original_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Circle, Rectangle, Arc\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import Dataset,DataLoader\n",
    "import torch.optim as optim\n",
    "from tqdm import tqdm\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "from sklearn.model_selection import KFold\n",
    "import random\n",
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "shots_df = pd.read_csv(\"data/shot_logs.csv\")\n",
    "better_shots_df = shots_df[[\"CLOSEST_DEFENDER_PLAYER_ID\",\"player_id\",\"LOCATION\",\"W\",\"FINAL_MARGIN\",\"SHOT_NUMBER\",\"PERIOD\",\"SHOT_CLOCK\",\"DRIBBLES\",\"TOUCH_TIME\",\"SHOT_DIST\",\"PTS_TYPE\",\"CLOSE_DEF_DIST\",\"FGM\"]]\n",
    "input_size = 13\n",
    "\n",
    "class MakeDataset(Dataset):\n",
    "    def __init__(self, features, labels):\n",
    "        self.features = torch.tensor(features.values,dtype=torch.float32)\n",
    "        self.labels = torch.tensor(labels.values,dtype=torch.float32)\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.features)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.features[idx], self.labels[idx]\n",
    "\n",
    "def shots_data_processing(df):\n",
    "    # select columns, numerical encoding\n",
    "    df_outcome = df[\"FGM\"]\n",
    "    if \"LOCATION\" in df.columns:\n",
    "        df.loc[:,\"LOCATION\"] = df[\"LOCATION\"] == 'H'\n",
    "        df.loc[:,\"LOCATION\"] = df.loc[:,\"LOCATION\"].astype(int)\n",
    "    if \"W\" in df.columns:\n",
    "        df.loc[:,\"W\"] = df[\"W\"] == 'H'\n",
    "        df.loc[:,\"W\"] = df.loc[:,\"W\"].astype(int)\n",
    "    df = df.astype(\"float64\")\n",
    "    # target encoding for player id, closest defender id\n",
    "    if \"player_id\" in df.columns:\n",
    "        df.loc[:,\"player_id\"] = df[\"player_id\"].map(df.groupby(\"player_id\")[\"FGM\"].mean())\n",
    "    if \"CLOSEST_DEFENDER_PLAYER_ID\" in df.columns:\n",
    "        df.loc[:,\"CLOSEST_DEFENDER_PLAYER_ID\"] = df[\"CLOSEST_DEFENDER_PLAYER_ID\"].map(df.groupby(\"CLOSEST_DEFENDER_PLAYER_ID\")[\"FGM\"].mean())\n",
    "\n",
    "    # scaling to [-1,1]\n",
    "    def scaling_func(x):\n",
    "        if not (x.max() == 1 and x.min() == 0):\n",
    "            return 2 * (x - x.min()) / (x.max() - x.min()) - 1\n",
    "        return x\n",
    "    df_scaled = df.apply(scaling_func).astype(\"float64\")\n",
    "\n",
    "    df_scaled = df_scaled.fillna(0)\n",
    "\n",
    "    return MakeDataset(df_scaled.drop(\"FGM\",axis=1),df_outcome)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DeepnnModel(nn.Module):\n",
    "    def __init__(self, input_size, hidden_layer_size, num_hidden_layers):\n",
    "        super(DeepnnModel, self).__init__()\n",
    "        self.num_hidden_layers = num_hidden_layers\n",
    "        self.layers = nn.ModuleList()\n",
    "        self.layers.append(nn.Linear(input_size, hidden_layer_size))\n",
    "        for _ in range(num_hidden_layers):\n",
    "            self.layers.append(nn.Linear(hidden_layer_size, hidden_layer_size))\n",
    "        self.layers.append(nn.Linear(hidden_layer_size, 1))\n",
    "        self.relu = nn.LeakyReLU(negative_slope=0.01, inplace=False)\n",
    "        self._initialize_weights()\n",
    "\n",
    "    def forward(self, x):\n",
    "        for i in range(len(self.layers) - 1):  # Exclude the last layer for activation\n",
    "            x = self.relu(self.layers[i](x))\n",
    "        x = self.layers[-1](x)\n",
    "        return x\n",
    "\n",
    "    def _initialize_weights(self):\n",
    "        # Apply He initialization to all layers with ReLU/LeakyReLU activations\n",
    "        for layer in self.layers:\n",
    "            if isinstance(layer, nn.Linear):\n",
    "                nn.init.kaiming_normal_(layer.weight, mode='fan_in', nonlinearity='leaky_relu')\n",
    "                nn.init.zeros_(layer.bias)\n",
    "        \n",
    "class LogRegModel(nn.Module):\n",
    "    def __init__(self,input_size):\n",
    "        super(LogRegModel,self).__init__()\n",
    "        self.fc1 = nn.Linear(input_size,1)\n",
    "    def forward(self,x):\n",
    "        f1 = self.fc1(x)\n",
    "        return f1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(initialized_model, train_data, num_epochs, print_fold_progress = False, epoch_progress_bar = False, print_roc_curve = True):\n",
    "    # 10 folds for cross validation\n",
    "    k_folds = 10\n",
    "    kf = KFold(n_splits=k_folds, shuffle=True)\n",
    "\n",
    "    acc = []\n",
    "    auc_scores = []\n",
    "    auc_labels = []\n",
    "    auc_probs = []\n",
    "    # Store and return models for test set validation\n",
    "    models = []\n",
    "\n",
    "    batch_size = 64\n",
    "\n",
    "    # k-fold cross validation\n",
    "    for fold, (train_idx, val_idx) in enumerate(kf.split(train_data)):\n",
    "        if print_fold_progress: print(f\"Fold {fold + 1}/{k_folds}\")\n",
    "\n",
    "        train_split = train_data.iloc[train_idx]\n",
    "        val_split = train_data.iloc[val_idx]\n",
    "\n",
    "        processed_train_data = shots_data_processing(train_split)\n",
    "        trainloader = DataLoader(processed_train_data, batch_size, shuffle=True)\n",
    "\n",
    "        # BCEWithLogitsLoss(), Adam\n",
    "        # Very important to deepcopy the model instead of shallow copying\n",
    "        model = copy.deepcopy(initialized_model)\n",
    "        criterion = nn.BCEWithLogitsLoss()\n",
    "        optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "        progress_bar = lambda x:x\n",
    "        # tqdm is acting up, doesn't seem to be working well\n",
    "        if epoch_progress_bar: progress_bar = tqdm\n",
    "        for epoch in progress_bar(range(num_epochs)):\n",
    "            model.train()\n",
    "            for i, data in enumerate(trainloader, 0):\n",
    "                inputs, labels = data\n",
    "                optimizer.zero_grad()\n",
    "                outputs = model(inputs)\n",
    "                loss = criterion(outputs.squeeze(-1), labels)\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "\n",
    "        models.append(model)\n",
    "        with torch.no_grad():\n",
    "            correct, total, probs, labels = validate(model,val_split,False,False)\n",
    "            acc.append(correct / total)\n",
    "            auc = roc_auc_score(labels, probs)\n",
    "            auc_scores.append(roc_auc_score(labels,probs))\n",
    "            auc_labels.extend(labels)\n",
    "            auc_probs.extend(probs)\n",
    "\n",
    "    # Calculate and print average accuracy and AUC across all folds\n",
    "    print(f\"Average accuracy: {sum(acc) / len(acc)}\")\n",
    "    print(f\"Average AUC: {sum(auc_scores) / len(auc_scores)}\")\n",
    "\n",
    "    if print_roc_curve:\n",
    "        # Plot the final ROC curve based on all the labels and probabilities across all folds\n",
    "        fpr, tpr, thresholds = roc_curve(auc_labels, auc_probs)\n",
    "        plt.figure(figsize=(8, 6))\n",
    "        plt.plot(fpr, tpr, color='b', label=f'ROC curve (AUC = {sum(auc_scores) / len(auc_scores):.2f})')\n",
    "        plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
    "        plt.xlim([0.0, 1.0])\n",
    "        plt.ylim([0.0, 1.05])\n",
    "        plt.xlabel('False Positive Rate')\n",
    "        plt.ylabel('True Positive Rate')\n",
    "        plt.title('ROC Curve')\n",
    "        plt.legend(loc=\"lower right\")\n",
    "        plt.grid(True)\n",
    "        plt.show()\n",
    "\n",
    "    return acc, auc_scores, models\n",
    "\n",
    "def validate(model,val_data,print_roc_curve=True,print_acc_auc=True,use_optimal_threshold=True):\n",
    "    # With smaller batch sizes, there may be no true/false cases at all, causing an error when calculating tpr/fpr\n",
    "    batch_size = 1000\n",
    "\n",
    "    processed_val_data = shots_data_processing(val_data)\n",
    "    valloader = DataLoader(processed_val_data, batch_size, shuffle=False)\n",
    "\n",
    "    correct, total = 0, 0\n",
    "    probs, labels = [], []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for data in valloader:\n",
    "            features, outcome = data\n",
    "            outputs = model(features)\n",
    "            probabilities = torch.sigmoid(outputs.squeeze(-1))  # Apply sigmoid to get probabilities\n",
    "            # Threshold at 0.5 by default\n",
    "            threshold = 0.5\n",
    "            if use_optimal_threshold:\n",
    "                fpr, tpr, thresholds = roc_curve(outcome.tolist(), probabilities.tolist())\n",
    "                # Calculate Youden's J scores\n",
    "                j_scores = tpr - fpr\n",
    "                # Find the threshold with the maximum J statistic\n",
    "                threshold = thresholds[np.argmax(j_scores)]\n",
    "            predicted = (probabilities > threshold).int()  \n",
    "\n",
    "            total += outcome.size(0)\n",
    "            correct += (predicted == outcome).sum().item()\n",
    "\n",
    "            probs.extend(probabilities.tolist())\n",
    "            labels.extend(outcome.tolist())\n",
    "\n",
    "    if print_acc_auc:\n",
    "        print(f\"acc: {correct / total}\")\n",
    "        print(f\"AUC: {roc_auc_score(labels,probs)}\")\n",
    "\n",
    "    if print_roc_curve:\n",
    "        fpr, tpr, thresholds = roc_curve(labels, probs)\n",
    "        plt.figure(figsize=(8, 6))\n",
    "        plt.plot(fpr, tpr, color='b', label=f'ROC curve (AUC = {roc_auc_score(labels,probs):.2f})')\n",
    "        plt.plot([0, 1], [0, 1], color='gray', linestyle='--')\n",
    "        plt.xlim([0.0, 1.0])\n",
    "        plt.ylim([0.0, 1.05])\n",
    "        plt.xlabel('False Positive Rate')\n",
    "        plt.ylabel('True Positive Rate')\n",
    "        plt.title('ROC Curve')\n",
    "        plt.legend(loc=\"lower right\")\n",
    "        plt.grid(True)\n",
    "        plt.show()\n",
    "\n",
    "    return correct, total, probs, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy: 0.6169759209270526\n",
      "Average AUC: 0.639271749382796\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average acc, auc for test set: 0.6101827268467905, 0.6315078766032591\n"
     ]
    }
   ],
   "source": [
    "input_size = 13\n",
    "\n",
    "model = LogRegModel(input_size)\n",
    "train_data = better_shots_df.sample(frac=0.95)\n",
    "acc, auc_scores, models = train(model,train_data,3)\n",
    "correct,total,labels,probs = 0,0,[],[]\n",
    "for model in models:\n",
    "    c,t,p,l = validate(model,better_shots_df.drop(train_data.index),False,False,True)\n",
    "    correct,total = correct+c,total+t\n",
    "    labels.extend(l)\n",
    "    probs.extend(p)\n",
    "print(f\"average acc, auc for test set: {correct/total}, {roc_auc_score(labels,probs)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dropped variable: CLOSEST_DEFENDER_PLAYER_ID\n",
      "Average accuracy: 0.6169185767400174\n",
      "Average AUC: 0.6391786290816638\n",
      "acc, AUC on test set: 0.6140871466500079, 0.6436330293503771\n",
      "-----------------------------\n",
      "dropped variable: player_id\n",
      "Average accuracy: 0.6106554056247822\n",
      "Average AUC: 0.6272666623340897\n",
      "acc, AUC on test set: 0.6012806496954552, 0.613526595034112\n",
      "-----------------------------\n",
      "dropped variable: LOCATION\n",
      "Average accuracy: 0.6218006415214988\n",
      "Average AUC: 0.6472325407123745\n",
      "acc, AUC on test set: 0.6287677651101047, 0.6490534709145312\n",
      "-----------------------------\n",
      "dropped variable: W\n",
      "Average accuracy: 0.6176909968525179\n",
      "Average AUC: 0.6405436815931834\n",
      "acc, AUC on test set: 0.6076838981727315, 0.6382154662396821\n",
      "-----------------------------\n",
      "dropped variable: FINAL_MARGIN\n",
      "Average accuracy: 0.61843080732695\n",
      "Average AUC: 0.6364399591727516\n",
      "acc, AUC on test set: 0.6301733562392628, 0.644398713971881\n",
      "-----------------------------\n",
      "dropped variable: SHOT_NUMBER\n",
      "Average accuracy: 0.6226390199808649\n",
      "Average AUC: 0.649125833866113\n",
      "acc, AUC on test set: 0.6214274558800562, 0.6498162219539599\n",
      "-----------------------------\n",
      "dropped variable: PERIOD\n",
      "Average accuracy: 0.6191705279509186\n",
      "Average AUC: 0.6437668019176602\n",
      "acc, AUC on test set: 0.6181477432453537, 0.6435798384426152\n",
      "-----------------------------\n",
      "dropped variable: SHOT_CLOCK\n",
      "Average accuracy: 0.6165814186188345\n",
      "Average AUC: 0.6404662101135465\n",
      "acc, AUC on test set: 0.617054505700453, 0.638525499339432\n",
      "-----------------------------\n",
      "dropped variable: DRIBBLES\n",
      "Average accuracy: 0.620124002151493\n",
      "Average AUC: 0.6446689391794853\n",
      "acc, AUC on test set: 0.6076838981727315, 0.6437031351548926\n",
      "-----------------------------\n",
      "dropped variable: TOUCH_TIME\n",
      "Average accuracy: 0.618841710384772\n",
      "Average AUC: 0.6456359013350628\n",
      "acc, AUC on test set: 0.6312665937841637, 0.6474772170170721\n",
      "-----------------------------\n",
      "dropped variable: SHOT_DIST\n",
      "Average accuracy: 0.602764919666052\n",
      "Average AUC: 0.6292263235024551\n",
      "acc, AUC on test set: 0.6108074340153054, 0.6307411083295065\n",
      "-----------------------------\n",
      "dropped variable: PTS_TYPE\n",
      "Average accuracy: 0.6220225329829228\n",
      "Average AUC: 0.6481354151367965\n",
      "acc, AUC on test set: 0.6301733562392628, 0.6583964045613867\n",
      "-----------------------------\n",
      "dropped variable: CLOSE_DEF_DIST\n",
      "Average accuracy: 0.613162106543977\n",
      "Average AUC: 0.6334767606043527\n",
      "acc, AUC on test set: 0.617054505700453, 0.6445849964272335\n",
      "-----------------------------\n"
     ]
    }
   ],
   "source": [
    "input_size = 12\n",
    "\n",
    "for var in [\"CLOSEST_DEFENDER_PLAYER_ID\",\"player_id\",\"LOCATION\",\"W\",\"FINAL_MARGIN\",\"SHOT_NUMBER\",\"PERIOD\",\"SHOT_CLOCK\",\"DRIBBLES\",\"TOUCH_TIME\",\"SHOT_DIST\",\"PTS_TYPE\",\"CLOSE_DEF_DIST\"]:\n",
    "    model = LogRegModel(input_size)\n",
    "    train_data = better_shots_df.sample(frac=0.95).drop(var,axis=1)\n",
    "    print(f\"dropped variable: {var}\")\n",
    "    _, _, models = train(model,train_data,3,print_roc_curve = False)\n",
    "    correct,total,probs,labels = validate(models[random.randint(0,9)],better_shots_df.drop(var,axis=1).drop(train_data.index),False,False,True)\n",
    "    print(f\"acc, AUC on test set: {correct/total}, {roc_auc_score(labels,probs)}\")\n",
    "    print(\"-----------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "variable: LOCATION\n",
      "Average accuracy: 0.5473754781007332\n",
      "Average AUC: 0.503948583261057\n",
      "-----------------------------\n",
      "variable: W\n",
      "Average accuracy: 0.5477043618724842\n",
      "Average AUC: 0.5\n",
      "-----------------------------\n",
      "variable: CLOSEST_DEFENDER_PLAYER_ID\n",
      "Average accuracy: 0.5813949624912148\n",
      "Average AUC: 0.6060515005222891\n",
      "-----------------------------\n",
      "variable: player_id\n",
      "Average accuracy: 0.5762580432884941\n",
      "Average AUC: 0.5996204152880473\n",
      "-----------------------------\n",
      "variable: FINAL_MARGIN\n",
      "Average accuracy: 0.5350961469540673\n",
      "Average AUC: 0.5338624316904258\n",
      "-----------------------------\n",
      "variable: SHOT_NUMBER\n",
      "Average accuracy: 0.5155590953185193\n",
      "Average AUC: 0.4974685647492956\n",
      "-----------------------------\n",
      "variable: PERIOD\n",
      "Average accuracy: 0.5150001011324388\n",
      "Average AUC: 0.5076673611811133\n",
      "-----------------------------\n",
      "variable: SHOT_CLOCK\n",
      "Average accuracy: 0.5585702459231501\n",
      "Average AUC: 0.5517459173382082\n",
      "-----------------------------\n",
      "variable: DRIBBLES\n",
      "Average accuracy: 0.52309573799259\n",
      "Average AUC: 0.5233931855640306\n",
      "-----------------------------\n",
      "variable: TOUCH_TIME\n",
      "Average accuracy: 0.510019042082998\n",
      "Average AUC: 0.4632305715554786\n",
      "-----------------------------\n",
      "variable: SHOT_DIST\n",
      "Average accuracy: 0.6121595956086012\n",
      "Average AUC: 0.6161121354132431\n",
      "-----------------------------\n",
      "variable: PTS_TYPE\n",
      "Average accuracy: 0.5476632948062203\n",
      "Average AUC: 0.553774943475634\n",
      "-----------------------------\n",
      "variable: CLOSE_DEF_DIST\n",
      "Average accuracy: 0.5120741741651382\n",
      "Average AUC: 0.49128414829224354\n",
      "-----------------------------\n"
     ]
    }
   ],
   "source": [
    "input_size = 1\n",
    "variables = [\n",
    "    \"LOCATION\",\"W\",\"CLOSEST_DEFENDER_PLAYER_ID\", \"player_id\", \"FINAL_MARGIN\", \"SHOT_NUMBER\", \"PERIOD\",\n",
    "    \"SHOT_CLOCK\", \"DRIBBLES\", \"TOUCH_TIME\", \"SHOT_DIST\", \"PTS_TYPE\", \"CLOSE_DEF_DIST\"\n",
    "]\n",
    "acc,auc = [],[]\n",
    "for var in variables:\n",
    "    model = LogRegModel(input_size)\n",
    "    train_data = better_shots_df.sample(frac=0.95)[[var,\"FGM\"]]\n",
    "    print(f\"variable: {var}\")\n",
    "    acc_scores, auc_scores, models = train(model,train_data,3,print_roc_curve = False)\n",
    "    acc.append(sum(acc_scores)/len(acc_scores))\n",
    "    auc.append(sum(auc_scores)/len(auc_scores))\n",
    "    print(\"-----------------------------\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in reversed(range(len(variables))):\n",
    "    if auc[i]<0.505:\n",
    "        variables.pop(i)\n",
    "        acc.pop(i)\n",
    "        auc.pop(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = range(len(variables))\n",
    "fig, ax = plt.subplots(figsize=(12, 6))\n",
    "bar_width = 0.4\n",
    "ax.bar(x, acc, width=bar_width, label=\"Accuracy\", color=\"skyblue\", edgecolor=\"black\")\n",
    "ax.bar([i + bar_width for i in x], auc, width=bar_width, label=\"AUC\", color=\"orange\", edgecolor=\"black\")\n",
    "ax.set_xticks([i + bar_width / 2 for i in x])\n",
    "ax.set_xticklabels(variables, rotation=45, ha=\"right\")\n",
    "ax.set_ylabel(\"Scores\")\n",
    "ax.set_xlabel(\"Variables\")\n",
    "ax.set_title(\"Average Accuracy and AUC Scores by Variable\")\n",
    "ax.legend()\n",
    "ax.grid(axis=\"y\", linestyle=\"--\", alpha=0.7)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy: 0.6282939433180852\n",
      "Average AUC: 0.6508077693578038\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average acc, auc for test set: 0.6324066843667031, 0.6539802740508978\n"
     ]
    }
   ],
   "source": [
    "hidden_layer_size = 256\n",
    "num_hidden_layers = 1\n",
    "batch_size = 64\n",
    "input_size = 13\n",
    "\n",
    "model = DeepnnModel(input_size,hidden_layer_size,num_hidden_layers)\n",
    "train_data = better_shots_df.sample(frac=0.95)\n",
    "\n",
    "acc_scores,auc_scores,models = train(model,train_data,3)\n",
    "correct,total,labels,probs = 0,0,[],[]\n",
    "for model in models:\n",
    "    c,t,p,l = validate(model,better_shots_df.drop(train_data.index),False,False,True)\n",
    "    correct,total = correct+c,total+t\n",
    "    labels.extend(l)\n",
    "    probs.extend(p)\n",
    "print(f\"average acc, auc for test set: {correct/total}, {roc_auc_score(labels,probs)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# hidden layers: 0, # neurons/layer: 1\n",
      "Average accuracy: 0.6090779104884184\n",
      "Average AUC: 0.6315976589612343\n",
      "--------------------------------\n",
      "# hidden layers: 1, # neurons/layer: 1\n",
      "Average accuracy: 0.6222114372800064\n",
      "Average AUC: 0.6413124715213965\n",
      "--------------------------------\n",
      "# hidden layers: 2, # neurons/layer: 1\n",
      "Average accuracy: 0.603471542904394\n",
      "Average AUC: 0.6351546061927796\n",
      "--------------------------------\n",
      "# hidden layers: 3, # neurons/layer: 1\n",
      "Average accuracy: 0.562454530901902\n",
      "Average AUC: 0.535616720534291\n",
      "--------------------------------\n",
      "# hidden layers: 4, # neurons/layer: 1\n",
      "Average accuracy: 0.5569497190848149\n",
      "Average AUC: 0.5278335102084405\n",
      "--------------------------------\n",
      "# hidden layers: 5, # neurons/layer: 1\n",
      "Average accuracy: 0.5930241325520617\n",
      "Average AUC: 0.5989422732327744\n",
      "--------------------------------\n",
      "# hidden layers: 6, # neurons/layer: 1\n",
      "Average accuracy: 0.5856454419028247\n",
      "Average AUC: 0.5773293159360844\n",
      "--------------------------------\n",
      "# hidden layers: 7, # neurons/layer: 1\n",
      "Average accuracy: 0.5478609145121263\n",
      "Average AUC: 0.49995655711686116\n",
      "--------------------------------\n",
      "# hidden layers: 8, # neurons/layer: 1\n",
      "Average accuracy: 0.584544099188494\n",
      "Average AUC: 0.593515814163199\n",
      "--------------------------------\n",
      "# hidden layers: 0, # neurons/layer: 2\n",
      "Average accuracy: 0.6141766900839175\n",
      "Average AUC: 0.6376584592496795\n",
      "--------------------------------\n",
      "# hidden layers: 1, # neurons/layer: 2\n",
      "Average accuracy: 0.5742373570911318\n",
      "Average AUC: 0.5879473609387682\n",
      "--------------------------------\n",
      "# hidden layers: 2, # neurons/layer: 2\n",
      "Average accuracy: 0.6175186386885949\n",
      "Average AUC: 0.6311981767200197\n",
      "--------------------------------\n",
      "# hidden layers: 3, # neurons/layer: 2\n",
      "Average accuracy: 0.6071414396027199\n",
      "Average AUC: 0.624545493499469\n",
      "--------------------------------\n",
      "# hidden layers: 4, # neurons/layer: 2\n",
      "Average accuracy: 0.6090467037874037\n",
      "Average AUC: 0.6251829538126848\n",
      "--------------------------------\n",
      "# hidden layers: 5, # neurons/layer: 2\n",
      "Average accuracy: 0.5987241031666304\n",
      "Average AUC: 0.6156924754739093\n",
      "--------------------------------\n",
      "# hidden layers: 6, # neurons/layer: 2\n",
      "Average accuracy: 0.5717073820795405\n",
      "Average AUC: 0.5887766138677416\n",
      "--------------------------------\n",
      "# hidden layers: 7, # neurons/layer: 2\n",
      "Average accuracy: 0.6063528974063104\n",
      "Average AUC: 0.6247948958343843\n",
      "--------------------------------\n",
      "# hidden layers: 8, # neurons/layer: 2\n",
      "Average accuracy: 0.614051807794233\n",
      "Average AUC: 0.6247423395157112\n",
      "--------------------------------\n",
      "# hidden layers: 0, # neurons/layer: 4\n",
      "Average accuracy: 0.6137316813445659\n",
      "Average AUC: 0.6343143559906845\n",
      "--------------------------------\n",
      "# hidden layers: 1, # neurons/layer: 4\n",
      "Average accuracy: 0.6230001252023991\n",
      "Average AUC: 0.6415590510402238\n",
      "--------------------------------\n",
      "# hidden layers: 2, # neurons/layer: 4\n",
      "Average accuracy: 0.6257564425426656\n",
      "Average AUC: 0.635665733926001\n",
      "--------------------------------\n",
      "# hidden layers: 3, # neurons/layer: 4\n",
      "Average accuracy: 0.6129195217831749\n",
      "Average AUC: 0.6293390650825526\n",
      "--------------------------------\n",
      "# hidden layers: 4, # neurons/layer: 4\n",
      "Average accuracy: 0.6165270392244715\n",
      "Average AUC: 0.6308067555850313\n",
      "--------------------------------\n",
      "# hidden layers: 5, # neurons/layer: 4\n",
      "Average accuracy: 0.6269589050654487\n",
      "Average AUC: 0.6487344757313019\n",
      "--------------------------------\n",
      "# hidden layers: 6, # neurons/layer: 4\n",
      "Average accuracy: 0.6168549635385664\n",
      "Average AUC: 0.6229575560376045\n",
      "--------------------------------\n",
      "# hidden layers: 7, # neurons/layer: 4\n",
      "Average accuracy: 0.6025501681208352\n",
      "Average AUC: 0.6064061953654761\n",
      "--------------------------------\n",
      "# hidden layers: 8, # neurons/layer: 4\n",
      "Average accuracy: 0.604283582958284\n",
      "Average AUC: 0.6191688818782566\n",
      "--------------------------------\n",
      "# hidden layers: 0, # neurons/layer: 8\n",
      "Average accuracy: 0.6181433842702349\n",
      "Average AUC: 0.6368129665114508\n",
      "--------------------------------\n",
      "# hidden layers: 1, # neurons/layer: 8\n",
      "Average accuracy: 0.625475374924602\n",
      "Average AUC: 0.6510023177834574\n",
      "--------------------------------\n",
      "# hidden layers: 2, # neurons/layer: 8\n",
      "Average accuracy: 0.6087109651461129\n",
      "Average AUC: 0.6288786524399175\n",
      "--------------------------------\n",
      "# hidden layers: 3, # neurons/layer: 8\n",
      "Average accuracy: 0.6249678247394697\n",
      "Average AUC: 0.6411666868211997\n",
      "--------------------------------\n",
      "# hidden layers: 4, # neurons/layer: 8\n",
      "Average accuracy: 0.5931491056918361\n",
      "Average AUC: 0.6067656665332075\n",
      "--------------------------------\n",
      "# hidden layers: 5, # neurons/layer: 8\n",
      "Average accuracy: 0.6251786573115219\n",
      "Average AUC: 0.6404677158682592\n",
      "--------------------------------\n",
      "# hidden layers: 6, # neurons/layer: 8\n",
      "Average accuracy: 0.6234685610703024\n",
      "Average AUC: 0.6411666206939943\n",
      "--------------------------------\n",
      "# hidden layers: 7, # neurons/layer: 8\n",
      "Average accuracy: 0.6239449137004021\n",
      "Average AUC: 0.6464085612654785\n",
      "--------------------------------\n",
      "# hidden layers: 8, # neurons/layer: 8\n",
      "Average accuracy: 0.6144578394060888\n",
      "Average AUC: 0.6317582705339029\n",
      "--------------------------------\n",
      "# hidden layers: 0, # neurons/layer: 16\n",
      "Average accuracy: 0.6238746207298369\n",
      "Average AUC: 0.648079232834921\n",
      "--------------------------------\n",
      "# hidden layers: 1, # neurons/layer: 16\n",
      "Average accuracy: 0.6234295607729846\n",
      "Average AUC: 0.6409319673319834\n",
      "--------------------------------\n",
      "# hidden layers: 2, # neurons/layer: 16\n",
      "Average accuracy: 0.6284190879526549\n",
      "Average AUC: 0.6506495953124063\n",
      "--------------------------------\n",
      "# hidden layers: 3, # neurons/layer: 16\n",
      "Average accuracy: 0.6090700309198829\n",
      "Average AUC: 0.6307651775156661\n",
      "--------------------------------\n",
      "# hidden layers: 4, # neurons/layer: 16\n",
      "Average accuracy: 0.6047599996102593\n",
      "Average AUC: 0.6273720661320766\n",
      "--------------------------------\n",
      "# hidden layers: 5, # neurons/layer: 16\n",
      "Average accuracy: 0.6141533690487597\n",
      "Average AUC: 0.6384907274992455\n",
      "--------------------------------\n",
      "# hidden layers: 6, # neurons/layer: 16\n",
      "Average accuracy: 0.6137783886562211\n",
      "Average AUC: 0.6317675281953055\n",
      "--------------------------------\n",
      "# hidden layers: 7, # neurons/layer: 16\n",
      "Average accuracy: 0.6169955013108571\n",
      "Average AUC: 0.6424665328716467\n",
      "--------------------------------\n",
      "# hidden layers: 8, # neurons/layer: 16\n",
      "Average accuracy: 0.6202281499405979\n",
      "Average AUC: 0.6438715918825076\n",
      "--------------------------------\n",
      "# hidden layers: 0, # neurons/layer: 32\n",
      "Average accuracy: 0.6249131683498139\n",
      "Average AUC: 0.6479156037321359\n",
      "--------------------------------\n",
      "# hidden layers: 1, # neurons/layer: 32\n",
      "Average accuracy: 0.623593571403738\n",
      "Average AUC: 0.6457084891699599\n",
      "--------------------------------\n",
      "# hidden layers: 2, # neurons/layer: 32\n",
      "Average accuracy: 0.6235701558600972\n",
      "Average AUC: 0.6458216072524836\n",
      "--------------------------------\n",
      "# hidden layers: 3, # neurons/layer: 32\n",
      "Average accuracy: 0.6227814435484188\n",
      "Average AUC: 0.6440901818320695\n",
      "--------------------------------\n",
      "# hidden layers: 4, # neurons/layer: 32\n",
      "Average accuracy: 0.6236794222997654\n",
      "Average AUC: 0.6566274150112178\n",
      "--------------------------------\n",
      "# hidden layers: 5, # neurons/layer: 32\n",
      "Average accuracy: 0.6206887922121986\n",
      "Average AUC: 0.6415265196112534\n",
      "--------------------------------\n",
      "# hidden layers: 6, # neurons/layer: 32\n",
      "Average accuracy: 0.6204390184868471\n",
      "Average AUC: 0.6456113775817842\n",
      "--------------------------------\n",
      "# hidden layers: 7, # neurons/layer: 32\n",
      "Average accuracy: 0.6173469600663612\n",
      "Average AUC: 0.645461221277116\n",
      "--------------------------------\n",
      "# hidden layers: 8, # neurons/layer: 32\n",
      "Average accuracy: 0.6230469568994125\n",
      "Average AUC: 0.6451813661454575\n",
      "--------------------------------\n",
      "# hidden layers: 0, # neurons/layer: 64\n",
      "Average accuracy: 0.6237028396726026\n",
      "Average AUC: 0.6483345615619887\n",
      "--------------------------------\n",
      "# hidden layers: 1, # neurons/layer: 64\n",
      "Average accuracy: 0.6216961233754466\n",
      "Average AUC: 0.647180529783658\n",
      "--------------------------------\n",
      "# hidden layers: 2, # neurons/layer: 64\n",
      "Average accuracy: 0.6274352125753695\n",
      "Average AUC: 0.6583661834034006\n",
      "--------------------------------\n",
      "# hidden layers: 3, # neurons/layer: 64\n",
      "Average accuracy: 0.6222269628896651\n",
      "Average AUC: 0.6495393568007988\n",
      "--------------------------------\n",
      "# hidden layers: 4, # neurons/layer: 64\n",
      "Average accuracy: 0.6244680559559972\n",
      "Average AUC: 0.6437644893599168\n",
      "--------------------------------\n",
      "# hidden layers: 5, # neurons/layer: 64\n",
      "Average accuracy: 0.6233904860883452\n",
      "Average AUC: 0.6503178596619988\n",
      "--------------------------------\n",
      "# hidden layers: 6, # neurons/layer: 64\n",
      "Average accuracy: 0.624983365592432\n",
      "Average AUC: 0.645865605655614\n",
      "--------------------------------\n",
      "# hidden layers: 7, # neurons/layer: 64\n",
      "Average accuracy: 0.6194628940246141\n",
      "Average AUC: 0.6498348637491482\n",
      "--------------------------------\n",
      "# hidden layers: 8, # neurons/layer: 64\n",
      "Average accuracy: 0.6215165121379805\n",
      "Average AUC: 0.6459605918517412\n",
      "--------------------------------\n",
      "# hidden layers: 0, # neurons/layer: 128\n",
      "Average accuracy: 0.6301213052350713\n",
      "Average AUC: 0.6562933080511086\n",
      "--------------------------------\n",
      "# hidden layers: 1, # neurons/layer: 128\n",
      "Average accuracy: 0.624210372785235\n",
      "Average AUC: 0.651033577123906\n",
      "--------------------------------\n",
      "# hidden layers: 2, # neurons/layer: 128\n",
      "Average accuracy: 0.6267167962829167\n",
      "Average AUC: 0.6548320834186241\n",
      "--------------------------------\n",
      "# hidden layers: 3, # neurons/layer: 128\n",
      "Average accuracy: 0.6251005792809041\n",
      "Average AUC: 0.6521897177634927\n",
      "--------------------------------\n",
      "# hidden layers: 4, # neurons/layer: 128\n",
      "Average accuracy: 0.62465544493673\n",
      "Average AUC: 0.6468866649986261\n",
      "--------------------------------\n",
      "# hidden layers: 5, # neurons/layer: 128\n",
      "Average accuracy: 0.6274351345296547\n",
      "Average AUC: 0.6512223577683063\n",
      "--------------------------------\n",
      "# hidden layers: 6, # neurons/layer: 128\n",
      "Average accuracy: 0.6301056125588043\n",
      "Average AUC: 0.65902014027136\n",
      "--------------------------------\n",
      "# hidden layers: 7, # neurons/layer: 128\n",
      "Average accuracy: 0.6297698928192101\n",
      "Average AUC: 0.6564979789713098\n",
      "--------------------------------\n",
      "# hidden layers: 8, # neurons/layer: 128\n",
      "Average accuracy: 0.6242494822246067\n",
      "Average AUC: 0.6540252541679152\n",
      "--------------------------------\n",
      "# hidden layers: 0, # neurons/layer: 256\n",
      "Average accuracy: 0.6290592743911851\n",
      "Average AUC: 0.6543097883274777\n",
      "--------------------------------\n",
      "# hidden layers: 1, # neurons/layer: 256\n",
      "Average accuracy: 0.6247335479663658\n",
      "Average AUC: 0.6553062513057532\n",
      "--------------------------------\n",
      "# hidden layers: 2, # neurons/layer: 256\n",
      "Average accuracy: 0.6301290878561954\n",
      "Average AUC: 0.6576409477728509\n",
      "--------------------------------\n",
      "# hidden layers: 3, # neurons/layer: 256\n",
      "Average accuracy: 0.6306287603019884\n",
      "Average AUC: 0.6584171360720774\n",
      "--------------------------------\n",
      "# hidden layers: 4, # neurons/layer: 256\n",
      "Average accuracy: 0.629184331673996\n",
      "Average AUC: 0.6610065187675132\n",
      "--------------------------------\n",
      "# hidden layers: 5, # neurons/layer: 256\n",
      "Average accuracy: 0.6240854972026038\n",
      "Average AUC: 0.6515364154942892\n",
      "--------------------------------\n",
      "# hidden layers: 6, # neurons/layer: 256\n",
      "Average accuracy: 0.6243431541548837\n",
      "Average AUC: 0.6514453557329046\n",
      "--------------------------------\n",
      "# hidden layers: 7, # neurons/layer: 256\n",
      "Average accuracy: 0.6276538716692603\n",
      "Average AUC: 0.6558566685442104\n",
      "--------------------------------\n",
      "# hidden layers: 8, # neurons/layer: 256\n",
      "Average accuracy: 0.6262093241434992\n",
      "Average AUC: 0.6556813161810133\n",
      "--------------------------------\n"
     ]
    }
   ],
   "source": [
    "input_size = 13\n",
    "train_data = better_shots_df\n",
    "\n",
    "total_acc = []\n",
    "total_auc = []\n",
    "for hidden_layer_size in [1,2,4,8,16,32,64,128,256]:\n",
    "    hls_acc = []\n",
    "    hls_auc = []\n",
    "    for num_hidden_layers in range(9):\n",
    "        model = DeepnnModel(input_size,hidden_layer_size,num_hidden_layers)\n",
    "        print(f\"# hidden layers: {num_hidden_layers}, # neurons/layer: {hidden_layer_size}\")\n",
    "        acc,auc_scores,_ = train(model,train_data,3,print_roc_curve=False)\n",
    "        print(\"--------------------------------\")\n",
    "            \n",
    "        hls_acc.append(sum(acc)/len(acc))\n",
    "        hls_auc.append(sum(auc_scores)/len(auc_scores))\n",
    "        \n",
    "    total_acc.append(hls_acc)\n",
    "    total_auc.append(hls_auc)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IuXPncvvtt/PII49w7733xuR5+umn6dmzJ/PmzeOKK67g6quvZsaMGQcuyP0kIwBCCCGEECIhVKhQddr56MhoR1lZWcwUoHhn/3+pcDhMRkYGzz77LBqNhqFDh1JeXs5DDz3EnXfeGc0zbNgw7rvvPgAOOeQQVq9ezbRp07j44osPWF32h3QAhBBCCCHE75bdbt+nawDS0tLQaDRUV8deJ1JdXU1WVlbcdbKzs9HpdNHpPgB9+/alqqoKv9+PXq8nOzubfv36xazXt29f3n777V8QzYEhU4CEEEIIIURCqFTqTl32h16vZ+jQoSxYsCCaFg6HWbBgAaNGjYq7zujRo9m0aRPhcDiatmHDBrKzs9Hr9dE869evj1lvw4YNFBYW7lf9DiTpAAghhBBCCAFMnjyZ5557jhkzZrB27VquuOIKPB4PEyZMAOCiiy6KuUj4iiuuoKGhgWuuuYYNGzbwwQcfcN999zFx4sRonuuuu46vv/6a++67j02bNvH666/z7LPPxuTpajIFSAghhBBCJMQvOVO/74Xvf7njx4+ntraWO+64g6qqKoYMGcJHH30UvTB4x44dMQ9Sy8/PZ968eVx33XUMGjSI3NxcrrnmGqZMmRLNM3z4cGbPns3NN9/MP/7xD4qLi3n88ce54IILfn2Mv5B0AIQQQgghhNhl0qRJTJo0Ke57ixcvbpc2atQovv76672Weeqpp3LqqaceiOodENIBEEIIIYQQCaFSqTrv2QQH2TMPDiZyDYAQQgghhBDdiIwACCGEEEKIBFHTeeej5Tx3R+STEUIIIYQQohuREQAhhBBCCJEQB9tdgLoL+WSEEEIIIYToRmQEQAghhBBCJISMACSGfDJCCCGEEEJ0IzICIIQQQgghEkKFGpXcBajLyScjhBBCCCFENyIjAEIIIYQQIiHkGoDEkE9GCCGEEEKIbkRGAA4A146FOLfNI+R3obfmk9b3PIyOkg7zhwJeGjbNxlO9nFDAg86USmrv8VjSB0XKK1uEu2wxgZZ6APTWHJJLTsOSPnDX+s00bJpDS/0agq0NaPQ2LBlDSC49A43O3Onx7s5Z8ynO6oWEAm70plzSC87CaCnsMH8o6KWh4n2aG38gFPKg06eQln8mFkf/dnkbq+ZTX/4/HBlHk57/p2i6q/YLmhqW4fOWoYR9FA9+AI22a+P+ibN8Ec4d83e1fR7pPc/FaC/uMH8o4KVh67s0160gFPCiM6aQ1uMcLKmRtnWVL8FVsYRA6662t2STUngqltQB0TJ2rniEVteGmHLt2UeR0fuCToiwYzNf+IAZT71DfU0jvfoXM+WByxl4aK+4ed974xPuvOrfMWl6g46l5e9EXz/9r9eZN/tTqirq0Om09Bvcg0m3XsjAob1j1vv042959uGZbPxxG3qDjqGHD+DxV2478AH+jJef/YDnnphNbXUjfQcUc9dDlzF4WPz4Z722gBuvaB//utq3AQgEgjxyz6ss/ngZZduqsNktjB4zmBvvvojM7NToOls2lvPA7S+y7Ou1BAJBevcvYvJtFzDqqEGdF2gcTZ/OxrVgJiF3A/rcHqScdTWGor5x8zZ//SH1r/0rNlGro/Cx+dGXIXcDje89Q+u67wi3NGPoMYiUs65Bl5EXzROoLafx3afxbVmFEgxg6juClLOuRmNP6ZQY92bbnHfYMmsmvoYG7CWl9L/yGpL69Iubt+zjD/nhkftj0tQ6PSe9/0n09QcnHBV33T6XXEHp2edR//0Kvr7xmrh5Rj/xDEm943/2nWHq1Nd46KEXqKqqZfDgPjz55O2MGBH/+/fSS+8wYcLNMWkGg57W1lXR14qicOedT/Dcc2/hdLoZPfpQnn76Lnr2LIrmaWhwctVV9/C//y1CrVbzpz8dz7//fStWq6VTYtybhgWzafhoJkFXA4b8UrIuuAZTSfzP3/n5h1ROfyAmTaXV0+fZtu9+0NVAzaxn8Kz+llBLM+Zeg8m64Br0mXl7FoeiKJQ9diOe1UvJm3QvtkOPPLDBJYiKThwBkPPcHZIOwK/UXLWUuvX/Jb3fnzE6SnBu/4TKZY+TP/petAZ7u/xKOEjlskfR6G1kDv47WmMywZZ61LsduGsNyaT0/BM6cyag0FTxJVUrnyJ/1B3orbkEW12EfE5Se52N3ppDoKWeurWvEmx1kTXkii6LvalhOXU7Z5NRMB6jpRBnzRIqNv6Hgv63odXZ4sZesfE/aLRWskr/ilbnIOhvQK1pf/De6tmOq/YL9KacOOX4sTj6YnH0pb78f50S275oqvmWuk2zyOh1PkZ7Mc6dC6j44QkKRtyNVh+/7St+eByNzkZW/8vR6pMI+hpQa03RPFpDEqklf0Rnyohso+orKlf/h/xht2GwtH0W9uwjSCn6Q/S1WqPvxEjbmzf7Mx65/XlufXgiA4f24rVpc7jy7Dt47+tppKQnxV3HajPz7tfToq9Vqtj3C0tzuOlffyevMIvWVh+vPf0eV5x1B3O+fZaUNAcAn/zvC/5x3VNcdetFjDhyEMFQiE1rt3dWmB16/+3PuO+WF7jn8SsZMqwXL/5nDhefeSefLHuatI7it5tZsOzptoTd4m/x+ljz/WauunE8fQcW4Wps5h9TnufSc//JnCWPRvNdcs49FJXm8Or792I0GnjxP3O45Jx7WPz9s6RnJndStLE8yxbSMPs/pI6fjL6wL02LZ1Hzn/8j5/ZX0Nji10FltJB7+8u7p0T/pSgKNc/dhkqjJf2yf6I2mnEveovqp64n59aXUBtMhH0t1Pzn/9DllJJ51WMAON9/gZpnbiHr+v+gUnfdH/mKxQtY++xUBlx1PUl9+rF19lt8c+sNjHnhNQxJ8ePXmi0c/cKr0deqPb78x74xO+Z17bff8MNj/yL7iKMBSO43oF2eDTNeoG7lMhy9+hyIsPbJm2/OZfLk+5k27W5GjhzM44/P4IQT/sb69R+RkZEadx273cr69R9FX+8Z+4MPPscTT7zCjBkPUFycx+23/5sTTvgbP/44F6PRAMAFF9xAZWUt8+e/SCAQYMKEW7jssjt4/fVHOi/YONxLF1Lz5lSyLpyMqaQfDfPfYsejN1B636to7fHbXm2yUHrfK7ulxH73dz51K2i05F39T9RGCw0f/5ftD0+m9N4ZqA2mmLIa5r/VfscpxC/0u+gaffrpp5x22mnk5OSgUql49913u2zbzm3zsecdiT33CPTWHNL7/RmVRk9Txedx87vLPycU8JA1ZCKm5J7oTGmYUnpjsOVH81gyhmBJH4TekonekkVqzzNRawy0OrcAYLDlkjXkSiwZQ9CZMzCn9iWlxx/x1H6PEg51SdwAzupFONIOx552GHpTNukF56BS62mq/zpufnf914SCHrJ7XIrJWoLOkIrJ1hODOTcmXzjko3rry2QUnhe3c5CUOZbkrOMwWIo6I6x95iz7BEf2EdizR6O35JDe64JI/JVfxs3vrvyCUMBD9oArMTl6RNo+qRcG625tnzYYS+pA9OZM9OZMUkvOQK0x4HNviSlLpdajNTiiy+6diK7wytPvcuaFJ3DG+eMo7V3AbY9cidFk4N3X53e8kkpFWmZydEnNiP2DefJZYzjs6CHkFWXRo08h1997Cc1NXjb+uA2AYDDEg7c8x3V3TeDsCSdR2COX0t4FnHBG158Fe+Gp9xh/8fGc/edx9OxTwL2PX4nJZOCtVz7pcB2VSkV6ZnLbslv8doeFV967h1POPIKSnnkcMqIPdz18OatXbKK8rBaAhno32zZX8PfJf6LvgGKKe+Rw490X0eL1sf7HrusEuRe9hW3UKVgPOwl9dhEp4yej0htp/mpuxyupQGNP3W1pO2sfrN2Jf9uPpIy/DkNhH3SZBaSccx1KwIdn2QIAfFtWE6yvIu3PN6HPKUGfU0LahTfjL1tP64blnR1yjK3v/Jf8E08l/4STsRUWMfDq69EYjJTN+6DjlVQqjCmp0cWQHDtqsft7xpRUqr/6nNTBh2DOjnT61TpdzPt6u4Pqrz4n//iT2x1Qd6ZHH32RSy89hwkT/kS/fj2YNu1uzGYj06e/3eE6KpWKrKz06JKZmRZ9T1EUHn/8ZW677QpOP30cgwb14eWXH6SiooZ33438ltau3cxHH33G88/fy8iRgzniiGE8+eRtzJz5ARUV1Z0e8+7q5/2XpKNOJenIkzHkFpF10fWo9Uacn+3lu48KrSN1t6Wt7f3VO2nZ/CPZF07GVNwXQ3YBWRdORvH7cH2zIKaU1h0baZj3X3L+OqWToksglSp6HcCBXqTD1LHfRQfA4/EwePBgpk6d2qXbVcJBfE3bMae2Df2qVGpMKX2jB+t78tauxJhUQu3a19m2+Dp2fHEHjVs+QFHC8behhGmqXEo45MeYVNphXcJBL2qtEZVa8+uC2kdKOIjPW4bJ3jY9Q6VSY7b1prV5a9x1PM7VGK3F1O54i63f38qONffTUPlxu9hrd7yF2dEfs7133HIOBpG234EpuW3oV6VSY07uQ6s7ftt76n/AaC+hduPrbP3iBnYsvZuG7XP33vbV30ba3h47paypZilbPp/MjqV3U7dlNuGQ/8AF9zMC/gBrv9/EyKMHR9PUajUjjx7CD9+u73C9Fk8LJw35KycMmsC1f76XTes6PmgN+AO8PeMjrHYLvfoXAbD2h83UVNajUqsZP/YaxvW7iInj7+zyEQC/P8DqlZsYPXZINE2tVjN6zGBWLF3X4Xre5haO6P83Rvf9K5edey8b1u7Y63aa3B5UKhV2R2SaQ3KKjZKeucx+YxFeTyvBYIg3XpxHarqDgUN6HJDYfo4SDOAvW4+x99Bomkqtxth7KL5tP3a8nq+FnXeMZ+ftZ1Pz7K34K9v2EUowEClH2zaKpVKrUWl1+DavasujApVW15ZHqweVCt+WtukknS0cCODauIG0Q4fF1DXtkKE4f1zT4XqhlhYWXng2Cy74E9/deTNN2+LvIwF8jQ3ULP2K/BNO6TBP9Vef429yk3f8Sb8skF/A7/ezbNkaxo07PJqmVqsZN+5wvvpqRYfrNTd7KSwcS37+0Zx++hWsWbMx+t7WrTupqqqNKdPhsDFy5OBomV99tYKkJDvDhg2M5hk37nDUajXffPPDgQxxr5RggNbtG7D0i/3uW/oNpWVzx20f9rWw8f/OYeP1Z1H2xC34ynf/7kf22ypd++9+y8ZVu5XRSvkz95D152vROuKPtAixv34XU4BOOukkTjqp63aEPwn5m0EJo9ljuofWYKfFUxV3nYC3jmDrOqzZh5F96DUEvDXUrn0NRQmRUto2pcPXtJPypfejhAOoNQayhlyJ3tp+OkykHk00bnkfe178eaSdIRT0AGE02tipPhqdDX9r/LMyAV8dwaYGrCnDyO5xOQFfHbU7/gtKiJScSPv9NLc/r+8NnR3CrxIKNANhNPo94tfb8Xs7aPuWWoKt9VgzR5I96CoCLTXUbngjEn/RadF8vuZydi7/V7Ttswf8Hf1u039smcPRGlPR6pPwe3ZSt/kdAt4qsgd0zfSvxno3oVCY1PTYM/ip6Uls27gz7jpFPfK464lr6NmviGa3h5enzuYvJ93I219MJTOn7Yzgp/OWMuWyh2j1+kjLTGbarH+QnBqZ/lO+LfK5PvPg61x/z9/Iyc/k5f/M5pLTb+a9b57Bkdx+2lln+Cn+Paf6pGUksXlDedx1Snrk8q+pV9NnQBFNbi/PPTGbs467kXnfPEV2blq7/L5WPw/eOYPTzjoKmz0yCqZSqXhlzj1cfv59DMwZj1qtIjU9iZfeuQtHsvWAxxlPyOOCcLjdvHuNLZlAdfwOjS6zgNTzp6DPLSHc4sG98E2qHp1Ezi0vok3OQJdZgCY5E+f/niPl3MgZVfeitwg5awm5GwAwFPVDpTfROOcZkk67FBQF55xnIRyO5ukKfrcLJRxqN9XHkJyCpyx+/Na8fAZNnoKtpJSgx8OWWTP58rorOerZGZjSM9rl3zn/I7QmM1lHdLw/L5v3AelDh8ddv7PU1TUSCoXIzIw9AM3MTGXduvgnPXr3Lmb69PsYNKg3LlcTDz88ncMPP5c1az4gLy+LqqraaBl7lllVVQdAVVUdGRmx3zetVktKiiO6flcINrkgHEKzx1QfjT0ZX2X8ttdn5ZM94UaM+aWEWjw0fDSTbfdNpOSel9ClZGDIKkSbmknNrGfJvvgG1AYj9R+/RbCxlqCzPlpO9cynMPUYgO2QIzo1xkRR7fqvs8oW8f0uRgD2l8/nw+12xyxdR0Gjt5Pe7yIM9iKsWSNILj4Fd9mSmFx6Sxb5o+4gb+Qt2PPHULN6Ov7minalhYMtVC5/Ap0lJ6YDcXBS0GhtZBSei9FSgC3lUJKzj8dV+wUAAX8jdWXvkFl8EWq17mfK+i1S0OhtZPT+M0ZbIbaM4SQXnoSr4tOYXHpzJvnDbiNv6E3Yc4+met1L+D1tbe/IOQpLSn8M1lxsmSPJ7DsBT91KAi1d98dwfw0e3ofTxh9Dn4ElDBs9kEdm3EJymoNZMz6KyTf8iEG8uejfzPjwQUYfO5QbL/kXDbVOAMLhyEjJ3647h3GnjabfkB7848lrUalUzH8v/pS7g8WhI/tw5vnH0G9QCSOPGMDTr91MaqqdN6Z/1C5vIBBk0sUPoigK9zzW1qlTFIU7r59GapqDN+fdz+xFj3DcKSO5dPy91FR13UHw/jIU98c68gT0eT0x9hxC+iX3oLE6aP4icv2OSqMl/ZJ/EKgpY+eU09hx/Qm0blyBsd/I6PC9xpZE+l/vomX1V5TdcBJlN55CuKUZfX6vg36IP7nfAPKOOxFHaU9SBw1h6B33oncksWPunLj5y+bNJeeY49DoDXHfb6mtoXbZt3sdIThYjBp1CBdddAZDhvTl6KNH8M47T5KensIzz8xMdNW6hLnHAJJGn4ixoCeW3kPIm3gvGlsSjYt3ffe1WvIm3oO/eicbrjqVdX8/Ae+6FVgGtn33m1Z8gWftcrLOm5TIUMTv0O9iBGB/3X///dx9992/uhyN3goqNSF/bAci6HOjMTjir2NwoFJpYq5411myCfldKOEgKnWkSVRq7a6LgMFgL8Ln2oZrxyek97soul442ErFssdRa41kDZkYXbcraLQWQE0o2BSTHgo0xb0AGECjs7eLXW/MIhR0R6cUhYJNlK19aLe1wrQ2b8ZV8xmlhz7aiXcK2D8anRVQE/LvEb/fjVbfQdvr27e93pxNyO9u1/Z6c+TMntFWiM+9DefOhWT0/nPccn+665C/pQadKf3XhvazklPtaDRq6msbY9Lra52kZezbhag6nZbeA0so21IZk26yGCkoyaGgJIdBw/pw2vDLmP3afP527dmkZ0bOApb2brtmQm/QkVuYRWV513V+foq/blfH5Cd1NU7SM5P2qYzIXY5K2LZH/IFAkKsufpDyshpe+9+90bP/AF8u+YGFH33Hih2vR9MHDLmCzxet5O3XF3LF5LN+VVz7QmNxgFrd7qx7qKlxn+/Go9Jo0ef1JFDbNlpiKOhNzk0vEG5pRgkG0diSqHz4CvQFbdMATX2Hk3vn64SanajUGtRmG2W3/BHzocccmOD2gd7uQKXW4HPGfvd9jQ3t5vV3RK3VYu/RE29F+9GihlXf49m5g0NvuavD9Xd+/CF6m53MUV17NjgtLRmNRkN1dX1MenV1PVlZ7Uex4tHpdBxySF82bYqcMc/KSo+WkZ3dNppRXV3PkCF9duVJo6Ym9vsWDAZpaHBF1+8KWpsD1BpC7ti2D7kbY+b1741Kq8VY0INATdtIqamoNyV3v0DIG/nua+1JbL3n75iKIt99z9rlBGorWD/p1Jiydk69A3OvQRROib272G9RZz4H4GA5ZjgYdctP5uabb8blckWXsrKyX1SOSq3FYCvEW782mqYoYVoa1mFMin8bUGNSDwLemph53wFvdaRjsJcDeEVRUMLB6OtwsIWKZY+iUmvIOmQSak3XnjFXqbUYzPm0uNtuR6koYbxN6zFa498G02QtIeCri429tSbSMVBrMdt6kd/vJvL73RhdDOYCbClDye9340H1Q460fQEtzti29zauazdf/ycmRymBltrY+FuqIx2DvXbeYtt+T77myPe3o47HgabT6+g7uAdLP22bfxsOh1n66fcMGr5v122EQiE2/biNtJ+5c42iKPh9kTnifYf0QG/QsW1T24FTIBCkoqyG7Lyumwqh1+sYMKQHXy7+PpoWDof5cskPHDJi3+7IEgqFWL9mOxlZbQcOPx38b9tcwStz7iE5NXZqYWuLDwC1OvaMt1qtjo6OdDaVVoc+v3fMhbdKOEzrhmUYiuLfBnNPSjiEv2ILmjhzmdUmKxpbEoGanfh3rMc8cHS7PBprEmqzjZb1ywk3OzEPPLxdns6i1ulw9OxF3Ypl0TQlHKZ+5XKS+rW/lXE8SihE09YtGFLax1827wMcPXtjL41/TYeiKJR9PJfccSeg1nbt+Tu9Xs/Qof1ZsOCraFo4HGbBgq8YNeqQfSojFAqxatUGsrMjB+7FxXlkZaXHlOl2N/PNN99Hyxw16hCcTjfLlq2O5lm48GvC4TAjR3bd7W9VWh3Gwl541sa2vWftckyl+9j24RC+nVvRJrVve43ZitaehL96J63b1mPdNd0n7ZTzKb57OsV3PR9dADLPnUj2X286AJGJ7qpbjgAYDAYMhvjDq/srqeg4alZPx2AvxOgoxrXjE5SQD1tO5A9X9aoX0BqTSO0ZuY+9I38Mrh0LqVs3E0fBMQS8NTi3foCj4NhomfUb38acOhCtKYVwsJXmqm9obVxPcsm1wE8H/4+hhHxkDryEcLCVcLAVAI3e1mUHykmZY6nZ9ioGSz5GcyHOmsUoYT+21JGR2Le+gkbvIC03MjXJnn4EzppPqSt7B0fGUQR8tTRWzceREZnrqtYYMexx20+VWo9aa4lJDwbchAJuAr7IWV9/SyVqjQGtPnnXyETXSMofR83alzDYijDainDuXBCJPztyQFK99kU0hiTSSv4IgD3naJzli6nb9CaO3GMItNTQuP1DHHltZzDrtszGktIfrSGFcMhHU81SWpwbyBl0NRC5jqCpeinm1AFotBb8nnJqN/0Xo6MnBmv7+0Z3lguvOIPbJz1GvyE9GHBoL16b9h4t3lZOP28cALdd+SgZ2alcffvFADzz0BsMHNabguIcmlzNzHhqNpU7a/njhccD0OJp5bnH/suYE0eQlpmCs97Nm9M/oKaynuNOj/yWrDYzZ/3lJJ7+1+tk5qaRk5fBjKcizxE4/vSuPRv6t0mnc8PfH2fgIT0YvOs2oF5vK2f9OfI7vv6yx8jMSeHGuyLxP/HATA4Z3pvCkmzcLg/P/vsdystqGX/xcUDk4H/ihQ+w5vstPP/f2wmHwtRWR840OpKt6PU6DhnRB0eShRv+/jhXTzkXg1HPmzM+Zuf2ao45YXiXxW4fezZ1r96PvqA3hsK+uBfPQvG1Yj0sch1P3cv3oUlKI/kPlwHg/HAGhqJ+aNNzCbc04/5kJqHGaqyj2qaweFYsRmN1oEnOJFCxhYa3n8Q86AhMfdviav76Q3SZBaitSfi2raFx1lPYxpyNLrOgy2IHKD7zHL5/+H6SevXG0bsv22a/RbC1hfzjTwZg5YP/xJiWRp+/Xg7AxldfIqlvPyw5eQSam9gyayYtNVXknxh7Rjfg8VD56WL6Xjaxw23Xr1xOS1UlBXus21UmT57AxRdPYdiwAYwYMYjHH5+Bx9PChAlnAnDRRTeSm5vJ/fdfD8A//vEUhx02hB49CnE63Tz00Ats317BJZecDUSua7n22ou4996n6dmzMHob0JycDM44I7Iv6du3lBNPPJJLL72dadPuJhAIMGnSPZx77ink5GR2afypJ5xDxfP3Yyzqg6m4Dw3zZxH2tZB0ROS7X/HcP9Emp5NxVuS7XzvnJUwl/dFn5BJqaabhwzcI1FeRdGRb+7m/XYTGloQuJRNf+RaqX38S26FHYB0Q+e7/dPegPelSM9GnZ3dB1J1PRgASo1t2AA4ka9YIQv5mGje/R9DnxmDLJ/vQa9HumgIUbK2PuU2b1phCztDrqFv/Jju/uguNIRlHwTiSitsuYg75m6hZ/QJBnwu11oTBlkf20Gsxp0bOMvjc2/G5Ihdd7fj8lpj6FBz5ADrTvg3H/lq2lEMJBZtpqJhLMODGYMojp+cVaHWRM5cBf2PM/FydPpmcnldSt/Md3D8+gEbnwJFxNMlZ4/Zru67az2msbJs7Xb4hMgSaUXgB9rSRByCyfWPLGE7I30zD1jkE/W4M1jxyBl0dfQZAoLWB3e/5rDOmkDPoauo2vYW74h9oDEk48o4hueDEaJ6Qv4nqtS8R9LvQaE3oLbnkDLoac8qus6sqDd7GtZHORsiH1piCNf1QUgpP7rK4AU7445E01rt4+oHXqKtppPeAEv7z37ujt/as3FmLarcz1W5nM/dc9xR1NY3Yk6z0HdSDGXMfpLR35OBNrVGzbeNOrp+5AGeDm6RkO/0P6cn0/z1Ajz5tD5a77q4JaDVqbrvyMXwtPgYM7c2zs+/FntQ1F8H+5NQ/HUlDnYvH7nuduupG+g4s4aW374re2rNiZ23MmXqXs5mbr36KuupI/AOG9GDW/H/Rs08k/uqKej6ZuxSAU0bHPvDp9Q/+yWFHDiQl1c6L79zFI/94lQtOvY1gMEjPPgU888at9B3Y8cPnDjTL0GMINTtxfvAioabIg8AyrnwwOgUo2Fgd87sPe5uof+NhQk0NqE1WDPm9ybpuKvrsomiekKuexnem7ppKlIp1xPE4TrwoZruB6h00znmWsLcJbUoWjhP+jG3s2V0S8+5yxhyL3+Vkw8vT8TU2YC/pwYh/PhydAtRSWx3z3Q80N7Hq8YfwNTags9qw9+zF4Y/9B1thUUy5lUsWoKCQM/ZYOlL20Qck9xuAtaDjhy12pvHjT6a2toE77niCqqpahgzpy0cfPR+9teeOHZWod3smQ2Ojm0svvZ2qqlqSkx0MHdqfL7+cSb9+bSMcN954KR5PC5dddgdOp5sjjhjKRx89H30GAMBrrz3MpEn3cOyxF0cfBPbEE13/8D/7iGMINjmpfXc6IVcDhvweFFz3UHQKUKChBnaLP+xppnLGQ4RcDajNNoxFvSi6ZSqG3KJonqCznuqZUwm6G9EmpeIYdQLpf7hoz00LccCpFEVREl2JX6u5uZlNmzYBcMghh/Doo48yduxYUlJSKCj4+bNDbrcbh8NB8TFPdvn91A8WqnpvoquQOLYDMxr0WzVrVvy7S3UHdv1vfvf3q4yd0f6Bdd3FgF7d+8zg+8d37dnzg0m/F+Pfqe33LtTiYcPEk3G5XNjtif/t/3Tsld7rGtSazvk7HA75qN3w74Mm5oPJ72IE4LvvvmPs2LHR15MnTwbg4osv5qWXXkpQrYQQQgghhDj4/C46AGPGjOF3MJAhhBBCCNHNqOm8e9J075G+vZFPRgghhBBCiG7kdzECIIQQQgghfnvkLkCJIZ+MEEIIIYQQ3YiMAAghhBBCiISQEYDEkE9GCCGEEEKIbkRGAIQQQgghREKoUKPqpPPRnVXu74F8MkIIIYQQQnQjMgIghBBCCCESQqVSdeI1AKpOKff3QEYAhBBCCCGE6EZkBEAIIYQQQiREZASgc87UywhAx2QEQAghhBBCiG5ERgCEEEIIIURCyHMAEkM+GSGEEEIIIboRGQEQQgghhBAJIc8BSAz5ZIQQQgghhOhGZARACCGEEEIkhFwDkBjyyQghhBBCCNGNyAiAEEIIIYRICBkBSAz5ZIQQQgghhOhGZARACCGEEEIkhNwFKDHkkxFCCCGEEKIbkRGA3aiaA6i03fMjSfq/IYmuQsI0vrgx0VVIqDOvcCa6CokTTnQFEkuVFUx0FRKmIjUt0VVIqNJzlia6CqKLhQMtia5CfCp1ZOmsskVc3fNoVwghhBBCJJxcBJwY8skIIYQQQgjRjcgIgBBCCCGESAiVSoVKpeq0skV8MgIghBBCCCFENyIjAEIIIYQQIiFUqDrxNqAyAtARGQEQQgghhBBil6lTp1JUVITRaGTkyJEsXbr3u2Y5nU4mTpxIdnY2BoOBXr16MXfu3Lh5H3jgAVQqFddee20n1HzfyQiAEEIIIYRIjE68C9AvuQ3om2++yeTJk5k2bRojR47k8ccf54QTTmD9+vVkZGS0y+/3+znuuOPIyMhg1qxZ5Obmsn37dpKSktrl/fbbb3nmmWcYNGjQL4nmgJIRACGEEEIIIYBHH32USy+9lAkTJtCvXz+mTZuG2Wxm+vTpcfNPnz6dhoYG3n33XUaPHk1RURFHH300gwcPjsnX3NzMBRdcwHPPPUdycnJXhLJX0gEQQgghhBCJoVJ17gK43e6Yxefzxa2K3+9n2bJljBs3LpqmVqsZN24cX331Vdx15syZw6hRo5g4cSKZmZkMGDCA++67j1AoFJNv4sSJnHLKKTFlJ5J0AIQQQgghxO9Wfn4+Docjutx///1x89XV1REKhcjMzIxJz8zMpKqqKu46W7ZsYdasWYRCIebOncvtt9/OI488wr333hvNM3PmTJYvX97hdhNBrgEQQgghhBCJoaLzTkeHI/8rKyvDbrdHkw0Gw4HbRDhMRkYGzz77LBqNhqFDh1JeXs5DDz3EnXfeSVlZGddccw3z58/HaDQesO3+WtIBEEIIIYQQv1t2uz2mA9CRtLQ0NBoN1dXVMenV1dVkZWXFXSc7OxudTodGo4mm9e3bl6qqquiUopqaGg499NDo+6FQiE8//ZSnnnoKn88Xs25XkSlAQgghhBAiMbrgGoB9pdfrGTp0KAsWLIimhcNhFixYwKhRo+KuM3r0aDZt2kQ4HI6mbdiwgezsbPR6PcceeyyrVq1i5cqV0WXYsGFccMEFrFy5MiEH/yAjAEIIIYQQQgAwefJkLr74YoYNG8aIESN4/PHH8Xg8TJgwAYCLLrqI3Nzc6Hz+K664gqeeeoprrrmGq666io0bN3Lfffdx9dVXA2Cz2RgwYEDMNiwWC6mpqe3Su5J0AIQQQgghRGL8gjP1+1X2fho/fjy1tbXccccdVFVVMWTIED766KPohcE7duxArW6bQJOfn8+8efO47rrrGDRoELm5uVxzzTVMmTLlgIXRGaQDIIQQQgghxC6TJk1i0qRJcd9bvHhxu7RRo0bx9ddf73P58croatIBEEIIIYQQiaGm865IlStdOyQfjRBCCCGEEN2IjAAIIYQQQojEUKlQDqJrALoLGQEQQgghhBCiG5ERACGEEEIIkRiqXUtnlS3ikg7AAeCsXIyzYj4hvxu9JY/04vEYbUUd5g8FvTTseI/m+pWEgl50hhTSis/Gkhy5H6yragmuqs8I+OoB0JuySck/Ofo+QNDvon77O3id6wiHWtGbMknOOxFr6qFxt9lZaua9Q9X/ZhJwNWAuKCV/wjVYe/SLm7du8Ydsm3Z/TJpKp2foK59EX4davex8/Rmc331OsMmFISObjBPPIuO406N5As56yl59Gveq7wi3ejFm55P9xwtJHjmmU2LcG9eOhTi3fkTI70Jvyyetz/kYk0o6zB8KeGnY+A6e6uWEAh50plRS+5yLJX3QrvIW4S5bTKClDgC9NYfk0j9gSR8YLcNdtoSmym/wubejhFopOuZJNDpzp8YZj2vTfFzr5xJqdaFPyif1kIswppR2mD/k99C4+i085d8R8nvQmdNIHXIB5uwhADSunYO3/Dv8TZWoNDqMqT1JGXQuelt2tIyKxf+ktXZdTLm2kmNIHzqhU2LcG9fm+bg27IrfkU/qkH2If81beCp2i3/QbvGvm4O3Yrf4U3qSMnCP+Jf8k9a6PeIvPob0Q7s2fveqD3GteJeQ14k+tYjUoy7BkNmzw/whnwfn16/h3fI1odZmtLZ0Uo74K+aioQC0VqzBteI9/DWbCXkbST9pCpaSke3K8TfspPGrl2mt+BHCIXQpeWSceCNaW3qnxRpP7cfvUPN+ZL9nKigl7+JrsHSw36tf8iE7nmm/3xsyI3a/V/HGM7iWte330k84i7Rxbfs9X3U55a/9B8/6HwgHA9gHjSTvL9egc6R0TpAd6Pa/+24ev/j9kA7Ar9RU9x11294mo+Q8jLZinJULqfjxCQoOuQutvv1jp5VwkIo1T6DR2cjqfRlafRJBXz1qbdsBnFafTGrhGeiMGYBCU83XVK6bRv7gWzCYcwCo3jiDcMhLdp8r0OgsNNV+S9X658kfdDMGa36XxN7w5QLKXplK4SXXY+nRj+q5b7Hx/hsY8Ohr6BzJcdfRmCwMeOzV3VJiu+dlL0+lac1yiifehiE9C/cP37J9+mPok1NJGnYEAFun/pOgt5ke/3cfOlsS9V/MZ/Pjd9HvvmcxF/fqrHDbaa5cSt26N0nvfyFGRwnO7fOpXPYY+Uf8E60hfttXfvcIGr2NzCFXoDUmE2zZo+2NyaT0+hM6cyag0FTxJVUrniT/8DvRW3MBCIf8mNMGYE4bQMPGt7sq3BjNZV9T//3rpB86AUNqKa4NH1H16YPkn/ggGqOjXX4lHKTq03+hNtjJHHU1GlMyQW8d6t06Lq2167D3GIchuQRFCdGw6i2qPv0XeSc8gFprjOazFY8hecCfoq/VGkOnxhpPc9nX1P/wOumHTMCQUopr40dUff4g+cfvJf7Pd8U/soP469ZhLxmHIaUEJRyiYc1bVH3+L/KO2yP+ojEk909c/J6Nn9Pw+YukjrkcQ2Yv3N+/T/X//kHu+U+iMSe1y6+EAlTPuQuNyUH6if+HxpJKqKkWtaEt9nDAhz61CGvfY6j98MG42w24qqh65xas/caRNOJc1HozgYYdqDS6Too0vsavFlD+6lTy/3o95h79qP3wLTY/cAN9H+l4v6c2Wej3yG77vT3mJZe/MpWmH5dTeOVt6NOzaPrhW8pefAxdciqOoUcQam1h0/3XYyospcetjwNQ+dYLbHnoJnr9YxoqddfM5pXfffeOv9OoVZGls8oWcf3mrwG4//77GT58ODabjYyMDM444wzWr1/fZdt3VizAkTkae+bh6M3ZpJech0qjp6nmq7j53TVfEgp6yO7zd0z2UnTGVEyOXhgsedE8lpRBWJIHoDdloDdlklp4OmqNAV/T1mie1qYtOLLGYrQVoTOmk5J/MmqtmVbP9k6P+SfVH/yXtGNOJW3MyZjyiii85HrUeiN1iz/oeCWVCl1S6m5L7Nmr5g2rST3qROz9D4mcBRv3B8yFpXg2r90tzxoyT/gT1h79MGTmkHPmxWgsVjxbN3RWqHE5t3+MPe8o7LlHoLfmkN7vwkjbl38eN7+7/HNCAQ9Zh0zClNwTnSkNU0pvDPa2DpslYwiW9EHoLZnoLVmk9jwTtcZAq3NLNE9S0XEkl5y815GGzuba8CH24jHYio9Cb88lbegEVBoDTds+jZu/aesSQn4PWaOvxZjWC50lHVN6XwxJhdE82UfdiK3oKPSOPAxJhWSMuIygtx5f47aYslRaA1pjUnRR60ydGGl8ro0fYi8aE6mvPZe0Q3fFv72D+Lftin/UXuI/Ylf89l3xDzs443et/B+2/sdh63ss+pR8UsdcjkproGntwrj5m9YuJNzaTMZJN2HM7ovOnoExtz/6tOJoHnPhoSQfdj6WksM63G7j169hKhxKyuEXYUgvQefIwlw8Im6nozPVzP0vqWNPJXXXfi//b9ejNhipX9Lxfk+1535vj7P2no2rST3yRGz9DsGQnk3asX/AVNC23/NsWIW/torCy2/BVFCKqaCUwituwbt1PU1rlndqvLvr9r/7bh6/+H35zY8ALFmyhIkTJzJ8+HCCwSC33HILxx9/PD/++CMWi6VTt62Eg/iad5Cce0I0TaVSY3b0obVpS9x1PA0/YLSVULt1Jp6G79ForVjTh5OcewIqVfv+mKKEaa5fRjjkx2hrO+Az2kporv8OS/IA1FoTzfXLUMIBTPauOQMeDgbwbN1A1hl/jqap1GrsA4fi2bCmw/VCrS38MOlslHAYS3Evcs+9DFN+24GAtdcAnMu+IG3syeiS02j6cQWtlWXkXzRptzz9afhqIY5DR6ExW2n8ehFKwI+t35BOiTUeJRzE595OcvHJ0TSVSo0ptR+tzs1x1/HWrMSYVErt2tfw1qxErbdiyz6MpOKTOm77qm8jbZ/U8RBzV1PCQXyN20jqc1o0TaVSY8rsT2v9prjreCqWY0ztQd3yGXgrlqM22LAWHE5Sn1Pjxg4QDrQAoNHH/o6bt39J8/Yv0BgdWHIOIanvGai1XXc2TAkH8Tm3kdR7j/gzfib+lB7UrZiBt3JX/PmHk9T7F8S/40uad+yKP/sQkvp0XfxKKIC/djOOoWdG01QqNca8Qfiq4p94adn6LYas3tR/+hzerUvRmOxYeh6J49A/olJr9m27SpiW7ctwHHIGVXP+gb9uC1pbJo6hZ8adKtRZwsEA3q0byPxD7H7PNmAo3o173++tvvpsCIcxF/cie/xlmPLa9nuWngNwLf+ClDGR/V7zjyvwVZVhHxjZ7ymBAKhUqHRtox0qnR5Uajzrf8A+cFgnRBtLfvfdO/5OdZA9Cbi7+M13AD766KOY1y+99BIZGRksW7aMo446qlO3HQo2A2E0e0z10ejs+Fuq464T8NURdK3Hmj6C7L4TCbTWUrtlJighUvJPjebzecrZueohlHAAtcZAdp/L0Zvb5gRm9b6Eqg3Ps/XbG0ClRq3WR/KYMjol1j0F3a7IHNw9hry1jhRay3fEXceYk0/R36dgLigl5PVQ9f5M1t1xJf0fnoE+NVLvggnXsP25h/jhyj+h0mhApabwsv/D1ndItJySa+9my7/vYuUlp6LSaFDrjZROvhdjVl7c7XaGkL8JlDCaPab6aPV2WjyVcdcJtNQSbFiLNfswsg+9hoC3htq1r6KEg6T02G2ub9NOyr+5L9r2WYdMRG/N6dR49kfItyv2PYa8NUY7gaaKuOsEPbW01qzFWjCKrCNvINBcTd3yGRAOktz/zHb5FSVM/cpXMaT2Qu9oGyGxFoxCa05Da0rG59xBw6o38TdVkXX4NQc2yL34xfHX7op/9K74V+6Kv18H8X8fJ/783eJ37aBh9a74R3VN/KHWXbHvcdZdY04i0Fged52Au5pg+SqsvY4i89TbCLoqqV/yLIRDJI0Yv2/b9bpQAq24ls8maeT5JI+6kJYdK6j98EE0Z/wDY27/XxvavtWjaS/7vYoO9nvZ+RRcNgVTQSmhFg81789kw51X0vfBtv1e3l+uoez5h1gz6U+g0aBSqcm/5P+w7trvmXv2R20wUvHGNHLGX4aiKFTMfAbCIQLO+k6N+Sfyu+/e8Yvfn998B2BPLpcLgJSUji+M8vl8+Hy+6Gu3293p9YpSFDQ6GxmlF0TOnFkLCfqdOMvnx3QA9KZM8gffQjjUQnP9Cqo3ziBvwORoJ6Bhx/8IB1vI6XcNGp0VT8NKqtY/T+6A6zFYcrsunv1g7TUAa6+2C5ktvQaw5voLqf1kDrnjLwGg5qO3ad74Iz3+7370aVk0r13JjumPoU9Oi57lqvjvC4Q8zfS69TG0dgfObz9jy7/vovddT2IuOHjOlLejKGj0dtL7X4xKpcbgKCLoa8S5dV5MB0BvySJ/1J2Egy00Vy+jZtUL5I6YclB1AvaboqA22Ekb9rdI7MnFBFsaca3/IO4fwrrlM/C7dpIz9vaYdHvJMdF/6x35aE1JVC55gEBzNTprZqeH8cvtiv/QPeLf+EHcDkDdihn43TvJOfpn4jcmUfnZQR6/EkZjcpA65u+o1BoMGaUEPQ24V7y7zx0AUAAwF4/AMSRyBtaQXoyvah1Na+Z1WQfgl7D0GoBlt/2etecAfvy/C6lbMIeccyL7vdp5b+PZ9CMl19+PPj2y39v50mPodu33dPYkiq+5m7Lpj1I7721QqUk+/FhMRb06PJN8UOjuv/vuHv++krsAJcTvqgMQDoe59tprGT16NAMGDOgw3/3338/dd9/9q7en0VoBNSF/bAciFHCj1bW/CBRAo3egUqljdtp6UxahgBslHESljjSJSq2Nns03WgvxNW/DWbmQjNILCLTW4qpaTP6Q26MXBRssebS4N+GqWkJG6fm/Orafo7U7QK0h4GqMSQ+6GtrN6++IWqvFXNQTX3XkzGHY76N85nOUXv9Pkg4dBYC5sBTv9k1UvT8T+8BhtFaVUzPvHfo/NCM6dchc2IOmdT9Q+/FsCi+54QBG2TGN3gYqNSFfbNsH/W40+vYXgwFoDA5UKk1M2+ssOYT8rnZtr7NEduoGRxE+11Zc2z8hvf9FnRTN/tEYdsXe6opJD7W60RiT4q9jdKBVa2O/97YcQq2xsUPkj6C3ciU5Y29Fa977d8mw6+4bXfmH8BfHr9ojfnsH8a+YgbdqJTlHH3zxa4y7Yvc6Y9JDXmeHc/E1lmRUam3MdB9dch4hrxMlFNini3g1RhuoNehSYkf5dMl5+CrXdrDWgaex/fr9nkqrxVwYu9+rfPM5iif/E8chkf2eqaCUlu2bqPlgZvTEh33QCPo/PpOg2wkaDVqLjVVXnIE+o2tODMjvvnvHL35/DuJTB/tv4sSJrF69mpkzZ+41380334zL5YouZWVlv2h7KrUWg7WAFlfb3FdFCeN1rY+Zr787k62EQGstihKOpgVaatDoHDE7g3YUBSUcBCJ3gQFQ7dm1Van56UxZZ1NrdViKe9G0ellbFcNh3KuXY+m1b2fjlHCIlrIt6JJSI6+DQZRQENWec/bUaghHPq+wv3VXWmwelVqNEu6a2CPb02KwF+JtaDv4UJQwLfVrO5yvb0zqQcBbE9v23qpIx2Avba+goIQDB67yv5JKrcWQXERLzY/RNEUJ01KzBmNqj7jrGNN6EWiujo29uQqNMSkau6Io1C2fgad8GTlH34zO8vPT2fzOyLSLjv4AdwaVWoshqYiW2j3ir91L/Km9CHj2iL8pTvwrZuCpWEbOkfsZvynpV0S071QaHfr0Ulp3/hBNU5QwrTt/wJDVO+46xqw+BFyVMbEHnRVozMn7fAcflUaHIaMHgcbYqRZBZwVaW9dMe4TIfs9c3IumNbH7vaY1yzH3/HX7vXZzldVq2O0z+4nWnoTWYqNpzTKC7kYcQ0f/8oD2g/zuu3f8neqnuwB11iLi+t10ACZNmsT777/PokWLyMvb+1xwg8GA3W6PWX6ppJxjcVd/jrvmK/zeSmq3vIES8mHLiJzJqd74EnXb343mt2cdRSjopW7rW/hbqvE0rKKx/CMc2UdH89Rtf5cW10YCrfX4POWR1+6N2NJHAJERA50xnZotr9PatI1Aay2N5Z/Q4lyHJWXwL45lf2Wecg61C9+nbsmHtJRvY/sLjxD2tZB2dOTC2K1T/8nON56J5q94+yVc3y/FV12BZ+t6tj51L77aKtKOiUx90pgtWPsOoey1p3GvWYGvpoK6xR9S/+k8koZHrucw5hRiyMpl+3MP07zpR1qryql6fybuVd+RNPyILosdIKnweJp2foq7/Av8zRXU/fhqpO1zI3+Qq1c9T/2Gttt0OvLHEgp4qFv3Bn5PFZ7a73FumYsjv214t37D27Q0rCfQUoevaSf1G96mtWE91uy2u6MEfS587h0EvDUA+Jt34nPvIORv7qLIwdHrJJq2LKZp22f43eXULX8JJejDWhRpp5ql02hY9WY0v730WEL+ZupXvoq/qRJv5Uqca+dg7zEumqd+xQyad3xJxmFXoNIZCbY6CbY6ox3eQHM1jT++i69xKwFPLZ6K5dQsfQZjWm8MSQVdFjuAo+dJNG1dTNP2XfGv2BV/4a74v51Gw+rd4i/ZFf/3u8W/fg720t3iXzmD5rIvyRixl/jX7hH/d7vid3Rd/I4hp9H04yc0r1uEv2En9YufQQn6sPWNfI9rP/k3jV+13fLSNuBEwq3NNHz2AgFnBd5t3+Fc9ja2gSdF84T9Lfhqt+KrjdzpLOiuwVe7lWBTbTSP/ZDT8Wz6gqY18wk4K3H/MBfvtu+wDTixiyKPyDj5HOoXvU/9px/SWr6NsumPEG5tIXXXfm/bf/4ZmZ+/S+U7L+H+IbLf825dz7ap9+KvqyJ1bOx+r+L1p2n6MbLfq1/yIQ2fzcMxrO06tvrFc/FsXIOvupyGzz9m67/vJP2kszHmdGHbd/fffTePX/y+/OanACmKwlVXXcXs2bNZvHgxxcXFP7/SAWRLG0Yo0EzDjvcJBtwYLHnk9Lsq+gyAgK+B3Seh6Qwp5PS7irqtb+FeeS8afRKO7LExdxIKBZqo3vRSZDqJxojekktOv6swJ/UFQKXWkN13EvXbZ1O57j+EQz50xnQyelwc87CwzpZy+LEE3U4q3ppOwNmAubAHPW96ODoU7qurjjmrFWxuYvtzDxFwNqCx2LCU9KLvP/6DKa8omqf0mjvZ+cazbH3qHoLNbgzpWeSeeynpux4EptZq6TnlQXa+8QybHrqZcGsLhsxciq+4haRdw+ddxZo9gpC/icZN7xL0uTHY88keeh1aQ2QKULClIWaURmtKIWfYddSte5OdX96JxpCMo3AcScVtB0Ihv5uaVS8Q9LlQ60wYrHlkD70Oc1rb2UV32WIaN8+Jvq5Y+i8A0gdMwJ7bNZ0ga/5hhHxNNK55m2CrC0NSAVlH/h/aXRfIBb317P6915pTyT7qRupXvkb5x7eiMSVj73kCSX3arntxb14AQOXi+2K2lT78UmxFR6FSa2mpXo1r4zyUoA+NOQVL3jCS+57R6fHuKRr/j7vidxSQdcQe8av2iP+IG6n/4TXKP9kVf48TSOq9W/xbdsX/6R7xD90t/prVuDbtit+UgiV3GMl9zuj8gHdj6XkEoRY3jd+8EXkQWFoxmafeHp0CFGyq2zUaGaG1pZH5hzto+Hw65TOvQ2tJwT7oFByH/jGax1e7mep374i+bvzixci2+owl/dirIv8uOYzw0ZfjWv4ODZ+9gDYph4wTb8SY07cLom6TPCqy36ucNZ2gswFTYQ9Kb3o4emvPQH01qt3OOoY8Tex4/iGCu/Z75uJe9Lo7dr9XdNWdVMx8lu1TI/s9fVoWOedcGvMgsNbKHVS8+SyhZjf69CyyTr+Q9JPP6bK4QX733T3+TiN3AUoIlaIoXTdvohNceeWVvP7667z33nv07t02BO1wODCZ9u0+uW63G4fDQcmIR1Fru+e9dZOuPngvoutsjS9uTHQVEkqx6xNdhcRpP8OiWwlnde6tkg9myaPTEl2FhHLN7rpnxoiDQzjQwrZ3L8Plcv2qmQ8HSvTYa8yTnXbsFQ62sGXxVQdNzAeT3/wUoKeffhqXy8WYMWPIzs6OLm+++ebPryyEEEIIIRJH1cmLiOt3MQVICCGEEEIIsW9+8x0AIYQQQgjxG9WZd+uRuwB16Dc/BUgIIYQQQgix72QEQAghhBBCJIY8CTghZARACCGEEEKIbkRGAIQQQgghREIoqFA66X79igwBdEhGAIQQQgghhOhGZARACCGEEEIkhtwFKCFkBEAIIYQQQohuREYAhBBCCCFEYshdgBJCRgCEEEIIIYToRmQEQAghhBBCJIZKFVk6q2wRl4wACCGEEEII0Y3ICIAQQgghhEgMuQtQQsgIgBBCCCGEEN2IjAAIIYQQQojEkLsAJYSMAAghhBBCCNGNyAiAEEIIIYRIDLkLUELICIAQQgghhBDdiIwACCGEEEKIxJARgISQEQAhhBBCCCG6ERkBEEIIIYQQiaGm805Hy2nuDkkHYHcGDWg1ia5FQjQ+uDzRVUgYldWU6CoklrYb7yHDSqJrkFC+RcsSXYWEcTX0S3QVEio4ID3RVUgYzTZXoquQEIo/mOgqiIOIdACEEEIIIURiqOjEawA6p9jfg2586k8IIYQQQojuR0YAhBBCCCFEYsiTgBNCRgCEEEIIIYToRmQEQAghhBBCJISiVqGoO+dUfWeV+3sgIwBCCCGEEEJ0IzICIIQQQgghEkOeBJwQMgIghBBCCCFENyIjAEIIIYQQIjHkLkAJIR0AIYQQQgiRGCoVdNbFujIFqEMyBUgIIYQQQohuRDoAQgghhBAiMX66CLizll9g6tSpFBUVYTQaGTlyJEuXLt1rfqfTycSJE8nOzsZgMNCrVy/mzp0bff/+++9n+PDh2Gw2MjIyOOOMM1i/fv0vqtuBIh0AIYQQQgghgDfffJPJkydz5513snz5cgYPHswJJ5xATU1N3Px+v5/jjjuObdu2MWvWLNavX89zzz1Hbm5uNM+SJUuYOHEiX3/9NfPnzycQCHD88cfj8Xi6Kqx25BoAIYQQQgiRGAfZRcCPPvool156KRMmTABg2rRpfPDBB0yfPp2bbrqpXf7p06fT0NDAl19+iU6nA6CoqCgmz0cffRTz+qWXXiIjI4Nly5Zx1FFH7X8lDwAZARBCCCGEEN2e3+9n2bJljBs3LpqmVqsZN24cX331Vdx15syZw6hRo5g4cSKZmZkMGDCA++67j1Ao1OF2XC4XACkpKQc2gP0gIwBCCCGEECIx1J14F6Bd5brd7phkg8GAwWBol72uro5QKERmZmZMemZmJuvWrYu7iS1btrBw4UIuuOAC5s6dy6ZNm7jyyisJBALceeed7fKHw2GuvfZaRo8ezYABA35pZL+ajAAIIYQQQojfrfz8fBwOR3S5//77D1jZ4XCYjIwMnn32WYYOHcr48eO59dZbmTZtWtz8EydOZPXq1cycOfOA1eGXkBEAIYQQQgiRGF0wAlBWVobdbo8mxzv7D5CWloZGo6G6ujomvbq6mqysrLjrZGdno9Pp0Gg00bS+fftSVVWF3+9Hr9dH0ydNmsT777/Pp59+Sl5e3i8O60CQEQAhhBBCCPG7ZbfbY5aOOgB6vZ6hQ4eyYMGCaFo4HGbBggWMGjUq7jqjR49m06ZNhMPhaNqGDRvIzs6OHvwrisKkSZOYPXs2CxcupLi4+ABG98tIB0AIIYQQQiSEourcZX9NnjyZ5557jhkzZrB27VquuOIKPB5P9K5AF110ETfffHM0/xVXXEFDQwPXXHMNGzZs4IMPPuC+++5j4sSJ0TwTJ07k1Vdf5fXXX8dms1FVVUVVVRUtLS2/+vP7pWQKkBBCCCGEEMD48eOpra3ljjvuoKqqiiFDhvDRRx9FLwzesWMHanXb+fP8/HzmzZvHddddx6BBg8jNzeWaa65hypQp0TxPP/00AGPGjInZ1osvvshf/vKXTo8pHukAHADOnYtwln1MyO9Cb8kjvdd5GO0dD++EAl4atr5Lc+1yQgEvOmMKaT3HY0kdCICrfDGu8iUEWusB0FtySCk6Jfr+7hRFofKHJ/A2rCFrwBVY0w/pnCA74Kr9DGf1QkKBJvSmHNLy/4TRUthh/lDQS0PFXDzOHwiFPOj0KaTm/RGLo1+7vI1Vn9BQ8T6O9KNIyz9z1/oeGio/osW9jqDfiUZrwZI0kOSck9FoTJ0WZ0ecFYtwls2PtL01j/TSc/fe9sFdbV+/oq3tS8/BkrKr7SuW4Krcre3N2aQUnoolJfZOAS3uzTRsfY/Wpq2gUmOw5JEz8BrUGn27bXYW17YFOLd8SMjnQm8vIK3/BRiTSjrMHwp4aVj/Np6qZYQCHnSmVFL7nYclYzAAjZvex1O1DH9zFSqNDmNyD1L7nI3emh0tI+CpoX7tm7Q0bkAJBzGnDySt/wVoDY5Oj3dPru0LcG79KBK/LZ+0fvsQ/4a38VQvJ+TfFX/f87BkDNpV3iLcZYsIeOsA0NtySe5xGpb0QdEy3DsW01T5DT7XdpRQK0XjnkKjM3duoHF4Gr7DU/81oWAzOkMm9uzj0ZtyO8wfDrXSVLOY1qZ1hEOtaHQO7JnHYbT1aJe3ue5LmmoWYU4ZjiPr+Gh60N+Iu/oTAt6dKEoQg7UUe9bxaLTWTolxb1wbPsa17gNCLS70yQWkDr0YY2pph/lDfg+NP/wXT9l3hPzN6CxppB56IeacIQA0rnkP787v8LsrUGn0GNN6kjLkXPT2nGgZtUtfoKV6NaGWRlRa464858Xk6QpN38zB/eUsQs2N6DNLSD75Sgx5vTvMH25pxrngJbxrvyDc0ow2KYPkEy/H1GvEfpXpK/sR54IZ+HeuA7UGfVYJ6Rf+E7Uu/lSOzuBeOw/X6v8RanGiTy4k9bAJGNLbf4d/EvJ5aFw+E+/2pYR8zWit6aSOuBhzfuTvdEvVj7hW/w9/3VZCLY1kHHMDlsLhsWW0OGn47nVayn8g7PdgzOpL6sgJ6BzZ8Tb529QF1wDsr0mTJjFp0qS47y1evLhd2qhRo/j66687LE9RlF9Uj84kHYBfqan6W+o2vUVG7wsw2otxli2g4vt/UzDyH2j19nb5lXCQiu8fQ6OzkdX/72gNSQRb61Hv9kdca0gmtfRMdKaMyDaqvqRy1X/IH347Bkvszt618xM67wkae9fcsJy6ne+SXnAORnMhzpolVG6aRn6/W9DqbO3yK+EglZueRqO1kVnyF7Q6B0F/I+o4B+6tnh24675Eb4qNNxhwEwq4SM09Hb0pi4C/gbodbxEMuMkqmdBpscbTVPMtdZtnkdHzfIy2YpzlC6hY/QQFw+7uuO1/eByN3kZW38t3tX0Dam1b/FpDEqnFf4y0vQJN1V9RueY/5B96W7TtW9ybqVz1BMn5J5HW41xUKjU+z05Uv/CR579Ec8U31K2dSfqAizAmleDcOp/Kbx4hf8z9aA3xY6/85iE0ejuZh05Ea0wm2FIX871vaViPvfBYjEnFKEqIhnVvU7n0EfKP+idqrYFw0EfF0ocx2PLJGXkjAA0bZlP17b/JHX0bKlXXzWhsrlxK3do3SR9wIUZHCc7t86n89lHyj7qv4/i/fTgS/yFXojUkE2ypR63bre2NyaT0OgudJRNQaCr/gqplT5I/+i70tsjBdTjkx5w2AHPaABo2vN1V4cZocf2Iu/oTHNknoTPl4KlfSsP2maT3+DsaraVdfkUJ0bD9ddRaM8l5f0KttREKuFBrjO3y+lsq8DYuR2vIiEkPh/00bH8drTGTlMILAGiqXULjjv+SWjyha7/727+ifsVrpA//K4bUUlzrP6Jq0QPkn/owGmP7jqgSClK16AHURjuZR1yNxpRC0Bv73W+tWYe95zgMqaUo4RANP/yXqkUPkHfKg6i1kc/JkFKMtehwtOY0wv5mGle/Q+WiByg47XFU6q757ntWL6Fx3nOknHYVhtzeuL9+l5pXbiXnqufRWJPaxx4MUPPyzagtSaSPvw2NLZWgqwa10bpfZfrKfqTmlduwHzme5JOvQKXW4K/a2rXtvuVL6pe+TNrhl2BI74l7zVyqPr6PvDMfQ2PqoN0/vheN0UHG2OvQmFMIeupQ69vaXQn60CcXYus5lpqFj7QvQ1GoXvAwqDVkHnsDKr0Z9+r3qZx3L3l/fAS1rv1vSIh99bu7BuCBBx5ApVJx7bXXdsn2nGXzceQcgT17NHpLDum9L0Cl1tNU+UXc/O7KLwgFPGQPvBJTUg90pjRMyb0xWPOjeSxpg7GkDkRvzkRvziS15I+oNQZ8ri0xZfmaymgsm09Gn4s7NcaOOGsWY08bhT11JHpTFukFZ0dir/8mbn53/TeEgl6ySv+GyVqCzpCKydYDgzn2zGE45KNm2yukF4xv1zkwmLLJKvkrlqQB6AxpmG29SMk5BY9rNYrS8UM3OoOz/BMc2Udgz9rV9j13tX3Vl3Hzu6u+IBT0kN3vSkyOHuiMaZiSesW2fepgLCkD0Zt2tX3xGZG2d7e1fd3mt3DkHkNywYkYLDnozVnY0oehUus6PeafOLd+jD3/KOz5R6K35ZI+8CJUGj1NZZ/Fze8u+4xQwEPWsKswpfREZ07DlNoHg70gmidnxPXY849Ab8vFYC8gY/DfCLbU43NtA6C1cSNBbx0Zgy/BYM/HYM8nY/Al+FzbaKlf2xVhRzm3zovEn7cr/v674t/ZQfw7PyPk95B16CRMyT/F3zsmfkvmECwZg9BbMtFbskjt9SfUWiOtzs3RPEnFx5NcegrGpI7PNnc2T/03mJOGYE4ajM6QjiP7ZFRqLS3O7+Pm9zauJBxqITn/bPTmfLT6JAyWQnTG2Ptsh8N+nOXv4cg+pV3nIODdSSjgIinnNHTGDHTGDJJyTiPQWonfs62zQo3Ltf5D7KVjsZUcjd6RR9rwv6LSGmjasiRu/qYtiwn5m8k68jqM6b3RWdMxZfTFkNw2Upo9dkq0PENyIRkjLyforcfXsDWax97jGEwZfdFZ0zGkFJM88GxC3nqCntpOjzkay5fvYB16ItZDjkeXUUjKqVeh1hloXjEvbv7mFR8Tbmkm/bw7MRT0R5uchbFoEPqskv0qs/GjZ7GNPB3HkePRZxShS8vHMuAoVNquG/F0r/kAW69jsfUciz4pj9TDL0Gl1dO0cVHc/E0bFxH2ecg89gaMmX3Q2TIwZfXDkFIUzWPOO4SUoediKRwRt4yguxJf7UbSRl2CIb0HekcOqYdfghLy49ka/xjjN0ml6txFxPW7GgH49ttveeaZZxg0aNDPZz4AlHAQX/MOkgtPiqapVGrMKX1pdW+Ju46n7nuMjlJqN7yBp24lGr0Na8YIkgtPjHsGU1HCNNd8Rzjkx+ho22mGQz6qfnye9J7nJ2T6gxIO4vPuJDmr7Wl5KpUak60XrR38Qfa6VmO0FFG7YxZe1yrUWiu2lKEkZR4bE3tt2SzMjn6Y7b1prPr4Z+sSDrWg1hhRqTQ/m/dAUcJBfE07SM7fo+2T+tDa1EHb1/+A0V5C7abX8dR/j0Znw5oxnOT8vbR97bJI29sjbR/0u/E1bcWWMYKdK/9FoKUWnTmL1KIzMDk6Hoo+kJRwEJ9rG8mlp0TTVCo1prR+tDo3xV3HW70CY1IptatfxVu9ArXehi33MJJKT+7wzH04GLk4Sq23RLeLSoVK3bbbUqt1oFLR2rARc1r/AxXiXinhID739g7i3xx3HW/NSozJpdT++Cre6pWR+HNGklQSP35FCdNc+S3hoC+hB/t7UpQQgdZKrGmHR9NUKhUGSzF+78646/iaN6Iz5+Gq/Ahf00bUWjMme38saaNiYndXfoTR2gODtZjmus/32G5w17bafuMqlRZQ4feWYbB2zR01lFAQX8NWkvr9Ybd6qDFlDqC1bmPcdTzlyzGm9qTuu5fw7lyG2mjHWng4SX1P6/DMfTjgBUCjjz+9KRxspWnrErSWdLTm1F8Z1b5RggH8lRuxHzk+mqZSqzGWHIK/LH4HvGX91+jz+9DwwVRa1n2FxuLAPHAs9iPORqXW7FOZoWYn/p3rsAwcS9Xz1xFsqESXlo/j2IsxFnbNQ5SUUBBf/RYcg85oq6dKjSl7IL6a+O3u3fEdhvSe1H81Hc+O79AYbVhLjsAx8PR9HrFRQru+95q2kzsqlRqVWkdr9XpsvY795UGJbu93MwLQ3NzMBRdcwHPPPUdycnKXbDMUaAYljGaP6R4anY2gzxV3nUBrLZ7aZSiEyR58NcmFp+Asm0/jtg9i8vmad7L506vYvORKaje8RvbAK9DvNv2nbtN/MTlKsaYPOeBx7YtQ0AOE0Whjp/potTZCAXfcdQK+ejzO74Ew2T0uJyXreJzVi2IO8psaluP37iQl59R9rEczjVUfY089/OczH0ChQDMQRqOPjV+jtxP0763tl6MoCtkDriK54GScOz+hcccebe8pZ/PnV7P5s4nUbnyN7P5/j7Z9sDUyP7xh+/vYs44gZ+DVGKwFlP/wGP6W6nbb7Awhf1Pke7/HVBetwUHI10Hbe2vxVH0HSpjsEdeR0vM0nFs+onHjnLj5FSVM3Y9vYEzuicEWuVeyMakEtcZA/bq3CId8hIM+6ta+CUqYkM95QGPcm2j8e/zutXo7oY5+97vHP+xaUnqchnPrPBo3/S8mn69pJ1s+voIt8y6jbs3LZB06KTr952AQDnoBBfUeU33UWgvhoCfuOkG/k1b3WkAhpWA81rQjaG74JuYgv8W1hkBrFbaMsXHL0JlyUan1uGsWooQDhMN+3NULAIVwsPkARffzQr5dbb/HVB+N0U6oNX7bB5tr8JQtBSVM1pgbSe5/Bq51c3GumR03v6KEqV/+Coa0XuiT8mPec22cz9a3/sq2t/5GS8X3ZI+9GZWma87jhbxuCIfbTfVRW5MINTfGXSfYWIn3x88hHCLjz/dgP/p8mr58G/eSN/a5zGBjJQCuxa9iHXoSGRfeiz67BzUzbiZQX35gg+xAyOeOtPseU300JgehFmfcdYLNNXi3f4OihMk67iaSBv8J15r3cX6/71P3dEk5aCxpNC57g5CvGSUUxPnDe4S89YS88T/z36SfrgHorEXE9bsZAZg4cSKnnHIK48aN4957791rXp/Ph8/ni77e8xHRnUpR0OhsZPS+EJVKjdFWSNDnxFk2j5Ti06LZ9OYs8ofdTjjUQnPNMqrXvkjeITegt+TgqVtJS+N68ofd1nX1PiAUNFor6QXjUanUGMz5BAMunNWLSMk+kaC/kfqd75Dd48rImd2fEQ61UrnpWXTGTFJyTuyC+v9aChq9jYxef25re78T586PSSncre1NmeQPvY1wsIXmuuVUr3+JvEHXo7fkRC8kcmQfiT1rNADp1gJanOtwV31JWvEfExLZz1PQ6O2kD/pLpO0dRQRbnTi3fEhKrzPa5a5b/Sr+pp3kjrolmqYx2Mk89EpqV7+Ma9snoFJhzRmJ3l7IQX8uQ9kV/4Dd42/EufUjUnqeHs2mt2SRP/quSNtXfUfND8+TO3LKQdUJ2H8Kao0lMlVIpUZnyiYUbMJT/xW29KMIBdy4q+aTUnhezOjO7jRaC8l5Z+Kq/JCqhm8BFSZHf7TGrN/AEL+C2mgnbfglqNRqDCnFBFsaca39gOSBf2qXu+67l/C7dpIz7o5279kKR2POGhhZf91cqr94gpzj7uzSi//3i6KgsSSR8odrUKk16HN6EnLX4f5iFo6xf97nMgCsw07GekjkonB9dg9at67As3weScf9tbNq/6soyq52P/yySLunlRDyNuBa/T+SDzl7n8pQqbVkHnM9dV9MY8frfwOVGlPOQEy5Qzq38qJb+F10AGbOnMny5cv59ttv9yn//fffz9133/2rt6vRWUGlJuSP7UCEAk0dTsvR6B2o1JqYoW+9JYuQ340SDkb/AKrUWvTmyIVwRlshvqZtOHcuIKP3hXgb1xNoqWXL59fGlF21ehrGpJ7kHXLDr47t50Qu9lMTCjbFpAeDTWh07S+CBNDo7KhUsbHrjJmEgu5dU4rKCAWb2bnu4d3WCtPavAVX7eeUHPJwdN1wqJWKTdNQa4xklfytS6f/RGKxAurI2eDdhPxutPq9tP0e8evN2fHb3rRH25cvJKPXn6Nl682xd4DQm7MItjYcqPD2SqO3Rb73e5ztD/pc7UYFousYktq3vTWbkM8VEztA7epX8NSsJHfUzWhNKTHlmNMHUDj2wcjnrtKg0ZnZ9sk1aM3pBzDCvYvGv8fvPuh3o+nod29o/7uPF79Krd11ETAYHEX4XFtxbf+E9AGJuc5nT2qtGVC1O9sfDnrajQr8RKON7Cd3j12rTyUc9ESmFLVUEg55qNvywm5rKfi9O/A2fEdW35sinSZrCRk9J0ZGIVRq1Boj1esfR2NPOvCBdkBj2NX2e5ztD7W6414ADKAxJqFVa2KmfejtOYRanSihYMwZ/LrvXsJbsYKcY2+PO7VHrTej1pvR2bIwpvZk29uX4S37DmtR54+Aasx2UKsJNTtj0sPNTjTW+KPuGmsKaDSo1G37Z116AeHmRpRgYJ/K1NhSouvtTpdWQNDVNdc/aAz2SLu37NHuLS40pqS462hNSbBHu+uScgm1tG/3vTGklZB7+oOE/V6UcBCN0U7F/25Fn9bxHcd+c9R03jmcg/zcUCL95j+asrIyrrnmGl577TWMxn27Iv7mm2/G5XJFl7Kysl+0bZVai8FaQEvjumiaooTxNq6Nztnek8nRg0BLLYrS9sS4gLdmV8dgLzsERYnMgQaSC04kf/gd5A+7PboApPU8h8w+f/lFsewvlVqLwZyHt6lt/qOihGlp2oDRUhR3HaOlmIBvj9h9tZGOgVqLydaLvL5TyOv7f9HFYM7HmjKUvL7/t8fB/9OoVBqySi/Zp9GCA02l1mKwFdDibJv7qihhvM51GG0dtL29tH3bt1TvW9vvmgOtNaai0Se1m+7j99agM6bEW/uAU6m1GBxFeOt+3K2KYVrq12JMin8dgjG5BwFvdWzsnqpIx2BX7IqiRA7+q5aTc9iN6PZyUK/R29DozHjrfiTka8KSOeTABLcPVGotBnsh3vrYtm+pW9vhfH1jck8C3po94q/e1THouO2V3X73BwOVSoPOmI1vt+t8FEXB59mG3hz/sfY6Ux4hf2PMbfBC/gbUWisqlQa9pYi0kktJK7kkuuiM2ZgcA0gruaTdNRJqrRm1xojPs41wyIPR1qtTYo1HpdFiSCmmpWpNNE1RwrRUr8aY1jPuOsb0XgSa9/juN1WhMSVFDwIVRaHuu5fw7PyOnGNuRWfNiFtWLAVQUMKBXxPSPlNpdeize9K6ZWVbDcJhWreuRJ/fN+46hoJ+BBsqUHZ7QmqgrhyNLQWVVrdPZWqSMtHYUgnUxV5jEqgvR5u0L5/Tr6fSaDGkltBauaqtnkqYlsrVGDLit7shszfBpj3a3VWJxpT8i6ZtqfVmNEY7AVclvvrNmAuG7X8gQuzmN98BWLZsGTU1NRx66KFotVq0Wi1LlizhiSeeQKvVEgq1vzOMwWBo91joXyop/zjclZ/hrvwSv6eS2g2voYT82LIj0zOqf5xO3eZ3ovntuUcTCnio2/gmfm81nrofaNw+F0fumGieus3v0OLcQKClDl/zzuhrW+ZIIDLX2mDNjVki6SnoTGm/OJb9jj1jDE11X+GuX4q/pYq6srdQwn5sqZF6Vm97lfrytjnOjvTRhIJe6nbOxt9ag8e1BmfVfBxpRwCg1hgxmLJjFpVaj0ZjxmCKnPEOh1qp2Pg0SshPRuF5hEOtBANuggF3zI62S+LPHYe78nPcVV/h91ZSu/H1SPxZkbNx1etepG5r2zxfe/bRhIIe6jbvavv6VTTu+BBHzphonrqtsyNt31qHz1Meee3agC0jcpcIlUpFct5xuMoX0ly7DH9LDfXb3iPQUoU964iui734eJrKluDe+Tn+pgrqVr+MEvRhy4/UoXrlc9Sveyua31E4NvK9X/M6/uYqPNXf49z0AY7CY9piX/0KzeVfkXnI5ag1JoKtLoKtLsIhfzSPu+wzWhs3E/DU0LTzS6qX/wdH8fExzwroCknFJ+yK/wv8zRXUrXkFJeTDlrcr/u+fo379rGh+R8FYQn4PdWvfwO+pwlPzPc7NH+AoaIu/fv0sWhrWE/DW4WvaSf36WbQ2rMeac1g0T9DnwufeQcBbA4C/aSc+9w5C/q6bB29JHYnXuQKv8wcCvjrclR+ihAOYkiI3X3CWz8Fd3XZnFEvKUMKhFtxVHxP01dPatJHmui8xJw8FQK0xRO/s89OiUutQaUzojG0HeF7n9/i95QT9jXidq2jc+Q6WlJFoDV1zEexPHL1PomnzIpq2fIrfVU7dty+iBH1Yi48GoOarp2lYOTOa395jHCFfM/XLXsHvrsRbvgLnmvew9zwumqf+u5do3vYFGYdPRKU1EmxxEmxxEg5GvvuB5hoa17yHr2ErQU8drbUbqP7iCVQaffRZAl3BdviZNC//kOaV8wnU7qDx/ScJ+1ujU3Pq3nkI5/zp0fzW4acSbmmm8cNpBOp20rLhG9yfzcQ6/LR9LlOlUmEbfRZN37yHd81nBOorcC6YQbCuDMuhJ3RZ7Pb+p9C0YSFNG5fgd+6k/svnI/u8nmMAqP30KRq+e70tf+/jCPmaafjmJQKuCrxly3H+8C72vm3PtggHWvHVb8NXvw2IXDfgq99GsLkumsez9StaKtcQaKrGs/1bqj7+J+aC4ZhzB3dJ3F1C7gKUEL/5KUDHHnssq1atikmbMGECffr0YcqUKWg0nTs1xJY5nFCgiYatcwj63RiseeQMujp6H/iAryHmC6gzppAz+BrqNv0X97d3o9En4cg7luTCtjnsoUAT1WtfjEyp0JrQW3PJGXwN5pT2D8tKJGvKoYSCHhorPyQYcGMw5ZLd4/LoMwCC/kZUuz2jQKtPJqfH36nb+S476x5Eo3PgyDiapMx9v5OBz1uGz7sdgB1rYq/1KOh/O7ouPBiwZQwnFGimYftubT/gZ9p+4NXUbX4L97J/oDEkRW7nmb9b2/ubqF7/EkH/rra35JIz8GrMyW1tn5Q3DiUcpG7zW4SCnsh2B16LztR102CsOSMJ+Zto3PAuQZ8Lg72A7BGTo1Pfgi31Mffo1ppSyRlxPXU/vsHOz25HY0zGUXwcSaUnR/O4d0QOGiu+/lfMttIH/Q37ro5FwFNFw/pZkQdpmdNI7nEajuLj6WrW7BGR+Df+FH8+2cOva4u/tSF2yosphZzhk6lbO5OdZXegMSTjKBpHUklb/CG/m5ofnifY6kKtM2Gw5ZE9fHLM3Y3cOxbRuKntwumKbx4AIH3gX7HndU0H0OToRzjkobl2CaGgB50hk5SCc6MP5AoFXOz+bBKNzk5KwXm4q+dTu+U5NFoblpThWNJG7dd2g756mqoXEQ61oNEnYU0bjSUl/u0TO5O1cBQhXxONq2YRbHVhSC4ka8wUtLsuEA1662N+91pLKtljb6J++SuUf3gzGnMy9t4nktS37SDYvekTACoXxO7T0kdehq3k6MhdX2rX417/EaGAB43RgSm9DznH3dnh1KPOYBlwNGGPC9fCVyIP7coqIePCe6PTdUKumtjfvSOdjAvvpfGjZ6l8+gq0tjRsh52B/Yiz97lMAPuoP6IE/TR+9AzhliZ0WSWkX3QfupSuewiateRwwq1uGlf8l1CLE0NKEZnH3xydAhT01MPuv3lrGlnH30LD0hmUv3cjGnMKjn4n4RjYds2Pr24zVR/9I/q6YenLkW31OJr0I6+MlNvixLX0FUKtTjSmZGw9jiJpcPtrR4TYXyrlYHw82a80ZswYhgwZwuOPP75P+d1uNw6Hg5Ij/x3zUKbuRHF5E12FhFFZu2eb/0SxHaQXEHaF8O9u97dfvNvj37a0OzANPrhOqHS1YJ+uHTk5mGi2xb9j0+9d2O9l+2sTcLlcv2rmw4Hy07FX4eWvxTwg7UAK+71sf+aCgybmg8lvfgqQEEIIIYQQYt/95qcAxbN48eJEV0EIIYQQQvwMBRVKJ83VV5BrADoiIwBCCCGEEEJ0I7/LEQAhhBBCCPEbIM8BSAj5aIQQQgghhOhGZARACCGEEEIkhloVWTqrbBGXjAAIIYQQQgjRjcgIgBBCCCGESIzOfGKvPAm4QzICIIQQQgghRDciIwBCCCGEECIx5BqAhJARACGEEEIIIboRGQEQQgghhBCJodq1dFbZIi4ZARBCCCGEEKIbkREAIYQQQgiREIpahdJJc/U7q9zfAxkBEEIIIYQQohuREQAhhBBCCJEYcheghJARACGEEEIIIboRGQEQQgghhBCJIU8CTggZARBCCCGEEKIbkREAIYQQQgiRGGo673S0nObukHw0QgghhBBCdCMyAiCEEEIIIRJDRSdeA9A5xf4eyAiAEEIIIYQQ3YiMAOwuEAIllOhaJITbszPRVUgYe+6ARFchoVQV7kRXIWH8f+iV6CoklKXWm+gqJM6G+kTXIKHCq7ckugoJo8vOTnQVEiPYkugaxKfqxOcAyF2AOiQjAEIIIYQQQnQjMgIghBBCCCESQ54EnBAyAiCEEEIIIUQ3IiMAQgghhBAiIRSVCqWT5up3Vrm/BzICIIQQQgghRDciIwBCCCGEECIx5EnACSEfjRBCCCGEEN2IjAAIIYQQQojEUKk68UnAcg1AR2QEQAghhBBCiG5ERgCEEEIIIURiyHMAEkJGAIQQQgghhOhGZARACCGEEEIkhowAJISMAAghhBBCCNGNyAiAEEIIIYRIDNWupbPKFnHJCIAQQgghhBDdiIwACCGEEEKIhFDUKpROmqvfWeX+HsgIgBBCCCGEEN2IjAAIIYQQQojEkCcBJ4SMAAghhBBCCLHL1KlTKSoqwmg0MnLkSJYuXbrX/E6nk4kTJ5KdnY3BYKBXr17MnTv3V5XZ2aQDIIQQQgghEuOn5wB01rKf3nzzTSZPnsydd97J8uXLGTx4MCeccAI1NTVx8/v9fo477ji2bdvGrFmzWL9+Pc899xy5ubm/uMyuIB0AIYQQQgiRGKpOXvbTo48+yqWXXsqECRPo168f06ZNw2w2M3369Lj5p0+fTkNDA++++y6jR4+mqKiIo48+msGDB//iMruCXANwADgrFuMsn0/I70ZvySO9dDxGW1GH+UNBLw3b36O5biWhoBedIYW0krOxpAwAwFW5BFflZwR89QDozdmk5J8cfT/QWs/2726LW3ZWn0uwpg09sAHuRUvjCrwN3xIOedAa0rFmHIvOlN1h/nCoFU/d5/ibNhIOt6LR2rFkjMVgLQHAU/cF3vqvYtbR6FNIKf5r2zad3+NzryXoq0EJ+0ntMQm1xtg5Af4M19ZPcG7+kJDPhd5eQNqAP2NMLukwfyjgoWHd23gqlxEKeNCZUkntfz6WzMiOonHj+3gql+FvrkSl0WFM7kFqv3PQW9s+04CnhvofZ9LSsBElHMCcPpC0gX9Ga3B0ery7c9Z8irN6IaGAG70pl/SCszBaCjvMHwp6aah4n+bGHwiFPOj0KaTln4nF0b9d3saq+dSX/w9HxtGk5/8pmu6q/YKmhmX4vGUoYR/Fgx9AozV3Snw/p+mbObi/nEWouRF9ZgnJJ1+JIa93h/nDLc04F7yEd+0XhFua0SZlkHzi5Zh6jdinMoONVVQ8/pe4Zaedcwvm/kcd0Pj2xlmzBGfVgkjbm3NJzz8bo7Wow/yhoJeG8v/R7Pw+ss/TJ5OWfxaWpDhtX/kx9eVzcGSMIb3grGh6OBygvuwdmhqWoShBzPa+pBeOR6uzd0aIe9Wdv/vN9d/QVPs5oWAzOmMWyTmnoDfndZg/HGrBVfUJLe4fCYda0OqScGSfjMneq11ed82nuKvnY00dRVLOyTHv+Tw7cFd/gt+7E1RqdMYs0osvRqXWHfAY98a1YyHObfMI+V3orfmk9T0Po2Nv+3wvDZtm46le3rbP7z0eS/qgSHlli3CXLSbQsuvvvTWH5JLTsKQP3LV+Mw2b5tBSv4ZgawMavQ1LxhCSS89Ao0vMvu+3yO12x7w2GAwYDIZ2+fx+P8uWLePmm2+OpqnVasaNG8dXX33VLj/AnDlzGDVqFBMnTuS9994jPT2d888/nylTpqDRaH5RmV3hF3UANm/ezIsvvsjmzZv597//TUZGBh9++CEFBQX0799+h/Z71lT7HXVb3yajx3kYbcU4yxdSsfoJCobehVbf/g+TEg5SsfoJNDobWX0vQ6tPIuirR61p+yFr9cmkFp2BzpQBKDRVf03l2mnkD7kFgyUHrSGZohEPxJTrrvqcxvL5mJO77vNvda+juXYxtsxxaI3ZtDQux7VzFinFf0WttbTLryghXDvfQq0xY8/5A2qdlVDAjVod+yPU6FNJyj9nt5TYLrwSDqK3FKO3FOOp+6wzQtsnzeXfUPfjTNIHXowxuQTnlo+p/OZh8sc+gNYQv+0rv3oYjcFG5rBJaI1JBL31qHfbibfUr8NefAzGpBKUcIiGdbOo/Pph8sfch1prIBz0UfH1QxjsBeSMuhGAhnXvULX0cXKPuB2VqmsG9ZoallO3czYZBeMxWgpx1iyhYuN/KOh/G1qdLW7sFRv/g0ZrJav0r2h1DoL+hpjv/U9aPdtx1X6B3pQTpxw/FkdfLI6+1Jf/r1Ni2xee1UtonPccKaddhSG3N+6v36XmlVvJuep5NNakdvmVYICal29GbUkiffxtaGypBF01qI3WfS5T40gn94bXY8ptXvYh7i9mYewxvLNDjmpqWEZd2WwyCsdjtBThrF5ExcapFAy4o+O23/BUZJ9X8rfIPs/fgFpjape3re1z271XV/Y2Xtcaskr/hlpjonbHf6na9Dx5fSd3Spwd6c7ffa9zFc7KD0nO+QN6cx7NdV9Ru3UGWb2vQaO1tsuvhIPUbp2BRmshteBcNDo7Ib8TVZwTNn7vTjwN36IzZrZ7z+fZQd22l7GlH0VSzimgUhNoqaKrn/LUXLWUuvX/Jb3fnzE6SnBu/4TKZY+TP/rejvf5yx5Fo7eROfjvaI3JBFti9/laQzIpPf+EzpwJKDRVfEnVyqfIH3UHemsuwVYXIZ+T1F5no7fmEGipp27tqwRbXWQNuaILo+88ahWoO+tP166vSH5+fkzynXfeyV133dUue11dHaFQiMzM2O9hZmYm69ati7uJLVu2sHDhQi644ALmzp3Lpk2buPLKKwkEAtx5552/qMyusN8f+ZIlSxg4cCDffPMN77zzDs3NzQB8//333HnnnQe8gj8nFApx++23U1xcjMlkorS0lHvuuQdFUbpk+87yBTiyRmPPPBy9OZv0Hueh0uhpqo7fq3NXf0ko6CG7798x2UvRGVMxOXphsLadQbGkDsKSMgC9KQO9KZPUotNRawz4mrYCoFKp0eodMUtz/UqsaUO79Ex4S+N3GB0DMToGojWkYc08DpVaR6trddz8ra5VhEOt2HPPQGfORaNzoDfnozVmxGZUqVFrLbstsX8ozSlDMaeORLuXkYau4NwyD3vB0dgLjkRvyyV90MWRtt/xadz87h2fEgo0kzX8akwpPdGZ0zGl9cHgKIjmyTnsBuz5kfIMjgIyhlxCsKUen2sbAK0NGwl668gYcgkGez4Gez4Zh1yKz7mNlrq1XRE2AM7qRTjSDseedhh6UzbpBeegUutpqv86bn53/deR732PSzFZS9AZUjHZemIwxx7ohUM+qre+TEbheXEPkJIyx5KcdRwGS1FnhLXPmr58B+vQE7Eecjy6jEJSTr0Ktc5A84p5cfM3r/iYcEsz6efdiaGgP9rkLIxFg9BnlexzmSq1Bo0tJWbxrv0Sc/8jURvaH0x3Fmf1wl1tPyrS9oXnRtq+roN9Xt1XhEJesksvw2Qr3a3tY88ah0M+qre8REbRee06B6FgC+66r0jLOxOzvTdGSwGZRX+m1bOF1uatnRZrPN35u99U9yWW5GFYUg5FZ8wgKfc0VGodnoblcfN7GpcTDnlJLTwfg6UQrT4Zg7UY/R777nDIR0PZLJLzzkAVp2PoqvwQa+ph2DOOQmfMRGdIx5w0EJW6aycxOLfNx553JPbcI9Bbc0jv9+fIPr/i87j53eWfEwp4yBoyEVNyT3SmNEwpvTHY2g5GLRlDsKQPQm/JRG/JIrXnmag1BlqdWwAw2HLJGnIllowh6MwZmFP7ktLjj3hqv0cJh7ok7t+DsrIyXC5XdNn9bPyvFQ6HycjI4Nlnn2Xo0KGMHz+eW2+9lWnTph2wbXSG/f713HTTTdx7771MnjwZm63tbMcxxxzDU089dUArty/+9a9/8fTTTzNjxgz69+/Pd999x4QJE3A4HFx99dWdum0lHMTXvIPk/BOiaSqVGnNSH1qbtsRdx9PwA0ZbCbWbZ+Jp+B6Nzoo1fTjJeSfEPXurKGGa65YRDvkx2uMPM7Y2b8fv2Ul66bkHJrB9oCghgq3VmFNGRtNUKhU6cwGB1oq46/ibN6Mz5tBcvQBf8ybUGhMGe1/MKSNiYg/5G6nf9DSoteiMOVjSj0STgGH+vVHCQXyubST3OCWaplKpMaX1p7Vxc9x1vNUrMSb3oHbVK3irVqA22LDlHkZSj1M6PHMfDrYAoNZZdm03ACpVzB8+tVoHKhWtDRswp3f+CJASDuLzlpGcfVw0TaVSY7b17vBgzONcjdFaTO2Ot/A4V6HRWrGmDCU5a1xM7LU73sLs6I/Z3puGyvgH04mmBAP4KzdiP3J8NE2lVmMsOQR/WfxOWMv6r9Hn96Hhg6m0rPsKjcWBeeBY7EecjUqt+UVl+is2EqjaTMopEw9sgHuhhIP4PGUkZx0fTVOp1JjtvWn1dNT2qzBaiqnd8WZb26cOIznruD3a/k3MjgGY7X1oqPgopgyfdwcoIUz2tilWelMWWn0yrc1bMVqLD3Ck8XXn774SDhJoqcCefmQ0TaVSY7SW4veWxV2n1b0OgzkfZ/n7tDStRa2xYE4ahC39yJjYnRXvY7T1wmgtxV2zOKaMULAZf8tOzEmDqdn8LEF/A1pDOo7McRj2Mu3qQFPCQXxN20kuaZuapFKpMaX0jR6s78lbuxJjUgm1a1/HW7sCtc6GLXskScUndfz3vuq7yN/7pNIO6xIOelFrjajUml8f2EGgK+4Carfbsdt//jgiLS0NjUZDdXV1THp1dTVZWVlx18nOzkan06HRtLVH3759qaqqwu/3/6Iyu8J+jwCsWrWKP/7xj+3SMzIyqKurOyCV2h9ffvklp59+OqeccgpFRUWcddZZHH/88V1ye6VQoBkItzs41ejsBP3uuOsEWuvw1C1HIUx2/4kk55+Ms3wBjWWxt4vyecrZ/OW1bP7iKmo3vUF238vRm+Of8XZXfYnOlIXJ3vEO40ALh1oApd1UH7XGQjjoibtOKODC17wBCOPIOxNz6ihaGr7Du9uZM60xG3v2STjyzsKWOY5QwIVzxxuEw/5OjGb/hfxNoITR7DHvXmuwE/K54q4T8NTgqfwWlDDZIyeT0vMPODd/xP+zd99RclRnwod/VdU59+Q8GgWEBAiJIJDIIHLGBowBYZJtcCCsMWB78XpZg1lsjJf1Z3AAbGPWxgSTowAhEFFCZCSUw+TQeTpV1fdHj1pqTY+QxMy00LzPOX2krr5Vfd+5VdV164bqW/ZY0fSmadD94f04gpOw+3J3Sx3BCaianZ5PHsDIpjCyKbo//juYBnqy+PcONz0bBww0S2F3B83qJZuJFl0nk+om3rcE0zSonfgtgrXHEup4kb7NLnQ29m8urz95JLP/hemJCBjGoK4+qieAHusruk62r43Ex6+CoVN13o34Dvs60YUPEZn/fzu8zdjiZ7FUNmFvmvqFY9pWenbjOW+Lsrf4yGaGOOele4j3vYuJSe2kywjWHUeofR59bZsu8qO97+TKvuGU4t+biYBiGdTnXbP4yGaLf+9IGMv7vqEnAAN1i64+qsUzsF8Mls30kQh/jIlBxbjz8VUdTqz7NaKbXeQnQu+T7m/FX3N08W2kc/t/pPNF3MH9qBg3F5ujlq5V9+THyY0GPR3LnfO36Nq71XN+opt4xyLAoHafKyibcBKhNc/Rt/KJgnSp6HpWzvsOK1/4Nt2f/JWa6Zdj8wzuBpbLR5S+lU/gaxi9MT9jic1mY99992XevHn5ZYZhMG/ePGbNmlV0nYMOOojly5djGEZ+2bJly6itrcVms+3QNkfDdlcAAoEAbW1tg5a/++67BVMejZbZs2czb948li1bBuS6Ir366qscf/zxQ66TSqWIRCIFr1Fjmmg2L1UTz8XhacZbuR/BxuMItxX2Zbc5q2mc8SMapv8QX+2hdCz7M+nE4L+7oaeJdb2Nr/qg0Ypgx5kmqubCU30MVkcNDt/uuMoPJBl6L5/E7hmP3TsZi6MSm7sFf8MZmEaKVGRpCTM+XEw0m4/KvS/EHhiHp/4AgpNOJrLmpaKpuz/4K+noeqr33dTPU7P7qN73O8Q7lrDq6W+z6pnLMDIJbP7mnfyBJyaaxUtV89dwuJvwlu1DsPYYwl2vAZBJ99G97mGqW+bmWjR2NaaJ5g5QdsoV2Oom4d7zMHyHfo3oO0/u0OaMTIr4By/hmXHs5ycuNTNXYahqPmeg7PclWHss4a5ct4lMuo/utQ9R3fKNXbPsx/K+b5poFjfB+lOxOetxBfbCW3kYsd63Acimw4TanqKs8cyhB/MOdOd1l+2Pu2wfbM46AnUnYLVXkOhbNFqR7KCBc/7Uudh94/DUzCTYciKRdfMLUtncNTTOuoGGA36Er/FwOj+8m3RscEu6ke2nbfH/YHXXUTaheGX5y2hjC8BIvbbX1VdfzR/+8Af+/Oc/88knn3DZZZcRj8e58MILAZg7d25BF6LLLruM3t5errjiCpYtW8aTTz7JTTfdxHe+851t3mYpbHcXoK997Wtce+21/POf/0RRFAzD4LXXXuMHP/gBc+fOHYk8btV1111HJBJh9913R9M0dF3n5z//Oeeee+6Q69x888387Gc/+8LfrVk9gJq7O7UZPRMpOgAYQLP5URS1oPnP5qxBz0QwjWy+a4eiWrA5c33jHZ5mUtHVhFpfpGpiYVyxnncxjDS+6gMYTbk+usqgu/2GHi86ABjILd8ids1WhqHHMU0dRRncnKlqDjRbED1T/C5oqWg2LyjqoDs/2VRkUKtAfh17AEXVCuK3eurQU+GCsgfo+uCvxDveo/6g67E4ywq246rak+ajbkVPRUFV0axuVj/3fSyuymGMcGiaxQ2o6NnCO556Jlp0ECTkWsUUpTB2m6MGPRvJd6vQs1HWfXLrZmsZJGMrCHcuYMI+t43aAOfPo7l8oKrosVDBciMWQvMEi6/jKQNNK2iyt1Y2YcT6MLOZ7d5m/8cLMDMp3NOP+sLxbI/cQE8VfYu73Xo2MuRsPJrVP7jsNzvnpeJrc2X/8S2brbWx7F9hwr6351pZzSx6NlHQCqBnI1gso9c9cCzv+7lxCSrGFnf7jWys6ABgANXqRaHwnG9xVGJkYwNdijZgZON0Lv/d5lskHV9DrOdN6vf8ab61yWIvPL9Z7JVk06PT6gmg2Ty5c/4WrftbP+cP3vet7lr0dHjQ731uEDDYfeNIhVcTXvsClVM3XVMZ2SSti25HtTiomf6dUR//MJacffbZdHV1ccMNN9De3s706dN55pln8oN4165di7rZqOXGxkaeffZZrrrqKqZNm0Z9fT1XXHEF11577TZvsxS2ew/aWKtpbGxE13WmTp2Krut8/etf5yc/KT415Uh64IEH+Nvf/sb999/PHnvswZIlS7jyyiupq6vjggsuKLrO9ddfz9VXb5o5IhKJDBohvi0U1YLd00R/aCme8ulArttGIrSUQO3hRddx+sYT7Xob0zTyJ4VMf2euYrDVA9rENLKDlkbaX8NdNm1Qk/xIUxQNi6OadGItdu+kXA5Nk0xiLc7AjKLrWJz1pCKfYJomykC1XM/0oWruohf/kJv5Qk+HsfuK/8CUiqJasPvHkej+GHdtbtpV0zTo7/4Y/7jiF2WOsknENrxeWPbx9oGKgWVgGybdH95HvH0RdbOuw7qVi3rNnivzRPfH6Kko7prif/fhpqgW7K5G+iPL8ASmDeTbIBFdSqCqeLO00zN+YPrGzWJPduYujlQLLu9uNE69rmCdztX3Y3NUEdiir3SpKRYrttpJJFcuwTVlNgCmYZBctQTPzOJdOOxNU4l/8BKmYaAM/HBkujegectQLLk7n9uzzdjiZ3FOPhDNHRiBCIemqBbs7kb6o0vxBHNT15qmQSKy7HPK/p2hy943mcY9flSwTueq+7A5qgnU5sYJ2F1NoGgD35vbz9PJDrLpvlHr/w9je99XVAtWZx3J+Eqc/ly3M9M0SMVW4i4vfgPK7moiEXq/IPZsqgfV4h34/ZxA9aTvFqzTu/4RrPaK/DgBzRpAtXjJpgq7GGdT3Ti8g6cSHSmKasHubSbR8wnuqtw+aJoG/b2f4m86oug6jsBEYm1vFpZ9oiNXMdjK771pFv7eG9l+Whf9GkW1UDPju6jartVSpChK/ppgJLa9I7773e/y3e9+t+hnL7/88qBls2bN4o03ik8EsC3bLIXtrgDYbDb+8Ic/cMMNN/DBBx8Qi8WYMWMGkyZNGon8fa5rrrmG6667jq99LTcAdq+99mLNmjXcfPPNQ1YAhpr/dUcE6o+ic9mfsXuacHjHEWp9EVNP4a3O9evqWHovmj1AxbjTAPDVHkqobT7dK/+Jv+5wMv2d9K1/Bn/dphNI9+p/4Q7ugcVehqEniXa9TX/4M+r2+F7Bd6f7O0lGllO7x+gNAtycM7gf0fansTqqB6YBXYRpZHD4c88riLQ9hWrx4KnM/TA6A3uTDL1LrPNFnMEZ6Ok+Ej1v4gzuk99mrPNlbJ4JaFYfRjZGvHshKAoO7+75NEY2jpGNo6dDQO6HQFVtqFZv0akFR0pg/LF0LvkD9kALjsB4wiufy5V9U26QXMe7v8fiCFI+5UwA/OOOILz6Bbo//Bv+lqPJxNsJffYE/pY5+W12f/BXYhtep2b/K1AtDrLJXIyq1YWq2QCIrF2AzVuLZvOR7Fue2974YwqeFTDisVcfQefq+7C7G3G4mgl1voxppPEOXAh0rPorms1PRX2umdpXeTChzlfoXvcw/qpDyaS66Gt/Hv/ARZOqObBvMfWhotpQLe6C5dlMBD0TIZPqAiDd34aq2bHYggN3Z0eHd/YZ9DzyS2z1k7DXTyb6+iMY6SSeGbnBsd0P34rFW07g6NzzKzz7n0T0rcfpe/pOvAecQrZ3A5EFf8d7wKnbvM2NMj2tpNZ8SOW5N45avJsLVB9J56q/Ync15acBNY0U3ooDAehY9Rc0q5+KhlxsvqpDBsr+QfxVh5FJdtHX9hz+qsOAbSt7zeLEVzGL7nUPo2puVM1B99p/4nC3jGoFAMb2vu+tmE3v+oexOeuxOeuJ9byOYaRxD5zDe9c9iGb14R8YJO4um0ms501CbU/hKT+QbKqHaNd8POW5fUXV7Kha4R1QRbWiaq78dKCKouCtPJhIx4tYnTXYHLXE+94lk+qmvOmcUYl7o8C4o+n88G7svmYc/hbCa1/InfPrcl1wOz74ExZHgPJJuec3+BsPJ7z2Rbo//Tv+piPJJDoJrXoSf9Omm0Q9nz2Eq3wvLM4yjGySWPubJPuWEhx/JbDp4t/UU1TvdQlGNomRTQK5luidpYIovny2uwLwyiuvsPvuu9PY2Fhw1zyTyfD6669z6KGjOzAlkUgUNMUAaJpWMBhjJHkr98s9qGPtE2TTEezuBur2/F6+C1Am1VvQCc1qL6Nuj+/RveqfRBb/F5o9gL/uCIINm/ry6pkoHcvuJZuOoFkc2Fz11O3xPVzBKQXfHe1YiMUewBUoXD5aHL7dMfUE8e7XMPREbmaGhq/muwAZmQibz9OsWX34G75KrPMl+lb/GdXiwRncB1fZpgchGdko0dYnMIwkqubE6qwn2HRuwVSg/aElBQ8LC6/7OwDemuPylY/R4Kk/IDcga+kjZFNh7L4mag/4t/wDubL9PSibxW9xllN3wA/o/uh+1s//CZojiH/80QQ2m0kosuZFAFpfL3zOQ+X0i/E15ioWmXgbvZ/+Ez0dx+qqIDjpZPzjR7cvuLdsH/RsjN7Wp8hmItidDdRNuizfDSST7ivc721B6iZdTvf6h4l8/As0qx9/1WEEa+YM9RVFhbteLRg8umHZbwCoaj4XX8XodYNz73kYRjxM+MW/5h7aVTOeqvP/K99dRw93Ftx5svgrqTr/v+h75ve0/e4yLN4KvAeehu/gM7d5mxvF330WzVeBY8I+lIK3bN+Bsn+SbCaK3VVP3aTvbCr7VC+bH/dWW5C63S6ne93DRD66Gc0WwF99OMEhBn0OpaLxK/Sg0L7ijwUPAhttY3nfdwX2wsjGiXTMG3gQWC0VLXMHusNCNhNm86GFFpufinFzCbc9TUfvb9GsXjzls/BuNpPQtvBWzMY0soTbnsbI9mN11lDZ8g0s9rLPX3kYeWpmoqdj9K14lGwqgt3bSO0+V2465yd7Co97Rxl1+15F99J/sP71/0CzB/E3zSHQsmmMop6O0vnhn8imwqgWJ3ZvA7X7XomrPDejWyqyhlQ4N8vQ2lcLW8qaDvkFVmfFSIc94kZjFiAxmGJu54T5qqpSXV3NI488woEHHphf3tHRQV1dHbo+uvPSfuMb3+CFF17grrvuYo899uDdd9/lm9/8JhdddBG33HLL52+AXBcgv9/P+ANvQ7WM3h3knUm4q/jUlWOBb7fRqzTsjJTWURwEv5NJnzJ6XQh2RrbHl5c6C6Vjjs5Nop1VchT7z+9s7LWlfYZMqRjZfla9+D3C4fA2TYk50jZee7Xc9hSqc2RasYz+OKuuPmGniXlnskNtR1/72tc46qijuPfeewuWj9bDtzZ3xx138NWvfpXLL7+cKVOm8IMf/IBvfetb3HhjaZrHhRBCCCHEttnZZgEaK7a7C5CiKFx//fUccsghzJ07l/fff59f/epX+c9Gm9fr5fbbb+f2228f9e8WQgghhBDiy2a7WwA23uU/44wzWLBgAQ8++CDHH388oVBouPMmhBBCCCF2ZSooI/TasX4uY8MX+tPMmDGDt956i1AoxFFHje581EIIIYQQQojtt90VgAsuuACnc9NA2ZqaGubPn89RRx1FU1PTsGZOCCGEEELsumQMQGls9xiAe+65Z9Ayu93On//852HJkBBCCCGEEGLkbFMF4P3332fPPfdEVVXef//9raadNm3asGRMCCGEEELs2lQl9xoJprQADGmbKgDTp0+nvb2dqqoqpk+fjqIoBVN+bnyvKMqoPwdACCGEEEIIse22qQKwatUqKisr8/8XQgghhBDii5InAZfGNlUAmpubi/5fCCGEEEII8eWyzbMALVu2jLfeeqtg2bx58zjiiCOYOXMmN91007BnTgghhBBC7LpkFqDS2OYKwLXXXssTTzyRf79q1SpOPvlkbDYbs2bN4uabb5an8QohhBBCCLGT2+ZpQN955x1++MMf5t//7W9/Y7fdduPZZ58FcrP/3HHHHVx55ZXDnkkhhBBCCLHrURQFZYRu1Y/UdncF29wC0N3dTUNDQ/79Sy+9xMknn5x/f/jhh7N69ephzZwQQgghhBBieG1zBaCsrIy2tjYADMPgnXfe4cADD8x/nk6nC6YGFUIIIYQQYmsUdWRforht/tMcfvjh3Hjjjaxbt47bb78dwzA4/PDD859//PHHjBs3bgSyKIQQQgghhBgu2zwG4Oc//zlHH300zc3NaJrG//zP/+B2u/Of//Wvf+XII48ckUwKIYQQQohdjzwHoDS2uQIwbtw4PvnkEz766CMqKyupq6sr+PxnP/tZwRgBIYQQQgghxM5nmysAABaLhb333rvoZ0MtF0IIIYQQohhpASgNGR4hhBBCCCHEGLJdLQBCCCGEEEIMF4URbAEYmc3uEqQFQAghhBBCiDFkuyoA2WyW//zP/2T9+vUjlR8hhBBCCDFGqMrIvkRx2z0I+NZbb2Xu3LkjlZ+SSoTaUTV7qbNREn5PU6mzUDKRzz4udRZKytuye6mzUDLWxR2lzkJpWcbur6Ppc5U6CyVl68qWOgslo3f2ljoLJWHoyVJnQexEtrsL0JFHHsn8+fNHIi9CCCGEEGIM2TgL0Ei9RHHbPQj4+OOP57rrruODDz5g3333LXgYGMApp5wybJkTQgghhBBCDK/trgBcfvnlANx2222DPlMUBV3Xv3iuhBBCCCHELk+eA1Aa210BMAxjJPIhhBBCCCGEGAVf6DkAyWQSh8MxXHkRQgghhBBjiKIqKCM0Xc9IbXdXsN2DgHVd58Ybb6S+vh6Px8PKlSsB+Pd//3f+9Kc/DXsGhRBCCCGEEMNnuysAP//5z7n33nv57//+b2w2W375nnvuyR//+MdhzZwQQgghhNh1ySxApbHdFYC//OUv/P73v+fcc89F07T88r333ptPP/10WDMnhBBCCCGEGF7bPQZgw4YNTJw4cdBywzDIZDLDkikhhBBCCLHrk1mASmO7WwCmTp3KggULBi1/8MEHmTFjxrBkSgghhBBCCDEytrsF4IYbbuCCCy5gw4YNGIbBww8/zNKlS/nLX/7CE088MRJ5FEIIIYQQu6KR7KsvLQBD2u4WgFNPPZXHH3+cF154AbfbzQ033MAnn3zC448/ztFHHz0SeRRCCCGEEGJMymazvPDCC9x1111Eo1EAWltbicViO7zNHXoOwCGHHMLzzz+/w18qhBBCCCGEquReI7XtL7s1a9Zw3HHHsXbtWlKpFEcffTRer5dbbrmFVCrFnXfeuUPb3e4WgAsuuIBXXnllh75MCCGEEEIIsW2uuOIK9ttvP/r6+nA6nfnlp59+OvPmzdvh7W53C0A4HGbOnDk0Nzdz4YUXcsEFF1BfX7/DGRBCCCGEEGOTzAK0dQsWLGDhwoUFz94CGDduHBs2bNjh7W53C8C//vUvNmzYwGWXXcY//vEPxo0bx/HHH8+DDz4o04AKIYQQQggxTAzDQNf1QcvXr1+P1+vd4e1udwUAoLKykquvvpr33nuPN998k4kTJ3L++edTV1fHVVddxWeffbbDGRJCCCGEEGODoo7s68vumGOO4fbbb8+/VxSFWCzGT3/6U0444YQd3u4X+tO0tbXx/PPP8/zzz6NpGieccAIffPABU6dO5de//vUX2bQQQgghhBBj2q9+9Stee+01pk6dSjKZ5Otf/3q++88tt9yyw9vd7jEAmUyGxx57jHvuuYfnnnuOadOmceWVV/L1r38dn88HwCOPPMJFF13EVVddtcMZE0IIIYQQuzYZA7B1DQ0NvPfee/z973/n/fffJxaLcfHFF3PuuecWDAreXttdAaitrcUwDM455xzeeustpk+fPijNEUccQSAQ2OFMCSGEEEIIIcBisXDeeecN7za3d4Vf//rXnHnmmTgcjiHTBAIBVq1a9YUyJoQQQgghdm2KoqCM0K36kdruaPrLX/6y1c/nzp27Q9vd7grA+eefv0NfJIQQQgghhNh2V1xxRcH7TCZDIpHAZrPhcrlGrwIA8M477/DAAw+wdu1a0ul0wWcPP/zwDmXkyyze8zaxntfRszGsjmr8Ncdhcw39bARDTxLpeIlk9FMMvR/N6sdfcwwO76RBaaNdrxHtfBF32Uz8tcfml+uZGJGOF0jFV2LqaSz2cjyVB+P0TRmRGIcS6nyFUMeL6JkINmc9lU1fxeFuHjK9nk3Q2/oEsb730fU4VlsZFY1n4PbvMShtX/vz9Gx4HH/VYVQ2fiW/PNz1GtHeRaQS6zCNFC17/wLN4hqR+D5Pom8xiZ63MPQ4FnsV3uo5WJ21Q6Y39CTxrgWkosswjCSaxYen+kjsngkAxLpeJdGzsGAdzVZG+fhL8u/7Q0tIhj8hm+rANNJUTPo+qjZ0i9xICa+ZR2jVM+ipMDZvIxVTz8URGD9kej2ToHfZQ8Q7FqOn41id5ZRPOQd31bSB7b1EZN1LZBLdANi89QQnnoy7clp+G5G1LxNte5NUeA2mnmTcnP9Fs5am7MMrXyC0/Olc/L5GKqadhyM4Ycj0eiZO78cPEW97Bz0zEP9e5+Ku3huAvmWPE29bRDrahqJZcZRNonzqWdi8uf0pk+hi7fM/KLrt6v2+g6d+5vAHOYRQ+3xCrS/kjntXPZUtZ+HwjBsyvZ5N0LvucWK9S9CzCaz2Miqav4I7uCcA4fZXCHcuIJPqBcDmrKWs/njcwU3nhWw6TM/aR0iEP8XQU9gc1QTrj8VTPmNEYy0mvPZFQqufRU+HsXkaqZhyDg7/5+z7yx/J7fsby37y2fl9O7zuJSLrXibT3wOAzVNHcPzJuCv3Glg/Ru/yx+jv+YhsshfN5sVdNZ3ghNNGff8Pd71KuPMl9EwUm7OO8obTP+ec309f21PEQ++j6wmstjLK60/F5Z86KG2ofR69bU/iqzyEiobTB9aP09f2LP3RpWTTfagWD+7AnpTVHo+q7Xj/5x0V6VpIuHM+ejaKzVlLef2p2N1NQ6bXs/2E2p8hEfoQXU9gsQUpqz8ZV5Hf6lDHS4TansZbcTDlDacM+tw0TTpX3k1/dCmV4+biDuw5rLGViowB2Lq+vr5Byz777DMuu+wyrrnmmh3e7nZXAP7+978zd+5cjj32WJ577jmOOeYYli1bRkdHB6effvoOZ2Qor7zyCrfeeiuLFi2ira2NRx55hNNOO60gzSeffMK1117L/PnzyWazTJ06lYceeoimpqEPyuHSH/6IcMfzBGpPwOqsJ977Jj1r7qdq0uVoFveg9Kah07P6PlSLm2DjV9EsXvRMGFWzD0qb7m8l0bcYi71q0GehDY9iGEnKGs9GtbjoD39I37qHsIy/eKsXoMMp2ruY7vWPUNV0Ng53M6HO+bR+9v9o2uMnWKyD56Y1jSytn/0/NIuHmgkXYbH6yaZ7UbXBP2DJ+BrCXa9hc9YV2U4at38Kbv8UejY8PiKxbYtk5BNinS/hrT4Gq7OWRO87hNY9QPn4S1CLlb2pE1r3AKrmwld/aq7ss2EUtfDiXbNVEGg6K/9e2WKyLtPIYvO0YPO0EO8qzVO5Y21v0f3JP6jc83wc/vGE1jxP29u30XjoTVjsvkHpTSNL29u/RLP5qJ5xORZ7kGx/D6p10w+4xRGkbLevYnVXAybRDa/RvugOGg/6D2zeXIXa0NO4KvbEVbEnvcseGq1wB4lteJPuj/6PymkX4AhOILTyWdpe/yWNR90ydPwLb0Wz+6je/7tYnEGyiR7UzS7e+nuW4ms5CkegBdM06P3kQdpev5XGI29GtdixOMtpPvY3BduNrHmZ0GdP46qetuVXjpho9yK61zxMVcvXcHjGEWp/idZP/pem6T8d+rj/5A40q5ea3S7BYg0MHPeblb09SHnjqVgdVYBJtOtN2pbdReNe12F35c4BHSv+gpHtp3byt9EsHqLdb9P+2Z9odFyL3d04WuETa3+L7qUPUDn1vIF9/wXaFt1O40H/NXTZL7oNzealeu9vY3Fs3Pc3lb3FHqRs0lewugb2/daFtC/5Xxpn3YDNU082GUZPhSjf7Uxsnjoy/T10f3If2WSYmumXjV7sfe/Ss+FRKhvPxO5qItz1Cu0rfk/jlOvQhij79hV3olo8VLd8Ay1/zh984Z6MryXS8zo2R+Hvl56JkM2EKas/BZujmmy6j+51D9KViVDd8o2RCrWoeN8Selsfp7zhDOzuJiJdC+hY+Sfqd78GzeoZlN40snSs+AOa1UPluPPRrD70TF/R+FOJdcR63sDqGPr3O9K1YFjjEV9ekyZN4he/+AXnnXcen3766Q5tY7unAb3pppv49a9/zeOPP47NZuM3v/kNn376KWedddaIXHDH43H23ntvfvvb3xb9fMWKFRx88MHsvvvuvPzyy7z//vv8+7//+1bHKAynWM8buIIzcAWnY3VU4q89EUW1kuhbUjR9IrQEQ09S1nQWdlcjFlsAu7sZq6OmIJ2hp+lb/wiBuhOLnizS/etwl+2PzVWPxRbEW3kIiuYgnWwfiTCLCnW8hL9iNr6KA7E5a6lsOgtFtRHteaNo+kjPG+jZOLUTL8XpGY/VXo7TOwn7Fq0lhp6iY9VfqGo+p2jlIFB9BMGao7G7x41EWNss0fsOTv80nIG9sNgr8NYci6Ja6Q9/UDR9MvQ+hp7E33A6NlcDms2PzdU0cNGziaKoaBZP/qVu0brhKtsPd/mBWB2DK0ejJbTqWXyNh+JrOASbt57KPeaiaDai64v/QEXWL0BPx6nZ57s4g5Owuipwlk/G7tt0znBXT8ddNQ2buxqbu4by3b6CanGQDK3Ipwm0HENwwok4AkPfaR8NoeXP4Gs+DF/zodh89VTu/Y1c/GuKV8gia15BT8eomfl9nOW7YXVV4qzYHbt/U/x1s36Ar+kQbL4G7P4mqmZcQra/h1QoN55KUVQsjkDBK962CE/9TFTL6LUAhdrm4a+aja9qFjZXLZUtX8sd952vF00f6XodPZugdrdv4fROwOoox+mbhN3dkE/jDu6FO7gnNmcVNmc15U2noKp2UrHV+TTJ6Er8NYfh8IzD6qigrOF4VIuLZHztSIdcILT6eXwNh+CrPxibp47Kqeflyr711aLpIxteRc/EqZn+ndy+76zAWTYZu3dTpcVdNR135Wb7/qQzUDU7ydBKAOzeemqmX467ajpWVxWu8imUTTydeNd7mMbgBwSNlHDnfHzlB+Itn4nNWUNF41dRVCvRnreKpo/2voWeTVAz/iIcnhas9jKc3olFz/lda/5GReNZg853NmctNeMvxO3fA6u9Aqd3EsHa44mHP8I0Ry92gHDXArzlB+At3x+bo5ryhjNy8fe+XTR9tPdtDD1BVcsFuf3WXobDM2HQja1c/P9HeeNXh2zVSCVaiXQtoHyzm0O7io0tACP12lVZLBZaW1t3fP3tXWHFihWceOKJANhsNuLxOIqicNVVV3HkkUfys5/9bIczU8zxxx/P8ccfP+TnP/7xjznhhBP47//+7/yyCRNG5+LANHQy/W14Kg7KL1MUBbu7hUz/+qLrJKPLsLnqCbc9TTKyDNXiwunfE0/FbJTNnlgRbnsah2cSds94ol2Df1hszkb6wx/j8ExC0RwkIx+BkcXuGropdjiZRpZUYh3B2qPzyxRFxeWdTDJWfAB4PPQhDk8LXWv/STz0AZrFg6dsX4I1cwpi71r7T1z+PXD5JtPb9uyIx7IjTFMnm2zHXX5gfpmiKNhczWT6ix+QqdgKrM46ou3Pk4otR9VcOHxTcJUfUBB/Nt1H9/LfgmLB6qzDU3kYmnXwncVSMY0sqcgaghNOzC9TFBVnxdSCi/XNJTqX4AhOoOvj+0h0LEG1efHWHUBg/AkFsee/wzSItb2NkU2V/GJ/S6aRJRVeTXC3k/LLFEXFWbkHyb7lRddJtL+Lo2wiXe//hUT7u7n4G2YRmHRi0fgBjEw/AKpt8J1FgFRoFenwWiqn7Vj/zx1hGllS8XUE6zd1R1QUFZd/d5KxlUXXife9j8PbQtfqfxDvez933FfsR7DumKHLvmcxhpHG4WnJL3d4xxPrWYw7uCeq5iTWsxjTyOD0De46OVJMI0squobg+E0P31EUFWfZlPzF+pYSXUtwBMbT9cn9JLreRbV68dYeQKDl+KHjb38HQ09vdd83sglUiwNF1b54YNsgd85fT6D6qPwyRVFxencjmVhddJ14+EMc7ma61z1EIvwhqsWDJ7gPgeojC2LvXv8QTt8UXL7dCHU8/7l5MfQkquZAUUYndsjFn05swF91RH6Zoqg4PJNIxdcUXac//DF2dzM96x8hEf4YzeLGHZyBv+rwgvh71v8Ll293nN5JhNrnDdqOYaTpXnM/5Q2nFW1lE8Pvt7/9Lbfeeivt7e3svffe3HHHHcycWbyb5b333suFF15YsMxut5NMJvPvY7EY1113Hf/617/o6emhpaWF73//+3z729/+3Lw89thjBe9N06StrY3//d//5aCDDhpirc+33RWAYDBINBoFoL6+ng8//JC99tqLUChEIpHY4YzsCMMwePLJJ/nhD3/Isccey7vvvktLSwvXX3/9oG5CI/L9egIw0SyFP9CqxU16oB/zlvR0H6lMCJd/L8qaz0FP9xJqexpMHW/VYQD0hz8kk2yjcrN+31sKNn6FvnUP0b70l4CKoloJNp2JxV42XOFtlZ6NAwaapfBkpFm9pJMdRdfJpLrJRnvxlO1H7cRvkUl107X2ATB1yupylbyNffsbphTv67yzMLK5st/ybpVqcZNN9BZdR8+E0BNhHL6pBBq/ip7uI9r+PGDgHqhEWp11+GqPx2IrQ8/Gife8Rt+a+ylrubBoN7FS0NNRMA00W2GlxGLz0R9rK7pOJtFFtv8TPHUHUrvflWQSnXR99FdMQ6ds0qn5dKnoeja8/nNMI4Oq2anZ57v57j87Cz01EL/dX7DcYvfTH91K/N2f4GmYRe2BV5OJd9L13p8xjSxluw/uOmmaBt0f/g1H2STsvoYiW8y1Klg9dTjKRu8CWM/GAGNQdw/N6iXdX7z1MZPsIZtahqdif2onX04m2UXX6n/kjvuGTZXIVGID6z/8JaaRRdXs1O52KTbXpu4QNZMupv2zu1n1zg9BUVFVG7W7fRObY3AXyZGip2PF9327j/74EPEnuskmP8VTeyC1+1yR2/c/+RumqVM2YVM/71R0PRveunnTvj/9cmye4q18ejpK38on8DUcOnzBfQ5dHzjnb1n2Fi+ZZGfRdbKpXpLp5XiC+1Az4VIyqW661z0Epk5wYExbrO9dUon11E/etucG6dkYofbn8ZXP+kLxbK8h47d6yKSKx59J95KNrcATnEH1+IvIprrpWf8vMHUCNbmbZ7G+JaT7N1C72/eG/O7eDY9jdzfjKjJWblews40B+Mc//sHVV1/NnXfeyQEHHMDtt9/Osccey9KlS6mqKn6+8fl8LF26dLPvLfziq6++mhdffJH77ruPcePG8dxzz3H55ZdTV1fHKacMHu+xuS2vZxVFobKykiOPPJJf/epX2x/ggO3uAnTooYfy/PO5GvqZZ57JFVdcwaWXXso555zDUUcd9TlrD6/Ozk5isRi/+MUvOO6443juuec4/fTTOeOMM5g/f/6Q66VSKSKRSMFrtJiYaBY3/roTsTlrcfr3wFtxMPG+xQDomTDhtucINpyOog5dP4t2voxhJClvPo/KCRfjKT+AvnUPkRni4nvnYKJZvFQ1fw2Huwlv2T4Ea48h3PUaAJl0H93rHqa6ZS6qai1xXkeAaaJqLrw1x2J11ODwTcFdMYv+zbqL2T3jcfh2x+Kowu5pIdDwVUwjSSq6dOjtfhmYJprNR+We38DuH4endibBCScRWfdyQTKbu4bGg/6Dhlk/wdd0BJ3v/5F0dENJsjysTAPN7qVy+oXYAy146g8guNspRFa/VDR59/t/IR3ZQPV+lxf93NDTxNa/ga959C4Ad5yJZvVSNf7rODxNeCv2JVh/LOGOwpZNm6OaxmnX07DnNfiqD6FjxV9JJzZVqHrXPYGRTVA35Xs07nktgdojaf/sT6QSO/v+MbDvT52L3TcOT81Mgi0nEllX+Btlc9fQOOsGGg74Eb7Gw+n88G7SscGtiUa2n7bF/4PVXVdQgdg5magWDxUDXV49wRkEauYQ6c5NdJBN99Gz/hGqxp23Ted8Q0/SvuKPWB3V+QrEzi13g7C88SvYXQ24g9PxVx9JtDvXTTabDtG74TEqm88ZMv5E+COS0eWU1e/sZb3ruO2227j00ku58MILmTp1KnfeeScul4u77757yHUURaGmpib/qq6uLvh84cKFXHDBBRx++OGMGzeOb37zm+y999689Vbx7nObMwyj4KXrOu3t7dx///3U1u74mM/tbgH43//933yzxo9//GOsVisLFy7kK1/5Cj/5yU92OCM7wjAMAE499dT8U4enT5/OwoULufPOOznssMOKrnfzzTcPS1elXP90ZeCu2Gb5ysYHtQpspFk8oGgFzX8WewVGNoZp6KT72zD0OF0r/rDZWibpxBrivW9TO/VH6JkQ8d63qZzwrXz/caujhlRiHfHedwjUnchIyw1wVtGz0YLleiY6ZBOlZvWhbBG7zVGDno3kuxTp2SjrPrl1s7UMkrEVhDsXMGGf24bsLjHacnf+lYGWgE2MbLzoAODcOu5BZa/ZyjH0OKapF23OVjUHmq0MPT14FoBS0WxeUFT0dGHFOZuODLornl/H7kdRC2O3emrRU2FMI5uv7CqqZWAQMNj940iFVxFe8wKVe14wQtFsP80+EH8qXLA8mwqjOYaI3xEYtO9bvYPjB+h6/y/E29+j/uAfYXEWb9GLt76NoafwNu548++OyJ3XVPRMkePeVryb2pDHfSYyqOw33s13eJpIxdYQan+JqvFfJ5PsItwxn8ZpP84PCra7G+iPrCDc/gpV488ZgWiLxGLzFN/3U5+z729Z9u5a9HSRfd81sO/7xpEKrya89gUqp27q4mVkk7Quuh3V4qBm+ne2epNouGnawDl/y7LPRosOAIZcy5CFLcreXo2ejea7FOnZGBs+vW2ztQySsZVEul6jZfp/59c19CRtK36PqtqpHn/hqHb/ga3En4kNagnPr2PxDi57R1VB/EY2RuvSzQf3G6Tiq4h2L6R575voj64gm+5l7Qc/Ldh21+q/EnG3UDvp87uQ7OxUJfcaqW0Dg2702u127PYik6+k0yxatIjrr79+0zZUlTlz5vD668XHOUGui09zczOGYbDPPvtw0003sccem1psZs+ezWOPPcZFF11EXV0dL7/8MsuWLePXv/71F4xwx2332aOsbNMPkqqqXHfddQAkEgmWLFnC7Nmzhy93n6OiogKLxcLUqYXTiU2ZMoVXXy0+IAvg+uuv5+qrr86/j0QiNDZu/ywSiqphddaSjq/G6dsdyPXNSsVX4S7bv+g6Nlcj/eEPMU0z30SUTfegWjwoqobd3ULlhG8VrBPa8BgWe0V+nIBpZAYyUHjEKCiAud1x7AhFtWB3NdIfWYYnkJuBxDQNEtGlBKqK35V0esYT7V2EaRr5E2Im2Zm7QFAtuLy70Tj1uoJ1Olffj81RRWCLcQKlpigaFkcN6fga7APTt5pmrqLmDO5TdB2rq4Fk+OOCstfTvagW95A/ZoaRRk+HUH07T9Ovolqw+5pJ9HyCuzoXq2ka9Hd/gr/5yKLrOIKTiLW9UVj28Y6BisHQpyHTNDGN7PAH8QUoqgW7fxyJro9x1+4LDMTf9TH+ljlF13GUTSK2fov4Yx1o9kA+ftM06f7gr8TbFlF30PVY3ZVD5iGy5hXcNTPQisw6M5IU1YLd3Uh/eCmestz0paZpkIgsJVBd/IaL0zueaPc7RY77rZc9bCp7w8hNNz3oHKCojNY5L/d1FuzegX2/Kjf9qGka9Pd+ir/piKLrOAITibW9WRh/Yvv3fSPbT+uiX6OoFmpmfBdVG91W0tw5v4H+6Ge4A3sN5NGgP/oZ/oqDi67jcLcQ61tcGHuqC82SO+c7vZNo2L1wGsOutX/Haq8qGCdg6Enalt+FolqonnBxSVqIFdWCzVVPMrY8P/2maRokY8vxVhS/7nG4xxHrW1IQfzbVnasYqBac3onUTb66YJ3utQ9gdVTlxwn4q4/AW17Y97x16W2U1Z+M0zd4KtUvI5URrAAM/LvlNd5Pf/pT/uM//mNQ+u7ubnRdH3QHv7q6esjZdiZPnszdd9/NtGnTCIfD/PKXv2T27Nl89NFHNDTkunDecccdfPOb36ShoQGLxYKqqvzhD3/g0EOLXy9tfo36eW677bbPT1TEsN0++OyzzzjkkEPQ9dEblW+z2dh///0L+l0BLFu2jObmoQfDDlXz2xGe8gPp2/AoVmctVmcd8Z63MI0MruDA3N7r/4Vm9eIbGDjlLtuXeO/bRNqfxV22P9l0L7Gu13CX5yoMqmZH1baYFUa1oWrO/N1+i70CzVZGuPUpfNVzUC1OkpGlpOIrKSv/2rDEtS0C1UfQufo+7O5GHK5mQp0vYxppvOUHANCx6q9oNj8VA02XvsqDCXW+Qve6h/FXHUom1UVf+/P4ByoMqubAvsXsCIpqQ7W4C5ZnMxH0TIRMqguAdH8bqmbHYgsWnXp1pLjK9iPS9hQWZw1WRy2JvndygxL9uR/HSOuTuUFvA2M7nIHp9PctJtYxD2fZPujpPuI9b+AK7pvfZrTzJeyeCWgWP0Y2Rqz7VVAUHJvNGa1nYxjZOHom1yqQTXWhqDY0q2/U5sUOtBxL5/t/xO4bhyPQQnj185h6Cm9D7kKg470/YHEEKZ/8VQD8TUcQXjOP7k/+D3/zUWTiHYRWPIm/eVO3wZ6lD+Kq3AuLoxxDTxJrfYNk71KC+286EWZTYfRUmEwi1+c2HV2PanFgcZTl7s6OksDE4+hc/AfsgRYcwfGEVzybi7/pkFz8i+7C4gxSPjU3Y4e/5UjCq16g+4O/4R9/NJlYO6HPHsffsmkQfff7fyG2/g1qDrgC1eIgmwwBoFpdqJotny4T6yDZs5TaA7f9B2I4BWqPonPFX7B7mnLTgLa9mIu9MjcgvmP5n9FsASqacmM7fNWHEup4he7VD+KvOYxMsou+1mfx1xye32b32kdxB6ZisZVhGEmi3e/QH/mMut2/A+RaDKyOSjpX3k9F8xloFjex3vfoD39KYPLo3gENjDuazg/vxu5rxuFvIbz2hVz8dbnWmI4P/oTFEaB8Uu7ZJf7GwwmvfZHuT/+Ov+lIMolOQquexN+02b7/2UO4yvfC4izDyCaJtb9Jsm8pwfFXApsu/k09RfVel2BkkxjZXEu8ZvOO2s0Rf9VhdK35P+yuRuzuJsKd8zGNNJ6BC9TO1fdjsfkoq8sNkPdVzCbc9So96/+Fr/JgsqluQh0v4KvMHSeq5sC2xbTVuXO+K788d/F/J6aRoWrcuRh6EkMfiN3iGdUbQ/7KQ+ha+wB2VwM2VyORrldzv3ll+wHQtebvWKx+ggNj2rwVs4h0L6R3w2P4Kg4ik+om1PEivoExX7n4C2cAzP3eu/LLLVYvFGlh0awBrKM05m9XsG7dOny+TTdMhusaEGDWrFnMmrVpTMrs2bOZMmUKd911FzfeeCOQqwC88cYbPPbYYzQ3N/PKK6/wne98h7q6OubMGXzj6N13392m7/4iTzoevfbDHRSLxVi+fNPMGqtWrWLJkiWUlZXR1NTENddcw9lnn82hhx7KEUccwTPPPMPjjz/Oyy+/PCr5c/r3wMgmiHbOzz8IrLz56/kuQHomUnCnXrP6KW8+l3D7c8RX3IVm8eEun4lniDsIxSiKRnnT14h0vEjv2n9gGmk0W5BA/alFHyY2Urxl+6BnY/S2PkU2E8HubKBu0mVYBmasyaT7CmK32oLUTbqc7vUPE/n4F7kHoFUdRrCm+F3ToYS7XqWv7Zn8+w3Lcs2nVc3n4qs4YBgi2zYO3xQMvZ9416v5B4EFGs/MdwHSMxFg87L3EWg8k2jHi/SvugfV4sUV3BdX+aY8G5kokdbHB2a5cGJ1NhBsPq9gsHF/35KCh4WF1v4fAN6a43EO3JkbaZ7ambmBiJ/9i2wqjN3XSO3+V2EZ6AaRTfYWdnNzllG3/9V0f/J31q+7Ac0exD9uDoHNZlPR0xE63/8j2WQY1erE7m2gdv+rcVVsav2IrH2JvuWbZkRoffMXAFTudRG+huJ3IUeCp/4A9FSEvk8fHoi/idoDf4BloAtQtn/L+Mupm3UN3R/ez/qXfoLmCOAffwyBSZu660VWv5iL6bWbC76rcsYl+AYqFgCRta9gcQZxVpXmIUDein3Rs1F61z1BNhPF7qqnbvfv5LsAZVJ9bL7fW+1B6nb/Dt1rHiLy/k1otgD+msMJ1h2TT6NnonQs/wvZTARNc2Ab2KYrkKv4KqpG7eTL6Vn3KG1L78TQU1gdlVRNOD//MLHR4qmZiZ6O0bfiUbKpCHZvI7X7XLnZvt9T8KNscZRRt+9VdC/9B+tf/4/cvt80h0DLptnt9HSUzg//RDYVRrUM7Pv7XomrPLfvpyJrSIVzswytffVHBflpOuQXWJ0VIx02AJ7gDPRsjL62Z8hmI9id9dRM+Ga+22c2U3jOt9iC1E78Fj3r/8WGT3+JZvXjqzyUQHXxlsJiUon1pBK5qV7XfXxTwWeNU38yqhfB7uD0gQeTPTfwILA6qsdfnO8ClU2H2Hzft9gCVE+4hN4Nj7Nh6a+xWH34Kg/GX3X4qOX5y0BVTFRlZFryNm7X5/MVVACGUlFRgaZpdHQUjqfs6OigpqZmiLUKWa1WZsyYkb927e/v50c/+hGPPPJIfhbNadOmsWTJEn75y18WrQC89FLx8WHDSTFNc1j+6u+99x777LPPsLcAvPzyyxxxxOCm1QsuuIB7770XgLvvvpubb76Z9evXM3nyZH72s59x6qmnDlpnKJFIBL/fT83uP9xpZloZbS7H0N0NdnWRePEpW8cKb8vupc5C6dhGtx/xzkbpiH1+ol2U6Rub5/qNzK7Rm/xiZ2OYO1e3wtFi6EnWfnAD4XB4my6GR9rGa68jH3oGi3tkWu+z8TgvfuW47Yr5gAMOYObMmdxxxx1AbrxpU1MT3/3ud/Pd3rdG13X22GMPTjjhBG677bZ8nE899VTBtPbf+ta3WLVqFc8999yOBfcF7fQtAIcffjifV0e56KKLuOiii0YpR0IIIYQQYjiMxiDg7XH11VdzwQUXsN9++zFz5kxuv/124vF4fq7/uXPnUl9fz80351pr//M//5MDDzyQiRMnEgqFuPXWW1mzZg2XXJKbyt3n83HYYYdxzTXX4HQ6aW5uZv78+fzlL3/Z5v7777zzDg888ABr164lnU4XfPbwww9vf5BsRwVgywcRbGnVquIPfxJCCCGEEOLL4Oyzz6arq4sbbriB9vZ2pk+fzjPPPJMfGLx27VpUdVMXz76+Pi699FLa29sJBoPsu+++LFy4sGCCmr///e9cf/31nHvuufT29tLc3MzPf/7zbXoQ2N///nfmzp3Lsccey3PPPccxxxzDsmXL6Ojo4PTTBz9HZlttcxegzYMdcmOKMqqDgIeLdAGSLkBjmXQBGrukC9DYJV2Axp6dtQvQ0Q8/g3WEugBl4nGeP2P7ugDtbKZNm8a3vvUtvvOd7+D1ennvvfdoaWnhW9/6FrW1tTs8rf02D5/f8kEExV5fxot/IYQQQgghdkYrVqzIDx622WzE43EUReGqq67i97///Q5vd+eZWF0IIYQQQowpG2cBGqnXl10wGCQazT2Arr6+ng8//BCAUChEIpHY2qpbJRUAIYQQQgghdiIbL/QPPfRQnn/+eQDOPPNMrrjiCi699FLOOeccjjrqqK1tYqt2+lmAhBBCCCHErmlnmwVoZzFt2jT2339/TjvtNM4880wAfvzjH2O1Wlm4cCFf+cpX+MlPfrLD25cKgBBCCCGEEDuR+fPnc88993DzzTfz85//nK985Stccskl2/Qsgm0hXYCEEEIIIURJqCP8+rI65JBDuPvuu2lra+OOO+5g9erVHHbYYey2227ccssttLe3f6Ht7/DfJp1Os379etauXVvwEkIIIYQQQnxxbrebCy+8kPnz57Ns2TLOPPNMfvvb39LU1MQpp5yyw9vd7i5An332GRdddBELFy4sWG6a5pf2OQBCCCGEEGL0yRiAbTdx4kR+9KMf0dzczPXXX8+TTz65w9va7grAN77xDSwWC0888QS1tbUoyi721xVCCCGEEGIn8sorr3D33Xfz0EMPoaoqZ511FhdffPEOb2+7KwBLlixh0aJF7L77GH56qBBCCCGE+MIUxUQZofn6R2q7o6W1tZV7772Xe++9l+XLlzN79mz+53/+h7POOgv3F3x68nZXAKZOnUp3d/cX+lIhhBBCCCFEcccffzwvvPACFRUVzJ07l4suuojJkycP2/a3uwJwyy238MMf/pCbbrqJvfbaC6vVWvC5z+cbtswJIYQQQohdl4wBKM5qtfLggw9y0kknoWnasG9/uysAc+bMARj09DEZBCyEEEIIIcQX99hjj43o9re7AvDSSy+NRD6EEEIIIcQYM5Lz9X+ZnwMw0ra7AnDYYYeNRD6EEEIIIYQQo2CHKkcLFizgvPPOY/bs2WzYsAGAv/71r7z66qvDmjkhhBBCCLHrUhVzRF+iuO2uADz00EMce+yxOJ1OFi9eTCqVAiAcDnPTTTcNewaFEEIIIYQQw2e7KwD/9V//xZ133skf/vCHghmADjroIBYvXjysmRNCCCGEELuujbMAjdRLFLfdFYClS5dy6KGHDlru9/sJhULDkSchhBBCCCHECNnuQcA1NTUsX76ccePGFSx/9dVXGT9+/HDlS4yyVCZa6iyUjN3mL3UWSirb2lnqLJRMb+SzUmehpKqCe5U6CyWTbg+VOguiRBRlbM4NY5g75zTtCiM3W480AAxtu//ml156KVdccQVvvvkmiqLQ2trK3/72N37wgx9w2WWXjUQehRBCCCGEEMNku1sArrvuOgzD4KijjiKRSHDooYdit9v5wQ9+wPe+972RyKMQQgghhNgFyZOAS2O7KwCKovDjH/+Ya665huXLlxOLxZg6dSoej2ck8ieEEEIIIYQYRttdAdjIZrMxderU4cyLEEIIIYQYQ0Zyvn55DsDQtqkCcMYZZ2zzBh9++OEdzowQQgghhBBiZG1TBcDv3zRLimmaPPLII/j9fvbbbz8AFi1aRCgU2q6KghBCCCGEGNtkDEBpbFMF4J577sn//9prr+Wss87izjvvRNM0AHRd5/LLL8fn841MLoUQQgghhBDDYrunAb377rv5wQ9+kL/4B9A0jauvvpq77757WDMnhBBCCCF2XeoIv0Rx2/23yWazfPrpp4OWf/rppxiGMSyZEkIIIYQQQoyM7Z4F6MILL+Tiiy9mxYoVzJw5E4A333yTX/ziF1x44YXDnkEhhBBCCLFrklmASmO7KwC//OUvqamp4Ve/+hVtbW0A1NbWcs011/Bv//Zvw55BIYQQQgghxPDZ7gqAqqr88Ic/5Ic//CGRSARABv8KIYQQQojtJrMAlcYOPwgM5MJfCCGEEEKIL5ttqgDMmDEDRdm2atTixYu/UIaEEEIIIcTYIC0ApbFNFYDTTjst//9kMsn/+3//j6lTpzJr1iwA3njjDT766CMuv/zyEcmkEEIIIYQQYnhsUwXgpz/9af7/l1xyCd///ve58cYbB6VZt27d8OZOCCGEEELsskZyvn55DsDQtvtv889//pO5c+cOWn7eeefx0EMPDUumhBBCCCGEECNjuysATqeT1157bdDy1157DYfDMSyZEkIIIYQQuz5l4DkAI/FS5DkAQ9ruWYCuvPJKLrvsMhYvXlzwILC7776bf//3fx/2DAohhBBCCCGGz3ZXAK677jrGjx/Pb37zG+677z4ApkyZwj333MNZZ5017BkUQgghhBC7JpkFqDR26DkAZ511llzsCyGEEEII8SX0hR4EJoQQQgghxI6SWYBKY5sqAGVlZSxbtoyKigqCweBWHwrW29s7bJn7soj3vE2s53X0bAyroxp/zXHYXPVDpjf0JJGOl0hGP8XQ+9Gsfvw1x+DwThqUNtr1GtHOF3GXzcRfe2x+uZ6JEel4gVR8JaaexmIvx1N5ME7flBGJcSjR7jeIdi1Az8awOWoI1J+E3dU4ZHpD7yfc/jyJ8EcYej8Wa4BA3Yk4fZMHpY10zifc/hyeitkE604s+CwVX0u4/XnSiXWgqNictVS0fANVtQ57jFszluOPdr9OuHN+LnZnLWX1p3xu7H1tz9If/ghdT2CxBimrPwmnb/dBacMdLxNqfwZvxUGU1Z886HPTNOlcdQ/J6DIqx52Py7/HsMa2LdKRj0mHP8DU+1FtZTjKZ6HZK4dMb+opUn2LyCZWY+opFIsHR/mBWAb+Zqm+xaRD7xaso1r9uBu+CoCRiRJf/0DRbTuqjsTqbhmmyD5fuGsBoY4X0TNRbM46Khq/gsPdPGR6PZugt/Up4qH30fU4VlsZ5Q2n4/ZPHZS2r/0FelufwF95KBWNZwysH6e37Rn6I5+STYfQLG7cgb0I1p2ApjlHLM6hjO3jfvRj71zxR1LxVQVp3WX7U9Zw2rDFta2i3a8T6Xxl4LxXQ3AbznuhtucG4k9gsQYIbuW8F25/Fm/FbIKbnfc6lv9+UPye8pmUNZw+fIGJMWebKgC//vWv8Xq9ANx+++0jmZ9Bfve73/G73/2O1atXA7DHHntwww03cPzxx9Pb28tPf/pTnnvuOdauXUtlZSWnnXYaN954I36/f1Ty1x/+iHDH8wRqT8DqrCfe+yY9a+6natLlaBb3oPSmodOz+j5Ui5tg41fRLF70TBhVsw9Km+5vJdG3GIu9atBnoQ2PYhhJyhrPRrW46A9/SN+6h7CMvxirs3ZEYt1SIvQ+obanCNafit3VSLT7NbpW3Uvt5KvQLJ5B6U0jS+fKe9Asbiqav45m9ZFNh1C1wbNHpRLrifW8jdVRM/iz+Fq6Vt2Lr+owgvUnASqZZDsKo9vZbyzHH+97j97WJyhvOB2bq5Fo12t0rvwTdZN/gGYtHnvHij/lYh93Lpatxr6OaO+bRWPfKNr9KoxyeW8uE1tJqudNHBUHodoryUQ+ItH+DO6Gr6IWuSA1TZ1E+zMomgNH1VGomgsjG0NRbQXpVGsAZ83xmxYom+5fKRY37sZzCvMRXUo6/AEWZ8PwBrgVsd7FdK//F5VNZ+FwNRPqnE/b8jtpnPojLFbvoPSmkaVt+e/QLF6qx38Di9VPNt1X9O+UjK8l0r0Qm7OuYHk2E0HPhCmvPxWbs4ZMupfutf8km4lQM/7CEYu1mLF83JcqdgB32X74q+fk3yujfLMHIN73Pn2tT1LWcBp2VyORrtfoXHk3dZP/bcjzXueKP6FaPFSO+zraVvb9VGIdsd63thL//gRqjs6/L0X8I0VlBMcAjMxmdwnbVAG44IILiv5/NDQ0NPCLX/yCSZMmYZomf/7znzn11FN59913MU2T1tZWfvnLXzJ16lTWrFnDt7/9bVpbW3nwwQdHJX+xnjdwBWfgCk4HwF97IsnochJ9S/BWHjQofSK0BENPUjH+QhRFA8BiCwxKZ+hp+tY/QqDuRKJdrw76PN2/Dn/tCfmWBm/lIcR63iSdbB+1CkC06zU8ZfvhKdsXgGD9qSQjS4n3LsJXddig9PG+RRh6P9UTv7VZ7MFB6Qw9Re/aByhrOI1I58uDPg+1PYW3YlbBd1gdQ995HSljOf5I96t4y2biKdsPgLKG0+iPfEqs9x381YcPSh/rfQdDT1Az6bLNYi8blM7QU3Sv+QflDWcQ7nix6Hen+1uJdC2gdtL3WP/xz4cvqO2QjnyI1TsZq3c3ANTyg8gm1pGJLsMe2HtQ+kx0GaaRwlV3MsrARb1a5GIZRUW1uIp+p6KoKFt8lk2sxupuGdWLgVDny/gqZuErPwCAyqYzSUQ+JtrzJsGaOYPSR3reRM8mqJ98Zb7srfbyQekMPUXn6r9S2XQ2fe3PFXxmd9ZSM/6i/HurvYKyuhPpWP1XTFPPb3c0jOXjvlSxAyiqDa3YMTOKot0L8JTtX3DeS0aWbuW8NxB/wXmvePw9n3PeU1VryeMXu5ZtHgMQiUS2KZ3P59vhzBRz8smFzf8///nP+d3vfscbb7zBxRdfXPDwsQkTJvDzn/+c8847j2w2i8UyskMcTEMn09+Gp2LThb6iKNjdLWT61xddJxldhs1VT7jtaZKRZagWF07/nngqZucvDADCbU/j8EzC7hlftAJgczbSH/4Yh2cSiuYgGfkIjCx219DN8MPJNLKk+1vxbnbSVxQVu3ciqcTaouv0Rz7F7mqkb8Nj9Ec+QdXcuIN74608tCD2vtbHcfgm4/BOHPRjoGdjpBPrcAX2pmP5XWTTPVjtlfhrjsbuHjcSoRY1luM3jSzpxAb8VYfnlymKisM7kVRiTdF1EpFPsLua6F3/KInIxwNdOKbjqzqsIPbeDY/i9E3G6Z1U9IfQMNJ0r/k7ZfWnluzH0DR1jFQ3Nv+0/DJFUdCcdRipzqLrZBNr0exVpHoWko2vQdEcWDwTsPmnFcRvZCLE1v4fKBqavQp72X6oRe6sAuipbox0L47y2cMb4FaYRpZUYn3Bhb6iqDi9u5GMry66TiL8IQ73OLrWPkgi/AGqxYO3bF8C1UcVxN617kFc/qm4fJMHVQCKMfR+VM0xqhf/Y/64L0HsGyX6lpDoW4Jq8eD07Y6v+gjULVrQRlLuvNeKb9B5bwLpIeP/GNvAea8/8gmaxY0rsPeg817fhkdx+nbH4Z04ZAUg3vce8b4laFbvQPxHjmr8I0kZwfn65TkAQ9vmK+RAILDVvv+maaIoCrquD0vGitF1nX/+85/E43FmzZpVNE04HMbn8434xT+AoScAc1DTp2pxk050F11HT/eRyoRw+feirPkc9HQvobanwdTzJ9b+8Idkkm1Ujr9kyO8ONn6FvnUP0b70l4CKoloJNp2JxT74rupIyMVuDIpds3jIJruKrpNN95JNh3AH9qZy3AVk0z30bXgM09TxVx8F5JqYM/2tVE+8rPg2UrkxJpGOefhrj8fmrCXe9y6dK++mZrfvY7VXDF+QWzGW49e3EnsmNXTsyXQf7uB0qlq+QTbVQ++GRzFNncDAxWS87z3S/RuonfTdIb+7b8MT2N1NJenzv5GpJwFzUDO+ojnRM+Hi62Sj6Nk2rO4JOGuOxchESPYsBNPAHtwHAM1eiaPyUFSrHzObIBV6l0TrE7gbzhjUVQhy3X9UawDNUT3sMQ5Fz8bJlX1h5cti8dKf7Ci6TibVQzb9GZ6yfamd+C0yyS661j2IaeqU1R4HQLR3MenEeup3v3ob8xGjr/05fKNY+YGxfdyXKnYAV2Aami2IZvGSSbYTbn+WbKqbinHnDlt8n2eo855q8W7lvNdHNr0yf97LpHro2/AvMHX8Bee9VmomfWfI73YHp6NZA2hWH5lkG6G2Z8ikuqkcd95whSfGoG2+Sn7ppZfy/zdNkxNOOIE//vGP1NcPPdh1uHzwwQfMmjWLZDKJx+PhkUceYerUwYPHuru7ufHGG/nmN7+51e2lUilSqVT+/ba2bgwHExPN4sZfd2LuDoCzFj0TJdbzOt6qw9AzYcJtz1E+7lwUdejiiXa+jGEkKW8+D9XiJBlZSt+6h6houQDrKF4QbBczF3uw4TQURcXmqkfPRIh0LcBffRTZdIi+1ieobLloK10acrV5d9nMfDO0zVlHKraCeO8iApsNlN7pjOX4B2Ivbzgjd9fQ1YCejRDpfIVAzRyy6RC9rY9TPf7iIWNPhD8mGVtB7W7fH+XMf3GmaaKoDuwVB6EoKpq9AlOPkw5/kK8AWDYfSGgrQ7NXElv3DzLxVdi8hQMmTSNLJr4Se2D6KEaxo3I3SSqbzh4o+0aymTChjpcoqz2ObLqPnvUPUzvx8m0azGroSdqW/x6ro5qyuuNGIf9fkBz3XzD23IDXjWzOGjSrl66Vd5NN9WAp0p1sp2EaaBY3ZQ2nb4o/GybSuQD/wHmvr/UJqsZvZ/wWH50r/0gm1VO0O92XjTwHoDS2uQJw2GGF/fs0TePAAw9k/Pjxw56pLU2ePJklS5YQDod58MEHueCCC5g/f35BJSASiXDiiScydepU/uM//mOr27v55pv52c9+9oXzpWouQEHPxgqWG9l40QFRkLtbgqIVNP9Z7BUY2RimoZPub8PQ43St+MNma5mkE2uI975N7dQfoWdCxHvfpnLCt7A6cgOErY4aUol1xHvfIbDFzBEjIRe7Oih2PRtDLTIYCsh12RgUe+VA7LnmZSMbp+Oz3262lkEqvppY9xs07PWzfLePjXFv2k7VkHdfR8JYjl/bSuxD7vdWL8oWsVvtVejZ6EDsGzCyMdqW3bHZWrnYo92v0zTtv0jGVpBN97Luw8Jjt2v1fdjd46iZ+K3hCnGrFM0BKLkZvDZbbur9RQf3AQP9+tWC+FVrAFPvH7IPu6LZc60BmcE3KLLxVWBksXgmfsFotk9uYgMVPRstzE82imYt3v1Ts/oGl72jGj0bGehStA49G2P9p7/cbC2DZGwl4a5XGT/jl/l1DT1J6/I7UTUHNeMvHtXuPzC2j/tSxb75uhvZBirLmXTvqFUAhjrvGdnooBax/DpWHyjqoPOescV5r33Z/26+xYHz3hs0Trtxq/Fnd5EKgCiNL8VzAGw2GxMn5n7o9t13X95++21+85vfcNdddwEQjUY57rjj8Hq9PPLII1itW7+LdP3113P11ZuamiORCI2NQ0/jNRRF1bA6a0nHV+en9DJNk1R8Fe6y/YvH4mqkP/xhvssUQDbdg2rxoKgadncLlRMKL2RCGx7DYq/IjxMwjcxABgqrtrnZIEanv5uiWvJ3oFwDU/mZpkEqtgJP+YFF17G5mkmE3sM0jfxJLRe7F0W14PBMoHqLu7u96x7Caq/EW5XrM6pZc83A2S2aXLOpbhwDAzJHw1iOX1Et2Fz1JKPL811xTNMgGVuOd4guGXZ3M/G+JQWxZ1JdaPnYJ1K725UF6/SsexCroxJfZa6/rL/qcDxbHFdty24nWHfSqE5/qygaqr0CPdmGdaD/tWma6P2tWH2DWyYBNHs1mfiKguPeyIRRNNeQF7GmkcHIRlC0wRf5mdgyLK6mISscI0VRLdhdDSSin+EO5MZAmKZBf3QZ/spDiq7jcLcQ61s0uOytPhTVgtO7Gw1Tri1Yp2vN/Vgd1QXjBHIX/79DUSzUTLhk1Kf8BTnuSxF7MZn+NoAhL7xHQu68V0cyumKL894KPOXFuyQXP+91F5z3ana7omCd3nUPYtnsvFdMJtkKsMsMCpbnAJTGl6ICsCXDMPJdeCKRCMceeyx2u53HHnsMh2Pw9GJbstvt2O2Dp93cEZ7yA+nb8ChWZy1WZx3xnrcwjQyuYG4mkL71/0KzevEN9Hd0l+1LvPdtIu3P4i7bn2y6l1jXa7jLcxc2qmZH1Qrv8iiqDVVz5u/+WOwVaLYywq1P4auek+8ClIqvpKz8a8MS17bwVh5Ez7qHsDnrsbkaiHYvxDDSuIO5Juqetf9Es/ryzdOe8pnEet4g1PoknopZZFPdRDpfxjtw8lQ1OzatsPuSqtpQLS5sA92aFEXBW3kI4Y55WB21WJ21JPoWk0114WkunCJxpI3l+H0VB9O97p/YXA0D0+G9immk890Tutf+A83qJzjQx9tbfiDR7tfp2/A43srZZFI9hDtfxlcxe1PszsLp7xTViqq58ss1q7foD57FFsA6SmNfNrL59iTZ/QqarWJgGtAPMc1sflag/q75qJoL+0CFxerbnXTkY1K9b2DzTcXIhEmH3sO62ViGZM+buYt6iwdTT5DqW4yCisVT2MpqZCLoyXac1aXp9hGoOpzONfdjdzXicDUR7pqPaaTxDswK1LH6PixWP+UD85j7Kw8i3LWA7vWP4K88hEyqi1D78/grDwVA1RzYt5i5TFFtaJorv9zQk7R+9jtMI031hPMx9CSGngRyrapDXSiNhLF83Jci9myqh3joPZzeyagWF5n+dvransLuHjfonDHSvBWH0LPun9hc9blpULtewyg47z2AxeojMHDe85QfMHDeewJv5SyyqZ5c/Fs97+X2/Y3LM6keEqElOL27o1pcpPvbCLU+id3dgm2UZvwTu6YvVAHY2qDg4XL99ddz/PHH09TURDQa5f777+fll1/m2WefJRKJcMwxx5BIJLjvvvuIRCL5/vyVlZVo2sg3Dzv9e2BkE0QHHohkdVRT3vz1fFcIPRMpuFOvWf2UN59LuP054ivuQrP4cJfPxFOx7YPZFEWjvOlrRDpepHftPzCNNJotSKD+1KIPExsprsA09GyccMc89GwUm6OWypZv5OdD1jPhgtgttgCVLd8g1PYU7cvuQLP68FbMxjtwIbCtvJUHYZpZQm1PYWQTWJ21VI6/cNT7go7l+N3BvdH1OKH253OxO+uoarkof4GeTYfYfJ5+iy1A9fiL6G19gujS32Cx+vBVHFR06sAvA6tnPKaRJNW3KNf1x16Oq/rY/B15MxvD2Cx+1eLBVXMsyd43iW94BEVzYfXvUTCTkKnHSXa9jKknUTQHmqMaV93Jg+7yZ6LLUDQ3mnPkx18V4ynbBz0bp6/tabKZCHZnPbUTv5V/BkA23VcwN73FFqRu4rfpXv8v1nf/d+7Bh1WHERi4KbItUol1+Rmm1n70XwWfNe3x76PaDWIsH/cliV3RSMVWEOteiGFksFj9uPx7FMzGM1rcwWkYeoxw+wsD571aqlouzJ/39HRoi30/QNX4C+lrfZK2pf+DZSD+7TnvKYpGMrpioLKRi9/p3xN/9RHDHl+pqIqJOkKz9ezodn/7299y66230t7ezt57780dd9zBzJkzi6a99957ufDCwueR2O12kslkwbJPPvmEa6+9lvnz55PNZpk6dSoPPfQQTU1NO5THL0oxTXOb/jpnnHFGwfvHH3+cI488Ere78GFXDz/88PDlDrj44ouZN28ebW1t+P1+pk2bxrXXXsvRRx/Nyy+/zBFHFD8IVq1axbhx47bpOyKRCH6/n5rdf1j0gVxjgVbkwSxibFBHuR/1zqQ38lmps1BSVcG9Sp2Fkkln46XOgiiR0Wwx2pkYepL1H/4sP1tiqW289rrm5X9h9wx+cOpwSMXi3Hr4adsV8z/+8Q/mzp3LnXfeyQEHHMDtt9/OP//5T5YuXUpV1eAHs957771cccUVLF26NL9MURSqqze1bq1YsYKZM2dy8cUXc8455+Dz+fjoo4848MADi25zNGxzC8CWT9Y977zRmX7qT3/605CfHX744Wxj/UUIIYQQQuxkdrZZgG677TYuvfTS/F39O++8kyeffJK7776b6667rug6iqJQUzN0l7Qf//jHnHDCCfz3f/93ftmECRO2P3PDaJsrAPfcc89I5kMIIYQQQoiSSafTLFq0iOuvvz6/TFVV5syZw+uvvz7kerFYjObmZgzDYJ999uGmm25ijz1yY7wMw+DJJ5/khz/8IcceeyzvvvsuLS0tXH/99Zx22mkjHdKQxmY7mBBCCCGEKDlF2dQKMNyvjUNSNo4R3fja/FlQm+vu7kbX9YLuOwDV1dW0t7cXXWfy5MncfffdPProo9x3330YhsHs2bNZv349AJ2dncRiMX7xi19w3HHH8dxzz3H66adzxhlnMH/+/OH7Q26nL+UsQEIIIYQQQmyLLad6/+lPf/q5z4zaVrNmzWLWrE1Twc6ePZspU6Zw1113ceONN2IYBgCnnnoqV111FQDTp09n4cKF3HnnnYOeszVapAIghBBCCCFKQht4jdS2AdatW1cwCHioqeArKirQNI2Ojo6C5R0dHVvt4785q9XKjBkzWL58eX6bFoul4OG1AFOmTOHVV1/dxkiGn3QBEkIIIYQQuyyfz1fwGqoCYLPZ2HfffZk3b15+mWEYzJs3r+Au/9bous4HH3xAbW1tfpv7779/wSxBAMuWLaO5uXkHI/ripAVACCGEEEKUxM72HICrr76aCy64gP3224+ZM2dy++23E4/H87MCzZ07l/r6em6++WYA/vM//5MDDzyQiRMnEgqFuPXWW1mzZg2XXHJJfpvXXHMNZ599NoceeihHHHEEzzzzDI8//jgvv/zysMS5I6QCIIQQQgghSmJnmwb07LPPpqurixtuuIH29namT5/OM888kx8YvHbtWlR1Uweavr4+Lr30Utrb2wkGg+y7774sXLiwoMvP6aefzp133snNN9/M97//fSZPnsxDDz3EwQcf/IVj3FHb/CCwXZk8CEweBDaWyYPAxi55EJgYi+RBYDvXg8B++uojOEboQWDJWJyfHXz6ThPzzkRaAIQQQgghREnsbC0AY8XYrAYLIYQQQggxRkkLgBBCCCGEKAlNyb1GatuiOGkBEEIIIYQQYgyRFgAhhBBCCFESMgagNKQFQAghhBBCiDFEWgCEEEIIIURJ7GwPAhsrpAVACCGEEEKIMURaAIQQQgghREkoIzgGQJExAEOSFgAhhBBCCCHGEGkBEEIIIYQQJaENvEZq26I4aQEQQgghhBBiDJEWACGEEEIIURLyHIDSkBYAIYQQQgghxhBpARAAGEa61FkomVB0damzUFJeV12ps1Ay5f7Jpc5CSUUTraXOQsnYbf5SZ6Gk0ploqbNQMlaLu9RZKAnDyJY6C0XJcwBKQ1oAhBBCCCGEGEOkBUAIIYQQQpSEpuReI7VtUZy0AAghhBBCCDGGSAuAEEIIIYQoCZkFqDSkBUAIIYQQQogxRFoAhBBCCCFESUgLQGlIC4AQQgghhBBjiLQACCGEEEKIklAZwRaAkdnsLkH+NkIIIYQQQowh0gIghBBCCCFKQlVMNHkS8KiTFgAhhBBCCCHGEGkBEEIIIYQQJaEycnej5S730ORvI4QQQgghxBgiLQBCCCGEEKIk5DkApSEtAEIIIYQQQowh0gIghBBCCCFKQloASkNaAIQQQgghhBhDpAVACCGEEEKUhKYwYs8B0KQFYEjSAiCEEEIIIcQYIi0AQgghhBCiJGQMQGlIC4AQQgghhBBjiLQADIN4z9vEel5Hz8awOqrx1xyHzVU/ZHpDTxLpeIlk9FMMvR/N6sdfcwwO76RBaaNdrxHtfBF32Uz8tcfml+uZGJGOF0jFV2LqaSz2cjyVB+P0TRmRGIcS63mLWPfCgdhrCNQevw2xz6M/sin2QO1xQ8T+KpGOebjLDyBQexwA2XSIjmW/Kbrtssav4vTvMTyBbaNM9FMy4Q8x9X5UWxm2splo9soh05tGmnTfYvT+tZh6CsXiwVa2PxZnAwDp0BIy4fcK1lEsPlz1p+ffG3o/6b53MPpbMc0sqtWH1TcNi7t5ZIIcQrx3EfHeNzGyMaz2Krw1x2Bz1g2Z3tCTxLrmk4wsxTCSaFYfvuo52D0TAYh2LSDe/WrBOpqtjMoJ38q/N40s0c559Ec+BkPH5hmPr+ZYNIt7ZILcikjXQsKd89GzUWzOWsrrT8XubhoyvZ7tJ9T+DInQh+h6AostSFn9ybiKHLOhjpcItT2Nt+JgyhtOGfS5aZp0rryb/uhSKsfNxR3Yc1hj+zyJvsUket7C0ONY7FV4q+dgddYOmd7Qk8S7FpCKLsuVvcWHp/pI7J4JAMS6XiXRs7BgHc1WRvn4S/LvTSNLrPMlkpFPwNSxuVvw1hyNWoKyj3a/QbRrAXo2hs1RQ6D+JOyuxiHTG3o/4fbnSYQ/wtD7sVgDBOpOxOmbPChtpHM+4fbn8FTMJlh3YsFnqfhawu3Pk06sA0XF5qylouUbqKp12GMcSiJ/3OfK3ldzNNbPPe5fIbXZce+tnrNZ2S8g3v1awTqarYyKCd/Mv88d9y+SzB/3LXhLcNzHet4k2vVq/vcuWHciNlfDkOlz5f4C/ZGP8+Xurz0Bp2+3QWkjna8Q6XgeT/ksAnUnFHyWiq8l0vEC6cR6UFSsjhoqWy5AGcVyH0nSAlAaO30FYMOGDVx77bU8/fTTJBIJJk6cyD333MN+++03KO23v/1t7rrrLn79619z5ZVXjkr++sMfEe54nkDtCVid9cR736Rnzf1UTbq86MnJNHR6Vt+HanETbPwqmsWLngmjavZBadP9rST6FmOxVw36LLThUQwjSVnj2agWF/3hD+lb9xCW8Rdv9Yd4OCXCHxJuf45A3YnYnA3Eet6ge/V9VO/23SFj7179V1TNTVnjmWhWH3omhKo6BqVNJzYQ712ExVFdsFyz+qiZ/G8Fy+J9i4h1L8TuGVyJGEnZ+CrSvW9jKz8QzVZJJvoxyc4XcNWdhqI5B6U3TZ1kx3MomgN7xeEoFhdmNgaqrSCdYg3gqD5m03sKz2Cp7gVgpLFXHYmiOsjGV5Lqno9iPRHNVj4ywW6hP/Ix0c55+GqOw+asI977Nn1r/0HFhG8WL3tTp3ft/6FpbgINZ6BaPBiZCMoW+73FXkGw6Zz8e2WLRspIxwukYisI1J+OqtqJdDxHaP1DlI+bOzKBDiHet4Te1scpbzgDu7uJSNcCOlb+ifrdr0GzegalN40sHSv+gGb1UDnu/IF9vw+1yH6SSqwj1vMGVsfQx3Gka8GwxrM9kpFPiHW+hLf6GKzOWhK97xBa9wDl4y8pejFumjqhdQ+gai589afmznnZMMoWx71mqyDQdFb+/ZZlH+t8kVRsBf76U1E0O9H2Fwhv+BfB5nNHJtAhJELvE2p7imD9qdhdjUS7X6Nr1b3UTr4KzVK87DtX3oNmcVPR/HU0q49sOoSqDT7vpRLrifW8jdVRM/iz+Fq6Vt2Lr+owgvUnASqZZPug88NISkY+Idr5Ir6aY7E660hsdtwPVfZ9a/+OqrnxN5yOZvGgFznuNXsFwaav5d9vWfbRjnkDx/1pKKqDaMdzhNc/TNm480cm0CISoQ8ItT1NsO4UbK4GYt2v07Xqz9RMvmLIcu9a9Wc0i5vypq/ljvl0CKVIuacT64n3vo11i987yJV79+q/4K08lEDdiaCoZPrbYRTLXeyaduouQH19fRx00EFYrVaefvppPv74Y371q18RDAYHpX3kkUd44403qKsb+k7ESIj1vIErOANXcDpWRyX+2hNRVCuJviVF0ydCSzD0JGVNZ2F3NWKxBbC7mwed8A09Td/6RwjUnVj0IiHdvw532f7YXPVYbEG8lYegaA7SyfaRCLOoWPcbuIP74A7OwOqoJFB30kDs7xZNnwi9i5Htp7z5bOzupoHYx2F1Do69d/3DBOpPHlQ5UBQVzeopeCUjn+L0TUXVCi+kR1om8jEW7ySsnkmotgC2slkoikYmtrxo+mxsOaaRwl55JJqjCtXiQXPUoNnKCtIpKKiaM//a8gfDSHVh8U5Bs1eiWr3YAnuDasNI9YxYrFtK9LyFK7A3rsA0LPYKfDXHoagW+kPvF03fH3oPU08SaPwKNlcDFlsAm7upyA+eimbx5F+qxZX/xNCT9Ifew1d91MB+U4u/9iQy/RtI928YwWgHC3ctwFt+AN7y/bE5qilvOANFtRLtfbto+mjv2xh6gqqWC3B4xmG1l+HwTBjUYmLoKbrW/B/ljV8tetwDpBKtRLoWUL7ZxfJoSvS+g9M/DWdgLyz2Crw1x6KoVvrDHxRNnwy9j6En8Tecjs3VgGbzY3M1YXUU3thQlK2VfYr+0Pt4qo7ENnC+9NUeT6Z/A5n+1hGNd0vRrtfwlO2Hp2xfrI4qgvWnoipW4r2LiqaP9y3C0PupGHcednczFlsQh6cF2xY3agw9Re/aByhrOK1o2YfansJbMQtf1WFYHdVYHZW4AnuhqKN3Hy/e8xbOwN44B457b81xubIf8rh/f+C4P2Og7Isf98o2HPfe6iOxDfxe+GpPHPXjPtq9EHdwP9xl+2B1VBGoPxlFtRLvXVw0fbxvMYaeoLz56/lytw9V7useJNhQ/MZRuO1pPOUH4qs6NFfu9tEv95G2sQVgpF6iuJ16D7rllltobGzknnvuyS9raWkZlG7Dhg1873vf49lnn+XEE08c9PlIMQ2dTH8bnoqD8ssURcHubiHTv77oOsnoMmyuesJtT5OMLEO1uHD698RTMRtF2VQfC7c9jcMzCbtnPNGuVwdtx+ZspD/8MQ7PJBTNQTLyERhZ7K7R6QaSi70Vb+XB+WWKomD3jM81UxbRH1mKzdVAqPUpkpGlqBYXLv9eeCoPKog91PYUDu8kHJ7xRDtf2Wo+0v2tZJLt+LdoMh1ppqljpHuw+vfKL1MUBc1Rh5HqKrqOnliHaq8i3fsG2cQ6FM2Bxd2C1bdnQfxGNkpi/QOgaKi2SmzBfVA3u8Ok2ivRE6tz3YZUG3piNZg6WpG7hiPBNHUyyXbcFbPzyxRFweYeR2aIH+Rk9DOsznoi7c+Riub2e4dvD9zlBxbErqf76PzsDhTFgtVZh7fqcDSrH4BMsh0wsLnH5dNb7OWoFh+ZxAZszqG7ng0n08iSTmzAX3VEfpmiqDg8k0jF1xRdpz/8MXZ3Mz3rHyER/hjN4sYdnIG/6vCC+HvW/wuXb3ec3kmE2ucN2o5hpOlecz/lDadhsXqHP7jPYZo62WQ77vID88sURcHmah7yQjwVW4HVWUe0/XlSseWomguHbwqu8gMKYs+m++he/lsYKHtP5WFoVl/us3zZbzq/5cu+f8NWu6AMJ9PIku5vxVt1WH6ZoqjYvRNJJdYWXac/8il2VyN9Gx6jP/IJqubGHdwbb+WhBfH3tT6OwzcZh3cikc6XC7ahZ2OkE+twBfamY/ldZNM9WO2V+GuOxr7Z8TCS8mVfMSu/7POO+9TAcR9tf45U9DMUiwunbyquLY77bLqPrs/+F0XRsDrr8VQdlj/uszvBcW8aWTL9rfgqD8kvyx3zE3LdsYpIDpR7aMMT9Edz5e4KTMvdrNv89671CRze3XB4JhQv9/71uAJ707ni92TTvVjslfir52Af5S6fYtezU7cAPPbYY+y3336ceeaZVFVVMWPGDP7whz8UpDEMg/PPP59rrrmGPfYY3f7fhp4AzEHNf6rFjZ6NFV1HT/fRH/kETJOy5nPwVh5CrOcNYps16feHPySTbMNXfeSQ3x1s/AqYOu1Lf0nbxzcRan2KYNOZWOxlQ64znDbGvmWzr/a5sX8MpkH5uK/jrTqUaM/rRLs2XeQnQh+S6W/DXz1nm/KR6HsXi71iq/1vR4KppwBz0N15RXNg6v1F1zGyUfT4ajBNHFVzsPmnkYl8TCa86e6Zaq/AXn5Q7vOyAzH1GMn2ZzCNTD6No/JwMA0S6/9OYu1fSfW8jr3ycNSBi6WRZmQHyl5zFSzXNDfGUGWfCZGMfgqmQbDxLNwVBxHvfbOg76/NWYe/7kSCjWfjqzkWPROmZ/V9GHpq4HvjuUrRFn9zzeLG0OPDG+RW6HocMNC2uADXrB70bLToOpl0L/HQB2CaVI+/iED1UUQ6XyHcsekiP9a3hHT/BgK1xw/53b0bHsfubsY1ymNdNsqXvaWw7FWLO1c+ReiZEKnoUsAk0PhV3BWzSPS+TaLn9Xwaq7MOX+3xBBrOxFt9DHomTN+a+z+37FWLa8jvHQm5854x6JyvWTwYmeL7fjbdSyL8EZgmleMuwF99RG58U+dL+TSJ0Ptk+lsJ1BxTfBupXoDcmKiy/ahs+QZWZx2dK+8mk+oenuA+x6bjvvCcr2pu9K2UfTL6KaZpEmg8C0/FQcR73yLevWm8h3XguA80noV34LjvXf23fNnrQ5b96B33G8tdHfRb7xny9y6b6SMR/hgTg4px5+OrOpxY92tEN7vIT4TeJ93fir/m6OLbSPcBEOl8EXdwPyrGzcXmqKVr1T1kRrHFd6SpysZnAQz/S1oAhrZTVwBWrlzJ7373OyZNmsSzzz7LZZddxve//33+/Oc/59PccsstWCwWvv/972/zdlOpFJFIpOA1WkxMNIsbf92J2Jy1OP174K04mHhfrhlRz4QJtz1HsOH0rTbxRTtfxjCSlDefR+WEi/GUH0DfuofIJDtGK5TttjH2QP3J2Jx1uPx74q08JN90nk2HCbc9Q1njGdvUvGkaGRKhD3AFZ4x01oeNojmxlc9Cs5djcbdg8+9FNrYs/7nF2YDFPQ7VVobFWY+jag6mkSYbX51Pkw69i2mkcVQdg6P2JKy+qaS65mMM/FjsnHIXDr7a47E6a3H6puIpP6igu5jdMwGHbwpWRxV2z3iCjWdhGqlcxeFLL3ejoLzxK9hdDbiD0/FXH0m0+w0gN7i9d8NjVDafM+SAzkT4I5LR5ZTVDx4UvFMzc5VFb82xWB01OHxTcFfMon+zbpJ2z3gcvt2xOKqwe1oINHwV00gOVBy+5MzceS/YcBo2Vz2uwLTcxWDPW0Cu7Ptan6Cs8aytDOrMPSTJXTYTT9m+2Jx1BOtOxGqvGLLr0c5h43F/HFbnQNmXz6Z/0HG/e/64DzSeiWmkSH3Zj/uN5V5/KjZnPa7AXngrDyM20E0wmw4TanuKssYzhy53c2O574+7bB9szjoCdSdgtVeQ6NuZy118GezUXYAMw2C//fbjpptuAmDGjBl8+OGH3HnnnVxwwQUsWrSI3/zmNyxevBhF2fZq3s0338zPfvazL5y/3B1QZdAdACMbLzooCHJ3ilC0giZAi70CIxvDNHTS/W0YepyuFZu3dJikE2uI975N7dQfoWdCxHvfpnLCt/L9aK2OGlKJdcR738kNFBphG2Pf8u6bvtXYvSiKWhC7dbPYM8lc7J3L79psrYHYe96ibo+fFKzbH/4Y08zgCuw9nKFtk9wgNgVTTxYsN/Vk0X6cuXWcsEX8itWPqfdjmjqKog1eR7WhWn2Y2Vwl1chEyEY/xVl7CqotNxZGs5WhpzrJRD/FXj5r0DaGW+7urzJwV2wTXY8PukO2aR0PClvu9+UYenzI2FXNgWYLog9UbFSLG0wdQ08W3A3Us/FBdyVHkqa5ARU9U3i3X8/E0CzFu+Xk9v3C+K2OKvRsFNPIkkqsx8jGaF26+QxXBqn4KqLdC2ne+yb6oyvIpntZ+8FPC7bdtfqvRNwt1E769nCFOKR82WcLy97IxoecjUe1uAed8zTbtpR92eeWvZFNjOosQLnznjronK9nY6hFBn8DuZaiQef8yoHzXq5LkZGN0/HZbzdbyyAVX02s+w0a9vpZvrVpy3ETFnsVeiY8LLF9nk3HfeE539DjQ87Gkzvu1R067jfe/daGLPvRO+43lvuWLZxGNjbk751q9Q6O3bGp3DP9GzCycTqX/27zLZKOryHW8yb1e/40X+6WLWaWs9gryaZHp9xHg6qYqCP0JOCR2u6uYKeuANTW1jJ16tSCZVOmTOGhhx4CYMGCBXR2dtLUtGnqPV3X+bd/+zduv/12Vq9eXXS7119/PVdffXX+fSQSobFx+7uQKKqG1VlLOr4ap293IDc9Xyq+CnfZ/kXXsbka6Q9/iGma+UpLNt2TO1GqGnZ3S8G0hwChDY9hsVfkxwnku4NsUenJzQYxOjt7LvY6UrGVhbHHVuIpn1l0nVzsHxTGniqMvWriZQXr9G14FIutAu8W4wQA4n3v4vBOLskUkIqiodrK0ZNtWFy5/c80zdx77+5F19HsVWTjKwviNzMRFM1Z9IcQcq0cRjaKRZswsEDfmIHC/Ixm2SsaVkcN6fhqHN7cdHamaZKOr8EV3LfoOjZnA/2Rj7fY73tzZT9E7IaRRk+HUP258s0NlFdz3zuwz2VTPRjZCNatTD073BTVgs1VTzK2PD/9pmkaJGPL8W42LmJzDvc4Yn1LME0jvx9nU925ioFqwemdSN3kqwvW6V77AFZHVX6cgL/6CLxbHFutS2+jrP5knL7C8+RIURQNi6OGdHwN9oGpe00zV0l3Bvcpuo7V1UAyXFj2eroX1eL+/LL35bo6WfJlvwbHwNSZ+bIfpbEfMFD2zjpSsRW4/Lm/uWkapGIr8Gw2LmJzNlczidB7hWWf7kEdKHuHZwLVuxW2YPeuewirvRJv1aEDEx8E0SxesluML8qmuvPH4EjbVPbFjvshyt7ZQDLyUZGy//zjXvPnLqwtO8Fxr6i5cSnJ+EqcBeW+Enf5AUXXsbuaSITe3+KY31Tuds8Eqid9t2Cd3vWPYLVX5McJaNYAqsVLdotuXqNZ7mLXtVNXAA466CCWLi1sAl62bBnNzbnBL+effz5z5hT2FT/22GM5//zzufDCC4fcrt1ux24fPO3mjvCUH0jfhkexOmuxOuuI97yFaWRwBXN3pfvW/wvN6sVXfRQA7rJ9ife+TaT9Wdxl+5NN9xLreg13ea7CoGp2VG2L2TFUG6rmzN/9sdgr0GxlhFufwlc9B9XiJBlZSiq+krLyrzFaPBUH0rf+X1idddic9cR63hiIfTqQO5lpFi/+mjkDse9HvPctwm1P4y4/AD3dQ7Tr1XyFoWjsihXV4hx05yub6iWdWEP5KE8BuDmrbyqp7ldRbeVo9goykU8wzSzWgXntU90LUDQXtoGLYot3Mpnop6T73sLq3R0jEyUd/gDrZvPAp/rexuJsRLF4MLMJ0uElgILFnRv8rlj9KBYvqZ7XsQX3Q1Ht6P3r0JOt2KuOGrXYXeUzCbc+gdVRk9vve9/GNDI4A9MACLU+jmbx4q06PJc+uA+JvkVEO57HFdyXbLqPePdCXGWbpvONdMzD4ZmEavVhZGPEuheAouAcuAhUNQfOwN5EOuahaE5U1Uak43mszvpRGwC8kb/yELrWPoDd1YDN1Uik61VMI413IJ6uNX/HYvUTrMv15/dWzCLSvZDeDY/hqziITKqbUMeL+AYmEFA1B7YtZsPKHfeu/HKL1QtFBv5q1gDWURr7A+Aq249I21NYnDVYHbUk+t7Jlf3AgPhI65OoFg+egYGyzsB0+vsWE+uYh7NsH/R0H/GeNwoqi9HOl7B7JqBZ/ANl/yooCo6BY0PV7DgD04h1voSqOXLTgHa8gMVZN2oDgDfyVh5Ez7qHsDnrsbkaiHYvxDDSuAfi6Vn7TzSrj8DAc1s85TOJ9bxBqPVJPBWzyKa6iXS+jHegtU7V7Ni0wllxVNWGanFhG5gtR1EUvJWHEO6Yh9VRm5t+tW8x2VQXnuZzGC3u/HFfm58C1jTSOAaO+3Dr46gFx/0M+vPH/X7o6V7i3a/j3Oy4j3a8iN0zMTdNZjaWexaIouAYqNRuPO6jHS/mZkVT7URLcNx7K2bTu/7hXLk764n1vD5Q7rnKT++6B9GsPvwD4zjcZTOJ9bxJqO0pPOUHkk31EO2an68o5n7vtpgNSbWiaq78LEm5cj+YSMeLWJ012By1xPveJZPqprxp9Mp9pKmMXH/0nbqfe4nt1BWAq666itmzZ3PTTTdx1lln8dZbb/H73/+e3//+9wCUl5dTXl4477nVaqWmpobJkwc/YGUkOP17YGQTRDvn5x8EVt789XyzoJ6JFNyt1ax+ypvPJdz+HPEVd6FZfLjLZ+IZ4s5hMYqiUd70NSIdL9K79h+YRhrNFiRQf2rRB2qNFJd/z4HYX84/GKVi3LmbYk+HC+aottj8lI87j3Dbs8SX/y73MKDyA/BUHjTUVwwp3vcumtWXf5hMKVjcLZh6kkxoCemBB4E5qubkuwAZ2TjqZvGrFjeO6jmke9+mP/oYisWF1TcFq2/TQ5zMbIJU9yu5h4RpDlR7Fc6aE/KDjRVFxVE1h3RoEcnOF8HMolq82MoPzj9MbDQ4fVNzZd+1AEOPY7VXEWw6K98ao2cibD5PtWb1EWw8m2jHPLpDf0KzeHGV7V8wm4yRjRJqfRRD789d+LoaKB93QcGAU1/1HKKKQmj9w7mHQXla8NVsekDeaHEHp6Nn4/S1PTfwILA6qsdfnG+yz6ZDULDvB6iecAm9Gx5nw9JfY7H68FUejH/gQunLxOGbgqH3E+96Nf8gsEDjmfmuOMXKPtB4JtGOF+lfdQ+qxYsruC+uze6cGpkokdbHB7p5OLE6Gwg2n1dQ9p6qIwGF8IZHMU0du3scniEGT44kV2AaejZOuGNeruwdtVS2fCP//Ac9Ey4451tsASpbvkGo7Snal92RexBWxWy8lYdu1/d6Kw/CNLOE2p7CyCawOmupHH8hFvvoPPsDBso+myA2cNxb7FUEm87e6nEfaDybWMc8evLH/X64Njvu9WyUcOtj+ePe6mqgbNzcgrL3Vh8FikJo/SO5sve04B1iwPRIcQX2wsjGiXTMG/i9q6WiZW6+3LOZMJtfblpsfirGzSXc9jQdvb9Fs3rxlM/Cu9lMQtvCWzEb08gSbnsaI9uP1VlDZcs3Rm3CD7HrUkzT3Kk7SD3xxBNcf/31fPbZZ7S0tHD11Vdz6aWXDpl+3LhxXHnlldv1ILBIJILf76dm9x8WfSDXWLBl95qxJBRdXeoslJTXNbp3UHcmDvvgZ4qMJYnk6MwgszOy2/ylzkJJpTPFZ6waC6wl6Da6MzD0JK0f/5xwOIzPNzqzxm3Nxmuvxz79J26v6/NX2AHxaIJTdj9zp4l5Z7JTtwAAnHTSSZx00knbnH6ofv9CCCGEEEKIL0EFQAghhBBC7Jo2ztk/UtsWxY3dfh9CCCGEEEKMQdICIIQQQgghSkKeA1Aa0gIghBBCCCHEGCIVACGEEEIIURKqMrKvHfHb3/6WcePG4XA4OOCAA3jrrbeGTHvvvfeiKErBy+FwDJn+29/+NoqicPvtt+9Y5oaJVACEEEIIIYQA/vGPf3D11Vfz05/+lMWLF7P33ntz7LHH0tnZOeQ6Pp+Ptra2/GvNmjVF0z3yyCO88cYb1NWVfvptqQAIIYQQQoiS2NlaAG677TYuvfRSLrzwQqZOncqdd96Jy+Xi7rvvHnIdRVGoqanJv6qrqwel2bBhA9/73vf429/+htVq3f6MDTOpAAghhBBCiF1WJBIpeKVSqaLp0uk0ixYtYs6cOfllqqoyZ84cXn/99SG3H4vFaG5uprGxkVNPPZWPPvqo4HPDMDj//PO55ppr2GOPPYYnqC9IKgBCCCGEEKIk1BF+ATQ2NuL3+/Ovm2++uWheuru70XV90B386upq2tvbi64zefJk7r77bh599FHuu+8+DMNg9uzZrF+/Pp/mlltuwWKx8P3vf3/7/jgjSKYBFUIIIYQQu6x169bh8/ny7+12+7Bte9asWcyaNSv/fvbs2UyZMoW77rqLG2+8kUWLFvGb3/yGxYsXoyg7z5PJpAVACCGEEEKUhgLKCL0YuN72+XwFr6EqABUVFWiaRkdHR8Hyjo4Oampqtikcq9XKjBkzWL58OQALFiygs7OTpqYmLBYLFouFNWvW8G//9m+MGzduR/9qX5hUAIQQQgghxJhns9nYd999mTdvXn6ZYRjMmzev4C7/1ui6zgcffEBtbS0A559/Pu+//z5LlizJv+rq6rjmmmt49tlnRySObSFdgIQQQgghRElsdqN+RLa9va6++mouuOAC9ttvP2bOnMntt99OPB7nwgsvBGDu3LnU19fnxxH853/+JwceeCATJ04kFApx6623smbNGi655BIAysvLKS8vL/gOq9VKTU0NkydP/kLxfRFSARBCCCGEEAI4++yz6erq4oYbbqC9vZ3p06fzzDPP5AcGr127FlXd1IGmr6+PSy+9lPb2doLBIPvuuy8LFy5k6tSppQphmyimaZqlzkSpRSIR/H4/Nbv/EFUbvoEhXyaKMnZ7g4Wiq0udhZLyukr/QJJScdiDpc5CSSWS3aXOQsnYbf5SZ6Gk0ploqbNQMlaLu9RZKAlDT9L68c8Jh8MFA2JLZeO110srH8DjdY3Id8SiCY4Yf9ZOE/POZOxe9QkhhBBCCDEGSRcgIYQQQghREpvP1z8S2xbFSQVACCGEEEKUhKKYKMrI9EYfqe3uCqRyJIQQQgghxBgiLQBCCCGEEKIkdrZpQMcKaQEQQgghhBBiDJEWACGEEEIIURIKoIzQrXppARiaVAA2E092oKi2UmejJOzWsTsnttXiLHUWSiprpEqdhZLJZvtLnYWSMk2j1FkomZ7wp6XOQklZNEeps1AymUys1FkoCdNIlzoLYiciFQAhhBBCCFESMgagNGQMgBBCCCGEEGOItAAIIYQQQoiSUJXca6S2LYqTFgAhhBBCCCHGEGkBEEIIIYQQJSFjAEpDWgCEEEIIIYQYQ6QFQAghhBBClISijOBzAKQJYEjSAiCEEEIIIcQYIi0AQgghhBCiJGQMQGlIC4AQQgghhBBjiLQACCGEEEKIkpAWgNKQFgAhhBBCCCHGEGkBEEIIIYQQJSFPAi4NaQEQQgghhBBiDJEWACGEEEIIURIyBqA0pAVACCGEEEKIMURaAIQQQgghRGkoJopijti2RXHSAiCEEEIIIcQYIi0AQgghhBCiJGQMQGlIC4AQQgghhBBjiLQACCGEEEKIklCU3Gukti2KkwrAMEiHPyIVfh9T70e1leEsn43mqBoyvamnSPa9Qza+ClNPoVo92MtnYXU1AZDsXUQ6tLhgHdXqx9N4VsGybLKDVO/b6KkuQEGzl+OqOR5FHb1i7e97l0Tv2xh6HIu9Ek/VUVidtUOmN/Qk8e5XSUc/wzCSaBYf7qojsHvGAxDvfo1Ez+sF62i2MspaLtr0naH3SEU+IZvqxDTSlE/8LqrmGJkAP0cq/CHJ0BJMvR/NVo6z4iAsjuoh0xt6imTvW2TiqzD1JKrVi7N8NlZ3MwD9vW+T6ltUsI5qDeBr+lr+fXTDo+jJtoI0Nt9UXJWHDmNkn6+/bwn9ve/ky95ddcTnln2i+zVS0eWYRhLV4sVTdTi2fNkvpL/njYJ1NFuQYMuFA+v3k+h+nUx8DXo2gqq5sHkm4Ko4CFWzj1ygQ4j1vEm0eyF6NobVUU2w9gRsroYh0xt6P+GOefRHPsHQ+7FYA/hrj8Pp3W1Q2kjXAiIdL+ApP5BA7fEAZNN9tC+7vei2yxrPwuXfY1ji2hZj/bjPRJeSDX80cM4PYi2biWavGDK9aaTJ9L1Ltn8d6CkUixtb2f5oznoA0qH3yIbfL1hHsfhw1p+af29komT6FqGnOsE00Jx12Mr2R9GcIxPkEMby7x1AMvQ+ydC7GHoCzVaBu/LQzz3n9/e+QTq2In/Od1Ucgs09DoBEz5sk+94uWEe1Bgg0nzdoW6ZpEmt7nExiLZ6aE/LnTiF2REkrAK+88gq33norixYtoq2tjUceeYTTTjsNgEwmw09+8hOeeuopVq5cid/vZ86cOfziF7+grq4uv41ly5ZxzTXX8Nprr5FOp5k2bRo33ngjRxxxxKjEkImtINnzBo7Kg9HsVaTDHxJvfxpP41moRU7MpqkTb38KVXXirJ6DqrkxsjEU1VaQTrUGcdWesGmBUthbK5vsINH2NPbgdBwVswEVI90zqtXdZORTYl0v462eg8VRS3/fYsLrH6Ss5SJUi3tQetPUCa//J6rmwld3CqrVg56JoKqFF2+arZxAwcm/MCbTyGJzt2BztxDvXjASoW2TdGw5/d0LcVYeisVRRSr0AfG2J/E2noNqGaLs255A0Zy4q49Gsbgxhyh7T93JmxYUKVObdwqOsv03JRnlH8FUZCnxrvl4qo/Kl31k/cMEWy5EtbgGpTdNncj6h1A0F766k1CtHoxMBEUtvIDTbOX4G7+62ZJN+72RjWNkY7iqDsViK0fPRIh1vICRjeOrP5nRlAh/SKj9WYJ1J2Nz1hPreYOu1X+lZrfvoVk8g0YnhwsAAEhDSURBVNKbRpau1X9B09yUN56NZvWiZ8KD4gdIJzYQ730H6xYXFZrVT+3kHxQsi/ctItr9Gg7PxOENcCvG+nGfja8m0/sOtvIDUG0VZKKfkOqch7PulKIX46apk+p4ATQH9opDUSwuzGwcRbUWpFOsfhzVR2++ZNM2jAypzhdQrcF8mkxoCanOl7DXHI8ySuf9sfx7B5CKfkai+1XcVYdjcdSQDC0h2voY/qZzhzzvRVsfRdWceGqOR7W4MbJRlEH7fhneuk2VvS3jz39/+D12xV7tKiPXH136uQ+tpH+beDzO3nvvzW9/+9tBnyUSCRYvXsy///u/s3jxYh5++GGWLl3KKaecUpDupJNOIpvN8uKLL7Jo0SL23ntvTjrpJNrb20clhlT4A6y+3bF5J6PZgjgqDkZRLGSiS4umz0SXYuopnDXHYHHUoFq9WJy1aPbywoSKgmpxbXptcacr1fMGNv+e2APT0WxlaLYAVs8EFEUbqVAH6e97B4d/Lxz+vbDYK/BUH42iWkmGPyyaPhn+AENP4qs/DaurHs3qx+ZqxLLl3SNFRbW4N3sVnlhdZfviKj8Ay1buOI6GVOh9bL4p2H27o9nKcFYeCoqFdPTTounTkU8x9RTummNzZW71YXHWDb5zqKhblH2RO3yqpSDNlj+oI62/bxEO/544/HtisZfz/9u78zgn6vvx46+ZybnZZO+TW0UE5L4EFFEoFBWP1qPWA7W13/aLv6+ItdVvVZRWwVqvVsWjKvVbrbXWq1CxiHiByiUqIAjIfS0Lu8km2Vwz8/sju9kNm0UUdgfJ+8kjjweZfGbyee8n+WQ+8zkmt2wsimo7SNmvaij7c1Nlb8/phM1Vkp6wRdk3xW5zFuPrcC7O3OPRHPk4PJ3xlJxKLPQVpmm0Zbgt1FUvxlMwCE/BAOyuUvIrz0FR7YRqPsmYPlT7CUainqIul+L0dMbmKMDp6YrDXZ6WztCj7N/+Two6nIuippe7oqhodm/aoz7wBW5f73btAcn2730isAabtzu23BNQHfk4Ck9BUTQSwY2Z0wc3YhpRnCWj0VylqLZcNFcZqqPwgJQqiuZu9miq843oXsxECEfxCFRHAaqjAEfxSIzYPoxI+/zWQXb/3gFEalfizOuN09cLzVFITskZoNiI1n2RMX008AWmHiG34izsDXW+3d0BW4veogM++xnq/ER0L/U1n+ApPbMNIhPZyNIegAkTJjBhwoSMr+Xl5TF//vy0bQ8//DBDhw5l69atdO7cmerqatavX89TTz1F3759AZg5cyaPPvooq1atory8PNOhjxjT1DGi1Tjz+6e2KYqCzd0BPVKVcZ9EaCs2VxmR6kUkwltQVBf23ONx5PdDadbqN+IB6rY8B4qG5irFVTgUteHKoqHXo0ersOceT2jHaxiJOlR7Hs7CIdhcbRtzI9PUSUT2kFM4LLVNURTsOZ2JR3Zm3CcW3IjdVUlwzwKiwQ2omhunryc5hUPTYtdjNezbMAtUG3ZXJZ6S09DsvjaP6ZswTR09uhdnwYDUtmTZdyQR2ZNxn3h4M5qrjPq9HxAPb0ZRXTi83XHm9z+g7P34Nz+LomhorjLchcNQ7d70Y9Wtx1+3HkVzY/d0xVUwsMUVxbbSWPbuwqGpbcmy70LigKFJjZJlX0Fwz9vEghsbyv4k3IVDWpT9/g2PN5R9BTklpx607E09iqI60o7R1kwjQbx+F76S01LbFEXFlXscsfC2jPtEAutw5nSidudc6gNrUW0ecvL64C05NS3vtbvm4vJ2x5V7PIGq9w6aj1j9TuKR3eRXnn1kAjsE8r3XMWL7seWdnNqmKAqqqwIjujfjPnp4G6qzhNj+j9HD21E0JzZPN2y+3mnxm4kA9dtfAkVDdRRjLxiY6lExTb3hzZqd8CoaoKBHq9DaoVGUzb930FjnV+EuGJTalvzsdyTRSiMsHtqEzVVOeO+7xEKbUDU3jtwTk/V1889+vJaaTU+jKDZsrnLcRcPRmtX5phEnuPs/eEpOz9jL9l0ncwCs8Z3qHfH7/SiKQn5+PgBFRUX06NGDZ599llAoRCKR4PHHH6e0tJRBgwYd/GBHgKlHALNFt6+iuTH0cMZ9jESAeGgTYJBTPh5nwQBi/s+J1TZdOdRcpbhLTien/Pu4i0dixusI7fwXphFLHiMeACBaswK77yRyyr+P5igmvHMuetzfJrG2iEOvB8wWlVGyizeUcR897ica/BIwyOv4A3KKhlO/fxnhZuO+ba4KfBUTyOt4Id6ysehxP7Vb/4bREPvRorHsD7xSo9rcmK2VfbyOeOgrwMRTcRauwkFEaz8lWtM0/tXmLCOn9AxyK87GXTIKI15H3c7XUmUP4PB2J6dsDLmVE3EVDCBW9yXhqrfbIszMcaTKPv0KrarltFr2RtxPNLieZNlfQE7RKdTvX079vo9TaeyuCrwV38fX8Qfklo1Bj/vxb/17q2VvJOoJ7/sIV16fIxXaIUl+t43UCUoj1ZaLnghm3CcRqyEcWINpGhR3vRxf6ekE9y2mbu+7qTTh2s+J1e8ir2zsIeUjVLMCm7MEZ8NY6vYg3/somet8F6Zen3mfRBA9tAVME1fpmdjz+hIPrCHh/zyVRnMW4ygaibN0DI7CYZh6iOjuNzGNeMPrJaDYiNeswDQSmEaceM1ywIRW3vdIy+bfO6ChfFvGn6z3Msevx/3EQhsxTRNvxURcBUOI1H5CpGZZKo3NVU5u2Vi8leeSU3I6RiJA3Y6X0+r8cPUH2N0VMuZfHFHfmUnAkUiEX//611x66aX4fMmrQoqi8NZbb3H++efj9XpRVZXS0lLmzZtHQUFBq8eKRqNEo9HU80Ag0Ob5b2KiqC5cxaclu/SdJRiJEDH/ZzgbrizYczo1S1+E5iylbuvfiAe/wuE7KfWK3dcTh7cHkPwBSUR2Eq9bh9bsyuxRxTRRtRxyy8ahKCp2VzlGIkj9/qV4ikcApCYFJpVgc1Ww/6sniAbW4c5v3xO9Iy/54+EuGZW8+tNQ9tHaT3EVDgbA7mk6mdMayj6w9TliwY04fT0BcPp6NaVxFqFoHkK7/oUe96PZ89o3pENkpsr+eyiKis1VhpEIEt6/jJzi4QA4crs126MEm6ucmq/+TCywDtcBZW/oUQI7XklOBGzY/+hmotk8yaE9iorDXYkeD1BXvQhf6RkkYn5qd71BcbcrD6knxzTihGs/x1favhO/vxX53qNoLhxFp6AoKqqzCFMPEw+swZ7fDyA1GTipANVZTP32l9FDm7F5u6NoLpwlo4jt/5hE3VpAQfN0RXEUcnSPCc/i3zug8SKRp/SMhnqvFCMRJFL7SaoH1dGwAAQAzmJsrnL8W/5CLLgBp68XsdAm4vXbyet0iUUxtD25D4A1vhMNgHg8zsUXX4xpmsyaNSu13TRNJk+eTGlpKe+//z5ut5s///nPTJw4kaVLl1JRkblbdMaMGdx5552Hna/kGE2lxZUfU69H1VpOCEruk4NiU9O6/1RHAaZej2nqGcc0KpoT1ZGHkUg2VBqvOqv2/LR0qj0fo5UrkEdaMg9Ki6t+hh5qtYtStXlASY9dcxRi6KFWY1c1F5qjAD1ec0Tzf7gay944oOyNRD3KQcpePTB+ewGmHj5I/E40e17qKlgmjWOpjXigXRoATWWfftXL0MMHLXtF0VqUvXlIZV+b/j5GjMD2l1FUB77Kc9t9HHDyu622+K4ZiWDGCcCQ7B04MH6bswQjEUwOKYrsxNBDVG14vPkRiYW3ENy3hA69b0vbN+xfg2nGyWk2HKM9yPfeSeY6P9LqajyK5m4Rv2rPg4PV+aoD1e7DSNSltmnuStwdLkheiVdUFNVBeNs/UHIyf+aOtGz+vUvmK/nZPzD+ZL2XOX5Vy/zZ/7o6X7Xno8dqAYiHt2PE/dR89WRauuDuN5I9Zx1/cHiBiax11A8Bajz537JlC/Pnz09d/Qd4++23mTNnDi+88AIjR45k4MCBPProo7jdbv7yl7+0esxbbrkFv9+femzblnnc7tdRFA3VWUyifkdqm2maJOp3trosmuYqx4gHME0ztc2I+5MVZSsnMqYRx4jXpU4sFZsXRcvBOKD704j7UW3eTIc44hRFw+YqIxbe2pRP0yQe3ordVZlxH5u7A3qsNi12PV6DqnkOEnsMPeZvMdzCaoqioTlLSIQPLPsdrS4JZ3OVo8f9B5R97SGUfaDVH1gAPVoNcNA0R1Jj2cczlL3NlbnRbf/WZV+bdmJp6FEC2/4Jioavw3ntvvoRJFdcsrsriAS/asqraRANbsKRdjWziTOnM4nY/rTJyoloNarNi6LacHqOo+yE/6bshJ+nHnZ3JTl5fSg74ect5jiEalbg9vZAa+fxwPK911AdhWkTb03TxIjsRnWWZNxHdZZixusO+N4HkhN9D/a9T9RlbFQomgtFdaDX7wIjgnaQpWePpGz+vYPGOr+UeH3T+ULys7+91bkINncFxjeu82MNsSW/2+6Cgfg6XYqv049SD4Cc4lPxHOJwwaNd4xyAtnqIzI7qHoDGk//169ezcOFCiorSVw4Ih5NXIFU1/cdRVVUMo/VVQZxOJ07nkVk1w5nXh/q976I5S9CcJcT8qzDNOPbc5Nre9VULUWweXI3dfb6exPyriexbjMPXGyMRIFa7EoevaQ3vyL6PsOV0QbXlYuhhojXLUVCw5x4PJIc+OfL7Et2/PLkigrMoub52vBaHt/0qBHfBYOp2v4HdVdawHOByTCOOq2GCXGDXv1FtueQ2rE/vzu9HpPYTglVv4y4YgB6rIbzvY9wFA1PHDFa9gyP3eDS7DyMRJFS9GBQFl7epKzi5HGQodYUkEa1GVR2odm/mFXPaiDO/L+GqhdicJWiuUqL+z8CMp7qpQ3veRrV5cBclJ0w683oT9a+ivnoRzryTMeJ+IrWf4Gw2obC++kPsni4otlxMPUxk/1JAwe5NLvOox/3E6zZg83RGUZ0Ysf3UVy9Gc2VYWaMNuQsGUbd7HjZXWXI5vJoVDWWf/BzX7XoD1ZaLp2GirCu/H5HalYSqFjYr+yW4m02iDlW9iyP3uIYrnyHC1YtBUXE2lL2hRwls/yemkcDXYQKmEUuNk02eTLXf9Qxv8Qj2b38Fh7tDwzKgH2IYMTwN8ezf/jKazUteeXLJRk/hEIL7l1C76w1yi4aRiO2nbu/75DZ8NlTNiaqlNxwVxYFqy2mxHGgiuo9YeAvFXS5rh0hbyvbvvc3Xi1j1IlRHUfKEOPAFppnA1lA/R6sXoWhuHA3x2bwnkqhbR7xmKTbvSZjxAHH/KuzNhrfEapajuTs2LA0cJt6w3KPN0zQsLhHcgGLPQ1FdGNG9xGqWYvP2TPYmtJNs/r0DcOX3J1T1FjZnaXJyc+2nYCZwepPDM4N75qNqHnIah7b5TiZS+xnh6vdw5fVFj/sbVtHqlzpmuPoD7J5uqDYvRiJE/f4lgIKj4f4gjSsDHUi1eY+6SfLiu8XSBkAwGGTDhg2p55s2bWLlypUUFhZSUVHBhRdeyIoVK5gzZw66rqeW9iwsLMThcDB8+HAKCgqYNGkSt99+O263myeffJJNmzZx9tntszKGPfd4TD1CtGY5ZiKM2nBzksYuQSMRQm02Ck215ZJTMYHovo8I7XgZRcvB4euNI7+pQjASIeqr3k51K2uuMjwdzkv7kXPm9QFTJ7LvI0wjmlySrOIs1HasEFy+kzD1MKHqRRh6GJuzhLyOF6Yqq+SwlabYNbuPvI4XEqxaSM3mv6DacnEXDCSn2RhOI1FH3c45GEYEVXNjd3eg4IA1lutrV6bdNMi/7QUAvOXfT52EtAdH7gmYeoT6mqWYiTCasxhPxdnNyr4ubQCiasslt/Js6qsXU7f9H6iaB2den7RVNQw9SGjPW6myt7nLye14QarsFUUjXr89eSMeM4Fq82DP7YaroO0nvTfn9PXA0MOEqxenyt7X8QepstfjdaSXvRdfxx8QqnqHms3PNpT9ANzN7mWgJ4LU7fx3quxt7g7kd7409fdMRKtSq23UbHo6LT8Fx/2kXec/5OSdjJEIEah6u+FGYOUUd70iNQQoEfPTPH6bI4/irlfg3zWPPRtmodm85Badgrfk1G/83qGaT9DsPpwNJ0jtLdu/9zZPV0w9Qrz209SNwJylZ6au1psHDI9SbR6cZWOI719GpO5fKLYc7L6TsDU7CTYTIWLV7ydXtdJcqM4SXOUT0pcCjQdI1HwCRgzF5sGe1wdbw4lne8nm3zsAp7c7pl5P/f4lGIkQmrMEb+XEpvgz1HveynMJV3+AP/ACqubBldcPV7PGr5EIEdz9Zip+u7sST6eL2rVRazWZA2ANxWzeN9XO3nnnnYw37Jo0aRJ33HEH3bp1y7AXLFy4kNGjRwOwbNkyfvOb37Bs2TLi8Ti9e/fm9ttvb3V50UwCgQB5eXl4u05q9/XUjxbOo3TyaHuIt+M40qORrZXxq9nAlcWfe4BovO7rEx2jQq0s15stbBbdRfloYFOPzAiA7xrTiFHz1RP4/f604dRWaTz3WrPzBby+tvkdqguE6VX5o6Mm5qOJpT0Ao0eP5mDtj0NpmwwePJg333zzSGZLCCGEEEK0A1VJPtrq2CKzo34SsBBCCCGEEOLIOaonAQshhBBCiGOXzAGwhvQACCGEEEIIkUWkB0AIIYQQQlhCUUwUpW3Wo2mr4x4LpAdACCGEEEKILCI9AEIIIYQQwhIyB8Aa0gMghBBCCCFEFpEeACGEEEIIYQlFST7a6tgiM+kBEEIIIYQQIotID4AQQgghhLCEzAGwhvQACCGEEEII0eCRRx6ha9euuFwuhg0bxpIlS1pNO3v2bBRFSXu4XK7U6/F4nF//+tf06dMHj8dDZWUlV155JTt37myPUFolDQAhhBBCCGEJtY0f39Tf//53pk6dyrRp01ixYgX9+vVj/PjxVFVVtbqPz+dj165dqceWLVtSr4XDYVasWMFtt93GihUrePnll1m3bh3nnnvut8jdkSNDgIQQQgghhADuv/9+rr32Wq6++moAHnvsMebOncvTTz/NzTffnHEfRVEoLy/P+FpeXh7z589P2/bwww8zdOhQtm7dSufOnY9sAIdIegCEEEIIIYQ1lKaVgI7045tOAojFYixfvpyxY8emtqmqytixY/nwww9b3S8YDNKlSxc6derEeeedx+rVqw/6Pn6/H0VRyM/P/2YZPIKkASCEEEIIIY5ZgUAg7RGNRjOmq66uRtd1ysrK0raXlZWxe/fujPv06NGDp59+mtdee42//vWvGIbBiBEj2L59e8b0kUiEX//611x66aX4fL7DC+wwSANACCGEEEJYRGnjB3Tq1Im8vLzUY8aMGUcs98OHD+fKK6+kf//+nH766bz88suUlJTw+OOPt0gbj8e5+OKLMU2TWbNmHbE8fBsyB0AIIYQQQhyztm3blna13el0ZkxXXFyMpmns2bMnbfuePXtaHeN/ILvdzoABA9iwYUPa9saT/y1btvD2229bevUfpAdACCGEEEJYRGnjf5Bcpaf5o7UGgMPhYNCgQSxYsCC1zTAMFixYwPDhww8pHl3X+fzzz6moqEhtazz5X79+PW+99RZFRUWH8Rc7MqQHQAghhBBCCGDq1KlMmjSJwYMHM3ToUB588EFCoVBqVaArr7ySDh06pIYRTZ8+nVNOOYUTTjiB2tpa7r33XrZs2cJPf/pTIHnyf+GFF7JixQrmzJmDruup+QSFhYU4HA5L4pQGgBBCCCGEsISiqChK2wxI+TbHveSSS9i7dy+33347u3fvpn///sybNy81MXjr1q2oatNxa2pquPbaa9m9ezcFBQUMGjSIxYsX06tXLwB27NjB66+/DkD//v3T3mvhwoWMHj362wV3mBTTNE1L3vkoEggEyMvLw9t1EopqTUvMak57ntVZsEw8EbQ6C5ay2XKszoJlXFn8uQeIxuuszoJlQpE9X5/oGGbTXF+f6BhlUzMP/zjWmUaMmq+ewO/3Wz7+HJrOvbbs/Qc+X9v8DgUCYbqUXHTUxHw0kR4AIYQQQghhkW+xYP83OrbIRBoAzWiqHUW1W50NS2TzVfCEnnk94GwRT9RbnQXLBMM7rc6Cpbw5Ha3OgmU8rrKvT3QMi2Vx749uxK3OgiXMLI1bZCYNACGEEEIIYYnk9f+2uVIv1/9bJ8uACiGEEEIIkUWkB0AIIYQQQlhE5gBYQRoAQgghhBDCEkfbMqDZQv4yQgghhBBCZBHpARBCCCGEEBaRIUBWkB4AIYQQQgghsoj0AAghhBBCCEsoDf/a6tgiM+kBEEIIIYQQIotID4AQQgghhLCE9ABYQ3oAhBBCCCGEyCLSAyCEEEIIISyi0nbXo+U6d2vkLyOEEEIIIUQWkR4AIYQQQghhCUVRUJQ2mgPQRsc9FkgPgBBCCCGEEFlEegCEEEIIIYRF5E7AVpAeACGEEEIIIbKI9AAIIYQQQghLyH0ArCE9AEIIIYQQQmQR6QEQQgghhBAWkfsAWEH+MkIIIYQQQmQR6QE4AiK1nxOtXYmhh9EcReSUnIbNVdZqekOPEtn/MbHgV5h6BNXuJaf4VOyeLgDU71tCpGZZ2j6qPZ+8Lj9ucSzTNAnumksivBVP+fdx5B53ZIP7GlH/KiK1KzH1ejRHEe7ikYcQ+xLioU2p2N1FI5pi37+UaM3ytH1Uez6+zj9KPa/b8Rp6ZFdaGoevFzklo45gZIcmFviCuH8Vpl6P6ijAWXQKmrOk1fSmHiVWu4JEeAumHkWx5eIsHIotpxMA0ZpPiPtXpu2j2PLwdPxB6rmRCBOrWYZevxPTjKPafTjy+mHzdG2LEFsVr1vbLPZCHIVDDx67ESNWswK9fmsqdkfhEGzujgDEalcS93+ato9i85HT4YLUc0OvJ1azDKN+J6aZQLX7sPv6Ymv4/LSXRN16EnVfgB5BceRjzx+E6ixqNb1pxEj4P0MPbwcjhmLzYMsfgOaubEqTCBP3f4oR2QWmjmLLxV44DNVRCIAe3oYe3IARrwEjhqNsPKqjoM1jzaS+5hPC+5di6CFszhJyS8dgd1e0mt7QI4SqPyBWtx7DiKDZfHhKz8DZUF+FqhcR3vdh2j6ao5DCbtc07F9PqHox8dBm9EQdqubGmXsCOcWnomrOtgu0Fe0dP0B97adEA1+QiFZhGjGKTrgOVXO1TYAHEfWvIur/NFXnu4pGYnOVtpre1KNEapYQD21O1fmuohHYczoDENm/jGhtyzrf2+mStG2JyG4i+5eiR6sABc1ZhKf8bBS1/U5jsjn2tiRzAKxh6adnxowZvPzyy6xduxa3282IESO455576NGjRyrN6NGjeffdd9P2+6//+i8ee+yxtG2zZ8/m/vvv58svv8Tn83HRRRfxyCOPtHkMsbr11FcvIqf0dGyuMiK1nxHcOQdf50tRbTkt0pumTnDn6yiam9zy8Sg2D0aiDkVN/xFTHYV4K89t2tDKzSyi/s+OaDzfRCy4gfrqxbhLRmFzlRKt/ZzQrrl4O12KanO3SG+aOqFdc1A0N56y76HYPJiJIIrqSEun2gvIrZzYtCFD7A5vT1yFQ5qSWFARxkNfEdu/BGfRCDRnCbHAaur3/IecDj9A1TLHX7/nPyiaC1fJGShaDqYeghbx5+MqG596rijpHXXR6vcxjRiusjEoqotEaCORve/gtk1EO8hJ6JGUCG0itn8pjqJT0BwlxOvWEKl6i5zK81FaiT3SELuzeDSKLQczEWwRu2LPx1U2run5AZV3tPp9MGI4S89siP0rotXvotjPRnO0T+x6eCuJ2k+wFQxGdRah160jtvcdnBVno2Q4ITNNnVjVOyiaE0fxSNDckAiDam9KY8SIVr2F5izDUXw6aE7MRF1aGswEqrMENacziZql7RFqRpHAWoJ738FbNhabq4L6mhX4t79EYbdrUG2eFulNU8e//R+oWg6+ynNR7bno8QDqAXWe5igiv9PFzbY0lb2RCGIkgnhKR2NzFKHHAwT3zEdPBMnrcF5bhZqRFfEDmEYCh6cbDk83QtXvt0VoXysW3EBk34e4S05Dc5YR9X9GaPdcvJ1+1GqdF9o9F0V1k1P2PVStld87ewGeinOaNhxQ5yciuwntegNnQX/cxSMBFT22r9XfxbaQzbGLY5OlDYB3332XyZMnM2TIEBKJBP/7v//LuHHjWLNmDR5PU0V67bXXMn369NTznJz0E+v777+f++67j3vvvZdhw4YRCoXYvHlzu8QQqf0UZ14vnL6eybyVnI4/tIVY3VpcBQNbpI8FvsDUo3g7/gBF0QDQ7L4W6RSUjA2I5hLRaiI1K/F1ugj/5tmHH8w3FK39DIevJ07fSQC4S0YRDzfGPqBF+lhgLaYeJbfD+anYyRA7ivq1saPavj5NG4v7V2P3nojd2x0AZ9EI9PrtJOrW48jv2yJ9om49phHFXXF200m93ZvhyAePX49W4Swanrra7sjvTyywBiO2r90aAPHAGmze7thzk7E7Coej128nHtyAI69Pi/SJ4AZMI4qr/Kym2G25LdIpKBl/TBsZ0b04Ck9pFns/4nVfYET3tVsDIFG3Fi33eGwNV2+VgiHokV3ooa+w+Xq1SK+HNoERxV42ttXYE4EvULQc7EXDmjYekEbzdAOSJ8NWqq9ZhiuvD66Gcs4t+x6x0FdE/KvIaZ7/BhH/5xh6hPzOP25W5+W1PLCiZjyBBrA5S9JO9DVHPp6SUwns+jemabRoJLclK+IHyCkcBEAsvPUIRPHtxPyf4/D1xOFtqPOLR5EIb03W+fkZ6vy6dZh6FE/leanY1Ux13tfU+ZF9H+LMOzntPTRH/uEF8w1lc+xtTe4EbA1LGwDz5s1Lez579mxKS0tZvnw5o0Y1DefIycmhvLw84zFqamq49dZb+de//sWYMWNS2/v2bXkCdqSZpo4e3Zt2oq8oCvacjiQiuzPuEwttxuYqI7z3feKhTSiaG0dud1wFA9J+xPS4n9pNs1EUGzZXGe6iU9IqD9OIE9o9n5ySUZacCDfG7mx2oq8oCjZ3RxKRPRn3iYc3o7nKqN/7AfHwZhTVhcPbHWd+/7TYjbgf/+ZnURQNzVWGu3BYi4ozXrcef916FM2N3dMVV8FAlOZXS9uYaeoYsX048po+Z4qioLkqGrppW0rUb0VzlhDd9yF6eCuK5sLmOQ57Xp/0+BMBQtteAEVDc5biKBiE2uxkUHOWkghtwubuBKqDRGgTmDqaK/N35EhrjN3e7EQ/GXslRnRvxn308DZUZymx/R+RCG9riL0bdt/JB8ReR3j7i6BoqI4SHAUD02JXnSXo4c3JYUOqAz28ud1jN2M1qN6mE31FUVCdZRjRfRn3Mep3oDiLSdQsQ6/fgaK60Dyd0bw9U7Eb9TtQXeXEqhdhRKtQNDdabndsuce3S1yHyjR1EpE95BQ2negm67zOxCM7M+4TC27E7qokuGcB0eCG5PAdX09yCoem13mxGvZtmAWqDburEk/JaRkvjjQy9CiK6mjXk/+jKf72lqrz8/untjXW+XordX4itBnNVUp99QckwltQVBf23BMy1vmBLf8HiobNVYarcCiqLVnnG3o9erQKe253gjtexUgEkr2khUOwuVofdnUkZXPs4th1VE0C9vv9ABQWFqZtf+655yguLubkk0/mlltuIRwOp16bP38+hmGwY8cOevbsSceOHbn44ovZtm1bm+fX1COAiaqln4ArmhsjEc64jxEPEAt9BaZBbsXZuAsGEa1dSaTZuHebqwxP2ZnkVp5DTsko9ESAuh2vYBqxVJpw9SJs7nIcud3aJLav0xR7+tVa1ebG1FuLvY546CvAxFNxFq7CQURrPyVasyKVxuYsI6f0jOTfpmQURryOup2vpcXu8HYnp2wMuZUTcRUMIFb3JeGqt9sizFaZehQwWwx3UTQ3pl6fcR8jHiQR2gKYuMq+hz2/H7HA6rRx75qzBFfxqbjKxuEsGoGRqKN+178xjXgqjatkNJgGoW3PE9ryF6L7FuMqPRO1nU4WmmJPH+6iaK7WY0/UoYc2g2niKh2LI68v8cAa4s2GsKnOYpxFI5OvF56CqQeJ7J6XMfbw9hcIb/0/ovs+xFkyut1ix4jRauxG5tjNRBAjvA0wcZScji2vN4m6deiBNWlp9OCG5LyIktFouSeQqF2R7D04ihh6PWC2uFKdHN4QyriPHvcTDX4JGOR1/AE5RcOp37+M8L6PUmlsrgp8FRPI63gh3rKxyQsgW/+G0ex7n5aPRJjwvg9x57X9hZ609z1K4rdCY53/jeq8RB3x0CbAxFM+AWfBQGL+z4jWNtX5mquUnJLReMrPwl18Gka8juDO11N1vhEPABCtWYbDdxKe8rPQHMWEds5Bj/vbJNYDZXPs7UNp44fI5KiZQWIYBlOmTGHkyJGcfPLJqe0//vGP6dKlC5WVlXz22Wf8+te/Zt26dbz88ssAfPXVVxiGwd13381DDz1EXl4et956K9/73vf47LPPcDgcLd4rGo0SjUZTzwOBQNsHmJKsRHJKRyevArhKMRIhIrUrcTeMabc3n9DoBM1Vhn/L/xELbsDp60UstIlE/Q58aeNFvwuSsbtLRiVjd5ZgJEJEaz/FVTgYALuncyq1RhGas5TA1ueIBTemhlk5mw2z0JxFKJqH0K5/ocf9mbvWjxrJE0dn0QgURUVzFicnfgZW4Wjo3rXldEzbQ3MUE9r+DxKhTdi9JwIQq/2kYQ7AeBTNhR7eQqTqHdwVE9AchS3e9WihaG4cRcMbyr4IUw8TD6zG0XBVrXEyMCSvTGjOEsLbXyIR2pwaZpWKvXQcaE708Faie99FLZ9g2YTYQ6K5sBUMScbuKMTUwyQCa7HlNdV1iqMAe34/AFRHAWbcTyK4ITX05zvLTF4kyS0bh6Ko2F3lGIkg9fuX4ikeAZCaDJtUgs1Vwf6vniAaWIc7P31ImaFH8e94Gc1ZRE7D/ke1Ixz/d4uJorpxF49qqPNKMBNhov5PcRU01Pk56XW+zVlKYOvzxINf4fCdBJgA6cNvnMUkIjuI161FK2w57OrokM2xi++Co6YBMHnyZFatWsUHH3yQtv1nP/tZ6v99+vShoqKCMWPGsHHjRo4//ngMwyAej/PHP/6RceOSkwf/9re/UV5ezsKFCxk/fjwHmjFjBnfeeedh5zl5FVDBOOCKt6nXtzosR9U8oKhpXYCaowBTD2OaetPY+LR9nGj2PIxYssWfCO/AiPup/erPaelCu98k6qrA2/H8wwvsEDTFnn71w0jUo2iZY1e0HNQDY7cfYuzx1htpjaswGPFAuzUAFM0JKC2u/ph6fcZJsMl93CgHxK/a8zH1+lbjVzQnqj0PI5GM34gHiNd9gbvyfLSGE17NUYge2UM8sBatHU6ImmKPpG039chBYz/wc6/Y8w4eu+pAtfswm8WeqFuLu+Lc1Mm+5ihEj1YRr1uLs2j4EYrwIFQHrcautjJ3QXOhckDsNh8YkabYNRfqAZ9dxe7DrN9+pCM4LMkeP6XF1W5DD7U6fl21ZarzCjH00EG+9y40RwF6vCb9fYwY/u3/RFEd5FWen3HftmR1/FZqrPO/WZ2Xg2I7oM5z5B+0zlc0J5ojDyOR/L1r7GFX7ekNfM2e327zYbI59vaQXAOobQakyCpArTsqhgBdd911zJkzh4ULF9KxY8eDph02LNni3bBhAwAVFclxcL16NV0VLikpobi4mK1bM0+WuuWWW/D7/anHtx0upCgamrOERP2O1DbTNImHt2NrZUyyzV2OEfdjmmZqmx6vTVYWrfyYmUYcIx5AafiBcRUMxNfpEnydLk49ANzFI8kpO/NbxfJNpWIPp8eeqN/R6jKgNlc5+gGxG4cY+4HDrJrTo9UAB01zpCmKhuooSluO1DRN9MguNGfmZeE0VxlGvC49/oS/oWFwkPgTgVSjyjQTyfdvUakpNF4tamsHi11tZRlQzVmKGQ+kxW7GA4cQe11Tg9LUGzOQnp92jl1xFGBEm8b9mqaJEd3T6jKgqqMYI5Fe7skVflxNkwOdxS0auWa8rtXGtFWUhnHKzSeiJuu8rdhdlRn3sbk7oMdqD6jzalA1z0HKPoYe86fN/zD0KP5t/0BRVPI6XGDJyl9Wxm+11n7vEvU70Fqt88ta/N4Zcf+h/d41fPYVmxdFy8E4YMiLHm+/v082xy6OXZY2AEzT5LrrruOVV17h7bffplu3r+/qXrlyJdB04j9y5EgA1q1bl0qzf/9+qqur6dIl89rgTqcTn8+X9vi2XPn9iAbWEA2sRY/tJ7z3XTATqe660J63qK9uWt/Z6euNoUeor/4APVZLPLSZSM0KnM2GAoSrFxGv34EeD5Co30Vw1xuAgqNhGIRqy0FzFqU9kttz23XSmDO/L7G6L4gF1qHHaqivfg/MOA5vj4bY36Z+38dN6fN6Y+pR6qsXNcS+hUjtJzjzeqfS1Fd/SKJ+ZzL2yG5Cu+cBCnbvCUCy4ovsX04iuhc9HiAe2ky4aiGaq6LdVsBpZM/rTbzuS+LB9RixWqL7FmOaCWwN5RTZ+x7RZvdzsHt7YBpRYvs/xoj7SYS3Ea/9DLu3ZypNdP8S9MhujHgdemQPkaq3k/F7kkMEVHs+is1LZN9i9Oje5JwS/yr0yE5szbqT2zx2Xy8SdV8SD27AiNcS2/8RppnAnpssp2j1+8Saz2vx9mi4D8CShti3E/N/jq3hewIQrVmajD0RRI9UEdm7EFCwNQyBUex5KDZvchJ1Q+zxwGr0yE60dozd5j0JPbgRPbQpGUvNMjASaA1lFNv3EfHapnkdttwTwIiRqF2BEQ+g1+8k0bCKUlOaHpixfSQCq5NlH9qMHtqILbcpjalHMWI1mA0NBTNRl3zeyhjktuIuGEzE/xkR/yoS0X0E98zHNOK4GuqwwK5/E9z7XlP6/H6YRoRg1dskYvuJBjcS3vdx2kphwap3iIW3ocf9xOt34N/xGigKrobPh6FH8W9/CdOI4y3/PqYRw0iEMBIhTNM45uMHMBIhEpEq9FgtkFwFLhGpatEL25YceX2I1a0lVpes8yPV72OacRy5yTo/XPU2kf1Ndb7Dl6zzI/sa6vzwFqK1n+DwNavz9yXrfCNeRyKym/CeNwElVZcoioIzvx9R/yriwa8afgOWYsRrsTf7+0js32UyB8AKlg4Bmjx5Ms8//zyvvfYaXq+X3buTK+fk5eXhdrvZuHEjzz//PGeddRZFRUV89tln3HDDDYwaNSq1ys+JJ57Ieeedx/XXX88TTzyBz+fjlltu4aSTTuKMM85o8xgc3u4YeoTI/iUYiTCas5jcynNSQ4CMeJDmH0DV7sVbOZFw9SKigb+jah6ceX3TfgyMRIjQ7vmpIRU2dwXeTj886PKIVnDknoCpR6ivWYrZELun4uym2BN1ad891ZZLbuXZ1Fcvpm77Pxpi75O2soKhBwnteatZ7OXkdrwgFbuiaMTrtxP1f5a8EZTNgz23G66CQe0ZOgB2z3GYeoRYzSepm2G5y8al8mokQqjNy96Wi7tsHNH9S4jveA3FloPd1yttNR0zESay953kjbI0F5qzjJyKc1KTThVFxV32PaI1y4nseavhb+DFWXxa6mZi7cHm6YapR4jXriTWELurdGyqO7xl7B5cZWOJ7V9Kfd3rDbH3xO5raviaiTDR6vdSsavOUtzlZ6XF7iodS6x2ebJh1BC7o+jUtPkDbU3L6ZyM3f956kZgjpLRqXyaevrwEMXmwVEymnjtJ+i75yU/194T0Zo1/FRnEfbiU0n4PyPhX41iy8WWPxCt2c3d9MgOEvuXpJ7H9y1O5sfXO+0z1NZcvpMw9TCh6kUYeji5RGfHC1NDYJI9GU1lr9l95HW8kGDVQmo2/yX5PSgYSE7h0FQaI1FH3c45GEYEVXNjd3egoPNlqbokEd1DoqHHaf+m9KGPhcdd265zf6yIH6C+dmXazcL8214AwFv+/VTjo6011vmRmmVNdX75Wc3q/AN+72y5eCrOIrLvQ4I7XkLVPDh8J6fV+WYiRLhqQVOd7yrH0+H8tN87Z17f5H1U9i3GNKJojiI8FWe3a7lnc+zi2KSYzfun2vvNW1mf9ZlnnuGqq65i27ZtXH755axatYpQKESnTp244IILuPXWW9Ou2gcCAW644QZefvllVFXl9NNP56GHHqJTp0M7IQoEAuTl5ZF/3E9b3JQqW7T3VbSjSUKPfn2iY1g2l71uZHfZe3Par+Ekji6xeJ3VWRDtzDRiBDY/g9/vP6yRD0dK47nX3pqF+HxtM6QpEAhSUnDGURPz0cTSHoCva3t06tSpxV2AM/H5fDz11FM89dRTRyprQgghhBBCHJOOmlWAhBBCCCFEtmnLsfoyB6A1R8UqQEIIIYQQQoj2IT0AQgghhBDCEgpqG94HQK5zt0b+MkIIIYQQQmQR6QEQQgghhBAWkTkAVpAeACGEEEIIIbKI9AAIIYQQQghLKA3/2urYIjPpARBCCCGEECKLSA+AEEIIIYSwhKIoKEob9QC00XGPBdIDIIQQQgghRBaRHgAhhBBCCGERlba7Hi3XuVsjfxkhhBBCCCGyiPQACCGEEEIIS8gqQNaQHgAhhBBCCCGyiPQACCGEEEIIi8idgK0gPQBCCCGEEEJkEekBEEIIIYQQlpD7AFhDegCEEEIIIYRo8Mgjj9C1a1dcLhfDhg1jyZIlraadPXt2qhHT+HC5XGlpTNPk9ttvp6KiArfbzdixY1m/fn1bh3FQ0gAQQgghhBAWUdv48c38/e9/Z+rUqUybNo0VK1bQr18/xo8fT1VVVav7+Hw+du3alXps2bIl7fXf//73/PGPf+Sxxx7j448/xuPxMH78eCKRyDfO35EiDQAhhBBCCCGA+++/n2uvvZarr76aXr168dhjj5GTk8PTTz/d6j6KolBeXp56lJWVpV4zTZMHH3yQW2+9lfPOO4++ffvy7LPPsnPnTl599dV2iCgzaQAIIYQQQghLKG3875uIxWIsX76csWPHprapqsrYsWP58MMPW90vGAzSpUsXOnXqxHnnncfq1atTr23atIndu3enHTMvL49hw4Yd9JhtTSYBk2ydAZhGzOKcWMc0DauzYJlsLnfI9rKPW50FSxl61OosCItke72XjRrLvPGc52gRCATb/NiBQCBtu9PpxOl0tkhfXV2NrutpV/ABysrKWLt2bcb36NGjB08//TR9+/bF7/fzhz/8gREjRrB69Wo6duzI7t27U8c48JiNr1lBGgBAXV0dAP7Nz1qcEyGEaD9y+i9E9qmrqyMvL8/qbOBwOCgvL6dTp9Pb9H1yc3Pp1KlT2rZp06Zxxx13HJHjDx8+nOHDh6eejxgxgp49e/L444/z29/+9oi8R1uQBgBQWVnJtm3b8Hq9liwZFQgE6NSpE9u2bcPn87X7+1tJYs/O2CG748/m2CG748/m2CG747c6dtM0qauro7Kyst3fOxOXy8WmTZuIxdq2N8o0zRbndpmu/gMUFxejaRp79uxJ275nzx7Ky8sP6f3sdjsDBgxgw4YNAKn99uzZQ0VFRdox+/fvf6hhHHHSACA5vqtjx45WZwOfz5d1FWIjiT07Y4fsjj+bY4fsjj+bY4fsjt/K2I+GK//NuVyuFktmWsnhcDBo0CAWLFjA+eefD4BhGCxYsIDrrrvukI6h6zqff/45Z511FgDdunWjvLycBQsWpE74A4EAH3/8Mb/4xS/aIoxDIg0AIYQQQgghgKlTpzJp0iQGDx7M0KFDefDBBwmFQlx99dUAXHnllXTo0IEZM2YAMH36dE455RROOOEEamtruffee9myZQs//elPgeQKQVOmTOF3v/sd3bt3p1u3btx2221UVlamGhlWkAaAEEIIIYQQwCWXXMLevXu5/fbb2b17N/3792fevHmpSbxbt25FVZsW0aypqeHaa69l9+7dFBQUMGjQIBYvXkyvXr1SaX71q18RCoX42c9+Rm1tLaeeeirz5s2ztPdDGgBHAafTybRp01odk3Ysk9izM3bI7vizOXbI7vizOXbI7vizOfbvmuuuu67VIT/vvPNO2vMHHniABx544KDHUxSF6dOnM3369COVxcOmmEfbelBCCCGEEEKINiM3AhNCCCGEECKLSANACCGEEEKILCINACGEEEIIIbKINAAs9sgjj9C1a1dcLhfDhg1jyZIlVmepXbz33ntMnDiRyspKFEXh1VdftTpL7WbGjBkMGTIEr9dLaWkp559/PuvWrbM6W+1m1qxZ9O3bN7UW9vDhw3njjTeszpYlZs6cmVoiLhvccccdKIqS9jjppJOszla72bFjB5dffjlFRUW43W769OnDsmXLrM5Wu+jatWuLslcUhcmTJ1udtTan6zq33XYb3bp1w+12c/zxx/Pb3/4WmYIprCQNAAv9/e9/Z+rUqUybNo0VK1bQr18/xo8fT1VVldVZa3OhUIh+/frxyCOPWJ2Vdvfuu+8yefJkPvroI+bPn088HmfcuHGEQiGrs9YuOnbsyMyZM1m+fDnLli3jzDPP5LzzzmP16tVWZ61dLV26lMcff5y+fftanZV21bt3b3bt2pV6fPDBB1ZnqV3U1NQwcuRI7HY7b7zxBmvWrOG+++6joKDA6qy1i6VLl6aV+/z58wG46KKLLM5Z27vnnnuYNWsWDz/8MF988QX33HMPv//97/nTn/5kddZEFpNVgCw0bNgwhgwZwsMPPwwk7zbXqVMn/t//+3/cfPPNFueu/SiKwiuvvGLpDTGstHfvXkpLS3n33XcZNWqU1dmxRGFhIffeey8/+clPrM5KuwgGgwwcOJBHH32U3/3ud/Tv358HH3zQ6my1uTvuuINXX32VlStXWp2VdnfzzTezaNEi3n//fauzclSYMmUKc+bMYf369SiKYnV22tQ555xDWVkZTz31VGrbD3/4Q9xuN3/9618tzJnIZtIDYJFYLMby5csZO3ZsapuqqowdO5YPP/zQwpyJ9ub3+4HkSXC20XWdF154gVAoxPDhw63OTruZPHkyZ599dtr3P1usX7+eyspKjjvuOC677DK2bt1qdZbaxeuvv87gwYO56KKLKC0tZcCAATz55JNWZ8sSsViMv/71r1xzzTXH/Mk/wIgRI1iwYAFffvklAJ9++ikffPABEyZMsDhnIpvJjcAsUl1dja7rqTvLNSorK2Pt2rUW5Uq0N8MwmDJlCiNHjuTkk0+2Ojvt5vPPP2f48OFEIhFyc3N55ZVX0u6aeCx74YUXWLFiBUuXLrU6K+1u2LBhzJ49mx49erBr1y7uvPNOTjvtNFatWoXX67U6e23qq6++YtasWUydOpX//d//ZenSpfzP//wPDoeDSZMmWZ29dvXqq69SW1vLVVddZXVW2sXNN99MIBDgpJNOQtM0dF3nrrvu4rLLLrM6ayKLSQNACAtNnjyZVatWZc046EY9evRg5cqV+P1+XnrpJSZNmsS77757zDcCtm3bxvXXX8/8+fMtvQW8VZpf8ezbty/Dhg2jS5cuvPjii8f88C/DMBg8eDB33303AAMGDGDVqlU89thjWdcAeOqpp5gwYQKVlZVWZ6VdvPjiizz33HM8//zz9O7dm5UrVzJlyhQqKyuzruzF0UMaABYpLi5G0zT27NmTtn3Pnj2Ul5dblCvRnq677jrmzJnDe++9R8eOHa3OTrtyOByccMIJAAwaNIilS5fy0EMP8fjjj1ucs7a1fPlyqqqqGDhwYGqbruu89957PPzww0SjUTRNszCH7Ss/P58TTzyRDRs2WJ2VNldRUdGigduzZ0/++c9/WpQja2zZsoW33nqLl19+2eqstJubbrqJm2++mR/96EcA9OnThy1btjBjxgxpAAjLyBwAizgcDgYNGsSCBQtS2wzDYMGCBVk1FjobmabJddddxyuvvMLbb79Nt27drM6S5QzDIBqNWp2NNjdmzBg+//xzVq5cmXoMHjyYyy67jJUrV2bVyT8kJ0Nv3LiRiooKq7PS5kaOHNliud8vv/ySLl26WJQjazzzzDOUlpZy9tlnW52VdhMOh1HV9NMtTdMwDMOiHAkhPQCWmjp1KpMmTWLw4MEMHTqUBx98kFAoxNVXX2111tpcMBhMu+q3adMmVq5cSWFhIZ07d7YwZ21v8uTJPP/887z22mt4vV52794NQF5eHm632+Lctb1bbrmFCRMm0LlzZ+rq6nj++ed55513ePPNN63OWpvzer0t5np4PB6KioqyYg7IL3/5SyZOnEiXLl3YuXMn06ZNQ9M0Lr30Uquz1uZuuOEGRowYwd13383FF1/MkiVLeOKJJ3jiiSeszlq7MQyDZ555hkmTJmGzZc/px8SJE7nrrrvo3LkzvXv35pNPPuH+++/nmmuusTprIpuZwlJ/+tOfzM6dO5sOh8McOnSo+dFHH1mdpXaxcOFCE2jxmDRpktVZa3OZ4gbMZ555xuqstYtrrrnG7NKli+lwOMySkhJzzJgx5n/+8x+rs2WZ008/3bz++uutzka7uOSSS8yKigrT4XCYHTp0MC+55BJzw4YNVmer3fzrX/8yTz75ZNPpdJonnXSS+cQTT1idpXb15ptvmoC5bt06q7PSrgKBgHn99debnTt3Nl0ul3nccceZv/nNb8xoNGp11kQWk/sACCGEEEIIkUVkDoAQQgghhBBZRBoAQgghhBBCZBFpAAghhBBCCJFFpAEghBBCCCFEFpEGgBBCCCGEEFlEGgBCCCGEEEJkEWkACCGEEEIIkUWkASCEEEIIIUQWkQaAEOKotXnzZhRFYeXKlVZnJWXt2rWccsopuFwu+vfvf1jHmj17Nvn5+QdNc8cdd3zt+1x11VWcf/75h5UXIYQQ2UMaAEKIVl111VUoisLMmTPTtr/66qsoimJRrqw1bdo0PB4P69atY8GCBRnTtHZC/s4776AoCrW1tQBccsklfPnll22Y2yNLURReffVVq7MhhBDiMEkDQAhxUC6Xi3vuuYeamhqrs3LExGKxb73vxo0bOfXUU+nSpQtFRUWHlQ+3201paelhHSPbxeNxq7MghBDfOdIAEEIc1NixYykvL2fGjBmtpsk0TOXBBx+ka9euqeeNV8XvvvtuysrKyM/PZ/r06SQSCW666SYKCwvp2LEjzzzzTIvjr127lhEjRuByuTj55JN59913015ftWoVEyZMIDc3l7KyMq644gqqq6tTr48ePZrrrruOKVOmUFxczPjx4zPGYRgG06dPp2PHjjidTvr378+8efNSryuKwvLly5k+fTqKonDHHXcc5C/39TINAZo5cyZlZWV4vV5+8pOfEIlE0l7XdZ2pU6eSn59PUVERv/rVrzBNs0UcM2bMoFu3brjdbvr168dLL72Uer2xJ2LBggUMHjyYnJwcRowYwbp16751LPv27ePSSy+lQ4cO5OTk0KdPH/72t7+lXn/22WcpKioiGo2m7Xf++edzxRVXpJ6/9tprDBw4EJfLxXHHHcedd95JIpFIva4oCrNmzeLcc8/F4/Fw1113UVNTw2WXXUZJSQlut5vu3btn/BwJIYRIkgaAEOKgNE3j7rvv5k9/+hPbt28/rGO9/fbb7Ny5k/fee4/777+fadOmcc4551BQUMDHH3/Mz3/+c/7rv/6rxfvcdNNN3HjjjXzyyScMHz6ciRMnsm/fPgBqa2s588wzGTBgAMuWLWPevHns2bOHiy++OO0Yf/nLX3A4HCxatIjHHnssY/4eeugh7rvvPv7whz/w2WefMX78eM4991zWr18PwK5du+jduzc33ngju3bt4pe//OVh/T0O9OKLL3LHHXdw9913s2zZMioqKnj00UfT0tx3333Mnj2bp59+mg8++ID9+/fzyiuvpKWZMWMGzz77LI899hirV6/mhhtu4PLLL2/RcPrNb37Dfffdx7Jly7DZbFxzzTXfOu+RSIRBgwYxd+5cVq1axc9+9jOuuOIKlixZAsBFF12Eruu8/vrrqX2qqqqYO3du6n3ff/99rrzySq6//nrWrFnD448/zuzZs7nrrrvS3uuOO+7gggsu4PPPP+eaa67htttuY82aNbzxxht88cUXzJo1i+Li4m8dixBCHPNMIYRoxaRJk8zzzjvPNE3TPOWUU8xrrrnGNE3TfOWVV8zm1ce0adPMfv36pe37wAMPmF26dEk7VpcuXUxd11PbevToYZ522mmp54lEwvR4PObf/vY30zRNc9OmTSZgzpw5M5UmHo+bHTt2NO+55x7TNE3zt7/9rTlu3Li09962bZsJmOvWrTNN0zRPP/10c8CAAV8bb2VlpXnXXXelbRsyZIj53//936nn/fr1M6dNm3bQ40yaNMnUNM30eDxpD5fLZQJmTU2NaZqm+cwzz5h5eXmp/YYPH572XqZpmsOGDUv721ZUVJi///3vU88b/x6N5RSJRMycnBxz8eLFacf5yU9+Yl566aWmaZrmwoULTcB86623Uq/PnTvXBMz6+vpW4wLMV1555aCxN3f22WebN954Y+r5L37xC3PChAmp5/fdd5953HHHmYZhmKZpmmPGjDHvvvvutGP83//9n1lRUZGWhylTpqSlmThxonn11Vcfcr6EECLb2axregghvkvuuecezjzzzMO66t27d29UtanjsaysjJNPPjn1XNM0ioqKqKqqSttv+PDhqf/bbDYGDx7MF198AcCnn37KwoULyc3NbfF+Gzdu5MQTTwRg0KBBB81bIBBg586djBw5Mm37yJEj+fTTTw8xwiZnnHEGs2bNStv28ccfc/nll7e6zxdffMHPf/7ztG3Dhw9n4cKFAPj9fnbt2sWwYcNSrzf+PcyGYUAbNmwgHA7zve99L+04sViMAQMGpG3r27dv6v8VFRVA8qp8586dDzXMFF3Xufvuu3nxxRfZsWMHsViMaDRKTk5OKs21117LkCFD2LFjBx06dGD27NmpieaQLMtFixalXfHXdZ1IJEI4HE4da/DgwWnv/Ytf/IIf/vCHrFixgnHjxnH++eczYsSIbxyDEEJkC2kACCEOyahRoxg/fjy33HILV111Vdprqqq2GIeeaXKm3W5Pe64oSsZthmEccr6CwSATJ07knnvuafFa40ktgMfjOeRjHgkej4cTTjghbdvhDqE6FMFgEIC5c+fSoUOHtNecTmfa8+Z/+8aT8G/yt2/u3nvv5aGHHuLBBx+kT58+eDwepkyZkjbhesCAAfTr149nn32WcePGsXr1aubOnZuW9zvvvJMf/OAHLY7vcrlS/z+wLCdMmMCWLVv497//zfz58xkzZgyTJ0/mD3/4w7eKRQghjnXSABBCHLKZM2fSv39/evTokba9pKSE3bt3Y5pm6kTySK7d/9FHHzFq1CgAEokEy5cv57rrrgNg4MCB/POf/6Rr167YbN++SvP5fFRWVrJo0SJOP/301PZFixYxdOjQwwvgEPXs2ZOPP/6YK6+8MrXto48+Sv0/Ly+PiooKPv744xZ/j4EDBwLQq1cvnE4nW7duTYujrS1atIjzzjsv1cNhGAZffvklvXr1Skv305/+lAcffJAdO3YwduxYOnXqlHpt4MCBrFu3rkXD6VCUlJQwadIkJk2axGmnncZNN90kDQAhhGiFNACEEIesT58+XHbZZfzxj39M2z569Gj27t3L73//ey688ELmzZvHG2+8gc/nOyLv+8gjj9C9e3d69uzJAw88QE1NTWri6OTJk3nyySe59NJL+dWvfkVhYSEbNmzghRde4M9//jOaph3y+9x0001MmzaN448/nv79+/PMM8+wcuVKnnvuuSMSx9e5/vrrueqqqxg8eDAjR47kueeeY/Xq1Rx33HFpaWbOnEn37t056aSTuP/++1P3FQDwer388pe/5IYbbsAwDE499VT8fj+LFi3C5/MxadKkw8rjpk2bWjTuunfvTvfu3XnppZdYvHgxBQUF3H///ezZs6dFA+DHP/4xv/zlL3nyySd59tln0167/fbbOeecc+jcuTMXXnghqqry6aefsmrVKn73u9+1mqfbb7+dQYMG0bt3b6LRKHPmzKFnz56HFacQQhzLZBUgIcQ3Mn369BbDRHr27Mmjjz7KI488Qr9+/ViyZMkRXSFn5syZzJw5k379+vHBBx/w+uuvp1Z5abxqr+s648aNo0+fPkyZMoX8/Py0+QaH4n/+53+YOnUqN954I3369GHevHm8/vrrdO/e/YjFcjCXXHIJt912G7/61a8YNGgQW7Zs4Re/+EVamhtvvJErrriCSZMmMXz4cLxeLxdccEFamt/+9rfcdtttzJgxg549e/L973+fuXPn0q1bt8PO49SpUxkwYEDa45NPPuHWW29l4MCBjB8/ntGjR1NeXp7xZmh5eXn88Ic/JDc3t8Xr48ePZ86cOfznP/9hyJAhnHLKKTzwwAN06dLloHlyOBzccsst9O3bl1GjRqFpGi+88MJhxyqEEMcqxTxw4K4QQgjRhsaMGUPv3r1b9CQJIYRoH9IAEEII0S5qamp45513uPDCC1mzZk2LuSRCCCHah8wBEEII0S4GDBhATU0N99xzj5z8CyGEhaQHQAghhBBCiCwik4CFEEIIIYTIItIAEEIIIYQQIotIA0AIIYQQQogsIg0AIYQQQgghsog0AIQQQgghhMgi0gAQQgghhBAii0gDQAghhBBCiCwiDQAhhBBCCCGyiDQAhBBCCCGEyCL/H/AfOPYjHFSXAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hidden_layer_sizes = [1,2,4,8,16,32,64,128,256]\n",
    "num_hidden_layers = list(range(9))\n",
    "\n",
    "total_acc_array = np.array(total_acc)\n",
    "total_auc_array = np.array(total_auc)\n",
    "\n",
    "def plot_heatmap(data, x_labels, y_labels, title, xlabel, ylabel):\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.imshow(data, cmap=\"YlGnBu\", aspect=\"auto\")\n",
    "    plt.colorbar(label=\"Value\")\n",
    "\n",
    "    for i in range(data.shape[0]):\n",
    "        for j in range(data.shape[1]):\n",
    "            plt.text(j, i, f\"{data[i, j]:.3f}\", ha=\"center\", va=\"center\", color=\"black\")\n",
    "\n",
    "    plt.xticks(ticks=np.arange(len(x_labels)), labels=x_labels)\n",
    "    plt.yticks(ticks=np.arange(len(y_labels)), labels=y_labels)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Plot heatmap for accuracy\n",
    "plot_heatmap(total_acc_array, num_hidden_layers, hidden_layer_sizes, \n",
    "             \"Heatmap of Accuracy\", \"Number of Hidden Layers\", \"Hidden Layer Size\")\n",
    "\n",
    "# Plot heatmap for AUC\n",
    "plot_heatmap(total_auc_array, num_hidden_layers, hidden_layer_sizes, \n",
    "             \"Heatmap of AUC\", \"Number of Hidden Layers\", \"Hidden Layer Size\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "nba-shot-prediction-og",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}