RPA_Tipos_de_Cambio_Mailing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EMyreKUzklIl"
   },
   "source": [
    "# 🤖 **RPA** - 📧 Mailing Automático de Reporte de Tipos de Cambio 💵"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "hyYGNq8YhWOc"
   },
   "source": [
    "## 📚 Instalación de librerías"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para instalar correctamente los paquetes necesarios, puedes utilizar el siguiente código quitando los numerales:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import sys\n",
    "# !conda install --yes --prefix {sys.prefix} pandas\n",
    "# !conda install --yes --prefix {sys.prefix} numpy\n",
    "# !conda install --yes --prefix {sys.prefix} matplotlib\n",
    "# !conda install --yes --prefix {sys.prefix} seaborn\n",
    "# !conda install --yes --prefix {sys.prefix} selenium\n",
    "# pip install webdriver-manager"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Por otra parte, también vas a necesitar el chromedriver, o el driver necesario para tu navegador web. Este Software se descarga simplemente de la web oficial, https://chromedriver.chromium.org/. Una vez descargado, se ubica en la misma ruta que el script como es en el caso del presente notebook, o dentro del script puede informar la ruta de la siguiente manera, obviamente, quitándo los numerales que convierten las líneas de código en comentarios:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ruta_webdriver = r\"C:\\ruta\\webdriver\"\n",
    "# driver = webdriver.Chrome(ruta_webdriver, options=chrome_options)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📚 Importación de librerías"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OxvzGXGMqW5k",
    "outputId": "4f2c590d-f13b-4a04-bb13-a84cfabb4c32"
   },
   "outputs": [],
   "source": [
    "# Manejo del tiempo para cronometrar el programa y manejar datos tipo fecha\n",
    "import time\n",
    "import datetime as dt\n",
    "from datetime import datetime\n",
    "comienzo = time.perf_counter()\n",
    "\n",
    "# Meses en español\n",
    "import locale\n",
    "\n",
    "# Manejar datos y cálculos\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Manejar APIs\n",
    "import json\n",
    "import requests\n",
    "\n",
    "# Webscrapping\n",
    "from selenium import webdriver\n",
    "from selenium.webdriver.support.ui import WebDriverWait\n",
    "from selenium.webdriver.support import expected_conditions as EC\n",
    "chrome_options = webdriver.ChromeOptions()\n",
    "chrome_options.add_argument(\"--headless\")\n",
    "chrome_options.add_argument(\"--no-sandbox\")\n",
    "chrome_options.add_argument(\"--disable-dev-shm-usage\")\n",
    "\n",
    "# Crear gráficos\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import matplotlib.font_manager as fm\n",
    "import matplotlib.ticker as ticker\n",
    "from matplotlib.ticker import FormatStrFormatter, FuncFormatter, MultipleLocator, AutoMinorLocator\n",
    "import seaborn as sns\n",
    "\n",
    "# No mostrar contraseña\n",
    "from getpass import getpass\n",
    "\n",
    "# Personalización del mail\n",
    "import mimetypes\n",
    "from email.message import EmailMessage\n",
    "from email.mime.image import MIMEImage\n",
    "from email.mime.base import MIMEBase\n",
    "from email.mime.multipart import MIMEMultipart\n",
    "from email.mime.text import MIMEText\n",
    "\n",
    "# Seguridad para el mailing automático\n",
    "import smtplib, ssl\n",
    "\n",
    "# Personalizar df como tabla html\n",
    "from tabulate import tabulate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "hfE601GR5bd4"
   },
   "outputs": [],
   "source": [
    "# Función para eliminar el signo peso\n",
    "def eliminar_peso(x):\n",
    "    x = x.lstrip('$')\n",
    "    return float(x)\n",
    "\n",
    "# Función para reemplazar la coma de un string y convertirlo a número\n",
    "def reemplazar_coma(x):\n",
    "    x = x.replace(',', '.')\n",
    "    return float(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fuentes,usaremos las variables para ingresar a las webs y para citar las fuentes\n",
    "web_dolarhoy = \"https://dolarhoy.com/\"\n",
    "web_bna = \"https://www.bna.com.ar/Personas\"\n",
    "web_ambito_mep = \"https://www.ambito.com/contenidos/dolar-mep.html\"\n",
    "web_ambito_riesgo_pais = \"https://www.ambito.com/contenidos/riesgo-pais-historico.html\"\n",
    "web_fed = \"https://www.federalreserve.gov/releases/h15/\"\n",
    "web_bcra = \"https://www.bcra.gob.ar/PublicacionesEstadisticas/Principales_variables.asp\"\n",
    "web_perfil= \"https://tiempofinanciero.com.ar/cotizaciones/euro-blue/\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fecha</th>\n",
       "      <th>TCC_Blue</th>\n",
       "      <th>TCV_Blue</th>\n",
       "      <th>TCC_Billete</th>\n",
       "      <th>TCV_Billete</th>\n",
       "      <th>TCC_Divisas</th>\n",
       "      <th>TCV_Divisas</th>\n",
       "      <th>Solidario</th>\n",
       "      <th>TCV_MEP</th>\n",
       "      <th>riesgo_pais</th>\n",
       "      <th>TCC_Euro</th>\n",
       "      <th>TCV_Euro</th>\n",
       "      <th>fed_tea</th>\n",
       "      <th>bcra_tea</th>\n",
       "      <th>Solidario / TCV Blue</th>\n",
       "      <th>TCV MEP / TCV Blue</th>\n",
       "      <th>TCV Euro / TCC Blue %</th>\n",
       "      <th>Variación Solidario</th>\n",
       "      <th>Variación TCV Blue</th>\n",
       "      <th>Variación TCV Euro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13/03/24</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>828.5</td>\n",
       "      <td>868.5</td>\n",
       "      <td>846.5</td>\n",
       "      <td>849.5</td>\n",
       "      <td>1389.6</td>\n",
       "      <td>1010.76</td>\n",
       "      <td>1645.0</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>5.33</td>\n",
       "      <td>122.36</td>\n",
       "      <td>0.342609</td>\n",
       "      <td>-0.023420</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.001153</td>\n",
       "      <td>0.014706</td>\n",
       "      <td>0.014427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12/03/24</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>827.5</td>\n",
       "      <td>867.5</td>\n",
       "      <td>845.5</td>\n",
       "      <td>848.5</td>\n",
       "      <td>1388.0</td>\n",
       "      <td>1036.19</td>\n",
       "      <td>1713.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>1109.0</td>\n",
       "      <td>5.33</td>\n",
       "      <td>122.36</td>\n",
       "      <td>0.360784</td>\n",
       "      <td>0.015873</td>\n",
       "      <td>0.087255</td>\n",
       "      <td>0.000577</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.030669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11/03/24</td>\n",
       "      <td>985.0</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>827.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>848.0</td>\n",
       "      <td>1387.2</td>\n",
       "      <td>980.98</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>5.33</td>\n",
       "      <td>171.46</td>\n",
       "      <td>0.380299</td>\n",
       "      <td>-0.023900</td>\n",
       "      <td>0.070647</td>\n",
       "      <td>0.001733</td>\n",
       "      <td>0.010050</td>\n",
       "      <td>0.004669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>08/03/24</td>\n",
       "      <td>975.0</td>\n",
       "      <td>995.0</td>\n",
       "      <td>825.5</td>\n",
       "      <td>865.5</td>\n",
       "      <td>843.5</td>\n",
       "      <td>846.5</td>\n",
       "      <td>1384.8</td>\n",
       "      <td>1004.58</td>\n",
       "      <td>1643.0</td>\n",
       "      <td>1038.0</td>\n",
       "      <td>1071.0</td>\n",
       "      <td>5.33</td>\n",
       "      <td>171.46</td>\n",
       "      <td>0.391759</td>\n",
       "      <td>0.009628</td>\n",
       "      <td>0.076382</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>0.010152</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>07/03/24</td>\n",
       "      <td>965.0</td>\n",
       "      <td>985.0</td>\n",
       "      <td>825.0</td>\n",
       "      <td>865.0</td>\n",
       "      <td>843.0</td>\n",
       "      <td>846.0</td>\n",
       "      <td>1384.0</td>\n",
       "      <td>992.30</td>\n",
       "      <td>1621.0</td>\n",
       "      <td>1038.0</td>\n",
       "      <td>1071.0</td>\n",
       "      <td>5.33</td>\n",
       "      <td>171.46</td>\n",
       "      <td>0.405076</td>\n",
       "      <td>0.007411</td>\n",
       "      <td>0.087310</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>-0.005051</td>\n",
       "      <td>-0.005137</td>\n",
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       "      <td>16/12/98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>760.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>14/12/98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>766.0</td>\n",
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       "    <tr>\n",
       "      <th>6503</th>\n",
       "      <td>11/12/98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>720.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "         Fecha  TCC_Blue  TCV_Blue  TCC_Billete  TCV_Billete  TCC_Divisas  \\\n",
       "0     13/03/24    1015.0    1035.0        828.5        868.5        846.5   \n",
       "1     12/03/24    1000.0    1020.0        827.5        867.5        845.5   \n",
       "2     11/03/24     985.0    1005.0        827.0        867.0        845.0   \n",
       "3     08/03/24     975.0     995.0        825.5        865.5        843.5   \n",
       "4     07/03/24     965.0     985.0        825.0        865.0        843.0   \n",
       "...        ...       ...       ...          ...          ...          ...   \n",
       "6499  17/12/98       NaN       NaN          NaN          NaN          NaN   \n",
       "6500  16/12/98       NaN       NaN          NaN          NaN          NaN   \n",
       "6501  15/12/98       NaN       NaN          NaN          NaN          NaN   \n",
       "6502  14/12/98       NaN       NaN          NaN          NaN          NaN   \n",
       "6503  11/12/98       NaN       NaN          NaN          NaN          NaN   \n",
       "\n",
       "      TCV_Divisas  Solidario  TCV_MEP  riesgo_pais  TCC_Euro  TCV_Euro  \\\n",
       "0           849.5     1389.6  1010.76       1645.0    1092.0    1125.0   \n",
       "1           848.5     1388.0  1036.19       1713.0    1076.0    1109.0   \n",
       "2           848.0     1387.2   980.98       1690.0    1054.0    1076.0   \n",
       "3           846.5     1384.8  1004.58       1643.0    1038.0    1071.0   \n",
       "4           846.0     1384.0   992.30       1621.0    1038.0    1071.0   \n",
       "...           ...        ...      ...          ...       ...       ...   \n",
       "6499          NaN        NaN      NaN        758.0       NaN       NaN   \n",
       "6500          NaN        NaN      NaN        760.0       NaN       NaN   \n",
       "6501          NaN        NaN      NaN        734.0       NaN       NaN   \n",
       "6502          NaN        NaN      NaN        766.0       NaN       NaN   \n",
       "6503          NaN        NaN      NaN        720.0       NaN       NaN   \n",
       "\n",
       "      fed_tea  bcra_tea  Solidario / TCV Blue  TCV MEP / TCV Blue  \\\n",
       "0        5.33    122.36              0.342609           -0.023420   \n",
       "1        5.33    122.36              0.360784            0.015873   \n",
       "2        5.33    171.46              0.380299           -0.023900   \n",
       "3        5.33    171.46              0.391759            0.009628   \n",
       "4        5.33    171.46              0.405076            0.007411   \n",
       "...       ...       ...                   ...                 ...   \n",
       "6499      NaN       NaN                   NaN                 NaN   \n",
       "6500      NaN       NaN                   NaN                 NaN   \n",
       "6501      NaN       NaN                   NaN                 NaN   \n",
       "6502      NaN       NaN                   NaN                 NaN   \n",
       "6503      NaN       NaN                   NaN                 NaN   \n",
       "\n",
       "      TCV Euro / TCC Blue %  Variación Solidario  Variación TCV Blue  \\\n",
       "0                  0.086957             0.001153            0.014706   \n",
       "1                  0.087255             0.000577            0.014925   \n",
       "2                  0.070647             0.001733            0.010050   \n",
       "3                  0.076382             0.000578            0.010152   \n",
       "4                  0.087310             0.000578           -0.005051   \n",
       "...                     ...                  ...                 ...   \n",
       "6499                    NaN             0.000000            0.000000   \n",
       "6500                    NaN             0.000000            0.000000   \n",
       "6501                    NaN             0.000000            0.000000   \n",
       "6502                    NaN             0.000000            0.000000   \n",
       "6503                    NaN             0.000000            0.000000   \n",
       "\n",
       "      Variación TCV Euro  \n",
       "0               0.014427  \n",
       "1               0.030669  \n",
       "2               0.004669  \n",
       "3               0.000000  \n",
       "4              -0.005137  \n",
       "...                  ...  \n",
       "6499            0.000000  \n",
       "6500            0.000000  \n",
       "6501            0.000000  \n",
       "6502            0.000000  \n",
       "6503            0.000000  \n",
       "\n",
       "[6504 rows x 20 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ruta_bbdd = r'Seguimiento Dólar Blue.csv'\n",
    "df = pd.read_csv(ruta_bbdd, index_col=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2TvyenJbkoKp"
   },
   "source": [
    "## 📝 Importación de Excel acumulativo "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Cuando olvide el bot un día, puedo insertar datos como diccionario\n",
    "# # Eliminar fila cuyo ID sea 0\n",
    "# new_data = {\n",
    "#     \"Fecha\": [\"06/03/24\"],\n",
    "#     \"Solidario\": [1352.8],\n",
    "#     \"TCC_Blue\": [960.0],\n",
    "#     \"TCV_Blue\": [990.00],\n",
    "#     \"TCV_MEP\": [981.21],\n",
    "#     \"TCC_Billete\": [824.5],\n",
    "#     \"TCV_Billete\": [864.5],\n",
    "#     \"TCC_Divisas\": [842.5],\n",
    "#     \"TCV_Divisas\": [845.5],\n",
    "#     \"TCC_Euro\": [1043.9],\n",
    "#     \"TCV_Euro\": [1076.53],\n",
    "#     \"fed_tea\": [5.33],\n",
    "#     \"bcra_tea\": [254.82],\n",
    "#     \"riesgo_pais\": [1621.0]\n",
    "# }\n",
    "\n",
    "# # Convertir el diccionario en dataframe\n",
    "# new_data_df = pd.DataFrame(new_data)\n",
    "\n",
    "# # Concatenar los datos nuevos a los anteriores\n",
    "# data_df = pd.concat([df, new_data_df], ignore_index=True)\n",
    "# data_df['Fecha'] = pd.to_datetime(data_df['Fecha'], format='%d/%m/%y')\n",
    "\n",
    "# # Ordenar las fechas en forma descendente y reiniciar el índice\n",
    "# data_df.sort_values(by=\"Fecha\", ascending=False, inplace=True)\n",
    "# data_df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "# data_df['Fecha'] = data_df['Fecha'].dt.strftime(\"%d/%m/%y\")\n",
    "\n",
    "# # Reemplazar el dataframe e imprimirlo para revisar\n",
    "# df = data_df\n",
    "# df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O6Dse0lNhvbU"
   },
   "source": [
    "## 🤖 Ingreso a la web y toma de datos "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 380
    },
    "id": "WIn5lVYaqijo",
    "outputId": "f74e7193-6277-4418-f036-b403f5ad9464"
   },
   "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>Fecha</th>\n",
       "      <th>TCC_Blue</th>\n",
       "      <th>TCV_Blue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14/03/24</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>1025.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Fecha  TCC_Blue  TCV_Blue\n",
       "0  14/03/24    1005.0    1025.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ingreso a la web dolarhoy\n",
    "driver = webdriver.Chrome(\"chromedriver\", options=chrome_options)\n",
    "driver.get(web_dolarhoy)\n",
    "\n",
    "# Toma de datos mediante xpath\n",
    "solidario = driver.find_element(\n",
    "    \"xpath\",\n",
    "    '//*[@id=\"home_0\"]/div[2]/section/div/div/div/div[1]/div/div[2]/div[6]/div/div[2]/div[2]')\n",
    "blue_tcc = driver.find_element(\n",
    "    \"xpath\",\n",
    "    '//*[@id=\"home_0\"]/div[2]/section/div/div/div/div[1]/div/div[1]/div/div[1]/div[1]/div[2]')\n",
    "blue_tcv = driver.find_element(\n",
    "    \"xpath\",\n",
    "    '//*[@id=\"home_0\"]/div[2]/section/div/div/div/div[1]/div/div[1]/div/div[1]/div[2]/div[2]')\n",
    "\n",
    "# Creación de fila nueva, la columna fecha toma la fecha de hoy y la convierte en serie de tiempo\n",
    "# - dt.timedelta(days=1) para restar un día\n",
    "dolar_hoy = pd.DataFrame(\n",
    "    {\"Fecha\":dt.datetime.today(),\n",
    "     \"TCC_Blue\":blue_tcc.text,\n",
    "     \"TCV_Blue\":blue_tcv.text},\n",
    "    index=[0])\n",
    "\n",
    "dolar_hoy[\"Fecha\"] = pd.to_datetime(dolar_hoy[\"Fecha\"]).dt.strftime(\"%d/%m/%y\")\n",
    "\n",
    "# Eliminación de $\n",
    "dolar_hoy.TCC_Blue = dolar_hoy.TCC_Blue.apply(eliminar_peso)\n",
    "dolar_hoy.TCV_Blue = dolar_hoy.TCV_Blue.apply(eliminar_peso)\n",
    "\n",
    "display(dolar_hoy)\n",
    "\n",
    "driver.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "RjkDBPTEoSRJ"
   },
   "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>TCC_Billete</th>\n",
       "      <th>TCV_Billete</th>\n",
       "      <th>TCC_Divisas</th>\n",
       "      <th>TCV_Divisas</th>\n",
       "      <th>Solidario</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>829.0</td>\n",
       "      <td>869.0</td>\n",
       "      <td>847.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>1390.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TCC_Billete  TCV_Billete  TCC_Divisas  TCV_Divisas  Solidario\n",
       "0        829.0        869.0        847.0        850.0     1390.4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Ingreso a la web Banco Nación\n",
    "driver = webdriver.Chrome('chromedriver', options=chrome_options)\n",
    "driver.get(web_bna)\n",
    "\n",
    "billete_tcc = driver.find_element(\n",
    "    \"xpath\",\n",
    "    '//*[@id=\"billetes\"]/table/tbody/tr[1]/td[2]')\n",
    "billete_tcv = driver.find_element(\n",
    "    \"xpath\",\n",
    "    '//*[@id=\"billetes\"]/table/tbody/tr[1]/td[3]')\n",
    "\n",
    "bna = pd.DataFrame({\n",
    "    \"TCC_Billete\":[reemplazar_coma(billete_tcc.text)],\n",
    "    \"TCV_Billete\":[reemplazar_coma(billete_tcv.text)]},\n",
    "    index=[0])\n",
    "\n",
    "#Cliquear botón que habilita el tipo de cambio divisas\n",
    "driver.find_element(\n",
    "    \"xpath\",\n",
    "    '//*[@id=\"rightHome\"]/div[1]/div/ul/li[2]/a').click()\n",
    "\n",
    "#Esperar dos segundos para que aparezcan los elementos, wait.until no sirvió\n",
    "time.sleep(2)\n",
    "\n",
    "bna_tcc_divisas = driver.find_element(\n",
    "    \"xpath\",\n",
    "    '//*[@id=\"divisas\"]/table/tbody/tr[1]/td[2]')\n",
    "bna_tcv_divisas = driver.find_element(\n",
    "    \"xpath\",\n",
    "    '//*[@id=\"divisas\"]/table/tbody/tr[1]/td[3]')\n",
    "bna_divisas = pd.DataFrame(\n",
    "    {\"TCC_Divisas\":float(bna_tcc_divisas.text),\n",
    "    \"TCV_Divisas\":float(bna_tcv_divisas.text)},\n",
    "    index=[0])\n",
    "\n",
    "bna = pd.concat([bna,bna_divisas], axis = 1)\n",
    "\n",
    "#Agregar dólar solidario según impuestos\n",
    "impuestos_solidario = 0.6\n",
    "bna[\"Solidario\"] = bna.TCV_Billete * ( 1 + impuestos_solidario ) \n",
    "\n",
    "display(bna)\n",
    "\n",
    "driver.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "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>TCC_Euro</th>\n",
       "      <th>TCV_Euro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1082</td>\n",
       "      <td>1114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TCC_Euro  TCV_Euro\n",
       "0      1082      1114"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "driver = webdriver.Chrome('chromedriver', options=chrome_options)\n",
    "driver.get(web_perfil)\n",
    "\n",
    "# tcc_euro = WebDriverWait(driver, 10).until(\n",
    "#     EC.presence_of_element_located(\n",
    "#         (\"xpath\", '//*[@id=\"app-body\"]/main/div[1]/div/div/div[24]')\n",
    "#     )\n",
    "# )\n",
    "\n",
    "# tcv_euro =  WebDriverWait(driver, 10).until(\n",
    "#     EC.presence_of_element_located(\n",
    "#         (\"xpath\", '//*[@id=\"app-body\"]/main/div[1]/div/div/div[23]')\n",
    "#     )\n",
    "# )\n",
    "\n",
    "tcc_euro = WebDriverWait(driver, 10).until(\n",
    "    EC.presence_of_element_located(\n",
    "        (\"xpath\", '//*[@id=\"price-content\"]/div[1]/table/tbody/tr/td[2]')\n",
    "    )\n",
    ")\n",
    "\n",
    "tcv_euro =  WebDriverWait(driver, 10).until(\n",
    "    EC.presence_of_element_located(\n",
    "        (\"xpath\", '//*[@id=\"price-content\"]/div[1]/table/tbody/tr/td[3]')\n",
    "    )\n",
    ")\n",
    "\n",
    "euro = pd.DataFrame(\n",
    "    {\"TCC_Euro\": [int(tcc_euro.text.replace(\".\",\"\").replace(\",\",\".\").replace(\"$\", \"\"))],\n",
    "    \"TCV_Euro\": [int(tcv_euro.text.replace(\".\",\"\").replace(\",\",\".\").replace(\"$\", \"\"))]},\n",
    "    index=[0]\n",
    ")\n",
    "\n",
    "display(euro)\n",
    "\n",
    "driver.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "SYD9EMHcRmDI"
   },
   "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>TCV_MEP</th>\n",
       "      <th>riesgo_pais</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1016.32</td>\n",
       "      <td>1596.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   TCV_MEP  riesgo_pais\n",
       "0  1016.32       1596.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "driver = webdriver.Chrome('chromedriver', options=chrome_options)\n",
    "driver.get(web_ambito_mep)\n",
    "\n",
    "time.sleep(2)\n",
    "\n",
    "mep = driver.find_element(\n",
    "    \"xpath\",\n",
    "    '/html/body/main/div/div[1]/div[1]/div/div[2]/div[1]/div/div/div[2]/span[1]')\n",
    "\n",
    "ambito = pd.DataFrame(\n",
    "    {\"TCV_MEP\":[reemplazar_coma(mep.text)]},\n",
    "    index=[0])\n",
    "\n",
    "driver.get(web_ambito_riesgo_pais)\n",
    "time.sleep(2)\n",
    "\n",
    "riesgo_pais = driver.find_element(\n",
    "    \"xpath\",\n",
    "    '/html/body/main/div/div[1]/div[10]/table/tbody/tr[1]/td[2]')\n",
    "\n",
    "riesgo_pais = pd.DataFrame(\n",
    "    {\"riesgo_pais\":[reemplazar_coma(riesgo_pais.text)]},\n",
    "    index=[0])\n",
    "\n",
    "ambito = pd.concat([ambito,riesgo_pais],axis = 1)\n",
    "\n",
    "display(ambito)\n",
    "\n",
    "driver.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>fed_tea</th>\n",
       "      <th>bcra_tea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.33</td>\n",
       "      <td>122.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fed_tea  bcra_tea\n",
       "0     5.33    122.36"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Ingresamos a la web de la FED para obtener la tasa de interés de EEUU\n",
    "driver = webdriver.Chrome('chromedriver', options=chrome_options)\n",
    "driver.get(web_fed)\n",
    "\n",
    "fed_tea = driver.find_element(\n",
    "    \"xpath\",\n",
    "    '/html/body/div[3]/div[6]/table/tbody/tr[1]/td[5]')\n",
    "time.sleep(2)\n",
    "\n",
    "fed_tea = pd.DataFrame(\n",
    "{\"fed_tea\":[reemplazar_coma(fed_tea.text)]},\n",
    "index=[0])\n",
    "\n",
    "time.sleep(2)\n",
    "\n",
    "driver.get(web_bcra)\n",
    "time.sleep(2)\n",
    "\n",
    "# Ingresamos a la web del BCRA para obtener la tasa de interés de Argentina \n",
    "driver = webdriver.Chrome('chromedriver', options=chrome_options)\n",
    "driver.get(web_bcra)\n",
    "\n",
    "bcra_tea = driver.find_element(\n",
    "    \"xpath\",\n",
    "    '/html/body/div/div[2]/div/div/div/div/table/tbody/tr[8]/td[3]')\n",
    "time.sleep(2)\n",
    "\n",
    "bcra_tea = pd.DataFrame(\n",
    "    {\"bcra_tea\":[reemplazar_coma(bcra_tea.text)]},\n",
    "    index=[0])\n",
    "\n",
    "# Concatenamos las tasas para crear las columnas a agregar\n",
    "tasas = pd.concat([fed_tea,bcra_tea], axis=1)\n",
    "\n",
    "driver.close()\n",
    "\n",
    "# Imprimimos ambos dataframes con dos renglones de diferencia\n",
    "display(tasas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Fecha</th>\n",
       "      <th>Inflación Mensual</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990-01-31</td>\n",
       "      <td>79.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990-02-28</td>\n",
       "      <td>61.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990-03-31</td>\n",
       "      <td>95.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990-04-30</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990-05-31</td>\n",
       "      <td>13.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>2023-10-31</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>2023-11-30</td>\n",
       "      <td>12.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>25.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>2024-01-31</td>\n",
       "      <td>20.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>2024-02-29</td>\n",
       "      <td>13.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>410 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Fecha  Inflación Mensual\n",
       "0   1990-01-31               79.2\n",
       "1   1990-02-28               61.6\n",
       "2   1990-03-31               95.5\n",
       "3   1990-04-30               11.4\n",
       "4   1990-05-31               13.6\n",
       "..         ...                ...\n",
       "405 2023-10-31                8.3\n",
       "406 2023-11-30               12.8\n",
       "407 2023-12-31               25.5\n",
       "408 2024-01-31               20.6\n",
       "409 2024-02-29               13.2\n",
       "\n",
       "[410 rows x 2 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtenemos la inflación mucho más rápido mediante api que mediante scraping\n",
    "bcra_api_url = \"https://api.estadisticasbcra.com/\"\n",
    "token = \"string_que_se_consigue_de_la_web_gratis\"\n",
    "consulta = \"inflacion_mensual_oficial\"\n",
    "url = bcra_api_url + consulta\n",
    "headers = {\"Authorization\": f\"BEARER {token}\"}\n",
    "response = requests.get(url, headers=headers).json()\n",
    "inflacion_df = pd.json_normalize(response)\n",
    "\n",
    "inflacion_df.columns = [\"Fecha\", \"Inflación Mensual\"]\n",
    "inflacion_df[\"Fecha\"] = pd.to_datetime(inflacion_df[\"Fecha\"], format=\"%Y-%m-%d\")\n",
    "inflacion_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_02eeb\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_02eeb_level0_col0\" class=\"col_heading level0 col0\" >Fecha</th>\n",
       "      <th id=\"T_02eeb_level0_col1\" class=\"col_heading level0 col1\" >Inflación Mensual</th>\n",
       "      <th id=\"T_02eeb_level0_col2\" class=\"col_heading level0 col2\" >Inflación Bimestral</th>\n",
       "      <th id=\"T_02eeb_level0_col3\" class=\"col_heading level0 col3\" >Inflación Trimestral</th>\n",
       "      <th id=\"T_02eeb_level0_col4\" class=\"col_heading level0 col4\" >Inflación Anual</th>\n",
       "      <th id=\"T_02eeb_level0_col5\" class=\"col_heading level0 col5\" >Inflación Agregada</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row0\" class=\"row_heading level0 row0\" >314</th>\n",
       "      <td id=\"T_02eeb_row0_col0\" class=\"data row0 col0\" >31/03/2016</td>\n",
       "      <td id=\"T_02eeb_row0_col1\" class=\"data row0 col1\" >3.00%</td>\n",
       "      <td id=\"T_02eeb_row0_col2\" class=\"data row0 col2\" >5.78%</td>\n",
       "      <td id=\"T_02eeb_row0_col3\" class=\"data row0 col3\" >10.12%</td>\n",
       "      <td id=\"T_02eeb_row0_col4\" class=\"data row0 col4\" >26.237867</td>\n",
       "      <td id=\"T_02eeb_row0_col5\" class=\"data row0 col5\" >19348.33%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row1\" class=\"row_heading level0 row1\" >315</th>\n",
       "      <td id=\"T_02eeb_row1_col0\" class=\"data row1 col0\" >30/04/2016</td>\n",
       "      <td id=\"T_02eeb_row1_col1\" class=\"data row1 col1\" >3.40%</td>\n",
       "      <td id=\"T_02eeb_row1_col2\" class=\"data row1 col2\" >6.50%</td>\n",
       "      <td id=\"T_02eeb_row1_col3\" class=\"data row1 col3\" >9.38%</td>\n",
       "      <td id=\"T_02eeb_row1_col4\" class=\"data row1 col4\" >29.109747</td>\n",
       "      <td id=\"T_02eeb_row1_col5\" class=\"data row1 col5\" >20009.57%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row2\" class=\"row_heading level0 row2\" >316</th>\n",
       "      <td id=\"T_02eeb_row2_col0\" class=\"data row2 col0\" >31/05/2016</td>\n",
       "      <td id=\"T_02eeb_row2_col1\" class=\"data row2 col1\" >4.20%</td>\n",
       "      <td id=\"T_02eeb_row2_col2\" class=\"data row2 col2\" >7.74%</td>\n",
       "      <td id=\"T_02eeb_row2_col3\" class=\"data row2 col3\" >10.98%</td>\n",
       "      <td id=\"T_02eeb_row2_col4\" class=\"data row2 col4\" >33.200353</td>\n",
       "      <td id=\"T_02eeb_row2_col5\" class=\"data row2 col5\" >20854.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row3\" class=\"row_heading level0 row3\" >317</th>\n",
       "      <td id=\"T_02eeb_row3_col0\" class=\"data row3 col0\" >30/06/2016</td>\n",
       "      <td id=\"T_02eeb_row3_col1\" class=\"data row3 col1\" >3.10%</td>\n",
       "      <td id=\"T_02eeb_row3_col2\" class=\"data row3 col2\" >7.43%</td>\n",
       "      <td id=\"T_02eeb_row3_col3\" class=\"data row3 col3\" >11.08%</td>\n",
       "      <td id=\"T_02eeb_row3_col4\" class=\"data row3 col4\" >35.969865</td>\n",
       "      <td id=\"T_02eeb_row3_col5\" class=\"data row3 col5\" >21503.75%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row4\" class=\"row_heading level0 row4\" >318</th>\n",
       "      <td id=\"T_02eeb_row4_col0\" class=\"data row4 col0\" >31/07/2016</td>\n",
       "      <td id=\"T_02eeb_row4_col1\" class=\"data row4 col1\" >2.00%</td>\n",
       "      <td id=\"T_02eeb_row4_col2\" class=\"data row4 col2\" >5.16%</td>\n",
       "      <td id=\"T_02eeb_row4_col3\" class=\"data row4 col3\" >9.58%</td>\n",
       "      <td id=\"T_02eeb_row4_col4\" class=\"data row4 col4\" >36.909440</td>\n",
       "      <td id=\"T_02eeb_row4_col5\" class=\"data row4 col5\" >21935.83%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row5\" class=\"row_heading level0 row5\" >319</th>\n",
       "      <td id=\"T_02eeb_row5_col0\" class=\"data row5 col0\" >31/08/2016</td>\n",
       "      <td id=\"T_02eeb_row5_col1\" class=\"data row5 col1\" >0.20%</td>\n",
       "      <td id=\"T_02eeb_row5_col2\" class=\"data row5 col2\" >2.20%</td>\n",
       "      <td id=\"T_02eeb_row5_col3\" class=\"data row5 col3\" >5.37%</td>\n",
       "      <td id=\"T_02eeb_row5_col4\" class=\"data row5 col4\" >35.556579</td>\n",
       "      <td id=\"T_02eeb_row5_col5\" class=\"data row5 col5\" >21979.90%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row6\" class=\"row_heading level0 row6\" >320</th>\n",
       "      <td id=\"T_02eeb_row6_col0\" class=\"data row6 col0\" >30/09/2016</td>\n",
       "      <td id=\"T_02eeb_row6_col1\" class=\"data row6 col1\" >1.10%</td>\n",
       "      <td id=\"T_02eeb_row6_col2\" class=\"data row6 col2\" >1.30%</td>\n",
       "      <td id=\"T_02eeb_row6_col3\" class=\"data row6 col3\" >3.33%</td>\n",
       "      <td id=\"T_02eeb_row6_col4\" class=\"data row6 col4\" >35.422630</td>\n",
       "      <td id=\"T_02eeb_row6_col5\" class=\"data row6 col5\" >22222.78%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row7\" class=\"row_heading level0 row7\" >321</th>\n",
       "      <td id=\"T_02eeb_row7_col0\" class=\"data row7 col0\" >31/10/2016</td>\n",
       "      <td id=\"T_02eeb_row7_col1\" class=\"data row7 col1\" >2.40%</td>\n",
       "      <td id=\"T_02eeb_row7_col2\" class=\"data row7 col2\" >3.53%</td>\n",
       "      <td id=\"T_02eeb_row7_col3\" class=\"data row7 col3\" >3.73%</td>\n",
       "      <td id=\"T_02eeb_row7_col4\" class=\"data row7 col4\" >37.163970</td>\n",
       "      <td id=\"T_02eeb_row7_col5\" class=\"data row7 col5\" >22758.52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row8\" class=\"row_heading level0 row8\" >322</th>\n",
       "      <td id=\"T_02eeb_row8_col0\" class=\"data row8 col0\" >30/11/2016</td>\n",
       "      <td id=\"T_02eeb_row8_col1\" class=\"data row8 col1\" >1.60%</td>\n",
       "      <td id=\"T_02eeb_row8_col2\" class=\"data row8 col2\" >4.04%</td>\n",
       "      <td id=\"T_02eeb_row8_col3\" class=\"data row8 col3\" >5.18%</td>\n",
       "      <td id=\"T_02eeb_row8_col4\" class=\"data row8 col4\" >36.626072</td>\n",
       "      <td id=\"T_02eeb_row8_col5\" class=\"data row8 col5\" >23124.26%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row9\" class=\"row_heading level0 row9\" >323</th>\n",
       "      <td id=\"T_02eeb_row9_col0\" class=\"data row9 col0\" >31/12/2016</td>\n",
       "      <td id=\"T_02eeb_row9_col1\" class=\"data row9 col1\" >1.20%</td>\n",
       "      <td id=\"T_02eeb_row9_col2\" class=\"data row9 col2\" >2.82%</td>\n",
       "      <td id=\"T_02eeb_row9_col3\" class=\"data row9 col3\" >5.29%</td>\n",
       "      <td id=\"T_02eeb_row9_col4\" class=\"data row9 col4\" >33.075635</td>\n",
       "      <td id=\"T_02eeb_row9_col5\" class=\"data row9 col5\" >23402.95%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row10\" class=\"row_heading level0 row10\" >324</th>\n",
       "      <td id=\"T_02eeb_row10_col0\" class=\"data row10 col0\" >31/01/2017</td>\n",
       "      <td id=\"T_02eeb_row10_col1\" class=\"data row10 col1\" >1.30%</td>\n",
       "      <td id=\"T_02eeb_row10_col2\" class=\"data row10 col2\" >2.52%</td>\n",
       "      <td id=\"T_02eeb_row10_col3\" class=\"data row10 col3\" >4.16%</td>\n",
       "      <td id=\"T_02eeb_row10_col4\" class=\"data row10 col4\" >29.496271</td>\n",
       "      <td id=\"T_02eeb_row10_col5\" class=\"data row10 col5\" >23708.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row11\" class=\"row_heading level0 row11\" >325</th>\n",
       "      <td id=\"T_02eeb_row11_col0\" class=\"data row11 col0\" >28/02/2017</td>\n",
       "      <td id=\"T_02eeb_row11_col1\" class=\"data row11 col1\" >2.50%</td>\n",
       "      <td id=\"T_02eeb_row11_col2\" class=\"data row11 col2\" >3.83%</td>\n",
       "      <td id=\"T_02eeb_row11_col3\" class=\"data row11 col3\" >5.08%</td>\n",
       "      <td id=\"T_02eeb_row11_col4\" class=\"data row11 col4\" >29.244087</td>\n",
       "      <td id=\"T_02eeb_row11_col5\" class=\"data row11 col5\" >24303.70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row12\" class=\"row_heading level0 row12\" >326</th>\n",
       "      <td id=\"T_02eeb_row12_col0\" class=\"data row12 col0\" >31/03/2017</td>\n",
       "      <td id=\"T_02eeb_row12_col1\" class=\"data row12 col1\" >2.40%</td>\n",
       "      <td id=\"T_02eeb_row12_col2\" class=\"data row12 col2\" >4.96%</td>\n",
       "      <td id=\"T_02eeb_row12_col3\" class=\"data row12 col3\" >6.32%</td>\n",
       "      <td id=\"T_02eeb_row12_col4\" class=\"data row12 col4\" >28.491209</td>\n",
       "      <td id=\"T_02eeb_row12_col5\" class=\"data row12 col5\" >24889.39%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row13\" class=\"row_heading level0 row13\" >327</th>\n",
       "      <td id=\"T_02eeb_row13_col0\" class=\"data row13 col0\" >30/04/2017</td>\n",
       "      <td id=\"T_02eeb_row13_col1\" class=\"data row13 col1\" >2.60%</td>\n",
       "      <td id=\"T_02eeb_row13_col2\" class=\"data row13 col2\" >5.06%</td>\n",
       "      <td id=\"T_02eeb_row13_col3\" class=\"data row13 col3\" >7.69%</td>\n",
       "      <td id=\"T_02eeb_row13_col4\" class=\"data row13 col4\" >27.497080</td>\n",
       "      <td id=\"T_02eeb_row13_col5\" class=\"data row13 col5\" >25539.11%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row14\" class=\"row_heading level0 row14\" >328</th>\n",
       "      <td id=\"T_02eeb_row14_col0\" class=\"data row14 col0\" >31/05/2017</td>\n",
       "      <td id=\"T_02eeb_row14_col1\" class=\"data row14 col1\" >1.30%</td>\n",
       "      <td id=\"T_02eeb_row14_col2\" class=\"data row14 col2\" >3.93%</td>\n",
       "      <td id=\"T_02eeb_row14_col3\" class=\"data row14 col3\" >6.43%</td>\n",
       "      <td id=\"T_02eeb_row14_col4\" class=\"data row14 col4\" >23.948697</td>\n",
       "      <td id=\"T_02eeb_row14_col5\" class=\"data row14 col5\" >25872.42%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row15\" class=\"row_heading level0 row15\" >329</th>\n",
       "      <td id=\"T_02eeb_row15_col0\" class=\"data row15 col0\" >30/06/2017</td>\n",
       "      <td id=\"T_02eeb_row15_col1\" class=\"data row15 col1\" >1.20%</td>\n",
       "      <td id=\"T_02eeb_row15_col2\" class=\"data row15 col2\" >2.52%</td>\n",
       "      <td id=\"T_02eeb_row15_col3\" class=\"data row15 col3\" >5.18%</td>\n",
       "      <td id=\"T_02eeb_row15_col4\" class=\"data row15 col4\" >21.664482</td>\n",
       "      <td id=\"T_02eeb_row15_col5\" class=\"data row15 col5\" >26184.09%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row16\" class=\"row_heading level0 row16\" >330</th>\n",
       "      <td id=\"T_02eeb_row16_col0\" class=\"data row16 col0\" >31/07/2017</td>\n",
       "      <td id=\"T_02eeb_row16_col1\" class=\"data row16 col1\" >1.70%</td>\n",
       "      <td id=\"T_02eeb_row16_col2\" class=\"data row16 col2\" >2.92%</td>\n",
       "      <td id=\"T_02eeb_row16_col3\" class=\"data row16 col3\" >4.26%</td>\n",
       "      <td id=\"T_02eeb_row16_col4\" class=\"data row16 col4\" >21.306645</td>\n",
       "      <td id=\"T_02eeb_row16_col5\" class=\"data row16 col5\" >26630.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row17\" class=\"row_heading level0 row17\" >331</th>\n",
       "      <td id=\"T_02eeb_row17_col0\" class=\"data row17 col0\" >31/08/2017</td>\n",
       "      <td id=\"T_02eeb_row17_col1\" class=\"data row17 col1\" >1.40%</td>\n",
       "      <td id=\"T_02eeb_row17_col2\" class=\"data row17 col2\" >3.12%</td>\n",
       "      <td id=\"T_02eeb_row17_col3\" class=\"data row17 col3\" >4.36%</td>\n",
       "      <td id=\"T_02eeb_row17_col4\" class=\"data row17 col4\" >22.759420</td>\n",
       "      <td id=\"T_02eeb_row17_col5\" class=\"data row17 col5\" >27005.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row18\" class=\"row_heading level0 row18\" >332</th>\n",
       "      <td id=\"T_02eeb_row18_col0\" class=\"data row18 col0\" >30/09/2017</td>\n",
       "      <td id=\"T_02eeb_row18_col1\" class=\"data row18 col1\" >1.90%</td>\n",
       "      <td id=\"T_02eeb_row18_col2\" class=\"data row18 col2\" >3.33%</td>\n",
       "      <td id=\"T_02eeb_row18_col3\" class=\"data row18 col3\" >5.08%</td>\n",
       "      <td id=\"T_02eeb_row18_col4\" class=\"data row18 col4\" >23.730810</td>\n",
       "      <td id=\"T_02eeb_row18_col5\" class=\"data row18 col5\" >27520.15%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row19\" class=\"row_heading level0 row19\" >333</th>\n",
       "      <td id=\"T_02eeb_row19_col0\" class=\"data row19 col0\" >31/10/2017</td>\n",
       "      <td id=\"T_02eeb_row19_col1\" class=\"data row19 col1\" >1.50%</td>\n",
       "      <td id=\"T_02eeb_row19_col2\" class=\"data row19 col2\" >3.43%</td>\n",
       "      <td id=\"T_02eeb_row19_col3\" class=\"data row19 col3\" >4.88%</td>\n",
       "      <td id=\"T_02eeb_row19_col4\" class=\"data row19 col4\" >22.643332</td>\n",
       "      <td id=\"T_02eeb_row19_col5\" class=\"data row19 col5\" >27934.45%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row20\" class=\"row_heading level0 row20\" >334</th>\n",
       "      <td id=\"T_02eeb_row20_col0\" class=\"data row20 col0\" >30/11/2017</td>\n",
       "      <td id=\"T_02eeb_row20_col1\" class=\"data row20 col1\" >1.40%</td>\n",
       "      <td id=\"T_02eeb_row20_col2\" class=\"data row20 col2\" >2.92%</td>\n",
       "      <td id=\"T_02eeb_row20_col3\" class=\"data row20 col3\" >4.88%</td>\n",
       "      <td id=\"T_02eeb_row20_col4\" class=\"data row20 col4\" >22.401908</td>\n",
       "      <td id=\"T_02eeb_row20_col5\" class=\"data row20 col5\" >28326.94%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row21\" class=\"row_heading level0 row21\" >335</th>\n",
       "      <td id=\"T_02eeb_row21_col0\" class=\"data row21 col0\" >31/12/2017</td>\n",
       "      <td id=\"T_02eeb_row21_col1\" class=\"data row21 col1\" >3.10%</td>\n",
       "      <td id=\"T_02eeb_row21_col2\" class=\"data row21 col2\" >4.54%</td>\n",
       "      <td id=\"T_02eeb_row21_col3\" class=\"data row21 col3\" >6.11%</td>\n",
       "      <td id=\"T_02eeb_row21_col4\" class=\"data row21 col4\" >24.699968</td>\n",
       "      <td id=\"T_02eeb_row21_col5\" class=\"data row21 col5\" >29208.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row22\" class=\"row_heading level0 row22\" >336</th>\n",
       "      <td id=\"T_02eeb_row22_col0\" class=\"data row22 col0\" >31/01/2018</td>\n",
       "      <td id=\"T_02eeb_row22_col1\" class=\"data row22 col1\" >1.80%</td>\n",
       "      <td id=\"T_02eeb_row22_col2\" class=\"data row22 col2\" >4.96%</td>\n",
       "      <td id=\"T_02eeb_row22_col3\" class=\"data row22 col3\" >6.43%</td>\n",
       "      <td id=\"T_02eeb_row22_col4\" class=\"data row22 col4\" >25.315466</td>\n",
       "      <td id=\"T_02eeb_row22_col5\" class=\"data row22 col5\" >29735.72%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row23\" class=\"row_heading level0 row23\" >337</th>\n",
       "      <td id=\"T_02eeb_row23_col0\" class=\"data row23 col0\" >28/02/2018</td>\n",
       "      <td id=\"T_02eeb_row23_col1\" class=\"data row23 col1\" >2.40%</td>\n",
       "      <td id=\"T_02eeb_row23_col2\" class=\"data row23 col2\" >4.24%</td>\n",
       "      <td id=\"T_02eeb_row23_col3\" class=\"data row23 col3\" >7.47%</td>\n",
       "      <td id=\"T_02eeb_row23_col4\" class=\"data row23 col4\" >25.193207</td>\n",
       "      <td id=\"T_02eeb_row23_col5\" class=\"data row23 col5\" >30451.78%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row24\" class=\"row_heading level0 row24\" >338</th>\n",
       "      <td id=\"T_02eeb_row24_col0\" class=\"data row24 col0\" >31/03/2018</td>\n",
       "      <td id=\"T_02eeb_row24_col1\" class=\"data row24 col1\" >2.30%</td>\n",
       "      <td id=\"T_02eeb_row24_col2\" class=\"data row24 col2\" >4.76%</td>\n",
       "      <td id=\"T_02eeb_row24_col3\" class=\"data row24 col3\" >6.64%</td>\n",
       "      <td id=\"T_02eeb_row24_col4\" class=\"data row24 col4\" >25.070948</td>\n",
       "      <td id=\"T_02eeb_row24_col5\" class=\"data row24 col5\" >31154.47%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row25\" class=\"row_heading level0 row25\" >339</th>\n",
       "      <td id=\"T_02eeb_row25_col0\" class=\"data row25 col0\" >30/04/2018</td>\n",
       "      <td id=\"T_02eeb_row25_col1\" class=\"data row25 col1\" >2.70%</td>\n",
       "      <td id=\"T_02eeb_row25_col2\" class=\"data row25 col2\" >5.06%</td>\n",
       "      <td id=\"T_02eeb_row25_col3\" class=\"data row25 col3\" >7.58%</td>\n",
       "      <td id=\"T_02eeb_row25_col4\" class=\"data row25 col4\" >25.192849</td>\n",
       "      <td id=\"T_02eeb_row25_col5\" class=\"data row25 col5\" >31998.34%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row26\" class=\"row_heading level0 row26\" >340</th>\n",
       "      <td id=\"T_02eeb_row26_col0\" class=\"data row26 col0\" >31/05/2018</td>\n",
       "      <td id=\"T_02eeb_row26_col1\" class=\"data row26 col1\" >2.10%</td>\n",
       "      <td id=\"T_02eeb_row26_col2\" class=\"data row26 col2\" >4.86%</td>\n",
       "      <td id=\"T_02eeb_row26_col3\" class=\"data row26 col3\" >7.27%</td>\n",
       "      <td id=\"T_02eeb_row26_col4\" class=\"data row26 col4\" >26.181539</td>\n",
       "      <td id=\"T_02eeb_row26_col5\" class=\"data row26 col5\" >32672.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row27\" class=\"row_heading level0 row27\" >341</th>\n",
       "      <td id=\"T_02eeb_row27_col0\" class=\"data row27 col0\" >30/06/2018</td>\n",
       "      <td id=\"T_02eeb_row27_col1\" class=\"data row27 col1\" >3.70%</td>\n",
       "      <td id=\"T_02eeb_row27_col2\" class=\"data row27 col2\" >5.88%</td>\n",
       "      <td id=\"T_02eeb_row27_col3\" class=\"data row27 col3\" >8.74%</td>\n",
       "      <td id=\"T_02eeb_row27_col4\" class=\"data row27 col4\" >29.298672</td>\n",
       "      <td id=\"T_02eeb_row27_col5\" class=\"data row27 col5\" >33884.98%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row28\" class=\"row_heading level0 row28\" >342</th>\n",
       "      <td id=\"T_02eeb_row28_col0\" class=\"data row28 col0\" >31/07/2018</td>\n",
       "      <td id=\"T_02eeb_row28_col1\" class=\"data row28 col1\" >3.10%</td>\n",
       "      <td id=\"T_02eeb_row28_col2\" class=\"data row28 col2\" >6.91%</td>\n",
       "      <td id=\"T_02eeb_row28_col3\" class=\"data row28 col3\" >9.16%</td>\n",
       "      <td id=\"T_02eeb_row28_col4\" class=\"data row28 col4\" >31.078595</td>\n",
       "      <td id=\"T_02eeb_row28_col5\" class=\"data row28 col5\" >34938.52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row29\" class=\"row_heading level0 row29\" >343</th>\n",
       "      <td id=\"T_02eeb_row29_col0\" class=\"data row29 col0\" >31/08/2018</td>\n",
       "      <td id=\"T_02eeb_row29_col1\" class=\"data row29 col1\" >3.90%</td>\n",
       "      <td id=\"T_02eeb_row29_col2\" class=\"data row29 col2\" >7.12%</td>\n",
       "      <td id=\"T_02eeb_row29_col3\" class=\"data row29 col3\" >11.08%</td>\n",
       "      <td id=\"T_02eeb_row29_col4\" class=\"data row29 col4\" >34.310316</td>\n",
       "      <td id=\"T_02eeb_row29_col5\" class=\"data row29 col5\" >36305.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row30\" class=\"row_heading level0 row30\" >344</th>\n",
       "      <td id=\"T_02eeb_row30_col0\" class=\"data row30 col0\" >30/09/2018</td>\n",
       "      <td id=\"T_02eeb_row30_col1\" class=\"data row30 col1\" >6.50%</td>\n",
       "      <td id=\"T_02eeb_row30_col2\" class=\"data row30 col2\" >10.65%</td>\n",
       "      <td id=\"T_02eeb_row30_col3\" class=\"data row30 col3\" >14.08%</td>\n",
       "      <td id=\"T_02eeb_row30_col4\" class=\"data row30 col4\" >40.373392</td>\n",
       "      <td id=\"T_02eeb_row30_col5\" class=\"data row30 col5\" >38671.34%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row31\" class=\"row_heading level0 row31\" >345</th>\n",
       "      <td id=\"T_02eeb_row31_col0\" class=\"data row31 col0\" >31/10/2018</td>\n",
       "      <td id=\"T_02eeb_row31_col1\" class=\"data row31 col1\" >5.40%</td>\n",
       "      <td id=\"T_02eeb_row31_col2\" class=\"data row31 col2\" >12.25%</td>\n",
       "      <td id=\"T_02eeb_row31_col3\" class=\"data row31 col3\" >16.63%</td>\n",
       "      <td id=\"T_02eeb_row31_col4\" class=\"data row31 col4\" >45.767049</td>\n",
       "      <td id=\"T_02eeb_row31_col5\" class=\"data row31 col5\" >40765.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row32\" class=\"row_heading level0 row32\" >346</th>\n",
       "      <td id=\"T_02eeb_row32_col0\" class=\"data row32 col0\" >30/11/2018</td>\n",
       "      <td id=\"T_02eeb_row32_col1\" class=\"data row32 col1\" >3.20%</td>\n",
       "      <td id=\"T_02eeb_row32_col2\" class=\"data row32 col2\" >8.77%</td>\n",
       "      <td id=\"T_02eeb_row32_col3\" class=\"data row32 col3\" >15.84%</td>\n",
       "      <td id=\"T_02eeb_row32_col4\" class=\"data row32 col4\" >48.354630</td>\n",
       "      <td id=\"T_02eeb_row32_col5\" class=\"data row32 col5\" >42072.68%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row33\" class=\"row_heading level0 row33\" >347</th>\n",
       "      <td id=\"T_02eeb_row33_col0\" class=\"data row33 col0\" >31/12/2018</td>\n",
       "      <td id=\"T_02eeb_row33_col1\" class=\"data row33 col1\" >2.60%</td>\n",
       "      <td id=\"T_02eeb_row33_col2\" class=\"data row33 col2\" >5.88%</td>\n",
       "      <td id=\"T_02eeb_row33_col3\" class=\"data row33 col3\" >11.60%</td>\n",
       "      <td id=\"T_02eeb_row33_col4\" class=\"data row33 col4\" >47.635160</td>\n",
       "      <td id=\"T_02eeb_row33_col5\" class=\"data row33 col5\" >43169.17%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row34\" class=\"row_heading level0 row34\" >348</th>\n",
       "      <td id=\"T_02eeb_row34_col0\" class=\"data row34 col0\" >31/01/2019</td>\n",
       "      <td id=\"T_02eeb_row34_col1\" class=\"data row34 col1\" >2.90%</td>\n",
       "      <td id=\"T_02eeb_row34_col2\" class=\"data row34 col2\" >5.58%</td>\n",
       "      <td id=\"T_02eeb_row34_col3\" class=\"data row34 col3\" >8.95%</td>\n",
       "      <td id=\"T_02eeb_row34_col4\" class=\"data row34 col4\" >49.230432</td>\n",
       "      <td id=\"T_02eeb_row34_col5\" class=\"data row34 col5\" >44423.97%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row35\" class=\"row_heading level0 row35\" >349</th>\n",
       "      <td id=\"T_02eeb_row35_col0\" class=\"data row35 col0\" >28/02/2019</td>\n",
       "      <td id=\"T_02eeb_row35_col1\" class=\"data row35 col1\" >3.80%</td>\n",
       "      <td id=\"T_02eeb_row35_col2\" class=\"data row35 col2\" >6.81%</td>\n",
       "      <td id=\"T_02eeb_row35_col3\" class=\"data row35 col3\" >9.59%</td>\n",
       "      <td id=\"T_02eeb_row35_col4\" class=\"data row35 col4\" >51.270692</td>\n",
       "      <td id=\"T_02eeb_row35_col5\" class=\"data row35 col5\" >46115.88%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row36\" class=\"row_heading level0 row36\" >350</th>\n",
       "      <td id=\"T_02eeb_row36_col0\" class=\"data row36 col0\" >31/03/2019</td>\n",
       "      <td id=\"T_02eeb_row36_col1\" class=\"data row36 col1\" >4.70%</td>\n",
       "      <td id=\"T_02eeb_row36_col2\" class=\"data row36 col2\" >8.68%</td>\n",
       "      <td id=\"T_02eeb_row36_col3\" class=\"data row36 col3\" >11.83%</td>\n",
       "      <td id=\"T_02eeb_row36_col4\" class=\"data row36 col4\" >54.819565</td>\n",
       "      <td id=\"T_02eeb_row36_col5\" class=\"data row36 col5\" >48288.03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row37\" class=\"row_heading level0 row37\" >351</th>\n",
       "      <td id=\"T_02eeb_row37_col0\" class=\"data row37 col0\" >30/04/2019</td>\n",
       "      <td id=\"T_02eeb_row37_col1\" class=\"data row37 col1\" >3.40%</td>\n",
       "      <td id=\"T_02eeb_row37_col2\" class=\"data row37 col2\" >8.26%</td>\n",
       "      <td id=\"T_02eeb_row37_col3\" class=\"data row37 col3\" >12.37%</td>\n",
       "      <td id=\"T_02eeb_row37_col4\" class=\"data row37 col4\" >55.874810</td>\n",
       "      <td id=\"T_02eeb_row37_col5\" class=\"data row37 col5\" >49933.22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row38\" class=\"row_heading level0 row38\" >352</th>\n",
       "      <td id=\"T_02eeb_row38_col0\" class=\"data row38 col0\" >31/05/2019</td>\n",
       "      <td id=\"T_02eeb_row38_col1\" class=\"data row38 col1\" >3.10%</td>\n",
       "      <td id=\"T_02eeb_row38_col2\" class=\"data row38 col2\" >6.61%</td>\n",
       "      <td id=\"T_02eeb_row38_col3\" class=\"data row38 col3\" >11.62%</td>\n",
       "      <td id=\"T_02eeb_row38_col4\" class=\"data row38 col4\" >57.401498</td>\n",
       "      <td id=\"T_02eeb_row38_col5\" class=\"data row38 col5\" >51484.25%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row39\" class=\"row_heading level0 row39\" >353</th>\n",
       "      <td id=\"T_02eeb_row39_col0\" class=\"data row39 col0\" >30/06/2019</td>\n",
       "      <td id=\"T_02eeb_row39_col1\" class=\"data row39 col1\" >2.70%</td>\n",
       "      <td id=\"T_02eeb_row39_col2\" class=\"data row39 col2\" >5.88%</td>\n",
       "      <td id=\"T_02eeb_row39_col3\" class=\"data row39 col3\" >9.48%</td>\n",
       "      <td id=\"T_02eeb_row39_col4\" class=\"data row39 col4\" >55.883643</td>\n",
       "      <td id=\"T_02eeb_row39_col5\" class=\"data row39 col5\" >52877.03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row40\" class=\"row_heading level0 row40\" >354</th>\n",
       "      <td id=\"T_02eeb_row40_col0\" class=\"data row40 col0\" >31/07/2019</td>\n",
       "      <td id=\"T_02eeb_row40_col1\" class=\"data row40 col1\" >2.20%</td>\n",
       "      <td id=\"T_02eeb_row40_col2\" class=\"data row40 col2\" >4.96%</td>\n",
       "      <td id=\"T_02eeb_row40_col3\" class=\"data row40 col3\" >8.21%</td>\n",
       "      <td id=\"T_02eeb_row40_col4\" class=\"data row40 col4\" >54.522874</td>\n",
       "      <td id=\"T_02eeb_row40_col5\" class=\"data row40 col5\" >54042.52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row41\" class=\"row_heading level0 row41\" >355</th>\n",
       "      <td id=\"T_02eeb_row41_col0\" class=\"data row41 col0\" >31/08/2019</td>\n",
       "      <td id=\"T_02eeb_row41_col1\" class=\"data row41 col1\" >4.00%</td>\n",
       "      <td id=\"T_02eeb_row41_col2\" class=\"data row41 col2\" >6.29%</td>\n",
       "      <td id=\"T_02eeb_row41_col3\" class=\"data row41 col3\" >9.16%</td>\n",
       "      <td id=\"T_02eeb_row41_col4\" class=\"data row41 col4\" >54.671597</td>\n",
       "      <td id=\"T_02eeb_row41_col5\" class=\"data row41 col5\" >56208.22%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row42\" class=\"row_heading level0 row42\" >356</th>\n",
       "      <td id=\"T_02eeb_row42_col0\" class=\"data row42 col0\" >30/09/2019</td>\n",
       "      <td id=\"T_02eeb_row42_col1\" class=\"data row42 col1\" >5.90%</td>\n",
       "      <td id=\"T_02eeb_row42_col2\" class=\"data row42 col2\" >10.14%</td>\n",
       "      <td id=\"T_02eeb_row42_col3\" class=\"data row42 col3\" >12.56%</td>\n",
       "      <td id=\"T_02eeb_row42_col4\" class=\"data row42 col4\" >53.800208</td>\n",
       "      <td id=\"T_02eeb_row42_col5\" class=\"data row42 col5\" >59530.41%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row43\" class=\"row_heading level0 row43\" >357</th>\n",
       "      <td id=\"T_02eeb_row43_col0\" class=\"data row43 col0\" >31/10/2019</td>\n",
       "      <td id=\"T_02eeb_row43_col1\" class=\"data row43 col1\" >3.30%</td>\n",
       "      <td id=\"T_02eeb_row43_col2\" class=\"data row43 col2\" >9.39%</td>\n",
       "      <td id=\"T_02eeb_row43_col3\" class=\"data row43 col3\" >13.77%</td>\n",
       "      <td id=\"T_02eeb_row43_col4\" class=\"data row43 col4\" >50.735877</td>\n",
       "      <td id=\"T_02eeb_row43_col5\" class=\"data row43 col5\" >61498.21%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row44\" class=\"row_heading level0 row44\" >358</th>\n",
       "      <td id=\"T_02eeb_row44_col0\" class=\"data row44 col0\" >30/11/2019</td>\n",
       "      <td id=\"T_02eeb_row44_col1\" class=\"data row44 col1\" >4.30%</td>\n",
       "      <td id=\"T_02eeb_row44_col2\" class=\"data row44 col2\" >7.74%</td>\n",
       "      <td id=\"T_02eeb_row44_col3\" class=\"data row44 col3\" >14.10%</td>\n",
       "      <td id=\"T_02eeb_row44_col4\" class=\"data row44 col4\" >52.342558</td>\n",
       "      <td id=\"T_02eeb_row44_col5\" class=\"data row44 col5\" >64146.94%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row45\" class=\"row_heading level0 row45\" >359</th>\n",
       "      <td id=\"T_02eeb_row45_col0\" class=\"data row45 col0\" >31/12/2019</td>\n",
       "      <td id=\"T_02eeb_row45_col1\" class=\"data row45 col1\" >3.70%</td>\n",
       "      <td id=\"T_02eeb_row45_col2\" class=\"data row45 col2\" >8.16%</td>\n",
       "      <td id=\"T_02eeb_row45_col3\" class=\"data row45 col3\" >11.73%</td>\n",
       "      <td id=\"T_02eeb_row45_col4\" class=\"data row45 col4\" >53.975860</td>\n",
       "      <td id=\"T_02eeb_row45_col5\" class=\"data row45 col5\" >66524.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row46\" class=\"row_heading level0 row46\" >360</th>\n",
       "      <td id=\"T_02eeb_row46_col0\" class=\"data row46 col0\" >31/01/2020</td>\n",
       "      <td id=\"T_02eeb_row46_col1\" class=\"data row46 col1\" >2.30%</td>\n",
       "      <td id=\"T_02eeb_row46_col2\" class=\"data row46 col2\" >6.09%</td>\n",
       "      <td id=\"T_02eeb_row46_col3\" class=\"data row46 col3\" >10.65%</td>\n",
       "      <td id=\"T_02eeb_row46_col4\" class=\"data row46 col4\" >53.078042</td>\n",
       "      <td id=\"T_02eeb_row46_col5\" class=\"data row46 col5\" >68056.43%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row47\" class=\"row_heading level0 row47\" >361</th>\n",
       "      <td id=\"T_02eeb_row47_col0\" class=\"data row47 col0\" >29/02/2020</td>\n",
       "      <td id=\"T_02eeb_row47_col1\" class=\"data row47 col1\" >2.00%</td>\n",
       "      <td id=\"T_02eeb_row47_col2\" class=\"data row47 col2\" >4.35%</td>\n",
       "      <td id=\"T_02eeb_row47_col3\" class=\"data row47 col3\" >8.21%</td>\n",
       "      <td id=\"T_02eeb_row47_col4\" class=\"data row47 col4\" >50.423509</td>\n",
       "      <td id=\"T_02eeb_row47_col5\" class=\"data row47 col5\" >69419.55%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row48\" class=\"row_heading level0 row48\" >362</th>\n",
       "      <td id=\"T_02eeb_row48_col0\" class=\"data row48 col0\" >31/03/2020</td>\n",
       "      <td id=\"T_02eeb_row48_col1\" class=\"data row48 col1\" >3.30%</td>\n",
       "      <td id=\"T_02eeb_row48_col2\" class=\"data row48 col2\" >5.37%</td>\n",
       "      <td id=\"T_02eeb_row48_col3\" class=\"data row48 col3\" >7.79%</td>\n",
       "      <td id=\"T_02eeb_row48_col4\" class=\"data row48 col4\" >48.412116</td>\n",
       "      <td id=\"T_02eeb_row48_col5\" class=\"data row48 col5\" >71713.70%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row49\" class=\"row_heading level0 row49\" >363</th>\n",
       "      <td id=\"T_02eeb_row49_col0\" class=\"data row49 col0\" >30/04/2020</td>\n",
       "      <td id=\"T_02eeb_row49_col1\" class=\"data row49 col1\" >1.50%</td>\n",
       "      <td id=\"T_02eeb_row49_col2\" class=\"data row49 col2\" >4.85%</td>\n",
       "      <td id=\"T_02eeb_row49_col3\" class=\"data row49 col3\" >6.95%</td>\n",
       "      <td id=\"T_02eeb_row49_col4\" class=\"data row49 col4\" >45.685007</td>\n",
       "      <td id=\"T_02eeb_row49_col5\" class=\"data row49 col5\" >72790.90%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row50\" class=\"row_heading level0 row50\" >364</th>\n",
       "      <td id=\"T_02eeb_row50_col0\" class=\"data row50 col0\" >31/05/2020</td>\n",
       "      <td id=\"T_02eeb_row50_col1\" class=\"data row50 col1\" >1.50%</td>\n",
       "      <td id=\"T_02eeb_row50_col2\" class=\"data row50 col2\" >3.02%</td>\n",
       "      <td id=\"T_02eeb_row50_col3\" class=\"data row50 col3\" >6.42%</td>\n",
       "      <td id=\"T_02eeb_row50_col4\" class=\"data row50 col4\" >43.424134</td>\n",
       "      <td id=\"T_02eeb_row50_col5\" class=\"data row50 col5\" >73884.27%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row51\" class=\"row_heading level0 row51\" >365</th>\n",
       "      <td id=\"T_02eeb_row51_col0\" class=\"data row51 col0\" >30/06/2020</td>\n",
       "      <td id=\"T_02eeb_row51_col1\" class=\"data row51 col1\" >2.20%</td>\n",
       "      <td id=\"T_02eeb_row51_col2\" class=\"data row51 col2\" >3.73%</td>\n",
       "      <td id=\"T_02eeb_row51_col3\" class=\"data row51 col3\" >5.29%</td>\n",
       "      <td id=\"T_02eeb_row51_col4\" class=\"data row51 col4\" >42.725867</td>\n",
       "      <td id=\"T_02eeb_row51_col5\" class=\"data row51 col5\" >75511.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row52\" class=\"row_heading level0 row52\" >366</th>\n",
       "      <td id=\"T_02eeb_row52_col0\" class=\"data row52 col0\" >31/07/2020</td>\n",
       "      <td id=\"T_02eeb_row52_col1\" class=\"data row52 col1\" >1.90%</td>\n",
       "      <td id=\"T_02eeb_row52_col2\" class=\"data row52 col2\" >4.14%</td>\n",
       "      <td id=\"T_02eeb_row52_col3\" class=\"data row52 col3\" >5.70%</td>\n",
       "      <td id=\"T_02eeb_row52_col4\" class=\"data row52 col4\" >42.306906</td>\n",
       "      <td id=\"T_02eeb_row52_col5\" class=\"data row52 col5\" >76948.55%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row53\" class=\"row_heading level0 row53\" >367</th>\n",
       "      <td id=\"T_02eeb_row53_col0\" class=\"data row53 col0\" >31/08/2020</td>\n",
       "      <td id=\"T_02eeb_row53_col1\" class=\"data row53 col1\" >2.70%</td>\n",
       "      <td id=\"T_02eeb_row53_col2\" class=\"data row53 col2\" >4.65%</td>\n",
       "      <td id=\"T_02eeb_row53_col3\" class=\"data row53 col3\" >6.95%</td>\n",
       "      <td id=\"T_02eeb_row53_col4\" class=\"data row53 col4\" >40.528070</td>\n",
       "      <td id=\"T_02eeb_row53_col5\" class=\"data row53 col5\" >79028.86%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row54\" class=\"row_heading level0 row54\" >368</th>\n",
       "      <td id=\"T_02eeb_row54_col0\" class=\"data row54 col0\" >30/09/2020</td>\n",
       "      <td id=\"T_02eeb_row54_col1\" class=\"data row54 col1\" >2.80%</td>\n",
       "      <td id=\"T_02eeb_row54_col2\" class=\"data row54 col2\" >5.58%</td>\n",
       "      <td id=\"T_02eeb_row54_col3\" class=\"data row54 col3\" >7.58%</td>\n",
       "      <td id=\"T_02eeb_row54_col4\" class=\"data row54 col4\" >36.414406</td>\n",
       "      <td id=\"T_02eeb_row54_col5\" class=\"data row54 col5\" >81244.47%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row55\" class=\"row_heading level0 row55\" >369</th>\n",
       "      <td id=\"T_02eeb_row55_col0\" class=\"data row55 col0\" >31/10/2020</td>\n",
       "      <td id=\"T_02eeb_row55_col1\" class=\"data row55 col1\" >3.80%</td>\n",
       "      <td id=\"T_02eeb_row55_col2\" class=\"data row55 col2\" >6.71%</td>\n",
       "      <td id=\"T_02eeb_row55_col3\" class=\"data row55 col3\" >9.59%</td>\n",
       "      <td id=\"T_02eeb_row55_col4\" class=\"data row55 col4\" >37.074689</td>\n",
       "      <td id=\"T_02eeb_row55_col5\" class=\"data row55 col5\" >84335.56%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row56\" class=\"row_heading level0 row56\" >370</th>\n",
       "      <td id=\"T_02eeb_row56_col0\" class=\"data row56 col0\" >30/11/2020</td>\n",
       "      <td id=\"T_02eeb_row56_col1\" class=\"data row56 col1\" >3.20%</td>\n",
       "      <td id=\"T_02eeb_row56_col2\" class=\"data row56 col2\" >7.12%</td>\n",
       "      <td id=\"T_02eeb_row56_col3\" class=\"data row56 col3\" >10.12%</td>\n",
       "      <td id=\"T_02eeb_row56_col4\" class=\"data row56 col4\" >35.629030</td>\n",
       "      <td id=\"T_02eeb_row56_col5\" class=\"data row56 col5\" >87037.50%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row57\" class=\"row_heading level0 row57\" >371</th>\n",
       "      <td id=\"T_02eeb_row57_col0\" class=\"data row57 col0\" >31/12/2020</td>\n",
       "      <td id=\"T_02eeb_row57_col1\" class=\"data row57 col1\" >4.00%</td>\n",
       "      <td id=\"T_02eeb_row57_col2\" class=\"data row57 col2\" >7.33%</td>\n",
       "      <td id=\"T_02eeb_row57_col3\" class=\"data row57 col3\" >11.41%</td>\n",
       "      <td id=\"T_02eeb_row57_col4\" class=\"data row57 col4\" >36.021400</td>\n",
       "      <td id=\"T_02eeb_row57_col5\" class=\"data row57 col5\" >90523.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row58\" class=\"row_heading level0 row58\" >372</th>\n",
       "      <td id=\"T_02eeb_row58_col0\" class=\"data row58 col0\" >31/01/2021</td>\n",
       "      <td id=\"T_02eeb_row58_col1\" class=\"data row58 col1\" >4.00%</td>\n",
       "      <td id=\"T_02eeb_row58_col2\" class=\"data row58 col2\" >8.16%</td>\n",
       "      <td id=\"T_02eeb_row58_col3\" class=\"data row58 col3\" >11.62%</td>\n",
       "      <td id=\"T_02eeb_row58_col4\" class=\"data row58 col4\" >38.281775</td>\n",
       "      <td id=\"T_02eeb_row58_col5\" class=\"data row58 col5\" >94147.91%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row59\" class=\"row_heading level0 row59\" >373</th>\n",
       "      <td id=\"T_02eeb_row59_col0\" class=\"data row59 col0\" >28/02/2021</td>\n",
       "      <td id=\"T_02eeb_row59_col1\" class=\"data row59 col1\" >3.60%</td>\n",
       "      <td id=\"T_02eeb_row59_col2\" class=\"data row59 col2\" >7.74%</td>\n",
       "      <td id=\"T_02eeb_row59_col3\" class=\"data row59 col3\" >12.05%</td>\n",
       "      <td id=\"T_02eeb_row59_col4\" class=\"data row59 col4\" >40.450901</td>\n",
       "      <td id=\"T_02eeb_row59_col5\" class=\"data row59 col5\" >97540.84%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row60\" class=\"row_heading level0 row60\" >374</th>\n",
       "      <td id=\"T_02eeb_row60_col0\" class=\"data row60 col0\" >31/03/2021</td>\n",
       "      <td id=\"T_02eeb_row60_col1\" class=\"data row60 col1\" >4.80%</td>\n",
       "      <td id=\"T_02eeb_row60_col2\" class=\"data row60 col2\" >8.57%</td>\n",
       "      <td id=\"T_02eeb_row60_col3\" class=\"data row60 col3\" >12.92%</td>\n",
       "      <td id=\"T_02eeb_row60_col4\" class=\"data row60 col4\" >42.490362</td>\n",
       "      <td id=\"T_02eeb_row60_col5\" class=\"data row60 col5\" >102227.60%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row61\" class=\"row_heading level0 row61\" >375</th>\n",
       "      <td id=\"T_02eeb_row61_col0\" class=\"data row61 col0\" >30/04/2021</td>\n",
       "      <td id=\"T_02eeb_row61_col1\" class=\"data row61 col1\" >4.10%</td>\n",
       "      <td id=\"T_02eeb_row61_col2\" class=\"data row61 col2\" >9.10%</td>\n",
       "      <td id=\"T_02eeb_row61_col3\" class=\"data row61 col3\" >13.02%</td>\n",
       "      <td id=\"T_02eeb_row61_col4\" class=\"data row61 col4\" >46.140361</td>\n",
       "      <td id=\"T_02eeb_row61_col5\" class=\"data row61 col5\" >106423.03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row62\" class=\"row_heading level0 row62\" >376</th>\n",
       "      <td id=\"T_02eeb_row62_col0\" class=\"data row62 col0\" >31/05/2021</td>\n",
       "      <td id=\"T_02eeb_row62_col1\" class=\"data row62 col1\" >3.30%</td>\n",
       "      <td id=\"T_02eeb_row62_col2\" class=\"data row62 col2\" >7.54%</td>\n",
       "      <td id=\"T_02eeb_row62_col3\" class=\"data row62 col3\" >12.70%</td>\n",
       "      <td id=\"T_02eeb_row62_col4\" class=\"data row62 col4\" >48.732013</td>\n",
       "      <td id=\"T_02eeb_row62_col5\" class=\"data row62 col5\" >109938.29%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row63\" class=\"row_heading level0 row63\" >377</th>\n",
       "      <td id=\"T_02eeb_row63_col0\" class=\"data row63 col0\" >30/06/2021</td>\n",
       "      <td id=\"T_02eeb_row63_col1\" class=\"data row63 col1\" >3.20%</td>\n",
       "      <td id=\"T_02eeb_row63_col2\" class=\"data row63 col2\" >6.61%</td>\n",
       "      <td id=\"T_02eeb_row63_col3\" class=\"data row63 col3\" >10.98%</td>\n",
       "      <td id=\"T_02eeb_row63_col4\" class=\"data row63 col4\" >50.187317</td>\n",
       "      <td id=\"T_02eeb_row63_col5\" class=\"data row63 col5\" >113459.52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row64\" class=\"row_heading level0 row64\" >378</th>\n",
       "      <td id=\"T_02eeb_row64_col0\" class=\"data row64 col0\" >31/07/2021</td>\n",
       "      <td id=\"T_02eeb_row64_col1\" class=\"data row64 col1\" >3.00%</td>\n",
       "      <td id=\"T_02eeb_row64_col2\" class=\"data row64 col2\" >6.30%</td>\n",
       "      <td id=\"T_02eeb_row64_col3\" class=\"data row64 col3\" >9.80%</td>\n",
       "      <td id=\"T_02eeb_row64_col4\" class=\"data row64 col4\" >51.808573</td>\n",
       "      <td id=\"T_02eeb_row64_col5\" class=\"data row64 col5\" >116866.30%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row65\" class=\"row_heading level0 row65\" >379</th>\n",
       "      <td id=\"T_02eeb_row65_col0\" class=\"data row65 col0\" >31/08/2021</td>\n",
       "      <td id=\"T_02eeb_row65_col1\" class=\"data row65 col1\" >2.50%</td>\n",
       "      <td id=\"T_02eeb_row65_col2\" class=\"data row65 col2\" >5.57%</td>\n",
       "      <td id=\"T_02eeb_row65_col3\" class=\"data row65 col3\" >8.95%</td>\n",
       "      <td id=\"T_02eeb_row65_col4\" class=\"data row65 col4\" >51.512938</td>\n",
       "      <td id=\"T_02eeb_row65_col5\" class=\"data row65 col5\" >119790.46%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row66\" class=\"row_heading level0 row66\" >380</th>\n",
       "      <td id=\"T_02eeb_row66_col0\" class=\"data row66 col0\" >30/09/2021</td>\n",
       "      <td id=\"T_02eeb_row66_col1\" class=\"data row66 col1\" >3.50%</td>\n",
       "      <td id=\"T_02eeb_row66_col2\" class=\"data row66 col2\" >6.09%</td>\n",
       "      <td id=\"T_02eeb_row66_col3\" class=\"data row66 col3\" >9.27%</td>\n",
       "      <td id=\"T_02eeb_row66_col4\" class=\"data row66 col4\" >52.544641</td>\n",
       "      <td id=\"T_02eeb_row66_col5\" class=\"data row66 col5\" >123986.63%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row67\" class=\"row_heading level0 row67\" >381</th>\n",
       "      <td id=\"T_02eeb_row67_col0\" class=\"data row67 col0\" >31/10/2021</td>\n",
       "      <td id=\"T_02eeb_row67_col1\" class=\"data row67 col1\" >3.50%</td>\n",
       "      <td id=\"T_02eeb_row67_col2\" class=\"data row67 col2\" >7.12%</td>\n",
       "      <td id=\"T_02eeb_row67_col3\" class=\"data row67 col3\" >9.80%</td>\n",
       "      <td id=\"T_02eeb_row67_col4\" class=\"data row67 col4\" >52.103761</td>\n",
       "      <td id=\"T_02eeb_row67_col5\" class=\"data row67 col5\" >128329.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row68\" class=\"row_heading level0 row68\" >382</th>\n",
       "      <td id=\"T_02eeb_row68_col0\" class=\"data row68 col0\" >30/11/2021</td>\n",
       "      <td id=\"T_02eeb_row68_col1\" class=\"data row68 col1\" >2.50%</td>\n",
       "      <td id=\"T_02eeb_row68_col2\" class=\"data row68 col2\" >6.09%</td>\n",
       "      <td id=\"T_02eeb_row68_col3\" class=\"data row68 col3\" >9.80%</td>\n",
       "      <td id=\"T_02eeb_row68_col4\" class=\"data row68 col4\" >51.072049</td>\n",
       "      <td id=\"T_02eeb_row68_col5\" class=\"data row68 col5\" >131540.40%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row69\" class=\"row_heading level0 row69\" >383</th>\n",
       "      <td id=\"T_02eeb_row69_col0\" class=\"data row69 col0\" >31/12/2021</td>\n",
       "      <td id=\"T_02eeb_row69_col1\" class=\"data row69 col1\" >3.80%</td>\n",
       "      <td id=\"T_02eeb_row69_col2\" class=\"data row69 col2\" >6.39%</td>\n",
       "      <td id=\"T_02eeb_row69_col3\" class=\"data row69 col3\" >10.12%</td>\n",
       "      <td id=\"T_02eeb_row69_col4\" class=\"data row69 col4\" >50.781526</td>\n",
       "      <td id=\"T_02eeb_row69_col5\" class=\"data row69 col5\" >136542.73%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row70\" class=\"row_heading level0 row70\" >384</th>\n",
       "      <td id=\"T_02eeb_row70_col0\" class=\"data row70 col0\" >31/01/2022</td>\n",
       "      <td id=\"T_02eeb_row70_col1\" class=\"data row70 col1\" >3.90%</td>\n",
       "      <td id=\"T_02eeb_row70_col2\" class=\"data row70 col2\" >7.85%</td>\n",
       "      <td id=\"T_02eeb_row70_col3\" class=\"data row70 col3\" >10.54%</td>\n",
       "      <td id=\"T_02eeb_row70_col4\" class=\"data row70 col4\" >50.636544</td>\n",
       "      <td id=\"T_02eeb_row70_col5\" class=\"data row70 col5\" >141871.80%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row71\" class=\"row_heading level0 row71\" >385</th>\n",
       "      <td id=\"T_02eeb_row71_col0\" class=\"data row71 col0\" >28/02/2022</td>\n",
       "      <td id=\"T_02eeb_row71_col1\" class=\"data row71 col1\" >4.70%</td>\n",
       "      <td id=\"T_02eeb_row71_col2\" class=\"data row71 col2\" >8.78%</td>\n",
       "      <td id=\"T_02eeb_row71_col3\" class=\"data row71 col3\" >12.92%</td>\n",
       "      <td id=\"T_02eeb_row71_col4\" class=\"data row71 col4\" >52.235967</td>\n",
       "      <td id=\"T_02eeb_row71_col5\" class=\"data row71 col5\" >148544.48%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row72\" class=\"row_heading level0 row72\" >386</th>\n",
       "      <td id=\"T_02eeb_row72_col0\" class=\"data row72 col0\" >31/03/2022</td>\n",
       "      <td id=\"T_02eeb_row72_col1\" class=\"data row72 col1\" >6.70%</td>\n",
       "      <td id=\"T_02eeb_row72_col2\" class=\"data row72 col2\" >11.71%</td>\n",
       "      <td id=\"T_02eeb_row72_col3\" class=\"data row72 col3\" >16.07%</td>\n",
       "      <td id=\"T_02eeb_row72_col4\" class=\"data row72 col4\" >54.995970</td>\n",
       "      <td id=\"T_02eeb_row72_col5\" class=\"data row72 col5\" >158503.66%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row73\" class=\"row_heading level0 row73\" >387</th>\n",
       "      <td id=\"T_02eeb_row73_col0\" class=\"data row73 col0\" >30/04/2022</td>\n",
       "      <td id=\"T_02eeb_row73_col1\" class=\"data row73 col1\" >6.00%</td>\n",
       "      <td id=\"T_02eeb_row73_col2\" class=\"data row73 col2\" >13.10%</td>\n",
       "      <td id=\"T_02eeb_row73_col3\" class=\"data row73 col3\" >18.42%</td>\n",
       "      <td id=\"T_02eeb_row73_col4\" class=\"data row73 col4\" >57.824907</td>\n",
       "      <td id=\"T_02eeb_row73_col5\" class=\"data row73 col5\" >168019.88%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row74\" class=\"row_heading level0 row74\" >388</th>\n",
       "      <td id=\"T_02eeb_row74_col0\" class=\"data row74 col0\" >31/05/2022</td>\n",
       "      <td id=\"T_02eeb_row74_col1\" class=\"data row74 col1\" >5.10%</td>\n",
       "      <td id=\"T_02eeb_row74_col2\" class=\"data row74 col2\" >11.41%</td>\n",
       "      <td id=\"T_02eeb_row74_col3\" class=\"data row74 col3\" >18.87%</td>\n",
       "      <td id=\"T_02eeb_row74_col4\" class=\"data row74 col4\" >60.575002</td>\n",
       "      <td id=\"T_02eeb_row74_col5\" class=\"data row74 col5\" >176593.99%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row75\" class=\"row_heading level0 row75\" >389</th>\n",
       "      <td id=\"T_02eeb_row75_col0\" class=\"data row75 col0\" >30/06/2022</td>\n",
       "      <td id=\"T_02eeb_row75_col1\" class=\"data row75 col1\" >5.30%</td>\n",
       "      <td id=\"T_02eeb_row75_col2\" class=\"data row75 col2\" >10.67%</td>\n",
       "      <td id=\"T_02eeb_row75_col3\" class=\"data row75 col3\" >17.31%</td>\n",
       "      <td id=\"T_02eeb_row75_col4\" class=\"data row75 col4\" >63.842516</td>\n",
       "      <td id=\"T_02eeb_row75_col5\" class=\"data row75 col5\" >185958.77%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row76\" class=\"row_heading level0 row76\" >390</th>\n",
       "      <td id=\"T_02eeb_row76_col0\" class=\"data row76 col0\" >31/07/2022</td>\n",
       "      <td id=\"T_02eeb_row76_col1\" class=\"data row76 col1\" >7.40%</td>\n",
       "      <td id=\"T_02eeb_row76_col2\" class=\"data row76 col2\" >13.09%</td>\n",
       "      <td id=\"T_02eeb_row76_col3\" class=\"data row76 col3\" >18.86%</td>\n",
       "      <td id=\"T_02eeb_row76_col4\" class=\"data row76 col4\" >70.841614</td>\n",
       "      <td id=\"T_02eeb_row76_col5\" class=\"data row76 col5\" >199727.12%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row77\" class=\"row_heading level0 row77\" >391</th>\n",
       "      <td id=\"T_02eeb_row77_col0\" class=\"data row77 col0\" >31/08/2022</td>\n",
       "      <td id=\"T_02eeb_row77_col1\" class=\"data row77 col1\" >7.00%</td>\n",
       "      <td id=\"T_02eeb_row77_col2\" class=\"data row77 col2\" >14.92%</td>\n",
       "      <td id=\"T_02eeb_row77_col3\" class=\"data row77 col3\" >21.01%</td>\n",
       "      <td id=\"T_02eeb_row77_col4\" class=\"data row77 col4\" >78.341978</td>\n",
       "      <td id=\"T_02eeb_row77_col5\" class=\"data row77 col5\" >213715.02%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row78\" class=\"row_heading level0 row78\" >392</th>\n",
       "      <td id=\"T_02eeb_row78_col0\" class=\"data row78 col0\" >30/09/2022</td>\n",
       "      <td id=\"T_02eeb_row78_col1\" class=\"data row78 col1\" >6.20%</td>\n",
       "      <td id=\"T_02eeb_row78_col2\" class=\"data row78 col2\" >13.63%</td>\n",
       "      <td id=\"T_02eeb_row78_col3\" class=\"data row78 col3\" >22.04%</td>\n",
       "      <td id=\"T_02eeb_row78_col4\" class=\"data row78 col4\" >82.994377</td>\n",
       "      <td id=\"T_02eeb_row78_col5\" class=\"data row78 col5\" >226971.55%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row79\" class=\"row_heading level0 row79\" >393</th>\n",
       "      <td id=\"T_02eeb_row79_col0\" class=\"data row79 col0\" >31/10/2022</td>\n",
       "      <td id=\"T_02eeb_row79_col1\" class=\"data row79 col1\" >6.30%</td>\n",
       "      <td id=\"T_02eeb_row79_col2\" class=\"data row79 col2\" >12.89%</td>\n",
       "      <td id=\"T_02eeb_row79_col3\" class=\"data row79 col3\" >20.79%</td>\n",
       "      <td id=\"T_02eeb_row79_col4\" class=\"data row79 col4\" >87.944950</td>\n",
       "      <td id=\"T_02eeb_row79_col5\" class=\"data row79 col5\" >241277.06%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row80\" class=\"row_heading level0 row80\" >394</th>\n",
       "      <td id=\"T_02eeb_row80_col0\" class=\"data row80 col0\" >30/11/2022</td>\n",
       "      <td id=\"T_02eeb_row80_col1\" class=\"data row80 col1\" >4.90%</td>\n",
       "      <td id=\"T_02eeb_row80_col2\" class=\"data row80 col2\" >11.51%</td>\n",
       "      <td id=\"T_02eeb_row80_col3\" class=\"data row80 col3\" >18.42%</td>\n",
       "      <td id=\"T_02eeb_row80_col4\" class=\"data row80 col4\" >92.345612</td>\n",
       "      <td id=\"T_02eeb_row80_col5\" class=\"data row80 col5\" >253104.53%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row81\" class=\"row_heading level0 row81\" >395</th>\n",
       "      <td id=\"T_02eeb_row81_col0\" class=\"data row81 col0\" >31/12/2022</td>\n",
       "      <td id=\"T_02eeb_row81_col1\" class=\"data row81 col1\" >5.10%</td>\n",
       "      <td id=\"T_02eeb_row81_col2\" class=\"data row81 col2\" >10.25%</td>\n",
       "      <td id=\"T_02eeb_row81_col3\" class=\"data row81 col3\" >17.20%</td>\n",
       "      <td id=\"T_02eeb_row81_col4\" class=\"data row81 col4\" >94.754565</td>\n",
       "      <td id=\"T_02eeb_row81_col5\" class=\"data row81 col5\" >266017.96%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row82\" class=\"row_heading level0 row82\" >396</th>\n",
       "      <td id=\"T_02eeb_row82_col0\" class=\"data row82 col0\" >31/01/2023</td>\n",
       "      <td id=\"T_02eeb_row82_col1\" class=\"data row82 col1\" >6.00%</td>\n",
       "      <td id=\"T_02eeb_row82_col2\" class=\"data row82 col2\" >11.41%</td>\n",
       "      <td id=\"T_02eeb_row82_col3\" class=\"data row82 col3\" >16.86%</td>\n",
       "      <td id=\"T_02eeb_row82_col4\" class=\"data row82 col4\" >98.690894</td>\n",
       "      <td id=\"T_02eeb_row82_col5\" class=\"data row82 col5\" >281985.04%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row83\" class=\"row_heading level0 row83\" >397</th>\n",
       "      <td id=\"T_02eeb_row83_col0\" class=\"data row83 col0\" >28/02/2023</td>\n",
       "      <td id=\"T_02eeb_row83_col1\" class=\"data row83 col1\" >6.60%</td>\n",
       "      <td id=\"T_02eeb_row83_col2\" class=\"data row83 col2\" >13.00%</td>\n",
       "      <td id=\"T_02eeb_row83_col3\" class=\"data row83 col3\" >18.76%</td>\n",
       "      <td id=\"T_02eeb_row83_col4\" class=\"data row83 col4\" >102.296555</td>\n",
       "      <td id=\"T_02eeb_row83_col5\" class=\"data row83 col5\" >300602.65%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row84\" class=\"row_heading level0 row84\" >398</th>\n",
       "      <td id=\"T_02eeb_row84_col0\" class=\"data row84 col0\" >31/03/2023</td>\n",
       "      <td id=\"T_02eeb_row84_col1\" class=\"data row84 col1\" >7.70%</td>\n",
       "      <td id=\"T_02eeb_row84_col2\" class=\"data row84 col2\" >14.81%</td>\n",
       "      <td id=\"T_02eeb_row84_col3\" class=\"data row84 col3\" >21.70%</td>\n",
       "      <td id=\"T_02eeb_row84_col4\" class=\"data row84 col4\" >104.192492</td>\n",
       "      <td id=\"T_02eeb_row84_col5\" class=\"data row84 col5\" >323756.76%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row85\" class=\"row_heading level0 row85\" >399</th>\n",
       "      <td id=\"T_02eeb_row85_col0\" class=\"data row85 col0\" >30/04/2023</td>\n",
       "      <td id=\"T_02eeb_row85_col1\" class=\"data row85 col1\" >8.40%</td>\n",
       "      <td id=\"T_02eeb_row85_col2\" class=\"data row85 col2\" >16.75%</td>\n",
       "      <td id=\"T_02eeb_row85_col3\" class=\"data row85 col3\" >24.45%</td>\n",
       "      <td id=\"T_02eeb_row85_col4\" class=\"data row85 col4\" >108.815719</td>\n",
       "      <td id=\"T_02eeb_row85_col5\" class=\"data row85 col5\" >350960.73%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row86\" class=\"row_heading level0 row86\" >400</th>\n",
       "      <td id=\"T_02eeb_row86_col0\" class=\"data row86 col0\" >31/05/2023</td>\n",
       "      <td id=\"T_02eeb_row86_col1\" class=\"data row86 col1\" >7.80%</td>\n",
       "      <td id=\"T_02eeb_row86_col2\" class=\"data row86 col2\" >16.86%</td>\n",
       "      <td id=\"T_02eeb_row86_col3\" class=\"data row86 col3\" >25.85%</td>\n",
       "      <td id=\"T_02eeb_row86_col4\" class=\"data row86 col4\" >114.180157</td>\n",
       "      <td id=\"T_02eeb_row86_col5\" class=\"data row86 col5\" >378343.46%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row87\" class=\"row_heading level0 row87\" >401</th>\n",
       "      <td id=\"T_02eeb_row87_col0\" class=\"data row87 col0\" >30/06/2023</td>\n",
       "      <td id=\"T_02eeb_row87_col1\" class=\"data row87 col1\" >6.00%</td>\n",
       "      <td id=\"T_02eeb_row87_col2\" class=\"data row87 col2\" >14.27%</td>\n",
       "      <td id=\"T_02eeb_row87_col3\" class=\"data row87 col3\" >23.87%</td>\n",
       "      <td id=\"T_02eeb_row87_col4\" class=\"data row87 col4\" >115.603956</td>\n",
       "      <td id=\"T_02eeb_row87_col5\" class=\"data row87 col5\" >401050.07%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row88\" class=\"row_heading level0 row88\" >402</th>\n",
       "      <td id=\"T_02eeb_row88_col0\" class=\"data row88 col0\" >31/07/2023</td>\n",
       "      <td id=\"T_02eeb_row88_col1\" class=\"data row88 col1\" >6.30%</td>\n",
       "      <td id=\"T_02eeb_row88_col2\" class=\"data row88 col2\" >12.68%</td>\n",
       "      <td id=\"T_02eeb_row88_col3\" class=\"data row88 col3\" >21.47%</td>\n",
       "      <td id=\"T_02eeb_row88_col4\" class=\"data row88 col4\" >113.395722</td>\n",
       "      <td id=\"T_02eeb_row88_col5\" class=\"data row88 col5\" >426322.52%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row89\" class=\"row_heading level0 row89\" >403</th>\n",
       "      <td id=\"T_02eeb_row89_col0\" class=\"data row89 col0\" >31/08/2023</td>\n",
       "      <td id=\"T_02eeb_row89_col1\" class=\"data row89 col1\" >12.40%</td>\n",
       "      <td id=\"T_02eeb_row89_col2\" class=\"data row89 col2\" >19.48%</td>\n",
       "      <td id=\"T_02eeb_row89_col3\" class=\"data row89 col3\" >26.65%</td>\n",
       "      <td id=\"T_02eeb_row89_col4\" class=\"data row89 col4\" >124.165226</td>\n",
       "      <td id=\"T_02eeb_row89_col5\" class=\"data row89 col5\" >479198.92%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row90\" class=\"row_heading level0 row90\" >404</th>\n",
       "      <td id=\"T_02eeb_row90_col0\" class=\"data row90 col0\" >30/09/2023</td>\n",
       "      <td id=\"T_02eeb_row90_col1\" class=\"data row90 col1\" >12.70%</td>\n",
       "      <td id=\"T_02eeb_row90_col2\" class=\"data row90 col2\" >26.67%</td>\n",
       "      <td id=\"T_02eeb_row90_col3\" class=\"data row90 col3\" >34.66%</td>\n",
       "      <td id=\"T_02eeb_row90_col4\" class=\"data row90 col4\" >137.885320</td>\n",
       "      <td id=\"T_02eeb_row90_col5\" class=\"data row90 col5\" >540069.88%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row91\" class=\"row_heading level0 row91\" >405</th>\n",
       "      <td id=\"T_02eeb_row91_col0\" class=\"data row91 col0\" >31/10/2023</td>\n",
       "      <td id=\"T_02eeb_row91_col1\" class=\"data row91 col1\" >8.30%</td>\n",
       "      <td id=\"T_02eeb_row91_col2\" class=\"data row91 col2\" >22.05%</td>\n",
       "      <td id=\"T_02eeb_row91_col3\" class=\"data row91 col3\" >37.19%</td>\n",
       "      <td id=\"T_02eeb_row91_col4\" class=\"data row91 col4\" >142.361055</td>\n",
       "      <td id=\"T_02eeb_row91_col5\" class=\"data row91 col5\" >584903.98%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row92\" class=\"row_heading level0 row92\" >406</th>\n",
       "      <td id=\"T_02eeb_row92_col0\" class=\"data row92 col0\" >30/11/2023</td>\n",
       "      <td id=\"T_02eeb_row92_col1\" class=\"data row92 col1\" >12.80%</td>\n",
       "      <td id=\"T_02eeb_row92_col2\" class=\"data row92 col2\" >22.16%</td>\n",
       "      <td id=\"T_02eeb_row92_col3\" class=\"data row92 col3\" >37.68%</td>\n",
       "      <td id=\"T_02eeb_row92_col4\" class=\"data row92 col4\" >160.613222</td>\n",
       "      <td id=\"T_02eeb_row92_col5\" class=\"data row92 col5\" >659784.49%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row93\" class=\"row_heading level0 row93\" >407</th>\n",
       "      <td id=\"T_02eeb_row93_col0\" class=\"data row93 col0\" >31/12/2023</td>\n",
       "      <td id=\"T_02eeb_row93_col1\" class=\"data row93 col1\" >25.50%</td>\n",
       "      <td id=\"T_02eeb_row93_col2\" class=\"data row93 col2\" >41.56%</td>\n",
       "      <td id=\"T_02eeb_row93_col3\" class=\"data row93 col3\" >53.31%</td>\n",
       "      <td id=\"T_02eeb_row93_col4\" class=\"data row93 col4\" >211.198472</td>\n",
       "      <td id=\"T_02eeb_row93_col5\" class=\"data row93 col5\" >828055.03%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row94\" class=\"row_heading level0 row94\" >408</th>\n",
       "      <td id=\"T_02eeb_row94_col0\" class=\"data row94 col0\" >31/01/2024</td>\n",
       "      <td id=\"T_02eeb_row94_col1\" class=\"data row94 col1\" >20.60%</td>\n",
       "      <td id=\"T_02eeb_row94_col2\" class=\"data row94 col2\" >51.35%</td>\n",
       "      <td id=\"T_02eeb_row94_col3\" class=\"data row94 col3\" >70.73%</td>\n",
       "      <td id=\"T_02eeb_row94_col4\" class=\"data row94 col4\" >254.061657</td>\n",
       "      <td id=\"T_02eeb_row94_col5\" class=\"data row94 col5\" >998654.97%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_02eeb_level0_row95\" class=\"row_heading level0 row95\" >409</th>\n",
       "      <td id=\"T_02eeb_row95_col0\" class=\"data row95 col0\" >29/02/2024</td>\n",
       "      <td id=\"T_02eeb_row95_col1\" class=\"data row95 col1\" >13.20%</td>\n",
       "      <td id=\"T_02eeb_row95_col2\" class=\"data row95 col2\" >36.52%</td>\n",
       "      <td id=\"T_02eeb_row95_col3\" class=\"data row95 col3\" >71.33%</td>\n",
       "      <td id=\"T_02eeb_row95_col4\" class=\"data row95 col4\" >275.982923</td>\n",
       "      <td id=\"T_02eeb_row95_col5\" class=\"data row95 col5\" >1130490.63%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x264d9040fd0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tomamos sólo los últimos 96 meses (8 años)\n",
    "meses_ocho_años = 8 * 12\n",
    "inflacion_df[\"Fecha\"] = pd.to_datetime(inflacion_df[\"Fecha\"], format=\"%Y-%m-%d\").dt.strftime(\"%d/%m/%Y\")\n",
    "\n",
    "#A la columna inflación la dividimos por 100 para que sea %, le sumamos 1 para poder multiplicar\n",
    "#Le aplicamos cumprod (multiplica acumuladamente) y le restamos el uno que sumamos para cada fila\n",
    "#Lo multiplicamos por 100 para que coincida con el formato del eje Y de antes\n",
    "\n",
    "# Inflación en ventanas de dos meses (bimestral)\n",
    "inflacion_df[\"Inflación Bimestral\"] = (\n",
    "    (inflacion_df[\"Inflación Mensual\"] / 100 + 1)\n",
    "    .rolling(window=2) # Trimestral\n",
    "    .apply(np.prod, raw=True)\n",
    "    - 1\n",
    ") * 100\n",
    "\n",
    "# Inflación en ventanas de tres meses (trimestral)\n",
    "inflacion_df[\"Inflación Trimestral\"] = (\n",
    "    (inflacion_df[\"Inflación Mensual\"] / 100 + 1)\n",
    "    .rolling(window=3) # Trimestral\n",
    "    .apply(np.prod, raw=True)\n",
    "    - 1\n",
    ") * 100\n",
    "\n",
    "# Calcula la inflación anual (12 meses)\n",
    "inflacion_df[\"Inflación Anual\"] = (\n",
    "  (inflacion_df[\"Inflación Mensual\"] / 100 + 1)\n",
    "  .rolling(window=12, min_periods=12)\n",
    "  .apply(np.prod, raw=True)\n",
    "  - 1\n",
    ") * 100\n",
    "\n",
    "inflacion_df[\"Inflación Agregada\"] = (\n",
    "    (inflacion_df[\"Inflación Mensual\"]/100+1)\n",
    "    .cumprod()-1\n",
    ")*100\n",
    "\n",
    "inflacion_df = inflacion_df.tail(meses_ocho_años).copy()\n",
    "\n",
    "# Imprimir tabla con formato en porcentaje\n",
    "inflacion_df.style.format(\n",
    "    {\n",
    "        \"Inflación Mensual\": \"{:.2f}%\",\n",
    "        \"Inflación Bimestral\": \"{:.2f}%\",\n",
    "        \"Inflación Trimestral\": \"{:.2f}%\",\n",
    "        \"Inflación Agregada\": \"{:.2f}%\"\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "tRxlSmzuh6K5"
   },
   "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>Fecha</th>\n",
       "      <th>TCC_Blue</th>\n",
       "      <th>TCV_Blue</th>\n",
       "      <th>TCC_Billete</th>\n",
       "      <th>TCV_Billete</th>\n",
       "      <th>TCC_Divisas</th>\n",
       "      <th>TCV_Divisas</th>\n",
       "      <th>Solidario</th>\n",
       "      <th>TCV_MEP</th>\n",
       "      <th>riesgo_pais</th>\n",
       "      <th>TCC_Euro</th>\n",
       "      <th>TCV_Euro</th>\n",
       "      <th>fed_tea</th>\n",
       "      <th>bcra_tea</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14/03/24</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>829.0</td>\n",
       "      <td>869.0</td>\n",
       "      <td>847.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>1390.4</td>\n",
       "      <td>1016.32</td>\n",
       "      <td>1596.0</td>\n",
       "      <td>1082</td>\n",
       "      <td>1114</td>\n",
       "      <td>5.33</td>\n",
       "      <td>122.36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Fecha  TCC_Blue  TCV_Blue  TCC_Billete  TCV_Billete  TCC_Divisas  \\\n",
       "0  14/03/24    1005.0    1025.0        829.0        869.0        847.0   \n",
       "\n",
       "   TCV_Divisas  Solidario  TCV_MEP  riesgo_pais  TCC_Euro  TCV_Euro  fed_tea  \\\n",
       "0        850.0     1390.4  1016.32       1596.0      1082      1114     5.33   \n",
       "\n",
       "   bcra_tea  \n",
       "0    122.36  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#nueva fila final con todos los datos tomados\n",
    "fila_nueva = [dolar_hoy, bna, ambito, euro, tasas]\n",
    "fila_nueva = pd.concat(fila_nueva, axis=1)\n",
    "fila_nueva"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vO2HzjEokgt3"
   },
   "source": [
    "## 🖥️ Concatenado de la nueva fila en el DataFrame acumulado "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concatenado de la fila nueva\n",
    "df = pd.concat([\n",
    "    fila_nueva,\n",
    "    df.loc[:]\n",
    "]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bajo paridad de tasas de interés de Irving Fisher a 3 meses\n",
    "bcra_tea_numerador = 1 + (float(df[\"bcra_tea\"].iloc[0])/100)\n",
    "fed_tea_denominador = 1 + (float(df[\"fed_tea\"].iloc[0])/100)\n",
    "division = bcra_tea_numerador / fed_tea_denominador\n",
    "fwd_oficial = float(df[\"TCV_Billete\"].iloc[0]) * division\n",
    "fwd_blue = float(df[\"TCV_Blue\"].iloc[0]) * division"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 235
    },
    "id": "3OfPxK2xSp_w",
    "outputId": "7e9d5807-e80e-4fe7-a56a-35aa4d29659f",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mmartinez\\AppData\\Local\\Temp\\ipykernel_28264\\3908910309.py:5: FutureWarning: The default fill_method='pad' in Series.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  df[\"Variación Solidario\"] = df[\"Solidario\"].pct_change(periods=-1)\n",
      "C:\\Users\\mmartinez\\AppData\\Local\\Temp\\ipykernel_28264\\3908910309.py:7: FutureWarning: The default fill_method='pad' in Series.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  df[\"Variación TCV Blue\"] = df[\"TCV_Blue\"].pct_change(periods=-1)\n",
      "C:\\Users\\mmartinez\\AppData\\Local\\Temp\\ipykernel_28264\\3908910309.py:9: FutureWarning: The default fill_method='pad' in Series.pct_change is deprecated and will be removed in a future version. Call ffill before calling pct_change to retain current behavior and silence this warning.\n",
      "  df[\"Variación TCV Euro\"] = df[\"TCV_Euro\"].pct_change(periods=-1)\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fecha</th>\n",
       "      <th>TCC_Blue</th>\n",
       "      <th>TCV_Blue</th>\n",
       "      <th>TCC_Billete</th>\n",
       "      <th>TCV_Billete</th>\n",
       "      <th>TCC_Divisas</th>\n",
       "      <th>TCV_Divisas</th>\n",
       "      <th>Solidario</th>\n",
       "      <th>TCV_MEP</th>\n",
       "      <th>riesgo_pais</th>\n",
       "      <th>TCC_Euro</th>\n",
       "      <th>TCV_Euro</th>\n",
       "      <th>fed_tea</th>\n",
       "      <th>bcra_tea</th>\n",
       "      <th>Solidario / TCV Blue</th>\n",
       "      <th>TCV MEP / TCV Blue</th>\n",
       "      <th>TCV Euro / TCC Blue %</th>\n",
       "      <th>Variación Solidario</th>\n",
       "      <th>Variación TCV Blue</th>\n",
       "      <th>Variación TCV Euro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14/03/24</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>829.0</td>\n",
       "      <td>869.0</td>\n",
       "      <td>847.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>1390.4</td>\n",
       "      <td>1016.32</td>\n",
       "      <td>1596.0</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>5.33</td>\n",
       "      <td>122.36</td>\n",
       "      <td>0.356488</td>\n",
       "      <td>-0.008468</td>\n",
       "      <td>0.086829</td>\n",
       "      <td>0.000576</td>\n",
       "      <td>-0.009662</td>\n",
       "      <td>-0.009778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13/03/24</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>828.5</td>\n",
       "      <td>868.5</td>\n",
       "      <td>846.5</td>\n",
       "      <td>849.5</td>\n",
       "      <td>1389.6</td>\n",
       "      <td>1010.76</td>\n",
       "      <td>1645.0</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>5.33</td>\n",
       "      <td>122.36</td>\n",
       "      <td>0.342609</td>\n",
       "      <td>-0.023420</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.001153</td>\n",
       "      <td>0.014706</td>\n",
       "      <td>0.014427</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12/03/24</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>827.5</td>\n",
       "      <td>867.5</td>\n",
       "      <td>845.5</td>\n",
       "      <td>848.5</td>\n",
       "      <td>1388.0</td>\n",
       "      <td>1036.19</td>\n",
       "      <td>1713.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>1109.0</td>\n",
       "      <td>5.33</td>\n",
       "      <td>122.36</td>\n",
       "      <td>0.360784</td>\n",
       "      <td>0.015873</td>\n",
       "      <td>0.087255</td>\n",
       "      <td>0.000577</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.030669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11/03/24</td>\n",
       "      <td>985.0</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>827.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>848.0</td>\n",
       "      <td>1387.2</td>\n",
       "      <td>980.98</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>5.33</td>\n",
       "      <td>171.46</td>\n",
       "      <td>0.380299</td>\n",
       "      <td>-0.023900</td>\n",
       "      <td>0.070647</td>\n",
       "      <td>0.001733</td>\n",
       "      <td>0.010050</td>\n",
       "      <td>0.004669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08/03/24</td>\n",
       "      <td>975.0</td>\n",
       "      <td>995.0</td>\n",
       "      <td>825.5</td>\n",
       "      <td>865.5</td>\n",
       "      <td>843.5</td>\n",
       "      <td>846.5</td>\n",
       "      <td>1384.8</td>\n",
       "      <td>1004.58</td>\n",
       "      <td>1643.0</td>\n",
       "      <td>1038.0</td>\n",
       "      <td>1071.0</td>\n",
       "      <td>5.33</td>\n",
       "      <td>171.46</td>\n",
       "      <td>0.391759</td>\n",
       "      <td>0.009628</td>\n",
       "      <td>0.076382</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>0.010152</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>758.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>6501</th>\n",
       "      <td>16/12/98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>760.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <th>6502</th>\n",
       "      <td>15/12/98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>734.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>14/12/98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>766.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>6504</th>\n",
       "      <td>11/12/98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>720.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>6505 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Fecha  TCC_Blue  TCV_Blue  TCC_Billete  TCV_Billete  TCC_Divisas  \\\n",
       "0     14/03/24    1005.0    1025.0        829.0        869.0        847.0   \n",
       "1     13/03/24    1015.0    1035.0        828.5        868.5        846.5   \n",
       "2     12/03/24    1000.0    1020.0        827.5        867.5        845.5   \n",
       "3     11/03/24     985.0    1005.0        827.0        867.0        845.0   \n",
       "4     08/03/24     975.0     995.0        825.5        865.5        843.5   \n",
       "...        ...       ...       ...          ...          ...          ...   \n",
       "6500  17/12/98       NaN       NaN          NaN          NaN          NaN   \n",
       "6501  16/12/98       NaN       NaN          NaN          NaN          NaN   \n",
       "6502  15/12/98       NaN       NaN          NaN          NaN          NaN   \n",
       "6503  14/12/98       NaN       NaN          NaN          NaN          NaN   \n",
       "6504  11/12/98       NaN       NaN          NaN          NaN          NaN   \n",
       "\n",
       "      TCV_Divisas  Solidario  TCV_MEP  riesgo_pais  TCC_Euro  TCV_Euro  \\\n",
       "0           850.0     1390.4  1016.32       1596.0    1082.0    1114.0   \n",
       "1           849.5     1389.6  1010.76       1645.0    1092.0    1125.0   \n",
       "2           848.5     1388.0  1036.19       1713.0    1076.0    1109.0   \n",
       "3           848.0     1387.2   980.98       1690.0    1054.0    1076.0   \n",
       "4           846.5     1384.8  1004.58       1643.0    1038.0    1071.0   \n",
       "...           ...        ...      ...          ...       ...       ...   \n",
       "6500          NaN        NaN      NaN        758.0       NaN       NaN   \n",
       "6501          NaN        NaN      NaN        760.0       NaN       NaN   \n",
       "6502          NaN        NaN      NaN        734.0       NaN       NaN   \n",
       "6503          NaN        NaN      NaN        766.0       NaN       NaN   \n",
       "6504          NaN        NaN      NaN        720.0       NaN       NaN   \n",
       "\n",
       "      fed_tea  bcra_tea  Solidario / TCV Blue  TCV MEP / TCV Blue  \\\n",
       "0        5.33    122.36              0.356488           -0.008468   \n",
       "1        5.33    122.36              0.342609           -0.023420   \n",
       "2        5.33    122.36              0.360784            0.015873   \n",
       "3        5.33    171.46              0.380299           -0.023900   \n",
       "4        5.33    171.46              0.391759            0.009628   \n",
       "...       ...       ...                   ...                 ...   \n",
       "6500      NaN       NaN                   NaN                 NaN   \n",
       "6501      NaN       NaN                   NaN                 NaN   \n",
       "6502      NaN       NaN                   NaN                 NaN   \n",
       "6503      NaN       NaN                   NaN                 NaN   \n",
       "6504      NaN       NaN                   NaN                 NaN   \n",
       "\n",
       "      TCV Euro / TCC Blue %  Variación Solidario  Variación TCV Blue  \\\n",
       "0                  0.086829             0.000576           -0.009662   \n",
       "1                  0.086957             0.001153            0.014706   \n",
       "2                  0.087255             0.000577            0.014925   \n",
       "3                  0.070647             0.001733            0.010050   \n",
       "4                  0.076382             0.000578            0.010152   \n",
       "...                     ...                  ...                 ...   \n",
       "6500                    NaN             0.000000            0.000000   \n",
       "6501                    NaN             0.000000            0.000000   \n",
       "6502                    NaN             0.000000            0.000000   \n",
       "6503                    NaN             0.000000            0.000000   \n",
       "6504                    NaN             0.000000            0.000000   \n",
       "\n",
       "      Variación TCV Euro  \n",
       "0              -0.009778  \n",
       "1               0.014427  \n",
       "2               0.030669  \n",
       "3               0.004669  \n",
       "4               0.000000  \n",
       "...                  ...  \n",
       "6500            0.000000  \n",
       "6501            0.000000  \n",
       "6502            0.000000  \n",
       "6503            0.000000  \n",
       "6504            0.000000  \n",
       "\n",
       "[6505 rows x 20 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crear columnas de cálculo de brechas y variación del día\n",
    "df[\"Solidario / TCV Blue\"] = df[\"Solidario\"]/df[\"TCV_Blue\"]-1\n",
    "df[\"TCV MEP / TCV Blue\"] = df[\"TCV_MEP\"]/df[\"TCV_Blue\"]-1\n",
    "df[\"TCV Euro / TCC Blue %\"] = df[\"TCV_Euro\"]/df[\"TCV_Blue\"]-1\n",
    "df[\"Variación Solidario\"] = df[\"Solidario\"].pct_change(periods=-1)\n",
    "df[\"Variación Solidario\"].fillna(0, inplace=True)\n",
    "df[\"Variación TCV Blue\"] = df[\"TCV_Blue\"].pct_change(periods=-1)\n",
    "df[\"Variación TCV Blue\"].fillna(0, inplace=True)\n",
    "df[\"Variación TCV Euro\"] = df[\"TCV_Euro\"].pct_change(periods=-1)\n",
    "df[\"Variación TCV Euro\"].fillna(0, inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wZZFGXIMkeF-"
   },
   "source": [
    "## 💾 Exportar a CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(ruta_bbdd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B8sO1d0UJBQQ"
   },
   "source": [
    "## 📈 Gráficos para adjuntar en el mail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "FLFCeGpHsvif",
    "scrolled": true
   },
   "outputs": [
    {
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       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6502</th>\n",
       "      <td>15/12/98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>734.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6501</th>\n",
       "      <td>16/12/98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>760.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6500</th>\n",
       "      <td>17/12/98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>758.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>08/03/24</td>\n",
       "      <td>975.0</td>\n",
       "      <td>995.0</td>\n",
       "      <td>825.5</td>\n",
       "      <td>865.5</td>\n",
       "      <td>843.5</td>\n",
       "      <td>846.5</td>\n",
       "      <td>1384.8</td>\n",
       "      <td>1004.58</td>\n",
       "      <td>1643.0</td>\n",
       "      <td>1038.0</td>\n",
       "      <td>1071.0</td>\n",
       "      <td>5.33</td>\n",
       "      <td>171.46</td>\n",
       "      <td>0.391759</td>\n",
       "      <td>0.009628</td>\n",
       "      <td>0.076382</td>\n",
       "      <td>0.000578</td>\n",
       "      <td>0.010152</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11/03/24</td>\n",
       "      <td>985.0</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>827.0</td>\n",
       "      <td>867.0</td>\n",
       "      <td>845.0</td>\n",
       "      <td>848.0</td>\n",
       "      <td>1387.2</td>\n",
       "      <td>980.98</td>\n",
       "      <td>1690.0</td>\n",
       "      <td>1054.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>5.33</td>\n",
       "      <td>171.46</td>\n",
       "      <td>0.380299</td>\n",
       "      <td>-0.023900</td>\n",
       "      <td>0.070647</td>\n",
       "      <td>0.001733</td>\n",
       "      <td>0.010050</td>\n",
       "      <td>0.004669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12/03/24</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>827.5</td>\n",
       "      <td>867.5</td>\n",
       "      <td>845.5</td>\n",
       "      <td>848.5</td>\n",
       "      <td>1388.0</td>\n",
       "      <td>1036.19</td>\n",
       "      <td>1713.0</td>\n",
       "      <td>1076.0</td>\n",
       "      <td>1109.0</td>\n",
       "      <td>5.33</td>\n",
       "      <td>122.36</td>\n",
       "      <td>0.360784</td>\n",
       "      <td>0.015873</td>\n",
       "      <td>0.087255</td>\n",
       "      <td>0.000577</td>\n",
       "      <td>0.014925</td>\n",
       "      <td>0.030669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13/03/24</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>828.5</td>\n",
       "      <td>868.5</td>\n",
       "      <td>846.5</td>\n",
       "      <td>849.5</td>\n",
       "      <td>1389.6</td>\n",
       "      <td>1010.76</td>\n",
       "      <td>1645.0</td>\n",
       "      <td>1092.0</td>\n",
       "      <td>1125.0</td>\n",
       "      <td>5.33</td>\n",
       "      <td>122.36</td>\n",
       "      <td>0.342609</td>\n",
       "      <td>-0.023420</td>\n",
       "      <td>0.086957</td>\n",
       "      <td>0.001153</td>\n",
       "      <td>0.014706</td>\n",
       "      <td>0.014427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14/03/24</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>829.0</td>\n",
       "      <td>869.0</td>\n",
       "      <td>847.0</td>\n",
       "      <td>850.0</td>\n",
       "      <td>1390.4</td>\n",
       "      <td>1016.32</td>\n",
       "      <td>1596.0</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>1114.0</td>\n",
       "      <td>5.33</td>\n",
       "      <td>122.36</td>\n",
       "      <td>0.356488</td>\n",
       "      <td>-0.008468</td>\n",
       "      <td>0.086829</td>\n",
       "      <td>0.000576</td>\n",
       "      <td>-0.009662</td>\n",
       "      <td>-0.009778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6505 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Fecha  TCC_Blue  TCV_Blue  TCC_Billete  TCV_Billete  TCC_Divisas  \\\n",
       "6504  11/12/98       NaN       NaN          NaN          NaN          NaN   \n",
       "6503  14/12/98       NaN       NaN          NaN          NaN          NaN   \n",
       "6502  15/12/98       NaN       NaN          NaN          NaN          NaN   \n",
       "6501  16/12/98       NaN       NaN          NaN          NaN          NaN   \n",
       "6500  17/12/98       NaN       NaN          NaN          NaN          NaN   \n",
       "...        ...       ...       ...          ...          ...          ...   \n",
       "4     08/03/24     975.0     995.0        825.5        865.5        843.5   \n",
       "3     11/03/24     985.0    1005.0        827.0        867.0        845.0   \n",
       "2     12/03/24    1000.0    1020.0        827.5        867.5        845.5   \n",
       "1     13/03/24    1015.0    1035.0        828.5        868.5        846.5   \n",
       "0     14/03/24    1005.0    1025.0        829.0        869.0        847.0   \n",
       "\n",
       "      TCV_Divisas  Solidario  TCV_MEP  riesgo_pais  TCC_Euro  TCV_Euro  \\\n",
       "6504          NaN        NaN      NaN        720.0       NaN       NaN   \n",
       "6503          NaN        NaN      NaN        766.0       NaN       NaN   \n",
       "6502          NaN        NaN      NaN        734.0       NaN       NaN   \n",
       "6501          NaN        NaN      NaN        760.0       NaN       NaN   \n",
       "6500          NaN        NaN      NaN        758.0       NaN       NaN   \n",
       "...           ...        ...      ...          ...       ...       ...   \n",
       "4           846.5     1384.8  1004.58       1643.0    1038.0    1071.0   \n",
       "3           848.0     1387.2   980.98       1690.0    1054.0    1076.0   \n",
       "2           848.5     1388.0  1036.19       1713.0    1076.0    1109.0   \n",
       "1           849.5     1389.6  1010.76       1645.0    1092.0    1125.0   \n",
       "0           850.0     1390.4  1016.32       1596.0    1082.0    1114.0   \n",
       "\n",
       "      fed_tea  bcra_tea  Solidario / TCV Blue  TCV MEP / TCV Blue  \\\n",
       "6504      NaN       NaN                   NaN                 NaN   \n",
       "6503      NaN       NaN                   NaN                 NaN   \n",
       "6502      NaN       NaN                   NaN                 NaN   \n",
       "6501      NaN       NaN                   NaN                 NaN   \n",
       "6500      NaN       NaN                   NaN                 NaN   \n",
       "...       ...       ...                   ...                 ...   \n",
       "4        5.33    171.46              0.391759            0.009628   \n",
       "3        5.33    171.46              0.380299           -0.023900   \n",
       "2        5.33    122.36              0.360784            0.015873   \n",
       "1        5.33    122.36              0.342609           -0.023420   \n",
       "0        5.33    122.36              0.356488           -0.008468   \n",
       "\n",
       "      TCV Euro / TCC Blue %  Variación Solidario  Variación TCV Blue  \\\n",
       "6504                    NaN             0.000000            0.000000   \n",
       "6503                    NaN             0.000000            0.000000   \n",
       "6502                    NaN             0.000000            0.000000   \n",
       "6501                    NaN             0.000000            0.000000   \n",
       "6500                    NaN             0.000000            0.000000   \n",
       "...                     ...                  ...                 ...   \n",
       "4                  0.076382             0.000578            0.010152   \n",
       "3                  0.070647             0.001733            0.010050   \n",
       "2                  0.087255             0.000577            0.014925   \n",
       "1                  0.086957             0.001153            0.014706   \n",
       "0                  0.086829             0.000576           -0.009662   \n",
       "\n",
       "      Variación TCV Euro  \n",
       "6504            0.000000  \n",
       "6503            0.000000  \n",
       "6502            0.000000  \n",
       "6501            0.000000  \n",
       "6500            0.000000  \n",
       "...                  ...  \n",
       "4               0.000000  \n",
       "3               0.004669  \n",
       "2               0.030669  \n",
       "1               0.014427  \n",
       "0              -0.009778  \n",
       "\n",
       "[6505 rows x 20 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tomamos las primeras filas que querramos mostrar y las primeras 14 columnas, es decir, sin variaciones\n",
    "cotizaciones_a_mostrar = 25\n",
    "# Copia del dataframe, para no reemplazarlo\n",
    "data = df.copy()\n",
    "# Damos vuelta el dataframe sólo para los gráficos\n",
    "# Si no lo damos vuelta, los gráficos van a empzar de hoy hacia atrás\n",
    "data = data.iloc[::-1]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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dQmpqKvLy8hAZGYmlS5di8ODB5fqNLFu2TBBca6/JujXP0dHR+PzzzwXrvcj59GWPD23ys8J/hYM7iUTC/64yMzMFD2HMzMzQpEkTNGjQQHA8hYaGCs5BGo0G06ZNE/xmbW1t0bhxY8F56ejRo1i7dm2J5dZSkec6bXDt7e2N+vXrC66N165dE7Q2ycnJwYQJEwSBsnY9rYP09HRMnjwZiYmJ/DI//fSTYB1PT0+89tprgutIWloaPv300zJ9foKoKijAJmosarUaLVu2xKlTp7Bz506cO3cOQ4cO5eenpqYKblhOnTolCK47dOiAM2fOYNeuXTh37hwWLVrEB4Tp6emCIPLGjRv86w8//BAnT57E33//jcOHD2Pt2rUQiURwdnZGvXr18PDhwzKVf8+ePXj69Cn/vmvXrjh79iz/WfQ1X9YSGhqKq1evAmA37t9++y1OnDiBnTt34tixY3xG9vz8/CI3/cURHh6OgwcP8u+7d++OM2fO4J9//sGpU6cENQg7d+7Es2fPALDaYG2thJ+fH0JDQ7F161bs27cPv/32m2AfhYNHLRzHISgoCKdOncK2bdtw+PBhwQMTjuPQr18/hIaG8rWmuhSuHdDFysoKGzduxJEjR3DkyBFs2LBBcBO2bt06QQ1tcaSlpeHbb7/l33t7e2Pv3r04ePAgjh49ii1btvCZ8TmOw/z585GTk1Pqdrt16ya40db9DgAIjtm2bduWqaazMFKpFFu2bMGOHTtw+vRpTJo0qdR1li5dyjddb9q0KX98nThxAvPnz+eX03ZPAID79+/zfUMtLS1x6tQp7Nq1C9u2bcOZM2fQokULmJubw9/fv0it7i+//MK/trS0xB9//IHDhw/j2LFjWLt2bbHNSwH2EEX3IcvAgQNx4cIF7Ny5ExcvXhS0CoiNjcWqVatK/fz6EIlEWL58OU6ePIn9+/fjn3/+EYyG8Ntvv/EZnBMTE5GZmQkTExOIRCIEBwdj79692L59O86cOQMvLy9+veJ+F4WpjPOQseHk5IRNmzahVatWRaZt2rQJ9evXL9f2OI7DlClTEBoaiu3btyM4OLjI/IULFyIkJASHDh3C2LFj+XnZ2dl49OgR//727dv8uaNv3758t5jjx49j4sSJ/HIJCQn8cfAyv4nyEhsbi3HjxiEvL4+f9uGHHwqWKTxEl6OjIxo3biw4B4WFhWHmzJll2mdSUpLgIWXz5s1x8uRJ7Ny5EydPnsT48eP5eRcuXMDNmzcBvPj59GWPj6CgIH5Z7d+qVauKtGKaPXs2XnvtNQDAv//+Czs7O4hEIjg5OSEkJATbtm3Drl27sHfvXsGDNt3f8vHjxwXnpREjRuDs2bPYvn07QkNDBa1MfvvtN8H3VhwVea6TSCRYv349Dh8+jN27d/NdObRovyuAPVzS/hZMTU3x+++/8+sdOHCAf4iTnp4uOB50z1kLFixASEgI/v77b5w4cQLz5s2DSCSCm5sbHB0d+XsKgjAGKMAmaiwmJiZYsmQJH4hJpVLMnj1bUJugHUsWYDWoWuzt7fHDDz8Invi+9957gvFaT5w4wdeA6zZrO378OHbt2sVfDNq1a4fr16/jzJkz2LBhAx/clsb58+f519bW1vjmm2/4vnPm5ub43//+JyifLiEhIfzrpk2bCpoNOzk5CW6qTp06VaYA8ujRo/xrExMTLFq0iC+PiYkJPv74Y4wdOxbffvstduzYAXt7ewDsAn/69GlcunQJwcHBgto57TJatDed+vjqq6/4Zuf29vZ4/fXX+XlSqRRz586FqakpAOZct59hRkZGsdv94IMPBEOetGjRQuAnOTm5TLX8e/bsEdwALV68WFDr8cYbbwiaMSclJeH06dOlbtfCwgI9evTg3+vWkGVkZAhaMfTr16/U7emjdevWaNq0KQB2c1T4eylMQkKC4KHFtGnTBOsMHjxYUGuqHS5IN9jMycnBmjVrcO3aNRQUFMDS0hLr1q3D9evXsXfvXsHwTQ8fPhS0bhg5cqTg5rNdu3YYPnx4seXV7QLg6+uLBQsW8AG5WCzG1KlTBdm4d+/eXaSpZ1l47733BF0uGjRogI8++oh/n52dzTf3d3Z2xr59+/Dvv//i8OHDaN26Nb9cfn6+oHavpN+FLpVxHnrVcXNzw+TJk/mHpy1atBAEk40aNcKgQYP49927dxesr1Ao+NcLFy7ElStXEBoaii+//JLfplqtLtL0Wfudvuhvorw8evQIw4cPF9QeduzYscjn8fLygr+/P6ysrDB37lycOXMG27dvx+nTp9GhQwd+udDQUEGAVRynTp0S1PLPnz+fP1eIRCJMmDABI0eOxNdff43t27fzfaUr63xaXgoKCjBlyhRBYDx48GDB+aZdu3Y4ceIErl69im3btqFWrVr8PBMTE8E1T/e3rHtNdXZ2xuzZs/naXktLS3z++eeYMGECfvrpJ/z1119l6h5Qkee6wYMHo23btvz7zp07C2qWdWu/jxw5wr/u0qWL4AGHp6enoCuFbs237u9i9+7dOHToENLS0gAAQ4YMwY0bN3DixAkEBwcLWloQhKGhPthEjcXb21twoQPYxe7NN9/kmyRFR0fz8+7cucO/fuutt/RmuO3cuTN/AeM4Drdv30a7du0wcuRIHDhwAHl5eXjw4AF/4Xdzc8Nrr72Gli1bolOnTrCzsytz+WNiYvjX9evXF1ykgefN0PTdVOgGhNevXxcktCqMUqlETExMkeaPhYmKiuJfe3t7FymPq6trkSZ+ushkMty9exf//PMPnwyqcI1McYG+lZVVkb5iut+Pl5dXkfLY2NjwNUMlDdfy5ptvFplWODlWbGwsGjRoUOw2AFZ7pcXR0ZGv3dClc+fOEIlEfM3vrVu30LVr1xK3C7Bm4tu2bQPAbpTDw8PRoEEDQfNwW1tbwQ1wedDNJ1AWCj9wGDNmTInLa2vF3Nzc8M4772D//v3gOA4bN27Exo0b+SajzZo1Q/v27QUPTwDh7xSA3v7DrVu31tuMMj8/H/fv3+ffd+jQQW9fxs6dO/PdHxQKBWJiYso9vrS+crVs2VLwPjo6WvA9mZqaQq1WY+vWrbh16xZu376N+/fvC47ZsjwAA1Ap56GdO3fyiSMLM2fOnHLXGBsbAQEBRYYKk8vlfCDUqFEjwTzdljOA/u9GJpPhwoULuHHjBsLCwnDr1i1kZ2cLltEGNS/6mygPUVFRGDVqlOB86+fnJ6gh1qLbMqvwZ/rqq68ED6XPnDmjN2Fb4X3rbqPwb8rS0hJz5swpsl5lnk/Lw1dffYVLly7x71u3bo0vvvhC77KWlpa4f/8+Dh48iDt37uDWrVt8gj8tuseLrpvAwMAix2FgYGC5zs0Vfa5r3LhxkWlOTk78Z9INzHUfQOzfvx/79+8vtpzR0dHIzc2Fubk5xo0bhytXrkCj0fB91UUiETw9PfHGG2+gdevWaN++vSCnC0EYAxRgEzWW4prK6j4F1W2iq/s0trgnpYWnZ2ZmAmA3aX/++Sd++uknnD9/nr85jo+PR3x8PPbv34+FCxdi6NCh+PTTT8uUrCQ3N5d/rS/zJoBi+5OVtcZLS1n60+luU18W9ZL47bff8Ouvvwr2Y2pqitdee63E/tFa9D3s0HVYOLgGUObxdQvfMOubVpam3GU5fszNzWFjY8Mvqz1+SqNp06bw9fXlb8gOHjyIBg0aCJqH9+zZk6/BLy/lCbiAspdbi66bb7/9Ft7e3vj777/5G/7c3Fxcu3YN165dw6+//go/Pz988803/A1e4eNZt9avpGkAq+XXDVbL+tvWrZksK/rOOYW3qzt80u3btzF//nzBwz2A1fjk5+fzGZPLSmWch+Lj44skS9JSXkfaQEiXF2kpUJGUdm4pfI4tKeFhdnY2lixZgl27dgkyc1tbWyMwMLDYriov8psoK5GRkRg5cqQg30i9evXw+++/l7u/uqurK2xsbPgWQWU5Pl/0ulGZ59OysnbtWuzcuZN/X7duXSxbtkxvfoo9e/Zg6dKlgmbMUqkUgYGBePjwod5ryMtcU/VR0ec6fS3kdK8xur/n8txzcByHjIwMmJubIygoCMHBwVixYgV/L8BxnCAZq1wux8SJEzFu3Lgy74MgKhsKsIkaS0pKit7puhdh3Zsr3SezxfX10e0TDQgvQA0bNkRwcDBSUlJw7tw5XL58GTdu3EBUVBQ4jkNeXh5+++03yGSyMg2xpfvEtrgmzsX1ydPtj9q6dWssW7asxH2V5eKu66q4gJzjuCI3oBs3buRrSmQyGSZMmIBWrVrB398fEomkTDVgpQXL5RlaqTDJyclFau8L33DoC+ALoxvgFXf85OTkCL7L4pr466N///68x0OHDuGDDz4QNA9/kezhWvQNt1Ke5f/4448Sa1p0AxapVIopU6Zg0qRJuHXrFs6fP49r167h1q1bvPfIyEh88MEHOHr0KKytrYscn/pupIv7Ldjb20MikfA1Ry/y2y4r+s45hcuq/R0lJCRg9OjR/PwePXqgb9++aNq0KWxsbPD5558XybpcFir6PFSR6AumC9fsVjWlnVtKS/any8yZM/lmv15eXhg3bhzefPNNeHp64uLFi4Lxpwvvo7y/ibLw+PFjjBkzRhBcBwYGIjg4uMSHamq1GsnJyXByciriR/fBQVnOu7rXDYVCAY1GU2Sb+q4blX0+LY2QkBD8+OOP/HtbW1v8+uuvet0fO3aMb71lYmKCsWPHIigoCA0bNoSZmRnat2+vN8DWdVPcNV6fm+Ko6HNdWbOWA+yeQxtkDxgwoNQ++rqfvVWrVmjVqhWePHmCs2fP4sqVK7hx4wbfii8rKwvff/89HB0dX+o6RxAVCfXBJmosUVFRRYZ+AMAn/wJYHyUtuv27Ll68qPeJrG7mZ4lEgsaNG4PjOCQkJODs2bPYtGkTsrKy0Lt3b3z99dfYv38/zp49K9i2bv/oktAN+m7duiWo0QbYzUp4eLjedXU/lzaTurW1Nf9348YN7N69G3fu3EFOTk6ZbiJ1m5DFxsYWCWiSk5Px2muvoVevXvjoo4/4/nm6CU0mTZqEDz/8EA0bNoSJiclLJ+2pCHSbPGrRPUYAlKmpsO53nJycrLdm/tixY4Kn/tp+z2WhT58+/A1tXFwcfvzxR/5m18fHp9w1W7qU50ZKuz9doqKiBMeXhYUFNm7ciNOnTyMhIYE/vpRKJcLCwrBv3z7s3r0bTZs2xaRJkxAcHIxLly7hq6++4reZnp7ON80s/ABEt8mmluL6X0okEsH6J0+e1NusV9tPHGA1+rpJxspKaGhokWnXrl0TvNf+Nnfu3MkH176+vvjpp5/Qrl07/ma3vL+NyjoPTZ06lR8yrPBf4a4U+tAdzklfEBEbG1umcpSEvprxqkY7BJeW77//HgMHDuRHJiju+3zR30RpZGdnY/z48YL9NmnSBBs2bCg2uL569SratGmDRo0aISgoqMg57MGDB4IWGGUZT133XJGXl1ektUZBQQFatWqFbt26YfLkyTh16hSAij2flvf4uH37Nj7//HN+PVNTU6xatQoeHh56l9+4cSP/euDAgZg+fTreeOMNmJmZoaCgoNgH0rpubt++XSSz/61bt/D666+jX79+mDFjhiChnj6q8lxXGN17jrt37wquB9bW1jhz5gz279+Pe/fuQaVSQSwWQ61WIzY2FqGhofj9999hbm6OgQMH4rvvvkNISAiOHTsmyO2h28+bIAwNBdhEjUWj0WD69On8U1yNRsOPbaqlffv2/GvdJ6Opqan49NNPBTeE27Ztwz///MO/7969O2xsbPD06VN07NgRY8eOxddff4358+cLaq2kUqmg2VZZg5mOHTvyrxUKBb788kv+ApyTk4Mvvvii2CZxnTp14l8nJSXh66+/5td99uwZ5s2bh2+++QajR4/G5MmTy1Set99+m3+t0Wgwd+5cfv8FBQVYunQpcnJyEBkZiSNHjvDNrHWfol+9epUPClNTUwVDOAFl72takWzZskUQGF2/fl2QWd3Ly0uQsKs4evXqJfhuZ8+eLehj9++//wr6PLq5uQkSyJSGg4MD2rVrx7/XTWZT1U/169atK7iRW7VqlaDP5Pr167Fy5UrMmDEDffr04Wvav/nmG7z77rv49NNP8cUXXwhu9MRicZGaGm1Nl3ZoHt3t6z4EOXbsGDZv3lxseXX9REVFYf78+fwDK41Gg59//lkw/Nx7771X5i4GuuzevVuQLPHBgwf47rvv+PfW1tZ8X1rdZFNPnjzhb541Gg22bt0qaJ1QlmbUlXUeell0b5DPnz8vqMk7f/68wHt50K09zcrK4s9vJeVbqEwK1xbqjk1///79IhmYtee6F/1NlMYPP/zAZ+8HWPPudevWldgap169elAoFIKyaVtlZGVlYeHChfyyZmZmgoR+xdG+fXvBd7Vw4ULeFcdxWLVqFVJTUxEdHY1jx47xTZBf9nz6osfHkydPMHHiRP44FYvF+P777wVjNxdG97u/efMm/xAiOzsbCxcuFBzzur/lLl268K8zMjIwf/58wTV++fLlUCqVCA8Px4kTJ4rtKqZLVZ3rCqN7zxEWFoZVq1bxDyiioqLw1VdfYcGCBRg+fDg/ysT169fx9ttvY/z48ViyZAkWL14sSGwnkUgED0eq6pxFEGWBmogTNZpbt26hU6dO8PX1RVJSEpKTk/l5Tk5OeP/99/n3Xbt2RevWrXHu3DkA7Olv27Zt4ePjg6SkJEFNgJOTE98krHbt2ujfvz8ffJ8/fx5BQUGoW7cuOI5DVFSU4KJROGtrcXTt2hV+fn588pC9e/fi9OnT8PDwwMOHD5GVlSVoDlZ4XX9/f/5hwtatW3HkyBHUqVOnSC3E1KlTy1SewMBA9OjRgx8m6tSpUwgKCoK3tzeePXsmcPvOO+/A09MTAKvl0PY9PHnyJDp06AAXFxdERUUVqZWv6uaiEokEeXl5GD9+PLy9vWFiYoKoqCjBTVhZm9E6Oztj8uTJWLFiBQCWyKVXr17w9vaGSqVCbGwsf7MglUqxaNGicveZHjBgAN+KQrstsVgsGCKtqpg2bRo+/vhjAGxInffeew8+Pj4oKCgQ1LQ0adKEf5A1btw47N+/H7m5uVCr1Zg0aRLq1KnDD8GSkJDAr2dnZydIGjZp0iQ+aZdCocCwYcPg4+OD3NxcvS1VdBk6dCj27NnD/5a2bduG/fv3w9vbG/Hx8YIapnr16hUZuqisaDQazJs3D6tWrYK9vT0ePHggaFI7btw4vrm7bg1ddnY2evfuDT8/Pzx9+rRIbWdZfheVdR56WZo1a8Y/DImNjUWfPn3w1ltvISEhgT/Xvgi6gbtCoUDPnj2h0WjwxRdfvHCyv5dBe/7Qft9Lly7Fzp07YWJigvv37xepRdV+py/zmyiOZ8+eCca6Bljf8eLOZTNnzkTDhg1hY2ODYcOG8UOVhYWFoXPnzvD29kZMTIygVdfUqVNLHW0AYNfKUaNGYd26dQBYTW3nzp3h6+uL9PR0wed74403+KSAL3s+fdHjY8aMGYLfn6WlJbZs2aJ3DPKAgADMnTsXfn5+/DkvLCwMHTp0EFynddH9LXfq1EmQh2THjh04evQoPD098fjxY8F5acSIEXrzBRSmqs51hRk8eDA2btzIu1uxYgX++usvuLi4ICIigv9dmJiY8MNANmvWDC1btuQfJu7ZswcnT56Et7c3cnNz8eDBA8EDCd3RNAjC0FANNlFjCQwMxBtvvIGCggLcvXtXEABaW1tjxYoVgmRWYrEYP/30k+Dim5eXh/DwcMEF19PTExs2bBAkCvnyyy8RFBTEv1cqlbhz5w7CwsIEN7Vdu3YtNeOyFqlUip9//hmurq78tPT0dNy6dQtZWVlwdXXFhAkTil33l19+4YNc7bq3b98WBNezZ88W1IqWxtdffy0YfkPbvFHXbdOmTQU105988omgCXpSUhLu3LmD3NxcSKVSwTAdVT02r6OjI6ZNmwaA3cBFRkYKguuxY8eiV69eZd7epEmTMH78eP5Ju0ajwYMHDxATE8PfDFpaWmLZsmWCYZnKSlBQUJFkXq1atTLI8CXdu3fHxx9/zNewaTQa3L9/XxBce3t7C8Yk9/b2xrJlywR9qh8/fozr168LbrStrKywYsUKQR6Cfv36YfDgwfx7bdAYFxcHExOTIg+KdGv+LCws8OuvvwoyEWuPXd0bzkaNGiE4OLhMN7L6GDx4MExMTJCYmIh79+4Jgut33nkHH3zwAf++f//+RZrP3r59mz/X6A6H8/jxY8G2iqMyzkMvy8iRIwXHZ0xMDLZu3YozZ87A2toa06dPf6HtFj5vxcbGIi4uTnAcVSW2trZFghXtOYXjONjb2wtyY2gz47/Mb6I49uzZU+R40Sar0/enm3Ni+vTpgkzc2t+JbqA4YsQIwbFcGh9//DF69+7Nv8/Pz0d4eLjg83l5eQn6PAMvdz590eOjcP/krKysYr1pR0eYPHmy4PvTvU4DEFzDdUdEEIvFWLFihWCECoVCgdu3bwvOSx07dizzg96qOtcVxsrKCuvWrRMketRe67XHokQiwQ8//CAYIvDHH39Ew4YN+fcKhQI3b95ERESEILgeMWIEBdiEUUE12ESNRSaTITg4GOvWrcPu3bvx5MkT2NnZoV27dpg0aZLgoqfFxsYGv/zyC06dOoW9e/fi+vXrSEpKgrm5OXx9fdGtWzcMGjRIcKMEsAQfa9euxeHDh7F3717cuXOHH8vR0dERgYGB6Nu3r6CZdVnw9PTEzp07sXbtWhw5cgSJiYlwdnZG165dMXHixBL7JLm6umLv3r34888/ERISgujoaGRlZcHGxgavvfYahg8fXqbaEF1kMhnWr1+P/fv3Y8+ePfyNl0wmg5+fH3r27ImBAwcKAuo2bdpg48aNWLNmDW7cuIGcnBzY2dnh9ddfx7hx4/Dvv//yQ8McP34cX3755UslLSsvkydPRpMmTRAcHIxbt25Bo9GgYcOGGDVqlKDZW1kQiUT45JNP0KNHD/zzzz84f/48njx5Ao7j4O7uzg/pVnhM3LIikUjQp08frF+/np9myKQvEydOROvWrbFlyxZcuXIFycnJEIlE8PDwQJcuXTB69OgiN3AdOnTAoUOHsGnTJpw7dw6xsbHIzc2FhYUFPD090bp1awwfPlyvo/nz5+P111/Hli1bEBkZCYlEgmbNmmHKlCmQSCRYuXIlv2zh1gGurq74888/cejQIRw4cADh4eFITk6GTCZD/fr10atXL/Tp06dcSa0K0717d/Tv3x/Lly/H9evXoVar4efnh8GDB6Nv376CoN/S0hJ//vknfv75Zxw/fhyJiYkwMzODr68v+vbti65duyIoKAgqlQpKpRKnT58u9XisrPPQy2Bvb48dO3Zg5cqVOHXqFFJTU+Hs7IwOHTpg/PjxgmGFykPz5s3x7bffIjg4GNHR0TAzM4OXlxfc3d0r+BOUnWnTpsHd3R1btmzhh1qrXbs22rZti/Hjx2PBggV8E/BDhw6hZ8+eAF7uN6GPf//994U/g4mJCVasWIGDBw9ix44duHPnDrKysmBnZ4cmTZpgyJAh5X44KJVK8f3336N79+7Ytm0bbt26xWeR9vHxwdtvv41hw4YVSZ74MufTqjw+AgICsG3bNqxcuRKXLl1CZmYmrK2t0ahRI4wcORK5ubl8V6zLly8jNTWVr2F3dnbG1q1bsX37dhw4cACRkZFQKpWwsbFBgwYN0K9fP/Ts2bPMic6AqjnX6aN+/fo4dOgQNm7ciJMnTyI2NhY5OTlwcHDAm2++iTFjxhRJhmlvb4+tW7di165dOHToEO7du4eMjAxIJBI4OTmhadOmGDhwYLnvVQiishFxxpD9gyCqiFmzZvFjtjZv3hybNm0ycIkIomL59NNPsW/fPgDsgce5c+fKnQW8OnL79m04OjrC0dFR7wOYK1euYNiwYfz7kJAQQQuOyiAuLk4Q9G7cuLFMib8IgiAIgqi+UA02QRDEK8KlS5f4PvAA0K1btxoRXAOsuWhiYiJEIhEcHBywefNmPvlcfn6+ILmYpaWloIk1QRAEQRBERUEBNkEQRDVmz5492Lt3L3Jzc3Hjxg0+qZ1YLMbIkSMNXLqqo0WLFti3bx84jkNycjJ69+4Nf39/AOC7P2jp1atXhTd/JAiCIAiCACjAJgiCqNZIpVK9QxlNmDCBDzBrAp999hmuX7/OZwzPz88XDA2mxd/fHzNmzKjq4hEEQRAEUUOgAJsgCKIaU7duXbi5ufFJaurWrYtBgwYJsvLWBFxcXLBnzx78+eefOH36NB4+fMhnP7ayskK9evXw9ttv47333oOZmZmBS0sQBEEQxKsKJTkjCIIgCIIgCIIgiAqAxsEmCIIgCIIgCIIgiAqAAmyCIAiCIAiCIAiCqAAowCYIgiAIgiAIgiCICoACbIIgCIJ4ATQajaGLQBAEQRCEkUEBNkEQBPFKc+DAAXTv3h2Ojo4wNTWFp6cnxo8fj9jY2BfaHsdx+O233zBkyBB+2qNHjyASiSASiXDx4sWKKnqZCQ0N5ff/9OnTKt//y6LrT/snkUggl8sRGBiItWvXVnmZRo0aBZFIhG7dulXJegRBEMSrAQXYBEEQxCvLJ598gnfeeQeHDx9GWloaLCwsEBsbi7Vr16JJkyYIDw8v9zZnzpyJsWPHCgJZqVQKNzc3uLm5GWQYMDMzM37/EomkyvdfkTg5OcHV1RW2trbIzs5GeHg4xo8fj9WrVxu6aARBEARRKhRgEwRBEK8kmzdvxk8//QSABcUKhQIZGRkIDQ2Fvb090tPT8cEHH5R7u9rxtXVxd3dHXFwc4uLi8Nprr7102ctLy5Yt+f07OTlV+f4rkr179yI+Ph4pKSmIiYmBn58fAGDZsmWGLRhBEARBlAEKsAmCIIhXkiVLlgAAevXqhSVLlkAmkwEA2rVrh6VLl6Jbt27o2bMnCgoK+HXOnDmD7t27w87ODjKZDK1bt8a+ffv4+aNGjcKvv/4KADh16hREIhFCQ0P1NhEv3ORZ9+/Ro0cAgKioKAwYMAC1atWCqakpatWqhWHDhuHJkyeCz3Lx4kV06dIF1tbWsLGxQevWrXH48GF+fnFNxGNiYjB69GjUrl0b5ubmaNCgAX744Qeo1Wp+mfnz50MkEmHAgAE4cOAAmjZtCnNzczRq1AiHDh0SlOPu3bt45513IJfLYWVlhbfffhuXL18WLHP06FG0a9cOjo6OkMlkCAgIwJIlS8BxXPm+QAAeHh7o06cPAAia9JfFW/v27SESiTBv3jx06dIFVlZWGDx4cLm8FyYvLw+zZs1CnTp1YGZmhoCAAPz000+lfrbExESMHj0a7u7uMDU1haOjI/r06YOIiAh+GYVCgRkzZsDHxwcWFhZwcXFBr169cOvWrXJ7IwiCIAwIRxAEQRCvGE+ePOEAcAC44ODgMq2zY8cOTiKRcAA4ExMTzsLCgt/GihUrOI7juOnTp3MymYwDwJmamnJubm7c+fPnuejoaH7ZCxcucBzHcW5uboI/kUjEAeCkUin39OlTLi8vj/Py8uIAcGZmZpyjoyO/jW7duvHlOnPmDGdqasqXSy6XcwA4iUTChYSEcBzHcSdPnuTXffLkCcdxHPfw4UPO3t6eA8CJRCLOysqKX2bAgAGcRqPhOI7j5s2bxwHgPDw8OIlEwllbW/PLyWQyLjk5meM4jouKiuJsbW05AJyFhQW/nLm5OXflyhWO4zjuxo0bnImJCb+unZ0dv6158+YV616fP47juIiICK5u3bocAK5+/focx3Fl9tauXTv+ezIxMeFkMhm3ePHiMq8/cuRIDgDXtWtXflqvXr04AJxYLOYcHBz473TGjBklrteqVSv+u3dycuLXCwgI4JcZOHAgv20nJyfeo4ODA/8dEARBEMYP1WATBEEQrxy6tZ116tQpdfn8/HxMnDgRarUavXr1QmpqKjIyMjB+/HgAwGeffYaEhAT8+OOPGDZsGIDnzbJbtmypd5vaJttxcXH47LPP+FrOH374AS4uLnj48CECAgLQrFkzJCQkICkpCT///DMA4Pz58/x2Zs6cifz8fLRr1w4pKSlITU1Fv379oFarERwcXOxn+uSTT5CamgofHx9ERUVBoVBg48aNAIB//vkHu3btKuJs+fLlyMjIwNatWwEA2dnZOH36NABW052eno73338f6enpSE9Px5dffonc3Fx8+eWXAFjtdUFBAd566y1kZGQgNTUVa9asQZcuXWBqalrq9wAAvXv3hru7OxwcHODv74+HDx8CAGbNmgUAZfamRSwWIzw8HCkpKZg4cWK519dy/Phx7Nu3D46Ojrh//z6Sk5Nx8+ZNmJubY9myZYiPj9e7XnJyMhwdHVG/fn1ERkYiMTGRbxVx7949pKWlAQAOHjwIANi3bx8SExPx5MkTdO/eHd27d0dcXFyZ3BEEQRCGR2roAhAEQRBERaM7hBZXhqbJ586dQ2JiIgDW11culwMAfvzxR/zxxx/Iy8vDoUOHMHbs2HKXJSQkBDNmzAAAjBgxAh999BEAICAgAIcOHUJBQQGuXbuGy5cv80FvZmYmAECpVPJNzqdNmwYrKysAwPr16/Hbb7/B1tZW7z7z8/Oxf/9+AMCcOXNQt25dAMDw4cOxbt06nDlzBrt27cK7777Lr2NtbY1JkyYBgGC6tizHjx8HABw5coTfXn5+PgDWRF2lUqFp06YAWJP2du3aoVOnTggKCsK+ffvKHGAnJSUBYInj7Ozs0LhxY0yfPp1vKl4Wb7q0adMGvr6+AFgyOBsbm3Ktr0X7+RUKBYKCgvjpBQUFUKvVOHnyJP/wRRdHR0fs2bMHarUat27dwi+//CJo3p+ZmQk7Ozu89tprOHv2LEaNGoUePXogKCgIa9asgaenZ5m8EQRBEMYB1WATBEEQrxyurq7868ePHxeZn5CQgIsXL/LBt7bfskgkgpeXF7+cpaUlXFxcAADPnj0rdznu37+PQYMGQa1W44033uD7bwPsIcDMmTPh4OCAli1b4rvvvuP7g2vLlZaWxj8scHR05Ne1t7cvNrgGgJSUFKhUKgDgg2Et3t7eej+Pk5MTRCIRABbcagNi7f6Tk5P5MsXHxyM+Pp4PhnNzc5GcnIzOnTvj999/h5+fH86dO4eFCxeic+fOqF27Nn7//fcyObtw4QI4jkNBQQFSU1MRGhrKB9dl9aZL7dq1Be/Lu74W7efPz8/nP398fDzfn72kWuYff/wRtWvXxuuvv44vv/xSEMhr/W7duhWDBg1CZmYmNmzYgLFjx8LLywtdunTh900QBEEYPxRgEwRBEK8cHh4e8PHxAQDs2bOnyPw1a9agZcuW8PHxQW5uLmrVqgWABVjaBGQAq0HWBqLaQE0bhJZGRkYGevfujfT0dDg7O2PXrl0wNzfn569btw7fffcdrKyscPfuXcTFxfFNrbXY2tpCLGaXat0myA8ePMDmzZtx/fp1vft2dHTkh+vSNrHWon1fOPCUSoWN2gp/Tu3yP//8MziOA8dxyMnJgUqlAsdxvMMRI0bg33//xf379xEcHIy+ffsiNTUVH3zwARISEkowVjbK4k0XCwuLl1pfi/bzN2vWjP/8HMchMzMTHMfxTdgLo23BkJ2djYsXLyIpKUnvkGOurq5Yt24dUlJScPToUcybNw8uLi44evQovvrqq1LLRxAEQRgHFGATBEEQryTagGffvn346quvoFQqAbCA+/vvvwcAtG7dGubm5mjVqhXs7OwAANOnT0dWVhYKCgrwySefIC8vDxYWFujRoweA54FoVlYWAPA1xbpoNBoMHjwY9+7dg7m5OXbv3l2kL7g2O7SpqSlcXV2hVCqxfv16wTZkMhlatGgBAHz/aJVKhblz52L48OGYMGGC3s9uYmKCLl26AAD+97//ITo6GgCwadMmnD17FgDQv3//sssEy74OsIcTz549g1qtxqhRoyCTyTBw4EAAwIwZMyCTydC+fXs4OTlhzJgx+PzzzwEAarUaqamp5dqnPsriTZfCDwrKu74W7ee/du0a34f6wIEDsLa2hpeXF6KiovSud/PmTQCARCJBnTp1oFKpsGbNGsH+YmNj4erqCmtra+zatQudO3fG3Llz8eabbwIA332BIAiCqAYYILEaQRAEQVQJEyZM4DNESyQSQSZtf39/LjExkV928+bNfHZn3SziIpGI+/XXX/nlfvzxR34b1tbW3K5du4pkwd66dSv/3szMrEhG8W3btnFbtmzhl7GwsODMzc359wD4zNGnTp3iM0pLpVI+i7hYLOYOHjzIcZz+LOJ3797lM30XziI+ZMgQ/vNos4j7+/sL3JmZmXEAuN9//53jOI67d++eIIO5dttisZjbvXs3x3Ecd/XqVc7S0pJ36OjoyDt96623OLVarfd7Ki6LuD7K6k2bRXz8+PEvtL6+bOBvv/02v5ydnR3/2fr3788vU3i9s2fP8uuYmpryDrV/V69e5TiO4wYNGiTYtu7xt3///hKdEARBEMYD1WATBEEQryxr1qzBP//8g44dO0Iul0OtVqNBgwaYO3cuzp8/DycnJ37ZoUOH4sSJE+jWrRs/ZnarVq2wf/9+fPjhh/xyI0aMQLdu3WBpaQlLS0uYmJgU2a+2thxgYyfr9tmNj49HdnY2hgwZgkWLFsHd3R0ikQh+fn74448/4OzsDAA4duwYACAoKAgnTpxAhw4dYGZmBolEgjZt2uDAgQPo3r17sZ89ICAAN27cwMiRI+Hi4oK8vDwEBATgxx9/xKZNm8rt0t/fH6dPn0bPnj15ly1atMCePXv4PtJvvPEGzpw5gz59+sDBwQFZWVmoW7cuPvnkExw4cIBv7v4ylNVbZay/c+dOfPrpp/Dw8EB2djY8PDwwZ84cbNmypdh1WrdujXXr1sHHxwcSiQRubm5YunQpXn/9dcH+Nm3ahCVLlqBBgwbIy8uDTCZDUFAQDh48iJ49e5bLEUEQBGE4RBxXhvSqBEEQBEEQBEEQBEGUCNVgEwRBEARBEARBEEQFQAE2QRAEQRAEQRAEQVQAFGATBEEQBEEQBEEQRAVAATZBEARBEARBEARBVABSQxeAIAiCIAiCIAiCqFpys/MQdT0GiY9ToC7QGLo4L43URAIXL0f4NPWAmYWpwcpBWcQJgiAIgiAIgiBqEAkPErF96SEU5Klg52INE7PqX+9akKdC6tMMWMjM8N5nPeDs4WCQclCAbSA0Gg0SEhJgZWUFkUhk6OIQBEEQBEEQBFEDUKvU+GPubji7O6DHB0GQ28kMXaQKQ5GShX1rTiIzPQsjFvYpU5zFcRwyMzPh6uoKsfjle1BTgG0g4uLiUKdOHUMXgyAIgiAIgiCIGoS9qQu6NxyId6YFQWZvbujiVDgZT7NxcNVpHAjbCoUqtczrPX78GO7u7i+9/+rfFqCaYmVlBYB9kdbW1gYuTVGePXsGFxcXQxfD4JAHBnlgkAcGeWCQBwZ5YJAHBnlgkAcGeWAYm4frx+/i3yPheHdw2Wp4K4K8vDyYmZlVyb40Gg0SzikwdfE4BLb2LXV5hUKBOnXq8PHZy0IBtoHQHszW1tZGGWDHxsYaZbmqGvLAIA8M8sAgDwzywCAPDPLAIA8MY/Vw99ID3D4dgYSHSVAXqCp9f9nZ2ZDJKq/5sVgihpO7Peq39MXrnRoYbbdLYzsezEzNYGJqAlNT/YnAVCoVBg4ciJSUFJw+fbrI/GfPnmHBggW4evUqzMzM0L9/f0ybNg1isRiPHz/GokWLcOXKFdja2mLSpEkYOHAgcnJyAADffvst9u/fD0tLS0ycOBEDBw4ssaxXr17FsGHDMHnyZEydOhUAcPPmTcyfPx+xsbFo1qwZvvnmGzg6OgrWk5pKYW5qXi7vFXX8UIBNEARBEARBEK84V0Pu4OTfF+ER4IpWvV+DqbkJKjseVSqVsLS0rLTtF+SrERfxBCf/uoiU+DR0Gdmm0vZVU1AoFJgzZw7Cw8OLrXWfOXMmrl27hkGDBuHevXtYs2YNPDw88O6772LatGl48OABhgwZglu3buGLL76Aq6srGjVqhI0bN2LTpk3o0aMH4uPj8cUXX8DPzw9NmjTRu5/c3FzMnTsXuj2ac3JyMGHCBJiamqJv377YunUrZs+ejXXr1lWKjxeBAmxCLw0aNDB0EYwC8sAgDwzywCAPDPLAIA8M8sAgDwxj81CQp8LZXdfQtEN9dB7WqspqejmOq/R9NevSENeOheHEnxfwZrfGsHMxnppiLcZ2PBSHUqlEx44dYWZmVmLLg2+++Qa5ubmwtLTEH3/8gStXrsDExATp6ekIDw/H22+/jVmzZiErKwtvvPEGduzYgVatWuHgwYOws7PDjz/+iAcPHqBnz544ePBgsQH28uXLkZCQIJh26dIlpKam4osvvsDw4cORnJyMkJAQZGRkwMbGpkJ9vCgvnyaNeCWJiooydBGMAvLAIA8M8sAgDwzywCAPDPLAIA8MY/PwOPIJCvIK8HrnwCptRp2dnV0l+2nc1h9SEwke3oqtkv2VF2M7HopDo9GgT58+2L17N2xtbYtdzs3NDT4+Pvj888/xxx9/oHv37ujVqxesrKxgaWmJW7du4d9//8WhQ4cAsM+fnZ2N6OhouLq6QiQS8cmeo6Oj9e7j1q1b2LBhAyZOnCiYrl1em4ysTp060Gg0iImJedmPX2FQgE3oJT8/39BFMArIA4M8MMgDgzwwyAODPDDIA4M8MIzNQ05mLgDAxrFikjiVFY1GUyX7MTGTwtLKAsr/PqexYWzHQ3HI5XJ8+eWXcHJyKtPyw4YNw/vvv49Dhw5h8+bNkEgk+PLLL5GamorBgwdj5cqVkMvlyMnJgUajQU5ODt/v28TEBAD4vtm65OfnY86cOWjVqhV69+4tmKdUKgGgTNsxFEbTRDwpKQktW7bE+vXr0b59+xKXvXPnDpo3b46DBw8Klv3uu++wYsUKpKWl4c0338Svv/4Kf39/AOwJ2pQpU7B3716oVCr06dMHq1evhlwu17uPS5cuYdq0aQgLC4OTkxO++OILjB07lp+/YcMGLFq0CE+ePEH9+vWxcuVKtGzZ8qU9GAvFealpkAcGeWCQBwZ5YJAHBnlg6POQlaFExJVoPL7Hag+rO2KJGHYu1vBvVhdu9fT3zaTjgWF0Hv7rwlpc5fXLJLXKzMzE/Pnzcfr0aYhEIgQFBWHBggWQyWSQSp+HGqNHj8b58+cRERGhtwxHjx7Fjz/+iPj4eNSpUweffPIJOnXqBKBsSa1EYuNMcAYY4fFQQXTt2hVvv/02Dh06hO3bt2PYsGF499130bJlSzx48ACNGjVCly5dIJfLIZVKYW5uzj9s0P43Ny86TNiaNWsQHR2NuXPn8k3EMzIykJKSwi9feDsWFhaV/nnLilHUYJ87d47/IkpDqVRi8ODBRZ5SbNiwAStWrMCRI0eQkpKCN954A/379+c7xU+ZMgWPHz/G/fv3cf/+fcTGxmLmzJl695GWloYePXpgxIgRSE9PR3BwMKZPn47Lly8DAEJDQzF16lRs2LAB6enpGDp0KHr37s0/UXkVqFWrlqGLYBSQBwZ5YJAHBnlgkAcGeWAU9pCSkIaN83bh1LZLyFPmw8zCFGaW1ftPLBYj4ko0/ly8Dxf33yiTh5pKdfKgUCjw8ccfIzw8vNhlZs6ciTNnzqB3797w9PTEmjVrsHv3bgDAypUrcfDgQXTr1g2tW7fGvn37EBwcDAD8sEzbt2/H+fPni91+SkoKPvnkE0ilUgwdOhQ5OTmYPn06cnNz+aRWqamp6Nu3L86dO4fZs2dXnIAqoDodD6XBcRyGDRuGTz/9FAAbfisvL4/vs71u3TqsW7cObdq0QU5ODtLT0+Hn5wczMzN4eHggISEBGo0GcXFxAABvb+8i+7h69SpUKhVGjRqF4cOHAwA2bdqE77//Hp6engDYUMcAEBcXB5FIxE83Bgxeg71hwwZ89dVX+O677/D++++XuvykSZPQr18/3LlzRzB93bp1mDRpEgIDAwEAS5Yswbp16xAaGooWLVpgy5YtCA0Nhb29PQCWIr5Dhw74/vvvi2Q33LFjBxwcHDB58mQAQMeOHTF06FCsWrUKzZs3x/r16/H++++jdevWAIDp06dj7dq12Lp1K0aPHq233NqDT4tCoSijIcMQFRWFhg0bGroYBoc8MMgDgzwwyAODPDDIA6OwhyN/nIW5zAwjFvSD3KbysihXNRzH4dzuf3Fm51XUbVwHzh4Ogvl0PDCqi4eXTWoFsCGozM3N0bNnTygUChw8eJAPrLOzs5GTk4Nvv/0WpqamxTaVfvLkCfLz89GoUSP07NkTUVFR/L1ydUhqVRrV5XgojszMTGzatAl+fn7o3LkzXFxc+KG2oqOjkZubi379+gEAnj59ii1btqCgoAB37tyBSCTCwIEDkZ2dja5du2L58uWYMWMGXzPdvXt3AMCJEydw7949DB06FFOnTkVqaioAIDU1FQsWLEC3bt0wdOhQeHt7w8bGBuvXr0dsbCyOHz+O1q1bG9WxYPAAu2vXrhg6dCikUmmpAfbGjRsRFRWF4OBgLFq0SDAvLCxMUCNtYmKCevXq4ebNm7C3t0dBQQEaNWrEz2/QoAFycnIQGRmJpk2bFtmW7rLa5bVP48LCwjBmzJgi82/evFls2RcvXowFCxYUmR4eHg65XI6AgABER0cjLy8PlpaWcHNzw/379wGwp14cx+HZs2cAAH9/f8TGxiInJwfm5ubw9PTkm9u4uLhALBbjyZMnAIB69eohISEB2dnZMDMzQ926dXH37l0AgJOTE0xNTREfHw8A8PHxQWJiIjIzM5GVlQWO4xAWFgYAcHBwgKWlJf+0yNvbG6mpqcjIyIBEIkH9+vURFhYGjuNgZ2cHKysrxMayRBOenp5QKBRIS0uDSCRCYGAg7t69C7VaDRsbG9jZ2eHRo0cAWKKC7Oxs/kfVsGFD3Lt3DyqVCtbW1nB0dMTDhw8BsOQGubm5SE5O5r+DqKgo5OfnQy6Xo1atWnxSCVdXV6hUKiQmJgIAAgIC8OjRI/5i4e7ujsjISN43wE4QCoUC+fn5iIuLg1KphLm5Oby8vHDv3j0AgLOzM6RSKX+S8PX1xdOnT5GVlQVTU1P4+vryT4QdHR1hbm7OP7GrW7cukpOToVAoIJVKERAQwD84sre3h0wm4317eXkhLS2tWN/W1tZ8cgcPDw9kZmYW69ve3p5PEFGnTh0olUqkpKQAAAIDAxEZGYmCggJYWVnB2dkZDx484L+//Px8JCUlAQDq16+Phw8f8k8tXV1d+WO2du3a0Gg0gmM2JiYGubm5sLCwgIeHh+CYFYlEePr0KX/MxsfHQ6lUwszMDN7e3lXiWy6X88esl5cX0tPTkZ6eDrFYjAYNGiA8PBwKhQJxcXGwtbXlj1kPDw9kZWVVyTFrLOcIhUKBiIgI+Pn51ehzhEKhwJ07d+Dn51ejzxFaD25ubjX6HJGZmYk7d+7A1tYWJjDF/VvR6DGuHcwsTZCbm8sHFtbW1sjKyoJGo4GJCRuHVpsIysLCAmq1ml/WysoK2dnZ0Gg0kEqlMDMz45c1NzcHx3H8w3u5XA6lUgmNRgOJRAJzc/MSl83JyYFarYZEIoGFhQWysrIAsBpHkUiE3FzWl1UmkyEvLw8qlQpisRgymQyB7eri4qHruHryJlr2fk1wjlAqlbhz5w5MTExq9DkiPz+fP36M4RyRm6NBQUEBW9ZECisrK/5+r2fPnpgwYQIGDx4MtVqNgoICqFQqFBQUQCQSwcrKCtbW1rCyssKECRNw7do1dO7cGe3atUNBQQH69++P8+fPY+TIkQCAN998EwMHDoRSqQTHcZgzZw4cHR0REBCAQ4cO8YGz7vHt6emJFi1aYMeOHdixYwekUil+/vln5Ofn89ckR0dHKBQK1KpVCxqNBuHh4QgMDOSP2by8XMTFxSEvr5HRnSMAds+v0Whga2tr8PuIxGeJ4DgN/13oO0doNBpwHIfs7Gykp6dj+fLl6N69O9q2bYvZs2dDrVbj4MGDkMvlmD59Orp06YKsrCxMmTIFjx49wp49e+Dk5ITFixfDz88PKpUKY8aMQXJyMg4cOABzc3MsXLgQPj4+UCgUOHToEPbu3Yt27dohICAAlpaWKCgo4K9dvr6+8PLygkajwapVq/C///0PO3fuxJtvvomvv/4aOTk5gmM2Ly8fMTExcHxsVeo5QvvbrTA4IwIAd/LkSb3z7t69y7m7u3MxMTF6l5VIJNzx48cF67Rp04ZbtGgRd+bMGQ4Ap1ar+XkqlYoDwJ05c6bIvsaOHcsNHz5cMG39+vWcj48Px3Ec5+PjwwUHBwvmDxs2jBs7dmyxny03N5fLyMjg/x4/fswB4DIyMopdx5CkpKQYughGAXlgkAcGeWCQBwZ5YJAHhq6HuMin3Hej13GJj19dN38u3sftX3uyyHQ6HhjG5uHO2Ujuu9HrOFWBqthlOnTowLVt27bE7Rw+fJj76quvOD8/P27Tpk0cx3Hc7du3ufbt23MrV67k5s2bx/n5+XGbN2/mOI7j/vnnHy4gIID7999/uZkzZ3J+fn56t6vRaLiFCxdyffr04bZt28a1a9eO69ChA6dUKrmff/6Z8/Pz486ePctxHMctW7aM8/Pz4y5evCjYxq+f/c2d3nGlzE6qEmM7Hi7sv86tnLqpXOts3ryZ+9///vfC+8zLy3vhdV+EpR8Ec/8eCyvTshkZGRUalxm8Brss5ObmYtCgQVi2bBk8PDz0LiOTyYr0gVYqlbCysuKbvCiVSj7JgHZZK6ui2RRlMhnS09P1bqukfRVOtqCLmZkZ31ymOqBSqQxdBKOAPDDIA4M8MIzZQ0ZSJh6FxSE3u/IztmYoMmBjXXlN0sQSEawc5KjbyB2m5qaVtp+XxZiPh6pE1wP3X/4XiVR/qpuXSSilTd6qi5ubG06cOFFisqniuHr1KoYNG4bJkydj6tSpAMqWUEosFkOjLpohmo4HxqvqQV9Sq9mzZ0Mmk2HKlCnQaDQ4ePAgtm/fju7du+O7777D22+/DXt7e75FRUxMDNzc3AQJ0I4ePYrNmzdj+fLl6NatG7Kzs7F48WKcO3euWiS1Ko3qfjwkJydjx44dWLVq1QtvQ3terAlUiwD7ypUriIyMxNixYwWZvN955x2MGDECq1evRsOGDREWFoZ33nkHAFBQUID79++jYcOG8Pf3h4mJCcLCwtCiRQsArJmGqakp/Pz8iuyvYcOGCAkJEUwLDw/n+05o91V4fo8ePSr0cxuSxMREODs7G7oYBoc8MMgDgzwwjNGDRq3BoeDTCL8YBbFYBBNzE4greazX3Lw8mFfig1ONhkNeTj6kJhJ0GdkGga3qVdq+XgZjPB4MQVk9KBQKzJkzB+Hh4XBx0Z+Je+bMmbh27RoGDRqEe/fuYc2aNfDw8MC7776Ljz76iF8uPj4e//zzD4KCggA8TzY1YMAAZGVlYd++ffDw8MC0adP07ic3Nxdz584V3PhqE0qZmpqib9++2Lp1K2bPno1169ZVqIdXnVfJA8dxGD58OGrVqoUffvihSFKr+Ph42NraguM4vmm5qakp7t+/j/T0dBw5cgRHjhzht9elSxccP36cH8dYuw3g+bjZukMxVYekVqVhlMdDOQJeR0dHbN++HRKJ5IV3V1BQUK0qG1+GahFgt23btkjWcJFIhP379/PDdI0ZMwbz5s1Dt27d4O/vj7lz58LFxQVBQUEwMTHBoEGDMGvWLGzbtg0AMGvWLAwePFjv0693330Xn3/+OZYtW4bJkyfj7Nmz2LJlC/bs2cPvq1+/fnjvvffQpk0brFq1Cs+ePeM79xMEQRBVy7k9/+LelYfoMqI16r/lC1Nzk0rfp0KhgLW1daXuIyM5E2d3XcOh4NOwr22L2t5lG5uUME4qIqHUpEmT+OVGjRoFLy8vzJkzB0DJyab0sXz5cr4vqpZXIaEU8fKUJ6lV69atERISgo8++ghKpRJKpRKdO3dGvXr1sGTJEv5ee8uWLbh8+TKWL18OBwcHQVKrFi1aQCKR4KeffkJUVBS2b98OBwcHvP766wBg9EmtqhsmZiYoyFNBo9ZALCnboFIvGlyr1RqIRICZidl//boBSRn3+aLk5xZArdLAxMwwoa5RDNNVHHK5HFu2bCnTsmPGjMH06dPRr18/ODk54fr16zhw4AB/QVq9ejXq1auHRo0awd/fH15eXoJmDoGBgfjf//4HgCXiOHr0KP/jHjduHFasWIEOHToAADp16oTVq1dj4sSJsLOzw19//YVDhw7xGcpfBQICAgxdBKOAPDDIA4M8MIzNA8dxCDt/H42D/NGkff0qCa6BqhnX1MbRCt3HBkFmY4F7F0sfytIQGNvxYCjK4kGj0aBPnz7YvXs3bG1ti13Ozc0NPj4++Pzzz/HHH3+ge/fu6NWrl2CZkJAQXLhwAZ999hlMTVkXAm2SqpEjR2Lq1Kl46623BC3/dLl16xY2bNiAiRMnCqZrE9xpaxfr1KkDjUbDJxoqDToeGNXdQ0ZGBpYvX87XPM+bNw+9evXCoUOHEBcXh88//xwDBgwAACxatAjvvPMOLl68iPDwcIwZMwajR4+Gvb09+vTpg27duqFbt25wc3MDAHTr1g0WFhYICQnB8uXLkZ6ejgYNGuDHH3+EjY0N/vrrL3h5eeGXX36BXC6HXC7H+vXr4ejoiJ07d6Jly5ZYvHixwdy8CIY6HvKU+VDlq5CRkgVVvgp5Sta83s3XGSqVGo/C4yt1/xq1Bs8eJeP26UjcPhOJ26cj8SwmWW/3korkwU2WbM6tnmGGRzOqGuzCbfOXLl3KXzRKW1YkEmHGjBmYMWOG3uWtrKywdu1arF27Vu/8wk2+mzVrhnPnzhVb1mHDhmHYsGHFzq/uPHr0CL6+voYuhsEhDwzywCAPDGPzkJOZi8zUbHjWd63a/ebklFgLWVGIxWK4+9XCs9jkSt/Xi2Bsx4OhKIsHuVyOL7/8sszbHDZsGOrWrYu///4bzZo1E9x3/Pbbb6hbty46derET3NwcICDgwP69++P5ORk/PXXX9i6dSuGDh0q2G5+fj7mzJmDVq1aoXfv3li+fDk/T7dpLgC+oqJwS8LioOOBUR09nDhxgn/t7u6Or776is+KbW1tje+//17vera2tli6dKneebrnySVLlmDJkiX8vMLvtYG4Pho3boydO3eW7wMZAXnKfEikYmRlKCG3sYRapYGZZdXk1MjPK8C+X07gyO9nkK3IgczaAl3HtEXvSZ3g4umI2t5OOLjuFNoNfBN1/GvDxKxiH06rNRokxabgWYzw2pV9R4mcrFw41XGARFxxdb0cOBTkqvAoLA5ndl6FZwM32LlUbiuz4jCqAFsXjuNw5coVLFu2zNBFqZFoh+eo6ZAHhj4PSkUObp2OwIObsVBmVn9PIhEgs7GETxMPNA7yh7msaLNKOh4YxuZBpVIDAKTFNAWrrIRSGRkZWLRoEU6ePAkAaN++PebOnau32XhJSaPKklDKxEwKdUHlPvF/UYzteDAUleFBX0IpgAVv169fx7Rp0yDSyTVQXLKpwgH2mjVrEB0djblz5/JNxDMyMpCSkvLSCaXoeGBUdw8VkdQKANRqdQWVqPxog9vszFzIrMyrNLgFSg5wTSswmNVoNMjPLUCeMh/5OQXIy8mHzNYSxzafw67lR/nlshU52LksBBzHoWWv12BhZY6Y8ARs+WYfJCZiACKA41jXbI4DBw7gWFdtjvvvNT/t+XKA8D0HQCwWwcXTEU8eJkKtp7ZaIhGjto+z4PxVUYjFItRt7IGeH7ar8G2XFaMNsEUiEdavX2/oYtRYLC0tDV0Eo4A8MAp7yErPxtbvDiIzTYm6jd3h7lerUk6SVYlGwyH9WQbO7b6Gu5ce4L1Pu8NCbi5Yho4HRnXyUJkJpZYtW4Y9e/Zg4MCByM7Oxu7duyGVSvHNN98Itl9S0qiXTShlDFSn46EyqSgPpSWUAsC3sGvbtq1g3eKSTRXm6tWrUKlUGDVqFD9t06ZNyMrKQufOnQG8eEIpOh4YRufhv0s0pylbYquKSGoFPG8BURVwGk77MassuC2OPGU+9v1yAjuXP0+arA1wwQEdBr+F2LsJLCDO/S8wVuYjLycf+bn5yMspQH5OPpuW+99r7TQ+mGbLF+QJM5Rb2cuw/NwXCPn9rN6yhfx+Fr0mdETIhrPITM2ulM/v7OGADoNb4KcP/yh2mVGL+kNmU7GZ4KUmUjjXsYeltWEzzBttgE0YFt3MjjUZ8sAo7OHKodvIyczFyPn9DNb8prJIepyKLf/bh+sn7qJV79cE8+h4YFQXD5WdUCorKwsdOnTA119/DQA4f/48/v333yLbLylp1PXr16t9QqnqcjxUNi/joTwJpQA2colEIinSr7O4ZFMABAmlpk6ditTUVABAamoqFixYgG7dumHo0KHw9vZ+qYRSdDww6tSpY7B966u5ldmwgD/tmQJOdcqWM+hlgmttYiupxKRKElvlZOVCkZqFvJx8pCVm4Njm88XW3r7Vowku7L8JtUoNdYEaapUaKpXm+esCtfC1SsMvq9L+107n19euo4GllTl+ODkLR34/o7esR34/g3cmdMAvM/6q8ADX1NwErj4uyEzNRrZCf7eObEUOsjNy0GloS2QkZ0EqlUBiIoZEKoHERMLeSyWQmrL/EqkYUhMJP18ilUAqFUNiKmX/daebsOVNzKRwquMAmbWF3nLIrC0Q2NIXUtNXMxR9NT8V8dJERkbyw5LVZMgDo7CH+9djENDC55ULrgHAqY49fJrUwYMbMUUCbDoeGNXFgzah1IQJEzBo0KBixyHVJt4ZPnw4Ll++XGJCqVWrVvE1gkuWLOGbgz948AAKhQJvvvlmke2XlDSqpHmNGzd+WQVVQnU5Hiqbl/GgTSjVu3dvdO7cGfPmzYNYLMahQ4cgl8sFCaUA1nzX1ta2SO30okWLYGpqijNnzkAqlfLJpgB2DO/atQs9e/ZE8+bN+XXi4uIAAL6+vmjUqBEAYP369Zg/fz6fUKpwq4zK8vAqwAe3GUrIbWRG0yy5xwftYWZhisuHb6HHuHaV2upMm9gq6XEq1Co1JFIJnDzsUcvTUW/Gak7DsQBW/TxgZUHtf4GtSifILeb/3YsPcP/fR4i89git+71Rau3t8S3nK6321sndHoqUrBID3Kw0JQJb1UN6ogKmFiYwszCFmYUp/9rU3BRmFiYwszSFqYUpzMxN2H8LE5hZmrHlzJ8vb2ZpChMzKcT/9WlW5atKDG5tHOR479PKHV44T5mPrmPaslr7QnQd0xZqlQbSqvtpVCkUYBMEUW6y05Wwq1U9atdeBAdXOzyOeGroYhAvSWUnlNLy5MkTTJw4ESKRCBMmTCgyv6SkUS+bUIqovrxoQikA+PXXX/VOLynZVOGEUrr7joiIEEx7mYRShhzntqb0uS2OEpslA2jT7w2c+Osikh6nwivQDaYWptDG2Rzflxb/9cPV6VPLut0CGtYvt+gy2vU5mJqbIitdifREhbBw5wFbZ2tYyM2Q+jQDGrUGGo0GGjVX5mbr+lCr1HgWk4zo23HISlPC93XPUmtvlRk56D42CJmp2UVqZgvX1koL1cw+r8GVQGL6vLZXWwMsNZFAaiqFtYO8xADX1skK01aNeOHPXboXjcGDWzNLU/SexK6ZR34zzG/CUFCATeilVi3DpLU3NsgDo7AHDhzEYv1Pv18moRQA/PXXX1i3bh1SUlLQrFkzLF68GM7OzqXO08fVq1cxbNgwTJ48GVOnTgVQtoRSYrEInKZoUg46HhivqofyJpQyNzdHXFwcRowYgYSEBCxatEhvzV1JSaNeNqGUMfCqHg/l5WU8VFRCKUOiDW6d7WpBla+qMcEtx3FQF6hRkK/CgbWhxfa57TSsFV+jW7hZskpba1tis2TNf/NUUBdo2DSdZsmmZlKM/mZA8c2SfzuDny/Nw5Zv9iL8QhTO7b7GclpptAmqXjzI1SI1kaBxkD9unYrgk08K5kslaNzOH7dOR0BVUHS+WCJ+3vxYqhvQat8/fy01kUAslcDUTAoXLyd0HtoKjdr5QyKRlFp7a+0gR98pb7/05y0JQ9feGktwa2pmgl7jO6Lv5M58Gdjx+uoG1wAF2NUKjuOQHJ+GrDQly+JXiaSnp0NhW7k1KBZyc7h4OJR5gHvC+HnZhFIHDx7E/Pnz0aZNG7Rp0wZbt27FN998g+XLl5c4Tx+5ubmYO3eu4KbhVUgoRVQsL5NQKjU1FSNGjMDTp0/x3XffoXfv3nr3oU0OpS9pVGJiYrHziOpPWWOWikooZSgqO7jVaDQoyFMh77+kT4LEUDn5qF3XCae2Xy62z+2bXRvh9D9XBIGpKl/9vPmxdnrB82na+bp9c1mw+1+g+99DBI1awyeVKq3P7dJxwZXWLLmOf21kJGeWWHOrSMmC1ESK3Oy8Mm1TJBY9r839L9iVFqrZ1dbeSqUS1K7rhP7Tu+H8nuvFbnPoF33QPvIpRGIRLK0sYGltDktrC1jIzSE1qZjjn2pvGbrBrTIzF5b/teqo6uBW+6BNI1FBaip9ZZuF60IBdjXhUVg8jm46V7TJTSWRm5vL16xUJpZW5mjV53W81rFBpe/rRXj69GmR2s2aSFk8VERCqb/++gt2dnZYs2YNsrKyMGjQINja2pY6Tx/Lly/nh5/RUlKyqbIk8KHjgVHdPVRUQql58+YhPj4egYGBiIuLw+rVqyGXyzFixAhBQqm33nqr2KRRJc2rLlT346GiSEtL4z2YWbA7SKUiBw61bcu0fkUklFKrNJBIxZWeUAoAsjOUsHOxLiVbMoeggc0RcSVamAU5pwB5OXn8+/ycAuTm5AuWyc8tQO5/mZLzcwuKLUdZMyaf2/1vpQW3tk7Wpfe5Tc2GdyN3JD1O5QNTbRIpqU7TY90kUlJT6fPaWz64FetZXwJzuRlsna1LbpbsbI0JPw6GSCR63uRZN2A2ETZ5Fr/AGMWl1R5b2VqiYRu/cm+3PBhDcAsIA1zdcbCrMsDVBrfWDnIAMGhwGx8fDzs7O8MVoAqhALsa8Cw2BbtWhMDVxwWdh7aCfW2bSq/1zczMhJWVVaVtn+M4ZKZk49bpezi2+TzMZWao38Kn0vZHVD4VkVDq3r17sLKyQp8+ffDw4UM0bNgQq1evLnVeYW7duoUNGzZgypQpghruVyGhFPHyVERCqdTUVJw6dQoAEBYWhrCwMACAi4sLRowYIUgo5enpWWzSKLlc/lIJpYyBmtznVrcMTrbOfK2mg5stLK3McffiA9Txr12p+y9vQqnCcBwHjVo3oVTh/4UTTmmQHJ+KsAtRuH02Au0GNi+h5vYs3pnQEZsX7amw4NbETPpfMiiW2MkjoDay0pQl97lV5KLv1LeRna4U9pflA9r/amdNpEWCXb1ZkgsHu6YSmJublhrczto4vkIcFEdpzZI1ag28G1ZulndjqD0GjK/2Nin9GWyd6tWI2luCAuxqwZ2zkTCXmaH/9K4V1nymNGQ2Fi/05LI8WNvL4errjMw0JW6cvGuUAbafX+U+Za0ulMVDRSSUys7OhkKhwIgRI/Dee+9h6dKl+Prrr7Fy5coS5+mSn5+POXPmoFWrVujdu7cgwH7ZhFJ0PDCqo4eKTihlb2+P8PDwYs+ThRNKlZQ06mUSShmSmtrntqxleLNbY5zafhmqAjX83vCChdycH4v4hdBJQsVxHDQaDiKRCKlP0pEUlypYNPFxChJjUmDtIIMiJZvP0MwCaTU0au75f7WmzG3ZNWoNUp9mIPxCFJJiU1DLywkZKSU3S85KV+LNbo2hSMl8Hhhb/JcR2ZwFyc/fm/CBM8ukbPI8s7Ile6/vN1dqn1t7GbqPCSrTZ3xRDN3nFjCOmltjKINuWQDjqL2lbj/V897hRaEAuxoQF/kU3o3cqyy4BljAUVIz34pCJBKh3mueOP7nBXAcV6nDRrwIcXFxqFu3rqGLYXAqw4O+hFLm5uYwNzfH3LlzAQCXL1/GxYsXAaDEebqsWbMG0dHRmDt3Lt9EPCMjAykpKS+dUIqOB0Z191BRCaWq6jypD0PX3Bo6uC0tW3Kv8R15HxzHFZsw6nlCqf/64Rao+LFtCyeRUhUI++w2aOWL83v+xa4Vevr9aji83jkQ+TkFOPHXBZz48wILrjnoyc7MPX/9X7pm4TL/ZV0pFASLJRLUCaiNx3cToNGTlFEsFqNOfVc8vvcEGnXRhFJFEUEsEUEsYU2Dha//e//f61pejug9uRMcXe1g52JTcs2toxXGLR5Yhv2/OMZQa2osgaUx1NwKE1spIbOu+qbRxkZ1v25WBDXJAQXY1QBVvgrmllXbBE9dpotxxWAuMwXHcSjIU8HU3LhOvtoaz5pORXkoLaGUj48PoqKikJOTAwsLC6hUKr75aUnzdLl69SpUKhVGjRrFT9u0aROysrLQuXNnAC+eUIqOB0Z191BRCaUqIuvui1BVwa02O3LhgFRqKsWR309jp56EUuCA9oOaI+buE2FiqDJkR9Ymlioc2AqDXQ3MLE0w9ecRJWZL7jW+I6a1XIS0JAXUerIVvyxW9jIsH/AFQv4opt/vH2fRa2JHxN5LqLR+v7XrOqPvlM5Y0H9lsct8/OsoXNp/A/l5KpjLzWAhM4eFlTksZGawkJvBQm4OCys23dTCpNSH3GKJGHJbGUzMnt8+Us3tc4yhzy1gHDW32jIkpj1DPUdqGl3dr5sVQU1yQAH2K8DLDIuUl5eH119/XdBfdsCAAZgzZw4yMzMxf/58nD59GiKRCEFBQViwYEGJNTYvNCySkdVa61IVid6qAy/joTwJpfr374958+Zh7NixCAgIwNmzZzF48OBS5+kmlJo6dSpSU1lzydTUVCxYsADdunXD0KFD4e3t/VIJpeh4YFhbWxu6CAKkJuxSVpCrv9+/PioioZSZiRk0Gk2lJ5TSqDXIzcpDVoYSKQlpOPH3Rf3ZkjUcXusUiCO/n/6vxlWlPzsyP9yPWm925OfLFq0VfZ4tWX9gqc2WvPazvw2eLdnCyhzJCWl6lyk2O7JO4ijd7MiFx8h19nBAdnrp/X4Hfd4DSkUun0SqcJ9f/f2AJcJ96u0HzN5zGq7E2mOvBm7wbVq5TVONMbg1hj63ipx02DpZ1/jA0thaJhoKun+oWQ4owK7mvOywSPfv34dKpUKPHj1Qr149AECDBg1gYWGBJUuW4ODBgxgwYACysrKwb98+eHh4YNq0aXr38yoOi+Tl5WXoIhgFL+OhPAmlBg0ahOTkZGzevBkPHjxAv379MGPGjFLn6SaUat68Ob/vuLg4AICvry8aNWoEAC+VUKqmHw/aZsk2lnYG6XOrWwbdptEWcjPYOMgREx4P/ze9K3X/FZFQiq+pLWBj2rLXqhKmqXDlyB08efgMIxf0Kz5b8n+1prdOR1RacGvnUoZsyWlKNGxTDynx6cLhfHSyIwuCR33ZkU10AlwTqSAINrM0hV1p2ZJdrPHpb2Mhloj5bbPtvHh25MKUpd9vx8EtX3o/JWEMtceA8QW3xtDn1tXV1XA7NyJq+nVTC3moWQ4owK7GVMSwSPfu3QMADBkyBD4+PrC3twfAAvfY2FiYm5ujZ8+eUCgUOHjwYInZYitjWCRDc+/ePTRs2NDQxTA45fXwogmlRCIRpkyZgilTppRrXuGEUrr7joiIEEx7mYRS0dHR/IOoqqam97ktrQyBrevhwr4bsK9ti8BWviyhVAWj/i+4fhqd9HyaSo2nD5MADnB0tUVWhvJ5oJyvKhQws7+yJpTiOA6ZadkIvxCFxNhkuNWrhczU7BKD25zMXAyd2xs5mbnCWlGpMKAtNjtyGYbtKS2wtHWywtSVI15MchkpNVuySgMnd/tKLQP1+y1aFgB4lvoE9RyoSTDdPzDIA4M81CwHFGBXYypqWCQAGDt2LPLy8tC4cWOsWLECMpkMgwcPxvnz5zFy5EgAwFtvvYWxY8fq3QcNi0QUR0UllDIk+obhqUnBbXkSSr0Iz4cJet58Wa3TZ1dVoIbc1hJHN54tpt8vh9bvNsOt0xHYs+oY9v96AhKpBCIRBMmkAG0iqeevwSeZ0kkwpee9SCyCQ21bPHuUDI2maIAsFovg4uWIZzEp0KiLNq0ujEgs0kkeJYZET0IpiETgOA5mlqb49Lex8G9Wt/QxZu1kCBrw5ot+FWWCAkvjKQNgPLXHWvLy8gyyX4IgCGOBAuxqTEUMi1RQUAAvLy8MGTIEqamp+OWXX7BgwQIsX74cDg4OcHBwQP/+/ZGcnIy//voLW7duxdChQwXbrcxhkQyNs7OzoYtgFNSurWcc1zLmd6qohFJViW5Ge2MPbt/5sANEYlGp2ZHVqv/m60kiVTg7skql5h8kSEzE6DqybckJpSZ0xKqPtiArLVuQjIqVQacfsJ59avsBl0Tp/X7ZWLs3Tt6ttKbRjm526DSkJdbP2l7sMt3GtsOFvddRkFfAJ4+ylFuwJFJyc1hYWcDyv4RS4jKMCiEWi2DtIIdnfVdITdnlmoLb5xhDYCnMlsw81PSm0XTdZJAHBnlgkIea5YAC7BqEvmGRFixYIFgmNDQUly5dgkgkwuzZsyGTyTBlyhRoNBocPHgQ27dvLxJgV+awSIZGKq3ZPxFtza2UMxXU3FrIzaFIzSrzdioioZRapYFEKq70hFIAoEjJgrnMDEpFDg6uDy221rTNu2/i3uWHRWpcBVmSC0oIdgv0TNcJds0tzTB784QSg9t3xnfAtFaLKjWhVIseTUpOKJWchdi7CXgc8aRC961NNlXL2wmZKSU3jc5OV+KNLg2R9iSjSNNobVPn5/179TSNLrYfMNuOmaUZ/F73wl//219s7XHbfm+gw6AWFeqgMMYY3Nb0wJJvvWGigdRUWuObRtf066YW8sAgDwzyULMc1JxPWkMpbVikn3/+GRqNBtOmTQPHccjOzoaFhQVyc3MRHx8PW1tbcBwHlUqFgoICviZal8ocFsnQJCQk8P3Sqxpj7nPr3cgdEZcfokWPJjCXVd4Qci+aUIrjOGg0Gp3a3ML/ta+LTs/KyMGZnVeREJWIIXN7lVpr+tfifZWbLTml5GzJmSnZsHWy5stQUnZkbeApNZEWmx25cDIqma0lbJ1KTihl42yFXhM6QKPhSs6OXDhLcwnZkcUSsSD7bGlNo20crfDht4MqyLx+KKGUkOfD8DylYXjArnPVIbdIZWPI66YxQR4Y5IFBHmqWAwqwX0HKMyxSdHQ09u/fj7i4OOTm5uLx48f48MMPAQCtW7dGSEgIPvroIyiVSiiVSj5grqphkWoqxt4suVWv1xH1bww2LdqDwJa+sHaQQyQufSiO5/1hdfrEalhAzIEDNBz+WwRSEwkykjKR+jRdsI3oO49hX8sWcltLpCUqoFFr+D+1muNfl7kNu7ZsGiArPRtRN2IRF/EUjm52pWZLzk5XonmPxkh/poDERFo0O/J/waPusD+62ZH5INRUXxAsgam5CeycbUoMLO1crDF/51Q+aK6I7MiFKS2w5NQc2rzbrML3qws1jS5aFsA4mgRTn1uCIAiCeA4F2K8g5RkWad68eRCJRAgNDQXAEqFpx8hetGgRTE1NcebMGUilUowZMwajR48GUHXDIhkaX1/fKt9nacFtj7HtUJCnKjS2rYbPVly0D67mefPlMjRLlppI0XtSp1L73GamZiHy3xjcDL2H5wmjtEmkuOcJonQD6jJmT5ZIJfBu7I7om4+h1pMwSiIRw7tJHUTfioNaVUL/XZEIEknhRFISiKUinWnP55tbmsG3qQeGzH4Htes6w9LKvNRa07HfDCzTZ3pRylJrWhkZs3UxhsDSGMoAGE/tsTFhiPOkMUIeGOSBQR4Y5IFBHmqWAxHHlfGOl6hQFAoFbGxskJGRAWtr6xKXDZ6zHT5NPNC+HH37tmzZgtjYWMyePfuFyqdUKmFpaflC65aXu5ceYP+vJ/HR6pEwNTeum9T4+Hg+C3tlwHEcFClZSI5PQ3JcGhSpWWg3sDkmvzm/2KBu5aWv8FHrryu1WfKM4DH4uE3xD0KWn/0CP4wNrpA+t2KJqEizZHf/2hj7vwH4pN3i4stw7gvcOn0PGpUG5lYWsJSbw9LaHJZW7L+F3AJmlqaCZsYlIRKxwF6XPGU+9v16Qm9w++7HXV46e3ZZyc8rwN7Vxw1ea8p3W9Dpc2uocbB1g9uqLoOWyj4/VBcePXpUo8Y3LQ7ywCAPDPLAIA8M8mDcDsoTl5UFqsF+BamIYZE0mtKHmXmV0d7Ey0zlLzUsk6pAjdQn6SyATmBBdHJ8GlIS2P/k+DQU5D0fXq2Of200aR9Qpj63yszc4pMylaVZsome6SYSWFpblNrn1tbZCgNmdAOn4Yr0o9U3dq6gDP9N05ahuAC4tD63ds7W6DSkVbm/k/JAtaZCjKHPrTE1jU5LS6MAG0BWVtkTHr7KkAcGeWCQBwZ5YJCHmuWAAuxqgFgs1ttMtjheZlgkbcZmMxMzaDSaKsnYrB2iRyJl+zHm5F6FA5qcrFy+9jn5v6A5Jf558Jz2TIHSGomIRCLYOlvB0c0e7gG1Sg1u7VysseTIp2WumX0RSmuWrFFzeLNro0rbP2AcfW4BYXCbpVBCbm1ZY7Mla6GGTwx9SR9rIuSBQR4Y5IFBHhjkgUEeapYDCrCrAfa1bZAQlViudV4kuH7RjM0vS3zUM9g4WkEilRh1ci+O4/DG2w2xa3kIkuPTkBSXBmUxNc26mJhJ4eBqB0c3Ozi62sLR3Q6ObvZwcLWFo7s9HGrZ8GPcastQemBZuT9dY6i5NYYy6JYFAKzt5RCLxTU+W3JN6kdVEuSBQR4Y5IFBHhjkgUEeGOShZjmgPtgGojxt/e9deYh9a06g/aAWeL1TgyL9RCsC9X/B9dPopCLzatV1gounY5lqsjmOA6d5ntRK+F44T6PWIPr2Yxxcdwru/rXQdVRbhG67hF06Yw5r6ffR23jrnddw9fBtQbIubaIulTapV/7zZF38dJ0EXqoCDdQFKqh0En+pC9RQqTSwsDLHt0c+w9QWC8rV/1lmYwFHN3sWOLvastdudnBws4WTmz2sHGTlzuxsbH1uszKUkNtY1vg+t3fu3EHDhg0Nsm9jgjwwyAODPDDIA4M8MMgDgzwwyINxO6A+2DUQ/2beeNKlIUK3XsKFfddh62RV4UPxcByHp9HJevtei8Vi1PJ2RHJCGjQqzX/LP28mqpstujz7y0pTIu1ZBtKTMvHw9mMM+KQbQooZczjk97PoNaEjDv9+utKSe9k5W0NRypjDSkUuRi3qD3NLUzi5s1roysjgbGx9bpPSn8HWifrcEgRBEARBEERJUIBdDRCJRGg/qAUatvbD/esxyErLBqep2IYHOco8HAo+Xez8/h93xf1rN5CeqCj3tkViEcRiNiySSMyGShL9NzySmaUZ3Oq5wM23FrLSlCWPOZyRg7dHtEFmStbzxF76kmnpJvjSyUytb3xi3URfJmZS2LmUPOawtb0MLd9pWm4HL4IxBZZWVlaG27kR4ejoaOgiGAXkgUEeGOSBQR4Y5IFBHhjkgUEeapYDCrCrCSKRCE517OFUx75Stq/KV+HALyeLDSx7T+qIBi19IRKLWJbqUrJEP89gLS5zMq7SskbbOMgxYHrXl/6sJVG2/s+VWgSjxNy8csdZri6QBwZ5YJAHBnlgkAcGeWCQBwZ5YJCHmuWgctNDE9UGbcZmfWizRtd/ywcBzeui3uteqNuoDjzqu8KtngtqeTnByd0edi42sHaQQ2ZjATMLU0hLGILpRcqgVlX+0GHaxFrvftwFMmsLACy4f/fjLug9qZPB+v4amri4OEMXwSggDwzywCAPDPLAIA8M8sAgDwzywCAPNcsB1WATAIwjY7MxlAEoNCyTTnKvqu7/TBAEQRAEQRBE9cJosognJSWhZcuWWL9+Pdq3b19kvkajwcKFCxEcHIy0tDR4e3vjyy+/xHvvvcfPt7a2BsdxglrTZ8+eQSaTITs7G1OmTMHevXuhUqnQp08frF69GnK5XG95Ll26hGnTpiEsLAxOTk744osvMHbsWH7+hg0bsGjRIjx58gT169fHypUr0bJlyzJ/3orOVldRGEPGZmMog5acnBxYWFgYZN/GhFKphKWlpaGLYXDIA4M8MMgDgzwwyAODPDDIA4M8MMiDcTuo6LjMKJqInzt3Di1btsSDBw+KXWbVqlXYuHEjQkNDkZWVhcWLF2Pw4MH8OuHh4SgoKEBaWhqysrL4P5lMBgCYMmUKHj9+jPv37+P+/fuIjY3FzJkz9e4rLS0NPXr0wIgRI5Ceno7g4GBMnz4dly9fBgCEhoZi6tSp2LBhA9LT0zF06FD07t0bSqWygs1UPWaWppCaSpGdnwmpqdQgga22DNYOcoOVQUtSUtFhy2oiycnJhi6CUUAeGOSBQR4Y5IFBHhjkgUEeGOSBQR5qlgODB9gbNmzAkCFD8M0335S43OTJk3H79m34+PggLy8PSUlJkMlk/JOQK1euoHHjxjA1LRqMKZVKbNmyBQsXLoS9vT2cnZ3x7bff4vfff9cbFO/YsQMODg6YPHkypFIpOnbsiKFDh2LVqlUAgPXr1+P9999H69atYWJigunTp8PR0RFbt24ttvx5eXlQKBSCP2MmJSXF0EUwCoz9e6oqyAODPDDIA4M8MMgDgzwwyAODPDDIA4M81CwHBu+D3bVrVwwdOhRSqRTvv/9+scuJxWLIZDKEhISge/fu4DgOP/30E2rXrg2ABdg5OTl488038ejRI9SvXx9LlixBq1atcP/+fRQUFKBRo0b89ho0aICcnBxERkaiadOmgn2FhYUJltUuHxwczM8fM2ZMkfk3b94stvyLFy/GggULikwPDw+HXC5HQEAAoqOjkZeXB0tLS7i5ueH+/fsAgFq1aoHjODx79gwA4O/vj9jYWOTk5MDc3Byenp6IiIgAALi4uEAsFuPJkycAgHr16iEhIQHZ2dkwMzND3bp1cffuXQCAk5MTTE1NER8fDwDw8fFBYmIiMjMzkZ2dDY7jEBYWBgBwcHCApaUlHj9+DADw9vZGamoqMjIyIJFIUL9+fYSFhYHjONjZ2cHKygqxsbEAAE9PTygUCqSlpUEkEiEwMBB3796FWq2GjY0N7Ozs8OjRIwBAnTp1kJ2djdTUVABAw4YNce/ePahUKlhbW8PR0REPHz4EALi7uyM3N5d/ItagQQNERUUhPz8fcrkctWrVQlRUFADA1dUVKpUKiYmJAICAgAA8evQIubm5sLS0hLu7OyIjI3nfAPD06VNkZmYiPz8fcXFxUCqVMDc3h5eXF+7duwcAcHZ2hlQqRUJCAgDA19cXT58+RVZWFkxNTeHr64vw8HAAbHgCc3NzPslD3bp1kZycDIVCAalUioCAANy5cwcAYG9vD5lMxvv28vJCWlpasb6tra0RExMDAPDw8EBmZmaxvu3t7REdHc37ViqV/AOVwMBAREZGoqCgAFZWVnB2dsaDBw/47eXn5/O1+vXr18fDhw+Rl5cHmUwGV1dX/pitXbs2NBqN4JiNiYlBbm4uLCws4OHhIThmRSIRnj59yh+z8fHxUCqVMDMzg7e3d5X4lsvl/DHr5eWF9PR0pKenQywWo0GDBggPD0dmZibi4uJga2vLH7MeHh7IysqqkmPWmM4RERER8PPzq9HniMzMTNy5cwd+fn41+hyh9eDm5lajzxFZWVm4c+cObG1ta/Q5Ijc3F3fu3IGJiUmNPkeoVCr++KnJ5wgAfJlq8jlCKpUiPDwcGo2mxp4jMjMzkZOTw99HGNM5QvvbrSiMpg82wIaiOnnypN4+2Fry8vIgkUhw6tQp9O3bF+vXr8egQYMwY8YMpKen87XUq1atwoIFC3Dr1i3Ex8ejbdu2UKvVEItZpb1arYZUKsWZM2fQpk0bwT7GjRuH/Px8bNy4kZ8WHByMxYsXIyoqCr6+vpgzZ44gyB4+fDjMzMywfv36Ysudl5fHv1coFKhTp47R9cEmCIIgCIIgCIKoKbySfbDLg5mZGaRSKTp16oThw4fjzz//BAAsXboUwcHBcHNzg4WFBT799FN4eHjgwIEDfD9s3ebg2tfap2u6yGSyIk3HlUolv2xp84srt7W1teDPmNE+kavpkAcGeWCQBwZ5YJAHBnlgkAcGeWCQBwZ5YJCHmuWg2gTYM2bMwIwZMwTT8vLyYG9vDwCYO3curl+/XmS+hYUF/P39YWJiwjdBAFjTbFNTU/j5+RXZV8OGDQXLapdv2LBhmeYTBEEQBEEQBEEQNY9qE2AHBQXhl19+wenTp6HRaLBv3z78/fff+OCDDwCwpyIfffQRnj59iry8PCxcuBAKhQL9+vWDpaUlBg0ahFmzZiEpKQlJSUmYNWsWBg8erHcIpnfffRdPnz7FsmXLUFBQgJMnT2LLli18k/AxY8Zgy5YtOHnyJAoKCrBs2TI8e/YM/fr1q1InlYn2wUVNhzwwyAODPDDIA4M8MMgDgzwwyAODPDDIA4M81CwHRh1gy+VybNmyBQDQp08frFy5EuPGjYOdnR0WLlyInTt3olWrVgCA33//HT4+PmjSpAkcHBwQGhqKY8eO8V/m6tWrUa9ePTRq1Aj+/v7w8vLis4IDLCnD//73PwCsk/3Ro0exfft2ODg4YNy4cVixYgU6dOgAAOjUqRNWr16NiRMnws7ODn/99RcOHTr0Sh042mb1NR3ywCAPDPLAIA8M8sAgDwzywCAPDPLAIA8M8lCzHBhVkrPC/Prrr7C3t8fAgQMNXZQKp6I701c0d+7coSbvIA9ayAODPDDIA4M8MMgDgzwwyAODPDDIA4M8GLeDGpPkjOM4XLlyBd27dzd0UQiCIAiCIAiCIAiiVIy6BvtVxthrsLOysiCXyw1dDINDHhjkgUEeGOSBQR4Y5IFBHhjkgUEeGOSBQR6M20GNqcEmDEtaWpqhi2AUkAcGeWCQBwZ5YJAHBnlgkAcGeWCQBwZ5YJCHmuWAAmxCLxkZGYYuglFAHhjkgUEeGOSBQR4Y5IFBHhjkgUEeGOSBQR5qlgMKsAm9SCQSQxfBKCAPDPLAIA8M8sAgDwzywCAPDPLAIA8M8sAgDzXLAfXBNhDG3gebIAiCIAiCIAjiVYf6YBNVQlhYmKGLYBSQBwZ5YJAHBnlgkAcGeWCQBwZ5YJAHBnlgkIea5YACbEIv1LCBQR4Y5IFBHhjkgUEeGOSBQR4Y5IFBHhjkgUEeapYDCrAJvdjZ2Rm6CEYBeWCQBwZ5YJAHBnlgkAcGeWCQBwZ5YJAHBnmoWQ4owCb0Qv3CGeSBQR4Y5IFBHhjkgUEeGOSBQR4Y5IFBHhjkoWY5oACb0EtMTIyhi2AUkAcGeWCQBwZ5YJAHBnlgkAcGeWCQBwZ5YJCHmuWAAmyCIAiCIAiCIAiCqAAowCb04uHhYegiGAXkgUEeGOSBQR4Y5IFBHhjkgUEeGOSBQR4Y5KFmOaAAm9BLZmamoYtgFJAHBnlgkAcGeWCQBwZ5YJAHBnlgkAcGeWCQh5rlgAJsQi9paWmGLoJRQB4Y5IFBHhjkgUEeGOSBQR4Y5IFBHhjkgUEeapYDCrAJvYhEIkMXwSggDwzywCAPDPLAIA8M8sAgDwzywCAPDPLAIA81y4GIq0mjfhsRCoUCNjY2yMjIqFFp6wmCIAiCIAiCIIyFio7LqAab0Mvdu3cNXQSjgDwwyAODPDDIA4M8MMgDgzwwyAODPDDIA4M81CwHFGATelGr1YYuglFAHhjkgUEeGOSBQR4Y5IFBHhjkgUEeGOSBQR5qlgMKsAm92NjYGLoIRgF5YJAHBnlgkAcGeWCQBwZ5YJAHBnlgkAcGeahZDijAJvRib29v6CIYBeSBQR4Y5IFBHhjkgUEeGOSBQR4Y5IFBHhjkoWY5oACb0Et0dLShi2AUkAcGeWCQBwZ5YJAHBnlgkAcGeWCQBwZ5YJCHmuWAAmyCIAiCIAiCIAiCqAAowCb0UqdOHUMXwSggDwzywCAPDPLAIA8M8sAgDwzywCAPDPLAIA81ywEF2IRelEqloYtgFJAHBnlgkAcGeWCQBwZ5YJAHBnlgkAcGeWCQh5rlgAJsQi8pKSmGLoJRQB4Y5IFBHhjkgUEeGOSBQR4Y5IFBHhjkgUEeapYDCrAJgiAIgiAIgiAIogIQcRzHGboQNRGFQgEbGxtkZGTA2tra0MUpAsdxEIlEhi6GwSEPDPLAIA8M8sAgDwzywCAPDPLAIA8M8sAgD8btoKLjMqrBJvQSGRlp6CIYBeSBQR4Y5IFBHhjkgUEeGOSBQR4Y5IFBHhjkoWY5oACb0EtBQYGhi2AUkAcGeWCQBwZ5YJAHBnlgkAcGeWCQBwZ5YJCHmuWAAmxCL1ZWVoYuglFAHhjkgUEeGOSBQR4Y5IFBHhjkgUEeGOSBQR5qlgOjCbCTkpLg6+uL0NBQvfM1Gg3mz5+POnXqQC6Xo1GjRti2bZtgme+++w7u7u6QyWRo3749IiIi+HnZ2dkYPXo0HBwcYGNjgxEjRiArK6vY8ly6dAktWrSAXC6Ht7c3goODBfM3bNgAX19fyGQyNGvWDBcuXHjxD2+EODs7G7oIRgF5YJAHBnlgkAcGeWCQBwZ5YJAHBnlgkAcGeahZDowiwD537hxatmyJBw8eFLvMqlWrsHHjRoSGhiIrKwuLFy/G4MGD+XU2bNiAFStW4MiRI0hJScEbb7yB/v37Q5vDbcqUKXj8+DHu37+P+/fvIzY2FjNnztS7r7S0NPTo0QMjRoxAeno6goODMX36dFy+fBkAEBoaiqlTp2LDhg1IT0/H0KFD0bt371dqfLeSvouaBHlgkAcGeWCQBwZ5YJAHBnlgkAcGeWCQBwZ5qFkODB5gb9iwAUOGDME333xT4nKTJ0/G7du34ePjg7y8PCQlJUEmk8HS0hIAsG7dOkyaNAmBgYEwNzfHkiVLEBsbi9DQUCiVSmzZsgULFy6Evb09nJ2d8e233+L333/XGxTv2LEDDg4OmDx5MqRSKTp27IihQ4di1apVAID169fj/fffR+vWrWFiYoLp06fD0dERW7duLbb8eXl5UCgUgj+CIAiCIAiCIAji1UFq6AJ07doVQ4cOhVQqxfvvv1/scmKxGDKZDCEhIejevTs4jsNPP/2E2rVrAwDCwsIENdImJiaoV68ebt68CXt7exQUFKBRo0b8/AYNGiAnJweRkZFo2rSpYF9hYWGCZbXLa5uJh4WFYcyYMUXm37x5s9jyL168GAsWLCgyPTw8HHK5HAEBAYiOjkZeXh4sLS3h5uaG+/fvAwBq1aoFjuPw7NkzAIC/vz9iY2ORk5MDc3NzeHp68s3hXVxcIBaL8eTJEwBAvXr1kJCQgOzsbJiZmaFu3bq4e/cuAMDJyQmmpqaIj48HAPj4+CAxMRGZmZnQaDTgOA5hYWEAAAcHB1haWuLx48cAAG9vb6SmpiIjIwMSiQT169dHWFgYOI6DnZ0drKysEBsbCwDw9PSEQqFAWloaRCIRAgMDcffuXajVatjY2MDOzg6PHj0CANSpUwfZ2dlITU0FADRs2BD37t2DSqWCtbU1HB0d8fDhQwCAu7s7cnNzkZyczH8HUVFRyM/Ph1wuR61atRAVFQUAcHV1hUqlQmJiIgAgICAAjx49Qm5uLiwtLeHu7s5nN6xVqxYA4OnTp8jPz0d+fj7i4uKgVCphbm4OLy8v3Lt3DwBr7iKVSpGQkAAA8PX1xdOnT5GVlQVTU1P4+voiPDwcAODo6Ahzc3PExcUBAOrWrYvk5GQoFApIpVIEBATgzp07AAB7e3vIZDLet5eXF9LS0or1bW1tjZiYGACAh4cHMjMzi/Vtb2+P6Oho3rdSqURKSgoAIDAwEJGRkSgoKICVlRWcnZ3x4MED5OfnIy0tDfn5+UhKSgIA1K9fHw8fPkReXh5kMhlcXV35Y7Z27drQaDSCYzYmJga5ubmwsLCAh4eH4JgViUR4+vQpf8zGx8dDqVTCzMwM3t7eVeJbLpfzx6yXlxfS09ORnp4OsViMBg0aIDw8nD8WbG1t+WPWw8MDWVlZVXLMGss5Ij8/HxEREfDz86vR54j8/HzcuXMHfn5+NfocofXg5uZWo88RKpUKd+7cga2tbY0+R0ilUty5cwcmJiY1+hwhl8v546cmnyMcHR35MtXkc4SbmxvCw8Oh0Whq7DkiPz8fOTk5fKxhTOeIis5wblTjYItEIpw8eRLt27cvdpm8vDxIJBKcOnUKffv2xfr16zFo0CBIpVKEhISgY8eO/LJt27ZF165d0b59e7Rt2xZqtRpiMau0V6vVkEqlOHPmDNq0aSPYx7hx45Cfn4+NGzfy04KDg7F48WJERUXB19cXc+bMEQTZw4cPh5mZGdavX19sufPy8vj3CoUCderUMdpxsJ89ewYXFxdDF8PgkAcGeWCQBwZ5YJAHBnlgkAcGeWCQBwZ5YJAH43ZQ0eNgG7wGu7yYmZkBADp16oThw4fjzz//xKBBgyCTyYo091YqlbCysoJMJuPfy+Vy/jWgP6OdTCZDenq63m1p5+vbl6OjY4nl1pb9ReE4DglPM5GSroRGU7nPRaKiHsDXV1Np2xeJRLCSmcLbw85oB50HWPI9Yz0ZVCXkgUEeGOSBQR4Y5IFBHhjkgUEeGOSBQR5qloNqE2DPmDEDALB06VJ+Wl5eHuzt7QGw6v2wsDC88847ANhYa/fv30fDhg3h7+8PExMThIWFoUWLFgBY02xTU1P4+fkV2VfDhg0REhIimBYeHo6GDRsK9lV4fo8ePSro0xbl1t2nWLPxKuKeVk3f7dycXJhbxFb6fuxtLTC0byN0aedb6fsiCIIgCIIgCIKoTKpNE/E9e/ZgyJAhOHToENq0aYMDBw7g/fffx9GjR9GqVSsEBwdj3rx5OHDgAPz9/TF37lzs3bsX4eHhMDExwfDhwxEXF8cP7fXee+/B09MTf/zxR5F9paSkwNfXF/PmzcPkyZNx9uxZ9OnTB3v27EGHDh1w/Phx9OvXD3v27EGbNm2watUqLFy4EFFRUXzAXxrlaYrw6HEaZiwKgV9dB7zbrT7ca1tDIqncWl+OAyqzYlmj4ZCcqsTh0CicuhSDmRNbo01zz8rb4QuiVqshkUgMXQyDQx4Y5IFBHhjkgUEeGOSBQR4Y5IFBHhjkwbgdVHQTcXBGBADu5MmT/HuZTMZt3ryZfx8cHMzVq1ePs7a25po1a8YdPnyYn6fRaLgffviB8/b25uRyOdehQwcuIiKCn69QKLgPPviAc3Fx4ezs7LhRo0ZxWVlZ/PwGDRpw33zzDf/+ypUrXKtWrTgrKyuubt263O+//y4o66ZNmzh/f39OJpNxzZs35y5evFiuz5qRkcEB4DIyMkpddu2Wq9yIj3dyeXkF5drHy5CZmVkl+9FoNNycJce4WYuPVsn+yktkZKShi2AUkAcGeWCQBwZ5YJAHBnlgkAcGeWCQBwZ5MG4H5YnLyoJRNRHnClWmL126FKampvz7MWPGFMnerUUkEmHGjBl8U/LCWFlZYe3atVi7dq3e+YWbfDdr1gznzp0rtqzDhg3DsGHDip1fkdyJSMTrDWvD1LTqvi6NpvL6X+siEonQ8g13rN1yDRzHGV1/bN3EdDUZ8sAgDwzywCAPDPLAIA8M8sAgDwzywCAPNcuBwcfBLg6O43DlyhV0797d0EUxOHn5KljJTEtfsAKRSqsumLeSmYEDkJenqrJ9lhVtgryaDnlgkAcGeWCQBwZ5YJAHBnlgkAcGeWCQh5rlwGgDbJFIhPXr1/NZv4niUalU6NevH4KCgvTOz8rKwqxZs9CiRQu0aNECM2fOhELBkqX5+/sX+evYsSPMzMxKXK8wN2/eRL9+/fDGG29g/Pjx/Ph8pc0DKrev98vi6upq6CIYBeSBQR4Y5IFBHhjkgUEeGOSBQR4Y5IFBHmqWA6MNsImyoVAo8PHHHyM8PLzYZf73v/9h165dePvtt9GqVSvs3r0b3377LQDgo48+4v8GDBgAAAgKCkJ2dnaJ6+mSk5ODCRMmIDU1FX379sW5c+cwe/bsUudVB+7fv2/oIhgF5IFBHhjkgUEeGOSBQR4Y5IFBHhjkgUEeapYDo+qDTZQPpVLJ1zaX1OwiPT0dHTp0wNdffw0AOH/+PP79918AwKRJk/jlRo0aBS8vL8yZMwe5ubklrqfLpUuXkJqaii+++ALDhw9HcnIyQkJCkJGRgevXrxc7z8bGpiJ1EARBEARBEARBGBQKsKsxGo0Gffr0wYQJEzBo0CCoVPr7MK9evZp//eDBAygUCrz55puCZUJCQnDhwgWsWrWKTyxXlvUAIDo6GgDg7u4OAKhTpw40Gg1iYmJKnNe4ceMX/ehVRu3atQ1dBKOAPDDIA4M8MMgDgzwwyAODPDDIA4M8MMhDzXJAAXY1Ri6X48svvyzz8k+ePMHEiRMhEokwYcIEwbzffvsNdevWRadOnQAIM7qXtB7AatIB8IG5iYkJANY8vKR51YGqyqZu7JAHBnlgkAcGeWCQBwZ5YJAHBnlgkAcGeahZDqgPdg0hLi4OQ4cORWxsLBYsWICGDRvy8x49eoTr16/jnXfe4YfJ0qbSL2k9Lebm5gCA/Px8wX8LC4sS51UHnj17ZugiGAXkgUEeGOSBQR4Y5IFBHhjkgUEeGOSBQR5qlgOqwa4BpKamYsSIEXj69Cm+++479O7dWzBfO95327Zty7WeFk9PTwDA48ePAbCgXCQSwdPTE4mJicXOIwiCIAiCIAiCeJWgAPsVJDMzE5s2bYKfnx86d+6Mr776CvHx8QgMDERcXBxWr14NuVyOESNGAADCw8MhkUgQEBDAb0Mul2PatGnFrnfixAncu3cPQ4cOxVtvvQUbGxusX78esbGxOH78OFq3bg0bG5sS51UH/P39DV0Eo4A8MMgDgzwwyAODPDDIA4M8MMgDgzwwyEPNckBNxF9BMjIysHz5chw5cgSpqak4duwYACAsLAzLly/H8uXLsX79en755ORk2Nra8v2kASAhIaHE9UJCQrB8+XKkp6dDLpdj/fr1cHR0xM6dO9GyZUssXrwYAEqcVx2IiYkxdBGMAvLAIA8M8sAgDwzywCAPDPLAIA8M8sAgDzXLAdVgvyKcOHGCf+3u7o6vvvoKsbGxsLe3x71790pc99dffy0yzdrausT1lixZgiVLlvDvGzdujJ07d+pdtqR5xk5ubq6hi2AUkAcGeWCQBwZ5YJAHBnlgkAcGeWCQBwZ5qFkOqAb7FSQ5ORk7duzAqFGjXngb2mzfNZ3qkoytsiEPDPLAIA8M8sAgDwzywCAPDPLAIA8M8lCzHFANdjVAIhZDpS57antHR0ds374dEomk3PtSqTUQi0QQS0yg0XDQcBykksp9DlOgYp9NKjW+5z0eHh6GLoJRQB4Y5IFBHhjkgUEeGOSBQR4Y5IFBHhjkoWY5ML6IhiiCWy0rRDxIKdc6LxJcq9UaPHiUipDTDxBy6gFCTj/Ag0epUJcjuH8R7kUlw9lBBqm0/GWubCIiIgxdBKOAPDDIA4M8MMgDgzwwyAODPDDIA4M8MMhDzXJAAXY1oG1zT0RGp2Df0YhKC3ZVag2iHqUi8mEKCgrUAICCAjUiH6Yg6lFquWrQywrHcbh2KwEnzkcjqAUN20UQBEEQBEEQRPWGmohXA9o090D4/SSs/fMa/tpzG64uVhCLRRW6Dw3HISo6FWoNV2SeRCyCr7c9xKKK2yfHAcmpSiSnKdG0QS2837thhW27InFxcTF0EYwC8sAgDwzywCAPDPLAIA8M8sAgDwzywCAPNcsBBdjVAJFIhA+HvoFOrb1x6UY8klOV4LiigfDLkJmdjz0hxTfdWPRpB8hlpsXOfxHq+zrijcauaOjvDEkl9/N+UUQV+FChOkMeGOSBQR4Y5IFBHhjkgUEeGOSBQR4Y5KFmOaAAu5ogEong6+0AX2+HStl+fr4aP667gPSMoin0bW3M8fnE1jA1rXmHy9OnT+Ho6GjoYhgc8sAgDwzywCAPDPLAIA8M8sAgDwzywCAPNcuBcVYbElVOgUqNaWNa6J03ZVRznDgXjSOhUVVcKoIgCIIgCIIgiOqDiKvotsZEmVAoFLCxsUFGRgasra0NXRwAQG6eCotXncGK3y4hPSMXtjbmmDa6BaaOaYG27/6Ge1HJ+GDI61j6ZVdYyc0MXdwqIS8vD2ZmNeOzlgR5YJAHBnlgkAcGeWCQBwZ5YJAHBnlgkAfjdlDRcRnVYBM85mZSfD6hNZ79+xmeXv8Uz/79DJ9PbA1Lcym6BPkAANb9+S8avb0aJ849NHBpq4b4+HhDF8EoIA8M8sAgDwzywCAPDPLAIA8M8sAgDwzyULMcUIBNCJBZmsLUVAJF2hOYmkogszSFpYUpli/ojtBto+DtYYuYuAx0en8jpnxxAFnZeYYucqWiVCoNXQSjgDwwyAODPDDIA4M8MMgDgzwwyAODPDDIQ81yQAE2UWbatfTCrZCJmDi8GQBg1YYraNLlF5y++MiwBatEjLUpS1VDHhjkgUEeGOSBQR4Y5IFBHhjkgUEeGOShZjmgPtgGwhj7YOuiUqkglRafNfzYmQcY+9lexMZnQCQCpo1pgf/N7ARLi4odysvQlOahpkAeGOSBQR4Y5IFBHhjkgUEeGOSBQR4Y5MG4HVAfbKJKuHfvXonzO7f1we2jEzFu8OvgOGB58CU07foLzl+NraISVg2leagpkAcGeWCQBwZ5YJAHBnlgkAcGeWCQBwZ5qFkOKMAmXhhrK3Os+643Dm0cCrdaVrgfnYq2/X/H59+EIDe3wNDFIwji/+ydd3gURR+A30vvuTQCJIHQQu9delGadJAqAqJUEUSRrggKiIUikV4EVEAQEEVAOkiTagglhBoghJS7S7+23x9r7iOkkMBdCpn3efIAO1t+9zK7udmZ+Y1AIBAIBAKBIE8RDWxBphQrVizH+7ZvVYGQv0bxVq+aGI0S85f+Te0Oyzh9PsKCEeYNufHwMiM8yAgPMsKDjPAgIzzICA8ywoOM8CAjPMgID0XLgWhgCzIlt3MklO6OrP22OztX96N4MReu3oimcbdVTJ23n9RUvYWitDwFda5IXiM8yAgPMsKDjPAgIzzICA8ywoOM8CAjPMgID0XLgWhgCzLlwYMHz3Vc51crcvmvUfTvVh2jUeKL745Sr9Nyzv37fOfLb57Xw8uG8CAjPMgIDzLCg4zwICM8yAgPMsKDjPAgIzwULQeigS0wO54eTmxc3JOty9/Ax8uJkGtRNOi8gk++PohWW3h7swUCgUAgEAgEAoEgO8QyXflEQV+mKyUlBQcHhxc+z+OYREZP+50tu0IBqFW1OOu+7UaNysVf+Nx5gbk8FHaEBxnhQUZ4kBEeZIQHGeFBRniQER5khAcZ4aFgO3hpl+l6/Pgx5cuX59ChQ1nus3TpUipWrIirqytBQUEEBwebyoxGIy4uLjg7O+Pi4mL6SUxMBCAxMZEhQ4bg5eWFu7s7gwYNIiEhIctrnTp1ioYNG+Li4kKZMmVYtWpVuvJ169ZRvnx5nJ2dqVevHidOnHgxAQWMyMhIs5zHx8uZzd+/wabgXnh5OHLhciT1Oi3n80VH0OsNZrmGJTGXh8KO8CAjPMgIDzLCg4zwICM8yAgPMsKDjPAgIzwULQcFooF9/PhxGjduTHh4eJb7bN++ncmTJ7Nu3To0Gg3r1q1j6tSpbN26FYDQ0FB0Oh1xcXEkJCSYfpydnQEYM2YM9+7dIywsjLCwMO7evcvHH3+c6bXi4uLo2LEjgwYNQqVSsWrVKsaPH8/p06cBOHToEO+99x7r1q1DpVIxYMAAunTpQlJSkpnN5B/ZvXx4Ht7oXI3L+0fTrV0ldDoj0+YfoFGXlVy+FmXW65gbc3sorAgPMsKDjPAgIzzICA8ywoOM8CAjPMgIDzLCQ9FykO8N7HXr1tG/f38+//zzbPd78OABkyZNolGjRigUCho3bkyrVq04cuQIAGfOnKFGjRrY2dllODYpKYmNGzfy2Wef4enpSbFixZg3bx5r1qzJtFG8detWvLy8GD16NDY2NrRu3ZoBAwawZMkSAFauXEnfvn1p0qQJtra2jB8/Hm9vbzZt2pRl/KmpqWg0mnQ/BZnMPL4ovj4ubFvRhw2LeqB0d+Dsvw+p03EZ84KPYTAYzX49c2AJD4UR4UFGeJARHmSEBxnhQUZ4kBEeZIQHGeFBRngoWg7yPV96u3btGDBgADY2NvTt2zfL/UaNGpXu31FRURw5coRvvvkGkBvYycnJ1K9fn9u3b1O5cmXmzp3LK6+8QlhYGDqdjurVq5uOr1KlCsnJyVy/fp1atWqlO/fly5fT7Zu2f9ow8cuXLzN06NAM5RcvXswy/jlz5jBz5swM20NDQ3FxcaFSpUrcunWL1NRUnJyc8PPzIywsDIDixYsjSRKPHj0CoGLFity9e5fk5GQcHBwoXbo0165dA8DX1xcrKysePnwIQIUKFXjw4AGJiYnY29tTtmxZrly5AoCPjw92dnbcv38fgHLlyhEVFUV8fDw2NjZIksTly5cB8PLywsnJiXv37gFQpkwZYmNjUavVWFtbU7lyZS5fvowkSXh4eODq6srdu3cBKF26NBqNhri4OBQKBQO618DfR8+Mb09z5HQkk+b8xY+/nmP2hPo0b1yFxMRE1Go1NjY2VKhQgatXr6LX63Fzc8Pb25ubN28C4O/vT0pKCtHR0ab/gxs3bqDVanFxcaF48eLcuHEDgJIlS6LX64mKknvMK1WqxO3bt0lJScHJyQl/f3+uX79u8g3ytAVra2u0Wi0REREkJSXh4OBAYGAgV69eBeQ1/WxsbEyZEcuXL09kZCQJCQnY2dlRvnx5QkPl+efe3t44ODgQESGvD162bFmio6PRaDTY2NhQqVIlQkJCAPD09MTZ2dnkOzAwkLi4uCx9u7m5cefOHQBKlSpFfHy8yXfVqlW5cuUKBoMBd3d3PD09uXXrFgABAQEkJSURExMDQNWqVbl+/To6nQ5XV1eKFStGeHg4kiQRFxeHVqvl8ePHAFSuXJmbN2+SmpqKs7MzJUuWNNXZEiVKYDQa09XZO3fukJKSgqOjI6VKlUpXZxUKhWnoUIUKFbh//z5JSUnY29tTpkyZPPHt4uJiqrOBgYGoVCpUKhVWVlZUqVKF0NBQDAYDERERKJVKbt++bfKdkJBAbGwsANWqVbNYnS0oz4jU1FSuXbtGUFCQRZ4RT9dZDw8Pk++AgAASExPzxPeznhGpqamEhIQQFBRUpJ8RaR78/PyK9DNCp9MREhKCUqks0s8IOzs7QkJCsLW1LdLPCKVSaao/RfkZ4efnZ4qpKD8jKlWqRGhoKEajscg+IyRJIjk52dTWKEjPiLR711wUqCRnCoWCgwcP0rJly2z3i4yMpFOnTnh7e/P7779jY2PDhAkTUKlUpl7qJUuWMHPmTC5dusT9+/dp1qwZBoMBKyu5095gMGBjY8PRo0dp2rRpuvMPGzYMrVbLDz/8YNq2atUq5syZw40bNyhfvjxTpkxJ18h+8803sbe3Z+XKlZnGnJqaSmpqqunfGo2GgICAApvkLCQkhGrVqln0GpIksW7LBd7/9E808ak42Nvw/Red6PV6VexsrFFpUlC6OaDTG3B2ytu3XolJWmxtrIlVJeGpdMqXGAoSeVEfCgPCg4zwICM8yAgPMsKDjPAgIzzICA8ywkPBdvDSJjnLKSdPnqR+/fpUrFiRnTt3mhYt//rrr1m1ahV+fn44Ojry4YcfUqpUKX7//XfTPOwnh4On/d3V1TXDNZydnTMMHU9KSjLt+6zyzLC3t8fNzS3dT1FHoVAw+I3ahPw1itealyMwQEmnNkHMCz6Gb535+Naej2+d+Xy59DgpqXm3vFdKqp4vvz+Ob535lKj7db7EIBAIBAKBQCAQCAof+T5EPDesXr2a9957j88++4wJEyakK5s6dSq9evWidu3apm2pqak4OjpSsWJFbG1tuXz5Mg0bNgTkodl2dnYEBQVluE61atXYu3dvum2hoaGmty7VqlUzDWd4srxjx45m+ZwFAW9v7zy7VkBJd/7cMJCwW7EsWnOK2QuPmMpU6hQ++/YwkgS9OlZm2+4rFo2lZ8cqbNl1mVmZxAAwcUSTItmTnZf1oSAjPMgIDzLCg4zwICM8yAgPMsKDjPAgIzwULQeFpoG9detWRo4cyc6dO2nXrl2G8pCQEI4ePcrmzZvx8PBg3rx5aDQaunfvjpOTE3369GHSpEls3rwZgEmTJtGvXz8cHR0znKtHjx5MnDiRBQsWMHr0aI4dO8bGjRvZsWMHAEOHDqV79+688cYbNG3alCVLlvDo0SO6d+9uWQl5SF6vU6dQKAj0V/Ld2tOZli9ec4qPRzZhybozRMdaJlu7t6cTH41owuIsYli0+hRTxzS3yLULOgV13cK8RniQER5khAcZ4UFGeJARHmSEBxnhQUZ4KFoOCnQD28XFhWXLljFgwABmzpyJXq+nZ8+e6fYZOHAgS5cuZc2aNUyYMIGaNWuSmJhIgwYN+Ouvv/D09AQgODiYCRMmUL16dbRaLV27duW7774znadq1aoMGDCAKVOm4OXlxb59+3j//feZMWMGPj4+LFq0iFatWgHQpk0bgoODGTlyJBEREVStWpXdu3ebrvUykJbIKS9RaVJQqVMyL1OnEKdOYdzbjXjwKN4i1y/p60qsKjnbGNTxKfh4OVvk+gWZ/KgPBRHhQUZ4kBEeZIQHGeFBRniQER5khAcZ4aFoOShQDeyn8619/fXXppTuly5dyvZYT09P1qxZk2W5q6sry5cvZ/ny5ZmWPz3ku169ehw/fjzL8w0cOJCBAwdmG5MgdyjdHFC6O2TawFW6O1DMy5mpYy3bg6zVGrKNwd216Lx9EwgEAoFAIBAIBLmjwCY5kySJM2fO0KFDh/wOpUhStmzZPL+mTm9g7NCGmZaNHdoQnd6QrzGMGdyAmDjLDE8v6ORHfSiICA8ywoOM8CAjPMgIDzLCg4zwICM8yAgPRctBgW1gKxQKVq5ciYuLS36HUiRJW18vL3F2smPy6GbMGN8CpbvcU6x0d2DG+BZMHt0sT5KLZRXD9PebM3ZIQ3oO38T1m3nvJr/Jj/pQEBEeZIQHGeFBRniQER5khAcZ4UFGeJARHoqWgwI1RFxQcNBoNPlyXQd7GyaOaMLUMc1Rx6fg7iqvg+1gn3dV9ckY4tRJeLg7kZyq4+0Pd3DibASvDVjP37++TcniRWeptfyqDwUN4UFGeJARHmSEBxnhQUZ4kBEeZIQHGeGhaDkQDexCRlJUFMmxMUhGo0Wvk3znFtFWCoudX6FQYOviiqu/PwpF+uuk9VSnJROzs7O2WBxZkRZDUkI0vj5lsLOz5vsvXufSlUeE3Yql3cANHPllCB7KjFnoX0bS1psv6ggPMsKDjPAgIzzICA8ywoOM8CAjPMgID0XLgUJ6OrOYIE/QaDS4u7ujVqtxc3t2T2h06GUurliK5u5tyweXhzj5FKNSn/6UbtUmv0PJEbfvxfFKt1U8jEqgaf1S7N34Jo6OtvkdlkAgEAgEAoFAIHgOctsuexaigZ1P5OY/UnPnDocnf4hb6UDKd+mOq38ACivLTp9PSIjHxcXVcheQJJJjorm9bw/3TxyjwYeT8Wv8iuWu95yEhIRQrVq1dNv+vfKI5r3XoFKn0LltENtW9MHGJu972fOSzDwURYQHGeFBRniQER5khAcZ4UFGeJARHmSEh4LtwNwN7KLTV1+IuX1gH9YODjT5dDY29vZ5ck1Jo8HVDBUsO1z9/fGpUZPUT9Tc3L2rQDawM6N6ZV9+W92PV/uv57e/rvPux7+x6quuGYa6CwQCgUAgEAgEgqJFgc0iLvg/MZdD8K1dN88a14Bp/XFLo1AoKNmwMdGhIRnWQS8IeHp6Zrq9aYPSbAruhbW1gjWbLzB57l95HFnekpWHoobwICM8yAgPMsKDjPAgIzzICA8ywoOM8FC0HIgGdiFAn5qCnYV7k5/G2jrvhjzbubmBJGFITc2za+aU7JaJ6/JaJVbM6wLAvODjfLP877wKK88Ry+XJCA8ywoOM8CAjPMgIDzLCg4zwICM8yAgPRcuBaGC/BOj1erp3707z5s0zLU9ISGDSpEk0bNiQhg0b8vHHH2dIlb93714qVqzIli1bAEhOTs7RcWls2bKFDh06ULt2bfr378+VK1dMZQ8fPmTEiBHUrFmT5s2bs2nTpnTHFuSh1Xfv3s22fEif2syd3BaACbP2sn7rxbwIK895loeigvAgIzzICA8ywoOM8CAjPMgIDzLCg4zwULQciAZ2IUej0TBu3DhCQ0Oz3OeLL77g119/5dVXX+WVV15h+/btzJs3z1R+4MABJk2alOvj0jh79izTpk3D1dWV3r17c+XKFUaPHg2AwWDgnXfe4ezZs/Tv3x9XV1c++eQTrl+/boZPXzCYOLIJH7zTGIChH+7gjwMvz2cTCAQCgUAgEAgEOUckOSvEJCUl0bp1a+zt7XF2ds5yP5VKRatWrZg9ezYAf//9N+fOnQMgODiYhQsXEhgYSGJioukYJyenbI97ksqVK7Njxw6KFStGeHg4f/zxB7a28tJV//zzD2FhYUyePJlevXoxcOBA4uPjCQgIMJsHSxIYGPjMfRQKBfOnvUpUTCIbtl2i1/DN7P/5LRrXLRyfMSfkxENRQHiQER5khAcZ4UFGeJARHmSEBxnhQUZ4KFoORA92IcZoNNK1a1e2b9+OUqnMcr/g4GCWLl0KQHh4OBqNhnLlygHg4+PDokWLGD58eLpjdDpdtsc9iZOTE5UqVSIsLIyBAweSmJjI/PnzAbh69SoAR44coUGDBrRr147Tp0/j6Oj4wp8/L1CpVDnaz8rKitVfdaVDq/Ikp+jp9NZGLl+LsmxweUhOPbzsCA8ywoOM8CAjPMgIDzLCg4zwICM8yAgPRcuBaGAXYlxcXJg+fTo+Pj452v/hw4eMHDkShULBiBEjAOjduzft2rXLsK9Op8v2uMzw9fVl9uzZuLm5MW7cOJKSkky94vfu3eOzzz6jbt26zJkzx9TwLujk5mFga2vNlqVv0KiOP3HqFNoNXM/d+zk/viBTlB6K2SE8yAgPMsKDjPAgIzzICA8ywoOM8CAjPBQtB7keIp6cnMzZs2eJjIzEYDBQrFgx6tWrh6urqyXiE5iJiIgIBg0axIMHD5g1a9YzF3pPSzyWm+MCAwNNQ83nzJnD33//beqpHjJkCL169aJKlSp0796dU6dOUalSJfN9QAthZZW7d1DOTnbsWtufZj1XcyUsmnYDN3B06xC8PbMewl8YyK2HlxXhQUZ4kBEeZIQHGeFBRniQER5khAcZ4aFoOcjxJ42Li+Ptt9/Gw8ODFi1a0KdPH/r370/btm3x8fFh6NChxMbGWjJWwXMSGxvLoEGDiIyM5Msvv6R3797PPMbV1TXHx+3cuZN27doREhICQHx8PADOzs6ULVvWFAPIGc8BHBwcXvhz5QVVqlTJ9TFeHk7s2fAmASXduHojmk5v/UhCYsFbgiw3PI+HlxHhQUZ4kBEeZIQHGeFBRniQER5khAcZ4aFoOchRAzsuLo5GjRqxdu1atFotkiSl+9Fqtaxbt44GDRoQFxdn6ZgFzyA+Pp7g4GD++usvAGbMmMH9+/epVKkSERERBAcH88MPPzzzHNkdd+DAAYKDg1Gr1VSqVIn79+8zceJE5s2bx+rVqylVqhR16tShcePG+Pn5sXLlSubMmcP06dNxcHCgWbNmFvdgDrLLzp4dASXd2bPhTTyVjpy+cJ9ewzej1erNHF3e8bweXjaEBxnhQUZ4kBEeZIQHGeFBRniQER5khIei5SBHDezPPvuMsLAwHBwcmDZtGocPH+bq1atcu3aNY8eOMWfOHDw9Pbl16xZz5syxdMyCZ6BWq1m4cCF79uwhNjbW1NC+fPkyCxcuZOHChaxcuTLbc8TFxWV73N69e1m4cCEqlYqgoCAWLFgAwM8//0ytWrVYsWIF9vb22NnZsXz5cipXrsyPP/6I0Wjku+++o2TJkpYTYEaMRuNzH1u5gg9//DAAJ0db9hwOZ8iEHS90vvyksMZtboQHGeFBRniQER5khAcZ4UFGeJARHmSEh6LlQCFJkvSsncqXL8+tW7f49ddf6dKlS6b7HDhwgLZt21K+fPmXao1jS6HRaHB3d0etVuPm5pbtvvveG0Hxeg2o/tbQHJ9/48aN3L17l8mTJz9XfMnJyXmW6Tvi2BHOfDufzhu3YFPAho5HRETg7+//Quf482AYnYf+hF5v5P23G/LtJ+1Nc9wLC+bw8DIgPMgIDzLCg4zwICM8yAgPMsKDjPAgIzwUbAe5aZflhBz1YD948AB7e/ssG9cArVu3RqlUEhER8cJBCV6M6Ohotm7dyuDBg5/7HHZ2duYLqBCT3fJnOaV9qwqs/aYbAAtXnWLukmMvfM68xhweXgaEBxnhQUZ4kBEeZIQHGeFBRniQER5khIei5SBHDeyUlBTc3d2fuZ+zszOpqYU7mVNBRGFtjaTP+fxdb29vtmzZQokSJXJ9LaNBj2Q0Iul1SEYjRoPl5w0b/lsSzMom10ntLc7t27fNcp4B3Wvw7SfycmhT5u1n5U9nzXLevMJcHgo7woPMw4cP8zuEAoGoDzLCg4zwICM8yAgPMsKDjPBQtBzkuEVja2tryTgE2eBSoiSxYbkbdm9tbZ3r6xgNBlTh4ahv38Ko02Fla4t7YBmU5cpj9RznyylxYddx9PYpkA1sczJuWGOiohOZs+QYwyftwtvDiW7tK+d3WIJCQmKSFlsba1SaFJRuDuj0Bpyd8nakSVoMru7F0WoN+RKDQCAQCAQCQUEmxy2a6OhoOnbs+Mx9BObH75Wm/LPgK27t/ZPAtq+hsMA6ckaDHlV4OHFPNOSNOp3p38py5bCyNm8DWJIkHv97iXuHDhDYroNZz20uSpUqZdbzff5xG6JiEln183n6jvmFvRvepHmjQLNewxJk5kGtSWHP4RucufgAdXxKPkRlXhQKBR7uDjSo5cdrzcvh4myfYR9z14eckpKq58vvj7NozSlU6hSU7g6MHdqQyaOb4WCfNy+mCkIMBY38qg8FDeFBRniQER5khAcZ4UFGeChaDnL8rSglJYU///zzmfsVtuRNhQH/ps2JvhzChWVLuLrpR1xK+pm9kS1JEnE3riNlkuFPYWWFR/kgs/7fSpJEckw0iZEP8a5Sjcp9+pvt3OYkISHBLMkO0lAoFCyd8zrRsUns2HuNzkN/4sgvQ6hZpbjZrmEJnvYQG5fElC/3ExOXTL0aJaka5FPo732jUeLBo3g2/vovh0/eYfZHrXF1Sd/INnd9yAmJSVq+/P44ny04bNqmUqfw2beHQYKBPWpw/Mxdi8bQpH4p1m+7xKzMYgAmjmhSJHuy86M+FESEBxnhQUZ4kBEeZIQHGeGhaDnIUQP7rbfesnQcgmxQKBTUGj6KUq3aEHnmFMnR0Zk2hF8EbWICYb9uzbK80ZQZ2Dm7mO+CCgXupQPxrV0Xnxo1C+zw8NjYWLMvKWZjY81P3/Wi3cD1HD19l/ZvbuD4tqGULe1p1uuYk6c9bPvzCpr4VBbN7EAJX9d8jMz83L4Xx0ef7+P3/dfp27V6urLExESzX0+SJB7HJHL7norbEf/9/Pd3dXwKezcOYtGaU5keu2jNKSaObMJHn+8jOjbJ7LEBeHs6cfvEOBZnFcPqU0wd09wi1y7oWOL5UBgRHmSEBxnhQUZ4kBEeZISHouUgR62aNWvWWDoOwTNQKBR4VayEV8VKFjm/QavlfPAiUlWqDGX2SiWNPp6KtcgsbjYcHW3ZubofLXqv5dKVR7w2YD3Hf30bXx8zvsSwICfPRdC8YemXrnENEBjgQYNafpw6f9/UwH6RuceSJBEVnZiu4fxkY/pOhIrklMyTCVarVIyo6ERU6syH4KvUKcSqkun9elXuRKie6/M+i9L+SmLikrONQR2fgo+Xs0WuLxAIBAKBQFCYeOFuw3v37vHo0SMqVqyIq+vL92W7qGDU66gzaiwnvvgsQ1ntEWN4cOoEyjLlcC2g69dZimrVqlns3Ep3R/5cP5BXuq8i/E4cHQZt4NDmwbi5Fqy1wCGjh1hVMn7FX95hPgEl3Pj36iPg2XOPJUni0eOEDA3n2/dU3Lmv5vY9FSmp2WfjVyjAr7gbgf5KAgOUpj8rBHpSsrgrSneHTBu4SncHfL1dCP68k0U8pKHVGrKNwd0143z1ooAlnw+FCeFBRniQER5khAcZ4UFGeChaDnI8kff06dN06tSJL774ApCHSnbr1o3AwEAaNmxIiRIl+Prrry0WqMCy2Do50+CjyTSeMgP7/9aps1cqaTR5BrVHj+Wv90bwQ8OahO34NX8DzWOuXr1q0fOX8HVl78Y38fFy4nxIJN2G/UxKis6i13wenvYgSWBtnfmca71eT/fu3WnePPthw3v37qVixYps2bLFtO3evXu8++671K5dm1atWqUr0+l0zJ49m0aNGtG6det0ZU9z8eJFunfvTt26dRk+fHi6BIzZlaVhZaXAaJRITNIy57ujfLbgsKlxmTb3+IvFR9h7JBynCp9Tou7XNO66in5jtjJ57n6WbTjLnsPhXL0RTUqqHoUCAkq60bR+KQb2qMG0sc1ZOb8Lf/00iBtHx5JyYxr3Tn/A0W1DWb+wB7M+as3bfevQvFEger2RsUMbZvo5xw5tiE5vyNazOdDpDVnGMGZwAw6fusONWzEWj6OgYennQ2FBeJARHmSEBxnhQUZ4kBEeipaDHPVgnz9/nhYtWqDVaqlYsSIAU6ZMYefOnaZ9kpKSmDhxImXLlqV79+6WiVZgUWwcHKg/YSINP55KsioOR6UHRp2OxMePsXV2JuVaLDv79qDG2+/Sct432Dq//ENC9blYf/x5qVDGi90/DKTlG2s5+PdtBr6/jU3BvbG2Nn+2+Oclpx40Gg1TpkwhNDQUX1/fLPc7cOAAkyZNyrB97NixhIeH079/fy5dusS0adMoWbIkTZo04YcffmD9+vV07NiR+/fvM23aNIKCgqhZs2a6cyQnJzNixAjs7Ozo1q0bmzZtYvLkyaxYsSLbssywtbHOcv7z4rWn+XhUU1yc7dDqDPgVd03fA/3E3/1LuGFn93wDhpyd7Jg8uhkgz3fOjwzeWcYwpCHvDWlIs56refQ4gS1L36BN07IWj6egkBfPh8KA8CAjPMgIDzLCg4zwICM8FC0HOfpm9sUXX5CamkqZMmVo27YtiYmJrFq1CoVCQadOnZg7dy5fffUVa9euJTg4WDSwCzG2TnKjOd5gxMXODms7O5TOzvQ7+DfHZ07nzDdfcmnVciKOHaHTup8oVrNW/gZsYfIq22HdGiXZsaovHQZtZOsfVxg97Xe+/+L1ApOZOycekpKSaN26Nfb29jhn8/IlODiYhQsXEhgYmC5pmEqlIjQ0lFdffZVJkyaRkJBA3bp12bp1K02aNOGPP/7Aw8ODb775hvDwcDp16sQff/yRoYF96tQpYmNjmTZtGm+++SbR0dHs3bsXtVrN+fPnsyxzd3c3nUOnN3InQsWDR/HZzj2OT9BybvdwfL2dn7sBnRMc7G2YOKIJU8c0Rx2fgrurvA52Xi6P9WQMcZpkPNwc0ekNJCXpcHe15+qNaNoNXM+CT9sz+q0GBabuWpKikg31WQgPMsKDjPAgIzzICA8ywkPRcpCjLrJjx45hZWXFoUOH6NixIwcPHiQpSc5YO2PGDKpUqcLixYuxtrbmwoULloxXkEc82dgAsLazo/nn8+j1+z6ci5cg9tpVfmzekLOLvjV7RvOChLe3d55dq3WTsmxc1AOFAg6fvEPI1Si0WgNR0YlotQYSk7R5FsvT5MSD0Wika9eubN++HeV/0wwyw8fHh0WLFjF8+PB0211dXXFycuLSpUucO3eO3bt3A3Djxg0Abt26RcmSJVEoFAQEBJi2PU3aNv//8gUEBARgNBq5c+dOtmUAKnUy5y8/5N+rj7hyIwYfLyeU7pnPiVe6O+CpdCSgpLtFG9dpODvZYWdnjY+XM3Z21vmyLFZaDG7O1qYYfLydObR5MG/2rIHBIPHe9N2MmLwLrfblf1Odl8+HgozwICM8yAgPMsKDjPAgIzwULQc5+lYYExODl5eX6UvtX3/9BYBSqaRevXoAODs7o1QqUavVzxXI48ePady4MStXrqRly5aZ7rN06VK+/fZbHjx4QIkSJRg3bhyjRo0ylX/55ZcsWrSIuLg46tevz7Jly0xD2hMTExkzZgw7d+5Er9fTtWtXgoODcXHJPGvzqVOnGDt2LJcvX8bHx4dp06bx9ttvm8rXrVvHrFmzePjwIZUrV2bx4sU0btz4uT57QeTmzZuZJiMo3botg85cYu/ItwnftZNDH3/A7b/20H75WpyLF+y1nJ+HrDxYil6dqrJxsZG2TcuyaM0pvlt7Ol+GBD9NTjy4uLgwffr0Z56rd+/eAGzbti3ddmtra6ZPn86MGTPo168fvr6+uLi4kJycDMhDv+3+y2Rva2tr2vY0aS//Mts3q7K7ETFoUu4Q919vtWQEJ0cbHkYlMHZoQ9N6z0+SNv/Zzs76mZ/5ZSM8PDxdfXBwsGXdt92pUcmXiV/sY/nGs1y9Ec0vy97I0+ziMXFJ7D92k/OXI0lItPwLqdi4WDw9LLe8npWVAh9PJxrXDaBFo9LY2BTMupbXz8mCivAgIzzICA8ywoOM8FC0HOTom7qbmxtqtZrU1FTs7e3ZtWsXCoUiXUP44cOHxMbG4umZ+y8bx48f56233iI8PDzLfbZv387kyZPZvXs3DRs25OTJk3Ts2BFfX1969uzJunXrWLRoEXv27KFcuXJMnTqVnj178u+//6JQKBgzZgz37t0jLCwMvV7PG2+8wccff8ySJUsyXCsuLo6OHTvy2WefMXz4cI4cOUK3bt2oXr06DRo04NChQ7z33nvs3r2bBg0a8N1339GlSxfu3LmDk5NTrj9/YcPJ25uum7dzaeUyDk0cz+19e1jXoAbtl6+lbPuO+R1eoafLqxWZF3yM2QuPmLalJdYCmDiiSb70XuYFPXr0oHHjxoSHh1O9enVee+0100swBwcHtFq50ZT2p4NDxt7ltG1P7+vo6JiuLDlFR2SU/ELwzv0EJNsUrKwUlPR1pWJ5L5o3LE35QM98n/9cWFAoFHw4oglVgnzoN2YrR07dof7ry9m5uh81Klv+5dvDR/FMnvsXick66lQrQWk/d6ysLDtMPTnZDUdHy2X9NxgkbkeoWLjqJCfPRfDxqCYFtpEtEAgEAoFAJkffDuvWrctff/3FiBEjUCqV3Lx5E4VCQY8ePQC4fv06I0aMAKB+/fq5CmDdunXMmDGDL7/8kr59+2a534MHD5g0aRKNGjUCoHHjxrRq1YojR47Qs2dPVqxYwahRo6hatSoAc+fOZcWKFRw6dIiGDRuyceNGDh06ZHoBMG/ePFq1asX8+fMzNIq3bt2Kl5cXo0ePBqB169YMGDCAJUuW0KBBA1auXEnfvn1p0qQJAOPHj2f58uVs2rSJIUOG5OrzF1T8n7Ecl0KhoOY7I/Bv2pzf3+rH438v8Wv3TtQe+R7Nv/gSm0waPoWRZ3mwBLY21ixeezrTskWrTzF1TPbZuS1BXnlYsWIFDx8+ZMaMGURGRqJSqUwv8kqVKsWDBw8wGo1EREQAUKZMmQznKF26NCBnJAeIiIhAoVBQunRpHj2Sl976+1QIiUY/boTfRqFQ4O8fQMVy3pT2c8fe3oawJzJiPzn3WKVJRvnf3OOi3LjOrj50bB3EyR3D6DL0J27cjuWVbqtYv6AH3TtUtmhMa7dcwNraimVzXsfTI29edOp0OtMoCEty5sJ9Plt4mL//uUfzRoEWv15uyY/nZEFEeJARHmSEBxnhQUZ4KFoOcjQHe/z48UiSxA8//MCiRYsAqFChAn369OHRo0dUrlyZw4fl3rX3338/VwG0a9eO8PBw+vTpk+1+o0aN4uOPPzb9OyoqiiNHjlC3bl0ALl++TPXq1U3ltra2VKhQgYsXLxIWFoZOp0tXXqVKFZKTk7l+/XqGaz19rrT9L168mKPyzEhNTUWj0aT7KcikpGSe2OlpvCpXof+RU9QZLf+/n/9+MRubNSA69LIlw8szcurBnKg0Kdkm1lLH531ML+IhPj6e4OBg09SS7IiMjGTjxo1Mnz6dkSNHolAoTEPK27VrR1xcHBMmTGDq1KkAdOjQAZCzkgcHB6NWq2nUqBHu7u6sXLmS2bNns3//fpo0aYI6QcJgUxJHJxd2/PoTv25aRui/p6lXvxGd29UkqKwX9lk0mtPmHluRkm/znwsSz6oPlSv4cGrnMNo2K0tiko4e725i9sLDSJJkkXh0OgP/XHpAx9YV8qxxDWAwWH6JNID6tfyoEOjJyfMReXK93JIfz8mCiPAgIzzICA8ywoOM8FC0HOSoC6Z9+/Zs2LCBTz75hPv379OqVSu+//57bGxs8PHxAeT5l8uXL6ddu3a5CqD4c8zbjYyMpFOnTtStW5f+/fsD8pf4pzMXOzk5kZCQQHx8PEC68rRe64SEhAznz+5cOSnPjDlz5jBz5swM20NDQ3FxcaFSpUrcunWL1NRUnJyc8PPzIywsDJAdSZJk6n2rWLEid+/eJTk5GQcHB0qXLs21a9cA8PX1xcrKiocPHwLyi5AHDx6QmJiIvb09ZcuW5cqVK4CcbMrOzo779+8DUK5cOaKiooiPjychIQFfX18uX5Ybyl5eXjg5OZl6BcuUKUNsbCxqtRpra2tafbUARVAl/p05leiQf9nQpB5BYycQ0KsPgYGBaDQa4uLiUCgUVK1alStXrmAwGHB3d8fDw4Pbt28DctKpxMREYmNjAXlR+qtXr6LX63Fzc8Pb25ubN28C8puwlJQU0zrGVapU4caNG2i1WlxcXChevLgpQVbJkiXR6/VERUUBUKlSJW7fvk1KSgpOTk74+/ubXrak1cnIyEg0Gg2enp5ERESQlJSEg4MDgYGBprX8ihUrho2NDQ8ePACgfPnyREZGkpCQgJ2dHeXLlyc0NBSQkzs4ODiYel/Lli1LdHQ0Go0GGxsbKlWqxJUrVyhbrgJKd4dMG9lKdwfcXO0B+UWPJEl4eHjg5uZmStRVqlQp4uPjs/Tt6elpSvYVEBBAUlISMTFyj23VqlW5fv06Op0OV1dXihUrRnh4OBqNBnt7e7RaLY8fPyYxMRFJku8fo9GIjY0N9vb2pqzgkiQhSRIajYYHDx6wcOFCOnbsSIMGDbC2tsbR0dE0f1qn06HVaklJSWHw4MHcu3ePnTt34uXlxaxZs6hTpw4ajYa+ffsSGxvLrl27sLe3Z8qUKdSoUYOkpCR27drF77//TseOHfH09GThwoXMmzePbdu2UbVabV7t/C4XQx8BNgx/bybbNy/l/D8HadCgPtOnT0erTcXa2toUk8FgJDk5hZCQEKysrKhSpQqhoaGoVCpSUlJQKpWmOluqVCkSEhLypM4WlGfE/fv3UavVBAUFZfuMWP55C6Z9ZcWPO28w/auDHDsdxqzx9ShZwgdXV1fu3r0LyKMOXuQZcfZ8CCp1PMW8HDAYDKZ66OjoiMFgME0TcHV1JTExMdM66+DggCRJpKamApjm/xsMBlOdTXvGp001SEpKMv3fZbWvvb09CoXC9MXC2dmZ1NRU9Ho9VlZWODs7m35H2dnZpauHzs7OaLVadDodvt6ORMcmERISAoCnpyfOzs4m34GBgcTFxZmeyZUrV86zZ8TNmzeJjo7Gz8/P9IwAqFy5Mjdv3iQ1NRVnZ2dKlixpqrMlSpTAaDSmq7N37twhJSUFR0dHSpUqla7OKhQKIiMjTXX2/v37JCUlYW9vT5kyZSz2TH7St4uLi6nOBgYGolKpUKlUpmfErVu3iI6ORqlUFulnxN27d4mOjsbW1vaZz4gnv0c8XWfN+YzIj+8RDx48MJ03KCgoz75HFLRnhEqlMnkoys8Io9FIbGwsRqOxyD4jNBoN7u7uprZGQXpGZNbh+kJIZuDw4cNSamrqC58HkA4ePJjtPidOnJD8/f2lfv36SSkpKabtbm5u0m+//ZZu3zp16kgLFiyQzp07JwFSfHy8qUyj0UiAdOHChQzXGDt2rNSzZ8902xYtWiTVqlVLkiRJqlGjhrR48eJ05T169JDGjRuXZdwpKSmSWq02/dy7d08CJLVane3nzS/+/fff5zouITJS+qVLe+krB6SvHJC29ewsJT5+bObo8o7n9fAiJCSmSjO+PiDh/0mGn+nz90sJiS9+r+WWpz10e/sn6ff913J8/IYNG6QvvvjC3GFlitFolKKiE6RT5yOknfuuSjv3yj9/HQ2XbtyOkVJT9c88x6ad/0oD3vslw/b8qA8Fkdx6WL7xH8m2zEwJ/0+kOh2WSnfvq8waz+OYBOn1wRulfy7ez7Rcp9NJ3bp1k5o1a5btefbs2SMFBQVJmzdvNm0LCgrK8NOqVStJkiQpNDRUeuedd6RatWpJLVu2THfc01y4cEHq1q2bVKdOHendd9+VHj/xXMyuLI2Fq05IEz77M9v48wtxX8gIDzLCg4zwICM8yAgPBduBWq02a7ssR0PEn0Xz5s1NWXktyerVq2nTpg3jxo3jxx9/xN7e3lRWrVo10xsQkHvFwsLCqFatGhUrVsTW1jZdeWhoKHZ2dgQFBWW4ztPnSts/LfPds8ozw97eHjc3t3Q/BZkqVao813HOvr70+PV3Ws1fgLWdHTd//40f6tfgzoFnDw8uiDyvhxfB2cmOyaObMWN8C9MSUUp3B6a935z3hjTkx+2X8jymF/EQHR3N1q1bGTx4sPkCygSd3sCtu3EcOnGbk+ciePQ4ASTw8XKmQS0/WjcpQ7nSni+U9Ts/6kNBJLce3ulfl/0/vYW3pxPn/n1I/deXc+LsPQtFlx6NRsO4ceNMPRJZceDAASZNmpRh+/vvv2/66dWrFyD/zgOYMmUKJ0+epE+fPpQoUYJp06Zx/PjxDOdITk5mxIgRxMbG0q1bN44fP87kyZOfWVZYEPeFjPAgIzzICA8ywoOM8FC0HORoiHjHjplnhrayssLOzg5PT0/q1avHoEGDLJZFe+vWrYwcOZKdO3dmOgx96NChfPLJJ7Rv356KFSsydepUfH19ad68Oba2tvTp04dJkyaxefNmACZNmkS/fv1wdHTMcK4ePXowceJEFixYwOjRozl27BgbN25kx44dpmt1796dN954g6ZNm7JkyRIePXpE9+7dLfLZ0zBoU9Dc+BetJhZJsuza0w/uP6CkX8nnPr5k3SA6fP8tZ76dT0LEPXYPaE/5bj2p0m8gVrY2KFBg4+SKW/nq2DoX3JcNN27cyPQljKV5MrGWOj4Fd1cHbt6No3mvNVwLj6a4jyudX62YZ/Fk5iGn02m9vb3ZsmUL1taWyX6ckJjKrXsqIh5q0Ovl+8LGxoqAkm4E+nvg4pz7l3+SJCfye5r8qg8Fjefx0Kxhac7seoeub//MpSuPaPnGWpbNeZ3Bb9S2UJTyEO7WrVtjb2+fYVrPkwQHB7Nw4UICAwNNQ8bTeHIpyMGDBxMYGMiUKVNQqVSEhoby6quvMmnSJBISEqhbty5bt241JcBM49SpU8TGxjJt2jTefPNNoqOj2bt3L2q1mvPnz2dZ5u7ubl4hFkLcFzLCg4zwICM8yAgPMsJD0XKQowb2n3/+iUKhyDJBjUKhYM2aNSxatIhjx44911JdmeHi4sKyZcsYMGAAM2fORK/X07Nnz3T7DBw4kKVLlzJ06FBUKhXdu3fn8ePH1K9fn99//92U4TU4OJgJEyZQvXp1tFotXbt25bvvvjOdp2rVqgwYMIApU6bg5eXFvn37eP/995kxYwY+Pj4sWrSIVq1aAdCmTRuCg4MZOXIkERERVK1ald27d5vtc2fG47MHub1zNUZdKgqFFViosZJGakoqdx3sn73jM6jQrhHJj8uSolKhj71B6Kq5uBQviZWdLZJBj0JhRfEmnQjoMDDTBk1+kzZ3Mz9IS6SVto5wpfLetGwUyNUb0QwYu5WTO4ZRJahYnsTytAc3V3sexyRmsXdGXqRxrTcYsVIo0OkN2NpYY5QkrK0UPIpO5Na9OKJjkkz7ujjbERigJKCE2wstZ/Q4NhE3l4z1Pz/rQ0HieT0EBnhw/NehDBr3K7/+eZUhE3bw79Uovpz6KtbWZhlQlQ6j0UjXrl0ZMWIEffr0Qa/XZ7pf2jM+MTExy97jvXv3cuLECZYsWWKaK+3k5MSlS5c4d+6caZnJtLlvT5I2VzEtg2pAQABGo5E7d+5kW1ajRo0XE5BHiPtCRniQER5khAcZ4UFGeChaDnLUwB40aFCWjR+dTmfK6H3t2jVmz57NN99881zBPN2A//rrr01Dzy9dyn5YrEKhYMKECUyYMCHTcldXV5YvX87y5cszLX96yHe9evUyHeqXxsCBAxk4cGC2MZkLza1Qbm5binft5vi16om9p6/FG6NJSUlmHY2QEBnJ438vYNTqUFhb4121Gg5KVx6f2U/EX5uxU3pT/JUOZrueuUhbg7mgsHBme67ceMzhk3foMvQnTv/2Tp5kTX7aQ93qJTh6+i69OlXBxfnFX8RkhcFgJPx2LLfuqdDpDNjaWlMmQEmZAA+uhD0mIVGLQgG+3i4EBijx9nR64XsjTpXMibMRtGmScQmwglYf8osX8eDibM8vy95g5reH+WzBYb5ZcYLQsMf89F1PlO4ZRxS9CC4uLkyfPv2Z+6Vlqt+2bVuW+6xevZqyZcvSpk0bQH5p9PHHHzN79mz69euHr6+vKdnZ0yQlyS+B0n6fpb34TU5OzrassCDuCxnhQUZ4kBEeZIQHGeGhaDnIUQN77dq1z9znzz//pGPHjuzcufO5G9hPIkkSZ86cYcGCBS98rsJO9NlDOHgWp2zPUXnWy/vk/HZz4FK8OA7uLYi6dIHk6GgeX7qIc4kSFG/ahaTIuzw+e7BANrCfJ8u9JbGzs2HL0jeo//pywu/E0Wf0L+z+YcAL9dbmhKc9dGtXiZPnIvjgsz20alyGYt7OWFmZt24aDEbuP4on4kH6Je3OXnqAf0k3XJzs0CSkUtzHmYQkLSHXol7oekajxINH8Rw4fgsbGys6tck4jKmg1Yf84kU9WFlZMXNCK6pVLMZb43/lz0M3aNR1JTtX9yOorLeZojQft2/f5vz584wdOzbdM7h37960aNGC8PBwqlevzmuvvZbpF4i0rONpb+/T/nR0dMy2rLAg7gsZ4UFGeJARHmSEBxnhoWg5yFEDOye0b98ed3d301INL4pCoWDlypVmOVdhJ/HBTdzKVcvTIdSJiYlmT8Rm4+hIiQaNUN0MJ+76VRIfPiRVpcLBN5DYkJNIBgMKCw99zy03btzINnldfuDj5czO1f14pdsq/jp6kw9n72XBp5Z9OfG0h1J+Sr74uA2//B7Kjr1XSUzWmf2aRqPEmYv3TfOqn8TGxor6Nf3M3qh3dbajQS0/+nSuhq9PxoZSQawP+YG5PPR+vSrlSnvQ9e2fuRYeQ4POK9gc3JvXWpQ3Q5TmI200U7NmzdJt//7774mNjWXGjBlERkaiUqlo2bJlhuNLly4NYFp6JCIiAoVCQenSpU3Lp2RWVlgQ94WM8CAjPMgIDzLCg4zwULQcmK2BDaDX6y2WyKgoY9TrsbZzyLJcr9fTu3dvYmJiOHLkSJb77d27l/fee4/Zs2ebhkRWrJgxUZafnx/bt29Hp9Mxb948du3ahZOTEyNHjjQd9zQXL17k008/5e7du9SrV4/PP/8cb2/vLMtKNm5K1IVz6BITSYy4gebeXXQpSdg5u+ZGTZGlRuXi/LCgOz3f3czCVaeoUcmXoX3r5GkMgQEefDhCTuak1xtynPQsp0RFJ+LfIOvRMCvmdTbNTzcHCgUWHwkgyEid6iX55/d36f7Oz5w4G0GHQRv5evprvP92I4u9VIyPj2f9+vUEBQXRtm3bZ+4fGhqKtbU1lSpVSrf90aNHbN68GZ1OR0hICAqFwvSMPHDgAFevXmXAgAE0atQId3d3Vq5cyd27d9m/fz9NmjTB3d092zKBQCAQCASFD7Nllfnxxx9JTEw0JWoR5A2WWoLGwcGBH374gfXr19O4cWO8vb2ZNm0aFy9ezHCO51mCxkGpxL9pc1wDAkCChIgIfunYFk3EPXRJiRi0WpIeR2HQatEl5TyZlrkpWfL5M6lbmh4dqvDpBy0BGDFlF8fP3LXYtZ7lwcbGGltb8/54eTqZlil7GqW7A96ezma9Xk4a1wW5PuQl5vbg6+PCwU2DGdy7FkajxPiZexj20U5SUzNPSvaiqNVqFi5cyJ49e3K0f3R0NEqlMsNylGPHjqVly5bs3LmT+Ph45s+fT7169QD5hebChQtRqVS4uLiwcuVKvL292bZtG40bN2bOnDkA2ZYVFsR9ISM8yAgPMsKDjPAgIzwULQc56sF+cpmSpzEYDKa37gqFgs6dO5stOEH2WHIJGkmS+OOPP/Dw8OCbb74hPDycTp068ccff1CzZs1053iRJWiK1ahF6uM7KGysSX2sxsbBgdNfzeP894tJVamwVyqpM2osDT6ajI1D1r34liKrrMMFhenvN+ffq4/Y+scVery7iX9+f5eAkubv+cprD/OXHieorBdjBjdg9sKMozLGDm2ITm94oTWtn4eCXh/yCkt4sLe3YfXXXalR2ZcPZ+9l9abzXLsZzdZlfTIdrp9bDhw4YPq7v78/M2bM4O7d9C+levToQY8ePTIcu2zZskzP6ebmlmXZ3LlzmTt3runfNWrUyDKJWnZlhQFxX8gIDzLCg4zwICM8yAgPRctBjhrYS5cuzXaoXlr27ypVqjBt2jTzRCZ4JpZcgkaj0XDr1i0CAwNRKBQEBAQA/19u5kledAkaJ29vitWoRe1OYzi/ZBEn5842nTtVpeLEF58BUH/CRGydzDckOCdERUVRrFjeLIX1PFhZWbH2m26E3Yrl0pVHdBv2M0e3DsHJMfdrP2dHXnmQJIkZXx1k9qIjVCrvzckdw7CyUrBo9SlU6hSU7g6MHdqQyaOb4WBv1hkuOaKg14e8wlIeFAoF499pTJUKPvQZvYXjZ+5R//Xl7Fzdj1pVS5jtOtHR0WzdupUlS5a80HlSU1PNnhCyMCLuCxnhQUZ4kBEeZIQHGeGhaDnI0TfU5s2bZ9rAtra2xt7enmLFilG3bl2GDh1q1qWdBNljySVoQB7enZOlY8yxBI21vT0lGjTktwFvZBrfueBFNPx4anYfs8ji4mzPjlV9qf/6Cs79+5ChE3bw05JeBXJd8eyQJInxM/9k4apTALzVqybubg5MHNGEqWOao45Pwd3VAZ3ekC+Na0He0a5leU7tfIcuQ3/i+s0YmnRfzbpvu9GrU9VM90+r608v9ZgV3t7ebNmy5YVzhlhZmX/t7qwwd44DgUAgEAgEliFH31IPHTpk4TAE+UlmS9C4uLjg4OCQYekYh0yGaZtrCZpUtZpUlSrTGFNVKpKjHyNJEq5+eTfP/+mkRgWVwAAPti57gzb9fmDTb5epWaU4k8c0e/aBOcTSHgwGI8Mn/caqn88D8N2sjowe3AAAZyf55UxaQrO8Hhb+JIWlPliavPBQsZw8gqHv6F/YeySc3iO2sHhWAkPeqI2tjTUqTQpKN/mFi7OjLQogJi7na0e/SONabzBipVBga+eA0ShhlCRsrC3b2I6JS8LVpWD2lov7QkZ4kBEeZIQHGeFBRngoWg7y7vW7oMCS2RI0SUlJlCpVigcPHmA0GomIiACgTJkyGY7Pbgma7Mqext7dHXulMtMY7ZVK7N2VrG9Um59aN+XC8u9Jio5+zk+cczIbEl9Qad4okO9mdQRg6pf7+W3fNbOd25IetFo9A97byqqfz2NlpWDtN91MjeuCRmGqD5Ykrzx4KB35fV1/xg9rRKXy3vTpXI15wcfwrTMf39rz8a0zny+XHgeFgipBPuw/fhO93mDRmAwGI+G3Y9l7JJy9h8PZeySc8NuxGAwZl5MzFw8fxfPv1SjqVDPfMHlzIu4LGeFBRniQER5khAcZ4aFoORDjLF9CzLEEjdFopF27dixcuJAJEybw4MEDADp0kNdbtsQSNEa9jjqjxprmXD9J7ZFjeXT+LMkxMSRHH+fBieMcnDCWwNfaU7nPAMq93gVbC0xPSE1NNfs5LcnwgfW4GBrJ9+v/of97Wzm5YxhVK774fBdLeUhO1tF75GZ+3x+Gra0VPy7umeUw4IJAYasPliIvPdjYWPPNJ+0ZOSiGRWtOpUt6p1Kn8Nm3h0GCFo1K8+X3fzP2k900qVsKD6Uj5p4l4ehgQ+TjBO7d16Tb/u+VRwT4ueFX3M2sPdkGg5Fb91TsOxpOMW9nWjQqmGtji/tCRniQER5khAcZ4UFGeChaDkQD+yUkbQmaLl265KiBndkSNNbW1rzzzjuo1Wp27NiBo6Mjs2bNonbt2oCcFO3XX3+lU6dOlC5dmpUrV/Lpp5+alpn5/PPPgf8vQZNZ2dPYOjnT4CM5Cdu54EWZZhF/N+we17b8zJVNG4m6cJ6bf+zi5h+7sHVxoUKX7lTuO4BSrdpgZWOeql0YcwosnNmBKzeiOXTiNl2G/sTpXe/g5fFin8MSHhISU+ky9CcO/n0bB3sbtq3oQ4dWFcx+HXNSGOuDJcgPD6X9lHy39nSmZYvWnGLiyA/oP2YbR07d4cftIdhYm7d1bWtjRe1qJTj370P0mfRW21hbUbd6Se5HarCxtcLBzgZ7O2vs7W2wfoFGt7OjLY3q+PNmz5q4ueb9Sgo5QdwXMsKDjPAgIzzICA8ywkPRcqCQcpoVRmBWNBoN7u7uqNVq3Nzcst334jfj8KhUl1Id38zx+Tdu3Mjdu3ezzBr+LAwGwwsnAMopMRePc2PTQup9+gPWdg7okhKxsrElVa3G3t0do06HbSbLkMVcvcKVnzdy5eeNaO7cNm138vWlUq++VO47AN+69V4o2VdhzRIcHZtI/ddXcPueijZNy/Dn+oE5Wuc5K8ztIU6VTMe3NnLyXASuLnbsWtOf5o0CzXZ+S1FY64O5yQ8PUdGJ+Naen2X57ZPjGDtjN2G3YpAkeV60OSlf2osvp7xKtbbBWe7z775RvDFqM1fC0k9fUbrZU8rPnVJ+7pQuqSTAz53S//27lJ877lk0nK2sFDg52mb6DEtM0maci+5k3tUDcoq4L2SEBxnhQUZ4kBEeZISHgu0gN+2ynJCjbr6DBw/SuHHjTBNcCQoe5liCJjEx0SwV7HlIW4rLyccHAGu7zL80elWqTNNPZ9Pkk1k8OHmCK5s2cu2XTSQ9esS5JQs5t2QhHuUrUKnvACr3HYBHufK5jiUsLIxq1ao9/4fJJ7w9ndm5qh+Nu61k/7FbTJi1l4UzOzz3+czpISo6gdcGrOdi6CM83B3Ys+FN6tfyM8u5LU1hrQ/mJj88KN0cULo7oFKnZCxzd6CEjys7VvWzaAxarSHbGMoHetK7UxUuX3/M7XsqbkeoiIlLRqVJRaWJ4tKVqEzPq3R3INBf+f+fgPR/urul/92bkqrny++Ps2hNwVi+TtwXMsKDjPAgIzzICA8ywkPRcpCj38Rt2rTBzs6OBg0a0LJlS1q0aEGTJk1Eg7uAYo4laPJy+ZkXRaFQ4Nf4Ffwav0Kr+Qu4vW8PV37eSPiuHcTdCOPE7E85MftTitdrQOW+A6jYqw/Ovr75HbbFqV7Zl/ULetDj3U0sWn2KGpV9ebtvnXyN6d4DNW37/cD1mzH4+jizb+Mgqld++f8vBC+OTm9g7NCG8pzrpxg7tCE6vcHiGeafFYPBaGTmhNbptscnpHInQm5s34lQc/u/v6c1wKNjk1CpU7igjuTC5chMr6t0d6C0nzuB/ko+Gd+SX/+8yqyF/4/BNBcdmDiiSb71ZAsEAoFAIMjhEPEFCxZw5MgRjh49SkxMDAqFAltbW+rXr0+LFi1o2bIlTZo0yXTpJUHm5GYowr+LPsRYsjSX/L355845YhPjkLDQyH5JAhRIkhGFwgqQMHumIECBAhcHF2r6VaOh0QXtvm00mPUTCjMOS9fGx3Pjt+1c+Xkjd/bvQzLK8yYV1taUbvMqlfsOoHznbti5uGR5jri4ODw8PMwWU37w2YJDfPL1IWxtrTi4aTBN6pfK9Tmio6Px9vZ+oTjCb8fSpt867kSoKeXnzl8/DaJCGa8XOmdeYw4PLwP55SElVc+cJUdZtDr/em7NHUNCYur/G973VNy5n74B/jgmybSvt6cTt0+Mw7/BN1n2oj/4ZwJHTt4hMEBJ2VIe2NpafqqPuC9khAcZ4UFGeJARHmSEh4LtwNxDxHM9B/vff//l8OHDHDx4kGPHjvH48WMUCgU2NjbUq1ePVq1aMXv27BcO7GUnV3Owf/yahdf/wLpcZRqXbUBJZQmsFeb/0iQhYdSmYNRqkZBQoMDKzg4rOwcUmLeRLSERkxjLifBTRN8O5S2bANp9uMys13iSxMhIrv2yiSs/byTy7BnTdhsnJ8q/3pXKfQdQuu1rWNvaApjmgaeo4nBQemDU60xD1wsbRqORPqN+4ZffQynm7cyZXe9Qyk+Zq3M8fvwYn/+G7D8Pl69F8Wr/H3gYlUCFMp789dOgXMdQEHhRDy8L+ekhbe6xOj4Fd9f8mXv8//nPySjdHC0aQ2KS1tQDrolPpWmD0gQ0+CbL/W/9PY7OQ38k5GoU1tYKypbyIKisF0FlvKhYztv095LFXV8oP8WTxMbG4unpaZZzFWbE80FGeJARHmSEBxnhoWA7yPcG9tOEhoZy6NAhDh8+zJEjR4iKisJgsOw6pC8DufmP/GlPML9e3MXc7rMIDKyOla35v8hJkoQhJRF9clKGMhtHJ6wdnM32ZSwNo0GP6nEEU3/9BD87N6a9vdis58+K2LDrXN30I1d+3ogq/IZpu6O3N7VGvUf9sRM4/c2XnM8ik3lhJDFJS5Puq7gY+oja1YpzbNtQnBxzXo9CQkKee97M2UsPaDdwPTFxyVSvVIy9G9+keDHX5zpXfvMiHl4mhAeZsLAwKlTI28z3Wq0B3zrzs+zBvn9mAq8P2cjp8/dJTNJleR5nJ1tTYzuo7H+N7//+/vSc76xIe9EQq0rCU+mUr4nWCgLivpARHmSEBxnhQUZ4KNgO8iXJWXZUqVKFKlWqMGrUKACuXbv2wkEJ0nNNq6JepWZ4GEB941+zDqN+EkNKIpm9b1EoFFg7mL/3VjIaUEjQolIL9tw5hSRJZm/EZ4ZnhSBemfYpjad+QuQ/Z7jy80au/fIzSVFRFKtWk9NfzeXk3P+PwkhVqUxrc9efMLFQ9mQ7O9mxY1U/6r++nPMhkQz5YAc/B/eyuO+jp+7w+pAf0cSn0qCWH7t/GIDnCy4ZJhAUFPJjTc9nzQOXJIkDPw9GkiQeRMZz/VYM12/GcC082vT3m3fjSEzScT4kkvMhGed9F/N2pmJZubEdVNaLimXlnu+ypTyw/28YfEFLtCYQCAQCQUFBLNOVT+TmTcmELZOpHVCT/vV7oU/QYNRrzR6PUa9DEx6SZblbuWpY2dia9ZoKa2tsnN35+/Y/fH94BWsGL8XeJn/S9xv1eiKOHaFEw0YsK+tHqkqVYR97pZKRdx5lmdW8MHD01B1a912HXm9k9ketmTq2eY6O0+l02Nrm7v9/7+EbdBv2M8kpelo0Ks1va/rj6lIwl2fIKc/j4WVEeJDJLw8vOg9cq9Vz656K6zf/a3zfjDb9/WFUQpbHWVkpKBOgZO233dl7+AazFh7JsM+M8S2KbKI1cV/ICA8ywoOM8CAjPBRsBwWuB1uQNygUYGVtg517xnluer2e3r17ExMTw5EjGb/wpLF3717ee+89Zs+eTe/evQG5B6ZOnTro9XrTfp2aNWLi0L7o9QaCN+3gr1PncHFzZ+TIkabjnubixYt8+umn3L17l3r16vH555+bEhlkVwZg/V/G8vx812NlY0Oplq1JehyVaeMa5J7sVLXatHxYYaRZw9Ismd2R4ZN2MW3+AapVLEbXdpWeedzdu3cpV65cjq/z6+4r9B3zC1qtgQ6tyrN1WR8cHQvmQzU35NbDy4rwIJNfHhzsbZg4oglTxzRPNxc9pz3HdnY2VCznTcVyGZPNaOJTCLsVm6Hhfe1mDAmJWtTxqdSuWpzOQ37M9NyLVp9i6picvbh72RD3hYzwICM8yAgPMsJD0XIgGtiFHI1Gw5QpUwgNDcU3m6WnDhw4wKRJkzJsDwsLQ6/X06FDBwJ9vdCqY6lQWl6T+Jd9h9n61xFea9mMh49jmDZtGuXKlKZOvQbpzpGcnMyIESOws7OjW7dubNq0icmTJ7NixYpsywoi9u5K7JXKLHuw7d3d8z4oM/PugHpcuvKIJevOMPD9bZzY/jbVKmW/VFZycnKOz79h20UGf7Adg0GiV6cqbFzUAzu7l+NRkxsPLzPCg0x+ekjrIfbxkqesmGuJMjdXB+rWKEndGiXTbZckicioBO5HalBrUjKdAw7ykmHq+BRTXEUJcV/ICA8ywoOM8CAjPBQtB4VnsWNBBpKSkmjdujXnz5/H2TnrLzPBwcGMHDky08x9V69eBWDAgAG8+c4IRo0aRZO6tQA4eOYCSnc3FnwXzMdD+wLw6w+rSXx4O11v86lTp4iNjWXYsGFMnz6dNm3acOzYMdRqdbZlBRGjXkedUWMzLas9Ygz6lJfj4fDtJ+1p9UogCYlaur79MzFxGZPbPUlO17xfuv4Mg8b9isEgMbh3LX76rudL07iGnHt42REeZIqSB4VCQQlfV+rV9MPb0xmle+afXenugLtr0fHyJEWpPmSH8CAjPMgIDzLCQ9Fy8MIN7Hv37vHPP/8QHx9vjngEucBoNNK1a1e2b9+OUqnMcj8fHx8WLVrE8OHDM5SlNbDffvttXmnSlCEfTEbvVxmf+m24FxWDn38A1rZ2VH6lNQB3Hz4i/tYVYv89gS5BbiTfunULAH9/fwACAgIwGo3cuXMn27KCiK2TMw0+mkzjKTOw/8+pvVJJo0nTqD1qLIcnfYhBl3Vm3sKCra01m7/vTZlSSm7ejeONkVvQ6bLO/l+6dOlnnnP+0uOMnPI7kgTvDWnAqq+6YGNj+TV485KceCgKCA8yRdVDWqK1zBgzuAGnzkc886Xdy0hRrQ9PIzzICA8ywoOM8FC0HOS4a+n06dPMnDmTJk2aMGXKFBITExkwYAC//fYbAI6OjsycOZMJEyZYLFhBelxcXJg+ffoz90ubN71t27YMZTqdjsDAQPr3709sbCxLly7ls1mz+Oqrr0hOTsbuv4ReTkoveX8rGxQ2NugS1MT8ewKn4qVJTJST4qTtm5bAIDk5maSkpCzLCio2Dg7UnzCRhh9PJVkVh6PSg+SYaH7p0o7HF85j1Olot3xNnmQ8tyTens7sWNmPxt1WcuD4LSbM2sOizzpmuu+1a9eyXFpBkiRmfHWQ2Yvk+f9TxjRj9sTWhd5PZmTnoSghPMgUVQ/OTnZMHt0MIH2itSENGTO4Ac17rSFVq+fXFX2pWaV4Pkebd2RWH0KvR/HHgTBCrkWRkqrP4sjCg7W1FSV9XWlavxSd2lTI9CVqUb0vnkZ4kBEeZISHouUgRw3s8+fP06JFC7RaLRUrVgRgypQp7Ny507RPUlISEydOpGzZsnTv3t0y0QrMzsyZM9P9+9ChQ5w6dQqj0YiDgwNarZyxPO1PZzcl3rWaEX/7KinRD0l6eBtDzIN0+6T96ejoaBoOkllZQSZtKa6Hag0VivniUqIkTWfMYnvvrlzesA4XP3+afjr7GWcp+FSv7MuGhT3o/s4mFq85TY3KvgzrVzfHx0uSxPiZf7Jw1SkA5kxqw6T/vngLBIKXlycTrcWpk/Bwl9fBvv9Qg1Zr4NZdFY27rmTV/K7061Y9v8PNF85cuM8X3x2lRDEXWr1SBrdCvooCgN5g5PrNGNZsPk9o2GM+HtUEKysx21AgEAieJEcN7C+++ILU1FTKlClD27ZtSUxMZNWqVSgUCjp16sTcuXP56quvWLt2LcHBwaKBXYj47rvvMBqNjB07FkmSSExMxNHREXt7e0qVKsWDBw8wGo1EREQAUKZMGaztHFAG1SLVxw/NrVBKeikBuPbPCZq/0oiIiAgUCgWlS5cmKioKkKcSAOnKCgNPDr0v26ETbRcvZd+odzg173Nc/fyp+c6I/AvOTHRrX5lZH7Zi+lcHGTX1dyqV86Zpg/T/P5kl0DMYjAyf9Burfj4PwHezOjJ6cIMM+71MZJdIsCghPMgUdQ9pidZsrXXY2VljZ2dNUDlvzux6h/7vbWXP4XD6v7eVs/8+YO7kti/dlJGnebI+SJLEqk3nqFaxGJ+Mb/HSffbjZ+4yN/gYIdeiqFE5/SiFon5fpCE8yAgPMsJD0XKQo9eOx44dw8rKikOHDtGxY0cOHjxoGvo7Y8YMqlSpwuLFi7G2tubChQuWjFeQA+Lj4wkODuavv/565r63bt1iyZIlTJw4kffff5979+7Rs2dPFAoF7dq1Iy4ujgkTJjB16lQAOnToAMhZyVf9tAWbwOo0bf0ars5ObNi6nekT3mf//v00adIEd3d3GjVqhLu7OytXrmT27NnpygoDT7+ZrzFkGI2nfgLA/nGjufHbjvwIy+xMHduc3q9XQacz0uPdTdy9r0pX/rQHrVZP/zFbWfXzeaysFKz9pttL37iGjB6KKsKDjPAg8/QSi54eTvy+bgCTRzcF4OvlJ2j/5gaiYxPzI7w848n6IGdcj6fLqxVfusY1wCv1AvD2cOL0hfsZysR9ISM8yAgPMsJD0XKQo08aExODl5cXAQEBAKaGm1KppF69egA4OzujVCoLbHboooRarWbhwoXs2bPnmft+8skndO7cmUOHDnHy5EnefPNNxo4dS0pKCu+88w6DBw/mxIkTREVFMWvWLGrXrg3Ia2ovXLgQTXw8JarUZvmy7/H0ULL76EnqVCrPxDd7oEtQ4+LiwsqVK/H29mbbtm00btyYOXPmWFqB2Xj48GGGbY2nfkK1wW8jGY38/lY/Hpw8kQ+RmReFQsGar7tRq2pxHsck0fXtn0lM0prKn/SQnKyjx7ub2LzrMra2Vmz+vjdv9a6VD1HnPZnVh6KI8CAjPMhk5sHa2oovJrVly9LeODvZsv/YLep1Ws75kJfX2ZMe4tRynpHixVzyKxyLolAoKF7MJdPl2sR9ISM8yAgPMsJD0XKQowa2m5sbarWa1NRUAHbt2oVCoaBly5amfR4+fEhsbGyh6Zl82Thw4ABHjshJpvz9/ZkxYwaenp7p9unRowfXrl0zJT0D+f/2q6++4vTp05w+fZpp06aZEpHZ2toyefJkTp48ycGDB3njjTdMx82dO5dr166ZhnrXqd+IHX/s4fje3/lq4hjc7ayI+fcEmluhVKtSmW3btnHu3DlWrFhBsWLFLK3DoigUCl5dvJQy7TuiT07m116diQ27nt9hvTDOTnbsWNUXHy8nLlyOZMiE7aaeKXt7ee5gQmIqnQZv5Pf9YTjY27BzVT96dqySn2ELBIICTK9OVTm5YxjlSntwJ0LNK91WsfHXS/kdlsVJ69S3yiLZo16vp3v37jRv3jzb8+zdu5eKFSuyZcsW07ZHjx4xatQoGjRoQLNmzViwYAFGoxGQp2O9++671K5dm1atWqU77mkuXrxI9+7dqVu3LsOHDyc6OjpHZWlYKRRIGbYKBAKBIEcN7Lp166LT6RgxYgTjx4/n5s2bgNxgA7h+/ToDBgwAoH79+hYKVZBToqOj2bp1K4MHD37uc2S3rnZWKBQKnHwD8KrVDAefkiBJJD28Q8yFo6TERGYYRlgYqFChQqbbrWxs6LxhM8Xr1iclJoZtXdqTGBmZx9GZn1J+SrYt74OtrRX/Xo3i8rUotFoD7h4lSNXqOXPxAQ+jEnB1sWPPhoG0b5W5n5eVrOpDUUN4kBEeZJ7loVolX87sepcOrcqTkqpn4NhtjP/0T/T6rJcGLIzktD5oNBrGjRtHaGhotvsdOHCASZMmZdj+8ccfc/ToUbp06ULp0qX5/vvv2b59OwBjx47l5MmT9OnThxIlSjBt2jSOHz+e4RzJycmMGDGC2NhYunXrxvHjx5k8efIzy3KCuC9khAcZ4UFGeChaDnLUwB4/fjySJPHDDz+waNEiQJbUp08fHj16ROXKlTl8+DAA77//vuWiLaoowJiLxqm3tzdbtmyhRIkSz33JtGzfz4O1nT3KCjXxqFIfawcnDNpUVNfOo7p6DkNKxuW5DP+9ebdSFLy5GQ8ePMiyzNbZme7bdqEsWw717Vts69EJ7UuwHnzTBqXZuKgnR34ZwqZdl/GtMx/f2l9RvM5XHPj7Fke2DuH4trdp3igwv0PNc7KrD0UJ4UFGeJDJiQcPpSO/renPtLFyj+2CVSd5tf96Hse8PPOyc+IhKSmJ1q1bc/78+WxfZAcHBzNy5Eh8fHwylH3++eds376dt99+m6pVqwLyiDOVSkVoaCjNmzdn0qRJLF++HICtW7dmOMepU6eIjY1l2LBhTJ8+nTZt2nDs2DHUanW2ZebyUBQQHmSEBxnhoWg5yFGLpn379mzYsIGyZctib29Phw4d2Lt3LzY2NqaHv4uLCz/++CPt2rV7rkAeP35M+fLlOXTo0DP33bp1K2XLlk23zWg04uLigrOzMy4uLqafxET5l3diYiJDhgzBy8sLd3d3Bg0aREJCQpbXOHXqFA0bNsTFxYUyZcqwatWqdOXr1q2jfPnyODs7U69ePU6csNw8XKWjO5HqR7k6xtr6+ZKqGIwGjEYjVjZWGI1GDMbn72GwV3rjXbMpLv7lQKEgNS6K6ItHSXxwC0kymvaL1DzC0c4BW2vb576WpUirP1nhVKwYPXb+iaOPD1Hnz/HbgN4YdLo8is5ydGxdgcVrTjF74RHTHDuVOoXZC4/w3ZrTlC3tkc8R5g/Pqg9FBeFBRniQyakHa2srZn3Umm3L++DibMehE7ep23EZZy+9HF+6cuLBaDTStWtXtm/fnm6Viqfx8fFh0aJFDB8+PEOZn58f5cqVY+LEiaxdu5YOHTrQuXNnXF1dcXJy4tKlS5w7d47du3cDcOPGjQznuHXrFiBPKQMICAjAaDRy586dbMtygrgvZIQHGeFBRngoWg5y3GXYv39/wsLCSEpK4vfff6dUqVLyCaysOHjwII8fP6ZPnz7PFcTx48dp3Lgx4eHh2e6n0+n48ssv6du3r2m+URqhoaHodDri4uJISEgw/aS9IR4zZgz37t0jLCyMsLAw7t69y8cff5zpdeLi4ujYsSODBg1CpVKxatUqxo8fz+nTpwF5rej33nuPdevWoVKpGDBgAF26dDFlVjc3dUrV4tL9EO7E3LXI+dMwGA3cir7NwetHTD+3om+/UCNbYW2NS6kgvGs2xc7NA8lgIP72VWIu/Y02XoUmWcORsGPULVUbRRZz1fKTtLnH2eFRrjzdt+7CxsmJ2/v2sG/UO4VyOPyT2NpYs3jt6UzLFq05he1LmBU3J+SkPhQFhAcZ4UEmtx66d6jMqZ3DCCrrxb0HGpr0WMUPv1ywTHB5SE48uLi4MH369Ex7pp+kd+/ez+ywGDhwIH379mX37t1s2LABa2trpk+fTmxsLP369WPx4sW4uLiQnJxx5Fja9xU7u/+WWvsv90pycnK2ZTlB3BcywoOM8CAjPBQtBzlaB/tZpCXpePToEePGjeOnn37K8bHr1q1jxowZpoZzdrz22ms4ODgwadIk1q9fn67szJkz1KhRw/QL4UmSkpLYuHEjhw4dMiX+mjdvHq1atWL+/Pk4OTml23/r1q14eXkxevRoAFq3bs2AAQNYsmQJDRo0YOXKlfTt25cmTZoA8hD65cuXs2nTJoYMGZLjz55TWgY15+/wk3zy2+fUKVULP2UJrK3M28AxGo1ExT8mUpO+p/woUNzNl2KuPi+cXl+SJHTaWJKjH2Aw6Ii7tINLcXdxcPemW63OL3RuS/H0SImsKFG/AZ03bGZ7765c3rAOV/8Amnwyy8LRWQ6VJiXT7LAg92Sr41Pw8cr9PP3CTk7rw8uO8CAjPMg8j4cqQcU4/ds7DHx/G7v+us5b47fzz6UHfD29Hba2hfMFXl7Xh3bt2vHqq6+ye/dutmzZwsCBA+nRo4epw6J69eq89tpruLhkzGTu4OAA/H86WNqfjo6O2ZblBHFfyAgPMsKDjPBQtBzkuIG9YMECFi9ezOPHj6lXrx7ffPMNtWrVMpUvWbKEadOmodFoctXAbteuHQMGDMDGxuaZDez169fj7+/P2rVrM5SdOXOG5ORk6tevz+3bt6lcuTJz587llVdeISwsDJ1OR/Xq1U37V6lSheTkZK5fv57ucwBcvnw53b5p+6cNE798+TJDhw7NUH7x4sUsY09NTTVlYQc5yUlOcbZ3YkqHiey7sp9/7pzjSuS1DD34L4pRMnLw2mF0Bn2GMltrG1pVbGG2OdJGnZaEO1cx3rtJgCqR+lYeSKXaILXsXuB6sW/evJnjpAxlO3Si7eKl7Bv1DifnzsalpB813xlh4Qgtg9LNAaW7Q6aNbKW7A+6uDvkQVf5z5coVqlWrlt9h5DvCg4zwIPO8HtzdHNixqi+fLTjMzG8Ps3jNaS6GPmLz973x9Sl8y1vlRX2QJIk333yT4sWL89VXX5m+W6SN1luxYgUPHz5kxowZREZGolKp0q34kkbaCiD37t0DICIiAoVCQenSpYmKisqyLCeI+0JGeJARHmSEh6LlIEcN7EWLFvHBBx+Y/n3o0CHat29PSEgIer2evn37cvToUSRJynUvZ/HixXO8b9p8oMxwdHSkYcOGfPbZZ3h6erJkyRLatWvHpUuXiP8v8dSTCUXSeq0zm4cdHx+fIfmIk5OTad9nlWfGnDlzmDlzZobtoaGhuLi4UKlSJW7dukVqaipOTk74+fkRFhYGyI4kSaK8TRnKlytDxYoVuXv3LsnJyTg4OFC6dGmuXbsGgK+vL1ZWVqa15ipUqMCDBw9ITEzE3t6esmXLcuXKFUCe42VnZ0d0dDTuxdypPLNulvF/0e1THLA3zcEqU6YMsbGxqNVqrK2tqVy5MpcvX0aSJDw8PHB1deXuXXlIe+nSpdFoNMTFxaFQKKhatSpXrlwh4cppYrd8TWrkbc5+NpjQLUupNfE7nDx8cHB0Qhuvwt7Ng3iNmqhYFfb29nh7e5uy2Pv7+5OSkmJaPqRKlSrcuHEDrVaLi4sLxYsXN809K1myJHq93vTFoVKlSty+fZuUlBScnJzw9/fn+nV5qS0/Pz8crBXYOTji5+mGUaclMV5DZIwcf2BgIFevXgWgWLFi2NjYmBI3BPUbSMTlEK4sWchf40bj5OtLalm5ge7t7Y2DgwMRERGA/CYvOjoajUaDjY0NlSpVIiQkBABPT0+cnZ1NX3ACAwOJi4vL0rebm5vp/6ZUqVLEx8dn8G0wGHB3d8fT09M0xy4gIICkpCRiYmIAqFq1KtevX0fpWYyxQxry2YLDGerC2CENiVOp8fZy5+bNm6YvdyVLljTV2RIlSmA0Gnn0SB4RUbFiRe7cuUNKSgqOjo6UKlUqXZ1VKBRE/peFvUKFCty/f5+kpCTs7e0pU6ZMlr7Lly9PZGQkCQkJ2NnZUb58eVNm3tz6dnFxMdXZwMBAVCoVKpUKKysrqlSpQmhoKBqNhoiICJRKJbdv3zb5TkhIIDY2FoBq1apx9epV9Ho9bm5uZq2zz3pGPOnbnM+I+/fvA1CuXDmioqLQaDRcu3aNoKAgLl++DICXlxdOTk6mOmuOZ0RanfXw8DD5DggIIDExMU98Z/WMSPu9pdFoCAkJISgoiIiICJKSknBwcMj2GWHOOpufzwidToerqyvFihUzefDz80Or1fL48WMAKleunKNnRK/XfKhRuTeDxm3nyKk71GoXzLblfXB1iC9Uz4j4+HhCQkJQKpUkJelJSU4mPiEenc4Rg8Fg6gl2c3MjISEBo9GIJEkYDAYePXrEpk2bqFKlCi1atDDtmzbdKG3otr29PZ6enuzatQt7e3vTc7V9+/YYjUbu3bvHpk2bSEpK4urVqygUCjp16oRWq+XgwYOEhobSu3dvGjRogJubGytWrCA8PJz9+/fTsGFDFAoFtWvXNpXduHGD/fv307hxYxQKhWnqXXx8PKmpqcTHy1PznnxGJCUlERISgq2tbZF+Rmi1WlP9KcrPCIPBYIrpeZ8RUPi/R4D8nd9oNKJUKovk9wiNRkNycjJRUVHEx8cXqGdE2r1rNqQcULNmTUmhUEj16tWTxo8fL5UpU0aysrKS5s6dK1WqVEmysrKSFAqFVK5cOengwYM5OWWmADk6fs2aNVLp0qWfuV+VKlWkxYsXS+fOnZMAKT4+3lSm0WgkQLpw4UKG48aOHSv17Nkz3bZFixZJtWrVkiRJkmrUqCEtXrw4XXmPHj2kcePGZRlLSkqKpFarTT/37t2TAEmtVj/zc+QFqbpUqcyUqpLneP8MP2WmVJUSU5Ok07f+Mft19akp0tW1X0i/veol7R9UV0qJeyxdWTVb+uP1AGlHS1fpj9cDpCurP5f0qclmv3bWMSVLV1Y/fwxGo1H6c8Tb0lcOSAs8HKX7J/62cMSWITlFJ834+oCkrDpHwv8TSVl1jjTj6wNScoouv0PLNyIjI/M7hAKB8CAjPMiYy8OVsCipYotFEv6fSPblPpPWbDpnlvPmFU96CLn6SHp98EYp4kHWv+NbtWolNWvWTJIkSbp3754UFBQkffjhh+n22bp1qxQUFCRt3rzZtE2tVksffvihVK9ePally5bSypUrJaPRKEmSJMXFxUnvvvuuVKNGDalNmzbSzp07Tcd9/PHHUlBQkHT79m1JkiTp4sWLUvfu3aXatWtLw4YNkx49emTaN7uyNKbM/Uv68vtj2XooyggPMsKDjPBQsB2o1Wqztsty1IN98+ZN7O3tOXjwIC4uLgwZMoSaNWsyffp09Ho9VlZWvP/++3z++ec5nqNjbqZOnUqvXr2oXbu2aVtqaiqOjo5UrFgRW1tbLl++TMOGDQH5LZKdnR1BQUEZzlWtWjX27t2bbltoaKhpWEO1atVMb1ueLO/YsWOW8dnb2xfoyf16o553mw1l/t5vM5QNazqYQ9eO8OaaYQxs2JdPX5+Ch7N5skhb29lT8a3J+LXqRaoqilvblnJ9/Zemcl2Cius/zAUkSnV4E83Ny1mfzAy4la3K3d3ruf7DvExigPL9xmHj4JTV4YC8Hviri5eSGPmQW3/+wa+9OtPv4N94VshY1woyDvY2TBzRhKljmqPSJKN0c0SnN+Bgb5bUDYWSzHI8FEWEBxnhQcZcHiqV9+H0b+/w5vu/snPfNYZM2ME/lx7wzYx22NkV/OdObj0cOHDA9Hd/f39mzJhh6o1Jo0ePHvTo0SPdNjc3N+bPn5/pOZVKJcuWLcu0bO7cucydO9f07xo1arBt27ZM982u7FmI+0JGeJARHmSEh6LlIEe/sRITEylRooQpUUblypUBMBgM+Pv7s3HjRpo1a2a5KHNASEgIR48eZfPmzXh4eDBv3jw0Gg3du3fHycmJPn36MGnSJDZv3gzApEmT6NevX6YvBHr06MHEiRNZsGABo0eP5tixY2zcuJEdO3YAMHToULp3784bb7xB06ZNWbJkCY8ePaJ79+55+pnNiZOdE+PayEndlh9djTpZjbujO+82G8r7bUax4K8lAGw49TN/Xt7HrC4z6F3XfHOmXUpVwKlEaU5P7Zdp+a1tyyjfdxwXvhyFVh1jlms+jZ27F21/CuHWtsy/nNzatpSggR/m6FxWNjZ03rCZze1aEXn2DNu6tKffwb9xzsWUiIKAs5P8MFTHPaSYdwXs7Apn8iFzcf/+fTw8iuYSZU8iPMgIDzLm9ODm6sCvK/swe9ERPvn6EEvWneFi6CO2LO1N8WKuZrmGpXgRD9HR0WzdupUlS5aYOaq8R9wXMsKDjPAgIzwULQc5amBLT82ttrH5/2G7du2iRo0a5o8MeTmLZcuWMWDAgGfuu2bNGiZMmEDNmjVJTEykQYMG/PXXX6as4cHBwUyYMIHq1auj1Wrp2rUr3333nen4qlWrMmDAAKZMmYKXlxf79u3j/fffZ8aMGab1KFu1agVAmzZtCA4OZuTIkURERFC1alV2795tulZhxcHWnrGtR/BB2zGoktQondzRG/U42jowucMEWlVsxgdbJnPt0XVG/vg+P53Zwvyen1O+mHmyAuoS1OgSVFmUqdBq4vCp15qkB7fMcr2ncSpZBq0mNtsYdIka7JXeOTqfrbMz3bft4qdWr6C6Gc62Hp3os+cQdq4F+0tiZjyZoE8gEAgsiZWVFTPGtaROtRIMGLuNY2fuUrfjcrat6EPD2lnnYilIpL17NuZwyUZvb2+2bNmCtXXheYlplCQKVlpSgUAgKBgoJOnZT38rKyv8/f3TDV2ysrLC19fXNMHdEixbtgxPT0969+5tsWvkFxqNBnd3d9RqNW5ubvkdTgZSUlJMS3U8iVavZcmh5Xy1dwEp+lTsbewZ32YMY9uMxN7mxYbAG3Va9vQon2kD19ZFSbttN7CytezwEkvEEBd+g59avULy48cEvtqOblt/w/q/dUULC8nJyfk2/aMgITzICA8ywoOMJT1cvxlNt2E/cyUsGjs7a4I/78TbfetY5FovypMeHkRqGD55FzPeb0H9Wn4Wv7beYMRKoUCnN2BrY41RkrCxNs/KH5khSRJvf7iDV+oFMKxf+gSp4r6QER5khAcZ4aFgOzB3uyzHT1+VSsWoUaNMPyAPHX9y26hRo0xrR78okiRx5swZOnToYJbzCXJHWhbBp7GzsWN82zEcm/gXrSu2IFWfytw9X9P8q3Ycv3Hiha5pNOgp0yPzZa3K9BiBMZMlxMxNtjF0f5eUmNy/UPIoV57uW3dh4+TE7X172DfqHXLwXqtAkZaBsqgjPMgIDzLCg4wlPQSV9ebUznfo3r4SWq2BYR/tZOTkXWi1lv99kFue9FDC1xW/4q7s2n8dg8G8y2o+jcFgJPx2LHuPhLP3cDh7j4QTfjvWotc9cfYej2OTaJDJywNxX8gIDzLCg4zwULQc5DhrSGJiYobEGQkJCem2SZKEQqEwyxwihULBypUrX/g8gucjbWmzrCjjHcjmd9fz64WdTN0+kxtR4XQJfoN+9Xszs/M0vFxyP1zexsGJCgPk5eBubVuKLkGFrYuSMj1GUGHAB1jbWX7t5Sxj6P4uZXqM4O8PXqfS0GmUaPp6rs5bon4DOm/YzPbeXbm8YR2u/gE0+WSWJT6CRXhWfSgqCA8ywoOM8CBjaQ+uLvb8suwN5i45xrT5B1i64R8uXX3E9hV9cXKyxdbGGpUmBaWbAzq9wZQ7Iq950oNCoWDIG7WZ891Rxs7YTaM6/ri5mD/RqcEoERkVz/1H6f8Pjp6+g5+vK8WLuWJtZb6B3HqDkes3Yzh94T6NavtTrWKxDPuI+0JGeJARHmSEh6LlIEcN7ObNm5stmZWgcGCbgyHMCoWCHrW70qZSSz77fS5r/97AT2e2sCf0L2Z2nka/+r1zXW+s7Rwo328cQQM/RJeowdbZDaNBlyeN68xiSI1XYe+qxKjXcm3tHOJvhfLPp4Oo98k6SjTrnKvzlu3QibaLl7Jv1DucnDsbFz9/ag4bbqFPYV5yUh+KAsKDjPAgIzzI5IUHKysrprzXnFpVi9P/va3EqpJRWCn48vvjLFpzCpU6BaW7A2OHNmTy6Gb5strB0x4a1vZn9ket2X3oBn8du0lSss7s1zQaJQ6fvI1On7G32tbGihaNArEyYwPbxtqKkr6uDOpZk86vBqXLz2O6rrgvAOEhDeFBpiB7iE9I5d+rj0hMMv8z6knuRUTzMDbcotdwc7WnWsVi+faiNY0czcEWmJ+CPgc7bTRCbjhz+ywfbJlE6MOrADQp14ives0hyLe8JULME570YDToOT9nOPf3b0FhbUPdGWsp2bxLrs/59+xPOfH5TBRWVnTZ9CvlX8/9OfKa56kPLyPCg4zwICM8yOS1hxu3Yrh9X83hk7eZvfBIhvIZ41swcUSTPP+ClZceYuOS2LzrMu1blqfMKwuz3C/qwkf4eDnnSUxpiPtCRniQER5kCqIHSZJY9fM5dv11HYMxD5qDEuRFZkQbayt6dqzMgO41cuzc3O0yszWwU1NT2bJlCytWrODw4cPmOOVLTUFvYIeEhJjW/c4NOoOOpYdXMW/P1yTrUrCztuP9NqMY12Y0DrZ51wttLp72YI5GtiRJ7B31DiFrV2Hj6EjvP/ZTslFjc4duVp63PrxsCA8ywoOM8CCTHx60WgO+deajUqdkKFO6O/Do3Ed5vqxgXnh4EKnhmxUnWLbxLA72Ntw+MQ7/Bt9k6SHy3IfY5/Ea4uK+kBEeZIQHmYLo4ZffL/PDLxcZ0L0GbZqWwcvDyaIvATQajUXbPJIk8TgmkT2Hw9m86zIj36xHx9ZBOY4tX5KcZUVoaCjjxo2jZMmSvPXWWxw7duyFgxIUUbFeiAABAABJREFUXmytbXmv9Qj+/vgAr1ZujdagZf7eBTT/6jUOXy/8dcPK2oY6k5fj1/YNJIOes58N5sHhHbk6h0Kh4NXFSynTviP65GR+7dWZ2LDrFopYIBAIXj5UmpRMG5UAKnUKKk1yHkdkWcJvxzJ80m+UabKQr5efICFRS0lfVx49TmDs0IaZHjNmcAP2HQ3nt33X8jhagUBQ0JEkiX1Hb9K6SRn6dKmGt6dzgethzy0KhYJi3i682bMmr9QN4K+jN/MtludqYKekpPDDDz/QtGlTqlevzuLFi4mLi0OSJKpWrWruGAX5gJeX1wsdX8ozgJ+GrWXNW0vxdStG+ONb9FjajxEb3+dxfLSZorQ8mXlQWFtTZ9KyF2pkW9nY0HnDZorXrU9KTAzburQnMTLSXGGbnRetDy8LwoOM8CAjPMjkhwelmwNK98xHRSndHXB2suPN97fxex5k8U7DEh4uXYmk/5hfCGqxmOUbz6LVGmhSP4Df1/Xnwp4RlC3tyeTRzZgxvoXJh9LdgRnjWjDu7UZ8NHsfXYb+xITP9uRZ5nVxX8gIDzLCg0xB85CYpOXBo3jqVCuRZ9e0s8u7aTu1qxYnzMKrKWRHrhrYISEhjB07lpIlSzJkyBD+/vtv03JDH374IRcvXuTSpUsWCVSQtzg5Ob3wORQKBV1qduLkxwcZ1nQwCoWCLWe30WhuSzac/BmjMX8qfW7IykNaI9v/1T5IRsN/jeztuTq3rbMz3bftQlm2HOrbt9jWoxPaApph0Rz14WVAeJARHmSEB5n88KDTG7LsuX1vcAP2Hb3Jhm2XeH3wj5R5ZQEzvz3EvQdqi8ZkTg/Hz9zl9cEbqfnaUn7aEYLRKNGhVXmO/DKEY9vepmPrIFNvk4O9DRNHNOHRuY+IuvARj859xMSRTXB1tqN9CzkHyjcrTtC81xruRKjMFmNWiPtCRniQER5kCpoHrdYAgKND5snX9Ho93bt3p3nz5tmeZ+/evVSsWJEtW7ak2x4ZGUnNmjX58MMPTdusrf8/bSc4OJiKFSty6tSpTM8bHR3N8OHDqVOnDt26dePixYs5Kksj7XPpdIZs47cUOWpgr127lldeeYWaNWuyZMkSVCoVdnZ2vPHGG6Z9Pv30U6pXr26xQAV5y71798x2LjdHN+b1mMWesTuo7lcVVbKa9zd/ROclvbgaWbCHRmfnQWFtTe2Pl+L/at//GtlDct3IdipWjB47/8TRx4eo8+f4bUBvDDrLZnF8HsxZHwozwoOM8CAjPMjkhwdnJ7vMe27Ht2DKe82pUakYH7zTGC8PR+490PDpN4cIbLyAzkN+5Ld919Drzf+l60U9SJLEnwfDaNFrDU17rOb3/WFYWSno07kq5/8czh8/DKRZw9KZHuvsZIednTU+Xs7Y2Vn/928bvv20Pb+u6IPS3YFT5+9Tq/1Sduy5+kJxPgtxX8gIDzLCg0xh8qDRaBg3bhyhoaHZ7nfgwAEmTZqUYXt4eDhDhw4lJSX9NJ7kZHnqzo0bNwgODs723FOnTuX48eN0796duLg4hg8fTlJS0jPL0sjv0e45amAPHTqUU6dOIUkStWrVYtGiRTx48ICff/7Z0vEJXiLqlq7NX+N2MavLdJztnDh56wwtv27P7D/mkawtmPPl7O2zX7dUbmR/n76RfejXXF3Do1x5um/dhY2TE7f37WHfqHcQyf0FAoEgezLtuR3RBAd7G8qW9uTrGe2IOP0BPy7uScvGgRiNErv+uk6XoT9R5pWFfPrNQYv3aucEg8HI5t9CqNtxGR0GbeTIqTvY2loxrF8drh4aw8/BvalV9fmHcXZrX5nzu4fToJYfKnUK3Yb9zPhP/8yzIeMCgaDwkJSUROvWrTl//jzOzlmvQBAcHMzIkSPx8fFJt/3UqVN06dIFgyHzl5gGg4EpU6ZkO987ISGBI0eO0KpVK6ZPn867775LXFwcJ06cyLasIJGrIeKOjo40atSIhg0b4uHhYamYBAWAMmXKWOS8NtY2jGr5Ln9/fID2VduiM+j49q/vaDq/LQevydnnk7RJaPVaohOi0eq1JGmTnnFW85MWg7K4xzNjMDWyX+snN7JnDeX+wW25ul6J+g3ovGEzCmtrLm9Yx9+fzXjRj2BWLFUfChvCg4zwICM8yOSnh8x6bp/EwcGWft2qc3DzYK4eGsOHw1/B29OJiIcaZn57mMDGC3h98EZ27r36wr3aufWg1epZ9fM5Krf6jj6jfuF8SCTOTrZ88E5jbh0fx4ovu1ChjHnmbQYGeHB06xA+eEdesWLBqpM07bGaW3fjzHL+JxH3hYzwICM8yOSXh8QkLVqtgajoRLRaA4lJ2mz3NxqNdO3ale3bt6NUKrPcz8fHh0WLFjF8+PB0262trRk/fjxLly7NcIyTkxNr167l6tWrDB06NMtz37lzB6PRiL+/PwABAQEA3Lp1K9uygkSOGtjjxo3D29ub5ORkli5dSqNGjahWrRrz58+3dHyCfCI2Ntai5/f38GPD0NWsG7ycEu7FuR1zlym/fooqSc2i/d9T6ZM6VJxRm0qf1GHRgaWk6FItGs+TpOhSWXRAjqHSJzmLQWFtTe2JwQS0649kNHBu9tu5bmSX7dCJtovlB9L1X38hNuw6Bq2WpMdRGLRadEmJL/S5XgRL14fCgvAgIzzICA8yhcVDxXLezJ/2GhGnP+DnJb1o3aQMRqPE7/vD6Pr2z5RutIAZXx147nnKOfWQmKTl2xUnKNtkIcM+2knYrVg83B34ZHwL7pwcz9cz2uFXwvxL2djZ2fD1jHbsWNUXD3cHzlx8QO0OS/l19xWzXqew1AdLIzzICA8y+eEhJVXPl98fx7fOfHxrz8e3zny+XHqclNSsR6+4uLgwffr0DD3TT9O7d2/atWuXYXu9evUYNmwYtrYZ53bfuHGDRYsWMWbMGEqXzny6C/x/KHlaUrS0cyUnJ2dbVpDIUQP7m2++4f79+2zevJnXXnsNhUJBaGgokyZNMnXxr1+/XtxELxFqteWHzSkUCl6v0YETHx9keLO3mfH6ZIIPr2D+vgWok+Xrq5PVzN/7LQv2L8mTnuwkbRIL9n/H/L25j0FhbU2tj5YQ0H7A/xvZB7bm6vo1hgyj9TeL6bPvCFd+XM/3pX35vpQv35f25czXX6JPyXxZGkuTF/WhMCA8yAgPMsKDTGHzYG9vQ58u1dj/81tcP/IeH42Qe7UfPIpn1sIjlHllAR0HbWDHntz1aj/LQ2xcEp8tOETpRt/ywWd7uB8ZT0lfV76e/hp3T43n0w9a4eVh+URIXV6rxPk/R9Cojj9qTSo93t3E+5/sJjWbL925obDVB0shPMgIDzJ57SExScuc747y2YLDpiUNVeoUPvv2MHOWHCUxOe/z/Xz66ad4eXnRpk0bYmJiAHj06BEJCQnp9kubnqnVatP96eDgkG1ZQcImxzva2NCrVy969erF/fv3Wb16NWvXrjV1yY8aNYqxY8fSunVrdu/ebbGABXnDk5n+LI2rgwtfdP+UVH0qo3/6INN9lh9dzdjWI2j77euoklQWiUPppGTn6M0sP7omyxg+aDsm23MorK2p9eF3ANz7cyPnPh8GgF/rnjmOo9qgIZz+eh4n5842bUtVqTjxxWcA1J8wEVunrOfFWIK8rA8FGeFBRniQER5kCrOHCmW8+HLqa8z6sDU79l5l+Y9n2X/sFrsP3mD3wRuUKObC233r8Hbf2gQGZD81LqsswQ8iNXyz4gTLNp4lIVH+MliutAcfj2rKoJ41sbfP8Vcxs1HaX8mRX4YwZd5+vlr2N4tWn+L4mbts/r43ZUt7vtC5C3N9MCfCg4zwIJPXHmxtrFm0JvMM3YtWn2LkwPp5Gg/A+fPnAejYsaNp20cffcScOXPo0aOHaVupUqVQKBSmxHARERGAPMw+u7KCxHM91f38/Jg+fTrTp0/nwIEDrFy5ku3bt5OSksLevXvNHaMgH6hcuXKeXzM+Jd7Ua/w06mQ10QkxpOhTuRVzxyLXr2znyOP46Gxj0KTE4+2S/Zy4/zeyFdz7cwNnP38bkPBr3StHcVjZ2nL++8WZlp0LXkTDj6fm6DzmJD/qQ0FEeJARHmSEB5mXwYO9vQ1vdK7GG52rceNWDCt/PseazRd4GJXA7EVH+HzxEdq1KM+7/evyetsgbG3//2U5MUmLrY01Lm6+aLUGdHoDzk52hN+O5culx1m75YJpSZyaVXyZPLoZvTpVwdo6V2lwzI6trTXzp71Gy8aBDBr3K2f/fUjtDstYNb8LvTpVfe7zvgz1wRwIDzLCg0xeetBq9UTHJZt6rp9GpU4hKjqRh1EJqDQpGAzGZz6P4uPjWb9+PUFBQbRt2/a54lq4cKHp76dPn2bjxo289957NGzYkKtXr3LgwAFatWpF5cqVadSoEYcOHWL27Nns27cPpVLJK6+8gpOTU5ZlBYkXfm3aunVrWrdujUqlYsOGDaxevdoccQnymcuXL1O16vP/gn0e3BzccHd0z7SB6+7ojq9bMRb3+Qq90TKZT22sbCju7pttDG4Orjk6lzxcPK0newNnTT3Zz25kp6pVpKpUmZepVKSq1Tg9Y26MucmP+lAQER5khAcZ4UHmZfNQvowXcye/ymcTWrFz3zWWbTzLX0dv8uehG/x56AbFi7kw9I3ajHyzHt5eznz5/XEWrTmFSp2C0t2BsUMaMm5YI7q8/ROh1x8D0LR+KSaPaUqHVhWyzZ6bH3RqE8SFPSPoO/oX/v7nHr1HbGHM4Dt8Ne215+pdf9nqw/MiPMgIDzJ54eFaeDTLN55l1/5rnNs9AqW7Q6aNbKW7Ax5KBx5Eavj36iN0eiNeHo74eDnj4+mEi7NdhueUWq1m4cKFdOnS5bkb2E2aNMHVVf4enbasVv369fHz82Pbtm0sXLgQHx8fKleuzFdffcW0adPYtm0bAQEBfPrpp6ZRQtmVFRTMNi5JqVQyZswYxozJfgitoHCQH8tE6Y163m02lPl7v81Q9m6zoRiMBmqXqmnRGJK0SVnGMKzpYFJ0KdjZ2GVyZEYUVlYZGtmSJOHfpne2x9m7K7FXKjNtZNsrldi5mT/5zbMQy4bJCA8ywoOM8CDzsnqws7OhV6eq9OpUlfDbsaz8+RyrN50nMiqBL747SoPafpy99IBZC4+YjlGpU/hswWGMksTnE9uwfOM/TB7dLMv1qwsKASXdObR5MNPmH+DL74/z3drT/H32HpuW9KJ8LjOZv6z1IbcIDzLCg4ylPKSk6Ni2+wrLfzzL4ZP/H+F59PRd3hvSkFkLDmc4ZuzQhjyKTkTp5oCtrRUGg5Go6ESiouVkuo4ONvh4ObPhx+14e8pTEv39/ZkxYwZ3795Nd64ePXqkG96dhr+/P9euXUu37UkHTx/39L+9vb0zzUT+rLKCQt5P/BEUCvJjGTYnOyfGtRkNyPOd1clq3B3debfZUMa1GY2DbfZrUlsyhmFNB/NusyGM2zyRb9+Yh7uje47Ol9bIVigU3N29nnNfvAOShH/bN7I8xqjXUWfUWNOc6yepPWIMt/fuJvLsPzSe+gnWmWRptARiWT4Z4UHmWdlFiwqiPsgUBQ/lAj2ZM6ktMz9oyW9/XWfTzhDaNi3L4A+2Z7r/d2tPE3nuQ7q1q5S3gb4AtrbWzJvyKi0alWbQuF859+9D6nRcxsovu/BG52o5Pk9RqA85QXiQER5kzO3hSthjVvx4lnW/XCRWJWfQtrJS8HqbIN4dUJdXm5WlZeNAFAp5zrVphM3Qhkwe3YykJC3lAj1pWNufSuW8eRyTxOPYRGJUySSn6Ll7X83d+2pQIDfEFSls2rSF778Pfu6YM8ss/rKikMSrpXxBo9Hg7u6OWq3GLR96JJ+FRqPJt7iStEnYWNmgSYnHzcEVvVGPk13eDv0wxZCswc3RDVWSmkFrh3Hm9jlqBdRg6/CNKJ2UOT6fZDRy8eux3P3jB7Cyos6kZfi/2ifL/fUpKZyeP4dzwYtIVamwVyqpM2osdd4bx08tXyH22lWK161Px7Ub8ShfwQyfOHsyqw+G1GRiLv2N6to5dAkai8dgaRQKBbauSpSV6uJVvTFWthlHKuTnfaFPScLK2gZdghpbF3eMBj02Dnl7X6TFoE1QY5dPMRQk8rM+FCSKqodHjxMoXuerLMujLnyEj1feJqQ0FxEP1fQd/QvHz8iJhEa+WY9vZrTDweHZX5CLan14GuFBRniQMYeHlBQdv/wRyvKNZzl6+v89yQEl3XinX12G9KmFf4n0HUBpOSLU8Sm4uzqYckTExiXx1gfbmfF+C+rX8jPtbzAYiYlL5nFsIo9jkohP+P8StUajATs7W7w8nPDxcqKYlzNOjrY5nvai1+uxscmbvt0jJ28zf9nfbPm+d46fW+Zsl4kGdj5R0BvYISEhVKuW8zfWLythYWFUqCA3YEPuh9J9aV9iE+PypJGtS0rEysaWVLUae3d3jDodts7OXPtlM/veG06qSoWtszMt5y+g+uC3LTqv7+n6oE9O5Orq2SQ+uIlrYGXsPXxQULDmFeYWSTKSEhNJwt3ruJWtRtCgiVjbpV/2Ib/uC4M2hbCNX3Nr2zJ0CSpsXZSU6TGCCgM+yBDjyxxDQUM8J2WKqget1oBvnflZznF8dO4j7OwKbwZlvd7AjK8OMmfJMQBqVS3O5u97U+EZQ8aLan14GuFBRniQeREPodejWPHjOdb9coG4/5431tb/761u16J8rpMmxiek0v+9rXw8sglNG2SzJnWKjujYJB7HJPI4NsmUsDENJ0dbfLyc8PFyxtvDKV0CyDT0BiNWCgVanQE7W2uMkoSNhZM87jsSzqI1p/h1RR9sbJ79HDZ3u0wMERcIsiE19f9v7qr5VWH7yE10/74vF+5dosfS/mwb8WOOG9kKKytqTlgECgV3f1/HubnDkZAIeLVvpvunLcWVltDM2k7uUa3Y6w1KNGzMn8MGce/IIfaNeodbu3/n1eAVOHl7v8CnzTkPDm8n5fEDqo2ei3PJgrU0wouiuXWFq2tm8+jkXko275Lf4aBPSeLGT99y/Yd5pm26BBXXf5gLQPl+4yzei1wQYhAICho6vYGxQxvy2beZz3HU6Q2FuoFtY2PNF5Pa0rxhad4c9ysXLkdSp8MyVszrTN+u1fM7PIHgpSY5+f+91cfO/L+3upSfO+/0q8OQN2rjV+L5G4IuznZ4ezgRci0q2wa2o4MtASXdCSjpjiRJaOJTTY3tWFUySck67kSouROhRqEApbuj3OD2dEbp5oAkSYTfjuXWPRU6nQFbW2vKBCgpH+hp0ZUU/r36iIASbjlqXFsC0YOdTxT0Huz4+HhTpr+iTGYeLj+4Qvfv+xKTGEtN/+q5amSD3JN96Ztx3Pl9LVhZUXvS0iwb2c86zz8LvubYp1Mx6nQ4Fy9B+xVrCWz7Wq7P9Sye9nDxm3G4BlaibI8RZr9WQSBs49doNbFUHfl5uu15eV9IRiOpcY+xdVWyt2cFdAmqDPvYuih57ZdrhARPRp+UYJE4bJxcqDZqDnt7VcwyhnbbbmQ6pP5lRzwnZYqyh5RUPXOWHM10jqNDPqxvbSnuP9TQb8wvpmGpwwfW5dsZ7XF0zDj0sijXhycRHmSEB5mcerh8LYrlP57lh60XTaNjrK0VdG5bkXcH1OW15uXM1jBdt+UC2/dcZczgBjRrUAo7u9w9s/R6w3/DyeUe7oREbbryBrX8UGlSuH4zJsOxQWW9KBfoafae7NRUPfuP32TZhrMM7FGD3q/nLHO76MEW5AkajUY8EMncQ9WSldk+8me6fd+XixH/PldPdo0PFoBCwZ1dazg/ZzhIEgGv9ctVbAorK+p/8BGlWrfljyEDiL16ha2d21Fn9Ps0mz0XGwfzDdt92oNWFY1T8YKdFfdFcCxeivi71zNsT0pKMtt9IRnlIelJkXdJfnRX/jPyLkmP7vy37R7O/uVp8PnPmTZsQe5FTo2LIvbfk8TfCjVLXE/jWqYKKbGPso1Bl6jBXpk3oycKEuI5KVOUPTjY2zBxRBOmjmmOSpOM0s0Rnd7wUjWuAfxKuHFg01t8+s0hvvjuKMs2nOXE2Qg2f9+biuXS3/vmfE4WZoryffEkwoNMdvdFcrKOLb9fZtnGs/z9zz3T9tL+7vLc6jdqUbK4+TvjBnSvzsOoeBasOsmSdadxdbHH6gWmG+r0BhKTdCQkaklJ1VG2lCdht2MwGDL25VpbKwgq42XW6Y3G/3rY9QYjbZqUoWfH/FuD/eX6DSAwG3Fxcfj5+T17x5ecrDxUyaSRvXX4Rjycc5YlUmFlRY3x8lJgd3at4fxcuSc4t41sAN9atRl4/B+OTJnIhWVLOLdkIXcP7afj2h/xqWaeYXxPe5AkIwqrzN866vV6evfuTUxMDEeOHMlQrtPpmDdvHrt27cLJyYmRI0fSu7e8dNm9e/eYNWsWZ86cQalUMmrUKFNZamoqderUQa///zrovXr14vPPP89wjYsXL/Lpp59y9+5d6tWrx+eff473f8PnsytLQ2FljWT8/zyjtORe7vY2GHXaHCX3SteAjrxD0n+N6LR/J0dFYNRpsz2HVhWNvUcxbF2UWfYe23v4Eth1GEZtxnmg5sDKzgEHz+LZxmDrXPBG4eQF4jkpU9Q9ODvJozfUcQ8p5l2hUA8Lzw4bG2tmT2xD84alGfj+Ni5deUS9TstZNud1+nevYUqmJCmc0GoNpmRKRZWifl+kUdQ9ZHdfhFx9xPIfz7J+26V0vdVdX6vEu/3r8mrzslhl8V3LHNjYWDNpdDMiHqo5HxKZLqHZi5LW2P1h68Us9xnUsyZuLuZbIUihUODqYkfd6iUp4Zu/L3VEA1uQKZZMmFWYyM5DlZKV2TFqU4ae7Fw3shUK7vy2Wm5kSxIB7frnOk5bJyfaLPiOMu06sGfEUKIvh7CxST2azZ5HndFjs2wM55Sc1geNRsOUKVMIDQ3F19c3031++OEH1q9fT8eOHbl//z7Tpk0jKCiImjVrMnbsWMLDw+nfvz+XLl1i2rRplCxZkiZNmhAWFoZer6djx46mxHNVqlTJcP7k5GRGjBiBnZ0d3bp1Y9OmTUyePJkVK1ZkW5YVBm0qCfdu4uxXBq1Gg7WdA4n3b+FaWh62nXj/JkmRd+TeZ9OP3ICW9LrsvVpZ41jMH8fipXD678fRtxROxUvjVLwUDj4lMeq0lOkxwjTf+UnK9BiBJBkp03VYttd5UfQpSVnH0P1dEu7fxLVU0AvXs8KGeE7KCA8yWm32L8xeFl5rUZ4Lf46g/3tbOXzyDrMWHaHj/9g77+goqjYOP9s3m02vJKQCofcuRQQEsdBUiogiotIEEQtdmiIiCEiTqiKfCoKIAoJKR5r0EFoKpEBIzybZvjvfH0vWhBRCTST7nMNJ2Dvl7i937sw79y2dIvhixSEWrnm0XeXvBMd1YaMy66A3mPls6cEi18V7bz7Gm+N+5YdfIu3bhga522OrH7ZxWLWKW5Hs4/cDo9HCxDm7SkwEObhvk0f3hWR5d8BBxaRu3bLFLDzq3E6H2lVq2VeyzyRF3p2R/c48m5G9ZRUnZw9DEASCnxpwV/0N7/YMrxw9w85hrxO7fSt7PhhD3I5tPLX8a9QBAXd1TCjbeNBqtXTs2BGFQoGzc8mlabZt24aHhwfz5s0jJiaGZ555hm3bthESEkJUVBRPPvkk48aNIzc3l6ZNm7Jx40batGnDhQsXAHjppZeoVq0anp6exR7/yJEjZGRkMGnSJAYOHEhaWho7d+4kOzubkydPltjm5lb05mI26LAajZz734+cXLbIXjKt8bCRNB/zHjlx5zn0XsmJ0ERiCU5+QTj5BZVoQIslpU/DYomUGgPeBSBu07JyyeAtVaqK70OvNwnrPZSDo5/COTCcxh8uRe5WeobhRwnHPGnDoYONyqRDgL8rf37/CtPn76VpgwDmLv+bmQv+9VjKytbbk799MLRNpVzJrkzjoTQqqw55WiOfLT3I9Pn/JkHMvy6sVoG+z9Xjp61R9OhSkzdfakrndg92tbo8eNQTQZaGw8B2UCznz5+ndu3yi12oKJRFh/tiZI+eC8DVLas49dlwgLs2sp39/Oi58VdOr1jG3nFjufrXH3zTogFdFq+gRo9ed3XMsuhgtVrp0aMHQ4cOpW/fvoVcuQsSFxdHaGgoIpGIoKAg+2cuLi6oVCrOnDnDiRMniImJASA6OhrAbmC//vrrGAwGGjRowMKFC6lSpUqR4wNUrVoVgKCgIKxWK1evXi21rUGDBrd+IawmM8e++JzDn860f2zIyuLwLNv/m495H7eIxsicXW4azvn/QnDyD0bpXeW2BnRZkMiVVO//DhEvv4cpT4PM2RWrxfRQy2MV7IMhJwuFiztWs4kbh35He/0qufGX2PtmO5pOXoNnvZYPrV8FEQSBvMRosi+fwazLe+Dnu379epHxdz8RicXI3b3wqNMCRQV+ceG4X9iobDpIpRKmv9cRvcHMoHc3F7vNwtVHmDiy/cPtWAWhso2HkqisOsikEhauOVJs26Kvj3Ltn7EkHn0XPx/1Q+7Zw8NZJWf8iHYAj3wiyFt5dL+Zg3vCYrHcfqNKQFl1qF2lls1dfInNyO61rD+bhn6P5x0a2SLgypZVRP8wH896rVD5BWHKzUamditT3K/9eCIRjd4cRlD7Dmx7bQApp06ypV9v6g16nSfmzEeuvrMJvSw6qNVqJk+efNvtdDod8pslx2Qymf0ziUTC5MmTmTJlCv3798fPzw+1Wo1OpwNssduhoaG89NJLZGRksGzZMqZNm8ayZcsKHV+r1QIUe47S2sBmoJnzNOhSEsm8eAKJTM7JpYuK/R4nly6i1bhJPP5V0TezD4L8v31+MrHyyNqd34fE9GxqePoilskJ7Pg86uAI/pn+KnkJ0Rwc/RS1hnxE9b73HppwJ1hNRi6t/Yzs6DNIlCpkand4wK6JYr2erJzkB3cCiwVDdhpXf11DyDOD8G/z9IM71z3guF/YqKw6aHIMxbqAgm3FLi1Ti8FgIjTIo1K5C1fW8XArlVWHLI2+1OsiN8/4SBvX+RRMBJmZrcXDTfVIJoK8lUf72zm4a4pzl62M3IkOtfxrsnn4j/Rc0pezSefofRdGdv135qHw9CW0xxvEbVpG3M/L78kl2KtWbV7ae5iD06dwbN5nRH69isT9e3l6zTqqNG9R5uPcz/GgVCrtsYr5P5U3M5737t2b1q1bExMTQ/369enSpQvqmy8Dpk2bVug4e/bs4ciRom+H84916zmcnJxKbJNLJeRdi0OXkoRZm4MhKw3BYsaQnY0hK6vY72HIysKQnY3Kx/fuhPgPo7wlQ71b9fo8vmwvp+eOJmnXT5xfPoX00/tpPH75Q1t5jf99HTlXL1JjwFg8ajd/KMa9TqfDycnpgZ7DrMsjaddPXN36NaqAUFzDiuYdKG8c9wsblVUHd1cl7m7KEuMs3VwUhHZZispJRpf21ejSvhqd2oTh6VG2F8b/VSrreLiVyqiDxWLFRS2/zXXx8DzQypv8EBHBkodc7vLIuoUX5NFy9ndw3/DwKJtR+KhzpzrU8o9g8/Af8VF7czbpnK1edm5GmfcXiURU6zOKuE3LuLT2M3vWZlNuFpe+/ZTL6+Zh1mvvqE8SuZz2Mz+lz++7UAdWJSsmmu+feIxDs2ZgLcGN+1bu53gIDg7m2rVrWK1WEhMTAQgLCwNgxYoVrFixgrZt26LT6cjKyiIiIgKARYsWsXDhQuCmK3BeXrHGTUiIrXxYQoKt1EViYiIikYiQkJBCbYLVQnxsNCKRCHVmPDlXLmDW5tjccl3cUVetjtzNDYW7e7HfQ+HujszZmaRDB++bNv8VihsPUpULTSatouHYhYjlSlKO/MHeIW1IP3vogfdHsFrJOPs3fi274Fm35UNbOc/3gHiQSJ2cCX76FRTuPqSfrphjzXG/sFFZdciPsyyOt19ryfGz18nJMxCflM3K70/QZ9gGvBt+RsvnVjDl810cOHoVk+nRW+WsrOPhViqbDpocPT1e/56d+2IYOaj4hYz8+OPKhvoOvSf/yzgMbAfFcuXKlfLuQoXgbnTIN7J9XXyIvBZF72X978jIFktlxP28vNi2uE3L7jqmN6h9B149doaaL/RFsFj4e/oUfuzSgewrcbfd917GQ05ODkuWLOHPP/8EoGvXrmRmZjJ27FgmTpwIQLdu3QBITk5m3bp1TJ48mWHDhiESiexluuLi4li8eDEffPABo0ePJiEhgeeffx6AXbt2sWTJErKzs2nVqhVubm6sXLmSmTNn8tdff9GmTRvc3Nxo2bIlrq6urFi2lImjhrF7336a162Ji8oJmYs7ruF18Wn2BKoqoSBVEr/7LxoPHVns92o8dCRX//qDHzq25bdX+pOTlHTXGv3XKGk8iEQiQp4dRLslu1AH1UCfdo2/33may/+bh2C1PrD+mLU5mHKzUYfWfGDnKI78kIMHjUgkQh1aC11K4kM5353iuF/YqKw65MdZThnzOO5utlU5dzclU8Y8zoSR7WjfMoSMsx+y/dsBjBnSiroRPggCHD2VxIwF+2j3/Bq8Gsym5+vfs/TbY8RcKfv9siJTWcfDrVQmHaLj0mnVYyVb/7rMR3N388HQNsVeF+NHtKuUif8q01ioMAZ2amoq1atXZ8+ePbfdduPGjYSHhxf5/LPPPqNq1ao4OzvToUMHLl68aG/Ly8vjtddew8vLCzc3N1555RVyc3NLPMeRI0do2bIlarWasLAwVq1aVaj9m2++oXr16jg7O9OsWTMOHXrwqzQO/jvcamTfyUq2KTe72HrDtrYsTHmau+6X0sODZ779nm6rvkXu4sK1Qwf5tkVDov63FkEQ7vq4pZGdnc2CBQvYsWMHAG+88QaDBg3i0KFDpKSkMGPGDBo3bgzA22+/TYcOHdiyZQs5OTnMmTOHZs2aAfDRRx/x3HPPsWfPHg4fPszAgQMZNWoUADt37mTBggVkZWWhVqtZuXIl3t7ebNq0idatW/Px9KnkJcViiDnFZ6OH4K5WsX3/YZrWrcX0iePwbtwOr/qtUfkHI5bKsRqNXD9yiL3jxtLk7XdoPWGKfSVb4e5O6wlTaPHBBFLOngGRiIsbfmBNw5ocnfsZlkpSqqc03KrVo/1Xe6n6ZF8Eq4XzK6ZyZPwLGLLSHsj5rBabJ4ZYUvyKstlsplevXrRvX3yyJZPJxMyZM2nVqhUdO3Zkw4YN9racnBzGjh1L8+bNadGiBe+99x55ebYEagaDgbp161KzZk37v/yXRrdy+vRpevXqRdOmTXnrrbdIS0srU1s+YokUwVI2jxMHDh42+XGWN068T/KJsdw48T4fDG1jj7NUOcl56okazPvoKSL/GkHi0XdZ/XkP+nWvh5eHEzm5Rn7ZeZHhE7dSvd1CqrVZwLDxv7H59/Nka4qPY3XgoCLx14FYWjy3gvOX0wjwc2HF7O64qBWlXhcOHl1EwoN6qr4DDh48yKuvvkpMTAy7d++mQ4cOxW5nMpn44osvmDhxIoGBgYXehHzzzTdMnDiRHTt2UK1aNfvvZ8+eRSQS8dprr5GQkMD69esxm8306dOHunXrsnjx4iLnyczMpHr16kyfPp233nqLffv20bNnT/78809atGjBnj176N69O9u3b6dFixYsWrSITz75hKtXr6JSlS2mSKPR4ObmRnZ2Nq6urncj2wOlpLJFlY171eHijcv0XNKXlJxU6lapzc/DfsBLXXx5qXysJiM7elcv1siWqd3psvHSfckenX0ljm2DB3LtpotzzRf60nnhUpTFuHPdqsPRyS8R8syr+LXqWqZzrVu3jvj4eMaPH3/P/S4rgtWCPiMFfWqSzbC7OdWJxGIUnn44+QYid/MuknTHYjQSuWI2F9d9SW6eJ/13/41UqUAslWHIzkLh5o7VZEJ2sxTZjZMn+GvMSK4fsb1k84ioScd5XxLa6cmH9l0fNmW9LgRBIGH7d5xZMBarUY/SuwpNJ63Gq2Gb+9ofQ3Y6p2YPo+ar43Gv2bhQW35t9j/++AM/Pz/27dtXZP9Vq1bx2Wef2Wuznz59mvXr19OwYUM++eQT1q5dywsvvEBubi7btm1jxIgRjBo1ilOnTtG3b98itdlvvYfpdDo6duyIXC6nc+fO/Pjjj7Ru3dpem72ktoLEblyKLiWRusM+vq/a3Q8c9wsbDh1s5OTk4OJS9jq+VquVk5HJ7NwXw4690Rz8JwGz+V+PF4lERKsmVe3x280bBiKRFL8+lKc1IpNKyNLocXdVYjJbym2l0DEebDzqOgiCwOJvjvLO1N+xWARaNArk5xV9CfAv/Gx/p9fFo0hFHgv32y4r9xXsb775hpdeeomPP779Q0OXLl3YvXs348aNK9K2YsUKhg8fTt26dVEqlXz66afEx8ezZ88etFot69atY/r06Xh6euLr68vs2bNZs2ZNsS5+GzduxMvLixEjRiCVSunYsSMDBgywG+MrV66kX79+tGnTBplMxpgxY/D29ubHH3+8d0EqCPkrNJWde9Whpl8Nfhm+Hl8XH85dP0/PpX1Jy00vdR+rxUxY76HFtoX1epOUo39yZv67WIz39lbfLTSMvjv30OajGYgkEi7+9CPftmhIwr49RbY1GAx3fZ60tDQ2btzIoEGD7r6zZUQQBIw5WWTHniP1n91kXzqFITMVBMHmAl6tHj7NOuIe0QiFu08R49pqNpP8z1Esej1SpRMvbNmBk6cnMpUzErmcXMEW0y4rUOfbr3ET+u86QNfla1D5+pJ56SIbn+3Clv4voImPf+DfuTwo63UhEokIfnog7ZftQR0cgT7tOgfffYZL3815oC7j+eTXZj958mSZa7N/8skn9s8A4uPjUSqVPPPMM/ZQBoVCAdjKz4CtNnu/fv0YPnx4sS+I82uzDxkyhMmTJ9OpUycOHDhAdnZ2qW3/FRz3CxsOHWzk5OTc0fZisZimDQIYP7Ideza8RsbZD/l1TX/efq0FNat5YbEIHDyWwEdz99C6xyq8G37Gi0PXs+J/x7mamGU/jt5g5rOlB/FrMge/xnPwazKHz5YdRG8oH88Px3iw8SjrYDSaGTr+N96evB2LReDl3g3Ys35QEeMa7vy6eBR5lMfCrZS7j0LXrl0ZMGAAUqmUfv36lbrt2rVrqVq1Kl9//XWRtnPnzvHhhx/a/y+TyahRowanT5/G09MTk8lE/fr17e116tRBp9Nx6dIlGjVqVORYBbfN3z7fTfzcuXMMHjy4SPvp06dL7LvBYChkpGg0d+/m+zDIyMggICCgvLtR7twPHSL8qvPL8PX0XNKXqOsX6LW0Hz8P+wFvdfHZlaVKFTUGvAvYYq4LZhGv9uJI9o/oSG78JTKjjtH0o69RB1a7676JpVJajZtESMcn2fbaALJiY1j/VEeaj3mfNh/NwGo2IZbKUAlWLEYjVrMJmepfQ8VsMXM5JRqNPqdUF/Oxn33AVV0CV2MT7rqvpWExGTFmpWLISsNi0Nk/l8jkyN19ULh7I1FIIO+67V8xiABTTAK+BikiqYQqLVrhejMpWj4pKSn4+hbNGi4Si6k3cBDVn+vJ3zM+4tSyRVzevJG4Hdto+eFEmr3zHtKbRtmjwJ3eJF3D6tB+2R7OzB9L4s7vubBqBumnD9JkwgoUHj4PqJf3XpsdoH///vz999+8+uqrALRq1YrXX38d+NfAfmi12SsojvuFDYcONu5VBxe1gmc71+TZzracClcSMvljfyw798Xw54FYsrL1/LQ1ip+2RgEQEe7F94teYPPOC8yY/2/ZxKxsPdO/sP3/g6FtHvpKtmM82HhUdUhNz+OFt9az78hVRCL4dHxn3h/apsRSdI+qDndCZdKg3A1sf3//Mm+b/wBSHDk5OUVWKFQqFbm5ufa3RgXb8125i4vDLu1YZWkvjlmzZhUpMwQQFRWFWq2mVq1axMXFYTAYUKlUBAYGcvnyZcCmkSAI3LhxA4CaNWsSHx+PTqdDqVQSEhJijzf38/NDLBZz/brNiKhRowbXrl0jLy8PhUJBeHi4/aHQx8cHuVxO0s3kTNWqVSMlJYWcnBxyc3MRBIFz584B4OXlhUqlsmdmDgsLIyMjg+zsbCQSCbVr1+bcuXMIgoCHhwcuLi7E31y9CwkJQaPRkJmZiUgkom7dupw/fx6LxYKbmxseHh52d/+goCDy8vLIyLDFK9erV48LFy5gNptxdXXF29ub2NhYwDYe9Hq9PV6xTp06REdHYzQaUavV+Pv7Ex0dDUBAQABms5mUlBQAatWqxZUrV9Dr9ahUKqpWrcqlS5fseoMt4ZZGo8FoNJKYmIhWq0WpVBIaGsqFCxcA8PX1RSqVcu3aNQCqV69OcnIyubm5yOVyqlevTlSU7SFgZZ/FDP5xGFHXL9Dti55sGvo9gs6KRqNBKpVSq1YtIiMj7ccNfXEENQaMxZibjdzFHV1eLleu38D1uWEYv5tJ9uXT7B7ShtAhMwjt0o+rV68CtizdOTk5Jert6elpf6gPCgpCq9WSrnKm6Zr/cWP1V0R+s5qYrVto/u4HnFiykJNLFmLIykLh7k7j4aNoPvYD9GaB3TGH2HF+E1qjDpFIhEgkwirYViXzbzD5RrdYJLa33fr/0rYVISpwsxKwmcHY2gUQCVasFhOCxXqz/eZeEgkiiRSRRIKQ8O9xBQT7eSRiCRarxd4Hi96A1WhEJZLyjCqYEGdXIiMjEYvF1KlTh6ioKDQaDYmJibi7u9vHbHBwMLm5ufYx23HuAtSPdyTy0+lknjzBwamTOLlqObXGfkjjvi/d1ZhVKBQEBAcglcjIzsvC3dkdo9nI9YTrGAyGhzJHKBQK/KtWQSGT4+rrhsFsxGIxkxSfhMFgKNMcoeg+Ch+fcNI2zCX1n138OaglvoOmUqtzr3uaI6IvX0ar1WIwGLBYLPYXAE5OTrz//vsYjUasN1fMc3NzsVqtSKVSFAoFeXl56HQ6ZDIZBoPBXhNdq9WSl5eHUqnEw8ODF154gevXr7Nx40b+97//0a9fP4xGI8HBwQwYMICUlBRWrVrFlClT+Oqrr+z3A4VCYb8Hmc3mQjVhMzIy7F5UZrMZvV6PRGIrX5Keno7FYsFoNGIymewvaPPnCE9PT5ydne16h4aGkpmZWeKc7Orqem9zRLrN86Zu3bpcunQJk8mEi4sLvr6+aDQaIiMjCQwMxGg0kpqaCkDt2rWJjY3FYDDg7OxMQECA/b5WpUoVrFZroTF79epV9Ho9Tk5OBAcHFxqzIpGI5ORk+5hNSkpCq9WiUCgICwu7qznZ29sbpVJpr2YQHh5OWlpasXOyp6cnarXafl8LDQ0lKyuLrKws+xyRk5NDZGQk7u7upc4RD/K+VhGeI7RaLZGRkchkMiIiIu7Lc0S/5yJ4+nF/0tJqExWdyeUEK7/8Hsnp8+lkZOmoWc2LL9cULdsIsHD1ESaMbMf58+dxdnZ+aM8RRqPRPn4iIiLuy3PEnY7ZijBHWCwWe58elTni2Ol4Rk8/RFJyHs5OUj4b15KeT4WTk5NT4hwBtmd+q9VaaecIjUaDTqez2xr3c464V1sj/9q9X1SIGOx8RCJRqTHY+Xz99ddMnTq1UAy2m5sb69at49lnn7V/1rRpU1555RXat29PkyZNyMnJsaeIz8nJwdXVlVOnTtGwYcNCxx89ejRJSUn89NNP9s++/PJLVq9ezcmTJ2nYsCFvvPEGI0f+m134+eefJzg4mC+++KLYPhe3gh0UFFRhY7AdPBgup8TQY0kfbmhSqO1fk5+H/YCPi/cdH0eXeo3jMweTceZvAEK6v069EbPuS2z25c2bkCiVXD9yiMOfzizS3nrCFK755LI27SRPte3HU/W64OPig4ji39reC4IgYDHkYdXrEQQBkUiEWKlEIldhzsvCWqDMhVgqRaxQIpYq7qhMkyAImPNyMOTlcSMvjb+uHmLPkZ8ZWaMrT7404Z76fuGH/7F3wnvk3bzpV3u2Ox0++wL3sKJJGktDbzIw/69FLN+/hmxdNm5ObrzZbjDvdBqBUvZwVsbvZx80cef5Z9or5F69CGIxNV8dT8SA9xBJ7q42Zmkx2Pl07NgRs9lcbAx248aNCQsLY9OmTej1eho2bEj79u1ZsWIFzz33HIIg8Ntvv2G1WmnVqhUBAQFs3ry5yHF69OhBfHw8J0+eLPR5foz3smXLeOKJJ5gzZw4rV65kw4YNHDt2rMS2givYFTkG24GD8iJbo+f4mWvUCPciuGXxz18AKafex8er5DARBw7Kyubfz/Py6E3kaU1UC/Fgy+r+1Iko6tnm4L/F/Y7BLvcV7PtFvXr1OHfunN3ANplMXL58mXr16lGzZk1kMhnnzp2jZUtbrcaoqCjkcrm9xu6tx9q5c2ehz6KioqhXr16hc93a/vTTT5fYP4VCYY/b+y9w4cIFatWqVd7dKHfutw41fKvxy/D19FjSh/PJF+3u4ndqZDv5BPDYvN+4uOYTLq/7nKtbVpF57ijNPvoadVCNe+tjz95YDAa2vTag2PYTSxaSPm8otRR1eCm4LUqJM2KTGUpwi7pbBEHAatAjKuDyDQLkaZGYrcikSixmLWKZHLFM8a9xZrXa/pURs06HVavDCTHVPQIJkXcm9uoZIl0k3Jqm7E7Gg0gkonb/AYQ/8xyHPpnOycULiPltC1f+2EGL98bRfOyHyIqp430rWqOWhbuWMmfnfPtn2bps5uz8AhB47bGBXM9OLvP3vRuquPmz+uBaPv+juD7AqI5DUcnLluARwDWsNu2X7uHswvdI+H0dF9d8TPqZv2kyYQVKz4f/oFJabfakpCTc3d1tL2LMZkwmE3K5zdV03rx5SKVSRo0adde12fNXFopr+6/guF/YcOhg42Hq4OaqpGPbcIxGC+5uSrKyi+YmcXdT4qp++M9fFX08WCzWh1J//OKlS9Qs5ln7fiGVipFK7+7l7J0gCAIfL9zH5M93A9CpbRjrl7yIp0fZ7n0VfTw8DCqTBo+MgT148GA++ugjnnrqKXupFD8/P9q3b49MJqNv376MGzeO9evXAzBu3Dj69+9f7MNQ7969+eCDD5g/fz4jRozgwIEDrFu3jl9++cV+rl69etGnTx/atm3L4sWLuXHjBr169Xqo3/lBUlKsYmXjQehwq5Hdc2lfNg/78Y6NbLFESu0hU/Bq8BgnZr2JJuYse996nIZjF1C104v31EeDJhtDVlbxbVlZXMlL5bkGTyGWStHeiP/XO/u+ImDO0xQb2y0SiZA6579hvHvD3mI0YNbZXHSlSicsSiUyZ1da1O3AscQzRbaX3MUKq8LVlQ6ffk79Vwez6923id+zi0MfT+Pcd9/wxJz5VHu2OyKRCK1RR0JmIgkZicRnJBCfkUimNpNZvaazfP+aYo+9fP8a3n5iGH2WDyQ978HUjvVy9uTkpEOsOFBSH1bzbufia4WXhtTJmcYfLsWrYVvOLhhL2vHd7H2jDU0mrsSnyeP32u1SycnJYe3atURERNC5c2e6du3KggULGDt2rN1tMD+hWZs2bdi5cyejR49Gq9Wi1Wrp3LkzYKvpuWPHDhITE9Hr9SQkJPDmm28CttrsFy5cYMCAAYVqs8fHxxeqzV5a238Fx/3ChkMHG+Whg8lsYdTglvaY64KMHNSC7Xui+fanU3zyYSdqVX9weR8KUhHHgyAI7NgTzc59MVx+SPXGtXlaVM4l5yi6HwT6u/BE6zBeeKZOiRnm7wWtzshr7/7C+t9si2tvv9aCuZO7IpOV/ZmgIo6Hh01l0qBCG9hqtZqvvvqKAQOKX0kryODBg8nKyqJXr16kpqbSvHlztm7dikxmq4u6ZMkSxo4dS/369TEajfTo0YNFixbZ969bty4DBgxgwoQJeHl58ccffzB69GimTJmCj48PCxcu5IknngCgU6dOLFmyhGHDhpGYmEjdunXZvn07np6ll1/6L+FwW7fxoHSo4VuNLcM30GNJHy4kX7prIxvAt0VnOqw4yPGZr5N++gAnZr5O+qkD1Bv5KRLF7VdIi0Ph5o7C3b1YI1vh7o5JJOCi9sQlOALBasFqub9vwa1GAyCQfvpgidt41KmFSHb3SWu0KTfIPHMaEHALDce9eg3EEgkisQSX9AsYzP/Ws9YatUjFUtz8bLHPZqv5jlZsAZThodRbvQTW/4+EuYvQXL3CL316khLhw+723sTIimYYrV2lFqk5qWTris8ona3LJiMvg/qBdbmcEnNH/SkrNXyrkZ6XXmofNPqcEpP23Y7gpwbgUasp/0x7lZwr5zn0XndqvjqOiJc/uCuX8bwbN0g5fRLTLTkx/NNTsFgsXNr0E+np6WyZNZNmzZoRrMmig48nmhZNObb9NxRyOeO7P4NzXAyX4mIY3LIZrglXOL9jGxKxhNdbNqethyuXNv3Ey40b4Bp/hahfNgHwarNmdAsO5NKmn9iz7juOHj1KQ4kIHx8fPu7fl/Xr13Ps61V0Ca9G/7aPcWmTLQyppDaRRIKTtw9+TZrelbYPC8f9woZDBxvloYOzSs74Ee0AW8x1VrYedzclo15ryZghrWjTezVRl1L5ZedFXuvTiKnvdqBqlQf7Eqsijocft0SybvNZWjYKZMQrzXFSyu6381kRDEYjCvmDSzBnMFo4dymF7385S8K1bN4ben/LQCZcy6bnkB84cfY6UqmYJR8/wxsv3fmcXBHHw8OmMmlQoWKwb+Wrr77C09OTF1+8t9W4ikhFr4Ot1WrLXNP7UeZB6xCdEkuPJX1I1tygpl8Em4f/gK/L3b1dt1rMXPpmFpe++xwEAdfwejT76BvUwXfuMm7S5nFs7mcc+mR6kbZW4yax0P0Sgzu9Qdd6nYu0m81mXnzxRdLT04uNdzWZTMyePZvffvsNlUrFsGHD7Nd4zZo1i2zv7+XBj59/hMFkotuwD7FY/nX/fuGFF4ot8Xf69GmmTp1KfHw8zZo14+OPP8bb29veNmXSJOLj46kbHsakMWOo2a59ocyfm0/9yvbIP/jq5YVljj3ONeSRmJlI/M0V6ISMRBIyk2y/ZyYWKs8mM1l57HgOrU7nILGCWQxHGrlw5rEAAv1CCPKsSrBHEBF+1enf/EVqT21arIHr5uTGhWknkEsfbHZco9lIrY+aPNA+mPVaIhe+T/z2tQB4N36cJpNWovT0u+2+hux0Ts4aikkSRNqFy4jEYqRKp1Jj8bOzszGZTPZxcTfk5wV4UFgtFsw6LRKZHL+6Qai9nStkDLbjfmHDoYON8tQhvw52do4eN5d/62BHXUphwuy/+GWnLUmTUiHl7ddaMG542zK7+N4pFW086PUmBozaxLOdInitb/G5Kh4EFovlrry/7pSde6P58uujLPn4GYIC7s/Lk0PHE+j1xg/cSM3D21PFxq/60L5V6F0dq6KNh/KgImtQaWKwBUHg2LFjzJ8/v7y7UimJjY21x5xXZh60DtV9w9kyYgPdF7/IxRuX6Lmk310b2WKJlFqDJ+PVsC3HPx6CJjaSvW+1p+G786n6ZN87OpZM5UyL98cDtphrexbxYW/TeNjbZHzUifTAc5jCWxcq26XRaJgwYQJRUVH4+RVvGH377besXbuWp59+mqSkJCZNmkS1kGCqeal4vffTdnfzTKuETb/8SssGdQC4kpSMxWKlY4vG1KxTF5mrJ3Xr1i1yfJ1Ox9ChQ5HL5fTs2ZMff/yR8ePHs2LFCnQ6HW+99RYSQaBTs6b8fvgIc79dy4r2xbsklxb/LAgCnWt3YNzPU0jISCyTi7abkxvBnlUJ8qhKUMeqqLVSxCs3k3f4OG1O5PBUipYOs9+gRq/n7Yab1qjlzXaD7fHOBXmz3WDMVjNyHqyBbbaaS+zDkLaD2HvpAFKJhCdq3r1rt1SpotEHi/Fq3I4z88aQdnIve4fcdBlv2uG2+6deiMck5NB4xDsEte+AVFlywj+j0cjp06epX78+ylK2ux0mk8nuJfWg0KakEPX9d0T//APhHSvmSrbjfmHDoYON8tQhvxRXfkIzudxm2NWJ8GXzqv78/U8842b9yf6j8cxZ9jcrvj/Bh8PaMGpwS1RO93cerWjj4eyFFIwmC091qP5Qz5uXl/dQFpI6tA7lq3XHOX7m2n0xsL9ef5K3xv+G0WihQW0/flnVj9Agj7s+XkUbD+VBZdKgwhrYIpGIlStXlnc3HDh44FTzCWPLCJu7+L0a2QA+TZ/412X81H5OfPIGaaf2U+/tz5Aqy/7mUKpU0nzsB7T8cCK6rEyc3D2wmkyknD2NxWTEnKcl8cB+fBs2xtnPD61WS8eOHVEoFEXK2BVk27ZteHh4MG/ePC5GRdKj9wts+m41I/v15JXnuiB390JdtQZvjhpDSEgI4957D3PGNaLjbWVg+vftS91WbfHyLl6fI0eOkJGRwaRJkxg4cCBpaWns3LmT7Oxsjh06RGZmJkN79aRPjx6YnNXs/OMPsrOzi415lYqlJcY/rziwhlEdhxUyrt2d3Ai6aUAHe1Yl2DPIvhod5BmIm1PRcwgvTST611/Y8/47aOKv8uuAFwnu2JmOcxfiVas2KrmKdzqNAOCn4z+jlCvRG/W80LTXQ8siXrAPy/evLrCS/xpvthvMM1/25nJqDGM6jeTDru8ildz9rSXoyX6412zCP1NfIScuikPv9yBi4AfUfGVciS7jgiCguZFFrZf6EtblKSxWC8YCLv5FEEODRg0QiUSlb3cbLFYLgvnBOoEpfbxp+vY7XPt7Dxkx8Q/0XA4cPOo81iyYvT+9xvbdlxk360/OXkhh/Kd/8eWao3w05nEG9238UBJmlQe5Wttc5+1ZMVcQ7xW5XIq7q5KcvLuf08GW/O2Dj/9g3opDAPR6qhbfzu+F2vm/k6jYQflToV3EH2Uquot4VlYW7u7u5d2Ncudh6hCbGkf3JX24np3Mk3U6seyl+ajkKjR6Da5K1zuO+xUsFi6unc2lb2eDIOASVodmU7/FJfjOs3lqNJpC4/SlpQN51rkegefTyIqNQSyVIlIquXTpEtWrVefvQ38jCAIdO3YscqydO3firFLRvGE9TDot+/45jae7C43r1Ubq5IJYJic5OZmTJ07SpGkTfH1tWaWjoqKIvxqPWCLGarHi5u5G48aNiyQqjIuL48L5CzRt1hRfX18uXrhIbGwsrVq1JCUpkdiEJOrVqE7VatW4dOkysbGxtH6sNR6enijcPajSrAXHvXV8e2oTX738JY1nti5Rl3Mf/cPllBg8nT0I8gjE1enur2WTVsvRubM5Nnc2FoMBsVRKk7fH0Hr8ZOQuLuhzc5DJFeizMlG6e2Ay6lGqH+7ckR+LrtFpcHWyjUkxYiZumcbXf38HwGPhLfnq5S8JcK9yT+cy67VELvqQ+K3fAODVqB3NPvoaidIZsUSKKTcbmdoNq8WMISuDTc91oun0T5E0rkueUXvP37U0/r1r/lub/UF5iotFYlyVriQuWkLGqSP0/KVoEqfyxnG/sOHQwcZ/RQeLxcr/Np9l8ue7uJpoC3+JCPfi4w868vzTde45/KOi6bDrYCxfrDzMzyv6FvsS4X6HdwUGBrJr1y67p8/OnTt5++23mTlzZomhn7cL7yqpLZ8h7//C461CGfh8w+IOf1uysnX0H7mR3/fY6khPHt2eqe92sNexvhcq2ngoDyqyBpXGRdxB+aLXFy11URl5mDqE+4SxZfh63ln/AYv6zWXJ3pWsPPD1XdccFkkk1Bo0Aa/6j3Hi4yHkxEWx763HaTBmHkFd+t9R37RabaEJR24G086DZOjAt2EjnLy9kchkhD3RCZFYjBAWjiAIBN1SmsNqtdLa2xcnhZyqfr6AgLVOI1ROToTeLIsEYI6Lo1PT5oSHh9sfcmQRtaiel4eHhwcWi4W0tDSUajVBwcGFzqGsXRfv5i0JDg5GrVajqFWHgLQ0Anx98dHrCdBoCA4KQu3igrJOPQLS0ggJCUGpVJCdmMDJdWtIIJur1TPxcvbEzcmtxNhjT2cP2tV47I60LAmZSkWbydOoO+AVdn8whtitv/LPF3NIPnaEXpt+4/j8zzlZwF2/yfBRtHh/fKmu0Pcb+wseA8hd5HbX9LkvzKJttda8s/5D/o49Qoe5T7FswAI61upw1+eSKlU0eu9LvBu14/S8dzBk3ACRmOjvvyBu01eYcrOQqd0J6z2UwK4DMUgl3DBqCBSJCfYMQib513XbbDbz2suDyMzI5LedW4ucy2wys2DefHb8vgMnJxWD3xhMj1497O0bN2zk29XfkJGZQaPGjflo6iS8vDy5lnSNOZ/N5dTJ07i6uTL4jdfp2btnsd8n8mwksz/+lMTERBo1bsykqZPw8rIlhtu9azdLv1zC9WvXCQgMYMSoEbTv8DggoDPqSc1NI0swYnWqmHV8HfcLGw4dbPxXdJBIxAx8viF9nq3Lsu/+YebCfVyKTefFoRto3jCAT8d3pmOb8Ls+/n9FB7j78K6IiAgaNmzI6NGj7dslJSXx008/0b59e8AWg71//37GjRtXah9uF95VUtv94lJsGt0Hf8/FmHSclFK+nteTPs/dP3fm/9J4eFBUJg0cBraDYklLS8Pf37+8u1HuPGwdwn3CWPPqMr7av4a5fyywf34vNYd9mnbg8ZUHOTFzCGkn93Jy1lukndxP/dGfl9ll/FYd3E7F4WT2osvir1C4uWHI0YAgIJJIULi6kX38OFarlTptbNk8rRYzFr0Wi0FPelAITgoFtevWRiRXkn7wb7y8vKjT0PbGWavVknz4MGFhYfZaxAB1bunT0aNH0el01Hm8cNxvfHw8luhowhs0wNvbG3l0NNb4eKqFh5Gj0yG6nlykrWr92kiVMvwQCEh4lrxxY6mVYCY1J/Whxz+7h1ej109biN2+lV1jR9F01Lscm/cZhz+dad/GkJVlT0DXfOwHheLgHwbJyclFVg56Ne5Og6r1ef3bYZxNOseLywfyTqcRjH/qvXtyGa/auQ/uEY3RpV0jbuNSLq39zN5mys3i0refYszNI08iQiVXEu4eiEQqB7EIEKHRaPho4hQuXriIn58frkqXIudYtW4VP37/o/3B8eNpM2lQpz4NGzZk27ZtzP74U9q2bUu7gHasX7+eBXMX8sXcOUwYN4nY2Dj69e1DZOQ5Ppn+MdVCwmnTpnAWW51Ox3ujx9oeDnv0ZP369cya9gnLly8nPT2dyeMmERISwksvvcSOHTuY+OFEjhw5glKpwEXujKtEToxETJa8YrquOu4XNhw62Piv6aBQSBn9eisG923M3OV/8/lXf3Ps9DU69fuWLu2r8en4zjSud+ceOf8VHe4mvCsmJoZnnnmGbdu20bBhQ4YPH27fbtCgQYSGhjJhwgQAli5dyrJlywgNDSUvL6/E45cW3nXy5MkS2+5HScOde6PpO+InsrL1VK3iyi+r+tGkfsA9H7cg/5Xx8CCpTBrc/2JxDhw4uCdclC6sPPB1sW3L969GKr5zY0Xp6UfrOZupOWgCiMUk/P4d+4c9Qc6VC3fVR4+EbALatMPZ3x+pkxNOnl6IpVIEiwV9ViaC1Zbp22oxY8rTYMzOwGKwvbl0UigwmszI1G4YzbbyXgWzSmZk2OKZ81f38omLiyMuLg6wxdyWlJk032Vcp9MhCALanBxEIhFOSiWuHrZSelqtllxDHppcjW2FXCpCQEAiluBXrRYhrdtRP9eFUO8Q3uk0gve7jLHHT7s5ufF+lzG802nEHZfquhPCuz3DoBPnCO3chZPLFhW7zYklCxFLH2ySrTuhmk8Yv4/azOA2rwAw/6/F9FjSh6Ss6/d0XHVwDbzqtybu5+XFtkdvXYNRJKCWKrFoczHmZGLUZJJ1I4lOHTty4vhxnJ1VCIIVoyajyL9tv/2Kh7s7sz6ayNTxHwDw2+ZNGDUZ/O+7tXi4u7Ng9seMfPN1fvh2DWNGvEV68nXOn79A29atGDtyGF9+PhuAn378AVNuFqbcbPu/v/fuIiMjg0EvD2DcmFE88Xh7Dhw4QNq1BBJiL2M0GqlXuxZdOz5OeGgIcpns5r4azNpcZCIJTnJnMk0P1vXdgYPKjItawdR3nyDmwGhGDmqBTCZm574YmnT7iv4jfiI6Lv32B/kPYrVa6dGjB5s3by7VfTcuLo6AgABEIhFBQUH2zwqyc+dODh06xPvvv4/8ZmkuLy8vFi5cyFtvvVVqP/KPVbVqVQCCgoKwWq1cvXq11LZ7QRAE5q88RLdX1pGVrad106r8s/XN+25cO6h8OAxsB8VSp86t64WVk/LQQaPX3Lbm8N0gkkio+eo4Hvt8CwpPP3KunGffsA7E/77utvveqoNCb8Y54N8bkFgqRenhiUSpBEFAEASbMZOdgVGbR+KNVLJytchd3fH1r4LRZCIqKooLF2wGfn6cNUBubi4ikQi1Wl3onFqtlri4OKKiojh37hw6nY4qVWyrCmlpaVy5cgWTyYSHhwcymYz4+HgunDtHRlYWbs7OqDw8UbmpkUqlXI2/Skx0NNlZGtSuatROznio3PFUeeCsUOEcEIBSbwZAKVMwquNQLkw7wcXpp7gw7QSjOg59KMnFpEolxtycYuuRg20lW5eehjYt7YH3pSClXRdKmZI5z3/M6leWolaoORx3jA5zu/Ln+d33dE6bwZlVbFueLgcEW1I6QRAQrFYEiwWrycwzT3Vh/bercXNxBcGWm+DWf1euxlPF3w+sVgL9be6RV65eteUxuHwZJ6WSFwa8QvvOXZkxazZiRDg7KVGpnDh7LoqTJ0+x448/AIiJjcNqMmE1GRHMJrBaiLtiewisGlQVsVRGcHAwVquVazfSqNegES1btODnLb/Sb+AgDh89xhdz56J290SqVCFVqZGqXJBIpJitJvt3Nuu1WE1GDJmpWE1GzPryM74d9wsbDh1s/Nd18PNR8+WMp7mweyQv9awPwA9bIqndcTEjJm4lOaVs9+D/ig5qtZrJkyfj41N6YlWdTmc3mvOrJ+h0ukLbrF69mvDwcDp16mT/bODAgXTt2vW2/dBqbXNYcecore1uMRjMDHl/C2Om7cBqFRj0YiN2/zgIPx/17Xe+C/4r4+FBUpk0cLiIOyiW6OhoIiLuPBnWo0Z56OCqdC017rc4F9c7wbtxex5fcdCWXfz4bk7NHkb6qf3UHz0XaQkxnkV1EN10wS3wiViMXK1GazQyddo0cnJy2LXlFywiEfHXb+Dv74+/VE6VKlVYtGgRu3fvRqlUMmjQoELJ0H777Tc2bdpEXl4ezZo1Y9asWfj6+hIREYFIJCI9PZ25c+dy/vx5zp8/D0BqairXr1/H19eXVatWsWhR0RXfCR9NoNtzTxMSEUpCbDxjR48lLy/P3o9C30UkLpjJyr5SnZuWg3eo1wMvi1UQhZs7Cnf3Yo1shbs7Clc3VtQKxT2sGtWe7U7408/hXbfeA63PXJbrokejZ6kfWJcha0dwOvEsfVe8wqiOw5nQ7b1C8dFlRaZ2Q6Z2L9bIlqpcIBekSmcU7v96PijcvZg6YyZWQcBoNmOxWAq156PT6xFEIv6JumRPVqY32bbVanXk5OTy5JNP0qx5C37++Wdmzf2C+bM/4cMxo5n+6RxefWs47u7uKJVKNLk2jwkh/0WTxYL2pltkfEISfx/9h8zMTAByMlOxGkIICgwkOTiYxzs8ztat25gybRpbt/6GqgTXf4tRX2wseo0B7yKRP7yY/Hwc9wsbxekgCAIZF86Tdi4Ss/7ujYGKglgixTkggCrNWiArwZ34URkP4SGerPvyed4f+hjjP/2L3/dEs+TbY3y94RTvvtGa94c+hqtLydfbo6JDPkqlEqPRlqE7/2fBe+eVK1c4efIko0aNKnT/ycvLK/LCvKTjFzx2/k8nJ6dS2+6GG6m5PP/Wjxw8loBYLOLzSV14Z0ircr9vPupUJg0cBraDYsmfvCo75aHD7WoOH4w5jK+LD3UDat/1OZSevrSevYnL/5vLha8/IWHH/8i8cJxmH32La1jR495Oh/wY68y0VD76+FPi4+Px9fFBEASkiKkeHo7BZFt9++677/j111/t8a6ff/45LVq0sMe7rlixgrZt2xIYGMiPP/7Ixx9/zIIFC5DJZNSpU4cNGzbYDev8zJ61a9emdm1bv1u1agUWC9abfd68fRvZ2RrqNayPVCxBKVXw/brvSUhIwM/P747qIOfm5pZ52/uF1WyiyfBR9pjrgjQe9jbXjx1Bl5aGLi2N68eOcOCjibiGhFLtme5Ue7Y7Vdu2R3KfazWX9boI9wlj+6ifmbJlJisPfM3CXUs4HHuUlQMXE+hxZy54VouZsN5DufTtp0XaqnbpT+Km7SAWIRIXDhswmUxcuHAB682whVvbwbYqkpenxcPDA41GA9hWSERiCQqFAolEwpAhQ1AqlVy8eJGjx48D8GSnjkhVLuhyc2jQsCFD3nwLV1c3FB4+tlV0qwXBasXJ2fZwqTca8fHyICfPthqjkCvY+cdf/PTzzwwfNpQunTqi0+nYsOEn9u78nY4d2iMSSxCJJQgWM8bsdLTJCcRv/9ZWHSD/O96MRQeo3v+dOyrHdz9w3C9s3KqDMSeHv2d8RGbMZWTOauRq9YNLN/+QsJpM6DLSEUuktBj7IVVatCyyzaM2HhrVrcL2tS+z51AcH37yJ0dPJTFz4T6Wrj3GxLfbM2xgM5TKonPsgzTWyoPg4GCuXbuG1WolMTERoFCelIMHDwLQrl27Qvvlz723IyQkBICEhAQAEhMTEYlEhISEkJKSUmLbnXLq3HW6D/6ehGsa3FwV/Lj4Rbo+hNrgj9p1cTdUJg0cBraDYinL28bKQHnoUFrN4TfavcazXz5PfEYCs3pNZ2Cr/nd9ExdJJEQM/ADP+o9xfOZgcq9eZP+wDtQfPZegpwYUOq6Hh0exx7BazFh0WixGPVqdjqd790GukOPs7IxILEYsl2PQ6rh+/Tp1IiIQBKHURCnff/89Hh4eLF26lNzcXPr27VsoJuzGjRvMnj0buVxeZKK2ClYMJgMREeHUCbbFhx2JPMPS1auZPHUKDWrVx6g30uWpLrdN5lIS5TEeZCpnWrw/HrDFXBeXRfytmCRif99KzNYtxO/6E83VK5xcspCTSxaicHMjtEs3qj39HGFdu6Es4W95J9yJDgqpgtm9Z9CmWitG/fg+R6/8w+Nzu7K4/xd0rdu5zMeRKlXUGPAuAHGblhVauVW1686RTb8X2cdisXDo0CHEYrF9Vbk4fH19ycjIoFatWkRFRQHY3SWrVKlCYmIiNWrUQCaToVarcXJSIRKJWL12HZdjrzBrxjTyDEY0Go3dG0MkFiO6+QLI28/f3p/a9RqQczMvQGjN2hw+cRoAJ6WSamGhSG5edxKJBMEqIFjNgBnBasGQmYrczZO4TV8V+z3iNi0j4uX3yqzp/cJxv7Bxqw4nliwkL+UGbT6aiU/9Bo+MwaVLT+fM6uUc/fxTnlz0FaoCIT7w6I6HDq3DOLxlCD//fp4Js//iYkw6707fwfxVh5k+9gle7t0AiURMntaITCrB1d0fo9GCyWzBWfXwvJ7uFzk5Oaxdu5aIiAg6d+5M165dWbBgAWPHjuXatWsAdOvWzb59VFQUEomEWrVqFTqOVFqyqbFr1y4uXLjAgAEDaNWqFW5ubqxcuZL4+Hj++usv2rRpg5ubW6ltpZH/t8jS6HF3VZKYrGHwe7+QcE1DRLgXW1b3p2Y171KPcb94VK+LO6EyaeCIwXZQLJUly9/tKC8dio/7HYYEWwkivdnAmA0f8tZ3b5Ojv7dVVe9Gbemw4iA+zTpiMeg49dlwTs56C7M+zx7n6a2SF4rzFFmtWIwGW/Iyoy15mUgipXv37vzyyxa7Uax090Dl6krdsDDEZhP6zMxSE6VcuHABlUpFjx49aN26NVOmTLEZGoKA1WplypQp+Pr60qVLF8Dmfmk0G8nWaUjPzUCrzUGqt62UW+VSvpj/JbVr1+alvv2RSWQIglCmZC4CAmJR0emxvMaDVKmk+dgPGHb1BsPiUxh29QbN3/3AXqJLHRBAg8Fv0GvjrwxPTKPH+s3Ue3UwKl9fDNnZXNzwA9teG8CSIB/WP9WR41/OJys25q77czc6dG/4DHvGbqdRUAMytVm8tOo1pv76MSaL6fY730QiV1K9/zt03RRN159j6bopmur9RyMpIR5eEAT8/f1p3rx5oc9zcnJYsmQJf/75JwCNGzcmNzeX9957jxkzZgDY93nssccwGAz069ePkSNHcujQITp16oTc3Zvk1HT27t3LhxMnM3jw64hEInr0sJX32rVrF0uWLCE7O5vatWujUqlYv349M2fO5NixY9SuXRuF0om69esjFov5+tu1fLF4Gb9u3YaLiwt1mzRH7uqBTO2GVKVGJJYgVbtj1KSXGItuys3CkJnClV9Xk37mbyxGQ5m1vRcc9wsbBXUw5eVx4/g/1HqxH74NGj4yxjWAk5cXTUa+g0gsJunQwSLtj/J4EIlE9O5Wh8g/h7Pis+cI9HchPimbQe9uptcbP5KbZ+SzpQfxazIH/yZz8Wsyh8+WHURvMJd31++Y7OxsFixYwI4dOwB44403GDRoEIcOHSIlJYUZM2bQuHFj+/ZpaWm4u7vb46TzUShKzleyc+dOFixYQFZWFmq1mpUrV+Lt7c2mTZto3bo1s2bNAii1rST0BrP9b+HXeA5+TeawZv1Jdnw3kNf7NebwL0MemnENj/Z1UVYqkwYOA9tBsURHR5d3FyoE5amDSq5CLpXjrfZCLpWjkqtwd3bn+yFf89GzE5CIJWw8+QudvniayKSoezqXwsOHVrM3Uev1KSAWk3XxBFaDgejvv2BH7+rs6F2NHb2rE/39F5hysiAvB53RZmyLZXLkrh54+Acy5aOPCiVKEYlEyF1cULq7IxKJsZqMtkQpN12Wb01UkpeXR1JSEm3btmXcuHFcvHiRadOnkZaXzroN/2Pfvn1MmjrJ/kY8PS+DLF02BrMBsdWK0mhboZQolZyKPM+lS5cYMmSI/eG2rMlc9CYDSllR1/HyHA8ylTMSuRyVjw8SubzE+EeZszPVn+tB12WrGBp3nf57DtHi/fF41amLYLGQsHc3ez4Yw6q61fm6SV32Tx7PtcOHsFosZe5LvpvenRLqFcK2tzfxZrvBAHy5exnPLX6RxMykMh9DqlQhlslRuHsjlsmRKkv2RJBKpURERBR5wLv1wbFLly5069aNQ4cOkZqayoABA6he3eYy+Nhjj/Hss8+SnJzMiRMn7P8XiUT07duXhg0bcvToUUwmE4MGDbK/oS/44KhUKnn77bfx9PRk06ZNNGrUiFdeeQWLxUJYWBivv/46rq6ufP/99wQGBjJ8+HBUKhViqQyJXIFUqUIkkeLk7Y/S0x+Z2r3Y7ytTuyNz8eDCqhkcHP0U258L4u93n+Pit7MfqMHtuF/YKKhDTlIiVosZrzp1y7FHDw6ZkxNuYeFo4otmca4M40EqlTCkf1Mu7xvF7AmdcXdT8nq/xsxeeoDp8/eSlW178ZyVrWf6F3uZtXg/edqK7x67a9cu9u3bB9gydk+ZMgVPT1v1DZlMxvjx4zl8+DC7d++mT58+hfb96quv+Pvvv4scM780V+/evbl48SIvvviive3TTz/l4sWLdlfvBg0asGnTJk6cOMGKFSsKJUAtra3IObVGZi3aX+RvMXPBPr5cc5T5U5/Cw/3u4rfvlspwXdyOyqSBw0XcgYP/GGKxmFEdh9EyrBlD1o4gJjWOLgu680nPqbzaesDdu4yLxUS8/B5eDR7DrNcSu3FJMTWHZ4Mg4Onuh9akI0ci4KV2LXa1tyBSpRKxVIohOwuFQoFep8OUl4f5pgttfhy0UqlEqVQyceJEBEHg0OFDHD16lIyMDL6cu5B2HdqjVDuhybHFycbHxxMQUAVnuQqxXg+CgEShQOHqxubNm1GpVIWymd4Oq9VKam4aepMeD9W9u1KXNyKxmICWrQho2Yp20z8hKy6W2K2/ErN1Cwn795J+Por081Ec/fxTVL6+hHd7lvCnnyO005PFGvAmbR5iqYwqri5YjEasZtMd1+BWSBXM6jWNx6q1YtQP73HsynEe/7wri1/6gqfqPnm/vnoR5s6da48FzH9wjI+PB2wPjgMGDGD+/PlYLBb27t1rj++XSqX06tWLuXPnAnDmzBmys20JCBs3bsz69evt5zh69Kg9gdmnn37Kp5/a4qLj4+MJDQ1l9erVeHt7Ex0dTXx8PBKJBIlEQpMmTRg0aFCRtuIoLRY9rPdb6FKT8G78OGmn92PMTCXt5F7STu7lIiCWK/Gs2wKvhm3xbtQO99rNkMgffDb8yojVbFuxLElfs9nMiy++SHp6ut2gKYjJZGL27Nn89ttvqFQqhg0bZjdMDAYDTZo0wWz+d1X0hRde4OOPPyYnJ4epU6eyb98+RCIR7du3Z9q0acWGxPz111/Mnz+f+Ph4goKCGDNmTKH58nZ9lMjlWE1l90B5FHFykvHBsLa8+VJTFAopg97dXOx2C1cfYfyIdhw7nURIoBs+Xs4V3qshLS2NjRs3snjx4vLuSpkRAE2OHqlEzMI1R4rd5suvjzBpVPuH2zEHlQ6Hge2gWAIC7iwB0aNKRdahZVhz9o7dwYjvx7Az6i/G/jSeA9F/M6/P7HvKNO7V4DGsJiMnZgwutj3u5+X4eDXCVe1FQvZ1kjQ3kElkFHxUMJqNWC1Woq6dL7K/bxVfEq9f42xCJNev2xKXuPm6EXXtPAFBASRcSeBk7CnkSgVZeVlIZVIOnzhMdnY2e3ftYe+uPfZj9e/Vj2X/+woPNzVYBUQSCTKLE0LedfYf3E+t+rWIzYgr0ofi+igAJosJq2DFVemCRe1ZZJ+KPB7KgntYOE1GjqbJyNE2d/2dvxOzdQtXdm5Hm5JC5DerifxmNRKFgpCOnan2THfCuz2LOiAAs17P0bmfcbKEOPA75bkG3WgQWJfXvx3OyYTTDFg1mOGPv8HkZ8Yhlz7YeMVbHxydnJwwGAwIgoBeb1vtyK/NrlKpyMvLs9ddFwTBbnwnJCQgEokICwsrc212AL1eb6vNfvO8JbUVR2mx6PlZxJt99DWCIJAbf4m0U/tJP32A9FMHMGSmkHZyH2kn99kNbo86zfFu1Bavhu3wqNPsrrKQ54d6VHbKOj9oNBomTJhAVFQUfn5+xW7z7bffsnbtWnsyyEmTJhEREUHDhg25fPkyZrOZp59+mho1agD/lr/58ssv2bZtGy+88AK5ubn8+uuvBAcHM2rUqELHj4+PZ/To0QQGBvLqq6+yefNmRo8ezbZt2wgODi5TH+9Vh0cJdzcnUtLy7Kult5KVrSc5JZfB7/1C5IUUXNRyqoV4Ui3Eg2ohnlQP/ff3qlVckUjuv4NpvkFvtRafi+JWvL292bBhQ4kv+8rK3Wb6Lg5BEDAYLej0JrQ6Ezq9+eZP2/+jLqawc28MT3eMKPVvkZ2jx8frzvOw3AuV8bq4lcqkgcPAdlAsBd+MV2Yqug6ezh6sG7yaJXuXM2PrbH4+9SunE8+y6pWlNKha766PW1rNYVNuFsis+Lr4EOBfk2ydBrO1sE5ikRhBJODq5Epubi6b1/9MWPVw2rRvwxOdO/L18jUsnPkFaekZAHR84nFcnVx5psezzJ/9BTPHzSSsWhhnjp+hbdd2OPuqee291+3H3//7PqIjLzN5xiTUeTr++fsoCddT6PP6a7g6uZKakkp2pi321dXJtdjvUbCP+UglMtydXLmqvkRx376ij4c7QenhQe2+/andtz8Wk4mkg/uJ+W0LMVu3kH0ljtjtW4ndvhWAF3/fRcLe3RyeNcO+vyEry57ZvPnYD+54JRsgxCuYbW9vYtpvn7Bs3yqW7F3Bkbh/WPnKYoI9H5zR5u7uzpw5c+yhBr6+vsTGxhIVFWU3sPPdD6tUqcLFixc5ffo0arWajIwMAgMDAVvd1pSUFHQ6HVarFZ1OZ3d1TEtLIzc3l8DAwEK12XU6HWlpafbPSmsrifxY9IiX38OUp0Hm7IrVYipkHItEIlxCauISUpOwHkNsBnfCZdJP7bcZ3TcN7vRT+0k/tR+YhVimwKNui5sGd1s86jQv1eA267WIJVIUFgNWkxGrxfzQM5hXJIqbH6wmA7q065i1OWC1otPr6fbSYBRyGc4qJwSLmZwrF4rs99vmTbi7uTJt1JvExSfQ58232fzDd4S7KTh1cA8APTu2JSyoKh7utkRPOVcuEHshCoVcTsem9dHk5rFt2zbQaoqc49yRfwitGsioIa/SulkTXCRWPl+ygmO7dqBs1bzkPopEiGUK5K4eJSYNfJTmyTvB3VWJu5uyWMPO3U2Jr7czErEIkQhyco2cOpfMqXPJRbaVyyWEBbkXMbyrhXgQFuSBQnH7R/dbk3uZzBa8PGyGbsI1DdVCi75ALo57Ma7NFitikQirVYTVKmAVBKS3eXFQxIDWmdDqTWh1ZttnehNWS/HjTqc3kZGlR5NrwNfbudS/hVsp5dUeFJX1uihIZdLAYWA7KJaUlJRS41sqC/8FHcRiMSOfGErLsOa8/u1wYtOu0HVBDz7u+RGvPTbwrtzQSqs5LFO7Y5VIbKtsciec5EXfTkslUhCgqkcgiXmJrFm+hu7du9O3Rx/eGzUWjAK/bN6MTCxi1IsvUMsswU0rMPS1t9BptHz/v+9JuBJPq46P8dzLPVA4KWj02L/JVM6fiCKay3QIDMWQlcWpfyL54/ARBr/1NlU9AslMtBnu1UOqU9UjsNjvWLCPZeW/MB7uBolMRnCHjgR36EiHOV+Qfj6KmK1biPltC1lxMVRp1oIt/XoXu++JJQtp+eHEuz63XCrn455TaVO9NSO/H8vx+JN0mNuNRf3n8nS9rmU6hlguwyoImPVlizE2mUxcvXoVf39/fHx8CA4OxmQycePGDcRiMbVq1bJnpw0ICMBoNJKUlEReXh7+/v6E31yxjqhRA7FYTHp6OmBzPc8vW1OwNrtKpaJhw4ZcvHiR5ORkPDw87Jl2pVJpiW0FsRoMWAvUns83ZBXutiQ9Ylnpq/4ikQiX4AhcgiMI7f46giCQlxB9c4V7P2mnDmDIuFHA4MZmcNdpjlcjm0t5QYO7otXirgjcOj9YjUZyE6Jx9vVB6uyCSCYHi5VnunTi9Zf78+rwd7BYLIgVRfW6mniN4KqBSJROBIWEAhB/LRmxQkn0VVsOhLcnTMVgNFKvdk0+mzYJf19fXuzVnaMnTzP0w8kANG/SiFcH9EcsLWwotW/flvbt29r/H3nxMgDh1cJBJi+5j4KAWZeLMTsdc54GoZhkkY/qPHk7TGYLowa3ZPoXe4u0jRrcEkEQOLVjGHq9ibiELGKuZhBzNZPoKxn23+MSMjEaLVyMSediTHqR44hEEBTgRrUQj5vGt2eh313UCntyr4VrjpCVrcfdTcmowS15/602uDjLWf/bOT4c3sbuifMgsFisxFzJIC4hC5PJgkxme2lQPdQTs8VKntZUYBW6bAb0vyKAk0KKk5MMlVKGykmGk1LGL39cIKSqG3s2vApQ6t/CZLYgl9/byvydUlmvi4JUJg0cBrYDB48IzUObsnfs74z84V1+P/cn72+cyIHoQ8zvM7vEVdySKD3OcygXth4udf9du3bZfy8u3nX8+PGMHz8ei9HIjVMn0KWmknLqJEkJ0dTtXJ8ZT9anSVAjsvUaYlJjixx/8keTcBqUij4tDbFczrzFS2w1Zm9Sv359Ll68WOY+OvgXkUiEd526eNepS8v3x6NNS8OQnY0hK6vY7Q1ZWRiys1HdJnHc7Xi6Xlf2jK3DkG9HcDz+JANXD2Fo+9f56NkJt3UZl6ldyHMSkRF5Fp7oUuw2rVu3tv/u5ORERESE3S1bLBZTo0YNu7ttQfJdwPMNZ0EQMOXlYczKRLBaCfXxpnpwMDLnwjGVBWuzA7i6uhbJZl6WNgCrxUJa5BnMAWVbdSoLIpEIdXAN1ME1CO0++BaD+wBpp/bbDO7TB0g/fYBL33yKWCbHo3Zz6o/+nGt7N1eoWtwVEaMmHbFMhmv1+ogltsctZ2D6p3MAEEulWAHnKqFF9tXp9Sid1ThXCcXpZu4Ao9W2rSBTEhoayksvvURGRgbLli1jztLVLFu2jMAauXh5e/P888+TlpbG999/z2/7DjNgwIAS+7l8+XJ+/2sPnTp1onE7Wwx2aX0UBAFDxg27oe3AhrNKzvgRthrQC1cXNm7Hj2iH8ubKs1Ipo3YNH2rXKDpnWixWEq5lE3M1k5irGTeN739/z9OaiE/KJj4pm91/Xymy/7ZvB/D38QRmLvg3Zj4/0ZogwFMdajB/1SGOnEykQW0/nIqp332vuLkoyNLoSU4tWuHEz0eNSikjLiGzxP1FgEwmQS6TIJNJUMgl9v/bPhMjLjDXGk0WIi+mcDUpmzcHNKWKn+15pyx/CwcOHhSOEeagWIpbQamM/Nd08HD24LvBq1m2byVTf/2EX07/xpnEs6x6dSkNq9Yv83FuF+d5cXv/Mh+rtEQpErkcl/p1yDp3CsWNbGTpOVTNFWMOq4JKocJL7YkIuJqRgMliQiaREeIRhDopi7y0NMRSCVWatyhkXD9I/mvj4X6g8vbGYjSicHcv1shWuLsjc3Ym+tdfCO/2DOJSap7ejmDPIH4b+RMzt81m8Z7lLNu3iiNx/7DqlSWEeAWXuJ9IJCKtmjdX/tiBT2h1gjt0RO5Sch4Co9HI9evXqV+/7NcEgCBYMeVpMeX9++AoWK32/8ucVYhuk/Dvzs4nkJOYyPkf1pGdkYq582P37di3UqzBnRhN2qkDNrfy0wcwpCeTc/UCqiqhFa4Wd0Wg4PxgzMnCajKgcPexG9d3glKpxGi0ZZ3O/5mfDHLatGmFtt2zZw9HjtgSOo0fPx5nZ2dGjhyJ1Wpl27ZtbNiwoUQDe9GiRXz5pa2c4SeffFKmvolEIhSefiASo7tRtKJAZZwn81EqpHwwtA0TR7YnO0ePm4vNPbusBp1EIiY0yIPQIA86tQ0v1CYIAilpeUUM7/xVcID2LUN46e2NxR77yzVHSDz6LqOnbudkZDK/74m+77HeCoWE3k/VZuO28xhNRStTyGUSnn+6Nr/vjUEuFePsLEetkqFWKXBWyVA7y1EpZRiK2bckZFIJoVXdGfRiI5o1/Ncj7V7/Fvebynxd5FOZNHAY2A6K5cqVK/YyNZWZ/6IOIpGIYY+/QfPQZgz5djhx6Vd5akFPpnefzJC2r5bZZbxgnKcxJwu5i3vhOM8S4u9upaREKTn6HGJS40jW3AAncPaTUyXNjNJgRRybitg1CImPM2HeoYR7h2EWLEhEYtKjzpFz/RoisRi/Js1Rut//bN+CINh88W7hvzge7gdWs4kmw0fZY64L0njoSK7+tZNf+vTCNTiExsPepv5rQ1DcdLG+U+RSOdO7T6Z1eEtGfv8uJxNO02FuN77s9znPNuhW7D5ikYjMJmE4h1Th7DerOPv1SsQSKaJSXCAFBK5zZ+ETAtiM6eKGvghkzuo7PGLpWC0WBKsFqdIJS+8nEPnfffLCO0UkEqEOqoE6qAahz71mM7iTYtDERGLKySw1R4MpT2N3Xa9MFJwfTDm2FTpJMe7fZSE4OJhr165htVpJTEwEsHtRLFq0CKvVyqhRo2x/l7w8eyKppKQk3N3dEQQBs9mMyWQqUpc4n5UrV/Lll1/SuHFjvvrqK3tYRFkQiUSIpVKMedoibZV1nszHWWXTW5eXjo9X8H1zRRaJRPj5qPHzUfNYs6IvHHPzDOTkGktN7pWZrWfUay1ISs65L326lUB/F4b0b8qqH04W267VmZg1rjMr5/R4IOe/lfy/RX5Cs4ftFl6Qyn5dQOXSwGFgOyiW/EQ/lZ3/sg7NQhqzZ+x2Rv4wlu2ROxn382QOxhxiYd85ZXYZz3fzTEjPpoanrz3OU+7qijYttcx9KWhcFzKsb+Lv6ke1amEorRJunPgHQ3Y2148dxrtufVwCqyISi5CYrYikYpy9fdGnp+NRPeKe3ZJLQpuagty1qEb/5fFwL8hUzrR4fzxgi7kumEW8+XvjOPv1Spy8vdHEX2Xv+Pf4++Op1HtlME1GjsY9LPw2Ry+ebvW6sGfs7wxZO4J/rp7g1a/fZOLTH/Bmu9eQS+Ro9Bpcla6YrWZclC7I5QqEbm146q33ST17GlNuUffEu8VqMmHI0SBTq0k9c7rE7fwaNkZcSnKyO0UkkaDy9sGnQUMmbf2YAGev+3bsO+6LSIS6anXUVatjNRlLzdEgc76zkJRHhYLzg3DTrZsSXrlYrVYMBgOWm/Xnc3JyWLt2LREREXTu3JkuXbqwcOFCXnnlFTIybKuT3brZXjDFxsaydetW/vnnH4xGIwkJCTz//POArW77H3/8wYABAzAajWi1Wjp37gzYwmIuXLjAgAEDSEpKYu7cuYhEInx8fJg3bx7e3t507tyZ2rVrk52dzaVLl9Dr9QiCgNFoLMZQFxX7orWyzpO3otFoHur51M4K5DJp6YnWvJyZNrbjA+2H0WipcAnGKgKO66JyaeAwsB0US36JmsrOf10Hd5U7a19byVf7VjH1t0/49cw2ziZFsvKVJTQOaljm49y6+uzXuCmJB/YR0fP5Ul1xC6LR5RCTGsuNHFtpLhEi/Fx9qeYTjovyXxfvgNZtSI86hy4jHbV/FbJiY8i+EofVZEIsk+EWEkpgm7ZIpPc/dgxAl5HB9SOHCOlUtCbzf3083AtSpZLmYz+g5YcT0WdlonT3wGoyIXNyosmwt6k/aAjnf1jHiUXzSY86x8klCzm59EuqP9eTpm+PIbBN2ztOuBfkWZXfRv7Ex9s+Y8e5P3ml1Uss3LWMlQe+JluXjZuTG2+2G8w7nUbQOKghf5zfRZvqrQl+/Il7/r5Ws5m8GzfIvZaILi0VtXMVgp/ohNxZXWztX7FMZhszgnBPbvLFcSTuGFfT4+nR8Jn7ety7pdQcDb3eJO3UPhDAt0Xncuhd+VHW+cFkMnHhwgV7XXaA7OxsFixYQPfu3encuTPdunXj3LlzHDt2DLlczoABAwgPt72sGjt2LKmpqZw7dw6AHj162MtwDR06lKysLM6fP49YLKZz585243vnzp38/PPPPPPMM2zYsMF+/p07d9r74e/vT0REBGfPnkUsFiORSDAajZw/f56GDct2z6jM82RBykOH2yVaexjJvSpCHyoijuuicmkgEkqqs+DggaLRaHBzcyM7OxvXYlbKypvi31ZXPh4lHU7En+L1b4cTn5GATCJjevfJvNF2UJmMnlt10CQksH/Sh0hVKoLbP4GTr2+J7rhag5Zr2dfJ1GYBtvUcD5UHAe5Vis1Ano9Zr0OXmkpuUlKRNnXVqqirBCC6x/qcBRGsVvKuXyNh724A2n/8Gapbsl0+SuPhXjCZTCWWkRIEgat//cHxL7/gys7f7Z/7NWlK05FjiHj+RSR3oWFCZiJrD//A3D8WFGl778l3aFO9FR9tmYlSpqRuQG1cla53kUFfQKQ3IsrTI9IZCq3OCXIp7lVDMObkoEtNKbKnk7cPUicncq9fQ+Xtg9LDs9gwgzvBKliIz0jkano8rcNbMrzDG0jEFePB1GLUc3ndvFtyNLxFeO+hHBjVldz4SwR2fJ66Iz5F6XlndZT/qxScH67s3MLBqRN5dt1m3MKq2bexWCwcPHgQsViM1WpFIpHQpk0bANatW0d8fDzjx4/nn3/+Qa/X06ZNG7RaLUeOHCEoKIgaNWpw7do1Lly4QJMmTVCpVIXmpNL2K0haWhpnzpwhIiKCqlWrEhkZSWpqKm3btiU7O7vEtoLX/V+j38KYlUy3b34pUYfKTHnpoDeYmbV4f7km96oIfahoOK6Liq3B/bbLKucod3BbLl26RL16d19H+VHhUdKhSXAj9ozdzts/vMfWs78z/ucpHIw+xMJ+c3BzKj327lYdXIOCaDfjUy79/BOxv2/FmFs0nivPoCUp6xqZ2n+zhXo5exLoHkC23Ins2/RXsFpJ/ucoVnPRZCdiqQT/Zi1KjbG9GxSubvg3a0FE7xeLGNfwaI2He+HixYsl6iASiQjt3IXQzl1IPx/FicULiFr3LTdOHGfb4JfZN/EDGg0bSYPX38LJs+xZsf1cfFl54Oti21YcWMOojsNIyEwkU5vFsSvHbWXYyoiz1opXlhnPbBNS879GtUEuJsNdSoa7FNQq+nm8iNVqJi3xIulnz2A2GJAqFHjVb4C3pxPxmzeSk2DLli+oFIhrV8clLBQXpRoXhQtOcqc7srnFIjGB7lXo2ehZmoU0qTDGNRTO0WDIyUJxM0dD/sp1bmI0Sbs2cuPIn9R5cyohz75236/XikZZ5gdBEPD39yckJIQTJ07YV5FvTQap1WpxcnJCJBLZk5tptbZ459yb4Q+nT5/GYrHg6upKvXr1UCqVpe5XkPzP8rdxcnJCEAR0Ol2pbaXVZ78THSoD5aVDRUjuVbAPmdlaPNxU5ZpgrCLguC4qlwaVd6Q7cFAJcXNy45tBy1m+fw0f/TqT385u50xSJCsHLqZpSOPbH6AArsHBNBttyzQuWK3kO8NEJp1jzs75bI+0uR2KRDJ6NnqWdzuPoqZ/RJmPr01N4auwgBLbuyxddV9jsEUi0SNvADxsvGrX4clFX9Fm6secWbmMU18tJvf6NQ5MmcDhWTOoO3AQTUaMxjOi5m2PpdFryNYV/1omW5eNRq9hTKeRpOSULTeAKDkd2f7TyA6eQXL931JDVhcV5tb1MLVriKVGkH0V2tfFh16Nu/P8speY9vyHtJ/YjtyMNNSe3uw9v4+pOz/j+5nrmTziaWr8dRlnvRW2J5LkK2dXazcSAhQopAqqegQS7FmVYI8ggjyr2n73tP3uq/Ypc21arVGLVCwtFIuukj9c97v8HA2Jt+RoqDfiU6o+2Y/Tc0eTfekkZ74YQ8KO/9Hg3QW4VascD1dgc4C41UlQKpUSEVF0Hrw1GaTFYrGPhfyf+ca4IAioVCoCAwMxmUxcuXKFS5cu0aBBg1L3K0h+7Hf+NvneHhaLpdQ2B/8NKkJyr/w+aLKS8fOpUSndwh1UXhwGtoNi8ff3L+8uVAgeRR1EIhFvtR9Mi7CmvP7NcK5mxPPMoueZ9txE3mw3uFi32tvpIBKLOZsYyZyd89kWucP2mURM70bdeffJUdS6A8M6HydPr1JLQzl5eiK+jy7iZeFRHA93w53qoPL2ptW4STQb8z4Xf/qR419+QerpU5xevpTTy5cS3u0Zmrw9huAOHUt063ZVuuLm5Faske3m5IaXsycjnnir1H5oU1K48NOPnP/+O5L/OWr/XOrkRPXnelK73wBCOndBUsIqndFs5EZOKv3XvoGXsye+rr6kaFJIz8vAzcmNAK+qjPjsWy7HR3F9zf8Qbd5NYIqRgb+kcjnUid0tXYkxxxZb2x24aYAHEOTxr9EdXOB3PxdfxGIxepOBhbuWsnz/miKx6EqZolQNHgQeHkUz+btHNKL9kl3E/bKCC6tmkBl1jH1vtiP8xZHUfHUcUifnh97PB03B60Iik4EgYL6DpD4Fc13ku5DDvwZyvsFbs2bhF1JpaWlkZmYiCIJ9P0GwYrUKhfYr7ly3nkMikZTaVhCzVotYWnQOdsyTNhw62ChufqiMOMZD5dLAYWA7cFBJaRzUkN1jtzH6xw/49cw2JmyeyoHoQ3zZ73PcVe5lPs6ZxEg+2/lFgRVrEb0bdWdsl9HU9Ktxm71LprTSUE2Gj8JqNt1VLK+D8kOqUFB3wCvUeWkgifv38s/CecRu+43Y7VuJ3b4Vn/oNaPL2GGr16Y9UUdhQNFvNvNluMHN2flHkuG+2G4zZakZO0fFgzM0l5tdfiPrhO67+9QfCzVU4kVhMSKcnqd1vANWf61mmZH0F+5Cel0F6XkaRPrQKb0Gr8BbQYRC5169z6JNpnF2zkhpXdNSIN+DT8xlEA57mmlhLfGYCCRmJxGckkpR1DYPZQExqHDGpccWeXy6R8/2QNfwde7RQLHq2Ltuuy6iOQx/6SnZJiCQSwnsPpUq77kQu+pDr+34h5scFXNvzMw3emYtfq67l3cUHhjogAMFqJOXUCbzr3PmqvZOTEwaDAUEQ7Jl38xMExcXZxkdYWBiCIGCxWJCIxejSUlHKZOh1Okx5eRhvZjAvLrFQflkvnU4H2LL7ikQi+3lLastHn5lJVmw0Qa3KnizTgQMHDioLDgPbQbEkJyfj7V356pjeyqOug5uTG2teXcaqg98w+ZcZbIvcwdm551j5yhKaFXAZz8zMLKLD6cSzfLbjC34/9wdgM6yfb9yDd58cdU+GdT6llYZq8f54pMqHX+rjUR8PZeVedRCJRAS170BQ+w5kRl/mxKIFRK5dQ+rZM+x48zX2Tx5HozeH03DIUHssvEqu4p1OIwD46fjPKOVK9EY9LzTtVWTl1mIycfWvPzj/wzqif92MuUAMqn/T5tTu/zI1X+iLs9+dJd8q2Ifl+1ffdvVYXaUKT365jCYj3+HA5PFE/7qZ1E2/It3+J23eHsM770611ws3WUxcz04mPiOR+Ayb4Z2QmWgzwDNtBriLUk3z0GYM/nZ4sf1bvn81YzqNIEeXg4vTw6uZfbvx4OQTQPNpa0k+tJ2zC95DdyOeI+NfpMrjPak/cjZK7yoPra8PkoI6SORyXHzUXNy4AbmrO4Gt29he4hTw0LCazVitViwmE2azmaTERJydnfH28cHbw4O4uDgiz5xGrzcgmM14e3hgMZnI1WSTciOFPI0Gi9WCNieHKt5emA0G3FROJKWkcu5cFCaLpdB+6Wlp5ObmEhAYiKtajUQk4kpsLHkaDanJybi5uyGGUttMOh2Zly5y7rtvECxGPKqHlapDZcahgw2HDjYcOlQuDRxZxMuJip5FPDIystIkIiiNyqTDqYQzDPl2OHHpV5GKpczvM5seDZ9FKpGSlZeFu7M7ZquZ+IxEZmz91G5Yi0ViejfuwdgnRxHhV/2+98ukzUMslWHIzkbh5mYrDeVcPu6llWk8lMaD0EGfmcmZ1Ss4ufRLcpMSAZAoFNTu/zJNR76Dd13b+fS5OcjkCnupMJNRj1LtiiAIXD96hPM/rOPixh/Rpf4bi+1erTq1+w2gVt+X8Kxx5+EKt5If/5ylzcZd5Vbm+Oekvw+yb+IHXDv8NwBO3t60GjeZhm8Mva03htliJi03DZFITJ2pTUvc7uSkv3l51etIJVLaVm9Nm2qtaRXe/LaJDO+FOxkPZl0eF7+ZReyGxQhWC1KVC7WHTCG0+5D7WhWgPCiogybuPFFfTUZwrcuNk6cK1MX+l6tXr4IAIaEhmEwm4uPjUavV+Pn5IQgC6enp5ObkIhKL8PDwsD8rWC1WUtNS0WltK8xOSgUe6n9LHWbl5pKns606e3p64upm2y8lJYWcnByCg4KRyWXo9XrS0tIwGU0onZT4+PggvVlirrQ2AGc/f7yCnXELDaN6v9El6lCZcehgw6GDDYcOFVuD+22XOQzscqKiG9gVOZX+w6Sy6aDRaXhn/Yecv36BX0f+xIr9a1hRoObwG20H8Ua713hu0QtEp8byfJOejH1yFDV8q93+4I8AlW08lMSD1MFiMnH5540cXziP5OPH7J/Xe3UwT3y+gGNfzOFkAY+GxsNH0Wz0u/w6oA9X//y3nq+Tjw+1XuhH7X4D8G/e4i5Kdt2e0sqVlYQgCET/+gv7J48j89JFANzCwmk77RNqPv/ibRPtGc1Gan3UpMRY9MiPjtJoRutC7utikZj6gXVpU60Vbaq3pnV4i/tqcN/NeMiOPsuZeaPJPP8PAO61mtDw3QW41fjvuhwX1EETd57zKz6iwZj5iOXOZFy8gElXNJt3Qf78809SbtzgpQEDynxOi9HIjRP/lNju36QZ4vt4rYolUtQBAbiFhXNh1QxkarciBrZjnrTh0MGGQwcbDh0qtgYOA/sRoaIb2LGxsYSHh5d3N8qdyqiDIAjEpMay/vjPxdYcHvvkaLrW6YSrk2ulMazzqYzjoTgehg6CIHDt0N8c//ILorf8TPcfNnHjxD8c/nRmkW1bjZuEX5OmbB00gBrde1G73wCCO3YuMVnZ/eJedLCazZz9ehWHPp5KXnIyAH5NmtH+k88IfvyJEvfTGrUs3LWs2Fj097uMYVTHoWRps/k75jB/xxzhQMyhIknVRCIRDQLr0aZaKx6r1orW4S3uKO/CrdytDoLFwpVfV3N+5TTMeRoQiwnvPYxagycidVLf/gAVjII6FDSwnXxKroaQT1paGm+++SaLFy+mSpXbu8wLgoAm/iougVW5uutPrCZTkW3EMhmhnZ9E9IDKu51fOb1YA9sxT9pw6GDDoYMNhw4VWwNHHWwHD4Xi6mZWRiqjDiKRiGDPoBJrDq888DXvPTkKubRivoV8kFTG8VAcD0MHkUhE4GNtCHysDZr4q6h8fPn9zUHFbnty2SKGxl1neHzKQw0fuBcdxFIpDYe8Re1+Azi+cB7HvpjDjRP/sOGpjoR17Ua7mbPxqVe/yH5liQNXyVW80LQXLzTtBcD17GT+jjnMwejDHIw9THRKDKcTz3I68SxL9q5AJBJRP6Aubaq3ok211ndscN9t+SaRREJYzzeo0u45IheN49qeTcT+tJjr+36h3ttzqNL2mbs6bnlR7HgQrOToc7mcEo3BbCh1/7Gz3+eKLp4rsfGlbmfSasmKicGYk4NnzVqYFHnkpCQU2c6lalXiTv6BYDbj5O19X7w4JCIJ/m5+BHlUtdUhKwbHPGnDoYMNhw42HDpULg0cBraDYlGWQwKpikhl1eH2NYdz8FZ7PeRelT+VdTzcysPWwTU4BG1qSrEl2wAMWVkYc3Lua130snA/dJCr1bSeMIUGr7/F4VkzOLPqK+J2bCdu5+/UfflVHps8HdegoMLnlSkY1XEo73YeiUafg6vSBbPVXGKJripu/jzfpCfPN+kJQLLmht3gPhBziOiUGM4kRXImKZKle1ciEomoF1DnX5fysBZ4OBcttZMfi+7m547RbLzrWtxKL3+affQ1N556ibPzx6JNvsqxyf3xb/ss9d/+DCffqnd8zPKg4HiQOjljEQSWH/iGY1mxxdaivlMEQcBqMmIxGm9+IkKSexyxRHrzcxMg2D6XyxBfPoVJpwOrFZFEilSpQCQqW53121HFzZ8umWlE+AYWaXPMkzYcOthw6GDDoUPl0qDCuIinpqbSunVrVq5cSYcOHYrdZtu2bXz44YfExsYSHBzMnDlzePbZZwFbnUZXV1uim4JvaW/cuIGzszN5eXmMHDmSLVu2YDab6dGjB0uWLEGtLt4N7ciRI4waNYpz587h4+PDpEmTeP311+3t33zzDTNmzOD69evUrl2bL7/8ktatW5f5+1Z0F3Gz2VwooUllpbLqcLs4zwvTTlTKFezKOh5upTx0sBiNLA3xK7Eu+rCrNx562bYHoUNm9GUOTJnApZ9/AkCqVNJ4xGhavDcOpbv7fT1XPsmaGxy66U5+MPowl1OiC7XnG9yPhbeiTfVWtK3WGoVMyfy/Ft33WtxmvZZL384mZv2XCBYzEic1tQZPJKzXW4glFfvaKzgeBEFg+kfdOOfpxCsd36RFWDNcFOq7XkW2GE0YczVYzWbAlgBQrnZBLJEgCAKCxYJIIkawCojEIgSLFZFEjEmrw6TNA0FAJBYjc3a5aWjfXT9MFjMxqbF8f+h7Yk/tZl6fzwhq1L5EHSozDh1sOHSw4dChYmtwv+2y+/Mq8x45ePAgrVu3JiYmpsRtLl++zPPPP8+MGTPIzs5m2rRp9OnTh6SkJACioqIwmUxkZmaSm5tr/+d8011w5MiRJCQkcPnyZS5fvkx8fDwffvhhsefKzMzk6aef5pVXXiErK4tVq1YxZswYjh49CsCePXt4++23+eabb8jKymLAgAF07979kXJ9uHDhQnl3oUJQWXXIr/dbHPn1fisjlXU83Ep56JBfF7048uuiP2wehA4e1Wvw3P820H/PIQLbtMOs13Ns7mxW1a3GPwvmYb5Zo9ikzcNiNKJNTcFiNNqMqLvE39WPXo27M/eFWRwet5uoqcdZOXAxrz02kAi/GgiCwNmkc3y1fxWvrHmDv2OPMO/PL5mzcz5SsYTaVWohFUuYs/ML5v+1GK3x7u+FUqWKOm9O4/Hl+/Go2xKLLpdzi8ezf1hHsi6euOvjPgwKjgeTxcRFLzVPV2tHB88auFvFSI1GJAbDHf0T6/VYMzOxpKchMRiRCwLOTipUSidkZjNivR5RTjZCZirW9FTISseanoqQmYooR4NSIkHtrEYhEiE1mRGybMcSa7V33BeJwYDCZKam0othtZ7G6uHFZVnRe4FjnrTh0MGGQwcbDh0qlwbl/hrhm2++YcqUKXz22Wf069ev1O3atWtHz549AejTpw9r1qxh+fLlTJs2jWPHjtGgQYNis9NptVrWrVvHnj178PT0BGD27Nk88cQTzJkzB5WqsEvbxo0b8fLyYsQIW5xbx44dGTBgAIsXL6ZFixasXLmSfv360aZNGwDGjBnD8uXL+fHHH3nttdeK7b/BYMBg+Df+SqPRlF0kBw4eMnda79eBgwdNRayL/iAJaNmKvn/sJXbbb+yfPI7081HsHTeWuB3b6PHjzxyb/3mhbOr3Uwc/V196Ne5Or8bdAUjJSeXvmMMciD5E1LULtK/Rlnl/LuKHgStoV6sdOempuHj5sO/CfubsXcK7nUfecx9cw+vSduEOrm79hvPLPyL78in2De9IWM83qTV4EjLniuf5VZDEzCRwdqFFncexmozkXbtyx8ewmk22zOMWm3u5WC5H6uSEOSe9wFYCpjxN8fHQItFNnUSAgMVgwKzX27YViZA6Od30+riz1WyRRIKPTxAR1ZpwOf0KHe/4mzlw4KAiYTHo0KUmIdxlPo2yYky+Qo7Lg00+KpJKcfKpikRevs+p5W5gd+3alQEDBiCVSks1sM+dO0f9+oUTvtSpU4fTp08DcOzYMXQ6Hc2bN+fKlSvUrl2bTz/9lMcee4zLly9jMpkK7V+nTh10Oh2XLl2iUaNGZTrXqlWr7O2DBw8u0p7fl+KYNWsW06ZNK/J5VFQUarWaWrVqERcXh8FgQKVSERgYyOXLlwHw9/dHEARu3LgBQM2aNYmPj0en06FUKgkJCeHiRVu5Fz8/P8RiMdevXwegRo0aXLt2jby8PBQKBeHh4Zw/fx4AHx8f5HK53QugWrVq9jqZZrMZQRA4d+4cAF5eXqhUKhISbIlUwsLCyMjIIDs7G4lEQu3atTl37hyCIODh4YGLiwvx8bZELSEhIWg0GjIzMxGJRNStW5fz589jsVhwc3PDw8ODK1euABAUFEReXh4ZGbYSM/Xq1ePChQuYzWZcXV3x9vYmNtaWEbdq1ar2Wp35f4Po6GiMRiNqtRp/f3+io22ujgEBAZjNZlJSUgCoVasWV65cQa/Xo1KpqFq1KpcuXbLrDZCcnIzBYMBoNJKYmIhWq0WpVBIaGmp/C+fr64tUKuXatWsAVK9eneTkZHJzc5HL5VSvXp2oqCgAvL29USqVJCbaavyGh4eTlpaGRqNBKpVSq1YtIiMjAfD09MTZ2dmud2hoKJmZmSXq7erqaqupCgQHB5OTk1Oi3p6ensTFxdn11mq1pKfbHtjq1q3LpUuXMJlMeHh4MOLxNxnTaSTZOlu93zydloQr8RgMBmrXrk1sbCwGgwFnZ2cCAgLsY7ZKlSpYrdZCY/bq1avo9XqcnJwIDg4uNGZFIhHJNzMp16hRg6SkJLRaLQqFgrCwsIeit1qtto/Z0NBQsrKyyMrKQiwWU6dOHaKiojAYDCQmJuLu7m4fs8HBweTm5j6UMVtR5giDwcDFixeJiIh4qHOEQqGg8ah3afHBBPTZWSjd3BHMZuKvXUOr1T70OcJgMBAZGUlERMQDmyNk9RvSZPU6krf/RtyKpTQe9jbH5n1WKJu6ISuLQ59MRwDqvDmc5IyM+zpHWLRmqkvDqF4rjLrP1yU9N4MNr60mcsECVj7d327kNxw2kg2jV5OjzyXjejrh4eH3PkeENafBvB1c/fZj0g5sIW7TMq7t3Yxv3/dxbvg4/t6eqF1cMWgykbu4Y9DrMFqxa/gw5wij0UhkZCTu7u5kGbPQ6XVI5C44+YdhNhoxGm0v2dVqNdq8PKyCgFQqQS6To9XZalorlQrMBiNZ0Zcx3KzlLlYocA6rhsrLG7lCjvZm/WuZ1YRM4UT6mYOUhLpOLfQmEzKp7aFWm51Nbmw05hwNZGqRurjiWiMCtYcHeXk2zwOFXA4iMBhssd7OKhV6gwGLxYJELEHl4kJubi5SZKRnpZOZmVlojhCLxURGRiKTyR76HJE/ZivCc4STk5N9/DzIOaIiP0e4uLjg7u5u71NgYCBGo5HUm2O7Mj1H+Pr6EhUVhdVqxd3dvUI8R/j7eHFi1aeY4qOQS23VBkwmm1eKUqnAaDRitQqIxSLkcjl6vW0Ok8mkhbZVKBSYTSYsVmuRbaVSKWKRCKPJhCBYiVQqMZvNWCxWRCIRSoUCnV5/c1sJYrEYo9F087jyAtvaYrj1ej2CUHRbuVyG1Wq12S9iKSGtn0Rfsy1WkbhMc0T+tXu/qDAx2GCL89q9e3exMdidO3fmscceY/r06fbPJk+ezKFDh/jzzz8ZO3YsWVlZTJ8+HU9PTxYvXsy0adM4c+YMSUlJtGvXDovFgvhmjVGLxYJUKmX//v20bdu20LmGDBmC0Wjk22+/tX+2atUqZs2aRXR0NNWrV2fChAmFjOyBAweiUChYuXJlsd+tuBXsoKCgChuDnZGRYV/tr8w4dLCRnZ2Nm9v9q5v7X8UxHmw4dLDxsHUwabWIxCKWhQWUGIv+VkwiG57tgjkvD7FUhlgmQyKX//tTKkMikyGWy20/83+XFv7Mvu3NfQt+5t+yJefWfs2RWUVLprUYP4kW736A2EmJTHJ/VypS/tnFmS/GoL0Whzo4graL/iD2pyXEbfoKU24WMrU7Yb2HUmPAu0jkD9+joeB4uJB8kem/fcrnL3xCgHvRsltms5kXX3yR9PR09u3bZ/88N/k6aefOYsjTsnLLr+w7fQZnFxeGDRvGiy++CEBi9EWmTZvKyXMXcHJW0611EwZ174JYLMZstrDkx1/48/BxVEolI0a/w4t9+hQ6tyAIaK5e4e8dv/Pljxu4np5O4wb1mT3vC3xuJgo8ffo0U6dOJT4+nmbNmvHxxx/j7e1d6Dgzt83GTenG2x2HlqhDZcahgw2HDjYqmg6CIHBh1QzyEmMI7PQCLmG1H/i8aTSakMsf7Aq2xagn+/IZru3ehFtEQyJefr9M+1XaMl3Ozs5FYpy1Wi0uLi4AzJ07t1Dbe++9x5o1a9i6davdlVur1dqTmuUfK3//W8+VdcvDS8FzldSXW28+BVEoFCgU/x232mvXrlWoiaC8cOhgIyEhwWFg4xgP+Th0sPGwdZCpVLfNpq5NTcWk0ZB2LvKB9MHJ25s3Llzh1NJFxbafXrqIVh9OpPfS/kx5djzNQ5vet3P7NuvIE6sPc+m7ObjXbkbshsVcWvuZvd2Um8Wlbz8FoHr/d5Aq7zyj+b1Q1vGg0WiYMGECUVFR+Pn5AWDW60k7F0less2z5LcjR9iy/wBPP/00SUlJTJo0ibAAf8Ld5XwwaSZnL8XybIfHuJqRwze//E4Vbw+6tW3JT3/sZeOf++jYojEpGi2TJk8momZNGjZsaD+/SCRC7ufP9DXfIBGJ6NSsKdsPHebdYcNY9fUaLBIpQ4cORS6X07NnT3788UfGjx/PihUr7qsOjzoOHWw4dLBR0XTQ3YhHExtJjZfG4lmv5UM5p0mjwekhLCqqq1ZH7upB7MalGDJTUXg83Aoj8B8ysOvVq8eJE4UTnERFRdGsWTMAJk6cyAsvvEDjxo3t7QaDAScnJ2rWrIlMJuPcuXO0bNnSvq9cLiciIqLYc+3cubPIuerVq2dvz3d5Ktj+9NNP3/sXdeDAgQMHDkpA4eaOwt29xBVsZ/8qdP5yGaa8PKwmExajEavJZPvdZLz504TVaLT9NN+yjdFY4mdWkwnngEB06WmlG/nJydQ8mMDQ/d3o0HMQk3tMuKO62qUhUThR+/UpWEwGTs0aWuw2cZuWEfHye/flfPcbrVZLx44dUSgU9iSsmsQE0s+fw2o0gUiER7Xq/L36azw8PJg3bx4XTh2nZ78BbP5hLSP79+KDwf0RuXrhFRrB2v/9wPETJ1H7BiKWyth19CRuLmo+mzmNazor3Xv05Jcf19GgQYNCmcOPHDlCRmYmEydOpGeHDmR98D4HT53m/I7fSTCYyMjIYNKkSQwcOJC0tDR27tzp8GRy4OARQhMXhUgixb32/XsJWpHwqNsSNi5FExeFj8fjD/38/xkDe+DAgcybN4/169fTu3dvNm3axJ49e1iwYAEAkZGR7N+/n/Xr1+Ph4cHs2bPRaDT06tULlUpF3759GTduHOvXrwdg3Lhx9O/fHycnpyLn6t27Nx988AHz589nxIgRHDhwgHXr1vHLL78AMHjwYHr16kWfPn1o27Ytixcv5saNG/Tq1eu+fFeLxVLInbw8CAwMfKSyot8td6ODQqFAIpE8oB6VD9WrVy/vLlQIHDrYcOhgozx0yM+mfuiT6UXamgwfhWAxE9i6zQPtg8VoLNXIV3n7EH4glippaeh//5zxc1bSqt9r9H1z8n0rNWbO1WDKLXp+sK1kGzUZSJxUyFQPLwSrLOPBarXSo0cPhg4dSp8XX8So15N6+hQACjc3fOo3ROHmRlxcHCFBQWSeO4pKY4tBTUhOReUfTP2mHZAonBg4cCBHjx6lW7duPD9wMCKRiMTUDwgNDcW5ajV8EmyVWWIuX0YTcxbX8HqIbobJFYyfdQ0OJqJRY/afPMW1lBTOx9ra/Dw97NtYrVauXr1KgwYN7osOlQGHDjYcOtioaDpYDXokcuVDLX+Y/1LxYSBVqhBJpFgNuod2zkLnL5ezlhG1Ws1XX33FgAEDqFWrFps3b+bDDz/k9ddfJyQkhI0bN9pXoNesWcPYsWNp2LAheXl5tGjRgj///NPujrFkyRLGjh1L/fr1MRqN9OjRg0WL/nVxq1u3LgMGDGDChAl4eXnxxx9/MHr0aKZMmYKPjw8LFy7kiSeeAKBTp04sWbKEYcOGkZiYSN26ddm+fft9cf3QaDRER0dTgULjHdwhIpGI6tWrV8jY+rslOTmZ0NDQ8u5GuePQwYZDBxvloUNFyKZ+OyNfm5ZK+NPPcfG3n1FmZFH9fCZpH83jy2lf4N/mMer27Eu1Z57DLST0rvsgU7shU7sXa2TL1O5IVS7seqUpXg1aE9T1JXyadkT0gF98lmU8qNVqJk2ahOZKHGaDHsFqq1ftUaMm7mHhIBJhzE5Hp9MiNhswajKQy2wxixa5E67hde3HevnllwkPD+eHH36gWbNmvPzyy+h0OuRyOWKJFPfgGgDoDUZ0KUlYjHrcIxojlsrsL47zK68obi42KINDMFy2JUjSXL5IxqUayG7WrdXpyvag6pgfbDh0sOHQwUaF1EFUcgWBkvJE5GMymZg9eza//fYbKpWqUJ6I3NxcZs6cye7duwHo0KEDEydORCqVIpFIaNKkCWbzvyX+XnjhBT7++OMi5ygtF0RZ8kSUJxXKwL7VqJw7d26hsltdu3ala9euxe7r6enJmjVrSjy2i4sLy5cvZ/ny5cW23+ry3axZMw4eLDkz58svv8zLL79cYvvdYLFYiI6OxsXFhSpVqtgTsjn472C1Wrl+/TrR0dE0aNAAqbRCXWJ3TW5ubnl3oULg0MGGQwcb5aWDVKmk+dgPaPnhRAzZ2Sjc3LCaTA+tVFlZjPynvlpNF8sK4g/t56elM8jYvR+vTBMp+w+Ssv8gu8eOwqd+A6o9051qz3THr0lT++pqWbBazIT1HmqPuS5IWO+3yDx3FH3aNZJ2bSRp10aU3lWo+mQ/grq+hEtIzfumRUHKMh6MOTmknj2NPjMTBBCJxVRt9zgylTMmTQa5CdEYNRkoZDJMZjMq/2AkXgEAOKkKr/507dqVJ598ku3bt7NhwwZefvlllEolRqMt+3f+T7WnNyKJBGNWOhmRR/Co3RTlzbFy67ZeQcH41awFW7dhNJrJvHyJ9FjbSnhxHn93q0NlwKGDDYcONkwmU3l3oUQEQbCHFQHk5GiYMWMG0ZFn8fXxsc1Xt7D2u7X8+M3XPPnkk1y/dp3pE8YT5u9PvXr1mDVjOlt//ZWePXqQp9WybdNGZGYTo0ePJjExEbHRwDNPPkm18GqALUP8refQ6XW8PWQIcrmMnl278vPPPzPx3XdZsGBBqW0SuRypSlUoJKY8qLBP/4IgcOzYMebPn1/eXXloGAwGBEGgSpUq9mRsDv57VKlSBY1Gw08//UTLli0JCwsr7y7dM8XVl6+MOHSw4dDBRnnqILtpbKluZn2WPOS+FDTy9VmZKN09ihj5YomE0LYdeK9tB2JS45i09G2y9hwg4oqOoGQjqWfPkHr2DIc/nYm6SgDhzzxHtWe6E9yh421fFkiVKmoMeBewxVzfmkVcLFPQftleEnasI/GvDejTrhP9/RdEf/8F7rWaEvTUAAI7Po/cxeO+aVLaeBCsVjJjosmKvoxgtSKWSpHI5bYK1WYDmeciMWpsD5gisZiqgQGkZGajDq1tL88TFhaGIAgMHDgQf39/Pv/8c3uFknzXy+DgYK5du4bVarWXFqoWURPPui3JvHAcszaHjLOHCPSzjZv8Ek6JiYmIRCJCQkLsJX3ynFRIFHKSkpMRiUS4GA1YLRbEt/EEcMwPNhw62KjsOpi0tooOVVxd7PksZKqH5ypdGoLVStT33xG/Zxe6NFvpNKvFSlRUFL5iEX1EAuLMFLYPeaXIvpmXLtFXbKVuciJ6k56agpEj779DQmAA3nFXeM1FSVicrfSVp9iC5Pdf2RtzgeysbJ4XjFSLuYAy6QpSqZScQ3vZ/nXhBVCNRkPHzBsEBgTiffEsapUUzd97+G3QAPK0eSW2SaQSnP38CX6ic7l6A1dYA1skEpVY8upRx7Fy/d8m/++XmZnJ9u3b6d27t71u7n+VihY7VF44dLDh0MFGZdch/yHRydsHsVhcqpFfzSeM/03+lQ1P/8zkLdPJS0uh2lU9nTU+uJ6/Ru71a5xZ+RVnVn6FzNmZkE5dqP5cD8K6Pm1/iXArErmS6v3fIeLl9zDr8pA6OWO1mOylZtxrNsa9ZmPqDP2YG4d3kLBjHSmHd5J14ThZF45zbvE4/Ns8S1DX/vg073TPsYgljQd9ViapZ05jzMkBQOXnh3fd+ohmfYrVYCDz3FFytTo2/XWA2vXr8/Tz/Xj6uRgWLFjA2LFj7fV6u3Xrhkgkws/Pz+6WGRcXh16vt+eA6dq1a7H7ydRunEk3curAHnq0b0GEWoarqwsrV64kPj6ev/76izZt2uDm5karVq1wc3Nj7YYNXO/UiSPnomgSEYH1RjKJB/bhU78hTqWExFX26yIfhw42KrMOZr2eo3M/42Q5hfPY+qBFLJFiys1GpnbDajHbKywkHouCs5mEdu6Cd70GSBUKzBYzQUlJ+FepwoULFxCsQqEqBPkoTp5AqVBSp04dLFYL6hMncHNzI6JGBK0LbKfT6ZBFncPd3Z1q4dVISIjHNyUFiViCxWrB2dmZatWqoZAXrrSUnJyMZ2ICNarXwN3dnaDERK4nX6dO7drk5OSW2KaQSEk5fZLzP65DZLhO6DMPUt2SqbAGtoM7I09rRCaVkKXR4+6qxGS24Kyq3G8NyxsfHx8uX77MjRs3/vMGdsEs+pUZhw42HDrYcOhgo6w6iEQi+jTrzZO1n2Dab7NYe+R7ItHi2y6McYE9CLyYRszWX8m9lkT0lp+J3vIzIrGYgFaP2VzJn+2OZ0Rh927BKiCIwWwwIVGCYC16XolcQUD77gS0744+I4WkP9cTv2MdObHnuLZnE9f2bELh6Wd3IXcNq31XOsTExFCjRg37/61WC5mXL/2fvfOOr+n+//jz7tybmx1JZIkVQlTt3RJ71KZUaav42jU6f3Rpq1TVKkVLlSqtompTo6jZ2iNmIomRvW+Su87vjyOXyE2skJDzfDw8uOfzOZ/zuS/nnHve5/MeZMWngyCgUKvxqB6KWqch/fIJrMYcEKzI5HLMju4sWrORzmYFL72iZfDgwaSmprJu3Tq0Wi2fffaZrUrKxx9/jFwuZ/Pmzej1et5991169uwJUOh+f+3azdq1f9D6xaZ4q5V8NWYws1auZ82aNTRq1MgWA6nX6/nhhx/45JNP+GPdOho1bsyEMWPg5nVMGRlcP/gPLkHlEax2xEa6LnKRdBAprTqYDJkcnv4VB+/IVZGTkmLLXVFv/LuPfSXbYszm0ooZRKxZkM/LJyspmYz4DJpMGkr5Nu3y7Od/6+9ryLFarfjUrZdvbG16Bs7OzvjUqYMgCJzPMODk5obPHdWcsrOziTx+HMfgqjxXpw5arZY0Zxesvsn4+flhMpmIjIwkVe+cL4FiVkQESTpHvJ5/Hnd3dwzuHqQ5u+D+3PPIU1IKbHNzc8O/aTOc/Pw5+MUHGDMyi1zX+0EysJ8BsnPMfPXdP8z+8RApqdm4ujgwemADPhjRDAfNo/0XT5gwgdOnT2MymYiKiqJiRTFeonfv3vTr148rV64wffp0oqKiAAgKCmLixIk4OjrSrFkzNm7ciK+vr2288PBwhg8fzo4dO/LER4SFheHg4IBKdbsAfbly5Zg9e/Yjzf9u1qxZw5QpUyhbtiyCIGA0Gunbty+vvfYaIMaBnD9/vkiOJZPJkMlkWCyWIhlPQkJC4lnAzdGNmS9/RZ96PRm36gPOx15g3OUlvBDSlGkT9+B0LYVLG9ZxeeOfxJ84zrX9+7i2fx97JryLW+VgKnbqQpWeL+NZrfoDrw45uHtRsfdIKvQaQdqlk0RtWc61HavISYrl8q+zuPzrLFyr1CKgbT/8WvZE7Xzv5KX2XEDjTp0k+cJ50jyicHBwRe/nh7OfL1k3I8mMFF3Bf/vmU7TeATj6VsBb48BHH31k+y1VqVR88MEHfPDBB/mO5+zszLRp0+zOpbD9pkyZwpQpUxCsVtIunyIEmP/BCBz9K6IPqJznN/m5555jzZo1efa3VK5M4rmzpMdEkxoRQWpkBDgkQlje42g0eVeiJCRKI3KlimPz7D/DHp03mwbvTXisxzdnG7i0YgYXlk61bTNlpNjyVqTFpqHUOhLwQvOHGl8uF41vwPb3nR64WVlZHD9+nOzsbKpUqYKzszMmk4kqVfK+JE1ISCDZTox3bjWeu4+hUCgKbcslsEVLjkzXkXz5CgGtH+orPhKSgV3CEQQBQ1bBiREsFoHpC/czaebftm0pqdlMmiF+Hj+4MQqF/UB/nVZ1zyQAuW+0Y2JiGDBggK1UGUBsbCwDBgxg0qRJhIWJv7A///wzgwcPZt26dbRp04YNGzYwZMgQ2z5r166le/fudo+7cOFC/P39820vasLCwpgyRbzBZGRk0KFDB5o0aVKq3ZjuRUnKzFicSDqISDqISDqIPKwODSvUZ/f4zXy7ewHTt81iz8V9vPB1G8a1GsmoD/6PJh9+SlpUFJc3refyxj+J/nsXyRcv8O+Mafg1asLl9X9wcMrntvEeZHVIJpPhUrkmNSrXpPrQz4k9tI3oLcuJPbiVlPPHSDl/jDPf/R/ejTsQ0LYvXvVb23Uht+cCWmvYKJx6dkAmk6N00OBZrSrmjARSLx4Tjy2Xi4a1XwWbO3tCQgKrV69m7ty5D6XlgyCTy3Gu9BwKjY6MmEtkxlzGkpOFS8VQZPKC46sVajVeNZ9H7+tL/KmTWE0mTi5fwF8nM3nhy6+RyUSjwtfFucTFmhYH0v1BpLTqkJOaYreUIYj3qpzU1ALDX4oCuUJJxJoFdtsi1szHtW5PlDrHh3ZV12q1ttxR2dnZAOh0ouu50Wi0GdchISE2L065XG4rEZibU8JisdgtbZubVDG3ekF2djYymcx23ILactG4uCBXqjDdZ/WDokYysEswgiDQtPti9v8bbbfd011H5IExzP7xkN322YsP8e7QJgQ1mklCUv5azk3qBbB39cCHzrT3yy+/0LBhQ5txDWJ2dTc3N4xGIz179mTSpEk2A9tisbBp0yZbLfL75f3336d+/fp0794duL3KPGfOHI4fP87Nmzfp1asXzZs356OPPiI5ORmdTseECRPuWbPTYDCgUChwcnLKs33OnDkAjBo1ChCN8qVLl+Lr68v06dPZv38/ZrOZdu3aMWLEiAf6Pk8jDk8oVqikI+kgIukgIukg8ig6qJVqxrUaRbfnX+Kd1RPYdX4PX26Zzqqjf/BNzy9pUqkRtYaOoNbQEeSkpRG5fSvRf++kXFgrtgx53e6YR+fNpt64d1lcsyqWnBzUej0qRz0qvR613kn8fGub2skJ9a02pxqdca75Eqnhh0k4tous65eI3r6GmB1r0Xp4EdD2lgv5rVJZBbmAHvzyM/zNKej9/VG6qMm6IZa9smdY5+Lp6cmqVavsPmjeDxarBRkyzFYzSrkSAQFFIcayTCZDH1gZhUZL6pXTZMdfx2rMxrVKLeTKwsPLdGW88G/2IvLL65BbIXrPbizZWRydO5tj380ptljTkoZ0fxAprTpoXFzRuLraNbI1rq5oXFwe27FzkuOxmk12yxiCuJJtzsoEqwVLtgGZSo1MrshjD5jNZj7++GPS0tI4ePAgZrOZmJgYHB0dKVOmDO7u7nz99deMHTsWjUZD27ZtGT16NADnz58nKyuL8PBwhg4dypgxY+jUqRN+fn4YDAZWrVrFX3/9RWpqKhUrVmTCBHE1PyEhgYyMDPz8/HBzc+PAgQNMmjSJ5ORk/P39GTx4MCqVCjc3N86cOcMXX3xBXFwcnp6e9OvXz1ZO2UYxZhKXDOwSTmHnho+XnriETFJSs+22p6RmE59owMdLb9fAflTCw8Np2LBhvu0dO4oZBerVq4fRaOT8+fNUqVKFffv2UaVKFcqWLWt3vCFDhuRxEe/RowcDBuTPXHgnWVlZbNy4EYBevXoxcOBA2rdvz/HjxxkzZgxbtmzJl8Fy586ddOnSBavVSmRkJF27dsXb2/u+vvPq1asxmUysXbsWk8nE8OHD2b17N82bN7+v/Z9WYmJicHV1Le5pFDuSDiKSDiKSDiJFoUN5zyBWDfmZtcf/ZMIfn3Ip7jKd5/Wmb71efPrSRDz07micnanSoxdVevTCEBdX6OqQISEehUpF8oWiCfmBDM5um4z83S9ROGhwrVCZPrsOFugCGvPjMjQfdeViwmV8fULRegfi6Fc+n2F9J49iXEckRHI1KRqTxYRKoaKcewDlPYMKNbIBtN7+yDUOpJw/hjE1SSzjVbUOiltJkArChIV4ZQ7t3vqQFl7Pc/TbWQ/tTfCsIt0fREqrDilXLlNr6Mg810UutYaOJHrPLmRyBeXCWhXJ8SzGbG7u20j0tl9IuXCcVstPotK72jWyVXpXFBotgmDFZBDLqMnkMuRKNXKlivSsbCZ++BFXr17FzU2stGAymbhy5Qo+Pj6UKVOG3bt3s2vXLurVq0diYiI///wznTt3JiQkhISEBE6ePGkrnxwfH09UVBQ+Pj5cvnyZFStWUL16dUJCQtizZw8///wz9erVIz4+nhs3buDl5cW5c+dYunQpFStWpHr16uzdu5dZs2bRtWtXrl+/zoIFC/Dw8KBly5YcOnSImTNn0qZNGwIDA4tEz0dFMrBLMDKZjL2rBxbqIq5SKnB1cbBrZLu6OODr7cTBdYPs7ns/LuL3mt+9yi90796d9evXU6VKFdauXWsrQm+Ph3ERf/755wHIzMwkMjKS9u3b27Y7Oztz5coVqlatmmefO13EExMTeeWVV1i7dq0tC2th7N27l7Nnz3LokOg1kJWVxYULF555A1tCQkLicSOTyeheqwthVV7ks41TWXLgZ1YcWcXWs38xqfOH9Knb0/abpXEtfHVI71OWTstXYUxNxZiZgSkjA2NG+q2/MzBlZmBMTxf/vvNzRkae/sb0dIRbeTQEASxmAUtGNlaLgCEutkAjn4QUqntUZMuNY1Sr3ZrqAVUfS13WXOP6UvwV2zaTxWT7fD9GtsbVE/fQBqSc+w+zIYPEUwdxC6mDSm9/hS01K5WfDvyCRbDQ6aWBuGuc2dD/Zbt9n0SsqYRESSP26H9sGfIGvTbvAJksj2dHreGjqT18FCtbNiPpfDjV+g3gxSnT0T2EK70gCCSfO0L01hVc37k6jzGdcv4Y5bv/L08Mdi7luw8l4WI0MrkCuUqFYDYjWAUsxhzSU1No07k7arUaR0dH1CoVVrMZBwcHgoODbW7Zmzdvxs3NjWXLlnH58mU6duzIpk2bqFmzJufOneO7774jKCiIyMhIQkJCaNKkCSaTiVWrVuHm5sbKlSvJyMjgxo0bthcwISEhhISE2P69bt06vLy8uHz5MidOnLDZHFeuXKFChQq88847NGvWjGXLlvH5559z+vRpycCWuD9kMlmh2cAzDUZGD2xgi7m+k9EDGzzWbOI1atTg1KlT+Y87ejTvvPMOAQEBdO3alVdeeYUhQ4Zw4sSJAhOzFIZMJrPVsjOZ8r5syI23sFfrThAEzGZzoWN7eHjw4osvcvLkyTwGtkwmsyVNuPO4FouFd955h7Zt2wJiKa7S4P5UoUKF4p5CiUDSQUTSQUTSQaSodXDVuTK915e8XLcH41a9z7mb5xm5YhwrD69iWs/JBHtXwmo2UXv4aNsq6Z3UHj4aq8WMR5WqdkZ/MARBwGI02gzuzBsxXP/7T1IunMTRp2yhRv6brYYxdcdsJm+bgZODHr1GD0VsYwuCwOX4CKzW/Mk05XIFFcuUv2/DXjCbyYqLwWLKQbZXhoOnL0qdPk8fi8VCfEY8cpmC0S2GUcbJE0P8PbwJ4uPIvHkT71q1kZWiMqTS/UGktOlgTE9nw4A+pFy+xMEpn9Ns0mQavj+RnNQUNC6uWE0mBEGgXFhrki6c5+zypURs3UTzr2YQ0qfffV2vWfHXidm+kugty8mIvmjbrvXyx7+NGMqi96+EW7W6gIyINfPzZRFPmfohMrkCtZMbgiAgmE1YzSZkRiOd2rVl0Ov96T9oKGaLBWNaEmaLhevXYqhetQpWs5mIiAiCgoKQyWQEBAQA2OKry5Qpw+zZs8nMzMyTcFGpVBIeHo6TkxNdunThypUrhIaGMm/evHzfUafTUbVqVQ4dOsSAAQPQ6XS2fs2bN8+zsHX8+HGgZJ1rkoH9lOOoU/PBiGaAGHNd1FnEC6N379507tyZnTt3EhYWhiAILFq0iKioKPz8/ADw9vamUqVKTJ8+nXbt2uVxAb9fXF1dbZm9t2/fbrePXq8nICCAzZs321zE4+LiCA4OLnRso9HI0aNH6dOnT75jHjhwAIATJ04QHx8PQMOGDfntt98ICwvDbDbz+uuvM2rUKFq1KhoXn5JKQkJCiXkrWJxIOohIOohIOog8Lh3ql6/LrvGb+e7vH/hq6zfsu3yAF79uy1sthzO25UjqvyM+uB19jDVmZTIZSo0GpUaD1sMDl3JB+DZsCkBOajK1ho3k4Jd2XECHjUQrKPiiy8ecu3meczfCyTHnFMmcchEESMtO46cDywvs0z60DU6a+3fPNvtWJ3LdD6RHngO5DP+WL1OmVjNbu1yuwMfZmzrlauHsIOYuuVesqYOrG8sa1kKlcySkTz9CXulfJC8/Sjol9f4gCAKpEVdIunAei9H42I+XkBCPp+djTOalVOJSLgiPkGol4gXOznGjSLl8CSf/ABpP/ASVo3j9pZotlFWrUdxahQ37ZjYhffqxbcRgEk6fYvPA/pxdvpRWc+bjWj6/oWjJyeLGvg1Eb1lO/NHdkJs5W6Ol7AudCWjbD89aL+TRQKF2oFLfMQS/+jamzDRUjs5YLSYxVEUmBwQycjJJzkzGYMxCQByzz6jXSLdaMQkWrIKFy+k3AAGdtwORKVchBbKyDFgwc/baWXLXuBJTEwm/eZ4azcT8Rzs27wDgRspNwm+eRxAgIzODtLQ0Xur5Es3bNWfp90t5b+J7vP9Z/soHAJkKAyPfHcmKH1cyYtQI5iz5Fgft7fv778t/Z8OGDTRo2gBcZZyPvYijWoebzg0Qiu2ckAzsZwAHjZJ3hzZhwsgXSE3PxsVJrIP9OI1rEN9QLV68mKlTpzJjxgwsFgvBwcEsXLgwT6r+nj17MmrUKFusdEHcHYMNYlmtvn37MmbMGF566SUaNmxImQKyLk6bNo1PPvmEefPmoVKpmD17tl0X9twYbJlMhtFopHHjxrYEarl07NiRbdu20aFDB6pXr061atUA6NOnD1evXqVr166YzWY6duz4zBvXAGlpacU9hRKBpIOIpIOIpIPI49RBpVAxOmwYXWp25N3VE/krfBfTts3kaNRxlry+EM/XXmbQ2++SkZSA3t2TqPirWBTyJ/Jwo9I5UmfUWwAc++7bO7KIj6TOqLdQ6XTI5XKq+4ZQ3ffhamvbQxAEtp3dwfw9P/DzwMV8vX02qVmp+fq5aF0Y2Lg/6nskLbsba+N+nPxmDFGbl8Gvy6iIO9WGfFrgg2ph3gS1ho8m8dxZLDk5ZCUkcOiryRz6ajI+deoR8kp/qvbq81gzKRcnJfH+kJOayv7PPiYl4jIyuRyFSv3YE0Hl5OSQ8BhLt1lNJqwWM1oPTxp/OAnnW6upxcG5Fcs58/NPyORyOiz5BYdb8csghiTenYOobP0GvLr/P/6d+TUHvviUqzu281OdUBpN+IQ6o8ciVypJPnuY6C2/cG3XasyZt88p9+caE9i2H2Vf7ILK0bnAOSlv5VPQuIou6HKVeD9QO7mSbTVz/sZ51CoNeo0exV3XuFwmR5AJOOndERDEt3qCgICAWqPBYraiU9/O6q110KJVOoBMhgyZ7d6jVqhwUIj/dtA4oNaoGT3uLZDBuVPnOHXsFDq1/bwPlSpUolKFSlhyLHw36zvOHT9HkxebALBs0VKW/bCUipUr8u7E99CpdVisFpIMycSmxpIjWFE53bvU4uNAMrCfEXLdwMt4iG/K1OqHS5ZSEP7+/uzcuTPf9pCQEJYsWVLovq1btyY8PLzQPvbGziUwMDBPPc7cbIO5Gb5zqVixIsuWLSv0ON27d89nTN9J7kq5m5sbS5cutdtn4sSJhR7jWUSplG4VIOmQi6SDiKSDyJPQoZxHICsH/8SfJzbywdqPeaNxf2bumMv07bPwcHTHy9mLuLQ4EjOTeKfNWEaHDS3wga2osFrMXN+1mvIv1qf+2zFkJ8bj4FGGuCM7ub5rNYEd+tseZosCQRDYevYvvto6gxMxYnjWvkv7Gdz0Db7ePjNf/8FNXyfLmPXABrZcqaLmO9+iK1uO8MWfc/nXWWTFRlPrg/l2k7SpdI739CYYejWWyxv+5NyKZURs28LN/45w878j/P3eOILatKNa3/5U7NT5mco4XhLvDwenfE52chKNJ3xCmedqIn8Cc8zIyECv19+740MiCAJJF85zfP5c/vl0Im2++wHFQ3hLPiopVy7z11vDAGg04WP8mzTN017Q+aBQqWjwzgcEd+vJX6OGErV7J3snvseJ+TPxCXZCMNyw9dV6BxLQti8Bbfri6Pdo7tCKMmXJkFlxc3CivFdFuy/QlAolCBDokfelhSAIBJULIjY2Fn8XXy5fvgxASKUq+Dv72Pq568QXDG5aZwJcfAGoWLECly5dxkPjis5Rj0qmQuugJdA97zH+/PNP5s6dy/Tp0wkNDUVhEe2aIJ9yBLoH8MMPP7D0h6XUqlWLBQsW4HJHVnar1cKVm5fIlFlRe/pQHJS8q19CQqLEcXeiuNKKpIOIpIOIpIPIk9JBJpPR5flOhFV5EaVSxYgV4wBIzEwiMTPJ1m/h3sWMazXysc9H6aCjXKfXuLj8G84t/BC5xgFrTjZ+rXpTud+4QjOGPwiCILDlzHambZtpM6wd1TrebPo69YPq8mJwM2QyGQv3LiY1KxUXrQuDmr7O4GZvMGjZCKZ2/4wKZco/0DFlMhnB/d9F6x3A8Wkjub57DdmJN6j/2S+oXTzsaOFAvfHv0uC9CeSkpqJxccFqMtkMZpVWS9VeL1O118sY4uII/20FZ1csI/bof1zZtIErmzagdnamSo/eVHulP36Nm5YId99HoaTdHzKuXyfpQjj1x7+Pd+06T+y4j9O4BvFc9ahSlbpjxrNz3CjiTxzHp269x3rMu7EYjWwc0Bdjejr+TV+wm9jvXueDk78fDccORqVIIuLvE6TF3CAt5gZu/jpCX32ZCp1fx6Nm0V0XUSnXkClVeGvdMBnSkcsV+XJECFYrCALmrAzS09NZvvJXgitVIqxFc1q3bMGcud/x9ttvc/2G+BKgTcswrKYccn3GrSZxZdtqNmE1ismYu73UiUmTpzDozTdtFYZe7tUTc1YGu/7ew/kLF+j7cm8qlw8kJiaGd955mxeaNuG339cQEODPcyHBnDr2L9OnT0cmk1GzRnWW/SRmK3+xWTNCqlTBajXjrXMHpYqolGtUKRLFHgzJwJaQkLgnp0+fJjQ0tLinUexIOohIOohIOog8aR2ctE4kZCTYdYsGMct1WnY6nvr8hmBRc2eMY056Chon19sxjo+IIAhsPrONaVtncvLaaUA0rAc1fYMRzYfgob/t+jg6bCjjWo0kLTsdZwcnUrPSGLxsBHsu/kO72V1Z/uZi6gU9uFEV0KYvDp6+HPnoVZJOHWDvqNY0/PJ3u6tnuaW4rqWkULlMGVus6d3ovLyoPfItao98i8Twc5z9ZRnnVvxMekw0p378gVM//oBzYDlC+r5KtVf64x5cHI/Hj05Juz+kXBFXGcs8V/OJHjctLQ1n54JdmIsK58ByaJxdSLly+Ykb2P9M+oib/x3Bwc2N9ot/Rn5HyT1ztgG5QklOWjIaZzesFrPNbVsQBJJOHyR66y9c37UGsyEdOVCupo70VBcSLl0nOcbA8eU78GzQE88ifOmUlp2BXK5A5+SO1WxCsFpshnF+ZKSlZTBn7ne81LEDYS1a8Obrr5Oamsb6jRtxcHDgkw8nUqvW87Y9BOF2omDBYsZiFI3t7p06EB8Xx8rfV3M5IpKunV9i7FujARnb/9rJuvXrad+2LZUrVWbGtK+YMWcOv/2+hpo1avDh/32ARuPAmj/+tCUiXrL0Z9txPD08CQkJQaHUotc5IZPJSM/OKDLNHgTJwJaQkJCQkJB4qnB2cMZF61Jg7LFOreXjP7+gc80O1A58/rGUyMol92E5JjGVyu5ej+wWbtew1jgyqMnr+QzrXHLd4XNfKpRx8mR+v9n0/eF1TsScouu8l/mu32w61+zwwPMpU/tFms7ZxqEPepIZfYm9I1vR4Itfcatm34jJjce8HzyqhtBs0mSafvI5Mfv2cPaXZVxYs4q0qKscmvoFh6Z+USritZ8EVrNYDaUgN3yz2UyvXr1ITExkz549+dpNJhNTp05lw4YN6HQ6hg0bZiu9Wljbnbzxxhvs37/fFo53NzNmzGD+/Pl5tv3999/4+Piwfft2vvnmG65du0ZAQADjxo2jZcuWtn4ymQyFRmP7nk+KyB3bOTJdLIXV5rtFeWLALcZsLq2YQcSaBXmzePcdS/TW5Vxe9S2Z126X2NOVDSKgbV/82/TFsWwQkdu3sn3UUNKuRrK2W0eq9HyZFtNm4uhTBG7Pt4xpmVyOQp0/Rt5qtfLVtGkYjUaUWkfKVarMRx99RFRUFEqtI3KNlV4v9+HFFmEoFArKlSuHUuto2/fSpUuU9Q/kx0WL8Pe5fd0KCNSsW5/n6ty+f9yIT6Rq1TJ89fXXfPX117btDZo0ZVIZL7KysnB1dSWgQkWUajUffzqJMePGc+HCBVtb1apV7eRdkmG9w9B/kjzd/jcSEhJPBHf34kkSUdKQdBCRdBCRdBApDh3MVjNDmg202zao6evsPr+Xb3fPp82szjSaGsasHfO4nnLDbv+iwtHx/rN120MQBDae2kKLb9rTf/EgTl47jaPGkTEtR3Bswn4+6vS+XeO6ILydvfhzxCraVmtFtjmHgUuH8t3f39sta3kvnMuH0GzuDlwqP48xJYH94zpxY98Gu30f5nyQyeUEvNCctvMXMfRqLB2XrqRC+47IFApu/neEXeNHs6CCL2t7dub86lWYs7Mf+BhPmqfp/pCWlsaYMWM4e/ZsgX2WLl3KsmXLaNSoEZ6enkycOJETJ07csy3X6Fm1ahX79+8vdB7nz5/H3d2dt956y/ZHr9eTmJjIuHHjUCqV9OvXj6ysLMaOHUv23efBY07YdjeGuDi2DBoAQM0hw6jc5Xa5V3O2gYvLp3Nh6VRbfWpTRgoXlk7h4i/T0Xj4kHntCgoHRwLavUqTmZtp+fNxqrz2AY5lgwAIat2W1/87Td0xbyOTyzn/+6/8WCuEU0sWPdR1fL+YTCbOnDlDenq6bVtCQgKrV6/m9ddfByAmJoaYmBjc3NxQq9WEh4eTmpqap83V1RW1Ws2lqGtkGAwAZGXnIAgCZTw9KF++PBUqVMDTTg1wi8XCqVOnMJlM+Pj4kJSUxLlz5+7ZVlKQDGwJCYl78qgPjs8Kkg4ikg4ikg4ixaGDTq1jTMsRvNNmLC5aMbmNi9aFd9qMZVyrkXg6edCrTne0Kgcuxl1i0sYvqflZQ3ov7M+aY+vIMmYV+ZweVger1cqGk5tpPr0dA34czKlrZ3DUODK21UiOTzzAhx0fzLC+E73GkaVvfM8bjfsjCAIT103ig7UfY7FTN/teOHj40GTWJrwatMGSk8WRj/px5ff89Wsf9XzIjdfutmYDQ69cp8W0mXjXroPVbObKxvVseLU384N82DZ8MDH79ohxorcwGTKxGI0Y4uPE+uWGzEeay6PwtNwfDAYDYWFhHDt2rNA5b9q0CTc3N7755hsmT55s23avNoVCQWxsLFOnTrVb2eVOwsPDCQ0NpXfv3gwaNIjhw4ej1+u5ceMGRqORGjVq0LFjRypWrHjPsR43gtXKliGvk3nzJh7VqvPilOl52uUKJRFrFtjdN2LtQrzqhlHnwx9pu+Yitd6bh0fNJnbjq1WOjrz45TT67TuCV63a5KSksG3YIH5r24KkC/Y9Ae4LuQws+e8DFouFAwcOkJqamicxm6enJ6tWrbJlQo+Li0OtVlOtWjVbfHlcXFyeturVq1P1VhWexNR05AolmdliaTj/gED8/PwICgqya2AnJydjNBoJDAwkODgYT09PkpKSMJlMhbblIggCWCxSmS4JCYmSS3R0dJ4MjaUVSQcRSQcRSQeR4tLBQaXJF3tstppxUDlQP6gu9YPqktbjc/48vpEVR37jYMQRdoTvZkf4bpwdnOleqzN96vWibrlaReJC/qA6WK1WNp7eytfbZnL6urhy6Khx5H/NBjLsxcG4O7rdY4T7Q6lQMq3HF5RzD+STDV/w/b4fiUm5xsJXv0Wn1j7YWFo99b9YyalZb3N1/WJOz30fQ2wU1Yd+gexW3GlcXFyRnQ/3G69de9RYnhs4mMPTv+LYY6yL/iA8LfcHq9VK586dGfx6f/r2fw2LxYLhZlS+fhFXrhAY4EdWbDQet5J0Xwo/i+FmVKFt2TnZTPz4Czzd3ahSqSJbduyyO35aejo3btwgKTGRJk2aoFGrGf2/N3mlZ3fKuTlSr9bzrF69mtWrV6NUKlmwYAEOxZh1/ujc2URs3YzSwYFOS1ei0t6+lrISroOAbeX6bkwZKZizDfiF9bjv43nXqk2/PYc4Onc2/0z6kJi9f7O03nM0eH8i9ce/V2DOg4JQuLqQk5mBISEB3R0GriAI+Pj4UK5cOY4ePWqLdQbxZUkuBoMBrVaLTCaz/T8Ybq1S39mmvaVLttmKUu9CdmwCACdPnsRiseDs7ExoaGi+/8vcsXK3a7VaBEEgKyur0Lbccr/pMTEYLUYUbsVzDUor2M8I5mwDVpORnOR4rCYj5mxDkYw7YcIEunTpQocOHQgNDaVLly506dKF5cuXA3DlyhVGjBjBSy+9xEsvvcSoUaOIjY0lIyODWrVqcf369TzjhYeHExYWls+1JSwsjA4dOtClSxc6derEa6+9Ztt3zpw5zJkzp0i+j4SEhITEs4NOrUOtVOOp90CtVOcrzeXs4MSrDfuwcdQajnywh7dbv4W/mx9p2WksOfAz7WZ3ocGU5sz461uuPWYX8lysVivrb61Yv75kCKevn0Wv0TOu1SiOTzzAhA7vFplxnYtMJmNU2FAWDZiHRqlh8+ltdJnXm/j0hAceS65Q8tzYGYQM+RSAK7/P4/TcDzAZ0rGajPi7Oxfpc0guufHag89H0nvrLkJfG4jayYm0qKu4lAvi8LQvOTh5EjkpKQDkpKRwYPIkDk/7slhXsks6jjotY197GYfMJLBawWrFnJWR709WdjYqhQJzVgYys+iabcjMvGfb1q3b2XfoMBPfGobi1nsse+Nfj47Cv6wPLzSqz0fjRhHgW5avv53PyRPHseYYKO9fluAKQXwwYjAebq589NFHZGUVvSfK/RB77Ch7JrwLQPOvZuBZPRRzVgZRW5bzz9iO/D2oCSq9Cyq9q939VXrXQmtXF4RcqaTuW+N4/egZgtq0w2I0sn/SRyxrWItrB+y73xuMBoxmIwkZCRjNRgxG8brUVK1EhimL8JXL8zyTK5VKgoOD0dyjdrnFYkF+a3U49+9cY/zOttyXl6bsbLIS4jEZDGgdHKhQoQJBQUGkpaVx4cIFu+PfOXbuOBaLpdA2AKvFQvivv5AuGHGo/GjlzB4WaQX7GaDAJApFUCbkiy++AMR4igEDBrBu3TpbW2xsLAMGDGDSpEmEhYUB8PPPPzN48GDWrVtHmzZt2LBhA0OGDLHts3btWrp37253tWDhwoX4+/sDsHjxYr766itmzpz5SPOXKBqCgoKKewolAkkHEUkHEUkHkadFhwplyvNB+7d5r+04/rl8kBVHVrH+5EYux1/h801T+WLzVzQPbkafuj3pUKPdA6/u3ksHq9XKhlNbmLZtBmdvhAOg1+j53wsDGfbCINyK2Ki2R9fnX8LH2ZtXF7/J0ajjtJ3VhV+HLKWyV8UHGkcmk1G571h03gFcXD6d4AHvcnnlLCLWLizy55B8x74Vrx3wQnPCZnxL5LatlGvZmi1DXrfb/+i82XbLJj1unpbrIivuGpZsA/qASsiUKmQyOc7lq+Xr5+DggEWmwLl8NVvss97NA+fy1QpsM7v4MHPRUlq3bk1AaB1MW3YBkCx3xM/PL48Lcu3y1dgR1t722btiVUaMGMGZmHjSZA78tn4zM2fOpHXzF8jKMTLzh5/4559/aNWq1eOUJx/GjAw2vtYXq8lEpc5d8a1bjWNTh3F99x9Ysm+9yJHJSL14gvLd/8eFpVPzjVG++1CsFvNDJ0R0KRdE9z82cX7Vr+x65y0Sz51lZVgTag4ZRrNJX6K55TmRbcph9s7vWLj3R1sJvyHNBjKm5QjkWgduNKvM1d07iDt5HM/qNVDcZVTfiIhAEKwcO3U83xwSLl4kTa1GfuQggtVK/KWLGBwd4dD+222HD2DOySE+6ioZGg2WW3kJ5ECsWoNCrSYt6iqJRhOWypXzjJ+cnER8fDxnjx7BUa8nPj6e5OQkwk8eIyvLUGCbUhBIOH2SnLRUYppUpH4x1EQHycAu8QiCgKWQt8CC1crl32bnuYBzkygAVOw9qsD4A4WD7pHc4n755RcaNmxoM64BXn31Vdzc3DAajfTs2ZNJkybZDGyLxcKmTZv47bff7jl2enq63ZiMKlWq2LJPrlmzhsOHDzNlyhTOnj3L5MmTyczMxMnJiY8//piKFR/sgUGiYJKTkx97LcunAUkHEUkHEUkHkadNB7lcTrPKjWlWuTFTu3/Gnyc3svLwKvZfOcSu83vYdX4PTg5OdHv+JfrW60W9oDr39VtZkA6iYb2Zr7bO4NxN8fdLr9Ez9IU3GfrCm0/EsL6ThhXqs2X0Ol7+vj+RiVG0n92VnwcuomGF+g88ll9YT9xrNCZizXwuLPvKtv3O55BKfcfYMq0XNSqtlspdumKIj7OtXN9NTkoKOampTzwD+dNwXQiCgDEtCY1bGVR6l0LP88DAQK5fv47VaiUmJgaA8uXLF9p28eJFUlJS2Lp1K1u3brWN1aZNG3bs2GFbVAE4fvw469ato0ePHoSGhpKRIZZX0mq1XLt2DRDdhpVaR8wK0TBVKZ68I+7OcaNIvngBBzcXNOZwDozrZGtz9K9IQNtX8G/dB513AK5VawMyItbML/IXTzKZjKq9+1CuVRv2/N87nP5pMScWfselP/+g5Yxv8evQjtk7v2Patpl4OLoTUrYqcWlxTNs2A4AKnuXICgngxddGEr1nN6kRV7Ca8mZg3/vnOqxWKx5du+U7/pFtW8jKyqJb126kJCezY+tWKlUojyI4mEP//EN2dg7de3TnxsWL/P3fUcr7lkUoX54L0dEgCFQJCsLJ35896zdgMpsp0717nvGvxUSzd89eatepQ9UqVTi8bx9R0VH06NGTuLjYAtp6oNU74df0BQJfbMHq3V8+ksaPgmRgl2AEQWDfqDYknzlkt13t4kGrFacLTqKwZj6V+rzFX31DMaYm5mt3D21Ik9lbH9rIDg8Pp2HDhvm2d+zYEYB69ephNBo5f/68rZh8lSpVbAkS7mbIkCGoVCqSk5MxGAysXLnyvuZhMpn4v//7P+bOnYufnx///fcf7777LqtXr36o7yWRn9TUVALuKD1RWpF0EJF0EJF0EHmadXBy0NOv/sv0q/8ykYlX+fXIalb++ztRSdEsPfgLSw/+QsUy5Xm5bk9ertsDfze/Ase6O6Ox6Aq+iWnbZtoMaycHJ/7XbGCxGNZ3UsmrAltGr6PfooH8F3WM7vNfYW7fb+hWq/MDj6Vx9SRi7UK7bRFr5lOp7xiOfNIftd4NrU8gOp9AdD7l0PkEonH3LpIkRBoXVzSurnaNbI2rq21F70nyNFwXgtmEYLGg0OZ/EZCens6yZcsIDg6mVatWtG3bllmzZjF+/HhbCF/79uKKc0Ft5cqV45tvvrHF7i5fvpzDhw8za9YsPDw82LlzJ+Hh4fTr1w9HR0dWrVrF33//TYcOHfjjjz/Q6/W0a9eOmzdvolAomDFjBpcuXWLVqlW4uTjzXLWQJ6QUmA3pHPzsPc4sWwKAZ1kjxqRrKB2d8WvenYB2/XCrXj/PM7VC7UClvmMIfvVtctJT0Di5YrWYitSrQ+vuTtv5iwjp+yrbRwwh5fIl/uzbgwqdOrP/eSMr+39Ps6rNSE+Mx8mjDHvC9zLt73lUbPYGWaZsXCtVLrDW/KR/j2M2m5kx+SuSYmP5+eefKe/nS+Oaz3FIoWHxmjVkx9wgLimZcEcXOr/8CsFBQfynd2XZ5i0ocqxEZpnFttcGEhIUxMZly/n72DHCKqhRGkxsExQMGTaM5uPH5zkf6ioUzAkLIzw5nTbO7qxMM9CgTUfazvyWjIyMAtrm5v0Cu4tM5gdGMrBLOIUZvxp3b3JS4gtNomBMTUDj7m3XwC6Kud0ri2P37t1Zv349VapUYe3atXbrIuZyp4v4kiVLGDZsWJ43ngURERFBZGQkw4cPt21LSkrCaDQWe5bJZ4U7E1uUZiQdRCQdRCQdRJ4VHYI8yvFeu3G802YM+68cYuWRVfx5YiOX4yOYvHkaX275mhcqN6VP3Z50eq69zYXcYDSglCtx8XbFaDZispg5eOUwH63/jPCbYmyhk4OTbcXaVedajN/yNmWcPPlj+K8MXT6ajae2MGjZCKKTrzGqxdAHevFuykgt/DkkJZ6MqIukR+QvASVXqdF6+aO9ZXDrfALReQfaPjt4+NyXAW41m6g9fDQHJk/K11Zr6Egyrl/DJaj8fX+nouBpuC5yI2/taXz+/HlmzZpFgwYN8PLyYsCAAaSmprJ27VqUSiX9+/dHoVCQk5PD4MGDSU1NZc2aNbZSWsnJySQnJ9O8eXNbdvLdu3cD4gr2pUuXWLZsGfv376devXrUq1ePGTNmMGPGDJYsWYKPjw9DhgwhOzubatWq8c033zBnzhxWrFhBpYoVGTOgN3r9483ULlitJBzfS/SW5VzdtobIf8VnaXd/NYFhbQho14+yTTuh0BQcTpLrvXEjJYMK7l4P7RZ+LwJfbMGrh47x+/vDuPHjL6RcvMDK7/ZwdO5sfujQ15b4r+awkax6azFrz2wmNi2OHeG7cdW54K5xxlmmQWOWYc4yYM40YM7OxmKxELF1M7FJSXy3eDEt6tQm1M2Vro0akJSYwM5//8NBrWbsgP7UbdgIlaMjw0OqIbi4snHrVlTA6F49CbkVMjGiZ3fkMhmHz55DoVLRv39/Ro8eDcC2bdtYu3YtHTt2pFy5cvzwww988sknrFmzhkaNGtnCVvV6fYFtJQWZ8DgLqUkUSFpaGi4uLqSmpuLsLCY6MBgMnDt3jpCQEHQ68YK8l4u4XKlia/dKdn/cVHpX2q65hNVsyr8jD+YinhuDvXPnTtu2uXPnEh0dzZQpU/L0HT16NO+88w4BAQHExsbyyiuvsHbtWrp06cK2bdtsGf7uJCwsjKVLl9oM7PT0dOrWrcuBAwdsCdVGjRpFlSpVCA8PRyaT8dtvv3H06FFef/113nnnHdavXw+ImsXGxuLj43Nf362oyf1/jIyM5OLFi7Rp04batWsXy1wkJCQkJB6ejJxM1p/cxMrDq9h3+YBtu16j53/NBvJWyxHM2ZU3xnFQ09cZ0uwNXvq2JzfT4hj6wpv874WBJcawvhuL1cKH6z5jwd5FALze+FWmdvsMpeL+1mCsJmOhzyFtVl/kxt71ZMZcxHAzCsPNKLJuRpEVF4Nwj3JhMqUKnXeAaHB7B6DzCby1Cn7LAHf3sWUvN2dnceirLzn23RybMVFr6EhqDR/Nb22bU2vYKGoOHvpg4jxDRO3eyX9zZtDl17XIb8U+W0xG0i6dQh9YOU/Srfj4eE6dOsXhw4dJSkqiY8eO6PV6AgMDOXXqFC4uLjg7O3P9+nX0ej116tQBxMzQaWlpeVy//f3988RaA0RFRXHp0iW8vb3Jzs4mNTWVOnXq4OLiUmhbLgXNe+uwQQQ0e5Fqr/R/JK0yrl0mZusvRG9bSVZsNIJVIPqMgZxMK+6Vy9N76y4cy5Z7pGMUJRfjLrPyyCp+/Xc1N1JvUsmgY9Ur84netZODUz7P17/BBxO5XNOLNSc38GXD0ajMVuRWOwPfgUKtZtOhQ9xISmbs0KGoHB1RanWoHR2Rq9V27QmrxUzK5cskX8yfxMytcjCuFSsiv8/7zMPQf/Eg+jfsS5tqLe/Z155d9ihIK9glHJlMhlJb8Bs6c7aB8t2H2mKd7iQ3iUJh+z8KvXv3pnPnzuzcudOWGXzRokVERUXh5ye60Xl7e1OpUiWmT59Ou3bt7BrX9jhw4ABly5bF3T1v7U9XV1fOnz9PcHAwO3bswM3NjQoVKpCamsqhQ4do0KAB69evZ/Hixfzxxx9F/ZVLLWfOnKF69erFPY1iR9JBRNJBRNJB5FnWQa9xpG+9XvSt14uopGh+PbKaFUdWcTUpilqBNZm1cx7Tt8+y9U/NSrV9XjTgO/zcytrqdJdUFHIFk7t9QjmPACas+5Ql+3/mesoNvu8/F73m3s8PVou50OcQwWrBv2VPu/tlx1+/ZXRfJSs2Kr8BbjaRee0Kmdeu2D22TKlC6+VPrfe+E5NKvViP+m/HkJ0Yj4NHGeIO72DP+yNIPHeWv0YPI+PGdRp/+GmRlGW7F0/zdZGcnExaWhoHDhxg/vz5ZGZmEhUVZXP/rlKlCnq9HplMRlRUFJmZmTg6OpKRkYGTkxO+vr4olUrkcjlmsznf+HfWUDYYDBw6dMhW3q2wtseJKTON67v/IHrrcpJO3X6ZpnR0IcvkQ07mv2hcXemxYTeOZQMfePyiPh9Ss1JZe2w9K46s4t+rR23bXbUutG/RF9+Gjfnz5e529z3+3beU/eMHNEdBY7xtWZuVckxKMCllmFQyjEoZVrUCnZMbmGVs+fZf5s2bh7ufv91x70auUOJasZI438gIrCYTcpUKl6DyuFashPwp8PJ4WCQD+ylH6aCjcr9xAI8liUJhlClThsWLFzN16lRmzJiBxWIhODiYhQsX2lLnA/Ts2ZNRo0axcePGQsfLjcGWy+UoFAq++eabfH3efvtthg8fjqenJ3Xq1CE5ORm1Ws2sWbOYPHky2dnZ6HQ6vv766yL/vqUZydFFRNJBRNJBRNJBpLToEOgewDttxzC+9Wj+vXqUGn6hjFgxzm7fH/Yt4e3Wo1Ern54wpf+98CZ+rr787+dRbDu7g85ze7Fi0BK8nb0K3e9hn0PkCqXNLRya5mu3WsxkJ9wg65YBbjO+Y3P/jkYwmzBnpuFSuSaHJ/TBlJGC2sVDDKFLisWYmojS0YWGH3zIwS8/4+CXn5Fx4zqt58y3reI+Lp7m60Kj0eDs7MxXX32FXq+3Gdaurq4kJiZy8+ZNvL29SU1NBUTPPbVaTXZ2NiaTiX379qFQKKhQoYLdvDv3W0P57rZ78oCaCxYLCcf3ELVlOTf3rseSc6vsl1yOV90wAtr2w2hyYE23lwBoM+8HnAMf3LgWp/bo54PFamH3+T2sOLKKTae3kWPOAcSXZC2rNqdvvV60rd4KjVJzz8R/pswMtF5e+NSth0rniFKnQyaXk5mTSZIhmaTMZDIMyWJpr+xkAIZ+NoJzqRe4YYrF3dENN50bLlpnFPKCDWW5QoFrxYq4VaqMxWRCoVIhWK1PxLgWKL5rUDKwnwHuTKJgykxD5ehc5EkU/P3987iH5xISEsKSJUsK3bd169aEh4cX2sfe2LmMGjXK9u9evXrZjeOuVasWq1atKvQYEg+Pm1vxJeMpSUg6iEg6iEg6iJQ2HeRyOfXL1yUhI4HUrFS7fVKzUknLTsdT7/GEZ/dodHquPX8M/41+i97gRMwp2s7qwsrBP1HVJ7jQ/e58DjFmpKLWuzzyc4hcoRTdwr0D8KjZJF97rgFuTEnAnJlmc1E3pibmyTtjzkyl7qhROPn589foYZxesghDXCydlv2KSvd4sptDybsu5ErRg9CcnY36HtnN/fz8iIuL49q1a1y7ds0WQ63RaHByciIqKoqoqCjbdovFQk5ODlqtFmdnZ9zd3YmOjubSpUs4OzvfcrkVkMnktv73U0P57rbCEAQBS06O7XuK39WAXKHElJGKSu8ienU66MiIvkj01hVEb1tBdvw1W399uSoEtO1HQOuXcfAsiyE+nqX1nwNB4LlB/yO4W497zqMgHuV8OB97kZVHfue3f1dzMy3Wtr2qTzB96/WiZ51u+Dh759nnXon/3MuUxRyhQuHuilp1+zrVO+jRO+gJdA9AEIQ8BneyIZkcs5HEzCQSM5MAUMgUuOhccNe54e5o3+AWbnmMWBUy5IAgf/weJGlZaVit1vvywnkcSAb2M0JuEgWNq1ja6nElUZAonRRFPMqzgKSDiKSDiKSDSGnVwdnBGReti10j20XrgrODUzHM6tGpF1SbrW+t4+XvB3A5PoL2s7uxbOAPNK3UqND9cp9DLGodcpX6sT+H3GmAW01GVHrXAuPAFQ461IoE2n+/mG0jhnJl0wZWdWhFt9Xr0Xo8npcgJe26cK0gli6NP3kCv8b5X1jciVKppE6dOqSlpSGXy0lLS+PChQsoFIoCt+v1eho1Es8RQRDAbObcxYvEX4tBZSyDUqtD5eiITCZDLpfbjObcv+80qgtqK4y0qKvkpKXavqfFmM2lFTOIWLPgDq+K/1GhxzAOT+xLRpQYF6zSu+IX1oOAdv1wrXq7JJ8gCGwZ8jqZN2/iEVKN5lPze1U+CA96PqQYUlhz7E9WHFnF0ajjtu1uOld61O5K33q9qOlfo8Bwh8IS/9UePppyXsEIgsA/lw7QMqSF3TFkMll+g9uYSVJmXoM7KTOJpMwkiL9lcGtdcHd0o4zeA72DnoiESK4mRWOymFApVJRzD6C8Z1ChK9+Pyt8X/0Emg1Df/DXdnwSSgS0hIXFPrl69SmhoaHFPo9iRdBCRdBCRdBAprTqYrWaGNBtoqyt7J0OaDcRsNaPm6XzZXd4ziM2j/qD/j4M4FHGEngv6Mfvlr+ld135M551EREQ88fOh0DjwbkOI/28XF36agsJBR53BvTm+dB03Dh1gZcum9Fi3BedyRZ+wqqRdF3pfX9yDq3Jy0QIUGg1eNZ8vsG9uzLWPj48tmRmINanPnz+fb7teryc1NZWbN29StqwPDnIFxltu3QqZHMFqxZQp1rVWOerQarXk5OQgCIKtvF1uct/C2uwhCAJJ58M5vmAeDm7ueD1fC3O2gUsrZnBh6VRbP7E2+1QQBEIGf8LVDUsIbNcP78bt7XpaHJs7m4gtm1BoNHRcuvKRvR3u53wwW8zsuuUCvvn0NowWIyC6gLcOaUGfer1pUy0MjVJzz+OpdI7Uf+cDAI7Om21L/Fd7+Gjqv/MBSgcHmlRqyI8HfuZqUjTVylZFo7r3uHfionUh25RNalY66VlppOakYzIbbe1VvauQYcwgJvlanv3O37xAgJsfvq6+9/Xy5H4RBIEccw4nY86w5+JeWlZtgbO2eF50SQa2hISEhISEhMQDolPrGNNyBAAL9y62ZREf0mwgY1qOwOEBH1ZLGh56d9YM/YXhv4xl3YkNDPvlLaKTYxjXatQTSRL2INwrDjz57L+4Vq1NSvhRko6sxbeyihuXnEg6H86KFo3p/ucWyoTWKOZv8fhp+P5E9n/+CQcmf4pMLkemUJCTFIva1RPFHeer2WImOioKpVKJVqsjLS0NrVZLqlcZu9szy5bFaDRy7VoMCoUSrVpNZnYWggCJHh7IZJBuyEKtUuLs7kFySjLJSUmEO+oxm83k5GST6OtHuINDoW25CFYL2Yk3Ubt6IkOO1WJG61mGRv83keRz/+Ia/DwRaxbY1SBi7ULarrlE2aadCtQp9vgx9kx4F4DmU78pknNDoyn4fnDuRjgrj/zOqv/WEpseZ9terWxVXqnfmx61u+LlVOaBj6l0cKDe+Hdp8N4EclJT0bi4YDWZUN7ScugLg/DUe7Lv0n7+Orfrwb/UXQgIZJtyyMhOx2DMwt/dj1MxZzDbqRaglCt4zj8UuazoDOxcvJ296Fm7G12ff6nIx75fpDJdxcT9lumSePp4Fst0paWllTh3t+JA0kFE0kFE0kGktOuQWwc7LSsNZ60zZqsZnfrZ+Q23Wq1M2vglc3bNB6B/g75M6/kFKoX9qiDFeT7YYm7vyEejdBBjMAVBIO7wds4v+ZKU8P8w5Vi5fj4bo8GC2tmZrr//SUCzF4tsLiX1uhAEgdTICJLOh5OTnEDE2gX4teyFrmxQnn5Hjhxm3bp1GDINVKtejT59+qDXO9ndrnNwICc1lePHj/PXP/tJSIjH09WFdg0bEuDtTWp6OrNXraZGhQoMGT0aQS7jjz/WcfjwIdRqNe3atadJE9Ft3WIxF9iWizkzjYi1CyjbvCsyQcCUeo2cuIuknD2Co39F6n+xkh2vPFegBm3XXrGFVN6NMSODnxvXIfniBSp26kKX39Y+0gul3PtDalYaLnfcH5Iyk1l9dB0r/13F8eiTtv4eju70rN2NPvV6UsOv+hN5mSW6fhvyrD4XBQkZSTSc2rzA9kPv/42HY9HmKlArNejU2gfWrajLdJUYAzs+Pp5GjRrxww8/0Lx5c7t9Nm3axHvvvceVK1cIDAxk2rRpdOp0+w3UV199xezZs0lOTqZevXosWLCAKlWqAKK7y8iRI/nzzz8xm8106dKFefPmoS8g0cOhQ4cYPXo0Z86coUyZMkycOJE333zT1v7TTz/x2WefcePGDUJCQpgzZ44t9uR+kAzsZ5dn0cC+du2arfRaaUbSQUTSQUTSQUTSQSQ2NhZvb+97d3xKWfTPT7y/5iOsgpWwKi+y+LX5ODnkf4Yq6edDrqF94acpJJw6wvXzWWSnW5ApFLSZN5/QAYOK5DglXQeAnJQEjn81nKoDJ+JSqWCD1B7GjAwMcbFkxt4kOzkZBAGFWk1gi5Zc3fkXVpMp3z5ylYqglq1ttcsfFMFqwZSeQkbMZY5PG0nmtcsI5rzH0QeF8OL83WzrWaXAmPy2ay4VmCNg69A3Of3TYvS+fgw4fOKRYvSzTTnM3PEtC/f+eNvDpekbDH1xEC/N7cnZG2ICYKVcSetqYfSt14vWIWFPVfWBwjCajVT9uHaBeSrCPz1aYr5rURvYRb8u/xD8888/NGrUiMuXLxfY5+LFi/To0YPPPvuM1NRUPv30U3r37s21a6Jf/08//cTs2bPZunUriYmJ1KlThx49etjS4o8cOZLo6GguXrzIxYsXiYqK4r333rN7rOTkZDp06MCAAQNISUlh0aJFjB07lsOHDwOwe/duRo0axU8//URKSgr9+vWjc+fO919GQELiKSM5Obm4p1AikHQQkXQQkXQQkXQQiY+PL+4pPFbebPIaywb+gE6tZef5v+n0bQ+up9zI16+knw8ymQzvBm1oOncHTaavIbRrExzdlAgWC1v/N5iNfduQnRR774HuQUnX4UERBIGspCQSw88S9fcuov/eReK5s2QnJYEgoHZxxrlcEFazGZeg8nbHcCkXhCE+noSzZ7DYMcDzHdNiwZiaSEbURZJOHyLu8F8knTlM5vUILNmZCGYTGndv/MJ68NzYmYT99B8tFh9EEATKdx9qd8zy3YditeSvzQ0Q/ttKTv+0GGQyOixZ/kjGtcFoYOaOb5m2babNwEzNSmXa9pnM+/t7Pmj3NjX8qjO56yec+eRffh64iI412pUYg7MoyM1TYY/cPBXPKsUeg/3TTz/x0Ucf8dVXX9GnT59C+zVr1oyuXbsC0Lt3b3788UcWLlzIp59+yvfff8/w4cNtRdynTJnC999/z+7du2nQoAHLly9n9+7duLu7AzB16lRatGjBtGnT8q0Wr169Gg8PD0aMEGOrwsLC6NevH3PnzqV+/fr88MMP9OnTx+ayMnbsWBYuXMivv/7KG2+8YXf+OTk55OTk2D6npaU9nGASEsVASYu3Ky4kHUQkHUQkHUQkHURKgw7tqrfmzxGreOWHNzh9/SxtZ3fh10E/Uc03xNbHwaHoSoQ+TmQyGV71W1OmXiuqHNzG9uFvEhd+jfA/thP3XwXqjh5J5b5jcfDweejxnxpuLUZl5GRiMN5eLLKazWQlJZGVEEdWQjyWnNtGsUwuQ+Pujs6zDLoyXigdHDADSaZ0rF4uWAxlSIuOwmoyIVepcA4IJMfTmav/HsSUmYki4iyuFSuj9/W9nbnbYsGUmYYpLRlTRgqmzFQE621HW41CibPOFY2bFw6evoSOnIpPk475tH6Y2uypkRFsH/U/QIxVf9RwAaVcycK9P9pt+2HfEs59epQONdo+0jFKOs96norCKHYDu23btvTr1w+lUlmogX3mzBlq1MibZKBatWqcOHHC1n7nirRKpaJy5cqcOHECd3d3TCZTnv2rVatGVlYWFy5c4Pnnn7+vYy1atMjWPnDgwHztuXOxx5dffsmnn36ab/vZs2fR6/VUrVqVqKioAve/F7YYsOw0nB2KLgZswoQJnD59GpPJRFRUFBUriuUPevfuTb9+/bhy5QrTp0+3zT0oKIiJEyfa3OTu1Z7LoUOHGDp0KIGBgQiCgNFopE2bNowZMwa5XE5YWBhLly7F39//kb/TkyI1VXxjGRkZiVqtxtPTEwcHB2JiYgCoUKECCQkJpKWloVQqqVq1KqdPnwbA3d0dR0dHoqOjAVG35ORkUlNTUSgUhISEcObMGQRBwM3NDWdnZ65evQpAYGAg6enpJCcnI5PJqF69OufOncNiseDi4oK7uzsREREABAQEYDAYSEwUa4ZWr16dCxcuYDKZcHJywsvLy+ZZkpycjNFotK3ShISEcOXKFXJycnB0dMTX15eLFy8CULZsWaxWK7Gx4ipAlSpVuHr1KtnZ2Wi1WgIDAzl//jwA3t7eyGQybt68CUDlypW5du0aBoMBjUZD+fLlbXXUvby8UCqVtsyllSpV4ubNm2RkZKBWq6lUqRJnz54FeGC99Xp9nvM0JSWFlJQU5HI51apV4+zZswiCQExMDK6urkRGRtr0zsjIIClJrAkZGhpKeHg4ZrMZZ2dnPD09uXLlCiDWk8/OziYhIQEQ7xuXLl3CaDSi1+vx8fHh0qVLAPj6+mI2m4mLExOeVK1alYiICHJyctDpdPj5+dn09vHxQRCEPHpHRUWRlZWFg4MD5cqVy6O3XC7nxo0bNr2vX79OZmYmGo2GChUqcO7cOQDKlCmDWq22eQpVrFiRuLg4BEHg/PnzBAcHc+bMGQA8PDzQ6XS2c7Z8+fIkJSUVeM7m1lIFKFeuHGlpaQWes25ubja9AwICyMzMfCJ6R0ZGkp2djU6nw9/fnwsXLtj0BnFF5/Tp0wQHBxMTE4PBYMDBwYGgoKAncs6WlHtErg5+fn6l+h4hk8k4ffo0rq6uz/Q94rkKocxuN413t31IVEo07ed0Z2bHKTSp1hhXZxecvVzIMRlJy0jD09Xj6bhHOPlR86dNxC2cwYlFS0iKNnDgq2+IXLcYp6ZdcW3Vj8CQ5x7oHuHt7W07f0rqPeLCpSsYDAb+uXSIvafWcDn+imhsW6xYzSasZrPN+AZAJkOuVCJXqUCuQBYrQyaXgYDNY1SuEMtsyWUyQAYI4t83BTgiw2I2YcnOQrBY4RzIFHLkCiVgRbBab/W/85AykCuRK5Ugk+Pl7E0zr+oE6F24kQ2JZ87YvUfExcXj89IgKvcbj9mQjlLnRHpqCinpmchkhjx6X4+OZme/nhjT0ijbsDFOnbtz+vTpB9Zbo9Xw+4G1nE0KZ1z70XZdo0FcyU7PSifqZhI5OTnP3D3izucIHx8fhjV7k7EtR5KalYqrzoX0zAyiI6OwWq0l5jki99otKkpMDDaIF9GuXbvsxmC3atWKxo0bM2nS7XpuH374IQcOHOCvv/5CqVSybds2wsLCbO3NmjWjbdu2NG/enGbNmuUpXm+xWFAqlezdu5emTZvmOdagQYMwGo0sXbrUtm3RokV8+eWXXLp0iUqVKvF///d/eYzs/v37o9Fo+OGHH+x+N3sr2AEBAUUSg203xqOI3w7FxMQwYMAAdu7cadsWGxtLjx49mDRpkk33n3/+md9++41169YRFxdXaPudbxwPHTrEt99+y7JlywAwGo306dOHt956ixdffPGpMrCfxRjs3POytCPpICLpICLpICLpIFLadEgxpDDgx8HEpyewfuTvfL93Cd/ve3zPIU+Kk4u/569RQxGsVnSuCspW1qLUagl66Q0qPcCK9tNwPgiCwC9fvspqeQK1Q16kYUBtXFQ6ZNx+PpMp5ChUauRqDXKl8r5W5gVBwGLMwmoyIkOGgIBcpUah1iJYzVhNJiw5OVjNt7NLy+QyZHKQyUWDW6ZUIVMokckVyGQyWw3mI5H/sfv4VhrHG3jrk3V2V6MflH0fT+DQV5PRuLgw4NCJBy7bdvraWVYc+Y3fj/5BQkYiHo7uHJt4gBqT6j8V8cdPiitXrlChQoXinoZdijoGu9hXsO8XR0fHfDHOBoMBJyene7Y7OjraPucmNcvtm7v/3cdKSUl54GN5etrPSAhiev7CUvQXhCAIGIxZBbZbBQtzdy9k2raZtm2pWam2upwjmg9GLrOfTOJhsuzdyS+//ELDhg3zvNR49dVXcXNzw2g03rO9MD1ycnIwm8143BX/smbNGg4fPsyUKWKty/79+zNy5EgaNGjAjz/+yPr16zGbzdSrV48PPvgApfKpOcVLNBZL/hILpRFJBxFJBxFJBxFJB5HSpoOrzpVV//uZ09fOsnDvj0zfPsvWdudzyOiwoU9VVvXnBg7G0duHDf1fxpCSxc1IOd6BBq6s/o7IPxdT7qU3RNdxz7KFjlOc54PJkIlcqSInNQWNiytWswmVztHWbjWbubZ/H5c3/Mna6P1UDWvHiGqdUeqckckVyFUqFGoNCrUGuVIBD/KsKAhYsg2Ys/PnJVI66JDdyvIu9hUXyK0Wca1PJpOhcnREqdXaPaZgMVPTOQCSEzlgPoigePRnvKjdOzk07UsAWs/9/r6N6/j0BFYf/YMVR1Zx+vpZ23YvpzL0qtMNgzGTIc0G2q6DO8mNP1ZTugzs0pSr6qmxPkJDQzl69GiebWfPnqVu3bq29jNnztiyiptMJi5evEhoaChVqlRBpVJx5swZGjRoYNtXrVYTHBxs91jbtm3Ld6zcAvG5x7q7vUOHDkXzZW8hCAId5nTncOS/dttz35AVFOOxcO9iRrUYSq3PG5GYmZSvvUH5emwcufqhjezw8HAaNmyYb3vHjh3vq/1uTp8+TZcuXbBarcTExFCnTh2qVq16X3PZv38/x44dY9WqVcjlcj766CN+/fVX+vXr9wDfSKIgXFxcinsKJQJJBxFJBxFJBxFJB5HSqINGqaGGX3V6Lexvt33h3sWMaTmcvy/sw8fFmwA3f3Rq7ROe5YNTseNL9Nq0g7U9OpEZn0SCgz/lGwdhuHqCiDXzubr+x3sa2oUtujxOzNnZHJ7+FcfmzSYnJQWNqyu1h4+m/tvvE733b86tXE7Elo1kJydjVMqIf7kSvZ0rYUxJwJKVfmuV+mFzIIuGsjkrM69reS4yGUqtI1ZTDsgUyBQKZDI5gsWCOSf7lou4uIqt0GhuuY7nH+OFKs34J/EiV5OiqVjGfkK1+8GQkMCmN14FQaDGwMFU6dGr0P5Gs5FtZ3ew8t/f2X52py1Jl1qhpl1oa/rW60VYlRdR3pp3aY0/LojSdI98agzs/v3788033/Dbb7/RvXt31qxZw+7du5k1S3xjOnDgQD7++GPatWtHlSpVmDBhAt7e3rzwwguoVCpefvll3n//fX777TcA3n//ffr27YtWm/9G3717d959911mzpzJiBEj2LdvH8uXL2fdunW2Y3Xr1o3evXvTtGlT5s6dS2xsLN26dSvy712Y8evl7EVCRkKhMR6JmYl4OXvZNbCLYm5qdcFv3+7VfjehoaE2F/HMzEwGDRrE/PnzGTly5D333bt3LydOnKB79+6AuAIurV4XHbnJAUs7kg4ikg4ikg4ikg4ipVWHtOy0Qp9D4tLjmbDuU87dKklURu9JgLs/ge7+BLj5i/92CyDg1mdHzcOvdhdlPhrfho3os/MfVr/UlrToKC7tNtNiyjfE//MbSacP3ja0O71Opb5j0ZbxBW7X4nbVKLCajFgtZpQOT2YF32TI5PD0rzg4+XY4ZU5KCgcmT0KwWvGuXYdzK34GwMHDg4AObXCvnEnlBi3xCnwOc1YGwgOsvAtWC2ZDBubMNEyZaQhWC/pyVUg9f6zAfZzKhyC3W0NdIDMujvSoSCwm0XDVunvgElQexS2PR5lcjkLriNmQguyALE9CtgdFEAS2/u8NMm/ewL1qCC2mzSyw36lrZ1hxZBW/H11LUubt7PC1AmrSp15Putfqgrudes4OKg2jw4YyrtVI0rLScdY6YbaaS6VxDaXrHlmiLRC9Xs+CBQvo168fVatW5Y8//uC9997jzTffpFy5cqxevdq2Aj1w4EBSUlLo1q0b8fHx1KtXj40bN6JSiRfxvHnzGD9+PDVq1MBoNNKlSxe+/fZb27GqV69Ov379+L//+z88PDzYvn07b731Fh999BFlypRh9uzZtGjRAoCWLVsyb948hg0bRkxMDNWrV2fz5s1FfuLIZDI2jlxdqIu4SqHERetSYIyHj7M3W0evs7vvo7qI16hRg1OnTuXbPnr0aN555517tgcEBBQ4tqOjI+3ateOff/7Jsz03DicX060yDxaLhddee80WF5+env50ZfAs4URERNg8OEozkg4ikg4ikg4ikg4ipVUHZwfnQp9Dyug9cde54eTgRHp2OvEZCcRnJHA06rjd8Tz1HgS4+RPoHiAa4bcM70B3f/zd/NFrHO3ul23KYfbO74o0H41Hlaq8svsAq7u0I+H0KbaNHkfnX9dS5XUF53/6kqRTB4hYu4CrG5YQ3P8dKvYexaUVM4hYs+C+MlcXNXKlimPzZtttOzb/W/53OYZGEz4msHlLfBs2Iik7lT9Wvo1SpUIml6NyvB17+vPPP7N48WISEhJsOZBWrljB3Hnz8o39/pt9ad+0AYv+2MLSdVvytK2a/gle7q62+WlcPDFbLEydOpUNGzag0+kYNmwYvXr1Qu3sjrZsAJ9/OJGtu/9Gq1HTt01rXu73Kq4VKyG/VT+7KJ7vjn33LVc2bUCh0dDxpxWo7sp9FJcez+//rWXFkVW2etUA3k5e9K7bnT71elLVp8o9j5P7gif5ZhKelT1KnVv4nZSme2SJMrDvzrc2ffr0PCugbdu2pW1b+yntZTIZ48ePZ/z48XbbnZycWLhwIQsXLrTbfrfLd926dfMZd3fy6quv8uqrrxbYXlTIZLJC3+YajIZ7xng8ytvgwujduzedO3dm586dhIWFIQgCixYtIioqCj8/v3u2F4bFYuHw4cO2smu5uLq6cvHiRVs259yMhg0bNmTmzJn07t0brVbL2LFjadasGa+99tpj+e4SEhISEhISt2vdFvQcIiDw5wjRezA1K5WopBiikqKJTooR/50cTUzyNaKSYkjNSiUhI5GEjESORduvzOLh6H5rBTyAwFsr4G2rt2L5wV+Ztn2mrV9RxYHrfX15efse1vXuSszev1nTpT3tFy2jyawtJBzbIxraJ/fjVKE6F5dP58Kyr2z7mjJSuLBUzBlTqe+Yx7KSnXLlMpHbt5J04Tx1Ro0h564cQrnkpKRgyjTQeOIn9xxzx44dfPbZZ9SuXYuwZk1ZtfYPhg96g8Hd2/Nmt9vhkJv2HSItI5O6DRrhUqk6V5PX4O7uzsudO2BMFT0n9drbLxZ0PuUQBIGlS5eybNkyOnTowLVr15g4cSLBwcHUrFmT5StXsmrjJtq1bs3VK5eZufI3Ar29CY2JxiOkOo4+D1c27U7iTp5gzwdvA/Dil1/j9VxNUSNzDlvP7ODXf1ex/dwuLFZxNV+tUNOhRhv61utN8+BmNhfwB+HORMcSzz4lysC+E0EQOHLkCDNnzizuqZRoirPGXJkyZVi8eDFTp05lxowZWCwWgoODWbhwIXK5/J7td5Mbgy2TyTCbzVSpUoXBgwfn6dOkSRPWrFlDu3btqFChAnXq1AHEWuXnz5/n5ZdfxmKxUL9+fSn+uggpzNugNCHpICLpICLpICLpIFJadXiQ5xAXrQs1/Fyo4Vfd7lipWalEJ10jKjnXAI8mKimG6OQYopNiSMlKJTEzicTMJI5HnwREg7tvvd4s3FdwPppxre4dalYYDq6u9PhzC5sH9ufC2t/ZOKAPhtib1B75Fp61XiDp9EFcK9fk+JRhdvePWDOfyv3Gk371AlovX5Ra/UPPxZieTtTunUTu2MbV7VtJuSKW0tR6etJs0mQ0rq52jWyNqyuae8TAWi1mTGnJ/L11EwAju7Wmor8vOcmx/LZ1N25Oet7o1QW1izunrsSw+I/NfPHFF9R4UVz8unAr91G/N4egTE/ElHgdq9mEXKlC51MOR/8KyOQKNm3ahJubG9988w2XL1+mY8eObNq0iZo1a9raZs6Zw6VLl+jUqRP7Tp+harlyxB79F62nJ5TzfWj9TJmZbBzQB4vRSIWOL1Hzf8M5Hn2SFUdWsfroHyQbbmtXJ7AWfev3otvzL+Gqc33oY0LpvT/cSWnSoMQa2DKZrMCSVxJ5yRPjkZ2Os0PRx3j4+/vnKdGVS0hICEuWLClwv3u159KgQQOOHSs4ZufOY8+ZM8dun2HDhjFsmP0fN4lHw2AwlKrkFAUh6SAi6SAi6SAi6SBSmnW48zkkNSsNF63zQz2HuGhdcPFzIdSvmt32tKw0opOv3WF4R2MVBJIykwqNA0/LTsdT72G3/X5ROjjQcdlKdOPf4viCuex6ZwwZN67T7LMv8ajRiJzkeEwZKXb3NWWkkJN0k38/HUB6xFnULh7ofALR+pRD5xOIzjsArU8guluf7zTABauVuOPHiPxrK5Hbt3L94H6xPvUt5Eolvo2aENSqDcb0NGoPH82BO2Kwc6k9fDRWswnFHZ6h5qwMTJmpZN6IJDEpDVNGKggCrrdCpLfv/xdZs8acjbwGQLKDJ/XrNAfgq3ETCQkJseW+SU1N5caNGyQlJdG02QtoNBrGjxvHq6/0Ra5UIQhWZHLRxTsiIoKgoCBkMpnN6IqIiMjXFhgYCECCyYxb5WBSrlwiKyGB5BuRZNy4Tk56eqH/Z/Zi8ve9M4ak8+FofXyIefUFXvi6Dedunrft4+Pszct1e9KnXk+CvSsVOv6DUJrvD7mUJg1KrIEt8WDkuj7l/oCU5hgPiaInMTGRsmULL0lSGpB0EJF0EJF0EJF0ECntOuQ+h6TcTKZMZc/H8hzirHWmutaZ6r5560sbzcZC48CdHfKXZH0Y5AoFYTPmoPfzY99H/8eRb74i8+YN2sxfhErvgkrvatfIVuldUbuWwWrMFuebmogxNZGUApKBydTOGC2OZCYZSYmKxZSZNxePa8VKlGvVhqBWbQl8sQXqO0rO1n/nfQRB4Nh3c2xZxGsNG3Vru5XYQ9tIPL6PhON7ib58nIznAsiKu4bJVfQsVDjo6PPyy+w5eZ4Vm3eyYvNOKlasCED2rbrV+/bt4/z583zzzTe2eOibN28SGBhIaGgozZo148cff+TLKVMIqVaN+vXrI+O252JWVpYtBDQ3V1JWVlaBbdnZ2bgHV8HJ35/Ec2dJjrpIVmICf/buimb8ZEL7v47sLs9IezH5/5PXRvnjMgQZLG4gELFHTJTsoNTQoUZb+tbrxYvBzVDI7Ze3fRRK+/0BSpcGkoEtISEhISEhISFRJBRHrGlhceCDmr7OhdhLVPUJfqjY2buRyWQ0eOcDHH3Ksm3YIM7+sgxDfBztFy+lfPehXFg6BbWLBxp3b3KSYjGmJlK++1AAWv58HFNGKobYaLJuRmG4eRXDzSgyr0USd+o0iRevkh6fidGQd2VWrgCtsxKdiwJHVyU6LyMqwymSD6eSHXVQXAX3KYdrSB2u71pD+RfrUf/tGLIT43HwKEPckZ1EbVyC1juQIx/2tY0rKGXIVWoc3L1wqfgcamd3FBqxus7va//g+PHjODg4cPz4cT777DP0enFlfe3ateh0Olq2bGkbq0qVKmzfvt322dnZmREjRnDkyBHq16+f5/s4ODhgNBoBbH87ODjcs02lc8SnTj1Mni4oDmrITkpi29A3Ofn9fMJmfEvZeuJxDEYDs3d+x7RtM28fNDYR86qfUQKer/chwmEf9YLq0LdeL7o+3wkXbelYWZV4MkgGtoSExD25O9lcaUXSQUTSQUTSQUTSQUTSQaQ4dCgoDnxw09cZ3OwNXvq2J+6ObizsPxc/16JZQQvt/zq6Ml6s79eLyO1bWdOlI93WrMOnaWcc/cqTlRCH1tOLzGsROJWrjEItusur9C44OzpjNilIPnqByL+OE7P3b8xZd6xSy2S4B1eiTNWKuPh7otZYyI6PISs2CmNaEqa0ZFLTkkm9eNy2i9rFg1YrTnP+x8mYMlLyGfkqvSutfzuHS5XauFQMxaNmE6hcnU07ZqAPqIy2zO3ks5cuXWLRokV06dKF0NBQVqxYAUBwcDCCIHDgwAHq1q1rM3wBjh8/zrp16+jRowehoaFkZGQA4OaWv3xVYGAg169fx2q1EhMTA0D58uXv2ZaL1sMDt0qVqTO6GXFff8fN/47wywsNqD7gDZpN+hKVhxsL94ox+R6O7njry/Di7nM4GAVulNUy9OuFvJCZQIVHqKH9oEj3h9KlgWRgS0hI3JMLFy5Qpcq9y1E860g6iEg6iEg6iEg6iEg6iBSXDgXlozl45Qg30mK5EHeJ5tPbMq/vTFpXCyuSY1Zo14Hem3eypntHTJkZyJVqzqxcxbF5s23u2bWHj6b+Ox+Qk5pK5F/biPxrK1f/2kZ6THSesfRlfW+7fYe1QufpafeYZkM6hthoDDev3loFF//I5EqMKQk2F/VcN/RcTBkpWLINvDh/t21bQkYi9nB2dmb9+vUcPXqUpk2bsnbtWho3bkxAQAA3b94kMTGRatXyxsk7OjqyatUq/v77bzp06MAff/yBXq+nWbNmpKens2zZMoKDg2nVqhVt27Zl1qxZjB8/nuvXrwPQvn17gELb7kQmk1Ot3wA69h/Dzv8bz8WVKziz9EdO/racWu+9h4+jJwt6fk2zqs0wxMfhNNKLK7u2M/LQN2RZjU/UuAbp/gClSwPJwJaQkLgnufXGSzuSDiKSDiKSDiKSDiKSDiLFqYO9fDRhVV9k17hNvLl0OCdiTtHnh9cY1WIYEzq8g0qheuRjlq3fgL679pMWGcHRb2dxcMrntraclBQOTJ6EIAh416rNhld729oUGg3+TV8gqFVbglq3xaNa9fuq76zUOeFcvhrO5fMngrOajIXGgav09+cG7eXlxeTJk5k+fTqrV6+mVatWfPLJJwDEx8cD4O3tnWefypUrM2PGDGbOnMlPP/1ExYoV+fDDD3FxcSE1NZVZs2bx0ksv0apVKwYPHkxqairr1q1Dq9Xy2WefUatWLYBC23Ixmo2kZafz7a75nI+9yGn3s/h2K0ObfSmUjTcSs3IV2//aw7F5s/mhQ1/by47nh43k97fXoi2imPwHQbo/lC4NJANbQkLinjg5Pfkfo5KIpIOIpIOIpIOIpIOIpINISdShvGcQm0ev5eM/v+D7fT8yZ9d3HIo4wvf9v8Xfze/eA9wD98rBuASWY8OAPnbbj303h/9disG3URPK1q1PUOu2+DVphkpXtHWxrRazLQ78bsp3H4rVYkauur/kc507d6Zz5875tteoUYPz58/b2QNat25N69at82wzGAz4+/vn2UelUvHBBx/wwQcf5BvDXpvRbCTZkEJSZjLJhmSuJV8nOjmGA1cO2fronquO0LU+nucyadagA8fnzubQXS87Dn35Ochk1B37NuifbDLgknhdPGlKkwaSgf2MYDJkIleqyElNQePiitVsQqVzLJKxY2JiaNmyJd27d+fLL7+0bU9ISOCFF15g2LBhHD58mJs3b6K748dCq9WycuVK1qxZw5QpUyhbtiwymQyj0UjlypWZPHkyjo5FM0eJx4uXl1dxT6FEIOkgIukgIukgIukgIukgUlJ10Cg1TOk+iSaVGjF65dscjvyX5tPbMbfvDNpWb/XI4+ekpdqtPw2icWfKzKTvzn2PfJzCUDroqNxvHCDW3jZlpKDSu1K++1Aq9xuHQu1wjxGKHo3mwUvG3m1Qp2dnICDY2gUENEoNnWt2pPNzHWhcsSHezrfPO4sxh01v9LM79vF5c2j03sQH/yKPSEm9Lp4kpUkDycB+BjBnZ3N4+ld2Y36UDkVzM3Vzc2Pv3r2YTCZb2YTNm4nHzdAAAQAASURBVDfj6upq6/P555/ToEEDu/uHhYUxZcrtN6pjx45l/vz5jB8/vkjmJ/F4uXz5MqGhocU9jWJH0kFE0kFE0kFE0kFE0kGkpOvw0nPtec6vOm8uHc6x6BO8sugNRjb/HxM7vvdILuMaF1c0rq52jWyNq9j2JFCoHajUdwzBr76NKTMNlaMzVovJrnEtl4mlrcxWc762R8VitSBDhtlqRiaTISAUWP7KaDaSZEghOTOZpMxk0nPy17fWa/S461xxd3TDaDFRqUwF3m79Vr6SbQA5qYW/7MhJTUVXpswjfb8HpaRfF0+C0qSBZGCXcARBwGwwFNhutVr4d+Z0Dk6eZNuWG/MDUOet8cgV9m9oSp3uvuJ9AHQ6HdWrV+eff/6hefPmAGzZsiWfK9D9Uq9ePfbs2fNQ+0pISEhISEhIPCzlPALZNGoNn26YzPw9i/h29wIORhzhh/5zCXD3f6gxrWYTtYePtj1/3Unt4aOxmk0o1E/GLVnpIHoTalzFRGkFuYU7OehRK9VEJUZTK6BmkR3fYrUQkRDJ1aRoTBYTKoWKcu4BlPcMQiFXiAZ1ZvKtVeok0nMy8o2h1+hxd3TDXeeGm6MrGuXtlfDDEf8C4KF3t3v8e77scJFKckk8XiQDuwQjCAIrw5py/eB+u+1aT08Gh0dybN5su+1H582m3rh3+b5qEFkJCfnafRs1oc+OvfdtZLdv357NmzfTvHlzrl+/joODA553ZLmcOHFiHhfxZs2a8fbbb+cbJyMjg23bttGoUaP7Oq5E8ePn9+gxas8Ckg4ikg4ikg4ikg4ikg4iT4sOaqWaL7p+QuOKDRm18m3+vXqU5tPb8W3fb2gf2uaBx1PpHKn/jhg3fPQxehQWJSqFiloBNdl+bieNKzakjJP9zOUPQq5xfSn+im2byWLiUvwVBMBd58qRq0fz7VeYQX0nmTkG1p3YQIC7Pz7O3nb7lKSXHbk8LdfF46Q0aSAZ2CWdQoxfR28fDPFxhbrBGBLicfT2sWtgPygtWrRgypQpGI1GNm7cSIcOHWwlFKBwF/GdO3fSpUsXAMxmM40aNeL1119/5DlJPBmMRmNxT6FEIOkgIukgIukgIukgIukg8rTp0LFGO2rcchk/GnWcVxe/yfAXB/Nhx/dRKx/MCFM6OFBv/Ls0eG8C2SkpOLi6YjWZSqRxnUu/Bi/z+capjFv1PlV9gnHTud73wos9BEHgYtxlrII1X5tcJqdimQpciY9AqVCgVevQqbTo1DqUBXhb3jluZo6BMzfOoZQr+L/27xTYtyS+7HjarovHQWnSQDKwSzAymYw+O/YW6iIuV6kKdYPRl/Xllb8P2t33QVzEQUxaVrduXf755x/++usvfvjhB5YsWXJf+94dgy3xdBEfH5+vJEdpRNJBRNJBRNJBRNJBRNJB5GnUIdA9gI0jV/PZxinM+/t75v39PQcjjrBowDwC3QMeaKzcBLPXU1Op7OX1xFdKHxRPvQeTunzIgcuHOHX9DPEF1MYujCyjgespN0nOSqGKd2V+OfxbgX1fb9QPjUqDg0o0crPNOWSbc+7rOA5KDd2ef+m+VtvvfNmRk5qKxsWlWF92PI3XRVFTmjSQDOwSjkwmQ1VIpm2TIfOebjCF7f+gdOjQgQULFuDl5VWq0u1LSEhISEhIPLuolWo+6/IRjSs2ZOSKcRyNOk7z6e2Z0+drOtZo98Dj5eTcn9FYEnB2cKJt9Vb3nU09Pj2B/ZcPsu/yAf65dJDzsRcA8HB0Z+Wgn1hz7E9Ss1Lz7eeideGzLh89sGfAw5L7siM3oVlJf9kh8ewgGdhPOU/aDeaFF17g/ffft+vefXcMNsD8+fOL9PgSxUNISP4snaURSQcRSQcRSQcRSQcRSQeRp12H9qFt2D1+C28uHc5/UccY8ONg/tfsTT556f8eyDB82nW4k7j0eNGgvnSAfy4f5ELsxXx9qpcNoXHFhiRkJDKk2UCmbZuRr8+QZgMxW82oKX2G7rN0PjwspUkDmSAIwr27SRQ1aWlpuLi4kJqairOzMwAGg4Fz584REhKSz1C9F7frYN92gynKlWuJ+yf3/zEyMpKLFy/Spk0bateuXdzTeiQuXrxI5cqVi3saxY6kg4ikg4ikg4ikg4ikg8izooPRbOTzTVOZu3shALUCarJowDzKeQTe1/5Psw6xaXG2Fer9lw8VaFA3qdSIJhUb0qhCgzwZvbNNOczcMZeFexeTmpWKi9aFIc0GMqblCBxUD14X+1ngaT4fioqSrIE9u+xRkFawnxEkNxiJx8nT5Or2OJF0EJF0EJF0EJF0EJF0EHlWdFAr1Uzq/CFNKjZixIqxHIs+QfPp7Znd52teeq79Pfd/mnS406D+59JBLsZdytcn1LcaTSo2okkl0aB2d3QrcDwHlYbRYUMZ12okKYZUXHUumK3mUmtcw9N1PjwuSpMGkoEtISFxTxwlbwhA0iEXSQcRSQcRSQcRSQeRZ02HttVbsXv8FgYtG8GRyP94fckQBjd9g087TyiwlBQUrw4GowGlXEladhrODs6YrWZ06tuekTfTYtl/+SD/XBKN6ktxl/PsL5PJbhnUDWlc8d4GtT1yj2dMy0HtrC6VbuF38qxdFw9DadJAMrAlJCTuia+vb3FPoUQg6SAi6SAi6SAi6SAi6SDyLOrg7+bH+hGr+GLTNObs+o7v9/3I4ch/WTRgHuU9g+zuU1w6ZJtymL3zOxbu/fEO9+w3GNnif3y7cwFrT6y3a1DX8K1O44oNaFKpEY3K18ftAQ3qgihzy7OytPMsXhcPSmnSQF7cE5CQkCj5XLyYP/6qNCLpICLpICLpICLpICLpIPKs6qBSqPjkpf9jxaAluDu6cSLmFC2+6cCfJzba7V8cOhhyDMzc8S3Tts20ZfFOzUpl2raZzN45n+f8Q7kUdxmZTMZzfqEMe3EQPw9cxKXPTrJr/Ga+6PoJHULbFplxDc/u+fCgSDqULg2kFWwJCQkJCQkJCQmJ+6BNtZbsHreFwT+P5FDEEd74aShvNnmNSZ0n2mo7A2g0RR9vLAgC8RkJRCXFEJ0UTVRSDFHJ0UQnxZCWlc6aYb+wcO+Pdvf9Yd8SznzyL78NWUadwOdx1bkW+fwkJCREJANbQkLinpQtW7a4p1AikHQQkXQQkXQQkXQQkXQQKQ06+Ln5sm74r3y5eTqzds5l0T8/cSTyP5a+8T0eeneUciWuPm4YzcZ88c+FIQgCcenxogGdHH3LkI4hKimaqOQYYpJiyDbbTxQVUrYq8ekJdutPg7iSnZmTScuqzR/2az8UpeF8uB8kHUqXBpKBLSEhcU+sVmtxT6FEIOkgIukgIukgIukgIukgUlp0UClUfNTpfRpXbMCwX94i25SNTq1j1g4xRtteeSqr1UpcRrzNaI5OvnbfBnQuMpkMX5eyBLr7E+AeQKCbP4Hu/lQoU56yLj64aF3sGtkuWhecHZwelxwFUlrOh3sh6VC6NJAM7GeEHIMRhVJOZno2jk4OWMxWNLqiydi4adMmFi5ciNVqRRAEXnrpJYYMGVJg//79+zNy5Eh0Oh0rV67kiy++yNMeExPDgAED2Llz533PYcKECfTp04caNWo89PeQeHhiY2OlRCVIOuQi6SAi6SAi6SAi6SBS2nRoFdKC3eO3EplwlQV7FzN9+yxbmxj/PANBsNKwQn36LRpIzj0MaLlMjq9rWQLdRAM6wM3vtjHt7o+vS1nUSvvPdwajgSHNBjJt24x8bUOaDcRsNT/xbN6l7XwoCEmH0qWBZGA/AxhzTKyfv5OtP+4lMy0LR2ctbQc2o/Pwlqg1qkcaOzY2lmnTprFmzRrc3NzIzMykf//+VKhQgVatWhW6b40aNYrMIL7bSJeQkJCQkJCQKAn4uZaljN6D/j8Ostv+/b4ljA4bjl7jiMliymNAi8azP4Futwxo17KoFA/37KZT6xjTcgQAC/cutruKLiEh8fiRDOwSjiAI5GQZC263CmxcuJs1s7bZtmWmZbFmpvi5w+DmyOUyu/tqtGpkMvttuSQnJ2MymcjKysLNzQ1HR0emTJmCRqPh5MmTfP7552RnZ+Pm5sakSZMoV66cbd9Dhw7x7bffsmzZMs6ePcuECRMAqFq1qq3PhQsX+Oyzz8jKyiIpKYlBgwbxyiuvMGfOHI4fP87Nmzfp1asXO3bsYOTIkTRo0ICFCxfyxx9/oFAoaNKkCe+88w4KheLeYko8NFWqVCnuKZQIJB1EJB1EJB1EJB1EJB1ESqsOadlp94x//vvtrXjqPR7agL4fHFQaRocNZVyrkaRlp+Ps4ITZai4247q0ng93I+lQujSQDOwSjCAIfNpjDhf+i7Tb7uTuyKx/JrL1x71227cu3kun/7XgrSafk56Uma89uG55Pv59ZKFGdtWqVWnbti2tW7cmJCSEBg0a0KlTJ3x9fXnjjTf45ptveP7559m8eTPjxo1j9erVdsd57733ePfdd2nWrBnfffcdhw4dAmDVqlUMHTqUJk2acO3aNTp16sQrr7wCQFZWFhs3iuUvduzYAcCePXvYtm0bq1evRqVSMWrUKFauXEm/fv0K/A4Sj87Vq1epVKlScU+j2JF0EJF0EJF0EJF0EJF0ECmtOjg7OBca/+yqcynQtbuoyU2q5qn3AHjibuF3UlrPh7uRdChdGkh1sEs6hRi/rmWcSUvMIDMty257ZloW6YmZuJZxfqQpfPjhh/z111/07t2ba9eu0adPHxYsWICTkxPPP/88AO3btycqKor09PR8+yclJREbG0uzZs0A6Nq1q63tvffeIzs7mwULFvD1119jMBhsbblj38mBAwfo1KkTWq0WpVJJjx49OHDgwCN9P4l7k52dXdxTKBFIOohIOohIOohIOohIOoiUVh3MVjNDmg2025Yb/1waKa3nw91IOpQuDaQV7BKMTCbj499HFuoirlQqcHTW2jWyHZ21uHk78+kfo+3uez8u4rt378ZgMNChQwd69+5N7969WbVqFX/++We+fQVBwGzO/wMik8kQBMH2+U537rfeegtXV1datGhBhw4d2LRpk61Nq9XmG8tqteY7rr1jShQt9v4vSiOSDiKSDiKSDiKSDiKSDiKlVQcp/tk+pfV8uBtJh9KlwVO3gh0eHk7btm1xdXUlMDCQL774wpb2fdiwYWg0GvR6ve3PwoULbfv+9NNPVKpUCUdHR+rWrVvoymdmZiZvvPEGHh4euLi4MGDAADIyMmztFy5coGXLljg5OeHr68vkyZMfy/eVyWQ46DQF/rGYrbQd2Mzuvm0HNsNitha4772MawAHBwe++eYbbty4AYhG9NmzZ3n++edJSUnh+PHjgJhp3MfHBzc3t3xjuLm54efnx19//QXAhg0bbG0HDhxg9OjRtGrVin379gFgsVgKnE/Dhg1Zv349WVlZmM1mVq9eTb169e75PSQejcDAwOKeQolA0kFE0kFE0kFE0kFE0kGkNOuQG/8c/ulRzk86TvinRxkdNrTUGtdQus+HO5F0KF0aPFUGdkZGBm3btiUwMJBr166xd+9efv31Vz777DMAjhw5wsKFC8nIyLD9yS0ntXv3bkaNGsVPP/1ESkoK/fr1o3Pnznlcku9k5MiRREdHc/HiRS5evEhUVBTvvfceACaTiU6dOlGvXj0SExPZuHEjc+fOZdWqVU9GiDvQ6NR0Ht6S7mPa4OgsvhlydNbSfUwbOg9v+ciluho2bMiIESMYMmQI7dq1o127dsjlckaNGsWMGTP44osv6NSpE8uWLWPGjPxlIXKZNm0a8+fPp2vXrkRFRdm2jxo1ih49etC+fXsOHz6Mv78/0dHRBY7TokULwsLC6NmzJx07dsTHx4f+/fs/0neUuDfnz58v7imUCCQdRCQdRCQdRCQdRCQdREq7Djq1DrVSTfKNJNRKtS0eurRS2s+HXCQdSpcGMuFO390SzpYtW+jWrRupqamo1aLh+Ouvv/LWW29x9epVnJ2dOXr0KNWrV8+376uvvopOp8uzoh0SEsK7777LG2+8kaevwWDA1dWV3bt307hxY0DMiN2iRQsSEhLYv38/3bp1IzEx0TaPqVOnsmnTJv7+++/7+i5paWm4uLiQmpqKs7Oz7bjnzp0jJCQEne7Bbsi5dbAN6dnoirgOtsSDkfv/GBkZycWLF2nTpg21a9cu7mk9EqdPnyY0NLS4p1HsSDqISDqISDqISDqISDqISDqISDqISDqISDqUbA3s2WWPwlO1gm2xWFCr1ahUt8sbyOVyYmNjOX78OCaTiY8++ghvb2+Cg4OZOnWqzX38zJkz+WoyV6tWjRMnTuQ7zsWLFzGZTHn6V6tWjaysLC5cuMCZM2cIDg62GdeFjZVLTk4OaWlpef4UJRqdGqVaibOHHqVaKRnXEkWKt7d3cU+hRCDpICLpICLpICLpICLpICLpICLpICLpICLpULo0eKqSnDVp0gStVssHH3zARx99RHx8PNOmTQPE2OzmzZszevRoVq5cybFjx+jWrRtyuZx33nmH9PR0HB0d84yn0+nyxFXnkpsJ+87+uSvKGRkZDzRWLl9++SWffvppvu1nz55Fr9dTtWrVPK7TEk8/qaliqY7IyEjUajWenp44ODgQExMDQIUKFUhISCAtLQ2lUknVqlU5ffo0AO7u7jg6Otrc5YOCgkhOTiY1NRWFQkFISAhnzpxBEATc3Nxwdnbm6tWrgBjjkp6eTnJyMjKZjOrVq3Pu3DksFgsuLi64u7sTEREBQEBAAAaDgcTERACqV6/OhQsXMJlMODk54eXlxeXLlzEajSiVSoxGI/Hx8YDoAXLlyhVycnJwdHTE19eXixcvAlC2bFmsViuxsbGAWPvw6tWrZGdno9VqCQwMtLkKeXt7I5PJuHnzJgCVK1fm2rVrGAwGNBoN5cuXJzw8HAAvLy+USiXXr18HoFKlSty8eZOMjAzUajWVKlXi7NmzAA+st16vt12DQUFBpKSkkJKSglwup1q1apw9e5bs7GxycnJwdXUlMjLSpndGRgZJSUkAhIaGEh4ejtlsxtnZGU9PT65cuQKAv78/2dnZJCQkAOKLuUuXLmE0GtHr9fj4+HDp0iUAfH19MZvNxMXFAWLJvIiICHJyctDpdPj5+dn09vHxQRCEPHpHRUWRlZWFg4MD5cqVy6O3XC635VWoXLky169fJzMzE41GQ4UKFTh37hwAZcqUQa1Wc+3aNQAqVqxIXFwciYmJJCUlERwczJkzZwDw8PBAp9PZztny5cuTlJRU4Dnr5ORk07tcuXKkpaUVeM66ubnZ9A4ICCAzM/OJ6B0ZGUl2djY6nQ5/f38uXLhg0xsgOjqa2NhYgoODiYmJwWAw4ODgQFBQ0BM5Z0vKPSJXBz8/v1J9j4iJiSE2NhZXV9dSfY9ISEggNjYWlUpVqu8R6enpNr1L8z1CpVLZ5lSa7xFeXl6cPXsWq9Vaau8RuXOMi4sjPT29RN0jcq/douKpchEHOHbsGOPGjePkyZNUqlSJ/v37M2rUKDIzM/O5VU+bNo1ff/2Vf//9l5o1azJ48GBGjhxpa+/RoweBgYH5YoePHTtG7dq1SU9PR6/XA6LR7ezszPHjx9m5cyfLly/n33//te2zfv16BgwYQHJyst155+TkkJOTY/uclpZGQEBAkbmIS5QcJBfxZxdJBxFJBxFJBxFJBxFJBxFJBxFJBxFJBxFJh5KtQal2ETcajZjNZnbu3EliYiKHDh1CoVBQrVo1tm3bxoIFC/L0z8nJsaWEDw0Ntb0hyeXs2bN2/6OrVKmCSqXK0//s2bOo1WqCg4NtbzruLA9V0Fi5aDQanJ2d8/wpiFy3domnE+n/T0JCQkJCQkJCQqJ08lS5iAuCQJs2bfj6668ZOHAgR48e5YsvvuDjjz9GEATGjh1LpUqVCAsL4+DBg8yaNcu2Oj1w4EC6detG7969adq0KXPnziU2NpZu3brlO45Op+Pll1/m/fff57fffgPg/fffp2/fvmi1Wlq0aIGnpyfvv/8+n3/+OefPn2f27Nl88cUXj/T9NBqxdNaNGzcoW7YscvlT9f5DAtG4vn79OoIgYDKZins6RUblypWLewolAkkHEUkHEUkHEUkHEUkHEUkHEUkHEUkHEUmH0qXBU2VgazQa1q1bx9ixYxkzZgxeXl689957DB48GIAZM2YwfPhwYmJi8PHx4dNPP+XVV18FoGXLlsybN49hw4YRExND9erV2bx5M+7u7gBMnjyZ5cuX21at582bx/jx46lRowZGo5EuXbrw7bffAqBUKtm2bRsjRozAx8cHvV7P6NGjef311x/p+ykUCipVqsSlS5eKPAmaxJNDEARiYmJsK9n3U2+8pHPt2jUqVKhQ3NModiQdRCQdRCQdRCQdRCQdRCQdRCQdRCQdRCQdSpcGT10M9rNCYb7+FouF/fv3c/z4ccqUKZMna/qTIikpyfbyoTTzMDqYTCYsFgtpaWlkZmbSpUuXp/6tXUmOm3mSSDqISDqISDqISDqISDqISDqISDqISDqISDqUbA2KOgb7qVrBLi0oFArq1KlDXFwcly9fxmKxPPE5ZGRk2DIUlmYeRQeNRkODBg2oWLFiEc/qyaPRaIp7CiUCSQcRSQcRSQcRSQcRSQcRSQcRSQcRSQcRSYfSpYG0gl1M3M+bkuzsbGJjY/NkH39SWCwWFArFEz9uSeNhdZDJZGi1Wnx9fZ+JWHqz2YxSKb2Pk3QQkXQQkXQQkXQQkXQQkXQQkXQQkXQQkXQo2RpIK9iliNy6c8XB6dOnCQkJKZZjlyQkHUTCw8NLrFvPk0TSQUTSQUTSQUTSQUTSQUTSQUTSQUTSQUTSoXRpIBnYxUSu40BJTWaWkZFRYuf2JJF0EJF0EJF0EJF0EJF0EJF0EJF0EJF0EJF0EJF0EJF0KNka5M6rqBy7JQO7mEhPTwcgICCgmGciISEhISEhISEhISFRuklPT8fFxeWRx5FisIuJ3HrJTk5O+co41atXjyNHjhTTzMS3OAEBAURHRxdJHMLDIukgIukgIukgIukgIukgIukgIukgIukgIukgIukgIukgaZBLQToIgkB6enqR5U6SVrCLCblcjr+/v902hUJRrCdfLs7OzsU6D0kHEUkHEUkHEUkHEUkHEUkHEUkHEUkHEUkHEUkHEUkHSYNcCtOhKFauc3n60xs/g4wYMaK4p1AikHQQkXQQkXQQkXQQkXQQkXQQkXQQkXQQkXQQkXQQkXSQNMjlSekguYhL5KOoU9U/rUg6iEg6iEg6iEg6iEg6iEg6iEg6iEg6iEg6iEg6iEg6lD4NpBVsiXxoNBo+/vjjUlUQ3h6SDiKSDiKSDiKSDiKSDiKSDiKSDiKSDiKSDiKSDiKSDqVPA2kFW0JCQkJCQkJCQkJCQkKiCJBWsCUkJCQkJCQkJCQkJCQkigDJwJaQkJCQkJCQkJCQkJCQKAIkA1tCQkJCQkJCQkJCQkJCogiQDOynkLi4OLp27Yqrqyuenp6MGTMGs9nM0KFD0ev1ef4oFAratm2bZ//GjRtz9OhRAL766iv8/f1xdHSkefPmnD9/3tYvMjKS7t27U6ZMGTw9PenatSsRERF5xlq4cCGjR48GYPXq1Tz//PM4OzsTFBTEp59+itVqzTf/7du3o1AoiIyMfCw6AGzatIlatWrh5OREzZo1Wbt2bb79nxUdAOLj46lUqRK7d++2bRs2bBgajSbP+bBw4cJnVoOCdChN58KJEydo3bo17u7u+Pj4MGDAABISEvL0OXDgAA4ODnb3Lw06zJ07l8qVK6PX66lcuTLffvttqdOhtP1WFHY+lKb7Q2G/mbncuHEDb29vlixZ8tA6HD9+nLCwMFxcXPD09KR///4kJiY+FTqcPHmSli1b4uTkhLe3N+PGjcunUWnQYeXKlYSEhODs7ExwcDDz58/Pt/+zokMu9p4f7mcupUGHXErD/aHIECSeOpo3by7069dPyMzMFC5fvixUr15d+Oqrr/L127p1q+Du7i6cPn3atu369etC+fLlBUEQhCVLlgh+fn7C6dOnhaysLGHcuHFC9erVBavVKgiCINSsWVN48803hYyMDCE9PV0YOHCgEBoamucYbdu2FXbu3Cn8+++/glarFdavXy9YLBbh7NmzQlBQkPD111/n6X/jxg2hbNmyAiBEREQ8Fh3+++8/QaVSCd9//71gMpmEPXv2CE5OTsKuXbueSR327dsnVKxYUQDyfMc6deoIS5YsKXC/Z0mDgnQoTeeCwWAQypYtK3z00UdCTk6OkJCQIHTo0EHo1KmTIAiCYLVahUWLFgl6vV6wd+svDTr8+eefgpubm/Dvv/8KgiAIhw8fFhwcHISdO3eWKh3u5ln+rShMh9J0fxCEez87WCwWoUWLFoJcLhd+/PHHPPverw45OTlC2bJlhUmTJgkmk0lITk4WWrZsKQwYMKDE6xAfHy94enoKkydPFoxGoxARESFUrlxZmDZtWqnS4dSpU4JOpxMOHDggCIIg/PPPP4JarRb27NnzTOogCPafH+5nLqVBh1xKy/2hqJAM7KeMixcvCoBw7do127aVK1cKgYGBefrl/lD8/PPPebbPnTtXGDNmjCAIgtCkSRPhiy++sLUZjUbByclJ2Llzp5CUlCS0bdtWuH79uq39xIkTAiAkJSUJgiAIKSkpgq+vr2A2m4Xff/9dGDt2bJ5jjR07VujcubPts8ViEcLCwoQPP/zwkS+CwnR47733hBYtWuTpP3ToUKF3797PnA5LliwRAgMDhZUrV+a5IWZnZwtqtTrPA/PdPCsaFKZDaToXwsPDhXbt2glms9m2bd26dYKzs7MgCILw+uuvCw0bNhSmT59u18AuLTqkpaUJgiAIJpNJ2LRpk6DVaoWjR4+WOh1yedZ/KwrToTTdH+7n2eHjjz8W+vfvL5QrVy7fA/T96iAIgpCRkSFYLBZBEAThypUrQsOGDYXx48fb+pdUHb7++muhcePGefpHRkYKV69eLVU6rF69WlCr1cI///wjWK1WYf/+/YJWqxUOHz78zOkgCAU/P9zPXEqDDrmUhvtDUSIZ2E8Zf/zxh+Du7p5n28mTJwVASE5Otm0bOHCg0K5du3z7t2zZ0vYW0tXVVfjzzz/ztNeuXVuYMWOG3WN/+OGHQlBQkO3zsmXLhIEDB9rtazAYhODgYOHTTz+1bfvkk0+EPn36CBEREY98ERSmw6BBg/Kt0gwfPlx47rnnbJ+fFR1u3LghmEwmQRCEPDfEQ4cOCTKZTOjevbvg5eUlVK5cWZgyZYrtpiYIz44GglCwDm+//XapORfs0b9/f5sBER0dLQiCIOzatcuugV1adBAE0ehSKBQCIIwbNy5P39KkgyA8+78V9sjVoTTdH+717LBz506hSpUqQnp6ut0H6IfRoXHjxgIgVKtWTbh586Zte0nVoU2bNsKQIUOE//3vf4K3t7dQoUIF4fPPP3/k382nTYeYmBjhhRdeEADbffLuFcNnRQdBKPj54X7mUlp0KC33h6JEMrCfMpYtWyYEBATk2Xbp0iUBsD1AX7lyRVCpVMKxY8fy9EtMTBT8/PxsPxYKhULYsWNHnj5NmzYVPvvss3zH/e677wStVits377dtq1bt27Chg0b8vVNS0sT2rRpI9SsWdO2WrR7926hUqVKQmpqapFcBIXp8PfffwtqtVr4/fffBZPJJOzbt09wd3cXKleu/MzpcCd33hC3bdsmtGjRQti9e7dgNBqFQ4cOCb6+vjZ3wGdVA0HIq0Oua1tpOxesVqswYcIEwdXVVTh58mSeNnsGdmnTwWg0CkajUThy5Ijg6+srTJkyRRCE0qdDafituJO7dShN94fCfjOvXr0qlCtXzhY6cfcD9MPqYDAYhKSkJOHll18WQkNDbV4EJVWH5s2bCyqVSli0aJFgNBqF48ePCwEBATYX8dKiw9WrV4XXXntN2L59u2A0GoUNGzYIjo6OwtatW585He6mIAPb3lxKiw6xsbGl5v5QlEgG9lPGmjVrBA8Pjzzbct86pqSkCIIgCBMnThSaN2+eb9/FixcLgwcPtn12dnYW1q9fn6dP7dq1hZkzZ9o+5+TkCMOHDxfc3d3zxClmZmYK3t7eQnZ2dp79w8PDherVqwthYWE2t7i4uDihXLlywsGDBwVBEIrkIriXDr/++qsQGhoquLu7C13+n707j4/52v84/prseyQiQYQgQqylWlqt3loqRSm1FVVtr5YqvahaW0VJUTRKq662v9ZWVNW1tdUFXSzVVovYYssiErLvy8z398cxI8tkI2Qin+fjkcck8/3Od868MzPJZ875ntO3rzZp0iTt/vvvv+tyyK+kT141TdMWLlyo3XvvvZqm3b0ZaFrRHKrbcyE5OVnr37+/1qBBgyLFtaaZL7CrYw5GCxYsMPVYVrccqsPfCqPicqgu7w8l/c3817/+pS1evNh0feF/oG8mh/xiY2M1QPvzzz8tPodOnToV2LZw4ULtvvvu0zSt+uQwcuRI7aWXXiqw7d///rfWv39/TdPurhwKM/d/lLm2aFr1yMFgMGiPPfZYtXl/qEhSYFcxZ86c0YACwym++OILrV69eqafg4KCtI8//rjIbXv37q3t3r3b9PODDz5o6rnRtBvnSXz//feapqlz8zp27Ki1bdtWO3/+fIFjffnll9rgwYMLXLdz507Nzc1NmzRpkmmYiaapT0odHR01d3d3zd3dXXN1ddUAzdXVVQsJCanwHOLj44ucezxo0CDt5ZdfvutyyC//H4atW7dqK1euLLB97ty52kMPPXRXZ6BpBXOobs+F8PBwLTAwUOvWrZt29epVs/uYK7CrSw5LliwpcH6tpmnanDlzTEVmdcnBqDr8rdC04nOoTu8Pxf3NBDR7e3vTfbm7u2s6nU5zdHTUevXqVe4cLly4oPn7+xc4Fz0iIkIDtIsXL1psDvXq1dMmT56stW/fvsD+ISEhpuuqSw59+vTRRo4cWWD/MWPGaEOGDLnrciiscIFdXFuqSw6XLl2qVu8PFUkK7CrooYce0oYMGaKlpKRo58+f11q0aKHNmjVL0zRNu3btmgZoZ8+eLXCb1NRUzcfHR8vJyTFdt3r1as3X11c7evSoaaa/gIAA0/DJdu3aaT169NAyMjKKtGHo0KHaxo0bTT8fOHBAs7OzM/vPWmEV9SlTcTkcPHhQc3Z21o4eParl5uZqX3zxhebo6KgdP378rszBKP8fhq+++kpzdHTUvv/+e9MkJV5eXtqaNWvu6gw0rWAO1em5kJCQoNWvX18bOXJkgXMGCytcYFenHI4cOaLZ2dlpGzdu1PR6vfbLL79oNWvW1DZu3FitctC06vO3oqQcqtP7g6aV/L9Dfvl7qMqbg8Fg0Nq1a6cNHjxYS01N1a5evar17t1be/zxxy0+h5MnT2r29vbaggULtLy8PO2ff/7RfH19tdDQ0GqVw+rVqzVHR0ftm2++0QwGg7Z3717N1dVV2759+12ZQ375/38oqS3VKYfC7vb3h4oiBXYVdOXKFW3AgAFazZo1tVq1ammTJk0ynbvw+++/a0CRP/BffPGFNmzYsALXGQwG7d1339UaNmyoubi4aI8++qh2+vRpTdM0bcuWLRqgOTg4aM7OzgW+Ll26pPn4+GipqammYz3xxBOaTqcrsq+5yXMq6kVQUg4ffvih5u/vr7m4uGjt27c39TDcjTkYFX5DXLlypRYYGKg5OTlpjRo10lasWHHXZ2Auh+ryXDDODu7k5FTkPvMrXGBXtxz+97//aa1bt9ZcXV21li1bmmbPrm45VJe/FaXlUF3eHzSt5L+Z+eX/B7q8OWiamlCxf//+mqenp1a3bl3t5Zdf1pKSkrTs7GyLz+HgwYPaww8/rNWoUUOrW7euNnfuXM1gMFS7HJYtW6YFBgZqrq6uWosWLW76fbIq5JBf/v8fSmpLdcqhsLv9/aGi6DRN0xBCCCGEEEIIIcQtsarsBgghhBBCCCGEEHcDKbCFEEIIIYQQQogKIAW2EEIIIYQQQghRAaTAFkIIIYQQQgghKoAU2EIIIYQQQgghRAWQAlsIIYQQQgghhKgAUmALIYQQQgghhBAVQApsIYQQQgghhBCiAkiBLYQQQgghhBBCVAApsIUQQgghhBBCiAogBbYQQgghhBBCCFEBpMAWQgghhBBCCCEqgBTYQgghhBBCCCFEBZACWwghhBBCCCGEqABSYAshhBBCCCGEEBVACmwhhBBCCCGEEKICSIEthBBCCCGEEEJUACmwhRBCiFtgMBgquwlCCCGEsBBSYAshhKgWdu7cyeOPP46Xlxd2dnY0aNCAl156iYiIiJs6nqZpfPLJJwwdOtR03cWLF9HpdOh0Og4ePFhRTS+zvXv3mu7/ypUrd/z+K0pGRgaLFi3i/vvvx93dHQcHBwICAhgzZgznz58vsv8ff/xBx44dcXBwwMPDg9mzZ/PWW2+h0+lo1qxZue77//7v/9DpdDg4OJTrdndL9kIIIW6NTWU3QAghhLjdJk6cyNKlSwGwsrLCxcWFiIgIVq1axaZNm/j1119p3rx5uY45ZcoUFi1axCOPPGK6zsbGBl9fXwDs7e0r7gGUkb29ven+ra2t7/j9V4SrV6/StWtXjh07BoCjoyMuLi6cO3eOc+fOsWbNGjZu3EivXr1Mt5kwYQKHDh3CysoKd3d3nJycsLa2xtfXl9q1a5fr/p2dnfH19S13gS2EEEKA9GALIYS4y61du9ZUXE+ZMoWUlBSSk5PZu3cvnp6eJCUlMWrUqHIfNyUlpch19erVIyoqiqioKNq2bXvLbS+vBx54wHT/tWrVuuP3XxGGDx/OsWPHcHJyYs2aNaSkpHDt2jXCwsK4//77SU9PZ8iQIVy8eNF0m6ioKACmTZtGbGwskydPZuLEiURFRbF3795y3f/AgQOJiooiPDy8Ah+VEEKI6kIKbCGEEHe1d955B4AnnniCd955B2dnZwAeeeQRFi9eTHBwML169SI3N9d0m59//pnHH38cDw8PnJ2d6dSpE9u3bzdtHzlyJB999BEA+/btQ6fTsXfvXrNDxI0/m/syFonh4eEMGDCA2rVrY2dnR+3atRk+fDgxMTEFHsvBgwd57LHHcHNzw93dnU6dOvHNN9+Ythc3TPnSpUs899xz1KlTBwcHB5o3b867776LXq837WMcUj1gwAB27tzJPffcg4ODA61atWL37t0F2nHy5El69+6Ni4sLrq6udO/encOHDxfYZ8+ePTzyyCN4eXnh7OxMs2bNeOedd9A0rdjf1Z9//sl3330HwKJFixg+fDg2NmqwXVBQEDt27MDFxYW0tDSWLFliyvvChQsAzJs3z5RrcUPEd+3aRadOnXBycsLT05PHHnuMQ4cOmbabGyKu1+t56623aNKkCQ4ODri5ufHAAw+wc+fOYh+L0Y4dO2jfvj0ODg54e3vz7LPPFvm9rlixgtatW+Pq6kqNGjXo2LEjW7duLfXYQgghLJAmhBBC3KViYmI0QAO0jz/+uEy32bJli2Ztba0Bmq2trebo6Gg6xrJlyzRN07QJEyZozs7OGqDZ2dlpvr6+2m+//aZduHDBtO+BAwc0TdM0X1/fAl86nU4DNBsbG+3KlStadna25u/vrwGavb295uXlZTpGcHCwqV0///yzZmdnZ2qXi4uLBmjW1tbad999p2mapv3000+m28bExGiapmnnz5/XPD09NUDT6XSaq6uraZ8BAwZoBoNB0zRNmzVrlgZo9evX16ytrTU3NzfTfs7Oztq1a9c0TdO08PBwrUaNGhqgOTo6mvZzcHDQfv/9d03TNO3o0aOara2t6bYeHh6mY82aNavY7ENCQjRAs7Ky0lJSUszuM3DgQA3QmjZtqkVGRmq+vr6m35ebm5vm6+urRUZGmh5P06ZNTbf94osvTPk7OjpqDg4OpjYeO3ZM0zRN+/TTT02/C6Pp06eb2l+rVi3TY7O1tdUiIiKKzX7z5s2m+6tRo4Zmb2+vAVqjRo205ORkTdM0bcWKFabbeXp6mp5XVlZW2g8//FDq81UIIYRlkR5sIYQQd638E5j5+fmVun9OTg5jxoxBr9fzxBNPkJCQQHJyMi+99BIAkydP5vLlyyxZsoThw4cDN4ZlP/DAA2aPaRyyHRUVxeTJk009uO+++y4+Pj6cP3+eZs2a0b59ey5fvszVq1dZvnw5AL/99pvpOFOmTCEnJ4dHHnmE+Ph4EhIS6NevH3q9no8//rjYxzRx4kQSEhJo3Lgx4eHhpKSk8PnnnwPw5ZdfFukpjYiIIDQ0lOTkZDZu3AhAeno6+/fvB1RPd1JSEkOGDCEpKYmkpCTeeOMNsrKyeOONNwDVe52bm0vHjh1JTk4mISGBDz/8kMceeww7O7ti22oc6u3t7Y2rq6vZfRo3bgxAZGSkaUh+vXr1ADVEPP/P+WmaxmuvvYamaaa2X7t2jQ4dOpCZmWnKxJzk5GSaN2/Ol19+SVxcHFFRUTg5OZGbm8uRI0fM3kbTNCZNmoSmaSxbtozExEQSEhLo3r0758+f58MPPwRUjzqo51Z8fDyJiYm8/PLL9O3bl8TExGLbJIQQwjLJJGdCCCHuWvmX0NJKGJps9OuvvxIXFwfAe++9h4uLCwBLlizh//7v/8jOzmb37t288MIL5W7Ld999x6RJkwAYMWIEr776KgDNmjVj9+7d5Obm8scff3D48GFT0ZuamgqoWbWNQ87Hjx9vKj5Xr17NJ598Qo0aNczeZ05ODjt27ABg+vTpNGrUCIBnnnmG//73v/z8889s3bqV/v37m27j5ubGyy+/DFDgemNbfvjhBwC+/fZb0/FycnIANUQ9Ly+Pe+65B1BD2h955BG6du1K586d2b59e4kFdl5eHgCZmZnF7mP8PZbl95nf6dOnTQX85MmTsbOzw87Ojm3btuHi4mI6dcAc4wce58+fZ/369fz888+m4fXGXAo7e/as6QOe+fPns2DBggL779mzhylTptC2bVt27tzJ+++/z+nTp/nXv/7Fv//970o5h18IIcStkx5sIYQQd626deuavo+MjCyy/fLlyxw8eNBUrBnPW9bpdPj7+5v2c3JywsfHB4DY2Nhyt+Ps2bMMHjwYvV7Pvffeazp/G9SHAFOmTKFmzZo88MADLFy40HQ+uLFdiYmJpg8LvLy8TLf19PQstrgGiI+PNxWtxmLYqGHDhmYfT61atdDpdICaFd1YEBvv/9q1a6Y2RUdHEx0dzdWrVwHIysri2rVrdOvWjU8//ZTAwEB+/fVX5syZQ7du3ahTpw6ffvppse019jwnJyebjlnYmTNngLKNSMgvPj7e9H3+DH18fEosrkEVw0FBQTRu3JhRo0Zx8uRJbG1tgeLXQTfmBOp5ZczKODmesdh/4403mDJlCi4uLvzvf/9j4sSJtGvXjmbNmhU5r10IIYTlkwJbCCHEXat+/fqmIcXbtm0rsv3DDz/kgQceoHHjxmRlZZmWdNI0rcAs1RkZGaZCtE6dOgCmIrQ0ycnJ9OnTh6SkJLy9vdm6dWuBCbT++9//snDhQlxdXTl58iRRUVGmodZGNWrUwMpK/cmOjo42XX/u3DnWrl3LX3/9Zfa+vby8TMt1FV4/2viz8fEYGScVMyr8OI37L1++HE3T0DSNzMxM8vLy0DTNlOGIESP4888/OXv2LB9//DFPPvkkCQkJjBo1isuXL5ttb7du3UzfG3uN84uOjjZN6pZ/37Lw9PQscByjv/76i40bN3Ly5Emzt0tMTOTJJ5/k1KlTfPjhhyQlJbF3717T6Ibi5M/1+PHjpqzS0tLQNI1Tp04BYGdnx5w5c4iIiODw4cMsXbqUtm3bcvr0aUaOHFmuxyiEEKLySYEthBDirjZ16lQAtm/fzptvvklGRgagCu5FixYB0KlTJxwcHHjwwQfx8PAA1NrKaWlp5ObmMnHiRLKzs3F0dKRnz57AjUI0LS0NuDG8OT+DwcDTTz/NqVOncHBw4Ouvvy7S8/rPP/8AqtCqW7cuGRkZrF69usAxnJ2d6dChA4Dp/Oi8vDxmzJjBM888w+jRo80+dltbWx577DFADVM2zra9Zs0afvnlFwCeeuqpsocJpnW/P/zwQ2JjY9Hr9YwcORJnZ2cGDhwIwKRJk3B2duZf//oXtWrV4vnnn+f1118H1IzcCQkJZo/dsWNHunbtCsCCBQv4+OOPTb35x44do3fv3mRmZuLo6MiECRPK1e6mTZua1ghfsGAB2dnZZGRk8OqrrzJkyBDeeusts7cLDw83PWd8fX2xtbVl8+bNptEOxfVgN2jQwDQKYv78+eTk5JCUlMQ999xDjRo1WLx4MZqm8cADD+Dk5MSkSZNo27Yt//nPfxg0aBCA6XQFIYQQVcidnlVNCCGEuNNGjx5tmqnZ2tq6wEzaTZs21eLi4kz7rl271jTzc/5ZxHU6nfbRRx+Z9luyZInpGG5ubtrWrVuLzCK+ceNG08/29vZFZhTftGmTtm7dOtM++We2Nn4ZZ+/et2+fafZqGxsb0yziVlZW2q5duzRNMz+T9cmTJ00zfReeRXzo0KGmx2Nu1m1N00wzX3/66aeapmnaqVOnCsxgbjy2lZWV9vXXX2uapmlHjhzRnJycTBl6eXmZMu3YsaOm1+uL/V1duXJFa926dYFMatasafrZwcFB27JlS4HbNGjQQAO0kJCQEh9P/qwdHBxMv1snJyftr7/+0jSt6CziqampBWZhN86gbvx69913i81+7dq1Zn+3NWvW1C5cuKBpmqZ9+OGHBWZrz3/8119/vdichBBCWCbpwRZCCHHX+/DDD/nyyy/p0qULLi4u6PV6mjdvzowZM/jtt9+oVauWad9hw4bx448/EhwcbDo398EHH2THjh28+OKLpv1GjBhBcHAwTk5OODk5mc7Jzc/Y8wmQnZ1tOg/X+JWens7QoUOZO3cu9erVQ6fTERgYyP/93//h7e0NwPfffw9A586d+fHHH3n00Uext7fH2tqahx56iJ07d/L4448X+9ibNWvG0aNHefbZZ/Hx8SE7O5tmzZqxZMkS1qxZU+4smzZtyv79++nVq5cpyw4dOrBt2zb69u0LwL333svPP/9M3759qVmzJmlpaTRq1IiJEyeyc+dO03B3c3x8fDh48CDvvvsu9913H9bW1qSmpuLv78+///1vjh49WmDytfIYOnQo27ZtM40GcHJyokePHvz444+midkKM54bfd999+Hg4ICLiwsvvfSSaWZ54+/HnGHDhrFp0ybuu+8+ABwdHXniiSfYv3+/qXd79OjRbN68mQcffBBbW1vy8vJo3bo1oaGhzJ8//6YepxBCiMqj07RyTsMphBBCCCGEEEKIIqQHWwghhBBCCCGEqABSYAshhBBCCCGEEBVACmwhhBBCCCGEEKICSIEthBBCCCGEEEJUAJvKboAQQgghhBBCiMqXmgG/HoPj5yEzu7JbUzxnR2jVCB5sqb63JDKLuBBCCCGEEEJUczHxMGMVxKdA0/rg7lzZLSpeUhqciYTanjBvFHjVqOwW3SAFdiUxGAxcvnwZV1dXdDpdZTdHCCGEEEIIUY3N+dye5HQbZj9voFaNym5N6WLi4c2Prajvnctrg3Nu+jiappGamkrdunWxsrr1M6ilwK4kUVFR+Pn5VXYzhBBCCCGEENWdtRs1H9zFqwOtuMf/WmW3pswOhfvwwZfpJB54HLRbG9MeGRlJvXr1brlNcg52JXF1dQXUL9LNza2SW1NUWFgYzZs3r+xmVDrJQZEcFMlBkRwUyUGRHBTJQZEcFMlBkRyUqpDDuctWzPzYkUF9DDSqW/HHT0tLw8XFpcKP63cB/rpsxeK1V6lb8+b6jVNSUvDz8zPVZ7dKCuxKYhwW7ubmZpEFtouLi0W2606THBTJQZEcFMlBkRwUyUGRHBTJQZEcFMlBqQo5OCaCtTU4Olhja1tw2549e1iyZAnR0dH4+fkxceJEunbtSkxMDLNnz+bAgQO4u7szduxYBg8eDMDatWv55JNPuHbtGg8++CCvv/46Hh4eRe43NzeXBQsWsGPHDpycnBgzZgwDBw4sdZuRg71qt5OTK7cacUWdtivLdAmzLP1TtjtFclAkB0VyUCQHRXJQJAdFclAkB0VyUCQHpSrnEB8fz8SJE7GxsWHYsGFkZmYyYcIEMjIyGDVqFH/88QdDhw7F1dWVWbNmcebMGX744Qfmzp2Lj48PTz/9NAcPHmT69Olmj//555+zZs0aHnjgAby8vJg5cyZ///13qdssmRTYwqzw8PDKboJFkBwUyUGRHBTJQZEcFMlBkRwUyUGRHBTJQanKOcTExJCTk0OrVq3o1asXjRs3xs7Ojt9//52zZ88yduxYxo4dy6pVq/j666/x8/PjwIEDAMyaNYtp06YxZMgQ/vrrL86fP1/k+Lt27cLDw4MlS5Ywf/5803WlbbNkMkTcAun1erKzK3fhuaysLDIyMiq1DZYgOzsbTdOq/UzvOTk3PzPj3URyUCQHRXJQJAdFclAkB0VyUCQHpSrnEBQURIcOHdiyZQtbtmzBxsaGjz76iHPnzgGwf/9+Fi5ciJWVFa+//jrNmjXD29sbgO3bt6NpGn/++ScA586do1GjRgWOf+HCBfz9/dHpdKYJoC9cuFDqNksmBbaFSUlJITw8HEuY3P3kyZOV3QSLcObMGfz9/bG3t6/splSa2zEpRVUkOSiSgyI5KJKDIjkokoMiOSiSg1KVc7CysqJJkyakpKQwbNgwVqxYwZtvvkn//v0BNWHznDlz2L59OyEhIdx///0MHTqUb775htWrV7N69WoaN24MYLbzLjMzEzs7OwBsr5/8nZmZWeo2SyYFtgXR6/WEh4fj6upKnTp1KmQdNnHzNE0jOzub6OhowsLCaN26NdbW1pXdrEpRu3btym6CRZAcFMlBkRwUyUGRHBTJQZEcFMlBqco57Nmzh7Vr1xIaGkpwcDDp6emEhISYtj/33HMMGDCA5s2b069fPw4dOkSzZs3YtGkTR48excHBgaNHjzJ37lyzHzQ4ODiYeviNlw4ODqVus2RVroL7888/6dy5MzVq1KBOnTq8+uqrZGdnM3r0aFxcXAp8WVtb06NHD9NtP/vsMwICAnB2dqZ9+/am8wPMSU9P57nnnqNmzZq4u7szYsQI0tLSTNvPnDlD165dcXV1pW7duqbzAm6FcThynTp1cHFxwcnJqdxfWJX/NvJl/svZ2RlPT08aNmyIwWBg+/btpKam3vLvuSqqyucOVSTJQZEcFMlBkRwUyUGRHBTJQZEclKqcQ3R0NKBqI7jRC92qVSsAEhISAMjLywNUARweHs4bb7xBXl4eLVu2NI2KDQwMLHL8+vXrc/nyZQwGA1FRUQA0bNiw1G2WrEoV2AaDgd69ezNgwAASEhL4/fff+fbbb1m4cCErV64kLS3N9PXVV19Ro0YNlixZAsDevXsZN24cn332GUlJSQwbNow+ffoUe57xK6+8QmRkJGfPnuXs2bNEREQwZcoUQE0Z37t3b+677z7i4+PZuXMnK1asYPPmzRXyOG+25zozG95Zpy5FxTH+PiIjI9m1axd6vb6SWySEEEIIIcTt16FDB6ytrVm6dCkLFizgk08+oWbNmrRr1w5fX19Wr15NSEgIb7zxBg4ODjz88MO4ubmxfft2Zs2axdy5c9m6dSv3338/fn5+pKam8sEHH/D9998D0KNHDxITE5k0aRIzZswA4PHHHy91myWrUgV2YmIiMTExGAwG0znKVlZWquc2n2vXrjFs2DCWLVtGixYtAFi9ejVDhgyhU6dO2NraMmHCBLy8vNi4cWOR+8nIyGDdunXMmTMHT09PvL29WbBgAZ9++ikZGRns27ePmJgY5syZg52dHW3btmX8+PEsX7789odQjMxs6Dcd5n2uLqXIrng1atQgNjbW9AledVK3bt3KboJFkBwUyUGRHBTJQZEcFMlBkRwUyUGpyjk0b96cJUuW4O7uzoYNG/D392flypW4urqyatUqgoKCWL9+PQaDgeXLl1O3bl28vb2ZP38+WVlZbNmyhW7duvHuu+8CkJycTGhoKN988w0Ao0aNYuTIkRw4cIC4uDjmzp1L27ZtS91myarUOdg1a9ZkwoQJTJo0iddeew29Xk/fvn2ZMGFCgf2mTJlC+/btGTZsmOm6EydO8PzzzxfYr3nz5mbXUjt79iy5ubmmoQ/GfTMzMzlz5gwnTpwgMDDQdNK9cXv+8xEKy87OLjAzeEpKStkfeCmMxfWeI+rnPUfUz1vng+Mtzss1Y8YMjh8/Tm5uLhEREaZJCgYNGsSwYcM4f/48ixcvJiIiAgB/f39mzpyJs7MzDz/8MDt37izwpnLq1ClefvllfvjhhwIzc3fp0gUHBwdsbW3R6/XUrFmTkJAQ6taty4YNGwB4+umnadq0KadPn+b9998HYNy4ccW2fdmyZTz44IO0b9/+1kK4zsrKCoPBUC17sI3Dfqo7yUGRHBTJQZEcFMlBkRwUyUGRHJSqnkNwcDDBwcFFrg8ICDD9n15Ynz596NOnj+lnYx1Ur149Tp8+bbre1taWadOmMW3atCLHKGmbJatSBbbBYMDR0ZHly5fz/PPPEx4eTr9+/UzDD0BN3b5mzRoOHz5c4Lapqak4OzsXuM7JyanAedX59wUK7G/sJU9LSyvXsYxCQkKYPXt2kevDwsJwcXGhWbNmpiI1P02DjCzzx7S2Ao2CxbVR/iJbB+gNRW/v5AClrT41b948AKKiohgxYgTbtm0zbYuNjWXEiBHMmTOHLl26ALB27VpGjRrFtm3beOyxx9ixYwcvvvii6TZbt26lf//+Zpe9WrVqFfXq1QPgk08+YeHChbz33ns8/fTTJTeyGL///jsdOnS4qduak5ycTGpqKnq9nuPHjwPg7e2NjY0Nly9fBtQbzZUrV0hLS8POzo6AgADCwsIA8PLywsHBwXQOSaNGjbh27RopKSnY2NjQrFkz03E9PT1xdnYmMjISUB9cJCYmkpycjLW1NUFBQZw4cQJN0/Dw8MDNzY1Lly4B6nyV1NRUEhMT0el0tGjRgpMnT6LX63F3d8fT09O0xIGfnx8ZGRnEx8cD0KJFC86cOUNubi6urq54e3tz7tw5UlJSsLW1JScnh6tXrwJq2Ybz58+TnZ2Ns7MzdevW5ezZswDUqVMHg8FAbGwsAE2bNuXSpUtkZWXh6OhI/fr1TW+uPj4+6HQ6rly5AkCTJk2Ijo4mIyMDe3t7GjZsyKlTp2573i4uLgU+KEpKSiIpKQkrKyuaN29OWFgYSUlJ5OTkUKNGDS5evGjKOy0tzXQOUsuWLTl16hR5eXm4ubnh5eVlWvexXr16ZGVlce3aNUB9MBceHk5OTg4uLi7Url3bdJ5W3bp1ycvLIy4uDoBmzZpx4cIFsrOzcXJywtfX15R37dq10TStQN4RERFkZmbi4OBAgwYNCuRtZWVFTEyMKe/Lly+Tnp6Ovb09jRo1Mp0rVatWLezs7EznXzVu3Ji4uDiio6NJTEwkMDCQEydOAOoDUCcnJ9NztmHDhiQkJBT7nHV1dTXl3aBBA1JSUop9znp4eJjy9vPzIz09/Y7kffHiRbKysnBycqJevXqcOXPGlDeoc+ri4uIIDAwkKiqKjIwMHBwc8Pf3vyPPWUt5jzDm4OvrW63fI86dO0dcXBw1atSo1u8RFy9eJC4uDltb22r9HhEVFWXatzq/RyQlJZlyqM7vEQaDgWvXrmEwGCz2PSIh0YqsLEdSU/Mw1HImMzMTvV6PtbU1jo6OpjrH3t4enU5HVpYqUJydncnOziYvLw8rKyucnZ1NtZRx37y8PGxt7U1LEZvb187ODmtra9Ms4U5OTuTm5pKbm4tOp8PV1ZXU1FQ0TcPW1hYbGxsyMzNJS7PCYHDkypWrpFy9dlPvEcbXboXRqpAvv/xSa9q0aYHr1q5dq3l6epp+njlzpvavf/2ryG1bt26tvf/++wWu69+/v/af//ynyL5//vmnBmipqamm61JSUjRAO3r0qLZkyRLt3nvvLXCb//3vf1qNGjWKbXtWVpaWnJxs+oqMjNQALTk52bRPenq6duTIES09PV3TNE0zGDTtoZc1zapz0a8eEzUtIUVdmttelv0eHqvuoywiIyO1Rx99tMB1S5Ys0SZNmlRk3x07dmhZWVna4cOHtd69e5uuz8vL0x566CHt8uXLRW7z6KOPapGRkaaf33vvPW3u3LmapmnasmXLtGXLlmmapmmBgYFFrvv111+1gQMHak8++aT20ksvabGxsdqXX36p3XPPPVq3bt20sLAwLSIiQnv++ee1J598Uhs0aJD2xx9/lO2Bazd+L2vXrtWWLl2qJSQklPm2d4tjx45VdhMsguSgSA6K5KBIDorkoEgOiuSgSA5KVcjh5EVNe2KKpl2Mqfhj5+Vp2qlL6rKihV1Q7b505eaPkZycXKQuuxVVqgc7IiKiwDBrUEMH8g/V3rJlC6+99lqR27Zs2dL0KapRWFgYPXv2LLJv06ZNsbW15cSJE6Ye0LCwMOzs7AgMDCQuLo4zZ86Ql5eHjY2NaXvLli2Lbbu9vf1NraNcXA/zsv/A0k1Fe64L23NE7bfsPxA0vNx3X6JTp07RsWPHItf36tULgPvuu4+cnBxOnz5N06ZN+eWXX2jatCl16tQxe7wXX3wRW1tbEhMTycjI4Isvvii1DYmJiSxcuJDPPvsMd3d3/ve///H222+zbNkyvv76a1555RWCgoIYNmwY06dPp0WLFkRERPDcc8/x7bffmn5/omTNmjWr7CZYBMlBkRwUyUGRHBTJQZEcFMlBkRyUqpCD0/XVr5IreKohvR5+PwXXkiExFe5rBhW56q2xvU63eFpsRapSk5z16NGDmJgY5s+fj16v5/z587z99tsMH64qx/j4eE6ePEnnzp2L3Pb5559n3bp1/PTTT+Tm5vLee+8RGxtLv379iuzr5OTE4MGDmTp1KlevXuXq1atMnTqVp59+GkdHRx599FG8vLyYOnUqWVlZ/P333yxbtowXXnihQh+vTgf73oeUb4p+1feGacOheymnF3dvr/ar7130GPveL32IeMnt0xX4cMOc/v37s337dkANDx84cGCx+65atYpt27axf/9+XnnlFcaMGYPBYGZsez5Hjx4lOjqaESNG0LdvX/773/8WGWqfnp7O33//zfTp0+nbty/jxo0jNzfXNPxNlM44rKa6kxwUyUGRHBTJQZEcFMlBkRwUyUGp7ByKO900v3q1wMMVfvmn4u43f3EN6vL3U+r6iqBpqr11vaCme8UcsyJUqe675s2bs2PHDmbOnMnChQtxd3dn+PDhzJo1C8B0Poivr2+R23bt2pUPPviAMWPGEBUVRYsWLdi9ezeenp4AzJ8/n3Xr1pl6uT/44AMmTZpEq1atyMnJoW/fvqZZwm1sbPjuu+8YO3YstWvXxsXFhfHjxzNy5MgKf8w6HTg7Fr9963zz52CDKq4rYqKz4rRq1Ypjx44VuX78+PFMnjwZPz8/nnzySYYOHcqLL77I33//zaJFi8p07KeeeoqQkBCSkpJK3E+v13PvvfeycuVKQC1CX3gCOYPBgJ2dXZHzx2vVqlWmtghM59lUd5KDIjkokoMiOSiSgyI5KJKDIjkolZmDcRnfacNLrgmsrKDvw/B/u1QB/GArcHNWczndDL0Bjp1Tvdb5xVyDqDho1VjNJ3UzNCApFfb/rb7GPHlrnYYVrUoV2ADdunWjW7duZre1b9/etHyXOcOHDzf1dhc2ffp0pk+fbvrZOPX8qlWrzO4fEBDAt99+W46W3x6O9uaL7NtdXIOaSbxPnz78+OOPdOnSBU3T+Pjjj4mIiDB9yOHj40NAQACLFy8mODgYW1vbMh37wIED1KlTx/QBSHHatGnDjBkzOHfuHI0bN+bjjz/m+PHjrFixAmtra/R6Pa6urvj7+7N161b69evHkSNHePXVV/npp59K7YEXSuGl8KoryUGRHBTJQZEcFMlBkRyUqpiDXg9ZORV8UGtn0jMr5lD2tlBVz+yrrOdD/pWGDoeVXhv0e1gV1Nt/Lf00VL0BcvMgT3/jMk+vhn/XdIMLMZCaUfztXZ2gcd1bK4x9PGH0k/B40TNWK1UVfZqK/AoX2XeiuAY1e+gnn3zCggULWLp0KXq9nsDAQFatWoWV1Y2PpAYMGMC4cePYuXNniccznoNtZWWFtbU1S5YsKVMbQkJCmDhxIgaDgZo1a7Jw4UIA/vWvf/Hmm28SEhLCokWLeOutt/jkk0+wtrYmNDRUiutyMM7uXt1JDorkoEgOiuSgSA6K5KBUpRyOnoWv9sM/56CUM/PKTdMaVGjPYtP60LMjPNqu4o55J1TG86G8y/gaDBCfAk3qwdDucCYCYuLhWgpcS1Lb4pPVMO/4FFVUm/PdEvj6Z/jmUOlt7HovjO57czWLk70qsC2p59pIp5XU5Stum5SUFNzd3UlOTsbNzQ2AjIwMTp48SVBQ0E190pWZDSFrSx8CIsrH+Hs5deoUV69e5dlnn8XDw6Oym3VHHT9+vMRJ/KoLyUGRHBTJQZEcFMlBkRyUqpLDH6dh3ufQ2Bc6t1Hn4FZkwZKenl5kaduboWmQlgmHwlSbRz+pCu2q4k4/HwoX1/l1bw+b5sLKr+GHP+BqEsQmwNXk8p8b7e4CPh7qq9b1ywdaqKHmA2aW3At+pzoEy8JcXXYrpAf7LuJoD1OHWcYTVQghhBBCWLZNP0KgH7z979sz/DolRU8F1CsmPe6HZV/CF99D8P3qvGGhaJoqlF2d4KkSits9R2DQG7DhLfjxD/g7vOD2mu43imbv618+HuDteeN6H0/wrgEOJdQclTlPVGWTAvsuY5xiX4iKVLt27cpugkWQHBTJQZEcFMlBkRwUyUGpCjlkZsPJS/DKU7fv3GYHh4r9x1Sng+73qZ7X85choIqMxPdvFFShx0tOg2Pn1SRix87f+Dq8Cj74uuzL+K59A46cvlE016oBthX0XKjMeaIqmxTYQgghhBBCVDPGyce8LGh5o7IwtjetgiZPu90ys2HhBuubOoUzLw/ORsE/hYrpS1fM7z9xuSqaD4eVPjzb2J7gDuVrU3lU1jxRlU0KbCFEqa5cuYKXl1dlN6PSSQ6K5KBIDorkoEgOiuSgVIUcjJMwWZk553rPnj0sWbKE6Oho/Pz8mDhxIl27diUmJobZs2dz4MAB3N3dGTt2LIMHDwZg6dKlpmVTjXbs2EGTJk0KXJednU27du3Iy7sxS9aAAQOYN28eubm5LFiwgB07duDk5MSYMWMYOHBggdtb4qRWxSnPLN6xCTd6pY0FddglyC5mZnc/b7XUVatG6qt1YzXc39bGsoZnG4vs6jRPlBTYd5vcDLCtektDCCGEEEKIyhcfH8/EiRPx9/dn2LBhfPvtt0yYMIGDBw8yatQoYmNjGTp0KPv372fWrFm0bduWwMBATp8+jaenJ88884zpWC4uLkWOf/bsWfLy8ujZs6ep+G7evDkAn3/+OWvWrKFnz55ER0czc+ZMAgMDadOmzU0/noysyjmFsqRZvKOuwq/H4Ph5NXv7sfMQl2j+OM6O0KqhKqZbN4aW1wtqD9fi79vShmc72sOkQbk42pdtud6qTgrsu0luJuwPgc7Twdaxslsj7iKBgYGV3QSLIDkokoMiOSiSgyI5KJKDUpVziImJIScnh1atWtGrVy/Cw8NJSUnh999/5+zZs0ybNo0BAwYwfPhwUlNT8fPzA+DUqVO0bNmSQYMG4ebmhp2dHQYz636dOnUKgKFDh9K4cWM8PT1N23bt2oWHhwdLlizh3Llz9OrVi127dt10gZ2ZDe+su/M9p8XN4m0ssje8pSZqy79dp4MA3+uFdKMbBbV/7ZubzM3Shmc72lefhaukwL5b5GbCur5wbg9EHoJh2yqkyJ4xYwbHjx8nNzeXiIgIGjduDMCgQYMYNmwY58+fZ/HixURERADg7+/PzJkzcXZ25uGHH2bnzp3UrVvXdLxTp07x8ssv88MPP6DLN8anS5cuODg4YGtri16vx8XFhVdffZUHHngAgFGjRvH222/j4+Njtp2lbRe3JioqikaNGlV2Myqd5KBIDorkoEgOiuSgSA5KVc4hKCiIDh06sGXLFrZs2YKNjQ0fffQR586dA2D//v0sXLgQKysrXn/9dZo1a0ZycjIxMTEkJCTQqVMn7O3tmTRpEgMGDCiyTJexwH7hhRfIzs6mdevWLFu2jDp16nDhwgX8/f3R6XSmwv3ChQs39TjKMzy7LDKyIDZRDeU2XhqXuIpNVD3QjX1h0cvw9Fslz+L99FuwcTas/x7sbFVB3dxf9VZXJEsanl2VXxPlJQX23SB/cQ3qcl3fCimy582bB6gXxYgRI9i2bZtpW2xsLCNGjGDOnDl06dIFgLVr1zJq1Ci2bdvGY489xo4dO3jxxRdNt9m6dSv9+/cvUFwbrVq1inr11HSQ//zzD6NGjWL9+vU0btyY//73vyW2s7Tt4tZkZGRUdhMsguSgSA6K5KBIDorkoEgOSlXOwcrKiiZNmpCSksKwYcNYsWIFb775Jv379wcgMjKSOXPmsH37dkJCQrj//vvR6XTUr1+fli1b8vDDD/Ppp58SEhJC06ZN6dix4ILVubm5+Pv7M3ToUBISEli5ciWzZ89m5cqVZGZmYmdnB4CtrRpOnJlZ/tnMShqebSwyNQ1SMwoWzXHG4vl6wRyXeGN7WSZV+2iymp27LLN4L96olte93cPXLWUZ36r8migvKbAtnaap86rN0VkDWsHi2ih/kY0ONDMrx9s63dJMEevXr6djx46m4hpg+PDheHh4kJOTw4ABA5gzZ46pwNbr9ezatYtNmzaVeuzWrVsTHBzM5s2bmTp1Kl26dOHzzz9n3LhxvPXWW7Rp0wZN0+jWrRtr1qxh+PDhfP755+j1eqZMmUJmZibW1tbMnDmTdu3asXv3bv7v//6PrKwscnNzmTNnDu3bt2fTpk2sXbsWvV5P48aNWbhwYYUvKXE3kEwUyUGRHBTJQZEcFMlBkRyUqpzDnj17WLt2LaGhoQQHB5Oenk5ISIhp+3PPPceAAQNo3rw5/fr149ChQzz77LPs2XPjf1E3NzfGjh3LX3/9VaTAnj17doGf9+7dy6FDhwCVW06OmtXLeFneLEsbnv3FbBgfClv2QlYxE4gVx8HuxhrQ3teXtvLJt050ZrYqZsszi/edYAnL+Fbl10R5SYFtyTQNVj8EEb8V3da4OwzeCBsHFy2ujYxFdnH71e8E//75povsU6dOFXnTBOjVqxcA9913Hzk5OZw+fZqmTZvyyy+/0LRpU+rUqVOm4wcGBrJ3794C1/Xt25cdO3bQpk0b/vrrL+rWrVtgCPr69evp2bMnI0aMYN++ffz555/cc889bNiwgQ8//BBPT0+2bt3K6tWrad++PYsXL2bPnj24ubmxYMECzp8/b5poQ9zg7+9f2U2wCJKDIjkokoMiOSiSgyI5KFU5h+joaADS09OBGz2PrVq1AiAhIQHANAu4g4MDR48eZdu2bTz11FO0bNmStLQ0QBXahS1fvhyDwcD48ePRNI309HQcHdWIy/r163P58mUMBgNRUVEANGzYsEztzswCgwZPzSx5ePaQWeoc6LgE9bOL441C2fv6V/6i2SdfIe1axr4pS5rF21JU5ddEeUmBbfGKeRX3Xg6/LSm+uDY6t0ft13s5hDat2JbpdKZhPMXp378/27dvp2nTpmzdurXIUgulHd/4hmvUu3dvBg4cyLRp09i5cyd9+vQpsL1z585MmjSJf/75h86dO/PMM89gZWXF8uXL+fHHH7l48SKHDx/G2toagK5duzJo0CC6dOlCjx49pLguhnHikupOclAkB0VyUCQHRXJQJAelKufQoUMHrK2tWbp0KeHh4WzevJmaNWvSrl07fH19Wb16NampqRw8eBAHBwcefvhh0tPT2bx5M/v27aNnz558/fXXuLi48NBDD5GamsqaNWsIDAykW7duXLhwgR07dhAVFUVWVhaRkZGm0Y49evQgNDSUSZMmcfnyZQAef/xxs+3U6+HgCfjhD/j+CKyaDGu+K9vw7KWbYNMcsLG+Pb27ljaLtyWoyq+J8rqJOenEHaPTqR7mN9KKfrnXV7OFN+5e8jEad1f7udcveoxb6L0G9UnmsWPHilw/fvx4IiMjAXjyySfZvXs3KSkp/P333wWGk5fm5MmTBAQEFLjOy8uLgIAADh06xE8//URwcHCB7Z06dWLXrl08+OCD7Nq1i9GjR5Oens6AAQOIiYnh/vvvZ8SIEab958+fz5IlS3Bzc2Py5MkFzjEXQgghhKhumjdvzpIlS3B3d2fDhg34+/uzcuVKXF1dWbVqFUFBQaxfvx6DwcDy5cupW7cuTZo0YenSpTg6OvLZZ5/h5eXFqlWr8PDwIDk5mdDQUL755hsAZs2axRNPPMHevXs5ePAgzzzzDOPHjwfUpLUjR47kwIEDxMXFMXfuXNq2bQuogZ1pmRARC5dioct/oNPL8ObHsP9vGPceTBykitiSGIdnuznf3qHTxiLb2J7qXFxXN9KDbel0OrBzLn77sG3mz8EGVVxX0Gzi5gwaNIg+ffrw448/0qVLFzRN4+OPPyYiIgJfX18AfHx8CAgIYPHixQQHB5smrCjNn3/+yffff89XX31VZNuTTz7JokWLaNWqFa6uBRcBnDVrFs2bN2fw4MF07NiRfv36cfHiRaysrHjppZfQNI3XX38dvV5PZmYmTzzxBJs2bWL06NHk5eVx8uRJ+vbte+vh3GW8vb0ruwkWQXJQJAdFclAkB0VyUCQHparnEBwcXKQTAyAgIIANGzaYvU337t3p3r1gx092djb16tXj9OnTpuvc3Nx49913zR7D1taWadOmMW3aNHX7XIi+BteS4FqyOsc5OQ1S0lWxXcMFutwLXe9VBWwNV8sanm1Js3hXtqr+migPKbCrOltH80X2bS6uAWrVqsUnn3zCggULWLp0KXq9nsDAQFatWoVVvgX7BgwYwLhx49i5c2eJx3vxxRextbVFp9Ph7OzM+++/b/Z87a5du/Lmm28yduzYItuef/55pkyZwvr167G2tuatt96iWbNmNG/e3LQU2EMPPcSRI0dwcHBg9OjRDB8+HAcHB9zd3XnnnXduPZi7kI2NvFWA5GAkOSiSgyI5KJKDIjkokoNibVPyqYSF6fWQkKoK6qvJqpDOT6cDD1eo7Qlz/w1Pd4XrZ/2ZWNrwbEd7eP1pPY721qXvfBerTq8JnaZp1WfVbwuSkpKCu7s7ycnJpgkgMjIyOHnyJEFBQTg5OZXvgPmX6roDxXV1Yvy9nDp1iqtXr/Lss8/i4eFR2c26o44fP15tzpspieSgSA6K5KBIDorkoEgOSlXI4VoSPP8OzHoO7q3YaXoAVSyHR0OAb9Ei2EjTVBF9NVm1JyFFTVSWn6sT1HIHrxrg6ab2e3GRKrDbBJg56HX5ZxOv7OHZVeH5cLtZcgbm6rJbUX0+SrjbGXuy989X51xLcS2EEEIIUW1lZJV8jrGbM9jaqHOaK7rA1uvh91NqWHdiKtzX7EaRnZGlrr+apC5z8wre1t7uRkHt5a6WxsovMk5dermX3AYZni0qixTYdxNbR+g8TYprUeEKTzZXXUkOiuSgSA6K5KBIDorkoFR2DpnZ8M66kgtLO1toFwjfHIJH26rzlytC/uIa1OXvp1QRf+IiRMUV3N/aCmq63yiqXRyLn4M3Jxe27AM/b6jrVXpbHO3VutSVXVxX9vPBElSnDKTAvtvYlnNouRBlcOXKlWq1fmFxJAdFclAkB0VyUCQHRXJQKjOH/EOjD4eVPDR65OMw47/w0ruq2PZwvbkFZgwGNbTbSgeXrqgJyAr73y9Qv7bqMTcYVCHt6qTaFnW15ONrGqRnwp9nISsHZo0seztv50zhZSWvi+qVgRTYQohSpaWlVXYTLILkoEgOiuSgSA6K5KBIDopHTd9Kud/8xTWoy37Tiy+yfWvBuy/D93/A0bM3hl+D6i3OzIbMHHWZnXPj56zr32dd/9nbAx65B/YdhcvXim9fXS/o0k6tP52WCVcSyv7YHO3VbbveC/V9yn47SyCvi+qVgRTYd5lMfRaO1hbwUZ24q9jZlW8W0LuV5KBIDorkoEgOiuSgSA6q8Fy8yfaOn/dbuLg2yl9k5+Sq3uLYRIhNgLhE9b3pMuH6ZZIqqMvq8CpY+x2ciy55v3PRMKSrGrZtCT3Ld4q8LqpXBlJg30WyDNksj/mccXWfxcFKZnIQFac6nTdTEslBkRwUyUGRHBTJQanuOZRneHZFyMuDlAw1EVj/GebXfoYbRfaGt2DS8uL3K8zNGXw81Je3J3jXAB9P1WPt46G+9/GAujXV+d6Hw0o+dvf21XPCser+uoDqlYEU2HeJLEM2z515nf0ph/kz7QSfBi6UIltUmLCwMItdWuFOkhwUyUGRHBTJQSlPDvHJ8McZSE5T55ZaIjtbCPSDoAblOyfXEp4Ppc2efbuUd3h2cbJzVA9ybMKN3mVjD3P+XufYBIhPgbA1qve4tKJ5zxFYugmWT4Anp98omn08VMFcuGj29ih/IWxu/Wmjyl4qqzJZwuuislWnDKTAvgvkL64B9qcc5rkzr1dIkT1jxgyOHz9Obm4uERERNG7cGIBBgwYxbNgwzp8/z+LFi4mIiADA39+fmTNn4uOjTo4pbbvRoUOHGD16NPXr11ePKSuLdu3a8dprr1GzZk2OHTvGF198wbx588y2s7TtQgghRGXTNPhkF2z7WRWtLo5gZVXZrSpK09Q5tjm56hzdOS9ArRqV3aqyKcvs2bfrfksbnp2cBhevmC+Urybd+D6pnKeqvroM1r9Zvt7j45+X+yGWiXFprMJZVOfiWlQ/UmBbOE3TyDRkmd1mhRXoKFBcG+UvstHAgKHI7R2tHNCV8rG0sWCNiopixIgRbNu2zbQtNjaWESNGMGfOHLp06QLA2rVrGTVqFNu2bSMuLq7E7YXvu2XLlqxZs8b0uBcvXsyrr77K2rVradWqFa1atSq2naVtF7fGy6sMa2FUA5KDIjkokoMiOShlyeGbQ6q4HhEMj3cAZwteVVPTIOwivLcZ5v4fhL5atp7synw+3Mnh2ZoGKemQmqFm3n5qZtmGZ7/1SdmGZ9tY3+hR9s7Xq1x4qLaPh1oP2tracnqPCxfZUlzL+yRUrwykwLZgmqbx5MmXOJJ2rMi2zm7382HAXMacfaNIcW1kLLI/DJjLmPCi+93n0pqtQStLLbKLs379ejp27GgqngGGDx+Oh4cHOTk5pW63ty/+nVan0zF+/Hg6derE6dOnSUpKYvny5cycOZP//Oc/7N69G4CDBw/y4Ycf8vLLL7N8+XLWrFnDpk2bWLt2LXq9nsaNG7Nw4UJsbGx46623OHv2LPHx8QQGBrJ48WJ0Oh2vv/46ERER5OTkMGLECIYMGXJTedzNHByq0UwkJZAcFMlBkRwUyUEpSw4//QX3B8GAf93+9twqnQ5aNITRfWH2p6rntWGd0m9XWc+HihierWmQkHKjJ7nE4dmJaij3ybWwanvZh2evmAjD5pgvmn3yDdP2cC3/6AZL6j02tiVkbfU857oweZ+sXhlIgW3hdJgvfuf5T+K/V74otrg22p9ymP9e+YJ5/pN4+J/BFdq2U6dO0bFjxyLX9+rVq0zbS2NnZ0eDBg04d+4cNWvWBKBp06bY2dlx8uRJgoKC2LlzJ3379i1wu8WLF7Nnzx7c3NxYsGAB58+fJz09HWtrazZu3IimaYwcOZL9+/djY2ODwWDg66+/JjY2lsWLF0uBbUZUVBQ1atSo7GZUOslBkRwUyUGRHJSy5HD+Mgx/7M60p6K0aqQuz18uW4FdGc+H0oZnfzVPLQd1LjrfjNkJN85zzj+Tdp6+fPc95UP4dHr5hmcf/KjcD7HMLKn32NEeJg3KxdHe9s7fuYWR98nqlYEU2BZMp9OxNWil2SHiVlgxru6z/Jl2osQiu7Pb/Yyr+yxocPbeHwtsK8sQ8dLaV9KU+6VtL+t9ODoWHEPXt29fdu7cSUBAAPv372fKlCmcOHHCtL1r164MGjSILl260KNHD5o3bw6Aq6sr69at49y5c5w/f56MjAwefPBB3n77bf7973/TuXNnXn/99VtqrxBCCFGc3Dw123Nhe/bsYcmSJURHR+Pn58fEiRPp2rUrMTExzJ49mwMHDuDu7s7YsWMZPPjGh+V5eXkMHDiQ+Ph49u/fb/Y+s7OzadeuHXl5eabrBgwYwLx588jNzWXBggXs2LEDJycnxowZw8CBAwvc3s5W9Wbn5hU+smUorrg22nNEza694S1Y/EXZhmfXcCk40VeBScA8C04EZpxMzVKGZ4Nl9R5HXjqNezWZ2EoIIymwLZxOp8PJuviTtD4NXGj2HGxQxfXtnE28VatWHDtWdPj6+PHjmTx5cqnb/fz8Sjx+Tk4O586dIyAggMuXL5uu7927N8OGDePee+/l3nvvxcXFpcDt5s+fT1hYGPv372fy5Mm88sorODs7ExoaynPPPcdTTz1FUlISmqbh4+PD7t27+fXXX/n555/p168fO3fuxM3N7SZTuTs1atSosptgESQHRXJQJAdFclBuNof4+HgmTpyIv78/w4YN49tvv2XChAkcPHiQUaNGERsby9ChQ9m/fz+zZs2ibdu2BAYGkpKSwvTp0wkLCysycWh+Z8+eJS8vj549e9KkSRMA0wfPn3/+OWvWrKFnz55ER0czc+ZMAgMDadOmzU09FoCGjZvf9G1Lk54JJy7CsfNw7BxMGAQf7yz78OwPJ8GLiwoWyoW/964B9jfRN2BJw7ON7Zk6rPKHZsv7gyI5VK8MpMCu4hys7M0W2be7uAY1k3ifPn348ccf6dKlC5qm8fHHHxMREYGvr2+p20ui1+tZunQp999/P35+fgUKbG9vb+rXr8/777/Pq6++WuB2mZmZPPHEE2zatInRo0eTl5fHyZMnTf9c9O/fn8jISA4fPswDDzzA9u3b+eWXX1iwYAGPPPIIBw4cICYmRgrsQq5du2aa4b06kxwUyUGRHBTJQbnZHGJiYsjJyaFVq1b06tWL8PBwUlJS+P333zl79izTpk1jwIABDB8+nNTUVPz8/MjIyKBLly7Y29vj7Oxc4vFPnToFwNChQ2ncuDGenp6mbbt27cLDw4MlS5Zw7tw5evXqxa5du266wM7MhgXrrW6519RggAsx8M+568X09YI6PLrgsmanLqme6fIMz96z9ObbVRpLGp4NlbNUWWHy/qBIDtUrAymw7wKFi+w7UVwD1KpVi08++YQFCxawdOlS9Ho9gYGBrFq1Cisrq1K3F3b8+HHT+dQGg4GWLVuyaNEis/fdt29fQkJC6NSpU4HrHR0dGT16NMOHD8fBwQF3d3feeecdkpKSmDhxItu2bcPJyYl27doRFRXFyy+/zA8//ECvXr2wtbWlR48eNG3atOLDquJSUlIquwkWQXJQJAdFclAkB+VmcwgKCqJDhw5s2bKFLVu2YGNjw0cffcS5c+cA2L9/PwsXLsTKyorXX3+dZs2akZaWRt++fRk9ejSDBw8uMPy7MGOB/cILL5CdnU3r1q1ZtmwZderU4cKFC/j7+6PT6Uyjyi5cuHBTj+NmZ/BOTL1RQP9zDo6fh2MXVG+1OT6e0LIhtG4MrRqrNcVleLblkvcHRXKoXhlIgX2XMBbZ71/+jHF1n63w4rpevXr8+OOPRa4PCgri//7v/4q9XWnbjTp06MBff/1V4vYOHTqYfu7Tpw99+vQxu33AgAEMGDCgwO19fHzYuXOn2WO/9957pbavurOxkbcKkByMJAdFclAkB+Vmc7CysqJJkyakpKQwbNgwVqxYwZtvvkn//v0BiIyMZM6cOWzfvp2QkBDuv/9+mjVrxhtvvFGm4+fm5uLv78/QoUNJSEhg5cqVzJ49m5UrV5KZmWmaK8XWVk1ElZlZTGVbgrLM4J2bB2cib/RKG4vpyDjzx7S3gxb+apK1Vo1UMd2qkSqwzbG04dmvDZbJvUDeH4wkh+qVQfV5pNWAg5U9r9QZcdt7rkX106xZs8pugkWQHBTJQZEcFMlBudkc9uzZw9q1awkNDSU4OJj09HRCQkJM25977jkGDBhA8+bN6devH4cOHSrXfc2ePbvAz3v37uXQoUOAWjYnJycHwHRZ3qV0SpvBe9McmPoRfLoLcnLNH6NBbVU8t24MLRtB60bQpB6U5/9xSxue7eYixTXI+4OR5FC9MijnCnvC0jlaW8AJN+Kuc/z48cpugkWQHBTJQZEcFMlBudkcoqOjAUhPTwcgIyMDUBOJAiQkJACYhoGXtwBevnw5y5YtA0DTNNLT002rc9SvX5/Lly9jMBiIiooCoGHDhmU+dllm8B70JswbBY+0AVcneLAlvNQHlk+Afe9Dwk44vxG2hcDcf8PgLhDkX77i2shYZM8YUbnFNcjrwkhyUCSH6pWB9GALIYQQQlSSDh06YG1tzdKlSwkPD2fz5s3UrFmTdu3a4evry+rVq0lNTeXgwYM4ODjw8MMPl3i81NRU1qxZQ2BgIN26dePChQvs2LGDqKgosrKyiIyM5MUXXwSgR48ehIaGMmnSJNNkoo8//nipbT5/WRXL728p+wzem+aAiyOYmYKlQlnK7NlCiOpLerCFEKXKP+tsdSY5KJKDIjkokoNyszk0b96cJUuW4O7uzoYNG/D392flypW4urqyatUqgoKCWL9+PQaDgeXLl1O3bt0Sj5ecnExoaCjffPMNALNmzeKJJ55g7969HDx4kGeeeYbx48cDMGrUKEaOHMmBAweIi4tj7ty5tG3btsgx9Qb45R8Y/S40eVp9PTNXLZPVvX3Jj884g7eb8+0vro0sYfZseV0okoMiOVSvDKQH+y6jZWSgc3Kq7GaIu0xpy8BUF5KDIjkokoMiOSi3kkNwcDDBwcFFrg8ICGDDhg0l3rbwBKT16tXj9OnTpp/d3Nx49913zd7W1taWadOmMW3atALX6w1qdu9rSXA1Cc5Fw+6DN7bbWENWjrpuyzx4aoZlzOBtSeR1oUgOiuRQvTKQHuy7iJaZSXrIYrSbmAFUiJJERkZWdhMsguSgSA6K5KBIDool5KDX39ztNA2S01UhfSgMvj0MB0+odaeT09X2+t4wfgD87x24tgP2vg9Du4OzgyqiC/dkV+fiGizj+WAJJAdFcqheGUgP9l1Cy8wkqe9gcvb8SO6h36mxbSO665OY3IoZM2Zw/PhxcnNziYiIoHHjxgAMGjSIYcOGcf78eRYvXkxERAQA/v7+zJw5Ex8fH4BStxsdOnSI0aNHU79+fTRNIycnh8cee4z//Oc/WFlZMWrUKN5++21+/fVXDh8+zDvvvEOXLl34/PPPqVevntm2p6amMnXqVFasWHHLOQghhBCWTK9XBXGAL1hbl75/Zrbqnb6WrL4Kz/Btbwte7uBVAxr7wrinILiDuSNZ3gzeQghRmaTAvgvkL64Bcvb8SFLfwRVSZM+bNw+AqKgoRowYwbZt20zbYmNjGTFiBHPmzKFLly4ArF27llGjRrFt2zbi4uJK3K7T6QrcV8uWLVmzZo16DDk5DBkyhHvvvZdHHnmE//73v+Vue3JyMidPnrypxy0K8vf3r+wmWATJQZEcFMlBkRyUsuRgZ6sK24qm18Pvp1ShnJgK9zUrWmTn5kF8yvVh38mQXmiwm7UV1HRTBbWXu5rETKeD7By1za6UVaeMRXbIWnXOdXUvruV1oUgOiuRQvTKQAtvCaZoG15fsKMLaGjStQHFtlL/IRqczP27MyalIkVse69evp2PHjqbiGWD48OF4eHiQk5NT6nZ7++L/+mZnZ5OXl0fNmjUBTL3V5hgMBhYvXsxvv/1GXl4ewcHBjB07ljlz5hAXF8fo0aNZuXIlO3bs4JNPPkGv1xMQEMDs2bNxcXG56cdfnSQmJkpWSA5GkoMiOSiSg6LpSp9Zq0k9+Dsc+nWuuPvNX1yDuvz9lCqy07MgJl5dl5SmhnrnV8MFatVQBbWHq/lJyI6G32h7aRztYfKQPBzt5d9LeV0okoMiOVSvDOQcbAumaRqJD3UnzsWnyFdSv6eL9FwXZiyytcxMkvo9XeQYiQ8/pgr4m3Tq1ClatGhR5PpevXphb29f6vbCjh8/Tt++fXniiSfo3Lkz3t7eZVqUfsuWLeTm5rJ161a++uorjh49yt69e3nzzTfx9vZm5cqVnDt3jvXr17Nhwwa2bdtGQECADB0vh+Tk5MpugkWQHBTJQZEcFMlB9Uov+sKm1N7prvfCn2fgs29UT/It/AkGihbXRsYi28Fe9Wgnpqr7cnKABj5wb1PocT881Bqa1oea7kWL65xcOHwSPvwaAv3Az7tsbbp04dStPai7hLwuFMlBkRyqVwbyEaOlK6aH2XX5YjKWLC+2uDbK2fMjGUuW47p8MfFNiy69cWtN02FnZ3fT2wvLP0Q8PT2df//736xcuZJXXnmlxNv9/PPPhIWFcejQIQAyMzM5c+YMAQEBpn0OHDjAxYsXGTRoEAB5eXn4+fmVuW3VnXVZTuirBiQHRXJQJAeluueQmX3j3OPDYSWfe9z1XnXe86YfYcteVdRalWEgmd4AeXr1pderfw2cHSH6KmRkFX87JwfwraXaaGcDtmX8r0/jxsC3JvXgjWfLdjuQ54OR5KBIDorkUL0ykALbgul0Ojx+/s78EHFra5ynv0buod9LLLLtunfBefproNPhnRZbcOMtDhFv1aoVx44dK3L9+PHjmTx5cqnbSypwnZ2dCQ4O5tdffy21HXq9nsmTJ9OjRw9ADUFxcHAgPj6+wD6PP/44b7zxBgAZGRnk5OSUemyhBAUFVXYTLILkoEgOiuSgWEIOGVmVs/Zx/uIa1GW/6SUX2UO6Qo/7YN/fEBmnepeT0q5/Ffo+MQ1S0oseY+Vr8NOfqje8NE3qwbPBYF/2z7sBVZAH+kF9n9L3zc8Sng+WQHJQJAdFcqheGUiBbeF0Oh2UsG5cjW0bix0mbte9S4XNJm7OoEGD6NOnDz/++CNdunRB0zQ+/vhjIiIi8PX1LXV7SfR6PYcPHzY7xLywjh07smnTJrp06UJeXh4jR45k3LhxtGzZkry8PAA6dOjAxx9/zOjRo6lVqxYhISE4Ojoyffr0CsnibnfixIky/S7udpKDIjkokoNS2TlkZsM76+78xFqFi2sjY5H95duw/Ve13FVsIsQlQmyC+j4xtXz3pdOp86R9PMDHU01WtnIyJKSYX3/aqHt7+Hjqnc2lsp8PlkJyUCQHRXKoXhlIgV3F6RwdzRbZt7u4BqhVqxaffPIJCxYsYOnSpej1egIDA1m1ahVWVlalbi/MeA62TqcjLy+Ppk2bMmrUqFLbMWTIEC5dusSTTz5JXl4evXr1olu3buj1enx9fRk6dCjr169n3LhxPPfcc2iaRuPGjZk6dertiOWudCvn6t9NJAdFclAkB6UycyjP8Ozy0jRIy7xRFMdeL5BdnaBPJxj0ZvHF7Z4jMGAmbHgLPtttfj9ra/CuoYpmb8/rxfP1AtrbQ30Zr/NyBxsz/7HlXxqrsMpaKkteF4rkoEgOiuRQvTLQadXp0VqQlJQU3N3dSU5Oxs3NDVDDlk+ePElQUBBOTk7lOl7+Cc/uRHFdnRh/L6dOneLq1as8++yzeHh4VHaz7qjo6OhSRx1UB5KDUpVySM+E3YfgUBjEJ6tzSytKZmYmjhX0PmtjDf614ZF7oFOrYqffsEip6Xm4Ot/5z+vN9SCXVlRqmuo9jstXMOf/PjZRnSNt/N7cpGUn18La72Ce+YUtCpgxAsY8CZt/ulEwGy893czP2l1eN5PD7VSV3h9uJ8lBkRwUycGyMzBXl90K6cG+Sxh7stPnv4vz9NekuBYVqiLebO4GkoNSVXJIz4Q3P4ZLsWrJotaN1Xq+FcVgsKuQAgkgJw/CLsLC9TDwUXimR8Uc93Yzzp5tacOzN89VRe0v/+Qbnn39MjevfPfl7Fiwp3n3QZj8tOoxL214tjGX8QPK/RDLzLj+tDGPyiyuoeq8P9xukoMiOSiSQ/XKoMoV2H/++Sf/+c9/+Oeff3B0dGTQoEEsXLgQe3t7/vnnHyZMmMDhw4dxcnJi2LBhLFy4EJvr46o+++wz5s6dS0xMDEFBQbz//vs88MADZu8nPT2dV155hf/973/k5eXRt29fPvjgA9P6bWfOnGHMmDEcPnwYV1dXXnnllUo/n1fn6IjztElSXIsKd+nSJVq2bFnZzah0koNSVXL47ne4eAUWvQyN6lb88VNS0iv8H4aNP8K679QSSt4WPlDmdg7Pzs0r2JMcmwhxCeDuoiYJK2149sA31PDsTT+a38/d5cbw61r5hmIXHqrt46EKbHMsaXi2scgOWXvnz0UvrKq8P9xukoMiOSiSQ/XKoEoV2AaDgd69ezN16lT27t3L5cuX6datG15eXowZM4auXbsyceJEvvnmG6Kjo3nssceoW7cur732Gnv37mXcuHHs3r2b+++/n+XLl9OnTx8uXbpkdjj2K6+8QmRkJGfPniUvL49BgwYxZcoUVqxYQW5uLr1796Z///7s3r2bEydO0Lt3b5o0acLAgQMrIZkbdOUcWi6EEHer309Bu8DbU1zfLn06wcYf1JD2JzqVvn9Vmj07KxvikooWzYW/j0tSw/nNObkW3v2i5J5jY3uWboLPZsCOA4XOb66h1oe+VYV7jo0qqwfZ0R6mDqvc4loIIUQVK7ATExOJiYnBYDCYTpS3srLCycmJzz77jMDAQKZNmwaAv78/e/bsMS1DtXr1aoYMGUKnTuo/lgkTJrBq1So2btzIc889V+B+MjIyWLduHXv37sXT0xOABQsW8Oijj7Jo0SJ+++03YmJimDNnDnZ2drRt25bx48ezfPnyCimwDQbDLR9DVBzj76M6T1dQv379ym6CRZAclKqSQ0IKBNzG070q6vzrAse0V+fmlmWWaUudPXvzXDWx176/CxbQ5pabKomVFdSqUfC85X1Hyz88+4Ve5X2EZWdpw7Mr48OWwqrK+8PtJjkokoMiOVSvDKpUgV2zZk0mTJjApEmTeO2119Dr9fTt25cJEybw9NNP07JlS0aPHs3XX3+Ns7Mzzz//vKngPnHiBM8//3yB4zVv3py///67yP2cPXuW3NxcWrVqVWDfzMxMzpw5w4kTJwgMDMTOzq7A9pCQkGLbnp2dTXb2jdlSUlJSiuxjb2+PTqcjJiaGOnXqmJ1pW9w5mqaRnZ1NZGQkBoOhwO+vuklNTa1W584UR3JQqkoOmqZmai5sz549LFmyhOjoaPz8/Jg4cSJdu3Zl6dKlrFy5ssC++/btY/PmzSxfvrzIcWbPns2QIUMKXJednU27du1MSwQCDBgwgHnz5pGbm8uCBQvYsWMHTk5OjBkzxuyHstZWpU/GdruGZ2saJKfl61XOt7yUpxuM7AmDyzg8e8dv8MuxgtttbW70JBuLZ+NQbO983/t4gqer+d8fyPBsS1ZV3h9uN8lBkRwUyaF6ZVClCmyDwYCjoyPLly/n+eefJzw8nH79+jFr1iwSEhLYunUrK1eu5P333ycsLIwnnngCe3t7XnvtNVJTU3EutJ60k5MTaWlpRe4nNVV1HeTf3ziMPC0trVzHMgoJCWH27NlFrg8LC8PFxYVmzZpx4cIFbGxsSElJMVuAi8qRkZHB6dOniY+PJycnB71ez/HjxwHw9vbGxsaGy5cvAxAQEMCVK1dIS0vDzs6OgIAAwsLCAPDy8sLBwYGoqCgAGjVqxLVr10hJScHGxoZmzZqZjuvp6YmzszORkZGAGpGRmJhIcnIy1tbWBAUFceLECTRNw8PDAzc3Ny5dugSoTwhTU1NJTExEp9PRokULTp48iV6vx93dHU9PTy5cuACAn58fGRkZxMfHA9CiRQvOnDlDbm4urq6ueHt7c+7cOVJSUnByciInJ4erV68CEBQUxPnz58nOzsbZ2Zm6dety9uxZAOrUqYPBYCA2NhaApk2bcunSJbKysnB0dKR+/fqcPn0aAB8fH3Q6HVeuXAGgSZMmREdHk5GRgb29PQ0bNuTUqVO3PW8XFxciIiJMeSclJZGUlISVlRXNmzcnLCyMpKQkNE2jRo0aXLx40ZR3WloaCQkJALRs2ZJTp06Rl5eHm5sbXl5enD9/HoB69eqRlZXFtWvXAPXBXHh4ODk5Obi4uFC7dm3Cw8MBqFu3Lnl5ecTFxQGY3iOys7NxcnLC19fXlHft2rXRNK1A3hEREWRmZuLg4ECDBg0K5G1lZUVMTIwp78uXL5Oeno69vT2NGjXi5MmTgFqKz87OjujoaAAaN25MXFwc0dHRpKWlERgYyIkTJwD1AaiTk5PpOduwYUMSEhKKfc66urqa8m7QoAEpKSnFPmc9PDxMefv5+ZGenl6mvHNyAsnNhZSULABcXV2JjIxk4sSJ1K9fn6effppvv/2WCRMm8MsvvxAWFoaHhweDBw/Gzs6OvLw8NE2jTZs2jB8/npycHAC2b99OcnIyQUFBpKSk4OLiQmZmJnq9ntOnT5OXl0f37t1p3LgxNjY2pv3WrFnDmjVreOyxx7h8+TIzZ86kSZMmNGrUCAA7Ozusra3JztYTHXWNjAzPIs/Zixcv4l27Pv1nWBUZnv3VPI24K5fIzMwskLe7uwd6nTtHw2KIT7VBZ+dDVGwOUbG5JKTakI0Hly5nEp9iTUKaLTm55qcwP7kWFpdjePbnMzW+2H0FN8dM6nnb0bJpLa7GnEWnK+U9QnOkhnN9Tp4s/j0iKSGBr+b5FMgBrhfX8zRiYy6RlpZ2R98jJg7MJjYmBhsbm2r9HhEdHU1iYiK2trYW/x5xq3lfvHiRrKwsnJycqFevHmfOnDHlHRsbS2JiIgCBgYFERUWRkZGBg4MD/v7+d+TvmiX8H5GUlGTKwdfXt9r+H2EwGEhOTsZgMFCjRo1q+R6RkpKCp6cncXFxpKamWtR7hPG1W1Gq1DJdW7ZsYcaMGaYXCcC6desYP348DzzwAElJSfzyyy+mbYsWLWLz5s0cPnyYNm3aMGrUKF555RXT9qeeeor69euzdOnSAvfz119/0a5dO1JTU02Tmhk/dTl69Cg//vgj69at48iRG3/Vt2/fzogRI0xvIoWZ68H28/MzOx28Xq/np59+4uTJk9SpU8c0zP1OSkxMrHZLUZkTFxeHu7s7AAkJCeh0Op577rlyL6NW1Z04cYIWLVpUdjMqneSgVJUcXloED7aCZ4NvXHf8+HGeeuopnnrqKYYOHUpoaCh//fUXv/zyC8HBwTRp0oSQkBDc3NwKjFIy+v3333nmmWeYN28ejz32GK6urgW2f/nll8yYMYO1a9fSuHFj02lGoP7mREdHc+DAAc6dO0evXr0YOXKkaaRVSe02Km54NqjicvNcWLkNfvzzxvDsq0mQpy9XdLg6FVyT2ccDHmgJTz4EA94ofXj2nepBzp9HZQ/Priqvi9tNclAkB0VyUCQHy86gWi/TFRERUWSYrq2tLXZ2djRv3pyffvqpwDa9Xm86b7Zly5amT0iMwsLC6NmzZ5H7adq0Kba2tpw4cYIOHTqY9rWzsyMwMJC4uDjOnDlDXl6eaYbysLCwEmfGs7e3x96+bH/1ra2tadKkCWfOnOH06dNlvl1FS0pKqpT7tTTp6emm8/4feOCB23LepaWz1DfEO01yUKpyDkFBQXTo0IEtW7awZcsWbGxs+Oijj8jOziYmJoaEhAQ6deqEvb09kyZN4tlnny1w+7lz5xIUFET//v3Nfvhp/AD4hRdeIDs7m9atW7Ns2TLq1KnDhQsX8Pf3R6fT4efnB2DqBSqLkoprKDg8+4cjcDS84HZPt+vLTeUrmgt/bxyqXVKRKsOzzavKr4uKJDkokoMiOSiSQ/XKoEoV2D169GDatGnMnz+fKVOmcOnSJd5++22GDx/O888/z7Jly1i4cCGTJk0iLCyM5cuX8/rrrwPw/PPP069fPwYNGsRDDz3EihUriI2NpV+/fkXux8nJicGDBzN16lQ2bdoEwNSpU3n66adxdHTk0UcfxcvLi6lTp/L2229z+vRpli1bxrx58yrssTZo0IDevXtz9uxZ0tPLOTNMBbhy5Qq1a9e+4/draYw52NjYULt2bdq0aVMpIwoq28mTJwkKCqrsZlQ6yUGpyjlYWVnRpEkTUlJSGDZsGCtWrODNN9/kgw8+oH79+rRs2ZKHH36YTz/9lJCQENq1a2eaj+OXX37h9OnTLFmyBJ1OR2pqapEe7NzcXPz9/Rk6dCgJCQmsXLmS2bNns3LlSjIzM0294ra2tgBkZmaW2ua8PEjJgPc2l3149po34MipG0VzrRpgZ1v+vMyR2bPNq8qvi4okOSiSgyI5KJJD9cqgShXYzZs3Z8eOHcycOZOFCxfi7u7O8OHDmTVrFnZ2duzbt4/JkycTEhJimkBm3LhxAHTt2pUPPviAMWPGEBUVRYsWLdi9e7dp+N78+fNZt26dqZf7gw8+YNKkSbRq1YqcnBz69u1rmuTGxsaG7777jrFjx1K7dm1cXFwYP348I0eOrNDHW79+/Uqbce/48ePVZq26kkgOil5fzvGldynJQanKOezZs4e1a9cSGhpKcHAw6enphISEEBUVxZ49e0z7ubm5MXbsWA4fPmwqsLdu3YqTkxNdu3YFzK8sUHiujb1793Lo0CEAHBwcTOdxGy8dHIqZ9lmDwyfVmtibfoI2jVXPdHlmz368Y5kiuSkye3ZRVfl1UZEkB0VyUCQHRXKoXhlUqQIboFu3bnTr1s3stg4dOrB///5ibzt8+HCGDx9udtv06dOZPn266WdXV1dWrVrFqlWrzO4fEBDAt99+W46WVy3G846rO8lBkRwUyUGpyjkYJ2MyjgzKyMgA4Pz588yePZunnnqKli1bmiatNJ4SomkaBw4coH379qai2NgLnd/y5csxGAyMHz8eTdNIT083HaN+/fpcvnwZg8FgmjSnYcOGBW6fnqnOmZ72kTp/2uhoOGzYA1vmwVMzZHi2JarKr4uKJDkokoMiOSiSQ/XKoMoV2OLOyD8xT3UmOSiSgyI5KFU5hw4dOmBtbc3SpUsJDw9n8+bN1KxZk86dOzNgwAD27dtHz549+frrr3FxcSE4WM00FhsbS3x8PM2bNzcdy9bWltTUVNasWUNgYCDdunXjwoUL7Nixg6ioKLKysoiMjOTFF18E1GlOoaGhTJo0yTSD7eOPP052Lly+BtFXISlNFdixiapg7fsQDHtMFc+21/9iW9rw7ClDDTjay7KSVfl1UZEkB0VyUCQHRXKoXhnIX0RhVnkm3rmbSQ6K5KBIDkpVzqF58+YsWbIEd3d3NmzYgL+/PytXrqRZs2YsXboUR0dHPvvsM7y8vFi1apXpHwLjsjI+Pj6mY2VkZJCcnExoaCjffPMNALNmzeKJJ55g7969HDx4kGeeeYbx48cDMGrUKEaOHMmBAweIjY1j4utzybVvy/dH4MQFVVwDuDjCv3tDzNew7k3o2fFGcQ03eo67t1c/V/bw7Avnwirnji1MVX5dVCTJQZEcFMlBkRyqVwbSgy2EEKJaCQ4ONvVM59e9e3e6d+9u9jatWrUyrQGaX7169Qpc7+bmxrvvvmv2GNbWtrwwehrBA6ZxJR70BohLUtvcnaFeLajjBdt/Vct0uZawGqAMzxZCCCEskxTYwizjEjLVneSgSA6K5KBIDoqTs2up+2gaJKer4d+Xr0F2br7b24NvLfXlchOr/1nK7NnyfFAkB0VyUCQHRXJQJIfqlYEMERdmGSf+qe4kB0VyUCQHpSrlYDDcnuPq9XAuWkdxk6KmZ8GZSNj7F/zyD1yIUcW1rQ00qA0PtoRH20HT+kWLa0PRycmLZQmzZ1el58PtJDkokoMiOSiSgyI5VK8MpMAWZsXHx1d2EyyC5KBIDorkoFSVHNxd1IRhFU2vh99PwdkodWkssnNy4WIM/HoMfvpTFdjpWWClgzo14b5m6nzpVo3A0w10uqLHzsmFxFQ1ZLyqqCrPh9tNclAkB0VyUCQHRXKoXhnIEHEhhBBVTkZW6b239zaFLXshNgF8KmjyUmNxfS1Z/XwtWf18b1P462zBgt7LXQ3/ru1ZcJKykvzwhyqy2zetmPYKIYQQ4s7SaZpWjsFooqKkpKTg7u5OcnIybm5uld2cIjRNQ2eue6WakRwUyUGRHJTKziEzu2yTeyWlwpSVkJENnduoScSsrW/+fg16OB0JKelFt7k5QxM/iIwDBzuo6QZ2RZfJLlZ2DoRdhN+OQ9d7YfyAm2/nnVbZzwdLITkokoMiOSiSgyI5WHYGFV2XSQ+2MOvMmTM0bSpdKJKDUt4criXBlYTynUt6J1lbqSG7nuV8D5Xng1KZOWRm31gD+nBYyctT1XCF+S/Cl/vgwHGIT1GTjuWnN0BunvrKyTPzfS442EPDOnA64sZSWmbvzwWC/NWQ8PKysVbnZj/XE/p0Kv/tK5O8LhTJQZEcFMlBkRwUyaF6ZSAFtjArNze39J2qAclBKWsOl67A+1vUuadVQYuG8OoAqF2zbPvL80GpXbdRpdxv/uIa1GW/6QWLbE2D5DSITbz+lQB5eeDnrXqUYxMhLuHG9oys0u/35FpY+x18e7jk/ZLSYNhjamZvS5h87E6R14UiOSiSgyI5KJKDIjlUrwykwBZmubqWvvxMdSA5KGXJISEFZq5WvXivPQ2N6tzacNzbKU+vJqja+ANMXwXv/wecy7BMkjwfVJG7eJPtHV172WCArFzon6+4NjIW2V/MhnFLYct+Ndy6PJwcwMcDvK9/+Xioc7aN12XnqKL5cFjR+8+ve/vquSa1vC4UyUGRHBTJQZEcFMmhemUgBbYwy9vbu7KbYBEkB6UsOfz8D6RnwvL/qNmbLZ2fNzRvAC8ugkMnoUu70m9T3Z8P5RmeXZq8PLiarHqY45KuX+brdY5NVD83qw8rJsLTbxVf3O45AkNmwYa31CRje46oc6J9rhfLtfIVzYULaB8PcHEqW5u3zi/Yg55f9/a3lkdVVt1fF0aSgyI5KJKDIjkokkP1ykAKbGHWuXPnaNmyZWU3o9JJDkpZcgi7oIZcV4Xi2qh2TWhUF06cL1uBbQnPh7LMnn07lGV4dnbOjWLZWCjHJZovoM2dD23OF2/B0k0l9xwb27N0E2yeq85nvh2FrqO9+SK7OhfXYBmvC0sgOSiSgyI5KJKDIjlUrwykwBZCVIjMHHAtY0+gJXFzVm2vCjKz4Z11d3YYcla2mqyu/4ySh2e/8A58/XP5jm1lpZayMg7Fzj8s29jTrNNZ1vDswkV2dS+uhRBCCFGQFNjCLF9f38pugkWQHJRbyWHPnj0sWbKE6Oho/Pz8mDhxIl27dmXp0qWsXLmywL779u2jdu3app+PHDnC8OHDGTt2LOPGjSty7NzcXBYsWMCOHTtwcnJizJgxDBw4sNRtN6synw8VNTxb09QyU6Ye5qSCk37l72lu0RA+nVb24dnpmfDTXzeKY+9CQ7G9PcG7xo3rvNzLfp6+JQ3PNhbZZVkqrDqQ90lFclAkB0VyUCQHRXKoXhlIgS3MysmpIl16t5nkoNxsDvHx8UycOBF/f3+GDRvGt99+y4QJEzh8+DCnT5/G09OTZ555xrS/i8uN8eVZWVnMmDEDrYRxxJ9//jlr1qyhZ8+eREdHM3PmTAIDA2nTpk2J226WrX3lrFlf2vBsTVOTzBU+fznOTNEcm1j2ScB2Lizf8Owv3wYne9UzXdEsbXi2oz28/rQeR3sLncnvDpL3SUVyUCQHRXJQJAdFcqheGUiBLcy6evUqPj4+ld2MSic5KDebQ0xMDDk5ObRq1YpevXoRHh5OSkoKAKdOnaJly5YMGjQINzc37OzsCtw2NDSUy5cvl3j8Xbt24eHhwZIlSzh37hy9evVi165dtGnTpsRtNyMzGxZusL5jPZZ6vepptrcrZXj2WzBsLnxzqHzHd3E0PyTb2NPs4wHODqqHVoZnm3fx/Mlqcz5ZSeR9UpEcFMlBkRwUyUGRHKpXBlJgCyFum6CgIDp06MCWLVvYsmULNjY2fPTRR2RnZxMTE0NCQgKdOnXC3t6eSZMm8eyzzwLwzz//8Nlnn/HKK68QGhpa7PEvXLiAv78/Op0OPz8/03WlbSuvihqenZtnfqZsc73O9zSB9W+aL66N9hyBIW+p4dlPvwW/nypYNBeYObvQ8OzyTJQmw7OFEEIIIcpGCmxhVlBQUGU3wSJIDsrN5mBlZUWTJk1ISUlh2LBhrFixgjfffJMPPviA+vXr07JlSx5++GE+/fRTQkJCaNeuHU2bNmX69Ok8+OCD9OnTp8QCOzMz09TzbWtra7qutG3lUdrw7Kzsgucvl1Q0J6SU/X5Dx5dvePaWearH+XawxOHZU4dVfnEt7w+K5KBIDorkoEgOiuSgSA7VKwMpsIVZ58+fp0mTJpXdjEonOSg3m8OePXtYu3YtoaGhBAcHk56eTkhICFFRUezZs8e0n5ubG2PHjuXw4cP8+OOPXLhwgRkzZpiGiCcnJxMfH0/NmjULHN/BwcF0To/x0sHBodRtZVW4uDY9rutFtrHnuLQiOD9r6+s9yfmGYpubFMzLXYZnl6QyliorTN4fFMlBkRwUyUGRHBTJQZEcqlcGUmALs7Kzsyu7CRZBclBuNofo6GgA0tPTAcjIyADUm+zs2bN56qmnaNmyJWlpaQA4Ojqyd+9e8vLyGDlypOk4a9asIS0tjXfeeafA8evXr8/ly5cxGAxERUUB0LBhw1K3leZqkiocB8wseXj202+pIvuZt+H4+YJFs3F4dq0aBWfS9nQr3yRgMjzbcsn7gyI5KJKDIjkokoMiOSiSQ/XKQApsYZazs3NlN8EiSA7KzebQoUMHrK2tWbp0KeHh4WzevJmaNWvSuXNnBgwYwL59++jZsydff/01Li4uBAcHExAQQEJCAgAJCQnMnj2b4OBghg0bRmpqKmvWrCEwMJBu3brRo0cPQkNDmTRpkqm3+/HHHwcocVthWTnwv1/h+9/hhz9UEfn+lrIPz940+/b1qFri8OzXhuThaC9/PuT9QZEcFMlBkRwUyUGRHBTJoXplIP8hCbPq1q1b2U2wCJKDcrM5NG/enCVLlvD++++zYcMGAgICePPNN2nWrBlLly7lvffe47PPPqNx48a88cYbeHp6cv/995tub+x5DggIoFWrVkRFRREaGsoTTzxBt27dGDVqFMnJyWzbtg1HR0fmzp1L27ZtAUrcZjBAUprqqb50BdbtUUtdGb26DL6YJcOzi2Nvo0f+fMj7g5HkoEgOiuSgSA6K5KBIDtUrA51W0iKz4rZJSUnB3d2d5ORk3NwqZ23dkhw/flyWn0FyMCpLDm9+rJZ+en3o7WmDXq/OX74ZmgZpmXAtWRXVCSmQp1fbPv8GtuyDAF/o1h66todH24KHa/HnYEPlFLmZ2ZYxPFteF4rkoEgOiuSgSA6K5KBIDorkYNkZVHRdJl0QQgiLp9dDeLQqgstaZGfnwNVkuJakCuusnILbbW3URGK1PeH1pyFkdNFjWOLwbEuYPVsIIYQQQpgnBbYwq06dOpXdBIsgOShlycHG+kavcEXS69X6zteSITEV7mtmvsjW6yE+RRXUV5MhNaPgdiudmmDMqwbUcgc3Z9DpVO+1l3vx929pw7MtYfZseV0okoMiOSiSgyI5KJKDIjkokkP1ykAKbGGWwWCo7CZYBMlBsXesUeo+db3gl3/UcGydrmLuN39xDery91OqyLayguS0G73UCakFz6MGVUR7uauZvD1dixbmOblw/jK0blxyO2T27ILkdaFIDorkoEgOiuSgSA6K5KBIDtUrg3IsFiOqk9jY2MpugkWQHNR5vws3WJNZyuoKnVqqc5vXfge5ebd+v4WLayNjkZ2rh1MRcDpC9Vxrmip8/byhbSB0vw86t4Hm/qrANldcf7obMrKgU6vS2+NoD5MG5Vb74hrkdWEkOSiSgyI5KJKDIjkokoMiOVSvDKQHWwhRrPyTfB0OK3lodJA/PNMD1nwLuw6qQtemHJOSGQygN4BmABsbiLoK6ZnF7+/sCL5equfaxkr9bGtTtt7z3Dw1e3hWDox5UrW1LCIvncbdQifoEEIIIYQQlU9mEa8klj6LeG5uLra2tpXdjEpnCTlkZFXOebfmZtAuy/nHl67AgRMQm6AK2NRMSMtQ50SnZqjZvPN/b/zZOAnZlKHwxxn4vpQ1qEHN+t2lHdiV86NCa2uoUxMebAm+tcp+O0t4PlgCyUGRHBTJQZEcFMlBkRwUyUGRHCw7A5lFXNwRly5dIiAgoLKbUekqO4fMbHhn3Z0/77e45an2HFHXfzVPnbt8OhLiEiA28fpXAsQl3vi5pB5oc2xt4FIcbJwNQ2aVvgb1nZ5srLKfD5ZCclAkB0VyUCQHRXJQJAdFclAkh+qVgRTYwqysrKzKboJFqMwcyjM8+1Zomjp3OjZRnZccUA8GzCy+uN1zBPrPgA1vwWsrSi6CARzswMcTfDzA26PQ5fXrfTzUPh6uN4Z4m1sey6iyZvKW14UiOSiSgyI5KJKDIjkokoMiOSiSQ/XKQApsYZajo2NlN8EiVFYOhXuQjT3HZS0q9Xo1GZixVzk2Ea4mmu9pjku8sbzWybWwcH3pRfOeI7B0E6ycDJOWF1M0X790dbq5WcUtbQ1qkNeFkeSgSA6K5KBIDorkoEgOiuSgVKkcctIh6hDEHoPccg5FLEGd+HhIqFlhx8PWCercA773g60FrGGaj5yDXUnkHOyqoTJyKG54Nqjicss8CLsAJy4WXzRfS1aThpVHDRfo1xkWj4XBFjQ8O38elb0GtbwuFMlBkRwUyUGRHBTJQZEcFMlBqTI5pMXBnmmQHgcejcDercLWXtU0DV1FreOqaZCVBEkXwa0edJsHTp43fbiKrsukwK4kll5gHz9+nJYyW/Idz6Gk4tqoe3s1PPvpt0reT6eDmm6FhmcX+t67xo1Le7uytaMyitzMbMtYg1peF4rkoEgOiuSgSA6K5KBIDorkoFSZHL5/A9JioOtccK1ToYdOSUmp+JonKQJ+eAO8msEj0276MDLJmRB3qYwsNaFZWYdnr3od3lhtvoD28QAvd7Xc1c2wtOHZjvYwdVjlFtdCCCGEEHetrBS48jd0GFvhxfVtU6M+BD0Jf6+F3CyLGSouBbYwy8fHp7KbYBHuZA4aMPlpNaFZaT3Yxp7cz2bcvvYULrIre3h2ZSxVVpi8LhTJQZEcFMlBkRwUyUGRHBTJQakSOaTHAhrUbHJbDm9vf5v+gazZBPQ5kBkPtr635z7KyaqyGyAsU4WdI1HF3YkcsnNg2ZfQaBAMfEMN/+7e3vy+d7rINRbZM0ZUbnFtKeR1oUgOiuSgSA6K5KBIDorkoEgOSpXIwXB9xlurov2ve/bs4fHHH6d169b06tWLH374AYClS5fStGnTAl9XrlwB4O+//6Zfv37ce++9vPTSS8THx5u929zcXN5++206duxIly5d2Lx5c5m2mRjba2y/BZAe7Crun3Pwyz8QdVXNHF1R4uOhZgVN9GdlBbVqQIfm0LE5WFtXzHHvhCtXruDl5XVbjq3Xw/rv4a1P4KJ6L+JSLBy/YHnDsycNysXRvgpMznGb3c7nQ1UiOSiSgyI5KJKDIjkokoMiOShVOYf4+HgmTpyIv78/w4YN49tvv2XChAkcPnyY06dP4+npyTPPPGPa38XFhczMTEaPHo2dnR1PPvkkGzduZMaMGXz66adFjv/555+zZs0aevbsSXR0NDNnziQwMJA2bdqUuM2SSYFdhW3+CdZ8q867bVYfbCvwt1nT1QWHChqSq9erwnHvX9C5DUwYVLWK7IqmabD7EMxYpT4gAahTE2Y9B889fuO8aUsanh156TTuVWFyDiGEEEIIUWFiYmLIycmhVatW9OrVi/DwcFJSUgA4deoULVu2ZNCgQbi5uWFnp2bM3bt3LwkJCcycOZNnnnmGa9eu8d1335GcnIy7u3uB4+/atQsPDw+WLFnCuXPn6NWrF7t27aJNmzYlbrNkUmBXUdFXVXE98FEY/liFzaBvotfbVngR/OsxWLBO9WQ/bNmvC5MmTSr2PJSDJ2DaR7D/b/WzuwtMGQrjnip6jrFxeLYlzJ5d0TlUVZKDIjkokoMiOSiSgyI5KJKDIjkoVTmHoKAgOnTowJYtW9iyZQs2NjZ89NFHZGdnExMTQ0JCAp06dcLe3p5Jkybx7LPPcuHCBQDq1asHgJ+fHwaDgUuXLtG6desCx79w4QL+/v7odDr8/PxM15W2zZLJOdhV1IET4GAHg7tUfHENkJWVVeHH7NQKAuqptlcV0dHRFXKcU5fgqZnQ6WVVXNvbwaQhEL4BpgwrfgIvS5k9u6JyqOokB0VyUCQHRXJQJAdFclAkB0VyUKpyDlZWVjRp0oSgoCDefvttatWqxZtvvklMTAz169ena9euhISE0KBBA0JCQjh27BgZGRkAph5t4xrgmZmZRY6fmZlZ7H4lbbNk0oNdRV1NgrpeYHebTovVV+QJ3fn414bIuNty6NvC+AZxs6KvwuxP4dPdYDCo89FH9IC3ngc/77IdwxJmz77VHO4WkoMiOSiSgyI5KJKDIjkokoMiOShVOYc9e/awdu1aQkNDCQ4OJj09nZCQEKKiotizZ49pPzc3N8aOHcvhw4dxuH6eaU5OToFLR0fHIsd3cHAosp/x9iVts2TSg11FaRpYm/ntFTfLn9GRI0do1qwZ77//vum6tWvX0qVLF1q3bs3o0aOJi4vDyqrowW95lj/AxloVmlXFzS4pkJgKU1dC4FD4eKd6zH0egqOfwMdTy15cW4rbtrRCFSM5KJKDIjkokoMiOSiSgyI5KJKDUpVzMPa+p6enAzc+LDh//jyzZ8/m+PHjAKSlpQGqiG7QoAEAkZGRAERFRaHT6UzX51e/fn0uX76MwWAgKioKgIYNG5a6zZJJD/ZdpKRZ/hwcHMjKymLGjBlomma6zQ8//MDcuXNp164d3bt3Z+PGjYwfP57169cXOf7dOMtfacr7Is7MhuVb4J11kKTeZ3ioFYSMhger8BxhVeHN7E6QHBTJQZEcFMlBkRwUyUGRHBTJQanKOXTo0AFra2uWLl1KeHg4mzdvpmbNmnTu3JkBAwawb98+evbsyddff42LiwvBwcHY2dnh7u7O6tWriYiI4IcffqBTp064u7uTmprKmjVrCAwMpFu3bvTo0YPQ0FAmTZrE5cuXAXj88ccBStxmyaQH+y5SeJa/xo0bm85bAAgNDTU9OY0OHDgAwKxZs5g2bRpDhgzhr7/+Mn0alV/+mfzmz59vuq60bVXZqVOnyrRfXp7qqW46DKZ+pIrrlg1hWwjsfb9qF9dQ9hzudpKDIjkokoMiOSiSgyI5KJKDIjkoVTmH5s2bs2TJEtzd3dmwYQP+/v6sXLmSZs2asXTpUhwdHfnss8/w8vJi1apVeHp64uLiwurVq/Hy8uKrr77igQceYMaMGQAkJycTGhrKN998A8CoUaMYOXIkBw4cIC4ujrlz59K2bdtSt1ky6cG+ixQ3y5+DgwP//PMPn332Ga+88gqhoaGm23h7q7HK27dvR9M0/vzzT0DN0FcdZvm7VZoGX/8MM/8LpyLUdfV91DnWw7tX7+XIhBBCCCFE1RccHExwcHCR67t370737t3N3qZ169Z89dVXpp+NS3vVq1eP06dPm663tbVl2rRpTJs2rcgxStpmyaQH+y5S3Cx/aWlpTJ8+nQcffJA+ffoUuM3QoUNp0aIFq1ev5sknnzSdP5GXl1fk+HfjLH+l8W8UVOy2/X/DQy/DgDdUce3pBu++DCfXwLPBd1dxbfwgprqTHBTJQZEcFMlBkRwUyUGRHBTJQZEcwNXZqbKbcMdIgX0XMc7yN3r0aAYOHMjIkSOJjo5m+PDhXLhwgRdeeME0RDw5OZn4+HhcXFzYtGkT69atY8uWLQwdOhQAFxeXIse/G2f5K0lmNizcYE1mdsHr/zkHvafAo+PhYJia5Xv6M2rJrQmDwaHqzmNRLBsbGewCkoOR5KBIDorkoEgOiuSgSA6K5KBUrRy00ncpL4MeXUI4GG7DKkXabWjvLapyBfaff/5J586dqVGjBnXq1OHVV18lO1tVQGPGjMHe3h4XFxfT16pVq0y3/eyzzwgICMDZ2Zn27dubzj82Jz09neeee46aNWvi7u7OiBEjTL27AGfOnKFr1664urpSt25d03nHlam4Wf7s7OzIy8tj5MiRPPPMMwCsWbOGRYsWER4ezhtvvEFeXh4tW7bk5MmTgJq1r7C7cZa/4mRmQ7/pMO9zdZmZDVfi4dl50O4F2H1Q9VC/1AfOrIe5/wb3op9J3DUKn7tfXUkOiuSgSA6K5KBIDorkoEgOiuSgVIkcbK8voZWVUrHHNegh+neIP6suK7rIzr7eXtuiS4BVlqr0cQoGg4HevXszdepU9u7dy+XLl+nWrRteXl688cYb/P7776xatYpnn322yG337t3LuHHj2L17N/fffz/Lly+nT58+XLp0CSenokMWXnnlFSIjIzl79ix5eXkMGjSIKVOmsGLFCnJzc+nduzf9+/dn9+7dnDhxgt69e9OkSRMGDhx4J6Iwq7hZ/l577TUSEhIASEhIYPbs2QQHBzNs2DDc3NzYvn07f/75Jw899BBbt27lwQcfxNfXt9Jn+cvIqpw1oI3F9Z4j6uc9R9TPG96C2AT1QdnAR1VR3aTenW+fEEIIIYQQZZabAbalDNF2qwf27oRFbOd7wx+czjhPlpZd8m1Ko2mQnQr6XPVzFpD0Gdi7gk53S4d2tHIgyDGAxyLO0cSlNjh53VpbK1CVKrATExOJiYnBYDCYlpqysrLCycmJ7Oxsjh07Rvv27c3edvXq1QwZMoROnToBMGHCBFatWsXGjRt57rnnCuybkZHBunXr2Lt3L56engAsWLCARx99lEWLFvHbb78RExPDnDlzsLOzo23btowfP57ly5dXaoFtnOXv/fffZ8OGDQQEBPDmm28WmKzM2LscEBBAq1atAJg/fz6LFy9my5YtdOvWjbfeegtnZ2diYmIIDQ3liSeeoFu3bowaNYrk5GS2bduGo6NjkVn+itt2MzKz1VJX04aD4x0ccl24uDbacwSefgs2zlZFdmDRDv67WkBAQGU3wSJIDorkoEgOiuSgSA6K5KBIDorkoFRqDrmZsD8EOk8vuZfXypo/A+9jeeoe6qXey2Pu7XGxcb3F+84Ap6LzOmFtU3rBX4rk3CSOJB1hgeEv/tOkPy1vsWCvSDpNs8CB6yWYOHEioaGh6HQ69Ho9ffv25auvvuLIkSN07NiRfv368csvv+Du7s4LL7zA5MmTsbKyom3btjz//POMGzfOdKynnnoKPz8/3nvvvQL38ffff3PPPfeQkpKCq6t6YqWmpuLm5sZff/3Fvn37+Pzzz/njjz9Mt9m+fTvPPPMMSUlJZtudnZ1tGsoOaiY9Pz8/kpOTcXNzK3cOH2yFc9Gw+JVy37RMMjIyzPbs36qytDt/kdu9PWydXzFFtqZBQgrEJqoiOS4J4hLUzzXd4fleMPjNosV1fhXZnqrk4sWL+Pv7V3YzKp3koEgOiuSgSA6K5KBIDkqVzEHTwGCmGLoFFy9dxL+Bf8UczMrmlns9K0ulPR9yM2FdXzi3Bxp3h2Hbii2yNU1j2sVFeBlsmODQGWvNUMrBNfWc0TTAcON7nQ6sbFVxXdLzycoG7JyAm/+d5lpZsTDjB/R2jrxZf/xNHyclJQV3d/ebrssKq1I92AaDAUdHR5YvX87zzz9PeHg4/fr1Y9asWXTu3Jl//etfjB8/ni+++IK//vqLfv36YWVlxeTJk0lNTcXZ2bnA8ZycnAqcV22UmpoKUGB/Y7GZlpZWrmMZhYSEMHv27CLXh4WF4eLiQrNmzbhw4QLZ2dk4OTnh6+vL2bNnAahduzaaphEbGwtA06ZNuXo1iaxsV9LT9Tg6Opru297eHp1OR1ZWlukxZGdnk5eXh5WVFc7OzqbHV9y+ALa29miaZtrXzs4Oa2tr08zgTk5O5Obmkpubi06nw9XVldTUVDRNw9bWFhsbG7P7ZmXZAQ6cPHkSvV6Pu7s7Hh4eXLx4ERcXF3zqNKDfdF2R4dlfzTOQlnINFxcXzp8/D6hp/tMzsgi/mEh8qg0uHo3551QscYmQmuVEeq4LF6MziE+1ISndnmvJOvL05l/EJ9fC4i9KLq6N7QlZC5MG5RJ56TTe3t7Y2NiYhsUHBARw5coV0tLSsLOzIyAggLCwMAC8vLxwcHAwjSJo1KgR165dIyUlBRsbG5o1a2Zaf9zT0xNnZ2ciIyMB8Pf3JzExkeTkZKytrQkKCuLEiRNomoaHhwdubm5cunQJUOfDp6amkpiYiE6no0WLFgXy9vT0NC2h5ufnR0ZGBvHx8QC0aNGCM2fOkJubi6urK97e3pw7d870xpOTk8PVq1cBtSzc+fPnyc7OxtnZmbp165qes3Xq1MFgMBR4zl66dImsrCwcHR2pX7++aYkGHx8fdDodV65cAaBJkyZER0eTkZGBvb09DRs2NK0feTvzdnFxISIiwpR3UlISSUlJWFlZ0bx5c8LCwkhKSsLGxoYaNWpw8eJFU95paWmm0zBatmzJqVOnyMvLw83NDS8vrwLP2aysLK5duwaoUSfh4eHk5OTg4uJC7dq1CQ8PB6Bu3brk5eURFxcHUO73iIiICDIzM3FwcKBBgwYF8raysiImJsaU9+XLl0lPT8fe3p5GjRqZ5mKoVasWdnZ2pvkdGjduTFxcHJcvXyY7O5vAwEBOnDgBQM2aNXFycjI9Zxs2bEhCQkKxz1lXV1dT3g0aNCAlJaXY56zxPcL4nE1PT78jeV+8eJGsrCycnJyoV68eZ86cMeUN6py6tLQ0AgMDiYqKIiMjAwcHB/z9/e/Ic9ZS3iOMOfj6+lbr94iYmBjS0tKoUaNGtX+PSEtLw9bWtlq/RyQkJJj+N7P094haWiw1Lv9EbsTv6LQ87O3s0ev15Onz0KH+j8vIyEADbKytsbGxIev6/4v2dnYYDAZyr68+4+ToRFZWJgZNw9ramlp5eaRfL4rt7OzQDBq5ebnX93UkKytL7WtlhZ2dHZnX/ye1s7VFA3Jz1b6Ojo5kZ+eQ5tSAHP/ueN/Xr0q9RxgMBsLCwjAYDHfkPcLFxYUGvt7o1j2pimtQl+v6Yhj6NbHxyXh6ehZ4j4jLucb5pPM84TMMnbs/2WkJkJeFlZaHjU5Dn5OJzpCLTstTl8WdS12vA6RdgfQ489vzc6qJwb0+GZlZ2NraYm1lTVZ2lul3npubS15eHjqdDmcnZ9LS1WvK1sYWa1s7MnM17tdl8FnSVk5HniE3Oeem3iOMr92KUqV6sLds2cKMGTMKLNa+bt06xo8fb/rjn9+iRYvYuHEjR44coU2bNowaNYpXXrnRdfrUU09Rv359li5dWuB2f/31F+3atSM1NdU0m7axB/vo0aP8+OOPrFu3jiNHblRj27dvZ8SIESQmJppt++3owQ6PhiW3oQdbr1fHDvCt+KWmlm+BCzHme7CLG54Nqud401z48Gv44QjEJaqe52vJYCjtA7ZCPFzB2wN8PG5cdmwBfR6CATOlB9ucM2fOEBgYWNnNqHSSgyI5KJKDIjkokoMiOShVJoeYv+Gn2eDuB/6dwdGzQg+fmZmJo2MFTT6VkwaRByH2H+g4DgIeq5jj3gF3/PmQv+e6sMbd4ektcGQ1XPhJFcJpsYSTxNz27Xi71kj8rGqU7X50VmBjD9b21y/t1HPItS5cPgLpV4u/rXMt8L0PrG6t2DiTeYF5kSuY32AyvvY+N3WMat2DHRERUaBIBbXmsp2dHV9//TWxsbG89NJLpm3Z2dmmF3XLli1Nn6IahYWF0bNnzyL307RpU2xtbTlx4gQdOnQw7WtnZ0dgYCBxcXGcOXOGvLw807T7YWFhtGzZsti229vbY29fcVWZk4Ma7mwciVFR9Hr4/ZQqXBNT4b5mFVNk6/WQnQuX4+HyNVi943qRnAA+njC2HwyeVXxxu+cIDHpDTTT20x9w7PyNbTodeLnfKJi9PdQxfQp9b/zZzrb4dm6dX3KRXx2La5BzqIwkB0VyUCQHRXJQJAdFclCqTA7HvgDPxtA9RJ0XW8EcKvof1aa94dfF8Pc6VShWkSHjd+T5oGmQmQA2DrC+n/niGtT1G56CwRvh7G6IOqSud3EAdFhZXT8/2sbeVDzv+eVPlqxaS/SVWPx8fZn4n3F07faYadj+kSNHGD58OGPHjmXcuHG8//77LF++vMhdh4wfTP+u9xUornNzc1mwYAE7duzAycmJMWPGmOa0KmmbkZVpiLnl9BlXqQK7R48eTJs2jfnz5zNlyhQuXbrE22+/zfDhw9E0jQkTJhAQEECXLl04ePAgoaGhpt7p559/nn79+jFo0CAeeughVqxYQWxsLP369StyP05OTgwePJipU6eyadMmAKZOncrTTz+No6Mjjz76KF5eXkydOpW3336b06dPs2zZMubNm3fHsrgnAL7ap9ZkblNBr9n8xTWoy99PFV9k5+khO0cVzsavnNyC1+Vcv8zTQ0o6/O8XOHkJNv104zgn18LijWUbnr10E6x9A/48e6No9nKHilpe0NHefJFdnYtrKP0DpOpCclAkB0VyUCQHRXJQJAelSuSQmwlxJ6DD2NtSXMONEaAVRqeDJsFwcR8kXgDPRhV37NtIn5WKlZN7xR3Q+Lu78g/EHlO9+lf+gVG/wt9rii+ujc7tgd+WwMC1cOk3cPEBOz0kfQUNHwX72qZd4+PjmTh7GP7+/gwbNpxvv/2WCZOncfhwVxwcbMnKymLGjBnkHxTdsWNHrK2tVdGfEsWW3XtJSs2gXZB/kZ7rzz//nDVr1tCzZ0+io6OZOXMmgYGBtGnTpsRtlqxKFdjNmzdnx44dzJw5k4ULF+Lu7s7w4cOZNWsWdnZ2LF26lJdffpmoqChq167N7NmzGT58OABdu3blgw8+YMyYMURFRdGiRQt2795tmiV8/vz5rFu3ztTL/cEHHzBp0iRatWpFTk4Offv2NX0SY2Njw3fffcfYsWOpXbs2Li4ujB8/npEjR96xLFo1gqAG6nzgnh3V97a38NvUG+DEBUgqdBr5+ctwJhKa+0PUVdWrnZunvvRlHJqtN8DVJPj1mBrWfU8A1K11o0A+cQFeHwqHw0ofnm2cVfyx+272kZaucJFd3YtrIYQQQtyFctIBTRU8VYmzt7rMruD1mm+X3ExsD7xb+ize5hgMkHxJFc/5i+n4s2BuErKd42HQFxB5qOQiu3H3G+1p/qS6LvMipFgV2TUmJoacnBxatWpFr169CA8PJyXlRvahoaFF1vm+7777uO8+9c/674cOseyzrcx7ZSD+TZoXGRa+a9cuPDw8WLJkCefOnaNXr17s2rWLNm3alLjNklWpAhugW7dudOvWzey2l156qcAQ8cKGDx9uKrgLmz59OtOnTzf97OrqyqpVq1i1apXZ/QMCAvj222/L0fKKZW39/+ydd3xUVfqHn2lJJr0SWkIISSB0sKAgChIXBJXFuoLiquv+BFEpiuBSRBCE1UCkCIgVEARZQRELFsQKKgLSCYSSSvokkzLt/v44mSEhPWTIxJzn8xnuzL13zj3z5c6dfO95z/vCC4/A2zvgi73w4a6Gt2VTIDEZ8o3V7+PnBZ3awqnUivupVcLY67Sg1ZQ9L1tqyz33dIdh/eCDF6B1UNXHcKXwbLvJXrDuypcKc0WCg12ntmBTInUQSB0EUgeB1EEgdRBIHQTNQ4eyEUdVZVO1c+dO4uPjSUlJISwsjMmTJzNkyBDH9ktDgkEYsTlz5vDzzz/j5+fHE088wciRIyu1XVpaSt++fbFYLmaYvvvuu3nppZfqFBLcXMLCgYpzoc/vqTGLNyX5wkCnH7w4In3hkKgjXRWewdC6J4T2vLhs1VW0P2ZbzXOwa+rHJcTGxtKvXz+2bNnCli1b0Gq1rFq1Cg8PDw4ePMi7777LhAkTSEhIqPL9c196idguXbjz3tEQHFNpznVSUhIRERGoVCrCwsIc62rb5so0O4MtuYjeHcaPgv+7AwxFIgy7vhSVwGtb4LNfat7vQi6M6A/xT4oyWyH+EOIHXnWo5KVWga9X7SPsrhaerXeHqffb0LtX/uFpaXh4eDR1F1wCqYNA6iCQOgikDgKpg0DqIGjOOmRnZzN58uSykOAxIiR40iT27t2Lh4dHlSHBVquVxx57jIyMDEaPHs3u3buZPXs2PXr0oGvXrhXaP3nyJBaLheHDhxMdHQ3g2McpIcHmosuuudyw416SaKwsizdjtonEYud/qWim889V3Y7GDUK6QmgPYaTtZto7tPqbDdWZ7HqaawC1Wk10dDQGg4ExY8awfPlyZs2axccff8zzzz9P//79ueOOO6o02D/88APHjx8nPj4egqKrTGhWXFyMm5ub6LZO51hX2zZXRhrsvwAajciM3VDm/QsOn649PHvev4Tp7Bze8GPVhquFZ5853QzmUF0BkpOT8ff3b+puNDlSB4HUQSB1EEgdBFIHgdRB0Jx1aEhI8G+//cbJkyeZPn06d999Nw888AAFBQWOqZjlsVcDGj16NJ06daqwT6OHBJuLYfeChoVnXw7VZfG2m+z7PoDf36y83S9MmGe7mQ7tKUZ9NTVk6K2OS012A8w1iGiGdevWkZCQwLBhwzAajSxYsIAHHniApKQk/vOf/zjOh/z8fLKzswkKEuGqH330EZ6engwZMoQCY1GV8/E9PDwwmUwAjqX9BlVN21wZabAl1Y4c25Hh2RKJRCKRSCQtg4aEBNtN8+7du1m0aBFqtZqpU6fy97//vVL79n0fffRRSktL6dmzJ6+99hpt2rRp3JDg+oRn1wWrBYoyoTDj4sOYAYUXLj4PiIRb5sMH99WcxfuD++Afm2DfOyITd+syU60PaHj/qsJusnfPb/BNBnude6NRzBEtKioCRF1zi8VSIQfV2rVrKSws5OWXX0ZRFH7++WeuvvrqCkb5UsLDw0lNTcVmszlqjnfs2LHWba6MNNgSwDXDs6eNaXpzHRnZPLJTOhupg0DqIJA6CKQOAqmDQOogkDoImrMODQkJtpuv8+fP8+KLL/LJJ5+wYMECrrrqKrp161ahfbPZTEREBKNHjyYnJ4eVK1cyZ84cVq5c2XghwTWFZ5c3mZZSRx3oKo2zsdy64myRFbsmnj4usnPXJYv3j6/CjdOdH76u05cdp2E3F/r164dGo2Hx4sUkJiayefNmgoKCeOaZZ8jJyQEgJyeHOXPmMGzYMMaMGQNARkYG2dnZjvB/Ly8vQGSWX7t2LTExMcTFxTF06FASEhKYMmWKYyT81ltvBahxmysjDbbEgauFZ3u6QARIVlYW4eFOjIlvJkgdBFIHgdRBIHUQSB0EUgeB1EHQnHVoSEiwXi/M28MPP8zdd99N165dGTVqFD///HMlgz1nzpwKr3ft2sWePaIWc6OEBNcWnv2PzfDZZDjyPyjJq1/bKrVILuYdKh5eoeDdqmwZKhKVDZxWvyzeV4LLMPFdu3YlPj6epUuXsmHDBqKiopg1axY9e/Z07GMfXY6KiqJHjx4AZGZmAhAaGgqI/0+9Xk9+fj4JCQncfvvtxMXF8dhjj5Gfn8+2bdvQ6/XMnTuXPn36ANS4zZWRBltSARmeXZHyc45aMlIHgdRBIHUQSB0EUgeB1EEgdRA0Zx0aEhJsH1W0j2jaM4RrtZWtxrJly7DZbDz11FMoioLRaHQY9MsKCbaYwGaG90fVHJ698R4xBzr/vHit1orSX3bT7DDO9ufltnkGV5moqxKNmMXbFRg2bBjDhg2rdnv79u05fvx4hXU9evSosM5sNqPX6yvtq9PpmD59OtOnT6/Ubk3bXBlpsCWV0LvDM/eZ0bs3IKHCX4yqfhhaIlIHgdRBIHUQSB0EUgeB1EEgdRA0Zx0aEhLcuXNn2rVrx5o1aygoKOCXX37Bw8ODAQMGVAoJTkpKYvv27SQnJ1NSUsL58+f597//DdQzJNhmg7T9cOorYWRvWwb736tbePZP8WIk22YV854bu/RXI2bx/qugr0vpob8IzffbL3Eqvt7SXAN06dKlqbvgEkgdBFIHgdRBIHUQSB0EUgeB1EHQnHVoaEjw6tWrmTlzJu+//z4REREsW7aM6OhokpOTK4QEz549G5VKxa5duwB48MEHeeqpp4A6hASbiyE/GfLOwTtxUJJ7cdsnT4iRaVcJz26kLN5XDnGTwUYt88wbgFWxctaUQkeP9mhUdYgAqAcX++s69dFVilLbbH2JMzAYDPj5+ZGfn19lyvqm5tChQ7I8FVIHO1IHgdRBIHUQSB0EUgeB1EEgdRA0Cx2MmfDRIzBkLrTp7ZRDGAyGy/s712qGomyRvduYBaZCMc/5q/+IutFuXhBxkzCvnW6BVl3BUuJa4dnm4svK4n2lyDLnMCVpPhPajOUan561v6GOWBUr+41HyTHnEajzp7dXbKOa7N35e3krYzPLOr2At8arQW00ti+TI9gSiUQikUgkEomk0fH2qqehVGxQnCfMf1GmSEJWfixQpQIPPzFHevAs6PUgaN0qtuFq4dk6Pebrn0HXhOa62FqCXlNzsrhgXSBhbm34Ku8Hent1Rae+fJtY3lwD5Jjz2G882mgmu8RWyjd5PxOtj2iwuXYG0mBLqiQwMLCpu+ASSB0EUgeB1EEgdRBIHQRSB4HUQSB1EDQPHcpCam1W5zRvs6LOOQVB0dUnBlMUMSptzBKGuigbbJaK+7h5gWcIeAWLJGNF2eATCm2vrmyu7bhYeHZmnpG2nn5NcuwSWynL0t7jybYP4aGuOXvxP0JuJyH1bZ4/+196e3XF5zJMqw0bZ0tSKLQWVdr2Q/6vdPBohxp1g9s3WAvZV3iIQmsRz7b/d4PbcQbSYEuqxNvbu6m74BJIHQRSB4HUQSB1EEgdBFIHgdRBIHUQNAsd3H1FBm1DMiVtunOy+AxFtgbUnK4Kmw2yjorRaMPvEBwL6jIzZTVBcT6U5ontVlPF96p1YpTawx/0fqD1AKy4q3KJwhdvg5j/jWctNzHsJtsFwrOb6nwosZXy8Imp7DbsZV/hYd6OWVSjye7uFcP0sPF8m/czB4xHKbaVOLYpioJZsVCqmDApZsw2M6U28dxkM1GqmDEpZjzVemL1nThYdIxsc261xwrSBdDbqysaVcNMtl7tQU+vWG72v55w97YNasNZSIMtqZJz5865/tyhK4DUQSB1EEgdBFIHgdRBIHUQSB0EUgeBS+hgLqq5/rHWDaVNbz48v54v1T9gUjVSWiYFsBRfHIkuApJ/EkbZUipKaVVAJUa4VZqLSwtQWPYoh1qB6zIu8KhvW7S+7Wvvi04PN05v8rnPTXE+lDfXALsNe3n4xNQKJtuiWMgx55NpySHLnEOm+eKy1GYix5JXti6XbEsuFqX2aIfve37AlqzP+ST36xr3KywtYlTQ35jQZmyt4evNDWmwJRKJRCKRSCSSvxLmYti9oNaR28+iurI97TC3qyMZ4Hc1/roAGpaNWRHGWgWYiiqHeYMYLdd5CuOPIl6rtWXh47UdU8FoKWRfwQE+8NyJtnVnHq1raa2abjL8RbnUXNuxm+xVUfN4JullduR+i1LPrOH+Gh+CdYGE6AIrLIO1AYToArEqNia0Gcu+wsOVjl+eG32vrVPYenNEZhFvIlw9i3hhYWHzCG9yMlIHgdRBIHUQSB0EUgeB1EEgdRBIHQS2UiNq9yZKumQurtPcY0VReO7MQqLd2/OY101QaqhojBVFJB1TbGKetmIDxVr1OrWbCNkuygFrSaVjOdB4iP0aUndapQF3Hz4xHebj/O9Z2umFZmPOiizFeGqdN4peYDVyrCiRPGsB1/n04d8nn6/V3L4eNZdxiTP53vArgVp/QnQBFw2ztpxx1gVUMNFu6rqV8q3O5NuPX1u4+pVEZhGXXBHy8vLkDyRSBztSB4HUQSB1EEgdBFIHgdRBIHUAzMWof3i5aeb9ljfXIJbrR1402aWFYMyAwgwKC86RUXqYu1t1K5sLXSoelrJHVaPQ1dHhBlGf2myseT+bEbRta058Vgu9S934MO8bzpWmEqPv2KA2riQltlKWp69tlNFai2LhTEkyR4oSOVZ8iqNFpzhalMh5UxogwrNXpq2v0VyDGMl+I30ja6IX4K52Q6tqfEvooXbn7ZhFlUy2q5lrZyANtqRK8vLyaN++DnNb/uJIHQRSB4HUQSB1EEgdBFIHgdRB0OJ1KG9wz+9xfuZqRRF1oUsLxMjw+6Mq13+2m+z7PoAP7nNsN7lpYcD1eLqnwyVed+fPfxK/7jNSLuQQ1jqYyY/cxZCB/UDjDlp3fvvzJA+Mn8oTj/2TJyeMB407m785wFtrXif9Qjaxke2Y+djfiY1sV7FhrxBKvcPp26MnFstFA3/33Xfz0ksvYTabWbhwIdu3b8fT05Nx48Zxzz33VGhCX2bMTJXmcrse9U0yVp5scy5HihI5WpzIsSJhpk8Un6ZEMVW5f2tdCJszd/BE2wddJjz7UpPdEsw1SIMtqQa1uuFp8/9KSB0EUgeB1EEgdRBIHQRSB0G9dCjJh9R9IiTXVdHoxChjYFS9wnld4nyoLbmX045by+hxXbHZoDgHCjMco82OR/nXxgtiGXGTMM9VmWs7p3YKc33fB7DlIbhwCPxagT4AfNqAPgq07qBxJ9tgZHL880R0CGfMmBF88eWXTFqwhr2j/o2HhwclJSX8Z+FEFEURJbTcffn999+ZMWsWvXr2ZODVPdi8YxdPLHiHb974z8U+eIVAu2s4eeQoFouF4cOHEx0dDUDXrl0BeO+991i7di3Dhw8nJSWFGTNmEBMTQ69evRzNqOo5R7wuNaCdQV2SjAGU2kycLD7D0eJEjhYlcqzoNEeLE7lgzq6yXb3agy76SGI9o4jVR9HFsxNd9J0I1F0sA1bVyLGdK21y7SZ7aeq7f9k515fidINdXFzM77//Tnp6OlarlVatWnH11Vfj4+Pj7ENLLgP7ha6lI3UQSB0EUgeB1EEgdRBIHQR10kFR4MA6OPyhmLuq9aBhCaWcjSJKJyk28I+Am+fUXhKpjCY/H+qY3Mspxy1vru2UN9nmIhFGfalpvtQ4F2XWrzb1bcvgp/jqzXX5vvwUD/dtLEs2lgtJL0FQDHjFOHZLO3cIk8lEj569GHHbbSSeOoXBcPFmUEJCAqmpqRWajo2NZdu2bbRq1YpTJ0+y47u96LTlhsXLzDVqDceOHQNg9OjRdOrUqULN8B07dhAQEEB8fDynTp1ixIgR7Nixo4LBrg/1qQHdmNSWZOyN6AW8nb6Zj3K+JLH4LFYq/3+rUNHBva3DSMd6RhHr2YkO7u1Q11LaytXCsz3U7kxoM7ZFmGtwosHOzc3lmWeeYf369ZjNFUM4dDodo0eP5pVXXqnwpZK4DkeOHGn6H0kXQOogkDoIpA4CqYNA6iCQOgjqpMOpnXBoE/T4B3S+TdT6dVVsVkg/CL+8BrtehFsX12kku0nPhysdnm0phdJ80HnVPnpsD9H+4tnajbAdfSB4h4qHV+jF596h4NWq3OvW4obC+T01t93pljrdeIiNjaVfv35s2bKFLVu2oNVqWbVqFR4eHhw8eJB3332XCRMmkJCQ4HiPp6cnXbp0Yc+ePYwdOxZPT09WzHlabCxnrgGHwX700UcpLS2lZ8+evPbaa7Rp04akpCQiIiJQqVSEhYUBkJSUVDe9LuFywrMberwCSyFeGk8ePTmt2hDt3Ya9PHZyOq9HzeWngn0cLz6Nv8aHWM8ouug7lRnpKDrrO+KlaXgkhquFZycdP91ifiucYrBzc3O57rrrSExMpKok5SaTiXfffZfdu3fz66+/EhAQ4IxuSC4Dm83W1F1wCaQOAqmDQOogkDoIpA4CqYOgTjqc+hraXg29xji/Q5eLWgNt+8C144XBzj8H/h1qfVuTnQ+NFZ5tKqoiLPtC1SPNJXnw9HH4ZWndR49vXwEb7qzaNJc3zl6tRJh+fRizrepRdKgxm/ilqNVqoqOjMRgMjBkzhuXLlzNr1iw+/vhjnn/+efr3788dd9xRwWDbCQ0NZd68eSxbtoyJ899g+7uL8WzXo0JCM7PZTEREBKNHjyYnJ4eVK1cyZ84cVq5cSXFxMW5uboAYkAMRDVtf6hqeXRtGaxGZ5etDW0RNaPtrx3NLDgVWI9/3/IB3Lmypc5Kx16PmUmQroY0uBFVDMqvXgiuFZ7ek3wqnGOwXX3yRkydPotfrmTJlCrfccguhoaGoVCoyMzP5/vvveeWVV0hKSmLBggUsWrTIGd2QXAb+/v5N3QWXQOogkDoIpA4CqYNA6iCQOgjqpENOIvR60Ol9aVRal4XmZifWyWA3yflQl/Ds4hzITaraOJc3zabC+h17x9Nwz/v1Gz2ecLD+n7Eu6PRVm+x6mGuAnTt3sm7dOhISEhg2bBhGo5EFCxbwwAMPkJSUxH/+8x9HiHh+fj7Z2dkEBQUBEBERQUREhOM9PxzN4G/hvSu0P2fOnAqvd+3axZ49ewDw8PDAZBJJvOxLD4/6zZ+uLTz7rZiF5JjzOV+aWmaY7QY6t4KZzrLkUmyroeRYFbxwNoGlnV6oV5Ixf5xbrtdVwrNb0m+FUwz2J598gkqlYsOGDdxxxx0VtkVHR9O/f3+uueYa4uLi2Lp1qzTYLkhL+hLUhNRBIHUQSB0EUgeB1EEgdRDUSQerGXSVzcLOnTuJj48nJSWFsLAwJk+ezJAhQxzbf/vtNx544AGeeOIJnnzySQC+/vprlixZwrlz5wgLC2PSpEkV3mOntLSUvn37NjhjM1o3QAV1zNh8xc+H6sy1nfLh2d++WLfwbK1H5TDsSiHaZUt9gAidb6TR48vmUpPdgGOnpKQAYDSKkltFRUUAuLm5YbFY+Oc//+nYd+3atRQWFtK/f3+WL1/Oq6++Svfu3SkoKADA06tyLfBly5Zhs9l46qmnUBQFo9GIXi/6Fx4eTmpqKjabjeTkZAA6dqx7Ka6aai+DMNmPnHiO16PmkpD6Tq0jzSCSilWsDR1QoTZ0iDbQUT/aV+ONSqVyqSRjQJMkebuUlvRb4RSDnZqairu7eyVzXZ6bb74Zf39/x5dH4lqcOXOG7t27N3U3mhypg0DqIJA6CKQOAqmDQOogaKgO2dnZTJ48mYiICMaMGcMXX3zBpEmT2Lt378WMzf/5T4Upd+fOnePpp5+mXbt2PPTQQ2zdupWnn36aHTt2EB4eXqH9kydPXlbG5vrirq5Hcq76YjFB1nHIOAgZf4rw9d/fqHt49h0rYcuD5cKwqwrRDgV3n3plTgcabfS4UbD3Zff8BiV769evHxqNhsWLF5OYmMjmzZsJCgrimWeeIScnB4CcnBzmzJnDsGHDGDNmDO7u7qSkpDB16lRuuukmNm7cSHh4OJ07d6agoIC1a9cSExNDXFwcSUlJbN++neTkZEpKSjh//jz//ve/ARg6dCgJCQlMmTLFMUp+66231trnQqsRs2JhTfoHdQ7PfjliKhNPzy0zx8IwB+kCKhjmEF1gg+ZBu1qSMVegJf1WOMVgl5SUEBoaWut+Xl5e5OfnO6MLEolEIpFIJC5PWlqayNjcowcjRowgMTGx1ozNp0+fJjIykmeffZaBAwcSEhLCvHnzOHToUCWDfSUzNmMuRvfzK5efwVtRoCBNGOn0skfGn5B1VEQB2EndJ0am6xOe/diPDe9XbTTC6HGj9uXG6Q06dteuXYmPj2fp0qVs2LCBqKgoZs2aRc+ePR372AfIoqKi6NGjBwBLliwhPj6ejRs30rt3b2bPno27uzv5+fkkJCRw++23ExcXx+zZs1GpVOzatQuABx98kKeeegqAxx57jPz8fLZt24Zer2fu3Ln06dOnUh8VReFw0Um+zPue7/N/ZZ/xEP19ruL1qLn1Cs/+qOvKeutTV1wtyZjkyuG0LOL2xASS5smlP9AtFamDQOogkDoIpA4CqYNA6iBoqA4Nydg8aNAgBg0a5Hi9f/9+ACIjIyu1f6UyNjc4g7fJCBcOCwOdfvCiqS7OqXp/d19o3RNCe0BoTyhIdZ3wbLjs0ePG7UvDM1APGzaMYcOGVbu9ffv2HD9+vMK6uLg44uLiKqwzm834+vpW2NfX15dXXnml6i7rdEyfPp3p06dXWK8oCkW2YrIteSQVn+dE8Rm+TppXYZ9zpSlsyfqcNdEv869qsnjLGtBNR0v6rXCawc7KymL48OG17iNxTQoLC/H1dW7SheaA1EEgdRBIHQRSB4HUQSB1EDRUh8vJ2AywevVqtm/fzpAhQ+jSpUul7VciY3OdMnjbbJCXdHE02m6mcxLFiPWlqNQQ3FmY6NY9L5pqv/Cqw7ddJTwbQKfHOmAqmqY01y6CStPwzNgmm4kcSz7Z5lxyLPmU2EoBMFgKsWHDW+3FIP9+3Oh7LQP9riHcva3jva4Unu2hdmdcqzEt2lxDy/qtcJrBLikp4fPPP691P2ekpJdcPjk5ObRt27b2Hf/iSB0EUgeB1EEgdRBIHQRSB0FDdbicjM3Lli1j6dKlxMbGMn/+/Crbd3bG5lozeP/jQ/h6Jux7U4xWV4V36MUR6dY9xTIktsqEcNXiSuHZwNHEsy1mvml1WBUrZ0pT6OjRHo1KU6f9cy0Gcix5ZJvzKLRWPF/UKhX+Wl+CdAF09GjPf8KeoKdX5ZtK4Hrh2WdOJLX486El/VY4xWA/9NBDzmhWIpFIJBKJ5C9FQzI2v/zyy6xZs4alS5fSp08fVq1ahZ+fX5XtOzNjc50yeG+8W8yTzjwKZ3dDq27CQIf2uDgq7V173p464Urh2c0Au+k1KXXLEF8frIqV/caj5JjzyLcW0NsrtpLJVhQFg7WQHEseOZY88iwGbJdEM/hovAjU+ROk9cdf64tGpSHNdAEPtTtuqpqno8rwbElT4RSD/fbbbzujWckVpKXfZbMjdRBIHQRSB4HUQSB1EEgdBA3VoSEZm48cOcKrr76KSqWiT58+rF+/HoDBgwfTvn17p2dsxpACGjf45bW6Z/D+x2bQeYHGacGTgstI7tWYuML3othaUmN5Jl+NN74ab04WJ9HXu1ujHbe8uQbIMeex33iU3l6xWBQrmeYcss255FryMSuWCu/1ULsTqPUnSOdPoNYPN7VbpfZPFCehQkUbt1a19sVVakC7wvnQ1LQkDZx8lavM+fPnycjIoHPnzvj4+Fzpw0vqyLFjx6qcy9XSkDoIpA4CqYNA6iCQOgikDoKG6tCQjM1z587FZrMB8NZbbzn2CwkJwcfHp9EzNqPYIHkPnPgMTn0FmUdECHZ9M3hfKS4juVdj0dTfixJbKcvS3qtx5FatUnONT0++yvuJKH0Efby6olapL+u4l5prO3aT3dOzMxnmLMd2rUpDgNavzFD746nWVzt9VFEUEkvO8r+sL+jqGYWftm4+whVqQDf1+eAKtCQNnGaw9+7dy5w5cxgwYADPP/88RqORMWPG8MknnwCg1+uZM2cOU6ZMcVYXJJeBxWKpfacWgNRBIHUQSB0EUgeB1EEgdRBcjg71zdg8c+ZMZs6cWe3+l5uxGcUGJXlgzIKiTMg5CSc+vbhdpRLbj2yF0R/B+6NcI4O3C9GU34sSW6lj7vG+wsM1zj2+L/g20k2ZvJb6Dp5qPb4a77rlR1LAhg2LYsWqiNrn7mo30kwXKLJVnyjPU62njVsrjNYidCod7mq3Oh3PnkXcYC2krVso/9d6dO19dCHkdbJlaeAUg/3HH39w0003YTKZ6Ny5MwDPP/88H3/8sWOfoqIipk6dSmRkJKNGjXJGNySXQUvJ8lcbUgeB1EEgdRBIHQRSB4HUQeASOtisoK49mVQlFEUkICvKLDPVWWAr98ewAni3htiREBkHkTeD58V62i6VwdtFiIipx1z2RqS8uQbYbdjLwyemVmuy3dVuPNvu35wuOc8h43EyLTkUWososBopsBopvGRpLFsWWI1YsDraea79//F7wSG+yf+51j7G+Q3gZv/rcVPXr6Svu8qdzp6RdNFH1ilpmivhEteHJqYlaeAUgz1//nxKS0vp2LEjcXFxGI1G3nzzTVQqFSNGjODll1/mlVde4Z133mHFihXSYLsgwcHBTd0Fl0DqIJA6CKQOAqmDQOogkDoImlwHmxWyT0JQdN1MtqVUGGljpliaLxl11OjAMxi8QiAgEvo/DdHVjLK7WAbvpqbEVsrrF9Zf8cRal5prO3aT/Wb0y+wrPMzhopNkWXLINOeQZb64zLLkYlGs1bReNV5qT0J0AWSb81jW6QX+nfifKutP27nR91pWRb/U5HOirzRNfn1wAVqSBk4x2D/88ANqtZpdu3YRFhbG9u3bKSoqQqVSMWvWLLp27crSpUtZu3Yt+/fvd0YXJJfJ6dOnW1QyguqQOgikDgKpg0DqIJA6CKQOAh11yMSs0YG5pPEPbrNCyq/CLJfkQbtrKptsmxWKc8Q+xkwoNVTcrlKDPhC8gsEzBDz8RCi4xSQSk9U22igzeAP1C89uKGabhSxLDlnmXDLNOWhQc5VPDx47Ob1ac7vbsJdHT07j9ai5LE9bW6MJ9tP4EKwLIEQXSLAukBCtWFZYV7b+0vnNVdWfttPUpbKaEnmdbFkaOMVg22s0hoWFAfDVV18B4O/vz9VXXw2Al5cX/v7+5OfnO6MLEolEIpFIJFcGczG6n1+p3VgGRlGSvo+97dpy0HiMAmvh5R9bUaAwHcyivBelQMGnIqTbZhGh3+YisJQgYr3LoXEX/dXqxVJ1FoqAItCqtHT0CON6o4Z2AIGdau+LTo/5+mfQNbG5ri17trOob3j2pe+1jyaLEeVcMi3lR5hzHc/zrBVvjnzf8wNeT1tXo2m29+eN9I282vE/rEp/X5hkXSBBuoAKJtq9iszddeXS+tN2WrK5lrQ8nGKwfX19yc/Pp7S0FHd3d7Zv345KpWLQoEGOfdLS0sjJySEwMLD6hiRNRvv27Zu6Cy6B1EEgdRBIHQRSB4HUQdDidShfC/r8nhpDo4siB/LfpASSzqYR5dedYLdgVFxO1mZFHN+rqv8DLbjrQe0N7vb51Goxsq3WgkojRq2rbFWhxFbC11nf8FnOCZ4OaU1P/w516pHRpODfhEm865I921nHrSk8e030y/xU8Dt/Go9XCM22Py+0FdXreBo0BOn8CdYF8sGFT5nQbiz7Cg/XGp5t12VOh4kN+Zh14lKTLc21vE5Cy9LAKQb7qquu4quvvuLxxx/H39+f06dPo1KpuPPOOwE4ceIEjz/+OADXXHONM7oguUxKSpwQwtYMkToIpA4CqYNA6iCQOghcQgdzUdOUZipvrkEs14+s1mRv91eR2rYzs71vpaMmBGxqoHwGZUU8lCqeK0q5fVQi3NxSIkK/q0PRgNajLPmZGsqb+bLmq3+vFbNXLMtU37IqqIAErGjr8GdjU54Pzg7PVhSFfGtBBXPspnLjBr+reOzk8zWGZ/+rLDz7rfTN1e7nptJVGE0OuSQk2x6uHaILxF/rW6mkliuFZ9tN9tLUd6/4zQ5XxCWuk01MS9LAKQZ70qRJ7Ny5k/fee8+xLjo6mvvuu4+MjAxiY2Md659++mlndEFymWRlZdG6deum7kaTI3UQSB0EUgdBs9ShKFvMPy2rHdwYGE6foLUmpnEa0+jAp02znLfa5OeDuRh2L7jy834vNdd27CZ79P/g9DeQcRhMBVBq4Dfvs1zXZhAd1QFgKhTGV7GWLetxbob2KDunc2vf16ct+LUXI9b1QaVC5+bFndYwZp1fwrGi03T3qv18b6rzoaHh2TbFRq4lv8Jocma5+c1Z5cK1s825mJSKc+1FePb6OodnL46cwXsX/ldpfnOILrDuJbKqwdXCsz3U7vxfyP0t3lyDC1wnXYCWpIFTDPawYcNYt24ds2fPJiUlhcGDB/P666+j1WoJCQkBwNvbm9WrVzN06FBndEEikUgkErhwFH5bDTmJjd50WHEJnG7EeZ5qHbTvB9dNADevxmv3r0w9wrPrhcUExgtQmHFxWZgBxgzwagXXjoON91Rd+xnE+vfvhPs+gF+WOfbLvuEa2ptUoGRXf2yVWoRw2x/2BGPl16m10LoX2MwiYVl1eIVAm94NK91VRpgiIgNyLHkNbsPZ1B6evYBv8n7mD+NhMsvNZc625JJtzsNK/TJn+2i8CC4bSd6W/RWPtxlTr/Dsqe3/r0Gfsy64Wnj2uZNnW0xiK4nEjkpRlJoChJzC7t27ue6663Bza3gSheaOwWDAz8+P/Px8l6wLZ7PZUKsvZ17YXwOpg0DqIJA6CJqNDnnn4PMp4Bcu6vf6tr8so3EpiqJc1mhTBawmyDgEhzaJEcdhr4gMzs0AxVSEys0FwrOh5vJQ5uKLRtluli810Pb1NY0MP30cDqyFXfNq7+OgGXDN/8EPr4C7L//0PclDbf/BYJ+rK5jlnd/uJv615aSkpBIWFsbkyZMZMmQImzdv5q233iI9PZ3Y2FhmzpzpiAJct24db731FlmZF+jfK4oXx99Dq8Byf094hUC7ayg1W+jbty8Wy8W61nfffTcvvfQSZrOZhQsXsn37djw9PRk3bhz33HNPpY/xzxPP8lCrOxnsf32tH/lKXh9KbSasipVHT06r1dy+HjWXcYkzq90vQOtHsDbgYobs8lmztRefB+kC0Ksr31irzuTbj3+lTW6JrdQlwrObze+Fk5E6uLYGje3LmsRgS1zfYJ84cYKYmEYKfWzGSB0EUgeB1EHQbHT4bQ2c+Q5GvgG6xs8oXFhYiLe3d+M2mvoHfDML/rYIWsXWvn9TYy5umrJM1YVngzDZ938IPy6GU19C4QVhmksL6ncMtUaMVnuFgne5R/trRT3o9++sfgTb3o9LzH5VRjU7O5tBgwYRERHBDTfcwBdffEFWVhZvv/02o0ePplevXvTu3ZvNmzcTEBDAN998w9dff8348ePp27cvPXv04IMPNtAloi0bF04QjZaZa9QaDh06xF133cXw4cOJjo4GoGvXrgwaNIg333yTRYsWMXz4cFJSUjhw4ACbNm2iV69eFT5KfQx2QWkhPu6N+72wKTaSTekcLUrkSFEix4pPcbQokXdi/suWrM9Zkvp2rW1MbPswY1vdyZbszwnWBpQz0YEEaQPQqS8/qLMqk92UI8hNlU29PM3m98LJSB1cW4PG9mVOCREfPnx4levVajVubm4EBgZy9dVXM3bsWDw9mzDVpKRaTCZTU3fBJZA6CKQOAqmDoNnokPGnMBlOMNcg7sY3Oq17gc5L9N3VDbazwrNBJPQqyQNDChiSxTI/WdRm7vtw7eHZG+4W4dnnfqw4PUDjdtEoX2qcy7/2aiVqQtc02jJmW80mv456pKWlYTKZ6NGjByNGjCAxMRGDwUBsbCzbtm2jVatWnDp1ih07dqDTiVrUP//8MwCzZ8+mS5cuqFTw9jvvcjr5ApGdu1Wog33s2DEARo8eTadOnSpUb9mxYwcBAQHEx8dz6tQpRowYwY4dOyoZ7LpSYitlZeb7lzVqarAUcqz4FEeKEjlaZqaPFZ2qMsv2f868ysqoefUKzx7f5oEG9asuuFp4dlOba2hGvxdORurQsjRwisH+/PPPUalUVDc4rlKpePvtt3nttdf44YcfZKkuF6TRR2WaKVIHgdRBIHUQNBsdLMXg7rwIIa3WCT+hajW4+4i+14Vmkj27AjarCMvOT65ooC9dmqsoW/T0cfjxlZpHju39+Ske7t0AF45cNM7uvo0Xeq/TV22y62GuAWJjY+nXrx9btmxhy5YtaLVaVq1ahaenJ126dGHPnj2OAYkVK1YA0KpVKwA++eQTFEVh3x/7xcfOUxNZzlzDRYP96KOPUlpaSs+ePXnttddo06YNSUlJREREoFKpCAsLAyApKalBctQ3g7dFsZBUkuww0keLEzladIoUU3qV+7updETrI4jVRxHr2YlYzyhi9VH4arxl9mwXptn8XjgZqUPL0sApBnvs2LHVzkszm81cuHCB3bt3c/z4cebNm0d8fLwzuiG5DFpKlr/akDoIpA4CqYOgWelQxW/Rzp07iY+PJyUlpc7zXat6z6BBgyq1ff78eebOncuvv/6Kv78/48ePd8xpret81zobQFfNnj1mK2SfgguHKo4+F6SI5wWpNZeWKo8+UMyd920nMmEn74EbnhMj5rWFZ9t1iRjY4I9aK5ea7HqaaxDRfdHR0RgMBsaMGcPy5cuZNWsWn376KXq9ntDQUObNm8eyZcuYOHEi27dvZ/To0Xz++eesWbOGNWvW0KlTJwCKtP6V8gyYzWYiIiIYPXo0OTk5rFy5kjlz5rBy5UqKi4sd+XDso+PFxXW8uVOO2jJ4Z5pzhIkuOlVmpBM5WXyGUqXqEa22bqHE6stMtGcnYvVRRHqEVxvG7aFyvezZT7R5sMWba2hmvxdOROrQsjRwisF+5513at3n888/Z/jw4Xz88cfSYLsgiYmJMusjUgc7UgeB1EHQnHXIzs5m8uTJREREMGbMGL744gsmTZrE22+/zYwZM+jVqxcDBw5k8+bNPPHEE3zzzTfVvuerr75yjCTaeeqppzh16hSjR4/m4MGDzJgxg7Zt2zJgwADee+891q5d65jvOmPGDGJiYhoWjuvM8OxLsYdrlxaCZyC8P6rm8Oz1fxfh2Z9PqX4/lVqUJfNtd9FAV1q2q/4zNVJ4dqNgN9kNnIu+c+dO1q1bR0JCAsOGDcNoNLJgwQJ+/PFH4uLiiIiIICIiwrH+p59+Ii4ujk2bNrF//348PDzYv38/c+fOxduncsTGnDlzKrzetWsXe/bsAcDDw8MRtmlfenjUL6y4tgzeK6Pm8dSpOVWOLnuq9XTRdyLWsxNdPaOI9Yyisz4Sf239I09cLTz79NFTzfY62Zg059+LxkTq0LI0cIrBrgvDhg3Dz8+PlJSUpuqCRCKRSFoYDZnvWt17LiUvL48jR45wyy23MG3aNAoLC7nqqqvYsmULAwYMaLz5rpcTnm3HZoPi7EuyZ1+omF27/HqrSYRnf/9y3cOz//4G7Hy+snH2ay/mO2su40+QRgrPbjR0eszXP4OuAce1/x1kNBoBKCoqciyHDh3Kq6++Svfu3SkoEEnavLy8SExM5M0332TkyJF0796dDRs2AFSZQGjZsmXYbDaeeuopFEXBaDSi14t+hoeHk5qais1mIzk5GYCOHTvWue81Zc4GYbIfT5zB61FzefHcUgqsRrqWC+8Oc2+DWtV4WYVleLZEInEFmsxgA1gsFjSaxiuZImk82rZt29RdcAmkDgKpg0DqIGjOOjRkvmt17/H396/Qto+PD56enhw8eJB9+/Zx6tQpQNy1BxpnvmtdwrMNaSKx16VmuXw5qqLMuodp2/nyORj1dv3Cs+9ZX79j1IdGCM9uTApKrAQ2YDp8v3790Gg0LF68mMTERDZv3kxQUBAxMTGkpKQwdepUbrrpJjZu3Eh4eDh9+/YlPz+fTz75hH379nHDDTfw0Ucf0b9/f8LCwigoKGDt2rXExMQQFxdHUlIS27dvJzk5mZKSEs6fP8+///1vAIYOHUpCQgJTpkwhNTUVgFtvvbXaviqKQlJpMt/n/8ot/gNYn7mtxuRiIEz2G+kbeanDlCuSdMtD7c6ENmOb3Fw35+tkYyJ1EEgdWpYGTWaw33//fYxGo8uma2/plK+X2ZKROgikDgKpg6A569CQ+a56vb7K9/zvf/9zzF8F0Gg0zJw5k1mzZnH//fcTGhqKt7e3Y07rZc93rak0FVQMz/7k1dpHmkHMca42q3aris/txvUvFJ7dmDT0e9G1a1fi4+NZunQpGzZsICoqilmzZtGlSxeWLFlCfHw8GzdupHfv3syePRt3d3datWrF/PnzefXVV9myZQtxcXG88MILAOTn55OQkMDtt99OXFwcs2fPRqVSsWvXLgAefPBBnnrqKQAee+wx8vPz2bZtG3q9nrlz59KnT58K/TPZzBRajazJ2MS0M4tILktC9nnud7weNbdeGbyvFK6QPbs5XycbE6mDQOrQsjRwisEeP358tdusVivnzp3j66+/RqVScfvtt9er7X379jFx4kQOHjyIXq/n3nvvZdGiRbi7X7xwp6Wl0bt3bxYuXMg///lPx/p3332XuXPnkpaWRmxsLEuXLuX666uu6Wg0GpkwYQIff/wxFouFkSNHsmLFCkcGvBMnTjBu3Dj27t2Lj48PEyZM4Pnnn6/XZ3FlLly4UGluYUtE6iCQOgikDoLmrEND5rvabLYq3/P9999X+g278847uf766zl16hQ9evTgb3/7m+N347Lmu5YWwA+L6h6ePXIVbPt3ZdNc3kh7hYBGVz8BwSXDs7lxepOaa7i878WwYcMYNmxYpfVxcXHExcVV+Z477riDO+64o9L69u3bc/z4ccdrX19fXnnllSrb0Ol0TJ8+nenTpzvWWRUreRYDOZZ8ss15FFgLyTBlcbr0vHiPSsvV3j25zrcPWeYcl8rg7Uo05+tkYyJ1EEgdWpYGTjHYK1eurDaLOOAo39W1a1dmzJhR53ZtNhu33XYb06ZNY9euXaSmphIXF0dwcDAzZ8507DNmzBiysrIqvHfXrl08+eSTfPbZZ1x77bUsW7aMO+64g7Nnz1ZZi3vChAmcP3+ekydPYrFYuPfee3nuuedYvnw5ZrOZ2267jTvvvJPPPvuMw4cPc9tttxEdHV11RliJRCKRuAQNme9qL3N06Xvso9DleeONN0hLS2PWrFmkp6eTl5fnyDZer/muigKp++DYJ3D8Y9AHiZHp+oRn/7MOI9gNxcXCs5ukVJkLYlWsaFT1m3qnKAoFViM5ljyyLXnkWQzYlIo13t3Ubgzzv4kxrUZynU9vPDUV/59dKYO3RCKRNDVOMdg33nhjlQZbo9E4QpuuuuoqHnnkkSrNbXXk5uaSlpaGzWZzmHS1Wl2hjRdffJH27ds75rfZWbNmDf/4xz8YMGAAAJMmTWL16tV88MEHPPzwwxX2LSoqYv369ezatctRo3vhwoUMHjyY//73v/z000+kpaXx4osv4ubmRp8+fXjqqadYtmzZX8Zgd+nSpam74BJIHQRSB4HUQdCcdWjIfFc/P78q39O/f/9K813T09NZv349ZrOZQ4cOoVKpHL8Ltc53tVmhKBsK0uCzyVCSe3GbSgXfvgj3b4ENd8nwbBekrt8LxUnHtypWkkqS6ejRvlaTXWwtcRjqHEs+Zpu5wnZ3tRuBWn+CdP4EaPxY776NB1qNZLB/1VF/rpbB2xVoztfJxkTqIJA6tCwNnGKw7fN8GpugoCAmTZrElClTeOaZZ7BarYwcOZJJkyYB8O2337Jx40Z+++23SmngDx8+zCOPPFJhXdeuXTlw4ECl45w8eRKz2UyPHj0q7FtcXMyJEyc4fPgwMTExFebede3alQULFlTb99LSUkpLSx2vq8pA60okJSURHR3d1N1ocqQOAqmDQOogaM46NGS+a3XvUavV5OXlVZjv+uSTT5KcnMzHH39MSEgI//3vf7n66quBaua79ugG+efLkpBlgs0CxbnCXOs8IWoodLkDYoaDd1lonYuFZ5uvf7ZB2bP/ahRbS/DRete4j5daT76l8X//rYqV/caj5JjzyLcW0NsrtoLJNtss5FryybbkkmPOp8hWce6/RqUhQOtHkNafQJ0/Xmq9Y6Ak31KAglJp1PpSZAbvijTn62RjInUQSB1algZNmkW8vthsNvR6PcuWLeORRx4hMTGRUaNGMXv2bJ588kkefvhhtmzZ4pjvVp6CggK8vLwqrPP09KSwsLDKfYEK+9tHyQsLC+vVlp0FCxZUqkUJcOTIEby9venSpQtJSUmUlpbi6elJu3btOHnyJCAKsyuKQkZGBgCdO3fm3LlzFBcX4+HhQYcOHRzzrUJDQ1Gr1aSlpQEQHR1NamoqRqMRd3d3IiMjOXr0KAAhISG4ubk5QiY7derEhQsXKCgooLCwkKioKA4fPgyImxuenp6cPy/mYHXs2JGcnBzy8/PRaDTExsZy+PBhFEUhICAAHx8fzp07B0CHDh0wGAzk5uaiUqno1q0bR48exWq14ufnR0BAAGfOnAEgLCwMo9FITk4OAN27d+fYsWNYLBZ8fX0JDg7m9OnTgJhnVlJS4pgO0LVrVxITEzGZTHh7e9O6dWtH9t62bdtisVi4cOECIO6inTlzhpKSEjw9PWnfvj0nTpxw6A2Qnp6OwWCgQ4cOJCcnU1RUhIeHBxEREY6Q0VatWqHVah2jUVFRUaSnp1NYWIibmxtRUVEcOXIEgODgYDw8PByhoZGRkWRlZWEwGNBqtXTp0oVDhw4BEBgYiJeXl0PviIgIcnNzq9Xb19eXs2fPAiIMtaCgoFq9AwMDHZmLw8LCKCoqIjs7G4Bu3bpx4sQJzGYzPj4+jpJFBoOB4OBgTCYTmZmZgMisfPr0aUpLS/Hy8qJt27aOc7ZNmzbYbLYK5+zZs2cpKSlBr9cTHh5e4ZxVqVSkp6c7ztmUlBSKiopwd3enY8eOV0Rvb29vxzkbERFBXl4eeXl5qNVqunbtypEjR8jLy0Ov1+Pv7+84Z8PDwyksLLwi56yrXCMyMzOx2WzExMS49DUixmQCs5mSshuaPj4+GI1G+vfvz4033oi7u7sj7NtkMjFw4ECuvfZaAEdyMoPBgEaj4W9/+xv9+/cHxLxplUqFWgXqwECOHj3q2Fen0/H66687fg/c3d0xmUyUlJQAMHXqVCaO/xcq4wV0pdkop75EpVwc01S0HljdfMmNeRCvkUvIyisU52xyDl26tOLQoUN4e3vTYfRWVO//3RGerYzZRmZuARcunLri14g/9h/C19eXdu3atchrhI+PD6FhrVmZ+T5PtH4QY24her2+ymtEoNmHvdqD3OR+LVpFg06nw83NzXEe6vV6rFarY46+/Zy12WxotdoK56zjPNSo2V8kzDVAjjmP/caj9PaKJdmYTqolg0KliPKoUOGt9sQXbwLUvrTyCsZYaAQT6FQazCqz45z9wfQbpSWlcMbESY+TNV4joqOj+XfwP0g/l0ZpaanLXyOc+XdEQUGB4/yJiYlpsX9HFBUVOfrUUq8RarUam83GkSNHsNls+Pv7t8i/IwwGA+3bt3d4DZ1O5zLXCPt3t7FQKYrirGilRmfLli385z//cXxJANavX89TTz3F1VdfzdChQ5k8eTIgTuwXXnjBkeSsV69ePPbYY0yYMMHx3rvuuovw8HAWL15c4Th//PEHffv2paCgwGHWCwoK8PX1Zf/+/XzzzTesX7+e3377zfGeTz75hLFjx5Kbm0tVVDWCHRYWRn5+Pr6+vpcnjBM4ffo0kZGRTd2NJkfqIJA6CKQOgmajw7Z/Q/gA6PNQ47dts0L2SQiKBnUtc14VmxiVLsyAwnQwGStud/cF79Yi8ZiHH3z8f3Xrt7nYJcKzm8354CTK14KuLTQ6sfgMC5NX0d69DYP8+hGkDbisOtA2xcqx4iQMVYyK+2p9ifGI4ETJGQwWAx5qD/y0PvhpfPDVeKNV1zzGUmIr5U/jcb7N/5lBfv34Z+jddepTSz8f7EgdBFIHgdTBtTUwGAz4+fk1mi9rViPY586dq2BSQSSZycnJ4bvvvmPPnj28+OKLgBBq/PjxfPjhh2zfvp3u3bs77pDYOXLkCMOHD690nM6dO6PT6Th8+DD9+vVz7Ovm5kZMTAwXLlzgxIkTWCwWtFqtY/ulYenlcXd3r5Dp3NVp165dU3fBJZA6CKQOAqmDoNnooFKD1Vz7fvXFZoWUX0VId0ketLumssm2Wi7WnTZmVOyHSg2eQRdN9aXm2GoW+9SGi2TPdoXzodha0iSlmcqbaxA1nx8+MbVakx2lj2Bi20f4MHsHq9I2YMWKTbFhVWzYsC+tYqnYsGLDplix2rcpNqxY8VTrae/emjMlKRRYjZWOY8dH40VHj/bYFFuthroqArX+3B44hFFBf6vze1zhfHAFpA4CqYNA6tCyNGhWBnvo0KFMnz6d+fPn89xzz3H27FnmzZvHM888w3//+98K+146gv3II48watQo7r33Xm644QaWL19ORkYGo0aNqnQcT09P7rvvPqZNm8amTZsAmDZtGvfffz96vZ7BgwcTHBzMtGnTmDdvHsePH+e1117jpZdecroGV4qTJ0/WeMOgpSB1EEgdBFIHQbPRwact5CSSY87jD+MRci35lbIj1xtFgdzTomwWgAko2gsBkaBYheEuyQdTodjXjloD7n7g4QvuPqDKBks25Ikbv1qVljD31vRUtca9KFv0vS64QPbspj4fSmylLEt774rO+y2xlqAAj5x8rlJ5KrvJXh31EktT3+Ongn0UWo0UWI0UWo0YbcUol5nq7KPYlezI+ZZv83+pcb8sSw5x/v15OPSeemvjptbhr/GtsSpMVTT1+eAqSB0EUgeB1KFlaeAUg/3tt99y/fXX172+Zx3p2rUr27dvZ8aMGSxatAg/Pz8eeOABZs+eXet7hwwZwooVKxg3bhzJycl069aNzz77zJElfP78+axfv94xyr1ixQqmTJlCjx49MJlMjBw5kmXLlgGg1Wr58ssveeKJJ2jdujXe3t489dRTFWpuSyQSicSJmItqN5fhA9j250L+d/RZtB6B+Gl96l3CqAKKApZiYaTdyq83Ql4maPUXt+sQo9BqrXiobKDKA0seWCo3bbKZybPk45mfyhR3haj21za8ny2I8iPI+woPNzhztaIoGKyFZJpzyDLnkGXJJcucQ2bZI8ucK5aWHDrrI1kSOZNxiTOrrP0MwmT/O/E/vB41lz8Tj/OH8XClfTRo8NF44q3xwkfjhZfGEx+NF94aL7w1nnirvSq+LtvPR+NJK10wz7V/nOPFSdX2AUSprGlh41p8wjGJRCK5kjhlDrZarcbNzY1rr72WQYMGcdNNNzFgwIBGN9zNmcaO9W9ssrKyCA4ObupuNDlSB4HUQSB1EDS5DnWce7zP8CcJiYu4w/s6bvW6Gk83P1Huqj4o9n9UwjzbrNXvq9aA1kMYbJW6bmHejuPYyChOZY3hK5IVA4u7xTcbU2Q0F+HVBCPpl4ZnQ8XayzbFRp7FIEyyJYfsMpNsN9GZZUY60yy2lSqmOh33+54fsCXrc5akvl3rvhPbPsy/Wt/H0aJTl5hoTzxU7vUeHb6UqjSw01Slspr8+uAiSB0EUgeB1MG1NWgWc7Dj4+PZvXs333//PT/88AMvvfQSOp2Oa665hptuuolBgwYxYMAA9HpZ1sNVaUa575yK1EEgdRBIHQQB3k14s9RcfLFE1fk9NZam+rlwPx0C+3BX6wdF6LbVRIUqxIoiymJVeJjF/Gmb+eK6gI5QYoCirNr75xkMnoH1M9cAai2h/rH8K6QLU88v5qDxGNf69KpfG01Aia2UFRnrrmh4ttlmwaJYagzPXhk1jydOzao1hPpSfDReBGsDCdYFEKILJFgXWLYMIEQrXnuq9Uxo+xD7Cg/XOnps16W/b98GfdbauLT+dPljN1UdanmdFEgdBFIHgdShZWngFIM9ceJEJk6cCMCff/7Jd999x7fffssPP/zAjz/+yIIFC9BqtVx99dUMHjyYefPmOaMbkssgIyODkJCQpu5GkyN1EEgdBFIHwFyM5seFTZO5ury5BrFcP7Jqk11aSLLhON28ukBpPlhLwVwClhIxEm0pEevq8ntfWgDtroLUfSKxWXV4hVSd8KwehAL+Gl+SS9Nd3mA3Vnh2eQqsRtJNmaSZLpBWtkw3ZzrWdXRvz8sdn6s1PPvxxBm8HjWXcYkzOWg8VsksB+sCywyzMNIhukCCdAHo1XW/eVSVsbVzJQ3upSa7Kc01yOukHamDQOogkDq0LA2cnuSsR48e9OjRw1Ee68iRI+zatYvvvvuO3bt388svv0iDLZH81SjJFxmUbZeZUMpZqNUig7O7T1P3pHlRj9Fjpx67PHaT/Y8P4fsFcPRjKEiBknysV3XHLVIPxv0V3rLz5z+JX/cZKRdyCAsNYvLDoxhyw7Vs/uJn3vpwB+kXsontHM3Mac8Q270naDxYnJDAypUrK7Tz3ZszaB3sL16UmevzKanMnTuXX3/9FX9/f8aPH88999wjPoLZzMKFC9m+fTuenp6MGzfOsa087mo3LEoVE7WroLlkz7YpNrIteTWa53RTJoW2okrvLc/rUXN5I31jjSPH9v68kb6RN6MX4ukkfVxp9Njel6Wp717RaAKJRCKRVKbJ62AfP36czp07N2UXmgRXn4NtNpvR6XRN3Y0mR+ogqLMOhhT4ZRlcOEzdhgabEJUaQnvC9U8Jc1QHWvT5UJXB7XRLw0221SxqQxdlQ3GOeFT1PKAj3DgNNt5b2VyXp9MtcN8H8MF9jv2mXtuHqzuN4V6f/iIBmdaDbEMJg/7+IBHhYdxwwwC++PIrsrKzefvttxk9ejS9evWid+/ebN68mYCAAL755hsAHn/8cQ4cOMCDDzwAhmQwFzH2thvw9vSoMHI9atQoTp06xejRozl48CC///47b731FgMGDODNN99k0aJFDB8+nJSUFA4cOMCmTZvo1aviSPXUpJe52rsH94aMqFHCEltpkxiq2ub9vhG9gI+yv+RHw28O85xhzsJcx5sGfhofWruF0MYthNa6ENq4tSp73YpOHh0IdQvikROVw8Mv7ceVMrn1qYPtbJrqhkt5WvR1shxSB4HUQSB1cG0NmsUc7PrQEs11c+DcuXN06tSpqbvR5EgdBHXSoTgPvpwObp7CtAZEiuzJrojNAtkn4NBm2Dkdhr8m+l0LLnE+1CV7dqMfs5bR4zFbwZgF+edrNsvFOVCUA8XZF8tc1cbTx+HHV2s21/a+/BQP922EggzwbQdpK8C7C4Rc79gtLeMQJpOJHr16M+L2kSSePoOhoIDY2Fi2bdtGq1atOHXqFDt27Kjwh8CxY8fo3r079953H77eXrhlHhDh4uXMdV5eHkeOHOGWW25h2rRpFBYWctVVV7FlyxYGDBjAjh07CAgIID4+nlOnTjFixAh27NhRyWDXBWeEZ5fHZDM7MmnbM2jr1e7c7H89j518vsbw7MdOTuf1qLnsyPmWXwsPOrapUBGiCxSGWVdmoN3KGWhdK9q4heCpqf2GjauEZ4NrjR43tbkGF7lOugBSB4HUQSB1aFkauOhfv5Kmpri4uKm74BJIHQR10uHs92AqgOFLRJInVycwEkJ7wMePQ8pe6Dio1rc0+flgLobdC67M/GdFEaH+Gh28P6p6g3tqJ6z/uxg9/nZO7Ub4Ujz8wTMI9IEXH/bXnoHCtA+cJkLSaxvBtuuir/78i42NpV+/fmzZsoUtW7ag1WpZtWoVnp6edOnShT179jB27Fg8PT1ZsWIFAPn5+aSlpZGTk8OAAQNwd3dnyuTJPDTiOgiKdsy59vHxwdPTk4MHD7Jv3z5OnToFQGJiIgBJSUlERESgUqkICwtzrKsv9Q3PtlNsK7lYbspunC3lMmqXW+ZZK98A+b7nB7yetr7O4dlLO83m54I/HMa5lS4YXSPddHOl8Gx7f/4v5H4Zmo0LXCddBKmDQOogkDq0LA2kwZZUiSypJpA6COqkQ8YhCOnaPMy1Hd92IgQ5/WCdDHaTng+NPf9ZUcQIbN4ZyD0jlpc+H78fDqyt++jxHa/DpvurNsrlX9ufe/jXPSHYmG1Vj6JDvULV1Wo10dHRGAwGxowZw/Lly5k1axaffvoper2e0NBQ5s2bx7Jly5g4cSLbt28nPT2d8PBwunfvzsCBA3n77bdZ8PLL9OmzgZ4hF/uv0WiYOXMms2bN4v777yc0NBRvb2/HHxXFxcW4uYkC2vbR8fr+wVFdeLbdZK+JXsDnubv5rfDPS+o459Q6v/lSNGjKEoKJRGCf5+xmXJsx9cqefXvgkHodsz64WnKvC+cz8I2SeR3k76ZA6iCQOgikDi1LA2mwJVXSoUOHpu6CSyB1ENRJB3MRuLtePoFacfcT5rUOdApv4+TOVEN9smfbURRRVir3DOQmVW2ga/vcnz4J926s3+jx4zWPbjYYnb5qk13PeeA7d+5k3bp1JCQkMGzYMIxGIwsWLODHH38kLi6OiIgIIiIiHOt/+ukn4uLi2Lnz4jF9fX154okn2Pvr7/Ts1adC+3feeSfXX389p06dokePHvztb3/D29sbEH9cmEyi1rJ9WZ8/OGqa+wzCZP+rLDz7w6zPqtzPTaW7mE1bW7kUlT27doguEH+tL+oqyo3J8Oyqkb8XAqmDQOogkDoIpA4tS4MrbrDPnz9PRkYGnTt3xsdH3ul1VY4fP0737t2buhtNjtRBcDk67Ny5k/j4eFJSUggLC2Py5MkMGTKEzZs389Zbb5Genk5sbCwzZ84kNjYWgMWLF1fO2Pzdd7Ru3brCupqyMtc1Y3OdMRej+v7lK1+eqrb5z6M/gtTfIXnPRfNsX5prGbFUqcCnLfhHiEfAJUu/MNC6N9ro8WVzqcluwLFTUlIAMBqNABQVFTmWQ4cO5dVXX6V79+4UFIgQaS8vL/bv38+2bdu466676N69O4WFhYAYDb+UN954g7S0NGbNmkV6ejp5eXkMGjQIgPDwcFJTU7HZbCQnJwPQsWPH2vtcmo672p23MzbXOTw7IXIWO3K/Jahc/eYQXSC+Gm9UKlUdlKoeVwzPntBmbJOHZ8vfC4HUQSB1EEgdBFKHlqWB0wz23r17mTNnDgMGDOD555/HaDQyZswYPvnkEwD0ej1z5sxhypQpzuqCRCJpYrKzs5k8eTIRERGMGTOGL774gkmTJvH2228zY8YMevXqxcCBA9m8eTNPPPGEI2Pz8ePHCQwM5MEHH3S0ZR8FLM97773H2rVrHVmZZ8yYQUxMDL169apxW725kuWpFAVKDSIJmD6g9vnP748S8593zat6P5+2ZYa5Y/UGujYaafS4UbD3Zff8Bt3s6NevHxqNhsWLF5OYmMjmzZsJCgoiJiaGlJQUpk6dyk033cTGjRsJDw+nb9++nDt3js2bN/Pdd98xfPhwtm7dire3N0OGDKGgoIC1a9cSExNDXFwc6enprF+/HrPZzKFDh1CpVI4bO0OHDiUhIYEpU6aQmpoKwK233lqpjwoK50vTeCX5Db7M+57DRSe50fdaXo+aW6/w7H+G3l0vbeqDq4Vnu0JyL4lEIpFIwEkG+48//uCmm27CZDI5soQ///zzfPzxx459ioqKmDp1KpGRkYwaNcoZ3ZBcBqGhoU3dBZdA6iBoqA5paWkiY3OPHowYMYLExEQMBkPdMzbfey++vr6OeauXUlNW5kbL2NyQ8OxLsdmgJBcKMy4+jBlQeKFsecl6S6nInv3bqrrPfx71Fux9vaKJ9guvm4GuC40wetxo6PRw4/QGHbtr167Ex8ezdOlSNmzYQFRUFLNmzaJLly4sWbKE+Ph4Nm7cSO/evZk9ezbu7u5ER0ezePFilixZwrvvvkunTp2YOXMmrVu3JjMzk4SEBG6//Xbi4uJ48sknSU5O5uOPPyYkJIT//ve/XH311QA89thj5Ofns23bNvR6PXPnzqVPHxFiblWs5FjyyTTnkFRynvczL/5eqlFTYivl+/xfeSt6IY+crLpEVUsOz3YF5O+FQOogkDoIpA4CqUPL0sApBnv+/PmUlpbSsWNH4uLiMBqNvPnmm6hUKkaMGMHLL7/MK6+8wjvvvMOKFSukwXZBqgp9bIlIHQQN1aHRMjZPmcJDDz1Uqf2asjI3SsbmupSnKkiHnFPlTHN5s3zh4tJWtxrADr54Fu58t37zn295qX7HqC+XOXrcuH1peKmyYcOGMWzYsErr4+LiiIuLq/I9t9xyC7fcckuFdSaTifbt23P8+HHHOn9/f1atWlV1l3U6pk+fzvTp08X7bSZSSjPIMueQbcnFqtgAsChW3FQ64vwHcIv/DQzx70+QLsDRjquFZ48PfaDFm2uQvxd2pA4CqYNA6iCQOrQsDZxisH/44QfUajW7du0iLCyM7du3U1RUhEqlYtasWXTt2pWlS5eydu1a9u/f74wuSC6TtLQ0goKCmrobTY7UQdBQHRotY/OCBfTt25cePXpUaL+mrMyXnbG5OnNtp3x5qh/+W7fyVB7+4B0qHl6h5Z63uvjcvs1el9tV5j/b0ekxX/8MuqY01y6CSlO/PxYURcFoKy7L7J1NvqViKSx3tRshukDauoVyT/BwxrQaWWU7rhaenXT8dIuZV1cT8vdCIHUQSB0EUgeB1KFlaeAUg52dnU1QUJBj1Oirr74CxJ19e6icl5cX/v7+5OfnO6MLEonEBWjUjM1791Yy2DVlZb6sjM2lhfDDwnqUp1oF2/5V0TRXMNKthIluSLi2K81/LuP46fN07+53xY/bEBQntWtVrJw1pdDRoz0aVfWlxmyKjTxLAZnmbDLNORTbSips99F4EaILIkQXiI/GC5VKhZdGj05V88+zDM+WSCQSicQ1cYrB9vX1JT8/n9LSUtzd3dm+fTsqlcqRSRVwhIAGBjajmrktiOjo6KbugksgdRA0VIfGzNis11c2kjVlZa5fxmYFMo/BsY/Fw82r/uWpHv66fuLUB1ea/0zz+V7oVFpKbKWN3q5VsbLfeJQccx751gJ6e8VWMNlmm4VsSy6Z5hyyzbmYlYvTA9QqFQFa/7IyWYHoNRWNsaIoFNtKcFdXnXegPK6SPbu5nA/ORuogkDoIpA4CqYNA6tCyNHCKwb7qqqv46quvePzxx/H39+f06dOoVCruvPNOAE6cOMHjjz8OwDXXXOOMLkguk9TU1DqVj/mrI3UQNFSHxszYPGzYsEoZm2vKylxrxmbFBsW5Yt703v/BzukVO//Vf+D+LbDhLtcIz3ah+c/N5XvRyaMDh4qOY1NsVdZzbgjlzTVAjjmP/caj9PaKJducx3lTGrmWfBTl4ti5Tq0luKy2dJDOH20No9NnSpMpsBqJ9AivU39cIXt2czkfnI3UQSB1EEgdBFIHgdShZWnglNnmkyZNQlEU3nvvPV577TVA3LW47777yMjIIDY2lu+++w6Ap59+2hldkFwm9hHHlo7UQdBQHewZm/38/NiwYQMRERGsXLnSkbEZcGRsfuONNypkbNbr9bz77rsEBwezevVqAgMDyc/PJyEhgc8//xwQWZn/+c9/8vPPP3PhwoUKWZmr3NazOxhSIXUfJH4J536CohwozgaNG0QPg9tXwDPnxNLdR5jaThWTWzXZCPJlZM9uTFzhe1FsLal1n4F+15BlzmVF2jqSSs5TYivFbLM0+FFiLeW3wj+5YMrGolgdjwumbH4r/BNvtScWRezrpnajnXsovbxj6e99FZ31kQRq/VEUqmy7yFrMvsLDvJ62nhBdILHALxiAAAEAAElEQVSena6Aio2DK5wProDUQSB1EEgdBFIHgdShZWmgUsrfZm9E3n//fWbPnk1KSgqDBw/m9ddfJzw8HJvNhk6nw9vbm9WrV3Pfffc54/Auj8FgwM/Pj/z8fHx9fZu6O5U4efJkiwrlqA6pg6BOOnw1E9y84cbnnNMJmxXU1c91rRZz0cWs3kXZYuTajsYNDm0Caync/row1FW2Uewy4dmuQFN/L0pspXWee7y34ADvZmyh0FZU9wMoYMWGRbFgVawogF7tTpopk2Jb9Yny9Go9bdxCsCkK2oacq0AH93Y81fYhgnXNZ/pUU58ProLUQSB1EEgdBFIHgdTBtTVobF/mNINdE7t37+a6666rtrZtS8DVDbbVakWjadgfiH8lpA6COungTINts0L2SQiKrt1kKwqU5ENhujDVpYaK2928yxKQtQZ9AHw9q279Nhe7RHi2K1BsLWmy0OQSW2m9s2dbFAsni8+QazFgVazkWfLJtuSTY84j25JHrkUs7a9zzHmYuThvOiFyJrvzf2VL9ue19u+uoGH8PehvdZpDXR6dSks7t9a0c29+dULldVIgdRBIHQRSB4HUQSB1cG0NGtuXOWUOdm3ceOONAGRkZDBx4kQ2bNjQFN2Q1MDRo0dl2RWkDnbqpINaU/9az3XBZoWUX8GYCSV50O6ayibbZoWirIsj1ZZy4cMqFegDL2b1dvO+5L2Wuo2My/JUgDC4y9Lea5LM1eXNNcBuw14ePjHVYbKLbSWkmzJJN2WSZsok3ZxJmulC2esLpJkyuWDOxoatliMJArX+tHYL4XTxeV6KeIZMc06F2tOXcqPvtSzqOK3Jk45daeR1UiB1EEgdBFIHgdRBIHVoWRo4zWAvWbKEpUuXkpmZydVXX018fDy9e/d2bF++fDkzZszAYDBIgy2RuDidI8Nq38mnLZz/GYO5gP1FR4WRUepmZKpFUSD3NJSW1Qs2AUV7ISASsEFxPpTmg6kAbOWOpdaAmw94+IGHL6hywZoL+cfEZpWaUF0wffWd8cpNgtAelQ5dFc2pPJUzKG9w9xUednrtZUVRKLGVUmQrQa/24NGTz1UyuHaTvTJqHo8nzqjRANvRoCHULZjWuhDauIXQ2k0s27i1KlvXilC34EqfrXzt6Utp6lrUEolEIpFIXAOnhIi/9tprTJw4scK6Vq1acejQISwWC//4xz/4/vvvURQFtVqNxeKEUS8Xx9VDxDMyMggNbX6hio2N1IG6h0ZnHOL7H6bwVmQbFM9gAmvJllw7iji2zVp5k1oDWg8xUm3frlKBWlv20ACq6j+SYiHXnIemMIPxSZlcNWQlBETU2qOWfD5cOnoM9TOVxdYScq0G8iwGci355FkueW69dL1Y9vPpzetRcxmXOLPW0ePXo+Yy8fRcEovPCrPsFkJbh4G+aJ6DdQE11q52pg5/RVry96I8UgeB1EEgdRBIHQRSB9fWoFmEiL/11luAKNc1cOBAtm7dytmzZ3nzzTd55513OHHiBIqiEBkZyZo1a5zRBcll0pLnx5enxetQPrnX+T01Jvc66xfAm5FtGKjvyj1eA/B1D6Imk1s1StlDJcyzWxXm2o7dZNssoFJDvQyTQm7JBd63fc/yKDcWevsSUod3ucL50BTzn6sylXBx9HhN9AK+zvuJg8Zj5FVhovMsBkoUU4OO/VLEFN5I31jryPRuw17eSN/I653mOlUfD7V7hZHslm6uwTW+F66A1EEgdRBIHQRSB4HUoWVp4BSDffr0adzd3fn222/x9vbm4YcfplevXsycOROLxYJarebpp5/mpZdeQq9v2fMZXZWUlBQCAgKauhtNTovWoby5BrFcP7Jak72nYD9ePuE8FPZ/aEsMIjO3TQHFIkaZbeWWigWs1rJtZesVi/DWgZ3EXOuirNr76BkMnkHCYNcHFQT4RvJoSE/2n1/I3oIDjAgcXOvbmvp8uBLzn0ttJlJMGSSXpmFWLPTz6cVjJ5+v1uDuNuzlXyen83rUXDZkflKjEdaqNARo/fDX+OKvFY8ArV+5576ObYFl64O0ATzZ9iH2FR6udQT7Ss0Lt5vsumYy/6vT1N8LV0HqIJA6CKQOAqmDQOrQsjRwisE2Go20adMGb2+RTCg2NhYQ2ePat2/P+vXrGThwoDMOLZH89TAXgc7zCh/zEnNtx2Gyt0J+MuQkOhKLpVh/J6p1L7QZR4S5tpSCzSRMc72OXQTtrobU30Vis+rwCqk64Vk98AAiPNqTXJre4DauFI01/7m8gT5f9kg2lS1L00g3X7yx8X3PD3g9bX2dR4+XRM5kY9Z2/DVlZrnMQNufe6k9UanqG9UgcLX5zx5qd/4v5P4Wb64lEolEIpFUxCkG2z632nEQ7cXDbN++nZ49ezrjsJJGpFOnTk3dBZegyXUwF8PuBVemNJTFBCW5Isv2+6Mqm2s7p3bC+r/DfR/A9gmO/cxdOuAVGA2qiiPPO385RPzaz0i5kENYmxAmP3Y/Q24awOZPd/HWxq2kZ2QS2yWGmf+ZTmy3XqBW8/XXX7NkyRLOnT1DWGgAkx4YzpB+3S42WmauzVYbC+cvYPv27Xh6ejJu3Djuuece0R+zmYULF1a5rTw6lRaLUrc8EJFdmuZ8qC17dnlKbSZSTRllhjmd86Y0zpemOgx0hjkbpZa7Hnq1B+Hubfkw8zPGtX2gXqPHT7f952V/3qq4NDS7/LGbKkRbZ2uSQhwuR5NfJ10EqYNA6iCQOgikDgKpQ8vSwClJztRqNe3bt+fcuXMV1oWGhpKWltbYh2uWuHqSs7Nnz9KhQ4em7kaT06Q6lB9F7nRLjfOfa6W0EAwpYEiufmm8AE8dgwNrYde82tscNAP6PgKfPAHerVgUXIJXu+t4Ivhu0LqDxp3s/EIGxQ0lIiKCG264gS+++IKsrCzefvttRo8eTa9evejduzebN28mICCAb775hnPnzjF8+HDatWvH0L/dwtb/fUhOnoEdy54lvE1whZHrN998k0WLFjF8+HBSUlI4cOAAmzZtolevXjVuK8+i5FV4qT15ou2DNX7cEltpk4QEVzf/GYS5fCN6Pu9m/I+deT9wvjSNDHNWnQx0mHsb2ru1Icy9tXju3oYwtzaEubchUOtfYaS5tj5cSYPbkDrYzkJeJwVSB4HUQSB1EEgdBFIHgdTBtTVoFknOAPLy8hg/fnyFdUajsdI6lUrF8uXLndUNSQMpKCho6i64BE2mQ13nPyuKmKtc3iznJ0NB2dK+vtRQt+N+NgnuXi8SmlU3gg3C8NtH1cfuEOuSV4HaE/zaO3ZLSzyHyWSiR48ejBgxgsTERAwGA7GxsWzbto1WrVpx6tQpduzYgU6nA0QOh8jISJ599lkGDhxISEgI816az6HEZMKjYiuEhe/YsYOAgADi4+M5deoUI0aMYMeOHfTq1avGbfXF2eWpLIqFbHMemeYcssw5ZFpy8FC5M9j/ulrnPz928nlej5rLD4bfSDeLkPr6GujacKXRY1ea/yyvkwKpg0DqIJA6CKQOAqmDQOrQsjRwmsE2Go2sWrWqwrrCwsIK6xRFkQbbRbGbnZZOk+hQ2/znf2yGb1+Eox9BQaqY61wX3H3Btz34thMm2Kds6dvu4nrPYFHuasy2qvsA9RpNj42NpV+/fmzZsoUtW7ag1WpZtWoVnp6edOnShT179jB27Fg8PT1ZsWIFAIMGDWLQoEGONvYfOAhAZLerK825TkpKIiIiApVKRVhYmGNdbdvqQ33Cs8tTajORZc51GOYscw6ZZY8scy6Z5hyyLWKZa8mvNOpc3/nPyzu9yLnSlAYZ6LrgStmzPdTuTGgztsnnP8vrpEDqIJA6CKQOAqmDQOogkDq0LA2cYrBvvPHGRv/jTnJliYmJaeouuARXXIfqzLWdUzth4z1i/nPGn5CbJAyxV6uLJtmxvOS5u0/d+6HTV22y6xmqrlariY6OxmAwMGbMGJYvX86sWbP49NNP0ev1hIaGMm/ePJYtW8bEiRMd86XtrF69mu3btzNkyBC6XPe3SgnNiouLHWUf7Bfu4uLiWrfVlbqUp/o8dzf7Cg8J41xmmLPMORishfU6lho1QTp/grWBhOgC+SznO8a1GVOv+c+BOr96HbO+uNLo8ZUuVVYV8jopkDoIpA4CqYNA6iCQOgikDi1LA6cY7F27djmjWckV5PDhw3Tv3r2pu9HkXFEdzEUioVlNodkgtv8UD/dtgtJ88G4DWifUFrzUZDdgHvjOnTtZt24dCQkJDBs2DKPRyIIFC/jxxx+Ji4sjIiKCiIgIx/qffvqJuLg4AJYtW8bSpUuJjY1l/vz5VWYL9/DwwGQS9ZXtSw8Pj1q31YWa5h1DxfJUH2Z9VuV+OpWWIG0AITphmoPLlvZ1wbqL2wK0fmiqqOUts2e7LvI6KZA6CKQOAqmDQOogkDoIpA4tS4MmS4FaWlrK5s2beeONN/juu++aqhvNG0WBxC/gzPdinq2tbpmQ60K4wQBHGyn5mkotElOFXQ+db3N+NuzmhqLAmd2Q+CXc8Ez95j/r/Z3bN7vJ3j2/QZnMU1JSADFlBKCoqMixHDp0KK+++irdu3d3zMvx8vICYM2aNSxdupQ+ffqwatUq/PyqHpkNDw8nNTUVm81GcnIyAB07dqx1W01kmnNQo+atjE11Ds9OiJzF9pxvCNKVN9MB+Gt8Lzuax5XmP9s5d/Jsi/mRlEgkEolEIqkPV9xgHzlyhNWrV7N27Vry8vKu9OH/OigK/LIUTn0FbXpD1FDQNN7cBg+zCXSNNCpqs0LeWfhzI6T8BjfPAV3Th3fWhaCgIOc1brPBsW3w/UJI3iPWpf4u5lhvvOey5z83Gjo91gFT0TTgmP369UOj0bB48WISExPZvHkzQUFBxMTEkJKSwtSpU7npppvYuHEj4eHh9O3blyNHjvDqq6+iUqno06cP69evB2Dw4MG0b9+etWvXEhMTQ1xcHEOHDiUhIYEpU6aQmpoKwK233gpQ47YKKMJUL0t9jy/zvmdf4WEG+l7D61Fz6xWe/UjryiXAGgtXmv8MTv5eNCOkDgKpg0DqIJA6CKQOAqmDQOrQsjS4Iga7pKSETZs2sXr1an7++WdAJDgD5ChIQ8k5JQxYvycgelijN68ym6GxkxFkHocvnoWz30PULY3btpMoPx+40bCUwoF18MN/Ieu4WKd1hz4Pw4Ap4OHXKPOfG5PCUht+Dbgn0rVrV+Lj41m6dCkbNmwgKiqKWbNm0aVLF5YsWUJ8fDwbN26kd+/ezJ49G3d3d7Zs2YLNZgPgrbfecrQVEhKCj48PCQkJ3H777cTFxfHYY4+Rn5/Ptm3b0Ov1zJ07lz59+gDUuM2m2MizGMg053CmNIVtORVvZuRa8vk67yfejF7Ioyefc4nwbFea/+yU70UzROogkDoIpA4CqYNA6iCQOgikDi1LA6fUwbZz6NAhVq9ezbp168jPz3eYapVKxZQpU3jwwQfp0aOHsw7v0lx2vbUD78PxT+CutaBp/PskBoPBOfW5v5wObl6ihnIz4NChQ413E6jEAL+tgp+WiOzfAB7+cO14uP4p8A6tuH9j1sG+TOqiQ13rSTcVZpvFkbU7y5yLRRFTKjZn7eDz3O+4ya8ftwTcQJz/DbR1a+V4X1VzsZtyBLnYWtLkCb4a9XvRjJE6CKQOAqmDQOogkDoIpA4CqYNra9As6mC/8847rF69mj17RNiroii4u7szcuRINm3aBMALL7zQou5kNDoleSJztBPMtVPxaSPCxVsSBenwcwL8+jqU5It1vu2g/yS4+t/VZ/e+zPnPfzWsirXKJGA1UWwtEaWxLNnkWgyUv5+oU+sI0QbQ2i2ESe0eYVK7R6tsw9XCs5vaXEskEolEIpFIqkftjEYfeeQR9uzZg6Io9O7dm9dee43U1FQ2btzojMO1UJQqsyrv3LmTW2+9lZ49ezJixAi+/vprADZv3sytt95Knz59GD16NEePHnW858CBA4waNYqrrrqK//u//yMrK6vKmx+FhYVMmzaNfv360a9fP5577jkMBgMAZrOZefPmcd1113HzzTezefPmqrut1oi+NxPqkhSrWrIT4ePHIT4Cvn9ZmOvgLjDqLZh0WoSD11Y6S6eHG6c3ubm+LB0aAatiJakkGatirXE/RVHItxSQWHyWnw37+MHwG8eLT5NjFhE0Xho9ER7tuManBzf5Xks3rxi8NZ64qWrON2A32RPbPtyk5tpVaOrzwVWQOgikDgKpg0DqIJA6CKQOAqlDy9LAKQbbjl6v57rrrqNfv34EBAQ481ASIDs7m8mTJ6PVahkzZgzFxcVMmjSJ33//nRkzZuDj48M999zD0aNHeeKJJwBRF/jxxx8nJyeHv//97/z4449Mnz4ds9lcqf358+fz0Ucfccstt9C/f3+2bt3KwoULAXjvvfdYu3Yt119/PcHBwcyYMYMDBw5c0c/vDHJycur/ppTfYeO9kBADv64Sc67DrofRW+HJw9D34fqV1dI1faRHXXRwU7lRbCtp9GNbFSv7jUdJKjnPfuPRSibbqljJNGVzpOgkuw2/srfgAEkl5ym0iozlAVo/YvQd6e97Ff19ryJa3xF/rZ8ju3exrRQ3de35BjzU7oxrNabFm2to4PfiL4jUQSB1EEgdBFIHgdRBIHUQSB1algZOiS+eOHEi69evJzMzk5UrV7Jy5UpiY2N56KGHnHE4SRlpaWmYTCZ69OjBiBEjSExMxGAwEBsby7Zt22jVqhWnTp1ix44d6MoSmO3Zs4ecnBxmzJjBgw8+SFZWFl9++SXZ2dm0b9++Qvt5eXkMHjyYefPmAfDTTz+xb98+AHbs2EFAQADx8fGcOnWKESNGsGPHDnr16nVlRWhk8vPzCQsLq31HRREZ3b9fCKe/vrg+ZjgMnAYdboDLLNfUlPi18q91n076cD7O/ppiWwl6deOEMdvNdY45D4Accx77jUfp7RVLriWf5NJ0si152BSb4z0alcZRXzpIG1Cjec63FHCmJJkbfa+pU3/OnEhy2flDV5I6fy/+4kgdBFIHgdRBIHUQSB0EUgdBc9LBej4Z085vsBw6glJc3GjtKrl5GAL8G609lZcX2p7dcbvlZjRtWjdau42BUwx2fHw8ixYtYuvWrbz55pvs3LmTI0eOMG3aNMeo0dq1a7nnnnsIDAx0RhdaJLGxsfTr148tW7awZcsWtFotq1atwtPTky5durBnzx7Gjh2Lp6cnK1asACApKQnAYabDwsIcdYMvNdj29wCcOnUKg8HANddc42gnIiIClUrluIDY227OdI6s5WJotcCRLcJYp/0h1qk10ON+uGEqtG7+SfxKbKWsytxQa+bq63z68En21yxJeYtRQUPp4NEODfWbM10eq2LlQNExcsvMtZ0Lpix+Vf6khz6GEsWEyWbCQ+1OsDaQEF0A/lo/1KqLwTkmW+VoDKti5VTJOT7M+gy92oOrvOv2/6TRNPzz/JWQOgikDgKpg0DqIJA6CKQOAqmDoLnoYDl2nMJZ81C5uaG9qg9qX59GGyDyKSlB59FIeWQUBVtePqbPd2L6fCde82ahjXSdEHSnZcjSarXcfffd3H333aSkpPDWW2/xzjvvOEzX+PHjeeqpp7j55pv57LPPnNWNFoVarSY6OhqDwcCYMWNYvnw5s2bN4tNPP0Wv1xMaGsq8efNYtmwZEydOZPv27RQViTBaNzcRsmwf2VbV8GVKS0tj3LhxqFQqHn/8cUCEml/aRnEj3vVqEszF6H5+peoEY+Zi+OMd+OEVyD0t1uk84erHRPIy/w5XvLvOoHwG7X2Fh2ucfxyiC2RKu3+xMv19FiS/Xqf2FUXBqlixYBVLxYYaFV4aPWmmTIps1Z9Dnmo9bdxCsCo2dCotNOD630oXxHPt/w9frXed9o+Nja3/Qf6CSB0EUgeB1EEgdRBIHQRSB4HUQdBcdChe9Raa9u3wnv8CqsYyw2U4I5uQrbCQwmmzKHnzXbxfesEJR2gYVyQFdbt27Zg5cyYzZ87km2++Yc2aNWzdupWSkhK+/PLLK9GFFsHOnTtZt24dCQkJDBs2DKPRyIIFC/jxxx+Ji4sjIiKCiIgIx/qffvoJj7Ivj8lkqrC01yG+lOTkZMaOHUtqaipz5851hMt6eHhUasOjkb+YV5TyJbLO77lYIqs4F/asgF9eA+MFsa9nEPR7Eq6bIJ7/Rbi0PNVuw14ePjG1RpMdo+/Ii+ET2Vd4mKTS8+Sa88mzFpBvLSDfUkC+xUCepey1taDKOdsJkTPZnf8r3+T/XGsfu3pG8fegv+GursecdkCr0hCqC6aDe7sabyZdyuHDh+nWrVu9jvVXROogkDoIpA4CqYNA6iCQOgikDoLmoIMtKwtL4mm8nn260c01QEFBAT4+tST3rSdqb2/cb7uVohVvYCssRO1dtwETZ3PFazzdfPPN3HzzzeTl5bFu3TreeuutK92FvywpKSkAGI1GAMfodFFREUOHDuXVV1+le/fuFBQUAODl5UWHDmKk9fz584Aw0OXDvMuTk5PD2LFjSU9PZ9GiRdxxxx2ObeHh4aSmpjrCy6EZZwssb65BLNePhPs/hP/9E459LNb7dxCZwPs+Imp7O4mmqHtcVe1nuGiy10Qv4NOcb/mt8E9RBsucQ5Y5h0xLDiW20nody02lI1gXSIgukGBtANnmPOZHPEOmOafS8ctzo++1LOo47YomHStf5qslI3UQSB0EUgeB1EEgdRBIHQRSB0Fz0MGWLZKQacLa17Jnw3CWBpqwdqAoKLl50FINth1/f38mTJjAhAkTmqoLfzn69euHRqNh8eLFJCYmsnnzZoKCgoiJiSElJYWpU6dy0003sXHjRsLDw+nbty9msxk/Pz/WrFnDuXPn+PrrrxkwYABBQUEUFBSwdu1aYmJiiIuLY9asWaSkpNCtWzeSk5NZsWIF3t7ejB07lqFDh5KQkMCUKVNITU0F4NZbb728D2QuuvIZtC8113ZO7YQNd8N9H8CX0yDiRuh+L2hqzz59OZTYSlmW9l6t858bikWxcMGcQ5rpAummTNSoGOh3DY+dfL5ac7vbsJd/nZzO61Fz+Sj7yyr306s9ygxzmXEuSzomngsjbTfVvhrvKkeRy9eevpSmqkUtqyEIpA4CqYNA6iCQOgikDgKpg0DqIGgWOtgNcBXzxXfu3El8fDwpKSmEhYUxefJkhgwZwtdff82SJUs4d+4cYWFhTJo0iSFDhgA4BlKzsrLo378/zz//PL6+vpXaNpvNLFy4kO3bt+Pp6cm4ceO45557at3mwN5fF7qJoVKawy2VvyAGgwE/Pz/y8/OrPNlqZc9yyDkFt8ZXWP3555+zdOlSUlJSiIqKYtasWfTs2ZOvvvqK+Ph40tLS6N27N7NnzyYiIgKAgwcP8sILL3DmzBmuuuoqXnrpJQICAsjIyGDIkCHcfvvtPP/88/Tv37/S3afQ0FB2796N2WzmlVdeYdu2bej1esaNG8e9995b535XwlwMu+dXPf+5sTGXQFEW6APg/VGVzXV5Ot1yMVzcyZQfRW6IoSy2lpBmznSYZ/syvdy6C+YcbFycDvB9zw/YkvU5S1LfrrX9iW0f5qFWd/FF3m6HmbYbaS9N49wYqWokvanMNYjvbYO+r38xpA4CqYNA6iCQOgikDgKpg0DqIGgOOliOHafg2Rn4Lo9HE34xkjU7O5tBgwYRERHBDTfcwBdffEFWVhZbtmxh1KhRtGvXjqFDh7J161ZycnLYsWMHJ0+eZPz48fTt25eePXvywQcf0LlzZz744INKx33zzTdZtGgRw4cPJyUlhQMHDrBp0yZ69epV47ba+l0fLtuXXUKTjWBLnMOwYcMYNmxYpfVxcXHExcVV+Z6ePXvyv//9r8I6g8FA+/btOX78uGPdsWPHqj2uTqdj+vTpTJ8+vYE9L0d1858b3F4J5J+DvDOQe6biMu8MFKTB08fht1U1m2sQ23fPhxunO3V0vab5z+4qN/KsBtJMmRWMc5q5opHOsxbU6VhiPnIIrd2C+Tj7a8a1GcO+wsO1hmfbR9UfbDWqUT5zVXio3SuMZDeluQY4d+6cLNOF1MGO1EEgdRBIHQRSB4HUQSB1EDRnHaorA5ySkkJkZCTPPvssAwcOJCQkhHnz5nHo0CFHGd/Zs2fTpUsXVCoVb7/9NqdPnyYyMrJC+zWV+m2uZYClwZZUibeX80doq6S6+c81mey6GOja+OIZuPM9YehrG8F28qh6bfOfV0bNZXzirBoNsB1PtZ7WbiG0cQuhtS6ENm6taOMmlvbXwbqACmWtwLXCs+0me2nqu04LlZdIJBKJRCKRVKa6MsA33HADgwYNcuy3f/9+ACIjIx35mD755BMURXEY7lOnTlUy2DWV+m2uZYClwW7OKFVn+r5sbFbUOacgKFrUdG7ktqulpvnP60fC6I8gaRec+7H+BtrNC/w7QkAE+EdcXNqf6wNFnb8x26ruA1yR8PDqzLWd3Ya9PJ44k9ej5jI1aSFJpedpowspM9EVzXNrt5Bq5zfXxqUjx3aaagTZQ+3OhDZjm9xc25MCtnSkDgKpg0DqIJA6CKQOAqmDQOogaM461FYGGGD16tVs376dIUOG0KVLF9q3b8/nn3/OmjVrWLNmDZ06dQIuJmAuT02lfptrGWBpsJsr7n5gzASbjUKlmAvmHKxKDea1rtiskHEIinOg4DiEdr9sk61CRZDOnwCtHxRmgJu3MMVF2eI4Kg207gUb7qx+9PjUTjE/+r4P4OeEyvu5eVU0zP4RENCxsoGuDZ2+apN9Bcy10VrEirR1tY5M7zbs5Y30jSREznRqdnFXC8++0pnUq8JgMDR6iYnmiNRBIHUQSB0EUgeB1EEgdRBIHQTNWYfaygAvW7aMpUuXEhsby/z58wHw9vZm06ZN7N+/Hw8PD/bv38/cuXPxriLLd02lfptrGeBmZ7D37dvHxIkTOXjwIHq9nnvvvZdFixbh7u7O8uXLWbJkCWlpabRp04ann366Qpbyd999l7lz55KWlkZsbCxLly7l+uuvr/I4RqORCRMm8PHHH2OxWBg5cqQjazbAiRMnGDduHHv37sXHx4cJEybw/PPPXxENAGh3DXlHNvDWkVkccjNjpRHMtQKYjWCziNeFQNHnoPOCqrypAmC7mLVPueQ5ingoCtisRJq1PJT4LRGpx2DX3IvtPH0cflhYt/nPP8XDne/AwfcrmmnPoLoZ6Lpwqcl2srlOKc1gdfoGzpWmsjhyRr3mPzsbGZ5dkdzcXNq1a9fU3WhypA4CqYNA6iCQOgikDgKpg0DqIGjOOlRXBtjNzY01a9awdOlS+vTpw6pVq/Dz8wMgMTGRN998k5EjR9K9e3c2bNgAQExMTKX2ayr121zLADcrg22z2bjtttuYNm0au3btIjU1lbi4OIKDg+nduzczZ85k586dXHXVVfz666/ceOONdOvWjcGDB7Nr1y6efPJJPvvsM6699lqWLVvGHXfcwdmzZ/H0rJysasKECZw/f56TJ09isVi49957ee6551i+fDlms5nbbruNO++8k88++4zDhw9z2223ER0dXTl1vJMwB0ayMDKAIksWY3zjiPKKQqu+zP9OU9FFc10etVYk9LKUgmIpM9G2MoNdOzbFRoopnR1537OogycvZmkJNtvEqLI+UGQWH/Ji/eY/3/BMgz5inbGbbCdmMj9edJoVaevYmvMllrLogwXnX2dN9Mv86+Q0l5j/DMJk/1/I/S3eXAMNCrf/KyJ1EEgdBFIHgdRBIHUQSB0EUgdBc9ahujLAHh4evPrqq6hUKvr06cP69esBGDx4MEFBQXzyySfs27ePG264gY8++ohrr72WsLCwSmWAayr165QywFeAZlWmKzs7m+DgYBYvXsyTTz5JWloaf/vb33j00UeZMmUKBQUF+Pj4YLFY2LlzJ3fddRc//vgjffr04YEHHsDT05PVq1c72ouNjWXq1Kk8/PDDFY5TVFSEv78/u3bton///gDs2bOHwYMHk5WVxU8//cSoUaPIzs52zAtYuHAhO3bs4LvvvqvTZ7ncdPB/FB5mScrbvBA8mo5WdzF/ua6O91IUBUoLwGqufh+NDtx9Ku+nKvtHpQKVuornavFa40aRmweTct5juN8NjAy9A9QVE2tVOwcbrmh5rIp9avxa3HsLDrAibR07835wrLvepy9PtHmAQX7XoVKpXK48lUQikUgkEonkr0tN5a6qKgO8bds21q1bV6mdefPmcc899/Dxxx/z6quvkp+fz4033sgLL7xAYGAgycnJjjLAr7zySo2lfutSBtgVy3Q1K4MNMHnyZBISElCpVFitVkaOHMn//vc/1GVm7fjx43Tr1g2r1crkyZN59dVXAejTpw+PPPIITz75pKOtu+66i7CwMJYsWVLhGAcOHKB3794V5ksUFBTg6+vLH3/8wXfffcd7773H77//7njPJ598woMPPkheXl6V/S4tLaW0tNTx2mAwEBYW1uD/yI2Zn/BrwUFejfxPvd9bAZsVsk+KR20ERUNgpBjp1ujEQ62rV2j2a6nvUGQtYVrY41XvUJXJbipzDRw9epTY2NjLbsem2Pgq70eWp63lt8I/ATE3/daAmxjX5gH6ener9J7LrYPdmDSWDs0dqYNA6iCQOgikDgKpg0DqIJA6CKQOguagQ2MY1ZqwD4Q2Nq5osJtdiLher2fZsmU88sgjJCYmMmrUKGbPns3cuWJOb2RkJMXFxRw4cICRI0fSqlUrnnvuOQoKCvDy8qrQnqenJ4WFhZWOU1Ag6geX398eRl5YWFivtuwsWLCAOXPmVFp/5MgRvL296dKlC0lJSZSWluLp6Um7du04eVKY3tatW6MoChkZGQB07tyZ8xdS0Om0GI1G9Hq949ju7u5iBLSkxPEZSktLsVgsqNVqvLy8HJ/P3d0djUaDJigaVUmeSJpWHV4hKEHRFJeUYrGo8dS5YzaZMZtLUKlU+Pj4UFBQgKIo6HQ6tFqtI8ufp6cnZrMZs9mMzqylhFKOHj2K1WrFz8+PgIAAzpw5A4h5FZ6jt6J6/++O+c+20Vs5l5KBWq0mODiY06dPA9C+fXtKSkrIysoCoGvXriQmJmIymfD29qZ169YkJiYC0LZtWywWCxcuXACgS5cunDlzhpKSEjw9PWnfvj0nTpxw6A2Qnp6OwWDAZDKRnJxMUVERHh4eREREOGqCt2rVCq1W6whbiYqKIj09ncLCQtzc3AiP7MDrh95lk/lzziki27kOLUM01zMp8lF8C/QYzhg4pj1Gly5dOHToEACBgYH4+fnxZtTLLE9fy4Q2YzFk5ZN44QIajYbY2FgOHz6MoigEBATg6+vL2bNnATFfpaCggNzcXFQqFd26daugd2BgoKPEQVhYGEVFRWRnZwPQrVs3Tpw4gdlsxsfHh1atWnHq1CkMBgO5ubmYTCYyM8V5Ehsby+nTpyktLcXLy4u2bds6ztk2bdpgs9kqnLNnz56lpKQEvV5PeHi4o8Z6aGgoKpWK9PR0AKKjo0lJSaGoqAh3d3c6duxYZ72joqI4cuQIAMHBwXh4eDjm7ERGRpKVlYXBYECr1VbS29vbm3PnzgEQERFBXl4eeXl5qNVqunbtypEjR8jLyyM5ORl/f3/HORseHk5hYSE5OTkAdO/enWPHjmGxWPD19W3Uc7Y+14hz585RXFyMh4cHHTp0qKC3Wq0mLS3NoXdqaipGoxF3d3ciIyM5evQoACEhIbi5uTnmX3Xq1IkLFy6Qm5vL8ePHiYmJ4fDhwwAEBQXh6enJ+fPnHd/lnJwc8vPzqzxnfXx8HHp36NDBcY5Vdc6Wv0aEhYVhNBqviN61XSNyc3M5dOgQMTExDb5GXM456+Xl5dA7IiKC3NzcavV25jXCrkO7du1a9DUiLy+PQ4cO4e/v36KvEQUFBRw6dAidTteirxHFxcWO86clXyNMJpOjTy35GmGz2Thy5Ag2m81lrxG2nFysJcWoCgrwtdkoLi7GarWi0Wguy2uoVCosFgueHh5YrdZq93Vzc0Oj0VTpH2ryGrbCQmxWK+np6eQa8ht0jbB/dxsNpRnx4YcfKp07d66wbt26dUpgYGCV+y9cuFDp2bOnoiiK0rNnT2Xp0qUVtt95553KxIkTK71v3759CqAUFBQ41hkMBgVQ9u/fr8THxytXXXVVhfd8/PHHir+/f7V9LykpUfLz8x2P8+fPK4CSn59f84euhrfTNyuzzyyptP7LL79Uhg0bpvTo0UMZPny48tVXXymKoihfffWVcttttyk9e/ZURowY4Vj/2muvKTExMZUeW5bNVJSjH4vHuZ8VxWpRCgoKlOeee0659tprlWuvvVaZOnWqo/8mk0mZO3eu0q9fP2Xw4MHKpk2b6tXvSpiKFOWrGWLZhJw7d65B7yu0GJVVae8rV/1xh9J2z3VK2z3XKTG/3qzMO7dMSSu9UK+2iizFDepDY9JQHf5qSB0EUgeB1EEgdRBIHQRSB4HUQSB1EDQHHczHjis5t92tWJLONHrbNotFMR89rtgslkZv23zkqOj3ufMNbiM/P/+yfNmlNKsR7HPnzlUIswZRE83NzY3Fixfzyy+/8MEHHzi2lZaWEhgYCIi7E/a7qHaOHDnC8OHDKx2nc+fO6HQ6Dh8+TL9+/Rz7urm5ERMTw4ULFzhx4gQWiwWtVuvY3r1792r77u7ujru7c8N7s7OzmTx5MhEREYwZM4YvvviCSZMmsWXLFp5++mnatWvHQw89xNatW3n66afZsWMH1113HRpNWRkuRWHLpg3kGQroGxsh1nmFQLtrQK1h/vz5fPTRR9xzzz0YjUa2bt2KVqvlpZde4r333mPt2rUMHz6clJQUZsyYQUxMDL169WrYh9Hp4cbpTRIWXp6AgIB67Z9lzuGtjM28m7GFPKu4I9dKF8S/Qu/jwVaj8NVWLk9QG65Qnqq+OvxVkToIpA4CqYNA6iCQOgikDgKpg0DqIGgOOqjKKiXZsnPQRDRe3W7FasX86+8omVnY8vLQXXMVKs3llQAujy1TjOarvL1q2fPKoa59F9dh6NChpKWlMX/+fKxWK6dPn2bevHk88MAD3HjjjWzdupVNmzZhs9n48ccfSUhIYNy4cQA88sgjrF+/nm+//Raz2cySJUvIyMhg1KhRlY7j6enJfffdx7Rp08jMzCQzM5Np06Zx//33o9frGTx4MMHBwUybNo2SkhIOHDjAa6+9xqOPPnqlJalAWloaJpOJHj16MGLECDp16uQI3YqMjGTGjBlMnjyZxx57DLPZzKFDh7jmmmsYP34848eP55prryUlI4vnx48hom1IBXMNkJeXx+DBg5k3bx6LFy/G39+fffv2AbBjxw4CAgKIj4931MDbsWPH5X2gRk4u1hDs4SS1cbYkhefP/Jd++0eRkPoOedYCItzbsyhiGj/32sITbR9skLl2Feqqw18dqYNA6iCQOgikDgKpg0DqIJA6CKQOgqbWQSkrrVUT6rZt0LQOxfTFVyiNlKKrvLkGUDKzxGtrI5QXBhSbDdPOb9BERqB2oZsYzWoEu2vXrmzfvp0ZM2awaNEi/Pz8eOCBB5g9ezZubm58+OGHzJgxg3/961906NCBhIQER6a5IUOGsGLFCsaNG0dycjLdunXjs88+c4xwz58/n/Xr1ztGuVesWMGUKVPo0aMHJpOJkSNHsmzZMgC0Wi1ffvklTzzxBK1bt8bb25unnnqKf/7zn02ii53Y2Fj69evHli1b2LJlC1qtllWrVnHDDTcwaNAgx3779+8HxFyS8sydO5fY2FjufGQy5CSKpGbqi3eYVqxY4Xhun5d7zTXXAJCUlERERAQqlYqwsDDHuuZOeHTNd/AOGY+zIm09n+R8jQ0bAL28Yhnf5gFuDbgJjarx7tBJJBKJRCKRSCT1QSkuxrjgVbyefwaVvvrIUJVKhcfoezAuXo7t+Rdw698PlZ9vvZIZVziu1Ybl2HHIy6+0zbz/INounVFpGjjWqyjY8vIxf/8T1hOJeE6f0rB2nESzyyL+V+Fys9W9k/EhZ0pSeKHD0451iqIwb948fv/9d8aMGcPy5ctRq9V8+umn6Mu+UKtXr+bVV1913HCw88MPP/Doo48SHx/PiBEjUKwWVJqq77+kpaXx0EMPkZyczKZNm+jevTuxsbH06tWLjRs3YrPZiI2N5dprr2Xt2rW19ttVKbGVsjT1XZ5s+1CF7N2KovBjwe+sSF3Hd4Y9jvWD/Poxvs2D9Pfp26zrHVZFfn4+fn5+Td2NJkfqIJA6CKQOAqmDQOogkDoImpsOiqJgS89AqSFhb0NozMzRKg8P1O3aorq0zGszoKnOB6W4mLyR92Ha+Q1ut9yM/7YPajTZAKaf91D6yWdYDh0RpXwvxWZDMZuh7KGYypb2dW7uaNq3xXLiJEqeodrjqPx90XbpXLlsbx1RaTRoe3TF/e+3o7uqT4PasNOis4hLambnzp2sW7eOhIQEhg0bhtFoZMGCBfz444/ExcWxbNkyli5dSmxsrCOM285HH32Ep6cnQ4YMAaDUbMGjCoOdnJzM2LFjSU1NZe7cuY555x4eHphMJgDH0sOj6ecON5TyJbL2FR7m7ZhF6FRaPsv9jhVp6zhgFBlU1ai5PXAI49uMobtX5ybutfMwGo3N6g8FZyF1EEgdBFIHgdRBIHUQSB0EzUmH0i+/pmTT/7BlXGj0ts1mMwU6XaO1p/bzxW34UDzuv6dZDWY0xflQ3lwDmHZ+Q97I+6o02bbCQmwZFxwPTXh7cHPDlpaG7UKmeGSIJWVZv6sj8OAvlLy/GdPnO2vcT8nJQXvvXXg9+zQqzwZMCy3LUO6KSIP9F8JeJsNoNAJQVDbfws3NjTVr1rB06VL69OnDqlWrKnzJFUXh559/5uqrr3aYYpPJVMkg5+TkMHbsWNLT01m0aBF33HGHY1t4eDipqanYbDZHOYOOHTs678M6kfLmGmC3YS8Pn5jKyqh5rL+wjQPGo3io3Lgv5Db+r/VoOni0a+IeO5+cnBzatm3b1N1ocqQOAqmDQOogkDoIpA4CqYOguehg+mYXRUtX4jawP26PP4oqsHHnsRYWFOLt0zg5aJRCI+Zf9lKyYTNYregfvL9R2r0SXOnz4VJzbcdusv02r6Nw1jxMn+zAmnEB6jBHuwI6HerQVlU+bJnZeM2YiuX3Pyodvzxut9yM96xptY6oN0ekwf4L0a9fPzQaDYsXLyYxMZHNmzcTFBSEh4cHr776KiqVij59+rB+/XoABg8eTGxsLBkZGWRnZ9O1a9cK7RUUFLB27VpiYmKIi4tj1qxZpKSk0K1bN5KTk1mxYgXe3t6MHTuWoUOHkpCQwJQpUxy1BW+99dbL+jzF1pIrnkH7UnNtZ7dhL48nzmBl1Dy+yN3Nzf7XE6wLvKJ9k0gkEolEImlMSrZuR9fvajyfneiU0UC1wYC2EUJu7eh6dkel1VL66ed4/ONuVI04Ot7csWZkYE1OQxsTRf5do6s1t6ad35B/zwP4ffAu+UePYU06IzZ4eqKxG+VWIahDQ6o20a1CUPn713q++G/7oEqTD9Q5XL25Ig32X4iuXbsSHx/P0qVL2bBhA1FRUcyaNYtt27Zhs4kEXG+99ZZj/5CQEGJjY8nMzAQgNDTUsc3X15fk5GQSEhK4/fbb6du3L1999RUAhw8fdiSDCw0NZezYsTz22GPk5+ezbds29Ho9c+fOpU+fhs+HKLGVsiztvUrznxsLRVHItuRyvjSN86VpqFAxyP86/n3y+Urm2o7dZL8ds8gpfXJlaipB15KQOgikDgKpg0DqIJA6CKQOguagg62wEGvSWTzuvMNpobaNMZ/1UnQD+1Py0SdYTyeh7RzT6O07g26XJBa+HJTiYixHjmH58zCWg4ewHDyE+eAhlMwsgo7/QdGixTWOHIMw2UXxy/Db9B627BxhnL0bt9qNSq+v0mT/1c01SIP9l2PYsGEMGzaswrqePXsyc+bMat/To0cPjh8/XmGd2Wqhffv2FdYfO3as2jZ0Oh3Tp09n+vTpDez5Raqa/1xfQ3upgU42pYtl2evzpjRKbBdrqn/f8wNWpq2v1lzb2W3Yy9LUd5nQZqxL1Ke+Uhw7dowuXbo0dTeaHKmDQOogkDoIpA4CqYNA6iBoFjoUFwOgcuLc4MLCQrwb2bip/YRpV4pLGrVdZ1HXLN6V3qco2M6ew3zwUAUzbT2RCGUDZxVQqTC+HI/P4oWY9/xaa3i2vT9qf/8GfKq6canJbgnmGqTBllSBVbFy1pRCR4/2V7zMVHXzny812YqikGPJc5hlh3EuTSfZJJ4X22q+8KpQ0dothDC3NnyW8x3j2oxhX+HhGk32jb7XOm1U3ZWxWCxN3QWXQOogkDoIpA4CqYNA6iCQOgialQ5VjF7v3LmT+Ph4UlJSCAsLY/LkyQwZMoSvv/6aJUuWcO7cOcLCwpg0aZIjQe7ixYtZuXJlhXa2b99OdHR0hXWlpaX07du3gkZ33303L730EmazmYULF7J9+3Y8PT0ZN24c99xzT639dVXKz4U27/m1WnNpMxgumug/D2M5eBjLn4dRDFVn4VYFBaLt2R1dz+5o7Y+uXRzJwlwpPNtuso3zX6n3TYbmijTYzRQVaixK41+8rYqV/caj5JjzyLcW0NsrtlFNtlmxoFFVnY6/pvnPD5+Yyuro+axIXcfned/V2UCH6oIJc29DmHsb2ru3IcytDe3dWxPm3oa2bqG4q90qvOftmEVV9gGEuW6J4eHgnBCv5ojUQSB1EEgdBFIHgdRBIHUQNGcdsrOzmTx5MhEREYwZM4YvvviCSZMmsWXLFp5++mnatWvHQw89xNatW3n66afZsWMH4eHhHD9+nMDAQB588EFHWwEBlZOmnTx5EovFwvDhwx3m254H6L333mPt2rUMHz6clJQUZsyYQUxMDL169Wrw51GKihqWpfoyqTaL99aNmH/9XZjuMiNtO3O26kZ0OrRdu6Dt0e2ike7ZHXXr0BrD+l0tPFul1+M+dWKLMNcgDXazpbVbMN8b9jZqIrDy5hogx5zHfuPRyzLZiqJgxYrZZsGsWDhsPIECvJvxP/KsBvIsBlrpgnggZCT/TvxPjfOf/33yeV6Pmst+4xFOFCc1yEDXhofavUqT3ZLNNUBwcHBTd8ElkDoIpA4CqYNA6iCQOgikDoLmrENaWhomk4kePXowYsQIEhMTMRgMpKSkEBkZybPPPsvAgQMJCQlh3rx5HDp0iPDwcI4dO0b37t2599578fX1xc3NDavVWql9+5TD0aNH06lTJwIDLyaN3bFjBwEBAcTHx3Pq1ClGjBjBjh07GmywGxqe3VAUmw2lsBCVVkve3/9RdRbvv/8Dvw/exfjSfytsV7dvJwx0mZnW9eyGpnNMgxO5uVp4tlmjoaWkpJMGu5lytXcPNmR+wobMT3ig1d9xU1/eKXupubZT3mQDmGxmzIoFs2JfXnxYbOXXXXyuKAo2xcZ+4xF2G34lw5zFh9mfOY7xfc8PWJn+fp3mP7+RvpHXo+aSY8mnXQMMdF241GS3dHMNcPr06WaRsMXZSB0EUgeB1EEgdRBIHQRSB0Fz1iE2NpZ+/fqxZcsWtmzZglarZdWqVdxwww0MGjTIsd/+/fsBiIyMJD8/n7S0NHJychgwYADu7u5MmTKFUaNGVRrNtxvsRx99lNLSUnr27Mlrr71GmzZtSEpKIiIiApVKRVhYGABJSUkN+hx1Dc+utR2rFVtWlqgDXa5WtONx4eJ6bc/u+L3/Fnl3P1BzFu/7HsLvw3WYvt2N2t8PbY9uqAMbvzqNK4VnN+fvRH2RBruZEqQL4NHQe3kzYxO/FPxxWfOlFUUhzZRJka242n081XpC3YLJMGXVuF9VWLGRZrpApikbFdDPuxeBOn/8tb74a3w5ZDzB+DYP1Gv+s7/WuaFXdpO9NPXdFjnnWiKRSCQSSctErVYTHR2NwWBgzJgxLF++nFmzZvHpp5+iLzNoq1evZvv27QwZMoQuXbpw/PhxwsPD6d69OwMHDuTtt99mwYIFdO7cmeuuu65C+2azmYiICEaPHk1OTg4rV65kzpw5rFy5kuLiYtzcxOCJrmzktri4fn93Qg3h2WUmWzGbKxjjGo1zVnbVScWqwOfbHRTFL6tbFu//JuA1fYrTw9dVer04TgsJz3YFpMFuxgz0u4YofQd+LThImimzQXOyzYqFnwp+58eC32vdd4DPVfTw6sxnF3bx/+zdd3hUVd7A8e9MeicdCAmhV2EBFcW10ERBRSyggorYABEF3NcKiKKoqyACdl0RWAHFVcHCosi6ooIIgnRCTwKkZ9Iz5bx/HDNLmBtSCJkJ8/s8Tx5l6sk3k0lO7r3nBpkDCTQHEOQTSJA5kCBTAIHmwD///ef/m/X/h5iDGNTkUnqGdqVNYFKVx4x42vHPgeYAxjcdJZNroEWLFu4egkeQDpp00KSDJh006aBJB60xd1izZg2LFy9m7ty5XHXVVRQVFTFr1izWr1/PgAEDmD9/PvPmzaNTp048//zzAHTo0IE1a9Y4HyM8PJwHHniAbdu2uUywZ8yYUenf69atY8OGDQAEBgZSXl4O4PxvYGDtDoU8dXJdoWKSHbHsQ/JH3FHtJLgSkwlTTPQp54l2PUe0KTKSkCceqdUq3g3BHcegn6oxf0/UlkywG7lm/nFcFz3gjB5jbLPbqpzYVqiY4AaY/Jma9OAZPV9VPPH454IsCyFN3f+m5G6lpY3jVBhnm3TQpIMmHTTpoEkHTTpojblDWloaAEVFRQAUFxcD4O/vz7vvvsu8efPo0aMHb731FhF/nuLr999/5/PPP+fGG2+ka9euFBYWAsaT4/nz5+NwOJg4cSJKKYqKipxbxpOSkkhPT8fhcJCamgpAq1atqh2zUgrHkaOYoqLIv/G2anbPvoOIZQvJv20Mtq1/VJ4kVzFxNsdEY/Kt+bTJk1bx9hSN+XuitmSCLaqc2FZoyAmupx3/nJWVRdOmTd3y3J5EOmjSQZMOmnTQpIMmHTTpoDXmDr1798bHx4c5c+aQkpLCxx9/THR0NIGBgbzyyiuYTCZ69OjBkiVLAOjbty8hISF8/PHH/Oc//2Hw4MF89tlnhIaGcvnll1NQUMCiRYto3749AwYM4ODBg6xatYrU1FRKS0s5evQo9913HwCDBg1i7ty5TJkyhfT0dACuvvpqw3E6srIpWbyU8m+/p/zb74lc+yUlL82p2e7Zs+cTsWIJ5rO0ZdfTVvH2BI35e6K2ZIItAM/aeizHPwshhBBCuEfnzp2ZPXs28+bN46OPPqJt27ZMmzaNzz//HMefxyK///77ztvHxsZy8803M2fOHF599VUWLlxImzZtmDp1KpGRkeTn5zN37lyuvfZaBgwYwPTp0zGZTKxbtw6A22+/nYkTJwJw7733kp+fz+eff05QUBDPPvssPXr0AEDZbDhycrHv2Yd9bwp5A6+tNO6CyY8TseQ9j9k929NW8RYNx6SUUu4ehDeyWCxERESQn5/vUedKPPlc1O7eelyfpyCrK4fDgdlsfN5ubyIdNOmgSQdNOmjSQZMOmnTQGkMHR2Ym+WPGE/rsVPz+0u2sPIey2zH51H0RXpWXr1fwzsxC5eaBw4Ej30LhkzNwHDmKb8+/4D+wH/4D+uJ/yUV6AbMqjsEG92xBViUlHrGKt7t58vdEfc/LPPOzFG5TsfX44eZ3uf3UVO6eXAOkpKS4ewgeQTpo0kGTDpp00KSDJh006aBJBz25tu/bjzI4F7bh7ZXCUVSE/dARrJs2U776W6w//oR9915Udo5eyTsoCHNCc3xaJhK59kuif/uRsBeeIWBAX+fktWLLsf/AfpUe311bkE1BQfj/7SGvnlyDd31PyC7iwkWgOYD7Y2+VXbP53wqW3k46aNJBkw6adNCkgyYdNOmgNYoOFVuWrdZ6f2hlt2P99TdUZhaOvDz8LuhluCVblZfjyMrGkZmlT4f154JqTn5+mKOjMMfGYIqNwRQcjOP4CUwREZiio6t8fk/bPXvvkSNecw7oqjSK74l6IhNsYSjnWDbhyWHuHobbhYaGunsIHkE6aNJBkw6adNCkgyYdNOmgeUIHVVx82tMzmcLDMQUFYkvZj98FverveU+aXAOozCysv/6mn8NkwpGdg/pzUq3yLcBJR6uazZgim2COidGT6ohwTKfsVmzfp7eE+jSNO+04KibZnrB7tie8HtzNmxrIBFsY8pZV/qojHTTpoEkHTTpo0kGTDpp00KSD5u4OqqSEolmvnHZiafL1xe+iCyn/cjX+F/fGJ7nlmT/vKZNr5+UVk+yePbDvP1DpelNYmJ5Mx0Rjjo467emwHNnZlC79BN92bTDHnX6CDXqSHfL4FLfvnu3u14Mn8KYGMsEWhlJSUrx+VxaQDhWkgyYdNOmgSQdNOmjSQZMOmjs7nLzIl3XDr6fdNTro7jsoPHAQy4OP4NM6GXNkE6jNQlRKgd2OcihMvj7Y09KhqLjKm5eEBOPTvDmOvDxMfr56C3tNzi+tFKqgENveFMwR4YQ88bcaD/F0W/EbinxfeFcDmWALIYQQQghRj9onJrrleU9dQbt8zVryho6ocpJtjogg7OXnsW7chHXzVlRhIdhsqKJiVGEhqrDoz49CVFERjsIiKCzCUaQvo7gEgKCHxmP9/Q+s//lvtWP0u/xS/C+/BPz8avW5mZs1JXhAX/z69MbsQWfgEeJUMsEWhpo3b+7uIXgE6aBJB006aNJBkw6adNCkgyYd9CS3/OXX8Gvg436rOj2Vc5L92VJsh49g35uC40SG60dGJo4Tmai8vNo9sdmM49hxmny2lPzht1d7DmpvPBe0fF94VwOZYAtDNpvN3UPwCNJBkw6adNCkgyYdNOmgSQfN2zvUZvfsM3oepVAWC44TGajycnySk8m/4dYqJ7fla9aSd/0tRCxbSOFD/3faSTCgV++Oi8UcH/e/j1P/HR+LT3wcpuho5wrhJ6/cfSpvnVyDfF+AdzWQCXYDW7BgAQsWLMBew3MCuktGRgZxNVg84lwnHTTpoEkHTTpo0kGTDlpNOyiHA/vOXVh/+x2Vn6+PYfVE/v74tGuL30UXYK7F6r+e8HqobvXss/a8tdw92+X+DgcqN9dwy7L9RKbL5ZSVARC9ZwvFL86udtJcvmYtxbPnE/b2PAom/g2fPyfJrhPnOEyRkZhMplo3OPX0WBW8eXINnvF94W7e1EAm2A3sgQce4IEHHsBisRAREeHu4QghhBCigSibjaIXXsG6YRPmyCaY42Jrt6BUQ1EKVVJC2ddrML23kNDnpuPbupW7R1UjNVk9+2w9b3W7Z1t37cW+a3fVu2dnZEItt/KZQkMpev5lwl59CeuGX6vdPbuiS+QXy+v0edZoTB52DmohGppJKU/90+m5rWKCnZ+fT7gHLtRgs9nwrcmqjuc46aBJB006aNJBkw6adNBq0qF0+QpKP/qY4Ecewq/PRXXaQtiQHNnZFD33dxy5eYS//0aNxuvO18PJk9yzOalT5eV/Hq+cgbLb8e3ckfwbbqt2chuxbCH5I+6sdkuzqUkTvWX5lK3KFR8+J+2uffKW+qom+RXP39CTXFVS4hHnoPYE8j7p2Q3qe17mmZ+lcLtDhw7Rtm1bdw/D7aSDJh006aBJB006aNJBq0mH8v/+hN8lF+N/ycUNNKozY46OJuieOyl4dBr23Xvw7dSx2vu46/Vwxrtnl5Q4tybbDRf/qvh3Jio313k/vXv2nBrvnh3+znwKH59e5eTZHBeLyd+/Tg08bfdsU1AQAX97yOsn1yDvk+BdDWSCLQyVlpa6ewgeQTpo0kGTDpp00KSDJh20mnRwpKUTcGX/BhhN/fFp3w4Ae2p6jSbY7ng9VLt79r+WYt3yO7Y/dpwycc5y/r8qKKjdk/r4YI6LpXjOAkJfeKZWu2dH/PMfdfk0a8TTds/ec+SI15z7+HTkfdK7GsgEWxgKdsPiIJ5IOmjSQZMOmnTQpIMmHbSadFA2u+G5f9esWcPs2bNJS0sjMTGRyZMn079/f7777jteffVVjhw5QmJiIpMmTaJ///9N0G02GzfffDPZ2dn88MMPhs9ZVlZGz549K63ge9NNN/Hcc89htVp58cUXWbVqFcHBwYwbN46bb7650v1Nvr5gMkENF2dt6NeDKi4h73rj3aLhz0n2ML16dtEzL5x+S7O/fxW7ZMdiiov9c1GwPxcBi4rCdNLx8560enbFJNsTds+W9wdNOnhXA5lgC0MtWrRw9xA8gnTQpIMmHTTpoEkHTTpode2QnZ3N5MmTSU5OZuTIkaxevZpJkyaxYsUKHnroIRISErjzzjv57LPPeOihh/jqq69ISkrCYrHwxBNPsHPnTuLj46t8/H379mGz2Rg8eDDt2umt0Z07dwbgww8/ZNGiRQwePJi0tDSeeuop2rdvT/fu3ev0uQAkxzet831PR1mt2PfsxbptB7Y/dmDbtp2w1/5O6QdLar579nuvUzTzxSp3zzaFh9f5uHhP3D075PEpbt89W94fNOngXQ1kgi0M7d27V3bpQTpUkA6adNCkgyYdNOmg1bXDsWPHKC8v57zzzmPIkCGkpKRgsVhIS0ujdevW/O1vf+PSSy8lNjaWmTNnsn37dmJiYujXrx8BAQGEhISc9vF3794NwG233UabNm2IiopyXvfVV18RGRnJ7Nmz2b9/P0OGDOGrr76q8wRblZRQ/MKZreCtlMJx/AS2bdv/9/HHDmw7d4PVWum2BVYrEcsW1mr37PC35tVpXDXhabtnu+NUZaeS9wdNOnhXA5lgCyGEEEK4SadOnejduzcrVqxgxYoV+Pr68tZbb/HXv/6VK664wnm733//HYDWrVvjcDgYOnQoY8eOZcSIEZV2/z5VxQT77rvvpqysjG7duvHaa6/RrFkzDh48SHJyMiaTicTERAAOHjxYp8/j5OOgrRt+rdHEUhUXY9u52zmRtv45mVZZ2Ya3N4WF4dutK77duuB7Xhd8u3UFf3/ZPVsI4VFkgi0MNW16dnbxamykgyYdNOmgSQdNOmjSQatrB7PZTLt27bBYLIwcOZIFCxYwbdo0vvzyS4L+nJy9/fbbrFq1iv79+9Oxo15obOrUqTV6fKvVSnJyMrfddhs5OTm8+eabzJgxgzfffJOSkhL8/1yx2u/PY8NLSkpq/TlUt4K3cjhwHD6iJ9DOjx3YU/aDw2EUBZ/2bf+cTHfF97wu+HXrirllUpW7cHva7tmBj06SyTXy/lBBOnhXA5lgCyGEEEK4yZo1a1i8eDFz587lqquuoqioiFmzZrF+/XoGDBjA/PnzmTdvHp06deL555+v9ePPmDGj0r/XrVvHhg0bAAgMDKS8vBzA+d/AwMBaPX51K3hHLF+E5c77KPviS8P7m2Ki8et+XqXJtG/njrWenHra7tl5paXEhIa65bmFEO4lE2xh6Pjx48TExLh7GG4nHTTpoEkHTTpo0kGTDlpdO6SlpQFQVFQEQHFxMQD+/v68++67zJs3jx49evDWW28RERFR68efP38+DoeDiRMnopSiqKjIuWU8KSmJ9PR0HA4HqampALRq1arGj13V5LpC+Zq15A+/nYhlC8kfdQ+O9GP/m0j/OZk2x8fVeWGxU3nS7tnyfaFJB006eFcDmWALIYQQQrhJ79698fHxYc6cOaSkpPDxxx8THR1NYGAgr7zyCiaTiR49erBkyRIA+vbtS6dOnap8vIKCAhYtWkT79u0ZMGAABw8eZNWqVaSmplJaWsrRo0e57777ABg0aBBz585lypQppKenA3D11VefdryquJjyH3/Gt2sXSt54p8YreEcsX4Q55OwvuuUpq2cLIbyXTLCFofbt27t7CB5BOmjSQZMOmnTQpIMmHbS6dujcuTOzZ89m3rx5fPTRR7Rt25Zp06bx+eef4/jz+OT333/fefvY2NjTTrDz8/OZO3cu1157LQMGDGD69OmYTCbWrVsHwO23387EiRMBuPfee8nPz+fzzz8nKCiIZ599lh49elR6PKUUlJZSumwFxX9/lfL1v0BZGf4D+9V6Be+G4gmrZ8v3hSYdNOngXQ1MSinl7kF4I4vFQkREBPn5+YSHh7t7OC4OHDhA69at3T0Mt5MOmnTQpIMmHTTpoEkHrSYdcq8bTvD4ewm4amADjapuVHExjswsHFnZODKzKXjoEex7U5zXm1sk4D+wH4G334J/7wvJu94zVvD2JPJ9oUkHTTp4doP6npfJFmxhqOIYMG8nHTTpoEkHTTpo0kGTDpondFB2OyYfn9rfr7wcR3YOjswsVFY26s/jwZ3MZvwuupDAkcPxH9gPn/btKh037UkreHsKT3g9eALpoEkH72ogE2xhqLariJ6rpIMmHTTpoEkHTTpo0kGrcYeztOOgstux79uPT7s21U6yld2Oys3DkZWFIzMblZ9feVwmM6bICMwxMZhiovFp24bgB+6rcsu7p63g7Qnk+0KTDpp08K4GMsEWhpKTk909BI8gHTTpoEkHTTpo0kGTDlpNOpgCA1CFhfX+3Mpux/rrb6jMLBx5efhd0KvSJFsphSoowJGZjSMrC5WdA3Z75bGFhmKKjcEcG4M5OgqTr/4VsWJrtikw4LRj8KQVvD2BfF9o0kGTDt7VwOzuAQjPtHv3bncPwSNIB006aNJBkw6adNCkg+Y4dbdqA75dO2PdsIn6XP7m5Mk1gMrM0v+227FnZmLd/Dvla9Zi/c+P2HfuQmVk6sl1QADmhOb4/qUb/gP64d/3Mvy6dsYnPs45uQYo/2WjHnvnjtWOxRQUhP8jE71+cg3yfVFBOmjSwbsayBZsIYQQQogzoEpKKH/5Nfyq2XIbcPWVFD77IsUvvIL/kKswx8diMtd9W4eyO7Bu2aq3SJ8s34IjNw+/bl2xn8jQ1/uYMUVGYo6KwhwThSk0xHkctSosQBUWnPTAClVSinXLVkqXLMPvwl6Y4+JqNKa9R4/StQ7n6xZCiHOFTLCFobga/iA910kHTTpo0kGTDpp00Ly9gyopcR57bN3w62mPPfa7oBchkydQsvCflP80o7pHBrsDZbOBzQY2O9hs+t8+Ppgjm2A/chRVUPUu56awUHxaJqLKrZj8/aGWk3mTrw9+fS4ieOK4Gt/H218PFaSDJh006eBdDWSCLQz5+spLA6RDBemgSQdNOmjSQfOEDqq42C3nPj55cg1QvmYteUNHVDnJVkrh260rwX97CNvWP3AcTdWrd2fnoHJysWfnorJzcOTk6K3O5eWGzxvx2VLKVn1D+df/rnaMfhf3JmjM7bXebdvk54tPcktMISG1up8nvB48gXTQpIMmHbyrgfd8pqJW0tPTiYqKcvcw3E46aNJBkw6adNCkg+buDqqkhKJZrzT4wlqnTq4rVEyyI1YsoeSt9ylf+x8cJzL0R0YmWK21eh5TSAjm+Lg/P2Ixx8eh8i2EzXkBx+EjhuefruA/sB/hr/29Qbu4+/XgKaSDJh006eBdDWSCLYQQwisomw0cjvp7QKsVVcUWxlrz9T2jY3G9VW12z67V41qtODKz/jcxPunDHBdL0Ni7yb/59iont+Vr1pJ/40gili2k/N/fYdv8e6XrTRERlSbMRh8+8XGY42JPuwXZ6PzTFeRUWUII4R4mVZ9LWYoas1gsREREkJ+fT3h4uLuH46K0tNSrzldXFemgSQdNOmiNqYMjI4PSpZ9g3bAJh6Wg+jvU5rEdDsz1OCn2SUzA/4pLCbjx+mrPY+xJHEVFmGu5G3F9MNqCfLpJpSotrbQlueL/7adOojMyXRcNO0n0ni2ULlpK0cwXqx1jyFOPEjxxHNaNm/43eY6LxRRw+lNe1UZtO5xtjen94WySDpp00KSDZzeo73mZTLDdxNMn2IcOHfKq89VVRTpo0kGTDlpj6eDIyaHgb0+BzYZ/v8sxt2herxPX0tIyAqs5N3BNqXIrtu07sf6wHr+/XkzIIw/Vy+OebaqkxC3nPa5q92zQk8uIT5ZQPHcB5f/+DscJPZlWFkvtnsTHR58T+uQty3Gx+PU+H/+rryT/htuq3T27oSa5J/dw95brxvL+cLZJB006aNLBsxvU97ys0e0ivnnzZh5++GG2bdtGUFAQw4cP56WXXiIgIIAVK1bw7LPPcuDAAaKiorjrrruYOnWqcwvDwoULefbZZzl27BidOnVi3rx5XHzxxYbPU1RUxIQJE/jiiy+w2WwMHTqU119/ndDQUAD27t3LuHHj2LhxI2FhYUyYMIEnnniiwTqcbYWFVa9K6k2kgyYdNOmgNZYOZau/RRUUEP76HMwxMfX++KUWC/71+AfSgCv7U3ZeF4pfe4PA4Tfgk5RYb499NpyN3bOVUqj8fMNdsx0nMjA3a0bwpAnkD69m9+yb9O7Z1v/+hH1fyv+u9PfHHFfVbtmxzt2yzfFxmKKjT7vbviftnm0KCqLJ58vc8seOUzWW94ezTTpo0kGTDt7VoFFNsB0OB9dccw2PPfYY69atIz09nQEDBhATE8PgwYO5/fbbWb58OYMHD2bPnj0MHjyY0NBQpkyZwrp163jwwQf5+uuvufDCC5k/fz7XXXcdhw8fJthg5dEJEyZw9OhR9u3bh81mY/jw4Tz66KMsWLAAq9XKNddcww033MDXX3/Njh07uOaaa2jXrh0333yzG8rUP39/f3cPwSNIB006aNJBaywdbL/9jt/5Pc/K5Bqo193DK/hfcSklb72H9bctNZpgN4bVs5XDgcrOdu5+rXfJzjSeRGdkVrlyNujds4tfee20W44rxlM8ez4RH/0D245dzkm0KSLCee7nM1UxqfWU3bNNQUGEPD7F7cdcN5b3h7NNOmjSQZMO3tWgUe0inp2dTUxMDHPmzOHBBx/k2LFjXHnlldx9990kJyezfv16Zs+e7bz95MmT2b9/P59//jmjRo0iODiYt99+23l9p06d+L//+z/uuuuuSs9TXFxMkyZNWLduHX369AFgw4YN9O3bl6ysLH766SeGDRtGdna288Xy4osv8tVXX/Gf//ynRp+Lp+8iXt/HFjZW0kGTDpp00BpLB8t9E/Qpiu66/aw8vlKq3iZrJ8u/5wH8L+1D0J0jT//8Hrt79mKKZr1C+df/1pPmzCyw22v1HKbwcMNFwHx79cD/8kvJv+FW2T3bQzWW94ezTTpo0kGTDp7dwKt3EY+OjmbSpElMmTKFRx55BLvdztChQ5k0aRJms5kbb7zReduSkhK+/PJLRo7Uv6Ds2LGDMWPGVHq8zp07s3XrVpfn2bdvH1arlfPOO6/SbUtKSti7dy87duygffv2lf4S07lzZ2bNmlXl2MvKyigrK3P+21LbY8Ea2M6dO+natau7h+F20kGTDpp00BpVB4Mf5mvWrGH27NmkpaWRmJjI5MmT6d+/PwA2m42bb76Z7OxsfvjhBwDmzZvH/PnzXR5n2rRpzp8xFcrKyujZsyc2m8152U033cRzzz2H1WrlxRdfZNWqVQQHBzNu3DjDvZ5M5uon7Wdl9+yyskqLf526GJi5RQIhT/ytBrtnjyJi2UJsv23BtvWP/31eUVEuE2afU45vdm5prmYhHNk923M1qveHs0g6aNJBkw7e1aBRTbAdDgdBQUHMnz+fMWPGkJKSwrBhw5g+fTrPPvus83YFBQXcdNNNBAUFMWnSJOdlIaeschocHGx4PEBBgV5p9uTbV+xGXlhYWKvHqjBr1ixmzJjhcvnOnTsJDQ2lY8eOHDx4kLKyMoKDg0lISGDfvn0ANG3aFKUUJ06cAKBDhw4cOXKEkpISAgMDadmyJXv27AEgPj4es9nMsWPHAGjXrh3p6ekUFRUREBBA69at2bVrFwCxsbH4+/uTlpYGQJs2bcjIyKCgoIDCwkKUUuzYsQPQf9wIDg7m6NGjALRq1YqcnBzy8/Px8fGhU6dO7NixA6UUkZGRhIWFceTIEQBatmyJxWIhNzcXk8lEly5d2LVrF3a7nYiICCIjIzl06BAAiYmJFBUVkZOjV3Dt2rUru3fvxmazER4eTkxMDAcOHACgRYsWlJaWkpWVBeg/cqSkpFBeXk5oaChNmzYlJUUfe9e8eXNsNhsZGRkAdOzYkUOHDlFaWkpwcDAtWrRg7969zt4Ax48fx2KxUF5eTmpqKsXFxQQGBpKcnMzu3bsBiIuLw9fXl/T0dADatm3L8ePHKSwsxN/fn7Zt27Jz504AYmJiCAwMJDU1FYDWrVuTlZWFxWLB19eXjh07sn37dgCioqIICQlx9k5OTiY3N7fK3uHh4Rw+fBiApKQkCgoKquwdFRXFwYMHnb2Li4vJzs4GoEuXLuzduxer1UpYWBhxcXHs37/f+fUrLy8nMzMT0HuAHDhwgLKyMkJCQmjevLnzNdusWTMcDkel1+zhw4cpLS0lKCiIpKSkSq9Zk8nE8ePHna/ZtLQ0iouLCQgIoFWrVg3SOzQ01PmaTU5OJi8vj7y8PMxmM507d2bnzp1YLBZSU1Np0qSJ8zWblJREYWFhg7xmPeU9wmKxsGfPHtq3b+/R7xFNy61gtWL98w+aYWFhHD16lMmTJ5OUlMStt97K6tWrmTRpEj/++CNlZWU89dRT7Ny5k/j4eIqKirDb7XTv3p2JEydS/ueuyytXriQ/P5+uXbtisVgIDQ2lpKQEu93Onj17sNlsDBw4kDZt2uDr60unTp2wWCwsWrSIRYsWceWVV5Kens5TTz1Fu3btaN26NaB3n/Px8aG0tIwTqakkFBe7vGYPHTpEYlwclutvcdk9O+KzpRzJyKCkpOR/vYtLiLTZCCkuJWPHTszZOUTa7ZSnHaM8/Rg+OTkEFhRiPXYcczWrrNd69+ylH3Dwv+spCw0hKLEFzVu2rPQeYTV6jyiwEGSzVvsekZOXR9xnSyt1gD+3oH+2jMMnTlBYWNig7xG+Ux7k8IkT+Pr6evV7RHFxMdu3b8fPz8/j3yPO5u8R5eXlztdP+/btvfb3CLvd7hxTQkKC1/4eAfp3fofDQZMmTbzyPcJisVBSUuKca3jSe0TF9269UY3IJ598ojp06FDpssWLF6uoqCjnv3fv3q26dOmi+vXrp3JycpyXd+vWTc2bN6/SfW+44Qb18MMPuzzP5s2bFaAKCgqcl1ksFgWo33//Xc2ePVv16tWr0n2++OIL1aRJkyrHXlpaqvLz850fR48eVYDKz8+v2SffwI4dO+buIXgE6aBJB006aI2lQ/69D6jiDxZXuuyPP/5Q7du3V48//rj6448/1D333KN69eqlioqKVK9evVSfPn1Ujx491KWXXmr4mBs3blQdOnRQn3zyiSopKXG5/uOPP1bt27dXGzduVNnZ2ZWuu+GGG1Tv3r2Vw+FQ+/btU+3bt1fPP/98jcZdwVFcrHIGXquOE+LykTPwWmXPy1f5D/+fymxznjoRGm94u9N++EaojIR2KqtHH5Vz1fUq7877lOX/nlKFr7ymSr/9XtkLC6t8/pPH4SgurumX6Yyc3KMhn9dIY/m+ONukgyYdNOmgSQfPbpCfn1+v87JGtQX7yJEjlXazBvDz83Puqv3VV19x6623cu+99/LCCy/g6/u/T69r167Ov5BU2LlzJ4MHD3Z5ng4dOuDn58eOHTvo3bu387b+/v60b9+ejIwM9u7di81mcz5Hdbs9BAQEEFCP57082zz1PHUNTTpo0kGTDlpj7tCpUyd69+7NihUrWLFiBb6+vrz11ls4HA6GDh3K2LFjGTFiRKVdvE/27LPP0qlTJ2644QbD21RsJbn77rspKyujW7duvPbaazRr1oyDBw+SnJyMyWQiMVEvYFaxFagmTnfsM/y5e/bNevfs/B27sO/XWzwIDHQ51ZRPvPFK2qbIyGqPK5fds4015u+L+iQdNOmgSQdNOnhXA8880rwKgwYN4tixYzz//PPY7XYOHDjAzJkzGTVqFL/88gvDhg1jzpw5vPzyy5Um1wBjxoxhyZIlfP/991itVl599VVOnDjBsGHDXJ4nODiYESNG8Nhjj5GZmUlmZiaPPfYYt956K0FBQfTt25eYmBgee+wxSktL2bp1K6+99hp33313Q6U46yp2h/F20kGTDpp00BpzB7PZTLt27ejUqRMzZ84kNjaWadOm4ePjw9SpU4mNja3yvj/++CN79uzhnnvuwWQyUVJS4nIbq9VKcnIyU6ZMYezYsWzbts15eFBJSYnzD8J+fn7Oy6rjyMjAnpFJ0fMv13z37GUfEr33d2Lz04krziT20E6iN6wj8ovlRLy7gNDnniZ44ngCR9yE/xWX4dupI+aoqBot2lYxqfUf2K/S5d6+enZj/r6oT9JBkw6adNCkg3c1aFQT7M6dO7Nq1Sq++OILoqOj6du3L9deey3PPfcczz//PFarlYkTJxIaGur8uPrqqwHo378/r7/+OuPGjSMyMpKPPvqIr7/+mqioKACef/55unTp4nyu119/nXbt2nHeeefRoUMHkpOTWbBgAQC+vr78+9//5o8//qBp06YMGTKEiRMnMnr06AZvIoQQoubWrFnD4sWLGTt2LDfffDOjR48mLS2N9evXV3vff/3rXwQHBzsXRDMyY8YMVq9ezZ133smkSZPo2LEjGzZsAPRf7yuO4674b1V/0VdlZZT8czm5g28gs3k7LKPuJnjyBJdJ7an8B/Yj5IlHMEc2wbddW8zh4WdlpfNTJ9nuXj3bHacqE0IIIYw0ql3EAQYMGMCAAQNcLv/iiy+qve+oUaMYNWqU4XVPPPEETzzxhPPfYWFhvP3225VO63Wytm3bsnr16hqOuvGpWHTH20kHTTpo0kFrzB0qFmMqKioC9GkZofrzcyql+Pnnnzn//POdk+JTF7sEmD9/Pg6Hg4kTJ6KUoqioiKA/J51JSUmkp6fjcDicf8lv1arV/57D4cCRlY097RglCx+pdD5oR14+5ev+S5PPlpF3veye7Yka8/dFfZIOmnTQpIMmHbyrQaObYIuGkZWVRVJSkruH4XbSQZMOmnTQGnOH3r174+Pjw5w5c0hJSeHjjz8mOjqanj17nvZ+J06cIDs7m86dOzsvKy8vx2azsWjRItq3b8+AAQM4ePAgq1atIjU1ldLSUo4ePcp9990H6MOc5s6dy5QpU5wr2F511VU48vJwpKZjTz8GZWWo/HwoL8endSsCR40gcOQIfNu3cz6v0THQ7tw9O+D/Hvb6yTU07u+L+iQdNOmgSQdNOnhXg0a1i7hoOJ5+nu6GIh006aBJB60xd+jcuTOzZ88mIiKCjz76iOTkZN58801CQ0NPe7+K08rEx8c7L7NareTn5zN37ly++eYbAKZPn861117LunXr+OWXX7j99tuZOHEiAPfeey+jR4/m559/5sTx4zw9YQJd8wqw/vcn7AcPQVkZ+Ptjiowk+Kn/IzplG6Eznqo0uQbP2z17z5+n9vF2jfn7oj5JB006aNJBkw7e1UC2YAtDpy4S562kgyYdNOmgNfYOV111FVdddVWV169d67r79Xnnnec8B2gFs9lMixYtKl0eHh7Oyy+/bPi4vg4H/3fLbUy59ApUbi4AqqgIfHwwx8djbtEcc2wMZSs+w7dtm9MeO+1Ju2c39tdDfZEOmnTQpIMmHTTp4F0NTEop5e5BeCOLxUJERAT5+fmEh4e7ezhCCHHOsdw3Ab9LLibozpFn5fGV3Y7Jx6fa2ziOn8Celo7KyALl0FeYTJhiovFJSMDcLB7TSb941GbcqrhYFvgSQgghzkB9z8tkF3FhaPv27e4egkeQDpp00KSD1mg6mExgt5+Vh1Z2O/Z9+1EGj6+Uwp6ZhXXLVsr//R22zb+jTmSAcmCKiMCnSyf8B/TF/6IL8UlMqDS51o/t0GOvAU+YXDea18NZJh006aBJB006aNLBuxp4z7Z6IYQQXsUUHY09Na3eH1fZ7Vh//Q2VmYUjLw+/C3qB2YzKt+BIS8eelq6Pp64YR3Aw5oTmehfwao71VkVFqJwczNFR9T5uIYQQQpx9MsEWhirOD+7tpIMmHTTpoHlCh5rsGu3X+3xKP1iMbe8+l4XC6vy8J02uAVRmFtZff8OvVw9su/c4L8ffH3PzpvgkJGCKbFLjc1GX/mslymbHr/f59TLehuAJrwdPIB006aBJB006aNLBuxrIBFsYMjq/qzeSDpp00KSD5u4OqqSEolmvVLu4V8CV/bGu/4XCR6fi26M7Pi0SoJpjpg2fTymwO8Bswn7gEBQUuNym7MvV+CS3xJGVhSkoEFNYGI7jJ7Bt3lqz5ygrw7ZjF/YDhwgaORxzTEytx+ku7n49eArpoEkHTTpo0kGTDt7VQCbYwtDRo0eJiIhw9zDcTjpotemg7HZsO3fhOJ4BDsdZHlkd+fjg07wpPp061njLIsjroYI7z3isSkqc54C2bvj1tKenMgUFETrjScrWrMW24VesGzc5X5PKoVBlZVBaiiotRZWUQVkpqqQUVVaGKi2FklJUaRmmyAj8L7mI8h9/wXH8eJVjMzdtiv/ll1S78JkhX198WiYRdNtw/HpfUPv7u5F8X2jSQZMOmnTQpIMmHbyrgUywhRD1xrr1D4r//iqO/MZxrkNzdBQhj0/Bt0N7dw+l0VAlJZS//Bp+bjg11MmTa4DyNWvJGzrCOclWZWU4MjJxnMhw+bCfyEBlZGL/898qOwdqeBKNqJ++pXTRUhz7/3na2zn2H8Dn1psJeXyKRyw+JoQQQoiGJ6fpchNPP01XYWEhodUsxuMNpINWkw72Y8cpmDAZ3y6dCLz9Vnxat6rblrwGoGw27PtSKPnHYuyHjxD+5lzMkZHV3s/bXw8nT3D9B/Y77dbjenvO4mIcFgvmsHDyht3inFyfzH9gPyKWfUj+iDsMr6+S2Yw5NgZzfBzmuFj9X6OPpBaYg4MrTe6NNFQTT+Pt3xcVpIMmHTTpoEkHTTp4doP6npfJFmxhKDc312O/CRqSdNBq0sH6w49gNhPyxN8wBQY20MjqxuTri2+njoQ89X9Y7rgP688bCRg8qNr7ecLrwV3nPa5u63GNH0cplMVSeQuzy1bn//3br09vIpYtrHJyXTGW/BF3ELFsIfkj78a2bfv/JseGE+dYfOLjMEVH1+qPQE0+X1blJNtbJ9fgGd8XnkA6aNJBkw6adNCkg3c1kAm2MJSfn09iYqK7h+F20kGrSQfb3hR8u3Ty+Mn1yczh4fi0bY1tb0qNJtjufj3UdHGvs/G8RhPLkyfZymrFkZp2ym7ZmYYT6JNPYVWdsPmvUDx7frVbpsvXrKV49nwiPlmM+Sz9AcIUFGQ4yfbmyTW4//vCU0gHTTpo0kGTDpp08K4GMsEWhnw8dNfehiYdtBp1KC/HZPCXyTVr1jB79mzS0tJITExk8uTJ9O/fHwCbzcbNN99MdnY2P/zwg8t9N23axKhRo3jggQd48MEHXa63Wq28+OKLrFq1iuDgYMaNG8fNN99c7XUnMwUF1njC587XQ20W96qX57PbceRbMAcEVLv1OG/oCCKWLaTg4UdrvIu2KSzspC3MVeyeHReLuXkzQp54BOuGX6vdPbsh/vBw6iTb2yfXIO+TFaSDJh006aBJB006eFcDOQbbTTz9GGwhaqtw6jOYQkMJeXSy87Ls7GyuuOIKkpOT+etf/8rq1avJyspi48aNlJeX88QTT7BmzRri4+NdJtilpaUMHTqUQ4cOMWHCBMMJ9nvvvcdLL73E4MGDSUtLY+vWrSxfvpzu3buf9rrqxl0VT9k9G+q21VSVl7vujm2wKJj9RAYqK5vo3ZspXbSUopkvVvvYIU89SuCdt5F37c3GE+X4OHxO+ndtO1a1Fb2uLc6UKimh6PmXG3xvAiGEEELULzkGWzSIHTt20KVLF3cPw+2kg1bXDseOHaO8vJzzzjuPIUOGkJKSgsViweFw0K9fPwICAqo8L+LcuXNJT08/7eN/9dVXREZGMnv2bPbv38+QIUP46quv6N69+2mvqwtP3j3bkW/BcfiIc4Vs48lzJio3t1bPXfDgI0QsXVirrccxuzbX6fOsjqftnm0KCsL/kYdkco28T1aQDpp00KSDJh006eBdDWSCLQzJjg2adNDq2qFTp0707t2bFStWsGLFCnx9fXnrrbdwOBwMHTqUsWPHMmLECGw2W6X7bdu2jYULFzJhwgTmzp1b5eMfPHiQ5ORkTCaT87iegwcPVntdbTXk7tlKKVRhIcpSgLlJkxrvnl049dma7Z7t6/u/hb9Ot3J2fCzmmBhMPj4es7iXp+2evffoEbpGdHXLc3sSeZ/UpIMmHTTpoEkHTTp4VwOZYAtDkTU4ZZE3kA5aXTuYzWbatWuHxWJh5MiRLFiwgGnTpvHll18ydepUw/tU7Drep08frrvuutNOsEtKSvD39wfAz8/PeVl119VGfayerZRC5eUZnp/ZceJ/52au2OJMSQnRe7ZQ9OZ7NV7cK+yNueTfOvqUXbJdJ9CmyEhMZnOtGnjS1uOKsXjC7tny/qBJB006aNJBkw6adNCkg3c1kAm2MCTHhWvSQatrhzVr1rB48WLmzp3LVVddRVFREbNmzWL9+vUMGDDA8D5vvPEGBw8e5Mknn3TuIp6fn092djbR0dGVbhsYGEh5eTmA87+Bf65ifrrraqra3bM/W4Yj4wS2/QcrnVrK5SMjE6zWWj134SNPEv7h27XaPTt6439q9Ry14Ulbj01BQYQ8PsXtu2fL+4MmHTTpoEkHTTpo0kGTDt7VQCbYwtDhw4fp2lV2fZQOWl07pKWlAVBUVARAcXExgHPLspFNmzZhs9kYPXq087JFixZRWFjICy+8UOm2SUlJpKen43A4SE1NBaBVq1bVXlcTp1tUC/6cZF+vd88ufnFOjXbPNkVE/G/3a4NFwCo+fOLjMP15bLqn7J4NnrX12B2LzZ1K3h806aBJB006aNJBkw6adPCuBjLBFkKcNb1798bHx4c5c+aQkpLCxx9/THR0ND179qzyPg8++CA5OTkA5OTkMGPGDK666ipGjhxJQUEBixYton379gwYMIBBgwYxd+5cpkyZ4tzaffXVVwOc9rrTUWVlqOISimfPq/nu2W++huWe8ZVXyj514hwXW6dzhHvS7tkV4/F/ZKLbtx4LIYQQQngimWALQ0lJSe4egkeQDlpdO3Tu3JnZs2czb948PvroI9q2bcu0adMINThfdoULL7zQ+f8VW57btm3LeeedR2pqKnPnzuXaa69lwIAB3HvvveTn5/P5558TFBTEs88+S48ePQBOe93JlALH8eMU/TmhLv9hPf6XXETEstqtnh219qs6NaoJT9o9G6DUZKLqfRC8h7w/aNJBkw6adNCkgyYdNOngXQ3kPNhu4unnwU5LSyMhIcHdw3A76aDVpENtziftbqqkFEdWFo7MLIpemkPZshWVrjfHxxH88ASCJtxH/g23ecTu2eA5516W7wtNOmjSQZMOmnTQpIMmHTTp4NkN6nteVrulZIXXyK3l+XLPVdJB84QOym6v+31tNuzHT2DdvpPy73+g/Nu12H7fhiMtHWx28PXF/6qBhL4yi6itvxBzbD8hj03GHBpKk8+X4T+wX6XHc+fu2Z6wuJcnvB48gXTQpIMmHTTpoEkHTTpo0sG7Gsgu4sKQyWRy9xA8gnTQ3N1B2e3Y9+3Hp10bTD4+1d/e4UDl5ePIzMKRlYXKzQflOOkWJkxNIjDHRuPTsgUhTz9B6JP/Z/hYnrZ7tics7uXu14OnkA6adNCkgyYdNOmgSQdNOnhXA9lF3E08fRdxIWqr8JlZAIROe7xeH1fZ7Vh//Q2VmYUpNga/C3q5TLKVUqiiIlRmFo7MbBzZOWCrfFosU0gIpphozLExmKOjMP25knnB/z2FuWk8IZMfPP04PGT3bCGEEEIIUX9kF3HRIHbt2uXuIXgE6aCV51uqvY1Pq2Rsu/agbLZ6e96TJ9cAKjNL/9tuR1mt2NPSsf6+jfLv1mH9/gds23fiOHFCT679/DE3b4Zvt674978C/36X49etKz7Nmjon16qkBHvKfnxatax2LHr17Idkco18X1SQDpp00KSDJh006aBJB006eFcDmWALQ/YzON71XCId9CS0/OW5qJKS097O/7JLUEXFFM99HUdW1pk/7ymTa+flf06yUQr70VQcR1OhpATMZkwx0fh06oDfpZfgP6g/fr164NMyyXC3avuJExS98hoohf8lF9VoTHuPHjnjz+tcIN8XmnTQpIMmHTTpoEkHTTpo0sG7Gsgx2MJQRESEu4fgEby9gyopcR57bN3w62mPPfZpmUTI3x7S549e91/MEeFQg+OlQYHdjrLZwWYDE5gCg7CnpqEKi6q8lyk0BJ+E5jgsBZj8/DAFB4G5hn8ztNn0/QL8CXnqUcxxcTW6m7e/HipIB006aNJBkw6adNCkgyYdNOngXQ1kgi0MRUVFuXsIHsETOqjiYrcsbHXy5BqgfM1a8oaOOO0k2//SS/Dr+RfKN2zCnrIfR24eKj8flW/BkW8x+P8ClMUCjv8tQBb6yvNY//sz1nX/rXaM5uuvJfCG6yCgdmdlNvn4YG7eDL/ze2IKDKzx/Tzh9eAJpIMmHTTpoEkHTTpo0kGTDpp08K4GMsEWhg4ePEjXrl3dPQy3c3cHVVJC0axXGnxhrVMn1xUqJtkR//oI6/pfsG3ZiuNERqUP+4kMVFY21HL9RFNkJOb4WOzpxwn/4C3yby4yPP90Bf+B/Yj45/sN2sXdrwdPIR006aBJB006aNJBkw6adNCkg3c1kAm2EB6qNrtnn8lznDwxxtcX/0v7kH/jyCont+Vr1pI/7FYili2k+OW5VU+CTSZ9THR8HGajj7jYSv9fsfBYhZNPjXUqd58qSwghhBBCCCNymi438fTTdOXn53vVsRJVcVcHoy3INZlUKqVQBQU4MjJdtiw7PzKynP+vCgoq3T96zxZKFy2laOaL1Y4x5KlHCbp/DMWvvWE8cY6JxuR7Zn/Dq2uHs0W+LzTpoEkHTTpo0kGTDpp00KSDJh08u0F9z8tkC7YwVFxc7LHfBA3JHR2q3T37048o/3Yttk2bcZzIxH7KBJrS0to9ob+/c3JcsmgpIX97COuGX6vdPbtit/Wwl2bW5dOsEVNQUKUt2e7eci3fF5p00KSDJh006aBJB006aNJBkw7e1UAm2MJQdnY2zZo1c/cw3K6hO1Q1ua5QvmYt+Tfo3bNLXn+nytuZQkJO2aIca7ybdnwcpvBwTCZTpft70u7ZFZPsoudfbvBj0U8l3xeadNCkgyYdNOmgSQdNOmjSQZMO3tVAJthCeAhVXEzRrFdOu+UY9CS7ePZ8wv/xJiXvLXSZMPvEx2EKCTmjsZy65biCu7Ygm4KCCHl8ihxzLYQQQgghPJocg+0mnn4MtlLKZaumN2rIDvb0Y5hCQ8i/aVS1u2c31CT35C3q7t492xPI94UmHTTpoEkHTTpo0kGTDpp00KSDZzeo73mZuR7GJM5Be/fudfcQPEJDdLCfOIHlwSlktexE/k2jiFi2EP+B/Qxv667ds0OeetTrJ9cg3xcVpIMmHTTpoEkHTTpo0kGTDpp08K4Gsot4I6aUovzf32H973rsqelgs9XbYwdbLOTX15Z1Hx/MsTH4XXQBAddcjSkwsH4etwFYrdaz9tiOggKKX55L8SvzUEVF+kKzGUd2jsftnu3/yESvn1zD2X09NCbSQZMOmnTQpIMmHTTpoEkHTTp4VwOZYDdSSilK5r1B2bfr8OveFf+B/TD51d+XM7ysnIAA/+pvWBMOB/bDRyn96GOsv24mdMaTjWaSHRYWVu+PqcrKKHnrPQpnvoTKzALA94JehL0wA/9+Vzhv50mrZx/Ly6Oll6z8eDpn4/XQGEkHTTpo0kGTDpp00KSDJh006eBdDWSC3UjZDxykbM33BD9wHwFXDaz3x/ez2/Hx8anXx7Tt3kPB/02l/L8/EVDFLtCeJi4urt4eSzkclP5zOYVTn8Vx6DAAPu3bEfrcNAJuvN7luBRPWj27Pjs0ZtJBkw6adNCkgyYdNOmgSQdNOmjSwbsayDHYjZR14yZMoSH4D+h7Vh6/qGKX5Xrk27EDvl06Yd3wa70/9tmyf//+M34MpRRlX/+bnJ6XYLn9HhyHDmNu1pSwN+cSvX0jgTcNq3LRB09ZPbs+OpwLpIMmHTTpoEkHTTpo0kGTDpp00KSDdzWQLdiNlMrNwxwXi8m3cX0JfZrFYz981N3DaDDWDb9S8Ng0rOv+C4ApPJyQRycR/ND4Gp9KyxQcfDaHKIQQQgghhKgnsgW7sVIKk8Eu3GvWrOHqq6+mW7duDBkyhO+++w6ArVu3MmzYMHr16sX9999PVlaW8z6LFy+mX79+dOvWjbFjx5KRkUGgwTHSVquVmTNnctFFF9GvXz8+/vjjGl1XiY8PNKIzwyUkJNTpfrY9e8m78TZyLuqrJ9cBAQRPmUjMgT8IeeJvZ3ye6oZW1w7nGumgSQdNOmjSQZMOmnTQpIMmHTTp4F0NZIJ9DsnOzmby5Mn4+voycuRISkpKmDRpEsXFxYwdO5acnByuv/561q9fz+OPPw7Ad999x7PPPkt8fDy33norv/zyCxMnTsTo9OgffvghixYt4uKLLyYmJoannnqKrVu3VntdY1ZeXl6r29vT0rHcN4HsLhdQ9ukXYDYTOHoUMXt/J+zl5zFHR5+lkZ5dte1wrpIOmnTQpIMmHTTpoEkHTTpo0kGTDt7VQCbY55Bjx45RXl7Oeeedx5AhQ2jTpg3+/v5s3LiRnJwc7rnnHqZOnUr//v358ccfyc/P5+effwZg+vTpPP7449xyyy1s2bKFPXv2uDz+V199RWRkJLNnz+b55593XlbddY1ZZmZmjW7nyM2l4LFpZLXtRsk7H4DdTsB1Q4je9gsR/3gTn6TEszvQs6ymHc510kGTDpp00KSDJh006aBJB006aNLBuxo0rgN4xWl16tSJ3r17s2LFClasWIGvry9vvfUW+/btA6BFixYAJCYm4nA4OHz4sHNFv5UrV6KUYvPmzQAcPHiQbt26VXr8gwcPkpycjMlkIjEx0XlZddedy1RJCcXz36Jo1iuo3FwA/C65mNAXZuD/1z5uHp0QQgghhBCiIckW7HOI2WymXbt2dOrUiZkzZxIbG8u0adOcK4L7++vzWvv5+QFQUlLCbbfdRpcuXXj33Xe5/vrrKSwsrPLxS0pKDB+juusas86tWxtermw2St7/kKz2f6Hw/55C5ebi06UTEZ8vI/K//z7nJtedOnVy9xA8gnTQpIMmHTTpoEkHTTpo0kGTDpp08K4GMsE+h6xZs4bFixczduxYbr75ZkaPHk1aWprz+opjHyr+GxQURGhoKMuXL2fJkiWsWLGC2267DQBfg9XJAwMDXR6jYjG0013XWKmSEopnvYI66Q8FSilKP19FdrfeWO4ejyM1DXNiC8L/8SbRW38h8LohVZ5yqzE7cOCAu4fgEaSDJh006aBJB006aNJBkw6adNCkg3c1aHQT7M2bN3PZZZfRpEkTmjVrxkMPPURZWVml2/z888+Gk7uFCxfStm1bQkJCOP/8853HHxspKirirrvuIjo6moiICO64445KW3f37t1L//79CQsLo3nz5s7jjt2pYjJdscW6uLgYgFatWgFw9Kg+PVZqaiomk4mWLVuSkpLC1KlTsdlsdO3alV27dgHQ2mDLbVJSEunp6TgcDlJTUys99umua4xUSQl5Q0dQNPNF8oaOQJWUYP1jB7l/HUj+9bdg37UHU1QUoS8/T8ze3wkaPcpwVfdzxanfY95KOmjSQZMOmnTQpIMmHTTpoEkHTTp4V4NGNcF2OBxcc8013HTTTeTk5PDrr7+yevVqXnrpJUBvXXz//fe58sorXb6I69at48EHH2ThwoXk5eUxcuRIrrvuOuck9FQTJkzg6NGj7Nu3j3379nHkyBEeffRRQJ+S6pprruGCCy4gOzubL7/8kgULFlR9aqoG0rt3b3x8fJgzZw4vvvgi77//PtHR0Vx++eVERETw7rvvMnPmTL777jsuueQSIiIiCA8PZ+XKlUyfPp1nn32Wf/3rX/Tp04eWLVtSUFDA66+/zrfffgvAoEGDyM3NZcqUKTz55JMAXH311dVeV1eqiq/N2VYxuS5fsxaA8jVryRs6Ap8WzTGFBENQECFPPELM/m2ETJmIqZFvqa+JkEZ2WrGzRTpo0kGTDpp00KSDJh006aBJB006eFeDRjXBzs3N5dixYzgcDudppMxmM8HBwQCMGTOGd955hxkzZrjc99133+WWW27hkksuwc/Pj0mTJhETE8OyZctcbltcXMySJUt45plniIqKIi4ujhdffJF//OMfFBcX85///Idjx47xzDPP4O/vT48ePZg4cSLz588/uwGq0blzZ2bPnk1ERAQfffQRycnJvPnmm4SGhvLuu+8SExPDp59+ysUXX8ysWbMAiIuL4/nnn6e0tJQVK1YwYMAAXnnlFQICAsjPz2fu3Ll88803ANx7772MHj2an3/+mYyMDJ599ll69OhR7XV1oUpK9MJhDXwc96mT6wrla9aSP+JOIj5ZQszR3YQ+9zTmJk0adGzu1Lx5c3cPwSNIB006aNJBkw6adNCkgyYdNOmgSQfvamBSRic89mCTJ09m7ty5mEwm7HY7Q4cO5dNPP8VsNpOamkqLFi1Yt24dffv2rXQu5x49ejBmzBgefPBB52U33ngjiYmJvPrqq5WeY+vWrfzlL3/BYrEQFhYGQEFBAeHh4WzZsoX//Oc/fPjhh/z222/O+6xcuZLbb7+dvLw8w3GXlZVV2qpusVhITEwkPz+f8PDwWncoXvAW9v0HCZv9Qq3vWxMWi6VO46pOTcZ98iTXf2A/mny+DFNQ0Bk9ryopwXEiA8eJDOx//teRkem8zJzYgtCnHiV/+O0uk+uT1dd4Gpvt27fTtWtXdw/D7aSDJh006aBJB006aNJBkw6adNCkg2c3sFgsRERE1HledqpGdZouh8NBUFAQ8+fPZ8yYMaSkpDBs2DDn7s0Vp6EyUlBQ4LJrQnBwsOGq2QUFBUDlXRkqtpIXFhbW6rEqzJo1y3DL+s6dOwkNDaVjx44cPHiQsrIygoODSUhIcJ5eq2nTpiilOHHiBAAdOnQgKzOLoNJSzEVFBAUFOZ87ICAAk8lEaWmp83MoKyvDZrNhNpsJCQlxfn5V3RYgJCgIpZTztv7+/vj4+DhXBg8ODsZqtWK1WjGZTISFhVFQUIBSCj8/P3x9fQ1vaystww/YtWsXdrudiIgIIiMjOXToEKGhoSTFx5NvsHt2+GdLyS4sJDQ0VC+SoBQJTSIpS00lL2U/5uwcEvwCyNq9G5WZhX++Bf/8AsrS0jBn52AuOv3u5tF7tlD8ymunnVxXjKfo+Zfxf2Qie48eJS4uDl9fX9LT0wFo27Ytx48fp7CwEH9/f9q2bcvOnTsBiImJITAw0HmMeuvWrcnKysJiseDr60vHjh3Zvn07AFFRUYSEhDiPm09OTiY3N5f8/Hx8fHzo1KkTO3bsQClFZGQk4eHhHD58GNDHwxcUFJCbm4vJZKJLly6VekdFRTlPoZaYmEhxcTHZ2dkAdOnShb1792K1WgkLCyMuLo79+/djsVjIzc2lvLzceR7DTp06ceDAAcrKyggJCaF58+bO12yzZs1wOByVXrOHDx+mtLSUoKAgkpKSnOdaj4+Px2Qycfz4cQDatWtHWloaxcXFBAQE0KpVK3bv3g1wVnuHhoZy5MgRZ++8vDzy8vIwm8107tyZnTt3YrFYSE1NpUmTJhw6dMjZu7CwkJycHAC6du3K7t27sdlshIeHExMT41zYo0WLFpSWlpKVlQXovU5SUlIoLy8nNDSUpk2bkpKSAui/9NpsNjIyMgBq/R5x5MgRSkpKCAwMpGXLlpV6m81mjh075uydnp5OUVERAQEBtG7d2rkWQ2xsLP7+/s71Hdq0aUNGRgYWi4U9e/bQvn17duzYob+HoqMJDg52vmZbtWpFTk5Ola/ZsLAwZ++WLVs6X2NGr9mK94iK12xRUVGD9D506BClpaUEBwfTokUL9u7d6+wN+gfy9u3bad++PampqRQXFxMYGEhycnKDvGY95T2iokNCQoJXv0cUFBSwfft2mjRp4tXvEcXFxWzfvh0/Pz+vfo8oLy93vn68+T3Cbrc7x+TN7xGgf+d3OBxe+x5hsVgoKSkhIyODgoICj3qPqPjerS+Nagv2ihUrePLJJ53fJABLlixh4sSJzm9swHALdvfu3bn33nuZMGGC87Ibb7yRpKQk5syZU+l5tmzZQs+ePSkoKCA0NBT43xbs33//nbVr17JkyRI2bdrkvM/KlSu54447yP3zXMinOitbsPftJ+zVl2p93+ooux37vv34tGtT7wt3Fb/2OvZDRwy3YFe1ezboLccRHy+mcOqzlK38CseJDKjt7uMBAZjj4/RHXKzz/33iY/Htfh5+F/Qib9itsgXbQHZ2NtHR0e4ehttJB006aNJBkw6adNCkgyYdNOmgSQfPbuDVW7CPHDnisniZn5+f8/zLp9O1a1fnX0gq7Ny5k8GDB7vctkOHDvj5+bFjxw569+7tvK2/vz/t27cnIyODvXv3YrPZnKez2rlz52l3ewgICCAgIKDacdaUKSICR2YWyuHAZK6/Q+mV3Y71199QmVk48vLwu6DXGU2ylVJQXo4qK0OVlWPbux/7/gMUPDbNuXu2T6uWhM6cTv6IO6qc3JavWUv+zaOIWLYQ++49lB/Sf2U1hYT8b9IcH1tp4nzqhyk8vNpTaDX5fNlpJ/neOLkGvfeIkA4VpIMmHTTpoEkHTTpo0kGTDpp08K4GjWqCPWjQIB5//HGef/55Hn30UQ4fPszMmTMZNWpUtfcdM2YMw4YNY/jw4fz1r39lwYIFnDhxgmHDhrncNjg4mBEjRvDYY4+xfPlyAB577DFuvfVWgoKC6Nu3LzExMTz22GPMnDmTPXv28Nprr/Hcc8/V++dcFb8LelK6bAXWH37E/4rL6uUxT55cA6jMLKy//uYyyVYOR6VJM5X+W4YqL4cyfT3l5fDnngT24xmUr16DPeUA5d+scT5e9J4tFM+eV6Pds4tnzydi+Yc4cnLxiY/DVM8rEpqCggwn2d48uQY4ceIEsbGx7h6G20kHTTpo0kGTDpp00KSDJh006aBJB+9q0Kgm2J07d2bVqlU89dRTvPTSS0RERDBq1CimT59e7X379+/P66+/zrhx40hNTaVLly58/fXXREVFAfD888+zZMkS51bu119/nSlTpnDeeedRXl7O0KFDnauE+/r68u9//5sHHniApk2bEhoaysSJExk9evRZ+9xP5dO+Hf5/vZjiV1/HumETPh3bY/Lzq/PjKbsD+759qHyLy3XWn37Bp20b7EdSUTm5YLfV7sEdDhw5uVg3bkLZbPjfdD2+Cc2dW5YdOXmEPDYF64Zfq909O+SJRzAFBZ3VFbxPnWR7++RaCCGEEEIIUTON6hjsc0l97Ouv7HbKPl+F9b8/YU9NA1stJ77Ox3Fg274DlZNX5W1MUU3w7dIJ245d+nYmwM8Pk78/+P/5X5d/+2Py98MUEIA5Pha/i3sTcMN1mP9cmd1lHNUcg93Qk1xVUkLR8y87J/XezGq14ncGf8A5V0gHTTpo0kGTDpp00KSDJh006aBJB89uUN/HYMsE203q+wtZV6q4mKJZr1A088Vqbxvy1KMEP/IQlJViio6u9wXQwHiS7c4tyFaLBT83fn08RUpKCm3btnX3MNxOOmjSQZMOmnTQpIMmHTTpoEkHTTp4doP6npfV3+pYolEyBQcT8sQj+A/sd9rbVeyebY4IxxwXd1Ym1/C/3bMrxuPu3bP3/Ln8v7erOJWbt5MOmnTQpIMmHTTpoEkHTTpo0kGTDt7VQCbYwmVSe6qGnuRWjCfkqUfdfuxzkJfvGl5BOmjSQZMOmnTQpIMmHTTpoEkHTTpo0sG7Gsgu4m7iKbuIn8zTds9WxcWYgoMb/HlP5snHizQk6aBJB006aNJBkw6adNCkgyYdNOmgSQfPbiC7iIuzxtN2z3b35Bpgz5497h6CR5AOmnTQpIMmHTTpoEkHTTpo0kGTDpp08K4GMsEWlXjS7tlCCCGEEEII0ZjIBFu4MAUFEfjoJJlcA/Hx8e4egkeQDpp00KSDJh006aBJB006aNJBkw6adPCuBjLBFobyvGilv9MxmUzuHoJHkA6adNCkgyYdNOmgSQdNOmjSQZMOmnTwrgYywRaGjh8/7u4heATpoEkHTTpo0kGTDpp00KSDJh006aBJB006eFcDmWALIYQQQgghhBD1QE7T5SaeeJquk5WVlREQEODuYbiddNCkgyYdNOmgSQdNOmjSQZMOmnTQpIMmHTy7gZymSzSItLQ0dw/BI0gHTTpo0kGTDpp00KSDJh006aBJB006aNLBuxrIBFsYKi4udvcQPIJ00KSDJh006aBJB006aNJBkw6adNCkgyYdvKuBTLCFIU/dhaOhSQdNOmjSQZMOmnTQpIMmHTTpoEkHTTpo0sG7Gsgx2G7i6cdg22w2fH193T0Mt5MOmnTQpIMmHTTpoEkHTTpo0kGTDpp00KSDZzeQY7BFg9i9e7e7h+ARpIMmHTTpoEkHTTpo0kGTDpp00KSDJh006eBdDTzzzwheoGLHAYvF4uaRGCssLPTYsTUk6aBJB006aNJBkw6adNCkgyYdNOmgSQdNOnh2g4px1deO3TLBdpOCggIAEhMT3TwSIYQQQgghhPBuBQUFREREnPHjyDHYbuJwOEhPTycsLAyTyVTpugsuuIBff/3VTSPTf8VJTEzk6NGjbj0+XDpo0kGTDpp00KSDJh006aBJB006aNJBkw7SoEJVHZRSFBQU0Lx5c8zmMz+CWrZgu4nZbKZFixaG1/n4+HjEwmfh4eFuHYd00KSDJh006aBJB006aNJBkw6adNCkgyYdpEGF03Wojy3XFWSRMw/0wAMPuHsIHkE6aNJBkw6adNCkgyYdNOmgSQdNOmjSQZMO0qBCQ3WQXcSFC08/hVhDkQ6adNCkgyYdNOmgSQdNOmjSQZMOmnTQpIP3NZAt2MJFQEAA06dP96oTwhuRDpp00KSDJh006aBJB006aNJBkw6adNCkg/c1kC3YQgghhBBCCCFEPZAt2EIIIYQQQgghRD2QCbYQQgghhBBCCFEPZIIthBBCCCGEEELUA5lgN0IZGRlcf/31NGnShJiYGB5++GFsNhtjx44lNDS00oePjw+DBg2qdP8+ffqwefNmAF566SVatGhBSEgIV1xxBXv27HHe7tChQ9xwww3ExsYSExPD9ddfz8GDBys91ttvv83EiRMBWLFiBX/5y18IDw8nOTmZGTNm4HA4XMa/Zs0afHx8OHTo0FnpAPDVV1/Ro0cPwsLC6N69O//6179c7n+udADIzMykbdu2rFu3znnZuHHjCAgIqPR6ePvtt8/ZBlV18KbXwtatWxk4cCBRUVE0bdqUO+64g6ysrEq3+fnnnwkMDDS8vzd0WLBgAe3atSM0NJR27doxf/58r+vgbT8rTvd68Kb3h9P9zKxw7Ngx4uPj+eCDD+rc4ffff6dfv35EREQQExPD7bffTnZ2dqPosG3bNvr3709YWBjx8fFMnjzZpZE3dFi6dCmdOnUiPDyc9u3b8+abb7rc/1zpUMHo94eajMUbOlTwhveHeqNEo3PFFVeokSNHqqKiIrV//37VpUsX9dJLL7ncbvXq1SoqKkpt377deVl6erpq1aqVUkqpDz74QCUkJKjt27erkpISNXnyZNWlSxflcDiUUkp1795d3X333aqwsFAVFBSoMWPGqK5du1Z6jkGDBqm1a9eqTZs2qaCgILVy5Uplt9vVzp07VXJysnr55Zcr3f7YsWOqWbNmClAHDx48Kx1+++035efnp9555x1ltVrVDz/8oMLCwtT3339/Tnb48ccfVZs2bRRQ6XPs1auX+uCDD6q837nUoKoO3vRaKC4uVs2aNVPTpk1TZWVlKisrSw0ePFhdc801SimlHA6Heu+991RoaKgyeuv3hg5ffPGFioyMVJs2bVJKKbVx40YVGBio1q5d61UdTnUu/6w4XQdven9QqvrfHex2u+rbt68ym83qH//4R6X71rRDWVmZatasmXrmmWeU1WpVubm5qn///uqOO+7w+A6ZmZkqJiZGPf/886q8vFwdPHhQtWvXTv3973/3qg5//PGHCg4OVj///LNSSqn169crf39/9cMPP5yTHZQy/v2hJmPxhg4VvOX9ob7IBLuR2bdvnwJUWlqa87KlS5eqpKSkSrer+EGxePHiSpcvWLBAPfzww0oppS655BL13HPPOa8rLy9XYWFhau3atSonJ0cNGjRIpaenO6/funWrAlROTo5SSqm8vDzVvHlzZbPZ1CeffKImTZpU6bkmTZqkrrvuOue/7Xa76tevn5o6deoZfxOcrsOjjz6q+vbtW+n2Y8eOVcOHDz/nOnzwwQcqKSlJLV26tNIbYmlpqfL396/0C/OpzpUGp+vgTa+F3bt3q6uuukrZbDbnZZ9//rkKDw9XSik1evRoddFFF6lXXnnFcILtLR0sFotSSimr1aq++uorFRQUpDZv3ux1HSqc6z8rTtfBm94favK7w/Tp09Xtt9+uWrZs6fILdE07KKVUYWGhstvtSimlDhw4oC666CI1ZcoU5+09tcPLL7+s+vTpU+n2hw4dUocPH/aqDitWrFD+/v5q/fr1yuFwqJ9++kkFBQWpjRs3nnMdlKr694eajMUbOlTwhveH+iQT7Ebms88+U1FRUZUu27ZtmwJUbm6u87IxY8aoq666yuX+/fv3d/4VskmTJuqLL76odH3Pnj3VnDlzDJ976tSpKjk52fnvRYsWqTFjxhjetri4WLVv317NmDHDednTTz+tbrnlFnXw4MEz/iY4XYd77rnHZSvN+PHjVbdu3Zz/Plc6HDt2TFmtVqWUqvSGuGHDBmUymdQNN9yg4uLiVLt27dQLL7zgfFNT6txpoFTVHR555BGveS0Yuf32250TiKNHjyqllPr+++8NJ9je0kEpPeny8fFRgJo8eXKl23pTB6XO/Z8VRio6eNP7Q3W/O6xdu1Z16NBBFRQUGP4CXZcOffr0UYDq3LmzOn78uPNyT+1w5ZVXqvvuu0/df//9Kj4+XrVu3VrNnDnzjH9uNrYOqamp6rLLLlOA833y1C2G50oHpar+/aEmY/GWDt7y/lCfZILdyCxatEglJiZWuiwlJUUBzl+gDxw4oPz8/NSWLVsq3S47O1slJCQ4f1j4+Pio7777rtJt/vrXv6pnn33W5XnfeOMNFRQUpNasWeO8bNiwYWrVqlUut7VYLOrKK69U3bt3d24tWrdunWrbtq3Kz8+vl2+C03X4z3/+o/z9/dUnn3yirFar+vHHH1VUVJRq167dOdfhZCe/If773/9Wffv2VevWrVPl5eVqw4YNqnnz5s7dAc/VBkpV7lCxa5u3vRYcDod68sknVZMmTdS2bdsqXWc0wfa2DuXl5aq8vFz9+uuvqnnz5uqFF15QSnlfB2/4WXGyUzt40/vD6X5mHj58WLVs2dJ56MSpv0DXtUNxcbHKyclRI0aMUF27dnXuReCpHa644grl5+en3nvvPVVeXq5+//13lZiY6NxF3Fs6HD58WN15551qzZo1qry8XK1atUqFhISo1atXn3MdTlXVBNtoLN7S4cSJE17z/lCfZILdyHz66acqOjq60mUVf3XMy8tTSin11FNPqSuuuMLlvu+//7669957nf8ODw9XK1eurHSbnj17qldffdX577KyMjV+/HgVFRVV6TjFoqIiFR8fr0pLSyvdf/fu3apLly6qX79+zt3iMjIyVMuWLdUvv/yilFL18k1QXYdly5aprl27qqioKDV06FA1ZcoUdeGFF55zHU52ur+8KqXUSy+9pHr16qWUOncbKOXawdteC/n5+eqGG25QLVu2dJlcK2U8wfbGDhVefPFF5xZLb+vgDT8rKlTVwVveH073M/OKK65Qr7zyivPyU3+BrkuHk504cUIBavPmzR7f4ZJLLql03UsvvaQuuOACpZT3dBg9erS6//77K113zz33qBtuuEEpdW51OJXR71FGY1HKOzo4HA515ZVXes37Q32SCXYjs3fvXgVU2p1i6dKlqkWLFs5/d+rUSb333nsu973mmmvU119/7fx3nz59nFtulPrfcRLffvutUkofm3fRRRepHj16qAMHDlR6rE8++USNGDGi0mVffvmlCg8PV1OmTHHuZqKU/ktpUFCQioiIUBERESosLEwBKiwsTM2aNaveO2RnZ7scezx8+HA1fvz4c67DyU7+wfCvf/1Lvfnmm5Wuf/bZZ9Vf//rXc7qBUpU7eNtrISUlRbVv314NGDBAZWZmGt7GaILtLR1mz55d6fhapZR65plnnJNMb+lQwRt+VihVdQdven+o6mcmoAICApzPFRERoUwmkwoKClJDhgypdYeDBw+q5OTkSseiHzlyRAHq0KFDHtuhRYsW6m9/+5s6//zzK91+1qxZzsu8pcN1112nRo8eXen248aNU7fccss51+FUp06wqxqLt3Q4fPiwV70/1CeZYDdCf/3rX9Utt9yiLBaLOnDggOrSpYuaPn26UkqprKwsBah9+/ZVuk9BQYGKj49X5eXlzsveffddlZCQoH7//XfnSn9t27Z17j7Zs2dPNWjQIFVcXOwyhttuu00tW7bM+e+ff/5Z+fv7G/6ydqr6+itTVR1++eUXFRISon7//XdltVrV0qVLVVBQkNq+ffs52aHCyT8YPv30UxUUFKS+/fZb5yIlMTExatGiRed0A6Uqd/Cm10JOTo5KSkpSo0ePrnTM4KlOnWB7U4dNmzYpf39/tWzZMmW329WPP/6ooqOj1bJly7yqg1Le87PidB286f1BqdP/7nCyk7dQ1baDw+FQPXv2VCNGjFAFBQUqMzNTXXPNNerqq6/2+A67du1SAQEB6sUXX1Q2m01t27ZNJSQkqLlz53pVh3fffVcFBQWpb775RjkcDrVu3ToVFhamVq5ceU52ONnJvz+cbize1OFU5/r7Q32RCXYjdPz4cXXTTTep6OhoFRsbq6ZMmeI8duHXX39VgMsP+KVLl6qRI0dWuszhcKiXX35ZtWrVSoWGhqq+ffuqPXv2KKWUWrFihQJUYGCgCgkJqfRx+PBhFR8frwoKCpyPde211yqTyeRyW6PFc+rrm+B0Hd544w2VnJysQkND1fnnn+/cwnAudqhw6hvim2++qdq3b6+Cg4NV69at1YIFC875BkYdvOW1ULE6eHBwsMtznuzUCba3dfjiiy9Ut27dVFhYmOratatz9Wxv6+AtPyuq6+At7w9Knf5n5slO/gW6th2U0gsq3nDDDSoqKko1b95cjR8/XuXl5amysjKP7/DLL7+oSy+9VDVp0kQ1b95cPfvss8rhcHhdh9dee021b99ehYWFqS5dutT5fbIxdDjZyb8/nG4s3tThVOf6+0N9MSmlFEIIIYQQQgghhDgjZncPQAghhBBCCCGEOBfIBFsIIYQQQgghhKgHMsEWQgghhBBCCCHqgUywhRBCCCGEEEKIeiATbCGEEEIIIYQQoh7IBFsIIYQQQgghhKgHMsEWQgghhBBCCCHqgUywhRBCCCGEEEKIeiATbCGEEEIIIYQQoh7IBFsIIYQQQgghhKgHMsEWQgghhBBCCCHqgUywhRBCCCGEEEKIeiATbCGEEEIIIYQQoh7IBFsIIYQQQgghhKgHMsEWQgghhBBCCCHqgUywhRBCCCGEEEKIeiATbCGEEEIIIYQQoh7IBFsIIYQQQgghhKgHMsEWQgghhBBCCCHqgUywhRBCeLwrrrgCk8l02o9Dhw7V+/OuW7fO+fjHjx+v98cHnI+/dOlSw+svu+wyTCYTM2bMOCvPf7Lk5GRMJhMvvPBCre5X3ecAMHr0aJevmdlsJiQkhI4dOzJ9+nSsVqvL7a+66qo6fz7u9PTTT7t8vn5+fkRFRTF48GC2bdtW68dcsWIFJpOJTp06sX///rMwaiGEEGfK190DEEIIIWoqJCSEJk2aGF7n69s4f6QlJCQAEBwc7HLdN998w+HDh/nnP//Jrbfe2tBDOysCAgKIiYkBwOFwkJeXx549e3jmmWfIzc3ltddeAyAqKoqEhARiY2PdOdwz5uvrS3x8PA6HA4vFQm5uLl9//TU//fQTu3btolmzZjV6nPLycl555RWuu+46PvzwQyIiIs7yyIUQQtRF4/xtRAghhFcaNWoUb775pruHUa9SU1OrvO6qq67i8OHDDTias++KK67gm2++cf67rKyMm266iVWrVvHWW2/x8ssv4+/vz+zZs5k9e7YbR1o/2rRpw+7duwFQSrF8+XJuueUW8vPz+cc//sETTzxRo8fx9/fnp59+OptDFUIIUQ9kF3EhhBDnjHHjxmEymejdu3ely5cvX47JZCIsLIyioiIAcnJymDhxIi1btiQgIIA2bdrw5JNPUlJSctrnMNp1+fjx487dgNetW+e8PC0tjTvvvJO4uDiCgoLo3Lkzr776Kkop522Mdq8uKSlh6tSptGvXjoCAAFq0aMGECRPIyclx3qZi9/WYmBj27t3LoEGDCA4OpkWLFrz00kvVtlqzZg29evUiMDCQzp07s3LlSsPb7dq1i2uuuYbQ0FDCwsIYOHAgGzdurPbxayogIIABAwYAeiutxWIBjDuXlZXx2GOPkZiYSEBAAB07dmTOnDmVeh47dox77rmHli1bEhgYSPPmzbnttttc/lDxww8/0Lt3bwIDA+nYsSOffPIJ119/PSaTiQ8++MB5u5p8LWrKZDIxYsQI51b5I0eOOK9bunQp559/PuHh4QQHB9OpUydefvlll/uf+lpZsGAB3bp1IywsjCZNmnDRRRfxr3/9q9ZjE0IIUU+UEEII4eEuv/xyBaj777//tLfbtGmTAhSg9u3b57z8uuuuU4AaM2aMUkqpnJwc1bp1a+dtw8PDnf/fp08fVVpaqpRS6vvvv3defuzYMaWUUnfeeacC1KBBg5yPf+zYMeftvv/+e6WUUpmZmSoxMVEBymQyqSZNmjhvM23aNOd9Ky776KOPlFJKlZWVqd69exuOrX379io7O7vS2IKDg1V8fLwKCQlRZrPZedtvvvmmyk7r169Xvr6+ClBms1mFhoYqHx8fFRAQoAA1a9YspZRSKSkpznEHBQU5xxIYGKh+/fXXKj8HI0bdrFarOnDggOrZs6cCVFJS0mlvf+211zrHHB0drUwmkwLUlClTnLe54IILFKB8fX1VbGys8vHxUYBq27atKi8vd75O/P39K33+ZrNZNW/eXAHqH//4R62+FkamT5+uANWhQwfnZQ6HQ7333nvOx3jxxReVUkp98803zs+lSZMmKjg42HmbpUuXVtl5wYIFzsuioqJUSEiI83P67rvvqhybEEKIs0e2YAshhGg03nrrLcMFzkaPHg1Ar1696N69OwCLFy8G9Jbqr7/+GoAxY8YA8Mwzz3DgwAGio6PZvHkz+fn5fPvtt87dcBcsWHDGY50zZw5Hjx4lLi6OvXv3kpub69wiuXDhQsrKygzv98Ybb7Bhwwb8/f1Zs2YN+fn5bNmyhejoaPbu3cvTTz9d6fbFxcX079+fnJwc9u7dS2hoKABfffVVlWN7/vnnsdlsJCUlsX//fiwWC48++qjLmJ5++mny8vK45ZZbyMvLIy8vj6lTp1JaWsrUqVPr1GX16tWVFv1q3bo1mzdvJiQk5LS7/3/33XesXLmSmJgY9u3bR1ZWFlu3biUwMJBXX32VtLQ0srKy+PXXXwHYunUrGRkZpKSkcNlll3HZZZc5F6p77rnnKC8vJykpiQMHDmCxWJg+fTrp6emVnrO2Xwsj+/fvp0WLFjRv3pzQ0FDuvvtuAOLj47nrrrsAOHToEBdccAFTpkwhJyeHvLw8Lr74YoDT7hZe8TX+29/+RnZ2Nrm5uYwfP56hQ4eSm5tb7diEEELUP5lgCyGEaDRCQkJISEhw+YiKinLepmICs2TJEkDvHm61WunQoQOXXHIJAJ9++ikA48ePp0ePHgD079+fESNGANTLLrbfffcdACNGjKBt27YATJgwgaNHj3Lo0CECAgIM71cxthEjRjh3nf7LX/7C+PHjqxzb5MmT8ff3p02bNvzlL38BoKCgoMqx/fzzz4D+/CtWDp86darLQnEVn8Pq1atp3bo1iYmJzknwunXrsNls1Yc4RUBAAE2bNsVs/t+vIFOmTGHHjh1cffXVVd6vYiwWi4XLLruMFi1acPXVV2O1WrHb7Xz//fdERkbSsmVLQB+/PnbsWH766SeWL1/Oe++9R2JiIgA//vgjAGPHjqVly5aYTCaeeOIJIiMjKz1nXb4Wp7LZbKSlpXHs2DHsdjsJCQncfffd/Pzzz85dxe+//342bNjAY489xqpVq3j66aedu7Sf7utY8dqdN28eQ4cOZf78+dxzzz18+umn3HjjjdWOTQghRP2TCbYQQohGY9SoUaSmprp8nLwY1siRIwkICCAlJYVff/2Vf/7znwDOrYWAc0tm69atKz1+q1atADhx4kS1Y1EnHfdrNNHMzs4GcK6YDTiP4T2duowtPj7e+f8Vq5E7HI4qnyM/Px+g0grWgYGBlcYKkJWVBUBubi5paWmkpaWRmZkJQGlpqfP62rjiiis4duwY+/fv57zzzgP03gaFhYWnvV/Fc5WXlzvHkpaWht1uB/RicT4+PnzzzTcMHjyYY8eO8dZbbzFy5EiaN2/OLbfcQmlpKQB5eXkANG/e3Pn4vr6+lf4N9fM66dChA0oplFKUlpaSmprKu+++63wMgAMHDjBgwADi4uIYNmwYX3/9NYGBgcDpv45Tp07l0UcfJTQ0lC+++ILJkyfTs2dPOnbsWK/HyQshhKg5mWALIYQ4p0RFRTFs2DBA7+L7448/4uPjwx133OG8TdOmTQE9sTlZxb9Pd+qkii2vJ+9OXTFhPXUcoBc6q2C1Wnnrrbf48ccfnZO9U9VlbCdveTaZTFWO/XRjKysrc/5RoELFc82fP985SSwpKcFms6GUco61LpKTk/nkk08IDg7mxIkTDBs2jOLi4ipvXzGW888/3zkWpRQFBQUopXjssccA6NixIx999BFZWVmsXLmSRx99lJCQEJYtW+Y8BVjFuE9ewd1ms7nsIn4mr5PaGDlyJN999x133nknOTk5bN68mQsvvLDa+/n7+/PMM89w5MgRNm7cyJw5c+jRowd79uxxHjYhhBCiYckEWwghxDmnYjfxDz74AKUUV111VaXJ0JAhQwB4/fXX2bJlC6B3QV6+fDnAaXevrdiNePv27c4toSevOl2hf//+AHz88cfs2rUL0MeQjx07lgEDBlS5WnnF2JYtW8a3334LwO+//84bb7xR7dhq6vLLLwfg7bff5tChQyilePrpp7FarYa3e+ONNzhx4gR2u53Ro0cTEhLCzTfffMbjaN++Pc8//zwA+/btO+1x3RVj+e2335wrnn/55ZeEh4eTnJxMSkqKc7fryMhItm7dyjXXXMO0adOcu+hnZGQAcOmllzo//8OHD+NwOJgxY4bLccsN8bUA2LZtG6D/8BEeHs7OnTudu8RXtQVbKcXFF19McHAwU6ZMoUePHjz88MMMHz680ucqhBCigbllaTUhhBCiFipWEQ8JCVEJCQmGHyevmu1wOFRycrJzheUVK1ZUerwTJ06oFi1aGK4OfcUVVzhXmzZaRfyrr75yXtakSRPVunVrFR0drYKCgiqtIp6RkaESEhKct42MjHT+/+OPP+4cC6esDF1cXKy6d+9uOLbOnTurvLy8KsemlFKDBg1SgLrzzjur7Pn777+rwMBA5wrnFatox8fHV1pFfPfu3So0NFQBysfHxzkWs9msPvvssyo/ByNGq4IrpZTdbld9+vRxPsfvv/9e5e0HDhxYqWfFyts33nijUkopm82mLrnkEudtYmJinCujBwYGqs2bNyullNq6davzcrPZrEJCQlRAQIAKCwtTgPrggw9q9bUwYrSKeFWq+rwAdc0111TZ+Y033nBeFhISUmml+v/7v/+r9nmFEELUP9mCLYQQotEoKiqqdPztyR8nbxE2mUzOY65jYmK49tprKz1OXFwcmzdvZsKECSQlJVFaWkqrVq146qmn+Prrr/Hz86tyDFdffTUvv/wyCQkJlJeX07ZtW3744Qfnsc8VYmNj+fnnn7ntttuIjo6mtLSULl26MHfuXJ577rkqHz8oKIj169fzxBNP0LZtW0pLS0lISODBBx/kxx9/JCIioi7pKunevTvffvstF1xwAX5+fiQlJbFixQrOP//8Srfr0KEDP/zwA0OGDCE0NBS73U7v3r35/PPPGTp06BmPA/Qu9++99x6BgYHY7XbGjh1b6fj2k3366ac88sgjJCUlUVRURFJSEk888YRzQTsfHx+++uorHnvsMdq2bUthYSGRkZFcffXVfP/9985Fwbp168aXX35Jjx498PPzo1WrVqxcudK5l0PF17IhvhYA77//Ptdddx3h4eH4+Phw9dVXO9cV+O9//1vlivNjx47l448/pk+fPvj5+WGz2ejWrRtz58517hkghBCiYZlUVT/FhBBCCCHOQW+88Qb79+8nNjaWCRMmEBISwr59++jRowdFRUVs3LiRCy64wN3DFEII0QjJBFsIIYQQXmXevHlMnDgR0Fupw8LCyMzMRClF165d2bp1a6XTiAkhhBA15Vv9TYQQQgghzh0TJkygqKiIJUuWkJKSQnZ2NvHx8QwcOJBZs2bJ5FoIIUSdyRZsIYQQQgghhBCiHsifaIUQQgghhBBCiHogE2whhBBCCCGEEKIeyARbCCGEEEIIIYSoB7LImZs4HA7S09MJCwvDZDK5ezhCCCGEEEII4XWUUhQUFNC8efN6WeRSJthukp6eTmJioruHIYQQQgghhBBe7+jRo7Ro0eKMH0cm2G4SFhYG6C9keHh4nR9n586ddO7cub6Gdc6QLq6kiTHpYky6uJImxqSLK2liTLoYky6upIkx6WLsTLtYLBYSExOd87MzJRNsN6nYLTw8PPyMJtihoaFndP9zlXRxJU2MSRdj0sWVNDEmXVxJE2PSxZh0cSVNjEkXY/XVpb4O25XzYLuJxWIhIiKC/Pz8M3pBOByOejlW4FwjXVxJE2PSxZh0cSVNjEkXV9LEmHQxJl1cSRNj0sXYmXapr3lZBfkKNXIpKSnuHoJHki6upIkx6WJMuriSJsakiytpYky6GJMurqSJMelizNO6yAS7kSsvL3f3EDySdHElTYxJF2PSxZU0MSZdXEkTY9LFmHRxJU2MSRdjntZFJtgNbMGCBXTu3JkLLrigXh4vNDS0Xh7nXCNdXEkTY9LFmHRxJU2MSRdX0sSYdDEmXVxJE2PSxZindZFjsN2kvvb1Ly0tJTAwsB5Hdm6QLq6kiTHpYky6uJImxqSLK2liTLoYky6upIkx6WLsTLvIMdiiEk875sBTSBdX0sSYdDEmXVxJE2PSxZU0MSZdjEkXV9LEmHQx5mldZIIthBBCCCGEEELUA5lgN3LNmzd39xA8knRxJU2MSRdj0sWVNDEmXVxJE2PSxZh0cSVNjEkXY57WRSbYjZzNZnP3EDySdHElTYxJF2PSxZU0MSZdXEkTY9LFmHRxJU2MSRdjntZFJtiNXEZGhruH4JGkiytpYky6GJMurqSJMeniSpoYky7GpIsraWJMuhjztC4ywRZCCCGEEEIIIeqBnKbLTeprOXibzYavr289juzcUB9d7OXllFksKIejnkZVe2ZfXwIiIjD7+JzxY8lrxZh0MSZdXEkTY9LFlTQxJl2MSRdX0sSYdDF2pl3q+zRd8hVq5A4dOkTbtm3dPQyPcyZdCo8dY/s//sGJ337DXl5ezyOrPf+wMJr36cN5d9+NX1BQnR9HXivGpIsx6eJKmhiTLq6kiTHpYky6uJImxqSLMU/rIhPsRq60tNTdQ/BIde1SkpPDf594ApPZTKeRIwlv2bJeth7Xld1qJWf3bg6sWkXBkSNcOmtWnccjrxVj0sWYdHElTYxJF1fSxJh0MSZdXEkTY9LFmKd1kQl2IxccHOzuIXikunY5+v33lFssXPnOOwRFRdXzqOqm2QUXEHveefw4dSrZO3YQ261bnR5HXivGpIsx6eJKmhiTLq6kiTHpYky6uJImxqSLMU/rIoucNXItWrRw9xA8Ul27ZGzZQtxf/uIxk+sKsd27ExgVxYktW+r8GPJaMSZdjEkXV9LEmHRxJU2MSRdj0sWVNDEmXYx5WheZYDdye/fudfcQPFJdu1iLiwn0sMk1gMlkIjAyEmthYZ0fQ14rxqSLMeniSpoYky6upIkx6WJMuriSJsakizFP6yITbCFqIOWLL/igWzdeDQ7mH127kvLFFy63sVutrH3oIRbExvJ2q1b88d57zusK09P57PrrmR8VxZsJCfw4dapzdfIyi4WvR49mQUwMC2Ji+OrOOynNy3N5fJNZvl2FEEIIIYTwZPIbeyPXtGlTdw/BI9Vnl7z9+1l5883Yy8ro9fDDlOXlsfLmm8nbv7/S7TbPncvm114jqX9/QuLjWX3PPRzbsAGAr+64g4PffEOnUaNo0rYtv8ycyY4PPwTg+0mT2LFwIW2HDSNpwAB2fvgh/3nkkXobfwV5rRiTLsakiytpYky6uJImxqSLMeniSpoYky7GPK2LLHImRDWyd+8mqmNHLnvpJVoNGkRIs2asnTiR45s20aRNG+ftdi9bRlBMDNd89BHZu3bxQZcu7F62jGa9ezPovfewlZTgFxLCb3PmkPrDD/j4+wNQmp1N62uuYdA77wBw5NtvSVu/3i2fqxBCCCGEEKLuZAt2I3f8+HF3D8Ej1WeXNkOGcOfWrbQaNAiA9J9/BiCqY8dKt8vds4fwli0xmUw0ad0agJw9ewCIaNmS6I4d+er22/ltzhw6DB9Op9tuA+D6zz7jhpUrAcjetYvS3FyiO3Wqt/FXkNeKMeliTLq4kibGpIsraWJMuhiTLq6kiTHpYszTusgEW4ha2PDCC+z+6CPaDh1KXPfula6zFhXhExAA4Nw6bS0qqnSbng8+SPexY9mzfDmb58+vdJ3l6FH+dd11YDLR+8knz+JnIYQQQgghhDgbZILdyLVv397dQ/BIZ6PLTzNm8N/HHyfuL39h0Pvvu1zvGxSEvawMANuf//U75bx87W+8kQELFhAYGckf777rvDz/0CGWXnYZefv3M/DNN2naq1e9j19eK8akizHp4kqaGJMurqSJMeliTLq4kibGpIsxT+siE+xGLjU11d1D8Ej13WXj3//OT08/TfOLL2b42rWG58lu0rYtlsOHUQ4H+QcPAhDVoQNKKZZefjlfjhoFgK20FFtpKf5hYQAUZ2ayrG9fCo4eZfCiRXS75556HXsFea0Yky7GpIsraWJMuriSJsakizHp4kqaGJMuxjytiyxy1sgVFxe7ewgeqT67nNiyhf8+9hiYTDTv04ctCxYA0OLSS0n973+JPe882g4dSvubbmL91Kmsuu02LIcPA9Bh+HBMJhOhCQns+uc/8QsJIXfvXmwlJXS5804A1tx/P5ZDh4jv2ZP8gwf5eeZMAsLD6TlxYr19DiCvlapIF2PSxZU0MSZdXEkTY9LFmHRxJU2MSRdjntZFJtiNXGBgoLuH4JHqs8v29993nrN60yuvOC+/8p13WD91Kp1HjaLt0KFc+OijlObksHPRInyDg7ny7bdpfvHFAAx4/XXMPj7sWb4c//BwLv/73znv7rspzspi32efAXBi82ZObN4MQGhCQr1PsOW1Yky6GJMurqSJMeniSpoYky7GpIsraWJMuhjztC4mpZRy9yC8kcViISIigvz8fMLDw+v8ODabDV9f+TvJqera5fvJk2nSpg09HnigRrff8vrr5KWk0Hf27Fo/V23VdmynkteKMeliTLq4kibGpIsraWJMuhiTLq6kiTHpYuxMu9TXvKyCHIPdyO3evdvdQ/BIDdGl6MQJtr/3Hr0mTTrrz1Uf5LViTLoYky6upIkx6eJKmhiTLsakiytpYky6GPO0LvInECFOYjKbUXZ7jW4bEh/PyI0bMfv4nOVRaQ6bDZNZ/iYmhBBCCCGEp5Lf1hu5uLg4dw/BI9W1S1BsLPmHDtX49g01ubaVlVGYnk5wfHydH0NeK8akizHp4kqaGJMurqSJMeliTLq4kibGpIsxT+siE+xGzhuOw7DZbPTs2ZMWLVoYXn/w4EGGDBlCWFgYLVu25N1333V2KS0txc/PD5PJ5Py456TTYL355pskJycTHBzMoEGDCGjfntx9+0hbv75BPreaUEqxZ9ky7GVlzkXT6sIbXit1IV2MSRdX0sSYdHElTYxJF2PSxZU0MSZdjHlaF88ajai19PR0ogzOyXyuyMvLY8yYMWzZsoWEhATD29x4443s2rWL8ePHs3HjRu69914cDgf33XcfO3bswGazMWLECLp27QpAjx49AFi2bBnjxo1j0KBBDBo0iLfffpuI8HAeuewyNrzwAhGtWhHesmWDbaU2Yrdaydm9m+KMDLrceSehzZrV+bHO9ddKXUkXY9LFlTQxJl1cSRNj0sWYdHElTYxJF2Oe1kUm2MJjFRUVkZycTGBgIGFhYYa3ycnJYcuWLQwbNoxXXnnFuQrgZ599xn333cfWrVsBGD9+PJ06dSI2NtZ53zfeeIOYmBi++OILLBYL999/P9HR0SS2aEHCJZeQ/vPPlGRmohwOlMPRoMc/Vzyf2deX+F69aHHZZcT++QcCIYQQQgghhGeSCXYj17ZtW3cP4axxOBzcfvvtPPnkk1x00UXYbDaX20RERBAaGsrGjRv56aef2LlzJwCpqakAzgn2oEGDKC0t5cILL+STTz4hMTGRrVu3EhERQffu3dm9ezfnn38+n332GWYfHxL69CGhT59Kz7X8vvvI3Lv3LH/WENu+PcPffrveH/dcfq2cCeliTLq4kibGpIsraWJMuhiTLq6kiTHpYszTupzTx2Bv3bqVgQMHEhUVRdOmTbnjjjvIysoCYMOGDfTu3ZvQ0FBatWrFe++9V+m+CxcupG3btoSEhHD++efz888/O6+z2+387W9/Iz4+nrCwMIYOHcqxY8ca9HOrcPz4cbc8b0MICwtj3rx5NG3atMrb+Pj4MG/ePDIyMrjkkkt4+umnCQ8Px2KxAFBeXk67du144YUXePLJJ9m4cSMP/Hke6YKCAg4fPsygQYN45ZVX2LZtGxMnTqzyuTL37iV927az/nG2JvHn8mvlTEgXY9LFlTQxJl1cSRNj0sWYdHElTYxJF2Oe1uWcnWCXlJRw9dVX06dPH44fP86OHTvIzs7mrrvuIjc3l8GDB3PHHXeQl5fHe++9x6RJk9i4cSMA69at48EHH2ThwoXk5eUxcuRIrrvuOoqLiwGYOXMm//73v9m0aRNpaWkEBQVVWjirIRUWFp6Vx63LwmIVLBYLo0ePJiYmhpiYGO68807y8vIAyM/P57bbbiMyMpKoqChGjRp1xp/D6NGj2b9/P6tXr+aPP/7Az8+P4OBgQO8GvnfvXh566CFmzpxJ9+7d+f777wEIDg4mLi6OV199lcmTJ3PVVVexdu3aMxpLQ6nL16eic3p6Otdffz1RUVEkJCQwdepUHA4HAFarlYceeojY2FjDPzydi87W91BjJ11cSRNj0sWVNDEmXYxJF1fSxJh0MeZpXc7ZCfaRI0fo3r0706ZNw9/fn+joaO6//35++OEHVqxYQXR0NA888AC+vr7069ePkSNHsmDBAgDeffddbrnlFi655BL8/PyYNGkSMTExLFu2zHn9o48+SmJiIuHh4cydO5evv/6aAwcONPjn6e/vX++PmZeXx/Dhw9myZUuVt7nxxhtZu3Yt9913H0lJSdx7772sWbMGgEmTJrFw4UKGDRvGgAED+PDDD3nkkUcAePrpp1m2bBk333wzV155JUuWLOHvf//7GY33pZde4sUXX+TKK6+kqKiI7OxsOnToAMCMGTOYPn06oFfjLigocE6+O3XqRFFRkfMPJ1arlaCgoDMaS0Oo69fn119/BeCOO+7gm2++YdSoUbRt25aZM2fy4YcfAjB37lxee+01+vfvT3x8PPfccw8bNmxokM/LXc7G99C5QLq4kibGpIsraWJMuhiTLq6kiTHpYszTupyzx2B36NCBr7/+utJln3zyCb169WLHjh2cd955la7r3Lmzc2vdjh07GDNmjMv1W7duJT8/n9TU1Er3j4+PJzIykm3bttG6dWvD8ZSVlVFWVub8d8UuzGeqvo85OJOFxd5//30GDhxIdnY211xzDe+88w4A3377Lev/PO1VSkoKQUFB3HrrreTm5rJs2TICAwNrNcb8/HzmzZvHeeedx9ChQzl69CgLFiygvLycTZs2YTKZmDRpEgB79uzho48+4uDBgxQXF3PgwAEee+wxAMaMGcPYsWMZNGgQ3bt3Z/Xq1YwbN66u6RrEmXx9Vq9ezV133cV7771HSUkJISEhzJkzhx9++MH5xrRs2TJiYmL46KOP2LVrF126dGHZsmX07t27IT/NBuVpx+14CuniSpoYky6upIkx6WJMuriSJsakizFP63LOTrBPppRi6tSprFy5kh9++IG5c+cSEhJS6TbBwcHO3QsKCgqqvL6goADgtPc3MmvWLGbMmOFy+c6dOwkNDaVjx44cPHiQsrIygoODSUhIYN++fQA0bdoUpRQnTpwA9B8Pjhw5QklJCUVFRfTs2ZM9e/YAerJvNpudx4S3a9eO9PR0ioqKCAgIoHXr1uzatQuA2NhY/P39SUtLA6BNmzYcOnSIwYMHM378eEaNGkVJSQnbt28nOjqa4OBgjh49it1uJzQ0lJ9//pnFixdz8OBBADZv3sz27dtZsGABYWFhbN++nQMHDpCbm8sFF1zA9u3bGTx4MN9++y39+vUDoE+fPowbN47t27cDkJiYSFFRETk5OQB07dqV3bt3Y7VacTgcFBcX88svvzB16lSGDx9O7969ueWWW9i2bRuLFy8mJiaGWbNmERgYSGlpKRMmTMBisbBy5UoAbrvtNoYPH47NZqNfv36MHz+epUuXsnPnToYOHVrpOP2K4znat29PamoqSbfeStj557N/6VK6PvQQAMd++AFbcTGJV10FwO533iFhwADCWrWiLCeHPe+/T7c/t96f+PlnSjMzaXnddQDs/eAD4i++mIgOHbAWFLBj/nz+8vjjBIaHk56eTkhICEePHgUgOTmZ3Nxc0tPTGTJkCK+88gq9evXCarWSlpZGeHg4hw8fBiAhIYGQkBDWr1/P4sWLKS0tBeC3337j6NGjxMTEcPDgQW644QY2bdrEtddeS7du3di+fTt79uwhKSmJHTt24OfnB8CmTZvYvn07CQkJlJeXk5mZCeg9AA4cOEBZWRkhISE0b97c+Zpt1qwZDoej0mv28OHDlJaWEhQURFJSUqXXrMlkcvZu164daWlpFBcXExAQQKtWrdi9ezcAcXFx+Pr6kp6eDug31OPHj1NYWIi/vz9t27Z1LnQXExNDYGCgc8G71q1bk5WVhcViwdfXl44dO7J9+3YsFgvJycmEhoZy5MgRZ++8vDzy8vIwm8107tyZnTt34nA4aNKkCU2aNOHQoUMAJCUlUVhY6PKatdlshIeHExMT49y7pUWLFpSWljpfY507dyYlJYXy8nJCQ0Np2rQpKSkpADRv3hybzUZGRgZAnd8jAgMDadmyZa3fIywWC23atHF5j8jIyKCgoAA/Pz/at2/Pjh07ACq9RwC0atWKnJwc8vPz8fHxoVOnTuzYsQOlFJGRkYSFhTl7t2zZEovFQm5uLiaTiS5durBr1y7sdjsRERFERkY6e1f1HnE2eh86dIjS0lKCg4Np0aIFmzZtIjw83LkuxKnvEcXFxQQGBpKcnHxWX7MAUVFRhu8RVfU++T0iKSmJgoKCKntHRUU539cTExMpLi4mOzsbgC5durB3716sVithYWHExcWxZcsWwsPDz9n3iIretXmPyMrKcp6X9Vx9jwDj3yNO9x5x/Phx58+Wc/E9Yu+fa6jU9j0iNTXV+Yfuc/E9Yv/+/QC1eo8oLy8nMTHxnH2PqOvvEVarlWbNmp2z7xF1/T3CZrMRGxtb5/eIvfW8/pFJKaXq9RE9jMVi4a677uK3335j5cqVnHfeeTz00EOkpaXxySefOG83b9483n//fbZs2UL37t259957mTBhgvP6G2+8kaSkJKZNm0ZUVBR//PGH87zKoF8U77//PkOHDjUch9EW7MTERPLz8wkPD6/z57d9+/ZK46hPycnJ2Gw25xvLyT744APuu+8+rFYrCQkJFBQUEBsb6/ymBjh69Cj9+vXj4MGDbNiwgV69evHbb79xww03MGbMGE6cOMEbb7zBggULGD9+fK3G9vrrr5OSksLs2bMNrz8bXRZccQXp27bV62Maad6tGw+sW1ft7Wr79YmIiHC+EQGsWLGCb7/9ljfffJN58+YxYcIEfHx8uOiii1i/fj0OhwMfHx8uv/xy1tVgPI3V2fweasykiytpYky6uJImxqSLMeniSpoYky7GzrRLxd6eZzovq3DOHoMNsH//fi644AIsFgubNm1y7tbdtWtX519LKuzcudP5hTnd9ZGRkSQkJFS6/vjx4+Tk5Jz2CxsQEEB4eHilj/oQExNTL49TW0YLi538OR06dIjLLruM/fv38+abb9KrVy8A7rrrLsLCwpg+fTrz588nMjLSuSt5TZ04ccK5MF1V3NXFUxh9fSIiIird5sYbb2TBggVERkY6F6kLCgpy/iGo4r8Vx6yfq7z9tVIV6eJKmhiTLq6kiTHpYky6uJImxqSLMU/rcs5OsHNzc+nXrx99+vRh9erVlcLfcMMNHD9+nFdffRWr1cr333/PkiVLnMddjxkzhiVLlvD9999jtVp59dVXOXHiBMOGDQP0JHHmzJkcPHiQgoICHn74YS6//HLatGnT4J9nbY9fri9GC4tV/AEjMzOTvn37cvToURYtWlRphfVDhw5RWFiIUgqr1Up5eTkBAQG1eu74+Hg2btxIYmJilbdxVxdPYfT16dq1K0opLr/8ckaNGgVAaWkppaWlzuO527Zty+HDh3E4HM7dvyoWjKtKdSuan27l+NOtRl9aWoqfnx8mk8n5cTZW6/f210pVpIsraWJMuriSJsakizHp4kqaGJMuxjytyzl7DPY//vEPjhw5wvLly/n4448rXVdYWMiaNWt46KGHmDZtGrGxsbz22mv07dsXgP79+/P6668zbtw4UlNT6dKlC19//TVRUVEATJs2DavVyqWXXkpBQQF9+/Zl+fLlDf45AqSmptKkSZOz/jw1WVjs3nvvBeD+++/n0KFD9OzZk4MHDzJz5kzCw8OZOHEiAwcO5NNPP2X48OEUFhZSVFTE9ddfX+vx+Pj4nPb6huriKWry9bn66qsxmUwkJCTwz3/+k5CQEPbu3UtJSQl33nknADfddBNTp07ltttucx6LNXz48CqfNy8vjzFjxrBlyxYSEhIMb1Oxcvzdd9+NxWJhyZIltGnThhkzZnDjjTeya9cuxo8fz8aNG7n33ntp2bIlAwcOZMeOHdhsNkaMGOHcO6RHjx71XM77Xis1JV1cSRNj0sWVNDEmXYxJF1fSxJh0MeZpXc7ZCfbkyZOZPHlyldeff/75zpWtjYwaNcq5le9Ufn5+vPDCC7zwwgtnPM7GIjc3l6lTpzJq1CiGDh3KjBkzOHjwIIsXL6ZZs2YsXryYv/71r2RlZfHZZ58BetGzzZs3A3phi4kTJ/LOO+8QGBjI119/jZ+fH1OmTGHKlClu/MzODTX5+nTr1g3Qx6/7+PiwfPlywsPD+fvf/87dd98NwKOPPkpOTg6LFi0iODiYt99+m4svvtjwOWuyojlUvXJ8davRb926FYDx48fTqVMnYmNj67maEEIIIYQQ9eucX+TMU9XXwfTFxcUNdoxsdQuLeZKz0cXTFjk7VXVfn/puUlBQwBNPPMGTTz7JRRddVOWCa1999RU33nijczXzfv36sXr1akwmE02aNCEiIoLly5ezc+dO7r33Xrp168bWrVt56KGHeO2115wrwl944YV88sknpz00oC4a8nuoMZEurqSJMeniSpoYky7GpIsraWJMuhg70y6yyJmopGKZ/rOtJguLeZKG6uIpavL1qe8mYWFhzJs3z3k6kqrEx8cTFxfH008/zbhx41i7di1vv/02Pj4+zJs3j4yMDC655BKefvppwsPDKSoqAvQpOtq1a8cLL7zAk08+ycaNG3nggQfq9XMA73ut1JR0cSVNjEkXV9LEmHQxJl1cSRNj0sWYp3U5Z3cR9xYWi6VBnqdiYbHqjn2ub8pux1SH56xrl7o+n7vV5OvTUK+VU528crzD4WDp0qW88847jB8/ntGjR9O/f3927drFBRdcQLt27Zx/OXzjjTcqPc6qVav4/vvv63187uri6aSLK2liTLq4kibGpIsx6eJKmhiTLsY8rYtMsBs5X9+G+xI29OQawOTjQ+bDD2M96fzaNVF+1VWkP/ZYre7j17Ytsa++Wqv7eJLqvj4N+Vo52aFDh4iKijJcOf6ll17iyJEjzJ8/n9TUVLKzsxkyZAgAM2bMwOFwMGPGDJRSFBQUnJXdotzVxdNJF1fSxJh0cSVNjEkXY9LFlTQxJl2MeVoXzxqNqLWOHTu6ewhnnTUlhfJTzktenagdOyg/S+NprBrqtXLqiuanWzn+dKvR79mzh48++oiDBw9SXFzMgQMHeKyWfzSpCW/4HqoL6eJKmhiTLq6kiTHpYky6uJImxqSLMU/rIsdgN3Lbt2939xA8UsZDD7l7CB6noV4rFSuaf/LJJwC888473HbbbXz33Xds3ry50srxM2bMYMiQISxevJj8/HznavSgF20bOXIkq1atYu3atTz44IM888wz9T5e+R4yJl1cSRNj0sWVNDEmXYxJF1fSxJh0MeZpXWQLthCNTEMfJ16T5zt06JDz/5OTk1mwYAEpf+7WHxUVxZIlSwzvFxUVxapVqwyva9KkCYsXL67boIUQQgghhHADmWA3clFRUfX+mJ44gautoN9/r9fH8yR1PS7dp1u3Wp9mrC7HpVesaF5xPnRPdza+h84F0sWVNDEmXVxJE2PSxZh0cSVNjEkXY57WRSbYHsRms3HhhReSkZFheD7h/Px8xo0bx9dff43JZGLw4MG8+OKLAKSnpzN+/Hh++OEHgoKCGDNmDDNmzMBsNmOxWJg4caJzS+GQIUOYO3cuTZo0MRxHXSdwdXG2FhbzS0ur98f0JHU5Lt1UVkZ5A3xN3bXifF2FhIS4ewgeSbq4kibGpIsraWJMuhiTLq6kiTHpYszTusgE20Pk5eUxZswYtmzZQkJCguFtnn76aZYtW8bdd9+NxWJhyZIlhIWF8cYbb3DHHXfw448/ct9997F161ZmzpxJmzZtGD16NJMmTWLhwoXcc889FBQU8OGHH+Ln58e7775b5XjqMoHzJJYhQwicO9fdw/AoDdmksUyuQS+0FhER4e5heBzp4kqaGJMurqSJMeliTLq4kibGpIsxT+siE2wPUFRURHJyMoGBgYSFhVV5u5SUFIKCgrj11lvJzc1l2bJlztMdvffee5SUlBASEsKcOXP44Ycf8Pf3ByA7O5trrrmGd955B4Bvv/2W9evXn/1PTAghhBBCCCG8iEywPYDD4eD222/nySef5KKLLsJmsxnebty4cXz77bf069cPgH79+vH0008D0LJlSwCuuOIK/vOf/zB8+HBuu+02gErHwu7atYvc3Fwuu+yys/cJeYAmK1a4ewgeR5oYS05OdvcQPJJ0cSVNjEkXV9LEmHQxJl1cSRNj0sWYp3WR03R5gLCwMObNm0fTpk1Pe7v4+Hji4uJ4+umnGTduHGvXrmXBggWVbvPggw8yduxYli9fzvz58ytdd/ToUa677jpMJhNPPvlkvX8enqS0a1d3D8HjnI0mym6v98ds6OfLzc2t98c8F0gXV9LEmHRxJU2MSRdj0sWVNDEmXYx5WhfZgt2I3HXXXYSFhTF9+nQcDgdLly5l8eLFlSbLN954I8OGDWPZsmW8++67TJgwAdCnUerbty+HDx/m7bffplevXu76NBpEaYcOhH/zjbuH4VHORpNzYUG8/Px8EhMT6/1xGzvp4kqaGJMurqSJMeliTLq4kibGpIsxT+siE+xG5NChQ0RFRaGUwmq1Ul5ejr+/P0oprrjiChITE1m8eDGlpaWUlpY6j+fOzMykb9++HD16lEWLFjFy5Eg3fyZnn6m01N1D8Dhnq0ljXxCvMS3I1pCkiytpYky6uJImxqSLMeniSpoYky7GPK2L7CLuwfLz85k5cyaff/45AAMHDuTw4cMMHz6c6/+/vTuPi7Lc/z/+HvYdHMQN98RcONmilqmlaWFZJi1aammamkvaek79Onay7JvpabFcyrSTpaVly6msY5vmKS21TMN9AVxQAdkGkP36/YHMEa8PCDhwXwPv5+PBoxyGmQ8vb27nYuaee+hQ5OTk4O6774bNZkNkZCTef/99TJw4EYMHD8bp06cxevRoAMDEiRORkJCAbt26IT4+HrNmzcJrr71m5bdW6yLefNPqEYzTEJoUFRXh8ssvR8uWLcXP22w27ePGG28EUPrzNmLECDRq1Ah2ux2jRo1Cdnb2eT/nylnKjiFy9Sw10blzZ5ffprtjExm76NhExi4ydtGxiYxdZKZ14QLbYOnp6ZgxYwZWr14NAHjrrbcwYsQIfP/99/j999/x6KOPYtCgQQCAhQsXYuTIkfjwww9x6NAhzJ07F+PGjUNqaqrzTc5+//13zJgxAzNmzMCcOXOs+rbqRMqUKVaPYJz63iQjIwPDhg3Dtm3bKrzOc8895/wYN24cAOCqq64C8L/T4N1555244YYbsGLFCsydO/e8n3PlLDfddJPLZ6mpnbX0qoTa+sUDALzxxhto27YtAgICEBMTg+PHj7t09tpq4u7YRccmMnaRsYuOTWTsIjOtC18ibpiEhATn/7dt2xYLFizAgTPHt9rtdqxYsaLc9ePi4gAAYWFheO+997Tba9y4MUpKSmpvYEMpL27a56rPTap6qru///3vzv8fOHAgoqKi8Ne//hWAfBo8Pz+/837OlbO88sorLp3lQiilXH6bGRkZGDt2LLZt24bIyEjxOs8995zz/xMSErB06VLtFw/jxo1DVlYWVqxYgYsuuggzZ87EqlWrMGnSJMTExCAmJgaLFy/G9OnT8eGHH7ps/tpoUh+wi45NZOwiYxcdm8jYRWZal/r7iLseOHnyJJYuXVruNFvnatSoUd0N5Eb8DPtNlgnqc5OqnuquzCeffILvv/8en332GZo2bQpAPg3e448/ft7PuXKWsvPau2qWC+HqfUtt/+Jh0aJFaNy4MT7//HNkZWVh4sSJCA8Pd+n3wP2tjF10bCJjFxm76NhExi4y07rwJeIGa9q0KTZv3lzpu+KFhITU4UTuw3f/fqtHME59blLVU92V+ec//4lOnTphyJAhzp8h6TR4ixcvPu/nXDlLGVfNciFcvW8p+8XDH3/8Abvdft7rl/3iYe7cueV+8VBcXIzrrrsOt99+e7lfLmzfvh2BgYHo1q0bIiIiMHHiRHi5+FUb3N/K2EXHJjJ2kbGLjk1k7CIzrQsX2IY737viJSYm1tEk7iVz6FCrRzAOm5Tav38/Nm3ahBEjRsBmszl/hs4+Dd78+fPRqFEjvPXWW+f9nCtnKePqWWpy3HOnTp2cn6/o2OasrCyMGTMGjRs3RuPGjTF69GhkZGSI91Hbv3hwOBxITExETEwMXnrpJezYsQPTpk2r0n1VFfe3MnbRsYmMXWTsomMTGbvITOvCBTYRNSjffPMNACAmJqbc5QkJCcjOzi53GryyZ04r+5zps9T0Ddf69u0LAM5jmzt16oR77rkH33zzDaZPnw4AePjhh7Fs2TLExsZi4MCBePfdd/HYY4/VPMgZNfnFQ0BAAJo0aYJXX30VjzzyCAYNGoQffvjhgmch61zIG+Ll5eXB29u73Ofuv/9+AEBSUhKGDh0Ku92OyMhIzJgxo0G+VwkREdUOHoPt5lq3bm31CEYK/fxzq0cwTkNskpmZiddffx1/+ctfcOuttwIAtm3bBk9PT3Tr1g3A/36Grr/+enzyyScYNmwYsrOzkZOTg6FnnvWv7HOunKWMq2a5kOOeX3/9dQCVH9t86tQp3Hzzzc5F7nfffYeff/65Wl0klf3iwW63i79c6Ny5M3bu3Inc3FwEBASgsLAQ/v7+FzzL2bi/ldVGlwt9Q7ydO3eiqKgIw4cPR3R0NADgsssuAwDce++9+OmnnzBhwgRs374ds2bNwkUXXYQxY8a4bH5uKzJ2kbGLjk1k7CIzrQsX2IZQxcWw1eAk6Q6Ho0bHHdT0/txFQfv28I2Pt3oMozTEJmWnuhs1apRzUXvixAmEh4c7F2ZlP0NvvfUW/Pz88PXXX8Pb2xuPPvooHn30UQCo9HOunKWMq2a5kDdcKygoAFB6bHNoaCi6deuGPXv2oHv37s43Xjz7DRh3796N9PR0XHPNNdXq4qpfPIwdOxYPPPAAYmJi0K1bN6xduxaTJk2q1iznU9P9bX3n6i6ueEO87du3AwAmT56Mzp07IyIiwnndpUuX4vTp0wgMDMQrr7yCDRs2wMfHx2XzA9xWKsIuMnbRsYmMXWSmdeEC2xA2T0+kPPQQCs+ckquqjsfGwvbpp9X6Gu8OHRDx6qvV+hp3czo6GsHff2/1GEZpKE0qO9UdAHz55Zflrp+eno7IyEjxNHhlKvucK2epyv1VZ5ay456r6uzjnnfu3InIyEg4HA5kZGRg+vTpGD9+PJ588klMmzYNH3/8sfPrjhw5giFDhsBms+Gpp56q8v0BrvvFw4QJE3Dy5Em8/vrr2LVrF8aMGYPZs2dXa5aqzFrRs6kNmau7uOKd+MsW2DExMcjLy0PPnj2xevVqtGrVCm3atAEA9OvXDz/++COGDRuGESNGuGx+gNtKRdhFxi46NpGxi8y0LlxgG6TwwAEUVPNUSiX9+1f7axqE4mKrJzBPA2xSlVPdnX2Mr9WzWKnsuOdnn33WecwqUHpss7+/P14980u5H3/8sdyxzQkJCejfvz8SExOxePFiXHHFFee9r9r4xYPNZsPTTz+Np59++rz3X1N1ta24G1d3uZBfDJUpKChAVFQUpkyZgpSUFDz//POYMmUKPj/rUJkHH3wQnTt3xhtvvIG+ffti6tSpFd5HUVERevbsieTkZBw9elT7vNSgTZs2SEhIQFJSEiZPnowNGzbA398fY8eOxcyZM+Hh4YHCwkI89thjeP/99xEUFIS///3vzvdBqI/4MyRjFx2byNhFZloXLrDdXJP5860ewUjsomuITcpOdVfZu/F37drVmFmsdO5xz2VdKju2OSUlBf3798eRI0fw3nvvYeTIkdW+X9N/8XC2utpW3I2VXc79xVCZRYsWlbvel19+iXXr1pW77Pbbb0dsbCxWrVqFJUuWVLjAvtDjwSs75nvevHl47bXXMHz4cCQkJOD+++9HdHQ0rrzyyhr1MB1/hmTsomMTGbvITOvCdxF3c6kTJlg9gpHYRddQm5xvQbt79+46muT8s9SVzMxMzJo1C//+97+dl5173HNZl7FjxyInJwcxMTGYOnUq1q5di9tuuw0AMHHiRCQkJKBbt26Ij4/HrFmz8Nprr1VrlrJfPLRq1cpF313tqcttxZ1Y2aWiN8SbOXMm/vGPfwAAlFJwOBwICAiAUgrXXnstRo0aBaD03cbz8vIqPNa77HjwjRs3nvd48LKPhIQEtGnTxnk8+NKlS/HHH3/g8ccfd77Co+yY71WrVqFx48b44IMP8Pbbbzsvq6/4MyRjFx2byNhFZloXPoPt5kpc/C659QW76NhEVtwAXzpfleOey7pUdGxzamqq81nn33//Hb///jsAIDIystrnnzblFw/n0xC3laqoqy7VeUO8vXv34oMPPkB8fDxyc3Nx6NAhPPHEE7DZbIiMjMT777+PwMBA7Nu3D6dPn8bo0aPF+6zp8eDz5s1z/ixVdsz33r170bFjR9hsNrRv3955WX3FnyEZu+gaQpOaHHrSokULHDt2zPnnTz75BLfffjveeuutcqcirOiwlPrKtO2l/pY21IIFC9ClSxf06NHDJbfnu2+fS26nvmEXHZvIQkNDrR6hTiQkJDj/AS877vnsd1b+8ssvcfLkSeefy7qUHduckpKCU6dOYenSpQgKCkLjxo1RUlICpVS5D+lBgolqco7lG2+8sdx1PvnkE9hsNixZssR5WWFhIaZPn46IiAi0a9cOS5curdXvw9UupEtl33t8fDwGDx6M4OBgtGnTplyzqir7xdDq1audl1X0hngLFy7EyJEj8eWXX+KHH37Agw8+iGeffbbc5z788EMcOnQIc+fOrfC457LjwZs1a1alGcuOB5dOnffggw/igQcewIcffoj5Zw7ZycnJcc5e9qx2Tk5Ole7LHTWU/W11sYuuvjfJyMjAsGHDsG3btgqv89xzzzk/yvZRAwYMcH7+iy++EE8veO+99+I///kPRo0ahQ4dOmDWrFl49913Xf49mMS07YXPYNexKVOmYMqUKcjKynLJxuB/5p1SqTx20bGJzG63Wz1CnavKcc/1uUtNj6kdNGiQ87KKHti48zG1F9qlsu/99ttvx+7duzF58mRs3rwZ48ePR5s2bXD99ddXOlNN3xAvLCwMy5cvr/Bz7733XqX3WxNnHw9edq74s0nHfPv7+yM/Px8AnP8NCAio8D4u5M3WKntDtbKXyZ/9DP24ceNq9IuQytTn/cqFYBddfW5yIaciLHvD0VmzZmHGjBmIioqCw+Eo93V1cSpC05i2vXCB7eYy7rwTTebNs3oM47CLrr43qem53ePj4xEdHe3S+6vr88xX9/6q8oZrNe1SEVOaXMgDm7Lzalf2wObsY2p3796Nrl27YtWqVcYvsF3RpaLvPSoqCtu2bUNsbCxeeukl5y+Y33777fMusM9m+hvinX08eNnPj1IK/fr1Q6tWrbB8+XLtmO8OHTogMTERJSUliI+PBwBcfPHF4u1f6JutVfYLkJ07d6KoqAjDhw93/txfdtllrglzFlfvV+oLdtHV5yYXcirCpKQk2O12NGvWDKtXr4bD4cB9991X7vp1cSpC05i2vXCBTUT1Qk3PJZ8XG4ukJ56o1tec71zyNZ2lJmp6Xvu6Pu7ZlCYX8sCm7BmAyh7YuOsxta7oUtH3HhoaiqCgIGzevBkbN27Erl27AMD536oy6Z34z3c8+P79+wHgvMd833HHHZgxYwZGjBiBxMREAMCwYcO0+7uQX4CUvdlaZb/8KTtv+OTJk9G5c+dyh48Q1SfnexUIALzxxhuYPXs2kpOT0bdvX7zzzjto3rw54uPjMXXqVGzYsAF2ux0zZsxwHvdcnVPuXcipCHeeOTVv2f2+8847FX5ddU5FSK7FBbabC/nqK6tHMBK76BpCk5qcSz6ooAAFZx4MWz2LSWrjXb1NaHIhD2yysrIAVP7ApibH1F7IA77MzExMmjQJX3/9NWw2G2666Sa88cYbCAoKqvTrzuWKLhV9756ennj99dcxYcIE9O7dG5GRkQgJCanRscYmLK6B879R4Nk/PwsXLoSnpyc+/PBDhISElDvm+29/+xvS0tLw3nvvISAgAIsXL0avXr20+7uQX4CU/Z1U9sufsgV2TEwM8vLy0LNnT6xevdrl+wF3OFuAFdhFVxtNqvIqkFWrVmHSpEmIiYlBTEwMFi9ejOnTp+PDDz+s9FCX2jo86NxTEVanS1VPRVgfmPYzxDc5c3OFwgMlYhcJm8jYRZabm2v1CJYre2AzYsQI2Gy2KjWp7jG1VXmjm7IHfJ06dcI999yDb775BtOnTwcAPPPMM1i1ahXuvPNO3HDDDVixYgXmzp173q+7EBV1qex7HzNmDA4ePIi1a9fizz//hLe3N0JCQi54lrpUnTcKPHtbKTvmOz09HYmJiXjsscecx0p7e3vj5ZdfRkpKChITEzF+/Hjxvmv6ZmtDhgxxXlbZL38KCgoQFRWF2bNn46mnnsLmzZsxZcqUKt1XdXC/ImMXnaubVPWUe4sWLULjxo3x+eef4/nnn8dvv/2GuXPnIi0tDdu2bcONN96Il156CWvWrAGAcqfXq41T7p17KsLzdanuqQjrC9N+hvgMtps7fdllCN6wweoxjMMuOjaR1fcuNT32+dSpU+IznbVxX6Y694FNVZpU55jaqr7s9+wHfFlZWZg4caLzTbQOHDgAf39/3H333UhPT8eqVavg5+d33q+7EBV1qex7nzNnDg4fPoz58+fj6NGjOHXqFAYPHnzBs1jlfMeD1+Tnx1XOfcarTGW/AFm0aFG52/jyyy+xbt06l89mZReTsYvO1U2q+iqQ7du3IzQ0FN26dcOePXvQvXt3fPbZZ+c91MUVhwdV5VSE5+tS3VMR1hem/QxxgU1EVI/V1bHpNT0W3BTVOcdyZap6TC1w4Q/4AGDSpEn47rvvcN111wEArrvuOjz++OPn/bqqqk6Xyr73I0eOYMGCBSgoKMDWrVths9kqfLbWHZh0PPi5zv0FSJnKfgEyc+ZMlJSUYObMmVBKweFwVPrKCyJ3U9XDYBwOBzIyMjB9+nSMHz8eTz75JKZNm4aPP/640kNdXHHKvfMdelJVlR2WQnWDC2w3F/Haa1aPYCR20bGJrCF0qcmxz+G7dqFAqVqayDxVeWDTtWvX895OVY+pBVzzgK9p06Zo0qQJxo4di5MnT2LRokVYvHgxJk+eXOnX1UaXyr73mTNnIj4+HsuXL0fz5s2xfPly9OnTp8L7NeVd5ytT2eK6KtuKK7jqFyB79+7FBx98gPj4eOTm5uLQoUN4oppv/lgVddXF3bCLzqomAQEB8Pf3d54O68cff8QPP/wAoPRQlwEDBmD37t3o0aMHoqKinIe6VPfwoDLVPRXhuV3GjBmjnTKytk5FaDLTfoa4wHZzaWPGIPxf/7J6DOOwi45NZOwiawhdqvvAZt++feVe7i09sCk7pvbll1922ZyVPeC77777EBwcjH/84x8oKSnBypUr8dZbb2Hy5MmVfl1latqlsu/dbrdXeM5qiSnvOl9T524rtaU6z3hV9guQhQsXwsPDw/l39OCDD+LZZ591+bx11cXdsIvOqiadO3fGzp07kZubi4CAABQWFsLf3x9A5Ye6VOfwoIpU5VSE3FZkpnXhAtvNFbvZm8TUFXbRsYmMXWQNrUtVHtgUFhbW3UBnqewBX0JCAux2O5RSKCwsREFBgXNhVdnXVZWVXUx41/maqs1tpbq/AClT2S9AwsLCsHz5cpfPei6rfoZMxy66umpy7qtAxo4diwceeAAxMTHo1q0b1q5di0mTJgGo/FCX6hweVJGqHHrCbUVmWhe+i7ib8zl0yOoRjMQuOjaRsYusoXUpe2BT2ak+6updWDMzMzFr1iz8+9//BgCMHTsWOTk5iImJwdSpU7F27VrcdtttAIDrr78eiYmJGDZsGIYOHYqcnBwMHTr0vF9XVSZ1cSd11aTsFyAPP/xwndzfheK2ImsIXYqKinD55ZejZcuWFV7njTfeQNu2bREQEIDJkyfj+PHj2nWuv/76cm/el5SUhKFDh8JutyMyMhIzZsxASUlJlecqexXI6tWrAQATJkzAzJkzsWfPHnzwwQcYM2YMZs+eDaD0UJfBgwdj+fLlyMzMLHeoy9/+9jc8/PDD+P7775GUlFTp4UGVOd/7OtTWtlLdv5+YmJgq/f3k5eXB29sbNpvN+VF2qktXMu1niM9gu7nAX36xegQjsYuOTWTsImuIXc73wKZJkyZ1Mse5L/udMGECTp48iddffx27du0q94Dvrbfegp+fH77++mt4e3vj0UcfxaOPPgoAlX5ddZjSxQo1PRa8pk2qe38mv9mapD5vKxeivne50PNPl1myZAm+++67cl9377334qeffsKECROwfft2zJo1CxdddJF2+M7ZKnsViM1mw9NPP42nn35a+7rKDnWpjcODJLWxrdTm38/OnTtRVFSE4cOHIzo6GgBw2WWXufx7MO1niAtsN5c+YgSazJtn9RjGYRcdm8jYRcYuuoMHDzofILjahTzgW7FihXiblX2dK9VmF6vV9FjwpNhYtPj002p9TU2PBbdqcV1UVISePXsiOTnZeX7wc73xxhuYPXs2kpOT0bdvXzzxxBPo378/kpKSMHnyZGzYsAH+/v4YO3YsZs6cCQ8PD+c5e89+V/1x48ZhyZIldfWt1bn6/DNU09MRDhgwAFdeeaXz88eOHcNjjz0GX19f55uIAcDSpUtx+vRpBAYG4pVXXsGGDRuc7+JdFVU5DMYkrt5WXHG6SKDiv5/t27cDACZPnozOnTsjIiLCZbOfzbSfIS6wiYiIDOJuD/jqu5ocC14ycKDbHj9eFTV9xgsA+vfvX+mzjnX1jBfVjZqejrBr165Yu3at8/MPPPAAWrRogUsvvRQffPCB8/I2bdoAAPr164cff/wRw4YNw4gRI6o8n7u9CsTVXHG6SKDiv5+yBXZMTAzy8vLQs2dPrF69utLDjuoDHoPt5oLPnO+SymMXHZvI2EXGLrqKFhKuVpXjnk1SV13cSX3++Sl7xmvjxo1Vfsbr+eefx2+//Ya5c+cCKH3W8Y8//sDjjz+OK664AsD/zh189jNeEydOxN///nfnOzUDgFIKyVnJOJJ2xNKP9Jx08fuuybGsZYu7rKwsjBkzBo0bN0bjxo0xevRoZGRklPvaTz75BDabrUrP6F/IcbWumqXsdITNmjWrdFaHw4HExETExMTgpZdewv79+zFt2jQAwPLly/HVV19h6dKlFT47/eCDD+KBBx7Ahx9+iPnz51d6X+eyYnGtiotr9HU13d9WdH81/fvZsWNHlf5+CgoKEBUVhdmzZ+Opp57C5s2bMWXKlBp9D5Ux7d8hPoPt5orDwqwewUjsomMTGbvI6nOXmh5XW1BQUGf3Z9UDPtO7uIva+Pkx5dzgF/KM11tvvQWg8mcdK3rGq2XLlvhw64f4+s+vcSrnVG18y9XWNrwthvcYjj5RpW90VdNn9v/2t7/h888/x8MPP4xly5bh/vvvh8PhwLvvvgtvb2/nAvaLL76o9Njis13ocbWunKUqzj2t4DfffIMffvgBKSkpeOihhxAbG4uIiAg4HA4AwIEDB9C2bVt4eZUuZW6//XbExsZi1apVWLJkCaZOneqy2WpDTQ8/yerSBad37arW17jiVIQVnfbxfH8/ixYtKnc7X375JdatW3dBs0hq+u9QbeEC283l9uyJoE2brB7DOOyiYxMZu8jqc5cLOa62uI6Oq7UCu7hObfz8mHJu8LJnvM7H4XAgIyMD06dPx/jx4/Hkk0/i8ccfx7fffuu8zoMPPojOnTvjjTfeQN++fTF16lTnM15TpkxBSkoKnn/+eUyZMgW3P3E7Vv+2GjdG34ie7Xoi0DcQNtgqmaD2KCicyj6F73Z/hzlr58DTwxOXNLukxseypqamAgBOnTqFm2++2fmLiO+++w4///wzAGDWrFmYMWMGoqKinIuYirjiuFpXzVJV555WMCcnB/7+/ti5cydOnTqFjz/+GB9//LHz+lFRUTh06BDGjBmDVq1aYfny5cjLy3Mew+8OanL4SdbAgfD76KNamqhiFZ32sbK/n/j4eCxbtgwlJSWYOXMmlFJwOBwICAhw+XwpKSlo2rSpy2+3prjAJiKiBofH1crYxWzudG5w6Rmvc5+5kp51lJ7x+uHHH+B7ky/uvOJO3Hv1vXX1LZxX7w698ffP/o7Ptn2G6JjoGj+zX/bu/mcf07p7926kp6fjmmuuAQA0a9YMq1evhsPhwH333VfpXK44rtZVs1TkfOef3rhxIyZNmoSuXbvio7MWlAsWLMD69evx0UcfoWnTpoiMjMT777+PwMBA7Nu3D6dPn8bo0aNrNBP9T1XPD17Z30+TJk2wd+9efPDBB4iPj0dubi4OHTqEJ554wsLvrG5wge3mGp/zDxGVYhcdm8jYRcYuOjaRsYuOTeRnvIKCgqCUQr9+/Sp81nHmzJnaM16+Eb7IK8zDNR2vsfi7Ks9ms6FvVF8sXL8Qvv6+NX5mf8GCBRgwYIDzOkeOHMGQIUNgs9nw1FNPAYDz3MHvvPPOee/jQl5lMG3atHLPRF7oLBU53+kIR48ejdmzZyMoKAh33HGH8+vKTpNVdtnChQvh6emJDz/8ECEhIZg7dy7GjRsn3qcph1lciLrat1T1dJFV+fvx8PBwXv7ggw/i2Wefdfm8nTt3dvltXggusN1c+vDhCH/vPavHMA676NhExi4ydtHVRpP68ICP24quITapyjNed999N2w2W6XPOkrPeN376L1IQxoaBTSy+LvUhfmHQSmF0wWn4eN1/tNDSc/sf//9987PJyQkoH///khMTMTixYudbwJXGyo6rra2ZqnO6Qj379+PoKAg7Tbeeeedcgv7sLAwvFfFnzVTDrO4ELW5b6np6SLPJv39LF++vFpznO8UgNKp/O644w589NFHiI+Px9SpU7FhwwbY7XbMmDHD+QshQD914DvvvIPmzZtXa76q4ALbzRXb7VaPYCR20bGJjF1k7KKrjSb14QEftxVdQ2xSlWe8Jk6cCKDyZx2lZ7yGTxiO2WtnV3jfRcVF6Pl/PZGclYyjc4UH5IV5CH4wGEXFZ51bu884LBm9BPEp8Zj6wVRs2LcB9kA7Ztw8A/f3LX1AXlhUiMdWP4b3f30fQb5B+Pvgv2Nc3/LPjtps1TsOXHpm39fXF0DpcaT9+/fHkSNH8N5772HkyJHVuu3qqui42rqY5XynIzz7XMqu5E6HWUjqat9i1ekiq/LmfNKp/Oxnutx+++3YvXs3Jk+ejM2bN2P8+PFo06YNrr/++krf1M/VuMB2cz5Hjlg9gpHYRccmMnaRsYuutpq4+wM+biu6htKkus94xcfHA6j8WUfpGa9fD/1a4QwZuRkY+85YbDu8DZFhFTwgT9qJouIiDO8xHNEtzpxbu3XpubVvf+N27D6+G5P7Tcbm+M0Y/+54tAlvg+u7XI9538/Da9+/huE9hiMhNQH3v3s/oiOjcWX7K89T5n+q8sx+2eJ14sSJSEhIwOWXX474+HjMmjULISEhztMhXaiqHldbF7Oc7/zTgYGBLrmf+qau9i1WnB+8qm/Od/ap/Dp37oyIiAjEx8cjLS0N27ZtQ2xsLF566SVkZWUhNDQUb7/9Nq6//vpK39TP1bjAdnNBtfBW9/UBu+jYRMYuMnbRsYmMXXQNsUlVnvFq0aKFS+8zJz8HbZ9oCz9vPwT7VfKA/MiZB+T9JqNz886ICI4AAKTlpGHb4W2IvSwWLw17CVmnsxA6LRRv//Q2ru9yPVZtWYXGQY3xwfgPsPv4bnT9R1es2rKqWgvsqjyzP2fOHKSmpjrb/f777/j9998BlJ7f11WL2qoeV1sXswCVn47Q1dtKfVGX+5a6Pl1kdd6cDyh/Kr8VK1YgNDQUQUFB2Lx5MzZu3IhdZ05nVvbfyt7Uz9U8auVWqc6k3WvOu2mahF10bCJjFxm76NhExi66htik7BmvVq1aVXid/fv3u/Q+S1QJ7rnqHvzx9B+wB1b80tntR888IH81Bk0eaYIr/+9KHEk7glD/UAT5BmFz/GZsPLARH24tfanoruOlD8j3ntyLNuFtYLPZ0D6ivfOy80lISHAeO1r2zH5EROmivuyZ/ZSUFJw6dQpLly7F8ePH0bhxY5SUlEApVe7j3GNQx4wZA6VUueNKXTlLUFBQrc1SHa7eVuqL+rxvKXtzvmbNmlV6vbJT+c2ePRtPPfWU86Xgnp6eeP3115GcnIzevXvjmWeeQUhICHJycgCUvqlfYmIiYmJi8NJLL2HHjh0u/YXR2fgMNhERERFdsLp+xivYLxivjzj/u2UXFBUgqkkUpvSfghRHCp7/6nlMeX8KPp/6OV6/+3VMeG8Cer/YG5FhkQjxD0FOfukD8pz8HPh6lR4f7ePp47ysOqw6ltX0WYhqSjqV3+bNmwGU/tJnwIAB2L17N3r06IGoqCiEhIQAOP+b+rkSF9huLmj9eqtHMBK76NhExi4ydtGxiYxddGwiq413662KRaPOeUC+40us21P6UtsxvcdgQOcB2H18N3q07YGov0chxL/0Abm/jz/yi0rfbKvsvwE+AdW676ocy1pXXaw4rramrNpWTMd9i3wqv7Jj9ufMmYPDhw9j/vz5OHr0KE6dOoXBgwcDqPxN/VyNC2w3p3zOf0qIhohddGwiYxcZu+jYRMYuOjaRlZSUWHK/Mz+fiRJVgpm3nnlAnu9wLpTn/GcODqcdxvwR83E07ShOZZ/C4L+UPiDvENEBiacSUVJSgvjU0jdou7jZxdW+//MtaOuyS10vrmt6asCaNqnrUx/WtdrYt5h+ushz35xPOpVf2Uu9jxw5ggULFqCgoABbt26FzWbD+PHjAaDSN/VzNS6w3VzO1VcjcMsWq8cwDrvo2ETGLjJ20bGJjF10bCI7efKk8/jf2pSZm4nXf3gdf2n5F9x66a3Ye3IvPtj8AeJT45FbkItDKYfwxI1PAACOpB3BgnULUFBUgK2JZx6Q9y19QH7HFXdgxr9nYMSSEUg8lQgAGNZ9mMvnrasuVqjpqQiTYmNR+Omn1fqa2joVoUlqY99i+ukiz31zPulUfmPGjAFQ+ux2fHw8li9fjubNm2P58uXo06cPAFT4pn61gQtsIiIiIqqyOn/Gq6S4WtdPz03HjH/PwKirRuHWS2/FwpEL4WHzwJc7zjwgv+5BPDvkWQDAzFtnIj41Hst/XY7moc2xfNxy9IkqfUD+t0F/Q1pOGt775T0E+ARg8T2L0euiXq795hqAmpyKsGTgQLc+faG7Me10kZWdAlA6lV9cXByA0vNhly28zyWdOrC2cIHt5sKXLLF6BCOxi45NZOwiYxcdm8jYRVffm9T0Ga9QPz8k5eVV62u8O3SAbdrd571ewuwE5/+3bdwWC0YswIHkMw/IA8Kw/P7l4tfZA+34cpr8gNzbyxsvD38ZLw9/uVozV9fFF1f/Zef1XX3/GaqphtilKm/OZ9rPEE/T5eYyb7nF6hGMxC46NpGxi4xddGwiYxddQ2hS9oxXdT5SoqKq/TU1ednqyayTWPrTUjx8/cO18J1XTBVX75n2MomJiS6/v5rO4uo5aqoh/AzVREPsUpVTANb0Z6i28BlsN1fUtKnVIxiJXXRsImMXGbvo2ETGLjo2kdW0i4et9PmgYlW1hVzTkKbY/NRmeHrU/svYi0qKAACeHp41fmY/IzYWSQ89VK2vOd+xrHV1XG1tHffMnyFZQ+1yvjfny6vmK2NqGxfYbs77xAmrRzASu+jYRMYuMnbRsYmMXXRsIqtpl2ahzQAAB5MPwt7OXqWvqYvFNVA6U5BvEAJ9S08TVJNjWT2jo2vl+FfTjqutDv4MydhFVlun26opvkTczYWsWWP1CEZiFx2byNhFxi46NpGxi45NZDXt0rJRS7Rs1BIfbP4AjjyHi6equcOnDmPtzrXodVEv2Gy2Gt8Otxcdm8jYRda6dWurRyiHz2DXsQULFmDBggUodtHxKqfGjUOTefNcclv1Cbvo2ETGLjJ20bGJjF10bCKraRebzYZHrn8EM/49A6PfHo2uLboi0DcQNtR8UXshSlQJTuWcwr6T+9Da3hqje42+oNvj9qJjE1l971LTMxTs3bsX0dHRdXZ/58MFdh2bMmUKpkyZgqysLISGhlo9DhEREZHxoppGYf7d8/Hf/f/F7uO7kVuQC6B0sVt2jHZdKFEl8LR5omlIU9wYfSN6te+FAN+AOrt/ovqspu8dkBcbi6QnnqjW19TmedO5wHZzQT/9ZPUIRmIXHZvI2EXGLjo2kbGLjk1kF9qlcXBjxF4ei1jElrt83Pxx2Hts7wXddlVcHHkxlk5d6vLb5faiYxNZQ+hSk/cOCPDzM+r9BrjAdnd1eBoGt8IuOjaRsYuMXXRsImMXHZvIaqnL3mN7sT1he63cdp3g9qJjExm7yAzrwjc5c3PZ115r9QhGYhcdm8jYRcYuOjaRsYuOTWTsImMXHZvI2EVmWhcusImIiIiIiIhcgAtsN2dftszqEYzELjo2kbGLjF10bCJjFx2byNhFxi46NpGxi8y0LlxguznHgAFWj2AkdtGxiYxdZOyiYxMZu+jYRMYuMnbRsYmMXWSmdeEC280Vtmxp9QhGYhcdm8jYRcYuOjaRsYuOTWTsImMXHZvI2EVmWhcusN2c16lTVo9gJHbRsYmMXWTsomMTGbvo2ETGLjJ20bGJjF1kpnXhAtvNha1ebfUIRmIXHZvI2EXGLjo2kbGLjk1k7CJjFx2byNhFZloXLrDdXOrEiVaPYCR20bGJjF1k7KJjExm76NhExi4ydtGxiYxdZKZ14QKbiIiIiIiIyAUaxAI7JSUFHTp0wPr1652XTZo0Cb6+vggKCnJ+LF682Pn5ZcuWoUOHDggMDET37t2xadMm5+eKi4vx+OOPo2nTpggODsatt96K48eP1+W35BT4yy+W3K/p2EXHJjJ2kbGLjk1k7KJjExm7yNhFxyYydpGZ1qXeL7B//vln9OrVCwcPHix3+ZYtW7B48WJkZ2c7PyZMmAAAWL9+PR588EEsW7YMGRkZGDlyJIYMGYLc3FwAwKxZs/DNN99g69atOHbsGPz9/XH//ffX+fcGAB7Z2Zbcr+nYRccmMnaRsYuOTWTsomMTGbvI2EXHJjJ2kZnWpV4vsJctW4YRI0bg+eefL3d5fn4+/vzzT3Tv3l38uiVLluCuu+5C79694e3tjYcffhiNGzfGqlWrnJ//29/+hlatWiEkJATz5s3D119/jUOHDtX693Qux8CBdX6f7oBddGwiYxcZu+jYRMYuOjaRsYuMXXRsImMXmWld6vUCOyYmBgcPHsTw4cPLXb59+3YUFhbi6aefRtOmTdGxY0e8+OKLKCkpAQDs3LkTf/nLX8p9TZcuXbB9+3ZkZmbi6NGj5T7ftGlTNGrUCDt27Khwlvz8fGRlZZX7ICIiIiIiovrDy+oBalOzZs3EyzMzM9GvXz9MmzYNK1euxLZt2xAbGwsPDw88/vjjcDgcCAwMLPc1AQEByM7OhsPhAIAKP1+RF154ATNnztQu37VrF4KCgtCpUyckDxiAgoED4X30KIK//x5po0cDAIJ+/BHw9ER2nz4AgPClS5E1eDAKmzWDR1YWigMDcerMS9QDN26EraAA2f36AQDs776L7P79UdCqFTzT0tBo1SokxcYiLS4OERER8PHxwbFjxwAAF110EZKTk+FwOFAYE4OwXbuQMm0aAMB/2zZ4Hz+OrJtuAgCEffQRTnfrhvyOHeFx+jQaL16M5KlTAU9P+MfFwefQIWQOGQIACP3sM+RHRSGva1fYiooQsWABUiZOhPLzQ2BGBkKys5GQkAAAaNWqFXJycpCWlgYAiI6OxolBg1A0cCB8DxxAwNatSL/rLgBAyNq1KLLbkdujBwAgYuFCpN19N4obNYJXSgqK7Hak3XMPACD4hx9Q4u+PnF69AACNFy9GRmwsiiIi4J2UhJC1a51dyrabEydOAAA6duyIo0ePovXddyO4e3ccXLkS0dOnAwCOb9iAotxctBo0CACw5623EDlwIILbtUN+Whr2vv02LnnsMQDAyU2bkJeSgjZnuux75x007dULoRdfjEKHAzvnz8elTz4Jv5AQJCUlITAwEEeOHAEAtG3bFunp6cjMzISnpydCAaRMmQLl5QW/nTvhu38/MocOLe39+ecoaN8ep6OjgeJiNJk/H6kTJkD5+CDzxhvhv307Mu68s7ThV1+hsHlznL7sstKGr72GtDFjUBwSAp9DhxCWloa4uDgAQGRkJAoKCpCSkgIA6Ny5Mw4dOoTM2Fh4XXwxgtatQ9q995Zus+vXQ/n4IOfqq0u32SVLkHnLLShq2hTeJ04gZM0anBo3rvS6P/0EFBcj+9prS7fZZcvgGDAAhS1bwuvUKYStXo3UiRPhERwMr+RkeHl5ISkpCQDQoUMHnDhxAtnZ2fDx8UGghweSz/zdBPz2G7ySk5F1440AgEYffojcyy5DflQUPHJy0HjJEiRPmwbl4wNH//7wSUxE5i23lG7fn36KvE6dkNe5M2wFBYhYtAgpkyZB+fjAb/duBBcWOru0bt0a2dnZ5bbZPXv2IDs2Ft5t2iBg2zakDxtW2vvrr1HUpAlyr7iitPf8+UgbNQrFYWHwiY9H0MaNSBs5snSb/e47lAQFIeeqq0q32TffRMYdd6AoPLzcPsIjOBg+qalQSuHkyZMAgIsvvhiHDx/G6dOn4efnB18/P2eXquwjUs98r9m9esEzIwOOG24obfj++8i56ioUtG8Pz6ws2N95x7mPCD55EoGZmc5ttl27dkhLS3Nus507d0bSrbeiZODAau0j/PbuhV9cHDJuv7204Zo1KIyMxOlLLwUANJk3D6njxqEkKMi5j0g987PcsmVL5OXlITU1FUDpL0oPHDiAgoICqN694X/8eLX2EcrHB8nTpyPov/8FAGT37Vu6ff/rX8iKiUFhixbwSklB2KefIvXMIUehR44gLS2twm22Q4cOSIqNRcnAgQjYsgVeaWnIiokp7b1yJXK7d0d+hw7wyM5G46VLnX+P/n/8Ae9jx5A1eHDpNvvxx8iLjkbexRfDlpeHiDff1PYRZV1at24Nh8OB9PR02Gw2dO3aFbt370ZxcTFsPXrAOz29yvuIwF9+cXYJ/uYbFIeFIbdnz9KGixYhffhwFNvt8DlypNw+otGhQ0hJSSm3zSYmJiIvLw/+/v5o3bq1s0tV9xFA6XF4HtnZzmcz7CtWIPvqq1HQrh08MzJgX74cKVOnlttHlHVp3749UlNTkZWVBS8vL3Tq1AlxcXHIi42Fb5Mm1dpHBK1b5/y7Cv3iCxS0aYPTl1wCKIUmr72G1PvvR0lgIHz373fuIzyCgxGQkVHhNhsUFARbSIjzdqu6jwAqfxzhdfIkQr/4otzjiMJmzZz7uKioKCQlJSEnJwe+vr5o3749du/ejbzYWPiFhFR5H+G/bRsCf/7ZOX9VH0d4BAcj2OFAVlaWuM2GhoaiaVBTzBg0AwDw0baP0MbeBj3blG6Hz/3nOUzvNx0hfiHYc3IPfj70M8b1Kv3357MdnyEiKAK92/cGAMz+djbGXz0e4YHhOJh6EN/u+RYP9HkAALBm5xp0adbF2aVTp05ISEhAXl4eAgIC0LJlS+zbtw95sbEI8PWt8j4icNMmBGzd6uxif+89ZF9zDQratIFnejrsH3yAlMmTS7fZs/YRHsHBCMvNFbdZALDb7TjdogVOnflZqMo+4nyPI0r8/eG7b1+5xxHhu3fj+PHjOHXmPMRdu3bFvn37UFhYiODgYDRp0sT5s1ydfUTA778jp0ePaj2OSL35ZqTFxSEqKgrHjh1Dbm4ufH190a5dO+zZswcA4N2pE0qAau0jzvc4AjYb/HfsKLePiNixA0ePHkVGRgY8PDzQpUsX7Nq1CyUlJQgLC0NYWJizS1X3EWXbS3afPtV6HJF6/fVIi4tDp06dEB8fj/z8fAQEBCAyMhL79+8HAPhcdBGK/PyqvI+oyuMIAAjYvLncPqLJ1q1ITEyEw+GAt7c3OnbsiJ07d5beZ3g4AgICnF2qs9YIWr8eWQMHVutxRH7fvkiLi0N0dDT27dsHV7IppZRLb9FQNpsN69atQ78zG8O55s6di1WrVmHr1q3o1q0bxo8fj6lnfsAA4Pbbb0fr1q3x9NNPw263488//0R0dLTz8+Hh4Xj77bdx6623irefn5+P/Px855+zsrLQqlUrZGZmIiQkBACQdPPNKDizkVVVxpAhCPv882p9jU/Xrmjx5ZeVXqcms9REbc1SG10W9OuHpEpepeAqLS65BFPOekM+iSlNajpLTbjTtlLTWWrClC5sUrez1AS71GwWNnHdLLXVpc+TfbA9YXu1brcmurXthp9e+KnS65jUpS62F3fbVvgz5LpZasLULllZWQgNDS23LrsQ9fol4hX57LPP8Oabb5a7LD8/H/7+/gBKn4Haec5f7K5duxAdHY1GjRohMjKy3OdPnDiBtLS0cgvuc/n6+iIkJKTchysUtGvnktupb9hFxyYydpGxi45NZOyiYxMZu8jYRccmMnaRmdalQS6wlVJ4+OGH8f3330MphU2bNmHevHmYeOYlZmPHjsWKFSuwbt06FBYW4tVXX8XJkycRGxsLALjvvvswa9YsxMfHw+Fw4KGHHsK1116Liy66qM6/F8+MjDq/T3fALjo2kbGLjF10bCJjFx2byNhFxi46NpGxi8y0LvX6GOyKxMbG4pVXXsHkyZNx9OhRNGvWDDNnzsSoUaMAAAMGDMDChQsxadIkHD16FF27dsXXX38Nu90OAHj66adRWFiIvn37wuFwoH///vjwww8t+V7sy5dbcr+mYxcdm8jYRcYuOjaRsYuOTWTsImMXHZvI2EVmWpcG8wy2Uqrc8dcTJ07E3r17kZOTg4MHD2LymTeQKDNq1KjSNyrKzsavv/6KK6+80vk5b29vzJ49G0ePHkVmZiY+++wzNGnSpK6+lXJSzjpOnP6HXXRsImMXGbvo2ETGLjo2kbGLjF10bCJjF5lpXRrMApuIiIiIiIioNnGB7eYCfvvN6hGMxC46NpGxi4xddGwiYxcdm8jYRcYuOjaRsYvMtC5cYLs5r+Rkq0cwErvo2ETGLjJ20bGJjF10bCJjFxm76NhExi4y07pwge3msm680eoRjMQuOjaRsYuMXXRsImMXHZvI2EXGLjo2kbGLzLQuXGATERERERERuQAX2G6ukUWnBzMdu+jYRMYuMnbRsYmMXXRsImMXGbvo2ETGLjLTunCB7eZyL7vM6hGMxC46NpGxi4xddGwiYxcdm8jYRcYuOjaRsYvMtC5cYLu5/Kgoq0cwErvo2ETGLjJ20bGJjF10bCJjFxm76NhExi4y07pwge3mPHJyrB7BSOyiYxMZu8jYRccmMnbRsYmMXWTsomMTGbvITOvCBbaba7xkidUjGIlddGwiYxcZu+jYRMYuOjaRsYuMXXRsImMXmWlduMB2c8nTplk9gpHYRccmMnaRsYuOTWTsomMTGbvI2EXHJjJ2kZnWxdgFdlJSEjZv3gwAUEpZPI3BbDarJzATu+jYRMYuMnbRsYmMXXRsImMXGbvo2ETGLjLDuhi3wP7yyy/RtWtXtGrVCldffTUA4Nprr8XChQstnsw1FixYgC5duqBHjx4uuT3/HTtccjv1Dbvo2ETGLjJ2AdHE9AAAUXRJREFU0bGJjF10bCJjFxm76NhExi4y07p4WT3A2dauXYuhQ4eipKTEeVlRURE2bdqEn3/+GSEhIRg1apSFE164KVOmYMqUKcjKykJoaOgF355PYqILpqp/2EXHJjJ2kbGLjk1k7KJjExm7yNhFxyYydpGZ1sWoZ7CfffZZKKXw1ltvoVmzZs7LH3roISil8PLLL1s4nZkyb7nF6hGMxC46NpGxi4xddGwiYxcdm8jYRcYuOjaRsYvMtC5GLbD/+OMPNGrUCOPGjYOnpycAwMvLC3PnzkVYWBj27t1r8YREREREREREMqMW2L6+vsjJyUFubm65yw8ePIjMzEwEBgZaNJm5wj791OoRjMQuOjaRsYuMXXRsImMXHZvI2EXGLjo2kbGLzLQuRi2wBw0ahIKCAgwePBhZWVkAgL/97W+45pprAAADBw60cjwj5XXqZPUIRmIXHZvI2EXGLjo2kbGLjk1k7CJjFx2byNhFZloXoxbYc+fORatWrfDjjz/C4XAAAP75z3/i+PHjaNq0Kf7v//7P4gnNk9e5s9UjGIlddGwiYxcZu+jYRMYuOjaRsYuMXXRsImMXmWldjHoX8cjISGzbtg0vv/wyfvjhB6SmpqJ58+a49tprMX36dNjtdqtHNI6toMDqEYzELjo2kbGLjF10bCJjFx2byNhFxi46NpGxi8y0LkYtsAGgUaNGeO655/Dcc89ZPYpbiFi0yOoRjMQuOjaRsYuMXXRsImMXHZvI2EXGLjo2kbGLzLQuRr1EHAD27duHDRs2AAAOHTqEm2++GT179sTixYstnsxMKZMmWT2CkdhFxyYydpGxi45NZOyiYxMZu8jYRccmMnaRmdbFqGew//vf/2LQoEEYOnQorrnmGtx9993YsmULAOC3335DSEgI7rrrLounNIvy8bF6BCOxi45NZOwiYxcdm8jYRccmMnaRsYuOTWTsIjOti1HPYD/zzDM4ffo0lFLYu3cvtmzZgqioKDzwwANQSuHVV1+1ekTj+O3ebfUIRmIXHZvI2EXGLjo2kbGLjk1k7CJjFx2byNhFZloXoxbYO3bsgL+/P5YuXYoffvgBADBu3DgsWLAAwcHB2L9/v8UTmsdvzx6rRzASu+jYRMYuMnbRsYmMXXRsImMXGbvo2ETGLjLTuhi1wM7NzUVQUBD8/f3x448/wmazoXfv3rDZbLDZbCguLrZ6RONkxMZaPYKR2EXHJjJ2kbGLjk1k7KJjExm7yNhFxyYydpGZ1sWoBXarVq2QmpqKN954A19//TWCg4PRvXt3zJ49G1lZWejQoYPVIxIRERERERGJjFpgjxw5EkopTJkyBQ6HA3fffTeUUnjqqadgs9kwceJEq0c0TugXX1g9gpHYRccmMnaRsYuOTWTsomMTGbvI2EXHJjJ2kZnWxagF9pNPPonHHnsMXbp0wahRozB37lz4+fmhffv2+Otf/4rx48dbPaJxCtq0sXoEI7GLjk1k7CJjFx2byNhFxyYydpGxi45NZOwiM62LUQtsLy8vzJkzB3/++SfeffddBAUFAQC2b9+OF154weLpzHT6kkusHsFI7KJjExm7yNhFxyYydtGxiYxdZOyiYxMZu8hM62LUebABoKCgALt374bD4UBJSQkAoKSkBOnp6fjuu++wYMECiyc0jFJWT2AmdtGxiYxdZOyiYxMZu+jYRMYuMnbRsYmMXWSGdTFqgb1582bceOONyMjIqPA6XGCX1+S116wewUjsomMTGbvI2EXHJjJ20bGJjF1k7KJjExm7yEzrYtRLxJ966imkp6dDKSV+DBkyxOoRjZN6//1Wj2AkdtGxiYxdZOyiYxMZu+jYRMYuMnbRsYmMXWSmdTFqgf3bb7/By8sLcXFxGD16NAYNGoS8vDzMmTMHANC1a1eLJzRPSWCg1SMYiV10bCJjFxm76NhExi46NpGxi4xddGwiYxeZaV2MWmDn5ubCbrejS5cuuOaaa7Bp0yb4+PjgscceQ0hICFatWmX1iMbx3b/f6hGMxC46NpGxi4xddGwiYxcdm8jYRcYuOjaRsYvMtC5GHYPdrFkzHDt2DL/99huuuuoqZGZm4j//+Q9atmyJnJwcFBYWWj2icQK2bbN6BCOxi45NZOwiYxcdm8jYRccmMnaRsYuOTWTsIjOti1HPYA8dOhTFxcW455570LlzZ7Rs2RKDBw9Gt27dUFJSgosuusjqEY2TPmyY1SMYiV10bCJjFxm76NhExi46NpGxi4xddGwiYxeZaV2Megb7hRdeQEpKCtSZt1p/4YUXMHr0aJSUlMDHxwfPP/+8xRMSERERERERyYxaYPv7+2PFihUoKioCAIwcORJXXHEF4uLi0L17d7Rt29baAQ0U8vXXVo9gJHbRsYmMXWTsomMTGbvo2ETGLjJ20bGJjF1kpnUx6iXiZby8/rfuj4iIwNChQ7m4rkBRkyZWj2AkdtGxiYxdZOyiYxMZu+jYRMYuMnbRsYmMXWSmdTFigZ2RkYG33noL8+bNw4EDBwAA27ZtQ6dOndCkSRPY7XY89dRTFk9pptwrrrB6BCOxi45NZOwiYxcdm8jYRccmMnaRsYuOTWTsIjOti+UvEd+1axeuu+46pKSkAACeeOIJLFu2DH/9619x+PBhAEB2djZmz56N4OBgPPHEE1aOS0RERERERCSy/Bnsxx9/HMnJyfD09ETLli1RUFCAUaNG4ciRI7jqqquwcOFCxMTEQCmFZcuWWT2ucSLmz7d6BCOxi45NZOwiYxcdm8jYRccmMnaRsYuOTWTsIjOti+UL7F9//RU2mw2//PILEhMTsWzZMhQVFcHDwwP//ve/8cADD2D16tXw9vZGYmKi1eMaJ23UKKtHMBK76NhExi4ydtGxiYxddGwiYxcZu+jYRMYuMtO6WL7AzsrKQkREBC6//HIAwLAz5zGz2+2IiIgAAAQGBiI8PBwFBQWWzWmq4rAwq0cwErvo2ETGLjJ20bGJjF10bCJjFxm76NhExi4y07pYvsAuKiqCt7e3888+Pj4AAF9f33LX8/T0dJ4fm/7HJz7e6hGMxC46NpGxi4xddGwiYxcdm8jYRcYuOjaRsYvMtC6WL7ABwGazWT1CnVmwYAG6dOmCHj16uOT2gjZudMnt1DfsomMTGbvI2EXHJjJ20bGJjF1k7KJjExm7yEzrYsQC2+FwYM6cOc4P6TKHw2HxlK4xZcoU7Nq1C1u2bHHJ7aWNHOmS26lv2EXHJjJ2kbGLjk1k7KJjExm7yNhFxyYydpGZ1sXy03QBpcdhP/nkk84/22w27TKlVIN6ppuIiIiIiIjci+UL7NatW3PhfAGCv/vO6hGMxC46NpGxi4xddGwiYxcdm8jYRcYuOjaRsYvMtC6WL7ATEhKsHsGtlQQFWT2CkdhFxyYydpGxi45NZOyiYxMZu8jYRccmMnaRmdbFiGOwqeZyrrrK6hGMxC46NpGxi4xddGwiYxcdm8jYRcYuOjaRsYvMtC5cYBMRERERERG5ABfYbq7xm29aPYKR2EXHJjJ2kbGLjk1k7KJjExm7yNhFxyYydpGZ1oULbDeXcccdVo9gJHbRsYmMXWTsomMTGbvo2ETGLjJ20bGJjF1kpnXhAtvNFYWHWz2CkdhFxyYydpGxi45NZOyiYxMZu8jYRccmMnaRmdbFyAV2Tk6O8/9Xr16NhQsX4tixYxZOZC7vo0etHsFI7KJjExm7yNhFxyYydtGxiYxdZOyiYxMZu8hM62LUAjs9PR3XXXcd/vrXvwIApk2bhuHDh+PBBx/E5Zdfjr1791o8oXmCv//e6hGMxC46NpGxi4xddGwiYxcdm8jYRcYuOjaRsYvMtC5GLbD/3//7f1i/fj3i4uKQkZGBxYsXw9PTE82aNUNKSgqeeeYZq0c0Ttro0VaPYCR20bGJjF1k7KJjExm76NhExi4ydtGxiYxdZKZ1MWqB/dVXX8HDwwOvvPIK1q1bh4KCAkyePBlxcXHw8vLCxo0brR6RiIiIiIiISGTUAjs5ORmNGzfG5Zdfjg0bNsBms+Haa69Fo0aN0KhRI6SkpFg9onGCfvzR6hGMxC46NpGxi4xddGwiYxcdm8jYRcYuOjaRsYvMtC5GLbDDw8ORmZmJzMxMfP3117DZbLjmmmuwZ88epKamolmzZlaPaB5PT6snMBO76NhExi4ydtGxiYxddGwiYxcZu+jYRMYuMsO6GLXAvvrqq5Gfn4/mzZtj//796NOnD3x9fXHVVVcBAAYPHmzxhObJ7tPH6hGMxC46NpGxi4xddGwiYxcdm8jYRcYuOjaRsYvMtC5GLbD/7//+Dy1btkReXh6CgoLw4osvIigoCADQsWNHzJgxw+IJiYiIiIiIiGReVg9wtg4dOmD//v2Ii4vDRRddhLCwMADAkiVLEBMTg+DgYGsHNFD40qVWj2AkdtGxiYxdZOyiYxMZu+jYRMYuMnbRsYmMXWSmdTHqGWwA8PX1xV/+8hds27YNK1euxI8//ohbb72Vi+sKZPFl8yJ20bGJjF1k7KJjExm76NhExi4ydtGxiYxdZKZ1MeoZbAD4/PPPMXHiRCQnJzsva9GihfNZbCqvkG/8JmIXHZvI2EXGLjo2kbGLjk1k7CJjFx2byNhFZloXo57B3rhxI+644w6cPHkSSikAgFIKx44dw6233orNmzdbPKF5vE6etHoEI7GLjk1k7CJjFx2byNhFxyYydpGxi45NZOwiM62LUQvsmTNnoqioCLfccgsOHjyIoqIiHDhwALfccgsKCgrwzDPPWD2icUK/+MLqEYzELjo2kbGLjF10bCJjFx2byNhFxi46NpGxi8y0LkYtsH/55Rf4+Phg5cqVaNeuHWw2G9q3b4/3338f3t7e+Pnnn60e0Tin7r/f6hGMxC46NpGxi4xddGwiYxcdm8jYRcYuOjaRsYvMtC5GLbABwMvLCz4+PuUu8/X1hZeXcYeLExERERERETkZtcDu1q0bTp8+jWnTpiEvLw8AkJ+fjwcffBCnT5/G5ZdfbvGE5gncuNHqEYzELjo2kbGLjF10bCJjFx2byNhFxi46NpGxi8y0LkYtsB999FEopfDGG2/Abrejffv2aNSoERYvXgybzYbp06dbPaJxbAUFVo9gJHbRsYmMXWTsomMTGbvo2ETGLjJ20bGJjF1kpnUxaoF966234pVXXoG3tzfy8vKQkJCAvLw8eHh44Pnnn8fQoUOtHtE42f36WT2CkdhFxyYydpGxi45NZOyiYxMZu8jYRccmMnaRmdbFuAObp0+fjhEjRmDNmjU4ceIEmjVrhkGDBqGZYec3IyIiIiIiIjqbUQvsZ599FiEhIXjooYcwZswY5+XFxcV47LHHEBoaihkzZlg3oIHs775r9QhGYhcdm8jYRcYuOjaRsYuOTWTsImMXHZvI2EVmWhejXiL+zDPP4JVXXtEuz8/Px1tvvYWXX37ZgqnMlt2/v9UjGIlddGwiYxcZu+jYRMYuOjaRsYuMXXRsImMXmWldLF1gK6Vw+eWXw9PTE56engCAo0ePOv9c9hEcHAyHw1HjU3WlpKSgQ4cOWL9+vfOyX3/9FVdeeSWCgoLQrl07LF26tNzXLFu2DB06dEBgYCC6d++OTZs2OT9XXFyMxx9/HE2bNkVwcDBuvfVWHD9+vEazXaiCVq0suV/TsYuOTWTsImMXHZvI2EXHJjJ2kbGLjk1k7CIzrYulC2ybzYZ58+ZBKQWlFGw2GwA4/3z2BwCMGDGi2vfx888/o1evXjh48KDzsvT0dNx000249957kZGRgaVLl+Lhhx/G5s2bAQDr16/Hgw8+iGXLliEjIwMjR47EkCFDkJubCwCYNWsWvvnmG2zduhXHjh2Dv78/7rfoBOeeaWmW3K/p2EXHJjJ2kbGLjk1k7KJjExm7yNhFxyYydpGZ1sXyY7D79u2LNWvWIDMzEyNGjEB4eDjmz5/v/LzNZoOXlxfat2+PSy+9tFq3vWzZMjz99NOYM2cO7rrrLuflH3/8McLDwzFlyhQAwHXXXYeRI0diwYIF6NmzJ5YsWYK77roLvXv3BgA8/PDDWLx4MVatWoX77rsPS5YswYsvvohWZ35bMm/ePDRv3hyHDh1C+/btL7BI9TRatapO789dsIuOTWTsImMXHZvI2EXHJjJ2kbGLjk1k7CIzrYsRx2DfeOONuOuuu/Cvf/0LixYtwvDhw50fw4YNw2233VbtxTUAxMTE4ODBgxg+fHi5y3fu3Im//OUv5S7r0qULtm/fft7PZ2Zm4ujRo+U+37RpUzRq1Ag7duyocJb8/HxkZWWV+3CF1EmTXHI79Q276NhExi4ydtGxiYxddGwiYxcZu+jYRMYuMtO6WP4M9tlGjx6N4uJibN++HQ6HAyUlJdp1rrnmmirfXkWn9nI4HAgMDCx3WUBAALKzs8/7eYfDAQCVfr3khRdewMyZM7XLd+3ahaCgIHTq1AnJAwagYOBAeB89iuDvv0fa6NEAgKAffwQ8PZHdpw8AIHzpUmQNHozCZs1QEBmJ4sBAnDrzEvXAjRthKyhwng/O/u67yO7fHwWtWsEzLQ2NVq1CUmws0uLiEBERAR8fHxw7dgwAcNFFFyE5ORkOhwOFMTEI27ULKdOmAQD8t22D9/HjyLrpJgBA2Ecf4XS3bsjv2BEep0+j8eLFSJ46FfD0hH9cHHwOHULmkCEAgNDPPkN+VBTyunaFragIEQsWIGXiRCg/PwRmZCAkOxsJCQkAgFatWiEnJwdpZ17qER0djRODBqFo4ED4HjiAgK1bkX7m1Qgha9eiyG5Hbo8eAICIhQuRdvfdKG7UCIXNmqHIbkfaPfcAAIJ/+AEl/v7I6dULANB48WJkxMaiKCIC3klJCFm71tmlbLs5ceIEAKBjx444evQoWt99N4K7d8fBlSsRPX06AOD4hg0oys1Fq0GDAAB73noLkQMHIrhdO+SnpWHv22/jksceAwCc3LQJeSkpaHOmy7533kHTXr0QevHFKHQ4sHP+fFz65JPwCwlBUlISAgMDceTIEQBA27ZtkZ6ejszMTHh6eiIUQMqUKVBeXvDbuRO++/cj88x54kM//xwF7dvjdHQ0UFyMJvPnI3XCBOS3b4/MG2+E//btyLjzztKGX32FwubNcfqyy0obvvYa0saMQXFICHwOHUJYWhri4uIAAJGRkSgoKEBKSgoAoHPnzjh06BAyY2PhdfHFCFq3Dmn33lu6za5fD+Xjg5yrry7dZpcsQeYtt6CoaVN4nziBkDVrcGrcuNLr/vQTUFyM7GuvLd1mly2DY8AAFLZsCa9TpxC2ejVSJ06ER3AwvJKT4eXlhaSkJABAhw4dcOLECWRnZ8PHxweBHh5IPvN3E/Dbb/BKTkbWjTcCABp9+CFyL7sM+VFR8MjJQeMlS5A8bRry27eHo39/+CQmIvOWW0q3708/RV6nTsjr3Bm2ggJELFqElEmToHx84Ld7N4ILC51dWrdujezs7HLb7J49e5AdGwvvNm0QsG0b0ocNK+399dcoatIEuVdcUdp7/nykjRqF4rAw+MTHI2jjRqSNHFm6zX73HUqCgpBz1VWl2+ybbyLjjjtQFB5ebh/hERwMn9RUKKVw8uRJAMDFF1+Mw4cP4/Tp0/Dz84Ovn5+zS1X2EamTJiG/fXtk9+oFz4wMOG64obTh++8j56qrUNC+PTyzsmB/5x3nPiL45EkEZmY6t9l27dohLS3Nuc127twZSbfeipKBA6u1j/Dbuxd+cXHIuP320oZr1qAwMhKnz/zStcm8eUgdNw4lQUHOfUTqmZ/lli1bIi8vD6mpqQBKf1F64MABFBQUQPXuDf/jx6u1j8hv3x7J06cj6L//BQBk9+1bun3/61/IiolBYYsW8EpJQdinnyJ1woTS7+3IEaSlpVW4zXbo0AFJsbEoGTgQAVu2wCstDVkxMaW9V65EbvfuyO/QAR7Z2Wi8dKnz79H/jz/gfewYsgYPLt1mP/4YedHRyLv4Ytjy8hDx5pvaPqKsS+vWreFwOJCeng6bzYauXbti9+7dKC4uhq1HD3inp1d5HxH4yy/OLsHffIPisDDk9uxZ2nDRIqQPH45iux0+R46U20c0OnQIKSkp5bbZxMRE5OXlwd/fH61bt3Z2qeo+AgACf/kFHtnZcAwcWHrdFSuQffXVKGjXDp4ZGbAvX46UqVPL7SPKurRv3x6pqanIysqCl5cXOnXqhLi4OOTFxsK3SZNq7SNK/P2df1ehX3yBgjZtcPqSSwCl0OS115B6//0oCQyE7/79zn2ER3AwAjIyKtxmg4KCYAsJcd5uVfcRQOWPI7xOnkToF1+UexxR2KyZcx8XFRWFpKQk5OTkwNfXF+3bt8fu3buRFxsLv5CQKu8j/LdtQ3FgoHP+qj6O8AgORrDDgaysLHGbDQ0NRdOgppgxqPQsMx9t+wht7G3Qs03pdvjcf57D9H7TEeIXgj0n9+DnQz9jXK/Sf38+2/EZIoIi0Lt96SsWZ387G+OvHo/wwHAcTD2Ib/d8iwf6PAAAWLNzDbo06+Ls0qlTJyQkJCAvLw8BAQFo2bIl9u3bh7zYWAT4+lZ5HxG4aROKg4OdXezvvYfsa65BQZs28ExPh/2DD5AyeXLpNnvWPsIjOBhhubniNgsAdrsdp1u0wKkzPwtV2Uec73FEib8/fPftK/c4Inz3bhw/fhynTp0CAHTt2hX79u1DYWEhgoOD0aRJE+fPcnX2EcUhIcjp0aNajyNSb74ZaXFxiIqKwrFjx5CbmwtfX1+0a9cOe/bsAQB4d+qEEqBa+4jzPY6AzQb/HTvK7SMiduzA0aNHkZGRAQ8PD3Tp0gW7du1CSUkJwsLCEBYW5uxS1X0EABQHBiK7T59qPY5Ivf56pMXFoVOnToiPj0d+fj4CAgIQGRmJ/fv3AwB8LroIRX5+Vd5HVOVxBAAEbN5cbh/RZOtWJCYmwuFwwNvbGx07dsTOnTtL7zM8HAEBAc4u1VlrlPj7I2vgwGo9jsjv2xdpcXGIjo7Gvn374Eo2VXaAswG2bNmCO++80/kA7Vw2mw1FRUU1um2bzYZ169ahX79+mD59Oo4dO4bVq1c7P//666/j7bffxrZt29CtWzeMHz8eU8/8gAHA7bffjtatW+Ppp5+G3W7Hn3/+iejoaOfnw8PD8fbbb+PWW28V7z8/Px/5+fnOP2dlZaFVq1bIzMxESEgIACDp5ptRcGYjq6rsXr0QdNYbsFWFT9euaPHll5Vepyaz1ERtzVIbXRb064ekSl6l4CotLrkEU856Qz6JKU1qOktNuNO2UtNZasKULmxSt7PUBLvUbBY2cd0stdWlz5N9sD1he7Vutya6te2Gn174qdLrmNSlLrYXd9tW+DPkullqwtQuWVlZCA0NLbcuuxBGvES8zIMPPojDhw+Lb3KmlBKf0a6J6Oho529Lyuzatcu5YK7s840aNUJkZGS5z584cQJpaWnlFtzn8vX1RUhISLkPV/DMyHDJ7dQ37KJjExm7yNhFxyYydtGxiYxdZOyiYxMZu8hM62LUAnv79u2w2Wx48cUXceDAASQlJeH48ePlPlzhtttuw4kTJ/Dqq6+isLAQ69atw4oVKzB27FgAwNixY7FixQqsW7cOhYWFePXVV3Hy5EnExsYCAO677z7MmjUL8fHxcDgceOihh3Dttdfioosucsl81VH2kgsqj110bCJjFxm76NhExi46NpGxi4xddGwiYxeZaV2MOga7VatWSElJweOPP16r9xMeHo5vv/0W06dPx9NPP42IiAi89tpr6H/mJOUDBgzAwoULMWnSJBw9ehRdu3bF119/DbvdDgB4+umnUVhYiL59+8LhcKB///748MMPa3VmIiIiIiIiMptRC+ynnnoKY8eOxVdffYWbzryZlquce6h59+7d8fPPP1d4/VGjRmHUqFHi57y9vTF79mzMnj3bpTPWRKP337d6BCOxi45NZOwiYxcdm8jYRccmMnaRsYuOTWTsIjOti1EvEY+Li0Pbtm0xZMgQdOvWDTfccANuuukm58fgM++WSv9T9u6hVB676NhExi4ydtGxiYxddGwiYxcZu+jYRMYuMtO6GPUM9ksvvQSbzQalFP7880/8+eef5T5vs9ksmsxcBe3bWz2CkdhFxyYydpGxi45NZOyiYxMZu8jYRccmMnaRmdbFqAX2vffey0V0NXlmZVk9gpHYRccmMnaRsYuOTWTsomMTGbvI2EXHJjJ2kZnWxagF9jvvvGP1CG7HzmYidtGxiYxdZOyiYxMZu+jYRMYuMnbRsYmMXWSmdTHqGOzDhw+f94PKS5k2zeoRjMQuOjaRsYuMXXRsImMXHZvI2EXGLjo2kbGLzLQuRj2D3a5du0o/b7PZUFRUVEfTEBEREREREVWdUQvsc0+lVd3PN0T+27ZZPYKR2EXHJjJ2kbGLjk1k7KJjExm7yNhFxyYydpGZ1sWoBfamTZvK/bmoqAiZmZlYtmwZfv31V3zzzTcWTWYu7+PHrR7BSOyiYxMZu8jYRccmMnbRsYmMXWTsomMTGbvITOti1DHYV155ZbmP3r1746abbsIHH3yAkpIS/OMf/7B6RONk3XST1SMYiV10bCJjFxm76NhExi46NpGxi4xddGwiYxeZaV2MWmBXRCmFgoICfPXVV1aPQkRERERERCQy6iXikydPLvdnpRTy8/Px66+/Ijk5GU2aNLFoMnOFffSR1SMYiV10bCJjFxm76NhExi46NpGxi4xddGwiYxeZaV2MWmC/8cYbsNls2uVlb242duzYuh7JeKe7dYNPUpLVYxiHXXRsImMXGbvo2ETGLjo2kbGLjF10bCJjF5lpXYx6ifg111yjffTr1w+33XYbFi5ciFmzZlk9onHyO3a0egQjsYuOTWTsImMXHZvI2EXHJjJ2kbGLjk1k7CIzrYtRz2CvX7/e6hHcjsfp01aPYCR20bGJjF1k7KJjExm76NhExi4ydtGxiYxdZKZ1MWaB/csvv+Dw4cNo3749unfvbvU4bqPx4sVWj2AkdtGxiYxdZOyiYxMZu+jYRMYuMnbRsYmMXWSmdbH8JeIZGRno1asXevfujbvvvhtXXnklbrjhBpw27DcRpkqeOtXqEYzELjo2kbGLjF10bCJjFx2byNhFxi46NpGxi8y0LpYvsB9//HH8+uuvUEo5P77//nv885//tHo09+DpafUEZmIXHZvI2EXGLjo2kbGLjk1k7CJjFx2byNhFZlgXyxfYa9asgc1mw0MPPYSdO3di3LhxUErh/ffft3o0t+AfF2f1CEZiFx2byNhFxi46NpGxi45NZOwiYxcdm8jYRWZaF8sX2GlpafD29saLL76Izp07Y8GCBfD29sbRo0etHs0t+Bw6ZPUIRmIXHZvI2EXGLjo2kbGLjk1k7CJjFx2byNhFZloXyxfYhYWFsNvt8Pb2BgD4+PjAbrcjLy/P4sncQ+aQIVaPYCR20bGJjF1k7KJjExm76NhExi4ydtGxiYxdZKZ1sXyBrZSCl1f5NzP38vJCSUmJRRMRERERERERVZ8Rp+nKyMjA5MmTy/0ZQLnLAMBms2HBggV1OZrxQj/7zOoRjMQuOjaRsYuMXXRsImMXHZvI2EXGLjo2kbGLzLQuRiywc3Jy8Oabbzr/rJQCAO0yLrB1+VFR8E1MtHoM47CLjk1k7CJjFx2byNhFxyYydpGxi45NZOwiM62L5Qvsa665Bjabzeox3FZe164I+e47q8cwDrvo2ETGLjJ20bGJjF10bCJjFxm76NhExi4y07pYvsBev3691SO4NVtRkdUjGIlddGwiYxcZu+jYRMYuOjaRsYuMXXRsImMXmWldLH+TM7owEXzJvIhddGwiYxcZu+jYRMYuOjaRsYuMXXRsImMXmWlduMB2cykTJ1o9gpHYRccmMnaRsYuOTWTsomMTGbvI2EXHJjJ2kZnWhQtsN6f8/KwewUjsomMTGbvI2EXHJjJ20bGJjF1k7KJjExm7yEzrwgW2m/Pbu9fqEYzELjo2kbGLjF10bCJjFx2byNhFxi46NpGxi8y0Llxg17EFCxagS5cu6NGjh0tuzy8uziW3U9+wi45NZOwiYxcdm8jYRccmMnaRsYuOTWTsIjOtCxfYdWzKlCnYtWsXtmzZ4pLby7j9dpfcTn3DLjo2kbGLjF10bCJjFx2byNhFxi46NpGxi8y0LlxgExEREREREbkAF9huLmTNGqtHMBK76NhExi4ydtGxiYxddGwiYxcZu+jYRMYuMtO6cIHt5gojI60ewUjsomMTGbvI2EXHJjJ20bGJjF1k7KJjExm7yEzrwgW2mzt96aVWj2AkdtGxiYxdZOyiYxMZu+jYRMYuMnbRsYmMXWSmdeECm4iIiIiIiMgFuMB2c03mzbN6BCOxi45NZOwiYxcdm8jYRccmMnaRsYuOTWTsIjOtCxfYbi513DirRzASu+jYRMYuMnbRsYmMXXRsImMXGbvo2ETGLjLTunCB7eZKgoKsHsFI7KJjExm7yNhFxyYydtGxiYxdZOyiYxMZu8hM68IFtpvzPXDA6hGMxC46NpGxi4xddGwiYxcdm8jYRcYuOjaRsYvMtC5cYLu5gK1brR7BSOyiYxMZu8jYRccmMnbRsYmMXWTsomMTGbvITOvCBbabS7/rLqtHMBK76NhExi4ydtGxiYxddGwiYxcZu+jYRMYuMtO6cIFNRERERERE5AJcYLu5kLVrrR7BSOyiYxMZu8jYRccmMnbRsYmMXWTsomMTGbvITOvCBbabK7LbrR7BSOyiYxMZu8jYRccmMnbRsYmMXWTsomMTGbvITOvCBbaby+3Rw+oRjMQuOjaRsYuMXXRsImMXHZvI2EXGLjo2kbGLzLQuXGATERERERERuQAX2G4uYuFCq0cwErvo2ETGLjJ20bGJjF10bCJjFxm76NhExi4y07pwge3m0u6+2+oRjMQuOjaRsYuMXXRsImMXHZvI2EXGLjo2kbGLzLQuXGC7ueJGjawewUjsomMTGbvI2EXHJjJ20bGJjF1k7KJjExm7yEzrwgW2m/NJTLR6BCOxi45NZOwiYxcdm8jYRccmMnaRsYuOTWTsIjOtCxfYbi5owwarRzASu+jYRMYuMnbRsYmMXXRsImMXGbvo2ETGLjLTunCB7ebS7rnH6hGMxC46NpGxi4xddGwiYxcdm8jYRcYuOjaRsYvMtC5cYBMRERERERG5ABfYbi74hx+sHsFI7KJjExm7yNhFxyYydtGxiYxdZOyiYxMZu8hM68IFtpsr8fe3egQjsYuOTWTsImMXHZvI2EXHJjJ2kbGLjk1k7CIzrQsX2G4up1cvq0cwErvo2ETGLjJ20bGJjF10bCJjFxm76NhExi4y07pwgU1ERERERETkAlxgu7nGixdbPYKR2EXHJjJ2kbGLjk1k7KJjExm7yNhFxyYydpGZ1oULbDeXERtr9QhGYhcdm8jYRcYuOjaRsYuOTWTsImMXHZvI2EVmWhcusN1cUUSE1SMYiV10bCJjFxm76NhExi46NpGxi4xddGwiYxeZaV24wHZz3klJVo9gJHbRsYmMXWTsomMTGbvo2ETGLjJ20bGJjF1kpnXhAtvNhaxda/UIRmIXHZvI2EXGLjo2kbGLjk1k7CJjFx2byNhFZloXLrDd3Kn77rN6BCOxi45NZOwiYxcdm8jYRccmMnaRsYuOTWTsIjOtCxfYdWzBggXo0qULevToYfUoRERERERE5EJcYNexKVOmYNeuXdiyZYtLbi/ov/91ye3UN+yiYxMZu8jYRccmMnbRsYmMXWTsomMTGbvITOvCBTYRERERERGRC3CB7eay+/a1egQjsYuOTWTsImMXHZvI2EXHJjJ2kbGLjk1k7CIzrQsX2EREREREREQuwAW2mwv/17+sHsFI7KJjExm7yNhFxyYydtGxiYxdZOyiYxMZu8hM68IFtpvLiomxegQjsYuOTWTsImMXHZvI2EXHJjJ2kbGLjk1k7CIzrQsX2G6usEULq0cwErvo2ETGLjJ20bGJjF10bCJjFxm76NhExi4y07pwge3mvFJSrB7BSOyiYxMZu8jYRccmMnbRsYmMXWTsomMTGbvITOvCBbabC/v0U6tHMBK76NhExi4ydtGxiYxddGwiYxcZu+jYRMYuMtO6cIHt5lInTLB6BCOxi45NZOwiYxcdm8jYRccmMnaRsYuOTWTsIjOtCxfYRERERERERC7ABbabC9y0yeoRjMQuOjaRsYuMXXRsImMXHZvI2EXGLjo2kbGLzLQuDXqBvWrVKnh5eSEoKMj5cc899wAAfv31V1x55ZUICgpCu3btsHTp0nJfu2zZMnTo0AGBgYHo3r07Nln0F+tx+rQl92s6dtGxiYxdZOyiYxMZu+jYRMYuMnbRsYmMXWSmdWnQC+wtW7bgnnvuQXZ2tvPjvffeQ3p6Om666Sbce++9yMjIwNKlS/Hwww9j8+bNAID169fjwQcfxLJly5CRkYGRI0diyJAhyM3NrfPvwXHddXV+n+6AXXRsImMXGbvo2ETGLjo2kbGLjF10bCJjF5lpXRr8Art79+7a5R9//DHCw8MxZcoUeHl54brrrsPIkSOxYMECAMCSJUtw1113oXfv3vD29sbDDz+Mxo0bY9WqVXX9LRAREREREZEhGuwCu6SkBL///jvWrFmDNm3aoGXLlpgwYQLS09Oxc+dO/OUvfyl3/S5dumD79u0AcN7PS/Lz85GVlVXuwxXs773nktupb9hFxyYydpGxi45NZOyiYxMZu8jYRccmMnaRmdbFy+oBrJKSkoLLLrsMd9xxB1avXo3U1FSMHj0ao0aNQvPmzREYGFju+gEBAcjOzgYAOByOSj8veeGFFzBz5kzt8l27diEoKAidOnVC8oABKBg4EN5HjyL4+++RNno0ACDoxx8BT09k9+kDAAhfuhRZgwejsFkzFAcHo8krr+DU/fcDAAI3boStoADZ/foBAOzvvovs/v1R0KoVPNPS0GjVKiTFxiItLg4RERHw8fHBsWPHAAAXXXQRkpOT4XA4UBgTg7Bdu5AybRoAwH/bNngfP46sm24CAIR99BFOd+uG/I4d4XH6NBovXozkqVMBT0/4x8XB59AhZA4ZAgAI/ewz5EdFIa9rV9iKihCxYAFSJk6E8vNDYEYGQrKzkZCQAABo1aoVcnJykJaWBgCIjo7GiUGDUDRwIHwPHEDA1q1Iv+suAEDI2rUostuR26MHACBi4UKk3X03ihs1Qom/PxovXoy0M8fUB//wA0r8/ZHTqxcAoPHixciIjUVRRAS8k5IQsnats0uzZs0AACdOnAAAdOzYEUePHkXru+9GcPfuOLhyJaKnTwcAHN+wAUW5uWg1aBAAYM9bbyFy4EAEt2uH/LQ07H37bVzy2GMAgJObNiEvJQVtznTZ9847aNqrF0IvvhiFDgd2zp+PS598En4hIUhKSkJgYCCOHDkCAGjbti3S09ORmZkJT09PhAJImTIFyssLfjt3wnf/fmQOHVra+/PPUdC+PU5HRwPFxWgyfz5SJ0xAfrt2CNqwAf7btyPjzjtLG371FQqbN8fpyy4rbfjaa0gbMwbFISHwOXQIYWlpiIuLAwBERkaioKAAKSkpAIDOnTvj0KFDyIyNhdfFFyNo3Tqk3Xtv6Ta7fj2Ujw9yrr66dJtdsgSZt9yCoqZN4X3iBELWrMGpceNKr/vTT0BxMbKvvbZ0m122DI4BA1DYsiW8Tp1C2OrVSJ04ER7BwfBKToaXlxeSkpIAAB06dMCJEyeQnZ0NHx8fBHp4IPnM303Ab7/BKzkZWTfeCABo9OGHyL3sMuRHRcEjJweNlyxB8rRpKGzeHCHffAOfxERk3nJL6fb96afI69QJeZ07w1ZQgIhFi5AyaRKUjw/8du9GcGGhs0vr1q2RnZ1dbpvds2cPsmNj4d2mDQK2bUP6sGGlvb/+GkVNmiD3iitKe8+fj7RRo1AcFgaf+HgEbdyItJEjS7fZ775DSVAQcq66qnSbffNNZNxxB4rCw8vtIzyCg+GTmgqlFE6ePAkAuPjii3H48GGcPn0afn5+8PXzc3apyj4iddIkFDZrhtDPP4dnRgYcN9xQ2vD995Fz1VUoaN8enllZsL/zjnMfEXzyJAIzM53bbLt27ZCWlubcZjt37oykW29FycCB1dpH+O3dC7+4OGTcfntpwzVrUBgZidOXXgoAaDJvHlLHjUNJUJBzH5F65me5ZcuWyMvLQ2pqKoDSX4QeOHAABQUFUL17w//48WrtI04+/ji8T5xA0H//CwDI7tu3dPv+17+QFRODwhYt4JWSgrBPP3WeMiT0yBGkpaVVuM126NABSbGxKBk4EAFbtsArLQ1ZMTGlvVeuRG737sjv0AEe2dlovHSp8+/R/48/4H3sGLIGDy7dZj/+GHnR0ci7+GLY8vIQ8eab2j6irEvr1q3hcDiQnp4Om82Grl27Yvfu3SguLoatRw94p6dXeR8R+MsvSH7kEXifOIHgb75BcVgYcnv2LG24aBHShw9Hsd0OnyNHyu0jGh06hJSUlHLbbGJiIvLy8uDv74/WrVs7u1R1HwEAgb/8Ao/sbDgGDiy97ooVyL76ahS0awfPjAzYly9HytSp5fYRZV3at2+P1NRUZGVlwcvLC506dUJcXBzyYmPh26RJtfYRhc2bozgsrHQb+OILFLRpg9OXXAIohSavvYbU++9HSWAgfPfvd+4jPIKDEZCRUeE2GxQUBFtIiHMbqOo+Aqj8cYTXyZMI/eKLco8jCps1c+7joqKikJSUhJycHPj6+qJ9+/bYvXs38mJj4RcSUuV9hP+2bcjv0AElwcGlDav4OMIjOBjBDgeysrLEbTY0NBRNg5pixqAZAICPtn2ENvY26NmmdDt87j/PYXq/6QjxC8Gek3vw86GfMa5X6b8/n+34DBFBEejdvjcAYPa3szH+6vEIDwzHwdSD+HbPt3igzwMAgDU716BLsy7OLp06dUJCQgLy8vIQEBCAli1bYt++fciLjUWAr2+V9xGBmzYh99JLofz9S7fZ995D9jXXoKBNG3imp8P+wQdImTy5dJs9ax/hERyMsNxccZsFALvdjtMtWuDUmZ+Fquwjzvc4osTfH7779pV7HBG+ezeOHz+OU6dOAQC6du2Kffv2obCwEMHBwWjSpInzZ7k6+whbXh4Cfv+9Wo8jUm++GWlxcYiKisKxY8eQm5sLX19ftGvXDnv27AEAeHfqhBKgWvuI8z2OgM0G/x07yu0jInbswNGjR5GRkQEPDw906dIFu3btQklJCcLCwhAWFubsUtV9BAB4ZGfDb+/eaj2OSL3+eqTFxaFTp06Ij49Hfn4+AgICEBkZif379wMAfC66CEV+flXeR1TlcQQABGzeXG4f0WTrViQmJsLhcMDb2xsdO3bEzp07S+8zPBwBAQHOLtVZa3hmZMD72LFqPY7I79sXaXFxiI6Oxr59++BKNqWUcukturEtW7bgyiuvxH333YfMzEysXr3a+bnXX38db7/9NrZt24Zu3bph/PjxmHrmBxAAbr/9drRu3RqvvPKKeNv5+fnIz893/jkrKwutWrVCZmYmQkJCAABJN9+MgjMbWVUlT5+OJvPmVetrfLp2RYsvv6z0OjWZpSZqa5ba6LKgXz8k7dhRrdusiRaXXIIp69dXeh1TmtR0lppwp22lprPUhCld2KRuZ6kJdqnZLGziullqq0ufJ/tge0LFryJ0lW5tu+GnF36q9DomdamL7cXdthX+DLlulpowtUtWVhZCQ0PLrcsuRIN9ifiOHTvwxBNP4OzfL+Tn58PDwwM9e/Z0/jalzK5duxAdHQ2g9Bmqyj4v8fX1RUhISLkPV/BMT3fJ7dQ37KJjExm7yNhFxyYydtGxiYxdZOyiYxMZu8hM69JgF9h2ux3z58/H3LlzUVRUhMOHD+Pxxx/HmDFjcMcdd+DEiRN49dVXUVhYiHXr1mHFihUYO3YsAGDs2LFYsWIF1q1bh8LCQrz66qs4efIkYmNj6/77+OCDOr9Pd8AuOjaRsYuMXXRsImMXHZvI2EXGLjo2kbGLzLQuDXaB3bJlS6xZswafffYZ7HY7unfvjh49emD+/PkIDw/Ht99+i48++gjh4eG4//778dprr6F///4AgAEDBmDhwoWYNGkSGjVqhA8++ABff/017HZ7nX8fZcflUHnsomMTGbvI2EXHJjJ20bGJjF1k7KJjExm7yEzr0mDf5AwArr32WmzcuFH8XPfu3fHzzz9X+LWjRo3CqFGjams0IiIiIiIicjMN9hns+iJgyxarRzASu+jYRMYuMnbRsYmMXXRsImMXGbvo2ETGLjLTunCB7ea8zpwaiMpjFx2byNhFxi46NpGxi45NZOwiYxcdm8jYRWZaFy6w3VzZ+VKpPHbRsYmMXWTsomMTGbvo2ETGLjJ20bGJjF1kpnXhApuIiIiIiIjIBbjAdnONVq60egQjsYuOTWTsImMXHZvI2EXHJjJ2kbGLjk1k7CIzrQsX2G4ut3t3q0cwErvo2ETGLjJ20bGJjF10bCJjFxm76NhExi4y07pwge3m8jt0sHoEI7GLjk1k7CJjFx2byNhFxyYydpGxi45NZOwiM60LF9huziM72+oRjMQuOjaRsYuMXXRsImMXHZvI2EXGLjo2kbGLzLQuXGC7ucZLl1o9gpHYRccmMnaRsYuOTWTsomMTGbvI2EXHJjJ2kZnWhQtsN5c8fbrVIxiJXXRsImMXGbvo2ETGLjo2kbGLjF10bCJjF5lpXbjAJiIiIiIiInIBLrDdnP8ff1g9gpHYRccmMnaRsYuOTWTsomMTGbvI2EXHJjJ2kZnWhQvsOrZgwQJ06dIFPXr0cMnteR875pLbqW/YRccmMnaRsYuOTWTsomMTGbvI2EXHJjJ2kZnWhQvsOjZlyhTs2rULW7ZsccntZQ0e7JLbqW/YRccmMnaRsYuOTWTsomMTGbvI2EXHJjJ2kZnWhQtsIiIiIiIiIhfgAtvNhX38sdUjGIlddGwiYxcZu+jYRMYuOjaRsYuMXXRsImMXmWlduMB2c3nR0VaPYCR20bGJjF1k7KJjExm76NhExi4ydtGxiYxdZKZ14QLbzeVdfLHVIxiJXXRsImMXGbvo2ETGLjo2kbGLjF10bCJjF5lpXbjAdnO2vDyrRzASu+jYRMYuMnbRsYmMXXRsImMXGbvo2ETGLjLTunCB7eYi3nzT6hGMxC46NpGxi4xddGwiYxcdm8jYRcYuOjaRsYvMtC5cYLu5lClTrB7BSOyiYxMZu8jYRccmMnbRsYmMXWTsomMTGbvITOvCBbabU15eVo9gJHbRsYmMXWTsomMTGbvo2ETGLjJ20bGJjF1kpnXhAtvN+e3cafUIRmIXHZvI2EXGLjo2kbGLjk1k7CJjFx2byNhFZloXLrDdnO/+/VaPYCR20bGJjF1k7KJjExm76NhExi4ydtGxiYxdZKZ14QLbzWUOHWr1CEZiFx2byNhFxi46NpGxi45NZOwiYxcdm8jYRWZaFy6wiYiIiIiIiFyAC2w3F/r551aPYCR20bGJjF1k7KJjExm76NhExi4ydtGxiYxdZKZ14QLbzRW0b2/1CEZiFx2byNhFxi46NpGxi45NZOwiYxcdm8jYRWZaFy6w3dzp6GirRzASu+jYRMYuMnbRsYmMXXRsImMXGbvo2ETGLjLTunCB7e6Ki62ewEzsomMTGbvI2EXHJjJ20bGJjF1k7KJjExm7yAzrwgW2m2syf77VIxiJXXRsImMXGbvo2ETGLjo2kbGLjF10bCJjF5lpXbjAdnOpEyZYPYKR2EXHJjJ2kbGLjk1k7KJjExm7yNhFxyYydpGZ1oULbDdX4u9v9QhGYhcdm8jYRcYuOjaRsYuOTWTsImMXHZvI2EVmWhcusN2c7759Vo9gJHbRsYmMXWTsomMTGbvo2ETGLjJ20bGJjF1kpnXhAtvN+W/fbvUIRmIXHZvI2EXGLjo2kbGLjk1k7CJjFx2byNhFZloXLrDdXMadd1o9gpHYRccmMnaRsYuOTWTsomMTGbvI2EXHJjJ2kZnWhQtsIiIiIiIiIhfgAtvNhXz1ldUjGIlddGwiYxcZu+jYRMYuOjaRsYuMXXRsImMXmWlduMB2c4XNm1s9gpHYRccmMnaRsYuOTWTsomMTGbvI2EXHJjJ2kZnWhQtsN3f6ssusHsFI7KJjExm7yNhFxyYydtGxiYxdZOyiYxMZu8hM68IFNhEREREREZELcIHt5iJee83qEYzELjo2kbGLjF10bCJjFx2byNhFxi46NpGxi8y0Llxgu7m0MWOsHsFI7KJjExm7yNhFxyYydtGxiYxdZOyiYxMZu8hM68IFtpsrDgmxegQjsYuOTWTsImMXHZvI2EXHJjJ2kbGLjk1k7CIzrQsX2G7O59Ahq0cwErvo2ETGLjJ20bGJjF10bCJjFxm76NhExi4y07pwge3mAn/5xeoRjMQuOjaRsYuMXXRsImMXHZvI2EXGLjo2kbGLzLQuXGDXsQULFqBLly7o0aOHS24vfcQIl9xOfcMuOjaRsYuMXXRsImMXHZvI2EXGLjo2kbGLzLQuXGDXsSlTpmDXrl3YsmWL1aMQERERERGRC3GB7eaCv/nG6hGMxC46NpGxi4xddGwiYxcdm8jYRcYuOjaRsYvMtC5cYLu54rAwq0cwErvo2ETGLjJ20bGJjF10bCJjFxm76NhExi4y07pwge3mcnv2tHoEI7GLjk1k7CJjFx2byNhFxyYydpGxi45NZOwiM60LF9hERERERERELsAFtptrvGiR1SMYiV10bCJjFxm76NhExi46NpGxi4xddGwiYxeZaV24wHZz6cOHWz2CkdhFxyYydpGxi45NZOyiYxMZu8jYRccmMnaRmdaFC2w3V2y3Wz2CkdhFxyYydpGxi45NZOyiYxMZu8jYRccmMnaRmdaFC2w353PkiNUjGIlddGwiYxcZu+jYRMYuOjaRsYuMXXRsImMXmWlduMB2c0Hr1lk9gpHYRccmMnaRsYuOTWTsomMTGbvI2EXHJjJ2kZnWhQtsN5d2771Wj2AkdtGxiYxdZOyiYxMZu+jYRMYuMnbRsYmMXWSmdeECm4iIiIiIiMgFuMB2c0Hr11s9gpHYRccmMnaRsYuOTWTsomMTGbvI2EXHJjJ2kZnWhQtsN6d8fKwewUjsomMTGbvI2EXHJjJ20bGJjF1k7KJjExm7yEzrwgW2m8u5+mqrRzASu+jYRMYuMnbRsYmMXXRsImMXGbvo2ETGLjLTunCBTUREREREROQCXGC7ufAlS6wewUjsomMTGbvI2EXHJjJ20bGJjF1k7KJjExm7yEzrwgW2m8u85RarRzASu+jYRMYuMnbRsYmMXXRsImMXGbvo2ETGLjLTunCB7eaKmja1egQjsYuOTWTsImMXHZvI2EXHJjJ2kbGLjk1k7CIzrQsX2G7O+8QJq0cwErvo2ETGLjJ20bGJjF10bCJjFxm76NhExi4y07pwge3mQtassXoEI7GLjk1k7CJjFx2byNhFxyYydpGxi45NZOwiM60LF9hu7tS4cVaPYCR20bGJjF1k7KJjExm76NhExi4ydtGxiYxdZKZ14QKbiIiIiIiIyAW4wHZzQT/9ZPUIRmIXHZvI2EXGLjo2kbGLjk1k7CJjFx2byNhFZloXLrDdXXGx1ROYiV10bCJjFxm76NhExi46NpGxi4xddGwiYxeZYV24wHZz2ddea/UIRmIXHZvI2EXGLjo2kbGLjk1k7CJjFx2byNhFZloXLrCJiIiIiIiIXIALbDdnX7bM6hGMxC46NpGxi4xddGwiYxcdm8jYRcYuOjaRsYvMtC5cYLs5x4ABVo9gJHbRsYmMXWTsomMTGbvo2ETGLjJ20bGJjF1kpnXhAtvNFbZsafUIRmIXHZvI2EXGLjo2kbGLjk1k7CJjFx2byNhFZloXLrAvQHJyMoYOHYqwsDA0btwYDz30EIqKiup0Bq9Tp+r0/twFu+jYRMYuMnbRsYmMXXRsImMXGbvo2ETGLjLTunCBfQGGDx+OoKAgJCUlYfPmzfjuu+/wyiuv1OkMYatX1+n9uQt20bGJjF1k7KJjExm76NhExi4ydtGxiYxdZKZ14QK7hg4cOID169djzpw5CAgIQPv27TFjxgzMnz+/TudInTixTu/PXbCLjk1k7CJjFx2byNhFxyYydpGxi45NZOwiM62Ll9UDuKudO3fCbrejRYsWzsu6dOmCw4cPIyMjA2FhYeWun5+fj/z8fOefMzMzAQBZWVnOy/JatUJhYWG15sj18kJ+x47V+pqSVq3K3a+kJrPURG3NUhtdAtu2RaM6OAQgsG1bt2lS01lqwp22lZrOUhOmdGGTup2lJtilZrOwietmqa0u7eztUJRf+/8+t7O3c6sudbG9uNu2wp8h181SE6Z2KfuvUqpat1ERm3LVLTUwy5cvx//7f/8Phw8fdl528OBBdOjQAUeOHEHLcw62f+aZZzBz5sy6HpOIiIiIiIjOQ1rD1QSfwa6hwMBA5Obmlrus7M/BwcHa9Z988kk88sgjzj+XlJQgLS0N4eHhsNlsNZohKysLrVq1wpEjRxASElKj23ClHj16YMuWLVaPYVQXNpGxi4xddGwiYxeZCV3YRMYuMnbRsYmMXWSu6KKUgsPhKPfK5AvBBXYNRUdH49SpUzh58iSaNm0KANi1axdatmyJ0NBQ7fq+vr7w9fUtd9m5LyOvqZCQECN+0Dw9PY2Yo4wJXdhExi4ydtGxiYxdZCZ1YRMZu8jYRccmMnaRXWgXaf1WU3yTsxqKiopCnz598NBDD8HhcCA+Ph7PPfccxo0bZ/VolpkyZYrVIxiHTWTsImMXHZvI2EXGLjo2kbGLjF10bCJjl4rxGOwLcPLkSUydOhXr1q2Dh4cH7r33Xrz44ovw9PSsk/vPyspCaGgoMjMzjfoNktXYRccmMnaRsYuOTWTsomMTGbvI2EXHJjJ2kZnYhS8RvwBNmzbFRx99ZNn9+/r64h//+If20vOGjl10bCJjFxm76NhExi46NpGxi4xddGwiYxeZiV34DDYRERERERGRC/AYbCIiIiIiIiIX4AKbiIiIiIiIyAW4wCYiIiIiIiJyAS6wa0lycjKGDh2KsLAwNG7cGA899BCKiorwwAMPICgoqNyHp6cnYmJiyn391Vdfjd9//x0AMGfOHLRs2RKBgYHo168f9u7d67xeQkICbrvtNkRERKBx48YYOnQo4uPjy93W4sWLMW3aNADAxx9/jEsvvRQhISFo27YtZs6ciZKSEm3+b7/9Fp6enkhISKiTLgDw1Vdf4bLLLkNwcDC6deuGTz/9VPv6+tqlTEpKCjp06ID169c7L5s0aRJ8fX3LbTOLFy8u93X1uYvUpCFvK9u3b8f1118Pu92OZs2a4d5770Vqamq562zatAl+fn7i19fHLpU1WbBgAaKiohAUFISoqCjMnz9f+/r62ASouEtD/3eosu2lIe9bKvv3uczx48fRtGlTvPPOO9rXV7XLH3/8geuuuw6hoaFo3Lgx7rnnHpw6darcbZnSpbImO3bswIABAxAcHIymTZvikUce0XrVxyZA5V1WrlyJzp07IyQkBB07dsQbb7yhfX197QLIj1mqMld9bgLIXcrUy/2KolrRr18/NXLkSJWTk6MOHjyounbtqubMmaNdb+3atcput6u4uDjnZUlJSapdu3ZKKaXeeecdFRkZqeLi4tTp06fVI488orp27apKSkqUUkp169ZNjRs3TmVnZyuHw6HGjh2roqOjy91HTEyM+uGHH9TWrVuVv7+/+uKLL1RxcbHatWuXatu2rfrnP/9Z7vrHjx9XzZs3VwBUfHx8nXT57bfflLe3t3rrrbdUYWGh2rBhgwoODlbr1q1rEF2UUuqnn35SF110kQJQ7vu+4oor1DvvvFPh19XnLlKThryt5ObmqubNm6unn35a5efnq9TUVHXTTTepm2++WSmlVElJiVq6dKkKCgpS0u69PnaprMnnn3+uGjVqpLZu3aqUUmrz5s3Kz89P/fDDD/W6yfm6nKsh/TtUWZeGvG9R6vyPW4qLi1X//v2Vh4eH+te//lXua6vaJT8/XzVv3lw9++yzqrCwUKWnp6sBAwaoe++918guFTVJSUlRjRs3Vv/3f/+nCgoKVHx8vIqKilJz586t900q6/Lnn3+qgIAAtWnTJqWUUj///LPy8fFRGzZsaBBdpMcsVZmrPjepqEuZ+rpf4QK7Fuzfv18BUMeOHXNetnLlStW6dety1yvbQS9fvrzc5QsWLFAPPfSQUkqp3r17q+eff975uYKCAhUcHKx++OEHlZaWpmJiYlRSUpLz89u3b1cAVFpamlJKqYyMDNWiRQtVVFSkVq9erR5++OFy9/Xwww+rIUOGOP9cXFysrrvuOjVjxgyX/6BV1uVvf/ub6t+/f7nrP/DAA2rYsGHOP9fXLkqV7jhat26tVq5cWW4HlJeXp3x8fMo98D1Xfe1SUZOGvK3s2bNHDRo0SBUVFTkv+/e//61CQkKUUkqNGTNGXXXVVeqll14SF9j1scv5mmRlZSmllCosLFRfffWV8vf3V7///nu9bqLU+buUaWj/DlXWpSHvW6ryuOUf//iHuueee1SbNm20B8JV7aKUUtnZ2aq4uFgppdShQ4fUVVddpR599FHn9U3pUlmTf/7zn+rqq68ud/2EhASVmJjo/HN9bKJU5V0+/vhj5ePjo37++WdVUlKiNm7cqPz9/dXmzZvrfZeKHrNUZa762kSpiruUqa/7FS6wa8Fnn32m7HZ7uct27NihAKj09HTnZWPHjlWDBg3Svn7AgAHO3/aFhYWpzz//vNznL7/8cvXKK6+I9z1jxgzVtm1b55/fe+89NXbsWPG6ubm5qmPHjmrmzJnOy5555hl11113qfj4eJf/oFXW5f7779eeWZk8ebK65JJLnH+ur12UKv0tWWFhoVJKldsB/frrr8pms6nbbrtNNWnSREVFRanZs2c7dyJK1d8uFTV57LHHGvS2cq577rnHuSg4cuSIUkqpdevWiQvshtLl7CZKlS6qPD09FQD1yCOPlLtuQ2milN5FqYb375CkrEtD3rec73HLDz/8oC6++GLlcDjEB8I16XL11VcrAKpLly7qxIkTzstN6VJZkxtuuEFNmDBBTZw4UTVt2lS1b99ezZo164L/bTa9iVKVdzl69Ki65pprFADnPvfcZwXra5eKHrNUZa762kSpyrvU5/0KF9i14L333lOtWrUqd9mBAwcUAOcD4EOHDilvb2+1bdu2ctc7deqUioyMdO6kPT091ffff1/uOn369FHPPfecdr+LFi1S/v7+6ttvv3VeFhsbq7788kvtullZWeqGG25Q3bp1cz7Ds379etWhQweVmZlZKz9olXX58ccflY+Pj1q9erUqLCxUP/30k7Lb7SoqKkopVb+7nOvsHdA333yj+vfvr9avX68KCgrUr7/+qlq0aOF82V5D6XJ2k7KXnDX0baWkpEQ99dRTKiwsTO3YsaPc56QFdkPoUlGTgoICVVBQoLZs2aJatGihZs+erZRqGE2UqrhLQ/x36GzndmnI+5bK/n1OTExUbdq0cR5qce4D4Zp2yc3NVWlpaWr48OEqOjra+aoCU7pU1qRfv37K29tbLV26VBUUFKg//vhDtWrVyvkS8fraRKnzbyujR49W3377rSooKFBffvmlCgwMVGvXrlVK1e8uZ6togS3N1VCaKFW+y8mTJ+v1foUL7FrwySefqPDw8HKXlf12LyMjQyml1N///nfVr18/7WvffvttNX78eOefQ0JC1BdffFHuOpdffrl69dVXnX/Oz89XkydPVna7vdyxhTk5Oapp06YqLy+v3Nfv2bNHde3aVV133XXOl6olJyerNm3aqF9++UUppWrlB+18XVatWqWio6OV3W5Xt956q3r00UdVz549lVL1u8u5KvvNp1JKzZkzR11xxRVKqYbT5dwmDX1byczMVLfddptq06aNtrhWSl5g1/cu52tS5sUXX3Q+I1nfmyhVeZeG+O9QmYq6NNR9S2X/Pvfr10+99NJLzsvPfSBcky5nO3nypAKgfv/9d6O6nK9J7969y31uzpw5qkePHkqp+ttEqcq7jBkzRk2cOLHc5+6//3512223KaXqd5ezSY/jpLmUajhNlPpfl5KSEnXDDTfU6/0KF9i1YN++fQpAuZcmrFy5UrVs2dL5586dO6ulS5dqX3vzzTerr7/+2vnnq6++2vlsi1L/O+bgu+++U0qVHj931VVXqcsuu0wdOnSo3G2tXr1aDR8+vNxla9asUSEhIerRRx91vmRDqdLfSPr7+6vQ0FAVGhqqgoODFQAVHBysXnjhhRqWKK+yLqdOndKOMx42bJiaPHmyUqp+dznX2TvmTz/9VL3xxhvlPv/cc8+pPn36KKUaTpezmzT0beXAgQOqY8eOauDAgSolJUW8jrTArs9dKmry8ssvlzt+Vimlnn32Weeisj43Uer820pD/HdIqYq7NOR9S0X/PgNQvr6+zvsODQ1VNptN+fv7q8GDB1e7S3x8vGrbtm25Y9MPHz6sAKiEhASjulT2mOXxxx9X3bt3L3f9F154wXlZfW1yvi5DhgxRY8aMKXf9SZMmqbvuuqvedznbuQvsiuZSquE0Uep/XRITE+v9foUL7FrSp08fddddd6msrCx16NAh1bVrV/WPf/xDKaVUamqqAqD2799f7mscDodq2rSpKigocF62ZMkSFRkZqf744w/nu+Z16NDB+ZLHyy+/XMXExKjc3FxthhEjRqhVq1Y5/7xp0ybl4+MjPqA6V239JquiLr/88osKDAxUf/zxhyosLFQrV65U/v7+Ki4urkF0OdvZO+ZPPvlE+fv7q++++875hiGNGzdW7733XoPqcnaThrytpKWlqdatW6sxY8aUO9bvXOcusOtzl8qabN26Vfn4+KhVq1ap4uJi9dNPP6nw8HC1atWqet1EqfNvKw3136HKujTkfYtSlT9uOdvZzzRVt0tJSYm6/PLL1fDhw5XD4VApKSnq5ptvVjfeeKNSyrwuFTXZvXu38vX1VS+++KIqKipSO3bsUJGRkWrevHn1vkllXZYsWaL8/f3Vf/7zH1VSUqLWr1+vgoOD1RdffNEgupQ5+zFLZXM1pCZKVf4Kzfq2X+ECu5acOHFC3XHHHSo8PFxFRESoRx991HkcwJYtWxQA7R/dlStXqpEjR5a7rKSkRP3zn/9U7dq1U0FBQap///5q7969SimlPv74YwVA+fn5qcDAwHIfiYmJqmnTpsrhcDhv65ZbblE2m027rvQGN7X1g1ZZl0WLFqm2bduqoKAg1b17d+ezAA2hy9nO3QG98cYbqmPHjiogIEC1b99eLViwQCnVsLqc26Shbitl7w4eEBCgzXC2cxfY9bnL+Zp8/vnn6pJLLlHBwcEqOjra+W7Z9blJVbo01H+Hzteloe5blKr83+eznf1AuLpdlCp9M8bbbrtN2e121aJFCzV58mSVkZGh8vPzjetSWZNffvlF9e3bV4WFhakWLVqo5557TpWUlNT7Jufr8tprr6mOHTuq4OBg1bVr1xrvc92xS5mzH7NUNldDaqJU1RfY9aGL7cw3TEREREREREQXwMPqAYiIiIiIiIjqAy6wiYiIiIiIiFyAC2wiIiIiIiIiF+ACm4iIiIiIiMgFuMAmIiIiIiIicgEusImIiIiIiIhcgAtsIiIiIiIiIhfgApuIiIiIiIjIBbjAJiIiIiIiInIBLrCJiIiIiIiIXIALbCIiIiIiIiIX4AKbiIiIiIiIyAX+P6U/fbqrMcZrAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x1400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creo un subplot de 3 gráficos verticales\n",
    "fig, ax = plt.subplots(\n",
    "    3, 1,\n",
    "    figsize=(10, 14),\n",
    "    sharex=False\n",
    ")\n",
    "\n",
    "# Estilo de gráfico, con ticks en X e Y\n",
    "sns.set(style=\"ticks\")\n",
    "\n",
    "titulos = [\n",
    "    \"Cotizaciones Paralelas\",\n",
    "    \"Cotizaciones Oficiales\",\n",
    "    \"Evolución del Riesgo País\",\n",
    "]\n",
    "\n",
    "ax0_palette = sns.color_palette(\"dark\")\n",
    "\n",
    "ax0_columns = [\n",
    "    \"TCV_Euro\",\n",
    "    \"TCV_Blue\",\n",
    "    \"TCC_Blue\",\n",
    "    \"TCV_MEP\",\n",
    "    \"Solidario\"\n",
    "]\n",
    "ax0_labels = [\n",
    "    \"TCV Euro Blue\",\n",
    "    \"TCV Blue\",\n",
    "    \"TCC Blue\",\n",
    "    \"MEP\",\n",
    "    \"Solidario\"\n",
    "]\n",
    "\n",
    "# Títulos general y para cada gráfico\n",
    "fig.suptitle(\n",
    "    f\"Tipos de cambio y riesgo país - últimas {cotizaciones_a_mostrar} cotizaciones\",\n",
    "    fontweight=\"bold\",\n",
    "    fontsize=18\n",
    ")\n",
    "for i, titulo in enumerate(titulos):\n",
    "    ax[i].set_title(\n",
    "        titulo,\n",
    "        fontweight=\"bold\",\n",
    "        fontsize=12\n",
    "    )\n",
    "\n",
    "for i, column in enumerate(ax0_columns):\n",
    "    sns.lineplot(\n",
    "        x=\"Fecha\",\n",
    "        y=column,\n",
    "        data=data.tail(cotizaciones_a_mostrar),\n",
    "        label=ax0_labels[i],\n",
    "        color=ax0_palette[i],\n",
    "        marker=\"o\",\n",
    "        ax=ax[0],\n",
    "    )\n",
    "\n",
    "# Gráfico de cotizaciones oficiales\n",
    "ax1_palette = sns.color_palette(\"bright\")\n",
    "ax1_columns = [\n",
    "    \"TCV_Billete\",\n",
    "    \"TCV_Divisas\",\n",
    "    \"TCC_Divisas\",\n",
    "    \"TCC_Billete\"\n",
    "]\n",
    "ax1_labels = [\n",
    "    \"TCV Billete\",\n",
    "    \"TCV Divisas\",\n",
    "    \"TCC Divisas\",\n",
    "    \"TCC Billete\"\n",
    "]\n",
    "for i, column in enumerate(ax1_columns):\n",
    "    sns.lineplot(\n",
    "        x=\"Fecha\",\n",
    "        y=column,\n",
    "        data=data.tail(cotizaciones_a_mostrar),\n",
    "        label=ax1_labels[i],\n",
    "        color=ax1_palette[i],\n",
    "        marker=\"D\",\n",
    "        ax=ax[1],\n",
    "    )\n",
    "\n",
    "# Definimos los colores de las columnas del riesgo país\n",
    "min_riesgo = data.tail(cotizaciones_a_mostrar).riesgo_pais.min()\n",
    "max_riesgo = data.tail(cotizaciones_a_mostrar).riesgo_pais.max()\n",
    "colores_barplot =[\n",
    "    'darkred' if x == max_riesgo else\n",
    "    'darkgreen' if x == min_riesgo else\n",
    "    'red' for x in data.tail(cotizaciones_a_mostrar).riesgo_pais\n",
    "]\n",
    "\n",
    "# Gráfico de barras del riesgo país\n",
    "sns.barplot(\n",
    "    x=\"Fecha\",\n",
    "    y=\"riesgo_pais\",\n",
    "    data=data.tail(cotizaciones_a_mostrar),\n",
    "    label=\"Riesgo País\",\n",
    "    palette=colores_barplot,\n",
    "    ax=ax[2],\n",
    ")\n",
    "\n",
    "# Etiquetas de datos para cada 5 valores y último valor en el primer gráfico\n",
    "for i, (column, label) in enumerate(zip(ax0_columns, ax0_labels)):\n",
    "    for j, (x, y) in enumerate(zip(data[\"Fecha\"].tail(cotizaciones_a_mostrar), data[column].tail(cotizaciones_a_mostrar))):\n",
    "        if j % 5 == 0 or j == len(data[column].tail(cotizaciones_a_mostrar)) - 1:  # Agregar etiqueta de datos cada 5 valores\n",
    "            ax[0].annotate(\n",
    "                f\"{y:,.2f}\",\n",
    "                xy=(x, y),\n",
    "                xytext=(0, 3),\n",
    "                textcoords=\"offset points\",\n",
    "                ha=\"center\",\n",
    "                va=\"bottom\",\n",
    "                fontproperties=fm.FontProperties(weight=\"bold\", size=9),\n",
    "                bbox=dict(boxstyle=\"round\", alpha=0.7, edgecolor=ax0_palette[i], facecolor=\"white\")\n",
    "            )\n",
    "# Etiquetas de datos para cada 5 valores y último valor en el segundo gráfico\n",
    "for i, (column, label) in enumerate(zip(ax1_columns, ax1_labels)):\n",
    "    for j, (x, y) in enumerate(zip(data[\"Fecha\"].tail(cotizaciones_a_mostrar), data[column].tail(cotizaciones_a_mostrar))):\n",
    "        if j % 5 == 0 or j == len(data[column].tail(cotizaciones_a_mostrar)) - 1:  # Agregar etiqueta de datos cada 5 valores\n",
    "            ax[1].annotate(\n",
    "                f\"{y:,.2f}\",\n",
    "                xy=(x, y),\n",
    "                xytext=(0, 3),\n",
    "                textcoords=\"offset points\",\n",
    "                ha=\"center\",\n",
    "                va=\"bottom\",\n",
    "                fontproperties=fm.FontProperties(weight=\"bold\", size=9),\n",
    "                bbox=dict(boxstyle=\"round\", alpha=0.7, edgecolor=ax1_palette[i], facecolor=\"white\")\n",
    "            )\n",
    "\n",
    "# Agregamos etiquetas de datos a las barras\n",
    "for i, bar in enumerate(ax[2].containers[0].get_children()[-len(data.riesgo_pais) :]):\n",
    "    height = bar.get_height()\n",
    "    annotation_color = 'darkred' if height == max_riesgo else 'darkgreen' if height == min_riesgo else 'black'\n",
    "    edge_color = annotation_color if annotation_color in ['darkred', 'darkgreen'] else None  # Set edge color for max and min values\n",
    "    bbox_props = dict(boxstyle=\"round\", alpha=0.7, edgecolor=edge_color, facecolor=\"white\") if edge_color else None\n",
    "    ax[2].annotate(\n",
    "        f\"{height:,.0f}\",\n",
    "        xy=(bar.get_x() + bar.get_width() / 2, height),\n",
    "        xytext=(0, 3),\n",
    "        textcoords=\"offset points\",\n",
    "        ha=\"center\",\n",
    "        va=\"bottom\",\n",
    "        fontproperties=fm.FontProperties(weight=\"bold\", size=9),\n",
    "        bbox=bbox_props,\n",
    "        color=annotation_color\n",
    "    )\n",
    "\n",
    "# data['Fecha'] = pd.to_datetime(data['Fecha']).dt.strftime(\"%d/%m\")\n",
    "# Personalizamos el eje X\n",
    "for axis in ax:\n",
    "    axis.set_xlabel(\"\")\n",
    "    axis.xaxis.set_major_locator(MultipleLocator(2))\n",
    "    axis.xaxis.set_minor_locator(MultipleLocator(1))\n",
    "\n",
    "# y separador de miles, por si la inflación se va demasiado, ya estoy adelantado\n",
    "for i in range(2):\n",
    "    ax[i].yaxis.set_major_formatter(FuncFormatter(\"{:,.2f}\".format))\n",
    "\n",
    "# Títulos y ticks eje Y\n",
    "y_labels = [\"ARS / USD\", \"ARS / USD\", \"Puntos Base\"]\n",
    "y_major_locators = [\n",
    "    MultipleLocator(50),\n",
    "    MultipleLocator(10),\n",
    "    MultipleLocator(500)\n",
    "]\n",
    "y_minor_locators = [\n",
    "    MultipleLocator(250),\n",
    "    MultipleLocator(25),\n",
    "    MultipleLocator(200)\n",
    "]\n",
    "for i in range(3):\n",
    "    ax[i].set_ylabel(\n",
    "        y_labels[i],\n",
    "        fontsize = 12,\n",
    "        fontweight=\"bold\"\n",
    "    )\n",
    "    ax[i].yaxis.set_major_locator(y_major_locators[i])\n",
    "    ax[i].yaxis.set_minor_locator(y_minor_locators[i])\n",
    "\n",
    "# Límites eje Y\n",
    "ax[2].set_ylim([0, data.tail(cotizaciones_a_mostrar).riesgo_pais.max() * 1.1])\n",
    "\n",
    "# Le damos estilo al grid del fondo\n",
    "grid_estilo = {\"color\": \"silver\", \"linestyle\": \"--\", \"linewidth\": 0.5}\n",
    "for axis in ax:\n",
    "    axis.grid(**grid_estilo)\n",
    "\n",
    "ax[0].legend(prop={'size': 8}, shadow=True)\n",
    "ax[1].legend(prop={'size': 8}, shadow=True)\n",
    "   \n",
    "# Guardamos el gráfico como imagen .jpg para enviarla por mail\n",
    "fig.tight_layout(pad=1)\n",
    "graficos_jpg = \"Gráficos Tipos de Cambios y Riesgo País.jpg\"\n",
    "plt.savefig(graficos_jpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#locale.setlocale(locale.LC_TIME, 'es_ES.utf8')\n",
    "locale.setlocale(locale.LC_TIME, 'en_US.utf8')\n",
    "\n",
    "inflacion_df[\"Fecha\"] = pd.to_datetime(inflacion_df[\"Fecha\"], format=\"%d/%m/%Y\")\n",
    "data[\"Fecha\"] = pd.to_datetime(data[\"Fecha\"], format=\"%d/%m/%y\")\n",
    "\n",
    "variacion_acumulada = data.merge(inflacion_df[[\"Fecha\",\"Inflación Mensual\"]], on=[\"Fecha\"], how=\"outer\")\n",
    "\n",
    "#Ordenamos por fecha ya que luego del merge, pone al final las fechas sin cotización pero con inflación\n",
    "variacion_acumulada.sort_values(\"Fecha\", inplace=True)\n",
    "\n",
    "# # Filtramos por el año actual\n",
    "# current_year = pd.Timestamp.today().year\n",
    "\n",
    "fecha_inicio = pd.to_datetime(\"2023-01-01\")\n",
    "variacion_acumulada = variacion_acumulada[variacion_acumulada[\"Fecha\"] >= fecha_inicio]\n",
    "\n",
    "#Creamos las columnas acumulativas\n",
    "columns_to_cumulate = ['Variación Solidario', 'Variación TCV Blue', 'Inflación Mensual']\n",
    "for column in columns_to_cumulate:\n",
    "    if column == 'Inflación Mensual':\n",
    "        variacion_acumulada[f'{column} Acumulada'] = ((1 + variacion_acumulada[column] / 100).cumprod()-1)*100\n",
    "    else:\n",
    "        variacion_acumulada[f'{column} Acumulada'] = ((1 + variacion_acumulada[column]).cumprod()-1)*100\n",
    "        \n",
    "fig, ax = plt.subplots(figsize=(10, 5))\n",
    "\n",
    "# Gráfico de cotizaciones oficiales\n",
    "palette = sns.color_palette(\"pastel\")\n",
    "ax2_columns = [\n",
    "    \"Variación Solidario Acumulada\",\n",
    "    \"Variación TCV Blue Acumulada\",\n",
    "    \"Inflación Mensual Acumulada\"\n",
    "]\n",
    "ax2_labels = [\n",
    "    \"Δ Solidario Acumulada\",\n",
    "    \"Δ TCV Blue Acumulada\",\n",
    "    \"Inflación Mensual Acumulada\"\n",
    "]\n",
    "\n",
    "for i, column in enumerate(ax2_columns):\n",
    "\n",
    "    if column == \"Inflación Mensual Acumulada\":\n",
    "        sns.lineplot(\n",
    "            x=\"Fecha\",\n",
    "            y=column,\n",
    "            data=variacion_acumulada,\n",
    "            label=ax2_labels[i],\n",
    "            color=palette[i],\n",
    "            linewidth=2,\n",
    "            marker=\"D\",\n",
    "            markersize=7\n",
    "        )\n",
    "    else:\n",
    "        sns.lineplot(\n",
    "            x=\"Fecha\",\n",
    "            y=column,\n",
    "            data=variacion_acumulada,\n",
    "            label=ax2_columns[i],\n",
    "            color=palette[i],\n",
    "            linewidth=2\n",
    "        )\n",
    "\n",
    "    # Etiqueta de datos para cada línea, se requieren diferentes frecuencias\n",
    "    bbox = dict(facecolor=palette[i], alpha=1, edgecolor=palette[i], boxstyle='square,pad=0.2')\n",
    "    label_frequency = 25 if column != \"Inflación Mensual Acumulada\" else 1\n",
    "    for idx, row in variacion_acumulada.iterrows():\n",
    "        if not pd.isna(row[column]):\n",
    "            # Etiquetar según frecuencia correspondiente\n",
    "            if idx % label_frequency == 0:\n",
    "                plt.text(\n",
    "                    row['Fecha'],\n",
    "                    row[column]*1.05,\n",
    "                    f'{row[column]:,.0f}',\n",
    "                    fontsize=9,\n",
    "                    color=\"black\",\n",
    "                    ha=\"center\",\n",
    "                    va=\"center\",\n",
    "                    bbox=bbox\n",
    "                )\n",
    "\n",
    "            # Etiqueta del valor máximo por línea/columna\n",
    "            bbox_max = dict(facecolor=palette[i], alpha=1, edgecolor=\"black\", boxstyle='square,pad=0.2')\n",
    "            if row[column] == variacion_acumulada[column].max() and idx == variacion_acumulada[column].idxmax():\n",
    "                plt.text(\n",
    "                    row['Fecha'],\n",
    "                    row[column]*1.05,\n",
    "                    f'{row[column]:,.0f}',\n",
    "                    fontsize=10,\n",
    "                    color=\"red\",\n",
    "                    fontweight=\"bold\",\n",
    "                    ha=\"center\",\n",
    "                    va=\"center\",\n",
    "                    bbox=bbox_max\n",
    "                )\n",
    "\n",
    "ax.set_title(\"Variaciones acumuladas\", fontsize=14, fontweight=\"bold\")\n",
    "\n",
    "# Para que los meses aparezcan en español\n",
    "locale.setlocale(locale.LC_TIME, 'es_ES.utf8')\n",
    "# Formatear eje de X\n",
    "ax.xaxis.set_major_formatter(mdates.DateFormatter('%b/%y'))\n",
    "ax.xaxis.set_major_locator(mdates.MonthLocator(interval=1))  # Mostrar un tick al mes\n",
    "ax.set_xlabel(\"\")\n",
    "\n",
    "# Formatear eje de Y\n",
    "ax.yaxis.set_major_formatter(FuncFormatter(\"{:,.0f} %\".format))\n",
    "ax.set_xlabel(\"\")\n",
    "ax.set_ylabel(\"Variación Acumulada en %\", fontsize = 12, fontweight=\"bold\")\n",
    "\n",
    "plt.grid(True)\n",
    "sns.despine()\n",
    "\n",
    "variaciones_jpg = \"Variaciones.jpg\"\n",
    "plt.savefig(variaciones_jpg)\n",
    "\n",
    "plt.legend(prop={'size': 8}, shadow=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\Anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n",
      "C:\\Users\\mmartinez\\AppData\\Local\\Temp\\ipykernel_28264\\4037767755.py:112: UserWarning: The figure layout has changed to tight\n",
      "  fig.tight_layout(pad=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Para que los meses aparezcan en español\n",
    "locale.setlocale(locale.LC_TIME, 'es_ES.utf8')\n",
    "\n",
    "# Crear un subplot con tres gráficos verticales\n",
    "fig, axes = plt.subplots(3, 1, figsize=(10, 14), sharex=True)\n",
    "inflacion_df[\"Fecha\"] = pd.to_datetime(inflacion_df[\"Fecha\"], format=\"%d/%m/%Y\").dt.strftime(\"%b/%Y\")\n",
    "\n",
    "fig.suptitle(\n",
    "    f\"Evolución de la Inflación Argentina en los últimos {meses_ocho_años} meses\",\n",
    "    fontweight=\"bold\",\n",
    "    fontsize=18\n",
    ")\n",
    "\n",
    "# Gráfico inflación mensual NO ACUMULADA\n",
    "sns.lineplot(\n",
    "    x=\"Fecha\",\n",
    "    y=\"Inflación Mensual\",\n",
    "    data=inflacion_df,\n",
    "    color=\"darkred\",\n",
    "    marker='o',\n",
    "    markersize=8,\n",
    "    ax=axes[0]\n",
    ")\n",
    "\n",
    "# Configurar los ticks del eje X para mostrar cada 3 meses\n",
    "axes[0].set_xticks(range(0, meses_ocho_años, 3))\n",
    "axes[0].set_xticklabels(inflacion_df.Fecha[::3], rotation=45)\n",
    "axes[0].set_xlim(0, meses_ocho_años - 1)\n",
    "\n",
    "axes[0].set_title(\n",
    "    \"Evolución de la Inflación Mensual (no acumulada)\",\n",
    "    fontweight=\"bold\",\n",
    "    fontsize=12\n",
    ")\n",
    "axes[0].set_ylabel(\n",
    "    \"Inflación Mensual\",\n",
    "    fontweight=\"bold\",\n",
    "    fontsize=12\n",
    ")\n",
    "axes[0].set_xlabel(\"\")\n",
    "\n",
    "axes[0].yaxis.set_major_formatter(FuncFormatter(\"{:,.0f} %\".format))\n",
    "axes[0].grid(True)\n",
    "sns.despine()\n",
    "\n",
    "# Gráfico inflación mensual ACUMULADA\n",
    "sns.lineplot(\n",
    "    x=\"Fecha\",\n",
    "    y=\"Inflación Agregada\",\n",
    "    data=inflacion_df,\n",
    "    color=\"darkred\",\n",
    "    linewidth=2,\n",
    "    legend=False,\n",
    "    ax=axes[1]\n",
    ")\n",
    "\n",
    "# Configurar los ticks del eje X para mostrar cada 6 meses\n",
    "axes[1].set_xticks(range(0, meses_ocho_años, 6))\n",
    "axes[1].set_xticklabels(inflacion_df.Fecha[::6], rotation=45)\n",
    "axes[1].set_xlim(0, meses_ocho_años - 1)\n",
    "\n",
    "axes[1].set_title(\n",
    "    \"Inflación Acumulada\",\n",
    "    fontweight=\"bold\",\n",
    "    fontsize=12\n",
    ")\n",
    "axes[1].set_ylabel(\n",
    "    \"Inflación Acumulada\",\n",
    "    fontweight=\"bold\",\n",
    "    fontsize=12\n",
    ")\n",
    "\n",
    "axes[1].yaxis.set_major_formatter(FuncFormatter(\"{:,.0f} %\".format))\n",
    "axes[1].grid(True)\n",
    "sns.despine()\n",
    "\n",
    "# Gráfico inflación mensual ACUMULADA logarítmica\n",
    "sns.lineplot(\n",
    "    x=\"Fecha\",\n",
    "    y=\"Inflación Agregada\",\n",
    "    data=inflacion_df,\n",
    "    color=\"darkred\",\n",
    "    linewidth=2,\n",
    "    ax=axes[2]\n",
    ").set_yscale(\"log\")  # Escala logarítmica en el eje Y\n",
    "\n",
    "# Configurar los ticks del eje X para mostrar cada 3 meses\n",
    "axes[2].set_xticks(range(0, meses_ocho_años, 3))\n",
    "axes[2].set_xticklabels(inflacion_df.Fecha[::3], rotation=45)\n",
    "axes[2].set_xlim(0, meses_ocho_años - 1)\n",
    "axes[2].set_xlabel(\"\")\n",
    "\n",
    "axes[2].set_title(\n",
    "    \"Inflación Acumulada (escala logarítmica)\",\n",
    "    fontweight=\"bold\",\n",
    "    fontsize=14\n",
    ")\n",
    "axes[2].set_ylabel(\n",
    "    \"Inflación Acumulada\",\n",
    "    fontweight=\"bold\",\n",
    "    fontsize=12\n",
    ")\n",
    "\n",
    "axes[2].yaxis.set_major_formatter(FuncFormatter(\"{:,.0f} %\".format))\n",
    "axes[2].grid(True)\n",
    "sns.despine()\n",
    "\n",
    "# Ajustar el espaciado entre los gráficos\n",
    "plt.tight_layout()\n",
    "\n",
    "# Guardamos el gráfico como imagen .jpg para enviarla por mail\n",
    "fig.tight_layout(pad=1)\n",
    "\n",
    "inflacion_jpg = \"Gráficos Inflación.jpg\"\n",
    "plt.savefig(inflacion_jpg)\n",
    "\n",
    "# Mostrar el gráfico\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 📮 Mailing automático"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "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>Fecha</th>\n",
       "      <th>Inflación Mensual</th>\n",
       "      <th>Inflación Bimestral</th>\n",
       "      <th>Inflación Trimestral</th>\n",
       "      <th>Inflación Anual</th>\n",
       "      <th>Inflación Agregada</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>mar./2023</td>\n",
       "      <td>0.077</td>\n",
       "      <td>0.148082</td>\n",
       "      <td>0.216967</td>\n",
       "      <td>104.192492</td>\n",
       "      <td>3.237568e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>abr./2023</td>\n",
       "      <td>0.084</td>\n",
       "      <td>0.167468</td>\n",
       "      <td>0.244521</td>\n",
       "      <td>108.815719</td>\n",
       "      <td>3.509607e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>may./2023</td>\n",
       "      <td>0.078</td>\n",
       "      <td>0.168552</td>\n",
       "      <td>0.258531</td>\n",
       "      <td>114.180157</td>\n",
       "      <td>3.783435e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>401</th>\n",
       "      <td>jun./2023</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.142680</td>\n",
       "      <td>0.238665</td>\n",
       "      <td>115.603956</td>\n",
       "      <td>4.010501e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>jul./2023</td>\n",
       "      <td>0.063</td>\n",
       "      <td>0.126780</td>\n",
       "      <td>0.214669</td>\n",
       "      <td>113.395722</td>\n",
       "      <td>4.263225e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>403</th>\n",
       "      <td>ago./2023</td>\n",
       "      <td>0.124</td>\n",
       "      <td>0.194812</td>\n",
       "      <td>0.266501</td>\n",
       "      <td>124.165226</td>\n",
       "      <td>4.791989e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>sep./2023</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.266748</td>\n",
       "      <td>0.346553</td>\n",
       "      <td>137.885320</td>\n",
       "      <td>5.400699e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>oct./2023</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.220541</td>\n",
       "      <td>0.371888</td>\n",
       "      <td>142.361055</td>\n",
       "      <td>5.849040e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>406</th>\n",
       "      <td>nov./2023</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.221624</td>\n",
       "      <td>0.376770</td>\n",
       "      <td>160.613222</td>\n",
       "      <td>6.597845e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>dic./2023</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.415640</td>\n",
       "      <td>0.533138</td>\n",
       "      <td>211.198472</td>\n",
       "      <td>8.280550e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>ene./2024</td>\n",
       "      <td>0.206</td>\n",
       "      <td>0.513530</td>\n",
       "      <td>0.707262</td>\n",
       "      <td>254.061657</td>\n",
       "      <td>9.986550e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>409</th>\n",
       "      <td>feb./2024</td>\n",
       "      <td>0.132</td>\n",
       "      <td>0.365192</td>\n",
       "      <td>0.713316</td>\n",
       "      <td>275.982923</td>\n",
       "      <td>1.130491e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Fecha  Inflación Mensual  Inflación Bimestral  Inflación Trimestral  \\\n",
       "398  mar./2023              0.077             0.148082              0.216967   \n",
       "399  abr./2023              0.084             0.167468              0.244521   \n",
       "400  may./2023              0.078             0.168552              0.258531   \n",
       "401  jun./2023              0.060             0.142680              0.238665   \n",
       "402  jul./2023              0.063             0.126780              0.214669   \n",
       "403  ago./2023              0.124             0.194812              0.266501   \n",
       "404  sep./2023              0.127             0.266748              0.346553   \n",
       "405  oct./2023              0.083             0.220541              0.371888   \n",
       "406  nov./2023              0.128             0.221624              0.376770   \n",
       "407  dic./2023              0.255             0.415640              0.533138   \n",
       "408  ene./2024              0.206             0.513530              0.707262   \n",
       "409  feb./2024              0.132             0.365192              0.713316   \n",
       "\n",
       "     Inflación Anual  Inflación Agregada  \n",
       "398       104.192492        3.237568e+05  \n",
       "399       108.815719        3.509607e+05  \n",
       "400       114.180157        3.783435e+05  \n",
       "401       115.603956        4.010501e+05  \n",
       "402       113.395722        4.263225e+05  \n",
       "403       124.165226        4.791989e+05  \n",
       "404       137.885320        5.400699e+05  \n",
       "405       142.361055        5.849040e+05  \n",
       "406       160.613222        6.597845e+05  \n",
       "407       211.198472        8.280550e+05  \n",
       "408       254.061657        9.986550e+05  \n",
       "409       275.982923        1.130491e+06  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tomamos sólo los últimos 12 meses para ver la interanual y\n",
    "inflacion_df = inflacion_df.tail(12).copy()\n",
    "\n",
    "# Tomamos el último valor de inflación interanual, para informar en el cuerpo del mail\n",
    "inflacion_interanual = inflacion_df[\"Inflación Anual\"].iloc[-1]\n",
    "inflacion_interanual =  \"{0:,.2f}%\".format(inflacion_interanual)\n",
    "\n",
    "inflacion_df[\"Inflación Mensual\"] = inflacion_df[\"Inflación Mensual\"] / 100\n",
    "inflacion_df[\"Inflación Bimestral\"] = inflacion_df[\"Inflación Bimestral\"] / 100\n",
    "inflacion_df[\"Inflación Trimestral\"] = inflacion_df[\"Inflación Trimestral\"] / 100\n",
    "\n",
    "# Imprimir tabla con formato en porcentaje\n",
    "inflacion_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "I2GnVlwgI_rN",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Declaración de variables, para que funcione tu contraseña de gmail, tenés que tener una contraseña para app\n",
    "# si no tenés, se crea en esta página myaccount.google.com\n",
    "email_sender = input(\"Tipeá tu usuario de gmail:\")\n",
    "email_password = getpass(\"Tipeá tu contraseña:\")\n",
    "\n",
    "email_receiver = [\n",
    "    receiver_string_list\n",
    "\n",
    "# Descomentar la siguiente línea para testear\n",
    "# email_receiver = email_sender\n",
    "\n",
    "# Tabular los días a mostrar del dataframe para que se vea en el cuerpo del mail\n",
    "cotizaciones = [\n",
    "    \"Fecha\",\n",
    "    \"tccblue\",\n",
    "    \"tcvblue\",\n",
    "    \"tccBillete\",\n",
    "    \"tcvBillete\",\n",
    "    \"tccDivisas\",\n",
    "    \"tcvDivisas\",\n",
    "    \"Solidario\",\n",
    "    \"MEP\",\n",
    "    \"RiesgoPaís\",\n",
    "    \"tccEuros\",\n",
    "    \"tcvEuros\",\n",
    "    \"FEDtea\",\n",
    "    \"BCRAtea\"\n",
    "]\n",
    "\n",
    "# Valores correspondientes a los últimos días, 14 columnas\n",
    "valores = df.iloc[:cotizaciones_a_mostrar, :14].values.tolist()\n",
    "tabla_cotizaciones = tabulate(\n",
    "    valores,\n",
    "    headers=cotizaciones,\n",
    "    tablefmt = \"html\",\n",
    "    numalign=\"center\",\n",
    "    floatfmt=\".2f\"\n",
    ")\n",
    "\n",
    "# Encabezados para tabular últimos 10 días del df y ponerlo en el cuerpo\n",
    "variaciones = [\n",
    "    \"Fecha\",\n",
    "    \"Solidario/TCV_Blue\",\n",
    "    \"MEP/TCV_Blue\",\n",
    "    \"Euro_Blue/Dólar_Blue\",\n",
    "    \"Variación_Solidario\",\n",
    "    \"Variación_Blue\",\n",
    "    \"Variación_Euro\"\n",
    "]\n",
    "# Valores correspondientes a los últimos días, 14 columnas\n",
    "valores = df.iloc[:cotizaciones_a_mostrar, [0] + list(range(14, df.shape[1]))].values.tolist()\n",
    "tabla_variaciones = tabulate(\n",
    "    valores,\n",
    "    headers=variaciones,\n",
    "    tablefmt=\"html\",\n",
    "    numalign=\"center\",\n",
    "    floatfmt=\".2%\"\n",
    ")\n",
    "\n",
    "# Encabezados para tabular últimos 10 días del df y ponerlo en el cuerpo\n",
    "inflaciones = [\n",
    "    \"Fecha\",\n",
    "    \"I. Mensual\",\n",
    "    \"I. Bimestral\",\n",
    "    \"I. Trimestral\"\n",
    "]\n",
    "# Valores correspondientes a los últimos días, 14 columnas\n",
    "valores = inflacion_df[[\"Fecha\", \"Inflación Mensual\", \"Inflación Bimestral\", \"Inflación Trimestral\"]].values.tolist()\n",
    "tabla_inflacion = tabulate(\n",
    "    valores,\n",
    "    headers=inflaciones,\n",
    "    tablefmt=\"html\",\n",
    "    numalign=\"center\",\n",
    "    floatfmt=\".2%\"\n",
    ")\n",
    "\n",
    "# Cuerpo del texto con código html\n",
    "html = f\"\"\"<!DOCTYPE html>\n",
    "<html lang=\"es\">\n",
    "   <head>\n",
    "      <meta charset=\"UTF-8\">\n",
    "      <meta http-equiv=\"X-UA-Compatible\" content=\"IE=edge\">\n",
    "      <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n",
    "   </head>\n",
    "   <body>\n",
    "      <p> 📅 <b><u>Resumen diario: </u></b> ℹ️<br></p>\n",
    "      <p>\n",
    "         <b>Costo comprar USD 200 blue 💰:</b> $ {(float(df[\"TCV_Blue\"].iloc[0] * 200)):,.2f} <br>\n",
    "         <b>Costo comprar USD 200 oficiales con impuestos:</b> $ {(float(df[\"Solidario\"].iloc[0] * 200)):,.2f}<br>\n",
    "         <b>La diferencia es de:</b> $ {(float(df[\"TCV_Blue\"].iloc[0] * 200 - df[\"Solidario\"].iloc[0] * 200)):,.2f}<br><br>\n",
    "         <b>La brecha entre solidario y blue es:</b> {float((1 - df[\"Solidario\"].iloc[0] / df[\"TCV_Blue\"].iloc[0]) * 100):.2f}%<br>\n",
    "         <b>El dólar solidario varió:</b> {float(((df[\"Solidario\"].iloc[0] / df[\"Solidario\"].iloc[1] - 1) * 100)):.2f}%<br>\n",
    "         <b>El dólar blue varió 💵:</b> {float(((df[\"TCV_Blue\"].iloc[0] / df[\"TCV_Blue\"].iloc[1] - 1) * 100)):.2f}%<br>\n",
    "         <b>El dólar divisas varió:</b> {float(((df[\"TCV_Divisas\"].iloc[0] / df[\"TCV_Divisas\"].iloc[1] - 1) * 100)):.2f}%<br><br>\n",
    "         <b>El euro varió un 💶:</b> {float(((df[\"TCV_Euro\"].iloc[0] / df[\"TCV_Euro\"].iloc[1] - 1) * 100)):.2f}%<br>\n",
    "         <b>Entre euro blue y dólar blue hay una brecha de:</b> {(1-df[\"TCV_Blue\"].iloc[0]/df[\"TCV_Euro\"].iloc[0])*100:.2f}%<br><br>\n",
    "         <b>El valor del dólar oficial futuro según paridad de tasas de interés es:</b> $ {fwd_oficial:,.2f}<br>\n",
    "         <b>El valor del dólar blue futuro según paridad de tasas de interés es 💸:</b> ${fwd_blue:,.2f}<br>\n",
    "         <b>El riesgo país hoy es de ☣️:</b> {(float(df[\"riesgo_pais\"].iloc[0])):,.0f}, es decir que Argentina debe pagar un {(float(df[\"riesgo_pais\"].iloc[0] / 100)):.2f}% de sobretasa respecto a un bono del tesoro estadounidense.\n",
    "      </p>\n",
    "      <img src='cid:image1'>\n",
    "      <img src='cid:image2'>\n",
    "      <img src='cid:image3'>\n",
    "      <p>Las siguientes muestran las últimas {cotizaciones_a_mostrar} cotizaciones de dólares, euro, riesgo país y TEA del BCRA 🇦🇷 y TEA de la FED 🇺🇸.<br>\n",
    "      Analizalas mientras te cebás unos mates 🧉:<br>\n",
    "      </p>\n",
    "      {tabla_cotizaciones}<br>\n",
    "      {tabla_variaciones}<br>\n",
    "      <p>La siguiente tabla muestra las últimas 12 publicaciones de la inflación, tené en cuenta que la interanual es de {inflacion_interanual}:<br>\n",
    "      </p>\n",
    "      {tabla_inflacion}<br>\n",
    "      <p><b><u>📚Las fuentes consultadas fueron:</u>\n",
    "      <b><br>Cotizaciones oficiales: </b>{web_bna}\n",
    "      <b><br>Cotizaciones paralelas: </b>{web_dolarhoy}\n",
    "      <b><br>Cotización Dólar MEP: </b>{web_ambito_mep}\n",
    "      <b><br>Riesgo País: </b>{web_ambito_riesgo_pais}\n",
    "      <b><br>Cotización Euro Blue: </b>{web_perfil}\n",
    "      <b><br>Tasa Efectiva Anual: </b>{web_bcra}\n",
    "      <b><br>Tasa Efectiva Anual EEUU: </b>{web_fed}\n",
    "      <b><br>Inflación Mensual: </b>{bcra_api_url}<br>\n",
    "      </p>\n",
    "      <p>\n",
    "         🤓 Muchas gracias por leer,<br>\n",
    "         🤑 Ojalá te sirva,<br><br>\n",
    "      <p> \n",
    "         👨🏽‍💼 En Linkedin encontrá mi historial laboral, académico y mis aportes en datos y comercio internacional: <b>https://linkedin.com/in/mauroemartinez/</b> <br>\n",
    "         👨🏽‍💻 Encontrá en GitHub mis proyectos. ¡Incluso este!: <b>https://github.com/mauroemartinez/</b><br><br>\n",
    "         🤖<i> Este es un mail automático creado por <b>Mauro E. Martinez</b>, cuya ejecución duró ⏰ {time.perf_counter() - comienzo:.2f} segundos. Escriba a martinezmauroezequiel@gmail.com ante cualquier duda, feedback, desuscripción u ofertas de colaboración.\n",
    "      </p>\n",
    "      </p>  \n",
    "   </body>\n",
    "</html>\"\"\"\n",
    "\n",
    "# Crear el objeto MIMEMultipart y asignarle los campos\n",
    "# A los receptores los ubico en Cco para que no se vean entre sí\n",
    "em = MIMEMultipart('related')\n",
    "em['From'] = email_sender\n",
    "em[\"To\"] = \"\"\n",
    "em['Cco'] = ', '.join(email_receiver)\n",
    "em[\"Subject\"] = f\"📈 Reporte automático de tipos de cambio  💱 - {dt.datetime.today():%d-%m-%Y} 📧\"\n",
    "\n",
    "# Crear el cuerpo del mensaje como un objeto MIMEText y adjuntarlo al objeto MIMEMultipart\n",
    "body = MIMEText(html, 'html')\n",
    "em.attach(body)\n",
    "\n",
    "# Crear una lista con los nombres de los archivos a adjuntar\n",
    "archivos_adjuntos = [graficos_jpg, inflacion_jpg, variaciones_jpg]\n",
    "# Iterar a través de la lista y adjuntar cada uno\n",
    "for archivo in archivos_adjuntos:\n",
    "    with open(archivo, 'rb') as fp:\n",
    "        msgImage = MIMEImage(fp.read())\n",
    "    # Definir ID de la imagen, tal como en el html, adjuntar\n",
    "    msgImage.add_header('Content-ID', f'<image{archivos_adjuntos.index(archivo) + 1}>') # define los nombres de la imagen agregando 1\n",
    "    msgImage.add_header('Content-Disposition', 'attachment', filename=archivo)\n",
    "    em.attach(msgImage)\n",
    "\n",
    "# # Adjuntar .csv\n",
    "# bd_filename = 'Seguimiento Dólar Blue.csv'\n",
    "# with open(bd_filename, 'r') as fp:\n",
    "#     bd_data = fp.read()\n",
    "# bd = MIMEText(bd_data, 'csv')\n",
    "# bd.add_header('Content-Disposition', 'attachment', filename=bd_filename)\n",
    "# em.attach(bd)\n",
    "\n",
    "# Crear un contexto SSL y establecer una conexión SMTP segura con el servidor de correo\n",
    "context = ssl.create_default_context()\n",
    "with smtplib.SMTP_SSL(\n",
    "    \"smtp.gmail.com\",\n",
    "    465,\n",
    "    context=context) as smtp:\n",
    "    smtp.login(\n",
    "        email_sender,\n",
    "        email_password\n",
    "        )\n",
    "    smtp.sendmail(\n",
    "        email_sender,\n",
    "        email_receiver,\n",
    "        em.as_string()\n",
    "        )\n",
    "\n",
    "# # para outlook\n",
    "# context = ssl.create_default_context()\n",
    "# with smtplib.SMTP(\"smtp-mail.outlook.com\", 587) as server:\n",
    "#     server.ehlo()\n",
    "#     server.starttls(context=context)\n",
    "#     server.ehlo()\n",
    "#     server.login(email_sender, email_password)\n",
    "#     server.sendmail(email_sender, email_receiver, em.as_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "La duración de la ejecución total fue de: 135.18 segundos\n"
     ]
    }
   ],
   "source": [
    "# Autoenvío confidencial con .csv\n",
    "email_receiver = [\n",
    "    \"martinezmauroezequiel@gmail.com\",\n",
    "    \"garciiamaximo@gmail.com\",\n",
    "    \"bibybsa@gmail.com\"\n",
    "]\n",
    "\n",
    "# Cuerpo del texto con código html\n",
    "html = f\"\"\"<!DOCTYPE html>\n",
    "<html lang=\"es\">\n",
    "   <head>\n",
    "      <meta charset=\"UTF-8\">\n",
    "      <meta http-equiv=\"X-UA-Compatible\" content=\"IE=edge\">\n",
    "      <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n",
    "   </head>\n",
    "   <body>\n",
    "      <p> 📅 <b><u>Resumen diario: </u></b> ℹ️<br></p>\n",
    "      <p>\n",
    "         <b>Costo comprar USD 200 blue 💰:</b> $ {(float(df[\"TCV_Blue\"].iloc[0] * 200)):,.2f} <br>\n",
    "         <b>Costo comprar USD 200 oficiales con impuestos:</b> $ {(float(df[\"Solidario\"].iloc[0] * 200)):,.2f}<br>\n",
    "         <b>La diferencia es de:</b> $ {(float(df[\"TCV_Blue\"].iloc[0] * 200 - df[\"Solidario\"].iloc[0] * 200)):,.2f}<br><br>\n",
    "         <b>La brecha entre solidario y blue es:</b> {float((1 - df[\"Solidario\"].iloc[0] / df[\"TCV_Blue\"].iloc[0]) * 100):.2f}%<br>\n",
    "         <b>El dólar solidario varió:</b> {float(((df[\"Solidario\"].iloc[0] / df[\"Solidario\"].iloc[1] - 1) * 100)):.2f}%<br>\n",
    "         <b>El dólar blue varió 💵:</b> {float(((df[\"TCV_Blue\"].iloc[0] / df[\"TCV_Blue\"].iloc[1] - 1) * 100)):.2f}%<br>\n",
    "         <b>El dólar divisas varió:</b> {float(((df[\"TCV_Divisas\"].iloc[0] / df[\"TCV_Divisas\"].iloc[1] - 1) * 100)):.2f}%<br><br>\n",
    "         <b>El euro varió un 💶:</b> {float(((df[\"TCV_Euro\"].iloc[0] / df[\"TCV_Euro\"].iloc[1] - 1) * 100)):.2f}%<br>\n",
    "         <b>Entre euro blue y dólar blue hay una brecha de:</b> {(1-df[\"TCV_Blue\"].iloc[0]/df[\"TCV_Euro\"].iloc[0])*100:.2f}%<br><br>\n",
    "         <b>El valor del dólar oficial futuro según paridad de tasas de interés es:</b> $ {fwd_oficial:,.2f}<br>\n",
    "         <b>El valor del dólar blue futuro según paridad de tasas de interés es 💸:</b> ${fwd_blue:,.2f}<br>\n",
    "         <b>El riesgo país hoy es de ☣️:</b> {(float(df[\"riesgo_pais\"].iloc[0])):,.0f}, es decir que Argentina debe pagar un {(float(df[\"riesgo_pais\"].iloc[0] / 100)):.2f}% de sobretasa respecto a un bono del tesoro estadounidense.\n",
    "      </p>\n",
    "      <img src='cid:image1'>\n",
    "      <img src='cid:image2'>\n",
    "      <img src='cid:image3'>\n",
    "      <p>Las siguientes muestran las últimas {cotizaciones_a_mostrar} cotizaciones de dólares, euro, riesgo país y TEA del BCRA 🇦🇷 y TEA de la FED 🇺🇸.<br>\n",
    "      Analizalas mientras te cebás unos mates 🧉:<br>\n",
    "      </p>\n",
    "      {tabla_cotizaciones}<br>\n",
    "      {tabla_variaciones}<br>\n",
    "      <p>La siguiente tabla muestra las últimas 12 publicaciones de la inflación, tené en cuenta que la interanual es de {inflacion_interanual}:<br>\n",
    "      </p>\n",
    "      {tabla_inflacion}<br>\n",
    "      <p><b><u>📚Las fuentes consultadas fueron:</u>\n",
    "      <b><br>Cotizaciones oficiales: </b>{web_bna}\n",
    "      <b><br>Cotizaciones paralelas: </b>{web_dolarhoy}\n",
    "      <b><br>Cotización Dólar MEP: </b>{web_ambito_mep}\n",
    "      <b><br>Riesgo País: </b>{web_ambito_riesgo_pais}\n",
    "      <b><br>Cotización Euro Blue: </b>{web_perfil}\n",
    "      <b><br>Tasa Efectiva Anual: </b>{web_bcra}\n",
    "      <b><br>Tasa Efectiva Anual EEUU: </b>{web_fed}\n",
    "      <b><br>Inflación Mensual: </b>{bcra_api_url}<br>\n",
    "      </p>\n",
    "      <p>\n",
    "         🤓 Muchas gracias por leer,<br>\n",
    "         🤑 Ojalá te sirva,<br><br>\n",
    "      <p> \n",
    "         👨🏽‍💼 En Linkedin encontrá mi historial laboral, académico y mis aportes en datos y comercio internacional: <b>https://linkedin.com/in/mauroemartinez/</b> <br>\n",
    "         👨🏽‍💻 Encontrá en GitHub mis proyectos. ¡Incluso este!: <b>https://github.com/mauroemartinez/</b><br><br>\n",
    "         🤖<i> Este es un mail automático creado por <b>Mauro E. Martinez</b>, cuya ejecución duró ⏰ {time.perf_counter() - comienzo:.2f} segundos. Escriba a martinezmauroezequiel@gmail.com ante cualquier duda, feedback, desuscripción u ofertas de colaboración.\n",
    "      </p>\n",
    "      </p>  \n",
    "   </body>\n",
    "</html>\"\"\"\n",
    "\n",
    "# No repetimos todo lo anterior, aprovechamos las variables\n",
    "em = MIMEMultipart('related')\n",
    "em[\"To\"] = ', '.join(email_receiver)\n",
    "em['Cco'] = \"\"\n",
    "em[\"Subject\"] = f\"📈 Reporte automático de tipos de cambio  💱 - {dt.datetime.today():%d-%m-%Y} 📧\"\n",
    "\n",
    "# Crear el cuerpo del mensaje como un objeto MIMEText y adjuntarlo al objeto MIMEMultipart\n",
    "body = MIMEText(html, 'html')\n",
    "em.attach(body)\n",
    "\n",
    "# Crear una lista con los nombres de los archivos a adjuntar\n",
    "archivos_adjuntos = [graficos_jpg, inflacion_jpg, variaciones_jpg]\n",
    "# Iterar a través de la lista y adjuntar cada uno\n",
    "for archivo in archivos_adjuntos:\n",
    "    with open(archivo, 'rb') as fp:\n",
    "        msgImage = MIMEImage(fp.read())\n",
    "    # Definir ID de la imagen, tal como en el html, adjuntar\n",
    "    msgImage.add_header('Content-ID', f'<image{archivos_adjuntos.index(archivo) + 1}>')\n",
    "    msgImage.add_header('Content-Disposition', 'attachment', filename=archivo)\n",
    "    em.attach(msgImage)\n",
    "\n",
    "# Adjuntar .csv\n",
    "bd_filename = 'Seguimiento Dólar Blue.csv'\n",
    "with open(bd_filename, 'r') as fp:\n",
    "    bd_data = fp.read()\n",
    "bd = MIMEText(bd_data, 'csv')\n",
    "bd.add_header('Content-Disposition', 'attachment', filename=bd_filename)\n",
    "em.attach(bd)\n",
    "\n",
    "# Crear un contexto SSL y establecer una conexión SMTP segura con el servidor de correo\n",
    "context = ssl.create_default_context()\n",
    "with smtplib.SMTP_SSL(\n",
    "    \"smtp.gmail.com\",\n",
    "    465,\n",
    "    context=context) as smtp:\n",
    "    smtp.login(\n",
    "        email_sender,\n",
    "        email_password\n",
    "        )\n",
    "    smtp.sendmail(\n",
    "        email_sender,\n",
    "        email_receiver,\n",
    "        em.as_string()\n",
    "        )\n",
    "\n",
    "# # para outlook\n",
    "# context = ssl.create_default_context()\n",
    "# with smtplib.SMTP(\"smtp-mail.outlook.com\", 587) as server:\n",
    "#     server.ehlo()\n",
    "#     server.starttls(context=context)\n",
    "#     server.ehlo()\n",
    "#     server.login(email_sender, email_password)\n",
    "#     server.sendmail(email_sender, email_receiver, em.as_string())\n",
    "\n",
    "print(f\"La duración de la ejecución total fue de: {time.perf_counter() - comienzo:.2f} segundos\")"
   ]
  }
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