Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Important Notes\n",
    "\n",
    "* Notebook was redone to model on a larger dataset than the initial one on which this project was performed\n",
    "* Changes still need to be made to allow certain algorithms to train and run in a feasible amount of time, given the newly large amount of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import f1_score\n",
    "# from sklearn.metrics import confusion_matrix\n",
    "from sklearn.dummy import DummyClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import and Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/wasilaq/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/numpy/lib/arraysetops.py:580: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n",
      "  mask |= (ar1 == a)\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('final.csv', index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['target_delay'] = df['arr_delay'].apply(lambda x: 1 if x > 15 else 0)\n",
    "df = df[df['dest_weather_severity'] != 'UNK']\n",
    "df = df[df['origin_weather_severity'] != 'UNK']\n",
    "df.drop(columns = ['canceled', 'num_of_flights'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['day_of_week', 'flight_date', 'reporting_airline', 'name',\n",
       "       'tail_number', 'origin', 'origin_city', 'origin_statecode', 'dest',\n",
       "       'dest_city', 'dest_statecode', 'dep_delay', 'arr_delay', 'diverted',\n",
       "       'set_elapsed_time', 'actual_elapsed_time', 'miles', 'diverted_landings',\n",
       "       'origin_timezone', 'dest_timezone', 'arrtime', 'deptime', 'set_arrtime',\n",
       "       'set_deptime', 'dep', 'dep_utc', 'arr_utc', 'origin_weather_type',\n",
       "       'origin_weather_severity', 'dest_weather_type', 'dest_weather_severity',\n",
       "       'target_delay'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>day_of_week</th>\n",
       "      <th>flight_date</th>\n",
       "      <th>reporting_airline</th>\n",
       "      <th>name</th>\n",
       "      <th>tail_number</th>\n",
       "      <th>origin</th>\n",
       "      <th>origin_city</th>\n",
       "      <th>origin_statecode</th>\n",
       "      <th>dest</th>\n",
       "      <th>dest_city</th>\n",
       "      <th>...</th>\n",
       "      <th>set_arrtime</th>\n",
       "      <th>set_deptime</th>\n",
       "      <th>dep</th>\n",
       "      <th>dep_utc</th>\n",
       "      <th>arr_utc</th>\n",
       "      <th>origin_weather_type</th>\n",
       "      <th>origin_weather_severity</th>\n",
       "      <th>dest_weather_type</th>\n",
       "      <th>dest_weather_severity</th>\n",
       "      <th>target_delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>NK</td>\n",
       "      <td>Spirit Air Lines</td>\n",
       "      <td>N605NK</td>\n",
       "      <td>BWI</td>\n",
       "      <td>Baltimore, MD</td>\n",
       "      <td>MD</td>\n",
       "      <td>MCO</td>\n",
       "      <td>Orlando, FL</td>\n",
       "      <td>...</td>\n",
       "      <td>12:12:00</td>\n",
       "      <td>10:10:00</td>\n",
       "      <td>2019-04-01 10:10:00</td>\n",
       "      <td>2019-04-01 15:10:00</td>\n",
       "      <td>2019-04-01 17:35:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-04-08</td>\n",
       "      <td>NK</td>\n",
       "      <td>Spirit Air Lines</td>\n",
       "      <td>N904NK</td>\n",
       "      <td>BWI</td>\n",
       "      <td>Baltimore, MD</td>\n",
       "      <td>MD</td>\n",
       "      <td>MCO</td>\n",
       "      <td>Orlando, FL</td>\n",
       "      <td>...</td>\n",
       "      <td>12:12:00</td>\n",
       "      <td>10:10:00</td>\n",
       "      <td>2019-04-08 10:10:00</td>\n",
       "      <td>2019-04-08 15:10:00</td>\n",
       "      <td>2019-04-08 17:35:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-04-15</td>\n",
       "      <td>NK</td>\n",
       "      <td>Spirit Air Lines</td>\n",
       "      <td>N637NK</td>\n",
       "      <td>BWI</td>\n",
       "      <td>Baltimore, MD</td>\n",
       "      <td>MD</td>\n",
       "      <td>MCO</td>\n",
       "      <td>Orlando, FL</td>\n",
       "      <td>...</td>\n",
       "      <td>12:12:00</td>\n",
       "      <td>10:10:00</td>\n",
       "      <td>2019-04-15 10:10:00</td>\n",
       "      <td>2019-04-15 15:10:00</td>\n",
       "      <td>2019-04-15 17:35:00</td>\n",
       "      <td>Storm</td>\n",
       "      <td>Severe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-04-22</td>\n",
       "      <td>NK</td>\n",
       "      <td>Spirit Air Lines</td>\n",
       "      <td>N619NK</td>\n",
       "      <td>BWI</td>\n",
       "      <td>Baltimore, MD</td>\n",
       "      <td>MD</td>\n",
       "      <td>MCO</td>\n",
       "      <td>Orlando, FL</td>\n",
       "      <td>...</td>\n",
       "      <td>12:12:00</td>\n",
       "      <td>10:10:00</td>\n",
       "      <td>2019-04-22 11:11:00</td>\n",
       "      <td>2019-04-22 16:11:00</td>\n",
       "      <td>2019-04-22 18:36:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Monday</td>\n",
       "      <td>2019-04-29</td>\n",
       "      <td>NK</td>\n",
       "      <td>Spirit Air Lines</td>\n",
       "      <td>N644NK</td>\n",
       "      <td>BWI</td>\n",
       "      <td>Baltimore, MD</td>\n",
       "      <td>MD</td>\n",
       "      <td>MCO</td>\n",
       "      <td>Orlando, FL</td>\n",
       "      <td>...</td>\n",
       "      <td>12:12:00</td>\n",
       "      <td>10:10:00</td>\n",
       "      <td>2019-04-29 10:10:00</td>\n",
       "      <td>2019-04-29 15:10:00</td>\n",
       "      <td>2019-04-29 17:35:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  day_of_week flight_date reporting_airline              name tail_number  \\\n",
       "0      Monday  2019-04-01                NK  Spirit Air Lines      N605NK   \n",
       "1      Monday  2019-04-08                NK  Spirit Air Lines      N904NK   \n",
       "2      Monday  2019-04-15                NK  Spirit Air Lines      N637NK   \n",
       "3      Monday  2019-04-22                NK  Spirit Air Lines      N619NK   \n",
       "4      Monday  2019-04-29                NK  Spirit Air Lines      N644NK   \n",
       "\n",
       "  origin    origin_city origin_statecode dest    dest_city  ... set_arrtime  \\\n",
       "0    BWI  Baltimore, MD               MD  MCO  Orlando, FL  ...    12:12:00   \n",
       "1    BWI  Baltimore, MD               MD  MCO  Orlando, FL  ...    12:12:00   \n",
       "2    BWI  Baltimore, MD               MD  MCO  Orlando, FL  ...    12:12:00   \n",
       "3    BWI  Baltimore, MD               MD  MCO  Orlando, FL  ...    12:12:00   \n",
       "4    BWI  Baltimore, MD               MD  MCO  Orlando, FL  ...    12:12:00   \n",
       "\n",
       "   set_deptime                  dep              dep_utc              arr_utc  \\\n",
       "0     10:10:00  2019-04-01 10:10:00  2019-04-01 15:10:00  2019-04-01 17:35:00   \n",
       "1     10:10:00  2019-04-08 10:10:00  2019-04-08 15:10:00  2019-04-08 17:35:00   \n",
       "2     10:10:00  2019-04-15 10:10:00  2019-04-15 15:10:00  2019-04-15 17:35:00   \n",
       "3     10:10:00  2019-04-22 11:11:00  2019-04-22 16:11:00  2019-04-22 18:36:00   \n",
       "4     10:10:00  2019-04-29 10:10:00  2019-04-29 15:10:00  2019-04-29 17:35:00   \n",
       "\n",
       "   origin_weather_type  origin_weather_severity  dest_weather_type  \\\n",
       "0                  NaN                      NaN                NaN   \n",
       "1                  NaN                      NaN                NaN   \n",
       "2                Storm                   Severe                NaN   \n",
       "3                  NaN                      NaN                NaN   \n",
       "4                  NaN                      NaN                NaN   \n",
       "\n",
       "  dest_weather_severity target_delay  \n",
       "0                   NaN            1  \n",
       "1                   NaN            0  \n",
       "2                   NaN            0  \n",
       "3                   NaN            1  \n",
       "4                   NaN            0  \n",
       "\n",
       "[5 rows x 32 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Origin Weather')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "precipitation_del_d = pd.crosstab(df['dest_weather_type'], df['target_delay'].astype(bool))\n",
    "precipitation_del_d.div(precipitation_del_d.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
    "plt.title(\"Destination Weather\")\n",
    "\n",
    "precipitation_del_o = pd.crosstab(df['origin_weather_type'], df['target_delay'].astype(bool))\n",
    "precipitation_del_o.div(precipitation_del_o.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)\n",
    "plt.title(\"Origin Weather\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='day_of_week'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dayofwk_del = pd.crosstab(df['day_of_week'], df['target_delay'].astype(bool))\n",
    "dayofwk_del.div(dayofwk_del.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='dest_statecode'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "deststate_del = pd.crosstab(df['dest_statecode'],df['target_delay'].astype(bool))\n",
    "deststate_del.div(deststate_del.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True, figsize=(15,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='origin_statecode'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "deststate_del = pd.crosstab(df['origin_statecode'], df['target_delay'].astype(bool))\n",
    "deststate_del.div(deststate_del.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True, figsize=(15,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='reporting_airline'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "op_airline_del = pd.crosstab(df['reporting_airline'], df['target_delay'].astype(bool))\n",
    "op_airline_del.div(op_airline_del.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True, figsize=(15,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = [\n",
    "    'day_of_week', 'reporting_airline', 'origin_statecode', 'dest_statecode', 'origin_weather_type', 'origin_weather_severity', 'dest_weather_type', 'dest_weather_severity'\n",
    "]\n",
    "\n",
    "data_types = dict()\n",
    "\n",
    "for x in categorical:\n",
    "    data_types[x] = 'category' \n",
    "\n",
    "data_types['miles'] = 'uint16'\n",
    "data_types['target_delay'] = 'uint8'\n",
    "    \n",
    "data = df.drop(\n",
    "    columns = [\n",
    "        'arrtime', 'set_arrtime', 'dep_utc', 'arr_utc', 'flight_date', 'name', 'tail_number', 'dep_delay', 'diverted', 'arr_delay', 'actual_elapsed_time', 'set_elapsed_time', 'origin', 'origin_city', 'dest', 'dest_city', 'deptime', 'set_deptime', 'dep', 'origin_timezone', 'dest_timezone', 'diverted_landings'\n",
    "    ]\n",
    ").astype(data_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['day_of_week', 'reporting_airline', 'origin_statecode',\n",
       "       'dest_statecode', 'miles', 'origin_weather_type',\n",
       "       'origin_weather_severity', 'dest_weather_type', 'dest_weather_severity',\n",
       "       'target_delay'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2151070 entries, 0 to 2155433\n",
      "Data columns (total 10 columns):\n",
      " #   Column                   Dtype   \n",
      "---  ------                   -----   \n",
      " 0   day_of_week              category\n",
      " 1   reporting_airline        category\n",
      " 2   origin_statecode         category\n",
      " 3   dest_statecode           category\n",
      " 4   miles                    uint16  \n",
      " 5   origin_weather_type      category\n",
      " 6   origin_weather_severity  category\n",
      " 7   dest_weather_type        category\n",
      " 8   dest_weather_severity    category\n",
      " 9   target_delay             uint8   \n",
      "dtypes: category(8), uint16(1), uint8(1)\n",
      "memory usage: 39.0 MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = pd.get_dummies(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# standardize miles so that its between 0 and 1 like all other features\n",
    "minimum = min(data2['miles'])\n",
    "maximum = max(data2['miles'])\n",
    "\n",
    "data2['miles'] = data2['miles'].apply(lambda x: (x - minimum)/(maximum - minimum))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drop dummy variables, but keep all variables for weather (null values already dropped)\n",
    "\n",
    "data2.drop(columns = ['day_of_week_Friday', 'reporting_airline_9E', 'origin_statecode_AL', 'dest_statecode_CA'], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['miles',\n",
       " 'target_delay',\n",
       " 'day_of_week_Monday',\n",
       " 'day_of_week_Saturday',\n",
       " 'day_of_week_Sunday',\n",
       " 'day_of_week_Thursday',\n",
       " 'day_of_week_Tuesday',\n",
       " 'day_of_week_Wednesday',\n",
       " 'reporting_airline_AA',\n",
       " 'reporting_airline_AS',\n",
       " 'reporting_airline_B6',\n",
       " 'reporting_airline_DL',\n",
       " 'reporting_airline_EV',\n",
       " 'reporting_airline_F9',\n",
       " 'reporting_airline_G4',\n",
       " 'reporting_airline_MQ',\n",
       " 'reporting_airline_NK',\n",
       " 'reporting_airline_OH',\n",
       " 'reporting_airline_OO',\n",
       " 'reporting_airline_UA',\n",
       " 'reporting_airline_WN',\n",
       " 'reporting_airline_YV',\n",
       " 'reporting_airline_YX',\n",
       " 'origin_statecode_AR',\n",
       " 'origin_statecode_AZ',\n",
       " 'origin_statecode_CA',\n",
       " 'origin_statecode_CO',\n",
       " 'origin_statecode_CT',\n",
       " 'origin_statecode_FL',\n",
       " 'origin_statecode_GA',\n",
       " 'origin_statecode_IA',\n",
       " 'origin_statecode_ID',\n",
       " 'origin_statecode_IL',\n",
       " 'origin_statecode_IN',\n",
       " 'origin_statecode_KS',\n",
       " 'origin_statecode_KY',\n",
       " 'origin_statecode_LA',\n",
       " 'origin_statecode_MA',\n",
       " 'origin_statecode_MD',\n",
       " 'origin_statecode_ME',\n",
       " 'origin_statecode_MI',\n",
       " 'origin_statecode_MN',\n",
       " 'origin_statecode_MO',\n",
       " 'origin_statecode_MS',\n",
       " 'origin_statecode_MT',\n",
       " 'origin_statecode_NC',\n",
       " 'origin_statecode_ND',\n",
       " 'origin_statecode_NE',\n",
       " 'origin_statecode_NH',\n",
       " 'origin_statecode_NJ',\n",
       " 'origin_statecode_NM',\n",
       " 'origin_statecode_NV',\n",
       " 'origin_statecode_NY',\n",
       " 'origin_statecode_OH',\n",
       " 'origin_statecode_OK',\n",
       " 'origin_statecode_OR',\n",
       " 'origin_statecode_PA',\n",
       " 'origin_statecode_RI',\n",
       " 'origin_statecode_SC',\n",
       " 'origin_statecode_SD',\n",
       " 'origin_statecode_TN',\n",
       " 'origin_statecode_TX',\n",
       " 'origin_statecode_UT',\n",
       " 'origin_statecode_VA',\n",
       " 'origin_statecode_VT',\n",
       " 'origin_statecode_WA',\n",
       " 'origin_statecode_WI',\n",
       " 'origin_statecode_WV',\n",
       " 'origin_statecode_WY',\n",
       " 'dest_statecode_CO',\n",
       " 'dest_statecode_FL',\n",
       " 'dest_statecode_GA',\n",
       " 'dest_statecode_IL',\n",
       " 'dest_statecode_NV',\n",
       " 'dest_statecode_NY',\n",
       " 'dest_statecode_TX',\n",
       " 'dest_statecode_WA',\n",
       " 'origin_weather_type_Cold',\n",
       " 'origin_weather_type_Fog',\n",
       " 'origin_weather_type_Hail',\n",
       " 'origin_weather_type_Rain',\n",
       " 'origin_weather_type_Snow',\n",
       " 'origin_weather_type_Storm',\n",
       " 'origin_weather_severity_Heavy',\n",
       " 'origin_weather_severity_Light',\n",
       " 'origin_weather_severity_Moderate',\n",
       " 'origin_weather_severity_Other',\n",
       " 'origin_weather_severity_Severe',\n",
       " 'dest_weather_type_Cold',\n",
       " 'dest_weather_type_Fog',\n",
       " 'dest_weather_type_Hail',\n",
       " 'dest_weather_type_Rain',\n",
       " 'dest_weather_type_Snow',\n",
       " 'dest_weather_type_Storm',\n",
       " 'dest_weather_severity_Heavy',\n",
       " 'dest_weather_severity_Light',\n",
       " 'dest_weather_severity_Moderate',\n",
       " 'dest_weather_severity_Other',\n",
       " 'dest_weather_severity_Severe']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(data2.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = data2.corr()\n",
    "mask = np.triu(np.ones_like(corr, dtype=np.bool))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dest_statecode_GA                  -0.064448\n",
       "reporting_airline_DL               -0.062796\n",
       "day_of_week_Tuesday                -0.015319\n",
       "reporting_airline_WN               -0.014066\n",
       "day_of_week_Saturday               -0.012873\n",
       "origin_statecode_SC                -0.011815\n",
       "origin_statecode_VA                -0.010443\n",
       "origin_statecode_FL                -0.010156\n",
       "origin_statecode_OH                -0.008953\n",
       "origin_statecode_UT                -0.007631\n",
       "dest_statecode_NV                  -0.007340\n",
       "origin_statecode_OK                -0.006196\n",
       "origin_statecode_TN                -0.006153\n",
       "origin_statecode_AZ                -0.005841\n",
       "origin_statecode_OR                -0.005476\n",
       "origin_statecode_GA                -0.004825\n",
       "origin_statecode_NM                -0.004301\n",
       "origin_statecode_NE                -0.004035\n",
       "reporting_airline_YX               -0.004019\n",
       "origin_statecode_IN                -0.004008\n",
       "origin_statecode_LA                -0.003399\n",
       "origin_statecode_WI                -0.003277\n",
       "origin_statecode_MS                -0.003237\n",
       "origin_statecode_CT                -0.003222\n",
       "origin_statecode_CA                -0.003156\n",
       "origin_statecode_AR                -0.002502\n",
       "origin_statecode_ID                -0.002397\n",
       "origin_statecode_KY                -0.002145\n",
       "origin_statecode_RI                -0.001691\n",
       "day_of_week_Wednesday              -0.001650\n",
       "origin_statecode_MO                -0.001572\n",
       "origin_statecode_MN                -0.001566\n",
       "origin_statecode_MI                -0.001413\n",
       "origin_statecode_NH                -0.001204\n",
       "origin_statecode_MD                -0.001098\n",
       "reporting_airline_OH               -0.000714\n",
       "origin_statecode_KS                -0.000671\n",
       "origin_statecode_WA                -0.000512\n",
       "origin_statecode_NV                -0.000246\n",
       "origin_weather_type_Fog            -0.000182\n",
       "origin_statecode_NC                 0.000748\n",
       "reporting_airline_NK                0.001030\n",
       "dest_statecode_CO                   0.001099\n",
       "origin_statecode_MT                 0.001220\n",
       "dest_statecode_WA                   0.001815\n",
       "origin_statecode_PA                 0.001848\n",
       "origin_statecode_WV                 0.002129\n",
       "origin_statecode_ME                 0.002267\n",
       "origin_statecode_TX                 0.002724\n",
       "reporting_airline_G4                0.002734\n",
       "origin_statecode_VT                 0.002781\n",
       "origin_weather_type_Cold            0.002794\n",
       "dest_statecode_FL                   0.002953\n",
       "origin_statecode_IA                 0.003148\n",
       "dest_statecode_NY                   0.003240\n",
       "origin_statecode_WY                 0.003366\n",
       "dest_weather_type_Hail              0.003614\n",
       "dest_weather_severity_Other         0.003614\n",
       "day_of_week_Sunday                  0.004486\n",
       "origin_statecode_ND                 0.004687\n",
       "dest_weather_type_Fog               0.004690\n",
       "origin_statecode_SD                 0.004874\n",
       "dest_weather_type_Cold              0.005532\n",
       "dest_weather_type_Storm             0.006417\n",
       "reporting_airline_MQ                0.006662\n",
       "origin_weather_type_Storm           0.006785\n",
       "dest_statecode_TX                   0.007258\n",
       "origin_weather_severity_Severe      0.007298\n",
       "day_of_week_Monday                  0.007565\n",
       "reporting_airline_YV                0.007716\n",
       "origin_statecode_MA                 0.008951\n",
       "reporting_airline_UA                0.010027\n",
       "origin_weather_severity_Other       0.010269\n",
       "origin_weather_type_Hail            0.010269\n",
       "reporting_airline_AS                0.011735\n",
       "origin_statecode_NY                 0.012254\n",
       "reporting_airline_EV                0.013853\n",
       "dest_weather_severity_Severe        0.013980\n",
       "reporting_airline_OO                0.014638\n",
       "day_of_week_Thursday                0.015893\n",
       "origin_weather_severity_Moderate    0.019762\n",
       "origin_statecode_CO                 0.020003\n",
       "reporting_airline_B6                0.020862\n",
       "miles                               0.021505\n",
       "origin_statecode_IL                 0.022997\n",
       "dest_weather_severity_Moderate      0.023269\n",
       "dest_weather_severity_Heavy         0.023886\n",
       "origin_statecode_NJ                 0.024062\n",
       "reporting_airline_AA                0.024316\n",
       "origin_weather_severity_Heavy       0.027445\n",
       "dest_statecode_IL                   0.031786\n",
       "reporting_airline_F9                0.031790\n",
       "origin_weather_type_Rain            0.056694\n",
       "dest_weather_type_Snow              0.058882\n",
       "dest_weather_type_Rain              0.078108\n",
       "origin_weather_severity_Light       0.078406\n",
       "dest_weather_severity_Light         0.084098\n",
       "origin_weather_type_Snow            0.090317\n",
       "target_delay                        1.000000\n",
       "Name: target_delay, dtype: float64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr['target_delay'].sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AYyCalZWFRqOhsrISHx8fGhoaUCgUjBkzhiVLlvDss89ibm5+zXsDjclsDLb/nszGENevNIbbJZGNEEIIIYQQN+2BCBb9/PyYN28efn5+uLi4YG5uzogRI5g0aRKtW7fGzs6O0tJSoPHk8eTJk0yaNMnknNOmTWPBggV8++23VFVVERkZqTvxu5ann36a4uJilEol/fv35/jx41hbW9O9e3f++te/4ufnR3V1Nb169aJjx46EhIQQERGBVqulqqqKsLD/BlRWVlYsWrSIkJAQNBoNFhYWeuv5+/sTExPD2LFjUalUKJVK3n//fQBee+01pk6dSufOnWnbtq3Jfc+fP5+QkBA++eQTbG1tsbS0pEuXLrRu3Vr3eG+HDh1099LHx4chQ4bw5ZdfXtd9ARPZUCNj9NvfCQEg/3/G6fW5frPJZDZUYxlUhRBCCCGEEI0UDVc/eyior6/Hz8+PTz75BJVKdbe386d29uxZ5s2bR0pKynWPkWBRCCGEEEKIe8MDcbJ4vYqKiggKCsLHx0cXKAYFBVFeXt7sOpVKdc2yExEREeTn5+u1JyYm6t4BbGk3u9fbYfv27cTHxxMREXHH1xZCCCGEEELcOgkWr/Loo4/qPTKZkJBwU3PdjSDpZvd6O4wcOZKRI0fe7W0IIYQQQgghbtIDUTpDCCGEEEIIIcSNkWBRCCGEEEIIIYQeSXAjhBBCCCGEEELPff/OolarZdSoUezcufO2zP/dd98RGxvLyy+/TEBAwG1ZA6C4uJjg4GA2bNhwzWvnzp1LaWkpp06dwtzcnIcffhh3d3fCw8NbbD/5+flERESQmpraYnMCFC9cotfmsGQhRfMj9Noffa+xzVhm05vNhiqZUoUQQgghhHgAgsXbbefOncyfP5+hQ4fe7a3oLFu2DID4+Hjs7Ozw8zMcNAkhhBBCCCGEMfdlsHj58mXUajWXLl3C0dERgD179pCQkEBDQwOXL19m2bJl7NmzhxMnThASEkJdXR3PP/88GzduxNLSUm/OS5cu8fbbb1NRUUFdXR2zZ8/mypUrZGdnc+jQIWxsbOjbt6/eOB8fHxITE2nbti0DBgwgNTWVxx9/nBdeeIH09HTS09PZunUrCoUCT09PAgICKCkpITw8HK1Wi6WlJVFRUbr56urqmD9/Pl27dmXq1Kk3dF/8/f2JiIjA1dWVtLQ0fvvtN2bOnElqaqreHrZv305iYiJmZmY8/PDD/OMf/+C3335DrVbT0NBAhw4ddPN+8803rF+/ntraWhQKBQkJCSQnJ9OxY0cmTZpEeXk5r776KhkZGTe0XyGEEEIIIcTdc18muPn8889xd3dn/fr1+Pr6AvDrr78SGxtLamoqI0eO5JtvvsHLy4sdO3ZQV1dHTk4OAwYMMBgoAqxcuZJnnnmG9evX88EHHxAWFsbQoUMZNGgQb7/9tsFAEWDo0KHk5OTw888/4+DgwA8//MDx48dxcnKisLCQr776is8++4z169eTlZVFQUEBMTEx+Pv7k5qaSmBgIHFxcQDU1taiVqvp06fPDQeKxhw/ftzgHrZu3UpgYCBpaWk899xzVFRUsGrVKkaPHk1qairDhw/XzXHixAk+/vhj0tLScHNzY9euXYwfP54vvvgCgK1bt+Lt7d0i+xVCCCGEEELcGfflyeKJEyfw8PAAoHfv3piZmdGxY0feffddrK2tOXv2LP369UOlUtG/f3927dpFRkYGb7zxhtE58/PzdQFPx44dUalUnDt37pp7GTlyJKtWrcLe3p45c+aQmppKQ0MDI0eOJC8vj9OnT/PKK68AUF5ezsmTJ8nLy2P16tUkJSXR0NCAmVnj13Ts2DFUKhVXrly5xTsETXmNjO0hNDSU1atXs27dOlxcXBg+fDgnTpxgwoQJAPTr14+0tDQA/vKXvxASEsJDDz1EQUEBffr04dFHH+Whhx7i+PHjbNmyhY8++uiW9yyEEEIIIYS4c+7LYNHV1ZX9+/czfPhwjhw5Qm1tLeHh4Xz33XeoVCpCQkJ0wdKECRNITEzkwoULdO/e3eSc+/bt47HHHuPs2bNcunSJ9u3bX3Mv7u7uFBUVUVZWxty5c1m9ejU7duzg008/pbCwEDc3N5KSklAoFCQnJ9OtWzdcXFyYMmUK/fr1Iz8/n7179wLw+OOP8/HHHzN+/HgGDRpkcr+GWFhYUFZWhqurK0eOHKFjx464uLgY3EN6ejozZ87kL3/5C++88w7fffcdrq6u/Oc//6F79+4cPHgQgP/7v//jww8/5P/7//4/AF599dVm9/ajjz6iY8eO2NraXtceHZYsNNjelMzGEGPJZ1wz0254zLX6hBBCCCGEeFDcl8Gin58f8+bNw8/PDxcXF8zNzRkxYgSTJk2idevW2NnZUVpaCjSePJ48eZJJkyaZnHPatGksWLCAb7/9lqqqKiIjI3Unftfy9NNPU1xcjFKppH///hw/fhxra2u6d+/OX//6V/z8/KiurqZXr1507NiRkJAQIiIi0Gq1VFVVERYWppvLysqKRYsWERISgkajwcLC4rrvS0BAAIsXL6ZTp048/PDDAEb30KtXL6ZNm8ZDDz2EtbU1Q4YMwcPDg7fffpuvvvoKBwcHAFQqFf369WPixImYmZnRtm1b3b0dPnw4kZGRxMbGXvcehRBCCCGEEPeGB77OYn19PX5+fnzyySeoVKq7vZ37SmVlJS+//DIajQal8vpejzVWOsNYO9xk6QyvCfrt2zaYnk9KagghhBBCiAfIfXmyeL2KiooICgrCx8dHFygGBQVRXl7e7DqVSsXKlStNzhUREUF+fr5ee2JiIlZWVi236avc7F7vhH//+98sWrSIN99887oDRSGEEEIIIcS944EOFh999FG+/PLLZm0JCQk3NVdEREQL7OjG3Oxe74R+/fqxZcuWu70NIYQQQgghxE2SIx8hhBBCCCGEEHokWBRCCCGEEEIIoeeBT3AjhBBCCCGEEELfA/3O4p3w3Xff0atXL5RKJStWrLit7zZmZ2dTUlLCxIkTm7X7+/sTERHBgQMHaNeuHcOGDWuxNSMiIti/fz9ffPFFs3atVsvQoUN59dVXee211657vrOf6WcX7fjSeE4tidNr77xQDUBx+Lt6fQ5RYQbbTfU5RIWZns9ERlZTfUIIIYQQQvwZyWOot9natWupqKigQ4cOtz0JzuDBg/UCxav5+Pi0aKBYWVnJzz//jKurKz/99FOzvm+//RZPT082b95MfX19i60phBBCCCGEuDPkZBHIyMhg06ZN1NfX4+/vT0pKCkqlkieffBK1Wk18fDwFBQWcO3eOS5cusXDhQp566ikyMzNJSUnBwsICJycnIiMj2bJli26uadOmcfToUUJCQoiNjSUkJIQNGzbg7e3N008/zbFjx1AoFHz00UeoVCoWL17MoUOHsLOz49SpU6xcuRIHBweDe163bh3bt2+nsrISGxsbEhIS2Lp1KwUFBfj6+jJjxgzat2/P4MGDdWPi4+Oxs7PDxcWFxMREzM3NKS4uxtPTkxkzZlBSUkJ4eDharRZLS0uioqKwt7c3et++/vpr/vrXvzJ48GDWr1/PgAEDdH0ajYawsDDOnz/P//7v//Lcc8+13BcmhBBCCCGEuO3kZPF3bdu2ZeXKlSQkJJCcnExaWhpnz55l9+7dAFhZWbF27VpiY2OJjIzkwoULxMfHk5KSQlpaGm3atCE9PV03V1paGkOGDKFHjx7ExMRgbm6uW+vy5ct4eXmxbt06Hn74YbKzs9mxYwcXL15k48aNREdHU1JSYnSv9fX1XLx4keTkZDQaDXV1dRw8eLDZNWVlZXzyySe8/vrrBuc4ffo08fHxpKenk5SUBEBMTAz+/v6kpqYSGBhIXJz+o59X02g0jB8/nmeeeYYjR45w9uxZAE6cOEFlZSXdu3dn3LhxrF+//hp3XwghhBBCCHGvkZPF3zk7O1NYWMj58+eZOnUq0BjUFRYWAjBw4EAAunbtym+//UZRURFubm6oVCoA+vfvz65du+jduzfOzs7XXO+xxx4DwN7eHq1Wy6lTp+jTpw8Atra2uLi4GB2rVCoxNzcnODgYa2trzpw5Q21tbbNrHBwcsLCwMDqHu7s7ZmZmmJmZYWVlBUBeXh6rV68mKSmJhoYGzMyM/3rk5+fz66+/8t577wGgUChIS0vjrbfeQqPRUFlZSWBgIAD//ve/OXnyJF26dLnmfRFCCCGEEELcGyRY/J1SqcTBwQF7e3vWrFmDubk5GRkZ9OjRg6ysLA4fPszYsWPJy8ujY8eOODg4kJ+fz5UrV7C2tmbPnj26IFGp/O+BrUKhwFDCWYVC0eznrl278uWXXwJQXl7OiRMnjO41NzeXrKwsXVDm4+Ojt8bVezDkj+sDuLi4MGXKFPr160d+fj579+41Ol6j0TBnzhwmTZoENJ5UTpw4kTfeeIOvvvqKzZs30759ewBWrlzJZ599RmhoqMk9QWMyG0OaktkY0pSY5nrbb7bPVMIaSWYjhBBCCCHuNxIsXsXW1pZXXnkFf39/6urq6Ny5M6NGjQLg6NGjTJ48mcrKSqKiorC1tWXmzJkEBASgVCpxdHRErVazbdu2ZnP27duXefPmERUVZXLtIUOGkJ2dja+vL3Z2dlhZWTV7dPVqXbp0oXXr1vj6+gLQoUMHSktLb/nzh4SEEBERgVarpaqqirAww0FTdXU1W7duJTMzU9fWqVMnunfvzrfffsvjjz+uCxShMbHO2LFjeeutt2jduvUt71MIIYQQQghx+0mdxevQlBjGz8/vtq2Rn59Pbm4uXl5eXLhwgdGjR/P999+bfJT0fmSsdEZxxHt67Q4R8wHjZSuKF0UbXMNh8YKbK51haoyRPZwImG5wD05rVxlsF0IIIYQQ4l4hJ4v3CHt7e+Li4khJSaGurg61Wk1OTg7Jycl61wYEBDBixIg7sq+goCDKy8ubtalUKlauXHlH1hdCCCGEEELcHRIsXoeZM2fe9jWsra0NBmAtWRfxZiQkJNzV9YUQQgghhBB3h5TOEEIIIYQQQgihR4JFIYQQQgghhBB6JMGNEEIIIYQQQgg98s7iHfDdd9/Rq1cvlEolK1asICIi4ratlZ2dTUlJCRMnTmzW7u/vT0REBAcOHKBdu3Yt8i5kRkYGH374IY8++ih1dXUolUpiYmLo3LkzJ0+eZNGiRdTU1GBhYcHy5cuxsbG55pylm7fotT38gjfFUe/rtTuEzwNuLHupqb7bkg31NcPvuzolxVM4e75eu+MH+llfhRBCCCGEuBvkMdQ7YO3atVRUVNChQ4fbGigCDB48WC9QvJqPj0+LJs0ZPXo0qampfPbZZ3h7e/PJJ58AEB4ezltvvcX69evx9fXlxIkTLbamEEIIIYQQ4vaTk8XfZWRksGnTJurr6/H39yclJQWlUsmTTz6JWq0mPj6egoICzp07x6VLl1i4cCFPPfUUmZmZpKSkYGFhgZOTE5GRkWzZskU317Rp0zh69CghISHExsYSEhLChg0b8Pb25umnn+bYsWMoFAo++ugjVCoVixcv5tChQ9jZ2XHq1ClWrlyJg4ODwT2vW7eO7du3U1lZiY2NDQkJCWzdupWCggJ8fX2ZMWMG7du3Z/DgwboxTTUjXVxcSExMxNzcnOLiYjw9PZkxYwYlJSWEh4ej1WqxtLQkKioKe3v767qH5eXl2NraUlVVxfnz5/n+++9ZtmwZPXv2RK1Wt8j3JIQQQgghhLgz5GTxKm3btmXlypUkJCSQnJxMWloaZ8+eZffu3QBYWVmxdu1aYmNjiYyM5MKFC8THx5OSkkJaWhpt2rQhPT1dN1daWhpDhgyhR48exMTEYG5urlvr8uXLeHl5sW7dOh5++GGys7PZsWMHFy9eZOPGjURHR1NSUmJ0r/X19Vy8eJHk5GQ0Gg11dXUcPHiw2TVlZWV88sknvP766wbnOH36NPHx8aSnp5OUlARATEwM/v7+pKamEhgYSFxcnMl7tnXrVvz9/fHx8eHjjz9m+PDhlJeX8+uvv/LXv/6VtWvXUl5ezubNm6/9BQghhBBCCCHuGXKyeBVnZ2cKCws5f/48U6dOBRqDusLCQgAGDhwIQNeuXfntt98oKirCzc0NlUoFQP/+/dm1axe9e/fG2dn5mus99thjANjb26PVajl16hR9+vQBwNbWFhcXF6NjlUol5ubmBAcHY21tzZkzZ6itrW12jYODAxYWFkbncHd3x8zMDDMzM6ysrADIy8tj9erVJCUl0dDQgJmZ6V+R0aNH604N//WvfzFz5ky2bNnCQw89pLtfzz33HLt37+bFF180fUOEEEIIIYQQ9wwJFq+iVCpxcHDA3t6eNWvWYG5uTkZGBj169CArK4vDhw8zduxY8vLy6NixIw4ODuTn53PlyhWsra3Zs2ePLkhUKv97aKtQKDCUdFahUDT7uWvXrnz55ZdA4yOdpt7zy83NJSsrC41GQ2VlJT4+PnprXL0HQ/64PoCLiwtTpkyhX79+5Ofns3fvXpNzXM3e3p6amhqsrKxwcnJi3759PPXUU+zdu5euXbte1xwPv+BtsL0pmY3Bvt+TzFxv+832mRyzZKHBdqekeKNjJJmNEEIIIYS4l0mw+Ae2tra88sor+Pv7U1dXR+fOnRk1ahQAR48eZfLkyVRWVhIVFYWtrS0zZ84kICAApVKJo6MjarWabdu2NZuzb9++zJs3j6ioKJNrDxkyhOzsbHx9fbGzs8PKyqrZo6tX69KlC61bt8bX1xeADh06UFpaesufPyQkhIiICLRaLVVVVYSFGQ+QoPEx1AMHDtCqVSsuX77M4sWLAYiOjmbx4sXU1dXh4OAg7ywKIYQQQgjxJyN1Fq9TU2IYPz+/27ZGfn4+ubm5eHl5ceHCBUaPHs33339v8lHS+01xZIxem8M7IZx8Uz/Y7LKi8X1KYyUtikIXG1zj0aWLjJa6MDWfydIZi6L1+xYv4NdBowzuoWvO1/y/l17Ta3f+rPHd0ROTZ+j1OaWsNDiXEEIIIYQQt4OcLN5D7O3tiYuLIyUlhbq6OtRqNTk5OSQnJ+tdGxAQwIgRI+7IvoKCgigvL2/WplKpWLlSghchhBBCCCHuVxIsXqeZMw0XV29J1tbWBgOwlqyLeDMSEhLu6vpCCCGEEEKIO09KZwghhBBCCCGE0CPBohBCCCGEEEIIPZLgRgghhBBCCCGEHjlZvM2+++47zp49S1lZGREREbd1rezsbNLT0/Xa/f39yc/PJyMjgx07drTIWhkZGQwZMgR/f3/dn6ioKH788UcmTZrU7Npz584xYsQI6uvrW2RtIYQQQgghxO0nCW5us7Vr1xIREYGrq+ttDxYHDx5sst/Hx6dF1xs9erRe/cSGhgYWLVpEUVERjz76KABffvklY8eORam89v9NFM6ap9fm+OH7RktqAEbLYBgqdQHXUQbjZkpnGOkrnLvQ4B4cly2haH6EXvuj7zW2GesrDF6gP9dy/bIdQgghhBBC3CoJFmk8Jdu0aRP19fX4+/uTkpKCUqnkySefRK1WEx8fT0FBAefOnePSpUssXLiQp556iszMTFJSUrCwsMDJyYnIyEi2bNmim2vatGkcPXqUkJAQYmNjCQkJYcOGDXh7e/P0009z7NgxFAoFH330ESqVisWLF3Po0CHs7Ow4deoUK1euxMHBweCe161bx/bt26msrMTGxoaEhAS2bt1KQUEBvr6+zJgxg/bt2zcLIJtqRbq4uJCYmIi5uTnFxcV4enoyY8YMSkpKCA8PR6vVYmlpSVRUFPb29jd0LxUKBePGjePLL78kKCgIgC+++ILExMSb/4KEEEIIIYQQd5wEi79r27YtS5cu5aWXXmLTpk20bt2at99+m927dwNgZWXF2rVr+fXXX5k7dy4pKSnEx8ezefNmVCoV0dHRpKenY21tTdu2bXUlMHr06EFERATm5ua6tS5fvoyXlxfh4eHMnTuX7OxsLC0tuXjxIhs3buT8+fOMHDnS6F7r6+u5ePEiycnJKJVKAgMDOXjwYLNrysrK2LRpExYWFmRnZ+vNcfr0aTIzM6murmbQoEHMmDGDmJgY/P398fDw4F//+hdxcXEsW7bM6D62bt3KgQMHdD+PGzeO559/Hh8fHyZPnkxQUBC//PILnTt3pmPHjtf3RQghhBBCCCHuCRIs/s7Z2ZnCwkLOnz/P1KlTgcagrrCwEICBAwcC0LVrV3777TeKiopwc3NDpVIB0L9/f3bt2kXv3r1xdna+5nqPPfYYAPb29mi1Wk6dOkWfPn0AsLW1xcXFxehYpVKJubk5wcHBWFtbc+bMGWpra5td4+DggIWFhdE53N3dMTMzw8zMDCsrKwDy8vJYvXo1SUlJNDQ0YGZm+tfD0GOogO708j//+Q+bN29m4sSJJucRQgghhBBC3HskWPydUqnEwcEBe3t71qxZg7m5ORkZGfTo0YOsrCwOHz7M2LFjycvLo2PHjjg4OJCfn8+VK1ewtrZmz549uiDx6nfzFAoFhhLOKhSKZj937dqVL7/8EoDy8nJOnDhhdK+5ublkZWWh0WiorKzEx8dHb41rvR/4x/UBXFxcmDJlCv369SM/P5+9e/eanMOU8ePH88UXX3DgwIHb/q6mEEIIIYQQouVJsHgVW1tbXnnlFfz9/amrq6Nz586MGjUKgKNHjzJ58mQqKyuJiorC1taWmTNnEhAQgFKpxNHREbVazbZt25rN2bdvX+bNm0dUVJTJtYcMGUJ2dja+vr7Y2dlhZWXV7NHVq3Xp0oXWrVvj6+sLQIcOHSgtLb3lzx8SEkJERARarZaqqirCwsJMXv/Hx1BVKpXu8du//e1vREVFXXdimyaOH75vsL0pmY3BviWGk8g0JZ9pqb6bGeO4TD/5TpOmZDY30ifJbIQQQgghxJ0idRavQ1NiGD8/v9u2Rn5+Prm5uXh5eXHhwgVGjx7N999/b/JR0vvRqZh/6rV1DnmLU9H67052XjAXMJG9dJHhwMph8YKby4ZqJOsqGM/IenL6HIN76LLqHxS+FarX7vjPpQAGs6g6LlvCiddn6bU7JX4IQFHoYr2+R5cuMri+EEIIIYQQ1yIni/cIe3t74uLiSElJoa6uDrVaTU5ODsnJyXrXBgQEMGLEiDuyr6CgIMrLy5u1XX2CKIQQQgghhLg/SbB4HWbOnHnb17C2tjYYgA0bNuy2r21KQkLCXV1fCCGEEEIIcXdc/8tkQgghhBBCCCEeGBIsCiGEEEIIIYTQIwluhBBCCCGEEELokZPF2+y7777j7NmzlJWV3fZ6g9nZ2aSnp+u1+/v7k5+fT0ZGBjt27Gix9Y4cOcLrr7+Or68vAQEBzJw5k7Nnzza7ZsuWLUycOLHF1hRCCCGEEELcGZLg5jZbu3YtERERuLq63vZgcfDgwSb7fXx8Wmyt0tJS1Go18fHxuLq6Ao2B8fvvv8+yZY1lLo4cOcLGjRu5kcNro2UrTJW6iHhPvy9ivsFSEtBYTsLYGJPzzXtHf673IwEoWhCp3xf9jsESGNBYBqNofoT+mN/rKxorg2GspAYYL98hJTWEEEIIIcTNkGARyMjIYNOmTdTX1+Pv709KSgpKpZInn3xSFxAVFBRw7tw5Ll26xMKFC3nqqafIzMwkJSUFCwsLnJyciIyMZMuWLbq5pk2bxtGjRwkJCSE2NpaQkBA2bNiAt7c3Tz/9NMeOHUOhUPDRRx+hUqlYvHgxhw4dws7OjlOnTrFy5UocHBwM7nndunVs376dyspKbGxsSEhIYOvWrRQUFODr68uMGTNo3759swCyqV6ki4sLiYmJmJubU1xcjKenJzNmzKCkpITw8HC0Wi2WlpZERUVhb29vcP0vvviC8ePH6wJFgBEjRjB8+HAALly4wPLly1mwYAHh4eEt+G0JIYQQQggh7gQJFn/Xtm1bli5dyksvvcSmTZto3bo1b7/9Nrt37wbAysqKtWvX8uuvvzJ37lxSUlKIj49n8+bNqFQqoqOjSU9Px9ramrZt2+rKYPTo0YOIiAjMzc11a12+fBkvLy/Cw8OZO3cu2dnZWFpacvHiRTZu3Mj58+cZOXKk0b3W19dz8eJFkpOTUSqVBAYGcvDgwWbXlJWVsWnTJiwsLMjOztab4/Tp02RmZlJdXc2gQYOYMWMGMTEx+Pv74+Hhwb/+9S/i4uJ0p4R/VFxcjIeHBwBVVVW8/vrrAJSUlPDtt98SFhZGaGgolpaWN/AtCCGEEEIIIe4VEiz+ztnZmcLCQs6fP8/UqVOBxqCusLAQgIEDBwLQtWtXfvvtN4qKinBzc0OlUgHQv39/du3aRe/evXF2dr7meo899hgA9vb2aLVaTp06RZ8+fQCwtbXFxcXF6FilUom5uTnBwcFYW1tz5swZamtrm13j4OCAhYWF0Tnc3d0xMzPDzMwMKysrAPLy8li9ejVJSUk0NDRgZmb818Pe3p7i4mKgMZBOTU0F4Nlnn+Xw4cOcPHmSiIgItFotx48f59133yUsLOya90UIIYQQQghxb5Bg8XdKpRIHBwfs7e1Zs2YN5ubmZGRk0KNHD7Kysjh8+DBjx44lLy+Pjh074uDgQH5+PleuXMHa2po9e/bogkSl8r95gxQKhcF39hQKRbOfu3btypdffglAeXk5J06cMLrX3NxcsrKy0Gg0VFZW4uPjo7fG1Xsw5I/rA7i4uDBlyhT69etHfn4+e/fuNTr++eef5/XXX2fw4MG6z33o0CGuXLlCr1692LZtG9B4AhkcHCyBohBCCCGEEH8yEixexdbWlldeeQV/f3/q6uro3Lkzo0aNAuDo0aNMnjyZyspKoqKisLW1ZebMmQQEBKBUKnF0dEStVuuCpCZ9+/Zl3rx5REVFmVx7yJAhZGdn4+vri52dHVZWVs0eXb1aly5daN26Nb6+vgB06NCB0tLSW/78ISEhutPAqqoqkwGevb09cXFxxMTEcPnyZbRaLSqVio8++uiW9tCUtOZ62+G/iWn+yFQSF2NjTM73vn4SG11ftH7yG/hv8hmDY35PZmOwz8jeTc3nsMRwMh1JZiOEEEIIIW6G1Fm8Dk2JYfz8/G7bGvn5+eTm5uLl5cWFCxcYPXo033//vclHSe9Hx4eN0Wtz25FJ8aJovXaHxQsAExlUDWQHhcagymR21ZvJyGqk7/+9PNXgHpzXfczJN9V67V1WxAFQOGueXp/jh+/z/3yn6M/1+RoAo9lVC98K1Z/rn0tNjhFCCCGEEEJOFu8RTSd1KSkp1NXVoVarycnJITk5We/agIAARowYcUf2FRQURHl5ebM2lUqlS+AjhBBCCCGEuD9JsHgdZs6cedvXsLa2NhiADRs27LavbUpCQsJdXV8IIYQQQghxd5jOgiKEEEIIIYQQ4oEkwaIQQgghhBBCCD2S4EYIIYQQQgghhB45WRRCCCGEEEIIoUcS3Nxm3333Hb169UKpVLJixQoiIiJu21rZ2dmUlJQwceLEZu3+/v5ERERw4MAB2rVr1yJJczIyMkhISCAzMxOVSgXAnDlz8PX1pXPnzgQHB7NhwwYA9u3bx4IFC/jwww/p3r27yXlPvf+BXlvnebNNl60wUCLDYclCiiPeM7iGQ8T8myudYWQdgKIF+jUYH41+hxOvGU6O5JQUT+Fs/XqOjh807rkweIF+3/JoTrz6hv5cnzbWtiwKXay/h6WLKJyrX3+xqV6jsTGAyf0JIYQQQoj7n5ws3mZr166loqKCDh063NZAEWDw4MF6geLVfHx8WjS7amVlJdHR+vUPr/bTTz/xzjvvsHr16msGikIIIYQQQoh7h5ws0nhKtmnTJurr6/H39yclJQWlUsmTTz6JWq0mPj6egoICzp07x6VLl1i4cCFPPfUUmZmZpKSkYGFhgZOTE5GRkWzZskU317Rp0zh69CghISHExsYSEhLChg0b8Pb25umnn+bYsWMoFAo++ugjVCoVixcv5tChQ9jZ2XHq1ClWrlyJg4ODwT2vW7eO7du3U1lZiY2NDQkJCWzdupWCggJ8fX2ZMWMG7du3Z/Dgwbox8fHx2NnZ4eLiQmJiIubm5hQXF+Pp6cmMGTMoKSkhPDwcrVaLpaUlUVFR2NvbG71vzz//PP/5z3/4/vvvee655/T6f/jhB5YsWUJSUhKdOnW69S9KCCGEEEIIccdIsPi7tm3bsnTpUl566SU2bdpE69atefvtt9m9ezcAVlZWrF27ll9//ZW5c+eSkpJCfHw8mzdvRqVSER0dTXp6OtbW1rRt21ZXM7FHjx5ERERgbm6uW+vy5ct4eXkRHh7O3Llzyc7OxtLSkosXL7Jx40bOnz/PyJEjje61vr6eixcvkpycjFKpJDAwkIMHDza7pqysjE2bNmFhYUF2drbeHKdPnyYzM5Pq6moGDRrEjBkziImJwd/fHw8PD/71r38RFxfHsmXLjO6jVatWvPfee7z++uv06dOnWV9hYSH/+Mc/0Gq1VFVVXfP+CyGEEEIIIe4tEiz+ztnZmcLCQs6fP8/UqVOBxqCusLAQgIEDBwLQtWtXfvvtN4qKinBzc9O9r9e/f3927dpF7969cXZ2vuZ6jz32GAD29vZotVpOnTqlC7hsbW1xcXExOlapVGJubk5wcDDW1tacOXOG2traZtc4ODhgYWFhdA53d3fMzMwwMzPDysoKgLy8PFavXk1SUhINDQ2YmV3718PJyYmAgAAWL16MQqHQtVtZWZGYmMh//vMf3nrrLTZs2KBbRwghhBBCCHHvk2Dxd0qlEgcHB+zt7VmzZg3m5uZkZGTQo0cPsrKyOHz4MGPHjiUvL4+OHTvi4OBAfn4+V65cwdramj179uiCRKXyv6+CKhQKDFUnuTqwgsYg9MsvvwSgvLycEydOGN1rbm4uWVlZaDQaKisr8fHx0Vvj6j0Y8sf1AVxcXJgyZQr9+vUjPz+fvXv3mpyjycsvv0xWVhZ5eXn4+voC8PDDD9O+fXuee+45cnJyiIyMvOb7jdCYzMaQpuQzBvuW6CdwgcZENkbHmJrPSJ+xdaAxmY0hTknxRseYShbjuNzwvWpKZmNwD78nptGba5l+Yp5rjQFJZiOEEEII8aCTYPEqtra2vPLKK/j7+1NXV0fnzp0ZNWoUAEePHmXy5MlUVlYSFRWFra0tM2fOJCAgAKVSiaOjI2q1mm3btjWbs2/fvsybN4+oqCiTaw8ZMoTs7Gx8fX2xs7PDysqq2aOrV+vSpQutW7fWBWYdOnSgtLT0lj9/SEgIERERukdHw8KMB1RXUygULF26FG9vb6Pzvvjii3zxxRc8//zzJucylrmzpbKXwu+ZUm9mvpsYYyjbKDQGacYyqILx7KpF8yP0299rbDOaFdZEFtebyfBqat9CCCGEEOL+IcEijVlCm4wdO5axY8fqXePp6Ymfn1+zNm9vb70A6eq5oLGcxJw5cwB0pSR27typ61er1QDk5+fz1FNPsWjRIi5cuMDo0aOxsbExuN/WrVuzdu1ak5+paS2A1NRUAGbO/G8ZhwEDBuj+3vRe5qOPPsonn3xict4mf/ycnTp14ueffza4vqWlJVu2bLmueYUQQgghhBD3BgkW7xH29vbExcWRkpJCXV0darWanJwckpOT9a4NCAhgxIgRd2RfQUFBlJeXN2tTqVS6BD5CCCGEEEKI+5MEi9fh6hO528Xa2tpgANaSdRFvRkJCwl1dXwghhBBCCHF3mM6CIoQQQgghhBDigSTBohBCCCGEEEIIPYoGQ3UdhBBCCCGEEEI80OSdxdvku+++o1evXiiVSlasWEFERMRtWys7O5uSkhImTpzYrN3f35+IiAgOHDhAu3btWuz9x9zcXOLi4tBqtdTU1DBgwADefPNNLCwsAPj6669Zt24dSqWS2tpaJk6ceM2SGU1+/dvf9dq67vqW4sgYvXaHd0IAEyUjFhmuVeiweMFNlZO4mdIZBc+/ZHAPLl98xonX9N+FbarLeHL6HL2+Lqv+Qf7/jNNrd/1mEwBFb4fr9T0aG8XJN9X6c62IaxwzT7/kxaPvN5bGODElSH9/axI4OSNYf76VyxvnM1AqxFQtRyGEEEIIce+SYPE2Wbt2LREREbi6ut7WQBFg8ODBJvv/WObiVvz2228EBwezYsUKnJ2daWhoYMWKFSxdupRFixaRk5PD559/zqpVq2jTpg1VVVXMmjULS0tLXc1KIYQQQgghxL3vgQ4WMzIy2LRpE/X19fj7+5OSkoJSqeTJJ59ErVYTHx9PQUEB586d49KlSyxcuJCnnnqKzMxMUlJSsLCwwMnJicjISLZs2aKba9q0aRw9epSQkBBiY2MJCQlhw4YNeHt78/TTT3Ps2DEUCgUfffQRKpWKxYsXc+jQIezs7Dh16hQrV67EwcHB4J7XrVvH9u3bqaysxMbGhoSEBLZu3UpBQQG+vr7MmDGD9u3bNwsg4+PjsbOzw8XFhcTERMzNzSkuLsbT05MZM2ZQUlJCeHg4Wq0WS0tLoqKisLe3N7j+l19+ybhx43B2dgZAoVDw5ptvMmzYMKqqqli3bh1qtZo2bdoAYGVlRUhICIsWLZJgUQghhBBCiD+RBzpYBGjbti1Lly7lpZdeYtOmTbRu3Zq3335bV6jeysqKtWvX8uuvvzJ37lxSUlKIj49n8+bNqFQqoqOjSU9Px9ramrZt2+rKX/To0YOIiAjMzc11a12+fBkvLy/Cw8OZO3cu2dnZWFpacvHiRTZu3Mj58+cZOXKk0b3W19dz8eJFkpOTUSqVBAYGcvDgwWbXlJWVsWnTJiwsLMjOztab4/Tp02RmZlJdXc2gQYOYMWMGMTEx+Pv74+Hhwb/+9S/i4uJYtmyZwT0UFRXx7LPPNmtTKBR06NCB3377jaKiIhwdHZv1P/roo5w+fdrEtyCEEEIIIYS41zzwwaKzszOFhYWcP3+eqVOnAo1BXWFhIQADBw4EoGvXrrpgyM3NDZVKBUD//v3ZtWsXvXv31p22mfLYY48BYG9vj1ar5dSpU/Tp0wcAW1tbXFxcjI5VKpWYm5sTHByMtbU1Z86coba2ttk1Dg4OuncHDXF3d8fMzAwzMzOsrKwAyMvLY/Xq1SQlJdHQ0ICZmfFfi44dO3Lq1KlmbXV1dZSWlmJnZ6frb9euna7/xIkTRk8qhRBCCCGEEPemBz5YVCqVODg4YG9vz5o1azA3NycjI4MePXqQlZXF4cOHGTt2LHl5eXTs2BEHBwfy8/O5cuUK1tbW7NmzRxckKpX/rUSiUCgwlGhWoVA0+7lr1658+eWXAJSXl3PixAmje83NzSUrKwuNRkNlZSU+Pj56a1y9B0P+uD6Ai4sLU6ZMoV+/fuTn57N3716j41944QVeffVVhgwZgpOTEw0NDSQkJDB48GCsrKzw9/fn/fffJyEhAZVKxeXLl3n//feZNGmSyX016brrW4PtTclsDPb9nphGr33xghseY3K+35PZ3EifyxefGR3TlMzGkC6r/mGwvSmZjSGPxkYZnuv3ZDYGx/yezMYQpzUJhuf7PZmNwfkkmY0QQgghxH3jgQ8WofFE75VXXsHf35+6ujo6d+6se7/u6NGjTJ48mcrKSqKiorC1tWXmzJkEBASgVCpxdHRErVazbdu2ZnP27duXefPmERVl+B/wTYYMGUJ2dja+vr7Y2dlhZWXV7NHVq3Xp0oXWrVvj6+sLQIcOHSgtLb3lzx8SEkJERARarZaqqirCwowHRY888gjvv/8+ixcvprKyktraWp5++mndmKFDh1JRUcFrr72GQqGgvr6eF198EU9Pz1vepxBCCCGEEOLOkTqLJjQlhvHz87tta+Tn55Obm4uXlxcXLlxg9OjRfP/99yYfJb2fnU3bqNfW0e9FiiPe02t3iJgPGC9bYajdVN+1ymDczJh874l67QCuW9KNlqYAODn1Lb2+Lh//k+MjX9Brd9u+GbjJ0hlGxgCcePUN/f19+pHRsh5gvHRGwfjJeu0umhS9NiGEEEIIce+Qk8W7zN7enri4OFJSUqirq0OtVpOTk0NycrLetQEBAYwYMeKO7CsoKIjy8vJmbSqVSpfARwghhBBCCHF/k2DRhJkz9YumtzRra2uDAdiwYcNu+9qmJCQYfl9NCCGEEEII8WAwnQ1FCCGEEEIIIcQDSYJFIYQQQgghhBB6JMGNEEIIIYQQQgg98s7ibfDdd9/Rq1cvlEolK1asICIi4ratlZ2dTUlJCRMnNs+66e/vT0REBAcOHKBdu3Yt8g7kiBEj+Pzzz/nLX/5CaWkpHh4eLF++XFdmZPjw4WzcuBEfHx8mT57M5MmNGTDz8/OJiIggNTX1mmucDHpbr61LQqzRLJsAxQuX6PU5LFloOhuqkTE3Ot81s6F6TTC4B9dtG4xmGwUT2VCHj9Vrd8tqrNNZOFe/PqTjsiWcnBGsP9fvtRJNZkOdPEN/fykrje4NTGRDfTFAr91l41oACl7Qr8Hpsnm9XpsQQgghhLiz5DHU22Dt2rVUVFTQoUOH2xooAgwePFgvULyaj49PiyXL+etf/8q+ffsA+N///V/+/ve/k52dDUBRURG2tra0b98egJSUFAoKClpkXSGEEEIIIcSd98CeLGZkZLBp0ybq6+vx9/cnJSUFpVLJk08+iVqtJj4+noKCAs6dO8elS5dYuHAhTz31FJmZmaSkpGBhYYGTkxORkZFs2bJFN9e0adM4evQoISEhxMbGEhISwoYNG/D29ubpp5/m2LFjKBQKPvroI1QqFYsXL+bQoUPY2dlx6tQpVq5ciYODg8E9r1u3ju3bt1NZWYmNjQ0JCQls3bqVgoICfH19mTFjBu3bt2fw4MG6MU21Il1cXEhMTMTc3Jzi4mI8PT2ZMWMGJSUlhIeHo9VqsbS0JCoqCnt7e4PrP/vss+zbt08XJM6ePZugoCAaGhrYs2cPgwYN0l07f/58QkND+eyzz1r2ixNCCCGEEELcEQ/0yWLbtm1ZuXIlCQkJJCcnk5aWxtmzZ9m9ezcAVlZWrF27ltjYWCIjI7lw4QLx8fGkpKSQlpZGmzZtSE9P182VlpbGkCFD6NGjBzExMZibm+vWunz5Ml5eXqxbt46HH36Y7OxsduzYwcWLF9m4cSPR0dGUlJQY3Wt9fT0XL14kOTkZjUZDXV0dBw8ebHZNWVkZn3zyCa+//rrBOU6fPk18fDzp6ekkJSUBEBMTg7+/P6mpqQQGBhIXF2d0DwMHDuQ///kPtbW1FBcX4+bmhru7O4cPH9YLFj08POjatSuJiYnX+BaEEEIIIYQQ96IH9mQRwNnZmcLCQs6fP8/UqVOBxqCusLAQaAyOALp27cpvv/1GUVERbm5uqFQqAPr378+uXbvo3bs3zs7O11zvscceA8De3h6tVsupU6fo06cPALa2tri4uBgdq1QqMTc3Jzg4GGtra86cOUNtbW2zaxwcHLCwsDA6h7u7O2ZmZpiZmWFlZQVAXl4eq1evJikpiYaGBszMjP9KtGvXDjMzM7Kzs+nXrx/Q+Bjsv//9b3799Vd69erV7Pr58+czbtw4HB0djd8UIYQQQgghxD3pgQ4WlUolDg4O2Nvbs2bNGszNzcnIyKBHjx5kZWVx+PBhxo4dS15eHh07dsTBwYH8/HyuXLmCtbU1e/bs0QWJSuV/D2kVCgWGkswqFIpmP3ft2pUvv2xMTlJeXs6JEyeM7jU3N5esrCw0Gg2VlZX4+PjorXH1Hgz54/oALi4uTJkyhX79+pGfn8/evXtNzjFgwACSkpKYNm0aAIMGDWLOnDk4OTnpra9SqYiMjCQ4ONhkIHy1LgmxBtubktkY0pSYRq/99+QzNzLmpucz0ue6bYPRMU3JbAxpShjzR03JbAxxXKafmAf+m8zGkKZkNoY4pay8ob2B8e+pKZmNwT5JZiOEEEIIcU96oINFaDzRe+WVV/D396euro7OnTvrsnsePXqUyZMnU1lZSVRUFLa2tsycOZOAgACUSiWOjo6o1Wq2bdvWbM6+ffsyb948oqKM/0McYMiQIWRnZ+Pr64udnR1WVlbNHl29WpcuXWjdujW+vr4AdOjQgdLS0lv+/CEhIURERKDVaqmqqiIszHhABI3vLX766acMGDAAgI4dO3L58mX+9re/Gbx+wIABeHl5cfTo0VveqxBCCCGEEOLOkTqLRjQlhvHz87tta+Tn55Obm4uXlxcXLlxg9OjRfP/99yYfJb3fnXhtpl6bU1I8xYui9dodFi8AjJetMFk64ybKYNzMmJNvqg3uocuKOKOlLsB4SYuT0+foz7XqH41jjJStKJr3jn77+5GNYxZE6vdFN15fGLxAf3/Lo02W2zBWdqRw1jz9uT58v3EdI32GyqiA8dNnIYQQQgjRsh74k8W7yd7enri4OFJSUqirq0OtVpOTk0NycrLetQEBAYwYMeKO7CsoKIjy8vJmbSqVipUrDT+WKIQQQgghhLj/SLBoxMyZ+idcLc3a2tpgANZSdRFvVkJCwl1dXwghhBBCCHH3PdClM4QQQgghhBBCGCbBohBCCCGEEEIIPZLgRgghhBBCCCGEHnln8Tb77rvv6NWrF0qlkhUrVhAREXHb1srOzqakpISJEyc2a/f39yciIoIDBw7Qrl27W34nsq6uDj8/PwICAhg9ejQAZ86cYdKkSXz22Weo1WrGjRvH888/D8A//vEPGhoaCA4OvubchjJgdkmINZplE+7tbKiGMn1CY7ZPU1lFjWUwNXZ/wHhm06L5Efrt70WYHAPGM7Kayq5qNBvqW6F67Y7/XApgtK9w9ny9dgDHD94z+ZmEEEIIIUTLkGDxNlu7di0RERG4urre1kARYPDgwSb7fXx8WmSdVq1a8d577zFlyhQGDhyInZ0dCxcuZN68eXTs2JHY2Fheeukl+vbtS0FBAfv372fNmjUtsrYQQgghhBDizpBgEcjIyGDTpk3U19fj7+9PSkoKSqWSJ598ErVaTXx8PAUFBZw7d45Lly6xcOFCnnrqKTIzM0lJScHCwgInJyciIyPZsmWLbq5p06Zx9OhRQkJCiI2NJSQkhA0bNuDt7c3TTz/NsWPHUCgUfPTRR6hUKhYvXsyhQ4ews7Pj1KlTrFy5EgcHB4N7XrduHdu3b6eyshIbGxsSEhLYunUrBQUF+Pr6MmPGDNq3b98sgGyqHeni4kJiYiLm5uYUFxfj6enJjBkzKCkpITw8HK1Wi6WlJVFRUdjb2xtc38XFhcDAQN599108PDx4+OGH+fvf/w7AI488woIFC5g7dy5arZY1a9bQqlWrlv/ihBBCCCGEELeNJLj5Xdu2bVm5ciUJCQkkJyeTlpbG2bNn2b17NwBWVlasXbuW2NhYIiMjuXDhAvHx8aSkpJCWlkabNm1IT0/XzZWWlsaQIUPo0aMHMTExmJub69a6fPkyXl5erFu3jocffpjs7Gx27NjBxYsX2bhxI9HR0ZSUlBjda319PRcvXiQ5ORmNRkNdXR0HDx5sdk1ZWRmffPIJr7/+usE5Tp8+TXx8POnp6SQlJQEQExODv78/qampBAYGEhcXZ/Kevfzyy1y4cIGUlBTCwsKa9Xl4eHDhwgX69OlDhw4dTM4jhBBCCCGEuPfIyeLvnJ2dKSws5Pz580ydOhVoDOoKCwsBGDhwIABdu3blt99+o6ioCDc3N1QqFQD9+/dn165d9O7dG2dn52uu99hjjwFgb2+PVqvl1KlT9OnTBwBbW1tcXFyMjlUqlZibmxMcHIy1tTVnzpyhtra22TUODg5YWFgYncPd3R0zMzPMzMywsrICIC8vj9WrV5OUlERDQwNmZqZ/PRQKBWPGjKGgoICHHnqoWV9cXBz/8z//Q05ODjk5OQwaNMjkXEIIIYQQQoh7iwSLv1MqlTg4OGBvb8+aNWswNzcnIyODHj16kJWVxeHDhxk7dix5eXl07NgRBwcH8vPzuXLlCtbW1uzZs0cXJCqV/z2wVSgUGEo4q1Aomv3ctWtXvvzySwDKy8s5ceKE0b3m5uaSlZWFRqOhsrISHx8fvTWu3oMhf1wfGh8tnTJlCv369SM/P5+9e/eanMOY7777jl9++YXU1FTGjh3L9OnTSU9PlxNGIYQQQggh/kQkWLyKra0tr7zyCv7+/tTV1dG5c2dGjRoFwNGjR5k8eTKVlZVERUVha2vLzJkzCQgIQKlU4ujoiFqtZtu2bc3m7Nu3L/PmzSMqKsrk2kOGDCE7OxtfX1/s7OywsrJq9ujq1bp06ULr1q3x9fUFoEOHDpSWlt7y5w8JCSEiIgKtVktVVZXeo6XXo6ioiKVLl7Ju3TrMzMxwd3dnypQpzJs3j08++eSaQWxTZs8/asp8arAvyvA+jbXfbN/NjHH88H2jY5oynxrse18/SykYvz/w3yymeu0msoQaGwPG92dsb2D8e2rKfHojfY4fvGd8b5L5VAghhBDitpM6i9ehKTGMn5/fbVsjPz+f3NxcvLy8uHDhAqNHj+b77783+Sjp/ejkDP3yGl1WLr+50hkGxjSNu1OlMwyVn4Brl6AwVhrCVMmI4gj94MohYr7J8hjFi6L1xyxeAEDp5i16fQ+/4G16D0a+J2N7AygMXqDX57g8msK5RgLPZUs4tUT/fdrOC9Um9yCEEEIIIW6MnCzeI+zt7YmLiyMlJYW6ujrUajU5OTkkJyfrXRsQEMCIESPuyL6CgoIoLy9v1qZSqVi5cuUdWV8IIYQQQghxd0iweB1mzpx529ewtrY2GIANGzbstq9tSkJCwl1dXwghhBBCCHF3SOkMIYQQQgghhBB6JFgUQgghhBBCCKFHEtwIIYQQQgghhNAjJ4vXkJ2dTXp6utH+jIwMduzY0SJr7d27l9zcXKP9Wq0WjUbTImv9cd6hQ4fe9Pjp06czbdq0Zm2vvfYa/v7++Pv74+fnR7du3cjPz7/VrQohhBBCCCHuEElwcw2DBw822e/j49Nia23atAlPT0+6d+9usL+srAyNRsP48eNbbM1bdfr0aa5cuUJtbS1FRUU8+uijACQlJemueeedd+jevTuurq7XnM9YmQeT5R+MlWswUM4Cbr4Mxs2MKVm1xuAe7KdPMfmZDJXceDQ2isLZ8/Xam+oRFoUu1h+zdJHJUhLGxgAUvhWqv9Y/l5ou+WHkM5m6d8bmK0n4WK8dwD5oqsnyHTdTdkQIIYQQQuh74IPFmpoaQkNDKS4upq6ujldffZW0tDRsbW0pLy/Hy8uLkydPolarWbFiBVlZWdja2lJZWcns2bPZs2cPdnZ2uLi4kJiYiLm5OcXFxXh6ejJjxgyj64aGhnLy5EmqqqoICAjAzc2NnJwcDh8+jJubGzt37mT79u1UVlZiY2NDQkICq1at4vjx4yQkJDB58mTCwsK4cOECAAsXLqRbt25oNBrS0tKor69n6NChzJo1i8zMTFJSUrCwsMDJyYnIyEiqq6tRq9VcunQJR0dH3b6OHTvGkiWNwUX79u2Jjo6mTZs2Rj/Hpk2bGDZsGFZWVnz22WeEhIQ06//ss88oKCjg008/vZWvSQghhBBCCPEHBw4cIC4ujtTU1GbtO3fuZMWKFZiZmTFu3DgmTJhwU/M/8MFieno6tra2xMXFUVFRgY+PDxYWFrpahhkZGQDk5uaSk5PDxo0bqampwdvbW2+u06dPk5mZSXV1NYMGDTIaLFZUVLB37142bNgAwO7du+nZsyeDBg3C09OTRx55hIsXL5KcnIxSqSQwMJCDBw8yffp08vLyCAoKIjY2loEDB/LSSy9x4sQJQkNDSUhIIDExkczMTCwtLVm2bBmnTp0iPj6ezZs3o1KpiI6OJj09Ha1Wi7u7O3PmzOHAgQP89NNPAISHhxMdHY2bmxsajYakpCTmzJlj8HPU19ezdetW0tPTMTMzw8vLi9mzZ2NlZQXAzz//zKeffsrnn3+Oubn5LX9XQgghhBBCiEZN/+5v3bp1s/aamhqWLl3Kxo0bad26NX5+fgwdOhQ7O7sbXuOBDxbz8/N55plngMZi866uruzevRtnZ2e965544glatWpFq1at6Nmzp95c7u7umJmZYWZmpguYDFGpVCxYsIDw8HAqKioYM2ZMs36lUom5uTnBwcFYW1tz5swZamtrm12Tl5fHjz/+yNdffw1AeXk5RUVFdO3aVbe2Wq3ml19+wc3NDZVKBUD//v3ZtWsX9fX1eHh4ANC7d2/MzMx0n3Px4sZHE2tqanBycjL6OXJycrh8+TJz584FGoPHLVu2MH78eM6ePcu8efP48MMP+ctf/mJ0DiGEEEIIIcSNc3R0JD4+nnnz5jVrz8/Px9HRkXbt2gHw5JNPsnfvXkaNGnXDazzwwaKrqyv79u1jxIgRVFRUkJeXh4ODAwqFotl1bm5upKamUl9fT21tLUeOHNGb649jjCktLeXw4cOsWLECrVaLh4cHY8eORaFQ0NDQQG5uLllZWWg0GiorK/Hx8aGhoQGlUkl9fT0ALi4ujBkzBm9vb86dO4dGo8HR0ZGCggKqq6uxsLBg1qxZhISEkJ+fz5UrV7C2tmbPnj26QHj//v0MHz6cI0eO6IJRZ2dnYmJi6NSpEz///DNlZWVGP8fGjRtZsmQJQ4YMARpPEpcsWcLYsWOZPXs2s2fP5vHHH7+ueyKEEEIIIcT9YsSCrFsa/1rvc82SbE6cOJGJEyc2u+bvf/87xcXFemMrKiqavUb20EMPUVFRcVP7eOBLZ1RXVxMeHk5hYSFarRZ/f38yMjKIiIjA1dWVjIwMCgoKUKvVrFy5kh07dmBjY0NJSQmLFy/mhx9+0L2z+Pnnn/OPf/wDgGeffZbdu3cbXLOhoYFFixaRl5eHUqlkyJAhTJ06lc8//5z169ezfPlyoqKiqK6uBsDCwoIXX3yRv//970yYMIG//e1vvPbaa4SFhfF///d/VFRUEBQUxLBhw8jIyODzzz9HoVDw3HPPMX36dLZs2UJKSgpKpRJHR0fefbcx2ci8efMoLS3FxcWFffv28e2333Lo0CFiYmKora1FoVDw7rvv6p2yAvz222/4+Piwc+dO3akkgKenJ97e3qxevZonnnii2ZjZs2fz1FNPtcj3JoQQQgghxL3qVoPF76KHX9d1xcXFBAcH615vg8bX55YtW0ZiYiIA0dHR9OvXj//5n/+54X088MHi9Tp37hzffPMNkyZNorq6Gi8vL1JSUujUqdPd3tp95VT0Mr22zgvmmszoaTR7qYExTePuVDZUQ5k+oTHbZ2l6hl77wxN9TM53cupbeu1dPv4nAIVzF+r1OS5bYjJ7afGiaP11Fi8A4MRk/XdunVJWms6GaiS76qmly/XaO4cGA3Dq/Q/0++bN5swnqXrtAI8E+lM4a55eu+OH7wMYzRhrLLsrwMnp+u/ldln1D4PrCyGEEEJcy8iwWwsWt79788FiTU0NXl5ebNiwAWtra3x9fVm5ciUdO3a84X088I+hXi8bGxsOHTrEuHHjUCgUjB8//pqB4o4dO0hOTtZrb0qe82dQXV1NYGCgXruzszORkfplEoQQQgghhHjQKa/v7bQWtWXLFq5cucLEiROZP38+gYGBNDQ0MG7cuJsKFEGCxeumVCpZunTpDY0ZNmwYw4YNu007ujMsLCz0UvEKIYQQQgghjLveXCa3ysHBQXeqeHW1hqFDhzJ06NBbnl+CRSGEEEIIIYRoQXcoVrztlHd7A0IIIYQQQggh7j2S4EYIIYQQQgghWtDoRTtvafzWxbf+CGlLkMdQhRBCCCGEEKIF3SdPoUqwaEx2djYlJSV6xS+bZGRk0K5duxZJYLN3717atGlD9+7dDfZrtVoyMzMZP378La/1x3lHjRrFzp03/j8fWVlZpKSkAFBVVUVgYKCudktubi5xcXFotVpqamoYMGAAb775JhYWFtec11hZhuKo9/XaHcIbyycYLXVhoCwENJaGaPHSGUZKexS9HW5wD4/GRpksQWGsDIaxshBgvGzF6Q9W6bV3mj29ccwC/Yy2j0Y37qsweIH+WsujTY4x1nc67kP9PahnAXD6nx/p9731BsUR7+m1AzhEzDdZ8sPQOIeI+RTNj9Df23uNbcbut6FSJfDfciVCCCGEEIbcqQQ3t5sEi0YMHjzYZL+Pj0+LrbVp0yY8PT2NBotlZWVoNJoWDxZv1r///W+Sk5NZvXo1Dz30EBcuXGDixIm4ubnRvn17goODWbFiBc7OzjQ0NLBixQqWLl3KokWL7vbWhRBCCCGEENfpgQ0Wa2pqCA0Npbi4mLq6Ol599VXS0tKwtbWlvLwcLy8vTp48iVqtZsWKFWRlZWFra0tlZSWzZ89mz5492NnZ4eLiQmJiIubm5hQXF+Pp6cmMGfrFzJuEhoZy8uRJqqqqCAgIwM3NjZycHA4fPoybmxs7d+5k+/btVFZWYmNjQ0JCAqtWreL48eMkJCQwefJkwsLCuHDhAgALFy6kW7duaDQa0tLSqK+vZ+jQocyaNYvMzExSUlKwsLDAycmJyMhIqqurUavVXLp0CUdHR92+jh07xpIljadj7du3Jzo6mjZt2hj8DBqNhsmTJ/PQQw8BjTUoNRoNbdu2Zc2aNYwbNw5nZ2eg8X9V3nzzTYYNG0ZVVRVWVlYt8v0JIYQQQghxr7obdRZvhwc2WExPT8fW1pa4uDgqKirw8fHBwsKCgIAARowYQUZGBtD4SGVOTg4bN26kpqamWf2SJqdPnyYzM5Pq6moGDRpkNFisqKhg7969uloou3fvpmfPngwaNAhPT08eeeQRLl68SHJyMkqlksDAQA4ePMj06dPJy8sjKCiI2NhYBg4cyEsvvcSJEycIDQ0lISGBxMREMjMzsbS0ZNmyZZw6dYr4+Hg2b96MSqUiOjqa9PR0tFot7u7uzJkzhwMHDvDTTz8BEB4eTnR0NG5ubmg0GpKSkpgzZ47Bz1FaWsqjjz7arK1du3YAFBUV8eyzzzbrUygUdOjQgd9++w0HB4cb+JaEEEIIIYT485HHUP/k8vPzeeaZZwBQqVS4urqye/du3YnY1dc98cQTtGrVilatWtGzZ0+9udzd3TEzM8PMzMzkyZlKpWLBggWEh4dTUVHBmDFjmvUrlUrMzc0JDg7G2tqaM2fOUFtb2+yavLw8fvzxR77++msAysvLKSoqomvXrrq11Wo1v/zyC25ubqhUKgD69+/Prl27qK+vx8PDA4DevXtjZmam+5yLFze+81ZTU4OTk5PRz9GpUydKSkqaPTb7888/Y2dnR8eOHTl16lSz6+vq6igtLcXOzs7onEIIIYQQQtwv7pNY8cENFl1dXdm3bx8jRoygoqKCvLw8HBwc9P4XwM3NjdTUVOrr66mtreXIkSN6c13v/xyUlpZy+PBhVqxYgVarxcPDg7Fjx6JQKGhoaCA3N5esrCw0Gg2VlZX4+PjQ0NCAUqmkvr4eABcXF8aMGYO3tzfnzp1Do9Hg6OhIQUEB1dXVWFhYMGvWLEJCQsjPz+fKlStYW1uzZ88eXSC8f/9+hg8fzpEjR3TBqLOzMzExMXTq1Imff/6ZsrIyo5/Dx8eHZcuWMWDAAKytrTl37hwLFizggw8+4IUXXuDVV19lyJAhODk50dDQQEJCAoMHD76uR1CbErz8UVMyG4N9vyeZ0WtfrJ+g5VpjTM5naswS/QQp0JjIxhhjnxUak6sYbP/AcNIXaExmY0hTMhuDY6L1k+zo1lpuOEGQqTHG+pqS2Rjse+sNg+0OEfrJfHR9pr5bI+OaktkYYux+SyIbIYQQQtwMeQz1T27ChAmEh4fj5+eHVqslKChI9+jp1bp164aHhwcTJkzAxsYGc3Nz3WncjerQoQNlZWX4+vqiVCqZMmUKZmZm9O7dm7i4OJYvX07r1q3x9fXVXV9aWkrfvn2pqakhNjaW6dOnExYWxoYNG6ioqCAoKAhbW1tef/11Xn75ZRQKBc899xydO3dm5syZBAQEoFQqcXR0RK1WAzBv3jz8/PxwcXHB3NwcgIiICEJCQqitrUWhUPDuu/qZP5v07duXCRMm6PZfVVVFcHCw7qTx/fffZ/HixVRWVlJbW8vTTz9NWJjxQOtqxZExem0O74QYzTYKN5a91FTfLWVDvYmMrKaycxrL/mos0ycYz8hq8t7dRJ+x78jUGGOZWgGjmU1NfX+mMrIa3YOpLK5G9mcym62JzySEEEIIcT94YINFCwsLYmKa/6P3hRde0P29KdvpuXPnaNu2LRs3bqS6uhovLy/s7e2ZOXOm7toBAwbo/r57926jayoUCiIj9f/B6uvrqwsQ165da3Dsl19+qfv7Rx/plxrw8fHRy9Dq7e1t8B3LDz74QK+tZ8+epKamGt37H40ZM0bvMdomTzzxBJ9++ul1zyWEEEIIIcT9RN5ZfEDY2Nhw6NAhxo0bh0KhYPz48XTq1MnkmB07dpCcnKzX3pQ858+gurqawMBAvXZnZ2eDAa8QQgghhBCi0X0SK0qweC1KpZKlS5fe0Jhhw4YxbNiw27SjO8PCwuKGThqFEEIIIYQQjZT3SbQowaIQQgghhBBCtKD7JFZE0dDQ0HC3NyGEEEIIIYQQ9wu/97JvaXza/MEttJNbIyeLQgghhBBCCNGC5DHUB0h2djYlJSVMnDjRYH9GRgbt2rVrkfcU9+7dS5s2bZoVvL+aVqslMzOT8ePH3/Jaf5x31KhR7Ny584bGZWRk8OGHH/Loo4/q2l555RXKy8spKCjQleu4XqeWxOm1dV6ovqlSCYbKTEBjqQmTZTBasATF6X/qZ66FxtqChbP16wE21VEsiV+t12c/cxpnEpP12h95/RUAo6U4TJXbKJqnXxexqf5j4Sz92paOH77P6bgP9dqb6iga+55OTAnSa3dak9C4TrB+zUTH5dEUvhWq1w7g+M+lJsuO3EgZjKY6mMY+6/976TWDe3D+LMnoGDD+XQghhBDiwXCfxIoSLF6PwYNNHwP/sWTFrdi0aROenp5Gg8WysjI0Gk2LB4u3YvTo0XpBoaGalUIIIYQQQjwIJFi8j9TU1BAaGkpxcTF1dXW8+uqrpKWlYWtrS3l5OV5eXpw8eRK1Ws2KFSvIysrC1taWyspKZs+ezZ49e7Czs8PFxYXExETMzc0pLi7G09OTGTNmGF03NDSUkydPUlVVRUBAAG5ubuTk5HD48GHc3NzYuXMn27dvp7KyEhsbGxISEli1ahXHjx8nISGByZMnExYWxoULFwBYuHAh3bp1Q6PRkJaWRn19PUOHDmXWrFlkZmaSkpKChYUFTk5OREZGUl1djVqt5tKlSzg6Our2dezYMZYsaTwpa9++PdHR0bRp0+b2fglCCCGEEEKIe4oEi0B6ejq2trbExcVRUVGBj48PFhYWurqITadkubm55OTksHHjRmpqagwWvD99+jSZmZlUV1czaNAgo8FiRUUFe/fuZcOGDQDs3r2bnj17MmjQIDw9PXnkkUe4ePEiycnJKJVKAgMDOXjwINOnTycvL4+goCBiY2MZOHAgL730EidOnCA0NJSEhAQSExPJzMzE0tKSZcuWcerUKeLj49m8eTMqlYro6GjS09PRarW4u7szZ84cDhw4wE8//QRAeHg40dHRuLm5odFoSEpKYs6cOUbv39atWzlw4ADQWJfyww/1H1MUQgghhBDiQSHvLN5H8vPzeeaZZwBQqVS4urqye/dunJ2d9a574oknaNWqFa1ataJnz556c7m7u2NmZoaZmRlWVlZG11SpVCxYsIDw8HAqKioYM2ZMs36lUom5uTnBwcFYW1tz5swZamtrm12Tl5fHjz/+yNdffw1AeXk5RUVFdO3aVbe2Wq3ml19+wc3NDZVKBUD//v3ZtWsX9fX1eHh4ANC7d2/MzMx0n3Px4sb3vmpqanBycjJ5/ww9hiqEEEIIIcSD6j6JFSVYBHB1dWXfvn2MGDGCiooK8vLycHBwQPGHb9nNzY3U1FTq6+upra3lyJEjenP9cYwxpaWlHD58mBUrVqDVavHw8GDs2LEoFAoaGhrIzc0lKysLjUZDZWUlPj4+NDQ0oFQqqa+vB8DFxYUxY8bg7e3NuXPn0Gg0ODo6UlBQQHV1NRYWFsyaNYuQkBDy8/O5cuUK1tbW7NmzRxcI79+/n+HDh3PkyBFdMOrs7ExMTAydOnXi559/pqys7FZu7w3pvNBw0NmUzMaQpiQzeu0R+glkdH2/J7O5ofmMtJvq6/TWG0bHNCWzMcR+5jSD7U3JbAwxlkDF1H1oSmZjSFOylj9qSmZjcD4j31NTMhuD6yyPNtz+z6XG1zGRLObRpYsMt/+ezMbgWkY+q/NnSTc8BiSZjRBCCPGgu96Y4F4nwSIwYcIEwsPD8fPzQ6vVEhQUZDBBS7du3fDw8GDChAnY2Nhgbm6uO427UR06dKCsrAxfX1+USiVTpkzBzMyM3r17ExcXx/Lly2ndujW+vr6660tLS+nbty81NTXExsYyffp0wsLC2LBhAxUVFQQFBWFra8vrr7/Oyy+/jEKh4LnnnqNz587MnDmTgIAAlEoljo6OupPAefPm4efnh4uLC+bm5gBEREQQEhJCbW0tCoWCd9/Vzxx6Pb744gt++OEH3c+pqak89NBDNzWXEEIIIYQQfxbK+yNWRNHQ0NBwtzfxZ3Hu3Dm++eYbJk2aRHV1NV5eXqSkpNCpU6e7vbX7RnFkjF6bwzshpstWGCmDYWhM0ziTpTOMzXcTYwrnGj5xdFy2xGjJCDBegqJ04xd67Q+/+HzjHhbpn9A5LF5gct/GykyA8ZIWxtYB4yVEbqZEh6FSF9B4QmhqPmP7M1XOwtD35LhsCSenvmVwD10+/qfRMWD8vpoqtyGEEEKI+0fgP3bf0vhP5jzbQju5NXKyeANsbGw4dOgQ48aNQ6FQMH78+GsGijt27CA5OVmvvSl5zp9BdXU1gYGBeu3Ozs5ERhp/jFEIIYQQQogHkYL742hRgsUboFQqWbrU+HtUhgwbNoxhw4bdph3dGRYWFqSmpt7tbQghhBBCCPGncJ+8sijBohBCCCGEEEK0JCmdIYQQQgghhBBCz30SK0qCGyGEEEIIIYRoSdM//NctjV81668ttJNbIyeLNyA7O5uSkhImTpxosD8jI4N27dq1yDuKe/fupU2bNnTv3t1gv1arJTMzk/Hjx9/yWn+cd9SoUezcufOGxmVkZFBQUKAryXH27FlGjhzJe++9x6hRo657npNBb+u1dUmIbbHspab6dPMZyeh5M3swlLUTGjN3msroaSxzp6EMoU31A4uj9LNqOoTPM71vI2MASjds1ut7eMILJrOK3tS9M5ZB1VQ2VCPZYsHEd2FijNHspQYywkJjVtibuQ+mvj9T8wkhhBDiz+V+OVlU3u0N/JkMHjzYaKAI4OPj02LJbDZt2kRpaanR/rKyMjQaTYusdTtkZGTg7+/PZ599dre3IoQQQgghxB2lVChu6c+9Qk4Wr1JTU0NoaCjFxcXU1dXx6quvkpaWhq2tLeXl5Xh5eXHy5EnUajUrVqwgKysLW1tbKisrmT17Nnv27MHOzg4XFxcSExMxNzenuLgYT09PZsyYYXTd0NBQTp48SVVVFQEBAbi5uZGTk8Phw4dxc3Nj586dbN++ncrKSmxsbEhISGDVqlUcP36chIQEJk+eTFhYGBcuXABg4cKFdOvWDY1GQ1paGvX19QwdOpRZs2aRmZlJSkoKFhYWODk5ERkZSXV1NWq1mkuXLuHo6Kjb17Fjx1iypPGEpH379kRHR9OmTZtr3seGhga+/PJLPvvsM9544w3y8vJwd3e/xW9HCCGEEEKIP4d7KN67JRIsXiU9PR1bW1vi4uKoqKjAx8cHCwsLXU3EjIwMAHJzc8nJyWHjxo3U1NTg7e2tN9fp06fJzMykurqaQYMGGQ0WKyoq2Lt3Lxs2bABg9+7d9OzZk0GDBuHp6ckjjzzCxYsXSU5ORqlUEhgYyMGDB5k+fTp5eXkEBQURGxvLwIEDeemllzhx4gShoaEkJCSQmJhIZmYmlpaWLFu2jFOnThEfH8/mzZtRqVRER0eTnp6OVqvF3d2dOXPmcODAAX766ScAwsPDiY6Oxs3NDY1GQ1JSEnPmzLnmffzXv/6Fu7s7tra2jBs3jvXr17N4sf5jfkIIIYQQQoh7lwSLV8nPz+eZZ54BQKVS4erqyu7du3F2dta77oknnqBVq1a0atWKnj176s3l7u6OmZkZZmZmWFlZGV1TpVKxYMECwsPDqaioYMyYMc36lUol5ubmBAcHY21tzZkzZ6itrW12TV5eHj/++CNff/01AOXl5RQVFdG1a1fd2mq1ml9++QU3NzdUKhUA/fv3Z9euXdTX1+Ph4QFA7969MTMz033OpiCvpqYGJyen67qPGzZsoLi4mMDAQGpqajh27Bhqtfq6TiWFEEIIIYT4s7uXHiW9FRIsXsXV1ZV9+/YxYsQIKioqyMvLw8HBAcUfvmw3NzdSU1Opr6+ntraWI0eO6M31xzHGlJaWcvjwYVasWIFWq8XDw4OxY8eiUChoaGggNzeXrKwsNBoNlZWV+Pj40NDQgFKppL6+HgAXFxfGjBmDt7c3586dQ6PR4OjoSEFBAdXV1VhYWDBr1ixCQkLIz8/nypUrWFtbs2fPHl0gvH//foYPH86RI0d0waizszMxMTF06tSJn3/+mbKysmt+nvPnz3PgwAGysrJo1aoV0PhY7ObNmwkICLjm+C4JsQbbm5Kh3EjfzYyBxkQkLTVfU8KaG+4zktikKRmKwT38npjmevdmagw0JrO5kb3BTd47I2NMfdamxDQ3spapMY8uXWSw3XF5tPExN3EfTH4mSWYjhBBC3Dfuk1hRgsWrTZgwgfDwcPz8/NBqtQQFBekePb1at27d8PDwYMKECdjY2GBubq47jbtRHTp0oKysDF9fX5RKJVOmTMHMzIzevXsTFxfH8uXLad26Nb6+vrrrS0tL6du3LzU1NcTGxjJ9+nTCwsLYsGEDFRUVBAUFYWtry+uvv87LL7+MQqHgueeeo3PnzsycOZOAgACUSiWOjo667KXz5s3Dz88PFxcXzM3NAYiIiCAkJITa2loUCgXvvms4u+jVvvzyS0aOHKkLFJvu67x58/D397/uIFoIIYQQQog/q/vl37xSZ/EmnDt3jm+++YZJkyZRXV2Nl5cXKSkpdOrU6W5v7U/PWGkBY6UI4DaUzriB+W6l/IOp+W6klEPT6Zex0hDG5gI4tSROr6/zwsb/QDD6mUzMZ2zM2dTP9do7+jf+B4ix+QzNpduDqTIYxu7DzZTOmGv4hNBx2ZKbug8my6UYmc/UvoUQQghxbwpeveeWxi+f9nQL7eTWyMniTbCxseHQoUOMGzcOhULB+PHjrxko7tixg+TkZL32puQ5fwbV1dUEBgbqtTs7OxMZafyRSiGEEEIIIcSfjwSLN0GpVLJ06dIbGjNs2LAWq8F4t1hYWJCamnq3tyGEEEIIIcQ97T55ClWCRSGEEEIIIYRoSffLO4sSLAohhBBCCCFEC1LeH7GiJLgRQgghhBBCiJYU8sm+WxofE/hUC+3k1sjJ4g3Kzs6mpKSEiRMnGuzPyMigXbt2LfJ+4t69e2nTpg3du3c32K/VasnMzGT8+PG3vNYf5x01ahQ7d+68oXEZGRkUFBSgVqsZOnQoX3/9NZaWljc0x+llCXptneYGmc7aeS9kQzXSdyp6mcE9dF4w12RmTENZOB2XLeFk0Nt67U21KY3twVQ2TZOfKTJGv++dEIoj3tNvj5gPGM9EWvhWqF674z8b3/s9/c+P9Po6vfUGxVHv67VDY23I4kX69Q8dFi8wuYei+RH67b/XNjR2v0+8NtPgHpyS4k1npjWS2dTU92dsPlP7NpWRVQghhBDiVinv9gb+bAYPHmw0UATw8fFpsUQ2mzZtorS01Gh/WVkZGo2mRdYSQgghhBBCtAyF4tb+3CvkZPEPampqCA0Npbi4mLq6Ol599VXS0tKwtbWlvLwcLy8vTp48iVqtZsWKFWRlZWFra0tlZSWzZ89mz5492NnZ4eLiQmJiIubm5hQXF+Pp6cmMGTOMrhsaGsrJkyepqqoiICAANzc3cnJyOHz4MG5ubuzcuZPt27dTWVmJjY0NCQkJrFq1iuPHj5OQkMDkyZMJCwvjwoULACxcuJBu3bqh0WhIS0ujvr6eoUOHMmvWLDIzM0lJScHCwgInJyciIyOprq5GrVZz6dIlHB0ddfs6duwYS5Y01oxr37490dHRtGnT5vZ+CUIIIYQQQvyJKW9zxFdfX09ERATHjh3DwsKCJUuW0KVLF13/mjVr2Lp1KwqFgunTp990qT4JFv8gPT0dW1tb4uLiqKiowMfHBwsLC109xIyMDAByc3PJyclh48aN1NTU4O3trTfX6dOnyczMpLq6mkGDBhkNFisqKti7dy8bNmwAYPfu3fTs2ZNBgwbh6enJI488wsWLF0lOTkapVBIYGMjBgweZPn06eXl5BAUFERsby8CBA3nppZc4ceIEoaGhJCQkkJiYSGZmJpaWlixbtoxTp04RHx/P5s2bUalUREdHk56ejlarxd3dnTlz5nDgwAF++uknAMLDw4mOjsbNzQ2NRkNSUhJz5sy5TXdfCCGEEEKIP7/bfTqYlZVFdXU16enp7N+/n/fee4+VK1cCcOnSJdauXas7aHr++eclWGwp+fn5PPPMMwCoVCpcXV3ZvXs3zs7Oetc98cQTtGrVilatWtGzZ0+9udzd3TEzM8PMzAwrKyuja6pUKhYsWEB4eDgVFRWMGTOmWb9SqcTc3Jzg4GCsra05c+YMtbW1za7Jy8vjxx9/5OuvvwagvLycoqIiunbtqltbrVbzyy+/4ObmhkqlAqB///7s2rWL+vp6PDw8AOjduzdmZma6z7l4ceN7UTU1NTg5OV3XfRRCCCGEEELcHj///DODBg0CoE+fPhw6dEjX17p1azp16kRlZSWVlZW3VMZDgsU/cHV1Zd++fYwYMYKKigry8vJwcHDQu8lubm6kpqZSX19PbW0tR44c0Zvrer+Y0tJSDh8+zIoVK9BqtXh4eDB27FgUCgUNDQ3k5uaSlZWFRqOhsrISHx8fGhoaUCqV1NfXA+Di4sKYMWPw9vbm3LlzaDQaHB0dKSgooLq6GgsLC2bNmkVISAj5+flcuXIFa2tr9uzZowuE9+/fz/Dhwzly5IguGHV2diYmJoZOnTrx888/U1ZWdiu395o6zQ0y2N6UzMaQpoQs19t+s303M6bzgrlGxzQlszHEcdkSg+1NyVBuZA9NyWxuZAw0JrMx2P57MpsbWaspmY0hnd56w/A64fOM722xfjKYa+2hKSmMIcbut1NSvPExy/WT7OjWMpJkxtT3Z2w+U/uWZDZCCCHEvUnBrR0tpqenk56ervt54sSJzfKmVFRU6A5/AFq1akVtba3uwMfe3h4vLy/q6uqYNm3aTe9DgsU/mDBhAuHh4fj5+aHVagkKCtI9enq1bt264eHhwYQJE7CxscHc3Fz35dyoDh06UFZWhq+vL0qlkilTpmBmZkbv3r2Ji4tj+fLltG7dGl9fX931paWl9O3bl5qaGmJjY5k+fTphYWFs2LCBiooKgoKCsLW15fXXX+fll19GoVDw3HPP0blzZ2bOnElAQABKpRJHR0fU6sZAbN68efj5+eHi4oK5uTkAERERhISEUFtbi0Kh4N13DWcYNcTPz0/3d29vb1599dWbuj9CCCGEEEL8mdxqncU/Bod/pFKpuHz5su7n+vp6XSySnZ1NaWkpO3bsACAwMJB+/frRq1evG96H1Fm8SefOneObb75h0qRJVFdX4+XlRUpKCp06dbrbW/tTK5ytf2rl+MF7FC/UP/lxWNJY7uBeLp1hqDwGNJ4qmiqdYazPVKkEo6UuTN07EyUojJUrMVmKw8haxspjgPGSEYb21rQ/U5/JULmSzgvmmrx3xu534SzDp5uOH75v+vszVkLESIkOU3swdb9N9pnYnxBCCCFur0Vr/31L4xcH9DPZ/+233/L999/z3nvvsX//fhISEkhKSgJg3759rF69mo8//hiFQsGMGTOYNGkSf/vb3254H3KyeJNsbGw4dOgQ48aNQ6FQMH78+GsGijt27CA5OVmvvSl5zp9BdXU1gYGBeu3Ozs5ERso/RIUQQgghhLiV9wSvx4gRI9i9eze+vr40NDQQHR3Np59+iqOjI8OGDeOHH35gwoQJKJVK+vXrx7PPPntT60iweJOUSiVLlxp/D8uQYcOGtVgNxrvFwsKC1NTUu70NIYQQQgghHlhKpVLvoMbV1VX391mzZjFr1qxbXkeCRSGEEEIIIYRoQbf6zuK9QoJFIYQQQgghhGhBt/sx1DtFEtwIIYQQQgghRAt697P9tzQ+7KU+LbKPW6W82xu4l2VnZzerb/JHGRkZupS0t2rv3r3k5uYa7ddqtWg0mhZZ64/zDh069IbHxcfHk5aWpvt56dKlvPHGG1RXV/PLL78wZcoUXnnlFcaPH8+aNWtacstCCCGEEELc0xQKxS39uVfIY6gmDB482GS/j49Pi621adMmPD096d69u8H+srIyNBoN48ePb7E1W0JDQwNLliyhvLycDz/8EDMzMyIjI4mJicHV1ZWamhp8fX0ZOHAgjz322DXnM1o642ZKXRgorwCNJRbuWOkMA+UaoLFkQ9H8CP323wuwGyvzYGqMsVIXN1s643Tch3p9ndSzTJfvMFY6w8hcgNHPZKrsyKmly/XaO4cGN+4h6n39PYTPM11mwtj9fjvc8B5io27qPpgst2FsDzdRquRm5xNCCCGEuNoDHSzW1NQQGhpKcXExdXV1vPrqq6SlpWFra0t5eTleXl6cPHkStVrNihUryMrKwtbWlsrKSmbPns2ePXuws7PDxcWFxMREzM3NKS4uxtPTkxkzZhhdNzQ0lJMnT1JVVUVAQABubm7k5ORw+PBh3Nzc2LlzJ9u3b6eyshIbGxsSEhJYtWoVx48fJyEhgcmTJxMWFsaFCxcAWLhwId26dUOj0ZCWlkZ9fT1Dhw5l1qxZZGZmkpKSgoWFBU5OTkRGRlJdXY1arebSpUs4Ojrq9nXs2DGWLGn8x2f79u2Jjo6mTZs2Rj9HQ0MDixYtora2lvfffx+lsvGg2s7OjvXr1+Pj40OPHj1IS0vDwsKiJb4yIYQQQggh7nn30OHgLXmgg8X09HRsbW2Ji4ujoqICHx8fLCwsdHUPMzIyAMjNzSUnJ4eNGzdSU1ODt7e33lynT58mMzOT6upqBg0aZDRYrKioYO/evWzYsAGA3bt307NnTwYNGoSnpyePPPIIFy9eJDk5GaVSSWBgIAcPHmT69Onk5eURFBREbGwsAwcO5KWXXuLEiROEhoaSkJBAYmIimZmZWFpasmzZMk6dOkV8fDybN29GpVIRHR1Neno6Wq0Wd3d35syZw4EDB/jpp58ACA8PJzo6Gjc3NzQaDUlJScyZM8fo/Vu9ejXOzs60atWq2XF5XFwcKSkpREREUFRUxOjRowkJCZGAUQghhBBCPBAkG+p9ID8/n2eeeQYAlUqFq6sru3fvxtnZWe+6J554glatWtGqVSt69uypN5e7uztmZmaYmZlhZWVldE2VSsWCBQsIDw+noqKCMWPGNOtXKpWYm5sTHByMtbU1Z86coba2ttk1eXl5/Pjjj3z99dcAlJeXU1RURNeuXXVrq9VqfvnlF9zc3FCpVAD079+fXbt2UV9fj4eHBwC9e/fGzMxM9zkXL258dK2mpgYnJyeT92/YsGG88847zJo1i5UrV/LGG2+g1Wo5fPgwb775Jm+++SYXL14kNDSU9PR0/P39Tc4nhBBCCCHE/eBeeu/wVjzQwaKrqyv79u1jxIgRVFRUkJeXh4ODg96X6+bmRmpqKvX19dTW1nLkyBG9ua73F6K0tJTDhw+zYsUKtFotHh4ejB07FoVCQUNDA7m5uWRlZaHRaKisrMTHx4eGhgaUSiX19fUAuLi4MGbMGLy9vTl37hwajQZHR0cKCgqorq7GwsKCWbNmERISQn5+PleuXMHa2po9e/boAuH9+/czfPhwjhw5ogtGnZ2diYmJoVOnTvz888+UlZWZ/Cxdu3YFICoqihdeeIEnn3ySvn378vbbb5OSkoKzszPt27enc+fOcqoohBBCCCHEn8wDXTqjurqa8PBwCgsL0Wq1+Pv7k5GRQUREBK6urmRkZFBQUIBarWblypXs2LEDGxsbSkpKWLx4MT/88IPuncXPP/+cf/zjHwA8++yz7N692+CaTe/55eXloVQqGTJkCFOnTuXzzz9n/fr1LF++nKioKKqrqwGwsLDgxRdf5O9//zsTJkzgb3/7G6+99hphYWH83//9HxUVFQQFBTFs2DAyMjL4/PPPUSgUPPfcc0yfPp0tW7aQkpKCUqnE0dGRd99tTMQyb948SktLcXFxYd++fXz77bccOnSImJgYamtrUSgUvPvuu3qnrE3i4+Oxs7PDz88PaMzmOnfuXDIyMigsLCQ2NlY3zxNPPEFoaKjuBFMIIYQQQoj7Wazml1sa//b4Xi20k1vzQAeL1+vcuXN88803TJo0ierqary8vEhJSaFTp053e2v3ncJZ8/TaHD9833RGz3s4G2ph8AKDe3BcHm0w0+ajsVEARrNmnopeptfeecFcAKMZQosjY/T39k5I476NZA4FOP3BKr2+TrOnm8zoaSy7qqnMoYVzF+r1OS5bYjITqbHMr2AiG6qJPRj7TIZ+H6Hxd9JkZlMjGUcN/T44Lo82uQeT+zaV4bWF5xNCCCHE9Vu28eAtjZ/74hMttJNbI0c918HGxoZDhw4xbtw4FAoF48ePv2aguGPHDpKTk/Xam5Ln/BlUV1cTGBio1+7s7ExkpP4/KoUQQgghhBCSDfWBolQqWbp06Q2NGTZsGMOGDbtNO7ozLCwsSE1NvdvbEEIIIYQQ4k/lfklwo7zbGxBCCCGEEEIIce+Rk0UhhBBCCCGEaEH3S51FSXAjhBBCCCGEEC3owy8O39L4Wc8/3kI7uTXyGOo1ZGdnk56ebrQ/IyODHTt2tMhae/fuJTc312i/VqtFo9G0yFp/nHfo0KE3PC4+Pp60tDSDfb/88gs9e/bkl19uLW2wEEIIIYQQfzYKxa39uVfIY6jXMHjwYJP9Pj4+LbbWpk2b8PT0pHv37gb7y8rK0Gg0jB8/vsXWvF02bNjAq6++ymeffUavXtdfJ6Zw9ny9NscP3vvTls4wVf6haH6Efvt7jW3GyhvcTNkDU+UQTN3X08sS9Po6zQ0yXYrDyHzGSmoARkuImPz+TJT8MHofbqJ0xsmgtw3uoUtCrOnSJ/dC6Qwj+zP5e2fs3pkYI4QQQgh998uJ3AMfLNbU1BAaGkpxcTF1dXW8+uqrpKWlYWtrS3l5OV5eXpw8eRK1Ws2KFSvIysrC1taWyspKZs+ezZ49e7Czs8PFxYXExETMzc0pLi7G09OTGTNmGF03NDSUkydPUlVVRUBAAG5ubuTk5HD48GHc3NzYuXMn27dvp7KyEhsbGxISEli1ahXHjx8nISGByZMnExYWxoULFwBYuHAh3bp1Q6PRkJaWRn19PUOHDmXWrFlkZmaSkpKChYUFTk5OREZGUl1djVqt5tKlSzg6Our2dezYMZYsafxHevv27YmOjqZNmzY3dE8vX77Mjz/+yLZt2/D29ub8+fPY2trexLcjhBBCCCGEuFse+GAxPT0dW1tb4uLiqKiowMfHBwsLC109xIyMDAByc3PJyclh48aN1NTU4O3trTfX6dOnyczMpLq6mkGDBhkNFisqKti7dy8bNmwAYPfu3fTs2ZNBgwbh6enJI488wsWLF0lOTkapVBIYGMjBgweZPn06eXl5BAUFERsby8CBA3nppZc4ceIEoaGhJCQkkJiYSGZmJpaWlixbtoxTp04RHx/P5s2bUalUREdHk56ejlarxd3dnTlz5nDgwAF++uknAMLDw4mOjsbNzQ2NRkNSUhJz5sy5oXv61VdfMWLECCwtLRk1ahQbN25k6tSpNzSHEEIIIYQQf1b3S+mMBz5YzM/P55lnngFApVLh6urK7t27cXZ21rvuiSeeoFWrVrRq1YqePXvqzeXu7o6ZmRlmZmZYWVkZXVOlUrFgwQLCw8OpqKhgzJgxzfqVSiXm5uYEBwdjbW3NmTNnqK2tbXZNXl4eP/74I19//TUA5eXlFBUV0bVrV93aarWaX375BTc3N1QqFQD9+/dn165d1NfX4+HhAUDv3r0xMzPTfc7Fixsfe6upqcHJyem67uPVNBoNrVq1IjAwkKqqKs6cOcNrr72GUnm/HMgLIYQQQghh3H0SK0qw6Orqyr59+xgxYgQVFRXk5eXh4OCg978Bbm5upKamUl9fT21tLUeOHNGb63r/B6G0tJTDhw+zYsUKtFotHh4ejB07FoVCQUNDA7m5uWRlZaHRaKisrMTHx4eGhgaUSiX19fUAuLi4MGbMGLy9vTl37hwajQZHR0cKCgqorq7GwsKCWbNmERISQn5+PleuXMHa2po9e/boAuH9+/cz/P9n7+/jorrOhf//MwMMkUAjU6wGkwkPI3q3JJ70rq1Hg0RNGgPFtCj4lGCMecBTxGCmGsQpiAoxICaCiameFmoi4CAm9E40HiWnIv2qxLvx8Sg/9RYB9UCsYqbFGRB+f1DnxMzsrQ4kKlzv18vXK6y1197XXnsgLPbe1/XEExw9etSxGA0ODmbFihUEBgayf/9+mpubb2k+jx8/ztWrVx13TQFmzZrFZ599xvjx429pX0IIIYQQQtyNtL1ktdjnS2fY7XbMZjNnzpzBZrPx3HPPUV5eTkZGBqGhoZSXl3Pq1ClMJhPvvvsuO3fuxN/fn3PnzrFkyRL+8pe/ON5ZLCkpYdWqVQCMHj2a6upql8fs7OwkPT2d2tpatFotjz/+OC+//DIlJSV88MEH5OXlsXTpUux2OwA6nY7Jkyfz1FNPER8fz2OPPcaLL75IWloaX331FVarlaSkJMaPH095eTklJSVoNBrGjh1LYmIif/rTnygqKkKr1WIwGFi+vCsZy4IFC2hqaiIkJITPP/+cTz/9lMOHD7NixQra29vRaDQsX77c6S7rNfn5+ZSXl+Pv7w/Avffey9ChQzEYDCQkJDi2++STTygrK+P3v/99j103IYQQQggh7lTv/Z//6tb4V37xv3ooku7p84vFm3XhwgW2bdvGjBkzsNvtREdHU1RURGBg4O0OrVf57w82ObUNnBFPQ8YbTu0PZHRlTr2Ts6G6ar/Wp5bJ8kLVX5z6vh8xSjV7qWIGVTeyXwKceW2xU59h5TL1eVCITynLLaCYXdXVmGvjlDKHgkpmWrWsogqZQ+t+bXIZw0Nrct3LhvpqqlO74a1s1RhU43bjnHo6G6ratRBCCCH6qt993L3F4svRd8Zisc8/hnqz/P39OXz4MJMmTUKj0RAXF3fDheLOnTspLCx0ar+WPOduYLfbmT17tlN7cHAwmZnyC6EQQgghhBDfpNX2jsdQZbF4k7RaLdnZ2bc0Zvz48Xf9e3o6nY4NGzbc7jCEEEIIIYS4a/SStaIsFoUQQgghhBCiJ/WW0hlSy0AIIYQQQgghhBNJcCOEEEIIIYQQPajw0+PdGv/8U0N7KJLukcdQhRBCCCGEEKIH9ZY6i7JYdGHXrl2cO3eOKVOmuOwvLy/nvvvu65HkNTU1Nfj5+TFs2DCX/TabjYqKCuLi4rp9rG/u9+mnn6aysvKWxuXn5/POO+/wn//5nwwcOBDoKisyZswYli5dyk9/+lMmTpzIj370Izo7O7Hb7UycOJFnn332pvbfmJ3n1DY4db5qyQh3ylZ8V6UzXJUvgK4SBqplMBTKEaiWSlAqe+DGGFAuy3D27bVO7YHzEgGV0hnzFzm1G/KyVMecK/idUzvA/Ukvq5dyUCohojYPCvtzt3yHYukMhXIkajGoXiO1vu/qM3QLJVvgf66TEEII0Zv1krWivLPoypgxYxQXigCxsbE9luV08+bNNDU1KfY3NzdjsVh65Fg9JSgoiK1btzq+/uSTT7j//vsdXxuNRjZs2MD777/PBx98wK5du255USqEEEIIIcTdSqPp3r87RZ+8s9jW1kZqaioNDQ1cvXqVWbNmUVxcjF6vp6WlhejoaOrq6jCZTKxZs4YdO3ag1+tpbW1l3rx57Nu3j4CAAEJCQli3bh1eXl40NDQQFRXFnDlzFI+bmppKXV0dV65cISEhAaPRSFVVFUeOHMFoNFJZWcn27dtpbW3F39+fgoIC1q5dy4kTJygoKGDmzJmkpaVx8eJFABYvXszQoUOxWCwUFxfT0dHBuHHjSE5OpqKigqKiInQ6HUFBQWRmZmK32zGZTFy+fBmDweCI6/jx4yxb1nV3o3///mRlZeHn56d4HlFRUWzbto3nn38egM8++4yxY8e63NbLy4uEhAQ+/PBDxo0bd6uXSgghhBBCCHGb9MnFYmlpKXq9ntzcXKxWK7Gxseh0OhISEnjyyScpLy8H4NixY1RVVVFWVkZbWxsxMTFO+zp79iwVFRXY7XYiIiIUF4tWq5Wamho2bdoEQHV1NeHh4URERBAVFcWgQYO4dOkShYWFaLVaZs+ezaFDh0hMTKS2tpakpCRycnIYOXIk06dP5/Tp06SmplJQUMC6deuoqKjA29ublStX0tjYSH5+Plu2bMHX15esrCxKS0ux2WyEhYWRkpLCgQMH2Lt3LwBms5msrCyMRiMWi4X169eTkpKiOH8BAQH069eP+vp6Ojo6GDRoEN7e3qrbX1vgCiGEEEII0dvJO4t3sZMnTzJq1CgAfH19CQ0Npbq6muDgYKftHn74YTw8PPDw8CA8PNxpX2FhYXh6euLp6ck999yjeExfX18WLVqE2WzGarUyceLE6/q1Wi1eXl7Mnz8fHx8fzp8/T3t7+3Xb1NbWsmfPHscjoC0tLdTX1zNkyBDHsU0mEwcPHsRoNOLr6wvAiBEj2L17Nx0dHURGRgIwfPhwPD09Hee5ZEnXe0dtbW0EBQXdcA6jo6P5+OOPaW9vJyYmhurqasVtGxsbGTRo0A33KYQQQgghRG/QS9aKfbN0xvvvv099fT2pqalYrVaeeeYZvL29yc/PJzQ0lPLyck6dOkVMTAzp6els3LjRsSjKzMy87jHUkpISVq1aBcDo0aMVF01NTU2UlpYyd+5cbDYbkZGR7N69G7PZzIQJExg4cCBmsxmLxUJrayuxsbGkp6djMBhITk6mrKyM7OxswsPDiYmJ4cKFC1gsFuLj45kxYwYfffQROp2O5ORkFi5cyKxZs/jwww/x8fFh+fLlDB48GIAvv/wSk8nE0aNHSUpKorKyksmTJ7N69WoCAwPZv38/zc3NTJgwweV55OfnExAQwC9+8QteeOEF7r33Xn7/+9+Tl5dHSEgIP/3pT5k/f77jDqrdbuell17ilVdecSzQhRBCCCGE6M1KPjvRrfFTxxp7KJLu6ZN3FuPj4zGbzUybNg2bzUZSUpLj0dOvGzp0KJGRkcTHx+Pv74+Xl5fjbtytGjBgAM3NzUydOhWtVssLL7yAp6cnw4cPJzc3l7y8PPr168fUqVMd2zc1NfHoo4/S1tZGTk4OiYmJpKWlsWnTJqxWK0lJSej1el566SWeffZZNBoNY8eOZfDgwcydO5eEhAS0Wi0GgwGTyQTAggULmDZtGiEhIXh5eQGQkZHBwoULaW9vR6PRsHy56yyiX+fn58egQYN48MEH0Wqvz5N04sQJnnvuOTQajWORfbMLRaVsjD2VvVSt71vJhqqSEVItk2VD5grn/f12oWoMjctynfoGLzbRkPGG85iMriyfqllm07Oc+5YsUo9bYX9ujXGRZRO6Mm2qxq00d2rZZ5WygLp7/W7hnG6UiVT1XN04J3f2584YV8e/mRiEEEIIcefok4tFnU7HihXX/0L5q1/9yvHfsbGxQFdJiO9973uUlZVht9uJjo7m/vvvZ+7cuY5tf/aznzn+W+1RTI1GQ2am8y9PU6dOdSwQ//jHP7oc+9FHHzn++5133nHqj42NdcR8TUxMjMt3LN9++22ntvDwcDZs2KAY+9d9/dzz8/Md/31tMQrwf//v/72pfQkhhBBCCNEbaXvJY6h9crF4s/z9/Tl8+DCTJk1Co9EQFxdHYGCg6pidO3dSWFjo1H4tec7dwG63M3v2bKf24OBglwteIYQQQgghxP/Q9JKXFmWxqEKr1ZKdnX1LY8aPH99jNRhvF51Od9N3GoUQQgghhBDX6yVrRVksCiGEEEIIIURP6i2lM7Q33kQIIYQQQgghRF/TJ0tnCCGEEEIIIcS3ZcvuU90a/6vHQnooku6Rx1Bv0q5duzh37hxTpkxx2V9eXs59993XI+8r1tTU4Ofnx7Bhw1z222w2KioqiIuL6/axvrnfp59+msrKylsal5+fzzvvvMN//ud/MnDgQKArk+yYMWNYunQpsbGxqjUov85VqYIH38hQT93vTumMHtrfjUpnnJn3ussYDG+/wZnXnEsFGFZ2xaXUd27t753a7098oSsGhRIZ7pZeaMzOc+obnDqfs7mrndoDTcmq+1Mq6wFwZv4ipz5DXpbq3NUv+K1T+4NvdiVfUiqdoVq24jdm576cpdTNme8yhofezVMcA8plK1ydk+HtN1RjUI1bpU/pM6T0Paa2P9UxCtfCVbujT21/KiVJhBBCiLtFb8mGKo+h3qQxY8YoLhShq3xFTyW22bx5M01NTYr9zc3NWCyWHjlWTwkKCmLr1q2Orz/55BPuv//+2xiREEIIIYQQt4dGo+nWvzuF3Fn8p7a2NlJTU2loaODq1avMmjWL4uJi9Ho9LS0tREdHU1dXh8lkYs2aNezYsQO9Xk9rayvz5s1j3759BAQEEBISwrp16/Dy8qKhoYGoqCjmzJmjeNzU1FTq6uq4cuUKCQkJGI1GqqqqOHLkCEajkcrKSrZv305rayv+/v4UFBSwdu1aTpw4QUFBATNnziQtLY2LFy8CsHjxYoYOHYrFYqG4uJiOjg7GjRtHcnIyFRUVFBUVodPpCAoKIjMzE7vdjslk4vLlyxgMBkdcx48fZ9myrrtE/fv3JysrCz8/P8XziIqKYtu2bTz//PMAfPbZZ4wdO7YHrowQQgghhBDidpDF4j+Vlpai1+vJzc3FarUSGxuLTqdz1EcsLy8H4NixY1RVVVFWVkZbW5vLwvdnz56loqICu91ORESE4mLRarVSU1PDpk2bAKiuriY8PJyIiAiioqIYNGgQly5dorCwEK1Wy+zZszl06BCJiYnU1taSlJRETk4OI0eOZPr06Zw+fZrU1FQKCgpYt24dFRUVeHt7s3LlShobG8nPz2fLli34+vqSlZVFaWkpNpuNsLAwUlJSOHDgAHv37gXAbDaTlZWF0WjEYrGwfv16UlJSFOcvICCAfv36UV9fT0dHB4MGDcLb27u7l0UIIYQQQoi7zrd9c7Cjo4OMjAyOHz+OTqdj2bJlPPTQQ47+P//5z6xZs4bOzk5+9KMfkZ6e7tYdS1ks/tPJkycZNWoUAL6+voSGhlJdXU1wcLDTdg8//DAeHh54eHgQHh7utK+wsDA8PT3x9PTknnvuUTymr68vixYtwmw2Y7VamThx4nX9Wq0WLy8v5s+fj4+PD+fPn6e9vf26bWpra9mzZ4/jEdCWlhbq6+sZMmSI49gmk4mDBw9iNBrx9fUFYMSIEezevZuOjg4iIyMBGD58OJ6eno7zXLKk6/2ltrY2goKCbjiH0dHRfPzxx7S3txMTE3NT7ygKIYQQQgjR23zbpTN27NiB3W6ntLSUL774gjfeeIN3330X6LohlZOTwx//+Ef0ej3r1q3j4sWL6PX6Wz6OZEP9p/fff5/6+npSU1OxWq0888wzeHt7k5+fT2hoKOXl5Zw6dYqYmBjS09PZuHGjY1GUmZl53WOoJSUlrFq1CkA1sUtTUxOlpaXMnTsXm81GZGQku3fvxmw2M2HCBAYOHIjZbMZisdDa2kpsbCzp6ekYDAaSk5MpKysjOzub8PBwYmJiuHDhAhaLhfj4eGbMmMFHH32ETqcjOTmZhQsXMmvWLD788EN8fHxYvnw5gwcPBuDLL7/EZDJx9OhRkpKSqKysZPLkyaxevZrAwED2799Pc3MzEyZMcHke+fn5BAQE8Itf/IIXXniBe++9l9///vfk5eUREhJySwluhBBCCCGEuNt9svd0t8ZH/SxItT87O5tHHnmE6OhoACIiIqiqqgKgqqqKLVu24OXlRX19PXFxcfzqV79yKw65s/hP8fHxmM1mpk2bhs1mIykpyfHo6dcNHTqUyMhI4uPj8ff3x8vLy3E37lYNGDCA5uZmpk6dilar5YUXXsDT05Phw4eTm5tLXl4e/fr1Y+rUqY7tm5qaePTRR2lrayMnJ4fExETS0tLYtGkTVquVpKQk9Ho9L730Es8++ywajYaxY8cyePBg5s6dS0JCAlqtFoPBgMnUlY1ywYIFTJs2jZCQELy8vADIyMhg4cKFtLe3o9FoWL7cdWbRr/Pz82PQoEE8+OCDaLWSO0kIIYQQQvRN3U1SU1paSmlpqePrKVOmXJds02q1Op4YBPDw8KC9vR1PT08uXrzI3r17HTeJZsyYwb/8y784PTF5U+chdxZvzYULF9i2bRszZszAbrcTHR1NUVERgYGBtzu0XkGp9IJbpS5cjOnW/twoneGqHAL8sySCSvkHpTIK5//wvlP7oFnPdsXgRukMtTIFSn1KxwHl6+dOuQZXpR/gxuUfGtKznGNYski9BIXCtahL+o3LGB4qyHGvdMarqU7threyVWNQjVvt+imV4nCjbIXqGIU+1dIZKuek+BlS+X4RQggh7jRb99V1a/zTP31ItT87O5vhw4cTFRUFdFVu2LVrF9BV8m/jxo2sXbsWgGXLlvHjH//Yse2tkDuLt8jf35/Dhw8zadIkNBoNcXFxN1wo7ty5k8LCQqf2a8lz7gZ2u53Zs2c7tQcHB5OZKb+wCSGEEEIIcc23/Yzdj3/8Yz777DOioqL44osvCAsLc/T96Ec/ora2lr/97W9873vf48CBA8THx7t1HFks3iKtVkt2dvYtjRk/fnyP1WC8XXQ6HRs2bLjdYQghhBBCCHHH+7azoT755JNUV1czdepUOjs7ycrK4g9/+AMGg4Hx48fz2muv8eKLLwIwYcKE6xaTt0IWi0IIIYQQQgjRg7r7zuKNaLVap6f7QkNDHf8dHR3tSH7THbJYFEIIIYQQQogepP2W7yx+VyTBjRBCCCGEEEL0oJ37z3Rr/Pj/beihSLpH7ix+w65duzh37tx1qWm/rry8nPvuu69H3kGsqanBz8+PYcOGuey32WxUVFQQFxfX7WN9c79PP/00lZWVtzQuPz+fd955h//8z/9k4MCBQFd22DFjxrB06VJiY2P529/+xooVKzh79ixXr17l/vvv5/XXX2fAgAE3dQylbJE9ng3Vjcymbo1RiUEtw6SrTKCGlctUM3AqxqCQHRSUM08CnM1d7dQXaEpWz2SpcCy145yZv8ipz5CXxbn895zaAe6f+4r6tXAnG6rCvNbNme8yhofezXMvG+q8153aDW+/oRqDatwqfYqfITcy06qOUcriqpYN1Z2MrGrZgyVTqhBCiDvMt/0Y6ndFiuF9w5gxYxQXigCxsbE9lqxm8+bNNDU1KfY3NzdjsVh65Fg9JSgoiK1btzq+/uSTT7j//vsB6OzsJCkpiSeffJINGzawceNGJk2axCuvvMLVq1dvV8hCCCGEEEJ8pzSa7v27U/S5O4ttbW2kpqbS0NDA1atXmTVrFsXFxej1elpaWoiOjqaurg6TycSaNWvYsWMHer2e1tZW5s2bx759+wgICCAkJIR169bh5eVFQ0MDUVFRzJkzR/G4qamp1NXVceXKFRISEjAajVRVVXHkyBGMRiOVlZVs376d1tZW/P39KSgoYO3atZw4cYKCggJmzpxJWloaFy9eBGDx4sUMHToUi8VCcXExHR0djBs3juTkZCoqKigqKkKn0xEUFERmZiZ2ux2TycTly5cxGP7ntvbx48dZtqzrrk///v3JysrCz89P8TyioqLYtm0bzz//PACfffYZY8eOBeDw4cP4+fnxxBNPOLYfNWoUBoOBmpoaRo4c6fZ1E0IIIYQQ4m6hvZNWfN3Q5xaLpaWl6PV6cnNzsVqtxMbGotPpHDUPy8vLATh27BhVVVWUlZXR1tZGTEyM077Onj1LRUUFdrudiIgIxcWi1WqlpqaGTZs2AVBdXU14eDgRERFERUUxaNAgLl26RGFhIVqtltmzZ3Po0CESExOpra0lKSmJnJwcRo4cyfTp0zl9+jSpqakUFBSwbt06Kioq8Pb2ZuXKlTQ2NpKfn8+WLVvw9fUlKyuL0tJSbDYbYWFhpKSkcODAAfbu3QuA2WwmKysLo9GIxWJh/fr1pKSkKM5fQEAA/fr1o76+no6ODgYNGoS3tzcA9fX1PPjgg05jHnzwQc6ePXtrF0oIIYQQQghxW/W5xeLJkycZNWoUAL6+voSGhlJdXU1wcLDTdg8//DAeHh54eHgQHh7utK+wsDA8PT3x9PTknnvuUTymr68vixYtwmw2Y7VamThx4nX9Wq0WLy8v5s+fj4+PD+fPn6e9vf26bWpra9mzZ4/jEdCWlhbq6+sZMmSI49gmk4mDBw9iNBrx9fUFYMSIEezevZuOjg4iIyMBGD58OJ6eno7zXLKk6z2ltrY2goKCbjiH0dHRfPzxx7S3txMTE0N1dTUAAwcOpLGx0Wn7uro6x5wLIYQQQgjR2/WSG4t9Lxvq+++/T319PampqVitVp555hm8vb3Jz88nNDSU8vJyTp06RUxMDOnp6WzcuNGxKMrMzLzuMdSSkhJWrVoFwOjRox2Lpm9qamqitLSUuXPnYrPZiIyMZPfu3ZjNZiZMmMDAgQMxm81YLBZaW1uJjY0lPT0dg8FAcnIyZWVlZGdnEx4eTkxMDBcuXMBisRAfH8+MGTP46KOP0Ol0JCcns3DhQmbNmsWHH36Ij48Py5cvZ/DgwQB8+eWXmEwmjh49SlJSEpWVlUyePJnVq1cTGBjI/v37aW5uZsKECS7PIz8/n4CAAH7xi1/wwgsvcO+99/L73/+evLw8QkJC+NWvfkV8fDxz5sxh3LhxQFfCoJUrV1JeXo6Hh8e3cEWFEEIIIYS4s+w+6HwD5VY89sjgHoqke/rcncX4+HjMZjPTpk3DZrORlJTkePT064YOHUpkZCTx8fH4+/vj5eXluBt3qwYMGEBzczNTp05Fq9Xywgsv4OnpyfDhw8nNzSUvL49+/foxdepUx/ZNTU08+uijtLW1kZOTQ2JiImlpaWzatAmr1UpSUhJ6vZ6XXnqJZ599Fo1Gw9ixYxk8eDBz584lISEBrVaLwWDAZDIBsGDBAqZNm0ZISAheXl4AZGRksHDhQtrb29FoNCxf7pxl8pv8/PwYNGgQDz74IFrt/+RI0mg0rF27lqysLN57ryuT5aBBg/jd734nC0UhhBBCCNFnyJ3FXu7ChQts27aNGTNmYLfbiY6OpqioiMDAwNsdWq9Wl/Qbp7aHCnLu2tIZrsohwD9LIrhRwqAhc4XzcX67UD1utblzo8+d0hmNWSud2gcveg1QKRmhMnfnCzc6tQ96fnrX/pRKLyi0g0rJCLXrpzYPSnPnTtkKlbjVrl9P7k917m5hvtX6vusYhBBCiG/TXw53787iqHC5s3hH8/f35/Dhw0yaNAmNRkNcXNwNF4o7d+6ksLDQqf1a8py7gd1uZ/bs2U7twcHBZGZK3TIhhBBCCCH6ClksKtBqtWRnZ9/SmPHjx/dYDcbbRafTsWHDhtsdhhBCCCGEEHctKZ0hhBBCCCGEEMJJL1krymJRCCGEEEIIIXqSppesFiXBjRBCCCGEEEL0oM+PnevW+J8Mu7+HIukeubOoYNeuXZw7d44pU6a47C8vL+e+++7rkXcUa2pq8PPzY9iwYS77bTYbFRUVxMXFdftY39zv008/TWVl5S2Ny8/P55133uE///M/GThwINCVPXbMmDEsXbqUv/3tb/z5z3/m8uXLNDU1YTQaASgsLLxhCQ3FLKDuZC91kZkTurJzqu5PIRujW5lDXWS/hK4MmGrZNG8lO+eDOUvV4854w7k943UAzq4scOoLfC2pa38KmVfdyQLa+ObbTu2DF8wDoDE7z7kvdT71C1xnrHzwzUz1bJpKmWndyaZ5B2RDdTebrWRDVY9Bbe6EEEII0UV74036pjFjxiguFAFiY2N7LJnN5s2baWpqUuxvbm7GYrH0yLF6SlBQEFu3bnV8/cknn3D//V1/AXnxxRfZsGEDixYtYuTIkWzYsIENGzZIrUUhhBBCCNEnaDSabv27U/TZO4ttbW2kpqbS0NDA1atXmTVrFsXFxej1elpaWoiOjqaurg6TycSaNWvYsWMHer2e1tZW5s2bx759+wgICCAkJIR169bh5eVFQ0MDUVFRzJkzR/G4qamp1NXVceXKFRISEjAajVRVVXHkyBGMRiOVlZVs376d1tZW/P39KSgoYO3atZw4cYKCggJmzpxJWloaFy9eBGDx4sUMHToUi8VCcXExHR0djBs3juTkZCoqKigqKkKn0xEUFERmZiZ2ux2TycTly5cxGAyOuI4fP86yZV1/ae/fvz9ZWVn4+fkpnkdUVBTbtm3j+eefB+Czzz5j7NixPXBlhBBCCCGEuLvdQeu9bumzi8XS0lL0ej25ublYrVZiY2PR6XSOmojl5eUAHDt2jKqqKsrKymhrayMmJsZpX2fPnqWiogK73U5ERITiYtFqtVJTU8OmTZsAqK6uJjw8nIiICKKiohg0aBCXLl2isLAQrVbL7NmzOXToEImJidTW1pKUlEROTg4jR45k+vTpnD59mtTUVAoKCli3bh0VFRV4e3uzcuVKGhsbyc/PZ8uWLfj6+pKVlUVpaSk2m42wsDBSUlI4cOAAe/fuBcBsNpOVlYXRaMRisbB+/XpSUlIU5y8gIIB+/fpRX19PR0cHgwYNwtvbu7uXRQghhBBCiLueVhaLd7eTJ08yatQoAHx9fQkNDaW6uprg4GCn7R5++GE8PDzw8PAgPDzcaV9hYWF4enri6enJPffco3hMX19fFi1ahNlsxmq1MnHixOv6tVotXl5ezJ8/Hx8fH86fP097e/t129TW1rJnzx7HI6AtLS3U19czZMgQx7FNJhMHDx7EaDTi6+sLwIgRI9i9ezcdHR1ERkYCMHz4cDw9PR3nuWRJ1ztObW1tBAUF3XAOo6Oj+fjjj2lvbycmJobq6uobjhFCCCGEEELcHfpsNtT333+f+vp6UlNTsVqtPPPMM3h7e5Ofn09oaCjl5eWcOnWKmJgY0tPT2bhxo2NRlJmZed1jqCUlJaxatQqA0aNHKy6ampqaKC0tZe7cudhsNiIjI9m9ezdms5kJEyYwcOBAzGYzFouF1tZWYmNjSU9Px2AwkJycTFlZGdnZ2YSHhxMTE8OFCxewWCzEx8czY8YMPvroI3Q6HcnJySxcuJBZs2bx4Ycf4uPjw/Llyxk8eDAAX375JSaTiaNHj5KUlERlZSWTJ09m9erVBAYGsn//fpqbm5kwYYLL88jPzycgIIBf/OIXvPDCC9x77738/ve/Jy8vj5CQEGJjYwHYu3fvdXMjhBBCCCFEX3Dg//ff3Ro/fMjAHoqke/rsncX4+HjMZjPTpk3DZrORlJTkePT064YOHUpkZCTx8fH4+/vj5eXluBt3qwYMGEBzczNTp05Fq9Xywgsv4OnpyfDhw8nNzSUvL49+/foxdepUx/ZNTU08+uijtLW1kZOTQ2JiImlpaWzatAmr1UpSUhJ6vZ6XXnqJZ599Fo1Gw9ixYxk8eDBz584lISEBrVaLwWDAZDIBsGDBAqZNm0ZISAheXl4AZGRksHDhQtrb29FoNCxf7pxR8pv8/PwYNGgQDz74IFqt5EoSQgghhBACes87i332zuLNunDhAtu2bWPGjBnY7Xaio6MpKioiMDDwdofWKymVhnCrdIaLdkefG+UI3InhfOFGlzEMen46Z+Yvcmo35HWV+1AqseCqHMgDS7r2o1Q+QC1u1XIECjGolX84t/b3Tn33J76gWmbizLzXnfoMb7/hsv1an2rZCoVSIWpxn3nNuWSCYeUy6l5+1WUMD/3uLcUxoFy24kzyAucxq99UjcGdchuA4ufLVUmSB9/MVN2f6hiFPtWyI2r7u4UyJtfKxij1uVv6RK1PCCGEuBmHTypXOrgZ4aE/6KFIuqfP3lm8Wf7+/hw+fJhJkyah0WiIi4u74UJx586dFBYWOrVfS55zN7Db7cyePdupPTg4mMxM17XLhBBCCCGEEL3nzqIsFm9Aq9WSnZ19S2PGjx/fYzUYbxedTseGDRtudxhCCCGEEEKI20QWi0IIIYQQQgjRgzS95NaiLBaFEEIIIYQQogf1ljqLkuBGCCGEEEIIIXrQ8bovuzV+6EMBPRRJ90i9AwW7du2itLRUsb+8vJydO3f2yLFqamo4duyYYr/NZsNisfTIsb6533Hjxt3yuPz8fCZPnkx7e7ujLT4+noaGBvbu3cu//uu/8txzz/Hss88ydepUPvnkk54MWwghhBBCiDuaVtO9f3cKeQxVwZgxY1T7rxWe7wmbN28mKiqKYcOGuexvbm7GYrEQFxfXY8fsrsbGRt577z1+/etfO/WNHDmSVatWAfD3v/+d5557juDgYP7X//pfN9yvOyUjFEtnuCiBAe6XwVAtt6EwxlWJAPhnaQE3UverlQ+4lRIi1871XP57Tn33z30FgMYVbzn1DV74qmq5DaU5Ovv2Wqf2wHmJgEqJDpW5a8xa6RzbotdUY1C7forlGtSunxvzoFoGQ6n0iRtlXkDlM+RO3GolVpTmzkW7Wt93HYM786oWgxBCCNEb9dnFYltbG6mpqTQ0NHD16lVmzZpFcXExer2elpYWoqOjqaurw2QysWbNGnbs2IFer6e1tZV58+axb98+AgICCAkJYd26dXh5edHQ0EBUVBRz5sxRPG5qaip1dXVcuXKFhIQEjEYjVVVVHDlyBKPRSGVlJdu3b6e1tRV/f38KCgpYu3YtJ06coKCggJkzZ5KWlsbFixcBWLx4MUOHDsVisVBcXExHRwfjxo0jOTmZiooKioqK0Ol0BAUFkZmZid1ux2QycfnyZQwGgyOu48ePs2xZ1y9I/fv3JysrCz8/P8XzePHFF7FYLIwdO5Yf/vCHitvde++9TJkyhW3btt3UYlEIIYQQQoi7XS/Jb9N3F4ulpaXo9Xpyc3OxWq3Exsai0+kctRDLy8sBOHbsGFVVVZSVldHW1kZMTIzTvs6ePUtFRQV2u52IiAjFxaLVaqWmpoZNmzYBUF1dTXh4OBEREURFRTFo0CAuXbpEYWEhWq2W2bNnc+jQIRITE6mtrSUpKYmcnBxGjhzJ9OnTOX36NKmpqRQUFLBu3ToqKirw9vZm5cqVNDY2kp+fz5YtW/D19SUrK4vS0lJsNhthYWGkpKRw4MAB9u7dC4DZbCYrKwuj0YjFYmH9+vWkpKQozp+Pjw9Lly7l9ddfp6ysTHWuv//973PkyJGbui5CCCGEEELc7SQb6l3u5MmTjBo1CgBfX19CQ0Oprq4mODjYabuHH34YDw8PPDw8CA8Pd9pXWFgYnp6eeHp6cs899yge09fXl0WLFmE2m7FarUycOPG6fq1Wi5eXF/Pnz8fHx4fz589f914gQG1tLXv27GHr1q0AtLS0UF9fz5AhQxzHNplMHDx4EKPRiK+vLwAjRoxg9+7ddHR0EBkZCcDw4cPx9PR0nOeSJV2PrbW1tREUFHTDORwxYgSjRo3i7bffVt3u7NmzDBo06Ib7E0IIIYQQoje4k9477I4+u1gMDQ3l888/58knn8RqtVJbW8sDDzzg9FcAo9HIhg0b6OjooL29naNHjzrt62b/ctDU1MSRI0dYs2YNNpuNyMhInnnmGTQaDZ2dnRw7dowdO3ZgsVhobW0lNjaWzs5OtFotHR0dAISEhDBx4kRiYmK4cOECFosFg8HAqVOnsNvt6HQ6kpOTWbhwISdPnuQf//gHPj4+7Nu3z7EQ/uKLL3jiiSc4evSoYzEaHBzMihUrCAwMZP/+/TQ3N9/UOaWkpDB58mSamppc9lutViwWyw0XlEIIIYQQQog7S58tnWG32zGbzZw5cwabzcZzzz1HeXk5GRkZhIaGUl5ezqlTpzCZTLz77rvs3LkTf39/zp07x5IlS/jLX/7ieGexpKTEkdBl9OjRVFdXuzxmZ2cn6enp1NbWotVqefzxx3n55ZcpKSnhgw8+IC8vj6VLl2K32wHQ6XRMnjyZp556ivj4eB577DFefPFF0tLS+Oqrr7BarSQlJTF+/HjKy8spKSlBo9EwduxYEhMT+dOf/kRRURFarRaDwcDy5V2JThYsWEBTUxMhISF8/vnnfPrppxw+fJgVK1bQ3t6ORqNh+fLlTndZr8nPzycgIIBp06YBcOjQIaZOncqnn35KY2Mjr776KkajEa1Wy9WrV0lISODnP/95T19CIYQQQggh7kinz/6tW+ODAvU9FEn39NnF4s26cOEC27ZtY8aMGdjtdqKjoykqKiIwMPB2h9YrnZm/yKnNkJflVibSHs+G6sYYVxkp4Z9ZKdWyoSplCFXJpulW3BlvOPdlvA6gmHFUNQaF69T4pvOd5cEL5qmOcRX3tdjV5q4hc4XzmN8uVM+mqbA/V59H6PpMujMPrrKrPvhmpmoM7mQBBZXPUA9lFb3R3Kl+9t3JhupO9mCVGNzKhurGNRdCCNE31Z3r3mLxofvvjMVin30M9Wb5+/tz+PBhJk2ahEajIS4u7oYLxZ07d1JYWOjUfi15zt3Abrcze/Zsp/bg4GAyM12noxdCCCGEEEKAVhLc9A1arZbs7OxbGjN+/HjGjx//LUX03dDpdGzYsOF2hyGEEEIIIcRdp5esFdHe7gCEEEIIIYQQQtx55M6iEEIIIYQQQvSg3nJnURLcCCGEEEIIIUQPOtd8qVvj7x/Qv0fi6C55DFXBrl27KC0tVewvLy9n586dPXKsmpoajh07pthvs9mwWCw9cqxv7nfcuHG3PC4/P5/Jkyc7ajQCxMfH09DQwN69e0lJSblu+9zcXMrLy7sdrxBCCCGEEHcDTTf/3SnkMVQFY8aMUe2PjY3tsWNt3ryZqKgohg0b5rK/ubkZi8VCXFxcjx2zuxobG3nvvff49a9/3aP7bUjPcmp7YMkit9Lcu9qXY393QukMN8oHqMZwC/PwwJJFqmNAuYyCW2VMFMpZqB2ncVmuUzvA4MUm9z4P7pRKcBHbtfh6an83vOZuHKen99dTYySG68cIIYTovTS95DnUPrtYbGtrIzU1lYaGBq5evcqsWbMoLi5Gr9fT0tJCdHQ0dXV1mEwm1qxZw44dO9Dr9bS2tjJv3jz27dtHQEAAISEhrFu3Di8vLxoaGoiKimLOnDmKx01NTaWuro4rV66QkJCA0WikqqqKI0eOYDQaqaysZPv27bS2tuLv709BQQFr167lxIkTFBQUMHPmTNLS0rh48SIAixcvZujQoVgsFoqLi+no6GDcuHEkJydTUVFBUVEROp2OoKAgMjMzsdvtmEwmLl++jMFgcMR1/Phxli3r+qWmf//+ZGVl4efnp3geL774IhaLhbFjx/LDH/6wh66KEEIIIYQQ4k7RZxeLpaWl6PV6cnNzsVqtxMbGotPpHLUQrz02eezYMaqqqigrK6OtrY2YmBinfZ09e5aKigrsdjsRERGKi0Wr1UpNTQ2bNm0CoLq6mvDwcCIiIoiKimLQoEFcunSJwsJCtFots2fP5tChQyQmJlJbW0tSUhI5OTmMHDmS6dOnc/r0aVJTUykoKGDdunVUVFTg7e3NypUraWxsJD8/ny1btuDr60tWVhalpaXYbDbCwsJISUnhwIED7N27FwCz2UxWVhZGoxGLxcL69eudHif9Oh8fH5YuXcrrr79OWVnZdX179uzhueeec3xdX19PcnLyrV0gIYQQQggh7lLa3nFjse8uFk+ePMmoUaMA8PX1JTQ0lOrqaoKDg522e/jhh/Hw8MDDw4Pw8HCnfYWFheHp6Ymnpyf33HOP4jF9fX1ZtGgRZrMZq9XKxIkTr+vXarV4eXkxf/58fHx8OH/+/HXvBQLU1tayZ88etm7dCkBLSwv19fUMGTLEcWyTycTBgwcxGo34+voCMGLECHbv3k1HRweRkZEADB8+HE9PT8d5LlnS9chkW1sbQUFBN5zDESNGMGrUKN5+++3r2keOHMmqVascX+fmun6cUAghhBBCiN7o234MtaOjg4yMDI4fP45Op2PZsmU89NBDTtu8/PLLjB8/nmnTprl1nD67WAwNDeXzzz/nySefxGq1UltbywMPPOB0YY1GIxs2bKCjo4P29naOHj3qtK+b/TA0NTVx5MgR1qxZg81mIzIykmeeeQaNRkNnZyfHjh1jx44dWCwWWltbiY2NpbOzE61WS0dHBwAhISFMnDiRmJgYLly4gMViwWAwcOrUKex2OzqdjuTkZBYuXMjJkyf5xz/+gY+PD/v27XMshL/44gueeOIJjh496liMBgcHs2LFCgIDA9m/fz/Nzc03dU4pKSlMnjyZpqamm9peCCGEEEKI3u7bvrO4Y8cO7HY7paWlfPHFF7zxxhu8++67123z1ltvcfny5W4dp8+WzrDb7ZjNZs6cOYPNZuO5556jvLycjIwMQkNDKS8v59SpU5hMJt5991127tyJv78/586dY8mSJfzlL39xvLNYUlLiuJM2evRoqqurXR6zs7OT9PR0amtr0Wq1PP7447z88suUlJTwwQcfkJeXx9KlS7Hb7QDodDomT57MU089RXx8PI899hgvvvgiaWlpfPXVV1itVpKSkhg/fjzl5eWUlJSg0WgYO3YsiYmJ/OlPf6KoqAitVovBYGD58q7kIwsWLKCpqYmQkBA+//xzPv30Uw4fPsyKFStob29Ho9GwfPlyp7us1+Tn5xMQEOD4C8WhQ4eYOnUqn376KY2NjdfNB3TdWQwJCenRpEBCCCGEEELcqf52sXuLNL3/91T7s7OzeeSRR4iOjgYgIiKCqqoqR/+2bdv4r//6Lzw9Pa/7vf1W9dnF4s26cOEC27ZtY8aMGdjtdqKjoykqKiIwMPB2h9Yr/fdG5xIhA6fHucyMOXixCVDJwKmWCfE7yoZ6Zv4ilzEY8rJoyHjDeUzG6137U+hztT9DXle2U1eZVx/MTleNWy07Z/1vzM59OUtpWPqm8/7MC7riVsj8eGbe685xv911jl9+usOpL+CpJziTvMCpHcCw+k3qX89wju2Nrjal+JTmB6B+wW+d+97MpO7XJpcxPLQmV3F+QHlez7ya6tRueCu7a4zC/lTjduP6qc2d0v5Uxyj0uZpT6JpX1eunlElW4Rq5HYPa3CnNgxvXXO36SaZUIYTovS5e+qpb47d/+sl1ZfymTJnClClTHF+npaXx85//3PF62eOPP86OHTvw9PSktraW1atXs3r1atasWdOtxWKffQz1Zvn7+3P48GEmTZqERqMhLi7uhgvFnTt3UlhY6NR+LXnO3cButzN79myn9uDgYDIzXZcTEEIIIYQQQkB3X1n85uLwm3x9ffn73//u+Lqjo8ORi+TDDz/kv//7v5k5cyaNjY14eXkxePDgG5YGdEUWizeg1WrJzs6+pTHjx49n/Pjx31JE3w2dTseGDRtudxhCCCGEEELcdb7tdxZ//OMf89lnnxEVFcUXX3xBWFiYo2/Bgv95Ouva62PuLBRBFotCCCGEEEII0aM0fLtv+j355JNUV1czdepUOjs7ycrK4g9/+AMGg6FHb1rJYlEIIYQQQggh7iJardbp1bDQ0FCn7ebOndut40iCGyGEEEIIIYToQV9dutSt8X79+/dIHN0ldxaFEEIIIYQQoif1kvtxslh0YdeuXZw7d04xA1F5eTn33XdfjzwPXFNTg5+fH8OGDXPZb7PZqKioIC4urtvH+uZ+n376aSorK29pXH5+Pn/+858pKSlxZFyKj48nLy+PxsZGEhISyMvLc9R8AYiJieFHP/oRb7zhXA7imxTLNaikmL+VUhdqfd0qnaEQX0N6lusYlixSPSd3UvcrlhxQKRGgWlZDIeW/O6UAzuaudmoPNCV3HUeh7IGrcinQVTJFtXyHwjmplkpQOKczr7kuY2BYucyteVAt/6A0326UeACVchJqnwc3PkO38jlx9LkTg1oJETdiUP2Z4kYMbn3/qf0MUDmWEEKIu0AvWSxqb3cAd6IxY8aopqqNjY3tsRdHN2/eTFNTk2J/c3MzFotz7cHbqbGxkffee89lX0hICB9//LHj6+PHj9Pa2vpdhSaEEEIIIcTt19HRvX93iD55Z7GtrY3U1FQaGhq4evUqs2bNori4GL1eT0tLC9HR0dTV1WEymVizZg07duxAr9fT2trKvHnz2LdvHwEBAYSEhLBu3Tq8vLxoaGggKiqKOXPmKB43NTWVuro6rly5QkJCAkajkaqqKo4cOYLRaKSyspLt27fT2tqKv78/BQUFrF27lhMnTlBQUMDMmTNJS0vj4sWLACxevJihQ4disVgoLi6mo6ODcePGkZycTEVFBUVFReh0OoKCgsjMzMRut2Mymbh8+TIGg8ER1/Hjx1m2rOsv3P379ycrKws/Pz/F83jxxRexWCyMHTuWH/7wh9f1DRs2jP/3//4fX331FX5+flRUVBATE8O5c+e6c8mEEEIIIYQQ37E+eWextLQUvV5PSUkJf/jDH3jrrbe4ePEiv/jFLygsLMTDwwOAY8eOUVVVRVlZGWvWrKG5udlpX2fPniU/P5/S0lLWr1+veEyr1UpNTQ0FBQWsX78eDw8PwsPDiYiI4De/+Q2DBg3i0qVLFBYWYrFYuHr1KocOHSIxMRGj0UhSUhJr165l5MiRbNiwgaVLl5KRkcGFCxdYt24dGzduZMuWLdjtdhobG8nPz6eoqIji4mL8/PwoLS2lpKSEsLAwPvjgA6ZOneqIzWw2k56ezoYNGxgzZozqeQD4+PiwdOlSXn/9dex2u1P/z3/+c7Zv305nZycHDx7k0UcfvdlLI4QQQgghxF2vs7OzW//uFH3yzuLJkycZNWoUAL6+voSGhlJdXU1wcLDTdg8//DAeHh6Oxd03hYWF4enpiaenJ/fcc4/iMX19fVm0aBFmsxmr1crEiROv69dqtXh5eTF//nx8fHw4f/487e3t121TW1vLnj172Lp1KwAtLS3U19czZMgQx7FNJhMHDx7EaDTi6+sLwIgRI9i9ezcdHR1ERkYCMHz4cMc7hydPnmTJkq73Y9ra2ggKCrrhHI4YMYJRo0bx9ttvO/XFxMSQkZHBgw8+yE9+8pMb7ksIIYQQQohepePOWfB1R58snfH+++9TX19PamoqVquVZ555Bm9vb/Lz8wkNDaW8vJxTp04RExNDeno6GzdupL29nZiYGDIzM697DLWkpIRVq1YBMHr0aKqrq10es6mpidLSUubOnYvNZiMyMpLdu3djNpuZMGECAwcOxGw2Y7FYaG1tJTY2lvT0dAwGA8nJyZSVlZGdnU14eDgxMTFcuHABi8VCfHw8M2bM4KOPPkKn05GcnMzChQuZNWsWH374IT4+PixfvpzBgwcD8OWXX2IymTh69ChJSUlUVlYyefJkVq9eTWBgIPv376e5uZkJEya4PI/8/HwCAgKYNm0aNpuNyZMn09TUxObNm2lsbHTMx7Rp0wgICGD+/PnU19fzySef3FSCGyGEEEIIIe52l8+f79b47w0a1EORdE+fvLMYHx+P2Wx2LHiSkpIoLy932m7o0KFERkYSHx+Pv78/Xl5ejrtxt2rAgAE0NzczdepUtFotL7zwAp6engwfPpzc3Fzy8vLo16+f4/HQAQMG0NTUxKOPPkpbWxs5OTkkJiaSlpbGpk2bsFqtJCUlodfreemll3j22WfRaDSMHTuWwYMHM3fuXBISEtBqtRgMBkwmEwALFixg2rRphISE4OXlBUBGRgYLFy6kvb0djUbD8uWus4h+k7e3N1lZWdc90npNVFQUH330EcHBwdTX17s1Z0IIIYQQQojbp0/eWbxZFy5cYNu2bcyYMQO73U50dDRFRUUEBgbe7tB6LaV08W6VulArW+FOGQw3Uu27ar/W17jiLaf2wQtfBW6tlINh5TLVMe6UmQA482qq87HeyuZs3hqn9sD5v+6KQaFMwJn5i5z3ldd1fZTiO/vWO07tAIGv/pt6CQqlkhEK7YDivNYlpriM4aG1q9y6FmeSFziPWf2magzulIwAFOfcrfIdamMU+lyVeYGuUi9q18KdsjFuxeDGvKpec6W43Si3ASrnJCU1hBDirnD5bPeSO34v8P4eiqR7+uSdxZvl7+/P4cOHmTRpEhqNhri4uBsuFHfu3ElhYaFTe0JCAk8++eS3FGnPstvtzJ4926k9ODiYzEznX4iEEEIIIYQQX9NL7sfJYlGFVqslOzv7lsaMHz++x2ow3i46nY4NGzbc7jCEEEIIIYS4K3V23jm1ErtDFotCCCGEEEII0ZN6STbUPllnUQghhBBCCCGEOklwI4QQQgghhBA9qKXuTLfG3/eQoYci6R55DFXFrl27OHfuHFOmTHHZX15ezn333dcj7yjW1NTg5+fHsGHDXPbbbDYqKiqIi4vr9rG+ud+nn36aysrKWxqXn5/Pn//8Z0pKShzlROLj48nLy+PNN9/kRz/6Ea+88goAVquVSZMm8fbbbyue3zX//cEmp7aBM+JpyHCu0fhAxuuAckZPV9kJoStDoWpmU6Xsqu5kZHXR7ohPJSNk85+2OvUNiHlaNROp0jkpZWkE5cyhoJyN0Z2MkGfmve485u2ua6qU5dJV5lDoyh6qFoPSZ0Uto6dS5tfTM+e4jCGo6F3FMaA8r3Vz5ju1P/RunmoM7mR+BRTnXDWrqFImWZUxStfCVTZW+GdGVrUY3Mio61YMap99pWyoatdcKZOsG5lfQSU7rto1V4lPCCHEd6yjd7yzKI+hqhgzZoziQhEgNja2x5LZbN68maamJsX+5uZmLBZLjxyrpzQ2NvLee+85tWdkZFBSUsKJEycAePPNN5kyZcoNF4pCCCGEEEL0Cp2d3ft3h+jTdxbb2tpITU2loaGBq1evMmvWLIqLi9Hr9bS0tBAdHU1dXR0mk4k1a9awY8cO9Ho9ra2tzJs3j3379hEQEEBISAjr1q3Dy8uLhoYGoqKimDPH9R0JgNTUVOrq6rhy5QoJCQkYjUaqqqo4cuQIRqORyspKtm/fTmtrK/7+/hQUFLB27VpOnDhBQUEBM2fOJC0tjYsXLwKwePFihg4disViobi4mI6ODsaNG0dycjIVFRUUFRWh0+kICgoiMzMTu92OyWTi8uXLGAz/c4v7+PHjLFvWdXemf//+ZGVl4efnp3geL774IhaLhbFjx/LDH/7Q0a7X6zGbzSxevJiUlBQaGhpYssT5r+5CCCGEEEL0Rp29JMFNn14slpaWotfryc3NxWq1Ehsbi06nc9RELC8vB+DYsWNUVVVRVlZGW1sbMTExTvs6e/YsFRUV2O12IiIiFBeLVquVmpoaNm3qetyyurqa8PBwIiIiiIqKYtCgQVy6dInCwkK0Wi2zZ8/m0KFDJCYmUltbS1JSEjk5OYwcOZLp06dz+vRpUlNTKSgoYN26dVRUVODt7c3KlStpbGwkPz+fLVu24OvrS1ZWFqWlpdhsNsLCwkhJSeHAgQPs3bsXALPZTFZWFkajEYvFwvr160lJcV2UHMDHx4elS5fy+uuvU1ZWdl3fuHHj+I//+A9SU1MpLi5Go9G4dY2EEEIIIYQQt0efXiyePHmSUaNGAeDr60toaCjV1dUEBwc7bffwww/j4eGBh4cH4eHhTvsKCwvD09MTT09P7rnnHsVj+vr6smjRIsxmM1arlYkTJ17Xr9Vq8fLyYv78+fj4+HD+/Hna29uv26a2tpY9e/awdWvXe20tLS3U19czZMgQx7FNJhMHDx7EaDTi6+sLwIgRI9i9ezcdHR1ERkYCMHz4cMc7hydPnnTcAWxrayMoKOiGczhixAhGjRrF22+/7dT3y1/+kitXrjBw4MAb7kcIIYQQQoheo5fUWezT2VDff/996uvrSU1NxWq18swzz+Dt7U1+fj6hoaGUl5dz6tQpYmJiSE9PZ+PGjbS3txMTE0NmZuZ1j6GWlJSwatUqAEaPHk11dbXLYzY1NVFaWsrcuXOx2WxERkaye/duzGYzEyZMYODAgZjNZiwWC62trcTGxpKeno7BYCA5OZmysjKys7MJDw8nJiaGCxcuYLFYiI+PZ8aMGXz00UfodDqSk5NZuHAhs2bN4sMPP8THx4fly5czePBgAL788ktMJhNHjx4lKSmJyspKJk+ezOrVqwkMDGT//v00NzczYcIEl+eRn59PQEAA06ZNw2azMXnyZJqamti8eTMPPPAAAHv37r1uXoQQQgghhOgLLtX+/7o1vn/YkB6KpHv69J3F+Ph4zGazY8GTlJTkePT064YOHUpkZCTx8fH4+/vj5eXluBt3qwYMGEBzczNTp05Fq9Xywgsv4OnpyfDhw8nNzSUvL49+/foxdepUx/ZNTU08+uijtLW1kZOTQ2JiImlpaWzatAmr1UpSUhJ6vZ6XXnqJZ599Fo1Gw9ixYxk8eDBz584lISEBrVaLwWDAZDIBsGDBAqZNm0ZISAheXl5AV2KahQsX0t7ejkajYfly15k8v8nb25usrCxHzEIIIYQQQvRpveSdxT59Z/FmXbhwgW3btjFjxgzsdjvR0dEUFRURGBh4u0PrdRTLB6ikmFcsW+GilAR0lZP4rkpnuCoRAP8sDaFWgkKhtIDaPDQuy3XqG7zYpFp2RG1/5/KdM93eP/cVGpa+6bw/c1eZi8aslc4xLHpNNd2/YqkLldInavs7u7LAqS/wtSTF0gagXCak7tcmlzE8tCbXvfIPaqUXlK65WtxqfUr7c6OUg+oYpXIbLuYUuuZV9fOgVALGjRIiqjGolZRRmgc3rrnaNVL7OaR0vu5cc7WfNUIIIb4dl47Vdmt8/2FhPRRJ9/TpO4s3y9/fn8OHDzNp0iQ0Gg1xcXE3XCju3LmTwsJCp/ZryXPuBna7ndmzZzu1BwcHk5np+hd5IYQQQggh+rxe8s6iLBZvglarJTv71gobjx8/vsdqMN4uOp2ODRs23O4whBBCCCGEuLv0ksdQZbEohBBCCCGEED2ot7zpJ4tFIYQQQgghhOhJHb3jMVRJcCOEEEIIIYQQPejiwcPdGu//iHNd99tB7iwq2LVrF+fOnWPKlCku+8vLy7nvvvt65L3Empoa/Pz8GDZsmMt+m81GRUUFcXFx3T7WN/f79NNPU1lZectj9+zZwzvvvENnZydtbW089dRTPP/882g0Gp577jkyMjIIDQ3l73//O6+88gpjxozh5ZdfvuF+6+bMd2p76N28ns+G6kZmU9UxClkNGzJXuI7htwvVszsqZUJUySKpmA1VLfOri/ge+O3Crv25k9lU4ViN2XnO+0rtutZKmRpdzTf881qoxK2YmVYt+6XCObnKXgr/zNDrTlZRtWyaShk93cjaCW5m03Qjo6dSn6t2tb4bZQhVnW+lGFyMuTbOrWyo7mRxVfvZpZYNVemc3Pgcq/6sUbkWQgghuqGX3I/T3u4A7lRjxoxRXCgCxMbG9lgCm82bN9PU1KTY39zcjMVi6ZFj9YTa2lpWrFhBbm4uGzZs4P333+fkyZP8+7//+3XbWa1WXnzxRZ5++umbWigKIYQQQgjRK3R2du/fHaLP3llsa2sjNTWVhoYGrl69yqxZsyguLkav19PS0kJ0dDR1dXWYTCbWrFnDjh070Ov1tLa2Mm/ePPbt20dAQAAhISGsW7cOLy8vGhoaiIqKYs6cOYrHTU1Npa6ujitXrpCQkIDRaKSqqoojR45gNBqprKxk+/bttLa24u/vT0FBAWvXruXEiRMUFBQwc+ZM0tLSuHjxIgCLFy9m6NChWCwWiouL6ejoYNy4cSQnJ1NRUUFRURE6nY6goCAyMzOx2+2YTCYuX76MwWBwxHX8+HGWLev6K3f//v3JysrCz8/P5TmUlJTwyiuv8IMf/AAAT09PXn/9dX71q1/x4osvAvDVV1/x+uuvM336dH71q1/1yDUTQgghhBDirtBL3lnss4vF0tJS9Ho9ubm5WK1WYmNj0el0jjqI5eXlABw7doyqqirKyspoa2sjJibGaV9nz56loqICu91ORESE4mLRarVSU1PDpk2bAKiuriY8PJyIiAiioqIYNGgQly5dorCwEK1Wy+zZszl06BCJiYnU1taSlJRETk4OI0eOZPr06Zw+fZrU1FQKCgpYt24dFRUVeHt7s3LlShobG8nPz2fLli34+vqSlZVFaWkpNpuNsLAwUlJSOHDgAHv37gXAbDaTlZWF0WjEYrGwfv16UlJSXJ5HfX09kydPvq7N19eX1tZWOv75jfGb3/yGgIAA/vu//9u9CySEEEIIIYS4rfrsYvHkyZOMGjUK6FrohIaGUl1dTXBwsNN2Dz/8MB4eHnh4eBAe7vyyaVhYGJ6ennh6enLPPfcoHtPX15dFixZhNpuxWq1MnDjxun6tVouXlxfz58/Hx8eH8+fP097eft02tbW17Nmzh61btwLQ0tJCfX09Q4YMcRzbZDJx8OBBjEYjvr6+AIwYMYLdu3fT0dFBZGQkAMOHD8fT09NxnkuWdL270tbWRlBQkOJ5DBw4kMbGRn74wx862qxWKzqdDq2268nm1157jccee4xJkybx4x//mJ/+9KeK+xNCCCGEEKI36S05RPtsNtT333+f+vp6UlNTsVqtPPPMM3h7e5Ofn09oaCjl5eWcOnWKmJgY0tPT2bhxI+3t7cTExJCZmXndY6glJSWsWrUKgNGjR1NdXe3ymE1NTZSWljJ37lxsNhuRkZHs3r0bs9nMhAkTGDhwIGazGYvFQmtrK7GxsaSnp2MwGEhOTqasrIzs7GzCw8OJiYnhwoULWCwW4uPjmTFjBh999BE6nY7k5GQWLlzIrFmz+PDDD/Hx8WH58uUMHjwYgC+//BKTycTRo0dJSkqisrKSyZMns3r1agIDA9m/fz/Nzc1MmDDB5XkcOXIEs9nMe++9x4ABA2hra+O3v/0twcHBvPzyy9cluPniiy949dVX2bx5M9///ve/nYsphBBCCCHEHeRvNf+3W+P1I37cQ5F0T5+9sxgfH4/ZbGbatGnYbDaSkpIcj55+3dChQ4mMjCQ+Ph5/f3+8vLwcd+Nu1YABA2hubmbq1KlotVpeeOEFPD09GT58OLm5ueTl5dGvXz+mTp3q2L6pqYlHH32UtrY2cnJySExMJC0tjU2bNmG1WklKSkKv1/PSSy/x7LPPotFoGDt2LIMHD2bu3LkkJCSg1WoxGAyYTCYAFixYwLRp0wgJCcHLywuAjIwMFi5cSHt7OxqNhuXLXWeiBPjRj35ESkoKKSkpXL16lfb2dp588knH+4pf9y//8i/Ex8fz2muv8fvf/95x51EIIYQQQoheq7N3vLPYZ+8s3qwLFy6wbds2ZsyYgd1uJzo6mqKiIgIDA293aL2SYgkKtfIPt1DqQq2vW6UzFPrUSi+c/8P7Tu2DZj0LqJQ9UCu9oJTuXy11v0oJCqWSFu6UAjj79lqn9sB5iYByyQ/V0gtqZRSUygeolX9QmO+6pN+4jOGhghz1cgQKc3Rm/iKndkNelmoM7pS6ABQ/K26V/HBnvtXKVrhTOkNtvm+hZMS1cW6VznDjmrtzrtCzn2Ol72W144CU1RBCiO74256abo3XjxzRQ5F0T5+9s3iz/P39OXz4MJMmTUKj0RAXF3fDheLOnTspLCx0ar+WPOduYLfbmT17tlN7cHAwmZnOvxQJIYQQQgghehdZLN6AVqslOzv7lsaMHz++x2ow3i46nY4NGzbc7jCEEEIIIYS4+/SShzdlsSiEEEIIIYQQPaizl7yzKItFIYQQQgghhOhJHb3jzqIkuBFCCCGEEEKIHnSh6i/dGv/9iFE9FEn3yJ3FG9i1axfnzp1jypQpLvvLy8u57777euQdxZqaGvz8/Bg2bJjLfpvNRkVFBXFxcd0+1jf3+/TTT1NZWXlL4/Lz8/k//+f/8IMf/ACAtrY2UlJS+NnPfgbAM888w49//GPS028+e97Z3NVObYGm5B7LXqrW921kQz33znqXMdz/by+qZiFUzESqko1RMZOsSsZT1UyN6VnO45YsojFrpVP74EWvqY5xlRXW8Fa2atyuskvCjbNSKsWgmvVRIXNo3Zz5LmN46N089QyTCvN6Zt7rzmPefkM1BtXslyp9ip8hN7Jfqo5RyuKqlonUnYysapmAbyF78LVx7mSZdSsbsbvZUN3JjqsQnztZeOHWssxeGyOEEKJ3kcXiDYwZM0a1PzY2tseOtXnzZqKiohQXi83NzVgslh5fLHbH888/z7Rp0wA4efIkJpOJLVu2sH//fsLCwtizZw9WqxVfX9/bHKkQQgghhBDfkQ55Z7FXaGtrIzU1lYaGBq5evcqsWbMoLi5Gr9fT0tJCdHQ0dXV1mEwm1qxZw44dO9Dr9bS2tjJv3jz27dtHQEAAISEhrFu3Di8vLxoaGoiKimLOnDmKx01NTaWuro4rV66QkJCA0WikqqqKI0eOYDQaqaysZPv27bS2tuLv709BQQFr167lxIkTFBQUMHPmTNLS0rh48SIAixcvZujQoVgsFoqLi+no6GDcuHEkJydTUVFBUVEROp2OoKAgMjMzsdvtmEwmLl++jMFgcMR1/Phxli3r+mt3//79ycrKws/P76bm8tKlS/j4+ABgsVh46qmnuP/++/nwww959tln3b1EQgghhBBC3F16yZt+fX6xWFpail6vJzc3F6vVSmxsLDqdzlETsby8HIBjx45RVVVFWVkZbW1txMTEOO3r7NmzVFRUYLfbiYiIUFwsWq1Wampq2LRpEwDV1dWEh4cTERFBVFQUgwYN4tKlSxQWFqLVapk9ezaHDh0iMTGR2tpakpKSyMnJYeTIkUyfPp3Tp0+TmppKQUEB69ato6KiAm9vb1auXEljYyP5+fls2bIFX19fsrKyKC0txWazERYWRkpKCgcOHGDv3r0AmM1msrKyMBqNWCwW1q9fT0pKiuL8FRYW8sknn6DVavne977H0qVLsVqt7N+/n2XLlmE0Gvn1r38ti0UhhBBCCNFndPaSBDd9frF48uRJRo3qeoHU19eX0NBQqqurCQ4Odtru4YcfxsPDAw8PD8LDw532FRYWhqenJ56entxzzz2Kx/T19WXRokWYzWasVisTJ068rl+r1eLl5cX8+fPx8fHh/PnztLe3X7dNbW0te/bsYevWrQC0tLRQX1/PkCFDHMc2mUwcPHgQo9HoeAx0xIgR7N69m46ODiIjIwEYPnw4np6ejvNcsqTrnZi2tjaCgoJU5+/rj6Fes3HjRjo6OnjllVeArsdn/7//7//jX//1X1X3JYQQQgghhLhz9PnFYmhoKJ9//jlPPvkkVquV2tpaHnjgATQazXXbGY1GNmzYQEdHB+3t7Rw9etRpX98co6SpqYkjR46wZs0abDYbkZGRPPPMM2g0Gjo7Ozl27Bg7duzAYrHQ2tpKbGwsnZ2daLVaOv75/HNISAgTJ04kJiaGCxcuYLFYMBgMnDp1Crvdjk6nIzk5mYULF3Ly5En+8Y9/4OPjw759+xwL4S+++IInnniCo0ePOhajwcHBrFixgsDAQPbv309zc/Mtz2lZWRlr165lyJAhAFRUVPDBBx/IYlEIIYQQQvQN33KdxY6ODjIyMjh+/Dg6nY5ly5bx0EMPOfoLCwv5+OOPAYiMjCQpKcmt4/T50hl2ux2z2cyZM2ew2Ww899xzlJeXk5GRQWhoKOXl5Zw6dQqTycS7777Lzp078ff359y5cyxZsoS//OUvjncWS0pKWLVqFQCjR4+murra5TE7OztJT0+ntrYWrVbL448/zssvv0xJSQkffPABeXl5LF26FLvdDoBOp2Py5Mk89dRTxMfH89hjj/Hiiy+SlpbGV199hdVqJSkpifHjx1NeXk5JSQkajYaxY8eSmJjIn/70J4qKitBqtRgMBpYv78rcuWDBApqamggJCeHzzz/n008/5fDhw6xYsYL29nY0Gg3Lly93ust6TX5+PgEBAdfdWTxy5AiLFy9my5Ytjjabzcbjjz9OeXk5999/f49cNyGEEEIIIe5UX+74rFvjA54Yq9q/fft2KisreeONN/jiiy947733ePfddwGor69n3rx5WCwWtFot06ZNIyMjQzGJppo+v1i8WRcuXGDbtm3MmDEDu91OdHQ0RUVFBAYG3u7QehWlsgdqKebv5NIZrlLPwz/Tz6ul1F/6pvP+zAsU5wegcVmuU9/gxSYaMt5wHpPRVcJBbV6V9qdUIkBtf2pjlNLzuyr5AV1lP1RjULp+amUKlMoeqF0/d+bBjXINqp99N87Jnf25NXcqpU/ciUH1M3QHxODO3PX09XOnfEePfz+r9AkhRF/y5fZbK0n3TQE/H6fan52dzSOPPEJ0dDQAERERVFVVAV2vkn311Vfo9XoAJk+eTE5OjuINIDV9/jHUm+Xv78/hw4eZNGkSGo2GuLi4Gy4Ud+7cSWFhoVP7teQ5dwO73c7s2bOd2oODg8nMlLpaQgghhBBCOOnmY6ilpaWUlpY6vp4yZcp1dd+/WZrOw8OD9vZ2PD098fLyQq/X09nZyZtvvskPf/hDtxaKIIvFm6bVasnOzr6lMePHj2f8+PHfUkTfDZ1Ox4YNG253GEIIIYQQQvQZ31wcfpOvry9///vfHV93dHQ4ElZC12tgixYt4t577yU9Pd3tOLRujxRCCCGEEEII4aSzo7Nb/27kxz/+Mbt27QK6klaGhYX9z7E7O/m3f/s3hg4dSmZmJh4eHm6fh9xZFEIIIYQQQoie9C2nhXnyySeprq5m6tSpdHZ2kpWVxR/+8AcMBgMdHR3s27cPu93ueI9x/vz5PProo7d8HElwI4QQQgghhBA9qPn/bOvW+AG/mNBDkXSP3FlUsWvXLs6dO6f4vHB5eTn33Xdfj7yXWFNTg5+fn2JKW5vNRkVFBXFxcd0+1jf3+/TTT1NZeWsZm65cuUJGRgZNTU20trYyYMAAlixZgr+/PzabjbfeeosDBw6g0Wjw8fEhMzNTymYIIYQQQoi+oZfcj5PFoooxY8ao9sfGxvbYsTZv3kxUVJTiYrG5uRmLxdLji0V3bd68mYCAAN54o6ssQ2FhIWvWrGHx4sUsX76ckJAQNm7cCMB//Md/8Oqrr16X0UlJ3a9NTm0PrclVLzNxC6US4J/p7O+E0hkKJSPU9ncu/z2n9vvnvtI1xkWpiQd+u9CtVPuAYskNt1LtL3BOnf/gm137qf+N2bkvZ6nLMdfGuVNKxZ3yHWdeTXUZg+GtbNXrp1jCQOFc1WJwt0yB4v7cKKOgOkahz1W7Wp/j+9mdsiPuxOBOKQ6179keLLcBKufkzjVX+/7r6c+DUikclXMVQghx5+rTi8W2tjZSU1NpaGjg6tWrzJo1i+LiYvR6PS0tLURHR1NXV4fJZGLNmjXs2LEDvV5Pa2sr8+bNY9++fQQEBBASEsK6devw8vKioaGBqKgo5syZo3jc1NRU6urquHLlCgkJCRiNRqqqqjhy5AhGo5HKykq2b99Oa2sr/v7+FBQUsHbtWk6cOEFBQQEzZ84kLS2NixcvArB48WKGDh2KxWKhuLiYjo4Oxo0bR3JyMhUVFRQVFaHT6QgKCiIzMxO73Y7JZOLy5csYDAZHXMePH2fZsq7/offv35+srCz8/PxcnkNAQABlZWX8+Mc/5qc//SnPPfccnZ2d2O12KisrWbLkf36ZePLJJ/nJT37SE5dMCCGEEEKIO99NJKm5G/TpxWJpaSl6vZ7c3FysViuxsbHodDpHHcTy8nIAjh07RlVVFWVlZbS1tRETE+O0r7Nnz1JRUYHdbiciIkJxsWi1WqmpqWHTpk0AVFdXEx4eTkREBFFRUQwaNIhLly5RWFiIVqtl9uzZHDp0iMTERGpra0lKSiInJ4eRI0cyffp0Tp8+TWpqKgUFBaxbt46Kigq8vb1ZuXIljY2N5Ofns2XLFnx9fcnKyqK0tBSbzUZYWBgpKSkcOHCAvXv3AmA2m8nKysJoNGKxWFi/fj0pKSkuz+Opp55Co9FQVlZGamoqYWFhLF68GH9/fwICAtBoNNdt7+/v7/Z1EkIIIYQQ4q7SzTqLd4o+vVg8efIko0aNArpqlYSGhlJdXe1UtPLkyZM8/PDDeHh44OHhQXh4uNO+wsLC8PT0xNPTk3vuuUfxmL6+vixatAiz2YzVamXixInX9Wu1Wry8vJg/fz4+Pj6cP3+e9vb267apra1lz549bN26FYCWlhbq6+sZMmSI49gmk4mDBw9iNBodBTtHjBjB7t276ejoIDIyEoDhw4c7arKcPHnScUewra2NoKAgxfP461//yr/+67/y85//nKtXr/LRRx+RmppKaWkply9fprOz87oFY0VFBU8//TReXl6K+xRCCCGEEKI36C05RPt0ncXQ0FA+//xzoOuOX21tLQ888IDTXTGj0cihQ4fo6OjAbrdz9OhRp319c4ySpqYmjhw5wpo1a/jd735HTk4O7e3taDQaOjs7OXbsGDt27OCtt97CbDbT0dFBZ2cnWq2Wjo6uv1CEhITw/PPPs2HDBt566y0mTpyIwWDg1KlT2O12AJKTk/n+97/PyZMn+cc//gHAvn37CA4OJjQ0lC+++AKAo0ePOhajwcHBrFixgg0bNvCb3/yGxx9/XPE8Pv74Y4qKigDw8PBg6NCh6HQ6vLy8eOyxx9iwYYNj261bt/LHP/5RFopCCCGEEELcRfp06Qy73Y7ZbObMmTPYbDaee+45ysvLycjIIDQ0lPLyck6dOoXJZOLdd99l586d+Pv7c+7cOZYsWcJf/vIXxzuLJSUlrFq1CoDRo0dTXV3t8pidnZ2kp6dTW1uLVqvl8ccf5+WXX6akpIQPPviAvLw8li5d6lj06XQ6Jk+ezFNPPUV8fDyPPfYYL774ImlpaXz11VdYrVaSkpIYP3485eXllJSUoNFoGDt2LImJifzpT3+iqKgIrVaLwWBg+fKu5B8LFiygqamJkJAQPv/8cz799FMOHz7MihUrHIvX5cuXO91lvcZqtbJ06VKOHTtGv3798PHxIS0tjdDQUFpbW8nOzqa2thaA++67j4yMDMmGKoQQQggh+oSmzRXdGv+DSRNvvNF3oE8vFm/WhQsX2LZtGzNmzMButxMdHU1RURGBgYG3O7ReRylbZEN6llP7A0sWAbeWvVSt71vJhurifOCf2T7dye6oktVQKXupWtyN2XlOfYNT53ftT+mc3MhkefbttU7tgfMSgVs7V+g633PvrHdqv//fXuyKYembzjGYF7iVFVYtBrcyerqRQVU182sPZ9N0J6On4ty5aFfr+65jcGde3bnmbl8/Nz4PbmUv7aF5vdEYd+dBCCHuVk1lH3Zr/A8m/7JH4uiuPv3O4s3y9/fn8OHDTJo0CY1GQ1xc3A0Xijt37qSwsNCp/VrynLuB3W5n9uzZTu3BwcFkZrr+JUwIIYQQQog+T7Kh9h1arZbs7OxbGjN+/HjGjx//LUX03dDpdNe9eyiEEEIIIYS4Cb3k4c0+neBGCCGEEEIIIYRrcmdRCCGEEEIIIXpQZ0fvqLMoCW6EEEIIIYQQogf990ZLt8YPnB7XQ5F0j9xZFEIIIYQQQoie1Evux8liUcWuXbs4d+4cU6ZMcdlfXl7Offfd1yOJbGpqavDz82PYsGEu+202GxUVFcTF9exfGWw2G08//TSVlZW3NG7mzJl0dHRw6tQp9Ho9/fv3Z9SoUQwcOJCCggIqKirw9fUFICUlhalTp/Kzn/3shvutS0xxanto7Sr1VPtKpS5cpGOHm0hn35OlM9RKL6iUwTjzmnPaeMPKZaolRBpXvOXUN3jhq+pxq+1vWa7z/habFMubgPI8nM1d7dQeaEoG4MyrqU59hreyXZYCgX+WA3GjlIpq+QCFc6qbM99lDA+9m6c6D0qf1zPzXndqN7z9hmoM7pRYAeXPkGr5DqVyG2pjFD7Hqp99N0qIqH2/uBWDOyUj3Ljm7pTbAJVzcuNzfGb+Iqd2Q16W6nEAxeukOkbpc6w237dQ5uXr44QQ4o7WSx5DlQQ3KsaMGaO4UASIjY3tsYynmzdvpqmpSbG/ubkZi6V7t7N7UlFRERs2bCAiIoLf/OY3bNiwgTlz5gDQ2tpKVpbzL/NCCCGEEEKIu0efvrPY1tZGamoqDQ0NXL16lVmzZlFcXIxer6elpYXo6Gjq6uowmUysWbOGHTt2oNfraW1tZd68eezbt4+AgABCQkJYt24dXl5eNDQ0EBUV5Vg4uZKamkpdXR1XrlwhISEBo9FIVVUVR44cwWg0UllZyfbt22ltbcXf35+CggLWrl3LiRMnKCgoYObMmaSlpXHx4kUAFi9ezNChQ7FYLBQXF9PR0cG4ceNITk6moqKCoqIidDodQUFBZGZmYrfbMZlMXL58GYPB4Ijr+PHjLFvW9Zfu/v37k5WVhZ+f3y3P6y9/+Uv++te/8tlnnzF27NhbHi+EEEIIIcRdTR5DvfuVlpai1+vJzc3FarUSGxuLTqcjISGBJ598kvLycgCOHTtGVVUVZWVltLW1ERMT47Svs2fPUlFRgd1uJyIiQnGxaLVaqampYdOmTQBUV1cTHh5OREQEUVFRDBo0iEuXLlFYWIhWq2X27NkcOnSIxMREamtrSUpKIicnh5EjRzJ9+nROnz5NamoqBQUFrFu3joqKCry9vVm5ciWNjY3k5+ezZcsWfH19ycrKorS0FJvNRlhYGCkpKRw4cIC9e/cCYDabycrKwmg0YrFYWL9+PSkpzo+F3oiHhwdvvPEGL730Ev/yL/9yy+OFEEIIIYS4m3V2yGLxrnfy5ElGjRoFgK+vL6GhoVRXVxMcHOy03cMPP4yHhwceHh6Eh4c77SssLAxPT088PT255557FI/p6+vLokWLMJvNWK1WJk6ceF2/VqvFy8uL+fPn4+Pjw/nz52lvb79um9raWvbs2cPWrVsBaGlpob6+niFDhjiObTKZOHjwIEaj0fHu4IgRI9i9ezcdHR1ERkYCMHz4cDw9PR3nuWRJ17sjbW1tBAUF3dQ8uhIUFERCQgJLlixBo9G4vR8hhBBCCCHuOp29453FPl064/3336e+vp7U1FSsVivPPPMM3t7e5OfnExoaSnl5OadOnSImJob09HQ2btxIe3s7MTExZGZmXvcYaklJCatWrQJg9OjRVFdXuzxmU1MTpaWlzJ07F5vNRmRkJLt378ZsNjNhwgQGDhyI2WzGYrHQ2tpKbGws6enpGAwGkpOTKSsrIzs7m/DwcGJiYrhw4QIWi4X4+HhmzJjBRx99hE6nIzk5mYULFzJr1iw+/PBDfHx8WL58OYMHDwbgyy+/xGQycfToUZKSkqisrGTy5MmsXr2awMBA9u/fT3NzMxMmTFCdw9dff52oqCjGjBkD4Jgzk8lEZ2cnzz//PLW1tbz11ls3leBGCCGEEEKIu935P7zfrfGDZj3bQ5F0T5++sxgfH4/ZbGbatGnYbDaSkpIcj55+3dChQ4mMjCQ+Ph5/f3+8vLwcd+Nu1YABA2hubmbq1KlotVpeeOEFPD09GT58OLm5ueTl5dGvXz+mTp3q2L6pqYlHH32UtrY2cnJySExMJC0tjU2bNmG1WklKSkKv1/PSSy/x7LPPotFoGDt2LIMHD2bu3LkkJCSg1WoxGAyYTCYAFixYwLRp0wgJCcHLywuAjIwMFi5cSHt7OxqNhuXLnbNK3gqNRkN2drbLx3aVKGXScycTqWo2VHf258aYcwW/cxnD/UkvcyZ5gVO7YfWbAIp9dS+/6tT+0O/eAlQyF6plY1Q5p7N5a5z6Auf/WjFLKihnNaxL+o1z3AU5XTEsfdM5BvMCGrNWOrUDDF70GucLNzq1D3p+elcMSplIFbKDAorxnYqb6TKGEEsRdb82OY9Z0zU3StkiT8/6N6f2oD+8oxqDagZOlayUrjK5PvRunluZMdXGKGWzdZX5Fbqyv6rGoPAZUv1+Ucgy62rMtXHuZAFV+xwrfv+5kUEVlM9JbX9K8an93FDL0Kv0PaN0zUH5Z1dPZe6FrnlV+zwIIcQdQR5DvfvpdDpWrFhxXduvfvUrx3/HxsYCcOHCBb73ve9RVlaG3W4nOjqa+++/n7lz5zq2/fpdM6W7itC1gMrMdP4FYerUqY4F4h//+EeXYz/66CPHf7/zzjtO/bGxsY6Yr4mJiXG5WHv77bed2sLDw9mwYYNi7K688cb15Q2+efxrdymFEEIIIYToM3rJw5t9erF4s/z9/Tl8+DCTJk1Co9EQFxdHYGCg6pidO3dSWFjo1H4tec7dwG63M3v2bKf24OBglwteIYQQQgghBL3mnUVZLN4ErVZLdnb2LY0ZP358j9VgvF10Ot0t32kUQgghhBCir+st2VC1tzsAIYQQQgghhBB3HrmzKIQQQgghhBA9qZe8s9inS2cIIYQQQgghRE879876bo2//99e7KFIukfuLKrYtWsX586dY8qUKS77y8vLue+++3rk3cSamhr8/PwYNmyYy36bzUZFRQVxcXHdPtY39/v0009TWVl5S+Py8/MJCAhg2rRpjrb4+Hjy8vJYtWoVTU1NNDY24uXlxQ9+8APCwsIwm51Tvn+TYvp5lRTzt1LqwtHnoqzGA8sW3/L+blQ6w1VKeFBOC//gG11tSud7bu3vndrvT3xBdYxq3CrzoJjOXiV1v9L+lMpwgHLpBVfnA13npDZ3SuerlrpfqYyCq1IEcBMlLZTKd6iUHFCKQTVule8Lpfjc+dypjlH6nnVxPnDjc1L6DLlT6kI1BrW5U5oHN665O+cK7pXCUSx9olI2RvXaKhxLdYzS51htvpWOo/YzQOXzoPZ9JoQQ35kOSXDT610rNK/km2UiumPz5s1ERUUpLhabm5uxWCw9vlj8Nqxc2VUfz9WCUgghhBBCiF6vlzy82acXi21tbaSmptLQ0MDVq1eZNWsWxcXF6PV6WlpaiI6Opq6uDpPJxJo1a9ixYwd6vZ7W1lbmzZvHvn37CAgIICQkhHXr1uHl5UVDQwNRUVHMmTNH8bipqanU1dVx5coVEhISMBqNVFVVceTIEYxGI5WVlWzfvp3W1lb8/f0pKChg7dq1nDhxgoKCAmbOnElaWhoXL14EYPHixQwdOhSLxUJxcTEdHR2MGzeO5ORkKioqKCoqQqfTERQURGZmJna7HZPJxOXLlzEYDI64jh8/zrJlXX9t7t+/P1lZWfj5+X27F0EIIYQQQghxR+rTi8XS0lL0ej25ublYrVZiY2PR6XSOWojl5eUAHDt2jKqqKsrKymhra3NZ5P7s2bNUVFRgt9uJiIhQXCxarVZqamrYtGkTANXV1YSHhxMREUFUVBSDBg3i0qVLFBYWotVqmT17NocOHSIxMZHa2lqSkpLIyclh5MiRTJ8+ndOnT5OamkpBQQHr1q2joqICb29vVq5cSWNjI/n5+WzZsgVfX1+ysrIoLS3FZrMRFhZGSkoKBw4cYO/evQCYzWaysrIwGo1YLBbWr19PSkrKLc2pRqO5pe2FEEIIIYTobXpL6Yw+vVg8efIko0aNAsDX15fQ0FCqq6sJDg522u7hhx/Gw8MDDw8PwsPDnfYVFhaGp6cnnp6e3HPPPYrH9PX1ZdGiRZjNZqxWKxMnTryuX6vV4uXlxfz58/Hx8eH8+fO0t7dft01tbS179uxh69atALS0tFBfX8+QIUMcxzaZTBw8eBCj0Yivry8AI0aMYPfu3XR0dBAZGQnA8OHD8fT0dJznkiVd7460tbURFBSkeB7e3t7Y7fbr2v7xj3+onrsQQgghhBB9QmfveGexT2dDff/996mvryc1NRWr1cozzzyDt7c3+fn5hIaGUl5ezqlTp4iJiSE9PZ2NGzfS3t5OTEwMmZmZ1z2GWlJSwqpVqwAYPXo01dXVLo/Z1NREaWkpc+fOxWazERkZye7duzGbzUyYMIGBAwdiNpuxWCy0trYSGxtLeno6BoOB5ORkysrKyM7OJjw8nJiYGC5cuIDFYiE+Pp4ZM2bw0UcfodPpSE5OZuHChcyaNYsPP/wQHx8fli9fzuDBgwH48ssvMZlMHD16lKSkJCorK5k8eTKrV68mMDCQ/fv309zczIQJE1yeR3V1Nb/73e/493//dzw9PTlz5gwvvvgi27dvd2wj7ywKIYQQQoi+yFVyv1txLRHg7dan7yzGx8djNpuZNm0aNpuNpKQkx6OnXzd06FAiIyOJj4/H398fLy8vx924WzVgwACam5uZOnUqWq2WF154AU9PT4YPH05ubi55eXn069ePqVOnOrZvamri0Ucfpa2tjZycHBITE0lLS2PTpk1YrVaSkpLQ6/W89NJLPPvss2g0GsaOHcvgwYOZO3cuCQkJaLVaDAYDJpMJgAULFjBt2jRCQkLw8vICICMjg4ULF9Le3o5Go2H5ctfZRKFrQbx//35iY2Px9fWls7OTFStWuDUnQgghhBBCiDtPn76zeLMuXLjAtm3bmDFjBna7nejoaIqKiggMDLzdofU6DUvfdGp7wLzAvdIZLtLSQ1dqenfKYPTUmGt97qSSVy35kem8WH/gtwvV0/P3cBp+pRjUxpx96x2nvsBX/40z8xc5tQMY8rJUr0Xjm2879Q1eME813b9SfKdfSnYZQ9C61S7jM+RlAcrz6qoUx0MFOaoxuFPaAFCMT7UEhVIJA7UxSiU6XLQ7+tT2p1R2RK38g0IMqqVr1D77Cn2q19yNsiOq35tulD5RmqMz8153bn/7DdXjgEr5DpUxip9jtZ93t1AmBP55LVQ+D3W/Njn1PbQmVzVuIYToaWdXFnRrfOBrST0USff06TuLN8vf35/Dhw8zadIkNBoNcXFxN1wo7ty5k8LCQqf2a8lz7gZ2u53Zs2c7tQcHB5OZ6br+lRBCCCGEEH1eL3lnURaLN0Gr1ZKdfWsFfcePH8/48eO/pYi+Gzqdjg0bNtzuMIQQQgghhLirSDZUIYQQQgghhBDOesmbftrbHYAQQgghhBBCiDuPJLgRQgghhBBCiB7UmJ3XrfGDU+f3UCTdI4+h3sCuXbs4d+4cU6ZMcdlfXl7Offfd1yPvJ9bU1ODn58ewYcNc9ttsNioqKoiLi+v2sb6536effprKysqbHmO1Wvn5z3/Of/zHf3Dvvfc62n/5y18yffp0/vSnPwHw17/+lUcffRSAhQsXEh4errpfpSyJPZ2JVHV/ChkK3YnBVZY/+GemP5XsgK4yaAW+lqSawbFxxVtOfYMXvurWuYJKZky1zLTuzJ3CGHfn7lay4zrO9RYyWd5UDErnpJaBU2F/qhlw3Tgnd/bn1ty5aO9ODKrZNO+AGNyZu56+frcyDzeaO7X4bufn7lqfO9mk3Z0HIYRwSy+5HyeLxRsYM2aMan9sbGyPHWvz5s1ERUUpLhabm5uxWCw9vlh0h6+vL2PHjuXTTz91zMHhw4f53ve+R3x8PPHx8UBXPUZJkiOEEEIIIfoUSXDTO7S1tZGamkpDQwNXr15l1qxZFBcXo9fraWlpITo6mrq6OkwmE2vWrGHHjh3o9XpaW1uZN28e+/btIyAggJCQENatW4eXlxcNDQ1ERUUxZ84cxeOmpqZSV1fHlStXSEhIwGg0UlVVxZEjRzAajVRWVrJ9+3ZaW1vx9/enoKCAtWvXcuLECQoKCpg5cyZpaWlcvHgRgMWLFzN06FAsFgvFxcV0dHQwbtw4kpOTqaiooKioCJ1OR1BQEJmZmdjtdkwmE5cvX8ZgMDjiOn78OMuWdf01t3///mRlZeHn5+fyHOLj41m5cqVjsbh582bFO7BCCCGEEEL0FZ1SOqN3KC0tRa/Xk5ubi9VqJTY2Fp1O56iHWF5eDsCxY8eoqqqirKyMtrY2YmJinPZ19uxZKioqsNvtREREKC4WrVYrNTU1bNq0CYDq6mrCw8OJiIggKiqKQYMGcenSJQoLC9FqtcyePZtDhw6RmJhIbW0tSUlJ5OTkMHLkSKZPn87p06dJTU2loKCAdevWUVFRgbe3NytXrqSxsZH8/Hy2bNmCr68vWVlZlJaWYrPZCAsLIyUlhQMHDrB3714AzGYzWVlZGI1GLBYL69evJyUlxeV5DB8+nJaWFs6dO8f3v/99/vKXv5CamtoTl0UIIYQQQghxm/X5xeLJkycZNWoU0PVoZWhoKNXV1QQHBztt9/DDD+Ph4YGHh4fLd+/CwsLw9PTE09OTe+65R/GYvr6+LFq0CLPZjNVqZeLEidf1a7VavLy8mD9/Pj4+Ppw/f5729vbrtqmtrWXPnj1s3boVgJaWFurr6xkyZIjj2CaTiYMHD2I0GvH19QVgxIgR7N69m46ODiIjI4GuRZ+np6fjPJcs6Xrfo62tjaCgINX5mzx5MhUVFTzwwAOMGzcOnU6nur0QQgghhBC9Xi95Z7HPZ0N9//33qa+vJzU1FavVyjPPPIO3tzf5+fmEhoZSXl7OqVOniImJIT09nY0bN9Le3k5MTAyZmZnXPYZaUlLCqlWrgK539aqrq10es6mpidLSUubOnYvNZiMyMpLdu3djNpuZMGECAwcOxGw2Y7FYaG1tJTY2lvT0dAwGA8nJyZSVlZGdnU14eDgxMTFcuHABi8VCfHw8M2bM4KOPPkKn05GcnMzChQuZNWsWH374IT4+PixfvpzBgwcD8OWXX2IymTh69ChJSUlUVlYyefJkVq9eTWBgIPv376e5uZkJEyYozl9LSwuzZ88mICCA119/3WlxqTYPQgghhBBC9EYNGW90a/wDGa+r9nd0dJCRkcHx48fR6XQsW7aMhx56yNG/adMmSkpK8PT0ZM6cOYwdO9atOPr8ncX4+HjMZjPTpk3DZrORlJTkePT064YOHUpkZCTx8fH4+/vj5eXluBt3qwYMGEBzczNTp05Fq9Xywgsv4OnpyfDhw8nNzSUvL49+/foxdepUx/ZNTU08+uijtLW1kZOTQ2JiImlpaWzatAmr1UpSUhJ6vZ6XXnqJZ599Fo1Gw9ixYxk8eDBz584lISEBrVaLwWDAZDIBsGDBAqZNm0ZISAheXl4AZGRksHDhQtrb29FoNCxf7jqj6DX33XcfISEhfPnllze8CymEEEIIIUSf8C2/s7hjxw7sdjulpaV88cUXvPHGG7z77rtAV1LMDRs2sHnzZmw2G9OnT2f06NFuPQHY5+8s3qwLFy6wbds2ZsyYgd1uJzo6mqKiIgIDA293aL3K2bfXOrUFzkv8bktn3ML+bjTmzPxFLmMw5GXxZeUup/aAcV3ZdxVLiCx90/k45gVdY5TSxauV28ha6dQ3eNFrAJx51fn9U8Nb2T1eMkJx7l5b7NQOYFi5THF+AJflLh58I0OxHaD+N2bnvpyl1CW6fl/3obWrXMZnWNl1/krne2ae818JDW93/eVRaX+q5TZU+hT3pzYPSp8htflW6HM1p9A1r6oxKJV/ULhGbsfgRhkT1WuuFLfaZ1+tBIXSOanFrTBHSt/LascBle8ltTEKMaiWrVA6jkrpE7XPQ13Sb5z6HirIUY1b8ftFSmoIIdzUkJ7VrfEPLHH9O+Q12dnZPPLII0RHRwMQERFBVVUVADt37uTPf/4zmZldP8N+/etf88orr/DII4/cchx9/s7izfL39+fw4cNMmjQJjUZDXFzcDReKO3fupLCw0Kn9WvKcu4Hdbmf27NlO7cHBwY4PoBBCCCGEEOJrulk6o7S0lNLSUsfXU6ZMua7qgNVqdeQkAfDw8KC9vR1PT0+sVut11QzuvfderFarW3HIYvEmabVasrOzb2nM+PHjGT9+/LcU0XdDp9NJnUQhhBBCCCFuRTcf3vzm4vCbfH19+fvf/+74uqOjw/GK3Df7/v73vyuWwrsRrVujhBBCCCGEEEK41NnR0a1/N/LjH/+YXbu6Xmn64osvCAsLc/Q98sgj7N+/H5vNxldffcXJkyev678VcmdRCCGEEEIIIXrSt5wW5sknn6S6upqpU6fS2dlJVlYWf/jDHzAYDIwfP57nnnuO6dOn09nZSUpKCt7e3m4dRxLcCCGEEEIIIUQPcpWQ7FZcS152u8mdxRvYtWsX586dU3xmuLy8nPvuu69H3k2sqanBz8+PYcOGuey32WxUVFQQFxfX7WN9c79PP/00lZWVNz3GbrczduxYdu3ahYeHB3/961+ZNm0amzZt4pFHHsFms/HEE0/w5z//mSeeeIKtW7fe1F80lLJFqmUNVMxe6mLMtXHfWTZUtYyeahkhlTIhqmTmU8ps2pC5wjm23y7siltlXs/lv+fUd//cV1QzCirtr3HFW86xLXwVUMn8qlCf6IGM19UzK7qRlVLpWrj6PELXZ1I1I6Q72TRvISPkjc4V3Mym6c7cKWVQdXE+8M9zciNDqFtZXNVicOcz5MY1dyd7MLj5eVAao5ZJ1p15deN7SfVclY6jlg1VJW5XWagNeVnqcSv9HHLjGgkhBPCt31n8rsg7izcwZswY1ZdLY2NjeyyJzebNm2lqalLsb25uxmKx9Mixukun0zFs2DD+67/+C4A///nPTJgwwfHs9F//+ld+8pOfoNXKR0wIIYQQQvQxHR3d+3eH6PN3Ftva2khNTaWhoYGrV68ya9YsiouL0ev1tLS0EB0dTV1dHSaTiTVr1rBjxw70ej2tra3MmzePffv2ERAQQEhICOvWrcPLy4uGhgaioqKYM2eO4nFTU1Opq6vjypUrJCQkYDQaqaqq4siRIxiNRiorK9m+fTutra34+/tTUFDA2rVrOXHiBAUFBcycOZO0tDQuXrwIwOLFixk6dCgWi4Xi4mI6OjoYN24cycnJVFRUUFRUhE6nIygoiMzMTOx2OyaTicuXL2MwGBxxHT9+nGXLuv6S2r9/f7KyshSzJ40ePZrPP/+c8PBw9uzZQ05ODq+99hpJSUns27ePiIiIHrxSQgghhBBC3CV6yZ3FPr9YLC0tRa/Xk5ubi9VqJTY2Fp1O56iFWF5eDsCxY8eoqqqirKyMtrY2YmJinPZ19uxZKioqsNvtREREKC4WrVYrNTU1bNq0CYDq6mrCw8OJiIggKiqKQYMGcenSJQoLC9FqtcyePZtDhw6RmJhIbW0tSUlJ5OTkMHLkSKZPn87p06dJTU2loKCAdevWUVFRgbe3NytXrqSxsZH8/Hy2bNmCr68vWVlZlJaWYrPZCAsLIyUlhQMHDrB3714AzGYzWVlZGI1GLBYL69evJyXFdWHyUaNGsWbNGn7xi1/Qr18/HnzwQTo7O/nb3/5GTU0NU6dO7YlLJIQQQgghhLgN+vxi8eTJk4waNQroqkkSGhpKdXU1wcHBTts9/PDDeHh44OHhQXh4uNO+wsLC8PT0xNPTk3vuuUfxmL6+vixatAiz2YzVamXixInX9Wu1Wry8vJg/fz4+Pj6cP3+e9vb267apra1lz549bN26FYCWlhbq6+sZMmSI49gmk4mDBw9iNBodRTtHjBjB7t276ejoIDIyEoDhw4c76rKcPHmSJUu63utoa2sjKChI8TyGDh3K6dOnqaqqctxFfOyxx9i7dy82m40f/OAHimOFEEIIIYTorTo7esedxT6fDfX999+nvr6e1NRUrFYrzzzzDN7e3uTn5xMaGkp5eTmnTp0iJiaG9PR0Nm7cSHt7OzExMWRmZl73GGpJSQmrVq0Cuh7RrK6udnnMpqYmSktLmTt3LjabjcjISHbv3o3ZbGbChAkMHDgQs9mMxWKhtbWV2NhY0tPTMRgMJCcnU1ZWRnZ2NuHh4cTExHDhwgUsFgvx8fHMmDGDjz76CJ1OR3JyMgsXLmTWrFl8+OGH+Pj4sHz5cgYPHgzAl19+iclk4ujRoyQlJVFZWcnkyZNZvXo1gYGB7N+/n+bmZiZMmKA4fyaTibNnz5KZmYnRaOSvf/0rb775Jv/7f/9vTCYTAOPGjbvpBDdCCCGEEELc7Vwl27oVhrysHoqke/r8ncX4+HjMZjPTpk3DZrORlJTkePT064YOHUpkZCTx8fH4+/vj5eXluBt3qwYMGEBzczNTp05Fq9Xywgsv4OnpyfDhw8nNzSUvL49+/fo5HuMcMGAATU1NPProo7S1tZGTk0NiYiJpaWls2rQJq9VKUlISer2el156iWeffRaNRsPYsWMZPHgwc+fOJSEhAa1Wi8FgcCziFixYwLRp0wgJCcHLywuAjIwMFi5cSHt7OxqNhuXLnbN8ft3o0aPJz8/HaDQCXUVAT506xauvvurW3AghhBBCCHHXkzuLfcuFCxfYtm0bM2bMwG63Ex0dTVFREYGBgbc7tF5FKcV7T5W6UOv7NkpnKNXYUUoLf6PU/aolRBRKZLidur8HSwGcK/idU/v9SS93HecW0vNf61OdB4U+t87VzeunWP6hp6+5G+fU03N3K2N6en/fdQzuXHN3yv64u7+eLt/xbV+Lb+v6Kf1M6envF3fnVQjRNyiV37pZhrddlxD7rvX5O4s3y9/fn8OHDzNp0iQ0Gg1xcXE3XCju3LmTwsJCp/ZryXPuBna7ndmzZzu1BwcHk5npugaWEEIIIYQQ4u4ni8WbpNVqyc7OvqUx48eP77EajLeLTqdjw4YNtzsMIYQQQggh7h6dd06txO6QxaIQQgghhBBC9KDekg1VFotCCCGEEEII0ZN6SVoYSXAjhBBCCCGEED2obs78bo1/6N28Hoqke7S3O4A7za5duygtLVXsLy8vZ+fOnT1yrJqaGo4dO6bYb7PZsFgsPXKsb+533LhxtzTGbrczevRorl69CsBf//pXhg0bxsGDBx37jIiI4I9//KNTQpy5c+eycePGngleCCGEEEII8Z2Qx1C/YcyYMar9sbGxPXaszZs3ExUVxbBhw1z2Nzc3Y7FYiIuL67Fjukun0zFs2DD+67/+i/DwcP785z8zYcIEdu3axSOPPMJf//pXfvKTn/Dcc8/x2WefOeL++OOPaWtrY/r06Td1HLdKZyilXU93Xcz0gSWL3Cud0cOp35XSu4NKOYkFv3VufzNTNQa3S2coxeBO6Yz3/uDUfv8rs7rGZDinhn4g43WX5wpd59vTJSgUSw64GYPitXg9w3nMGxlux/1dXT935ttVu1rfdx3DHVE64zsq5aD6s+YOLgGj+vNTLQaF77Pv6ueGu/sTQvQyveThzT63WGxrayM1NZWGhgauXr3KrFmzKC4uRq/X09LSQnR0NHV1dZhMJtasWcOOHTvQ6/W0trYyb9489u3bR0BAACEhIaxbtw4vLy8aGhqIiopizpw5isdNTU2lrq6OK1eukJCQgNFopKqqiiNHjmA0GqmsrGT79u20trbi7+9PQUEBa9eu5cSJExQUFDBz5kzS0tK4ePEiAIsXL2bo0KFYLBaKi4vp6Ohg3LhxJCcnU1FRQVFRETqdjqCgIDIzM7Hb7ZhMJi5fvozBYHDEdfz4cZYt6/qfV//+/cnKysLPz8/lOYwePZrPP/+c8PBw9uzZQ05ODq+99hpJSUns27ePiIgINBoNWVlZzJgxg0cffZS1a9dSVFTUg1dQCCGEEEKIO5wkuLk7lZaWotfryc3NxWq1Ehsbi06nc9Q+LC8vB+DYsWNUVVVRVlZGW1sbMTExTvs6e/YsFRUV2O12IiIiFBeLVquVmpoaNm3aBEB1dTXh4eFEREQQFRXFoEGDuHTpEoWFhWi1WmbPns2hQ4dITEyktraWpKQkcnJyGDlyJNOnT+f06dOkpqZSUFDAunXrqKiowNvbm5UrV9LY2Eh+fj5btmzB19eXrKwsSktLsdlshIWFkZKSwoEDB9i7dy8AZrOZrKwsjEYjFouF9evXk5KS4vI8Ro0axZo1a/jFL35Bv379ePDBB+ns7ORvf/sbNTU1TJ06FYD777+f5ORkpkyZQl5eHnq9vtvXTQghhBBCiLtFp5TOuDudPHmSUaNGAeDr60toaCjV1dUEBwc7bffwww/j4eGBh4cH4eHhTvsKCwvD09MTT09P7rnnHsVj+vr6smjRIsxmM1arlYkTJ17Xr9Vq8fLyYv78+fj4+HD+/Hna29uv26a2tpY9e/awdetWAFpaWqivr2fIkCGOY5tMJg4ePIjRaMTX1xeAESNGsHv3bjo6OoiMjARg+PDheHp6Os5zyZKuR5Ta2toICgpSPI+hQ4dy+vRpqqqqiIiIAOCxxx5j79692Gw2fvCDHzi2/eUvf0lOTo7jmEIIIYQQQoi7S59bLIaGhvL555/z5JNPYrVaqa2t5YEHHkCj0Vy3ndFoZMOGDXR0dNDe3s7Ro0ed9vXNMUqampo4cuQIa9aswWazERkZyTPPPINGo6Gzs5Njx46xY8cOLBYLra2txMbG0tnZiVarpaOj668SISEhTJw4kZiYGC5cuIDFYsFgMHDq1Cnsdjs6nY7k5GQWLlzIyZMn+cc//oGPjw/79u1zLIS/+OILnnjiCY4ePepYZ6PExwABAABJREFUjAYHB7NixQoCAwPZv38/zc3Niueh0Wgcj75mZna9jzFmzBjefPNNfvrTn97UXAghhBBCCNHr9ZJ3Fvtc6Qy73Y7ZbObMmTPYbDaee+45ysvLycjIIDQ0lPLyck6dOoXJZOLdd99l586d+Pv7c+7cOZYsWcJf/vIXxzuLJSUlrFq1Cuh6n6+6utrlMTs7O0lPT6e2thatVsvjjz/Oyy+/TElJCR988AF5eXksXboUu90OdCWTmTx5Mk899RTx8fE89thjvPjii6SlpfHVV19htVpJSkpi/PjxlJeXU1JSgkajYezYsSQmJvKnP/2JoqIitFotBoOB5cu7ErYsWLCApqYmQkJC+Pzzz/n00085fPgwK1asoL29HY1Gw/Lly53usn7dli1byM/Pp7KyEoCrV68yatQoVq9ezc9+9rPrtlWbEyGEEEIIIXqr0y8kdWt80O8LeiiS7ulzi8WbdeHCBbZt28aMGTOw2+1ER0dTVFREYGDg7Q6tV1PK2udW9lIX7Wp9N8yuqpbNT2F//2/GSy5jCP5gHXUvv+rU/tDv3gKg7tcm5741uZyKm+nUHmLpSiCklEm2+cP/49Q+4Je/6Bqjkl3V1Q+5oN8XcOY158x9hpVdc6N0/c7/+wan9kGznwOgaXOFU98PJk2kacufnNoBfvCrGM4kL3COYfWbADQuy3XqG7zYpJoRsjFrpfOYRa+5zNQKXdlaXWXbfWDJIkA5E+KZV1Od434ru2uM0mdf5ThKcYPyZ1JtHv7/7N17XM93//jxx0cHjVBhLIdRxmxma+MytisTds1hZkhKGdq6HBplTUMlp6jQRuRMhSSyg+Eadl1O++Jim+Y8TKgmlLMOn3r9/ujy+V326fOWz5K4nvfbze2m1/vzfr2f79f7/Xl/evV+f57PC1OijNcJHaudxdVU5slS2u8u03r/mZPR01RGXa3jp3XuZ81daLTsmY//bvI9BpA5y/hD3fETfzJnzzNuHzMSuE82TRPjauoYgelxMOe8A9PvpQuTI423ExasGbdmRlYTx6K0MYWScdXM4mriPZMR9YVRe4OxozVjMGd8tGLQ6i9r/hKjZc+M+NCoTQjxeDg7ZMSfWr/J8vnlFMmf8z/3GGpZ2dvbc/jwYfr27YtOp8Pd3f2+E8Xt27ezYsUKo/a7yXMeBwUFBUZ1EqHkcdW7j54KIYQQQgghNEg21CdblSpVmD59+gOt07lzZzp37vyQIqoY1tbWJCYa3wUSQgghhBBC/G+RyaIQQgghhBBClKcn5Jt+MlkUQgghhBBCiHKkip+MOouS4EYIIYQQQgghypGpJIdl1XTV4nKK5M+RO4tCCCGEEEIIUZ6ekPtxMlkso/z8fLp162aoL1gWycnJ9OnTBysrqzKvc/XqVXbt2sW7775r8jVbt26ldevW1KtXr8z9lkVgYCADBgwwqpd4P61atcLFxcXws7OzM+Hh4WbVWTSVFt6sUhel9PVn+tNM1W5qHY3yAedGf2bU3viLktT3v69YbbSs/mAvzo0Zb7zO7JIU7aZS1muVrdAqiZA+fIzRsmfjZvP78pXG/Q3xLunPxBiZKsMBplPWX0xIMmoHqDfIU3scTJQ30CqVYGqd0sqbQEmJE63zIfNz43TXjgEjNEsYmCqxoBm3xvEzWfZAq+SHqXINZpS6KG37d2PQKmFg8v1soiyE5joa5XM0SzmYGAetUiWm+tM6Rpkz5xgtcwwaBWiU79Doz1RZm8wvFhhvZ/QwwHS5DdAoJ6FVQsTEeWxqfzS3U8qYwn9KymiVMjJVtsKM94vW+1yrjJA5nyOmzqHS0u9XlpT6Qognn0wWH6KFCxfSu3fvB1rnxIkTfP/995qTxYSEBMLDw8t9smiuWrVqSQZVIYQQQggh7npCvrMok0UNt27dIigoiOvXr9O4cWOgZDI3dWrJXwzt7OyIiIigsLCQgIAAlFLk5+czadIkDh8+zKVLlwgMDGT+/NL/Avjdd9+xePFiLC0tefrpp4mJiWHBggUcP36c5ORkXFxcmDFjBkVFReTm5hIeHs7169c5duwYwcHBrF69muTkZDZu3IhOp6N79+4MGjSIs2fPEhISQmFhITY2NsTExHD79m3Gjx9PUVEROp2OkJAQnn/+eVatWkVKSgp169blypUrABQWFjJx4kTS09MpLi4mICDgge82CiGEEEII8T9LHkN98q1Zs4bmzZsTGBjIoUOH2LdvH6GhoURERNCsWTNSUlJYsmQJLi4u2NnZERUVxalTp7h9+zbu7u7ExcURExNjsv+NGzfi6+vLO++8w5dffsnNmzcZNmwYa9aswcPDg02bNhEcHEyLFi345ptvSE1NZerUqbRs2ZLw8HDOnTvHpk2bWL265JHFIUOG8OabbxIdHY2fnx+urq5s376do0ePsnbtWgYNGkSXLl04duwY48ePZ9GiRSQkJPDNN9+g0+no06cPACkpKdjb2xMREUFubi7e3t58++23Jvfj2rVr+Pj4GH4ODg6mVatW5XQUhBBCCCGEeLw8KdlQZbKo4ezZs3Ts2BGAl19+GUtLS06fPs2kSSXfKygsLKRJkya4urpy9uxZRowYgaWlJcOHDy9T/+PGjWPhwoWsXLkSJycnunTpcs/yp59+mvnz52NjY8OtW7ewtbW9Z/nJkyfJzMxk8ODBQMmkLT09nd9++83wHcLOnTsDMH36dNq2bQtAy5Yt+f333zl37hzNmjXD2toagNatWxv6PXjwIGlpaQDo9XpycnJwcHAodT/kMVQhhBBCCCGePFI6Q8OKFSu4fPkyQUFBHD16FH9/fxwcHJgzZw6Ojo4cPHiQS5cuUatWLXJzc+nevTs//fQTs2fPJjExkc6dO7Nx40aeeuqpUvv//PPP8fHxoXbt2oSFheHi4kLDhg1ZuXIlX3zxBe+//z4zZ87E2dmZOXPmkJGRQWRkJIMGDSIsLAy9Xk90dDRLlixBp9OxYsUK3n77baZNm8bAgQPp0KEDX3/9NdeuXePf//437733Hp07dzY8xrpixQo8PT356quvsLKywt3dneDgYE6cOMHt27cZNmwYeXl5xMXFMXr0aKpUqVLqfphKZGNOghshhBBCCCEed2f6DfpT6zutSyinSP4cubOowdPTk7Fjx+Lp6YmTkxNWVlaEh4cTHByMXq9Hp9Mxbdo07OzsGDNmDElJSej1ekaOHAlAmzZt8PPzIyEhAZ1OZ9R/69at+fvf/0716tWpVq0ab731FgUFBZw8eZIVK1bQq1cvRo8eTc2aNalfvz65ubkAuLi4MHbsWJYtW0b79u3x9PSkoKDAkCF17NixhIWFERcXh42NDdHR0XTq1InQ0FCWLVuGXq9n2rRpODg48NFHHzFgwAAcHBwMk9oBAwYQEhKCt7c3N2/exMvLy+REUcvVq1cNj7YCDB06lJ49e2quc6qTcWKfZv/8xmRmTsBkpkatTKSamfRM9KeVRdLUstPvepQag/M3yfzm7WfU3nTlIgDShwUaLXt2QQyHfr1o1P7ycyWJjkxlKDw3aqxRe+M5JZlitbL5/eb1oXF8q5doZkLMmD7baFmDcWNIHxlk1P7svJLsg6WNkfM3yaVmDoWS7KFa+2Ty+E2ONG4PCwYw2Z+pC73TugTNzKamMkxmr//aqP3pvr0A01lFtbJ2amWFNXWOa2b0NJUF1ESmSNDI4qr1/jOR9Rg03s9aWSQfIIvr3f5MZawF0+8lrayiD5LR8+7xMyfLrFZ2XFMZljWvdxrvC1Prab7/TGW01rh+/r54hdGy+h8NLnVfoWR/tY6fqfeF1vvF5Hmscd6l+39qtOzZ2GjA9DUgI/Jzo/YGwQGA6XPodHd3o3bnTSkA/DZgqNGypmuWGbUJIR6B4ifjfpxMFjVUrVqVL74wTvFe2iOXy5cvN2qLjDT+EP5vbm5uuLm5GbVv3rzZ8P8hQ4YYLQ8MDCQwsGQy8eGHH/Lhh/f+Uv/ss88SHx9/T5udnV2pMfbr149+/foZtUdFlV52ojSm7h4eOXKkzH0IIYQQQgjxxFDynUVRBgUFBfj6+hq1N23alMmTjf9iWVnFxsayb98+o/aIiAgaNWr0CCISQgghhBCiknpCvuknk8WHzNra+olI/uLv74+/v3FhdSGEEEIIIcSTSSaLQgghhBBCCFGO1BPynUXJhiqEEEIIIYQQ5chUksOycv4muZwi+XPkzqIQQgghhBBClKcn5M6iTBbNkJ+fT7du3fj+++/LvE5ycjJ9+vTBysqqzOtcvXqVXbt28e67xuUk7tq6dauhZEZ5CgwMZMCAAbRr1+6B1rtbW3Hu3LnUqVMHT0/PB1rfVLkGrXT/ptK7l9autey+pTM00tybWpYRZZxNF6DB2NGc/fBjo/YmS+YCmCwVYqrMBMClrzYZLav7XneT6fTBdIkAgJz9B4yWOfyljXYpABPp8X9favy93fq+PgBcTFpntKyeZz9O9+hv1A7g/O1azVIcvy9fabytId4mU9mD6ZT1Z4eW/j3dJstiNcfBVKkJUyUewHSphKwFxmnwnxlWki5fs6yGidIepZ2TDcaOLonbxPssc+Yco3bHoFEl2zFRjqC0cwFKzgfNc8hE6YWs+UuM2p8ZUXK9MFWOoLS+7vandSwe5JpyvxIUmqUptM5JU6UzNOI2VQpHq8yE1jJT72etkjumyudolksxNd4ax8+cUjiaZX9MvGe14tYqY5I1d6HRsmc+/rtmGRpT43Cmt5dRu9OXqwE0SzCZKkUlhKggT8jDmw9ePE+YZeHChRQXP1gK3RMnTtx3QpqQkMDNmzf/TGhCCCGEEEIIYUTuLJbRrVu3CAoK4vr16zRu3BgomcxNnVryl0A7OzsiIiIoLCwkICAApRT5+flMmjSJw4cPc+nSJQIDA5k/3/ivuADfffcdixcvxtLSkqeffpqYmBgWLFjA8ePHSU5OxsXFhRkzZlBUVERubi7h4eFcv36dY8eOERwczOrVq0lOTmbjxo3odDq6d+/OoEGDOHv2LCEhIRQWFmJjY0NMTAy3b99m/PjxFBUVodPpCAkJ4fnnn2fVqlWkpKRQt25drly5AkBhYSETJ04kPT2d4uJiAgICHvhuoxBCCCGEEP9THvAmUWUlk8UyWrNmDc2bNycwMJBDhw6xb98+QkNDiYiIoFmzZqSkpLBkyRJcXFyws7MjKiqKU6dOcfv2bdzd3YmLiyMmJsZk/xs3bsTX15d33nmHL7/8kps3bzJs2DDWrFmDh4cHmzZtIjg4mBYtWvDNN9+QmprK1KlTadmyJeHh4Zw7d45NmzaxenXJoylDhgzhzTffJDo6Gj8/P1xdXdm+fTtHjx5l7dq1DBo0iC5dunDs2DHGjx/PokWLSEhI4JtvvkGn09GnTx8AUlJSsLe3JyIigtzcXLy9vfn2228rZMyFEEIIIYR4HD0pOURlslhGZ8+epWPHjgC8/PLLWFpacvr0aSZNKvnuTWFhIU2aNMHV1ZWzZ88yYsQILC0tGT58eJn6HzduHAsXLmTlypU4OTnRpUuXe5Y//fTTzJ8/HxsbG27duoWtre09y0+ePElmZiaDBw8G4Nq1a6Snp/Pbb7/h4uICQOfOnQGYPn06bdu2BaBly5b8/vvvnDt3jmbNmmFtbQ1A69atDf0ePHiQtLQ0APR6PTk5OTg4ODzQ+AkhhBBCCPE/4wmZLErpjDJasWIFly9fJigoiKNHj+Lv74+DgwNz5szB0dGRgwcPcunSJWrVqkVubi7du3fnp59+Yvbs2SQmJtK5c2c2btzIU089VWr/n3/+OT4+PtSuXZuwsDBcXFxo2LAhK1eu5IsvvuD9999n5syZODs7M2fOHDIyMoiMjGTQoEGEhYWh1+uJjo5myZIl6HQ6VqxYwdtvv820adMYOHAgHTp04Ouvv+batWv8+9//5r333qNz586Gx1hXrFiBp6cnX331FVZWVri7uxMcHMyJEye4ffs2w4YNIy8vj7i4OEaPHk2VKqV/3fXPJrgRQgghhBDicXfq7ff/1PrNvttQTpH8OXJnsYw8PT0ZO3Ysnp6eODk5YWVlRXh4OMHBwej1enQ6HdOmTcPOzo4xY8aQlJSEXq9n5MiRALRp0wY/Pz8SEhLQ6XRG/bdu3Zq///3vVK9enWrVqvHWW29RUFDAyZMnWbFiBb169WL06NHUrFmT+vXrk5ubC4CLiwtjx45l2bJltG/fHk9PTwoKCgwZUseOHUtYWBhxcXHY2NgQHR1Np06dCA0NZdmyZej1eqZNm4aDgwMfffQRAwYMwMHBwTCpHTBgACEhIXh7e3Pz5k28vLxMThT/aNGiRaSkpABQvXp1EhONM2L+kalsjKYyA8KDZS81LDOR5U+rP80YTGSxS/f/tNQYno2N1uzPVMbDdL8A474WfV6yjonMfJmzYo3aHT8pyfJ5+TvjBEp13nYDIH1YoPG2FsTw+4rVRu31B5dk6zO1T6VlFW2yrCQuU5kVS8vGCiUZWU1l+tTqTzP7pYmMhxcT15QaQz2fAdpZEk1sy1SWRjB9Dplz7oPp7Jya552pDJwa2UtNnXeljQGUjINWVlFT29LMRGpGRmRzsqFqZb80dU3ROkbmHAuta5epsTOVsRZMn6ugMa5aWVxN9Kd5DpnIUlraGMB/zkmNzKYmM9NqjJ2pa4pm1mONzMIm30tax9zEts4OGmbU3iRhAYDmZ8IZ9w+MljmlxJvMOi6EKGeP4DuLeXl5fPrpp1y5coXq1asTGRlp9DRgZGQkP/74I3q9Hg8PD/r3Lz37/F0yWSyjqlWr8sUXxh8MpU2Ali9fbtQWGWn84fXf3NzccHNzM2rfvHmz4f9DhgwxWh4YGEhgYMkv9R9++CEffnjvh8Czzz5LfHz8PW12dnalxtivXz/69etn1B4VVXr68tLs2bMHgI8//piPPzYuDSGEEEIIIcQT7xE8vJmUlETz5s35+OOP+fbbb5k/fz4hIf//j+h79+7l3LlzJCcnU1BQQI8ePfjb3/5GrVq1TPYpk8UKVFBQgK+vr1F706ZNmTy59L+gVkaxsbHs27fPqD0iIoJGjRo9goiEEEIIIYSoRIorfrJ48OBBw40jV1dXoyoMLi4utGzZ0vBzUVERlpba00GZLFYga2vrMj2KWdn5+/vj7196sXIhhBBCCCH+1yn15x5DTU5OJjk52fCzh4cHHh4ehp9TUlKMnh6sXbs2NWrUAEq+Anbjxo17lletWpWqVatSWFjIZ599hoeHB9WrV9eMQyaLQgghhBBCCFGJ/HFy+Efu7u64u7vf0+bv78+tW7eAkhrxNWvWNFrv2rVrjBo1ir/85S/8/e9/v28cZctUIoQQQgghhBCibIrVn/tnhldffZUdO3YAsHPnTl577bV7lufl5TF48GD69u1rSMJ5P1I6QwghhBBCCCHK0a9/7fan1n9u1+b7v+gP7ty5Q3BwMJcuXcLKyopZs2ZRt25doqKieOedd/jxxx+JjY2953uL98s5IpNFUalcXJ1i1FbPy127RICp9O5apTO0+nuAUhz3W6e0VPvwn9ICGinZr1/MNlpWs97TZER+btTeIDgA0CjXoFFmQittvqkyAedGf2bc3xclqfRNpaY3VYYDMFlW4+yQEUbtAE2WzzdZHgNMl8HQKntgKp195sw5pcbgGDRKM3W/qXHNmm+cnv6ZESVfRDdZ6kIrbq3jZ6qEgRklW8wpr1BayRYoKduiVcLAVKkXzX19gJIRUFI2wpz+NN9/JtYx57wDTJa70LpumLx2aZS60CyDYaosijmlVLTiNhFDaevcXU/r+Jm65mlew01cN7IWLDNqf2bY0JJ1tMqiPEA5kPuWztAoPZQ+Msho2bPzZgLw28CPjJY1XbVYuxSHxrVaCPFgfn3zb39q/ed2/6OcIvlz5DuLGvLz8+nWrRvff29ci86U5ORk+vTpg5WVVZnXuXr1Krt27eLdd981+ZqtW7caaieWp8DAQAYMGEC7du3KvM6FCxf429/+RnJyMq1atQJKUvVevnyZVq1aMXv2bNavX4+1tTUAM2bMwMLCgk8/Lb3moBBCCCGEEE+SyjLZ+7PkO4vlbOHChRQ/YBHOEydO3HdCmpCQwM2bN/9MaOXK1taWcePGUVBQcE97p06deOmllwypen/88UcOHDhAQEDAI4hSCCGEEEIIYS65s/gHt27dIigoiOvXr9O4cWOgZDI3dWrJo1Z2dnZERERQWFhIQEAASiny8/OZNGkShw8f5tKlSwQGBhrVNbnru+++Y/HixVhaWvL0008TExPDggULOH78OMnJybi4uDBjxgyKiorIzc0lPDyc69evc+zYMYKDg1m9ejXJycls3LgRnU5H9+7dGTRoEGfPniUkJITCwkJsbGyIiYnh9u3bjB8/nqKiInQ6HSEhITz//POsWrWKlJQU6taty5UrVwAoLCxk4sSJpKenU1xcTEBAgObdxmeffZY2bdoQExNDcHDwPcvGjx9Pnz596NKlC1OnTmXmzJkPdKdVCCGEEEII8ejJZPEP1qxZQ/PmzQkMDOTQoUPs27eP0NBQIiIiaNasGSkpKSxZsgQXFxfs7OyIiori1KlT3L59G3d3d+Li4oiJMf18/8aNG/H19eWdd97hyy+/5ObNmwwbNow1a9bg4eHBpk2bCA4OpkWLFnzzzTekpqYydepUWrZsSXh4OOfOnWPTpk2sXr0agCFDhvDmm28SHR2Nn58frq6ubN++naNHj7J27VoGDRpEly5dOHbsGOPHj2fRokUkJCTwzTffoNPp6NOnD1BSq8Xe3p6IiAhyc3Px9vbm22+/1RyrgIAA+vXrx4EDB+5pt7W1ZcqUKQwePJhPPvkEJyenP3lUhBBCCCGEEBVNEtz8QWhoKB07dqRLly4AvP3221y5coUXXngBKLkD16RJEyIiIkhISGDnzp1YWloyfPhwXFxccHNzY/PmzVStWrXU/rOysli4cCGnTp3CycmJsWPHcuTIEdasWUNMTAwHDhwgMTERGxsbbt26ha2tLTNmzMDHx4fw8HBOnDhBZGSk4a7ntWvXCAwMJDIykpSUFEMhToAePXqwevVqatWqBUCHDh2IjY1l6dKlzJs3D4AJEybQq1cvNm/ezMGDB7GzswMgOzubpKQkHBwcjPbhwoULjBkzhrVr13LkyBE++eQT+vfvz61bt/j4448Nr7vfWAghhBBCCCEqL/nO4h84Ozvz888/A3D06FH0ej1NmzYlMjKSxMREPv30U9566y327dvH008/zbJlyxg+fDizZ88GQKfTaX5nMTk5mY8//piVK1cCJYlrqlSpYlhn2rRpjBo1isjISJo3b87dubxOp0MphZOTE82aNSMhIYHExET69OlDixYtcHZ25pdffgHg66+/JjExEWdnZ8Ndv2PHjlGnTh2aNGnCqVOnyMvLo6ioiGPHjgHg5OREjx49SExMZPHixbzzzjuGiaOWF198kZ49e7J48eIHH2whhBBCCCFEpSWPof6Bp6cnY8eOxdPTEycnJ6ysrAgPDyc4OBi9Xo9Op2PatGnY2dkxZswYkpKS0Ov1hsKWbdq0wc/Pj4SEBHQ6nVH/rVu35u9//zvVq1enWrVqvPXWWxQUFHDy5ElWrFhBr169GD16NDVr1qR+/frk5uYC4OLiwtixY1m2bBnt27fH09OTgoICQ4bUsWPHEhYWRlxcHDY2NkRHR9OpUydCQ0NZtmwZer2eadOm4eDgwEcffcSAAQNwcHDgqaeeAmDAgAGEhITg7e3NzZs38fLyokqVsv0tYdiwYfzzn/8sl/E3lSJcK9W+yXTxpaT0B9NlNf5U6QwT5QNu/Of4/VENe3vOfvixUXuTJXMBSPcLMFr27KLPOf2uh1G78zfJgOlU7en+xllon42N1lwH4MquH4yW1f5rB+0U+CbGIX34GOMY4kr+wPLbgKFGy5quWcbpXp5G7QDOXydx7dx5o/ZajUtqBJkqq6FVtsJUOvvS0tJDSWp6U6UuwHQKfK3z2FQMmqUNzChpYaodICPqC6NlDcaO1owh83Pj72c7Bowo9ThAybEwpxyIWaUups4sNYYGIUGllkVxDBoFaJTC0RpvE2OkWV5Bq4yCifemqdIioFHuRqt8jsb7OWvuQqNlz3z8d5NlPcDM8iumrrmlvMeg5H2mdU7+vjTRaFl9Xx/tc8hUqQutEh0aJXxMjoPW9dPE9eFMHx+jdqfUkn3UKoNx6u33jZY1+24Dp3v0N2p3/nYtoFFu44PhxtuJjzNqE0I8eWSy+AdVq1bliy+Mf2FKTDT+8Fm+fLlRW2Rk6R9ud7m5ueHm5mbUvnnz/y+8OWTIEKPlgYGBBAaW1D/68MMP+fDDD+9Z/uyzzxIfH39Pm52dXakx9uvXj379+hm1R0UZ15kqTcOGDVm7dq3hZ0tLS9avX2/0ugcpOSKEEEIIIYSoXGSy+BAUFBTg6+tr1N60aVMmTzb+S3JlFRsby759+4zaIyIiaNSo0SOISAghhBBCCFFRZLL4EFhbW5d6J/Jx4+/vj7+/8WOhQgghhBBCiCefJLgRQgghhBBCCGFESmcIIYQQQgghhDAij6GKSuXi6hSjtnpe7mZlIi1tnbvraWWENCsbqollpWUBhZJMoL9fumrUXr+uHWA6M59mBk4T2ffMydgHlJoJ9Nl5MzUzqJrK7qiVPdFUfFrZNDWzMZrKaqiRgTMj8nOjZQ2CA8hOTi01hqc9+pARMct4nfGflMRg6liMGmsc95ySxFKmxk4rbq3MmKbOFc2MkKYyOGodc1PnqkY2Yq2MkCbHQSNzqKnMmKVt5+62zMmuqpnF1dQ6Zuyr1jKtdUydx1rHT/P6YGJ/zbkOaY7DA6xzdz3Nc9JEf6Vlx20QUnKdM7WvZh8/U+eDGeuYyhIOaGea9vrQaFnT1Us0M6imDws07m9BjMnPA0AypQrxhJPHUEuRn59fasZSLcnJyRQWFj7QOlevXuWbb77RfM3WrVu5ePHiA/VbFoGBgaUmr9Eyfvx4Nm3aZPi5W7duTJr0/z/cPvvsM7Zt2wZAWloarVq1Ii0trXwCFkIIIYQQQlQomSyWk4ULF1JcXPxA65w4ceK+5SUSEhK4efPmnwmt3LzxxhscPHgQgHPnztG4cWP+/e9/G5b/+OOPvP766wCsXbuWIUOGsHr16kcSqxBCCCGEEOLPkcdQ/+PWrVsEBQVx/fp1GjduDJRM5qZOLXkcx87OjoiICAoLCwkICEApRX5+PpMmTeLw4cNcunSJwMBA5s83LlAN8N1337F48WIsLS15+umniYmJYcGCBRw/fpzk5GRcXFyYMWMGRUVF5ObmEh4ezvXr1zl27BjBwcGsXr2a5ORkNm7ciE6no3v37gwaNIizZ88SEhJCYWEhNjY2xMTEcPv2bcaPH09RURE6nY6QkBCef/55Vq1aRUpKCnXr1uXKlSsAFBYWMnHiRNLT0ykuLiYgIIB27dqVug/t27dnyZKSwuY7duzAzc2N77//nlOnTlG1alXq1auHra0tt27dYu/evXz77be8++675OTk4ODgUN6HTAghhBBCCPEQyWTxP9asWUPz5s0JDAzk0KFD7Nu3j9DQUCIiImjWrBkpKSksWbIEFxcX7OzsiIqK4tSpU9y+fRt3d3fi4uKIiYkx2f/GjRvx9fXlnXfe4csvv+TmzZsMGzaMNWvW4OHhwaZNmwgODqZFixZ88803pKamMnXqVFq2bEl4eDjnzp1j06ZNhjt1Q4YM4c033yQ6Oho/Pz9cXV3Zvn07R48eZe3atQwaNIguXbpw7Ngxxo8fz6JFi0hISOCbb75Bp9PRp08fAFJSUrC3tyciIoLc3Fy8vb359ttvS90HBwcHdDodN27cYOfOnUyePBm9Xs/OnTupVasWf/3rXwHYtGkTXbt2pWrVqnTr1o1169bh5+dXzkdMCCGEEEII8TBJNtT/CA0NpWPHjnTp0gWAt99+mytXrvDCCy8AJXfgmjRpQkREBAkJCezcuRNLS0uGDx+Oi4sLbm5ubN68mapVq5baf1ZWFgsXLuTUqVM4OTkxduxYjhw5wpo1a4iJieHAgQMkJiZiY2PDrVu3sLW1ZcaMGfj4+BAeHs6JEyeIjIw03PW8du0agYGBREZGkpKSQo0aNQzb6tGjB6tXr6ZWrVoAdOjQgdjYWJYuXcq8efMAmDBhAr169WLz5s0cPHgQOzs7ALKzs0lKSjJ5JzA6OprWrVuzePFi1q1bx/nz54mKiqJ69ep88MEHtGzZkv79+2NhYUG1atXIy8vj999/Z+vWrVSpIk89CyGEEEII8biQO4v/4ezszM8//0yXLl04evQoer2epk2bEhkZiaOjIwcPHuTSpUvs27ePp59+mmXLlvHTTz8xe/ZsEhMT0el0mt9ZTE5O5uOPP6Z27dqEhYWxdetWGjZsaFhn2rRpzJw5E2dnZ+bMmUNGRgYAOp0OpRROTk40a9aMJUuWoNPpWLFiBS1atMDZ2ZlffvmFDh068PXXX3Pt2jWcnZ05cOAAnTt35tixY9SpU4cmTZpw6tQp8vLysLKy4tixY/Tq1QsnJyfq16/PsGHDyMvLIy4uzjBxLM0bb7xBbGwsf/nLXwBo1KgRV69e5fLlyzz//POcOHGCoqIi1q5da1hnyJAh/POf/6Rz587lcKSEEEIIIYQQFUEmi//h6enJ2LFj8fT0xMnJCSsrK8LDwwkODkav16PT6Zg2bRp2dnaMGTOGpKQk9Ho9I0eOBKBNmzb4+fmRkJCATqcz6r9169b8/e9/p3r16lSrVo233nqLgoICTp48yYoVK+jVqxejR4+mZs2a1K9fn9zcXABcXFwYO3Ysy5Yto3379nh6elJQUEDr1q2pV68eY8eOJSwsjLi4OGxsbIiOjqZTp06EhoaybNky9Ho906ZNw8HBgY8++ogBAwbg4ODAU089BcCAAQMICQnB29ubmzdv4uXlpXkH8LXXXuPIkSOMHj3a0Pb8889z8+ZNdDodKSkpvPfee/es4+7uzqpVq8o0WTSVVtys0hkaqfvLvXSGidT9ZsfwAOVA7le+Q3OdiRHGyyaVlKUwuU/mxKC1HRPrlHYuQBnOB1MxmDHemjGYc/zMWceMsYMHOyfve+6X0zp31yvvc99UGRPNGMp57DKmzzaOYdwYs85VMPMaUI7nnVZ8FXbtKuXch5Lzv8LiNnfsTJQXMeu807oGlPdnmTnjYCI+rfefEOLxIZPF/6hatSpffPGFUXtiYqJR2/Lly43aIiONPxj+m5ubW6nlODZv3mz4/5AhQ4yWBwYGEhhYUvfoww8/5MMP762b9OyzzxIfH39Pm52dXakx9uvXj379+hm1R0VFacb+36pWrcqhQ4fuaZswYYLh/yEhxh8C3bt3p3v37mXehhBCCCGEEOLRk8liOSooKMDX19eovWnTpkyeXPodisooNja21BqMERERNGrU6BFEJIQQQgghhKhoMlksR9bW1qXeiXzc+Pv74+/v/6jDEEIIIYQQQjxCkp5SCCGEEEIIIYQRKZ0hhBBCCCGEEMLIE/UY6s6dO8nKysLDw6PU5ampqdSqVeuRlXDIzMzk+PHjuLm5GeonOjs7l0vfJ06c4Pr167Rt27Zc+itNq1atcHFxMfzs7OxMeHh4uW7DnOxtD5JB9e56ptbRjMFEpjowM5umGRnuzMmMWaGZECsqG2o5Z2M8PzbMeDtRk0vNsgn/ybRpRnbH85+FG29nRrjZcWuNw8POLGxOxkXDMjMyK5rKeAoa+1reGVnLKSPk/cYOTO+T1rXL1Hls9jXA1DhoXAvNOu9MjZ2551AFZZLNmDrTaFmDkKCS/szJJl2O7z/N/ioom7S5x08IUbk8UZNFV1dXzeV9+vSpoEhKt3fvXs6cOVNqVtQ/67vvvqNOnToPdbJYq1atJ+I7mUIIIYQQQoj7e6wmi4WFhYwbN44LFy5QVFTEkCFDSEpKwsHBgWvXrtGjRw/S09MJCgpi3rx5bNu2DQcHB+7cucPo0aPZv38/derUwcnJicWLF2NlZcWFCxfo3r07w4cPL3Wb8fHx6PV6fH19CQsLw9rampCQEOLi4mjYsCHNmzdn6tSSv5DZ2dkRERFBtWrVCAsL4/fffyc7Oxs3NzdGjRrFokWLyMvLM9ydmzdvHpcvX+bOnTvMnj2bRo0aMWvWLA4cOEBxcTGDBw+mW7du+Pj4GPZx6dKlWFhY3BPjxYsX2bBhA1ZWVrz44otMnjyZdevWARAQEMDQoUP57LPPaNOmDb/++iu1atVi9uzZWFlZMXHiRNLT0ykuLiYgIIB27do98HFZtmwZ3377LZaWlrRp04ZPP/2UnJwcgoKCKCgooGnTpuzdu5etW7c+cN9CCCGEEEKIR+OxmiwmJyfj4ODAzJkzuXnzJn369MHa2ppBgwbRtWtXUlNTATh+/Di7du1i3bp1FBYW8u677xr1lZmZyddff01BQQF//etfTU4Wu3btyvjx4/H19eW3334jLy8PgF27drFo0SKGDh1KREQEzZo1IyUlhSVLluDu7s4rr7yCu7s7+fn5uLq6EhgYiJ+fH2fOnKFz586sWLGCjh078t577zF37ly2bNlC8+bNuXDhAklJSeTn59O/f3/eeOMNAHr27EnXrl1LjbFevXq8//771KlTh9atW2NjY8OpU6eoU6cOFy5coHXr1uTl5fHuu+/Stm1boqKiSE5OpmrVqtjb2xMREUFubi7e3t58++23Jsf/2rVr+Pj4GH4ODg7GysqKzZs3s2bNGiwtLfn444/55z//yf/93//RuXNnBg4cyJ49e9izZ0/ZDrIQQgghhBCiUnisJounT5+mQ4cOANja2uLs7MyePXto2rSp0eteeuklLCwssLCwoFWrVkZ9NW/eHEtLSywtLbGxsTG5TUdHR/Ly8khLS8PZ2ZmsrCzS0tKoUaMGtra2nD59mkmTJgEldz6bNGmCnZ0dv/zyC3v37sXW1paCgoJS+74bV506dbh8+TInT57kyJEjhgmZXq8nIyMDwGgftbi7u5OamoqjoyO9evUCwNLS0vCI6quvvsrOnTupUqUKBw8eJC0tzbC9nJwcHBwcSu23tMdQN2/ezMsvv4yVlRWA4e7l6dOnef/99w1tQgghhBBCiMfLYzVZdHZ25sCBA3Tt2pWbN29y8uRJGjZsiE6nu+d1zZo1IzExkeLiYvR6PUePHjXq64/raOnYsSPR0dF88MEHZGZmMnXqVNzd3YGSSVxkZCSOjo4cPHiQS5cukZqaSo0aNZg8eTLp6emsXbsWpRRVqlShuLjY5HacnJxo164dU6ZMobi4mPnz59OoUaMyxavT6Qx9v/POOyxbtgw7Ozu++OILoGQiePz4cZ5//nkOHjxIs2bNAKhfvz7Dhg0jLy+PuLg47Ozsyjwud2Nevnw5er0eCwsL/v3vf9O7d29yc3P56aefaNmyJT///PMD9SmEEEIIIYR49B6r0hkFBQWEhoZy7tw58vPz8fHxITU11ZBVNDU1lTNnzhAUFERcXBzbt2/H3t6erKwsJk2axA8//GD4zuKaNWuIiYkB4I033tB8TPL06dP06tWLffv2kZ2dTc+ePdm9ezcODg4cPnyYyMhI9Ho9Op2OadOmodfr+eSTT6hRowbW1tZkZWURHx/PlStXCAwMZNSoUaxZs8YQd1JSEpcvX8bf358ZM2bwyy+/cPv2bbp06YK/v3+ZMqf+61//IioqirCwMF5//XWmTp1KTk4Os2fPBsDNzQ0XFxcyMzNxdHRk+vTpAISEhJCZmcnNmzfx8vKif//+JrdhapyWL1/Opk2bKC4u5rXXXmPcuHHk5uYyduxY8vPzefrpp/nll1/47rvvynSchRBCCCGEEI/eYzVZLKsrV66wZcsWBg4cSEFBAT169CA+Ph5HR8dHHVqFmTRpEm+//Tbt27cHSiaLmzdvpmrVqhWy/R07dmBvb0/r1q354YcfWLBgAQkJCfddrzzLVmimzTcn3bg5MZib+t2MEiLluU65x1BOJTrurldhqd/NLb1gThmFckxzr9lfeZ935f3+e8jn/p/qz5zSNeYevwcoJ3HfY2FG3FrLyr18TnlfPx/xuW92f+U43uUeQznFfTd2c8dVCFHxHqvHUMvK3t6ew4cP07dvX3Q6He7u7vedKG7fvp0VK1YYtd9NnlMZZGZmEhwcbNTetm1bRo0aZfh56NCh2NvbGyaKDyI2NpZ9+/YZtUdERBgeiS2Lhg0bMn78eCwsLCguLmbChAkPHIsQQgghhBDi0XkiJ4tVqlQxPGZZVp07d6Zz584PKaLy4ejoWKY6h8uWLTNq+/7778u0DX9/f/z9/R84tj9ydnYmOTn5T/cjhBBCCCGEeDSqPOoAhBBCCCGEEEJUPjJZFEIIIYQQQghh5IlMcCOEEEIIIYQQ4s957O8s7ty5U/O7campqWzfvr0CI7pXZmam4fuCPj4+nD59utz6PnHiBP/+97/Lrb8/unDhAq+++io+Pj74+PjQv39/Bg8ezLVr10yu86jHWwghhBBCCFE+HvsEN66urprL+/TpU0GRlG7v3r2cOXMGNze3cu/7u+++o06dOrRt27bc+76rWbNm9yTVmTVrFuvWrcPX17fU1//Z8b4wOdKorWFYMJmz5xm1O44ZCcD58ZONljWKCNNO262Vxt2csgflnTrcVAxa5TvKsWSE1jJzxuH8uElG7Y2mT9Rep5TjCiXH1tQxB42x0zjm5z4xTsveeNZUzo0ZX2oMjWdHmFwHMHmczIpbY7zN6s/Ee0yrP611TJ4nU6KM2gEaho41rwRFeZeuKe8SFOX4fgHImDrTaFmDkCDN99L5T0ONl0VPIWP6bOO+xo0picGcY1HKsW0YOrZkHRPnSnmVHTEsM+c81joWJs4vzfNEaxzMKCFiVtxa701T+1ROpajudx0q7VyFkvNV69pl6toqJTWEeDQq/WSxsLCQcePGceHCBYqKihgyZAhJSUk4ODhw7do1evToQXp6OkFBQcybN49t27bh4ODAnTt3GD16NPv376dOnTo4OTmxePFirKysuHDhAt27d2f48OGlbjM+Ph69Xo+vry9hYWFYW1sTEhJCXFwcDRs2pHnz5kydWnLRsrOzIyIigmrVqhEWFsbvv/9OdnY2bm5ujBo1ikWLFpGXl4eLiwsA8+bN4/Lly9y5c4fZs2fTqFEjZs2axYEDByguLmbw4MF069YNHx8fwz4uXboUCwuLe2K8ePEiGzZswMrKihdffJHJkyezbt06AAICAhg6dCifffYZbdq04ddff6VWrVrMnj0bKysrJk6cSHp6OsXFxQQEBNCuXbsyHQulFFlZWTRu3BgomTgePnyYq1ev8vzzzzN9+nTmzp37wOMthBBCCCGEqHwq/WQxOTkZBwcHZs6cyc2bN+nTpw/W1taG+oepqakAHD9+nF27drFu3ToKCwt59913jfrKzMzk66+/pqCggL/+9a8mJy9du3Zl/Pjx+Pr68ttvv5GXlwfArl27WLRoEUOHDiUiIoJmzZqRkpLCkiVLcHd355VXXsHd3Z38/HxcXV0JDAzEz8+PM2fO0LlzZ1asWEHHjh157733mDt3Llu2bKF58+ZcuHCBpKQk8vPz6d+/P2+88QYAPXv2NFnjsV69erz//vvUqVOH1q1bY2Njw6lTp6hTpw4XLlygdevW5OXl8e6779K2bVuioqJITk6matWq2NvbExERQW5uLt7e3nz77bcmx//UqVP4+Phw9epV8vPzeffdd3n//fe5efMmNWvWZPny5RQXF9OjRw8uXrxo1ngLIYQQQgghKp9KP1k8ffo0HTp0AMDW1hZnZ2f27NlD06ZNjV730ksvYWFhgYWFBa1atTLqq3nz5lhaWmJpaYmNjY3JbTo6OpKXl0daWhrOzs5kZWWRlpZGjRo1sLW15fTp00yaVPJ4RWFhIU2aNMHOzo5ffvmFvXv3YmtrS0FBQal9342rTp06XL58mZMnT3LkyBF8fHwA0Ov1ZGRkABjtoxZ3d3dSU1NxdHSkV69eAFhaWhoeUX311VfZuXMnVapU4eDBg6SlpRm2l5OTg4ODQ6n93n0MNS8vj2HDhlG7dm0sLS2pWrUqOTk5jBkzhmrVqnH79m0KCwvvWbes4y2EEEIIIYSofCr9ZNHZ2ZkDBw7QtWtXbt68ycmTJ2nYsCE6ne6e192d1BQXF6PX6zl69KhRX39cR0vHjh2Jjo7mgw8+IDMzk6lTp+Lu7g6UTOIiIyNxdHTk4MGDXLp0idTUVGrUqMHkyZNJT09n7dq1KKWoUqUKxcXFJrfj5OREu3btmDJlCsXFxcyfP59GjRqVKV6dTmfo+5133mHZsmXY2dnxxRdfACUTwePHj/P8889z8OBBmjVrBkD9+vUZNmwYeXl5xMXFYWdnd9/xsLGxYebMmfTu3ZtXX32VjIwMsrKy+Pzzz8nJyWHr1q38MbHug4y3EEIIIYQQonKp9KUzCgoKCA0N5dy5c+Tn5+Pj40Nqairh4eE4OzuTmprKmTNnCAoKIi4uju3bt2Nvb09WVhaTJk3ihx9+MHyHbs2aNcTExADwxhtvsGfPHpPbPX36NL169WLfvn1kZ2fTs2dPdu/ejYODA4cPHyYyMhK9Xo9Op2PatGno9Xo++eQTatSogbW1NVlZWcTHx3PlyhUCAwMZNWoUa9asMcSdlJTE5cuX8ff3Z8aMGfzyyy/cvn2bLl264O/vj4+Pj+G1pvzrX/8iKiqKsLAwXn/9daZOnUpOTg6zZ5ckM3Bzc8PFxYXMzEwcHR2ZPn06ACEhIWRmZnLz5k28vLzo379/qf1fuHCBMWPGsHbtWkPb119/zcqVK5k7dy4jRozAxsYGnU5HXl4e48aNM3u8hRBCCCGEEJVLpZ8sltWVK1fYsmULAwcOpKCggB49ehAfH4+jo+OjDq3CTJo0ibfffpv27dsDJZPFzZs3U7Vq1UccWdmZypCWtXC5Ufszfx8CmM7mp5VN06yMdGZkL9XMBmdGhlCzsrhqxa21zFQmPXOywpZTFte72zInC6jWOuc/CzdeNiOcc6M/KzWGxl/MICPqC6P2BmNHl8RgYp+0MqiaikFz7DTOB1MZMDWzJz5AVsq754mp87i02O7GZ845pDXemTPnGC1zDBpV6thBGTLTmshyaeoYgUYWUDPGG8zMSmniWGREzDJqbzD+E8D0eQIaWS61xs7UOGidQw+Qhfduf1rXT5PZNDWyl5o8j83IHAqms9lqZsA1cZzM+ezRis+s7MbmHD+tbOBmnMea65jIBCyE+PMq/WOoZWVvb8/hw4fp27cvOp0Od3f3+04Ut2/fzooVK4za7ybPqQwyMzMJDg42am/bti2jRo0y/Dx06FDs7e0NE8UHERsby759+4zaIyIiDI/ECiGEEEIIIf63PDGTxSpVqhgesyyrzp0707lz54cUUflwdHS8p86hKcuWLTNq+/7778u0DX9/f/z9/R84NiGEEEIIIcSTq8qjDkAIIYQQQgghROUjk0UhhBBCCCGEEEaemAQ3QgghhBBCCCHKj9xZFEIIIYQQQghh5LFIcLNz506ysrLw8PAodXlqaiq1atV6ZMlqMjMzOX78OG5ubmWqj/ggTpw4wfXr12nbtm259FeaDRs2sGHDBpRSFBYW4u/vz5tvvnnPflUUk2m7y6lkBNy/FIA5KevNKf9gTkkLs2Iwt3RGOfZXXusYlmmdD+bE8ADp4v9UDBV5/MxIgV+uY1fe535liKEij5+p61p5n/uVYRwe4LoP5pdfKff3i1YMFfX+q6hrQDmNt9ayh3EstK4BQoiyeSwmi66urprL+/TpU0GRlG7v3r2cOXPmoUyqvvvuO+rUqfPQJos3btxg/vz5fPvtt1hbW3Px4kXc3d3517/+9VD3SwghhBBCCFG5VYrJYmFhIePGjePChQsUFRUxZMgQkpKScHBw4Nq1a/To0YP09HSCgoKYN28e27Ztw8HBgTt37jB69Gj2799PnTp1cHJyYvHixVhZWXHhwgW6d+/O8OHDS91mfHw8er0eX19fwsLCsLa2JiQkhLi4OBo2bEjz5s2ZOrXkL1J2dnZERERQrVo1wsLC+P3338nOzsbNzY1Ro0axaNEi8vLycHFxAWDevHlcvnyZO3fuMHv2bBo1asSsWbM4cOAAxcXFDB48mG7duuHj42PYx6VLl2JhYXFPjBcvXmTDhg1YWVnx4osvMnnyZNatWwdAQEAAQ4cO5bPPPqNNmzb8+uuv1KpVi9mzZ2NlZcXEiRNJT0+nuLiYgIAA2rVrV+o4WFtbU1hYSFJSEp06daJx48Zs27YNpdQ9+/XMM88wZcoULCwsqFq1KlOmTKG4uJjhw4djZ2eHq6srO3fupEWLFvz6669Uq1aNNm3asHv3bq5fv86yZcuoVatWeZ0yQgghhBBCiIesUnxnMTk5GQcHB9asWcPy5cv5/PPPyc3NpWfPnqxYscIwiTp+/Di7du1i3bp1zJs3j0uXLhn1lZmZydy5c0lOTmbJkiUmt9m1a1d27doFwG+//cahQ4cA2LVrF506dSI0NJSJEyeSmJiIq6srS5YsISsri1deeYWlS5eybt061qxZg4WFBX5+fvTs2dPwGGzHjh1JSEjA1dWVLVu2sGPHDi5cuEBSUhIJCQksWLCA69evAxjt43+rV68e77//PoMHD6Z169bY2Nhw6tQprl69yoULF2jdujV5eXm8++67JCUl4eTkRHJyMikpKdjb27Nq1Srmz5/P5MmTTY5D1apViY+PJz09nQ8//JBOnTqxbt06o/0KCQkhLCyMlStX4unpyYwZMwC4dOkSS5cu5aOPPgKgdevWxMfHU1BQgI2NDcuXL6dZs2b8+9//vu95IIQQQgghhKg8KsWdxdOnT9OhQwcAbG1tcXZ2Zs+ePTRt2tTodS+99BIWFhZYWFjQqlUro76aN2+OpaUllpaW2NjYmNymo6MjeXl5pKWl4ezsTFZWFmlpadSoUQNbW1tOnz7NpEmTgJI7n02aNMHOzo5ffvmFvXv3YmtrS0FBQal9342rTp06XL58mZMnT3LkyBF8fHwA0Ov1ZGRkABjtoxZ3d3dSU1NxdHSkV69eAFhaWhoeUX311VfZuXMnVapU4eDBg6SlpRm2l5OTg4ODg1GfFy9eJC8vj7CwMKBk4vzhhx/y2muv3fO67OxsWrZsCUDbtm2ZNWsWAA0bNsTa2trwuhdffBGAmjVr0qxZM8P/8/Pzy7yfQgghhBBCiEpAVQKJiYkqIiJCKaXUjRs3lJubm+rWrZs6deqUUkqp9evXq+joaHX8+HHl4eGhioqKVH5+vnr77bfV3r171Zw5c9Tq1avV3r17VUBAgKHfDh06aG53/vz5ytvbW23dulXFx8crd3d3tXbtWqWUUn379lUZGRlKKaUOHDigNm/erOLj41VUVJRSSqmzZ8+q559/XhUXF6sNGzaoyMhIpZRS3t7ehrhXr16t5syZo7Zt26ZCQkKUUkoVFRWpuXPnqhs3btzzWlPmzp2rVq5cqZRSKi8vT/Xq1UsNGjRI5ebmKqWU6tSpkzp27JhSSqkZM2ao+Ph4FR8fr+Li4pRSSt25c0fNnj1bFRUVldr/4cOH1bvvvqtu3LihlFIqPz9f9e3bV50+ffqe/Xr//fcN29m6dasaNmyYOn/+vHJ3dzf09d/7ExAQoPbu3auUUmrq1Klq48aNmvsphBBCCCGEqFwqxZ3F/v37ExoaiqenJ/n5+fj7+5Oammr0uhYtWtCxY0f69++Pvb09VlZWWFqavwtvv/02sbGxxMXFkZ2dzYwZM1iwYAEA4eHhBAcHo9fr0el0TJs2DWdnZz755BN+/vlnrK2tefbZZ8nOzqZ58+bExcUZ7qr9kZubG/v378fLy4vbt2/TpUsXbG1tyxRjq1atiIqKwtnZmddff522bduSk5ODnZ2d4TWLFy8mMzMTR0dHAgMDAQgJCcHb25ubN2/i5eVFlSqlP3H84osv4uPjg7e3NzY2NhQVFeHu7o6TkxN5eXmG/Zo6dSpTpkxBKYWFhQUREaVnqfuzTGU7y5g606i9QUhQyTrmZLKsqGxwWtkYzemvgrKAmt1fRY1dBWXTPD++9Ee4G0WEmbdP4TOM28M/K/e4NfuroGyamu+/ijrvKjIbY0VlQzUnA6cZx1xrWaU4fo8wI/LjcP2EB8sUXlEZVMvUnxkx/L5itdGy+oO9yIj8vNQYGgQHaPYnhPj/KsVk0dramsjIyHva3n//fcP/72Y7vXLlCjVr1mTdunUUFBTQo0cPnnnmGT7++GPDa/87kcuePXs0t+vs7MyRI0eAksdfjx49aljWqlUrEhMTjdb5+uuvjdrq1avHP/7xDwB69OhhaPf09DT8f9y4cUbrldb/H7311lu89dZbhp/vTub+W0REBFWrVr2nLSoq6r593+Xu7m7UJ8ALL7xg2C+AVatWGb1m7dq1hv//9/7ExMQY/j9hglx8hRBCCCGEeNxUisliWdnb23P48GH69u2LTqfD3d0dR0dHzXW2b9/OihUrjNoHDRpE165dH1KkDyYzM5Pg4GCj9rZt2zJq1CjDz0OHDsXe3p727ds/8DZiY2PZt2+fUXtERASNGjV64P6EEEIIIYQQT7bHarJYpUoVpk+f/kDrdO7c2ZCltLJydHQs013GZcuWGbV9//33ZdqGv78//v7+DxybEEIIIYQQ4n9TpSidIYQQQgghhBCictEppdSjDkIIIYQQQgghROUidxaFEEIIIYQQQhh5pN9Z3LlzJ1lZWXh4eJS6PDU1lVq1aj2y7xxmZmZy/Phx3Nzc8PHxITw8HGdn50cSy4M6duwY27dvx9/fn61bt9K6dWvq1atX5vVTU1M5c+YMQUFB97T7+/sTGxtb6joXLlxgzJgx92RIhXvH8X5MlRbIil1k1P6Mv1/JOqbSbGul7S7vtOsm+jO79IKplOdapRfMSZP+CMtg3G8drbErbVmjiLCS/swYh3OfhBgtazxrKudGjS01hsZzorRjMHGczn8aarxO9JSSdco7df8U44zIDUPHcmFypHF7WLB2DBrnnalxKG37hhjMeP9lRMwyam8w/pOSdUztaynbubsts95/5qxj7vvPxD6dHzfJqL3R9ImA6fO4tPIBDYIDNLcDGueD1jn0AMfi7jE3eQ5pHT+NsTs/Nsy4v6jJ5l33tdbRGgcT7xnN/sw577RiMNWfOdcAjRhMnZOltRuWaVw/z40Zb7Ss8ewIzRhGzTdO4DdnRDtu5OaWGkMNe3vtfdKIT4j/NY/0zqKrq6vJiSKUlMx4lMlp9u7dy48//vjItv9ntGzZ0pDQJiEhgZs3b5ZLv6Ymiloe53EUQgghhBDif9VDvbNYWFjIuHHjuHDhAkVFRQwZMoSkpCQcHBy4du0aPXr0ID09naCgIObNm8e2bdtwcHDgzp07jB49mv3791OnTh2cnJxYvHgxVlZWXLhwge7duzN8+PBStxkfH49er8fX15ewsDCsra0JCQkhLi6Ohg0b0rx5c6ZOLflrkp2dHREREVSrVo2wsDB+//13srOzcXNzY9SoUSxatIi8vDxcXFwAmDdvHpcvX+bOnTvMnj2bRo0aMWvWLA4cOEBxcTGDBw+mW7du+Pj4GPZx6dKlWFhYGMW5atUqvvzyS6pUqcJLL71ESEgIWVlZhIaGkp+fT9WqVZkyZQpbt27l+vXr+Pv7U1BQQK9evfj6669JTk5m48aN6HQ6unfvzqBBg/jss8+4evUqV69exdfXl02bNvHee+9x7NgxgoODcXd35+zZswQHB1NUVETv3r1Zt26dUY1GLW+88QZ79uwhLS2NSZMmUb16dWrXrk3VqlXx9/cnJyeHESNGcOnSJVq0aMGkSZPuGcfKnplWCCGEEEIIUeKh3llMTk7GwcGBNWvWsHz5cj7//HNyc3Pp2bMnK1asMEyijh8/zq5du1i3bh3z5s3j0qVLRn1lZmYyd+5ckpOTWbJkicltdu3alV27dgHw22+/cejQIQB27dpFp06dCA0NZeLEiSQmJuLq6sqSJUvIysrilVdeYenSpaxbt441a9ZgYWGBn58fPXv2NExwOnbsSEJCAq6urmzZsoUdO3Zw4cIFkpKSSEhIYMGCBVy/fh3AaB//KDU1ldDQUJKTk3FyckKv1xMZGYmPjw+JiYn4+voyc+ZM3nvvPTZv3oxSiu3bt9OpUyfOnTvHpk2bWL16NatWrWLbtm2cOXMGgNdff501a9ZQs2ZNAN566y1atmxJZGQkPXr0YPv27RQVFbFr1y7atWv3QBPF/zZx4kRmzJhBQkICjRs3NrTfvHmT6dOnk5yczP/93/9x9epVo3EUQgghhBBCVH4P9c7i6dOn6dChAwC2trY4OzuzZ88emjZtavS6l156CQsLCywsLGjVqpVRX82bN8fS0hJLS0tsbGxMbtPR0ZG8vDzS0tJwdnYmKyuLtLQ0atSoga2tLadPn2bSpJLn6AsLC2nSpAl2dnb88ssv7N27F1tbWwoKCkrt+25cderU4fLly5w8eZIjR47g4+MDgF6vJyMjA8BoH/9o+vTpLFu2jKioKF555RWUUpw8eZKFCxeyZMkSlFJYWlpSq1YtWrZsycGDB9mwYQPBwcGcOHGCzMxMBg8eDMC1a9dIT0+/73ZtbW1p27Ytu3fvJjU1lREjRmjGqCU7O5vnnnsOgNdee41NmzYB0KhRI2rVqgVA7dq1uXPnjtnbEEIIIYQQQjxC6iFKTExUERERSimlbty4odzc3FS3bt3UqVOnlFJKrV+/XkVHR6vjx48rDw8PVVRUpPLz89Xbb7+t9u7dq+bMmaNWr16t9u7dqwICAgz9dujQQXO78+fPV97e3mrr1q0qPj5eubu7q7Vr1yqllOrbt6/KyMhQSil14MABtXnzZhUfH6+ioqKUUkqdPXtWPf/886q4uFht2LBBRUZGKqWU8vb2NsS9evVqNWfOHLVt2zYVEhKilFKqqKhIzZ07V924ceOe15oyZcoUlZeXp5RSaujQoWrfvn1q5MiR6uDBg0oppU6dOqWSkpKUUkrt27dP+fv7Kx8fH6WUUseOHVNDhw5VxcXFSimlli9frjIyMlRwcLDasWOHUkrdM2Y+Pj7q119/VUop9fPPP6uRI0cqLy8vzfjuHps/ujv2ffr0MfQ5Z84cFRwcrM6fP6/c3d0Nr3V3d1fnz5+/ZxyFEEIIIYQQj4eHemexf//+hIaG4unpSX5+Pv7+/qSmphq9rkWLFnTs2JH+/ftjb2+PlZUVlpbmh/b2228TGxtLXFwc2dnZzJgxgwULFgAQHh5OcHAwer0enU7HtGnTcHZ25pNPPuHnn3/G2tqaZ599luzsbJo3b05cXBwvvvhiqdtxc3Nj//79eHl5cfv2bbp06YKtrW2ZYmzRogVeXl5Ur16devXq8fLLLxMcHEx4eDj5+fnk5eUxYUJJZre//OUvhIaGGr6n+fzzz9O+fXs8PT0pKCi4b6ZTFxcXxo4dy7Jly3j55ZdJT09n4MCB943xyy+/5IcffjD8nJiYaPj/xIkTGT9+PNWqVcPKykpz+/89jj169LjvdoUQQgghhBCPnk4ppR51EFeuXGHLli0MHDiQgoICevToQXx8PI6Ojo86tCdOcXExnp6eLF26tMwT29KsWrWKbt264eDgQExMDFZWVobsq3+GyVTfGundTaZdLyUVOpShDMZDLhlxdz2zYjAn9bsZcYOZpRzMKTtiztiZcyy0Uu1/Fm60rNGMcDKmziw1hgYhQdqlMx6gHIhhHTPG26z+tM6hcizfUd7nvmbpjPI+hx7xua+1TOuYmzqPzS6/YkYZBbPOuwcoGVGW/h7kM+FhXO/AzDIY5ox3RV2Py2nsDP1p7NPZzByjZU0cHTTXufTlRqNldXv3JHPmnFJjcAwaZdaxMFWeRogn2SOts3iXvb09hw8fpm/fvuh0Otzd3e87Udy+fTsrVqwwah80aBBdu3Z9SJE+mMzMTIKDg43a27Zty6hRoyo8nvPnz+Pv70+fPn0ME0V/f3+uXbt2z+tsbW2Ji4vT7Kt27doMHTqUatWqUaNGDWbMMK4pJYQQQgghhHh8VYrJYpUqVZg+ffoDrdO5c+dKn13T0dHxnkc3H7VGjRrx1Vdf3dNmTt1EgHfeeYd33nmnPMISQgghhBBCVEIPtXSGEEIIIYQQQojHk0wWhRBCCCGEEEIYqRQJboQQQgghhBBCVC4V+p3FnTt3kpWVhYeHR6nLU1NTqVWr1iP7LmJmZibHjx/Hzc0NHx8fwsPDcXZ2fiSxPKhjx46xfft2/P392bp1633LaZTm+PHjzJw5k/z8fAoLC2nXrh0jR47E2tqaEydOcP36ddq2bYubmxubN2+matWq5b4fJjOxhRsn0GkY/lnJOuWdjdFUf+bEYGY2uArLYvcIM5tWeCbZcspCqLXM0F95ZkI09/iVY2bFypANtbzW+VP9mZONuLyzaZpzLMyIW2uZWedQOWXhLVMMj/jcN7u/8s5o/Yg/R8r7M7jr+G1G7VsjumiuUxk+R4R43FXoY6iurq4mJ4oAffr0eaRJa/bu3cuPP/74yLb/Z7Rs2dJQuiIhIYGbN28+0PqXL19mzJgxTJgwgcTERJKSkrCysjIkHvruu+84depUuccthBBCCCGEqJzK9c5iYWEh48aN48KFCxQVFTFkyBCSkpJwcHDg2rVr9OjRg/T0dIKCgpg3bx7btm3DwcGBO3fuMHr0aPbv30+dOnVwcnJi8eLFWFlZceHCBbp3724oSP9H8fHx6PV6fH19CQsLw9rampCQEOLi4mjYsCHNmzdn6tSSv/bY2dkRERFBtWrVCAsL4/fffyc7Oxs3NzdGjRrFokWLyMvLw8XFBYB58+Zx+fJl7ty5w+zZs2nUqBGzZs3iwIEDFBcXM3jwYLp164aPj49hH5cuXYqFhYVRnKtWreLLL7+kSpUqvPTSS4SEhJCVlUVoaCj5+flUrVqVKVOmsHXrVq5fv46/vz8FBQX06tWLr7/+muTkZDZu3IhOp6N79+4MGjSIzz77jKtXr3L16lV8fX3ZtGkT7733HseOHSM4OBh3d3fOnj1LcHAwRUVF9O7dm3Xr1pV6R/Crr76ib9++NG3aFACdTsfIkSPp3LkzFy9eZMOGDVhZWfHiiy8CEB4ezoULF4CSjKrVqlVj4sSJpKenU1xcTEBAAO3ataNnz540adIEKysrYmJi/vxJJoQQQgghhKgQ5XpnMTk5GQcHB9asWcPy5cv5/PPPyc3NpWfPnqxYscIwiTp+/Di7du1i3bp1zJs3j0uXLhn1lZmZydy5c0lOTmbJkiUmt9m1a1d27doFwG+//cahQ4cA2LVrF506dSI0NJSJEyeSmJiIq6srS5YsISsri1deeYWlS5eybt061qxZg4WFBX5+fvTs2dNwd7Njx44kJCTg6urKli1b2LFjBxcuXCApKYmEhAQWLFjA9evXAYz28Y9SU1MJDQ0lOTkZJycn9Ho9kZGR+Pj4kJiYiK+vLzNnzuS9995j8+bNKKXYvn07nTp14ty5c2zatInVq1ezatUqtm3bxpkzZwB4/fXXWbNmDTVr1gTgrbfeomXLlkRGRtKjRw+2b99OUVERu3btol27diYfHT1//jyNGze+p02n01G3bl0KCwt5//33GTx4MK1btwagb9++JCYm0qBBA/bs2UNKSgr29vasWrWK+fPnM3lySVHk27dvM2LECJkoCiGEEEII8Zgp1zuLp0+fpkOHDkBJYXdnZ2f27NljuFv136976aWXsLCwwMLCglatWhn11bx5cywtLbG0tMTGxsbkNh0dHcnLyyMtLQ1nZ2eysrJIS0ujRo0a2Nracvr0aSZNmgSU3Pls0qQJdnZ2/PLLL+zduxdbW1sKCgpK7ftuXHXq1OHy5cucPHmSI0eO4OPjA4BerycjIwPAaB//aPr06SxbtoyoqCheeeUVlFKcPHmShQsXsmTJEpRSWFpaUqtWLVq2bMnBgwfZsGEDwcHBnDhxgszMTAYPHgzAtWvXSE9Pv+92bW1tadu2Lbt37yY1NZURI0aYfG29evUM+3JXUVER2dnZ1KlTR3Ns8vLyOHnyJAcPHiQtLc0wNjk5OWUaGyGEEEIIIUQlpMpRYmKiioiIUEopdePGDeXm5qa6deumTp06pZRSav369So6OlodP35ceXh4qKKiIpWfn6/efvtttXfvXjVnzhy1evVqtXfvXhUQEGDot0OHDprbnT9/vvL29lZbt25V8fHxyt3dXa1du1YppVTfvn1VRkaGUkqpAwcOqM2bN6v4+HgVFRWllFLq7Nmz6vnnn1fFxcVqw4YNKjIyUimllLe3tyHu1atXqzlz5qht27apkJAQpZRSRUVFau7cuerGjRv3vNaUKVOmqLy8PKWUUkOHDlX79u1TI0eOVAcPHlRKKXXq1CmVlJSklFJq3759yt/fX/n4+CillDp27JgaOnSoKi4uVkoptXz5cpWRkaGCg4PVjh07lFLqnjHz8fFRv/76q1JKqZ9//lmNHDlSeXl5acaXlZWl3nnnHfXbb78ppZQqLi5Wn3/+uQoLC1NKKTV37ly1cuVKpZRSnTp1MuxLdHS0Wr9+vYqPj1dxcXFKKaXu3LmjZs+erYqKiu55rRBCCCGEEOLxUa53Fvv3709oaCienp7k5+fj7+9Pamqq0etatGhBx44d6d+/P/b29lhZWWFpaX4ob7/9NrGxscTFxZGdnc2MGTNYsGABUPLduuDgYPR6PTqdjmnTpuHs7Mwnn3zCzz//jLW1Nc8++yzZ2dk0b96cuLg4w/fy/sjNzY39+/fj5eXF7du36dKlC7a2tmWKsUWLFnh5eVG9enXq1avHyy+/THBwMOHh4eTn55OXl8eECSVZuP7yl78QGhpq+J7m888/T/v27fH09KSgoOC+mU5dXFwYO3Ysy5Yt4+WXXyY9PZ2BAwdqxle/fn2ioqKYNGkSd+7cQa/X85e//MUQU6tWrYiKijKZHXbAgAGEhITg7e3NzZs38fLyokoVKeMphBBCCCHE4+qR1Fm8cuUKW7ZsYeDAgRQUFNCjRw/i4+NxdHSs6FCeeMXFxXh6erJ06dIyT2wfpUqRdt1Uynoz0s+fHz+51BgaRYSZl4bfnHXMTPVtVvmHcoxbc+zKOfX7+c/CjbczI1w77bo5+2RiO5pxm3Huay0r9/IPD/B+MSyroPdfuZfO0IrbRGkds8semFH+weR5XN6lM8qpjJA5pUruxl7upTPMWCcjYpbRsgbjPynpz9T5UNnLH5Vn2RGN6+eVf+0yaq/91l8BiE5JM1r2qXtr7XP/01CjZY2ip3B+3KRSY2g0fSK/r1ht1F5/sBdgehxMbQcodVuNpk+UkhrisVehdRbvsre35/Dhw/Tt2xedToe7u/t9J4rbt29nxYoVRu2DBg2ia9euDynSB5OZmUlwcLBRe9u2bRk1alSFx3P+/Hn8/f3p06ePYaLo7+/PtWvX7nmdra0tcXFxFR6fEEIIIYQQovJ6JJPFKlWqGOr3lVXnzp0faQ3GsnB0dCQxMfFRh2HQqFEjvvrqq3vaYmNjH1E0QgghhBBCiMeJfKlMCCGEEEIIIYQRmSwKIYQQQgghhDDySBLcCCGEEEIIIYSo3B7JdxbLU35+Pt26deP7778v8zrJycn06dMHKyurhxhZiZUrV+Lt7U1qaipnzpwhKCioXPrNz8/n66+/xt3dvVz6K01xcTGLFi1i586dWFhYABASEkKLFi1Kff2FCxcYM2YMa9euvad95syZODk50adPn/tu06wMjg+Qvc2wrLwzIU6JMl4WOtb8jKwVlM3PrKyU5Z197wGySN6Nr6IyCpodQ3lmhDQ3E+IjjqHcM5GWdwzllN3xvutUZCbLJyyGynoOVfjYmXkNLy2jdKOIMDKmzjRqbxASpNlfeWdD1cwka8616wF+d4CH9FlmTtwav9sIUVn8Tz6GunDhQoqLiytkWw8ry+ilS5dISUl5KH3ftWTJEnJzc1m5ciWJiYl8+umnjBgxgsLCwoe6XSGEEEIIIcSj91jeWbx16xZBQUFcv36dxo0bA3DixAmmTi35q46dnR0REREUFhYSEBCAUor8/HwmTZrE4cOHuXTpEoGBgcyfP9+o79zcXAYPHsxXX33Fzz//zEcffcS+ffvIzs5mwoQJLFiwgIkTJ5Kenk5xcTEBAQG0a9eOLVu2sGrVKvR6PTqdjtjYWJKTk7l27Rrh4eG0bt2aQ4cOMXToUHJycvD09MTDw4P9+/cTExODhYUFjRo1YvLkyXzzzTesX7+e4uJiRo0aRfv27Y3iXLBgAadOnSI2Npbdu3czZcoUnnvuOXbs2ME///lPateuzZkzZ7hy5QrXr18nJCSENm3asHnzZlasWEGVKlV47bXXNO90Jicnk5qaSpUqJX9TaN26NevWrcPKyoqjR48yZcoULCwsqFq1KlOmTLln3X/84x/ExcXh4OBAYWEhTk5OZh9vIYQQQgghRMV7LO8srlmzhubNm7Nq1SoGDBgAQGhoKBMnTiQxMRFXV1eWLFlCWloadnZ2LF68mLCwMG7fvo27uzt169YlJiam1L7t7e2xs7MjKyuLnTt38swzz3D48GG2b99Oly5dSElJwd7enlWrVjF//nwmTy55zOPs2bMsWrSIpKQkmjVrxu7duxk+fDi1atUiPDwcAEtLS5YuXUpsbCzx8fEopQgNDSU2NpaVK1dSr149NmzYAEDNmjVJSkoqdaIIMGzYMJo1a4a/vz/u7u6G9davX294NNXGxoaEhASio6OZPHkyV69eZe7cuaxYsYKkpCQuXrzInj17TI5zXl4etWrVMhofKHkcNSwsjJUrV+Lp6cmMGf//UcLCwkJmzJjB8uXLWbp0KTY2NprHUwghhBBCCFH5PJZ3Fs+ePUvHjh0BePnll7G0tOT06dNMmjQJKJmsNGnSBFdXV86ePcuIESOwtLRk+PDhZeq/a9eu7Nixg59++gk/Pz/27NnDTz/9REREBLGxsRw8eJC0tDQA9Ho9OTk51K5dm+DgYKpXr86ZM2d45ZVXjPp94YUX0Ol01K1bl7y8PHJycsjOziYgIAAomZx16NCBZ599lqZNm5Z5PLp160afPn3w9fXl4sWLvPjii3z//fe8/vrrADz33HNcvnyZc+fOkZOTg5+fH1Byh/bcuXO88cYbpfZbs2ZNbt68ia2traFt69attG/fnuzsbFq2bAlA27ZtmTXr/3//ICcnh1q1ahkmli4uLmXeFyGEEEIIIUQloR5Dy5cvV9HR0UoppY4cOaI6deqk+vbtqzIyMpRSSh04cEBt3rxZ/fDDD+rbb79VSin1448/Km9vb6WUUm5ubur27dsm+8/KylIDBw5U/v7+6urVq6pPnz7qgw8+UEopFR8fr+Li4pRSSt25c0fNnj1bXb9+Xb355ptKr9crvV6vfHx81Lp165RSSrVv314ppdT69esNMefl5alOnTqpoqIi9c4776jr168rpZTatm2b+uGHH+55rSkZGRmqb9++hp/DwsJUYGCgWrlypVJKqTlz5qhp06YppZQ6ceKE6t27t7py5Yrq3bu3KigoMMR09OhRk9tYuHChmj59uiouLlZKKXXw4EHVqVMnlZ+fr95//3117NgxpZRSW7duVcOGDVPnz59X7u7uqrCwUHXq1ElduXJFKaXUsGHD1Pr16zX3RwghhBBCCFG5PJZ3Fj09PRk7diyenp44OTlhZWVFeHg4wcHBhu8MTps2DTs7O8aMGUNSUhJ6vZ6RI0cC0KZNG/z8/EhISECn0xn1X79+ffLz83n99depVasWlpaWvPXWWwAMGDCAkJAQvL29uXnzJl5eXtja2vLqq6/i4eGBpaUlNWvWJDs7GwBnZ2eCgoLo0KGD0XaqVKnChAkT8PPzQylF9erViYqKIisr675jULt2bQoLC4mOjubTTz+lf//+eHl5GR55BTh27BgffPABd+7cYcqUKTg4ODB48GB8fHwoKiqiQYMGdOvWzeQ2fH19+eKLLwz7ZWlpSVxcHNbW1kydOpUpU6aglMLCwoKIiP+f0cvS0pKwsDB8fX0N4yeEEEIIIYR4vEidxSdEWloaK1euJCqqpITD3LlzqVOnDp6eno84sgdzYXKkUVvDsGDN9NLnx00yWtZo+sRS04ZDSepwUynFQaOUg1b5DhOpsbViMFVuAzTKVphTOkMjbbfWODzIuBrGzlS6cTPGTittvmYM5qR+NzXe5qZdr6jSJ+aUlCnv1O8PsM7d9cq9fEB5l84oz/IBZoydZgxmpOE3u3xOeZYyKu9zqDLEXd7XAFPnkMZnhanrNMD5z8KNl80I5/zYMOP2qJJr6rlPQoyWNZ411eTnImCyv4zps43aARqMG6M5DqWt12DcGPNKMGldw805FhrjYDIGjbhN7StovzeFqEj/s7d8CgoK8PX1NWpv2rSpIWlNZRAeHs7p06eN2hcvXmxIHLNy5UrWrVvH559//sD9p6WlER0dbdTerVs3vLy8Hrg/IYQQQgghxJPhf3ayaG1tTWJi4qMO477++7FSU7y9vfH29r6n7eOPPy5T/61bt34sxkEIIYQQQghRsR7L0hlCCCGEEEIIIR4umSwKIYQQQgghhDAiCW6EEEIIIYQQQhh54r6zmJ+fT7du3fj+++/LvE5ycjJ9+vTBysrqIUZWYuXKlXh7e5OamsqZM2cICgoql37z8/P5+uuvcXd3L5f+SuPj40N4eDjOzs6GbWqN9aVLl5g3bx7h4eG4ubmxefNmqlatqrkNk9n8yil7IvyJzHzllElPK777Zuc0ZxzKO5tfeWeyfICsgX8qhnJap7z7e2jH7xHHUFnHrjLE8Dgcv8oQgxw/86+fWsvMWsdEpvIHjdvQn5xDmuuY258QD4M8hgosXLiQ4uLiCtlWXFzcQ+n30qVLpKSkPJS+zVW3bt0yJegRQgghhBBCVD5PxJ3FW7duERQUxPXr12ncuDEAJ06cYOrUkr9q2dnZERERQWFhIQEBASilyM/PZ9KkSRw+fJhLly4RGBjI/PnzjfrOzc1l8ODBfPXVV/z888989NFH7Nu3j+zsbCZMmMCCBQuYOHEi6enpFBcXExAQQLt27diyZQurVq1Cr9ej0+mIjY0lOTmZa9euER4eTuvWrTl06BBDhw4lJycHT09PPDw82L9/PzExMVhYWNCoUSMmT57MN998w/r16ykuLmbUqFG0b9/eKM4FCxZw6tQpYmNj2b17N1OmTOG5555jx44d/POf/6R27dqcOXOGK1eucP36dUJCQmjTpg2bN29mxYoVVKlShddee83sO5379+8nNjYWpRS3bt1i1qxZWFlZMWbMGNauXWtWn0IIIYQQQohH54m4s7hmzRqaN2/OqlWrGDBgAAChoaFMnDiRxMREXF1dWbJkCWlpadjZ2bF48WLCwsK4ffs27u7u1K1bl5iYmFL7tre3x87OjqysLHbu3MkzzzzD4cOH2b59O126dCElJQV7e3tWrVrF/PnzDTUaz549y6JFi0hKSqJZs2bs3r2b4cOHU6tWLcPdNktLS5YuXUpsbCzx8fEopQgNDSU2NpaVK1dSr149NmzYAEDNmjVJSkoqdaIIMGzYMJo1a4a/vz/u7u6G9davX294NNXGxoaEhASio6OZPHkyV69eZe7cuaxYsYKkpCQuXrzInj17NMc6ODgYHx8ffHx8GDp0qKH9119/JTo6msTERN5++222bNlSxqMnhBBCCCGEqIyeiDuLZ8+epWPHjgC8/PLLWFpacvr0aSZNmgRAYWEhTZo0wdXVlbNnzzJixAgsLS0ZPnx4mfrv2rUrO3bs4KeffsLPz489e/bw008/ERERQWxsLAcPHiQtLQ0AvV5PTk4OtWvXJjg4mOrVq3PmzBleeeUVo35feOEFdDoddevWJS8vj5ycHLKzswkICAAgLy+PDh068Oyzz9K0adMyj0e3bt3o06cPvr6+XLx4kRdffJHvv/+e119/HYDnnnuOy5cvc+7cOXJycvDz8wNK7tCeO3eON954w2TfkZGRRt9ZBKhXrx7Tpk2jWrVqXLx4kVdffbXM8QohhBBCCCEqnydisujs7MzPP/9Mly5dOHr0KHq9nqZNmxIZGYmjoyMHDx7k0qVL7Nu3j6effpply5bx008/MXv2bBITE9HpdJrfWezSpQtBQUHY29vz17/+laFDh1KjRg3q1KmDk5MT9evXZ9iwYeTl5REXF4eVlRVz5szhX//6FwBDhgzhbtLZ/04+q9Pp7tmOvb099evXZ/78+dSoUYPt27dTrVo1srKyqFJF+yZwlSpVDPtQrVo12rVrx7Rp0+jVq5fhNUeOHOG9997j5MmT1KtXj4YNG/LMM8+wbNkyrKysSE1NpWXLlg809neFhoaydetWbG1tCQ4ORpLsCiGEEEII8Xh7Ikpn5OfnM3bsWLKzs3FycuLAgQPMmjWLyMhIw3cGp02bhp2dHWPGjEGv16PX6xk5ciRvvvkmwcHBZGZmkpCQYDSBu8vd3Z3evXszcOBAPDw86NatG4MHD6agoICQkBAyMzO5efMmXl5euLu7ExAQQEZGBpaWltSsWRMXFxeGDx+Oj48P9erVo0OHDoZsqP+dVXT37t3MmzcPpRTVq1cnKiqKHTt23Ddzan5+Pv379+fNN9/k008/5ciRI3h5ebFr1y5q1qzJ3Llz2b9/P1WqVOHOnTuEhYXRqlUrvvrqK5KSkigqKqJBgwZMnz6dp556qtRtaGVDnT59Onv37uWpp56iTp062NnZMWzYMMN3FsuaDVUIIYQQQghROTwRk0VhLC0tjZUrVxIVFQXA3LlzqVOnDp6eno84Mm2m0nZnxS4yan/Gv+Tx2fOfhRstazQjvNRU31CS7tus1OFapTPCZxgvC/9MO223iXW0+suYOtOovUFIyR8RTKXTPj9uklF7o+kTATg/frLxsoiwkv5M7K/mOqZi0FjHVHznPw01agdoFD2l/MuOmOivtLjvxq4Zg6mU5+aknzcjbq1l5VUC5rFIWW9m6n5T56tZY2fGeWJ2f+Yc83J6X9zvGqC5HVPv2fI+fuZcNzTGrtzHwUR850aNNWpvPCdKM26AjMjPjZY1CA7Q/Bw5FzDOeFufTzfZl1bcpa1zdz3NY2Hq81RrX6O+MN7O2NGl9mXoz4xzUnMczIhb8/cAU++/Uj4bG0VP0VxHiD/jiXgMtTwUFBTg6+tr1N60aVND0prKIDw8nNOnTxu1L168GBsbG6CkluO6dev4/PPPH7j/tLQ0oqOjjdq7deuGl5fXA/cnhBBCCCGEeDzJZPE/rK2tSUxMfNRh3FdZ6hZ6e3vj7e19T9vHH39cpv5bt279WIyDEEIIIYQQ4uF6IkpnCCGEEEIIIYQoXzJZFEIIIYQQQghhRBLcCCGEEEIIIYQw8tjdWczPz8fNze2B1klOTqawsPAhRXSvlStXApCamsrMmcYZx8yVn59PSkpKufVXmvT0dPz8/Bg6dCj9+/cnOjpas/6kEEIIIYQQ4sn1P5HgZuHChfTu3btCthUXF2eUXKY8XLp0iZSUFNzd3cu977tmz56Nt7c3rq6uKKXw9/dn+/btdO3a9aFt849MpX3OnD3PqN1xzMiSdUylKC8lXTWYX0bBnPTzWqUXzClpce4T4xTYjWeVxGuqLINWuQbN1O8mUnqblS5eI4aM6bONljUYN0az9ILW2JkcB3PS5puZuv9BSjnct+yBVmp1c8oHmFPCoJzKFEAZxs6c998DxH13W+V1PvyZ0gtmnQ/lXXbEjOuaWWVjyumY342vokqImDPeYGYJHxPr/L4kwai9/oeDADTLKJwb/ZnRssZfzCBrwTKj9meGDdXsT2u8z40Zb7yd2RGlXqeh5Fpt1nlsRgmK0kpqwH/KapgRg2b5KnOuAeaUrtH6/DP1u8jYMON1oipPpn9RuT0Wk8Vbt24RFBTE9evXady4MQAnTpxg6tSSN5KdnR0REREUFhYSEBCAUor8/HwmTZrE4cOHuXTpEoGBgcyfP9+o79zcXAYPHsxXX33Fzz//zEcffcS+ffvIzs5mwoQJLFiwgIkTJ5Kenk5xcTEBAQG0a9eOLVu2sGrVKvR6PTqdjtjYWJKTk7l27Rrh4eG0bt2aQ4cOMXToUHJycvD09MTDw4P9+/cTExODhYUFjRo1YvLkyXzzzTesX7+e4uJiRo0aRfv27Y3iXLBgAadOnSI2Npbdu3czZcoUnnvuOXbs2ME///lPateuzZkzZ7hy5QrXr18nJCSENm3asHnzZlasWEGVKlV47bXXCAoKMjnOderUYcOGDVSvXp3WrVvz+eefY2lpyb59+1i8eDFWVlZcuHCB7t27M3z4cC5cuMD48eMpKipCp9MREhLCvn370Ov1+Pr6EhYWhrW1NSEhIcTFxdGwYUPefffdcjorhBBCCCGEEA/TY/EY6po1a2jevDmrVq1iwIABAISGhjJx4kQSExNxdXVlyZIlpKWlYWdnx+LFiwkLC+P27du4u7tTt25dYmJiSu3b3t4eOzs7srKy2LlzJ8888wyHDx9m+/btdOnShZSUFOzt7Vm1ahXz58831Fw8e/YsixYtIikpiWbNmrF7926GDx9OrVq1DOUtLC0tWbp0KbGxscTHx6OUIjQ0lNjYWFauXEm9evXYsGEDADVr1iQpKanUiSLAsGHDaNasGf7+/ri7uxvWW79+veFuo42NDQkJCURHRzN58mSuXr3K3LlzWbFiBUlJSVy8eJE9e/aYHOfg4GBefvllZs+eTYcOHRg3bhw3btwAIDMzk7lz55KcnMySJUsAiIqKYtCgQaxatYoJEyYwfvx4unbtyq5duwD47bffOHToEAC7du2iU6dOZTvgQgghhBBCiEfusbizePbsWTp27AjAyy+/jKWlJadPn2bSpJJb8YWFhTRp0gRXV1fOnj3LiBEjsLS0ZPjw4WXqv2vXruzYsYOffvoJPz8/9uzZw08//URERASxsbEcPHiQtLQ0APR6PTk5OdSuXZvg4GCqV6/OmTNneOWVV4z6feGFF9DpdNStW5e8vDxycnLIzs4mICAAgLy8PDp06MCzzz5L06ZNyzwe3bp1o0+fPvj6+nLx4kVefPFFvv/+e15//XUAnnvuOS5fvsy5c+fIycnBz88PKLlDe+7cOd54441S+927dy+DBw9m8ODB3Lp1i8jISObPn0+nTp1o3rw5lpaWWFpaYmNjA8Dp06dp27YtAC1btuT333/H0dGRvLw80tLScHZ2Jisri7S0NGrUqIGtrW2Z91EIIYQQQgjxaD0Wk0VnZ2d+/vlnunTpwtGjR9Hr9TRt2pTIyEgcHR05ePAgly5dYt++fTz99NMsW7aMn376idmzZ5OYmIhOp9NM1NKlSxeCgoKwt7fnr3/9K0OHDqVGjRrUqVMHJycn6tevz7Bhw8jLyyMuLg4rKyvmzJnDv/71LwCGDBnC3aSy/51cVqfT3bMde3t76tevz/z586lRowbbt2+nWrVqZGVlUaWK9k3eKlWqGPahWrVqtGvXjmnTptGrVy/Da44cOcJ7773HyZMnqVevHg0bNuSZZ55h2bJlWFlZkZqaSsuWLU1uIzo6GhsbG/7yl79QvXp1mjZtSm5ubqn7cve4HDhwgM6dO3Ps2DHq1KkDQMeOHYmOjuaDDz4gMzOTqVOnPtTvWgohhBBCCCHK32NROiM/P5+xY8eSnZ2Nk5MTBw4cYNasWURGRhq+Mzht2jTs7OwYM2YMer0evV7PyJEjefPNNwkODiYzM5OEhIRSJz0A7u7u9O7dm4EDB+Lh4UG3bt0YPHgwBQUFhISEkJmZyc2bN/Hy8sLd3Z2AgAAyMjKwtLSkZs2auLi4MHz4cHx8fKhXrx4dOnTgzJkzBAUFkZ+fT7du3fj+++/ZvXs38+bNQylF9erViYqKYseOHYbXao1B//79efPNN/n00085cuQIXl5e7Nq1i5o1azJ37lz2799PlSpVuHPnDmFhYbRq1YqvvvqKpKQkioqKaNCgAdOnT+epp54qdRunT59m6tSpXLt2DWtraxo2bEh4eDhHjhxhzZo1hkd533jjDfbs2cOFCxcIDQ2loKAAvV5PSEgIL730EqdPn6ZXr16G73727NmT3bt34+Dg8OdPBiGEEEIIIUSFeCwmi8JYWloaK1euJCoqCoC5c+dSp04dPD09H3Fkf055ZkHTzIRoRnZHs7KAmpuNsTyzaZqRbU0zhvLOxvgA+3o3vgqLwcxzqMIyWZoTQ3mfd+U9dpXh+D3ic/9/LoYHeL8Y+qvE1y4o52uAVkZkjXEw67OsPLMRl3dG5PK+BlRQdtxyf/9V0O8OQtz1WDyGWh4KCgrw9fU1am/atKkhaU1lEB4ezunTp43aFy9ebPiu4MqVK1m3bh2ff/75A/eflpZGdHS0UXu3bt3w8vJ64P6EEEIIIYQQT6b/mcmitbU1iYmJjzqM+7qbSVWLt7e3US3Hjz/+uEz9t27d+rEYByGEEEIIIcSj9ViUzhBCCCGEEEIIUbFksiiEEEIIIYQQwogkuBFCCCGEEEIIYUTuLAohhBBCCCGEMFIhCW7+u85gWSUnJ9OnTx+srKweYmQlVq5cibe3N6mpqfetd1jZBAYGEhkZyeXLlzl+/Dhubm4PtP7dmol37dy5k02bNjFjhnGq7opgKu1zxtSZRu0NQoI01yktLTb8J6V3RZXO0EodrpXK2lQq8vJO9V3epTgecqpvQwwVVXakvGMo77GroFIqT1LZijL194jHTmtZRb1nzY6hMpTPqQyla8qzhIjWeGuV1TD1OaKxjqnPOc11xk0yXmf6xFL7MvRX3mNnal/L+zpUQeU2NPsz4/33+5IEo/b6Hw4CICt2kdGyZ/z9NMdO/G+ptHcWFy5cSHFxcYVsKy4urkK28zDExMRgbW3N3r17+fHHHx91OEIIIYQQQognxEObLN66dYvhw4czcOBAQzmIEydO4OPjg4+PDx9//DE3btwgJyeHQYMG4ePjQ//+/Tl27BgpKSlcunSJwMDAUvvOzc3lvffeA+Dnn3+mbdu2FBcX8/vvv+Pr60thYSHjx49n4MCBeHp6sm/fPgC2bNmCj48Pnp6eeHl5kZOTQ1xcHNeuXTPEeOjQIYYOHUrv3r1JTk4GYP/+/Xh6euLt7c24ceMoLCwkNTXV0P///d//lRrnb7/9xoABA/D29sbLy4usrCwAZs2ahaenJx4eHmzevJmcnBy6devG3a+PTp48ma1bt5Y6Xvv27cPd3R0vLy++/PJL3NzcuH37NosWLWLjxo1s27aNLl26UFRUBEB0dDSbNm0y6xhu3rwZDw8PPD09mTmz5M7e77//zrBhwxgyZAg9e/Zk27ZtHD9+HB8fH8N6f//73zl06BD9+vUztAUEBJCWlmZWHEIIIYQQQoiK99AeQ12zZg3NmzcnMDCQQ4cOsW/fPkJDQ4mIiKBZs2akpKSwZMkSXFxcsLOzIyoqilOnTnH79m3c3d2Ji4sjJiam1L7t7e2xs7MjKyuLnTt38swzz3D48GF++eUXunTpQkpKCvb29kRERJCbm4u3tzfffvstZ8+eZdGiRTz11FOEhYWxe/duhg8fzsqVKwkPDyc1NRVLS0uWLl1KRkYGfn5+9O/fn9DQUFavXk3t2rX5/PPP2bBhA5aWltSsWVPzruQPP/xA69at+fTTTzlw4AA3btzg5MmTXLhwgaSkJPLz8+nfvz9vvPEGLVq04MCBA7z88svs27eP8ePH4+XlZTReHTp0ID8/n5SUFADmzJmDhYUFfn5+nDlzhi5durB161Z2797Nm2++yc6dOxk9erTJGK9du3bPRO/q1au8+OKLXL16lblz57J+/XqeeuopPv30U/bs2YNOp2PIkCG0a9eOH3/8kblz57J8+XIKCgrIyMjAysqK3NxcXn75ZWxsbDh16hR16tThwoULtG7d2syzSQghhBBCCFHRHtpk8ezZs3Ts2BGAl19+GUtLS06fPs2kSSXPthcWFtKkSRNcXV05e/YsI0aMwNLSkuHDh5ep/65du7Jjxw5++ukn/Pz82LNnDz/99BMRERHExsZy8OBBw50svV5PTk4OtWvXJjg4mOrVq3PmzBleeeUVo35feOEFdDoddevWJS8vj5ycHLKzswkICAAgLy+PDh068Oyzz9K0aVPNGPv168fixYv58MMPqVGjBoGBgZw8eZIjR44YJmh6vZ6MjAz69+/Phg0buHTpEm5ubibHC7jvdt3d3UlMTKS4uJgOHTpgbW1t8rW1atUiMTHR8PPd7yyeO3eOnJwc/Pz8gJI7xefOnaNNmzbExcWxbt06dDoder3esK9ffvkl1tbW9OnTxxBHamoqjo6O9OrVSzNmIYQQQgghRCWjHpLly5er6OhopZRSR44cUZ06dVJ9+/ZVGRkZSimlDhw4oDZv3qx++OEH9e233yqllPrxxx+Vt7e3UkopNzc3dfv2bZP9Z2VlqYEDByp/f3919epV1adPH/XBBx8opZSKj49XcXFxSiml7ty5o2bPnq2uX7+u3nzzTaXX65Ver1c+Pj5q3bp1Siml2rdvr5RSav369YaY8/LyVKdOnVRRUZF655131PXr15VSSm3btk398MMP97zWlG+//Vb98MMPSimlvvnmG/XZZ5+pbdu2qZCQEKWUUkVFRWru3Lnqxo0bqri4WPXu3VsNHTpUpaenK6VUqeO1d+9eFRAQYNhGp06dVF5entqwYYOKjIw0tL///vtqxIgR6uTJk5oxdujQ4Z6fd+zYoYKDg9WVK1dU7969VUFBgWFsjh49qkaOHKn+9a9/KaWUWrdunRo4cKBSSqmbN2+qfv36qf79+6sbN24YxrBXr15q0KBBKjc3VzMOIYQQQgghROXy0O4senp6MnbsWDw9PXFycsLKyorw8HCCg4PR6/XodDqmTZuGnZ0dY8aMISkpCb1ez8iRIwFo06YNfn5+JCQkoNPpjPqvX78++fn5vP7669SqVQtLS0veeustAAYMGEBISAje3t7cvHkTLy8vbG1tefXVV/Hw8DA8QpqdnQ2As7MzQUFBdOjQwWg7VapUYcKECfj5+aGUonr16kRFRRm+f6ilVatWBAcHExcXR3FxMePGjeOFF15g//79eHl5cfv2bbp06YKtrS0Af/vb3/jhhx9o3LgxQKnjdTfmP2revDlxcXG8+OKL9OjRg3fffZctW7bw3HPP3f9glcLBwYHBgwfj4+NDUVERDRo0oFu3brzzzjtERUWxaNEi6tevT25uLgDVq1fn+eefR6/XG/anatWqtG3blpycHOzs7Mq0XVOZ2LLmLzFqf2bEhyXrmMjEdmFyZKnbaBgWrJ3Z1FRGOq2scyaWaWZi08qqZkYWO5MxaGROMzV2Wv2VV1bY+2VvO/9pqFE7QKPoKWZlBzQnC6FmNj9zMkJqHb9yzL4HGuexOdn8yikDoNayxz4banlnQjQnM6Y5cZtxbM26BlTg8TOZ0dOcDNRmbMfc/h7kOnR3O5oxlPIZ2DAsmMzZ84zaHceU/O51bvRnRssafzGDjIhZRu0Nxn9SEoOJz5GM6bON2gEajBtj3jVc41zNiPzceDvBAaVmUoeSbOrmvC8yor4w7mvsaLPj1vxMMDFG5z4JMWpvPGuq5jojY/catc/zfx2A6xeNf6+sWe9pzWzEWuedePI8tMli1apV+eIL4zfVfz/yeNfy5cuN2iIjS/9F/7/d/d4eYEhGA2BtbU1UVJTR60uLx1RMVatWNZT6ePPNN3nzzTfvWX73UUstjRs3Jikpyah93Lhxpb5+2LBhDBs2zPBzq1atjGJr2rQp7dq1M/x8N8YXXniBf/zjH4b2oqIi3N3d7xvjf5fNAHB1dcXV1RWA9957z5BI6K6ePXvSs2fPUvuaMmWKUVtZ4xBCCCGEEEJULhVSZ9FcBQUF+Pr6GrU3bdqUyZNL/4v/oxAeHs7p06eN2hcvXoyNjU2Fx/PZZ5+RnZ3NggULgJKJ9MaNG41eN2bMGFxcXB5aHEOHDsXe3p727ds/tG0IIYQQQgghHo5KPVm0trYu9a5fZXO37EZlMWPGvY81eHh44OHhUeFxLFu2rMK3KYQQQgghhCgfD63OohBCCCGEEEKIx5dMFoUQQgghhBBCGNEppdSjDkIIIYQQQgghROXyQN9ZzM/Pp1u3boYMnGWRnJxMnz59sLKyeuDgHtTKlSvx9vYmNTWVM2fOEBQU9NC3WV4CAwOJjIzk8uXLHD9+HDc3twdav0WLFnh4eNyT+Gfq1Kl8//33ZT5e/fv3Z/bs2TRs2PCBtv3ftm7dSuvWralXr55Z65tK9V1a+usGISXH12TpjFLSVcN/0peXd/r5ByhbYejPnNIZWuuUY6p9MDP9fAWlza+o8h2VovyDGXGbHYM55/4DrFOmGMrz+Gm9/8r7GmDO+6+8x8Gccg2VoYTIA4yd1rKHcvwqQekTc0p0gOlruGYpjrHG5Q8aRU3WLPFgch2tz2CtsTPxe4BZ65gbg6nz2MR2wMyyTVr9mTgffr901ai9fl07zbgvbdxi1F635zsAZM6cY7TMMWiU2e+/0speNYo2zpYvHh8P/THUhQsXUlxc/LA3A0BcXFyFbOdhiImJwdramr179/Ljjz8+8Pp2dnYcOHAAvV4PlJSs+OWXX8o7zPtKSEjg5s2bFb5dIYQQQgghRPm6753FW7duERQUxPXr1w3F4k+cOMHUqSV/VbCzsyMiIoLCwkICAgJQSpGfn8+kSZM4fPgwly5dIjAwkPnz5xv1nZuby+DBg/nqq6/4+eef+eijj9i3bx/Z2dlMmDCBBQsWMHHiRNLT0ykuLiYgIIB27dqxZcsWVq1aZShWHxsbS3JyMteuXSM8PJzWrVtz6NAhhg4dSk5ODp6ennh4eLB//35iYmKwsLCgUaNGTJ48mW+++Yb169dTXFzMqFGjSi3z8NtvvzFu3DgsLS0pLi5m1qxZPPPMM8yaNYsDBw5QXFzM4MGDadeuHQMHDmTTpk3odDomT55M+/btady4sdF4HT16lJkzZ2JlZUX//v2ZM2cOGzduZNGiReTl5fHKK68wY8YM/vGPf2BhYUF0dDQvvvgi3bt3L/1AWlryl7/8hT179tCxY0d2795Nhw4d+OqrrwA4evQoU6ZMwcLCgqpVqzJlyhQcHR2JiYlh165d1K9fn9zcXABu3LjBhAkTDD+HhITQokULOnXqhJOTE87OzvTr148ZM2ZQVFREbm4u4eHhXL9+nWPHjhEcHMzq1asNJTt0Oh3du3dn0KBB9z0hhRBCCCGEEJXDfe8srlmzhubNm7Nq1SoGDBgAQGhoKBMnTiQxMRFXV1eWLFlCWloadnZ2LF68mLCwMG7fvo27uzt169YlJiam1L7t7e2xs7MjKyuLnTt38swzz3D48GG2b99Oly5dSElJwd7enlWrVjF//nzDI5Znz55l0aJFJCUl0axZM3bv3s3w4cOpVauWoYyFpaUlS5cuJTY2lvj4eJRShIaGEhsby8qVK6lXrx4bNmwAoGbNmiQlJZmsB/jDDz/QunVrli9fzscff8yNGzfYsWMHFy5cICkpiYSEBBYsWIClpSUtWrTgwIEDFBQUsG/fPjp16lTqeEHJY72rV6+md+/eAFhYWODn50fPnj3p0qULr732Grt376aoqIidO3fSpUsXzWPVs2dPNm3aBMDGjRt59913DctCQkIICwtj5cqVeHp6MmPGDH755Rf+/e9/s27dOqKiorh16xYACxYs4PXXXycxMZEpU6YYxjQrK4uZM2cyfvx4Tp06RXBwMPHx8Xz00Uekpqby1ltv0bJlSyIjIzl37hybNm1i9erVrFq1im3btnHmzBnN+IUQQgghhBCVx33vLJ49e5aOHTsC8PLLL2Npacnp06eZNKnke2KFhYU0adIEV1dXzp49y4gRI7C0tGT48OFlCqBr167s2LGDn376CT8/P/bs2cNPP/1EREQEsbGxHDx4kLS0NAD0ej05OTnUrl2b4OBgqlevzpkzZ3jllVeM+n3hhRfQ6XTUrVuXvLw8cnJyyM7OJiAgAIC8vDw6dOjAs88+S9OmTTVj7NevH4sXL+bDDz+kRo0aBAYGcvLkSY4cOYKPj48htoyMDPr378+GDRu4dOkSbm5uJscLuO923d3dSUxMpLi4mA4dOmBtba35+tdee41JkyaRm5vL1atXadCggWFZdnY2LVu2BKBt27bMmjWLs2fP0qpVK6pUqYKtrS3NmzcH4OTJk+zdu5fNmzcDcO3aNaBkcm9vbw/A008/zfz587GxseHWrVvY2treE8vJkyfJzMxk8ODBhj7S09NxcnLS3AchhBBCCCFEJaHuY/ny5So6OloppdSRI0dUp06dVN++fVVGRoZSSqkDBw6ozZs3qx9++EF9++23SimlfvzxR+Xt7a2UUsrNzU3dvn3bZP9ZWVlq4MCByt/fX129elX16dNHffDBB0oppeLj41VcXJxSSqk7d+6o2bNnq+vXr6s333xT6fV6pdfrlY+Pj1q3bp1SSqn27dsrpZRav369Iea8vDzVqVMnVVRUpN555x11/fp1pZRS27ZtUz/88MM9rzXl22+/VT/88INSSqlvvvlGffbZZ2rbtm0qJCREKaVUUVGRmjt3rrpx44YqLi5WvXv3VkOHDlXp6elKKVXqeO3du1cFBAQYttGpUyeVl5enNmzYoCIjIw3t77//vhoxYoQ6efKkZowdOnRQSikVHR2tRo0apdasWWPY97v9HDt2TCml1NatW9WwYcPU0aNHlYeHhyoqKlK3bt1Sb7zxhjp//ryKiIhQX3/9tVJKqcuXLxuOwd1tKKVU79691alTp5RSSn3xxRdq7NixSimlfHx81K+//qqOHTumhg4dqoqLi5VSJefR3TEQQgghhBBCVH73vbPo6enJ2LFj8fT0xMnJCSsrK8LDwwkODjZ8Z3DatGnY2dkxZswYkpKS0Ov1jBw5EoA2bdrg5+dHQkICOp3OqP/69euTn5/P66+/Tq1atbC0tOStt94CYMCAAYSEhODt7c3Nmzfx8vLC1taWV199FQ8PDywtLalZsybZ2dkAODs7ExQURIcOHYy2U6VKFSZMmICfnx9KKapXr05UVBRZWVn3nVC3atWK4OBg4uLiKC4uZty4cbzwwgvs378fLy8vbt++TZcuXQx31/72t7/xww8/GL7jWdp43Y35j5o3b05cXBwvvvgiPXr04N1332XLli0899xz940T4N1336Vfv373ZEWFksyoU6ZMQSmFhYUFERERNGrUCFdXV/r168fTTz9N7dq1ARg2bBgTJkxg7dq13Lx5E39/f6Pt9OrVi9GjR1OzZs17vu/o4uLC2LFjWbZsGe3bt8fT05OCgoI/lSFVCCGEEEIIUfGkzmIlt2TJEuzs7OjXr9+jDqVClGfqd820z+akPDenbIVW+YAKSoFfKdLmm5Pm3tzjV0HlOx5lDGU6fo84hso6dpUhhsfh+FWGGOT4mX/91Fpm1jpaJR7K+zNYziGz++s6fpvRsq0RXSq2dJQZvzuIyu2B6iyaq6CgAF9fX6P2pk2bGt0Be5TCw8M5ffq0UfvixYuxsbGp8Hg+++wzsrOzWbBgAYAhu+gfjRkzBhcXl4oOTwghhBBCCPEEq5DJorW1NYmJiRWxqT/lbtbPymLGjHsLvHp4eODh4fGIohFCCCGEEEL8L7lv6QwhhBBCCCGEEP97ZLIohBBCCCGEEMKIJLgRQgghhBBCCGHkoX1nMT8/n27duvH999+XeZ3k5GT69OmDlZXVwwrLYOXKlXh7e5OamsqZM2cICgp66NssL4GBgURGRnL58mWOHz+Om5vbA62/Y8cOli1bhlKKvLw8vL296dWr10OK9sGYzAKqkYnt/HjjJEmNIsJKbb+7TDOTV/gM42Xhn5mVtUwzBq3scuZkQzUjA5mpsdNaprWOyaxqWplkzcmkZ05mt/LOBmdORsHyzj5bThl6/1Q2v/LOBFwZjl9FZUIs73OoHMdOsz9zzodKno2xMmSyNLUsI2KWUXuD8Z8AcP6zcKNljWaUtJkaB83r/qehxsuip5AxdaZxDCElvzedHzfJeJ3pE8mYPtuoHaDBuDGa42Bqf8363C7lGgll+Dw19RlsYjua62jtaylj1GDcGACyk1ONlj3t0Ydpq382ap/g9UpJDKZ+FxkbZtTeKKrkPDB1PmSv/9qoHeDpvr0038+mtmXqPNGKW1QOleox1IULF1JcXFwh24qLi6uQ7TwMMTExWFtbs3fvXn788ccHXn/ixInMnTuXhIQEli1bxhdffMGVK1ceQqRCCCGEEEKIx1W5ThZv3brF8OHDGThwoCGz6IkTJ/Dx8cHHx4ePP/6YGzdukJOTw6BBg/Dx8aF///4cO3aMlJQULl26RGBgYKl95+bm8t577wHw888/07ZtW4qLi/n999/x9fWlsLCQ8ePHM3DgQDw9Pdm3bx8AW7ZswcfHB09PT7y8vMjJySEuLo5r164ZYjx06BBDhw6ld+/eJCcnA7B//348PT3x9vZm3LhxFBYWkpqaauj///7v/0qN87fffmPAgAF4e3vj5eVFVlYWALNmzcLT0xMPDw82b95MTk4O3bp14+5TwJMnT2br1q2ljte+fftwd3fHy8uLL7/8Ejc3N27fvs2iRYvYuHEj27Zto0uXLhQVFQEQHR3Npk2bTB6nGjVqkJCQwK+//kr16tXZvHkztWvX5saNG/+vvfMOi+L83v69SNFYQbE3xBZNiFFji2JAgthRqQL2QhQbaCzYULEhltiwxI6ioEaj2FBjjbH3KArWGEUFVARZyrx/8O5+QXba2WWE/J7PdXkJy5x9ntmdnZ17zn2eg1GjRmnHv3fvHo4dO4ZJkyZpY3v27Ik3b97g4MGDcHd3h6enJxYuzLnjuGzZMgwcOBAeHh6Ii4vDli1b4O7uDg8PD2zevFn0+GEwGAwGg8FgMBiFB4OKxYiICNSvXx/h4eHw8PAAAEydOhXTp0/Hli1bYGtri3Xr1uHGjRsoV64c1q5di2nTpiE1NRWurq6wtLTE4sWLdT63ubk5ypUrh3///RenTp1ClSpVcOvWLRw7dgwODg6IjIyEubk5wsPDsXLlSm3/xkePHmHNmjXYvn076tatizNnzuCnn35C2bJltWLR2NgYv/76K5YvX45NmzaB4zhMnToVy5cvx9atW1GpUiXs2bMHAFCmTBls374drVu31jnPc+fOwcbGBhs2bNCKvZMnT+LZs2fYvn07Nm/ejLCwMBgbG6NBgwa4dOkS1Go1/vrrL9jZ2el8vYAcW++2bdvg7OwMAChWrBiGDh2Krl27wsHBAc2aNcOZM2eQlZWFU6dOwcHBgfd9Wr9+PdLS0uDv74+2bdti9erV4DgOYWFhaNWqFbZs2YJZs2ZhxowZ+OGHH3D16lWkpqbixo0bqFGjBooVK4Zly5Zh48aN2L59O16+fImzZ88CAOrUqYOIiAhwHIfo6Ghs27YN4eHhiImJQXx8vLwDisFgMBgMBoPBYHw2DFqz+OjRI7Rv3x4A8M0338DY2BhxcXEICsrxKWdkZKB27dqwtbXFo0ePMHz4cBgbG+Onn36S9Pw//vgjTp48iatXr2Lo0KE4e/Ysrl69ijlz5mD58uW4fPkybty4AQDIzMxEYmIiypcvjwkTJqBkyZKIj49HkyZN8j1vo0aNoFKpYGlpiY8fPyIxMREJCQkYM2YMAODjx49o06YNatWqBSsrK8E5uri4YO3atRg8eDBKly6NsWPHIjY2Frdv34aPj492bv/88w/c3NywZ88evHr1Cvb29ryvFwDRcV1dXbFlyxZkZ2ejTZs2MDU11bnd27dv8fz5c4wfPx7jx4/Hy5cvMXLkSDRu3BixsbE4f/48Dh48qN22WLFi6NixI44cOYJr167B1dUVT548QWJiIoYOHQogJ6P85MmTPPOMjY3F8+fP0b9/f+1zPX78GHXq1BHcDwaDwWAwGAwGg1FI4AzIhg0buJCQEI7jOO727ducnZ0d17t3b+6ff/7hOI7jLl26xB08eJA7d+4cd+DAAY7jOO7KlSuct7c3x3EcZ29vz6WmpvI+/7///st5eXlxfn5+XHJyMterVy+uX79+HMdx3KZNm7hVq1ZxHMdxaWlp3KJFi7h3795xbdu25TIzM7nMzEzOx8eHi4qK4jiO41q3bs1xHMft2rVLO+ePHz9ydnZ2XFZWFufk5MS9e/eO4ziOi4mJ4c6dO5dnWz4OHDjAnTt3juM4jvv999+5iRMncjExMdyUKVM4juO4rKwsbtmyZdz79++57OxsztnZmRs4cCD3+PFjjuM4na/X+fPnuTFjxmjHsLOz4z5+/Mjt2bOHmz9/vvbxnj17csOHD+diY2N555eQkMDZ2dlxr1690s7np59+4k6fPs3NmTOH27dvH8dxHPf69Wvt6/nkyRNuwIABXM+ePbmsrCzuzZs3nLOzM6dWq7Wv4Z07d7hffvmF27ZtG8dxHPf3339zAwcO5LKzszmOyzk2NPvFYDAYDAaDwWAwCj8GzSx6enri559/hqenJ+rUqQMTExPMmDEDEyZMQGZmJlQqFYKDg1GuXDn4+/tj+/btyMzMxIgRIwAAzZs3x9ChQ7F582aoVKp8z1+5cmWkp6ejVatWKFu2LIyNjfHDDz8AADw8PDBlyhR4e3sjJSUFffr0QalSpdC0aVO4u7vD2NgYZcqUQUJCAgDA2toa48aNQ5s2bfKNY2RkhMDAQAwdOhQcx6FkyZJYsGCBtv5QiK+++goTJkzAqlWrkJ2djUmTJqFRo0a4cOEC+vTpg9TUVDg4OKBUqVIAgI4dO+LcuXOoWbMmAOh8vTRz/pT69etj1apVaNy4Mbp06YJu3brh0KFDqFevHu/8LC0tERgYiGHDhsHY2BhZWVn44Ycf0LZtWzRu3BiBgYHYuXMnUlJS4OfnBwCoUaMGAMDe3h5GRkawsLBA//794ePjg6ysLFSrVg2dOnXKM07Dhg3RunVreHp6Qq1Ww8bGBpUqVRJ9/RgMBoPBYDAYDEbhgPVZ/A+xbt06lCtXDi4uLp97KgwGg8FgMBgMBqOIU2B9Fqmo1WoMGjQo3+NWVlbaRWsKAzNmzEBcXFy+x9euXYvixYsrPp+JEyciISEBYWFhAHJ6Vu7fvz/fdv7+/vj222+Vnh6DwWAwGAwGg8EoYrDMIoPBYDAYDAaDwWAw8mHQ1hkMBoPBYDAYDAaDwfhvwMQig8FgMBgMBoPBYDDywcQig8FgMBgMBoPBYDDywcQig8FgMBgMBoPBYDDywcQig8FgMBgMBoPBYDDywcQio1CSnZ0tabtff/0ViYmJBTwbw5Genv65p0Dm3r17Oh/fu3evwjMxDPHx8Tofv3LlimisWq3G8+fPoVar9ZpDWlqaXvEMw/FfXBic7zOrFC9fvpS8bVZWVgHORD537tz53FMgER0d/bmn8J/mxIkTeX4vDK+3Wq3m/SfGgQMH8OHDB1nj3bx5kzpVPHr0CCdPnsSLFy8kn3MpMQzDwlpnMAoN+/btQ7FixaBWq7FgwQIMHjxYZ8/N3Gzfvh379u2DpaUlevfuDVtbW6hUKsGYYcOGwdXVFXZ2dihWrJjk+fXq1Qvdu3eHs7MzypUrJzkOAJ4+fYrw8HDs27cP586d07nN27dv8ejRI9jY2GDPnj24desW6tatCzc3Nxgb626J6ujoCGdnZ7i4uKBixYqy5pSbuXPnYtKkSYLbdOzYEWvXrkXNmjUB5HxBBQUF4ebNm9i3bx9v3OHDh7F161Y8f/4cVatWRZ8+fdCpUyfe7X18fHjfw82bN+t8fPHixRgzZkyeuDdv3uDnn3/Gr7/+qjOmYcOG8Pb2xqRJk/IcB3379uUdJyMjA3PnzsXJkydhaWmJhIQEtG/fHpMnT4aJiQnvPvHh4uKCqKgoSdumpKRgz5492L59u+wLlI0bN6J///68f7979y4aNmyIjIwM7Ny5E6ampujduzeMjKTfTzx+/DjCw8N5X28Nf/zxBw4cOIDk5GRUrlwZnTt3RuvWrXm3b9u2Le/fzpw5w/s3juNw8eJFPH/+HFWqVEGLFi0Ezw1C77sQd+/exeHDh5GUlITKlSvDyckJtWvX5t3+t99+4/2bs7Mz79+EPp9z587V+biTkxM8PDwE3/tPWbt2LYYMGZLnscTERIwbNw7r16+X9Bznz59HeHg4rly5grNnz+rc5sWLFxgzZgxWr16NsmXL4vfff8eWLVuwbNkyVKpUSWfMxYsXecf87rvveP82e/ZsTJkyRdLcNVCOB6GLc1NTU52PR0REwMPDQ9Y4Qsg5p2jo168fNm3axPv30NBQjBkzRtb3pS6knCMcHR3z3DgwNjZGlSpVMH78eDRu3FhnTFhYGNatW5enx7XQuQEA3r9/jwsXLuS5gdu5c2fe7U+cOIErV67gwIED6Nq1K4CcGxzHjx/HwYMHBccKCAhAaGio4DYaKOcHe3t7qFSqfEJKpVLh2LFjguOFhYXh9OnTKF26NH788Ud06NBB9Ppm7Nix+Oeff9C9e3d0794dZcqUEdxew9atW3H06FG8ffsWzs7OePLkCaZNm2bwGIbh0X0FymB8BjZv3oy1a9fC398fJ0+exMCBA0XFoqenJzw9PXH//n2EhYVh+vTp6N27N/r27YuyZcvqjPn555+xa9cuLFu2DG3btoWrq6vgxZ2GjRs34vfff4evry+qVKkCV1dXtGnTRjDm5MmT2Lp1K65cuYKhQ4cKfhH4+/vDw8MDISEhePv2Lezs7HDx4kVMmDCB94smIiICe/fuxZAhQ1C9enW4ubmhffv2ovuS++KE4zjExcXh+vXr2ufURXBwMIYPH45ff/0VqampGDNmDL777jvBC5PffvsNBw8exIwZM1CjRg08evQIISEhSEtLQ69evXTGBAUF5fn97t27mDNnjvYLWhcqlQoDBw5EaGgoLCwscObMGUybNk3w+GnatCnUajX69++PX375Bebm5trXg48VK1agfPny2i/grKwsrFixAkuXLsW4ceN44/iQcq/uwYMH2Lp1Kw4dOgRHR0fMmzdP9jj79+/nFQwbNmxAdHQ0tm/fjvnz52tF/Zw5c0QvsJOTkxEZGYmdO3eiZs2acHFxEdw+PDwcp06dQt++fVG+fHk8f/4cq1evxpMnT+Du7q4zRuyiTxevX7/GsGHDUKtWLVSvXh3Hjx/HvHnzsHr1ar1uqnzKwYMHsXbtWnh4eOCrr77C8+fPMXLkSIwePRoODg46Y+Li4rQ/HzhwAF26dAEA0Ztct27dwsePH9G9e3d8++23ko6dXbt2YcGCBRg0aBDmzZsHS0tL0ZhTp06hdOnS2nPEpUuXMH78eN73R0Nqaqr2ZsarV68wdepUwQvk6dOnY/DgwdrzdLdu3WBsbIzp06cjLCxMZ8z27dvz/K5SqXD+/Hmo1WpBIRkbGys4d0Ph5OSU56Jd87PQRfu5c+dw6tQpzJkzR/ZNSF1Q7v+npKQI/j0jIwOenp4ICQlBrVq1ZD233HNEq1at4OTkhObNm+Pq1auIjIxE7969MXv27Hzvv4bo6GicPn0aJUqUkDyvgQMHwtraWit0VCqVoFhs2LAhkpOTYWZmBisrK22M0PeSBrVajbt378LKykr7Oee7eaA5P1y7dg0lSpTAt99+i5s3byIzM5NXLB4/flx0Dnz4+vrC19cXN2/exOzZszF9+nTcunVLMGbx4sV4+/Yt9u/fj9GjR8PCwgJubm5o2bKlYNyBAwcQHh6Ofv36oX///ujdu7fo/CgxjAKAYzAKCV5eXlxiYiI3YsQIjuM4zt3dXTTm7du33LZt2zh3d3du8ODB3JEjR7iDBw9Kin3z5g3n7+/PNW7cmOvfvz935coVSfN88OAB5+/vz7Vq1YpzcXHhjhw5km+bX3/9levatSsXEBDAnTt3jhs4cKDo83p7e+f5X4OUfeE4jrt+/To3bdo0ztnZmVuxYoXgtvv27eP69evHxcbGck+fPuXc3Ny4Z8+ecc+ePROMO3/+PNe5c2fOwcGBO3HihOic+vTpw6Wnp+d5LCUlRdI+ZWdnc2FhYZyTkxP3119/iW5//Phxrlu3btzUqVO5Hj16cLGxsYLb+/j4cBzHceHh4ZyjoyP3999/cxyX//XPjYeHh87HXV1dReeni969e/P+7dChQ5yPjw/n4eHB7dy5k+vbty9pDI7juF69evH+zc3NjcvMzOSysrK4li1bcm/fvuU4Tvi4u3nzJjdx4kTO0dGRW7x4seBrlht3d3cuMzMzz2Pp6emcl5cXb8yePXt4//Gh+dzl5uTJk9yYMWN4Y1q3bs35+/vr/MeHh4cH9+HDhzyPvX//XvLrIXU7Dffu3eNCQkI4Hx8f7pdffuEePXokKe6vv/7iOnToIGmfUlJSOE9PT27Pnj3c6tWrOUdHR+7atWuCzz9z5kyuc+fO3KJFi7iHDx9ygwYNEp0T33vO9xn7lKSkJG7MmDFcv379uOfPnwtu6+joyEVEROj8x0fjxo2577//Xuc/PrKzsyXN/VP279/PdevWjTt9+jQpPjdC5xR9Yi5fvsz16tWL27FjB5eenq79xwf1HPHpdprzXp8+fXhjfvrpJ9mv/YABA2RtryErK4t78+YN988//2j/idG1a1fOzs5O+8/e3l405tNrBqH5BgUFcRyXcy53d3fP80+M2bNnc25ubtywYcO4zZs3c/Hx8aIxHJdzHbRgwQKuR48e3KxZs7igoCAuICBAMMbd3Z3Lzs7WfvdK+axTYhiGh2UWGYWGGjVqwN3dHZMmTcLy5cvRoEED0RgXFxd0794dixYtQtWqVbWP//3337wxJ0+exJ49exAXF4cePXpg8uTJyMzMxJAhQwTtlOHh4di7dy9KlSoFFxcXzJs3D5mZmXBzc8OPP/6YZ9v169ejS5cu6NWrFxo0aCDJvmVsbIwbN26gadOmuHjxIr777jtcvnxZshXQxsYG2dnZUKlU2Lt3L4YPH867bbdu3WBtbY2QkBBMnDgRZmZmqFatmugYLVu2xJQpU7BkyRI0a9ZMdPtixYrlu4NasmRJUTvTo0ePMHHiRNSvXx9RUVEoWbKk6Fh169aFmZkZzp07h06dOsHa2lo0BgD69OmDBg0aaDNCQhkePqspn01YQ2hoaL7n5ThOsKZrwoQJ6Nu3LwYMGABzc3McOXJEcAxA2AbHh+b9uH37NmrUqKG9084JZCg8PDwwaNAg/P777zA1Nc1nW+TDxMQk33tvamoqeDxMmTIFVatWhZ2dHczMzCRlTl68eJHP2mpra4uVK1fyxlSsWFE0e/YpxsbG+OKLL/I8VqpUKcl2PbFs4qfUr19fm8G+ePEiQkND8eLFC+zcuZM3Ji4uDosWLUKLFi0Eba4aSpYsiTVr1mDgwIEwMTHBrl27UKpUKcGYy5cvo3Hjxvjmm29Qs2ZNSfvF9z5KeX9PnjyJOXPmoG/fvvDy8hLdPiMjA69evRLdLjfffvsttmzZIiumffv2+O6779CuXTu0bdsWFSpUkBTXpUsXNGzYEO7u7pKtlHz27OTkZN6YHTt25HuM4zhJdf9NmzbFzJkz0b9/f6xevVo0Y0o9R5iammL79u349ttvcfXqVZiamuLWrVuCNa0ZGRno1q0b6tevDyDncyVm+2zbti22b9+OunXrah8TsjJrmDFjBs6dO4cKFSpoXwM+N46G33//HQCQlJSEcuXKSfp8JCYm4t27dyhTpgySkpIE31fNd/2iRYtEn/dT1Go1zMzMUKVKFVStWlWS88LV1RXFixeHm5sbRo8erf2OF3OCdenSBV5eXnj+/DmGDBnC677ITdeuXWXHMAwPE4uMQsPcuXPx4cMHlCxZEl999ZUky9Thw4fznHgTEhJQsWJFjB07ljdm37596NOnD1q0aJHn8ZEjRwqOlZCQgNDQUNSoUUP7mImJCWbOnJlv2+PHj+Pw4cMIDg7Gx48fkZaWhvfv36N06dK8zx8UFISpU6ciMTERq1evRsmSJWFlZYXZs2cLzuuff/7R2j3r1KkDNzc3TJ8+XTAGABo1aoQFCxYgMDAQSUlJotu7u7trbVVPnjxBjx49YGlpKfhlmZ2drX1PNaSkpAh+8W/ZsgUbN27EpEmTYGtrC+B/IojPurN371788ssvmDx5Mtq3b4+FCxfCx8cHixYt4q1/MjMz0/7crFkzbN26FaNHjxZcFMTMzAxPnjzR1m0CwJMnT/Jc4OmiTp06Oh/39/fnjTly5Ah2794NLy8v1K9fX9J79KkNToPQxYlKpcLDhw+xZ88e2NvbA8gR60KCZ9u2bYiMjETXrl3x448/IjU1VXRuQvMQEginTp3CgQMH8Mcff6BKlSro1q2bqN1JTq2lhtKlS+c7J4jBtz9SF+iikJKSgqNHj2L//v1IS0tD9+7debdds2YNIiIiMHXqVNjZ2Ul6/ocPHwIAAgMDMX78ePz555/aC2qN/e5TfvvtN1y5cgWRkZGYN2+e1toudMPGxsYGmzdvRt++fbWPbdmyRfAmYWpqKubMmYO4uLg89dNiVKtWDX5+fpK21YeYmBhcvXoVFy5cwNixY5GRkYEWLVqgXbt2gkIkKioKq1atwpQpUyQJeoBmz+YTzHwlARoyMzOxYsUKREdHY8WKFZI+J9RzxMKFCxEWFobjx4+jXr16WLBgAW7cuIHg4GDeGKlCNDeXLl3KY19WqVSSxOLdu3dx9OhRWTd6Ll68iKCgIGRlZcHJyQlVq1aFq6urYIyvry+cnZ1RtmxZvH//HlOnTuXdVnNTIjMzE4cOHUJGRgaAnGsWXdcnudGUfdy4cQMhISEYPXq0qA116tSpsLGxyfe4WL26p6cn2rRpg9jYWFhZWeW5wc9HmzZt0Lp1a21Mw4YNRWMYhoctcMMoNNy/fx/Tp0/Hu3fv0L17d9SrV0/0Amfp0qXYvn07MjIy8PHjR9SuXRsHDhwQjMnIyMCtW7eQmZkJjuOQkJAgqe4gKSkJZ8+ezRM3bNgw0bjHjx9j586dOHjwIL766iv88ssvgtunp6cjOTkZ5cqVyyNodOHt7Y3Xr1/DxcUFPXv2RPny5UXn8ynZ2dm4ffs2vv76a8Ht/vnnH96/8WUlDx48iJ07d2LcuHGoWbMm/vnnHyxYsAC9evXifc01ggX438W42F1sHx8fLFy4MI8wPHr0KObPn4+YmBjB/cpNRkYG1q9fz/u+3r17FwEBAXBxcUGNGjXw7NkzREZGYsGCBbyLLwA5iyNIvVjXxZ9//omdO3fi+vXr6NixIyZMmEB+rk+5ceMGZs2ahQoVKmDhwoW4ffs2xo8fj6VLl6JJkyaCsampqThw4ACioqKQnZ2NHj16wNvbm3f7Zs2aoV69enke0wiLS5cuic71yZMn2LdvH65cuYLGjRsjICBA53YTJ05Ex44d87zmf/zxB37//XfejMOvv/4qemf8UzQXMrnhOA5//fUX78Iu/v7+WkF//vz5PPFC2ZDo6GhER0fj+fPncHR0RNeuXVG9enXB+Y0aNQpBQUHaelwp+Pj46HxcpVJJWvAlJSUF+/bt09Yy7969W+d2arUawcHBOH78OCwtLfH27Vu0a9cOEydO5L35Ym9vj/T0dPTo0SNfNl/oxsu4ceOwcOFC0bnnJjIyUvSCXojExERcuHABmzdvRnx8PM6fP69zu8GDB4PjOAQHB6Ny5cqSn3/mzJnahT7u3LmDRo0akecqRvfu3WFjY4NJkyZJcnnkRu45Asj5rN6/fx9WVlaCmSTNeVVXxlTMJdC/f39s3LhR0j7kxs/PD/PmzRPNtufGy8sLK1aswMiRI7Fu3Tp4enryfi5yk5mZicTERJQvX16SW8HFxQU//vgj/vrrL1SsWBGpqami1xvr16/H6dOnkZaWhh9++AE//vijqCvH19cXycnJ2u/xT90Vn/Lq1SukpKRgwoQJWLBgATiOQ3Z2NiZMmCC6GJOnpydvrSpDOZhYZBQa+vXrh5kzZ2LKlClYunQpBg8eLHpC7dGjByIjIzFnzhwMGDAAQUFBopZPX19fZGRkICEhAVlZWahYsaKkLw1vb2/UqVMHsbGxMDMzQ4kSJXgXYtCFWq3G0aNHtQtafMq///6LX3/9FRYWFujQoQNGjhyJzMxMBAUFoV27djpj/vrrL9Esiy5evnyJhQsXIiQkBI6OjkhNTUVqaipWrVrF+3zUlQhPnz6N8PBwPH36FJUrV4a3t7egcNJYb+SgySh/ytOnT/NkgvlQq9X4/fffER4eDrVajf379/Nu++bNG+zdu1e7P87OzrzZSw25V1YcM2YMlixZIjonXSQlJWHv3r28i9UI2ZD4LqYzMjLy2GvVajVUKhXev38PCwsLyXO7d+8eoqKiEBgYyLsN5YZDbp49e4YDBw7g2LFjqFy5Mu+FUGJiIkaOHInSpUujZs2aePbsGd68eYNVq1bx7hNlFcILFy7wxvBlXygxQM4CG3Xq1NHeWc+d2eATmc+fP+d9Pil39aVy4cIFzJs3DyVLlsSsWbO0C4ZJETEZGRlITk6Gubm5qJ17z549vH/r2bMn799yvw4qlQpmZmaix3bPnj1RtmxZuLm5wdHRUXRuQM4iRCdPnsSpU6cA5Fgd27VrBxsbG95s99atW0XFky58fHy0NlmpK7dSVxb+448/8MMPP8ia37Fjx9C6des8QuLevXuIjIwUXDgrNDQUjx8/RtOmTXHp0iVUr14dEydO1Lntnj170LNnTyxfvjzf38QyycHBwWjSpAm+/PJL7WeJL3MO/M9Z8+bNG3z48EH7vSLFhqp5rzTvU+73jm8cXYiNo3n+SZMmYe7cuejTpw+2bdsmGLN582Y4OjrKulEB5AjAvXv3IiYmBtbW1oKZ35iYGGzatEm76jaQ4/749ttvMWbMGMFxBg0aBGtra1hZWWk/Q3LLBRj6w2yojEJFrVq1oFKpYGFhIekOpqWlJUxNTfHhwwfUqlVLa78QIikpCTt27EBgYCCmTp2KAQMGSJobx3GYOXMmJk2ahODgYPTp00dSXEJCAiIiIrBr1y40bNiQVyz+/PPP6NatG96+fQsfHx/88ssvqFy5MiZMmMArFo8ePaoVd/v27dNa0kaMGIEVK1bwzik4OFi7baVKlbBlyxbcunULixcv5hWL1JUI27Vrh5YtW+LVq1fa90uI3r17o3z58mjbti1sbW112l0+pU+fPpg4cWK+u9BiQvHZs2cIDw/HwYMHwXEcFi9ejKZNmwrGJCcnY+DAgQByMl1v374VFYu578m9efNGcFsNuuocxRC62OFj7NixWLZsWZ5V+i5evIjx48fjjz/+kPw8DRo0EF11Uug44ROLr169wsGDB3Hw4EF88cUX6NKlC9avXy94Z9/CwgLh4eG4desWnj59CicnJ9H3lVIbKZbZM1QMwN82RoixY8fmW53z8ePHeP/+vaDVbP369dixYwfS0tJgYmKCPn36CGZdFy9ejJCQECQnJ2PRokVaES8kFJOTk7Fy5UpMnDgRSUlJ8PX1hampKYKDg3lt21LdHJ/y6euQmpoKtVqNkJAQ3vPLnj17cPv2be3K2fb29nB3dxe0vrq6uqJTp04IDQ2V/D6vWbMGV69elV3nmBup9/zFrKv//POPzs/hrVu3eI8XPlEWExODBQsWoFKlSmjXrh3atWuHhg0biq6wfPHiRa0o6tevH9zc3Hi31dwg8PPzQ0JCQh7Xjxh3797F3bt3tb+LZc4p9YAaatasidDQUCQnJ2PNmjWCN2r0GUelUuHVq1f48OGD9gawGC1atMCIESPw8uVLVKhQAcHBwYIuGQ2ZmZlQq9XIzs4WzXo6ODjAwcEBJ0+elLRae26+/fZbANK/NxkFAxOLjEJD2bJlERERgbS0NBw4cEBSdqly5cqIiopCiRIlEBoainfv3onGaCxOaWlpKF68uOQL8mLFiiE9PR1paWlQqVSiTaQvXLiArVu34u+//4aRkREiIiJQpUoV3u2zs7O1X4yHDh1Cq1atAEDQ4pH74jwqKkorAMVeh7dv3+YTVl999ZXgEuq5v8SSk5MRFBSEevXq8fZ4A/7Xl/DUqVOoUKGCpL6ER48exbNnz3DhwgVs27YNQUFBqF27Ntq1a8eb4dm6dSumTp2KP/74A4GBgZKWUPf19UVKSgp69OiB/fv3Y8yYMaKC4vDhw1i0aBGioqJQunRpvH79GpMmTcL48eMF7VK5jzGpxxvfBbMQQtkVIKddwaetSapVq6a1BwHAqlWrsGvXLtm2PQCizZ0pgqx9+/awsrJCp06dUKFCBWRkZGit5nx3mD/NEj558gRPnjwBwJ8lpNRGUsQYVcBRRGZue55arcYvv/yCDx8+YO3atbwxGzduxMOHD7UL26SkpGDOnDlYt24dBg8erDPGxMREa11btmyZpLlNnz5du0jW7Nmz4e3tjfr16yM4OJi39uns2bMksajLpvjkyRNMmjQJ4eHhvHGNGzdG48aNoVarERMTg3nz5iE9PZ13ftu2bcOpU6cwbtw4lCxZUiuShGx91DpHyjlFjEmTJukUTJ8K2LS0NKxdu1awFlTzvaA5l2/atAlPnjxB7dq1BbNQmZmZyM7OhpGRkbb8QIzJkyfj2rVrSEtLw8ePH1GjRg3BRZ+AnPMK3/GsC42I/rTfqYmJCSpXrgwvLy/edl1BQUGIjIxEs2bN8MUXXwiuQ6AZ58WLF9r63Nq1a4v2QQZyRPPRo0fRo0cPODg4oEePHqIxwcHBCA4ORsOGDfH3338jKChIUgZTrVbDxcUFGzduFLWhaihbtiymTZuWp6ZSrM7Rz88P586dw9OnT/HNN9+Qbogy9IeJRUahYc6cOQgLC4O5uTlu3bol+IWiYebMmfj333/h5OSEPXv2SGp86+joiOXLl6Nhw4Zwc3OTfKLz8vLCxo0b8f3336N9+/aCq4H26tULderUgYeHB1q1aoWhQ4cKCkUAee7O5c6qConS3BfbuX8W+4LNvWrmunXrtD+L1UgC8lYi1PQl1NQNSu1LWL16dVSvXh0NGjTAlStXcOjQIWzatIn3Qr9y5cpYu3Yt9u7dCy8vrzx2K6FapmLFiuHjx4/aVWTF0GRcNAsVNW3aFNu2bcNPP/0kKBafPn2KRYsWgeM47c9i86MIPzE0i5fkZtKkSdqm5S9fvkSJEiWwe/du2VZgKVAE2U8//aR9b16/fi1pHIootbCwgI+PD3x8fLS1katXrxasjaSIMUoMQBeZQE4WZeLEiWjdujV27dolmN0/fPgwwsPDtZavUqVKISgoCN7e3pIurqUu7vPq1Sv07dsXKSkpuHfvHpydnaFSqZCWlsYbk5yczJsdE7JY6kLqqq1Ajhvl2bNnePXqleBNnG+//RbffvstRo8ejTdv3uD06dOYNm0aXrx4wVtvbWpqipYtW6Jly5Z56hx37tzJW+cIAFeuXNHuc3Jycp79pyx+A/BnKHP35b18+TKmTJkCLy8v+Pr6ij5neno63r59iw8fPqBYsWKijqHOnTvD09MT33zzDW7cuCHY+1DD3bt3ceDAAUybNg1jx47F6NGjRWNOnTqFAQMGSF65WEN6ejpq1KiB5s2b4/r167h58yYsLCwwYcIE3rKUtLQ0mJub45tvvgGQ0ztQbCGjKVOmwNPTE9999x0uXLiAwMBAbNq0STDmu+++095g6NChg6T94ThOaw398ssvJdmtAwMDJa1W/ykzZszA4MGDcfjwYdSvX1/S6t2LFi3CixcvEBcXB1NTU6xZs0av7CuDBhOLjM9O7gvY3A1XNctM60JXfVHp0qVx69atPEth6yK3wGnfvr22vkaMjh07an/u1KmToA3OxsYGV65cwalTp1CpUiVJFyW6BAXHcXj27BlvDPXucunSpfHo0SPUrl1bKxAfP34sKJwpKxH+9ddfeeyrxYoVw6hRowStRYcPH8bJkydx7do11K9fH7a2tli8eLHokt6JiYk4efIkypYtK+nuY1hYGP7991/s2rULrq6uSE1NxalTp9C2bVve+iJTU9N8x2T58uVFRfaoUaN0/qwPuoQflSlTpmDatGnIysoSXRABoC/BTxFkYqsU6xLNFFGaGyMjI5iYmCAlJQWPHz8W3V6OGKPGUERmdnY2wsLCsH//fsycORPNmzcXnZeJiUm+49/ExETwIvLly5fYsWOHth1M7rnyZX812f+LFy+iefPm2vOXkFhMTEzkXcBMrljMysrC+/fvef+elpaGw4cPY8+ePXj37h1cXFzw66+/Ct5E4TgOf//9Ny5duoRLly7h0aNHaNCggeBCObrqHH/++WdR+73YDQI+S6kQQt8hGRkZWLRoEf7880+EhoaK1qLOmjULFy5cQLVq1WBra4vx48dLqh8fOHAg2rZti/j4eLi4uGjbYQhhbm4OlUqF1NRUyXXWSUlJaNeuHapXrw6VSiWp9hDIOQY1YqVdu3YYOHAgxowZI3jjdMSIEahWrZo2Qyvluzo9PV0r+BwcHLBhwwbebe3t7Xmfk+8mhYZixYrhxIkTaN68OS5evCjp3PXs2TMsWLAAGRkZ4DgOycnJ2vYgQpibm6Nr1644e/YsRo4cKalW9/LlywgPD4ePjw969uzJFrv5TDCxyPjsaFZ0+xShGoK4uDgAwLVr11CiRAl8++23uHnzJjIzM3nv2AnZOISslD4+PrwnYr75zZgxAx8/fsTBgwcxdepU3L9/H9u2bUPnzp15BTCfoBC6WKYITCBnkZURI0bA1dUVtWrVwtOnTxEZGSloPezatat2JcJPVzDjy45R+hKOHTsWTk5OWLZsmeReiQcOHMDChQsxePBgSX3XNFSpUgV+fn4YMWIETp8+jaioKEybNo23Vk+lUuHjx495VmtMS0sTrZUtiCyhodBc2H/55Zc4deoUZs+erV2xlO9Cn7oEf27kCjI+dIlmiiil1EZSxBglJjdyRKa7uzueP3+OwYMHIy4uTnve1PxNF5pFPHKvrPz69WvBdiTdunXTHhO5fxaiYsWKWLRoEc6cOYPhw4cjJSUFmzZtEsxYWFlZCZ6r+fj05oZarcbx48cF3QAODg6wt7dHQECApLppIEc8fPnll2jTpg1GjBghKftCqXOUAp+llMKdO3cwadIktGvXDpGRkbzn9dz8+eefqFmzJjp06ABbW1vRuu7cq7tmZmbCyclJ8vwaN26MX3/9Vds2S+iGgwY5i9PlJiUlRdsWJi4uDh8+fEBSUpJgfSDHcbKP26ysLNy7dw8NGjTAvXv3BAXmoUOHtOMMGjRIUl9nDXPmzMH8+fMRGhoKa2trzJo1SzRmyZIlmDlzJiIiItCyZUucO3dO0lhGRka4f/8+0tLSEB8fj7dv34rGZGVlIT09XVv6Q2mLxNAfJhYZnx25jY8BaC/4Bg0ahDVr1mgf1yw8oguNnUXT8Ldp06a4efMmbt68KTiW5iJ+xYoV6NChA5o1a4YbN27gxIkTgnHFixdHz5490bNnT8TFxWlrCjV3kD9FTFDoWrSGIjCBnIUnNm7ciN9++02bfVm7dq3gimhiz6kLSl/CkydP4uTJk1i6dCmePXuGpk2bwtbWFq1ateK9MN68eTPWr19PrmdQqVSwtbWFra2ttpBel4Dr27cvhgwZgn79+qFGjRp48eIF1q1bR1rNMDeGzBLKJfeFvYuLS77HdCG20iCf+KUIMn2RKkoptZEUMUaJAWgiU7OYxIcPH0TrSTX89NNPGDJkCHx9fbUrya5atUrQzk05HmbMmIFdu3bB19cXDg4OuHbtGpKSknhvHgKQbRnU8OnxbGZmhiFDhqBNmza8MUeOHBG0TOrap+PHjwuKd10xlDpHKVAWuueLcXNzQ8mSJXHx4kVtaxWxhvTR0dF4+vQpTp48ialTpyI5ORktWrRA+/btddZiPnjwQPvzvHnzZAldf39/pKSkoHjx4jh16pRoGygg51iSWxMI5NzcHj9+PBISElClShVMmzYN0dHROi25GptljRo1cPXq1TwLx4hl8KZOnYrJkyfj1atXqFixoqCIy/1cRkZGkrKDGqpVqybJTZKbihUr4ttvv0VERAR69eoluEpxbiZOnIj79+/Dx8cH48aNy+Mk46Nfv37o1asXEhMT4erqKnlBQoZhYWKR8dkZNWoUfvnll3w2IpVKhdOnTwvGJiYmalstJCUlITk5mXdbzYqiGzZs0DbxbdasmejJR1Oj8vr1a63g/PHHH2WJXGtra0yYMEF7wSW2WqkudC1aQxGYGiwtLXmbGeuKo4wVEBCAn376SWdfQj4sLS3h4uICFxcXZGVl4dSpU1i9ejVGjRqFa9eu6YzZvn274B1HOa+3JqOiS8A5ODigfPny2LlzJxISElCtWjUEBASI9iMsLOi6GKQKPyH4xC9FkFGgiFJKbSRFjFFiAJrIpLy3rVq1wvz58xEREYGoqChUrlwZs2bN0quPn67jwczMLM+K0k2aNMnzOdI1N7EWR3zHKuV1EKut07VPYhfpumIodY5SkGJ11PTz1aBZVO1TDh8+LPg8fJbXGjVqwNvbG87Ozjh37hw2bdqE8PBwXL16Nd+2fPX3Qrx//x5RUVEoU6YMevbsCSMjI1StWhUjR44UtZRSagKBnBKTT1t68YlTJyenPD1VNQj1DAZysmlffvkldu3apRXBUmoJKYSFhWHdunV5buCK1byamJjg4sWLyMzMxOnTp5GUlCRprF27dmnboEjpMwkALVu2RJs2bfD48WNUr15dVjsnhuFgYpHx2dHc1QoKCsLu3buRnp4uOdbX1xfOzs4oW7Ys3r9/j6lTp4rGpKam4s8//8TXX3+Nq1evyhovMjISNjY2uHr1qiQrzqdoYqSs2voplBXvKONQ43TFNGzYEJs3b8bevXtx9uxZVKpUCevXrxe0JCUmJuLy5cu4dOkSrl69imLFiqFly5aC/ZjErCnU10EXmos7XShpJxW6oEpJScHatWuRkJAAOzs7NGjQALVq1ZJlT9JgyKwnRZBRoIhSSm0kRYRQxTlVZArB997Wq1eP91yq1DFOOe6ox+rnzOxT6hz15cKFC5g5cyaysrLg5OSEqlWrwtXVFSNGjNC5vVjtoy7L66FDh3Dp0iVcuXIFRkZGaN26Nfz8/HgXhqPU348ePRpfffUV7ty5g3///RcVKlTA8uXLMWHCBNFYOTWBAP9NbYBfXB0/fhwAcOPGjTxW5r/++ot3nNjYWIwYMQJRUVEoW7Yszp8/j3nz5iEsLIx3PYbcx+/Hjx/x6NEj7feDmNsmOjoap0+flrSCuIagoCDEx8fjp59+wtKlS/HTTz9Jinvw4IHsPsqDBw9GjRo14ObmxoTiZ4SJRUahYcGCBZg1a5asE0nHjh3RoUMHJCYmonz58pJsSsHBwQgJCcHDhw9Rr149zJ8/X9JYCxcuRFhYGA4dOoS6deuSWgtoMNRS5wU1DiWOL6Z8+fK89mBdGT8PDw+0adNGW/djiFU5lXq9C+KikyL8Jk+eDFtbW1y8eBEVKlRAYGAgtm7dSrrBYUgogkwMXaK5IESpUuKFL6YgMsAUPqew+i9CqXOUgtDNpKVLl2Lr1q0YOXIkfH194enpqZc41TXW6dOn8f3332P48OE6L/I/zUZSVnf98OED/P39wXEcnJycUK1aNezduzdPvS0fcmoCgf/d1P50LkK1ipcuXUJcXBw2bNigdTBlZ2cjPDwc+/fv1xkTHByMRYsWadtwODg4wMLCArNnz+bNrOe2bpuZmWlv9Ij1jgRyVh4XKgvRRaVKlRAfH4/Lly9jxIgRkss/4uLi0LJlS1hYWGhfb7Es5u7du3Hz5k3s3r0bixYtgoODg2RxyjAcTCwyCg316tVDixYtZMWcPXsWGzduzJMdFDs5Wltbk4rbLS0tMWbMGKhUKsTExLBCawOgK+N35MgRwRiKhbewI3RhRxF+ycnJcHFxwb59+9C0aVPJ7Qw+N0JCRI5oLghRWthhIq5oQqlz1IVUSymQ48QoV64cVCoVzMzMRC23YugSWmKtrz7NRlJWd9W8bpr9WLVqlaT2T8D/agITEhJQqVIlwd6HQvTt2zffgm8aypQpg1evXkGtVmvrZlUqFcaPH8/7fNnZ2flsrU2bNhVcRE2sJCYiIiJP+5PcZGRkoFu3btpVZ1UqlWgLMmo7C7F1HvioV68emjRpgidPnuDSpUuk52DoBxOLjEJDhw4d4O7unqePldgKYnPnzsXkyZMFF2b5FIpHH8hZpfOHH37A1atXkZ2djaNHjyoqWvga/hZllLTWyoWyQIQYlCwhVfhpatpevHhBXhiESkG8dobMljJhpSwFcTx8bgy1gAylzjE3ci2lQE6PydDQUCQnJ2PNmjWoWrWqtB0wIHJfP11W19zfH+XKlZMsFIGc8+KuXbu0v0dHR2v7DcpBaD/q16+P+vXrw83NDZmZmahQoYLo+813fs/MzJQ9Nw3R0dG8YpFv3QIhqO0s7t+/j+nTp+Pdu3fo3r076tWrBzs7O8GYSZMm4fr16+jYsSOCgoIMumIwQzpMLDIKDVu2bMHgwYO1Dc+lUKVKFcEV7XRB8egDQEJCgrZtxJYtW9C/f39Z8bkREn4vX75ESEgIEhMT4eTkhAYNGuCbb77BsmXLDDqOoeOUErMUgSk0N0PW94lBFTxyhV9gYCAmT56MuLg4jBo1CtOnTyfPWanaSDGKara0MGNoEWfI48GQq3nqE0fZJyU/FxRLaVBQECIjI9GsWTN88cUXktolCEF53eWex3WNcfv2bXh4eIDjODx48ED7s9AqrSdOnMCVK1dw4MAB7UI72dnZOHbsmHYBO0PtR0ZGBubOnYtTp06hQoUKSEhIwA8//IBJkybxnvNtbW0xf/58DB8+HKVLl8aHDx+wfPlywUyxGHzvT0xMDBwcHPD+/XusXLkSpqamGDZsmOjzUdtZzJ49G3PnzsWUKVPg4uKCwYMHi4rFH3/8EXPmzFGslIShGyYWGYWGChUqyD5Zly9fHtOmTUOjRo20JxOxVRUpHn0g58R/5MgR1K1bF4mJiZIWmaAIv6lTp2LAgAFYuXIlmjdvjokTJ2Lnzp0GH4caZ0gxa2goczNkxkrsookieCjCr0GDBvl6y4lRFGoji1q2VEnBI4RSIo5yPCgpxpQ6xpX8XMixlF68eFH7c926dbULply/fl1nOws+5FheDYUusbBv3z7BGF3W1YYNGyIpKQlmZmbaWjuVSoUuXboIPldoaGi+OXAch5cvX/LGrFixAuXLl0dMTAyAHJG1YsUKLF26FOPGjdMZM3ToUKxduxY9e/bEx48fUbZsWTg7O2PQoEGC8xNC12u3cOFCPH78GHZ2dpg1axZKlCiBSpUqYcaMGYKrlQP521nIuXFeq1YtqFQqWFhYSLI/V69eHV5eXrKykQzDw8Qio9BQvHhxDBo0KI/wE+rtBUBrSZCziAXFow/krMoVHR2NiRMnYsuWLRg+fLhoDEX4ffz4Ea1bt8aqVatQp04dSdYayjjUOOpYujB0NpIyN4qA0+fiW67gkSP87O3t81wYGBsbIzMzE6ampjh48KBgbGGojRQSIobMlgqhlHgxdPapMIg4yvGgpBhT6hhXMgsux1KqsQs+efIEGRkZ+Prrr3Hnzh2ULFlSUisoiuWVD0PcFKGs0lqlShX06tULzs7O2ibxJiYmqF27tuBz5S6PyY3QNcpff/2Vx6JZrFgxjBo1Cm5ubrwxKpUKQ4cOxdChQ3X+Xaj+UA6XLl1CREQEMjMz8ccff+DkyZMoUaIEPD09RWM7depEamdRtmxZREREIC0tDQcOHJC0eF1wcLDsbCTD8DCxyCg0UE4Afn5++OOPP3D//n1YWVnBwcFBNIbi0QcAR0dH1KtXD/fu3YO7u7tg+wcNFOFnZmaG06dPIzs7G9euXZPUYJcyDjWOEqOUtZb6OsgVcNSLVTmChyL8Dh06BI7jEBQUBA8PD9jY2ODOnTvYtm2b6D4pWRtJESKUbCkfhhZWSgqewiziAPnHg5JiTMlj3FBZcDFRJcdSqlmEZOjQoVi5ciWMjY2RlZXFK0w+RZ9VVPXNRhoqS3/27FkEBgbi6NGj2LFjB3799VdYWFjA1dVVcF8ofYb5Piv69EwUqj/kQ9froMnq3bhxA/Xr19eW5QgtpDNp0iTev4mtLwEAc+bMQVhYGMzNzXHr1i3RRZA0yM1GMgwPE4uMQoPYyVgXoaGhePz4MZo2bYrffvsNly9fFu2x9Pz5c9L8tm7diqNHj+Lt27fo2bMnHj9+nGfJal1QhN+sWbMwf/58JCUlYf369ZgxY4ZoDGUcahwlRilrLWVulIwV9aJTjuChCD/N/j59+lTb16tRo0aSF3RRqjZSjhDRJ1uqlLBSUvAUZhFHPR6UFGNKHOOUGLnHqj6WUs3KnECONTIxMVF0fgBtFVVqNtIQVldd9ssVK1YgMjISJiYmWLt2LTZs2IAqVarAx8dHr/YhuhZeMzMzw5MnT1CzZk3tY0+ePCGVwWgQE81//vknnjx5gm+++QZWVlYwMzPTufqqsbExzpw5gz179sDR0RFAzjEllO3LXSYUEhIiuKprbnJfc/Xp00f7c2pqap73WBeUbCTD8DCxyCjSXLx4UVvI3q9fP0F7hwbNxYKmGXK5cuXg7OwsGnfgwAGEh4ejX79+6NevH3r37i0aQxF+lStXxuLFi0W303ccahwlRilrLWVu1IyVnItOiuDRR/iVLl0aS5YsgY2NDa5evQpLS0vRGKVqIwF5QkSfbKlSwkrJmMIs4ijHg1JijBpH2SdKjNxjVR9LqYuLC7p06YL69evj/v37kjOLlFVU5WYjDWl11YWxsTEsLS3x9OlTmJiYoFatWgCgdyssXcI0ICAAP/30E1xcXFCjRg08e/YMkZGRojWBcsfRwNfSQvP9kZvAwEAsWrQIFSpUgKenJ06fPo2QkBAsWbKE9/nbtWun/XnNmjV5fhdi7NixAHLOXR8+fNAedxUqVMCePXsEY6nZSIZhYWKRUaTJzMxEdnY2jIyMtKugiREQEKD9meM4Sat/abZVqVTaMaRkrSjCj9LagzIONY4So5S1Vs7c9MlYyb3o1EfwUITfwoULERERgT/++AN169YV7TsIKFcbqUGqENFHNCslrJQUPEDhE3H6HA9KiTG5cZR90ud1kHus6mMp9fLygpOTE54+fYpatWrB3NxcNAagraIqNxupj9X1U3Rl4VQqlbZOr23btgCADx8+4OPHj6QxhGjYsCE2b96MvXv34uzZs6hUqRLWr18vqYSFgpyWFjVr1swjDNu1a5dH/InVRspZnVTzmRsxYgTmz5+PUqVKITU1VXRNisTERFhYWGDcuHE4ceIEzMzMRDORjIKBiUVGkaZz587w9PTEN998gxs3bkhaTVWtVmt/fvXqFZ49eyZprK5du8LLywvPnz/HkCFDJNVHUoQfpbUHtXckJY4So5S1Vs7c9BFwci9W9RE8FOFnZmYGMzMz7U0UIZSujQRoQokimgFlhJWSgqcwijjK8aCkGFPqGNf3c0HJNFMspffu3cPkyZPx4sULWFpaYs6cOWjUqBHv9vpYXuVmIylWVw1SrKs9e/ZE586dkZmZiU2bNiE2Nhbjx4+Hj4+P5HHkUL58eQwcOFDn33TVOYohdD6ntrTQBaU2UowXL16gVKlSAIAvvvgiz7H7Kb///jt++eUXREdHY/Xq1Th9+jQsLS1x7do1SYsLMgwLE4uMIs3AgQPRtm1bxMfHw8XFRbvCqRBOTk5QqVTgOE67AqsUvL290bp1a8TGxsLKykpSA1+K8KO09qD2jqTEUWKUstbKmRtFwOmbUaMIHjnCT8PUqVNRpkwZtG3bFhcuXMCUKVN4rU+fozaSIpQoormghZXS2SegcIo4yvGgpBhT6hjX53NBzTRTLKWzZ89GcHAwGjZsiL///htBQUG8fQkB/SyvcrORGnGZlJQk2eoqx7rq7OwMBwcHmJqawtTUFAkJCZg7d65WLOtqt5EbtVqt88YlZWVvXXWOuZFaf6hBn5YWn6Lru0aTiQVyhHnu36XcnG7bti28vb3x1Vdf4fr164I33MPDw7F3716YmJggIiICu3fvRoUKFeDh4cHE4meAiUVGkWT58uX5Hnvw4AGOHDkCPz8/wdjjx4/rPWZcXBxiYmJEx6IIP0prD2rvSEocJUYpay1lbnIEnL6ZA4rgkSP8NDx+/Bjh4eEAAAcHB8E7xErWRuojlCiiuaCFlZKCpzCLOA1yjgclxZjS9b+UGGqmmWop1dzs/PLLL0VX56RYXqnZSI24bN68uWSrq1zrqia7BQAVK1ZExYoVtb/rareRm969e6NVq1ZwdXXNc3OasrK3oeoPNWhaWjx58gTVq1eXfCxInZvYd/b169fxzTff8P597NixuHXrFh4/fgxnZ2fBG+5mZmb44osv8ODBA1hYWGjfI31rSxk0mFhkFEm2bt2KMmXKoEuXLqhcubKki8enT59i3rx5WLp0Ka5du4bRo0fjiy++QEhICJo0aSIaX6FCBQA5d9zu3Lkjqf6JIvworT2ovSMpcZQYpay1lLnJEXD6ZtQogkeO8NOQnp6OtLQ0lChRAh8/fkRWVpZojBK1kfoIETmiWSlhpaTgKcwiTgPlZohSYowaR9knOTH6ZprlWkqBnAvuEydOoHnz5rh48aLk1bPlWF6p2cjMzEykp6drz1lSauP0sa5+ith5ee/evTh9+jSWL1+OpKQkdO/eHZ07dzZ4Swc59Ycarly5gqCgILx58wYVK1ZEcHAwvvzyS4POS4jQ0FBBof3ixQusWbMGDx48gJWVFSZNmqTtlf0pKpUKKSkpOHz4MGxtbQEAb968QWZmZoHMnSEME4uMIsmZM2dw+vRp7N+/H3///TccHR3RsWNHwRP2rFmz4O7uDmNjY8ydOxcLFixA3bp1MW7cOEkNiT+9SB88eLBojBzhd+LECdjZ2SE+Pj7fF2SLFi0MNo6+cZQYpay1lLlRBBz1YpWSJaQIv759+6JHjx6oV68eHjx4gFGjRonGFHRtJKCfEJEjmpVeUEiJmKIg4iifpYIWY/rGUfZJToy+bgW5llIgZ4XJ+fPnIzQ0FNbW1pKyd4A8yyt1AR5/f3/UqVMHtra2uHLlCiZNmoSFCxcKxlCsq3yIiVMjIyOteImKisKWLVuwa9cudO3aFd7e3uRxP4VSfzh79myEhoaibt26iI2NxbRp00SPBT4M1dcyN1OmTIGnpye+++47XLhwAYGBgdi0aZPObQcMGIBu3bqhTJkyWL9+PW7cuIExY8Zg6tSpsufF0B8mFhlFEmNjY9jZ2cHOzg4fPnzA0aNHERAQgBIlSvBaF1NTU9GhQwckJSXhxYsX+P777wFAcn+z3Bdlr169EuzXSBF+ycnJAIDXr19Lmg91HGqcPmK2oK21+syNIuCoF6uULCFF+HXv3h3NmzfHmzdvUL58eUkXTwVdG5kbihCRI5qVFlZKCp7CLOIox0NBizF94yj7JCdGX7cCIM9SCuQsLuLu7o7vv/8eW7duRenSpSWNQ7G8yl2AJzk5GePGjQOQc47M3ZePD4p1lcqCBQtw7NgxtGjRAkOGDIGNjQ2ys7PRq1cvXrFIqXOk1B+WLl1aa/etX7++5O9PubWRfIgJ7fT0dHTo0AFAznu7ceNG3m3bt2+PEydOaH83MTHBzp07tQ4vMcsrw7Awscgo8ty+fRtXrlzB8+fPtQJQF5rWC3/++ad2lTSO4/D+/XtJ40ybNk37c/HixTFhwgTebSnCr0mTJnj48CG6dOkiOYYyDjWOOhZQ8NZafeZGEXDUi1VKlpAi/JYvXw61Wg1/f3+MGjUKX331legd/YKujcwNRYhQRLNSwkpJwVOYRRzleChoMaZvHGWfKDFUtwLFUurv74++ffsCyBEs48ePx+rVq0XjKJZXuQvw1K1bF5cvX0azZs1w7949VK1aFRkZGeA4jnffKNZVPsSOdSsrK+zevVvrYnr37h3KlCmjcx0FDZQ6R0r9Yfny5REYGIhWrVrh9u3byM7O1tbBuru764yh1EZSycrKwr1799CgQQPcu3dPVmyZMmXy/C5meWUYFiYWGUWSGzdu4MCBAzh37hyaNGmCrl27IigoSPBLol69eggICMCtW7cwa9YsJCQk4JdfftG5vHZuZs+ejSlTpkiyqmqgCD+NGNWs1Jr7f76TImUcahwlRilrLfV1AGgCjnqxShE8FOF3/Phx7N69GwDwyy+/wMPDQzRGqdpIgCZEKKJZKWGlpOApzCKOcjwoJcaocZR9osRQM80US2laWhrs7OwAAN26dUNkZKSksSiWV7nZyMuXL+PMmTMwMTFBRkYGAKBjx45QqVQ4duyYzhiKdVWDlHYbQE6GNCUlBZGRkfjuu++QkJCA7OxsTJgwAVFRUby1dwCtzpFSf1inTh0AOcd5qVKl0KJFC8H2FACtNpIPKTbUyZMnIyEhAZUqVdIrA0yxyTLoMLHIKJK4ubnB2toa7dq1g4mJCc6ePYuzZ88CAG+j1wkTJuDUqVPo378/vv76a9y7dw9169YV7a8UGxsre34U4acRo+vWrZNUD0kdhxpHiVHKWkt9HQCagKNerFIED0X4qVQqrfVJc1deDKVqIwGaEKGIZqWElZKCpzCLOMrxoJQYo8ZR9okSQ800Uyylmu/Mb775Bjdv3pS1wqRcy6vcbOTcuXNlZ7Uo1lU57TaAHNvjpk2b8PDhQ23dnJGRUZ72EXxQ6hwp9Ydv3ryBm5ubrEVtKLWRhw4dgoODQ773v1u3boJxjRo1wq5duyTPTQh9sscM+TCxyCiSzJ07V3aMSqVC+/bttb83aNAADRo00P7O1yD35cuXvEua81k7KMJPw6lTpzBgwABJTZmp41DiKDFKWWv1eb0pAo56sUoRPBTh5+HhobXwxsfHS8rSKlUbCdCECEU0KyWslBQ8hVnEUY4HpcQYNY6yT5QYaqaZYimdPXs25s+fj+DgYFhbW2PmzJmi4wA0y6vcbOSGDRvwzz//oHv37ujevXs++6EuKNZVue02HBwc4ODggJMnT+a5jpACpc6RUn9oZ2eHsLAwvHz5Uvv65W4TogtKbeStW7ewcuVKfP/993BxcYG1tTWAnJv4uvh0xd/c8GWLGYULJhYZRZKePXsK/p1P+AnB1yA3IyND1MrBhxzhpyEpKQnt2rVD9erVoVKpoFKpRO8oUsahxsmJUdJaK3duGigCjnqxShE8FOHn6uqKDh064OnTp6hRowYsLCxEY5SqjQRoQoQimpUSVkoKnsIs4ijHg1JijBpH2SdKDDXTTLGU1qpVC+PHj8fjx4/RsGFDVKpUSdJY1FVU5WQjFy9ejLdv32L//v0YPXo0LCws4ObmhpYtW/LGUKyrctttrFy5EsOHD8fevXuxb9++PH8TW6yNUudIqT+0tbWFra0tEhMTERwcjJCQEHTs2BHDhw9HzZo1dcZQaiPHjRsHf39/nDp1CkuWLMGrV6/g5uaGbt26wcTEJN/29vb2uHXrFtq0aYNu3bqhWrVqomOIwWyoysLEIuM/CZ/wE4Lvzle1atXg5+dHmgdF+IWFhSkyDjVOToyS1lrq/lAEHPVilSJ4KMLv/v37mD59Ot69e4fu3bujXr162gtKPpSqjQRoQoQimpUSVkoKnsIs4ijHg1JijBpH2SdKDDXTTLGUbt26FUePHsXbt2/Rs2dPPH78OM8CbnxQLK+UbOTr16/x/PlzJCUlwdraGocPH0ZkZCRvHSLFuiq33Ya9vT0AoHPnzpKynYB+dY6U+sO4uDjs3r0bJ06cQMuWLREeHo7MzEyMGTNGezx+CqU2kuM4nDlzBr/99ps2C5yUlARfX1/8+uuv+bafMmUKsrOzcebMGaxatQpv376Fg4MDOnXqJHo8UC2vDAPDMRj/QXx8fAwWExAQQJ7Hs2fP8v3jY+fOnRzHcdzChQu50NDQPP8MOY6+cZQYHx8fLjMzU9KcNKxdu1bW9tS59erVi0tPT+c4juPUajXn5uYmGrNs2TLt+zJy5Ehu9erVkua3c+dOztHRkfPz8+M6d+7M7dmzRzQmNjaW8/T05Lp06cKtXr2aO378uGhM3759uUePHnHe3t7cmzdvuJ49e4rGfLqNu7u7aAzltdPwzz//cDdu3OD++ecfyTFv3rzhrl27xr1580bS9nv37uV+/PFHbvjw4ZyjoyO3f/9+0RjKe6tUjAa5rx3lvaXMj3I8UOZGiaHGUfaJEkM5VjmO4x49esT99NNPXKdOnTg/Pz/u8ePHojEeHh5cVlYW5+3trZ2vFPr37689/+zbt48bOnSoaMyzZ8+4kSNHcl26dOFGjRolek52cXHhvL29uX379mlfQ47juIEDB/LGjBkzhnN1deW2bNnCvX37VtK+ZGRkcNu2beNmzJjBbdmyJc9YQnh4eEjajuM47ujRo5y3tzfXvHlzztvbm/P29ub69u3LLV68WDR2xowZ3J07dySPpZnbrl27uNTU1DyPb926lTemZ8+e3P379zmO47h79+5J+kw4ODhwEydO5C5dupTn8YkTJ0qaZ1JSEjd69GjOxsZGdNuQkBCuW7du3Lx587gHDx5Ien6G4WGZRQZDBLFV1XRZXiMjI+Hq6oqIiIh8GUu+BXgqV64M4H93FKVAGYcaRx0LKHhrrT5zo2SsqBk1SpZw9uzZmDt3LqZMmQIXFxcMHjxYNEsI5FjNVCoVLCwsRC1WgHK1kQAtw0PJliqVHVMqBlCu7lUpyzRlbpQYahxlnygx1EwzxVLK/X+nhuZcKSXbB9Asr3KzkVOnTs2TJbxw4QJatGihM2OlgWJdpbbbKFu2LDZt2gQrKyttFpdvkRt96hwp9Yft2rVDr169tL+HhoYiICAAXl5evDGU2siJEydq+yUCQHR0NDp37iy4lkR2djbOnj2LAwcO4O+//4atra2k40eu5ZVRMDCxyPg/T0ZGBkxMTAQb5Aqhy/JKEX7t2rUDkPMlfPPmTWRmZoLjOCQkJPDGUMahxlHHAgreWqvP3CgCjnqxShE8gHzhV7ZsWURERCAtLQ0HDhyQZJtSqjYSoAkRimhWSlgpKXgKs4ijHA9KiTFqHGWfKDFUay3FUtqlSxd4eXnh+fPnGDJkCBwcHETHAWiWV6kL8Fy6dAkPHjzAxo0bMWDAAAA5K3Vu27YN+/fvFx1HrnWV2m7D3Nwcd+/exd27d7WP8YlFfeoc5dQfRkZGIioqCnFxcTh16hSAnNcuMzMTAQEBguPIqY08ceIErly5ggMHDuD69evacY4fP47OnTvzjjFjxgxcunQJLVq0gJubG5o2bSo4p9xwMi2vjIKBiUXGfwopwm/t2rXai4R79+5h4sSJ2LNnj2CDXCF03ZGkCD8Nfn5+yMjIQEJCArKyslCxYkV07dpV57bUcShxlBh9Mn5yBKY+rzdFwFEvVimChyL85syZg7CwMJibm+PWrVuYM2eOaIxStZEAXSjJFc1KCSslBU9hFnGU40EpMUaNo+wTJYaaaT5w4ADCw8PRr18/9OvXD7179xaN8fT0RJs2bRAbGwsrKyvJWUzKKqpSs5FlypTB69evoVartbV5KpUK48ePFx3D1dUVxYsXh5ubG0aPHq3NlA4aNIg3htJuA8ipj3z48CGePHmCBg0aoGLFirzbUuocNcipP+zRowdat26N1atXw9fXF0BOrWj58uUBQHu+0IWc2siGDRsiOTkZZmZmsLKyApDzHvFdn2iIiIhAuXLlcOTIERw5ciTP386cOSMY6+joiObNm8PHxwfNmjXTPv7gwQPBOIaBUdb1ymAYljVr1mh/vnv3Lufs7CwaM378eG7btm3cunXruK5du+bz3ctFqD5y2LBh3MCBA7muXbtynTp14vr16yf6fJr6lsmTJ3NpaWmSaiQo41Dj5MScOnWK4ziO2717d75/fOhTu0nZH0p9H8fJr5/TjMVx/ztmNDVDQrx//54LCQnhhgwZws2bN49LTk4WjVmxYkWe3xcuXCgao1RtJMfRajdHjhzJbd++nXNxceH279/PDR8+XDSGWlNJeW+VilGq7pUyP8rxQJkbdX+UOsYpMdRj1d3dncvOztaeU4S+LxISErj4+HjO1dWVe/jwIRcfH889ePCA6927t6SxOI7j4uPjuRMnTnD//vsvl52dLbq9t7c3d+bMGe79+/fcuXPnRM/JL1680Pn4smXLeGOuX7+e5/e//vpLdF5Tp07VfvffvXuXCwgI4NRqtWjt4pYtW7i+fftyPXr04DZu3MgFBQWJjiWnzjF3jNz6Qz6ErlHk1EZmZGRwHMdxaWlpXHp6ep5/+nDt2jXev8XExOT5/cCBA3qNxaDBMouMIs39+/exfft2pKam4rfffsOMGTNEY+bNm4dx48YhMTERu3btklyvQSEpKQk7duxAYGAgpk6dqrXXCKGpGUhLS0Px4sUl1VJQxqHGyYlR0lord265kZuxombUKFnCzZs3a++AA/+rQ9GFLjtSdnY2MjIyRO1IStVGArQMDyVbqlR2TKkYQLm6V6Us05S5UY9VJY9xuTHUTLMcS6k+jeUBmuVVbjaSr+bywoUL+R7Tx7pKabcB0DK5cuocNVDqD/ngBNwHcmojJ0yYgNDQUHTu3Fl7XcL9//pXffolhoaG5lvpnGp5ZRQMTCwyijRyhJ+7u7v2BJeRkYF79+5paymktJn4FCmWV4rwc3R0xIoVK9CwYUO4ubnhiy++EI2hjEONo8QoYa2lzo0i4KgXnXIED0X4idmRxFCiNhKgCRE5olmDUsJKScFTmEUc9XhQQoxR4yj7RImhWmvlWEr1WXAFoAklak/HT9ElePSxrlLabWjmIXdxIDl1jvrUH/Ih9D0opzZSU2c5evRo9OjRgzQXXeh6b6mWV0bBwMQio0hCEX6LFi3Sa0xKrSNF+Hl5eWm/kNq3b49atWoBAGJiYnjvGlPGocZRYigZPzkCU5+5UTJWAO1iVY7goQg/U1NTVK9eHdOmTcOtW7e0Qvvy5cuir51StZGAPCGiT7ZUKWGlZExhFnGU40EpMUaNo+wTJUbusarp4TdhwgQsWLAADRs2RHZ2NgYOHIioqCjBscqWLYtp06ZpM2oJCQmSFguhCCVqT8dP0SV46tevj/r168PV1VWnCF2+fDlvj+QNGzZoF0zp3r275GOIsjiQnDpHfeoPKVB6M0ZGRhpULOp6by0tLdGzZ0906tRJ0kJKjIKFiUVGkYQi/KpVqwYAePnyJUJCQpCYmAgnJyc0aNBA+zchKJZXivAD/nfybNCggfaxzZs388ZQx6HEUWKUstZS5kbJWMm96KQIHn2E38iRI2ULbcoFLuW10yBViOiTLVVKWCkpeIDCK+Iox4NSYowaR9knSozcY1UfS+mMGTMwePBgHD58GPXr14darRaNAWhCiZKNlIsc66oGSrsNIH8mt2HDhqLzkyOYNef9WbNm6fz74MGD81k2xRCyoU6ZMgWurq7w8/NDiRIltI8LvU9qtRrOzs55bLViq7vKpaAsrwwaTCwyiiT6CD+N8Fi5ciWaN2+OiRMnYufOnaJjUmsd5Qo/PoRO+PqMQ4mTG6OktVbq3PTJWMm96NRH8FCEH0VoK1UbCcgTIvqIZkAZYaWk4CmMIk6f40EpMSY3jrJP+n4u5Byr+lhKzc3N0bVrV5w9exYjR46Et7e3pDjKKqpys5Ga7XU9LhexGLntNoCc8gg7Ozu4urpq7ZFiGFIwC+3T+fPn0apVq3yPa/oo6oJSG5n7M2QIdO1TQVleGTSYWGQUaSjC7+PHj2jdujVWrVqFOnXqwMzMTHB7Q9c6ArQvPqliSd9xqHFCMUpaa6XOTR8BJ/diVR/BU9CLJH2O2kiKUKKIZqWElZKCpzCKOMrxoKQYU+oY1+dzoY+1Vq6l1MjICPfv30daWhri4+Px9u1bwe31sbzKzUYOGjQI69evz/f4ggULBON0IXTeo7TbAIC9e/fi+PHjmDdvHtLT09GrVy90795dMIZi3+VDaJ+WLVumUyxOnz4932P61EY2atQIa9euRUJCAuzs7PLcmBXi0KFDcHBwgLFxXunRrVs33hhDW14ZNJhYZBRp5Ao/ADAzM8Pp06eRnZ2Na9euiZ649a111AVF+Ck5DiVOLEYpa63UuVEEnL6ZA4rgKehFkpSujQRoQokimgtaWH2O7FNhFHGU40FJMabUMa7P54KaaaZYSidOnIj79+/Dx8cH48aNE8106WN5lZuNLFOmDGJiYvJYHK2srFClShXRseQwderUPAvcXLhwAS1atBAV2qampnByckKFChWwefNmrFq1SlQsUuy7FFQqFUaMGJHntePrZ6xPbeTkyZNha2uLixcvokKFCggMDMTWrVtF53fr1i2sXLkS33//PVxcXGBtbQ0AcHNz441RwvLKEIeJRUaRRq7wA4BZs2Zh/vz5SEpKwvr160VrD/WtdTQU1CxhYaagrLVykCPg9M2oUQQPJcPaoUMHVKpUSSu0P72TmxslayP1EUoU0VzQwkpJwVOYRZwGOceDkmJM6fpfSgw100yxlF67dg2urq4AgN27d4vWwFEsr9Rs5Js3b7Bp0ybt7yqVSnR+cqyr+rTbAHIWzTl06BAaNWoEHx8ffPfdd6IxlDpHPoS+M+XYW/WpjUxOToaLiwv27duHpk2bIjs7W9KY48aNg7+/P06dOoUlS5bg1atXcHNzQ7du3WBiYsIbw/j8MLHIKNLIFX5ATh+/UaNGyV7Km1rrqAuhE/7z58/z/G5sbAxzc3PJPQOljmPouMJsrRWKkSPg9M2oUQSPHOEXGxuLly9fYuHChdrl47OysrBo0SLs3btXcBwlaiP1ESJyRLNSwkpJwVOYRZwGys0QpcQYNa6ge9Hqm2mWYyndv38/jh8/jr/++gvnz5/XjhUbG6strRBCjuWVmo3csmUL3r9/j3/++Qc1atSQVGcsx7qqT7sNIOc12L59O0qXLi1pe4BW50ipP+zWrRv27NmD58+fo1WrVqhXr57kOX6K2PdsXFwcAODFixcoVqyY5Oc8c+YMfvvtN+1KtElJSfD19eU9jqiWV4ZhYWKRUaShCD/qUt4UyytF+A0bNgwvX76ElZUVHj16hBIlSiAzM1PwDhtVYFLiDClmKQgJLMrcKAKOerEqR/BQhN+7d+8QHR2NN2/e4MCBAwByXq8+ffqIzq2gayMB/YSIHNGs9IJCSsQUBRGn1KrHlBhqXEH3otXXrSDHUtquXTtYWloiOTkZ7u7u2rFq1KghaSw5llfqAjyHDx/GqlWrkJWVBScnJ6hUKgwfPlwwRo51VZ92GwDw448/IigoKI/D6JtvvhGcH6XOUU79Ye6/VaxYEefOncPXX3+NCRMmYO3atYLj8CF0zE6ZMgWTJ09GXFwcRo0aJTin3Dg6OqJ58+bw8fFBs2bNtI8/ePCAN4ZqeWUYFiYWGUUaivCjrkxGsbxShF/16tWxadMmWFhY4O3bt5gyZQpmzZqFIUOGwNnZ2WDjUOOoY+nC0NZaytyU6hsJyBM8FOHXvHlzNG/eHLdv30bjxo0B5GQOpPSpKujayNzIESIU0ay0sFJS8BRmEafUqsfUlZKVOsblxOjrVpBjKS1btixatmyJFi1a4MOHD1CpVDh69KjkDBTF8ip3AZ4NGzZg586dGDRoEIYPH47evXuLikWKdZXSbgMApk2bJtthRKlzlFN/qOHJkycIDg7GpUuXYG9vjzVr1ghuT+XJkyfYvn277P6HEydORIcOHbS/R0dHo3Pnzpg7dy5vDNXyyjAsTCwyijQU4UddmYxieaUIvzdv3sDCwgJAzhft69evUa5cOcETM2UcahwlRilrLWVucgScBrkXnRTBo4/wi4uLw6NHj6BWqxESEoJBgwaJrvJX0LWRuZEjRPTJliolrJQUPIVZxFGOh4IWY/rGUfaJEiP3WNXHUurv748ffvgBV69eRXZ2No4ePYoVK1aIzlHuKqqA/AV4ihUrBlNTU+13dO7ef3xQrKt8iN3ApDiMKHWOlPYaWVlZSExMhEqlQkpKil7N7IVehz///BNLly6Fvb09XFxcRDPTJ06cwJUrV3DgwAFcv35dO9fjx4+jc+fOonOhWF4ZhoWJRUaRhiL8qCuTUSyvFOHXuHFj+Pv7o0mTJrh27Rq+/PJLREdHC9qSKONQ4ygxSllr5cxNn/o+uRed+ggeivDbvHkz1q5dC39/f/zxxx8YOHCgaIxStZGAPCGij2hWSlgpKXgKo4jT53hQSozJjaPskz6vg9xjVR9LaUJCAnr06IGoqChs2bIF/fv3F40B5K+iCsjPRjZr1gz+/v54+fIlpk2bhq+//lp0DIp1lQ+xzxPFYUStc5RbfzhmzBh4enri1atXcHd3x+TJk0VjKLWRU6dOhVqtxrFjxzBz5kxkZGRg48aNvNs3bNgQycnJMDMz09ZsqlQqSZlzquWVYViYWGQUaSjCz8PDg7QyGcXyShF+06dPx7FjxxAXF4cePXqgffv2iI+Ph52dnUHHocZRYpSy1sqZmz4CTu7Fqj6ChyL8NHe7S5YsCVNTU2RmZvJuq3RtJEATShTRrJSwUlLwFEYRRzkelBRjSh3j+nwu5B6r+lhKMzIycOTIEdStWxeJiYn48OGDaAwgfxVVQH42UrNaZqNGjWBtbS34vaeBYl2lQnEYUeocKfWHLVq0wOHDh5GYmKi9aSoGpTYSAG7cuIEzZ87gzZs36Nixo+C2lpaW6NmzJzp16iQ720m1vDIMCxOLjCINRfi5u7vDysoKjo6OqF27tuSxKJZXivBLSUlBeno6KlasiKSkJPz222+CVlLqONQ4SoxS1lo5c6MIOH0zahTBI0f4aahZsybc3d0xadIkLF++XHAFOaVrIwGaUKKI5oIWVkpnn4DCKeIox4OSYkypY1yfzwU100yxlA4ePBjR0dGYOHEitmzZIiqs9LG8ys1GPnv2DPfv38fHjx9x+/Zt3L59W3DBGYBmXZXTbiM3lStXxsKFC8FxHK5duybJYUSpc6TUH0ZGRmLTpk1IS0vTPnbs2DHBGEptZOfOndGwYUO4uroiODhYdF4TJkxAaGgoOnfurH3NNa+/2PzkWl4ZBQMTi4wiDUX47d69G3FxcTh27Bj69++P8uXLS6rXoFheKcJv+PDhqFixonYlNynZEMo41DhKjFLWWsrc5Ag4fTNqFMEjR/hpmDt3Lj58+ICSJUviq6++gqWlJQAgJiYmX/ZdydpIfYQSRTQXtLBSUvAUZhGnQc7xoKQYU7r+lxJDzTRTLKWOjo5wdHQEAIwePVr7+PTp0xEUFJRve30sr3KzkQEBAWjXrh0qVKgg+twaKNZVOe02chMcHAxra2s8f/4ct2/fRoUKFTB//nzBGEqdI6X+cPv27Vi9erX2fC8FSm1keHg4TExM8OzZM6Smpore2AgNDQWQc6z16NFD1lhyLa+MgoGJRUaRhiL8/v77b5w7dw5//fUXAMDa2lrSWBTLK0X4cRyHhQsXSpqTPuNQ4ygxSllrKXOTI+D0zahRBI8c4ZcbzSIPuS8cNm/ezBujRG2kPkJEjmhWSlgpKXgKs4jTQLkZopQYo8ZR9klOjL6ZZqqlVBcPHz7U+TjF8krNRhYvXlw0k/gpFOuqnHYbubl58yYCAwPh4+ODLVu2oF+/fqJjUeocKfWH5ubmqFatmuh2uaHURl64cIFUIxoZGSlbLALyLK+MgoGJRUaRhiL8vL29UaNGDYwdO1ZW/yeK5ZUi/Bo0aIDr16/jyy+/1D4m9uVCGYcaR4lRylpLmRtFwFEvVilZQs3cAOnCjw8hi1VB10YC+gkROaJZ6QWFlIgpCiKO8lkqaDGmbxxln+TE6OtWkGsp1Qc5lle52UiNUK1QoQL279+PRo0aaW/2iTWyp1hXKe02gJzP3K1bt1C9enWo1WpJ4pxS5yin/nDRokUAALVajUGDBuV57cQspZTaSGqNqFqthrOzcx6Brsk68iHX8sooGJhYZBRpKMLvr7/+wuXLl3HmzBmsX78e5cuX155shaBYXinC78KFCzh+/Lj2dym+fso41DhKjFLWWsrcKAKOerFKzRLqgtKjUuh1L+jayNxQxbZU0ay0sFJS8BRmEUc5HgpajOkbR9knOTH6uhXkWkr1QY7lVW42MvdicTt27ND+LEXEUayr1HYbPXr0QFBQEObMmYOQkBCtEBaCUucop/5QI6Y/FdVSvmcptZGUGlEApF7Mci2vjIKBiUVGkYYi/N69e4eXL1/i+fPnSEtLQ9WqVSWNRbG8UoTfvn37JM1H33GocZQYpay1lLlRBBz1YlUTA+ifJZRqNZZKQddG5oYqlHQhJJqVElZKCp7CLOIox0NBizF94yj7RImh3kDhg89Sqg8Uy6vUbOSWLVsA5PTky+0eiY6OFh2DYl2lttvw8vKCl5cXACAwMFD7+PLly3nnQKlzlFN/2LNnTwDAzJkz84jun3/+WfQGK6U2slmzZggICJBVIwoAjRo1wtq1a5GQkAA7OztJnz+q5ZVhWJhYZBRpKMJv8ODBcHBwgK+vb567nGq1WjALRbG8yhF+mhO9u7t7PiEQERFhsHH0jaPEKGWtpb4OcgUc9WKVD0qW0NDjKFUbCegntj9FSDQrJayUFDyFWcRp5gVIPx6UEmP6xFGOcbkxhryBQkXsPESxvErNRp44cQJXr17F/v37cfXqVQA5GdZjx47xNm7Xx7pq6HYbFy5c4P0bpc5RTv1heHg4Vq1ahbdv3+LIkSPax6Vco1BqIzU1ol9++aXkGlEAmDx5MmxtbXHx4kVUqFABgYGB2Lp1q2CMkm1RGPwwscgo0lCE3+7du3mfS8juIsfyShF+mhPg/PnzYWJion1cqC8VVWBS4vQRswVtrdVnbnwUhLDig5IlpAhMsUbfStRGAoYX23woJayUFDyFWcTxIXY8KCHG9I37FMrnTyjGkDdQ+MjKykJWVhb8/f2xePFicBwHjuMwZMgQbN68WefqoLmhWF6lZiP5Grd36dKFdz76WFepVko+hN5bOXWOlPpDTbYzLCwMvr6+suZN6c348uVLVK1aFdWrV8e6detQuXLlPN/TfCQnJ8PFxQX79u1D06ZNkZ2dLRpj6PeJQYOJRUaRhir8dCH25S/H8koRfhzH4eHDh5gwYQIWLFgAjuOQnZ2NadOmISoqymDjUOOoYwEFb63VZ258iAk4Q110UhESfmFhYVi3bp220TcAnDlzBvb29rLHMXRtJKCcEFFyQSGlYgq7iNOFUjdDqBn6gjjG5cYocQNl165dCAsLw+vXr+Hk5ASO42BkZITmzZsDQJ5zpxyELK9Ss5FVqlRBz5490aNHjzxWyISEBN7n1se6Smm3IYTQeyunzlGf+kMPDw/s378fmZmZ4DgOCQkJGDZsmGAMpTdjQEAA/Pz8sG3bNnTs2BFz5szRvhdixMXFAQBevHiBYsWKiW5PtbwyDAsTi4z/JAXx5S/H8koRftevX8emTZvw8OFDTJs2TftF3rZtW4OOQ42jxChlraW+DoamIC5WKcIvOjoap0+fNshdWEPXRmowlBAREs2fe0GhgoopaiKOgqHFWEHEGRJDuxV0vU9ubm5wc3NDVFQUXFxc9J6zFORmI5ctW4bt27cjIyMDHz9+RO3atbWrxH4KxbqqgdJug4qcOkd96g/9/PxQp04dxMbGwszMTNL5n9KbUaVS4bvvvkNYWBi6dOmCnTt3SoqbMmUKJk+ejLi4OIwaNQrTp08XjaFaXhmGhYlFxn+Sgvjyl2N5pQg/BwcHODg44OTJk5JXdqWMQ42jxChlraW+DkIolW0AhAUPRfhVr149j7hUmsIimoHPK6yUFDyFWcQpNTclKYgbAXKOVX0spV999RWuXr0KIyMjLFq0CL6+vmjdurXs/dEHvmzk8ePHcerUKcyZMwcDBgwQXNGVYl3VQGm3AeS8h7o+A5TjQVedoz71hxzHYebMmZg0aRKCg4MltV+h9GbMzMxESEgImjdvjvPnzyMjI0NS3JMnT7B9+3bJK/0CdMsrw7Awschg/H/ETvZyLK8U4adhzZo1kmOo41DiKDFKWWv1eb35EKvvo0ARPBThl5GRgW7duqF+/foAci6gxPpZ8VEQtZF8CAkRQ2ZL/4vipTCLOMrxUNhtqJR9MuTroI+ldMaMGZg6dSqWLVuGsWPHIiQkRHGxyIelpSVMTU3x4cMH1KpVS1CIUKyrGijtNgBg0KBBOoX4ggULZD0PoPu91af+sFixYkhPT0daWhpUKhWysrJ4t9WnN+PcuXNx9uxZuLq6IiYmRruyq9gigX/++SeWLl0Ke3t7uLi48PbczI0+lleG4WBikfGfRNdJeOvWrfD29sb169fxzTff5Pt73bp1DTaWBjnCT4NKpcKIESPyNK4VO3lTxqHGyYlR0lpL3R8l6/sogoci/IYMGSL5+cVQqjZSDENmSwtzDZ2SQtbQIs6Qx4NSYkwsjrJPhnwd+I5VfSylpqamqFevHjIyMtCkSRNZmR5dGPKYrVy5MqKiolCiRAmEhobi3bt3ojFyrKsaKO02AKBMmTKIiYnJ8/1sZWWlbfEkB6HzEKX+0MvLCxs3bsT333+P9u3bo1mzZrzb6lMbWbt2bW2v6dx2X7G1IqZOnQq1Wo1jx45h5syZyMjIwMaNGwXHolpeGYaFiUVGkYQi/LZs2YLq1atj8eLFGD9+fJ6/tW3bVpJ/XhdCJ1eK8OvduzdpDnLHocbJiVHSWkvdH0NmrMQuVimCR47w0yz0EB8fn++4bNGihWDs566NBIQvOg2ZLaWglHihCp7CIOIox4OSYkypY9zQnwshKJZSlUqFn3/+Gba2toiOjhZd2EbfVVTlMHPmTLx48QJOTk7Ys2ePpM+4HOuqPu02AODNmzfYtGmT9ncpK69SoNQfduzYEUDOqqOdOnVCqVKleLfVpzaSDyk3DW7cuIEzZ87gzZs32vkKQbW8MgwLE4uMIglF+I0fPx5HjhzBmzdv8t111Ke2TQiK8OvWrRt27NiBBw8eoHbt2vD09CyQcahxlBglrLXUuVEEHPViVY7goQi/5ORkAMDr16+l7oqWwlAbKSREDJktNbSw+tzZJ6BwiDjK8aCkGFPqGDfk50LsApxiKV28eDFu3ryJ9u3b4/z587yremsoiFVU+fYrNTUVO3bs0DZul/Lccqyr+rTbAHKuPd6/f49//vkHNWrU0NaZCkGpc6TUH168eBFBQUHaBvZVq1aFq6urzm31qY3kQywr2blzZzRs2BCurq4IDg6W9JxUyyvDsDCxyCiSUISfRoQcP34c9vb2SExMRLly5QrUgkMRftOmTUOZMmXw/fff48KFC5gyZYpoPQRlHGocJUYpay1lbpSMFfViVY7goQg/zd3iFi1a5PniNjY2xosXL1C5cmXeWCVrI+UIEX2ypXwYWlgpKXgKs4ijHA9KijGljnFDZsHFMs0US6mpqSmuXLmCQ4cOwc7ODm/fvkW5cuV4t6dYXqnZSErjdjnWVX3abQDA4cOHsWrVKq0gU6lUok3iKXWOcuoPNSxZsgRbt27FyJEj4evrC09PT16xqE9tJJXw8HCYmJjg2bNnSE1NxRdffCEaQ7W8MgwLE4uMIok+wu+LL75Ahw4dULp0abx79w6zZs3C999/z7u9PrWOFOH3+PFjhIeHa/fTw8NDdJ8o41DjKDFKWWspc6NkrORedFIEjz7Cb+nSpXj9+jUaN26MO3fuwMTEBGq1Gq6urhg8eLDOGCVrI+UIEX2ypUoJKyUFT2EWcZTjQUkxptQxTomhZprlWkoBmiAD5FleqdlISuN2OdZVfdptAMCGDRuwc+dODBo0CMOHD0fv3r1FxSKlzlFO/aEGIyMjlCtXDiqVCmZmZpKynpTaSD7EsuAXLlyQLbSpYzEMCxOLjCKNXOEH5FxIb9u2DZUqVcLLly/h5+cnGKNPrSNF+GnuJpYoUQIfP36UdEeRMg41jhKjlLVWztz0yVjJvejUR/BQhF/x4sWxb98+mJmZQa1WY+TIkVi2bBm8vb15Y5SqjQTkCRF9RLNSwkpJwVMYRZw+x4NSYkxuHGWf9HkdqJlmuZZSgCbIAHmWV30W4JHbuF2OdVWfdhtATsbP1NQUKpUKKpVK0vtFqXOUU3+ooWbNmggNDUVycjLWrFkj2AtaA6U28vz582jVqlW+x8UWCaQIbT4KQ2/U/0swscgo0sgVfkDOyb5SpUoAgEqVKsHMzExwe31qHSnCr2/fvujRowfq1auHBw8eYNSoUaIxlHGocZQYpay1cuamj4CTe7Gqj+ChCL+kpCTtcW1qaoqkpCSYmprqvChUujYSoAklimhWSlgpKXgKo4ijHA9KijGljnF9PhfUTLNcS6kGuYJMM5Zcy6vcBXgojdvlZEr1abcBAM2aNYO/vz9evnyJadOm4euvvxaNodQ5yqk/1BAUFITIyEg0a9YMJUqUwKxZs0THodRGLlu2TKdYFHuvKEKbUThgYpFRpJEr/ACgVKlS2LJlC7777jtcvHgRZcuWFdxeH8srRfh1794dtra2ePbsGapXry7pi58yDjWOEqOUtVbO3CgCTt+MGkXwyBF+Gjp06ABPT0/Y2Njg5s2bsLe3x7Zt21CvXr182ypdGwnQhBJFNBe0sFI6+wQUThFHOR6UFGNKHeP6fC6omWaKpXTKlCkIDAzUCrIZM2aIjqOZk1zLq9wFeO7fv4/169dLElQaKJlSSrsNIKcU4tSpU2jUqBGsra3z1D3yQalzlFN/qGHYsGFwdHTEyJEjYWFhITovgFYbSV19vVmzZggICJAltPlgNlRlYWKRUaSRK/wAICQkBCtXrsTixYthbW2NOXPmSBqLYnmlCL8rV64gKCgIr1+/RqVKlRAcHIwvv/zS4ONQ4ygxSllrKXOTI+D0zahRBI8c4adhxIgR6NChA+Lj49G7d2/Ur18fiYmJOrOzStZG6iOUKKK5oIWVkoKnMIs4DXKOByXFmNL1v5QYaqZZjlCyt7fX7j/HcbCwsMDr168REBCAgwcPio5FsbzKzUY+ffoUQ4cORenSpeHo6Ah7e/sCyZTKabeRm2fPnuH+/fv4+PEjbt++jdu3b4v2a6TYLyn1h3PmzMGxY8cwefJkqNVq/PDDD+jbt69gDKU2krr6ukZof/nll5KFNtXyyjAsTCwyijQU4Ve6dGlMmDAh3+MjRozAihUreOMolleK8Js9ezZCQ0NRt25dxMbGYtq0aYiIiDD4ONQ4SoxS1lrK3OQIOH0zahTBI0f4RUZGwtXVFaGhodr53b17F4D4nV8laiP1ESJyRLNSwkpJwVOYRZwGys0QpcQYNY6yT3JiDLHar1ShdOjQIXAch6CgIHh4eMDGxgZ37tzBtm3bJI1DsbzKzUb6+vrC19cXN2/exOzZszFt2jTcunVLMIZiXZXTbiM3AQEBaNeuHSpUqCBpe4Bmv6TUH1aqVAlff/013r17h5iYGERHR4uKRUptZLdu3bBnzx48f/4crVq1ErxxmZuXL1+iatWqqF69OtatW4fKlSuLfj9TLa8Mw8LEIqNIQxV+uhBabhugWV4pwq906dLau2b169eXVMdCGYcaR4lRylpLmRtFwFEvVuUIHorw01zI16pVS3IdkoaCro0E9BMickSz0gsKKRFTFEQc5bNU0GJM3zjKPsmJ0detIMdSqulL9/TpU9jY2AAAGjVqpG1ULwbF8io3GxkcHIwbN27A3NwcXbt2xbx580TnRbGuymm3kZvixYuLZhI/hVLnSKk/bNGiBapWrYqhQ4diw4YNKF26tGgMpTZy+vTpqFixIs6dO4evv/4aEyZMwNq1a0XHCggIgJ+fH7Zt24aOHTtizpw52lYmfFAtrwzDwsQi4z+J1BN/bsRW16JYXinCr3z58ggMDESrVq1w+/ZtZGdna5sHu7u7G2wcahwlRilrLWVuFJsn9WJVjuChCL927doByFlZka+PGR8FXRuZGzlChCKalRZWSgqewiziKMdDQYsxfeMo+yQnhnqs6mMpLV26NJYsWQIbGxtcvXoVlpaWgttroNQGys1GqtVqmJmZoUqVKqhatSoqVqwoOgbFuiqn3QYAraCuUKEC9u/fj0aNGmlff82qqnxQ6hwp9Ydr1qzB6dOnERUVhUOHDqFNmzai5RuU2sgnT54gODgYly5dgr29PdasWSNpfiqVCt999x3CwsLQpUsX7Ny5UzSGanllGBYmFhn/SQpiWWWK5ZUi/OrUqQMgp2avVKlSaNGiBV69emXwcahxlBilrLWUuckRcBrkXnRSBI8+wo+vr5cQBV0bmRs5QkSfbKlSwkpJwVOYRRzleChoMaZvHGWfKDFyj1V9LKULFy5EREQE/vjjD1hbW2PkyJGiMRrk1gbKzUZqagdv3LiBkJAQjB49WtSGSrGuymm3AeQsuKZB850CSGuDQalzpNQfNmnSBFWqVEHFihWxf/9+7NmzR1QsUmojs7KykJiYCJVKhZSUFEmL/QFAZmYmQkJC0Lx5c5w/f16S9ZdqeWUYFiYWGQyJUCyvFOHn5+eHc+fO4enTp/jmm29gZWUlanmljEONo8QoZa2VMzd96vvkXnTqI3gowu/NmzdYunQpnj59iurVq8PCwkL0gkap2khAnhDRRzQrJayUFDyFUcTpczwoJcbkxlH2SZ/XQe6xqo+l9IsvvsDAgQNFt/sUyiqqcrOR69evx5kzZ5CWlob27dtLGoNiXZUrYjV2SU2NqYbo6GjRsSh1jpT6Q2dnZ5ibm8PBwQELFy7Uls0IQamNHDNmDDw9PfHq1Su4u7tj8uTJkvZp7ty5OHv2LFxdXRETE4P58+cDyMkma47nT6FaXhmGhYlFxv85+E5MUmyluhCyvFKE36JFi/DixQvExcXB1NQUa9asEa3zoIxDjaPEKGWtlTM3fQSc3ItVfQQPRfh5enpi6dKlaNOmDWJjY9GrVy/ebZWujQRoQokimpUSVkoKnsIo4ijHg5JiTKljXJ/PBTXTTLWUykHfVVTlZCONjY0RHByMKlWq5Hk8JiYGDg4OOmMo1lW5IvbEiRO4evUq9u/fj6tXrwIAsrOzcezYMXTu3FkwllLnSKk/3Lhxo0777fTp03lXe6XWRh4+fBiJiYmSLbIAULt2bdSuXRsA8rxmgwcP5v1Oo1peGYaFiUVGkYYi/Hr37o1WrVrB1dVV29MKyFl1i4KQ5ZUi/C5fvozw8HD4+PigZ8+e2L59u+gcKONQ4ygxSllr5cyNIuD0zahRBI8c4adh06ZN2L17N0qWLImUlBT069cPzs7OOrdVujYSoAklimguaGGldPYJKJwijnI8KCnGlDrG9flcUDPN+lhKpaKP5VVuNoltpZsAABreSURBVJIve7Z582ZesUixrgLyRGzDhg2RnJwMMzMz7TlbpVKhS5cuvDH61DlS6g/56jSFss2U2sjIyEhs2rQJaWlp2seOHTsmKVYXQj0TqZZXhmFhYpFRpKEIv7179+L06dNYvnw5kpKS0L17d3Tu3FnWSmpSoQi/rKwspKenaxvkSjk5UsahxlFilLLWUuYmR8Dpm1GjCB45wk+DSqXSHs+lSpUSfK2VrI3URyhRRHNBCyslBU9hFnEa5BwPSooxpet/KTHUTDPVUioHiuVV32zkpwgJCop1VW67jSpVqqBnz57o0aNHnu/khIQE3hh96hwp9YcUKLWR27dvx+rVqw2WxRa64U61vDIMCxOLjCINRfgZGRnB1tYWABAVFYUtW7Zg165d6Nq1K7y9vQ06P4rw69u3L3r16oXExES4urqif//+BTIONY4So5S1ljI3OQJO34waRfDIEX4aatSogXnz5qF58+a4dOkSatasKRqjRG2kPkJEjmhWSlgpKXgKs4jTQLkZopQYo8ZR9klOjL6ZZiWRY3nVt6fjpwgJCop1ldJuA8i5Eb19+3ZkZGTg48ePqF27Ng4cOKBzW33qHCn1hxQotZHm5uaoVq1agcznU6iWV4ZhYWKRUaShCL8FCxbg2LFjaNGiBYYMGQIbGxtkZ2ejV69egmKRYnmlCL/w8HBs374djx490l5oiEEZhxpHiVHKWkuZG0XAUS9WKVlCivCbO3cuduzYgXPnzsHa2hoBAQGiMQVdGwnoJ0TkiGalFxRSIqYoiDjKZ6mgxZi+cZR9khOjr1tBSeRYXvXt6SgHinWV0m4DAI4fP45Tp05hzpw5GDBgAG8tIKBfnSOl/pCCnNpIzfevWq3GoEGD8thq9bmxIZQ1NrTllUGDiUVGkYYi/GrXrq29YNdgZGSE5cuXC45FsbxShJ9KpcKkSZNkNaGljEONo8QoZa2lzI0i4KgXq5QsIUX4GRsbw8vLS3S73BR0bWRuKEJEjmhWWlgpKXgKs4ijHA8FLcb0jaPsk5wYfd0KSkKxvBpqAR4hQUGJobTbAABLS0uYmpriw4cPqFWrlmD7B0qdowZK/SEfQq+DnNpIzT58er6R2qrs/PnzaNWqVb7HNQvZ6cLQllcGDSYWGUUaivBr0aIFtm7dqj3JJyQkYObMmahevbrgWBTLK0X4UZrQUsahxlFilLLWUvdHroCjXqxSsoQU4UehoGsjc0MRIhTRrJSwUlLwFGYRRzkeClqM6RtH2SdKDNWtUNgx1AI8AwYMkB0jJGIo7TaAnExwVFQUSpQogdDQUMHV0Cl1jhSysrKQlZUFf39/LF68GBzHgeM4DBkyBJs3bxa8CSGnNrJnz54AgJkzZ+apx/z5558lff6WLVumUywK1YsqaXll8MPEIqNIQxF+48aNw48//ogrV66gYsWKSE1NlTQWxfJKEX6aE7IcKONQ4ygxSllrKXOjCDjqxSpF8CiFUrWRAE2IUESzUsJKScFTmEUc5XhQSoxR4yj7RImhZpoLO3KzkWFhYVi3bl2eVklnzpyBvb29QedFabcB5AilFy9ewMnJCXv27EFoaKhojJw6Rwq7du1CWFgYXr9+DScnJ3AcByMjIzRv3hwAYGJiwhsrpzYyPDwcq1atwtu3b3HkyBHt49bW1pLmqVKpMGLECEk3cwvK8sqgwcQio0hDEX5ffPEFhg0bhkePHmHu3Lno06ePpLEolleK8KNAHYcSR4lRylpLmRtFwFEvVpXKElJQqjYSoAsluSglrJQUPIVZxFGOB6XEGDWOsk+UGGqm+b9GdHQ0Tp8+jRIlSuj9XEL2S2q7jdTUVOzYsQMJCQmws7MTFGIa5NQ5UnBzc4ObmxuioqLg4uIiK1ZObaSXlxe8vLwQFhYGX19f2fOUczNXX8srw7Awscgo0lCEn0qlwqtXr/DhwwekpqZKzixSax0Zylpr5UIRcNSL1cKMUrWRAF0oyUUpYaWk4CnMIo5yPCglxqhxlH2ixCh1A6WwU7169TxZRX0Qsq5S2m0AwOTJk2Fra4uLFy+iQoUKCAwMxNatWwVj5NQ5iiEkgL/66itcvXoVRkZGWLRoEXx9fdG6dWvB56PURnp4eGD//v3IzMwEx3FISEjAsGHDROferVs37NmzB8+fP0erVq0Ee4jqa3llGBYmFhlFGorw8/PzQ0xMDHr06IEff/wR3bt3lzQWtdaRoay1VgkKs52UipJZT6XEtlLCSknBU5hFHAWlxJg+cUqg1A2Uwk5GRga6deumXUROpVKJWj0p1lVKuw0ASE5OhouLC/bt24emTZsiOztbdJ/k1DnqU384Y8YMTJ06FcuWLcPYsWMREhIiKhYp+Pn5oU6dOoiNjYWZmZnkLPD06dNRsWJFnDt3Dl9//TUmTJiAtWvX6txWX8srw7Awscgo0sgRfp82CTYxMYGZmRn++OMPTJgwQXQsaq0jg4ZSFl4KhfmisyiglBBRSlgpKXgKs4hj0PkvuhUoDBkyRHYMxbpKabehIS4uDgDw4sULSe1O5NQ56lN/aGpqinr16iEjIwNNmjSR3G9ZLhzHYebMmZg0aRKCg4Mll/I8efIEwcHBuHTpEuzt7bFmzRrebfW1vDIMCxOLjCIJRfjxNQmW0pIBoNc6MhiMvCglRFgGmFFU+C8eq3LQNK6Pj4/PV5fWokULwVhDWlfFWnRMmTIFkydPRlxcHEaNGiW4kqcGOXWO+tQfqlQq/Pzzz7C1tUV0dLSkekoKxYoVQ3p6OtLS0rSrlUshKysLiYmJUKlUSElJkSRmqZZXhmFhYpFRJKEIP74mwfHx8ZLGpNY6MhiMzwMTVoyiwv/1YzU5ORkA8Pr1a9mxFOsqH2ILqNy/fx/r168XbJn1KZQ6R0r94eLFi3Hz5k20b98e58+f164oSkFINHt5eWHjxo34/vvv0b59ezRr1kzSc44ZMwaenp549eoV3N3dMXnyZNEYquWVYViYWGQUSfQRftQmwdRaRwaDwWAwGPxoyg5atGiRR7AZGxvjxYsXqFy5Mm8sxbpK5enTpxg6dChKly4NR0dH2Nvb8y4So4FS50ipPzQ1NcWVK1dw6NAh2NnZ4e3bt7xz06c2smPHjtr96tSpE0qVKiW6P0DOe3v48GEkJiZKWt0coFteGYaFiUVGkYYi/OQ2Cda31pHBYDAYDIY4S5cuxevXr9G4cWPcuXMHJiYmUKvVcHV1xeDBg/Nsq491lQ8xG6qvry98fX1x8+ZNzJ49G9OmTZPUckNunSOl/lBOBlOf2siLFy8iKCgIWVlZcHJyQtWqVeHq6io6v8jISGzatAlpaWnax44dOyYYQ7W8MgwLE4uMIo1c4QfIbxKsb60jg8FgMBgMcYoXL459+/bBzMwMarUaI0eOxLJly+Dt7Z1PLOpjXeVDqN0GAAQHB+PGjRswNzdH165dMW/ePNHnpNQ5UuoP5WQw9amNXLJkCbZu3YqRI0fC19cXnp6eksTi9u3bsXr1asluLoBueWUYFiYWGUUaucKPgr61jgwGg8FgMMRJSkrStg0xNTVFUlISTE1NdQoffayrlHYbAKBWq2FmZoYqVaqgatWqqFixoug+UeocqfWHcjOYlNpIIyMjlCtXDiqVCmZmZpL3y9zcHNWqVZO0rQaq5ZVhWJhYZDAkQq11ZDAYDAaDIU6HDh3g6ekJGxsb3Lx5E/b29ti2bZtgA3c51lUNlHYbABAUFAQAuHHjBkJCQjB69GhRGyqlzlFO/aGGKVOmIDAwUJvBnDFjhuj+UGoja9asidDQUCQnJ2PNmjWoWrWq4PYaoatWqzFo0CA0atRIK+79/f0FY6mWV4ZhUXFiBm0GgwEgZ/nriIgIPHr0CNbW1vD09NRmHRkMBoPBYOjP3bt3ER8fj7p166J+/fpITEyEubk570qlgwYNwsqVK3VaV3fu3KkzZvjw4VixYoXo6qefsn79epw5cwZpaWlo3749fvzxR8mN4jV1jrdv3xYVmKNGjYKtrS12796NcePGYdGiRbz1h7rWVcjIyICZmRkOHjwoOE7fvn2xbt06/PTTT/j111/h4+ODLVu2CMZkZmYiMjISsbGxqFOnDtzd3QWvhfbs2aPzcZVKBWdnZ8GxvLy8sGLFCowcORLr1q2Dp6cndu/eLRjDMDwss8hgSEQJyyuDwWAwGP/XiIyMhKurK0JDQ7XC5+7duwDEs09yrKsaqO02jI2NERwcjCpVquR5PCYmBg4ODjpjKHWOcuoP+dZV2LZtm+g4lNrIYcOGwdHRESNHjpS0qqnGLjxz5kxMmzZN+/jPP/8sKhaplleGYWFikcFgMBgMBoPx2dDUF9aqVUtSrV1uKNZVaruNvn376nx88+bNvGKRUucISK8/5FtX4eHDh6JjUGoj58yZg2PHjmHy5MlQq9X44YcfeF8XAAgPD8eqVavw9u1bHDlyRPu4lIysXMsro2BgNlQGg8FgMBgMxmdn4MCBgj3++JBqXdW024iIiMj3N3d3d/K8pdg3NXWOV69eFbWhxsbGYtq0aYiLi4OVlRVmzJiBRo0aCcYMHz4c9evX166r8PTpUyxZskQwJiUlBWvXrkVCQgLs7OzQoEED1KpVSzAGyLHUnj17FjExMTA2NkZERIRoTFhYGHx9fUW3y41cyyujYGCZRQaDwWAwGAzGZ6dMmTKIiYmBlZWVtreglZWVzm0p1tWCaLcBQLD28dM6R6GFZz6tP7SwsMDr168REBAgWn9IaSUmpzejhhYtWqBq1aoYOnQoNmzYgNKlS4uOAwAeHh7Yv38/MjMzwXEcEhISMGzYMMEYuZZXRsHAxCKDwWAwGAwG47Pz5s0bLF26FE+fPkX16tVhYWGBzZs369yWYl3Vp90GFTl1jvrUH1LWVZBTG6lhzZo1OH36NKKionDo0CG0adMGHh4eonF+fn6oU6cOYmNjYWZmJmklWrmWV0bBwMQig8FgMBgMBuOz4+npiaVLl6JNmzaIjY1Fr169eLdt164dgJw2GHKtq5R2G0IIVXTJqXPUp/6QitzejE2aNEGVKlVQsWJF7N+/H3v27JEkFjmOw8yZMzFp0iQEBwejT58+ojGVKlXC119/jXfv3iEmJgbR0dFMLH4GmFhkMBgMBoPBYHx2Nm3ahN27d6NkyZJISUlBv379RFfMlGNd1VC8eHHs27dPZ7sNilgcMGCA7BghgalUX2dKb0ZnZ2eYm5vDwcEBCxcuRKVKlSSNVaxYMaSnpyMtLQ0qlQpZWVmiMVTLK8OwMLHIYDAYDAaDwfjsqFQqbXuEUqVKaVtiCCHHuqqB0m4DyFmkZd26dShevLj2sTNnzsDe3l50np8iVOdIqT+Ugz61kRs3bkS5cuXyPT59+nQEBQXxxnl5eWHjxo34/vvv0b59ezRr1kx0nlTLK8OwMLHIYDAYDAaDwfjs1KhRA/PmzUPz5s1x6dIl1KxZUzRGjnVVA6XdBpBjeT19+rSkejt9KOi+zvrURuoSigBErbIdO3YEkFMn2alTJ5QqVUp0LKrllWFYmFhkMBgMBoPBYHx25s6dix07duDcuXOwtrZGQECAaAzFujpixAh06NAB8fHx6N27t7bdhqenp2Bc9erV82QV9eFzdq77HLWRFy9eRFBQELKysuDk5ISqVavC1dVVMIZqeWUYFiYWGQwGg8FgMBifHWNjY3h5ecmKkWNdpbTbyE1GRga6deuG+vXra8cODQ2VNV8NlDpHQ6NUbSQALFmyBFu3bsXIkSPh6+sLT09PUbFItbwyDAsTiwwGg8FgMBiMIokc6yql3UZuhgwZIjvGkHWOhqagayNzY2RkhHLlykGlUsHMzEwr8IWgWl4ZhoWJRQaDwWAwGAxGkUSOdZXabuPEiROws7NDfHx8voVpWrRoIRirVJ0jBUPWRorZamvWrInQ0FAkJydjzZo1qFq1qkHGZRQ8TCwyGAwGg8FgMIokFOuq3HYbycnJAIDXr1/Lnp8h6xw/J1lZWcjKyoK/vz8WL14MjuPAcRyGDBmCzZs3i4rvoKAgREZGolmzZihRogRmzZql0MwZ+sLEIoPBYDAYDAbj/wxy22307NkTQE4WMXdm0djYGC9evNDaW3VhyDrHz8muXbsQFhaG169fw8nJCRzHwcjICM2bNwcAmJiYCMYPGzYMjo6OGDlyJCwsLJSYMsNAqLjPuRwTg8FgMBgMBoOhINHR0Vi6dCmsra0RGxsLPz8/0RVUgZxega9fv0bjxo1x584dmJiYQK1Ww9XVFYMHD9YZc+HChXyPiVlXCzNRUVFwcXGRHffy5UscO3YMp06dglqtxg8//IC+ffuS5uDj44MtW7aQYhnyMfrcE2AwGAwGg8FgMJRC025j5cqV+O233yQLj+LFi2Pfvn1YtGgR9u3bh6pVq+L333/HkSNH8m174sQJAEB8fDwePnyY519R5quvvsLVq1dx/fp19OvXD3/++aekuEqVKuHrr79GkyZN8O7dO0RHR/Num5WVBbVaDT8/P2RkZECtViM9PV0rLuXUmzL0h9lQGQwGg8FgMBj/Z5DTbiM3SUlJ2m1NTU2RlJQEU1NTZGdn59tWnzrHwsyMGTMwdepULFu2DGPHjkVISAhat24tGteiRQtUrVoVQ4cOxYYNG1C6dGnebfW1vDIMCxOLDAaDwWAwGIz/M8hpt5GbDh06wNPTEzY2Nrh58ybs7e2xbds21KtXL9+2+tQ5FmZMTU1Rr149ZGRkoEmTJtoFgsRYs2YNTp8+jaioKBw6dAht2rSBh4eHzm3d3Nzg5uZGtrwyDAurWWQwGAwGg8Fg/J8hMzMTO3bsQFxcHKytreHm5iY5W3X37l3Ex8ejbt26qF+/PhITE2Fubp6vpYYGSp1jYaZfv34wNzfHt99+C0tLS0RFRUm2hb58+RJ//PEH9u/fD7VajR07dghuf/fuXaSlpcHIyAiLFi2Cr6+vpCwmw7AwschgMBgMBoPBYPAQGRkJV1dXhIaG5hOF/v7+grGDBg3CypUrYWZmBrVajZEjR2LZsmXw9vbGzp07C3LaBUJiYiJu3ryJ9u3b4/z582jYsCHKlSsnGufs7Axzc3M4ODjAwcEBlSpVEo3x8PDQWl59fX0REhKC8PBwA+wFQw7MhspgMBgMBoPBYPCgsYzWqlULxYoVkxUrp86xKGBqaoorV67g0KFDsLOzw9u3byWJxY0bN+rcbvr06QgKCuIdi2J5ZRgWJhYZDAaDwWAwGAwe2rVrByCn5YbclTjl1DkWBSZPngxbW1tcvHgRFSpUQGBgILZu3SoaxycohVaHValU+Pnnn2Fra4vo6Gi2sM1ngolFBoPBYDAYDAZDhDJlyiAmJgZWVlbaLJeVlZVgzIgRI9ChQwfEx8ejd+/e2jpHT09PJaZscJKTk+Hi4oJ9+/ahadOmBZohXbx4cR7L66JFiwpsLAY/TCwyGAwGg8FgMBgivHnzBkuXLsXTp09RvXp1WFhYYPPmzTq31VXnePfuXQDidY6Fnbi4OADAixcvZNty5UC1vDIMCzP/MhgMBoPBYDAYInh6ekKtVqNNmzb4+PEjevXqxbtt7jpHKyurPP+KMlOmTEFgYCD+/vtvjBo1CpMmTSqwsSZPnowaNWrg8ePHWssrQ3lYZpHBYDAYDAaDwRBh06ZN2L17N0qWLImUlBT069cPzs7OOrfVp86xMGJvb6/NkHIcBwsLC7x+/RoBAQE4ePAg+XmFmjIoaXll8MMyiwwGg8FgMBgMhggqlQolS5YEAJQqVUq7yqkQmjrHuLg4PHz4UHBBl8LMoUOHEB0djZYtW2LJkiU4fPgwli9fjmbNmgnGZWVlQa1Ww8/PDxkZGVCr1UhPT0ffvn0BQFRIK2V5ZfDDMosMBoPBYDAYDIYINWrUwLx589C8eXNcunQJNWvWFI2RU+dYmDE1NQUAPH36FDY2NgCARo0aiYrfXbt2ISwsDK9fv4aTkxM4joORkRGaN28OAIIrnGosr3FxcRg1ahRmzJhhmJ1hyELFCeV/GQwGg8FgMBgMBjIzM7Fjxw7ExcXB2toabm5uou0coqOjsXTpUlhbWyM2NhZ+fn681tWiwPDhw1G/fn3Y2Njg6tWrePr0KZYsWSIaFxUVBRcXF0ljfGp5NTExQUZGBszMzPSyvDJoMLHIYDAYDAaDwWAUAO7u7li/fn2eOsddu3Z97mmRSU1NRUREBB49egRra2t4enpqs45C3L17F2lpaTAyMsKiRYvg6+uL1q1b69xWrVaD4zgEBQXBw8MDNjY2uHPnDrZt24bZs2cbepcYIjAbKoPBYDAYDAaDUQBQ6hwLM1988QUGDhwoO27GjBmYOnUqli1bhrFjxyIkJIRXLFItr4yCgYlFBoPBYDAYDAajAKDUOf4XMTU1Rb169ZCRkYEmTZrAyEh8jc3SpUtjyZIlWsurpaWlAjNlfAqzoTIYDAaDwWAwGAUApc7xv0i/fv1gbm6Ob7/9FpaWloiKihJdCZVqeWUYFiYWGQwGg8FgMBgMRoGRmJiImzdvon379jh//jwaNmyIcuXKfe5pMSTA+iwyGAwGg8FgMBiMAsPU1BRXrlzBpEmT8O7dO7x9+/ZzT4khESYWGQwGg8FgMBgMRoExefJk1KhRA48fP0aFChUQGBj4uafEkAgTiwwGg8FgMBgMBqPASE5OhouLC4yNjdG0aVNkZ2d/7ikxJMLEIoPBYDAYDAaDwShQ4uLiAAAvXrxAsWLFPvNsGFJhC9wwGAwGg8FgMBiMAiM2NhbTpk1DXFwcrKysMGPGDDRq1OhzT4shASYWGQwGg8FgMBgMhsGxt7eHSqUCAHAcBxMTE2RkZMDMzAwHDx78zLNjSMH4c0+AwWAwGAwGg8Fg/Pc4dOgQOI5DUFAQPDw8YGNjgzt37mDbtm2fe2oMiTCxyGAwGAwGg8FgMAyOqakpAODp06ewsbEBADRq1AgPHz78nNNiyICJRQaDwWAwGAwGg1FglC5dGkuWLIGNjQ2uXr0KS0vLzz0lhkRYzSKDwWAwGAwGg8EoMFJTUxEREYFHjx7B2toanp6e2qwjo3DDxCKDwWAwGAwGg8FgMPLB+iwyGAwGg8FgMBgMBiMfTCwyGAwGg8FgMBgMBiMfTCwyGAwGg8FgMBgMBiMfTCwyGAwGg8FgMBgMBiMfTCwyGAwGg8FgMBgMBiMf/w8bzAvIyOf+MwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(style = 'white')\n",
    "f, ax = plt.subplots(figsize=(15, 12))\n",
    "cmap = sns.diverging_palette(10, 250, as_cmap=True)\n",
    "sns.heatmap(corr, mask=mask, cmap=cmap, square=True, linewidths=.5, cbar_kws={\"shrink\": .5}, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data2.drop(columns = ['target_delay'])\n",
    "y = data2['target_delay']\n",
    "\n",
    "X_tv, X_test, y_tv, y_test = train_test_split(X, y, test_size = 0.20, random_state = 42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class Imbalance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1743790\n",
       "1     407280\n",
       "Name: target_delay, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2['target_delay'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "RUS = RandomUnderSampler(sampling_strategy = 1, random_state=42) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using F1 score\n",
    "* False positives and false negatives are equally important"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dc = DummyClassifier()\n",
    "\n",
    "pipeline = make_pipeline(RUS, dc)\n",
    "cross_val_score(pipeline, X_tv, y_tv, cv=5, scoring='f1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adjust Class Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = LogisticRegression(class_weight='balanced', max_iter=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.3349798 , 0.33585776, 0.33512887, 0.33566146, 0.33475327])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(lr, X_tv, y_tv, cv=5, scoring='f1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "lr2 = LogisticRegression(max_iter=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.33459844, 0.33551768, 0.33520688, 0.3351407 , 0.33520131])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline = make_pipeline(RUS, lr2)\n",
    "cross_val_score(pipeline, X_tv, y_tv, cv=5, scoring='f1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*TO DO:*\n",
    "* *plot F1 score against different probability thresholds*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optimal method: **logistic regression with adjusted class weight & logistic regression with undersampling perform similarly**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* KNN shows very slow performance given the amount of data. Unable to find optimal k value\n",
    "* k = ~ square root of total samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-43-abd983a6996b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mknn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mKNeighborsClassifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_neighbors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m1400\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mpipeline\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmake_pipeline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRUS\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mknn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpipeline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_tv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_tv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'f1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[1;32m    438\u001b[0m     \u001b[0mscorer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_scoring\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscoring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m     cv_results = cross_validate(estimator=estimator, X=X, y=y, groups=groups,\n\u001b[0m\u001b[1;32m    441\u001b[0m                                 \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'score'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m                                 \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    244\u001b[0m     parallel = Parallel(n_jobs=n_jobs, verbose=verbose,\n\u001b[1;32m    245\u001b[0m                         pre_dispatch=pre_dispatch)\n\u001b[0;32m--> 246\u001b[0;31m     results = parallel(\n\u001b[0m\u001b[1;32m    247\u001b[0m         delayed(_fit_and_score)(\n\u001b[1;32m    248\u001b[0m             \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscorers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1039\u001b[0m             \u001b[0;31m# remaining jobs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1040\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1041\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1042\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1043\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    857\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    860\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    775\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    776\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 777\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    778\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/utils/fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[1;32m    618\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    619\u001b[0m         \u001b[0mfit_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 620\u001b[0;31m         \u001b[0mtest_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merror_score\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    621\u001b[0m         \u001b[0mscore_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_time\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mfit_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    622\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mreturn_train_score\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_score\u001b[0;34m(estimator, X_test, y_test, scorer, error_score)\u001b[0m\n\u001b[1;32m    672\u001b[0m             \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    673\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 674\u001b[0;31m             \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    675\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    676\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0merror_score\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'raise'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m     85\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscorer\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_scorers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     86\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscorer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_BaseScorer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 87\u001b[0;31m                 score = scorer._score(cached_call, estimator,\n\u001b[0m\u001b[1;32m     88\u001b[0m                                       *args, **kwargs)\n\u001b[1;32m     89\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\u001b[0m in \u001b[0;36m_score\u001b[0;34m(self, method_caller, estimator, X, y_true, sample_weight)\u001b[0m\n\u001b[1;32m    234\u001b[0m         \"\"\"\n\u001b[1;32m    235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 236\u001b[0;31m         \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod_caller\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"predict\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    237\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0msample_weight\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    238\u001b[0m             return self._sign * self._score_func(y_true, y_pred,\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/metrics/_scorer.py\u001b[0m in \u001b[0;36m_cached_call\u001b[0;34m(cache, estimator, method, *args, **kwargs)\u001b[0m\n\u001b[1;32m     51\u001b[0m     \u001b[0;34m\"\"\"Call estimator with method and args and kwargs.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcache\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     54\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/utils/metaestimators.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m         \u001b[0;31m# lambda, but not partial, allows help() to work with update_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m         \u001b[0;31m# update the docstring of the returned function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m         \u001b[0mupdate_wrapper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/pipeline.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X, **predict_params)\u001b[0m\n\u001b[1;32m    417\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtransform\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwith_final\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    418\u001b[0m             \u001b[0mXt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 419\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msteps\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mpredict_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    420\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    421\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mif_delegate_has_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelegate\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'_final_estimator'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/neighbors/_classification.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m    195\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'csr'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    196\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 197\u001b[0;31m         \u001b[0mneigh_dist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mneigh_ind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkneighbors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    198\u001b[0m         \u001b[0mclasses_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    199\u001b[0m         \u001b[0m_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_y\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/neighbors/_base.py\u001b[0m in \u001b[0;36mkneighbors\u001b[0;34m(self, X, n_neighbors, return_distance)\u001b[0m\n\u001b[1;32m    703\u001b[0m                 \u001b[0mkwds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meffective_metric_params_\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    704\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 705\u001b[0;31m             chunked_results = list(pairwise_distances_chunked(\n\u001b[0m\u001b[1;32m    706\u001b[0m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit_X\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduce_func\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreduce_func\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    707\u001b[0m                 \u001b[0mmetric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meffective_metric_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/metrics/pairwise.py\u001b[0m in \u001b[0;36mpairwise_distances_chunked\u001b[0;34m(X, Y, reduce_func, metric, n_jobs, working_memory, **kwds)\u001b[0m\n\u001b[1;32m   1631\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mreduce_func\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1632\u001b[0m             \u001b[0mchunk_size\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mD_chunk\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1633\u001b[0;31m             \u001b[0mD_chunk\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreduce_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD_chunk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1634\u001b[0m             \u001b[0m_check_chunk_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD_chunk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunk_size\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1635\u001b[0m         \u001b[0;32myield\u001b[0m \u001b[0mD_chunk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/neighbors/_base.py\u001b[0m in \u001b[0;36m_kneighbors_reduce_func\u001b[0;34m(self, dist, start, n_neighbors, return_distance)\u001b[0m\n\u001b[1;32m    580\u001b[0m         \"\"\"\n\u001b[1;32m    581\u001b[0m         \u001b[0msample_range\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 582\u001b[0;31m         \u001b[0mneigh_ind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margpartition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_neighbors\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    583\u001b[0m         \u001b[0mneigh_ind\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mneigh_ind\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0mn_neighbors\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    584\u001b[0m         \u001b[0;31m# argpartition doesn't guarantee sorted order, so we sort again\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36margpartition\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36margpartition\u001b[0;34m(a, kth, axis, kind, order)\u001b[0m\n\u001b[1;32m    830\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    831\u001b[0m     \"\"\"\n\u001b[0;32m--> 832\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'argpartition'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkth\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    833\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    834\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/numpy/core/fromnumeric.py\u001b[0m in \u001b[0;36m_wrapfunc\u001b[0;34m(obj, method, *args, **kwds)\u001b[0m\n\u001b[1;32m     56\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mbound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     59\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0;31m# A TypeError occurs if the object does have such a method in its\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "knn = KNeighborsClassifier(n_neighbors = 1400)\n",
    "pipeline = make_pipeline(RUS, knn)\n",
    "cross_val_score(pipeline, X_tv, y_tv, cv=5, scoring='f1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Random forest is slow to train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adjust class weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(class_weight='balanced')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-72-30ac0842df60>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcross_val_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrfc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_tv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_tv\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'f1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_val_score\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, error_score)\u001b[0m\n\u001b[1;32m    438\u001b[0m     \u001b[0mscorer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_scoring\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mscoring\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    439\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 440\u001b[0;31m     cv_results = cross_validate(estimator=estimator, X=X, y=y, groups=groups,\n\u001b[0m\u001b[1;32m    441\u001b[0m                                 \u001b[0mscoring\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'score'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mscorer\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcv\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    442\u001b[0m                                 \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_jobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     62\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[0;34m<=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     64\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     65\u001b[0m             \u001b[0;31m# extra_args > 0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36mcross_validate\u001b[0;34m(estimator, X, y, groups, scoring, cv, n_jobs, verbose, fit_params, pre_dispatch, return_train_score, return_estimator, error_score)\u001b[0m\n\u001b[1;32m    244\u001b[0m     parallel = Parallel(n_jobs=n_jobs, verbose=verbose,\n\u001b[1;32m    245\u001b[0m                         pre_dispatch=pre_dispatch)\n\u001b[0;32m--> 246\u001b[0;31m     results = parallel(\n\u001b[0m\u001b[1;32m    247\u001b[0m         delayed(_fit_and_score)(\n\u001b[1;32m    248\u001b[0m             \u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscorers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1039\u001b[0m             \u001b[0;31m# remaining jobs.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1040\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1041\u001b[0;31m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1042\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1043\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    857\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    860\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    775\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    776\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 777\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    778\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/utils/fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/model_selection/_validation.py\u001b[0m in \u001b[0;36m_fit_and_score\u001b[0;34m(estimator, X, y, scorer, train, test, verbose, parameters, fit_params, return_train_score, return_parameters, return_n_test_samples, return_times, return_estimator, split_progress, candidate_progress, error_score)\u001b[0m\n\u001b[1;32m    591\u001b[0m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    592\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 593\u001b[0;31m             \u001b[0mestimator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfit_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    594\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    595\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    385\u001b[0m             \u001b[0;31m# parallel_backend contexts set at a higher level,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    386\u001b[0m             \u001b[0;31m# since correctness does not rely on using threads.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 387\u001b[0;31m             trees = Parallel(n_jobs=self.n_jobs, verbose=self.verbose,\n\u001b[0m\u001b[1;32m    388\u001b[0m                              \u001b[0;34m**\u001b[0m\u001b[0m_joblib_parallel_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprefer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'threads'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    389\u001b[0m                 delayed(_parallel_build_trees)(\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1042\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1043\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1044\u001b[0;31m             \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1045\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1046\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[0;34m(self, iterator)\u001b[0m\n\u001b[1;32m    857\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    860\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    775\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    776\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 777\u001b[0;31m             \u001b[0mjob\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    778\u001b[0m             \u001b[0;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    779\u001b[0m             \u001b[0;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[0;34m(self, func, callback)\u001b[0m\n\u001b[1;32m    206\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 208\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    209\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, batch)\u001b[0m\n\u001b[1;32m    570\u001b[0m         \u001b[0;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m         \u001b[0;31m# arguments in memory\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    260\u001b[0m         \u001b[0;31m# change the default number of processes to -1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m             return [func(*args, **kwargs)\n\u001b[0m\u001b[1;32m    263\u001b[0m                     for func, args, kwargs in self.items]\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/utils/fixes.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    220\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    221\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mconfig_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 222\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/ensemble/_forest.py\u001b[0m in \u001b[0;36m_parallel_build_trees\u001b[0;34m(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)\u001b[0m\n\u001b[1;32m    167\u001b[0m                                                         indices=indices)\n\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m         \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcurr_sample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m         \u001b[0mtree\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    896\u001b[0m         \"\"\"\n\u001b[1;32m    897\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 898\u001b[0;31m         super().fit(\n\u001b[0m\u001b[1;32m    899\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    900\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/envs/fdp/lib/python3.8/site-packages/sklearn/tree/_classes.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[1;32m    387\u001b[0m                                            min_impurity_split)\n\u001b[1;32m    388\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m         \u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mis_classifier\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "cross_val_score(rfc, X_tv, y_tv, cv = 5, scoring='f1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Oversampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc2 = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_val_score(rfc2, X_tv_rs, y_tv_rs, cv = 5, scoring='f1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*TO DO:*\n",
    "* *SVM & naive bayes?*\n",
    "* *grab smaller subset of data to optimize algorithms (namely, to find best hyperparameters for random forest and KNN) and test these additional models*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Comparison\n",
    "\n",
    "* Compare optimized models by using the ROC curve "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_val_score(final_rfc, X_val, y_val, cv=5, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_lr = LogisticRegression(class_weight='balanced', max_iter=200)\n",
    "\n",
    "cross_val_score(final_lr, X_val, y_val, cv=5, scoring='roc_auc')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Best model: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Performance\n",
    "\n",
    "* Assign variable 'best' to best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_pred = best.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "f1_score(y_test, test_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_matrix(y_test, test_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exporting Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_prob = best.predict_proba(X_test)[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = data2\n",
    "results['prediction'] = test_pred\n",
    "results['pred_prob_delay'] = pred_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.to_csv('results.csv', index = False)"
   ]
  }
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