Bank_Customer_Churn_Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ce2ae1f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from scipy.optimize import minimize\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import StandardScaler, RobustScaler, MinMaxScaler\n",
    "from sklearn.impute import SimpleImputer\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import ScalarFormatter\n",
    "\n",
    "\n",
    "from leverage_scores import compute_leverage\n",
    "\n",
    "from row_sampling import leverage_sampling, l2s_sampling, uniform_sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e3e7872",
   "metadata": {},
   "source": [
    "# **Data cleaning and pre-processing**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "066b8078",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10000 entries, 0 to 9999\n",
      "Data columns (total 12 columns):\n",
      " #   Column            Non-Null Count  Dtype  \n",
      "---  ------            --------------  -----  \n",
      " 0   credit_score      10000 non-null  int64  \n",
      " 1   age               10000 non-null  int64  \n",
      " 2   tenure            10000 non-null  int64  \n",
      " 3   balance           10000 non-null  float64\n",
      " 4   products_number   10000 non-null  int64  \n",
      " 5   credit_card       10000 non-null  int64  \n",
      " 6   active_member     10000 non-null  int64  \n",
      " 7   estimated_salary  10000 non-null  float64\n",
      " 8   churn             10000 non-null  int64  \n",
      " 9   gender_Male       10000 non-null  uint8  \n",
      " 10  country_Germany   10000 non-null  uint8  \n",
      " 11  country_Spain     10000 non-null  uint8  \n",
      "dtypes: float64(2), int64(7), uint8(3)\n",
      "memory usage: 732.5 KB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('Bank Customer Churn Prediction.csv')\n",
    "df = df.drop('customer_id', axis=1)\n",
    "df = pd.get_dummies(df, columns=['gender'], prefix='gender', drop_first=True)\n",
    "df = pd.get_dummies(df, columns=['country'], prefix='country', drop_first=True)\n",
    "df.info()\n",
    "\n",
    "X_raw = df.drop('churn', axis=1).values\n",
    "y = df['churn'].values\n",
    "\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(X_raw)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6fbc70c",
   "metadata": {},
   "source": [
    "# **Define the necessary functions for gradient, NLL etc.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2636f229",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Logistic function (sigmoid)\n",
    "def sigmoid(z):\n",
    "    return 1 / (1 + np.exp(-z))\n",
    "\n",
    "# Negative log likelihood\n",
    "def neg_log_likelihood(theta, X, y):\n",
    "    z = np.dot(X, theta)\n",
    "    return -np.sum(y * z - np.logaddexp(0, z))\n",
    "\n",
    "def lev_neg_log_likelihood(theta, X, y):\n",
    "    z = np.dot(X, theta)\n",
    "    log_sum_exp = np.logaddexp(0, z)\n",
    "    return -np.sum(w*y[ind] * z[ind] - w*log_sum_exp[ind])\n",
    "\n",
    "def l2s_neg_log_likelihood(theta, X, y):\n",
    "    z = np.dot(X, theta)\n",
    "    return -np.sum(w_l2s * y[ind_l2s] * z[ind_l2s] - w_l2s * np.logaddexp(0, z)[ind_l2s])\n",
    "\n",
    "def unif_neg_log_likelihood(theta, X, y):\n",
    "    z = np.dot(X, theta)\n",
    "    log_sum_exp = np.logaddexp(0, z)\n",
    "    return -np.sum(y[ind_unif] * z[ind_unif] - log_sum_exp[ind_unif])\n",
    "\n",
    "\n",
    "\n",
    "# Gradient of negative log likelihood\n",
    "def gradient(theta, X, y):\n",
    "    z = np.dot(X, theta)\n",
    "    return np.dot(X.T, sigmoid(z) - y)\n",
    "\n",
    "def lev_gradient(theta, X, y):\n",
    "    z = np.dot(X, theta)\n",
    "    return np.dot(X.T[:,ind], np.dot(np.diag(w),(sigmoid(z) - y)[ind]))\n",
    "\n",
    "def l2s_gradient(theta, X, y):\n",
    "    z = np.dot(X, theta)\n",
    "    return np.dot(X.T[:,ind_l2s], np.dot(np.diag(w_l2s),(sigmoid(z) - y)[ind_l2s]))\n",
    "\n",
    "def unif_gradient(theta, X, y):\n",
    "    z = np.dot(X, theta)\n",
    "    return np.dot(X.T[:,ind_unif], (sigmoid(z) - y)[ind_unif])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c91d51b8",
   "metadata": {},
   "source": [
    "# **Run the full-data model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a99e0dd7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Full data misclassification rate:  0.352700\n"
     ]
    }
   ],
   "source": [
    "initial_theta = np.zeros(X.shape[1])\n",
    "\n",
    "result_opt = minimize(neg_log_likelihood, initial_theta, args=(X, y), \n",
    "                              method='BFGS', jac=gradient)\n",
    "\n",
    "est_probs_opt = (sigmoid(np.dot(X, result_opt.x)))\n",
    "opt_pred_labels = (est_probs_opt > 0.5).astype(int)\n",
    "opt_misclassification_rate = np.mean(opt_pred_labels != y)\n",
    "print(f\"Full data misclassification rate: {opt_misclassification_rate: .6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e0c85d1",
   "metadata": {},
   "source": [
    "# **Run the sampling algorithms for varying sample sizes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a924e694",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Relative Error Estimated Probs Leverage for s = 100: 0.3069448853049243\n",
      "Mean Relative Error Estimated Probs L2S for s = 100: 0.3319680182847709\n",
      "Mean Relative Error Estimated Probs Uniform for s = 100: 0.28433211312028217\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 100: 0.399965\n",
      "Mean L2S Misclassification Rate for s = 100: 0.39429499999999995\n",
      "Mean Uniform Misclassification Rate for s = 100: 0.38952000000000003\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 100: 0.08855001764869455\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 100: 0.10408381053057061\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 100: 0.9908829151005349\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 100: 0.11050036338155618\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 100: 0.13745888790029137\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 100: 0.09698697596532484\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 200: 0.22479567162135253\n",
      "Mean Relative Error Estimated Probs L2S for s = 200: 0.1974247634251987\n",
      "Mean Relative Error Estimated Probs Uniform for s = 200: 0.2127925783825067\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 200: 0.38777999999999996\n",
      "Mean L2S Misclassification Rate for s = 200: 0.37574\n",
      "Mean Uniform Misclassification Rate for s = 200: 0.382995\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 200: 0.04653940267544944\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 200: 0.043671133338630375\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 200: 0.9808826362475566\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 200: 0.05406848305765066\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 200: 0.04180646597907386\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 200: 0.0474889149320481\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 300: 0.18280186730368694\n",
      "Mean Relative Error Estimated Probs L2S for s = 300: 0.17790099755481245\n",
      "Mean Relative Error Estimated Probs Uniform for s = 300: 0.18323228310384873\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 300: 0.37603000000000003\n",
      "Mean L2S Misclassification Rate for s = 300: 0.37977999999999995\n",
      "Mean Uniform Misclassification Rate for s = 300: 0.37107000000000007\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 300: 0.03198646563669458\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 300: 0.033903999404157704\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 300: 0.9713593606809315\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 300: 0.0338588358655517\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 300: 0.03242229527351172\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 300: 0.03574691804616109\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 400: 0.1532809719762344\n",
      "Mean Relative Error Estimated Probs L2S for s = 400: 0.15098608548968548\n",
      "Mean Relative Error Estimated Probs Uniform for s = 400: 0.15231000455573834\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 400: 0.3735\n",
      "Mean L2S Misclassification Rate for s = 400: 0.36158999999999997\n",
      "Mean Uniform Misclassification Rate for s = 400: 0.37406999999999996\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 400: 0.021789250219929376\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 400: 0.030344491594963163\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 400: 0.9617886723584789\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 400: 0.023188166609849993\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 400: 0.02438376809024598\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 400: 0.024224788098188586\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 500: 0.12794256407907756\n",
      "Mean Relative Error Estimated Probs L2S for s = 500: 0.13356840462197833\n",
      "Mean Relative Error Estimated Probs Uniform for s = 500: 0.1334574567630496\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 500: 0.368575\n",
      "Mean L2S Misclassification Rate for s = 500: 0.36556\n",
      "Mean Uniform Misclassification Rate for s = 500: 0.36609\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 500: 0.018526362880092098\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 500: 0.028179364309651744\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 500: 0.9521846224811077\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 500: 0.016784870091574526\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 500: 0.01807501573871912\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 500: 0.018249683077016285\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 600: 0.11846447392816384\n",
      "Mean Relative Error Estimated Probs L2S for s = 600: 0.11150493050349455\n",
      "Mean Relative Error Estimated Probs Uniform for s = 600: 0.11906010025327476\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 600: 0.366735\n",
      "Mean L2S Misclassification Rate for s = 600: 0.366755\n",
      "Mean Uniform Misclassification Rate for s = 600: 0.36564500000000005\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 600: 0.01746447058572906\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 600: 0.015165983918013515\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 600: 0.9427624030217585\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 600: 0.01377259040237921\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 600: 0.012232062876207895\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 600: 0.01407644770080226\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 700: 0.11170637992293922\n",
      "Mean Relative Error Estimated Probs L2S for s = 700: 0.10971026149000632\n",
      "Mean Relative Error Estimated Probs Uniform for s = 700: 0.10457631491952776\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 700: 0.36695\n",
      "Mean L2S Misclassification Rate for s = 700: 0.362405\n",
      "Mean Uniform Misclassification Rate for s = 700: 0.36080999999999996\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 700: 0.019456393467287645\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 700: 0.022335723543786566\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 700: 0.932953152172808\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 700: 0.012294734644625405\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 700: 0.012357969438141362\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 700: 0.011142519097115323\n",
      "============================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Relative Error Estimated Probs Leverage for s = 800: 0.09982872340528207\n",
      "Mean Relative Error Estimated Probs L2S for s = 800: 0.0987928278542413\n",
      "Mean Relative Error Estimated Probs Uniform for s = 800: 0.10763924928125104\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 800: 0.36251\n",
      "Mean L2S Misclassification Rate for s = 800: 0.36070499999999994\n",
      "Mean Uniform Misclassification Rate for s = 800: 0.365225\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 800: 0.019653800230773674\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 800: 0.01907677715456243\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 800: 0.9241763868913834\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 800: 0.01003584752034246\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 800: 0.009779726441213017\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 800: 0.01195397350814114\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 900: 0.0995461404638627\n",
      "Mean Relative Error Estimated Probs L2S for s = 900: 0.0918898220830885\n",
      "Mean Relative Error Estimated Probs Uniform for s = 900: 0.09555593814082877\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 900: 0.36552500000000004\n",
      "Mean L2S Misclassification Rate for s = 900: 0.36029000000000005\n",
      "Mean Uniform Misclassification Rate for s = 900: 0.357615\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 900: 0.019750701869978925\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 900: 0.018666682719345795\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 900: 0.9154281969765543\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 900: 0.00982117961123734\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 900: 0.008591048620236155\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 900: 0.009645961955228105\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 1000: 0.10200205561171216\n",
      "Mean Relative Error Estimated Probs L2S for s = 1000: 0.09036927902234472\n",
      "Mean Relative Error Estimated Probs Uniform for s = 1000: 0.09061329845874558\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 1000: 0.36523999999999995\n",
      "Mean L2S Misclassification Rate for s = 1000: 0.36221500000000006\n",
      "Mean Uniform Misclassification Rate for s = 1000: 0.3625749999999999\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 1000: 0.01416867800285121\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 1000: 0.010937890503242245\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 1000: 0.9054664906295831\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 1000: 0.010241805716910595\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 1000: 0.007837935596845777\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 1000: 0.0081483258090734\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 1100: 0.0902350937739422\n",
      "Mean Relative Error Estimated Probs L2S for s = 1100: 0.08945979650631555\n",
      "Mean Relative Error Estimated Probs Uniform for s = 1100: 0.0899487617592182\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 1100: 0.362125\n",
      "Mean L2S Misclassification Rate for s = 1100: 0.359265\n",
      "Mean Uniform Misclassification Rate for s = 1100: 0.35957999999999996\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 1100: 0.014393709488084449\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 1100: 0.01385018005603087\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 1100: 0.8962700865718871\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 1100: 0.007999611427055076\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 1100: 0.00791573743390347\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 1100: 0.008338324078289777\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 1200: 0.08852597938428348\n",
      "Mean Relative Error Estimated Probs L2S for s = 1200: 0.07571280180290149\n",
      "Mean Relative Error Estimated Probs Uniform for s = 1200: 0.09147767122038243\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 1200: 0.36178999999999994\n",
      "Mean L2S Misclassification Rate for s = 1200: 0.36142\n",
      "Mean Uniform Misclassification Rate for s = 1200: 0.363535\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 1200: 0.014697681670616608\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 1200: 0.012348199412749606\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 1200: 0.8872707182005002\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 1200: 0.007883293633652077\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 1200: 0.00561351035504526\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 1200: 0.008329465234708839\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 1300: 0.07767133852585681\n",
      "Mean Relative Error Estimated Probs L2S for s = 1300: 0.07375752820195428\n",
      "Mean Relative Error Estimated Probs Uniform for s = 1300: 0.07719134445703466\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 1300: 0.360785\n",
      "Mean L2S Misclassification Rate for s = 1300: 0.35790000000000005\n",
      "Mean Uniform Misclassification Rate for s = 1300: 0.36261499999999997\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 1300: 0.012263138882150835\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 1300: 0.011344132887353465\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 1300: 0.8790015054896081\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 1300: 0.005991066155966864\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 1300: 0.005378942244735515\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 1300: 0.005914965781730724\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 1400: 0.08024376014074745\n",
      "Mean Relative Error Estimated Probs L2S for s = 1400: 0.07369660793429053\n",
      "Mean Relative Error Estimated Probs Uniform for s = 1400: 0.08215580880375287\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 1400: 0.357865\n",
      "Mean L2S Misclassification Rate for s = 1400: 0.35955\n",
      "Mean Uniform Misclassification Rate for s = 1400: 0.36039999999999994\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 1400: 0.012119669676832204\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 1400: 0.014112800335888945\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 1400: 0.8700573472627082\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 1400: 0.006433392393650978\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 1400: 0.005394571872356202\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 1400: 0.0067956597685119744\n",
      "============================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Relative Error Estimated Probs Leverage for s = 1500: 0.07718409731934532\n",
      "Mean Relative Error Estimated Probs L2S for s = 1500: 0.06685202801845512\n",
      "Mean Relative Error Estimated Probs Uniform for s = 1500: 0.0714201266920916\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 1500: 0.36117999999999995\n",
      "Mean L2S Misclassification Rate for s = 1500: 0.354305\n",
      "Mean Uniform Misclassification Rate for s = 1500: 0.361415\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 1500: 0.009823833798838869\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 1500: 0.011896877621130703\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 1500: 0.8607671555390624\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 1500: 0.005911217782740925\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 1500: 0.004303990698455817\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 1500: 0.005052906567946469\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 1600: 0.07479491280667355\n",
      "Mean Relative Error Estimated Probs L2S for s = 1600: 0.07079726807746725\n",
      "Mean Relative Error Estimated Probs Uniform for s = 1600: 0.07184366304386541\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 1600: 0.358465\n",
      "Mean L2S Misclassification Rate for s = 1600: 0.35744500000000007\n",
      "Mean Uniform Misclassification Rate for s = 1600: 0.35710000000000003\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 1600: 0.010969762576642023\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 1600: 0.012066370849754883\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 1600: 0.8528361326659155\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 1600: 0.005564981866382048\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 1600: 0.005164245699898695\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 1600: 0.005029423298966803\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 1700: 0.07562427084096489\n",
      "Mean Relative Error Estimated Probs L2S for s = 1700: 0.06325847047409815\n",
      "Mean Relative Error Estimated Probs Uniform for s = 1700: 0.0694998614787016\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 1700: 0.35935000000000006\n",
      "Mean L2S Misclassification Rate for s = 1700: 0.35628000000000004\n",
      "Mean Uniform Misclassification Rate for s = 1700: 0.35847999999999997\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 1700: 0.010153043919375217\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 1700: 0.008782717863843388\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 1700: 0.8453825475880306\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 1700: 0.005589169975637849\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 1700: 0.003968286263589736\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 1700: 0.0048383577478910795\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 1800: 0.06562060584919639\n",
      "Mean Relative Error Estimated Probs L2S for s = 1800: 0.06375202556499206\n",
      "Mean Relative Error Estimated Probs Uniform for s = 1800: 0.062224165608234835\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 1800: 0.35957500000000003\n",
      "Mean L2S Misclassification Rate for s = 1800: 0.35597\n",
      "Mean Uniform Misclassification Rate for s = 1800: 0.35680999999999996\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 1800: 0.010861660852323653\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 1800: 0.012116569510424585\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 1800: 0.8364056284515305\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 1800: 0.004280257723522708\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 1800: 0.0041198508575956885\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 1800: 0.003853627657982489\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 1900: 0.06736335301763906\n",
      "Mean Relative Error Estimated Probs L2S for s = 1900: 0.06081798197307732\n",
      "Mean Relative Error Estimated Probs Uniform for s = 1900: 0.07042986315116026\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 1900: 0.35708\n",
      "Mean L2S Misclassification Rate for s = 1900: 0.3568699999999999\n",
      "Mean Uniform Misclassification Rate for s = 1900: 0.36095\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 1900: 0.007904315440110693\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 1900: 0.005347385186214522\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 1900: 0.8278165378654627\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 1900: 0.004449705648781879\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 1900: 0.003885204311339982\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 1900: 0.004961232233497584\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 2000: 0.06803790580972874\n",
      "Mean Relative Error Estimated Probs L2S for s = 2000: 0.057301665152799316\n",
      "Mean Relative Error Estimated Probs Uniform for s = 2000: 0.06047329890893684\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 2000: 0.35833000000000004\n",
      "Mean L2S Misclassification Rate for s = 2000: 0.35523\n",
      "Mean Uniform Misclassification Rate for s = 2000: 0.35844\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 2000: 0.009995855432865752\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 2000: 0.008986921221694\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 2000: 0.819343205854983\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 2000: 0.004642366249511814\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 2000: 0.003270612240532614\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 2000: 0.0036031462651578846\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 2100: 0.06652787985840272\n",
      "Mean Relative Error Estimated Probs L2S for s = 2100: 0.05596946583918235\n",
      "Mean Relative Error Estimated Probs Uniform for s = 2100: 0.06313965910674894\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 2100: 0.35752\n",
      "Mean L2S Misclassification Rate for s = 2100: 0.35611000000000004\n",
      "Mean Uniform Misclassification Rate for s = 2100: 0.356\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 2100: 0.01006705938915686\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 2100: 0.007942878623731674\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 2100: 0.8102574676365046\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 2100: 0.004386477724002809\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 2100: 0.003177090191561457\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 2100: 0.0039031035795175978\n",
      "============================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Relative Error Estimated Probs Leverage for s = 2200: 0.06248578697977394\n",
      "Mean Relative Error Estimated Probs L2S for s = 2200: 0.05699085422106874\n",
      "Mean Relative Error Estimated Probs Uniform for s = 2200: 0.060461851428250515\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 2200: 0.36008\n",
      "Mean L2S Misclassification Rate for s = 2200: 0.35597500000000004\n",
      "Mean Uniform Misclassification Rate for s = 2200: 0.35775499999999993\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 2200: 0.008763418901029937\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 2200: 0.008751755484369123\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 2200: 0.8028761382437262\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 2200: 0.003979509802805952\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 2200: 0.003155931649093777\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 2200: 0.0035721868840962066\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 2300: 0.06628239767172343\n",
      "Mean Relative Error Estimated Probs L2S for s = 2300: 0.05538145565617605\n",
      "Mean Relative Error Estimated Probs Uniform for s = 2300: 0.056455172502080465\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 2300: 0.35692\n",
      "Mean L2S Misclassification Rate for s = 2300: 0.35659\n",
      "Mean Uniform Misclassification Rate for s = 2300: 0.356635\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 2300: 0.013809232454443317\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 2300: 0.010906939206711565\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 2300: 0.7941286019054851\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 2300: 0.00450958975711445\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 2300: 0.0029763598431996356\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 2300: 0.003112494096256672\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 2400: 0.06355770611356772\n",
      "Mean Relative Error Estimated Probs L2S for s = 2400: 0.05688985066012172\n",
      "Mean Relative Error Estimated Probs Uniform for s = 2400: 0.05989974614975241\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 2400: 0.35622999999999994\n",
      "Mean L2S Misclassification Rate for s = 2400: 0.356745\n",
      "Mean Uniform Misclassification Rate for s = 2400: 0.35835500000000003\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 2400: 0.009727418230838067\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 2400: 0.007681000741664142\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 2400: 0.7871220716798298\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 2400: 0.003922247062010531\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 2400: 0.0031422431674197116\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 2400: 0.003558034283719916\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 2500: 0.0580739723013594\n",
      "Mean Relative Error Estimated Probs L2S for s = 2500: 0.05149660297922541\n",
      "Mean Relative Error Estimated Probs Uniform for s = 2500: 0.05020689050125141\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 2500: 0.356085\n",
      "Mean L2S Misclassification Rate for s = 2500: 0.35595\n",
      "Mean Uniform Misclassification Rate for s = 2500: 0.3582\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 2500: 0.008337177125470201\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 2500: 0.007140736493448289\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 2500: 0.7786609009863457\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 2500: 0.0033686974784740828\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 2500: 0.0026109446215830416\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 2500: 0.002513109485657074\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 2600: 0.06182959995812024\n",
      "Mean Relative Error Estimated Probs L2S for s = 2600: 0.052780875248810186\n",
      "Mean Relative Error Estimated Probs Uniform for s = 2600: 0.050533538315484884\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 2600: 0.357855\n",
      "Mean L2S Misclassification Rate for s = 2600: 0.354185\n",
      "Mean Uniform Misclassification Rate for s = 2600: 0.35714500000000005\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 2600: 0.007434666843072623\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 2600: 0.00725513774659593\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 2600: 0.7712110765563086\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 2600: 0.0037085671247889967\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 2600: 0.002750955492812791\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 2600: 0.002507592338325368\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 2700: 0.05828747211549014\n",
      "Mean Relative Error Estimated Probs L2S for s = 2700: 0.04841235367861918\n",
      "Mean Relative Error Estimated Probs Uniform for s = 2700: 0.05282876769311347\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 2700: 0.35896\n",
      "Mean L2S Misclassification Rate for s = 2700: 0.356495\n",
      "Mean Uniform Misclassification Rate for s = 2700: 0.35877\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 2700: 0.00785420587992033\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 2700: 0.008713960356553635\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 2700: 0.763804259296246\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 2700: 0.003277066242625165\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 2700: 0.002391209598007641\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 2700: 0.002770508333914533\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 2800: 0.057288312202875225\n",
      "Mean Relative Error Estimated Probs L2S for s = 2800: 0.04728335918793084\n",
      "Mean Relative Error Estimated Probs Uniform for s = 2800: 0.05212421469222904\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 2800: 0.35867499999999997\n",
      "Mean L2S Misclassification Rate for s = 2800: 0.355135\n",
      "Mean Uniform Misclassification Rate for s = 2800: 0.356215\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 2800: 0.006910114369398213\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 2800: 0.00795915812587742\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 2800: 0.7566952441047803\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 2800: 0.0032470408819152523\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 2800: 0.0022108522794960345\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 2800: 0.0026977238870305254\n",
      "============================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Relative Error Estimated Probs Leverage for s = 2900: 0.05470887894420097\n",
      "Mean Relative Error Estimated Probs L2S for s = 2900: 0.048849033545647895\n",
      "Mean Relative Error Estimated Probs Uniform for s = 2900: 0.05250805984333644\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 2900: 0.356755\n",
      "Mean L2S Misclassification Rate for s = 2900: 0.355345\n",
      "Mean Uniform Misclassification Rate for s = 2900: 0.356395\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 2900: 0.006048228632740255\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 2900: 0.006999626373653654\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 2900: 0.7479511052299763\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 2900: 0.0029217296086714575\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 2900: 0.002336986692491716\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 2900: 0.0027321471985119984\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 3000: 0.056093772878258785\n",
      "Mean Relative Error Estimated Probs L2S for s = 3000: 0.0478527464659375\n",
      "Mean Relative Error Estimated Probs Uniform for s = 3000: 0.04727197784628375\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 3000: 0.35919\n",
      "Mean L2S Misclassification Rate for s = 3000: 0.35582\n",
      "Mean Uniform Misclassification Rate for s = 3000: 0.35695\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 3000: 0.006141407350451457\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 3000: 0.0080403366247201\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 3000: 0.7413938703423643\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 3000: 0.0031302148556272085\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 3000: 0.0023219082772762134\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 3000: 0.002263420735366602\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 3100: 0.05723947568225287\n",
      "Mean Relative Error Estimated Probs L2S for s = 3100: 0.048584235602845374\n",
      "Mean Relative Error Estimated Probs Uniform for s = 3100: 0.049568869602839155\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 3100: 0.3593100000000001\n",
      "Mean L2S Misclassification Rate for s = 3100: 0.35375500000000004\n",
      "Mean Uniform Misclassification Rate for s = 3100: 0.355085\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 3100: 0.007559286738723907\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 3100: 0.008255292245854074\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 3100: 0.7349542794361951\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 3100: 0.0032199222557988084\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 3100: 0.0023025509712801687\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 3100: 0.0025471142679523327\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 3200: 0.05790284649787758\n",
      "Mean Relative Error Estimated Probs L2S for s = 3200: 0.044075633016234524\n",
      "Mean Relative Error Estimated Probs Uniform for s = 3200: 0.05205833681620322\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 3200: 0.356015\n",
      "Mean L2S Misclassification Rate for s = 3200: 0.35428499999999996\n",
      "Mean Uniform Misclassification Rate for s = 3200: 0.35636499999999993\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 3200: 0.007503855466835932\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 3200: 0.00656035515518665\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 3200: 0.7266321749240773\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 3200: 0.0032315639928087217\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 3200: 0.0019753446309531096\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 3200: 0.002685133224145779\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 3300: 0.049534036618821345\n",
      "Mean Relative Error Estimated Probs L2S for s = 3300: 0.04536183547234072\n",
      "Mean Relative Error Estimated Probs Uniform for s = 3300: 0.049857123345768625\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 3300: 0.35679999999999995\n",
      "Mean L2S Misclassification Rate for s = 3300: 0.353985\n",
      "Mean Uniform Misclassification Rate for s = 3300: 0.35449499999999995\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 3300: 0.006550316595930432\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 3300: 0.006770627417259503\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 3300: 0.7193113792094811\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 3300: 0.0024033235706804627\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 3300: 0.002179114010784624\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 3300: 0.0023937139101683994\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 3400: 0.05268666740133414\n",
      "Mean Relative Error Estimated Probs L2S for s = 3400: 0.042578879627580814\n",
      "Mean Relative Error Estimated Probs Uniform for s = 3400: 0.04687020503497513\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 3400: 0.35594499999999996\n",
      "Mean L2S Misclassification Rate for s = 3400: 0.3544649999999999\n",
      "Mean Uniform Misclassification Rate for s = 3400: 0.35623499999999997\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 3400: 0.005931296830923008\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 3400: 0.008585145378468132\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 3400: 0.711978694294873\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 3400: 0.002763394402996159\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 3400: 0.001822579710521427\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 3400: 0.002234252288528775\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 3500: 0.04862337667063671\n",
      "Mean Relative Error Estimated Probs L2S for s = 3500: 0.04200763178922049\n",
      "Mean Relative Error Estimated Probs Uniform for s = 3500: 0.04821277267844798\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 3500: 0.357755\n",
      "Mean L2S Misclassification Rate for s = 3500: 0.35500499999999996\n",
      "Mean Uniform Misclassification Rate for s = 3500: 0.35552999999999996\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 3500: 0.0068997253365048776\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 3500: 0.00787293964071959\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 3500: 0.7056725078002702\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 3500: 0.0023045311150097134\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 3500: 0.0017298601980025388\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 3500: 0.0023206599925538443\n",
      "============================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Relative Error Estimated Probs Leverage for s = 3600: 0.049359842614476124\n",
      "Mean Relative Error Estimated Probs L2S for s = 3600: 0.04221706261265699\n",
      "Mean Relative Error Estimated Probs Uniform for s = 3600: 0.042975513515541824\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 3600: 0.35656499999999997\n",
      "Mean L2S Misclassification Rate for s = 3600: 0.35483000000000003\n",
      "Mean Uniform Misclassification Rate for s = 3600: 0.35647\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 3600: 0.0074084953847338\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 3600: 0.008511683266534831\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 3600: 0.6982229527247417\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 3600: 0.0023678565099866625\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 3600: 0.0017460056242359259\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 3600: 0.0017902757328949921\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 3700: 0.0498680167424989\n",
      "Mean Relative Error Estimated Probs L2S for s = 3700: 0.0415285734895231\n",
      "Mean Relative Error Estimated Probs Uniform for s = 3700: 0.04081469042488673\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 3700: 0.35723\n",
      "Mean L2S Misclassification Rate for s = 3700: 0.35234\n",
      "Mean Uniform Misclassification Rate for s = 3700: 0.355845\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 3700: 0.0033133849432194183\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 3700: 0.008109498134211461\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 3700: 0.6904102305766284\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 3700: 0.00241204241732366\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 3700: 0.0016843437602921652\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 3700: 0.0016589606756155617\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 3800: 0.04874308222772904\n",
      "Mean Relative Error Estimated Probs L2S for s = 3800: 0.03829778399934189\n",
      "Mean Relative Error Estimated Probs Uniform for s = 3800: 0.04716705635431702\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 3800: 0.358015\n",
      "Mean L2S Misclassification Rate for s = 3800: 0.35256\n",
      "Mean Uniform Misclassification Rate for s = 3800: 0.35257499999999997\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 3800: 0.00679557987040012\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 3800: 0.00845349142152914\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 3800: 0.6855576833506304\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 3800: 0.002323813240672381\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 3800: 0.0015175919452027132\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 3800: 0.002207484104282853\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 3900: 0.045334607650635204\n",
      "Mean Relative Error Estimated Probs L2S for s = 3900: 0.04240132882253234\n",
      "Mean Relative Error Estimated Probs Uniform for s = 3900: 0.0439592858727557\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 3900: 0.357395\n",
      "Mean L2S Misclassification Rate for s = 3900: 0.354535\n",
      "Mean Uniform Misclassification Rate for s = 3900: 0.356205\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 3900: 0.006001224065039693\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 3900: 0.007627129241617699\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 3900: 0.6775646829525687\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 3900: 0.0020230454922710852\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 3900: 0.0018396229210234974\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 3900: 0.0019030909958717157\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 4000: 0.048901293198273424\n",
      "Mean Relative Error Estimated Probs L2S for s = 4000: 0.04073026141235892\n",
      "Mean Relative Error Estimated Probs Uniform for s = 4000: 0.043676214386055366\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 4000: 0.35531999999999997\n",
      "Mean L2S Misclassification Rate for s = 4000: 0.35308500000000004\n",
      "Mean Uniform Misclassification Rate for s = 4000: 0.356255\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 4000: 0.00600044425926124\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 4000: 0.005983634391986071\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 4000: 0.6708508436288653\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 4000: 0.0022929605858701086\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 4000: 0.0017157332006002082\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 4000: 0.001942829951037152\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 4100: 0.045013550439226696\n",
      "Mean Relative Error Estimated Probs L2S for s = 4100: 0.039645671512923156\n",
      "Mean Relative Error Estimated Probs Uniform for s = 4100: 0.0423434523187612\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 4100: 0.35577000000000003\n",
      "Mean L2S Misclassification Rate for s = 4100: 0.35224\n",
      "Mean Uniform Misclassification Rate for s = 4100: 0.355175\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 4100: 0.00696552195934628\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 4100: 0.008764648754662818\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 4100: 0.6648864905683334\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 4100: 0.001975195564141957\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 4100: 0.0015622874888646394\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 4100: 0.0017611258825869093\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 4200: 0.047628743668129955\n",
      "Mean Relative Error Estimated Probs L2S for s = 4200: 0.03983337534092497\n",
      "Mean Relative Error Estimated Probs Uniform for s = 4200: 0.04512273325044781\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 4200: 0.35496000000000005\n",
      "Mean L2S Misclassification Rate for s = 4200: 0.35242999999999997\n",
      "Mean Uniform Misclassification Rate for s = 4200: 0.35755\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 4200: 0.006895304374106959\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 4200: 0.00862403504432822\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 4200: 0.657644983676865\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 4200: 0.0022420616411924577\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 4200: 0.0016084426096866188\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 4200: 0.002010560243462227\n",
      "============================================================\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Relative Error Estimated Probs Leverage for s = 4300: 0.04556830083712779\n",
      "Mean Relative Error Estimated Probs L2S for s = 4300: 0.03741198778303025\n",
      "Mean Relative Error Estimated Probs Uniform for s = 4300: 0.04387963934723348\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 4300: 0.35668\n",
      "Mean L2S Misclassification Rate for s = 4300: 0.35326\n",
      "Mean Uniform Misclassification Rate for s = 4300: 0.35529\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 4300: 0.006668159332936287\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 4300: 0.008758880301826845\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 4300: 0.6524096881811927\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 4300: 0.002022586141794556\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 4300: 0.0013689307499948674\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 4300: 0.001909965155968556\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 4400: 0.04408330812768176\n",
      "Mean Relative Error Estimated Probs L2S for s = 4400: 0.040460598361994574\n",
      "Mean Relative Error Estimated Probs Uniform for s = 4400: 0.04271653063112903\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 4400: 0.35565499999999994\n",
      "Mean L2S Misclassification Rate for s = 4400: 0.35384000000000004\n",
      "Mean Uniform Misclassification Rate for s = 4400: 0.357625\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 4400: 0.005387697363161565\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 4400: 0.01024837091452204\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 4400: 0.6449342297727371\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 4400: 0.0018833960371653646\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 4400: 0.001599077162191064\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 4400: 0.001762595760649156\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 4500: 0.040444259805278535\n",
      "Mean Relative Error Estimated Probs L2S for s = 4500: 0.03672364361350743\n",
      "Mean Relative Error Estimated Probs Uniform for s = 4500: 0.04089340626476086\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 4500: 0.355535\n",
      "Mean L2S Misclassification Rate for s = 4500: 0.352615\n",
      "Mean Uniform Misclassification Rate for s = 4500: 0.35512\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 4500: 0.0036462633382832203\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 4500: 0.01178298725634913\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 4500: 0.6394365612342261\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 4500: 0.0016163292898487293\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 4500: 0.0013329165422515071\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 4500: 0.001706721539778631\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 4600: 0.0390953241381417\n",
      "Mean Relative Error Estimated Probs L2S for s = 4600: 0.03810962073706984\n",
      "Mean Relative Error Estimated Probs Uniform for s = 4600: 0.040745437280792655\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 4600: 0.35518\n",
      "Mean L2S Misclassification Rate for s = 4600: 0.353225\n",
      "Mean Uniform Misclassification Rate for s = 4600: 0.35706000000000004\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 4600: 0.0059689410608318606\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 4600: 0.009775163859700845\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 4600: 0.6320802672433248\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 4600: 0.0014977289999681058\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 4600: 0.001488651158278152\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 4600: 0.0017120511749482363\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 4700: 0.04194973930254829\n",
      "Mean Relative Error Estimated Probs L2S for s = 4700: 0.03789603408244355\n",
      "Mean Relative Error Estimated Probs Uniform for s = 4700: 0.03813043134068251\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 4700: 0.35662000000000005\n",
      "Mean L2S Misclassification Rate for s = 4700: 0.35048500000000005\n",
      "Mean Uniform Misclassification Rate for s = 4700: 0.35602999999999996\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 4700: 0.005768320713224305\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 4700: 0.009187790272163102\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 4700: 0.6259717055288172\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 4700: 0.0017155471779301032\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 4700: 0.0014474901127475963\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 4700: 0.001412540776331436\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 4800: 0.0437339212962196\n",
      "Mean Relative Error Estimated Probs L2S for s = 4800: 0.03562415591556886\n",
      "Mean Relative Error Estimated Probs Uniform for s = 4800: 0.03591030202310685\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 4800: 0.35514\n",
      "Mean L2S Misclassification Rate for s = 4800: 0.35219999999999996\n",
      "Mean Uniform Misclassification Rate for s = 4800: 0.3559150000000001\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 4800: 0.0057768190639169124\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 4800: 0.009045603469845332\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 4800: 0.6191896173430014\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 4800: 0.0018815338239530394\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 4800: 0.001279767733685464\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 4800: 0.001280534853508992\n",
      "============================================================\n",
      "Mean Relative Error Estimated Probs Leverage for s = 4900: 0.04211450338702691\n",
      "Mean Relative Error Estimated Probs L2S for s = 4900: 0.0346079420037566\n",
      "Mean Relative Error Estimated Probs Uniform for s = 4900: 0.037999151946868225\n",
      "-----------------------------------------------------------------------\n",
      "Mean Leverage Misclassification Rate for s = 4900: 0.35576500000000005\n",
      "Mean L2S Misclassification Rate for s = 4900: 0.35081\n",
      "Mean Uniform Misclassification Rate for s = 4900: 0.35388000000000003\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage Negative Log Likelihood for s = 4900: 0.006933932883116468\n",
      "Mean Relative Error L2S Negative Log Likelihood for s = 4900: 0.010985643982563789\n",
      "Mean Relative Error Uniform Negative Log Likelihood for s = 4900: 0.6131742728331485\n",
      "-----------------------------------------------------------------------\n",
      "Mean Relative Error Leverage True Negative Log Likelihood for s = 4900: 0.0017377467153233614\n",
      "Mean Relative Error L2S Negative True Log Likelihood for s = 4900: 0.0011768695581726202\n",
      "Mean Relative Error Uniform True Negative Log Likelihood for s = 4900: 0.0014319255172792304\n",
      "============================================================\n"
     ]
    }
   ],
   "source": [
    "s_values = range(100,5000,100)\n",
    "\n",
    "# Number of times to run the analysis for each s value\n",
    "num_runs = 20\n",
    "\n",
    "# Initialize lists to store mean relative errors\n",
    "mean_rel_est_probs_lev_list = []\n",
    "mean_rel_est_probs_l2s_list = []\n",
    "mean_rel_est_probs_unif_list = []\n",
    "\n",
    "mean_lev_misclassification_rate_list = []\n",
    "mean_l2s_misclassification_rate_list = []\n",
    "mean_unif_misclassification_rate_list = []\n",
    "\n",
    "mean_rel_lev_neg_log_likelihood_list = []\n",
    "mean_rel_l2s_neg_log_likelihood_list = []\n",
    "mean_rel_unif_neg_log_likelihood_list = []\n",
    "\n",
    "mean_rel_lev_true_neg_log_likelihood_list = []\n",
    "mean_rel_l2s_true_neg_log_likelihood_list = []\n",
    "mean_rel_unif_true_neg_log_likelihood_list = []\n",
    "\n",
    "\n",
    "# Initialize lists for standard deviations\n",
    "\n",
    "std_rel_est_probs_lev_list = []\n",
    "std_rel_est_probs_l2s_list = []\n",
    "std_rel_est_probs_unif_list = []\n",
    "\n",
    "std_lev_misclassification_rate_list = []\n",
    "std_l2s_misclassification_rate_list = []\n",
    "std_unif_misclassification_rate_list = []\n",
    "\n",
    "std_rel_lev_neg_log_likelihood_list = []\n",
    "std_rel_l2s_neg_log_likelihood_list = []\n",
    "std_rel_unif_neg_log_likelihood_list = []\n",
    "\n",
    "std_rel_lev_true_neg_log_likelihood_list = []\n",
    "std_rel_l2s_true_neg_log_likelihood_list = []\n",
    "std_rel_unif_true_neg_log_likelihood_list = []\n",
    "\n",
    "\n",
    "\n",
    "for s in s_values:\n",
    "    \n",
    "    rel_est_probs_lev_list = []\n",
    "    rel_est_probs_l2s_list = []\n",
    "    rel_est_probs_unif_list = []\n",
    "    \n",
    "    lev_misclassification_rate_list = []\n",
    "    l2s_misclassification_rate_list = []\n",
    "    unif_misclassification_rate_list = []\n",
    "    \n",
    "    rel_lev_neg_log_likelihood_list = []\n",
    "    rel_l2s_neg_log_likelihood_list = []\n",
    "    rel_unif_neg_log_likelihood_list = []\n",
    "    \n",
    "    rel_lev_true_neg_log_likelihood_list = []\n",
    "    rel_l2s_true_neg_log_likelihood_list = []\n",
    "    rel_unif_true_neg_log_likelihood_list = []\n",
    "\n",
    "    for run in range(num_runs):\n",
    "        \n",
    "        \n",
    "        # Pick the indices and weights for leverage, l2s, and uniform sampling\n",
    "        ind, w = leverage_sampling (data=X, size=s)\n",
    "\n",
    "        ind_l2s, w_l2s = l2s_sampling(data=X, size=s, k=20)\n",
    "\n",
    "        ind_unif = uniform_sampling(data=X, size=s)\n",
    "        \n",
    "        \n",
    "\n",
    "        # Calculate parameters using leverage, l2s, and uniform methods\n",
    "        result_lev = minimize(lev_neg_log_likelihood, initial_theta, args=(X, y), \n",
    "                              method='BFGS', jac=lev_gradient)\n",
    "        \n",
    "        result_l2s = minimize(l2s_neg_log_likelihood, initial_theta, args=(X, y), \n",
    "                              method='BFGS', jac=l2s_gradient)\n",
    "\n",
    "        result_unif = minimize(unif_neg_log_likelihood, initial_theta, args=(X, y), \n",
    "                              method='BFGS', jac=unif_gradient)\n",
    "\n",
    "        \n",
    "        # Calculate estimated probabilities and predicted labels\n",
    "        est_probs_lev = (sigmoid(np.dot(X, result_lev.x)))\n",
    "        est_probs_l2s = (sigmoid(np.dot(X, result_l2s.x)))\n",
    "        est_probs_unif = (sigmoid(np.dot(X, result_unif.x)))\n",
    "\n",
    "        lev_pred_labels = (est_probs_lev > 0.5).astype(int)\n",
    "        l2s_pred_labels = (est_probs_l2s > 0.5).astype(int)\n",
    "        unif_pred_labels = (est_probs_unif > 0.5).astype(int)\n",
    "\n",
    "        # Calculate misclassification rates and relative errors\n",
    "        \n",
    "        rel_est_probs_lev = np.linalg.norm(est_probs_lev - est_probs_opt) / np.linalg.norm(est_probs_opt)\n",
    "        rel_est_probs_l2s = np.linalg.norm(est_probs_l2s - est_probs_opt) / np.linalg.norm(est_probs_opt)\n",
    "        rel_est_probs_unif = np.linalg.norm(est_probs_unif - est_probs_opt) / np.linalg.norm(est_probs_opt)\n",
    "        \n",
    "        lev_misclassification_rate = np.mean(lev_pred_labels != y)\n",
    "        l2s_misclassification_rate = np.mean(l2s_pred_labels != y)\n",
    "        unif_misclassification_rate = np.mean(unif_pred_labels != y)\n",
    "        \n",
    "        rel_lev_neg_log_likelihood= np.abs(neg_log_likelihood(result_opt.x,X,y)-lev_neg_log_likelihood(result_lev.x,X,y))/neg_log_likelihood(result_opt.x,X,y)\n",
    "        rel_l2s_neg_log_likelihood= np.abs(neg_log_likelihood(result_opt.x,X,y)-l2s_neg_log_likelihood(result_l2s.x,X,y))/neg_log_likelihood(result_opt.x,X,y)                    \n",
    "        rel_unif_neg_log_likelihood= np.abs(neg_log_likelihood(result_opt.x,X,y)-unif_neg_log_likelihood(result_unif.x,X,y))/neg_log_likelihood(result_opt.x,X,y)\n",
    "\n",
    "        rel_lev_true_neg_log_likelihood= np.abs(neg_log_likelihood(result_opt.x,X,y)-neg_log_likelihood(result_lev.x,X,y))/neg_log_likelihood(result_opt.x,X,y)\n",
    "        rel_l2s_true_neg_log_likelihood= np.abs(neg_log_likelihood(result_opt.x,X,y)-neg_log_likelihood(result_l2s.x,X,y))/neg_log_likelihood(result_opt.x,X,y)                    \n",
    "        rel_unif_true_neg_log_likelihood= np.abs(neg_log_likelihood(result_opt.x,X,y)-neg_log_likelihood(result_unif.x,X,y))/neg_log_likelihood(result_opt.x,X,y)\n",
    "\n",
    "        \n",
    "        # Append to lists\n",
    "        \n",
    "        rel_est_probs_lev_list.append(rel_est_probs_lev)\n",
    "        rel_est_probs_l2s_list.append(rel_est_probs_l2s)\n",
    "        rel_est_probs_unif_list.append(rel_est_probs_unif)\n",
    "        \n",
    "        lev_misclassification_rate_list.append(lev_misclassification_rate)\n",
    "        l2s_misclassification_rate_list.append(l2s_misclassification_rate)\n",
    "        unif_misclassification_rate_list.append(unif_misclassification_rate)\n",
    "        \n",
    "        rel_lev_neg_log_likelihood_list.append(rel_lev_neg_log_likelihood)\n",
    "        rel_l2s_neg_log_likelihood_list.append(rel_l2s_neg_log_likelihood)\n",
    "        rel_unif_neg_log_likelihood_list.append(rel_unif_neg_log_likelihood)\n",
    "        \n",
    "        rel_lev_true_neg_log_likelihood_list.append(rel_lev_true_neg_log_likelihood)\n",
    "        rel_l2s_true_neg_log_likelihood_list.append(rel_l2s_true_neg_log_likelihood)\n",
    "        rel_unif_true_neg_log_likelihood_list.append(rel_unif_true_neg_log_likelihood)      \n",
    "\n",
    "\n",
    "    # Calculate mean relative errors for each s value\n",
    "    \n",
    "    mean_rel_est_probs_lev = np.mean(rel_est_probs_lev_list)\n",
    "    mean_rel_est_probs_l2s = np.mean(rel_est_probs_l2s_list)\n",
    "    mean_rel_est_probs_unif = np.mean(rel_est_probs_unif_list)\n",
    "                              \n",
    "    mean_lev_misclassification_rate = np.mean(lev_misclassification_rate_list)\n",
    "    mean_l2s_misclassification_rate = np.mean(l2s_misclassification_rate_list)                          \n",
    "    mean_unif_misclassification_rate = np.mean(unif_misclassification_rate_list)\n",
    "    \n",
    "    mean_rel_lev_neg_log_likelihood = np.mean(rel_lev_neg_log_likelihood_list)\n",
    "    mean_rel_l2s_neg_log_likelihood = np.mean(rel_l2s_neg_log_likelihood_list)\n",
    "    mean_rel_unif_neg_log_likelihood = np.mean(rel_unif_neg_log_likelihood_list)\n",
    "    \n",
    "    mean_rel_lev_true_neg_log_likelihood = np.mean(rel_lev_true_neg_log_likelihood_list)\n",
    "    mean_rel_l2s_true_neg_log_likelihood = np.mean(rel_l2s_true_neg_log_likelihood_list)\n",
    "    mean_rel_unif_true_neg_log_likelihood = np.mean(rel_unif_true_neg_log_likelihood_list)\n",
    "    \n",
    "    # Calculate standard deviations of errors for each s value\n",
    "    \n",
    "    std_rel_est_probs_lev = np.std(rel_est_probs_lev_list)\n",
    "    std_rel_est_probs_l2s = np.std(rel_est_probs_l2s_list)\n",
    "    std_rel_est_probs_unif = np.std(rel_est_probs_unif_list)\n",
    "                              \n",
    "    std_lev_misclassification_rate = np.std(lev_misclassification_rate_list)\n",
    "    std_l2s_misclassification_rate = np.std(l2s_misclassification_rate_list)                          \n",
    "    std_unif_misclassification_rate = np.std(unif_misclassification_rate_list)\n",
    "    \n",
    "    std_rel_lev_neg_log_likelihood = np.std(rel_lev_neg_log_likelihood_list)\n",
    "    std_rel_l2s_neg_log_likelihood = np.std(rel_l2s_neg_log_likelihood_list)\n",
    "    std_rel_unif_neg_log_likelihood = np.std(rel_unif_neg_log_likelihood_list)\n",
    "    \n",
    "    std_rel_lev_true_neg_log_likelihood = np.std(rel_lev_true_neg_log_likelihood_list)\n",
    "    std_rel_l2s_true_neg_log_likelihood = np.std(rel_l2s_true_neg_log_likelihood_list)\n",
    "    std_rel_unif_true_neg_log_likelihood = np.std(rel_unif_true_neg_log_likelihood_list)\n",
    "    \n",
    "    \n",
    "    # Append mean values to the lists\n",
    "    \n",
    "    mean_rel_est_probs_lev_list.append(mean_rel_est_probs_lev)\n",
    "    mean_rel_est_probs_l2s_list.append(mean_rel_est_probs_l2s)\n",
    "    mean_rel_est_probs_unif_list.append(mean_rel_est_probs_unif)\n",
    "                              \n",
    "    mean_lev_misclassification_rate_list.append(mean_lev_misclassification_rate)\n",
    "    mean_l2s_misclassification_rate_list.append(mean_l2s_misclassification_rate)\n",
    "    mean_unif_misclassification_rate_list.append(mean_unif_misclassification_rate)\n",
    "    \n",
    "    mean_rel_lev_neg_log_likelihood_list.append(mean_rel_lev_neg_log_likelihood)\n",
    "    mean_rel_l2s_neg_log_likelihood_list.append(mean_rel_l2s_neg_log_likelihood)\n",
    "    mean_rel_unif_neg_log_likelihood_list.append(mean_rel_unif_neg_log_likelihood)\n",
    "    \n",
    "    mean_rel_lev_true_neg_log_likelihood_list.append(mean_rel_lev_true_neg_log_likelihood)\n",
    "    mean_rel_l2s_true_neg_log_likelihood_list.append(mean_rel_l2s_true_neg_log_likelihood)\n",
    "    mean_rel_unif_true_neg_log_likelihood_list.append(mean_rel_unif_true_neg_log_likelihood)\n",
    "    \n",
    "    # Append standard deviation values to the lists\n",
    "    \n",
    "    std_rel_est_probs_lev_list.append(std_rel_est_probs_lev)\n",
    "    std_rel_est_probs_l2s_list.append(std_rel_est_probs_l2s)\n",
    "    std_rel_est_probs_unif_list.append(std_rel_est_probs_unif)\n",
    "                              \n",
    "    std_lev_misclassification_rate_list.append(std_lev_misclassification_rate)\n",
    "    std_l2s_misclassification_rate_list.append(std_l2s_misclassification_rate)\n",
    "    std_unif_misclassification_rate_list.append(std_unif_misclassification_rate)\n",
    "    \n",
    "    std_rel_lev_neg_log_likelihood_list.append(std_rel_lev_neg_log_likelihood)\n",
    "    std_rel_l2s_neg_log_likelihood_list.append(std_rel_l2s_neg_log_likelihood)\n",
    "    std_rel_unif_neg_log_likelihood_list.append(std_rel_unif_neg_log_likelihood)\n",
    "    \n",
    "    std_rel_lev_true_neg_log_likelihood_list.append(std_rel_lev_true_neg_log_likelihood)\n",
    "    std_rel_l2s_true_neg_log_likelihood_list.append(std_rel_l2s_true_neg_log_likelihood)\n",
    "    std_rel_unif_true_neg_log_likelihood_list.append(std_rel_unif_true_neg_log_likelihood)\n",
    "                              \n",
    "\n",
    "    # Print the mean relative errors for each s\n",
    "                             \n",
    "    print(f\"Mean Relative Error Estimated Probs Leverage for s = {s}: {mean_rel_est_probs_lev}\")\n",
    "    print(f\"Mean Relative Error Estimated Probs L2S for s = {s}: {mean_rel_est_probs_l2s}\")\n",
    "    print(f\"Mean Relative Error Estimated Probs Uniform for s = {s}: {mean_rel_est_probs_unif}\")\n",
    "    print(\"-----------------------------------------------------------------------\")                          \n",
    "    print(f\"Mean Leverage Misclassification Rate for s = {s}: {mean_lev_misclassification_rate}\")\n",
    "    print(f\"Mean L2S Misclassification Rate for s = {s}: {mean_l2s_misclassification_rate}\")\n",
    "    print(f\"Mean Uniform Misclassification Rate for s = {s}: {mean_unif_misclassification_rate}\")\n",
    "    print(\"-----------------------------------------------------------------------\") \n",
    "    print(f\"Mean Relative Error Leverage Negative Log Likelihood for s = {s}: {mean_rel_lev_neg_log_likelihood}\")\n",
    "    print(f\"Mean Relative Error L2S Negative Log Likelihood for s = {s}: {mean_rel_l2s_neg_log_likelihood}\")\n",
    "    print(f\"Mean Relative Error Uniform Negative Log Likelihood for s = {s}: {mean_rel_unif_neg_log_likelihood}\")\n",
    "    print(\"-----------------------------------------------------------------------\") \n",
    "    print(f\"Mean Relative Error Leverage True Negative Log Likelihood for s = {s}: {mean_rel_lev_true_neg_log_likelihood}\")\n",
    "    print(f\"Mean Relative Error L2S Negative True Log Likelihood for s = {s}: {mean_rel_l2s_true_neg_log_likelihood}\")\n",
    "    print(f\"Mean Relative Error Uniform True Negative Log Likelihood for s = {s}: {mean_rel_unif_true_neg_log_likelihood}\")\n",
    "    print(\"=\" * 60)\n",
    "    \n",
    "\n",
    "\n",
    "# Convert lists to arrays    \n",
    "\n",
    "# mean\n",
    "mean_rel_est_probs_lev_array = np.array(mean_rel_est_probs_lev_list)\n",
    "mean_rel_est_probs_l2s_array = np.array(mean_rel_est_probs_l2s_list)\n",
    "mean_rel_est_probs_unif_array = np.array(mean_rel_est_probs_unif_list)\n",
    "                              \n",
    "mean_lev_misclassification_rate_array = np.array(mean_lev_misclassification_rate_list)\n",
    "mean_l2s_misclassification_rate_array = np.array(mean_l2s_misclassification_rate_list)\n",
    "mean_unif_misclassification_rate_array = np.array(mean_unif_misclassification_rate_list)\n",
    "\n",
    "mean_rel_lev_neg_log_likelihood_array = np.array(mean_rel_lev_neg_log_likelihood_list)\n",
    "mean_rel_l2s_neg_log_likelihood_array = np.array(mean_rel_l2s_neg_log_likelihood_list)\n",
    "mean_rel_unif_neg_log_likelihood_array = np.array(mean_rel_unif_neg_log_likelihood_list)\n",
    "\n",
    "mean_rel_lev_true_neg_log_likelihood_array = np.array(mean_rel_lev_true_neg_log_likelihood_list)\n",
    "mean_rel_l2s_true_neg_log_likelihood_array = np.array(mean_rel_l2s_true_neg_log_likelihood_list)\n",
    "mean_rel_unif_true_neg_log_likelihood_array = np.array(mean_rel_unif_true_neg_log_likelihood_list)\n",
    "\n",
    "# stdev\n",
    "std_rel_est_probs_lev_array = np.array(std_rel_est_probs_lev_list)\n",
    "std_rel_est_probs_l2s_array = np.array(std_rel_est_probs_l2s_list)\n",
    "std_rel_est_probs_unif_array = np.array(std_rel_est_probs_unif_list)\n",
    "                              \n",
    "std_lev_misclassification_rate_array = np.array(std_lev_misclassification_rate_list)\n",
    "std_l2s_misclassification_rate_array = np.array(std_l2s_misclassification_rate_list)\n",
    "std_unif_misclassification_rate_array = np.array(std_unif_misclassification_rate_list)\n",
    "\n",
    "std_rel_lev_neg_log_likelihood_array = np.array(std_rel_lev_neg_log_likelihood_list)\n",
    "std_rel_l2s_neg_log_likelihood_array = np.array(std_rel_l2s_neg_log_likelihood_list)\n",
    "std_rel_unif_neg_log_likelihood_array = np.array(std_rel_unif_neg_log_likelihood_list)\n",
    "\n",
    "std_rel_lev_true_neg_log_likelihood_array = np.array(std_rel_lev_true_neg_log_likelihood_list)\n",
    "std_rel_l2s_true_neg_log_likelihood_array = np.array(std_rel_l2s_true_neg_log_likelihood_list)\n",
    "std_rel_unif_true_neg_log_likelihood_array = np.array(std_rel_unif_true_neg_log_likelihood_list)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00fe9174",
   "metadata": {},
   "source": [
    "# **Plot relative error estimated probabilities for the various sampling**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "e27a4df1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Smooth the curves using a rolling average with window size\n",
    "window_size =1\n",
    "\n",
    "smoothed_lev_prob = np.convolve(mean_rel_est_probs_lev_array, np.ones(window_size)/window_size, mode='valid')\n",
    "smoothed_l2s_prob = np.convolve(mean_rel_est_probs_l2s_array, np.ones(window_size)/window_size, mode='valid')\n",
    "smoothed_unif_prob = np.convolve(mean_rel_est_probs_unif_array, np.ones(window_size)/window_size, mode='valid')\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.plot(s_values[window_size-1:], smoothed_unif_prob, label=\"Uniform Sampling\", color='b')\n",
    "plt.plot(s_values[window_size-1:], smoothed_l2s_prob, label=\"L2S Sampling\", color='g')\n",
    "plt.plot(s_values[window_size-1:], smoothed_lev_prob, label=\"Leverage Score(this work)\", color='r')\n",
    "\n",
    "plt.xlabel(\"Sample Size (s)\", fontsize=18)\n",
    "plt.ylabel(\"Relative error est. probs.\",fontsize=18)\n",
    "plt.legend(fontsize=18)\n",
    "plt.grid(True, which='both')\n",
    "plt.yscale('log')  # Set y-axis to log scale\n",
    "# plt.xscale('log')\n",
    "\n",
    "plt.xticks(fontsize=18)\n",
    "plt.yticks(fontsize=18)\n",
    "plt.savefig('plots\\\\prob_churn.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2ce0b76",
   "metadata": {},
   "source": [
    "# **Plot misclassification rates for the various sampling methods**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "963b4959",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WVqHqy+Vy9OrVC5s3b8bTp0+RmZmJpKQkpKSkYPXq1fDx8cGJEycwbdq0PK8zfPhwBAUF4ebNm2L977//HjKZDCdOnMBXX32Fd955Bz169MCDBw+QmJiI5ORk8bqbN2/GoUOHjF7/hx9+wPnz57FixQqkpKQgISEBjx49wptvvgkA+PLLL/HHH38U6rkXxPTp0/HkyRPs2rULqampSElJwa1bt9C8eXMIgoDx48cjKSlJr96QIUPw119/wc7ODqtWrdKJuV+/fhg/fjzi4uLMHi9RgQhULiUlJQkAhKSkJLNdMysrS9i1a5eQlZVl2gXq1RMEQBAOHDBbTFQ2FbktEQnF247S09OFv/76S0hPTzdaRqHQ/JdWnh4KhdlfSsHf318AIAwZMiTfsqtXrxYACACEyMhInXMzZswQz82YMcNg/TfeeEMAIHTo0EHvnLZ+mzZt9M7du3fP6H0FQRDS0tKESpUqCQCEAQMGGLz3999/L17jwoULRmOfNGmSwfqCIAhDhgwRy61fv95gnBKJRCxz4sQJvTKHDx8Wzz9+/NjovQy5deuW4OTkJNZ3c3MTevfuLcycOVPYu3evkJCQUKjr5XbhwgUBgODg4KD3bysyMlK878svvyxkZGTo1Q8NDRXLdOzYUVCr1XplWrVqJQAQhg8frneuTZs2Yv1Vq1bpnVepVELr1q0FAEK9evWM1jfU/rTnpk+fLiQkJAgqlUo8p/03YGNjI/z99996dWNjYwVbW1uDv/cTJ06IMa9bt85gzCEhIWKZ1atX65WhskmlUum1pZwK8ncqp+LIDQRBENgzSSWHi/AQEVER2djY4OOPPzZ4Trvl1rVr18x6z4MHD+LFixcAYHT445gxY1ClShUAwMaNGw2WkUql4vDcvPj5+WHgwIF6x2vUqIGaNWsCAFq1aqUzpFSrTZs2sLGxAVD41yEwMBDHjh1DkyZNAAAJCQnYtWsXPv/8c3Tt2hXu7u5o27Ytdu3aVajrajVu3Bienp5ITU0V52caMnHiRPE55NS5c2fx+6lTp0IikRgtk9dz9/X11esNBjS/n88//xwA8Ndff+ltMVdUb775Jl566SW945UrV0ZwcDAA/bi3bt0KQDMn8p133skzZiJL4JxJKjlchIeIyhF7e0ChsHQU5mVvb+kI8vfyyy8bXXHVx8cHAMTEz1wiIiIAaJKQOtq/ZbnIZDK0a9cOv/32m1g+t1q1asHT0zPf+zVu3NhgogQAXl5euHv3rpjwGYrDw8MDT58+RUJCQr73yu3VV1/F+fPnERERgT179uDMmTO4cuUKoqOjoVarcezYMRw7dgxDhw7FL7/8ohdnVlYWfvnlF+zYsQM3btzAixcvkJmZqXefvOZlaofb5ubl5SV+b+z5a8vk9dy1i+UY0rp1a8jlcmRnZyMiIgINGjQwep3CatasmdFzxtqudjGl1q1bG425ZcuWYsxEJY3JJJUcJpNEVI5IJMC/2+9RCcprcRu5XPO2xtxvqmNjYwEAVatWzbNctWrVdMrnVpBEEijYcyxIGWOrmhZE48aN0bhxY/HnBw8eYPv27Zg7dy7i4+OxZs0aBAUF6ezdGRsbiw4dOuj06Nna2uosahQXFwe1Wq2zKmtuxp6b9nkVpExezz2v36ONjQ3c3d0RExNj9PdoKlN+Z9q5kNpk0xDtqsLR0dFmiJKocDjMlUoOk0kiogpHu2pnQfYlzLnfY16rfVqKsZ6hgpYryAqzpVX16tXx0Ucf4dixY+Lv5ueff9YpM3HiRFy/fh3u7u745ZdfEBUVhfT0dMTFxYmLGmmTIsGCqz0V9PdYGmhfp/xituTrSRUbk0kqOdpk8sEDzaquRERU7nl4eAAAnj59mm9ZbRmpVIpKlSoVa1yFoe1RfJzPauTaoZu5txQpT+rVqyfO1fwnxxoISqUSO3bsAKBZ+XbYsGHw9vbWqZtzCxZLymuIbWZmJp4/fw6g4D3JxUkbw7Nnz4yWyRkzUUljMkklx9MTcHHRLBp4966loyEiohKg3evw8uXLeQ5tBICTJ08CABo2bAgrK6tij62gtMM9nzx5gttGRteoVCocOXIEgPH5fOWFds5qzkVy4uLikJGRAUAz79KQkydPimUs6dixY0Z78k6cOCEOk845zNdSXnvtNQCamI05deoU50uSxTCZpJIjkXCoKxFRBfP2228D0Axh/f77742WO3XqFE6cOAEA6NevX4nEVlAdO3aEu7s7AOOruf74449i79GAAQNKKjSzCg8Pz3ee5dOnT8U9HLWJDgA4OzuLQzGvXr2qVy87Ozvf/SVLyqNHjwzuRapWqzF79mwAQN26dc26+I6ptHtfPnjwABs2bNA7LwiCGDORJTCZpJLFZJKIqEIJDg5G3759AQCff/45PvvsM53hoklJSVi1ahV69OgBQRBQo0YNjB071lLhGmRnZycmkRs3bsT777+PmJgYAJokecmSJZgwYQIATfKs7Y0tayZPnowaNWpg6tSpOHnypM481xcvXuDnn3/G66+/jpSUFADARx99JJ53dHREy5YtAQCTJk1CeHg41Go1AODGjRvo1q0bIiIi4FAKVq1ycXHB6NGj8dNPP4k9pY8fP8aAAQPE3uVZs2ZZMkRRq1at0LFjRwDAyJEjsWbNGnF13CdPnuCdd97BiRMnYF8WlmKmconJJJUsJpNERBXO6tWr0b17d6jVasyZMwd+fn5wcnJCpUqV4ObmhhEjRiAhIQF16tTBnj174OzsbOmQ9XzwwQeYOHEiAE0vZJUqVVCpUiW4uLhg3LhxUCqVCAkJwU8//WThSE1nZWWFJ0+eYN68eWjVqhUcHBzg6uoKBwcHuLu7Y+TIkXjw4AGsra2xePFidO/eXaf+okWL4ODggKdPn6J9+/awt7eHs7MzGjRogCNHjuCnn34S59Ba0pgxY9C4cWOMGjUKzs7OqFSpEvz8/LBlyxYAmg89+vTpY+Eo/7N27Vq89NJLSEtLw7Bhw+Dk5AQ3Nzf4+vpi8+bNWLp0qfi62traWjhaqmiYTFLJYjJJRFThODk54X//+x/+/PNP9O/fHwEBAeL2EF5eXujcuTNWrFiBq1evIjAw0NLhGrVw4UKEh4ejb9++8PLygkKhgJOTE0JCQvDLL7/g4MGDeW7/UNodOXIEu3fvxqRJk9CqVSt4eXkhPT0dSqUSHh4eaNGiBaZNm4a///4b48aN06sfFBSE8+fPo1+/fvDw8IBarYaTkxP69euH06dPIzQ01ALPSp+1tTUOHz6M2bNnIzAwEJmZmXBxcUH79u3x559/4uuvv7Z0iDq8vb1x4cIFfP7556hTpw6kUinkcjm6deuG8PBwjBw5EklJSQAAV1dXywZLFY5E4FrC5VJycjJcXFyQlJRktk94lUol9uzZg27dupm+MMLly8Brr2kW4/l3iBBVPGZpS1ThFWc7ysjIQGRkJAICAvhJfwWgVquRnJwMZ2dnSKX8nL28atu2LY4dO4YZM2YYnftaVJZoS3fu3EGdfz+sf/ToEXx9fUvkvlS88mtLhf07VRy5AcCeSSpptWtrvsbGAomJFg2FiIiIqKybM2cOAM22LUwkqaQxmaSS5egI/LthMYe6EhEREeXt1q1bGDFiBI4fPy4ufqQ9PmzYMKxevRoAMHXqVEuFSBWY3NIBUAVUpw7w7JkmmWza1NLREBEREZVaGRkZWLVqFVatWgVAsxqtUqlEWlqaWGbcuHGlZk4qVSzsmaSSx0V4iIiIiAqkZs2a+Oabb9ClSxcEBAQgOzsbKpUKvr6+6NevHw4dOoTFixdbOkyqoNgzSSVPu1Ifk0kiIiIqIUePHrV0CCZxcnLCRx99pLOvJ1FpwZ5JKnnansl//rFsHEREREREZDImk1Tycg5z5c40RERERERlEpNJKnkBAYBMBqSlaRbiISIiIiKiMofJJJU8KyugRg3N95w3SURERERUJjGZJMvgiq5ERERERGUak0myDO2KrlyEh4iIiIioTGIySZbBnkkiIiIiojKNySRZBpNJIiIiIqIyjckkWYY2mbx/H1AqLRsLEREREREVGpNJsgwfH8DBAVCpgMhIS0dDRERERESFxGSSLEMi4VBXIiIiIqIyjMkkWY42meSKrkREROVGWFgYJBIJ2rZtW6hzRFT2MJkky2HPJBFRuadNHiQSSaHqPX/+HKtXr8agQYNQr149ODg4wMbGBtWqVUPv3r2xc+fOfK+hVCrxyy+/oFu3bqhatSpsbGzg4uKCOnXqICQkBJ9++in27t2LjIwMk56bQqHA4sWL0a5dO3h5ecHa2hqVKlVC3bp10blzZ3z55ZcIDw+HSqUy6fpERKWd3NIBUAXGZJKIiIzw9vZGdna2+LOtrS2srKzw9OlTPH36FL///ju6du2Kbdu2wd7eXq/+48eP0a1bN9y4cUM8Zm1tDZlMhnv37uHOnTs4evQo5s6di8OHD+O1114rVHzXrl3D//3f/+Hx48c6MQqCgH/++Qe3bt3CgQMHAACRkZGoXr16IV+B8snDwwOBgYHw8/OzdChEZAbsmSTLYTJJRERGZGdno2nTpvjhhx9w7949pKenQ6FQIDIyEsOHDwcA7N27F++9955eXZVKhV69euHGjRuwt7fH119/jUePHiEjIwMvXrxAWloazp8/j7CwMNSsWbPQsaWkpKBbt254/PgxPDw8sHjxYsTGxiI9PR0JCQlISUnB8ePHMXnyZFSpUqXIr0V58sEHH+DWrVtYu3atpUMhIjNgzyRZjjaZfPYMUCgAR0fLxkNERKVGeHg4QkJC9I5Xr14dP//8M+RyOX788UesX78es2fPhq+vr07dy5cvAwBWrVqF/v3761zDxsYGTZo0QZMmTTB9+nRkZGQgMzOzwLFt2rQJT58+BQD873//Q/PmzXXOOzg4oFWrVmjVqhVmzZpV4OsSEZU17JmkEiMIgu4BV1fA01PzPXsniYgoB0OJZE7a3kkAiIiI0Dl35coV8ftevXrleR2JRAIbG5tCxaa9vqenp14imZtcLodcrv/Z/T///IMFCxagQ4cOqFmzJuzs7ODs7IxXX30Vn3/+OeLj441es3r16pBIJFizZg3S0tIQFhaGunXrwt7eHj4+PggNDUVkjm234uPjMWXKFNSpUwd2dnbw9vbGiBEjEBMTY/D6uRfJ2bJlC9q0aYNKlSrBwcEBQUFBWLp0qUlzQfNagGfo0KGQSCQYOnQoAGDbtm1o27YtKlWqBHt7e7zyyitYvHgx1Gq10esLgoDVq1cjODgYTk5OcHFxQbNmzbBy5UoIgqB3DyIqGvZMUrFbfmE5vjz2Jd6o+wZ+6P6D7sk6dYDYWE0yWcj5KkREVHHZ2tqK3+eV1Dx58gS1a9culhgSEhKQlpZmcM5mfjp37oyHDx8C0CS0Li4uSEpKwpUrV3DlyhWsWbMGhw8fRmBgoNFrJCUlITg4GNeuXYOtrS0kEgmioqKwfv16HD58GKdPn4ZarUaHDh0QGRkJe3t7qNVqxMTEYNWqVTh+/DgiIiLg7Oxs9B5TpkzB/PnzIZFI4OrqioyMDFy6dAmXLl3C7t278fvvvxc6GS+IDz74AMuWLYNUKoWzszPS09Nx9epVTJgwAZcuXcKvv/6qV0elUuGdd97B5s2bAUCMOSIiAufPn8fRo0dhbW1t9liJKjL2TFKxk0qkiEmNwZPkJ/onOW+SiIhMcPToUfH7Bg0a6Jxr2rSp+P37778vDkk1F+31lUolhg8fjhcvXhT6Gs2bN8eSJUtw9+5dZGRkICEhARkZGTh06BCaNm2Kp0+fYuDAgXleIywsDMnJyThw4ABSU1ORkpKCAwcOwMPDA1FRUZgyZQoGDBgAFxcXnDlzBqmpqVAoFNi8eTPs7e1x584dzJ8/3+j1r1y5gvnz5+ODDz5ATEwMXrx4gYSEBHz99deQSCTYv38/Pv3000I/9/z88ccf+Omnn7Bw4UIkJCQgISEB8fHxGDFiBABg7dq1CA8P16u3YMECMZGcNGkS4uLixJhnz56NTZs24Y8//jB7vEQVGXsmqdhVcdIsPhCtiNY/yWSSiMooQRCQpkyzdBhmZW9lX+gtPCwhMTERc+bMAQC0atVKr/euTZs26NixIw4ePIjw8HD4+/ujadOmaNasGYKCgtCsWbMi9Vb2798f33zzDW7cuIFNmzZhx44daNmyJZo2bYqgoCA0b95cZw6nIZs2bdI7Zm1tjfbt2+Pw4cOoVasWLl26hJMnT+L11183eI3MzEwcPHgQtWrVEo917NgR8+bNw/Dhw7FlyxZ4eXnh5s2bcHd3BwBYWVmhX79+uHHjBr7++mts2rQJM2fONHj9pKQkhIaGYsmSJeIxZ2dnfP7558jMzMTMmTOxZMkSfPzxx/Dx8cn3dSuohIQErF69Wmcoqru7O3766SdcvnwZFy9exMaNG9GuXTvxfFpamtgmhg8fjm+//VYn5k8//RSZmZn48ssvzRYnETGZpBLg7egNAIhSROmfZDJJRGVUmjINjnPK18Jhik8VcLB2sHQYeVKr1QgNDUVUVBRsbGx0Ep2cdu7cicmTJ+Onn36CUqnEmTNncObMGfG8v78/hg0bhokTJ8KxkAvA2djYIDw8HB9++CG2bNmCrKwsHDlyBEeOHBHL1K1bF6NGjcLo0aMLPQzU0dERbdq0wZYtW/JMJvv27auTSGp17txZ/H7UqFFiIpm7zNdff4179+4hNTUVDg6Gf+/Tp083ePyTTz7Bt99+i/T0dGzfvh0ffvhhQZ5agfj6+mLw4MEGz/Xs2RMXL17EtWvXdI7v378fycnJAIBp06YZrPvRRx9hwYIFSEsrXx8CEVkSh7lSsaviqOmZjFHEQC3kmjSv/TT5n3+A3Av0EBER5TJ+/Hjs3r0bAPDDDz+gUaNGBss5ODhg2bJlePLkCVauXInQ0FDUrVsXMpkMAPDw4UOEhYXhlVdewb179wodR+XKlbFp0yZERkZi8eLF6NevH2rWrCn27P7999+YOHEigoOD8fz5c4PX2L17N95++23UqFEDDg4OkEgk4mPLli0ANHM+jck5nDcnLy8v8fsmTZrkWyYxMdFgGV9fX4PJKqDp7QsKCgKgvwBSUTVp0gRSqeG3qNoe0NxDiy9dugQA8PPzQ0BAgMG6Tk5OYsxEZB7smaRi5+Wo+YOlVCvxIv0FPOw9/jtZsyYgkQDJyZqFeHL8cSMiKs3sreyh+FRh6TDMyt6q8AvJlKSPP/4YS5cuBQB89913ePfdd/Ot4+npiZEjR2LkyJEAAIVCgfDwcCxYsAAnT55EZGQkBg4ciIMHD5oUk7+/P8aNG4dx48YB0AzR3Lt3L+bMmYMbN27g8uXLeO+997Bt2zaxjlqtxqBBg7Bx40bxmFwuh5ubm7hATFJSEjIyMpCammr03k5OTgaP51w9tiBllEqlwTJVq1Y1eu+c52NjY/MsV1jGYgb+izt3zHFxcQCQ73Db/J4TERUOk0kqdtYya7jbueN5+nNEK6J1k0kbG6B6dSAyUjPUlckkEZUREomk1A8JLU8mT54szoNbsGABJkyYYNJ1HB0d0bNnT/zf//0fOnTogCNHjiAiIgLXr19Hy5Ytixynm5sbBg4ciN69e6Nx48b4+++/sXPnTrx48QKVKlUCoNn7cuPGjZDJZJg2bRpCQ0NRo0YNnd640NBQrF+/Xn9brRJUFubPamlfp/xituTrSVQecZgrlQhx3mQK500SEVHhfPLJJ1iwYAEAYP78+fj444+LfE2pVCquDgoAd+7cKfI1c7K3t8egQYMAaHoic15fu/jOiBEj8OWXX6JWrVp6wzqjow0sWlfC8hpiC0BcJddTu2e0BWljePbsWZ7l8jtPRIXDZJJKBFd0JSIiU3z88cf45ptvAGgSyU8++cRs18658E5x7JVo7PqPHz8GALz66qsG6ykUCpw7d87s8RTW48ePjc4nTUlJwcWLFwEAjRs3LsmwDHrt372qHz58iAcPHhgso1AoxJiJyDyYTFKJyHNF15yL8BAREf3r448/Foe2fvPNNwVOJG/cuFGgvSXXrl0rfp97r8q8nD9/Pt+9JbOzs/Hbb78B0CwGlHP7EhcXFwDA1atXDdb9+uuvkZKSUuB4itPXX39t8Lh2JVe5XI433nijhKPS16lTJzg7OwMAZs+ebbDMd999x5VcicyMySSVCO2KruyZJCKquOLj4/N85FxVdMqUKWIiuXDhQnz00UcFvs/Ro0dRo0YNvP3229i6dSuiov77IDMjIwMnT55Ez549sX37dgCaLTb8/PwKfP0tW7bA398f7777Lnbv3q2zWmtaWhr27t2LkJAQnD9/HgAwevRo2NnZiWW6dOkCAPjpp5+wcuVKZGVlAdAMbZ04cSLmz59vcDuPkubi4oJff/0V48ePR3x8PABNj+Ts2bPFJHPs2LGlYlEbBwcHTJkyBYDmdZ08ebKY8KekpGDevHkICwuDm5ubJcMkKne4AA+ViALtNXn3LqBSAf8u205EROVL5cqV8zzfqFEjXLlyBY8ePcL8+fMBaOY2zps3D/PmzTNa7+OPP9aZR2llZYWsrCxs2bJF3GLD1tYWdnZ2SEhI0KnbqVMn/Pzzz4V6HlZWVlAoFFi9ejVWr14NQDNH0srKCklJSTplQ0ND9XrKPvroI2zbtg23bt3Ce++9h9GjR8PZ2RlJSUkQBAHvvfceMjIy8OuvvxYqLnN75ZVX0KxZM8yfPx9Lly6Fq6srkpKSoFKpAAAdOnTA3LlzLRpjTpMnT8bly5exbds2LFiwAN9++y1cXFyQnJwMlUqF0NBQSCQSrF27Fra2tpYOl6hcYM8klYg8eyZ9fTWruiqVwMOHJRwZERGVNmq1Wuf7mJiYPB8Khe4WLe+99x6uXr2KefPmoVevXqhVqxZkMhmSkpLg5OSEevXqYfDgwdizZw/2798vDo8sqNmzZ+Ps2bP48ssv0aVLF1SvXh2CIEChUMDFxQWNGjXCe++9h5MnT2Lt2rWwsrLSqe/q6orTp09jwoQJqF69OmQyGeRyOdq2bYuNGzdixYoVpr94ZjZv3jxs2rQJLVu2hFqthrW1NV555RUsXrwY+/btK1VJmVwux5YtW/Dzzz+jadOmsLOzQ3Z2Nho3boyff/4Za9euFXu/XV1dLRorUXkhEbhGcrmUnJwMFxcXJCUlFfqPpDFKpRJ79uxBt27d9P4w5udI5BG0W9sOge6BuPXBLf0CDRoAN24Ae/cC/w7/ofKrKG2JSKs421FGRgYiIyMREBBQqt4sU/FQq9VITk6Gs7Oz3qqqFVFYWBi+/PJLtGnTBkePHrV0OGYjCAL8/Pzw5MkTrF27FqGhoWa/B9sSmUt+bamwf6eKIzcA2DNJJSTP1VyB/xbh4bxJIiIiKgbr1q3DkydPIJfL0b59e0uHQ1QuMJmkEqGdM5mUmYR0Zbp+Ae28Sa7oSkRERCYaMGAAtm3bJi4YBAAxMTGYO3cuRo4cCQAYPHgwfHx8LBUiUbnCBXioRLjYuMBWbouM7AxEK6IR4BagW4AruhIREVER7d27F5s2bQJgeFGkVq1a4bvvvrNUeETlDnsmqURIJJKCrejKZJKIiIhM9P3336N///4IDAyEjY0N0tLSULlyZXTs2BGrVq3C4cOHzTpfjKiiY88klZgqjlXwIPFB3ntNPnoEpKcDOfbjIiIiopITFhaGsLAwS4dhksGDB2Pw4MGWDoOowmDPJJUY7SI8USkGeibd3QHtRsJ375ZgVEREREREZAomk1RivB00w1wN9kxKJP+t6MpFeIiIiIiISj0mk1RixJ5JQ3MmAc6bJCIiIiIqQ5hMUonRLsBjdK9JJpNERERERGUGk0kqMVUc2TNJRERERFReMJmkEsOeSSIiIiKi8oPJJJUY7ZzJGEUMVGqVfoHatTVfnz/XPIiIiIiIqNRiMkklxtPBExJIoBJUeJ5uIFm0twd8fTXfs3eSiIiIiKhUYzJJJUYulaOyQ2UARvaaBDjUlYiIiIiojGAySSWK8yaJiIiIiMoHJpNUoriiKxERERFR+cBksgzp06cP3Nzc8Oabb1o6FJPl2zMZGKj5ymSSiIiIiKhUYzJZhowbNw5r1661dBhFIvZM5jdn8s4dQK0uoaiIiIiISofnz5/D1dUVlStXRmpqqknXaNu2LSQSCcLCwixSv6IYOnQoJBIJhg4dWqh6jx49go2NDWrVqoWsrKziCa6EMJksQ0JCQuDk5GTpMIpE7JlMNdIz6e8PWFkB6enAkyclGBkRERWHsLAwSCQSSCQSS4dCJejcuXMYMWIE6tatCycnJ9jY2MDX1xeNGzfGu+++izVr1uDx48eWDrNUCgsLQ1JSEiZPngwHBwedc0ePHkVYWBjWrFljmeDILPz8/DBs2DDcu3cPy5Yts3Q4RVJmksnly5ejYcOGcHZ2hrOzM4KDg7F3794866SkpGDChAnw9/eHnZ0dWrRogQsXLpg9tuPHj6NHjx7w8fGBRCLBrl279Mr88MMPCAgIgK2tLYKCgnDixAmzx1EWaPeaNNozKZcDNWtqvudQVyIiojJFEARMmDABzZs3x6pVq3Dr1i2kpqbCwcEBcXFxuHjxIlavXo1hw4bhiy++sHS4pc7t27exYsUKVK5cGWPHjtU7f/ToUXz55ZfFnkz6+fkhMDAQHh4exXqfiuyzzz6DlZUVZs6cicTEREuHY7Iyk0xWq1YNc+fORUREBCIiItCuXTv06tULN2/eNFpnxIgROHjwINatW4fr16+jU6dO6NChA54+fWqw/KlTp6BUKvWO37p1C9HRRnrSAKSmpqJRo0ZYunSpwfObN2/GhAkTMG3aNFy+fBmtWrVC165d8ejRI7FMUFAQ6tevr/d49uyZ0fuWRfnOmQS4CA8REVEZ9d1332Hx4sUAgF69euH06dPIyMjAixcvkJGRgfv372PVqlXo0qULZDKZhaMtfRYuXIjs7GwMGTIE9vb2Fotj7dq1uHXrFj744AOLxVDe+fn5oVu3bnjx4gV+/vlnS4djMrmlAyioHj166Pw8a9YsLF++HGfPnsXLL7+sVz49PR3bt2/H77//jtatWwPQDBvYtWsXli9fjpkzZ+qUV6vVGDt2LGrXro1NmzaJ/8Hdvn0bISEhmDhxIiZPnmwwtq5du6Jr165GY1+4cCGGDx+OESNGAAAWLVqE/fv3Y/ny5ZgzZw4A4OLFiwV8JQpHqVQaTJBNvVbOr6bwsNV8whWliDJ6HWmtWpABUN26BbWZYqfSxRxtiag425FSqYQgCFCr1VBz/naRCIIgfl9aX0ttjNrfOZlGEAQsXLgQANC5c2fs2LFDPKd9Xf39/TF06FAMHToU6enp5e71LkpbUigU2LBhAwBg4MCBBusX9t8T23TxKur/HQMHDsTvv/+OlStXYuLEiTrTAfK7tlqthiAIUCqVBfpgprjec5ktmYyLi8PDhw+RlpYmJm/FRaVSYevWrUhNTUVwcLDBMtnZ2VCpVLC1tdU5bmdnh5MnT+qVl0ql2LNnD1q3bo3Bgwdj3bp1iIyMRLt27dCzZ0+jiWR+srKycPHiRUydOlXneKdOnXD69GmTrpmXZcuWYdmyZVCpVACAAwcOmP2TrYMHD5pcN12VDgBQZCmw/X/bYSez0yvjl5mJVwHEnzqFs3v2mHwvKv2K0paItIqjHcnlcnh7e0OhUJT5xREsLTMzU/w+OTm50PX//PNPbNiwAZcuXcLz589hb2+PevXq4c0330RoaCisrKzEsnFxcahXrx6ys7Px22+/oVu3bkavO2vWLHzzzTcICAjApUuXAGimx2hdu3YNK1euxKlTpxATEwOpVIrq1aujS5cuGD16NNzd3fWuOXfuXMybNw8tW7bE7t278ccff2DNmjW4fv06nj9/jsmTJ4vvB+7cuYO9e/ciPDwcDx8+RHR0NKysrFC9enV06tTJ6D20lEolfvrpJ2zcuBH379+HtbU1Xn75ZYwcORK9evXC//3f/+HUqVOYMmWK3nuQojzHvMTHx4ujvzp27Fig37exN7ja3poDBw7g3r17SE9Ph6enJ2rXro3u3bujb9++cHFx0av3v//9D+vXr8fly5eRmJgIV1dXvPrqqwgNDcX//d//GbzXmDFjsHHjRgwYMADLli3DunXrsGHDBty+fRsJCQlYtmwZBg4cKJaPiYnB8uXLcejQITx69AhZWVnw9vZG69atMWbMGLz00ks6bamgfv31V6SkpCAwMBABAQE6r9+jR4/QqFEj8edjx47pJRA548zOzgag+feXlJSEtWvXYt26dbh9+zYEQUDdunUxfPhwvP322wZjyav9ZGdnY/369di2bRv+/vtvJCcnw8nJCe7u7mjQoAHatWuHQYMGFfh5BwcH49atW5g3bx5GjRqlc+78+fPo3LkzAKBnz5749ddfdc4rlUoEBAQgNTVVpwNJKykpCT/++CP+/PNPREZGQqlUwsfHB23atMG4ceNQvXp1gzG5ubkB0LSnwMBAsSPo2bNnSE9PR0JCgnh/7VdD7T08PBxDhgyBQqHAgAED8P3330Mu/y/9atOmDZycnHDnzh3s2bMHrVq10ruGsbaUlZWF9PR0HD9+XPx95yUtLS3fMiYRiuj3338XXn31VUEqlQpSqVSQyWQ651+8eCF07txZ6Ny5s6BQKIp0r2vXrgkODg6CTCYTXFxchD///DPP8sHBwUKbNm2Ep0+fCtnZ2cK6desEiUQi1KlTx2idhw8fCv7+/sLbb78t+Pn5CYMHDxbUanWBYwQg7Ny5U/z56dOnAgDh1KlTOuVmzZqVZxyGdOrUSfDw8BDs7OyEqlWrCufPnzdaNikpSQAgxMfHC1lZWWZ5pKamCrt27RJSU1OLdB2HWQ4CwiD8Ff2XwfPKw4cFARDUNWqYLXY+StfDXG2Jj4r9KM52lJycLNy8eVNITU0VVCoVH0V4TJ8+XQAgAChUvaSkJKF79+5iXQCCs7OzIJFIxJ+Dg4OF+Ph4nXrdunUTAAh9+/Y1eu3s7GwhICBAACDMmDFDyM7OFhISEoTs7GxBpVIJX3zxhc597O3tBWtra/HnKlWqCBEREUafa5s2bYSJEycKAASJRCK4ubkJMplMmD59uljW399fvJ5EIhFcXV117lm1alXhr7/+Mhh/cnKy0Lp1a7GsTCYT3NzcxPpTpkwR2rRpIwDQuWfOh6nPMa9HdHS0WP/TTz81uc3s3btXcHNzE68ll8sFV1dXnbawfft2nTrp6elCv379xPNSqVRwc3MTpFKpeKx///5CRkaG3v0GDx4sABBCQ0OFvn376tVftWqVWPb3338XHB0dxWtaWVkJDg4O4s/W1tbC8uXLxbZUmEefPn0EAMKoUaP0zj148EDw8vIS72VlZSV4eXnpPDZs2CCW1/7+p02bJvTs2VN8HZ2dnXVexy+++MJgLMbaT1ZWltChQweda7i4uAg2NjY6xwrzvD/44AMBgNC7d2+9c19//bV4TXd3d73X9fjx4wIAwcbGRlAoFDrnrl27JlSrVk2sb2trKzg5OYk/29jYCFu2bDEYk7bMjz/+KHh5eenVz912Bg8erHeNX3/9VbCyshIACJ988onR59++fXuDZXL/v5T7kZqaKty8eVNITk4u0N+1+Ph4AYCQlJRUqPwjP0VKJufMmSNIpVJBIpGID6lUqleuT58+glQqFbZs2VKU2wmZmZnCnTt3hAsXLghTp04VPDw8hJs3bxotf/fuXfE/W5lMJjRp0kR45513hLp16+Z5n2PHjgkAhBo1aghKpbJQMRpLJk+fPq1TbubMmUJgYGChrl0Y2mTSnA0mKytL2LVrl5CVlVWoemq1INy4IQi3b2t+rrm4poAwCMcfHDdcISpKEABBkEoFISOjiFFTaWRqWyLKqTjbUXp6uvDXX38J6enpxgup1YKgUJSvRyE+PC2oGTNmiG/MCqN3794CAKFWrVrChg0bhOTkZEEQNL+b33//XahRo4b4BjSnzZs3i28UExISDF77xIkTYkz37t0TVCqVkJCQIKhUKuG7774TAAhOTk7CnDlzhKioKEEQBCE7O1uIiIgQ2rVrJwAQqlWrJqSkpBh8rtpkY/LkyUJsbKwgCIKQkZEhPHjwQCz79ttvC0uWLBHu3r0rZGZmCoKgeZ9z6NAhoWnTpgIA4bXXXjMY/3vvvScmPPPmzRPjiIuLE8aNGycAEJOvGTNm6NUvynPMjzZJd3JyEg4cOFCouoIgCJcuXRJsbW0FAMLLL78s7NmzR/w3npqaKly4cEH46KOPhEOHDunU++ijj8TE/IsvvhB/9y9evBA+++wz8fc9ZcoUvXsOGTJE/L3J5XLhm2++Ed8/paSkCM+ePRMEQRDOnTsnJtzvvfee8PfffwvZ2dmCIGg6I8aMGSMmbefOnSv0c/f09BQACKtWrTJaRtvG2rRpk+e1tMmgm5ub4OLiIqxZs0ZIS0sTBEEQHj9+LPTo0UNsQ7e1b9AM1M/dftatWycmVj///LPYPtRqtRATEyPs2LFD6Nu3b6Ge944dO8RYVSqVzjltoqVNgi9fvqxzXpts5n49kpOTxbZYtWpV4c8//xSvfeXKFaF58+bi/xNXrlzRi0nbXhwdHYXAwEDh8OHDYv1//vlHLKdtO0OGDNGpv2DBAjE3WrRoUZ7PX9s+mzVrpnM85/9LhhTo71QOxZEbCEIRksmzZ88KUqlUsLa2FhYtWiQ8f/5c8Pb2NphMbt++XZBIJMKgQYOKFGxu7du3F0aNGpVvOYVCIf5H0K9fP6Fbt25Gy0ZHRwuBgYFCjx49BG9vb+GDDz4oVEy5k8nMzExBJpMJO3bs0Ck3btw4oXXr1oW6dmGUpmRyyhRNbjh6tObn1395XUAYhC03jHy4oFYLgpOTptJffxUxaiqNmEySOVg8mVQoNP9PladHEUcQGWJKMrl7924BgODt7S08efLEYJnHjx+LvTQ532Cmp6cLLi4uYq+CIaNGjRIACK+//rogCP+9aYuJiRHs7e0FiUSil6xoKZVKISgoSAAgfPfdd0af66RJkwr8fHNLSUkRe0NOnDihc+7hw4dib9vXX39tsL72Da6hZCAuLq5IzzE/v/32m3hvAIK/v78QGhoqLFq0SDh16pSQkc+HxK+//roAQKhdu7aQmJhYoHs+efJEkMvlgrZH1JBJkyYJ2h497XtCrZyv1/fff2/0Pk2aNBG0vXnGfPjhhwIAoWfPngWKXevevXtiDBEREUbLFTaZBCCEh4frnc/IyBB8fHwEAMLMmTON1s/dfkaPHi1oe0/N5cWLF2Kbvnjxok6MdnZ2gr29vfDxxx8LAIRvv/1Wp25ISIgAQAgLC9M5PnfuXPH3ff36db17JicnC9WrVxcACN27d9c7r33tnJ2dhcePHxuNPXcyqVarxVEJ1tbWwqZNm/J9/lu3bhU/hNB+sCQIZSeZNHk1V+1KXZ9++inGjx+PSpUqGS3bpk0bADD7thyCIOjMxTDGwcEBVapUQUJCAvbv349evXoZLBcfH4/27dujbt262LFjB8LDw7FlyxZ8/PHHJsdobW2NoKAgvTk9Bw8eRIsWLUy+blnSsqXm64EDmq/aFV2jFEa2B5FIuKIrEVEFpl3ZMDQ0FFWrVjVYplq1aggJCQEA7N+/Xzxua2uLt956CwCwbt06vXqZmZnYsmWLeP2cNmzYgLS0NDRu3Bjt27c3eF+5XI4BAwbo3TcnqVSKKVOmGH1++XF0dBTfO+Ve52H79u1Qq9Wwt7fHxIkTDdbPa8uN3377zSzP0ZiBAwdi06ZNqFatGgDg4cOHWLduHSZMmICWLVvCzc0N/fv3x9WrV/Xq3rlzR3y+s2fPNjgn0pDt27cjOzsbtra2RueHfv7557CxsYFSqcS2bdsMlnFzc8N7771n8NzVq1dx4cIFWFlZ4aOPPjIai7ZNHT58WFy/oiByrt5fuXLlAtfLT8uWLcV/JznZ2NiIcxGvXbtW4Ou5uroCQJ67HBSWm5ubOB80PDxcPH727Fmkp6ejZcuW6NKli975zMxMnDlzBgD0nuPmzZsBAG+++Sbq16+vd08nJydxPZS9e/ciKSnJYGyhoaFiW86PUqnEO++8g++++w5OTk7Ys2eP0TmpOWm3X8nOzkZcXFyB7lWamLwAj/Yfe0GWDHZ3d4ejo6PRLTkK4rPPPkPXrl3h6+uLlJQUbNq0CUePHsW+ffsAAEuXLsXOnTtx+PBhsc7+/fshCAICAwNx9+5dfPLJJwgMDMSwYcP0rq9Wq9GlSxf4+/tj8+bNkMvlqFu3Lg4dOoSQkBBUrVrV6H/aCoUCd+/eFX+OjIzElStXUKlSJfj5+WHSpEkIDQ1F48aNERwcjJUrV+LRo0d4//33TX49ypK2bTXbR967p3lUcdTsNZnn9iCBgcDFi8A//5RMkEREhWVvDygUlo7CvCy4FUFO2vcYK1euxNq1a42W074BfPjwoc7xwYMH4+eff8apU6cQGRmJgIAA8dzu3buRmJgIGxsb9OvXz+B9b9y4AW9vb6P3TU9PN3hfrVq1asHT09No/ZyxrFu3DhcuXEBMTIzBBTKePHmi87N2saDGjRvrbWivVbNmTfj6+uLx48d658z1HPPy9ttv44033sChQ4dw6NAhnD9/HlevXkVKSgrS09OxefNmbN++HT/88ANGjhwp1tMuTCiTyfJcJT+3iIgIAECTJk3g7OxssIybmxsaN26MU6dOieVza9KkCaytrQ2e075uarUagYGBRmPRJpCpqal4/vx5gdoBAJ0kIq8OmsJq1qyZ0XM+Pj4ANIsdFVS3bt0wd+5c/PHHH+jatSsGDx6MNm3aiNcyVbt27XD58mWEh4eLnTjaxLFdu3Zo0aIFbGxsxMVm5HK5uO2MnZ2dzvPMysoSE+QOHToYvWfHjh0BaH6nly5dMph0t9T2iOQjJSUF3bp1w6FDh+Dl5YW9e/fi1VdfLVDdnL/vuLg4ox+glVYmJ5OxsbFwcnIq8GamVlZWUBThj25MTAxCQ0MRFRUFFxcXNGzYEPv27RMbQnx8PO7du6dTJykpCZ9++imePHmCSpUqoW/fvpg1a5bOym9aUqkUc+bMQatWrXT+I2nQoAEOHTqU54pmEREROg1w0qRJAIAhQ4ZgzZo1ePvtt/H8+XN89dVXiIqKQv369bFnzx74+/ub/HqUJU5OQIsWwPHjwMGDgPfL+fRMAuyZJKLSTyIBjLyZJ9MplUrEx8cD0PwdN9ZjkFPuJOz1119HQEAAIiMjsX79ep2eOm1vZc+ePcVeFq2oKM3fpfT0dDGZKsx9tfJLINRqNQYNGoSNGzeKx+RyOdzc3MT3IElJScjIyEBqaqpOXW3Skd+b96pVqxpMJrU9YEV9jvmxsrLS2TpNrVbj6tWr+PXXX7Fs2TJkZ2dj9OjRaNq0qdgrpe3t8vDwMJooGxIbGwsA+b4J1/YwacvnltfvTfu6qVQqxMTEFCiuwrx2GRkZ4vc2NjYFrpcfJycno+e0q4oWZsuI119/HfPmzcPnn3+Offv2iZ061apVQ4cOHTB48GCDSVl+QkJC8O233+LEiRNisnjkyBEAmmTSzs4OzZs3x7FjxxAREYHmzZuL57WJptaLFy/EpD6vNpGzx9GUNpFTzm1w/ve//xU4kQQ0O01o5WwHZYXJw1zt7e2RlpZWoD1VkpOTkZiYKC6za4pVq1bhwYMHyMzMRGxsLA4dOiQmkoBmD8kHDx7o1OnXrx/u3buHzMxMREVFYenSpXkOmejYsaPeViIA8Morr8DX19dovbZt20LQzD/VeaxZs0YsM2bMGDH+ixcvFvv2KaVNp06arwcOFLBnkskkEVGFlHNo4KZNmwz+fc3r7y0ASCQScWuCnENdnz9/jj3/bjk1ePBgo/d+//33C3Tf3O87tPLb823VqlXYuHEjZDIZpk+fjjt37iAzMxMvXrxAdHQ0oqOj8eabbwKAzr6COX/OuR+dIbnrmfs5FpZUKsWrr76KRYsWYeXKlWIsq1ev1iub33MzpqD1jJXL6/emfd1eeumlPF8vlUqFhIQEqFQqo9tOGJKz00K77URp9cknnyAyMhLfffcdevfuDU9PTzx58gRr1qxBu3bt8NZbbxV6T8PWrVtDLpdDoVDg/PnzSEtLw7lz5+Di4oKgoCAAmqQS+K/HMmfPpTF5tYmc50xpEzm1adNGzBVGjBhRqOGqOXuGC7sdT2lgcjJZp04dqFSqAo2z3r59OwRB0NkfhyoWbTJ5+DBQ2e7fnskU9kwSEZEuW1tb8YPf69evm3wdbbJ4584dnD17FoBmHpVSqUTlypXFOVg5eXl5Ffm+BbFp0yYAmjedX375JWrVqgWpVPctmbE5adqekpxz7Awxdl47tLW4n2NeBg8eLPbG/JNjOkuVKpoPm+Pi4vR6ZPOifU0M9cTmpB0ybMqcRO3rdv/+/ULFVlA5YyrMsFNL8fHxwYQJE7Bz507ExMTg2rVrGDFiBABg27ZtWL58eaGu5+TkJCaN4eHhOHnyJLKystC6dWsxodP2eIaHhyM1NRXnz5/XOa5VqVIlsU5ebSLnuaLOU61evTqOHj0Kf39/XLt2DSEhIUZ7O3PL+fs253zZkmJyMtmjRw8IgoC5c+fmWe7u3buYOnUqJBIJevfubertqIx77TXAzQ1ITgbiHxagZ7J2bc3XmBigAEOciIio/NDOU9q6dWuBRkAZUqtWLQQHBwP4r3dS+3XAgAE6G4fnvu/Zs2dNmitYUNo3scaGwikUCpw7d87guddeew2AZoqNsaTm/v37Rt9El9RzzItMJhNHguUcnqhdmFClUmHv3r0Fvl7jxo0BaF4TY8OiExMTdeZWFpb2dcvKysLOnTsLXT8/derUEdvk/fv3jZbTfuhgrOfZUho0aICffvpJfJ1yLzxZEDmTxZxDXLWaN28Oe3t7nD59GocPH4ZSqYSjo6Pe79Pa2hoNGzYEAJ21VHI7dOgQAM1rqv13VRQ1atTAsWPHEBAQgJs3b6Jt27YFWqgoMjISgObDlNxD78sCk5PJDz/8EJ6enti6dSuGDRuGW7du6Zy/f/8+Zs+ejSZNmiAuLg7Vq1fHu+++W+SAqWySyQDtHOgbZzSf7sWmxiJbnW24grMz8O8nlOydJCKqWEaNGgUAuH37NhYsWJBn2dTUVGRlZRk8p+2d3Lx5M/766y+xh9LQEFcAGDRoEOzs7KBSqTB27Ng8V+NUq9VITEzM76kYpO15NbSiKQB8/fXXSElJMXjujTfegFQqRWpqqriyfm6zZs0yeu/Q0NBie45ZWVliEpCX//3vf+JQzpxv4mvVqiVOA/rss8+QnJxcoPv27dsXcrkcGRkZmDdvnsEys2fPRmZmJqysrNC3b98CXTenxo0bi8n/tGnT8h3GWNjeRQcHB/G10Pa4GaJdYMjUtldU+e2ioO1xLujw0Jy0ieOZM2fEDxNyJpNWVlZo2bIl0tPTMXv2bACaOZyGPhjq378/AE0v6Y0bN/TOKxQKzJ8/H4BmUaGCrhycH39/fxw9ehQ1a9bE33//jbZt2+Y7ikD7wVFZnQJncjLp7OyM33//Hc7Ozli7di1efvllsTvX0dERtWvXxhdffIGkpCS4u7tjx44dZp1QTGWPdqjrqQOVIZVIIUBAXGoe/xlzqCsRUbkSHx+f50P7BrlXr17o06cPAGDq1KkYPXo0buf4W5CVlYVz585hypQp8Pf3Nzqc7O2334a1tTWeP3+OIUOGAADq1q0rDqfLzdvbWxxx9eeff6Jjx444deqUmHAJgoBbt25h4cKFqF+/Pnbv3m3S66AdYvvTTz9h5cqVYjIcHR2NiRMnYv78+UbnTvn7+2P48OEAgOnTp+Obb74RFzh8/vw5Jk2ahF9++cVoD0dxPsesrCy0a9cOr732GhYuXIhr166J11Wr1Xj48CG+/PJL8Y2+s7OzODRSa/HixbC1tcWdO3fQsmVL7Nu3T5x/p51H9/7774u9SoBmkZXx48cDAObOnYsZM2aIbSkxMRFffPGF+KHEpEmTxOG0hSGRSLBixQrY2Njg0aNHaNasGbZt26azyM7Tp0+xfv169OnTx+gWJXlp27YtABjtlQYgbnNx8+ZNcfXbktS7d2+8++672Lt3r05C++LFC8ycOVPsCezWrVuhr92yZUtYW1sjIyMDV69ehYeHBxo0aKBTRptcal8jY4v9jB49GgEBAVAqlejatSv27t0rjnK4fv06OnfujMjISFhbW2PmzJmFjjUvfn5+OHr0KGrXro1//vkHbdu2zXNHC+1z0W4HVOYUdaPKBw8eCG+++aYgk8kEiUSi85BKpUKfPn2E+/fvF/U2VEjFsTFpUTcIf/BAsye2VCoIXvO9BYRBuPjsovEKI0dqKkyfbmLEVFoV52bzVHEUZzsq7GbQZJx2k/WCPBo1aiTWS01NFfr3769z3sHBQXBzcxM3ONc+njx5YvT+b7zxhk7ZOXPm6JXJvTn4/PnzBZlMJtaxtrYW3N3dBSsrK51rrV+/3uBzzW9D+YSEBOGll14SryOVSgVXV1dBIpEIAIT33ntPbzP0nFJSUoTXX39drC+TyQQ3Nzex/ueffy60bt3a6PMtynPMS2pqqs41tbFVqlRJ77qenp7C8ePHDV5n//79gouLi1jWyspKcHNz06m/c+dOnTqZmZlCv379dF7T3G1lwIABBv+/yOu1zu3AgQOCu7u7zvNzd3cX7O3tdeIbPnx4gV83rcuXLwsABDs7O6Pv35RKpRAYGCjex83NTfD39xf8/f2FrVu3iuXatGkjABBmzJhh9H55tVdj9bXHtQ9nZ2fB2dlZ59ibb74p/lsqrFatWonXeeutt/TOnz17Vude58+fN3qt69evC1WrVhXL2tra6sRqY2Oj85rlpC1z5MiRPOPNq+08ffpU/F3VrFlTePTokV6Zf/75R/z3Fxsbq3Mu9/9LuRX271Rx5AaCIAgm90xq+fv7Y+vWrYiLi8Pu3buxcuVKLF++HDt37kR0dDR27Nihs78TVVz+/prtI9VqwF7NFV2JiMg4e3t7bNy4EUeOHEFoaChq1KgBtVoNhUIBT09PtGvXDvPnz8edO3fyXP4/55BWqVQqrvKal08++QS3bt3CxIkT0bBhQ9ja2iIxMVGcnzV58mScPn0aAwcONOm5ubq64vTp05gwYQKqV68OmUwGuVyOtm3bYuPGjVixYkWe9R0dHXH48GEsWLAADRs2hLW1NQRBQJs2bbBjxw58/fXXYq+RsR7K4niO9vb2iImJwdq1azFixAgEBQXBxcUFSUlJkMlkqFatGjp37ozFixfjzp07aNWqlcHrdOrUCXfu3MG0adPw6quvws7ODunp6ahevTo6d+6MH3/8UW8FT2tra3H/yq5du8Ld3R0pKSlwd3dH165dsWPHDmzYsMHg9nCF0bFjR9y9exdz5szB66+/DhcXFyQmJkIqlaJevXp49913sWHDBnz//feFvvYrr7yCpk2bIj09XWeriZzkcjkOHz6MESNGoHr16khNTcXDhw/x8OHDIm3BV1BLlizBvHnz0K1bN9SuXRuCICA9PR0+Pj7o2bMntm/fjq1bt+otKFVQOXsaDa3S2rhxY3Gor7Ozc55zHevXr4+bN28iLCwMr7zyCuRyOTIzM1GzZk28//77uHnzprhqcnHw8fHB0aNHUbduXdy7dw9t2rTRm6f822+/AQD69OlTJhffAQCJIJSyGbxkFsnJyeJ/4MY28C0spVKJPXv2oFu3bib/ZzxuHLBkCeA7tRse2+7Fzz1+xvDXhhsu/McfQK9emtV7Ll4sQuRU2pijLREVZzvKyMgQN7s3tGUUlS9qtRrJyclwdnY2+U1waaJQKODu7o6srCwcP37caNJG5lfUtrR27VoMGTIEISEh4tYXVD4JgoDatWvj3r17OHbsmN6cyfzaUmH/ThVHbgAUYc7kV199hYULFxa4/Pfff4+vvvrK1NtROaGdN5nwqJA9k/zMg4iIqEAWLlyIrKwsVKpUyaSVS8ly3nnnHdSrVw9HjhzJcyEeKvu2bNmCe/fuoXPnzmV28R2gCMlkWFgYvvnmmwKX/+677/Dll1+aejsqJ9q2BaysAEX0v3tNKvLYa7JGDc0ysAoFEJVHOSIiogokJSUF/fv3x759+3QWQXn48CE++eQThIWFAQAmTJjAnvUyRiaTiauMan+PVP6o1Wp89dVXkEql+a5YXdrpr6VLVIwcHYEWLYBjigL0TFpbAwEBwN27wD//AD4+JRQlERFR6aVSqbB582Zs3rwZgGbDdwA624n07dsXn376qUXio6Lp3r07Fi1ahMTERCgUCjg6Olo6JDKzZ8+e4a233kJAQIDeirVlTYklky9evOCnYwRAM9T12G8F6JkENENd794F7twBjCz/TEREVJE4Ojpi6dKlOHjwIG7cuIG4uDikp6ejSpUqaNy4MQYPHoy+fftCIpFYOlQykXarEyqfqlWrVm56nkskmdy6dStSUlIQGBhYErejUq5TJ2Dain97JlPy6JkENMnknj2ankkiIiKCXC7H2LFjMXbsWEuHQkQVXIGTycWLF2Px4sU6x+Li4lCjRg2jdQRBQGJiIpKTkyGRSNC9e3fTI6Vy49VXAVe5NxIBPE2OgiAIxj891X4Awe1BiIiIiIhKlQInk4mJiXjw4IHOMZVKpXfMmPbt22P69OmFiY3KKZkMaN/MG9sBZKrTkZKVAmcbI0sUc69JIiIiIqJSqcDJZO/evVG9enUAmh7Hd999Fy4uLli0aJHROlKpFM7Ozqhfvz5q1qxZ1FipHOnW0QHb7zoBNimISokynkxqeybv3weUSs1SsEREREREZHEFTiYbNWqERo0aiT+/++67sLOzw5AhQ4olMCrfOnYEcLkKYJOCO1HRCPQwMp/WxwewtwfS0oDIyP96KomIiIiIyKJM3mdSrVbj2bNn5oyFKhBfX8BerVnR9fD5PFZ0lUj+SyC5CA8RERERUalhcjJJVFTVXDUrup67mc+KrlyEh4iIiIio1DHb1iCCICAhIQGpqakQBMFoOT8/P3Pdksq4ur7euB0D3HwUBUHQdEIaxEV4iIiIiIhKnSInk7t378b333+PM2fOIC0tLc+yEokE2dnZRb0llRNBdarg9xggWRWNu3eB2rWNFNT2THKYKxERERFRqVGkZHLy5Mn49ttv8+yJzKmg5ahi8HfXDHOFUxQOHMgjmWTPJBERERFRqWPynMl9+/bhm2++gVwuxzfffIObN28CACpXroy7d+/i5MmTmDFjBipVqgQPDw/873//Q2RkpNkCp7LP21GzAA8co3HgQB4FtclkVBSQnFzscRERERERUf5MTiZ//PFHSCQSfPHFF5g0aRLq1q0LAJDJZKhRowZatGiBGTNm4MqVK3BxccHw4cNhY2NjtsCp7Kvi+G/PpGMUwsM120ga5OICeHlpvr9zp0RiIyIiIiKivJmcTJ4/fx4AMHLkSJ3juYeyVqtWDUuXLkVsbCzmzZtn6u2oHBJ7Jh3ioUhT4uzZPApzexAiIiqgoUOHQiKRYOjQoYU6R0REhWNyMvn8+XPY29vDS9tjBE2vpKFFeDp27AhbW1v8+eefpt6OyiF3e3fIpf9O23WMKdhQV86bJCIqU8LCwiCRSAr0KO0MPRepVApnZ2dUq1YNLVq0wNixY7Ft2zZkZWUVWxyJiYkICwtDWFgYEhMTi+0+RET5MXkBHmdnZ73E0cXFRdwexMHBQTwulUohl8vx9OlT0yOlckcqkcLLwQtPU54CjtE4eLAavv7aSGHuNUlEVObl/AC6rMv5XNLT0/Hs2TM8ffoUZ86cwQ8//AB3d3d8/fXXeP/9982eKCcmJuLLL78EoOlpdXV1Nev1iYgKyuSeyapVqyI9PR0JCQnisTr/9h6dOnVKp+ydO3egUCggl5ttW0sqJ6o4/Tdv8sIF4MULIwU5zJWIqMyLjo7O81GW5Iw7KSkJSqUS165dw7fffouAgAA8f/4cY8aMwaBBg7iaPRGVWyYnk40bNwYAXL9+XTzWsWNHCIKAzz77TPyjEBcXh5EjR0IikYh1iLS08yar1ImGWg2EhxspmLNnkn+UiYiolJHJZGjQoAEmTZqEGzduoH///gCADRs2YO7cuRaOjoioeJicTPbs2ROCIGDjxo3isbFjx8LV1RWXL1+Gn58fqlatiipVquDEiRMAgE8++aToEVO5ol3R1f/lKAAwPm+yRg1AKgUUCs0WIUREVG61bdsWEokEYWFhRsto5y+2bdu2xOIqKHt7e/z666949dVXAQBz587Fi1xDb9RqNU6dOoWpU6eiefPmqFatGqytreHu7o42bdpgxYoVUBpY5rxt27YICAgQfw4ICNCZw5nz9TD1HkREBWXyuNOOHTti9erVOuP0PT098eeff2LAgAF49OgRov590+/g4IBvvvkGXbp0KXLAVL5oeybd/TU92QcOaDoe9aaXWFsDAQHAvXua3kkfnxKOlIiIqOCsra3x2Wef4a233kJycjJ27dqFd999Vzz/6NEjvP766+LPcrkc9vb2ePHiBY4fP47jx49jw4YN2L9/P+zs7MRy2v274+PjAQAeHh6QyWQ654t6DyKigjK5Z9LW1hZDhgxBr169dI4HBwfj3r17OH78OH777Tfs3r0bT58+xXvvvVfkYKn80fZMSpyjYG0NPHyYx1aSXISHiIjKkC5duoiJ3rFjx3TOyeVy9OrVC5s3b8bTp0+RmZmJpKQkpKSkYPXq1fDx8cGJEycwbdo0nXo7duzAhQsXxJ8vXLigM39zx44dRb4HEVFBFcuKODKZTOeTMCJjtD2T8RnReP11zZzJAwf+W29HR506wJ49XISHiKiM8vb2Nnru8OHDePnll0swmuLn6OiIGjVq4M6dO7h3757OuWrVqmHXrl0G6wwdOhT169dHkyZNsHLlSsyePRu2traFvn9J3IOIKjaTk0mpVAqpVIpbt26hVq1a5oyJKhDtaq5RKVEY3em/ZPKDDwwUZs8kEZUyK1euhEKhsHQYZuPo6IhRo0YV2/VjYmKMniuvc/e0w05zz5nMT+PGjeHp6YnY2FhcuXIFzZs3N3tsJXEPIirfTE4m7ezsYGVlxUSSikTbMxmtiEbHbgKmTpXgyBEgK0szTVIHtwcholJGoVAgJSXF0mGUGRVxi4y8nnNWVhZ++eUX7NixAzdu3MCLFy+QmZmpV+7Jkycm378k7kFEFZfJyWS1atX4Hw8VmTaZzFRlwj8wEZUruyEuDjh7FmjdOldhbTJ5/z6gVAJWViUbLBFRLo6OjpYOwazK2/MpDbT7cbu7u+scj42NRYcOHXS2WLO1tdVZUCcuLg5qtRqpqakm3bsk7kFEFZvJyWT37t2xePFiHDt2DG3atDFnTFSB2Mpt4WrrisSMRMSmRaNjRzds2KAZ6qqXTFatCtjbA2lpQGSkkYmVREQlpziHhFLZp1AocP/+fQBAzZo1dc5NnDgR169fh7u7OxYsWICuXbvqzSn19fXFkydPTO7RLYl7EFHFZvJqrp9++ikqV66M0aNHi1uAEJlCu6JrlCIKnTppjhncb1Ii4VBXIqIKQC7XfNadkZFhtExSUlJJhWOyffv2QaVSAYDO/o9KpVJcdXXp0qUYNmyYXpKnUqnE7T9MURL3ICIyuWfy77//xqxZszBx4kTUq1cPoaGhaNmyJTw9PXX2O8qttV53E1V03o7e+Dv+b828yY6aYxERwPPnQK5RQZpFeK5c4SI8RETlmJubGwDg8ePHRsucO3eupMIxSVZWFmbPng0AcHFxQe/evcVzcXFxYqL86quvGqx/8uRJo8m0VPpfX4CxHsWi3oOIqCBMTibbtm0LSY6d5ZctW4Zly5blWUcikSA7O9vUW1I5lXNFV58GQP36wI0bwOHDQL9+uQqzZ5KIqNxr1KgRtm3bhv379yM1NRUODg4658PDw3HmzBkLRZe/9PR0DB8+HJcvXwagGc3l6uoqnnd2doZEIoEgCLh69SoCtauV/ys7OzvPvR+dnZ3F7xMTE42WKco9iIgKwuRhroDm07DCPNRqtbnipnLE20Ez7CZKoRkunedQV20yyZ5JIqJyq1+/fpBKpXj+/DkGDBggLviXnp6OX3/9FX369BG33Cgt1Go1bty4gYULF+Lll1/Gxo0bAQChoaGYPHmyTllHR0e0bNkSADBp0iSEh4eL75Fu3LiBbt26ISIiQi+J1nJ1dUXVqlUBAKtXrzb4QX1R70FEVBAmJ5NqtdqkB1Fu2p7JaEU0AN1kUm/0DveaJCIq9+rUqSP2mv3vf/+Dr68vXF1d4ezsjKFDh6Jdu3YYM2aMRWP09vYWH25ubrCyskKDBg3w0UcfITIyEh4eHlixYgXWrl2rM5JLa9GiRXBwcMDTp0/Rvn172Nvbw9nZGQ0aNMCRI0fw008/wcPDw+j933//fQDAkiVL4OjoCD8/P1SvXh39+/c32z2IiPJTpJ5JInPQbg+i7Zls1Uqzx+TjxwZGs2p7JqOigOTkEoySiIhK0ldffYV169ahefPmcHBwgEqlwiuvvIIVK1Zgx44dea7PUBJiYmIQExOD2NhYZGdnw9vbG82bN8fo0aOxbds2PH36FO+9957R+kFBQTh//jz69esHDw8PqNVqODk5oV+/fjh9+jRCQ0PzvP9nn32GxYsXo3HjxrCyssKTJ0/w8OFDREdHm+0eRET5kQhcC7pcSk5OhouLC5KSknTmVhSFUqnEnj170K1bN1iZcY/Hw/cPo8O6DqhXuR5ujrkJAOjQQTNn8vvvgQ8/zFXB2xuIidGs0hMUZLY4qOQUV1uiiqU421FGRgYiIyMREBAAW1tbs16bSh+1Wo3k5GQ4OzvrLG5DVFhsS2Qu+bWlwv6dKo7cAGDPJJUCYs9kyn9bzBRo3iQX4SEiIiIishgmk2Rx2jmTCRkJyMzOBPBfMnnkCJCVlasCF+EhIiIiIrI4JpNkcW62brCWWQP4bxGehg0BT08gNRXQW/2di/AQEREREVkck0myOIlEIg511SaTUinQsaPmvN5QVw5zJSIiIiKyOCaTVCrkXtEVyGPeZM5hrlw/ioiIiIjIIphMUqlQxVF3r0ngv57JixeB+PgchWvW1HRdKhSaLUKIiIiIiKjEMZmkUsHQiq5VqgD162s6H48fz1HY2hoICNB8z3mTREREREQWwWSSSgVDPZMA8Oqrmq960yO5CA8RERERkUUxmaRSwdCcSQCoXVvz9c6dXBW4CA8RERERkUXJi3qBlJQU7N69G9euXcOLFy+gVCqNlpVIJFi1alVRb0nlkHavydw9k/kmk+yZJKJiJHCRLyIiKoVKy9+nIiWTa9aswfjx46FQKMRjhp6YRCKBIAhMJskoYz2TRnNG7TBX9kwSUTGQSjUDd1QqlYUjISIi0qf9+6T9e2UpJieT+/fvx/DhwyEIAmxtbREcHAwfHx/I5UXu7KQKSDtnMkYRA7WghlSi+Yeh7ZmMjQWSkgAXl38raLPM+/cBpRKwsirhiImoPLOysoKVlRUUCgUcHR0tHQ4REZGOlJQU8W+VJZmc+c2fPx+CICA4OBi///47PDw8zBkXVTBejl4AAKVaiRfpL+Bhr2lPTk6AlxcQE6MZ6tq48b8VqlYF7O2BtDQgMvK/5JKIyAwkEgmcnJyQmJgIFxcX2NnZWTokIiIiAEB6ejqSk5Ph6uoKiURi0VhMTiYvXrwIiUSCNWvWMJGkIrOWWcPdzh3P058jWhEtJpOAJk/USyYlEs2JK1c0Q12ZTBKRmXl4eCA9PR2PHj2Cs7MznJycIJPJLP6Hm8xPrVYjKysLGRkZFh8yRmUb2xKZS+62JAgCVCoVUlJSkJycDBsbm1KRg5mcTGZnZ8PR0RG1teMQiYrI29Ebz9OfIyolCvU964vHa9cGTpwwsgjPlStchIeIioVMJoOvry/i4+ORkpKCxMRES4dExUQQBKSnp8POzo4fFlCRsC2RuRhrS1ZWVnB1dYWHhwdkMpkFI9QwOZmsWbMm/vnnH6hUqlLxRKjsq+JUBTfjbhpd0ZWL8BBRSZPJZPDy8oKnpyeUSiXUarWlQ6JioFQqcfz4cbRu3dri84+obGNbInMx1JakUimsrKxK1QcVJieTgwYNwtSpU7F371783//9nzljogoqvxVduT0IEVmKRCKBtbW1pcOgYiKTyZCdnQ1bW1smAFQkbEtkLmWlLZk8mHvChAlo0qQJxowZgzt67/KJCk+7omtePZM6O89oeyaZTBIRERERlTiTeyY3btyI0NBQTJ8+HY0aNcKbb76JZs2awcnJKc96gwcPNvWWVM4Z65msWVPzNTEReP4cEOcaa7PMqCggORlwdi6ZQImIiIiIyPRkcujQoeJ4XUEQ8Ntvv+G3337Ls45EImEySUYZ65m0twd8fYHHjzVDXcVk0tUV8PTUbEJ55w4QFFSyARMRERERVWAmJ5N+fn6lavInlX1iz2RKlN652rX/SyaDg3OcCAzUJJP//MNkkoiIiIioBJmcTD548MCMYRBpVnMF9HsmAU0yGR5uYHpknTqafUM4b5KIiIiIqERxN1UqNbQ9k0mZSUhXpuucM7qiKxfhISIiIiKyCCaTVGq42LjAVm4LwPiKrka3B+Fek0REREREJcrkYa653bx5ExEREYiNjYVEIkHlypXRpEkT1KtXz1y3oHJOIpGgimMVRCZGIkoRhQC3APFc7u1BxOm6Ofea1DlBRERERETFqcjJ5P79+zF58mTcuHHD4PkGDRpg/vz56NSpU1FvRRWAt6M3IhMj9Xoma9QApFIgNRWIjgaqVPn3RM2amhMKhWaLEB+fkg+aiIiIiKgCKtIw16VLl6Jbt264ceMGBEGAVCqFp6cnPD09IZPJIAgCrl27hq5du2LZsmXmirlC6tOnD9zc3PDmm29aOpRipV2EJ/eKrtbWQPXqmu91hrpaWwMB//Zgct4kEREREVGJMTmZvHr1KiZMmABBENC0aVPs2bMHCoUCUVFRiIqKQkpKCvbs2YPg4GAIgoAJEybg2rVr5oy9Qhk3bhzWrl1r6TCKnbeDZhEeQyu65hzRqoOL8BARERERlTiTk8mFCxdCrVajR48eOHnyJLp06QIbGxvxvI2NDbp06YLjx4+jR48eUKlU+O6778wSdEUUEhICJycnS4dR7MSeSYXhvSYBLsJDRERERFQamJxMHjt2DBKJBIsXL4ZMJjNaTiaTYdGiRQCAI0eOmHo7LF++HA0bNoSzszOcnZ0RHByMvXv3Gi2fnZ2Nzz//HAEBAbCzs0ONGjXw1VdfQa1WmxyDIdpk2cfHBxKJBLt27TJY7ocffkBAQABsbW0RFBSEEydOmDWO8kK7PYixvSaBPJJJ9kwSEREREZUYk5PJmJgYuLi4oLp2IlseAgIC4OrqipiYGFNvh2rVqmHu3LmIiIhAREQE2rVrh169euHmzZsGy8+bNw8rVqzA0qVL8ffff2P+/PlYsGABlixZYvQep06dglKp1Dt+69YtREfrJzcAkJqaikaNGmHp0qVGr7t582ZMmDAB06ZNw+XLl9GqVSt07doVjx49EssEBQWhfv36eo9nz54ZvW55VMXReM9kvsNc2TNJRERERFRiTF7N1c7ODmlpacjOzoZcnvdlsrOzkZaWBnt7e1Nvhx49euj8PGvWLCxfvhxnz57Fyy+/rFf+zJkz6NWrF7p37w4AqF69OjZu3IiIiAiD11er1Rg7dixq166NTZs2ib2tt2/fRkhICCZOnIjJkyfr1evatSu6du2aZ+wLFy7E8OHDMWLECADAokWLsH//fixfvhxz5swBAFy8eDGfV8A0SqXSYIJs6rVyfi0OHrYeAIDolGi9+2g+t7DC3bsCMjOzIdV+FBIQACsAwv37yE5LA6ysii0+Mo+SaEtU/rEdkbmwLZG5sC2RuZi7LRVXmzQ5maxbty7Onj2Lbdu2oX///nmW3bp1K7KyshAUFGTq7XSoVCps3boVqampCA4ONljm9ddfx4oVK3D79m3UqVMHV69excmTJ8Uht7lJpVLs2bMHrVu3xuDBg7Fu3TpERkaiXbt26Nmzp8FEsiCysrJw8eJFTJ06Ved4p06dcPr0aZOumZdly5Zh2bJlUKlUAIADBw4UKYk35ODBg2a9Xk4vlC8AaIa5/u/P/0Em+W8ItUolgVz+f8jMlGLt2iPw9EzXnFCr0d3GBvLMTBxbswapVasCAA4/Pww7mR1auLYotnipaIqzLVHFwXZE5sK2RObCtkTmYq62lJaWZpbr5GZyMvnWW2/hzJkzGDNmDDw8PNChQweD5Q4dOoQxY8ZAIpGgX79+JgcKANevX0dwcDAyMjLg6OiInTt3ol69egbLTpkyBUlJSXjppZcgk8mgUqkwa9YsDBgwwOj1fXx8EB4ejtatW2PgwIE4c+YM2rdvjxUrVpgcc3x8PFQqFby8vHSOe3l5GR06a0jnzp1x6dIlpKamolq1ati5cyeaNGmiV27s2LEYO3YskpOT4eLigk6dOsHZ2dnk+HNSKpU4ePAgOnbsCKti6v3LVmdj+M3hUEONZm2bwdPBU+d8zZoS/PMP4OvbDu3bC+Jx2UsvAVevom2VKhC6dcPFqItYsnoJrGXW+KLfF7CR2+S+FVlQSbQlKv/Yjshc2JbIXNiWyFzM3ZaSk5PNEJU+k5PJ0aNHY9WqVbh58yY6d+6M4OBgdOjQAVWrVoVEIsHjx49x+PBhnDlzBoIgoH79+hg9enSRgg0MDMSVK1eQmJiI7du3Y8iQITh27JjBhHLz5s1Yv349NmzYgJdffhlXrlzBhAkT4OPjgyFDhhi9h5+fH9auXYs2bdqgRo0aWLVqFSQSSZHiBqB3DUEQCnXd/fv3m3RfKysrs/9nVhzXFK8NK1R2qIzY1FjEZ8SjqmtVnfN16mimRkZGynVHswYGAlevQn7/PmBlhWUXNfuaZqmykJCVAF8732KJl4qmONsSVRxsR2QubEtkLmxLZC7makvF1R5NTiZtbGywf/9+vPHGGzh//jxOnz6NM2fO6JQRBE3PUbNmzbB9+3ZYW1sXKVhra2vUqlULANC4cWNcuHABixcvxo8//qhX9pNPPsHUqVPFIbgNGjTAw4cPMWfOnDyTyZiYGIwaNQo9evTAhQsXMHHixDwX7cmPh4cHZDKZXi9kbGysXm8laXg7eiM2NRbRimg0QiOdc9oVXfNahCdaEY3NNzaLp6IV0fB1YTJJRERERGROJq/mCmiGhZ4+fRqbNm1Cnz59UK1aNVhbW8Pa2hrVqlVDnz59sHnzZpw6dQo+Pj7milkkCAIyMzMNnktLS4NUqvv0ZDJZnluDxMfHo3379qhbty527NiB8PBwbNmyBR9//LHJMVpbWyMoKEhvvPPBgwfRogXn8hlSkBVd89oeZEXECijV/00yNrTNCBERERERFY3JPZNaUqkU/fr1K/J8yPx89tln6Nq1K3x9fZGSkoJNmzbh6NGj2LdvHwBg6dKl2LlzJw4fPgxAs/rrrFmz4Ofnh5dffhmXL1/GwoUL8e677xq8vlqtRpcuXeDv74/NmzdDLpejbt26OHToEEJCQlC1alVMnDhRr55CocDdu3fFnyMjI3HlyhVUqlQJfn5+AIBJkyYhNDQUjRs3RnBwMFauXIlHjx7h/fffN/fLVC4UZa9J4Z9/sCLiFgDAwcoBqcpUJpNERERERMWgyMlkSYmJiUFoaCiioqLg4uKChg0bYt++fejYsSMATa/ivXv3xPJLlizBF198gTFjxiA2NhY+Pj547733MH36dIPXl0qlmDNnDlq1aqUzHLdBgwY4dOgQ3N3dDdaLiIhASEiI+POkSZMAAEOGDMGaNWsAAG+//TaeP3+Or776ClFRUahfvz727NkDf3//Ir0m5ZXYM5mi3zOpTSbv3weyswFxV5p/k0lJdDTSngNVK1dFx5odsebKGiaTRERERETFoMwkk6tWrcrzfFhYGMLCwsSfnZycsGjRIqNbgRiiTUxze+WVV4zWadu2rTg3NC9jxozBmDFjChxLRSb2TKbqJ4FVqwJ2dkB6OvDgAfDvFFrA1RWCpycksbGo/QLo220MMrIzNNdhMklEREREZHYFSiaPHz8OALC3t0fjxo11jhVW69atTapHFUcVJ+M9k1KpJoG8fl0z1FVMJgGkVK8C59hY1H8hx8jXRmLrX1sBGE5KiYiIiIioaAqUTLZt2xYSiQQvvfQSbt68qXOsMCQSCbKzswsfJVUoec2ZBDRDXa9f16zo2rXrf8cjHJPRDsAbsvqo7FA53+sQEREREZHpCryaqyAIeiuhCoJQqEdeK6kSaeW1mitgeEXXJ8lPcED2AADQOlOTRGqTSUM9nEREREREVDQF6pk0lAQyMaTiok0CFVkKKLIUcLR21DlvaEXX5ReW4293zdxVt0exOteJVkRDEIRC96QTEREREZFxRdpnkqg4ONk4wcHKAUDe24Pcvq35mq5Mx48Xf8Rt7YK7t28DggAvBy/N+ex0pGSlFHfYREREREQVCpNJKpXyGqKqHeb66BGQmQlsvLERz9OfI7u6HwSpFFAogKgoOFg7wMnaCQDnTRIRERERmZvJyaRUKkXVqlULXD4gIAByeZnZiYQsTLuiq6Ek0NMTcHIC1Grg3j0B35/7HgAwKvgDSAICNIX+7bbM6zpERERERGS6IvVMFmR/xaKUp4pL7Jk0sAiPRPJf7+TOy8dxNeYq7K3sMeK1Ef+d+OcfneswmSQiIiIiMq8SG+aalZUFqZSjaqlgtCu65rU9CABsitT0SoY2DIWbnRsQGKg58W/PJJNJIiIiIqLiUSLZXWJiImJjY+Hq6loSt6NyIK+eSeDfZNL1AW5m7wIAjGs2TnNC2zOpTSYdmEwSERERERWHAk9ivHbtGq5cuaJzLD09HWvXrjVaRxAEJCYmYtu2bVCr1XjttddMDpQqlvx6JuvUAdDkBwgSNTrU6IB6letpTmh7JjnMlYiIiIioWBU4mdy5cye++uornWPJyckYNmxYvnW1e/xNmjSp8BFShZTXaq4AUC0gFXjtJwDA+Gbj/zuh7Zm8fx9QKplMEhEREREVkwInk66urvDz8xN/fvjwIaRSKapVq2a0jlQqhbOzM+rXr49Ro0ahVatWRYuWKoz8VmG9pFoP2CUCL2qijU+3/074+AD29kBaGnD/PpNJIiIiIqJiUuBkcvz48Rg//r8eIKlUisqVKyMyMrJYAqOKTZsExqbGIludDbn0v6YqCAJW3dAsvINzH+L+PSkaNfr3pFSq6Z28cgW4fRvejTUfdjCZJCIiIiIyL5MX4JkxYwY++ugjc8ZCJKpsXxlSiRQCBMSlxumcOxx5GH/F/QVptiNwZSju3MlVOcciPDmTUpVaVQKRExERERFVDAXumcxtxowZ5oyDSIdMKoOngyeiFdGIUkSJw14B4Ptzml7J2oph+CfTRbtw639y7DVZ2aEyJJBAJagQnxYPL0evEnoGRERERETlGzd+pFLL0Iqu917cw+7buwEAXSp9AAD6PZM59pqUS+Wo7FBZ7zpERERERFQ0JvdM5nT69GmcPHkST548QWpqKgRBMFhOIpFg1apV5rglVQCGVnRden4pBAjoWqsrguV1sBgGkskcPZPa68SmxiJaEY1GaAQiIiIiIiq6IiWTd+7cwcCBA3Hp0iWd49qtQAwdYzJJBZW7ZzIlMwW/XPkFgGY7kMrJmnJGh7lGRwPJyfB29Ma1mGvsmSQiIiIiMiOTk8nnz5+jXbt2ePr0Kby8vNCmTRts2bIFdnZ26Nu3L6Kjo3Hu3DmkpKTAw8MD3bt3N2fcVAGIPZMKTc/kr1d/RXJmMgLdA9GxZkekKjTl4uKApCTAxeXfiq6ugKcnEBsL3LnD7UGIiIiIiIqByXMmFy1ahKdPn6JZs2a4d+8eNm3aBABwcXHB2rVrceDAATx79gyffPIJ4uPjYWdnh9WrV5stcCr/cu41qRbU4sI745qNg1QihZMT4K3JE/Mc6mpo7iURERERERWNycnkn3/+CYlEgtmzZ8Pe3t5gGQcHB8ybNw/jx4/Hjz/+iK1bt5ocKFU8OXsm99/djzsv7sDFxgWDGw0Wy9SurfmqN9Q1xyI8Ys9kKpNJIiIiIiJzMTmZvHfvHiQSCVq1aqVzPCsrS6/s1KlTAQArV6409XZUAeXsUVx8bjEAYPirw+Fo7SiW0XZA5tUzyWGuRERERETmZ/KcSaVSCRcXF8jl/13C3t4eKSkpemW9vLzg4uKCa9eumXo7qoC0SeDDxIe4n3AfEkgwtulYnTLansm8tgdhMklEREREZH4m90z6+PggKSkJ2dnZ4jEvLy9kZ2fj/v37OmWVSiWSk5ORlJRkeqRU4WiTQJWgAgD0DOyJGm41dMoYHeaac84knAAwmSQiIiIiMieTk8mAgAAIgoDHjx+Lx5o0aQIAWL9+vU7ZNWvWQK1Wo2rVqqbejiogB2sHOFk7iT+PazZOr0zOYa4625vWrg34+wOpqag+exkAIDEjERnZGcUZMhERERFRhWFyMtmpUycAwL59+8RjoaGhEAQBM2fOxNixY/HTTz/hgw8+wAcffACJRILevXsXOWCqWLQrutb3rI+Q6iF652vW1HxNTASeP89xQi4H/t3T1Oan1eh+3woAEKOIKc5wiYiIiIgqDJOTyV69esHX1xcnTpwQj3Xv3h39+/dHdnY2VqxYgffffx/Lly+HUqnESy+9hOnTp5slaKo46rhruh4nNJsAiUSid97ODvD11XyvN9S1fXvggw8AAD//roZrOoe6EhERERGZi8kL8NSpUwcPHjzQO/7bb78hJCQEmzdvxuPHj+Hi4oIuXbrgo48+gou4qzxRwSztuhRDGg1B37p9jZapUwd4/Fgz1LVFi1wn580D9u+H9507WLIHiBoaVbwBExERERFVECYnk8ZIJBKMHDkSI0eONPelqQLyd/WHv6t/nmVq1wYOHzawoisA2NsDa9dC1SIYg64D+//YA7zUu1hiJSIiIiKqSEwe5kpUWmgX4dEb5qrVvDkOvPkKAKDVrPVADOdNEhEREREVVZGSyeTkZCgUinzLKRQKJCcnF+VWREYZ3Wsyh4gR3XHVC7BPTgdGjcq19CsRERERERWWycnkjh074ObmhlGjRuVbdtCgQXBzc8Mff/xh6u2IjMqZTBrLEStXqobQPoBSJgH++AP49deSC5CIiIiIqBwyOZncunUrAGD48OH5lh05ciQEQcCWLVtMvR2RUQEBgEwGpKYCUUbW1/F29MZ1b+Cnnv/udTp+PPDoUckFSURERERUzpicTF6+fBkAEBQUlG/Zli1bAgAuXbpk6u2IjLK2BqpX13xvbKhrFUfNfpXftpQCzZsDycnAsGGAWl0yQRIRERERlTMmJ5NPnz6Fk5MTXF1d8y3r6uoKJycnPH361NTbEeVJO9TV2CI83o7eAICn6TEQfv1Vs0FleDiwbFkJRUhEREREVL6YnExKJBIolcoCl8/OzoZKpTL1dkR50q7oaqxn0svRCwCQqcpEkp8nMH++5sSUKXksA0tERERERMaYnEz6+voiIyMD169fz7fs1atXkZ6ejqpVq5p6O6I85beiq63cFq62rgCAaEU0MGYM0L49kJ4ODB4MZGeXTKBEREREROWEyclk27ZtIQgCZsyYkW/ZsLAwSCQShISEmHo7ojzlN8wV+G+oa7QiGpBKgdWrAWdn4Ny5/3oqiYiIiIioQExOJj/88ENIpVL8/vvvGDRoEGIMbAQfExODgQMH4vfff4dUKsW4ceOKFCyRMdphrvfuGV9TRyeZBABfX+D77zXfh4UBV68Wb5BEREREROWIycnkSy+9hFmzZkEQBGzcuBH+/v5o2bIlBg4ciHfeeQctW7aEn58fNm/eDACYOXMm6tWrZ7bAiXLy89Os6pqZCTx+bLiMXjIJaIa49uoFKJVAaCiQmQmVCti2DTDw+QgREREREf1LXpTKU6ZMgbOzM6ZOnYqUlBScOXMGZ8+eBQAI/+4e7+zsjPnz52PUqFFFj5bICJkMqFEDuHVLM9TV31+/jLeDgWRSIgFWrgROnQKuXwfCwjDTdg7CwoD+/YGNG0smfiIiIiKisqZIySQAjB49GgMGDMC2bdtw+vRpREdr3qhXqVIFLVq0wFtvvQVnZ+ciB0qUnzp1NMnknTtAx47657U9k1GKKN0Tnp7Ajz8CfftCmD8fR+U9ALTA0aOAIGjyTSIiIiIi0lXkZBLQ7CM5YsQIjBgxwhyXIzJJfiu6GhzmqvXGG8CgQZCsX4+VWUPwCq4gOtoBjx9rhtASEREREZEuk+dMEpU2+a3ommcyCeDcoCV4gqqojbtYYj9Fc+yc2cMkIiIiIioXmExSuaFd0dWUnkm1GvjwC1e8i18AAO+mLUN7HGIySURERERkRIGGuX711VcAAA8PD4wZM0bnWGFNnz7dpHpE+dH2TEZGahZntbLSPa9NJuNS45CtzoZc+l/zX78euHABcHLqhLQ3RsP+1+UIQxg+O9+hpMInIiIiIipTCpRMhoWFQSKRIDAwUEwmtccKi8kkFRcfH8DODkhPBx48+C+51PKw94BUIoVaUCMuNQ5VnKoAABQKYOpUTZlp0wD7QdMgrF2B14VTiLnwCNnZfpCbZXYxEREREVH5UaC3yK1bt4ZEIoFfjpVItMeISgupVJNAXrumGeqaO5mUSWXwcvBClCIK0YpoMZmcNw+IitJsLTJhAgCbqkDr1sCxY+iVsQk3bkzGK6+U9LMhIiIiIirdCpRMHj16tEDHiCwtZzJpiLejt5hMAsDDh8A332jOLVgA2NhovpcMGAAcO4YB2Ihz55hMEhERERHlVqAFeP744w8cOHCguGMhKrLCrug6eTKQkQG0bQv06ZOj4JtvQiWV41VcwcP9t4ovYCIiIiKiMqpAyWTv3r0xbNgwnWPt2rXDW2+9VSxBEZmqMCu6njgBbNmiGR67aBGgM2rb3R3xr3UCAPie3Fh8ARMRERERlVEF3hpEEASdn48ePYpTp06ZPSCioihoz2SUIlozPxLAiBFAo0b6ZW2HDQQAtI/biOQkQb8AEREREVEFVqBk0t7eHgkJCcUdC1GRaXsmHz3SDF/NTZtMnr0RjUuXAGdn4OuvDV/LZXAvpEvsUAd38M/GS8UUMRERERFR2VSgZLJOnTrIysrCwoULkZaWVtwxEZmscmVNgigIwP37+ue1yeSVu5o5k198AXh6GrmYoyMuV+sBAFD/xqGuREREREQ5FSiZHDRoEARBwCeffAInJyfIZDIAQExMDGQyWYEfcm7WR8VMIsl7qKs2mVTaRKFWLWDcuLyvF9d+AACgZsRmQK02Z6hERERERGVagZLJCRMmYOzYsZDL5RAEQZw/qf2+MA+i4pbXIjzKRE0yCcdofPstYG2d97UqD+6KRLjAI+MJhBMnzRsoEREREVEZVqCuQqlUiiVLlmDOnDm4desWUlNTERISgkqVKmH79u3FHSNRoWh7Jg0lk9/P8gZeBmCTgnadUwE45HmtV5rZYLPkDQwTViP1541wbNPa7PESEREREZVFhRp36ujoiMaNG4s/W1tbo02bNmYPiqgojA1zPXYM+GOrE1DHDrBKR2xaDBxtauR5LXt74HyNARh2bzWsft8KKL8HrKyKKXIiIiIiorKjwFuD5LZ69WosWrTIjKEQmYehYa4qFTB+PABI4CT5b6/JgpC2D0EMPGGT8hw4dMi8wRIRERERlVEmJ5NDhgxBv379zBkLkVloeyafPQMUCs33q1cDV68CLi7AS9UKl0w2CZZjC/5t6xu5qisREREREVCEZJKotHJzA9zdNd/fvQskJwPTpml+njEDqOZauGSyWTNgIzSrugo7dwLp6WaPmYiIiIiorCnQnMl3330XAFClShXMmjVL51hhSCQSrFq1qtD1iAqrTh3gzBnNUNcNG4DYWM2xsWOBuwerACh4MhkYCNx0CsaDFH9UVzwE/vwTePPN4gyfiIiIiKjUK1AyuWbNGkgkEgQGBorJpPZYQbb70JZjMkklpXZtTTK5dy+wfr3m2MKFmq1AtHtNFnjOpBRo0lSCTYf7YyrmaYa6MpkkIiIiogquQMnk4MGDIZFIUKVKFb1jRKWRdt7k6tWar507A926ab4vbDIJ/DvU9fAATTL5559AUpJmAiYRERERUQVV4J7JghwjKi20K7oCgEym6ZXUfvZhajI5Gw1xz6Yuamb+DezcCQwdasaIiYiIiIjKFi7AQ+WStmcSAEaPBurV++9nU5LJpk0BQIJfMzUL8XBVVyIiIiKq6JhMUrkUGAh4egLe3kBYmO65nMlkQeb8Aprr+PkBG/5d1RWHD2tW9SEiIiIiqqCKJZlUqVRYunQpevXqhTfeeIOL7lCJs7cHbtzQPLTbhGh5OngCAJRqJV6kvyjwNZs1A+6hFqKqNgZUKmDrVnOGTERERERUppicTK5evRoymQz9+vXTOzdgwACMHz8eu3fvxq5duzBq1Cj079+/SIESFVblyvqJJADYyG1Qya4SgMLPmwSAfW4c6kpEREREZHIyuX//fgDAO++8o3P86NGj2LZtGwRBQIsWLdChQwcAwNatW/H7778XIdSKrU+fPnBzc8Ob3JLCLExdhAcAlsS+DUEiAU6dAh49Ko7wiIiIiIhKPZOTyStXrgAAWrZsqXN87dq1AICRI0fixIkTOHDgAL788ksIgsAVYItg3Lhx4mtLRWdKMvnaa5qVYS/HVkVWs9aag5s2FUd4RERERESlnsnJZHx8PGxsbODh4aFz/NChQ5BIJBg3bpx4bOzYsQCAiIgIU29X4YWEhMDJycnSYZQbpiST9vZAgwaa72825FBXIiIiIqrYTE4mk5OTYWtrq3MsKioKT548gaenJ15++WXxuJubG5ydnREXF2d6pACWL1+Ohg0bwtnZGc7OzggODsbevXuNlq9evTokEoneQ5vcmsvx48fRo0cP+Pj4QCKRYNeuXXplfvjhBwQEBMDW1hZBQUE4ceKEWWOgwvF2KHwyCfw31PUP6zcBuRy4cgW4dcvM0RERERERlX4mJ5MuLi5ISkpCWlqaeOzYsWMAgBYtWhiskzv5LKxq1aph7ty5iIiIQEREBNq1a4devXrh5s2bBstfuHABUVFR4uPgwYMAgLfeestg+VOnTkGpVOodv3XrFqKjjScdqampaNSoEZYuXWrw/ObNmzFhwgRMmzYNly9fRqtWrdC1a1c8yjHfLigoCPXr19d7PHv2zOh9yXRiz2SqacnkkWvuQKdOmh/YO0lEREREFZDc1Ir169fH8ePHsWXLFgwdOhSAZr6kRCJBmzZtdMomJSUhOTkZderUKVKwPXr00Pl51qxZWL58Oc6ePavTE6pVuXJlnZ/nzp2LmjVr6sUHAGq1GmPHjkXt2rWxadMmyGQyAMDt27cREhKCiRMnYvLkyQbj6tq1K7p27Wo07oULF2L48OEYMWIEAGDRokXYv38/li9fjjlz5gAALl68mMczN51SqTSYIJt6rZxfyzIPO83w7KjkqEI9n1dfBQArREQIyFryFqz37IGwYQOyp00DJJLiCbYcKk9tiSyH7YjMhW2JzIVticzF3G2puNqkycnkgAEDcOzYMYwdOxbnzp1DdHQ09u3bBxsbG73tQs6cOQMAqF27dtGizUGlUmHr1q1ITU1FcHBwvuWzsrKwfv16TJo0CRIDb/qlUin27NmD1q1bY/DgwVi3bh0iIyPRrl079OzZ02giWZD7Xrx4EVOnTtU53qlTJ5w+fdqka+Zl2bJlWLZsGVQqFQDgwIEDsLe3N+s9tD28ZdnjlMcAgDvRd7Bnz54C11OpADu7bkhLs8LPcZXxnrU1ZHfv4tSSJUiqVau4wi23ykNbIstjOyJzYVsic2FbInMxV1vKOZrUnExOJocPH45t27bh0KFDWLlyJQRBgEQiwcyZM+Ht7a1TduvWrQZ7LE1x/fp1BAcHIyMjA46Ojti5cyfq1auXb71du3YhMTFR7EU1xMfHB+Hh4WjdujUGDhyIM2fOoH379lixYoXJ8cbHx0OlUsHLy0vnuJeXV55DZ3Pr3LkzLl26hNTUVFSrVg07d+5EkyZN9MqNHTsWY8eORXJyMlxcXNCpUyc4OzubHH9OSqUSBw8eRMeOHWFlZWWWa1qKX6wfZtybgTRJGrp161aous2by3DkCCB16QxJjx7A9u1o9fQp1DkWnaK8lae2RJbDdkTmwrZE5sK2ROZi7raUnJxshqj0mZxMymQy7Nu3Dxs3bsTp06fh6uqKbt266W0VkpWVhaioKLRu3TrPoaAFFRgYiCtXriAxMRHbt2/HkCFDcOzYsXwTylWrVqFr167w8fHJs5yfnx/Wrl2LNm3aoEaNGli1apXBnszCyn0NbfJdUNp9PQvLysrK7P+ZFcc1S5qvmy8AID49HpACVrKCP5/mzYEjR4CICDnef+cdYPt2yLZuheybbwCpydOQK6Ty0JbI8tiOyFzYlshc2JbIXMzVloqrPZqcTAKaoaHvvPMO3nnnHaNlrK2tCzWMMD/W1tao9e9wwsaNG+PChQtYvHgxfvzxR6N1Hj58iEOHDmHHjh35Xj8mJgajRo1Cjx49cOHCBUycOBFLliwxOV4PDw/IZDK9XsjY2Fi93koqOZXsKkEulSNbnY3Y1FhUda5a4LraRXjOnQOwtCvg7Aw8eQKcPAm0bl08ARMRERERlTJlvhtFEARkZmbmWWb16tXw9PRE9+7d8ywXHx+P9u3bo27dutixYwfCw8OxZcsWfPzxxybHZ21tjaCgIL3xzgcPHjS66i0VP6lECi8HTTJf2O1BmjbVfL15E0hR2gJvvKE5wFVdiYiIiKgCKbZkcvfu3Rg/fjwmTZqEQ4cOmeWan332GU6cOIEHDx7g+vXrmDZtGo4ePSr2jC5duhTt27fXqaNWq7F69WoMGTIEcrnxjli1Wo0uXbrA398fmzdvhlwuR926dXHo0CGsWbMG3333ndG6CoUCV65cwZUrVwAAkZGRuHLlirj1x6RJk/Dzzz/jl19+wd9//42JEyfi0aNHeP/994v4ilBRaLcHiVJEFapelSqAry8gCMDFiwAGDNCc2LoV4OptRERERFRBmDzMdceOHfj444/RqVMnvQVqJk2ahMWLF4s/L168GJMmTcKCBQtMjxSaIaihoaGIioqCi4sLGjZsiH379qFjx44AND2L9+7d06lz6NAhPHr0CO+++26e15ZKpZgzZw5atWoFa2tr8XiDBg1w6NAhuLu7G60bERGBkJAQ8edJkyYBAIYMGYI1a9bg7bffxvPnz/HVV18hKioK9evXx549e+Dv71/o14DMR9xrspA9k4BmqOvjx5qhrm0/agd4egKxscChQ4AZ5gYTEREREZV2JieTf/zxBx4+fIhWrVrpHL906RIWLVoEQLOYjbW1Ne7evYuFCxeie/fuaNu2rcnBrlq1Ks/zYWFhCAsL0znWqVMnCIJQoOtrk9LcXnnllTzrtW3bNt97jBkzBmPGjClQHFQyippMbtv277xJuRx46y1g2TLNUFcmk0RERERUAZg8zPXChQsAoDes9JdffgEA9OnTB/fv38ft27cxduxYCIKAn376qQihEplXUZJJ7bzJc+f+PaAd6rpzJ5CeboboiIiIiIhKN5OTybi4OMjlcr09JQ8cOACJRIIpU6ZA+u82CZ999hkA4MyZM0UIlci8ipJMBgUBMhnw7JlmIVcEBwN+foBCAfz5p5kjJSIiIiIqfUxOJhMTE+Ho6Khz7Pnz57h79y5cXV3RVNt1A6BKlSpwcHBAVFThFjohKk5FSSYdHID69TXfnz8Pzf6S/ftrDnBVVyIiIiKqAExOJh0dHZGUlARljtUrT548CQAIDg7WK29lZZXnaqpEJa0oySSQa79J4L+hrn/+CSQlFTE6IiIiIqLSzeRk8qWXXoIgCNizZ494bPPmzZBIJHqL8qSlpSEpKUlvSCyRJZk9mWzUCHjpJSAzE5g8WbN3CBERERFROWVyV+Ebb7yBs2fPYsSIEbh16xaioqKwefNmSKVSvPXWWzplL1y4AEEQEBAQUOSAicxFm0ymKlOhyFLA0doxnxq6tCO5IyIAlQqQySTAnDnAG28AK1dqtgv5+mtzh01EREREVCqY3DP5wQcfoGHDhnj+/Dk+++wzfP/99xAEAR9++CFq1KihU3bHjh2QSCRo3bp1kQMmMhdHa0cxgTSld7JuXcDREUhNBW7e/Pdg797ADz9ovp85E/h3mxwiIiIi+n/27jq8qaSLA/AvqQsFSpFCkSKLa9HFneKwOCyuH7LYLg7F3d3dpcji7ixW3LVQWkqBuqRNzvfHkNxK2iZpanDeffI0m3vv3Ek6pPfcmTnDfjYGB5OWlpa4cuUKpkyZgkaNGqFjx47Yvn07FixYEGM/hUKBixcvIk+ePGjQoEGSK8yYMSVlqKuJCVChgnh+82a0Df37i0ASAIYNA7ZuTWItGWOMMcYYS3uSlBHH1tYW48ePT3Afc3Nz3Lt3LymnYSzZ5LDNgVffXiVp3uT582LeZO/e0TaMHQv4+YmeyR49gEyZgGbNjFFlxhhjjDHG0gSDeyYZ+xkkNQmPet6kJgmPmkwGzJ8P/PmnmFDZrh1w+XISasoYY4wxxljawsEk+6XlsDFORtfHj4Hg4Fgb5XJg/XqgaVMgPFz85F56xhhjjDH2kzDKwo8fP37EtWvX8PHjR4SEhIASWBJh4sSJxjglY0ah7pn0DvI26PicOQEnJ+DjR5HVtVatWDuYmQF79gANG4qeyUaNgCtXgIIFk1ZxxhhjjDHGUlmSgkk/Pz/0798fBw8eTDCABAAigkwm42CSpSmaYa4hhvVMAqJ38uNHkYQnTjAJAFZWwOHDYuP9+0D9+sDVqyISZYwxxhhjLJ0yeJhrSEgIatWqBXd3d5iZmaFChQogIpiZmaFq1aooUKAAiAhEhMyZM6NmzZq8NAhLc5I6ZxKQhrrGmTcZXaZMwMmTokfy3TvRU/n9u8HnZIwxxhhjLLUZHEwuX74cT548QeHChfHmzRvcuHEDAGBvb49Lly7hxYsXePv2Ldq1awd/f380atQI58+fN1rFGTMGYwST8SbhiS17duDUKcDREXj0SMyhDAkx+LyMMcYYY4ylJoODSXd3d8hkMsycOROOjo5a98mbNy927dqFdu3aYezYsTh79qzBFWUsOaiDyc/Bn6EilUFluLiIXDteXuKRIGdnEVBmygRcuwa0aQMoFAadlzHGGGOMsdRkcDD57NkzAECjRo1ivB4ZGRln3+nTp4OIsHTpUkNPx1iyyGaTDQCgJCW+hn41qAxbW6BECfE80d5JQOx87BhgbQ2cOAF07w6oDAtkGWOMMcYYSy0GB5Ph4eHIlCkTLCwsNK9ZWloiOM76CICzszMyZsyImzdvGno6xpKFmYkZHKwdABhn3qTOTbxKFWD/fsDUFNi5E/jrLyCRJFZaBQSI5UZOnhTPGWOMMcYYSyEGB5PZs2dHYGAgVNF6VLJmzQqFQoGPHz/G2FepVCIkJARfvxrW88NYckrReZPRNWoEbNkCyGTAsmXAlClx9wkOFvMr//0XWLoUGDECaN0aKFcOyJxZDJctW1aU1a2bwfVnjDHGGGNMXwYvDZI3b158+PABnz59gpOTEwCgTJky+PjxI9zd3TF48GDNvocPH0ZUVBRy5cqV9BozZmQ5bHPgke8jo/RM3r4NKJWAiYmOB3bsKLK6DhwIuLnhze3TyG+dC3j7Vjz8/BIvw8FB7HfkiJi0yf/OGGOMMcZYCjA4mKxbty6uXLmCc+fOoWvXrgCA9u3b48iRIxgzZgzCw8NRpkwZ3L9/H9OmTYNMJoOrq6vRKs6YsTjaigRSSQkmixUTcyeDg4EnT4CSJXU/9mX7+jh3Mif6Hf6E/P9ejbtD5sxAvnzi4ewc82e+fOLE1asDV66Ins4xYwx+H4wxxhhjjOnK4GCyWbNmcHNzw/bt2zXBZMeOHbFu3TpcuHABo0eP1uxLRMiRIwfc3NySXGHGjM0Yw1xNTIDy5YELF8RQV12CSSLCurvrMPTkUISWDcWDKOC3r4Bz2dpo3nCICBjz5hVDWRPTo4cIJjduBEaPFkNnGWOMMcYYS0YGz5ksW7YsVCoVTp48qXlNJpPh6NGjGDNmDJydnWFqaoosWbKgS5cuuHHjBnLmzGmUSjNmTJpgMsTwYBLQLwmPX6gfWu9pjb7/9kVoZCjq5K+DYpOWYagr0KfAY0Q2awKULq1bIAkAbduK7LAvX4olRxhjjDHGGEtmBgeT8bGyssL06dPx6tUrREREwNfXF1u2bEGePHmMfSrGjMIYPZOA7kl4Tr46iZIrS+Lgs4Mwk5thbv25OP3nafR16YvsNtnhG+KLoy+P6nfyDBlEQAmI3knGGGOMMcaSmdGDScbSG3Uw6R3knaRy1D2Tjx6JuZOxhUWG4a/jf6HR9kbwCfZBUYeiuNnnJkb+PhJymRxmJmboVlpkZF3vsV7/CvToIX7u3g2EhBj4LhhjjDHGGNMNB5Psl2esnslcucRDpQL69QP27QPUq+E8+PwAFdZWwJKbSwAAgyoMwp2+d1AmR5kYZfQs2xMAcOzlMXgFeulXgRo1gPz5RSS7f3+S3gtjjDHGGGOJ0SkBj6enp9FOyMNdWVqjDia/h39HRFQELEwtDC6rYUNgwwZgxw7xgEyFXH8sgk/xMVDKFMhmnR2bWm6EayHtmY0LOxRGtTzVcMXzCjbf34yx1cfqfnKZDOjeHZg4UQx1/ZEYizHGGGOMseSgUzDp7OxslJPJZDJERUUZpSzGjCWzZWaYyc0QqYrE55DPyJPR8BseK1cCLVoAZ88Cx6964WWx7vAqcEZsfN4M34+vw5xj2XC3HlC3rsgAaxrrX2Gvsr1wxfMKNnhswOhqoyGX6TGAoFs3YNIkkVb2zRvRU8kYY4wxxlgy0OkqlYiM8lCpVMn9fhjTm0wmM9pQV3NzoHlzoEa//fjarhRQ4AzMZVao4rcKuS4fQqR/Nly4AIwfD1SpAmTJIoLPpUvF+pREQNtibZHBPANef3+NS+8v6VeBPHlElAoAmzcn6b0wxhhjjDGWEJ16Jt++fZvc9WAsVeWwzYEPgR+SHEwGK4Ix5PgQbLwnMqq6OLpge+vtKOxQGLRErNxx9ixw5gxw/jzw/Ttw+LB4ACKJz5UrNuhQogPW3l2L9R7rUStfLf0q0aOHOMHmzaKXUs5ToxljjDHGmPHpFEzmzZs3uevBWKoyVs9kr8O9sOfxHsggw+hqo+FWyw3mJuYAxJTG334TjwEDAKUS8PCQgsuLF8WyIhcviqGua++uxb4n+7DUdSkyWWbSvRKtWgEZMwLv34uIVd1TaUxKpTjP/ftA0aJA8eLiUaIEUKwYYGtr/HMyxhhjjLE0hbssGINxgsl3/u+w78k+AMDJLicxo+4MTSCpjYmJmDM5ahRw+rTInQMAO3cCFXNVRPGsxREeFY6dD3fqVxErK6BDB/E8udac3LQJOHIE8PQETp4EFiwAevUSXasZMgD58gFNm4o3t2ULcOcOEBqaPHVhjDHGGGOpgoNJxmCcYHLlrZVQkQr18tdD/QL19T6+Y0fxc/9+QKGQoVfZXgCSuObk/v1AQEC8u4WGAteuibmaOgsOFpM+AWDkSGDNGmDIENEDmkN8jnj/Hjh6FJgzRyQFKl9e9FYWLAi0aAH5hAmw+PZN//fFGGOMMcbSDIODydOnT8Pe3h6dOnVKdN/WrVvD3t4e58+fN/R0jCWrpAaTYZFhWOexDoBYQ9IQNWoAjo6Av7/o7Puz9J8wk5vhjvcd3Pe5r19hFSuK4afh4cDu3fHu1qcPULUq4O6uR9nz5gE+PiJT7LRpopDFi8VYXW9vwM9PjNVdsQIYOBCoVQtwcBAR6+vXwOHDMJk9G1UnTAA4uzNjjDHGWLplcDC5a9cuBAQEoKO6OyUB7du3h7+/P3bt2mXo6RhLVo62jgAMDyZ3PdqFb2HfkDdjXjT9ralBZZiYAO3a/ShvF+Bg7YAWRVoAMKB3UiaTeifjGer65QuwZ494fvKkjuV++gTMnSuez5oFWGhZkzNLFhEZDxgALFsm5m1++QJ8/gycOwcsWQLKkgUZvLwg27RJr7fFGGOMMcbSDoODyRs3bgAAqlSpkui+dX8kALl+/bqhp2MsWSWlZ5KIsPTmUgDA/yr8DyZyE4Prob43c+gQEBICzVDXbQ+2ITwqXL/C/vxTRKg3bgBPn8bZvH271DH4459z4saPF2Njf/8daNNGv/pkywbUrg0MHgzV2LEAAJOpU3kuJWOMMcZYOmVwMPnx40dYW1vDwcEh0X0dHBxgbW0NLy8vQ0/HWLKKHkySXhMIgRsfb8DDxwOWppaa4M9QFSuK0aOhoSK/Tf389ZHbLje+h3/HwWcH9SssRw7A1VU8j9UDSBSzw/LRIyAoKJHy7t+Xypk/X/R+GkjVty9CsmWDzNtbDJFljDHGGGPpjsHBpFKphEyPi0m5XI6wsDBDT8dYsspumx0AEBYVhsCIQL2OVfdKdizREVmssySpHjKZlIh1507ARG6C7mW6A0hiIp4tW2LMT/TwAB48EKNUs2cHVCrg9u0EyiESyXaIxFjcypX1r0t0FhZ41rmzeD5rFvD1a9LKY4wxxhhjKc7gYNLR0REhISF49epVovu+evUKwcHByJ49u6GnYyxZWZtZw87CDoB+Q129g7yx98leAMCgioYl3olNHUwePw58/w70KCMCwjNvzuCd/zv9CmvaVCS/8fEBTpzQvLxhg/jZqhVQs6Z4nuBQ1+PHRYIdc3MR/BnBx+rVQaVKAYGBwIwZRimTMcYYY4ylHIODyWrVqgEA5syZk+i+s2fPhkwmQ/Xq1Q09HWPJzpB5k2vvrkWUKgpVnKqgnGM5o9SjZEmgeHEgMlJkWXXO7Iy6zmLe8UYPPdeNNDcHunQRz3+Maw0PB3bsEC/16CF1MsYbTEZFAX//LZ4PGQI4O+tXh/jI5VCqg8hly8RyIowxxhhjLN0wOJgcMGAAiAjr16/H2LFjoVAo4uyjUCgwZswYrF+/XnMMY2mVvsFkpDISq26vAgAMrjjYqHVRJ+LZuVP8VM/F3HhvI5QqpX6FqYe6HjkC+Pnh8GHR4+nkJJaGjB5Map0uun498OQJYG8P/EicYyxUvz5Qpw6gUAATJxq1bMYYY4wxlrwMDiYrVqyIwYMHg4gwe/ZsODk5oXPnzhg7dizGjRuHzp07w8nJSdNzOXDgQJ0yvzKWWvQNJt2fucM72BvZbbLjj2J/GLUu6qGu586JEaqtirZCZsvM+BD4AWfenNGvsFKlgHLlRFfn9u2axDvduolkr2XLAmZmgK8v8PZtrGODgqQgb9IkIHPmJL2vOGQyadjs1q1iIidjjDHGGEsXDA4mAWDhwoX4+++/IZPJ4Ofnh127dmH27NmYNWsWdu3aBT8/P8jlcowePRqLFi0yUpUZSx45bPQLJtWJd/q59IO5iblR61KgAFChgkiMs28fYGlqic4lRcKapCTiUazZiFOnxEvdu4uflpYi1gS0DHWdPVtEmQULAv37639eXVSoIJL6EAFjxiTPORhjjDHGmNElKZiUy+WYPXs2nj59irFjx6JmzZooXLgwChcujFq1amHcuHF49uwZZsyYAbk8SadiLNlpeiZDEg8m7/ncwxXPKzCVm6Jf+X7JUp84Q13LiaGuB58dhF+on36FdeoEmJvD/Ml9lFJ5oEYNER+qaZ03+eGDWAIEAObMEfMvk8u0aYCpKXDsGHDhQvKdhzHGGGOMGY1RIrxChQph2rRpOHfuHJ48eYInT57g7NmzmDp1KgoUKGCMUzCW7PQZ5rr85nIAQOuirZEzQ85kqU/79mIU6LVrIjdNmRxl4OLogkhVJLY92KZfYfb2oBYtAAA9sFEzjVJNazA5frzI1lO9OtCypcHvQyeFCgF9+4rno0bFM3mTMcYYY4ylJdxdyNgPugaT38K+YfvD7QCMn3gnupw5pWU7du0SP9WJeNZ7rAfpGXA9qSgiyM7YjjbNImJsUweTHh5AWBiAu3fF2pSA6J3UY01Zg02YANjYADdvAgcOJP/5GGOMMcZYkiRbMPnw4UMsXLgQS5YswfPnz5PrNIwZja7B5EaPjQiLCkPp7KVRNXfVZK2TOhGPOpjsWLIjLE0t8cj3EW59uqVXWQsfN4AXciILvsH2/JEY2/LmBbJnF6uAeNwlYMQIsaFTJzGnMSXkyCGdd+xYkTCIMcYYY4ylWQYHk+fOnUOdOnUwVstSAQsWLEDZsmUxcuRIDBs2DCVKlMDSpUuTVFHGkps6mPQN8Y13+Q2lSokVt1cAAAZVHARZMvfYtWkjphLeuwc8ewZkssyENsXaAADW39U9EU9ICLB7nwm2oKt4YWPM9SplMql30nfDv2LeooUFoF4HMqWMGAFkzQq8eAFs2GDUoo8eFctZBgcbtVjGGGOMsV+WwcHk3r17cfHiReTLly/G6y9fvsSoUaOgUqlgbm4OKysrKJVKDBs2DB4eHkmtL2PJJptNNshlcqhIFW+Cm+OvjuPN9zfIZJkJnUp2SvY6ZckCNGggnsdec3Lno50IUYToVM6+fSKIOpfnx2TJEyeAT59i7FO5MmCKSFTY+7d4YehQ0WWZkuzsxHBXAHBzE1GwEXz6JKZ9Dh4spmeuWSN6YRljjDHGmOEMDiavXbsGAHB1dY3x+tq1a6FUKlGzZk34+fnh+/fvaNOmDVQqFVasWJG02jKWjEzkJshqnRVA/ENdl91cBkAEdNZm1ilSr+hZXYmAmnlrokDmAghSBGHfk306laHuiKzV9zfg99/FmiNbt8bYp3JloA/WIlfQc8DBIfWW6ejXD3B2FgtsLl5slCJXr5aCRx8fcYpSpYAjRzjXD2OMMcaYoQwOJn19fWFiYgInJ6cYr584cQIymQwTJ06EjY0NzMzMMHPmTADApUuXklZbxpKZeqird7B3nG0vvr7AydcnIYMM/6vwvxSrU4sWYi3Ily9FghyZTIaeZXsC0G3NydevgYsXxVDWbt2gWXMSGzfGiKTKFwrAZEwCAPgPmwxkzGj096ITc3OxVAgg1rn003MZlFgUChFMAiJ+XrJE9Pg+fQo0bw7Urg3c0m/6KWOMMcYYQxKCyW/fvsHOzi7GnLGgoCA8fvwYNjY2qKlOQwmgQIECsLS0xMePH5NWW8aSWUJJeFbcEj3rTX5rgvyZ86dYnTJkAJo2Fc/VQ127le4GuUyOy56X8eLriwSP37RJ/GzQAHByAtCuHWBlBTx/HmMtENulM5EVfniGwjhXoI/x34g+OnQAypQBAgOTPG9z/37g82fA0VEstzJ4sAiwx4wRQfrFi0DFiuKUb94Yp/qMMcYYY78Cg4NJS0tLBAQExFie4Nq1ayAiVKpUCXJ5zKKtrKwMryVjKSS+YDJYEYyN98RY0UEVBqV4vdRDXXftEiNUc9nlgmtBMcR8g0f8iWqUSmDzZvFcs7aknZ3I7ANI41/fvwcWLQIA/I25uH7bzMjvQE9yueiVBIDly4F37wwuapkYmYz+/QGzH28rY0YRo754IXprZTJg926gSBFg2DDg69ekVZ8xxhhj7FdgcDBZsGBBqFQqXLx4UfPagQMHIJPJUK1atRj7KhQKBAQEIHv27IbXlLEUEF8wufX+VgRGBKKQfSHUL1A/xevVuLHoofz4Ebh6VbymTsSz+f5mRKm0Z5M5dw748AHIlEkMl9VQR5a7dgGhoWIpjogIeBetjX/RNHqHZeqpXx+oW1eMU5040aAi7t4Frl0TQWTfvnG3584tem49PETPbWSkiKkLFADmzgXCw5P0DhhjjDHGfmoGB5NNmjQBEaFXr17YvXs3Fi1ahE0/xtO1bt06xr4eHh5QqVTIkydPkirLWHLTFkwSEZbdEt1bAysMhFyWbMuzxsvSEmjVSjxXrznZ9LemyGaTDT7BPjj28pjW49Qdj506iTI0atYE8uUDgoLEeM8dOwCZDBHT5gGQ4fbtNLDMo0wGzJolnm/bBty/r3cRy5eLn23aiGUs41O6NHDypHiUKgUEBAD//AMULizmWapUBtSfMcYYY+wnZ/BV8fDhw5E7d268ffsWnTp1wogRIxAZGYl27dqhZMmSMfY9dOiQ1h5LxtIabcHkhXcX8OTLE9iY2aB7me6pVDNpqOvevSIzqZmJGbqWEutGakvE4+8PuLuL55ohrmpyOdC9u3i+ZIn4+eefyNOyHDJnFj1yDx4Y/S1o+PkBSqUOa3SWLy8mOhLpnV3261cRIwPAIB1HJjdoIHozN20S80s9PYGuXUU1OEkPY4wxxlhMBgeTmTJlwrVr19CzZ08UKVIElStXxvTp07E11nIDCoUCGzZsABGhdu3aSa4wY8lJWzCp7pXsWrorMlqmUoZTiBGfDg7Aly/A2bPitV7lxFDXoy+OwjsoZgbaXbtEUFiiBODioqXAbt2k55aWwPTpkMuBSpXES8k11PXmTSB3blMsWKCtUlpMmwaYmgLHjwPnz+t8ng0bxPsvWxaoUkX3+pmYiI/mxQtg5kwxxdTDA2jUSASXjDHGGGNMSNJ4vVy5cmHdunV4/Pgxrl69ijFjxsDU1DTGPubm5vDx8YFKpULdunWTVFnGklvsYNIzwBMHnx0EIIa4piYzM6BtW/FcndW1iEMR/J77dyhJGScRj3qIa48eYsRoHPnyiXmJADBixI9Ur2K9SSD5gsn160Wv5NWruXDunA69kwULioUhAWDUKJ0WhlQqAfWytoMGxfP+E2FlBYweLTK/li8PfPsmOkkVCv3LYowxxhj7GaX85C/G0jB1MBkQEYCwyDCsur0KKlKhdr7aKJ6teCrXTixfAYjhq+rkMH3LicwybhfdcPj5YQDA48eiB9DUFOjSJYECN20S6V4nTdK8lJzBZFSUWKpDbdQoEyiVOhw4YQJgYyPGmkYvIB7HjokEsPb20vBgQzk4AHv2iAywN27oPdqWMcYYY+ynxcEkY9FktMgICxMLAMD7gPdYe3ctAGBwxcGpWS2NatVEB2JgoBj1CQBdSnVBxxIdEaWKQps9bXDs5TFNr2TTpkC2bAkUmDOnmBRoJi0Foh7m+uqVGFJrTOfPA19tL0H2TzaYuo7A/fsybNumw4HZswMjR4rnY8cCwcEJ7q5eDqRXL9HDmFTOzlJP74IFwKFDSS+TMcYYYyy9M018F+DSpUsAAGtra5QvXz7Ga/qqUaOGQccxlhJkMhly2ObA+4D3WPLfEviF+iG3XW40K9wstasGQOTNad8emD9fDHVt1QowkZtgS6stiFJFYe+TvWi9uzUsLxwG0CBu4h0dZMoEFC0KPH0K/PefCEiNZc2BZ0CHliCr74iquBDw6IJx48qibVvA2jqRg0eMEGNXX74U3YRFi4rJoOXLi59lygDW1nj+HDh1SgxtHTDAeHVv1QoYOlQsHdK9u5hHmS+f8cpnjDHGGEtvdAoma9WqBZlMhiJFiuDx48cxXtOHTCZDVJT29fAYSyvUweS6u+sAAAPKD4CpXKd/KimiY0cRTB45Ilb2yJABMJWbYnvr7VCSEgeeHkBEoxbIpPoXrq6GzVOuXFkEkzduGC+Y9PL3xQGrxoDVd5jITKFEFCxbjIDX6rNYsECG8eMTKSBDBjEst29fwMtLjOV9/BjYskVsl8uBokXhryqPQXCB1e8ucM5eBkBiUaruZs8W61bevCmC+suXAXNzoxXPGGOMMZau6DzMlYigirXYGhHp9Yh9PGNpkWMGRwBApCoS5ibm6F2udyrXKKZy5UROmvBw4PBh6XUzEzPs/GMncgQ0A8zCEdy8Ga56XTDoHMaeNxkWGYZ665tDlfEt5AHOON/lEsxkZgh3PA8UPoJZswAfn8TLQePGwMePIpg8fFjM9WzaVCwiqVIBjx+j0tPNWIohmHO1qghAS5YUXYlLlwJPniTpfZibi/mTmTOLgPKff5JUHGOMMcZYuqZTMKlSqaBSqfD06dM4r+n7YCyty2EjrW7foUQHZLXJmoq1iUsmk5LKqLO6qvl/NceXFXuBF40RJQtD0x1NccXzit7nUAeTN29CtwQ5CVCRCl3cu+BZ8H9AWGa0VRxD5dzl0TxrcwCAZfORCAlXRM8BlLicOYFmzQA3N9FF6+0NeHnhWP/DcMMknLdpAlIHmI8eiSRDQ4aIdVL69ROLUBoob15RHAAsXgwcOGBwUYwxxhhj6Ron4GEsFnVGVyDtJN6JTR1MnjwZMy7avh1QRljA5fV+NCjQACGRIXDd7orrH67rVX7x4iJ5alCQGO6aFP+c/gcHnh4AosyBXQfR748iAIA22dsgm3U2hNu8BMqvxLp1YtSqocgxJ/6+1AyT4YaHM/6F7EeAiUOHgIkTxTIoRMCaNcBvvwFr14pg0wDNmkn5gHr2BN68MbzejDHGGGPpFQeTjMXinNkZAFApVyWUz1k+lWujXdGiQOnSMZfaIAI2/Fhqslc3SxxsfxB1nesiWBGMRtsb4abXTZ3LNzEBKlYUz5My1HXFrRWYf32++J9DG5EtrAbUObisTKwwueZkAIBZ/clQWXzD338bfq4LF8QoVhsboFu3Hy/mzAk0bw5Mngw6eRK4dEkMe/32Tcy9rFIFuHPHoPPNmCEODwgA2rUDIiIMrztjjDHGWHrEwSRjsXQo0QHzG8zH7ja7U7sqCVKvOake6nrnjhjRaWkpei6tzKxwuONh1MxbE4ERgWiwtQHufNI9cErqvMmjL45i8HHRs1vOfyrwsBPatBGBqlr30t1RMltJRJp+h6zWFBw/Dpw+bdj51MuBdO0qkr1GFxoZihqbaqD4w/4Iun4RWLhQzKe8eROoUAEYOBD4/l2v85mZAbt3i7Us79yReiqN4c4doFYt4PffRe8wY4wxxlhapHMw6enpaZQHY2mduYk5hlcZjryZ8qZ2VRKkDiYvXgQ+fZLWQWzVSizvAQDWZtb4t9O/qJanGgIiAlB/a33c87mnU/lJCSbvet9F+33toSIVupXqgTebxwEA2raNuZ+J3AQLGi4AAMgqLgeyPMfIkfrP0/T0BA4eFM8HDoy5jYgw4OgAXPG8gidfnmD1/fVijY/nz4FOnUSX7ooVYujrxo16DX3NnRvYulU8X7YM2LtXv3rH5u8PDBok4tuLF4Hr10XAyhhjjDGWFukcTObLlw/Ozs5JeuTPnz853wtjv5R8+cQwSyIR0OzYIV6PvbakrbktjnU6hipOVfA9/DvqbamHh58fJlq+Oph88kQM5dTVh4APaLqjKUIiQ1Avfz38Yb4a/t9lyJ4dqF497v718tdD09+aQiWLgmnjf/DggbTah65WrxYxYO3aYr5ndGvvrsWW+1KBC64vQERUBODoKCaZnj8PFCsG+PmJCZDVqgH37ul87saNgVGjxPNevYBXr/SrOyB+h9u2AYULA8uXiwB4d6Z+OI162LWWuyYZY4wxljbpNcxV36VAtD0YY8ajTsQzebLo1cqdG6hTJ+5+GSwy4Hjn46iYqyK+hn1F3S118eRLwstkZMsG5M8vAp2bOk63DAgPQOMdjeEd7I3iWYtjX9t9OLDPDADiDHGNbm79uTCVmyKqwGHA+RzGjQNCQnQ7Z3i4yKkDiF696G5/uq0Zajul1hQ42TnBO9g7RnCJWrVE8Dh3rphwef064OIisr/6++tUh2nTRAwaFCR6X8PDdas7IIL12rWBP/8EfH2BIkWAu/MvoJ3/GtTDWTS+OSnJSZAYY4wxxpKDXsGkTCaDs7MzJk+ejPPnz+v9OHfuXHK9D8Z+SW3bAnI5EBYm/r9bt/gDtoyWGXGyy0m4OLrgS+gX1NlcB8/8niVYvj5DXSOVkWi7ty0e+T5CDtscONb5GKzkGTXDT9u1i//YIg5FMKD8AACAebPh8PZRYv78xM8JiKGlfn6Ak5PItaP2Lewb2uxpA4VSgeaFm2N8jfEYUWUEAGDOtTlQqqKNpTUzE5Menz0D2rcX3ZxLl4quwi1bRESdAFNTYNcuwMFBxKXDhyde75AQYPRokUjp4kXAygqYORO4f49Q9pCbZr8hWILjs+7r9mGkQ/7+QN26wLhxqV0TxhhjjOmNdNS4cWMyNTUlmUxGcrmcChYsSNOnT6ePHz/qWgRLQQEBAQSAAgICjFamQqGggwcPkkKhMFqZLOnq1iUS0Q7Rq1eJ7/819CuVWVWG4AZynOdIL/xexLvvkiWi3MaNEy5TpVJR70O9CW4g6+nWdNvrNhERHT0qjs+RgygqStpfW1vyC/GjTLMyEdxAKLuObGyIPn1K/P1UrCjOMX269JpSpaTG2xsT3ED5F+en72HfiYgoKCKI7GfbE9xAux/tjr/QM2eIihSRPthq1YieP0+0LidOEMlk4pBdu7Tvo1IRubsT5ckjFd+iBdHbtz92OHdOvGhuTr6lxC/3pmkVUoQrE/8w0qH586XP4fBh/Y7l7yRmLNyWmLFwW2LGYuy2lByxARGRzj2TR48ehaenJ2bMmIFChQrh9evXmDBhAvLlywdXV1fs3bsXCoUi+aJexphW6mUw6tYFChRIfH97K3uc/vM0SmYrCe9gb9TaXAvTL03HVc+rUChj/huO3jOZUOfcrCuzsM5jHeQyOXb9sQsuOV0AAHv2iO0JDXFVy2KdBRNrTAQAmDUcj5DIIEycmPAxN2+Kh7k50Lu39PqMyzNw7OUxWJpaYn+7/chkmQmAmD86pOIQAMDMKzPjH3pfty5w/z4waxZgbQ1cuQK4uia6/kfDhsDYseJ5797Aixcxt795I9aobNVKJA3Klw84fFgkD8qXD+JDdnMTO/fpg8yHNyNYZosKUdfxaMTGhD+MdEi97Kdav356J9VljDHGWCrSa5iro6MjRo8ejWfPnuHy5cvo3r07rKyscPLkSXTo0AGOjo4YMmQI7t69m1z1ZYzF0qULcOiQlIBHFw7WDjjT9QyKZS2GT0GfMP78eFTbWA2ZZmVC/a31Mf3SdFzxvIKiJRSwsBDLMsaXWGbnw50Ye05EUEsaLUGzws0AiLhLlyGu0Q2sOBAF7Qsi0tIHqDYbGzYADxPIFbR8ufjZvr2Y4wkAp1+fxsTzIgpd0XgFyuQoE+OYwZUGw8bMBvd87uHk65PxF25uLjLrPH0qkvW8eQMsWZLoe3BzA2rWBIKDxTDksDDxWUybJpIDHT0qRtWOGwc8fiyCS40LF8RamObmwOjRMM2bCxdqibU4C6z5R4zn/YlcuiSS6traAoUKAd7eug0RZowxxljaYPA6k1WrVsX69evh4+OD9evXo2rVqvj+/TuWLVuGChUqoHTp0liyZAm+fv1qzPr+0lq1aoXMmTOjTZs2qV0VlobIZGKuoDqY0lU2m2y41vMalrkuQ5tibeBg7YCwqDCceXMG48+PR/WN1ZFtQSZY9q0H1JiGzReuiCyo0VzxvILuh7oDAIZVHoaBFaV1OU6fFllgHR2BqlV1q5O5iTnm1JsDAJBXmw9VBk/8/bf2fb98EfMUASnxzoeAD+h0oBMIhN5le6NH2R5xjrO3skc/l34ARO9kovLkAWbMEM+nTRNZchJgaioC+6xZgQcPRKBbsiQwYYJIzFOnjnh92jTR6akRq1cSTk4AgIJLhuA+SsEu8htCB49KvL7pyOrV4menTsCmTaItb9oEHD+emrVijDHGmK4MDibVrK2t0aNHD1y6dAkvX77EmDFjkCtXLjx8+BDDhg3D7NmzjVFPBmDIkCHYou+aCYwlIKNlRgysOBB72+6F70hfPP7fYyxvvBxti7VFVuusCIsKQ0CWs0CdCZj+qToyzc6EulvqYurFqXB/6o4Wu1pAoVSgVZFWmFt/boyy1WsuqpME6aplkZaombcmVPJwyOqPwcmTwEktHYjr1gEKhViTsWJFQKFUoN2+dvAL9UPZHGWxtPHSeM8xrMowmMnNcOn9JVz7cC3xSnXtCpQrBwQGApMmJbp7zpwioJTJgCNHgJcvgRw5xGtnzoiMrXHE6pVUK1LCFCtLrgQAWO/aAFy9mnh90wE/P2D/fvG8b1/g99/F8p+AiKX1WY6GMcYYY6kjycFkdAUKFECPHj3QqVMnmJubG7NoBqB27drIkCFDaleD/aRkMhmKZS2G/1X4H/a03YPPIz/j8f8eo7fjcuBRO5hGZEN4VDjOvT2HiRcmovWe1vgW9g0Vc1XEttbbYCKXJkVGH+Latq3+9VjQcAFkkIFK7ABy/YeRIwFltOSrUVHAShFfaXolR54aiRsfbyCTZSbsa7cPlqaW8Z7Dyc4JXUt3BSDmeyZKLgcWLhTP16xJeOztD/XqAbNni9VGhgwRiWI7dhQBZhxEUpDat6+mV1Kt4tDfsQ69xK4DBgCRkYnXOY3bskXcDHBxEQ9A9NYWLAh4eYnkuowxxhhL24wSTIaGhmLTpk2oUaMGChcujLlz50KhUKBEiRKoW7euMU6BlStXolSpUrCzs4OdnR2qVKmC4zqMhfLy8kKXLl2QJUsWWFtbo0yZMrhz545R6qR26dIlNGvWDDlz5oRMJsNB9VV0NCtWrICzszMsLS3h4uKCy5cvG7UOjBmbOric1Ph/wL7dUM3xwe0eT7Ci8Qq0K94OOWxzoGS2kjjc4TCszaxjHHvqlOjEy5lT9Djpq5xjOXQrIzILmTQZjkePCJs2Sdv//Rf48EEsxdGuHbDr0S4svSl6Ire22or8mfMneo5/qv4DGWQ48uIIHvk+SrxSNWoAf/whlg0ZPjzR5UIA4O+/xdqTixcDGTMmsOP588Dly3F6JdXatgWmWM+GH7JA9vChTnM307LoiXf69pVet7YG1q8Xz9etE0Ol0yQfHzEpljHGGPvFJSmYvHLlCnr27IkcOXKgV69euHLlCjJlyoQBAwbg1q1buH//Pho2bGiUijo5OWHWrFm4ffs2bt++jTp16qBFixZ4/PhxvMd8//4dVatWhZmZGY4fP44nT55g/vz5yJQpk9b9r169ikgtd/yfPXsGHx+feM8TEhKC0qVLY9myZVq37969G0OHDsW4cePg4eGB6tWrw9XVFZ6enpp9XFxcUKJEiTiPT58+xXtexlKCkxOQKxegUsoQ8q4oBlQYgN1tdsN7hDceDHiA7LbZ4xyjzuKq7xDX6KbXmQ5rM2soc14Diu/F+PHS9bv6n1qfPsCboCfofVikch1bbSya/tZUp/J/y/Ib2hQT849nX9VxOP6cOSLgO3NGZNLRgdaeyOiiz5Xs21d82LFkyADU75AFo/CjnpMmAR8/6lbnNCh64p2OHWNuq1FD6m3u3VsE42nKnTuAszNQvbroImeMMcZ+Yab6HuDl5YXNmzdj06ZNeP36NYgIcrkc9evXR48ePdCqVatkGeLaLEbKQ2D69OlYuXIlbty4geLFi2s9Zvbs2cidOzc2bpRS6ufLl0/rviqVCgMHDkShQoWwa9cumPxYx+DFixeoXbs2hg0bhn/++Ufrsa6urnB1dY237gsWLECvXr3Q+8faBYsWLcLJkyexcuVKzJwpEoAYu7dULTIyUmuAbGhZ0X+yX0fFiiZwd5fj6lUlqlRRJbhveDhw6JApABlat45CZGTcHjxd2lJWy6wYWXkkplyeAlPXUfBZ1ByzZ5uhbVsVzp41g1xOaN/1O1rvbo2QyBDUzlsbE6pN0Kt9jqw8Enuf7MXOhzsxodoEOGdyTviA3LkhHzQIJgsWgEaMQFSdOiI1axLIzp+H6eXLIHNzRI0YEe8Q1q5dZai9oQd6yzegSsg1qIYMgXL37iSdO7WsXGkCQI4OHZSwtFTFectTpgBHj5ri7VsZRo5UYtky7W0uxb+TIiNh2rMnZOHhwL17iNq0CaRem4ela/z3jRkLtyVmLMZuS8nVJnUOJvfs2YONGzfizJkzUKlUICIUKFAA3bt3R7du3eAUa45PclIqldi7dy9CQkJQpUqVePc7fPgwGjZsiLZt2+LixYvIlSsX/ve//6FPnz5x9pXL5Th27Bhq1KiBrl27YuvWrXj79i3q1KmD5s2bxxtIJkahUODOnTsYHWvoWoMGDXDtmg6JP/S0fPlyLF++HMofE8xOnToFa2vrRI7Sz+k0O/aMJZeMGQsAKIHDhz+jWLFbCe5782YOBAVVQpYsYfj69RSOHYt/38TaUjFlMWQxy4Kvtu+ASosxd+4InDnzBUBOVKj4CYNOdsBz/+ewN7NHN9tuOHkigaU+4lEmQxncC7qHoXuGop9Tv0T3N3VxQb2MGWHx4gWe/fUX3jTVrSdUKyJUHTcODgDe1quHh/fvi/Utte+KnLnqoJ/XSnjIysHE3R3/TZ0KX/WEw3QiMNAc+/c3AAAUKXIZx45pz7TTs6cDJkyoijVrTODkdAOlSsW/LEpKfScV2rsXxR480Py/YuxYnM2UCaok3lBgaQf/fWPGwm2JGYux2lJoaKhRyolNRvGu2h2TXC6HTCaDtbU12rZtix49eqB69erJUqn4PHz4EFWqVEF4eDhsbW2xY8cONG7cON79LS1FAo7hw4ejbdu2uHnzJoYOHYrVq1eja9euWo/x9PREjRo1ULlyZVy/fh21atXCpk2bIEt0rJogk8ng7u6Oli1bAgA+ffqEXLly4erVq/g92uSxGTNmYPPmzXj+/LmO7x5o2LAh7t69i5CQENjb28Pd3R0VKlTQum9gYCAyZswIPz8/2NnZ6XyOhERGRuL06dOoX78+zPji6Zdy9aoMtWubIkcOwvv3UQkO3ezWzQQ7d8oxZIgS8+bF36Oka1va9nAbeh7pCZOoDFAufAmEiGG1AzYtxMp3w2EqN8XZLmdRxSn+G0sJufj+Iupvrw9LU0u8/N9LrcN2Y5OvXQuTgQNBmTMj6ulTwN7eoHPLzp+HacOGolfy+XOtQ1yjmzdPjrFjTbDDcTg6ei8E5c+PKA8PwMrKoPOnhoUL5Rg1ygTlyqlw44YywX0HDZJjzRoTODsT7tyJgq1tzO0p+p307BlMy5eHTKFA1IoVMJk2DbJPn6BcuBCqgQMTP56lafz3jRkLtyVmLMZuS4GBgXBwcEBAQIDRYgPAgGGu1tbWuHDhAi5cuKD3yWQyGV6/fq33cWqFCxfGvXv34O/vj/3796Nbt264ePEiihUrpnV/lUqF8uXLY8aPNeLKli2Lx48fY+XKlfEGk3ny5MGWLVtQs2ZN5M+fH+vXr9c5kExI7DKISO9yT2pbHyERZmZmRv8yS44yWdpWsaJYP9HHRwYfHzPkyaN9v7AwsRQGAHToYAIzMxPtO/6gS1vqVrYbVtxZgdufbgO1JwH/rkLeatex1lOMFphXfx5qONfQ+z2p1S1QF5WdKuPGxxtYcXcFZtSdkfhBffsCq1ZB9vAhzGbMEBl2tHjv/x77nuxDX5e+yGARKxMzkUhfCkDWty/M4hmCH12PHmK9yr7ek9Em+x6YvXkDs3nzxLjQlBQZCXh6Am/eAK9fi8ebNyKoXroUsNSeSZdISrDTr58cZmYJT6idNw84cQJ4+1aGSZPM4s07lOzfSSoVMGCASD/bqBFM+/cXk4H794fJzJkw6dNHpO1l6R7/fWPGwm2JGYux2lJytUe9UmMQEb58+YJ3794Z/EgKc3NzFCxYEOXLl8fMmTNRunRpLI7nIg4AHB0d4wSaRYsWjZH4JrbPnz+jb9++aNasGUJDQzFs2LAk1dnBwQEmJiZxEvj4+voie/bEe0AYSwusrYHSpcXzGzfi3+/kSZEkJ3duoFIl45xbLpNjQYMFAACZy1og33kENmyLKFUU2hVvhyGVhiSpfJlMhtFVxTD05beWIyBchwUOTU2BBaJOWLFCrPsRy4uvL1BlfRWMPD0Sc6/NjbNdk8HVwkJrBldtcuQAmjQBgpEBOyv9+O6bPRt48UKn4/USFATcuycWg5w7F+jfH6hfHyhQQPSEFiwINGgggqx584ADB0QK1smT4y3y0iVRVW2Jd7TJkEEUCYgY9dIl47w1va1cKdb3tLUFVq0SWZV69gTy5wd8fdN9dl3GGGPMUDr3TE7SYaHulEZEiIiIiHd71apV4wwjffHiBfLmzat1fz8/P9StWxdFixbF3r178fLlS9SqVQsWFhaYN2+eQXU0NzeHi4sLTp8+jVatWmleP336NFq0aGFQmYylhsqVRSLLGzfEchza7N0rfiYli6s21fNWxx9F/8D+p/th0qM+viuVKJylMNY1W2eUkQPNCjdDsazF8OTLE6y8vRKjq+kQ3NWrBzRtKtYpGTlS/Pzhud9z1N5cG97B3gCAA08PYErtaL2HsdeVTGR4a3Q9ewKHDwP/3GiNLg0bQX7yBDBwoFiPJSmfRVQUsHEjsGmTiPj84p+jCED0PubPLx4FCogst3Pnioy3rVsDWobgr14tfnbqJAJFXdSvL7K6rlsn3vuDB+LmRorx9JSC/ZkzAfXfDzMz0SPcpYt4z/37A5kzp2DFWKpQKIBv38Tj61fpeZkyQNmyqV07xhhLeZROjBkzhi5dukRv376lBw8e0NixY0kul9OpU6eIiGjp0qVUp06dGMfcvHmTTE1Nafr06fTy5Uvavn07WVtb07Zt2+KUr1QqycXFhRo3bkwRERGa1x88eEBZsmShBQsWxFu3oKAg8vDwIA8PDwJACxYsIA8PD3r//j0REe3atYvMzMxo/fr19OTJExo6dCjZ2NjQu3fvjPHRaBUQEEAAKCAgwGhlKhQKOnjwICkUCqOVydKPbduIAKIqVbRvDw0lsrUV+1y/nnBZhrSlV19fkflUc4IbyGa6DT32faxH7RO35d4Wghso+9zsFKoI1e2gZ8+ITE3Fm/7xXfTsyzNynOdIcAMVX16cTKeYEtxAL/xeSMedOSOOsbAg+vhRr3oqFETZs/845cpXogyAaOdOvcrRUKmI9u0jKlxYlBP94eBAVKkSUadOROPHE23cSHTpkqizUhm3rI4dxXHFixOFh8fY9OULkbm52Hz7tn5V9PcncnISxw4bJr2e7N9JKhWRq6s4cdWqcd9zVBRRiRJi+9ixyVMHliIU377R7aFDKWruXNHWBwwgat+eqF49orJlifLmlb7gtD2srYl8fFL7bbA0gK+VmLEYuy0lR2xARJRugsmePXtS3rx5ydzcnLJmzUp169bVBJJERJMmTaK8efPGOe7IkSNUokQJsrCwoCJFitCaNWviPcepU6coLCwszuseHh7k6ekZ73Hnz58nAHEe3bp10+yzfPlyTf3LlStHFy9e1O2NG4iDSWZsr16JayZz8zhxAhERHTggtufJI67BE2JoW5p6cSqZTzWn3Y9263WcLhRRCsq7MC/BDbTi5grdD/zrL/HGS5Sgp94PKce8HAQ3UMkVJck32JfqbalHcAPNuTJH7K9SEVWrJo4ZPNiguv79tzi8eXMimjxZ/E+OHCLq0sfZs0QVKsQMHufNI/LwIDLku+PLF6Js2URZ48bF2DRvnnjZxUX/YomIjh0Tx8tkRFeviteS/TtJfQfF3Jzo6VPt+xw8yMFEehceTir1TQFdHjIZkb09UcGC4mZLzpzi9REjUvudsDSAr5WYsXAwyVIVB5PM2FQqEWsARDduxN3eoYPu11NJaUuRyki9j9HV0v+WEtxAzoucdT/P169EmTMTAfRPazuCG6jUylL0JeQLEREtv7mc4Ab6ff3vYv8k9EqqPXkiijAxIfJ+F05UqJB4YcgQ3Qq4c4eoQQPp4tjGhmjiRMMCyNj275cq96MLUqUi+u038fLq1YYX3b27KOO330RPeLJ+J33+TJQlizjhtGnx76dSEVWsqN/n/yv4+pXo/HmiDx8Sv7uU2kaNIgIoIkMGUrZrRzRwINGECUSLFhFt2UJ09KgYbvHihXhfUVExjz9+XPz+rayIvL1T5z2wNIOvlZixpJdg0oizmhhjPzOZTMybBOIm4YmexTW++ZTGYirXOwm1znqW7Yms1lnx1v8tdj/ardtB9vb4/Pf/AAAjTgTid7viONv1LBysHQAAzQs3BwBc/3AdPkHegJubOE7PuZLRFS0KVKkCKJXAlt0WwPLlYsOyZcDdu/Ef+OoV0KED4OIi5liamQGDBolsrJMnA8ZIFd66tWgESqVIP6tQ4OJF/RLvxGfBAsDRUZSV7NP4//pLzIkrVQpIaJ1hmQz4kTEcq1YB798nc8XSOCJg2zaRoKl2bZGNK3NmoFo1Ma90+XLg4kXx2aYF166Jub4APAYOhHLbNvHvaMoU0Qb+/BNo3Fh8+RUqJDIWm8TKUt2wocg4FhamKYsxxn4VHEwyxnQWXzB5/DgQEiJyk8Sz9Gm6YG1mjaGVhwIAZl2dBdJhGd6nX57CRb4WTx2AbKHAac/amkASAJzsnFAhZwUQCLe3zQGuXBEZXEeNSlJde/USPzdsAKhefaB9e2kJC1Ws9T19fID//U9Eobt/BMmdOokstEuXAsbOLL1sGeDgADx8CEyfjjVrpFPqmnhHm8yZpSQ+8+cDN28mPfmSVkeOALt2iSxS69eLoDshdesCdeqI5CwpvUxLWuLtDbRsKQKw798RYpMVZGICBASIbLirV4ubF7Vqifbh6CgyLA0bJj7n//4TWYRTSmgo0L07oFJB1bkzfNRfcPqSyaQsxitXin9vjDH2i+BgkjGms/iCyT17xM+2bZOWUDQt+F+F/yGDeQY88n2Eoy+PJrjvky9PUHtzbXiF+2J5e2cAgPXy1aKnL5pWRVoBBORbtFm8kIReSbV27cTShs+fi84VLFggIrWbN4G1a8VOAQHA+PEi2+rKlSJjq6sr4OEBbN8uMrEmh6xZxZIpAGjGDLza6wEA6Ncv6UU3ayYSqKpUQJ8+JlAojPxnLCBABOQAMGIEUL68bsdNny5+btokfim/EiLRnooXBw4fhtLEDONl05Ex5BPG/RUiUvDu2AGMHQs0by61Ox8f4MwZYNEikbK3cmXRO+7sLNqtDjdzkmTMGODlSyBXLijVS/0YqkEDUf+wMJHdlzHGfhVGHTTL0gyeM8mSQ0CAyD0BSFODQkJE7hGA6OZN3cpJ623pn1P/aOY5quKZ7/XY9zFlm5uN4AYqs6oM+QV/keYhtm4dY98nvk+oTlcxP1FlYUHk5WWUevboIU7Xs+ePFxYtEi9kykQ0fbpIEqKeF1m5MtGFC4mWqS1Jq8H++IMIIA+UpkplIxLfX0dfv0oZbf/447lx21H//qLgggVF49ZH8+bi2HbtjFeftM7bm6hFC00783N2oZKyh5pmF23qbExBQUT//Ue0fr1I0Vu/PpGjY8xEN5MmJV+9z52TznPihHG+k06cEOVZWvLcyV9YWv/7xtIPnjPJGPvp2NmJzgdAjEgDgGPHxGixfPl078RJ64ZVGQYLEwtc+3ANVzyvxNn+2Pcxam+uDd8QX5TNURZn/jyDLDYOYuylXA4cOCDmhf1Q1KEI5lyxAgC8bFMbyJnTKPXs2VP83L0bCA6GWG+yTBnA3x8YN06sf1e0KODuLrova9aMt6yoKDHaMGNGYPFio1QPtGw5vsuzoAzuY5nTLOMUCjFtbeVK8dzdvSDu3DFSd/jFi2LeIyB6d/Vd0HLqVNE1v2eP6P39mal7I4sVAw4dAszMcLf1NDi+vY6HVAJ9+khTZ3v1AiIjYx1vawtUrCga8YIFYg7vp09ifdOFC8U+kycDW7YYv+6BgWI+LyBGCTRsaJxy1b2T4eHcO8kY+2VwMMkY00vsoa5794qf7dql/yGuajlsc6BHGXGxOfPKzBjb4gSSXc8gi3UWsbFECWks57Bh4koaAM6ehcubMISbAAtrmButnlWrAr/9Juar7tkDwNRUzEuztgacnMQ8tAcPxDy2BH45/v5AkyZitGFwMDB0qHECyovPsmOAahkAwOX4VOD+/aQX+kOrVsCkWuexXdUJB9rsRkSoMmkFhoUBffqI5337inl9+ipVSsowNH580uoTjVIJDB4sphiWKAHUqyeG+o4cCcybJ/LdnDkDPHokYrHYU2aNzsdHJFrq0gX4/h0oVw77x95BefdxiIQZBgwQMfnSpSLwv39f1FMnWbKIBjh6tPj/3r2BCxeMW/+RI0WipHz59KiYDnjuJGPsV2TUfk6WZvAwV5Zc1q8XI7lq1SIKDpaGuN66pXsZ6aEtvfr6iuST5QQ3kIe3BxERPfz8kLLOyUpwA5VdVZa+hn6Ne6CvL1HGjOJD2bBBLItQtSoRQIsqgTLMyEDhkVoW6jTQrFniVFWrRnvR359Ix8/25UuiIkWkpRLVS7wAREuXJq1uHTsSASq6m6+VKLBsWZ3rlSClkmjaNFLJ5ZrK+tr/RrR5M1GkgUvH/FgegnLm1H+9zuhevhRjOwGiy5cNL+cHpVJaEkXXh6kpUa5cYk3PJk2IevUi2rXLCCt0qFREO3ZIw6fNzIimTqU1yxWacw8ZEvM8mzdLK+E8e6bnG2/bVhq2Hd86n/pSL1gKxBj2bbTvJJWKqEoVUf7QoUmsLEuP0sPfN5Y+pJdhrhxM/qQ4mGTJ5fFjKfDYsUM8d3bW70I1vbSljvs6EtxA7fe2jxFIlltdTnsgqTZ3rvhgcuQgOnBAM1eyzAQxx/LYi2NGq+OnT1LsotfFOollANVxgZMT0d274vc4Zox0vb1ihWH1+vKFyNxclHH/pLd0ooTWbNS14IYNNRV87lyZ/BBtbmj+/ETr1hFF6DFH884d6UM8dChp9SMi6ttXlFW9epIiOJWKaMAAae7hunVEp08Tbd0qmtiIEUSdOxPVrUtUvLi0LGZ8j3r1iF6/NrAyPj5ErVpJhZUtS3T/Pi1bJr00bFjct6tSSb+u6tX1nJMbGirm+qp/r76+Blb+h2/fxM0CgOivv2JsMup30smT0tzJT5+SXh5LV9LL3zeW9nEwyVIVB5MsuSiVRHZ24lpJ3aM1apR+ZaSXtnTf5z7BDSSfLKcss7PoFkgSEYWHExUoID4cdUQ1ZAj1P9Kf4Abqe7ivUevZtKk4xT//6H7M2rWiBwsgqlgx5jWvSiXKUgcJq1frX6d588SxLi4/Xti2TerNevhQ/wKJiK5cEV1uPy7UI9eupYMHD1Kvdv70D2aRn0lWqdJ58hCtXCl+FwlRKIjKlBHHtG9vWL1i+/BBdMX9SO5iCJVKBIuASHq1bZtux0VEiNPfukV05Ij4PY8YIeIagMjKSgSiOnfgxu6NNDUlmjyZSKHQ5HsCiP7+O/64+d07IhsbA29OfP4s7lYBoscvLEzPAqLp0kWU89tvcZIrGfU7iXsnU5+/P9HMmUQlSogvyH379LvBlATp5e8bS/s4mGSpioNJlpzq14/Z43Hnjn7Hp6e21Hh7Y4IbCG4gl9Uu9C30m24H/uiR1Izx8/Kik69OEtxA2edmpyhllNHq6O4uTpM9e+KjSKOiRA+SumodOogOoNiiBzOACEp0pVKJ63WAaM2aaC+qs526uOg3HFWlEhGQuvfwt9+I7t/XtKPPnxWUMyeRNYLJvcZ8KdUrIILPJUu0v0kiohkzxH729iJwMZbhw0W55coZ1Ds5caJhn318Xr4kql1bKrNcOdETnaBv30RmYvVBZcoQ3btHRNLNAoBo7NjE3+LixWLfDBlEsKuXJ0/EUFd1wG9IyuH9+8XxcjnR9etxNhv9O+nUKe6dTA2+vkTjxklTDaI/smQhGjxY/MFK8pjv+KWnv28sbeNgkqUqDiZZcpowQfr7XKCA/n+X01NbuvHhBplOMaUKayroHkgSiQ+lVi1pIhkRRURFkN1MO4Ib6KrnVaPVUaEgypYt8VGaAQFEjRtLv7spUxL+3alUomNF3Tu2YYNu9Tl/Xhxja0sUGBhtw6dPUlAwY4ZuhX37JgWh6uj3R6HR29Hx41I9L58KFQGkuhdTPeR4/nwx0Vft2TOpB3HLFp2qo1CIearbtyfS7n19xQcAiF4RPajnwQIiCDMWlUrMeVb/CkxMRA+01hVQgoKIKlWK0xsZu34TJ+r27z8qSuqsa9LEgGv5c+ekrvSxY/U71teXKOuPXuvRo7XuYvTvJJWK6PfftQ6pTbe+fRNf/josMZTiPD3F96yVldQ4ixUTd2JGj5aGN6sfJUuK7wMfH6NXJT39fWNpGweTLFVxMMmS09Gj0t/keK7NEpTe2pJPkA9FKg1I7OLrK4ZaRusVU8/D/PvU30asodSL2KKF9u2vX4t5deqhjnv26FauSiWu0dSB2qZNiR8jEu+IqYNxqDOymJuLCbgJuXmTKF8+af+VK2NEIbHbUe/eYldnZxELUXi4OCZPHqnBZs0qoqGAAKJq1cRrjRrpFN0olUSdOklFuboSffyYwAHq7sUiRUQ0pYMlS6TyZ87U6RC9eXuLpTCj3xA6cybaDuHhYiKmusc22kKR06ZJx02erN95Hz+WRn3v2GFAxTdtkk6+fr1ux6hUUu9qiRLxDntOlu+kn6l3MiREuhsgk4m7ECk0bDRBz56JBXfVNxoAogoVxHCN6D3YUVFEx4+Lnm31DST1HZVmzcRIEiO9n/T2981QSiXR7t1Ez5+ndk1+XhxMslTFwSRLTn5+0t/iRIfKafErt6U9j/YQ3EAFlxQklRGHWqkTI5mYxF0v/dIlIgcHsT1nTv0y7xKJ6/GBA6XryK1b4983euIdrcOfVSrRNQWIyZrahruqVCKVrJmZ2C9/fq2FxW5HAQFS3DhgQLQdIyJE9pr8+aWGq+7BsLERk/p0+Az695c66tTXo5kyifmMWn+V/v7SXMONGxM9x7p1UvUmTEh09yQ7fFgkXlKfs0cPoq+fI6Xgy9aW6L//iEi8Pzc3ad/p0w075+TJ4ngHB9FW9KYeFmFqGisCjsf27dL+CXxZJct30s/SO6lQSEMa1JNvAaLy5cX46dRw967I9iuTSfWpU0dkqErse/XbN3GTSd3zHn0Y7JAhUiYyA6XG37dTp8TNrcTuzxmTeqi7oyPR9+8pd95fCQeTLFVxMMmS24oVRLNnG/Y391duS4HhgWQ+1ZzgBnr0+ZFRy65cmQhWflR0SlMafmI4hShCaONGKSZzcUmkJy0B0YMpuVxco2sTJ/GONh8/SnOaZs+OuS0gQFoSAhAZROO5UtHWjs6elQ49fTrWAZGRomdUPaET0Hn9k9GjpWB6505x0Va+vFRM69bxJBudM0fskDdvgsmAtm+XrouHD0/WKV0xBAQQDRokzi2DknZY9iACSGVuLj5MEnUZP156r7F/ZfqIiBAdhIDIh6M3lUrqHs6YMeGrZy8vaUzvlCkJFpts30mnT6fv3kmlkqhrV+kGzNWrYv5p5szSDYdNm1KuwV66JKKm6EFg8+Za58Hq5MkT0cvq6BizzFKlxBhzA9pDSv99+/hR+nUUKRJzJH9yuXdPumkIEPXsmfzn/BVxMMlSFQeTLC371duSOqnP1ItTjVru2rVEaDhUkzDIfmIRguMdAkR8pnVunB6USqI+faSActeumNu1Jt6Jz4YNYkcLC2kNwXv3iAoWlHqSFi5M8CI1vnak7kXNnTueJSOjosQ431WrdErmMnOmdNEUPbOtQiFiFPUIu6xZxWi5GEJCpAvVeALXAwek3EL9+6fcdXl0V6+oaJO9yMwUBTlNdXGn9+9FXdSBNEC0YEHSz/Xff6L9AGLZR72FhUlDlPPl0z7vTaWSgg4Xl0SDgmT7Toq2zqx67nS6MnKkNOTh33+l1z09iWrWlBpGx45JW581ISqVaCjq37n6C6hzZ8MzQ8cWGSnO0a5dzCipd2+9i0rJv28qFVGDBjHj4F69kvecoaHSlIny5aWbYCdPJu95f0UcTLJUxcEkS8t+9ba09s5aTXZYY3r04T1hvOj1NBlrL4LKCWZUd9IsUkQaJ3usUikuVtTXl9HnXsabeEcblUrMVQREl+rq1dLY0dy5deppiK8dBQVJI1qTesd85UrpIm3uXO373L0r8nmo9+vSRYyk01ixQmzInj1Ot8GxY1LPcbduhiUqNYpokyF7mmzS/B6j5z5assR4p1NnFM6dW4e2os2XL9KNh4oV494pUY8ZtrAgepT4CIBk/U5S907+yOqcbqjXywW0T5aOihLtRn0nxNnZ8B5Cbfz9iZYvj/mPy9ycqF8/olevjHee2L5+FXdN1FGSnmsjpeTfN/U6r5aW4mtGXeXYN/qM6a+/pK8zX1/p//PkMfDfMosXB5MsVXEwydKyX70t+QT5kMxNRnADefp7Gq3c3od6iwCyW22ClR/JO7TW9FLW3FiT3vu/N8p5lEqi7t2lgHL/fvG6OvFOv346FuTpKdaKiH5bvUkTMSlXBwm1o0uXpAur6B0q+og+9HTcuIT3DQ8nGjNG6nHLmVPk+yAiMbZTvVZitKw6585JU9DatdNvtRSjWr5c+vwXLaInT6TONPVj+XLjnjI4WPpIBg0ysJAXL6Q5qX/8IUXib99KmXTjuwMQS7J+J6lUUq9aeumdVCfKAsRQ7YRcuyYlyjIxEQGmjgmntLp1S/QIWltLdbCxEb2kKRmMq5cNMjPTK0hOqb9vz55JU7/VWZ/HjRP/b2dH9OaN8c958qT0K1GPKoj+b/l//zP+OWN7+FAkAUyNERwpjYNJlqo4mGRpGbclomobqhHcQEtuGKe757nfczKZbCKCR6drlCMH0Y0bKlp/dz3ZTLchuIEyzsxIOx/uNMr5oqKI/vxTGpG6dm0iiXfis3atdBE6e7ZeXXOJtSN175ejo+hs0Mfhw1KHy8CBul+4XL8ec0pm374/7tZv2SJeyJSJ6Pt3unpVXB+rp3yl2j+F6BHzxImal5VK0StburTuS8LoS91hJ5MRXbliYCGXLkkN7++/RcXVC2pWrapTUBMVRXTmTCQNH36LwsKS6Rdx5ozUO2noxOWU8u+/UuMfMUK3Y/z9pbtJgFgWSZ8FRYOCxNj4cuVi3sUoVkxESjG6+lNI9EzAOXPGzWwWj5T4+6ZQiKS1gEi8rP7ajIyUku5Wrmzc75UvX6QR+wMHxtwWfa56cq4cc+eOdI+hRg1phsTPioNJlqo4mGRpGbclonlX5xHcQHU21zFKeR32dSC4gZruaEr37sUMnl5+fUmV1lbS9FJ2OdCF/MOSPr8pKkpMW4p+7Zdg4h0tHnjfp8Wja9PurfqvMZNYOwoNJSpcWNSrc2fdyz13Thpx26WL/kNPQ0KkoV/4Ma3vwtkocWEMUHiu/DTGYj5lwjeqX19MAUwVR45IQcOgQalyq7+HyPdDRYok4XPYtk36sNXzJK2tE8w0qlSKOHTQILEEqfrwfv2ikudjiN47OXiwccpLjopeuyZ1d/35p36NX6USPZrqXuHMmbVMIo7l3j0xUTj6CAULC/EP9tKl1O9+CgwkKlpU1Kt6dZ2is5T4+6bOrJwpU9yY/e1bKb+Zvkuyxid6XF20qPb59/36ie0FCiR9fr42Hz/GXS7UzEzcA0u179BkxsEkS1UcTLK0jNsS0auvr8Tcxskm5Bei27DO+NzzvqcJFO9539O6jyJKQRPPTST5ZDnBDZR3YV66/P5yks5LJO6Ed+gg/XFPNPHODx8CPlD3g901w33hBpp8Qb+FC3VpRzduSENPE7uuJRLJYdTXwi1aJG3o6blzIomr+rNZ0vYSRWbIpHkhTG5Fkd16xViyIkoZRT5BPnTP+x4df3mcNnpspJmXZ9Jfx/+iUadH0etvrw2vUHQXL0pjbDt3TrXJmt++iblXgMgYa7ApU2JeZS5bFmcXpVIkI/3rr7gXpZkyqUgmUxEgliFNFsbonVQoRMBWtKho2F27ikVkjeHxYyktaOPGhndrvXwZM9Vxv34xo4uQENHdHXtpjt9+I5o/X+dh7inm2TMxblTHGwHJ/fft5k3pHlB8WbX37JF6/X8kZY6fDjcm1q+Xgrf4VtgJCJCWGho+PPH3oY+gIKKyZaXO6vv3pRWm1E3n3DnjnjMt4GCSpSoOJllaxm1JKLmiJMENtPne5iSV03RHU4IbqMO+Donue9XzKjkvcia4geST5TTu7DhSRCXt9xAZKebKNGyYeFp6/zB/Gn16NFlOs9QEkVXWVdE8dzvvpvN5dW1H6mykWbPGs3zHDw8fStfSdesa5253YKCUARdyBVnnOk19HPvQ44wxoxmP/DY0oFNGspggBdfaHiaTTejPA3/SE98nhlfqzh3p4rhZs1QcYyvs2ycNl75/38BCVCppCYto4/5UKnFDYdiwmGtqAqL3pls3Mf8qOFhBvXo90GyL7yI9SVQq0btlSO9kWJjIsKKemxj9YWoqeveSMnzW01P6gCpXTvr6EhERYsmN6MNVjxwRc0bV3Wbq6KRdOxEJpHYvZEIOHpTqvGVLgrsm59+3kBBptEW7dgl/ZOrvHUfHBNZ0/fpVjIvNlUs0ei0FvnolDclPbFmgY8ekINZYuZiUSnFjT/0drp4LqlKJ747oq7p065bAe42MFDcx9F1oORVxMMlSFQeTLC3jtiRMODeB4AZqtauVwWVc87ymCTKe+z3X6ZiA8ADq6t5VE6BUWFOBXvi9MLgOuoiIiqDFNxZTltlZNOettqEa3fhwg4iIZl2epXdAqWs7Cg+X1jZs00b7BdirV9Jwx0qVxJ1wY7nnfY9arhxG8lFZpcBwEqhqD9COEiCFXAoMvG1AU2uAyoxzoNIrS1PDrQ2pm3s3GnV6FDXc2lBzvMxNRm33tCUPbw/9KvPsmbgiA8TSDqGhxnuj0XwO/kynX58mlY4BQqtWokrlyyehNzgqiujCBVKFhdOtWyJfS/SeYUCMpuzSRcyJjb7sp7ot/fVXlCbGSZaeDvXkMl17JwMDRQKc6GNxs2YViZwuXIi5LoSlpegSSuiOiTZ+fmKcsXoMozF7Bk+fjruGIyAytsyaRfT5s/HOldzUi61aWiY4MTw5/74NGiQFiInNAw8JkUboNm2q5XsvIEBkQo7+e6lXL8bw8MjIH+sX//i60CWvUrduUlMyxg25v/+W/slcvRp3u7+/uJmpnvqdJQvRxo2x3m9QkNSVaWZGtGNH0iuWAjiYZKmKg0mWlnFbEu5+uktwA1lNs6IQhf6TTFQqFdXaVIvgBup9SP/10HY/2k2ZZmUiuIGsp1vT2jtrdb7416eOux/tpvyL82sCoSLLitChZ4finGv2ldl6BZT6tKM7d6T1IHfGykH08aPU4VOypP7JerTxDfalRdcXUZlVZWL0LGaY6kC/r61B7fa2oyHHhtCMSzNo58kF9GJIF4rIkVW6qDMxEZOUYvXY3PK6RS13tYxRZtMdTTVBeYLevxdrcQAi0YmRLyjUvod9p7wL8xLcQO32ttOpbXt5SR1W8+bpdh6Viuj7d9GjfPy4yOU0YoSUWVL9sLERuWHc3eO/uFW3pfBwBbVtK/VcGmsZwxiV/tE7qRo4iF69iucC3c+PaNIkqascEL+7pUvjTki7cCFm+l1bW6IJE3Rb9zE4WBpu6uQkeiiN7NxuXzqToQVFwoTOZmxF7gNOkp9vaq2BkwRRUdKc3Lx54w26k+vv26lT0q/4xAndjrl3T5r/rc74SkSiDdWoITbY24uITb2jhYXIyBsRoZmbmTGj+PrQxdev0r2PpM7ZVOdn02W0wPXrRKVKSfvXqiXunZG3t5jMH/uGhjEWzU1mHEyyVMXBJEvLuC0JKpWK8izMQ3ADHXx6UO/jT78+TXADmU81N3iJEU9/T01ACjdQi50t6PCzw/Qh4EOSA8uL7y5SxbUVNWVnn5udVt9eTZHK+LueogeUk85PSrB8fdvRpEnStZM6MaOvr3T3vkABnRM2ahURFUHuT92pxc4WZDrFVPM+zKeaU5s9bejI8yMJDylWKMRkp+iLwauHCC5bFiP4e+DzgDrs66CZAws3UL0t9ejC2wvaf2+fP0tpZgsX1r/3Sg+d9neKEey6rHahjwGJ98Kpl4a0shI9xX5+4mL433+JVq0Syx506yZGsRYuLA290/awthbDAPfv163zNXpbCguTcuU4OSVD8tUfvZMKuTnlxEdq0CDaSGMvLxERR39zv/0muloiIuIvU6USYwyjZ0PNnFn0YMYashoV9WMUsEIhBUf29kRPkjB0Wotnz2LOazOFQvPc3JyobVtR5aSsIpLivn0jVYECRABF1q5Pnz9FkaenWKXmwQMxn/HcuUhau/aEUf++ffsmRqIC+i+/sXSp9Jl7eJDokm/YULxoZ0d0+7bY8eVL0TP545cUkq8o1ZRfIkD/jjx3d+memLp4fZ09K90AnDRJt2MUCtGRr84hVdLsKX3L9ONOoYODNGla3RDVGaDTKA4mWariYJKlZdyWJEOODSG4gbq5d9PrOJVKRRXWVCC4gYYeH5qkOihVSppzZQ6ZTTGLEQRkmZ2F6m6uSyNPjqRt97fRo8+PEgwE1R77PqZmO5ppyrGZbkOTL0ymoAjdxo7OuTJHp4BS33akUEhJHJo1Ex036mvvXLlEFkRDeHh70F/H/yKHOQ4xPr/ya8rTsv+WGZZg6eFDogED4kZMWbKI6LdWLaL27elbnz9pf+dy1LeFnJp1AFXsDWo7uzydfOAuBZX+/tIbz507WXqf1Lbd36YZdr3o+iLNZ+I4z5H++/hfgseqVER16khzruILFGM/7O1Fj0STJiLXy549+k/5i92Wvn6VRn6WKmXcTtxXL1V0y0b0Ci3BIHFN+8drUvXtJy1zAhCVKSPejD7RlnoimfoOCSAyHC1eTBQeTu/eiZsmWbMoyaNEFynyNtYENxKBz9ChUiBgaipG3756RbRkidQU1Y9cucQarS+Sd6S9Xv79V3RmFS4sRi3kyCEyp1paEpXEAwqGWJ9iJkZpbZMmJkoaOzYqxlDqpFCvuvLbb/q3bZVKLD8EEBX/TUGRTVtKv/fYa/KoVETbtpEyazbNmzlXoJdBQ5/bt5f+/SR0H0SbZ8/E5w2I967vfc03b4hGVLxEXyF69t+aFaQb234M31WpxBBr9S/rzz9Tfd54fDiYZKmKg0mWlnFbkpx/e57gBrKfba9ToKbm/tRdE6h9DjbOvCMPbw/q5t6NSq4oKa1ZGethOc2SKqypQH0O96HlN5fTVc+rmiDxU+An6nO4j6a3zGSyCQ34dwB5B+nf3adLQGlIO3rwgMgkz01ClwYkH21PGFyQTPtVoTprmlOvQ71o9OnRNP/afNpybwsdf3mc7ny6Q57+nhQWGXN85Ofgz7Tg2gIqtbJUjM8nx7wc9Pepv+nR50d6v2et/P3FFbg6stHjEWwhp+Dc2UmlHtqaNSvRc93m1Rri7fe3ZDfTLsYw5Tff3lCJFSUIbiCLqRa0/UHCY9VevZJyAwFE2bKJgL95c9EjM2OGyH9y7pwIPoy1BIG2tvT2rTRcr149/S+ItTl8WAwZrIVzonfLxJx2yjpQJEykN121quiyS8rIgKgo8UHlz68pNypnbhqTdS2ZQkHzMFz0jsKUZlQ/RleuJD3/TWSk6EC3t5feSrNm2puch4fIxZMlS8xmW62ayBwaGJi0uiRFZGTcubaxH+2xU/M/HUz3UsaMImbPm5fI2Vml2a94cZEhOil27pR6+Qwty8+PKHfOKNqGTqIwCwuRXTgeQ7p8pdXoI71hBweRRViPRuLrKw4DiCbrkajbz0/c8ADEfE2D5l3u2kWqHzdmbplVJgf4EkDUvbvIv/PmDVHIik2kUqfFbdjQuBPljYSDSZaqOJhkaRm3JUmkMlKTlOb82/M6HROljKLiy4sT3EDjzo5LlnqFRYbRba/btO7OOhp0dBBVXV+VbGfYag0wZW4y+m3pb2Q93VrzWstdLenpl6StKD336twEA0p929Fzv+fUZk+bBLOlJvSwnWFLzoucqeyqsnGGsbbb246OvTim1w0BvahU4urs4UNxEbhjB9HChaJLp2dPkWGjQgWKzONECnOTOFe/Clvr+HP6G0GkMpKqrq+qyc4b/XMIDA+M0VM99sxYUqriH1rm5ydWuzBWr44u4mtLd+5IncNduxoecEVFiWG66l/J71VUFF6pRozf0XE0pH9HXTLCu4lGoSBatYqUjrk05/lsIiXz6YItmiqUK0e0aZNhF+8nT2qWUdUEUadOJX5ceLjoSG3cWFrCBxCfeffuYgWblE7yunu3FD+dPy8CuHv3RG/Zu3dEPj7iHk/k0BFSZR9JN48UCgX9889/lDWrCCrlcjGa0pBcVx8/Sj10Eycm4U2pVOTVpLfmBsLFv4/Eu+uBA9LogLtLLotfpvoXU6eOXjek1IGwmZm4kZeYiAhpKmfevOKz1otKRTR3rlTfVq3ou1cIDRigfbRDE/kxTS/zY5vy1LHuZ+rSRdzocHMT9/G2bRP3dow+f1oHHEyyVMXBJEvLuC3F1P1gd4IbaMixITrtv/X+VoIbKNOsTPQ97HvyVi4apUpJL7++pD2P9tDYM2Op8fbG5DjPMUbAVXldZaOsX6kWPaCceG5ijPmAurYjr0Av6nu4r6a3VeYmo5z/60oZi96kpYcv0f4n+2nVrVU09eJUGnJsCHXc15HqbalHpVeWppzzc8YZ/qt+VFpbiVbcXEFfQ42QsceYVCr68uk1zdvcn+r3s6bmHUA5R8rJ/al7sp1y6sWpIsHQjAxa18KMUkbR6NOjY8zN1XXYc3JTqVR088NNmrx1MkVo6X48dkxa18+QtTC/fCGqX1+6gB0y5Ecv561bIlvQH3/Qsh63Nb1PuiZX0VVgIFH18qE0FAvoizxakqd58+jePaJevaQlR/GjA3v8eN3mij57Ju5jqI/NkkWsYGJIRl4vLzHFUz21V/0oWFD0VqYElYqoQgVx3kTn6UVGSuOyCxUS2aBI+l769ElBnTpJ7+O33+KOKk2sLupkveXLJ2EkpkqlmSeolMmpLXaTnZ20xEZ0Xl5Sb/GoUT9ejIgQQwLUjcTcXERaOtztUamkZT0Sy9SsUokbCIDIuqx38BYVRTRwYMx/aNGGiF+7JmYH5MolzakEiCriBn2BeNMvUJCc8TreHumRI/Wsk5qBvZ4cTLJUxcEkS8u4LcV08OlBghsoz8I8iSa9UUQpNJlRZ16emUI1TJhPkA+deHki/uQvSTTv6jytAWVi7eh72Hcac2YMWU2zipH59IHPA1KpdB+2qFKpyD/Mn15+fUnXPK/R4WeHk9zrmlK+h32nzvs7a3pQz70x/noX/338TxOob7mX8Bp8W+5tIfOp5gQ3UKmVpejd93dGr48uAsMD6cCTA9T7UG/KOT+npn1Mvzhd6/7q5EAA0Zo1up/n5k2iPHmkKWrxJTJRqcSSJYBIxurhof970iY0VFxAAz9y7NwMEj3aGzbE2O/LFxHIRV+L09RUzHu7ejVu76C2eZHDhonXk0qlEufs3Vt8Fur6JHW4qC4uXRLnsrTUcdUSX18pQ3LTpkRKZZzvpUOHpNVRZDIR18WY9+jrKxpG9+5ikmb79kQPHtCyZVJdnibl6yZal3jU+k30++/ifytVihmgKpVS8Fq2rJbvx1evpMQ9gKjr+fOJnt7LS+pdTWidypkzSdOTe/y4nu8xJESaGCqTiTaeiNBQUbeHD4lubn1GIVnziukBttlpSY+71K+fSBJVr17Meb6JLDMqiYgQ3bxNm4roWJfsyrFwMMlSFQeTLC3jthRTiCJEE/Dc+RT/+mVERKturdJkRg2OSOLC4umItoAyvnYUFhlGc6/OJfvZ9ppjfl//u1F7TNOTSGWkZjkR2xm2dMvLeIt2B0UEUYHFBQhuoPZ72+t0M+H6h+uUfW52ghso65ysKfJ7UalU9MT3Cc27Oo/qbK4Tp7fZcpolwQ0knyynU6+0j8+cOFGau3b0aGLnE0GnOp9OoUKJ97RERBDVri32d3TUfSmG+CgUUq9hhgy6rdUeGUm0d69m9RLNw8VFTJkLDiZavjzmXMemTX8swZAMgoNJs1RL48bJc47o1L1offvqcdCtW9KyGm5uWr+Xvn0j6tHjx5BPRFBHx/P0ruPomNl3Yz3c5a2pNDxoyZIkvKEZM6Qyly8nIjFUV70Mz5gx0q6LF0vBa7yJfVUqMXY1e/aYXa5VqoiG0K2buKswbRrRypUigdSZM3Rosgc5wZMymYfQs6dxvyP27ZOKW7ZMz/f4+bO0XqalpSjMEF5e0toiGTKIdLLRqGNyC4tE/i09eCA+A/WEUfXDgHpxMMlSFQeTLC3jthRXq12tCG6gCecmxLtPqCJU04uy9L+lKVi7tGH+tfmai/8J5yZQREREjHYUpYyiDXc3UO4FuTX7FVtejA4+PZgsPabpSVhkGNXZXEeTpfeJr3GWgeh5sCfBDZR7QW76Fqp7t5SnvyeVXVWW4AYym2JGG+5uSPwgPYUoQujf5//S//79H+VblC/OMOVCSwrRX8f/opOvTlJgaCDVW1JPkwzrzbe4YwCjD8Ozto7/gjI0VAocAKKWLXXvlPj+XZqiVry4ZuSk3qKiiDp0kK6vL17UvwwPDzEdVx0nqXsg1c+LFRNzJZPby5fSMOPk7J18/lyaV6d3T+CGDZoPJtLdPebfN5VKFLh4MX2u2ISCZVrWtCldWkys3L+flG3akRLSBD9V8xaGra+hjg4BsV5GNHv3Sp14Z86IKZ/q37NOwdz370T9+8cbCCf0CJdZkCpnTpGtuEcPejtmNVW0uEcmiKRBg/R8j8+fS0mmsmQRXdpJ4e8vdeWbmRHt2qXZFBUlLXXj5BRrPue3byJYL18+5vvNkUOMFzawa5mDSZaqOJhkaRm3pbg239tMcAOVXFEy3n3UvXN5F+al8MgUzE6ShkQPKMeeHkvu7u4iqHx6kIotL6bZ5rTAiTbc3UBRyvS0iF3yCgwP1Cwnk2t+riQPMd33eJ9mDuqFtxf0Pj44IjhGQqThJ4Yn6fcVpYyiF34vaOl/S8l1m6umt1H9sJhqQQ23NqTFNxbTC7+Y61AoFArac2APlV9dnuAGKrOqDIUo4qaKVSik+Y/ZssWdd/bmjTQkTi4XKxDoex/D05MoZ05RRu3a+meRVanEEFH19fCxY/odH9uXL6KDS73Oob29CDgMmRdpKHUQn5y9k+rYqFkzAwsYMEAEfxkz0oW5cylyxw7xi1CPc472CLDKRlvQhTpjK7nk8o4xT9bNjagYHtFes46kip41pmlT3aPp6OOy45n82bev1AtesqR47uqqZ3t9/14Mdd23T3TFz5wpJhb27Cm6eatVE3cdsmcnVfQ7EVoeoSY2pKpRUwRfBw6InsKEXLkipQ7On994marDwojatJGi7cWLNZv8/aU5vTWrRVHk0ZPirk30Oy5mZkStW4v1ZZL4j4SDSZaqOJhkaRm3pbi+hn7VzDt79fVVnO0B4QGarK8bPTamfAXTkAXXFmgChEZLG1GVdVU0/595Vmaae3UuhSoMSJ34C/gS8oWKLiuq6ZkzdFmZDwEfKPOszAQ30JgzYxI/IB5KlZLczrtpfn+u21zJP0x7N55KpSKfIB+6/uE67Xiwg6Zfmk69D/WmelvqUYHFBbQmSsqzMA/1P9Kfjjw/kuCwcPV30mu/15R1TlaCG6jLgS5ae7QDAkRHEn5MG1MvwXf0KFFmsawdOTgkuPJCojw8pPmCXbrofoGvUhGNGCEFs3v2GF6H2BQKkd3WgKlfSZbcvZNfvkj5ZS7of19EiIggzYTE2A9zc6K6dUUP4b17REolnT0r8i+pd+nZU2TAVb/PHTtI9Gh16RIz1W2jRgmvC7pjh9TFOmJEvI0nJCTmcqQODkTe+q/ipDuVitYtCKC8eEu/W96mt4sO0pps4+g06lKQPIP2zy13bhHYzZtHdPmytBbQvn1SAFexoo4TXPUQO5nP6NGaz/HVyVc023w8vUfumHUtWVLM1fT1NVo1OJhkqYqDSZaWcVvSTj0Mcd7VeXG2qS+4iywrknzLT6Qj0QNKuIGsplnRmDNjUjS7bXr1IeAD5V2YV9MDF1/wFh+lSqlpqy6rXSgiKukLMO55tEczb7jIsiK048EOmnd1Hg08OpAab29MRZcVjZFIKb6H2RQzqrWpFs25MocefX6k8/Dm6N9J59+e19zYWXxjsdb9vbykvCtVqxJNmCBdv1esKHoXk+rkSSmwGKfjCkBTpkjXtimVATWlJGfvpPpzc3FJ4lIkXl6k+pHFSFW0qJg7d/x4vAuiBgWJpKOxl61o3z7Wji9eiPmI6gYBiC7y2OlhDx6U9unfP9E38+CBFJMdOmT429aVUimNIlWfN1s2onevo8RY2/XrRZdpqVIxA2j1w8REbFN/YC1aGG+x2dhUKqLp06Vzt2olVf7H4xsy0ePaA8Uw5GSYSsHBJEtVHEyytIzbknZLbiwhuIGqrq8a4/UvIV8ow4wMBDfQnkdG7GpI5xZeXUh2U+2oz6E+5BWYyJAoFsMLvxeUbW42ghuo+obqWod0xke9XIv1dGt69sV4mVfufLpDuebnSjBYlLnJKPeC3FRjYw3q5t6N3M670SaPTXTx3UXy9Pc0eJhs7O8k9c0Kk8kmdPGd9gmHjx5JiUzUjwEDjLs+ZrSpeLR6dcL7Llok7btokfHqEF2oIjTVho4nV+9kWJgIaACRWyapFH5+dHzjRr3+vl2+LJI0AWKI89f4Vht69Up0YUYfMlqnjuhOPXFCyvjUtauI3HTw338GZE9NglevxJxjdUAZbydrUJAYQjtrlgjk1Clx1Y+BA2Ms/ZFs1q+PGcTLZEQNGtD+tjvJAmFkbp5wR3FScDDJUhUHkywt47aknae/p+aC2SdImt0/8uRIghuo7KqyCS72/qvhdpQ0Ht4eZDfTjuAGarK9CSmiEv8cPbw9NMNJ19zWY40MHX0K/ETt9rajyusqU8d9HWnMmTG05vYaOv36NL36+soovaDaxG5LKpWKOu3vRHADZZubjT4EfNB63Pnz4vrd0lJkO00OkyZJnTLxZZFdv1661p082bjn/xT4iZb9t4xqbapF8slyMplsQk4LnKjyusr0x+4/aMixITTnyhza/mA7XXh7gV59fZVsw8yTo3dy7VpRZp48xpkHauj3UmiouHnwKu4sh7jevCHq0ydmUKnuyWvTJmUntBpg2zaxfuj+/XocpFKJLv89e4jOnUuWnsB4HT0qgvapUzVplpVKEePix7zTT5+Mf9r0EkzKiIjAfjqBgYHImDEjAgICYGdnZ5QyIyMjcezYMTRu3BhmZmZGKZP9mrgtxa/8mvK4430Ha5utRe9yveEV6IWCSwsiPCocxzodg2sh19SuYprB7SjprnheQYOtDRAWFYZOJTtha6utkMvkWvcNjQxF+TXl8dTvKVoWaYkD7Q5AJpOlcI2Th7a2FBoZiirrq+DB5weolKsSLna/CAtTizjHvnsHmJsDOXMmT92IgJ49gU2bABsb4OJFwMVF2r53L9ChA6BSASNGAHPnAkn9tXgFeuHA0wPY+2QvrnheAUH/S0V7K3s42TkhV4ZcyJUhF0rnKI2+Ln1hbmJucL1evQKKFAGUSuC//4CKFQ0uCoD4zEqUAJ4+BebPB4YPT1p5QAp/L71/D8yaBaxfD0RGAk2aAAcOiAbJkl1QEFC5MvDkCVClCnD+PGAR9yvCYMZuS8kRGwCAqdFKYowxlmStirTCHe87cH/mjt7lemPapWkIjwpHtTzV0Khgo9SuHvvJVMtTDfva7UOLXS2w4+EOZLbMjKWuS7UGiX+f+htP/Z7C0dYRa5ut/WkCyfhYm1nDvb07yq8pj/+8/sPg44OxptmaOPvly5e89ZDJgDVrAC8v4PRpES/cuCHOe/w40LmzCIr69ElaIPkh4AP2P92PfU/24eqHqzG2VXaqjDZF26B10dYwNzGHV5AXvAK98DHwo3ge9OP5j9fCosLwLewbvoV9w4PPDzTlvPr2CosaLTL4syhYEPjzTxFYT54MHD1qcFEAxOf39ClgZwf07p20slJF3rzAypXA2LEium7WjAPJFJQhA3DoEFChAnD9OjBokPi3+pN/NcbBwSRjjKUhLYu0xPjz43HmzRnc97mPdR7rAAAz6sz46S/eWepoXKgxtrTcgs4HOmP5reWwt7LHlNpTYuzz74t/seL2CgDAppab4GDtkBpVTXH5M+fHjj92oPH2xlh7dy0q5KyAPi59UrweZmbAvn1A9erAgweAq6vokOrQQXRItW8vYgp9vyLe+7/Hvif7sO/pPtz4eCPGtqq5q6JNsTb4o+gfyJ0xd4xtuexyAbm0l0lE8A/3jxFgPvN7hnnX52HJf0vQtlhbVM1TVb+KRjNuHLB1K3DsGHDzZtJ6J+fPFz/79hUBZbqVO7d4sBRXsCCwcyfQuDGwbp0YNdC/f2rXKmVpH8vCGGMsVRTLWgwF7QtCoVSg+a7miFJFoVHBRqiet3pqV439xDqW7IjljZcDAKZemopFNxZptn0O/oyeh3oCAIZVHoYGBRqkRhVTTaOCjTCtzjQAwKDjg/Dfx/9SpR52diKAcnICnj0DWrYEwsOBpk1FcGViols5r7+9xtyrc1FxbUXkW5wPI0+PxI2PNyCDDNXzVMeSRkvwcdhHXOl5BUMrD40TSCZGJpMhs1VmlMhWAo0KNkKvcr0wt8Fc9CjTAwRCz8M9ERYZpv8H8IO6dxIQvZOGuntXDEs0NQWGDIm5bcfDHai5qSYuvrto+AnYL6NRI2DmTPF88GDg8uXUrU9K42CSMcbSEJlMhlZFWgEAPAM8AQDTak9LzSqxX8SACgM0bW3YyWHYfG8ziAg9DvXAl9AvKJW9FGbUnZHKtUwdY6qNQasiraBQKvDHnj/wOfhzqtQjVy4RUKp70WrXBvbsET2X8YlURuL82/MYeWokiiwrgoJLC+KfM//g1qdbkMvkqJWvFpa5LoPXcC9c6nEJgysNFj2PRja/wXw42jrixdcXmHwxCVEgRO+kiYnUO2lQfX70SrZvH7NT79zbc+jq3hWX3l9Cg20NsOfxniTVlf0a/vkHaNcOiIoC2rQBPn5M7RqlHA4mGWMsjWlZpKXmeZtibeCS0yX+nRkzorHVx2J4ZZGFpNfhXuji3gXHXx2HhYkFdrTeAUtTy1SuYeqQyWTY1HITijgUgVeQF9rubYtIZaTB5YVFhuHQs0MYemIoZlyegaMvjuJj4EfokhOxZEng0iVgzhwxX8vKKu4+n4M/Y9O9TWi7ty0c5jqgzpY6mH99Pp5/fQ5TuSnqONfByiYr8Wn4J5zvdh4DKw6EYwZHg9+PLjJbZcbKJisBAPOuzcPtT7cNLiupvZMfPgC7d4vnI0ZIr7/69gpt9rSBkpTIYZsDCqUC7fe1x8LrCw2uK/s1yGTAhg1AqVKAry/QqpUYOfAr4DmTjDGWxlR2qoyC9gXxMfAjptSakvgBjBmJTCbDvAbz8C38Gzbd24QdD3cAAObWn4vi2Yqncu1Sl52FHQ62P4gKayvgsudljDw1EotdF+t8fLAiGMdeHsP+p/tx9MVRhESGxNnH3soepbOXFo8c4mexrMXiZJEtXVo81FSkgoe3B46+PIqjL4/iltetGBlYs1pnReNCjdGkUBM0KNAAGS0z6v8BGEGLIi3QoUQH7Hq0Cz0P9cTtvrcNzu6alLmTS5aIjLB16gBly4rXAsID0Hxnc3wP/46KuSriXNdzGHVmFJbfWo7hp4bjQ+AHzGswL95sx4zZ2AAHDwLlywO3b4u5kxs3/vwJeTiYZIyxNEYuk+Naz2sIVgTDObNzaleH/WJkMhnWNlsL/3B/HHx2EK4FXTGo4qDUrlaaUNihMLa22oqWu1tiyc0lqJCrArqU6hLv/v7h/vj3xb/Y92QfTr4+ifAoqasit11uNP2tKQIjAnHP5x6e+T3Dt7BvOP/uPM6/O6/Zz1RuiiIOReIEmdZm1jj95jSOvjiKY6+OwSfYJ8a5yzmWQ5NCTdCkUBNUyFUhzQRBSxotwZk3Z/DQ9yFmXp6JSbUmGVSOoZldAwNFxk1A6pVUqpTouL8jnvo9Ra4MuXCw/UHYmNtgqetS5LbLjdFnR2PhjYXwCvLClpZbtC4RwxgAODuLoecNGwKbNwPlysWdk/uz4WCSMcbSoKw2WZHVJmtqV4P9okzlptjbdi+uel5FldxVOJNwNC2KtMD46uMx7fI09DnSB8WzFkdZx7Ka7X6hfjj07BD2P92PM2/OIFIlDYctkLkA/ij6B/4o9gcq5KwQ43MNjwrHky9PcN/nPu5//vHwuY/v4d/xyPcRHvk+wvaH2zX7yyCL0ftoY2aDBgUaoEmhJnAt5IqcGZJp4cskymqTFctcl6HD/g6YdnkaWhdtjZLZSxpUliG9k+vWiYCyaFGROAUARp8ZjeOvjsPK1AqHOhzSDPmVyWQYVW0UctnlQs9DPbHn8R58Dv6Mgx0OIpNlJoPqzH5+desC8+YBw4aJtUtLlhTzm39WHEwyxhhjLA5TuSlq5quZ2tVIk9xqueGO9x0cf3UcrXa3wr+d/sWl95ew/+l+XHx3EUpSavYtlrWYCCCL/oFS2UvFG5hbmlqinGM5lHMsp3mNiPAx8KMmsFQHmS+/vgSBUNC+oKb3sUbeGummx6xd8XbY+WgnDj0/hJ6He+J6r+swlet/Sapv72RkJLD4x8jkESMAuRzYfG8z5l2fBwDY2GKj1jnqXUp1QQ7bHGi9uzUuvr+Iahuq4Xjn43pnuv1ZqEiFq55Xcfj5YVTLUw0tirRI7SqlOX/9JTIGb90KtG0rhr0m95q0qYWDScYYY4wxPZjITbC99XZU+PT1fQAANR9JREFUWFsBr7+/RsmVMXvWyuQoowkgi2YtavB5ZDIZcmfMjdwZxZBYtRBFCPzD/ZMl62pKkMlkWNFkBS6+v4jbn25jwfUF+KfqPwaVpU/v5L59gKcnkC0b0LkzcO3DNfT9ty8AYEKNCWhfon28x9bLXw+XelxC4+2N8fjLY1RZXwXHOx83uFc1PXrk+wjbH2zHzkc78T7gPQBg3vV5GFhhIOY1mPfLJujSRiYDVq8GnjwRS/m8ePHzBpNpYwA9Y4wxxlg6ktkqM9zbu8PW3BYAUClXJcypNwevh7yGRz8PjK8xPkmBZEJszG3SbSCpljNDTixsKLKkTjw/Ec/9nhtUjq6ZXYmk5UAGDQJ8IzzRardY7qVVkVZwq+WW6LnK5CiD672uo6hDUXgFeaHaxmo4//Z8oselZx8DP2Lu1bkovao0Sq4siVlXZ+F9wHtkMM+ARgXFOOHlt5aj6oaqeP3tdSrXNm2xsgLc3YHr14EGP/HyvBxMMsYYY4wZoGT2kng1+BW8hnvhRu8b+Lvq38ifOX9qVyvd6Fa6GxoWaIgIZQR6He4FFakMKkeXdScvXQLu3BEX+F17h6DFrhbwDfFF6eylsaXVFp0TFOXNlBdXel5BtTzVEBgRiEbbG2HXo10G1Tut8g/3x7q761B7c23kWZgH/5z5Bw8+P4CZ3AwtCrfAnjZ78HnkZxzvfBzHOx9HFqssuOt9F+XWlMO+J/tSu/ppSu7cYs7kz4yDScYYY4wxA2W3zZ5mk92kdTKZDGuarYGtuS2ufriK5TeXG1SOLr2T88S0SHTrrsKIy91wz+ceslpnxaEOhzS9y7qyt7LH6T9P44+if0ChVKDj/o6Yf22+TuuEplXhUeHY/2Q/Wu9ujezzsqPPkT648O4CCIQaeWtgddPV8Bnpg4MdDqJt8bawMhMLnDYq2Aj3+t9D1dxVERgRiLZ722LwscGIiIpI5XfEUgoHk4wxxhhjLFXkyZgHc+rNAQCMPjsab7+/NaichHonnz0D/v1XzGMzrTsF+5/uh5ncDO7t3ZE3U16DzmdpaondbXZjSEWx7sPI0yMx7OQwg3tXU8v1D9fR61Av5JiXA232toH7M3colAqUzFYSs+rOwvuh73Gx+0X0dekLeyt7rWU42TnhfLfzGF11NABg2a1lqLqhKt58f2P0+qpIhShVlNHLZYbjYJIxxhhjjKWafuX7oWbemgiNDEXvI70N6uFLqHdywQLxs3zXvVj2SGxc1XQVquapmpRqw0RugkWNFmFu/bkAgMX/LUZn985QqBRJKjcl/PfxPzTc1hC/b/gdG+5tQEBEAHLb5caoqqPwoP8DPBjwAKOqjUKejHl0Ks/MxAwz683E0U5HkcUqC+5430HZ1WWx/8l+o9T34eeHGHFyBBznOyLPwjz4GPjRKOWypONgkjHGGGOMpRq5TI51zdfBytQK596ew7q76wwqR1vvpK8vsGULAMe7eFCgGwBgWOVh6Fm2p1HqLpPJMPL3kdjeejvM5GbY/2w/JryagFufbhmlfGO78+kOmu5oisrrK+PU61MwlZuiR5keuNj9It4NfYdZ9WYlKUNt40KN4dHPA7/n/h2BEYFos7cN/jr+l0HDXv1C/bDkvyVwWeOCUqtKYcGNBfAN8YV3sDdGnhppcB2ZcXEwyRhjjDHGUlVB+4KYVmcaADFk1JCeJ229kytWABFmPjDv2gIRqjA0KtgIc+rPMVa1NTqV7IQTXU7AzsIOz0Ofo+qmqmixqwUefH5g9HMZ4sHnB2i1uxXKry2Poy+PwkRmgh5leuDFoBfY0GIDauStoXMSosTkzpgbF7pdwD+/i+Veltxcguobq+s0hDlSGYnDzw+j9e7WyDk/J/468Rfuet+FmdwMrYu2xvLGyyGXybH78W5ceHfBKPVlScPBJGOMMcYYS3V/VfoLlXJVQmBEIPr928+g4a7ReycvXgSWrQoH2reCwuojCmcpjJ1/7ISpPHmWWa/jXAd3et9BHfs6kMvkOPz8MMqsKoOO+zvixdcXyXLOxDz58gTt9rZD6VWlcfDZQcggQ5dSXfB04FNsaLEBzpmdk+W8ZiZmmF1/Nv7t+C/srexx69MtlF1dFu5P3bXu/+DzAww/ORy5FuRCi10t4P7MHZGqSLg4umBJoyX4NOIT9rfbj/9V+B/6u/QHAAw+PhiRyshkqT/THQeTjDHGGGMs1ZnITbChxQaYm5jj2Mtj2P5wu95lRO+dbNmK8LVKPyD3DWS2zIwjHY8gk2Um41Y6lrwZ82JIniG41+ce2hVvBwJh16NdKLa8GHod6oX3/u+T9fxqL76+QOcDnVFiRQnsfbIXANC+eHs8/t9jbG21FYWyFEqRejT5rQk8+nmgilMVBEQEoPWe1hh2YhgUSgW+hHzB4huLUW51OZReVRoLbyzEl9AvyGaTDSOqjMCD/g9wu+9tDK40GA7WDpoyp9aZiixWWfDI9xFW3FqRIu+DxU9G6TmPMYtXYGAgMmbMiICAANjZ2RmlzMjISBw7dgyNGzeGmZmZXseuWbMGwcHBRqkH+zmEh4fD0tIytavB0jluR8xYuC2lHUGKIARGBEIOObLbZtd7+GVUFPD5MwDzIMAyEADgYO0ACxOLZKhtXNHbUqQqEoERgQiPCtdstzGzgZ2FndGGlUanVCkRqAhEaGSo5jVLU0vYWdjBTK7ftZuxBUQEIFghrgVNZCZQkjLGditTK1ibWcPSNPF/hyGRIfAP94cMMuSwzZEsn6Ux2draom/fvnodk5Trbm2SIzYAgOTp52csluDgYAQFBaV2NVgaExnJw1NY0nE7YsbCbSntsIO42A0JDjHseM21sniiCFVAgZTLshq9LZn/+E/aKIKh5GIKU83nBwCIEutIhiM8/oNSgBxyqV7aurKigMioSERCt3+HSW0jzDg4mGQpwtZWvwWB2c+PewGYMXA7YsbCbSltiVRFwjfEFwCQySIT5HI5lCololRRmkfsnq04CLCU2yKLbcYUqLEkobYUoYxAYEQgFEoR2Mogg625LTJYZIAMsgTLVZJS8xmonytJ+kzULEwskNEiI8xMUrcnMj5KUiIsMgyWppZJmr+qUCrwJfQLACCrdVaYm5gnckTq+ZmvgzmYZClC36599nMz9tAN9mvidsSMhdtS2jTh3ARMuzwNSGBVCQsTC+TPnB/5M+dHgcwFUMC+gOZ5vkz5YGVmlXIVhm5tiYhw7OUxjD8/Hvd87gEKwN7EHqOqjkIRhyLwDPDEh4AP8Az0hGeAeHwK+gQVqRI8d13nuphSewp+z/17MryztKnHoR7YdG8TXDK64L/e/8FEbpLaVfrlcDDJGGOMMcbSnPE1xuPax2t4+PlhjIAxf+b8KGBfAAUyF4BjBsc0P18uNplMhia/NYFrIVfsf7IfEy9MxDO/Zxh1ZlSCx5mbmCO3XW7kyZgnziN/5vwoaF8whd5B2jGr7iwceHoAd7zvYIPHBvRx6WP0cxx9cRTzrs/DsMrD0Lxwc6OXn95xMMkYY4wxxtIcC1MLnO16NrWrkWzkMjnaFm+LVkVbYfuD7Vh2axkAiADRTgSJuTNKwWM2m2zpLnBObtlts2NKrSkYenIoxpwdgz+K/QF7K3ujlX/q9Sm03tMaCqUCF99dxLwGIqiUyRIekvwr4WCSMcYYY4yxVGIqN0W3Mt3QrUy31K5KujSw4kCs81iHR76PMOHcBCxvstwo5V7xvIKWu1pCoVSgQOYCeP39NUacGoFX315hieuSZFuvNL3h2xuMMcYYY4yxdMlUboqlrksBAKvurBLzUJPorvddNNnRBGFRYWhUsBGeDHyC+Q3mQwYZVt5eiWY7myEwIjDJ5/kZcDDJGGOMMcYYS7dq5auF9sXbQ0UqDDo2CETa1h7RzZMvT9BgawMERgSiRt4a2N9uP8xNzDG8ynAcaH8A1mbWOPHqBKptqAbPAE8jvov0iYNJxhhjjDHGWLo2r8E8WJtZ4+qHq9j+cLtBZbz5/gb1t9bH17CvKJ+zPI50PAJrM2vN9pZFWuJi94vIYZsDD30fotK6Srj96bax3kK6xMEkY4wxxhhjLF1zsnPChBoTAAB/n/5b72GoXoFeqLelHj4FfULxrMVxovMJ2FnYxdmvfM7y+K/3fyiZrSR8gn1QY2MNHHx20BhvIV3iYJIxxhhjjDGW7g2rPAyF7AvBJ9gHUy9O1fm4LyFfUH9rfbz1f4sCmQvg9J+nkcU6S7z758mYB1d6XkGjgo0QFhWG1rtbY8H1BUkaXptecTDJGGOMMcYYS/csTC2wuNFiAMCi/xbh6ZeniR4TEB6Ahtsa4qnfUzjZOeFM1zNwzOCY6HF2FnY40vEIBpQfAAJhxKkR+N/R/yFKFZXk95GecDDJGGOMMcYY+ym4FnJF88LNEaWKwpATQxLsLQxRhKDJjibw8PFAVuusOPPnGeTLlE/nc5nKTbG88XIsaLAAMsiw6s4qNN3R9JfK9MrBJGOMMcYYY+ynsbDhQliYWODMmzNwf+audZ+IqAi02t0KVz9cRSbLTDj952kUdiis97lkMhmGVRmmyfR68vXJXyrTKweTjDHGGGOMsZ9G/sz58U/VfwAAw04OQ2hkaIztUaoodNjfAaffnIaNmQ2Odz6O0jlKJ+mcLYu0xKXul365TK8cTDLGGGOMMcZ+KqOrjUaejHngGeCJWVdmaV5XkQo9DvXAwWcHYWFigcMdD6OyU2WjnNMlp8svl+mVg0nGGGOMMcbYT8XazBoLGy4EAMy5Ogdvvr8BEWHg0YHY9mAbTOWm2Nt2L+o41zHqebVlet37eK9Rz5GWcDDJGGOMMcYY++m0KtIK9fPXR4QyAkNPDMWoM6Ow6s4qyCDD1lZb0axws2Q5b/RMr4UdCqNe/nrJcp60gIPJdKRVq1bInDkz2rRpk9pVYYwxxhhjLE2TyWRY4roEpnJTHHlxBHOvzQUArGm2Bh1KdEjWc6szvV7vdR2ZrTIn67lSEweT6ciQIUOwZcuW1K4GY4wxxhhj6UIRhyIYWmmo5v8XNFiA3uV6p8i5ZTIZMllmSpFzpRbT1K4A013t2rVx4cKF1K4GY4wxxhhj6cakWpMQrAhGOcdy6OPSJ7Wr81NJNz2TK1euRKlSpWBnZwc7OztUqVIFx48fT/AYNzc3yGSyGI8cOXIYvW6XLl1Cs2bNkDNnTshkMhw8eDDOPitWrICzszMsLS3h4uKCy5cvG70ejDHGGGOMsZhszW2xsulKDiSTQboJJp2cnDBr1izcvn0bt2/fRp06ddCiRQs8fvw4weOKFy8Ob29vzePhw4fx7nv16lVERkbGef3Zs2fw8fGJ97iQkBCULl0ay5Yt07p99+7dGDp0KMaNGwcPDw9Ur14drq6u8PSUFjN1cXFBiRIl4jw+ffqU4PtjjDHGGGOMsdSQboa5NmsWM9vS9OnTsXLlSty4cQPFixeP9zhTU1OdeiNVKhUGDhyIQoUKYdeuXTAxMQEAvHjxArVr18awYcPwzz//aD3W1dUVrq6u8Za9YMEC9OrVC717i/HZixYtwsmTJ7Fy5UrMnDkTAHDnzp1E62iIyMhIrQGyoWVF/8mYobgtMWPgdsSMhdsSMxZuS8xYjN2WkqtNpptgMjqlUom9e/ciJCQEVapUSXDfly9fImfOnLCwsEClSpUwY8YM5M+fP85+crkcx44dQ40aNdC1a1ds3boVb9++RZ06ddC8efN4A8nEKBQK3LlzB6NHj47xeoMGDXDt2jWDykzI8uXLsXz5ciiVSgDAqVOnYG1tbdRznD592qjlsV8XtyVmDNyOmLFwW2LGwm2JGYux2lJoaKhRyoktXQWTDx8+RJUqVRAeHg5bW1u4u7ujWLFi8e5fqVIlbNmyBb/99hs+f/6MadOm4ffff8fjx4+RJUuWOPvnzJkT586dQ40aNdCpUydcv34ddevWxapVqwyus5+fH5RKJbJnzx7j9ezZsyc4dFabhg0b4u7duwgJCYGTkxPc3d1RoUKFGPsMHDgQAwcORGBgIDJmzIgGDRrAzs7O4PpHFxkZidOnT6N+/fowMzMzSpns18RtiRkDtyNmLNyWmLFwW2LGYuy2FBgYaIRaxZWugsnChQvj3r178Pf3x/79+9GtWzdcvHgx3oAy+tDTkiVLokqVKihQoAA2b96M4cOHaz0mT5482LJlC2rWrIn8+fNj/fr1kMlkSa577DKISO9yT548qfd5zczMjP5llhxlsl8TtyVmDNyOmLFwW2LGwm2JGYux2lJytcd0k4AHAMzNzVGwYEGUL18eM2fOROnSpbF48WKdj7exsUHJkiXx8uXLePf5/Pkz+vbti2bNmiE0NBTDhg1LUp0dHBxgYmISpxfS19c3Tm8lY4wxxhhjjKUX6SqYjI2IEBERofP+ERERePr0KRwdHbVu9/PzQ926dVG0aFEcOHAA586dw549ezBy5EiD62hubg4XF5c4451Pnz6N33//3eByGWOMMcYYYyw1pZthrmPHjoWrqyty586NoKAg7Nq1CxcuXMCJEycAAMuWLYO7uzvOnj2rOWbkyJFo1qwZ8uTJA19fX0ybNg2BgYHo1q1bnPJVKhUaNWqEvHnzYvfu3TA1NUXRokVx5swZ1K5dG7ly5Yq3lzI4OBivXr3S/P/bt29x79492NvbI0+ePBg+fDj+/PNPlC9fHlWqVMGaNWvg6emJ/v37G/lTYowxxhhjjLGUkW6Cyc+fP+PPP/+Et7c3MmbMiFKlSuHEiROoX78+ANGr+Pr16xjHfPz4ER07doSfnx+yZs2KypUr48aNG8ibN2+c8uVyOWbOnInq1avD3Nxc83rJkiVx5swZrQl71G7fvo3atWtr/l89H7Nbt27YtGkT2rdvj69fv2LKlCnw9vZGiRIlcOzYMa31YIwxxhhjjLH0IN0Ek+vXr09wu5ubG9zc3GK8tmvXLr3OoQ5MYytTpkyCx9WqVQtElOA+//vf//C///1Pr/owxhhjjDHGWFqVrudMMsYYY4wxxhhLHRxMMsYYY4wxxhjTGweTjDHGGGOMMcb0xsEkY4wxxhhjjDG9cTDJGGOMMcYYY0xvHEwyxhhjjDHGGNMbB5OMMcYYY4wxxvTGwSRjjDHGGGOMMb2ZpnYFWPIgIgBAYGCg0cqMjIxEaGgoAgMDYWZmZrRy2a+H2xIzBm5HzFi4LTFj4bbEjMXYbUkdE6hjBGPhYPInFRQUBADInTt3KteEMcYYY4wxlhYEBQUhY8aMRitPRsYOT1maoFKp8OnTJ2TIkAEymcwoZQYGBiJ37tz48OED7OzsjFIm+zVxW2LGwO2IGQu3JWYs3JaYsRi7LRERgoKCkDNnTsjlxpvpyD2TPym5XA4nJ6dkKdvOzo6/IJlRcFtixsDtiBkLtyVmLNyWmLEYsy0Zs0dSjRPwMMYYY4wxxhjTGweTjDHGGGOMMcb0xsEk05mFhQUmTZoECwuL1K4KS+e4LTFj4HbEjIXbEjMWbkvMWNJLW+IEPIwxxhhjjDHG9MY9k4wxxhhjjDHG9MbBJGOMMcYYY4wxvXEwyRhjjDHGGGNMbxxMMsYYY4wxxhjTGweTLFFBQUFwc3NDyZIlYWtri4wZM6JChQqYP38+FApFalePGUFoaCiOHz+OadOmoXXr1sibNy9kMhlkMhnc3Nx0KuPz588YMWIEChcuDCsrK9jb26N69epYt24ddMnz9fr1a/Tr1w/Ozs6wtLREtmzZ0LBhQ+zfv1+n89+9exddunSBk5MTLCws4OjoiFatWuHcuXM6Hc+M4+vXr9i4cSO6dOmCYsWKwcbGBhYWFnByckLLli3h7u6eaBncltjdu3cxefJkNG/eHEWKFEGWLFlgZmaGLFmyoGrVqpg+fTq+ffuWYBncjlhCZs2apfk7J5PJEtyX2xIDgE2bNsVoM/E9zpw5E28ZP2VbIsYS8O7dO8qXLx8BIABkbW1NFhYWmv8vW7Ysffv2LbWryZLo/Pnzmt9p7MekSZMSPf727duUJUsWzTG2trZkamqq+f8GDRpQeHh4vMcfPXqUrK2tNfvb2dmRXC7X/H+PHj1IpVLFe/zatWtjnC9jxowkk8n0eg/MOKL/HgCQpaUl2djYxHjN1dWVQkJCtB7PbYkREQ0cODBOO8qQIUOM1xwcHOjatWtaj+d2xBLy7NkzsrS0jNGe4sNtialt3LiRAJBcLqfs2bPH+7h06ZLW43/WtsTBJItXVFQUlSxZkgCQo6MjnT59moiIlEol7dq1S/OH3dXVNZVrypLq/PnzlDlzZqpbty79/ffftHPnTsqRI4dOXy7+/v6afYsUKUK3bt0iIqKIiAhatmwZmZmZEQAaMGCA1uPfvHmjCTaqVq1Kz58/JyKioKAgmjhxouZLbvbs2VqPv3btGpmYmBAAatmyJX348IGIiPz8/Khfv36a43fv3m3gp8P0AYAqVqxIK1asoNevX2tef/v2LfXq1Uvz++jSpUucY7ktMbXNmzfT3Llz6fr16/T9+3fN60FBQbRp0ybKmjUrAaBs2bKRv79/jGO5HbGEKJVKqlq1KgGgKlWqJBhMclti0amDybx58+p97M/cljiYZPFat26dpnFpu/u7Y8cOzfYzZ86kQg2ZsURFRcV5LW/evDoFk+PHjycAZGVlRW/evImzfcaMGQSATExMNF9+0XXp0oUAUI4cOWJcNKr17dtXcwdOWy94tWrVCACVLFmSFApFnO0NGzbUfPlre5/MuM6dO5fg9uh/tDw9PWNs47bEdHXy5ElNO9q2bVuMbdyOWEIWLVpEAKhz5840adKkBINJbkssuqQEkz9zW+JgksWrevXqBIBq166tdbtKpSJnZ2cCQF27dk3h2rHkpmswmSdPHs3wCm2CgoLI1taWANDEiRNjbAsODiYrKysCQJMnT9Z6/Nu3bzV/7Dds2BBj2+vXrzXbNm/erPX4CxcuaPZJLNBhye/mzZua38eBAwdibOO2xHQVEBCg+V3MmjUrxjZuRyw+6t6dLFmykK+vb6LBJLclFl1SgsmfuS1xAh6mVWhoKK5evQoAcHV11bqPTCZDo0aNAACnTp1KsbqxtOP58+fw9PQEEH87sbW1RfXq1QHEbSdXrlxBWFhYgsfny5cPRYsW1Xr86dOnNc/VbTG2atWqIUOGDFqPZynP0tJS81ypVGqec1ti+rh8+bLmeYECBTTPuR2xhPTp0wchISFYsGABsmbNmuC+3JaYsfzsbYmDSabV06dPoVKpAAAlSpSIdz/1Nh8fn0Qz67Gfz6NHjzTPdWknT548iff44sWLJ3r848ePtR6fLVs2ZMuWTeuxJiYmKFKkiNbjWcq7cOGC5nnJkiU1z7ktscRERETg3bt3WLZsGf78808AQMGCBdGsWTPNPtyOWHzWrl2Ls2fPol69eujatWui+3NbYvH58uULXFxcYGtrCysrK+TPnx9dunSJ8fctup+9LXEwybT69OmT5nmuXLni3S/6tujHsF+Dvu0kMDAQwcHBcY7PnDkzrK2tEz0+dhtT/39C507oeJay/P39MXPmTABA9erVUbhwYc02bkssPpaWlpDJZLC0tISzszMGDx6M79+/o2rVqjh79iwsLCw0+3I7Ytp4eXnh77//hpWVFVavXq3TMdyWWHxCQ0Nx9+5dmJubQ6VS4e3bt9i+fTtq166Nnj17IioqKsb+P3tb4mCSaRUUFKR5nlDDjb4t+jHs15DUdqJ+ntCx0bfHbmNJPZ6lHJVKhT///BPe3t6wsLDA0qVLY2zntsTikyNHDmTPnh02Njaa12rXro1FixYhT548MfbldsS06devHwICAuDm5ob8+fPrdAy3JRZbzpw5MWnSJNy/fx/h4eH49u2bZlpYvXr1AAAbN27EsGHDYhz3s7clDiYZY4wlu7/++gv//vsvAGDFihUoXbp0KteIpRfv3r2Dj48PgoOD8fnzZ8ybNw/37t1DxYoVMXHixNSuHkvjtm3bhqNHj6JMmTIYPnx4aleHpWMNGjSAm5vb/9u706CorrQP4P+moVmaTUAxWBFcRgU1qLiMSkbNiI6iosSoccMkqAkxE43iUtHRxA8O6pjUaByNjvuUiCYmMW4JCs4QEw3jMu5ri0JAjQrY7DTP+4Hq+9LQ3XQrisH/r+pW3b5nued2n2p4+txzLl566SXljgi1Wo1evXrh4MGDiIqKAlD5N+7KlSv12dSnisEkmWWchAtUDudbUjWtahl6PjxuPzHuWytbNb16H3vc8vR0zJo1C6tWrQIAfPLJJ3jzzTdr5GFfIls0adIEM2fOxIEDB6BSqbB48WLlRwqA/YhM3blzB9OnT4darca6devg6Ohoc1n2JbKHg4MDli9fDqDyTpw9e/YoaQ29LzGYJLMCAgKU/aysLIv5qqZVLUPPB3v7iaenJ9zd3WuUf/DggdUvOWP56n3M+Nraua2Vpydv9uzZ+Nvf/gYAWLZsGaZPn242H/sS2aN79+4IDw8HAHz++efKcfYjqmrOnDm4d+8epkyZgnbt2kGv15tspaWlSt7qx9iXyF6tW7eGn58fAOD69evK8YbelxhMklnBwcFwcKjsHlVXkarOmNa0aVP4+Pg8lbbRs6PqqmS29JOQkBCL5a2tHmYsX30VM2P5O3fu4O7du2bLGgwGXLx40Wx5erLi4+OxbNkyAMDSpUsxa9Ysi3nZl8hexsUirl69qhxjP6KqdDodAOAf//gHPDw8amzGBcEAKMdmz54NgH2J6k5D70sMJsksNzc39O7dGwBw4MABs3lEBAcPHgRQeR85PX/atm2rLIBhqZ8UFBQoz4Sr3k/Cw8Ph6upqtXxGRgYuXLhgtnxERISyb6n8Dz/8oEwmZz99embNmqXc8rN06VLEx8dbzc++RPYy/vJf9ZYs9iOqK+xLZK9r167h119/BQC0aNFCOd7g+5IQWbB+/XoBICqVSn766aca6Tt27BAAAkCSk5ProYX0JAUGBgoAWbhwodV88+fPFwDi5uYmOp2uRnpCQoIAELVaLZcuXaqRPn78eAEgL7zwguTm5tZIf+eddwSAeHh4yP3792ukh4eHCwAJDQ2V0tLSGumDBg0SABIYGCjl5eVWr4XqxsyZM5XvhuXLl9tcjn2JRETKy8uloqLCap7k5GRRqVQCQGbPnm2Sxn5Etlq4cKHyXWUO+xIZ1fadVFFRISNGjBAA4uDgIBcvXjRJb8h9icEkWVRWViYdO3YUANKsWTMlYDQYDJKUlCSenp4CQAYNGlTPLaW6cP/+fbl7966yvfjiiwJA4uPjTY4/fPjQpFxubq40bdpUAEhISIikp6eLiEhJSYmsXr1aNBqNAJB33nnH7HmvX78uWq1WAMjLL78sly9fFhERvV4vH330kfIPY0JCgtnyR48eFbVaLQAkOjpaMjMzRUTk3r17ypcrANmxY0ddvVVkxezZs5X3fMWKFXaVZV8iERGdTiehoaGyZs0auXbtmsk/cTdv3pQlS5Yon7OPj49kZ2eblGc/IlvVFkyyL5GRTqeTbt261fheMhgM8uOPP8rAgQOVz8Ncf2jIfYnBJFml0+kkKChI6WRubm7i4uKivO7cubPZX0Dot8c4ElnbFhMTU6Nsenq6+Pr6Knk8PDzEyclJeT1gwAApLi62eO69e/eKm5ubkt/Ly0v50gMgkyZNsvqr4Lp168TR0VHJ7+3trXyx2jK6SnUjIyNDec8dHBzE39/f6rZs2bIadbAvkU6nM/nO0Wg04ufnp/wjZdxatGghJ06cMFsH+xHZorZgUoR9iSpV/15ydnYWPz8/cXZ2Njn+xhtvSFlZmdk6GmpfYjBJtcrPz5e//OUv0qFDB9FqteLh4SFhYWGyfPlyKSkpqe/mUR15nGBSRCQnJ0dmzJghv/vd78TFxUW8vb0lPDxc1q1bJwaDodbzX716VSZPnixBQUGi0WjE19dXIiIiZNeuXTa1/7///a+MHTtWmjVrJhqNRvz9/WX48OFy6NAhe94GegzV/9jWtln6w8W+9HwrKSmRpKQkiYuLk7CwMAkICBCNRiOurq7SvHlzGTp0qKxfv14KCwut1sN+RLWxJZgUYV8ikcLCQlm5cqWMHTtWQkJCpHHjxuLo6Cju7u7Srl07efPNNyUtLa3WehpiX1KJiICIiIiIiIjIDlzNlYiIiIiIiOzGYJKIiIiIiIjsxmCSiIiIiIiI7MZgkoiIiIiIiOzGYJKIiIiIiIjsxmCSiIiIiIiI7MZgkoiIiIiIiOzGYJKIiIiIiIjsxmCSiIiIiIiI7MZgkoiIqJ6lpqZCpVJBpVLVd1PsFhQUBJVKhU2bNtV3U+xy7949eHt7o3HjxigoKHjs+hITE6FSqTBx4sQ6aB0R0W8Dg0kiInpqRAQ7d+7EiBEjEBgYCFdXV7i7u6NVq1YIDw/HBx98gN27dyM/P7++m/pcKSsrw4YNGzB48GA0a9YMzs7O8PLyQps2bdCvXz/MmzcP+/fvR3FxcX03tc4sWrQIeXl5mD17NrRa7WPXN2rUKISEhGDbtm04ceJEHbSQiOjZpxIRqe9GEBFRw5ebm4vhw4fjyJEjyjFHR0d4enoiPz8f5eXlyvGNGzdi0qRJ9dDK+pGamop+/foBqAy4n6Zbt25h8ODBOHv2rHJMo9FAq9UiLy8PFRUVyvGUlBT07dvXpPwf//hHZGVlYcmSJRgxYsTTavZjuXz5Mtq3b49GjRrhxo0bcHNzq5N6ExMT8frrr6Nfv344fPhwndRJRPQs48gkERE9FRMnTsSRI0egVqsxc+ZMXL58GSUlJbh37x6Kiopw+vRpJCQkIDQ0tL6b+twwGAyIiorC2bNn4ebmhsWLF+PmzZsoLi7G/fv3UVhYiOPHj2PRokVo1aqV2ToOHTqEixcv/mYCSQBYsWIFysvLERMTU2eBJACMHDkSjRs3RkpKCtLT0+usXiKiZxVHJomI6Im7cuUK2rRpAwBYsmQJ5s6dazV/UVERXF1dn0bTngn1NTL5/fffY8CAAQCA7du3Y8yYMRbzighKS0vh7Oz8tJr3ROj1egQEBODhw4c4depUnf94MW3aNHz22WeIjY3FunXr6rRuIqJnDUcmiYjoiTt16pSyHxUVVWt+c4HknTt3sGHDBkRHRyM4OBheXl5wdXVF69atERsbi3Pnzlmsb9KkSVCpVMqts5s2bULPnj3h5eUFHx8f9O/fH//+97+V/OXl5Vi5ciXCwsLg6ekJLy8vDB482OJcuOoL6KSnp2PkyJF44YUX4OLigtatWyM+Ph65ubm1XrslBoMBmzZtwsCBA+Hv7w+NRoPGjRtj4MCBSExMfKQg1J7PRaVSmQ0kLS3A07dvX+U9sbYFBQWZPV9OTg7mzp2L0NBQeHl5wcXFBS1btkRsbCzOnz9v76Uqtm/fjocPHyI4ONhqIHns2DGMGzcOLVq0gIuLC7RaLQIDA9GnTx8sXrwYmZmZZsuNHTtWOY9er3/kdhIR/SYIERHRE5aUlCQABIB89913j1RHTEyMUgcA8fT0FEdHR+W1s7Oz7Nq1y2rZmJgYZd/R0VE8PDyU8o6OjrJnzx4pLi6WAQMGCADRaDSi1WqVPG5ubpKenl6j/pSUFCXPV199JRqNRmmjcR+ABAYGik6ns1renJycHOnRo4fJ9Xt5eZm8HjZsmJSUlNj1ni5dulQpf/nyZbvKGgUGBgoA2bhxo8nxESNGiL+/v8XN2dlZeU+q27Nnj7i7uyttc3JyMvkcNBqNbN68+ZHaGx0dLQBk6tSpFvNs2rRJVCqVSd/y9PQ0eb+rX69RaWmpuLi4CADZt2/fI7WRiOi3giOTRET0xHXr1k0ZtTPOl7RXixYtMH/+fJw8eRJ6vR55eXkoKSnB2bNnMW7cOJSUlCAmJga//PKLxTq+/vprJCUlYe3atcjPz0d+fj4uXryIsLAwlJeX47333sOsWbOQnp6OpKQk6PV6PHz4EOnp6WjVqhUKCwvx/vvvW21nTEwMevXqhfPnzyMvLw8FBQXYsWMHGjVqhIyMDIwaNQoGg8Hm6y4tLcXQoUNx7NgxdOnSBXv37kVBQQFyc3Oh1+uxefNmNGnSBN988w3mzJljc70A0L17d2X/7bffRlZWll3lrfnyyy+Rk5Njdtu/fz8cHR0BAJGRkSbljh8/jldffRV6vR5Tp07FhQsXUFRUBL1ej4yMDMTFxaG0tBRvvfXWI81LTEtLA2B67VUVFhbivffeg4hg/PjxuHr1KoqLi5GXlwe9Xo/09HTEx8ejSZMmZss7OTmhS5cuAGCy2BQRUYNU39EsERE9HyZPnqyM6qhUKuncubPExcXJP//5Tzlz5oxUVFQ8Vv2RkZECQBYvXlwjreqo5rZt22qkX7t2zWQk6j//+U+NPIcOHVLSb926ZZJWdWSxTZs2UlhYWKP8999/r+RJSkqyWL66VatWCQBp37695Ofnm7329PR0UalUotFo5Pbt22bzWBIREaGcW61WS8+ePWX69OmydetWm0YrLY1MWpKZmSkBAQECQPr37y9lZWUm6d26dRMAsmDBAot1/PnPfxYAEhUVZdM5ja5du6Zcq7kRZhGRY8eOCQDRarU12mard999VwDIH/7wh0cqT0T0W8GRSSIieipWr16NBQsWQKvVQkRw8uRJrF69Gm+99RY6duyIpk2b4oMPPsDt27cfqX7jCJdx5Mmc5s2bK3PaqmrZsqWyWunLL7+M8PDwGnn69OmjzBn83//+Z/Ec8fHxZud89u/fH7169QJQ+QgJW61fvx4AEBcXBw8PD7N5wsLC0L59e5SWliIlJcXmugFg9+7diIuLg5OTEwwGA3788Ud8+umnmDBhAtq0aYOgoCB89NFHdfLsT71ejyFDhuCXX35BcHAwdu7cqYxQAsDp06fx888/w8nJCTNnzrRYz8SJEwEAycnJdo3yVh21bty4sdk83t7eACpHhO/du2dz3VX5+fnVOB8RUUPEYJKIiJ4KR0dHfPzxx8jKysLWrVsRGxuL0NBQaDQaAJUL7HzyySfo0KEDjh8/braO06dPIy4uDi+99BI8PT3h4OCgLOQSFxcHABYXRgGArl27KrfbVufv7w+g8pZcc9RqtRIkPHjwwOI5XnnllVrTbL098+HDh0rgumDBAjRt2tTidunSJQBARkaGTXUbabVafPbZZ8jMzMTnn3+OCRMmIDg4GGq1Wqlv0aJF6NSpE65du2ZX3VUZDAa8/vrrOHXqFPz8/PDtt98qgZuR8YeAiooKtG3b1uK1/ulPfwIAFBQU2BXw3b17V9n38fExm6dVq1Zo164dysrK0KNHDyQkJODUqVN2Ba3Guquej4ioIXKsPQsREVHd8fLywvjx4zF+/HgAQHFxMdLS0vD3v/8de/bswa+//opXX30VV65cgYuLi1Ju1apVeP/991FRUQGgcnVRLy8vZbSwqKgI+fn5KCgosHhuSyN7AJQRMlvylJWVWczTrFmzWtPu3LljMU9VOTk5yvXev3/fpjKFhYU25auuSZMmmDx5MiZPngygchTx8OHDWLZsGdLS0qDT6TBmzBj8/PPPj1T/jBkz8O2338LZ2Rm7d+9Gy5Yta+QxjuQZDAabR6jtud7i4mJl39IjTtRqNRITEzFixAjodDrMnTsXc+fOhZubG3r16oXo6Ohan09pHJmuej4iooaII5NERFSvXFxc0L9/f3zzzTeIiYkBUDm6eODAASXPhQsXMH36dFRUVOC1117D8ePHUVxcjAcPHiiLuqxYsQLA031O45NWdTTsp59+gojUui1atKhOzu3u7o5hw4bhyJEjyjMw09PTTR4nYqtVq1Zh5cqVACpv2zV3GzHw/9fbrl07m65VRCw+WsQcX19fZd/a6HJoaCguXryIL774AlOmTEGHDh1QVFSE5ORkxMXFoV27djhz5ozF8sbAv+r5iIgaIgaTRET0zJgyZYqyb7xtEwB27doFg8GA4OBgJCYmolu3bsrtsUY5OTlPrZ3WWFsR1ZhmaSXQ6oy33gKwGrw8SQ4ODoiNjVVeV/1cbLFv3z5Mnz4dADB//nxlRNqcpk2bAgCuX79udYT5UVWdJ1nbSK9Go0F0dDTWrl2LM2fO4O7du1izZg18fHxw69Yt5YcPc4x1W5qXSUTUUDCYJCKiZ4a7u7uyX/U2xFu3bgGoHDFycDD/pys5OfnJNs5G1hbAMaZ17drVproaNWqEkJAQAPYt2lPXLH0utTl9+jRGjx4Ng8GAUaNG4eOPP7aav3fv3gAqF7/ZvXv3ozXWijZt2ii3Kl+/ft2usr6+vpg6dSoSEhIAACdPnrQ4X1On0wEAgoODH6O1RETPPgaTRET0xOl0OpueLbl582Zl3/isPqByniVQOTpn7jbW/fv3IzU19fEbWgeWL19udq5cSkoKfvjhBwDA6NGjba7POFp76NChWgNKW+dVGp09e9amZ0tu2bJF2e/cubNNdWdnZ2PIkCHQ6/Xo0aMHNm/ebHHxI6OuXbsq9X/44Ye1LmBj7/VqtVqlX1la5KmkpMRqHVVX6jUuUlTdsWPHAFSuAExE1JAxmCQioifu3LlzCA4ORmRkJLZs2YIbN24oaWVlZTh58iTeeOMNZd5j9+7dTebVGVfvPHfuHN59910liCgoKMDatWsxcuTIZ2Z+WnZ2NiIjI5XbQcvLy7Fr1y6MHDkSQGWQHB0dbXN9b7/9Nnr06AEAmDBhAubPn6+M1AKVC9CkpqZi2rRpyuNNbJWamoqWLVti9OjR2LlzJ7Kzs5U048JIw4YNwxdffAEAGDlyJAIDA2utt6SkBEOGDEFmZiaaN2+Or7/+2mQxJUtUKhXWrFkDZ2dn3Lx5Ez169MCuXbtMFtnJysrCtm3bEBERgTlz5th1vQDQt29fAP8f8FWXmJiI3r17Y+3atSajlwaDAQcPHsTcuXMBAD179qyxGi0A3L59Gzdv3gTAYJKIngNP86GWRET0fDpw4IDysHjjptFoxMfHR1QqlcnxLl26SFZWVo06xowZY5LP29tb1Gq1AJCwsDBZuXKlAJDAwMAaZWNiYgSAxMTEWGxjnz59BIAsXLjQYp7AwEABIBs3bjQ5npKSorTrq6++EicnJwEgXl5e4uzsrKQ1b95crl+/XqPequXNuXv3rrzyyism1+/p6Sne3t4m75+jo6PFtpuzZs2aGp+Li4uLNGrUqMbxAQMGSF5enk3viU6nU8pptVrx9/e3uHXt2rVGnd999534+voqdajVavH19RU3NzeTNsXGxtp1vSIiJ0+eFADi6upq9no2btxocg5nZ2fx9fUVBwcH5VhAQIBcuHDBbP1r164VANKpUye720ZE9FvDR4MQEdETN3DgQFy5cgX79u1DWloazp49i8zMTOTm5sLNzQ0BAQHo3LkzoqOj8dprr5mdF/mvf/0Lv//977FhwwZcunQJBoMBHTt2xOjRozFjxgxs3769Hq6spqioKBw9ehR//etfkZaWhqKiIrRo0QLR0dH48MMP0ahRI7vr9PPzQ3JyMvbs2YOtW7fi2LFjyuNFmjVrho4dOyIyMhLDhw+3q96pU6eiZ8+eOHDgAI4ePYpz584hOzsbeXl58PDwwIsvvoiuXbtizJgxGDRokN3tBipHj60tpmNuxDIiIgJXr17FmjVrsHfvXpw/fx65ublwdXVFSEgIevbsiaioKERERNjdnk6dOqF79+44fvw4vvzyS0yaNMkkfdiwYdiyZQtSUlJw4sQJZGdn4/79+/Dw8EDbtm0xdOhQTJs2zeyoJFDZT4HK95aIqKFTiTSgNdSJiIjqQWpqqvL4DP5ZffZt2bIFMTEx6NevHw4fPlxn9d64cQMtW7aEh4cHMjMzrT6zlIioIeCcSSIiInqujBs3DiEhIUhJSbG4EM+jSEhIgIhg3rx5DCSJ6LnAYJKIiIieK2q1GkuXLgUALFq0qE7qvHXrFjZs2IDmzZsrz9UkImroOGeSiIiInjuRkZH49NNPkZubC71eb/IszUeRkZGBefPmoV+/fjatXEtE1BBwziQREdFj4pxJIiJ6HjGYJCIiIiIiIrtxziQRERERERHZjcEkERERERER2Y3BJBEREREREdmNwSQRERERERHZjcEkERERERER2Y3BJBEREREREdmNwSQRERERERHZjcEkERERERER2Y3BJBEREREREdnt/wBjRkxIx1fxAwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Smooth the curves using a rolling average with window size\n",
    "window_size =1\n",
    "\n",
    "smoothed_lev_mis = np.convolve(mean_lev_misclassification_rate_array, np.ones(window_size)/window_size, mode='valid')\n",
    "smoothed_l2s_mis = np.convolve(mean_l2s_misclassification_rate_array, np.ones(window_size)/window_size, mode='valid')\n",
    "smoothed_unif_mis = np.convolve(mean_unif_misclassification_rate_array, np.ones(window_size)/window_size, mode='valid')\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "optimal_misclassification_rate = np.full(len(s_values[window_size-1:]), opt_misclassification_rate)\n",
    "\n",
    "\n",
    "\n",
    "plt.plot(s_values[window_size-1:], smoothed_unif_mis, label=\"Uniform Sampling\", color='b')\n",
    "plt.plot(s_values[window_size-1:], smoothed_l2s_mis, label=\"L2S Sampling\", color='g')\n",
    "plt.plot(s_values[window_size-1:], smoothed_lev_mis, label=\"Leverage Score (this work)\", color='r')\n",
    "\n",
    "plt.plot(s_values[window_size-1:], optimal_misclassification_rate, label=\"Full Data\", color='gray', linestyle='-',linewidth=2)\n",
    "\n",
    "\n",
    "plt.xlabel(\"Sample Size (s)\", fontsize=18)\n",
    "plt.ylabel(\"Misclassification rate\",fontsize=18)\n",
    "plt.legend(fontsize=18, loc='upper right', bbox_to_anchor=(1, 1))\n",
    "plt.grid(True, which='both')\n",
    "plt.yscale('log')  # Set y-axis to log scale\n",
    "# plt.xscale('log')\n",
    "\n",
    "plt.xticks(fontsize=18)\n",
    "plt.yticks(fontsize=18)\n",
    "plt.savefig('plots\\\\mis_churn.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15b2f199",
   "metadata": {},
   "source": [
    "# **Plot relative error subsampled NLL for the various sampling methods**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "bcc70e89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Smooth the curves using a rolling average with window size 5\n",
    "window_size =1\n",
    "\n",
    "smoothed_lev_NLL = np.convolve(mean_rel_lev_neg_log_likelihood_array, np.ones(window_size)/window_size, mode='valid')\n",
    "smoothed_l2s_NLL = np.convolve(mean_rel_l2s_neg_log_likelihood_array, np.ones(window_size)/window_size, mode='valid')\n",
    "smoothed_unif_NLL = np.convolve(mean_rel_unif_neg_log_likelihood_array, np.ones(window_size)/window_size, mode='valid')\n",
    "\n",
    "# Plot the mean values for misclassification rates with smoothed curves\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "\n",
    "plt.plot(s_values[window_size-1:], smoothed_unif_NLL, label=\"Uniform Sampling\", color='b')\n",
    "plt.plot(s_values[window_size-1:], smoothed_l2s_NLL, label=\"L2S Sampling\", color='g')\n",
    "plt.plot(s_values[window_size-1:], smoothed_lev_NLL, label=\"Leverage Score(this work)\", color='r')\n",
    "\n",
    "plt.xlabel(\"Sample Size (s)\", fontsize=18)\n",
    "plt.ylabel(\"Relative error subsampled NLL.\",fontsize=18)\n",
    "plt.legend(fontsize=18)\n",
    "plt.grid(True, which='both')\n",
    "plt.legend(fontsize=18, loc='upper right', bbox_to_anchor=(1, 0.8))\n",
    "plt.yscale('log')  # Set y-axis to log scale\n",
    "# plt.xscale('log')\n",
    "\n",
    "plt.xticks(fontsize=18)\n",
    "plt.yticks(fontsize=18)\n",
    "plt.savefig('plots\\\\subsampled_NLL_churn.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acaf6c1c",
   "metadata": {},
   "source": [
    "# **Plot relative error full-data NLL for the various sampling methods**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "69881306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Smooth the curves using a rolling average with window size 5\n",
    "window_size =1\n",
    "\n",
    "smoothed_lev_true_NLL = np.convolve(mean_rel_lev_true_neg_log_likelihood_array, np.ones(window_size)/window_size, mode='valid')\n",
    "smoothed_l2s_true_NLL = np.convolve(mean_rel_l2s_true_neg_log_likelihood_array, np.ones(window_size)/window_size, mode='valid')\n",
    "smoothed_unif_true_NLL = np.convolve(mean_rel_unif_true_neg_log_likelihood_array, np.ones(window_size)/window_size, mode='valid')\n",
    "\n",
    "# Plot the mean values for misclassification rates with smoothed curves\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "\n",
    "plt.plot(s_values[window_size-1:], smoothed_unif_true_NLL, label=\"Uniform Sampling\", color='b')\n",
    "plt.plot(s_values[window_size-1:], smoothed_l2s_true_NLL, label=\"L2S Sampling\", color='g')\n",
    "plt.plot(s_values[window_size-1:], smoothed_lev_true_NLL, label=\"Leverage Score(this work)\", color='r')\n",
    "\n",
    "plt.xlabel(\"Sample Size (s)\", fontsize=18)\n",
    "plt.ylabel(\"Relative error full-data NLL.\",fontsize=18)\n",
    "plt.legend(fontsize=18)\n",
    "plt.grid(True, which='both')\n",
    "plt.legend(fontsize=18, loc='upper right', bbox_to_anchor=(1, 1))\n",
    "plt.yscale('log')  # Set y-axis to log scale\n",
    "# plt.xscale('log')\n",
    "\n",
    "plt.xticks(fontsize=18)\n",
    "plt.yticks(fontsize=18)\n",
    "plt.savefig('plots\\\\fulldata_NLL_churn.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e32e9931",
   "metadata": {},
   "source": [
    "# **Plot the standard deviations**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4dda42c8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Smooth the curves using a rolling average with window size\n",
    "window_size =1\n",
    "\n",
    "smoothed_std_lev_prob = np.convolve(std_rel_est_probs_lev_array, np.ones(window_size)/window_size, mode='valid')\n",
    "smoothed_std_l2s_prob = np.convolve(std_rel_est_probs_l2s_array, np.ones(window_size)/window_size, mode='valid')\n",
    "smoothed_std_unif_prob = np.convolve(std_rel_est_probs_unif_array, np.ones(window_size)/window_size, mode='valid')\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plt.plot(s_values[window_size-1:], smoothed_std_unif_prob, label=\"Uniform Sampling\", color='b')\n",
    "plt.plot(s_values[window_size-1:], smoothed_std_l2s_prob, label=\"L2S Sampling\", color='g')\n",
    "plt.plot(s_values[window_size-1:], smoothed_std_lev_prob, label=\"Leverage Score(this work)\", color='r')\n",
    "\n",
    "plt.xlabel(\"Sample Size (s)\", fontsize=18)\n",
    "plt.ylabel(\"Stdev. relative error est. probs.\",fontsize=18)\n",
    "plt.legend(fontsize=18)\n",
    "plt.grid(True, which='both')\n",
    "plt.yscale('log')  # Set y-axis to log scale\n",
    "# plt.xscale('log')\n",
    "\n",
    "plt.xticks(fontsize=18)\n",
    "plt.yticks(fontsize=18)\n",
    "plt.savefig('plots\\\\stdev_prob_churn.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "633a7d9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Smooth the curves using a rolling average with window size\n",
    "window_size =1\n",
    "\n",
    "smoothed_std_lev_mis = np.convolve(std_lev_misclassification_rate_array, np.ones(window_size)/window_size, mode='valid')\n",
    "smoothed_std_l2s_mis = np.convolve(std_l2s_misclassification_rate_array, np.ones(window_size)/window_size, mode='valid')\n",
    "smoothed_std_unif_mis = np.convolve(std_unif_misclassification_rate_array, np.ones(window_size)/window_size, mode='valid')\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "\n",
    "\n",
    "plt.plot(s_values[window_size-1:], smoothed_std_unif_mis, label=\"Uniform Sampling\", color='b')\n",
    "plt.plot(s_values[window_size-1:], smoothed_std_l2s_mis, label=\"L2S Sampling\", color='g')\n",
    "plt.plot(s_values[window_size-1:], smoothed_std_lev_mis, label=\"Leverage Score (this work)\", color='r')\n",
    "\n",
    "\n",
    "plt.xlabel(\"Sample Size (s)\", fontsize=18)\n",
    "plt.ylabel(\"Stdev. misclassification rate\",fontsize=18)\n",
    "plt.legend(fontsize=18, loc='upper right', bbox_to_anchor=(1, 1))\n",
    "plt.grid(True, which='both')\n",
    "plt.yscale('log')  # Set y-axis to log scale\n",
    "# plt.xscale('log')\n",
    "\n",
    "plt.xticks(fontsize=18)\n",
    "plt.yticks(fontsize=18)\n",
    "plt.savefig('plots\\\\stdev_mis_churn.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "da73ab76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Smooth the curves using a rolling average with window size 5\n",
    "window_size =1\n",
    "\n",
    "smoothed_std_lev_NLL = np.convolve(std_rel_lev_neg_log_likelihood_array, np.ones(window_size)/window_size, mode='valid')\n",
    "smoothed_std_l2s_NLL = np.convolve(std_rel_l2s_neg_log_likelihood_array, np.ones(window_size)/window_size, mode='valid')\n",
    "smoothed_std_unif_NLL = np.convolve(std_rel_unif_neg_log_likelihood_array, np.ones(window_size)/window_size, mode='valid')\n",
    "\n",
    "# Plot the mean values for misclassification rates with smoothed curves\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "\n",
    "plt.plot(s_values[window_size-1:], smoothed_std_unif_NLL, label=\"Uniform Sampling\", color='b')\n",
    "plt.plot(s_values[window_size-1:], smoothed_std_l2s_NLL, label=\"L2S Sampling\", color='g')\n",
    "plt.plot(s_values[window_size-1:], smoothed_std_lev_NLL, label=\"Leverage Score(this work)\", color='r')\n",
    "\n",
    "plt.xlabel(\"Sample Size (s)\", fontsize=18)\n",
    "plt.ylabel(\"Stdev. relative error subsampled NLL\",fontsize=18)\n",
    "plt.legend(fontsize=18)\n",
    "plt.grid(True, which='both')\n",
    "plt.legend(fontsize=18, loc='upper right', bbox_to_anchor=(1, 1))\n",
    "plt.yscale('log')  # Set y-axis to log scale\n",
    "# plt.xscale('log')\n",
    "\n",
    "plt.xticks(fontsize=18)\n",
    "plt.yticks(fontsize=18)\n",
    "plt.savefig('plots\\\\stdev_subsampled_NLL_churn.png', dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "78d658ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Smooth the curves using a rolling average with window size 5\n",
    "window_size =1\n",
    "\n",
    "smoothed_std_lev_true_NLL = np.convolve(std_rel_lev_true_neg_log_likelihood_array, np.ones(window_size)/window_size, mode='valid')\n",
    "smoothed_std_l2s_true_NLL = np.convolve(std_rel_l2s_true_neg_log_likelihood_array, np.ones(window_size)/window_size, mode='valid')\n",
    "smoothed_std_unif_true_NLL = np.convolve(std_rel_unif_true_neg_log_likelihood_array, np.ones(window_size)/window_size, mode='valid')\n",
    "\n",
    "# Plot the mean values for misclassification rates with smoothed curves\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "\n",
    "plt.plot(s_values[window_size-1:], smoothed_std_unif_true_NLL, label=\"Uniform Sampling\", color='b')\n",
    "plt.plot(s_values[window_size-1:], smoothed_std_l2s_true_NLL, label=\"L2S Sampling\", color='g')\n",
    "plt.plot(s_values[window_size-1:], smoothed_std_lev_true_NLL, label=\"Leverage Score(this work)\", color='r')\n",
    "\n",
    "plt.xlabel(\"Sample Size (s)\", fontsize=18)\n",
    "plt.ylabel(\"Stdev. relative error full-data NLL\",fontsize=18)\n",
    "plt.legend(fontsize=18)\n",
    "plt.grid(True, which='both')\n",
    "plt.legend(fontsize=18, loc='upper right', bbox_to_anchor=(1, 1))\n",
    "plt.yscale('log')  # Set y-axis to log scale\n",
    "# plt.xscale('log')\n",
    "\n",
    "plt.xticks(fontsize=18)\n",
    "plt.yticks(fontsize=18)\n",
    "plt.savefig('plots\\\\stdev_fulldata_NLL_churn.png', dpi=300)\n",
    "plt.show()"
   ]
  }
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