiqzJCMuQlIx3L56XUwHhBD8be4Vvu07i2/xCgDfXn6DB2P38KH+7+NPp77L+eIMhmvhk1UOhvcwGuzj9ewVEnoYWZKQJCg7Bj5VY58/Re7SDMN5lR954u+1LVGh/gY/Vc5Sc22GgzH6lAABpUzeqhHXA80xZYwKjySHiOsBEr61XR5X/40qlcrK/ovO008/3fHL1xsI8Z7BEdyL13hqYG9zYnt+8hpfv3wJTVEIqgqTxQJTpSIP9vZxvZDnUjYD1Pc+jiZ7eGqwc3pzvlbjL89dZKlcYU8kjCxLzJfKZGo1RhIx5gol5oolsuUaqVCQHzg4SqRDfxDbdfnOhQlmsgWGk1E0RaFqWpyZWSTs1/mJx+5juVRBliR6wgGMapWvvXGOYqmMXC1SyeqEQmFioSDzZZODB5L0RULULBufptAXDTGfyaPKEjXLJtjIKJPqhpl+VW122gTqwtOyd9VA11Qm5rPULJu9/fFmWnfQpzO9lGcuXeTAUHviRifu9Kqh1dyzWCwSCoWIRCIdLVlSqdRtcTtu0E3hskYvmVv97J/85Cf5+Z//eR577DGeeOIJPve5z1Eul5vZZj/3cz/H0NAQn/70pwH4x//4H/NHf/RH/Pqv/zr/9J/+Uy5dusTv//7v82u/9mvbHsMdF5mt0KgTqVQqnDhxAsdxePrpp7ek+FsRmbcKrzJePEVQCeFT6imvJadA1j+HT4phWCcJqxkkFJAUhDAJKXNMlL9JUv+pNee7UJrglcIZFCEx6F+xoHAMTuQvMFXJc72cp1eP0afEqLomZwqTHA4P0euLMlFZIqT4yIgKmm1zPHaY+ZNXmhl0qyf66UqOP7n2JleKy9jCJaEHGXXh4Vg/b1QXKVoGfkUlb1aJ+wJ8YM89636hV3cwPXHiBIODg82Vo+k4FE2DkKa37Zesdv4tGAYvTk8R1nX6Q2Fc1yWu+5kpFZkrFfmZo/czUchTc2z6giEOJ1Lr7r9cXM6wUCpzIHXDiXkkHuNyOkNPsJf3jI2wVK4wPTHBgZ4Uh3o6T8CLhTIz+QKD8UgzZBXQNRJBP5fm0zw2Mshw4oZ465EIwWiMfaj0RYOUS2XK5TJzc/PMFitcVG3k4T1Mly2WSwaKIjOSihD3qczlSvTFQvhVlf8/e/8ZJVd+nveivx1q78o5dM5AIwMDYJAmMA05pC4l0hZtmpZMWZJ1fI8Xz/Eyr30kXct0OkqWjw51JS1zWbacZEm0raNIcihyyBnODDEzyI3U3egcK+e80/1QXYVudCMNMBzQi89a+IDu3rV3Ve39f//v+z7v8xRqDVyqgkOSMU0LUWq9h2pTQ5Ul3I6tAbGp64iigGqTt8wNiaKAJAoUq9stqXfCex1kNsOyLGRZJhwOd8rL7Vmndj9HEISOJEsoFHpXjcSepHLZ41Jg/vSnP00qleILX/gC8XicI0eO8NJLL3VK20tLS1vec39/P1//+tf5B//gH3Do0CF6e3v5+3//7/OzP/uz7/gavq+CDLQWrosXL9LV1cXevXsfeufxMEHmRvEyggCqdNtXxS15KQpF3DUR2Z3HRARapS5JULGLNm5Vvsk+7w+hSluH7abK8zQtDbt1OyA4JJVss8Ct8iJRpQ+n3HqInJJKSPGwUkvzU4MfYqqyznRpDbtkclDtI7RoEuuPsWvXrm0PRlVv8m9vnWW+nKXL7sEmSmSaVV5t5PgJxzHGu/o5m1qgqNU5FRniA7Exdnnvzr9vM/IWFxeZnp5m79699PX1YZgm31la4PWVZQqNOi6bwsmeXj4wOIwiSVuCDECyWqHQqDPYcbZs/Txod5Ct17CJEs/13d/CAKBUbyAKbBO4tMsy2VqdAb+PAb8Pez6L6x56YA1dRzfMbeZjqixTbTZp6gaVRpOFdJ6aphNw2gm4Hcyn84iC2KH1NnUdK5nB7XLz0pVbJAslfA47qsPOwnoSybTY3R1kPV+hUG3gcSh87MgucqUaq5mWHYJhWXidKp6+CLlCFTwOipUGyVwZt1NhMBZgOVXY9h4M08KhPNij/CQFmZ3KU+1Zp7Y7aKlUIpPJsLa2xtTUFE6n810zEnuSymXtnszjwOc+97m7lsdeeeWVbT87ffo0b7755mM5NzwBQeZBy2XtJrNhGAwPDzM+vl1X7EHwMEGmbtYQ2XoTm4YJWLhEkGUVw7JhCRayIOOQnAiYNM0qRX2dyB1BRjP1DQLB1ofdAgzLQJW2LoaqqFDUathlhb/edwbLsnjttdeoJWscOnSI7u6dLY0n8ussVnIMugKdOZoeh5d0Lsu5whr/fPwYH+ra9cALTnumZXZ2dstg63eWF/njqZuosoxPVSk3m/zZrSmqusZf2b13W5CxyzKqJFPXddybMq+G0RKcdNwlazFMkyvxJFfWExTqrQCiShKm1SoJbg40dV0n4rr9cN75/uqazkwyS7JUwS5LhFxO3KqyRXYfWsOYPX4P8UKJ70wtkq/UETf6JyG3A7ddYT6VI+h2YJgW+WqNse4ITruKzVPnmaEBavUalXKFXKFIqqZj5FIc74ri8vqIBnw4VYWGprOSKZIr1VBtEt1BLyJweXqNi9Or5Mo1FFnGwk08VwYBEtkyIV/rPabzFTxOle7wzpTunb7LJynI3OtaNqsdj4yMdIzEMplMR9zT7/d3spxHbZQ/SeWy70fdsrvhPQ8yD4JGo8HExAT1eh2Hw/FIlqRtZeAH2bUMOcc4l38dyzIRBJFGvUGpWkSQREYC+1m3ZrALCjbxNrNHM5uIgoRd3N5kHnb18kbmMjpa52HXrdYsiUf2UNJqqOrtQFPSa7hklajqQ9d1JiYmaDQaDA4O3jXAAOQaVYBtsi8OUSLZKHfO/SAPZKPR4MaNG1iWxZkzZ7BvTNrXdI3Xlpdw2Gz0uFvB1KOo2KpVzq2t8Xz/IE5JYrlZ59WlRQJOJ3tCIUYDASYSCQZ9fhRRoKZrpKpVzvT24b+LRfK35xZ4dX4JWRSxyzLnV9Zx2mTsssx8Nk+Xp+WYmSxX8Kkq+2Nbs7J2kCvXm3zt2i3mUrlO5uBSbARcDtKlCjVNx2GTKdYbqLLMeFeYN2dWqGs6o7HWPacbJouZPKPRIENhP6vZIpIgcGKkj0P9Mb52+RYeu4Isy3jcHjzu1vxNcuIGot2FXquwsLbC2oaTZSgUYjAcYLRr6z3tddlxu1RGe0P43K1S7Wq6iGqTcao2EtlSi9XmcXJgJIbPZUfXTeKZIrliDVkSiQTchPxbF94nLcg8TOZwp5FYezYnk8k8FnHPJ6lc9oMg8z1ELpfj8uXLBAIBnnrqKc6dO/dIcv/tncqD3ODH/WeYqdwk20xjaQKa3kR2SITqMXbJJ2kK10g2JhEFEREbhtWkaVbpdxzHY2vVPA3LQKSlNHzQu4s97iHOla+RauaQBJGmpTHk6mFAHeGbqaskGwVckkrVaNAwND4SO4ytCWcvne3IeNyvLh1SnQi0ZGY2B5qaZTCmPHgzsVAocPHiRbxeLwulAv9jborFYoGI08mIL0ihUSfs2JrSB+x2FooF5nI5zq4u83ohR3DqBpIoMuD18dHRMRq6wVw+h2boyILIwUiUF4e22yoApCtVzq2u47fbCTpbmUbU7WI2k6Xb48GnKqwWy1hYdHk8PD/cT493Z02wiZUEM8ksQyF/p/+SLFUoVuucHOljOVtsqTBHAhzojYIF6UqNgeAmzr8kEnA5yFZqfOxQa26oqetYVqvE5lRtJIuVLedtB7ne7i4O9Me2iCS2m91tuf1QKIRsU1lJFegKuvF3lAEEuoIeUvkqh8a6sG2U9wIeBzZZoqkZXLyxwtJ6DsuiRVJRZPaPxtg9FN1yLU9KkHmUif87xT0Nw+jM5iwsLGxxB20TCO53ricpk2nPyfzPgCc2yFiWxcLCAjMzM+zevZuBgYEW6+YRLZg3M9TuR5UMKVE+EfqbfHXmT0hJ68Q8XRz0HYNbKhIyZ0L/K6+lf4OctohpGYiCTFTdw4ngTzFTXuEbqfPMVVaxiwong/t5IXKczw7+CM3FMlpIAklgn2eE08HDuGUHPsXNd9I3KGs13LKdj8ae4rDQzZtvvkl/fz+7d+/uCBXeCwcDPQy5gsyW00TtHhRRItOoIAsST3vungFtxtraGtevX2dsbIyUBF+7dQ37so5HUVks5rkYX0fTDZyyDeemHWNF01AlibfXV7meThGQZMb8AUxgvpDn2wsL/N2jx1gsFMhWK3htCqP+wF0n2uPlMuV6s6Oe3PlunE4qWpPPHj1ITdMxLIuw04lN2vo6m0t204k0Xru6xfcl4nYyl87hsat86vg+DNPs/H42mQXL6pQ42xAFAcM0Wyy05SRT6xk0w8DvtBNyOzBMk1ylht9px7Qs1vNlXDaJgQ01gM0iibt27dom7KmZsJLQCAW8uO0K8kZAsckihtEqMUUDW3e5y/E886tZusIelI1h0EK5zs25FJGgh4D3NpvwSQkyj1O7TJKkTgYzNjbWEZ7MZDKsrq7u6Omy0/U8KZlMuVz+QSbzuLDTTaZpWmcG4+mnn96iAPqoQUYUxY3Zlvu/RjqdZurKDGdiH9pCMnh77m0Mw8Bj6+ajXf+SRP06FSONR+4iqu5htrLGl+b/hJJexSU7KOhl/mL9dZZrCf6XoU+wR+/jdO/pbTfRR2JHeH9kP0Wthluyszy/yI2F6xw8eLBD0X6QYU6HZOPv7j7DHyxc5FYxRcEyCSlOTrkD7HPsPCTahmVZTE9Ps7y8zJEjRwiGQvzBm9+hbhgcCoQ639dauUSx2WC9UsImiXgVlYqmsVYusS8SYaVYJOp0kS8UsbCwSTL9Hi8LxRzr5TJ7QmE0r2/HEoVmmFS1Jk6bDUWSEEVhQ1b/dnDQTANZFFEkaUdq8p3vCdpDmTssahYdO2lx0zliXjc+p510uUrUe9sBNFuusr83ylszK0wsJwm4VNx2G+lShWShQl/IS65SYz6VRxDAa1cIh114HTtfp91ux7K5qYg6pseFW7bwFNdZXImTScVxOpy43W50ZNxO+46Dl6uJAnbV1gkwAD63neV4jmyhgl2RWUkUmFwo0BSS7B6V6Qp/7+jBO+Hd1C67m7hnPB5nenq6U3Zve7q0yS1PSpCpVCo7qiR/P+I9DzJ3olAocPnyZdxu947U3IeV+98J9wtUlmUxNzfH3Nxch0l15/HthV4UJLodh7b8/pup85T0Kt3224tyzWhwvTjPdHnpnoFCEW34RJi4MkGlUtk26PigigHdDi//YM/7WK8VqZs63Q4vU1ev35NkoWkaV65coVardajhiWqZtUoZr7RVxyvqdFFqNhj3h0lWKyQqFeyyzNGubt43MMi/vXTxdoazcUpFktAMk/pdvj/Tsnh7eZU3l1cp1Bu4VYWnumNEXS6WC0UG/T4kUaSm6eRqdT4wMoj6gIN7giAwFg3yxswyIZejkznlqnVcdmVH2X23XeHESC+vTS0yn8qjyCL1pk7U56Y/6OPbN+bp9rlwbQxBulSF5UwBy7L45PE9ZMo1ZFEkYLdx5WLxrtc2MRPn8swahmEiSSJNTcfl9NDf56ap62A0WU3lqVRqjMScLMwK2zTBWgF0x3dOta7x5uVF1lNFMoUG4lqOVKHBvtEu9o3Fdjroe4LvVeZwL3HP6elpGo1GR+OvVqthv0OW6L3A46IwPwl4YoKMZVksLy8zNTXF6Ogow8PDO37Rj2rBDPcOMu0sqlQqceLEiS0Ck23cS2TTsExmK6u45K03qkNSyWtllqoJlHscXy6XuXjxIk6nk9OnT29rXj6MLI0gCPQ4twpk3i3ItM/rcrk4depU57w2QUIWRQyLtqRy632aJqok8cNjuzFMi7OrKxQbdQJ2OxYQdblYLbUWVmsjyqRrNXyqnW53a0Zmfn4eTdMIh8P4fD7eWFzhK1MzKLKER1UoNZp8bXqWw91dmFjM5/JAy03zQCzCmcF7++/cicN9XazkSsyn8yiy1DEdOzPaT9i9M130YF8Mv8POTDJLqd6gy+tmV1eIZLFCXdPpCWwNTl6HSrZSw6UqhDe8ZGobhm87IVuscm0hgdNu6/RfDMNkKZFntC+EQ7WRzlcY6I0x3BXA7xDJ5W5rgrWFPb0OkbVkE91rdoY7y9UGqiJRLDVYSxXpi/moFOL0RL1ohsj0QoqemBe/571RhH6vpP43i3tutmTP5/NMTEwgy/IWAsE7nXR/FPyg8f8YIQgCuq5z/fp1MpkMR48e7Rj67IRHLZfd6zXaFgHtBf5uN9e9RDZFhI2AUtryc9Nq/b1dUhGkxo7HJxIJJiYmGBwcZNeuXTs+gKIoomm31aF10+Tt1DLn06tUdY39/ijPdg113CfvPHan86ZSKa5cucLAwAD2WJQ/nm03+F083dXDeCDIS+vrNE0DRZIxLYuVUoEBrx+7ZOM/3bzMUqGATZK4nknxxsoyR6JdJCtllrQmjmqVhtWStPn42G7cksyFCxeo1+t4PB6uX79OTdP4Zr4Mso0ubxBJlvGqKqlKlcVcnp88dphkpcUACzkdDAcC2/ovm2FZFqu5EhPxHJIkY7p8DEUC/MjhcaYTGVbzRZyKjZFwYJty8p3oD/noD/moNTWy5Rq6YeKwyciiSEPTUTeVqGpNHZ9ja9/nXtljulChWm8S7brdc5IkEY9LpVRp8KFjY9v6KMHgbU2wtrBnPpWmlCtxKZnE63FjdzhQbDLjwxHW4kXcTgVJEmmnld6NUlquUHtPg8x7XZ4SBAGn00ksFmNmZoZnn322U1pbWlrqBPKdnCvfTTwuV8wnAe95kCmVSpw/fx5VVXnmmWfuy5ySJGnLIvtOsFMm0m50380i4H7HtyEIAqcC+/mz+GtUjRqyWEUz89TNBm7Zz37PIJPi9S2L/Wahyc39l7u9fvtYy7L4L7cu8s21GQzLQhJELqRXOZtc4vMHnyVsd9312Pbx8/PzzM7OcuDAAQqKzG9cfItUrYJTtnE9k+Lc+iof7B+iy6awUMh3ju1yuflr4/t5eXGOpUKBsUCwU4JaKRW5mUnzo3v28UeZHJIgMOzzcaqnn/0+P2fPnsXlcvH00093rutWPEHjzfPYm02WlpdRFAWnw4HDbidVb1DXdY50P5h0kGVZnJ1Z4ezMMuupNJIoMlNosL8vyosHxzg62M3RwQcjQLRf79LCOufn1ijWGsiSSF/QR9DtYCVboifgxm6TyVfr1JoaZ3YPbJGKab/HnSC0xUHvCCStfsV2W4TNUBSl03fYt8/iYDbH1MwKi6sp6oUCgagXu+BqZVIb+neteLcxm3Y7MX1P8CT5ybSzKlmW7yvuuZlA8LgGJu/EDzKZx4hKpUIsFmNsbOyBbjhJku5ZfngQbM5ETNNkcnKS9fV1jhw58kDOc/ezC/hg5BhL1Tg3Kq8jWq2MRhIEFKnJS6k/YlDc3Tle0zQmJlr9l9Ont5MB7sTmbGSqkObb6/P4bHb8G5mLbhrcKqT55uoMf2P08LZj27tqwzA6zqEnTpzA4/Xye+e+S7ZeY/emBv9KqcjZ9WU+6ArQu+8AuWYDr6JyMBJDESUmM2nCTucWdli3y81cPocqyXw0GOb48afxeb1kMhnefust+vr62L279RlomtZiS/n9hHy+DRKBQq1Wo1qtEo/HqRsmK3NzOBo9hEKh+5Yv1vIl3pxdxq7I9PtcSJKEw+tiYjnBQMjH4YEHC1Zt3FxL863r8yiyRJfPTVM3mF5PE/O5GIn6WcuVaOoGbrvKqbE+DvQ9eMM26nfjdqpki7XOgKWmG5RrTQ6MPPh1CoJAJBQkEgryLK37qs2ualRSzKyU6O8OYJpmqydRrOFyqoT8791u+Ulyxrxb0/9Occ9yuUw2myWZTHLr1i1UVe30xx6XuGd7BugHmcxjQnd3911l8XfC4yyX1ev1LRpoD7orEUXxnuQDu6RwMtTHckNHEDzIgg2HpGJaOtPlG8iKypi5q1Oec7lcO/Zf7nbudqCYzKeoGxq9ztt9AVmUcNkUzqdXtwWZdiZTq9W4dOkSoihy+vRpVFUlWa2wWMwTdW6do+lyuVko5MibAn8l1rNlgS/vYGN9+1y3zykKQqffthORAsDvsLM/FuGNxWVsotRyVlRU8oLE00E/fcEAKysr3Lx5s9OHCIfDOwooLmUKVBoa3X4PqUpLHdmp2LBJIrcSmfsGmaZuIIlCJ3BeXYoDEPO1NgA2WaJf9rGWK/H8niHO7B6goekEXA58zu3Mr3uVy3xuO0fGergwtcJCPNdRLxjpCbKr78Gfizths9mIxWLEYjGGR3bx+vlpZhYSZAsNMhPTuJx2juztRWtUMOzyezIf8qRlMve7FkEQ8Hg8eDweBgcHMQyjQyDYPO/UznIeRdyzXC5vIfx8P+M9DzIPi8fV+C8Wi0xNTRGJRNi3b99DPWSSJNG8xwILMF2+gSgI+Gy3B/kkQaFqVInLK2SzWa5du/ZA5bk2co0aE+U0pUqRkWa9pdS7w/plWXT8YjajLS569uxZotEoocEBbpUKBDQ7iighCsI2t82WbIuIhMVqqci5ZJzZfI6A6uDpnh7GgyHOrq0QsDs6C2S8UibocDIaCHAemJ2dJZfLbZGkydfqXFheZSHTmlHZH4vw4bFh6prGzVSG9VIZuyxzsDvKJ/eO43fYGRkZ6fQh2kKdgnCbaRUKhbDZbJimxU6fpigI6MbdM9DVTJErc+vEs2UUm8Se/gj7BiLkKi0xy81Q5JY9QrWpsav7/urH9/p+9wxGCHodrGeKNDWDkM9FX8S7hY78KHDYbbz/1B7GR3t55dU32L9/Hx6nhNGscPPmTTRNIxAIdD7Hd6sEtBmWZT1xQead6CDeTdxzszto+9/DiHv+oCfzHuJRMxnLsqjX66TTafbt20dfX99D7zYexMLZ5O6/102d1dVVDh8+vM0a9W7X/I21Gf773FXipQK6rvH620U+0D2Ky6aQaVQ7/ZeGoVPVNU5GtmcLpVKJdDrN0O5dvF7Oc+7NV6lqGg6bjSORGINePxOpBB5FQdrImFZLJXpcbsRmid+6eJ5kvYrLpjCtZ7iYWOeZvn76PT5u5TLYxBZjy60ofHxsN06xRTcvlUqcOnWqs3glyxX+0/nLLGRyyBvyLueWV/no7lE+c/gAK8US+Vodr6rS7/du0Sbb3IcwTZNisci1+WVeOXuFTLlKT8BDLBTEMk0q9dZGwMJCN0zqms5IdGdJorVMkZfOT1OsNvC57FTqTV69Ok+2VCXotrOQyhN0326QNzQdURDw3GX25WERDbi3DVjeC9Vqk3iySKOh43Da6Ip4sdvvngnbZImBbj99UQf7xro6w4htf5dsNksqleLWrVsdZYl2CejdyHLaJd8nvVz2MLhT3LNYLJLNZlldXeXmzZsdd9A2geBun2u7XPaDTOYx4WFvskcJMm2jrWq1Sm9vL/39D0eB3XwNnZ6KaXAlv0iyUSSkuDniH0SVbIy6xrlZuopuasiibeNvNXRdI1ALMzg4+EABBuBaLsF/uXURE4teh4datUpBq/PVlSlORvp5M7nMTDGz4d0pcCjYxQu9Y53j232ntjnU27US31ycI+J0EnI4KWtNvrOyxJFoF4NeH3P5HAhgWhBxOPnU+D7+47e/TcrS2R2KdBb9ZKXChfg6f+/o0ywW8iwVCwTsdo7EuumyKZw9exZBENi3b9+W3fGrswssZPOtKf6N3WyiVOFbc4vs74rS7/PS77u/4KMoiiyVGpxdK1JBQfaoTOYrLBTWsJlNrqbTSJKIItvIaTDWFWJ/7849t6sLCYqVBkObWF6uepPp1QxHxrpZyRRZy5UIuhw0DYNkscJYV5CB0M5GaJvxsN7s90M6U+b85SVyuWrHSjQadnP8qUF83rszxdrXcaelb9vfZWBgAF3XyefzZDIZpqenaTabWyRvHlWEso3OnNkTlMk8zmsRRRG/34/f7++Ie7aznM3Z42YCQftzbTQa6Lr+g8b/e4V3OoxZLpe5dOkSdrudrq6uhxbP24x2JpNqFPmN6ZeYrSQ6syD9jhD/+66Pcth7nKvFiyxW58DYUDLWNcJWjAPOpx7q/G8kF6nqGiPeILVaDVGAXqeXuVIWj03l/zj8PJfTa9QNnTFfiBORfpxy6/WbzSaXL19G0zSGhoaIFwu8ubZC0OEgYG8tSH7VDhbMF3L8g2OnWS2XSFTK+FQ7R2PduG0K61qTyB1ZRdjpZDafJV+v8+LI7aCWTqd580JLCmd9fX3Lw9vUDa7HUwSdrYHI9oYh4nYyl8kxn813NMruh2pT47XJRUzLYnQjQ7HCfuZSeYYjfl4Iunjrxgz1pkaEKoOim3R8HSsU2mIIZZgm69kSXtfWrMRlV0jmK/iddj58aJTzs2tkKlVsksThgRjP7RncxiK7GzafK5kpUyjVsckiXREvLseDz2EYpsm1G2sUSzX6ev23rb/jBW5Oxzl5bOiuQaAdZO4VJDb7u7RnSNo06bm5OZRNwp6P0ujeKeC9l3i3dcs298g2Z4+ZTIbZ2dmOuOf6+jq9vS2jvscRZH77t3+bX/u1XyMej3P48GF+8zd/kxMnTtz3uD/8wz/kM5/5DJ/4xCf4kz/5k0e6hu+7IPNOejLr6+tcu3atM38yNTX1yPpnpmnyH+dfZbq8TkT1oogymqmzWE3zO3Pf4p/t/xQ/1vt3uFh4i0vp8xSLBfZ4DvDi6MeZvTl3T3ZaSWuwVi3ilhV6nF6y9WrHglhA2JiJFBAFkVyzxoFAjAOB7VlRqVTqCFwePXqUlZUVSppGTdeIOLfWe12KjXyxhmFZfHBgeMvvarrW6teYW3fk1oaulyTe1gdbWlpienqaffv20dvbSyKR2LaTFwTAbClhtxWxLVrtpYfZJMfzZTKVKv3B29mEIAhEPU4SxQoff2qcsNyiBvf19XUe6vn5eWw2W2d37vf7sSsy2dJW1qJhmghCq/+yuzfMeHeYfKWOIkv4XTsrRt8LTU3nrStLzC5l0Dfuv6DPycnDg/TG7p8RAeTzNTK5CqGguxMsRFEk4HeSSJWoVJu4XTuX8B4kyGxGe4bE6XR2RCjvJez5ME6O/zOWyx4Ud2aPmz/XX/zFX+TChQv4fD5+7dd+jY997GOcOHHiHQXAL3/5y3z+85/nS1/6EidPnuSLX/wiL774IlNTU/eUrFlYWOAf/sN/yHPPPfcob7OD9zzIvJvlMtM0mZ6eZmVlhUOHDnXKU48qTSOKImm9zPXiKj6bE0VsfYw2USaouJmvJJmrJBhxRgmluhlfPsKhQ4c6X+zdejqGZfIni9f56vIUBa2OIkocCMSI2N00DbPVlN/w3zGs1v8H3f4drzEej3P16lWGh4cZHR3tuIp6pBb7rNhsbHGdLDYaOG3KNlVlAIdsY8TuYK5RJ2y4sUktk7bVcomI08l4MIxpmty8eZNEIrGlwa9ZFpfWEuSW46iyxN5YhH3RMN+eXcCrKoi0Fr94qYJXVejzuDEMo3O994Ig7OxH1NYoa8+ZwNZ6eVuxdy2e5MbkNKbeRG5KpLM1FAlCPje6YbGWLRLxueiPtAKAIktEfQ/fjG1f39R8ism5JLGwB4faIijE00XeurLID71vL3b1/tmttTHUKolbnxtRFLFM656lubv9TtMM0ukytWoTVZUJRzyo6val4W7Cnul0moWFhS2/DwQC98zW2/TlJyXIvJeDoZs/t5dffpmXX36Zn/zJn2RycpLf+q3fwrIs/vAP/5AXX3zxoV7313/91/mZn/mZjtXyl770Jb7yla/wu7/7u/zcz/3cjscYhsGP/diP8c//+T/ntddeI5/PP+rbe++DzMOiHWTupybbaDQ6ZaI7LZofh5JzzdTQLQOnsLXUYRMkdMskXytz4eYy9XqdU6dObUl97zZ5/5ert/ivs5dRJYmI3UXD0DmbXGLUGyLmcDNXzOGTbJQNjWIxR7/bzzOxwS2vsXmwsx1YddOk2mxiAg5B5Lm+Af50ZgoAj6JQbjbJ1+t8ZGiMqHPnRfS4x49oE1gs5jek5Fuy/p/asw+7IHD+/PnOZ91uKpcbTf5yLUV6NYPNZsMCvj2zwKmBXob8XhZyBWRJRDdMnDYbH9k1QsBh3/LdiKLY+XcnegIeol4Xa/kSAyFfS/jUNEmVqhzuj+Fz2EnucI9UGhqXl/LcWi1iWjIRj5v+LoVyY53phWWmLQGHw05vJMDz+wdx3MNZ82Ewt5zB5VBwbAQTURSIhT2sxgvEUyWG+raTEjTdYGk5y8paHsMwiUW8OB0K2XyVaPh2YziXq9AV8+Jy3s5iTNPaEmh3ymQqlQaXLy+TWC90fh8KuTlydAC//94ss2JJY2m1QTYn4nTECPgUJMlgfn5+R6n97cOmT0apDJ4smX+Px4Oqqnz5y1/GNE0uXLjA8PDw/Q/chGazyYULF/j5n//5zs9EUeSFF17g7Nmzdz3uX/yLf0E0GuWnf/qnee21197xe9iMJyLIPKg7Jmz1g7nbTdH2oAkGgxw7dmxb3fhxKDl7DRs+m4OiVtviaFnS69gFmcT1ebq9IU6fPr3j+TVNY72e4rvZK8TrGcKKn1dXWtPxMUdr8VAlGZsosVzJ87fHjnM1m+B6Zh0DizOxQf768MEtU/1tY7M2m8vhcvHVuVt8a3GeQqOBw7LYJ6v87ac+hIDAa6uLZGpVHLLCx0d381d37bvre/bZbPzdAweYb9RYK5Vw2WwciXXhE0TefPNN3G43R48e3fJe35hfYqFcY1csSsDrwbIskuUKby2t8NMnniJdrbFSKOJSFA50RRgK+DvUVsMwME2z86/9ubd3v6IoosoyH9o3zFcu32Im2frsTMuiL+jl2fHBHd9HQ9N56fwtZtezBN0ta4DZRIFcxcGPPHeCj1omC6sJKuUSsl5l7uYVsuv+LY3vdwLLagUM2x02z5IoYsGO1GrDMDl/aZGZuRQ2uaVGvbKaw+lUAIGVtRyqIlNv6nhcdvbs6tqwyMiwuJChVtcIhVwMDYWJRDw7BpnpqQRrqzm6u33IsoRhmMTjBa5fX+P06dEtQWoz1tbzvHVugXpDw+1SSGcqJFNlDh3o5eTJXdTr9U55cnl5GUEQOgEnGAw+UYOY8OQZlrU3xZIkPVAP5U6k02kMw9hGLorFYkxOTu54zOuvv86///f/nsuXLz/0+e6FJyLIPAzagUXX9W1BxrIsFhcXuXXr1hYPmp1e41EzGZsl8kPdT/H7S28Qr+dxSip1o0lT1zhYD7BneISRkZG76o/NaWv82+mvUNarCICJRQkdVRkCbtfnHbINvdYSo/z5w+9jIRXn2tVrfPzgc1teu1qtcvHiRVRV7eiu/bfJ6/zx9E0UScKlKCRKJebrWQYW5/hr4/v56PAYmVoNv93eav7fA4Ig4JRlno0MdH6WSqV4c0PzbCettcurcVRRRJHETvAIOezMZvOslcp8cGz77qz9Gps3E+2AU9d0ZuNZspUaDsXGru4QI5EAP3bmELfiGUr1BkG3k/HuEB777R395g3MfDzHYiLPYMTfWfC9TpW5eI6byynef2iYrg2TsjaVNJPJkE6nmZmZweFwbOnl3GthSqRLLMfz5AslMtk60e4eZpfT+Dy3xVNLlQZ2Ve74vWxGPFFkbiFDNHy7fKUbJqvrefbubhmXVaoNvF4H/T0BAn4n16+vcf36KjabhKLIzM+niccLnDgxgt+vbvmMa9Um8XiBQMDV8ayRJJFw2EM6VaJQqBIIbM9sTdNi6lYCTdPp6/F3fp4vVLk1k2SwP4TTaaenp4eenp4OnbcdcG7cuIHT6cQ0TQqFwgMZir3beJIymXK5/FD9rceBUqnE3/pbf4vf+Z3feajh+AfB912QuZsfTFtkM5vNbukJ7ITHEWQMw+CHuo7glBReik+QbhTxGDbGGkF+/NCH6IrenZ5sCfAt7RJlqUrQ5m0xhCyLspaiLi1jEUWg9dDVDQ1ZEFtul4JAyO7CueG02UZ7MLGnp4fx8XFEUSRbr/GtpXlcitIpgammxUKjwdcX5nj/wDAeRcWjPNicx52aae1gvn//fnp6enY8Rt9onLf7CHCbTfSgmWu7VFao1vnjc5PMxLMYVoswEHI5+NiRXYzGAjw93N157XstWPlyHdO0tmQUgiDgUm2sZ7aKmm52X2zTe9se820a6ubd+WYjrBszCd6+ukS11kTXdVKpIvuVEk67jcX1HB6nSlMz0HSDQ7u7Ce5QmsrkKhiGuaU/Iksiqk2m3tA5cXRoy9+Xy3Xm5pJ4PHa8G0HL73eytpZnZibJ0aO9nfcFLWKDYZg47pivkWUR0zAxjJ2/o1qtSb5Q20aX9nkdrKwVKJZqG9lWC5vpvG09sOXlZZaXl5mYmMCyrA6V90GcX98NPAlinW08DkmZcDiMJEkkEoktP08kEjtqI87OzrKwsMAP//APd37Wft5lWe6o478TPBFB5mHKZTu5Y1YqFS5duoTNZuPMmTMPJLL5qOWydnP6A9H9nPGN8fblC1imwbFTR+97gyTNHAWrjFfexBASBLyyi5xWJtFIEpQjNEydZK3Mfn+MQ8Huzrl3WuzvlGtZLRUpNBr0e27PmwiCgEuUyNVrJKplhn13D8Q7vec2E+zGjRukUqlthnJ34mBPlCsLi5TKZWRZRlEU8rU6TpuN4eCDnxvgjaklptYzDEb8KHJLCXopleOb1+fpD/tQN9S82+9zc2lt872l2qQWk+2Onl5d0/E6733f3CkRX6lUyGQyJBIJpqencTqdLW01u5vz11YQBYGh3hbt3GgUyOSr7B2J4nQorKeK+Dx2xgbCjPTf1oozTYtmU0eWxQ3V5O0wTBNZ3v67YrFOtdqkt3frZ+v12snlKtTrGqWSxsSVFep1Db/fgd1uI1+o0uW4nT0XCjXcHjsez9bsttHQaTZ1BFFAlkQ0feszpGkGsix2sqK7QVEUAoEAyWSSU6dOUSqVyGQyrK2tMTk52RlaDIVC3zPVY8MwHmms4XGinck8ChRF4dixY7z88st88pOfBFpB4+WXX+Zzn/vctr/fs2cPV69e3fKzX/iFX6BUKvEbv/Eb73imEJ6QIPOw2BwkEokEV69e7YguPqjI5qNmMu3yT7FY5NKlS/h8Pg4eO/hgcwOi0Jmr2Qy3TaVhNkCUSNRLKKLEiUgfP7P7JDax9eC2g4xpmly/fp10Or1j5uZWFFRJoqbreDbpjWmWiUeScNseziNDEAQ0TePcuXMYhtHq+exgYduGZVmc6u/lQtci08k0K4UiliBiVxSeHxmg3/fg08x1TefmSpKA24my8fmKgkBfyMdypsB6ocru7tCWHk77+11Jl5hLFHlrsUIs4KY/5ifocbCcKtAT8iKKAtliiyI+3v/gZYLNNNTBwUE0TetkOZeuXWXqVo7BngAFUUOSJCRRwONSSecrfOrk2I69juXVHDen4+TzVVRVJhRwY7NJFIq3s4ZqraVi0Nvlb/2/2iSTaemzGYaJJIrouonNtslFVDORJYnlpSzTUwVqlTVkWWR21kBVbYDF6moep9NGva4hiiL79vV0MihNM7gxuc78Qppm08DtUpElkUyugt1uQ1VkDMMkkSrR3eUjcB/CANzOHDYbig0PD28R9tysetwuUdrtD08dfxA8SZnM41Jg/vznP89P/MRPcPz4cU6cOMEXv/hFKpVKh2322c9+lt7eXn75l38Zu93OgQMHthzf3kDe+fOHxfdtkNF1nampKZaWlu4rj38nHsb4627HA6ysrDA5OXlPk7Wd0KdGceOgbNQICHJnt10xqnQ5gvyjg58iVavhtikMuPxbXredUbz11lsAnD59escHb8jrZzwY4mJiHVn04pBt1A2DvKbxfLRr25zM/dCmKAcCAQ4evHcwbTfuAw47//sHnuHCyjpTyQzoGj2KRFBv8p1XXyUQCHQG/+7VUDcME8O0kKXtTXPTstA3Foj299Kev3nj8jRfeXsG1enC7xVYiGe5uZjg0Gg3C4k8K+kWo8rjtPPsgUFGu3eWnHkQ2Gw2otEo0WgU0R5irXADu12iUChQrdUQEMjlcvi97o0Fbet7WV7N8Z2zMxiagcdjp1bXmJxJ4PPYaTYNllZyG+eR2LMrRl+Pn7m5FFevrlIq1UEAp1PBMCySiSJd3T4kSaTR0CiV6uzeHePWdAJJEunrD3Q+p5XlHD29QXw+B7lchXDITd9AiK6u2xnwpctL3JyK4/HYcbsUSuU69ZqGx6uSybZKeoIgEAl7OHKw/64Z2GbcjV1259BiuVwmk8l0bJOdTueWLOdx9VGepJ7M43LF/PSnP00qleILX/gC8XicI0eO8NJLL3XIAEtLS9+TwPpEBJmHbXAJgsDk5CSWZT2QPP6deNRMpn29U1NTD2wPsBmKZOOUuZez4iRZrYgkiBiWiV1S+FTPC0TtXqL2nWVVyuUNVWGnkwMHDnQeDMuyWCwWKDUa9Hg8hBxOfurgUzQMg1vZDLplIZgmo3Ynn9l38L7XWKjVWc4XUGQZj6lTrVaJxWIcOXLknt/X5mxCFEX8Dgcf2jXCh3aNbPm7SqVCOp0mlUptKTVFIhHsTjc311IsJFuWx7t7IvQGPdxcTeFzqp3zZ8o1vA6VnsDWz0oURebmF/na2av4An5G+2KdzHMpkWcxnuOvnNlDIl/BArqC3o4r5eNALOwh4HMjigIDA2HK5TKrq2sUy3U8qsZ3v/tGZ6EMhULIso2b03EMzaCn2w+AB7CrDaq1JiePDaPpOpYJwYCTSNhDNlvh4qUlAHp6/QgCZLMVGk0Nr8dBfL1FNZdlkZGRMH6/k3Kljtt9+5EXRRGvz0G10uDZ58Z2LHPNziR587uzWJaJaFooITfRiIdEsohdVTj5dB+Nho6iSEQjHhTlwZaUB2GXbVY9btsmt7Ocxy3s+SSxyx5HuayNz33uczuWxwBeeeWVex77H//jf3ws1/BEBJmHQT6fp1qt4vV6efrpp9+RrMWjBJn2/A3A0aNHCQYffvcriiLDZozTY0/zauoC8WaamBriudBRdrlb7K21WpY/WXuTC/lZJEHkTHAPp6QhlifnANi7d28nwCSrFf79lYvcTKdpGDpuReGDA8P89X37+cenn+NmOk2uUcOum5TnFwja713m+supWb4xNUuuVkdrNHAaGh/sjXC4q+uuC0OmXOX1mQUmVhNIosjR/m7OjPRvUy9uo91QHxwc7CweqVSK85cu89pChlTDQlVVbDaFt2ZW2NMTwee0M5vI4rYrNJo6CPDBAyNbhCvbc0JXpuaxu30Mdoe3DP1FA26y5ToN3WK0J9QJPs1mc0sv51EWHL/HwZG9PZy7usziWg5Db5IpNnn6qb184MQoWBrpdLpjXaDanczNlfD6PFi0VBQA3C6VbK6CJAuMDm/N1NfW89RqTfr7b99/oZCbarVJ30CA7piPpmbgdquEQm7WVnMbf7XD93eXtX5lIcOb35kmES8SCbmIr+TJpcsMjkVwu1Wq1QZ+nwPXXRQG7oV3Up6SZbmTLT5uYc8nrVy2k+379yu+b4KMZVkdTxKn00lvb+871k1qy8Lcb6DzThQKBS5duoTf3yph3Y9goJsm59LLXEivYloWh4LdnIm2aNUTlRx/eqXMcqVJzNHD0YFdjLpazbVEPc+/mPwyqXoBRZSxsPjjlbO8pl/mnxz4NJNXrnXOYZgm/+biOa4kE3S5XIRlJ4V6nT+5NYlbUfjE7j0c2mC6FYtFzs3N3/Oazy2t8f9M3ESRRNymQcXQqNpUvhXPcmS8wU5+kvlqjd95/RwzqSxuRcEC/ujyDW6lMvzU6aPY7yNZv3nxyIouyktV+oIyWqNBo16iVhe5WKny8eN7qGoWy9kCvoidQwNd7NtkENY2YiuVShw5fIjZN6YwTBMbtxcb02p9/3a7gqqqnf5Nm9TQ3nwYhgmCgKrY3tHic3B3N0Gfk6X1PNlsgbBb44Uzuzd0yhy43G7c3gjVah2tWYWbBZaWVshnk7jcLtwuN4rqQNpgk92JRl3fsSzVnnUJBF3MziS5cX0NQYBYzIuiyGQyt11lDcOkUKhx4EDftiym2dCZvr6GTZbwB50odhtuj518rkpirUAg6sGuyg+cudyJRx3GfNzCnoZhPFHlsrZ+2f8M+L4IMoZhdJrcx44dY3Fx8ZF6Ku2byTCMBw5U7V3n2NgYQ0NDvPzyy/fMhnTT5P93/Q1eXruFZrW0ub62MsXp6ABjso//kVtFbThx22zMFDL81tUs+UaNT+86yNcTF0k1CgQVNwJCywnUksmrTaaEJHCbXjiZTTOVSdPr9uDcYMeEnU6apsHLi/N8bHQXysb7fRAW33fnl9ANA7VZx7QsBrq7sUSRqwtL3Exl2T00tOXvLcvirfkVZlM5hkOBjrd9XdO5vp5iYjXBiaEHf2BurqbwOB1EQ60SmGEYVKtVptfS3Jic4mCXj+d7W32cYDDYaaAvJ7L8+Stvkyo2GB3qw1U36Qq6WUkVGYj6EcWWGkAyX2b/YIzARvZzZy+nUm1w+eYqUwtJDN2kv9vPgV1dxMKeLYOg94MgCPR1+enr8pPL5bh5s9IRwsxkK7x1YYFEsoium7jdKj093Ug2F26njGU1SSRTJFNluru8VMppslmLYlGnUmmiqDJ2hw1db1GQ28HGNE10zcDtVPnuG7dYXcnjcqlYwLWrq4iSiWkKLC9lkSQRwzDp6fUztmu7jlWpUKNUrNHT66dU10ikS/g9dhxOhWymjKBIPHN6dAvB4EHRaOisLOVIJooEfGm6un2o97ApeBA8qrDnk5bJfC88fb5XeCKCzL12GNVqlUuXLiFJEmfOnMFut7OysvLI7DB4sCCz2Z75qaee6gwq3c+C+a3UEt9cu4XHpuKxtTKeqq7xRmKRt40WBXfA00qJg0CqVuFP5yf5yMAY14pLLdMxCyrVSqt27vSQ1opMl9fYLdg6587WajQNoxNg2nDbFErNBqVmg9CGHtmDEB5W83nqpSJej5tQMIjQEeaEYr2x5W/bDf7JeArVJnUCDIDdJmOaFovZ/EMFGdgqkilJEh6PB59PY9++MQ72BjrKtVevXsXv99PExp+enaJqCPTEIszHC8yu59kzECHsczIfz7buMQv6wj6eO7SzUrFuWHzrrVluLaTwuFQkSeLarQTrqRI/9Pw4/g2G16OU1RpNndffnCGeLBGLeLDJEvlCjUSqSCTsoVptUm/IuNxhjg8MMDbkJpst8NJLE+QLGqqq4rA7iUR8uJw2VlfyeH0t4kexUKOry4dpWayt5unpDXSozrrfwcJ8gu5uJ0eeGqDR0PH5HfR0+3dc4EVRQBBFLBPGhsMgCOTyFaq1JpIgcGBvD7t3PZhVxWYU8lXOvznPzHRLcqmUv0U44ub4qRECwcfTh3gYYc9gMIjb7X6iGv+Pi132pOCJCDJ3QzKZZGJigt7e3s6QIbR2LY8icNnekd4vULX7L7qub7Nnvp9x2bnUCrpldgIMgFO2kaib5JtlusStpbaA6mC9UmKukMMpqeiGQaVeQVFaZZ224rFDVhHF25lM1OlClWXKzSbuTVTlUrNBxOnaMmx5ZyZjmCaTyTTL+SJOm0y3TYJiAVO2EQ6FOqt9UzcQRJHAxhS9ZVlkKlWwLDyKDadiwzC3Z0gWFuo9ZiYsy2I5U2Q+mQVgOBpgT0+YW/EMTd1A2Ti2UK1jV2SGY0GCwZYHR1ugcXFxkT95/Tr5apOegBOb2cDvdlI3YCGe40ef20+x2qBca+Jz2dnVG8Jp37lPtLSWY245TV+Xr+NKGfA5mV/JMrOc48xTvo76wObvvl7XWVrNk86UcTlV+vsCdEVvkxE2l2XX1gskkiX6uv2dDCQUdFFf13DYbbzvzC4KpRqqTSYa8SBJIm+9PYfdEWZfj9JamMslpqbnCPidDA9HqdU1FEVh//4edu3q4vq1VWRZ2jJLI8sSgihgmrB3387Ds5vhDTgJhV2kEkViPQH2745RKNVZW86ya7yL06cffjDPsixuXF0lGS8QijjRdJnubh/rK3muXl7m2feP31XG5lFwN2HPTCbTEfbUdb2jPvBez8v8IMh8D7BZ5HGnifLHwQ67X5DI5/NcunSJYDC4hcW1+RrulRUY1s6/a+cF2h2LctM0kEURhywzboa5qN8C1YliU1pUTqOOIsqcDOwmKc60FjrLpM/nYX84wvn1dcJOBw7ZRr5eRzNNPjI82imVwW36s2VZ1DSdf//WRS6txmkaOo1GA0XXeWHvLurJHAu5AhGXk6ZhkK5U6XU52BX0M5PK8BdXp5hNtbKD8WiYoZCfSytxCrU6Pod9IwjVcNps7O3amXlnmhZ/OTHDazcXqDZbfQKnYuP4aC97esJMr2eQBKFFXZZFntk9yGDYv+U1stksi8srGIqbXcMRnDaBarVCMpVqLRpNgbklP88/Nf5A8xXZQhXTtLbYHguCgMuhsJ4steZd7pC7yReqfPu1adbiBWRZxDAsrt5c5dTxYfaNb+9g1esaFta2forDbqNQrBPwO7fMmdTrGstLOQIBF16fA6/PSzQWpbu7zvJSCrdbwOEoI0kykiRTqeSRZaHVU7rzMzdMbLa7Z161SoN0soRpmHgDTvYc6qN5bpH15dZ3bQFju2IcPr6zLtz9UC41SKzlCYZdVCrFlk2EJBKOekgnixQLVfw7yNg8btzpYJnP55mYmCAej7OwsHBPYc/vBR4XhflJwRMRZDZ/ic1mk4mJCarVKqdOndrRglSSJJrN5iOd815B4s7+y930x+4VpA4Fu/nG2gx1Q8O+IaDZNA0EQWS3N8R8KkvTMFAkCc00SFYrjPtD1BdXiRYkno/u5+3SHKlmEREBVbLxie6THPAO8JfCLf5w7RyvFWap6Q267QF29/hJZHUqtSo+1c5fGd7LR4a37jY3S7p8Y3qWt5ZW6PK4aFYMqlg0nE6uZgv86KG9vDa3SKJUQRIFTg32sVs0ydfq/I8r06TKVcIuB5Yl8PbiKmvFEsf7u7m8miBdqWJZ4FYVXtw3xlhkZ/bd9HqaV67P4VQVuvytBypfrfPmrWU+ffogBwe6WEzlsMkyu7tDjPeEO7tc0zS5PjnN6uoqx556iluVKWoNvcNYC4ctGvUGtZUkhVyW119/fePnYSKRCD6fr/Od6rpBvanjUG0oNqmlMH0HIaTR1HE5t+5u272c6ZkU8WSJwf4QoihgmSapTJlzlxboirrxuO2kM2XWkzUWl7OodhlRFGk29S1N80qtwegOAdnc8N1R7thd2+0qTpeL0bFdNOsaly7Nc+P6Mpp2C0WxqFYEdKNOd3cIRbGRz9dQVIlgcOdd+upihqvnFykVaq0M1G5jZLyLE8+NkUmVaNR1HE6FSLd3Y4Dz4WFuzDuJUmu+SRDbOnUCpmHdVcbm3YQoigSDQQRB4NChQ0iSdE9hT0V5uCHmh0WbNfeDIPMuoc3e8nq9nD59+q5p66NmMnd7jfbAYTwe5+jRo4RCoXsef69M5rmuIV6Nz3EutYIotMzGdMvkQKCLnxp+in/56kvEqy2tLAvoc7p51lDRmk2OPX2C6cWrNMtZapRRRRvvi+3nU73PAPAXzDGTKGKTZGyCxGwlgSpl+enD72efu5+o07WldNZGZ2E1DN6YX8YuS1QLBSwLumIxBFFgIVtAliV+7oXnSFeqKJJE0OngypUrvLmWIlGqMBa+3XD3OhTmM3meHxvkmbFB5tI5REFgdzREf8B7113g5Fqahm7QF7pNPw64HOTKdeaTOX701H5OjPVtO24llee/f+NNFlMFwqEw1cl1BqJ+zk2t4HXZsW8s3Olyg4HuCB99/3HsNrEjctmmnwcCQdIlWEpUqdZ1PC6Vkf4QPo+d9VSRWNiDKAjkSzUQYNfQ9ua4phksLGfx+5zYNrIfSxSJhr0sreaIJ0pcubbKlauLZLIF1pITREJuvG6V1Xie0IYwZb5QQ1VtjI+2ehzpdJm19Ty6bhIIOPEHnKyt5nG7b5c+8/kqHo+dUqHO+XPzCKJAT3eMel2jUKgQCFqUyxXOnUsgSxKBgIfd4xFc7u3PTblYZ+LcAs2GRnd/AFEUKJfqTF1dxRdwMjDycHNgd6KQr6I1DRwuBX/ARS5bBsFCEFqbnnyu2srSvO/ONP/90GYWiqKI3X5vYU+Px9Mpvb1bwp7lcvkHQebdQDt7eJDp+Uc1HWu/xuYg02g0uHTpEqZpcubMmXtKpsD9Mxm7ZOP/e/iDfGP1Fm8mFzEsi6cjfbzYuxuHIPFj7m58B/eQrFdRdRN5JcFAOMa+ffv49Wuv87XlaZyygk+KUWk0+bOFWbyyl1NdPdyy8jhFO25b66F0SgqZZoWXklf4od6DWyyS77xmaEnOl+t1yoUiEbeTQKAVNFr9Gou6pmGTJLq9rSzSsqyWSN7qOoZm0mjUUVU7oiggiSKiILBeKPHC+MhdM5eGprOYzmOYFv0hLw1N3/E6JVGgpu383cYzeX7rf3yLbKXBSG8Xgijy1s1lesJeRnuCLCZar4/VmlX58PFdeDb0yLq6uujqaknhFwoFvv3mTb57aQlMA4/bTjarsLyeZf+uHnLFGktreQBcDhsnDw0yNnAXyRlrayYuCK2GuSiKLK8VmLi2iNYsMb6rB4fDwVq8iN9nZ9dwiHiqTK2uEQm7Obivh94eP5OTcc6fX6BcabQ2J6JIKOTCbrextJTF6VRo1DVESeCpIwPMz6UQRZHYxoS+y61iUyQ0zeCFF45QrzdbA7xCg0olR6WiceH8RYymDUOTcTgdaA2NYqFK3+BtDTW3x06lVGd1MfOOg0yl0uDy+UXWVrJoTQOXWyUQdCFJImtrJVS7jKXnUFSZPft7sL1DOvSjol1CvjNg7CTs2WasvZvCnj/IZN4F5PN5pqam7ps9tPFOLJjvxOYg0+6/hEIh9u/f/0AskwfJppyyjU8M7uMTg1t9WgzDQBFETkf7SCQSTM5PMj4+zsDAAKuVIt+JL+CxqfiUVhBxyDYy9SpfWZrE7bDQMZGsrQubU1JYq+UpajX8ys70x/YCksukUatldEnqBBiAUqOJKsv0+28PgrWn98fGxhiL51ibXSKfz2MYBqpqx25X0XQDv+Puu9DJtRR/cu4m8XwZ07IIuhz0h30Yprmlwa8ZBrppMhLdLp5ZqVT442+8QaGmc3R8uPMd+Zx2FhI5ju/u49TeQVKFCqpNYqw3RNCz/XMQBAFFdZIswNBAL36PSqVSoVKpkEhluXilwMee24Uw1I3H46Ur4iXkv90nKJZqrK61glkk5GagP8Dlqyt4vXakjUUqk6vgdqvMzq1SrRTZMz6I2+3Gsiz6egKsxQucPhHh+NFBNG1DC0yWyGRKnL+wgCDC0FDrOWg0dOLxAgf292KzSaTTZTw9PgaHwgT8Dq5OLOO5IwPweOysLOcQRYHdm/pCiUSCmZk5VheqzEyu06g3WvexLmITZXr6tw4wyjaJelXjnaCYr/KNP7/CylKWcMxHOOqhUmmwtJBhz/5uRLlOraozOhalfyhMrPu9Gz5sVyTu99wrikJ3dzfd3d1YlvWuCHu27SUe18T/k4AnIsgEAgGef/75B2Z1PM5y2fLyMpOTk+zatYvBwcEHbvI9iv5Z++abnJwklUpx7NixjnLAciVPVdfocmzdybhsCrlGjVfmFqkZBg2titpo4rOruBQF3TJQJRt2yUauWeEb8ZvMllL4FAcfiI7jMBys5gssVetw4wZ//cRR/mBylrlsDp9dpWkY1DWDZ4cH2BW5PQnf/pxlWeb9+3ZzPd2SKwk7VKq1GouZPKJlYKXWmZmRCIfDW3oe6VKFP3jjKvlKjZ6gF1EQSBUrXF9OEva4WErnO6oA5UaT8e4whwdvT7dbltXZOTYEhUhI2bIYSJLYEbl8Zv8ge7j/rjtfqlOuNIiG3MiyhM/na1FawxFW13OYloBVTRHPrdAoBahszF8sr5Z44+1ZisUaFq1m/dBAmO6Yn6XlHDZba/bErtqIBiQWF/L09/d0dqWCIKAotg06tUjA79kyCLq2nqdUrNE/EMDc0ANTVRmHw0YmW+FHfvgwAKVSHV03WoOaikyjrm2R1m80dGyKvGPvJJeqU8pKHDy8C0mWqFYqLMzFmbmZQHy7SXdPALfHg9vlolpuMDL+4JqAbawvZnjjGzeYmFjB4bCxXm1SL9cZGIui6QaZVJmxPSGcTsdDOz6+G2jf4w8TFN4tYc9arYZpmjv2or9f8UQEGeChaIOPI8iIosjy8jLVavWBM6jHdQ2a1todFgqFbdTokOpEESXqho5Lvr1w1A2NYr3BfK6GGJSwJIOGAemqiW4ZNNH5cNcBUvUS/3jiT1mvFbA2tJ6/PH+RQCmIlRIxdY29PTb+t64u/rdwmG9MzzKdzOB3OHh2eIAP7hpGgM7iB7cp3+PREH/tqf38xbVp1koVBASGumP8yP5dDDoVUqkUly5dQhCETpP9aqJIulRhJBbslMe6Ax7mElm6Ah5O7urj2lICC3j//mFOjPXhsitU603enFzmjYkZUpksx8YHiYQDJBa2+mNYloVhWrjuQkvejGqtiSxLOFQZxSZRb2gom4YJG00Dj8fJ/r2tmZjN+mqXLt/gys0iNsXOQG9rgrxcbjI9m+C5U2OM74oydStJoVilXs1TLsvs3zvKerK85Roq1QaqKuP3O7YNgkpii2bcpppblgUmYIGm6RTyVS5dWmZlOYOuW/j9DhxOhbXVPIoi43KrNBoaiUSRoaEwofDWjYplWWRTVVSbH2UjAHm8HvYfclMtgGAINOsC6/kkuWyZQNiNToBsNntfg7bOfVptcu3cAtVKA6dHJRz2YOgm2WQRp1vFHXBSqTRoNsDlejIGDts6ao/SX7mfsOdms7t7CXtWKhWAH5TLHjceliL4qEGmXq9TKBQQRXGLJ/3DXsM7yWSKxSIXL14E4ODBg9sme8e8IQ4EYpxPryAADslGRW+SqVexaTJD7gCZkkbCn8KQTXRTo9A0ORUZ5m8NnuH/nnyZ1VqekOJCEkRytRplvUpCTTLu6EavyywUSvzOmxf4Jx95H3/vmRMtpg+352jasyDtgcM2BEHg+bFBjvR1dRr8Y5EgTqW1YMViMRZSOS7NLjG7ksW5FGc+XaBUbFB22nA4HJ3NhF2RqTSafPjQGB8+NLblM2jqBl9+9Spv35zD0poEgwGuLOfwOCogQCJXJuJzYWERz5bxuVT2DNw9g1lcy3FuYom1ZItmvG+si96Yl8n5lq2xw26jWm+SzlZ4al9vZ+hys76aIC9y/dZV/B6J9Xgcy7RwupzUqhaLy2l6ugKsJ3Ksx1MtingzSDisYbNJLK9m8XocNJs6lWqTA/t6iIZbO1VdNyiV6iiKTCzmw+W0U6+1NMdM00Q3DMrlOqNjEV59dZKVpRyhsBunUyabay1I0ZiPUrFGNltBlkUGB0OcODm8bebEsqzW7NMdj5soCsR6fXT3BJAkyGWqdHf34g7INBsaN27cwDCMLaKed+s/ZJMlSvkq0d4AyWylw6JTHAq5VBlBFglGPIjS3TMHramjNXTsTgXxARSdHxWPe9r/UYQ9y+XyhuzRe0OCeDfwRASZh8WjNP5zuRyXL19GlmV6enreUYCB+zf+d0I8Hufq1auMjIywsLCwY3AVBIH/z6Hn+NUrr3I1EydpVnDZFHa5IuRKGg6bDWfJwa7SKA1Xg6JWQzUd/JP3fRILkwu5RZySgiSImFhUmhqCDpbNQgjbUNZ1Qh43C9k8k8k0B7tjnQzjXgFmM7x2lSN9W8solmXxzWuzfO3KLSr1JghgkySCTi+ykqdSrZHP55FlGbtqp1jV6BnZzh4DmFxK8vaNWVwy9A8MYLPZME2LuXiW0Z4g5WqThUQWBIGQx8mHj47RE9pZtXolnudPv3mNYqVG0OtE00y+c26Wkb4Q40MRltZyJNIGik1i71iMZ4+P7Pg6pgkOp4Pu7jAWLYp0pVImncly6fJNLiIhCBbRkIPe3j4M02RlLc+BvT0YhkUqXcLpVHjqUD8H9vYgCALTU3GuXFmmUKxhkyWGR8KMjISZmk5QKNSQZZF6XaO/P0TA7+LaxCrdvX5sG0OW0Yib1bU8DofMM88epFyqo9pthMPuzhxOJlVi4VaSXKaCZtaw2SS0hrGFQl0tN7A7FA4cG2BtKUs6XqJQbZLPWrg8KgePjdA14CGTybC+vs7U1BQul6sTcLxe71abBdPC620Jc8bjhVbANAyK1SbeiIex3THKtcS2hV1r6sxcW2PxVgKtoeH2ORnd30P/aORdnVV5txWYH1TYc3V1FbfbjdPpfCzX89u//dv82q/9GvF4nMOHD/Obv/mbnDhxYse//Z3f+R3+83/+z1y71tJFPHbsGL/0S790179/GDwxQeZh3DHfSeN/s8Dm7t27KZVKD3y+nfAwgW7zcOmhQ4eIxWIsLy/fNRNKFivIeRuOsh0Zk2FvgGdCA/xxYgptw5FTtiS8ZhitUiLmcWOXZMp6A8u6vVHVNA1d11u+K4KFJYAogCxBQahwPrPMaCSAU1a2SfQ/7EO9mM7ztSu3kASBkWgAQRCoNJqs5ct43S4qukEo6EHTNFZzRWyWAdlVJibMjuaUoig0m02+e2ECTdMZHB7slBVEUcBlVxAsgf/14ydZSuURBZHBmP+epbIrk2sUyjVG+m6XQ91OheV4nk+9eJhTR4YoVRp43SpdYe+W3X+zqVOtNXHYFUJBF/KGP4uq2rDb7SiKSqEMPo+NyaklYhEnzWaThYX5VuPWEsjlKvyNH32aRlPHJkudxX92Nskrr0whiODztbKcy5eXGRuL8oH3j7O8nKPR1Onp9jEyEmVxIY0giDid6kYPx8KyTJwOhVSyhNttw+NRNr671vWvr+T47renKBaqOBwK6XQOTWswMOQjsVZAkkQsszWvsudAL42axvTVVQIhN84NunQuXWbi/ALh2CGGhoYYGhra0n+4cmWCfKqK1ZBxuz1Eu0KoDhuVQiv7km0i6WSZXK5G72CQE8+MMjwW5cqV9W332LVzC0xfWcbtdWB3qhQyZc6/MoUgQP/odgr548L3UrfsbsKe6XSaz3/+8ywtLeH1evn1X/91fuiHfog9e/a8owD75S9/mc9//vN86Utf4uTJk3zxi1/kxRdfZGpqimh0+2f5yiuv8JnPfKYj3fWrv/qrfOQjH+H69euPLNb5xASZh0HbmfJBbw7DMLh58ybJZLLTZJ+cnHwsFsz3g67rTExMUC6XtwyX3o04sJQv8H+/cZZMtUbE5QYLljNFvlGbI+S0s1Is4TItbKbZ0S374MgQkijitdnZ6+3iQm4JybDQNA1FlmmgIyMSsDysKHFuSjM0PDr/YTXHn2Uu83dGT/GR2Hjnut7JTX1zLUW53mR0I8AAuFQFuywR87mIeT3ciqexBImnxgb58MFRejwqqVSqM4Pgdrup1WoosoRn0+64DW3Dj97rsnPA9WAN6eV4Hu8dUvR21YZhWOSKNXYNbS+zGYbJ5avLTFxfo1pt4HAo7BvvYngwzK25JA67jWbTIJuv4vPaaNSKuNwuhkeGYEOcsVKpUCxkadQKXLggdnpU7dLIjetrmKZJb0+LSedwKKiKjeWlLEeODPCBD+zZck1Op4JAq7wmyxKSJAAizaZBrMvXKd+270nTtJi4sEC5VGNguPUeZdVgdSmNKMDJZ8fIZipIkkh3r5+uvgBvfnsKURQ6AQYgEHazNJcisZYnsNHjafcfIpEoWn6W5dU5KpUSC7Ukjfp1HE47TqcTt8dN0ONG0k12jUU4/aG9hLp8G9e39dktZCsszSQJRDy4NmyfnW6V5Fqe2etr9A6F37XS2XupW7ZZ2PPChQv83u/9Hr/0S7/Eyy+/zC/8wi8QjUb5i7/4Cw4evL8H1Gb8+q//Oj/zMz/TccH80pe+xFe+8hV+93d/l5/7uZ/b9vf/9b/+1y3//3f/7t/xR3/0R7z88st89rOffedvkO/jIAMPlubW63UuXboE0InS7ddoNBr3OvS+13C/nky1WuXixYuoqsqpU6e2TAvfLci8Mj9PqlJlOHDbEdOp2FjI5fnw2AjL+SJTa3GymkbY4+GHxnfx0d2tnoYgCHx26CQ3MitkjSqqTUEQTdDAU/VSosm8J4eBhVtSCSh28s0aX5x8lbDNxbFQ/5YAoxsGy5lii3ob8nWoxjtBM8xWuf+OACWJIpIo8pPvf4p8tY5umITcztvDnF4vo6OjJBIJrl+/jqIo+GxNmtUStxYb9EUD2O12SrUmpmlyaPjh2E5el8pKqbblZ6ZpggAOdefb/8LlJV47O4PDbsPjtlOpNXn9zVmOHx3k9PFhvv7tm6QyZUTBpFDQGB3qwueVKJVqeD0OnE4niqJSqgqcPj5ILOYglUoxOzuLqqr4fAHW1tI4XVtLtS1l5VaP5k709Abo6vaxupInGvNgs0nkclVEUWTPnh4URenYV5imST5XIZMq4fU70HUdQWgFHo9PpVRq4PU72XWHhlmzoSPt8B23iCDb79X0Wp6Zq2tEewI43S2qdK1SZ2FmnUC3nXKpSDmdo3cwyu4DXXiCW31/Nj+7lVKderVBOLa17Ony2CkXajQ3ejTvBp4UwzJRFAkEAkQiEb761a9Sq9X4zne+w8jIziXcu6HZbHLhwgV+/ud/fstrv/DCC5w9e/aBXqNaraJp2jvyy7oTT0yQeZhy2eYgcy9WWjab5fLly0QiEfbt23cH9fXRyAP3y2TS6TRXrlyhp6dni7jn5uMbmsa51VWWCgX8djsnentZKhRQJGmr5bIgIEsidV3nFz/yIb765lvoosiZ/fvp8d6mOtbrdUqTy/wdxwHmAybTlSRBxUUPYVaWa0w0lzFFE79sx7/hr+G32ck0q3x1/SbHwwPcKMT52vpNprMpShkNR86OQ1eJ+Vz8yPG9W+jFmzEU9iNLEtWGhnODuWSYJpWmxt7eVk094Nre/zJNi9cu3uCVCzdxe/0c2h3juf1d+GJLvHR+mhsLa5imhduhcnxXL3t6ts/Q3AsHdnezsJojX6zh89gxDJO1ZJFwwMVw/3ZGYa3W5OqNVVxOhXCotXN3OhUkUWD6VoL+/iCqKjM66KVeK+HzRymWdRQFCqU6uXwNSRLQdZORwTBHDg7icqn09/eTyZRIpzLUG2VK5Ryrq3G6e/yt8onLjWmBIApb6MhtqKrM8+8b582zM8TjBXTNxOuz8/TTIwwNtwZF2/eYKIo4HHZkWe6wpkzTpF6vt8hqlolpmWTTJZZm0+QyZTxeB6pDpl5rbrEPaNQ1JFnCv4NCciZZRNN0nO7bTWqHy04w6KO3O8KJH99DqVQinU6TySRZfH0Or9dLKBTqMCxrlQaGbmKzSdhsLTr2ZlXoRk1DddiQbRLFXIX4UpZGrYnH76R7MITqePTA8yTJ/G92xXQ4HLz44osP/RrpdBrDMDo2y23EYjEmJycf6DV+9md/lp6eHl544YWHPv+deGKCzMOg/eDcrSdiWRZLS0tMT08zPj5Of3//th32O2ncb8bdMhnLslhcXOTWrVvs3buXvr6dm9sV0+RfnT/PfLmMsRFcu91uhv0BNNPYop9lWRa6YRJxuVAkid0BP5IkbQkwbUmeYDDID+8/sS391w+afOHyV8muZ/Ha2xbGLWkPWRBZqeX5y/VJfn3yFWq6Rl3TMUULNSxzpD7IWq7Ef3ntMn7nKQYj/m3vZ19vhKcGuzg/t4Yit7KXSkNjKOLnzO6BHT8D0zT5va99l1cnFnC6XFTLGl95c5LLM+t89iNHOTI+xMxqhkq1ikPQkfUqZ89+F7fb3Sk/eb13l64B2L+ri2yhyqUbqyystoQeYyEPH35mN27ndoZUsVynUmkSumNR9bjtrK7luD65jmU2aNQb9Pb2YrfbcTiaFEs1njk5SrFYp6np9HT52TUSweFQyOUqvHl2luXlLLrRkoo5cGAPU1NrNJs6+VyB5eV1alWLwaEwDsd2/TSAYNDFRz92iGy2jKYZBAItJQBoNc2nb6wzMxWn2dDoGwzhCzhZX8njdNtJpVNUqxUkwUO414PW1Hn9L2+Qz1awOxSaG7M1iiKzupjB6VIxDJNmQ2d0TxddvduDu3AXS832VlEUxc4M0ujoKI1Gg0wmQyaTIZcpMnnhDZplUBUHXb1hFFUmtZYn0u1DsdsoF2tUy3V2H+4juZrj4qtTlLIVRKkl9NrVH+T4B/fi9jspZMqszqcpF6p4/E56RyJ4H1Bs80kyLHsSpv1/5Vd+hT/8wz/klVdeeSwst+/LIAN3b/4bhsGNGzdIp9McP36cQGDnne+jqgbsFKRM0+yYqz399NP4/f67Hv9yMsmNYpE+vx+7LKObJmvFIg3DwGVTWC2ViG7saJLlCj67ynODLfVbQRC2BLg2a+1ekjyyKDLqDfNKXMAyLZAsQADLwrAseh0+vjTzXZqmjmrJaIaJKok0BJ0pdZ33B/cwn8rznZl59kphHJLMHm+s5XsDyJLEjz97hLFYiIsLazR0gwN9Uc7sGiDk3j4PYZomr711ke9eX6I7EiIaul2rn4/neO3qPJ96/iBh79aFoi3tkUqluHjxIqIodmraoVBomz+QJIq8/8QYB3Z1k8yUsMkS/d1+7JsGFTPZMvFkCVkS8fscqKpMra6hbiqn1eoagiSQyWZxqjA0PNARrXTYZdJZg0jIzfEjWxWKm02dl1++ydJihkjEgyxL5LIV8rkqu3Z1k06VKJcbhIJePEMyw8NOrl2bQGsaaA0FTIVINMTQSJRIrEVMCIe3DuoZhsl3X5lm8toKDoeCbJO4cn4Bl9uO26MycXEaTdOIRqJEuv2ceHY3E+cWKBfr9I+EEWhVEVLrBRAs9h/tJx0vYVNk+kfCDI5GkDYYbYZuUCrUkCSRYNSDTZGpluo4N/oozYaGoZl0DWwvs6iqSk9PD9FIjLe+OU2jCE6vQq1Z4vK5OKqqEu4OkFw3EJBwulX2Hh2kfzTCa39+hUa1Sd9YtGXToZusLaSYvrxEz2iUt1++SSlbQVYltKbB7PU1Tr6wj0iPf9t17HQvPomZzDtFOBxGkiQSia0zZYlEgq6ue5eb//W//tf8yq/8Ct/85jc5dOjQI11HG09MkHkcszK1Wq0zDHj69Ol7RuFHmdhvn3/z8Zt7P/c7d7HR4HqphFuWsW8sirIo0uXxkKpU+OT4Ht5eWSVRbg3yRV0u/taRw4yFgp1rbw9Lzs7OMj8/32Gt3Q2WZfFi1zj/beYcqUYZl27DJspU0bFLNnZ7oryanMUj2ynrTVr7VAGbJVERG5TEOllvkd8pvIF4qVXC63f4+T/2fZD9vlY93m6Tef++Yd6/795T3JqmceXKFebjWVSnm0jwdh1eFEV8Ljs3F5Pohol8R7N3s7RHW6Y9nU53TMzaNe1wOLxl/iAccBG+Y2drmhZvvDXLpYllypUGggDBgAuPW2VlLY8kCSg2iUS6TDZXJhoScDklXC7/FlXkYqmOy6ni820PpktLGVZXc/T1BTsukt3dfpaWMwjAX/3R4+TzVRRFIhz2IIoCpWKNr/3peeZm4tTrDZrN63j9Tk4+M8bxk+O43e4tz0tiLc/s1DrRLh+OjVJbIORifiaJy6ez53CYvt4h3B4HfQMhTNMkkywRjvlQbAqmZYIFoZiXxGqecJeXA8cGOt9H+1lZnUtz88IChY1sItYXoG84zPJcqjUDIwKWwNB4F73Dd9F6A5KrOXKJCrv3D+P3ezv3xOzkCjY7uMMmWBZdPQG6hz1kkwUK6TKx/mDnfUuyiC/kZmU+RXK9QK1cp28s0im7r81nuP72HM//yJH7BpAnybDscUjKKIrCsWPHePnll/nkJz8JtN7jyy+/zOc+97m7Hvev/tW/4hd/8Rf5+te/zvHjxx/pGjbjiQkyD4s7g0yLTnmFaDTKvn377ntjPc6eTKFQ4OLFiw+sfVbTNAzLwn7HNdpEEd00GQsF+cyhg0ynMwCMh0M4Ni1qLan4JleuXCGfz3dYaw1NZ2IlQbxQwmtXeWqwG++Gv4tpmsRUN//n0Y/zG5PfYaWap6E3cFsyTzfCTFxbpi7rqMjIogh39MfiQoFlTwabIBGUHJiYzFey/OOJr/K7Jz9D8C56aXei7XTqcrnYMz7OlfWr2/7GsiyETVTcu6Et0x4MBtm9ezfVarUzoT89PY3T6dwi73/nPTE1k+DsuTncLpWhgSCmaRFPFmk2dMZGoly9scLyWgHdMBAFHZvk5ehTu5maTbKeKOBx26nWmlSqTU4dH+4McW5GqVTHsqxtNsUup0o609I426yuDHB9YoVkosqe/UPIsoSuaywtJLn49gK1Rha3x74le8tlKui62Qkw0Mroy5UchqXw4z/9sS33ZLnYUpa2NjyNREEEASRRwmazYVdVsskyt66tkYoXcHvt+AIOFm6uYxoWgbAbw4D5m+vEBoKc/NBecqkShmES6fbTNRBEvoctc7lYxzDMjuoAbDDWusMoqo2P/vDT5PP5jgPq+mKGtfUcshu8Xg+qonRUKGrlBpVSg3D3bSkjQRAIdnnIJIqUclV8oXuXn56Uxj88vnLZ5z//eX7iJ36C48ePc+LECb74xS9SqVQ6bLPPfvaz9Pb28su//MsA/Oqv/ipf+MIX+P3f/32GhoaIx+MAHbr1o+D7Pshs7oHs2bOH/v7+hzr+Uc5vmiZra2tcv379nt4zdyLkdBKx21mv19ncei40GrgVhZFAAKfNxpHu7amtZVncSGZ5bWaeJiKn9u5mHyLNcpXffvktpuPpliQJ0O3z8DPvO85YNNAhVRwNDfC7Z/4ms+UMdU3jO+cWeHtuhapeQR8ySGtlHKINURBoGjqmZOE27MybGZAtAoqjFYQQ8dskco0aL8en+VT/YS7kVng5Pk1Rq7Pf183HevYQ2BR88vk8ly9fpru7m927d5Mt1fC67CRzZaKB1u5c0w2K1QYfGu/rCE4+KJxOJwMDA535g7a8/8TEBKZ5ex4nHA5js9m4ObWOZVkdkzBJEujp8rGwlMFul5FtMt1dHiyjgt8XQDcV5heznDw6xPxShlKpjtOhcOLoEE8d2vm+c23Qp9vU4zaq1SbdO4hCGobJ/EwSr9fR+XtZtjE00sPqUoaxkQOEoirpdJrp6WkajQaVgkipVKLR8KCqKs2mxvLyEggSwyOtWaNSocraUksNORB2E+vxMzeVwOlWO2Z2qXiBYMRDs6Fz9ps3qZTrON0q8eU8F1+bQVFEDp1sZakyEOsPkFzJsefIAIfu45TZbOhkE8XO5yxwO8i1Ua80CcVaPbZm2aK8biLUPQz1+imu3CKxmiadTiHLEi6Xm0qmSf9oN/lM5f43xz3wpJXLHlbiaid8+tOfJpVK8YUvfIF4PM6RI0d46aWXOtWOpaWlLe/53/ybf0Oz2eRTn/rUltf5p//0n/LP/tk/e6RreWKCzDsplzWbTa5evUomk7ln/+Vuxz+qu2az2eTGjRscOXKESOTB5dBlUeRD3d38t8VFlgsF3IpCXdfRTZOP795Nv+/uirS//+ZFfv/8NXTLwuvx8N8v3+S7C6sMeLzcWE3SF/Ci2mQM02QpW+A/vHaRf/LDz2NXbOimSTxfwqnI7PZEeH1qkUsLCbpDPlxqhABOroorVNGwSQKGZSHoIo51O5meInbZhm1T+aqtFBCvl/hP8+f4zwvn0EwTC4vXUnP8+do1/q+nPkGPw0c8HufGjRvs2rWrsxEIeZ28eHwXX31rivn1bOceGOsN8fyhRxNOlGV5i5ZUsVgklUqxuLjI9evX8fl8rKxmEAWBttoKtO9DgbnFNM1mE1Wu4wsFW/eWZTG/lMUmS/zNv3p8o29j26J/dicGBkJ0d/lZWc0RjXix2URyuSqyLO5og9zWLBPukIQRWu2z1g7eUsisWawvigiyHZdHBEHn8sVJgmEXpmkgCgo+j5exPd3MTyd485VJCtkqCC115a6+AMGom5X5zIYts4k/4OKpUyPcuLhErdqgf5PE/+psikqpgalb2JTWnJogi5iGSTFXplH3k1orkE+VkG0ysf4ggUird7Q0k2Ti7Cy5dKv86/Y5QLBIruTpHWwxx/LZCoLYKrVNXlhk4rszNJsasiSh6QZ21YFdcWCaFrqpkV4pICgmdTlLuV4jN5VjaLwXu701rJqNl+geDOIJOLEsi1yiSK3SwOG2E4hudbt80splg4PvzHn0Tnzuc5+7a3nslVde2fL/hYWFx3LOnfDEBJmHhSAIzM7O4nA47tsD2QmPKnA5OTmJaZo888wz76iGejwSwaEoXKxUWMjn6fF4+MjoKB/fvfuux1y5Nct/O3el5f+uKvj9XgzTZCGTZzaRZcjrQ90wzxJFkW6vm9V8kelEhlK9yV9cmSJVrmITJQ4PdFGuNECgo4Lcb4ZwaXauN1fxB+x0yW7i01WaeQMhAFWxiWQIeB2t3a+5YTGtiBK/t3AegIDSKhkZlslKtcB/mH2bv+kaY35+ntj4CIuKTj6/zn5fF6IgcGb/IH1hHzcWE9SaOj0hLweHYzgfQPDyQSEIAj6fD9XuBNGLw13HJmm4XVlm5+NozSJutwun04Us2RBFgWq1SrlcYGykB29bEVcQEEWoVFtCmx73/Rcmu93Gh17Yyxuvz7C2lkc3DHw+B8eODjGysYjH1/IszadpNnXCUS/dvQGuT6zg2xDRBMhlK7g8KjabxNf/9ArpZBG3147WNEiuGYyMDpLLFZmfXcFmkxHEJrEei0x+letvpRAFiYHRcKvEVG2yOp/m2LO72H+4n2KhhtOl0jcURpIEMoki/jtKTMGoh/npBLVKE1V1USrWKBdrlIp1BEni/Lcmmbuxhq4ZLWdUr4PDz44R6Qny9ss3aTR0ohtN+HSi2KJg+53k0iV0zcTtc3DkmTHcXgdv/+V17E6FWH+rB2noBqtzaYb2dePxO6mW6wQjXnpHI8iqwPS1ed785nXOvzGBoiqoikqsN8ze40M0qhoXX73J2lyKZl1DcSj0jUV56vk9nbkb0zS3EUbeK1QqlW16ht/veDI+2YdE2zjI6/Vy4sSJd5TqvtMgUy6XuXjxYieovdMbQhRFDvj9fOrUKTTTxHaPSfu2LM3Ll65h2RQibkfHfloSRdx2laVyHlFo00ctLKslg2+YFhMrSV6ZnEc3TfxOO5ph8OrUApgWLnnrnFHQdNGXjXDY3cX0coawbCMy4mJNdHGFZcpGA7OqI4siDdEkrLiQEGiaBj7b7UAvCSI2UeTb61OctMmcC+l8c/JrNEwdSRAZdgX5pwc/wrA7xEDMz0DM/44+xztRLjdYXslimhZdMS+hjcVybiHNN165SSrd8rTxuFTGx3ppaCrxRAGr1CSeKFCpNunr9qDaLFS7C8+merRhmFgWBP0P952Hwx5++EeOkMm0qMfB4G3q8dXLS5z9zi1q1Ubn+492+QiF3SwvZFBUGV03sMkST58ZZXk+TSZVYnCTnle5VGd1KUmkH178kacIhyL4Q04cLpGLb00RX0vh9ktoi2VcrlaNXXXYWJlP88kfP9W5TsuyqNc0JFlE17Y+G/6IB3kuRSZeZH05Q2qtSLlYxem289Y3bmLUNfrGwih2BYtW5nD59Wm6hiMUchUGdkVbVBJBINYXYHZmjr6xCCN7etE1A1/QhcOlMnttlWq5Qd/o7SxKkiVcPjvlfJXnPn6YUq7C+kKGpak4noCT/UfGGRkbYmkmTmI9jW41kRw6N2cmSEyXySyV6R/pItoXpFquM3N5GcUmc+xD+za+V+Ndt1V+UDwJFObHjScmyDxIucyyLBYWFpiZmcHv9xMMBt9xLbXdU9lpHuFuSCaTTExMdOr+r7zyykMdvxmiKKJpWstj5B6pumEYXL16lUKhwO7du3n93AQWbBlclUURRZbIVet47GpneiFfreNRbUzF0zR0nYGQv3OM3WZjLpnF1Lcah9WaGqIooJsW5XqDoUhLJqbHCqJpFpPSGk2xxe4ZkNz8MN1MzS9gmAaGoCNtDABiWRiajoXATJeNryxfxyaIeGQ7umVyq5zm5y9/lf90+jOo0uO5DW9OrvPqd6bI56tYVqsfcuzoIPv39/DVb1yjUKzR0+VDlERyuQqXr63w3OlRenuCrKzmCIWDuOwaQZ+BLKvEkytcnJghGvaiqnbKFY3engC7x+6to2VZFs2Gjmy7rVUmigKRyFbqcT5b4dx3ZxElgcGNrEbTDFaXsjz19BD7DvYRX8vjdKkMjYTp6Q/y5f/wBh6ffcs9Z1lN1tYSHD5xnGfff2TLObqi3USjGXoHg5QrZSrlCsvLy1RKGuWKm9WVNXKJJvOTcaqVBj0DIXxBF0uzKZxuFZsiYxgmjZrG3qcGqOQrLEwlcXlUBnfF6BoIcf3cPFgwerC3834jPQEWp+Mkl/PINgnTbLmutiFJIo2qRrhrh9LwXYeyBVZmkpx7+SbFXKXlHCoI9I5FOfXiAQ6cGOUAo53vILGaYu67ryHZLVbjK6SySivAemSWbsXZe2IYp8fxRJXLfhBk3kMYhsG1a9fIZrM8/fTTrK+vP5IFczs4GYZx31TZsizm5uaYm5vjwIEDdHd3d879TpkpkiRRr2+XD9mMer3OxYsXkSSJ06dPk2s0+R9Xp8jX6zg31hjDNCnW65wc6iOVLbOUKeBWbdT0FiniowfHeG16CY/9Dv0um4xDsRG0O1jNFrHJEuaG8OJTQ9247Upn5wktQvOQHkZKywRjKj9z5ml2eSIIwIX1eV65/hUqehNF0xA2KK8aJu+LjfHVxBQiAs4NfxxFkBBRWakVOJte4FR4kD9evsrX16eoGhpPB/v5zNBT9Dn9D/x5ptMlXv7WTZoNnf6+VmDM5at89+wM6VyZTK7CYF+oI2cTCrpZXs2ynijy1z95jJW1HK+9folyuUbfgYPs29PH+J4sL3/nOovLaXL5IrGwg72jdirlAnY1tOPCNDMVZ+LiEtlMGadbZf/BPg4c6e8svpuxtpqjVKwxsInua7NJuNwqa8s5nv/QXg4f21qfVxSJWq3Z+X8+lyOZTBIKhYjFtge/YNSDqtpo1DT8Ph9+X8uRdObmKl19Xr7xZ28zez2Jy+XA63OTWs/j9jkJRtwk1/IbwQHCMS9Pv283r3/tKjZVIRTz4nC3hnrdHgfpeIFirkog7KZea1ItN6hVmkT7g1SKjQ0Gm4VlWlQqVQzdxO23U681WJlNsb6QRkDAE3CiOhUKmTL+th2CZlAu1Bna083FV6do1Jv072rNy2hNnaWpOMGolyPP3S41C4KALCm4nG7CPX4EUaRSKVMul8lns5RzNaIXXQyN9dFsNp+Yxv//bK6Y8H0SZNq0V1mWOXPmDKqqkkwmOyWjd4L2AnG/WZl2JpHP5zl58iReb4vXvzlIPYzhWhv3m9Np06LD4TD79+9v+bgrCn/tqQP8p7MXiFeqlBAxLYuhoJ+/9/zTJPMlvjW5wGI6T5ffw/vGh3h21wDXVpKs5opbZF00w0ASBf7qib3ousXEUhxZFDky2MXp3QO8NbPCqzcW0DZKNe3PSm9aPNM1zLg3SqFcp9pociDczyf6D/Bnq9epmQaWoWEBbmT2FCXeqFc35l02BkBpZV8YsFYr8I+vfI03M4utzwX445U8r6Zm+e3jP8qg68HIHHPzaYqFGkNDt33qgwEX5XKD+YXWTMqd/iqqaqNYqnN5Yon//sdnqVSa+Hx+El+7ztStND/8sYP81I+9j3Kl0VJd0GodefY7Z3IcDgdTN9b45teuYhomHo+dQrbCt/7yGuVSnWfeP77tmi2zJZl9ZyYsiALGXe6NXft6eO2bN6hXG1SqJbLZLKrNjz/qoWeHAchot4/xg71cu7BIqVBDtkmUSw16+yMcOTbC6395nT37RhFkg1KpTMMosjCXZnRvN8feN4ZoKTjdKt0DIQQBDN3CH3F3hi8BAjEPa0sZGg2NpZkka0sZKsU6WtPAHXDhcKusLWYIRjzUajWmb84zONrDwGgXb3/zBrcmWt5JotRyFnW6FGq6QSlf3Shrm/SPRfEGWpP9PcPhzmdmU2Q8QReLk3EOnBpFtklUijUa1WbLK8hlp5yv4Y94Ok6WqugiGjaIdoVYmF1icXKNG9o83X0LjB8eoWeg6z0JOm0bgP+ZXDHh+yDItDXAuru72bNnT+fLl2WZWq12n6Pvjs1B4m5oD3e2M4nNRk1tteJHsWC+27Hr6+tcu3ZtR1r0D+3fhVcw+cbEDQJdXQwHAzwz3IfPrtLtdXNksAfTNFnOFynUGxQbTd6/Z5j//MZlMuUqAaeDpmEQL5ToD/g4NdqPS1V48Q7jsOMjPbw+ucDkehq3XUUUBIrVOv0hH0cGuviDv7zMtbk4mm7gdzt4/ugIfT1O/mLpKqbTxsmuET7Rsx+lqvHfJ5bJaDXQTExrIzuSBAQBynqTtzKL2EUZRdwIZpZFtlHjv8yf5xcOfBjTsriQXeFWKUVAcfB8dHSLayi0JusRb2vgVapNNK2VzYm0AkxT01E2iBGtv2nQ3+vjv/3RG5imwOFDI4iiRKOhcWNynZ5uH8+eHsPT0eZyEAwGGR8f77hmJpNJpqamcNidXDmfpV43GRyOIgoCXj/kcxWuX13hwJF+ZFliZipOKlHE4VQIhFw4nXbyuQr+jUFRwzApFersO9C7Yxl2z8FeUvEC59+cpFyuEA6H8fvdnHn/OG7PdvKLIAiceN9uQlEPMzfXqdc0du3vZfeBXjLxAvWqRnSj/+H3B7Ask3Vnmny6Sqa4jmnqBKUgStZo+cfEvKzMpfBsGjx1ulX8YTdL00ly6RKyTUS2yXQNhGjUNRS7jcGxGEvz6yTiCQ49vYvnXzxGpVBl4UacWG8QxS5jWRaNepPkSo79J4dRVQWtqaPYFWJ9AarlBuzAvBNFAdMyqVUa3Hx7jsXJdZoNDafbjs1uo5Aqo+sGDrdKrdSgUdc49oE9BL1+pl9fZXUijd1pJzVT4NrZaUZP9DC0u69j0nY3g7Z3Az8ol72LuPOB2tx/2UkD7HFQkO/1Gm1xzXsNdz6K/tlOQabd4F9cXLwrLVoQBPbGwtAX4ZlnTm3zgEmWyvzKN1/nZjKDhUXAaeeF3SN87NAY35laYjlXQBZFRiJBfuq5ox1m2Z1wqQp/7yMn+fqVW5yfX8MwLT64f4QXDo7w1dcmmZhZJ+Bx4nGqZIsVfv9r53iq387/9eFPbL1uN/z4rhP85tTrlJsGlm6BYGGIFj5NZT4ebw0rirdLT6IgIAsCZ9OLFLU6P3v5L5jIr3fmf4LKG/yjoffhSEjU6hqhkAu3W0UURMqVBgsrWbK5Krph0GjoHD08QH9vgPmlDH6fE1kSyeWruJ02Mqk1NF1g356BznesqjZcLoVrN9Y4c3J0WwYEW10zNU1jcWGNQnYZw9KYn28xhNwuN26Pg9XlHIvzKa5dXmF1OYski5i6icttJxLzkIwXW1ItsoTW0OkbDLL/8M5zNzabRKRfYJ8WpDv2FC63k76BIN5NZATTNKmWG8iyhN2pIMsS4wf7GD+49RkqZMqt7GSTIKYgiCiKHX/Qx7PPnmL66iJXz83wemIKm9PCH/RQqdWYvVknFPXTbOg0ak3e//EjTF1ZpN5oEox4CUQ8BKNeLNNifTnD7mNdNBUbz//IBxkZG0QQBOaur2JaFo5NApuSLCErZZo1ndEDfVz6zjSJpSxXDBO7S6Ve0yhkKvg3bAcs06KYrbD7yAATb0wzdWGRYNSD2++gnK9RzJTpHYuhawa1SgO338nhg30M7evmtT++SC5ZJNjvJRqJ4na5WZtPQkHBYXewtrbG5OQkDruTSDRCJBK+r1beo6Jarf4gyHwvoOs6165dI5/Pc+LECXw7zI08apC512ssLy8zOTnJ+Pg4AwM7izu2j3+UTGbzuXVd5+rVqxSLRU6ePHnPlLmdQbWPbweYfKXGz/zBn7NeLiNZAqIgUG1o/NGVm/ztE0f4l3/1gyxlCjgVG7tiQeT7NDsDLgd/48wh/tqpA633K4rcWk4zvZSiK+jFabeBZdGoVcjUamT1AKHQdjmRH+09xDcvTHNVioMNRESiDSdd005WtAK616Bu1JFluSNjYmGhiBK/Pf0GV3JrqGJLicDCIl2v8E8mXuID1wawmSKCKBDr9eKPOTh/YZFaXcdpV7BMC4fDRiJVZHw8RiziZXo2iaYZ9Pd4cNnLOJxdpDKpbbtjSRLRNvpa2/yKN8EwTERRorevm2is5XzocEpUqhWyuSyVlTp6Q+Dt794klagxPBrr6IAl4wWymQrv//A+4ut56jWNnr4AY+NdO2YlhmFw5coVms0mH/1/vW9HRtTSrQRX3pwjmywi2SRG93Rz+MzoFqXkNroHggSjHuLLObr7A4iSSK3apFKscejEEDcuLHLu25OYhoXHEaBcqFJsGgzsCrK6nGJhYQFfwMuBY8M8dXqM+HIGj8/VCQCtDxIqlSo3r0/xkR95dothligJ25r8giC2+jeWyNvfuEl8MUOoy4coQT5VplKqYSwblPMVFNVGvaYR6vLSPRDkza9fJdztw7WhvBCM2TANk1q5zoc/cwrTtJBtIrpmUkyXSa/mCHX5WUuUW/NHokC4J0A+XSLgDuMQPBRuzTCzuMaktYgrqhAdCRCJRjrWyW2l68cReEzTpFKp/KAn826j7cFis9m2lag241EsmDe/xuaF3jRNJicnWV9f75ib3QuPkslsDlB3luXuRae0LKtDGrhw4QLRaLRjhvVb336L9VIZVZZQxFYjv6kblOoNXpqc4RMHxjk2tH0A8L7XuimLyxQqaLqJ027DNE2y2QyGYdLbFaZY1ak1NFx3yK+vJgtEltx8wr+fhqMlwOk17RQCNZKlMkqgjIGFZJpohoFhWWiCyTFnlG/Epzbo0BsB0QJRg5qoY46KRHUvr6lL/LltBiSwn5LonnXhy0uEwm66Yl5qdY3pmSR/9yef4/3P7mZtbZ25uWn27j2EILq4MVmgVG7g3VjYTcuiWKzz9LHBHRv2AIV8lUvnFpiZaslvjI130T8Y5OrlZewOH+FQCK/Hx/JimlhUJZHIU6/XWVlpbmRBTkIRD6tLWWSbxAdfPLDjeRp1jfWlDNVKg7XEAv6wi+PHj+9IVlmZT/HtP7tMvabhC7nQNYMLr9+ikKvy4R89us0rxu5QOPOhfbzxjeusLrQkjGSbxJ7D/fQPR/jaH7yFalcIRlsbnnCXl+W5FA7Fx//7H32ATDpLLp8jm81w9s3vUqwVKKaa2D0yqqoiIJBMpCkUcrxw9IPbHBljfUFkm0ylWOsEhtpG/0tWJFIrOfrHokhya/jT4bKj6ybBqIdwj49KqU6kx8fQnh4qhRq1SoPIHVYQTq+daqlBs66xvphm+uIC1WIdQRRIr+Vw+RyYm7xtWsxIWJ9Pcv27MzTrGt6Al0atSX21iRx14hh0MHntFrOXl6hldTw+D3uOjXD4zF7sOyh7Pyiq1SqWZf2gJ/NuQRAEUqkUExMTd/Vg2YxHVVGGrUGm2Wxy6dIldF3n9OnTDzT/8qiZTFvg8dKlSx3Pm3u957YGmdPp5JlnnunodN26dQtLVnh7bhlBaA1HIrTKToosUdcNMpUqhXqDqPvRvnKvy/7/Z+8/4+Q4rzNv+F/V1TnnyQkTMMg5kgQzKQaJtEQFB8myLXm9ki2vZHtt73rl59mgx+vXXjmtZXlXtmyJlkQFUswZBEECRAYGmJxj55y7q+r90DONGWCQGGRKP1/fMOhQXV1d577PuQKiKJAvFEkl42g0Gnw+L8FYFrddh34xNz6bL5HOFrBbjBRLFSqyjFmjwyZfXFFrtRosGR0f9K/nyfAFCoqKKlRXtu1aB+sKBp4uldAIAjLiYlGvZsgLEuTEMo9bBolIOVAABdLWMoWtKdam6mhRHED12spki+RyJRLxEG8d68df10wuL9HaYmHzpkaOn5wilcqj02nI5kr4vFZ2bF1deZ3NFHn6R6eZmYxgtVdvjkcPj1Df6GBNl5/ZmRjhYApRFGnr8HHTbWt56kenUBUVnUEgl80SDIaq/mIphXA4TNsa92WLi/npKIefPU9gLkY4EsFkNrDv9s2grr5qHjw9Qz5bpGlZVLHRrGdqJMj8VHTVCOPGNjcPfGI381NRSqUKDpcZf5OTmdEQ6VSepvaLrU9BEHC6LQRn4xRyJbw+D16fB+iiUCjgNI9y8MdnOHt8ALPdiIBAPJJh+75eejevoVyqMDcWIhZKVWc2rS7W7Wyj//gk0UCyGuGhEejZ2oLZWj2vS4VxabdgsRnR6rTc/MBWpgbnmRpa4PiL/RjM1V11KpHFYjNe9DZLFzCaDUz0z3Hq1QH0Bi0mm5FsKk9kLk65UMbgA2HxdxddSODy21kYD1MplWlcRldPx7IsjMZo620hPVFBSegwGfUkYyle+M7rnDt2gT0PbMbn9+JyuW6YEJTNVu1x/q1d9h5hySJm7dq115Up/W60y5Z2IqlUilOnTuFwONi+fft1q3/f6UymVCpx/Phxurq6aG1tveqWW1VVZFmuzV+MRiPNzc00NzdTqVS4MDENpydBVSmUSkgaDaIoIApVZb5BknC8C9kQXc0e6pwmLozNUeey4nQ6iacLFMsV9mxoRZYVnj08wIkLMxQKZcwmPZu7G7AY9cRSObzOiz+geDKP323h36/bzx3pLg6GRslVymx2NnCnvxutqOFbh6eZyyURK5Vq60oFGQUBgQIVwlIOrSqCupi2qArIGoXTpgVaMg4A0tkCFrOeoaFhXnx5iHJFz+DIJKI4RUuzmwfv20idz07/4ALZXImtm5rZsqm5Flp2KYYHFpiditLc5qm1vhxOM7NTUTZuaWHX/k5SiRwGk47G5qr7clu7lzMnJ2lp92A2m/GqsDAXRdKUKMtpDh06hM1mqxl6Shodh57pIzgXoyLmaO7wYtJbOXd0ApfHxsadbZddH+H5RG1HsAS9QVu9xhM5oJo7k4hkkLQaHJ6qX5zJoqdz/codrqgREQUBZbEduARZVhA1wmU7PIPBwPa9G/C4vfQdG2d0cJJcPkvHei8GT5ETx04wdTZKdDaLIFQNWM12Iztu6+XOR3YSmImiVGR0Rj31LS5SseoNV67IK3ZguXSBpjU+zh8Z4+SrA4tqfQ25bJF8Kk8+U6Su1Y3OpCObzJNL5+nc2sLImSlMVgMuf7X1brYZKWSLLEyESeZKmDQ2UAQsDhOd21o580o/VtfK79/iNJEYCTJwbJzwbJy23sbaorCQLzI3HiAVypHLT9Lf318LaHO73Zc5Z6+GXC6HJEk/UaLBTwLvmyKj0+k4cODAdYui3q2ZTCwWo6+vj46ODjo6Om6ot/p2dzKqqjI3N0exWGT79u3X9D1bGu4vH/AvhyRJrG1rocPjIRUNUlBlBFVBrqiUF4fle+q8qHIFrhKhfD2Ix6L0uGWUio9otsJcOIXFpOfOnV3ctLmNJ145z2snx7Ga9NgsRjK5Iq8eH6WtwclCOMVMIIHJoCWTK6LXSxzYuQadVmKbq4ltrpWD6Xy+xJZ0HTNqgiwVNKoAIigacMckIuUYGKqFR1HVGtValhUCmjSFQpl0pkChUKa1Ucvrb4xTKOqq6ZY6iVKpwvhEmJdeHeRjH9nB5ksG41dCcCGBRhJrBQao/Tu4kGDbrnYamla2bbbv7iC4kGR6IopWp0EuK+iNWu66awObt7XWAr3C4eqsIzqfZ6Q/jMEm4rQ78HqrmqR8tsTQ2Rk27Fi5KBEEAYvdSGA2tuJ95Uq1IBtNOsYuzHHq9RESkTQajUh9q5vdd67D5VsZeQzgb3LirrMRnItT31LVF5VLFRLRDFv2dV4xkbKly0dRTSI5M+zYeTdur4tcLseRF88wdGYao02D0WLAYrFQyBQ4eXCQBz99M37FxcmDA0QWkgiigNNrxWjWMzcexlPvQNJqiIfTGEx6/E1Ojr/cj8liwLEocFUUlYkLc5htBuSKSjyYxmjWseVAD95GB+deH8Jd50BRlNoup77NiyKryMYcjWv8uOoctHTXY3WaOH94mHKxvOKzVUoVNJJIIpjCZDWs6DoYjHq0kg67ycmG3dWd3VJA29TUFBqNplZwlmY5l2IpS+a9JBb8a+B9U2SgerO8kQjmd1JkVFWlUCiQTCbZsmXLZf3i68Hb2cksDfgTiQRarfaqBWbJLPHSAf+ljxEEAb1W4r4t3QRezxAq5SgiI6tVEeUGl4M9DiOvv/76itXy0upKVVWGZyNcmAxSkRXWNLjZ1FFXu2kvvc/U1BTj4+Ps37WVD3m8TAfj5Apl/C4rbruJaCLLqYE5HFYjjsV2h9GgJRLPkEjn+eBtG+gbXiCazNK7xs+eTa30dlw5A+flg4MUTxTY3dLIgDtCWiwhVKA356Yn6GLQGkYlR0WuIAoigqCi0YgICOjLGuLJPCajljqPisehY3LSgN9vqLX0dDoJr9fK5FSEcCSDz3t9vXCDUYeySua9IisYr9CTd3ksfPCRHYwMLhBcSGK26Ono8tPcWnXcXQr0amioUtAPPnecfGEGs8tCMpGgXKrOckRJIJ8roshqlW48Ha0KLT0WejY3MTcZIRHJVGcyJZnAbKwW3PXak2eolGVcXiuVisLYhXlymSL3/+Lemo/XEvQGLXvuWMfrz/YxMxaqDcZbu/1svoLjsqqqDA0NEQqFuPnWfbUBttlsJhOu0NLehKfOTjabJZPNkKukmRmY57kfysTn86gVAV+ju6rWn42jN2ipa/GQjFRjBOweC5v2daJUFHKpPM3dF13KRVHAVWdDoxG56xN7qJRkDEYtgkakVCihN+oo5kto9RfvMflsAbPdgKPHTPf6VqYHAhx+/CRWlxmTzcDcSACDWY/RYqBSqhCYjFLX7kGr1zI7Erzss6OqaBevLYPBQGNjI42NjbW2eDQaZXx8vGbQulR0lgrLuxFYtoS/+Zu/4U//9E8JBAJs3ryZv/qrv2LXrl1XfPxjjz3GH/3RHzE5OUlXVxd/8id/wn333feuHMv7qsjcCJZb/d9o5a9UKpw7d45SqURra+vbKjBLx3AjO5l8Pl8jNWzevLkWcrYalorL0g9iOYNFVhReuTDOKxcmiKZztHkdfGBLF3dtWINGFHm+b4SFVAZJI3LH2nYe2bUBvVZifCHCD988x/nDY6hKhXV+G3dtXkP/Qo43+ucplKtECkkU2dJVz69+YAcGXXXAv3Tz2L59e43t11a/khgRTeTIFcrUe1eujC0mPfF0ntYGJ/u3tl/Xd5ZI5OgfWMDtsNAhGNkSq6cgVMgli1CB229fS9PMPGPCcRS9ilGjBVRKiowgqNxk9HLrXh+FQgqnw4nH28qxU0drBWYJOq2GZFmheMmqdQmKojI8sMBA3yzJZJ7GZicejxW9QUs0nMa1yKSKRTPoDVo6Fnv4C7NxZsbDVQ+1JifN7R5sdiPbd3dc8TPnMkVi4TTpTJJ4KozH66KhzoekFchks6QzGeYmorR0uzl9oo+hE0ESoSxyRcVo1tG5oZGt+zsZOjvD7FgYjSRS3+xi393rOX9sgkKuVJvL6AG9Ucv8RITp0SDdq0QVNHV4eeAX9zAzFqaYL2F3mWnq8KLTV6+Jsb5Zhk9PkY5n8Ta50DoqiEaFnTt3YjSubNtVnTEERI2I1WbFarNSV1ePWJomFS4QmI1g8+kJhkqYLWbcDRZC00laevy0PLAZuSJjd1vQ6iTGz8/CJe7ZUKUzC1oRo1nP8NAUQ6cmKeSK2N0WjCYj0UAcvUGHfk7VDgAA5C1JREFU3qQjlykQCyZp39hAMh3m4PeOUciUMNkMxAJVpwOrw0QqmmVuNEQ8nEKRVfQmHe5GJ5VyhXQ8i9VpRlEUIvMJrE4LdW2XsyuX5x51dXWRz+dru5yJiQm0Wi3f//73a3Hi7xTf/e53+eIXv8jXvvY1du/ezVe/+lXuuecehoaGVr3Xvfnmm3ziE5/gK1/5Cg888ACPPvooDz30EKdOnWLDhtUJKTeC91WRWVpVXw+WtpvXYwuzHEvsNb1eX+19vwP31RvZycTjcU6fPo3f76e3t5d8Pn/F5146f7n0hvydN/t48tQQAqDXSpyeXGBoIcJv3LmLO9Z3cGBtG+lCEZNOW3Nlnoum+Mtn3iKQyGAxGKjICicXskynBomHs2glAZfNjF6vR0Hk1PA8a5unOLCpjXPnzlEsFtm1a9dlN4/lsFn06HVV7YrVfHFFny+WMWglrObqTOhKBUaWFXK5EgaDttbm8nqrN3EBAaOqRWMUCIcz+H02dmxro2Hexf/X/woFuQwIiKLILZ52ftHWy/GjA6TSZXS6JD2dFSRJJRbPULfMjDORzGOzGa44f3nrjRFef6XquK3Xa5mbjuJwmeleV8/4SIjpiQgAFquB/bf20Nzm5uhrQxx/fYRctupIodNJrN/WzO33b1qRKbMERVE5c2SMM0fHCC1EyWTTrN3Qzpq1bqZGglisBrR6HWqpwpruNrbe3MyRFy4wNxnB4TVis9tQlApnjoxyy/2befjTNxELp9FqNfganUhaDbHQ2ct2WZJUJYhkk1VRczqeJTgTQxAF6ts8mCwGrHYT67ZdToA4c2iIEy/3gyCgN2o5+tJpRB088tl7MRqNFPMlZkaCpJM5zBYDdc1u+o6M4vLZEBfnOZlkDqvdgs1lQaho8TU5a/5q8XiceDjLwLkh6tbYcHsvRmv7W9zYXGYi83F8TdWFTqVUIRXLsvPOdZx9fYjTBwcxWQ0YzHoicwlKpTIOj5V0PEdkIYHeoKNjYyMaV4nMqTJyTqWx6+INOB5MoSgKG2/p5ugz59DqtHibXNUW67kZ9EYd2XSeeDiFAFhdFrbdvg6759q7YaPRSFNTE01NTciyTDgcJpvN8vTTTxMKhbjnnnu47777uO++++jq6rrhRfSf//mf85nPfKYWUPa1r32Np59+mm984xv8/u///mWP/4u/+Avuvfdefvd3fxeA//pf/ysvvvgif/3Xf83Xvva1G3rv1fC+KjI3gqXZzY0UmSX3gCX22sDAwLsSXHYtzM3N0d/fT3d3Ny0tLVUWzSK77NJV/bUKTDCZ4aXzYxh1Us0mxmk2MJ9I86MT/ezoaETSiCssZABeOjdKIJGm2eOo5cAUygbGAzGMWi0dfieFYpFsJktFrlAoKBw8OYghH8RoNLJjx45rsmX8biu97X5O9FdZbiaDjkyuSCKd59Yda7CvotVY+synz85w9Ng4sUQOo0FLb08dBr1ENlNC57z4/WYyRUxGHfbFAfc9DWvZ7m7mteAY2UqJzc4GnBmBb/zTQVIp0On1VCpFpmencNpFpudTRKNxHHYLsiIgihpuvbkb4yozhvFgmL8df5PpjRm0Gg3dBRfbMj6CEwmy6SIf++ReFuYSADQ0OXG5LcxNRTl2aASdXqKt3gFUdyh9x6dobHGzfuvluquhszMcfv4CZbkA2hJtHU2EZjNoWnXsvm0tYwMLlIsVOnvr2bCzvdqqK+vYvKMXRa2QzWbJZrMkkhkOPnsMR71EXYN/RevF6bUSnk+seF+5IoMKJquBc2+Ocuq1IdKJHIIADo+F3XdtoGsVUWg6kePC0XGMFgMOj4X5hQXsdUbEkp7BE9M4fQ5ee/wUgcloddEogN1tweo0Mz0SxGjWUyyUKRfLbNzbidGiJzAVRSNpsNvt2O12FFlhNDeNzWWpRWs7HI5aq3f77b0ce+ECU4MLCGJ1l9+6tp6GDi8vPXoEu8daS8S02E0Ep6PojVpu/chOivkyogST86MYtQ70ZDA2GJAkqfabdHiszI+HmBqYp1Qs072jrUaGKOSKROcTbL9rPXqjDo1GxN/iwWy/8gLsStBoNNTV1fH1r3+db3/72/yf//N/uP/++3nmmWf4/d//fcbHx2louH7ZQalU4uTJk/zBH/xB7W+iKHLnnXdy5MiRVZ9z5MgRvvjFL6742z333MPjjz9+w59nNfzUFpmlG/D1FInl6ZnL3QPeCTvsep6vqirDw8PMzMywdetWPB7PiucuPWapkFxrwA8wEYqTKZRodF3cVguCgMNkYCGRIZLOUue4fDXVPxPGqNPVCgxUTTIVRaWoymi1OrRaHVaLFVmukA/EiCdSZLM6xkNZnjv7IhVVQ29bPTdtbqPeffm2XhAEHr5jI4qqMDgRJpbMYdRr2bupjftuXnfF83Ty9DQ/fuYsqCpWq4FstsihN0ZwOc2k0nlUVcVk1pHLlkhnCty8vwubzcjcbJwzp6eZn4vjcJjYtbkJWx6+9/gbJFPQ1uZHr6v24BcCSRT0fPjhdZw+M0k4nESjkele48bpqJBIJLDbL0b4xoo5Pn/2RwTcmVpL5qhljjF9nPtSbczNxtAbtGy45CY8PR4mnyvhX5yDQNV2JRbJMD4UuKzIqKrK+ZOTZDIpdGZoaWrGoNdjtVYIzCXYc9tatt/UhSwr6BfjAUb6ZpErS//WYjQa8Xg8WM1p0qk0oWCYkeERsvEKRp2ZptZ62nvrmBxcIDgTw+mzUikrRBYS+JpciKLAkef70Oklmjt9sMhUO/z0GVx+G+5LnJJjgSSZZI6GDi+zc3OoikJzcwv5TInoQoI3nj7L/HiYxk4fklT1HpsbC1HX4mLdjvWcfXOUeCiFpNUwem4Wb6MTg0XH3HgYd50dVVWJzCeob/Gx/84d2F0W8vl8jbI/NjaGXq+na6+PYkpFrzXg9jto6vITWvRNa+5yrDhmq9NMKpbFbDVi85h45dlDGCQTTZ1eBHEOuSzXXEAAipUSWp2WWCBZY+wpsoICaPVaKhUZVVbp2vLuBIxBlcLs8Xj4whe+wBe+8AXy+fxVOwerIRKJIMtyLQFzCX6/n8HBwVWfEwgEVn38UgTzO8X7qsi8nXTMaxUJRVG4cOECkUiEnTt34nA4av8nSRLFYvHtHGrt/a+0k1ma+2QyGfbs2XMZ932pyCyxXa414F+CXiuhEUUqslKz5weoyAqSKGLQrv6Vmg06KrHUir+pqopOq0GsQK5YxqiTKJQr5PJ50rk8t+7uJqOz8eqJforFEqqicH5sjoMnB/nMA9vZ2N12GRvQZjHwyx/axXwoRTKTx2U34Xdbr/h5ymWZI8fGEID6xZW/1WIgmcpTKJTZuqWFiYkI8UQOk0HHLTd3c+CmbibGw/zo+ydJJHOYjDrm5xKcOT1Oc6uWbF6P222qzV8EQaDOb2N2Lo7dYeELn7+Xcrk6u4nFYoTDYc6cOQNQWyl/Pz5MWM4iKhfFqAoqYW2OQWuMdQn3ounnSlTZXJdDI4mUSpdfq6VShcnxWWS1TFNTO7rF3aJWJ6HICtl0AUmrIZcpMDMWQtJqqm0gk45MKo9lGWU5nynStqaRjtYuXvnhSWZGoxQK87xZPo+rwUz7ukais3lC83F0Wi2tPXXsuXMdF94ap1ysUN+yFPsr4G92MTkUYGoocFmRkXQaEFSmpqbR6jQ0NTchihoqpQqVikxwOoqnwVFrDWo0It5GJ/FQmkqPQiFXwuW3Y3dbKBXKTPTP4al34PRYiQVTVfv+Di87bu/F7rKgKArR2SSh6RSgp6upF71DIhaLUlQipEtJdHKFcAQQRXR6iWK+hGFZy7aYL6HTa8lksjz+F8+SjRQxGSxMHgtQKpSr7hBWQ+28R2bi+FrcaLQS8+MhpEWXclWpEi4UWQFRpVwu136v79RU81LfshstMO9XvK+KzI3iWqr/QqFQG66vlp55LSfka0EUxVXfP5/Pc/LkSfR6PXv27FlVwb90c65UKrUd2dJw/2rFdl2jl3qnhblYmnqHBY0oUqrIJHIFbu1tx2Fe/cLc19PM0FyETKFY8ysLp7K4rSa6PC76xgPEc3kKpQqyLOMwG3C4XbxybASryUhrnRsVlWKxxFQgzmMvnyI6P4nb7cbr9eL1emufUxAEGv12Gv1XjpFeQjpdIJ7IYbOt/G5sVgNTiRg93XXcfcc60pkCVosBs1mPoqgcPjRMKpWntbXquhyLxUgHCsRjRhStiCSt/MELi4PiSrla1HWLBWh5RPPYyCzHjgwzPXWeF3pDyCYFDSKKolaH1lQjqSf0ST60dj2GVVps/saqPUshX6r9f6UiU8yXaV1zMTNmYarqWhyOz6EzCEiCo1ZgoKr0lyQNFpuB4wcHOfPGKOlkHlEU8NTZcfmszE1EKOSqzKlUPItWK7F+eytvPtNHaCZG+9omJK2GYr7M9OgCpYxM734vwbkoNruV9q469BYNmVQenf7yW4EoVltDSygVy6gqOH0WyuRJhnJs3LEWUdRQKpRJRNJ0bm5hdjR4mbuApNVQKcsMnpxCVdVa6qXBpENn1BJdSHLzg1swWatZOQ6vtRq6Jyu89cwZzh+pqu8BdHqJDfu62H3fFtauXctY/zSDp0c5GxlB1Kvk5Dyh8xFauhqwO23kUlUPs403d/LUt18gNpOhe0MHJquRQq7IzHAAVJXgZIRCtkgslKrunu0m3A0OKmWZbKqAxW5CpRobbfdYaVxTXf0vX+gu2SK9nYLzbljKeDweNBoNweBK9lswGKSurm7V59TV1d3Q428UP9VF5mqq/yUlvdvtZv369avqb94pDVqj0VwWN7A04K+rq1vhGn0plgpJpVK5oZWQXivx2dt38tcvHGUhka69Vk+Dh0/s33jF5x1Y387oQoyjwzPEMwVUwGrU8dH9G9nY7OM//t/nKFUqaEXw2K2Iosj3Dp1DKKt0NVRXuAICBr0ev9tOToGNm7eSSSWYnZ1lYGAAu91eKzgmk+m6dqYGoxa9TqJYrGBeNpguFquOyUajFrNZj3nZqjSdyrMwn8Dlqv4gw+EwmXwBo9vJbCxLU5Oz6mxsN9aOIZXKYzLqaFzWxlqOuZkYzz7RTzSSxmA0QuUie0lRVGRZoWqjoGIzGdhzU9eqr9Pe7aNnYyMDZ2fR6jRkjBXSlSLrOuvp3VSlGB986hwLM1Fi0TgGs5YNW7uYn4wSmI3jcJspFStEQ2nW9NaTzxQ58kI/BpOOpg4viqwQmIljtRvYdnMXUyMhSoUyja0eNu3pwGTRszAdxdfkQtJWr3m9UYuvwUUmXGHLpq1IO0XC4TCBhSCTkxNEUnGCgRhmpx6zxYwoCNVkTBVcPhvJSJozrw8xNVjNcJK1BTo2N5ALqCwsEh9EjUDHhkb23ruRl76bJxpMUr+MaRUPpbA4zZTypcsEowajDrkik0sXaOleeWObHV6g741h7G4rlkUT0EwyR9/hYeo7fGRTeY4+e458poggVjsDZpcZyS8xcmEcuaJgdVjoWN9EshImGyzR3tWCaZFibzDpaWj3ko5nWbtrDSdfvoAoinibnSSjGaILCYwWA5l4llggAYDNZWHXPRvxNrhrM5wlJuhSuxsudiSu97f9bhQZnU7H9u3befnll3nooYeA6jl5+eWX+fznP7/qc/bu3cvLL7/Mb//2b9f+9uKLL7J37953dCxLeF8VmXerXbY0aL+Wkv7dKDLLn790s72WseYSRFFkZmaGhoaGG7q4ehu9/I+P3cWJ8TmSuSINTitb2+prTLLVoJU0fPbunRzY0M7IQhSdJLK5tZ56l5Vnjg2SzuZodRlxOhxoNJpF7UwUeTGzffkpVJSqHmUumOPU2SDBcAqX00iXVosmFmNsbAyDwVArOHa7/Yo/MpNRx8YNTbz2+jA6nYTZpKNUkgkEU6zp8NKyyB4qFMqEQ2n0egmjSYu4aGAZDAaJ5EqE8gqFWJRKWaYQFtAJIhNTESxmA+VSBVWFfXvXUOe/fJakqipvHBwmHs3QuphVskEpcphZFFR0+upAWF5sbe6w2Jidm6BQ9OB2u1cQTyRJwz0PbUXfYuCvgkeY02ZAgDc1EfIzWvTPZYjMJ1ClIp4GK3qNmcmhEOu2tRANpUjEqjuSjTvb2HtHL6/++Ayqqtb8w0RRQ0Obi9mxCA6PlT13raNcrGAw6RBFkemRIJWyjPaSnYlWL1EqFCgWysxNJDh/dIxEJIPFbqS5qZlUoET/6TEMFgmDTk+lKNC5oRlvk5MXv3OU2dEQFoeRUDhIOadi1Tu5/aO7SUXS5HNVenNDhw9Jq2HrgR4O/ugUMyPBqr1+pohOL7H91h76j08QWUjWhvJQ3SGJorjCjXkJsyNBlIpSKzBQHeTHAkmGTkwQmI6ikURae+uBqqPB7GiQ3fduouVTdUSCUXKlDOligkwsTzaTI+fIoi9q0euqoWt6k554OEU8VG3VbdjfXXPeziRyZJI59j24FVEjVkWs7V6si9EMS9f18nyqJeLOUtEBaoXmagUnm81eU5h9PfjiF7/Ipz71KXbs2MGuXbv46le/SjabrbHNPvnJT9LY2MhXvvIVAL7whS9w4MAB/uzP/oz777+f73znO5w4cYKvf/3r7/hY4H1WZG4UqxlcDg8PMzs7e9mg/Xqef6NYzhC70oB/NSytfHp7ewkGg7z11lsrbsoOh+OaBdduMnDHhtVFcVc+XoHeJi+9TRcv5EKhwOkLQ6iA0+EkWSySyFd3OoIWlLJKKJHB76wKNyuyTCpbpKvBxXd/dIJCqYLZoCMcTTM+FeOhezdx662ba+r1s2fPAhdnHU6ni5nZBKFQCoNRS09XHbfe3E0qlWdwOEAkmkWSRNrbPXzogS2IosBbR8d44/AIyWQeSRJpa/fi8Zg4eXIMu8dIKK9QURS0qoDFYsDrthBL5mhodaGpqFgtBjZtaGLzxqZVz2smXWBuNobDdVFtvSnrZcKQYE6frmpvRAFBFLnN18Gn2/YTi0RrrCeXy7UivEyV4K+Kxwjos2iq+aLk5TJ/P/kWW7HRrdVhNBrw+arpjnOTEXLpAh/9zC2k4jm0Ogmbs3pTTSdy6I0rWX2iKIIAhVwJrVYik8gzNRRYbDNZsNiNJCIZ3MsKaiKSwdfgYG4sxGs/Po2qqFhsRsJzcQLTUTbtW8u6rTITA3Pk83lsbXqMDSVefvo1Rs4u0NRdRywaxVPnwuPxMDMSZG40yNYDay87nx3rG9EbdQydniK6kKBpjZ+erS3VXYoq8MoPTxANJrG7LJSKZcKzcVq66mjsuPwGK1fkVU2wRVEgFkqRjudoWXtx96PVSVgdJibOz7L77o3YfWZOnDhBW1sbvh1+Iv3PEAslSGdTaDQazGYzlXw1biAaSGB1mVdEO1gcJiKBBKgqvTuvrHFa/t0sn7cuZ4uu1larfZ9Ui0x7e/s13+Na+NjHPkY4HOa//Jf/QiAQYMuWLTz33HO14f709PSKQrdv3z4effRR/vN//s/84R/+IV1dXTz++OPvikYGfgaKzNJMpFwuc+bMGQqFAnv37r2uncG7UWQqlQqnTp0im81e830vVfDX19fT0NCALMuX3ZSXCo7bvXrM77uBdDpdbSnaLej1OSbjCSK5PMqiVkmWVbwWIxqNyFQwUd36A631TorxIrKi0rLoeuvGTDCc5pXDQ2zd2ITP58Pnq1rfJ5NJwuEwAwPDHHpjlnCkjCho0Ol0+Hw2fu6hbXzswzuYnYsTjWexmPS0tbqRJA3nzs7wzNPn0GhEPG4LpbJM39lpNFKJxiYHU+E86WwRg6RBb9DS0OjEajGQzZcRtBp+89duueZ5WFphLtdoSarIA4EOTpTm0W8347SbOeDr4FZ/J5Io4nG56e7uJpfLEQ6HCQaDDA0NYTabGdIXmc8l0Qhijc0nChpKSoULzSm25lvwuC+mOxqMOpKxLFqdhNtvI5PKEw0ksbnM1DW7CEzHcPsv5piUSxVEQcDuMnHi4CAnDg6SWdS62J1m3PU25sYizOcjGM16sqk8OoOWDXvWcPLVATSiiL+1ukO0uy1EFhJM9M/x8d+6m5vurxZ2rU6iVCrx7LdfR1YqhELBxXOkkMvn0GpFQrMxKmWZkTNTjPXNUMyXae7207ujg8YO76pFo2dbC4VckXNHRglMR9HqJDo2NHHT/ZuQtFUm2sJ4iMh8HK1Oi9VlRlWrg/slK5tSoYwiq7jrHEQDycveQxAEVEWtXd+NjY20t7ej0WjYd892Dv/4JBpJg0YnEAvGSSUytG7xkQmVUIpgdZvQaavvtRQ/femM6XpwrV3O0r1raXfzbtr8f/7zn79ie+zgwYOX/e2RRx7hkUceeVfe+1K8r4rM222XZTIZTp06hdlsZu/evdetm3mnRUaWZRKJBE6nk717915VR3Jpv3b5gL/qZHzxppxIJAiHwwwPD1MsFlcdrr9ThMNh+vr6aG9vp3uDhzcmn2M2nkCn0aCTNFRkBVGjUkZlz9Y2MskCw9MRRFHAZzcxNJ2kzrmSKu10mIjEMiwEU3S0emqf0+Fw4HA4mJgqkUjO43KaAZl8Ps/4RIZ/+laGz/27W2lqdNHcdNFFQFVVjh+fQFFUGhqqq3JZraCRipRKGu6+ZxtvXZghdWaSercVq81QG+hrJZF0rnBd56Jq8eLj9LFJzBY90qK1fDSQYauzjk/vvHVVHQ1ALJBl+EyERDiLy+fE6rEymbmAsHj8Cst0UCqUjCpG20W2naqqZNMF2nvqyKYLvPn8eUbOz1EuVrC7zLStrcPmNDE7FsbhsSBXFBLRDB299QgCHHn+PDq9REuXD1WFyEKCwHSMbQd6CM3GSMWydKxvZP2udsxWI6lYtub3tQSHx0JwJk48nKJhmeuyTqfD7rJSKBRpdPkxGU1ks1mi0SiBmRjmeonHv/EC0+eDaHU6JK2GqYF5Js7P8YFP3YTNdbnAVRRFtt7SQ/fWVhLhNDqDFk99lTpeKpQ59IPjDJ+eoFys3oAtDhM2t4XAVAStTkKgysjr3NLK5pt7mB0LkoykcSy6TMiyQjqeo21DAydPnKQSFzh3dow3k334W9z07u7k1o/s4sLRUTKJHL46H5v2rKNtYwNjg9McfeoMhcEcJqsJk8lEIVHG5rLUhvzvBFfb5SzpW9asubHuxE8D3ldF5kYhSRKpVIqxsTFaWlpuWB37TopMLBZjZGQESZLYvn37NS36l1vEXO2xgiDgdDpxOp10dXWRzWYJh8OXDdd9Pt91xRGshpmZGUZGRli3bl2NQdLV7GE6kURdzKDRakT8dgulisybw9NUkmVyhRIajchsuCpGNBgk/PqLN6xKRUYjimi1l6/6KhWZM2dnsFqNeBdV0bIsk8vlmZuL8+xzb9LedpE44HK5UFWIRjKYzdUbfDabJRaL4/G6iUYKFIsVdm1v5/xkCMuyAqOoKtlCid2tq2sYouE0x98cY2QwgKTVsHFLMzt2dRAJpZmdrhpMqqqKw2nmjns3XLHADJya4qUfniKXKaDTaxnrn8feZ6bpPh9qepKlb7m6Gq6KEvWyhsBYFI/fgSSJJKNZzFYD67a18OL3TzBwehqnx4LVaSIRy3Dq9RG239xNIpImOBdH1Ihsv6WHHbd2c+ylfkrFCnUt1cIsCOBrdDI1FEAUBR7+zIGa5gogGcsg6aSqvf2yz1QqVJB0GnQGLYHpKBeOjhGYiqDRC2RKcTx+F0pewODQYzAYEMoSza1G/PVejj3fh6BTsZhM6E1mvC47s2NB+o+Ns+feTVe8Bs1WA+ZLgtkGj49z4ego3iYXJquh6mE2HQWK7L5nE5GFBAAtPfWs2dyC3qhj883dnHipn3Q8gEZbZbl5mh0UtAlKkyoz58JIWg0Gk57hU5PMDAe48+f3cd+v3Mrrj59gvG+GC0fHGD49hb/Fw9Z9G5gbC5JN5EguhBG0Kp61JsZnRvEWvO9aHPPyXU6lUuHzn/88Wq2WT3ziE+/4td9v+KktMqqqkslkSKVSbNq0ifr6+ht+jbcrxly64Tc2NhKLxa6rwFxNYHklCIKAxWLBYrHQ3t5OoVAgHA4TDocZHR3FZDLVCs71xMIuzY4WFhbYtm3bCs2Qx2bCZTXiMBurAVE6LRqNyHwsxWQgTp3ORJOv+nhFUbmQCTAcjmPS6UimC2Ty1byW9V11NNY5LnvvUlmmWJLRLSMnaDQaLBYzJnOR3t71rOmwL7bVBiiXy7hcLvQGCIUKaDTVSAaPx4MkaRHFIjabka5uP91tXvrHgpiNWjSiSDpbwOuycNP2y3vo8WiW7/7TEeZn4lisBmRZ4fknz9K7sZEPf2I3E6MhopE0JrOerrV1eFdxKIZq++bNFy9QLsu0dPpr53d2IozvLQO2Hj3pchEUtdp+XNy53mZswOotE1gIoNPqaGz1sv+uTWg0IuODC/ibnBgXmXRGs575qShzUxE+9hu3UsiW0CxGKkPV9l7SrhILrhHIZ4tUyjKj52YYvzBPRZZp7a6jod3L0OkpdAYteqOOUrFMaD5O1+ZmCtkiz/3zGySjGSS9SGA+iM1mo3tzO9lkntmxEABWh5lb7t1eDfSy2Gnq9pPL5chms4TCIZKZLMcOnqZ1c3WxkAimKeRKOL02bFew7wEYOjGBwaTDZL1oP+RrdjMztIDJZuTeuy9nT26/fT3eRheT/fPks0UcfjPJcog6fz0njw1hdZhxLH6HTr+d2eEFzh4aJDwf58Kbozh8VjyNzqoNzvACLT31fODTB0iGq5k3jZ0+NIZq1tXMzAz9/f1YrVY8Hg8ezzuPY5Zlmd/6rd/i+PHjHD169LpiTn7a8FNZZGRZpq+vj0wmg9/vf1sFBm7c4HLJZXZubo5t27YB1bbT1R7/dgvMajAYDCsyZKLRKKFQiFOnTlVpl4sFx+VyXVb4ls5ZNpvFVtfOS2dmEIQZ1rf76Wn2sqHFzyvnx9GIAobFlly5IlMsVZAUAbf94q5JFAUavHbmQ0lODc0hV6rnUJI0zEfTnBmYY9v6lbb5RoOWhno7Q8NBbDZD7VxkMkUMei0NDU70eiORcJTxUQFFFSnkC3i9IsNDCdIp8HodFEsywUCG1jYPnV1+dFqJX/nwbl45OsKJCzNUyjK7N7dxx96uVXU6p49PsjAbp6XdXTtHxUKZ4f4FtuyIsfWSnJYrITiXIB6uDtOXIAgCbp+N5Fya39m5j6/MHiSrW2yPKipdURuf3r2ftlt9zEzNEwyEyBXTzIaGyYRVMqksdZdEBFhs1SF+qVip2q9Mx8im8lidZqRmI7OTOSwUsVEtTJWKjKKAu87GKz84Qd+R0ep3phEZOjlJ4xofLV1+AlNR5IqMKIm0dNex/77NHH7iFKlYFneTjVAoSPemdip5iM4nefBXbyGXKaCqVe8wq8PE2derhJHliyGA6co8BqOO/rODnH5hkGykhKSRcHodbLm5l90f2LzqjKOYLyJdYmAqitWI5kp59cVg1U6mgda1DaTTaU6ePElXZydCVksmmaWle6Uli8NnIzQdIRpIYrIZakXPYNJT1+ZhYSKMViex5UDviufZbDbWrFlDqVQiEokQiURqA/SlgnMp0/BaUBSFL37xixw6dIhXX331Z7LAwPusyFzPTXh5VHFzc/NlOpUbwY04OZfLZc6ePUs+n2fPnj2YzWYSicSqRWppwH8ti5h3AkmSakJCRVGIx+MrdgFutxufz4fH40GWZc6cOYMgauiPSbxx6C1K5Qog8NSbg9y2bQ0fvW0T2zoaODk+D9lCTR/S5LKTDucue3+dToMkiZhNEi6rEZ1OwmbVE45lefyFc/R2+jHqL86oBEHg5v1dzM7FmZmNY7UaKJUqFApldu9sx2Y18M/ffJOJiXBVXa0oDA0KNDXp2bbdx8xMkVg0i6wkqau3sG2Hm3w+g05nx2rW86E7NvDAbetQFXVVE8olTIwGMRh1K4qw3qBFUVQW5uKs23R9mTKiKCAIVeff5Vj69/gzw+wcEbDtbUIxafAXjORG07wR6aPtt+6ko7OVjs5WZFkmmUxy7vgwuXyOsdFxLDYLZlN1JpDPFnF6LBSyRZ751lGmhwOkK0WOb0sz4ynALnhRjdIdtXDLlJd8pEBLlx+9QcuFt8Zx++21nUGV3hvijo/sYNed68kkc5itBhrX+CgXKwSmo+jMVSGf3+/HYrGgWlRmhgNkknmauvyMnJ7ijadOo9NrcfqsmG0GYoEk7kW3hkKuhCoL7DywmYm+Gchq8TXaKCtFQsEgT/3zPNFkhL33brtsMdTS08CpV/px+u01hlc2lUdn1OG5gr5pCYl4gud+8DLluECsr4TBrEOuKJRLFXSGi9dhuVhBEEXKxQrGS9p1eqOOSlkmn7nyLE+n062IZEgkEkQikVX91a6mFVMUhf/4H/8jzz//PAcPHqT1Cq3dnwW8r4rMtRCLxThz5kzNyXhqaop8Pv+2X2856+NqDK5cLsfJkycxGo3s2bOnNuBfbaZzJUHWewlRFGvZFD09PWQyGUKhEFNTU1y4cKG20sxp7Bw6dwqzQYfXYUZVVdK5Ii+fHGF9m58vPrCfgxcmODY6i6wobGtvYGdHI1/551eJJnN4HVWKr6KoRJNZNKpAS50D6zJ9g9dlIRTNMD4dobPFSzicxmCQcLstdHf5+fmP7ebNI6PMzsVxOkxs39bK3t0dHH59mPHxMI2N1YAqRVGYnQkyNJThNz53Fz/f7iMcTiNpBERNdTW5ZAWznIl3tQIDYDTpqFRWLgyWFgVa3dV/DqqqUipWkCQRf7MTb4ODwEycpkVtjaIoRIIpjA6Bhdk4nZ0tOFQbVEMeMTdoCC8kWZiOIUkipw6PMDMWxmw10LuthU3bupgYCiAoGpLJFNPj81RK0LXVwwuPvcXY+QXqmly82h1n1rjsRijAiDuDIIj8+0072HJzN+ePjlEuV2oFBqr0Xr1By9RQgO23rVypK7JCoVC1oO/oaauxnOSKshhYVubpb7zGRP9ctQMgK2j1Er4mF+lElqmBeRCqu9nene3Y3GbmRoM0rqmrscIaGhqYGV1g4swctiY9siKvoH+v29vJzPAC0wNzWJxmKqUKpUKZDfu7qVskJOQzBcKz0SpZptWDVledy373a08SGUljNplIavPkM4VqDk1Fpn19M5JWQyqeITAVYcfd64mHs4Rmoiv0N5lkDoNJf11OyrDSwr+7u5t8Pk84HK4VHb1eX9vlOJ3OFfebP/qjP+Lxxx/n1VdfpaPj2tTon2b81BSZ6elphoaGVggdr6b4vx4sd3K+UpGJxWKcPn265ty8fOV16UznRgb87xUEQcBqtWK1WrHb7Zw7dw6n04miKDxz6CTZbB6boTpslCQJm9lAOpfk1PAs23sauXdLF/duWalm//CBDTz60hlmw0k0ooisKLjtZmTKiKJAvlQmnMiSKZSrfl6yQt+5WZ7+wRkSyRySJLKm08cDD2ymq9NH5xovlYqCRiPWVqwDAwsYDFKVxlqRiUQimM06VFXL/GySdeuaaKl5a1WtMBRFIZlMEgyGOH3yAqVSkcbmKkvP6/WuOqBdt6mJ4f4FMqkCFlt1uBwNpzFbDHT2XNlGY3okyPHXhpifiKDVS6zf0cbuO3o5+MRZpkaCNc2UxiDT0OmkkhfR6VcSBkRRRFVVAjNRjr0yRDKaxuIwkUnmmR0P07u1he5NzcxNRKCkp67eSluvB7ND5PDT5zCatcwA06bsZcenCjDiTrPu9i7shkWtxyqpGYqqImqqxzrZP8/ouRnymQJ6uwiGEnqNCa20RN9VCE5HcDc4iAVTjJ+fpbHTXyvGyUiaaCDJbY/sopirzoC8jU6au+sZOzeNXJFXJGiKoojLY6dcktm5fRdTQ7OcermP+YkjaC0aOre10HtbK95xJ+HpOAazne5tbazd2YEoilw4PMTx586SDKcQJRFvk5st9/QyNDpMcipPfZO/1v5SZIXhUxPIFZnZkQXC8wlS0QwGi4GhU9N4G10ossLCRBib20IxVyIdy7Jxf3ctPuBGYTQaaWlpoaWlBVmWicViRCIRBgYGKJVK/PCHP6S9vZ1oNMp3v/tdXnnlFbq7u9/We/004X1VZFZb8SuKwsDAAIFAgO3bt+NyXbwA3g2dC3DF15iZmWFwcJC1a9fS3Hy55fmSMn6pqLyb85d3ipmZGYaHh1m3bl1tZvXSUIqZ5FytRSMKIjpdNeWxULyyB9ytW9dQ77HxVv80sVSOVr+TvRta+fYPj9M3GiBVLFEqywhi1ZdLFARefH2IOqMRh91IuSxz7swMmXSBz/76AbRa6TIGmihWbVzKpTKRSASj0YjdbiebqeabrAZRFEnFyhx7eZ75mRiyLDPnK9OxIY2oHcRqtdZ2OUspoBu2NDM7HePsiSli0QygYrEaufWe9dQ3OlY/l2MhHv/HN0gnctic1RbWwSfPsGFnOx/5zM2MDy6QiGaIp0L4Wqzs2b+LH6XeYHokhNGir10LkUBVgDg1HCIZy9Dc5a/9XzqZY3xwgU98/nZEsep95vbZsDpMTA0HcDomcfrNDJjjV/yeFFSeefkEm6z12D3WmqeZbVGdXsiXqJRl2tc18NbzfRx9ro9KSaZQypOMp1jT20rbWhuh6RiyooCq4vLbOfDQdg49cRLTooHkEuweK1MD8xRzRbbdttJl2+ayoDNoyaULK3ZT6UQOf4ubyf5ZXv3OUQrZImaLlWwkx+DLUwi3iZjqtTQ12hd3OA5EjcBU/yyHHjuKIAo0rPEjywozw3OMDI6w5bZNSEgrSAWiplqEJK0Gm8dCZCFJ67pGvI0u8tkikwNztPTUIwgCyUganUHHnvs3X/Y53i40Gk3t2lsiKb3++ut8+9vfZmRkhO7ubr797W9z//33s3v37vdMC/d+wPuqyMDK4LJSqcTp06epVCrs27fvMlfSd1pklqy9L32NpSTI+fn5ywrbciwVqSWTS1VV/9ULjKqqjIyMMD8/z7Zt23A6Lw6SN7TXc3Z0AaPJjNVipVwuk8sXKJaKKNkIfX19tdbFpQPMnmYvPc0rxXUP3L6eY4OzZPMldJIIioBBKyHLCqFigbVNbvSShMGgRa+XmJ6KMTwcZP36RsLhNJMTETQagc4uPxvWNzI8tECplMbptGO1Wkmm8tW0yTWrW22EFhL88J+OEI9mcXotqIrE7GSKSlnDx39tP6VKlf49MTGBTqer/ejv/eBmNm1tYXaxbdXe6cO7it3MEk4eGiEVz9LS6at9t2argeFzs2y9qYvN+zo4ffo0LsnP5s2bkSSJfXevJx5OMz0SrLK4CmVMFgN77ujl9WfOYXOuzHK32IzEQ2nC88nL4o0dHitmq5FKQaHJ7QJiVzzWgWeHmStP4vbbaen2MzUUIBZKA1UroN7tbfgaHfzg8ZMYTDpUWwU5VWBdRxehyThdG1vZddcG4qEUBrOett4GrA4Twqre0tTU+IqiMDMUYH6sarTY0OmnbX0TA8fGcXqt6Ew6UtEMAL2713Di+T6UikLL2upg3ouLhYkQsdEcd37oA2Tz1e9uZGSEvr4+Jt6cJxaJ07m5vUpVlkuoxgrENagFERAum60qsgJaDbFAkqbuepyLLDODWY8gQCKc5uO/8wFEUURv1K3Ydb2bWGpX22w2wuEwr7zyCgsLCzz99NM8+OCDPP300+zZs+c9ee/3A953RWYJqVSKU6dO4XA42L59+6qsjWu5MF8PLm15LQ34l5wDrqZFWVp9lMtlJEn6Vy8wsixz/vx5MpkMO3fuvEw9fNOmNo5cmGJsPlrzOSuWFTZ1NfPRe7eRTScYHx/n/PnztV651+u9zL16CXVeGzabAc2ieFGn1WC3GpifjZNXZeL5Ih6TSC5fQhQEZEUmFs3ywvPnOXxomGy2CAjYHUa2b6/HbldJJiXi8TKxWBS9XuKmm7poWyXSFuDs8Sli4QzNazzLbv56ZidijPYH2HNrT81RYcnS//z58yiKgtvtpr27WlAvFdHKskJ4IYkiK3jqbMxNhLE5Vg5xjWY94fkk81Nh5iPjmM1mNm7cWFt4tHb5+chnbuHCySnC8wkcHgu9W1toaHVz9KV+cpmVEROKUr1BrjYXsrvMbNjVztEXLmAo6/C69ESMRdRl3VhBgYaoFpuoIuhLzEwuUFHK3PfJfQRnqsr8xnYv7esaGDg+TjaVx+zRkk1naWioR6fTYXWVmByY57ZHdrFmYxOR+TjR+TiVUoXOzc289qMQ5VJlRbvMaDFQ1+rh4Pfe4uyhQUqFCqCiN+pYt6eTHXesZ/TcNJl4VQS65UAvTp+NRChVIwssweV3EAsmSYTS+Fs9uFwuenp6yGazjL/yQ2RBZnJqEknSUqmUcTgcZEsFLDYjVpeZyFwc72Krq1Qok03l6djYzOCJCdyXEAfMdhPh2Ri5VIGGjrcXv369UFWVv/7rv+bP/uzPeP7559m1axcAn/jEJ2ru6z/LeF8WmUAgQF9fHx0dHXR0dLxnBpdLr7E0pM9ms5w6dao24L8aHXGpTSZJEidOnKgp9t8pb/7tolgscubMGURRZOfOnas6A9jNBr70sVt44fgwJ4fmQIBda5u5a2cXdrMB/B46OztrVimBQIChoaFa28nn82E2X1yBC6KATiuhd2qxLUYro6potRpy5QqJVJ656TilkoyKiqjCyGiQoYEF9HqJxmYnqqIyMxPh2WfD/OpnbkVVJEaHgyRSeRoaHezas2aFOn50LMzZvhmy2SLTQ0EE7crCLooiggiR4MXsnEtbF6lUinA4zOTkJBcuXMDpdNb+Pzyf5pXHT7MwEwdVxVNnp1gsI19CoVXkqkXI5PQ4W/Z20dPTQzycRqMRsburrTlfoxNf40pKMsD6HW0cfPIsZrsBo0lfnX3MxHD5rLT1rK4s33fvRgwmHeeOjnHHeQ+vbIwSMl0c/tenDDwcb8PQqqFQKKA3pJkanePM2VN0b2rD662rFtTFApFOp8Gop6GhoVZkVVVFEAWyyRwHv3+c8fOzlPIljBYDHRuaaO2pY3o4gEYjLg7+tey8ewPZRI7TBwdweC46JafjWc6/OcJD//5Odt67kWK+jMVhQquTiMzFECWRSkVm+VUqV2Q0GhHNJa1Us9lM96ZO0sEcnnoXgYUF9Ho9iXiSWChJXs3Qs6+F86+NM9Vfva5FUaBzSwtbb+tlon+OXKqAzntxMZFL5dGbdCsG/+8FVFXl61//Ov/jf/wPnn322VqBWcLPcptsCe+rIrPU6pmcnGTz5s34fFdfYbzTwT9cLFTRaJQzZ87Q2NhIT0/PVQvF8gH//v37a/ThU6dO1W5oPp8Pp9P5Exn+ZzIZTp8+jcPhYP369Vd9T6fVyMdu38zHbt98xceYTCZaW1tpbW2t6QJCoRATExOogoTWaGVNayON9T7Wtvk4fmEGyyItWAVErQZBFQgupNBqRLQaDcViGUUj8uaJCSySRF29HVRIZVLo9QqKoiccKlBXZ2d4JEgslmWgf57jRye49fa13HrbWl45OMgTPz5DoVhGr9eSjOeQixXcfhsmw8UbpaqoWK9w8xAEoRbx29nZWWMEhcNhTp84z8kX5ijmVOqaXOh1OgKzcYq5EoIAZmsOi92IIitMj4eoCHk27tiJATv/8r9eJDhbnR+1dPm55YNbVxSYUrHqt2Uw6dh+oJvQfIKRvlnClcSipb6Vuz6yA9MVIqq1Oondd65n683d5LMlvmjWMZyPMhIKcubRczRixWiTQKgOoPU6PaW0Qu/adVjsWkYGxnlt8gh2hw3JoEHUgkG01ApMpVQhk8ixcV8XB79/nPNHRvE0OvA0OEgncpx7Y5idd29k3e5O5idC6PVaWtc10tbbwIvfegNFXumUbHWaiQWSTPXP0b6hiVggyfk3higVyvha3PiaXUwPVI03NVKV7BGei7FmUwvuegeZRJbRUxNE5+NYHGa8LW50Fi0Xjg3S2tWEyWQmmojRu62bto3NpLJJ6rdbKMSMmAwm2npaWLttDXqjjrU72jn+Qh+CKGC2Gcml8iTCabbfsW5V65t3C6qq8o//+I98+ctf5qmnnmLfvn3v2Xu9n/G+KjJQ/WJWS5JcDTeic7naawSDQebn51dEM1/t+JbrX3Q63ap6lQsXLiDLMh6PB5/Pd8NCretFNBrl3LlztLS0XHXX93axpAvweP384KWzHDw+QiY3icBZ1jZZ2bm+mfEZPfOhFKJYpTfbbEbsokQom0QAZEXBbjdR32BjaDiEZKkaGCYSCcqVMl6vl/m5JMFAksOvj5DLFfEvaiXi8SzPPdNHLJLhiafOUiyU0UoiFV0Fp93E/EKC/tEgW9c2oKgq4YUkdqeZ3k3XJ2xbzgjKhc5RLszjqjOSziTICAIGq4FsRsZXbyefLREPpylXKihikXs/tguv089jf/MKqXgWl9+GIqsMnJgkHs7wiS/chVyROfLCBYbOTKGq1Tba3ns28KFP7WN6NERkIYnOqKVjbX2tMC7FDwcmI0g6DS099bUMFp1ei25Rf7RBX8c6m4+iM8TU0EIt8AsgFkphd1lo7Wqg740R+l6ZIRnLkMlOYXHr8bZZCY6FCQeiGAx6tFodbb0NtHTX8cQr/XgbHZgXBbh2twW5LDNyepL9D2xh476V7MNKWV7hXLwEQahSn48/f443f3yKwmLmC4KAv9WDu97J7EgQoSrppL7dx00P7yS2EOeZr7/M/FiwGlxWUdBbtej9Emu3d1FMlklH0zjrnKzb3UNrSxsWh4n8lnxNKBmOzpE6HsHn99G9p4VKRWbk1BTB6QgGk55tt69j/we3vY1fxPVBVVW+/e1v8/u///s88cQT3HLLtY1af1bxvioygiDQ09Nz3Sr85eyut3NzVRSFYrF4zQH/Eq6l4L9Ur7LUlhkbG6vNOZbote+G0eXs7CxDQ0P09vbS0NBw7Se8Azz24lmePTyAQS/hctooFMpcmMljNMW4d5uLvvEYubKI3+vkwM4efvDYaQyqBpNRh6gRMRq1i4mUGvKFEtFoFACP27NouwK5fIlUMk9Ts7N2bt1uC5MTEZ584gzZYhmbRY8oCBSLZeJRGafTRC5XYm4mhkYUcfus3PXBzfjqHciywvC5WUbOz1Epy7R2+Vm3vRWjafVzn0rkMRgNtbZasVgkn89RkYvkKyn23tVDNlEmkYqx/7btdHS28eL3jpGIpmnpqqsds8liYG48RN/RMYbOTDM5tIDdZUEQBc4dHWN+KsrHP38H7WvraV+70q1ClhVef/wUp18bIJcqgAAun53bP7qLnu1tlx2zKIrsuXs9kYUE08MBTBYDxXwJSSux/75NTPUvcPiJ0xjMOvR2EcFggqIGg2rj4V/fycj5SeKROGaXnjXbPYSCQfLZwmXiR6PVQDqaIZsqYLQYyGcLpCIZTFYDzd31nH9jmFKxXCuASymWZruJt545g1Yn4V/vqf3f/FiQfR/cxq4PbCaTzGJ1mGnb0ITJauTZv3+Z+dEgLesa0WhEcrk8g6eGaJAa+KX/9iBvPXWac68NEJ6J8Oq/RDj9Uh+3PLKH3j1d1PnqCA4kmH0jQSKWwuqfxdNpxeozsfn+NgxaM81tDXgb3LxXUFWVxx57jC9+8Yt8//vf5/bbb3/P3uunAe+rInOjWB5hfKM37aVogEqlQkdHxzULzNLu5Xopype2ZZaMLufm5t6x0aWqqoyOjtZyc6517O8UyXSe10+NYzRocSwlCuokRFFgaDbDJx++mf17qLWdZsfOo6o5Mtk8LpeptoOTZQWjUYdaKhCJ5Kmrc5NM5kmnCrS1e3A6zSBcTmUvlyrk8yW0BgmNpuoBZjTqyGaLVVeCVjc//9Gd6HRamtrcNQX/8987wYlDQ8iVatjY2SNj9J+a4iO/dnPNH2w5HG4LiqygKtXZhMFgQK/Xk4qU2bBlDS6/GVkMU+eyEYmFECZUZserDgLLj1mzGP08dGaK6dEQTYthXlC14p8eDXLu6Bi3PXT5Snrw+DhHnz2H2WGkuacORVEJTUd58dEj+JpcOFdhwbX1NvBzv34rfUfHCEzHcLgtrNvZTuemJv7lT58BAUrkQYDm1qaqPmQ8jFFv4uO/8UCNYhsOh5kYniaVSTA+XMBT78JkNKHT6cgkcpjtRkxWPUefPs2ZgwNkknn0Rh1rNjXT2tvAZP9claG1KFrt3t6GViuRTeZp6b24CNIZtJisBiYvzHHgI7tXfJZsMsfUhVlcdfZagQkEFmhf30ZqIcvJ585x9tULGCwG6tq9qIpKaDrCK4++gavewalX+jl7cAC9SYdWLxEaTlCKy9z5qXVoLBCJRDjbfxrbrK2mzF+it79bePzxx/nc5z7Hd7/7Xe6999537XV/WvG+KzI36qIMV9a5XAnZbJaTJ09iNptxuVxXHb5dmgHzdhlkZrMZs9lMW1sbxWKRcDhMKBRidHQUs9lcKzhWq/Wqry/LMhcuXCCVSrFr1653LX/iagjGMhSKZezWlRRyk1FHNJElGM2wobOuZuRZLBaRdIN894dnGJ9YwGrRI0la0tkyRoPCB+5fz8J8hdOnpklnCtjtRppb3disRgSgUlGQFm/UqqpSKJQx6iQUSUOxLGPQSbXhbjZX4vYNjfRuWqljmhwKcOrwCFaHCau9etylYoWRc7OcOTLG3jvX1V5fkRU0koa1W5o5cWiY2ckw3rpqcFw0mMLqMFLXZiYej7Njxw5MJlOtoCYyEQILcbRmAZPJiF5vAPWiOwAqtQIDVbKEwahjdnx1z7sLb40jCFXaMoBGI1DX6mF6aIGx8zPs8K9f9XkN7d4VNv1QXRjFwymyhQx2s7kakiYKiKIGFcilq24Z5WKFxEIGnWDilttvorAg8NYL54jMx1GFCKV8BVHVcOAjO+k7PMTB7x/HaNbj8tspZIucfnWAdXvWcOcv7GOibxZBgI5NLazb3cn5N4ZWPV5BFFFW+d3WOhOiQDaXIxgILFq0mEktZJi8MEulIuNaNGEVNAJ17T4m+2Y49dJ5Bo6P42642Opz+u1MD8wz/NYU93/mNjo6OigWi7X8psnJSSRJqhWca90ProWnnnqKz372s3zrW9/igQceeNuv87OE912RuRFcSedyNUQiEc6ePUtTUxPd3d309fVd8fmXKviXZ8C8E+j1epqammhqaqJcLteMLk+cOIFWq60VHIfDsWKIXyqVqh5kgsCuXbvetWyZa8FpNaLVaiiWKuiW3TALxTI6rYRzcV6QzRWZmo+j00rcfNNGjCYbTz/fx/RsjGwuh04rsKbdicdrYnQkgKIqWK0GBFHklZcHaGpy4quzMT8fx243IYoCiUQOu92IpiCjs+uZCaXJ5EoIgkCxVKG+3s4dl1ikAEyNBCkVy9TZLw7fdXoJrUFi6OwMO27p5vjBIc68MUouW6R5jZfdd/TyoV/ay8uPnyE4VxU9ehtstG90UpCTbNm8lcm+IBeOjZPPlmjrreeWu/byXORNooEUKWO62oJNK7j9Dpo7fSxMRS9r55ZKlVrhuxSZZO6y6GRBrF53xVzVp0+uyIRmYqiqiq/ZvaKILUe5XKYs5inlKvjW+WsR2sVCGY1GxOG1MXRygkM/PEF0IQECeJtc7H9gK2abkYG3xsnnikhOkcZ1brAWeOZbR1ErAo56M5Jeg91kRaPVMNk/x/4PbmfLgV4m++cITIY589oAZqsBg1lPOpatiSUrZZlMIse2O6oFs5AtMtE3TT5TwFXnoH6Nn77DA0h28PmrC6/QdASHz45Gq6m15FaeJIjMxSjmStS1XSy2giBgc1uYGZ6vstckDXq9foX/WDweJxKJMDQ0RLFYxOVy1YrOlej7q+G5557j05/+NN/4xjd4+OGHr/t5P+v4qS4ycGM05iVrmuUD/iU7kEvxXhtcLkGr1VJXV1ezSYnFYoRCIfr6+lBVtUYc0Ov19PX1YbPZWL9+/U+U+uh1Wdi6tpE3Tk8gigJGg5ZCoUwqU2DXhhYavDZeODzIU6/0k84WEEWBRr+DTzywrTpTmYkgSaA36BkezxOODBBbSGIySZjNRvR6LRqNkdnZOLfeupamRifDw0EqZYXe3np27+7glR+fJRRIssZvJ5YtkEkX8XpsfOYzB/BfRUi5GlRV5ZlH3+LEoWH0BgmtXsv54xNMDgf56K8f4Je/dDfBuThyRSaaDpDJpNiyZQev/fAMJ18dQBAFJJ3EzEgQf7OTm+/byvlj4yTCGWSlgqdey5qdbvJChIpaZHJknoZWLzqtlngkjaTVsG5H26rH1tzpZ34stKIwlQplRI2Au97BxIU5Dn7/GOGZKCrgbXRyy8/tpHNzy4rXKRQKnDx5knV7O1BzM8yPh3B4rVRKFRKhNGs2N6PTS/zw71+lkC3iWWTCVVtzb/Lx37mPXXdvIpeuOj7rjTrioSSnfzCOKEE6lSYSiaDX6zEajWQSWaILcY4+c4b+o6NUSlX9mtluxOmzE52Pk4yk0EjVzJeWtfVsunkt86MBnvu/r1ZzY1QVjVaDxWOiJBRQ4jrSQo7YTBK9Uc/uB7aRimWYGZxDUdQa2aBcqiCIAs46B3NjYRRZQdRcXJyVi2VMNtuKvy1h+Rz1SkmnSwJlu91+xfvAK6+8wic/+Un+7u/+jo9+9KM3dD3+rON9V2TeTjrmtQSZiqIwODhIIBBgx44dK1Twqz3/3bbov14stw1fii0OhUIMDg5SLBYxmUy43e5rGnq+F/il+3dU7WFGFkhlCui1ElvXNvLLH9zJsXPTfPfp0wgIOO0mZFlhcjbGn/3fVykl8xiNAk2NPnRaLcVihcnpKHpBS53fTbFYJJfLUalUKJdlBgen+dLvfAC5IlCRFex2Y9VC32Xm+cfPMDcdw2vUs67Nx+33b2DtxtXZgK1dfnR6LelkfkW7rFyo4K13cOaNUZweC5bF/3O4zcyOR3jzxX7aeurwNzk4d+4chUKBnTt3EppJcPaNEexuS42qqygKM8Mh2tfl+fQfPEBgOoooijS0V/UouVwOu+ksh358loEzY4gaEYfLyt67N9J5hePedFM3o2enmR5cwO6xIldk0vEcXVuacXisPPYXz5OKZfAuFoXAVISn/+9rfOxLH6BuMY10ydDV7Xazb18vrc1THHu+j0gggaSV2Hb7Om764FaOPd9HOp6lZW197RpvWONjamCeoZOT3PTBbZisBhRFoVKWMVmN2JzWWlGSKzK5XI5YJEE6n+bgc4eZPhWirs2H01OVH0TnE8RCSfY/vIPYQoJirkhTTz29u9agN+n50V88S3A6QkNnPZJWQzwSZ/j0GLvu2Up7bwsDR0aIzMcp5Eucfvk87Rtb8DS5mLowi9NvQ5EVkpE0bRua2fvANubHQwQmwtS1exE1IrlUnnymyP4PdV/XHHV5W3upy7DcjHV5W22J/n3o0CE+8YlP8Jd/+Zf8wi/8ws+8uPJG8b4rMjeKa+1klgb8xWKRPXv2XDZk12g0lMvl2r9vdMD/XmEptjibzVIqlWqxrDMzMwwMDOBwOGpMtUvtdt4LWM16fuvnb2Y6kCAcz+C2m2lrqLLADh4dpVJRqFuMwNVKGrxukZHxEFpFoaWxDklTvdT0i62gQrmCpNUiabWYLRYUWaZQiFGplHnjjTdqK0hRrLZLmts8/Mpv3UEkmEKWFbx1tqs6Lrf11LHtpi5OHBoiEcnUbH+6NjXh8lopFsp4Gy7mzQiCgM1pYn4iQi6Tp3/wAoVsibbmTiollfnxMKV8Cd+yvBdRFLE4jIz2zfCBX9xLe+9Khp/JZOKOB/ey+5YtjA/MEo8lkMwKRTnL4cOHqyy2goboVIpSsYK/2U3X1lY+9O9u59gLfcwOB9EZtNz0wa3svGs9Z14bJB5M0trbULsuG9f4mRqcp/+tMepaPbUocr/fT3d39cbas72Nzs3NpGJZdHot5sXCmgin0Oq1K65xQRCQtBqSkTT5bIGTL57n/JsjlAplWtc20Njp59zrQ2i0GqxOM4IiouYFdt+2jWgohkYrki2kSE8nMRqNGG0GIlNJBEHgvl+9dcX5mTw/Q2AiRF2HH0mrIZfNkkwnaGyvI7GQxn6LjVQsS6VUweqyEJqOMjcaZN3eLpq6Gpjqn0WjldjzwDa237MZm8vCHZ/Yz8uPvsHscKDqQmHQsunmtWy6Ze2NXvIrugxLi75wOMz4+Djf/OY3efLJJ9m+fTuPPvoof/qnf8qnP/3pfyswq+CnvshcTZC59IOzWCxXVPBrNFWF9Ls14H+3oKoqY2NjzMzMsG3bthqDrKOjY4WAcHh4GIvFUnMcWK7If7chCAKt9U5a61eq2AORdK14LB17PpdDVVU0krZWYJag10uUFZVUKo9tcZ5TKitIksQdd2xlz9722pzq5MmTKwSuHv/1CVxFUeCej+6gY20dIxfmKZcqtHVXKcwjfXPVvBxZRSNdPFflUgWDScfx4ycYORkiNlPk1fwIRrMOp8eCrMCipKMGWVaQtCs/e6UkI+k0te/BYjeyac9FbcnSHOCNZ07yxuNnyKeLaLVaDAY9Pdva+fDn7+ZDn72NUrGMKIo1lX48mETSai4rCjq9llggQTwa5/FvPktyJo9eM8PEhiDbbl+Pr8WNRtLg9NmQK9V5iNGix9PgpP/o2IrWnKqoyGUFh8fK039/kP6jo5isBiSdxJlDAzi9NtbtWcPMUIDgZBidQcfGm3q4/eN7eex/PYPT7cTX5KJYKpHP5Uin0sTiMYYGhvD32vF4PLVrtJgvIVdktDqJbDZDOBTG5/OhlCCfyXPs6TMUMgWa114sqolwisnzs/zSlz/MHb94E4VskfBMhMhsrMp029yCv9XN5IU5SoUy3iYnjV1171gUvbToczgcdHV1UVdXRzgc5qmnnqJQKPDVr36V0dFRHnroIW6++eZ39F4/a3jfFZm30y5brcgsbXGbm5trK7qrPf+9GPC/XSxnkO3cufMyYepyAWG5XK4VnImJCfR6fa3gXK2H/G6iwWejb2gBbNUbaDqdRlFVjEY9WhnyhTLGRUV+vlBGr5fY0tvIzESE2WSV4aTRiGza1Myu3R1otdKKOVU8HicUCtUErkuBbG63+zLfseXQaETWbm1h7daV84o16+px+20EZmLUtbjQaETy2SKZZB53q5bRE2Emz8WwOkw43Gay6QIjfXMoskx4IYG3wVG7SRayJW66rwNFVjh1cJDTBwfIJHJ4G53svHMDPTvaVtVTibKW0SMLOB0u2rstFAp5UskMxw+eo6ItcPtH91RZVXoT5VIFSavBVWenUl4pPlZVlVKhjMGq5R//5HuEBpPYHFYqOoWjT59m9MwUj/yHD+BpcnHy5QucevkCmWQOq8PMmi0t2L1W5kaCuOod1diD+QSeRicmm5HhU5P4Wtw1urfDa2V6cIE1myU+9V8eJhFOYbIacS+ej/YNTcwOB1AUFb1ej16vRycZkP0CXZs6iMfjjI6MUs5UcLnceDxuzHYTgekgRSWPz+/DZDIzOzuPv81DIpTC7l3JtrR7rEwPzBOajjA7vMDhHx4jEazulDxNLm77+f10bWtnw/731kI/kUjw6KOP8p/+03/i13/913n55Zd56qmn+Id/+Id/KzKX4H1XZG4Ul85UVFVlenq6ZnN/rUhTURQpl8v/6u2xJSwxyIDrYpBptdoaU2bJHiccDtdYaEueXO+Umnk13LGvm6GJMMFICpEKgiBSrAi0Nrpocds43z9PLJ6rEiwEgR1bmvnVX9zPxESYg68OMTUdRaeT8PqspNN59PqLoVHLB7Nr166tCVwnJiY4f/48Tqez1ja8EhNo9Pwcpw+PEAkk8Tc52XZzN/f/wh6e+uejzE9WRaGCCPZ6DRt3tvPa94awu8zYF9lQeqMOBIF0PAsqzAwHQRCqRWx7KztuW8fBH5zg8BOnkHQaDGY94+fnmB0N8sCvHrhMIQ8w2T9HKpahpbsOQRTQ63XY7XZ0goHIeJpIKMIrP3qT+f4YSlGlrtXH+l3d1Zz6kQCexVZleD6O0aYnkgwQn8zS2F5fmxm5651MDc5x4oVz2Lw2Xv3eMbT6qkYlFkwSfPIMm27qJh3PVgfvQMfGZm75uR3MDi1QKVdW6IkEQcBiNzI1MM/9v2ZBURSGT0xw7rUBHH4b3VvbGTs7w/TgPCabkUpZplyssOnmHnbdupXARJgLT44x2T9NoVDE4NQhGTQEJ2NY7Rby+iKRqQQmi4GdH9jKwe+8SfmSCIpKqYKoEYgtxDny41OoqkLDGj+KqhKaivDc/3kF5396GE/je6cd6+vr44Mf/CC/+7u/y5e+9CUEQeChhx7ioYcees/e86cZPxNFZmkns5Q9EwwGLxvwrwZVVWsxykePHv1XN7nMZrOcPn36bTPINBpN7TMsRcOGw2EGBwcpl8u1oeVqzsPvBNvWN/GRe9bx/WdOUSiD0aBjXZuLX/zQDuq9Nl57Y4QfP3+OUDSDXi8RimUZGAmyMBvnfP98NYNGFHj6mT5OnJzi1z97gKamy7+7SwWulzKBltqGywV2p14f5ql/PkIhV0Jv0DI9EqT/5BQP/+rNfOYP72Pk/ByRcJxYaoHte9cjlI0UcucuM7a0WA2UCmXu/9R+MokcpUKZuhY3azY2kUnkOPXqwCKTqjqXcnisLEyEOfLMWXp3dlxGM5aX0jkvucy0WglJL5Gbh5njcSqyjCDBwIlRBk+PseGmTiSDjUQ4hSCIOOosuHuMOAxOpois8A8TRAGr08LQyQkEjYTRYqg5H1udZiLzcaYH5/mV//fD5DMFBEHAVWdHFEVC0xEEqC2+llAqVnA3GJnqn+PJr71EZC5eFdAiUNfu5Y6f38fcWJDxvhl0ei1rd69h0009pKMZnvir54jMxfA0uRAEgbmxecpCiW33rGduOMjc7Cw6vR6v30GhkKdzaxvHnz2DyWbEYNYjl2UWJkLUt/uIB1PkMwVa11UXkRqq8QJT52cZPjH+nhWZ/v5+HnjgAX7zN3+TP/iDP/hXX5T+NOB9V2TebrtsaQdQLpfZu3fvNYfhS8N9q9XKgQMHajOAU6dOIUnSFbUq7xVisVhNv9PZ2fmOL95Lo2GXIpmXnIevx8r/ehGNRtHJYX7nV/YjGewY9Foa/dVWXaFQ5vDxMRLpAi6nGY1GYGI6wt984zXEkoJB0uDxVAfwiqKwsJDi2ef62LmpmeNvjhKLZGhqdbPv1h7au1Y6FK9m5LkksNPpdNitTp5/7DSVskxTR1U7oaoqC1MxXn38FL/+5Q/S2GUnVphm3/ZtNDU1EQ0k0Ru15LPFFSabuWwRvUFLa089dtdKAWxoJkY2maOxc+Xx2T1WYoEUqVgGl99OIpwiHkxhthtp6PBitOhJxbK1HZOiKKRiWbbcupaTL17AaL5YFFgDs2MBguMx9nxsLfFQCqPRiMao0ruul8hIGui7rCjIZRmNTkMula8x0pZgc5mJBpIkw+nLjr1jYwsOn53AZIS6Vg+CKJBJ5CiXKvTuXsOL//Q6sUCS5rX11biMiszscIATL/bxsd994DIl/8BbI4RnI7Ssa0IURbKZDFqnBjWmo7Gxib137ObJv32B4GyYgTdH6Xt9EE+LE1u9jYWpECICgiDia/Fw9y8f4PXvv4XOsHKhJAgCokYkE788OfTdwNDQEA888AC/9mu/xpe//OV/KzDXifddkblRSJJEoVDg6NGjWCwWtm3bdl0W/ZcO+JebXF6qVVkqOO9Vy2l+fp6BgQF6enquadD5drA8knnNmjWXWfnbbLYVVv5v59iXJ3Aux6m+GaZmYng9FrSLbDCTUcfUTAy5UGH9mos3N1EUsVr1HDs6ztDRCVRFQWfQsjAbZ+DcLD//mVtYt2n187Nk5Lk8P+bc8WHmp0NYHHri8XjVJsZgwOWzEllIcuHMMNHUAhs2bMDvrx6Hu85Oz5ZWTh6qKtVNFj3ZdJFULMueu9dfVmAA9CYdGq1EuVheEXxVKpaRdBpEUeS5b77O2deHyGcK6AxVK5b1ezo589og6XgWrU4iny1Q1+altbeevjeG8beszNHx1DlJRjP0dm4g7o/w5vMnKKUqjL01R/0aHxqDwPx4kIYOP6JYnTPlMgV237+Zc4dHKebLaJcJGYv5qteY0WqgUpYZPzdNeDaGwayna2sbd3/yJl74p8PMjgQBFb1Jz/Y7N+BtdBGcjuJrcdcKmkbS4K53MDOwQDyYwlVnX3Hs8UASjVQ9F5l0hmgsit/vJy6nCI4HGXprjGwyT/eWNYiiSLFQZLJ/hvoOI41bPaQjWbz1HtbvXouv1YOvzcfwiYkV8ylFVlBVFdclOTXvBkZHR3nggQf4hV/4Bf77f//v/1ZgbgA/9UWmWCwSCARob2+nq6vrmhb9SzsYWH3Af6lWJZFI1LQqSy0nn8+3anrkjUJVVcbHx5menmbLli243e+dad9yXLoDWLK4GR8fx2g01grO1dqGqqoyOTnJ5OTkVY99PpBEhVqBgUVGlFZDMltCVWH5W1QqCqlEDrfXhsfnqL3XwkycF588S8/6BjSriOqWUMyXSMaymG1Wurq7cLmGMVl1KIJMKp1GjsdRZZFSvsLExAS33LEHg87M9HAAo8WAp97OvZ/YjaooDJ2dIRXPYjDp2XnbWu788A4A8pkC/cfGCc/GMVr0dG1tpaHdw/RQgIYOD5JWopArkQin2XXPRk69eoE3njyNzWXB3+KhkC1y7vAw6/d08tBv3M7AsXFyqQItvfVsvnkthWwRabFoLW+zlYvVwLDJsSme/YdXKSYUJI2EIiuEB9P4u5yM981y7mgUnU6H0Wxkw75ubv3IbjLJAn2Hh5G01ZlRPlskFkiw5UAveqOWx/5/TzN8agK5IlejB+oc3Pdrt/Hp/+fDTJyfpVwsU9fmob7Dx+SFWVRFuex7EKVqNpNckWvfW7WoarF5LMhlpcY28/l8GAwGyvkIiqwSmYtR3+GrFS29QY+v0UtmPs/Pf/HDTAxM8eaTx3nrR2eQTBpaehvQmkUmzk/ja6qarMbm49R3+OnZuTJZ9J1icnKSBx54gIcffpg//dM/fVc7G1/5ylf44Q9/yODgIEajkX379vEnf/In9PT01B5TKBT40pe+xHe+8x2KxSL33HMP//t//+/awmg1qKrKl7/8Zf7+7/+eRCLB/v37+du//Vu6ui6fD77X+KktMqqqMjU1RSAQwGaz0d19dTbJcoGlIAjXdaEIgoDT6cTpdNLd3U06na7djM+fP19jOb0dV2VFUbhw4QKJRGJVBtlPCjqdjsbGRhobG6lUKivahlfKxlFVlcHBQUKhEDt27MBqtV7x9W2L+e7LFdpQnRfodRLxeBb3YrtIlhUS8RxaUcDlqe4Y8oUyhWIFrVFLYDZGPJrB47tc4S/LCoef7ePoS/1V40aDlk171uCps7EwHaOhzYPdZqNYKDI+NI+70YjRJvHEN19h8lyUSlHFYNTRubGJ+z+5nw//+q2EFxKkYjmcHgvuxZV5PJjie3/xPNNDAVSqlN8jz5xlz32bKJdlFiYjNeX62h3t7Ll3I//833+M2WbE4a2eJ4vDhCAKTJyf4dYP7+SR315poqgoCs1ddYycnaKh3YdWL1HMl4gFk7Ru8vPmM8cpxGTa1jYh6SRUVSUwGSYXKvO5//FJRs5PEg1FwSDjatEzMj7M5rs6yWcLzAwFKM9W0Ool1u7o4M5P7OXoU6fpPzKCv92LYTFELTAR4tlvHOTX/r+Ps/GmbrKpPDNDC6RiE3gbnTi8NqILCfyLAlBVVYkHEjSs8eP02xh8a5S3njpFaCaK3qRnzZZWtGaRsXMTdKxvQyNILIwFsTgttKxrYvTM1GVdAkmroVwsM3Z6iue+8SrpWAarw0YunWf49Snq1rpRdDLTU7MYDQbWbGvjzl+4Beu7mBEzMzPD/fffzwc+8AH+8i//8l1vnb/22mt87nOfY+fOnVQqFf7wD/+Qu+++m/7+/lpX4T/8h//A008/zWOPPYbdbufzn/88P/dzP8cbb7xxxdf9n//zf/KXf/mXfPOb36S9vZ0/+qM/4p577qG/v/8dt8dvFIK6xNt9H6FYLF71/xVFob+/n3A4TH19Pdlslu3bt1/x8e+Fgj+bzRIKhQiHw6RSqRsSR5ZKJc6ePYuiKGzZsgW9/nJH4H9tLKcOh8PhWjaOx+MhEAiQz+fZunXrNT9rJJbhv/75s6TSBdwuCxpRIJ0pUChW2LGhmaHz8xQWbeEVRcVmMVCKZfH7bSwEU8ST+cU2CBj0En/8/32Y9s7Lw+xee+osz/7LW2h1EmabgUK+TDaZp2tjE8lohuhiUmYhn8dkl/iV33uQhfEoT/zDIRAUNHoBVYZiRqZnayuf/fLDq4o9n/i7Vzn63Dka13iRtNUbfHA6isVu5lf++CECU1GyqTwuv4223gbioRRf/4PvYXGYMC4LJFNVlZnhAL/4+w/Ss6P9svcJz8V44muvMD8WQlFUNBoBW52Jtt0e+p+ZQaPR4PRdbEnJFZn5sRAf+937Wb+3q/Ye6XS6RnFPpdJUUqATDDSvaaRzYxuqovI3v/VN8rkSngbnJa8X5JEv3Y9ckXn50TdJhKrn0OGz0bqukdFTkxSyxerOKFPA6jLz4L+7E7lc4fG/fI5ivoTNY6WYKxEPxTH5dbhdbjKRHEpFrrbf7tnM+pt6+Nb/8wNAqBlfqqrK9OAcXds7qJQqjJ+ZoqnnojNBPFiNx/7Uf3uEVDpJPBEnW6qKbt1uN16v95oU92thYWGBe++9l5tvvpm///u//4m4bITDVa3Qa6+9xi233EIymcTr9fLoo4/ykY98BIDBwUF6e3s5cuQIe/bsuew1VFWloaGBL33pS/zO7/wOAMlkEr/fzz/+4z/y8Y9//D3/HMvxvtzJLKmzV0OpVOL06dPIssyePXuIRqOkUqlVHwvvnUWM2Wymvb2d9vZ2CoVCreU0PDyM1WpdIY5cjlwux+nTp7FYLGzYsOF9G7+6GnV4YWGB/v5+FEXB7XYTi8WuuYvzuCz8yif28c+PvUU0lkFVwWjQcs9t6/jIg1sYHQnxw8dPMTwaQlFVNAYNkknH6GiIfFlGK4lIOolCvkxZVvjR90/y279374pdUTFf4uiL/Wh1Ep766o3XaNYjaUXmJsL84hfuIjgXY/DCKJIe7n/kVlweO09/4wgmkxF/kwtlMT8mlUgzcHqcHz36NOu2r1kxiyvkigyenMDuttQEmIIg4G1ysTAeZm4sVKMry7KCIFRZXCabkUwyv6LIZBf/bfdWb8IDx0aZG60q/Lu3tdPS28An/9OHGO+bIZ3IksolECwVNq7byNBz85etqEVRrF7rFZlULMOpF/sYPjmBVifRu6eTrbdvRRGUWsGZiYwTfSuA3eogly2g0628GYua6uvNjQU49dIFKqUK9YvkichcnOET49zyyG4iM7HFHY2bzQfW0dDp45t/9BjFfJmmnqoDQkaXJpFSEQoSH/r39zHZN82RH58kk8hx6LG36H9zhIZOP2NnppgdzqE36cmlctg9Njbfuo6n/+4lHL6VrVuHz8b04DyR6Rjrb6q2lpar8pdT3JdYlTcSqREMBrn//vvZtWvXT6zAQLUYADXx9cmTJymXy9x55521x6xdu5aWlpYrFpmJiQkCgcCK59jtdnbv3s2RI0f+rchcDel0mlOnTmGz2di4cSOSJF1R8b804P9JWMQYDAaam5tpbm5eEVe8NONY2uHIssy5c+doaGi45vzo/YRq2JiOaDSKx+Ohvb2qyF+ejbP0GVf7IW9e30hXxwP0DwcolSp0tHqoW2x5jY6HGZ+MIEkiRoOORCJPPl8iK8toKgoVFUDBbNbj8VuZnIgweH6OTCxLcC6O2WqgvsVFJpXHbKvexEulCpl0dTecTRfI54sYfWU2uhrZunVrNR8lmSedyGFebOeJgoDRYMBYZ6CUVfG5G5AkqebM63a7sZrtNSffS8+PSnXwHJyO8uaTpxk+PYkkadiwv4v1ezt5/UcniS4kqgmO2SLJcJqtt/VicZj4l//5JKOnJ1EVFRU4+vQZbn1kFzc9vJOeHe0MDg5SiMD27bsxGo209lbTLm2LQWhQXdlbnGacXhvf+cqPmeyfxWDWo8gKY2enmDg/w0e+eP8KEe9Sa1SwyEz2L9Ak1mMymTAY9KTjOYxmA8lwmmwyR/OyXYSvxc3M0ALpeJYPfe6uFecik8gRno1iX4wqyKTTJBIJmjuaCE1EGT8zybFnz1DIFvEuEhvCs1EyySw3f2Q386MhUpEU6/Z2seWODbjrnUhaTc1wcwlyWUajEdEuY5hdqspf7owxMjKCyWSqUfgdDscVf3+RSIQHH3yQTZs28c1vfvMnVmAUReG3f/u32b9/Pxs2bAAgEAig0+lwOBwrHuv3+wkEAqu+ztLfL53ZXO057yV+aopMKBTi3LlztLa2rqD4rqb4v3TA/5MUWS5nOS2fcZw4cQJFUWorq58mpFIpTp8+jd/vp6enp+rzZbOt2MUt/ZDNi7klPp9vRRiUyahjxyVOwdlskZdeGUCjEXE5qzs+i0VPJJImI4m47Ga0kgadXsJqNyCKIsmZGI/+3WvkollARVUXNSylCmKuRDJVIDCfpLxMGX/0jTNsP9DG9u3ba2QNo1mH1WEiFkphsV8sjIVcCa1WQ1NrPWt6Gunu7q61RkOhIKqhxNxoEkHrx2QyIkkSiXAKi82I2W7k0T95iuB0BIvTTDFf4pXvvkXn5hZu+bkdnHt9iHgohc6oY899m7nj43t465kzjJycoK7dW6PkxgIJDv3wOGu2tBLLhUkkEuzYsaPWmtz/0A7mx8NMD81jMOmrLDathts+uofJCzNM9c/Q2FVX220VskUGjo4yfGKc9fuqs8vlvly2zzn5lz/5MeGJKKI+TqlQQiNKbL97A7JcLaqXWtlotBpSkcxl14reqEVv1JPPFiCVqrV7REGDqBGZHQmQiqRpWde4zJTTz/TAPMV8iY///geJB5MMHRtj6NgY3mY3XdvaOfbMGUx2E3qjDkWuzox8LR5a11+ZjbncGWPptxgOhzl79ixw0exyeTR6LBbjwQcfpLOzk29961vvSWT6lfC5z32O8+fPc/jw4Z/Ye/4k8L4sMsvbZUssptHRUTZs2HAZTXY1xf9yi5ifhMblSpAkCZ/PRzabRRCEWmDSuXPnAGpDdbfb/a96nFdDJBLh3LlzdHR00NraelmxXr6LK5fLNa3K8ePH0Wq1tR3OanqjhUCSbLaI3bZyrmOzG4lEMqgaEV/9xblDNlOklC8TyxRpaavmqKiqSmg+SbFQIZ3Kk8yU0EgadDoNxXwZRVQZOB3mwUduW3HD0Egadt25jif/8TDRYBKbw0yxWCYWTNG1qZm2tXUUCyUuHBll/MIckqShe2srH/139/OdP3+umk0vqaiygNGo59aP7GTs7DTB6QhN3Re9smxuCxMXZtlx1wb+3Z98jEQ4jcVhwrY4nL7w5ggGi36F5sPptzM9uMDBp9+gbXsdO3fuXDG3a+qq4xf+4IOcPtjP7HAAm9vCxv3d9Ozs4B//6DF0Rt0KPzWDWY9cUZgZmq8VmeVoW9/ML/3nD3P8+XNMD8yiN+to2ujH3Wnl9Iv9xGMJzEkjZpMRrVZbZZCVZeraqgzMqQuzDL41Sj5doKHTT/euDl75l9eRUiKNbQ2IgoaFsSANnXVodRoknXS5KadOQyJQLS4//t8vEA8ka8F0jV11tKxrYn40gCJXF47uBif3/tptKyjjV4MkSTWZwvK22tjYGEePHuWrX/0qBw4c4KWXXqKpqYnvfve7P7G8JoDPf/7zPPXUUxw6dGiFjKGuro5SqUQikVixmwkGg9TV1a36Wkt/DwaDK+6XwWCQLVu2vCfHfzW8L4vMEpYYWJFIhJ07d162ZYSVO5l/LYv+K2GJoBCPx9m5c2eNhdXb2/ueUqPfLVxLA3MptFot9fX11NfX17Qq4XB4hd5oaeWo0WgwmXRIkki5LKNdRtWVK9X2mKooxCIZTBY9xUKZTLqARlVxuU01aq+qgstrJTSfAI2IkioiygoVBXRGEV+Tg3xGpu/kFP5GB8loBq1OwmI3seuu9eRzRY692E8kmESrk9iwp4MHPnUT5VKF7/z5cwweH68tWI6/eJ5d92zk1/74I5w5NMj04AIaA/i7HOj9FQ790zlktUKpVEKv19fMKxVZJTAZYeutvZguSReVK3Kt5bUEVanGIRcLRXbs2EF8Psn4uWlUVaV1XSMNnXX4Wtzc88nLPbKqK/1V5pmqilanrUZ3n55k5OQEpUKJlrWNrNvXTVN3HU3dl9+0OprX8A9TjzE/EkBr1qDRaCimKzS0+1i/r4ujT57ipX86VHUMEEXU5xRsdRYcrRYqcZXQxGL8QWcdD/7GXQyfHOfcocFaxHX10FTKxQo2r5Vn/v5l0rEMLb0NCKJAuVhmemCOPQ9u4+YP7yI6H8doNdC1rf1ts8gubasFg0EOHDjAE088wcjICMVikT/+4z/mwQcfZPfu3e9pu0xVVX7zN3+TH/3oRxw8eJD29pUkkO3bt6PVann55Zf58Ic/DFRFodPT0+zdu3fV12xvb6euro6XX365VlRSqRRvvfUWv/Ebv/GefZYr4f1xN1sFywf8e/fuvSLtbqnIqKpa29G8HwpMuVzm7NmzVCoVdu3atWIlejVq9JIa/+1So98NXK8G5mpYoj97vd4V2TjDw8MUi8UaU62jw8OFCwtIkohOJ1EuyySSedZvaGRrbwMHXxkkHEpRkRX8dTYKsoJGFMnlSwQDKVLpAgKgEcBX78AvaTAYJfKFHDanBYvZzFwmxnj/PF87OkJwOoqoEenZ1srdP7+X239uB7vuWE9koeqZ5W2s9urffPoMA8fG8TY5a6vlTCLHsRf66N3Zzgc+edOKz6soCmMHI/QHxkgkEiiKUhV/6vWoqoLeqENVVWZHAoycmqRSrtDUVU/ntjZe/8FxXP5qW0pRVGYm5tDqJW65dx9Hf3yKQ997i0wyiwAYrUZ237+Vuz51S1U5n8oxfHycXLpQbS1tb2fg2Ci5VB6TzVg99+E0BrOejs0tvPyt1zn8g+MU8yUEUeDYM2fofPUCH/uPH6xFFi+Hv9nLJ//Th3nt+8eYvDBLqVSiYY2Flh1+3nzjTd74hzNoJS1NaxsQRZFENM7UwCx3fOIm9nxgB+HZKIVMAZPdhFyRWbevm9MvnWd2eAH3IpstMh/DXe/A6rQQnU/Q0OmrFSCtXovdY2Po+Dj3/sptrN3decPX4rVgNpt58803aWho4ODBgxw+fJgnn3ySj370o7XgsvcKn/vc53j00Ud54oknsFqttZmJ3W7HaDRit9v51V/9Vb74xS/icrmw2Wz85m/+Jnv37l0x9F+7di1f+cpXePjhhxEEgd/+7d/mv/23/0ZXV1eNwtzQ0PCv4q/2viwymUyG48ePY7fb2bhx41VXEhpNVQBWqVRQVfV9UWCWGGRms5mtW7de9fiX5hs2m43Ozs5a/392dnZFbsySeO29xo1oYK4Xl64clz7jzMwMHa0K8/MSkWi66k4sirS2uvnlX9qL3WbkXN8swVAKRIFwOEMuVySWzIMsUyrJaCUN5YpMUVFJZYpQqaDRl3G6bRiNRmRZoVwqM3hsDJ1atbCvVGROvNxPeC7BZ/7fh7HYjbXwsiX0HxtHoxVXtGMsDhPxYJKxvlm6t7YRXUgwfGqScqlCc1cdO27fyNSFBUySCb1VSyFfYGEihIJC2ZDhR19/ljMvDFHIVEkJkk5izaYWGjt9zI4E0eo1JGJJRFHk9o/uR1Dg1X95E0nS0LK26tGViqR540fHaeppwGQz8qO/eK6WkilpNXRsbmXd3i6Gjo0RmY2BAEaLgf0/txONJPLmEycxmHXUtVeZYqVCmeET45x4/iwHPrqXdCzDwNFRUpEUDr+d3j1dNKzx8/Hfe4B0rGrXYl10PXj98aMUsyUM9Vrm5+bQLJJwfA0eJs/O8oFP38FbT5/iwhvD5NJ59EYdbRuaufOTN3Pi+bMExkOoKrRtaOGOn99PKppGvcQWB0CjFamUZMolGf31E8SuC7lcjo9+9KMIgsCTTz6JxWLhkUce4ZFHHrksNvu9wN/+7d8CcOutt674+z/8wz/wy7/8ywD8r//1vxBFkQ9/+MMrxJjLMTQ0VGOmAfze7/0e2WyWz372syQSCW666Saee+65n7hGBt6nOpmTJ09iMBhYs2bNNRX85XKZQ4cOYTKZ8Pl8+P3+G6IqvttIJBKcOXOG+vr6q0YMXA8KhcLiwDlEIpG4KjX63YAsy/z/2zvv8KiqrQ+/01t6rwQIoYfeu4DUQIIIiCgoXJUr9oLlw4JX5drb9So24IqFDgICSpfeSwKBEEgjyaRnMjOZvr8/hoyEXgJBzPs8PJqZc+bsM+Wss9de6/c7fPgwZrP5inpgaoLKykry8vTs2ZdOXl4pQUHedGjfkMiIcDZvTGfxwr34+2lRnvGrKSqooFBfjqTSjvrMmowECf5BXlRabQinHY1SRWCwDy6XoLzMjLA7UFisxMSFej4Pu81BQU4J9zw9kHZ9mp03rq9fXUTGkdMet8kqso/n0WdkJ0KiAlg1e7Pnwus2x2qCRqdi/4ajVJosCMDHX0evuzug8pXy01srcDoc+IR4o9VowCmhTF9B//u6I1fK2LF2D1pvDX2Gd6dl98b8Nmczm+fvoF6z6kri2cdyadW7KfkZRegziwlvGIJMLsNisqLPLKTnyE406RhLzrFcpHIZDeOjiW4awR8Ld7Lqm/XUaxZZ7XupzywkODqIYf+8kwXvLacgq/iM6CWENQxh9NThhDc8vzdpz+qDLP54FdHNIigvL8doNKJQKCjLL0epVtK0SyNS1qcRHBWIl78Oi8lKQWYRTTs34v7pIynNK8dmtWO32pHKpKg0Kr596SekUgn+oe61OCEEWUdzadY5jvteu6tGL/oWi4UxY8ZgNBpZvXo1vr6+l9+pjqvmlpzJtGrVylMZdjGq1l8kEgldu3aluLgYvV5Peno6Op2O0NDQG27idS75+fkcOXKEuLg4oqOjr/v11Gq1pzrmQvIvVQHH29v7us+xSmBUIpHQsWPHGlVpvhQajYaGDevTsGH9aiKXO3fu5PffsnA6HUhlf3qFBQZ7UVxcgUyrRCWTIlfI8PXXoVBLMGabadE6Bo1SSU5mMVKJhHZdY8k5lIndXH2Gq1DKEQIKT5decFxN29fnxIFM7NY/9b7MFRbkCjl+QV78+t0mbFYHEbGhSKUSjOVm9q5LIXFyX/7x5t1kHctDKpfSqFU9AsJ82TBvBwqJkphmUVhtViyVFqxWKyaLiT0bDtDjgdYMmdKL+Ph4z528xWThQh+rTC4j72QBhTmlhNUP9pRUq3UqvAO8SNl6nAHje9K4ffX8vsvlfhfP/a5IJBKcdie/frWOguxiIuPCkMn/FL1c/e0GHnhzNA67k+O70jmdlo9CrSA4KgCdn5bT6blINRAaEoJUKsNcYKVpx4Yc33kSp9SO0VqBo8yORqMhKDqAjORsck/oqSg28fv/NlGUU4JEIiEkxl0tdnxXOmZDJUqNAnN5JX4hvvS8u1ON/o6tViv33XcfpaWl/P7773UB5gZySwaZy32Zzu1/UavVHmmUqgqngoICTp06hVqt9gScmrgYX2w8GRkZnDp1ivj4eIKDg2v8GBeSf9Hr9ezZs8dTxVWlGn2153irNIieK3K5ds1CkJRTYTQiwV1YoFQq3f0RMom781+AudKMyWRCpVLRqGkkSWM7cfRQDqkHc9wpVKWCyuKKasdyuVwgRDVp/LNp37cFx/ZmkHYg09OYKJFIaNenGTaLnYoyM1GN/pwZeflqMZaaOLj5GN0S2hIRW/3Ov6rPQyaTodVo0Wq07hslUx6GciM2m42SkhL27d6Pt8aHerFRRDUOZ/evB7HbHB53TKfDid1mJygqEH1mCbJzLAQUKgUOmx2b1YFKq6Ks0EBJbile/jpimkei0ird8ixnFs0dNgfmCgvNuoZwePNRgqMCkMn/FL0MivQn62gOWam5bPxxK6k7T+A8I0Tp5avDK1SNPq8CrUVLYWUJdquD6MbhdE/oRNahPAKCA5ArpVRaLJSVuq0LjMVm9m06xKHfjmIxWQmMDEC4BLlp+VSUmOg7rgeZKTkYiito2aMZHQa2Iqrx5QtPrhS73c4DDzxAbm4u69atu6wlSB3Xx18uyFyuguzcCqeqgHP2xTg0NLTGXCOrPGyKi4urVZDdSM4ux3S5XOfV/1cVDVxJafSFemBuBWQyGe07NGTVikN4e3khhAub3U5ZqQGJFGTCRXFhOUq1FJvNhnAq8PKS07pDfbZvSGXxnO2YTe61D5vFjt1oRZVXRmCoL5UmC4W5ZQSG+dKi04XFFLXeasZNHcrBP45xbG8GhjITodGBtO/fghMHMpFw/vdUrpRjrrBc8PWim0YgV8ioNFo8nf82q52ykgq6JLamW5furJ69nrVrdmA0mFHq5LToFUdQfT9yjuei89ViMVopL64gJCaItn1bcOpQNhUlRnwC3d85IQSGIgMN4uuh1Cj49at17P3tEOYKt0hlo3YNaNmzCQc3HKW0wIBMLsVhddAgPpqmnRpxcEPKeUFLJpfhdLjYu+YQyVuPExITiFqrcsvipJ2mpLSEux4fQlnumabNZpG07t0MjbcG7wAvDMUVhNQLQqVWg58fJfpShA+cOHSS05l5hDYMwu60olZriIgLI/toLg6bg/HT776er89FcTgcTJo0ifT0dDZs2FDjorSbN2/mvffeY+/eveTl5bFkyZJqi+0X+329++67PP/88xd87vXXX2f69OnVHmvSpAmpqak1Nu4byS0ZZC5G1ezlSkuUZTKZ52JcVVJbUFDgSQtVBZxr9Yyx2+0cOnQIu91Op06damVRTSqVeqq4LlYaHRoaWq3hrIrL9cDUNn37N+fQgWxO55SiULgLPIRQ0LlrPbRyJ3u2nkSvd2C3uws+mraMoMJQydLvd2C3Owg9Uylmt9rJPumgsLSS0xlFWC1uO2OvAC/SDufQse/5azLgXjCPaRbBjlWHyT1ZQEZKLgc2HSMowg8k7sZNtdZdGOByuRWH2/VtDoCp3MyRHSeoKDYSGOFPXLv6xPdowoFNR90zBQkUF5QS2SiUYQ8M4NeZ69ixcr/baybah/IiAwdXHaN5v0YIrZOj6zKorLCd6cI3sXzmBqKahJO2L4PKCgsqrRJjmQmtt4aed3Xkj4U72ThvO15+OkKiA7GYrBzckEKL7k0YPTWB1J0nsJhshMeGEN+zGb5B3viF+FKaX+4RvQS3kkBgZACZKdmotErUWneVZEVFBQpvGa4SBXKpgkGT7mDnyn3sX5vMzuX7iOvQgKZdGrFt8W4Ksorw8tNhMVkwlpronNCO8sIKioMM6HQ6LBYLBkOFuzzabuX0qbwbsujudDqZPHkyKSkpbNiw4YZkHEwmE61bt2bixIncdddd5z2fl5dX7e9Vq1YxadIkT3nyxWjRogVr1671/H2rtDlcCbfkwr/T6TyvwfJsD5gLSfRfDWeLPxYUFFTzjLnSxsjKykr279+PRqPxSNzcSlSJI1adY2VlZbXS6KKioqvqgaktSkpMbNqQSkryaZRKOR061qdr91iOpCSzaO5hsk4akSCQKyTYz0iNSO1OomKCUJzVkFhSWEFpfjkqhx1vPx3eflqM5WZkChn3PTuI+C6x2Cx2hMuF6syF1GF38vnzP5OZmkdIlD8KlXumUpJvwMtHjcVkQa1TIZPLMJaZCI4MYMKrSdgqbfz87+UUZLstjaVSCdFNwhn17FBOpeSwd90h8k7n07xzHIPH9cNutfPFU3NQqBT4BP7Z+1GQVYRvsA+NO8Wydu4WtP5qkAskQoKp2EJQRAA9R3QkZetxjGUmIhqF0XVYO+o1jeCjh752p6LOEr00Gyoxlpl4+P37EEKwbu4WMlNykEgkxHVoQHB0IFsW78JWaUfjpcJcYUHjrSbp8UH8NnsTxjITQZEBGAwGzGYzQUFB5J0oYODEOzidlsehTUdRaRTI5DJM5WZC6wfTqlczju5Io6LEiEqronXfFtwxpitr525h48/bPEUIQggs5koyU0/T/M5YmveNvWBH/rXidDp5/PHH2bp1Kxs3brysLXtNIJFIzpvJnEtSUhIVFRWsW7fuotu8/vrrLF261GPLXhPcjMq5Km6tK+MZzj75G9HBf67449l3/w6Ho1pj5IXWJ8rLyzlw4AChoaE0btz4luzWv1Rp9JEjR5BIJERFRd3y+eiAAB0jRrZnxEi3yrbdbmf//v3kZldQWuggJNQHjWc24SLzZBFOmxNDeRkymQylUoVSqcRusWO12mnUNAzVme56rbeavIwi1i/cQ9rO46RsPY7L6aJh6xj6jOlKpdlGbnoBwVH+no58nY8Gm8WOTCbljjGdObIjHZvFTudBreg+vB1BEf58/sT/KMgq8lR92a12TiXn8PvcLSQ81geLrozhsXdQr55bZidl23HMhkqimvhVO3cvfx3lRRXsX3cE30BfgiL8cQkXNqsVhVJJbkY+WfpM+jzSgcCAQEJCQ5DJZBSdLqGywoL2nLJsjbeaotwSMlKy2bJoF0WnS/AL8UW4BHvWHCKyURiJUwa6XSyziolsHE7jDg2Ja9+AzJRsNi/YiVwjxWKzEBQUhNVkQ6lWIFwujmw7TnBUgCcVGBDmR/bxPOw2B1M+e5CTBzM5vvck+lOF/P6/P4iIDcUv2IfcE3oCI/wQAopyyoiKjeDuh5JQess8HfmHDx++LidXl8vF008/zebNm9mwYcNNCTBXgl6vZ+XKlcyZM+ey26alpREREYFaraZr167MmDHD8/25WpxOp+e6VllZecOrSG/JIFPFzRC4PLcx0mAwUFBQwIkTJ0hOTvYEnODgYORyOXq9npSUFBo1anTNH3JtoNPpiImJobKyEovFQnh4OAaDgS1bttzw0uiawmKxeGaPKlkADkc6as2fVXBSqRQfXw0lBQ4Uci1qjQybzUZ5eRllpSZkEoFEKjx3cUIIVFolB/84RoZK4B3ghVQmZf+6ZLJTc+k6oiMOu9NTPl2FSqPAYrbRZXBrBk9wd91XfTdPHsoi71QBwdGBnqovhUqBf4gPR3am4ddcSbsubfDR+nLiQAYaLzVaHw1KtQKLyVpNqdlqsqJQyXE6XCjOjEEqkaJWa1Cp1JiLrUSGRaFUKjl6JJX1yzbj4+NDXKtY1N4qzOWV1RQGzIZKNDo12am5FOWUENUkwqNm7eWnJTddT6XRwpCH+vHL579xYt8p0vacZNO8HbTq3RRtkNrdRBkaSEFmMS6ni44DW+Nyuo3Kzh67VCZF563h+J6TNOsSx4L3V1CmL3d7xNid+AR40WlIG9IPZlKQVYwEiGkRxaCJfQiJdq+TnN1XdbaTq5eXlyfzcLY+3oVwuVxMnTqV3377jY0bNxITE3PV37sbxZw5c/D29r5gWu1sOnfuzOzZs2nSpAl5eXlMnz6dnj17kpycfNVrwGcHmOeee46wsDAefvhhfHzO92iqKW7ZIFMbEjESiQRfX198fX09d/96vZ6MjAxSUlLQaDRUVlbSvHlzIiIibvh4apIqBejKyko6derkuXs5uzQ6PT3d0290I6vxrgWz2czevXsJCAigWbNmlOqPApznrCmTSfH21VBptmIxS5ArZNgqpfj6e2EvraCstAyLxYnZ6MRuc+GwOZHY7TRpFgFIKCs2gVJJxvF8gg9kotapMJVXVqtCqyg1ExIV4FZBPuf9sVbacDqc1RwtAZzCiaHcQL3IGI6sS2fr0r2Yys0olHJiWkYREhNE9tFcQmICUWlVmMrMVJSa6DWqC/qsYo7tPlnteCZDJSqtkrhWDSnPM7BzVgpFp4vdfWM+u/EKUlOkL8FisxAQ4o/D6qBUX06bvi2oKDGi1Cir2SVUBcTcE/kc2nSE43tPERDmh5e/DkNxBb//sJmmdzSg3R2tyErJRaNT0apPc9rdGc+2pbvdCtLnpGAcdgdqnYrV326gvNBAVONwJFJ3cM8/VUjKtuNM+ewBSvLKkEolhDYIuaDrqU6nQ6fTUb9+9TL3zMxMFAqFZ4ZztrEeuAPMtGnTWLZsGRs3bqRhw4ZX+a27sXz33XeMGzfusjOzwYMHe/6/VatWdO7cmZiYGObPn8+kSZOu+HhCCE+AGTRoEIWFhcyYMaOarcqNSKPdkkFm8+bNyGQyWrVqhVwur5ULnUQiwcvLCy8vLxo0aEBycjJFRUVotVqOHDlCXl6e52J8K5qOnc2lemDOLY2+UDXetZZG1xRVFXDh4eEei4T4tvXw8t5DaYmJgEB3L5TN6sBqdTAoqS3hYT7s/iMNY0Ulcc0i6NCjEfM/+Y2MEwWYTHacLhfCJXA6XEiQkpFVirW8EpvVvRbotDvZ+Vsy7fu24MTBbKwWO2qNEmO5GYlEQo/EducFEoDIRmF4n0lzVRlwVVZWos8upF6TSMpzzKyZvRm1VkVQVAB2i53UHSeIaBRKbNsYslNzsVntaLw0tB/Ymv739yT7WB45x/M5nZaPl78Om8WOtdJGhwHxyGRSFn64EovRQmhMMBKgKLcUW7GL1v1acGL/KTKPZ6HUKGnSJZa+D3Rj84+7sJ8jnV+17llptHLqcDahMUHutSkhUHrLcRY7MJ+2MeyNAZ5AUHVBatIxlo3+2yg6XUJQZAASiQSzoRKH3UlMiyi2L9tDQLifRypGIpEQGOFP8ekS8k8W0LD1lc8uzi5zd7lcHn28lJQUHA4Hvr6+7N69mxEjRvDll1/y888/s2HDhlqxHb4Uf/zxB8eOHWPevHlXva+fnx+NGzfmxIkTV7Vf1e/3xRdfRK/Xs27dOo9vTUFBAQ6Hg4iIiBoPNLdkkNmwYQMfffQR/v7+JCYmMmLECDp27Fgrax92u53Dhw9jtVrp1q0barXa41NRNX2v8lMJCQm5KV3yV8PV9MDI5XKP/PvZ1XgHDx50m3OdSVEEBATctM+ipKSEgwcP0qBBA+rXr+95PCjEm1H3d2XenG0U6t09MFKphGYtIxmc1AYvbzU97mxOabGR/JxSNDoV9zx5J2899iMOhwu5XIpMIUXuJcVUZqaoxIrUYkWlliKXybG5XNjtLirKzfQb05l9G49irbQRVi+IHolt6dC/xQXH6xPoRY+7OvLbnD/IO1kAcigrLMM/2I+B9/dmw0/bkMllBIT7Ae6mULlSTkFWEUMeGoVKreTAhiMU5hRjM9s4uPEobfu1YNz/JbFlyR5yjuXiH+pD+zvj6Z7Ugd/nbMJYYqzmGhkaE0TOsTxCwoO5d+pICnOK0OfqMVQYOLD/AMLfhsNlJy9DT0hUladLCd7+XgRFBeA841qJEJSXG7BaLQRHBGMsM2MxWsg9kc+WRTvJOpqLT5A3nQa3of/4Xqz9/g9yjrmrpxQqOW37taR17+ZsX7bnAu/U9dcbSaVSjwZe06ZNqaioYP/+/XzxxRdMnToVpVLJY489Viv6f5fj22+/pX379rRu3fqq9zUajaSnp3P//fdf07FPnjxJv379CAgIYMOGDWzYsIHvvvsOh8PBf//738um766WW7K6DNwXx99++41FixaxYsUKdDodw4cPJykpia5du96UhsHKykoOHDiASqXyzKrOxWq1UlhYiF6vp7S0FC8vr2pqA7VJ1QwgLCzsuiRuXC6XpziisLCwWnFETVT+XIyCggKSk5Np0qTJRRdr83PLOLA7g8pKGzENgolvG41CKcduc7B4zja2rj9KudGKDdB5qXAarfiq5CiVctRaJU67k8wjp6m0OtFKXShkApvVjtPuxC/cG4VSyeQZo2jYvB5Wsw0vX815pmXn4nK52L8uhfULtpKboadx64b0ubsr9ZtHMeO+z5HKpR6p/ypOp+WT9PhATh7M4uCmI26V4jOfV/Oucdz/2l2otCrsNgcyudQT5L9/YxGHNhwholF1g6q8kwU06xLHiCcHs/TTXzm++yQ2qx2tt4b4vk1w4GDnLwcwl1eiUCgIDPdn2D8H4O3nxXcv/0xAuB8WWyVWm43AgACKckoJjgpk4KQ+/Pz2EkzlZnS+OqyVVlwOJz3v7kLXxI4c35OO3eogvEEwMfH1UGmUfPHkHDKSs4lsFPZnuuxkAUHRgTz55T+q2RxcL0IIPvzwQ7755hvGjRvHvn37WL9+PXFxcezYseOG97EZjUbPDKNt27Z8+OGH3HHHHQQEBHjWcA0GA+Hh4XzwwQdMnjz5vNfo168fI0aM4LHHHgPcayfDhg0jJiaG3NxcXnvtNQ4cOMCRI0cuW4Z9oVnJc889x7p162jUqBFpaWm0adOGXr16sXv3blavXs3BgwdrdI3mlpzJAGi1WpKSkkhKSsJisbB27VoWL17M2LFjUSgUDBs2jKSkJHr06HFDJFCqKshCQkJo0qTJRe/cVSoVUVFRREVFYbfbPQGnSvqlKuBcboGypqnJHhipVEpAQAABAQE0adIEg8HgqfxJTk4mMDDQU41XU3eNVTYD8fHxhIScr5tVRViEH4MS25z3+K8L9rB2+UEqhMDqdHf3m0vNIAQ2IC7QG6lUglwhxzfEB0tOqbsrXyKQy2UERwai81dTUmDg0MHDFJvz3JVNJVoCgwPx9nPfQJzYn8G2JbvJPHoa/xAfOg5uQ/tBrfGP1dHx3qa0bTvWI1nidLrwD/Ul71RBtSBjrbQhlUkpySvj4KYjBIT5ota58/RWs42U7cfZv/4IXRLaIpNLyUzJwVhqIqReEGH1g9nn+HPtEtxWAQ67g9D6wfw8YwlHt6cREO6HT5A3FSVGti/aR8Kjd/LavOdI3Z1GeUU5Cl8JZZJCXMJKYD1fTh3KRuuvJjgsiLKCCpwOJ52HtWXTz9swGyqJjPtz5lReVMHuVQfoOrwDLXs0Yd33f7Dp5224XC4ad2hI+wHxlBUayD6Wh1wpw2F34hPoxeB/3FHjAeazzz7jww8/5LfffqNjx46A+6K+devWm9IovWfPHu644w7P38888wwAEyZMYPbs2QD8/PPPCCEYO3bsBV8jPT2doqIiz985OTmMHTuW4uJigoOD6dGjBzt27LhsgDl7kb+kpAS5XI6Xlxf//Oc/UavVbNu2jTfffJO2bdsSGRmJy+UiJyenxmd+t+xM5mLY7XY2btzIwoULWbp0KU6nk2HDhpGYmEifPn1q5A2quoOOjY2lXr1613SBPnt9o6ioCKVS6Qk4Pj4+NzTgnD59mtTUVFq0aHFRY6Oawmg0egKr0WjE39/fU413rc2pVRI9rVu39uSMrwZLpY1XH51LQakZg82BVCZBKpEgXAKbzYlEAlEBXoT4aXE6XRTmlmEqM6HBgU+ADolMhs3mwlhuIijMl2mzJnF4Ryq/zd1GfkYhEqmUuPb1aN6uARu/34Gp3IzWW4PNYgMBLfvH0bB3JO3atTvvwrZjxX4WffQrCqUc70Av7BY7pfpy4to3wDfYhz2rDxIZV/0zy03X06pnU4ZO7s+8d5aRmZKD3WJ3+6q0b0juiXxK8ssJCHMHs5L8cvzDfBk8sQ/z31uOT4BXtcqvwpxifAK9eW72P8lMyWHDT1vJTs1FqZbTuHtDVGEy9v1yhNIsAxKk+AX50uOuTnQa0pb3H/gvCqUCL/8/Z+lCCE4fz2PU8wnsXLGPU4ez8fLXIZFKqTjT8X/XMwlkp55Gn1lEUIQ/re9ocd55Xg9CCL766iumT5/OqlWrLuq18nfh7JuOF198kb1795KZmUmHDh148skn6dy5MzabDaVSicvl4vTp0wwYMIAhQ4bwwQcf1OhY/nJB5mwcDgdbtmxhwYIFLF26FLPZzJAhQ0hMTKR///5XfZETQpCVlUV6ejotW7a85B301eB0Oj02zIWFhchksmpqAzUVcIQQnDp1iszMzGu+QF8PVWtVVarRPj4+nnWcK0kdCiE4ceIEp0+fpl27dtc8ZS/IK+dfT/5EidVOpd2JXP7nLNRhcyIkoJZICJTJEAgCQ3zo1DuOLUv2kJ9ThsXqwOV0IZFK8AvypmPfZpzYlYapwoJPgA6bxUZ5iRGb2YJM2AltGOzxjsnP0mOz2nlhzhTqNXY7HFYaLUgkEtQ6FS6Xi80LdrJ1yR4MxUbkKjlx7eoz/NE7+W3OZnas2E/UuUHmhJ74nk0xlhlJ23uKoCh/VBoVxjIThmIj7QbEU2moJOdYHgJ38cGgiX0oKyjn+9cXnqf7ZSo3YzFbGfnMUBZ/9CvGUhNe/jrsVjtlJQbqt4vgsY//QcbRLHKz83Ep7Xj7e+Hr5cu8aSuQCCn+Z4oa3O+pg4LMQjoNbcf2ZXsIrR+M/IzWmsvl4vTxPAY82IeEyXde0+d5OYQQzJ49m5deeokVK1bQq1evG3KcvyKPP/44ixcv5uuvv8bhcDBt2jTMZjO7d+/G39+f3NxcVq1axUcffUSzZs1YsGBBjY/hlk2XXQlyuZw+ffrQp08fPv30U7Zv386iRYuYOnUqJSUlDBo0iKSkJO68887LXuRcLhfHjh2joKCA9u3b16gqa1VQCQkJ8VTEnLugHhoael4J5tXgcrlITU2lqKioxnxgrpazPdWvtjS6SgOupKSEjh07Xtd6lq+/Fq2XikKztdrjVW6MKo2SuNhguraLwcdfS6vODfH20+K0O1k8cyNKlRyttxqfAB3CBX8sP4BKJqjfJMw9bl8tGp2a43szCI30RalQYDabMZSXI1VLcFUIck8UIEXG2jmbSNt7CokEmnVrzIAH+tBnTFc6D21LYXYxGm8NQZH+7iqtDg3ZvepgNX0zy5lz8A/zJWVbKkHRAR5pF+8AL+w2B6cOZPHC3CkYS922A4GR7u/RyUOZKNSKaq8HYK6oxDvAi12/HsBYaiIizn1ehvJyNE4VJekGijJKadW5JfVjYzi6M42yzDIIMeLXwJtjm07iwI63nxcKuYKCzEKCogM9FWpVAQbcqVaVRsmpQ1nX/HleCiEEc+fO5cUXX+SXX36pCzBncfToUbZt28ayZcvo0KEDc+fOJTs7m9mzZ+Pv74/D4UAikZCZmck999zDtGnTbsg4/tJB5mxkMhk9evSgR48efPDBB+zevZtFixbx2muv8dBDDzFgwAASExMZPHjweRdgh8PBoUOHsFgs1XpIbgRnV8Q0a9bMI2+TkpKC0+msJm9zpcUN5/bA1IaG2rlcTWm0y+UiOTkZk8lEx44dr3v8KrWCHgOakzt3JxbhwukSSIQ7kClVChRKGf0Gx9P7jqbY7X+W8upzSvHy0xIaVX0GmHNCj1yrqBYYlWp3WtZmdaLVaqkoN2MsswJSrJU29mzby/wPsjEVmfEL8UMmlbB92R6yU3N59NMH8Qn0Os8npmXPprTp05z9G1IoyS8D4S47bdmjCeGxIdgtjvM87dU6FRajFbOhEv8wX1J3nGD/2sOovdQ0796YRm3qk7LtOP6hvm59sxIjNoudTkPasnn+drwCvJBI3GuQDoedsKhQ9CcLOH08n5K8MpZ9tprywgpAoPFS07Z/PIpuSk4cyKAkz+0A6h/qS8/7OlKcUe4JNGe/V3abw2N0VpMIIZg/fz7PPvssixYtqrYWUhNcTuzygQceOK9bf+DAgaxevfqSr/v555/z3nvvkZ+fT+vWrfnss8/o1KnTdY/37BSZW+dPkJeXR/Pmzfnhhx949NFH+frrr0lMTKSsrIzZs2czYcIEXnzxxRvqwXXbBJmzkUqldO7cmc6dO/Pvf/+bgwcPsnDhQt555x3++c9/0r9/f4YPH87QoUMpLi7m5Zdf5qmnnrqpPirgvoCcvaB+tkWxzWarJm9zsQquqh4YqVR608d/pVyuNLpqmw4dOtRYz9HguztgNtlYuuwAlTYHSEAul6FQyYlvFUVsgyBmv7+awztPIYSgZccGlBZVXHAmKZVJcdid1R6TSEGlVWIzW0k/mo2huBJw+7LIFTIydxVhLDITEOmLzW6l0iKQaOSkH85iz+r99B3Xk4zkLA5uPIK5vJLIuDDa3dmKe15OpEWPJqTuPIFLCJp0aEir3s0oyCpCrVNhNlRWs0k2lZnxD/VFJpfy9XNzObYr3aPx5zvLh6GP9EOtU5G668SZijANPUd3pfc93di5Yh+GkoozAcaBv79bch8kWEyVrPpmHRaTlfCGbjtkQ7GRnSv2MfqF4dw5vg/6zELMRjMyrQST1YhFXYFL6uR0ei4h9YJRKOSUF1YgV8ho0y++Rj7Xs1myZAmPPfYY8+bNY+DAgTX++pcTuwR3U+OsWbM8f1/u+ztv3jyeeeYZvvzySzp37szHH3/MwIEDOXbs2HWn56u+u2+//TYhISF069aNZs2a8eGHH/L+++/zxRdfMGbMGMA9y9mwYQPdunWrkQB3Kf7SazJXixCClJQUFi5cyOLFizl69Cg6nY527drx9ddfExYWdkt0uAshMBqN6PV6j7hlVQVXcHCwJ5CYzWb27duHj48PLVu2vCU11C6F1Wpl9+7d1QRQL6cbd7UU5Jezbk0yp04Vo9Ypadu2Hi1bRPDZtCXkZ5Wg1qqQSKDSZEWukOG0WAmLCvCUKdttDnJPFSJ1OQkM9sInQIfT4aI4twyVTondaiLnaAESiRSpTIJSrSQ4KpC8UwVoNHLqN4skK70AY7kFp8uF0+7EN0RL9xGtOLwmFZvZhkQqRQJENgnnH++MIyjy/LU0IQT/e20Be38/jJefDpVWianMjMPhJOnxgZTpy/lt1kaCo9yKAS6Xi4LMInyCvHhu9hSslVby0vMpPl2KTC6jQasYDm0+wrLPV6EN0BAWGYpwCQoyi/AP9aXdnfGsmbWxWhUZQN5JPY07xjLx7bEs/mglBzakYDFaUWkUNGrfgKD6fvyxcBcVxUYkUglevjq639WJu54YWqNtBytWrODBBx9k7ty5jBgxosZe92JcSOzygQceoKysjKVLl17x63Tu3JmOHTvyn//8B3DPOKKjo3n88cd58cUXr2lsZ88cly9fzn333ceGDRto164d99xzD/Pnz+fVV1/l9ddfB9yVZsOHDycqKoqff/75mo55NfytgszZ/PLLL4wdO5bevXuj1+s5dOgQPXv2JCkpiWHDhhESEnJLBBzAI29TUFCA0WgkICAAb29vTp8+XSM2z7VBZWUl+/btw9vbm5YtW7rXBM7oxhUUFGCxWC4YWGuCNfN3s/ibzQSE+nq6111OFwW5ZWi1CuyVNo+Ips1ip36zCBo2Dmb3msOYKyxn7IF9aDkghqLMUvYsS0Xno0GhkuPlr0OhVJC2/xQ2sxW/UF9KC00oVDKkUgkWkxW5SgHCiVYDukAtKrUKuUxOaU453e/qxL3T7nJL829MQZ9RiM5XS+s7WqDz0fDr1+s5sCEFW6Ud70Aveo7sRNfE9rw1+hPMhkoCw/8UPHU6XeSfKuC+10aCgGWfrcZwxrxNpVMR3ToMY7mJ0lOGMzM1CQHhvoyemkja3pOs/d9mIuOqFw0UnS4hOCqAhq1j2PjzNnwCvdH6aLCYrJTqy2jTtyUjnx3Ksd3plJcZ0IWqcKkcCCGq9VZdT8BZvXo1999/P7NmzWL06NHX/DpXw8WCzNKlS1Eqlfj7+9O3b1/efPPNi3rU2Gw2tFotCxcurPY6EyZMoKysjGXLll3XGFeuXEl2djYGg4GpU6d6Hh88eDAHDhxg5MiRyGQydu3aBcD27duv63hXym2ZLrsc69ev595772XOnDncfffdCCE4efIkixYt4qeffuLZZ5+la9euJCYmMnz4cCIiImr1Iq7T6WjYsCENGzaksrKSkydPkpGRAbh9PbKzswkJCbkl1mKuBKPRyL59+zw9SFXv7bm6cQUFBWRlZXHkyJEaKY2u4lRqHkgk1eRRBCCTS4mMDSGueTiHtqUjkUpo2zOOPknt8PHX0WtEB47sOEHGkdOUV5YhXLiVcXWnCKkXVO0YOh8tNrOV0kIjcrkMmVSKzWpHrpDj5aulJL+M+nGRqDQyrFYrVqsVh9TBjlV7aNavEb99vons1NwzszxY+7/NjP2/uxj5zFAGPtgHU7kZv1BfVBol1kobdov7tc/GrU3m1gnbttQt4R/W0H3zpM/Rk7zxGBNeH0NY/VByT+QjhCAkOojwBiEYS00eL54q+2khBBaThfBGYez7/TA6P53HYbMqhXd8dzqVFVa6DuvgGYcQwpMKTktLO09V+WpSpOvWrWP8+PHMnDmTUaNGXeUnX7MMGjSIu+66iwYNGpCens7LL7/M4MGD2b59+wWDaFFREU6nk9DQ6o2zoaGh121AZjQaeeihh8jPzz9Pz2zVqlX83//9H8eOHUMikTB48GBeffXV6zre1fC3DDI9e/Zk+/btxMe788QSiYTY2FimTp3K888/T3Z2NosXL2bx4sW88MILdOzYkcTERBITE6+5b6amKCkpIT8/n/j4ePz8/Dw9KsePH8fHx8ezoH4jF/Kuh7KyMvbv30+9evVo2LDhBd/Ls3XjqgJrQUGBR8bnes9T562GMym60lIzZeVmnE6BcLmwWB0k/aM3dz3c57z9CrNLWDXnDwpyi5FKpBz7I4fgSH8kUinmCgtab3fwczldADRqW5/jB7Kw2+w47A7kSjnB0YFnFmXdMw25XI1crkCnA4lDisPhYPU360ndlU5ApC9aLy1KuZLi06UsfP8XGrWtj5e/Di9/nSfNqNIoqd8ymkObj+ITeJaIZrkZlUaJqcyEqdRM+Bm76PLyclReKoRdwsH1R+jyQXuS/0hl5/K9WExWlBoF8T2bEd00gsyUHHR+WqQyGRXFFQSE+9G8a2MOrD3sCTBVqHVqDMUVlBWUV+uBkUgk+Pn5eTS3qm4gcnNzSU1N9XyewcHBl6wq3Lx5M/feey+fffYZ48aNq/XZ+z333OP5//j4eFq1akVsbCwbN26kX79+N20cVqsVLy8vduzYwbhx41i/fj0pKSm0aNHCk0p76623qjVn3kz+lkFGoVB4Asy5SCQS6tWrx1NPPcWTTz7pqSpZtGgRr7zyCq1bt/YEnNjY2Jv2Ra+abWVlZdG2bVtPD0x0dDTR0dGekmG9Xs+JEyfQ6XTV1AZuBapUCOLi4oiOjr7i/TQaDTExMcTExGCz2TwptarzrAo4V6qq0L5XE3auTyUnqwTzGUFMzswYTqYXsmbRXgaP6lBtH3OFhbnvrqAwtwT/MF98vL2w2ezkZxbjF+RFRUkF5UUGbFYHlWYb3v46Bj7QG8vM9ZQXVuDtr0Xro0WhklOcV4ZcLsNmssAZdWeXy4WpzEzrO1pw8kAmIRFBqLyV2Gw2TCYTMp2E/MwC9m08SIsuzVg/9w/2r03G5XLRsmdT2t4ZT0ZKNqdP5KPz0WC3OnDYHXROaIfGWw0SPAHG5XLh5+dHibmUihIjK2euZe3/NqHxVuMT7IXFaGX7L3tp068Fve/pxsGNKbgcLlr3bUH/+3sRHB2IxluDuaLSY/Dmfo/MqHWqC64pnY1Op6NBgwY0aNDAI8tUpSChVqs9Aedsi/StW7cyevRo3n//fR544IFaDzAXomHDhgQFBXHixIkLBpmqdUa9Xl/tcb1ef81N0wsWLCA1NZWHH36YevXqMW/ePAYPHsy4ceNYvHhxNeXp2ggw8Ddek7lahBAUFhaydOlSFi1axIYNG2jWrBmJiYkkJSVVS/vUNGf3wLRt2/ayPTB2u72a2oBGo6l1+f78/HxSUlJq1Imz6jwLCws9qgpV53n2BepchBAs/HoT82dvQ7gEUokEiVSCt58WiUSCzkfNjG8eQIKgotSMb5AXu9cd5ptXFuIf5oOPz5/vv6HYiFQmpWdiO5Z/sxGjwYJCKUelVaLz0RAbH03avgxkMikabxUWsw1bpZ3IBoEUnszHbrPjcgrMFRa8/b0Y9XwCy//7G3KlDK8z0jXCJbDarOSe0NNhVEtO7siiJKscnY8OuUKO2VBJRKNQhj82iIPrk0k/kImXv45OQ9rQfUQnDqxP4X+vzkfpI0cql+Ln5wcCctPz6TK8A0e2HsNus+MX8mdvmLHUhMPm4Pk5U/AP8+X47pOcOuzudWnSKZZju07w2+xNZ/xwtNWsle9//drSWA6Hg+LiYs/nuWnTJo4dO0b79u157733eOutt3jsscdqTZX9ci6XOTk51KtXj6VLlzJ8+PALbtO5c2c6derEZ599Brh/2/Xq1eOxxx67poX/6dOn8+abb/LGG28wceJEQkND0ev1DBo0CLvdzpIlS2pdgfpvOZO5FiQSCSEhITz88MM89NBDlJaWsmzZMhYtWsS7775Lw4YNPYrRzZs3r7FKr6oemKoenitZj1AoFISHhxMeHo7T6bxgj0poaOglL8Q1SXZ2NmlpabRu3ZqgoKDL73CFnHueVaXRVbYGF2tylUgkxLWuh85rHwqlHOmZBk2FSobd5sBosDD3w9Uc35WOxWxD461C7StFKpHi7V19VihTyHA63NI0Urmc2FbRyOQyhBCUFVZw6shp7ry3K/s3HqWixISXj5au97ah/71dSdlyjJ9mLOV0mh6pXIal0s6ST9cgV0gpLzSg83UHPYlUgrXCRkCIP5EhURzIS8U/3BencOISDryCteQczyXn2Gkm/GsMZfpy9q07TEleGduW7aFp51j8or05nZpPQLA/huIKjCUmAiL8ad4ljr1rDuIdWP28NN5qCrNLKMgqYuPPW9m2dA9Wiw1wrw91HtaO/hN6sWvFfgzFFSjVSnqO6sKwRwdc8+cpl8sJDQ0lNDQUl8uFTCYjLS2N999/H4vFwoYNG/Dx8SEhIeGii+s1ydlilwCnTp3iwIEDnraD6dOnM3LkSMLCwkhPT2fq1Kk0atSoWjn1uWKXzzzzDBMmTKBDhw506tSJjz/+GJPJxIMPPnhNY3zttdfw8fHh9ddfx2az8fDDDxMeHs7atWtJSEigS5cu7NmzhwYNGlzfm3Ed1AWZa6Cqv+XBBx/kwQcfpLy8nOXLl7N48WL69OlDZGQkSUlJJCYm0qZNm2sOODabjf379yOTyejQocM1VVjJZDLPD/dCF+Kz5W1qugT67BRfu3bt3HfQNwiZTOZZSD5bNfrsJtfg4GBPykLnrUahcisxn+186bC7qDRa2PHrIbQaBTKllMK8EsiRIJXIqhmYCSEwlplp0DyS1L2n0HqrPaXPEolbkqYwpwS/UF9envMIhhIjhmIjIAHhFr80lVuIjAtD6+NuAC4vqqCi1IxSrST3hB6NlxqbxYZEIqHffT0p0xuQSaX4BfghhMDhcGC12XAKJ1t+3YHET7Dx6+1UFJmQIEEAGn8l3e/vQKtuLTm08QgOu9PjVeMX4m7StBgt1Ro9LUYrKrUCfUYhWxbtQuOjITAyACEEpjIz25fuYeK/x9JvXE9K8srwDfLGN7jmlHulUim+vr5s2rSJF154gaFDh/LLL7/w2Wef8f3337N27doaO9bFuJTY5RdffMGhQ4eYM2cOZWVlREREMGDAAP71r39VK2Q4V+xyzJgxFBYW8uqrr5Kfn0+bNm1YvXr1ecUAlyI3N5eAgADPDefTTz+NTCbj5Zdfxm638+ijjxIREcHKlSt54oknakwe61qpS5fVMBUVFfz6668sXryYX3/9laCgII9FwdV44tzoHhiXy+VRGygoKEAIUU1t4HqPJ4TwyPS0a9eu1taFhBAXLI0OCgrm6xkbyEovxCdAi1zunsWUFZtwmq2EeCtRaRUYTUbUajWWCru7/FgmQS6XolArsBitaLzV3P/SMOa+swKXU1TrbBdCUJBdwj3PDKJhyyjmfbCK7ON5uJwC/xAfZBIozNQTGhNcbZ+8k4V0GBCPSiPn5KEsVBolTTo24o5x3Vn3v82sm7vlgiKaLXo1Ju+kntPH89AFaVGqlDjtDsr1FbTs1pxnv5uM0+Gk0mihILMImVxKdNNIFn2wgo3ztuMdoEPrraHSZMVQaHB396sV7Fyxj/CG51gJpOvpMqwdE/415oZ8bkeOHGHw4MFMmTKF1157rdqM22q13vJGgTXJ2X0wx48fp1mzZnz55Zfcd9991dRJPvvsM5588klee+01xo8fX6uzl7Opm8nUMN7e3owZM4YxY8ZgNptZs2YNixYtYsSIEXh7ezN8+HASExMv6YlTXl7ucYK8UT0wUqmUwMBAAgMDadq0qefOPzU1tZpfzLU0RbpcLlJSUjAYDHTs2LFWjdwuZKldUFBATk42LXv4UlpSTlmxEanU7dESHOpD+Uk9crUMo8mIRqNBpVQhEe4UWN+RHTm25yTlRUaa9KzPHaM70aR9A3avTWHP2hR0fhpPgDaWmVFrlUQ0COarl+dTmF2Cb5A3UpmU4vwyDEUV6DSK88YrAbQ+GgZN6sOPby4hbe8pslPz2LZsD407xqLSKinVl+MX4p45VJSYkMll1G8WTeq2E0TUD0OukmMwGEACam8Vx/alsXPDbiwldtbO+oPS/HKkUglhDUJIfHwQDruTfWsPU5RTglKjpN2AVox5IZEf31py4QpAqQSL2XZDPrNjx46RkJDAQw89dF6Agct31d8OnC0RU3X+mZmZNG7cmMmTJ/P0008jlUoZO3asp8Ly0Ucf5aOPPmL69OmoVCpeeOGFW6JAoi7I3EC0Wi0jRoxgxIgRHk+cRYsWMXbsWJRKJQkJCYwYMYLu3bt7UmE7d+7EZDIRGxtLTMyV29JeDxKJBH9/f/z9/WncuLHnzv/EiRMkJyd7Ak5wcPBlDcqcTicHDx7EZrPRsWPHW8qV8NzS6NatK2nTIZfdm1Mp1JcRFhlAWLA/P87Ix1Baga+/t2f8dqsdhVJO/3s6M/Kx/hhKjGSknMblEthtDoZM6EHGkRwKc0qQK+Q4HU5kchn9xnQmN72AotOlBEUFeHpzAsP9MJaaqaiw4nK6kJ553GaxI5FKiGkeyXcv/Uza3lP4Bnnj5a/DVGZm3++HadKhATnH89zOmwLUOiV97ulGw9b1cTkFEpkUo9GIRCrF388Pq9pKWWE5x/eeZPP/duC0u/AN9kEul5OVeprvpy/k+TmPcucDvSk+XYpvsDdhDdz9NHHtG3BgXTIOm8MjfOmwORAuQeMODS/8Rl8HJ06cICEhgXHjxvHmm2/eEhfJ2kAqlVJcXMxTTz3F999/z6ZNm5g0aRIrV67k888/R6vVMnnyZOx2O/fffz86nQ6TyURSUhJNmzYlMTHxlnnv6oLMTUKtVpOQkEBCQgJ2u50NGzawcOFCHnzwQVwuF0OHDsXLy4tvvvmG1atX37QAcy4XuvPX6/VkZGSQkpJSrQv/3ABit9vZv38/UqmUDh063DDHzJpCo9EQFxdLXFysp5Q2KzMLr2AVRZkGVGorUqkUu9WJucJCtyGt8Qn0YtXszaz5fitmQ6V79hMVwH0vD+PJj+9ny/J9pB/KxttfR8c7W9K+b3MWfLwG+LP5E9zvs3eAjoriCvIzikAiwWioxG514OWv49SR05w6lEVAmB8qrft99gvxoTi3lKLcMp748h+c2H+K/FOFKBRywhqEEBDmh0+QF/nZBfgGe+F7xiqhothIaHQwtkInUuQE1/fDbrdjs1lRessoyC5gw4I/GPHYUNL3Z7By5u+U5JZSr0UUnYa0o0F8tCdtB26TtYatY+g4uE2Nfh4ZGRkkJCRw11138d577/3lZJJqmgMHDrBhwwY6derEvn37+OKLL2jSpAkA7733Hkqlkqeeeoq8vDzatGnDtm3bWLt2LW+//fYt1ZhdtyZTyzgcDjZv3swLL7zAgQMHiIuLo127diQmJtKvX79b6stSlWoqKCigoqLC04UfEhKCEIJ9+/ah1WqJj4+vtZr86+H06dMcO3aMsIAo5n2wlpwTBdhtDhRKGQ3jI5nwf8PIOVLA7OlLkcokePnpsFrtGEpM+Ad58eoP/8TvAovfa3/azoKPVxMU4e+ZsQAUZJcQ2yoaidPJnnUpIARabw0KtQKbxY7DYiW2ZVS11zIbKrFb7bw87wmWfrKagxuOYLfaQSLBJ8iL4MZ+pO/IQCqRo9aqsJqtqHVq7nk5ifVzt3D6RD6BEX9KzzjsdnLT9TTu1QCVl5zk1SeQSqSodWpsZisaHw2jnx9OSX4ZB9anIJFA6zta0Ht0V3yCas5OIjs7m4EDBzJo0CD++9//1niAuZSist1uZ9q0afz666+cPHkSX19f+vfvz7///W8iIiIu+pqvv/4606dPr/ZYkyZNrrt7/2w++eQTnn76aWJjY0lLSwPAYrF4rgsfffQRX331FRaLBZVKxZIlS2jWrFmNHb8muLVvNf8m/PTTT+Tn57Nnzx4MBgOLFi3iueeeo6ysjEGDBpGYmMiAAQNqvYv/7Ca6KoOy/Px8UlNT3Xfm3t7ExcX9JQNMVlYWJ06coE2bNgQEBPDqrFiO78+ipKAclbcUlb+EtJOprPp6F1aLBf8wPwqKTVRWOhACjLkVfP5/i5n6n/tQKKv/rDr0b8Han7ZTeLoUv2BvZDIphlITCpWcfvd0Zsl/fsc/xKfaxb8wpwRzufmMcrL7c3c4nBjLzQSE+bFzxX72rD6Ed6AO/zBfXE4XuafyMRlNjH9tDIc3HaUgq4iw+iF0SexA6z7NSdl6jIzk7GoLyVKZDKVSSeNmjdg0fzsKtQK1lwKH04kmUE1FoZF1P/zB/81/+rrKky9FXl4eQ4cOpW/fvnz++ec3ZAZzKUXlqiKbqmbr0tJSnnzySYYPH86ePXsu+botWrSoVulWU7P3qjWZ4OBgHn30UbZv307Xrl1Zt24dWq2WyspKNBoNTz/9NHfffTcOhwNvb+8abRGoKepmMrWMXq9n4sSJzJw5k6ioP+9aXS4Xu3btYtGiRSxZsoT8/HzuvPNOkpKSGDRoUK2Ykl0Ig8HA3r178fX1PSPTUoqXl5dHbeB6zMduFqdOnSIjI4N27dpd0qzO5XLx/JD3KC+uwOyQYLMLpBK3RpjDIVCqFYx8pDcjLyBJk3Ygk5/eW4k+sxiXy4VPgBeDJvSkcdt6vD3+SzQ6tSctBuB0OMlIyUGtkuET6IWh1IzJYEEIQYMWUbgcDioNZgLD/XEJgbGiAiGgstTKfa/eRcfBbVjz7Qa2L9+L1WylfotoWvRowq9frcNaacMvxAeXS1CmL8c/1JeBD97BT28tJjgmCKlUisvlwmazYSwzYjXbuOffw4iLj/VIv9RUvl+v1zN48GA6dOjAnDlzbsoNypU0Vu7evZtOnTqRmZlJvXr1LrjN66+/ztKlSzlw4ECNje3sBf+zH1u3bh0vvPACMpmMjRs3en5X27dvp2HDhldVAn2zqZvJ1DKhoaGsXLnyvMelUildunShS5cuvPPOOxw4cICFCxcyY8YMJk+eTP/+/UlMTGTIkCE3ranyXEpKSjh48CANGjSgfv36gDv1UCVvc/LkSTQaTTV5m1tlMRKq2z1fiZuoVColpmkE+zcdw+EEmUyCRCJBuFyAC4lUsG7JXroNbM7JA9mYDZVEN42gcfv6xLWJ4f/mPELGkdPYrA5imoaj9dZQlFuKTCbF4XByds2Uy+nCJ9Cb8JgAUvecwmFzIpNL8fLVUaIvx1hqIjDUG5cQVFRUIJVI8Pb2wlJmw1hm4pvnf+DQpiMo1UrkShnJW9yzmN73dGX/2mRK8sqQSiXENI9i1PPDqDRakcpkuBwupEp3pZ1arcahdqKQK6lXP5ry8nJOnjx5UemXq6WwsJBhw4bRunVrZs+efUvNgMvLyz2aa5ciLS3NLZKqVtO1a1dmzJhx0aB0Oaq0xXJzc/njjz8AaNasGa1ataJPnz68//77vPjii3Tv3p158+axdOlS/vvf/7Jly5ZrOt7Nom4m8xdDCEFycrLHE+f48ePccccdJCUlMXToUAICAm7KhbygoIDk5GSaNm160bz12Y6YVbIvVQHHx8enVgPO2X087du3v+IZ1+Gtx/n4mR8pNzo8QcbpdCFXyPAK0FBptKJyVmI32d3Ww2oFLbo14aEZY6pZIJ/Nf576ngObUgkM93VXpjndfjXhDYPpOCCexZ+swTtQi1qjcqsJuASnUnKQSSEw2huZVIqXlxfWShvGUhNDHu7Lyi9+R+erQ61Tec63IKuQVr1b8MhH48k6ksOpw1lYTFa8/HS06NaYTx/9jqKcIoKi3X1SDruTouxi2t3Zin9+MgFwXwiLi4spKCigsLDQk9IJCQkhICDgilNdJSUlDBkyhNjYWObPn3/TzQIvNZOxWCx0796dpk2b8sMPP1z0dVatWoXRaKRJkybk5eUxffp0Tp8+TXJy8lVnGqpmMCdOnGDIkCGo1WrUajUnT55k9uzZJCQk4HQ62bFjBy+++KLHC2vWrFn07dv3qo51s6kLMn9hqi6UixYtYvHixRw+fLiaJ05wcPANuZBXLZC3bNnyiruJz704yeXyahbMNzPgCCE4cuQIpaWltG/f/qr7eBZ/uZ55X2xEOF1IpBKUagV+wd5YTDYMJUZ0woZ3oBaH04HVZMVqdtD7no7cOzXxgutqBdnFfPHcj5w+8adwYkCYL/94azTrf97OrlUHCT7HEjo/sxCzwYxaJUXnraOizIzVYickOpB2/Vuyfcmu8+wHKkrcOmvTlz/Pty/86HbfdLpter0DvOg9uivblu2hvNDg2ScyLozJH0+o1jBaxdnKCoWFhTgcDk/14aXcXMvKyhg2bBjh4eEsWrTopve9XCrI2O12Ro4cSU5ODhs3bsTH58pVDMrKyoiJieHDDz88T27/StDr9XTr1o0hQ4bw2WefkZ2dTatWrSgvL+ebb75h4sSJbsUFk4kDBw4QERFRTQDzVqUuyNwmVEm4LFy4kCVLlrB37166du1KUlISw4cPJzw8vEYu5BkZGZw6dYrWrVt7lKCvFpfL5ZG3KSgouKTOWE3jcrlITk7GaDTSrl27a6rec7lcvP3P7zl+KBuNToVaq8RqtmM0mBHmSiIjfT1VZC6Xi1J9OQqtnKRXe+Lt401QUDBqqYbA4ACPnIzFZOXg5lT0mUX4hfjQ9o7mePvr+Pm9Ffw+dwvBkX/OUF1CkHdKT0RcMAohI3XPKYRLoNap3N4vQuCw2YiKDa1WzVaSX+Z+7f7xrP52A35B3h4XzeK8Urz9vXhi5j9I35dBeVEFYfWDads/3jPGSyHOpO2qAo7JZCIgIMCTVqsKJAaDgaSkJHx9fVm2bFmtVE9eLMjY7XZGjx7NyZMnWb9+/TXpo3Xs2JH+/fszY8aMq9pPCMEXX3zB7t27PXbOPXv2RKVS0bJlSz799FPmzp3Lvffee9Vjqm3qgsxtiBCCrKwsjyfO9u3b6dSpk0feJjo6+qoDjhCCtLQ08vLyaNu27VXd4V2Ks++GCwoKPDpjoaGhBAQE1Gie3ul0cvjwYSorK2nfvv11NYqWFBj49u2VpB3KxuFwolQpCIv0JXtvOsHR1YOvqdwMwNu/PsfWFbv4bdYflOSWIVfIaNm7CXc/M5TImAvfBJw8nM2Hj3yLw+bAN8gbARTlFQMSHn1/HEs+WUPeqUICw93ac0IIinPLMJYZ8fFVExQZgEQqwVxRSUWxkeFTBvLHoh2YyiurlVu7nC6KckuZ8MYoeo3ues3vi+ecTSaPhH9ubi7//ve/ueOOO9i9ezcqlYoVK1bUWrXkhYJMVYBJS0tjw4YNBAefP3O7HEajkXr16vH666/zxBNPXPX+hw8fJi0tjbvuuotx48aRn5/PkiVLKCkpoUOHDpSUlPDJJ5/w+OOPX/Vr1yZ1C/+3IRKJhJiYGJ5++mmeeuopcnNzz/PEqRLwvJhx2Nm4XC6OHj3q+bLXZMWYVCr1qNo2adLE46B47NgxbDYbQUFBhIaGEhgYeF3loU6nkwMHDuB0Oq9ZbPRsAkJ8eO6je8hJL6Ss2Eh4vQCKsov56J8ZWMxW1No/10LMFRYat29A6o50ln7wO3arA523jkqzjV0rUzh19DRJL/UmLDyMkJCQarO5hvHRjH52CIs/XUNRXhk2mw2NTsXwR/oTFBFA0elSfAK8qkmQ+AV747A7UKiV6DOLMJusOOxOVBolaQcyMZZVolBUD96SMy6aFpP1ut6XKnQ6HTqdjvr161NcXMyIESOYP38+x44do0mTJrz99tskJSXRvn37m5IqvZSicnh4OHfffTf79u1jxYoVOJ1O8vPzAQgICPDcjJyrqPzcc88xbNgwYmJiyM3N5bXXXkMmkzF27NjLjudCBmLx8fE0bdoUvV5Penq6R2HZbrfTv39/GjZsWOuy/ddCXZC5zZFIJERGRvLYY48xZcoUCgoKPJ44b7zxBs2aNfMEnAt54px999+xY8cbmt4420ExLi4Oo9Ho+cElJydXUxu4miBht9s9qtPt2rWrsV4GiURCdKMQohu516UCQn1o0TWOg5uOYjXZkCtlmCssqLUqBj3Qi+X//R2bxY5fiB8FRSZsNnBJFJw+aWLjt0cY8UwgycnJVFZY8PHypWHT+gQHB9NrZCeadmnIrz+vRaVU0X9EL4KjAslOzXV/XufkIoQQKDVK7nkpiXnvLsdiceDnp0OhVpC89Rh2qwO5HHRn/HPAna5TKOXUj79yM7krRafTsXXrVnx9fcnMzGTbtm0sXbqUfv36sWvXLk8X+43kUorKr7/+Or/88gsAbdq0qbbfhg0b6NOnD3C+onJOTg5jx46luLiY4OBgevTowY4dOy47C3I4HMjlcsxmM9988w1FRUX4+fkxefJktFothYWF7Nq1C4vFAsCaNWtIT0/ngw8+IDIy8nrfiptOXbrsb4oQgpKSEo8nzrp164iNjfWYsDVv3pySkhLee+897rrrLtq1a3dTK4DOxWg0elJqRqPRk+8PCQm5ZNrLZrOxb98+lEolrVu3vuFlspVGC79+u5Ftv+zDZrERFRfO4Im9ady+Ac/e8S+3KrTZhdXmRCIBiQScDhdKtZJeSe2Q2qzsW5eMzWrDK0hLq6FxtL8znoqKCnx9fYmPj/fMWlwuF/8a/RmZR08TFO6PRCpxp8vyygiOCqDnXR1Y+NGvbsXnMxYEwiXQZxUhAeRyCVovDXab20Wz/YDWTP54fI2uiVmtVsaNG0dhYSG//fYb/v5/NpzabLZbStvuZlBVRVaVso2KikKj0WAwGEhNTfXcTE2ZMoUvvvjCE7i++eYbxo8fX9vDvybqgkwdCCGqeeKsWbOGsLAwnE4nISEhrFy58paxcAaorKxEr9dTUFCAwWDAz8/PE3DOnmlZrdZqUjc3UwvLYXdit9pR61SeMucXB8ygSG/AZBVIpHhmIU6nC7W3GrvFjsruNkkTSKiosCJTSOl6T3Oa9aqP0+k8bzH92O6TfPnsD5QXVyABhAAvPy0T3xrNzpX72bFiH0GR/tXGVqo3EBjpT0T9IE4ezETro6H7iE7c+UDvap4y14vNZmP8+PFkZWWxbt26m2I0dqtxoeZKIQRjxozBaDTy66+/AjB48GAyMjLYtGkTISEhGAwGFi5cyOnTp+ncuTMDBtwYtYWbwW0VZG6GltDfgcOHDzNgwAB0Oh2FhYUEBAQwfPhwRowYQYcOHW4p4UKLxeJp/iwrK8PHx8djv5ySkoKfn1+NOpVeD8v+8xsLP/uNSqfE3WODxJ2bl8vQeKupKDERHKDGahcYjTaEELhcArlCxripQ+g3tiP5eXpOHj2F1WkhNNIdWCVWGft+O4I+s4igCH+6DGtHZKNQfnhrKWu//4PAiOol4sW5ZbS5ozlP/HciLpfLbS9Qw+sidrudSZMmkZqaes0L6X91zpbv+eabb7jvvvtQq9WYTCYSEhJ444036NmzJ2PGjCElJYXVq1cTFRXFnj17aNq06S11Y3c93HZrMjdKS+jvQmpqKgMHDmT06NF89NFHWCwWVq9ezaJFi0hKSsLHx4dhw4aRlJREly5dar1LW61WEx0dTXR0NDabjcLCQvLy8khLS0OhUKDRaDCbzbfED3bQpD6kHcxix4ZjOB3ui7tMLsMvxJeKUhMAAikVxkrPPhKJe6az9KuNmEuN7F9zkJK8UqQyKU26NaTj6HisrkrCOvgQPziW0NBQT2FGx0Gt+WPhTgzFRnwCvEACpjIzUpmUrsPbA9yQ4OtwOJg8eTJHjhxh/fr1f/sAM2rUKNauXcuYMWNQq9WoVO6y8fz8fCZOnMjBgwf59ddfiYqKwmAwMHfuXDp06MC99957S9wcXS9//TM4B7lcTlhYmOffrSgYdysTGhrKq6++yscff4xUKkWr1XLXXXfxww8/kJ+fz+eff47JZGLMmDE0btyYp556ik2bNuFwOGp76CiVSnx9fTGZTERFRdG4cWOMRiM7d+5k27ZtnDhxAoPBQG1N3lUaJc/MnEjr7nEoNUq0vlr8Q31x2NzvnUIiMJndMxgJAonUPcOQy6VYTDaWzdyAPqMQuVKOcAkOrDnCppl76NG9BzExMZ5z3bp1K2lpaYTE+nPX04ORK+WU5JdTnFuGQMLAB3vRfkD8DTlHp9PJ448/zt69e/n9998JCwu7/E5XyebNmxk2bBgRERFIJBKWLl1a7XkhBK+++irh4eFoNBr69+/vUTC+FJ9//jn169dHrVbTuXNndu3adU3jOzvA3HfffezatauaCoBMJiM+Pp4nnniCzZs3s3z5ck9T5datW1m9ejXh4eG3RYCB2zBd9t577+Hr61sjWkJ1XBybzebxxFm2bBlCCBISEkhKSqJ37961sqBrMBjYt28f0dHR1UqznU6nR96msLAQhUJBSEgIoaGhtaL7Ziw38/27v7J/YyoOhxO1VknnAS3Zu3wP+flGXBIJUikgQLjAO1BHRakZqctJRMSfciU2ix2zoZKnvnqI1n2a43Q4Sd11gvxsPaogOXapFZlMhlKoKUwrQ6PW0KxLHJGNav7CD+71h6eeeor169ezcePGG/a7W7VqFVu3bqV9+/bcdddd5/W8vPPOO8yYMYM5c+bQoEEDXnnlFQ4fPsyRI0cuWh05b948xo8fz5dffknnzp35+OOPWbBgAceOHbtiVYtzmTJlCosWLfL4MAEUFRWRnJxMq1at6N27N3K5nDfeeIPo6GiOHz/OI488wvPPP8/LL798Tce8FbmtgkxNagnVceVUeeIsWLCAZcuWUVlZSUJCAomJifTt2/emdHWXlZWxf//+amKdF8LpdFZTG5DJZNXkbW7m3WNpgYHSwgpCovzx8tWyd90hPnr8f1RaBBLc6s5qLzU6Hw2FOaVolRICg6o3MJbklzPmheE07xbHzGfmkndSj8vhQqVV0nNUFwb9sxfFJW45HyGEx+U0MDCwRlOdLpeLqVOnsnLlSjZs2HDT5E7ObawUQhAREcGzzz7Lc889B7jFLkNDQ5k9ezb33HPPBV+nc+fOdOzYkf/85z+e84mOjubxxx/nxRdfvOpxrVu3jhEjRjBmzBi+/vprwK33161bN+666y7effddcnNzGTVqFGVlZWRnZ9O8eXOGDx9+WwUYuM2CzLlcr5ZQHVeP0+lk69atHouC8vJyBg0aRFJSEnfeeecN6fIuLi7m4MGDxMXFER195X0eLpeL0tJST8ARQnjEHgMDA29qwKmsrGTPnj04jVJ+eGMdpvJKVFolUpkUu9WOzWxFLXHiH/znzZLT4aS8sIIH3hrDiv/+TkFWEV4BXsjlMiqNFmwWO2NeHM7Qh/t5KgirztVms1XTGbue8nSXy8X//d//sWjRIjZs2HBTGwbPDTInT54kNjaW/fv3V+t56d27N23atOGTTz457zVsNhtarZaFCxdWmxFNmDCBsrIyli1bdtXjMhgMzJgxgz/++IO+ffvy2GOP0bdvX1q2bMn333+PTCZDKpViNpvR6/WUlpYSEhJSze7jduH2SPpdBD8/Pxo3blyt07eOG4tMJqNXr1588sknZGRksHr1aqKjo5k2bRr169fnvvvuY+HChVRUVNTI8QoLCzl48CBNmza9qgAD7kXvwMBAmjVrRq9evWjdujVyuZzU1FQ2bdrE4cOHPVI3NxKz2cyePXsIDg6m96BuvDLnYToPikehkiNXyOgyqBVDxnVBOJyYKyoRQuCwOygrqCAg3B8EFGYX4xvsg1KlQCqTovPVIpVKWP/DFk8FWdXvoXv37nTs2BEvLy9P2ey+ffvIycnBar26jn8hBG+88Qbz5s1j7dq1td6RXtWpf66/SmhoqOe5cykqKsLpdF7VPhfC5XJ5/uvj48O0adPo27cva9asIS4ujri4OH7++WcUCoXHs0er1dKgQQPatWt3WwYYuA2ry87GaDSSnp7O/fffX9tD+VsilUrp2rUrXbt25d1332X//v0sXLiQt956i0ceeYT+/fuTlJTEkCFDrkn6Pz8/n5SUFFq2bHndpk0SiQR/f3/8/f1p3LgxBoOBgoIC0tLSOHz4sCfNFBwcXKMVi1UBJjQ0lMaNGyORSKjXOIwnP7wXq8WORAJKlQK71Y6lopLdqw5QkluGVCYlJCaIR94fx7HdJ5FIJZ6GyyoUagWGYiO2SrtH8r/qXL29vfH29iY2Nhaz2UxBQQG5ubmkpqbi6+vrSSFeSqFaCMGMGTOYNWsWGzZsoGnTpjX2vvyVEEJgsVg871VVANHpdLz00kuoVCoqKioIDw/HZDKh0+kuKCtzu3JbBZnr0RKq48YilUpp37497du35+233yY5OZkFCxbw0Ucf8eijj9K3b18SExNJSEjA39//sgGn6oLYqlWrGi+RlUgk+Pr64uvrS6NGjTCZTOj1ejIyMjyLuFUB53oKHEwmE3v37iUsLIy4uLjzzlml/jOFpVApePj9+xj8j75kpGSj89XSskdTlGoF5UXuWaHd5qhm/WyrtBEZF4ZSc+lUmFarpX79+tSvX9/Td1QVYL28vDwB52xHTCEEH374IV988QXr1q2jZcuW1/w+1CRV1Wx6vZ7w8HDP43q9/jzJmCqCgoKQyWTo9fpqj+v1+iuqjmvdujWjRo3ilVde8TxWFWg0Gg3PP/88QghWrlzJ1KlTeeutt/Dz87tgo+btyG21JnPPPfewefPmalpCb731FrGxsbU9tDoughCC1NRUj0VBcnIyvXr1IjEx8aKeONnZ2aSlpdGmTZtrthu4Vkwmk2ddo6KiAn9/f89F+Gp8UUwmE3v27CEiIoJGjRpdV4Wb3Wpn+siPyEzORuOlQaaQUXmm1+aBN8fQZ8y1qSpXuZwWFBRQXFyMSqXil19+YfDgwRw8eJD333+fNWvW0LFjx2se+/VysYX/5557jmeffRZwr4+EhIRcduG/U6dOfPbZZ4A75VWvXj0ee+yxSy78P/zww+zfv5/du3cD7vfs7PWtqkBit9v54IMPWLFiBS1atOCtt97627RX3FYzmZ9//rm2h1DHVSKRSGjWrBmvvPIK06ZNIz09nYULFzJ37lyeeeYZunXrRmJioscT54033iAsLIwxY8Zc1hr3RqDT6WjQoAENGjSgsrKSwsJC8vPzOXbs2BWnmYxGI3v37q2RAAPuWc6TX07if68t5Oj2NOwmO75B3gx5qB+9R3e59tdVKIiIiCAiIgKn00lmZibp6emMHj0ah8PB8OHDqaio8Ag+3iwupahcr149nnrqKd58803i4uI8JcwRERHVFvXPVVR+5plnmDBhAh06dKBTp058/PHHmEwmHnzwwUuORa1W06hRIwCmTZtGs2bNGDdunOf5qhmNQqHg+eefRyaT8fXXX7NmzZpq293O3FYzmTpuH4QQZGZmVvPEad68OVlZWcycOZNhw4bVqn3zuVitVo+8TWlpKV5eXh6r6bOtEaoCTGRkJLGxsTV+DoXZxZgMZsIbhKDS1qzjpBCCWbNmMW3aNKZNm8aJEydYunQpdrudZcuW0aNHjxo93sXYuHFjNUXlKiZMmMDs2bMRQvDaa6/x1VdfUVZWRo8ePfjvf/9L48aNPdvWr1+fBx54gNdff93z2H/+8x/ee+898vPzadOmDZ9++imdO3e+6DiEEHz66afMmjULrVZLcnIy2dnZ+Pr6nrdt1YxGCMHatWu58847r+9N+AtRF2RqiM2bN/Pee++xd+9e8vLyzmsQq/rif/3115SVldG9e3e++OKLWq/G+SvgcrmYPHky8+bNo02bNmzbto02bdp4LAoaNGhwSwWcqjSTXq+npKQErVZLSEgIXl5eHDlyhHr16l2Rj8+thBCC77//nueff55ffvnFc5Gv8p1v0aJFrcwsbwUaNWpEfn4+jzzyCG+88cZF/Zb+Lmsw5/L3O+MbhMlkonXr1nz++ecXfP7dd9/l008/5csvv2Tnzp3odDoGDhzo8Yyo4+I8+eST/Pbbb+zbt4+NGzeSnZ3NpEmT2LhxI+3ataN79+688847HDt2rNYkY86mKs3Utm1bevfuTYMGDSgrK+PQoUOA+2JTm/I2V4sQgnnz5vHcc8+xcOHCarMImUxG9+7d/5YBxmazefqrRo8ezZo1a/jggw/Iy8u74PZ/xwADdTOZG0JNdSHX4WbFihW0bt36vD6YKk+cKhO2devWERcX51GMbtas2S3xw66oqGDv3r1ERUXh4+PjkbeRy+XV1AZu1ZnN4sWLeeSRR5g/fz5Dhw6t7eHUKpeajbz00kssXbqU0aNH8/DDD/8lDcZuBHVB5gZQE13IdVwdVR3tv/zyi8cTJzo6msTEREaMGEGrVq1qJeBU6anFxMTQoEEDz+Mul6uavI1EIiE4OJjQ0NBq9su1zfLly5k4cSI//PBDtfTv35GzA8ymTZvIyckhKCiI6OhomjdvDsCrr77KggULGDVqFA899NBVNwjfjtxW1WW3KtfShVzH1VHV0T5+/HjGjx+PwWBg5cqVLF68mAEDBhAcHOwJOO3bt78pF3GDwcDevXsvqKcmlUoJCgoiKCiIpk2bUlZWRkFBASkpKTidTk/ACQgIqLWmvVWrVjFx4kRmz5590wJM/fr1yczMPO/xRx999IKp6NmzZ59XAaZSqW5IGrrqO/PWW2/xySefEBYWRkZGBq1atWLs2LFMmTKFN954A6VSyU8//URxcTGvvfbaNQts3i7UBZk6bkt8fHwYO3YsY8eOxWQyeTxxhg8fjq+vL8OHDycpKYnOnTvfkIt4eXk5+/btu6xgJ7gvXgEBAQQEBNCkSROPxtixY8ew2WwEBQURGhpKYGDgTSsVXrduHRMmTOCrr77i7rvvvinHBNi9e3c1GZ/k5GTuvPNORo0addF9fHx8OHbsmOfvG5l2XLFiBR988AE///wzffr0ITU1lZkzZzJr1izkcjmPPPII06ZNw2azkZOT87cPMFAXZG4K19KFXEfNodPpGDlyJCNHjqSyspLff/+dRYsWMXr0aNRqtceErXv37jVyEa8KMA0bNiQmJuaq9q2akfn5+REXF4fRaESv15Oenu7xf69SG7geUctLsXnzZu69914+++wz7r333pu6VnSuesO///1vYmNj6d2790X3kUgkN8S35kIcOnSINm3aeOyQW7VqxdNPP83rr7/Or7/+yoQJE1Cr1bzxxhs3ZTx/BW6NxO9tToMGDQgLC2PdunWexwwGAzt37qRr12vrxq7j2tBoNAwfPpw5c+aQn5/Pd999h9PpZPz48TRq1IgpU6awdu1abDbbNb1+WVkZ+/btIzY29qoDzLlUaYw1atSIbt260blzZ3x8fMjKyqomanmtY70QW7duZfTo0XzwwQc88MADtVqMYLPZmDt3LhMnTrzkOIxGIzExMZ41uJSUlBs2Jm9vb89aGrjXAhs1asT999/P8uXLSU9Pv2HH/qtSF2RqCKPRyIEDBzhw4ADwZxdyVlYWEonE04X8yy+/cPjwYcaPH39eF3IdNxelUsmgQYP45ptvyMvL46effkKlUvHII4/QsGFDHnnkEVatWnXF+f0qT5tGjRrdEMMuLy8vGjZsSJcuXejWrRsBAQHk5uayefNm9uzZQ1ZW1nWtRezatYu7776bt99+m4ceeqjWq92WLl1KWVkZDzzwwEW3adKkCd999x3Lli1j7ty5uFwuunXrRk5Ozg0ZU8uWLUlLS2PBggU4HA7PexQWFkZ8fHyd3fsFqKsuqyFqogu5jlsDp9PJli1bPJ44BoOBwYMHk5SURP/+/S/oiVNaWsr+/fuv2tOmJrBYLJ4qtbKyMnx8fDyl0Vfq37Nv3z6GDRvGK6+8wtNPP13rAQZg4MCBKJVKli9ffsX72O12mjVrxtixY/nXv/5VI+M4t2z5nXfe4eWXX+bNN9+kX79+hIWF8cwzz5CRkcH27dtvWBrzr0pdkKmjjkvgcrnYsWOHJ+AUFBQwcOBAEhMTGTRoEF5eXqxatYqTJ0+SmJhY654gVQ2CBQUFlJSUVFNR9vLyuuA+hw4dYsiQIUydOpUXXnjhlggwmZmZNGzYkMWLF5OYmHhV+44aNQq5XM5PP/10TccWQiCRSDh16lS1svOqx8EtQTNjxgwcDgeBgYEoFAo2bdr0t2xKvRx1QaaOOq4Ql8vFvn37WLhwIYsXLyYnJ4fu3buzZcsWXnrpJZ599tlb4gJdhd1up6ioiIKCAoqKitBoNJ6iAW9vb6RSKUeOHGHQoEE8/vjjvPrqq7fM+F9//XVmzpxJdnb2VaWgnE4nLVq0YMiQIXz44YfXfPxly5bx7LPPsmTJEuLj4y+4TVpaGsXFxVitVjp27HhDXF9vC0QdtyWbNm0SCQkJIjw8XABiyZIl1Z6fMGGCAKr9GzhwYO0M9i+I0+kUX331lVAqlaJly5ZCqVSKQYMGiS+++EJkZ2cLo9EoTCbTLfPPYDCIkydPih07dogZM2aI0NBQMWrUKBEaGipeeukl4XK5avst9eB0OkW9evXECy+8cN5z999/v3jxxRc9f0+fPl2sWbNGpKeni71794p77rlHqNVqkZKSctXHrXoPcnJyRFJSkvjyyy8vuV0dV0bdKtVtSpWW2sSJE7nrrrsuuM2gQYOYNWuW5++r8UP5u7N+/Xqefvppvv76a+6//36OHj3KwoULmTlzJk888QS9evUiKSmJYcOGERQUVOszBJlMRmhoKKGhoTRs2BCZTMacOXMoLy9nzpw5GAwGRo4cSc+ePWt98Xrt2rVkZWUxceLE857Lysqqtj5SWlrKQw89RH5+Pv7+/rRv355t27Z5OvCvBolEwqFDh5g5cyYVFRWeMuULbVfHlVOXLvsbcK7MDcADDzxAWVkZS5curbVx/ZVZvnw5BoPhPE8QIQQnTpzwmLDt37+fbt26kZSUxPDhwwkLC6v1i1RGRgaDBg0iKSmJ9957jz/++INFixaxYsUKDh48eNON4G4lvvjiC958803Ky8tZvXr1TbMvuJ2pCzJ/Ay4WZJYuXYpSqcTf35++ffvy5ptvEhgYWHsDvc0QZzxxFi1axOLFi9m5cyedO3cmMTHRUyRwswNOdnY2AwcOZNCgQfz3v/+tNisQZy1s/1240Dn/+OOPvPbaa8THx/Pqq6/WNUxfJ3VB5m/AhYLMzz//jFarpUGDBqSnp/Pyyy/j5eXF9u3ba00r63ZGCMHp06dZvHgxixYtYtu2bbRt29YTcG6GJ05eXh4DBw6kV69efP3113/7z9npdCKTycjNzaWoqMjz/gD89NNPvP/++7Ru3ZonnniiLtBcB3VB5m/AhYLMuVQpRa9du5Z+/frdvMH9DRFCoNfrWbJkCYsWLWLTpk20bNnSE3AaN25c4wEnPz+fwYMH06lTJ2bPnv23DzBVvS8HDhzgvvvuQyqVUlFRga+vLzNnzqRz58788MMPfPTRR7Rq1YpHHnnkki6ZdVycuo7/OgBo2LAhQUFB1bzT67gxVGlt/fOf/+T3338nLy+Pxx57jJ07d9KlSxc6d+7MW2+9xZEjR2rE2KywsJBhw4bRpk0bZs2adVMCzOuvv45EIqn2r2nTppfcZ8GCBTRt2hS1Wk18fDy//vrrDRufVColKyuLwYMHM2LECLZu3cq2bds4dOgQa9euBWDcuHFMnTqVLVu28NVXX1FZWXnDxnM7U1ddVsNUXRT+arntnJwciouLqwl41nHjkUgkBAUFMWnSJCZOnEhZWZnHE+fDDz+kXr16JCYmkpSUdE2eOMXFxQwbNozGjRszd+7cm1o51qJFC88FG7jksbdt28bYsWOZMWMGCQkJ/PjjjyQlJbFv3z5atmx5Q8a3Y8cO2rdvz7/+9S8qKysZMWIECQkJHmNBgNGjRyOVSmnevDkajeaGjON2py5ddptiNBo9s5K2bdvy4Ycfcscdd3gk5adPn87IkSMJCwsjPT2dqVOnUlFRweHDh+tKmW8RDAYDK1asYPHixaxevZqQkBCP6+eVeOKUlZWRkJBAREQEixcvRqlU3qSRu2cyS5cu9Wj5XY4xY8ZgMplYsWKF57EuXbrQpk0bvvzyyxsyxn/9619s2LCB9evX07lzZwICAli4cCE6nY4lS5awe/du3n777Rty7L8TdemyGuStt95i0qRJJCcnX/D5mxnP9+zZQ9u2bWnbti0AzzzzDG3btuXVV19FJpNx6NAhhg8fTuPGjZk0aRLt27fnjz/+qAswtxA+Pj7ce++9LFy4EL1ez7vvvoter2fYsGE0b96cqVOnsm3btmr+K1UYDAZGjBhBcHAwCxcuvKkBpoq0tDQiIiJo2LAh48aNIysr66Lbbt++nf79+1d7bODAgWzfvr1GxnKh315SUhI2m41GjRrh5eXFsmXL0Ol0ABw9epRDhw5RVFRUI8f/O1OXLqsh8vPz2bJlC2vWrMFgMLBgwYLztrmZKbQ+ffpcMqitWbPmpo2ljutHp9Nx9913c/fdd1NZWclvv/12nifOiBEj6NatGxaLhZEjR6LT6Vi6dClqtfqmj7dz587Mnj2bJk2akJeXx/Tp0+nZsyfJycl4e3uft31+fv4Nc451OBzI5XKsVitGoxGtVotGoyEqKooGDRqQkZHBmDFjUCqVVFRUMH/+fN555x0WL15MUFDQdR//705dkKkhtm3bhkqlIjIy0qPCarPZUCqVGAwGfv75Z8LCwhg+fHgtj7SOvzoajcZTiWaz2Vi7di2LFi3ivvvuQyKRoFAoqF+/Pr/88kutrSMMHjzY8/+tWrWic+fOxMTEMH/+fCZNmnTDj3/06FGEEDRv3hy5XE5xcTEJCQmYTCYKCgo8a2DvvPMOjzzyCO+99x4fffQR4eHhHDlyhG+//bauyrKGqEuX1RC7d+/GbrfTrVs3QkNDMRgMnhTFt99+y+TJk0lKSuJ///tfLY+0dpgxYwYdO3bE29ubkJAQkpKSqlnmgluyfsqUKQQGBuLl5cXIkSPR6/W1NOK/BkqlkiFDhvDtt9+Sl5fHrFmzCA8PZ/ny5RdVXa4N/Pz8aNy48UWrF8PCws77rPV6/TU5XgoheP755+natSuHDh3C6XQyYMAAwsPDmT59OlOmTGH16tU8/fTTVFRUMGfOHP773/+SkJDApEmT+OWXX26q5fRtz03USbtt0ev1Yvjw4WL8+PFi9erVonv37sJmswkhhJg3b57o3LmzaNWqlRg6dKg4ePBgtX0dDodwOp21MeybysCBA8WsWbNEcnKyOHDggBgyZIioV6+eMBqNnm0mT54soqOjxbp168SePXtEly5dRLdu3Wpx1HXUFBUVFcLf31988sknF3x+9OjRIiEhodpjXbt2FY888sg1Hc9gMIhBgwaJqKgosW7dOjFhwgRRWlrqeX7FihWiV69eYuLEiX+L319tUhdkaoCVK1eKjh07innz5okVK1aIxo0bCyGE2Llzp+jWrZuYNm2auPvuu8WDDz4oHA6HEEIIk8lUm0OudQoKCgQgNm3aJIQQoqysTCgUCrFgwQLPNkePHhWA2L59e20Ns45r5NlnnxUbN24Up06dElu3bhX9+/cXQUFBoqCgQAhxvpry1q1bhVwuF++//744evSoeO2114RCoRCHDx++5jGYTCYxaNAgIZFIREREhDh16lS153/44QehVqvFiRMnrvkYdVyeunRZDbBjxw4UCgWdOnXCZrPRtWtX/vjjD9566y06dOhAv379yM3NpW3btshkMsrLy/nf//5Hr169uP/++9mwYcMFX9flct3kM7l5lJeXA3jEGPfu3Yvdbq9WYdS0aVPq1atXYxVGddw8cnJyGDt2LE2aNGH06NEEBgayY8cOgoODAbeacl5enmf7bt268eOPP/LVV1/RunVrFi5cyNKlS6+rR0ar1bJo0SIefPBB8vLy2L17d7VimP79+xMSEkJ2dva1n2gdl6Vu4f86KSkp4ciRI9SvX5/69etTUVHB3r17OXHiBM2bN2f69Ol89dVXSKVSunXrBrh7WKKiopg0aRJbtmxh/PjxPPzww7zyyivVXvtqG+/+KrhcLp566im6d+/uuYjk5+ejVCrPcxasqQqjOm4uP//88yWf37hx43mPjRo1ilGjRl3zMauqyM7+f61Wy5dffonBYOCRRx5BrVbTr18/tFotO3fupKys7JZau7odqQsy18nWrVvJzMzkgQceANyLjikpKcTFxfHGG2+g0WjYvHkzsbGxtGrVCoDIyEhCQ0ORy+VMmDCBefPm8X//938MHz6c1q1bo9fr+frrrxkyZAjt2rWrdrxz/cb/ikyZMoXk5GS2bNlS20Op4zbB5XJ5AswLL7zA6dOn6dmzJ8OHDyc8PJwFCxYwduxYRo0aRadOnWjTpg3Lli3j5ZdfpkOHDrU8+tubuiBTAwQFBdG1a1fAXa554MABLBYLYWFhrF27lqKiIvr164dCoUCv1zN79mw2btyIw+FgwIABjB07lpycHBwOB+C+e//111/Jzs5m5syZAJjNZjQazRUFGOFea/NoRt1KPPbYY6xYsYLNmzcTFRXleTwsLAybzUZZWVm12cy1VhjV8fei6ncxZMgQMjMzadGiBY8//jh//PEHTz75JB07duSnn37iiSee4D//+Q99+/Zl4cKFdOzYsZZH/jegdpeEbj+qrFmr/jt9+nTRt29fz+L1Qw89JMLDw8W0adPEv//9b9GrVy8hkUhEZGRktQXIxYsXi4iICGGz2YTNZhP33nuvqFevnti7d+8Fj1dYWCiWL18uUlNTLzqm2sTlcokpU6aIiIgIcfz48fOer1r4X7hwoeex1NTUuoX/Oi5JVSGNEO5iklGjRom8vDwhhBDbt28XsbGx4u677xY7duzwbDdmzBjx2Wef3fSx/l2pCzLXydlf8nOxWq2iT58+IiEhQZjNZiGEEEFBQeLtt9+uduEfPHiwGDRokKfyRggh0tLSRHx8vHjllVdEr169RIMGDcSePXsuGjC2bt0qvL29hUQiEaGhoeLJJ58UR44cqaGzvH7++c9/Cl9fX7Fx40aRl5fn+Vf1vgjhLmGuV6+eWL9+vdizZ4/o2rWr6Nq1ay2Ouo5bmbN/e9u2bRMrVqwQDz30ULWS5O3bt4tGjRqJu+++W2zdurU2hvm3py7I3GCOHTvmKdM1mUxizJgxYtCgQaK0tFQUFxeL7777TqjVavH+++9X+9EUFxeLPn36CJlMJp588knPLOdCQcbpdIqZM2eKkJAQUVRUJJYsWSKGDx8uWrRoId58882bc6KXAbjgv1mzZnm2qaysFI8++qjw9/cXWq1WjBgxwnNXWkft8Pbbb4sOHToILy8vERwcLBITEy84Wz6bWbNmnfc5q1SqGh3XuTdpMTExQiKRiMDAQLFs2bJq2+7du1fExsaKXr16iaysrBodRx2Xpy7I3GS2bt0qGjduLHx8fMTDDz8sGjZsKCIiIqqlhPR6vRg0aJAICAgQAQEBoqKi4pKvWVRUJEaNGiXuvPNOz2NOp1PMnj1bREVFiU8//fSGnU8dtzdX0kR7LrNmzRI+Pj7VZqz5+fk1NqazZyrz588XXbt2FYcPHxaLFy8WHTp0EMOGDROrVq2qts+ePXvExx9/XGNjqOPKqQsyN5iLpbcOHz4s0tLSxHPPPSdat27t6UZeunSpaNu2rejRo4dYsWKFiIyMFIcOHbrkMfbs2SOio6M9swK73e55bsqUKSIuLk5YLJbzxuJwOG6J9Zo6/jqc20R7IWbNmiV8fX1v+Fi++OILMXLkyGrBY+vWraJXr15i6NChYuXKlTd8DHVcnr92LexfgHOru6pk2Vu2bEmjRo1477332LhxI35+fnz33XdMmjSJgQMHsmjRIoYOHUpMTIyn5+BCzZlCCA4cOIDBYGDkyJEAyGQyz3GaNm2KXq9n9+7d541FJpMhkUhuqgXBrcSV6Kn16dPnPIfHyZMn19KIa59zm2gvhtFoJCYmhujoaBITE0lJSbnuY1d9T4UQHD9+nPnz57N27dpqFgLdunXj/fffp7Kyks8++4z58+df93HruD7qgsxN5lzrWyGEp2R34sSJpKSkMGPGDEJCQgC3ZPr27duxWCwXLF8uLS1l3bp1tGvXDm9vb1wuFxKJpNpxjEYjjRo1AtxOibNmzWLcuHG8+uqrZGRknBd8XC7X3yLwbNq0iSlTprBjxw5+//137HY7AwYMwGQyVdvuoYceIi8vz/Pv3XffraUR1y4XaqK9EE2aNOG7775j2bJlzJ07F5fLRbdu3cjJybmu41d9TyUSCY0bN+att96ib9++LF26tFrzZ8eOHXn//ffJy8ujoKDguo5ZRw1Qm9OoOqpTlbo6O+ecnJwsunfvLnJyci64z/79+0X9+vXFl19+KYRwp8qq9i8pKRH33nuvaNSokRBCiIyMDDFo0CDRpk0bMW3aNNGrVy8RGxsrfvvtt4uO6ezXO3est1uq7UKpoN69e4snn3yy9gZ1CzF58mQRExMjsrOzr2o/m80mYmNjxbRp0657DB9++KHo1auX57u3c+dOMXr0aNGzZ0/xww8/VNv29OnT1328Oq6fupnMLUTVndrZM5YWLVqwZcsWIiMjz9tenEmVGY1G/vGPfwDumVLV/tu2bWP16tXce++9AHz00UccP36crVu38q9//YtNmzYxfvx4nn/+eex2O+DWYXvjjTc8f8vl8gvOoG7FRs/r5WKpoB9++IGgoCBatmzJSy+9hNlsro3h1SpVTbQbNmyo1kR7JSgUCtq2bXtRmf8rxeFw4OPjQ3FxMUlJSTgcDjp16sSzzz5LZGQkX375Jd9//71n+4iIiOs6Xh01RG1HuTouzaX6cMrLy8XDDz8sJBKJ+OKLL8Tx48eF2WwWWVlZIjk5WTRt2lTUr19f6PV6UVhYKKKjoz2lqAkJCeL3338Xv/zyi+jXr59ITU0VTqdTTJ06VUgkEvHdd9+J+Ph48fjjj4vMzMxqx125cqUYN26cKC4uFkL8OfP6KytLO51OMXToUNG9e/dqj8+cOVOsXr1aHDp0SMydO1dERkaKESNG1NIobz6Xa6K9EhwOh2jSpIl4+umnr2q/C82gKysrxbx580SbNm3EwIEDRWVlpRDCXfxy3333icaNG4uNGzde0zjruDHUBZm/OCaTSXz66aeidevWQqVSiQ4dOogBAwYILy8v0a5dO7F582YhhPtHKJFIxIkTJ8TmzZvFY489Jho0aCCUSqXw9/cXR44cESaTSbRv3174+fmJN998UyxYsEC0adNGjBs3TrhcLuFwOMSpU6fEK6+8IoKCgoQQ1dNmjRs3Fp999lm16ra/CleaClq3bp0A/jby8FfSRHuubP/06dPFmjVrRHp6uti7d6+45557hFqtFikpKVd0zHPTsGvXrq32t9VqFfPnzxft2rUTAwYM8Nzc7Nq1q66T/xakLsjcRhiNRrFw4ULx1VdfiUOHDlUzaVq/fr2Ijo6udpdnt9vFjh07xNy5c4UQQmzcuFFIJBLx66+/en7o69atExqNRmzbtk0IIcTdd98tJBKJiI+Pr2bAtmvXLhEVFeV5rb8SU6ZMEVFRUeLkyZOX3dZoNApArF69+iaMrPbhCppoe/fuLSZMmOD5+6mnnhL16tUTSqVShIaGiiFDhoh9+/Zd9lhV37mqAOZ0OsXu3btFZGTkeeZlFotFfPvtt8LHx0cMGzZMlJSUXP/J1nFDqAsytwEOh+OSaTUh3GmGoUOHinHjxnmaO8+ecTidTvH888+L2NhYIcSfP3ibzSa8vLw8zW0rVqwQWq1WJCQkCLVaLYYNGyaEEOKFF14QXbt2FWlpadVe81bmWlJBW7ZsEcB5Dqd11AwHDx4Uvr6+Ijk5WQjhbkx+9913Rdu2bcXDDz9cbdv8/HzRrFkz4ePjI5555pnaGG4dV0CdCvNtwNnlyhezAlCr1bz88ss8+uijNG/enOHDhxMdHU1kZCR33303JpOJ3377jb59+wLuRVaFQsHatWupV68e4C402LZtG6GhoSxfvhwhBJmZmQD8/vvv3Hnnnezfv5/c3Fx69ep1y1sSTJkyhR9//JFly5bh7e3t8a3x9fVFo9GQnp7Ojz/+yJAhQwgMDOTQoUM8/fTT9OrVy2PbUEfNotPpaN++Pb169WLdunW0adOGiRMnolQqmTVrFpMmTeLbb78F3N/H9u3b8+mnn1Yzu6vjFqOWg1wdtcDChQvF/fffL+677z5P+fKWLVuERqMRffv2rSZj8+CDD4oBAwaIEydOiMLCQtGlSxcxefJkIYTwLLomJycLlUolmjZtKiZPniyio6NF48aNxe7du2/+yV0FXCYVlJWVJXr16iUCAgKESqUSjRo1Es8//7woLy+v3YHf5pw8eVLcddddQqvVip07dwoh3OX4n332mWjVqpXo0aOH+Oabb0SPHj3E0KFDa3m0dVyOuiDzN+Ji/S5CCPHyyy+LgIAA8dBDD4mFCxeK3Nxc8fnnnwu5XO7pwVm/fr3w9fUVW7ZsEUK48+JCCPHqq6+KevXqia+//lrYbDZRXl4u+vTpIx544IGbdGZ1/BWpSvEajUZhs9mqPZeeni5GjRol1Gq15/tWXl4uli9fLvr06SPatm0rhg8ffsunZOuoS5f9rahKX1VJzlTJypSWlvLLL7/w8MMPM3z4cIYOHYrT6aRevXo88cQTPPLII7hcLnbt2oWXlxfdu3dHCIFKpQJg5cqVJCQkcM8996BQKFAoFAQFBVFSUlJr51rHrY9MJuPQoUM8+uijhIaGMmnSJPz8/OjWrRsNGzZk5syZKBQK7rjjDlavXk3fvn0ZOnQoCQkJFBQUEBwcfNv1at2O1AWZvyHnStscOXKEI0eO8OOPPxIfH09JSQl79uzBz8/PI0cjlUo5efIk9evXB/5sHE1OTiY/P59u3bpV80rPzs6me/fuVFZWotFobs6J1fGXwmaz8fHHH7Nt2zakUimnTp0iLy+Pli1b0rJlS/7xj3/w+OOPExISwqBBg1i/fj09evQA8Mgu1XHrc2uvzNZxUzh69ChBQUHEx8d7Ov07dOjgCTBV9OjRg+zsbBITE9m+fTsAS5YsISoqqpqW1a5duygvL6dFixZ1AaaOi6JUKnnmmWcYP348CQkJJCQk8Pvvv9OuXTt27NjBoEGDGDduHGlpachkMnr16sXevXtre9h1XCV1QaYO/vGPf3Dy5EnALSMDXFAg8/7772fp0qUEBgayevVqAP73v//RokULzwwHYPny5QQFBdG6desbP/g6/tK0bNmS5557Dl9fX3777TeOHj3KO++8w/bt25k3bx7//ve/kUgkNGrUCC8vL2JjY2t7yHVcJRJxoatJHXVcAU6nk48++ohGjRqRlJTkebx3797Ex8fz9ttv4+PjU3sDrOMvw7Fjx3jnnXc4cuQI9957L0888YTnOYfDgRACu92OVqutxVHWcS3UzWTquCpcLle1woHnnnuuWoA5evQo6enpxMXF1QWYOq6YJk2a8PLLL9OyZUt++uknPv3002rPKxSKugDzF6VuJlPHNSPcJfCeqjUhBBKJBL1ejxCCsLCwWh5hHX81Tp06xYwZMzh69CgDBw5k2rRptT2kOq6TuiBTR41SFWjqqONayc7O5qWXXqK4uJh58+bVzYj/4tQFmTrqqOOWIy8vD7lcTnBwcG0PpY7rpC7I1FFHHXXUccOoW/ivo4466qjjhlEXZOqoo4466rhh/D+gELgQ1LzCnAAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3356,25 +806,32 @@
     }
    ],
    "source": [
-    "plt.scatter(y_test, y_pred, alpha=0.5, c=color_column_test, cmap='viridis')\n",
-    "plt.xlabel('Actual')\n",
-    "plt.ylabel('Predicted')\n",
-    "plt.title('Actual vs Predicted')\n",
+    "results_train_df = pd.DataFrame(results_train, columns=['max_depth', 'n_features_to_select', 'mse_train'])\n",
     "\n",
-    "plt.xlim(0, max(max(y_test), max(y_pred)))\n",
-    "plt.ylim(0, max(max(y_test), max(y_pred)))\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "xs = results_train_df['max_depth']\n",
+    "ys = results_train_df['n_features_to_select']\n",
+    "zs = results_train_df['mse_train']\n",
+    "\n",
+    "ax.scatter(xs, ys, zs, c=zs, marker='o')\n",
+    "\n",
+    "ax.set_xlabel('Max Depth')\n",
+    "ax.set_ylabel('Number of Features')\n",
+    "ax.set_zlabel('MSE')\n",
     "\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 446,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZkAAAGHCAYAAABieS8lAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOz9d3Rk+X3di35OqpyRc2h0npme1DPTPaQYxExRlmxKshxomXdJlC3xms9+endZy5aDJEuU7avr9xT8bMmiAvnkK5tUsiiJQUOKEzkzDXQ30GjknCvnOun9UX1qCmiEqgLQQA/PXmvWmgZw6pw6deq3f9+0t2CapokNGzZs2LBxDBBP+gJs2LBhw8bbFzbJ2LBhw4aNY4NNMjZs2LBh49hgk4wNGzZs2Dg22CRjw4YNGzaODTbJ2LBhw4aNY4NNMjZs2LBh49hgk4wNGzZs2Dg22CRjw4YNGzaODTbJ2LBhw4aNY4NNMjZs2LBh49hgk4wNGzZs2Dg22CRjw4YNGzaODTbJ2LBhw4aNY4NNMjZs2LBh49hgk4wNGzZs2Dg22CRjw4YNGzaODTbJ2LBhw4aNY4NNMjZs2LBh49hgk4wNGzZs2Dg22CRjw4YNGzaODTbJ2LBhw4aNY4NNMjZs2LBh49hgk4wNGzZs2Dg22CRjw4YNGzaODTbJ2LBhw4aNY4NNMjZs2LBh49hgk4wNGzZs2Dg22CRjw4YNGzaODTbJ2LBhw4aNY4NNMjZs2LBh49hgk4wNGzZs2Dg22CRjw4YNGzaODTbJ2LBhw4aNY4NNMjZs2LBh49hgk4wNGzZs2Dg22CRj44HDNE10Xcc0zZO+FBs2bBwz5JO+ABvfWTAMA1VVyeVyiKKIJEnIsowsy0iShCAIJ32JNmzYOEIIpr2dtPEAYEUvmqZhGAalUqnyc9M0EQShQjqKoiBJkk06Nmy8DWCTjI1jh2maqKqKruuVn5VKJURRrPweylHOTtKpjnJs0rFh4+GDTTI2jhW6rqOqKoZhIIoigiBUUmZ7EYZNOjZsvH1gk4yNY4FpmmiahqZpmKZZIRigki4TBKEmkqgmHcMwKsfYpGPDxumHTTI2jhyGYZBIJJBlGUVR7iOTeklmJ6zoxjTN+0jHqufIsryN2GzYsHEysLvLbBwZrEVfVVVu3bpFb28vnZ2dR36eanKSJGkb6RQKhW1/I0kSTqcTSZJs0rFh4wRgk4yNI0F1egx4oIv5XqSTyWS4ceMG169fr9R0FEWppNds0rFh4/hhk4yNQ8OKXnRdryzcgiCc2LBldRQDIMvyrpHOzhkdm3Rs2Dh62CRjo2HsnH2pXqT3I5kHuZBbnWm7RTqGYVRIRxTF+xoJbNKxYePwsEnGRkPYOfuyc0E+KJI5ycV7L9LRdR1d1ykUCjbp2LBxRLBJxkbdsLrDdkYv1TjJdFn1NdT6d1bNBriPdIrF4raajpVma7Q7zoaN7yTYJGOjZlgLr6qq982+7MRpIJlGsR/paJq2reazU3fNJh0bNrbDJhkbNeGg9NhOnBaSOYpr2It0NE2rKBdYpFOtu2b9vQ0b38mwScbGgaiOXmrdrZ8WkjkO2KRjw0btsEnGxp7YbfalnjrHSZPMg0pdHUQ6QEXhIBgM2qRj4zsKNsnY2BWGYRCPx1laWuLcuXN1L4ingWROCruRztLSEtlslvPnz1d+v1N3zSYdG29H2CRjYxuqpWHy+Tzr6+tcuHCh7tc5LSRzGq6hmnQURdl2j61IZyfpWN1rNmw87LBJxkYFO4v71gxJIzgNJHOaFunqe1GtRmD9ziKdauFQi3Rs11AbDzNskrEB7C4NI4riQ00yDwtqIR3bNdTGwwqbZL7DcZA0jGEYDb3ufiSjqirz8/N4PB7C4TCKojR8/bXA6oo7adTTNFEL6dheOjYeBtgk8x2Mg2ZfjiOSSSQSDA8P43A40DSNXC6H3+8nHA4TDocJhULbFtjD4DQtuoeJ6qpJp9rArVQqbVMjsEnHxmmETTLfodjNFnknDpvyqj7WNE3m5uaYmppiaGio4jNTKpWIx+PE43HGx8cplUoEAoEK6QSDQbvrqgrVmmtgk46N0w+bZL7DsHP25SBpmEbTZaIoViKkUqnErVu3SKfTXL16lWAwWBnudDqdtLe3097eXpHit0hnZWUFTdMIhUIV0vH7/XUvlg9buqyR16wmHeu/YrFIqVQCbKtqGycHm2S+g2Dl9S3iOGi48igimXg8zsjICIFAgOeff77SwrvX+dxuN263m87OTkzTJJvNVkhnfn4eYBvpeL3eh2KxfFBNEPu5hhaLRYrFIrquk8lkaGtrsxWmbRw7bJL5DkB14Xi/9NhOVA8T1rsACYJAMplkZWWFc+fO0dvb29Br+Hw+fD4fPT09mKZJOp0mHo8TjUaZnp5GkqQK4YTDYdxu97bGhdOEk7ie3Ugnn88zNjZGMBis/I3tGmrjuGCTzNsc9QpbVsP6u3pJplgssrq6SqlU4plnnqksZoeFIAgEAgECgQB9fX0YhkEqlSIej7O+vs7ExAQOh4NwOEwkEsHr9R7JeY8Cp6Wd2yIdQRAqUaXtGmrjOGGTzNsYhmGQz+d5+eWXee655+puFbYWFSv6qQXRaJSbN2+iKAotLS1HRjC7QRRFQqEQoVCIgYEBdF0nkUgQj8dZXFwknU4DMDExQSQSeSDt0g8DqjcNtbiG2qRj4zCwSeZtiJ2zL9lstqGddHW6rJZzTk9PMzs7y/nz5ymVSmSz2brPeRhIkkRTUxNNTU0AFYIVBIHZ2Vlu376Nz+fb1i4tyw/uK3BaFuX9ItP9SKdYLNquoTbqhk0ybzPsTI9Zi2gjXWLV6bL9UCwWGRkZoVAo8OyzzxIIBJidnT3xFJH13oeGhpAkaVu79OTkJIVCYduMjqWQfBw46XtRjXrSnzubQ2zXUBv1wiaZtxH2m31pZJGrTpftha2tLW7evElTUxNPPvnktsjgoHM+qNZi6zocDgdtbW20tbUB5UgnkUgQi8UYGxtD0zSCweC2dum344zOYe677Rpqo17YJPM2QPXsy262yKIoHnkkYxgG09PTzM3NcfHiRbq6urad82HQLrPapTs6OjBNk1wuV4l0FhcXMQxjW7u0z+c71EJ5WhbZoyT3egzcdqbXbHxnwCaZhxyGYaBp2r7dY40OVVoLxE6yKBQKjIyMUCqVeO655/D7/bsee9IkU89CKggCXq8Xr9dLd3c3pmmSyWQqpDM7O1tpNLBIx+Px1HyO0zIQCsd7LfWQju0a+p0Bm2QeUlTPvhxki3yUGmSbm5vcvHmT1tZWnnrqqT0L56eBZA4DQRDw+/34/X56e3sxDKMyo7O5ucnU1BSKomyb0XG5XAe+5mnAgyS8g0jH+pmqqhXdOpt03l6wSeYhRL22yI2my6zXNgwDwzCYnJxkYWGBS5cu0dXVdeBxp4VkjuI6RFEkGAwSDAbp7+9H13WSySTxeJzl5WXGx8dxuVzbSMfhcBzB1R89TjKq2o10YrEYd+/e5erVq8DuEjg26Ty8sEnmIcNuvi8H4TALviiKFAoFRkdH0TSNa9eu4fP5jvWcDwMkSSISiRCJRADQNK0yozM/P8/o6Cher7dCONbndRpwmlJ31nXYrqFvX9gk85BgP9+Xg3CYSMY0TUZGRmhvb+fixYs1t/ieBpJ5kAuRLMs0NzfT3NwMlEVBLdKZnp4ml8tVIpujtjSoF4ZhnKpF2mpWAds19O0Im2QeAhxGGgYaK/wbhsHExASapjE0NMTQ0FBdx58GkjlJOBwOWltbaW1tBWBsbKyyO7csDarbpQOBwAONdE7ToryfokQtpGO7hp5u2CRzymEYBslkkps3b/Lss8829OWpt/Cfy+UYGRnBMAwcDkclJVQP9iMZQRC2LRDHjdNAdqIo4vP5OHPmTEWk0upcW1paQtf1Q1sa1IrTlC6D+iKrWknHtjU4PbBJ5pTCSo9ZEUwymTzUAF2tkcza2hq3b9+ms7OT8+fP8+KLLzY8yLnbcaZpsri4yPj4OEBlNx+JRI58YT1tC0u1XpjH48Hj8dDV1fXALQ1OI8k0utmoJh3bwO10wiaZU4id6bFqDanjimQMw2B8fJyVlRUeeeQR2tvbgcbTXrtdp6ZpjI6OEovFePzxx5EkiWQySSwWY2FhAWBbd1Y9cyinHfvdQ0E42NJAluX72qUbvTenjWSqazKHQbXmmvW6YJPOScMmmVMGK3qpnn2p/tI08mU4KJLJZrOMjIwAcP36dTwez7ZjGyWZ6nOm02mGh4dxOp1cv3694pzp9/srw4/pdJpYLLbrHEokEsHpdNZ9HXA60mX1QBB2tzSIxWKsrq5y9+5dnE7nNtKp596cNpI5rkaE/UjHdg19cLBJ5pRgv9kXa5fXaFphv0hmdXWV0dFRurq6OH/+/H2vfxi1AAvLy8uMjY3R39/P0NAQgiBU2lOr/95aWHfOoSwtLXHnzh08Hk9Fsj8UCj10sv2NLlzVlgbAfZYGY2NjeDyebaSz3705bSRzVJHMQagmnb1cQ3cT+7QVpg8Hm2ROAXbaIu/8wlWTTCPYrYVZ13XGx8dZW1vj0UcfrYhG7nZso5GMruvcunWLjY0NnnjiiUp7r/X7/VA9h3LmzBlUVa2IWVotwYFAYF8F5dO0MBxlNLXT0sC6N5b8zUGWBqeNZA5TkzkMqjdyO0nHsgzv7Oy0XUMPCZtkThC12iIflmR2RiOZTIaRkRFEUeT69eu43e59j21kgbS8R7LZLM8///x9kiv1vqZlgtbS0gKU9dOsQvlOBWWrieA7BTvvTbWlwcTEBMVicRshW2rJpwWnZW6nmnSs4Vlrk2UbuDUOm2ROCPXMvlgP/2EiGWtRX1lZYXR0lN7eXs6ePXvgDrIRkrFScKIo8swzzxzLLtXlctHR0XGfgvLOJgKgMgh50gvBgzr/bpYGFumsrKygqiqKojA3N3cqLA1OKpLZD7qub5OzsV1DG4dNMieARqRhDqs/pmlaJXV15cqVypDgUZ63ukNtaGiIhYWFB5Zrr1ZQNgyDTCZTaSIYHh7G4XBUopx6C+VHgZNsPrAsDTo7OzFNk4mJCdLpNOl0moWFBUzTPFJLg3rxoGoy9cAwjF3Tr7ul12zX0P1hk8wDxElJw+i6zuLiIl6vd9fU1X6oNZLJ5XIMDw8D5Q61UqlUmfd40BBFsdJEMDMzwzPPPEOxWLyvUG4RTjgcfqA2zCcJQRBQFAWfz8eFCxf2tDSobiJwu93HukjutqCfNGqJrnYK0+7nGvqdTDrfGd+sU4DDSsM0QjKmabK8vMzm5ibBYJCrV6/WvWOshWQ2Nja4efMmnZ2dXLhwAVEUK23YpwGiKN7XRGAtqtPT0+Tz+YoNcyQSIRAIHMuid1oWlurCvyDsbmkQi8VYX19ncnKybkuDemEYxqnrFGxE0HQ3hWnbNdQmmQcCXddZWlpCEARaW1uPZdZlJzRNY2xsjK2tLZqbm/H5fA2lJPY7r6VvtrS0xOXLl+no6Nh23Gkgmd3utaIo23TFrCaCWCxWUZsOBoOVSOcolAhOw72wsF93WbWlwcDAwAOxNHhY0mX1Yi/SOcg19O1GOjbJHCOqZ1+i0SiSJO3ZKnwQ6mkl3jn4ODc3dyRNA9UoFAoMDw+j6zrXrl3D6/Vu+/1pIZlasFsTQSwWIx6PMzc3hyAI24ZCG00fnZaFo54W5nosDSKRyH3t0rXgtHSXVeM4rBnqIZ23k2uoTTLHhN1mXw6z6NaSLjNNk6WlJcbHx7cNPh6mM203srDcMdva2vaU/z+IZB50YblWVDcR9PT0bHPE3NjYYHJy8sSbCA6Lw0QO+1kaTE1NbUs97jW/dJTXc1x4EB1vtZKOLMt8/etf5+LFi5w9e/ZYr+k4YJPMEWOv2RdJku6bcq8HB5GMpmncvn2beDzOk08+WRnUs45t9NzVZGGaJlNTU8zNzR3ojvkwRTL7YS9HzFgsVmkiqDYn26uJ4DTdi6McxtxpaVA9v3Tnzp2aLA1OWwuz9R1+0M0Ie5GOqqr8/M//PD/5kz9pk8x3OvYr7ltaXY1iP5JJJpOMjIzgdru5fv36fTvrw0YyVovmyMgIxWKR55577sBhx9NCMkcdMe1MHx3URBAMBrcZcp0GHGd6amfqcaelgWEY20jH7/efunTZXsobDxrVpJPNZmtypD2NsEnmiHDQ7IskSQ0v9LA7yZimycLCAhMTEwwODjI4OLinYsBh7JdzuRwvvfQSkUiEJ598sqac+2lKlx0namkiCIVCFItFXC7XqZB0eVDXIAj7WxpY9S4Ap9NJMBg8Fcrbp4VkLFj37WFVsbBJ5pCodfblMNEE3E8yqqpy+/ZtEokETz311L7GYo3O2JimSSqVIpVKcenSJbq7u2teAKy/Oy2L6oPCXk0E8/PzLC8vs7q6uq2ec9wzKHvhpM5ZbWlgDc3eunWLdDrNt7/97fssDfaTPDouWN+V0zS7k8vl7muueVhgk8whUM/sy2EjmWqSSiQSjIyMVIYrD2ohbSR1VSqVuHnzJtlslvb2dnp6euq+XjgdJHNSqG4iiMfjlZpEPB5nfX2diYmJivNoI5L9jeK0fCbW0KyiKAwMDNDU1FRplz4KS4NGoev6qWsjttNl34GwfF9qndw/ipqMruvMzc0xOTnJ0NAQ/f39xyJJE4/HGRkZIRgM0tnZ2dD1VpPMTlj2zsVisbLARiKRYxnIO00LhSAI9zUR7JTsP2w7cC04LSRjweouq1YagHIzi0U6uyk1HJfdw2lrRDAMwyaZ7yRUz75YX47j1h6zsLy8jK7rPP3005UvYi2oNZIxTZP5+XkmJyc5e/YsfX19TE5ONtSZthfJVLc/9/T0kEwmK/MWOwvmpyldcVjsdv93k+y36hWTk5MUCgX8fn9lUa1uIjjstZwmktlrUZdleU9Lg5mZmUqdorpd+ihI+bTJ3ORyOQC7JvOdAMMw0DStIWmYw6TL4vE4W1tbuFwurl+/XveEdS0Ep6oqt27dIpVKcfXq1YpBVqNdYjtJprr9+fLly7S2tqKqaqVgbmmLxWIx7ty5g6qqlan7SCRyKNHG09DlBgdHVbs1EVhDoSsrK5UmAouIG70nDwvJ7MROSwPrmYnH49y9e/c+S4NGSfk4BjEPg2w2C2DXZN7OsIr7pVKpkqut90vaqPbY7Ows09PTlR1bIxIeB507mUwyPDyMz+e7j8QabVioJplSqcTIyAiFQqHS/mw5gFpwOp20t7fT3t6+69S9lUqpLpjXcx0PI1wuF52dnRX15J2dWZZjZr1NBKeRZBq5nupnBu63NKj2GKrH0uC0RTLZbBZFUR66oV8LNskcACs9Nj8/z8rKCs8880xDX4h6Saa68P7MM8+wsrLS8I58r2jENE0WFxe5e/cuZ86cYWBg4L73dthIJpFIMDY2RigU4oknnqi5/Xnn1H0qldpWEHa5XJUo52GwYj5sNLVbZ1a1kOXOJoJIJLLnhuS0zaUc1cT/TksDy2MoHo9XLA2qmwi8Xu+u9+G01WQymcye1/owwCaZfVA9+2Kluxr9oOshmVgsxsjICKFQiOvXr6MoCmtraw03Dux27mqFgP1aoA8rhzM8PMy5c+fo6+s71L2zPO4HBgYq+lmWFbM1ALlX7eJhSZfVg92ELGttIni7RDL7oXqj0t3dvc3SIBaLMTMzs6elwWlMl3k8npO+jIZhk8wu2G32RZblIx+m3O28MzMzzMzMcO7cOXp7e7cpBhyFNAyUBTRv3Lixp0LAfsfWAovAAB577LFKOuOosFM/q3oAcmVlBV3XK7ULS5rjpHHc11BPE8FpsmGAB6NdJgj7WxpYkWA4HD5VBAxvtS+ftuuqFTbJ7MBesy9H1YK8F4rFIjdv3iSfz/Pss88SCATuO/4wSsrWsUtLS9y5c2ebgOZ+qLcmk8lkuHHjRoW4dr6P6tc9KuwcgMxms5V6jq7rjIyMVFJrkUjkoc1t14OdTQTV9YpcLsfdu3fZ2NioRH8ntYhZm4AHHTnsZ2mwvr5OoVDg5Zdf3tYufVhLg0aRzWYf2qI/2CSzDYZhUCqVdp19OewwpXX8bqmKaDTKzZs3CYfDe9YtDmu/bBgGt27dYnNzkyeeeKISBRyEetJlq6ur3L59m76+PoaGhvjqV7/6wHfM1bWL3t5evvnNbzI4OEihUGB5eZk7d+48kFmUva7tpFBdr8jlcrS1tSEIArFY7ETcMC1Yz/RJ79KrNelkWSaZTNLZ2VlpsshkMvh8vsr9eZDPjXXuhxU2yfBWesxKI+zWmnwUkYx1rurOK6ut98KFC/vKthxGcLJYLFIqlcjlcly/fv3I7ZcNw+Du3bssLy9z5cqVyu75NIhkWmkSSzF6tzRSIBCoLDC1diDVi5O+DzvhdrtpaWnZs4mgetJ+vyaCw+K06YRB+ZqOw9KgUeRyObsm8zCjVmmYoxC4hLc6VwqFAjdv3qxZ1bjRSMaKLgRBaNh+eb/zVpuXXb9+fduXYT+SOamd615ppFgsVlEJrm6VPg2CjUeNndH0fk0ECwsLlSaC6tTRUe3irefjNJGM1ehTjaOwNGgUdiTzEKM6ejlo9sWKZBrtzLEeOOsLfPPmTZqbm2tWNa6XZAzD4M6dO6ytrXHu3DkmJiYatl/eiyii0SjDw8N7mpedlkhmP+xse81kMsRiMTY3N5mamkJRlJrago/iWh4UDnqGdzYRWLv4WCy2Lfo77NAjnJ50WTVqaWGuxdLAaj45bM3rYZaUge9QkqmWhgEOJBh4S5H1sCQzNTXFysoKFy9epKurq+bXqodkcrkcw8PDCILAtWvXABgfH6/7mq3z7iSK6i64ixcv0t3dveuxD5vcf3UHUl9f364GZVZe3qrn1JoiOWmyrUa9z/DOXXz1gmpJHVUPhdazoFrty6fpWTAMo67NxEGWBrOzswiCsK3mVU+E/DArMMN3IMnsZotcC6ojkUZ2bcViESjv/q9du1b3zqRWkllfX+fWrVt0dnZy4cIFRFEkn89Xunjq/TLvJApVVbl58yaZTGbXLrj9jj0pNHoNOw3KSqVSJbVmyZhUS9/4/f5TtVjuhcPOpeyM/qwFdbcmgkgksq86w2m0Xj7snMxelgbVEXI9lgbZbLYipfMw4juGZPayRa4V1o5V1/W6p8s3Nja4desWgiDw2GOPNRT6HkQyhmEwMTHB0tISjzzyyLbZlN2aDmpFdU1mp/zMQffhNJDMUS76DoeDtrY22traKikSq1V6YWEB4L56joXTNAB5lNdSixKB1URg3ZfqKOG0qQ/A0U/8W5YGgUCA/v5+DMOoy9Igk8kwODh4ZNfzoPEdQTKmaZJMJlFVFZ/PVzfBwFuLVb11kYmJCRYXF7l8+TLj4+PHohiQz+cZGRlB13WuXbt2X2i9n+z+QbBIZnFxkfHx8T3lZ/bCSZPMcaE6RdLd3V1ZXKu9YpxOZyXKOawC91HiOAlvvyYCS227uhVYkqRTF8kct3ZZLZYGXq+XaDRKLBY7knTZL/zCL/DFL36R8fHxyhD2Zz/7Wc6fP7/nMf/1v/5Xfud3fqcyWP3UU0/x7/7dv+OZZ56p69xve5KxopelpSXy+TyPPfZYQ68jCAKSJNXcxpzP5xkeHsYwjEp6bGJiouE26L26vKql83crvsP2zrZ6vzymaVIsFpmcnOTJJ5+sFINrwWElaY4KD+IaqhfXaq8YK4WUzWaZnp4mk8kce8vrQXiQUdVBTQT5fB5BEJiZmSESiRxpV1ajeNCyMntZGgwPD/Mrv/IrLCwscPv2bdbW1njve9/LO97xjrqzId/4xjf4iZ/4Ca5evYqmafz0T/80H/jAByqEthteeOEFfviHf7gy9vDZz36WD3zgA4yOjlZGAmp6f3Vd6UOEndIwsiwfas4F6q+LdHR0cOHChcpiclRT+1AmjKmpKebn57l06dK+H3o1ydSDbDbL+Pg4pmnWPV9j4TSQzElg5+L66quvEg6HKRaLjI2NVRSCj8LKoBGcVIpqZxPB6uoq09PT5HI5lpeXj7Qrq1GctECmZWnwqU99ik996lO85z3v4dlnnyUWi/GP/tE/IhKJ8MYbb9T1mn/+53++7d+f+9znaG1t5Y033uC7vuu7dj3m85///LZ//8Zv/Ab/83/+T772ta/xiU98ouZzvy1JZrfZl8POuQAHRjLVQ4mXL1+mo6PjvuMPQzLWgl09Y1NLE0Ej6TKLKJubm9E0rSGC2SuSsbTNotFoJVcfiUQaOsdBOC35fkvks7W19UArg+O6FxZOU31IURQUReGRRx65TxKo3iaCo8Jpk/rP5XJ84AMf4G/8jb8BlGujh4X1GnsJ4+51Haqq1nUMvA1JZi9b5HpSXXthv0jEahsGdq2LHHR8reeORqOMjIzUNWNjtYjWcm7DMJicnGRhYYFHH30Ut9tNLBZr6JrhfmKztM0cDgeXLl0imUxWip9ut3ubfP9RD/ydNKxncS8rg1gstu1eVLdKH6WVwWkimerusp2SQNUWD2trawc2ERwVTpMKs7UhqR7WDgaDh3pNwzD4zGc+w/PPP88jjzxS83H/x//xf9DZ2cn73ve+us73tiGZnbMvu2mPHZZk9nqNtbU1bt++va1teDccVn9M13XefPPNAyVo9jr+oMW2WCwyMjJCqVSqREipVKrhRXpnJGNFRz09PZw5cwZN0+7LQ1cP/D2M7cF7Yb97WG1lAOVIz5qxqMXKoJFrOS33cr/usp0WD9V1rt2aCI5qY3LaIpmjFsj8iZ/4CW7fvs23vvWtmo/5xV/8RX7/93+fF154oe4o+21BMjtnX3Yb7jqOSEbXde7evcvKysp9bcN7Hd/INZRKJUZHRzFNk2eeeaahncxBBBePxxkeHiYSiWyLkA7bhmzN50xMTFSio/b29vuuZae1rmU4FYvFKu3B1UrKx5lOOmnIsrztXlRbGVTXLSzSqdfQ6rSRTK2EuVsTgUXGExMT2+yXD9NEcNI1mZ04yon/n/zJn+RP//RP+eY3v7nnEPVO/If/8B/4xV/8Rb761a821Dj1UJNMPbMvRx3JZLNZhoeHEUXxPs2uvdBIJGMt/tbQ40EaZ3thP3fMubk5pqamOH/+PD09PdvuYb1S/zvPqaoqr7/+OoVCoa4h1OoJ6urZi3pTa6dlMYXGr2UvK4NoNMr09DSyLG+TvtnPyqDRodzjwmGGMavnlmC7Dt1hmghOU7pM13Xy+fyhScY0TT796U/zpS99iRdeeIGBgYGajvulX/olfv7nf56/+Iu/4Omnn27o3A8tydQqbGnhqEjGMAxWVlYYHR2lp6eHc+fO1bUTq3XBrl78z507R0dHB1//+tcb3mXt5Y5569YtkskkV69eraRrdh7XaCSj6zoTExNEIhGuXbvWcCpj5+yFlU6qNbV2GmoyR3UNu9UtLOmbWqwMrOs4LSRzlFHDbkoEuzURWIS8WxOBtXE9LSSTzWYBDk0yP/ETP8EXvvAF/uiP/gi/38/a2hpQru9Y9+ETn/gEXV1d/MIv/AIAn/3sZ/mZn/kZvvCFL9Df3185xnr+asVDSTLVtsi1DlYeVqofyl/M5eVlcrncNkn7WlFrJKOqKrdu3SKVSlUWf+vaD+spY2GnO+ZeBVQrAqp397u0tEQmk6G9vZ0rV64c6aK2M51kTd7vllo7DQRj4TgW9p2DfQdZGVi5/dNEMsdxLfs1EVRP2VuEYzURWM/LaanJHBXJ/Pqv/zoA7373u7f9/Ld+67f4kR/5EQAWFha2keuv//qvUyqV+PjHP77tmH/1r/4V//pf/+uaz/1Qkcxutsi1PqD7mYbVAkt7SJZlrl+/3lArZS0ks1O6xVr8G1Ec2Hlu6wtkRWK1uGNWtz/Xct8Mw2BsbIz19XX8fj/Nzc3HvqC53W66urp2Ta0VCgXu3LlDS0vLAzcpOwnsZmVg7eYXFxcrz8Dq6irNzc0nbmXwoLTLdjYRWFP2O5sIrHrnadmcZLNZnE7nobsLa3k/L7zwwrZ/z83NHeqcFh6ab1u96bGdkCSp4Xz08vIyY2NjeDwempubG+7V3y+aMk2ThYUFJiYmdpVuaXSgsvp4TdMYHR1lbW2Nxx9/vCbRvWrds4NgqRwAXL9+ndu3bz/wL+vO1NrLL79MW1sbqqret7Nvamp6YF1rJ7VoVROwaZqVGl80GmV2dvZIrQwawUlpl+2csreaCLa2tgB46aWXKs/JUfvD1AOrs+y0RJ6N4KEgGV3X2dzcxOv1oihKQze8WuCy1odF0zTu3LnDxsYGjz/+OFtbW4daLPaKZKzhxHg8zlNPPbXnsNNhWqBN0+Tu3bvIssy1a9dqdtqrdZBzN2+Z0yKQGQwGK4tJdWptcXERYNsQ5HEO+530QmFZGQBcuXIFoLKbt8zJfD7fNnOy404bnZb6h9VE4Pf72djY4LnnnqtEgDv9Yay044P4PDOZzEPtigmnnGSqZ19ee+01nn/++YZ3WvWqKKfTaYaHh3E4HDz//PO4XC5isVhlDqfRa9h5/M7ayH6dQY0W4Tc3N8nlckQiEZ566qm6vtQHkYxpmszOzjI9PX2ft8xpIZlq7JVas4b9qocgw+Hw2y61Vl34F0VxTyuD8fHxitvjcc4qnTapf4v0dkaAmUxmmz9MLU0ERwGrffmkNyiHwan9BhmGgaZplfSSLMuHkoWxvlQHFf9N06x06fT393PmzJnKl0CSJEqlUsPXsDMSWVpa4s6dOwwMDHDmzJkDH6R6IxnTNJmammJubq7ypan3C71fmq66O223+Z3TQDL7Yb+utampqfuK5oFAoOEv+2m5D/t1l9VjZXBUUd9pk/rfbRCz2syuuomguqV+tyaCo8BRD2KeBE4dyVTPvlTbIh/nxL4Fq2YRjUZ54oknaG5u3vb7w3aoWSShaRpjY2NsbW3tep6Djq8FpVKJkZER8vk8zz33HHfu3GlY6h/2lodxuVx7dqedFpKp9Rr261o7itTaaVhMa7U73svKYKdPjHUvwuFwQ8VpwzCOVDLnsKglnb6bQsNedgb1OqjuhE0yR4z9bJF3SzXVi/1IJpVKMTw8jMvl4vnnn981bXVYkU1RFCkWi7zyyisoilK3snGtJGPJhAeDQa5du1apYzVy7buRzNraGrdu3aKvr4+zZ8/uuWCdhkX1MNiZMrF2r1ZqzeVybVtk90utnQayhcPZh++M+qyFdXZ2ltu3b2+TvgmFQjVFzac1XVYPZFmmubm5slncy0HVinLqaSKwSeYIUT37YqW2qnFcApemabK4uMjdu3cPTFsdNpJJpVIkEgn6+/s5e/ZsQ6mr/Yii+r0MDQ3R399feS+HGaq0IhJLPHNxcZHHHnusMmm933EnbdZ1lA6Qu6XWqvXFqlNrfr//VC2eFo5q2n/nwlosFisLq2VlUC19s1dd4WFIl9WL3ZQIdmsiqEUW6CglZU4KJ04ytc6+HEe6TFVVRkdHicfjNRlyNRrJ6LrO+Pg4a2treL3efd3o9sN+JGOl+mKx2K4daodVgC4Wi9y+fZtSqcRzzz1X04NfS0rmYcVuqTVrkd0ttQan4/0el6SM0+mkvb2d9vb2bVYGlmmb1WRg3RMrgj8t3WUWjkNSZq8mglgsxvT0NJIkbavnVKdhM5mMTTKHQT2zL0dhOlZNMtbQo9frPbCry0IjkYxlASAIAufPn2d5ebmha4e9axyZTIbh4eFKCm6393LY+siNGzfuE89s9Hot1CpxohsGmWIJSRTxOesvqD6IVNVOSZN0Ok00Gq3UL6A8Ua2q6ol2rT0I3bJ6rAys5orTguMmvVqaCFwuF6qqMjMzQyqVOnQLcyPWywB/8Ad/wL/8l/+Subk5zp49y2c/+1k+8pGP1H3+EyMZwzAolUo1T+4fVSSjaRpzc3NMTk7W7VdfbyRjWQB0dXVx/vx5tra2Dl3T2Xm8VR/p7e3dNwXXSCRjmiZLS0vouk53dzcXLlw4MnsB0zQpFAq4XC5MYC2VYT2VQTcMmn1eukJ+FEliMZ5kbH2TRL6ILAh0BgM82tlaM9mcRPQgCAKBQIBAIFBJrb300ksIgnDiqbWTEMfcz8rAsqXe3Nzc1sV3UtHNg5b536uJ4IUXXuD/+r/+L2ZnZ2ltbcXtdvO+972Pd7zjHXXXaBqxXn7ppZf44R/+YX7hF36B7/me7+ELX/gC3/d938ebb75ZlwcNnBDJWBFMPdIwR1H4FwShspt8+umnK3pPtaLWSKbaIbPaAuCoutN2nuM46iO6rjM2Nsbm5iayLNPZ2Vn34rQXyWiaxs2bN9nY2MDhcBATFDZKBk63G1mSuLsWpb85RE8kyKvzy+iGQcjtQtMN7m5Gyasq33WmD1k6PWmW/SDLMpIk0dvbSzAYPDC1dtzujyedtqtONWYyGZqbm5FlmXg8zq1btw5tZXAYnLQCs1Xr+vjHP87HP/5xPvGJT6AoCvF4nE996lMUi0VWVlbquh+NWC//p//0n/jQhz7ET/3UTwHwsz/7s3zlK1/hV37lV/jP//k/1/ee6vrrI4JV2K9nV3XYSMbqhNmv5baWazhoobakVQzDuM8h8yi60wzDoFAoMDw8jK7re7pw7nZsrWmjfD7PjRs3EASB69ev88orrzTcmbZf6/O1a9dY3IwyMjaFWSpSjG/hcXtwerzcWcyzmspQ0DV6Q/fmbxRwKTIrqTRr6QzdodrSLKehs6v6GnZLrVW3BtfTtdbIdZw0yVTDNE2cTidtbW2V+2HVLBqxMjgsTluNqFgs8q53vYvPfOYzmKbJ5ubmoT+/WqyXX375Zf7pP/2n2372wQ9+kD/8wz+s+3wnli7bzVhsPzQayVRL5vt8Ppqamg6lGrAf0W1ubnLz5s1t0irVOEzx3To+nU4zNTVFc3Mzly9frjm0rzWS2draYmRkhPb2di5evFiJNBudsak+bmNjg5s3b25zxtQVJ8FwmL6mEKqqkk5nyGTSbG7FGVtYYKA5jNfQ8Pl8OB1OFEnCME1yqkqqUGQpniJVKOBzOukOBQh5Tq+Z2W7Pe3Vqrb+/v5IusYrCR51aO43dXNXvZ2fNQtf1Ss3CGl72er3bWqWPkoRPmytmJpPZppxdr/L7TtRqvby2tnZfdqStra0i918PTry7rFbIskyxWKzrmFKpxK1bt0in01y9epWVlZVDL/K7kYxhGExNTTE/P8/ly5fp7Ozc8/jDaI/lcjk2Nja4dOlS3fbLB0UypmkyMzPDzMzMkcnDVNsETE9PMzs7yyOPPEIg0kQsV0A0Dcpvofw+crpO0gTV7SXY7sJZLAAGiUSCleUVHE4Hbo+XHCKZfIEX1raIZvIokohqGExsRHluoJvOoH/bNTxM2NkaXCgUjnQg9LRFMgeRniRJla6rM2fOVKwMYrHYNjfMoyLhk06X7UQ2m23YqHA3NGK9fFicaCRTD+pNl8XjcUZGRggEAjz//PMoisL6+vqhUm67KTkXCgVGRkZQVfVA58dGScbyl8nlcnR1ddHT01P3a4iiiKqqu/7OqpGkUqk95WEaTZdpmsaNGzdIp9M8ffUZZlNZvnVrglxJRRYEwm4nkiAwsRFlNZ2hqGkYJqQKRS60NuNzKXgdDrodDhKZFIubMRStxDde26QgKgy2NhHw+nG5XCwn0owsr9Pq9yLvs1BohkE8l8cwIeJxoTyAnWujKTuXy0VnZ+eeqTWn00lTU1Nlsvyg6fnTRjL1DmMeZGUAVOo5FgnX835PmwLBUQ5j1mO93N7ezvr6+rafra+vH2gxvxsemkim1nRZtWDj2bNn6evr26YacFjtMSjvdmRZJhqNMjIyQnNzM0899dSBYbtFMvV80S0lAq/XS1tbW8Opvv3an9988819zcsaHeRUVZW1tTVCoRDXrl1jbD3K8MIqIY+b9oCPVD7PQjyJLEtMbMYwTAOfw4EpwNmWCJIo0OLzYhgmW/k8kuzkqXNDnGkO88L4DKgl8vkcm5ubiKKIy+NhPpNmo6OZznCIRL7AZCrD0tIqvapOfzhESdN5c3GVzWwO0zQJe9xc6WqjPxJq5LbWhcMu7rWk1qype8vGYOcCftpI5rDpu50zKBYJb25uMjk5icPh2FbfOuj7o+t6XSocxwkre3ES1svXrl3ja1/7Gp/5zGcqP/vKV77CtWvX6j7/Q0UyB0UhxWKRW7dukc1md92RH7Z5oFrJeW5ujtnZWS5cuFBz6so6vta8r5WDHhwcZHBwkDt37hxqoHKv9uda5GHqJZmNjQ1WVlbwer089dRTFDSN6c0YAbeLkMeFaZoEXOUC7koyTZPPTcTtwkQg4HIS8riI5wqous4HL5whkS+QL2n4XU4kUcDhUPD5fXgcCqZpkMvliCdSrMfivPbaa7i9XiaKKsvxBC2mwGKxxJsra6CbSIJIq8+DKAhsZXO8PLuE16HQ4nu45Dv2S61ZHvfVqTWPx3MqSeao0lM7SVjX9V01xfazMjhthf+jiGQasV7+J//kn/Cud72L//gf/yMf/ehH+f3f/31ef/11/st/+S91n/+hSZcdNIwZi8UYGRkhHA5z/fr1XUPew5KM9fANDw9TLBZ59tln6xokq1Y03o9kdF3nzp07rK+vbxPQPExNpzrlZRgGExMTLC0t1dz+XCvJVCs/t7a2VnxlCqpGoaQR8ZUfas0wyJZKyKJIUdNxyhL9TeFtz4UAmEA8V+Dm8jqbmRwAzT43DllmK5ulWwkiCiIej5eEavBEWyvvGOjij2+OEo+naRIFSKeQdY3pgkpWN/nI+SHke/e/I+BnLpZgLpak2eshms0Ty+aRRIG2gK+h4c9qrKTSTEVjvBZLUFpZ5bFuiRbv8fiD7JVa29jYYHJyEqfTidvtRtM0VFU9FWmh49QukyRpV2Myy8pAVdWKpphVzzmNJHPYmkwj1svXr1/nC1/4Av/iX/wLfvqnf5qzZ8/yh3/4h3XPyMDbIJKpLiqfP3+enp6ePQnsKNqgrde5fv163V0ttbhbVisE7LR5PsycjZXyKhaLDA8PV2pIteySaq3JqKrKzZs3yWazPHX1Kjen51najJLxLNDs8+JUJNKFIhtaloVYglyxhGaUU2StHh+JfIGwp/x+dcMgVSxyIdjMS7NLpPJFmv3l360ms0iigEuWmY8lcEgSqm4QdDu50tWGikBBkrnY38vG8hI+3z0flPQG64kUN+8atIeC+Hw+vD4fTlkimS/wxuIq42tb5FUVEAi6nDzd18lgc33zVBYmtqJ8Y26BvKoSK2m8sbbBYi7P+wYH6A4e75T7Xqm1paUlSqUS3/rWtyqptZMcgHyQi/peVgaWBbPV8aooCvl8/tjnlQ6CpmkUi8UjSZcdhJ3WywA/8AM/wA/8wA8c6tzwkJNMsVjk5s2b5PP5mqKKRkmmug1aFEXOnj3bUNukRX57LdhWi29nZycXLly478t32EimWCzy0ksvEQ6Ha6ohVZ/3oAfVqu14PB6euvoMr8wv8+2lNdSiSm55HVEQ8DkdzMXiLKfSeB0KApAvqciShFuWyasqyVgRRRIpaTrdoQBOSSSWzdPfFKzcv+6wzFw0wYW2JnxOJ6lCEb/TQVc4QMjtIp4vVL9zHA6FYDBEr6gQNUVaWlsQVJX19XWKCwukENGbI0yYIm3BAG0BH4ZpspnO8tr8MhGvm5C7vjx9UdN4fWUV0zQZCIcpbWzQFwywXijy5uoanQE/G5ksc4kk2VKJFq+XwUgI3zHZH1upNcuj6fLly/um1uotmDeKk2qp3svK4Pbt26RSKV555ZUjsTI4DDKZDICtXfagsLPwv7W1xc2bN2lqauKJJ56oacFsJBLY2QZ948aNQ6kZ73YNpmkyOTnJ/Pw8jzzyCB0dHXtefyMkY5omiUSCRCLBhQsXtjVD1Hrd+73n9fV1bt68WantTKxHmVyP0ebzoCpF+pvCZApFNrM5DAEUSUISJCQRLrS3EPK4KKgaV3s7SRdLFDWdVr+HnlCQG0urKPJ2VQhBEFAkCc0wudTRct/1BF1O2vxe5mPJbfdAMw06An5UQSIcCRBoamYtmcKrqeRyedYSKfRElJTPV87de7wspbOspTJ1k0w0lyeWL9DpK6c6TMq1kGaPh/VMljdWVrmxskZWVZFFEXVtna5NP+8fGiRyjDtoqyazM7WWyWSIRqPbUmvHvcBanZqnIT1lWRkoisLg4CChUGhPK4NIJEIwGDz2685ms4BNMg2j0RZmXdeZmZlhbm6Oixcv0tXVdWzaY8lkkhs3buD3+yudV4eVhtl5DcVikZGREYrF4rG0QOu6zujoKJubm/j9fvr7++u+5r3SZdX1l0cffbTS3rgYT+KQJBymTMksRxU+l5OlRAoBuNrXhSKKiFAmEARm8wl8TgcX27eTRsDlRNW2d+SZpomqG/hdu+/6RUHgqc4OUoUit1cL5JUsW5pB2O3mPYN9bKZzrKUyGKbJQHOExzrbuLG4ir8pR0ASyWQz5R3+0jIJA2YVaFXEmj1SrHsmImCYBiBaP0Q3TTTD4M2VVUDgTKScitMNk9l4nJHVdd4z2F/TORrBboX/6gHI6oK5paB8+/bt+xxCj2KBtTYup4FkLFjpu92sDKxW6dHR0ZqtDA6DbDaL2+0+VcOhjeChiWSsSOXb3/42qqry3HPP1V0QqzVdZpomCwsLTExM3OfLclTSMFCe5RkeHiYcDtekblwvyVj1HSvF18i0rnXenZFMdf3l2rVrmLJCLJvD73Ii3huuNDFJF1VmY3EEBPKqiiJLlHRLi6wcmeZKKk5Zxr3LbrknHOTuepSlRJoWX7lgvpnOEfG66QkH7/t7C+1+Hx85P4QUj+H0++nt6KA/HCTsdmO2mWSKJQzTLF+vINAe9LMQT9HaFMLj9dLa2kauWGR+Ywu3KHDnzp2KgnJ1t9ZeC0uL10Orz8NaOkPPvS5H3TDYzOVpcnuI5nP03RNFBJBEgYjHzVwiSUHTcB2TSnMt3WU7C+aFQqFSMLe0xY4itVarS+eDxF7DmE6nk46ODjo6OjBNk2w2W7kn+1kZHAbZbHbfZ+xhwUNDMvF4HCh30Dz99NMN1URqIRlN07h9+zbxeHxXEc2jELm0WqAnJyfvm+U56NhaScaSuOno6ODChQtsbGwcqp5TTTLpdJobN27g9Xp59ImneH15g/loAu2ekGXQ46KoaSzEkyzGk/gMKGk6pmnyeHcHmWIRRRTwKBIFVWM9k+NcSxNNXje6YbCRzlIoaXhdDlp8Ht5xppcbS2ts3esu6wj6eLy7/cAUVtDlYijgo6OjnY6Ot4bIBEHA79quf3WmOcxCLMF8LIHf6cQwTbKlEo/19fCOoV4kQSCbzRKLxSqaWoqiVAYhd6aUZFHkek83X5+dYyaeYK1QREim6WsKMxSJ8I25+YY+i8OikRZml8u1bYHNZDLbZlEaTa2d1kjmoMhBEAR891KqO60MVlZWKlYG1R4xjaxX1ZIyDzNOfbrMcmNcWFgA4Pz58w1rFR1EMtbgozWYeBwWzIIgMDU1RS6Xq1sJuhaSqZaHuXTpEl1dXZXzHtYZE96arenv76dvYICv3plhejNGq8+LIonEc3mi2RwmJpPRJIJhEETA51AIedxopsFgU5itbI7lZBqHJHOhtZln+jpJF0q8PL3IciKFahg4JZmB5hDPDnbzgQuDJO4V9ENuF1IdaataEHA5edfZfiY3oizGU8iSyBPd7ZxpCVfUA6yFxdLUqk4pjY6O3pdS6gz4+RsXzjGfSPJyfIunB/u40N6GCYysrbORzdLhL6dHdcMklsvzREf7sUUxcPhhzOrUWl9f36FSaw9TJLMfquX6BwcHK1YGuw3J1pNutFwxT9P9aQSnOpLJ5/OMjIygaRrXrl3j5ZdfPpTcvyULs7Nt0jRNlpeXuXPnzrFaMGcyGQqFQqUFul412YNaiS35mXQ6fV+33WE703RdZ2JigoWFhcpszUIsyUIsQW84iEMu7/7cDoX5WJKcqtET8mEUS3Q1hQi5XYS9bmajCZq85egkls6gGSatPg9OWeZrUzPMRhN0Bf04FZlcSWVsdROXIvPsYDdNDc6X1EquIbeLq31dPN17sK3BbimlnRpj1qIyFImw6XFzvilSSQk+19PFN+cWmI7GkCUJVdfpCvi50rH/zNJhcdTDmDvvg1W7qCW1Zj2PpyWS2W1taAQ7XVOtZ2M3K4P90q5HKSlzkjhRktlvd72xscGtW7e2KRof1h2zemLfepA0TWNsbIytra2aLZgbuYaVlRVGR0dRFIWBgYGG5Mr3ayW2Ulgej4dr167dJ59xWAXolZUVgG3Wy9liCd0wKwRj3OsW8jsVVtNpgm4XHkVisOUtSXEB0AydWCbPm4trbKSzKJJEm8/DQjRJe8CHUyk/lh6HQpPXw+xWgse623A76u9w0gyD+USa+eISDkmkKxSgPbB/t04ji/DObq2dboemaTI+PU3e7UJ0uQi7PXzo7BlW0pkH0sJs4bgn/nfWLvZLrblcrlNDMPDWZuSoC+27dfLtZmVgpdesteGoSOab3/wm//7f/3veeOMNVldX+dKXvsT3fd/37XvM5z//eX7pl36JyclJgsEgH/7wh/n3//7fH7g+7oZTF8lY0+iLi4v3KRof1cS+rusoinKfbXEtxbp6F2vDMBgfH2dlZYUrV64wNzfXcNpqr3Ovrq5y+/Zt+vv7GRoa2lNSvpHzptNpNjY2cDqdPP7U0yync2SiqfKci1Du5MqrKuvpLEvJFJqhY+gm7UE/+UIO1Xjr8yrei0JNE/5ibJK1rS2avV5Ep4tvL6yQyhXpCG5v5nDKEslCgZKm43YopAtF5uNJ8qpG0O2kNxzcM71UUDXe2IgTN5J4vF7AxO1QeLa3iyvd9Qv91QpBEAgGgwSDQQYGBlBVlT/6q6/z8uoyy+k0pmHgdDo5E47wNy9dprO7fkO4RvEgZWUOSq1ZLbozMzOEw+EH0ha8H6pt4I8Lu1kZJJPJisDn2NgYpmnyB3/wBwQCgUNbL0OZrK5cucInP/lJ/ubf/JsH/v2LL77IJz7xCX75l3+Zj33sYywvL/PjP/7j/OiP/ihf/OIX6z7/qSKZXC7HyMgIhmFw/fr1+1j8KEjGSjlZkUVfXx9DQ0M1P1j1XINlYGaaJtevX8fj8bCwsHBk+mPV7phXrlzZ12uikUjGqr94vV5Ej48/vzPNSiJDWewFWvxe/E6FF2cWSJdKuBUFTTdIF4uEfW7CLidzyST+ZArThIKmcbGtmc14ksnZeTp8LnLJBKpaQpYdRFN5ptY3udDRVlkIk/kiAbcLr9PBUiLF16dm2crkK9fYGwnw/nODBHfZIExsRllM5xlsa6a1qVz7imZzfHtxha5wgOZjknfZCVGSuJnNIjSFudbRia5pJDMZJmNRvvDSi1yPNFV2spFI5FiH/k5Su2xnam1ra4vR0VHy+fyJDoRaOIn0nSRJlfdrWRmMj4+Tz+f56le/ytbWFu9617t4//vfz/ve9z6uXr1ad6T14Q9/mA9/+MM1//3LL79Mf38///v//r8DMDAwwKc+9Sk++9nP1nVeC6cmXba2tsbt27fp7Ozk/Pnzu97Io7BgFkWRu3fvEovFePzxxyt501pRa+HfMv/aaWB2mLRV9bH1ysPUqz9mRZNXrlxhc3OTl5c2yEhO+ppCSKKIbhjMRxO4nTIGJoooIQB+l4NLHS3kNZWgU+GJ5hDegA9REBloChHC5P/7+jDNQT/9PV0ICKiqSiaTYTG9zOTiEsnNLcIBH4LixOVycXmoGxOTb80uEM8VGGgKlW0EdIO5WII3Fld579n7lWWnt+I4JBGlatFo8nqY2Yqzmsw8MJJZzWaIaSqPen1IkogkOWhxRlC8HoqaRk9vP6VMpiLieBwzKRZOk0CmoijIsszly5fvS61NTU2hKEpFUfpBTNxb6gMnGU0pisKjjz7Kb//2b/Nv/s2/YWFhgfe973189atf5dd+7deYmpo6kuhmP1y7do2f/umf5s/+7M/48Ic/zMbGBv/jf/wPPvKRjzT0eiceyVSnkx555JF9/QoOG8lks1kMwyCbzd6nC1YrDir8V2up7TT/so4/LMlY8zWRSKRmeZhaz1sqlRgZGaFQKFTqL3Ora6xn8gz0NFW6uiRRpD3oZ3IjSsTnYSASRjcM3A4FRZKIZnPEslme8Ll556MXK9I8d6amGOjupCDK5cXOBMWhEAgFOdcLF1ub2Iqn2Eok0XJpjHySpSmVrY0AC1txBloilUVSlkSavB7ubkTp9wfxuRxE/B5kqXyNqq6z13JqNJiyTBeKrCczGCa0+D2EvQc/Q5phYACKuH3jJIsiecAbDNJ777nfrXBeHeUcRk8rr6rMJ9OUVJWOfKFuFYOjRrWkTC1da8c9cX/aDMsymQwdHR382I/9GD/2Yz/2wDYIzz//PJ///Of5oR/6IQqFApqm8bGPfYxf/dVfbej1TpRkstksN27cAKikk/bDYQr/VqQkSRLnz59v+Mu6XzRVKpUqA4p7aakdpjvNMgF7/fXX65qvsY49KJJJp9O8+eab+P1+rl27hmhZE5jlFlv5XuNBqlCkoGmI915TMAVcirytrbio6bgUGcE00HWd126MEI1FuX71aboLGn95Z4pYNk/Y7ULVDVaSaToCPt55th9ZFBheWefW2gaxbI7X0gXEeJqlWAIzFcfv8+H1evF6vcRTWZaiKZSMiUdx0Bz00NLuYzIb47XkCjPFBHpO4fFgEFkUSRWKOGWZNn/9BdW7q1t8e3aZVK6ICficDq70tnGlt33fz6HF7cEjikQLebqqVJ238nl6/H5CVU0gOwvnO03KrPkLq0hca+pkOhrnm9PzTK2sYhoGsxo81d3B0z0Prh60E/t1cu3XtXb79u2aO7SO6npOAtlslkikqmnmAX1OY2Nj/JN/8k/4mZ/5GT74wQ+yurrKT/3UT/HjP/7j/OZv/mbdr3eiJDM5OUk4HOb8+fM1fbiNRDLVdYtHHnmE6enphgvvsDdJWBI0gUBgT6sBaHzORtd1JicnMQyDZ599tq75Guu69zuv1TwwMDCAt7mNv5qYZymexKXIeNQ8PofMSjJFulhiLZNB1XSKqkZH0M9gKMJSIklXqLyQpwtFcqrK1bYIybVVfvWP/pyNXJH29nbU2VWe7OvkuYFuRhZXmY2VhzgdDon2sJ+CrrEWz/DN2QUUUaTN76focbOcSiMHQzj9XhyUtdgm5xaZSZfo8Plo9TlxudzcjW7xxdW7SIoIqkm+pPHixirrxRKXAy2IosgTPe11k8xWOscrU0sYmPQ1h4CyBcFrMytEfB56m/ZWH/AoCufdHjZ0g9lEAo+ikCmV8CkOnuvs3nPmZzcl5Z3Ww9UL7V4zFYl8ga9PzpItqXR43IiCiSgIfGtuibDHzVBzZJezHz/q0S3bq2tta2urMhh72LqWruunSsLFmpN50PiFX/gFnn/+eX7qp34KgMceewyv18s73/lOfu7nfm5PbcW9cKIkc+XKlboW/HpJZrfC+9zc3JFqj5mmyeLiInfv3uXMmTMMDAzsu+NoJF2Wy+W4ceNG5XXrJRjrvDuto+GtYVer/mI43fzprQkSuTwBt5NstsTtjS0iDpnVZJq5eJKIx40kiLgVBwbgdztwO2QW4glME1yKzFM9HTSZKn+1vElRdnG+vw9ZEpndihPN5vneK+cZjAT55uwCI+vrpFWVv5qb583VNTRNR0SgI1DuNHMpMk5JZrS0SU43waHgijSTzZsEtAKDfi8rS0sYhsG0orKYzdLp8OH3uuhS3CQMnblUEocmMRiOoOk68VwetWRQULV7abb9i8xLsRTpQonB1rfufcTrJp0vMLeVqJBMMl9gLpYgp2oEXE4Gm0I4JYlBp4vnhoa4m0wQzxcYCoV5rLWNvuDe5ATltF62WEKRJFzK9vmLXC5X2d3Pzc1tKyJHIpFKG/tcPEEsl+dMU5jNUh4opxnT8SQTm9ETI5lGI4f9Umtzc3OMjo42lFo7bZHMUbhiNnrenSl4i3wb2aCfKMnUmzqqp/Bvzdm0t7dz4cKFyk06ig4163hLfLLWGZudx9eCanmY/v5+vvnNbzaUm60Wl7T+v7r+YjUP/OXYFPFsnsGWtwzESpkM66k0jmCAMy1hTBO8DoW2gA8EgVg2z996/BLpYglV12nyeCgmYnz19dskVIN3Xj6DdK9O4nU6mNmMc3dti/aQlztbW/icDlq8HkxgLZ3l1sYGj+7olHMpMiG3k6e6OjCslF0gSKiplYHmcHl3m8/x+tJdjIJGupRCoYQsmChiuX06XyqR1zX++PYEfzI8wVlfGEUUcTlkelvDPH22szKjsxhLMr66xUYqS8hTTulZXXXVUCSJQkktHxNP8pW7M2xmc5VaUE8oyPvPlzce58IRHm2vfRc4sxUvW0VnciiSyNnWJp7u7cB7jzx2StUnk8nKMOjY2FhloY0W1MozYJogiuWrc0oimWLjduSHxWFl/jfyWcaTWxR0jXa3jwuDg5w9e3Zbam10dBRd12tKrdXqWPugcFRzMplMhqmpqcq/Z2dnKzXd3t5e/vk//+csLy/zO7/zOwB87GMf40d/9Ef59V//9Uq67DOf+QzPPPPMtpGSavzIj/wIv/3bv82nPvUp/vN//s/bfnfi3WX1QJIkVFXd92+qZWh2ztnA4bXHrEgmm80yPDxcmd6vVRBPFMUD3wNsbyCw3kepVF4QGvkyVBumiaJIKpWqpPeuXbuGLMtohsFSLEnI49r22ficCrOqjmQYXO5oqwxfQlmTLFUoy72cbWmqNHKsrq4SbutAiE9XCAbKn7nbIbOZyZLWixR0je5QuXYlAB1+H7fW1llJpemvEpAsahqSKDLUEqE/XP756+5lxhY2Kq/rdXtwOVyISoHO5giKCOuJOPFiAROT1WKSnKaSLWlkSyp5Q+W5tk7i2QILd+YAk+cv9TGzGedrYzPkSxpep8LsZoJkPo+mGfQ0BVEqNtomBU2jLehD0w2+OV3ufjtzz+FTMwzmoglenV/GTX3P+3wsyV/cmaagakTukdyrs0uk8gU+cvnsfSk2URQrOllnzpyhVCpVFtqt5RVWN+LI+SyiaeJwOMoijyWVzsDhXBcPg8NEDsPRVf5wbpxYKY+AgCQIXAq38LcHH8W3i5jlXqm1cDhcifhOW+H/KFwxAV5//XXe8573VP79T//pPwXgH/yDf8DnPvc5VldXK7JdUCaMdDrNr/zKr/DP/tk/IxQK8d73vvfAFuaenh5+//d/n1/+5V+u1LwLhcLJd5fVg4MK/4VCgZGRkUpb726h5lGoKBcKBV5++WW6u7s5d+5cXQ9mLSRnKRxnMpltDQS12jfvhupIxpoRGhwcpKu3l9VUFlEQaPaX5V0S94YfNzIZ0sUS2VQG3dTxOR0kCwVafG/trpKFAn6nE7/TyfjyOn/52hukiyWevnQBRVUxzfvbZguqTsjtIqbmK7pgb11nWcG4UNLYyGQJuV2UNJ3VdJpml4fl5QSz8zFagh5ag14W3U4WN5OEfWVxTb+mIDoEDEnA5XSiCSKqLCIhYugigq6h54uImIxtbpJI5Gh3+ymVNP7kzbs0h7zcXtmgpOn036u9AKwmBea3kkxvxGjyehBFgWSuQGfYz5nWCGvpDBvpDB3Bt+oisijS4vMwH0vQp9W3sbm9sk6upDLQ9NY1eBwK01txlpNpevdRoIayC2R7ezvt7e2cOXsOffgWoyvrUMhjpjNMbcXoCAVplcvNJI3qAR4GjXrJJEsF/mRhgryucTbQVLb31jVuRtfp9gb5UPdQ5W+rxSyrhx8tN8zq1NpJGajthaOKZN797nfvm+b63Oc+d9/PPv3pT/PpT3+6rvM8+eSTTE9P88UvfpG/+3f/LgBf/OIXHy6S2S/VZZmYNTc379vWe5h0mWEYrK6uksvlePzxx/dtt94LB5FctcLxzgaCWuyb94J17MTEBKurqzz++ONENfjCazeJZvIIgkB7wEuTz8NsNMbEZpR0sQhANJXBJcL1cJDlZLpsmex0kCmWyJdU3n12gDen5/niK28gOxx0tbXx5tI6smkgC7AQTdIRKs/KbKazuBWFodYmllIJ3lxeQzeMys5cMwz8TidPd3aQKaisptM4RImQ7IKUwWQ6hiyLzKzEaIv4eKS/jeWtJNF0HkkQ+BsXz+NbdDCVjpM2iqRQEUwBpy7TFvDhdzko6Wn0UomSqpLWCoTTBookkygW+Yvhu0hOhciO1uQWn49sUeViRwupfBHTNHlqoJNLnS34XA6iuTy6yX0RhiSK6KZJPZ+Ybhisp7MEdvjluBQZVTdI5AoHkkw1nIrM9z52me6mCC+OjiNKEk+0NNHtkIitLLM8PUUwGKzs7v1+/4m7Yq5nMmxmc7gUmb7gW9EjwGQqRrSQYyDwVkrXJcn4HU6Go6u8v2sQSdi7a816n1DuWrOaKTY3N9F1nZGRkSPtWmsEVgT2sBmWffKTn+S3fuu3KiTz3/7bf3v40mW7uUpWz6UcZGLWKMlYUVI+n8flcjVEMLB/4d+KMPYS6TzIvnk/WCm6THqM61dz5PUFXh2PkCi00BH0lyOcZJp0oYQoiqym0vidjrLXis+DbOokC0WeH+xjajNKulgi4HTyjsE+vFqJ33jlDZpCIc73doNQ/lzurqzjkkXaAl7WklkM0yTidfPMQDc9kSBeh8zN0AYz8QShe+nGeL7AYCTExy6eQxQEYrkCqqrz6q0FRI9IxO++dw9M5tcTtAS9vOuxAfJFFUkScSoyF7pa+MrtKRYTScKqwVpJJS2bhLwuBAQQKHvauFy4FIUmT5B0Oo9P1JhfWgFZoBAO0hYJ4fV6y2laQ8elKDzR10FrwItpmtsIpdXvIeJxsZnJVhoWTNNkM5OlLxzErWZqN9cTRXxOB2upzLafa7qBKAh4GtBw8zkdfNdgH8FsCpfHw/kzZyq/2+l1b3mjWP81orNXjZKuM5dIkFVVmtxuegKBSkv9zkhG1XW+Mj3DyPoG2VIJSRToCgT4nrNnK6k9zTAwoeJbZEEWRUqGgWGaSDUuLU6nsxLxzc/PE4/HCYfD2+wcdkutPQg8jAKZf+/v/T3++T//58zPl60sXnzxxYcvkqku/BeLRW7evEk+n99zLmW316iXZGKxGCMjIzQ1NXHmzBlu375d97Vb2I1krDbrlZWVfVUIrGnkekmm3F79Jr3tX2Gw5yUEvYRTN/ieIYmFzEeZTH4cEOiLhJhY36Jo6jze3VGefREEKBXJZzMk8gWafR6e7e8ir6q4ZJm5mRleG5/EEwpztrcT63svCAJhj4v5LYMPPXqOXFHFME2a/R7cinJPSNPB37hwlhtrG0xsRQF4Z18PT3W9VdzuCirMrcXJFlV6W9/avYuiQMjnYnkrhW6YeKp2/ZGAh++/eploIsvt0TtsCCZfWd9gPZPFJcvIsoRDENEMEwkBVTXxuN2c6YiQLpZwybAUi2NubmCsaigOJ2lD4FxnK80+N6IglPN6VXArCs/2dfH1yTlmtuK4FZlsSSXsdvF0dwez8fW6PrNHOlpZiqfKs0QeFyVdZzmZpjPgpyd88HO+FyRBuE/rze1209XVRVdX1zZvFEuZ3OfzVRbaetxBAdYyGf54YoKFZBLdNHHJMpdbWvjo2bO71mTeWFnlpcUlWr0eOv0+SrrOQjLJn05M8g8efwynLNPtDeCVFRKlAmHnvU2HaRIv5nm+rfe+oddaYRgGDoeD3t7eA1NrD8KC+ahqMg8SLS0tfPSjH+Vzn/scpmny0Y9+9OEjGYsgrIU/HA7zxBNP1JxTliSpUkA/CNaU+tTUFOfPn6enp4dUKnXo7rRqkrAiJMvO4KCB1HJNJ0e2+AqGkcQhn8MhX95zl2xFR5fOJwg4vg44gAhFvYggZDkT/FMSpSE2co8Tdk7z/oEv43NsUDR72Ci8n6TaTyKhkTPNSn1FkSQwDG6OjJDNZnni8cdZGZtBN4zKAGf5/gkIArhkmbBn9+HXsNvN+84M8K7+PgSBSo1G0w0y+SIOWUIQBATKwprVb9M0TYSqL3m+qGIYJh6XgkOR6GgJsNXk4XwwSKA1wten53BKEpFWN3dWNtlIZQlLToJeF21hH7Io4hedvPeRAW4srLEQTaKqKrlCkYBi4M3GefmllyqLTFNT07ad7eWOVnxOJxMbURL5Aq1+LxfammlyO5nd91O9Hxfam0kVi9xYXOPbCysk8gUcskRH0M9GOruvK+h+2K0zMVUqkimVCDldeBRlmzeKqqqVKGdsbAxN02p2B1UNgz+ZnGQ2kaAvGMQhSWRKJV5fWSHodHKG7dkM3TC4sbaG16FUtOgckkRfMMhiKsVcIsn55ia6PH6ea+3mhbU5kqUCDlEirZbo9Ph5R1tvQ/cF7q917kytVTdTWBbMtd6LelEqlVBV9aFLl0E5ZfaTP/mTAPzqr/7qw5Uuk2UZTdOYmZlhenqac+fO0dvbW9fr1BrJqKrK7du3SSaTXL16ldC9TqejaBywzm/JwzQ1NXH58uWaivlOzzyx4i9jFjcAA0Fw4HZcp8X3bxHFt0LrndGRQ/oPmCWdguZGkQ0kQSKvupEdGbzCV0lm5nlf95dQxPIkP8xw1vw2b0T/EcnkIIliie5mF51BP5lMhhs3buB2u7l27RqGINC6tM5yIk1fJIggCKi6TjSXp93tqEmiX7nXgWaaJlMrMcbm1knliiiSREezH49TYTOZpS1c/tJpuk4yW+Sps53kCiVuT6+zvJXCNExaw14uDbbRGn6rCP+hc0O0+rwMr66TV1We6uskly6haAJBr4u8Wo6Qnx7opLcpREfIz1IsRSyfI2MW6Qj56XIGiG0lSKeTLC0tVXb5TU1NeLwBnC4PbV4vfRe2E4AVfdfznIqCwHP93SRyeRbjSdoCPpo8buZiSWK5u3zs8jn6q5oCdmIrnSOWzeOUJbrCgYrUTjXJ5DWNr87PcGNjnYKm4XM4eLa9k3f19FXIXlEU2traaGtr29apVYs76GIyyWIqRW8ggOPes+1zOAi5XNzc2KArEtmWclQNg9y9CHn7syGhGyb5qvv4kZ5zdHoDDG+tklaLPNfazTMtXbR7Gtv5m6bJUiZNXC2hbm1wJhTBveM6qpsp9roX1RbMh0mtWQrVDyPJfOhDH6JUKiEIAh/84AcfrkjGMAxKpRKLi4s888wzBA8YZNsNtXR3VXuzXL9+fdvDYh3fqI6QRXLz8/NMTEzURZSGmaO563cxzBSy2ARImOTJFV8gLv4Xmnz/D6C8CxoeHqZUKvHcc89xcy1KkDnaPSYFVUVUNZR7EYKqG2Bu8pEzk0iopIrldJYggENMcS7wO3zt7mdQDZPnByLoma9zY3ySdfURTEeQ6Pgc5zuaefe5Ab4yNs30RhxBLGfNBptCBFK7t2vv9X5n1+J86/YcsigS8DopqTrjC5uEfS4kBObW4pXIpq8tRF9biBdvzrOylaIp4EaUReZW48Qzed7z5Ft1B0USebaniyc72yloOh5FJpEtMLEaZTWRxutUONMWYbA1cu/vJdJKnr/YHGO1kKKwrOIsyFxW2+mRw3S3d3D1wmWSyQQ3RueZnL+NquqEgn4unOng6ccG8d4T4DxogK2k6aRz5ajN73mr/hHL5pnYiNEdDlQaEVopz898e2GlQujVUHWdb00sMLa8Sa6klmsa4QDvuThQqSVZx/zZzCTfWl4k4nLT7PaQKhb5s9kpREHgPb39u35m9biD5jUNVdcrBGPBJcvkNI2ipuGrIiWnJNHu8zMRjRKpkn1KF0u4ZZnmqmhYFkWebu7k6ebd5zbqQUHT+KOpCf56cQ7VNAlkUnT6/Hz/2fP0B0O7HrNf19rCwsK2OaVwOFx3mjGTKdfjHraaDJTXuDt37lT+/6EhmUQiwc2bNyvT+40qsh4UiSwvLzM2NrZn8b168rURkjFNk0KhwMzMzIH2y6ZZHgAUhPI586UXkZQ4Ak0IQvmjE/BgmiWyxf9F2PuPSSWzjE38Pv7wFkMd59gqdPDi9CrPdQ7S6bmDIoGJSEnTEQUTAcipQfyOFYqGB5eioJsmXkUB00/ImeCdfXmC5hTnXf9vDCPBE+cgp/p5eemHeGP+CqMrG7zv0hl+8OojzG7FKagaIbeLVreDb78a3fP97bx/hmFyd3ELASoRi9uh4JAlEtkC1y/1UtIMVE0j6HPT2eRnaSPJWjRNb2vwrYFPt4P5tQRzq3Fkti/yiiRVOpWa/B6u+XdPTy7k4nx+/nWyWhGP7iSdLpIQ0xQdGhE8jE9vUFJ1HA6JVMnB2aEzCBhsbsV57eYci4sLnO1rquTu93oW7i5tcWtunVS2gCyL9LWGePpsNz63g81sjnSxxGBVKzVAk9fNaipDTlUrtSsLtxbXeX12mSa/h/aQj5KmMx9N8LWxGT5+9VLlud3M5bi5uUGL20v4XmrKLcuYWZNXV5d5rrP7vp38ThzkDprUNEqZDEuGQUcoVElpxwoFegMBXPesNywIgsAzXZ0sJpPMxhOE3S6KmkayUOKprg66jqk+8eLyIq+uLuOTZEJuN95AgIVUki9N3uUfPf5UTXbY+6XWqtOMVpTj9Xr3XT9yuVzZYuMUze3Ug+r6+KknGdM0mZ+fZ3Jykv7+fqanpw81lbtXukzXdcbHx1lbW9u3+L6bu2atyGazjI+PV/xy9uraKelRljOfJ1r8BqapEXQ+TZf376AbMQBMtr9/QXBgmgWWV4bZzP4SkZ55JEkgmoOS5ibo/ltsFT9ApvjXBDxpDNOJLhhIQomcHqIkXkMSx3EgYiKgG0ZZHkaWAImrXSkU8/8GU0c13Oimic+R4bsHfheXs5+7G81MLv8VF4PjPBZcBKkfwfm3yJcG9t3FG4ax7feqppPMFvC5t98Xt1NhI5FFkkQudzdXfp7MF7izukmskKMTf+WuCIKA26kQTeZoq1MH1TBMVjaS/N9zN5jLx+h3R8jmSrgEmZDDxZqRZs2R5ly4lZdmFiioOi0eD61uBy5FJhjw0ZTKAdDV3UQmnWRiYgKA27dvVxZlt9vNzFqMvx6dQ5YkIgEPRVVndH6DQknj/U8OoYgiolge6qxu4VV1A0US75sx0gyD28ubeJwOgvcUlh2yRE8kyGoizXIsVSGZeLFAVlVp8WzfKQccTmLFPMliAbdcX6pmN3fQ6OgoL6+ssBaN4nU4UCUJn9vNMx0dEI/f9x062xTh+y9e4LXl5XuNGgrPDnXzbPf+XaONQjMM3lxfw684EEsqgiCgiCK9/gCL6RQziTiXmuuzA4G9U2uxWIyZmZltbpi7pdYymcyJtU83gt1mbSyc6pqMVRdJJBI8/fTT+P1+pqenDzWZuxvJ5HI5hoeHEQThQAuAne6atWJjY6Myx1MqlfYkGM3IMh7/abLaJKLgBESiha+TLt1iyP8PAQkoAG8tAIaZRSu1EY/9HsHWOSQxiCg4y5GQsMXjvf+duc0n+KORv8c7L7xId3ACE5G7sUe4sfkxgt4zlIwv4pSSlPTy6wqCCWaGktnJevQFups0BDmMXtTKaTZDwSFm6PV/A598jidafgNBNcqVee0mZukvkJR/AVhKzVugvkxZ2/9ZonGFGzduUFBNDMVDMBikv6sVr0shkSkQqEobFVUNWRJxO8v32zRNXptb5ttzK8xvJtiIZYjqRR5ta6Xt3qBoUdXweRwIFGv+jEzT5PbkGqNTa8yKMQzJZCOXoVDUCAXK2mYiAneLUV5Jb7BSSGMa0JL0cDYd5om2djqDfjwuB4lUHl8gRHdXB8VikRdffJFQKMTW1hZTU1M4HA7ubKnkdZHBrhZEUcKpyDgViYXNJGvxDN2hAB0BP8vxND2RAJIoklc14rkC7zzTi3PHDlvTDQolFZeyo5Ygl2saBe2tNG/A4cAty2RL5VZ0Cxm1hEdWCDi2P59FTWNiPcZSPIksSQw0hRhsCd+r390Pyx307z77LOdWV3l9eZlYOo3HNOkRBGLj48iyjM/nI5PJbNvZn29u4lxThJyq4qiKPC1spLNsZXM4ZZnecOC+39cD1TAo6hpOWaJUlZ1QJAndNMnrh/OvgtpSa9UdfD6f70Stl4vFIv/23/5bfu/3fo+1tTU6Ojr4mZ/5GT75yU82dA0nHsnsJUGfSqUYHh7G7Xbz/PPPV6QwoP4Fvho7ScbSBmtvb+fixYsHkle1u2YtME2Tqakp5ubmuHz5Mn6/n83NzT3/Plp4gaw2hSJGEAVrUfVRMjaJFidQ8+eQfHfRDR1BUDCMLKpmkt56F+GOv0Q1ZPJFMMwCDklCFEI4pCi6+RIvr3bxUvR7cDvKswTPDJxlI5dFF0xuxv4OTzb/JoqYQhEFFFEiryr89qtXeff5b6EZUChq5RkEgHvDbl55le6WN5BEFYMgoiiV28DMFJL6/0EQ/jEUfx/yvwpmHhMwDJnoynsQvD/E7FKCaCJKPr+EJNyiOeClYEgYukZrOEBR1dlIZOhvD9MaKn/pJjdifGNiHpcic7GzBVE1WUtm0HSD5/u6SaeLaIZBa8RHLpa+7/kqqBqxXB6HJBFyu5hdj7O4mSCVLbKynKAz4qfbGWJLzeBTHKSzRbLZIpIsEtcLbBRKGHkJpykhSBCXC0xoMaQ1Ab/LiV7UcTpkXPcaHqyFq7e3l/7+fnRdZ2MryuvLN8nnc0xMxPF4PHi9Xny+coprcmGLBSFGWJPZNARmthKIooAsCFzqaOFqX+e291PSdDwOmWa/h7mtBCHPWzJHmUKprP3mcZG9t5C2e31cbGrmtdUVdNPAUE02s1lSaokP9A9uS5XlSyr/6/Yk42tbFXuHN+ZXeHagi3ef6993s6hIEte7u3m2s5OSYeC6Rwi5XI7R0VEKhQKvv/76rirKO1OBqq7ztYlZbq5skC2WkEWRrpCfD18aqswm1QuXJNHp83MnuoXHNBHuPdfJYhGPrNDmOfqayH6pteHhYf7+3//7DA0NIcsyY2NjXLp0qeGIpl7rZYAf/MEfZH19nd/8zd9kaGiI1dXVQzU7nTjJ7IRpmiwtLTE+Ps7g4CCDg4PbjI2OQnvMKtxXL/57Cb/thlqvwfKXyeVyPPfcc/j9fnK53L4fWEYdB4wKwQAIgoiAQlq7RTH2Y3i9X0MQX8LQi+RzYYzix7h04RNMRb9ESTPRdA1BKBeUFUlAkQSWkqtsFVsISjKpgoh8Txzxkc5WJjaivDA3xJ2NH+eZjjcYjGRZi4f53Ve7cXoeQTOXEYUVDMPAMMAUQBHK70Ez3TjEJLrhxqlI1gUDHgRzk67mVyH/l4CBiQ9N18DMMdj5VV663caUeJa2Xj+XPEEc2jIrm1ncZpjlpSVm5iHo8zLQ1czTZzsqnUh3VjfRDYOWe3L9g11NKOsSi/EU3xifRxEkgn4nXx+bxaPleOpMecE1TZPh5XVemV0ikSsgiQJ60UBRy7MjyXSe5fUkJcFksLOJSWGTqJnD6ZHYLORYyqYpoqPlFOSSQMjjwiHJxPM5knKRlUKG2c04IdHJYxc6cDnLXy/DMElmVe7Ob+JQZNqb/LS3tjDQ28VGIkuz30k2myWTybCyus5CtEB0aZPWUAC3203QkOgKexkaaKHF76EnXLZUKKga355ZZmx5E1XXCfvctAW8OGSZhWiSoMdJUdVJ5Ys80ddBW8DLUtVu/XvPnMM04YXpOaKZHDICbZKX5FKON10rPDlU9poZW91kfHWT7kiwEj0l8wXemF/lTEuE3sjBDTiSKOKu2sB5vV7cbjehUIiurq5KA8F+7qBvLq7y8uwSzT4P7X4vqm6wEE/yv0Yn+cTVKzhkqaLJtt/QqmmarGWzJAoFgk4nz3d2s5BKsRiL0eFwks6kyagqz3d10+1vfCapVlSn1i5cuMCf/Mmf8Bu/8Rt8+ctf5urVq4TDYT7wgQ/wcz/3c3R1ddX12vVaL//5n/853/jGN5iZmamQYH9/f13n3IlTRTKapjE2NravqvFhVZStgc7XX3+dfD5fWfzrfY2DmN0SoPT5fFy7dq0See0luW9BFr1UUkxVvzfRkIUAJfzI2j/C1P4B05Mj9Pc/wcC5M8xuxdlKt9Pkm0UQPFhTkbqRxzBFsuoALU4HbpeTrqYwrX4vC/EUzw/28kxfNxvpDJJ4id7QD7E0M83v3xhHCYTobW9lKvFuevzfxqPkKKiOe3nrAgXNxa21s3QP3cCpSDvnEwFoDt4FU8PER0nVABNJ9qMZKXqa/5ovboW53vwlPtA1gkfSMQyRZPFJHP5/ydKGRjQeYyMa4y+/+SK9rWE621rZSKa2pYoCXhcX+1uJlgqousnZjjBhr5tcUeX2agLZ6eTc0BnG16N8eXQSWRRp9XvYSGYZWV6jOxjgHe09eBSZVKrASixFa8DLu/1DvF5aYF5OkHdouDUHhqYgaKCLkDFUnIKBappktRIL+RTNgpu/+9xjXBxsA8p1pldvL3BzLk1UnQNBIOx38eyjvVzoaWEtliGZ1wj5gyhuH9GijNedpqfFj1bKk4knkBUXG+k0j3VG6O8LVaL/r43OMLKwRsjjwu2QWUuk2UhmudLbRjxbYDOTxa3IPNHXwRN97ZXjrOfK53DwdKCdJSPO4y0tNLnd+GQH8UyeN6dX6WoO0B72M7kRw6ko2+550O1iK51jOZGqiWR2gzWMWa0wANsNyr75xpukSyptkTA3Yhmc96JPKKcBe8NBFhMp5mIJXIrMS7OLLCXSiKLA+ZYIzw/0bovqsqUSfzg5wdjWJjlVxS0rXGpu4fvOnuePk0lKAgSdLr67b4Brnd27XvdxQhRFnnzySa5evcry8jJf/vKXefHFF/nLv/zLBzKY+cd//Mc8/fTT/NIv/RK/+7u/i9fr5Xu/93v52Z/92YaNHk+cZKwH35q9cDgc+6oa1yP3vxuy2Sy6riPLMtevX29IGPCgSMbqUNsZiVnHwt4ilxHnd7GW+2M0M4FMCADdzCEg0Oz+bhYFgeXlZTKZDFeuvIfm5nIhfDWZYWzlu3nHud9DkRIYhhNB0JEEjfnYOQThMc42pQkFQwSC5d2ZLAnE83muDfTQHQpQLBa5ceMGqqbT1NaOKJXvTTw/wJdvfz/vf/QbOKQtHJKEZvayVPhR+tqvIEt/hcQWmEo5ijFNMHMgdiAKOmBSLKmIooCiOMioKhIm7a4c/9uZ1/hg1+voJuR1GYcATZ7XEcV/xaLj5xhPqMQKEm2eNdaLs6SWBilES8wmcgiFCD6fF4/bQ1HXyZc0zjdF6Ai95UMTdTuY20yRyBa4sbSKYZp0Bsu/13UDv+IgWSqxmc2hu3Q0n0EppbGVznEl3Ear4ed/RO/Q7hV5tKWDbybn2DCzGDmdeKGAjIhHkXHJCn6HE80toLsFZLn8OU8tRhmf28LjlOjrDGOYsB5N89roIh947hzXL/Vwa26D9UQGRRJp9nlodjjp7y4Xm1W1RCaTZXZpg1feuEV8Y55IJIKmuLizvEF7yIfvntum3+VkIZpkM53jb129RFHVcMjbaxqGYRDLFEhpUVwOmZnVKEHZQbf/LaII+9zMrMVZ3kpREgxWMxkyaunI7X/30i5zOp2Empt5eSvBmKGQLKkYSxusJtI0yyJGOlWxOXC73RimyXIizc3VNaLZAi1eN7ph8vLcEpuZHH/7yUcqdao/m57m1eVl2n0+2rw+MqUSr60uo4gCH4i00HfmDK1NTXsayT0oWLplTqeT9773vbz3ve99IOedmZnhW9/6Fi6Xiy996UtsbW3xj//xPyYajfJbv/VbDb3miZMMvDWV3tfXx9DQ0L51kUYtmE3TZGFhgbt37wJlt7dGu9T2knaplrnfq0OtunFgt/P7HZfo8f0IS5nfoWSUazciCs3uDxAU383t5CuIoliW53c4yZdU3A4FSRRYT11kdvMnafb9KR7nLJrmZW7rKqOr70HDwMVb9S/TNNENk5Dbharr3Jxd5KXhmwQCft795BU2F1a5tbxOs8+DIMDkxkXyU+9HMCb48KMX6Iw8xeOt99q5S/8vzOzPgJm6Z7kigODGdP4zUrk/IhKYRhAFTETyJY2SoeKVYSHdwne1v46BQMkoG6AJhogsmqDdYmz+a3gkiR949ncJONcBA9Vw8NS57+N333yG1UQaZyZLsVQiowtIOrR4nJiY5S6xTIb5fJaMavDX0/OspTL4q+yPBQQkUSQjFnlBnaJoahSbVIoOnWJOJ7ziAEEg4PDQEvDicTgI626W9TSGZKJpZS2xtFnCW3BwLtRMxOfixuIaV3rakUWR6cUtXA6JgiLe+/wF2pr8LK6XGxYe6W9nqLOZZLaAQ5GYmttkdOotCRpFcRAOO8iWBM72NXOxL0QsFuPN6UXml9cg7Kfg8eL1enC5XATcTqKZHKqm43Vur2kUVY0bi3GyS3kQFRRZJJktEHTd34RSMnX+Ym6G/LzOajLDVjpLUi3yeHsbTkkmmS/gVGS6Qo2nk/aT+n9hap7XFlZo9/voDPrJFous5kskRZGLTU3k8nk2NzdJl0roosyd+QVWcwUutLdWmhH8LgezsQSTmzEe7Wwlms9za3ODZo+n0uwQcDrRTZPbW1v4TbigKCdOMHByrpgW8X/+85+vtN7/n//n/8nHP/5xfu3Xfq2haObESWZ0dPRAza5qNJIu0zSN0dFRYrEYjz/+OG+++eahLJh3u4ZCocDw8DC6ru8rD1OLknKn9wcJO58jXnwF09QIOK6gZTt5+eXXyrMJ7R28OLfC7ZUNNF2nJxzkTGsTfpeDl2Za2Mz+EAW1gGaYaLrA+y42ky2pLMTjODwaDlVlNZmmxedlIBLmd7/xKi9PTOPx+vFKGsuv3eJ8WzN+p5OZrRg+WSJZLGHGszze8xTt4YuVAimA4PhuEDsxi38IxjyI/eD8fmZnRZY3n6G79Q0kMY2qy4CARyqSU12MJi7wXZ23UHWpHPxgIooCkqRgGml8yizvvvA1XHISzVAwTQVJKNHm+h/8b9fa+NrMVZbiKUxDp8ebYnohyfziErFNhRXdYEvTyBdLgMA3ZhbQDYOQy0nTvSHJgMeJhs6KO4FTEGkRvbglhS13lkQ4h7vNwbOtvRhLMpPRGEVVx5GW6JT8rCsZioKOALhLMj26n3P9Tai6TqZYoqBqeB0KRVVH3rGZEMXyMKmml58BSRTYSGS4u7jFeizNVjSLwynTHilHXPl7pmNdrcGK5IvoDzNVEPA4RNRCgZXVVUzDJC9ItAT9GJoKO2oSN6ZXmd7KMtQdoLUpREHVWIun2Urm6G4J4LgX1eeKKrO5JIYpcLYlQrvXy8jKOpPxGJliiQF/CFkSeKa/i+5D6KjtJfWfKZa4tbZBk8dD4B4Bep1OzrY0Mba2yWI2T2cgAC4P+UyWi0Evq/EEuWSa+WK+HOW43Xg8HkwTYrk8AFm1VPb/2dG15VUUUsUiOdM8NaZl1pzMg0ZHRwddXV3bZrsuXrxYqZWfPXu27tc8cZJpamqiv7+/Zoasl2QymQzDw8OVNJxVG7FSZo1gZyRjdYU0NzcfKA9Ta3eaW+7FLZd1mMoSJq9z9uxZovE4X59eYq1oEPK4UESRW8sbLCVSNPu9vDK/hGoYuGUZWRDweWQ2sznedaafdGyLrVyeoijTEw7x3WcHePnmbV66O82F3m6a75mBbaaz3F3f4t3nB5iPJljYiiEJAu+9OMiz/bt70gvyRQT5IlC+t7du3SKRSJDMhfjy2Kd5qvcPaPfPYQIz6R5+7c51TLMfAwVZVNF0EUkUcCkyAho6Ik2eKC45iaqXW7kBVMOJJBZoUv6I73/875PPv4BT/XWc4iLGZZjZOsOf3PkIa8sigqYjaAZNXgctishsOo9mGOSSq7icEg5RIeMposkaEc1HUdMRBYH+QJiiQ2PZkaKnPcSzosZ8IslcLE6upNLq8kFBwKuX6HP5CTqclEo6mJAuFmnxe8vvQxDoag0yPL5U3tTc22Fn8yUcikTI78Y0TV4cnWd4eg2XQ8blVCiaOq9PrTDYGsTndCJJAuf7W+iqEgjtjgTpaw4ztxWnq6mZtjaJzUSKTDxJi2zy2quv4vF4KrUOt9fH+NImXoeE113+DrgUmfNdLdyYWWFiKUrEX04zpdUSugMGI6FKh9eVrnYCbiepfJHLnS080tnKQHMIURBQNZ2ZjTir8TSyJNLbHKKnKXBgam2vdFm2pFLUNMI71oTuoJ9EPk+r30dRK7cdf/DSWZ7t6+LP7kxSXFqn3esil8+RSCZZW18nqpnEI17iTQGCbjc+h4NUqUhL1Xc/WSziUxS8qnZqhh8zmcyJRDLPP/88f/AHf7Dt/BMTE4iiSHd3YzWqEyeZjo6Oui2Ya/371dVVbt++TW9vL2fPnq08QIIgHFmHmjUoaglo1pKzrlVJ2TAM7ty5w9raWqURYnR5jdlYivM9XZU8c8jjYnozRkHX6QwHCLlcGKaJ3+Ug7HUzvRmjpGt875keNIeLnp4eIi4Ho7ducWtxlY729grBALT4vUyub2GYJn/76qNsJZJ8+9Ui7z43cOA15/N5vvatV8mrBo8/epn5l19hM9OKKf0r3I4EAgaTWZnF3Bw5vcRrsSs83/xtXIKOLLqQBA0oUtB70QwR0xSxCAYo13tQEIx1tpJ/TTM/jSioFDUJAZP+yDh/54klvrXwd/G4WmiSDVp9LgxNpaCmWHCXW74N3cQhKgRbXLTipUP2Y1IuJkuSgK6brORTAFxobeb7L1/gK3enWd1KI2oaj4ZbKRU01rNZsqhIokg0l0M3TZ7o6agMSp7tbWZueYvR5RLRZDmNVSxpPHKmnZawl7V4hjsLm7QEPZUh1IjfzejcBh6fkyuDnbRGfLS3+LeRuyyJvP+RM3x1dJrlWJqSruN3OXj/4xd557k+NFXl1Tsz/NnNeWKp23hlgXgRPKKJXmWg5nc76IgEeGKgA03XkUQRh0cmtjC7rTtLFkV6QkE2lRxXB7voutcyXFQ1/nxkkjvL5efFNE3cDoXnhrq5fn5/scq9Ipmg20nA5SRZKG67hkS+SGfAzyeuPoZDksr/3XNpvdzeytjaFmnNoDkcIRg0WYgn6DIN2p2Oig1zh2HweiZFUVUJuz1k1BLpUokP9A/gXFjck2TW01niuTxeh0JXKLDnfNBRIZvN0rrDgrwR1Gu9/Hf+zt/hZ3/2Z/mH//Af8m/+zb9ha2uLn/qpn+KTn/zkw1v4rxe1FP4tccjl5WUee+wx2tra7nuNo+hQGxkZIR6PHygPsxO1kIyVfrPUAawPOFVU0Qxj28CdIJTnM+YTSSI+N307hBMVSSJVKNEmSTR53QQlgddfew2v10t3Ty9rqeyu12AY5UKv1+moyZ9jbWOLz3/5r0mUBHyBEIuvTbC6miUUKWtmFbXyPepxwjvc3UwX47wc/5t0++CMZwRJKAASpngB1fmvMIU/xUTHNHVAxDDL6TRR1NHpJJv6HC1BFdVwYhhlFV9RkGjyZPjQ+bu8PtdG2KXgdbsIhkO86tzAlEXOuXwU8iUKpkrcSGMi4XdHSBU1lhPpcspLKCC6m9hMZWkJeHm0o5ULLU38L8dd5lZi9AXDCBGBkZV15mIJwkE3QbeLp/s6eLTrrcWhKejhu57oJxVdwSlLBD1OBrubONNTdnSMpXLlwVH3W2knURTpbC6LWl4534kovnXzVU1ndS1JNJ5FlkTeNdCLdhaKmk7E565onP312DKvzW3hcHhoa/ezlcoQSybImiqOtTWi0SgejwddlAm4HVw910XYd+8ZKxR5aX2ZRKFIi/ettG+iUMDvdFRkaADGljcZXdqkM+TH5ZDJqyrL8TR/OTpNV1OAvh2SONufr91rMi5Z5pmeTv5sfIqVZJqAy0m2VCJdLPGuM31EdlH0PtcS4f3nBnhxdonZWAIBgdaAjw+cP8NQS6TSXNS9tQWzs9zY2iQqxAh5PFztaKdgFvnD9BpfHXmRJ1s7eXdHP81uDwVV48t3phhd3SBTUnHJEmeaI3zP5bN7KosfBXK53JFEMvVaL/t8Pr7yla/w6U9/mqeffpqmpiZ+8Ad/kJ/7uZ9r+BoeOpKpxYK5ujayW17zsCRjGAYzMzMV98p6TZ1EUUTTNJbzU8zkbqEaJTpcAwx5H0cRHSQSCW7cuEFTUxOXLl0ilisQK6QqaRjMcmdUulQini/nm3NFlfaAl6yqbnOa1A0DVTfoDPoRMwnS6TTT09P09/czNDREbmqB6c04mv5W7SBTLKFIUqWoe1DbNZSbN/7gq68SK8mc6+vE63ZQVDUmZzTMTJ61WJqulmDlmoQ8/M3BC7zzkT7gu5laG2Yzfoto1ofTdYVHels42/e3Kal/ilOKoRkyiiiiSBoCAguZj9Ls/h0ERMot3+UivFVpu9Aa48UZg3hKI+h3sVRMkBHyDPmaafX7IFxO6y2mYyzn09xYX0TLCzhlGdMp4BRknCkH/2t4gh++/ihOWUaRJd73xBCvKPMsbiQplXT6fQGe7euktz1EOppnYzzG16aS9PZGGOxvxuGQCQfcnO308p73PHLffZMlCQEBwzDvIxOvy7HtZ6WSxmtvzjG3ELv3eYDTKfPIxQ4une+ofDabySy3FjaI+N0E77XvNgU85FWd1bUter0h3E6ZeDpLPBWjL+RgbnKc9D01Zb/XyzNdnfzl9EzZBdWhkC6WKOga7x7o2xZdTKxu4ZAlnIrETDTOfCxJXlPJFlR+81tv8pEnz1ESDGRR5HwkQpP7LdLazxnz2b5yWva1hRVS9yKa5/t7uNa/e8pGEASe7e/mQlszq6kMsijSHQpUNmOCIOD3+/H7/ZwZGCBTLLK0sUE2leT/t3iXqfk0DkTUZJwvpRKMxTb5yUee4Y25FV6dW6LF56XN7yOnatxe3QDg7z396LHJvhxV4f/dDVgvX7hwga985SuHPreFEyeZo3DHtBCNRhkZGaGlpYVLly7tWRs5DMlsbGwQi8UIBoNcvXq1oRyuKIncyH6VscRLaEYJE7iZ+mtuu17kSf2jzNyd5+zZs7jCTXz+tVvMRRPohkGTz0OHbBJ0yrw8t0hGVVENHVXXMU14/4UzhHAztRWj+d4OdCubozcc5HJ7C2PDS6xE44S6+8g4fCRyBR7vaWdifYvpzRhuRUE3DHTD5On+zorHvSAIJDSNPx4fZz6ZJOR282RHB5fvNWpMTEwwNTuP6Q7R2+TB6y7n8Z2KTLPXgemSKek6MyvRe3bEBt0tQR4/Ux6AfeXuIi+NZ4FBnIpMprDM9GqM77l6gebgryDk/zUuaQbQATe64+8zk3oPZz1/SMBZnug37hGgZXQs0wISrGcKmHERAxG3S6alqiFDkiSavAEkRUFNwpqRwRBBKZl05RVaMBhfWGGsq4nHB8upUJ/bwXufGmIrmSVfVPG5nSiCwEuvzLAVzeD3OSkUNd54Y55YLMezV/v3/ZJ3twSI+N2sxFJ0RgKIokC2UCJfVHnuYs+2v51fjDE7F6Wt1Y/DcW8oMpVnbHyN9tYgTREv+YLK3fkN4qkcbb1vNdIIgkB/S4hsMkGT30M8V0KTHAz29XC5O4LHIxGPx5mZmUFRFDpCYb6ro43xZJqsqhJyu3i2p4tnu7cPLRuGiSgIrKUzTGzGcEgiTR43hm5wO7nJzZe2aAl6kUSBiMvN9w6d47l7A4X7dZeJgsAzvV080dVOpljC7VBqEqoMul0V3bb94HM6udDTwyvrArFNmfO+DlKxGC5BJlco8ObiHF/Ia0QTBv4qLTivQ6Ez6Gd6K85yMk2b38vERpTlZBpZFDnTHKGvwbmhajyMrph74cRJpl5IklSxErZgmiYzMzPMzMxw4cIFuru79yWvRlQDqhUCGpHurkZOiTFR+BaSJBGQy2kTzVCZTd+hkBT5yJN/D48/wH978U3mownagz4USWQznWM+nabN6+ROJoGmGzglCbez3Lq6ms7wvY+epzsZZHJzCxB4rr+H6/1dzN4d543FdeaLJmJ+maI+T9jj5kOPnOUHn36Em0vrTG1GcUgSFztauNzZSt4ocTM2z3R8kz/LrOCZ0PA6nJQ2N3l9ZYWPDg3RnEqVpSueeJLZVyZwOXboackm60KWvFfAI8n0u0M83dnFUGcTTkVkI5HmzZkVfC5nxVq51TSZ3YjzxvQynVcvYHp/D9OYBNKY4jkQ/ATda7y+8hyd/i8iCCUMJETTxKmoqIZEKvMengwFmVPXaAv5iXSEKRRyZPUSQemtRSih5ul0B4nEg2SVEn6Pg4DoAt0gl8vhyo0ipr/F8qQLVbyON3CFcDhMa/itXeat0WW2ohl6ut/ynC/6NBYWY/T3NREKKns+j16Xg3c+2s9f35pjYSOBiYlDlnl0sJ2Lfdtz8osrcRxOuUIwAMGAm4WlOJtbaTa20oxPrDO3lWA5lkQomfR1h3HfI33VMAm7Zc53R3hlah1RFEkXSvz13WW6mwJ89OnzPOJxkkwmiUajtGbSuNQCitdLZ0sz7bvUIs60R5jeiBMt5AETn9NBSdUpyiYF2UDQIKI4afZ7Wc1k+NLEXXoCAbr8/j1rMtVQJOlY01Lz6QSGAR6HgxTQ3NxMUdXIJKJMFTKYKR3J0NHSKdxud/k/p4s1rawh99dT89xc3UA3TDBNPE4H7xnq411n+w91XSfVwnwceChJJn8vRQRlEc2bN2+SyWRq9pip13hspzzM0tLSobR8Es4VNLOET2wpd5qZBvlcEQTQ2uI0NTUxvLjKQizJQHO4YjjVEwkykkgyl0zTGwnS5PFgmOUvttfpYHIzylYmx996/BK50j0i1lTefPNNNos6UzmDnKlTKmTJqiVmkgmmYjH+n9/9PM8P9fL80FuF2on0Kr85+3XWC8mycVe4SIcTzijncQgKy4kEv/fKqzzjixAIt7E+t0rR1NHSebz3rJBjpTxv6hk0Q0YyHSS0IhuZPE26jyvucsPHRiJLplBioPWtmpYgCER8bpa2UhRKGi6HjCmd23YPz7Y18cr0e/ny5CzvO3MDj1J+v3lV4bdvfJCNuELE0DDdIgupLGuaQckBE84tuvwBfLKDpFpAFAQ+2HGepWyG6fU4TfekapBFvvvc/+LRlr9CutdybPJlFrbew7dG308gEKwoKm9spPF4HNuIxOmU71kZ5wkF99fZ628P0xz0srSVLA/CBjy0h/3bUmUApmHuqqoAsLqeZHk1idulcL6nmU01z8JmAsMwOD/URknXSeWKtHgV3pxZRxZFznaUFTV0w2B2Pc7rU8t88Imz26bvLfn+aDTKjRs3KtP5vmCImA4ZVcXrVhhd30QQIJEtlD9Dt1DWztMFtHvRTqfPx2Q8zp3oFl1+/77pssPANE3W81nSpRKtHi9Bx97pbKckY1JOBZvAXCzBVjZHXM2jKl7a3f6yU6fTQf7ebE6qqCIqCjdn5nhzPUZvUwjPvS68rUyOF6bmOdMSobvBGSLTNE+shfk4cOIkc5h0WbV0Sz0eM/Wky6xz+P3+ijyMVVNpGIKBSfm9a5pGNpdDkWVcDjfmPU2wZL4ImBWCseBxKCyncnSF5Ip2lwVRECjcuy6PQyEajTI8PExnZyfxAiSmV4hrJWSHE69DwSHJrKUzfO7bN/jFjpZK/rqoq3xu7gXWCgnanEESGQ3F0IiTYE5foEftJBuNsZIrcZMSPWYSVTdI5wqImoCJScDj4uXYEmlT55FQCwOhMLppshhP8Idjd+iQPFzpbkeWhEpNQqrqLtB0A1kqtzXvBo9D4Ym+bn7lGx/mr2af5kLLPPmCyPjKeVIlD6KQpNQUIKVpdLp8tDeFaSq4uZldJi9rKB6JHk+Y97ed41pTH5NajPlokqVYirDXxUDw2zzW+nXK8jwypgACOn0tf0Wk7btZi7UTjUaZn59ndjZNoSjhUFrxer1IklRJ4cmyWNNMls/t4ELP/nNivqCb4ckVSqJJa8CLQ5bIZIs4nTLJZAFRFIiEy8/E493tjAjrLEVT6IpAKODm0d424stZFnNFznS8dS5JFIn4PcysxSukbqFavr9Mmimmllb4v198g+VkFlmWkRQFRSl35LUGfTT53Izmo5RKhbL9dlVdRBAgXSgyOrPOjZkkaWmWsz2tnOluQpEPP6OSKBb475Oj3IpuUNQ1/A4H7+rs5yP9Qyji/a9/OdLCV5Zm2Mhn2SqqZNU0gmjilGXaFB+ZfIlUoYAciRAMBDFdbrLZLI82BZnZ2CSdTLOlFXG73XjcbsJuFzPRBDPReMMkA+WusAchI/MgcOIkUy+swr9lf7ubdMtBqJVk9pKHkSSJUql04PGqoTKcHGE0fQfVVDnrHeJq6ClCRger3CFTTKMVDNwuF5Iik1AztIsDpAvFyhBaUdWIFwrEcnkEyoNlrW4nmmGg6npFMqSk6RiGSX8kvK21+uLFi3R3dzPy6k1SqopqGrRYOWuprF+1mspwd2OLK13tANxJL7NaSNDuCiIiIQCCKaKgsKZuIG0IlCQHuqDSEwnQ4ytHj9F0js1kFr/HSV5VSZklWt1OOkI+iqrGwlqCZDZP3Cjy31+8yUJXjGcudtMS9LAcT9PdVE7HlFSNRK7A8xd69114/G4nXeEAbYEOUuYVnF6JgW6VRL5AWi2hOaBN9OC85wIacbu5rHdCAT755BVaPT6ke0Ol59qb+OiVc7w6vUQsk+fS0Ctl8jNlNKNMEqIgIYkaHuEv6ep6H11dXRiGQaRpnm9+a4L5hRUkUcflcpMvijhcLkRFrHTpNQrdMHhxdIE3Z1ZYzGUZH4sTcDkYDAcJe92cP9vG4lIct+utTVaLz8M7zvQwOrPGhYEOHj3XQXdTgP++MrvrOcqR2v4QRZFgMMjtsTnSopNHBptB10hnMqSyCUqaQS6bIugQcBoC6WKJXn+gUs8oaBqCCatLSeY2N1lJ5ElMrvLCrQU6W/x88JlzXOhuuW9jVSsM0+S3x0d4Y3OVVpeHiNNFolTgj2bHcUoSH+obuu+YoUCE7+k7y/+cHGVNL6JIJh5RYdAV4qKvhbgjz3oqi9chkykWcSsKH718nncM9vKbr9xAc3qIOBXy+TzxeBxVVUloBqur6ySbQwQCB88L7Qa7JnOCEASBdDpNOp3eU0TzIBxEMtXzKU888URFH8xCLTUdzdT5g5X/ya30aLkrC4Hp7DS3Urd53HyEYL6LTXkWh9tBztTJ5groRQ+vLfiZHHuF60O9dAR8fHN6vuJpUdI0DMOgoyVIb2sTExtb+O6F6ZliiUvtLVxqbeLWrVtEo1GuXr1KKBQCoCcSIqfrOKsiA8MoO2M6ZKkyFQ2Q10sYpoEslBfngNNFOp9HUnUMUyPSEuHGYoyAw0mr+60vQsTnJp4t8PjZLs51NDH3cobp5WXWMhni6SKlgobf46SgGTQHvEwvR5FEeM+jg3xleIrZ9Xj5/gow2Bb+/7P3n0GW3ed5L/pbca+dY+c8PT09PRGTMDMAQRAAwUyJVqDCsU0dibpXtqxTLrnqWvdYrpLKVXIdW7q2fK5FX135UrItSjYlURQlkQQYQGQMJqfuns559855r7zuh929pxsTEURCOn6+EOzZYe0V/u//Dc/zcHz0/srYPdEQUb+vRZDcyurCIR9ZvUF3NESu0cAniexcPv2KTNUwUZDbAWYbB/s7Ge9JUW7qpNyW5prrtjmUOK6HKLi4TmHXvXBgYgjXU5idzVCtNVjNlshVa0iKR+Hr84wPWgTVOJZlvSOLisnlLG9MrRALapw6OECx3GApXaLgWTx7Yj97hzqo102WVgrEorcHG0QXusMhToz20rM12RfTZKKSRqZcozu+peHmuhSqDY7v7b2jpwZQqxtUazqSJHJ+aZ3nr88helAuN/GJEioicS1K07PoCAfIl6tIlk5CFdAdnY1yCVGWqVkmvWqIxqZObyLM/LpHtVbHdT0uz9cpNJqcnRjio8f3vqOsZqFSZLKQoy8QIqi0notOfxDbdfne+hJP9Q/jk3b/PkEQ+NjAXpSGxRc2avTFk3RpIRJyyz8o7PNR9Zn88OFxUsGWNfl2Zra/M8l8rkh3JNxW+CjVGxilClEJrly5ArDLwuBemow7sV0u+5+ZzHuEtxPlG40Gs7Oz2LbNBz/4wYe6YHfD/YKMrutcunQJz/PuKQ/zMD2d6do016s3CYoBfFIrK3E8h9XmKpIhsr92miOHTjNVv8hCIYtZHSDlHqMjEiFTrfOtyTlGO+PYuC2HREEg5tcISAKbDZ0fG+xlf2eKaxubCMDT+/ZwuCvJtcuXADh79uyu83O4v5OkX2O9VidotCbadMsmGtAI+X27NL0GAykCso+K3SSqBOgI+MlXyjQ8Az9B0g0LRRQZ8ceQBZFMtU5VN1rlOstClkQURcLRHVabBkWzgGE6KJKIYbpEFB8DwQiWYLOyWeaxw8P85BOHmU8XmckUWCqVWKiU+S+vXeHEcC8nh3vvcIEEiPk1HtszwLem5lnMl9oBJOrXeGrfMN+YmqNWrRKVbv+2clMnGQrcVa8LWiTHZChAYX2c7tAMouixrWgtCB6uJ1Co7WNnpVKSRI4c6mOwP8G16XWypsnhoTAfP/bnjKReRRBcPE9gc+NrLOZ/jlh8gGQySSgUeqj7f3I5gyyJxLZ4LF2pCB2JMIuZIpbYkqkZHUmxkS6T3qwQjfixbIdiqc7QQBItqDK9mkOg1dN5bHyAl6dXmd8sIIsiluPSl4xwYnS3jLzjuFy5scbMXIZ6w2S9UWe5WcWSHIKqQqHUwLAcUiF/ywDNNjk6to+9ezq4srTOjXSaDb3GZr5IWBZ5JNVBQFcoeBblpkGhadMb9uFTZIqVBooocX1pkz09cQ4MvH0iYkFv0nRs+t7i6BlWVCqGwZXlNJ7hoakKe7rjhLeM8QRBYE8oSp/op0sMkVQC2K5LtlYnXa0BYDrOHQMIxwd6mM7kmcsV25OZLh5PjO/hI4fHEWiV2wuFAhsbG0xPT+9SYYjFYnedgDUMA9u2/2fj//uNzc1Nrl27RiLRIla90wAD9w4yDzsC/TCZzHx9Addz2gEGwHM8LMMi48vyka4Psze1F724l4szNxnraE2ZIUJPNMxctsC19QzDyTgdoVaDP6iqNBsNbq43Wa/U+PShcZ7aYuEXi0UuX7xAKpW667FH/BqfObCHL12dpmHZBFSFnmgYRBiKR5noul2j7/MnOJsY4zuZG9QtHauho6ouATVEh9mHJ0oMxqLUSgYXltfJ15u4nkfTtPErMmVD55W5ZZpVi4QoY0hgiy6m5+IYHo/H+whICk3Fw3ZcLNslEQkgqiI3M1ksxyWiqeRqDb56eYpyU+ejB/diWDYNwyKoqW2m9xOjg8QDfq6spik0mhzo6eT4QA9DiSib1Tp/faWA5brIAYOybuC6HmeHBx7opnhz9SkSe7+NT2m28yAB0M0A89kPc3Q3vxdBEIjHA9RMi3BY45Mn/oSR5CtbTWURcOlJTBKN/BcuzP1jrtyYRVUkhvq7SKVSJBKJO2SOtvlO1aaJ7y1ul9tDAYbZynL7emKcOTXC5K005XITWRY5sL8HWxX4wxeuUN5qyNeLRX5qTOJHzx5kdiNP3TDpioXZ15sk/Bbb61tzGS5eXSES1oinglybyeM2XUQ/VBoGIgJBv0LTcVA0maisMb2Y4VIuQ9O2kQWFsOMnjJ8PDg9wsjvK82/cYiNdwJZaWnWC58CWcXYri/JY2iy9oyCT0Pz4JZm6bRFSbm8sSoZBtWLwfHoGgVaPLBUN8NETYwx3tQZOgorMvmiYhYaO5bosl8ts1uqYtkMsoPGHl6/zyf1jPD58e6w86tf46ZOHubK2yWy2gCpLHOju4HDPbZHOaDRKNBplZGQEy7IoFosUCgWmpqawLItYLNYOOtvuoPV6ixz9boPMO3HF3MYrr7zCk08+yaFDh7h8+fK7Oo73fZBxXZeZmRmWl5c5dOgQgUCACxcuvKvPfGuQ8DyPxcVFZmdnH2oE+mEyGfEtpRjDMGg0Gsiags+9/QDUDAsR4Y7v02SZTLNO2O8jvGPXLWxJ6bs7msnb/amxsTGGhoban6VbdlvmA+BUbyf1eo1lV6TYbDlnDsSjfObQfoI+laZpcXV1k4VcEb+U4IQ4wtXyDJJfI1hXEZqd1OsSfgWKls5Co4hVtYm4Kn5FIRpQiQb9vDi9iCtB0h8gYEawgxoLhQqu46E4IkFPaQkXVhqE/SohTca0bV6dXca0HbojIRRZJB70U6g3OTe/hmO4LKRb2mFhTeX4aC8nRnuRRJHDvZ0c7r1zUfrI/lEq+RyT+TJNy6Y7HOLMcB/H+3vue+0AJLmfP3n1n/Dssa/TEb4JwGblIM9d/BgT43d/v+d5lKpNkhGd4eSrrQCztYC6nogIBOQrbJZWWC8ncRyLTCXLQEcRwTWJRCLE4nEyDY/ZzQq1pkV3IoQqi2wWdZJhP4btkKs0qG1xaXaWt4YGEvT3xqg3TGRZZLNc589euYEsiQx3xnE8h0vZTb5zeYGfeuoRnjx0b5kg23aYnt3E71eJRQMUGzou0BEL4jXqbNomiKAKMrplEnAVjnV1MrmWRXBk4qpKoVTDth2arsPXL84x8fGzPHHmEWrOJBulMlR1arUahmXjIiE4ASzn4QYl7oaRSJwDiQ7OZ9bp9AfwywolQydTrdPR1OhPRfEpMq7rsZIr8+3L8/z9p4/iU1p93lNdSYb8Yf7y5i1Wy1UiPpWRRIzheIx8o8HXp2cZSyXoDN1OYyOajydGB3li9P4SOgCKotDZ2UlnZ2e7JLbtm7PNT/rmN7/ZtmC+l8juw+KduGIClEol/uE//Ic888wzbG5uPvgND8APPMjcbzE3DIMrV65gGAZnz55t+4G/G7Y+7JamsW2b69evUyqVdvUw7oeHyWTGgnt5rfA6TbuJa7hYloU/5Kfq1tCqnVyv5kj29pEKBfBoMfazjVZdWhIFTMthoreDlVJlSwywdamadsv1ck8yvstaYGd/qlhr8tLUIjdXM7ge7OmM88TEMJIkcSQZ48cPHWa9Um0FmVjLO75umHzpjSvc2MgiIlCr19CbTZ499CifPX2SX/vTP2VFFNnfmcSxXZolA8d0cX3gsyQkB+I+P/v7OpnPFjBx6QwHsQSB7kCYqBhkcT1P0TIoVpt49Zb225mD/aiKRLZc48ZKhlrDZCNbQZEkuhNhuhMh3pxbo1Bo0B+LENZUSjWd//G9q1y9tsrhvk76+uL09sbuGPlVZYmTPSmOdiXpHxoi6FPvWna7G4YHEswsDPHV13+BjqQCLmzkDPyaQn/P3e8RQRBIxoM0qzcQBAfX2/1dngcCLkFlDVtIsFZqUFptslaN89kPH8Qv2nz74gwX5jdRJJFIKMCtdJq8Y9N0bC7k0yi2gGiBbbtEgxqvTC2TigXpjLYWPkkSiYRbWf70zSUMy6Y32RpHxhVJBBRKDZ35dKFtZ303mJZD07DawwQ+WUKRRFw8wigIMtSwMVyXkKxyqrObHn+Qc/YacVcmk6vh11SCAZWQ47JaqvD8uRl+7pOnODLWS+FyA8PysAM+NL9GIqziOCbZYo1KxGNy0iWZTBKPx5EkmfVSFdN26AgH7si4PM/bcoGV+Nz+I2iyxLVchoppEpAVBp0wewPxdjYoigJ9yQiruTKr2TKjvUncLammTx0Y4+L6BooiMRC9rRjQFQoyVygxkyvsCjLvFIIgEAwGCQaDDAwM4DgO5XKZr3zlK/z5n/85tVqNxx57jI9+9KN89KMf5fTp029b0PftumJu4xd+4Rf46Z/+aSRJ4s///M/f9vvfih94kLkXisUily9fJh6Pc/z48fYJ3ilO+U4ndrY/Y6dC89mzZx9aHuZhMpm9wVGOho7weu4NXFwUv0rDquDUwlybVLnuLXGp0OBD+4bpjoR4YX4Bc0ugULdsfIrMGW2AcI+PmxtZZKml3WUaJnvDAUbjUc6fP49pmrt6Rw3D4r+/do3ZdJ6AplI3TRaniizminx8vDWGGvKp7OvYPTDx5uIa19czDMQjVIpFZMHD39vDzVyFxWyRVcNsEVAFgVy1QaVm4JdlGthEAn5inkqtqlOtGwi0ppvyjSZai6NGZzyIbts4hTJhVaU/HuXQaDf7BjvwPI/JlSUyxRqiKBLR1Jay70aeSqNJtWkw3B2jMxrEshyaJZ1SrsHlkoFQsZmZyXDoUC9Hj94pUCoIAqokPBQLfCe6OyKcOT7MpZurrGw0EASRZCzAySMDxCL3JgdOjHTy2qUUnicg4OF5W26UAJ6HJwislULc2lI/CGgKM6s5vvLiFJ94bD9FR2XfniE0CVaLRZaqRaqmRVBWaDouhuPQ5QtyuK+L7mSYtXyFl64v8MlT+ylUGqiyRDIaaA3INHTUHWU2b6vwJ4kiTWM3oTlfbbC+paI8lIrh88mEgxrFUoNQ0LdVXg0xvZFHE0WGkjFWM2U8SWCsM8FINMZKuojfp1LXLXyyhKpu23FDQFOpVHQyuRofPDZCR8SH8c0KpuojoCkEtBbP6JnTezi9p4NKqcjCwgKvXrjCzaJJ2fIQZYVkJMTpsX4e3zeIIMD1lQxvzq+RqzaIBTRO7Onl8xPHyep1apZJSFD48gs3UN4ysSaJAq4H1pbdwk4TwdY96NulD7gN513w4+4HSZJIJBL85m/+JufOneMnf/In+Uf/6B/x3HPP8YUvfIHf//3f55Of/OTfyHfvxBe/+EXm5+f5b//tv70rvbKdeF8EmW13TGDX+O2+ffsYHBzctXBs3wj3cpZ8GEiSRKPR4PXXX2dgYGCXQvPD4GEymVKxRGImxtMdH6KZ0Mk1akwv2ISavSQiDrbTkoN5fnKOgWQUSZIIyTICrYmgWEBjOpvjZ88c50hvN9OZHJIg0utXqWws89Lrr9IZ2x2AAW6uZZjbzIMMU/lca2wUWFuooooOH+i6e533+loGVRTJZzKAQF9/H7IkM5XO8p2r8+QqBmWzjOhIlKutmn9QVKiZFnggyxKu55Et1wkEFT68f5RvTc8zmy9Ao0HBsDBx+OET+/n0gTEURUa3HBzXpdw0mFzJ0RuJkGnWESSRkCJTbRrMZ0sEVYXucADXdVlNF1nLVwiEVHA94h0hRMtjcjJNf3+CVOq9a5aOjXQw0BsjV6wjCgKpRAhVuf89N9gTx7JPsVp4hP7EZQRcBFEArzWmtlke4MJ8lIBPbpcxbcejVNP53pV5VtIlQoqCqkisOiaKptHvD1DXTQRsNNHDQAdPxzFVUmE/F2fX2dyoYloOsiQy0BnlA8dG6E1EmF5tqSOLW2VWx/WQgUSktSlxXY8XJxd5bXaFqm4iAh2RIB89Osb+sS5efn2OTK5KJKTR7Q+QUap4PhFBlQiHNCQH/I7IynqRRCzA432DfOPKLOpWT8TzPEqGQVTzEZVaagCSKDLUHeWjhzrp3XuQxUxrqnCwI8bengSKLNHVkcK0HS5++01yRpaIImDpNVarFVY3M5j1GsFwiG9cm8d1PcJ+lfVSlZXzk9R1iw9ODLHdNutLhplcyREJ+NprSbHWJOxX6dpSbnAcp23DcbCrg2/PLpAK+NsagGW9Nb48nIi9Z/fXvVCv14lEInzuc5/jZ37mZ3Bd9135Xz0sZmZm+JVf+RVeeumld2yDcje8L4LMNmzbbvuQ3EvZeDuw2Lb9joKM53kt1m6lwtGjR+nu7n7bn3G/TGbbgfPWrVu75P//7OINJguL9HTGKBaLiK5LVzTMzGaeq+ub7O1I0BEK4OFtlcYEZrJ51stVPrx/lLMjA1wrpPnPV15hppYlGPDzhKqhFQtUygYeMJyKkSlVKesGOaOBLIkk/BqeB5vVGq8ubXAgMnDX4zZ0nVwux2AyRkeqY0vqxiWdr2IXLUKGSE40WdgogOuhyCKCIuC3ZQzDpmy3uCmqYXBiTy9SzaXb0lg3Bep1k5G+KCcHejk12MNStsK5W6vMZvKs6jUMHCp1naFIlJQUpNTUqbktwmpAUegPh2maFgu5MtNrGUzbQag08csKpm3TEQ5SLDXI52t3DTIP84CmNyssLOWo1gySiSB7hlPEYwE0n0J/d+zhbw5gdCCFbf0bTP3/iSZdAM/FE6BQH+IPvvsTmLZLJLAdYFoeNkGfwrVbaWqmie5vuTWuCTUiAQ1PFVFUBcmTCfkU8s0mWd2g2ZxhvOsG+xIWtdphYsFDCKLErZUcTdPmqVN7uLmcYSFdIBkOYDk2uZrJoyMxxnpbmezNtQzfvblAwKcw0hFrOYoWq/zlxWn+1yeP8dije7g5vUGl2nLu/LEnDtHbH2t5FvkULN2mXGn9W293FAePmdUcM5kCDVobsbCqMh6Jo9piOwv0PA9FlhjvTzHen7rreZzPFNkoN9jX340qtwiuhmGwsJnjxRuz1HUDSxDpT8bwSwKxeJhstcFrsyvENBXH8YgEfBwb7WUlV+HiShq/KqN6Iqos8fiBobby9M5N6weGB5jJFZgrlAgoMtbWs/7kyBAD0XdOsHxY1Gq19hAA8I7lq94OHMfhp3/6p/n1X/919u3b9+A3vA28b4JMtVrl0qVL+P1+Hn/8cVRVvevrtncb76QvY5omV65caRvyvJMAA7un02zX4cXcJK/kpilbDZJNhbFmmGdOPrYrSFYNs61yvDNzk0SBhu1seZnsKG1sL4xbSdx0Kcu/OvdNco0aYUHGp/n58/mb/NXkNGN6BzIiYc1HKuQn32ziCB7RrXMoCKBJMqbnslhu3PF71tfXkaoFFM1PNJ5o39zL2RKGYbNvJEkqY7Po98g5BrWGARYklQBPdgwiWLBcKJH0B/j4gb00sk1eujmPX1OINyRkQaQZM/jm4hxfmZqiWTTxuxIrRpWaZYLTYvjPlov0hsIc6+3Gchws00GTZLpTYV6aWqTQ1BERUBCxPQ/Hc7mSzfJBzYfrtHZ79xNdvBdm57O88sY8zaaJT5WZXcgyO5/lyQ+M0d354EXFcVyW0iXy5To+VWaoO040FMNVvkDTncGoTTE1WyGQ/Ah18yq6UcC0bDwPak2TVCxApa7jWi59iQhlwyDq9yPVG5TrOp7n0ZeKUqrrLUMvx+VI35v848eeQ9oajxbFlzg/e5S/Ov9pVDXI3FKaR8a6+aEzE5ybXmUpW8TzYDQV4FOnxwn4WkHu2vJma9oq3MpsREmgPxlhfrPI7GaB03v7GRlK0miYqKq8i/C5jf63PEY//9QJvvyda+QbTaJBjbisYtddRvd10JFsbQIe5jrVmgae57UnCQVBQNM0upJJGqZFQHMIyGDbFplMpiUNI8rM5RvkM1XCPg1JFAhEVDa9BhtOnUbZIqL5eGZ4hDP7b2+4dh5PZyjI5x99hHMr68zmigRUhUd6unikt+tvTHV5J34QkjLVapXz589z6dIl/sk/+ScA7QxKlmWee+45nn766Xf02e+LILO+vs7169fb8vP3u5CCILwjFeVyucylS5eIRqPs37+f+fn5d3y82+Uyz/P44uILvJC9get62IbJLC7L4QTd9TxeroIqS4x3pRhMxLiwuE5F11mqVCk2mmzYLrbjcmygm+lsYVeDv7BlkLS3I4lt2/x/33iBtVqVlC+EZRhIroCre5SEJnIUkq6fatNgLtPExsWyna1AJdA0W9wVnyLTtG7L4Xiex61bt1hZWeHvfeA0gdk1bqazSELL7yZfadDtD9IdDtHM5Xg8kiQvusy6BVRBpEcKEqhLOK5LfyBMPBVkPl0gv1Hl2EgvsiRSrmc5bxUpL+cY6U6QrTQo6zphRcV1PbqDIWzHJV9tIDsCm40aI5EoKdVPul5jYriDxw4N8ersCgIgyiKW5ZAKBkgENPJNncVMiWhAIZ7wY9s2giC0NyM7A/rdoBsWF6+s4HkeQwOJ9nlZWS9x9doaXU+H73s/Ng2L596Y4dZyDmfroUxEAjxzai97+5N44hhNr5NK4xqHj6X42GP7+cPnLpEp1YkGfPSkQiRiQS5OrjLUEWWwO86N9Szlpo7miWQdiw5FZiAVwe+TubScpj+4wS89/o02SVQQWr/vxOgVmu4+zt06zXo2z2vnznNkbzeHu5OcHhtHlmSuXLpAInx7aqncNO4cj946b9v6d4ostYcJHgaDvQl+6tlHuDGdJpOvoioyY4dSHBjrbg9nPIxuWSyoIYoCumXv6o9UmgZ9iTD5ehNFlohuGe4ZhsHF2XVqDQNXqaBINrYk8935dfwBleMjvUiCQLbe4MpmhmOZHId6WlOJjuPsIsomAwE+Pr4Xxh/6Z79n2M5kvp+IRCJcu3Zt199+53d+h+985zv8yZ/8CSMjDzYsvBd+4EHG8zxyuRxHjx59aCe4txtktkd8R0dHGRkZoVAovKNMyPFcHM9ta1PN1NK8nJvCh4ynm6hqEM0fYK6a4bevfovOYh8IkAoGefbAKPGgxvfmlrBsG89zyZl5Qj4fvdEwsixxM53dEsz00GSZZ8b3kFJl/uK7L/BGKYshCGSbemvePptvBRrZ40phk1BNa/mpOB4d4QDr5SqlehMEAZ8iM5CK0tB1olsjr8VanW+/fh5D1/no46dJxWP8g44OrqymWcgVUSWJellnZanQ1pySBYnRYASx7HJ0rJcDw10sbxb57twi+apBtWKzki6D5+HLqxxMJikHFKoVG6Xh0CxUcG2XiKSQtZpEhVaNXJElfIpMSFPJNRvM54r4YiJH9nTz1COjOJ5HXzJCT7wlA5PP1WjWTIyG1SovBR0+dHIvnR1RXNfFdd329d3eDNxr55zP1ymVG/R23xZWFQSBRCzAZrZCrW4QDt17gb18a52bC5v0psJoPgXX81jPVvj2mzMUC3VW1ooUSzUco8T+A3WOTfQRiWh8/bVpSnUdWZGoGSZd0RDd0TCxgMap4V4K9SY1w+RyLkMg6GOpVEEA+uIRfnz0BRxPQBZ2B08PONz/CtOZj9FlSZw+uZeo5rX4X5OzrBQN8lWdDescJyeG2TfQyWAyylK2tGuQxtyyoe4IByjVdc7NrHJzNQvAgf4OTu/rb/vU3Au9XVF6u6KYpo0kiUhbjfe6bjK5mmV2ZYNKscJgvkJf8u7Z4nBHnL1dCSbXsqTCAVRZplhvokgiT+wfYnI9x5vza/gUGU2RadoeZcNmsDPB4cFedF3nViGH4ZgIFYtMJkssHCTl11ip1PjezCL1ekseSq80GLiPwdr3E++VAvPbccUURZFDh3b7HXV2dqJp2h1/f7v4gQcZQRA4evTo21I1ftgg47ouN2/eZHNzc5c8zMPaH2+jYZt8ZfUy38vMoDsWewIp+jAwy6vUzSZBUyQUDOHTNEoNHcv0wFdjpDOG4Imslso8d3OWoF/Bryr4RAHHtunvSOJTZM6vbvBLHzzNicFe5nNFClYDQRUomCW++tIMkyYIgoIi24RElYbjoDsOTcfFxSXoeIRUBcfzyOkNgpbKkcFuNkpVopqGX1WoGgYjyRj9AYHvXp/hf7x8nrrtEotGWXr5Cj90bD+H+rs4s2eAM3taZYTFjQJ/slGlWGmwrW5VrulIosjYQIrh3gQrtQo5vclQZ5yqaeLJLU7KtVyGTs1PCYeApqEIEIsGaRQrNA0TF4eqp5MQFURJQhIFBjqiyDWZJ0eG+cj4HrriLUa87bgkQn42SlW6k2GS4QCVqk6uXEOxVZ59coKDY7dNu7bT/FwuRzqdZnR0tD2y3spsYHY1z/xagVy+TqZcJxEPEAzcni7cqTe2strycWnUTTo6w+zd00E06sd1PSYXM4T8KtpW+UkUBHqSYd68vsLGaonueBjPsVleb/D1707ysQ9NMNqX5Gc/dZKldAnDsklEAiwu5rl0c41kzEWVJbqjITZzVZ7uG+T4o4NULIugqrCwWqBDa3C3QpMogKaUWNksMdyTYO9gF6oiYcsBFmdrrFd1jKbNGzeXOHdzgbP7utkz0ElEU5jPFEmE/DiuR6mus68nSU8szJdfvc7cZqGtpfeda/Ms50r81AeOtNW274edtgTFWpM/efUGS5kSpmlQrdXJf/cyHzu+l2N77pQQkiWRHz65n7Dfx62NPLVaa7x/or+TVDjIhw9FqOkmc5sFbNfFsBxCqsJEVwpZlgiFgvhNHb/RxOeKyIpCo9GkUCySrpssb+SZnc+gKAq23uDUiMXw8Mgdo/Dfb/ygXDH/pvADDzLwzpSYH6SC3Gw2uXz5Mp7n7bIv3n7/w2Yyjufy76e/wxv5RTRRQRFFLpZWuIjJ6WUL27KJhFMoW/2PSlMHARRBQhRaJZv+eJRb6Rx2zeNATweK61CpVunrSuF6Hgu5AhuVGicGe7lcSfNKZoVKs46hG0QCQTAlBn0xZpwsNdtE2PIcaWChOCKdhFpTMJ6LoEJdNHl0pI9CTWcxX0QUBE4Mj3CiK8ErFy7xneuvoWga8VSEYlNnfa3GRqXK//7JJ+mJ3dZLGuqOc/bQEG/cXCFdalK3S8RjEU4dGGC0rxWwr69mUCSJmXyejWqNpmehOxYlx+BCPo0/pGKYNkFFIxELIygyy5kSPhdEx6PcbGKZLQWCdKlIPBTkmYlhuncchyyJnBkb4M/O3WS1UCEW0LAlD88n8oGJAY7sCDDQ2kSsrq4yPT3NgQMH6Orqamc3juPywvk5Lt/a2HqtwHq5RvFGk8ePDOP3teRBCqU642NdLC3lOXd+CdtyUFSJxaUc8/NZnv7QONFYAMt27xB0bDQtylWd/sEIPZ0Rms0mesNHvtjgxq0NPnR2jICmMjF8O3OPBTVyxTor6RKi0FKlDgZUzhwbZnwH+72aaTCf6eLs3uk771VXYL04yME9XTx2dBhVaWXcr95YplzXGetLsbFhMTwyzHq2zHzBZLhTZ59mMV1vkM/r+P0aj+0b4MmJYSbXcsxvFhnpuG03kQwHmN8scnM1w6m9d3epvBdenVpufV5nDL3ZpCw5uJ7Lt6/MM9qdIHKX7CgS0PjMqQlurmb4q4u3yNeavD6zzPXlTU7s6eUnzhxitVCm1DBQJJGXLi9gmg5sVZuCioLtuEQ0Hx2JOJIoUqw3KJVWScoKISwcXce0XV6dW2Pf4ArHR+8chf9+4r0ql70TV8yd+LVf+zV+7dd+7V0fx/siyLxdPMiCeVseprOzk4mJiTum0N5OkLleWudiYYWkGiQgtwJJSFSZbaZZbtZIRqKUPZ2k1zKmslwHT3Dp8joQtrr24la5yXU9XK9FmNteljzPA0FAEgVeWV/mW8tzSKZFwhOJd/WyaTSZrRSIVzQkWaLua2UBPgFERyBQ8WELHlWxQUat40Y9iqLOH61d45eOneEnTh5CFkX8qsLU1BSzhSqCT6MqwnIujyQIOF5riuzfvPIS//ypJ/B5MovZIqIocHish5G+JN/6nk5nZyeH94/Qm7qtLOt6LgW9yWajTkCRCasqFUGn2NRZqJQZdWIIAoRiGj5VpkMKkq3X0esOccFH1TNRggqhgEpQkTioCkxfvEAumSSVSpFKpfD5fBwb7sH1PF67tUKh3sQnSzx1YA9PHRzZtSBsm8utra1x/Phx4vE4q/kyb9xabe14TZt8tsZoKkY05G9JFCky16Y2uDy1Rk8iDB7EEwG6O6NcvLiET5Xo6W6VdFzX5dpamsblKT71+EEGu6JcntkgHvHftj8utPSuOhO3d6OCIBCN+FhZL+2yx95GJKTx8Q9NsLiaJ1eo49cUBvvidCZ3iyQOJ6O88MZpasffJOhrIone1nXYymSiv8AnRybary/XDdYLVVLRQGsCZOsYu5JhljNlkr2DnDp+lGKxyHomS6lYxK5sMDvV4PJ6HTy3XeqCVsCXBJGVXIVTd4oa3xOm7TC1miMe1JAlsW1Y1hUNsZgpsZgpcWT47oM4umnzrWvz5KoN+pMRFEmk1NB5cWqReMjPY/tuN/Atw+FbF2dZyZYJaip2wyEkqXgKVHQDWRSZyRZRRJFHBvuJ+nzYts3m5iabdZPnXr+IvrlCcsuKeqfcj+24rOUqmJZNRyxE7D5l1HeDer3+UL5Yf1vwtzLI3CtIvFUeZmDg7uO62yPID0PoXKjnsT2nHWAsy6JSqeBHoqqKfDh8iG/mLrJo5lAlEVexUet+hpRWP8bzPJpWq6E/1hFjKpPDF9C2PNo9Nio1kgE/ezsS/Pal16jVanSrfhKpOKIkEXVcTNeh7BoMulEs3SGrlxEVhQ40ApKMJ3pktDquBGHZh+U4VB2D/3TjTf73Ex/kQKyDGzdusLGxQcP20EUo6joxTcMRXdJqhbLQ5LvVWa4/v0HU1OhshJGFlljkp46NM94fZWCgk96O3Tf/gd5Ovj412xpJ3Qrmfr+CK7RY4geGuzgZHeBybpPZQgHX9QioKkdDHYwF4kRTAfzRFvFtX0eSsE+lWq2Sy+VYW1tjcnKScDhMKpViLJXiyDMnqeommioT9O0u1ziOw/Xr16lWq5w6dYpgMMhKrsyXXrpCrtwgEvCxka+wXqig+hXi0SB40JEIMTQYRxElRvekWMqU2KjWmPnOVYq5OsfGW6Wcsmvw3eYqy6EKdjbN915Z51Sih1jEz/xakXCwxQOp6SapkJ/QVjnJdT0My6GhW6Ri6h3uktvwawoTe+8/8djZHeVox17+2/P/mI+e/jNGu1oDLOVqirXi5xk58MRb7nUBURBazo074tq2bbIkioii2DZgg5ZIbD6fx1ueJF8ooNoN/P4AgS1nSMd176rWfD+0TMF2mq5tPXs7ToXtuMynC6znW1bGI91xepNhZjcLrBcrDKai7YwqHvTTMCwuzK9xZm9/u8R1YqwXvypzaXaDYq3JSGecp46PslAtMZXJYVoOnYEAcUEltqV/qChKy6U2FKRvoJeJiSHy+TwLCwvcuHGDSCSCpwS4sFgiU9GxHYdwQOPkvj6eODx0x4bh3aLRaNDX1/fgF/4twd+ZILPNsSmXyw90yNzObBzH2UU60m2b2VIBQYC90QQ+WSawpeDreC6mbrSbcsWqQbGmc365SEDqwdMqhAIKH+0bZa7U4NpGjqZl0bBa2mRnhvv5qZOH+S/nrnArnaFWb1LP5on6NX7o8H7sRoOFtVVUWSaVSrZ3nOWmgYyAIotULBNZFJEMCdEWOTHcQ92wWHUqSIhEBRXDdoj4fIxGE6zUKzy3NENjdgnLsjh69ChvbnyPK4U6miohCJBWy1RlA8kWEG2RYqNJSdIJx3wcVlp9na+8eZOzHRr9noflOFxZSzOXK7RIdfEYsYCPlVIVCaO1kNAag/UEGOqN88OH9zNbKHBzI8vNqXWcgoXPcVgWckTSGk+c3cv4wG3FyUgkQiQSYc+ePZimSS6XI5fLsby8jCiK7QxHTSTaE0HbEkSCIPDoo4+2R+Bfm14mV6kz2t0azbZNh2KxznqpymA8guN6zOWKrBerBHwy5Xkb2fToToSwLZdyXefK/AaPjPbwHXGdVaeKH5ngltDidzNL/NDYPsaaKVY2SwTiCh88OszNm2nSmQqCJLC4miOTr6JsWjx6dIh60yQUeDh1ibfC55M5cXqE8695PP/t/42XfDVkySGSGOLME2N3vD7s9zHSHefS7AY9MT/C1sZnPV+lKxaidytTchyXW9MbvHF1iWLDoKcrypHRvWwaIoLnIoouhWKR4uoGSDJhTKrV6kMrSfsUmb3dCc7NrBEP+tuTj/lKK/gnghp/+O3L3FrPt97geQT9Pp48PIwreZiOe4eBnV9VqOsmjusiirfHnA8Od3FgqLNtfCcIAh9gkHJTx3Zd5jeL/OkbN7Fsp20p4LgupgMjXYk7nEE30hm+9MI1VnMVEkGVSChIs9Hgu5dmiQR8HB+7vyXF20W9Xn/XumXvJ7wvgsy7cceEVg3z0qVLaJrGY489dk+Ozc73w+4g8/rGKr93/TwLtQIOLp1amF88fJqTHYPElQBLpSwRRyIaiWCKHjXXIV5V6YlG0JQEltPF0maJAnCkr4frGzlqholPkvCrCuu1GrcyBX7xiUd5ZXqW85PTHD88weHeLrxqmQsXLnCyb5Dncus4noe0dU7qpokiShwKd+IYHlXTxCeZRAMRDnd0MtgV57cvv4atOxg4hHwKQ8kYsiShAJcW53hq+AjHjx/HMAxGIhr+qkFVN3AUl6po4DgesiDieB6yK+FXJZacEgfUTvriEWY2CyyXHcZNi9977SLnV9dw3FYmpskyQ6kYTdtGk2VUSSIR9ONXFbL1BnuScURBYF8ySWGhwlzGo78nharKLZvcbJVXz83T2x1tT3E1DItri2nmNvLIosi+/g4OHTyIKAiUSiXy+Txzc3Ncu3aNWKxlDLW2vo4XDnBgfLwdeLZthWOB26WsSFAj4FOpGCbrpSpr5So1wwLXQ5JEJjeyDCSjjEeCRAIaxUKDTKnGtVKWjWidCAq26RKJ+Uj6A9jNOm8W1/m3jz2LuqMsm4oE+epz17kxmW7dq1sZUyZf4/lXbvFDTx/cVYZ6O+gbSBCO+NlYLWKaDuGIRk9/HJ/v7o/zE4eHKVQbzK/lKJUN7I0iqUiAZ46P4lNa1+GF703xlXOTlGyrJV80v0rsko9TRwbJWwYl3cSTA6Q6IhzuiRKRHC5evIgkSe0s6G5K0tto6hadmh/JgavzG/gk0C2TDsXPaDLGH3ztAtfWMoR9Kp2JMANdUXKNJr//2mXEgMRqpULOarAvlaRjawEuNw3296buanK2PbW4E9vSQoF+hStLaW6t5whpPkRBYL3SZGKghyODu+W1NU1DFzUsQePYRAeubdNoNrCbTQqFOn/14nmi4kFSqRThcPg9IU6+V9Nl7xe8L4LM24Usy+3Gfzqd5vr1629LHmabP7EdqOZKBf6PCy+ypBdgayy0UKvzK+e+wb8+/gxPmAm+To2mX0C364iIBGo+RrltWaxIEn2xCAvZAsulCoOJKD2RMAggILBervD85CyPDvXx2MgASi7NUxN7mZycZHNzkxMnTvCI38fMuRdZqBQJKT5cz6OBRQSVfcEEBAQ2G3VW6o2WBHnIz+mRfiZWU8ys5nFdl6Zls1aq0uH3UahWOdjZzyOPPIIgCJimSVxT+NSRcb5y5SZ1z8QUHARPwBZaXhguHqor4ooOumejijKSIFCsm/z5pRley+fpi0boiLce9EKjyWatzlhnkmytgSZL1C2LqmlycrCXA90tCwHHdZmdzxAOae2JI0EQ6EyFWV7LM7WUYXgoiSbJfPmla0yt5lqCjJ7H5YU0C+k+fujMRHuXOTY2RrPZZGlpie/NT/OSXiBftFE2pjgQ7+DnDj7KnmiixQ0ybxuyhQIq3R0RSisZbqxkqOgmCgLhoIYqygQUhUJDJ1ut0xMNMzSYpNo0WSlV0AMWitUiLFqeS7Wuo8kSZUOnahrEfVr7/hsZSNLVGaFYbRDyyzSbVfbv68UwbRZXC6ymywz13alo8bCIRP1Eors11OaX80zPb1KuGvR0hJkY66IzGSYZCfCTHzrCxalFbtya59iRcfb2Jdp8mVymyvOXZql4NmFFoVY1UBzYKNd49fw8P/30cWK9IUQRBlMtDTlBEHBdl1KpRKFQaJeWotEoiURil19OJlfl+Zdukc6U8bvQ0MHwLEb7wkwM93H9xjqrhSqaouCTZTYyFQzTphywWaxV6Fda7pobtTol3eBwRwee7eFXZM6Ovf0mvV9V+OyZQ7wxs8r1lU1cPI50hfn0yfG7jmbX9VbgVWUZZLllMxIHX6CGYRrUGw3Wr15tcaS27s9kMvnQWoh3fN//DDI/eGxPl01PT7O8vMzhw4ffFnt/m6y3HWS+tTzPkl5AFMEvtoT6HM+jYjX5zfPf4d8ceILP7PkQF0urNGyTmBDg9268hi+5m/2syhJV08KzLEY7Ertu/mQwQKZaI12p0qmpOI7Dm2++ieM4nD17tj399s9PfoBvLM5yPrOGLEo81TPM0lyJyY0cm1aDmmXSNAwCuHxvZQnDc1hYLaEJMrbsICORqVTZrJUZjEf5sSMnd8lTeJ7Hjx8/SLHZ4GurtxBo1ewlQUT0PGzPpWjqhDyFhmkSiKiUazp62SAjF6mKLtQdGnWDge4oiYCffKPJ4d4uUsEANzZa02ZH+ro4Ndh727fFazlLvrXkUbUNrjULTJ6/RGjSh+BAvaRzqLerzfSuNU0uzK5xcKiLsd4keaOBLErUCwXOL83zLWo0fBJRScO0TF5PrzCbSfOL/QcZiCisZItEAj6CvhYB1JMEumJhVjMlfIpMLKAhAJlibUsyR2hpsgHxeJDu7ghdkSgFcR3ddlE8kWKlSami4/g99nYk0QRxFxHUdlxqDYO+nhg+GbLZZoux7lNwbJdKreXvYtsOlZqBokiEg+9sUQK4dHOVF9+Yw7JdNJ/M8nqB6YUMn/jQAfp7YgQ0lQODKWS9wKP7d0+FrawX2Ww0CWoK1VLrOP2aimiKVG2bK9dX+ImR4wwN7RZVFUWxvaju3buXZrNJoVAgn8+ztLSEJEnEEwku3CiymdcZ6ksgSSK27TJ5awW/AflMbev7FGzdxeeTkSSRjWqNimsTkGQimo/OWJC1go+FQomlcoUnRwZ5fHyQ8d67S9I8CBG/j2ePjPLhw3sAePnll0mG716iSoT9yJKIbtq7elF1w2Ksv5Mjhw/jeR7VapV8Ps/6+nrboGw7w4vFYg+d5bxXI8zvF7wvgsw7GRfMZDLIsty2AHi72Kk/drO0iY1LRLwtoOe5LoILBcUhNtDPwmYFn6kxHO1gIBHlT7ULFBtNUju0jAr1JlG/huW5mLbTZu9Da7pGFiV8sky9XsdxHLKug6+nkyvFPEeVLhRBJJuvk9L9fCq+j309KUY648x25/mXX/8OFVMnKKsEVZehzhSLxRJz2QIJReNseIArjTRFs4EruPgEhR8eOsSR5O3gu32TRzUfP3HyCK8UVzFdC0O2EO2WWjICuIKHUpG4VcqxHCyD4dEpK9T84OCiiBL5Yp2QXyUZDyIAiizy4fE9fHh8zz3Ot8jgQIJLV1aIRQOIooDhOHx3dZmcozOuhQgoClc30ji2y4idaAeZkF8lXarx4soC/7+NC8xXC5iGSb8j05nspF50GA7H2teu03FZqhWZxWC/TyXiNbm1WEWUFXyqSioaQvUHMKItCf327rWuU6nqWE0XkVb2lS/VCQZ8PHZomOtX82yoDTRZRhEk6o5F07A4oKYI+Hy7iaCeR0CTyRTqqGGl3fC2bAdBFAj4FabnMpy/ukyx1ECWJUaHkpw5Pnxf8ufdUKsbvHllGUWW6O1q9SI9z2N5rcibV5fp6462lQ/u9qy5Arh4mKaD63r4ta371vMQJRHLdVlcyt8RZN4Kv99PX18ffX197SxnZn6N2YUNFMkjndbx+wMEg0GiEZVMvknDrREK+EgJDvlaE9f1kGUR3bMxHZewohDUFBRJYrgjjl9TkASBn/3Q8XdcbtyJndyqe2khDnbG2NuX5OZShmQ4gKpIFKpNVEXmxFhf+3O2e4nbBmXbXjE3b97EcRzi8Xg7y9lJqdgJz/P+Zybzg0a5XGZlZQVJkjh79uw7Vgvd2deJa/72PLkH2JbVZohLksS/+86rlMp6i4mvyBwd6OZwKsaFQp2FXJGw5qNpWtiuy8cPjTFfKHF1LY1PafUoTMdhs1rjaH831Ku8ee0az1UKZMw6+vpyi8AXDDFImPX1CoZlY3suYc3HR4+OsacvQSDo45F4ayEu5LJE/CqiInFjPUMq6SddqGEUW4MBPtWH31PoVcJUmwYLmy2V2/7Ebd0oWRaJ+jU65SArepG0VUGQwedJuIZAuKm1dMXqHn1qkM6gjIVE0awhSQKCKFCqNtGCrQVgdNuz5D44eqCP9Y0yS6t5/JrKYr1MTm8y2pWga4sXk/BrrJVrLFfKxHe4n1ZFgz8vTOLKHqrtYjk2M4rLjcISMTnwFqVuEUEUMQM+Hj/4KCeOPcKVmSVmV9M061U0QedqqUF30Md8xaJuWgQUGU2VKcsCEb8Pz3BZSZeIhTQ+eHwPluWyvxEnltBYdWrUPQu/JDNihImXlPZ9uD216Louh/Z18/wrt8gVdFzHo1ZvMrNeIBD0cWFmncXZLKokE4/6sSyHi9dXqdQMfvgjh5AfwuPecV2WM2WuzWywsFni4PBtd9Nt1YJ0ttpWLbhXkBkb6SAe9LNWrKCwveh66J5L1K8R9akY+v15aW/FdpYzaEmkkgViUQ3bahn3FUtFTMvFMDyCrkm96dEXj5CtNijUdSQRLMfBcT3iIf8u/ozluHTFw+9JgNnGgzTvZEnk02f3Ew36mFrOUWua9CbDnJkYYHzg7pmUoih0dXXR1dXVDhz5fJ5MJsPMzAyaprWznJZnzu3rva2t+G7wdl0x/+zP/owvfOELXL58GcMwOHjwIL/2a7/GRz/60Xd1HPC3LMisrKwwNTVFMpm8YzLs7WJnkPnMyAR/uTpJ1TJQvRaXwBFaVsiKJVFoNBmMR5G3zL1en1vlSMTH3zs0ynS5yUa5Sn8iwuOjQ5zZ00+2VqduGCwUSq2oJcBoKsGJqJ/JyUnS8QjX08sMh4L0BEPYrstULsfVeppxIU7e0LEch/V6jc1Xa/wvTzyC47pEfD5kSWzLtmtyq7QwWyhQNXQ0WSaqtXxbyqbJ165N88rlRQq1lihmPOinw2vwtOvSHQzRF4owVyowQAyv6uELyOi2jW65qK6ELUPFMGnaNrboJ+55hCWRnN7Ecz0M3UWsSpwe6udgd8e9T/YWEvEgn3z2IFMzm6ylS+T9Foma0d59AyTCQdYrLfO2bZTqOhmtho5FypIQBJGueAoXuF7apGQ26Q/e/gzP81pcF19rt6j5fJw+tI/Th/bheR7zyxtMr5/H03UijkXBMKgJIgitAPOLnzpLbzSM47p0JcME/SqvX17C70k87R+i6pronk1E9FFs1HGc24S37YUqX2tSxUUMCyzNF4j6wywvbFB3HOKyy1+/MYVlOhwf6SHgV8EPmqawuFpgZb3EyOD9s4amYfHVVyeZWs5SrjVZL1SoOiZHBruIB1u/29keU95akO8VZKLRAJ9+bILff+4CRb2J0XRwRQGfT2Z/dwpBh56ed8bbSMSDRKN+qnWD7o6WFbHneVyfXCAaluiOwoXJdSpljYFwkIAosl6uMxqPY8YFHLEVTAVRpNTUsVyHM8MPJoF6nsfUQpabc2nKNZ2+zihHxnvoSd0pYbNNJ7hfOSvs9/HJ0/t58oiJadtEAtpdBw7uBkEQCIVChEIhhoaGsG27bcN869YtTNMkGo2yvr5Ob2/vexJk3q4r5osvvsizzz7Lb/zGbxCLxfjiF7/Ipz/9ad544w2OHTv2ro7lfRFkHlQucxyHyclJMplMe0pqZWXlHX3XVCHHc4tzvLmxyLBZ59MThzjV3csPde/lK6tT6IKHLLYW8g41QLwUYDAebfcWQpqPsGkxXazyyaNhnj1+BNt1kbcEGQG6I2F++ZnHubGRodhoElYVnGwap1bh1OnT/Okr30MVRCJqq/+jSBKKK5JzTW7pRSK0POwtx2G1UuXl6SVifh+5RoPucAhoSaMU9CYdfh8LuRKKLBPUNBy3JboZUhWubWxyOJRiqCMGQLpY43y2yoc3i4wPdPGj+yf4jxfeZKNax/RcLNPC8TzUhkBEUnEkFwJQsyzWTZPeVAfHHZv5aomVhk5Ph4+Pj/Tx9MHx272XByAWDXDmZEts7zvTCyy9WWOLjwpAKhIgkJOxbZe5dAE8UBUJIQGibSP5VMKhlmilCPglBdeFzUaNlBbAxSPdqBH3+Xm8+05LXEEQGB3q5ej+Ya7PpTnW20O12WCjUCJfarA/GSTq1fDLrZ3m9g6zpyOMIkvUmyZhv0oYFcdxqesmjw7sDggX5tb46rkp0vkizUaTUCKE6VPxyg5jqTixkMa16jqu5DGdzhP1q4R8PhS5JXdU3urX3A+vXF/i8uwGPckQPYkwlu6QK9e5vprl7N5+8KBQqnPsYH8riHHvIAPwzJl9RCMaX/rGZdLFKlG/j8FYFH8D+vpj7NnzznofPlXm5JFBXnhthuW1In6/QqNpoioyhyd6OHJkjFT3Glcn18gVqri2xaGkxgdPDmAHA3xzfo3VcgXX9Qj5VJ7eN8JjI3fnv+3Ea1eW+N75lteM5pNZ3SxzazHLDz99kMGe3QMX22Xzh7EOCflV4MFyOveDLMt0dHTQ0dEy7Gs2m+TzeX73d3+XL3/5yyiKwr/+1/+an/iJn+CZZ54hEnn79gJv1xXz3//7f7/r///Gb/wGX/3qV/na1772dyPI3A/NZpNLly4hCEK7Qb65uXlfxr7jukzmcqxWK4RVlUe6uwkqKufTa/y/zr9G2TBwLZNsNs3NaolPdPZypiZwYOIxXq5uUjCaHEp0MiDG+FZ+/g7LXp8sUXM9jK1G790WWE2ROTHY2pVcvHiRYDDIkeNnsICGbaFuBYpteB7YnocoCQS3uDmKJNEwLBbzJX740Qmem5ljsVDGtCxK5TKyB/t9EtVwENNpTZaJAqRCQSQXVvUK0fBt46XeZISV9Q2uLaUZH+jiVE8f/48zKs/Nz/LS5CJew0Fz5Zb6rui11JV7Y9TqBgu5IsvlOvuiCXpVmWPdAc4e7sBoVjn32msEg0E6OjpIpVJEo9E7FjPTdnh5bok3F9dpmBYHejo40t9FZzjIUqFEXzSCJApkaw36klGeGR1GcSVkSaQjKLM5n6YiSYTDkTZ/z/M8NEnmWEcvq9Uqq/UKoiDQ6Q/y9/omsBsuddm8q8bWh0+PYVg2i+tFHBeSkQQn9o3y+OFujEaVW7duYRgG8XicVCpFIpHg0L4eLk+uURBFZEmkoVsM9bb+vo1CtcFfvDlFvlgiIjmM7R2gabm8emMJ2REw6zaaT8b1wCdKGLZDoW4Q8vmwLLslba+0hgjELaLkW2HaDlfn00SDvvZvG+lP4Dgum8Ua1+bTxP0aQ30JHj06tOMeu7+vzskDgxwY6WZ6Ks3cXAbX9RgeTjEx0UPwXQwlHNzXTcCvMDW7SaHYoL8nxkLa5rXZTd5cLuP3KRw4MMD+wU6Cmows3O5nnPU5VPxhtECI/QO97O15sNx+qdrkzesraD4Jf0hFEgQ64gGWNkq8cXWZge7Yrs/YXku+H74tb4UgCAQCAQKBAF/4whf49V//dY4cOUIsFuNf/It/wWc/+1l+53d+h5//+Z//vh6X67pUq9U2X+jd4H0dZHK5HFeuXKG7u5uJiYn2TXA/WZiaafJ/vvkGlzbTmI6DiEBvOMw/PnmSL01ep2qajERjlEolVFUlXa/x1YUZ/uOznyIairAn24eAwN7OBPlag1eUFaq6QWSHfW+xodMZ9KM+YF4hk8lw9epVBgcHGRsbQxAEZM9jKBplLZdt2+G2fhPgemg7LonltHzLBRGOdHUxGI/y4vwSk0t1OhSZQ5EQH370JJkX38D2XPyy3MrCZLi4uY6jOUjS7YMUaPnXlBu3d8oHUh0cSHXw9/ce5tuv3OLrUzOUDBvPr9DTGSEVD5KMBqnaFgFVJRb2c/xAP0f29xDfcle0LIt8Pk82m21vCFKpFB0dHSQSCURJ4r++cYVX55db/B1R5Os3Zri6tsnHDu7lu7cW2ChXWzX4gManDu/j6fGWXMz6+jqTk5N8YvgAv5+eJK83SPj8uJ7Hpl4jpmr83/efIqL4mCrlaBgWt2ZyvPDaAs85s0T8Pj44McxTB/fsEj6Mhf385EceYSldpFo3iAQ1Brtj7dLS+Pg49XqdXC5HNpvl1q1bBH0aR0fD5KousqyyZyDJgb3dRHY06qfWsqyks6T8El1dPYiSRHoli2U5eKKIT5XQDRvdsBAAW/RwPA/LdklnqvR0RhnsjZHLV7kxlWZltYjmVxnf28WB/d34VAXLdjBtZ5dLZzSscWBvF1PLWQ7t6+HYWA8jA8m2cCfczmSauoVlO4QCvjvEIAN+lWPHBjl27M4s8N1gZCDJyFbG9/y5GWYulYmENDrDfmpNk1euL6EoEs+eahFKt9UHdk6src9Mszk/+0BeTjpXZalUpiLbVIsmogAd/gADwRDrmQqNpkUwcHvjsW078IMIMndDo9Hgt37rtwgGgywtLT2Q9/c3gd/8zd+kVqvx2c9+9l1/1vsiyLx1Z+J5HgsLC8zNzTExMUF//+4a7P2CzJ9M3uD1tTW6gkGCqorlOKxVKvy7N16naDdJ+beaxN6WzakkU5NEvrm4wuRilkK91b9IBgP86PEDnBju5cVbi6yUKhi2g27bJIN+Hu3vuefO0PM85ufnmZ+f59ChQ/T03N7pCoLAp/aOc35hgeVKmWQgiOE4OBKEUDEbFnWlJffv4RENaCSCAaJ+jQO9nTzSmeJlu0kgEGgz288M9/ONqRkkYENuMFfL09QsBB98x5nnhNjLkNRyPXQ86IndWe9NJUL8+CePISUV/uLyFEOpKCG/DxAwXJtwSOMnPnCUR0fvrIcrikJ3dzfd3d2tck+5TC6XaxMmK5LKSwubpMIhYqEt21/PYz5bZKVY5p9/5APMZ4vYrstgIkrU32pSz83Nsby8zCOPPEIikaDpl/nq8iQrjTIiAkktwM/vO8VgKAbAyVQf/5/nz3FjNUNnNERMbqli/8X5KfyqwmPjuxdOSRLZ03fv3kcwGCQYDLbr6Pl8nlwuR1DJ4bo6fkGmWlbwKS19NcuymL41g+s6dHe37KtL1Sblmk5IU2mYFpIkEgrKbTKrh4teMynSZGQwxVOPj2Hb8M3vTJPerBAMqBTLTZZXCqQ3yzz9wX34ZJHueIiZ9cIuS2HTcehJRXj69F66E+E7fk+tYXB+MsfLN17HcVr9ptNHB9k79M5KYe8ElbrOpZl1gppEIuxH8ylovpbu3+WZDc4cHCS8Qw3hbhNrOyVf7sbL2ajUmGuWEWWRkKrieh7L1QqFepPjiU5Eafd6806M7v6mUKvVkCSpxcUBhoaGHvCO9x5f+tKX+PVf/3W++tWvPrT9yv3wvggyO/Ew8jD3UmFuWhYvr6wQVlWC6u2SU28kwkqlhCN5BBUVwzAwLRNFVghGI5QqZb47NU9Y8DGYiOIB6XKNL527ys9/8CQXVza4tr6J47r4ZJmmbTNbqjKWvPPYdh7/6dOn71pPfbSnj08nO5lTZTKGjl+W+amDh6nldJ6/MQdui2gW0HxYnsPBvk56Y2EKhQKXLl1CVVUGBwfbO5wfP3IQgL9euMW0kW1Nq/nDmKZNxTZ41V0GF6yqQzKgcnDg7k16QYAPHxllvlBgJl3Ar5t4Hui2xZH+bo4MPpiLJIoi8XiceDzeJkz+ybnLNAyDumth1Kr4NA2/phHyqVxfz/DTp44w0XP7mFzX5caNG5RKJU6dOtVugv7UnqM83TPKZCmDIkocSXQTVm4vSHObBWbSefoSEfxqawffFQ2xmq/wyvQSp8f637HOlCzLu6aF3qqvFgwGMQyDZEClI5mgYTpE/DKGaeO5LZHJiM9HoaHjkyUMz8FzPT71+ASPTwyh+WS6OsJIosh3X5pmM1tlz3AHotjacNRrOtOzGSb2ddHbE+XUeC+ruTILG0WiIQ3DctBNm8cODrZ963fCsh2+88YiU4tlhgf9aD6ZxbUCm/kqP/KRwwz3vfuyyMOgVNNp6BZ+Vd61uQwHVDaLdUq15q4gsxM7eTnAHbwcWZZJJBLc2MjhiaC4AupWr1QWBDK1BmpUxe/bzW9zHOd9E2QajQaBQOAHdjx//Md/zOc//3m+/OUv8+EPf/g9+cz3TZARBKFtwfwgeZh7qTDrjoPhOG1+xTYUUURAoC8YYqGYJ+J6aKqKLMuk6zVkV0Q0BXq7bu/++mJh5rJF/uLKNGVd59RwHz5JQhJFKrrBxXSevbEwO+18Go0Gly5dQlGU+x6/7TiETJkz4S78qQCHB7sZTMaoGyY+WebySpqGaeJTZI719PBTp46wsrLC9PQ0+/fvJ5vN7sqiNEXmH5w4ypS9ycZmmf5QFFUS0S2bfKVBxqxzTc8wIEUJBwSWi2UGuzt2PeTbHI+AKvNzT57g9dlVrq1uIokCRwd6OLO3f5c74b1gWDZzmwUsx2UwFUVxBaKhGMFgiO5EFNM00fUmhWKBfNNCjoZYX19vaZGpatsi23VdHn300a0MwWFjOU+jahAMazw+OISi3NkHK9abLTLiW44zpKmU6jq6Zd8hqvlOsJMTsWfPHorFIpcvX0aWZYKuRRyd+Y0Kfp8PzxWp2hbJUIDDXZ2UGzq5agNsj/HBFD/xzNE77tfFpTyRsL9dyhIFgXDYT65Qp1QxGRn2MTHUhSxJvDG1wnq+SjyocexoDyfH++7a4F9cLbC8UaEj7icRbWWToYDK0nqJy5Nr37cgEw740HwylZJNZMch1nULTZUJ+x++93OvLGd2I0NAcnFMl2LZRpIkRLFl3ldxLb743AV8qszBwU4ODXe97zKZHxRH5o/+6I/42Z/9Wf74j/+YT37yk+/Z575vgszGxgbXrl1jaGio3b+4F7adKd96c0R9PvrCYWYKeaK+23XyimEQkBUe00IUKVBTZcqWCZZJXyxOjy9Izmjs+g5BEJBFgZlsDkGApm0xXyqi2zZBRaFh28wVymwUq3z7+iyvLCxSrVV4eu8gn3vmTPsYl4tlpjZziILARHcHqYCfP/zeZV64lUUL1FFkmZevLfLDpw9wZnyQn338OOulKrl6g5hfoz8WZnp6mo2NDU6cOEEikSCfz9/VdK1iGwQUBXWrr6ApMslIgEyhjinZiKLActHgP792mbxh85ljE+3xze2gXd/ygvnAxCAfO3Kn4OL9cGs9x5dfv85GqYahW9hNmx5PJaVq6OisuDDUGcPv12gYFtVimeN9naysrHDz5k1CoRDNZpNIJMKxY8eQZZlyoc6rz91gY6XYUg4WoXcoyeMfOUg4dpuhvZ4uc+3KKhvpMrV8k85UiM5Ui09RN0y6Y6GHCpJvF4VCgStXrjA4OMiePXtwXZcDuTwvX5/j0sIGVaNBr6YQsMAvQDIVI6H40E2bT3/gwB0BBkD1yW1FgG1sbyoUWWwPBBwY6WF8sBPTsreGIVrXcjvL336dKLYUChzXRd1xDgRBIBRQWdusPJQieaHcoFRpEvSrdCZDuJ7H0kqBTLaKJIkMDybpSN5/gYyH/UwMdfLc+iaBgEUg6FJrmhQqTR47NEgsfHeS4oOwM8s5sF6mubhOwqdQKNXQDRNRFCg3La7Pb1CKRnFdj8tzGzx2YJAP7Ot6qMmy7wfq9fp74iXzdlwxoVUi+9znPsdv//Zvc/r0adLpNNAK5O/WduB9EWRc12VpaYkjR47Q1dX1wNfvFLjcGWREQeCH9o3z/z5/jsVSiajmw7Ad6qbBflllwh/k8Wc+wZ9OTzO5vk5Elvj542eZ3yjyZ5s3cD2vLcPuuC6O6xEK+FirVliqVbBdF0kQKBsGlmUzXSjzz/7460zV8lhbvh7Xr0/zYr7Ev//Mx3h+ap5vTs5SN1sWr2Gfjz2RKEtLRSKaRDTiB1mmUtf56huTjHYn6YgG6YtH6ItHME2TCxcuYJomZ8+ebSuz3svZcyya4mYps2vBSFdqLZa53hp5lryWvPvzk7OcGu6lOxLCdV1Mx+G/zl3h+bVZGo5FQFb4SN8Y/3DvUVTpwbdJqd7kD1++Qq7aoCsUZDWXp1JrUFMNon6N/pqPebOG4TgoSisjPLNnkB8/+wh+VWFzc5MbN26gqirlcpnXXnuNZDLJzMU8mysV+oZSKKqMZdqszGe58PIMH/rUUQA2Nst89a+vkC/Wiak+cnqD2orRaugn/NiOy5l9A9xayjKzksOyHIZ7ExzY03VH6eTtIJ1Oc+PGDfbv308y2cmb5xaYmtqg0bSIp4J87snHGOiLMDu/yrdemWZufgUPkUQsxJljI4zvuXvZcmJfN89/d5JG0yTgV1tCopkK0YifwYHdGYckSfi3noedRNDt/94OOIrccjZ13d19RN2w6UyE7xtgTMvmO6/Pcv1WmvqWzcJIXwLJg4XFPJbV2qCEQz4+cHqURw7fn8fykUfHWFpeIld3Wc20pssenejnmZNvw6DmPnh0Tz/X17IYgsjQQBeO43F1eQPPhW5NQjCq+FUftify6o1FBqLq+yaTaTQaBIPBd22a9nZdMX/3d38X27b5xV/8RX7xF3+x/fft178bvC+CjCiKnDlz5oEjltvYGWS2FXe38Vj/AJIg8JczMyxXygRFkQOKyg+PTxDp6uU/vfgmK8UKumFRcQz+8/cu8iPHDtAdDbOQLZIKBfCAfK1BXzzCwb5O/uPrbyKLAuGt8pflOBiWxc1SmcZW094nSoi0pFIupdP8i298h1rNwK/IjCRiAOTqDb41s8CAGqaiW0ym09RVG1vyUBsiPZei/NKTpxEEgWK5zF+/+jqq5uejp08RCNze4d0ryHxiYB8vpRdYqVeI+zQsx2VTryGaAr66gu5YNHQTR2gQCLlMpbN0BFsKxb83c4GvLk3ilxWCskLDtvjv89cwXYd/PPHoA6/J1eVNMpU6w6kYpWKDZtOkMxqiYBqkbZ0nR/qJzqeJxuMM7e1gOBFloqcDRZLY2Njg5s2bjI+P09/fj+M4FItFFmZXmLw6h6PC4moFV1WJB4L0JoOsLeYpF+pEE0GuXF+lWGqwZyhJtx3lei5Hulpno1glFPbxqePjVHNNnrs2jb0l63J+cpXx2U4+++wRAg9hI/xWLC0tMTc3tzVumuAbX7/Gzcl1qrpJvtygfsXkhddm+MDZvXz6o0f4pwfGSGfKZHN5LKvGS9kF/ttfXsJSJI4muvjRfUcYTbaarIcmeklvVpiaSbOZrYLnEY34efLxMWLRe0vA75y+BNoSN57nMdIXJ+SXyBR1YnETRZEoV1oqFkfGe+75mQAvX1jgtUtLxKN++jujNA2LVy4uYDVtTkz0EQj4WnbXhTovvzFHf2+M1H0yGr9P4cxYkkiiC8UfIhLUSEUDD7WwNg2LG3ObzK20qgNjQx0cHO1ClkRqjdZm7khfJ585Ns7zN+dZKbTG2mUkxpNJ9nSlsG0bXddp6k2Ws0VevzrNkb4ImUzmvkrS3w/8oFwxX3jhhXf9nffC+yLIwLb3+sMFmbYI4T0smE/39XOqp5fJ2RlWF5c4vDXh9VvfepWVYpmhZIxmQ8HQdYqNJt+8McPPPX6cb1yfZTabR0Dg1HAfP/TIBOvVKoosYdsOddNqEwejqkq+oeOItJWKAXyShO44XFhbp1cLISGwlC2hKjKJkB/TsVmuldEdk3poy0jKg6po88e3rtPVEaIHmd979Rw1RFSfjxcK3+FHjxzg6bEWifFeQWZvNMmvHnuKP7h1kblKgZpuojYkEo0AYdkHMri2RaVp4skigtf6rJze4Ftr8wRkpc2S98sKRaPJt9Zm+YmRQyS124ub53nc2shzcy2D5Tjs6UxQqutbxyZgmq3rIogCiihSty0EQaDD76fX8/PpI+Ptz9meIDt69CipVGvKSZIkUqkUnqWgda5zyatQMHUco45XLhAQRA47AQr5IpF4gOXVIuFQa8oqqCg82t1NNWmxuJrnEwfH6U/F+f3XzxMN+trjxqZlM7W4yaXpdR4/OvxQ9137t9+61S5fRqNR5uYyzM1mQBbJVZv4VJlQyEexWOeNC4soqsSPfuoYvd0xerqi/Pb11/mmXcKVXHAc/nJjjhc3Fvm51CiHevrp6OjgI09PcHCih0y2hqJIDA0kiMfensfIdqnMdV3SG6scGgmyXoqRyddwXJdQwMdjjwyyb6SloHG3Md5G0+Tq9AaRkK9dygoFfHiuR92w2O6MCoJAKhFkaaXA0mrhvkFm+zymYsGWd9Jd/m0jU2mV5gI++nuiSKJI07D4s29fY3IhgyS2eGZXZza4PJUgKCmsbZQBGOyL89jJEU58opeVQgVZFPnr16bIV1oqErIsEwqFCAaDVCyRRDyOKHr3nVj7fuHvmm4ZvI+CzNvF/SyYtye8KpUKZ8+cIRKJsFmpMZPJt4iKoogoiHhAdzTEermK43n8b8+codjQEQWBqL+1aFUtg+5wCFGAhmkhCiIhRaZcqZATgC3m+U6IgOm4ZCp1Sl4TAQEPj81SFVEUqeomTsDDE8CH2Apcrottu/zXi5eQaxaCojGQiCFuERR//81LpIIBjvR2tWXW74ajyR5+68wnyDTr/NGLV3ixskjFMXF9XtsG2nIdBK/laikIAuuNCk3HIqXdzpY8D0RXIKvX+cNzl3lycISDA51IosjXLk7z3LVZmkZLqVgSRTojAVzXxbKdlu6W1xIZNRyH/mDroTENm8jWQum6Ljdv3qRYLHLy5EnC4TtHbsMxP9NCg4Ju0hMKbzk8OqxXatySdW4tTLG8Pke9VqXe9EjEAohbJlUhWSEsqUSDGosbBQzTpq/j9qSfqsj4FJnJhc2HDjKu63L9+nUqlQqPPvpou3yZz9ewLIdcrYEsim2GfcDvQxYFFpbzrKfLDPTGmSzl+PbaPCFZJbI1ReV6Hiu1MldkkzHD4PLly0CLK9Lf0zJoe2vG/rDYdgut1+t84tknECWF1XQJw7RIxQJEQr5dPbntQLP9v/WmiW7YRN8i2iltbQpN6/YzuO10adt3vzd34l4yLo2myTe+N8mthSy6YaMoEsN9CT759AFmVnLcnN+ktyOCb0sNuVRp8t035ugMBRjpbZUSb05vkCvU+Oynj3Oor5Udro/28rU3pjAsG99WX6pYa/WX9vYmUV2do0eP3nNi7UF+Oe8V3qtM5v2E902QebfGZduo1+tcvHgRTdM4e/Zse8LLdl1c77bUvNAqUSMJLc8Sx231MRLB3Y3HsWSS/liElVKZsY4kuq5TKpVwJJGQIFHxHFw8JLZ7Oa3gISPg2A6BgNZ6IIFq08DyHGRBRBcsJFfE2SJk+mQJXI9MvU5M9nOkM9nuD/VFwywUSnxvbpEjvV27bArudS67AiFSvgAJnx/RFSkZrUzD2iqhDGkqZqWEocokfAFUUUJ3bBRRwvMgV2mQbdRx8bgwuc70VJ4Te3o4OzHI89dm0WSZ3i1Ry6ZpsVasEvapLOXKhH0qniqyXqkR9vsYDobZ3Cij+VX27uvCsiyuXLmCbdvtCbK7oWAY6H6RgC6h1w2krYwyKqsocR97jjxCUhWpNKZ54eV5mo0K8VgIvz9ApWqTiIcYGUySn159yLvq3tg+ZsdxdjlvQitg2a6LYdm7CJKu4+IPa5imQ7VmAHC9sInh2KR8t+8z23KRbIFzm2l+LHqIM6f3oRsNcrkcS0tL7d31tiPow+6ubdvm8uXLuK7LqVOn2oFqz1tkcLb7N47j3FaR3oJfkwn6FSp1Hb92O9D5tv575+9tNE0UWaKr88EyKNsEyLfixXNzXJlcpzMZoisVxjBtpuczSJKApwjIktgOMACNhknTsNA6VSLhViAMBX0srRaYmk1z5kQr+z893s/cep6p1VyLBO2Bpso8dWQPnRGFarW1YXoYXs42GfS96J28Fds9mb9LeN8EmbeLuwWZbYb93QzMuiMh+mIR5rIFAqqCIIh4nkumVicR9DOSuruBlCpLfO7kI/zOq+eY2khj2xaaFmAwFmQ46vHVjSxV10L0XLbiFqoi0Sn6cTWPmmWiiq2syRE9NE8iGfUz45QAkEURVW7xfjwBVEVBRNo1gIAgoMkSG5Ua0CqDWJb1wHN0ZLibczOrJFQNPRSiYuiYlosPgU8eGmVlZZXnXr1EQQef32NDqSEEBQQHcvU6juQyKMY42NlFw7B4c26dsmHQMC26o7dTer+qtCbZwgGGUjEmVzPEkkHCVZUeR8XO68TiQU6e2UM8qXHu3DmCwSDHjh2771SP47poQZWwX8WsmliGTSii4Y9oVF0b14NEIsHHn30Uvz/GpavLlCp1NnM5VNllfI/CxsYy8aCCqkhU6jqR4O1ymW7Z7B9+MNlM1/X2aP3djnloKEk8HmQlV8FxPHyqjGFYeHgEwz5kRSK6tQC+VaKoXjcoFhtUPRPVE/mrv7zCnpEOPv6JI+zdu5e9e/ei63rbgnphYQFZltsBZ6e+2k6YpsnFixdRVfWB5/levRzXdfEpAkf3d/Pt1+bYzFUIh3w09ZbkzWBPjFyuSsNvYtsOtuNw9EAfA32xB57Tu40N1+oGk7ObxCJ+gltZnuZT6OoIs7haINUVhrdU1BtNsyUCukO5QBRbrpiZXK39t6Cm8g+eOcaNpU2Ws2UUSWL/QIrRngSLi4t3zarux8tZXFz8G8ly/me57H2EnUFmu7a/sLBwB8O+/XpR5DNH9/O7L51nPldEoaXu2+nz80NH9xO6D39ifzLOj3QluCpCtKuLgUSc8WiYpekpPnTqBP/mhVdYqpUBgWTAz0fGRlmYyeHXZDJGk0yzDh50BQP4PZl9/Sk25mvUZQdNkjFMExcIBDTiAT9C06OkN1mr1SgbBoIAkidwvL/lJX6vnsxbcWiom6cO7+Gl6wuYho2GTDKg8qFDIzx5bIyvvHKDKxmPhq7jdwXEhMOKXgRZwMKjX4xwTGidy4BPQRQElrNlWlXC3Ts4URBQJJHPPXmMStNoyZaoCrlMFdfx6OiKoBt1zp07R09PD/v27XvgLjDu1/CLMulajeGuGJrWEqJfK1ZIhQIMbpFhZVnimSf3c3Cil2yu1UPr64nSbLYIk8XsOjG5wdJaGVGW0Xw+EAQmhjo5vr/vvsewrT2XSqXYv3//HYtRtW4wvZbDCItUfS7VapNSUyfqU4jFgliOw/jeLnq2lKYf7ejjvypXyBkNYpJGqdjA9lwEVeCw10Ffb5y5uQyXLy3zgSdaI+SaptHf309/fz+u61IsFsnlcnfoq6VSKQKBAM1mk4sXLxIOhzl06NDbnpzaqZnmui6PHduDgMjFm2sUK000VeaDp4Y5vK+HuYU8SysFVFVmfG8nB8d7HorwerdyWVO3MEyb6FvGmDVVJm859CQjLG4UaepWO6tytp6DneU8z/OwLIdQyHfH55wY62t7wGzjYXky348sp1artSV1/q7gfRNk3km5zLZtbNvm6tWrVKvVezLst/HIQA+//OzjvDSzyMxGhm7Z43956gyH++49Nl2tVrl48SIdkQj/9Mzp9m6lXC4z7zicHh3gT0d/kly9QUU36I6EUEWRf5t/ieVcic5QgKZtUTR01ms1BiNR/v4Tx6jXyzyf26Bs6kiSSFDzMRiP8jNHH+FL569waXMTTwBFaJlGeXjcKuZpWtZDBxlBgE+d3MfBgRSLmTKCAHt7kgykokytZHntxjLxcIChrlYWN26YTGczKCEoFJsMKhJ1rYTj96NpGpIoENF86I5NTTcJbU1l2Y5L07Q4vKUIENlBqOvbGrlNp9PcvHmTsbExBgYerKK7tFrgr79zEyvdpGjUyZfqxMN+/AGVgE/hU4+Mt1n9rd8q0NMVbS/mAJFIgK6uLg54HkePlrhwfZ4bs+s0mg2GehKcGI/h2iaep9z1/nsrB+atr1nLlvmjb1ziynKaQlPH8ly8gIAlemh+mWRHiHgqRKQjyMXpNcaHOugPRfmHY4/wB7cusVIrUxdMVEWihyDH6Ww5ZIY0pqc3OPvY6B2+KaIotheyu+mraZqGaZokEgkOHjz4rkdzRVHE51P50Jl9nDo6RLWmo6kyfk3G8zw6EkHOnBjc1ct5KzzPo9YwkQSBwJZm2N3KZdGwRiSkUantLs1VajpBv8qjhwdomBbXZtZxtkaxJUVqqVA3bZxQ65nI5GqEQj72jz6YDrF9LG+XJ/M3leU0Go0fiJTM3yTeN0Hm7UKWZRqNBq+99tod/Zf7YW9Hgr0dCSqVCm+++SZH+u8tlZJOp7l27RojIyOMjo6+xRhL2rXQp4IBUsHb0z+fPLmf//j867y2voaDh+S1pq2ydpP/cfE6z/YnGNMEaskEaCpdoRBn+/vpCoV4dXGZ6/kcgrfFLvf56AgFWK/WeGNljRHpwUFmJ0diT3eS0Z7d+lRz6wUM22k3nwGCPpUOKUxE1vAH60QCKjgOtVqNbK5AyXA5PZSgLxnk/NIm+VoDSRQxLJvRrgQfGL9TVNHzPBYXF1lYWODw4cN0dDzYd6beMPnat66TzVc51NvJgB1lulCkWNWZ6O7gxx4/xMG+h9dUEgSBRDzOs0+c4NknTrTLT9lsljfeeANVVdsK0olEAlEUd3Fg+vruzHZc1+OvXp5iZj1P3bEJ+lV8kki1YaL6JKyAwKZikd7IcX01C0BHPMiPPnWYvzcywYF4B396+RrXMmuMRZKMCTFUWgudKIm7PGruh536avl8nitXrhAIBCiXy7z44oskEgk6Ojp2ec7X6wY3pjZYXM6jqhJjo13sH+t6oBFY0O8j6PdRrjYplHWiYQ1VkXc7gm5hOxvayFR49dwCy2tFBAH2DKf4wKk9d80eVFXm1NFBvvniFBuZCuGgj0bTpKlbPH5qDx2JEJ956iAH9nSyuF5EFAX29CUwmzYvn5tndaMEQCIW5INnRuntfjgSYcvE790the9VllOv19sDJX9X8Lc2yFiWxeLiIkNDQw9Venkr7iey6Xkes7OzLC4u3pMgut18vxdT+shQN6nOEFqjhF+S8asKiZAfD3hhaoZoX4JjwwMcPXr0jvem63UGY1EiPh8eoMkyoiCwWCyyWCqxJ5W4Z5DZZvBvN3Pvt7u8GwQgFfIz0B3lwtw6oiAgShq2IjHREWAkqlItlyChkDZAVn0cGx3gsX2Dt22Mt+C6LpOTk+TzeU6dOnXXCbK7YW4pSyZXZaCnpYqckgOkegOsb5bpdjQO9D44UN0PO8tPjuNQKBTI5XJMTk5iWRaaplHMVwj6Olm+VSO/scjIvi7CkdtlnEyxxkq6hKAIOE2P8JZlseKX2PR0TLPBTK5Mpz/AgXiSLl+A1UyFr7xwnV/67OOMx1L83w6d5L9fc5FLEmp8ux/iUS43OHly5G25P+ZyOa5evdrOFLf11bLZLCsrK9y4cYNIJEIgEOW1C5usbVRRlNZG6drNdU4dG+IjTx+4Q5V5J+oNk2+9PM3kbBrDtAkHNR59ZJCzJ0aQoT08sH0PZrIV/vRrl8gVGiTiATwPLl1dIZOt0p+y75plnTg0gCyJnL+2TLmiEwr6ePzUHh492trAKLLEwdFuDo7u3hzuHelgbSvI9HZH2xN+DwPHcd5TpeN3k+W8Fz2Zt+uKCS2ezC//8i9z48YNBgYG+NVf/VV+5md+5l0dxzbeN0HmYYPEdgAolUp0dnYyPj7+jr5vOxN5a5DYWX47c+bMPRfG7fT6XkHGtB02a3WGUjESW0RK23YoFAuYjoPtD91z9xTXNFbLFfzKbpl2gJCq3rNctpPtDdwzwADs6UmgSCK1prllxNTSHTNthyN7ejg9McB4XwdXF9NYjsvBwU5Oj/UT9vt2LczZbBa7sMriTLOdDaiqimVZXL16FcuyePTRR9uqsg+DRtPCg/Yi67oetuOgqhLVmrHL5OzdQpKktoHUtjDn7PQK56/kma8vYSoiIUHmcEcnP/3Zs/Rulf8cx8VxXdwdx+LgkhF1dBxwQJVEaq7NpXKGR+M99HVEWM2UmV3Jc2Ssh1QqzCPHh3jj9TmqNR1ZltCbJt090bcltb+ddR04cICenpY6eK2ioygao6OjjI6OYpomuVyO7744yZVrK6QSGpoSxB/w4zgiF6+usH9fN8ODSRzX5WY6S67WIBkMcKCnA0kQ+Nq3rnP55iqJWIB4NEClpvPN700hyxJnjg23z2frmrncnFkgV6gz1B+HrenLoF9hdb2I5N59hFkUBY4d7OfweA9N3cLnU3ZNsN0Lfk1h78g723y8k3LZ28HDZDmNRgNVVX8grpgLCwt88pOf5Bd+4Rf4wz/8Q7797W/z+c9/np6env/r2S9vL1z1ep3u7u53tfvYvsF32jjX63UuXbqEz+d7YPntXtI27X8XBTRFptJsjQ4bpkmpWELVfAREmZDmu2cm9eTIMNc3s60GsuZreafU6kQ0jUf7+xAb9TsykZ27yJ2/717YP9jBqf39nJtcJVepIyDgei4Hhjo5ua8PnyLzgYkhPjBxZ31458K8f//+XTvmmzdvEg6HaTabBINBTpw48bY5HqlEsGUK1jTJVxqsZyuYttOyKz7wYOvdd4JtDky5XGY+L3FVsnGTPgTPo+Y4pHOrZP/zX/IPfuwEnZ0dJKMxOmJBcmuN1rl3XWqijeG5qIKIjYdPkgnJChXLZKlRJhXrwvU8dOs2ifjs2VE6O8PM3NqkXjfo748zcaCX2AOIl57nMbdZ4IXLk6xtpDl1cJxoIsXyfJaLr82R2Si3rAzGuzn5+F7CUT+9vb2YziL9fd3Eoz4azQbFYhHbsilVHC5evoWojfHF89e5lcljbTm+7utM8CP793NrIUNnKkxox+RXOlvh3OUljh8a2BUMRFEkm6vh9/uQZBk8cD0XWRAxbZvlvMm1+Q1G+1KEA9odRFBZlgiHvj96Yt9PFeZ7ZTlf/OIX+Q//4T/g8/n4gz/4AwKBAB/+8IffkW7Y23XF/E//6T8xMjLCb/3WbwEwMTHByy+/zL/7d//u/1pBZnvKJxAIcPbsWRYWFjC3NMHeCXbuuOC2QVpfXx/79u174E23M0jdbRGVRJEnRgf58qUb5MsVLL1JMBSiaNqkQkEmOhK4evOO9wE8tWeYhWKRb88tsFRuTXPF/Rr/6/FjDMaipPXmrkxmZwZzv+zlrcf32ScPM97fwc2lTRzXY6w/xbG9vQTehp7XTkXi0dFRMpkM169fR1EUKpUKb7zxRjvDicfjDzyvhWoDHZdUd5hLV1aoNA0USWpPES1vljh3dYkzjww/1PFVmwbz6QLQGnq4m0vmTg7M8NB+/s8XvoEniyRVX1uJoiAZzNsepWKTcnkGXdcZiMmspAVynkC+1qShOriqiygoaIrQ6qnRUgEvWybVhkHAp9Kbup0di6LA2FgXY2MP16TexnNXZ/njly5RrjUIBPwsXpjh9Zk0nRs2Tt0ingxh2y4XX5ujmK/xqc+eQvXJiFvj9JpfQ/NrJBIJLMuiObdJrVblN//qW8xWdbpCAZLBAC4C19ezNOsWgm7Sldqd2YeCPqp1Y8tSefdUWCSiYZp2m6QpIbGSKzBVyBOK+PmD564QDWk8c2wPZw8MtPs4328DsR+kCvN2lvOrv/qr/PIv/zJPPPEEsViMf/kv/yU/+ZM/ya/8yq/wr/7Vv/obPYbXXnvtDln/j370o/zTf/pP35PP/1sRZDY3N7l69eouhWZZlmk2775IPwy2byrbtllbW2N2dpYDBw7ctcl7r/ffj3kP8MkD+7g0O8/1dB5F09B1i1QowOfPniDmWeQb9bu+TxJFPn/yOB/eu4fpbB5Vknikp7tddttZLttJonvYALMNWZI4sa+PE/se7jc/CNsil75oJ+tVl1y1TtyRoFRvW2Ynk8l2I3pnpmjZDl99Y5JXJpeob/nYZEWTgCLik2WCAZWezkhLN+vSAscP9u9SFL4bXp1c4i/emKJQ3TKiiwT4zJkDnB6/Pd2m6zoXL17E7/dz7Ngxzk+uUndtwj61fS4FQSAkKVQdAzXWwaOHh9pTXT5F5MK0xVIZsrKAIwv0xsMEfSobuQp13cIQHPyuRL7c5LEjQ/R1vDtV27VCmf/+0kVMw2BisBtFUTFth2sLaXqaEh/ekh8CCIR8rMznWFnIMrq/h/1jXSws5jAMG5+vdf5qdYtEIsLBEwf49rk36Y4pKEC1UsH1XHyIzOZzDEtBmrq1q9/RaJoEAyoB7c6NyYF9PVy7uU4mWyWZDJIvVTg/v4Ygy+wd6CQYUMmV6/zVG7foiIXY15+86/CAbtpML2bZLNQI+BQm9nTScRfPnHeKv+ly2cPC7/dTLpf5Z//sn3H27FkWFxff1Rr3sEin03f0nbu6uqhUKjSbTfz+d6aMvY33TZC5V3N6uwF/+PBhurtvN/vu17h/2O8TRZHJycm2TMjbTU3vx7w3TZPJa1f5RE+CHz35COl6k4Cqcmygh0TAz8rKyn0DlCAIjMTjjMTvJImKoojtOEynszQMk4F4hFjA/33VWNoJz/NYWlpifn4e29/B188tUd9if5u2Q2csxOc+dpxUSCGbze5isW+X3V64ucJfvDGJKkvEg34ahkUTh2gyyKGeLnyKjCCAT5cpVXQK5QbdqXuPq8+u5/kfL13Ddlz6kxE8IFOq8ccvXqUzFmKkK972L9rJgYnG/ciKREO3EP0iiiiCAIZpIcsSiWSLjb1zqutjT7U86WdW1/iP529QadbwiQE6ohqbNR3Z8hiPJfnUsXHOHBq863WqVprcvLbG8lIeTVMZ29/F2Hj3Hc1/13X55msXqTR09g/0tEu9qiwh2B55yd7VJ1RVGc91KRVaG5qjh/pZWM5za2YT12tdO80n89ijewjFNGzHJRoMtBQowmFs26bWbFKr1nG9GlOzFbqSYWKxMIbp0miafPD0KKp651IyMpjk2Q9N8OJrM8wtbLJSLCLIMgf3dLfZ+V2JMIvpIlfmNjmyt28XEdTzPPKlGv/juavMrxW27jX43gU/n3nqEIfH7i/s+bB4v5mWbfdkhoeHf7AH8x7hfRNk3oqd/Ze7NeDfbZDRdR3P82g2mzz22GP3lDa5H946xryN7dJeKBTisbNn79rgf5A0zP2wWq7yX6ZWqN/awHY9opqPjx8c4zNH93/fA43rukxNTZHL5Thw6Ci/87UL2I7LSE+r5ux6HkvpIt944xb/6DNn2mW1nWPEV65P8ftvbtAwXVRRIi/X8ftkNEmkqOvU7ZahFYBu2qiqhP8BysnnZ9eo6SYjXbeDdE8izGKmxMXZNaKKx5UrVxgaGmJkZKR93iRXRBBF8o5OuWygihJ+UcKSYaK3k5HuO829FEVpu2bGevr4vVfOs1IsY9o6YVnggyM9PLlnmEg0xN2uTrnU4Kt/coHV5QI+TcaxXaZurHHk1BDx4RjLmyVURWJisIPK5jJNXScQDN6x+1ZkCbu5WwnCddyt8ljrfGmawo986hFuzWZYXS+iKBJ7hlMMDyapGSZhzUdF1+kItYKpLMvYgkRXIs4v/b2zfOu715lZyJCdW8Onihwa72ak149lWXeUjQVB4OTRQeJhePX1q3Q4Q8xkKiTiu2VTfIpEvtIKgm8lgr56ZZrZlTy9HRFUpeXRtJGr8hffu8FQT4xI6N3tsre/5/0QZFzXfc/8ZN4Ouru72dzc3PW3zc1NIpHIu85i4H0aZLYX6WAwyNmzZ+/e87iHBfPDoFgscunSJURRZP/+/e8owMDdA0U2m20T+O5nvnavAPUg1HSD3339KsvVBkm/j4imUTcM/vvF68QDfj60b/id/JS3Dc/zuDizyl+/fIly3eDEwb246yUKlcaucpAoCCQjARbTRQrVJslIq6G9c4z40vIb1M01VElAElxs2yFvGtgy2K5HvtEgGfDTaJoUyg0ePz5yh2DjW5Et1TAMm7XNVgM8Fm5ZDkuiwEo6yyV98w4OTL5c50vPXSIqqjS1luxM03UxRdjTEefzHznVZrN7nsfGWonV5dYOu38wQU9fjEO9XfzbH/kYM9kCpu0QRODbr0zyu1+9TFM3iId9fODoEB84PtbuUV2+sMTKcoHB4WQ7c8kX6/zxi9cQr6qIsgiex59/7xKnRpM8+/gJrn39dSpNoz027rgu+EQ6DR/FXI1YMoTjOKRXiyQ7wwzvvc0rUlWZQwd6OXSgd9c5C2s+nt2/hy9fuslGpUpQVWmYFqbj8KMHx9jTm+Lnf/pJNnNV6g0TRbIx9RprqytMT03eVV9tbW2NpcVZPv6RM0yuV5n79hUcx23/zv8/e/8dJtdd3v3jr9Om1+19tavVSqvei225V2xjmxCIQwIB8pCQ2AkhhCfJl3xJru/zhCT8gFRIIE8wiSmOAZvgXmVhy7LVpdVqi7b3aTu9nfb7YzSjXWlXWlWLPLyvy9flXe2cOTNz5tyfz32/i2maZPMaDVW+sz5DTTc4PhDA53FgtxVydUygpsLN2HSM3uEAa5fVnmXqeaG4Vtpl6XSBRLJYqv/lwo4dO3juuefm/O7ll19mx44dl+X410yRKYVsnRJALlmyhLa2tgVv0udyYT4XRkdH6e7upr29nZGRkUXHC8yH2YWiKDo8efIkq1atoq6u7pyPXaxqfzZM02Tf0BhTiTRtNVXomkY+l0NU88TzOj/ed5j1VT5M2cpUNInLbqGhzDPnPcyqGgfGJhiPJfDbbWxtqsdrXzy9uIif7jnOk7sOYSDgdbt55fAANkUmm1+48IvzfJaRWJqB0TAumwUdcFgUYkaOuJBDozA5358MMBmP02LzsG55HXdef27aeiyRYWgozHQ4QVyUEASBaSVBQ62PdCaLno6x7qYbStECRRzsnWB6JsnK+iqWqCpTqRQZTSWdynNjazMrTwlADcPkzde7OfDOAJlM0VhRYdO2Vm64ZQWKJLGyphJN1/nmj9+ha2gGv89HpSQQiCR4ad8wiXiUOr+F8vJyjhwaxe5QEEWBQCjBdDDBcCrBNHmqRCfNjWVk0nEykkRvSON9ssKNK5p5rXOAWDqLLIlkVZ2l9RXcsr6W4SMTjA4GkSSRqjofO+9YhdO9uM/4wbUrcFgUXj4xQDSTpcLl4PYVrdzdUQgUEwSBmsq5bcqF/NVsNhuJRIK1a9dSUVHBeoebnx0dZDgQpdzjQBQEIvE0fredbR1nu0Dounm6IAkCgiCULI0EQcBEKH0H55vlLLbgXCvtslSqsJu7VArzhaZi/vZv/zb/+I//yOc//3k+8YlP8Nprr/Gf//mfPPvss5d0HkVcM0WmmNOx2ITMC22XFds6k5OTbNy4kfLycsbHxy+p5Vbcyei6zvHjxwmHw4ue7VxokSkO+IPJFGBiURRQFOx2O6ZpYMQSBFMZvv6T1zgRTKEi4rBZWNVUy0dv3kClx0kgmeL/9/oe+oIROLUqfNLVxe/fuI1VtYtX0A+MTvHU7sPYrBaaaysLBAjTZHBqhpymMRWOU1/pPUWMMAnF0qxvq8U3z+4jlcmhqgaNPg8DMzGiao4ZS8HLTdChzGErmFtqGlUVBi0VOSbGhqmoqMDn8817c3j5Zz2kIxl8dhsZXcMmy4Wwq6EpmspsPHTL9rMKDMB0JEE+rxEJp5BlkRaPF0kSGSdGJnW6DTXUH+DdPSdxOq1U1RRuuPFYhnf3nKShqYzWU0yx3uEQfSMh6qu8Jedgl9PG8OQMSdPLli2rCQaDpFIpZiIFY89gJE8WCFh1dMEgGEkRT2aoKHOwrLWGkWCM3rEQH75uDa3VZezvL7QFV9RXcP3yZspdDo7VeOkfDOJ0WNmxuRWPZ/EtD0kUuWflMm5f3koqr+K0KCjnWeXruoEoyXP81U6cOMHk5CQWi4WjR4+W/NV+5ebVvHp4iJPjIUwT2hsruWtrO43z7GRsVpkl9WUc7h7H67KVFksz8Qwuh5WW+oIma7aLdFEIWuxyLGaXc620y9LpNLIsX3RnpYgLTcVsaWnh2Wef5Q/+4A/4u7/7OxoaGvjXf/3Xy0JfhmuoyOi6TiqVYseOHYuq5BdSZHKnMjo0TZsTY3yxLavZ55DL5Xj33XeBwrZzsaLDCykyRXqyYRjUeApRuaqul778giCimuCxOeiJqdgcDjyCSTKT5WfH+piYnub37trCE30jdE+HqHW7scgFWvBEPME39uznKw/ciXUR1hqBQIBX3tqHIcg0niowcKot5nYwI2RQZJnByRlkSUQzDGrK3bxvx/zzojKvE5fDgpxTaSv3cSwRKtjwGKBIMs1VZdisMiORGJaGWpa3tzI1Oc0br7yDmtcoq67A4fZSV1uJ3+Ngz+s9PPvaMQzDoMxrJWW3kDY1wMCqmTywbS2t81gJpdN5+nqmmA4kiIVSiKKIw6HQ3FhGXtWpnKVbGeibRlV1vP7Tv/P6HERn0gz0TZeKTDCaQjeMOdb0AG6HlclQHIfDSWurm5tv03nx2aNMhOOopkHK0DBMAUEUEEwTw4BEWicazyKYArpuFuKrlzWyfdnpHUA6m+exn7xLV/90IXJbEDg0MMWH71lPS/2FmS4qkoTPfu7iksrkeX3/SQ6eGEfVdFrry7hp01KM7AzBYJBt27bhcrlIp9Ol+dvMzAwr/XY2NDTh8/lZ0lBTyB+aB4IgcMuWNsamogyNR3DYLeTyGqIocOuWNqrLC22lcyWCLmaXc620y4pZMpc6V73QVMziYw4dOnRJz7sQrpkiI8syGzduvKAI5sUUmXg8zsGDB/H5fGzatGnOEP5SyQOGYdDb20tlZSWrV6++oAt1MUWmuCornqMoimxsqmVJuY/+4AxlTjuKJBHNZJBFETNnYJFlanyFIu33mZRlckzFEuw61subw2OFWZaaQxQsyJJEtdvFZCxB52SATY0Lt/iKDLLjfX1UNTRgmRjgTOm9YZr4XDY+dc8WuoeDRJMZais8bGyvL81izoTTbmHH2iU8/9YJ7BYZv9NOJpfCJohU+d3YTtFsZUkkks6iZSS69oSZGE0zFIwTy08iWUXsTglyJi5TwBBAlCTUaB57SqS8XMbitqBqCrWnorCTqRxjk1EUWaSpoYw33uolHkzjtClopolFKbgLHD85TUtTOZtXnL6Z5/P6vPYrhVTQ09eT21HQ2Wi6gTyLJZbJqdRXeUrHWLexmSNHRukfi6DJAoYpYtFN8kohEM8wDPL5PKNTYSrKPbTWnU0+AHjtnT4OdY9TXebGYbeg6QYTgShPvniE3/+1G88qdpcCTdN5/NkDHDs5idNeuJYOnBjjSPcwO1f6uPvW60oD7NlMPE3TCIfDhEIhxodPMjrYW/JXc7m8yIqC3XbasLS51s/HH9zKu8dGGJqM4HHa2LCinvXLF6bdn0kemE3zP9MRo/gduxZ2Mv8dA8vgGioyF4pigVjI1gVgcnKSzs5OWltb53XRvZQiMzk5STwep6qqirVr116Ud9q5isyZCv5iH9quKPzBrTv49tuH6J4OkcrnKXM4uGN5Ky+821PSPgAICLjsNsLJLP7aehyhGSRAUzXS6TSiKGJRFPKaRia/cD6NYRjsOXyUH3f1Mq6DYUZJSlmyYYPlFeWlG2k0mWHnmiW0N1bS3rh4i49btrYhyyJ7jw5jj6cKSZteN5WnWEiGaaLqBg1uNy//+CDh6ThJ0SRpglWREQ0Bm2xnfHoGw61gkQ1i2Txem0IinkG2OCjz+zFMaKjx8ta+AXa93UcsnkGSBCr8LmKRNFU+F5UWN33BGVJ5FWQBdJMbVy0ppS5CwVn6yMFhVFVHOaVyV1UdwzBLrtMAHUuqqK1wMzYdpbrcTTqrEphJktc0btt6et7ocFi4/e5VDI6GmcnkyKcyOCWThCSSxwTTRBUglVXpsKoER3oRs1EqKirwegttybyqcbBrDLfDWtKxyJJIbaWXyVCckyMhVrXNbwabVTXeHh5jIBTBYbGwrbme1vL585WK6B4OcmIwQE25G9sp8a6pZRkLpEmZzQveLGVZLjHxiv5q/f1j/PCpfQwNRZFlmSVLKrjtlpW0L2tAEATqq7w8dNuaxVxKZ+FcWTlFduns319tIehs/HfMkoFrrMgU1dWLQXFHMtsWpojifGd0dJR169ZRVTX/vOFiikxRuzM8PIzP58Pv91/U9vZcFObzCSxrvW7+5K6dTMaSZFSVOp8bRRTZe3yEUDyFe5bVfiavosgirRVl1Ho9DEei1HncIICazxNKphB1jeDJHo6l4yWhZJHRp2kab7yzj386GZLB/AAAtR5JREFU0kfUMPDZbditCpoFhrMJ9CkDh6AgCNBSW8b7ts4dymdVjf3DE4xGYrhtVra11FPpnnsDkiSRm7e0cd36JQwFZ/jq7neYSiSJZ3MIgsBMOkuV20mL6eDg1CA1jX4GuyexKiI2RSadzJOJ53BYLBiiQntDGScmg0TTOUzBJBjNIDpmuHFLK9OBGM+9ehxJEqiv8aLpBuOTUQLBJKuXVlPucuB32Ihn82iGzmAgyuGJaSZfTlNlc+ATCrk6NfV+xkci2E9Z12fSeVqXVdO+8rR2w25T+NW7N/KDFw9xoHecRDqHKAq47BZeP9iP3+Ngx5qCbU/Lkkral9dw8NgwgmjgdDhwyRKhdBrBriBLIjdvXMon3r+ZWHSGqekAA4MHUWSByspK7C5vgd59hkBVlkQMwyxFZZ+JaCbLl155kxPTQYxT372fdHbzsS3ruKdj2fwXLzAZjBdC5awKpgkzMxFUVaOmsoyhqdiCj5uNgqjaxrsHgkxOGVRWVpJX83R3BxgaDrDzuhpaW+pK4WyXIxRs9i4nnU5z/PhxamtrzyISXSh54HIgnU7jcDjeM73blcI1VWQuBLO9w2ZffGfqa861MrjQIqNpGseOHSMej7Nt2zYGBwcveqZTbJeduRNbrIJfEIRS/HERt65u5Xs/O0IgnsJrt5LTdMKJNGubq+loqOSX1A7+9o29DIZn8Nht5DUdJJkPrF/Fre0tBAIBDnf1kkylaK6tpMzv593jAzzVM8W4kENBJJLL4HYYtJR5GYnG8Xtd7KxpoK7cw4a2ujkFLpxM85WX99A7HS7RT5861MVvbF9Pi8eH02HB7z3dRrMoMu11lfzxHTfw/QPHODEdAtNgU2MtH964mlhXCAGBrKoTi2dQ8xpiYaGPxSIhKxKabiAaOo12gbTDQziWo7zcyU07m/E7dZ569i0iMypN9WUFNbtFpqm+jKlAgrHpGGV+J5Io4rJZ2DM+QcjMkJiYZm/fKPmcRrlgoVl04nbaaF1Zi5osWBst66hh7YZm7Ge4/zbV+Fi3oo6ukQDV5W7KPQ4URWQ6kuSpNzpZWl9OVZkLSRJZvcrPyZNDJHJWookckijgs1vxOOwsaSzn7s3L2Puzk7x7eIRANIXNrlBb46bdzOCIxdCyMQIhnaZaPzabDVlWSKRy2G0KdZXzC1efOnqC41MBql0uLHJBhxJMpfmPfUdZX19DrWd+Oq3NqoBZoE5HZ6LoukZlZQVT4SRux/yD61AwQTiUxO6wUN/gR5JEuronGRmdobHBX5rN1FSXMTQUQtd9KIpCf38/x44dw+fzUVFRQWVl5SXfjIvBbmVlZXR0dJTcO2bvci6EPHA5cDnMMa9F/NwWmeKHPrtILEZfc+YxFltkiheloigl88xLEVQWL9bZRWZ2z/hCLWIAbl3TSlbVeO3YAOFkGosss2N5Iw/fsI7RySj79gziiQikTI1gOsGSSj+/vHkVt7e3MhqK8XTnFCcnI+i6jn96nGXuCd7pjxElj2gFuyxjmBBLZbEqEm6bFU0y+ZWbz44rAHjywHFOTAap8ZwiGpgGA1MR/tdTu1ht+nBaLKxqr+HeW1fhcp6+MS0p9/End+5kJp3BNMF/ykCxdyqDicnoYJhsWgWRU87FKghgSiJ6Jk80HKKpvgY1L1BnUbn3Q5tpX13o4b99NIUrEyOTyRCNRpEVGbvNjsupYGIyMhbB47YzGI8xnU1T63fhsFjIRHI4FIU4OrZyG1pcZ2Qyyv/4xM6zxIVn4mD3OB6njdpTvl+aZmCTJQKRJEd6J7h92zL6+vrIZsL8/qP3MDQS5+DxUSbDCewOC2uW19Fc5eXZHx/k2Mlp4pk8giBgtUrEE1lCkQwffnATH76vnsd/up+RySiyaGCYAggS161fQnX52TcvwzR5o38YWRApjpgEQaDC6WAiluDA6CT3rZq/yKxqrcbnttE3OIHfZaGiopxkOo+mG2xZNZeOrOY1XnnxOJ1HRkmlclgsCg3NZdz7/nUEgwlMkznDf0EQsNktRKJ52tvbaW9vJ5PJlMgD/f39WK3WkibH7/df0Dw0k8mwf//+ktNDibxyxixn9n9XY5fzXggxrwauqSJzKZkwgUCAo0eP0tjYuOh8GVmWyeVy5/27onizpqZmTgTvpbDTZl/MxTbh7AH/xazSJFHk/s0ruHV1K1PRJG67hSqvi5l4mn//6T4CkSTNfg8too9QNIktKNHq8BJLZfnHZ/cyOZOgzGVHNQ1OTkYYDVuQRQsVToVEPkFezSMggAkziTROt40Kx/z02Exe5d3BcVxWK5ZTN5BYPIOaUskJJrpLQDQE3j4wSCar8rEPbj3rNfvPOHZzezVl1R563ujFZZNJUhDyyYqIZJHI6xpeu4BdcjITyuH22rn+9g6WrTpNaGiqL2cqkKSy0l8QAmZzJJMpDD1Pc4MbDMikMyQlHa/HRkOtn4HBYMlVO5fPEcplWV1VzshYhJP9AbZubuFcSOdUZFlENwwGpmaYCMdQdQPDMPnBT/eRHh3HXy2xefNmnE4nNTUVbN/aChTJH/C977zF+GQcTQSvx45FEUmn8mhZjXQ6x8/2nuR//Pr12G02dh8YYHw6iiSaLK11UufNsXv3bsrLy0s3ZkVR2N01yGBgBlXXmUlk8NitVPtcyKeuzfw5FlBuh8KaRiuxuExGlxmbjmOzKly/fgnb18yNKdi75yTv7DmJr8xJU6WLXFajv3eaZ54+TNOyqgKd/owdvapqeDynmZp2u53GxkYaGxvPygDK5/NzXtu5GJ7FAlNZWcny5csX/J7NRx4oFpwrtcv5xUzmGoQkSaiqSn9/PwMDA6xevZra2sX7GS2G4VUUby5fvpymprlfHlEUL9p1oLjy0jSttCMrDvfPV2B0w6BvOMTI5AwWRWLl0hqqyk5fnE6bhaWz7E+O9EwwHU7SVOMvMZrqK70MT87w7rERymrcTM0kaCz3ks1lyOYytNSUMxCIY2p5VlRVMGlkUQ0dCyKGrhdioDMGLUpB1V1ZWTnH8DKv62jGaVaVYZrMxDKIgoAkgSiL+Gx2FFmku3+a0ckoTXXnHjZbbQrrr2vjyMFRBExEXScnicg2BdPUsZvwmT+4C6tiR1N1qut8eM6wzN+6vpkTfVOMTcYo8znQdYFUVmDNyiV86L7VJOIzTE4GCB0dIZsyyGTSaJqOcHqpjwml91FVz7+T7WiuYtehfiLpDKPBGOgGoihgiALDySTfe6mHu67rwO4O0dEuoygSkiSW3rtd757k5eODJHWNvKFSodiwIGGzW8ik85RVupgOJEhnVFYuraGjtRpV05ElCVEUzgow6+rqYiIDL/YFkQ3IiYViFklmyGs6Fd6Cd1lH9dlaIij48h06dIi2Bh933LyNk6MR8qpGY7WPxhrf3GKR1zhycASH04rXW1g02OwKtfVexkbCrFrXgMdjY2o6TlWlG1EUiERSKLLE6pXzM8hmR02YpkkymSQUCjE5OUl3dzdOp7PUVisSI6Aw8zhw4MB5C8yZuFSK9GLxi53MNQhJkujr6yOTybBt2zY8noUNExd6/ELtLsMw6OnpYWJioiTenO/xFxs3ULzANU27oJVQLq/x3ecOcKh7AlUrnLvXZeOhW9ewfe382eAz8QKDZjbtVhAE7FaFqVCCrKUw8E2lkuRyOXy+Qi/cYc0QV7PoOZMqw8awmigEcmHitFj44PqV3FxXwdjYGCdOnJhjeOm222mp8HFsPIDbakHXC3YxhgiKIOKXC+0xh91CcCZFJJo+b5ExTROr20ZVkx+vx4bNZkGQBKamg8SjKerramlprcc6jyNwEUsay/nQ/Rt57a1epoNxRFFk/eoG7rq5g6pyN7U1lbS3txOzdfLd3UdIpbOARiajo5uFBUGF1U4qncOiSNQuIuL3po2tHOwb5/DQFIZWKLy6YSJoBqppEpDhuVdP8MbxUXJ2AbffhsdlZ/uKRpyyzNNvHGdGzSNgkpJNskaWSgx8QqGoq5qO1ypjsRR1UwIWRSYaTJBJ5XB67AyPxhjoi2KYEo1NS3hptJtsXqXaIjFqaOQFHVEQiGazCLLAvauWsaLq7CKTy+VKkRtr1qxBFEW2rCrcGCOhBAfeOkk+p1FR7aFleQ3ZrEo2o2K3z/1MLBYZVdWxWxXed/caXn61i7HxGTBN3G4bN9/UjmSTONI9QW2lh6p52n3F1+p2u3G73bS0tKCqKqFQiHA4zOHDhwEoLy/H4/EwNDRETU3NRSXpzsZCFOmFQgMX+93+RZG5CriQDz6TyZDNZhFFkR07dly0weV8RUZVVQ4fPkwul5sj3lzs4xcLURQZHR2lrq5u0RfXm4cG2Xd8lHKvE6e94Oc0HUny41eP0lJfVhKozYb/lOLbMMxSoTHNAuOopsKNxa4UTBcFeU5/WxQF6ss99EwGSasqLkkkJxkgCNRJdq5rbKZtSQ1tS5eSy+UIBoOlnrnNZmNLhZv+QJixmTg2i0xOKHwB2xx+3PIpVlZWxarIeM9jezI4GubFN04wPD7DdDIF4RirmivRcwnUrIbT5mbd5tZzFpgiOpbV0N5aRThaWDH7PGc7WN+2vp3j42GOjwaweBwktBSJvIbPFAkPTjGDzJo19dRUn9tnKpbJ0huOUNngQRifRtBABMy8jmmAJIIhQtRiEMynMFWwpjNUVag8taeLfFajxuXAZpEJalk0CfKYjJMjmVepVCzohsmGNQ0op9qSqXiGt549zGDnONl0nslEjpwo4vYXhH4HDwwx4MxSUenB57ThyWQYzySIa3kEzWSN08n9zbXkcrk5rafiXNLr9bJy5co5N84TR0Z55SeHic2kEYQCY7Cto5a7P7gJX5mTwFQM16zPOJ3KY7Mr+Muc1DX4aW2pZGg4hGGYKHaZF97s5uk3T6CqOg6Hhc2rG3no9jWl1zgbBSqyitUqoygKtbW1pYTQWCzG5OQkfX19pZ+Hhobm+KtdCs5HkS4WnMW01VKpFJWVlxYtfi3imioyi0UkEuHQoUMoikJLS8tF2zDMVyRmkwe2b99+TtrkxfiPwWkGWUdHB9PT07zzzjvYbLbSLsDn8y148e87PooiSzhPsZgEQaC6zMXIVJTj/VPzFpl1y+t4Y38/o9MzVPicqJrBTDyN025hXXs1g8P9OC0SWUHBAEzdIJxIY7cq3L9lBY+9crBg8yIIlNkUqrxOYpksz+3vYXVzNb3jId7oHGQ8HKfa52LnypVUOyWCwSB317g5EIgTUjUaPE6MsE6takPTDLI5lWAkyar2Gprr5xcYAkyHEvzHj/YRmklS5nNQU+9jaCjIu71j1FsUGusr6VjTgLXeyd//6C2C0SQNlT5uXNdCR/NC9HWRqnneqyKcVgu/d+8O3u4Z5djwFLmsipwBM5Inmc0Qz+fYPzDOwS/9J2uWVXLn9Suor6uZ41p7YGiCb7z+LuFTbai03UARBKRoocAYgoAhgomJZhMxBRNZENA1g3g8i9/nIJDOYMnphPQsGiaCBgigixBTDPw2mc3rm1jWWMGzPzrAcH+Q6aEgWjJD69LCaw+ORpExqap0U9lQRiad40gwwYwlg99lx+dw4HM40DSd8Uic65sbmBqf5K3XDmKoEpVV5axY08jI2CAVFRUlNlYRiVia1356hGwmT/PSggtELqvSfWyMmgY/m7e18NzTh5maiOL22sllVeKxLOs3NlNb7wPA5bKyelU9qqbzt995g8GxCDUVbqwWmVgyyxvv9uNz2+f41pmmSdfxcd7Z008wmMBut7BhUzPbdyxFscgIgoCiKAQCAZqbm2loaCgJQQcHB1EUpZRvVFZWdllU/+cSgs7XViv+PxSKTEvLued7P4/4uSoypmkyOjpKT08PK1asKAVhXSzOLDKLdVAu4kLZZWcq+Gtra6mrq0PXdcLhcOn5gVLBKS8vn3Pxp7P5OepxOL0DzC0wH/B7HPz6/Zv595/up3NgqjAslyRa6nwcOdbJ+uWNPNLUyhNvdhKIpjBME7/LzoPbVoJpYrcotNWVA8IpFlJhLjEciPKz40P8x2uHCKTTpHSVrK7zoyMneGDDCh65ezsrV67kvliMYDDI5NQ07x6ZYjQQJxpL4nLaWL+ygQfvWjOvgr6IfUdGCEaSNDcUNEmqqlJbbSUck9h8XQd33rSCwyPT/NvzB1BVHZtVZjQQ41j/JB9/32Y2LW9gJp4mkcxR7neWCvT54LRauH3tUrY11TJwYoJsKk+8xuClwwPkEfGX+cll8xzojhCOHWbbchdut6swn3J5+Ppr7zKTzlBzyuI/n1OJ63kMm4k9I6ILJoYAqIClkKQpCgIIAppmkM9r6LpJKJfBVATsQsHNQNMNMAU8Hhs1zWVsXNnAv//7mySjGdyyxMTYDKIs4pzJoAkmikXGIkBoMkp5rQ+7w0q1YGE6m2cmmcHnLNDZp6MpGit8XNexnFefOsRQT45MNsfR7BRvvnKcddfVUlZWRiAQmKNbGeoLEI2kaGypKF2LVpuC02Wj6/AIn/zDu8CEd9/uJzqTxmKV2Xnzcm646ey5SO9gkNHJKHVVnpLmx+e2k89r7D08xC3b2gqRBqrO6y8d55VXuhBEgfIKN/F4hheeO0p0Js19D6wnlUpx4MAB6uvrWbp0KYIgzPFXm5kp2N/09PSQy+VK/moVFRULdi8uBOfb5ZxJHvhFu+wq4Fw3dcMw6OrqIhAIsHnzZvx+P6FQ6LIUmQt1UJ79+MXuZM7s184e8EuSRFVVFVVVVZimSTQaJRgM0tvbSy6XK622KisrWbGkijcO9FPuM0uuxulsvmCPUuNb8Pk9Lht53cDpsFJX5UUSTKZCM+xRXdywo45VtX7WLKmhbyKMbhi01Zbjtlt5u3ukwH4zQJJmDXT1Qj/9mXe7CaTTxPQ8BiaKLJLVNJ480EWZx86vXb8en8+Hz+dj2bJlbNqYYmBonIHhSdRcivoag0hoEkmoxO12z3sNjE9FsVikUyvkHJGZCB6PGx0VwSphdVl5/p0eRAEaq72l93s8FOfHb3RyvGuC433T5FUNl8PK9ZtauOP65WcFgs2Hoe5JXvrPfYQDcQSgT82RkgVWddSjWGVw23G77IQTWRpbV+N3CQSDQZ452MV4KEKV24GuaQgC+G0ieU0i7wQpXxD4SHkBRRNQLRSmXaaJLIsFNppuIJommgAIICIiSGAIIBvgsVqYiCT4q2+/Rjiawm5TsGsiFouIy14Ypnv8DkxAschoeR1d1RElkRrTSnmZh4ipMxqKo0giS6r8/Obtmzj05kl6j09S1+gH0WRycopswiQwaLJyjVTSrfj9fiorK0mnMqVrejYkWURVC9+v9ZuaWbW2gUQ8g81uwXFKxGqaJmpeQ1YkRFEkkc6hG+ZZolKbVSGdVcnlNWYCCV54cj9vvjNAJqviclqRgMaWCpKJHEePjLJ6bQ0jo71zCsxsiKJIeXk55eXlmKY5x1+tt7cXh8NRKjgLGbFeKM61y8nn8xw4cIClS5de8vNca7imisxCyOVyHDp0CMMw2LFjR6klcbF2/0UUM2k6OzsJhUIXnI652J3MmRYx57pgBUHA7/fj9/tZtmwZqVSKYDBYGq77LVZcNpGh8TAel72w4tU0NqxoYMWShZ2U93eNEphJ0lpfTjabIZFI0FJfQSCa5Z3OEZpr/dgtCmuXzLUeWdNcTYXXyeRMgtoyN5Ioks6ppHMqG1rr2Ns7SspUMTGxn1rZyqJIRtX4r0M93LOunXLX6VWh0+lkzap21qxqJ5/Pl3Zww8PDyLJcKqZlZWWl98nvdaBqBpl0hploFJ/Pi91uJxybweu2MTw9QzSZpWYWw04QBPxuB91DAaZGolT5XPg9DhKpLM++3oUkidxxnsiAXCbPq08dIBZJ0ri0EhOBrv5xjIxKeDpGTVOBDOKwWwjNpOgZDOJAIhzJkNacyHIM6dQK1TAKvnLVPoVsVqc+LaLlDSJ6hiQ6Yq7QMkMUsFpkVN1ANQw8soyR04maBjmzMJy3iCI2wyCaySFlBJSkhsuqgCgSyucRXRJOXS/Y3kgiApDJ5LE7LEiKRCaTR0Tg4evXUdNaxnAoitOisLy+AjWn89Nj4/jKHJiCQWBqGr/Pi6PWyfREDJviZ9V1HXNuyqOTE0SjM2i9WWobK7Db7eRzGuFAnNVbW4hEUvj9DhRFpuzUAN80Tfo7xznyZi/BiRmcbjurtreiWWRymTxjYxHKy10lcWsinaOh2ossCrzwn/sY6AsgSAJenwNMk+B4FKtVprreT2ggwJtv7ue661cu6qYtCMKC/mrHjp0yWz3lr1ZeXn7JLskwd5ejaRqPPPIIiqLw8MMPX/KxrzVc80UmFotx8OBBysvLWbVq1ZzW0aUO3jVNI5/Pk0wmL8hBefbzL8bkcjEK/vkgCAIulwuXy0VLSwvZbJZgMIgkSew9NsZ0LIHb6eDWLW3ced3Kc67MA5EkoiCQSibIZLOUlflRFAuWlMp4cGEbEJfdysdv38i/vXyAiUgCKFiVbGqr45a1rbzZM0ReN7DMfm4TJATSqspQKDqnyMyGxWIpDWlnty9OnDiBqqqlHdyq9ire2tfL8FiQpoZKLFYrk4E4HpeNdR31JPMqoiign2FEmcrkyWRVWmt9+NyFhYnV4iIQTrLnwCA3bll6TtPI0f4gockYtU3lJTNFiyKRU3XiM2mq6v2IUmHHkc2pvLGrB0kt7ETCRp6UI49DkpAFA4fDUYgTjiVpdlnZttLDcH8KKzCSyaAZJjZJRlME0ppWsK4pd7PV42dk3xBdTpjRVWyChEWAtGkimCblihVDgRkjT84EQzHR3SJ9aZ3qlI7HrpCyyQQiKaweB+NjEQQEVq9tYOXqOhSLTLXvdHHOqDl0zQDBYHo6gt/vx+N2YxqFbBc1V2jxOBwOmpqacEguht8OQTLP0HCUwRNTiFaFnAaGIjMaz/DOkVEaGsu4YWc7mzY1IwgCfYdHeOH7e1GzKi6vg/B0jO9+czeazUJWMQmoCaamY1RVepDtMqIgcNOWpYwPhpkcjVDfXEaoO49uGNhtCrpmEJ5O4PRaSaWTNDYuu+hdwXz+arPp3263u7TL8Xg8F/SdPhO6rvN7v/d77Nu3j717984J0fvvgmu6yIyPj9PV1UVbWxtLliy5rAaX8Xiczs5OALZu3XpRQ7/z7WQupcDMB5vNVhKk3bqzsNoKBAKEQiHefedtKisrqaqqmrMLKMLrspFKp7FJCuXlZUiSXGhVqDpV/nMLwNYsqeEvPnIbRwamSOXyNFR46WisRECgsdzLcDKOIYpIgAnkNR1FFrErCjZlcZfY7PZF27JlnByYJBIOkxwaJpVMsKLZSv+4QCCcRpJylPsd3HfbauprfGi6Tl25h5HpKA2VBXdjTTeIxNNYRImyMyJ6XQ4LiXSOeDJLZdnCr11TdXTDYDSfZSCaIqVrCApkRQNF09FPRRuPB2Lk0yqiTaKpqawQ7GUYTEyOMGUmqSp3IyGQyOUp97r51B3bqbVJjIxMMDQwRSJnMDSjEU2p5E2w2hW2rWnm/TesRE3k+K/JBMJ4hHGXlWk9T84waSrzYEoSbkVhLKeRShlICMgIGIJJ2i4RQcDI5VnW6GfL9W0oHjuCINK6rIoVHbUo8xRYq82CoeY42TNBQ3MFdlthgRCdSeHy2Kg+NagHSCcyvPidnzE5EGDF8joi0TTHugJkEhqS34YuCqSSGXK5PNlslulADFkWWbOmgf27utHyGvWnyAn5iEB0OIqUz7BufRMTqTTjkUKhaV9Ww323rWLLmiY69w+jawY2m0JluZOR8SiyJCLJItlsnpN9E7S0VnL9DesXdd2dD4Ig4PF4SrHh+Xy+FM42MjKCKIqlgnOh/mqGYfDZz36W3bt38/rrr/+3LDBwjRWZ2fYqvb29jI2NsX79+gVpfcU8lwtFMX2zqamJwcHBi+63LrSTKQ74L8Ui5nyYvdpaaBdQVVVFRUUFuq6jqGGcNpmcoaAbYJg6oWgKp93C1lVN530+j8PGztVLzvr9J27fTOd3Q4SzaTSt8DplScRilWmq8LK8toK8pvPuyTGOjwWQRIENS2rZsKRu3mF/30CAn77UyWQghmEYWBWDNe1u7rp5LRNTIfoGp7DbbKxa3khjvasww5AkfvX29fzrM+8yGoxRPGpdhYecmCOb00rOxFCYYTlsyhwrm/lQ3eBn2i7QHw4hiCKKKJA3DbKKiYzAZChR2G3aLMgWndqqU6taE1LJJMtUiWlZQVEsmMDaxhoe2tDBhuaCYLi2tpYtWwyi0SjT0wF6B8fJZvO0NlVRW1OFRQJPjZf3f+JGjrzVx3DPJFglWtc0sG3nCv7pyT10DwVIoxd2cLqJbhoIgMMiY6+0c8sDO1ixtBbHIpIxk9E0P/yn5xk51o+akxnonGZ6OEp5nR+L3cL1t3Xgn6VXGTg6yuRgkIbltUiyRFYHl9eOnFPJSBLuMgeCaJKNZkmm88RzOR7//i4+lFnNxHAAX9lpdl8gkkaQRQRdx8jpLK8pp6XCx/BwiOuXN3DdhgLrqrzKjdWmkE7maKrzkctphGfSJJNZRMlg1cpGfu1jN5fcsS83LBYLdXV11NXVYRiFzy4UCl2wv5phGPzP//k/efHFF9m1axfNzfNr3P474JoqMlBQEx85cqSkUTkX2+JCdzJFB+WhoSHWrVuHz+crmVxezE5mvudfSJB1JTF7F7B8+XKSySSBQKCQ/3L8OIIgUO5x8cmHtvP8npNMR5KYpkm518H9N65kWdP8yu7FoL2+gi8+dDN/89ybhNMZFFnEqsg0lHn59K1b0TSdrz33NgcGxtGNQpzyy0f7uXV1C//j1i1zCk0glODxH+1jJpamzOcgFo0yHVVBsHDjDWXccF0r27dqpTlOUWxXnON8/ldv5MjJKWaSGSp9Lja01fH40/vp7J2kwu/EZlVIpLKksyq3bF+G/ZRFfTSWZjqUwG5TqJnFajJtEiGngBk1sRkmogimZmDKEpW1Xj500zpcDitOReE//mMPnLLcicaihBIpEqKCFs2zrsXPLVuXsqWj8awFjSiKlJWVUVZWxooVy0mn0wUm3in1ustVYKttvqOdWz6wac61dOOmpZwYDpBTdWxWBcMwUVUNu0VhaUM5oWSarAgZTScVSVLuc855v1PxDEMnJhgdDJFM5ug9Osh49xgrNzQjW+xMTMSYmoiRjad5/0e2s27rXHptIpIEAaRT2pVMVsU0Ie+QCHkFsBdmdUaZiC2t4dBlhqazvPTWCLlknGgsSkWND6fTQT6vI5zKdZHkwntkkSWscoGwUERtUxnL1jRw9N1B3F47jVVuBFVD1rNcd/tKfvnXbsZyGXNzzoXZn13RXy0YDJaKzkL+aoZh8Gd/9mc8/fTTvP7667S2tl6V832vcE0VmWw2y969e3G5XOfVqMCFDf5nOyhv374dt9tdeqyu65elXXYhA/4rhdkKaK/XW4q+NQyDmekBblvlIi8U7DZWLm2Ys8K/UOi6wQuvdbF33yC1aQmP4sDndXLL9mXcvKoFl9XC84d7OTAwjt9px3bqy5/M5nitc5DNrfVsaj3dIjh4dITwTIr6ajczMzPYbAo1NZWMTkTZd2SY+lrfWTu4WCxG74lhDr79Dqqm0raijh1tDVRWVmK1WvnV92/kyecO0zsYJJbIYrcp3Lp9GbdfvxxV03nuleP87N2TjM0kSWsqNpvCjZtaeei2NUxFk2CVaawvIxXLoKk6To8dxWlBFaB9aRV15R5UVaOiwkUgEMdmM4iksozndNK5HHarzLGRKd4aHcPqs1Bb5ubmFS28b+0y7Ja5wtHZA+glS5bMIUaMjIwgSVJplVxWVsbmlQ2E42v5x5/sJafpWGQJv89JTZmb6VCCWDLDP/7LLkDA67XT1lLJ+25ZSUdbDUMnJnjpe2/T0zXJTDSLpunkc3kcLhuT0zlWrPCycmUNS1vKCI3PUFHmOGvm5/DY4dSiqpBNJKGKEPBLqIqIxTTRDNAlyLglHHkRhyQzFcqxZFkz2b5pBEMkHk+QzyRJJFTKyuwotkLjVVULu7LZGT2CIHDnL23E63fQdWiEVCKNw6lz+wNbufmuLe+pTb7dbqepqYmmpqZ5/dV+/OMf09LSQjgc5oknnuC1116jvb39PTvfq4VrqsjYbDba2tqora1d1MWy2J3MfA7KcLoIXOxcR5KkUmuseJwr1R67UIyOjtLb28vKlStLfm5Fy41gMEgoMMSBmfHSLsDv919wUdz1Vh8vvX4Cu12hptxNNqcRGU4xap/Gsb7w5dl3chygVGAAXDYr0XSOQ4OTbGqtRzcKZpGBcBIwCUci2Gw2vB4PCAIWRSIQTJz1/IIgcHjvOHt3D5BOZtE1g5GefsbWzlDZIOPxeKisrORX3reaeNoglc5TUeak3FfYHb+06wQv7epiKpslp+tIgkAyleOlt3sZC8a496aVSJKA3WXDO8tpOZ7Ogm5iPbXjURSZO27v4Nvf2cXMTJ6wXBjeu6wWKuo8nFTjpDQVKZpHNQwee/MQXRMB/uTencjnWNzMR4wIhUIlXUdZWRmrGyv54M2reH5fH06bBb/LzvBomGg6i6xCOqshIqCqOql0nq4TEyx1u4j0B0DVyKgGFoeMmNNR0yayKBIOJZmestPQ6EOxKuiqXhr4z0bLmkbK6/yM901T2ViGx2UhZTNRZRErhZhoTBAxMRSBlGzQWuVFlEWiusH6LUsZ7Z5Az4tUOFzky3IYisjg4AQAhiHStqyaFSvnMh5tdgs33buWjs117N93kI6VW2hpvbZEjPP5q/3sZz/ju9/9Ln19fbS3t/Pd736Xe++9l23btl0T8c9XCtdUkREEgfr6+ssawVx0UK6urqajo2POjVQQhEsiDxSPVTS5LMa4vpcFxjRN+vr6Sp5rfv9pP7DZlhuGYRCJRAgGgxw/fhxd10ur5IqKivPuIvN5jT37BrBaJMpP3YALxo4C/YMhhkbCtC6pQD1l/DkfUtk833/5MHuPjxRyYDSdcDzBssZy3C5XwYzSNMmrOpXzeFf1HJ/gZ690YbMpNC4ptPxCgQRD3Wm233AdNqdJMBhkcHAQi8VSmO3pEoZRoH7vPTBIFpO8YeCwFsLILLJEXtUYmYoyFYhT43czHo5TV1YgFKiaTjSZZduKxlKkdD6fJ52a4K47WghFFX7yTg/VXjvVFW6G1ARZQ8Mpy6iagcuiIMoiB4YmODA8ybbWhkV9rrNbou3t7SVq++TkJDXCDBsaXPQG0owHoqQzecokK1o6j12R0TWDbEYln8mDKKDPZDCDSZBEtKyK2yHg9XtQ01EyiSyeKg/BUJKGRh/RQByX30ll/dm+cp4yF3d+dCdv/ngf0yNhDMPAW+0kYmpYDZF0vtAuEwUBUwDZLlNR4SKZyqFqOrd+eAupYJJoKIHdacVV4ebQgWFOHB9H1TTqGhzUNii8886ekianoqJAkY5GoxzvOsbqtSvOMq691lBkiXo8HoLBIK+99hqTk5M8++yz3H///Tz77LNs3779vT7NK4ZrqshcKIoc84VQ1JbM56BcxKVkwhRXH6qqIsvye15gdF2ns7OTZDLJli1bzjnPms2KWbFiBfF4nGAwyMDAAJ2dnSVdQGVl5bzU7mQ6RyqVO6vd5rBbCIVTzMTSmKZJa5mPA/3jRAQRr9NWYAGphdV1/1CId0MpnDYLhqYRnEmgyzAdzaFYChky4UgSj8vGpnVnf35dh0fR8jr+WTfAiio3IwMhBnuD3Hbv2pKjQrGgdnZ2YhgGssXF2FiYSCZHztSRDBOLRS4ITvOF6OqhiRl+485N/Muz7zIRjheeQICWGj+/cvNaoNDiLdoQ3bFlCzOJDG8PTqDIElabwkw6d0o0W7guBEHAblGIprP0TIYWXWRmYza1vbqihuN7emgKnMST1xhTNabSBrIdpnIaaTWPYZpoRiEywGaT0WURu0UirenkdIMKmxtJlnG4bSRjaTKJLLIAE/0BDMPguvs34FnAoLJ+aTUf/IN7CIyE0XWDd8aD/HDPccrsVrI5jclAvFCcBZMyZ2EQHk9kaW4sw+uxU+Zz0risunS8e+p83H3f2tLrhILdSigUIhAI0NPTg81mI5vNlpiW1zpM0+Qf//Ef+cpXvsKLL77I1q1bAXj44YdL7uv/nfFzX2TmKxCLcVCefYyL9R8rqLNl9u/fX1LsXypv/mKRy+U4fPhwwRV3y5Y5tvvngyAIeL1evF4vbW1tpeHz1NQUPT09uN3uEj3a6SyYLLqcNpxOK/FEBucsllY6k8dqKRhePv3MYY69O4ypaYylokyGRZwOKxarTKPfQ3gsQY3fhablSat5ljZWMBpIoMkC0VgaVTcQrBJ1rWWMhuNUlLtw2AqvK5XNc2Q8yKCYYyY6Q53dTrnFWnBSEAUy6dPu2Ge2LmKxGE//57tEphNkFDAsApmMiprXsNotpxYL4HZaWdlcxZ//+m3s7xsnmsxQW+Zm47J6HFaFVCpV0nAV/bzKPA6WNVVwqGcCh01BEgRMs+CQoMgSdqtSSgm1zGP2eCFIzqR49l9eZqhzFEmW0HUDwRAQZQVd0NDyGqZZ8GkzC76m5FUdi6mS1Qx0wLAqhDMqNYoEgkBlQxm5nE6Z30Z9WzWrdiyjY9u5B9OSLFHbWqAiyxVOXj02QCyVpcLjJJ1VCcbTSIKAR5QZn5zBYpG5+bplSAu0Z8/8/swWSgaDQY4ePYrb7WZycpLJyck5FOLzBRVebZimyTe/+U3+8i//kueff75UYIr479wmK+KaKzLFttNiMN/gX1VVjhw5QjabPaeDchEX0y6bPeC//vrrS/ThgwcPlm5oVVVVFzXnuBgkk0kOHTqEz+dj1apVl/ycDoeD5uZmmpubS7qAQCDA4OAgVqu19Pqu29LCf71wjPBMCrfLRi6nEplJs2pFLcFQkjfe6sPltHK9v5bRXIqxRBIpY/Ch61eiZnVeHEuQz2dL8QKyLONza/jddu7b1sETrx0mls4x0zPGuz1jNFR5eeSXr8dmkfnKkz+jMxohbeYJJXWG0yna3W6W2lyYJtTO096BwvWVSZmMnkzQWOljJJEijo4pQk4zUFM5XC4LVkVic0dhl+F327ljY9uc48RiMQ4dOkRDQ8Mc2xJBEPjgbWsJzqQYD8aQTQENHYskFzJ/BJhJZXBYFLZexC5mNo7tPsHA0REal9chn5p5haZiTE8kUDUZCQEUEcMwMSkw+wwDcokcsiQg6mBgktJ0RoNJPJKIxWFl9fYGPviJG87K4lkMavxufuverTz+ymGmZxKIVonqMhc+ZFyCQm2zl5uua2PtAlkx58LMzAzHjh1j+fLlNDQ0lBYMRcPLzs7OyxrRfKkwTZPHHnuML37xizzzzDNcd91179m5vJe45orMhWC295ggCBfkoHzmMRaLM/UvFotlXr3K7DlHVVXVBQu1FotwOMzRo0dpamqitbX1sn+pZusCzjTyFAyTNcu99A0lCIWTWC0ym9Y38YF71/P4D95BQMB7KmagzeFlqd3D6NgMVdiI2zWy2Sw5Scbn851uPWoGbqeVVw72EU/nqC13Y2CSzObpnwzz+PMHqKnycnI8TH2Vl+BEjFxWRZcNTsxEMfIZVi6roWPdwjfwsaEwmXSOtuYyLCGZ/micuKmBJIAALpfCsmqZZKCfLhJnufSGw2GOHDnC0qVL59U31Fd5+fzHbmZ/1xjjwRi7x0aZSqWI53LEcznsFpmHt62htfLc+TnnQ9/BAZweR6nAAFTUeKmZjhE0TLJ5g6woYggCkiIhaAZkNCQdHC6ZbLrgLKAoIoIksXptA1t2trN2yxJcnvkTTyORFJ3Hx0mn8lRXe1i5sg6rde51va61lhUfr+TkRBhNN1haW4bdopDLa9htykVdo5FIhMOHD7N8+fKSaFEQhJIvXltbWymieTaFuDjHuVoLviJM0+S73/0uf/zHf8xPfvITbrzxxqv23Ncafu6LTLFtFQqFOHLkyAXFLxePsdgicz4F/5l6leKco7+/vzTnqKqqOitF8mIxNjZGT08PHR0dizb1vBScaeQZi8VoaAiwpGGSUDhJdXU5y5Y2YLcVsueLIVpFFE1BE8ksFmsCmyKioiAIBcuWZDqPiUlrbRmv7jtJmdfBZCJJIJVGNwqf864TQ7gHraRSeTLJgiO11WVBz2qkBZPq1dV88OHrcJxDaCnJ0qkRicCSKh/15R5m0lmmZpLYbQqf+Z07aajxloxKi2yuom/V+Pg4K1euPOd77nHauHVLYffzsL6e/UMTdE+GsMoy25Y2XHKBOfWOFnYoZ8AlCazZ0MiRA8O4qjxgkRBN6O2ZIpI1kO0WTFPG6VYoq3QgKQbRmRxtm8toXuHBYit8bqZpMtg1wYmDwySjaXSbwvHBMKl0vmDYKQi0tFbw67+2A98Zux6rIrOquXrO7y6WLl8s6itWrDjnez5fRHNxwadpWsmmqKKi4rJ8/xaCaZo8+eSTfPazn+WHP/wht9566xV7rp8HCOZie1NXCaqqLnpGomkar7zyCsuWLWNgYOCCHJSL2L9/P9XV1ecdIBZ3LxdLUS6ygQKBAPF4vJQiWVVVdcG24kVR6djYGOvWraOsbOEslqsB0zRLry8YDBKPx9l/OMbxrhnssoRsUfD6HDjcVgKhBNs2+Fi9soZg1s5TPztOIlOYn1gtMtevaWZDex1f+/6bGApMxAuqelkseITlNB0ME4suYJVkDMNEEKCy3EVON/jIHeu5f0fHOc83OpPin7/yMrmsSmVNYYam5jUmRme46c6V3PPghnlf38mTJwkGgwClz8/l9tE9NMPYZBS7TWFdRx0tDQvPAC8n9v70AK9//y0a2mtRisLSQIx8VuWBR+/m3ReO0n9oGMUuMzMzQyqlEXX6cPmdeHwOvD4HVptCMpkjmczw0C91gJAhHo/j8XiY7ElwbPcwumaCJNIbSSMoEitW1WF3WsnnNU5ORHDXuvBWOGmq8XHTxlbaGi5e3HsmFltgzoUihbgolCy+vmJb7XKEl83GU089xac+9SmeeOIJ7rvvvst23J9XXHM7mQs1kAQYHh5my5Yt+Hy+C36+8+1kzsyAuVgG2WyRXTFFMhAIcPLkSZxOZ6ngLGR3X4Su6xw/fpx4PM7WrVuvifyJM408Q4Eoe15+gUw8SwKQgMB0DItDoanZSUNNGd0Hkgz09lNp6DTUemhqr6K1uZIN7bWFlpnDQk84AghYTrWpNBME08QUwJQEZLnwWeTzOoFIksoKNysXCCmbDZ/fyV3vX8fzTx9idDBcWJWLAss6arj+lhXzPiYYDDIzM8PmzZtxOBwFavTwBD98+S2CM3lEUUKSJV7Z08MH7lzHrTuWXb43eAGsubGD0RPjDBwbQRQFDN3AYrew7b6NNK9soKqxgiO7u9j9/F7MCjdN25vwZnSCkzHsTgsWq0w6nScSTrJ2XQNbtq4uxCnkcvSdGOL1F/eRyKk4XBasgoIpC4iqRmgyRmNbFcFclikzx/hElho1z8mxEPu6RvmtD2xnQ/ul+3CFQiGOHj1KR0dHSet1MZgtUG5tbSWXy5XavkNDQ8iyPEfkeinD+GeeeYZPfepTPP74478oMKdwzRWZxaJo/w+ULGIuBucqMmcq+GdnwFwKrFZrKTxJVdWS0eX+/ftRFKVUcM7Mscjn8xw+fBhBENi6desV3fJfCo7tGyETybGhvZbJaIpoMoumakiZHM0VfnY900tiJk9ZpQdRgM7eCd4dmsRT7iykH25rZ+eGVo6/FAJA0wR0s7C7lQWxoLlAIqPpiEIhX8XQDdY2V9Nau7hd3YatLdQ2+Ok5PkE2k6e61kfHmvqz4ptN06S3t5epqSk2b96M213w22poaOCtI0HiGZGaKi+moZPLZYnF4nz/v96hpkymfWnjFZnDFeH0Orj/d++id/8AUwPTKHaFltVNNK8qzKNEi0DOlSbdVE04rBKcimPoBlldQwvGcc9ksFplVq2u54FZljWmKfJfz/YyljWRLTaSWRPDyKNpBnbTIBKM4q6yMxhLYAJWRARVh5zGdDLHf/x0P2s+U3tWuN6F4HIVmPlgtVrn+I/NJ3ItFp0LcWZ/4YUX+PjHP86//du/8dBDD13Wc/55xs9lkYnH4xw8eBCfz4eiKJf0RV4oQvlKG1wWoSgKNTU11NTUlASSgUCAY8eOYZpmiThgtVo5duwYHo/nrMiDaw09neNY7QplHjt+t41EMkUsFiOXUMhGFCLBGdx+mVQmyrAgMCMZkDcxVZ14KssPXj7Mh29bR2Olj6mZBCICdosVt91CIJRAF6DN6yWnakSzOTTDwCIp3LP57KTFc6GmzkdNnW/Bfy8G5UWjUbZs2TKnrakbBvuPjeKwW3A5C793u9148nnGp6P87J0uJkb7S3qjoojwcsPmtLL2pg7W3jS3RZjNZjlw4AAnRzNMBHJUlLuwnaJPz8TSqDmNe+5ax7KlVTSeco4u4s23T9I/EkYRBdw2BRNI5TXyuoqJhCAKTIaipDQVwQDTFAgHNCRJRDUMevuneeXlTu6+e+1FvaYiTXnVqlXU1NSc/wGXgDNFrkX6/vT0ND09PaUuQ0VFBV6vd8Hr67XXXuOjH/0o//Iv/8KHPvShK3rOP2+45orM+W4SRQfl1tZWWltb2b179zkFmefDfILOy23Rv1jMFkgWB+uBQIDu7m5yuRwOh4Py8vKLNvS8WijoMgqWIslkkmQiQWV5OYFcgkxKxe1xUlPjZ2AqQjCXRDBMBMMknchQ4/GSyBi8euAkD12/ku+/cRRFlnDbLOQ1HVMSUDQoU6ykRYlgOkMil0eRJf7h2bf55eQabjuDbnwx0HWdo0ePks1m2bJly9lBVWYhBlk849pQlILrcmtrG9dtbJxzwyqaXVZWnk4B1XWDdDaP3aogz9LNFBY55ll+Ybph0NsfYHwyit1uYc2KOjxnOCyn02kOHDhAeXk5U/sSWCwytlMzG0EQ8HsdjE9GyWPS1Hz2/OjAoWEcLitGOk8uq2K1KzgtMtm8RjZvYGKSCmTQvRKCZoJhItilU2aZAppu8vZrJ9h5QztOV+HcVFXj3XcGOXx4hFxWZfmKWnZc10ZZ2dx2b7HArF69murq08QBNafRe3iYsZPTSIpM66p6lnTUXlbG2JneccUuQygUKpmxzm6rFTU5u3fv5uGHH+bv//7v+chHPvLfXlx5objmisxCME2T/v5+BgcHWbt2bekCvNTgMkmSUFW19POlDvgvF4r0zFQqRT6fLwUwjY6OcuLECXw+X4mpdiVWyJeCVRuaGOydJhAIoetqIaI3UUhmrG+uoOvoGAODIUaTSXS3gGyCbkAup5PN5NBVlalAjhplCfduXMruE6PMpDIoksSW9gaESJ6J6TgjagrdNLEpMtUVbqKpLN9+8QDlXgfrl549JNZ1g0g4hdUq4/Eu/J6pqlq6qWzevHlegZ8kiaxeVsObBwbwuu0ld+NEKofdqrBsSeUcvdFs37jh4WEkSWIsZHKkb4ZURsPttHLTtjZu2tLGm2+f5O19A6TTeVqaK7j1puW0t1WTzuT59g/e5nj3JJpe2H2X+Rz82ge3snpF4fUWafzV1dW0t7fzxLODc5yXs3mNTF4lr+nk8/MvznJ5DYtFpqrez/RohFQihyCAYJi4RUBVcdrt5CWBtGkg6AboIFkksrqOS4Dg8DSvvbiXTTva8fvLePI/D3Bw/xCiKCDJIoODIY4dHeN//NZNlJ9yEyju4M8sMLlMnqe/uYvuA0OFDB/D5J0Xj7H97jXc+sErZ4o5u8tQXPQVXTG+853v8NOf/pRNmzbxve99jy9/+ct8/OMf/0WBmQc/F0VmPgflIi5HBHM2m71sA/7LhWJRHR0dZePGjSUGWWtra8lSvJhH7nK5StTioiL/vcS6bUt4e/cRhvrCOBxOJkei2OwK192+kpb2ajqPjjI9HcPqVpAEMDGRpIIyXtclLLbC7yV0mpQUv7yqDN3ioKm2mpWtjeTyOt946m2mjg1Q5nLgdlmRRBEXViYjCV492H9WkTlycIRXX+wkFEggySIrVtZxz/vXleKAi8jlchw8eBCbzcbatWsX3DFqmk69x4GaVjl+YgKHXcFiU1AUiZu3tdF8hhj0TN+4p148yAtvdaLrOhZZIJFI8f3/2s8rb3STjmRRFAmLReLw0VFODgT5zY9dT3f/NIc7xyjzO7DbLOiGQSCY5PEf7uMLf3A3hp7j+ZfeIp6xMBlNEs+MsLytil1v9aFqOhPhBOF4GlXTMQ1488ggWzY2U1c1N3K8o72G13b30lDnw2pXSETTpDN5LJpBXTZDrc+Hp8LNcCrLW+E4mlUijYnFMHFbLKx1e0nkY9jtNkZGRnjllf289WYAn9+B1+NClmUMw2B8fIa33uzj/Q9sYHp6ms7OTtasWUNV1VzyxtG3+uh6d4CqhjJsjsIcMhZO8s5LnbStbaR5+eWd2cyH2ZqcZcuWUVNTQzAY5JlnniGbzfK3f/u3nDx5kgcffJCdO3de8fP5ecI1V2TOvEFmMhkOHTqEJElzHJSLuBw7GV3Xr8iA/2Ixm0G2ZcsWXK65N8LZluKqqpYKTlGRXyw45+ohXynk83mOdx3juruWcMvdm5kcnUGWJdpW1tG6vLA6XbK8hrGpGFJaR7IIqBYRqywhmDATz+D0WLn3+pVs3byhNJgNBAIEJ0fYPTZIeXk5glTIUPGdIRq0yBKTkbmOzSeOT/DE43vJ5VQcTivRZJZXd3UzOBzij/7kfVhPtZLS6XRp1rdy5coFWzGmafKTJ/ez92d9lOkCsiCSSOSwGAIf+MB67rh5xTnf95yq8/bhMRx2OxVlzoLNfj5PIBTnxMA0jV4bTqcbq8WC12NnfDLKi692MRlJYLMp2E9Z60iiSFWFi+lggr0Hejna2cuJgTS6ISAIU4iiQGtTBVUVbnqHg8RzeUQERAGcbhtToST/8oM9fOF37kSZ1aq78YZ2unqmGJuYwWG3YMgSkt1CVaWTUO8YmiTQpOk0OawEJwTGciqGaFJf56LaZmdmZAa3z8H2netxuW0Epw8iCmEk0SQcDiNJElarFVkW6Do+zrbtdXR2drJ27dp5AwpP7B9EscilAgPgLXcR7Z1isGviqhSZMxGNRvne977H//P//D/81m/9Fq+++irPPPMM3/72t39RZM7ANVdkZuNcDspFnM8k83wQRbGkzXmvdy9wmkEGLIpBpijKvIr8IgutOAO4VGrmYlCMVHC5XKzetLrwfDvO/rtlHTX09EzicdupwWDUyJLUNfKajg2Rnetb+aVbVgNzB7OzjTzJp8nlcsQScWwWK4pSUJLnNZ2GCs+c59uzu5dMJo/FZaF/aoa8poMJB7vG+ft/fJXP/N4dZLOFAlNTU3NeMe/QQJD9ewdwe+zUeQozB0M3GBuNkA2lFvTkKiIQShBP5vC4CnMeWZaQZTsup85MTEWxWVHzeVLJJJIkoUgCA4NBBKs0pxgU3h8BTdfZd+AEgxN5bDYbPo+9QEXOa5wcCnLbznZGI3Gyuo7dquBx2/F77Wi6zuhklON9U6zvOE05rq7y8NufvJGf7emju3caSZGYzmUYiCeJWyVG0llOahrrXHaW13nJHp0km9fJ5OKcyIQKuiPD5Lvf2s0vf3QHNrsVxaKUGKC5XJ5cLkcqmSIsqyXHioUYooZuIMyTooppYugX7jt4qTh27Bjvf//7+aM/+iP+8A//EEEQePDBB3nwwQev+rn8POCaLTJFB+X29naampoW/NJfyk7GNE2cTifRaJS9e/e+5yaXqVSKQ4cOXTSDbLYivxgNGwwG6e7uRlXVOVb+l9tIMB6PlxYEy5efm+W1bHktTtcJJEWk1ueixnQTSmYIRpK879a1fOD9G+d93Gwjz49W1tIdeIFANImqZTD0JFnNxG61cH1HfclqCGB8NIJskRiZKkQ6O07tXFLpPEe7Jnjp1SM4rDGWLFnCkiVLzvvZD/UHyeVUKqtOt21FScTpstJ1dIwHP7TlnI93Oqwoskg+r2GdZQlTFJY6bDY8ThuGYZLP5wmG4ohaHpfDynQoj0URsFotCIJALJZGV/M43TXoRqBUYKAgblUUia7eKRx2Kx63DZfjNIHBIsrohkk0kQEgn1WJTMewOSxUV3n44IObAPjhK0c59uoxyrx2nIJAeDJKLqdySDNYn9Opc0tkPWVMh9NUVnupqvUgShK9xyf40ePvcOdD67HbLczMpPH7HdhsVkBEkrNUVVoJDWkMHDnGHscxlq9toK6hhsrKypIGbNm6JgaOT6CpGvKpDJ90IotiUWhqv7LsszPR1dXFfffdx6OPPsqf/MmfvOeL0p8HXJNFpru7m/Hx8fM6KMPFF5nicN/tdnPTTTeVtCoHDx5EluUFtSpXCpFIhCNHjtDQ0EBbW9slX7xnRsMWI5mHhoY4fvz4ea38LwRFVXZLS8uibtJLWivYefNydr/ezehwGBAQJYHta5dwzx2rF/WcFV4nf/ihG/n2CwcYDUYxRZMaj8LO9goSU4O8FSkEspmyA80uEphKo+o6DqtSMmGVRAFBMHltVyd/8MitNDQszrBSkkTmcXPB0E3kRWTLV/idrG6vZe/hYRRFwmZVyKsaOVXHbbeSiGdxnXKDBhEEmbvuWEdtjYNvP/Euo+MRrBYBwywIUTeva6SpoYojJwJnvfeSKGAaJn6vnelQYk6RyeRUFFmkqszFwVePs+fZQ8RCSRRFYum6Zm771R14y128dXgQq0XCZlGwlisIokg8nCCh6eT8Du55aCuvvtFPk9eJbxZbrLzSzVDfNIJucvsdK3nlpS7GxmaAQkvYYdFJDOU5cnzi1CNMIqM6W+8qzCNtNhsVFRU0ra5kSUctQycmsFgVdMMA02TdzuW0rLp00edi0dPTw3333cdv/uZv8sUvfvEXBWaRuOaKTDweJxwOs3379kWp2S908L/QgH+2yeWZWpViwblSLaeJiYlS7s1ib3QXgtmK56VLl55l5V9MkCwSBy7m3GcncC7mfO65fx3tK2rp6ZpAVXWaWypYtbbhgvLZlzdW8pefvIvhQBRdN2iq9mGRC4uO8clpvvPMAY4NBMlkNLKigagYWDERDZNcTkOWBURRR1E8p4SxOj29U0QiKcr8TpYvr0GZp2gsW1GL02UjEk5RVl4gWuRzGpmMyk2bzjbMnA+/cv9G4sksJ4dDRKIZRFFgaXMFN29eyosvH2dyqpBfI8si61Y3cOetK3E4LHi8Hl7Z3U133wSGnmPT6mramkQS6SCGoZFIZnC7bICAbhhksio7t7VRXe3hP/7rAMFIErfTSl7VSaRyrG6vRZuO8dy3dyMI4K1wk8+pHN7VRXImxcN/fD+ZnFZqAQqA2+/E5XMyGYyx5c51LFvRyPMv9+Byz52P2ewKM+EkyWSO2+9YxbJlNZzomiAUjpBNR5jsTJHPazS0VpSsfSZHwkRG4L6P3Fxq/Q5MnaRuox1nVSMzkxm8Xjcrt7SycmvrWRTvK4WTJ09y33338ZGPfIT//b//9y8KzAXgmisyXq+X66677oIMLnO53KL+drbAEuYf8J+pVYlGoyWtSrHlVFVVtaj0yMWcz8DAACMjI6xfv/68u7bLhTOt/IsWNwMDA9jt9lLBOVfb0DRNhoaGGBoauqhzFwSBtvZq2tqrz//H54AoCrTUzGVySZLEC++O0Dkcw+1yUe4VGJ+KksQklM/hzhVSPF0eCd1UWL2qkVAowbcfe4vh4UL4liiKNDeX84mPX09FhXvO8Wvrfdx292pefvYoY8ORklnk8pW1XH/z8kWdt9/j4HO/eSs9AwEC4QR+r4OVy2pQZIn1qxvo7Jognc7TUO+jva26dDPtWFaDy6bS3aSzYcM6KioqyOfzTE8H6BtOcLwvRDAkYLEo6IZAY52fW29op7LMharpvPRWL7FEBkWWuGFTKx+6Zx1Pf+0FDM2grq3A6rI5rVjtFoZOjDPcNUZHSxVvHRzAzKgIAtgcVgxJQLHItDVX4itz4vLYScWz2GcN55OJws8VVQVN0JKWCizWPN3dIazGUgbe3k9t42khqGKRcfucdB0c5u5f3jzHjLU4iwsGg6RSKVRbnInJcSoqKq64tdLQ0BD33XcfDz30EF/+8pcva2fjS1/6Ej/+8Y/p7u7Gbrdz3XXX8dd//dcsX376Ospms/zhH/4hP/jBD8jlctx11118/etfn0PzPhOmafLFL36Rb33rW0SjUa6//nq+8Y1vsGzZlbc7OhPXnEGmaRb60IvFwMAA8Xic9evXn/e4RYGlIAgXfKGYpkkikSAQCBAIBEin05SXl1+0q7JhGBw/fpxoNMqGDRvOYpC9F9A0rdQ2DIVCC2bjmKZJd3c3gUCAjRs3zqGUXwuIxNJ87u+eAUw8zkIr0DRgcCRMJpen0hSxywK5nIHXa+MjH9rAvnen6DoxRWWlG6tVJpfTCAYTrFnTwO/+zi1nFVvTNBkeDNHdOU4+p1HfVMbqdY1n2dJcbgwNDTE4OMiGDRvOGpTnVZ033znJW/tOEk+kqPRLLF/ipqW5pjSLM0yBYCSJ02HB73Gg5jX+/tF/xwR8lXM/x9GeSe79zZvpHwnxxDs95EwTySiYZSp2Czdtb+f3Hr4BURTY9WInz/34IIoi43RZyWZU0qks229s54MfLeSojI+P09PTw/r16+k9OMUPvr6Lyho3Tp8T5dQONh5No2sGf/ClD+B0z9/GLVr6B4NBIpFIaWFUWVmJ1+u9rEVgdHSUu+++mzvvvJNvfOMbl711fvfdd/Mrv/IrbNmyBU3T+NM//VM6Ozvp6uoqFc9Pf/rTPPvsszz22GN4vV4eeeQRRFHkrbfeWvC4f/3Xf82XvvQlvvOd79DS0sKf/dmfcezYMbq6ui65PX6huOaKDLDonQkUzDFDoRCbNm1a8G+uhII/lUoVaLWnXIcvRByZz+c5cuQIhmGwfv36s9Xk1wDmUIeDwVI2TkVFBVNTU2QyGTZs2HDNCUEB+kZCfOHrLyCaIApgsyo4XVY0TWdkIkKFIFHhcrGsrZoNa6tIxGb43vc7sVhkPB4HVqsFRVZIpXNksxqf/6O7qTuH/czVQHHXW9RNeTyeRT0mkUiUdgDJZBKfz1e6ITscDkzT5P984Ukmh0LUtpymD6s5jemRENe/fyN7nz2M6rAQsEuENR1RN/ClVX7n03ew7vrCilvXDfa83s2eXT0k41msNoWN21u47d61WK0KY2Nj9Pb2sn79eia6gjzzf3bR1R1CwMThtFLTWkV5rY+xwRAdG5r46GduX9T3tLgwKhYdoGTpf6lJmZOTk9x9993s3LmTb33rW1fFZSMYDFJVVcUbb7zBjTfeSCwWo7Kyku9973t88IMfBAoz646ODt5++222b99+1jFM06Suro4//MM/5HOf+xxQCNmrrq7mscce41d+5Veu+OuYjWuuXQYXlo65GBflK2ER43Q6aWlpoaWlhWw2W2o59fb24na754gjZyOdTnPo0KECzXf16mvWHmY+6vDk5CRdXV0YhkF5eTmRSOSyZeNcTpzoHCceSxdsWQrBMdjtFiw2cNpl/vz37qOxtrzUgurvD2C3D+J0KhiGRiwWRwBEUSabNUgkMoDvPXs9pmnS19fH5OQkmzdvXvSuVxAEPB4PHo+HpUuXzhHx9vX14XA4qKysZPn2JYz3BwhPRvFWuFFzKsHRCI3La8ml8+QzKk3NFcwOwxgNTzJwaKRUZCRJZOftK9m2s514NI3TbSu1zkZHR+nr62PDhg1kZ1Se/ear5DN5Gpt9TE0lSabzDHaNMxNOUd1Qxk3vW7Po76ksy6V56mxVfjEp0+/3z0nKXCymp6e599572bp161UrMFAoBkBJfH3gwAFUVeX2228v/c2KFStoampasMgMDg4yNTU15zFer5dt27bx9ttv/6LIXCgWGvwXB/xXwyLGZrOVwpJmxxUXZxzFHU7RD6uuro5ly5b93AwPBUHAYrEQDoepqKigpaWFcDjM+Pg4J06cwOv1ll7jhWbjXG5MTkZ5/bVuPKJCTFARBBERSKSyyJrI3devYEnDXMFfdbUX16n46LIyN6YJmqoSDMURBIOTJ4+RyUyXZnFXs6gWW5OhUOgsk84LxZki3uJgXXUkqV3jZbwzTLwvgd1ho219E3f/xk28/cxBhHk6RKIkksuc3da2WGUqqk/vskZHRzl58iQbN27E5/Px6nNvkZhJ0dRRcGRwOi2EQmlmQkmqq5z8+mdup6Hl4vJozlTlL1RUKyoq8Pl8C37/QqEQ999/P2vXruU73/nOVSswhmHwmc98huuvv57Vqwssy6mpKSwWy1mt0erqaqampuY9TvH3Z85szvWYK4mf+yIz307mzAH/1RRZzo4rnj3j2L9/P4ZhlFZWP0+YTwPj8Xjm7OKKX2Sn01naxV3uMKjFoOvEJOl0jvZqP6OJJKFMlryugwB+xconHth21mNcLis339TOT585SiCQwG5XyGRURFHh3vet4cYbWwkEAoyOjtLV1XXViupsF+jNmzdf1tbkme7f6zdEGe4fZbB7BFPQaV3ZjCpmqF5SjmmCmlNLwWiaqqGrOkvOQx8eGRmhv79/zvwoMZNEkk9/HysrnFRWOJnsN1nS5LvoAjMfZhfV4nexGB0Op80uZ0ejRyIR7r//ftra2nj88cevaFTDmfjd3/1dOjs7efPNN6/ac14NXJNF5kLbZbMV/2dmwFzNXO8zIcsyVVVVpFIpBEEoBSYdPXoUoDRULy8vf0/P81wo5nq0trbS3Nx8VtGYvYubbQK5b98+FEUp3Yyvlt5IVQsLDkkQaHI5cWoahlVBMGQ8dhvOWRHAum4wPhUFQeC221Zit1vY/bNeYrEMlZVubtzZzs6dy5AkEZfLRWtra6moDgxP8OyuQzjsdjauaqK5oe6yingNw+DYsWOkUqn5XaAvI0RRxOn0kM45CCZdqJqKMJgmnRkhrsdwVikMdI3i9jmRZJlsMsuSVQ2sub59wWPOV2AAKhvK0VUDQzcQT7UrTcMkn1WpaTnbUuZyYaG2Wn9/P3v37uVv//Zvuemmm3jllVdoaGjgiSeeuKo71kceeYRnnnmG3bt3z5Ex1NTUkM/niUajc97H6enpBWMQir+fnp6eIyuYnp4+L0HqSuCaLDIXgtk7mffKon8hFFeiMzMzbNmypcTC6ujouKLU6MuFC9XAzDaBnJ2xPltvVFw5XqkWxNLWSiyKTCKRRdUyOKwW7A4nU5NRVm2uQxAE0qkcP3nuCK/tPUkyk8dmk2ms8/PwA5v40z+5l2xWxWZT5mgwkoks7+45SU/XBMOJJMOJJKppYpozvHZ4mus6fKxsPj1ULysru+iiqus6R44cIZ/Ps3nz5it+s8vlNP71m2/QeWyM4tqu86jJ6jUNfPQ3dtLWtoy3nz1I74FhDFNj1a2tXH/fZuwLsL+Gh4cZGBhg48aNeL1zzTdX37CcQ692Mto9ib+m4K03MxWlot7Pmp3zp5JebpzZVpuenuamm27iJz/5CX19feRyOf78z/+c+++/n23btl3Rdplpmjz66KM89dRT7Nq1i5aWljn/vmnTJhRF4dVXX+WXfumXgIIodGRkhB075vFsAlpaWqipqeHVV18tFZV4PM4777zDpz/96Sv2WhbCNckuK3qJLQbJZJI9e/Zwxx13lHY077XBJRRew5EjR9A0jQ0bNiy4Ej2TGp3JZCgrK7toavTlwGwNzNq1ay9ZvzM7GycQCJDL5UqtisrKystqcWOaJt/619d5a08fsqJgt9vI5zSqqt387m8XqMh/9/cv0zkSQjcNJEFAkiTsToXyMhd/+uid1FV7yWRVDh0fYyaWxu2wsPelEwwPhEiLBhNiIRrC57LjL3cxk8xgt8h89le3YxEK0doXa+OjaRqHDx/GNE3Wr19/2e1/5sNbb/bx+L/vwV/mAFFgOppiJplF0wy2b27htz6+k3K/c87CIRgMYhjGnJaToiglivV8BaaIycEAu/9zL0PHxzCBxvZabvrQdhrar77RJRTuIR/4wAdQFIXvfe97vPnmm/z0pz/ltddeKwWXXSn8zu/8Dt/73vf4yU9+Mkcb4/V6S+3RT3/60zz33HM89thjeDweHn30UQD27NlT+vsVK1bwpS99qZTI+dd//df81V/91RwK89GjR39BYS5C07RFq/gzmQxvvPEGt912G6ZpXhM7mCKDzOl0smbNmgtaCRWp0YFAgEQiUaJGV1VVXZWL40prYEzTnPMak8kkfr+/1Dq8HBY3Bw8eIplyMjScJJXKsby9hht3tlNV5eYb//Aau/b3k8EoOD8LkM/rBXsXl5X337GaLeub+cfv7GY6WHBzzmZUtLTKqho/Q7k04XweCwKGYVJd48VmVwhEknzgtrU8fM+GOdThQCBAKpUqvcZzUdxVVS05jq9fv/6KrKBN0+TAoRHe3ttPJJKiubmc0HScvp5pKqpc9E1GyOY1JEFA1QwUi8zqjlo+/8gdp5wETh/nTIGkzWYjl8st6KZ85nkkIklMEzzlV392V0Q6neaDH/wguq7z/PPPz2Huzfa/u1JY6Pjf/va3+Y3f+A3gtBjz+9///hwx5ux2mSAIcx5TFGN+85vfJBqNcsMNN/D1r3+d9vaFW5xXCj/XRcY0TVRVZffu3TgcDqqqqqiurn5PGU7RaJTDhw9TW1t7Xjff8yGbzZZuxtFo9JzU6MsBXdc5duwY6XT6qmlgZjOAZmZmLikbp5hJslB7LxRM8L//v58ykUqT0XSspxyNTdNE03TsLhtb1jcTiqYYGg1TXuZEkSSGh0Nk8hqVThspySCtaVgliXxOw1fmxF/mZCqc4Laty/j0h65b1GssFtUiOSKfz3Pw4EGsVus5c2wuFc8+f5T/euYomqYjKxJqXiebVbGIAjaPlYlIEuupwXw+r+F02pAsEh/5pc3cdcvKBY/b09PD2NgYLpeLRCJRii2urKx8zwxnz4dsNsuHP/xhkskkL7zwwoI7r1/g0nBtNP8vAsX5iyAI7Nixg3A4zPT0NP39/TidTqqrq696iNfU1BRdXV0sW7aMxsbG8z/gPLDZbCV2zHz2L8WbcTHK91JQjBgQBIEtW7ZclTYNzGUAFenfF5ONU9RinGsVnVd1DN3EKktkVH3OStUwTAzTJJdV6e6dQld1MokcLpcVwQQJiGVVXG4LSbQ5xJSCiwTUlLlIJXM4nJY553omdbhIcR8eHkZRFMrKyohEIng8HtasWXPFCBKRSIoXX+5ClASqyws0Y9M0GRmJkMzkyYkmAoVVsWGYgIDbbSWT1+gbDHLXLfMft7+/n8nJSbZu3Yrb7Z5DADl48CCSJJVmjn6//5rQhuVyOX7t136NmZkZXn755V8UmCuIn8udTJGirOv6We2x2V/iUCiEzWYrFZzLcTNe6HyKveg1a9act1VwqSjSMaenpwmFQiUWV9E1+kJf47UoEC1m4xQ/x2I2TtGodLbFzeDgIMPDw/NarcyGpul86f97hqGxMGFdwzBNFElE03R0E6oq3VjyBsPRBJZT11XBx6xgNilKIq3lHvrTSfK6gQj4y5zkNR3FFGjMSgiqQX1zObfft5aV68+90NB1vWRSmstpCIjU1Rd247NptZcL7+4b5J//dTdVle45kcypVI7p6TgqBlldRxZFBARcLiu1dT6mgwluvn4Zn3j47EFzMb11IZFo0TmiuJNTVbWkyL/amqMiVFXlox/9KMPDw7z66qtXzTPw/1Zck0VG1/UFg8guhEGm63qp4ASDwdLNuLq6+rKlRhqGwYkTJwiHw2zYsOGq+3gZhlHi/wcCAYASaWAx1OgLyYF5r1DMxil+jrOH6sUb2GLnRwf2DfL4v79NOJkmruuopwgm1RVuNrbXcmjvICGxsMBRJPGUl56OKQqIpkmDZCWDQUTWER0KLrcNMa2hTGfw2W0oikQilsHhsvKJ37+NFasX1pKkUilef+1teo8nmBhNo2k6VTUOOtZ6cXuFOQSQy0FhPnBomK//yy4qyl1zmXPJHLpusH59Iy/u7kYSRXw+O263jUQqh64Z/N7/uJm1K0+/lmI8+Pj4OJs2bVqUC4FpmiSTyVLBSSQSeL3eUlvtShtdQmGB9olPfILu7m5ef/31y74g3L17N1/+8pc5cOAAk5OTPPXUU3PCzBb6fv3N3/wNf/RHfzTvv/35n/85f/EXfzHnd8uXL6e7u/uynfeVxM9Vu6wosFwsRVmSpBI3vsiMCQQCpbZQseBcrIZDVQupfqqqsnXr1qvO2oCCxqH4JV2IGr3Qyvh8GphrBbOzcZYvX04ikWB6epru7m40TcPn8xGLxbBYLOe9GW/a0oLdYWX3rh6GR8JYnApr1jRw7z1r+Je/fw2bIlNpszCdTJPTdCRRQMfEqsjcvr0NlyFiscqsXNtAVZOfwZ5pnvjn3XgrPCVzTKfbytRYlNef71ywyCQSCfa89S5vvjZFJJjD5bYiSRJDAzHiUY3f/N2dyBaVyclJuru7S/O44s34Yj6rjuW1lPmcRCIpKipcpZ1aIpll0/omPvmxG3B4bOza00cmp5HJJbHZZN53+yrWnFLow8UVGJgbOVHUHBXbasX8mOK1fDE78vNB13V++7d/m+PHj1+RAgOFhcO6dev4xCc+wQc+8IGz/n1ycnLOz88//zyf/OQnS/TkhbBq1SpeeeWV0s/XisxhMfi5ONOFMmAuBEVH4crKyjnmj2dmxixWGJnJZDh06BB2u53NmzdfEx+6IAj4/X78fj/t7e0lanR/fz+dnZ1zVsahUOiCc2CuBQiCgNPpJJlMYrPZ6OjoIBaLlW7GHo9nzs14PqxcVcfKVXVn/d7lsqLrBg0eNxZJJJDKoBkmCgLbOhr4nd+8+azrrnPPILpuzHFfFgQBh8vKSH8QXTfOyjyJxWIcPHiQRFQhGs5TVeNBlgt/43JbmZqIceCdUT7869tZsmRJaR4XDAYZGBjAYrGQjAiM9yfQVGhdXsPWncvwlZ/7Zu9wWPjwL2/mP767l+npePFkqav18UsPbUIUBX71A5vZsbmFrp4pBBFWr6ijuaGsdAzTNDl58iQTExOsX7fhkkg2NpuNhoYGGhoa0DStRI8+lyL/YqHrOo8++ij79u1j165dCwoZLxX33HMP99xzz4L/fubz/uQnP+GWW26htbX1nMeVZfmynvPVYM4V8d7fGefB7Bd/JRT8Z5o/zl79a5o2Rxg533wiFotx+PBhqquraW9vvybV+rPNEdva2kq04bGxMbq6uhAEgYaGBvx+//kPdg2hSPMVBIHNmzejKIXs+DOzcfr7++f4xi2G4bRpawuHD46QiGep8tipdNiYmUmDYvLQvevnfbzTXdg5FRXspmkSSWaZSKSQrTLfffJdbtm5nMb6wvs8MzPDgYOHcLirCIanMQyjVGCg6BMnMdAfKP3OYrFQX19PfX09uq7zw+/sZtdzx8nl8oiCwIE9vbz5Sie/8yf3UlNfxrmwedMS6up87D8wTCyeoa7Wy5ZNS/B47KXnb22uoLX5bHuXolHn0T1dRE/meOMb/47NaWHzHWu4/v2bsDoufr5SdMco5sfMVuQfO3bskpJcDcPgD/7gD9i9ezevv/469fVXL03zXJienubZZ5/lO9/5znn/tq+vj7q6Omw2Gzt27OBLX/oSTU1NF/W8uq6X7muZTOaKs0ivyZmMYRioqnrOAf+VQJH7X6QNZ7PZUsGprKxElmWmp6c5fvw4bW1tF/0hv1cwDIPu7m6CwSC1tbXE4/GrQo2+XMhms6Xd40L6I8Mw6D8ywskjw6RSKTy1NmTf6ZtYZWXlnGyc2TBNk6efPMAbr50gm1URALvDwt33reWOe+Z3Bo5H03z5z35CNJzC4lAYnUmQwsSkEIzmdNnwee08+qmbqSxXePrZPRzpTRNL5kmn8qhplbYqH27b6Rv09GSMtRua+PRnbp/zXJqmM9Qf5J+/9BySKODxO9FUlUwmS2AyTttqP+/7lfWlm/HlHKqbpklvby/H3j7B0Z8OkoplcfsdqDmNXDrPhltX8vAf339FFlypVKq0k4vFYvNSwBeCYRh8/vOf55lnnmHXrl3n3TFcTgiCcNZMZjb+5m/+hr/6q79iYmLinIXz+eefJ5lMsnz5ciYnJ/mLv/gLxsfH6ezsvOAZ8OwC87nPfY6amho+9alPLSo64mJxzRaZfD7/nlrEFEWD09PTJUGd3W4nk8mwcuVK6urObrdcyyg6QJ+ZAzN79R8Oh0t6oyvJxrsYpNNpDhw4QFlZGR0dHfPezHRN5+mvv8L+F4+SzxVU+Va7he33bWDHB9aWSCCGYcyxuJndijFNk/HRGXq7JxEEgY7VddTU+s55bl1Hxvg/f/8qw+MR0nYJRAFJEBBFAatVRrErNNV7KXNp7DkWRTcMnA4rikUiHE4hAiury7ApMvFYBjWv8xu/dSObtxVuiNmsyk+fO8pbe04SCSdJxzIsqfJQ5jx9YwqHk6iYfOATG7FZ8iSTl2+oXiwwgUCAnhcnOLF3gIZl1aVrIx3PkIyl+c2//DCtay6dun8uzKa5h8NhFEUpvcYzFw+GYfCFL3yBJ598kl27dl31VMjzFZkVK1Zwxx138A//8A8XdNxoNEpzczNf/epX+eQnP7nox81ukd19990Eg0G+9KUvsW3bthKF+0q00a7Jdtnu3buRJIm1a9ciy/J7cqMTBAGXy4XL5aKlpYXOzk5CoRAOh4Ouri4mJydLN+NrMXRsNs6lgZnditE0rXQj3r9//yVToy8Xigy42trac0YkHHuzl3eeO4y7zEWVtzAriEeS7Pmvgyxbv4SObR2lbJyFZlUWi4WGpjIams7ddpqNlesaqF9awUQshSQWaNGSJBaMH/M6FrtMT980GJDFRAaS8SyyIlHmcxCJphkJxPCIMg6nlTvvXcOmrQUPK8Mw+NrfvszR4+NYLTICkDdMBsNJQKDMaSWYyjKcyqJj8p3vH6Wu1seHHlyPTI6piQAnT54s2dyfL1b7TJimSU9PD8FgkHVr1vH6Px3B7XfMebzDYycyHWOif/qKF5nZLueGYZTmOMePH0fTNLxeL/v27eOhhx7in//5n/nBD37A66+//p7EDp8LP/vZz+jp6eGJJ5644Mf6fD7a29s5efLkBT2u+Jn98R//MdPT07z66qul3JpAIICmadTV1V32QnNNFpnXX3+dr33ta/j9fh544AEeeughtmzZ8p7MPlRV5dixY+RyOa677jpsNltJwV3UOBSt36uqqq65pMgL0cAUh4s1NTVz2HhHjhxZUKdypRGJRDhy5AgtLS0sWbLknH/b9XYfhm7g8p4eRnvKXMTDCU68e5KObUsRBAGv14vX62XZsmWlWdWZ2TgX8lmapsnE2Awut410JlvSoAiigKmaZFI5VNVEsojICAVqNKCpOppm4HLbqCl34zFEIuEU+94ZRLHKbN3Wyjf/7hXeOT4OpkleFLHaZCySiKrpTMXTiCIMRZJohoHXY8Nht3Dy5DT/6y+fo16QcFoVNly/lGW3N5JIFQgHsxmJZWVlC14Ts7NsNm/ejNVqxea0EAsl5vydoRdo4Dbn1V1siaJYSmtdsWIFiUSCQ4cO8Y1vfIPPf/7zWCwWHnnkkWsuVA/g//yf/8OmTZtYt27dBT82mUzS39/Pr//6r1/Ucw8MDHDbbbdRVlbG66+/zuuvv86//du/oWkaX//61+dlxV0Krsl2GRRuji+99BI/+tGPeOaZZ3A6nbz//e/nwQcfZMeOHVdFMJjJZDh8+HDJ6mM+hksuVzBEnJ6eLlmGzHYbeC9R3AHU1NRcksXNmTqV2eSIKyEaLCIQCNDZ2cny5csXNax97Is/omvvSWpbq+b8fvzkFFvuXMOH/+i+cz5+djZOJBK5oGyc//2FpxkdCRHUNTTdQJFEDMMkr2pIFhlRN3G6rUSzOZRZQk8Ai0PBqoI1byJaJGLZPKph4JAl9LRKVgCLLGKYJqpuYFpEdMNE1E1siGQxsVtkqut95NIqwekYGlBrs+A1C67T63cs5Tf/592YpnmW5qi8vLxEdCnuck3T5MSJE0xPBvAplQiGSMOyag6/0c3z//YG5bVeHG47hm4wPRLGW+Hmkb/7dTxli6MzXymYpslXv/pV/vVf/5WPfOQjHDx4kNdee41ly5axd+/eK65jSyaTpR3Ghg0b+OpXv8ott9xCWVlZaYYbj8epra3lK1/5Cr/927991jFuu+02HnroIR555BGgMDu5//77aW5uZmJigi9+8YscPnyYrq6uRXnEnXndfu5zn+PVV1+lra2Nvr4+1q9fz4033si+fft44YUXOHLkyGWd0VyTOxkAh8PBgw8+yIMPPkg2m+WVV17hxz/+MQ8//DCKonD//ffz4IMPcsMNN1wRC5Qig6yqqorly5cvuHK3Wq0lGqaqqqWCU7R+KRacqx3gdTk1MGfqVIrGiMV20+yb1OVaNRZjBtasWUNVVdX5HwAsXdfEsTd70PIasqVwaRdnM0tWn7+Ns9hsHK/Xi37KPLL4vl5/UztP/EcYv6IQNvLkVB3dKNCXvW4bQkLFa7cRz+XRDBNJPMWcBCTVQMyB1WtjIppCNwqEl2xWRxBMBB0Ew0S3ieSLTDRZwJAFcqKIU5SorvYiSiKBmSgCIIkCyCI+jwOLVebEwREGe6ZoXVE757NMJpMli5vjx4/j8xXiCmKxGH2HBul5dZzwRAxDN3B67Gy+cw3rb1pB1zv9RKZiIAiUVXt56JE7r4kC8w//8A989atf5aWXXmLLli1A4ab+1ltvXRWh9P79+7nlltP+O5/97GcB+NjHPsZjjz0GwA9+8ANM0+Thhx+e9xj9/f2EQqHSz2NjYzz88MOEw2EqKyu54YYb2Lt373kLzOwhfyQSQZZlXC4Xn/70p7HZbOzZs4f/9b/+Fxs2bKC+vh7DMBgbG7vsO79rdiezEFRVZdeuXfzwhz/k6aefRtd17r//fh544AFuvvnmy/IGFVfQS5cupamp6aJu0LPnG6FQCIvFUio4V9owcHx8nO7ublatWnXF9ABFFBXc09PTJUfl4s34YsWpRYuedevWlXrGi0Eqlubb/+8PGegcw2q3FFpMWZX2TS189P99CLvr4s6n2DqcnJxi9zPH6D8SRNcEGpZUcNcHt7JuRxu6bvDjH7zLW2/0EppJksXA5rByw852btjexjf//lUMw0S3ikwl06h6QVTs89hxaAJWRKYyGfKajoSAqmoU7MMEBExMAwzbKQLMKbdxp8tKNqeimAJ1FR5EUWR8qHBz0gRodNupddpOkRki3PXhrdz+/nW4Fngfim3gwcFB4pEkb3zrEJm4Sm1zBVabjXgkSSaZ5aFH76JmSQUT/dPYnDaWb265JgrMN7/5Tf7iL/6C559/fsGslf9bUCRMQWEGc+DAAYaHh9m8eTO///u/z7Zt28jn81gsFgzDYHx8nDvvvJP3ve99fOUrX7ms5/JzV2RmQ9M03nzzTZ588kmefvpp0uk073vf+3jggQe4/fbbL/gmVzALLCT6rV69etEr6PNhtg9XMBhEkqQ5bgOXq+DM9vG60Bv05UDxJlV0jfZ4PKU5zmJah0Wh3/j4OBs3bryoLXs8kuSd5w5z/O2TCAKsuWE52963Hqfn0mZlpmny/X96jT0vdSFKIIjmqeG9wJ0Pr2PHbaupqKhg/75OerrGWLVqBes2tWA9FVn8/E+P8MzTh8jlCuaaeUwqq9x89g/v4u/+5kXCsTShXA5JFNDyOoZuUPxiCqKAIYAhCxSvFKfTittlJRhMYOgmigZWi4SW19EBqyzRbLcQn8kQUTXygoDTbcNf7uTGne380i9tIjgd5/jhUXTdoL2jliVtlXR1dRGLxVCnJX70tRfwVDlRNfUUU85GPJBiSUc9v/9PH7+k9/NywjRNHnvsMf7kT/6EZ555hhtvvPG9PqVrBo8++ig//vGP+da3voWmaXzhC18gnU6zb98+/H4/ExMTPP/883zta1+jo6ODJ5988rKfw891kZkNXdd5++23+dGPfsRTTz1FJBLh7rvv5sEHH+SOO+44703OMAx6enoIBAKsX7/+irmyFhkxRS1OcaBeXV29oH5jscctDmnfCw+1M3Gh1OiiB1wkEmHjxo3v+TzrTIwPhfja//whsiLh8p4qWCZMjoaprHNz58c6SKWSiKLIkiVLqK+vn7PIMU2TnhOTHNw3RCqZpWVpFVuvW4rHY+eHP3iXn/zkMFFDRRJFtJxaKjAYgAiSaZKziEgIuNw2PF47wckYqmGiiAKyBnnNAExkAyokidRMhrQioMkiggmKLOHw2hBFkWUtFQQHw2TSeQQBFItM20o/m2+oZuu2LfzsRwd47l9fp76tGtOEfD5HNpcjMhVFUkQ++ZUPXBNJrqZp8vjjj/O5z32O//qv/5rTqvq/HSdOnODXfu3X+Jd/+Rc2b97M448/zqOPPspjjz3GAw88gKZpBINBvvGNb2CxWPjCF75wRc7jv02RmQ3DMNi3b1+p4ExMTHDnnXfywAMPcM8995x1A9Y0jaNHj5LNZq9ajgoUviBFe5tAIICu63PsbRZLbpitgdm4ceN74qF2LpzZOjyTGm0YBp2dnaRSqWvy/AHeee0E//G1l6lu9M8pkOlEFjWv89DvbMAQ89TW1jIzM1MSuc7eyS20Y00ms3z9717hrSPDaKaJqZsImAUWmQmCZmDJaaTcCg67hZo6H9Fwkng8gymJ1Nlt1FitpFWNyHSC2koXQ/0hcrpB3mVBFAQUi4Shm8iKhM1lIRHNUOOyF9IwTYiEY2QzKp989DZ23raKoz/r5jt/8WMq6vxYbKfJABP9AZZtXsIdn9pWCiu7FDX+pcA0Tf7zP/+TRx99lB/96Efcddddl/X45zO7/I3f+I2z1Pp33XUXL7zwwjmP+0//9E98+ctfZmpqinXr1vEP//APbN269ZLPd3aLrLjovP322zl58iRPPfUUn/70p/nWt77Fhz/8YaLRKI899hgf+9jHsFqtVzSD65od/F8KRFFk27ZtbNu2jb/6q7/iyJEj/PCHP+Sv//qv+fSnP83tt9/O+9//fu69917C4TB/+qd/ymc+85mrmqMCBd767CFsMaK4t7eXfD4/x95modViUQMjiuJVP//F4nzU6OLfFGmylxumaRIOJsCE8qqLE5janVZESUTXDGTldPFX8zq5fBbNyLN9+9bSTLAoGgwEAgwODpbMH+fLxnG5bHz2f95D848P8NQLR8mk8+h5rdD2kgRaHVbSUznCshXTLjMTS5PK5NEFAackUmm1IIkCbqtCRhYpq/YQjqSxiQLhvIblVAiZIBTC2dSchm6Y2F0WBARSmRSKVcTQLRx6Z5idt62iY+tSlqxq4OThYbzlLmRFJhZOYHfbuPVDO2hra6WtrY10Oj2Hzr/Ywno58NRTT/HII4/wxBNPXPYCA+c3u4SCqPHb3/526efzXb9PPPEEn/3sZ/n/t3feYVGcXRu/l6X33kG6KCAIKoKKRo2A0uy9RF/LGyzRWBJfeyxJNLElsSXBJGosgKBoUEFFBSw0ERFEUAHp4LIsbdvz/cG3ExZBAYFFnN91cSk7sztnlt2553mec+5z+PBhuLq6Yt++ffD09ERmZuZ7T8+LBGbnzp3Q1dWFu7s7+vTpgx9//BF79uzBoUOHMHXqVAANo5wbN27A3d29QwTubfTIkUxLEELw+PFjBAcHIzQ0FE+ePIGSkhKcnZ1x7Ngx6Ovrd4sKd5ElushtoLa2lsrg0tHRoYSkpqYGSUlJUFVVhb29fbf0UHsb9fX1ePDggZgB6rt849pKTmYRwk7cxcvsUhAAphbaGD9rMCxt22YKWl/Lxfcrz6C44DV0DNTBlJZCXQ0XRfllsBtshKWbprQo8K3tjQMAz1+W4XpMJu7dykBdWRWU6vmQExJo6KjAZ9EnyK+tR3zicxTkVaC+rBoWOmqQY/7b4bP4FQsDPWyQfO85mHLSyKusAYPBAFOKASIkDRlvsg1dPW2NtVBfXwuBQAAVZRW8rqiGSS8trN/VcEFllbJx5fgtPL77DAKeALqmWhg13R32Q5pv4du4Gr+srAxycnJirsod+fmMiIjAZ599hhMnTlB97TuT5qr3582bBxaLhbCwsFa/jqurKwYOHIiffvoJQMOIw8TEBMuWLcNXX33VrtgapylfvHgRs2bNwo0bN+Ds7Ixp06bh7Nmz2LRpE7Zs2QKgIdPMz88PxsbGOH36dLuO2RY+KpFpzIULFzB9+nQMHz4cxcXFSE1NxbBhwxAQEABfX1/o6up2C8EBIGZvw+FwoKmpCRUVFbx69apD2jxLgtraWiQlJUFFRQX29vZgMBhv+MY1J6xtoaSQhR82huN1GQcq/7+OUlVZA3VNZaz6xh/6Rm0zB332+BVO7ItCeTEbBARcLheGFur44pupUNdq3RpYS71xmo5YBQIhslPzkJ9dDEVlefQdZCGWwZWdUYift0eg/v9bQBMArDIOlJTlsHSTL/765SbycspQLSsFTl3DWo/g/1suM2WYQD0fuqqygBSBirIKCIDSIja8A5wwcdZgsZirK2tQX8eDmrbKG47SLdF4xFpWVgZCiFht1fvcQERGRmL27NkICgrClClT2v06baElkQkLC4OsrCw0NDQwcuRIbN++vcUmaFwuF4qKiggODhZ7nblz54LFYiE8PPy9Yrx06RLy8vLAZrOxdu1a6nFvb2+kpKRg4sSJYDKZuH//PgAgPj7+vY7XWj5Kkbl+/Tr8/Pxw/PhxTJo0CYQQ5OTkUGs4CQkJcHNzg7+/P/z8/GBoaNhtLuK1tbXIyclBQUEBAFApw7q6ut1yLaM5OBwOkpKSqBqkpu+tyDdOJDjtTY2+8Pd9RJx5AB19NaoKnxCCksJKeE90xvjZbnieVYz4GxkoK2LDwEQTQ0bZwtC05U6J1VV1eHj3GdIfZUDHUB3e44dDVq59U5SEEKodQ+P1jdY2Kntw+ykunLqH12UcAICWriomznNHv4HmyEx7hd/3RaG8nINKgRBcQsCQYkBNXREuLqbITc9DSUEVVFSUIMVkoLaGBx09Fazc6ANd/Y5NehG5KovOs66uTmwdpy1TpNHR0Zg+fToOHz6MmTNndtn3sjmROX36NBQVFWFubo7s7GysX78eysrKiI+Pb1ZECwoKYGRkhLi4OLEU67Vr1yImJgb37t1rd3wcDgc2NjYoKirCggULcOzYMbHt//vf/5CZmQkGgwEHBwds2rSp3cdqKx+lyPB4PGRkZMDBweGNbYQQ5OXlITQ0FKGhoYiLi8PAgQPh7+8Pf3//dtfNdBSNa2DU1dWpGhVRyrBIcDpzIe99YLFYSE5OhqmpKSwsLFr1XtbW1lKCU1lZ2erz/HnnJTxKeAmdJhfNsmI2+jgaY/Antjhx6AZqq7mQkmJAKCBQUVfAwi/HoI9j88WbdXV1SEpKomx6OnIKqKamhroQNz7Ptxlc1tfx8CKrGAwGA2bWepCV+3ftrrSoEg/uPENpUSUYskyYWOrCxlYfpaUvUV7KRnEuAykPXkDAJ7DvbwJPfycYGnd+6wfRDURpaSnYbHarzhNoWIifPHkyDhw4gHnz5nXp9/BdZpdAg12LpaUloqKiMGrUqDe2d5bI1NfXQ05ODrm5uZg5cyYKCgpw4cIF2NnZiU2lNS7O7Eo+SpFpLYQQKqskJCQEt2/fhqOjIyU4lpaWXfZBF422cnNzm62BEaUMFxcXU5Yojd0GugMiFwJra2uYmLTPSJHL5VKC8y7rl1NHY3Dzchp0Df5daBeNZNxH2uJJaj5YFRxo6TQkAxBCUFbEhomlDtbvnvzG1FBtbS0SExOhrq6Ovn37duoamMiuSOQ2LEoBf1dvHKGQoLaWC3l5mTfiFwqFePjwIerr6+Hi4gIZGRkIhaI+TZK5cWp8nhUVFZCXlxdzVhCdZ2xsLCZOnIjdu3dj0aJFXX6j1xqRAQAdHR1s374dixcvfmNbZ0yXnTt3DhkZGVi0aBH09PRQUFAAb29vMBgMhIaGdmlrg5agRaaVEEJQWlqKsLAwhISE4MaNG+jTpw/8/f0REBDQ7LRPR9HWGhiRJYpoPlzUvEuS9v1FRUV4/Phxh3bibGz9InJVEJ2nmpoacjKLcGBbBHg8AdQ1G+6QKyuqwZSWgs+UgQj9Kx5qGopi2WJ1tVzU1fLw9feTYGz2b+Ou6upqJCUlUWaMXfke8vl8scQBUTFvY3t7QghiojNw5XIqKsqroaQshxGj+sDb1xEyMkwqzZ3L5cLZ2blbZiGKzlP094yJiUFmZiZcXFywe/du7NixA0uXLpWYK/u7RCY/Px+mpqYICwuDn59fs/u4urpi0KBBlL2/UCiEqakpli5d2q6F/61bt2L79u3Ytm0b5s+fDz09PRQXF8PLyws8Hg/nz5+XuAM1LTLtQFTfEh4ejpCQEERFRcHCwoJyjO7Iu1zRxUFUw9PWdReBQEAJTmlpKVWjoqen90YqbWeRl5eHrKws9OvXD9rab3Zc7AgaLzSXlpZSGVy5TzmIDn8MNqsWAIGquiL8pg+Clq4qDmy72CqRqaqqQlJSEgwNDWFlZSXR6dLGrcMb98bJSKvEpfDHIASQl5cBj8uHQCDEyDF2mDnPDQ8fPgSfz0f//v27pcA0RSgUIjY2FkeOHEF0dDRqa2vh4+MDf39/+Pj4tLi43pG8zexSU1MTW7duxcSJE6Gvr4/s7GysXbsWVVVVePToEbXO1NTs8syZM5g7dy6OHDmCQYMGYd++fTh79iwyMjKgp6fXrjj37t2LLVu2YNWqVVi0aBEMDAxQXl4OHx8fPH36FAkJCTA3N++YN6Ud9Mg6mc5GVN/y2Wef4bPPPkNlZSUuXryI0NBQjBgxAkZGRggICIC/vz+cnJzaLThcLhfJyclgMplUq+G2wmQyoaenBz09PbELsai/TGN7m46e/mk8xefs7Ax1dfUOff3GMJlMaiG5cQaXomYdRk42Rh2bCTU1NTgP7g11DWXU1XKhpqn0xnRZFasWJpY6MDBpmI6srKyk1pDMzc0lngDStHU4m81Gfn4hrlxOQ309D6pqcpCWZkBeQQG11VzcicmEUS8GVFRl4OzsLNHq/LYgJSUFNTU1xMTEYN26dRg3bhwuXLiAgwcP4q+//kJUVFSnx/A2s8tDhw4hNTUVf/zxB1gsFgwNDTFmzBh88803YokMTc0up06ditLSUmzatAlFRUVwcnJCZGRkmwSmoKAAmpqa1A3nypUrwWQysX79evB4PHz++ecwNDTEpUuXsHz58g6zx2ov9Eimg6mqqsLly5cRGhqKy5cvQ1tbm2pR0JaeOJ1dA9P0jpgQIuY28L7HEzW7KikpgbOzs8TWhZprqS1KjX6ZWYm/j91uceH/9evXSElJoZysuyu5L8uxdf15yMoxISVFwOfzG1qWM6RQVcXFuAArTJv56QcjMACQnp4Ob29vBAYGYvPmzWLiLlro/lhovHj/9OlT9OnTB4cPH8asWbPE3EkOHjyIFStWYPPmzZgzZ45ERy+NoUWmE6mpqcGVK1eonjgqKirw8/ODv7//W3viiO6eu6oGpnGPEVGHvPcpihQKhXj8+DHYbDacnZ27TSO35lKjudXSyM2qQi1HCONe2lQKc3l5OR4+fAgbGxsYGxtLOvS3Ul7Gwfovz4IBQPH/G4cJhUKwqziorxPAy88E1r31qJuIrm470VYyMzPh7e2N+fPnY8eOHd061s6isUWMiJcvX6JXr14IDAzEH3/8gf3792P69OlUhqVAIIC1tTVevHiBnTt3Yt26dd3ivaNFposQ9cQJCQnBhQsXICsrCx8fH4wfPx5DhgyhpsLu3buH6upqWFpaSuTuubk7f5Hg6OjovPNuWCAQ4OHDh9QCc3fsSiiipdRoJpOJp0+fvjNJoYZTj4fx2aisqIaeiQbsB5pDRqbrU0QB4OAPV/HgXg5UVRUgI8tEVVU1qjk8WFjqYuN2f7x+XUGZlcrKylKCI8m22s3x7NkzeHt7Y9q0adi9e/cH52LRkZSXl+OLL77AX3/9hZiYGCxYsACXLl1C7969sWbNGuzbtw8HDx7E7NmzoaSkBDabjS1btsDW1hb+/v7tXuPpaGiRkQA8Hg83btxAcHAwwsPDIRQKMW7cOCgrK+PXX39FZGRkp/sJtQbRnb/IbaC6ulqsCr+pgPB4PCQnJ0NKSgpOTk4f1PSMKJU2Ly8PHA4H8vLyMDQ0bPHOP+dJIY7tuozyYjaAhnU6s956WLLRFxraXT81WFHOwU8/XsPznFLU13PBAGBkrIWlqz6FaaMsuaYJEgDELG4kUUch4sWLF/Dy8oK/vz/279//UQsM0FB4OnfuXBgaGiIpKQmHDh3CwoULqe3/+9//8MMPP2DdunVwcnJCXFwcrly5gvv373erwmxaZCQMn8/HrVu3sG7dOqSkpMDa2hrOzs7w9/fHqFGjutWHpfFUU1VVlZjbACEESUlJUFRUhIODg0QvVu3l1atXyMzMhJ2dHYRCIZUyLCcnJ5YazecLsW3JXyjKrYCmjgqY0lLgcvlglXMw6BNbLFo/TiLx19XVI/z8LVSy6uHQrzecXcwgJ99yskjTVsxcLrfZVsxdQV5eHjw9PeHl5YVffvmlwwXmbY7KPB4PGzZswOXLl5GTkwM1NTWMHj0a3377LQwNDVt8zS1btmDr1q1ij/Xu3RsZGRkdFvf+/fuxcuVKWFpaIisrC0DDrIjourB3714cPXoUdXV1kJOTw/nz59GnT58OO35HQIuMhOHz+fjvf/+LyMhIREREgM1mU/Y2LBaLurMbM2ZMt6riFzUoE7kNMBgMyoesu/WCaQ25ubl49uwZnJycxApdmzack5KSAqcMCDuaCDUNJcjK/jtaq66qAyHAN7/Pg5rm29+DKlYNEu5k4XUpBzoGanAZag1F5fYvZvP5fCQlJYHJZMLJyanNIi8yZRWdZ0d1OW0NhYWF8PT0hIeHB44dO9YpNyj//PMPYmNj4eLiggkTJoiJTGVlJSZNmoSFCxfC0dERr1+/xooVKyAQCJCQkNDia27ZsgXBwcFimW7S0tIdkqYvWpM5deoU4uLiEB8fD1lZWURHR0NRURG1tbXUWmdeXh74fD5UVFQ6rUTgfaBFRsIUFxdj/vz5OHLkiNgCs1AoxP379ynBKSoqwqeffoqAgAB4eXlJvCmZCDabjcTERKipqVH1Q8rKypTbwIcgOM+fP8eLFy/g7Oz81mZ1ooy8W/+k4Pyx+1BWl4OsrAxkZGQgLS2D+lou6uv42PjLTOibtNyVNCejEId2XMbr0irqMR1DdQRu8hErAG0tomlKaWlpODo6dshFummXUxUVFTHrl45axykuLoa3tzcGDBiAP/74o0tGwK0prHzw4AEGDRqEly9fwtTUtNl9tmzZgrCwMKSkpHRYbM0t+AuFQkRHR2PdunVgMpm4efMm9b2Kj4+HhYVFt1l/aQ5aZD4AhEIhUlJSqBYFL1++xOjRo+Hv74+xY8d2WVFlUyoqKvDw4UOYm5vDzMwMQMMFr7G9jYKCgpi9TXdaZG7c7tnFxaXVwl386jW2f34SQiKErDwTPB4PQqEQdRw+dAzUsOXYXCgoNH/nL+ALsS3wJPJflEFDW5nqUcMq58DGwRhrd08Cg8GAQCBE/vOG+gojMy1ISzd/8eXxeEhKSoKMjEyHCUxTGvfGKS8vb9H6pa2UlpZi3LhxsLOzw8mTJ7tsDa81IhMVFYUxY8ZQnoDNsWXLFuzevRtqamqQl5eHm5sbdu3a1aIovQuRt1hBQQFu374NAOjTpw/69esHHo+H27dv46uvvgKXy8WZM2cQFhaGX375BXfu3Gm3TVNXQIvMBwYhBGlpaZTgPH36FJ988gkCAgIwbtw4aGpqdsmFvKSkBGlpabC1tW1x3rppR0xZWVlKcN7mv9UVNK7jcXFxafOI69RP13Hz4kMwmQzIyMmgtroODAbg7msBY1sVymVYV1dXrKYjK+0V9qwLgYKynJiZZV0NFzyuABt/moHKimqcOnQTxa9YIAB09FUxZeEwOLmK+1CJBEZWVhaOjo5dslDe3PRhS71x3kZFRQXGjh0LS0tLnD17tsubBb5NZOrq6jBkyBDY2tri5MmTLb7OP//8Aw6Hg969e6OwsBBbt27Fq1evkJaW1uaZBtEI5tmzZxg7dizk5eUhLy+PnJwcHD9+HD4+PhAIBLh79y6++uorqhdWUFAQRo4c2aZjdTW0yHzAiC6UISEhCA0NxaNHj8R64ujo6HTKhVy0QG5vb9/qauKmFydpaWmxFsxdKTiEEKSnp+P169dwcXFpVx0PnyfA9fBk3Lr8CFWsWugZqePTiS4YMNwGdXV1LbpGZ6eX4MDGcKhqKIDZaHTCreejllOPBWu98NdP18GprIWSqjwYDAY47FooKslh9bcTYW7TMC3C4/GQmJgIOTm5LhOYpjTtjcPn88USB1oambBYLPj6+sLAwAAhISFdXlj5NpHh8XiYOHEi8vPzcfPmzRZHMc3BYrHQq1cv/Pjjj1iwYEGb4youLoa7uzvGjh2LgwcPIi8vD/369UNlZSV+/fVXzJ8/n8r4TElJgaGhYbcwwHwXtMj0EEQWLsHBwTh//jwSExPh5uaGgIAA+Pn5wcDAoEMu5C9evMDz58+bdYJuLUKhkEqjLSkpoXzG9PT0KMPHzkIoFCItLQ0cDgfOzs7vvaAtFBLweQLIyDKbfX9FqdEi12gpyOLcwRQI+QRqmsrA/z+FVc6BmoYSXDxscCUkERra/04tEkLwupSDEeP6Ye6KUeByuUhKSoK8vDz69evXLVJ9W9sbh81mIyAgAGpqaggPD5dI9mRLIsPj8TBlyhTk5OTg+vXr7fJHGzhwIEaPHo1du3a16XmEEBw6dAgPHjyg2jkPGzYMcnJysLe3x4EDB3DixAnMmDGjzTFJGlpkeiCEEOTm5lI9ceLj4zFo0CDK3sbExKTNgkMIQVZWFgoLC9G/f/823eG9jcZ3wyUlJRAIBJTgdHTdhkAgwKNHj1BbWwsXF5cuLxQVuUZfPHUXdy5nAQSQlZeBkE8gKyuDaUuGIzXhBZLjs6GpIz7dwiqvhmUffaz+djwSExOpVPHuIDDNUV1dTVn4FxQU4Ntvv8Unn3yCBw8eQE5ODhERERLLlmxOZEQCk5WVhRs3bkBHR6fNr8vhcGBqaootW7Zg+fLlbX7+o0ePkJWVhQkTJmDmzJkoKirC+fPnUVFRgQEDBqCiogL79+/HsmXL2vzakoQWmR4OIQQFBQVUT5w7d+7A0dGRMvBsTeMwoVCIJ0+eoKKiAs7Ozp2WMda4g2JJSQm4XC60tbWhp6cHLS2t91oYFggESElJgUAgkLgTMSEEd64+xrWwRJQUsKCiIQM7V30MGm6Lu1dzcfvyE2jovDmScRttC4dh6lBSUuoUP7vOory8HL/99hvOnj2LzMxM9O7dGxMmTEBAQABcXFy6ZKr0bY7KBgYGmDRpEpKSkhARESGWqaWpqUndjDR1VF69ejV8fX3Rq1cvFBQUYPPmzUhJSUF6evo7RaqlBmI8Hg8VFRXw9/fH5s2b4e3tjfLycgQGBsLCwgIeHh7w8vLqqLelS/hwSrJp2gWDwYCRkRGWLl2KwMBAlJSUUD1xtm3bhj59+lCC01xPnMZ3/wMHDuzU6Q0GgwF1dXWoq6vD2toaHA4HxcXFyM7ORlpampjbQFtEgsfjUa7T3cGJmMFgYJinPYZ52gMQNytVNxACUkKUFVdCRU0B0kxpcKpqISsvDU1jfHACAzTEHBsbCzU1Nbx8+RJxcXEICwvDqFGjcP/+ffTu3bvTY3ibo/KWLVtw4cIFAICTk5PY827cuIERI0YAeNNROT8/H9OnT0d5eTl0dHQwdOhQ3L17950Cw+fzIS0tjZqaGvz6668oKyuDuro6lixZAkVFRZSWluL+/fuoq6sDAFy5cgXZ2dn44YcfYGRk9L5vRZdDj2Q+UgghqKiooHriREdHw9LSkmrC1rdvX1RUVGD37t2YMGGCxBtdiQoFRcaWovl+XV3dt057idYvRBlY3d2JgBCC21cf4tyvsWBV1IAQAmU1eQz4xAgDh1t16ymy5qivr8fMmTNRWlqKq1evQkPj3/bOXC63W3vbdQaiLDLRlK2xsTEUFBTAZrORkZFB3UwFBgbi0KFDlHD9+uuvmDNnjqTDbxe0yNBQ01SinjhXrlyBvr4+BAIBdHV1cenSpW7TwhloKBQU+amx2Wyoq6tTgtN4pFVfXy9mdfNBXZzreHiWXoDKSjaqeSWQlpGCQCBodjG9u8LlcjFnzhzk5uYiOjq6SxqNdTeaK64khGDq1KngcDi4fPkyAMDb2xsvXrxATEwMdHV1wWazERwcjFevXsHV1RVjxoyRRPgdQo8Sma7wEvoYePToEcaMGQMlJSWUlpZCU1MTfn5+GD9+PAYMGNCtLtZ1dXVi9jaidGE1NTU8fvwY6urqHdqptCupq6uj3BTs7OzEXKPZbDbU1NQoce0u7RRE8Hg8LFiwABkZGe1eSP/QadwH5tdff8WsWbMgLy+P6upq+Pj4YNu2bRg2bBimTp2Kx48fIzIyEsbGxkhISICtrW23urF7H3rcmoydnd0bXkI0rScjIwOenp6YMmUK9u7di7q6OkRGRiIkJAQBAQFQVVWFr68vAgICMHjwYIlPP8nLy8PExAQmJibgcrkoLS1FYWEhsrKyICMjAwUFBdTU1HxwX1iRwIhEksFgQFFREWZmZjAzMxNLjc7KyoKysjKVldeRti/tgc/nY8mSJUhPT8f169c/eoGZPHkyoqKiMHXqVMjLy0NOTg5CoRBFRUWYP38+Hj58iMuXL8PY2BhsNhsnTpzAgAEDMGPGjA/y5qgpH/4ZNEFaWhr6+vrUT3c0jOvO6OnpYdOmTdi3bx+kpKSgqKiICRMm4OTJkygqKsLPP/+M6upqTJ06FTY2Nvjiiy8QExMDPp8v6dAhKysLNTU1VFdXw9jYGDY2NuBwOLh37x7i4uLw7NkzsNlsdPfBe11dHRISEsQEpilycnIwNjaGs7Mzhg8fjl69elHnGhsbi6ysLLBYrC4/V4FAgGXLliExMRHXrl2Dvr5+hx/j1q1b8PX1haGhIRgMBsLCwsS2E0KwadMmGBgYQEFBAaNHj6YcjN/Gzz//DDMzM8jLy8PV1RX3799vV3yNBWbWrFm4f/++mAsAk8mEg4MDli9fjlu3buHixYtUUWVsbCwiIyNhYGDQIwQG6IEik5WVRVXCzpw5E7m5uZIO6YNCQ0MDS5YsafbCJi8vD19fXxw/fhxFRUU4fvw4CCGYO3cuLC0tERgYiGvXroHL5Uog8oZCv4SEBBgbG1N2N46OjhgxYgQsLS1RW1uLhIQE3LlzB5mZmRK5CL8LUYyampotCkxTZGRkYGBgQJ2rjY0N6uvrkZycjNu3byMjIwPl5eUQCoWdGrtQKMTKlStx+/ZtREVFdVomVHV1NRwdHfHzzz83u/3777/HgQMHcPjwYdy7dw9KSkrw9PSksrWa48yZM1i1ahU2b96MpKQkODo6wtPTEyUlJW2OT/Q3CwwMRFRUFJKSkqj3oqysDDExMdi2bRu0tbWhoqKCjIwMpKSk4OzZs5gxYwbmzJmDUaNGtfm43ZUetSbTkV5CNK1H1BPn3LlzCA8PR21tLXx8fODv74+RI0d2SVU3i8VCcnKymFlnczRu2lVSUgImkylmbyPJu0eRwGhra8PW1va9p7wap0aXlJSAEEJ1OdXS0urQqU6hUIi1a9fi0qVLuHHjRpfZnTQtrCSEwNDQEF9++SVWr14NoMHKX09PD8ePH8e0adOafR1XV1cMHDgQP/30E3U+JiYmWLZsGb766qs2xxUdHY3x48dj6tSpOHbsGIAGvz93d3dMmDAB33//PQoKCjB58mSwWCzk5eWhb9++8PPzw/r169vxTnRfepTINOV9vYRo2o5AIEBsbCzVoqCyshJeXl4ICAjAp59+2ilV3uXl5Xj48CGsra3b5Ebb3EVYZPaopaXVpYIjEhgdHZ1m65Xel+YKXTuqQZlQKMT//vc/hISE4MaNG7C2tu7AyN9OU5HJycmBpaUlkpOTxWpehg8fDicnJ+zfv/+N1+ByuVBUVERwcLCYC8DcuXPBYrEQHh7e5rjYbDZ27dqF27dvY+TIkVi6dClGjhwJe3t7/PXXX2AymZCSkkJNTQ2Ki4vx+vVr6OrqirX76Cn0uOmyxqirq8PGxoaq9KXpfJhMJjw8PLB//368ePECkZGRMDExwYYNG2BmZoZZs2YhODgYVVVV736xVlBaWoqHDx/C1ta2zXbnUlJS0NLSQp8+feDh4QFHR0dIS0sjIyMDMTExePToEWV105nU1NR0qsAA/xa62tjYYMiQIRg4cCCUlZWptNmkpCTk5+ejvr6+Ta9LCMG2bdtw5swZREVFdanANEdRUREAvNFfRU9Pj9rWlLKyMggEgjY9pzlE05FCoRCqqqrYsGEDRo4ciStXrsDa2hrW1tY4ffo0ZGRkICUlBaFQCEVFRZibm8PZ2blHCgzQA7PLGsPhcJCdnY3Zs2dLOpSPEikpKbi5ucHNzQ3ff/89kpOTERwcjB07dmDx4sUYPXo0AgICMHbs2HZZ/xcVFeHx48ewt7d/76ZNDAYDGhoa0NDQgI2NDdhsNpW59ejRI2qaSUdHp0MzFkUCo6enBxsbmy7JChN1MVVRUYGlpSVqampQUlKCgoICZGRktDo1mhCCXbt2ISgoCDdu3ICtrW2nx94dIYSgrq6Oeq9EAqKkpISvv/4acnJyqKqqgoGBAaqrq6GkpNSirUxPpEeNZFavXo2YmBi8ePECcXFxGD9+PJhMJqZPny7p0D56pKSk4OLigl27diEjIwP3799H//79sXfvXpiZmWHSpEn4888/UVFR0arF+IKCAqSnp6Nfv34d3hWQwWBATU0N1tbWcHd3h6urK3XXf/PmTSQnJ+PVq1fvneBQXV2NhIQE6Ovrd5nANIcoNXrQoEEYOnQo9PX1UVZWhtjYWNy9exc5OTngcDhifxdCCH788UccOnQI165dg729vURib4oom624uFjs8eLi4hYz3bS1tcFkMtv0nMY4Ojpiz549Yo+JhEZBQQFr1qzBjBkzkJycjLVr14LFYoHJZHZ6IkZ3oUetyUybNg23bt0S8xLasWMHLC0tJR0aTQsQQpCRkUG1KEhLS4OHhwf8/f1b7ImTl5eHrKwsODk5tbvdQHuprq6m1jWqqqqgoaFB3fW3pQJfJDCGhoawsrLqVh1DRYi6nIo6YsrJyeHChQvw9vbGw4cPsWfPHly5cgUDBw6UWIwtLfyvXr0aX375JYCG9RFdXd13LvwPGjQIBw8eBNAw5WVqaoqlS5e+deF/0aJFSE5OxoMHDwA0vGeN17dEFf88Hg8//PADIiIiYGdnhx07dnw05RU9arrs9OnTkg6Bpo0wGAz06dMHGzduxIYNG5CdnY3g4GCcOHECq1atgru7O/z9/ameONu2bYO+vj6mTp0KdXX1Lo9XSUkJ5ubmMDc3R21tLUpLS1FUVITMzMxWTzNxOBwkJiZ2a4EBGlKjDQ0NYWhoCIFAgJcvXyI7OxtTpkwBn8+Hn58fqqqqKMPHrqKxozIAPH/+HCkpKdDU1ISpqSm++OILbN++HdbW1jA3N8fGjRthaGgotqjf1FF51apVmDt3LgYMGIBBgwZh3759qK6uxmefffbWWOTl5WFlZQUA2LBhA/r06YOZM2dS20UjGhkZGaxZswZMJhPHjh3DlStXxPbryfSokQxNz4EQgpcvX4r1xOnbty9yc3Nx5MgR+Pr6dquLs6gCX5QppKysTLWabtwaQSQwRkZGsLS07Fbn8C4IIQgKCsKGDRuwYcMGPHv2DGFhYeDxeAgPD8fQoUO7JI6bN2+KOSqLmDt3LlW7tXnzZhw9ehQsFgtDhw7FL7/8AhsbG2pfMzMzzJs3D1u2bKEe++mnn7B7924UFRXByckJBw4cgKura4txEEJw4MABBAUFQVFREWlpacjLy4Oamtob+4pGNIQQREVF4dNPP32/N+EDghaZDuLWrVvYvXs3EhMTUVhY+EZTJNEH/9ixY2CxWBgyZAgOHTok8WycDwGhUIglS5bgzJkzcHJyQlxcHJycnKgWBebm5t3qYi2aZiouLkZFRQUUFRWhq6sLZWVlpKenw9TUtFV9fLoThBD89ddfWLNmDS5cuEBd5EV95+3s7CQysuwOWFlZoaioCIsXL8a2bdta7LfUnFnmx8DHd8adRGdUIdM0sGLFCly9ehVJSUm4efMm8vLysGDBAty8eRPOzs4YMmQIvvvuO2RmZnaLCn7RNFP//v0xfPhwmJubg8ViITU1FUDDxeZDsLcRQQjBmTNnsHr1agQHB4uNIphMJoYMGfJRCgyXy6Xqq6ZMmYIrV67ghx9+QGFhYbP7f4wCA9AjmU6ho6qQaRqIiIiAo6PjG3Uwop44oiZs0dHRsLa2phyj+/Tp0y2+2FVVVUhMTISxsTFUVVVRUlKC0tJSSEtLi7kNdNeRTWhoKBYvXoyzZ89i3Lhxkg5HorxtNPL1118jLCwMU6ZMwaJFiz7IBmOdAS0ynUBHVCHTtA1RRfuFCxeonjgmJibw9/fH+PHj0a9fP4kIDpvNRlJSEnr16gVzc3PqcaFQKGZvw2AwKBdlDQ2NbiGOAHDx4kXMnz8fJ0+eFJv+/RhpLDAxMTHIz8+HtrY2TExM0LdvXwDApk2bcO7cOUyePBkLFy5sc4FwT6RHZZd1V9pThUzTNkQV7XPmzMGcOXPAZrNx6dIlhIaGYsyYMdDR0aEEx8XFpUsu4mw2G4mJic36qUlJSUFbW5vyKWOxWCgpKcHjx48hEAgowdHU1JRY0d4///yD+fPn4/jx410mMGZmZnj58uUbj3/++efNTkUfP378jQwwOTm5TpmGFn1mduzYgf3790NfXx8vXrxAv379MH36dAQGBmLbtm2QlZXF33//jfLycmzevBm6urodHsuHBC0yND0SVVVVTJ8+HdOnT0d1dTXVE8fPzw9qamrw8/NDQEAAXF1dO+UiXllZiaSkpHcadgINFy9NTU1oamqid+/elMdYZmYmuFwutLW1oaenBy0trS5LFY6OjsbcuXNx9OhRTJo0qUuOCQAPHjwQs/FJS0vDp59+ismTJ7f4HFVVVWRmZlK/d+a0Y0REBH744QecPn0aI0aMQEZGBo4cOYKgoCBIS0tj8eLF2LBhA7hcLvLz8z96gQFokekSGlchGxgYUI8XFxeLTZ/RdA5KSkqYOHEiJk6ciNraWly7dg0hISGYMmUK1b4gICAAQ4YM6ZCLuEhgLCws0KtXrzY9VzQiU1dXh7W1NTgcDoqLi5GdnU31fxfZ27yPqeXbuHXrFmbMmIGDBw9ixowZXbpW1LTB2bfffgtLS0sMHz68xecwGIxO6VvTHKmpqXBycqLaIffr1w8rV67Eli1bcPnyZcydOxfy8vLYtm1bl8TzIdA9Jn57OObm5tDX10d0dDT1GJvNxr179+Dm5ibByD4+FBQU4Ofnhz/++ANFRUX4/fffIRAIMGfOHFhZWVE9QNprGcNisZCUlARLS8s2C0xTRB5jVlZWlL2NqqoqcnNzxUwtO7J/T2xsLKZMmYIffvgB8+bNk2gyApfLxYkTJzB//vy3xsHhcNCrVy9qDe7x48edFpOKigq1lgY0rAVaWVlh9uzZuHjxIrKzszvt2B8qtMh0EBwOBykpKUhJSQHwbxVybm4uGAwGVYV84cIFPHr0CHPmzHmjCpmma5GVlYWXlxd+/fVXFBYW4u+//4acnBwWL14MCwsLLF68GP/880+r5/dFPW2srKxgamra4fEqKyvDwsICgwcPhru7OzQ1NVFQUIBbt24hISEBubm577UWcf/+fUyaNAk7d+7EwoULJZ7tFhYWBhaLhXnz5rW4T+/evfH7778jPDwcJ06cgFAohLu7O/Lz8zslJnt7e2RlZeHcuXPg8/nUe6Svrw8HBwe63Xsz0NllHURHVCHTdA8EAgHu3LlD9cRhs9nw9vZGQEAARo8e3WxPnNevXyM5ObnNPW06grq6OipLjcViQVVVlUqNbm3/nqSkJPj6+mLjxo1YuXKlxAUGADw9PSErK4uLFy+2+jk8Hg99+vTB9OnT8c0333RIHE3Tlr/77jusX78e27dvx6hRo6Cvr49Vq1bhxYsXiI+P77RpzA8VWmRoaN6CUCjE3bt3KcEpKSmBp6cn/P394eXlBWVlZfzzzz/IycmBv7+/xHuCiAoES0pKUFFRAWVlZUpwlJWVm31Oamoqxo4di7Vr12LdunXdQmBevnwJCwsLhIaGwt/fv03PnTx5MqSlpfH333+369iEEDAYDDx//lws7Vz0ONBgQbNr1y7w+XxoaWlBRkYGMTExH2VR6rugRYaGppUIhUIkJSUhODgYoaGhyM/Px5AhQ3Dnzh18/fXX+PLLL7vFBVoEj8dDWVkZSkpKUFZWBgUFBSppQEVFBVJSUkhPT4eXlxeWLVuGTZs2dZv4t2zZgiNHjiAvL69NU1ACgQB2dnYYO3Ysfvzxx3YfPzw8HF9++SXOnz8PBweHZvfJyspCeXk56uvrMXDgwE7p+tojIDQ9kpiYGOLj40MMDAwIAHL+/Hmx7XPnziUAxH48PT0lE+wHiEAgIEePHiWysrLE3t6eyMrKEi8vL3Lo0CGSl5dHOBwOqa6u7jY/bDab5OTkkLt375Jdu3YRPT09MnnyZKKnp0e+/vprIhQKJf2WUggEAmJqakrWrVv3xrbZs2eTr776ivp969at5MqVKyQ7O5skJiaSadOmEXl5efL48eM2H1f0HuTn55OAgABy+PDht+5H0zroVaoeishLbf78+ZgwYUKz+3h5eSEoKIj6vS39UD52rl+/jpUrV+LYsWOYPXs2njx5guDgYBw5cgTLly+Hh4cHAgIC4OvrC21tbYmPEJhMJvT09KCnpwcLCwswmUz88ccfqKysxB9//AE2m42JEydi2LBhEl+8joqKQm5uLubPn//GttzcXLH1kdevX2PhwoUoKiqChoYGXFxcEBcXR1XgtwUGg4HU1FQcOXIEVVVVVJpyc/vRtB56uuwjoKnNDQDMmzcPLBYLYWFhEovrQ+bixYtgs9lv9AQhhODZs2dUE7bk5GS4u7sjICAAfn5+0NfXl/hF6sWLF/Dy8kJAQAB2796N27dvIyQkBBEREXj48GGXN4LrThw6dAjbt29HZWUlIiMju6x9QU+GFpmPgJZEJiwsDLKystDQ0MDIkSOxfft2aGlpSS7QHgb5/544ISEhCA0Nxb179+Dq6gp/f38qSaCrBScvLw+enp7w8vLCL7/8IjYqII0Wtj8WmjvnU6dOYfPmzXBwcMCmTZvogun3hBaZj4DmROb06dNQVFSEubk5srOzsX79eigrKyM+Pl5iXlk9GUIIXr16hdDQUISEhCAuLg79+/enBKcreuIUFhbC09MTHh4eOHbs2Ef/dxYIBGAymSgoKEBZWRn1/gDA33//jT179sDR0RHLly+nheY9oEXmI6A5kWmKyCk6KioKo0aN6rrgPkIIISguLsb58+cREhKCmJgY2NvbU4JjY2PT4YJTVFQEb29vDBo0CMePH//oBUZU+5KSkoJZs2ZBSkoKVVVVUFNTw5EjR+Dq6oqTJ09i79696NevHxYvXvzWLpk0LUNX/NMAACwsLKCtrS3WO52mcxB5bf33v//FtWvXUFhYiKVLl+LevXsYPHgwXF1dsWPHDqSnp3dIY7PS0lL4+vrCyckJQUFBXSIwW7ZsAYPBEPuxtbV963POnTsHW1tbyMvLw8HBAZcvX+60+KSkpJCbmwtvb2+MHz8esbGxiIuLQ2pqKqKiogAAM2fOxNq1a3Hnzh0cPXoUtbW1nRZPT4bOLutgRBeFD21uOz8/H+Xl5WIGnjSdD4PBgLa2NhYsWID58+eDxWJRPXF+/PFHmJqawt/fHwEBAe3qiVNeXg5fX1/Y2NjgxIkTXZo5ZmdnR12wAbz12HFxcZg+fTp27doFHx8fnDp1CgEBAUhKSoK9vX2nxHf37l24uLjgm2++QW1tLcaPHw8fHx+qsSAATJkyBVJSUujbty8UFBQ6JY6eDj1d1kPhcDjUqKR///748ccf8cknn1CW8lu3bsXEiROhr6+P7OxsrF27FlVVVXj06BGdytxNYLPZiIiIQGhoKCIjI6Grq0t1/WxNTxwWiwUfHx8YGhoiNDQUsrKyXRR5w0gmLCyM8vJ7F1OnTkV1dTUiIiKoxwYPHgwnJyccPny4U2L85ptvcOPGDVy/fh2urq7Q1NREcHAwlJSUcP78eTx48AA7d+7slGN/TNDTZR3Ijh07sGDBAqSlpTW7vSv1PCEhAf3790f//v0BAKtWrUL//v2xadMmMJlMpKamws/PDzY2NliwYAFcXFxw+/ZtWmC6EaqqqpgxYwaCg4NRXFyM77//HsXFxfD19UXfvn2xdu1axMXFifVfEcFmszF+/Hjo6OggODi4SwVGRFZWFgwNDWFhYYGZM2ciNze3xX3j4+MxevRoscc8PT0RHx/fIbE0990LCAgAl8uFlZUVlJWVER4eDiUlJQDAkydPkJqairKysg45/scMPV3WQRQVFeHOnTu4cuUK2Gw2zp0798Y+XTmFNmLEiLeK2pUrV7osFpr3R0lJCZMmTcKkSZNQW1uLq1evvtETZ/z48XB3d0ddXR0mTpwIJSUlhIWFQV5evsvjdXV1xfHjx9G7d28UFhZi69atGDZsGNLS0qCiovLG/kVFRZ3WOZbP50NaWhr19fXgcDhQVFSEgoICjI2NYW5ujhcvXmDq1KmQlZVFVVUVzp49i++++w6hoaHQ1tZ+7+N/7NAi00HExcVBTk4ORkZGlAsrl8uFrKws2Gw2Tp8+DX19ffj5+Uk4UpoPHQUFBSoTjcvlIioqCiEhIZg1axYYDAZkZGRgZmaGCxcuSGwdwdvbm/p/v3794Orqil69euHs2bNYsGBBpx//yZMnIISgb9++kJaWRnl5OXx8fFBdXY2SkhJqDey7777D4sWLsXv3buzduxcGBgZIT0/Hb7/9RmdZdhD0dFkH8eDBA/B4PLi7u0NPTw9sNpuaovjtt9+wZMkSBAQE4M8//5RwpJJh165dGDhwIFRUVKCrq4uAgACxlrlAg2V9YGAgtLS0oKysjIkTJ6K4uFhCEX8YyMrKYuzYsfjtt99QWFiIoKAgGBgY4OLFiy26LksCdXV12NjYtJi9qK+v/8bfuri4uF0dLwkhWLNmDdzc3JCamgqBQIAxY8bAwMAAW7duRWBgICIjI7Fy5UpUVVXhjz/+wC+//AIfHx8sWLAAFy5c6NKW0z2eLvRJ67EUFxcTPz8/MmfOHBIZGUmGDBlCuFwuIYSQM2fOEFdXV9KvXz8ybtw48vDhQ7Hn8vl8IhAIJBF2l+Lp6UmCgoJIWloaSUlJIWPHjiWmpqaEw+FQ+yxZsoSYmJiQ6OhokpCQQAYPHkzc3d0lGDVNR1FVVUU0NDTI/v37m90+ZcoU4uPjI/aYm5sbWbx4cbuOx2aziZeXFzE2NibR0dFk7ty55PXr19T2iIgI4uHhQebPn/9RfP8kCS0yHcClS5fIwIEDyZkzZ0hERASxsbEhhBBy79494u7uTjZs2EAmTZpEPvvsM8Ln8wkhhFRXV0syZIlTUlJCAJCYmBhCCCEsFovIyMiQc+fOUfs8efKEACDx8fGSCpOmnXz55Zfk5s2b5Pnz5yQ2NpaMHj2aaGtrk5KSEkLIm27KsbGxRFpamuzZs4c8efKEbN68mcjIyJBHjx61O4bq6mri5eVFGAwGMTQ0JM+fPxfbfvLkSSIvL0+ePXvW7mPQvBt6uqwDuHv3LmRkZDBo0CBwuVy4ubnh9u3b2LFjBwYMGIBRo0ahoKAA/fv3B5PJRGVlJf788094eHhg9uzZuHHjRrOvKxQKu/hMuo7KykoAoMwYExMTwePxxDKMbG1tYWpq2mEZRjRdR35+PqZPn47evXtjypQp0NLSwt27d6GjowOgwU25sLCQ2t/d3R2nTp3C0aNH4ejoiODgYISFhb1XjYyioiJCQkLw2WefobCwEA8ePBBLhhk9ejR0dXWRl5fX/hOleSf0wv97UlFRgfT0dJiZmcHMzAxVVVVITEzEs2fP0LdvX2zduhVHjx6FlJQU3N3dATTUsBgbG2PBggW4c+cO5syZg0WLFmHjxo1ir93WwrsPBaFQiC+++AJDhgyhLiJFRUWQlZV9o7NgR2UY0XQtp0+ffuv2mzdvvvHY5MmTMXny5HYfU5RF1vj/ioqKOHz4MNhsNhYvXgx5eXmMGjUKioqKuHfvHlgsVrdau+qJ0CLznsTGxuLly5eYN28egIZFx8ePH8Pa2hrbtm2DgoICbt26BUtLS/Tr1w8AYGRkBD09PUhLS2Pu3Lk4c+YM/ve//8HPzw+Ojo4oLi7GsWPHMHbsWDg7O4sdr2m/8Q+RwMBApKWl4c6dO5IOhaaHIBQKKYFZt24dXr16hWHDhsHPzw8GBgY4d+4cpk+fjsmTJ2PQoEFwcnJCeHg41q9fjwEDBkg4+p4NLTIdgLa2Ntzc3AA0pGumpKSgrq4O+vr6iIqKQllZGUaNGgUZGRkUFxfj+PHjuHnzJvh8PsaMGYPp06cjPz8ffD4fQMPd++XLl5GXl4cjR44AAGpqaqCgoNAqgSENa22UZ1R3YunSpYiIiMCtW7dgbGxMPa6vrw8ulwsWiyU2mmlvhhHNx4XoezF27Fi8fPkSdnZ2WLZsGW7fvo0VK1Zg4MCB+Pvvv7F8+XL89NNPGDlyJIKDgzFw4EAJR/4RINkloZ6HqDWr6N+tW7eSkSNHUovXCxcuJAYGBmTDhg3k22+/JR4eHoTBYBAjIyOxBcjQ0FBiaGhIuFwu4XK5ZMaMGcTU1JQkJiY2e7zS0lJy8eJFkpGR0WJMkkQoFJLAwEBiaGhInj59+sZ20cJ/cHAw9VhGRga98E/zVkSJNIQ0JJNMnjyZFBYWEkIIiY+PJ5aWlmTSpEnk7t271H5Tp04lBw8e7PJYP1ZokXlPGn/Im1JfX09GjBhBfHx8SE1NDSGEEG1tbbJz506xC7+3tzfx8vKiMm8IISQrK4s4ODiQjRs3Eg8PD2Jubk4SEhJaFIzY2FiioqJCGAwG0dPTIytWrCDp6ekddJbvz3//+1+ipqZGbt68SQoLC6kf0ftCSEMKs6mpKbl+/TpJSEggbm5uxM3NTYJR03RnGn/34uLiSEREBFm4cKFYSnJ8fDyxsrIikyZNIrGxsZII86OHFplOJjMzk0rTra6uJlOnTiVeXl7k9evXpLy8nPz+++9EXl6e7NmzR+xLU15eTkaMGEGYTCZZsWIFNcppTmQEAgE5cuQI0dXVJWVlZeT8+fPEz8+P2NnZke3bt3fNib4DAM3+BAUFUfvU1taSzz//nGhoaBBFRUUyfvx46q6URjLs3LmTDBgwgCgrKxMdHR3i7+/f7Gi5MUFBQW/8neXk5Do0rqY3ab169SIMBoNoaWmR8PBwsX0TExOJpaUl8fDwILm5uR0aB827oUWmi4mNjSU2NjZEVVWVLFq0iFhYWBBDQ0OxKaHi4mLi5eVFNDU1iaamJqmqqnrra5aVlZHJkyeTTz/9lHpMIBCQ48ePE2NjY3LgwIFOOx+ank1rimibEhQURFRVVcVGrEVFRR0WU+ORytmzZ4mbmxt59OgRCQ0NJQMGDCC+vr7kn3/+EXtOQkIC2bdvX4fFQNN6aJHpZFqa3nr06BHJysoiq1evJo6OjlQ1clhYGOnfvz8ZOnQoiYiIIEZGRiQ1NfWtx0hISCAmJibUqIDH41HbAgMDibW1Namrq3sjFj6f3y3Wa2g+HJoW0TZHUFAQUVNT6/RYDh06RCZOnCgmHrGxscTDw4OMGzeOXLp0qdNjoHk3H3Yu7AdA0+wukS27vb09rKyssHv3bty8eRPq6ur4/fffsWDBAnh6eiIkJATjxo1Dr169qJqD5oozCSFISUkBm83GxIkTAQBMJpM6jq2tLYqLi/HgwYM3YmEymWAwGF3agqA70Ro/tREjRrzR4XHJkiUSiljyNC2ibQkOh4NevXrBxMQE/v7+ePz48XsfW/Q5JYTg6dOnOHv2LKKiosRaCLi7u2PPnj2ora3FwYMHcfbs2fc+Ls37QYtMF9O09S0hhErZnT9/Ph4/foxdu3ZBV1cXQINlenx8POrq6ppNX379+jWio6Ph7OwMFRUVCIVCMBgMseNwOBxYWVkBaOiUGBQUhJkzZ2LTpk148eLFG+IjFAo/CuGJiYlBYGAg7t69i2vXroHH42HMmDGorq4W22/hwoUoLCykfr7//nsJRSxZmiuibY7evXvj999/R3h4OE6cOAGhUAh3d3fk5+e/1/FFn1MGgwEbGxvs2LEDI0eORFhYmFjx58CBA7Fnzx4UFhaipKTkvY5J0wFIchhFI45o6qrxnHNaWhoZMmQIyc/Pb/Y5ycnJxMzMjBw+fJgQ0jBVJnp+RUUFmTFjBrGysiKEEPLixQvi5eVFnJycyIYNG4iHhwextLQkV69ebTGmxq/XNNaeNtXW3FTQ8OHDyYoVKyQXVDdiyZIlpFevXiQvL69Nz+NyucTS0pJs2LDhvWP48ccfiYeHB/XZu3fvHpkyZQoZNmwYOXnypNi+r169eu/j0bw/9EimGyG6U2s8YrGzs8OdO3dgZGT0xv7k/6fKOBwO/vOf/wBoGCmJnh8XF4fIyEjMmDEDALB37148ffoUsbGx+OabbxATE4M5c+ZgzZo14PF4ABp82LZt20b9Li0t3ewIqjsWer4vLU0FnTx5Etra2rC3t8fXX3+NmpoaSYQnUURFtDdu3BArom0NMjIy6N+/f4s2/62Fz+dDVVUV5eXlCAgIAJ/Px6BBg/Dll1/CyMgIhw8fxl9//UXtb2ho+F7Ho+kgJK1yNG/nbXU4lZWVZNGiRYTBYJBDhw6Rp0+fkpqaGpKbm0vS0tKIra0tMTMzI8XFxaS0tJSYmJhQqag+Pj7k2rVr5MKFC2TUqFEkIyODCAQCsnbtWsJgMMjvv/9OHBwcyLJly8jLly/Fjnvp0iUyc+ZMUl5eTgj5d+T1ITtLCwQCMm7cODJkyBCxx48cOUIiIyNJamoqOXHiBDEyMiLjx4+XUJRdz7uKaFsDn88nvXv3JitXrmzT85obQdfW1pIzZ84QJycn4unpSWprawkhDckvs2bNIjY2NuTmzZvtipOmc6BF5gOnurqaHDhwgDg6OhI5OTkyYMAAMmbMGKKsrEycnZ3JrVu3CCENX0IGg0GePXtGbt26RZYuXUrMzc2JrKws0dDQIOnp6aS6upq4uLgQdXV1sn37dnLu3Dni5OREZs6cSYRCIeHz+eT58+dk48aNRFtbmxAiPm1mY2NDDh48KJbd9qHQ2qmg6OhoAuCjsYdvTRFtU9v+rVu3kitXrpDs7GySmJhIpk2bRuTl5cnjx49bdcym07BRUVFiv9fX15OzZ88SZ2dnMmbMGOrm5v79+3QlfzeEFpkeBIfDIcHBweTo0aMkNTVVrEnT9evXiYmJidhdHo/HI3fv3iUnTpwghBBy8+ZNwmAwyOXLl6kvenR0NFFQUCBxcXGEEEImTZpEGAwGcXBwEGvAdv/+fWJsbEy91odEYGAgMTY2Jjk5Oe/cl8PhEAAkMjKyCyKTPGhFEe3w4cPJ3Llzqd+/+OILYmpqSmRlZYmenh4ZO3YsSUpKeuexRJ85kYAJBALy4MEDYmRk9Ebzsrq6OvLbb78RVVVV4uvrSyoqKt7/ZGk6BVpkegB8Pv+t02qENEwzjBs3jsycOZMq7mw84hAIBGTNmjXE0tKSEPLvF57L5RJlZWWquC0iIoIoKioSHx8fIi8vT3x9fQkhhKxbt464ubmRrKwssdfszrRnKujOnTsEwBsdTmk6hocPHxI1NTWSlpZGCGkoTP7+++9J//79yaJFi8T2LSoqIn369CGqqqpk1apVkgiXphXQLsw9gMbpyi21ApCXl8f69evx+eefo2/fvvDz84OJiQmMjIwwadIkVFdX4+rVqxg5ciSAhkVWGRkZREVFwdTUFEBDokFcXBz09PRw8eJFEELw8uVLAMC1a9fw6aefIjk5GQUFBfDw8Oj2LQkCAwNx6tQphIeHQ0VFhepbo6amBgUFBWRnZ+PUqVMYO3YstLS0kJqaipUrV8LDw4Nq20DTsSgpKcHFxQUeHh6Ijo6Gk5MT5s+fD1lZWQQFBWHBggX47bffADR8Hl1cXHDgwAGxZnc03QwJixyNBAgODiazZ88ms2bNotKX79y5QxQUFMjIkSPFbGw+++wzMmbMGPLs2TNSWlpKBg8eTJYsWUIIIdSia1paGpGTkyO2trZkyZIlxMTEhNjY2JAHDx50/cm1AbxjKig3N5d4eHgQTU1NIicnR6ysrMiaNWtIZWWlZAPv4eTk5JAJEyYQRUVFcu/ePUJIQzr+wYMHSb9+/cjQoUPJr7/+SoYOHUrGjRsn4Whp3gUtMh8RLdW7EELI+vXriaamJlm4cCEJDg4mBQUF5OeffybS0tJUDc7169eJmpoauXPnDiGkYV6cEEI2bdpETE1NybFjxwiXyyWVlZVkxIgRZN68eV10ZjQfIqIpXg6HQ7hcrti27OxsMnnyZCIvL0993iorK8nFixfJiBEjSP/+/Ymfn1+3n5KloafLPipE01ciyxmRrczr169x4cIFLFq0CH5+fhg3bhwEAgFMTU2xfPlyLF68GEKhEPfv34eysjKGDBkCQgjk5OQAAJcuXYKPjw+mTZsGGRkZyMjIQFtbGxUVFRI7V5ruD5PJRGpqKj7//HPo6elhwYIFUFdXh7u7OywsLHDkyBHIyMjgk08+QWRkJEaOHIlx48bBx8cHJSUl0NHR6XG1Wj0RWmQ+Qppa26SnpyM9PR2nTp2Cg4MDKioqkJCQAHV1dcqORkpKCjk5OTAzMwPwb+FoWloaioqK4O7uLtYrPS8vD0OGDEFtbS0UFBS65sRoPii4XC727duHuLg4SElJ4fnz5ygsLIS9vT3s7e3xn//8B8uWLYOuri68vLxw/fp1DB06FAAo2yWa7k/3Xpml6RKePHkCbW1tODg4UJX+AwYMoARGxNChQ5GXlwd/f3/Ex8cDAM6fPw9jY2MxL6v79++jsrISdnZ2tMDQtIisrCxWrVqFOXPmwMfHBz4+Prh27RqcnZ1x9+5deHl5YebMmcjKygKTyYSHhwcSExMlHTZNG6FFhgb/+c9/kJOTA6DBRgZAswaZs2fPRlhYGLS0tBAZGQkA+PPPP2FnZ0eNcADg4sWL0NbWhqOjY+cHT/NBY29vj9WrV0NNTQ1Xr17FkydP8N133yE+Ph5nzpzBt99+CwaDASsrKygrK8PS0lLSIdO0EQZp7mpCQ9MKBAIB9u7dCysrKwQEBFCPDx8+HA4ODti5cydUVVUlFyDNB0NmZia+++47pKenY8aMGVi+fDm1jc/ngxACHo8HRUVFCUZJ0x7okQxNmxAKhWKJA6tXrxYTmCdPniA7OxvW1ta0wNC0mt69e2P9+vWwt7fH33//jQMHDohtl5GRoQXmA4UeydC0G9KQAk9lrRFCwGAwUFxcDEII9PX1JRwhzYfG8+fPsWvXLjx58gSenp7YsGGDpEOieU9okaHpUERCQ0PTXvLy8vD111+jvLwcZ86coUfEHzi0yNDQ0HQ7CgsLIS0tDR0dHUmHQvOe0CJDQ0NDQ9Np0Av/NDQ0NDSdBi0yNDQ0NDSdxv8BzaR1QIUxZGIAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3384,25 +841,32 @@
     }
    ],
    "source": [
-    "plt.scatter(y_test, y_pred, alpha=0.5, c=color_column_test, cmap='viridis')\n",
-    "plt.xlabel('Actual')\n",
-    "plt.ylabel('Predicted')\n",
-    "plt.title('Actual vs Predicted')\n",
+    "results_test_df = pd.DataFrame(results_test, columns=['max_depth', 'n_features_to_select', 'mse_test'])\n",
     "\n",
-    "plt.xlim(0, 3*10**7)\n",
-    "plt.ylim(0, 3*10**7)\n",
+    "fig = plt.figure()\n",
+    "ax = fig.add_subplot(111, projection='3d')\n",
+    "\n",
+    "xs = results_test_df['max_depth']\n",
+    "ys = results_test_df['n_features_to_select']\n",
+    "zs = results_test_df['mse_test']\n",
+    "\n",
+    "ax.scatter(xs, ys, zs, c=zs, marker='o')\n",
+    "\n",
+    "ax.set_xlabel('Max Depth')\n",
+    "ax.set_ylabel('Number of Features')\n",
+    "ax.set_zlabel('MSE')\n",
     "\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 447,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -3412,464 +876,618 @@
     }
    ],
    "source": [
-    "plt.scatter(y_test, y_pred, alpha=0.5, c=color_column_test, cmap='viridis')\n",
+    "X_test_selected = best_model[1].transform(X_test)\n",
+    "y_pred_test = best_model[0].predict(X_test_selected)\n",
+    "\n",
+    "color_column = df_data['Gmina_miejska'] * 0 + df_data['Gmina_miejsko_wiejska'] * 10 + df_data['Gmina_wiejska'] * 5\n",
+    "\n",
+    "color_column_train, color_column_test = train_test_split(color_column, test_size=0.2, random_state=0)\n",
+    "\n",
+    "min_val = min(min(y_test), min(y_pred_test))\n",
+    "max_val = max(max(y_test), max(y_pred_test))\n",
+    "plt.plot([min_val, max_val], [min_val, max_val], 'r--')\n",
+    "\n",
+    "plt.scatter(y_test, y_pred_test, alpha=0.5, c=color_column_test, cmap='rainbow')\n",
     "plt.xlabel('Actual')\n",
     "plt.ylabel('Predicted')\n",
     "plt.title('Actual vs Predicted')\n",
     "\n",
-    "plt.xlim(0, 3*10**6)\n",
-    "plt.ylim(0, 3*10**6)\n",
+    "plt.xlim(0, 0.75*10**7)\n",
+    "plt.ylim(min(y_pred_test), 0.75*10**7)\n",
     "\n",
     "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 448,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "|--- Wplywy_z_oplaty_eksploatacyjnej <= -7751.83\n",
-      "|   |--- value: [132625880.92]\n",
-      "|--- Wplywy_z_oplaty_eksploatacyjnej >  -7751.83\n",
-      "|   |--- Wplywy_z_oplaty_skarbowej <= 4494.45\n",
-      "|   |   |--- Wymeldowania_ogolem <= 56.50\n",
-      "|   |   |   |--- Ludnosc_ogolem <= 3786.50\n",
-      "|   |   |   |   |--- Wplywy_z_oplaty_skarbowej <= 3336.50\n",
-      "|   |   |   |   |   |--- Dochody_podatek_rolny <= 52241.90\n",
-      "|   |   |   |   |   |   |--- value: [223720.88]\n",
-      "|   |   |   |   |   |--- Dochody_podatek_rolny >  52241.90\n",
-      "|   |   |   |   |   |   |--- Zameldowania_z_miast_ogolem <= 5.50\n",
-      "|   |   |   |   |   |   |   |--- value: [146790.02]\n",
-      "|   |   |   |   |   |   |--- Zameldowania_z_miast_ogolem >  5.50\n",
-      "|   |   |   |   |   |   |   |--- value: [147649.40]\n",
-      "|   |   |   |   |--- Wplywy_z_oplaty_skarbowej >  3336.50\n",
-      "|   |   |   |   |   |--- Wymeldowania_do_miast_mezczyzni <= 4.00\n",
-      "|   |   |   |   |   |   |--- value: [4988446.69]\n",
-      "|   |   |   |   |   |--- Wymeldowania_do_miast_mezczyzni >  4.00\n",
-      "|   |   |   |   |   |   |--- Dochody_z_najmu_i_dzierzawy <= 47510.35\n",
-      "|   |   |   |   |   |   |   |--- value: [8029322.26]\n",
-      "|   |   |   |   |   |   |--- Dochody_z_najmu_i_dzierzawy >  47510.35\n",
-      "|   |   |   |   |   |   |   |--- value: [7792324.25]\n",
-      "|   |   |   |--- Ludnosc_ogolem >  3786.50\n",
-      "|   |   |   |   |--- value: [35264017.53]\n",
-      "|   |   |--- Wymeldowania_ogolem >  56.50\n",
-      "|   |   |   |--- value: [106444841.13]\n",
-      "|   |--- Wplywy_z_oplaty_skarbowej >  4494.45\n",
-      "|   |   |--- Wplywy_z_oplaty_skarbowej <= 12330.00\n",
-      "|   |   |   |--- Dochody_podatek_od_dzialalnosci_gospodarczej <= -156.50\n",
-      "|   |   |   |   |--- value: [64551082.93]\n",
-      "|   |   |   |--- Dochody_podatek_od_dzialalnosci_gospodarczej >  -156.50\n",
-      "|   |   |   |   |--- Dochody_podatek_lesny <= 1523.00\n",
-      "|   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym <= 393.00\n",
-      "|   |   |   |   |   |   |--- value: [50040444.36]\n",
-      "|   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym >  393.00\n",
-      "|   |   |   |   |   |   |--- value: [593983.01]\n",
-      "|   |   |   |   |--- Dochody_podatek_lesny >  1523.00\n",
-      "|   |   |   |   |   |--- Dochody_podatek_PCC <= 206681.93\n",
-      "|   |   |   |   |   |   |--- Bezrobotni_mezczyzni <= 112.00\n",
-      "|   |   |   |   |   |   |   |--- Wplywy_z_oplaty_targowej <= 1527.50\n",
-      "|   |   |   |   |   |   |   |   |--- Zameldowania_z_miast_ogolem <= 26.50\n",
-      "|   |   |   |   |   |   |   |   |   |--- Zameldowania_mezczyzni <= 6.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Zameldowania_kobiety <= 6.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1029930.53]\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Zameldowania_kobiety >  6.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1142691.81]\n",
-      "|   |   |   |   |   |   |   |   |   |--- Zameldowania_mezczyzni >  6.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_lesny <= 8673.77\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1080165.70]\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_lesny >  8673.77\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 11\n",
-      "|   |   |   |   |   |   |   |   |--- Zameldowania_z_miast_ogolem >  26.50\n",
-      "|   |   |   |   |   |   |   |   |   |--- Wymeldowania_mezczyzni <= 27.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_srodkow_transportowych <= 144168.41\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_srodkow_transportowych >  144168.41\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3266211.60]\n",
-      "|   |   |   |   |   |   |   |   |   |--- Wymeldowania_mezczyzni >  27.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wymeldowania_na_wies_ogolem <= 34.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wymeldowania_na_wies_ogolem >  34.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [886572.54]\n",
-      "|   |   |   |   |   |   |   |--- Wplywy_z_oplaty_targowej >  1527.50\n",
-      "|   |   |   |   |   |   |   |   |--- Dochody_dofinansowanie_inwestycyjne <= 500.00\n",
-      "|   |   |   |   |   |   |   |   |   |--- Ludnosc_ogolem <= 3521.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wplywy_z_oplaty_targowej <= 9416.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [2852332.52]\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wplywy_z_oplaty_targowej >  9416.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [2869988.58]\n",
-      "|   |   |   |   |   |   |   |   |   |--- Ludnosc_ogolem >  3521.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_spadkow <= 6173.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [4041277.35]\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_spadkow >  6173.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
-      "|   |   |   |   |   |   |   |   |--- Dochody_dofinansowanie_inwestycyjne >  500.00\n",
-      "|   |   |   |   |   |   |   |   |   |--- Zameldowania_ze_wsi_mezczyzni <= 11.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_z_uslug <= 254898.80\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_z_uslug >  254898.80\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
-      "|   |   |   |   |   |   |   |   |   |--- Zameldowania_ze_wsi_mezczyzni >  11.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wymeldowania_na_wies_ogolem <= 21.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3043529.06]\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wymeldowania_na_wies_ogolem >  21.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1660371.13]\n",
-      "|   |   |   |   |   |   |--- Bezrobotni_mezczyzni >  112.00\n",
-      "|   |   |   |   |   |   |   |--- Dochody_podatek_od_srodkow_transportowych <= 112158.71\n",
-      "|   |   |   |   |   |   |   |   |--- Zameldowania_z_miast_kobiety <= 29.50\n",
-      "|   |   |   |   |   |   |   |   |   |--- Wymeldowania_na_wies_mezczyzni <= 11.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_razem <= 2784626.12\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_razem >  2784626.12\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1011703.86]\n",
-      "|   |   |   |   |   |   |   |   |   |--- Wymeldowania_na_wies_mezczyzni >  11.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Zameldowania_z_miast_ogolem <= 19.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Zameldowania_z_miast_ogolem >  19.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [5361057.90]\n",
-      "|   |   |   |   |   |   |   |   |--- Zameldowania_z_miast_kobiety >  29.50\n",
-      "|   |   |   |   |   |   |   |   |   |--- value: [17138312.52]\n",
-      "|   |   |   |   |   |   |   |--- Dochody_podatek_od_srodkow_transportowych >  112158.71\n",
-      "|   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_fizycznych <= 2183203.00\n",
-      "|   |   |   |   |   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_produkcyjnym <= 1325.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- value: [16479146.65]\n",
-      "|   |   |   |   |   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_produkcyjnym >  1325.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_razem <= 1710148.12\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_razem >  1710148.12\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [19699871.32]\n",
-      "|   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_fizycznych >  2183203.00\n",
-      "|   |   |   |   |   |   |   |   |   |--- Ludnosc_kobiety <= 3141.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- value: [5923600.26]\n",
-      "|   |   |   |   |   |   |   |   |   |--- Ludnosc_kobiety >  3141.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- value: [6925131.43]\n",
-      "|   |   |   |   |   |--- Dochody_podatek_PCC >  206681.93\n",
-      "|   |   |   |   |   |   |--- Bezrobotne_kobiety <= 57.00\n",
-      "|   |   |   |   |   |   |   |--- value: [22537878.79]\n",
-      "|   |   |   |   |   |   |--- Bezrobotne_kobiety >  57.00\n",
-      "|   |   |   |   |   |   |   |--- value: [24414621.52]\n",
-      "|   |   |--- Wplywy_z_oplaty_skarbowej >  12330.00\n",
-      "|   |   |   |--- Wymeldowania_na_wies_mezczyzni <= 20.50\n",
-      "|   |   |   |   |--- Dochody_podatek_lesny <= 15.00\n",
-      "|   |   |   |   |   |--- Dochody_dofinansowanie_razem <= 257331.97\n",
-      "|   |   |   |   |   |   |--- value: [3008745.87]\n",
-      "|   |   |   |   |   |--- Dochody_dofinansowanie_razem >  257331.97\n",
-      "|   |   |   |   |   |   |--- value: [70149861.07]\n",
-      "|   |   |   |   |--- Dochody_podatek_lesny >  15.00\n",
-      "|   |   |   |   |   |--- Turysci_zagraniczni <= 3835.00\n",
-      "|   |   |   |   |   |   |--- Wplywy_z_oplaty_targowej <= 1021444.50\n",
-      "|   |   |   |   |   |   |   |--- Wojewodztwo_Opolskie <= 0.50\n",
-      "|   |   |   |   |   |   |   |   |--- Turysci_zagraniczni <= 620.50\n",
-      "|   |   |   |   |   |   |   |   |   |--- Dochody_z_najmu_i_dzierzawy <= 60187.61\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_z_najmu_i_dzierzawy <= 59932.48\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 16\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_z_najmu_i_dzierzawy >  59932.48\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [23878760.16]\n",
-      "|   |   |   |   |   |   |   |   |   |--- Dochody_z_najmu_i_dzierzawy >  60187.61\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_ogolem <= 6881.45\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 31\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_ogolem >  6881.45\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
-      "|   |   |   |   |   |   |   |   |--- Turysci_zagraniczni >  620.50\n",
-      "|   |   |   |   |   |   |   |   |   |--- Dochody_podatek_rolny <= 1169820.38\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wojewodztwo_Lubelskie <= 0.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wojewodztwo_Lubelskie >  0.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [10510654.58]\n",
-      "|   |   |   |   |   |   |   |   |   |--- Dochody_podatek_rolny >  1169820.38\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Ludnosc_w_wieku_produkcyjnym <= 7303.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [16143701.14]\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Ludnosc_w_wieku_produkcyjnym >  7303.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [9428912.87]\n",
-      "|   |   |   |   |   |   |   |--- Wojewodztwo_Opolskie >  0.50\n",
-      "|   |   |   |   |   |   |   |   |--- Gestosc_zaludnienia <= 0.04\n",
-      "|   |   |   |   |   |   |   |   |   |--- value: [29647883.87]\n",
-      "|   |   |   |   |   |   |   |   |--- Gestosc_zaludnienia >  0.04\n",
-      "|   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_spadkow <= 66477.52\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_dzialalnosci_gospodarczej <= 19548.15\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 5\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_dzialalnosci_gospodarczej >  19548.15\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
-      "|   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_spadkow >  66477.52\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- value: [11839160.68]\n",
-      "|   |   |   |   |   |   |--- Wplywy_z_oplaty_targowej >  1021444.50\n",
-      "|   |   |   |   |   |   |   |--- value: [16775284.13]\n",
-      "|   |   |   |   |   |--- Turysci_zagraniczni >  3835.00\n",
-      "|   |   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym <= 2067.50\n",
-      "|   |   |   |   |   |   |   |--- Ludnosc_w_wieku_poprodukcyjnym <= 1294.00\n",
-      "|   |   |   |   |   |   |   |   |--- Dochody_podatek_od_srodkow_transportowych <= 350567.75\n",
-      "|   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_nieruchomosci <= 10820965.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wymeldowania_na_wies_ogolem <= 22.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wymeldowania_na_wies_ogolem >  22.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [5838.01]\n",
-      "|   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_nieruchomosci >  10820965.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- value: [523333.48]\n",
-      "|   |   |   |   |   |   |   |   |--- Dochody_podatek_od_srodkow_transportowych >  350567.75\n",
-      "|   |   |   |   |   |   |   |   |   |--- value: [3219570.01]\n",
-      "|   |   |   |   |   |   |   |--- Ludnosc_w_wieku_poprodukcyjnym >  1294.00\n",
-      "|   |   |   |   |   |   |   |   |--- Bezrobotni_ogolem <= 326.25\n",
-      "|   |   |   |   |   |   |   |   |   |--- value: [9680488.61]\n",
-      "|   |   |   |   |   |   |   |   |--- Bezrobotni_ogolem >  326.25\n",
-      "|   |   |   |   |   |   |   |   |   |--- value: [6533125.31]\n",
-      "|   |   |   |   |   |   |--- Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym >  2067.50\n",
-      "|   |   |   |   |   |   |   |--- value: [68031871.39]\n",
-      "|   |   |   |--- Wymeldowania_na_wies_mezczyzni >  20.50\n",
-      "|   |   |   |   |--- Dochody_podatek_rolny <= 3939909.12\n",
-      "|   |   |   |   |   |--- Turysci_zagraniczni <= 14.50\n",
-      "|   |   |   |   |   |   |--- Dochody_podatek_od_srodkow_transportowych <= 510970.09\n",
-      "|   |   |   |   |   |   |   |--- Dochody_podatek_od_dzialalnosci_gospodarczej <= 574395.34\n",
-      "|   |   |   |   |   |   |   |   |--- Bezrobotni_do_25_roku_zycia <= 287.25\n",
-      "|   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_ogolem <= 4332.75\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_w_relacji_do_sredniej <= 97.90\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 27\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_w_relacji_do_sredniej >  97.90\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
-      "|   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_ogolem >  4332.75\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_prywatnych <= 1913257.88\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_prywatnych >  1913257.88\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 7\n",
-      "|   |   |   |   |   |   |   |   |--- Bezrobotni_do_25_roku_zycia >  287.25\n",
-      "|   |   |   |   |   |   |   |   |   |--- value: [4226674.84]\n",
-      "|   |   |   |   |   |   |   |--- Dochody_podatek_od_dzialalnosci_gospodarczej >  574395.34\n",
-      "|   |   |   |   |   |   |   |   |--- value: [11386418.96]\n",
-      "|   |   |   |   |   |   |--- Dochody_podatek_od_srodkow_transportowych >  510970.09\n",
-      "|   |   |   |   |   |   |   |--- Wplywy_z_innych_lokalnych_oplat <= 4156267.12\n",
-      "|   |   |   |   |   |   |   |   |--- Wplywy_z_innych_lokalnych_oplat <= 4121841.12\n",
-      "|   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_w_relacji_do_sredniej <= 111.65\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Ludnosc_na_1_km2 <= 233.25\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 21\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Ludnosc_na_1_km2 >  233.25\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 17\n",
-      "|   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_w_relacji_do_sredniej >  111.65\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Bezrobotni_ogolem <= 485.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3730137.10]\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Bezrobotni_ogolem >  485.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [307553.43]\n",
-      "|   |   |   |   |   |   |   |   |--- Wplywy_z_innych_lokalnych_oplat >  4121841.12\n",
-      "|   |   |   |   |   |   |   |   |   |--- Wplywy_z_oplaty_eksploatacyjnej <= 14062.80\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- value: [4671836.69]\n",
-      "|   |   |   |   |   |   |   |   |   |--- Wplywy_z_oplaty_eksploatacyjnej >  14062.80\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- value: [863032.31]\n",
-      "|   |   |   |   |   |   |   |--- Wplywy_z_innych_lokalnych_oplat >  4156267.12\n",
-      "|   |   |   |   |   |   |   |   |--- Gestosc_zaludnienia <= 3.88\n",
-      "|   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_ogolem <= 3962.87\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wskaznik_urbanizacji <= 45.15\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wskaznik_urbanizacji >  45.15\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 22\n",
-      "|   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_ogolem >  3962.87\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wplywy_z_oplaty_eksploatacyjnej <= 5666409.75\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 22\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wplywy_z_oplaty_eksploatacyjnej >  5666409.75\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1478204.01]\n",
-      "|   |   |   |   |   |   |   |   |--- Gestosc_zaludnienia >  3.88\n",
-      "|   |   |   |   |   |   |   |   |   |--- value: [2622969.18]\n",
-      "|   |   |   |   |   |--- Turysci_zagraniczni >  14.50\n",
-      "|   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_prywatnych <= 115357.00\n",
-      "|   |   |   |   |   |   |   |--- Bezrobotni_do_25_roku_zycia <= 231.75\n",
-      "|   |   |   |   |   |   |   |   |--- Dochody_podatek_rolny <= 751889.41\n",
-      "|   |   |   |   |   |   |   |   |   |--- Miejsca_noclegowe_caloroczne <= 26.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- value: [2137619.22]\n",
-      "|   |   |   |   |   |   |   |   |   |--- Miejsca_noclegowe_caloroczne >  26.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_dzialalnosci_gospodarczej <= 21053.86\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 2\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_od_dzialalnosci_gospodarczej >  21053.86\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 4\n",
-      "|   |   |   |   |   |   |   |   |--- Dochody_podatek_rolny >  751889.41\n",
-      "|   |   |   |   |   |   |   |   |   |--- Wymeldowania_do_miast_ogolem <= 103.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- value: [6335790.21]\n",
-      "|   |   |   |   |   |   |   |   |   |--- Wymeldowania_do_miast_ogolem >  103.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Obiekty_caloroczne <= 1.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3478051.49]\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Obiekty_caloroczne >  1.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [2808492.26]\n",
-      "|   |   |   |   |   |   |   |--- Bezrobotni_do_25_roku_zycia >  231.75\n",
-      "|   |   |   |   |   |   |   |   |--- Zameldowania_mezczyzni <= 87.50\n",
-      "|   |   |   |   |   |   |   |   |   |--- value: [9530029.04]\n",
-      "|   |   |   |   |   |   |   |   |--- Zameldowania_mezczyzni >  87.50\n",
-      "|   |   |   |   |   |   |   |   |   |--- value: [14823699.73]\n",
-      "|   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_prywatnych >  115357.00\n",
-      "|   |   |   |   |   |   |   |--- Wojewodztwo_Opolskie <= 0.50\n",
-      "|   |   |   |   |   |   |   |   |--- Dochody_z_uslug <= 4892.30\n",
-      "|   |   |   |   |   |   |   |   |   |--- Wymeldowania_kobiety <= 110.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- value: [726982.20]\n",
-      "|   |   |   |   |   |   |   |   |   |--- Wymeldowania_kobiety >  110.00\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- value: [7756910.77]\n",
-      "|   |   |   |   |   |   |   |   |--- Dochody_z_uslug >  4892.30\n",
-      "|   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_w_relacji_do_sredniej <= 70.60\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wskaznik_urbanizacji <= 58.35\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [3473795.48]\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wskaznik_urbanizacji >  58.35\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [4769144.63]\n",
-      "|   |   |   |   |   |   |   |   |   |--- Wynagrodzenie_w_relacji_do_sredniej >  70.60\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wplywy_z_innych_lokalnych_oplat <= 566851.53\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 9\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Wplywy_z_innych_lokalnych_oplat >  566851.53\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 23\n",
-      "|   |   |   |   |   |   |   |--- Wojewodztwo_Opolskie >  0.50\n",
-      "|   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_fizycznych <= 9089692.00\n",
-      "|   |   |   |   |   |   |   |   |   |--- value: [12432814.85]\n",
-      "|   |   |   |   |   |   |   |   |--- Udzialy_w_podatkach_dochodowych_od_osob_fizycznych >  9089692.00\n",
-      "|   |   |   |   |   |   |   |   |   |--- Zameldowania_z_miast_kobiety <= 140.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_lesny <= 120971.51\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- truncated branch of depth 3\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- Dochody_podatek_lesny >  120971.51\n",
-      "|   |   |   |   |   |   |   |   |   |   |   |--- value: [1383897.89]\n",
-      "|   |   |   |   |   |   |   |   |   |--- Zameldowania_z_miast_kobiety >  140.50\n",
-      "|   |   |   |   |   |   |   |   |   |   |--- value: [4817436.62]\n",
-      "|   |   |   |   |--- Dochody_podatek_rolny >  3939909.12\n",
-      "|   |   |   |   |   |--- value: [9110050.20]\n",
-      "\n"
-     ]
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
    "source": [
-    "print(export_text(model, feature_names=feature_names))"
+    "results_train_df = pd.DataFrame(results_train, columns=['max_depth', 'n_features_to_select', 'mse_train'])\n",
+    "\n",
+    "heatmap_data = results_train_df.pivot(index='max_depth', columns='n_features_to_select', values='mse_train')\n",
+    "\n",
+    "sns.heatmap(heatmap_data, annot=False, fmt=\".2f\", cmap=\"viridis\")\n",
+    "\n",
+    "plt.title('MSE')\n",
+    "plt.xlabel('Number of Features')\n",
+    "plt.ylabel('Max Depth')\n",
+    "plt.show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 449,
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "results_test_df = pd.DataFrame(results_test, columns=['max_depth', 'n_features_to_select', 'mse_test'])\n",
+    "\n",
+    "heatmap_data = results_test_df.pivot(index='max_depth', columns='n_features_to_select', values='mse_test')\n",
+    "\n",
+    "sns.heatmap(heatmap_data, annot=False, fmt=\".2f\", cmap=\"viridis\")\n",
+    "\n",
+    "plt.title('MSE')\n",
+    "plt.xlabel('Number of Features')\n",
+    "plt.ylabel('Max Depth')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.32120 — Wplywy_z_oplaty_eksploatacyjnej\n",
-      "0.15728 — Wymeldowania_ogolem\n",
-      "0.07384 — Wplywy_z_oplaty_skarbowej\n",
-      "0.07115 — Dochody_podatek_od_dzialalnosci_gospodarczej\n",
-      "0.07098 — Ludnosc_kobiety_w_wieku_produkcyjnym_mobilnym\n",
-      "0.06455 — Dochody_podatek_lesny\n",
-      "0.04225 — Dochody_dofinansowanie_razem\n",
-      "0.02294 — Ludnosc_kobiety_w_wieku_produkcyjnym_niemobilnym\n",
-      "0.01696 — Dochody_podatek_PCC\n",
-      "0.01605 — Ludnosc_ogolem\n",
-      "0.01528 — Turysci_zagraniczni\n",
-      "0.01449 — Dochody_podatek_od_srodkow_transportowych\n",
-      "0.01294 — Gestosc_zaludnienia\n",
-      "0.01121 — Dochody_z_najmu_i_dzierzawy\n",
-      "0.00803 — Bezrobotni_mezczyzni\n",
-      "0.00639 — Udzialy_w_podatkach_dochodowych_od_osob_fizycznych\n",
-      "0.00608 — Dochody_podatek_rolny\n",
-      "0.00596 — Wymeldowania_na_wies_mezczyzni\n",
-      "0.00590 — Wynagrodzenie_ogolem\n",
-      "0.00527 — Wymeldowania_kobiety\n",
-      "0.00507 — Wplywy_z_oplaty_targowej\n",
-      "0.00475 — Zameldowania_z_miast_kobiety\n",
-      "0.00388 — Saldo_migracji_na_1000_ludnosci\n",
-      "0.00365 — Bezrobotni_do_25_roku_zycia\n",
-      "0.00357 — Wojewodztwo_Opolskie\n",
-      "0.00292 — Zmiana_liczby_ludnosci\n",
-      "0.00276 — Wynagrodzenie_w_relacji_do_sredniej\n",
-      "0.00266 — Dochody_podatek_od_spadkow\n",
-      "0.00202 — Ludnosc_w_wieku_poprodukcyjnym\n",
-      "0.00195 — Udzialy_w_podatkach_dochodowych_od_osob_prywatnych\n",
-      "0.00188 — Wplywy_z_innych_lokalnych_oplat\n",
-      "0.00156 — Wojewodztwo_Lubelskie\n",
-      "0.00134 — Wymeldowania_na_wies_kobiety\n",
-      "0.00106 — Udzialy_w_podatkach_dochodowych_razem\n",
-      "0.00088 — Ludnosc_kobiety_w_wieku_poprodukcyjnym\n",
-      "0.00088 — Dochody_z_uslug\n",
-      "0.00079 — Powierzchnia\n",
-      "0.00066 — Dochody_podatek_od_nieruchomosci\n",
-      "0.00062 — Zameldowania_z_miast_ogolem\n",
-      "0.00057 — Zameldowania_z_miast_mezczyzni\n",
-      "0.00055 — Zameldowania_mezczyzni\n",
-      "0.00054 — Zameldowania_ze_wsi_mezczyzni\n",
-      "0.00047 — Ludnosc_w_wieku_produkcyjnym\n",
-      "0.00042 — Miejsca_noclegowe_ogolem\n",
-      "0.00036 — Dochody_dofinansowanie_inwestycyjne\n",
-      "0.00036 — Ludnosc_mezczyzni_w_wieku_produkcyjnym\n",
-      "0.00035 — Wymeldowania_na_wies_ogolem\n",
-      "0.00032 — Ludnosc_kobiety_w_wieku_produkcyjnym\n",
-      "0.00030 — Wojewodztwo_Podkarpackie\n",
-      "0.00028 — Wymeldowania_do_miast_ogolem\n",
-      "0.00027 — Wymeldowania_do_miast_kobiety\n",
-      "0.00027 — Dochody_z_majatku\n",
-      "0.00023 — Bezrobotne_kobiety\n",
-      "0.00021 — Wymeldowania_do_miast_mezczyzni\n",
-      "0.00021 — Ludnosc_w_wieku_produkcyjnym_mobilnym\n",
-      "0.00021 — Bezrobotni_ogolem\n",
-      "0.00021 — Ludnosc_w_wieku_przedprodukcyjnym\n",
-      "0.00018 — Ludnosc_na_1_km2\n",
-      "0.00017 — Obiekty_ogolem\n",
-      "0.00017 — Wymeldowania_mezczyzni\n",
-      "0.00014 — Ludnosc_mezczyzni_w_wieku_poprodukcyjnym\n",
-      "0.00014 — Zameldowania_ze_wsi_kobiety\n",
-      "0.00012 — Wskaznik_urbanizacji\n",
-      "0.00011 — Zameldowania_kobiety\n",
-      "0.00011 — Miejsca_noclegowe_caloroczne\n",
-      "0.00010 — Bezrobotni_powyzej_50_roku_zycia\n",
-      "0.00007 — Zameldowania_ze_wsi_ogolem\n",
-      "0.00006 — Wojewodztwo_Warminsko_Mazurskie\n",
-      "0.00006 — Dochody_razem\n",
-      "0.00005 — Ludnosc_mezczyzni_w_wieku_produkcyjnym_niemobilnym\n",
-      "0.00004 — Saldo_migracji\n",
-      "0.00004 — Wojewodztwo_Slaskie\n",
-      "0.00004 — Wojewodztwo_Pomorskie\n",
-      "0.00003 — Ludnosc_mezczyzni_w_wieku_przedprodukcyjnym\n",
-      "0.00003 — Dlugotrwale_bezrobotni\n",
-      "0.00002 — Ludnosc_mezczyzni_w_wieku_produkcyjnym_mobilnym\n",
-      "0.00002 — Wojewodztwo_Lodzkie\n",
-      "0.00001 — Turysci_ogolem\n",
-      "0.00001 — Ludnosc_kobiety\n",
-      "0.00001 — Obiekty_caloroczne\n",
-      "0.00001 — Ludnosc\n",
-      "0.00001 — Ludnosc_w_wieku_produkcyjnym_niemobilnym\n",
-      "0.00001 — Wojewodztwo_Mazowieckie\n",
-      "0.00001 — Dochody_podatek_odrebne_ustawy\n",
-      "0.00001 — Wojewodztwo_Dolnoslaskie\n",
-      "0.00000 — Zameldowania_ogolem\n",
-      "0.00000 — Wojewodztwo_Lubuskie\n",
-      "0.00000 — Ludnosc_mezczyzni\n",
-      "0.00000 — Wojewodztwo_Podlaskie\n",
-      "0.00000 — Ludnosc_kobiety_w_wieku_przedprodukcyjnym\n",
-      "0.00000 — Wojewodztwo_Wielkopolskie\n",
-      "0.00000 — Wojewodztwo_Malopolskie\n",
-      "0.00000 — Wojewodztwo_Zachodniopomorskie\n",
-      "0.00000 — Wojewodztwo_Kujawsko_Pomorskie\n",
-      "0.00000 — Wojewodztwo_Swietokrzyskie\n"
+      "0.3853 — Dochody_podatek_od_nieruchomosci\n",
+      "0.2161 — Dochody_podatek_od_srodkow_transportowych\n",
+      "0.0911 — Powierzchnia\n",
+      "0.0670 — Wynagrodzenie_ogolem\n",
+      "0.0581 — Dochody_podatek_PCC\n",
+      "0.0424 — Dochody_razem\n",
+      "0.0292 — Dochody_z_majatku\n",
+      "0.0286 — Dochody_podatek_od_spadkow\n",
+      "0.0277 — Dochody_podatek_rolny\n",
+      "0.0225 — Dochody_podatek_od_dzialalnosci_gospodarczej\n",
+      "0.0156 — Wynagrodzenie_w_relacji_do_sredniej\n",
+      "0.0107 — Dochody_podatek_odrebne_ustawy\n",
+      "0.0057 — Dochody_podatek_lesny\n"
      ]
     }
    ],
    "source": [
-    "feature_importance = dict(zip(feature_names, model.feature_importances_))\n",
+    "feature_importance = dict(zip(feature_names, best_model[0].feature_importances_))\n",
     "for feature, importance in sorted(feature_importance.items(), key=lambda x: x[1], reverse=True):\n",
-    "    print(f'{importance:.5f} \\u2014 {feature}')"
+    "    print(f'{importance:.4f} \\u2014 {feature}')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 46,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_14224\\3704736663.py:1: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
+      "  X_test['Przewidziana Suma'] = y_pred_test\n",
+      "C:\\Users\\micha\\AppData\\Local\\Temp\\ipykernel_14224\\3704736663.py:2: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
+      "  X_test['Suma'] = y_test\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Powierzchnia</th>\n",
+       "      <th>Wynagrodzenie_ogolem</th>\n",
+       "      <th>Wynagrodzenie_w_relacji_do_sredniej</th>\n",
+       "      <th>Dochody_podatek_lesny</th>\n",
+       "      <th>Dochody_podatek_PCC</th>\n",
+       "      <th>Dochody_podatek_od_dzialalnosci_gospodarczej</th>\n",
+       "      <th>Dochody_podatek_od_nieruchomosci</th>\n",
+       "      <th>Dochody_podatek_od_spadkow</th>\n",
+       "      <th>Dochody_podatek_od_srodkow_transportowych</th>\n",
+       "      <th>Dochody_podatek_rolny</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Bezrobotni_mezczyzni</th>\n",
+       "      <th>Bezrobotni_ogolem</th>\n",
+       "      <th>Bezrobotni_powyzej_50_roku_zycia</th>\n",
+       "      <th>Gmina_miejska</th>\n",
+       "      <th>Gmina_miejsko_wiejska</th>\n",
+       "      <th>Gmina_wiejska</th>\n",
+       "      <th>Odleglosc_Warszawa</th>\n",
+       "      <th>Odleglosc_od_centrum_decyzyjnego</th>\n",
+       "      <th>Przewidziana Suma</th>\n",
+       "      <th>Suma</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1319</th>\n",
+       "      <td>100.0</td>\n",
+       "      <td>3779.10</td>\n",
+       "      <td>72.9</td>\n",
+       "      <td>93981.92</td>\n",
+       "      <td>365408.23</td>\n",
+       "      <td>5990.13</td>\n",
+       "      <td>3065899.23</td>\n",
+       "      <td>44610.00</td>\n",
+       "      <td>189528.36</td>\n",
+       "      <td>910440.39</td>\n",
+       "      <td>...</td>\n",
+       "      <td>142.0</td>\n",
+       "      <td>367.0</td>\n",
+       "      <td>81.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>261.0</td>\n",
+       "      <td>77.0</td>\n",
+       "      <td>1860350.76</td>\n",
+       "      <td>1860350.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2619</th>\n",
+       "      <td>102.0</td>\n",
+       "      <td>3463.10</td>\n",
+       "      <td>71.6</td>\n",
+       "      <td>99953.98</td>\n",
+       "      <td>104129.00</td>\n",
+       "      <td>12044.14</td>\n",
+       "      <td>2395129.05</td>\n",
+       "      <td>14909.00</td>\n",
+       "      <td>307592.55</td>\n",
+       "      <td>79375.79</td>\n",
+       "      <td>...</td>\n",
+       "      <td>57.0</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>32.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>198.0</td>\n",
+       "      <td>91.0</td>\n",
+       "      <td>1498567.97</td>\n",
+       "      <td>1498567.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>898</th>\n",
+       "      <td>125.0</td>\n",
+       "      <td>4786.11</td>\n",
+       "      <td>86.7</td>\n",
+       "      <td>3707.00</td>\n",
+       "      <td>187923.00</td>\n",
+       "      <td>2366.98</td>\n",
+       "      <td>1150221.59</td>\n",
+       "      <td>55974.84</td>\n",
+       "      <td>197984.69</td>\n",
+       "      <td>981887.41</td>\n",
+       "      <td>...</td>\n",
+       "      <td>71.0</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>33.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>100.0</td>\n",
+       "      <td>46.0</td>\n",
+       "      <td>486083.96</td>\n",
+       "      <td>486083.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>615</th>\n",
+       "      <td>172.0</td>\n",
+       "      <td>3764.14</td>\n",
+       "      <td>77.9</td>\n",
+       "      <td>256320.29</td>\n",
+       "      <td>81812.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>1006100.88</td>\n",
+       "      <td>34139.00</td>\n",
+       "      <td>35029.00</td>\n",
+       "      <td>241699.70</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>98.5</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>162.0</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>3493097.22</td>\n",
+       "      <td>3493097.22</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1317</th>\n",
+       "      <td>86.0</td>\n",
+       "      <td>3779.10</td>\n",
+       "      <td>72.9</td>\n",
+       "      <td>50013.00</td>\n",
+       "      <td>361760.03</td>\n",
+       "      <td>12538.98</td>\n",
+       "      <td>5595064.42</td>\n",
+       "      <td>35481.32</td>\n",
+       "      <td>286227.23</td>\n",
+       "      <td>689321.45</td>\n",
+       "      <td>...</td>\n",
+       "      <td>93.5</td>\n",
+       "      <td>287.0</td>\n",
+       "      <td>73.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>249.0</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>1860350.76</td>\n",
+       "      <td>1860350.76</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>3289</th>\n",
+       "      <td>110.0</td>\n",
+       "      <td>4195.22</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>14385.61</td>\n",
+       "      <td>196259.08</td>\n",
+       "      <td>3860.54</td>\n",
+       "      <td>1036514.37</td>\n",
+       "      <td>20938.34</td>\n",
+       "      <td>148191.22</td>\n",
+       "      <td>858593.83</td>\n",
+       "      <td>...</td>\n",
+       "      <td>38.5</td>\n",
+       "      <td>87.5</td>\n",
+       "      <td>23.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>197.0</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>183762.63</td>\n",
+       "      <td>183762.63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2771</th>\n",
+       "      <td>131.0</td>\n",
+       "      <td>4515.28</td>\n",
+       "      <td>99.7</td>\n",
+       "      <td>188729.01</td>\n",
+       "      <td>245472.61</td>\n",
+       "      <td>14341.25</td>\n",
+       "      <td>3335288.19</td>\n",
+       "      <td>57052.58</td>\n",
+       "      <td>267934.40</td>\n",
+       "      <td>48568.21</td>\n",
+       "      <td>...</td>\n",
+       "      <td>135.0</td>\n",
+       "      <td>326.5</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>338.0</td>\n",
+       "      <td>83.0</td>\n",
+       "      <td>913353.54</td>\n",
+       "      <td>913353.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1269</th>\n",
+       "      <td>32.0</td>\n",
+       "      <td>3400.75</td>\n",
+       "      <td>75.1</td>\n",
+       "      <td>37752.65</td>\n",
+       "      <td>125139.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>990652.07</td>\n",
+       "      <td>9266.00</td>\n",
+       "      <td>202607.60</td>\n",
+       "      <td>86466.96</td>\n",
+       "      <td>...</td>\n",
+       "      <td>67.5</td>\n",
+       "      <td>139.0</td>\n",
+       "      <td>32.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>309.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>108165.60</td>\n",
+       "      <td>108165.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1246</th>\n",
+       "      <td>126.0</td>\n",
+       "      <td>3681.50</td>\n",
+       "      <td>76.1</td>\n",
+       "      <td>238373.86</td>\n",
+       "      <td>319604.16</td>\n",
+       "      <td>1755.85</td>\n",
+       "      <td>3169234.07</td>\n",
+       "      <td>55944.22</td>\n",
+       "      <td>233611.73</td>\n",
+       "      <td>41946.11</td>\n",
+       "      <td>...</td>\n",
+       "      <td>119.0</td>\n",
+       "      <td>350.5</td>\n",
+       "      <td>91.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>312.0</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>26439.77</td>\n",
+       "      <td>26439.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>519</th>\n",
+       "      <td>159.0</td>\n",
+       "      <td>3691.03</td>\n",
+       "      <td>76.3</td>\n",
+       "      <td>240844.33</td>\n",
+       "      <td>138434.00</td>\n",
+       "      <td>5983.54</td>\n",
+       "      <td>1326617.29</td>\n",
+       "      <td>19542.30</td>\n",
+       "      <td>63370.83</td>\n",
+       "      <td>542170.27</td>\n",
+       "      <td>...</td>\n",
+       "      <td>186.0</td>\n",
+       "      <td>387.5</td>\n",
+       "      <td>73.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>166.0</td>\n",
+       "      <td>55.0</td>\n",
+       "      <td>2563499.46</td>\n",
+       "      <td>2563499.46</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>214 rows × 102 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Powierzchnia  Wynagrodzenie_ogolem  Wynagrodzenie_w_relacji_do_sredniej   \n",
+       "1319         100.0               3779.10                                 72.9  \\\n",
+       "2619         102.0               3463.10                                 71.6   \n",
+       "898          125.0               4786.11                                 86.7   \n",
+       "615          172.0               3764.14                                 77.9   \n",
+       "1317          86.0               3779.10                                 72.9   \n",
+       "...            ...                   ...                                  ...   \n",
+       "3289         110.0               4195.22                                 76.0   \n",
+       "2771         131.0               4515.28                                 99.7   \n",
+       "1269          32.0               3400.75                                 75.1   \n",
+       "1246         126.0               3681.50                                 76.1   \n",
+       "519          159.0               3691.03                                 76.3   \n",
+       "\n",
+       "      Dochody_podatek_lesny  Dochody_podatek_PCC   \n",
+       "1319               93981.92            365408.23  \\\n",
+       "2619               99953.98            104129.00   \n",
+       "898                 3707.00            187923.00   \n",
+       "615               256320.29             81812.00   \n",
+       "1317               50013.00            361760.03   \n",
+       "...                     ...                  ...   \n",
+       "3289               14385.61            196259.08   \n",
+       "2771              188729.01            245472.61   \n",
+       "1269               37752.65            125139.00   \n",
+       "1246              238373.86            319604.16   \n",
+       "519               240844.33            138434.00   \n",
+       "\n",
+       "      Dochody_podatek_od_dzialalnosci_gospodarczej   \n",
+       "1319                                       5990.13  \\\n",
+       "2619                                      12044.14   \n",
+       "898                                        2366.98   \n",
+       "615                                           0.00   \n",
+       "1317                                      12538.98   \n",
+       "...                                            ...   \n",
+       "3289                                       3860.54   \n",
+       "2771                                      14341.25   \n",
+       "1269                                          0.00   \n",
+       "1246                                       1755.85   \n",
+       "519                                        5983.54   \n",
+       "\n",
+       "      Dochody_podatek_od_nieruchomosci  Dochody_podatek_od_spadkow   \n",
+       "1319                        3065899.23                    44610.00  \\\n",
+       "2619                        2395129.05                    14909.00   \n",
+       "898                         1150221.59                    55974.84   \n",
+       "615                         1006100.88                    34139.00   \n",
+       "1317                        5595064.42                    35481.32   \n",
+       "...                                ...                         ...   \n",
+       "3289                        1036514.37                    20938.34   \n",
+       "2771                        3335288.19                    57052.58   \n",
+       "1269                         990652.07                     9266.00   \n",
+       "1246                        3169234.07                    55944.22   \n",
+       "519                         1326617.29                    19542.30   \n",
+       "\n",
+       "      Dochody_podatek_od_srodkow_transportowych  Dochody_podatek_rolny  ...   \n",
+       "1319                                  189528.36              910440.39  ...  \\\n",
+       "2619                                  307592.55               79375.79  ...   \n",
+       "898                                   197984.69              981887.41  ...   \n",
+       "615                                    35029.00              241699.70  ...   \n",
+       "1317                                  286227.23              689321.45  ...   \n",
+       "...                                         ...                    ...  ...   \n",
+       "3289                                  148191.22              858593.83  ...   \n",
+       "2771                                  267934.40               48568.21  ...   \n",
+       "1269                                  202607.60               86466.96  ...   \n",
+       "1246                                  233611.73               41946.11  ...   \n",
+       "519                                    63370.83              542170.27  ...   \n",
+       "\n",
+       "      Bezrobotni_mezczyzni  Bezrobotni_ogolem   \n",
+       "1319                 142.0              367.0  \\\n",
+       "2619                  57.0              130.0   \n",
+       "898                   71.0              135.0   \n",
+       "615                   30.0               98.5   \n",
+       "1317                  93.5              287.0   \n",
+       "...                    ...                ...   \n",
+       "3289                  38.5               87.5   \n",
+       "2771                 135.0              326.5   \n",
+       "1269                  67.5              139.0   \n",
+       "1246                 119.0              350.5   \n",
+       "519                  186.0              387.5   \n",
+       "\n",
+       "      Bezrobotni_powyzej_50_roku_zycia  Gmina_miejska  Gmina_miejsko_wiejska   \n",
+       "1319                              81.5              0                      1  \\\n",
+       "2619                              32.5              0                      0   \n",
+       "898                               33.5              0                      0   \n",
+       "615                               25.0              0                      0   \n",
+       "1317                              73.5              0                      0   \n",
+       "...                                ...            ...                    ...   \n",
+       "3289                              23.5              0                      0   \n",
+       "2771                              75.0              0                      0   \n",
+       "1269                              32.5              0                      0   \n",
+       "1246                              91.5              0                      1   \n",
+       "519                               73.0              0                      0   \n",
+       "\n",
+       "      Gmina_wiejska  Odleglosc_Warszawa  Odleglosc_od_centrum_decyzyjnego   \n",
+       "1319              0               261.0                              77.0  \\\n",
+       "2619              1               198.0                              91.0   \n",
+       "898               1               100.0                              46.0   \n",
+       "615               1               162.0                              48.0   \n",
+       "1317              1               249.0                              79.0   \n",
+       "...             ...                 ...                               ...   \n",
+       "3289              1               197.0                             126.0   \n",
+       "2771              1               338.0                              83.0   \n",
+       "1269              1               309.0                              55.0   \n",
+       "1246              0               312.0                              86.0   \n",
+       "519               1               166.0                              55.0   \n",
+       "\n",
+       "      Przewidziana Suma        Suma  \n",
+       "1319         1860350.76  1860350.76  \n",
+       "2619         1498567.97  1498567.97  \n",
+       "898           486083.96   486083.96  \n",
+       "615          3493097.22  3493097.22  \n",
+       "1317         1860350.76  1860350.76  \n",
+       "...                 ...         ...  \n",
+       "3289          183762.63   183762.63  \n",
+       "2771          913353.54   913353.54  \n",
+       "1269          108165.60   108165.60  \n",
+       "1246           26439.77    26439.77  \n",
+       "519          2563499.46  2563499.46  \n",
+       "\n",
+       "[214 rows x 102 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([1860350.76, 1498567.97,  486083.96, 3493097.22,  571715.87,\n",
+       "       1028236.29, 4252230.31, 3304757.1 ,   89784.  ,  905697.19,\n",
+       "       1035082.51,   43758.91, 1754503.74, 1181942.75,   29737.5 ,\n",
+       "        113582.96,  115037.1 ,  122196.66, 3052544.65,   29928.  ,\n",
+       "        590971.38,   36054.23, 2602729.18,  701604.12,  144384.92,\n",
+       "       4688787.43, 3483121.16,  501415.56,   62322.53, 4711234.74,\n",
+       "        100580.97,  571292.08,  884181.41, 2322080.77,   36297.77,\n",
+       "       3435512.15,  350776.06, 1790128.78, 2563499.46,  276609.  ,\n",
+       "       1187850.12,   54795.97, 2516963.67,   26439.77, 1780651.04,\n",
+       "       1900488.13,  596683.9 ,   45304.07,   39659.65,   77247.93,\n",
+       "        456071.25,  688016.43,  614119.12,   53798.55,   31818.33,\n",
+       "        518641.88,   77554.07,  370119.47, 1995853.47,  766096.1 ,\n",
+       "        672921.52,   14382.12,   40444.31,   36465.76, 2874827.68,\n",
+       "         75721.97,  665436.59, 1572715.87, 2360742.42,  317790.93,\n",
+       "        252263.98,   53527.5 ,  354722.08,  634649.02, 1741560.58,\n",
+       "       1032024.64,   69323.36,  269989.33, 1196987.75, 1174307.6 ,\n",
+       "         65352.18,   39132.19,  444907.31,   68280.63, 5982228.28,\n",
+       "        631640.54, 1237415.  , 1365031.79,  843874.12,  532083.12,\n",
+       "        698800.88,  561422.18,  597143.38, 3845249.2 , 1899389.61,\n",
+       "       4913480.94,   46025.06, 1241526.23,  215991.46, 1238429.57,\n",
+       "        876422.69, 2108206.97, 1212092.02,  404277.79,  627054.31,\n",
+       "       1930677.18, 1207596.26, 6009755.61,   37393.52, 2826740.85,\n",
+       "         32875.04,   54712.8 , 1096763.79,  444887.4 ,  701447.65,\n",
+       "       2438554.01, 3469490.2 ,  545834.72, 4386259.35,  716524.  ,\n",
+       "        239990.51,   24409.58,   29277.49,   41269.91,  110053.1 ,\n",
+       "       1119850.85,  521292.01,   78713.16,   27896.66, 1016449.03,\n",
+       "        266041.56,   30507.42,   51702.71,  115062.65,  183762.63,\n",
+       "        913353.54,  108165.6 ])"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "X_test['Przewidziana Suma'] = y_pred_test\n",
+    "X_test['Suma'] = y_test\n",
+    "display(X_test[np.abs(y_test - y_pred_test) < 1])\n",
+    "display(X_test['Suma'][np.abs(y_test - y_pred_test) < 1].drop_duplicates().values)"
+   ]
   }
  ],
  "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "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": "3.11.3"
+   "name": "python"
   }
  },
  "nbformat": 4,