Section 8.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up PySpark enviornment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark import SparkContext\n",
    "sc = SparkContext()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark import SQLContext\n",
    "sqlContext = SQLContext(sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import *\n",
    "from pyspark.sql.types import *    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the Data\n",
    "Now that the PySpark enviornment has been setup, the next step is to load in the data from Hadoop. Dataframes can be loaded using PySpark in a fashion that is similar to pandas. The variable `inferSchema` tells PySpark  to guess what the datatype of each column in the dataframe is. If the option is set to ```False``` then all columns will be loaded in as strings. We'll start with 2014 green taxi trips."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "green_2014 = sqlContext.read.csv(\"hdfs://namenode:8020/am2786/***/Taxi_2014_green.csv\", header=True,inferSchema=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "yellow_2014 = sqlContext.read.csv(\"hdfs://namenode:8020/am2786/***/Taxi_2014_yellow.csv\", header=True,inferSchema=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Selecting only trips where credit cards were used"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is only information on how much was tipped if the trip was payed using a credit card. That being said any other type of trips have to filtered out. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "green_2014_credit = green_2014.filter(green_2014.Payment_type == 1)  # Selecting only trips paid via credit card"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "yellow_2014_credit = yellow_2014.filter(yellow_2014.payment_type == 'CRD') # Selecting only trips paid via credit card"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Subsampling data for XGBoost model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "XGBoost models can take a while to run. To get around this we'll only look at a subsample of the datasets. For both datasets we'll choose a value of n = 100,000 trips. PySpark doesn't have a method for randomly sampling an *exact* number of rows and converting hundreds of millions of rows of data to a pandas dataframe is time-costly, so we'll take fractional subsamples using PySPark first, then convert those smaller dataframes to pandas dataframes  where the exact number of 100,000 rows can be selected.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "green_2014_subsample = green_2014_credit.sample(False, 0.1, seed=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "yellow_2014_subsample = yellow_2014_credit.sample(False, 0.01, seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "green_2014_subsample_use = green_2014_subsample.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "yellow_2014_subsample_use = yellow_2014_subsample.toPandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "green_2014_subsample_use = green_2014_subsample_use.sample(n = 100000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "yellow_2014_subsample_use = yellow_2014_subsample_use.sample(n = 100000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning data\n",
    "Now it's time to clean the data. If you've looked at any of the other jupyter notebooks for this project you'll notice that this is the same `taxi_data_cleaner` function that I've written for those, except this one is done in python format as opposed to PySpark. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def taxi_data_cleaner_pandas(data):\n",
    "    \n",
    "    data.columns = map(str.lower, data.columns)\n",
    "    \n",
    "    # Getting rid of rides with 0 passengers\n",
    "    data = data.loc[data.passenger_count > 0]\n",
    "    \n",
    "    # Getting rid of rides with impossibly low fares\n",
    "    data = data.loc[data.fare_amount > 1.]\n",
    "    \n",
    "    #Making a feature which measures the % a driver was tipped \n",
    "    data[\"tip_percentage\"] =  100.*(data[\"tip_amount\"])/(data[\"total_amount\"] - data[\"tip_amount\"])\n",
    "\n",
    "    \n",
    "    # Adding columns that give the month , day of the week and hour of pickups and dropoffs\n",
    "    \n",
    "    data['pickup_datetime'] = pd.to_datetime(data['pickup_datetime'])\n",
    "    data['dropoff_datetime'] = pd.to_datetime(data['dropoff_datetime'])\n",
    "    \n",
    "    data['pickup_hour'] = data['pickup_datetime'].dt.hour\n",
    "    data['dropoff_hour'] = data['dropoff_datetime'].dt.hour\n",
    "    data['pickup_month'] = data['pickup_datetime'].dt.month\n",
    "    data['dropoff_month'] = data['dropoff_datetime'].dt.month\n",
    "    data['pickup_dayofweek'] = data['pickup_datetime'].dt.dayofweek\n",
    "    data['dropoff_dayofweek'] = data['dropoff_datetime'].dt.dayofweek\n",
    "    \n",
    "\n",
    "    return(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can run the `taxi_data_cleaner` code and set our input and target variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "green_2014_subsample_use = taxi_data_cleaner_pandas(green_2014_subsample_use)\n",
    "yellow_2014_subsample_use =taxi_data_cleaner_pandas(yellow_2014_subsample_use)\n",
    "\n",
    "\n",
    "target_green = green_2014_subsample_use[\"tip_amount\"]\n",
    "target_yellow = yellow_2014_subsample_use[\"tip_amount\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['pickup_dayofweek', 'pickup_month', \n",
    "           'dropoff_dayofweek', 'dropoff_month', 'dropoff_hour', \n",
    "           'tolls_amount', 'pickup_neighborhood', 'dropoff_neighborhood', \n",
    "           'passenger_count'\n",
    "           ]\n",
    "\n",
    "\n",
    "green_2014_subsample_use= green_2014_subsample_use[columns]\n",
    "yellow_2014_subsample_use  = yellow_2014_subsample_use[columns]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mapping neighborhood names to integers for XGBoost model\n",
    "\n",
    "Now we get to the part where we need to finesse a little. The columns `pickup_neighborhood` and `dropoff_neighborhood` are in string format. These need to be converted to integers. One of the many great advantages of gradient boosting models is that since we're not fitting to any particular function, there's no need for one-hot encoding. We can simply map each neighbeorhood to an individual number. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "neighborhoods_list = set(list(yellow_2014_subsample_use.dropoff_neighborhood.unique()) +\n",
    "                         list(yellow_2014_subsample_use.pickup_neighborhood.unique())  + \n",
    "                          list(green_2014_subsample_use.pickup_neighborhood.unique())  +\n",
    "                          list(green_2014_subsample_use.dropoff_neighborhood.unique()))\n",
    "\n",
    "neighborhoods_number_list = np.arange(len(neighborhoods_list))\n",
    "neighborhood_mapper = dict(zip(neighborhoods_list,neighborhoods_number_list  ))\n",
    "\n",
    "yellow_2014_subsample_use_done = yellow_2014_subsample_use.replace({\"pickup_neighborhood\": neighborhood_mapper})\n",
    "yellow_2014_subsample_use_done = yellow_2014_subsample_use_done.replace({\"dropoff_neighborhood\": neighborhood_mapper})\n",
    "\n",
    "\n",
    "green_2014_subsample_use_done = green_2014_subsample_use.replace({\"pickup_neighborhood\": neighborhood_mapper})\n",
    "green_2014_subsample_use_done = green_2014_subsample_use_done.replace({\"dropoff_neighborhood\": neighborhood_mapper})\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we need to convert the target variable 'tip amount' from a continuous into a discretized form. To do this we create an array of evenly spaced tip values, then for each value in the target variable, we map it to the closest value from the evenly spaced tip value array. For example, a tip of `$1.75` gets mapped to `$2.00` if the tip value array is `python np.arange(0., 20., 2)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This function\n",
    "def feature_mapper(feature, feature_map_values):\n",
    "    \"\"\"\n",
    "    feature: feature in question\n",
    "    feature_map_values: values that the continuous values in 'feature' will be mapped to\"\n",
    "    \"\"\"\n",
    "    final = []\n",
    "    for i in feature:\n",
    "        i_use = feature_map_values[np.abs(feature_map_values  - i) == np.min(np.abs(feature_map_values  - i))][0]\n",
    "        final.append(i_use)\n",
    "    return(final)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_green_binned  = feature_mapper(np.array(target_green), np.arange(0, 12., 2))\n",
    "target_yellow_binned  = feature_mapper(np.array(target_yellow), np.arange(0, 12., 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# XGBoost modeling\n",
    "Great now we can start modelling!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/am2786/anaconda3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBRegressor\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.cross_validation import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn import model_selection\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_green = green_2014_subsample_use_done\n",
    "Y_green = target_green_binned \n",
    "X_train_green, X_test_green,Y_train_green, Y_test_green = train_test_split(X_green,Y_green,test_size = 0.3, random_state = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_yellow = yellow_2014_subsample_use_done\n",
    "Y_yellow = target_yellow_binned \n",
    "X_train_yellow, X_test_yellow,Y_train_yellow, Y_test_yellow = train_test_split(X_yellow,Y_yellow,test_size = 0.3, random_state = 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = XGBClassifier()\n",
    "model.fit(X_train_green, Y_train_green)  \n",
    "y_pred_green = model.predict(X_test_green) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the model completed a dataframe containing the feature importances of every feature can be produced for green taxis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances_green = pd.DataFrame(model.feature_importances_)\n",
    "feature_importances_green['feature'] = columns\n",
    "feature_importances_green['importance'] = feature_importances_green[0]\n",
    "feature_importances_green = feature_importances_green.drop(0, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The same can be done for yellow taxis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = XGBClassifier()\n",
    "model.fit(X_train_yellow, Y_train_yellow)  \n",
    "y_pred_yellow = model.predict(X_test_yellow)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importances_yellow = pd.DataFrame(model.feature_importances_)\n",
    "feature_importances_yellow['feature'] = columns\n",
    "feature_importances_yellow['importance'] = feature_importances_yellow[0]\n",
    "feature_importances_yellow = feature_importances_yellow.drop(0, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting feature importances\n",
    "Now let's plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAMuCAYAAAAALprUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xtczvf/+PFHJVHhModKIizSgWXO\nhizn9M2o2YSZbU3kkMMw580+zOYwFRpzzD6b48KcPowkneYUIYdos9gnLJJV1PX7w+96f7pcVylK\nc3neb7duXK/36/1+v16v9/t6P6/36/V6X5dRRkaGGiGEEEK88IzLuwBCCCGEKB0S1IUQQggDIUFd\nCCGEMBAS1IUQQggDIUFdCCGEMBAS1IUQQggDIUFdiDJy584dJk6ciKurKzVq1EClUnH27NnyLpYo\nwqFDh1CpVHz11VfPtJ2HDx+iUqnw9vYupZKVj9JqD/H8FCuoq1SqIv82bNhQ1uXU4uTkhJub23Pd\nZ2nRvNlr1KhR3kUpcykpKahUKkaNGlXeRSkX06ZNY8WKFbi4uDBu3DgmTZpErVq1nmsZNMeg4J+1\ntTUODg5069aNiRMnEhsbW2r7mzNnDiqVipiYmFLbpnh2muNS3L/yur7eu3cPOzs7VCoVH3/8cbmU\n4Z/E398flUrFH3/8Uex1KpRkB5MmTdKb7urqWpLNCPFS2Lt3L46Ojvz73/8u76JoXSQfPnzIX3/9\nxZkzZ/juu+9YsWIFXbt2ZdmyZc/9Q8c/TevWrYmPj6dmzZrPtJ0KFSoQHx+Publ5KZXs2XTs2BET\nExOttFOnTrFnzx6aNWtGr169tJZVr14dKL32KK4tW7aQmZmJkZERERERfPnll6hUqueyb0NRoqA+\nZcqUsiqHEAYlLy+P//73vzRt2rS8iwI8ukjre/9euXKFESNGsH//fnx8fNi3bx9mZmblUMJ/BnNz\ncxo3blwq2yqt7ZSGzp0707lzZ620devWsWfPHpo3b17otb0026M41qxZg4mJCSNGjCA4OJgffviB\n4cOHP7f9G4IyGVPPyspiwYIFdOjQgTp16mBra0v37t3ZunWrTt6cnBzCwsLw8fHBxcWF2rVrY29v\nT9++fTlw4IBWXs34TlpaGleuXNHqLtJ08T6py7dnz546Xd8Fx40SEhLw9fXF3t5ep9vj2rVrjB8/\nnubNm1O7dm0aNGjAu+++y8mTJ5+1ybTKffnyZQYNGoS9vT12dnb079+f8+fPA5Cens6oUaNo0qQJ\nVlZWvPnmmxw5ckRnewW7QcPDw3njjTeUbtdRo0bx3//+V285Ll68iL+/P46OjtSqVQtHR0eGDx/O\nlStXitzHDz/8wJtvvomtrS1ubm7MmTOHFi1aALB+/XqtY/Xjjz8CJTv2Gpqhl6ysLKZOnaqs16JF\nC4KDg1Gr9X/rcUJCAkOHDtWqV79+/YiIiNDJGx8fz+DBg2ncuDG1atXC2dmZoKAgbty4oXfbjyt4\njkVGRir1Lji+mp+fz8qVK3F3d8fW1pY6derw5ptvsnr1ap06FByfvX79OiNHjsTR0ZFXXnlFacun\n1aBBAzZv3kyjRo04deoUa9eu1clT3PPeycmJr7/+GoBevXop9X78/VaS60Nx3puac+Lu3btMmjQJ\nJycnrK2t6dixI7t27VLacP78+bRo0QIrKyvc3Nz47rvvitxfQZpj+uDBA7766ivc3NyoXbs2Li4u\nzJ49mwcPHmjlL2xMveB7ZuvWrXTp0gUbGxsaNGjAhx9+WOg59uuvv9K3b1/q1q1LvXr16Nu3L8eO\nHSvz4Y4ntUd2djafffYZrq6u1K5dGzc3N+bPn09ubm6J93X69GlOnDjBm2++yZgxYzA1NWXdunV6\n8xZs3z///JOAgAAcHByoU6cOPXv2VIaU7t27p1wnrKysaNeund73PEB2djYLFiygXbt2WFtbY2dn\nR69evfTmf9JcA31DxOvWrVOuf5GRkfTu3RtbW1vq1avHgAEDuHjxok79Nm7cCICzs3Oxh0ZKdKde\nHBkZGfTp04czZ87w2muv4efnh1qtZv/+/QwbNozk5GStT4U3b95kypQptGnThi5dulCzZk1u3LjB\n7t278fHxISQkBD8/PwDs7e2ZNGkSS5cuxcTERGvMpXnz5s9c9tjYWObPn0/79u0ZPHgw6enpmJqa\nAnDixAn69etHRkYGXbt25f/+7/+4efMmP//8M927d1eC2rO6evUqHh4eNG3aFD8/P1JTU9m5cyen\nTp1i37599O3bF5VKxVtvvcXt27fZunUrPj4+HDt2DFtbW53tLVmyhEOHDvHWW2/RrVs3jh49yvr1\n6zly5AgHDhzglVdeUfImJCTQr18/7t27R+/evWncuDEXLlzgxx9/ZPfu3URERPDaa6/p7GPx4sVE\nRkbSs2dPOnXqRFZWFh07duTu3bt8++23Ot17zs7OQMmOfUEPHjygb9++pKen061bN0xMTNi5cyfT\np08nJyeHCRMmaOVftWoVEyZMwNTUlF69etGwYUPS09M5fvw4q1at0rrwrl27lqCgICpXrkyvXr2o\nU6cOly5dYu3atezdu5f9+/dTp06dIo+hn58fHTt25KuvvqJ+/fq88847wKPzF0CtVvPBBx+wbds2\n7OzsGDx4MAA7d+4kKCiI2NhYwsLCdLZ769YtunXrRtWqVfHy8sLIyKhUusstLCwIDAwkKCiITZs2\n4e/vrywryXk/YsQIfv75Z2JiYvDz86Nu3boAGBv/796hpNcHjaLemwC5ubn07duXzMxMPD09ycnJ\nYcuWLQwePJiffvqJpUuXkpiYSNeuXTE1NeWnn35i/Pjx1KxZs0ST2YYNG0ZCQgIeHh5YWlqyb98+\nFi1axK1bt1iyZEmxtxMWFsaePXvo1asXb7zxBgkJCWzevJkzZ85w+PBhKlasqOSNiorCx8eHvLw8\nvL29qV+/PmfOnKF379507Nix2PssC0OGDOH06dN4eXlRoUIFfv75Z/71r39x8uRJvv/++xJta82a\nNQAMHDiQmjVr0rVrV3bv3k18fDytW7fWu85ff/1F9+7dUalU9O/fX7km9uvXj//85z8EBgaSmZlJ\nr169lHPi/fffZ//+/cpNBzy6wXjrrbeIiYnB0dGRjz76iPv37/PTTz/x3nvvMXHiRKZOnfrU7VTQ\nzz//zK5du+jWrRvDhg3j3Llz7N27lxMnThAXF0f16tUxNjZm0qRJ7Nixg7NnzzJixAiqVKkC/G9o\npFAZGRnqJ/0BakA9adIknb/Q0FCtvG+//bYaUM+ZM0cr/fr162p3d3e1kZGROjo6Wkm/ceOG+uzZ\nszr7vHr1qrpx48bqGjVqqP/880+tZXXq1FE3aNBAb1mPHz+uBtSDBw/Wu7xt27ZqExMTrbSffvpJ\nqeOSJUt01klPT1fXr19fXalSJfXu3bu1liUlJamtrKzUderUUf/3v/99YlvevHlTDeiUQVNuQD1r\n1iytZZ988okaUKtUKvWHH36ovn37trIsJCREDahHjRqltc6ECRPUgNrMzEwdFRWlteyjjz5SA+qh\nQ4cqabdv31Y3atRIDahXrVqllf/bb79VA+qmTZuq//rrL519WFhY6OyjOMfiaY89oO7Ro4f6+vXr\nSnpycrK6SpUqapVKpb5165aSfuTIEbWJiYm6evXq6ri4OJ19nTlzRvl/XFyc2tTUVP3qq6+qz58/\nr5Vv69atamNjY7W3t/cTj3HB49y5c2edZcuXL1cDajc3N/Uff/yhpF+7dk3drFkzNaBevXq1zrYA\n9cCBA7Xq96Q/zTEo7P2i+UtISFAD6goVKijn19Oc95pz4vH8mr+SXh+e9N4seE707t1b63zZvn27\n8r5p2bKlOjU1VVl27NgxdYUKFdRubm56rwVTp07VSm/btq0aULdo0UJ99epVrWNWv359tYmJifrS\npUtPPP6a9qlatao6JiZGSf/rr7/Uffv2VQPqdevWKem3bt1S169fXw2ot23bprWt+fPnK21TWHsX\n9bdkyZIi35/FaQ8HBwet9rh+/bq6RYsWakC9cuXKYpclLS1NXbVqVbVKpVKO4fr165VzvrD3F1Do\nNVGlUhV6Tjz+Pv7000/VgLpnz57qmzdvKunJyclqW1tbtZGRkXr//v1PbJeC5+Tj7zlNe1eoUEG9\nY8cOrWWjRo3S+77QvF+SkpKK3ZYl6n7/8ssvdf4KfhpLT09n8+bNtGrVisDAQK11K1euzKxZs1Cr\n1WzZskVJr1Spkt47H5VKhZ+fH7du3SqV7u3icHNzY8iQITrpu3btIjU1lYCAANq1a6e1zNbWlsDA\nQNLS0vR2g5dUgwYNGD16tFbawIEDgUfjtLNnz9a68xkwYADGxsacPn1a7/beffddnYmMU6dOpUqV\nKmzcuFHpNjx69CiXL1+mXbt29OvXTyv/22+/TatWrTh37hzx8fE6+xg2bNhTTZZ8lmM/f/58Kleu\nrLy2srKiZ8+eZGRkcPnyZSV91apV5OXlMXnyZJo0aaKzHc3dJMDKlSt58OABX375JdbW1lr53nzz\nTbp168bPP/9MVlZWietaUHh4OACzZs3CwsJCSbe0tGTmzJkAersdK1WqxJw5c3QmPJUGGxsb4FG3\n3507d4DSP++f5vqgUdh7s6B58+ZpzQfo1KkTdevWJSMjg9mzZ1OtWjVlWaNGjWjVqhVJSUnk5+cX\nq/wAs2fP1pq4ZWlpqdxFl+Q6FRAQoDXfwsjIiPfeew+A48ePK+lHjx4lNTWVLl260KVLF61tfPDB\nBzRo0KDY+ywLkyZN0mqPypUrM2PGDOB/53lxbNmyhbt37+Lj46McQ00X/08//aSck4+ztLQs9JqY\nkZHBl19+qXNO2Nra6lwvw8PDMTY25osvvqBChf91YFtZWTFhwgTUajXr168vdn2K4uvrq9PDMnTo\nUED72D+tEnW/Z2RkFLn82LFj5Ofno1armTt3rs5yzThLcnKyVnpSUhJLliwhNjaWGzdukJOTo7X8\n+vXrJSnmU3v99df1pickJACQmpqqt16XLl0CHtXrWbvgmzVrpnWCAkqAefXVV7WCAICpqSk1a9Yk\nLS1N7/Y6dOigk6ZSqXB2diY2NpaLFy/i5OTEqVOngEcnvT6dOnUiISGBxMRE2rRpo7WssHYrjqc5\n9q+88gr169fXSdcMPxQ8TzXHrlu3bk8siyZvVFSU3g8vt2/f5uHDh6SkpDzTEx+JiYmYmJjoPTZv\nvPEGRkZGJCYm6iyzt7fXGi4pTQXH8Y2MjIDSP++f9voATz7HatSoQb169XTSra2tuXbtmt7hORsb\nG3Jzc7l58ya1a9d+YvkBveOZ+s670tqO5jxo27atTn4TExNatWqld77L86LvHG7fvj3GxsZ6z+HC\naD7EFhxuMzU1xcfHh7CwMDZt2sSHH36os56Dg0Oh18S8vDzs7Ox01rGxseHMmTPK64yMDH777Tfs\n7Oxo1KiRTn7NNbEk9SlKaZ1DhSnVMfXbt28DjyZ1/Prrr4XmK3inExsbS9++fcnPz6dz58707t0b\nS0tLjI2NlUcuHr/Ql5XC3tiaeumbyFPQs97BAVStWlUnTfPJUd8yzfLHJ+poFFYnTfrdu3e1/n38\nDlXDysoKQO8n5uJeEB/3tMe+4B1XQZp2ysvLU9I05dXciRZFc5wXL15cZL5nPc6ZmZnUrl1b645A\nw8zMjOrVq5dqOxeHZoKWqamp0r6lfd4/zfVB40l1L+q9YWJigqWlpd5lQKHvncc9aTsFz7sn0XcO\n69uO5n35pPdxedE3p6NixYqoVCql7E+SlJREQkICTZs21Ql4fn5+hIWFsWbNGr1Bvajj/niwL7is\n4DHXvNee5tr3NIp77J9WqQZ1TQOPHj2azz77rFjrfPXVV2RnZ7N7926dLr758+ezZ8+eEpVBc5f7\n8OFDvcuLOjCaO5THaeq1ceNGunfvXqLylLfCZrlr0jV10/z7559/6s2vSdf3Jiqs3Z6ktI+9Ppo3\n0PXr12nYsGGReTV1S0tLK9Pni6tUqcKtW7fIy8vT6UrPzc3lr7/+0jsZ5mnbuTiioqIAaNGihbKf\n0j7vn+b6oFGWdf8n00yOetL7uLykp6frfGDOzc0lIyOj2M+XaybInTt3rtB1zpw5w7Fjx56pV7Aw\nmmtESa59mvOxsDhz9+7dcvvOh1J9pK1ly5YYGRmV6PGKlJQUatWqpXNRB4iOjta7jrGxcaGfaDQn\nhb5v4Llz5w4pKSnFLptGq1atAF7Ib8nS14YZGRkkJSVhbm6Og4MD8KjbHyh0fFSTXpKnDDQfsAo7\nVk9z7EtKc+z+85//FDtvWR9nV1dXHj58qHc/R44cQa1Wl8rTHMWVlZVFaGgo8Gi8T+Np2qOoY/40\n14eXneZ9qe9b//Ly8pQhkvKi73169OhR8vPzlbIXJTs7m40bN2JiYsLgwYP1/mmGdvQ9blkaVCoV\ndnZ2XLt2jatXr+os13zgLfieLCrOXLhwgXv37pVK2Z50DdW7Tqns+f+ztramf//+JCQksHDhQr0F\nSUlJ4bffflNe16tXj5s3b3Lu3DmtfKtXryYyMlLvfl555RVu3rypt2tWpVLRsGFDjh49qvPc35Qp\nU56qK79Pnz7Uq1ePsLCwQp+fjouLIzs7u8TbLmv//ve/dSaFfPHFF2RmZuLr66s8FtShQwcaNmzI\nkSNH2Llzp1b+LVu2EBcXR5MmTQp9tEQfzd3mtWvX9C5/mmNfUh988AEmJibMmzePCxcu6CwvOBfB\n39+fChUqMHnyZL0f/nJzc0slIA0aNAh4NOnq77//VtKzsrKUO1jNY25l7cqVK/j6+nL58mWdyWhP\nc95rxvz1HfOnuT687Dp06EC9evU4ePAgBw8e1Fr23Xfflet4OjzqUSs4Dvz3338r57C+x1Eft23b\nNu7cuUO3bt0IDg7W+7dq1SoqV67M1q1byczMLJN6DBo0iPz8fKZPn641cTI9PV357gXN+xbA0dER\nS0tLdu7cya1bt5T0+/fvM3ny5FIrV1Hvp8KU+nPqCxYsICUlhc8++4zvv/+etm3bKs8fJycnc+LE\nCdauXatMahkxYgSRkZH06NGDvn37UqVKFY4fP058fDz/93//x/bt23X20blzZxITE+nfvz/t2rWj\nYsWKNGvWjB49egCPuvfGjh1Lt27d6Nu3L6amphw5coT8/HycnJz0TsQpipmZGevXr8fHx4f+/fvT\ntm1bXF1dqVSpEteuXeP48eOkpqZy+fJlKlWq9OyNWIrefPNNpW2trKw4evQocXFx2NvbK7NU4dEn\nwmXLltGvXz+GDBmCp6cnDg4OXLhwgV27dlG1alWWL19eom7QatWq4ebmRlRUFP7+/jRq1AhjY2M8\nPT1xcnJ6qmNfUs7OzsyfP5+JEyfSsWNHevfuTYMGDbh9+zbHjx+nevXqypdLNG3alCVLljBmzBja\ntGlD165dadSoETk5OVy7do2YmBhsbGyeObC/88477N69m+3bt9O2bVs8PT1Rq9Xs3LmT33//HV9f\nX50nEJ7VX3/9pUxOy8vLIyMjg9OnT5OQkEB+fj7du3dn6dKlWs9HP81536lTJ4yMjJg1axZnzpyh\nWrVqGBsbM378eKDk14eXnYmJCcHBwfj6+uLr64u3tzf29vacOXOGQ4cO0bVrV/bv368zufZ5lc3e\n3p727dtrPad+9epVevfujY+PzxO3obn7LurJBpVKhZeXFxs3bmTz5s28//77pVYHjbFjx3LgwAF2\n7NjBG2+8QdeuXZXn1G/evMn48eOVnit49N7w9/dn4cKFdOzYEU9PTx4+fMiBAwews7MrtbkOnTt3\nZunSpYwaNQovLy8sLCyoXr263vkFGqUe1KtVq8bu3btZvXo1W7ZsYfv27eTk5FCrVi0aNWrE3Llz\ntWZY9+jRg++//54FCxawdetWTExMeP3119m5cycXL17Ue2GfNGkSmZmZ7N27l5iYGPLy8hg8eLAS\n1DWPByxbtozvv/8elUqFp6cnM2bM4N13332qejVv3pzo6GhCQ0PZu3ev8giElZUVr732GlOnTv1H\nfkfx6NGj6d27N8uXL+fy5ctYWloyaNAgZsyYofNNX23atOGXX37h66+/5vDhw+zevZsaNWrg4+PD\npEmT9M4MfZIVK1YwdepU9u3bx507d1Cr1dSrVw8nJ6enOvZP44MPPsDZ2Zng4GCioqLYuXMnNWrU\nwMXFRXmMSGPgwIG4uroSGhqqfEGPubk5NjY29OvXj7feeuuZy2NkZMTq1av57rvv2LBhA6tXrwag\nSZMmjB07tkwuWprHe+DRBalKlSrY29vzwQcf4OPjo/NEg0ZJz3snJydCQ0MJDQ1l5cqVZGdnY2Ji\nogT1kl4fxKML+86dO/niiy/Ys2cPxsbGvP766+zatUt5bEwz9v68rVu3ji+//JLNmzfz559/YmNj\nw5QpUwgKCnriDUBycjKxsbFYW1sr1+7CvPfee2zcuJE1a9aUyfvDzMyMiIgIQkJC2LJlC2FhYZia\nmuLq6sr8+fP1fsieNm0a5ubmrF+/njVr1mBlZYWvry+ffPJJqY399+jRg88++4z169ezdOlScnNz\nlW8fLIxRRkaG/u/VFC+0OXPm8PXXX+udhCaEMAweHh6cOnWKa9euPddewp49e5KQkKDV9Sz+GeT3\n1IUQ4h/s/v37ep/aWbduHceOHaNr167/uGE/UX5KvftdCCFE6dH8HoS7uzsNGjTgwYMHJCYmEhcX\nh0qlYs6cOeVdRPEPIkFdCCH+wTRPDURHRxMVFUVOTg5WVlYMGjSICRMmKD8UJATImLoQQghhMGRM\nXQghhDAQEtSFEEIIAyFBXQghhDAQEtSFEEIIAyFBXQghhDAQEtSFEEIIAyFBXQghhDAQEtSFEEII\nAyFBXQghhDAQEtSFEEIIAyFBXQghhDAQEtSFEEIIAyFBXQghhDAQEtSFEEIIAyFBXQghhDAQEtSF\nEEIIAyFBXQghhDAQEtSFEEIIAyFBXQghhDAQEtSFEEIIAyFBXQhR6latWoVKpWLLli1a6Y0bN6ZV\nq1blVKoX08SJE1GpVJw9e7a8iyJeABLUhXiJqNVq+vfvj0qlYuXKlYXmS0xMpHbt2jRt2pTbt28/\nxxKWj+zsbFQqVYn+Hv/AIsQ/QYXyLoAQ4vkxMjJi6dKltG/fnunTp9OxY0eaNGmilSc7Oxt/f38e\nPHjAsmXLeOWVV8qptM9PhQoVmDRpkk76+vXrSUtLY/DgwdSpU0drWdOmTZ9L2SZOnMhHH32Evb39\nc9mfeLFJUBfiJWNlZUVwcDADBw7kww8/5MCBA1SsWFFZPn36dM6fP8/IkSNxd3cvv4I+RxUqVGDK\nlCk66QcOHCAtLY0hQ4aU27BB7dq1qV27drnsW7x4pPtdiJdQ7969ef/99zl9+jSff/65kn7gwAFW\nrlyJi4sLM2bM0FonLS2NiRMn4ubmhpWVFfb29vj4+BAdHf3M5cnOzmbBggW0b98eGxsb7Ozs6N69\nO99//71Wvvz8fBo2bIiLi4vONpo3b45KpWLmzJla6ceOHUOlUjF27NhnLqc+sbGxBAUF0bZtW+rV\nq4e1tTWtW7fms88+4969e1p5//zzTxo3bkydOnVITk7WWpaXl0evXr1QqVT8+OOPSnphY+r79u2j\nX79+ODo6Urt2bRo3bkzXrl3517/+VSb1FC8GCepCvKS++OILHBwcCAkJITIyktu3bzNy5EjMzMxY\nuXIlZmZmSt6TJ0/SsWNHvvvuOxwcHPD396dXr17Ex8fj5eXFxo0bn7oc2dnZeHt78/nnn5OXl8cH\nH3zAO++8Q2pqKiNGjNAKxsbGxnTs2JFr165x+fJlJT01NZXU1FQADh06pLX9yMhIADp37vzUZSzK\n0qVL2bdvH87Ozrz33nsMGTIECwsLFi5ciJeXFzk5OUpeKysrvv32W7Kzs3n//ff5+++/lWVffPEF\nMTEx+Pn5MWDAgCL3+cMPP/D2229z6tQpPDw8CAwMpHfv3lSsWJEVK1aUST3Fi0G634V4SZmbm7Ni\nxQq6devGiBEjcHZ25saNG8yfPx9HR0clX25uLu+99x73799nz549tG7dWln2xx9/0KVLF8aNG0e3\nbt2oXr16icuxcOFC4uLi6NGjBxs2bKBChUeXpU8//ZRu3bqxZs0aevToQa9evYBHwTkiIoLIyEga\nNWoE/C9wd+nShcjISP766y+lLJGRkRgZGdGxY8ena6gn+Oyzz6hXrx7Gxtr3SAsWLODzzz/nxx9/\nZMiQIUq6u7s748aN4+uvv+aTTz4hODiYX375hUWLFuHo6MhXX331xH2uXbsWY2NjDh48SL169bSW\n3bp1q3QqJl5IcqcuxEvstddeY+rUqfzxxx/s27eP7t274+/vr5Vn165dpKamEhAQoBXQAWxtbRk5\nciT37t1jx44dT1WGDRs2YGxszL/+9S8loANUr16dadOmAY+CmIbmjrvgHXlkZCQqlYrRo0eTn5/P\n4cOHAcjJySEuLg5nZ2dq1qz5VOV7Ent7e52ADhAQEICJiQm//PKLzrIpU6bQrl071q9fT3BwMB9/\n/DGVKlVi9erVmJubP3GfxsbGGBsba/WmaNSoUePpKiIMgtypC/GSGz16NMuWLePPP//UGl/XiI2N\nBeDq1avMnTtXZ/mFCxcAuHjxYon3ffPmTf744w/s7OyUu+6CNBP1EhMTlbRGjRpRt25doqKiyM/P\nx8jIiMOHD9OxY0fat29P5cqVOXToEN7e3sTGxpKdnV2mE/6ys7P59ttviYiI4OLFi2RmZqJWq5Xl\n169f11nHxMSE7777jo4dOzJ9+nQAlixZUuwZ9QMGDCA6Opr27dvTr18/OnToQOvWrXVm6IuXjwR1\nIV5yxsbGyuz3SpUq6SzXPKe+devWIreTlZVV4n3fvXsXeDTWrI9KpcLMzEzJp9G5c2c2bNhAYmIi\npqampKen4+7ujpmZGW3atFG64zV37GU1np6fn0///v2Jjo6mcePGeHt7U6tWLUxNTYFHQwsFx9QL\nqlOnDi1btmTv3r3UrFmTt99+u9j7HTJkCNWqVSMsLIzVq1cr4+ivvfYa06dPx8PD49krJ15IEtSF\nEEWqVq0aABs3bqR79+6luu310hASAAAgAElEQVSqVasCj2aF65ORkUFOTo5Ol7ImqB86dEgJoJq7\n8c6dOzN79mx+++03ZXm7du1Ktdwav/zyC9HR0Xh7e7NmzRqMjIyUZZmZmXz55ZeFrrtmzRr27t1L\njRo1uHnzJlOmTGHRokXF3re3tzfe3t7cu3ePY8eOsWfPHlavXs0777zDL7/8gqur6zPVTbyYZExd\nCFEkzfPZMTExpb7tmjVrYmtryx9//EFKSorOcs0dd/PmzbXSNXfekZGRHD58mLp16yrd95rgvmPH\nDk6ePEnLli2xtLQs9bIDSpm9vLy0AjpQ5KN+SUlJTJ48GRsbG6Kjo2ndujWrV69m27ZtJS6DpaUl\nnTt3Zu7cuUycOJEHDx6wb9++Em9HGAYJ6kKIInl7e2NnZ0dYWBj79+/Xm+fEiRNkZGQ81fYHDRpE\nfn4+06ZNIy8vT0nPyMjgiy++ANCaPQ6PuuubNm1KbGwsR48e1Roz1zyvvnjxYvLy8sqs6x1QZp5H\nRUVppV+7dk2Z5Pe4rKws3n//fXJzc/n222+xtrbmu+++Q6VSMWbMGK5cufLE/R44cEBvt76mx0Pf\nBDrxcpDudyFEkczMzAgPD8fHxwcfHx/atGmDq6srlStXJi0tjZMnT3L58mXi4+NRqVQl3n5QUBAH\nDx5k165ddOjQgW7dupGdnc2OHTu4ceMGQ4cOVR5nK6hTp06cO3cOQCuoa55l18zGL8ug3qVLF5yc\nnFi7di0pKSm0aNGCtLQ09u7dS7t27UhLS9NZZ/z48Vy4cIHJkycrj9nZ2dmxdOlSBg4cyLBhw9i7\nd6/Wt/w9btSoUfz999906NABOzs7KlSowMmTJ4mKiqJOnTq88847ZVZn8c8md+pCiCdq3rw5R48e\nJSgoiLt377Jhwwa+++47jh8/TpMmTQgJCaF+/fpPte1KlSoRERHBtGnTMDIyYsWKFXz//ffY2dkR\nGhrK4sWL9a6nCdZGRkY6gVvz2sLCgpYtWz5VuYrDzMyMrVu3MnDgQFJSUli+fDknT55k1KhRrF+/\nXqdLPjw8nB9++IEOHTrwySefaC3r3bs3AQEBnDhxQufb/B736aef0qlTJ5KSkli3bh1r1qzh5s2b\njB8/nsjIyDJ7fE/88xllZGSon5xNCCGEEP90cqcuhBBCGAgJ6kIIIYSBkKAuhBBCGAgJ6kIIIYSB\nkKAuhBBCGAgJ6kIIIYSBkKAuxAviaX4F7UUm9TVsL1t9nxcJ6kIIIYSBkKAuhBBCGAgJ6kIIIYSB\nkKAuhBBCGAgJ6kIIIYSBkKAuhBBCGAj5lTbxwqt2veS/4S2EMGx3bDLKuwjlQu7UhRBCCAMhQV0I\nIYQwEGUe1AcMGEBAQEBZ76ZYYmNjad++PbVq1cLT07PQtLIWEBDAgAEDSrSOq6srwcHBReZRqVRE\nREQ8S9Geaf9lxdbWlg0bNpTLvoUQ4kVSobwL8DxNnjwZFxcXNm7ciIWFRaFpZW3evHmo1TKVQQgh\nROkq1+733Nzc57q/lJQUOnXqRN26dalevXqhaWWtWrVqqFQvxuSu532MhBBCPL1SDer3798nICAA\nW1tbHBwcWLBggdZyV1dX5s6dy8iRI6lXrx4fffQRAElJSXh7e2NtbY29vT0BAQHcuXNHWU/TXf3V\nV1/h4OCAra0tI0aM4O+//1by5OTkMHnyZBwcHLCysqJr167ExMQAkJqaikql4u7duwQGBqJSqdiw\nYYPetKJERUWhUqmIjIzEw8MDGxsb3N3dOXnypFa+uLg4evfujY2NDU2bNmXcuHHcvXtXpz4aWVlZ\nfPzxx0q7LVy4UO+wRXZ2NmPHjsXOzg4nJyeWLFmiU8Y///yTt99+GxsbG1xcXPjxxx+1lhe3rRcv\nXoyTkxNOTk7F3v/vv/+On58fdevWpW7dugwaNIg//vhDK8/q1atxc3OjVq1auLm5sXbtWq3lKSkp\neHp6YmVlRcuWLdmzZ4/eYyGEEEJXqQb16dOnc+jQIdatW0dERASJiYkcPXpUK8/SpUtp3Lgxhw4d\nYsaMGdy/fx8fHx8sLCw4cOAA4eHhxMfHExgYqLVedHQ0Z86cISIignXr1nHw4EFmzpypLJ8xYwbb\ntm0jJCSEw4cP4+TkhI+PDzdu3KBu3bokJydjbm7O3LlzSU5Opm/fvjpp/fr1K1Y9Z8+ezcyZM4mM\njOSVV17B399f6U5PSkqiX79+9OrViyNHjrB+/XpOnz6tU5+Cpk2bRnR0NOHh4Wzfvp0zZ84oH0ge\nbzsnJyciIyMZM2YMM2bMID4+XivP3Llz6dWrF1FRUQwdOpThw4dz4sQJgBK1dVJSEps3b9Yaoy9q\n/2q1Gj8/P9LT09m+fTs7duzgxo0b+Pn5KW2zY8cOJk6cSEBAADExMQwfPpzx48eze/duAPLz8xk0\naBD5+fns27ePkJAQ5s2bR05OTrGOixBCvOxKbUz93r17rF+/npCQEDw8PAAIDQ3VutMDaN++PWPG\njFFer127lqysLMLCwqhSpQoAixcvxsvLi5SUFBo2bAiAsbExoaGhWFpa4uTkxKxZsxg1apQS2Fet\nWsWSJUvo0aMHAIsWLeLw4cOsXLmSadOmYWVlhZGREVWrVsXKygoACwsLnbTimDp1Kp06dQLgk08+\noWfPnqSlpWFra8uSJUt46623GDVqlJJ/wYIFdOrUifT0dGrVqqXTbuHh4SxfvpwuXboAEBwcrNNu\nAG+++Sb+/v4AfPzxx4SFhREZGUnr1q2VPF5eXrz//vsATJgwgaioKJYtW8a3337Lpk2bitXWZmZm\nhISEYGZmVuz9Hzp0iDNnznDixAnq168PwMqVK3FzcyMyMhJ3d3dCQkIYMGCAso1XX32VkydP8s03\n39CrVy8OHTrE+fPnOXXqFHZ2dsD/PqQIIURJlPSnXR0cHMqoJM9XqQX1K1eukJubqxVgLC0tcXZ2\n1srn5uam9To5ORlnZ2clyAC0adMGY2Njzp8/rwQaZ2dnLC0tlTytW7cmNzeXK1euAPDgwQPatm2r\nLDcxMaF169acP3++tKqoKFgna2trANLT07G1teXUqVOkpKSwbds2JY/mTvXKlSs6Qf3KlSs8ePCA\n119/XUmzsLDQG9Qfb0tra2vS09O10lq1aqXzet++fUDx27pp06Y6Af1J+09OTsbGxkYJ6AD29vbY\n2Nhw/vx53N3dSU5Oxs/PT2sb7dq1U+7Uk5OTqVOnjhLQAVq2bImxsTx5KYQoGUMJ0iVVakG9uLO5\nH59hXtR6RkZGJdq3vvzF3UZJmJqa6mxfU4b8/HyGDBnCiBEjdNazsbHRSSvJLPiC+9XsuyTrF7et\nC3sKoKj9q9XqQtu6YHpRx0ieCBBCiGdTardADRs2xNTUlISEBCUtKyuLs2fPFrmeo6MjSUlJZGZm\nKmlxcXHk5+fTpEkTJe3s2bNkZWUprxMSEqhYsSINGjSgYcOGVKxYUWscOi8vj/j4eK1tPA/Nmzfn\n3LlzNGzYUOevcuXKOvk17Xb8+HEl7f79+09st8L8+uuvOq81bVDctn4ajo6OpKWlkZqaqqRdvXqV\n69ev4+joCECTJk2IjY3VWi8mJkZZrtnGtWvXlOXHjh0jPz//mcomhBAvi1IL6paWlgwePJhZs2Zx\n8OBBzp07R2Bg4BMvyL6+vpibmzN8+HCSkpKIjo4mKCgILy8vpTsYHgXpwMBAzp07x8GDB5k9ezbv\nvfceFhYWWFhYMGzYMGbPns2+fftITk5m3LhxpKen8+GHH5ZWFYtlzJgxHD9+nKCgIKUrfs+ePYwd\nO1ZvfktLSwYNGqRMvDt//jyjR48u8s63KDt27GDt2rVcvnyZhQsXEhkZqcyiL25bPw13d3dcXFzw\n9/fn5MmTnDhxgo8++ojmzZsr8w9GjRrFjz/+yIoVK7h8+TJhYWFs2rSJ0aNHK9to3Lgxw4cPJzEx\nkfj4eD799FMqVHipvk5BCCGeWqleLT///HOysrIYNGgQlStXxt/fn/v37xe5jrm5OVu2bGHKlCl4\neHhgZmZG7969mTdvnla+Dh064OjoiJeXF3///TdeXl7Mnj1bWa75/8iRI7lz5w7NmjVj8+bNypj3\n8+Li4sKuXbuYM2cOffr0IS8vD3t7+yK/rU7Tbu+++y4WFhaMGDGC//73v1SqVKnE+588eTLbt29n\n0qRJ1KxZk9DQUFq0aAEUv62fhpGRERs2bGDSpEn06dMHgM6dOzN//nzlw0mfPn2YP38+wcHBTJky\nBTs7OxYsWKBMhDM2NiY8PJzRo0fTtWtX6taty5w5c5RHH4UQQhTthfiVtoCAAG7fvq3zzLWhysnJ\nwdXVlVGjRmnNohf6ya+0CSEe97L+Spv0a/4DnDp1igsXLvD666+TmZnJN998w71794r93LwQQggB\nEtS1BAUFsXHjRr3L3n77bRYtWlRm+w4NDeXSpUuYmJjg6urKrl27sLW1LbP9CSGEMDwvRPf785Ke\nnq41M7ygKlWq6DxjLsTzdPHixZfq2Vupr2F72er7vMidegG1atWSwC2EEOKFJV/VJYQQQhgICepC\nCCGEgZCgLoQQQhgICepCCCGEgZCgLoQQQhgICepCCCGEgZCgLoQQQhgICepCCCGEgZCgLoQQQhgI\nCepCCCGEgZCgLoQQQhgICepCCCGEgZCgLoQQQhgICepCCCGEgZCgLoQQQhgI+T118cKrdl1V3kV4\nLlpaAtfLuxTPjyHU945NRnkXQbxk5E5dCCGEMBAS1IUQQggDUW5B3dPTk4kTJxYrb1RUFCqVilu3\nbpVxqcpeQEAAAwYMKNE6rq6uBAcHF5lHpVIRERHxLEV7pv2XFVtbWzZs2FAu+xZCiBdNuY2ph4eH\nU6HCyzekP2/ePNRqdXkXQwghhAEqt6havXr18tp1uapWrVp5F6HYcnNzqVixYnkXQwghRDGVWfe7\np6cnQUFBTJo0ifr161O/fn2mT59Ofn6+srxg93tubi6fffYZLi4u1K5dm+bNm7N8+XK9287JycHP\nz49OnTqRnp5OamoqKpWKEydOaOUr2CWtybNp0yZ69uyJlZUVrVq14pdffilWfTRDAJGRkXh4eGBj\nY4O7uzsnT57UyhcXF0fv3r2xsbGhadOmjBs3jrt37yrLH+9+z8rK4uOPP8bW1hYHBwcWLlzIgAED\nCAgI0NpudnY2Y8eOxc7ODicnJ5YsWaJTxj///JO3334bGxsbXFxc+PHHH7WWJyUl4e3tjbW1Nfb2\n9gQEBHDnzh2dsi1evBgnJyecnJyKvf/ff/8dPz8/6tatS926dRk0aBB//PGHVp7Vq1fj5uZGrVq1\ncHNzY+3atVrLU1JS8PT0xMrKipYtW7Jnzx69x0IIIYR+ZTqmvmnTJvLz8/nPf/7D4sWLWbt2LUuX\nLtWbNyAggB9++IEvvviC+Ph4goOD9d7V3r17l/79+/PXX3+xc+dOatWqVaIyzZw5k48//pioqCjc\n3d0ZOHAgaWlpxV5/9uzZzJw5k8jISF555RX8/f2V7vSkpCT69etHr169OHLkCOvXr+f06dMEBgYW\nur1p06YRHR1NeHg427dv58yZM8TExOjkW7p0KU5OTkRGRjJmzBhmzJhBfHy8Vp65c+fSq1cvoqKi\nGDp0KMOHD1c+6Ny/fx8fHx8sLCw4cOAA4eHhxMfH65QtOjqapKQkNm/erDVGX9T+1Wo1fn5+pKen\ns337dnbs2MGNGzfw8/NT2mbHjh1MnDiRgIAAYmJiGD58OOPHj2f37t0A5OfnM2jQIPLz89m3bx8h\nISHMmzePnJycYh8bIYR42ZVp97uVlRXz58/HyMiIxo0bc+nSJZYuXaoTSC5fvsyWLVvYvHkzXbt2\nBcDe3l5nezdv3sTf3x8bGxvWrFlDpUqVSlymYcOG8dZbbwHw5Zdf8ssvv7Bq1SqmTZtWrPWnTp1K\np06dAPjkk0/o2bMnaWlp2NrasmTJEt566y1GjRql5F+wYIHSo/D4B5B79+4RHh7O8uXL6dKlCwDB\nwcFad8gab775Jv7+/gB8/PHHhIWFERkZSevWrZU8Xl5evP/++wBMmDCBqKgoli1bxrfffsumTZvI\nysoiLCyMKlWqALB48WK8vLxISUmhYcOGAJiZmRESEoKZmVmx93/o0CHOnDnDiRMnqF+/PgArV67E\nzc2NyMhI3N3dCQkJYcCAAco2Xn31VU6ePMk333xDr169OHToEOfPn+fUqVPY2dkB//uQIsSL6uLF\ni2Wa/0X3T6qvg4NDeRehVJRpUG/ZsiVGRkbK69atW/PFF19odUcDJCYmYmxsTMeOHYvcXr9+/WjW\nrBnr169/6kl2rVq1Uv5vbGzM66+/zvnz54u9vrOzs/J/a2trANLT07G1teXUqVOkpKSwbds2JY/m\nTvXKlSs6Qf3KlSs8ePCA119/XUmzsLDQG9QL7lez7/T09ELrpnm9b98+AJKTk3F2dlYCOkCbNm0w\nNjbm/PnzSlBv2rSpTkB/0v6Tk5OxsbFRAjo8+lBmY2PD+fPncXd3Jzk5GT8/P61ttGvXTrlTT05O\npk6dOkpAh0fnj7GxPHUpXlwlCRQXL140mMBSHC9bfZ+Xf8T08+LOBu/Rowfbtm0jKSmJ5s2bK+ma\nC3/B7Tx48KB0C/n/mZqaKv/XfGDR7Dc/P58hQ4YwYsQInfVsbGx00koyC77gfjX7Lsn6ReUt+MHL\nwsKixPtXq9Va2yhs2/ryPN6GQgghnl6Z3gYdO3ZM62KdkJCAjY0NVatW1crXvHlz8vPziYqKKnJ7\nU6dOZdiwYfTt25fExEQlvWbNmgDcuHFDSTt9+rTebfz666/K/9VqNcePH6dJkybFr1QRmjdvzrlz\n52jYsKHOX+XKlXXyN2zYEFNTU44fP66k3b9/n7Nnzz7V/gvWTfNaUzdHR0eSkpLIzMxUlsfFxZGf\nn//M9Xd0dCQtLY3U1FQl7erVq1y/fh1HR0cAmjRpQmxsrNZ6MTExynLNNq5du6YsP3bsmDKxUggh\nxJOVaVC/ceMGkydP5uLFi0RERLBkyRK9d7GNGjXirbfeYvTo0URERHD16lWOHj3KDz/8oJN3+vTp\nvP/++/Tt21cJ3JUrV6ZVq1Z88803nDt3jri4uELHyFetWkVERAQXL15k8uTJ/P777wwbNqxU6jtm\nzBiOHz9OUFCQ0hW/Z88exo4dqze/paUlgwYNUibenT9/ntGjRxd551uUHTt2sHbtWi5fvszChQuJ\njIxUZtH7+vpibm7O8OHDSUpKIjo6mqCgILy8vJSu96fl7u6Oi4sL/v7+nDx5khMnTvDRRx/RvHlz\nZf7BqFGj+PHHH1mxYgWXL18mLCyMTZs2MXr0aGUbjRs3Zvjw4SQmJhIfH8+nn376Un6XgRBCPK0y\nDeq+vr7k5+fj4eHB6NGjGTx4sN6gDrB8+XJ8fHyYPHkyrVu3ZsSIETpj7xozZszgvffew9vbWwns\nISEhwKMJXUFBQYUG9ZkzZxIaGsobb7yhzAK3tbUthdqCi4sLu3bt4rfffqNPnz688cYbfPbZZ0XO\n0P/8889p164d7777Ll5eXjg7O/Paa6891STAyZMns337djp06MCqVasIDQ2lRYsWAJibm7NlyxYy\nMzPx8PBg4MCBtGrVSmm3Z2FkZMSGDRuoUaMGffr0wcvLi9q1a7Nhwwblw0mfPn2YP38+S5cupU2b\nNixfvpwFCxYoE+GMjY0JDw8nPz+frl27Mnz4cCZMmKB3fF8IIYR+RhkZGWUymOnp6YmTkxNfffVV\nWWy+xFJTU2nevDkHDx7Ezc2tvItTqJycHFxdXRk1apTWLHpRuJflV9rEi6ckv9L2sk0ce9nq+7xI\n32Y5O3XqFBcuXOD1118nMzOTb775hnv37tGvX7/yLpoQQogXjAT1/y8oKIiNGzfqXfb222+zaNGi\nMtt3aGgoly5dwsTEBFdXV3bt2lVqQwJCCCFeHmXW/f6iSU9P15oZXlCVKlVK/M11QpS2l627Uupr\n2F62+j4vcqf+/9WqVUsCtxBCiBeafF2XEEIIYSAkqAshhBAGQoK6EEIIYSAkqAshhBAGQoK6EEII\nYSAkqAshhBAGQoK6EEIIYSAkqAshhBAGQoK6EEIIYSAkqAshhBAGQoK6EEIIYSAkqAshhBAGQoK6\nEEIIYSAkqAshhBAGQoK6EEIIYSDk99TFC6/adVV5F+G5aGkJXNdOu2OTUS5lEUL8M8mduhBCCGEg\nJKgLIYQQBkKCejkLCAhgwIABhb4WQgghikuCeiny9PRk4sSJ5V0MgyHtKYQQJSNBXQghhDAQEtRL\nSUBAANHR0axYsQKVSoVKpSI1NZXo6Gg8PDywsrLCwcGBKVOmkJubW+ztRkdH07VrV2xtbalXrx4e\nHh6cPXv2ievdvn2bDz74ACcnJ6ytrWnbti3h4eFaeTw9PRk3bhxTp07F3t6eRo0asWzZMnJycpgw\nYQL16tXDxcWFH374QWu9pKQkvL29sba2xt7enoCAAO7cuaPVFo8PIcydO5d27drp5Fm2bBlNmzal\nfv36jBgxgvv37xfZnkIIIQonQb2UzJs3j9atW+Pn50dycjLJycmYmpri6+tLs2bNOHz4MMHBwWzZ\nsoXZs2cXa5sPHz5k4MCBtG3bliNHjrB//36GDx+OiYnJE9fNzs6mefPm/PDDD8TGxjJ8+HCCgoKI\njIzUyrdp0yYsLS05cOAAY8eOZcqUKfj5+dGoUSMOHTrEO++8w+jRo7l+/dGzVPfv38fHxwcLCwsO\nHDhAeHg48fHxBAYGlrjNYmJiOHfuHD/99BOrV69m586dLF++HNDfnnXr1i3xPoQQ4mUiz6mXkmrV\nqmFqaoq5uTlWVlYAfP7551hZWbFgwQKMjY1p0qQJM2fOJCgoiKlTp2Jubl7kNjMzM7lz5w49e/ak\nQYMGADRu3LhY5alTpw6jR49WXg8dOpTDhw+zefNmOnfurKQ7OjoyZcoUAAIDA1m8eDEVKlQgICAA\ngEmTJvHNN98QHx+Pt7c3mzZtIisri7CwMKpUqQLA4sWL8fLyIiUlhYYNGxazxaBKlSosXLiQChUq\n0KRJE/r27UtkZCTjxo3T255C18WLF8u7CGXK0Ov3OKlv+XFwcCjvIpQKCeplKDk5mVatWmFs/L8O\nkXbt2pGbm0tKSgouLi5Frl+9enUGDhxI//796dy5M506daJv377FumPNy8tj0aJFbN26levXr5Ob\nm0tubi5vvPGGVj5nZ2fl/0ZGRtSqVUsrzdTUFJVKRXp6ulInZ2dnJaADtGnTBmNjY86fP1+ioN6k\nSRMqVPjfKWhtbc2vv/5a7PWF4VyI9Ll48aJB1+9xUl9RGqT7vQyp1WqMjIz0Liss/XFLly5l//79\ntG/fnt27d9OyZUsOHDjwxPWCg4MJCQlh9OjRREREEBUVhaenp854vqmpqU65CgZaTVp+fr5Sp8Jo\n6mRsbKyT7+HDhzr59e27qO0LIYQomgT1UlSxYkXy8vKU146OjiQkJCgBER6NI1esWFHpTi8OV1dX\nxo4dy88//8wbb7zBv//97yeuExMTQ8+ePXnnnXdo1qwZDRo04NKlSyWrkB6Ojo4kJSWRmZmppMXF\nxZGfn0+TJk0AqFmzJjdu3NBa7/Tp0yXe1+PtKYQQomgS1EtRvXr1OHbsGKmpqdy6dYsPPviAGzdu\nMH78eJKTk9m7dy+zZ8/mo48+euJ4OsDVq1eZNWsWcXFx/Pbbbxw+fJikpCQleBbl1Vdf5fDhw8TE\nxHDhwgUmTpzIb7/99sx19PX1xdzcnOHDh5OUlER0dDRBQUF4eXkpXe+dOnUiMTGR9evXk5KSwjff\nfENsbGyJ9/V4exb8cCSEEEKXBPVSNGrUKCpWrEjbtm1p1KgRDx48YNOmTSQmJtKxY0cCAwPp378/\nM2bMKNb2zM3NuXTpEkOHDqVly5aMGDECX19fxo4d+8R1J06cSIsWLfD19aV3796Ym5vj6+v7rFXE\n3NycLVu2kJmZiYeHBwMHDqRVq1aEhIQoeTw8PJg0aRJz5szB3d2d3377jQ8//LDE+3q8PX///fdn\nLr8QQhgyo4yMDBnEFC+0l+VX2vQx5F9pe9kmUkl9RWmQO3UhhBDCQMgjbS8oHx8fYmJi9C4bN24c\n48ePf84lKj+GfLdakNzZCCGeRIL6C2rJkiVkZ2frXVa9evXnXBohhBD/BBLUX1B16tQp7yIIIYT4\nh5ExdSGEEMJASFAXQgghDIQEdSGEEMJASFAXQgghDIQEdSGEEMJASFAXQgghDIQEdSGEEMJASFAX\nQgghDIQEdSGEEMJASFAXQgghDIQEdSGEEMJASFAXQgghDIQEdSGEEMJASFAXQgghDIT89Kp44VW7\nrirvIpSJOzYZ5V0EIcQLRu7UhRBCCAMhQV0IIYQwEBLUhRBCCAMhQf05GDBgAAEBAeVdDABiY2Np\n3749tWrVwtPTs9C0wkRFRaFSqbh169bzKK4QQogSkIlyL5nJkyfj4uLCxo0bsbCwKDRNCCHEi0fu\n1MtZbm7uc91fSkoKnTp1om7dulSvXr3QtH+i591WQgjxopGgXsru379PQEAAtra2ODg4sGDBAq3l\nrq6uzJ07l5EjR1KvXj0++ugjAJKSkvD29sba2hp7e3sCAgK4c+eOsl5AQAADBgzgq6++wsHBAVtb\nW0aMGMHff/+t5MnJyWHy5Mk4ODhgZWVF165diYmJASA1NRWVSsXdu3cJDAxEpVKxYcMGvWnFcebM\nGTw8PLCxscHd3Z2TJ09qLd++fTvt27endu3aODs78/XXX6NWq7XaITg4WGsdT09PJk6c+MS2EkII\noZ8E9VI2ffp0Dh06xLp164iIiCAxMZGjR49q5Vm6dCmNGzfm0KFDzJgxg/v37+Pj44OFhQUHDhwg\nPDyc+Ph4AgMDtdaLjo7mzJkzREREsG7dOg4ePMjMmTOV5TNmzGDbtm2EhIRw+PBhnJyc8PHx4caN\nG9StW5fk5GTMzc2ZO2EOQjQAACAASURBVHcuycnJ9O3bVyetX79+xarn7NmzmTlzJpGRkbzyyiv4\n+/srQfvkyZMMHTqUPn36cPToUWbOnMmiRYv49ttvS9yej7eVEEKIwsmYeim6d+8e69evJyQkBA8P\nDwBCQ0NxcnLSyte+fXvGjBmjvF67di1ZWVmEhYVRpUoVABYvXoyXlxcpKSk0bNgQAGNjY0JDQ7G0\ntMTJyYlZs2YxatQoJbCvWrWKJUuW0KNHDwAWLVrE4cOHWblyJdOmTcPKygojIyOqVq2KlZUVABYW\nFjppxTF16lQ6deoEwCeffELPnj1JS0vD1taW0NBQOnTowKeffgrAq6++yuXLl/nmm2/4+OOPS9Sm\nj7fVy+TixYvFSjNkUl/D9k+qr4ODQ3kXoVRIUC9FV65cITc3l9atWytplpaWODs7a+Vzc3PTep2c\nnIyzs7MS0AHatGmDsbEx58+fV4K6s7MzlpaWSp7WrVuTm5vLlStXAHjw4AFt27ZVlpuYmNC6dWvO\nnz9fepX8/wrWydraGoD09HRsbW1JTk6me/fuWvnbtWvHl19+yd27d6latWqx9/N4W71MHr/IXLx4\n0WAuPMUh9TVsL1t9nxfpfi9FBceMi/L4DPOi1jMyMirRvvXlL+42SsLU1FRn+5oyqNXqQvepSTc2\nNtap98OHD3Xyy2x8IYQoPgnqpahhw4aYmpqSkJCgpGVlZXH27Nki13N0dCQpKYnMzEwlLS4ujvz8\nfJo0aaKknT17lqysLOV1QkICFStWpEGDBjRs2JCKFSsqE+MA8vLyiI+P19rG8+Do6EhsbKxWWkxM\nDLa2tkpvRM2aNblx44ayPDs7mwsXLjzXcgohhKGRoF6KLC0tGTx4MLNmzeLgwYOcO3eOwMBA8vPz\ni1zP19cXc3Nzhg8fTlJSEtHR0QQFBeHl5aV0vcOjIB0YGMi5c+c4ePAgs2fP5r333sPCwgILCwuG\nDRvG7Nmz2bdvH8nJyYwbN4709HQ+/PDDsq66lpEjRxIdHc3cuXO5dOkSGzduJDQ0lNGjRyt5OnXq\nxKZNm4iKilLaSd+duhBCiOKTMfVS9vnnn5OVlcWgQYOoXLky/v7+3L9/v8h1zM3N2bJlC1OmTMHD\nwwMzMzN69+7NvHnztPJ16NABR0dHvLy8+Pvvv/Hy8mL27NnKcs3/R44cyZ07d2jWrBmbN29Wxryf\nl9dee401a9Ywb948Fi5cSO3atRk7diz+/v5KnqCgIH777Tf8/PywsLBg/PjxXL9+/bmWUwghDI1R\nRkZG8QaCRbkKCAjg9u3b/Pjj/2PvzuOqqP7Hj79AxAW068LuxqaGCq64EZhYhsRXMHG3khLFrahQ\nTAU3QsUyBbdMcsFyN8ylNEUFBffUXFBT0dxDEbdEvfz+8Md8uoEIxjq8n4+Hjwf3nDNn3u8Z9X1n\nzlzuiuIOpcQpK1+9WtYeLJJ81a2s5VtU5Pa7EEIIoRJy+13oCAwMZOXKlTn29ejRgxkzZhRxRC/2\n7ytaIYQoq6SolxJz584tkv18/vnnDB8+PMe+f36OXgghRMkjRV3oMDExwcTEpLjDEEII8RJkTV0I\nIYRQCSnqQgghhEpIURdCCCFUQoq6EEIIoRJS1IUQQgiVkKIuhBBCqIQUdSGEEEIlpKgLIYQQKiFF\nXQghhFAJKepCCCGESkhRF0IIIVRCiroQQgihElLUhRBCCJWQoi6EEEKohHz1qij1Xrmqyfc2dyzS\nCiESIYQoXnKlLoQQQqiEFHUhhBBCJaSoFxBPT0+CgoLyNDY+Ph6NRkNqamohR1W65OcYCiGEyE7W\n1AtITEwMBgZyOPMiPj4eLy8v/vjjD2rUqFHc4QghhGpIFSog1apVK+4QhBBClHFy+z2PPD09CQwM\nZNSoUdStW5e6desybtw4tFqt0v/PW8cZGRlMnDiRxo0bY2pqipOTE/Pmzctx7kePHtG3b19cXV25\nefMmKSkpaDQaDh8+rDNOo9EQGxsLoIxZtWoVb731FmZmZrRq1Yrt27fnKZ+sJYCtW7fi5uaGubk5\nHh4eXL58mYSEBNq3b4+VlRU9e/bk1q1bynZarZZp06bRqFEjTE1NadeuHRs3blT6s+KKjY3F29sb\nCwsLWrduTVxcnNLv5eUFgK2tLRqNhoCAAJ35J06ciI2NDXZ2dowdO1Y5xkIIIXInRT0fVq1ahVar\nZevWrXz99dcsXryYOXPm5Dg2ICCA5cuXExYWxr59+4iMjOSVV17JNi49PZ133nmH27dvs2HDBkxM\nTPIVU2hoKIMGDSI+Pp4OHTrQp08frly5kuftw8PDCQ8P59dffyUtLQ0/Pz+mTZvGzJkz2bBhAydP\nniQ8PFwZP3fuXCIjIxk/fjx79uzB09OT/v37c/ToUZ15J0+ezKBBg0hISKBZs2b4+flx7949atWq\nxZIlSwBISkoiOTmZKVOmKNutWrWKcuXKsWXLFiIiIpg7dy5r167N1zERQoiySop6PpiZmTFt2jTq\n16+Pj48Pw4cPz7Go//HHH6xZs4ZZs2bRtWtX6tWrh6urK71799YZ99dff+Hl5YWxsTFr166latWq\n+Y7Jz88PHx8f6tevz9SpU7GysiI6OjrP248ZM4Z27drRuHFjBgwYwN69e5k4cSItW7akWbNm9O7d\nm4SEBGV8VFQUw4YNw9fXFzs7O8aMGUPbtm2JiorSmXfIkCF4eHhga2tLSEgIt2/f5tixY5QrV05Z\nqjAxMcHMzEznzU6DBg0YM2YMdnZ2+Pj48Nprr7Fz5858HxchhCiLZE09H1q2bImenp7y2tnZmbCw\nMNLT03XGHT16FH19fV577bVc5+vWrRuOjo4sXbr0pR+ya9WqlfKzvr4+LVq04NSpU3nevlGjRsrP\npqamObbdvHkTeHZX4erVq7Rp00ZnjrZt27Jly5bnzmthYQGgzJPXeADMzc3ztF1+nTlzpsDnLAql\nNe6XJfmqW0nK197evrhDKBBS1AtBZmZmnsZ17tyZdevWcfz4cZycnJR2fX39bPM8fvy4YIP8/8qX\nL6/8nPWG5d9teVnT/uebnefNm5fj8s/tsrbN6/HMj9L4D/jMmTOlMu6XJfmqW1nLt6jI7fd8OHjw\noE6B2b9/PxYWFtlumzs5OaHVaomPj891vjFjxuDn54e3t7fOmnTNmjUBuHbtmtJ27NixHOc4cOCA\n8nNmZiaHDh2iQYMGeU8qH6pWrYqFhQVJSUk67YmJifnap6GhIQBPnz4t0PiEEKKskyv1fLh27RrB\nwcF8+OGHnDhxglmzZuX4y1JsbW3x8fFhxIgRhIeH4+TkxJUrV7h48SK9evXSGTtu3DgyMzPx9vYm\nNjaWJk2aUKlSJVq1asXMmTOxtrYmPT2dCRMm5BhTdHQ0dnZ2ODg48O2333Lp0iX8/PwKJX+A4cOH\nEx4ejq2tLU2bNmXFihUkJiayY8eOPM9Ru3Zt9PT0+OWXX/Dw8KBixYoYGxsXWsxCCFFWSFHPB19f\nX7RaLe7u7ujp6dG/f3+GDBmS49h58+YRFhZGcHAwqampWFpaPndsSEgImZmZdO3aVSnsUVFRjBgx\ngo4dO2Jtbc306dPp0qVLtm1DQ0OZPXs2R44coXbt2sTExGBlZVWgef/T4MGDuXfvHqGhody4cQN7\ne3uWLFmCo6NjnuewtLRk9OjRTJ48mREjRtCrVy/mzp1baDELIURZoZeWllbwC5Yq5OnpiYODAxER\nEcUdCvDs895OTk7ExcXRrFmz4g6nWJWVb2kra2uQkq+6lbV8i4qsqQshhBAqIUVdpQIDA7Gyssrx\nT2BgYHGHJ4QQohDI7XeVunnzJnfv3s2xr0qVKvn+zXWi+JW125WSr7qVtXyLijwop1ImJiZSuIUQ\nooyR2+9CCCGESkhRF0IIIVRCiroQQgihElLUhRBCCJWQoi6EEEKohBR1IYQQQiWkqAshhBAqIUVd\nCCGEUAkp6kIIIYRKSFEXQgghVEKKuhBCCKESUtSFEEIIlZCiLoQQQqiEFHUhhBBCJaSoCyGEECoh\n36cuSr1XrmryNf6ORVohRSKEEMVLrtSFEEIIlZCiLoQQQqiEqoq6p6cnQUFBeRobHx+PRqMhNTW1\nkKPKn8OHD6PRaEhJSSnS/V6/fh0fHx8sLS3RaPJ3O7uglNRzIoQQpYWq1tRjYmIwMFBVSkUmMjKS\na9euER8fj7GxcXGHI4QQ4iWoqgJWq1atuEMotc6dO4eTkxO2trbFHYoQQoiXVKpuv3t6ehIYGMio\nUaOoW7cudevWZdy4cWi1WqX/n7ffMzIymDhxIo0bN8bU1BQnJyfmzZuX49yPHj2ib9++uLq6cvPm\nTVJSUtBoNBw+fFhnnEajITY2FkAZs2rVKt566y3MzMxo1aoV27dvz3NOv/76K61atcLMzAwPDw/O\nnj2r03/r1i0++OADHBwcMDc3p02bNsTExCj9P/zwA9bW1jx69Ehnu4EDB9KrVy/l9XfffUezZs0w\nMTGhWbNmLF68WOlr0qQJmzZtYvny5Wg0GgICAggNDaV79+7KmMWLF6PRaFi7dq3S1rlzZ6ZPn668\n3rx5M25ubpiZmeHo6MikSZPIyMhQ+jMyMggNDcXBwQFLS0tef/11tm3b9txj8+9zIoQQInelqqgD\nrFq1Cq1Wy9atW/n6669ZvHgxc+bMyXFsQEAAy5cvJywsjH379hEZGckrr7ySbVx6ejrvvPMOt2/f\nZsOGDZiYmOQrptDQUAYNGkR8fDwdOnSgT58+XLly5YXb/fnnn/Tt25cOHToQHx+Pv78/oaGhOmP+\n/vtvnJycWL58OUlJSQwePJjAwEB27twJgLe3N1qtlk2bNinb3Llzhw0bNtC/f38AfvrpJ4KCgggI\nCCAxMZHBgwfz6aefsnnzZgDi4uLo0KEDPj4+JCcnM2XKFFxcXEhKSuLJkycAJCQkUKNGDeLj4wF4\n8OABhw4dwsXFBYBt27bh7+/PwIEDSUpKIioqitjYWCZOnKjENXToUHbv3s2CBQvYs2cPvXv3plev\nXhw7dizbsfmv50QIIcqiUnf73czMjGnTpqGnp0f9+vU5e/Ysc+bMYdiwYTrj/vjjD9asWcPq1avp\n1KkTAPXq1cs2319//YW/vz8WFhYsWrSIihUr5jsmPz8/fHx8AJg6dSrbt28nOjqasWPH5rpddHQ0\ntWrVypZPWFiYMsbS0pIRI0Yor99//3127drF6tWrcXNzo1KlSvTo0YOYmBglhtWrV1OlShU6d+4M\nQFRUFD179sTf3x8AOzs7fvvtN2bOnImHhwc1a9akQoUKVKxYETMzMwDatm3L33//zaFDh3B2dmb3\n7t0MGzaMZcuWAbB3717Kly9PixYtAJg+fTrDhw+nX79+AFhbWzN+/HgGDRrEpEmTuHDhAqtXr+bo\n0aPUrl0bAH9/f3bs2MGiRYv48ssvC/Sc5ObMmTMFOl9RKs2xvwzJV91KUr729vbFHUKBKHVFvWXL\nlujp6SmvnZ2dCQsLIz09XWfc0aNH0dfX57XXXst1vm7duuHo6MjSpUtf+iG7Vq1aKT/r6+vTokUL\nTp069cLtkpOTc8znn54+fcqMGTNYu3YtV69eJSMjg4yMDOUKGeDdd9/Fzc2Ny5cvY2VlRUxMDL17\n91bySU5Opm/fvjrztm3bVrlSz4mxsTFOTk7KFfrdu3cZOHAgU6dO5erVqyQkJODs7Ez58uUBOHLk\nCIcOHWLmzJnKHFqtlocPH3L9+nWOHDlCZmYmbdq00dnPo0ePcHV11WkriHOSm9L6j/fMmTOlNvaX\nIfmqW1nLt6iUuqKeV5mZmXka17lzZ9atW8fx48dxcnJS2vX19bPN8/jx4yKPMTIykqioKKZMmYKD\ngwPGxsZMnDhRZ425SZMmODk58f333+Pp6cnhw4f55ptvdOb55xuH3Nr+ycXFhfj4eGrUqEHbtm0x\nNjamefPmJCQkkJCQwBtvvKGM1Wq1jBo1Cm9v72zz1KxZE61Wi56eHtu3b1feCGT595X4886JEEKI\n3JW6on7w4EEyMzOVgrR//34sLCyoWrWqzjgnJye0Wi3x8fHK7fecjBkzhmrVquHt7U1sbCyOjo7A\ns0IEcO3aNWVsTmu/AAcOHMDNzQ14VqgPHTpE165dX5hLw4YNWb9+fbZ8/ikxMZG33npLeegtMzOT\ns2fPZns24L333mPmzJmkpqbSpk0bnXfADRo0ICkpSVljz5q3YcOGucbn4uLCggULeOWVV5Q7Ay4u\nLmzZsoVDhw4xYcIEZayTkxOnT5/GxsYmx7kcHR3JzMzk+vXr2a7M/+1550QIIUTuSt2DcteuXSM4\nOJgzZ84QGxvLrFmzGDJkSLZxtra2+Pj4MGLECGJjY7lw4QJ79uxh+fLl2caOGzeOAQMG4O3trRTu\nSpUq0apVK2bOnMnJkyfZu3fvc9fIo6OjiY2N5cyZMwQHB3Pp0iX8/PxemMuAAQO4ePGiTj7fffed\nzhg7Ozt27dpFYmIip0+fJigoiIsXL2ab65133uHGjRtER0cr69pZhg8fzooVK1iwYAF//PEH8+fP\nZ9WqVTpr9Tlp27YtGRkZ/PTTT8oyhouLC2vXrtVZTwcYOXIkq1evJiwsjBMnTnD69GliY2MJCQlR\n8ujRowdDhgxRzsfhw4eJjIxk/fr12fad0zkRQgiRu1JX1H19fdFqtbi7uzNixAj69++fY1EHmDdv\nHt27dyc4OBhnZ2eGDBmSbe09S0hICO+99x5du3ZVikhUVBQAHTt2JDAw8LlFPTQ0lNmzZ+Pi4sK2\nbduIiYnBysrqhbnUrl2bpUuXsm3bNlxcXJgzZ062p9+DgoJo3rw5vr6+dOnShcqVK+Pr65ttripV\nquDt7Y2hoaHywFyWt99+m2nTpjFnzhxat27NvHnz+PLLL/Hw8Mg1PmNjY5o2bYqRkZFytezs7IyB\ngYHOejqAu7s7K1euJCEhAXd3d9zd3ZkxYwa1atVSxsyePZu+ffsSEhJCq1at6NmzJ7t376ZOnTo5\n7j+ncyKEEOL59NLS0vK2+FwCeHp64uDgQERERHGHAjz7nLqTkxNxcXE0a9asuMOhe/fuWFpaMmvW\nrOIOpUiVlW9pK2sPFkm+6lbW8i0qpW5NXWR3+/ZtduzYwfbt20lISCjucIQQQhQTKeqFKDAwkJUr\nV+bY16NHD2bMmFEg+3F1dSUtLY2QkBAcHBwKZE4hhBClT6m6/V7a3Lx5k7t37+bYV6VKFfktaSJf\nytrtSslX3cpavkVFrtQLkYmJiRRuIYQQRabUPf0uhBBCiJxJURdCCCFUQoq6EEIIoRJS1IUQQgiV\nkKIuhBBCqIQUdSGEEEIlpKgLIYQQKiFFXQghhFAJKepCCCGESkhRF0IIIVRCiroQQgihElLUhRBC\nCJWQoi6EEEKohBR1IYQQQiWkqAshhBAqId+nLkq9V65qcmy/Y5FWxJEIIUTxkit1IYQQQiWkqAsh\nhBAqIUVdCCGEUAkp6qLE8vT0JCgoqLjDEEKIUkOKehnz5MkTMjMzizsMIYQQhUC1Rd3T05PAwEBG\njRpF3bp1qVu3LuPGjUOr1QKwYsUKXn/9dWrVqoWdnR3vvfceV65cUbZ//PgxI0eOpGHDhpiamtKo\nUSPGjx+v9K9fv5527dphbm5OvXr16NKlCzdu3FD6N2/ejJubG2ZmZjg6OjJp0iQyMjKU/iZNmhAR\nEcHHH39M7dq1cXBwYNasWTo5nD17li5dumBmZkbLli3ZsmULVlZWLFu2TBlz5coV/Pz8lBx79OjB\nH3/8ofSHh4fTtm1bli1bRtOmTTE1NeX+/fu5HrvMzEwiIyNp3rw5pqamODg4MGHCBKX/+PHjdO3a\nVck9ICCAO3fuKP0BAQH07NlTZ86sOP49Zu7cubz66qvUrVuXIUOG8ODBA6V/9+7dLFiwAI1Gg0aj\nISUlJde4hRCirFNtUQdYtWoVWq2WrVu38vXXX7N48WLmzJkDQEZGBqNHjyYhIYEVK1aQmprKBx98\noGw7b948Nm7cyMKFCzl48CDR0dHY2dkBcP36dT744AN69+7N3r172bRpE7169VK23bZtG/7+/gwc\nOJCkpCSioqKIjY1l4sSJOvHNmTMHBwcHdu7cyUcffURISAj79u0DQKvV0q9fPwwMDNi6dStz5sxh\n6tSpPHr0SNn+wYMHeHl5UaFCBTZu3MjWrVsxMzOja9euSnEESElJYfXq1SxatIiEhAQqVqyY63Gb\nOHEiERERBAYGkpSUxKJFi7CyslL22b17d4yMjNi2bRsxMTHs27ePYcOG5fv8JCYmcvLkSX788Ue+\n++47NmzYwLx58wCYMmUKzs7O9O3bl+TkZJKTk6lVq1a+9yGEEGWJqj+nbmZmxrRp09DT06N+/fqc\nPXuWOXPmMGzYMPr376+Mq1evHl999RXOzs5cvnwZKysrLl26hK2tLe3atUNPT4/atWvTunVrAK5e\nvcrjx4/p2rUrderUAcDBwUGZb/r06QwfPpx+/foBYG1tzfjx4xk0aBCTJk1CT08PgI4dO+Lv7w/A\noEGDmD9/Pjt37sTZ2Zm4uDjOnDnD2rVrsbS0BOCLL76gc+fOyn7WrFlDZmYmc+bMUeb8+uuvsbOz\n45dffsHHxwd49gZm/vz5mJqavvCY3bt3jzlz5hAeHq4cIxsbG5ydnYFnb5Tu37/P/PnzqVKlirJP\nLy8vzp07h42NTZ7PT5UqVfjqq68wMDCgQYMGeHt7s3PnTj755BNeeeUVypcvT+XKlTEzM8vznP90\n5syZl9quJFNjTrmRfNWtJOVrb29f3CEUCFUX9ZYtWyrFDsDZ2ZmwsDDS09M5d+4cU6dO5dixY6Sl\npSnrzH/++SdWVlb06dMHHx8fWrRoQceOHXnjjTd444030NfXp0mTJnTo0IF27drx+uuv06FDB7p2\n7UrNmjUBOHLkCIcOHWLmzJnKvrVaLQ8fPuT69euYm5sD0KhRI514zc3NuXnzJgCnT5/GwsJCKegA\nzZs3R1//fzdXjhw5QkpKSrYr2AcPHnD+/HnltaWlZZ4KOkBycjKPHj3Czc3tuf2NGjVSCjpA69at\n0dfX59SpU/kq6g0aNMDA4H9/Bc3NzTlw4ECet38RtfwjzXLmzBnV5ZQbyVfdylq+RUXVRf15MjMz\neeedd+jQoQPz58/HxMSE1NRUPDw8lHXvpk2bcvToUbZt28auXbsICAigcePG/Pjjj5QrV45169ax\nf/9+tm/fztKlS5kwYQIbN26kSZMmaLVaRo0ahbe3d7Z9ZxV+gPLly+v06enpKW8u8vIwm1arpUmT\nJkRHR2frq1atmvKzkZFR3g5MHvabW3/WGyh9ff1s4548eZJtfG75CyGEyD9VF/WDBw+SmZmpFJv9\n+/djYWHB+fPnSU1NZdy4cdSrVw949uDbv1WpUgVvb2+8vb3p06cPnTp14ty5c9jZ2aGnp4ezszPO\nzs6MGjWKNm3asG7dOpo0aYKTkxOnT5/O11XrvzVo0ICrV69y9epVLCwsADh8+LDyoB+Ak5MTq1ev\npnr16mg0Of+q1JfZb4UKFdi5cye2trbZ+hs2bMiyZcu4e/eucrW+d+9etFotDRo0AJ69cTl27JjO\ndv9+nReGhoY8ffr0JbIQQoiySdUPyl27do3g4GDOnDlDbGwss2bNYsiQIdSqVYsKFSqwYMECLly4\nwC+//MIXX3yhs21UVBSrV68mOTmZc+fOsWrVKqpWrYqlpSX79+8nIiKCQ4cOcenSJTZt2sTly5eV\nojZy5EhWr15NWFgYJ06c4PTp08TGxhISEpLn2F9//XXs7e0JCAjg2LFj7N+/nzFjxmBgYKC8SfH1\n9cXU1JQ+ffqQkJDAhQsX2L17N2PGjNF5Aj4/qlSpwuDBg5kwYQIxMTGcP3+egwcPsnDhQmWflStX\nZvDgwRw/fpzdu3cTGBiIl5eX8ibG1dWVo0ePsnTpUs6dO8fMmTNJSkrKdyx16tTh4MGDpKSkkJqa\nqvOGRgghRHaqLuq+vr5otVrc3d0ZMWIE/fv3Z8iQIdSsWZO5c+eyceNGWrduzdSpUwkLC9PZtkqV\nKsyaNQt3d3fc3Nw4duwYq1atonLlylStWpW9e/fSs2dPWrRowdixYwkKClI+xuXu7s7KlStJSEjA\n3d0dd3d3ZsyYka+nt/X19YmJieHRo0e4u7sTEBDAp59+ip6envL0euXKldm0aRP16tXj/fffx9nZ\nmYCAANLS0v7TlXtoaCgff/wxERERODs78+677yof96tcuTJr1qzh7t27uLu706dPH1q1akVUVJSy\nvbu7O6NGjWLy5Ml06NCBixcv8uGHH+Y7juHDh2NoaEibNm2wtbXl0qVLL52TEEKUBXppaWmqXMT0\n9PTEwcGBiIiI4g6lwBw7dozXXnuNHTt20LRp0+IOp8QoK9/SVtYeLJJ81a2s5VtUVL2mXtr99NNP\nGBkZYWNjw8WLFxkzZgyNGzfGycmpuEMTQghRAklRL8Hu3bvH+PHjuXz5MhqNBhcXF7744gudj+nl\n16VLl2jTps1z+5OSkqhdu/ZLzy+EEKL4qPb2u8jZkydPuHjx4nP769Spo/PZcVFylLXblZKvupW1\nfIuK/O9dxhgYGPynj9oJIYQouVT99LsQQghRlkhRF0IIIVRCiroQQgihElLUhRBCCJWQoi6EEEKo\nhBR1IYQQQiWkqAshhBAqIUVdCCGEUAkp6kIIIYRKSFEXQgghVEKKuhBCCKESUtSFEEIIlZCiLoQQ\nQqiEFHUhhBBCJaSoCyGEECoh36cuSr1XrmpybL9jkVbEkQghRPGSK3UhhBBCJaSoCyGEECpRKot6\nz549CQgIKO4wAEhKSqJdu3aYmJjg6en53Lb8OHz4MBqNhpSUlIION1fXr1/Hx8cHS0tLNJqcb2kX\ntvj4eDQaDampqcWyfyGEKM1kTf0/Cg4OpnHjxqxcuRIjI6PntpUGkZGRXLt2jfj4eIyNjYs7HCGE\nEPlUKq/Uc5OR6wCoygAAIABJREFUkVGk+zt37hyurq7UqlWLatWqPbetNDh37hxOTk7Y2tpiZmZW\n3OEIIYTIpxJf1B88eEBAQABWVlbY29vz5Zdf6vQ3adKE8PBwhg4dSp06dRg4cCAAx48fp2vXrpib\nm1OvXj0CAgK4c+eOsl1AQAA9e/YkIiICe3t7rKysGDJkCA8fPlTGPHr0iODgYOzt7TEzM6NTp04k\nJiYCkJKSgkajIT09nWHDhqHRaFi2bFmObS/y66+/0qpVK8zMzPDw8ODs2bM6/bdu3eKDDz7AwcEB\nc3Nz2rRpQ0xMjNL/ww8/YG1tzaNHj3S2GzhwIL169VJef/fddzRr1gwTExOaNWvG4sWLdY7jpk2b\nWL58ORqNhoCAAEJDQ+nevbsyZvHixWg0GtauXau0de7cmenTpyuvN2/ejJubG2ZmZjg6OjJp0iSd\nN1oZGRmEhobi4OCApaUlr7/+Otu2bXvusXn06BF9+/bF1dWVmzdvvvBYCiFEWVbii/q4cePYsWMH\nS5YsITY2lqNHj7Jnzx6dMXPmzKF+/frs2LGDkJAQHjx4QPfu3TEyMmLbtm3ExMSwb98+hg0bprPd\n7t27+f3334mNjWXJkiXExcURGhqq9IeEhLBu3TqioqLYtWsXDg4OdO/enWvXrlGrVi2Sk5OpXLky\n4eHhJCcn4+3tna2tW7duueb3559/0rdvXzp06EB8fDz+/v46MQD8/fffODk5sXz5cpKSkhg8eDCB\ngYHs3LkTAG9vb7RaLZs2bVK2uXPnDhs2bKB///4A/PTTTwQFBREQEEBiYiKDBw/m008/ZfPmzQDE\nxcXRoUMHfHx8SE5OZsqUKbi4uJCUlMSTJ08ASEhIoEaNGsTHxwPP3nAdOnQIFxcXALZt24a/vz8D\nBw4kKSmJqKgoYmNjmThxohLX0KFD2b17NwsWLGDPnj307t2bXr16cezYsWzHJj09nXfeeYfbt2+z\nYcMGTExMcj2WQghR1umlpaVlFncQz3Pv3j1sbGyIioqiR48eSpuDgwOenp7MnTuXJk2a4ODgwIoV\nK5TtFi9ezLhx4zh+/DhVqlQBnj2A5eXlxaFDh7CxsSEgIICNGzdy4sQJZf14xYoVDB8+nPPnzwNQ\nr149Zs2aRe/evQF4+vQpLVq0oHv37owdOxYAKysrpk2bRt++fZX959T2PBMnTiQ2NpYDBw6gp6cH\nQEREBGFhYRw5coS6devmuJ2fnx9GRkZERkYCEBQUxLlz51izZg0ACxcuZOrUqZw4cQIDAwM6d+6M\nnZ0ds2fPVuYICAjg/Pnz/Pzzz8CzBxCrV6/O3LlzlWNdr149Nm3ahLOzMw4ODgwcOJBly5Zx4MAB\n4uLi6Nu3LykpKZQvXx4PDw9ef/11Ro4cqexjw4YNDBo0iD///JMLFy7QvHlzjh49Su3atZUxffr0\nwcLCgi+//FI5T3v37sXf3x8LCwsWLVpExYoVn3sMn/c59QP39r/w+AshBIC9vX1xh1AgSvSDcufP\nnycjIwNnZ2elzdjYmEaNGumMa9asmc7r5ORkGjVqpBR0gNatW6Ovr8+pU6ewsbEBoFGjRjoPhDk7\nO5ORkaEU9cePH9OmTRulv1y5cjg7O3Pq1KkCyzE5OZmWLVsqBT0rjn96+vQpM2bMYO3atVy9epWM\njAwyMjKUK2SAd999Fzc3Ny5fvoyVlRUxMTH07t0bAwMDZT//fpPRtm1b5Uo9J8bGxjg5OSlX6Hfv\n3mXgwIFMnTqVq1evkpCQgLOzM+XLlwfgyJEjHDp0iJkzZypzaLVaHj58yPXr1zly5AiZmZk6xxSe\n3WJ3dXXVaevWrRuOjo4sXbpUySG/1PKPNMuZM2dUl1NuJF91K2v5FpUSXdQzM/N2E+HfT5jntt0/\ni2de9p3T+LzOkZ/95CYyMpKoqCimTJmCg4MDxsbGTJw4UWeNuUmTJjg5OfH999/j6enJ4cOH+eab\nb14Y94tycXFxIT4+nho1atC2bVuMjY1p3rw5CQkJJCQk8MYbbyhjtVoto0aNwtvbO9s8NWvWRKvV\noqenx/bt25U3Aln+fSXeuXNn1q1bx/Hjx3Fycso1RiGEEM+U6DV1Gxsbypcvz/79/7uNev/+fU6c\nOJHrdg0bNuT48ePcvXtXadu7dy9arZYGDRoobSdOnOD+/fvK6/3792NoaIi1tTU2NjYYGhoqD8bB\nsyvmffv26czxXzVs2JCDBw/qFPd/5guQmJjIW2+9Ra9evXB0dMTa2jrbw3QA7733Ht9//z1Lliyh\nTZs2Ou+CGzRoQFJSUrZ5GzZsmGt8Li4u7N27l7i4OOXOgIuLC1u2bNFZTwdwcnLi9OnT2NjYZPtj\nYGCAo6MjmZmZXL9+PVu/paWlzn7HjBmDn58f3t7eHD169AVHUQghBJTwom5sbEz//v0ZP348cXFx\nnDx5kmHDhqHVanPdztfXl8qVKzN48GCOHz/O7t27CQwMxMvLS7n1Ds+K9LBhwzh58iRxcXFMmDCB\n9957DyMjI4yMjPDz82PChAls2bKF5ORkPvnkE27evMmHH35YYDkOGDCAixcvEhwczJkzZ4iNjeW7\n777TGWNnZ8euXbtITEzk9OnTBAUFcfHixWxzvfPOO9y4cYPo6Gj69eun0zd8+HBWrFjBggUL+OOP\nP5g/fz6rVq1ixIgRucbXtm1bMjIy+Omnn3jttdeAZ0V97dq1lC9fnhYtWihjR44cyerVqwkLC+PE\niROcPn2a2NhYQkJClDx69OjBkCFDiI2N5cKFCxw+fJjIyEjWr1+fbd/jxo1jwIABeHt75/ggnRBC\nCF0l+vY7wKRJk7h//z79+vWjUqVK+Pv78+DBg1y3qVy5MmvWrGH06NG4u7tToUIFunTpwpQpU3TG\ntW/fnoYNG+Ll5cXDhw/x8vJiwoQJSn/Wz0OHDuXOnTs4OjqyevVqzM3NCyy/2rVrs3TpUsaMGcOi\nRYto2rQpoaGh+Pv7K2OCgoJISUnB19eXihUr0qdPH3x9fbOt7VepUgVvb29iY2Px8fHR6Xv77beZ\nNm0akZGRjB49mtq1a/Pll1/i4eGRa3zGxsY0bdqU06dP4+joCDxb8zcwMNBZTwdwd3dn5cqVRERE\nEBUVhYGBAba2tvTp00cZM3v2bKZPn05ISAhXrlyhWrVqNG/eXHnD8G8hISFkZmbStWtXYmNjadKk\nSd4OrBBClEEl+un3whQQEMCtW7d0nppXg+7du2NpacmsWbOKO5QiU1a+pa2sPVgk+apbWcu3qJT4\nK3WRN7dv32bHjh1s376dhISE4g5HCCFEMZCiXsgCAwNZuXJljn09evRgxowZBbIfV1dX0tLSCAkJ\nwcHBoUDmLC3UdkUuhBAvq8zefi8qN2/e1HkK/5+qVKkivyVN5FlZu10p+apbWcu3qMiVeiEzMTGR\nwi2EEKJIlOiPtAkhhBAi76SoCyGEECohRV0IIYRQCSnqQgghhEpIURdCCCFUQoq6EEIIoRJS1IUQ\nQgiVkKIuhBBCqIQUdSGEEEIlpKgLIYQQKiFFXQghhFAJKepCCCGESkhRF0IIIVRCiroQQgihElLU\nRan3ylVNcYcghBAlghR1IYQQQiWkqAshhBAqIUVdCCGEUAkp6vnUs2dPAgICijsMAJKSkmjXrh0m\nJiZ4eno+t6008fT0JCgoqLjDEEKIUkmKeikWHBxM48aNOXz4MDExMc9tK4ni4+PRaDSkpqYWdyhC\nCKEaUtQLUEZGRpHu79y5c7i6ulKrVi2qVav23DYhhBBlgxT1XDx48ICAgACsrKywt7fnyy+/1Olv\n0qQJ4eHhDB06lDp16jBw4EAAjh8/TteuXTE3N6devXoEBARw584dZbuAgAB69uxJREQE9vb2WFlZ\nMWTIEB4+fKiMefToEcHBwdjb22NmZkanTp1ITEwEICUlBY1GQ3p6OsOGDUOj0bBs2bIc23KTdbW8\ndetW3NzcMDc3x8PDg8uXL5OQkED79u2xsrKiZ8+e3Lp1S9lOq9Uybdo0GjVqhKmpKe3atWPjxo1K\nf1Z8sbGxeHt7Y2FhQevWrYmLi1P6vby8ALC1tUWj0egsaWi1WiZOnIiNjQ12dnaMHTsWrVabr3Mn\nhBBlkRT1XIwbN44dO3awZMkSYmNjOXr0KHv27NEZM2fOHOrXr8+OHTsICQnhwYMHdO/eHSMjI7Zt\n20ZMTAz79u1j2LBhOtvt3r2b33//ndjYWJYsWUJcXByhoaFKf0hICOvWrSMqKopdu3bh4OBA9+7d\nuXbtGrVq1SI5OZnKlSsTHh5OcnIy3t7e2dq6deuWpzzDw8MJDw/n119/JS0tDT8/P6ZNm8bMmTPZ\nsGEDJ0+eJDw8XBk/d+5cIiMjGT9+PHv27MHT05P+/ftz9OhRnXknT57MoEGDSEhIoFmzZvj5+XHv\n3j1q1arFkiVLgGfPACQnJzNlyhRlu1WrVlGuXDm2bNlCREQEc+fOZe3atXk7aUIIUYYZFHcAJdW9\ne/dYunQpUVFRuLu7AzB79mwcHBx0xrVr146PPvpIeb148WLu37/P/PnzqVKlCgBff/01Xl5enDt3\nDhsbGwD09fWZPXs2xsbGODg4MH78eIYPH64U9ujoaGbNmkXnzp0BmDFjBrt27eLbb79l7NixmJmZ\noaenR9WqVTEzMwPAyMgoW1tejBkzhnbt2gEwYMAARo4cyY4dO2jatCkAvXv3Zv369cr4qKgohg0b\nhq+vr7L9nj17iIqK4ptvvlHGDRkyBA8PD+DZm5Tly5dz7Ngx2rZtqywNmJiYUKNGDZ14GjRowJgx\nYwCws7Nj8eLF7Ny5k+7duz83hzNnzuQ539KsrOSZRfJVt5KUr729fXGHUCCkqD/H+fPnycjIwNnZ\nWWkzNjamUaNGOuOaNWum8zo5OZlGjRopBR2gdevW6Ovrc+rUKaWoN2rUCGNjY2WMs7MzGRkZnD9/\nHoDHjx/Tpk0bpb9cuXI4Oztz6tSpgkvy//tnTqampjm23bx5E4D09HSuXr2qExtA27Zt2bJly3Pn\ntbCwAFDmyWs8AObm5i/cTi3/IHNz5syZMpFnFslX3cpavkVFbr8/R2ZmZp7GGRkZ5Xk7PT29fO07\np/F5nSM/ypcvn23+f7flZU3737HlNG9ejus/t8vaNq/nQwghyjIp6s9hY2ND+fLl2b9/v9J2//59\nTpw4ket2DRs25Pjx49y9e1dp27t3L1qtlgYNGihtJ06c4P79+8rr/fv3Y2hoiLW1NTY2NhgaGioP\nxgE8ffqUffv26cxRHKpWrYqFhQVJSUk67YmJifmKzdDQEHiWlxBCiIIhRf05jI2N6d+/P+PHjycu\nLo6TJ08ybNiwF16x+vr6UrlyZQYPHszx48fZvXs3gYGBeHl5Kbfe4VkxGzZsGCdPniQuLo4JEybw\n3nvvYWRkhJGREX5+fkyYMIEtW7aQnJzMJ598ws2bN/nwww8LO/UXGj58OFFRUaxevZqzZ88SFhZG\nYmJitocBc1O7dm309PT45Zdf+Ouvv7h3714hRiyEEGWDrKnnYtKkSdy/f59+/fpRqVIl/P39efDg\nQa7bVK5cmTVr1jB69Gjc3d2pUKECXbp00Xm6G6B9+/Y0bNgQLy8vHj58iJeXFxMmTFD6s34eOnQo\nd+7cwdHRkdWrV2Nubl7wiebT4MGDuXfvHqGhody4cQN7e3uWLFmCo6NjnuewtLRk9OjRTJ48mREj\nRtCrVy/mzp1biFELIYT66aWlpcliZRELCAjg1q1brFixorhDUYVXrmq4Y5FW3GEUurL2YJHkq25l\nLd+iIrffhRBCCJWQoq5igYGBWFlZ5fgnMDCwuMMrMGXhKl0IIfJC1tSLQVGtHX/++ecMHz48x75/\nfo5eCCGEOkhRVzETExNMTEyKOwwhhBBFRG6/CyGEECohRV0IIYRQCSnqQgghhEpIURdCCCFUQoq6\nEEIIoRLyG+WEEEIIlZArdSGEEEIlpKgLIYQQKiFFXQghhFAJKepCCCGESkhRFyXWt99+i6OjI2Zm\nZri5ubFnz55cxyckJODm5oaZmRlOTk5ER0cXUaQFIz/5Xrt2jQ8//JBWrVpRvXp1AgICijDSgpGf\nfNevX4+Pjw+2trbUqlULd3d3Nm3aVITR/nf5yTchIYE333wTa2trzM3NadWqFZGRkUUY7X+T33+7\nWRITE6lRowZt27Yt5AjVS4q6KJHWrl1LcHAwn376Kbt27cLZ2RlfX18uXbqU4/gLFy7Qo0cPnJ2d\n2bVrF5988gkjR44kNja2iCN/OfnN99GjR1SvXp2PP/6Yli1bFnG0/11+8929ezeurq6sXLmSXbt2\n8cYbb9CvX788F4vilt98jY2NGTRoEJs2bSIpKYnPPvuM8PBwvv322yKOPP/ym2uWtLQ0Bg8ejJub\nWxFFqk7ykTZRIrm7u9OoUSNmzZqltDVv3pyuXbsSGhqabXxoaCg//fQThw4dUtqGDx/OqVOn2Lp1\na5HE/F/kN99/6tmzJ9WrVy+yb/8rCP8l3ywdO3akbdu2hIWFFVaYBaYg8u3Xrx8VKlRg4cKFhRVm\ngXjZXPv160fjxo3JzMxk/fr1JCYmFkW4qiNX6qLEycjI4LfffqNjx4467R07dmTv3r05brNv375s\n493d3Tl8+DCPHz8utFgLwsvkW5oVVL737t1Do9EUdHgFriDyPXLkCPv27aN9+/aFEWKBedlcv/32\nW27cuEFQUFBhh6h68tWrosRJTU3l6dOn2b421sTEhBs3buS4zY0bN+jQoUO28U+ePCE1NRVzc/PC\nCvc/e5l8S7OCyHfBggVcuXKFnj17FkaIBeq/5Ovg4MBff/3FkydPGDVqFH5+foUZ6n/2MrkeP36c\nqVOnsnXrVsqVK1cUYaqaFHVRYunp6em8zszMzNb2ovE5tZdU+c23tHvZfGNjYwkJCWHhwoXUqVOn\nsMIrcC+T76ZNm7h//z4HDhwgNDSUunXr0qtXr8IMs0DkNddHjx7xwQcfMGnSJOrVq1dE0ambFHVR\n4tSoUYNy5cple2f/119/ZbsCyGJqaprjeAMDA6pXr15osRaEl8m3NPsv+cbGxjJ48GDmzZtHly5d\nCjPMAvNf8s0qdI0aNeLGjRtMmTKlRBf1/OZ67do1Tp06xdChQxk6dCgAWq2WzMxMatSowapVq7Ld\nyhe5kzV1UeIYGhrStGlT4uLidNrj4uJo3bp1jts4OzuzY8eObOObNWtG+fLlCyvUAvEy+ZZmL5vv\nunXrGDRoEHPmzKFr166FHWaBKajzq9VqycjIKOjwClR+c7W0tGTPnj3Ex8crf/z8/LCxsSE+Ph5n\nZ+eiCl015EpdlEhDhw5l0KBBtGjRgtatWxMdHc21a9cYMGAAAIMGDQJg/vz5AAwYMIAFCxYQHBzM\ngAED2Lt3L99//32p+AgQ5D9fgKNHjwKQnp6Onp4eR48exdDQkIYNGxZ9AvmU33zXrFnDoEGDmDRp\nEu3ateP69evAsyJSrVq14kkiH/Kb7/z586lbty729vbAs4/0RUVF8cEHHxRPAvmQn1zLly+Pg4OD\nzvY1a9akQoUK2dpF3khRFyVSt27duHXrFhEREVy/fp1XX32VlStXKmuof/75p874evXqsXLlSj7/\n/HOio6MxNzdn6tSppeaKLr/5Ari6uuq8/vnnn6lduzbHjh0rkpj/i/zmGx0dzZMnTxg9ejSjR49W\n2tu3b8/GjRuLNPaXkd98nz59yvjx47l48SIGBgbUq1eP0NDQEv+gHLzc32VRcORz6kIIIYRKyJq6\nEEIIoRJS1IUQQgiVkKIuhBBCqIQUdSGEEEIlpKgLIYQQKiFFXQghhFAJKepCCCGESkhRF6IEiI+P\nR6PRPPfP/v37Cz2GqKgofvjhh0Lfz3/15MkTNBoNffr0Ke5QCsxvv/1GeHg4ly5dKu5QRCknv1FO\niBKke/fuvPHGG9nabWxsCn3fc+bMwd7ent69exf6voSuo0ePMnXqVDp06EDt2rWLOxxRiklRF6IE\ncXJyKhXfEZ5fGRkZZGZmUqFCheIOpUS5e/cuVapUKe4whIrI7XchSqHVq1fTuXNnatWqhYWFBZ06\ndWL9+vXZxq1atYqePXvSuHFjTE1NsbW1pV+/fpw4cUIZk3U7+8qVK+zcuVPntv/ly5eV/uHDh2eb\nf8mSJWg0GhITE5W2yZMno9FoOH36NMHBwbz66quYm5tz6NAhZcy2bdvw9vamTp06mJmZ0b59exYt\nWvSfjomDgwNdu3blyJEjeHl5YWVlhZ2dHSEhITx9+pSHDx8yevRoGjZsiLm5OZ6enpw5cybHfHbt\n2sXkyZNp3LixEt+PP/6Y435jY2N54403sLS0xMrKCg8PD37++efnxvfbb78pubu5uTF58mRGjBgB\ngIeHh3Lss473nTt3mDRpEh07dsTGxgZTU1OaN2/OxIkTefjwoc4+duzYgUajYcWKFSxZsoTWrVtj\nampKkyZNiIyMzDH+3377jXfffRd7e3tMTU1p3LgxH374IRcuXNAZVxjnTBQ8uVIXogR58OABqamp\nOm2GhoY6V3Pjx4/n66+/5s033+Tzzz9HX1+f9evX8+677/LVV1/pfOnHggULMDEx4f3338fU1JRz\n586xaNEiOnfuzK5du7C2tqZcuXLMnz+fUaNGYWFhwccff6xs/1++Ac3Pzw8jIyOGDRuGnp4epqam\nACxcuJDPPvuM1q1bExQURKVKldi2bRsff/wxKSkphIaGvvQ+//zzT7p160b37t3x8fHh119/Zdas\nWZQvX54jR47w5MkTAgMDuXnzJrNnz6Zfv34kJiair697fTNu3Dj+/vtvBg4ciFarJSYmhvfff5/5\n8+fr3EnJOm4NGjRg1KhRaLVali1bRq9evYiMjKR///468168eJH/+7//o1u3bnh7e3P//n1cXV25\nfv06S5cuJSgoCDs7O+B/Sy6XL18mJiaG//u//8PX1xcDAwPi4+OZMWMGv//+OytXrsx2HBYsWMDN\nmzfp378/VatWZfny5YwbN45atWrh4+OjjNu4cSPvv/8+xsbGvPvuu1hbW3P9+nW2bdtGcnKy8n3u\nhXnORMGSL3QRogSIj4/Hy8srx75u3boRHR0NwMGDB3F3dycoKIgxY8bojOvZsyeJiYmcPHkSIyMj\nAO7fv6/8nOXEiRO4ubkxYMAApk2bprQ7ODhgb29PbGyszvgnT55Qs2ZN+vfvn+1qb8mSJYwYMYLN\nmzfTtm1b4NmV+vTp03F1dWXdunWUK1dOGX/58mWaNWuGj4+PztfIAnz22Wd89913HD58WPlGr5xk\nxdOlSxe+//57nfivXLnC0qVLdY5l+/btOXHiBG+//TZLlixBT08PePZg4NixY4mNjcXNzU0nn7p1\n6xIfH0/VqlUBSEtLo127dvz999+cOHGCihUrcuvWLRo1aoSVlRVxcXHKG687d+7g4uLCnTt3+P33\n35U5suKbPXs2ffv2feFxzJKRkYG+vj4GBrrXYBMmTGDGjBns2LGDpk2bAs+u1L29vbGysiIxMVHZ\n971792jSpAkNGzZk8+bNOm3ly5dn165dmJub68yv1WrR19cvkHMmio7cfheiBHn//ff58ccfdf58\n9tlnSv/KlSvR09Ojd+/epKam6vzx8PAgPT2dAwcOKOOzCnpmZibp6emkpqZiZmaGjY0NBw8eLNRc\nhgwZolPQAX788UcyMjLo169ftvjfeustnj59ys6dO196n7Vq1cr25qht27ZkZmbi7++vFHSAdu3a\nAfDHH39km+fDDz9UCiKARqPh/fff59atW8pSw7Zt23j48CGDBw/WuZPyyiuv4O/vT3p6Ort27dKZ\n18TEJN8PIhoaGioF/fHjx6SlpZGamkqHDh0AcjyPffv21Ynf2NiYFi1a6OT666+/cvv2bYYPH56t\noAPK3YvCPmeiYMntdyFKEFtbW+U/65ycPn2azMxMmjdv/twxN2/eVH7+7bffCAsLY8+ePdy/fz/b\nvgpT1m3kfzp9+jTAc+9KgG78+ZV1u/ifNBoNAHXr1s2x/datW9m2qV+/fra2Bg0aAChrzSkpKQA0\nbNgw29hXX31VZ2wWa2vrbLf68+Kbb75h0aJFnDp1Cq1Wq9OXlpaWbXxOx6F69eo6uWYVeEdHx1z3\nXdjnTBQsKepClCKZmZmUK1eOVatWPbc4ZBWUlJQUunTpQrVq1Rg5ciS2trYYGRmhp6fHyJEjefz4\ncZ72+c+r23978uTJc/sqVaqUY/zwv7X+nFhbW+cprpz8+85AXvqyYvqn3HLObbsXyemYvMjMmTMJ\nDQ2lU6dODB48GHNzcwwNDbl06RLDhw/PVuQhb7lm/fyiXAv7nImCJUVdiFLE1taWHTt2ULdu3Rde\naa9fv54HDx6wevVq5VYzPPtPOjU1Vef2LDz/P/dy5cpRtWpVbt++na3v31eieYkfoGbNmrnekShu\nycnJvPnmm9na4H9XwVmF7NSpU7i4uOiMPXXqlM7Y/2LFihVYW1uzatUqnXOU0xP2+WFvbw88+4y8\nq6vrc8eVlnMmnpE1dSFKkawnrydMmMDTp0+z9d+4cUP5Oetq7d9XlNHR0dmesIdn6+85FW549h/7\n3r17+fvvv5W2W7du5fs30Pn4+GBoaMgXX3yhM1eWtLQ0MjIy8jVnYfj222+5e/eu8jotLY1FixZR\nvXp15UG2jh07UqlSJebPn6+ztJGens6CBQuoWrWq8gDeixgbGwPkePz19fXR09PTOY+PHz/m66+/\nfqncsri7u1OtWjUiIyO5fv16tv6s/ZWWcyaekSt1IUoRZ2dngoKCiIiIwNXVla5du2Jubs61a9c4\nfPgwcXFxXLt2DYDOnTszadIk/P39lQe/9u7dy6+//pptfRmgZcuW/PDDD3zxxRfY29ujr69Ply5d\nqFSpEgMHDmTIkCHKx6rS0tJYvHgxdevWzdd6ap06dYiIiCAwMJDWrVvTo0cPateuzV9//cXx48fZ\ntGkTBw6yrMnWAAAgAElEQVQcwMrKqsCO2cvQaDR06tSJPn36kJmZydKlS7ly5Qpz5syhYsWKwLM1\n6tDQUIKDg+nUqRO9e/dGq9Xy/fffk5KSQmRkZJ5/sUzz5s3R09Nj+vTp/PXXXxgZGWFtbU3z5s3p\n2rUrYWFh+Pr64unpSXp6OitXrvzPv8jH2NiYWbNmMWDAANq1a0f//v2xtrbm5s2bysfVOnfuXGrO\nmXhGiroQpcyYMWNo1qwZ33zzDbNnz+bhw4eYmpry6quvMnXqVGWcra0tK1euZPLkyXz55ZeUK1eO\nNm3asGnTJj7++ONsV2ehoaGkp6czf/580tPTyczM5Pjx41hZWdG7d2+uX7/OwoUL+fzzz7GxseHz\nzz/nyZMnOk/b58V7771H/fr1iYyMJDo6mvT0dGrUqIG9vT1jx46lZs2aBXKc/otJkyaxa9cuvvnm\nG27evImdnR3R0dF069ZNZ9zgwYOxsLAgKiqK8PBw9PT0cHR05IcffsDDwyPP+6tXrx6zZs1i1qxZ\nfPrppzx+/Jj+/fvTvHlzPvnkEwCWLVvG6NGjMTU15Z133qFHjx46yyovw8vLi82bN/PVV1+xePFi\n7t+/j6mpKe3atdN5ALA0nDPxjHxOXQgh/r/cPi8uRGkga+pCCCGESkhRF0IIIVRCiroQQgihErKm\nLoQQQqiEXKkLIYQQKiFFXQghhFAJKepCCCGESkhRF0IIIVRCiroQQgihElLUhRBCCJWQoi6EEEKo\nhBR1IYQQQiWkqAshhBAqIUVdCCGEUAkp6kIIIYRKSFEXQgghVEKKuhBCCKESUtSFEEIIlZCiLoQQ\nQqiEFHUhhBBCJaSoCyGEECohRV0IIYRQCSnqQgghhEpIURdCCCFUQoq6EEIIoRJS1IUQQgiVkKIu\nhBBCqIQUdSGEEEIlpKgLIYQQKiFFXQghhFAJKepCCCGESkhRF0IIIVRCiroQQgihElLUhRBCCJWQ\noi6EECXQzz//jEajITIysrhDEaWIQXEHIIQoXufPn2fhwoXEx8eTkpLCvXv3MDIywsbGhjZt2uDr\n60vz5s2LO8xC4+npye7du/M8vnfv3sydO7cQIxLi5emlpaVlFncQQoji8dVXXxEWFsbTp09xdHSk\nZcuWVKtWjbt373LixAn279/Po0ePmDBhAh999FFxh1soli1bxsWLF3XaEhIS2L17N+3bt8fFxUWn\nr0mTJrz99tuFHtf9+/e5fPkyJiYmVKtWrdD3J9RBrtSFKKNmzJjBxIkTqVWrFt9++y1t2rTJNubW\nrVvMmzePu3fvFkOERaNv377Z2sLDw9m9ezcuLi6MHj26GKICIyMj6tevXyz7FqWXrKkLUQalpKTw\nxRdfYGhoyKpVq3Is6ADVq1fn888/Jzg4WKc9PDwcjUbDsmXL2Lx5M2+99Ra1a9embt26OuN+++03\n/Pz8aNiwISYmJjRo0AB/f3/OnTuX4/7+/vtvIiMjcXNzw8rKCktLSzp06EB0dDSZmbo3FVNSUtBo\nNHh6epKamspHH31EgwYNMDU1pU2bNixZsuQ/HKEXu3TpEpMnT6ZTp07Y29tjYmKCg4MD/v7+nD17\nVmdsZmYm3bt3R6PRsGjRomxzZR1Pf39/pe15a+qnT59myJAhODk5YWZmRr169WjTpg0jRozg2rVr\nhZKrKD3kSl2IMmjZsmU8fvwYX19fXn311ReONzDI+b+KH3/8ke3bt/Pmm28yYMAArl+/rvStXLmS\nIUOGYGhoiIeHB1ZWVpw7d441a9bw888/s+H/sXf3cTXf/+PHH0UuKtsxUokhIkWtIVdJWz4fLH2E\nXExs2KRDmVguhpLxK1dzUa5msyGfIRefMNvXRiUpmWvREbnYRj6NuQplTr8/fHt/HSVlkXM877eb\n2+2c1/t18Xy/ztbzvF+v9zln+3acnJyU+rdu3cLHx4eDBw/i5OTEwIEDAdi1axdjx47lwIEDxe5l\n37hxg65du1KlShX+9a9/kZeXR1xcHKNHj8bY2JhBgwaVdXpKJT4+nqVLl9KpUydcXFyoXr06p0+f\nVs7v559/Vq60jYyMWLZsGZ06dWLixIm0bt2aFi1aALBnzx7mzJlDkyZN+OKLL0oc8/z587z77rvk\n5eXxz3/+Ex8fH+7du8evv/7K5s2bGTx4MFZWVs/lfIV+kKQuxCsoNTUVgE6dOv2tfn7++WdiY2Pp\n0qWLTnlWVhZBQUHUq1ePHTt2ULduXeVYUlISPj4+BAYGsmfPHqX8s88+4+DBg0ybNo0xY8Yo5Xl5\neQwePJjvvvsOb29v3nvvPZ2xTpw4wZAhQ5g3bx6VKlUCYOTIkXTs2JFFixY9t6TetWtXfH19MTU1\n1Snfv38///rXv5gxY4bOakHt2rVZsWIFPXv2ZOjQocTHx3P37l38/f0xMTFh5cqVmJublzjmpk2b\nuH37NgsXLuTDDz/UOXb37l20Wm35naDQS5LUhXgFFV5RP5psC/3666/ExMTolJmbmxMUFFSkbvfu\n3YskdICvv/6avLw8/t//+39FxujUqRPdu3dn+/btnDp1iubNm/Pnn3/y3Xff4eTkpJPQAapWrUpo\naCg7d+5k/fr1RZK6qakpM2bMUBI6gL29Pe3atSM5OZlbt25Ro0aNp8xI2VlaWhZb3rZtW9q0aUNC\nQkKRY25ubowfP56IiAjGjh3LH3/8QXZ2NvPmzdNZtXgSY+OHO6bVq1cvcqy4MvHqkaQuxCuocH/a\nyMioyLHffvuNWbNm6ZTVqVOn2KTeunXrYvvfv38/APv27ePo0aNFjufk5AAP94ebN2/OwYMH+euv\nvzA2NiYiIqJI/b/++guAzMzMIscaN25c7BWujY0N8HB5/nkkdYCtW7eyevVqjh49yp9//qnEWai4\nNxQhISHs27ePDRs2ANCzZ08++uijUo33r3/9i9mzZzNq1Ci+//573nnnHVxdXWnevHmxr6V49UhS\nF+IVZGlpyenTp7l06VKRY+3bt+f69evKc5VK9cR+6tSpU2z5tWvXAIiOji4xjtzcXJ36R44c4ciR\nI0+sf/v27SJlr732WrF1C6/cHzx4UGIMz2ru3LnMmDGDWrVq4eHhgY2NDdWqVcPIyIj//Oc/aDQa\n8vPzi7QzNjamR48eJCYmAg+3CkqrcePG7Nq1i9mzZ7N7927i4uIAsLa2ZuTIkQQGBkpyf8VJUhfi\nFdSuXTuSkpLYs2cPgwcPfuZ+npRAChPtuXPnSvUZ68L6/v7+zJ49+5njeVHu3r3LvHnzqF+/PgkJ\nCdSqVUvneHFL74U0Gg3Tpk2jZs2a3Lhxg08++YTdu3cX2Zt/EgcHB7799lv++usvTpw4QWJiIitW\nrGDq1KkYGxszatSov3NqQs/JR9qEeAX5+flRuXJl4uLi0Gg05d5/mzZtgIfL76XRunVrjI2NSUlJ\nKfdYnofLly9z9+5dOnbsWCShX79+nZMnTxbb7u7duwwdOpS7d+/yzTffMHbsWDIyMggJCSlzDJUr\nV+att97ik08+Ue6B2L59e9lPRhgUSepCvIIaNmzIhAkTyM/Px9fXV9kDf9yjy/Bl4e/vT5UqVZgy\nZQqnT58ucvzBgwckJSUpz2vXrk3//v05fvw4ERERRfamAX7//fdi+6oINjY2VK5cmYMHD3Lv3j2l\nPC8vj3Hjxj3xy3rGjx/PyZMnCQ4OxsPDg0mTJtGhQwfWrl3L+vXrnzpuWloaf/zxR5Hy//73vwBU\nq1btGc9IGApZfhfiFRUSEkJBQQGRkZF07dqVt956i1atWinLwhcvXlSWkTt06FCmvu3s7FiyZAmj\nRo2iffv2dOnShcaNG/PgwQN+//139u/fT15ens7Xs86ePZusrCxmzZrF+vXr6dChA5aWlly5coUz\nZ85w4MABZs6c+VJ8y1rVqlUZOnQoK1asoEOHDnTt2pV79+6RmJjIvXv3aNeunfKxwUKxsbGsWbOG\ndu3a8dlnnwEP9/2//vpr3NzcGDduHK1ataJJkyZPHHfNmjWsW7eOjh07Ymtri0ql4vz58/zwww9U\nrly52JsZxatFkroQr7Dx48fTp08fVq5cyZ49e4iNjSU3Nxdzc3MaNWrEkCFD6NevH61atSpz376+\nvrRo0YLFixeTmJhIfHw81apVw8rKii5dutCzZ0+d+jVq1GD79u2sWbOG2NhYtm/fzr1797CwsODN\nN98kNDQUHx+f8jr1v23mzJlYWVmxbt06Vq5cSc2aNfH09GTKlClMmDBBp+7Zs2cJDg6mZs2afPXV\nVzofv7O2tmbZsmX069ePIUOG8PPPPz/xinvgwIFUqlSJtLQ0jhw5wr1797CyssLb25vAwMBSfSxO\nGDb5QRchhBDCQMieuhBCCGEgJKkLIYQQBkKSuhBCCGEgJKkLIYQQBkKSuhBCCGEgJKkLIYQQBkKS\nuhBCCGEgJKkLUUGK+xlRfSGxVwx9jh30P359IEldCCGEMBCS1IUQQggDIUldCCGEMBCS1IUQQggD\nIUldCCGEMBDy06tC76k+VVV0CEKIl8z1udcrOoQKIVfqQgghhIGQpC6EEEIYCEnqQgghhIF47km9\nf//+qNXq5z1MqaSmptKhQwcsLCzw8vJ6Ytnzplar6d+/f5natGzZkqioqBLrqFQq4uLi/k5of2v8\n58XGxoa1a9dWyNhCCKFPXqkb5SZOnEiLFi3YsGEDZmZmTyx73iIjIykoKHghYwkhhHh1VOjye35+\n/gsdLysrC3d3d+rVq0fNmjWfWPa8vf7666hU+nHH9ot+jYQQQjy7ck3qd+7cQa1WY2Njg52dHfPm\nzdM53rJlSyIiIhg1ahRvvvkmw4cPByA9PZ2ePXtiZWVFw4YNUavV3LhxQ2lXuFw9Z84c7OzssLGx\nYeTIkdy9e1epk5eXx8SJE7Gzs8PS0pIuXbqQkpICwIULF1CpVNy8eZPAwEBUKhVr164ttqwkSUlJ\nqFQqEhMT8fT0xNraGg8PD44cOaJTb//+/bz33ntYW1vTvHlzxo4dy82bN4ucT6Hc3FxGjBihzNsX\nX3xR7LbFvXv3GDNmDPXr18fBwYFFixYVifHKlSv069cPa2trWrRowfr163WOl3auFyxYgIODAw4O\nDqUe/9dff8XPz4969epRr149Bg0axO+//65T55tvvsHFxQULCwtcXFxYtWqVzvGsrCy8vLywtLSk\ndevW/Pjjj8W+FkIIIYoq16Q+depUEhISWL16NXFxcRw7dox9+/bp1FmyZAlNmzYlISGB0NBQ7ty5\ng6+vL2ZmZuzatYuYmBjS0tIIDAzUaZecnMyJEyeIi4tj9erVxMfHExYWphwPDQ1ly5YtREdHs2fP\nHhwcHPD19SU7O5t69eqh0WgwNTUlIiICjUaDj49PkbLevXuX6jzDw8MJCwsjMTGRN954A39/f2U5\nPT09nd69e9O9e3f27t3LmjVrOH78eJHzedSUKVNITk4mJiaGrVu3cuLECeUNyeNz5+DgQGJiIp98\n8gmhoaGkpaXp1ImIiKB79+4kJSUxZMgQAgICOHz4MECZ5jo9PZ2NGzfq7NGXNH5BQQF+fn7k5OSw\ndetWtm3bRnZ2Nn5+fsrcbNu2jZCQENRqNSkpKQQEBDBu3Dh++OEHALRaLYMGDUKr1bJz506io6OJ\njIwkLy+vVK+LEEK86sptT/327dusWbOG6OhoPD09AVi8eLHOlR5Ahw4d+OSTT5Tnq1atIjc3l+XL\nl1OjRg0AFixYgLe3N1lZWdja2gJgbGzM4sWLMTc3x8HBgWnTphEUFKQk9pUrV7Jo0SK6du0KwPz5\n89mzZw9fffUVU6ZMwdLSEiMjI1577TUsLS0BMDMzK1JWGpMnT8bd3R2A8ePH061bNy5duoSNjQ2L\nFi2iV69eBAUFKfXnzZuHu7s7OTk5WFhYFJm3mJgYli1bxjvvvANAVFRUkXkDePfdd/H39wdgxIgR\nLF++nMTERFxdXZU63t7eDB06FIBPP/2UpKQkli5dypdffklsbGyp5rpq1apER0dTtWrVUo+fkJDA\niRMnOHz4MA0aNADgq6++wsXFhcTERDw8PIiOjqZ///5KH02aNOHIkSMsXLiQ7t27k5CQQEZGBkeP\nHqV+/frA/71JEUKIsijrz7za2dk9p0herHJL6ufOnSM/P18nwZibm+Po6KhTz8XFRee5RqPB0dFR\nSTIAbdu2xdjYmIyMDCXRODo6Ym5urtRxdXUlPz+fc+fOAXD//n3atWunHK9UqRKurq5kZGSU1ykq\nHj0nKysrAHJycrCxseHo0aNkZWWxZcsWpU7hleq5c+eKJPVz585x//59WrVqpZSZmZkVm9Qfn0sr\nKytycnJ0ytq0aVPk+c6dO4HSz3Xz5s2LJPSnja/RaLC2tlYSOkDDhg2xtrYmIyMDDw8PNBoNfn5+\nOn20b99euVLXaDTUrVtXSegArVu3xthYPnkphCgbQ0nSZVVuSb20d3M/fod5Se2MjIzKNHZx9Uvb\nR1mYmJgU6b8wBq1WywcffMDIkSOLtLO2ti5SVpa74B8dt3DssrQv7Vw/6VMAJY1fUFDwxLl+tLyk\n10g+ESCEEH9PuV0C2draYmJiwoEDB5Sy3NxcTp48WWI7e3t70tPTuXXrllK2f/9+tFotzZo1U8pO\nnjxJbm6u8vzAgQNUqVKFRo0aYWtrS5UqVXT2oR88eEBaWppOHy+Cs7Mzp06dwtbWtsi/6tWrF6lf\nOG+HDh1Syu7cufPUeXuSX375pcjzwjko7Vw/C3t7ey5dusSFCxeUsvPnz3P58mXs7e0BaNasGamp\nqTrtUlJSlOOFffz222/K8YMHD6LVav9WbEII8aoot6Rubm7O4MGDmTZtGvHx8Zw6dYrAwMCn/kHu\n27cvpqamBAQEkJ6eTnJyMsHBwXh7eyvLwfAwSQcGBnLq1Cni4+MJDw/nww8/xMzMDDMzM4YNG0Z4\neDg7d+5Eo9EwduxYcnJy+Pjjj8vrFEvlk08+4dChQwQHBytL8T/++CNjxowptr65uTmDBg1SbrzL\nyMhg9OjRJV75lmTbtm2sWrWKs2fP8sUXX5CYmKjcRV/auX4WHh4etGjRAn9/f44cOcLhw4cZPnw4\nzs7Oyv0HQUFBrF+/nhUrVnD27FmWL19ObGwso0ePVvpo2rQpAQEBHDt2jLS0ND777DMqV36lvk5B\nCCGeWbn+tfz888/Jzc1l0KBBVK9eHX9/f+7cuVNiG1NTUzZt2sSkSZPw9PSkatWqvPfee0RGRurU\n69ixI/b29nh7e3P37l28vb0JDw9Xjhc+HjVqFDdu3MDJyYmNGzcqe94vSosWLdixYwczZsygR48e\nPHjwgIYNG5b4bXWF8/b+++9jZmbGyJEj+e9//0u1atXKPP7EiRPZunUrEyZMoHbt2ixevJi3334b\nKP1cPwsjIyPWrl3LhAkT6NGjBwCdO3dm9uzZypuTHj16MHv2bKKiopg0aRL169dn3rx5yo1wxsbG\nxMTEMHr0aLp06UK9evWYMWOG8tFHIYQQJTO6fv36S7+RqVaruXbtWpHPXBuqvLw8WrZsSVBQkM5d\n9KJ48tOrQojHvao/vSrrmi+Bo0ePcvr0aVq1asWtW7dYuHAht2/fLvXn5oUQQgiQpK4jODiYDRs2\nFHusX79+zJ8//7mNvXjxYs6cOUOlSpVo2bIlO3bswMbG5rmNZ0j09R15Zmam3n7sRmKvGPocO+h/\n/PpAL5bfX5ScnBydO8MfVaNGjSKfMRfi79DnP3ASe8XQ59hB/+PXB3Kl/ggLCwtJ3EIIIfSWfFWX\nEEIIYSAkqQshhBAGQpK6EEIIYSAkqQshhBAGQpK6EEIIYSAkqQshhBAGQpK6EEIIYSAkqQshhBAG\nQpK6EEIIYSAkqQshhBAGQpK6EEIIYSAkqQshhBAGQpK6EEIIYSAkqQshhBAGQn56Veg91aeqig5B\nvISuz71e0SEI8cLJlboQQghhICSpCyGEEAZCkroQQghhICosqXt5eRESElKquklJSahUKq5evfqc\no3r+1Go1/fv3L1Obli1bEhUVVWIdlUpFXFzc3wntb43/vNjY2LB27doKGVsIIfRNhd0oFxMTQ+XK\nr959epGRkRQUFFR0GEIIIQxQhWXVmjVrVtTQFer111+v6BBKLT8/nypVqlR0GEIIIUrpuS2/e3l5\nERwczIQJE2jQoAENGjRg6tSpaLVa5fijy+/5+flMnz6dFi1aUKdOHZydnVm2bFmxfefl5eHn54e7\nuzs5OTlcuHABlUrF4cOHdeo9uiRdWCc2NpZu3bphaWlJmzZt2L17d6nOp3ALIDExEU9PT6ytrfHw\n8ODIkSM69fbv3897772HtbU1zZs3Z+zYsdy8eVM5/vjye25uLiNGjMDGxgY7Ozu++OIL+vfvj1qt\n1un33r17jBkzhvr16+Pg4MCiRYuKxHjlyhX69euHtbU1LVq0YP369TrH09PT6dmzJ1ZWVjRs2BC1\nWs2NGzeKxLZgwQIcHBxwcHAo9fi//vorfn5+1KtXj3r16jFo0CB+//13nTrffPMNLi4uWFhY4OLi\nwqpVq3SOZ2Vl4eXlhaWlJa1bt+bHH38s9rUQQghRvOe6px4bG4tWq+Wnn35iwYIFrFq1iiVLlhRb\nV61Ws27dOmbOnElaWhpRUVHFXtXevHmTPn368Oeff7J9+3YsLCzKFFNYWBgjRowgKSkJDw8PBg4c\nyKVLl0rdPjw8nLCwMBITE3njjTfw9/dXltPT09Pp3bs33bt3Z+/evaxZs4bjx48TGBj4xP6mTJlC\ncnIyMTExbN26lRMnTpCSklKk3pIlS3BwcCAxMZFPPvmE0NBQ0tLSdOpERETQvXt3kpKSGDJkCAEB\nAcobnTt37uDr64uZmRm7du0iJiaGtLS0IrElJyeTnp7Oxo0bdfboSxq/oKAAPz8/cnJy2Lp1K9u2\nbSM7Oxs/Pz9lbrZt20ZISAhqtZqUlBQCAgIYN24cP/zwAwBarZZBgwah1WrZuXMn0dHRREZGkpeX\nV+rXRgghXnXPdfnd0tKS2bNnY2RkRNOmTTlz5gxLliwpkkjOnj3Lpk2b2LhxI126dAGgYcOGRfr7\n448/8Pf3x9ramm+//ZZq1aqVOaZhw4bRq1cvAGbNmsXu3btZuXIlU6ZMKVX7yZMn4+7uDsD48ePp\n1q0bly5dwsbGhkWLFtGrVy+CgoKU+vPmzVNWFB5/A3L79m1iYmJYtmwZ77zzDgBRUVE6V8iF3n33\nXfz9/QEYMWIEy5cvJzExEVdXV6WOt7c3Q4cOBeDTTz8lKSmJpUuX8uWXXxIbG0tubi7Lly+nRo0a\nACxYsABvb2+ysrKwtbUFoGrVqkRHR1O1atVSj5+QkMCJEyc4fPgwDRo0AOCrr77CxcWFxMREPDw8\niI6Opn///kofTZo04ciRIyxcuJDu3buTkJBARkYGR48epX79+sD/vUkR4llkZmY+07GXnT7HDi9v\n/HZ2dhUdQrl4rkm9devWGBkZKc9dXV2ZOXOmznI0wLFjxzA2NqZTp04l9te7d2+cnJxYs2bNM99k\n16ZNG+WxsbExrVq1IiMjo9TtHR0dlcdWVlYA5OTkYGNjw9GjR8nKymLLli1KncIr1XPnzhVJ6ufO\nneP+/fu0atVKKTMzMys2qT86buHYOTk5Tzy3wuc7d+4EQKPR4OjoqCR0gLZt22JsbExGRoaS1Js3\nb14koT9tfI1Gg7W1tZLQ4eGbMmtrazIyMvDw8ECj0eDn56fTR/v27ZUrdY1GQ926dZWEDg//+zE2\nlk9dimfzpD/SmZmZevsHXJ9jB/2PXx+8FLefl/Zu8K5du7JlyxbS09NxdnZWygv/8D/az/3798s3\nyP9lYmKiPC58w1I4rlar5YMPPmDkyJFF2llbWxcpK8td8I+OWzh2WdqXVPfRN15mZmZlHr+goECn\njyf1XVydx+dQCCHEs3uul0EHDx7U+WN94MABrK2tee2113TqOTs7o9VqSUpKKrG/yZMnM2zYMHx8\nfDh27JhSXrt2bQCys7OVsuPHjxfbxy+//KI8Ligo4NChQzRr1qz0J1UCZ2dnTp06ha2tbZF/1atX\nL1Lf1tYWExMTDh06pJTduXOHkydPPtP4j55b4fPCc7O3tyc9PZ1bt24px/fv349Wq/3b529vb8+l\nS5e4cOGCUnb+/HkuX76Mvb09AM2aNSM1NVWnXUpKinK8sI/ffvtNOX7w4EHlxkohhBBP91yTenZ2\nNhMnTiQzM5O4uDgWLVpU7FVs48aN6dWrF6NHjyYuLo7z58+zb98+1q1bV6Tu1KlTGTp0KD4+Pkri\nrl69Om3atGHhwoWcOnWK/fv3P3GPfOXKlcTFxZGZmcnEiRP59ddfGTZsWLmc7yeffMKhQ4cIDg5W\nluJ//PFHxowZU2x9c3NzBg0apNx4l5GRwejRo0u88i3Jtm3bWLVqFWfPnuWLL74gMTFRuYu+b9++\nmJqaEhAQQHp6OsnJyQQHB+Pt7a0svT8rDw8PWrRogb+/P0eOHOHw4cMMHz4cZ2dn5f6DoKAg1q9f\nz4oVKzh79izLly8nNjaW0aNHK300bdqUgIAAjh07RlpaGp999tkr+V0GQgjxrJ5rUu/bty9arRZP\nT09Gjx7N4MGDi03qAMuWLcPX15eJEyfi6urKyJEji+y9FwoNDeXDDz+kZ8+eSmKPjo4GHt7QFRwc\n/MSkHhYWxuLFi3Fzc1PuArexsSmHs4UWLVqwY8cOLl68SI8ePXBzc2P69Okl3qH/+eef0759e95/\n/328vb1xdHTkrbfeeqabACdOnMjWrVvp2LEjK1euZPHixbz99tsAmJqasmnTJm7duoWnpycDBw6k\nTZs2yrz9HUZGRqxdu5ZatWrRo0cPvL29qVOnDmvXrlXenPTo0YPZs2ezZMkS2rZty7Jly5g3b55y\nI5yxsTExMTFotVq6dOlCQEAAn376abH7+0IIIYpndP369eeymenl5YWDgwNz5sx5Ht2X2YULF3B2\ndq4DhZAAACAASURBVCY+Ph4XF5eKDueJ8vLyaNmyJUFBQTp30Ysnk59eFcV50k+v6vPNWvocO+h/\n/PpA1jYr2NGjRzl9+jStWrXi1q1bLFy4kNu3b9O7d++KDk0IIYSekaT+v4KDg9mwYUOxx/r168f8\n+fOf29iLFy/mzJkzVKpUiZYtW7Jjx45y2xJ4FTzpiuxlp89XLfocuxCG7Lktv+ubnJwcnTvDH1Wj\nRo0yf3OdEE+jz4lRYq8Y+hw76H/8+kCu1P+XhYWFJG4hhBB6Tb6uSwghhDAQktSFEEIIAyFJXQgh\nhDAQktSFEEIIAyFJXQghhDAQktSFEEIIAyFJXQghhDAQktSFEEIIAyFJXQghhDAQktSFEEIIAyFJ\nXQghhDAQktSFEEIIAyFJXQghhDAQktSFEEIIAyE/vSr0nupTVUWHoLg+93pFhyCEeIXJlboQQghh\nICSpCyGEEAZCknoFU6vV9O/f/4nPhRBCiNKSpF6OvLy8CAkJqegwDIbMpxBClI0kdSGEEMJASFIv\nJ2q1muTkZFasWIFKpUKlUnHhwgWSk5Px9PTE0tISOzs7Jk2aRH5+fqn7TU5OpkuXLtjY2PDmm2/i\n6enJyZMnn9ru2rVrfPTRRzg4OGBlZUW7du2IiYnRqePl5cXYsWOZPHkyDRs2pHHjxixdupS8vDw+\n/fRT3nzzTVq0aMG6det02qWnp9OzZ0+srKxo2LAharWaGzdu6MzF41sIERERtG/fvkidpUuX0rx5\ncxo0aMDIkSO5c+dOifMphBDiySSpl5PIyEhcXV3x8/NDo9Gg0WgwMTGhb9++ODk5sWfPHqKioti0\naRPh4eGl6vOvv/5i4MCBtGvXjr179/Lzzz8TEBBApUqVntr23r17ODs7s27dOlJTUwkICCA4OJjE\nxESderGxsZibm7Nr1y7GjBnDpEmT8PPzo3HjxiQkJDBgwABGjx7N5cuXAbhz5w6+vr6YmZmxa9cu\nYmJiSEtLIzAwsMxzlpKSwqlTp/jPf/7DN998w/bt21m2bBlQ/HzWq1evzGMIIcSrRJJ6OXn99dcx\nMTHB1NQUS0tLLC0t+frrr7G0tGTevHk0a9aMbt26ERYWxooVK5Qr0pLcunWLGzdu0K1bNxo1akTT\npk3p27cvzZo1e2rbunXrMnr0aJycnGjYsCFDhgzB29ubjRs36tSzt7dn0qRJNG7cmMDAQGrVqkXl\nypVRq9XY2toyYcIECgoKSEtLAx6+CcjNzWX58uU4Ojri5ubGggUL2LZtG1lZWWWasxo1avDFF1/Q\nrFkz3n33XXx8fJQ3HcXNZ2nezAghxKtMvnzmOdJoNLRp0wZj4/9779S+fXvy8/PJysqiRYsWJbav\nWbMmAwcOpE+fPnTu3Bl3d3d8fHxKdcX64MED5s+fz+bNm7l8+TL5+fnk5+fj5uamU8/R0VF5bGRk\nhIWFhU6ZiYkJKpWKnJwc5ZwcHR2pUaOGUqdt27YYGxuTkZGBra3tU2Mr1KxZMypX/r//BK2srPjl\nl19K3f5llJmZ+Vzrv0wk9oqhz7HDyxu/nZ1dRYdQLiSpP0cFBQUYGRkVe+xJ5Y9bsmQJarWaXbt2\n8cMPPzBjxgzWrl2Lp6dnie2ioqKIjo4mMjISBwcHzM3NmT59upKcC5mYmBSJ69FEW1im1WqVc3qS\nwnMyNjYuUu+vv/4qUr+4sUvqXx+U5Q9DZmam3v4hkdgrhj7HDvofvz6Q5fdyVKVKFR48eKA8t7e3\n58CBA0pChIf7yFWqVKFRo0al7rdly5aMGTOG77//Hjc3N7777runtklJSaFbt24MGDAAJycnGjVq\nxJkzZ8p2QsWwt7cnPT2dW7duKWX79+9Hq9Uq2wK1a9cmOztbp93x48fLPNbj8ymEEKJkktTL0Ztv\nvsnBgwe5cOECV69e5aOPPiI7O5tx48ah0Wj4n//5H8LDwxk+fDimpqZP7e/8+fNMmzaN/fv3c/Hi\nRfbs2UN6enqp9tSbNGnCnj17SElJ4fTp04SEhHDx4sW/fY59+/bF1NSUgIAA0tPTSU5OJjg4GG9v\nb2Xp3d3dnWPHjrFmzRqysrJYuHAhqampZR7r8fl89M2REEKIoiSpl6OgoCCqVKlCu3btaNy4Mffv\n3yc2NpZjx47RqVMnAgMD6dOnD6GhoaXqz9TUlDNnzjBkyBBat27NyJEj6du3L2PGjHlq25CQEN5+\n+2369u3Le++9h6mpKX379v27p4ipqSmbNm3i1q1beHp6MnDgQNq0aUN0dLRSx9PTkwkTJjBjxgw8\nPDy4ePEiH3/8cZnHenw+f/31178dvxBCGDKj69ev6/cmpnjl6euvtOnz/qLEXjH0OXbQ//j1gVyp\nCyGEEAZCkrqe8vX1xcbGpth/8+bNq+jwhBBCVAD5SJueWrRoEffu3Sv2WM2aNV9wNBWrLEveQghh\nyCSp66m6detWdAhCCCFeMrL8LoQQQhgISepCCCGEgZCkLoQQQhgISepCCCGEgZCkLoQQQhgISepC\nCCGEgZCkLoQQQhgISepCCCGEgZCkLoQQQhgISepCCCGEgZCkLoQQQhgISepCCCGEgZCkLoQQQhgI\nSepCCCGEgZCkLoQQQhgI+T11ofdUn6r+dh/X514vh0iEEKJiyZW6EEIIYSAkqQshhBAGQpL6C9C/\nf3/UanVFhwFAamoqHTp0wMLCAi8vryeWPUlSUhIqlYqrV6++iHCFEEKUgeypv2ImTpxIixYt2LBh\nA2ZmZk8sE0IIoX/kSr2C5efnv9DxsrKycHd3p169etSsWfOJZS+jFz1XQgihbySpl7M7d+6gVqux\nsbHBzs6OefPm6Rxv2bIlERERjBo1ijfffJPhw4cDkJ6eTs+ePbGysqJhw4ao1Wpu3LihtFOr1fTv\n3585c+ZgZ2eHjY0NI0eO5O7du0qdvLw8Jk6ciJ2dHZaWlnTp0oWUlBQALly4gEql4ubNmwQGBqJS\nqVi7dm2xZaVx4sQJPD09sba2xsPDgyNHjugc37p1Kx06dKBOnTo4Ojoyd+5cCgoKdOYhKipKp42X\nlxchISFPnSshhBDFk6RezqZOnUpCQgKrV68mLi6OY8eOsW/fPp06S5YsoWnTpiQkJBAaGsqdO3fw\n9fXFzMyMXbt2ERMTQ1paGoGBgTrtkpOTOXHiBHFxcaxevZr4+HjCwsKU46GhoWzZsoXo6Gj27NmD\ng4MDvr6+ZGdnU69ePTQaDaampkRERKDRaPDx8SlS1rt371KdZ3h4OGFhYSQmJvLGG2/g7++vJO0j\nR44wZMgQevTowb59+wgLC2P+/Pl8+eWXZZ7Px+dKCCHEk8meejm6ffs2a9asITo6Gk9PTwAWL16M\ng4ODTr0OHTrwySefKM9XrVpFbm4uy5cvp0aNGgAsWLAAb29vsrKysLW1BcDY2JjFixdjbm6Og4MD\n06ZNIygoSEnsK1euZNGiRXTt2hWA+fPns2fPHr766iumTJmCpaUlRkZGvPbaa1haWgJgZmZWpKw0\nJk+ejLu7OwDjx4+nW7duXLp0CRsbGxYvXkzHjh357LPPAGjSpAlnz55l4cKFjBgxokxz+vhcPS+Z\nmZnPfYyXadzyILFXDH2OHV7e+O3s7Co6hHIhSb0cnTt3jvz8fFxdXZUyc3NzHB0ddeq5uLjoPNdo\nNDg6OioJHaBt27YYGxuTkZGhJHVHR0fMzc2VOq6uruTn53Pu3DkA7t+/T7t27ZTjlSpVwtXVlYyM\njPI7yf/16DlZWVkBkJOTg42NDRqNhn/+85869du3b8+sWbO4efMmr732WqnHeXyunpeK+B86MzNT\nb/+QSOwVQ59jB/2PXx/I8ns5enTPuCSP32FeUjsjI6MyjV1c/dL2URYmJiZF+i+MoaCg4IljFpYb\nGxsXOe+//vqrSH25G18IIUpPkno5srW1xcTEhAMHDihlubm5nDx5ssR29vb2pKenc+vWLaVs//79\naLVamjVrppSdPHmS3Nxc5fmBAweoUqUKjRo1wtbWlipVqig3xgE8ePCAtLQ0nT5eBHt7e1JTU3XK\nUlJSsLGxUVYjateuTXZ2tnL83r17nD59+oXGKYQQhkaSejkyNzdn8ODBTJs2jfj4eE6dOkVgYCBa\nrbbEdn379sXU1JSAgADS09NJTk4mODgYb29vZekdHibpwMBATp06RXx8POHh4Xz44YeYmZlhZmbG\nsGHDCA8PZ+fOnWg0GsaOHUtOTg4ff/zx8z51HaNGjSI5OZmIiAjOnDnDhg0bWLx4MaNHj1bquLu7\nExsbS1JSkjJPxV2pCyGEKD3ZUy9nn3/+Obm5uQwaNIjq1avj7+/PnTt3SmxjamrKpk2bmDRpEp6e\nnlStWpX33nuPyMhInXodO3bE3t4eb29v7t69i7e3N+Hh4crxwsejRo3ixo0bODk5sXHjRmXP+0V5\n6623+Pbbb4mMjOSLL76gTp06jBkzBn9/f6VOcHAwFy9exM/PDzMzM8aNG8fly5dfaJxCCGFojK5f\nv166jWBRodRqNdeuXWP9+vUVHcpLR19/pU2fbxqS2CuGPscO+h+/PpDldyGEEMJASFIXOoKDg7Gx\nsSn2X3BwcEWHJ4QQogSyp64nli5d+kLG+eyzzwgKCir22KOfo3+ZVMTSuRBCvIwkqQsdFhYWWFhY\nVHQYQgghnoEsvwshhBAGQpK6EEIIYSAkqQshhBAGQpK6EEIIYSAkqQshhBAGQpK6EEIIYSAkqQsh\nhBAGQpK6EEIIYSAkqQshhBAGQpK6EEIIYSAkqQshhBAGQpK6EEIIYSAkqQshhBAGQpK6EEIIYSAk\nqQshhBAGQn5PXeg91acqnefX516voEiEEKJiyZW6EEIIYSAkqQshhBAGQpJ6OfHy8iIkJKRUdZOS\nklCpVFy9evU5R6VfyjKHQgghipI99XISExND5coynaWRlJSEt7c3Z8+epVatWhUdjhBCGAzJQuWk\nZs2aFR2CEEKIV5wsv5eSl5cXwcHBTJgwgQYNGtCgQQOmTp2KVqtVjj+6dJyfn8/06dNp0aIFderU\nwdnZmWXLlhXbd15eHn5+fri7u5OTk8OFCxdQqVQcPnxYp55KpSIuLg5AqRMbG0u3bt2wtLSkTZs2\n7N69u1TnU7gF8NNPP9G5c2esrKzo3r07v//+O3v37qVjx47Y2NjQv39/rl27prTTarXMnj0bR0dH\n6tSpQ4cOHfj++++V44VxxcXF4ePjg7W1NW3btiU+Pl457u3tDUDjxo1RqVSo1Wqd/qdPn46trS1N\nmjRhypQpyhwLIYQomST1MoiNjUWr1fLTTz+xYMECVq1axZIlS4qtq1arWbduHTNnziQtLY2oqChe\nf/31IvVu3rxJnz59+PPPP9m+fTsWFhZliiksLIwRI0aQlJSEh4cHAwcO5NKlS6VuHxERQUREBD//\n/DPXr19n2LBhzJ49m4ULF7J9+3ZOnTpFRESEUn/p0qVERUUxbdo09u3bh5eXF4MHD+bYsWM6/c6Y\nMYMRI0awd+9eXFxcGDZsGLdv36ZevXqsXr0agNTUVDQaDZGRkUq72NhYKlWqxM6dO5kzZw5Lly5l\n8+bNZZoTIYR4VcnyexlYWloye/ZsjIyMaNq0KWfOnGHJkiUEBgbq1Dt79iybNm1i48aNdOnSBYCG\nDRsW6e+PP/7A398fa2trvv32W6pVq1bmmIYNG0avXr0AmDVrFrt372blypVMmTKlVO0nT55Mhw4d\nABg6dCjjx48nISGBt956C4D333+frVu3KvWjo6MJDAykb9++Svt9+/YRHR3Nl19+qdQbOXIk3bt3\nByA0NJR169Zx/Phx2rdvr2xVWFhYFNlTb9asGZMnTwagSZMmrFq1isTERHx9fUs9J5mZmaWuW9H0\nKdbHSewVQ59jh5c3fjs7u4oOoVxIUi+D1q1bY2RkpDx3dXVl5syZ3Lx5U6fesWPHMDY2plOnTiX2\n17t3b5ycnFizZs0z32TXpk0b5bGxsTGtWrUiIyOj1O0dHR2Vx3Xq1Cm2LCcnB3i4qnD58mXatWun\n00f79u3ZuXPnE/u1trYGUPopbTwAVlZWpWr3KH35nzMzM1NvYn2cxF4x9Dl20P/49YEsvz8HBQUF\nparXtWtXUlNTSU9P1yk3NjYu0s/9+/fLL8BHmJiYKI8L37A8XlaaPe1H3+w8qd/SzMuj7QrblnY+\nhRDiVSdJvQwOHjyok2AOHDiAtbU1r732mk49Z2dntFotSUlJJfY3efJkhg0bho+Pj86edO3atQHI\nzs5Wyo4fP15sH7/88ovyuKCggEOHDtGsWbPSn1QZvPbaa1hbW5OamqpTnpKSUqYxq1SpAsCDBw/K\nNT4hhHjVyfJ7GWRnZzNx4kQ+/vhjTp48yaJFi4r9spTGjRvTq1cvRo8eTUREBM7Ozly6dImLFy8y\nYMAAnbpTp06loKAAHx8f4uLiaNmyJdWrV6dNmzYsXLiQRo0acfPmTcLDw4uNaeXKlTRp0gQHBwe+\n+uorfv31V4YNG/Zczh8gKCiIiIgIGjduzFtvvcX69etJSUkhISGh1H3Ur18fIyMj/ud//ofu3btT\nrVo1zM3Nn1vMQgjxqpCkXgZ9+/ZFq9Xi6emJkZERgwcPZuTIkcXWXbZsGTNnzmTixIlcvXqVunXr\nPrFuaGgoBQUF9OzZU0ns0dHRjB49mnfffZdGjRoxd+5c3nvvvSJtw8LCWLx4MUePHqV+/frExMRg\nY2NTruf9qICAAG7fvk1YWBj//e9/sbOzY/Xq1Tg5OZW6j7p16zJp0iRmzJjB6NGjGTBgAEuXLn1u\nMQshxKvC6Pr167JhWQpeXl44ODgwZ86cig4FePh5b2dnZ+Lj43FxcanocCqUvv5Kmz7fNCSxVwx9\njh30P359IHvqQgghhIGQpG6ggoODsbGxKfZfcHBwRYcnhBDiOZA99VJ69KtQXwYNGjTg+vUnLzN/\n9tlnBAUFFXusRo0azyusCqEvy+1CCPG8SVI3UBYWFmX+ylkhhBD6TZbfhRBCCAMhSV0IIYQwEJLU\nhRBCCAMhSV0IIYQwEJLUhRBCCAMhSV0IIYQwEJLUhRBCCAMhSV0IIYQwEJLUhRBCCAMhSV0IIYQw\nEJLUhRBCCAMhSV0IIYQwEJLUhRBCCAMhSV0IIYQwEJLUhRBCCAMhv6cu9J7qU5Xy+Prc6xUYiRBC\nVCy5UhdCCCEMhCR1IYQQwkDoZVLv378/arW6osMAIDU1lQ4dOmBhYYGXl9cTy8ri8OHDqFQqLly4\nUN7hlujKlSv06tWLunXrolKpnt7gOUhKSkKlUnH16tUKGV8IIfSZ7Kn/TRMnTqRFixZs2LABMzOz\nJ5bpg6ioKLKzs0lKSsLc3LyiwxFCCFFGenmlXpL8/PwXOl5WVhbu7u7Uq1ePmjVrPrFMH2RlZeHs\n7Ezjxo2xtLSs6HCEEEKU0Uuf1O/cuYNarcbGxgY7OzvmzZunc7xly5ZEREQwatQo3nzzTYYPHw5A\neno6PXv2xMrKioYNG6JWq7lx44bSTq1W079/f+bMmYOdnR02NjaMHDmSu3fvKnXy8vKYOHEidnZ2\nWFpa0qVLF1JSUgC4cOECKpWKmzdvEhgYiEqlYu3atcWWPc3PP/9MmzZtsLS0pHv37pw5c0bn+LVr\n1/joo49wcHDAysqKdu3aERMToxz/7rvvaNSoEXl5eTrthg8fzoABA5Tn33zzDS4uLlhYWODi4sKq\nVat05nHHjh2sW7cOlUqFWq0mLCwMX19fpc6qVatQqVRs3rxZKevatStz585Vnv/www907twZS0tL\nnJyc+Pzzz3XeaOXn5xMWFoaDgwN169blnXfeYdeuXU+cm7y8PPz8/HB3dycnJ+epcymEEK+ylz6p\nT506lYSEBFavXk1cXBzHjh1j3759OnWWLFlC06ZNSUhIIDQ0lDt37uDr64uZmRm7du0iJiaGtLQ0\nAgMDddolJydz4sQJ4uLiWL16NfHx8YSFhSnHQ0ND2bJlC9HR0ezZswcHBwd8fX3Jzs6mXr16aDQa\nTE1NiYiIQKPR4OPjU6Ssd+/eJZ7fb7/9hp+fHx4eHiQlJeHv768TA8C9e/dwdnZm3bp1pKamEhAQ\nQHBwMImJiQD4+Pig1WrZsWOH0ubGjRts376dwYMHA7Bt2zZCQkJQq9WkpKQQEBDAuHHj+OGHHwCI\nj4/Hw8ODXr16odFoiIyMxM3NjdTUVP766y8A9u7dS61atUhKSgIevuE6dOgQbm5uAOzatQt/f3+G\nDx9Oamoq0dHRxMXFMX36dCWuUaNGkZyczIoVK9i3bx/vv/8+AwYM4Pjx40Xm5ubNm/Tp04c///yT\n7du3Y2FhUeJcCiHEq87o+vXrBRUdxJPcvn0bW1tboqOj6devn1Lm4OCAl5cXS5cupWXLljg4OLB+\n/Xql3apVq5g6dSrp6enUqFEDeHgDlre3N4cOHcLW1ha1Ws3333/PyZMnlf3j9evXExQUxLlz5wBo\n2LAhixYt4v333wfgwYMHtGrVCl9fX6ZMmQKAjY0Ns2fPxs/PTxm/uLInmT59OnFxcfzyyy8YGRkB\nMGfOHGbOnMnRo0dp0KBBse2GDRuGmZkZUVFRAISEhJCVlcWmTZsA+Prrr5k1axYnT56kcuXKdO3a\nlSZNmrB48WKlD7Vazblz5/jxxx+BhzcgvvHGGyxdulSZ64YNG7Jjxw5cXV1xcHBg+PDhrF27ll9+\n+YX4+Hj8/Py4cOECJiYmdO/enXfeeYfx48crY2zfvp0RI0bw22+/cf78ed5++22OHTtG/fr1lToD\nBw7E2tqaefPmKa/T/v378ff3x9ramm+//ZZq1ao9cQ4f/Zz6gREHnjrnQgjxODs7u4oOoVy81DfK\nnTt3jvz8fFxdXZUyc3NzHB0ddeq5uLjoPNdoNDg6OioJHaBt27YYGxuTkZGBra0tAI6Ojjo3hLm6\nupKfn68k9fv379OuXTvleKVKlXB1dSUjI6PczlGj0dC6dWsloRfG8agHDx4wf/58Nm/ezOXLl8nP\nzyc/P1+5Qgb44IMP6Ny5M7///js2NjbExMTw/vvvU7lyZWWcx99ktG/fXrlSL465uTnOzs7KFfqt\nW7cYPnw4s2bN4vLly+zduxdXV1dMTEwAOHr0KIcOHWLhwoVKH1qtlrt373LlyhWOHj1KQUGBzpzC\nwyV2d3d3nbLevXvj5OTEmjVrlHMoDX36HzMzM1Ov4n2UxF4x9Dl20P/49cFLndQLCkq3iPD4HeYl\ntXs0eZZm7OLql7aPsoxTkqioKKKjo4mMjMTBwQFzc3OmT5+us8fcsmVLnJ2d+fe//42XlxeHDx/m\nyy+/fGrcTzsXNzc3kpKSqFWrFu3bt8fc3Jy3336bvXv3snfvXv7xj38odbVaLRMmTMDHx6dIP7Vr\n10ar1WJkZMTu3buVNwKFHr8S79q1K1u2bCE9PR1nZ+cSYxRCCPHQS72nbmtri4mJCQcO/N+Sam5u\nLidPniyxnb29Penp6dy6dUsp279/P1qtlmbNmillJ0+eJDc3V3l+4MABqlSpQqNGjbC1taVKlSrK\njXHw8Io5LS1Np4+/y97enoMHD+ok90fPFyAlJYVu3boxYMAAnJycaNSoUZGb6QA+/PBD/v3vf7N6\n9WratWun8464WbNmpKamFunX3t6+xPjc3NzYv38/8fHxysqAm5sbO3fu1NlPB3B2dub06dPY2toW\n+Ve5cmWcnJwoKCjgypUrRY7XrVtXZ9zJkyczbNgwfHx8OHbs2FNmUQghBLzkSd3c3JzBgwczbdo0\n4uPjOXXqFIGBgWi12hLb9e3bF1NTUwICAkhPTyc5OZng4GC8vb2VpXd4mKQDAwM5deoU8fHxhIeH\n8+GHH2JmZoaZmRnDhg0jPDycnTt3otFoGDt2LDk5OXz88cfldo5Dhw7l4sWLTJw4kczMTOLi4vjm\nm2906jRp0oQ9e/aQkpLC6dOnCQkJ4eLFi0X66tOnD//9739ZuXIlgwYN0jkWFBTE+vXrWbFiBWfP\nnmX58uXExsYyevToEuNr3749+fn5bNu2jU6dOgEPk/rmzZsxMTGhVatWSt3x48ezceNGZs6cycmT\nJzl9+jRxcXGEhoYq59GvXz9GjhxJXFwc58+f5/Dhw0RFRbF169YiY0+dOpWhQ4fi4+NT7I10Qggh\ndL3Uy+8An3/+Obm5uQwaNIjq1avj7+/PnTt3SmxjamrKpk2bmDRpEp6enlStWpX33nuPyMhInXod\nO3bE3t4eb29v7t69i7e3N+Hh4crxwsejRo3ixo0bODk5sXHjRqysrMrt/OrXr8+aNWuYPHky3377\nLW+99RZhYWH4+/srdUJCQrhw4QJ9+/alWrVqDBw4kL59+xbZ269RowY+Pj7ExcXRq1cvnWM9evRg\n9uzZREVFMWnSJOrXr8+8efPo3r17ifGZm5vz1ltvcfr0aZycnICHe/6VK1fW2U8H8PT0ZMOGDcyZ\nM4fo6GgqV65M48aNGThwoFJn8eLFzJ07l9DQUC5dukTNmjV5++23lTcMjwsNDaWgoICePXsSFxdH\ny5YtSzexQgjxCnqp735/ntRqNdeuXdO5a94Q+Pr6UrduXRYtWlTRobww+vorbfp805DEXjH0OXbQ\n//j1wUt/pS5K588//yQhIYHdu3ezd+/eig5HCCFEBZCk/pwFBwezYcOGYo/169eP+fPnl8s47u7u\nXL9+ndDQUBwcHMqlT32hT1fnQgjxPL2ySb3wC1aet88++4ygoKBijz36Ofq/S24kE0II8com9RfF\nwsJCvt5UCCHEC/FSf6RNCCGEEKUnSV0IIYQwEJLUhRBCCAMhSV0IIYQwEJLUhRBCCAMhSV0IIYQw\nEJLUhRBCCAMhSV0IIYQwEJLUhRBCCAMhSV0IIYQwEJLUhRBCCAMhSV0IIYQwEJLUhRBCCAMhSV0I\nIYQwEJLUhd5TfapC9amqosMQQogKJ0ldCCGEMBCS1IUQQggDIUldCCGEMBAGldS9vLwICQkpdzpd\nPgAAIABJREFUVd2kpCRUKhVXr159zlGVzeHDh1GpVFy4cOGFjnvlyhV69epF3bp1UakqZn/6ZX1N\nhBBCX1Su6ADKU0xMDJUrG9QpvTBRUVFkZ2eTlJSEubl5RYcjhBDiGRhUBqxZs2ZFh6C3srKycHZ2\npnHjxhUdihBCiGekV8vvXl5eBAcHM2HCBBo0aECDBg2YOnUqWq1WOf7o8nt+fj7Tp0+nRYsW1KlT\nB2dnZ5YtW1Zs33l5efj5+eHu7k5OTg4XLlxApVJx+PBhnXoqlYq4uDgApU5sbCzdunXD0tKSNm3a\nsHv37lKf088//0ybNm2wtLSke/funDlzRuf4tWvX+Oijj3BwcMDKyop27doRExOjHP/uu+9o1KgR\neXl5Ou2GDx/OgAEDlOfffPMNLi4uWFhY4OLiwqpVq5RjLVu2ZMeOHaxbtw6VSoVarSYsLAxfX1+l\nzqpVq1CpVGzevFkp69q1K3PnzlWe//DDD3Tu3BlLS0ucnJz4/PPPyc/PV47n5+cTFhaGg4MDdevW\n5Z133mHXrl1PnJvHXxMhhBAl06ukDhAbG4tWq+Wnn35iwYIFrFq1iiVLlhRbV61Ws27dOmbOnEla\nWhpRUVG8/vrrRerdvHmTPn368Oeff7J9+3YsLCzKFFNYWBgjRowgKSkJDw8PBg4cyKVLl57a7rff\nfsPPzw8PDw+SkpLw9/cnLCxMp869e/dwdnZm3bp1pKamEhAQQHBwMImJiQD4+Pig1WrZsWOH0ubG\njRts376dwYMHA7Bt2zZCQkJQq9WkpKQQEBDAuHHj+OGHHwCIj4/Hw8ODXr16odFoiIyMxM3NjdTU\nVP766y8A9u7dS61atUhKSgLgzp07HDp0CDc3NwB27dqFv78/w4cPJzU1lejoaOLi4pg+fboS16hR\no0hOTmbFihXs27eP999/nwEDBnD8+PEic/N3XxMhhHgV6d3yu6WlJbNnz8bIyIimTZty5swZlixZ\nQmBgoE69s2fPsmnTJjZu3EiXLl0AaNiwYZH+/vjjD/z9/bG2tubbb7+lWrVqZY5p2LBh9OrVC4BZ\ns2axe/duVq5cyZQpU0pst3LlSurVq1fkfGbOnKnUqVu3LqNHj1aeDxkyhD179rBx40Y6d+5M9erV\n6devHzExMUoMGzdupEaNGnTt2hWA6Oho+vfvj7+/PwBNmjThyJEjLFy4kO7du1O7dm2qVq1KtWrV\nsLS0BKB9+/bcu3ePQ4cO4erqSnJyMoGBgaxduxaA/fv3Y2JiQqtWrQCYO3cuQUFBDBo0CIBGjRox\nbdo0RowYweeff8758+fZuHEjx44do379+gD4+/uTkJDAt99+y7x58/72a5KZmVmqei8TfYy5kMRe\nMfQ5dnh547ezs6voEMqF3iX11q1bY2RkpDx3dXVl5syZ3Lx5U6fesWPHMDY2plOnTiX217t3b5yc\nnFizZs0z32TXpk0b5bGxsTGtWrUiIyPjqe00Gk2x5/OoBw8eMH/+fDZv3szly5fJz88nPz9fuUIG\n+OCDD+jcuTO///47NjY2xMTE8P777yvno9Fo8PPz0+m3ffv2ypV6cczNzXF2dlau0G/dusXw4cOZ\nNWsWly9fZu/evbi6umJiYgLA0aNHOXToEAsXLlT60Gq13L17lytXrnD06FEKCgpo166dzjh5eXm4\nu7vrlD3ra6Jv/1NmZmbqXcyFJPaKoc+xg/7Hrw/0LqmXVkFBQanqde3alS1btpCeno6zs7NSbmxs\nXKSf+/fvv/AYo6KiiI6OJjIyEgcHB8zNzZk+fbrOHnPLli1xdnbm3//+N15eXhw+fJgvv/xSp59H\n3ziUVPYoNzc3kpKSqFWrFu3bt8fc3Jy3336bvXv3snfvXv7xj38odbVaLRMmTMDHx6dIP7Vr10ar\n1WJkZMTu3buVNwKFHr8Sf9JrIoQQomR6l9QPHjxIQUGBkpAOHDiAtbU1r732mk49Z2dntFotSUlJ\nyvJ7cSZPnkzNmjXx8fEhLi4OJycn4GEiAsjOzlbqFrf3C/DLL7/QuXNn4GGiPnToED179nzqudjb\n27N169Yi5/OolJQUunXrptz0VlBQwJkzZ4rcG/Dhhx+ycOFCrl69Srt27XTeDTdr1ozU1FRlj72w\nX3t7+xLjc3NzY8WKFbz++uvKyoCbmxs7d+7k0KFDhIeHK3WdnZ05ffo0tra2xfbl5OREQUEBV65c\nKXJl/rgnvSZCCCFKpnc3ymVnZzNx4kQyMzOJ+//t3XlcFfX+x/EXi5iCXtxYBEW2UHDFPCj6E/No\n/tRLoImkSamZCopXStwFzYWUcgFESSXXSjQVC7tlXBRQXDK3XNByzV0MUTQRD78//DG300EDZT1+\nno8Hjwdnvt+Zec+M8jkz35lzEhOJiooiKChIp5+joyN9+vRhzJgxJCYmcu7cOXbv3s2XX36p03fa\ntGkMGTIEX19fpXDXqFGDdu3asWjRIk6cOMHevXufOEYeHx9PYmIip0+fZuLEiVy8eJGhQ4f+7bYM\nGTKECxcuaG3PZ599ptXHycmJ1NRUMjIyOHXqFKGhoVy4cEFnWW+88QbXr18nPj5eGdcuFBwczPr1\n61m2bBm//vorcXFxbNiwQWusvigdOnQgLy+Pr7/+WhnG6NSpE5s2bdIaTwcYP348GzduZPbs2Rw/\nfpxTp06RmJhIWFiYsh39+/cnKChIOR4HDx4kOjqarVu36qy7qGMihBDi6apcUffz80Oj0aBWqxkz\nZgwBAQFFFnWApUuX0q9fPyZOnIhKpSIoKEhn7L1QWFgY77zzDj4+PkoRiYmJAaBr166EhIQ8saiH\nh4ezePFiOnXqRHJyMmvXrsXGxuZvt6VRo0asWbOG5ORkOnXqRGxsrM7d76Ghobi7u+Pn50evXr2o\nWbMmfn5+OsuqVasWvr6+mJiYKDfMFfrnP//JvHnziI2NxcPDg6VLl/LJJ5/Qs2fPp+YzMzOjdevW\nmJqaKmfLKpUKY2NjrfF0ALVaTUJCAunp6ajVatRqNQsWLMDW1lbps3jxYt566y3CwsJo164d/v7+\n7Nq1i8aNGxe5/qKOiRBCiCczyM7OLt7gcyXQu3dvXF1diYyMrOgowOPn1Fu1akVKSgpt2rSp6Dj0\n69ePhg0bEhUVVdFRylXh165mf5xdwUlKpirfNCTZK0ZVzg5VP39VUOXG1IWu33//nR07dvCf//yH\n9PT0io4jhBCigkhRL0MhISEkJCQU2da/f38WLFhQKuvp3Lkz2dnZhIWF4erqWirLrEqq2hm6EEKU\nlSpV1JOSkio6ghY7Ozuys59cUCZPnkxwcHCRbbVq1Sq1HDLeLIQQAqpYUa9qGjRoIB9vKoQQotxU\nubvfhRBCCFE0KepCCCGEnpCiLoQQQugJKepCCCGEnpCiLoQQQugJKepCCCGEnpCiLoQQQugJKepC\nCCGEnpCiLoQQQugJKepCCCGEnpCiLoQQQugJKepCCCGEnpCiLoQQQugJKepCCCGEnpCiLqo883Hm\nmI8zr+gYQghR4aSoCyGEEHpCiroQQgihJ6SoCyGEEHpCirqotHr37k1oaGhFxxBCiCpDivoLJj8/\nn4KCgoqOIYQQogzobVHv3bs3ISEhTJgwATs7O+zs7Jg2bRoajQaA9evX8+qrr2Jra4uTkxPvvPMO\nly9fVuZ/+PAh48ePp2nTplhYWODm5sb06dOV9q1bt+Lp6YmVlRVNmjShV69eXL9+XWn/9ttv8fLy\nwtLSkpYtWzJz5kzy8vKU9hYtWhAZGcnYsWNp1KgRrq6uREVFaW3DL7/8Qq9evbC0tOSVV17h+++/\nx8bGhnXr1il9Ll++zNChQ5Vt7N+/P7/++qvSHhERQYcOHVi3bh2tW7fGwsKC3Nzcp+67goICoqOj\ncXd3x8LCAldXV2bMmKG0Hzt2DB8fH2XbAwMDuX37ttIeGBiIv7+/1jILc/y1z5IlS2jWrBl2dnYE\nBQVx7949pX3Xrl0sW7YMc3NzzM3NOX/+/FNzCyHEi05vizrAhg0b0Gg0bN++nYULF7Jq1SpiY2MB\nyMvLY9KkSaSnp7N+/XqysrJ49913lXmXLl1KUlISK1as4MCBA8THx+Pk5ATAtWvXePfddxkwYAB7\n9+5l27ZtvPnmm8q8ycnJDB8+nPfee489e/YQExNDYmIiH374oVa+2NhYXF1d2blzJ//6178ICwtj\n3759AGg0GgYNGoSxsTHbt28nNjaWuXPn8uDBA2X+e/fu4e3tTfXq1UlKSmL79u1YWlri4+OjFEeA\n8+fPs3HjRlauXEl6ejovvfTSU/fbhx9+SGRkJCEhIezZs4eVK1diY2OjrLNfv36YmpqSnJzM2rVr\n2bdvH6NHjy7x8cnIyODEiRNs2bKFzz77jG+++YalS5cC8NFHH6FSqXjrrbfIzMwkMzMTW1vbEq9D\nCCFeJMYVHaAsWVpaMm/ePAwMDHj55Zf55ZdfiI2NZfTo0QQEBCj9mjRpwvz581GpVFy6dAkbGxsu\nXryIo6Mjnp6eGBgY0KhRIzw8PAC4cuUKDx8+xMfHh8aNGwPg6uqqLO/jjz8mODiYQYMGAWBvb8/0\n6dMZMWIEM2fOxMDAAICuXbsyfPhwAEaMGEFcXBw7d+5EpVKRkpLC6dOn2bRpEw0bNgRgzpw59OjR\nQ1nPV199RUFBAbGxscoyFy5ciJOTE9999x19+vQBHr+BiYuLw8LC4m/32d27d4mNjSUiIkLZRw4O\nDqhUKuDxG6Xc3Fzi4uKoVauWsk5vb2/OnDmDg4NDsY9PrVq1mD9/PsbGxri4uODr68vOnTt5//33\n+cc//kG1atWoWbMmlpaWxV6mEEK8yPS6qL/yyitKsQNQqVTMnj2bnJwczpw5w9y5czl69CjZ2dnK\nOPNvv/2GjY0NAwcOpE+fPrRt25auXbvSvXt3unfvjqGhIS1atKBLly54enry6quv0qVLF3x8fKhf\nvz4Ahw8f5qeffmLRokXKujUaDffv3+fatWtYWVkB4ObmppXXysqKGzduAHDq1Cmsra2Vgg7g7u6O\noeF/L64cPnyY8+fP65zB3rt3j7NnzyqvGzZsWKyCDpCZmcmDBw/w8vJ6Yrubm5tS0AE8PDwwNDTk\n5MmTJSrqLi4uGBv/95+glZUVP/74Y7Hn/6vTp08/87wVpSpmLiTZK0ZVzg6VN7+zs3NFRygVel3U\nn6SgoIA33niDLl26EBcXR4MGDcjKyqJnz57KuHfr1q05cuQIycnJpKamEhgYSPPmzdmyZQtGRkZs\n3ryZ/fv385///Ic1a9YwY8YMkpKSaNGiBRqNhgkTJuDr66uz7sLCD1CtWjWtNgMDA+XNRXFuZtNo\nNLRo0YL4+Hidtjp16ii/m5qaFm/HFGO9T2svfANlaGio0y8/P1+n/9O2/1lUtf+Up0+frnKZC0n2\nilGVs0PVz18V6HVRP3DgAAUFBUqx2b9/P9bW1pw9e5asrCymTZtGkyZNgMc3vv1VrVq18PX1xdfX\nl4EDB9KtWzfOnDmDk5MTBgYGqFQqVCoVEyZMoH379mzevJkWLVrQqlUrTp06VaKz1r9ycXHhypUr\nXLlyBWtrawAOHjyo3OgH0KpVKzZu3EjdunUxNy+dj0l1cXGhevXq7Ny5E0dHR532pk2bsm7dOu7c\nuaOcre/duxeNRoOLiwvw+I3L0aNHteb76+viMDEx4dGjR8+wFUII8WLS6xvlrl69ysSJEzl9+jSJ\niYlERUURFBSEra0t1atXZ9myZZw7d47vvvuOOXPmaM0bExPDxo0byczM5MyZM2zYsIHatWvTsGFD\n9u/fT2RkJD/99BMXL15k27ZtXLp0SSlq48ePZ+PGjcyePZvjx49z6tQpEhMTCQsLK3b2V199FWdn\nZwIDAzl69Cj79+9nypQpGBsbK29S/Pz8sLCwYODAgaSnp3Pu3Dl27drFlClTtO6AL4latWoxcuRI\nZsyYwdq1azl79iwHDhxgxYoVyjpr1qzJyJEjOXbsGLt27SIkJARvb2/lTUznzp05cuQIa9as4cyZ\nMyxatIg9e/aUOEvjxo05cOAA58+fJysrS+sNjRBCCF16XdT9/PzQaDSo1WrGjBlDQEAAQUFB1K9f\nnyVLlpCUlISHhwdz585l9uzZWvPWqlWLqKgo1Go1Xl5eHD16lA0bNlCzZk1q167N3r178ff3p23b\ntkydOpXQ0FDlMS61Wk1CQgLp6emo1WrUajULFiwo0d3bhoaGrF27lgcPHqBWqwkMDOSDDz7AwMBA\nuXu9Zs2abNu2jSZNmjB48GBUKhWBgYFkZ2c/15l7eHg4Y8eOJTIyEpVKxdtvv6087lezZk2++uor\n7ty5g1qtZuDAgbRr146YmBhlfrVazYQJE5g1axZdunThwoULDBs2rMQ5goODMTExoX379jg6OnLx\n4sVn3iYhhHgRGGRnZ+vlJ5H07t0bV1dXIiMjKzpKqTl69Cj/8z//w44dO2jdunVFx6k0Cr+hLfvj\n7ApOUjJVeXxRsleMqpwdqn7+qkCvx9Sruq+//hpTU1McHBy4cOECU6ZMoXnz5rRq1aqiowkhhKiE\npKhXYnfv3mX69OlcunQJc3NzOnXqxJw5c7Qe0yupixcv0r59+ye279mzh0aNGj3z8oUQQlQcvS3q\nSUlJFR3huQ0YMIABAwaU6jKtra1JS0t7antVU9UuuwshRFnR26IuimZsbPxcj9oJIYSovPT67nch\nhBDiRSJFXQghhNATUtSFEEIIPSFFXQghhNATUtSFEEIIPSFFXQghhNATUtSFEEIIPSFFXQghhNAT\nUtSFEEIIPSFFXQghhNATUtSFEEIIPSFFXQghhNATUtSFEEIIPSFFXQghhNATUtSFEEIIPSFFXQgh\nhNATUtSFEEIIPSFFXQghhNATUtRLyN/fn8DAwIqOAcCePXvw9PSkQYMG9O7d+4nTqpLevXsTGhpa\n0TGEEKJKkqJehU2cOJHmzZtz8OBB1q5d+8RplVFaWhrm5uZkZWVVdBQhhNAbUtRLUV5eXrmu78yZ\nM3Tu3BlbW1vq1KnzxGlCCCFeDFLUn+LevXsEBgZiY2ODs7Mzn3zyiVZ7ixYtiIiIYNSoUTRu3Jj3\n3nsPgGPHjuHj44OVlRVNmjQhMDCQ27dvK/MFBgbi7+9PZGQkzs7O2NjYEBQUxP3795U+Dx48YOLE\niTg7O2NpaUm3bt3IyMgA4Pz585ibm5OTk8Po0aMxNzdn3bp1RU57msKz5e3bt+Pl5YWVlRU9e/bk\n0qVLpKen07FjR2xsbPD39+fWrVvKfBqNhnnz5uHm5oaFhQWenp4kJSUp7YX5EhMT8fX1xdraGg8P\nD1JSUpR2b29vABwdHTE3N9ca0tBoNHz44Yc4ODjg5OTE1KlT0Wg0JTp2QgjxIpKi/hTTpk1jx44d\nrF69msTERI4cOcLu3bu1+sTGxvLyyy+zY8cOwsLCuHfvHv369cPU1JTk5GTWrl3Lvn37GD16tNZ8\nu3bt4ueffyYxMZHVq1eTkpJCeHi40h4WFsbmzZuJiYkhNTUVV1dX+vXrx9WrV7G1tSUzM5OaNWsS\nERFBZmYmvr6+OtP69u1brO2MiIggIiKCH374gezsbIYOHcq8efNYtGgR33zzDSdOnCAiIkLpv2TJ\nEqKjo5k+fTq7d++md+/eBAQEcOTIEa3lzpo1ixEjRpCenk6bNm0YOnQod+/exdbWltWrVwOP7wHI\nzMzko48+UubbsGEDRkZGfP/990RGRrJkyRI2bdpUvIMmhBAvMOOKDlBZ3b17lzVr1hATE4NarQZg\n8eLFuLq6avXz9PTkX//6l/J61apV5ObmEhcXR61atQBYuHAh3t7enDlzBgcHBwAMDQ1ZvHgxZmZm\nuLq6Mn36dIKDg5XCHh8fT1RUFD169ABgwYIFpKamsnz5cqZOnYqlpSUGBgbUrl0bS0tLAExNTXWm\nFceUKVPw9PQEYMiQIYwfP54dO3bQunVrAAYMGMDWrVuV/jExMYwePRo/Pz9l/t27dxMTE8Onn36q\n9AsKCqJnz57A4zcpX375JUePHqVDhw7K0ECDBg2oV6+eVh4XFxemTJkCgJOTE6tWrWLnzp3069ev\nyPynT58u9rZWNpK9Ykj2ilNZ8zs7O1d0hFIhRf0Jzp49S15eHiqVSplmZmaGm5ubVr82bdpovc7M\nzMTNzU0p6AAeHh4YGhpy8uRJpai7ublhZmam9FGpVOTl5XH27FkAHj58SPv27ZV2IyMjVCoVJ0+e\nLL2N/H9/3iYLC4sip924cQOAnJwcrly5opUNoEOHDnz//fdPXK61tTWAspzi5gGwsrJ66nxV9T/j\n6dOnJXsFkOwVp6rnrwrk8vsTFBQUFKufqalpseczMDAo0bqL6l/cZZREtWrVdJb/12nFGdP+a7ai\nlluc/frn+QrnLe7xEEKIF5kU9SdwcHCgWrVq7N+/X5mWm5vL8ePHnzpf06ZNOXbsGHfu3FGm7d27\nF41Gg4uLizLt+PHj5ObmKq/379+PiYkJ9vb2ODg4YGJiotwYB/Do0SP27duntYyKULt2baytrdmz\nZ4/W9IyMjBJlMzExAR5vlxBCiNIhRf0JzMzMCAgIYPr06aSkpHDixAlGjx79t2esfn5+1KxZk5Ej\nR3Ls2DF27dpFSEgI3t7eyqV3eFzMRo8ezYkTJ0hJSWHGjBm88847mJqaYmpqytChQ5kxYwbff/89\nmZmZvP/++9y4cYNhw4aV9ab/reDgYGJiYti4cSO//PILs2fPJiMjQ+dmwKdp1KgRBgYGfPfdd9y8\neZO7d++WYWIhhHgxyJj6U8ycOZPc3FwGDRpEjRo1GD58OPfu3XvqPDVr1uSrr75i0qRJqNVqqlev\nTq9evbTu7gbo2LEjTZs2xdvbm/v37+Pt7c2MGTOU9sLfR40axe3bt2nZsiUbN27Eysqq9De0hEaO\nHMndu3cJDw/n+vXrODs7s3r1alq2bFnsZTRs2JBJkyYxa9YsxowZw5tvvsmSJUvKMLUQQug/g+zs\nbBmsLGeBgYHcunWL9evXV3QUUYGq8k1Dkr1iVOXsUPXzVwVy+V0IIYTQE1LU9VhISAg2NjZF/oSE\nhFR0PCGEEKVMxtQrQHmNHU+ePJng4OAi2/78HL0QQgj9IEVdjzVo0IAGDRpUdAwhhBDlRC6/CyGE\nEHpCiroQQgihJ6SoCyGEEHpCiroQQgihJ6SoCyGEEHpCPlFOCCGE0BNypi6EEELoCSnqQgghhJ6Q\noi6EEELoCSnqQgghhJ6Qoi6EEELoCSnqotJavnw5LVu2xNLSEi8vL3bv3v3U/unp6Xh5eWFpaUmr\nVq2Ij48vp6S6SpL96tWrDBs2jHbt2lG3bl0CAwPLMWnRSpJ/69at9OnTB0dHR2xtbVGr1Wzbtq0c\n02orSfb09HRee+017O3tsbKyol27dkRHR5djWm0l/TdfKCMjg3r16tGhQ4cyTvh0JcmflpaGubm5\nzs+pU6fKMbH+kaIuKqVNmzYxceJEPvjgA1JTU1GpVPj5+XHx4sUi+587d47+/fujUqlITU3l/fff\nZ/z48SQmJpZz8pJnf/DgAXXr1mXs2LG88sor5ZxWV0nz79q1i86dO5OQkEBqairdu3dn0KBBxS5I\npamk2c3MzBgxYgTbtm1jz549jBs3joiICJYvX17OyUuevVB2djYjR47Ey8urnJIW7Vnz79mzh8zM\nTOXH0dGxnBLrJ3lOXVRKarUaNzc3oqKilGnu7u74+PgQHh6u0z88PJyvv/6an376SZkWHBzMyZMn\n2b59e7lkLlTS7H/m7+9P3bp1y+3reYvyPPkLde3alQ4dOjB79uyyilmk0sg+aNAgqlevzooVK8oq\nZpGeNfugQYNo3rw5BQUFbN26lYyMjPKIq6Ok+dPS0vD29ubXX3+lXr165RlVr8mZuqh08vLyOHTo\nEF27dtWa3rVrV/bu3VvkPPv27dPpr1arOXjwIA8fPiyzrH/1LNkrk9LKf/fuXczNzUs73lOVRvbD\nhw+zb98+OnbsWBYRn+hZsy9fvpzr168TGhpa1hGf6nn2fZcuXXBxceH1118nNTW1LGO+EOT71EWl\nk5WVxaNHj3S+C75BgwZcv369yHmuX79Oly5ddPrn5+eTlZWFlZVVWcXV8izZK5PSyL9s2TIuX76M\nv79/WUR8oufJ7urqys2bN8nPz2fChAkMHTq0LKPqeJbsx44dY+7cuWzfvh0jI6PyiPlEz5LfysqK\n+fPn4+7uTl5eHuvXr8fHx4dvvvmm3N9U6RMp6qLSMjAw0HpdUFCgM+3v+hc1vTyUNHtl86z5ExMT\nCQsLY8WKFTRu3Lis4j3Vs2Tftm0bubm5/Pjjj4SHh2NnZ8ebb75ZljGLVNzsDx484N1332XmzJk0\nadKknNL9vZLse2dnZ5ydnZXXKpWKCxcuEB0dLUX9OUhRF5VOvXr1MDIy0nmHf/PmTZ0zgUIWFhZF\n9jc2NqZu3bpllvWvniV7ZfI8+RMTExk5ciRLly6lV69eZRmzSM+TvbAwurm5cf36dT766KNyLeol\nzX716lVOnjzJqFGjGDVqFAAajYaCggLq1avHhg0bdC6Fl6XS+nfftm1bNm3aVNrxXigypi4qHRMT\nE1q3bk1KSorW9JSUFDw8PIqcR6VSsWPHDp3+bdq0oVq1amUVVcezZK9MnjX/5s2bGTFiBLGxsfj4\n+JR1zCKV1r7XaDTk5eWVdrynKmn2hg0bsnv3btLS0pSfoUOH4uDgQFpaGiqVqryiA6W3748ePYql\npWVpx3uhyJm6qJRGjRrFiBEjaNu2LR4eHsTHx3P16lWGDBkCwIgRIwCIi4sDYMiQISxbtoyJEycy\nZMgQ9u7dy+eff14hjyaVNDvAkSNHAMjJycHAwIAjR45gYmJC06ZNK33+r776ihEjRjBz5kw8PT25\ndu0a8PgPfZ06dSp19ri4OOzs7JTLwLt27SImJoZ33323XHOXNHu1atVwdXXVmr9+/fo31vOGAAAM\nTElEQVRUr15dZ3p5Kem+j42NpXHjxjRr1oy8vDwSEhJISkpi9erVFZJfX0hRF5VS3759uXXrFpGR\nkVy7do1mzZqRkJCgjNP+9ttvWv2bNGlCQkICkydPJj4+HisrK+bOnVshZ40lzQ7QuXNnrdf//ve/\nadSoEUePHi2XzH9W0vzx8fHk5+czadIkJk2apEzv2LEjSUlJlTr7o0ePmD59OhcuXMDY2JgmTZoQ\nHh5e7jfKPUv2yqak+R8+fMi0adO4cuUKL730ktL/tddeq4j4ekOeUxdCCCH0hIypCyGEEHpCiroQ\nQgihJ6SoCyGEEHpCiroQQgihJ6SoCyGEEHpCiroQQgihJ6SoCyGEEHpCiroQlUBaWhrm5uZP/Nm/\nf3+ZZ4iJieGLL74o8/U8r/z8fMzNzRk4cGBFRyk1hw4dIiIigosXL1Z0FFHFySfKCVGJ9OvXj+7d\nu+tMd3BwKPN1x8bG4uzszIABA8p8XULbkSNHmDt3Ll26dKFRo0YVHUdUYVLUhahEWrVqVe7fQ14e\n8vLyKCgooHr16hUdpVK5c+cOtWrVqugYQo/I5XchqqCNGzfSo0cPbG1tsba2plu3bmzdulWn34YN\nG/D396d58+ZYWFjg6OjIoEGDOH78uNKn8HL25cuX2blzp9Zl/0uXLintwcHBOstfvXo15ubmZGRk\nKNNmzZqFubk5p06dYuLEiTRr1gwrKyt++uknpU9ycjK+vr40btwYS0tLOnbsyMqVK59rn7i6uuLj\n48Phw4fx9vbGxsYGJycnwsLCePToEffv32fSpEk0bdoUKysrevfuzenTp4vcntTUVGbNmkXz5s2V\nfFu2bClyvYmJiXTv3p2GDRtiY2NDz549+fe///3EfIcOHVK23cvLi1mzZjFmzBgAevbsqez7wv19\n+/ZtZs6cSdeuXXFwcMDCwgJ3d3c+/PBD7t+/r7WOHTt2YG5uzvr161m9ejUeHh5YWFjQokULoqOj\ni8x/6NAh3n77bZydnbGwsKB58+YMGzaMc+fOafUri2MmSp+cqQtRidy7d4+srCytaSYmJlpnc9On\nT2fhwoW89tprTJ48GUNDQ7Zu3crbb7/N/Pnztb6MZNmyZTRo0IDBgwdjYWHBmTNnWLlyJT169CA1\nNRV7e3uMjIyIi4tjwoQJWFtbM3bsWGX+5/mWtaFDh2Jqasro0aMxMDDAwsICgBUrVjBu3Dg8PDwI\nDQ2lRo0aJCcnM3bsWM6fP094ePgzr/O3336jb9++9OvXjz59+vDDDz8QFRVFtWrVOHz4MPn5+YSE\nhHDjxg0WL17MoEGDyMjIwNBQ+/xm2rRp/PHHH7z33ntoNBrWrl3L4MGDiYuL07qSUrjfXFxcmDBh\nAhqNhnXr1vHmm28SHR1NQECA1nIvXLjA66+/Tt++ffH19SU3N5fOnTtz7do11qxZQ2hoKE5OTsB/\nh1wuXbrE2rVref311/Hz88PY2Ji0tDQWLFjAzz//TEJCgs5+WLZsGTdu3CAgIIDatWvz5ZdfMm3a\nNGxtbenTp4/SLykpicGDB2NmZsbbb7+Nvb09165dIzk5mczMTOV75svymInSJV/oIkQlkJaWhre3\nd5Ftffv2JT4+HoADBw6gVqsJDQ1lypQpWv38/f3JyMjgxIkTmJqaApCbm6v8Xuj48eN4eXkxZMgQ\n5s2bp0x3dXXF2dmZxMRErf75+fnUr1+fgIAAnbO91atXM2bMGL799ls6dOgAPD5T//jjj+ncuTOb\nN2/GyMhI6X/p0iXatGlDnz59tL56FmDcuHF89tlnHDx4UPlmr6IU5unVqxeff/65Vv7Lly+zZs0a\nrX3ZsWNHjh8/zj//+U9Wr16NgYEB8PjGwKlTp5KYmIiXl5fW9tjZ2ZGWlkbt2rUByM7OxtPTkz/+\n+IPjx4/z0ksvcevWLdzc3LCxsSElJUV543X79m06derE7du3+fnnn5VlFOZbvHgxb7311t/ux0J5\neXkYGhpibKx9DjZjxgwWLFjAjh07aN26NfD4TN3X1xcbGxsyMjKUdd+9e5cWLVrQtGlTvv32W61p\n1apVIzU1FSsrK63lazQaDA0NS+WYifIjl9+FqEQGDx7Mli1btH7GjRuntCckJGBgYMCAAQPIysrS\n+unZsyc5OTn8+OOPSv/Cgl5QUEBOTg5ZWVlYWlri4ODAgQMHynRbgoKCtAo6wJYtW8jLy2PQoEE6\n+f/3f/+XR48esXPnzmdep62trc6bow4dOlBQUMDw4cOVgg7g6ekJwK+//qqznGHDhikFEcDc3JzB\ngwdz69YtZaghOTmZ+/fvM3LkSK0rKf/4xz8YPnw4OTk5pKamai23QYMGJb4R0cTERCnoDx8+JDs7\nm6ysLLp06QJQ5HF86623tPKbmZnRtm1brW394Ycf+P333wkODtYp6IBy9aKsj5koXXL5XYhKxNHR\nUfljXZRTp05RUFCAu7v7E/vcuHFD+f3QoUPMnj2b3bt3k5ubq7OuslR4GfnPTp06BfDEqxKgnb+k\nCi8X/5m5uTkAdnZ2RU6/deuWzjwvv/yyzjQXFxcAZaz5/PnzADRt2lSnb7NmzbT6FrK3t9e51F8c\nn376KStXruTkyZNoNBqttuzsbJ3+Re2HunXram1rYYFv2bLlU9dd1sdMlC4p6kJUIQUFBRgZGbFh\nw4YnFofCgnL+/Hl69epFnTp1GD9+PI6OjpiammJgYMD48eN5+PBhsdb557Pbv8rPz39iW40aNYrM\nD/8d6y+Kvb19sXIV5a9XBorTVpjpz562zU+b7+8UtU/+zqJFiwgPD6dbt26MHDkSKysrTExMuHjx\nIsHBwTpFHoq3rYW//922lvUxE6VLiroQVYijoyM7duzAzs7ub8+0t27dyr1799i4caNyqRke/5HO\nysrSujwLT/7jbmRkRO3atfn999912v56Jlqc/AD169d/6hWJipaZmclrr72mMw3+exZcWMhOnjxJ\np06dtPqePHlSq+/zWL9+Pfb29mzYsEHrGBV1h31JODs7A4+fke/cufMT+1WVYyYekzF1IaqQwjuv\nZ8yYwaNHj3Tar1+/rvxeeLb21zPK+Ph4nTvs4fH4e1GFGx7/Yd+7dy9//PGHMu3WrVsl/gS6Pn36\nYGJiwpw5c7SWVSg7O5u8vLwSLbMsLF++nDt37iivs7OzWblyJXXr1lVuZOvatSs1atQgLi5Oa2gj\nJyeHZcuWUbt2beUGvL9jZmYGUOT+NzQ0xMDAQOs4Pnz4kIULFz7TthVSq9XUqVOH6Ohorl27ptNe\nuL6qcszEY3KmLkQVolKpCA0NJTIyks6dO+Pj44OVlRVXr17l4MGDpKSkcPXqVQB69OjBzJkzGT58\nuHLj1969e/nhhx90xpcBXnnlFb744gvmzJmDs7MzhoaG9OrVixo1avDee+8RFBSkPFaVnZ3NqlWr\nsLOzK9F4auPGjYmMjCQkJAQPDw/69+9Po0aNuHnzJseOHWPbtm38+OOP2NjYlNo+exbm5uZ069aN\ngQMHUlBQwJo1a7h8+TKxsbG89NJLwOMx6vDwcCZOnEi3bt0YMGAAGo2Gzz//nPPnzxMdHV3sD5Zx\nd3fHwMCAjz/+mJs3b2Jqaoq9vT3u7u74+Pgwe/Zs/Pz86N27Nzk5OSQkJDz3B/mYmZkRFRXFkCFD\n8PT0JCAgAHt7e27cuKE8rtajR48qc8zEY1LUhahipkyZQps2bfj0009ZvHgx9+/fx8LCgmbNmjF3\n7lyln6OjIwkJCcyaNYtPPvkEIyMj2rdvz7Zt2xg7dqzO2Vl4eDg5OTnExcWRk5NDQUEBx44dw8bG\nhgEDBnDt2jVWrFjB5MmTcXBwYPLkyeTn52vdbV8c77zzDi+//DLR0dHEx8eTk5NDvXr1cHZ2ZurU\nqdSvX79U9tPzmDlzJqmpqXz66afcuHEDJycn4uPj6du3r1a/kSNHYm1tTUxMDBERERgYGNCyZUu+\n+OILevbsWez1NWnShKioKKKiovjggw94+PAhAQEBuLu78/777wOwbt06Jk2ahIWFBW+88Qb9+/fX\nGlZ5Ft7e3nz77bfMnz+fVatWkZubi4WFBZ6enlo3AFaFYyYek+fUhRDi/z3teXEhqgIZUxdCCCH0\nhBR1IYQQQk9IURdCCCH0hIypCyGEEHpCztSFEEIIPSFFXQghhNATUtSFEEIIPSFFXQghhNATUtSF\nEEIIPSFFXQghhNAT/wd1L0g6czkNGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a212a7c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "font_axis = 18\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "plt.figure(figsize = (7,12))\n",
    "plt.suptitle('Feature Importance for Determining Tip Amount', fontsize = 20)\n",
    "plt.subplots_adjust(left = 0.35, right = 0.9,hspace=0.3, wspace=0.5, top = 0.91, bottom = 0.1)\n",
    "\n",
    "ax1 = plt.subplot(211)\n",
    "plt.title('Yellow Taxis')\n",
    "feature_importances_yellow.sort_values(by = 'importance', ascending = False)[::-1].plot(ax = ax1, kind = 'barh', color = 'gold', legend=None)\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Feature Importance', fontsize = font_axis)\n",
    "plt.yticks(np.arange(len(feature_importances_yellow)), list(feature_importances_yellow.sort_values(by = 'importance', ascending = False)['feature'])[::-1]);\n",
    "\n",
    "\n",
    "\n",
    "ax2 = plt.subplot(212)\n",
    "plt.title('Green Taxis')\n",
    "feature_importances_green.sort_values(by = 'importance', ascending = False)[::-1].plot(ax = ax2, kind = 'barh', color = 'darkgreen', legend=None)\n",
    "plt.ylabel('')\n",
    "plt.xlabel('Feature Importance', fontsize = font_axis)\n",
    "plt.yticks(np.arange(len(feature_importances_green)), list(feature_importances_green.sort_values(by = 'importance', ascending = False)['feature'])[::-1]);\n",
    "\n",
    "plt.savefig('Final14.png', dpi = 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting error in tip amount predictions\n",
    "Every output value in our test set has a corresponding predicted value. We can plot the distribution of the difference between thse two to get an idea of how well we fit the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWcAAAKJCAYAAABnHujiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a212f6160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "tip_max = 5.\n",
    "\n",
    "x_yellow = Y_test_yellow - y_pred_yellow\n",
    "x_green = Y_test_green - y_pred_green\n",
    "\n",
    "plt.figure(figsize = (5, 9))\n",
    "plt.suptitle('Error in Taxi Trip Tip Amount Predictions')\n",
    "plt.subplots_adjust(left = 0.15, hspace=0.45, wspace=0.4, top = 0.88)\n",
    "\n",
    "ax1 = plt.subplot(211)\n",
    "plt.title('Yellow Taxis')\n",
    "sns.kdeplot(x_yellow, color = 'gold', shade=True)\n",
    "plt.xlim(-tip_max, tip_max)\n",
    "plt.xlabel(r'$y - y_{pred}$ (dollars) ')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "ax2 = plt.subplot(212)\n",
    "plt.title('Green Taxis')\n",
    "sns.kdeplot(x_green, color = 'darkgreen', shade=True)\n",
    "plt.xlim(-tip_max, tip_max)\n",
    "plt.xlabel(r'$y - y_{pred}$ (dollars) ')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "\n",
    "plt.savefig('Final13.png', dpi = 1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the stanbdard deviation of these distributions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.76313263732\n",
      "2.06710640768\n"
     ]
    }
   ],
   "source": [
    "print (np.std(x_yellow))\n",
    "print (np.std(x_green))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}