pickel-cancer-rick/project-cancer-classification-pca-nn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Laden der Rohdaten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Laden der 'kirp' Liste aus der Pickle-Datei\n",
    "with open('rick.pickle', 'rb') as f:\n",
    "    data_frame = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Aktiviere Cuda Support"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "device = \"cpu\"\n",
    "if torch.cuda.is_available():\n",
    "    print(\"CUDA is available on your system.\")\n",
    "    device = \"cuda\"\n",
    "else:\n",
    "    print(\"CUDA is not available on your system.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA Klasse zu Reduktion der Dimensionen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import Dataset\n",
    "import torch\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from typing import List, Tuple, Dict\n",
    "\n",
    "\n",
    "class GenomeDataset(Dataset):\n",
    "    \"\"\"\n",
    "    Eine benutzerdefinierte Dataset-Klasse, die für die Handhabung von Genomdaten konzipiert ist.\n",
    "    Diese Klasse wendet eine Principal Component Analysis (PCA) auf die Frequenzen der Genome an\n",
    "    und teilt den Datensatz in Trainings- und Validierungsteile auf.\n",
    "\n",
    "    Attributes:\n",
    "        dataframe (pd.DataFrame): Ein Pandas DataFrame, der die initialen Daten enthält.\n",
    "        train_df (pd.DataFrame): Ein DataFrame, der den Trainingsdatensatz nach der Anwendung von PCA und der Aufteilung enthält.\n",
    "        val_df (pd.DataFrame): Ein DataFrame, der den Validierungsdatensatz nach der Anwendung von PCA und der Aufteilung enthält.\n",
    "\n",
    "    Methods:\n",
    "        __init__(self, dataframe, n_pca_components=1034, train_size=0.8, split_random_state=42):\n",
    "            Konstruktor für die GenomeDataset Klasse.\n",
    "        _do_PCA(self, frequencies, n_components=1034):\n",
    "            Wendet PCA auf die gegebenen Frequenzen an.\n",
    "        _split_dataset(self, train_size=0.8, random_state=42):\n",
    "            Teilt den DataFrame in Trainings- und Validierungsdatensätze auf.\n",
    "        __getitem__(self, index):\n",
    "            Gibt ein Tupel aus transformierten Frequenzen und dem zugehörigen Krebstyp für einen gegebenen Index zurück.\n",
    "        __len__(self):\n",
    "            Gibt die Gesamtlänge der kombinierten Trainings- und Validierungsdatensätze zurück.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, dataframe: pd.DataFrame, n_pca_components: int = 1034, train_size: float = 0.8, split_random_state: int = 42):\n",
    "        \"\"\"\n",
    "        Konstruktor für die GenomeDataset Klasse.\n",
    "\n",
    "        Parameters:\n",
    "            dataframe (pd.DataFrame): Der DataFrame, der die Genome Frequenzen und Krebsarten enthält.\n",
    "            n_pca_components (int): Die Anzahl der PCA-Komponenten, auf die reduziert werden soll. Standardwert ist 1034.\n",
    "            train_size (float): Der Anteil der Daten, der als Trainingsdaten verwendet werden soll. Standardwert ist 0.8.\n",
    "            split_random_state (int): Der Zufalls-Saatwert, der für die Aufteilung des Datensatzes verwendet wird. Standardwert ist 42.\n",
    "        \"\"\"\n",
    "        self.dataframe = dataframe\n",
    "\n",
    "        # Umwandlung der Krebsarten in numerische Werte\n",
    "        self.label_encoder = LabelEncoder()\n",
    "        self.dataframe['encoded_cancer_type'] = self.label_encoder.fit_transform(dataframe['cancer_type'])\n",
    "\n",
    "        # Anwenden der PCA auf die Frequenzen\n",
    "        self.dataframe['pca_frequencies'] = self._do_PCA(self.dataframe['genome_frequencies'].tolist(), n_pca_components)\n",
    "\n",
    "        # Teilen des DataFrame in Trainings- und Validierungsdatensatz\n",
    "        self._split_dataset(train_size=train_size, random_state=split_random_state)\n",
    "\n",
    "    def transform_datapoint(self, datapoint: List[float]) -> List[float]:\n",
    "        \"\"\"\n",
    "        Transformiert einen einzelnen Datenpunkt durch Standardisierung und Anwendung der PCA.\n",
    "\n",
    "        Diese Methode nimmt einen rohen Datenpunkt (eine Liste von Frequenzen), standardisiert ihn mit dem \n",
    "        zuvor angepassten Scaler und wendet dann die PCA-Transformation an, um ihn in den reduzierten \n",
    "        Feature-Raum zu überführen, der für das Training des Modells verwendet wurde.\n",
    "\n",
    "        Parameters:\n",
    "            datapoint (List[float]): Ein roher Datenpunkt, bestehend aus einer Liste von Frequenzen.\n",
    "\n",
    "        Returns:\n",
    "            List[float]: Der transformierte Datenpunkt, nach Anwendung der Standardisierung und der PCA.\n",
    "        \"\"\"\n",
    "        # Standardisierung des Datenpunkts\n",
    "        scaled_data_point = self.scaler.transform([datapoint])\n",
    "\n",
    "        # PCA-Transformation des standardisierten Datenpunkts\n",
    "        pca_transformed_point = self.pca.transform(scaled_data_point)\n",
    "\n",
    "        return pca_transformed_point.tolist()\n",
    "\n",
    "    def _do_PCA(self, frequencies: List[List[float]], n_components: int = 1034) -> List[List[float]]:\n",
    "        \"\"\"\n",
    "        Wendet PCA auf die gegebenen Frequenzen an.\n",
    "\n",
    "        Parameters:\n",
    "            frequencies (List[List[float]]): Die Liste der Frequenzen, auf die die PCA angewendet werden soll.\n",
    "            n_components (int): Die Anzahl der Komponenten für die PCA. Standardwert ist 1034.\n",
    "\n",
    "        Returns:\n",
    "            List[List[float]]: Eine Liste von Listen, die die transformierten Frequenzen nach der PCA darstellt.\n",
    "        \"\"\"\n",
    "\n",
    "        # Standardisieren der Frequenzen\n",
    "        self.scaler = StandardScaler()\n",
    "        scaled_frequencies = self.scaler.fit_transform(frequencies)\n",
    "\n",
    "        # PCA-Instanz erstellen und auf die gewünschte Anzahl von Komponenten reduzieren\n",
    "        self.pca = PCA(n_components=n_components)\n",
    "\n",
    "        # PCA auf die Frequenzen anwenden\n",
    "        pca_result = self.pca.fit_transform(scaled_frequencies)\n",
    "\n",
    "        return pca_result.tolist()\n",
    "\n",
    "    def _split_dataset(self, train_size: float = 0.8, random_state: int = 42):\n",
    "        \"\"\"\n",
    "        Teilt den DataFrame in Trainings- und Validierungsdatensätze auf.\n",
    "\n",
    "        Parameters:\n",
    "            train_size (float): Der Anteil der Daten, der als Trainingsdaten verwendet werden soll.\n",
    "            random_state (int): Der Zufalls-Saatwert, der für die Aufteilung des Datensatzes verwendet wird.\n",
    "        \"\"\"\n",
    "\n",
    "        class SplittedDataset(Dataset):\n",
    "            def __init__(self, dataframe):\n",
    "                self.dataframe = dataframe\n",
    "\n",
    "                # Umwandlung der Genome Frequenzen in Tensoren\n",
    "                self.genome_frequencies = torch.tensor(dataframe['pca_frequencies'].tolist(), dtype=torch.float32)\n",
    "\n",
    "                # Umwandlung der Krebsarten in numerische Werte\n",
    "                self.label_encoder = LabelEncoder()\n",
    "                self.cancer_types = torch.tensor(dataframe['encoded_cancer_type'].tolist(), dtype=torch.long)\n",
    "\n",
    "            def __getitem__(self, index):\n",
    "                # Rückgabe eines Tupels aus Genome Frequenzen und dem entsprechenden Krebstyp\n",
    "                return self.genome_frequencies[index], self.cancer_types[index]\n",
    "\n",
    "            def __len__(self):\n",
    "                return len(self.dataframe)\n",
    "\n",
    "        # Teilen des DataFrame in Trainings- und Validierungsdatensatz\n",
    "        train_df, val_df = train_test_split(self.dataframe, train_size=train_size) #, random_state=random_state)\n",
    "        self.train_df = SplittedDataset(train_df)\n",
    "        self.val_df = SplittedDataset(val_df)\n",
    "\n",
    "\n",
    "    def __getitem__(self, index: int) -> Tuple[torch.Tensor, int]:\n",
    "        \"\"\"\n",
    "        Gibt ein Tupel aus transformierten Frequenzen und dem entsprechenden Krebstyp für einen gegebenen Index zurück.\n",
    "\n",
    "        Parameters:\n",
    "            index (int): Der Index des zu abrufenden Datenelements.\n",
    "\n",
    "        Returns:\n",
    "            Tuple[torch.Tensor, int]: Ein Tupel, bestehend aus einem Tensor der transformierten Frequenzen und dem zugehörigen Krebstyp.\n",
    "        \"\"\"\n",
    "\n",
    "        print(self.train_df.shape)\n",
    "        print(self.val_df.shape)\n",
    "        \n",
    "        if index < len(self.train_df):\n",
    "            row = self.train_df.iloc[index]\n",
    "        else:\n",
    "            row = self.val_df.iloc[len(self.train_df) - index]\n",
    "\n",
    "        pca_frequencies_tensor = torch.tensor(row['pca_frequencies'], dtype=torch.float32)\n",
    "        cancer_type = row['encoded_cancer_type']\n",
    "\n",
    "        return pca_frequencies_tensor, cancer_type\n",
    "\n",
    "    def __len__(self) -> int:\n",
    "        \"\"\"\n",
    "        Gibt die Gesamtlänge der kombinierten Trainings- und Validierungsdatensätze zurück.\n",
    "\n",
    "        Returns:\n",
    "            int: Die Länge der kombinierten Datensätze.\n",
    "        \"\"\"\n",
    "        \n",
    "        return len(self.train_df) + len(self.val_df)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Definition des neuronalen Netzes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class CancerClassifierNN(nn.Module):\n",
    "    \"\"\"\n",
    "    Eine benutzerdefinierte neuronale Netzwerkklassifikator-Klasse für die Krebsklassifikation.\n",
    "\n",
    "    Diese Klasse definiert ein mehrschichtiges Perzeptron (MLP), das für die Klassifizierung von Krebsarten\n",
    "    anhand genetischer Frequenzdaten verwendet wird.\n",
    "\n",
    "    Attributes:\n",
    "        fc1 (nn.Linear): Die erste lineare Schicht des Netzwerks.\n",
    "        fc2 (nn.Linear): Die zweite lineare Schicht des Netzwerks.\n",
    "        fc3 (nn.Linear): Die dritte lineare Schicht des Netzwerks.\n",
    "        fc4 (nn.Linear): Die Ausgabeschicht des Netzwerks.\n",
    "        dropout (nn.Dropout): Ein Dropout-Layer zur Vermeidung von Overfitting.\n",
    "\n",
    "    Methods:\n",
    "        __init__(self, input_size: int, num_classes: int):\n",
    "            Konstruktor für die CancerClassifierNN Klasse.\n",
    "        forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "            Definiert den Vorwärtsdurchlauf des Netzwerks.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, input_size: int, num_classes: int):\n",
    "        \"\"\"\n",
    "        Konstruktor für die CancerClassifierNN Klasse.\n",
    "\n",
    "        Parameters:\n",
    "            input_size (int): Die Größe des Input-Features.\n",
    "            num_classes (int): Die Anzahl der Zielklassen.\n",
    "        \"\"\"\n",
    "        super(CancerClassifierNN, self).__init__()\n",
    "        # Definieren der Schichten\n",
    "        self.fc1 = nn.Linear(input_size, input_size)  # Eingabeschicht\n",
    "        self.fc2 = nn.Linear(input_size, input_size//2)        # Versteckte Schicht\n",
    "        self.fc3 = nn.Linear(input_size//2, input_size//4)         # Weitere versteckte Schicht\n",
    "        self.fc4 = nn.Linear(input_size//4, num_classes) # Ausgabeschicht\n",
    "        self.dropout = nn.Dropout(p=0.5)       # Dropout\n",
    "\n",
    "    def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
    "        \"\"\"\n",
    "        Definiert den Vorwärtsdurchlauf des Netzwerks.\n",
    "\n",
    "        Parameters:\n",
    "            x (torch.Tensor): Der Input-Tensor für das Netzwerk.\n",
    "\n",
    "        Returns:\n",
    "            torch.Tensor: Der Output-Tensor nach dem Durchlauf durch das Netzwerk.\n",
    "        \"\"\"\n",
    "        x = F.relu(self.fc1(x))\n",
    "        x = self.dropout(x)\n",
    "        x = F.relu(self.fc2(x))\n",
    "        x = self.dropout(x)\n",
    "        x = F.relu(self.fc3(x))\n",
    "        x = self.dropout(x)\n",
    "        x = torch.softmax(self.fc4(x), dim=1)  # Oder F.log_softmax(x, dim=1) für Mehrklassenklassifikation\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "import torch.optim as optim\n",
    "from IPython.display import clear_output\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pickle\n",
    "\n",
    "class ExperimentationalExperiments():\n",
    "    \"\"\"\n",
    "    Diese Klasse dient zur Durchführung und Verwaltung von Experimenten im Rahmen\n",
    "    des maschinellen Lernens, insbesondere für die Krebsklassifikation.\n",
    "\n",
    "    Attribute:\n",
    "    results : Dict\n",
    "        Speichert die Ergebnisse der durchgeführten Experimente.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self) -> None:\n",
    "        \"\"\" Konstruktor der Klasse. Initialisiert 'results' als None. \"\"\"\n",
    "        self.results = None\n",
    "\n",
    "    def run_single_experiment(self, train_loader: DataLoader, valid_loader: DataLoader, n_pca_components: int, n_epochs: int = 200, learning_rate: int = 0.0005, verbose: bool = True, experiment_num: int = None) -> Tuple:\n",
    "        \"\"\"\n",
    "        Führt ein einzelnes Experiment mit dem spezifizierten DataLoader, PCA-Komponenten und weiteren Parametern durch.\n",
    "\n",
    "        Parameter:\n",
    "        train_loader : DataLoader\n",
    "            Der DataLoader für den Trainingsdatensatz.\n",
    "        valid_loader : DataLoader\n",
    "            Der DataLoader für den Validierungsdatensatz.\n",
    "        n_pca_components : int\n",
    "            Anzahl der PCA-Komponenten, die im Modell verwendet werden.\n",
    "        n_epochs : int, optional\n",
    "            Anzahl der Epochen für das Training (Standardwert ist 200).\n",
    "        learning_rate : float, optional\n",
    "            Lernrate für den Optimierer (Standardwert ist 0.0005).\n",
    "        verbose : bool, optional\n",
    "            Gibt an, ob der Trainingsfortschritt angezeigt werden soll (Standardwert ist True).\n",
    "        experiment_num : int, optional\n",
    "            Nummer des Experiments.\n",
    "\n",
    "        Rückgabewerte:\n",
    "        Tuple\n",
    "            Ein Tupel bestehend aus Listen der Trainings- und Validierungsverluste sowie der Genauigkeiten.\n",
    "        \"\"\"\n",
    "        if not isinstance(n_pca_components, int):\n",
    "            raise TypeError(\"n_pca_components must be an integers!\")\n",
    "\n",
    "        model = CancerClassifierNN(input_size=n_pca_components, num_classes=3)\n",
    "        model.to(device=device)\n",
    "\n",
    "        # Verlustfunktion\n",
    "        criterion = nn.CrossEntropyLoss()\n",
    "        # Optimierer\n",
    "        optimizer = optim.Adam(model.parameters(), lr=learning_rate)\n",
    "\n",
    "        # Listen, um Verluste zu speichern\n",
    "        train_losses = []\n",
    "        valid_losses = []\n",
    "        train_accuracies = []\n",
    "        valid_accuracies = []\n",
    "\n",
    "        for epoch in range(n_epochs):\n",
    "            model.train()\n",
    "            train_loss = 0.0\n",
    "            correct_predictions = 0\n",
    "            total_predictions = 0\n",
    "\n",
    "            for i, (inputs, labels) in enumerate(train_loader):\n",
    "                inputs, labels = inputs.to(device), labels.to(device)\n",
    "                optimizer.zero_grad()\n",
    "                outputs = model(inputs)\n",
    "                loss = criterion(outputs, labels)\n",
    "                loss.backward()\n",
    "                optimizer.step()\n",
    "                train_loss += loss.item()\n",
    "\n",
    "                # Berechnen der Genauigkeit\n",
    "                _, predicted = torch.max(outputs, 1)\n",
    "                correct_predictions += (predicted == labels).sum().item()\n",
    "                total_predictions += labels.size(0)\n",
    "\n",
    "            # Durchschnittlicher Trainingsverlust und Genauigkeit\n",
    "            train_loss /= len(train_loader)\n",
    "            train_accuracy = correct_predictions / total_predictions\n",
    "            train_losses.append(train_loss)\n",
    "            train_accuracies.append(train_accuracy)\n",
    "\n",
    "            # Validierungsverlust und Genauigkeit\n",
    "            model.eval()\n",
    "            valid_loss = 0.0\n",
    "            correct_predictions = 0\n",
    "            total_predictions = 0\n",
    "\n",
    "            with torch.no_grad():\n",
    "                for inputs, labels in valid_loader:\n",
    "                    inputs, labels = inputs.to(device), labels.to(device)\n",
    "                    outputs = model(inputs)\n",
    "                    loss = criterion(outputs, labels)\n",
    "                    valid_loss += loss.item()\n",
    "\n",
    "                    # Berechnen der Genauigkeit\n",
    "                    _, predicted = torch.max(outputs, 1)\n",
    "                    correct_predictions += (predicted == labels).sum().item()\n",
    "                    total_predictions += labels.size(0)\n",
    "\n",
    "            # Durchschnittlicher Validierungsverlust und Genauigkeit\n",
    "            valid_loss /= len(valid_loader)\n",
    "            valid_accuracy = correct_predictions / total_predictions\n",
    "            valid_losses.append(valid_loss)\n",
    "            valid_accuracies.append(valid_accuracy)\n",
    "\n",
    "            # Aktualisieren des Graphen\n",
    "            clear_output(wait=True)\n",
    "            fig, ax1 = plt.subplots()\n",
    "\n",
    "            # Zeichnen der Verlustkurven\n",
    "            ax1.plot(train_losses, label='Trainingsverlust', color='r')\n",
    "            ax1.plot(valid_losses, label='Validierungsverlust', color='b')\n",
    "            ax1.set_xlabel('Epochen')\n",
    "            ax1.set_ylabel('Verlust', color='g')\n",
    "            ax1.tick_params(axis='y', labelcolor='g')\n",
    "\n",
    "            # Zweite y-Achse für die Genauigkeit\n",
    "            ax2 = ax1.twinx()\n",
    "            ax2.plot(train_accuracies, label='Trainingsgenauigkeit', color='r', linestyle='dashed')\n",
    "            ax2.plot(valid_accuracies, label='Validierungsgenauigkeit', color='b', linestyle='dashed')\n",
    "            ax2.set_ylabel('Genauigkeit', color='g')\n",
    "            ax2.tick_params(axis='y', labelcolor='g')\n",
    "\n",
    "            # Titel und Legende\n",
    "            plt.title(f'Experiment #{experiment_num}: Trainings- und Validierungsverlust und -genauigkeit über die Zeit mit \\n{n_pca_components}-Hauptkomponenten, Lernrate: {learning_rate}')\n",
    "            fig.tight_layout()\n",
    "\n",
    "            # Legende außerhalb des Graphen\n",
    "            ax1.legend(loc='upper left', bbox_to_anchor=(1.15, 1))\n",
    "            ax2.legend(loc='upper left', bbox_to_anchor=(1.15, 0.85))\n",
    "\n",
    "            # Fortschritt anzeigen, falls angegeben\n",
    "            if verbose:\n",
    "                print(f'Experiment #{experiment_num} mit {n_pca_components} PCA components: Epoch [{epoch+1}/{n_epochs}], Trainingsverlust: {train_loss:.4f}, Trainingsgenauigkeit: {train_accuracies[-1]:.4f}, Validierungsverlust: {valid_loss:.4f}, Validierungsgenauigkeit: {valid_accuracies[-1]:.4f}')\n",
    "\n",
    "        # Plot speichern\n",
    "        name = str(experiment_num) + \".png\" if experiment_num is not None else \"single_experiment.png\"\n",
    "        if not os.path.exists(\"Experiments\"):\n",
    "            os.makedirs(\"Experiments\")\n",
    "        if not os.path.exists(f\"Experiments/{str(n_pca_components)}\"):\n",
    "            os.makedirs(f\"Experiments/{str(n_pca_components)}\")\n",
    "        plt.savefig(f\"Experiments/{str(n_pca_components)}/{name}\", bbox_inches='tight')\n",
    "\n",
    "        return train_losses, valid_losses, train_accuracies, valid_accuracies\n",
    "\n",
    "    def run_single_pca_experiment(self, train_loader: DataLoader, valid_loader: DataLoader, n_pca_components: int, n_experiments: int, n_epochs: int = 200, learning_rate: int = 0.0005, verbose: bool = True) -> List:\n",
    "        \"\"\"\n",
    "        Führt eine Serie von Experimenten mit verschiedenen Konfigurationen für die PCA-Komponenten durch.\n",
    "\n",
    "        Parameter:\n",
    "        train_loader : DataLoader\n",
    "            Der DataLoader für den Trainingsdatensatz.\n",
    "        valid_loader : DataLoader\n",
    "            Der DataLoader für den Validierungsdatensatz.\n",
    "        n_pca_components : int\n",
    "            Anzahl der PCA-Komponenten, die im Modell verwendet werden.\n",
    "        n_experiments : int\n",
    "            Anzahl der durchzuführenden Experimente.\n",
    "        n_epochs : int, optional\n",
    "            Anzahl der Epochen für das Training (Standardwert ist 200).\n",
    "        learning_rate : float, optional\n",
    "            Lernrate für den Optimierer (Standardwert ist 0.0005).\n",
    "        verbose : bool, optional\n",
    "            Gibt an, ob der Trainingsfortschritt angezeigt werden soll (Standardwert ist True).\n",
    "\n",
    "        Rückgabewerte:\n",
    "        List\n",
    "            Eine Liste von Ergebnissen der einzelnen Experimente.\n",
    "        \"\"\"\n",
    "        if not isinstance(n_pca_components, int):\n",
    "            raise TypeError(\"n_pca_components must be an integers!\")\n",
    "\n",
    "        results = []\n",
    "\n",
    "        for n in range(n_experiments):\n",
    "            res = self.run_single_experiment(train_loader, valid_loader, n_pca_components, n_epochs=n_epochs, learning_rate=learning_rate, verbose=verbose, experiment_num=n+1)\n",
    "            results.append(res)\n",
    "\n",
    "        return results\n",
    "            \n",
    "\n",
    "    def run(self, n_pca_components: List[int], n_experiments: int, n_epochs: int = 200, learning_rate: int = 0.0005, batch_size: int = 64, verbose: bool = True) -> Dict:\n",
    "        \"\"\"\n",
    "        Hauptmethode zum Ausführen von Experimenten mit verschiedenen Anzahlen von PCA-Komponenten.\n",
    "\n",
    "        Parameter:\n",
    "        n_pca_components : List[int]\n",
    "            Eine Liste von Anzahlen der PCA-Komponenten, die in den Experimenten verwendet werden sollen.\n",
    "        n_experiments : int\n",
    "            Anzahl der durchzuführenden Experimente pro PCA-Komponentenanzahl.\n",
    "        n_epochs : int, optional\n",
    "            Anzahl der Epochen für das Training (Standardwert ist 200).\n",
    "        learning_rate : float, optional\n",
    "            Lernrate für den Optimierer (Standardwert ist 0.0005).\n",
    "        batch_size : int, optional\n",
    "            Batch-Größe für das Laden der Daten (Standardwert ist 64).\n",
    "        verbose : bool, optional\n",
    "            Gibt an, ob der Trainingsfortschritt angezeigt werden soll (Standardwert ist True).\n",
    "\n",
    "        Rückgabewerte:\n",
    "        Dict\n",
    "            Ein Wörterbuch, das die Ergebnisse der Experimente für jede Anzahl von PCA-Komponenten enthält.\n",
    "        \"\"\"\n",
    "        if not isinstance(n_pca_components, list):\n",
    "            raise TypeError(\"n_pca_components must be a list of integers!\")\n",
    "\n",
    "        plt.ioff()\n",
    "        self.n_pca_components = n_pca_components\n",
    "\n",
    "        results = {}\n",
    "\n",
    "        for n_pca_comps in n_pca_components:\n",
    "            genome_dataset = GenomeDataset(data_frame, n_pca_components=n_pca_comps)\n",
    "            train_dataset = genome_dataset.train_df\n",
    "            valid_dataset = genome_dataset.val_df\n",
    "\n",
    "            train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n",
    "            valid_loader = DataLoader(dataset=valid_dataset, batch_size=batch_size, shuffle=False)\n",
    "\n",
    "            res = self.run_single_pca_experiment(train_loader, valid_loader, n_pca_comps, n_experiments, n_epochs=n_epochs, learning_rate=learning_rate, verbose=verbose)\n",
    "            results[str(n_pca_comps)] = res\n",
    "\n",
    "            self.plot_and_save_results(res, n_pca_comps)\n",
    "\n",
    "        self.results = results\n",
    "\n",
    "        # Speichern der Daten in einer lokalen Datei\n",
    "        if len(n_pca_components) > 1:\n",
    "            with open('Experiments/results.pickle', 'wb') as f:\n",
    "                pickle.dump(self.results, f)\n",
    "        else:\n",
    "            with open(f'Experiments/{str(n_pca_components[0])}/results_{str(n_pca_components[0])}.pickle', 'wb') as f:\n",
    "                pickle.dump(self.results, f)\n",
    "\n",
    "        plt.ion()\n",
    "\n",
    "        return results\n",
    "\n",
    "    def plot_and_save_results(self, results: List[Tuple], n_pca_components: int) -> None:\n",
    "        \"\"\"\n",
    "        Erstellt und speichert Plots für die Ergebnisse der Experimente.\n",
    "\n",
    "        Parameter:\n",
    "        results : List[Tuple]\n",
    "            Eine Liste von Tupeln mit den Ergebnissen der Experimente.\n",
    "        n_pca_components : int\n",
    "            Anzahl der PCA-Komponenten, für die die Ergebnisse geplottet werden sollen.\n",
    "\n",
    "        Keine Rückgabewerte, da die Methode Plots speichert.\n",
    "        \"\"\"\n",
    "        \n",
    "        # Mittelwerte und Standardabweichungen berechnen\n",
    "        train_losses, valid_losses, train_accuracies, valid_accuracies = zip(*results)\n",
    "\n",
    "        train_losses = np.array(train_losses)\n",
    "        valid_losses = np.array(valid_losses)\n",
    "        train_accuracies = np.array(train_accuracies)\n",
    "        valid_accuracies = np.array(valid_accuracies)\n",
    "\n",
    "        avg_train_losses = np.mean(train_losses, axis=0)\n",
    "        avg_valid_losses = np.mean(valid_losses, axis=0)\n",
    "        avg_train_acc = np.mean(train_accuracies, axis=0)\n",
    "        avg_valid_acc = np.mean(valid_accuracies, axis=0)\n",
    "\n",
    "        std_train_losses = np.std(train_losses, axis=0)\n",
    "        std_valid_losses = np.std(valid_losses, axis=0)\n",
    "        std_train_acc = np.std(train_accuracies, axis=0)\n",
    "        std_valid_acc = np.std(valid_accuracies, axis=0)\n",
    "\n",
    "        # Erstellen von Plots\n",
    "        epochs = range(1, len(avg_train_losses) + 1)\n",
    "\n",
    "        # Plot für Verluste\n",
    "        plt.clf()\n",
    "        plt.plot(epochs, avg_train_losses, label='Mittlerer Trainingsverlust', color='r')\n",
    "        plt.fill_between(epochs, np.subtract(avg_train_losses, std_train_losses), np.add(avg_train_losses, std_train_losses), color='r', alpha=0.2)\n",
    "        plt.plot(epochs, avg_valid_losses, label='Mittlerer Validierungsverlust', color='b')\n",
    "        plt.fill_between(epochs, np.subtract(avg_valid_losses, std_valid_losses), np.add(avg_valid_losses, std_valid_losses), color='b', alpha=0.2)\n",
    "        plt.title(f'Mittelwert und Standardabweichung der Verluste für {n_pca_components} PCA-Komponenten')\n",
    "        plt.xlabel('Experiment Nummer')\n",
    "        plt.ylabel('Verlust')\n",
    "        plt.legend()\n",
    "        plt.savefig(f\"Experiments/{n_pca_components}/average_losses.png\", bbox_inches='tight')\n",
    "        plt.clf()\n",
    "\n",
    "        # Plot für Genauigkeiten\n",
    "        plt.plot(epochs, avg_train_acc, label='Mittlere Trainingsgenauigkeit', color='r')\n",
    "        plt.fill_between(epochs, np.subtract(avg_train_acc, std_train_acc), np.add(avg_train_acc, std_train_acc), color='r', alpha=0.2)\n",
    "        plt.plot(epochs, avg_valid_acc, label='Mittlere Validierungsgenauigkeit', color='b')\n",
    "        plt.fill_between(epochs, np.subtract(avg_valid_acc, std_valid_acc), np.add(avg_valid_acc, std_valid_acc), color='b', alpha=0.2)\n",
    "        plt.title(f'Mittelwert und Standardabweichung der Genauigkeiten für {n_pca_components} PCA-Komponenten')\n",
    "        plt.xlabel('Experiment Nummer')\n",
    "        plt.ylabel('Genauigkeit')\n",
    "        plt.legend()\n",
    "        plt.savefig(f\"Experiments/{n_pca_components}/average_accuracies.png\", bbox_inches='tight')\n",
    "        plt.clf()\n",
    "\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Starten der einzelnen Experimente, da in einer Schleife RAM Probleme auftreten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "e1 = ExperimentationalExperiments()\n",
    "results = e1.run([1024], 10, n_epochs=500)\n",
    "del e1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "e2 = ExperimentationalExperiments()\n",
    "results = e2.run([512], 10, n_epochs=500)\n",
    "del e2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "e3 = ExperimentationalExperiments()\n",
    "results = e3.run([256], 10, n_epochs=500)\n",
    "del e3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "e4 = ExperimentationalExperiments()\n",
    "results = e4.run([128], 10, n_epochs=500)\n",
    "del e4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "e5 = ExperimentationalExperiments()\n",
    "results = e5.run([64], 10, n_epochs=500)\n",
    "del e5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "e6 = ExperimentationalExperiments()\n",
    "results = e6.run([32], 10, n_epochs=500)\n",
    "del e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "e7 = ExperimentationalExperiments()\n",
    "results = e7.run([16], 2, n_epochs=10)\n",
    "del e7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lesen der Daten und Erstellen der Mittelwerte und anschließender Auswertung"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pickle\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from typing import List, Dict, Tuple\n",
    "\n",
    "def load_results(path: str) -> Dict:\n",
    "    \"\"\"\n",
    "    Lädt und konvertiert die Ergebnisse aus Pickle-Dateien in den spezifizierten Verzeichnissen.\n",
    "\n",
    "    Argumente:\n",
    "    path (str): Der Pfad zum Basisverzeichnis, das die Ergebnis-Unterverzeichnisse enthält.\n",
    "\n",
    "    Rückgabe:\n",
    "    Dict: Ein Dictionary, in dem jeder Schlüssel einer PCA-Komponentenanzahl entspricht und\n",
    "          dessen Werte Achtertupel aus Durchschnitt und Standardabweichung von Trainingsverlust,\n",
    "          Validierungsverlust, Trainingsgenauigkeit und Validierungsgenauigkeit sind.\n",
    "    \"\"\"\n",
    "\n",
    "    results = {}\n",
    "\n",
    "    # Über alle Ordner im Basispfad iterieren\n",
    "    for directory in os.listdir(path):\n",
    "        full_path = os.path.join(path, directory)\n",
    "\n",
    "        # Überprüfen, ob es sich um einen Ordner handelt\n",
    "        if os.path.isdir(full_path):\n",
    "            pickle_file = f'results_{directory}.pickle'\n",
    "            pickle_path = os.path.join(full_path, pickle_file)\n",
    "\n",
    "            # Überprüfen, ob die Pickle-Datei existiert\n",
    "            if os.path.isfile(pickle_path):\n",
    "                # Pickle-Datei laden\n",
    "                with open(pickle_path, 'rb') as file:\n",
    "                    results[directory] = pickle.load(file)\n",
    "\n",
    "    converted_results = {}\n",
    "    for values in list(results.values()):\n",
    "        key = list(values.keys())[0]\n",
    "        value = list(values.values())[0]\n",
    "        converted_results[key] = value\n",
    "\n",
    "    return converted_results\n",
    "\n",
    "\n",
    "def calculate_means_for_n_last(results: Dict, n_last: int) -> Dict:\n",
    "    \"\"\"\n",
    "    Berechnet Durchschnittswerte und Standardabweichungen für die letzten `n_last` Ergebnisse.\n",
    "\n",
    "    Argumente:\n",
    "    results (Dict): Ein Dictionary von Ergebnissen, wie von `load_results` zurückgegeben.\n",
    "    n_last (int): Anzahl der letzten Ergebnisse, die zur Berechnung verwendet werden sollen.\n",
    "\n",
    "    Rückgabe:\n",
    "    Dict: Ein Dictionary mit Schlüsseln als PCA-Komponentenanzahlen und Werten als Achtertupel,\n",
    "          bestehend aus Durchschnitt und Standardabweichung von Trainingsverlust, \n",
    "          Validierungsverlust, Trainingsgenauigkeit und Validierungsgenauigkeit.\n",
    "    \"\"\"\n",
    "\n",
    "    assert results is not None\n",
    "    assert n_last <= len(list(results.values())[0][0][0])\n",
    "\n",
    "    means_and_stds = {}\n",
    "\n",
    "    for key, value in results.items():\n",
    "        train_losses, valid_losses, train_accuracies, valid_accuracies = zip(*value)\n",
    "        \n",
    "        train_losses = train_losses[:n_last]      \n",
    "        valid_losses = valid_losses[:n_last]\n",
    "        train_accuracies = train_accuracies[:n_last]\n",
    "        valid_accuracies = valid_accuracies[:n_last]\n",
    "\n",
    "        avg_train_loss = np.mean(train_losses)#, axis=0)\n",
    "        avg_valid_loss = np.mean(valid_losses)#, axis=0)\n",
    "        avg_train_acc = np.mean(train_accuracies)#, axis=0)\n",
    "        avg_valid_acc = np.mean(valid_accuracies)#, axis=0)\n",
    "\n",
    "        std_train_loss = np.std(train_losses)#, axis=0)\n",
    "        std_valid_loss = np.std(valid_losses)#, axis=0)\n",
    "        std_train_acc = np.std(train_accuracies)#, axis=0)\n",
    "        std_valid_acc = np.std(valid_accuracies)#, axis=0)\n",
    "\n",
    "        means_and_stds[key] = (avg_train_loss, std_train_loss, avg_valid_loss, std_valid_loss, avg_train_acc, std_train_acc, avg_valid_acc, std_valid_acc)\n",
    "\n",
    "    return means_and_stds\n",
    "\n",
    "\n",
    "def plot_results(results: Dict, show_lines: bool = True) -> None:\n",
    "    \"\"\"\n",
    "    Stellt die Ergebnisse als Fehlerbalkendiagramme dar. Jedes Diagramm zeigt Mittelwert und\n",
    "    Standardabweichung von Trainings- und Validierungsverlust sowie -genauigkeit. \n",
    "    Fügt zusätzlich eine rote Linie für den höchsten Genauigkeitswert und den geringsten Verlustwert hinzu,\n",
    "    mit einer Beschriftung, die den Schlüssel des entsprechenden höchsten bzw. niedrigsten Werts anzeigt.\n",
    "\n",
    "    Argumente:\n",
    "    results (Dict): Ein Dictionary von berechneten Mittelwerten und Standardabweichungen,\n",
    "                    wie von `calculate_means_for_n_last` zurückgegeben.\n",
    "    show_lines (bool): Ein flag, das angibt, ob die Maximal- / Minimallinie gezeichnet werden soll.\n",
    "    \"\"\"\n",
    "    # Schlüssel sortieren\n",
    "    sorted_keys = sorted(results.keys(), key=lambda x: int(x))\n",
    "\n",
    "    # Listen für das Plotten vorbereiten\n",
    "    mean_train_loss = [results[k][0] for k in sorted_keys]\n",
    "    std_train_loss = [results[k][1] for k in sorted_keys]\n",
    "    mean_validation_loss = [results[k][2] for k in sorted_keys]\n",
    "    std_validation_loss = [results[k][3] for k in sorted_keys]\n",
    "    mean_train_accuracy = [results[k][4] for k in sorted_keys]\n",
    "    std_train_accuracy = [results[k][5] for k in sorted_keys]\n",
    "    mean_validation_accuracy = [results[k][6] for k in sorted_keys]\n",
    "    std_validation_accuracy = [results[k][7] for k in sorted_keys]\n",
    "\n",
    "    # Plotten\n",
    "    plt.figure(figsize=(12, 8))\n",
    "\n",
    "    # Verluste\n",
    "    plt.errorbar(sorted_keys, mean_train_loss, yerr=std_train_loss, label='Trainingverlust', fmt='o', linestyle='--', alpha=0.8)\n",
    "    plt.errorbar(sorted_keys, mean_validation_loss, yerr=std_validation_loss, label='Validierungsverlust', fmt='o', linestyle='--', alpha=0.8)\n",
    "\n",
    "    # Genauigkeiten\n",
    "    plt.errorbar(sorted_keys, mean_train_accuracy, yerr=std_train_accuracy, label='Trainingsgenauigkeit', fmt='x', linestyle='--', alpha=0.8)\n",
    "    plt.errorbar(sorted_keys, mean_validation_accuracy, yerr=std_validation_accuracy, label='Validierungsgenauigkeit', fmt='x', linestyle='--', alpha=0.8)\n",
    "\n",
    "    # Gestaltung\n",
    "    plt.xlabel('Anzahl der PCA Komponenten')\n",
    "    plt.ylabel('Werte')\n",
    "    plt.title('Trainings- und Validierungsverlust und -genauigkeit')\n",
    "    plt.grid(True)\n",
    "\n",
    "    # Höchste Genauigkeit und geringster Verlust\n",
    "    highest_accuracy = max(max(mean_train_accuracy), max(mean_validation_accuracy))\n",
    "    lowest_loss = min(min(mean_train_loss), min(mean_validation_loss))\n",
    "\n",
    "    # Schlüssel für höchste Genauigkeit und geringsten Verlust finden\n",
    "    highest_acc_key = sorted_keys[mean_train_accuracy.index(max(mean_train_accuracy))] if max(mean_train_accuracy) > max(mean_validation_accuracy) else sorted_keys[mean_validation_accuracy.index(max(mean_validation_accuracy))]\n",
    "    lowest_loss_key = sorted_keys[mean_train_loss.index(min(mean_train_loss))] if min(mean_train_loss) < min(mean_validation_loss) else sorted_keys[mean_validation_loss.index(min(mean_validation_loss))]\n",
    "\n",
    "    plt.legend()\n",
    "\n",
    "    # Linien und Text für höchste Genauigkeit und geringsten Verlust\n",
    "    if show_lines:\n",
    "        plt.axhline(y=highest_accuracy, color='r', linestyle='-', alpha=0.5)\n",
    "        plt.text(0.95, highest_accuracy, f'Höchste Genauigkeit (PCA: {highest_acc_key})', verticalalignment='bottom', horizontalalignment='right', color='red', fontsize=8, transform=plt.gca().get_yaxis_transform())\n",
    "\n",
    "        plt.axhline(y=lowest_loss, color='r', linestyle='-', alpha=0.5)\n",
    "        plt.text(0.95, lowest_loss, f'Geringster Verlust (PCA: {lowest_loss_key})', verticalalignment='top', horizontalalignment='right', color='red', fontsize=8, transform=plt.gca().get_yaxis_transform())\n",
    "        \n",
    "        plt.savefig('Endergebnisse_mit_Linien.png')\n",
    "    else:\n",
    "        plt.savefig('Endergebnisse_ohne_Linien.png')\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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gscceu2rdQQqU9r05t7wZjUaqV69+weOnpaV5fcYLylfBZ+/cwTLPfb/37t2Lqqq8+OKLRd6nl156CSj6PpXmO/BSv2cvpCxlusDrr7/OkiVLWLBgQZGxC/bs2YOqqiQkJBS5Dzt27ChyD4QQoqykT7oQQkCxNXIZGRm0bduWoKAgXnnlFeLj47FYLGzatIlRo0aVqka6pJHA1VL0J76Ufc/l5+fH999/z7p161i2bBkrVqxg7ty5tG/fnlWrVqHX64mOjvbaZ/r06cUO0gQUGZSuwLkDPRW42BHRyyoiIoJOnTqxcOFCPvjgA7755huys7Pp37+/lubLL79k8ODBdO/enX//+99ERESg1+t5/fXX2bdv31XJJ6CVnwkTJpTYD/7cBxbFlVNFUYotE2V9Ly6mXEVERLBlyxZWrlzJ8uXLWb58OdOnT2fgwIFeg8X16dOHcePGcfLkSQIDA/n666/p16+fNqtBwb24//77SxyD4Nx+u6WtRT9fWS18L+rUqcOuXbtYunQpK1asYOHChUyZMoUxY8ZoU2aVVWnPXeByvjfF6dmzJ//73/+05UGDBhUZXO18Ct6nkSNHamMJnOvcoPdC11SW79my3s9LkZiYyIoVK3jrrbdo166d12jxbrcbRVFYvnx5sec993MrhBBlJUG6EEKUYP369Zw6dYpFixbRpk0bbX1ycrIPc3VWREQEFoul2NGfi1un0+no0KEDHTp04J133mH8+PE8//zzrFu3jo4dOxZpslyvXr0Sz11QG3buyOuFm7CWVZUqVVi7di05OTleP3J37dpVpuP079+fFStWsHz5cmbNmkVQUBBJSUna9gULFlC9enUWLVrk9aO/oCawrHkGT81a4ZrMEydOXLC1REFXhqCgIDp27FjmcxcIDQ0ttvvExb4XBddU2nJlMplISkoiKSkJt9vNo48+yocffsiLL76oBWx9+vRh7NixLFy4kMjISLKysujbt692jPDwcAIDA3G5XJd0L4oTGhpa7AwBBw8eLFL77O/vT58+fejTpw92u52ePXsybtw4Ro8ejcViKTFIvBznLo3C5a2gFQKAw+EgOTmZRo0anXf/iRMnepXLgm4yVapUYd26dUWmnjz3/S7Is9FovGzvU1m+Zy/2fpa1TAPccsstPPzww3Tr1o17772XxYsXaw+V4uPjUVWVatWqUbNmzfNe37nKWoaEEDcmae4uhBAlKKghKVyLZbfbmTJliq+y5EWv19OxY0eWLFnC0aNHtfV79+5l+fLlXmnT0tKK7F9Qe5ufnw94+sAX/ju3Zr2wKlWqoNfrvfp+A5d0b+68806cTif//e9/tXUul4vJkyeX6Tjdu3fHarUyZcoUli9fTs+ePb1qwYp7X3/99Vd+/vnnMue5Y8eOGI1GJk+e7HW8SZMmXXDfZs2aER8fz9tvv01OTk6R7edOU1WS+Ph4du7c6ZV+69atF91UOyYmhvr16/P555975et///sf27dv90p76tQpr2WdTqfVeBeUK/DUUjdo0IC5c+cyd+5coqOjvQIyvV5Pr169WLhwIX/++WeRPJX2XhQnPj6eX375xWu0/aVLlxaZKuvcazGZTNStWxdVVXE4HADavPSlnRawtOcurebNmxMeHs7UqVO9jjljxoxS5alZs2Zen/G6desCnlpjh8PBxx9/rKV1u9188MEHXvtHRETQrl07PvzwQ1JSUooc/2Lep7J8z17s/SxLmS6sY8eOzJkzhxUrVjBgwACtVr9nz57o9XrGjh1bpJWDqqpFylJhBQ9BSluGhBA3JqlJF0KIErRq1YrQ0FAGDRrEk08+iaIofPHFF5et6enl8PLLL7Nq1Spat27NI488gsvl4v3336d+/fps2bJFS/fKK6/w/fff07VrV6pUqUJqaipTpkyhUqVK2nzbZREcHMy9997L5MmTURSF+Ph4li5dekl9MZOSkmjdujXPPvssBw4coG7duixatKjMfVIDAgLo3r271i+9cFN3gG7durFo0SJ69OhB165dSU5OZurUqdStW7fYYPl8CuZ7f/311+nWrRt33nknmzdvZvny5doUXSXR6XRMmzaNLl26UK9ePYYMGUJsbCxHjhxh3bp1BAUF8c0331wwD0OHDuWdd94hMTGRYcOGkZqaytSpU6lXr16Jc4dfyPjx47n77rtp3bo1Q4YMIT09XStXhe/RAw88QFpaGu3bt6dSpUocPHiQyZMn07hxY+rUqeN1zD59+jBmzBgsFgvDhg1Dp/OuJ3jjjTdYt24dN998Mw8++CB169YlLS2NTZs2sWbNmmIfNJXGAw88wIIFC+jcuTO9e/dm3759fPnll16DMgLccccdREVF0bp1ayIjI9mxYwfvv/8+Xbt2JTAwEPAEuQDPP/88ffv2xWg0kpSUpAXvF3vu0jIajbz22ms89NBDtG/fnj59+pCcnMz06dMvqma+QPfu3WnRogVPP/00e/fupXbt2nz99dfaPS9c+/vBBx9w66230qBBAx588EGqV6/O8ePH+fnnnzl8+DBbt24t07nL8j17KfeztGW6uHtT0IUjKCiIDz/8kPj4eF577TVGjx7NgQMH6N69O4GBgSQnJ7N48WKGDx/OyJEjiz2en58fdevWZe7cudSsWZOwsDDq169P/fr1S3/ThBDXv6s7mLwQQvhWSVOwlTT10oYNG9RbbrlF9fPzU2NiYtRnnnlGXblyZZFpnkqa7mrChAlFjsk50++UNAXbY489VmTf4qYgWrt2rdqkSRPVZDKp8fHx6rRp09Snn35atVgsXmnuvvtuNSYmRjWZTGpMTIzar18/dffu3cVed2mcOHFC7dWrl2q1WtXQ0FD1oYceUv/8889ip2Dz9/cvsn9x133q1Cl1wIABalBQkBocHKwOGDBA3bx5c6mnYCuwbNkyFVCjo6O9pmNTVc90UePHj1erVKmims1mtUmTJurSpUuLvIeqeuEp2FTVM1XV2LFj1ejoaNXPz09t166d+ueffxZ5r0qatm7z5s1qz5491QoVKqhms1mtUqWK2rt3b3Xt2rVF7lVJU+h9+eWXavXq1VWTyaQ2btxYXbly5SWVSVVV1Tlz5qi1a9dWzWazWr9+ffXrr79We/XqpdauXVtLs2DBAvWOO+5QIyIiVJPJpFauXFl96KGH1JSUlCLn2LNnjwqogPrjjz8Wex3Hjx9XH3vsMTUuLk41Go1qVFSU2qFDB/Wjjz4qch/nz59fZP+S7vHEiRPV2NhY1Ww2q61bt1Z///33ItN2ffjhh2qbNm209yE+Pl7997//rWZmZnod69VXX1VjY2NVnU5XqunYSnPukq6puKnEVFVVp0yZolarVk01m81q8+bN1e+//77EqfhK68SJE+p9992nBgYGqsHBwergwYPVDRs2qIA6Z84cr7T79u1TBw4cqEZFRalGo1GNjY1Vu3Xrpi5YsEBLU/BZ+e2337z2Le49Ku33rKqW7n6WdN9KU6ZL+pxMmTJFBdSRI0dq6xYuXKjeeuutqr+/v+rv76/Wrl1bfeyxx9Rdu3ZpaYr7Xvnpp5/UZs2aqSaTSaZjE0IUS1HVclQlJIQQ4rLo3r07f/31lzaCuBCXQ+PGjQkPDz/vlGvi+rFkyRJ69OjBjz/+SOvWrX2dnStCyrQQojySPulCCHGNO3eqtD179vDtt98WmTZIiNJyOBw4nU6vdevXr2fr1q1Srq5T536PFIwHERQURNOmTX2Uq8tHyrQQ4loiNelCCHGNi46OZvDgwVSvXp2DBw/y3//+l/z8fDZv3lxkPmUhSuPAgQN07NiR+++/n5iYGHbu3MnUqVMJDg7mzz//pEKFCr7OorjMHnjgAfLy8mjZsiX5+fksWrSIn376ifHjxzN69GhfZ++SSZkWQlxLZOA4IYS4xnXu3JnZs2dz7NgxzGYzLVu2ZPz48RKgi4sWGhpKs2bNmDZtGidOnMDf35+uXbvyxhtvSDBznWrfvj0TJ05k6dKl2Gw2atSoweTJk3n88cd9nbXLQsq0EOJaIjXpQgghhBBCCCFEOSF90oUQQgghhBBCiHJCgnQhhBBCCCGEEKKcuOH6pLvdbo4ePUpgYCCKovg6O0IIIYQQQgghrnOqqpKdnU1MTAw63fnrym+4IP3o0aPExcX5OhtCCCGEEEIIIW4whw4dolKlSudNc8MF6YGBgYDn5gQFBfk4N+fncDhYtWoVd9xxB0aj0dfZEeWclBdRVlJmRFlJmRFlJWVGlJWUGVFW10qZycrKIi4uTotHz+eGC9ILmrgHBQVdE0G61WolKCioXBc4UT5IeRFlJWVGlJWUGVFWUmZEWUmZEWV1rZWZ0nS5loHjhBBCCCGEEEKIckKCdCGEEEIIIYQQopyQIF0IIYQQQgghhCgnbrg+6aWhqipOpxOXy+XTfDgcDgwGAzabzed5EeVfeS8ver0eg8EgUx8KIYQQQghxHhKkn8Nut5OSkkJubq6vs4KqqkRFRXHo0CEJbMQFXQvlxWq1Eh0djclk8nVWhBBCCCGEKJckSC/E7XaTnJyMXq8nJiYGk8nk02DH7XaTk5NDQEDABSe8F6I8lxdVVbHb7Zw4cYLk5GQSEhLKXR6FEEIIIYQoDyRIL8Rut+N2u4mLi8Nqtfo6O7jdbux2OxaLRQIacUHlvbz4+flhNBo5ePCglk8hhBBCCCGEt/L3S74cKI8BjhDXA/lsCSGEEEIIcX7yi/kKsTlcJE3+kaTJP2JzlL9BvIQQQgghhBBClD8SpAshhBBCCCGEEOWEBOlXiNutkmd3kW1z8OeRTNxu1ddZKrOqVasyadKkUqdfv349iqKQkZFxxfJ0OV1r+RVCCCGEEEJc/yRIvwJ+2nuSBz7/nYNppzmcnscTszczaPpGftp78oqcT1GU8/69/PLLF3Xc3377jeHDh5c6fatWrUhJSSE4OPiiznete/nll2ncuLGvsyGEEEIIIYS4hsno7pfZT3tP8tzi7WTbnOgUBUUBq0nPjpRsnlu8nfE9GtCqRsXLes6UlBTt9dy5cxkzZgy7du3S1gUEBGivVVXF5XJhMFz4rQ8PDy9TPkwmE1FRUWXax1ccDoevsyCEEEIIIYQQRUhNeinZHK4S/+xON+Bp4v7f/+0j2+YkItCEogAKmA06IgJN5OQ7eX/dXq+m7yUdsyyioqK0v+DgYBRF0ZZ37txJYGAgy5cvp1mzZpjNZn788Uf27dvH3XffTWRkJAEBAdx0002sWbPG67jnNndXFIVp06bRo0cPrFYrCQkJfP3119r2c5uPz5gxg5CQEFauXEmdOnUICAigc+fOXg8VnE4nTz75JCEhIVSoUIFRo0YxaNAgunfvDsBHH31ETEwMbrfbK2933303Q4cO1Za/+uormjZtisVioXr16owdOxan0+mV9//+97/cdddd+Pv7M27cuCL3sbia8EmTJlG1alWva2zRogX+/v6EhITQunVrDh48yIwZMxg7dixbt27VWjDMmDHjfG+bEEIIIYQQQhQhNemldO/Un0vc1qxKKC/fVY+/jmaxLzWH0/lOku1O8s8E7/tPnkZBwa2qbDmUwV9Hs2hQydMkfNhnv5GV5yxyzG+euPWy5v/ZZ5/l7bffpnr16oSGhnLo0CHuvPNOxo0bh9ls5vPPPycpKYldu3ZRuXLlEo8zduxY3nrrLSZMmMDkyZPp378/Bw8eJCwsrNj0ubm5vP3223zxxRfodDruv/9+Ro4cycyZMwF48803mTlzJtOnT6dOnTq89957LFmyhNtvvx2Ae++9lyeeeIJ169bRoUMHANLS0lixYgXffvstAD/88AMDBw7kP//5D7fddhv79u3Tmum/9NJLWl5efvll3njjDSZNmoTBYGD//v1luodOp5Pu3bvz4IMPMnv2bOx2Oxs3bkRRFPr06cOff/7JihUrtIcdN2qzfyGEEEIIIcTFk5r0yygt147DpXpq0IuhAG5VJS3XflXzBfDKK6/QqVMn4uPjCQsLo1GjRjz00EPUr1+fhIQEXn31VeLj471qxoszePBg+vXrR40aNRg/fjw5OTls3LixxPQOh4OpU6fSvHlzmjZtyuOPP87atWu17ZMnT2b06NH06NGD2rVr8/777xMSEqJtDw0NpUuXLsyaNUtbt2DBAipWrKgF8mPHjuXZZ59l0KBBVK9enU6dOvHqq6/y4YcfeuXlvvvuY8iQIVSvXv28DyJKkpWVRWZmJt26dSM+Pp46deowaNAgKleujJ+fHwEBARgMBq0Vg5+fX5nPIYQQQgghhLixSU16Kc1/uGWJ23RnovIwqwmjXiEmxA+zQcf+k6cBqF7RH0VRsDlc5NpdhFlN2r6fDLrpymb8jObNm3st5+Tk8PLLL7Ns2TJSUlJwOp3k5eXxzz//nPc4DRs21F77+/sTFBREampqiemtVivx8fHacnR0tJY+MzOT48eP06JFC227Xq+nWbNmXs3b+/fvz4MPPsiUKVMwm83MnDmTvn37otN5njFt3bqVDRs2eDVhd7lc2Gw2cnNzsVqtxd6DsgoLC2Pw4MEkJibSqVMnOnbsSO/evYmOjr6k4wohhBBCCCFEAalJLyWLUV/in8nguY31YoKIjwgg80zzdaXgP0VBATLznNSICKBeTNAFj3u5+fv7ey2PHDmSxYsXM378eH744Qe2bNlCgwYNsNvPX8tvNBq9lhVFKdJf/ELpVbVs09ElJSWhqirLli3j0KFD/PDDD/Tv31/bnpOTw9ixY9myZYv2t337dvbs2YPFYtHSnXsPzqXT6Yrk7dwB5qZPn87PP/9Mq1atmDt3LjVr1uSXX34p0/UIIYQQQgghREmkJv0y0ukUHmkbz3OLt5OanY9bVVHwDA6XmeckwKznkbbx6HQltIe/ijZs2MDgwYPp0aMH4Al0Dxw4cFXzEBwcTGRkJL/99htt2rQBPDXgmzZt8hrAzWKx0LNnT2bOnMnevXupVasWTZs21bY3bdqUXbt2UaNGjUvKT3h4OMeOHUNVVZQzrSO2bNlSJF2TJk1o0qQJo0ePpmXLlsyaNYtbbrkFk8mEy1W2Qf+EEEIIIYQQojAJ0i+zVjUqMr5HA95ft5ffDqShqpBrd1EnOpBH2sZf9unXLlZCQgKLFi0iKSkJRVF48cUXz1sjfqU88cQTvP7669SoUYPatWszefJk0tPTtSC5QP/+/enWrRt//fUX999/v9e2MWPG0K1bNypXrsw999yDTqdj69at/Pnnn7z22mulzku7du04ceIEb731Fvfccw8rVqxg+fLlBAV5Wj4kJyfz0UcfcddddxETE8OuXbvYs2cPAwcOBDyj4ScnJ7NlyxYqVapEYGAgZrP5Eu+QEEIIIYQQ4kYizd2vgFY1KjJtYHOqhPlTKdSPyf2a8NmQFuUmQAd45513CA0NpVWrViQlJZGYmOhVO321jBo1in79+jFw4EBatmxJQEAAiYmJXs3UAdq3b09YWBi7du3ivvvu89qWmJjI0qVLWbVqFTfddBO33HIL7777LlWqVClTXurUqcOUKVP44IMPaNSoERs3bmTkyJHadqvVys6dO+nVqxc1a9Zk+PDhPPbYYzz00EMA9OrVi86dO3P77bcTHh7O7NmzL/KuCCGEEEIIIW5UilrWDsLXuKysLIKDg8nMzNRqSAvYbDaSk5OpVq1akSCxrGwOlzZt2/yHW15UP3O3201WVhZBQUHaIGnXO7fbTZ06dejduzevvvqqr7NzTbkWysvl/IyJS+dwOPj222+58847i4wfIURxpMyIspIyI8pKyowoq2ulzJwvDj2XNHe/QixG/WWf6/x6dPDgQVatWkXbtm3Jz8/n/fffJzk5uUhtuRBCCCGEEELcCMpndZu4Yeh0OmbMmMFNN91E69at2b59O2vWrKFOnTq+zpoQQgghhBBCXHVSky58Ki4ujg0bNvg6G0IIIYQQQghRLkiQLoQQQogLsjltDFwxkMzsTNo725frfn9CCCHEtUyauwshhBBCCCGEEOWEBOlCCCGEEEIIIUQ5IUG6EEIIIYQQQghRTkif9CvFkQefdva8HroCjH6+zY8QQgghxFUk4xgIIcTFkZp0IYQQQgghhBCinJAg/UpR3eDIhfwsSNkKbrevc3Re7dq1Y8SIEdpy1apVmTRp0nn3URSFJUuWAHDgwAEURWHLli1XLI/Xi8L3TQghhBBCCCEKkyD9Stj/P5jdD9KSIeMfWDAUvuzpWX8FJCUl0blz52K3/fDDDyiKwrZt28p0zN9++43hw4eXOn1cXBwpKSnUr1+/TOcRl2b9+vUoikJGRoavsyKEEEIIIYS4DCRIv9z2/w+WjoDUv0GnA50BjFY4/pdn/RUI1IcNG8bq1as5fPhwkW3Tp0+nefPmNGzYsEzHDA8Px2q1ljq9Xq8nKioKg+Hihzmw2+0Xve+14Hq/PiGEEEIIIcSlkyC9tBx5Jf85zwRfbjf8+C7kZ4N/JJ7bq4DBAgGRnvU/TPRu+l7SMcugW7duhIeHM2PGDK/1OTk5zJ8/n+7du9OvXz9iY2OxWq00aNCA2bNnn/eY5zZ337NnD23atMFisVC3bl1Wr17tlb645u5//vknXbp0ISAggMjISAYMGMDJkye17e3atePxxx9nxIgRVKxYkcTExGKPk5GRgaIorF+/Hjhbe7x27VqaN2+O1WqlVatW7Nq1yytPr732GhEREQQGBvLAAw/w7LPP0rhxY237+vXradGiBf7+/oSEhNC6dWsOHjzI7t27URSFnTt3eh3v3XffJT4+/pKu71zF1YRv2bIFRVE4cOAAAAcPHiQpKYnQ0FD8/f2pV68e3377LQcOHOD2228HIDQ0FL1ez6OPPlrkHEIIIYQQ1wKb00b/Ff2Zkj0Fm9Pm6+wI4TMSpJfWp51L/lv9oifNsa1wcg/k50DaPnDZPH+n9kLaXk//9MO/e9IVmNWn+GOWgcFgYODAgcyYMQNVVbX18+fPx+Vycf/999OsWTOWLVvGn3/+yfDhwxkwYAAbN24s1fHdbjc9e/bEZDLx66+/MnXqVEaNGnXefTIyMmjfvj1NmjTh999/Z8WKFRw/fpzevXt7pfvss88wmUxs2LCBqVOnlum6n3/+eSZOnMjvv/+OwWBg6NCh2raZM2cybtw43nzzTf744w8qV67Mf//7X2270+mke/futG3blm3btvHzzz8zfPhwFEWhZs2aNG/enJkzZ3qdb+bMmdx3331X7foKPPbYY+Tn5/P999+zfft23nzzTQICAoiLi2PhwoUA7Nq1iyNHjvD6669f1DmEEEIIIYQQ5YNMwXY55Z4CtwNQALWYBIpnQLncU5f91EOHDmXChAn873//o127doCnqXuvXr2oUqUKI0eO1NI+8cQTrFy5knnz5tGiRYsLHnvNmjXs3LmTlStXEhMTA8D48ePp0qVLifu8//77NGnShPHjx2vrPv30U+Li4ti9ezc1a9YEICEhgbfeektLU1B7XBrjxo2jbdu2ADz77LN07doVm82GxWJh8uTJDBs2jCFDhgAwZswYVq1aRU5ODgBZWVlkZmbSrVs3rXa8Tp062rH79+/P+++/z6uvvgrA7t27+eOPP/jyyy8v6fouxj///EOvXr1o0KABANWrV9e2hYWFARAREUFQUBBZWVmXdC4hhBBCCCGEb0mQXlpDV5S8TdF7/rVWAJ0RgmM9TdxP7fWsr1ADFOVMU/ZcT7oC9829LNmrXbs2rVq14tNPP6Vdu3bs3buXH374gVdeeQWXy8X48eOZN28eR44cwW63k5+fX+o+5zt27CAuLk4L0AFatmx53n22bt3KunXrCAgIKLJt3759WhDbrFmzMlylt8L97KOjowFITU2lcuXK7Nq1q0jT7xYtWvDdd98BnuB28ODBJCYm0qlTJzp27Ejv3r214/Tt25eRI0fyyy+/cMsttzBz5kyaNm1K7dq1r9r1FXjyySd55JFHWLVqFR07dqRXr15lHmNACCGEEEIIcW2Q5u6lZfQr+c9g8qSJagQVE8CW6VlWCv7OvLBlQsWannQXOu5FGDZsGAsXLiQ7O5vp06cTHx9P27ZtmTBhAu+99x6jRo1i3bp1bNmyhcTExCs6kFlOTg5JSUls2bLF66+gb3sBf39/r/10Ok+RLNxs3+FwFHsOo9GovVYUBfA0zS+t6dOn8/PPP9OqVSvmzp1LzZo1+eWXXwCIioqiffv2zJo1C4BZs2bRv3//S76+c5Xmeh944AH279/PgAED2L59O82bN2fy5Mmlvk4hhBBCCCHEtUOC9MtJp4Nb/w/MAZBzzNO0XVU9NejZKWAO9GzXXZnb3rt3b3Q6HbNmzeLzzz9n6NChKIrChg0buPvuu7n//vtp1KgR1atXZ/fu3aU+bp06dTh06BApKSnauoJgtiRNmzblr7/+omrVqtSoUcPr73yBa3h4OIDXuS5m7vVatWrx22+/ea07dxmgSZMmjB49mp9++on69etrQTl4mrzPnTuXn3/+mf3799O3b99Lvr5zlfZ64+LiePjhh1m0aBFPP/00H3/8MQAmk+cBkcvlKvU5hRBCCCGEEOWXBOmXW/W20G0SRNT1jOLudnqauEfWg27verZfIQEBAfTp04fRo0eTkpLC4MGDAU+/6NWrV/PTTz+xY8cOHnroIY4fP17q43bs2JGaNWsyaNAgtm7dyg8//MDzzz9/3n0ee+wx0tLS6NevH7/99hv79u1j5cqVDBky5LwBpZ+fH7fccgtvvPEGO3bs4H//+x8vvPBCqfNa4IknnuCTTz7hs88+Y8+ePbz22mts27ZNq3FPTk5m9OjR/Pzzzxw8eJBVq1axZ88er37pPXv2JDs7m0ceeYTbb7/dq7n/xV7fuWrUqEFcXBwvv/wye/bsYdmyZUycONErzYgRI1i5ciXJycls2rSJdevWafmsUqUKiqKwdOlSTpw4ofW5F0IIIYQQ4np3vc4IIEH6lVC9LfSbDWHVIKQy3PMp3L/oigboBYYNG0Z6ejqJiYlaUPnCCy/QtGlTEhMTadeuHVFRUXTv3r3Ux9TpdCxevJi8vDxatGjBAw88wLhx4867T0xMDBs2bMDlcnHHHXfQoEEDRowYQUhIiNbEuySffvopTqeTZs2aMWLECF577bVS57VA//79GT16NCNHjqRp06YkJyczePBgLBYLAFarlZ07d9KrVy9q1qzJ8OHDeeyxx3jooYe0YwQGBpKUlMTWrVu9mrpf6vUVZjQamT17Njt37qRhw4a8+eabRa7X5XLx2GOPUadOHTp37kzNmjWZMmUKALGxsYwdO5Znn32W6OhonnnmmTLfKyGEEEIIIUT5oaiFO8PeALKysggODiYzM5OgoCCvbTabjeTkZKpVq6YFcxfNkXd2KrWhKy6qn7nb7SYrK4ugoKAyBX6ieJ06dSIqKoovvvjC11m5Iq6F8nJZP2PikjkcDr799lvuvPNOrzEehCiOzWlj4PKBZGZmsqDXAgL9An2dJVHOSZkRZSVlRpTVtVRmzheHnktGd79SjH7w0P98nYsbVm5uLlOnTiUxMRG9Xs/s2bNZs2YNq1ev9nXWhBBCCCGEEKJEEqSL65KiKHz77beMGzcOm81GrVq1WLhwIR07dvR11oQQQgghhBCiRBKki+uSn58fa9as8XU2hBBCCCGEEKJMymfHVSGEEEIIIYQQ4gYkQboQQgghhBBCCFFOSJAuhBBCCCGEEEKUE9InXQghbkA2p42BKwaSmZ1Je2d7mYJNCCGEEKKckJr0K8TmtNFnaR/6LO2DzWnzdXaEEEIIIYQQQlwDJEgXQgghhBBCCCHKCQnSRYmqVq3KpEmTSp1+/fr1KIpCRkbGFcvTtezAgQMoisKWLVtKvc+MGTMICQm56HPKeyKEEEIIIcS1xadB+vfff09SUhIxMTEoisKSJUsuuM/69etp2rQpZrOZGjVqMGPGjCuez7KYv3s+C3cvLHbbwt0Lmb97/mU/p6Io5/17+eWXL+q4v/32G8OHDy91+latWpGSkkJwcPBFne96FxcXR0pKCvXr179q5zz3PbnUoF8IIYQQQghxZfk0SD99+jSNGjXigw8+KFX65ORkunbtyu23386WLVsYMWIEDzzwACtXrrzCOS09HTrm7Z7Hkr1LvNYv3L2QebvnobsCtzwlJUX7mzRpEkFBQV7rRo4cqaVVVRWn01mq44aHh2O1WkudD5PJRFRUFIqilPkabgR6vZ6oqCgMhqs3XqO8J0IIIYQQQlxbfBqkd+nShddee40ePXqUKv3UqVOpVq0aEydOpE6dOjz++OPcc889vPvuu1c4p56B4Er6c7gcWrpeNXvRo0YPFuxeQFpeGm7Vzfxd85m7ay49avQgKT6pVMcti6ioKO0vODgYRVG05Z07dxIYGMjy5ctp1qwZZrOZH3/8kX379nH33XcTGRlJQEAAN910E2vWrPE67rnN3RVFYdq0afTo0QOr1UpCQgJff/21tv3cptUFtbYrV66kTp06BAQE0LlzZ1JSUrR9nE4nTz75JCEhIVSoUIFRo0YxaNAgunfvrqVZsGABDRo0wM/PjwoVKtCxY0dOnz5d6v3dbjevv/461apVw8/Pj0aNGrFgwYIi+V67di3NmzfHarXSqlUrdu3apaUpzf0qrjVISEiI1tqjuObuX3/9NQkJCVgsFm6//XY+++yz8zZPP3HiBM2bN6dHjx7k5+d7XZu/vz+33nprsdeWkZHB+vXrGTJkCJmZmZfcykIIIYQQQghxZVxTU7D9/PPPdOzY0WtdYmIiI0aMKHGf/Px88vPzteWsrCwAHA4HDofDK63D4UBVVdxuN26322vboOWDSjxH44jGjLpplLa8bP8yMvMzOZ57nOO5x/lP5n8INYeyeM9idp7ayZiWY7S0j699nGx7dpFjzu46G1VVAbQ8lUZBunP/ffbZZ3nrrbeoXr06oaGhHDp0iM6dO/Pqq69iNpv54osvSEpKYseOHVSuXFk73rnnHjt2LG+88QZvvvkm77//Pv379yc5OZmwsDCvcxb85ebmMmHCBD777DN0Oh0DBw7k6aef5ssvvwTgjTfeYObMmXzyySfUqVOH//znPyxZsoR27drhdrtJSUmhX79+vPnmm3Tv3p3s7Gx+/PFHXC4Xbrf7gvsDjB8/npkzZzJlyhQSEhL4/vvvuf/++6lQoQJt27bV0j3//PNMmDCB8PBwHn30UYYOHcoPP/wAeMpNae5XcWWn8P0ovJycnMw999zDk08+ybBhw9i8eTPPPPNMifscOnSIxMREbr75ZqZNm4Zer/e6tho1arB69WoGDhxIeHi417W53W5uueUW3n33XV566SV27NgBQEBAQKnL1uXgdrtRVRWHw4Fer79q5xVFOZwO7TvG4Sz6fSjEuaTMiLKSMiPKSsqMKKtrqcyUJW/XVJB+7NgxIiMjvdZFRkaSlZVFXl4efn5+RfZ5/fXXGTt2bJH1q1atKtKU22AwEBUVRU5ODna73Wuby+UqMV92u10L/gvSBhmDOM5xbV2QMQiXy4Xd4Z3W6XQWe+zCabKziwbxJbHZbKiqqu2fm5sLwKhRo7j55pu1dNWqVaNatWra8siRI1m4cCHz5s3T+qG73W5sNptXXvr27UvXrl21Y06ePJn169fTsWNH7VzZ2dnodDpsNhsOh4MJEyZo5xo6dCgTJkzQjjl58mRGjBhBhw4dABg3bhzLli3D6XSSlZXF3r17cTqddOzYkbCwMMLCwqhSpQput5usrKwL7p+fn8/rr7/O4sWLadGiBQA9e/Zk/fr1fPDBBzRp0kTL9+jRo2nSpAkAjz/+OH369CE1NRWLxVKq+wWQl5fndb9UVdXuYU5ODuDp5lGQ9xo1avDCCy8AEB0dzaZNm5g4caLXPVRVlT/++IMePXrQrVs3Xn/9dU6fPl3std1333388ssvRa6t4HgmkwlAK/sF9/Fqsdvt5OXl8f3335e624W4Mhyqg6wcz3v/3drvMCoyT7o4PykzoqykzIiykjIjyupaKjMFv8tL45oK0i/G6NGjeeqpp7TlrKws4uLiuOOOOwgKCvJKa7PZOHToEAEBAVgsFq9tn9/5eYnn0Ct6jPqzBWJa4jQW7F7A5C2TAagSVIVeCb24O/5udIoOk96kpZ3SaUqxx7QYLKiqSnZ2NoGBgaXuU2yxWFAURbu2gmDstttu87renJwcxo4dy7fffktKSgpOp5O8vDxOnDihpdPpdFgsFq/9mjdvri0HBQURFBRETk4OQUFB2rkCAwMJCgrCYrFgtVpp1KiRtn+1atW0c2RmZpKamlokb82bN8ftdhMUFESrVq3o0KEDt956K3fccQedOnXinnvuITQ0tFT7//XXX+Tm5tKzZ0+v+2S322nSpIlXvm+55RbtOPHx8YCnTERERJTqfgH4+fl5LSuKot3DgIAAAPz9/QkKCuLAgQPcfPPNXulvvfVWJk6c6HUPbTYbXbt2pV+/fl5dO8pybYWPV7h8XG02mw0/Pz/atGlT5DMmri6b08bc1XPJysqifYf2BFoCfZ0lUc5JmRFlJWVGlJWUGVFW11KZKUvF2DUVpEdFRXH8+HGvdcePHycoKKjYWnQAs9mM2Wwust5oNGI0ej9pcblcKIqCTqdDp/Purm81lX4AteUHlrM0eSmhllBCLaH0qNGDxXsXY9Kb6FWzV6mPW9AMuSBPpVGQ7tx/AwMDvY7xzDPPsHr1at5++21q1KiBn58f99xzDw6Hwyvduec2m81Fthecp/A5C/6MRqNXer1ej6qqxaYvfMzC78Pq1av56aefWLVqFR988AEvvvgiv/76K2FhYRfcv+CJ1bJly4iNjfW6VwXXUrBv4Wsr3BRbp9OV6n4VPm+Bgu3FXW9x6Yu7h2azmY4dO7Js2TKeeeYZ7TrOvTa3201OTg4BAQH4+fmV+J4UPs/VVnDdxX3+xNXlUlza59dokPdDXJiUGVFWUmZEac3fPR8dOrpW71qkzCzcvRA3bu6tea+PcynKo2vpe6Ysebum5klv2bIla9eu9Vq3evVqWrZs6aMcFVUwinvPhJ6EWkIB6F6jO71r9mbe7nklTs92tW3YsIHBgwfTo0cPGjRoQFRUFAcOHLiqeQgODiYyMpLffvtNW+dyudi0aZNXOkVRaN26NWPHjmXz5s2YTCYWL15cqv3r1q2L2Wzmn3/+oUaNGl5/cXFxpc5rae5XeHi416B4e/bsOW+zllq1avH77797rSt8LQV0Oh1ffPEFzZo14/bbb+fo0aMlXlv16tXPe20mk+m8XTeEEEIIIa42X8yOJK5tvpj2+mryaU16Tk4Oe/fu1ZaTk5PZsmULYWFhVK5cmdGjR3PkyBE+/9zT1Pzhhx/m/fff55lnnmHo0KF89913zJs3j2XLlvnqEopw46Z3zd50rd6VxXsXa+sLatDdXL1Bus4nISGBRYsWkZSUhKIovPjii1d1ALECTzzxBK+//jo1atSgdu3aTJ48mfT0dO2J2K+//sratWu54447iIiI4Ndff+XEiRPUqVOnVPsHBgYycuRI/u///g+3282tt95KZmYmGzZsICgoiEGDSh4QsLDS3K/27dvz/vvv07JlS1wuF6NGjTrvE7OHHnqId955h1GjRjFs2DC2bNmijQR/bvcGvV7PzJkz6devH+3bt2f9+vVERUV5XVurVq04evQoW7duJTg4uNhrq1q1Kjk5Oaxdu5ZGjRphtVrLNM2eEEIIIcTlVvA7ee6uuaTb0tGh4+v9X/N18tf0rtm7SEtUIQoe7Djc3oOxFTzY6V2zt49ydnn4NEj//fffuf3227Xlgr7jgwYNYsaMGaSkpPDPP/9o26tVq8ayZcv4v//7P9577z0qVarEtGnTSExMvOp5L0lBU5ziplErT18w77zzDkOHDqVVq1ZUrFiRUaNGXdUBxAqMGjWKY8eOMXDgQPR6PcOHDycxMVFrbh4UFMT333/PpEmTyMrKokqVKkycOJEuXbqUan+AV199lfDwcF5//XX2799PSEgITZs25bnnnit1PktzvyZOnMiQIUO47bbbiImJ4b333uOPP/4o8ZjVqlVjwYIFPP3007z33nu0bNmS559/nkceeaTYLhoGg4HZs2fTp08fLVA/99qCg4Np2rQpzz//fLHnbNWqFQ8//DB9+vTh1KlTvPTSSzINmxBCCCEuG7fqJt+Vj91lJ9+V7/Xa7rJTwa8CcYGeFn+5jlxWHVyF3WXH7rIT5R/FnvQ9uFU3H//5MR2rdNR+P9tddqZtnwaAwpnKjEJ1GgkhCXSs0lHLwyfbP/HKV+EKkMqBlbmj6h3a8md/fYZLdWnHVVC09FH+USRWPRtrzNs1j3xX/tm0hY5bwa8Cnat21pa/3vc1uY6zrSoLHzfYHOx13BUHVpBjz/G6PkVRUFCwGq1eadcfWk9mfmaR8ysomPVmr2v76ehPpNvSi82DUWfU7hnAH8f/4GTeySLnL9ivQ5UOWtrtJ7ZzIu/E2feiUF4UFG6NvRW9zvN7fFfaLo7nHi9ybQXLLaJaaON77c/Yz7HcY8WmA8+sWma9mV41e5FmS+OLv78gx56DWTVfVw92FLVgzPobRFZWFsHBwWRmZhY7cFxycjLVqlW75EGtbE4bg1Z4ajI/6/wZFkPZj1cw8nZQUJDP+hBfbW63mzp16tC7d29effXVq76/r40bN46pU6dy6NChMu97LZSXy/kZE5fG5rQxcPlAMjMzWdBrAYF+5XegFVE+SJkRZSVlpvxQVRWn6plVxajzBEN5zjwOZx/G7rZ7BdEFr2uE1KBWWC0ATuadZO6uuUUC7oLXHSp34O4adwOQkpPCiPUjSsxL56qdGVJ/CADptnQeXvOwVz53pu0EwKw3M7TBUB5p9AjgCeiHrBxS4nFbxbTiX03/BXiC9H7L+pWYtllkM5656Rltuf+3/XG6i591pn6F+rzY8kVtedjKYeQ4copNWyOkBuNuHactP7LmEdJsacWmrRxYmQltJ2jLI9aNIOV0SrFpI6wRTG4/WVse9f0oDmQdKDZtsCmYj+74SFt+6aeXtHt6LovewmddPtOWx/0yjm0ntxWbVoeO2d1ma8sTf5/IxmMbi00L8GWXL7XA+/3N7/PDkR9KTPvJHZ8QYPIMsPzxto9Z88+aEtN+0OEDKvpVBDwPV778+0uO5x7HgIH40Hj61u5bbgP088Wh57qmBo67llgMFuZ2m+vrbJR7Bw8eZNWqVbRt25b8/Hzef/99kpOTue+++67K/r42ZcoUbrrpJipUqMCGDRuYMGECjz/+uK+zJYQQQoirQFVVnG5nkRrnUEsoweZgAE7lnWL7ye0lBsitYlrRILwBAPsz9zNt27Ria7LduOlfpz93xd8FwNGco7yw4YUS89YroZcWpOe78vn+8Pclps3Iz9BeF57FyKw3Y9KZMOlNmPVmjHqjNmYTgJ/Bj3Zx7TDpPNu3ndjG3oy9uN1uQi2h5DvztbRGnZH+dfp73TsVVXtdKbCSV5761OqjzZ+tcjYtQLR/tFfa7jW643J7j9lTkD7CGuG1PrFqInaX3StNwXnCreFeadtXbs9p++li8xBqDvVK2zq2NRm2jCLnV1WVQJP3A67mUc2pGlS1yHFVVcVq9O7G2KBiAypYKhQ5Lpx9YFOgToU6+Bn8iqRVVbVIV8zqwdW9Hmycey+8Wi4EVaZBfgPOVZBWp5ytXIryj6JOWJ2iac8cv3Cew/3CaR7VnOXJy0EFvU5fbgP0spIgXfiUTqdjxowZjBw5ElVVqV+/PmvWrNH6nF/p/X1tz549vPbaa6SlpVG5cmWefvppRo8e7etsCSGEEBet8Ejd57rWRurOd+WTbc/2CnYLB8q1wmpptXoHMg+w4eiGYpt42112eib0pHFEY8DTrPi9Te9hd9m9gqYCDzZ4UGuGfCTnCP/d+t8S8xgTEKMF6U63k32Z+857PQX8DH5E+EVg1Bsx682egFpv0oLqgibpACHmEPrX6e8JtnVn0xb8W8HvbBAYagnliy5fYNQZLziFsMVg0WrKF+5eyIGsA1SwVECXr2NgnYF8nfw1cbvj6FWzF0a9UXvAcCE6RUfPhJ4XTnhGWcpj71ql7+tcluNeqbT31Lyn1GnLcs96JPQoddq74u8q9XuXFJ9EUnxSqdLeWf1OsuxZrDm4BpfLhcvtYuHuhddFoC5BuvCpuLg4NmzY4LP9fe3dd9/1mvtcCCGEuNZdyQGdXG6XFvj6G/215rQn805yOPtwkUA635WPw+2gbaW2RPpHArAldQsrD6z0CqLzXflas+8nmjyhBdM/HfmJqdumlpiffzX9lxakH8s9xtf7vi4xbeFmz3pF7xUwAxgUgxYw65WzY+uEmkNpHN64SGBcEFgX1HYDxAbEMuqmUVqt9bnBd+HulzEBMUzuMJnS8Df6lylALlybXhqFZ0datGcRmfmZ3FX9LixGC/N2zwPK19hOonxYuHshi/YsIsQcgi5fR/f47tdNeZEgXQghhBBCXDa9avbC5XYxe9dsMmwZGFWjNqBT20ptMRvMLN6zuNga5/vr3q81L159cDVf7/vaa7tLPdsk+ZVWr2gB6q8pv/L535+XmKeaoTW1IP2U7RSbUjeVmLZw8GzWmzHqjN4BcqHm24HGs82QY/1j6Va9m9c+WoCsN1E9uLqWtnZYbSa3n6wdy6Q3YdAV/7M8LiiO0TeXrpWdv9GfppFNS5W2PCk8O9KiPYu09eVtdiRRflzvD3YkSBdCCCGEEBekqio5jhwy8jOI8o/S+ob+kvILG1M2kpGfof2ddpwmx57DKdsp9OhZsm8JfWv3xY2bL/7+osRzJMUnaUG6zWkjNTe12HQKild/2FBzKFWDqnoFxYUD68J9cuuG1eXhhg97pStc8xxmCdPStoptRavYVqW6P3FBcQyoO6BUaS0Gy0UNKny9ulZmRxLlx/X+YEeCdCGEEEKIG5jD5SDTnkmIOUSrzf3j+B9sOr6J9Px0MvMzSbd5/i0YIfzddu8SExADwKHsQ2w4WrTrWbg1nNS8VK8BnTambOTW2Fu1gcIKB9MmvUlrOg6e0bprhtb0qpEueG3QGbz6O5clmI4OiCY6IPrCCYUQ5db1/mBHgnQhhBBCiOuMqqqcdpzGz+CnzVW8/cR2tp7Y6hV4Z+RnaNNJvdPuHWIDYgHPKOElTYMUYAzwmvu5YcWGWPQWQi2hhJhDtL9v9n3DnvQ9RQZ0ahHdolTXUMGvgteAZEIIcaOQIP0KcdtsHOx/PwBVZn6JTuaEFkIIIcQlcrgd6BW9NmXRrrRdbD+53dPM3JahBeAZ+Rk43A4mtp2oTU21M30n3+z/ptjjGhQDOfazcz/Xq1APanqakQebgwk1hxJiCSHYFKwN1lagVlgtr8HLwNNfdPHexdflgE5CCHGlSZAuhBBCCOFD584rvC9jHzvSdnjVdhcE4dmObN5u+7Y2PdafJ/9k/u75JR47055JJTxBep2wOnSt1pUQi6emuyDwDjGHEGAM8Go+XrdCXepWqHtR13O9D+gkhBBXmgTpAoB27drRuHFjJk2aBEDVqlUZMWIEI0aMKHEfRVFYvHgx3bt358CBA1SrVo3NmzfTuHHjq5JncZaiKCxcuJD27duXKv369eu5/fbbSU9PJyQkpMznk/dbCCHK5lDWIXZn7CbDluE1wFrB8vjbxmuB99YTW5m7a26Jx8rMz9TS1gitQYfKHbyamRcOwgvXetevWJ/6Fetf2Qvl+h/QSQghrjQJ0i+z9LnzQKcQnJRUdNv8+eBWCe1z8fODFicpKQmHw8GKFSuKbPvhhx9o06YNW7dupWHDhqU+5m+//Ya/v3+p08fFxZGSkkLFihUvnFhcdikpKQQHB5Ofn3/hxJfBue/3pQb9QghxLUrJSSE5M5n0/PQiQXdGfgZjWo7Rgunfj//OnF1zSjxWhi1DS1stuBqtYlp5Au1z+nmHWDy13gUahTeiUXijK3uhZXS9D+gkhBBXmgTpl5tOIWPOXFSHw2t1+vz5ZMyZS0jfPpf9lMOGDaNXr14cPnyYSpUqeW2bPn06zZs3L1OADhAeHl6m9Hq9nqioqDLtcy673Y7JZLqkY9yooqKicLvdVy1Ivxzvt/Att83G8Pf24nK5ULvawC/wwjsJcQM4mXeSQ9mHtNHM0/PTtb7eGfkZPHPTM1ow/UvKL6UOvOMC42ga0VQLtoMtZ/p5n1kO8zs77VeTiCY0iWhyZS9UCCFEuaXzdQauFW6brcQ/1W7X0oXeey/BvXqSMW8+zlOnUN1u0ufOI332bIJ79ST47rtLddyy6NatG+Hh4cyYMcNrfU5ODvPnz6d79+7069eP2NhYrFYrDRo0YPbs2ec9ZtWqVbWm7wB79uyhTZs2WCwW6taty+rVq73SHzhwAEVR2LJli7buzz//pEuXLgQEBBAZGcmAAQM4efKktr1du3Y8/vjjjBgxgooVK5KYmFjscTIyMlAUhfXr1wOeWltFUVi7di3NmzfHarXSqlUrdu3a5ZWn1157jYiICAIDA3nggQd49tlnvZpmr1+/nhYtWuDv709ISAitW7fm4MGDpd4fYNq0adSpUweLxULt2rWZMmVKkXuyaNEibr/9dqxWK40aNeLnn3/W0pw6deqC78257wVA48aNefnll7VlRVFYsmSJtvzTTz/RuHFjLBYLzZs3Z8mSJUXua2G5ubl06dKF1q1bk5GRUepr27JlCwcOHOD2228HIDQ0FEVRGDx4cLHnEUIIX8jMz2THqR38fPRnlicvZ/bO2fx3y395/dfXeeb7ZzicfVhL+8PhH3hj4xt8uO1D5uyaw8oDK/n12K/sTt9Nam4q6bZ0LW1MQAx1wurQKqYVd1a7k/tq38ejjR7luZuf4602b1EzrKaWtnlUc0a1GMVDjR6iT+0+dK7amZujb6ZWWC0i/SO1OceFEEIIqUkvpYKR2ovj17QpUc8/py1nLV2GKzMD5/HjOI8f58S776IPCyNz4SLyd+wk+tVXtLSHHnkEd1Z2kWNWW7ig1HkzGAwMHDiQGTNm8Pzzz2sDv8yfPx+Xy8X999/P/PnzGTVqFEFBQSxbtowBAwYQHx9PixYXngbF7XbTs2dPIiMj+fXXX8nMzDxvX3XwBNbt27fngQce4N133yUvL49Ro0bRu3dvvvvuOy3dZ599xiOPPMKGDUXnV72Q559/nokTJxIeHs7DDz/M0KFDtePMnDmTcePGMWXKFFq3bs2cOXOYOHEi1apVA8DpdNK9e3cefPBBZs+ejd1uZ+PGjdq9u9D+BWnGjBnD+++/T5MmTdi8eTMPPvgg/v7+DBo0yCufb7/9NgkJCTz//PP069ePvXv3YjAYsNlsNGvW7KLfm+JkZWWRlJTEnXfeyaxZszh48OB536+MjAy6du1KQEAAq1evxmq1lvrawNP0feHChfTq1Ytdu3YRFBSEn5/fReVdCCFKK9eRS2puKhn5Gd6Dq535G95wuDad2Hf/fHfeGu9TtlPaCOiR/pFUDaqqNS0vPLhaqDlUqxkHuDn6Zm6OvvnKXqgQQogbjgTpV4g+NBTnseOeBUXBEBp6Rc83dOhQJkyYwP/+9z/atWsHeJq69+rViypVqjBy5Egt7RNPPMHKlSuZN29eqQLBNWvWsHPnTlauXElMTAwA48ePp0uXLiXuUxDcjR8/Xlv36aefEhcXx+7du6lZ01O7kJCQwFtvvaWlOXDgQKmvedy4cbRt2xaAZ599lq5du2Kz2bBYLEyePJlhw4YxZMgQAMaMGcOqVavIyfFML5OVlUVmZibdunUjPj4egDp16mjHvtD+AC+99BITJ06kZ8+eAFSrVo2///6bDz/80CuQHTlyJF27dgVg7Nix1KtXj71791K7dm1iY2Mv6b0pzqxZs1AUhY8//lhr+XDkyBEefPDBImmPHTtGnz59SEhIYNasWVp3g9JeG3iavoeFeZppRkRESJ90Ia4z83fPR4eOrtW7Ftm2cPdC3Li1PsiXyuFycMp26mwz80LTimXYMhhUbxAxAZ7/D606uIrZO0tuFXYi94QWpIdbw4myRmnBduG/UEso1YOra/u1imlFq5hWl+V6hBBCiIshQXopVZn5ZYnbFJ13r4HKn35C+tx5nNj/LigKpipVCO7Vk5CePeGctHH//e9lyV/t2rVp1aoVn376Ke3atWPv3r388MMPvPLKK7hcLsaPH8+8efM4cuQIdrud/Px8rFZrqY69Y8cO4uLitAAdoGXLlufdZ+vWraxbt46AgIAi2/bt26cF6c2aNSvDVXor3M8+OjoagNTUVCpXrsyuXbt49NFHvdK3aNFCq8UPCwtj8ODBJCYm0qlTJzp27Ejv3r2141xo/9OnT7Nv3z6GDRvmFfw6nU6Cg4NLlc/atWtf8ntTnN27d9OwYUMsFotX3ovTqVMnWrRowdy5c9Hr9WW+NiHE9U+Hjnm752HPz/Uax2DhoVXM2z2P3jXPPxiqW3WTbc/WarsLAvCCubx71+xNdIDnu/Hb5G+ZtXNWicfqkttFC9LDLGEEm4K9armDzcGEWjz/Vg2qqu13a+yt3Bp766XfDCGEEOIqkCC9lHSFAp4LyfzmGzK/WoI+LAxDaCjBvXqSuXARitFI6L3etQ1lOe6FDBs2jCeeeIIPPviA6dOnEx8fT9u2bXnzzTd57733mDRpEg0aNMDf358RI0ZgL9SX/nLLyckhKSmJN998s8i2gkAVKDKCvO7MQ4yCOWMBHOcMwlfAaDzbf6+gmbrbXfppXaZPn86TTz7JihUrmDt3Li+88AKrV6/mlltuueC+BTXqH3/8MTff7N3UsSDYLU0+J0yYcMH3RqfTed0PKPmelFXXrl1ZuHAhf//9Nw0aNCjztQkhrn8Fo3HP2TGbRjXc3LQLvj64jK8PfUuXal2oX7E+v6b86jWy+d017ibK3zO45NL9S5m5Y2aJx29bqa0WpIdaQjHrzUVGMy8IwAuapAO0qdSGNpXaXMErF0IIIXxDgvTLTBvF/Z57yFzomRs0pGdPFKORjDmeOU/PDdQvl969e/Ovf/2LWbNm8fnnn/PII4+gKAobNmzg7rvv5v77Pf3q3W43u3fvpm7duqU6bp06dTh06BApKSlagP3LL7+cd5+mTZuycOFCqlatisFQ+mJWMKp8SkoKTZp4RrYtabCz86lVqxa//fYbAwcO1Nb99ttvRdI1adKEJk2aMHr0aFq2bMmsWbO45ZZbLrh/ZGQkMTEx7N+/n/79+5c5fwVK896Eh4eTkpKiLWdlZZGcnFziMWvWrMnMmTPJz8/HbDaXeO0Ab7zxBgEBAXTo0IH169dTt27di7q2gmbyLperVOmFENeWxKqJ5NlymFlzIr/Eq9j3zARFYcWBFaw4UHT6zxbRLbQgPcQcgoJCoClQC7YLNzuP9j/74PbW2Fu5LfY27YGmEEIIcSOSIP1yc6uE9O1DcFKSFqRDocDcrZaw46ULCAigT58+jB49mqysLG2E7YSEBBYsWMBPP/1EaGgo77zzDsePHy91kN6xY0dq1qzJoEGDmDBhAllZWTz//PPn3eexxx7j448/pl+/fjzzzDOEhYWxd+9e5syZw7Rp00qskfXz8+OWW27hjTfeoFq1aqSmpvLCCy+U6T6Ap2/3gw8+SPPmzWnVqhVz585l27ZtVK/u6XeYnJzMRx99xF133UVMTAy7du1iz549WlB+of3B07/8ySefJDg4mM6dO5Ofn8/vv/9Oeno6Tz31VKnyWZr3pn379syYMYOkpCRCQkIYM2bMeWu077vvPl588UWGDx/Os88+yz///MPbb78NUOwP37fffhuXy0X79u1Zv349tWvXLvO1ValSBUVRWLp0KXfeeSd+fn7FdnUQQlwbVFXlYNZBNqduZnPqZvak7+H/Gj6O3g0uHRh1RgLMgRh1Rm1gtcLNzSOtkdqxWsa0pFVMKwy6C//k0Cky6YwQQgghQfplFtrH0zevuGnUrlQNemHDhg3jk08+4c4779T6kL/wwgvs37+fxMRErFYrw4cPp3v37mRmZpbqmDqdjsWLFzNs2DBatGhB1apV+c9//kPnzp1L3CcmJoYNGzYwatQo7rjjDvLz86lSpQqdO3fWmrSX5NNPP2XYsGE0a9aMWrVq8dZbb3HHHXeU/iYA/fv3Z//+/YwcORKbzUbv3r0ZPHgwGzduBMBqtbJz504+++wzTp06RXR0NI899hgPPfRQqfYHeOCBB7BarUyYMIF///vf+Pv706BBgwuOfF9Yad6b0aNHk5ycTLdu3QgODubVV189b016UFAQ33zzDY888giNGzemQYMGjBkzhvvuu8+rn3ph7777rlegXtZri42NZezYsTz77LMMGTJEm21ACHHtsDltbDuxzROYn9jsNdUYwML9X+HSgd4FZr2F7vHd6Vu77wVrvWVqMSGEEKJsFPXczq7XuaysLIKDg8nMzCQoKMhrm81mIzk5mWrVqpUYzJSW22bTpm2rMvPLi+p77na7ycrKIigo6IKBrbiwTp06ERUVxRdffOGT/a+085WXmTNnMmTIEDIzM306Pdrl/IyJS5Obk8FP3dvjcrlouWgNQaHhvs6SuMpUVcXutmPWe7rF/JP1D//+/t/adrPeTP2K9WkS0YQj2UdYtm8pjZbv5aZdKife/T++Obyc3jV7a33WhTiXzWlj4PKBZGZmsqDXAgL9An2dJVHOSZkRZXUtlZnzxaHnkpr0K0RnsZRprnNxeeXm5jJ16lQSExPR6/XMnj2bNWvWsHr16quyv699/vnnVK9endjYWLZu3arNUS/zlwtxY7O77Px96m82pW5ic+pmEkISeLLpkwDEBcZRJ6wOVYOq0jiiMfUq1MOoN7Jw90KWH1hOz+p3E773XVy4uKtKV/ysgczbPQ9AAnUhhBDiMpIgXVyXFEXh22+/Zdy4cdhsNmrVqsXChQvp2LHjVdnf144dO8aYMWM4duwY0dHR3HvvvYwbN87X2RJC+EBqbiqbUjexJXULf578E4f77OwQDrcDVVVRFAVFUXi51ctF9nfjpnfN3nSJ6cBPvKutLwjM3ZR+Vg0hhBBCXJgE6eK65Ofnx5o1a3y2v68988wzPPPMM77OhhDCB9yq22sAtom/T+RA1gFtOcwSRtOIpjSJaEK9ivUu2Kf83pqe8VRyczKKbJMadHE+bpuN4e/txeVyoXa1QTluhiqEuDZZDBZmdp7Jt99+i8Vw/XSllCBdCCGEuMal2dLYkrqFLalb+Dvtbya3n4yfwdO9pXlUc/wMfjSJaEKTiCbEBcbJFGdCCCFEOSZBejFusLH0hLhq5LMlxOXhVt3sSd+jTZFWuKYc4K+Tf9E8qjkA9yTco9WGCyGEEKL8kyC9EKPRM01Mbm6uDLAlxBWQm5sLnP2sCSEuzqqDq5j+53RtWUEhPiReqy2vFlzt7DapNRdCCCGuKRKkF6LX6wkJCSE1NRXwzKXtyx83brcbu92OzWaTKdjEBZXn8qKqKrm5uaSmphISEoJer/d1loQo99yqmwNZB9h83FNb3qFyB26vfDsAjcMb42/0p1F4I5pENKFReCOCzcE+zrEQQgghLgcJ0s8RFRUFoAXqvqSqKnl5efj5+UlNiDgvVVVxnTyJ2+3GGB6OUs6C9AIhISHaZ0wIUVSuI5dtJ7exOXUzW1K3kJGfoW0LtYRqQXqUfxQfd/oYvU4eeAkhrh/X6yBgQpSVBOnnUBSF6OhoIiIicDgcF97hCnI4HHz//fe0adNGmgeL83Ln53Pk3UlkZ2dT7YP3Mfn7+zpLRRiNRqlBF+I88px5DF893GuKNIveQsPwhjSOaEzj8MZe6SVAF0IIIa5PEqSXQK/X+zyg0Ov1OJ1OLBaLBOnivNyA/tQplMxMzCYTZos8fRaivLI5bfx16i82p27mtOM0/2r6LwD8DH5UD65Otj1b61teO6w2Rr18/wshbgxum41D9/WncmYm7vbtQX7/ihuUBOlCCCHEFXbs9DG2pG5hU+om/j71t1ZbrkPHsPrDCDAFADD65tHa1GlCCCGEuDFJkC6EEEJcQdO2T2P1wdVe68L9wrXacrPerK2XAF0IIYQQEqQLIYQQl8GpvFPavOUD6g4gyt8zSGK1oGroFT21w2rTJKIJjSMaUymgkgwIKoQQQohiSZAuhBBCXASX28Xu9N3aSOwHsw9q2+pXrE+Xal0AaBXbipYxLbEarb7KqhBCCHFdul7HMZAgXQghhCijfRn7GPfrOE47TmvrFBQSQhO0ecsLSBN2IYQQQpSFBOlCCCFECdyqm+TMZDalbiLMEkaHyh0AiAmIId+ZT6AxkEYRjWgS0YSG4Q0JMgX5OMdCCCGEuNZJkC6EEEIUkmPPYduJbWw+4WnGnmXPAqBacDUtSPcz+PFGmzeIDYhFp+h8mV0hyp30ufNAp2Du0qnotvnzwa0S2qe3D3ImhBDXBgnShRBCiDMm/DaBTcc34catrbPoLTQK99SWq6qqDfgWFxjnq2wKUb7pFDLmzMWan+e1On3+fDLmzCWkbx8fZUwIIa4NEqQLIYS44eQ58/jz5J/sSttF/zr9tcDbrDfjxk2lgEo0jWxK44jG1AytiVF3fQxEI8TVEHrvvQCkzZqF9bSTbIvC6a+XYvt6GSF9+2jbhRBCFE+CdCGEENc9VVVJOZ2iTZG2I20HTrcTgDaV2lA5qDIA99a8l761+xJhjfBldoW44lSXCzU/H9Vux51vR7V7Xqt2O+h0WGrV0tLm/PADzlOnUPPtWhrVno/bbkdntlDx4Ye0tKnvvEv+3r2e454+TXCGg2Ag873JmKpU9QrQM7/6CsfRoygWP3RWKzqrHzo/P23Zr3EjFJ2nO4nbZkMxGFAM8tNVCHH9k286IYQQ17VfU35l5o6ZHM897rU+whpB04immPQmbV10QPTVzp4QgOdBEk4nqsuFzmLR1ufvT8adl1soQC4IrPPRBwQQ0Latljbts89wpqVrwbfq8KRT7Q4M4eFEPf+clvbIiBE4jqYUmxdDZCRxUz7QljO//gb7/v3FptUHB0GhIN2Vno7zuOezpvP3P5vQraIP8h5YMXfzZmzb/yz+huj1VJ07R1s88d5/yN24EcVoRGe1olj90Fk8Qb3OaiVi5NMoZ6Zeyv3tN5wnTpwJ9j3bFYvF8yDAzw99aKgW/AshRHkkQboQQojrxoncE2xO3UzN0JpUDa4KgMVg4XjucQyKgToV6tAkoglNIpoQ7R+tNXMXJbuRBwFTVRUcDtwFtcf5ZwNknZ8fprg4LV326tWodkeRQFq12zHGxBLSo7t23KPPP487O8erRlrNt4PbjaVuXaJffUVLe/y1V3FlZhWbP1O1al5B+ulfN2oBchEup9eiYjIXem1CMZtRTEZ0ZjP6ihW90lqbNcUUF3cmjQnFbEJnNqOYzOj8rV5pw4YOQbXbUUwm0pZ9Q87eXagqmGJjsN5ys1fawI6d8GvQAPfpXNx5ebhteai5ebjzPH3ZC38+3Xm5nnvtcODKzITMzLMH0umgUA179vr15P7ya/H3Aagy80uUMw9CTs2YQd6mzZ4a/DMBvc7Pis7PguLnR0jPntpDE/s//+DKytZq/D37WFGMRvkuEUJcVhKkCyGEuGY53U52pe9iS+oWNh3fxOGcwwB0q95NC9LrhtXl6WZP0yC8gcxZfjHK2SBgqqqC242i13uWnU4chw+fabJdTIAcFYVfw4YAuO120mZ8dramuVCArObn49eoIWEDB3qO63BwoN99oKrF5sPavDmRo58FPMFk2iefojqdxaa11K/vFaQ7jhzFnZ1d/PXZ872WDVHR6PwDvANkkxnFbMYQFemVNrhHd1RbfqEg+kzwbTQVCaajX30FDIZSBZihffued3th5mrVAE/5yF27jtMBBrItCg1730PO18swVKyoNXkPuLV1qY8b9cILuG023KdzUfNyPa9z83Dn5noeChS6BkvtOig6vSfwz8tFzcvzpM3LQ83PRzGffUDhPHECx5EjJZ43pEcP7XXmN9+Q8926oon0enR+fsROehdDaCgAWatWYfvzLy3Y1/l5N+e3NmuKzs/zfeQ+fRpVVdFZLNKcXwgBSJAuhBDiGpTryGXqtqlsO7GNPOfZ4FGHjlphtagUUElbZ9QbaRHdwhfZvC6UZhAw1e1GdTjOBr/5nuBXHxSEoUIFAFw5p8n9baMnIHacTVMQIFsa1CegtSdoc6ank/rWhLOBdH6+V3/owM6JVHzwQcAT4Bx5emSJ+Q9o20YL0gGyV64sMa0hslDQazCAopwN0nW6swGyyYQuKNBrX+stt4CqegfIZ9IaIr3HOIh4+ilQdOjMpkK12J70OpP3IIUx48eVmN9zBXUq2tqhJDqr9cKJLlLBA5zQe+9FWbAQv8xMwnreQ641gIw5cwHKPHicYjCgDwhAHxBwwbTBSd1K3FZ4hgaAsP79CepypyfwLxTMFwT3SqGuB/qgYIyxsYW22zwbXC7cOTne3RT27OH0hg0l5iPuow+1ID193nyyli71XKfJ5Kml97d6muv7+RH+xOMYwsMByNu2jfw9ezzBvt/Z5v6eZSvGiHAUk6nE8wohrg0SpAshhCjX3KqbvRl7Sbelc3O0p7msn8GP3Wm7yXPmEWQKonFEY5qEN6FheEMCTBf+ES8uTFVVXGlpOI6moA8KxhAbQ9CunQRkwenFX1Ghf3+szZtzoG8/VIej2GME9+xBWP/+ALizMjn5/gfFpgNQjAYtSAfI37275LzZ7Wf3M5vRBwd7Al2z+WzgeyZANlWPL3QOIyG9e3sCocIBstFTQ20ICzubVlE8gVRBmgvUcEb834jzbi/Mr0GDUqe9JrlVQvr2ITgpiYwFC7XVWmDuLr51wtVwbqsBY0wMxpiYUu0bNuB+wgbcry2rbjeqzXYmuM/1CugD2rbDVLWqZ3tBc/4zgb07N9frIUnh8qza7bjsdk+T/rOZ1l7mbtpE1jdLS8xj7KR3tW4YGUuWkPXtt2ea7/uh+Fm8mvMH33WXFvw7jhzxDOJ3JuC/2s35C7rVBCclFd12nXerEaI4EqQLIYQod7Lt2Ww9sZXNqZvZmrqVbEc2gaZAboq6CZ2iQ1EUhjUYRqg5lOoh1dEpMgjUxSpoaltQQ2k/dIgT7/0HR0oKqs12Np3bhaKCAmAwEHrvvdgPHy4SoCsGg1YzrCvUrFhnteLXpEmhJttnA2nFbMKckKCl1QcEEDHqmbMBsuls4K0zm1D8znZb0FksVP70k1Jdq6IoZfqhX9B0WZRNwT12Fyo/2rbraPo1RafzBLVWK5xpMVLAr349/OrXK9VxKj40nArDhpbYnL/wgHvmhAQCOrT3BPtnHg6otrMtAHSFPhvuzExcp9JwkVbseQMLtbzI+eknrZVDEXo90a++iqVWTQBO//wz2Wu/00bkVyyWM835Pct+TZtqnx1XTo72UOK8zfnPdKs59/vEV91qhPA1CdKFEEKUG+v+Wcd3h75jb/pe3Li19VaDlfoV6pPryNVqym+KuslX2bzmqC4XjpRjOFOOYj9yBGdKCo6jKTiOHMGVmelV462zWrEnJ3t21OkwRkViiI4m/+gR3Lt34VYAp5P0+fMJ6dGDSv+d4t3/uYRRs/UhIUS98Hyp8qsYjfi3kC4K4sZR2ub8Aa1be7U4OZ/gu+/G/7bbcOfmeQL/3IIafRvuvFz0hR5C6YODMdeocWZ70eb8OsvZB26OI0fI27y5xPNGv/aqFqTn/O9/pH06/ex1nmnOr1g9/fQrPDAMS61ahN57L85jxzn10Ue482zodDpOvPEGtt9+J6BtW0yVKuFMTz8b/Gdl4Tx5CsV4ZlwFoxFFr9deI4P5iWucBOlCCCF8IteRy/aT22kc0Riz3vMDMOV0CrvTPc2cKwdW1kZirxlaE71O78vslnuFm6c7Uo5iqBiOtWkTAJynTnHkX/8qcV9XRob2Wh8WRuSzozDGxGCIiEAxGkmfP5/Tv/1GdtCZQcB63H3RfYuFEFeHPiQEfUhIqdIG3XEHQXfc4bWucHP+wrX51ptuQh8W5hmIr5h+/IWDf9xuFKNRqyEvaM6vjc7vcmlJzfHVQVFwZ2VhBHKWfYs+LIy8TZvI27SJyOef04L03N9/5+QHU0q8noinn8K/VSsATm/cyKmPPvYE8IZCgfyZgRNDunfHr3FjAPKTk8lescJru2Iwavv4NaiPqWpVT9YzM7Ht2nV2u8noaUlk9PyrDw7WpiFU3Z6HzjL13+VzvXeRkCBdCCHEVaGqKkdyjrA5dTObUzezM20nLtXFqJtG0TSyKQC3xd5GhDWCxhGNqehX8QJHvLG58/PJXLwEx9Gjnr9zmqf7t2qlBemGihXRBwehr1ARY3S0px9ubIzndXS013zWiqJgvelsK4WC5qYBvXqQPGMSuFz439UNyyUMAiaEKP+8mvMXYqpSBVOVKqU6RnBSEsFJSagOR6Em/Ke14N9YubKW1lilCqH33ceJ9/4Dbje6gAACO3RAdTpQHQ6vBwWKwYA+LMwzYOWZ7TjPBvyFp+RT8/JwpaeXmEdX+/baa2fqCbLXrC0xbYWHhmtBuv3AAVLffKvEtGGDB2kBZP6ePaQ897xnAMpza/5NRoK6JRGU6HlI4jh+nFMfTzsT8BuKPDDwa9RI+2535Zzm9A/fF/tAQTEaMISHY4yK8twHl8vT+sBk1B4kFOTjmnSdd5GQIF0IIcQVdfz0cZbuX8qW1C2k5qV6bYv2j8buPjtoUlxQHHFBcVc7i+WO6nDgTE09G4AfPYrjaAqmqlWoMGwY4PmRmrF4kfcPU50OQ2QExpgYzDXP9vFWdDriPvnk4pp/nhkEzNylE8yYpK0uD4OACSGuDYrRiN5oRB8YWGIav3r1yNu6FcVgwO1yoQ+viLlmQrEPAQPatCGgTRuvdarbDU4nqtPpCWwLjtusGTETJmgBPQ4H6pl0qsOBuWYtLa0xNpaQvn20oF91OM7s50lrjIk9e00WC+aaNb22qw67Jw92h9dUf6rjzPSMbrdnFox876kW3Xm5Z19nZ5+3O4HOz3I2SM9I59S0ksfkCOrWjQpDBnvSpqdz+NFHizmgDkWvJ7BTJyoMG+rJw+nTHH3+hbMBv8FwNqg3GrDUr09Q586ea3O5SJ81W9tWkK7gwYExMhJL3bra6Ww7dnjGJjAYPIN2FuxnNBYZy+R8CspF+uzZuNIzQKcja8kScpZ8pc08ci2TIF0IIcRllZqbit1lp1KgZxo0l+pi1cFVABh1RupWqEuTiCY0Dm9MdEC0L7PqUwXN0915eZgqee6V6nZz5P+ewnH0KLjdRfZx55+tKVf0ekK6d0dntWojVBc0Ty/OxfbPLGgumJuTUXTbNf4jSAhRfqTPn0/GggXoQ0PJ0+mI6NmzTK11FJ0OzgwyWVhpp+4DMFWKxVTK7zVLrVrEvD6+dGnr1KbyjOlnAvlzAnqnUxtlH8AQHk7Fxx8786DAWeghgOe1pU4dLa3ObMa/5S2ebV5pPQ8XDBXOzlihulwoJhOq0+n9/xe32/OAQz27zm234zh0qMTrKTx4p2q3k7lkSYlp/Vu11IJ01e0m5YUXS0zr16SJ19gl/zzwILhd3gH9mRYI5oQaVBg2DNXhIPWtCZhcLrIWLSLsvvuui/83SZAuhBDikjjcDnae2snm1M1sObGFIzlHuDn6Zp5q9hTgqS3vXqM7NUNrUq9CPSwGywWOeP3J378fx+HDnv7i5zRPN9esqf3QU3Q6zw8StxvFYvEE34Wbp1eq5HXc0H79fHE5QghxWWlNlO+5xzNtX2YmQd27Y7BYrotuNYpef95WBIXpg4MJvP32UqU1hIcTMXJkqdIaIyOpOnsW4AnYvQJ6u8NrcEC9vz9RL79U6IFC4W4FToyxZ1sUoNMR1K2b13ZtP6dT6x4AgMuFMSYa1e7dmkF1OLTxCwpzZWYW+8AaQPHz/JYI6dmT1Alve9admXnkeiBBuhBCiIuy7p91bErdxLYT27C5ztbw6tDhdDu1ZUVR6Ff7+g4mz22errpVQnp017anvj0R5/HjRXcsCMoLiXj6aXRBQehDQ2V0YiHEjeFMt5rgpCRPkH6GdKu5MhS9/rx90RWTCb8GDUp1LJ3ZrDWpv+B5jUYqTZ5c7DbV7fYaSBCg0nuTvFsI2M8+MChoHZGxaBGonvKhnpl55HoI1CVIF0IIcUEut4vDOYepEnR2sKC1/6xlT8YeAIJNwTSJ9IzE3qBiA/yN/iUd6rqRsXAhtl27cBw9ivN4qtfTfn1wkFeQbqldC2eFME+T9OhoTLGxGKJjMEYWbZ7uVesghBA3gIJuNe5Cg19q266DgEtcmKLTeR5cF2KMiTnvPpfaRaI8kyBdCCFuIPN3z0eHji4xHYpsW7h7IW7c3FvT8z+2zPxMtp7YyubUzWw7sY1cRy7TEqdpAXinKp20KdKqBldFp1wfU8u4c3O9BmvTasfzbV41AHl//olt23ZtWbFYzjZNj4lBdbu16XbCn3zyql+HEEIIcb263rtISJAuhBA3EB065u2ehz0/l/BC6xfuXsi83fO4o8odLNi9gM2pm9mXsQ+Vs00MA4wBHMk5Qs3QmgC0jWt7lXN/+agOB47jqThTU7VRcgGOvzWB3F9/LXE/d14eujMD5gQlJuJ/S8szQXk0+rAwaZ4uhBBCXA3XeRcJCdKFEOIG0qtmLwDm7JhNoxpuGuxTWZi8hBVH19C7Zm+CzEFM2z5NS18lqApNI5rSJKIJNUJqoNdde/Op5u/fT/6evWcGaztapHl6lZlforN4BqApmIdXHxKiBd+Fm6cXnlLH/5Zbrv7FCCGEEOK67yIhQboQQtxgetXsxem8TD5q+C6Lm0PFffMZ3ughetXsxcm8k9wcdTONIxrTKLwRFfwq+Dq7F6Q1T09JwXHEE4SHP/6YNg1P1vLl5Hy3rsh+Bc3T3dnZWpAe2rcPYQPuR+d//fepF0IIIUT5JEG6EELcYP46+Re/HN9InglUwOl2ajXsFf0q8lTzp3ybwWKoDgfo9Vof7+zv1pGzbh2Oo0dxZWQUSR9yTy9MlSsDYKldG1dGptZX3Bjj6TdeXPN0fUjIlb4UIW4YOouFuLlz2P7tt9S33HhTLwohxMWSIF0IIW4QNqeN2Ttns+LACtJyT6FTISQHgiuGsnD3Qi1Q9xVVVXGlp3tqw1OOnv33TPP0SpPe1eZmdaWnYfv7b21ffXAwxthYLQDXFZqPNrBDBwI7FB0oTwghhBCiPJIgXQghbgB70vfw/ub3OZZ7jHRbOi7VRdfNOlrugBPvJjFv9zyAqxKoF26e7tewIfrgYAAyFy0ifdbsEvdzHD2qBenWFi0whId7asajo6V5uhBCCCGuGxKkCyHEDcCtujmeexy7y45Fb6FfrQGEf/wuLlzcVaUrftbAKxKo2w8fJu+PP7z6ixdunh75/HNYmzYFwBAZBTodhsiIM8F3jFcTdX1YmLafKS4OU1zcZcunEEIIIUR5IUG6ENcBm9PG/qxkXG4X8U4bZgIvvJO47mXbswk0ecpCrbBa/Kvpv8icvwCj3kTLTkn8xLta2l41exGy6nfU/RuhlEF6kebpZ+YTD+nZE0vt2gDY9+0j7fMviuxb0Dxd0Z8dLd6/xU34z5qJYjReymULIYQQQlzTJEgXQojrjMPlYP7u+aw8sJI3bnuD6IBoAFrGtCQ95jAZc+aSHfK11z7p8+dTfd0eQvr2KXI8d24uKIo2P3jeX3+RNuMzHClHUfOKTn1ibdJEC9JN1arh37q11lf8fM3TC0ZjF0IIIYS4kUmQLoQQ15H9Gfv5YMsHHM45DMAvKb/QI6GHtr1g7tC0WbOwnnaSbVE4/fVSbF8tJbBzZ8xVq5L51VdFmqdXGD6coMQ7AFAMBuz793sOeG7z9NgY/OrV085nqlyZiKf+7ypdvRBCCCHEtU+CdCGEuA443A4W71nM4j2LceMm2BTMAw0eoEV0C1RVxX06F1d6Gq60NAwVKmKMr07Anl1Yc+D04q8Ivr092StXkr1yZbHHd546qb02Va5M5LOjMETHYIyMkObpQgghhBCXkQTpQghxDVOdTg78s505v3xI5vFDhAYbSGjUjqH1h+J3IofDTzyB81Qaan6+135utwtFBRTAYCBs0EBO//orxuhor6nMjDExGKKi0QecbZ6u8/PDetNNV/dChRBCXPd0Fgtxc+ew/dtvqW+x+Do7QviMBOlCCFEOFa791lksGMLDAXCkppL2yac409NwnUrDlZlJui2NFrZ0dIqOoKQkmjXzNC93+jlxHE3Rjqnz90cfFoYhLAzb4X9Q9+zybHA6yVq1miozv0RRlKt+rUIIIYQQ4iwJ0oUQ4ipTnU5Uh0MbiM2VkUHm19/gTDuFKy0dV3qaV+138F1JhA0aBICi15P7++8UVIIDhPhVwB5sJapSbUKrNdPOow8NJWrsWAxhoegrVEBnNgOeQeJOb95ETqCBbItCZI+7yZg7F5SzfdaFEEIIIYRvSJAuhBBXgDs3l9O//IIrzRNwu9LScKal4Tp1CldWFkHdulJh8GAAVLebzK++KvY4uoAAKFS7rYQEs/+uxmx3/sODbUdiqRiBLiiI6jpdkX0VnQ6/+vW81qXPn0/GnLkE9OpB8oxJ4HLhf1c3LNYAMubMBSRQF0IIIYTwJQnShRCilFS7nfzkZFynTuFMSz8TeJ/CdSoNZ3oa/q1aEXbffQC48/M5+cGUEo/lSs/QXutDQgjq2hV9WCiGChW0Jun6sDCt9hvgaM5RpmyZwp4KewBoYk6hbUjNsl2EWyWkbx/MXTrBjEnaai0wd6tlO54QQgghhI9cr+MYSJAuhLihefp+n/YE3qfSzjQ1P9vs3FK/AcFJ3QBwnT5NynPPl3gs57Hj2mt9cDB+TZqgDw05J/CugCEsFF1QkJZW0emoMHRIicd1q26WJy9n9s7ZONwO/Ax+DK43mDaV2pT5ekP79AYgNyej6DapQRdCCCGE8DkJ0oUQ1y3V4cCZno4rPd0rCDdVqUJA27YAuDMz+WfYAyUeQzGdrcnWBwdjiI7y/BtWEHiHog+rgD4sFGNU1Nn9dDqiXig5oC+tY6ePMXXrVHak7QCgYcWGPNToISr6VbzkYwshhBBCiPJHgnQhxDVHVVXcOTlnm52np6EPC8PapAkArpwcjjz5JK7MrGL392/VUgvSdUFBYNCjs/hhqBCmBdyGM/+aqlTR9lN0OuLef//KX2Ah0/+czo60HVj0FgbUHUCHyh1kBHYhhBBCiOuYBOlCiHJFdTi0wFsxmzFXqwaAOy+P4+PHa4OwqQ6H137+LW/RgnSd1Yor5zQAisGAPiysUOAdhjkhQdtP0emo+sUXKCbTVbrCshlafygz/prBkPpDiLBG+Do7QgghhBDiCpMgvZxy22wcuq8/lTMzcbdvD0ajr7MkxCUpqP1WnU4MoaEAuO120j79VAu8XelpXrXf/i1vIWLkSAAUsxnb7t3gdGnbdYGB2gBrpvga2npFpyN24tvog4PRBQZesOa5vAToqqry3T/fkZqXSr/a/QCI9I9kVItRPs6ZEGAxWKgeVI3MzEzMhutncB4hhBCivJEgXQhxyVS3G+XMFGCq00nW8uXa4GsFo5+70tNRHQ6st9xM5L//DXhqubPXrfMKvAEUoxF9aCi6QO/B1SKefhp9QAD6ChUwhIaeN7g2xcVdgSu9ck7mneTDrR+y7eQ2AG6KvIkaoTUusJcQQgghhLjeSJAuxDUsfe480Cme6bTO3TZ/PrhVbTTvS6G63eRt2XJ21PO0U15zf/vVr6fVeKPXk/7lTFSns/hj5du114pOR9j9A9D5WbRRz/UVKqALCCi29tu/RYtLvpbyRlVV1h9az2d/f0aeMw+jzki/2v2oHlLd11kTwsv1Os2NEEIIUd5IkC7EtUynkDFnLtb8PK/V6fPnkzFnLiF9+5S4q6qqOFNTC835XSjwTk/DXLUqFR44M+q5opD65lslBt7OU2naa0VRCOzUEQwGz9RjoWHagGyG0JAitd8F05vdiNJsaXy87WM2pW4CICEkgUcbP0pMQIyPcyaEEEIIIXxFgnQhrmEF81qnzZqF9bSTbItCzlffYFvyDYGJd2CuXp2sVau0ZufGiAhC7rlH2//Iv0YUGYBNU6gJuqIoWBo1BNBGPS8857e+ovd0YFpwL0rkcrt46aeXSM1NxaAz0KdWH7pV74ZO0fk6a0IIIYQQwockSBfiGhd6773Y8/PIefMNrDmQ9d776ENDyVmzlpw1a73SmmvU0IJ0RVEwREWh2u2emu5CNd76sFCMkZFe+0Y999xVu6YbgV6n596a97I8eTmPNn6UuMBrqw+9EEIIIYS4MiRIF+Ia5zx1isDud3HsrTdABQx6DKGh6IODz5l6LBRjTKzXvpUmveubTN+gfj76M34GPxpHNAbgttjbaB3TGr1O79uMCSGEEEKIckOCdCGuUa6MDNK++JLTGzZgvq2VJ0AHDFFRhNx7D6F9+/o2g0KTZc/i0+2f8nPKzwSbgpnYbiKBJs/UcHpFAnQhhBBCCHGWBOlCXGNUt5vsVatJnzUL9+nTONPTcSxawukAA9kWhch7epIxfwHo9VqfdeE7vx37jY+3fUymPRMdOjpW6YhF5pgWQgghhBAlkCBdiGtI/t69nPr4Y/L37vOs0OvRWf0I6NubgzMmgcuF/13dsFgDyJgzF0ACdR/Jsecw/a/p/HjkRwAqBVTiscaPydRqQgghhBDivCRIF+IakfbZZ2R+sxRUFZ2fH6H978OVmQl6vWee9BmTtLRaYO5WfZPZG1yWPYtn/vcM6fnp6NBxV427uCfhHox6o6+zJoQQQgghyjkJ0oW4RugCAkBVCWjbhtABAzCEhmrbcnMyiqSXGnTfCTIFUb9iffZl7OPRxo+SEJrg6ywJIYQQQohrhATpQpRT9kOHUB1OzNWrARCclISlTh0sdev6OGeiONtObKNSYCXCLGEADK0/FIPOgElv8nHOhBBCCCHEtUSCdCHKGXdeHhnzF5C5dCmmSrHEvPUWisGAYjJJgF4O5Tnz+PLvL1nzzxqaRjTlmZueQVEUrEarr7MmhBBCCCGuQRKkC1FOqKpK7q+/curTT3GdSgPAEBmF22ZDHxDg49yJ4vx18i+mbp1Kal4qABHWCFyqC4MiX61CCCGEEOLiyC9JIcoBx7FjnJr2CXmbNwNgiIigwrChWJs393HORHFsThuzds5i5YGVAET4RfBwo4epV7Gej3MmhBBCCCGudRKkC+Fj9oMHOTrqWVSHA8VgILj73QT37InObPZ11kQxjuYc5Y2Nb3A89zgAHSt35P669+Nn8PNxzoQQQgghxPVAgnQhfMxYuTLmhAQw6Kn4wAMYY2N9nSVxHmGWMFRUwixhPNzoYRqFN/J1loQQQgghxHVEgnQhrjLnqVNkzJ9P2IAB6Pz9URSFyGdHoVitKIri6+yJYhzMOkhcYBw6RYfFYOGZm56hgqWCDA4nhBBCCCEuOwnShbhKVKeTrG+/JX3uPFSbDcVkpsLQIQDo/P19nDtRHIfLwbzd81i6bymD6g2ic7XOAMQFxvk4Z0IIIYQQ4nolQboQV4Ftxw5OfvQRjn8OAWCuVYuA29v5NE/i/PZl7GPKlikczjkMwKHsQz7OkRBCCCGEuBFIkC7EFeTKyCDty5nkrFsHgC4wkLCBAwho1w5Fp/Nx7kRxHG4Hi3YvYsneJbhxE2wK5sGGD3JT1E2+zpoQQgghhLgBSJAuxBWUPmeuFqAHdupIaP/+6AMDfZwrUZJDWYeYvGUyB7MOAtAqphVD6g8hyBTk45wJIYQQQogbhQTpQlxmqtut1ZKH9OmN4+hRQvv3x1Krpo9zJi7EoTo4nH2YQGMgwxoMo2VMS19nSQghhBBC3GB83t72gw8+oGrVqlgsFm6++WY2btxYYlqHw8Err7xCfHw8FouFRo0asWLFiquYWyFK5so5zalp0zjxzjvaOkNoKNGvjJUAvRzLsedor6sHV+eJJk8wsd1ECdCFEEIIIYRP+DRInzt3Lk899RQvvfQSmzZtolGjRiQmJpKamlps+hdeeIEPP/yQyZMn8/fff/Pwww/To0cPNm/efJVzLsRZqqqS8/33HHnySbKWr+D0z7+Qvz/Z19kSF+Byu1iydwmPrX2MA5kHtPUtY1oSbA72XcaEEEIIIcQNzadB+jvvvMODDz7IkCFDqFu3LlOnTsVqtfLpp58Wm/6LL77gueee484776R69eo88sgj3HnnnUycOPEq51wID/vhwxx76WVOvPcfXJmZGGNiiHr5JczVq/k6a+I8juQcYcxPY5i9czY2l40fjvzg6ywJIYQQQggB+LBPut1u548//mD06NHaOp1OR8eOHfn555+L3Sc/Px+LxeK1zs/Pjx9//LHE8+Tn55Ofn68tZ2VlAZ6m8w6H41Iu4YpyOxyoqgp48qorx3m9Ebnz88lauIjspd+Ay41iMhHUsyeB3bqiGI1XvWw5HQ4oKC9OZ7ku277kVt0sP7Cc+Xvm43A7sBqsDKgzgNtibrvh7pmUGXExCsqJlBdRWlJmRFlJmRFlda2UmbLkz2dB+smTJ3G5XERGRnqtj4yMZOfOncXuk5iYyDvvvEObNm2Ij49n7dq1LFq0CJfLVeJ5Xn/9dcaOHVtk/apVq7BarZd2EVeQ4nAQd+aBwnfffYdqNPo4R6IwxeEgesliDFnZ5FWrRlrbNrjMJli92if5cdpzCXC7AVi/fj0GU/kt276S4c7gO9t3HHMdA6CyvjLtLO3I2ZrD8q3LfZy7q0/KjLgUq330XSeuXVJmRFlJmRFlVd7LTG5ubqnTXlOju7/33ns8+OCD1K5dG0VRiI+PZ8iQISU2jwcYPXo0Tz31lLaclZVFXFwcd9xxB0FB5XdaJbfNxuGZs8jKyqJ9+/aYZdoun3OeOIG+QgVt5HZbtWqodjt+zZv7OGeQl5PBxqlv4XK7adeuHUEhFX2dpXJn+YHl5O3MI1IfSf/a/bm90u0oiuLrbPmMlBlxMRwOB6tXr6ZTp04Y5eGxKAUpM6KspMyIsrpWykxBi+7S8FmQXrFiRfR6PcePH/daf/z4caKioordJzw8nCVLlmCz2Th16hQxMTE8++yzVK9evcTzmM1mzGZzkfVGo7Fcv4lul0sLIMp7Xq93qt1OxldfkblwEWFDhhCUeAcAxmbNfJyzsxxGIxSUF4NByssZbtWNTvE8VOlWoxuZjkwSqyYSYY3wcc58T8qMuBTy/yVRVlJmRFlJmRFlVd7LTFny5rOB40wmE82aNWPt2rXaOrfbzdq1a2nZ8vxTH1ksFmJjY3E6nSxcuJC77777SmdX3KDytm7lyNNPkzFnLqrDgW37Nl9nSZSCqqqsPria5358jnyXZ0wKnaJjQN0BEqALIYQQQohyzafN3Z966ikGDRpE8+bNadGiBZMmTeL06dMMGTIEgIEDBxIbG8vrr78OwK+//sqRI0do3LgxR44c4eWXX8btdvPMM8/48jLEdch56hRpMz7j9E8/AaAPCSFsyBD8W7fycc7EhZzMO8nUrVPZfnI7AGsPruXO6nf6OFdCCCGEEEKUjk+D9D59+nDixAnGjBnDsWPHaNy4MStWrNAGk/vnn3/Q6c5W9ttsNl544QX2799PQEAAd955J1988QUhISE+ugJxPcr5cQMnp/4XNc8GOh1BXboQ2qc3On9/X2dNnIeqqqw/tJ7P/v6MPGceRp2RfrX70blaZ19nTQghhBBCiFLz+cBxjz/+OI8//nix29avX++13LZtW/7++++rkCtxIzNGR6Hm2zHXqkWFBx/AXE3mPC/v0mxpfLTtIzanbgYgISSBRxs/SkxAjI9zJoQQQgghRNn4PEgXwtdcWVnY/t6B/y03A2COjyf6tVcxJyRoI7mL8u2Lv79gc+pmDDoDfWr1oVv1btqAcUIIIYQQQlxLJEgXNyzV7SZ77VrSv5yJ25ZH7MR3MFWKBcBSq5aPcyfK4v4695Ntz2ZQvUHEBcb5OjtCCCGEEEJcNAnSxQ0pf38ypz76iPw9ewAwVa2K6rD7OFeitH46+hP7MvYxoO4AACr4VeCFW17wca6EEEIIIYS4dBKkixuK+/Rp0ufMIWvFSnC7UfwshPbtR1CXzih6va+zJy4gy57FJ9s/4ZeUXwBoEtGE+hXr+zhXQgghhBBCXD4SpIsbhupyceSZUTiPHQPAv3VrwgYPwhAW5uOcidLYmLKRadunkWnPRK/o6ZHQg1ph0i1BCCGEEEJcXyRIFzcMRa8nsFNHctaupcKDD+LXsKGvsyRKIceew/S/pvPjkR8BiAuM49HGj1I9uLqPcyaEEEIIIcTlJ0G6uG65bTYyFi7Er1Fj/OrXAyC4WzeC77wTxWTyce5Eaaiqyiu/vMLBrIPo0HFXjbu4J+EejHqjr7MmhBBCCCHEFSFBurgund64kbRPPsV58iS5G38jduLbKAYDikGK/LVEURTuSbiHObvm8GijR6kRWsPXWRJCCCGEEOKKkohFXFccx1NJ+/QTcn//AwBDxYqE3d9fgvNryJbULTjcDm6KugmAFtEtaBrZFINO3kMhhBBCCHH9k1+94rqg2u1kfvMNGfMXoDocYNATfNddhPTqhc5i8XX2RCnkOfP44u8vWPvPWgKMASSEJBBiCQGQAF0IIYQQQtww5JevuC7kbtpM+qzZAFjq16fCgw9gqlTJx7kSpfXnyT+ZunUqJ/JOAHBb7G1YDPJwRQghhBBC3HgkSBfXLNXl0uY2t97cAv82t2Ft2hT/W29FURQf506URp4zj9k7Z7PywEoAIvwieLjxw9SrUM/HORNCCCGEEMI3JEgX1xzV5SJr+Qr+n737jq+qvv84/jrnruRmb0IIhL03shyIinvvPVpHrdZBbavW1mpbO2zVDn9uW6t14KrbigOqooAsAdl7BQjZ687z++NkMjQBwrlJ3s/H4zzIPfd7bz4hh5D3+a7y99+j6+9/jysxEcMwyL75ZqdLk1aoCdfws//9jO3V2wGY0mMKlwy8hHh3vMOViYiIiIg4RyFd2pXaFSvY9fgTBNevB6Bi+oeknnWmozXJ/ol3xzMsaxgLdizgB8N+wNCsoU6X1KmYcXE8fnMfysrKeEXrNoiIiIjEDIV0aRciFRWUPPccFR9+BICZmEjaJReTdNxxDlcmrbGyZCWpvlSy/dkAXDrwUi4ecDF+j9/hykREREREYoNCusS8io8/pvhfzxKtqAAg8ZjJpF96Ka6UFIcrk5YKRUJMWzmNt9e8zcCMgdw1/i5Mw9TicCIiIiIiu1FIl5hXu2wZ0YoKvD26k3HttcQNGOB0SdIKa0rX8H8L/4/NlZsByIjPIBQN4XP5HK5MRERERCT2KKRLzIlWVxMNBHCnpQGQfumleHsUkHzSiQ2ruUvsC0VDvLbyNf6z+j9EiZLiTeGaYddwWJfDnC5NRERERCRmKaRLzLAsi6rPZ1H8z3/i7dWTnDvuwDAMXCkppJx6itPlSSsU1RTxxzl/ZEPFBgAmdp3I94Z8jyRvksOViYiIiIjENoV0iQmhLVsoevJJar9eDEB4WxzRigpcyckOVyb7I8WbQsSKkORN4uqhVzM+d7zTJYmIiIiItAsK6eKoaCBA6auvUvbGGxCOYHg8pJ57Dimnn47h9TpdnrTClsotdPF3wWW68Lg8TB0zlURPIik+LfAnIiIiItJSCunimODmzWz/7X2Ed+wAIH7UKDKu/j6enByHK5PWiEQjvLnmTV5Z+Qrn9juXs/qeBUBeYp7DlYmIiIiItD8K6TGqNhRh9c4qIhGD3qEIHXEdbHd2NpgmrswMMr73ffxjD8MwDKfLklbYXLGZRxY9wurS1QCsK1+HZVn6PrYDce44/n3iv3n33Xe1FZ6IiIhIDFFIl0PGCoWo+OQTko49FsPlwvR6ybn9Z7izsjDjFBLak6gV5Z217/DSipcIRUP43X6uGnIVR+YdqYAuIiIiInIAFNLlkKhZvJhdTzxJaMsWCIdJPvlkALz5+Q5XJq1VWFXI/y38P1aUrABgRNYIrh12LRnxGQ5XJiIiIiLS/imkS5sKl5RQ/MwzVH36GQCulBTMFC0k1p6Fo2HWlq0lzhXHFYOvYHL+ZPWei4iIiIgcJArp0iasSITy99+n9IUXidbUgGGQfOIJpF54Ea7EBKfL63DMuDgev7kPZWVlvNIGUweqQ9X4PX4AuiV140cjf0SvlF5k+bMO+ucSEREREenMFNKlTex6/HEqPvwIAF+f3mRcey2+3r0drkpay7IsPtz4Ic8ve547xt1Bv7R+AIzLHedwZSIiIiIiHZNCurSJ5JNOomrOHNIuvIikKcdhmKbTJUkrFdUU8eiiR1lctBiAjzZ+1BDSRURERESkbSikywGzolEqZ8wgUlJC6jnnAOAtKCD/sccwvV6Hq5PWsiyLGZtm8Mw3z1ATrsFrerl44MWcUHCC06WJiIiIiHR4CulyQALr1rHriScJrFgBLhf+cePwdusGoIDeDu2q2cUTi59gwY4FAPRL68cPh/+Q3MRchysTEREREekcFNJlv0Srqyl5aRrl774L0ShGfBxpF1yAp0sXp0uTA7Bw50IW7FiAx/RwQf8LOKXXKZiGpiqIiIiIiBwqCunSKpZlUfX5LIr/+U8iJSUAJEycQPqVV+LO0D7Z7ZFlWQ1bqB2TfwxbKrYwuftk8pO0h72IiIiIyKGmkC6tEi0vp+jRR7BqanHndiHj+9/HP3Kk02XJfrAsiy+2fsGba9/kl+N/id/jxzAMLh98udOliYiIiIh0Wgrp8p2sUAjD4wHAlZJC+iWXEKmoIPXMMzE077xdKguU8dSSp5i9bTYA7657l3P7netwVSIiIiIiopAu36p63jx2PfkUmT+4jvjhwwF7ezVpv2Zvm82Ti5+kPFiOy3BxVt+zOLPPmU6XJSIiIiIiKKTHrEC4lpC5DUwIRGoP+ecP79zJrqf/QfWcOQCUvv56Q0iX9qkiWME/lvyDz7d+DkB+Uj4/HPFDeqX0crgyERERERGpp5AuzVihEGVvv0Ppyy9jBQLgcpFy6qmknqeh0O3dC8tf4POtn2NickafMzin7zl4XB6nyxIRERERkSYU0qVB7bJlFD36GKHNmwGIGzSQjGuuwdu9u8OVycFwQf8L2Fq5lUsHXkqftD5OlyMiIiIiInuhkC4NImVlhDZvxpWSTPoVV5Bw1FENW3NJbHp55cuYmJzS65Q9nnto3kNsLN/IA5MfACDFl8KvJv7qEFcoIiIiIiKtoZDeiVnRKKEtW/Dm2/th+8eNI+Pq75Nw5JG4EhMdrk5awsRk2spphKKhhnM14Rp+/vHP+WLrF6TFpTG3cC6HdTnMwSpFRERERKSlFNI7qdqVK9n1+BOEi4ro9re/4kpKwjAMrdzezpzT7xwAXlrxEiW1JYStMNd+dC1bKreQHpfOxQMvZljWMIerFBERERGRllJI72QilZWUPPdvKj78ECwLMyGB4IaNxA8Z7HRpsp/O6XcOVaEqHpz3IBErQmVlJd2Tu/Prw3/NoIxBTpcnIiIiIiKtoJDeSVjRKJUzZlL87L+IllcAkHj00aRfdimu1FRni5MDtqJkBRErAkCKN4XnTn6OeHe8w1WJiIiIiEhrKaR3AlY4TOE991L7zTcAePLzybjmauIHq/e8o0jzpWEYBqZlkhaXxrtr320YCi8iIiIiIu2HQnonYLjdePLyCKxdS9r555F8yikYbn3r2yvLsvhsy2dYWBzV7SheXfkqcwrnkB2fjRkwObP3mUxbOQ1AQV1EREREpJ1RUuuALMui+osv8BYU4OnaFYC0Sy8h9dxzcGdmOlydHIiimiKeXPwkC3YsIM4Vx7qydby77l3O7ns2r616jbJAGaf3Op04T5yCuoiIiIhIO6SQ3sGEtm5l15NPUbNoEfEjRpBz188xDMPeUk3bqrVbUSvK9A3TeX7Z89RGanGbbs7scybBSJDz+53PKb1O4bVVrzW0rw/mUaJOlSwiIiIiIvtBIb2DiAaDlL32OmWvv44VDmN4PPj69YNoFFwup8uTA7C1ciuPf/04y4qXAdAvrR/XDbuObkndGtrUhmv3eJ160EVERERE2h+F9A6gev58dj35FOHt2wGIHzGCjO9/r2Gou7Rf5cFy7vj0DmojtcS54rho4EUc3+N4TMN0ujQREREREWkDCuntXOXnn7PzgQcBcGWkk3HVVfjHj8cwDIcrk4Mh2ZvMlB5T2FSxiauHXk2WP8vpkkREREREpA0ppLdzCYcdRml+Pv6RI0g9/3zMeO2N3Z4FI0FeXfUqR+QdQX5SPgAXDrgQl+HSjRcRERERkU5AIb2dqVm6lIr3/0vWzTdhuN0YXi959/8Rw+NxujQ5QMt2LeOxrx9jW9U2lhYt5d7D78U0TNym/pmKiIiIiHQW+u0/xpS8NA1MA447ptn5SGkpW2+/ndoVK3Gnp+Mb0J+UU04BUEBv56pD1byw/AU+2PABAGm+NM7oc4bmnYuIiIiIdEIK6bHGNCh98SW81VX2Y8si8MkM1j/9DKHCQlzp6SQdfzyJkyY5W6ccFAt2LODxrx+nuLYYgGO7H8slAy8hwZPgcGUiIiIiIuIEhfQYk3beeQAU/fvfpFZG8EQsqh5+FKusDF+/fnT97W/w9e3rcJVyMHxV+BX3f3U/ANn+bK4bdh1DMoc4XJWIiIiIiDhJIT0GpZ13HtXVVaT8/g8YFkSNclLPOJ0ud9+NYWoIdEcxMnskfVL7MCB9AOf3Px+fy+d0SSIiIiIi4jAlvhjlP/00wi6DqAmu7t3JveceBfR2blfNLp5e8jShSAgAl+ninon3cNmgyxTQRUREREQEUEiPWdVvvgVA2GVgYFHy8ssOVyT7K2pFmb5hOlNnTOW/6//La6tea3hOK7eLiIiIiEhTSggxqOTll6l+/T+Ux5tU+F0MO+N0Sl98CWicsy7tw7bKbTz29WMsK14GQN/UvkzMm+hwVSIiIiIiEqsU0mNMycsvU/riS/jPOpOKpx8EIO60U3AnJSuotyORaIR31r3DtBXTCEVD+Fw+Lux/ISf2PFFbq4mIiIiIyD4ppMeaqEXqhRfY+6TXhXRoEsyjlkOFSWs8+82zvLf+PQCGZg7l2mHXku3PdrgqERERERGJdQrpMSbtgvMBKCnZtedz6kFvN07udTKzC2dzfr/zOTr/aAzDcLokERERERFpBxTSRQ6CFcUr+GbXN5zV9yzA3vf8r8f8FY/pcbgyERERERFpTxTSRQ5ATbiGF5a/wAfrP8DCol96PwZnDAZQQBcRERERkVZTSBfZTwt3LOSJxU9QVFMEwNH5R9MjqYfDVYmIiIiISHumkC7SShXBCp5Z+gyfbvkUgOz4bK4ddi1Ds4Y6XJmIiIiIiLR3CukirRC1ovxq1q/YXLkZA4OTep7EBf0vIM4d53RpIiIiIiLSASiki7SCaZic1fcsXl/1OtcNv45+af2cLklERERERDoQhXSRb2FZFh9v/JhkXzKHdTkMgMO7Hs643HFaGE5ERERERA46hXSRfSisKuTxrx9n6a6lpHhTGJg+kERvIoZh4DEU0EVERERE5OBTSBfZTSQa4d117zJtxTSC0SBe08sZfc7A7/E7XZqIiIiIiHRwCukiTWws38ijix5lTdkaAIZkDOHaYdeSk5DjcGUiIiIiItIZKKSL1NlRvYM7Pr2DsBXG7/Zz2aDLmJw/GcMwnC5NREREREQ6CYV0kTrZ/myOyDuCqlAV3xv6PdLj0p0uSUREREREOhmFdOm0asI1vLryVU7seSKZ8ZkAXD3satyGW73nIiIiIiLiCIV06ZQW7VzEE18/wc6anWyp3MLPxv4MQNuqiYiIiIiIoxTSpVOpDFby7DfPMmPzDACy47M5seeJzhYlIiIiIiJSRyFdOgXLsphdOJunFz9NWbAMA4OTep7E+f3PJ94d73R5IiIiIiIigEK6dBL/2/w//m/R/wGQl5jHdcOuo396f4erEhERERERaU4hXTqF8V3H88aaNxifO56z+pyFx6W55yIiIiIiEnsU0qVD2l61nffXv89lgy7DNEx8Lh9/OOoPWhhORERERERimkK6dChRK8p7697jpRUvEYgEyIzP5JRepwBauV1ERERERGKfQrp0GJvKN/Ho14+yunQ1AIMyBjE6Z7TDVYmIiIiIiLScQrq0e6FoiP+s/g//WfUfwlaYeHc8lw28jMndJ2MaptPliYiIiIiItJhCurR7jy16jE+3fArA6JzRfH/I98mIz3C4KhERERERkdZTSJd277Tep7Fk1xIuH3g5E7pOwDAMp0sSERERERHZLwrp0u4s3rmYzZWbOannSQD0SO7B3475mxaGExERERGRdk8hXdqNymAlzy57lhmbZmBiMjB9IAUpBYBWbhcRERERkY5BIV3ahdnbZvP0kqcpDZRiYDClYAo5CTlOlyUiIiIiInJQKaRLTCutLeXpJU8zu3A2AF0TuvKD4T+gf3p/hysTERERERE5+BTSJWaFoiHu/OxOdtXuwsTkjD5ncE7fc/C4NLRdREREREQ6JoV0iVke08PpvU9nxuYZ/GDYDxrmn4uIiIiIiHRUCukSM6JWlPfXvU+P5B4MzhwMwPEFxzOlxxRcpsvh6kRERERERNqeQrrEhE0Vm3hs0WOsKl1Ftj+bP036Ez6XD9MwQduei4iIiIhIJ6GQLo4KRUO8sfoNXl/9OuFomDhXHKf3Pl1bqomIiIiISKekkC6OWVO6hkcWPcKmik0AjMoexdVDryYjPsPhykRERERERJyhkC6OWF+2nrs+u4soUZK8SVw1+Comdp2IYWhsu4iIiIiIdF4K6eKIHsk9GJE9gnh3PFcMvoIUX4rTJYmIiIiIiDhOIV0OiapQFa+sfIWz+55NkjcJwzCYOnqq9jwXERERERFpwnS6gIcffpiCggLi4uIYN24cc+bM+db2Dz30EP379yc+Pp78/HxuvfVWamtrD1G1sj++KvyKH8/8Me+ue5dnv3m24bwCuoiIiIiISHOO9qS/9NJLTJ06lUcffZRx48bx0EMPccIJJ7BixQqys7P3aP/8889z++238/TTTzNx4kRWrlzJlVdeiWEYPPDAAw58BfJtygJl/HPpP5m1dRYAuQm5HJ1/tLNFiYiIiIiIxDBHQ/oDDzzANddcw1VXXQXAo48+yjvvvMPTTz/N7bffvkf7WbNmcfjhh3PxxRcDUFBQwEUXXcTs2bMPad3y7SzL4tMtn/LM0meoDFViYnJq71M5r995eF1ep8sTERERERGJWY6F9GAwyLx587jjjjsazpmmyXHHHccXX3yx19dMnDiR5557jjlz5jB27FjWrl3Lu+++y2WXXbbPzxMIBAgEAg2Py8vLAQiFQoRCoYP01Rx84XBjbaFwJKZr3d1769/jueXPAdAjqQfXDLmGnik9IWrviy4HXygcwrKsho/b0/Uizqm/TnS9SEvpmpHW0jUjraVrRlqrvVwzranPsZBeVFREJBIhJyen2fmcnByWL1++19dcfPHFFBUVccQRR2BZFuFwmB/84Afceeed+/w8v/vd77jnnnv2OP/BBx/g9/sP7ItoQ9U11STXfTxz5v/wx8durburtWqxqiwGegYyIjKCZZ8vYxnLnC6rQwtZIcor7RtQH3/0MR5D8/2l5aZPn+50CdLO6JqR1tI1I62la0ZaK9avmerq6ha3bVeru8+YMYP77ruP//u//2PcuHGsXr2am2++mV//+tf84he/2Otr7rjjDqZOndrwuLy8nPz8fI4//niSk5P3+ppYUFq6izmP/B6ASZOOIitzzzn6sWJr5Vb+t+V/XNDvgoZ9zk+LnKaF4Q6h2nAtL01/ifLyco459hiS4pKcLknagVAoxPTp05kyZQoej/69ynfTNSOtpWtGWkvXjLRWe7lm6kd0t4RjIT0zMxOXy8X27dubnd++fTtdunTZ62t+8YtfcNlll3H11VcDMHToUKqqqrj22mv5+c9/jmnuuVi9z+fD5/Ptcd7j8cT0N9HtbqzN43bFZK3haJi31rzFK6teIRwN0yO1B0d1OwogJuvtyCJGpOEGiccd29e2xJ5Y/3kosUfXjLSWrhlpLV0z0lqxfs20pjbHtmDzer2MHj2ajz76qOFcNBrlo48+YsKECXt9TXV19R5B3OVyATTMx5VDY23pWu789E5eXPEi4WiYEVkjGJQxyOmyRERERERE2jVHh7tPnTqVK664gjFjxjB27FgeeughqqqqGlZ7v/zyy8nLy+N3v/sdAKeddhoPPPAAI0eObBju/otf/ILTTjutIax3FFHLYmNCVyrdCSRtryIjw8I0DafLIhgJ8vLKl3l7zdtEiZLkSeLywZdzZN6RDT25IiIiIiIisn8cDekXXHABO3fu5Je//CWFhYWMGDGC999/v2ExuY0bNzbrOb/rrrswDIO77rqLLVu2kJWVxWmnncZvf/tbp76ENjFrdRF//XA5K/pfSsRw8coba+g3u4jrJ/VmYp9MR2t7YN4DLNixAICJXSdy5eArSfGlOFqTiIiIiIhIR+H4wnE33ngjN954416fmzFjRrPHbrebu+++m7vvvvsQVOaMWauLuPP1xVTUBvFFanFHI3g8WSzbVsGdry/mvrOGOhrUT+99OhvKN/D9Id9nTJcxjtUhIiIiIiLSETk2J132FI1aPDJzDZWBMFmJXrzRMCYWcR6TLsk+KgMRHpm5hmj00M2//6rwKz7Z+EnD40EZg/jr5L8qoIuIiIiIiLSB/epJLy0t5ZVXXmHNmjX85Cc/IT09nfnz55OTk0NeXt7BrrHTWLq1nDU7KknzezEMi5DhpsrjJ96ycBsGqX4Pa3ZUsnRrOUO7te0Q87JAGc8sfYbPt36O1/QyKGMQOQn2NARtrSYiIiIiItI2Wh3Sv/76a4477jhSUlJYv34911xzDenp6bz22mts3LiRf/3rX21RZ6dQXB0kFLHwukyi0Qhl3mTCppstZUFyU1z4XCZlUYvi6mCb1WBZFp9t+Yxnlj5DRagCE5MTe55Ialxqm31OERERERERsbU6pE+dOpUrr7ySP/7xjyQlJTWcP/nkk7n44osPanGdTbrfi8dlEIxEcZuQHKqg1JtMOGKxuaSG5Dg3HtMg3e9tk89fVFPEk4ufbFgYrkdSD64bfh29U3u3yecTERERERGR5lod0ufOnctjjz22x/m8vDwKCwsPSlGd1eCuyfTOTmTZtgoyE1x4oyEyAyVUJydRHYxSVBUkJ9lHfnr8Qf/cNeEabv/f7VSEKnCbbs7tey6n9T4Nt+n42oIiIiIiIiKdRqsXjvP5fJSXl+9xfuXKlWRlZR2Uojor0zS4flJvEn0udlYGCZoesCy7B91t4jYNfG4Xt7y0kMKy2oP6uePd8RxfcDz90vrxhyP/wFl9z1JAFxEREREROcRaHdJPP/107r33XkKhEACGYbBx40Z+9rOfcc455xz0AjubiX0yue+sofTNjCfo8lLuTaImGGFEfiq/PWsI/XISyU2JIzvJd0CfJxwN8/qq11lXtq7h3Nl9z+aeiffQLanbgX4ZIiIiIiIish9a3VX65z//mXPPPZfs7GxqamqYNGkShYWFTJgwgd/+9rdtUWOnM7FPJv2Te/H6lb+mwu1n9L1/4Ihh/TFNg1OGdiUQjmCaBgDBcJSqQJi0hJbPU19btpZHFz3KhvINfLntS+474j5cpks95yIiIiIiIg5rdSpLSUlh+vTpfP755yxatIjKykpGjRrFcccd1xb1dVqmYdCjagtgMTAnviGUx3tdxHtdDe2e/nwdn68uYuqUfozsnvat7xmMBHll5Su8teYtokRJ9CRySq9TMI1WD6gQERERERGRNtDqkP6vf/2LCy64gMMPP5zDDz+84XwwGOTFF1/k8ssvP6gFyr7VhiIs3VpOaXWIu99cyrmju3Hx2O64XXuG7mW7lvHY14+xrWobABNyJ3DVkKtI8bXtfusiIiIiIiLScq3uQr3qqqsoKyvb43xFRQVXXXXVQSlKWibO4+JP5w3jxCFdsCx4+avN3PHaYnZUNF9Ubumupfzqi1+xrWobab40bhtzG7eMvkUBXUREREREJMa0uifdsiwMw9jj/ObNm0lJUeg71HxuFzdM7sPwbqn89eNVLC+s4OYXFnLzcX0Z3ysDgIHpAxmQPoC8xDwuGXgJCZ4Eh6sWERERERGRvWlxSB85ciSGYWAYBsceeyxud+NLI5EI69at48QTT2yTIuW7HdE3kz7Zifzx/eUs37GDuz55mBezbyUzMQnTMLlr3F14XB6nyxQREREREZFv0eKQfuaZZwKwcOFCTjjhBBITExue83q9FBQUaAs2h+Uk+zhzYiW//+Jf+Mwa3t3wOpcPttcIUEAXERERERGJfS0O6XfffTeRSISCggKOP/54cnNz27IuaaVdNbt4aslTzNs+jyQ/5Cf1ZGLXiQDM21BCZSDMpH5ZDlcpIiIiIiIi36ZVc9JdLhfXXXcdy5Yta6t6pJWiVpSPN37Mc8ueoyZcg9twc3bfszm9z+l4TA8lVUEemL6C8powX28q5ZqjehHncX33G4uIiIiIiMgh1+rV3YcMGcLatWvbohbZD6+sfIUnFj9BTbiGvql9+f1Rv+ecfufgMe3h7cnxHk4akothwAffbOfH0xaxcVe1w1WLiIiIiIjI3rQ6pP/mN7/htttu4+2332bbtm2Ul5c3O+TQOrb7saTFpXHl4Cu59/B7yU/Kb/a8yzS4dHwP7j1jCKl+DxuLq7l12kL+u7QQy7IcqlpERERERET2ptVbsJ188skAnH766c22Yqvfmi0SiRy86mQP68vWM2/7PM7pZy/SlxGfwd8m/+07F4YbkZ/K3y4ayQPTV7JgYyl//3g1izaVMnVKP9yuVt+rERERERERkTbQ6pD+ySeftEUdUufllS9jYnJM2vhm50OREHd9fhcLdywkNS6V3qm9GZE9Amj5yu2pfi+/Om0wr87fzHNfbsDndimgi4iIiIiIxJBWh/RJkya1RR1Sx8Rk2sppVHctoUvduTUVa3ngq5+wrmwdaXFpjMsdR0Fywf69v2lw3ph8huen0j3d33C+NhTB5zabjY4QERERERGRQ6vVIR3g008/5bHHHmPt2rW8/PLL5OXl8eyzz9KzZ0+OOOKIg11jp1I/jP2Fpf9m8ACDoAfmLvgNZaFy8hLzuGPcHYzLHXfAn6dfTlLDx9Goxe/fW47bNLj5uL4kxWlPdRERERERESe0eqzzq6++ygknnEB8fDzz588nEAgAUFZWxn333XfQC+yMzul3Dqd3P5n3Rxl8MMKkNFjG+K7jee7k5w5KQN/d2qIqFm0uZfa6Ym56YQHLtmkBwPamNhRhzY4qtlcbBMJaF0JEREREpL3ar9XdH330UZ544gk8nsYe18MPP5z58+cf1OI6s9O6n0RSDZhR6Orvwt+O+RuJ3sQ2+Vx9shP503nDyU2Jo6gyyO2vfs20rzYRjWr19/Yizh1HbtXtJG2/EZ8rzulyRERERERkP7U6pK9YsYKjjjpqj/MpKSmUlpYejJoEeHvTe3gikF0GHtPDqytfbdPP1zsrkb9cOJKj+2cRteDZLzbwq7eWUlodbNPPKyIiIiIiIo1aHdK7dOnC6tWr9zj/2Wef0atXr4NSVGf36spXeWPDu0xcbnHL2xFO63YS01ZOa/OgHu91MXVKP246ti9et8mCjaX87t3lbfo5RUREREREpFGrF4675ppruPnmm3n66acxDIOtW7fyxRdfcNttt/GLX/yiLWrsVF5d+SrTVk7jjB4n02WlHZBP6XYCSSkZTFs5DWhcXK4tGIbBlEE59M9J4sEPV3LNUT3b7HOJiIiIiIhIcy0O6evWraNnz57cfvvtRKNRjj32WKqrqznqqKPw+Xzcdttt/OhHP2rLWjuFKFHO73c+x6SN5yseaDhfH8yjRA9JHd0z/Dxw/vBmW7LNWl1E35wkspJ8h6QGERERERGRzqbFIb1379706NGDyZMnM3nyZJYtW0ZFRQWVlZUMGjSIxMS2WdSsszmv33kAlOzcssdzbdmDvjdNA/rqHZXc/8EK4twubjmuL+N6ZRzSWkRERERERDqDFs9J//jjj7niiitYu3Yt1157LQUFBZxxxhk89dRTvPPOO2zfvr0t6xSHJfhcFGQkUBkI85t3lvHkp2sJRQ5Nr76IiIiIiEhn0eKe9KOPPpqjjz4agNraWmbNmsWMGTOYMWMGzzzzDKFQiAEDBrB06dK2qlUclJsSzx/OGca/vljPGwu38sbCrXyztZyfnNif3JR4p8sTERERERHpEFq9ujtAXFwcxxxzDHfddRf33HMPN910E4mJiSxfrpXAOzKv2+TqI3tx1ykDSfS5WbWjkptfXMinq3Y6XZqIiIiIiEiH0KqQHgwG+d///sc999zD5MmTSU1N5Qc/+AElJSX8/e9/Z926dW1Vp8SQcb0y+OtFIxmUm0xNMEJhWa3TJYmIiIiIiHQILR7ufswxxzB79mx69uzJpEmTuO6663j++efJzc1ty/okRmUl+bjv7KF8vHwHxw7IbjgfjVqYpvEtrxQREREREZF9aXFP+qeffkpGRgbHHHMMxx57LFOmTFFA7+Rcpr2nen0orw1FuO3lRUz/ZjuWZTlcnYiIiIiISPvT4pBeWlrK448/jt/v5w9/+ANdu3Zl6NCh3Hjjjbzyyivs3Kl5yZ3df5cWsmpHJX/9aBUPTF9JTTDidEkiIiIiIiLtSotDekJCAieeeCK///3vmT17NkVFRfzxj3/E7/fzxz/+kW7dujFkyJC2rFVi3GnDunLZhB6YBsxYsZNbXlrAmp2VTpclIiIiIiLSbuzX6u5gh/b09HTS09NJS0vD7XazbNmyg1mbtDOmaXD+mHx+d/YwMhO9bC2t5baXF/HWoq0a/i4iIiIiItICLV44LhqN8tVXXzFjxgw++eQTPv/8c6qqqsjLy2Py5Mk8/PDDTJ48uS1r7VSMOB8PX+LGwuIpn8/pclplUNdk/nrRSB76cBVz1hXz+P/WUlEb5uJx3Z0uTUREREREJKa1OKSnpqZSVVVFly5dmDx5Mg8++CBHH300vXv3bsv6pJ1KivNw1ykDeevrbbw2fzPHD85xuiQREREREZGY1+KQfv/99zN58mT69evXlvVIB2IYBqcP78oJg3PwuV0N5xduKmVYXoq2ahMREREREdlNi0P6dddd15Z1SAfWNKDPWlPE795dzqjuqdw6pR+pfq+DlYmIiIiIiMSW/V44TmR/BEJRvG6T+RtLuenFhSzaVOp0SSIiIiIiIjFDIV0OqckDsnnw/BF0T/dTUhXkF28s4bkvNxCJavV3ERERERERhXQ55Lpn+Pnz+cOZMigHy4KX5m7irv8spqgy4HRpIiIiIiIijlJIF0fEeVzcdGxffnx8P+I9LpZsKWfl9gqnyxIREREREXFUixeOE2kLR/fPpm9OEnPXFTOxd6bT5YiIiIiIiDhKPeniuLzUeM4cmdfweFdlgF+9uZTCsloHqxIRERERETn0FNIl5jw6cw3zNpRw04sL+GxVkdPliIiIiIiIHDIK6RJzrjmqFwO6JFETjPCH95fz8CerCYQjTpclIiIiIiLS5hTSJeZkJ8Xxu7OHct6YbgC8v6SQ217+mk3F1Q5XJiIiIiIi0rYU0iUmuV0ml08o4J4zBpPq97C+qIpbX1rI0q1lTpcmIiIiIiLSZhTSJaaN6p7GXy4cybBuKWQn++idleh0SSIiIiIiIm1GW7BJzEtP8PLrM4ZQWhMizuMCIBq12FZeS15qvMPViYiIiIiIHDzqSZd2wTQN0hO8DY9fX7CFHz0/n3e+3oZlWQ5WJiIiIiIicvAopEu7Y1kWK7ZXEIpYPDpzDb9/bzmVgbDTZYmIiIiIiBwwhXRpdwzD4I6TBnD1kT1xmQaz1uzilhcXsKKwwunSREREREREDohCurRLhmFwxog87j93GDnJcWwvD/DTV7/m1XmbiUY1/F1ERERERNonhXRp1/rmJPGXC0dwRN9MolGLZ7/cwJbSGqfLEhERERER2S9a3V3avQSfm5+e0J/h3VKIRCE/3e90SSIiIiIiIvtFIV06BMMwOHFIbrNza3ZWMmddMReMycc0DYcqExERERERaTmFdOmQguEof3hvOdvKavl6cxm3Hd+PjESf02WJiIiIiIh8K81Jlw7J6za5aGx34jwmS7aUcdOLC5i3odjpskRERERERL6VQrp0WJMHZPPQhSPpmZlAeU2YX735Df/4fB3hSNTp0kRERERERPZKIV06tLzUeP503nBOGWbPV39t/hZ+9upiKmpDDlcmIiIiIiKyJ4V06fC8bpMfTOrNHScNwO91keBzkeDVcgwiIiIiIhJ7lFSk05jYJ5Pe2Yn43GbDau+BcAQDA69b96tERERERMR5SibSqeQkx5Hq9zY8fnzmWm57eRFbSmscrEpERERERMSmkC6dVml1kNnrillXVMUtLy7gk+U7nC5JREREREQ6OYV06bRS/V7+cuEIhnZLoTYU5YHpK3now5XUhiJOlyYiIiIiIp2UQrp0ahmJPn5zxhAuHtcd04CPlu3g1pcWsq6oyunSRERERESkE1JIl07PNA0uGtud3541lPQEL5tLarj3raWEtJ+6iIiIiIgcYgrpInWG5KXw1wtHMqYgjRsm98Hj0j8PERERERE5tLQFm0gTKX4Pvzx1EIZhNJybt6GY5DgPfXOSHKxMREREREQ6A4V0kd00Deg7ymu5/78rqA1FuXJiAWeM6NrseRERERERkYNJ43lFvoXf52Z4fiqRqMVTn63j3re/oawm5HRZIiIiIiLSQSmki3yLRJ+b208cwA+P7o3HZfDV+hJufnEBS7aUOV2aiIiIiIh0QArpIt/BMAxOGprLn84bTl5qPLsqg/z89cW8OGcjlmU5XZ6IiIiIiHQgCukiLdQrK5EHLxjBMQOyiVpQWhPS/HQRERERETmotHCcSCvEe13cOqUf43qlM6ZHesP5SNTCZSqwi4iIiIjIgVFPush+mNg7E6/b/ucTjVr84o0lPDNrPeFI1OHKRERERESkPVNPusgBmrexhMWby1i8uYwlW8r4yQn9yU6Oc7osERERERFph9STLnKADitI5/aTBuD3ulheWMFNLy7gizW7nC5LRERERETaIYV0kYPg8D6Z/PWikfTNSaQqEOG+d5fx6Mw1BMMa/i4iIiIiIi2nkC5ykOQkx/GHc4Zx1sg8AN75ehsPfbjS4apERERERKQ9UUgXOYg8LpPvHdGTu08bRGailwsOy3e6JBERERERaUcU0kXawJiCdB6/fAw9MhIazs3bUExtKOJgVSIiIiIiEusU0kXaiMfV+M9r2bZy7n17GT+etogNu6ocrEpERERERGKZQrrIIRC1LFLiPWwsrubWlxby/pJCLMtyuiwREREREYkxCukih8Dgrin89cIRjO6RRihi8fAnq7n/vyuoDoadLk1ERERERGKIQrrIIZLq9/LLUwdx5cQCTNPg01VF3PTCQlZtr3C6NBERERERiREK6SKHkGkanDO6G384ZyjZST62l9eyrFAhXUREREREbG6nCxDpjAZ0SeahC0fwwdLtnDYs1+lyREREREQkRqgnPUbFuV34PCYeE3wel9PlSBtIivNwzuhuGIYBQE0wwp2vL2bp1jKHKxMREREREacopIvEiBfmbGTx5jLufG0x0+ZuIhrV6u8iIiIiIp2NQnqssqL0DAQYXluDuX0JRKNOVyRt7KKx3ZncP4uoBc9+uYFfvrmEkqqg02WJiIiIiMghpJAei9bOxDPtCn68czs3Fu8i/o3r4bmzYe1MpyuTNhTvdXHrlH7cfGxffG6TRZvKuOnFBSzYWOJ0aSIiIiIicogopMeatTPh7Vswdy6j1jAoMV1YngTYvhTevkVBvYMzDIPjBuXw4AUj6J7hp7Q6xN1vLuXj5dudLk1ERERERA4BhfRYEo3CZw9CoBIrsQsh08Q0AE8cJOVCoNJ+XkPfO7z8dD8PnD+cE4d0ISXew6juaU6XJCIiIiIih4BCeiwpXARFqyA+DQwDfzRKl3AYo2onWGH7fNEqu510eD63ixsm9+HvF48i1e9tOL92Z6WDVYmIiIiISFuKiZD+8MMPU1BQQFxcHOPGjWPOnDn7bHv00UdjGMYexymnnHIIK24j1bsgGgK3DwCvZa/ubdSWQfE6qCmFcK3dTjqNlHhPw8czV+7k5hcX8sT/1hIMa0SFiIiIiEhH43hIf+mll5g6dSp333038+fPZ/jw4Zxwwgns2LFjr+1fe+01tm3b1nAsWbIEl8vFeeedd4grbwP+DDA9EA4AUOJysdPlwvL4AQsCpRAog6VvQMkGR0sVZ2wqrgbgzUVb+ekri9haWgNANGpRE4xQE4alW8u1fZuIiIiISDvleEh/4IEHuOaaa7jqqqsYNGgQjz76KH6/n6effnqv7dPT0+nSpUvDMX36dPx+f8cI6V2GQ2ZfqCmBul70oGFiJXeFlHzABFccbFsEX/zd2VrFEZeO78EvTh1Eos/Nmp1V3PLiQh6ZsYar//UVG0uqKQ7CLdO+5op/zGHW6iKnyxURERERkVZyO/nJg8Eg8+bN44477mg4Z5omxx13HF988UWL3uOpp57iwgsvJCEhYa/PBwIBAoFAw+Py8nIAQqEQoVDoAKpvG8aEH+F69zaoKMQbjRLCwArVYAUqICmXyMRbMIqWER1yPtTXX1NiD4HP6ONs8XJIjOyWxIPnDeGBD1czZ30JD05fgcs0MbDvusV7TJZtK+eO1xbz6zMGMqFXhtMlS4yq/xkYiz8LJTbpmpHW0jUjraVrRlqrvVwzranPsCzLsXGxW7duJS8vj1mzZjFhwoSG8z/96U+ZOXMms2fP/tbXz5kzh3HjxjF79mzGjh271za/+tWvuOeee/Y4//zzz+P3+w/sC2gjmRXf0KfwDcyalbgtC58rhcq4PFblnEpR0qA92vfa8T75xbMoSuzPxoyjqYjPc6BqOdTCUfjjIhc7a7F3AQAMA3Li7I/LQtDVb/GDgdGG50VERERE5NCrrq7m4osvpqysjOTk5G9t62hP+oF66qmnGDp06D4DOsAdd9zB1KlTGx6Xl5eTn5/P8ccf/51/Oc45mdqa7/Hb548mIRzipuMeIa3XUYw19j47wfxsOcbKZSRbhfQqfRErcSzREZdCzuBDXLccSku2lGMuX0jXOIOoZVFcHSQSiZCUnITLNIkLRSgPRigYMYIhebF6rYuTQqEQ06dPZ8qUKXg8nu9+gXR6umaktXTNSGvpmpHWai/XTP2I7pZwNKRnZmbicrnYvn17s/Pbt2+nS5cu3/raqqoqXnzxRe69995vbefz+fD5fHuc93g8Mf1NjIS9rPP5iLjdmHkj8Hj3/BoaTP4ZjLwYFjwHqz6ALXNxbZkLeaNg1BXQdcQhq1sOnfJglHDEIj3JCwYUVwcBiERhR0UtGYlewlGL8mA0pq91cV6s/zyU2KNrRlpL14y0lq4Zaa1Yv2ZaU5ujC8d5vV5Gjx7NRx991HAuGo3y0UcfNRv+vjcvv/wygUCASy+9tK3LbB9S82HyHXDhv2HAqWC6YMt8WPc/pyuTNpLu9+JxGQQjzbdiK6oKUB2MsHFXNeFIlNT42P1hJSIiIiIizTm+uvvUqVN54okneOaZZ1i2bBnXX389VVVVXHXVVQBcfvnlzRaWq/fUU09x5plnkpGhRbGaSe4Kk34CFz4Pg8+E4Rc1Ple0GjZ+2bByvLRvg7sm0zs7kZLqEFY0Qn5kEwXWVjL8Hnxug1DUIhSJMu2rjewor3W6XBERERERaQHH56RfcMEF7Ny5k1/+8pcUFhYyYsQI3n//fXJycgDYuHEjptn8XsKKFSv47LPP+OCDD5wouX1I6gJH3Nr83NwnYeMXkNUfRl4GPQ4H0/H7NLKfTNPg+km9ufP1xeyoCOK33HisCFbUwu0ySfe7SPV7+HpzOTc+v4DvHVHACYO7YBhaRU5EREREJFY5HtIBbrzxRm688ca9Pjdjxow9zvXv3x8HF6Vvn6JRSCuArQtg5wr44C5I7wWjLoOeRyust1MT+2Ry31lD+fvHK1m9zkcFLnyhCANzk7l+Um96ZCbwlw9XsmxbBQ9/sgaXaTJlUI7TZYuIiIiIyD7EREiXQ8A0YfwPYPiFsPhlWPIaFK+FD++BtH/C2Oug4HCnq5T9MLFPJiNyfXxy/32UROLoc/aDjO3XDbNu37Xfnz2Mt77eysyVO5nUL8vhakVERERE5NsopHc28akw9hoYdgEsedU+SjZA1Q6nK5MDYBoG/VzbiBAhLzexIaCDPSz+jBF5nDasa8P5cCTKP2et58yReWQmfsvOASIiIiIickhpjHNnFZcMY66Ci16E8T+E/qc0Prf+M1j2FkRCztUnB13T4P7a/C28sXArN/x7PtO/2a7pIyIiIiIiMUIhvbPzJcLwC8DttR9Ho/DlI/C/P8GLF9vD4sNBZ2uUg25C7wz65iRSHYzw149Wcc9b31BUGXC6LBERERGRTk8hXZqzIjDoTPBnQOUO+Pwv8MKF8PXLENI2Xh1Ffrqf+88dzhUTC3C7DOZtKOHG5+fz8XL1qouIiIiIOEkhXZpzeWDYefYw+MNvhsRsqN4FX/zdDutrPna6QjlIXKbBuaO78ZcLRtI3O5GqQIQHp6/iuS83OF2aiIiIiEinpZAue+f2wpCz4cLn4ajbICkXakrAl+J0ZXKQdc/wc/95w7lsfA/8XheTB2Q7XZKIiIiISKel1d3l27k8MPA06HcSbJoNeaMan1vwb4gEYMi59kJ00m65TIPzD8vnlGG5JPgafyx8snwHI/JTSUvwOlidiIiIiEjnoZAuLeNyN99HvbYcFjwHoWp7vvqQs2HoefYWb9JuNQ3oy7aV8+CHK0nwuvnB0b05qm8mhmF8y6tFRERERORAabi77B9vIkz6KWT0toP6gufg+QvsleGri52uTg4Cv9dFQUYClYEwf/rvCn7/3nJKq7XSv4iIiIhIW1JIl/1jmtB7Mpz9JJzwW8jqD+FaWPSiHda1wFy71yMjgQfOH85FY7tjmgaz1uzihufn8+mqnU6XJiIiIiLSYSmkx6g4dxwvBJJ5cpdJnMvndDn7ZppQcASc9Ric9AfIGWxv45Y1sLGNtvRqt9wuk4vHdefB84dTkJlAeU2YP76/gr9+tMrp0kREREREOiTNSZeDwzCg+3jIHwcl6yE5t/G5j+4FTzyMuARS8hwrUfZfr6xEHjh/OC/N3cTLX21iQJckp0sSEREREemQFNLl4DIMSO/Z+Lh8a+PQ9xXvQd8pMPJSSO3uTH2y3zwuk0vH92BSvyy6pcU3nF+9o5KsJB8p8R4HqxMRERER6Rg03F3aVnJXOONhu4fdisLK/8K0K+DDe6B4ndPVyX7IT/c3rPJeFQjzm3e+4cbn5zNrTZHDlYmIiIiItH8K6dL2ugyBk/9oz1vvcbgd1td8DC9fCes/d7o6OQClNSESvG5Kq0P87t3l/Om/KyivDTldloiIiIhIu6WQLodO9gA48T445ynoeRTEp0He6Mbng9XO1Sb7JS81ngcvGMG5o7thGjBz5U5u+Pd8Zq/d5XRpIiIiIiLtkuaky6GX2QeO/zUEKsETZ5+LRuGNH0JCNoy63O59l3bB6za5YmIBE3pn8OD0lWwuqeE37yxjcv8sbjq2L26X7gWKiIiIiLSUfnsW5/gSGz8uWgklG2DTbHjjBnh7Kmxd6Fhp0nr9cpL4y4UjOXtUHqYBoailgC4iIiIi0krqSZfYkD0ALngOFv4bVr4PW+bZR+5wGHUF5I2yV46XmOZ1m1x1eE8m9M4gN7lxBfjy2hCmYZDo04+cWFEbinDuI19SVubimCkRPB6tzi8iIiISC9TNJbEjJQ8m/RQufB4GnQEuD2xbBO9MhcKvna5OWmFAl2RS/HbosyyLhz9ezY3Pz2fehmKHK5MGoRoeLLuFJyJ3QajG6WpEREREpI5CusSepC5w5FS48AUYcg7kDoMuwxqfL90EluVcfdIqFYEw63dVsasyyK/e/Ia/fLiKqkDY6bJERERERGKSQrrErsQsOPwmOPUvjUPdg1Xwn+vh1ath7Qx7wTmJaclxHv5y4UjOGNEVw4APl23nhufnM29DidOliYiIiIjEHIV0iX1mk8u0aBVEI7BrNUy/G165ClZ/qLAe4+I8Lq4+shf3nTWULilxdb3qS/nbR6uoCUacLk9EREREJGYopEv70nUEXPwSjL4CvIlQsh4++jVMuwxWvG8HeIlZQ/JS+NtFIzlteC4ACzaVYqGpCyIiIiIi9bTUsrQ/cckw5nsw9HxY+hp8PQ3KNsPMP0DOYEjNd7pC+RZxHhfXHtWbib0zAfB77R9DlmURCEeJ87icLE9ERERExFEK6dJ++RJh1OUw5Fz45g2o2tk8oG+aa2/h5vY6V6Ps05C8lGaP/7t0Oy9/tYmbju3L8PxUZ4oSEREREXGYQrq0f14/jLio+bnSTfDeT8GfDsMvggGngifOmfrkO0WjFu8s3saOigB3/WcJJw/N5cqJBcR71asuIiIiIp2L5qRLx1S5A/wZUFUEs/4GL1wIC1+AYLXTlclemKbBH88ZxolDugDw7uJt/OiF+SzeXOZwZSIiIiIih5ZCunRM3UbDhc/DkT+GpFyoKYHZj8ILF8D8ZyFU43SFspt4r4sbJvfh12cOISvJx/byAHe+vphHZ66hNqQFAUVERESkc1BIl47L7YVBp8MFz8HRt0NKN6gth69fAksriseqEfmp/P3ikQ296u8t3sbGYo2AEBEREZHOQXPSpeNzuaH/SdD3eFjzMYQD9jx2sMP6klehz3EQn+pomdLI73Vzw+Q+TOidwfqiKvrlJDU8Z1kWhmE4WJ2IiIiISNtRT7p0HqYL+k6Bgac2ntv4ZeOc9S8fhepi5+qTPYzqnsbZo7o1PN5UXM2NLyxg6VbNVRcRERGRjkkhXTo3Tzxk9rPnqC96wQ7rs/5mLzgnMefZLzewcVc1d7y2mCc/XUsgrLnqIiIiItKxKKRL59Z1BJz9OJz0B8geZA+FX/yKHdY/e1ALzMWYm47ty3EDc7AseGPhVm56YQHLtpU7XZaIiIiIyEGjkC5iGNB9PJz5f3DKn6HLUIiEYNvX4PI5XZ00kehzc/Nxfbn7tEGkJ3jZWlrL7a9+zT8+X0cwHHW6PBERERGRA6aF40TqGQZ0GwN5o2HbQvucWXcfK1Rjb+E25FxIzXesRLGNKUjn7xeP5IlP1/HJ8h28Nn8L2UlxnDIs1+nSREREREQOiEK6yO4MA7qObH5u2Vuw9D/wzZvQ+xgYeSmk93SkPLElxXmYOqUfh/fO4INvtjds2SYiIiIi0p5puLtIS+QOhx6HgxWF1R/CK1fB9F9C0WqnK+v0xvXK4BenDsJl2tuyBcNRfvfeMlZtr3C4MhERERGR1lNIF2mJrP5w4n1wzpPQ8yh7f/W1M+HV78N/fw5RrTIeK16dv5lZq3dx28uLePaL9ZqrLiIiIiLtikK6SGtk9oXjfw3n/cMe9m4YYJj2HuwSE04emsuRfTOJWjDtq83cOm0hq3eoV11ERERE2geFdJH9kd4LjrsbznsGxl3XeL58G7z7E9i60LHSOruUeA8/PXEAd5w0gJR4Dxt3VfPjaYt49ssNhCLqVRcRERGR2KaF40QORFqP5o8X/hs2zbGP3OEw6grIG2X3uMshNbFPJoO7pvDo/9bw2aoips3dRHlNiBsm93G6NBERERGRfVJPusjBNOISGHgamG7YtgjemQpv3AgbZ9vz2OWQSvF7+NmJA/jZiQPISY7j3NHdnC5JRERERORbKaSLHEzJuXDUbXDRizDkbHB5YfsSeO+n8M6PFdQdckTfTB67bDQ5yXEN516dt5m1OysdrEpEREREZE8K6SJtITELDr/ZDuvDzgd3HGQPbBz2blkQ1fzoQ6l+izaAhZtK+ees9dw6bREvztlIWHPVRURERCRGKKSLtKWEDJhwA1z8Igy/sPH8lvn2XuurP1RYd0BBhp8JvTOIRi3+PXsjt728iPVFVU6XJSIiIiKikC5ySMSngS+p8fHil6FkPXz0a5h2Gaz8r/ZaP4RS/V7uOGkAPz6+H4k+N2t2VnHLSwuZNncTkaimJIiIiIiIcxTSRZxwzM/hsO/bwb1sM3xyH7x0KSx/ByIhp6vrFAzD4Oj+2Tx8ySjG9kwnErV49ssN3PvWUqdLExEREZFOTCFdxAm+JBh1OVw8zd5nPT4VyrfCzD/C+3c4XV2nkp7g5a5TBjJ1Sj8SfC6OGZjjdEkiIiIi0olpn3QRJ3n9MOJiGHwWLHsLFr0AfY9vfD4cBCxw+xwrsTMwDIPJA7IZXZBGkq/xx+KiTaWkJ3jJT/c7WJ2IiIiIdCYK6SKxwBNvrwI/6Ax7j/V6K96Bec/Yi84NPN0O9dJmkuM8DR+X1YT40wcrqAqEuWRcD84amYfZZIV4EREREZG2oOHuIrHE7QPT1fh4zcdQUwJfPgIvXAALnoOgViE/FKJRi95ZiYQiFv+ctZ6fvvo1m0uqnS5LRERERDo4hXSRWHbKgzDpZ5CcB7XlMOcJeP4C+Oof9uN6VhSfVUsC1ZjbF2tbt4MgLcHL3acN4qZj+xLvdbGisIKbXljA6ws2E9UK8CIiIiLSRhTSRWKZyw0DToYLnoXJP4fU7hCogHn/hE//bLdZOxPPtEvIjRbShSLi37oOnjsb1s50tPSOwDAMpgzK4eGLRzGqeyqhiMXTn63nZ69+TSCsLfNERERE5OBTSBdpD0wX9DseznsGjrsb0nvZ89TXzoS3b8HcuZQoEMbE8iTA9qXw9i0K6gdJVpKPX50+mBuP6UO8x0W3ND8+t+u7XygiIiIi0kpaOE6kPTFN6H0M9JoMlmVv1xaoxDI8eIgQwYRoCOJSoWaX3dtecKT9OjkghmFwwuAujOyeit/b+KOzqDJAMByla2q8g9WJiIiISEehkC7SHhkGbFsIRasgPg2jaicW4CKKUbkdDMCKwoZZ8K8z4Mq3Gl+78gOIhiExG/wZ9p/eBIe+kPYnOymu4WPLsvj7x6tZvKWMKyYWcOrQXK0ALyIiIiIHRCFdpL2q3mX3mrt9WMndCNdUYBDF5YnHsCIQDgF7mTc9/xko29z8nMcPiVmQ3tseTl+vcIm94nxCpt07byiANlUbihKKRAmGozzxv7V8saaIm4/tR5eUuO9+sYiIiIjIXiiki7RX/gwwPRAOgMtHFBMLAzMpD1wuCNVAsAKOuLn567odBsldoXKHHfQDFRCqhpIN4N4tXM74XWOgd3ntsJ6QZf+Z1hNGXdbYtrYMvEmdamh9vNfFr88YwvtLC/nH5+tYsqWcG5+fz5WHF3DyEPWqi4iIiEjrKaSLtFddhkNmX3uRuISc5s9Zlr2/es5g6HVM8+eOuKX542A1VBdB1c49P4c/3d6XvaYEIkEo32ofAFn9m4f0//zQfs6f0RjkE7LsIzUfCo444C85FpmmwclDcxndI42HPlzFki1lPDZzLbPW7OInx/cnLcHrdIkiIiIi0o4opIu0V6YJR9wKb9+CUVWIQRQLIFwDgXLwJdnPf1fPttcP3u729m67O/1v9p/hoN3rXrWz7ihqPo+9/qaAFW1s01RW/+Yh/T83QLjWng/fNMwnZENSDqR025+/EUflJMfx2zOH8O6Sbfzz8/XsqgwQ79UK8CIiIiLSOgrpIu1Zr0lw6kNEZ/4Js2qWPeA9VG33oB9xq/38weD2QnKufeyNYcAVb9tBvWonVO2wg3x9YE9s0tNvWbBrlT1Mf9fqPd8rawCc/Vjj409+B4a5W5iv66mPS4mpefKmaXDqsK6M7pFGdTBCnMcO6dGoRXF1kMxEn8MVioiIiEisU0gXae96TSKUM5Idf56AEQ2RftpjJPQ54tDPDTdNSMiwDwZ8e9szH7HDe+UOe6h95c7GQN+0R9+yYM3H9lD7vckZAmc+3Ph44fN1C93V9conZEJ8+iH/u8hNab4d21tfb+XfX27ke0cUcMLgLhgxdGNBRERERGKLQrpIR2CYBIw4InhIzRka24u3GQZk9LaP72JZcOSPm/TO76r7cyfUlEJ8avO2X/1jz0Bf3wvf7TCY9NPG8+s/t6cEJGTZ8+jdbTN33LIs5q4vpiYU4eFP1vD56l386Ng+zbZyExERERGpp5AuIrHLNKH/iXt/Lhy057XXi4Zh0Ol273z9UPvqXfY8+cod9urz9SwLPvxV80Afn9Y4lL7LUBhxUeNz5Vvt5z3Ne8hbwjAM7j19CG8u2sq/vljPwk2l3PjvBVx9ZE+mDMpRr7qIiIiINKOQLiLtk9vbvPfb5YGJP2reJhqB6mJ7SL3paTwfroXsgY2L4EWC9nz6mhIoWtl8JIJlwbQr7Da+JLvXPTG7cV58Zr/mi+JZ1h7z5E3T4MyReYwpSOMvH65ieWEFf/t4NbPW7OLGY/porrqIiIiINFBIF5GOy3RBYpZ9NOWJh9P/an9sWXYve9OF7hKatA9Wgum2Q3qgwj5K1jc+3/OoxpBuWfDMafaCdgmZjfPi63rou6V04w/nDOM/C7fw3JcbWLiplIrasEK6iIiIiDRQSBeRzs0w7Lnt8amQ2WfP531J8L337P3i63ve68N85Q67J71ebWljkC/bvOd79TwK8/hfc/aobhzWIw3znVvIW9C1IcQH49LxpnQBf6Yd7l2ePd/jYLGi+KxaDEKY2xdDkgOLDYqIiIjIHhTSRURawptgH2kF+27jS4GLXmg+L77pdnTpvRqa5vuDULMS1q4EoDYcZWtpDZmJPpLi3Bg9j4Ljf203jkZh1l/32juPZz8WoFs7E8/MP5EbLQQsXG9dZ+9lfzC37RMRERGR/aKQLiJysJgmJHe1j+/ijocp9zb0yi9asoJScys7K0vIC1aT7knHX9+2thSWvr739/ElQb8TYeKN9uNoFFa+Z/fGJ9YFeW9i4zz5tTPh7VswaysIYxDFwPQkwPal8PYtcOpDCuoiIiIiDlJIFxFxgieuWRgeM9bi1fmbeWT2RqKRCMkrDb7fdQdH98vCMF0w8tI9e+dDNfbQeqzG960thZl/bP653HGNve+b5kCgEiuxC1Z1OQZRe295by5UbIPPHoSCIzX0XURERMQhCukiIjHAZRqcPyafcT3TeejDVazeUckDH6xk1uoibpjch9Sx1zR/gWU1zpN3NxnyHg1D9/GNc+YDFfZq9mWb7ZXrK7dDYpe6nnULL2GM0vV2b7vLC0UroHARdB15KL98EREREamjkC4iEkN6ZCRw/7nDeHX+Zl6Ys4kv1xYzPL+IU4ftNoTeMMCXaB9NJWbDSX9ofBwONC50t+pDmP2I3XNugR3TwYhGIFBmB/9oGD7+DQw51+7pb8nQfRERERE5aBTSRURijNtlcsFh3RnbM4P3lmzj5CG5Dc9ZloWx2z7s3/5mPkjpZh8A8/5pB3eXDwuDIB68yXm4wjX2VnTRCJRuhNmP2gvlDTrdfl2w2t7Szq3t4kRERETakiYdiojEqJ6ZCfzw6D6Yph3Ka0MRfvLK13y6auf+vWGX4ZDZF2pK7F7zeu54e6E5jx+6HQaTfgb54+yj3sr34Z+nwns/sxexK992AF+ZtEe1oQhnPfIlf/raRW0o4nQ5IiIiHZZ60kVE2ok3F21lRWEFf3x/BZ+v3sX1k3qT4m/FXuqmaW+z9vYtGFWFGETtJefCNRAot1eKP/p2e5j70HObv3bHMogEYeOX9gH2dnTdJ9hz4LsMtXvaRUREROSAqCddRKSdOGtkHheOzcc04PPVRdzw/Hw+X13UujfpNQlOfYho5kBMLNxEMULVkDMYTn1w39uvTb4Tzn0axl0HucPAMKFkPSx6Ad75sb04Xb1IaL+/RhEREZEWC9XgeupYJi3/pb3rTQehnnQRkXbC4zK5ZFwPxvXM4KEPV7JhVzW/f285R/bN5LpJvUmJb2Gveq9JhHJGsuPPEzCiIdJPe4yEPkd8+7ZrhgEZve1jxMVQWw6b59q96tGQPX+93ps/sofTdx9v97Rn9tOWbiIi8t1CNbieOoFJZWUwZTJ4WjFaTKQDUUgXEWln+mQn8sD5I3hp7kZembeZT1cV4XaZTJ3Sr+VvYpgEjDgieEjNGdr6EB2XDH2OtY+mastg53I7pO9cbi9U50+H/PF2aO82pnmgFxEREZFmFNJFOoA4j4veWQmUl5UR59G84M7A6za5bEIB43tl8NRn67hiQg+nS7LFpcAlr8Cm2bDxC9g8D6qLYcW79tH7GDjubrtt/eJ1rVmtXkRERKSDU0gXEWnH+uYk8ftzhjU799Rn6xjcNZnxvTKcKSohEwacYh/hIBQutgP7xi/s3vR6Jevhv3faq8h3nwBdR4Lb60zNIiIiIjFCIT1WeeKJfP8jZr77Lid74p2uRkTaiXkbSvjPgi38Z8EWJvfP4pqjepEU5+CcPrcXuo22j4k3QjTa+NzGL6F8q72l29LX7T3Y80Y3zmVPzHaubtlTqIYHy24hEolA6HAgzumKREREOiSt5CMi0oEMzUvhnFF5mAZ8smInNzy/gDnrip0uq1HTue+DzoATfgsDT4WELAgHYMMs+PQB+Pd5sHWhY2WKiIiIOEU96SIiHYjXbXLl4T0Z3zuDh6avYktpDb9++xuOGZDNNUf1ItEXQz/2vX4oOMI+LAt2rakbFv8lFK+F7IGNbec/a5/rPgHyx0J8qmNli4iIiLSlGPptTUREDpYBXZL5y0Uj+PeXG/nPwi18vHwHm4qr+fP5wzEMg6hlsTKSS0kkjtJtlYxNSsU0HVzAzTAgs499jLrM3uvU7Wt8fvWH9hz2NR/bbbMHNQ6Lz+ijxedERESkw1BIFxHpoHxuF987oicTetv7ql9wWD6GYTBrdRF//3glqwMXEbJc+F5bTt+crVw/qTcT+2Q6XbZt97U4jvpJYy/7rtWwfal9zH0KsvrD2Y87U6eIiIjIQaaQLiLSwQ3MTebvF4/C4zKZtbqIO19fTHFVgHgrSjrVWJ5slm2r4M7XF3PfWUNjJ6g31WWIfYy9Bip3Ngb2LfMgvXdju2gUPvoV5Ay1e9pT8x0rWURERGR/KKSLiHQCHpdJNGrxyMw1VNSGCUcsSkkkhJt0A7okeyksD/LIzDWM75Xh7ND375KYBYNOt49wEIKVjc8VrYS1M+3ji79DSjc7rOePh9zh2uJN5FAK1eB66gQmlZXBlMngcXCnCRGRdkSru4uIdBJLt5azZkclqX4PSXH2Pdoq4thcGmD9rhqiVpRl28pZsKnE4Upbwe0Ff3rj48RsmHCjvZWb6YayzbD4FXj3NvjX6bDiPedqFREREWkB9aSLiHQSxdVBQhGLNLeLuEQDX2Up5VY8ASOOSNSiOhAlHLWY/s12Rvewg69lWRjtaVE2fzoMO88+gtWw5St7WPzGL6F6FyTlNrYtXGIPm+8+wV6IztR9axEREXGeQrqISCeR7vficRkEI1G8LvAZIbII4U7vQjAKpdUhKgNhxvVs7Jn+cNkO3lq0lcN6pjO2IJ2+2YmxPRS+Ka8feh5lH5YFRasgvVfj86s/hKWvw4LnIC4Z8sfZQ+O7jbUfi4iIiDhAIV1EpJMY3DWZ3tmJLNtWQXZi449/w4B4j4syI8xhBelM6pfd8Nzc9cWsK6piXVEV0+ZuItXvYXSPNMYWpDOieyp+bzv5b8QwIKtf83PdDoPaMtg8F2rLYdV0+zBMyBkEJ/4efEnO1CsiIiLfzYpCqBp3pAYKv4aCiR1iZFw7+e1KREQOlGkaXD+pN3e+vpgdFUH8lhuPFaE2FKU8ECbR5+KHR/du1lN+w9F9mNArgznri5m3oYTS6hAfLdvBR8t24HEZ/Pvq8cR7XQ5+VQeg4HD7iEZg+5K6YfFfQPE6qNoF3sTGtsvehvg0yBu15/ZwIiJycHTQwCVtZO1M+PTPGCXr8UejGK9fY2/LesSt0GuS09UdEIV0EZFOZGKfTO47a6i9T/o6HxW48IUiDMxN3us+6Sl+D5MHZDN5QDbhSJRvtpUzZ10xc9cXkxLvaRbQH/pwJclxHsb2TGdgbjKu9jIs3nTZK7/nDodx10FFIVRut3vfwQ7xsx+FQAW4PJA7wh4W330CpOQ5WrqISIfRgQOXtIG1M+HtW+z/mw2TKGB6EjC2L7XPn/pQu75uFNJFRDqZiX0yGZHr45P776MkEkefsx9kbL9u3znX3O0yGdYtlWHdUrn6yF5UB8MNz1XUhvhk+Q6iFry+YAsJPheje6RxWEE6o3ukkRTXjrZeSupiH/VC1dDnOLunvWKbPTx+81yY9Td7i7chZ8OQc5yrV0SkvevggUsOsmgUPnsQApWQ2MX+07DAE2evR1OxzX6+4Mh2OxJDIV1EpBMyDYN+rm1EiJCXu3+LwTWdj+51m9x2Qn/mritm7voSKgNh/reyiP+tLMI04JzR3bh8QsFB/AoOIV8SHHGLvfhc6QbYONseFl/4tb3FW215Y9tglf3LZv44SMhwrGQRkZhnWfZN0Opd8PGvoaYMkrs2BC4jVGVvpVm5A975MYz5XuMIp+SuMOiMxvea+ySEA/Z7Nn4C+4/EHBh2fpO2T9k3A5q2qX9dQiaMuryx7VdPQ3VxY7smLyEuBcZd26TtP+xam71n3ccev/3/SL35/4LSjU3qbfIatw+Ovr2x7YLnYNfqvbc1DJhyb2PbhS/Y07caam369wEc9yt761KARS/B1vl7tql//+N+Bd4E++PFr9j/7+2r7TG/aNwOdenrsOaTPdvUv3byzyG5bqeVZW/Bivf3bFP/+OjbIa3A/njF+/Z7Y9nfk02z67Za3QSRIFA3ss8w7OlpRaugcBF0HUl7pJAuIiIHzOd2cWTfLI7sm0U0arG8sIK564uZs76YjbuqyU6Ka2i7vbyWNxZu4bCCdAZ3TcHrbid3uQ3D/mUhrQCGX2D/IrnlK8jo09hm81cw8w/2x5n9GofFZw1ot3fzRURaLByAmhKoKYXaUjvkdRlqPxcJwX9/bj9fW2q3iQTtoF68DnyJjSEc7DZY9jz1kvUw7x922AU7eDUN6Uv/0yR47yZ7YPOQvvL9ujC9F2kFzUP6mk/sML03SbnNQ/qGWVC0cu9t49Oah/TNc2Hb13tv64lvHtK3fW0H0r3ZfYvUHd/A+s/23hZodrNh12p7hNi+REKNH5dusP9/a0nb8q2wbdG3tA00fly5o/lNhd2Fm7StKYady+2PA+X2tWNFAcP+02iyPo7bZ18/1bv2/d4xTiFdREQOKtM0GNQ1mUFdk7liYgHby2tJ8DX+dzNnXTFvLdrGW4u2Ee9xMbJ7KocVpDOmII1Uv9fBylvJlwi9jm5+znTbgXzncvuXtaKVdo9JfKq9tduoyyE134lqRURaLxK2d8FoGqzjU6HbmLrnQ/DmTXXPl9mBu6keh8OJdSHddMO2hc2DF9jhyjDAtdvP/7hUMLB7VwMV9ntl9rPbJndt3nbouU3e12geXhOymrcdci4EK+uaGnb7evGpu7U9u3G0VMN71v1Z38vc0PYcO0g2bVP/OrevedtBZ9pDsXdvC/Y6Kc3anmHf8G1q91qatu122N7bGIb9Pag34JTGXua9ta2/IQLQ/2ToMmy3NjQ+brptad8TIHvQvuts+v3oc6z9Pd1X26bf556TGrdRLVoFH90D7nh7ZEDpJog2uQERDoDpAX/7HdGmkC4iIm0qJzmu2eM+2YlMGZTD3PXFlFaHmLVmF7PW7MIwoG92ErdO6Uu3NP8+3i3G1a8YXz8Ub+MXdu9DTSms+sAerlmvaLXdu57Wc8/eEBGRthCN2r2Q9aG6psQ+ErKgZ11oDAfh1e/ZP7f21jvdY2JjSHd5oHgthGsbn3d57IAdn9o4rBnsn3NH324Hq/g0+/m4VNi5DF68dI/Qa/kzMEwXhGrsID/6in0PXR59Zcv/DoZf0PK2g89qedv+J7a8bZ9jW9624PCWt63/vrRE1xH20RLZA+2jJTL72EdL1I9Oa4mUvMbFWruNhSWvwval9rQDwwQi9nOWZV/TOYOhy/CWvXcMcjykP/zww9x///0UFhYyfPhw/va3vzF27Nh9ti8tLeXnP/85r732GsXFxfTo0YOHHnqIk08++RBWLSIi+2tgbjIDc5OJRi3W7Kxk7voS5q4vZvWOStbsrCQjobHHYc66YgwDhnVLweduR1u9+dOh/0n2EQnD9sWw/Zvmv7B+9TRs+NyeL9l9nD0svusoe+EbEZGWsCx7LYz6Xu760F1bZi+A2XeK3S4chOfPt89b0T3fp/uExpDu9kJVkR2OwQ5AcSl2qI5Pg4zezV875V57sa64uue9Cfu+8dj7mD3PdRkOmX3twJWYs+fX1wEClxxkpmmv+v/2LVBZaF/TlmVfs4HyurVkbm3X08wcDekvvfQSU6dO5dFHH2XcuHE89NBDnHDCCaxYsYLs7Ow92geDQaZMmUJ2djavvPIKeXl5bNiwgdTU1ENfvIiIHBDTNOibk0TfnCQuHtedXZUB1hZVNdvW7fnZG1izswqv22R4t1TG9kxjTEE6mYm+b3nnGONy270/TXuALMs+7/La271986Z9uLx2u4IjYNDpztUsIs5pOq+76TDzmhJ7uszA0+raBeGfJzefD9xU9/GNId3trVtYrS6g+5IaQ3dc6p69pKf82R7uHJ8GvuRvDzvdx+33lwp0isAlbaDXJHvV/0//DBtmYRLFCFXbN3Q6wLZ9job0Bx54gGuuuYarrroKgEcffZR33nmHp59+mttvv32P9k8//TTFxcXMmjULj8fezqegoOBQliwiIm0kI9FHRpPwHY1aDMhNpqwmRFFlkLnr7f3ZYQ09MxOY1C+Lc0Z3c67gA1G/Im+oFrYusIfFb/zSDuybZttDR5uG9J0rIL23HexFpH2xLHsBq/rF1HYP4Gk9Ydh5dttwEJ46ft/v1X18Y0h3e+15t5FQXaBObezNjk+1e6ebOvP/7MAdl/LdP0tyBu/HF3oAOnjgkjbSaxJ0G4P12CSqK8rxnfUEnoKJHeKGjmP/2weDQebNm8cdd9zRcM40TY477ji++OKLvb7mzTffZMKECdxwww288cYbZGVlcfHFF/Ozn/0Ml2vvwyADgQCBQOMCFeXl9uIPoVCIUGgfdx5jRH19sV6nxIBQCLNu24pQKAS6ZuQ7NP25EgrH7s/D70/szvcm5LOxuIa5G0r4akMJK7dXsnZnJXkpvoa6Lcti7oYShnRNbrY1XOxzQdcx9jHuRijdgLFpNiRmY9V/T2pKcb12LXj8WHljsPLHYXUba/8ifgi1l2tGYkhH/r/JitqrfteWYtSFb6O+x7u2FCujL9aIS+y2kSCuZ8/e91t1G0t04Jl1jwxcnng7eNfN67bi0hqGm1vpvRt/NgCc/ZTd0+zexzSZpm2T6m5qRi2IxuD3In8inDsc86ljqamswHXaI7gLJtjD7TvStSMHVziC6Y4n7AphZgyESMQ+YlBr/t907DeZoqIiIpEIOTnN557k5OSwfPnyvb5m7dq1fPzxx1xyySW8++67rF69mh/+8IeEQiHuvvvuvb7md7/7Hffcc88e5z/44AP8/vaxMNH06dOdLkFinBkNcmTdDaiPPv6YqNmOVsgWR4RDQQbX/Sf2ySczcXti/5rxA0f5YExXWFdhkFhazLvvrgKgqBb+udKFaUB+gkXvZIteSRap7WhUfKMkoAaWvwtAcvVGhlQE8URKYeebsPBNACri8tiV2I8dyUOp8Wa2eVXt8ZoRZ7W7/5usKMk1m/FGKvFEqvBEqvGGK/FEqvGEK6mIz2dd1nEAGFaYo1bcu8+3Kk7sy+KtjTfSJlYGsQwXQVcCIXcCIVdCw8dVpZnsevfdhrautO8RMbyN87ojQFXdURSCle/SUZnRIEfWhMEVzydLdxJd9r7TJUmMa08/Z6qrq7+7UZ321N1ANBolOzubxx9/HJfLxejRo9myZQv333//PkP6HXfcwdSpUxsel5eXk5+fz/HHH09ycvJeXxMrQqEQ06dPZ8qUKQ3D+0X2KlSDWfhXKsrLOfaYY/D4Y/vaFufVVldSuOz3RCIRJk+eRFLyoe2VPdgWbS5jQO06tpXVUgrMq7KPbmnxjOmRypSB2XRNjXe6zP1nXQs7V2Bu+hJj82woWkUyleSF5hMddAxWv5PsdvXbCnkTD3oJHe2akUMgWIW56ffUVFZw3OCsxl7RQykaxtjyVcNiakb9cPPaMoyaEqwuw4iOv8FuGwnh+udeVug2AA9YOb0YeGLjQsWuqpfsdSTiUrDqhplbcakQl0JiSnfy80Y1vod14qH/2tsj/T4jrdWOrpn6Ed0t4VhIz8zMxOVysX379mbnt2/fTpcuXfb6mtzcXDweT7Oh7QMHDqSwsJBgMIjXu+edE5/Ph8+3Z1eKx+NpN8G3PdUqTgkTrbvjrutFWiLS5BrxuNv/NTOmZyajCzLYUlrD3PXFzFlXwjdby9hSWsuW0kJGFWTQI8v+Gkurg7hMg6S4dvY15w2zD66Fql0NW7yZBYdD/fdv2Ycw+xHoMtRerbn7eEjtcVC2eOto14y0sbUzicz8E9Hi9fiwcL11PWZW//2fX2xZTXqWQ7B2xm6rmZc2zvXOGw2TflLX1oDpd+37ff3puOqvZY8HsvrVBe/U5gurxadCctfGtgCXTGv91yHfQb/PSGu1n2umNbU5FtK9Xi+jR4/mo48+4swzzwTsnvKPPvqIG2+8ca+vOfzww3n++eeJRqOYdQsCrFy5ktzc3L0GdBER6TwMw6Bbmp9uaX7OGtmNykCY+RtKmL+xhCFdUxravTp/C28u3MKgrskcVpDOYQXpdEuLx2hPe5UnZMCAk+2jqeI1EI3A1oX28eUjkJRrh/XuE+zwosXnpK2tnQlv34JZW0EYgygGpifB3mLr7VvsBcIKjoRIsHHLwXAQlr/dfAuxph93Hw/H1IdtAz7+zb4/f2Vh48cut70XtMvXfG/u+DT7SNxtN6FznjxYfwsiIvvN0f+pp06dyhVXXMGYMWMYO3YsDz30EFVVVQ2rvV9++eXk5eXxu9/9DoDrr7+ev//979x888386Ec/YtWqVdx3333cdNNNTn4ZIiISgxJ9bo7ql8VR/bKand9WWkPUgiVbylmypZx/fL6eLilxjC1I57Ce6QzLS8E021Fgb+ro22HkZY2rxW9bCBXbYOnrsPwduOKtxpAeqgFPO54CIIdGNApWBFyexselG+yAHQ7YvdqRQN3joH0D6bMHIVCJlZiDWV2CiWUPM7ciULYFXr7CHuHR51g49pf2+5ou+Pwv+66jurjxY5fb3qbQ7Wve013/cULzf/Oc9i3vKyISgxwN6RdccAE7d+7kl7/8JYWFhYwYMYL333+/YTG5jRs3NvSYA+Tn5/Pf//6XW2+9lWHDhpGXl8fNN9/Mz372M6e+BBERaWfuOnUQ28tr7S3d1hXz9ZYyCstqeXPRVv63aifPXDW2oW1tKEKcZ++7h8SslDwYeq59hGpgy3w7tEcjjb2WAG/cYK9QXT8sPmeIHZT2xYris2oxCGFuXwxJR3SIbW7alUjY3sorEmwSkoONR1IuZPS22wYq4Js3mz/ftH3e6MatvGrL4K1bdmtb92c0DP1PhqPrftcK18LLV+67xuwBULTKDsyGiQt7sUEjWGnP7TaAQCWEqu2h6fVMF/Q70b5G9xhqnrbnbgYn/PYA/zJFRGKX42Pebrzxxn0Ob58xY8Ye5yZMmMCXX37ZxlWJiEhHlpMcx6nDunLqsK7UBCMs3FTK3PXFJMe5G3rRo1GLa5+dR3aSj7EF6YwpSKNnZkL7GhbviYeCw+2jqZoSKF5nh/TidbDweXsbp26H2aE9/7DmoWjtTDwz/0RutBCwcL11HRzI/OL2KBqFUJXdcxwO1PUehxqDb3JXSKpbU6dqF6ybWdfLHNyzbY8m35PSjTDzD42huFmgDsHwC2DM9+y25Vtg2uX7rnHoeTCx7neqUC3MeXzfbb0JjSHdMKF47b7bRhq3ssXts7cDc/vsudv1h9trDyk33fb2Xm4fWBDBBVi4/JkY7roVy2tKYPKdMOC05p9n8h2IiEgMhHQREREnxXtdTOidwYTeGc3ObyiupqQqSElVkBWFFTz75QYyEr0cVpDO2J7pDOuWgs/dznrZ68WnweX/gU1zYdOX9tD4QAWs+dg++h4Px/zcbrt2Brx967fPLz4UQT0abT6suunHybl2cASoKIRtX+/Wtknw7XOc3dsLsP0bmPfPvfQ01w3hHnsd9K9b7XvrAnhn6l5LA2DCDTDsfPvjysJvH7qdkNUY0qNhKFyy77bhJgG5IRD77OHnrvo/684lNtnW1uu3e8CbPt80UKf1bGzrSYBTHmjSti5w13/cdA9u0wVXvLnvercugGVv2XW7fERwYWFhxqWCy2WP7nDHQ1qB1kcQEdkH/XQUERHZi56ZCfzjqsP4an0Jc9cXs3BTKbsqg7y/pJD3lxRy7uhuXDGxwOky919cCvQ9zj6iUdjxTeNc9h4T7DbRKHxyH5RtxvKl1o1WNuwwl5AJldvh0z/bi4CZJpRvtXtkmw6rbgjJAXs4c3JX+703z4Plb+0ZuOtfd+SPoX4LqxXvw4zf7ftrOfaX9vxmgJ3L4ZNvGQqd3qsxpAcq7FXy96V+SztonJNtGI3huGnw9SY0to1Ps29c1AfdhrZ1wTp3WGPbxC5w/K/3DNz17+tLamybnAtXT993vU15ExqHqH8X04Ruo1vW9rt0GQ6Zfe2bOAk5zZ+zLLsXPWew3U5ERPZKIV1ERGQfMhN9nDikCycO6UIgHGHJljLmrCthzrpdjCloHA7+1fpi/vXFBg7rmc64nun0yUpsX4vPmSZ0GWIfY6+xwxRA4SIoWgmGgRGswE0YAKNknT232IraobhwEXQdCes/hy/+vu/PkzO0MaRXbIM1n+y7baCi8WPXbtvWmO4mPb5e+3E9f6Y9bL/p802Db3qTHuSM3nD0HXsG7vr2CZlNah8CV39of67vmvKQ3BWm3Pvtbep5/dDzqJa1bQ9M054G8fYtGFWFGESxAMI1ECi3bzoccavWMxAR+RYK6SIiIi3gc7sY3SOd0T3S+cGkXs2em7O+mHVFVawrqmLa3E2k+j2M6ZHOYQVpjOyeRry3nQ2Lrw+h1bvA9EBKPlagCqtyOwZWfSMw3PbQ8Opd9qnELDvMNhs2XTd02u21V/6ulzMYDr+p+bDqpm1Tuze2LTjSHmJd//y3BbwuQ+CUP7Xs60zIbBzO/l1ME1CwbJFek+DUh4jO/BNm1Sx7dfdQtf0970zrGIiI7CeFdBERkVbaffG4S8b2YECXJGavK2bBhlJKq0N8uGw7Hy7bjss0ePzy0WQnxe3j3WKYP8MOxoYLErIIVRZjYeFN743L5bbnFwer7HYAvY62j5ZI79m8V/vbuOtCvLQfvSYRyhnJjj9PwIiGSD/tMRL6aEcAEZGWUEgXERE5QCl+D8cMyOGYATmEIlGWbi3nq/XFzF5XjGVBVqKvoe0/Pl8HwGEF6QzMTcYVy8Pi9zm/2ND8YvluhknAiCOCh9ScoQroIiItpJAuIiJyEHlcJiPyUxmRn8r3j+hJRSDc0PMejkR5b0khNcEIr83fQqLPzZiCNA4rSGdk91SS4jzf8e6HmOYXi4hILPPEE/n+R8x8911O9sQ7Xc1Bo/9VRTqC+h9QA+6190UWkZhgGAbJTYK3Bdw4uQ+T+2eR6HNTGQgzY8VO7v/vCi59cjaPzFjjXLH7Uj+/OHMgJhZuoo3zi099UPOLReTg0e8zIoB60kVERA4Zj8vkqH5ZHNUvi0jUYnlhOXPXFTN3fQkbi6tJ8zcG+qpAmBfmbGRMQTqDuybjcTl4X13zi0VERA4ZhXQREREHuEyDwV1TGNw1hSsP78n28lq8TYL4go2lvLFwK28s3Eq8x8XIHqmMLUhndI80Uv0OLKKm+cUiIiKHhEK6iIhIDMhJjtvtsY/jBubw1YZiSqtDzFq9i1mrd2EY0C8niR9M6kWf7CSHqhUREZG2opAuIiISg/rmJHFzThLRqMWanZXMXlfM3PXFrN1ZxYrCCpLjG4fGL9lSRm0owtBuKfjc7WxPdhGROrWhCOc+8iVlZS6OmRLB44mxxTRFDhGFdBERkRhmmgZ9c5Lom5PEpeN7UFQZ4Jut5c32XX9l3mbmbSjB67ZXlj+sIJ0xBWlkNtn6TURERNoHhXQREZF2JDPRx1H9spqd65YWz4ZdVRRVBpmzrpg564oB6J2VwPheGVw4trsTpYqIiMh+UEgXERFp564+shffP6In64qq+Gp9CXPWF7NyewVrdlbh97m5sEnbRZtK6ZeTRLxXw+JFRERikUK6iEhn5Inn1pSHKCsr413tRdshGIZBr6xEemUlcv5h+ZRWB5m3oYSkJvu0l1YH+cUbS3CZBkPzUjisIJ2xPdP3WLRub6KWxcpILiWROEq3VTI2KRXTNNrySxIREemUFNJFREQ6oFS/l2MH5jQ7t6MiQE5yHIVltSzYWMqCjaU8/r+1dE/3c1hBGscMyKF7hn+P95q1uoi/f7yS1YGLCFkufK8tp2/OVq6f1JuJfTIP1ZckIiLSTEddbFAhXUREpJPol5PE45eNZktpDXPqVov/Zms5G4ur2VhcTX66vyGkVwbCWJbF4s1l3Pn6YipqQyQQIIkIliebZdsquPP1xdx31lAFdRERkYNIIV1ERKQTMQyDbml+uqX5OXtUNypqQyzYWMqcdcWM7pHW0G76N4X847N17KgIUBUI0yXZBzVhADweE7/PQ2F5gEdmrmF8rwwNfRcRETlIFNJFREQ6saQ4D0f1y9pjxfgNu6qpDkbZVRXENAw2ldbiiqYTRxBfTZj0RBepfg9rdlSyYFMJo7qnYRgK6iIiIgdKIV1ERET2cMtx/SjISOAXbyzBYxrUhCKEcVFJPDXVITIS4/C5TMqiFo/OWMuuqgDpCV7S/F77zwT7z3S/l2MHZivAd0ZaoFJEZL8opIuIiMhe9c5KJMnnJsHnxm1C+fYdBC03rrhEAAKRKB7TIBCOEIpYbC8PsL080Ow9/F4Xxw1qXMDuN29/w9qiqrow7yEtwUtGk3A/uod65EVEpHNTSBcREZG9Gtw1md7ZiSzbVkF2ohu/EcBPAE+CB8uyKK0OMTA3iccvG0NZbYjiqiAlVUGKq+0/d1UFce0WuHdUBNhZd+zO73Xx0nUTGh4/8MEKNpfUNPTKNwR7v5eMRC99spPa/O9ARETkUFNIFxERkb0yTYPrJ/XmztcXs6MiiN9y47Ei1IailAfCJPpcXD+pN3FeF3FeV4v2W//laYPYVRm0A321HeRLquzHXrfZrO26XdWsL6ra6/sk+Fy8eG1joH905hoKy2rtIfZNQn1Gov1xZqLvwP4yREREDhGFdBEREdmniX0yue+sofY+6et8VODCF4owMDd5v/ZJz0z0tTgw33pcX3ZWBCipDlJcFaK4KkBxVYiS6iBxHleztku3lu8z0Cf63Lxw7fiGx89+sZ7iqlDDcPt0v5f0RPvPVL93j5sFIiIih5JCuoiIiHyriX0yGZHr45P776MkEkefsx9kbL9ubb7tWq+sRHplJbao7feP6Mn28tqGYfb1w+6Lq4Ikx3matf1yXTEbd1Xv9X2S4tw8f01joH913mYqA+HGMF/fS5/gwed27fU9REREDoRCuoiIiHwn0zDo59pGhAh5uYkxty/6iPzUfT5nWVazxxeMyaewrLbZ3Pn6UJ/m9zZr+/HyHWws3nug75ISxxOXj2l4/P6SbQTC0eYr3Pu9xHsV5kVEpOUU0kVERKRD2321+N33hK9nWRa1oWizcycO6cLW0pqGQF8/7D4UsfYYFv/Wom17DfTxHhc9Mvzcf97whnOfrtpJJGo1mzsf73FpZXsREVFIFxEREQE7zO/e633a8K57tLMsi6pghOpguNn5Cb0z6JHhtxfEq7QXxqsNRakJRagJRZq1fXHOpj0Cvc9tkp7gpXu6n7tOHdRwfsHGEgzDIN1vD7NP9LkV5kVEOjCFdBEREZFWMAyDRJ+bRF/zX6MuHd9jj7Y1wQjF1UGC4eY99IO6JpPi91BcaQ+zrwlGCISjbCurxbfbonhPfLqWTcU1DY89LqOhB757up8fHdu34blV2yvwuEzSErwkxzkb5qNRi5pghJqwvbDf2F6+mJsmISISixTSRURERNpIvNdFnjd+j/M3TO7T7HFtKNLQA7+7bml+AIqrglQFIoQiFtvLA2wvDxDYLfw/MH0lm0vsQO92GaT5G/eXz0/3c/mEgoa228pqiPe4SI7zHPTwPGt1EX//ZDUbS6qJROGWaV/TNydpv3YEEBHpbBTSRURERBwW53GRmxJPbsqegf7Okwc2fBwIRyitDjUMp3ftFq6T4twkxbmpqA0TjljsrAiwsyIAwPbyQLOQfs+b37CltAbTNEjze+qG09uL3nVLi+eMEXkNbStqQyR43S0K87NWF3Hn64upqA1jGGACfq+LZdsquPP1xdx31lAFdRGRb6GQLiIiItJO+NwucpJd5CTH7fX5P55rL04XDEcprbG3oCuuClJSFSLe23yhOwsLw7CHpe+qbN6L3ysroVlIv+3lRRSW1ZLi95Lut/eXz6hbwb5rSjyTB2QD9nv934w1VAbC5CT7qCoKYxn2TQi/101heYBHZq5hfK8MDX0XEdkHhXQRERGRDsbrNslOiiM7ae9hHuCxy8YQjkQprQnVrVxv987v2sve8mU1IaIWlNRtV8fOqobnemUlNIT0pVvL+WpDMQYGWyK1hCIWlgVFlXavv8uENTsqWbq1nKHdUpizrphI1MLnMfG5TXxuF3Gexj+TdqtDRKQzUEgXERER6aTcLpPMRB+Zib5vbff81eMprQk19MzXB/riqiAZCY17yxdXB4lELVyGPTQ/allgQXltCAMDr8vA63FRXG332j82cw076obj765bWjyPXDq64fEdr33NjvJAXaB31YV6E5/HRUaCl+sm9W5o++E326kMhPG5TeI8dW0bwr+LPtmJDW2D4Shu01DPvojEDIV0ERER+W6eeG5NeYiysjLe9ew5b1o6NtO0V5RPbxLI9ybd7yUjwYfPbWCaJltKqolELdL89uuiUQuXaW8nB9AnO5H0BC+BcJRA2F7hPhCKUhuO4NttH/qdFcF9Bvquqc1HDLyxaCvri6r22jbV7+HZ749rePyL/yzhm23leFyGHf6b9Oonx7v5zZlDG9934RZ7BX53kxsFTXr+j+yb1dB2R0Ut4YjVcCPB5zZxm4a2z/sW2hFAWqujXjMK6SIiIiJyUAzumkzfnESWbasgO8leaM6yIM3vwTQMCssD9MtOZHDXZADuaLIo3u6iUavZ41+dPojqYIRAqEmgD9uPfZ7mgX5sQRoFGX4C4Si1oeav2X0ofyBs72EfiliEImEqm9wHSIlv3vbLtbtYsqV8r/V63c1D+qMz1jJ3fXGzNqZBw42AZ64a2xAmXpyzkeWFFXuMEqgfBXDGiDy8dTct1u6spKwmtMcNhfqPvS6zXd4I0I4A0lod+ZpRSBcRERGRg8I0Da6f1Js7X1/MjooAUcuek14bilBeGyHR5+L6Sb1b1NO1e5v6reha4rImq9h/l9+fM6xZ4G/aq281v0/AcQNzGNQ1hUCo/iZB4+t2X2nf4zKI97jqhv3b56IW1IQihKLRZl/fqh2VzNtQss8azxzZuIjf6wu2MGPFzn22ff6acQ1z+Z/9Yj2z1uzabdh/402AKyYWNLRdsqWMTcXVu90oaFwjIDc1Do/LvlFgWdZBvRGgHQGktTr6NaOQLiIiIiIHzcQ+mdx31lD+/slq5q4vJgpUByMMzI3NHq44jz1PHb57kbpjB+a0+H3rRwlYlkU4atUN5bfDfSjSfH/7M0fkMb5Xxl5vFARC9pz5epmJPnpmJjR7PhCOEIrYdwJ8bldD252VQTaX1OyzxkvH92j4+LPVRbzz9bZ9tn3i8jF0SbGnFTwzaz1vLtq6Z29+3fD/Hx3Tt2EHgnkbSvh6c+ke0wPqbxQMzE3mkZn2jgBZSV6qAvaOAB6XSVaiix0VAf768SoG5CaRHOfBXXejoDYUoSZoj4Kov5diNbmrkhTnaRh9UBOMUBkI17Wta9PkBkxyvKfuGrDblteG6t6P5q8BUuO9xHsb29avr1D/uZve2ElL8JLocze03bnbdI2m75uW4G0Y5VEbirC1tGa3to3qt0usb7u5pHqPz930fevXnAiEI6yrmwayt7bpCd6G71swHGXVjopvfd+8VHvqUzgS5Ztt5Xu0rf8w3e+le4Z9ky0atVi0uXSPr6n+dal+D72zGteMmLehmLqlLRpEoha/f285pTUhuqbEdchdJBTSRUREROSgmtgnkxH5qZz2t88oq6jkofOHMbZXVrv9hflAGIaBx2XgcZkNgW13Q7ulMJSUFr3fFRMLuGJiwR7no3U3Ajyuxr/jCw/L5/hBOQTCEWpDe44W8Hsb6ynI8DOhd8YeowTqX9d0jQD7RsOe0wPqhZtMVfhmaxmvzd+yz6/n+qN7s2ZHJWl+LxW1YQKRKFiwobgaA4OoZTFvQwnnP/olf7t4JANz7akS7y8p5KnP1u3zfX995hBG5KcCMHPlDh7+ZM0+2951ykDG9coA7CkND0xfuc+2t53Qn0n97GkNCzaW8Lv3lu+z7Y3H9OGEwV0A+GZbOb96c+k+215zVC9OH94VgDU7K7n91cX7bHvZhB6cPyYfgM0lNdz60qJ9tj1/TLeGkSU7KwL85OWv99n2jBFdufrIXoC92OO31XDikC7cMLkPYI8O+fnrS/bZ9uj+Wfz4+P6AfW388o19/z1M6J3BnU2mwdz71jfsNvOFmmCEDcVV+L2uPUZ0GIZBqt/TbBeJ9kghXUREREQOOtM0iPe6CLrtueqdMaAfSvV/3011TY2na2rLFno8cUguJw7JbVHbyycUcNaovL32+gfCkYaFAQEGdU3mzJF5+7xREKwL/F6Xyd6W+jOwe1nD0eajDwzDnuO/5wv2PGkYBu66mxe7P2sYzRfzc5kGcU3WODB2e4Vrt7b+ur/z+tNN2ze9YeJxGSTHN0av+nb1r/O6zCZtTVL9nob6mtZtGBDvcTVpa5CZ2Pj3vXv7hCY3htymSU5y050cmtfQdL0Gl2E0W5Cx6ddlGDTUB2AaBt3T9zEdxaDZDhCGAT0zE/be1IDspOY7TfTOTmzoZa+vYHt5LZtKqolzN7/e6/lcJmVRq2GUQ3ukkC4iIiIiIi0W73XtcUNgX0b3SGd0j/R9Pr94cxkel0EwEiXN76W4KkgkEqFXZgIu06AmFKU6EOZvF41q6EUHOGNEHmeMyNvn+zZ1wuAuDT3a3+Woflkc1S/ruxsC43pl8NJ1E1rUdli3VP599fgWte2Xk9RsB4Jv0yMjgX9cNbZFbbukxPHkFYe1qG1agpfHLhvTorYJPjcPXzKqRW09LpO/XjSyRW0BHjh/xB7nFm8u47pnv2p2A6KpQCSKp8kuEu2R+d1NREREREREDr7BXZPpnZ1ISXWo2ZxysHvQS6tD9G6yI4DIt18zVoe4ZhTSRURERETEEfU7AiT6XHvsCFBYHmjVjgDSOXSGa0YhXUREREREHFO/I0C/nCQsi2Y7ArT3rbSkbXT0a0Zz0kVERERExFHaEUBaqyNfMwrpIiIiIiLiOO0IIK3VUa8ZDXcXERERERERiREK6SIiIiIiIiIxQiFdRKQTivO4eP368dw2LEKcp2V73YqIiIhI21NIFxEREREREYkRCukiIiIiIiIiMUIhXURERERERCRGKKSLiIiIiIiIxAiFdBEREREREZEYoZAuIiIiIiIiEiMU0kVERERERERihEK6iIiIiIiISIxQSBcRERERERGJEQrpIiIiIiIiIjFCIV1EREREREQkRiiki4iIiIiIiMQIhXQRERERERGRGKGQLiIiIiIiIu1OnMfF69eP57ZhEeI8LqfLOWgU0kVERERERERihEK6iIiIiIiISIxQSBcRERERERGJEQrpIiIiIiIiIjFCIV1EREREREQkRiiki4iIiIiI4zrqSt0iraWQLiIiIiIiIhIjFNJFREREREREYoRCuoiIiIiIiEiMUEgXERERERERiREK6SIiIiJy0GkRMBGR/aOQLiIiIiIiIhIjFNJFREREREREYoRCuoiIiHwnDV0WERE5NBTSRURERERERGKEQrqIiIiIiIhIjFBIFxEREREREYkRCukiIiIiIiIiMUIhXURERERERCRGKKSLiIiIiIiIxAiFdBEREREREZEYoZAuIiIiIiIiEiMU0kVERERERERihEK6iIiIiIiISIxQSBcRERERERGJEQrpIiIiIiIiIjFCIV1EREREREQkRiiki4iIiIiIiMQIhXQRERERERGRGKGQLiIiIiIiIhIjFNJFREREREREYoRCuoiIiIiIiEiMUEgXERERERERiREK6SIiIiIiIiIxQiFdREREREREJEYopIuIiIiIiIjEiJgI6Q8//DAFBQXEK9z8LwAAHRpJREFUxcUxbtw45syZs8+2//znPzEMo9kRFxd3CKsVERERERERaRuOh/SXXnqJqVOncvfddzN//nyGDx/OCSecwI4dO/b5muTkZLZt29ZwbNiw4RBWLCIiIiIiItI2HA/pDzzwANdccw1XXXUVgwYN4tFHH8Xv9/P000/v8zWGYdClS5eGIycn5xBWLCIiIiIiItI23E5+8mAwyLx587jjjjsazpmmyXHHHccXX3yxz9dVVlbSo0cPotEoo0aN4r777mPw4MF7bRsIBAgEAg2Py8vLAQiFQoRCoYP0lbSN+vpivU6JDbpepLV0zfx/e3ceVVXV/gH8i8yzDDKaaCKCJUhgCKjgEJCoWJnkSIWaviqkhab+CE1zNmnUSMLX2QYtxdIcQCyUVHKpvIqCBAYqjiCQitzn94fLk1dUoJR7te9nLdbi7r3PPs++51lcnnvOuZcaijlDDcWcoYZizlBDPSo505D4dEREHmIs91VSUgJnZ2dkZmbC399faZ84cSJ27dqFrKysWtvs2bMHJ06cgKenJ8rKyrBgwQJkZGQgJycHzZs3rzV+2rRpmD59eq32pUuXwsTE5MEuiIiIiIiIiOgOVVVVGD58OC5fvgxLS8v7jn3kivQ7VVdXw8PDAwMHDsSMGTNq9d95Jr24uBjt2rV7MAsgIiIiIiIiqqdTp07d9eTy7TR6ubutrS10dXVx9uxZtfazZ8/CwcGhXnPo6+vD29sbeXl5d+03NDSEoaGh8tjMzAynTp2Cubk5dHR0/n7wjaC8vBxPPPEETp06BQsLC02HQ1qO+UINxZyhhmLOUEMxZ6ihmDPUUI9KzogIrly5AicnpzrHarRINzAwgI+PD3bs2IF+/foBAFQqFXbs2IGxY8fWa46amhocPnwYvXr1qtf4Jk2a1PnOhbaxsLDQ6oQj7cJ8oYZizlBDMWeooZgz1FDMGWqoRyFn6rrM/RaNFukAMGHCBERFRcHX1xfPPvssEhMTUVlZiddeew0AMGzYMDg7O2P27NkAgPfeew+dOnWCq6srLl++jPnz56OwsBDDhw/X5DKIiIiIiIiI/jGNF+mRkZE4d+4c3n33XZw5cwYdOnTAli1blK9VKyoqQpMmf31T3KVLlzBixAicOXMGVlZW8PHxQWZmJu8zJyIiIiIiokeexot0ABg7duw9L29PT09Xe7xo0SIsWrSoEaLSPENDQyQkJKjdU090L8wXaijmDDUUc4YaijlDDcWcoYZ6HHNGo5/uTkRERERERER/aVL3ECIiIiIiIiJqDCzSiYiIiIiIiLQEi3QiIiIiIiIiLcEinYiIiIiIiEhLsEjXsIyMDPTp0wdOTk7Q0dHBd999V2vM0aNH0bdvX1haWsLU1BQdO3ZEUVFR4wdLWmHx4sXw9PSEhYUFLCws4O/vjx9//BEAcPHiRYwbNw5t27aFsbExWrRogZiYGJSVlWk4atK04uJiDBkyBDY2NjA2Nkb79u2xf//+u44dNWoUdHR0kJiY2LhBksbc77WouroakyZNQvv27WFqagonJycMGzYMJSUlanMcP34cERERsLW1hYWFBTp37oy0tLRGXgk1htmzZ6Njx44wNzeHnZ0d+vXrh9zcXLUxwcHB0NHRUfsZNWpUrbmWLVsGT09PGBkZwc7ODmPGjGmsZVAjmjZtWq18cHd3V/qTkpIQHBwMCwsL6Ojo4PLly2rb//7774iOjkarVq1gbGyM1q1bIyEhAdevX2/kldDDUldNJCJ499134ejoCGNjY/Ts2RMnTpxQ+huaI3l5eTA3N0fTpk0f4qr+PhbpGlZZWQkvLy98+umnd+3Pz89H586d4e7ujvT0dBw6dAjx8fEwMjJq5EhJWzRv3hxz5szBgQMHsH//fnTv3h0RERHIyclBSUkJSkpKsGDBAhw5cgTLli3Dli1bEB0dremwSYMuXbqEwMBA6Ovr48cff8T//vc/LFy4EFZWVrXGbtiwAXv37oWTk5MGIiVNud9rUVVVFbKzsxEfH4/s7GysX78eubm56Nu3r9q43r1748aNG9i5cycOHDgALy8v9O7dG2fOnGmsZVAj2bVrF8aMGYO9e/di27ZtqK6uRkhICCorK9XGjRgxAqdPn1Z+5s2bp9b/wQcfYOrUqXjnnXeQk5OD7du3IzQ0tDGXQo3oqaeeUsuHn3/+WemrqqpCWFgYpkyZctdtjx07BpVKhc8//xw5OTlYtGgRlixZcs/x9OipqyaaN28ePvroIyxZsgRZWVkwNTVFaGgorl69CqBhOVJdXY2BAweiS5cuD3VN/4iQ1gAgGzZsUGuLjIyUIUOGaCYgemRYWVnJ0qVL79r31VdfiYGBgVRXVzdyVKQtJk2aJJ07d65z3B9//CHOzs5y5MgRcXFxkUWLFj384Ejr3O216E6//vqrAJDCwkIRETl37pwAkIyMDGVMeXm5AJBt27Y9zHBJC5SWlgoA2bVrl9IWFBQksbGx99zm4sWLYmxsLNu3b2+ECEnTEhISxMvLq85xaWlpAkAuXbpU59h58+ZJq1at/nlwpHXufB1SqVTi4OAg8+fPV9ouX74shoaGsmbNmnvOc68cmThxogwZMkRSUlLE0tLyQYb+wPBMuhZTqVTYvHkz3NzcEBoaCjs7O/j5+d31knj6d6qpqcHatWtRWVkJf3//u44pKyuDhYUF9PT0Gjk60hYbN26Er68vXn75ZdjZ2cHb2xtffPGF2hiVSoWhQ4ciLi4OTz31lIYipUdFWVkZdHR0lMsEbWxs0LZtWyxfvhyVlZW4ceMGPv/8c9jZ2cHHx0ezwdJDd+uWKmtra7X2VatWwdbWFk8//TQmT56MqqoqpW/btm1QqVQoLi6Gh4cHmjdvjgEDBuDUqVONGjs1nhMnTsDJyQlPPvkkBg8e/I9v3SwrK6uVc/R4KigowJkzZ9CzZ0+lzdLSEn5+ftizZ889t7tbjuzcuRNff/31Pc/YawsW6VqstLQUFRUVmDNnDsLCwvDTTz/hhRdewIsvvohdu3ZpOjzSoMOHD8PMzAyGhoYYNWoUNmzYgHbt2tUad/78ecyYMQMjR47UQJSkLU6ePInFixejTZs22Lp1K0aPHo2YmBj897//VcbMnTsXenp6iImJ0WCk9Ci4evUqJk2ahIEDB8LCwgIAoKOjg+3bt+O3336Dubk5jIyM8MEHH2DLli13va2CHh8qlQpvvvkmAgMD8fTTTyvtgwYNwsqVK5GWlobJkydjxYoVGDJkiNJ/8uRJqFQqzJo1C4mJifjmm29w8eJFPPfcc7zP+DHk5+en3IK3ePFiFBQUoEuXLrhy5crfmi8vLw8ff/wx3njjjQccKWmjW7dN2dvbq7Xb29vf85aqu+XIhQsX8Oqrr2LZsmXK65e24qk1LaZSqQAAERERGD9+PACgQ4cOyMzMxJIlSxAUFKTJ8EiD2rZti4MHD6KsrAzffPMNoqKisGvXLrVCvby8HOHh4WjXrh2mTZumuWBJ41QqFXx9fTFr1iwAgLe3N44cOYIlS5YgKioKBw4cwIcffojs7Gzo6OhoOFrSZtXV1RgwYABEBIsXL1baRQRjxoyBnZ0ddu/eDWNjYyxduhR9+vTBvn374OjoqMGo6WEaM2YMjhw5onZ/MQC1N4fbt28PR0dH9OjRA/n5+WjdujVUKhWqq6vx0UcfISQkBACwZs0aODg4IC0tjfemP2aef/555XdPT0/4+fnBxcUFX331VYM/N6e4uBhhYWF4+eWXMWLEiAcdKj0G7pUjI0aMwKBBg9C1a1cNRlc/PJOuxWxtbaGnp1frDKmHhwc/3f1fzsDAAK6urvDx8cHs2bPh5eWFDz/8UOm/cuUKwsLCYG5ujg0bNkBfX1+D0ZKmOTo63vfvyO7du1FaWooWLVpAT08Penp6KCwsxFtvvYWWLVtqIGLSRrcK9MLCQmzbtk3tLMTOnTuRmpqKtWvXIjAwEM888ww+++wzGBsbq12xQY+XsWPHIjU1FWlpaWjevPl9x/r5+QG4eXYLgPLGze1/m5o1awZbW1v+j/Mv0LRpU7i5uSn5UF8lJSXo1q0bAgICkJSU9JCiI23j4OAAADh79qxa+9mzZ5W+W+6XIzt37sSCBQuU/3Wio6NRVlYGPT09fPnllw93EQ3EM+lazMDAAB07dqz1tSbHjx+Hi4uLhqIibaRSqXDt2jUAN8+gh4aGwtDQEBs3buQ3ARACAwPv+3dk6NChavd5AUBoaCiGDh2K1157rdHiJO11q0A/ceIE0tLSYGNjo9Z/617jJk3U3/tv0qSJclUYPT5EBOPGjcOGDRuQnp6OVq1a1bnNwYMHAfxVnAcGBgIAcnNzlQL/4sWLOH/+PP/H+ReoqKhAfn4+hg4dWu9tiouL0a1bN/j4+CAlJaXW3xt6fLVq1QoODg7YsWMHOnToAODm/7tZWVkYPXq0Mq6uHNmzZw9qamqUx99//z3mzp2LzMxMODs7N8pa6otFuoZVVFSovYtYUFCAgwcPwtraGi1atEBcXBwiIyPRtWtXdOvWDVu2bMGmTZuQnp6uuaBJoyZPnoznn38eLVq0wJUrV7B69Wqkp6dj69atKC8vR0hICKqqqrBy5UqUl5ejvLwcwM0zFLq6uhqOnjRh/PjxCAgIwKxZszBgwAD8+uuvSEpKUt5htrGxqVV06evrw8HBAW3bttVEyNTI7vda5OjoiP79+yM7OxupqamoqalR7gG0traGgYEB/P39YWVlhaioKLz77rswNjbGF198gYKCAoSHh2tqWfSQjBkzBqtXr8b3338Pc3NzJR8sLS1hbGyM/Px8rF69Gr169YKNjQ0OHTqE8ePHo2vXrvD09AQAuLm5ISIiArGxsUhKSoKFhQUmT54Md3d3dOvWTZPLo4fg7bffRp8+feDi4oKSkhIkJCRAV1cXAwcOBHDznuMzZ84of4cOHz4Mc3NztGjRAtbW1iguLkZwcDBcXFywYMECnDt3Tpn7zjOp9GiqqyZ68803MXPmTLRp0watWrVCfHw8nJyc0K9fPwCoV454eHio7XP//v1o0qSJ2udpaA0Nf7r8v96tr5q48ycqKkoZk5ycLK6urmJkZCReXl7y3XffaS5g0rjXX39dXFxcxMDAQJo1ayY9evSQn376SUTunU8ApKCgQLOBk0Zt2rRJnn76aTE0NBR3d3dJSkq673h+Bdu/y/1eiwoKCu75dyUtLU2ZY9++fRISEiLW1tZibm4unTp1kh9++EFzi6KH5l75kJKSIiIiRUVF0rVrV7G2thZDQ0NxdXWVuLg4KSsrU5unrKxMXn/9dWnatKlYW1vLCy+8IEVFRRpYET1skZGR4ujoKAYGBuLs7CyRkZGSl5en9CckJNw3p1JSUu6Zd/R4qKsmUqlUEh8fL/b29mJoaCg9evSQ3NxcZfu/kyPa/BVsOiIiD7rwJyIiIiIiIqKG480cRERERERERFqCRToRERERERGRlmCRTkRERERERKQlWKQTERERERERaQkW6URERERERERagkU6ERERERERkZZgkU5ERERERESkJVikExEREREREWkJFulERET3MW3aNHTo0OG+Y4KDg/Hmm28+8HmJiIjo34dFOhERabU9e/ZAV1cX4eHhmg7lkdGyZUvo6OhAR0cHpqameOaZZ/D111+rjSkvL8fUqVPh7u4OIyMjODg4oGfPnli/fj1ERG3smjVroKurizFjxtR7/4mJicpjEcHbb78NCwsLpKen/9PlPRaWLVuGpk2bajoMIiLSQizSiYhIqyUnJ2PcuHHIyMhASUmJpsPRKtXV1ffse++993D69Gn89ttv6NixIyIjI5GZmQkAuHz5MgICArB8+XJMnjwZ2dnZyMjIQGRkJCZOnIiysjK1uZKTkzFx4kSsWbMGV69ebVCMNTU1iI6OxvLly5GWlobg4OAGr5OIiOjfhEU6ERFprYqKCqxbtw6jR49GeHg4li1bptafnp4OHR0d7NixA76+vjAxMUFAQAByc3OVMbefVb7955ZJkybBzc0NJiYmePLJJxEfH3/X4nfFihVo2bIlLC0t8corr+DKlSsNWsucOXNgb28Pc3NzREdH37XYXbp0KTw8PGBkZAR3d3d89tlnSt/vv/8OHR0drFu3DkFBQTAyMsKqVavuuT9zc3M4ODjAzc0Nn376KYyNjbFp0yYAwJQpU/D7778jKysLUVFRaNeuHdzc3DBixAgcPHgQZmZmyjwFBQXIzMzEO++8Azc3N6xfv77ea7527RpefvllbN++Hbt374aPj4/SHhMTAzs7OxgZGaFz587Yt2+fst2t47p161Z4e3vD2NgY3bt3R2lpKX788Ud4eHjAwsICgwYNQlVVlbJdcHAwxo4di7Fjx8LS0hK2traIj49XuzLg0qVLGDZsGKysrGBiYoLnn38eJ06cUPpvneHeunUrPDw8YGZmhrCwMJw+fbrBx2r9+vXo1q0bTExM4OXlhT179ijre+2111BWVqbk47Rp05Tn5u2334azszNMTU3h5+endvVBfeMjIqJHmBAREWmp5ORk8fX1FRGRTZs2SevWrUWlUin9aWlpAkD8/PwkPT1dcnJypEuXLhIQEKCMKS0tldOnT8vp06fljz/+kE6dOkmXLl2U/hkzZsgvv/wiBQUFsnHjRrG3t5e5c+cq/QkJCWJmZiYvvviiHD58WDIyMsTBwUGmTJmijAkKCpLY2Nh7rmPdunViaGgoS5culWPHjsnUqVPF3NxcvLy8lDErV64UR0dH+fbbb+XkyZPy7bffirW1tSxbtkxERAoKCgSAtGzZUhlTUlJy1/25uLjIokWL1NosLS1lwoQJUlNTI1ZWVjJy5Mh7P/G3iY+Pl/79+4uIyMcffyzdu3evcxsXFxeZMWOG9OjRQ9q2bStFRUVq/TExMeLk5CQ//PCD5OTkSFRUlFhZWcmFCxdE5K/j2qlTJ/n5558lOztbXF1dJSgoSEJCQiQ7O1syMjLExsZG5syZo8wbFBQkZmZmEhsbK8eOHZOVK1eKiYmJJCUlKWP69u0rHh4ekpGRIQcPHpTQ0FBxdXWV69evi4hISkqK6OvrS8+ePWXfvn1y4MAB8fDwkEGDBilz1PdYubu7S2pqquTm5kr//v3FxcVFqqur5dq1a5KYmCgWFhZKbl65ckVERIYPHy4BAQGSkZEheXl5Mn/+fDE0NJTjx4/XOz4iInq0sUgnIiKtFRAQIImJiSIiUl1dLba2tpKWlqb03yrmtm/frrRt3rxZAMiff/5Za76YmBhxcXGR0tLSe+5z/vz54uPjozxOSEgQExMTKS8vV9ri4uLEz89PeVxXke7v7y//+c9/1Nr8/PzUivTWrVvL6tWr1cbMmDFD/P39ReSvwu/W83E/txfp165dk1mzZgkASU1NlbNnzwoA+eCDD+qcp6amRp544gn57rvvRETk3LlzYmBgICdPnqxz/wYGBmJjY1Prua6oqBB9fX1ZtWqV0nb9+nVxcnKSefPmicjdj+vs2bMFgOTn5yttb7zxhoSGhiqPg4KCxMPDQ+2NnEmTJomHh4eIiBw/flwAyC+//KL0nz9/XoyNjeWrr74SkZtFMADJy8tTxnz66adib2+vPK7vsVq6dKnSn5OTIwDk6NGjyn4sLS3V5igsLBRdXV0pLi5Wa+/Ro4dMnjy53vEREdGjjZe7ExGRVsrNzcWvv/6KgQMHAgD09PQQGRmJ5OTkWmM9PT2V3x0dHQEApaWlamOSkpKQnJyMjRs3olmzZkr7unXrEBgYCAcHB5iZmeH//u//UFRUpLZty5YtYW5urraPO+e/n6NHj8LPz0+tzd/fX/m9srIS+fn5iI6OhpmZmfIzc+ZM5Ofnq23n6+tbr31OmjQJZmZmMDExwdy5czFnzhyEh4fX+lC4+9m2bRsqKyvRq1cvAICtrS2ee+45fPnll3VuGxISgsrKSsyaNUutPT8/H9XV1QgMDFTa9PX18eyzz+Lo0aNqY28/rvb29sotCbe33XkcOnXqpHY7g7+/P06cOIGamhocPXoUenp6asfCxsYGbdu2Vdu3iYkJWrdurTy+/Xg35FjVJy9vd/jwYdTU1MDNzU1t7l27dqnNfb/4iIjo0aen6QCIiIjuJjk5GTdu3ICTk5PSJiIwNDTEJ598AktLS6VdX19f+f1WgaZSqZS2tLQ0jBs3DmvWrFErnPbs2YPBgwdj+vTpCA0NhaWlJdauXYuFCxeqxXL7/Lf2cfv8/1RFRQUA4IsvvqhVzOvq6qo9NjU1rdeccXFxePXVV2FmZgZ7e3vleWnWrBmaNm2KY8eO1TlHcnIyLl68CGNjY6VNpVLh0KFDmD59Opo0ufd7/T169MC4ceMQEREBlUqFDz/8sF5x3+7O4/qwj8Pd9ntrP7fe3GjIsaorL+9UUVEBXV1dHDhwoNZct39OwP3iIyKiRx+LdCIi0jo3btzA8uXLsXDhQoSEhKj19evXD2vWrMGoUaPqNVdeXh769++PKVOm4MUXX1Try8zMhIuLC6ZOnaq0FRYW/vMF3MHDwwNZWVkYNmyY0rZ3717ld3t7ezg5OeHkyZMYPHjwA9mnra0tXF1da7U3adIEr7zyClasWIGEhAS1N0GAm4WikZERysrK8P3332Pt2rV46qmnlP6amhp07twZP/30E8LCwu4bQ0hICDZt2oS+fftCRPDRRx+hdevWMDAwwC+//AIXFxcANz+lft++fQ3+rvm7ycrKUnu8d+9etGnTBrq6uvDw8MCNGzeQlZWFgIAAAMCFCxeQm5uLdu3a1Wv+B3WsDAwMUFNTo9bm7e2NmpoalJaWokuXLn97biIierSxSCciIq2TmpqKS5cuITo6Wu2MOQC89NJLSE5OrleR/ueff6JPnz7w9vbGyJEjcebMGaXPwcEBbdq0QVFREdauXYuOHTti8+bN2LBhwwNfT2xsLF599VX4+voiMDAQq1atQk5Ojtql29OnT0dMTAwsLS0RFhaGa9euYf/+/bh06RImTJjwQON5//33kZ6eDj8/P7z//vvw9fWFvr4+du/ejdmzZ2Pfvn1YsWIFbGxsMGDAALXLxwGgV69eSE5OrrNIB4CePXsiNTUVffr0gUqlwieffILRo0cjLi4O1tbWaNGiBebNm4eqqipER0f/47UVFRVhwoQJeOONN5CdnY2PP/5YuTKiTZs2iIiIwIgRI/D555/D3Nwc77zzDpydnREREVHvfTyIY9WyZUtUVFRgx44d8PLygomJCdzc3DB48GAMGzYMCxcuhLe3N86dO4cdO3bA09MT4eHhf+s5ISKiRwuLdCIi0jrJycno2bNnrQIduFmkz5s3D4cOHapznrNnz+LYsWM4duxYrTPGIoK+ffti/PjxGDt2LK5du4bw8HDEx8crX4f1oERGRiI/Px8TJ07E1atX8dJLL2H06NHYunWrMmb48OEwMTHB/PnzERcXB1NTU7Rv3/6BnF2+k7W1Nfbu3Ys5c+Zg5syZKCwshJWVFdq3b4/58+fD0tISX375JV544YVaBTpw8xgMHToU58+fh62tbZ376969OzZv3ozevXtDRLBgwQKoVCoMHToUV65cga+vL7Zu3QorK6t/vLZhw4bhzz//xLPPPgtdXV3ExsZi5MiRSn9KSgpiY2PRu3dvXL9+HV27dsUPP/xQ6xLy+3kQxyogIACjRo1CZGQkLly4gISEBEybNg0pKSmYOXMm3nrrLRQXF8PW1hadOnVC7969G/I0EBHRI0xHeBMTERERPQaCg4PRoUMHJCYmajoUIiKiv42f7k5ERERERESkJVikExEREREREWkJXu5OREREREREpCV4Jp2IiIiIiIhIS7BIJyIiIiIiItISLNKJiIiIiIiItASLdCIiIiIiIiItwSKdiIiIiIiISEuwSCciIiIiIiLSEizSiYiIiIiIiLQEi3QiIiIiIiIiLfH/OsJ1IvyhwGwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Ergebnisse laden\n",
    "base_path = 'Experiments'\n",
    "loaded_results = load_results(base_path)\n",
    "\n",
    "# Ergebnisse verarbeiten und plotten\n",
    "m_a_s = calculate_means_for_n_last(loaded_results, 200)\n",
    "plot_results(m_a_s)\n",
    "plot_results(m_a_s, show_lines=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TODO MNIST datenstaz mit wget ohne tensorflow oder pytorch einlesen"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "rl",
   "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": "3.8.18"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}