diff --git a/BERT.py b/BERT.py
index 0561b75..ebeeeb9 100644
--- a/BERT.py
+++ b/BERT.py
@@ -3,10 +3,12 @@ import random
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from torch.utils.data import DataLoader
+from torch.utils.data import DataLoader, Subset
 from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
 from transformers import BertForSequenceClassification, AutoTokenizer
 import numpy as np
+from datetime import datetime
+import json
 
 import Datasets
 import dataset_helper
@@ -53,20 +55,16 @@ if __name__ == '__main__':
         # Config
         "max_len": 128,
         # Training
-        "epochs": 10,
+        "epochs": 1,
         "patience": 7,
         "batch_size": 32,
-        "learning_rate": 0.001,
+        "learning_rate": 1e-6,
         "weight_decay": 5e-4 ,
         # Model
-        "filter_sizes": [2, 3, 4, 5],
-        "num_filters": 150,
         "dropout": 0.6
     }
 
     # Configs
-    MODEL_NAME = 'BERT.pt'
-    HIST_NAME = 'BERT_history'
     GLOVE_PATH = 'data/glove.6B.100d.txt'
     DATA_PATH = 'data/hack.csv'
     FREEZE_BERT = False
@@ -74,6 +72,11 @@ if __name__ == '__main__':
     TEST_SIZE = 0.1
     VAL_SIZE = 0.1
 
+    N_MODELS = 2
+
+    models = []
+    timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+
     # Daten laden und vorbereiten
     embedding_matrix, word_index, vocab_size, d_model = dataset_helper.get_embedding_matrix(
         gloVe_path=GLOVE_PATH, emb_len=EMBEDDING_DIM)
@@ -96,42 +99,77 @@ if __name__ == '__main__':
     val_loader = DataLoader(val_dataset, batch_size=params["batch_size"], shuffle=False)
     test_loader = DataLoader(test_dataset, batch_size=params["batch_size"], shuffle=False)
 
-    # Modell initialisieren
-    model = CustomBert(dropout=params["dropout"])
-
+    subset_size = len(train_dataset) // N_MODELS
     device = ml_helper.get_device(verbose=True, include_mps=False)
-    model = model.to(device)
 
-    criterion = nn.MSELoss()
-    optimizer = optim.Adam(model.parameters(), lr=params["learning_rate"], weight_decay=params["weight_decay"])
-    early_stopping = EarlyStopping.EarlyStoppingCallback(patience=params["patience"], verbose=True, model_name=MODEL_NAME)
+    for i in range(N_MODELS):
+        model_name = f'BERT.pt'
+        hist_name = f'BERT_history'
 
-    hist = ml_history.History()
+        if N_MODELS > 1:
+            model_name = f'BERT_{i}_ensemble.pt'
+            hist_name = f'BERT_{i}_ensemble_history'
 
-    # Training und Validierung
-    for epoch in range(params["epochs"]):
-        ml_train.train_epoch(model, train_loader, criterion, optimizer, device, hist, epoch, params["epochs"], bert_freeze=FREEZE_BERT, is_bert=True)
+            subset_indices = dataset_helper.ensemble_data_idx(train_dataset.labels, N_MODELS, i, methods='bootstrap')
+            train_dataset_sub = Subset(train_dataset, subset_indices)
+            train_loader = DataLoader(train_dataset_sub, batch_size=params["batch_size"], shuffle=True)
 
-        val_rmse = ml_train.validate_epoch(model, val_loader, epoch, criterion, device, hist, is_bert=True)
 
-        early_stopping(val_rmse, model)
-        if early_stopping.early_stop:
-            print("Early stopping triggered.")
-            break
+        model = CustomBert(dropout=params["dropout"])
+        model = model.to(device)
 
-    # Load best model
-    model.load_state_dict(torch.load('models/checkpoints/' + MODEL_NAME))
+        criterion = nn.MSELoss()
+        optimizer = optim.Adam(model.parameters(), lr=params["learning_rate"], weight_decay=params["weight_decay"])
+        early_stopping = EarlyStopping.EarlyStoppingCallback(patience=params["patience"], verbose=True, model_name=model_name)
 
-    # Test Evaluation
-    test_labels, test_preds = ml_train.test_loop(model, test_loader, device, is_bert=True)
+        hist = ml_history.History()
 
-    hist.add_test_results(test_labels, test_preds)
+        # Training und Validierung
+        for epoch in range(params["epochs"]):
+            ml_train.train_epoch(model, train_loader, criterion, optimizer, device, hist, epoch, params["epochs"], bert_freeze=FREEZE_BERT, is_bert=True)
 
-    # save training history
-    hist.save_history(HIST_NAME)
+            val_rmse = ml_train.validate_epoch(model, val_loader, epoch, criterion, device, hist, is_bert=True)
 
-    # RMSE, MAE und R²-Score für das Test-Set
-    test_mae = mean_absolute_error(test_labels, test_preds)
-    test_rmse = np.sqrt(mean_squared_error(test_labels, test_preds))
-    test_r2 = r2_score(test_labels, test_preds)
-    print(f"Test RMSE: {test_rmse:.4f}, Test MAE: {test_mae:.4f}, Test R²: {test_r2:.4f}")
+            early_stopping(val_rmse, model)
+            if early_stopping.early_stop:
+                print("Early stopping triggered.")
+                break
+
+        # Load best model
+        model.load_state_dict(torch.load('models/checkpoints/' + model_name, weights_only=False))
+        models.append(model)
+
+        # Test Evaluation
+        test_labels, test_preds = ml_train.test_loop(model, test_loader, device, is_bert=True)
+
+        hist.add_test_results(test_labels, test_preds)
+
+        # save training history
+        hist.save_history(hist_name, timestamp)
+
+        # RMSE, MAE und R²-Score für das Test-Set
+        test_mae = mean_absolute_error(test_labels, test_preds)
+        test_rmse = np.sqrt(mean_squared_error(test_labels, test_preds))
+        test_r2 = r2_score(test_labels, test_preds)
+        print(f"Test RMSE: {test_rmse:.4f}, Test MAE: {test_mae:.4f}, Test R²: {test_r2:.4f}")
+
+
+    if N_MODELS >1:
+        # Ensemble Prediction
+        ensemble_test_preds = ml_train.ensemble_predict(models, test_loader, device, is_bert=True)
+        ensemble_avg_preds = np.mean(ensemble_test_preds, axis=0)
+
+        # Save ensemble predictions as json
+        ensemble_preds_path = f'histories/ensemble_preds_BERT_{timestamp}.json'
+        with open(ensemble_preds_path, 'w') as f:
+            json.dump(ensemble_avg_preds.tolist(), f)
+
+
+        # Test Evaluation
+        test_labels = test_dataset.labels
+        
+        test_mse = mean_squared_error(test_labels, ensemble_avg_preds)
+        test_mae = mean_absolute_error(test_labels, ensemble_avg_preds)
+        test_r2 = r2_score(test_labels, ensemble_avg_preds)
+
+        print(f"Ensemble Test RMSE: {test_mse:.4f}, Test MAE: {test_mae:.4f}, Test R²: {test_r2:.4f}")
\ No newline at end of file
diff --git a/CNN.py b/CNN.py
index 13571f3..52d56f1 100644
--- a/CNN.py
+++ b/CNN.py
@@ -3,9 +3,11 @@ import random
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from torch.utils.data import DataLoader
+from torch.utils.data import DataLoader, Subset
 from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
 import numpy as np
+from datetime import datetime
+import json
 
 import Datasets
 import dataset_helper
@@ -57,7 +59,7 @@ if __name__ == '__main__':
         # Config
         "max_len": 280,
         # Training
-        "epochs": 25,
+        "epochs": 5,
         "patience": 7,
         "batch_size": 32,
         "learning_rate": 0.001,
@@ -69,14 +71,17 @@ if __name__ == '__main__':
     }
 
     # Configs
-    MODEL_NAME = 'CNN.pt'
-    HIST_NAME = 'CNN_history'
     GLOVE_PATH = 'data/glove.6B.100d.txt'
     DATA_PATH = 'data/hack.csv'
     EMBEDDING_DIM = 100
     TEST_SIZE = 0.1
     VAL_SIZE = 0.1
 
+    N_MODELS = 1
+
+    models = []
+    timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+
     # Daten laden und vorbereiten
     embedding_matrix, word_index, vocab_size, d_model = dataset_helper.get_embedding_matrix(
         gloVe_path=GLOVE_PATH, emb_len=EMBEDDING_DIM)
@@ -95,53 +100,83 @@ if __name__ == '__main__':
     val_loader = DataLoader(val_dataset, batch_size=params["batch_size"], shuffle=False)
     test_loader = DataLoader(test_dataset, batch_size=params["batch_size"], shuffle=False)
 
-    # Modell initialisieren
-    model = EnhancedCNNRegressor(
-        vocab_size=vocab_size,
-        embedding_dim=EMBEDDING_DIM,
-        filter_sizes=params["filter_sizes"],
-        num_filters=params["num_filters"],
-        embedding_matrix=embedding_matrix,
-        dropout=params["dropout"]
-    )
-
-
+    subset_size = len(train_dataset) // N_MODELS
     device = ml_helper.get_device(verbose=True, include_mps=False)
-    model = model.to(device)
 
-    criterion = nn.MSELoss()
-    optimizer = optim.Adam(model.parameters(), lr=params["learning_rate"], weight_decay=params["weight_decay"])
-    early_stopping = EarlyStopping.EarlyStoppingCallback(patience=params["patience"], verbose=True, model_name=MODEL_NAME)
+    for i in range(N_MODELS):
+        model_name = f'CNN.pt'
+        hist_name = f'CNN_history'
 
-    hist = ml_history.History()
+        if N_MODELS > 1:
+            model_name = f'CNN_{i}_ensemble.pt'
+            hist_name = f'CNN_{i}_ensemble_history'
+        
+            subset_indices = dataset_helper.ensemble_data_idx(train_dataset.labels, N_MODELS, i, methods='bootstrap')
+            train_dataset_sub = Subset(train_dataset, subset_indices)
+            train_loader = DataLoader(train_dataset_sub, batch_size=params["batch_size"], shuffle=True)
 
-    # Training und Validierung
-    for epoch in range(params["epochs"]):
-        ml_train.train_epoch(model, train_loader, criterion, optimizer, device, hist, epoch, params["epochs"])
+        model = EnhancedCNNRegressor(
+            vocab_size=vocab_size,
+            embedding_dim=EMBEDDING_DIM,
+            filter_sizes=params["filter_sizes"],
+            num_filters=params["num_filters"],
+            embedding_matrix=embedding_matrix,
+            dropout=params["dropout"]
+        )
+        model = model.to(device)
+    
+        criterion = nn.MSELoss()
+        optimizer = optim.Adam(model.parameters(), lr=params["learning_rate"], weight_decay=params["weight_decay"])
+        early_stopping = EarlyStopping.EarlyStoppingCallback(patience=params["patience"], verbose=True, model_name=model_name)
 
-        val_rmse = ml_train.validate_epoch(model, val_loader, epoch, criterion, device, hist)
+        hist = ml_history.History()
 
-        early_stopping(val_rmse, model)
-        if early_stopping.early_stop:
-            print("Early stopping triggered.")
-            break
+        # Training und Validierung
+        for epoch in range(params["epochs"]):
+            ml_train.train_epoch(model, train_loader, criterion, optimizer, device, hist, epoch, params["epochs"])
 
-    # save training history
-    hist.save_history(HIST_NAME)
+            val_rmse = ml_train.validate_epoch(model, val_loader, epoch, criterion, device, hist)
 
-    # Load best model
-    model.load_state_dict(torch.load('models/checkpoints/' + MODEL_NAME))
+            early_stopping(val_rmse, model)
+            if early_stopping.early_stop:
+                print("Early stopping triggered.")
+                break
 
-    # Test Evaluation
-    test_labels, test_preds = ml_train.test_loop(model, test_loader, device)
+        # Load best model
+        model.load_state_dict(torch.load('models/checkpoints/' + model_name, weights_only=False))
+        models.append(model)
 
-    hist.add_test_results(test_labels, test_preds)
+        # Test Evaluation
+        test_labels, test_preds = ml_train.test_loop(model, test_loader, device)
 
-    # save training history
-    hist.save_history(HIST_NAME)
+        hist.add_test_results(test_labels, test_preds)
+
+        # save training history
+        hist.save_history(hist_name, timestamp)
+
+        # RMSE, MAE und R²-Score für das Test-Set
+        test_mae = mean_absolute_error(test_labels, test_preds)
+        test_rmse = np.sqrt(mean_squared_error(test_labels, test_preds))
+        test_r2 = r2_score(test_labels, test_preds)
+        print(f"Model: {model_name} Test RMSE: {test_rmse:.4f}, Test MAE: {test_mae:.4f}, Test R²: {test_r2:.4f}")
+
+    if N_MODELS >1:
+        # Ensemble Prediction
+        ensemble_test_preds = ml_train.ensemble_predict(models, test_loader, device)
+        ensemble_avg_preds = np.mean(ensemble_test_preds, axis=0)
+
+        # Save ensemble predictions as json
+        ensemble_preds_path = f'histories/ensemble_preds_CNN_{timestamp}.json'
+        with open(ensemble_preds_path, 'w') as f:
+            json.dump(ensemble_avg_preds.tolist(), f)
+
+
+        # Test Evaluation
+        test_labels = test_dataset.labels.to_numpy()
+        
+        test_mse = mean_squared_error(test_labels, ensemble_avg_preds)
+        test_mae = mean_absolute_error(test_labels, ensemble_avg_preds)
+        test_r2 = r2_score(test_labels, ensemble_avg_preds)
+
+        print(f"Ensemble Test RMSE: {test_mse:.4f}, Test MAE: {test_mae:.4f}, Test R²: {test_r2:.4f}")
 
-    # RMSE, MAE und R²-Score für das Test-Set
-    test_mae = mean_absolute_error(test_labels, test_preds)
-    test_rmse = np.sqrt(mean_squared_error(test_labels, test_preds))
-    test_r2 = r2_score(test_labels, test_preds)
-    print(f"Test RMSE: {test_rmse:.4f}, Test MAE: {test_mae:.4f}, Test R²: {test_r2:.4f}")
diff --git a/Transformer.py b/Transformer.py
index 38bdf49..41c8a49 100644
--- a/Transformer.py
+++ b/Transformer.py
@@ -1,11 +1,14 @@
 import math
+import random
 
 import torch
 import torch.nn as nn
 import torch.optim as optim
-from torch.utils.data import DataLoader
+from torch.utils.data import DataLoader, Subset
 from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
 import numpy as np
+from datetime import datetime
+import json
 
 import Datasets
 import dataset_helper
@@ -14,6 +17,12 @@ import ml_helper
 import ml_history
 import ml_train
 
+SEED = 501
+random.seed(SEED)
+np.random.seed(SEED)
+torch.manual_seed(SEED)
+torch.cuda.manual_seed_all(SEED)
+torch.backends.cudnn.deterministic = True
 
 class PositionalEncoding(nn.Module):
     """
@@ -102,7 +111,7 @@ if __name__ == '__main__':
         # Config
         "max_len": 280,
         # Training
-        "epochs": 25,
+        "epochs": 1,
         "patience": 7,
         "batch_size": 32,
         "learning_rate": 1e-4, # 1e-4
@@ -113,17 +122,19 @@ if __name__ == '__main__':
         'hiden_dim': 2048,
         'num_layers': 6
     }
-    # TODO set seeds
 
     # Configs
-    MODEL_NAME = 'transfomrer.pt'
-    HIST_NAME = 'transformer_history'
     GLOVE_PATH = 'data/glove.6B.100d.txt'
     DATA_PATH = 'data/hack.csv'
     EMBEDDING_DIM = 100
     TEST_SIZE = 0.1
     VAL_SIZE = 0.1
 
+    N_MODELS = 2
+
+    models = []
+    timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+
     # Daten laden und vorbereiten
     embedding_matrix, word_index, vocab_size, d_model = dataset_helper.get_embedding_matrix(
         gloVe_path=GLOVE_PATH, emb_len=EMBEDDING_DIM)
@@ -142,55 +153,83 @@ if __name__ == '__main__':
     val_loader = DataLoader(val_dataset, batch_size=params["batch_size"], shuffle=False)
     test_loader = DataLoader(test_dataset, batch_size=params["batch_size"], shuffle=False)
 
-    # Modell initialisieren
-    model = TransformerBinaryClassifier(
-        embeddings=embedding_matrix,
-        nhead=params['nhead'],
-        dim_feedforward=params['hiden_dim'],
-        num_layers=params['num_layers'],
-        positional_dropout=params["dropout"],
-        classifier_dropout=params["dropout"],
-    )
-
+    subset_size = len(train_dataset) // N_MODELS
     device = ml_helper.get_device(verbose=True, include_mps=False)
-    model = model.to(device)
 
-    criterion = nn.MSELoss()
-    optimizer = optim.Adam(model.parameters(), lr=params["learning_rate"]) #, weight_decay=params["weight_decay"])
-    early_stopping = EarlyStopping.EarlyStoppingCallback(patience=params["patience"], verbose=True, model_name=MODEL_NAME)
+    for i in range(N_MODELS):
+        model_name = f'Transformer.pt'
+        hist_name = f'Transformer_history'
+        
+        if N_MODELS > 1:
+            model_name = f'Transformer_{i}_ensemble.pt'
+            hist_name = f'Transformer_{i}_ensemble_history'
 
-    hist = ml_history.History()
+            subset_indices = dataset_helper.ensemble_data_idx(train_dataset.labels, N_MODELS, i, methods='bootstrap')
+            train_dataset_sub = Subset(train_dataset, subset_indices)
+            train_loader = DataLoader(train_dataset_sub, batch_size=params["batch_size"], shuffle=True)
 
-    # Training und Validierung
-    for epoch in range(params["epochs"]):
-        ml_train.train_epoch(model, train_loader, criterion, optimizer, device, hist, epoch, params["epochs"])
+        # Modell initialisieren
+        model = TransformerBinaryClassifier(
+            embeddings=embedding_matrix,
+            nhead=params['nhead'],
+            dim_feedforward=params['hiden_dim'],
+            num_layers=params['num_layers'],
+            positional_dropout=params["dropout"],
+            classifier_dropout=params["dropout"],
+        )
+        model = model.to(device)
 
-        val_rmse = ml_train.validate_epoch(model, val_loader, epoch, criterion, device, hist)
+        criterion = nn.MSELoss()
+        optimizer = optim.Adam(model.parameters(), lr=params["learning_rate"]) #, weight_decay=params["weight_decay"])
+        early_stopping = EarlyStopping.EarlyStoppingCallback(patience=params["patience"], verbose=True, model_name=model_name)
 
-        early_stopping(val_rmse, model)
-        if early_stopping.early_stop:
-            print("Early stopping triggered.")
-            break
+        hist = ml_history.History()
 
-    # save training history
-    hist.save_history(HIST_NAME)
+        # Training und Validierung
+        for epoch in range(params["epochs"]):
+            ml_train.train_epoch(model, train_loader, criterion, optimizer, device, hist, epoch, params["epochs"])
 
-    # save training history
-    hist.save_history(HIST_NAME)
+            val_rmse = ml_train.validate_epoch(model, val_loader, epoch, criterion, device, hist)
 
-    # Load best model
-    model.load_state_dict(torch.load('models/checkpoints/' + MODEL_NAME))
+            early_stopping(val_rmse, model)
+            if early_stopping.early_stop:
+                print("Early stopping triggered.")
+                break
 
-    # Test Evaluation
-    test_labels, test_preds = ml_train.test_loop(model, test_loader, device)
+        # Load best model
+        model.load_state_dict(torch.load('models/checkpoints/' + model_name, weights_only=False))
+        models.append(model)
+        
+        # Test Evaluation
+        test_labels, test_preds = ml_train.test_loop(model, test_loader, device)
 
-    hist.add_test_results(test_labels, test_preds)
+        hist.add_test_results(test_labels, test_preds)
 
-    # save training history
-    hist.save_history(HIST_NAME)
+        # save training history
+        hist.save_history(hist_name, timestamp)
 
-    # RMSE, MAE und R²-Score für das Test-Set
-    test_mae = mean_absolute_error(test_labels, test_preds)
-    test_rmse = np.sqrt(mean_squared_error(test_labels, test_preds))
-    test_r2 = r2_score(test_labels, test_preds)
-    print(f"Test RMSE: {test_rmse:.4f}, Test MAE: {test_mae:.4f}, Test R²: {test_r2:.4f}")
+        # RMSE, MAE und R²-Score für das Test-Set
+        test_mae = mean_absolute_error(test_labels, test_preds)
+        test_rmse = np.sqrt(mean_squared_error(test_labels, test_preds))
+        test_r2 = r2_score(test_labels, test_preds)
+        print(f"Test RMSE: {test_rmse:.4f}, Test MAE: {test_mae:.4f}, Test R²: {test_r2:.4f}")
+
+    if N_MODELS >1:
+        # Ensemble Prediction
+        ensemble_test_preds = ml_train.ensemble_predict(models, test_loader, device)
+        ensemble_avg_preds = np.mean(ensemble_test_preds, axis=0)
+
+        # Save ensemble predictions as json
+        ensemble_preds_path = f'histories/ensemble_preds_Transformer_{timestamp}.json'
+        with open(ensemble_preds_path, 'w') as f:
+            json.dump(ensemble_avg_preds.tolist(), f)
+
+
+        # Test Evaluation
+        test_labels = test_dataset.labels.to_numpy()
+        
+        test_mse = mean_squared_error(test_labels, ensemble_avg_preds)
+        test_mae = mean_absolute_error(test_labels, ensemble_avg_preds)
+        test_r2 = r2_score(test_labels, ensemble_avg_preds)
+
+        print(f"Ensemble Test RMSE: {test_mse:.4f}, Test MAE: {test_mae:.4f}, Test R²: {test_r2:.4f}")
diff --git a/cnn_bootstrap_agg.py b/cnn_bootstrap_agg.py
index 36e6599..06f02ce 100644
--- a/cnn_bootstrap_agg.py
+++ b/cnn_bootstrap_agg.py
@@ -1,101 +1,159 @@
-import pandas as pd
-import numpy as np
+import random
 import torch
 import torch.nn as nn
-from torch.utils.data import DataLoader, Dataset
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
-import matplotlib.pyplot as plt
-import matplotlib.patches as mpatches
-from tqdm import tqdm
-from dataset_generator import create_embedding_matrix
-from EarlyStopping import EarlyStopping
 import torch.optim as optim
-from torch.utils.data import DataLoader, Dataset, Subset  # Import Subset
-#from utils import tokenize_and_pad, HumorDataset, evaluate_model, bootstrap_aggregation
-def train_model(model, train_dataset, val_dataset, criterion, optimizer, epochs, batch_size):
-    train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
-    val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
+import matplotlib.pyplot as plt
+from torch.utils.data import DataLoader, Subset
+from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
+import numpy as np
 
-    model.to(device)
-    history = {'train_loss': [], 'val_loss': [], 'train_r2': [], 'val_r2': []}
+import Datasets
+import dataset_helper
+import EarlyStopping
+import ml_helper
+import ml_history
+import ml_train
 
+SEED = 501
+random.seed(SEED)
+np.random.seed(SEED)
+torch.manual_seed(SEED)
+torch.cuda.manual_seed_all(SEED)
+torch.backends.cudnn.deterministic = True
+
+class EnhancedCNNRegressor(nn.Module):
+    def __init__(self, vocab_size, embedding_dim, filter_sizes, num_filters, embedding_matrix, dropout):
+        super(EnhancedCNNRegressor, self).__init__()
+        self.embedding = nn.Embedding.from_pretrained(embedding_matrix, freeze=False)
+        
+        # Convolutional Schichten mit Batch-Normalisierung
+        self.convs = nn.ModuleList([
+            nn.Sequential(
+                nn.Conv2d(1, num_filters, (fs, embedding_dim)),
+                nn.BatchNorm2d(num_filters),  # Batch-Normalisierung
+                nn.ReLU(),
+                nn.MaxPool2d((params["max_len"] - fs + 1, 1)),
+                nn.Dropout(dropout)  # Dropout nach jeder Schicht
+            )
+            for fs in filter_sizes
+        ])
+        
+        # Fully-Connected Layer
+        self.fc1 = nn.Linear(len(filter_sizes) * num_filters, 128)  # Erweiterte Dense-Schicht
+        self.fc2 = nn.Linear(128, 1)  # Ausgangsschicht (Regression)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, x):
+        x = self.embedding(x).unsqueeze(1)  # [Batch, 1, Seq, Embedding]
+        conv_outputs = [conv(x).squeeze(3).squeeze(2) for conv in self.convs]  # Pooling reduziert Dim
+        x = torch.cat(conv_outputs, 1)  # Kombiniere Features von allen Filtern
+        x = torch.relu(self.fc1(x))  # Zusätzliche Dense-Schicht
+        x = self.dropout(x)
+        return self.fc2(x).squeeze(1)
+
+def train_model(model, train_dataset, test_dataset, criterion, optimizer, epochs, batch_size):
+    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
+    
+    test_losses, train_losses = [], []
+    train_r2_scores, test_r2_scores = [], []
+    
     for epoch in range(epochs):
         model.train()
-        total_loss = 0
-        all_train_preds, all_train_targets = [], []
-
-        for inputs, targets in train_dataloader:
-            inputs, targets = inputs.to(device), targets.to(device)
+        running_loss = 0.0
+        running_r2 = 0.0
+        
+        # Training
+        for inputs, labels in train_loader:
+            inputs = inputs.to(device)
+            labels = labels.to(device)
+            
             optimizer.zero_grad()
-            outputs = model(inputs).squeeze()
-            loss = criterion(outputs, targets)
+            outputs = model(inputs)
+            loss = criterion(outputs, labels)
             loss.backward()
             optimizer.step()
-            total_loss += loss.item()
-
-            all_train_preds.extend(outputs.detach().cpu().numpy())
-            all_train_targets.extend(targets.detach().cpu().numpy())
-
-        train_r2 = r2_score(all_train_targets, all_train_preds)
-        train_loss = total_loss / len(train_dataloader)
-        history['train_loss'].append(train_loss)
-        history['train_r2'].append(train_r2)
-
+            
+            running_loss += loss.item()
+            running_r2 += r2_score(labels.cpu().numpy(), outputs.cpu().detach().numpy())
         
-        model.eval()
-        val_loss = 0
-        all_val_preds, all_val_targets = [], []
+        train_losses.append(running_loss / len(train_loader))
+        train_r2_scores.append(running_r2 / len(train_loader))
+        
+        # Test
+        model.eval()  # Set model to evaluation mode
+        test_loss = 0.0
+        test_r2 = 0.0
+        with torch.no_grad():  # No gradient calculation for testing
+            for inputs, labels in test_loader:
+                inputs = inputs.to(device)
+                labels = labels.to(device)
+                
+                outputs = model(inputs)
+                loss = criterion(outputs, labels)
+                
+                test_loss += loss.item()
+                test_r2 += r2_score(labels.cpu().numpy(), outputs.cpu().detach().numpy())
+        
+        test_losses.append(test_loss / len(test_loader))
+        test_r2_scores.append(test_r2 / len(test_loader))
+        
+        print(f'Epoch {epoch + 1}/{epochs}, Train Loss: {train_losses[-1]:.4f}, Train R²: {train_r2_scores[-1]:.4f}, Test Loss: {test_losses[-1]:.4f}, Test R²: {test_r2_scores[-1]:.4f}')
+    
+    return train_losses, test_losses, train_r2_scores, test_r2_scores
 
-        with torch.no_grad():
-            for inputs, targets in val_dataloader:
-                inputs, targets = inputs.to(device), targets.to(device)
-                outputs = model(inputs).squeeze()
-                loss = criterion(outputs, targets)
-                val_loss += loss.item()
-
-                all_val_preds.extend(outputs.cpu().numpy())
-                all_val_targets.extend(targets.cpu().numpy())
-
-        val_r2 = r2_score(all_val_targets, all_val_preds)
-        val_loss /= len(val_dataloader)
-        history['val_loss'].append(val_loss)
-        history['val_r2'].append(val_r2)
-
-        print(f"Epoch {epoch+1}/{epochs}, Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}, Train R²: {train_r2:.4f}, Val R²: {val_r2:.4f}")
-
-    return history 
-
-
-def bootstrap_aggregation(ModelClass, train_dataset, num_models=3, epochs=5, batch_size=32, learning_rate=0.001):
+# Bootstrap Aggregation (Bagging) Update
+def bootstrap_aggregation(ModelClass, train_dataset, test_dataset, num_models=5, epochs=10, batch_size=32, learning_rate=0.001):
     models = []
-    all_histories = []  
-
+    all_train_losses, all_test_losses = [], []
+    all_train_r2_scores, all_test_r2_scores = [], []
+ 
     subset_size = len(train_dataset) // num_models
 
     for i in range(num_models):
-        print(f"Training Model {i+1}/{num_models}...")
-
+        print(f"Training Model {i + 1}/{num_models}...")
         start_idx = i * subset_size
         end_idx = start_idx + subset_size
         subset_indices = list(range(0, start_idx)) + list(range(end_idx, len(train_dataset)))
-
         subset = Subset(train_dataset, subset_indices)
 
-        
-        val_indices = list(range(start_idx, end_idx))
-        val_subset = Subset(train_dataset, val_indices)
-
-        model = ModelClass()
+        model = ModelClass(vocab_size, EMBEDDING_DIM, params["filter_sizes"], params["num_filters"], embedding_matrix, params["dropout"])
+        model.to(device)
         criterion = nn.MSELoss()
         optimizer = optim.Adam(model.parameters(), lr=learning_rate)
 
-        history = train_model(model, subset, val_subset, criterion, optimizer, epochs, batch_size)
-        all_histories.append(history)  
+        train_losses, test_losses, train_r2_scores, test_r2_scores = train_model(model, subset, test_dataset, criterion, optimizer, epochs, batch_size)
+        
         models.append(model)
+        all_train_losses.append(train_losses)
+        all_test_losses.append(test_losses)
+        all_train_r2_scores.append(train_r2_scores)
+        all_test_r2_scores.append(test_r2_scores)
 
-    return models, all_histories
+    # Plot für alle Modelle
+    plt.figure(figsize=(12, 6))
+    for i in range(num_models):
+        plt.plot(all_train_losses[i], label=f'Model {i + 1} Train Loss')
+        plt.plot(all_test_losses[i], label=f'Model {i + 1} Test Loss', linestyle = 'dashed')
+    plt.title("Training and Test Loss for all Models")
+    plt.xlabel('Epochs')
+    plt.ylabel('Loss')
+    plt.legend()
+    plt.show()
 
+    plt.figure(figsize=(12, 6))
+    for i in range(num_models):
+        plt.plot(all_train_r2_scores[i], label=f'Model {i + 1} Train R²')
+        plt.plot(all_test_r2_scores[i], label=f'Model {i + 1} Test R²',  linestyle = 'dashed')
+    plt.title("Training and Test R² for all Models")
+    plt.xlabel('Epochs')
+    plt.ylabel('R²')
+    plt.legend()
+    plt.show()
+
+    return models, all_train_losses, all_test_losses, all_train_r2_scores, all_test_r2_scores
+
+# Ensemble Prediction
 def ensemble_predict(models, test_dataset):
     dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False)
     all_predictions = []
@@ -104,160 +162,64 @@ def ensemble_predict(models, test_dataset):
         for inputs, _ in dataloader:
             inputs = inputs.to(device)
             predictions = torch.stack([model(inputs).squeeze() for model in models])
-            avg_predictions = predictions.mean(dim=0)  # Mittelwert über alle Modelle
+            avg_predictions = predictions.mean(dim=0)
             all_predictions.extend(avg_predictions.cpu().numpy())
 
     return np.array(all_predictions)
 
-import matplotlib.pyplot as plt
+if __name__ == '__main__':
+    # Hyperparameter und Konfigurationen
+    params = {
+        # Config
+        "max_len": 280,
+        # Training
+        "epochs": 2,
+        "patience": 7,
+        "batch_size": 16,
+        "learning_rate": 0.001,
+        "weight_decay": 5e-4 ,
+        # Model
+        "filter_sizes": [2, 3, 4, 5],
+        "num_filters": 150,
+        "dropout": 0.6
+    }
 
-def plot_training_histories(histories, num_models):
-    epochs = range(1, len(histories[0]['train_loss']) + 1)
+    # Configs
+    MODEL_NAME = 'CNN.pt'
+    HIST_NAME = 'CNN_history'
+    GLOVE_PATH = 'data/glove.6B.100d.txt'
+    DATA_PATH = 'data/hack.csv'
+    EMBEDDING_DIM = 100
+    TEST_SIZE = 0.1
+    VAL_SIZE = 0.1
 
-    fig, axes = plt.subplots(1, 2, figsize=(14, 5))
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 
+    # Daten laden und vorbereiten
+    embedding_matrix, word_index, vocab_size, d_model = dataset_helper.get_embedding_matrix(
+        gloVe_path=GLOVE_PATH, emb_len=EMBEDDING_DIM)
 
-   
-    for i in range(num_models):
-        axes[0].plot(epochs, histories[i]['train_loss'], label=f"Train Loss Model {i+1}")
-        axes[0].plot(epochs, histories[i]['val_loss'], linestyle='dashed', label=f"Val Loss Model {i+1}")
+    X, y = dataset_helper.load_preprocess_data(path_data=DATA_PATH, verbose=True)
 
-    axes[0].set_title("Train & Validation Loss")
-    axes[0].set_xlabel("Epochs")
-    axes[0].set_ylabel("Loss")
-    axes[0].legend()
+    # Aufteilen der Daten
+    data_split = dataset_helper.split_data(X, y, test_size=TEST_SIZE, val_size=VAL_SIZE)
 
-    for i in range(num_models):
-        axes[1].plot(epochs, histories[i]['train_r2'], label=f"Train R² Model {i+1}")
-        axes[1].plot(epochs, histories[i]['val_r2'], linestyle='dashed', label=f"Val R² Model {i+1}")
+    # Dataset und DataLoader
+    train_dataset = Datasets.GloveDataset(data_split['train']['X'], data_split['train']['y'], word_index, max_len=params["max_len"])
+    val_dataset = Datasets.GloveDataset(data_split['val']['X'], data_split['val']['y'], word_index, max_len=params["max_len"])
+    test_dataset = Datasets.GloveDataset(data_split['test']['X'], data_split['test']['y'], word_index, max_len=params["max_len"])
 
-    axes[1].set_title("Train & Validation R² Score")
-    axes[1].set_xlabel("Epochs")
-    axes[1].set_ylabel("R² Score")
-    axes[1].legend()
+    # Bootstrap Aggregation (Bagging) Training
+    models, all_train_losses, all_test_losses, all_train_r2_scores, all_test_r2_scores = bootstrap_aggregation(
+        EnhancedCNNRegressor, train_dataset, test_dataset, num_models=2, epochs=params["epochs"], batch_size=params["batch_size"], learning_rate=params["learning_rate"])
 
-    plt.show()
-
-
-
-# 1. Gerät automatisch erkennen
-device = torch.device('mps' if torch.backends.mps.is_available()
-                      else 'cuda' if torch.cuda.is_available()
-                      else 'cpu')
-print(f"Using device: {device}")
-
-# 2. Daten laden
-data = pd.read_csv('data/hack.csv')
-
-# 3. Filtern humorvoller Texte
-humor_data = data[data['is_humor'] == 1].dropna(subset=['humor_rating']).copy()
-
-# 4. Einbettungsmatrix erstellen
-embedding_matrix, word_index, vocab_size, d_model = create_embedding_matrix(
-    gloVe_path='data/glove.6B.100d.txt', emb_len=100
-)
-print(f"vocab_size: {vocab_size}, d_model: {d_model}")
-
-# 5. Tokenisierung und Padding
-def tokenize_and_pad(texts, word_index, max_len=50):
-    sequences = []
-    for text in texts:
-        tokens = [word_index.get(word, 0) for word in text.split()]
-        if len(tokens) < max_len:
-            tokens += [0] * (max_len - len(tokens))
-        else:
-            tokens = tokens[:max_len]
-        sequences.append(tokens)
-    return torch.tensor(sequences, dtype=torch.long)
-max_len = 50
-train_texts, test_texts, train_labels, test_labels = train_test_split(
-    humor_data['text'], humor_data['humor_rating'], test_size=0.2, random_state=42
-)
-train_input_ids = tokenize_and_pad(train_texts, word_index, max_len=max_len)
-test_input_ids = tokenize_and_pad(test_texts, word_index, max_len=max_len)
-
-# Labels in Tensor konvertieren
-train_labels = torch.tensor(train_labels.values, dtype=torch.float)
-test_labels = torch.tensor(test_labels.values, dtype=torch.float)
-
-# 6. Dataset und DataLoader
-class HumorDataset(Dataset):
-    def __init__(self, input_ids, labels):
-        self.input_ids = input_ids
-        self.labels = labels
-
-    def __len__(self):
-        return len(self.input_ids)
-
-    def __getitem__(self, idx):
-        return self.input_ids[idx], self.labels[idx]
-dataset = HumorDataset(train_input_ids, train_labels)
-
-# 7. CNN-Regression-Modell
-def create_cnn(vocab_size, embed_dim, embedding_matrix):
-    class CNNRegressor(nn.Module):
-        def __init__(self, vocab_size, embed_dim, embedding_matrix):
-            super(CNNRegressor, self).__init__()
-            self.embedding = nn.Embedding(vocab_size, embed_dim)
-            self.embedding.weight.data.copy_(embedding_matrix.clone().detach())
-            self.embedding.weight.requires_grad = False
-            self.conv1 = nn.Conv1d(embed_dim, 128, kernel_size=3)
-            self.conv2 = nn.Conv1d(128, 64, kernel_size=3)
-            self.dropout = nn.Dropout(0.5)
-            self.fc = nn.Linear(64, 1)
-
-        def forward(self, x):
-            x = self.embedding(x).permute(0, 2, 1)
-            x = torch.relu(self.conv1(x))
-            x = torch.relu(self.conv2(x))
-            x = self.dropout(x)
-            x = torch.max(x, dim=2).values
-            x = self.fc(x)
-            return torch.sigmoid(x) * 5
-
-    return CNNRegressor(vocab_size, embed_dim, embedding_matrix)
-
-# 8. Bootstrap Aggregation mit CNN
-models, histories = bootstrap_aggregation(
-    lambda: create_cnn(vocab_size, d_model, embedding_matrix),
-    dataset,
-    num_models=5,
-    epochs=10,
-    batch_size=32,
-    learning_rate=0.001
-)
-# **Plot Training & Validation Loss & R²**
-plot_training_histories(histories, num_models=5)
-
-
-# Vorhersagen mit Ensemble
-predictions = ensemble_predict(models, HumorDataset(test_input_ids, test_labels))
-actuals = test_labels.numpy()
-
-# 9. Metriken berechnen
-mse = mean_squared_error(actuals, predictions)
-mae = mean_absolute_error(actuals, predictions)
-r2 = r2_score(actuals, predictions)
-
-print(f"MSE: {mse:.4f}, MAE: {mae:.4f}, R²: {r2:.4f}")
-
-# 10. Visualisierung
-tolerance = 0.5  # Toleranz für korrekte Vorhersagen
-predictions = np.array(predictions)
-actuals = np.array(actuals)
-
-correct = np.abs(predictions - actuals) <= tolerance
-colors = np.where(correct, 'green', 'red')
-
-plt.figure(figsize=(8, 6))
-plt.scatter(actuals, predictions, c=colors, alpha=0.6, edgecolor='k', s=50)
-plt.plot([0, 5], [0, 5], color='red', linestyle='--')
-
-green_patch = mpatches.Patch(color='green', label='Correct Predictions')
-red_patch = mpatches.Patch(color='red', label='Incorrect Predictions')
-plt.legend(handles=[green_patch, red_patch])
-
-plt.xlabel("True Humor Ratings")
-plt.ylabel("Predicted Humor Ratings")
-plt.title("True vs Predicted Humor Ratings (Correct vs Incorrect)")
-plt.show()
+    # Ensemble Prediction
+    test_predictions = ensemble_predict(models, test_dataset)
 
+    # Test Evaluation
+   # test_labels = np.array([y for _, y in test_dataset])
+    
+    test_mse = mean_squared_error(test_dataset.labels.to_numpy(), test_predictions)
+    test_mae = mean_absolute_error(test_dataset.labels.to_numpy(), test_predictions)
+    test_r2 = r2_score(test_dataset.labels.to_numpy(), test_predictions)
+    
+    print(f"Test RMSE: {test_mse:.4f}, Test MAE: {test_mae:.4f}, Test R²: {test_r2:.4f}")
diff --git a/dataset_helper.py b/dataset_helper.py
index 47fb36a..d72ecc9 100644
--- a/dataset_helper.py
+++ b/dataset_helper.py
@@ -8,6 +8,7 @@ import torch
 import regex as re
 
 def load_glove_embeddings(glove_file_path, emb_len=100):
+    print('Loading GloVe embeddings...')
     embeddings_index = {}
     with open(glove_file_path, 'r', encoding='utf-8') as f:
         for line in f:
@@ -99,4 +100,39 @@ def split_data(X, y, test_size=0.1, val_size=0.1):
     for key in ret_dict.keys():
         print(key, len(ret_dict[key]['X']), len(ret_dict[key]['y']))
 
-    return ret_dict
\ No newline at end of file
+    return ret_dict
+
+def ensemble_data_idx(labels, n_models, cur_models_idx, methods='bootstrap'):
+    if methods == 'bootstrap':
+        # Calculate the size of the subset
+        subset_size = len(labels) // n_models
+        # Calculate the start and end index of the subset
+        start_idx = cur_models_idx * subset_size
+        end_idx = start_idx + subset_size
+        # Calculate the indices of the subset
+        subset_indices = list(range(0, start_idx)) + list(range(end_idx, len(labels)))
+        return subset_indices
+    
+    if methods == 'shuffle':
+        subset_indices = np.random.permutation(len(labels))
+        return subset_indices
+    
+    if methods == 'random':
+        subset_indices = np.random.choice(len(labels), len(labels), replace=False)
+        return subset_indices
+    
+    if methods == 'flatten_normal_dist':
+         # TODO: test this and plot if it works
+        subset_size = len(labels) // n_models
+        std_range = 1
+        mean = np.mean(labels)
+        std = np.std(labels)
+        # Randomly select samples arounnd the mean in the std
+        del_subset_indices = np.random.choice(np.where((labels >= mean - std_range * std) & (labels <= mean + std_range * std))[0], size=subset_size, replace=False)
+        subset = np.delete(labels, del_subset_indices)
+        # TODO i dont think this really uses the indices
+        subset_indices = np.where(np.isin(labels, subset))[0]
+        return subset_indices
+
+    else:
+        raise ValueError(f"Unknown method: {methods}")
\ No newline at end of file
diff --git a/ml_helper.py b/ml_helper.py
index 89da6b7..ba838b2 100644
--- a/ml_helper.py
+++ b/ml_helper.py
@@ -4,6 +4,7 @@ import nltk
 import time
 import json
 import os
+import re
 
 def get_device(verbose=False, include_mps=False):
     """
@@ -39,7 +40,7 @@ def save_model_and_hyperparams(model, model_prefix_name, rmse, hyperparameters,
         json.dump(hyperparameters, f)
     print(f"Hyperparameters saved to {hyperparameters_path}.")
 
-def get_newest_file(path, name=None, extension=".pth"):
+def get_newest_file(path, name=None, extension=".pth", ensemble=False):
     """
     Get the newest file in a directory.
     """
@@ -49,13 +50,35 @@ def get_newest_file(path, name=None, extension=".pth"):
     if name:
         files = [f for f in files if name in f]
 
+    if ensemble:
+        files = [f for f in files if "ensemble" in f]
+
     # Sort files by modification time
     files.sort(key=lambda x: os.path.getmtime(os.path.join(path, x)), reverse=True)
     
     # Get the newest file
     if files:
-        newest_model_path = os.path.join(path, files[0])
-        return newest_model_path
+        if not ensemble:
+            newest_model_path = os.path.join(path, files[0])
+            return newest_model_path
+        else:
+            # Extract timestamp from the newest file's filename
+            regex = r"(\d{4}-\d{2}-\d{2}_\d{2}-\d{2}-\d{2})"
+            newest_stamp = None
+            ret_files = []
+            for file in files:
+                match = re.search(regex, file)
+                if match:
+                    newest_timestamp = match.group(1)
+                    if not newest_stamp or newest_timestamp > newest_stamp:
+                        newest_stamp = newest_timestamp
+                if newest_stamp:
+                    ret_files.append(os.path.join(path, file))
+            if ret_files:
+                return ret_files
+            else:
+                print("No File found in the directory")
+                return None 
     else:
         print("No File found in the directory")
         return None
diff --git a/ml_history.py b/ml_history.py
index 70f6768..3d552e4 100644
--- a/ml_history.py
+++ b/ml_history.py
@@ -99,11 +99,12 @@ class History:
 
         return history_to_save
 
-    def save_history(self, hist_name):
+    def save_history(self, hist_name, timestamp=None):
         directory = "histories"
         if not os.path.exists(directory):
             os.makedirs(directory)  # Create the directory if it does not exist
-        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        if timestamp is None:
+            timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
         filepath = os.path.join(directory, f"{hist_name}_{timestamp}.json")
 
         # Needed for saving the history to a json file: 
diff --git a/ml_plots.py b/ml_plots.py
index 969a1e4..96f58e8 100644
--- a/ml_plots.py
+++ b/ml_plots.py
@@ -1,6 +1,11 @@
 import numpy as np
 import matplotlib.pyplot as plt
 import seaborn as sns
+import matplotlib.cm as cm
+import scipy.stats as stats
+import matplotlib.gridspec as gridspec
+
+from sklearn.linear_model import LinearRegression
 import os
 import time
 
@@ -11,23 +16,23 @@ def save_plot(plt, plot_name):
     time_stamp = time.strftime('%Y%m%d-%H%M%S')
     plt.savefig(f'plots/{plot_name}_{time_stamp}.png')
 
-def plot_training_history(hist_data, title='Training History', save=True):
+def plot_training_history(hist_data, colors, title='Training History', save=True):
 
     epochs = range(1, len(hist_data['train_loss']) + 1)
 
     fig, axs = plt.subplots(1, 2, figsize=(12, 5))
 
     # Plot accuracy
-    axs[1].plot(epochs, hist_data['train_rmse'], label='Train RMSE')
-    axs[1].plot(epochs, hist_data['val_rmse'], label='Validation RMSE')
+    axs[1].plot(epochs, hist_data['train_rmse'], label='Train RMSE', color=colors['blue'])
+    axs[1].plot(epochs, hist_data['val_rmse'], label='Validation RMSE', color=colors['green'])
     axs[1].set_title('RMSE')
     axs[1].set_xlabel('Epochs')
     axs[1].set_ylabel('RMSE')
     axs[1].legend()
 
     # Plot loss
-    axs[0].plot(epochs, hist_data['train_loss'], label='Train Loss')
-    axs[0].plot(epochs, hist_data['val_loss'], label='Validation Loss')
+    axs[0].plot(epochs, hist_data['train_loss'], label='Train Loss', color=colors['blue'])
+    axs[0].plot(epochs, hist_data['val_loss'], label='Validation Loss', color=colors['green'])
     axs[0].set_title('Loss')
     axs[0].set_xlabel('Epochs')
     axs[0].set_ylabel('Loss')
@@ -41,10 +46,10 @@ def plot_training_history(hist_data, title='Training History', save=True):
         save_plot(plt, title)
     return plt
 
-def plot_distribution(true_values, predicted_values, title='Distribution of Predicted and True Values', save=True):
+def plot_distribution(true_values, predicted_values, colors, title='Distribution of Predicted and True Values', save=True):
     plt.figure(figsize=(10, 6))
-    plt.hist(true_values, bins=20, color='skyblue', edgecolor='black', alpha=0.7, label='True Values')
-    plt.hist(predicted_values, bins=20, color='salmon', edgecolor='black', alpha=0.7, label='Predicted Values')
+    plt.hist(true_values, bins=20, color=colors['green'], edgecolor='black', alpha=0.7, label='True Values')
+    plt.hist(predicted_values, bins=20, color=colors['blue'], edgecolor='black', alpha=0.7, label='Predicted Values')
     plt.title(title)
     plt.xlabel('Score')
     plt.ylabel('Frequency')
@@ -55,15 +60,15 @@ def plot_distribution(true_values, predicted_values, title='Distribution of Pred
         save_plot(plt, title)
     return plt
 
-def plot_predictions(true_values, predicted_values, title='True vs Predicted Values', threshold=0.3, save=True):
+def plot_predictions(true_values, predicted_values, colors, title='True vs Predicted Values', threshold=0.3, save=True):
     plt.figure(figsize=(10, 6))
     # Difference between predicted and true values
     correct_indices = np.isclose(true_values, predicted_values, atol=threshold)
     incorrect_indices = ~correct_indices
     # Plot
-    plt.scatter(np.array(true_values)[correct_indices], np.array(predicted_values)[correct_indices], color='green', label='Correctly predicted')
-    plt.scatter(np.array(true_values)[incorrect_indices], np.array(predicted_values)[incorrect_indices], color='red', label='Incorrectly predicted')
-    plt.plot([min(true_values), max(true_values)], [min(true_values), max(true_values)], color='blue', linestyle='--', label='Ideal Line')
+    plt.scatter(np.array(true_values)[correct_indices], np.array(predicted_values)[correct_indices], color=colors['green'], alpha=0.5, label='Correctly predicted')
+    plt.scatter(np.array(true_values)[incorrect_indices], np.array(predicted_values)[incorrect_indices], color=colors['red'], alpha=0.5, label='Incorrectly predicted')
+    plt.plot([min(true_values), max(true_values)], [min(true_values), max(true_values)], color=colors['blue'], linestyle='--', label='Ideal Line')
     plt.xlabel('True Values')
     plt.ylabel('Predicted Values')
     plt.title(title)
@@ -72,4 +77,87 @@ def plot_predictions(true_values, predicted_values, title='True vs Predicted Val
     # save plot
     if save:
         save_plot(plt, title)
-    return plt
\ No newline at end of file
+    return plt
+
+def plot_residuals(labels, preds, colors, title='Residuals Plot', save=True):
+    residuals = np.array(preds) - np.array(labels)
+    
+    fig = plt.figure(figsize=(14, 6))
+    gs = gridspec.GridSpec(1, 2, width_ratios=[4, 1])
+
+    # Main plot
+    ax0 = plt.subplot(gs[0])
+    ax0.scatter(labels, residuals, label='Residuals', color=colors['blue'], alpha=0.5)
+    
+    # Fit linear regression model to residuals
+    labels_reshaped = np.array(labels).reshape(-1, 1)
+    model = LinearRegression()
+    model.fit(labels_reshaped, residuals)
+    trend_line = model.predict(labels_reshaped)
+    
+    # Plot trend line
+    ax0.plot(labels, trend_line, color=colors['red'], label='Trend Line', linewidth=2)
+    
+    ax0.set_xlabel('True Values')
+    ax0.set_ylabel('Residuals')
+    ax0.axhline(y=0, color='k', linestyle='--')
+    ax0.set_title(title)
+    ax0.legend()
+
+    # Side plot for distribution of true values
+    ax1 = plt.subplot(gs[1], sharey=ax0)
+    ax1.hist(residuals, bins=30, alpha=0.5, color=colors['blue'], orientation='horizontal')
+    ax1.set_xlabel('Frequency')
+    ax1.set_title('Distribution of residuals')
+    ax1.yaxis.tick_right()
+    ax1.yaxis.set_label_position("right")
+
+    plt.tight_layout()
+    # save plot
+    if save:
+        save_plot(plt, title)
+    return plt
+
+def plot_qq(labels, preds, colors, title='Q-Q Plot of Residuals', save=True):
+    residuals = np.array(preds) - np.array(labels)
+
+    # Generate a Normal Q-Q plot
+    fig = plt.figure(figsize=(8, 6))
+    ax = fig.add_subplot(111)
+    stats.probplot(residuals, dist="norm", plot=ax)
+    
+    # Set colors
+    line = ax.get_lines()
+    line[0].set_color(colors['blue'])  # Data points
+    line[1].set_color(colors['red'])   # Fit line
+    
+    plt.title(title)
+    # save plot
+    if save:
+        save_plot(plt, title)
+    return plt
+
+def plot_val_preds(val_preds, val_labels, colors, title='Histogram of Validation Predictions', save=True):
+    plt.figure(figsize=(10, 6))
+    plt.hist(val_labels, bins=20, alpha=0.5, label='True Values', color=colors['green'],)
+
+    cmap = cm.get_cmap('coolwarm', len(val_preds))  # Use 'coolwarm' colormap for gradient from red to blue
+    for epoch, preds in val_preds.items():
+        color = cmap(len(val_preds) - epoch )  # Get color from colormap
+        plt.hist(preds, bins=20, alpha=0.5, label=f'Epoch {epoch}', color=color)
+
+    plt.xlabel('Predicted Values')
+    plt.ylabel('Frequency')
+    plt.title(title)
+    plt.legend()
+    plt.grid(axis='y', linestyle='--', alpha=0.7)
+    # save plot
+    if save:
+        save_plot(plt, title)
+    return plt
+
+
+####################################################################################################
+############### Comparison Plots ###################################################################
+####################################################################################################
+
diff --git a/ml_train.py b/ml_train.py
index aadf31d..394fc02 100644
--- a/ml_train.py
+++ b/ml_train.py
@@ -85,3 +85,29 @@ def test_loop(model, test_loader, device, is_bert=False):
             test_labels.extend(labels.cpu().detach().numpy())
 
     return test_labels, test_preds
+
+def ensemble_predict(models, test_loader, device, is_bert=False):
+    for model in models:
+        model.eval()
+    
+    test_preds = []
+    with torch.no_grad():
+        for batch in test_loader:
+            if is_bert:
+                input_ids = batch['input_ids'].to(device)
+                attention_mask = batch['attention_mask'].to(device)
+                predictions = [model(input_ids, attention_mask=attention_mask).float().cpu().detach().numpy() for model in models]
+            else:
+                X_batch, y_batch = batch
+                X_batch, y_batch = X_batch.to(device), y_batch.to(device).float()
+                predictions = [model(X_batch).float().cpu().detach().numpy() for model in models]
+            
+            predictions = predictions
+            test_preds.append(predictions)
+            
+    #check if predictions are empty lists
+    if not test_preds[0]:
+        raise ValueError("No predictions were made in ensemble prediction.")
+    
+    test_preds = np.concatenate(test_preds, axis=1)
+    return test_preds
\ No newline at end of file
diff --git a/model_comparison.ipynb b/model_comparison.ipynb
index fcbc1d0..9d010ac 100644
--- a/model_comparison.ipynb
+++ b/model_comparison.ipynb
@@ -2,21 +2,300 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
+   "execution_count": 11,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "# TODO: compare"
+    "import json\n",
+    "\n",
+    "import ml_helper\n",
+    "import ml_plots\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "COLORS = {\n",
+    "    'red': '#B56576',\n",
+    "    'blue': '#187795',\n",
+    "    'green': '#3F9A73' , #'#47B887', #'#38686A',\n",
+    "    'brown': '#DBCFB0',\n",
+    "    'yellow': '#D5C67A'\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading histories/CNN_history_2025-02-16_03-37-28.json\n",
+      "Loading histories/Transformer_history_2025-02-16_03-39-21.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load latest data if keyword is in the file name\n",
+    "hist_name_cnn = ml_helper.get_newest_file('histories/', name='CNN', extension=\".json\", ensemble=False)\n",
+    "print(f\"Loading {hist_name_cnn}\")\n",
+    "\n",
+    "hist_name_transformer = ml_helper.get_newest_file('histories/', name='Transformer_history', extension=\".json\", ensemble=False)\n",
+    "print(f\"Loading {hist_name_transformer}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hist_cnn = None\n",
+    "with open(hist_name_cnn, 'r') as file:\n",
+    "    hist_cnn = json.load(file)\n",
+    "\n",
+    "labels_cnn = hist_cnn['test_labels']\n",
+    "preds_cnn = hist_cnn['test_preds']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hist_trans= None\n",
+    "with open(hist_name_transformer, 'r') as file:\n",
+    "    hist_trans = json.load(file)\n",
+    "\n",
+    "labels_trans = hist_trans['test_labels']\n",
+    "preds_trans = hist_trans['test_preds']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def save_plot(plt, title):\n",
+    "    pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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LCgpizJgxvPvuuyQlJdGlSxemT59e5rgTEw+vW7eOnTt3UlRUxJw5c2r6q4hILTt+/HiZ13/88QerV6+mb9++xgQyyNatW0lISGDkyJFGRxGRamSxWJg9ezaHDx/m5ZdfPmm7ESNG8O233+Ln58egQYPO+HNOFMGSk5OrnFVE6h89viciDYLFYuGKK67gk08+4cCBA44V8lJTU1m2bBkXXXQRfn5+ABw7dozg4GDHsT4+PrRq1YqkpCSg8hMPi0j90KJFC0aPHk2LFi04ePAgr7zyCm5ubjz88MNGR6sVO3fuZNu2bcyZM4eIiAiGDRtmdCQRqWZ9+/bl/PPPZ968eUyYMKFMH+eEG264gaSkJNq2bYubm9tJz/X111/Tt2/fcquCrl69GqDCqRREpOFSUUpE6p3FixezZs2acvunT59OfHw8F110EXfddRcuLi68+uqrFBYW8swzzzjadejQgb59+9KjRw+CgoLYunUrH330Effccw9QOvHwZZddxtChQ+nQoQMuLi6sWLGi3MTDIlI/DBgwgHfffZeUlBTc3d3p1asXTz75JK1btzY6Wq346KOPmDlzJm3btuXdd9+t8H9WRcT5PfTQQ9x4443ExcVx5513lnvf39+/3Kjxitx7773k5+dz7bXX0q5dO4qKivjuu+94//33iY6OZsyYMWXaHzp0iHfeeafceXx8fLjmmmuq+nVExEloTikRqTdOzFVwMklJSRw9epTJkyezefNmbDYbPXv25IknnnAslQ7wxBNPsGrVKvbu3UthYSFRUVHceuutPPTQQ7i6unLs2DGmTZvG+vXrSUpKckw8/OCDD3LjjTfWxlcVEREROWOnmn/TZrPRpk0bAPbs2cNll11WZk6pilQ0p9SaNWv48MMP+e677/j7778pKiqiWbNmDBw4kClTptCoUSPH8dHR0Rw8eLDCc0dFRXHgwIGz/MYiUtepKCUiIiIiIiIiIrVOE52LiIiIiIiIiEitU1FKRERERERERERqnYpSIiIiIiIiIiJS61SUEhERERERERGRWqeilIiIiIiIiIiI1DoVpUREREREREREpNa5GB2gttlsNg4fPoyvry8mk8noOCIiIlLH2e12cnJyaNy4MWZzw72fpz6UiIiIVFZl+08Nrih1+PBhIiMjjY4hIiIiTiYpKYmmTZsaHcMw6kOJiIjImTpd/6nBFaV8fX2B0gvj5+dncBoRERGp67Kzs4mMjHT0IRoq9aFERESksirbf2pwRakTw839/PzUoRIREZFKa+iPrKkPJSIiImfqdP2nhjsxgoiIiIiIiIiIGEZFKRERERERERERqXUqSomIiIiIiIiISK1rcHNKiYg0JDabjaKiIqNjiNRprq6uWCwWo2OIiEgtsVqtFBcXGx1DxKlVV/9JRSkRkXqqqKiI/fv3Y7PZjI4iUucFBAQQHh7e4CczFxGpz+x2OykpKWRmZhodRaReqI7+k4pSIiL1kN1uJzk5GYvFQmRkJGazntYWqYjdbic/P58jR44AEBERYXAiERGpKScKUo0aNcLLy0s3IkSqqDr7T4YWpTZu3Mizzz7Ltm3bSE5OZsWKFVxzzTWnPGbp0qU888wz/PHHH/j7+zNw4ECeffZZgoODaye0iIgTKCkpIT8/n8aNG+Pl5WV0HJE6zdPTE4AjR47QqFEjPconIlIPWa1WR0FK/+8ocvaqq/9k6K3zvLw8unbtyvz58yvVfvPmzYwcOZL//Oc/7Nq1iw8//JAff/yRcePG1XBSERHnYrVaAXBzczM4iYhzOFG81RwjIiL104l/33WzTqT6VEf/ydCRUgMHDmTgwIGVbr9lyxaio6O57777AGjevDl33HEHTz/9dE1FFBFxahqWLlI5+rsiItIw6N97kepTHX+fnGqSkV69epGUlMTq1aux2+2kpqby0UcfMWjQIKOjiYiIiIiIiIjIGXCqolTv3r1ZunQpw4YNw83NjfDwcPz9/U/5+F9hYSHZ2dllNhERkcpISUnh8ssvx9vbm4CAAKPj1Dkmk4mVK1caHUNERERqkfpHp6b+0ZlxqqLUb7/9xv33309sbCzbtm1jzZo1HDhwgDvvvPOkx8yePRt/f3/HFhkZWYuJRUTkTPTt25cJEyYYHcPh+eefJzk5mYSEBPbu3Wt0nCo5cOAAJpPplFtcXFyVzp2cnHxGj+FXJDo6mnnz5p3VOUREROoz9Y+qnzP0j07k8PLyonPnzrz++utl2mzYsAGTyURgYCAFBQVl3vvpp58cx//TokWL6Nq1Kz4+PgQEBNC9e3dmz57teH/69OkVXot27dqd1fc5FUPnlDpTs2fPpnfv3jz00EMAdOnSBW9vby6++GIef/zxCpchnDx5MhMnTnS8zs7OVmFKRMSJ2e12rFYrLi41/yvsr7/+okePHrRu3brK5ygqKqrVCeeLi4txdXV1vI6MjCQ5Odnx+rnnnmPNmjV8+eWXjn3+/v6On61WKyaTCbP59PetwsPDqym1iIiInA31j07NGftHM2fOZNy4ceTn5/Phhx8ybtw4mjRpUq7g5evry4oVKxg+fLhj3xtvvEGzZs1ITEx07Fu8eDETJkzgxRdfpE+fPhQWFvLLL7+wc+fOMufr2LFjmesA1Oh/V041Uio/P7/cfwQnlh202+0VHuPu7o6fn1+ZTURE6p7Ro0fzzTff8MILLzjuyhw4cMBxF+iLL76gR48euLu7s2nTJv766y+uvvpqwsLC8PHx4bzzziv3CzQ6Oponn3yS2267DV9fX5o1a8Zrr73meL+oqIh77rmHiIgIPDw8iIqKctwtio6O5uOPP2bJkiWYTCZGjx4NQGJiIldffTU+Pj74+fkxdOhQUlNTHeecPn063bp14/XXX6d58+Z4eHgApUO5X331Va688kq8vLxo3749W7Zs4c8//6Rv3754e3tz4YUX8tdff5X5Dp988gnnnHMOHh4etGjRghkzZlBSUuJ432Qy8corr3DVVVfh7e3NE088UeZ4i8VCeHi4Y/Px8cHFxcXxes2aNURERLBq1So6dOiAu7s7iYmJ/PTTT1x++eWEhITg7+9Pnz59+Pnnn8uc+5/D00/ccVy+fDn9+vXDy8uLrl27smXLlir81/D/XnnlFVq2bImbmxtt27bl7bffdrxnt9uZPn06zZo1w93dncaNGzsWQwFYsGABrVu3xsPDg7CwMG644YazyiIiIlLb1D9quP0jX19fwsPDadGiBY888ghBQUHEx8eXazdq1CgWL17seH38+HHee+89Ro0aVabdqlWrGDp0KP/5z39o1aoVHTt2ZPjw4eWuzT+vw4ktJCTktHmrytCiVG5uLgkJCSQkJACwf/9+EhISHNW8yZMnM3LkSEf7IUOGsHz5cl555RX27dvH5s2bue+++zj//PNp3LixEV+hnC0/f88Ha5ZzMOmA0VFERBzsdjslhUWGbCe7afBvL7zwAr169WLcuHEkJyeTnJxcZmTro48+ylNPPcXu3bvp0qULubm5DBo0iPXr17N9+3YGDBjAkCFDytwRApgzZw7nnnsu27dv56677mL8+PHs2bMHgBdffJFVq1bxwQcfsGfPHpYuXUp0dDRQOux5wIABDB06lOTkZF544QVsNhtXX3016enpfPPNN8THx7Nv3z6GDRtW5jP//PNPPv74Y5YvX+74HQcwa9YsRo4cSUJCAu3atWPEiBHccccdTJ48ma1bt2K327nnnnsc7b/99ltGjhzJ/fffz2+//carr75KXFxcuc7D9OnTufbaa/n111+57bbbKnW9/yk/P5+nn36a119/nV27dtGoUSNycnIYNWoUmzZt4vvvv6d169YMGjSInJycU57rscceY9KkSSQkJNCmTRuGDx9eppN4JlasWMH999/Pgw8+yM6dO7njjjsYM2YMX3/9NQAff/wxzz//PK+++ip//PEHK1eupHPnzgBs3bqV++67j5kzZ7Jnzx7WrFnDJZdcUqUcUvPSjh1l9Tdr+fzrNUZHEZEGRP0j9Y9Opa70j2w2Gx9//DEZGRkVji679dZb+fbbbx1/xh9//DHR0dGcc845ZdqFh4fz/fffc/DgwUpegdph6ON7W7dupV+/fo7XJx6zGzVqFHFxcSQnJ5f5yzN69GhycnJ4+eWXefDBBwkICODSSy/l6aefrvXsJ/PaD5+z3+U4ZpOZqMhoo+OIiABgLSpm3b1TDfnsK16ahYv76Ydn+/v74+bmhpeXV4XDnmfOnMnll1/ueB0UFETXrl0dr2fNmsWKFStYtWpVmY7LoEGDuOuuuwB45JFHeP755/n6669p27YtiYmJtG7dmosuugiTyURUVJTjuNDQUNzd3fH09HTkiY+P59dff2X//v2ODuGSJUvo2LEjP/30E+eddx5QeodxyZIlhIaGlvkOY8aMYejQoY4svXr1YurUqfTv3x+A+++/nzFjxjjaz5gxg0cffdRxp6tFixbMmjWLhx9+mGnTpjnajRgxosxxZ6q4uJgFCxaUuZ6XXnppmTavvfYaAQEBfPPNN1x55ZUnPdekSZMYPHiwI3/Hjh35888/qzQXwXPPPcfo0aMdf34TJ07k+++/57nnnqNfv34kJiYSHh5OTEwMrq6uNGvWjPPPPx8ovWPr7e3NlVdeia+vL1FRUXTv3v2MM0jt+GXvLp7ZG09IsYXB/QYYHUdEGgj1j9Q/OhWj+0ePPPIIU6ZMobCwkJKSEoKCghg7dmy5do0aNWLgwIHExcURGxvL4sWLKyzCTZs2jeuuu47o6GjatGlDr169GDRoEDfccEOZJ9J+/fVXfHx8yhx7yy23sHDhwpNmPRuGjpTq27cvdru93HZiQrG4uDg2bNhQ5ph7772XXbt2kZ+fz+HDh3nnnXdo0qRJ7Yc/CT9L6T8sGfla5U9EpDqde+65ZV7n5uYyadIk2rdvT0BAAD4+PuzevbvcncAuXbo4fjaZTISHh3PkyBGg9GZHQkICbdu25b777mPdunWnzLB7924iIyPL3KHs0KEDAQEB7N6927EvKiqqXIfr31nCwsIAHCN7TuwrKChwrBS7Y8cOZs6ciY+Pj2M7cac0Pz//pNfmTLm5uZXJBpCamsq4ceNo3bo1/v7++Pn5kZubW+76nuo7npjr8cT1PlO7d++md+/eZfb17t3bca1vvPFGjh8/TosWLRg3bhwrVqxw3HW8/PLLiYqKokWLFtx6660sXbq0zDWTuqVZeFMAMi1WbDabwWlERJyH+kf1t3/00EMPkZCQwFdffUXPnj15/vnnadWqVYVtb7vtNuLi4ti3bx9btmzh5ptvLtcmIiKCLVu28Ouvv3L//fdTUlLCqFGjGDBgQJnfvW3btnU80XZimzlz5imzng2nmujcGfi5ekJJFpn5uUZHERFxsLi5csVLswz77Org7e1d5vWkSZOIj4/nueeeo1WrVnh6enLDDTdQVFRUpt0/J7WE0o7XiV+855xzDvv37+eLL77gyy+/ZOjQocTExPDRRx9Va9aKspxYDaWifSfy5ebmMmPGDK677rpy5zoxF8OpPq+yPD09y63OMmrUKI4dO8YLL7xAVFQU7u7u9OrVq9z1/bdTfZ/qFhkZyZ49e/jyyy+Jj4/nrrvu4tlnn+Wbb77B19eXn3/+mQ0bNrBu3TpiY2OZPn06P/30k5avroMiGzcFO5SY4UhaKuGNyi9eIyJS3dQ/+n/qH5VndP8oJCSEVq1a0apVKz788EM6d+7MueeeS4cOHcq1HThwILfffjv/+c9/GDJkCMHBwSc9b6dOnejUqRN33XUXd955JxdffDHffPON4yk2Nze3kxa/aoKKUtXM390LSiCrSHdjRaTuMJlMlRoibjQ3NzesVmul2m7evJnRo0dz7bXXAqUdlAMHDpzxZ/r5+TFs2DCGDRvGDTfcwIABA0hPTycoKKhc2/bt25OUlERSUpLjbuBvv/1GZmZmhR2Es3XOOeewZ8+eWu0YnLB582YWLFjAoEGDAEhKSiItLa1WM7Rv357NmzeXmahz8+bNZa61p6cnQ4YMYciQIdx99920a9eOX3/9lXPOOQcXFxdiYmKIiYlh2rRpBAQE8NVXX1XYiRVjubt74FdiJtvVxoFDiSpKiUitUP/o5NQ/qphR/aPIyEiGDRvG5MmT+eSTT8q97+LiwsiRI3nmmWf44osvKn3eE38+eXl51Zb1TKkoVc0CPH0hD7KKjhsdRUTE6URHR/PDDz9w4MABfHx8Kuz4nNC6dWuWL1/OkCFDMJlMTJ069YxH5MydO5eIiAi6d++O2Wzmww8/JDw8/KQjaWJiYujcuTM333wz8+bNo6SkhLvuuos+ffqc9RDxisTGxnLllVfSrFkzx/P+O3bsYOfOnTz++OPV/nn/1Lp1a95++23OPfdcsrOzeeihh/D09KyRzzp06FCZCU+hdIj/Qw89xNChQ+nevTsxMTF8+umnLF++3LGKUFxcHFarlZ49e+Ll5cU777yDp6cnUVFRfPbZZ+zbt49LLrmEwMBAVq9ejc1mo23btjXyHeTsBZncyKaAv48kn76xiEgDov5RWQ2lf/Rv999/P506dWLr1q0VXtdZs2bx0EMPnXSU1Pjx42ncuDGXXnopTZs2JTk5mccff5zQ0FB69erlaFdSUkJKSkqZY00mk+PRyupm6JxS9VGgtx8AObZCg5OIiDifSZMmYbFY6NChA6Ghoad8Pn/u3LkEBgZy4YUXMmTIEPr3719ulZHT8fX15ZlnnuHcc8/lvPPO48CBA6xevbrMZI//ZDKZ+OSTTwgMDOSSSy4hJiaGFi1a8P7775/R51ZW//79+eyzz1i3bh3nnXceF1xwAc8//3yZCUdryhtvvEFGRgbnnHMOt956K/fddx+NGjWqkc967rnn6N69e5nt888/55prruGFF17gueeeo2PHjrz66qu8+eab9O3bF4CAgAAWLVpE79696dKlC19++SWffvopwcHBBAQEsHz5ci699FLat2/PwoULeffdd+nYsWONfAc5e6GupY9ZHM48anASEZG6Rf2jshpK/+jfOnTowBVXXEFsbGyF77u5uRESElLukcMTYmJi+P7777nxxhtp06YN119/PR4eHqxfv75MIWvXrl1ERESU2Wry2prslV2Lsp7Izs7G39+frKws/Pz8qv38X3//DTN+/ZSQYgsf3VV3VgUUkYaloKCA/fv307x58zLP1otIxU71d6am+w7Ooqavw3PLFvBZ3j56WxrxxG0PV/v5RaRhU99IpPpVR/9JI6WqWbB/IAB5Jq0cIyIiIlJZjf1DADhSqMViREREGgoVpapZaGDpEpfHLXbHstQiIiIicmpNQsIBOGbXFAgiIiINhYpS1SzkxLOYJjiWXrurFImIiIg4q6jGzQDIslixWnVjT0REpCFQUaqaubq64fm/ftTRDBWlRERERCqjSXhjTHawmiE5VSvwiYiINAQqStUAb7sFgGOZ6QYnEREREXEOrq5u+JeUdk0PHv7b4DQiIiJSG1SUqgE+uAKQnp1pbBARERERJxJscgfg76OHDU4iIiIitUFFqRrga3EDICMv2+AkIiIiIs4jxNUHgOQsTYEgIiLSEKgoVQP8XD0ASM/PMTiJiIiIiPMI9/YHICUv09ggIiIiUitUlKoBfu7eAGQV5hucRERERMR5RPiHAnC0KNfgJCIiIlIbVJSqAQGepUPPs4uPG5xERESMEBcXR0BAgNExRJxOZGg4AMfshQYnERGR6qb+kVRERakaEOjlC0COVR0qEZG6bPr06ZhMplNuVTFs2DD27t17Vtk2bNiAyWQiMzPzrM4j4kwiG0cCkO1io7i4yOA0IiINkzP0j05soaGhDBo0iF9//bVMu9GjR2MymbjzzjvLnePuu+/GZDIxevRox76jR48yfvx4mjVrhru7O+Hh4fTv35/Nmzc72kRHR1d4LZ566qmz+k4NnYpSNSDILwCAXHuxsUFEROSUJk2aRHJysmNr2rQpM2fOLLPvn4qKKvc/yZ6enjRq1KgmIovUa43DGmO2gc0Eh1O0Ap+IiBGcoX+0Z88ekpOTWbt2LYWFhQwePLhcjsjISN577z2OH///J5gKCgpYtmwZzZo1K9P2+uuvZ/v27bz11lvs3buXVatW0bdvX44dO1am3b+vQ3JyMvfee2+1fKeGSkWpGhDqHwxAnslqcBIREedis9l45plnaNWqFe7u7jRr1ownnngCgAMHDmAymVi+fDn9+vXDy8uLrl27smXLFsfxJ4aFr127lvbt2+Pj48OAAQPKdZ5O8PHxITw83LFZLBZ8fX0dr2+66SbuueceJkyYQEhICP379wdg7ty5dO7cGW9vbyIjI7nrrrvIzc0tl+OE6dOn061bN95++22io6Px9/fnpptuIien6gtiZGRkMHLkSAIDA/Hy8mLgwIH88ccfjvcPHjzIkCFDCAwMxNvbm44dO7J69WrHsTfffDOhoaF4enrSunVr3nzzzSpnEakuLi4uBFgtABw8nGRwGhGRukH9o/IaNWpEeHg455xzDhMmTCApKYnff/+9TJtzzjmHyMhIli9f7ti3fPlymjVrRvfu3R37MjMz+fbbb3n66afp168fUVFRnH/++UyePJmrrrqqzDn/eR1ObN7e3qfNKyenolQNCAkKASDfYsdqLTE4jYgI2O128ouKDdnsdnulc06ePJmnnnqKqVOn8ttvv7Fs2TLCwsLKtHnssceYNGkSCQkJtGnThuHDh1NS8v//1ubn5/Pcc8/x9ttvs3HjRhITE5k0aVKVr91bb72Fm5sbmzdvZuHChQCYzWZefPFFdu3axVtvvcVXX33Fww8/fMrz/PXXX6xcuZLPPvuMzz77jG+++eashnuPHj2arVu3smrVKrZs2YLdbmfQoEEUF5eO0r377rspLCxk48aN/Prrrzz99NP4+JTOeXji+n7xxRfs3r2bV155hZCQkCpnEalOQSZ3AP4+lmJwEhGp79Q/cv7+UVZWFu+99x4Abm5u5d6/7bbbytx4W7x4MWPGjCnTxsfHBx8fH1auXElhoabgqW0uRgeoj0KCSkdK2U2QkZlBSHCowYlEpKE7XlxCy9hFhnz2XzPH4eXmetp2OTk5vPDCC7z88suMGjUKgJYtW3LRRReVaTdp0iQGDx4MwIwZM+jYsSN//vkn7dq1A6C4uJiFCxfSsmVLAO655x5mzpxZ5fytW7fmmWeeKbNvwoQJjp+jo6N5/PHHufPOO1mwYMFJz2Oz2YiLi8PXt3TewVtvvZX169c77nSeiT/++INVq1axefNmLrzwQgCWLl1KZGQkK1eu5MYbbyQxMZHrr7+ezp07A9CiRQvH8YmJiXTv3p1zzz3X8R1E6opG7j78ac3ncGaa0VFEpJ5T/8h5+0dNmzYFIC8vD4CrrrrK8V3/6ZZbbmHy5MkcPHgQgM2bN/Pee++xYcMGRxsXFxfi4uIYN24cCxcu5JxzzqFPnz7cdNNNdOnSpcz5HnnkEaZMmVJm3xdffMHFF198yrxychopVQPc3T1w/9+Te0fT1aESEamM3bt3U1hYyGWXXXbKdv/sHERERABw5MgRxz4vLy9Hh+tEm3++f6Z69OhRbt+XX37JZZddRpMmTfD19eXWW2/l2LFj5Ofnn/Q80dHRjg7X2ebavXs3Li4u9OzZ07EvODiYtm3bsnv3bgDuu+8+Hn/8cXr37s20adP45ZdfHG3Hjx/Pe++9R7du3Xj44Yf57rvvqpRDpCaEeQcAcCQ/y9ggIiJ1gPpHFfv222/Ztm0bcXFxtGnTxjFa699CQ0MZPHgwcXFxvPnmmwwePLjC0eHXX389hw8fZtWqVQwYMIANGzZwzjnnEBcXV6bdQw89REJCQpntxE0+qRqNlKoh3jYLhRYrxzLTjY4iIoKnqwt/zRxn2GdXqp2nZ6Xaubr+/13FE6u/2Gy2Ct8/0eZMhsj/27/nCThw4ABXXnkl48eP54knniAoKIhNmzbxn//8h6KiIry8vE6b+0Suf+aubmPHjqV///58/vnnrFu3jtmzZzNnzhzuvfdeBg4cyMGDB1m9ejXx8fFcdtll3H333Tz33HM1lkekspoEhEL2Xo4W556+sYjIWVD/yHn7R82bNycgIIC2bdty5MgRhg0bxsaNGytse9ttt3HPPfcAMH/+/JOe08PDg8svv5zLL7+cqVOnMnbsWKZNm1Zmlb6QkBBatWp12nxSeRopVUN8/lfvS8vKMDiJiEjpL3gvN1dDtsouG9y6dWs8PT1Zv359DV+Ns7Nt2zZsNhtz5szhggsuoE2bNhw+XLurhLVv356SkhJ++OEHx75jx46xZ88eOnTo4NgXGRnJnXfeyfLly3nwwQdZtOj/H1EIDQ1l1KhRvPPOO8ybN4/XXnutVr+DyMk0CS29w59ur9xqTiIiVaX+UfUxsn909913s3PnTlasWFHh+wMGDKCoqIji4mLHpOyV0aFDB8fjgVJzVJSqIb6W0knWMvI09FxEpDI8PDx45JFHePjhh1myZAl//fUX33//PW+88YbR0cpo1aoVxcXFvPTSS+zbt4+33377pEPGq8Ovv/5aZoj4jh07aN26NVdffTXjxo1j06ZN7Nixg1tuuYUmTZpw9dVXA6XzOqxdu5b9+/fz888/8/XXX9O+fXsAYmNj+eSTT/jzzz/ZtWsXn332meM9cQ4bN25kyJAhNG7cGJPJxMqVK0/ZPjk5mREjRtCmTRvMZnOZeT/qmujGUQBku9gqvcy4iEh9pf7R6Xl5eTFu3DimTZtW4egvi8XC7t27+e2337BYLOXeP3bsGJdeeinvvPMOv/zyC/v37+fDDz/kmWeecfSrTsjJySElJaXMlp2dXWPfrSFQUaqG+Lp4AJCZX/XlvkVEGpqpU6fy4IMPEhsbS/v27Rk2bNhZzXdQE7p27crcuXN5+umn6dSpE0uXLmX27Nk19nmXXHIJ3bt3d2wn5nB488036dGjB1deeSW9evXCbrezevVqxzB4q9XK3XffTfv27RkwYABt2rRxTDTq5ubG5MmT6dKlC5dccgkWi8Wxco04h7y8PLp27XrKxxD+qbCwkNDQUKZMmULXrl1rON3ZCWsUhoutdMGYpMOJRscRETGc+kend88997B7924+/PDDCt/38/PDz8+vwvd8fHzo2bMnzz//PJdccgmdOnVi6tSpjBs3jpdffrlM29jYWCIiIspsp1thUE7NZD+bB0mdUHZ2Nv7+/mRlZZ30P8rqMPvtl1hbcJA+ro2ZMXpijX2OiEhFCgoK2L9/P82bN8fDw8PoOCJ13qn+ztRW36GqTCYTK1as4JprrqlU+759+9KtWzfmzZt3Rp9Tm9fhhgWPkOZqZVrnIfS7oE+NfpaINAzqG4lUv+roP2mkVA0J8PQBILv4uMFJRERERJxLsLm0Y/t3WrLBSURERKQmafW9GhLk7QcZkFNSaHQUERERkTNWWFhIYeH/92Nqc86MUHdf9pTkkZJ1rNY+U0RERGqfRkrVkCBffwBy7MUGJxERERE5c7Nnz8bf39+xRUZG1tpnh3sHAJCSrwVjRERE6jMVpWpIsH8QAHmmEoOTiIiIiJy5yZMnk5WV5diSkpJq7bMbB4YCkFacX2ufKSIiIrVPj+/VkJDAYADyzXZsNhtms+p/IiIi4jzc3d1xd3c35LMjGzWGA5Bu0jQIIiIi9ZmKUjUkNLj0Dp/NDFnZmQQGBBmcSEREROqL3Nxc/vzzT8fr/fv3k5CQQFBQEM2aNWPy5MkcOnSIJUuWONokJCQ4jj169CgJCQm4ubnRoUOH2o5/Ws0aNwMgx8XO8YICPLVSloiISL2kolQN8fT0ws0KRRY4cuyoilIiIiJSbbZu3Uq/fv0crydOnAjAqFGjiIuLIzk5mcTExDLHdO/e3fHztm3bWLZsGVFRURw4cKBWMp+J0OBQXK1QbIGkQ4m0adnG6EgiIiJSA1SUqkFeNjNFFhtpGem0NTqMiIiI1Bt9+/bFbref9P24uLhy+07Vvq4xm80E2lw4YikhMeVvFaVERETqKU10VIN8/lfzS8/OMDiJiIiIiHMJNpc+snfoWKrBSURERKSmqChVg3zNbgCk52k5YxGRhig6Opp58+YZHUPEKTXy8AXgcFaawUlERKQ6qX8k/6SiVA3ydSm9w5eZn2NwEhER59C3b18mTJhQreccPXo011xzzSnbmEymU27Tp0+v0mf/9NNP3H777VU69oSauCYiziDMJxCAIwXZBicRETGW+kfl9e3b15HDw8ODNm3aMHv27DKPqh84cACTyYTFYuHQoUNljk9OTsbFxQWTyVRmbsUVK1ZwwQUX4O/vj6+vLx07dixz7ePi4iq8Fh5akKPKNKdUDQpw94ICyCrIMzqKiIicQnJysuPn999/n9jYWPbs2ePY5+Pj4/jZbrdjtVpxcTn9r9DQ0NDqDSrSgDQNDIOM30grVj9KRMQIdb1/NG7cOGbOnElhYSFfffUVt99+OwEBAYwfP75MuyZNmrBkyRImT57s2PfWW2/RpEmTMouCrF+/nmHDhvHEE09w1VVXYTKZ+O2334iPjy9zPj8/vzLXAUoLeFI1ho6U2rhxI0OGDKFx48aYTCZWrlx52mMKCwt57LHHiIqKwt3dnejoaBYvXlzzYasgwKP0L2lW0XGDk4iI1H2jR4/mm2++4YUXXnDcdTpx52rnzp0MHDgQHx8fwsLCuPXWW0lL+/9Hej766CM6d+6Mp6cnwcHBxMTEkJeXx/Tp03nrrbf45JNPHOfcsGFDuc8ODw93bP7+/phMJsfr33//HV9fX7744gt69OiBu7s7mzZt4q+//uLqq68mLCwMHx8fzjvvPL788ssy5/338HSTycTrr7/Otddei5eXF61bt2bVqlVndd0+/vhjOnbs6PidOGfOnDLvL1iwgNatW+Ph4UFYWBg33HDDaa+bSF3QtFEEAOmmYoOTiIgYR/2jk/Py8iI8PJyoqCjGjBlDly5dyhWQoHRl2jfffLPMvjfffJNRo0aV2ffpp5/Su3dvHnroIdq2bUubNm245pprmD9/fpl2/7wOJ7awsLDT5pWKGVqUysvLo2vXruX+kE9l6NChrF+/njfeeIM9e/bw7rvv0rZt3VzbLsC7dC6EnJJCg5OISENnt9s5XlxoyFbZFb9eeOEFevXqxbhx40hOTiY5OZnIyEgyMzO59NJL6d69O1u3bmXNmjWkpqYydOhQoPQu3vDhw7ntttvYvXs3GzZs4LrrrsNutzNp0iSGDh3KgAEDHOe88MILq3QNH330UZ566il2795Nly5dyM3NZdCgQaxfv57t27czYMAAhgwZUuaOW0VmzJjB0KFD+eWXXxg0aBA333wz6enpVcq0bds2hg4dyk033cSvv/7K9OnTmTp1qmPlta1bt3Lfffcxc+ZM9uzZw5o1a7jkkkuAU183kbogumkUAHkudvLycg1OIyL1kfpH9aN/ZLfb+fbbb/n9999xc3Mr9/5VV11FRkYGmzZtAmDTpk1kZGQwZMiQMu3Cw8PZtWsXO3furOS3l+pg6ON7AwcOZODAgZVuv2bNGr755hv27dtHUFAQUFplrauCfAIAyLEXGRtERBq8gpIiBsY9ZshnfzH6CTxd3U/bzt/fHzc3N8ddrxNefvllunfvzpNPPunYt3jxYiIjI9m7dy+5ubmUlJRw3XXXERVV+j+xnTt3drT19PSksLCwzDmrYubMmVx++eWO10FBQXTt2tXxetasWaxYsYJVq1Zxzz33nPQ8o0ePZvjw4QA8+eSTvPjii/z4448MGDDgjDPNnTuXyy67jKlTpwLQpk0bfvvtN5599llGjx5NYmIi3t7eXHnllfj6+hIVFUX37t2B0s7qqa6biNEC/ANxt0KhBRIPJdK+TQejI4lIPaP+kXP3jxYsWMDrr79OUVERxcXFeHh4cN9995Vr5+rqyi233MLixYu56KKLWLx4Mbfccguurq5l2t177718++23dO7cmaioKC644AKuuOIKbr75Ztzd///PKisrq8yjiwAXX3wxX3zxxUmzysk51UTnq1at4txzz+WZZ56hSZMmtGnThkmTJnH8eN18PC7YPwCAPKzGBhERcWI7duzg66+/xsfHx7G1a9cOgL/++ouuXbty2WWX0blzZ2688UYWLVpERkZGtec499xzy7zOzc1l0qRJtG/fnoCAAHx8fNi9e/dp7wR26dLF8bO3tzd+fn4cOXKkSpl2795N7969y+zr3bs3f/zxB1arlcsvv5yoqChatGjBrbfeytKlS8nPzweotesmUlVms5kAW+n/MCSmHDpNaxGRhkX9I7j55ptJSEhg8+bNDBw4kMcee+ykI75uu+02PvzwQ1JSUvjwww+57bbbyrXx9vbm888/588//2TKlCn4+Pjw4IMPcv755zv6TwC+vr4kJCSU2V5//fVTZpWTc6qJzvft28emTZvw8PBgxYoVpKWlcdddd3Hs2LFyz4ieUFhYSGHh/z8+l51deyu4hAQEA5BnsWGz2TCbnaoGKCL1iIeLG1+MfsKwzz4bubm5DBkyhKeffrrcexEREVgsFuLj4/nuu+9Yt24dL730Eo899hg//PADzZs3P6vP/idvb+8yrydNmkR8fDzPPfccrVq1wtPTkxtuuIGiolOPjv33XTmTyYTNZqu2nP/k6+vLzz//zIYNG1i3bh2xsbFMnz6dn376iYCAgFq5biJnI8TiSSrFHEpPNTqKiNRD6h+dPSP7R/7+/rRq1QqADz74gFatWnHBBRcQExNTrm3nzp1p164dw4cPp3379nTq1ImEhIQKz9uyZUtatmzJ2LFjeeyxx2jTpg3vv/8+Y8aMAUpvmpz4XDl7TlWUstlsmEwmli5dir+/P1D66MINN9zAggUL8PT0LHfM7NmzmTFjRm1HBSAkuBEAVjNk52QT8L+RUyIitc1kMlVqiLjR3NzcsFrLji4955xz+Pjjj4mOjj7pii4mk4nevXvTu3dvYmNjiYqKYsWKFUycOLHCc1aHzZs3M3r0aK699lqgtHP4zyWFa0P79u3ZvHlzuVxt2rTBYrEA4OLiQkxMDDExMUybNo2AgAC++uorrrvuulNeN5G6oJGHL7uKsknOrtq8ayIip6L+Uf3pH/n4+HD//fczadIktm/fXuFqeLfddht33XUXr7zySqXPGx0djZeXlxaCqUFONXQnIiKCJk2aOApSUNoht9vt/P333xUeM3nyZLKyshxbUlJSbcXF18cHl/8Vd9PS007dWEREiI6O5ocffuDAgQOkpaVhs9m4++67SU9PZ/jw4fz000/89ddfrF27ljFjxmC1Wvnhhx948skn2bp1K4mJiSxfvpyjR4/Svn17xzl/+eUX9uzZQ1paGsXF1bOSV+vWrVm+fDkJCQns2LGDESNG1NiIp6NHj5YbJp6amsqDDz7I+vXrmTVrFnv37uWtt97i5ZdfZtKkSQB89tlnvPjiiyQkJHDw4EGWLFmCzWajbdu2p71uInVBuG/pHKJHCrIMTiIiYhz1jyrnjjvuYO/evXz88ccVvj9u3DiOHj3K2LFjK3x/+vTpPPzww2zYsIH9+/ezfft2brvtNoqLi8vMm2W320lJSSm31db3rG+cqijVu3dvDh8+TG7u/6/AsnfvXsxmM02bNq3wGHd3d/z8/MpstcnbWnqJ0zKP1erniog4o0mTJmGxWOjQoQOhoaEkJibSuHFjNm/ejNVq5YorrqBz585MmDCBgIAAzGYzfn5+bNy4kUGDBtGmTRumTJnCnDlzHAtpjBs3jrZt23LuuecSGhpabmRRVc2dO5fAwEAuvPBChgwZQv/+/TnnnHOq5dz/tmzZMrp3715mW7RoEeeccw4ffPAB7733Hp06dSI2NpaZM2cyevRoAAICAli+fDmXXnop7du3Z+HChbz77rt07NjxtNdNpC5oEhgKQFpJ3Zw/VESkNqh/VDlBQUGMHDmS6dOnV1ggcnFxISQk5KQjy/r06cO+ffsYOXIk7dq1Y+DAgaSkpLBu3Tratm3raJednU1ERES5rapzhDZ0JruBaz/n5uby559/AtC9e3fmzp1Lv379CAoKolmzZkyePJlDhw6xZMkSR/v27dtzwQUXMGPGDNLS0hg7dix9+vRh0aJFlfrM7Oxs/P39ycrKqpUC1YgF/+WwaxEPt45hUN8zX1lJRKQqCgoK2L9/P82bN8fDw8PoOCJ13qn+ztR236GuMuI67PjtF+7fvATPEhNfjH+2Vj5TROon9Y1Eql919J8MHSm1detWxx1fgIkTJ9K9e3diY2OB0uWq/zlLv4+PD/Hx8WRmZnLuuedy8803M2TIEF588UVD8leGn7l0Arv0XA07FxERETkTUY0jATjuYic7p/YWqxEREZHaYehE53379uVUA7Xi4uLK7WvXrh3x8fE1mKp6+bq4gz2XjPwco6OIiIiIOJWAgEA8SqDABRIPHaRTu85GRxIREZFq5FRzSjkjf7fSFQGzCnJP01JERERE/i3IXrpMeGLKIYOTiIiISHVTUaqG+Xv4AJBVpAk6RURERM5UsMULgEMZmkBWRESkvlFRqoYFevkCkFNSaHASEREREecT5lnal0rO1krGIiIi9Y2KUjUsyMcfgBxbkcFJRKQhMnCBVRGnUtHS0VI3hPsGA3C0QPNzisjZ07/3ItWnOv4+GTrReUMQ5B8IQC4lBicRkYbE1dUVk8nE0aNHCQ0NxWQyGR1JpE6y2+0UFRVx9OhRzGYzbm5uRkeSf2ka1AjSIM2qqRBEpOrc3Nwwm80cPnyY0NBQ3Nzc1D8SqaLq7D+pKFXDQgJK7+7lWVSRF5HaY7FYaNq0KX///TcHDhwwOo5Inefl5UWzZs0wmzWIvK5pGt4E9kKGudjoKCLixMxmM82bNyc5OZnDhw8bHUekXqiO/pOKUjUsJCgEgBIz5OTm4uvjY3AiEWkofHx8aN26NcXF+h85kVOxWCy4uLjojnkdFdUkGoACC2RkphMYEGRsIBFxWm5ubjRr1oySkhKsVqvRcUScWnX1n1SUqmF+vn5YbGA1Q9qxIypKiUitslgsWCwWo2OIiFSZr48PXiUm8l3sHDyUqKKUiJwVk8mEq6srrq6uRkcRETTReY0zm814W0svc1qmVo0REREROVOB9tL/eUxK1SM3IiIi9YmKUrXAm9JRCseyMo0NIiIiIuKEQl28ADiUfsTgJCIiIlKdVJSqBb6m0pno03MzjQ0iIiIi4oQaefoBkJKbbnASERERqU4qStUCXxd3ADLzcgxOIiIiIuJ8IvxKVzM+UqC+lIiISH2iolQt8HfzBCCzINfgJCIiIiLOp0lQGADHbMcNTiIiIiLVSUWpWuDv4Q1AVqE6UiIiIiJnqllEUwAyzCXYbDaD04iIiEh1UVGqFgR4+QKQXaKilIiIiMiZimrSDIAiC6RnaDVjERGR+kJFqVoQ5O0PQI6tyOAkIiIiIs7H09MLnxITAAf+TjQ4jYiIiFQXFaVqQZBfIAC5lBicRERERMQ5BdlLVzP+++hhg5OIiIhIdVFRqhaEBAYBkG/WHAgiIiIiVRHi6gXA4YyjBicRERGR6qKiVC1oFBwKlM6DcPx4vsFpRERERJxPmGfpdAjJuRkGJxEREZHqoqJULfD3C+DEIKmjx3R3T0RERORMhfsHA3C0MMfgJCIiIlJdVJSqBWazGS9b6eScaVoxRkREROSMRQaHA3DMVmBwEhEREakuKkrVEm+7CwDHstINTiIiIiLifCLDmwKQYSnBZtM8nSIiIvWBilK1xNfkCkB6TpbBSUREREScT7MmkWCHEjMcSUs1Oo6IiIhUAxWlaomvizsAGXmaB0FERETkTLm7e+BXUtp1TTyUZHAaERERqQ4qStUSP1dPADKOqyglIiIiUhVBuAGQdDTZ4CQiIiJSHVSUqiUB7l4AZBXlG5xERERExDmFunkDcDjjiMFJREREpDqoKFVLArx8Acgp0YoxIiIiIlXRyNMfgJS8TGODiIiISLVQUaqWBHqXdqJyrEUGJxERERFxTo0DggE4WphrcBIRERGpDipK1ZIQ/0AAcikxOImIiIiIc2oSHAHAMbtGnouIiNQHKkrVkuCAIADyzFaDk4iIiIg4p6iIpgBkWqxYrbrRJyIi4uxUlKoloUEhABRaoLBQd/dEREREzlTTxk0x2cFqhpQjqUbHERERkbOkolQtCQwIxGQv/Tkt/ZixYURERESckKurG/4lpd3XA4cSDU4jIiIiZ0tFqVpisbjgZTUBkJaeZnAaEREREecUZHIH4FBassFJRERE5GypKFWLvO0WAI5lpRucRERERMQ5hbh6A3A4Uzf5REREnJ2KUrXIx+QKQFp2hsFJRERExJlt3LiRIUOG0LhxY0wmEytXrjztMRs2bOCcc87B3d2dVq1aERcXV+M5a0K4dwAAKXnqT4mIiDg7FaVqka+ldLh5Rn6OwUlERETEmeXl5dG1a1fmz59fqfb79+9n8ODB9OvXj4SEBCZMmMDYsWNZu3ZtDSetfhH+pYvHpBXlGZxEREREzpaL0QEaEj9XTyjJJPN4rtFRRERExIkNHDiQgQMHVrr9woULad68OXPmzAGgffv2bNq0ieeff57+/fvXVMwaERkaAYfgmF2rGYuIiDg7jZSqRf7uXgBkF+rOnoiIiNSeLVu2EBMTU2Zf//792bJly0mPKSwsJDs7u8xWFzSLaApAlouNkpISg9OIiIjI2VBRqhYFefkCkF2sO3siIiJSe1JSUggLCyuzLywsjOzsbI4fP17hMbNnz8bf39+xRUZG1kbU02oc3gSzDWwmOJT8t9FxRERE5CwYWpSqyiSdJ2zevBkXFxe6detWY/mqW6C3HwA51iKDk4iIiIic2uTJk8nKynJsSUlJRkcCwMXFBX9r6YrGiSpKiYiIODVDi1JnOknnCZmZmYwcOZLLLrushpLVjCC/AAByKTY2iIiIiDQo4eHhpKamltmXmpqKn58fnp6eFR7j7u6On59fma2uCDaVLh6TlJZscBIRERE5G4ZOdH6mk3SecOeddzJixAgsFssZja4yWnBAEAB5JqvBSURERKQh6dWrF6tXry6zLz4+nl69ehmU6OyEuvnwpy2f5Kw0o6OIiIjIWXC6OaXefPNN9u3bx7Rp0yrVvi5N0hkaWLqE8XEXKC7WI3wiIiJSNbm5uSQkJJCQkADA/v37SUhIIDExESh99G7kyJGO9nfeeSf79u3j4Ycf5vfff2fBggV88MEHPPDAA0bEP2th3gEApOZlGRtEREREzopTFaX++OMPHn30Ud555x1cXCo3yKsuTdIZHBQC9tKfj2UcMyyHiIiIOLetW7fSvXt3unfvDsDEiRPp3r07sbGxACQnJzsKVADNmzfn888/Jz4+nq5duzJnzhxef/11+vfvb0j+s9U4oPRG39FirWgsIiLizAx9fO9MWK1WRowYwYwZM2jTpk2lj5s8eTITJ050vM7OzjasMOXi4oKn1cRxFztHjh0lvFGEITlERETEufXt2xe73X7S9+Pi4io8Zvv27TWYqvZENoqAJEi3FxodRURERM6C0xSlcnJy2Lp1K9u3b+eee+4BwGazYbfbcXFxYd26dVx66aXljnN3d8fd3b22456Ut93Mcawcy8owOoqIiIiIU2oW0QyAbBcbRUVFuLm5GZxIREREqsJpilJ+fn78+uuvZfYtWLCAr776io8++ojmzZsblOzM+JhcScNKenam0VFEREREnFJ4ozAsNrCa4e/kv2kR1cLoSCIiIlIFhhalcnNz+fPPPx2vT0zSGRQURLNmzZg8eTKHDh1iyZIlmM1mOnXqVOb4Ro0a4eHhUW5/XeZrdgcKyMgzbsJ1EREREWdmsbgQaLWQZrZyMDlJRSkREREnZWhRauvWrfTr18/x+sTcT6NGjSIuLq7cJJ31gb+bJ5RkkXk8x+goIiIiIk4ryOxBGnkcSksxOoqIiIhUkaFFqapM0vlP06dPZ/r06dUbqob5u3lBCWQV5hsdRURERMRphbr5sNeaR3KWVjQWERFxVmajAzQ0AV4+AGQXHzc4iYiIiIjzCvcJBCA1P9PYICIiIlJlKkrVskAvPwCyrUUGJxERERFxXo0DQgFIK9bocxEREWelolQtC/ILACDXXmxsEBEREREnFtmoMQDHKDQ4iYiIiFSVilK1LNg/CIA8k9XgJCIiIiLOK6pxJAA5LnaOFxQYnEZERESqQkWpWhYSGAxAvsVOSUmJwWlEREREnFNoSCNcrYAJkg7Vr9WaRUREGgoVpWpZaHDp/AeY4FiGVosRERERqQqz2UyArXQh6cTUvw1OIyIiIlWholQtc3Nzw+N/T+6lpacZG0ZERETEiQWbPQA4lJZqcBIRERGpChWlDOBlswBwLDPd4CQiIiIizquRuy8AydkafS4iIuKMVJQygC+uAKTnZBobRERERMSJhfsEAnDkeJbBSURERKQqVJQygK/FDYD0XHWgRERERKqqcWAjAI4W5xucRERERKpCRSkD+LmWzn+QeTzX4CQiIiIizisyLAKADFORwUlERESkKlSUMoC/mxcAWYV5BicRERERcV5RTaIAyHWxk5+v0VIiIiLORkUpAwR4+gCQXXTc4CQiIiIizisoIAi3/61qfPDQQWPDiIiIyBlTUcoAgd5+AGRbCw1OIiIiIuK8zGYzgbbSBWQSk/82OI2IiIicKRWlDBDkGwBAjr3Y2CAiIiIiTi7EUjpX56H0VIOTiIiIyJlSUcoAwf6lyxfnmawGJxERERFxbqHuvgAkZ6cbnERERETOlIpSBggODAYg32LHai0xOI2IiIiI84rwDQLgSEG2wUlERETkTKkoZYDQ4EYA2E2QkZVpbBgRERERJ9Y4sLRflVai1fdEREScjYpSBvD08MD9f0/upaWnGRtGRERExIk1C2sCQIZJc3WKiIg4GxWlDOJlswBwLOOYwUlEREREnFdUk2YA5LvYycnRI3wiIiLOREUpg/jgAsCxnExjg4iIiIg4sYCAQDz+N0XnwUMHjQ0jIiIiZ0RFKYP4mt0AyMjVHT0RERGRsxFodwUgMfWwwUlERETkTKgoZRA/Vw8AMvJzDE4iIiIi4txCLJ4AHEpPNTiJiIiInAkVpQzi7+YFQFZhnsFJRERERJxbI08/AFJy0g1OIiIiImdCRSmD+Ht6A5BVpOWLRURERM5GuG8QAEeOawS6iIiIM1FRyiCBXqV39LJLCg1OIiIiIuLcmgSFAZBm1c0+ERERZ6KilEGCfPwByLUXG5xERERExLk1C2sMQIZJ/SoRERFnoqKUQYL9S4eZ55lKDE4iIiIi4tyimkQBUOACmZkZBqcRERGRylJRyiChQcEA5Jnt2Gw2g9OIiIiIOC9fXz+8SkwAHDyUaHAaERERqSwVpQwSGtwIAJsZsrOzDE4jIiIi4twC7a4AJKYeMjiJiIiIVJaKUgbx8vLC1Vr689H0NGPDiIiIiDi5EBcvAA5nHDE4iYiIiFSWilIG8raVXv60zGMGJxERERFxbo08Slc2Ts5JNziJiIiIVJaKUgbyxgWA9KxMY4OIiIiIOLkIv9JFZI4W5hicRERERCpLRSkD+ZrdAEjPyzQ2iIiIiIiTaxIUBkCatcDgJCIiIlJZKkoZyM/FA4CMPN3RExERETkbzSKaApBhLtbKxiIiIk5CRSkD+bl5ApBZkGtwEhERERHnFtUkCoAiC6Rnal4pERERZ6CilIECPLwByC46bnASEREREefm5eWFT4kJgIOHDhqcRkRERCpDRSkDBXr7ApBtLTQ4iYiIiIjzC7SXzteZlJpscBIRERGpDEOLUhs3bmTIkCE0btwYk8nEypUrT9l++fLlXH755YSGhuLn50evXr1Yu3Zt7YStAcE+AQDk2oqMDSIiIiJSD4S4egFwOOOIwUlERESkMgwtSuXl5dG1a1fmz59fqfYbN27k8ssvZ/Xq1Wzbto1+/foxZMgQtm/fXsNJa0aQf+nSxblYDU4iIiIi4vzCPP0BSMnNMDiJiIiIVIaLkR8+cOBABg4cWOn28+bNK/P6ySef5JNPPuHTTz+le/fu1Zyu5oUEBgOQb7Fhs9kwm/U0pYiIiEhVRfgFw/EDHCnUysYiIiLOwKmrIDabjZycHIKCgoyOUiWhQSEAlJghL08r8ImIiIicjSYhYQAcsxUYnEREREQqw9CRUmfrueeeIzc3l6FDh560TWFhIYWF/z+ReHZ2dm1EqxRvbx9cbKVFqaPpafj6+hkdSURERMRpNQtrCrsg01yiUegiIiJOwGl/Uy9btowZM2bwwQcf0KhRo5O2mz17Nv7+/o4tMjKyFlOemtlsxsta+keQlnHM4DQiIiLiTObPn090dDQeHh707NmTH3/88aRti4uLmTlzJi1btsTDw4OuXbuyZs2aWkxbOyKbNAM7FFvgaJomOxcREanrnLIo9d577zF27Fg++OADYmJiTtl28uTJZGVlObakpKRaSlk5PlgASMtKNziJiIiIOIv333+fiRMnMm3aNH7++We6du1K//79OXKk4kLMlClTePXVV3nppZf47bffuPPOO7n22muddrGYk/H08MC3xATAwcN1q88nIiIi5TldUerdd99lzJgxvPvuuwwePPi07d3d3fHz8yuz1SU+ZjcAMnIzjQ0iIiIiTmPu3LmMGzeOMWPG0KFDBxYuXIiXlxeLFy+usP3bb7/Nf//7XwYNGkSLFi0YP348gwYNYs6cObWcvOYF4w5A0pHDBicRERGR0zG0KJWbm0tCQgIJCQkA7N+/n4SEBBITE4HSUU4jR450tF+2bBkjR45kzpw59OzZk5SUFFJSUsjKyjIifrXws5R2nDLytEqMiIiInF5RURHbtm0rM1rcbDYTExPDli1bKjymsLAQDw+PMvs8PT3ZtGlTjWY1QoirFwCHM48anEREREROx9Ci1NatW+nevTvdu3cHYOLEiXTv3p3Y2FgAkpOTHQUqgNdee42SkhLuvvtuIiIiHNv9999vSP7q4OfmCUBmQZ7BSURERMQZpKWlYbVaCQsLK7M/LCyMlJSUCo/p378/c+fO5Y8//sBmsxEfH8/y5ctJTk4+6ecUFhaSnZ1dZnMGYV7+AKTkZhicRERERE7H0NX3+vbti91uP+n7cXFxZV5v2LChZgMZINDDB4ogu+i40VFERESknnrhhRcYN24c7dq1w2Qy0bJlS8aMGXPSx/2gdLGYGTNm1GLK6hHhHwL5+zlalGt0FBERETkNp5tTqr4J8PYFILukwOAkIiIi4gxCQkKwWCykpqaW2Z+amkp4eHiFx4SGhrJy5Ury8vI4ePAgv//+Oz4+PrRo0eKkn1PXF4s5mSYhpdcg3aa+lYiISF2nopTBgrwDAMixFRkbRERERJyCm5sbPXr0YP369Y59NpuN9evX06tXr1Me6+HhQZMmTSgpKeHjjz/m6quvPmnbur5YzMlERUQCkGGxYrWWGJxGRERETkVFKYMF+QcAkIc6TSIiIlI5EydOZNGiRbz11lvs3r2b8ePHk5eXx5gxYwAYOXIkkydPdrT/4YcfWL58Ofv27ePbb79lwIAB2Gw2Hn74YaO+Qo1pGtEUkx2sZkg5knr6A0RERMQwhs4pJRASEAxAntlmcBIRERFxFsOGDePo0aPExsaSkpJCt27dWLNmjWPy88TERMzm/7/3WFBQwJQpU9i3bx8+Pj4MGjSIt99+m4CAAIO+Qc1xc3PDr8RMlquNxOREmkQ0MTqSiIiInISKUgYLDQoBoNgC+fn5eHl5GZxIREREnME999zDPffcU+F7/14cpk+fPvz222+1kKpuCDK5k8Vxko4kc+oHGkVERMRIenzPYH5+/pwYJHX02BFjw4iIiIjUA6Gu3gAczkwzOImIiIiciopSBjObzXjbTAAcTT9mcBoRERER5xfm7Q9Aal6msUFERETklFSUqgO87aVPUR7LSjc4iYiIiIjzi/AvnR7hcGGWwUlERETkVFSUqgN8TK4ApOeq4yQiIiJyts5r1w2THQ66FrJ2Y7zRcUREROQkVJSqA/xdPADIyM82OImIiIiI82vdojWXejQF4JWd8eTn5xucSERERCqiolQd4OfmCUDW8TyDk4iIiIjUD/dfext+xWYyXW28tOJNo+OIiIhIBVSUqgP83UtXiMkq0l08ERERkerg5+vH2HZ9AFibu5/df+w2OJGIiIj8m4pSdUCgly8A2cUFBicRERERqT+u7DuQ9lYfbGZ49stl2Gw2oyOJiIjIP6goVQcE+vgBkGMrMjiJiIiISP1hNpt5aOBIXGywz+U4H6xZYXQkERER+QcVpeqAYN8AAHIpMTaIiIiISD3TIqoFVwW0BWDJwe9JzzhmcCIRERE5QUWpOiA4MBiAfLPV4CQiIiIi9c8d19xKaLEL+S525qxYbHQcERER+R8VpeqAkKAQAAotcLxA80qJiIiIVCd3dw/u7TEYgM0lqXy//QeDE4mIiAioKFUnBPoHYLKX/nz02BFjw4iIiIjUQ5f0vJjzTSFggnlbVlJcrLk8RUREjKaiVB1gsbjgZTUBcEzzHIiIiNQ7R46c+qZTSUkJP/74Yy2labgevHoMHiWQ4lrM4lXLjI4jIiLS4KkoVUf42C0AHMvKMDiJiIiIVLeIiIgyhanOnTuTlJTkeH3s2DF69eplRLQGJSw0jOFNewDw8dGdJB5KNDiRiIhIw6aiVB3hY3IFID0n09ggIiIiUu3sdnuZ1wcOHKC4uPiUbaRm3DLoRpoVu1NkgTmfv2V0HBERkQZNRak6ws/iDkBGXrbBSURERMQIJpPJ6AgNgsXiwoN9hmKyww5TFms3xhsdSUREpMFSUaqO8HPzBCDzeK7BSURERETqt64du3KpR1MAXtkZT35+vsGJREREGqYqFaWSkpL4+++/Ha9//PFHJkyYwGuvvVZtwRoaf3dvALKK1CkSERGpb0wmEzk5OWRnZ5OVlYXJZCI3N5fs7GzHJrXr/mtvw6/YTKarjZdWvGl0HBERkQapSkWpESNG8PXXXwOQkpLC5Zdfzo8//shjjz3GzJkzqzVgQxHg6QNAdnGBwUlERESkutntdtq0aUNgYCBBQUHk5ubSvXt3AgMDCQwMpG3btkZHbHD8fP0Y27YPAGtz97P7j90GJxIREWl4XKpy0M6dOzn//PMB+OCDD+jUqRObN29m3bp13HnnncTGxlZryIYgyMcfjkKOtcjoKCIiIlLNTtzMk7rlyn4D+eKPn9htyeXZL5fxessZmM2a3UJERKS2VKkoVVxcjLt76cTcX375JVdddRUA7dq1Izk5ufrSNSBBvgEA5FB86oYiIiLidPr06WN0BKmA2WzmoYEjuX3NAva5HOfDtSsYNvB6o2OJiIg0GFW6FdSxY0cWLlzIt99+S3x8PAMGDADg8OHDBAcHV2vAhiI4IAiAfLPV4CQiIiJS3UpKSigsLCyzLzU1lRkzZvDwww+zadMmg5JJi6gWXBVQ+vjkWwe+Jz3jmMGJREREGo4qFaWefvppXn31Vfr27cvw4cPp2rUrAKtWrXI81idnJiQoBIACCxQV6RE+ERGR+mTcuHHcd999jtc5OTmcd955zJ8/n7Vr19KvXz9Wr15tYMKG7Y5rbiW02IV8FztzV2rScxERkdpSpaJU3759SUtLIy0tjcWLFzv233777SxcuLDawjUkwYHBmOylPx89dtTYMCIiIlKtNm/ezPXX//9jYUuWLMFqtfLHH3+wY8cOJk6cyLPPPmtgwobN3d2De3sMBmBTcQo/bv/R4EQiIiINQ5WKUsePH6ewsJDAwEAADh48yLx589izZw+NGjWq1oANhYuLC55WEwBpGjYuIiJSrxw6dIjWrVs7Xq9fv57rr78ef39/AEaNGsWuXbuMiifAJT0v5nxTCJjg+S0rKC7WyHUREZGaVqWi1NVXX82SJUsAyMzMpGfPnsyZM4drrrmGV155pVoDNiTedgsAx7LSDU4iIiIi1cnDw4Pjx487Xn///ff07NmzzPu5ublGRJN/ePDqMXiUQLJrMYtXLTM6joiISL1XpaLUzz//zMUXXwzARx99RFhYGAcPHmTJkiW8+OKL1RqwIfExuQKQnp1pbBARERGpVt26dePtt98G4NtvvyU1NZVLL73U8f5ff/1F48aNjYon/xMWGsbwpj0A+PjoTv4+nGRwIhERkfqtSkWp/Px8fH19AVi3bh3XXXcdZrOZCy64gIMHD1ZrwIbEz+IGQEZ+tsFJREREpDrFxsbywgsv0LJlS/r378/o0aOJiIhwvL9ixQp69+5tYEI54ZZBN9Ks2J0iCzz7WZzRcUREROq1KhWlWrVqxcqVK0lKSmLt2rVcccUVABw5cgQ/P79qDdiQ+Ll6ApCZr+H7IiIi9UmfPn3Ytm0b9913H2+++SaLFi0q8363bt144IEHDEon/2SxuPBgn6GY7LDDlMXajfFGRxIREam3XKpyUGxsLCNGjOCBBx7g0ksvpVevXkDpqKnu3btXa8CGxN/dC0ogqyjf6CgiIiJSzdq3b0/79u0rfO/222+v5TRyKl07duXSbV+zvvBvFu6M5+Jze+Pl5WV0LBERkXqnSkWpG264gYsuuojk5GS6du3q2H/ZZZdx7bXXVlu4hibA0xfyIKvo+Okbi4iIiNPYuHFjpdpdcsklNZxEKuv+a2/jp7cfJ8PVxvwVb/LQzXcbHUlERKTeqVJRCiA8PJzw8HD+/vtvAJo2bcr5559fbcEaokBvP0iDHFuh0VFERESkGvXt2xeTyQSA3W6vsI3JZMJqtdZmLDkFP18/xrbtw9x9X/NF7n6u/GM37VtXPNJNREREqqZKc0rZbDZmzpyJv78/UVFRREVFERAQwKxZs7DZbJU+z8aNGxkyZAiNGzfGZDKxcuXK0x6zYcMGzjnnHNzd3WnVqhVxcXFV+Qp1UpBfAAC59mJjg4iIiEi1CgwMJDIykqlTp/LHH3+QkZFRbktPTzc6pvzLlf0G0t7qg80Mz3257Iz6uSIiInJ6VSpKPfbYY7z88ss89dRTbN++ne3bt/Pkk0/y0ksvMXXq1EqfJy8vj65duzJ//vxKtd+/fz+DBw+mX79+JCQkMGHCBMaOHcvatWur8jXqnNCAIADyTOrwiIiI1CfJyck8/fTTbNmyhc6dO/Of//yH7777Dj8/P/z9/R2b1C1ms5mHBo7ExQZ/uRxn6eoPjI4kIiJSr5jsJxtDfgqNGzdm4cKFXHXVVWX2f/LJJ9x1110cOnTozIOYTKxYsYJrrrnmpG0eeeQRPv/8c3bu3OnYd9NNN5GZmcmaNWsq9TnZ2dn4+/uTlZVV51YKTDmSzE2fzAE7fHnbU7i4VPnpShEREakm1d13SExMJC4ujrfeeovCwkJGjRrFjBkz6vzv/brch6ppL76/iOXZe8AONwV14vbrRmI2V+neroiISINQ2X5DlX6bpqen065du3L727VrV6NDz7ds2UJMTEyZff3792fLli019pm1KTgwuPQHExxLTzM2jIiIiNSIZs2aERsby5dffkmbNm146qmnyM7ONjqWnML460bR2xIGJngvYyfT3ppLcXGR0bFEREScXpWKUl27duXll18ut//ll1+mS5cuZx3qZFJSUggLCyuzLywsjOzsbI4fr3jFusLCQrKzs8tsdZWrqxueJaU/H81QUUpERKS+KSwsZNmyZcTExNCpUydCQkL4/PPPCQoKMjqanIKrqxuzRj/IsMCOYIdvS1K4+40nyMzKNDqaiIiIU6vSOPFnnnmGwYMH8+WXX9KrVy+gdBRTUlISq1evrtaAZ2v27NnMmDHD6BiV5m23cBwrxzI12amIiEh98eOPP/Lmm2/y3nvvER0dzZgxY/jggw9UjHIiZrOZ8TeMoelXq3lx71fsteRx+9KneGbInURHRhsdT0RExClVaaRUnz592Lt3L9deey2ZmZlkZmZy3XXXsWvXLt5+++3qzugQHh5OampqmX2pqan4+fnh6elZ4TGTJ08mKyvLsSUlJdVYvurggysA6dmZxgYRERGRanPBBRfwxRdfcN999zFjxgyio6PZtGkTq1atKrNJ3Tfk0kE81Xs4PiUmjriWcPdn8/kx4SejY4mIiDilKs+o2bhxY5544oky+3bs2MEbb7zBa6+9dtbBKtKrV69yI7Hi4+Mdo7Uq4u7ujru7e43kqQm+FjeggIy8uvuYoYiIiJy5xMREZs2addL3TSYTVqu1FhNJVfXo3IMFQaE8/MkrpLgW898f3ueetBSuiRlidDQRERGnYuiyIbm5uSQkJJCQkADA/v37SUhIIDExESgd5TRy5EhH+zvvvJN9+/bx8MMP8/vvv7NgwQI++OADHnjgASPi1wg/Vw8A0vNzDE4iIiIi1cVms512y8nR735n0qxJM167dTLtrT6UmGHe/m946YPXsdlsRkcTERFxGoYWpbZu3Ur37t3p3r07ABMnTqR79+7ExsYCkJyc7ChQATRv3pzPP/+c+Ph4unbtypw5c3j99dfp37+/Iflrgp+7NwBZhfkGJxEREZHaUFhYyNy5c2nRooXRUeQM+fn68dLYKfR1bQzAx1m/8983n6WwsMDgZCIiIs6hyo/vVYe+fftit9tP+n5cXFyFx2zfvr0GUxkrwNMH8iG7uOLVBEVERMT5FBYWMn36dOLj43Fzc+Phhx/mmmuuYfHixUyZMgWLxVKvRn43JC4uLkwfPZHXV7zN0qM7+N52lLsWP8GzN00gKDDY6HgiIiJ12hkVpa677rpTvp+ZmXk2WQQI9vaDY5BjLTQ6ioiIiFST2NhYXn31VWJiYvjuu++48cYbGTNmDN9//z1z587lxhtvxGKxGB1TzsLYa2+l6cZw5v62lr9cjnP7u8/w1MBxtGreyuhoIiIiddYZFaX8/f1P+/4/54CSMxfkGwBArr3Y2CAiIiJSbT788EOWLFnCVVddxc6dO+nSpQslJSXs2LEDk8lkdDypJgMuuZyIkDCmbniHNFcr9615lcfOu47e5558UR4REZGG7IyKUm+++WZN5ZD/CfYPAiDPpNV3RERE6ou///6bHj16ANCpUyfc3d154IEHVJCqh7p26MIrgffz0Ir5HHYtInbbx9yRlszQAad+4kBERKQhMnSicykvJCgEgHyLHau1xOA0IiIiUh2sVitubm6O1y4uLvj4+BiYSGpSk4gmvDryv3S2+WE1w4Kk75j77kKtzCciIvIvhk50LuWFBJVOiGk3QUZmBiHBoQYnEhERkbNlt9sZPXo07u7uABQUFHDnnXfi7e1dpt3y5cuNiCc1wNfHh3lj/8vspfP5sjCJVbl/cviNp5h160Q8PTyMjiciIlInaKRUHePu7oH7/57cO5qeZmwYERERqRajRo2iUaNG+Pv74+/vzy233ELjxo0dr09sZ2L+/PlER0fj4eFBz549+fHHH0/Zft68ebRt2xZPT08iIyN54IEHKCgoOJuvJadhsbgwZeT9jAnrgckOW0ln/OLHOZp2xOhoIiIidYJGStVB3jYLhRYrxzLTjY4iIiIi1aC65+V8//33mThxIgsXLqRnz57MmzeP/v37s2fPHho1alSu/bJly3j00UdZvHgxF154IXv37mX06NGYTCbmzp1brdmkvFFXDafJd2E8+8tqDrgWcPsHc5h9xRjatWpndDQRERFDaaRUHeTzv1phWlaGwUlERESkLpo7dy7jxo1jzJgxdOjQgYULF+Ll5cXixYsrbP/dd9/Ru3dvRowYQXR0NFdccQXDhw8/7egqqT4xF17K3H5jCCg2k+FqZUL863zzw0ajY4mIiBhKRak6yNdSOhFqRl6WwUlERESkrikqKmLbtm3ExMQ49pnNZmJiYtiyZUuFx1x44YVs27bNUYTat28fq1evZtCgQSf9nMLCQrKzs8tscnY6tu3IwhseILLYnQIXmJGwimWrPzQ6loiIiGFUlKqDfF1KJ7/MzM8xOImIiIjUNWlpaVitVsLCwsrsDwsLIyUlpcJjRowYwcyZM7noootwdXWlZcuW9O3bl//+978n/ZzZs2eXme8qMjKyWr9HQxXeKIJXRz9GN3sANjO8dugHnl46X6sui4hIg6SiVB0U4F66Ek9mYb7BSURERKQ+2LBhA08++SQLFizg559/Zvny5Xz++efMmjXrpMdMnjyZrKwsx5aUlFSLies3Ly8v5vznUQZ6RgPwRf5+Jr3xFMePq+8nIiINi4pSdVCAhw8A2cXHDU4iIiIidU1ISAgWi4XU1NQy+1NTUwkPD6/wmKlTp3LrrbcyduxYOnfuzLXXXsuTTz7J7NmzsdlsFR7j7u6On59fmU2qj8XiwiO33MPtjc/HbIPtpkzuePMJUo+mnv5gERGRekJFqToo0NsXgJySQoOTiIiISF3j5uZGjx49WL9+vWOfzWZj/fr19OrVq8Jj8vPzMZvLdvssFgsAdru95sLKaY0YPJRp3a7CwwqJroXc8eEcdu/9zehYIiIitUJFqTooyNcfgBx7scFJREREpC6aOHEiixYt4q233mL37t2MHz+evLw8xowZA8DIkSOZPHmyo/2QIUN45ZVXeO+999i/fz/x8fFMnTqVIUOGOIpTYpw+PS9hXsxYAostZLramPDVYr7assHoWCIiIjXOxegAUl6wfxAAeSZNeCkiIiLlDRs2jKNHjxIbG0tKSgrdunVjzZo1jsnPExMTy4yMmjJlCiaTiSlTpnDo0CFCQ0MZMmQITzzxhFFfQf6lXat2vBbwIJM+eJGDrgU8/utnHDqWwq1X3mR0NBERkRpjsjewMdvZ2dn4+/uTlZVVZ+dGSPz7ICO/eAmzDb4c90y54fYiIiJSe5yh71AbdB1qx/Hj+Ux5Zx7bSAegv0cUD48Yj8Wie8kiIuI8KttvULWjDgoNDgXAZoas7Exjw4iIiIhIrfH09OLZ/zzKld4tAFhbcJAH3phNXl6uwclERESqn4pSdZCnpxdu1tKfjxw7amwYEREREalVZrOZSSPu4s4mvTDb4BdTFne89STJKclGRxMREalWKkrVUV620j+atIx0g5OIiIiIiBFuGnQ9M7pfg2cJ/O1axJ3Ln2fn778aHUtERKTaqChVR/n8bw769OwMg5OIiIiIiFEuPv8iXrjidoKKLWS52nhww1t8ufkro2OJiIhUCxWl6ihfszsA6XlZBicRERERESO1admG14ZNonmJB4UWeGLXauI+WWZ0LBERkbOmolQd5efiAUBmfo7BSURERETEaCHBoSwYM4XzTMHYTRB35GceXzKPkpISo6OJiIhUmYpSdZS/uycAWQV5BicRERERkbrA08ODp297hGt8WwPwZeHfTHjjSXJytTKfiIg4JxWl6qgADx8AsoqOG5xEREREROoKs9nMhJvu4J5mvbHYYKc5mzuWPMmh5ENGRxMRETljKkrVUQHevgDklBQanERERERE6pob+l/LzB7X41Vi4rBrEeNXvMCOXTuMjiUiInJGVJSqo4J8AgDIsRcZG0RERERE6qTe5/bixQF3EFJsIdvVxksbPzY6koiIyBlRUaqOCvYPACAPq7FBRERERKTOatW8Fa8Nf5jzTMHMuna80XFERETOiIvRAaRiIQHBAORZbNhsNsxm1Q9FREREpLygwGCeHTvZ6BgiIiJnTJWOOiokuBEAVjNk52QbnEZEREREREREpHqpKFVH+fr44Pq/J/fS0tOMDSMiIiIiIiIiUs1UlKrDvGylfzxpmccMTiIiIiIiIiIiUr1UlKrDfP435Vd6VobBSUREREREREREqpeKUnWYr9kNgPTcLIOTiIiIiIiIiIhULxWl6jBfF3cAMvJzDE4iIiIiIiIiIlK9VJSqw/zdPAHIKsg1OImIiIiIiIiISPVSUaoO8/fwASCr6LjBSUREREREREREqpeKUnVYoJcvADklhQYnERERERERERGpXipK1WFBPv4AZNuKDE4iIiIiIiIiIlK9VJSqw4L8AwHIo8TgJCIiIiIiIiIi1atOFKXmz59PdHQ0Hh4e9OzZkx9//PGU7efNm0fbtm3x9PQkMjKSBx54gIKCglpKW3tCA4MByLPYDE4iIiIiIiIiIlK9DC9Kvf/++0ycOJFp06bx888/07VrV/r378+RI0cqbL9s2TIeffRRpk2bxu7du3njjTd4//33+e9//1vLyWteaHAjAErMkJOrFfhEREREREREpP4wvCg1d+5cxo0bx5gxY+jQoQMLFy7Ey8uLxYsXV9j+u+++o3fv3owYMYLo6GiuuOIKhg8fftrRVc7Ix9uHE4Ok0o5VXKQTEREREREREXFGhhalioqK2LZtGzExMY59ZrOZmJgYtmzZUuExF154Idu2bXMUofbt28fq1asZNGhQhe0LCwvJzs4uszkLs9mMt7X0jygt85jBaUREREREREREqo+hRam0tDSsVithYWFl9oeFhZGSklLhMSNGjGDmzJlcdNFFuLq60rJlS/r27XvSx/dmz56Nv7+/Y4uMjKz271GT/HEF4LPtGw1OIiIiIiIiIiJSfQx/fO9MbdiwgSeffJIFCxbw888/s3z5cj7//HNmzZpVYfvJkyeTlZXl2JKSkmo58dm5qf3FAHxTnMzSz983OI2IiIiIiIiISPVwMfLDQ0JCsFgspKamltmfmppKeHh4hcdMnTqVW2+9lbFjxwLQuXNn8vLyuP3223nssccwm8vW2dzd3XF3d6+ZL1ALBvcbwO8p+/k09y8W//0Trbc35/zu5xsdS0RERERERETkrBg6UsrNzY0ePXqwfv16xz6bzcb69evp1atXhcfk5+eXKzxZLBYA7HZ7zYU10ISh4+hk88Nqhlnff8ih5ENGRxIREREREREROSuGP743ceJEFi1axFtvvcXu3bsZP348eXl5jBkzBoCRI0cyefJkR/shQ4bwyiuv8N5777F//37i4+OZOnUqQ4YMcRSn6huLxYUnR0wgtNiFHBc7j65YwPGCAqNjiYiIiIiIiIhUmaGP7wEMGzaMo0ePEhsbS0pKCt26dWPNmjWOyc8TExPLjIyaMmUKJpOJKVOmcOjQIUJDQxkyZAhPPPGEUV+hVvj5+vHkgP9w77pXSXItZMbSF3hyzEPlRo2JiIiIiIiIiDgDk72+PvN2EtnZ2fj7+5OVlYWfn5/Rcc5Y/KYvefK3NdhNcEtIV8Zee6vRkUREROo1Z+87VBddBxEREamsyvYbNMzGyVx+UQw3BLYHYNnRHWz84VuDE4mIiIiIiIiInDkVpZzQ+OvHcA6B2Ezw1M+rOJh0wOhIIiIiIiIiIiJnREUpJ2Q2m5k14n4iil3Jd7Hz6KevkpeXa3QsEREREREREZFKU1HKSXl7+zB7yB14lZhIdi1m6rIXsNlsRscSEREREREREakUFaWcWHRkNI+ecxVmO/xMBq98/KbRkUREREREREREKkVFKSd3Sc+LGRHaFYCPMnYTv+lLgxOJiIiIiIiIiJyeilL1wG1X38wF5lDsJpizcw1/7PvD6EgiIiIiIiIiIqekolQ9YDabmXbz/TQrdqfAAv9d8wbZOdlGxxIREREREREROSkVpeoJTw8PZl97Fz4lJo66lvDfZfOwWkuMjiUiIiIiIiIiUiEVpeqRJhFNiL3gRiw22GnO5oUPXjc6koiIiIiIiIhIhVSUqmfO734+tzU9D4BVuX/y2derDU4kIiIiIiIiIlKeilL10M2Dh9HHtTEAL+75il17dhmcSERERERERESkLBWl6qnHRtxDyxJPiiwwdf0S0jOOGR1JRERERERERMRBRal6ys3NjSdvuAf/YjPprlYmv/8iJSWa+FxERERERERE6gYVpeqxsNAwZvQZgYsN9ljyePbdV4yOJCIiIiIiIiICqChV73Xr2I3xzS8GYG3BQT5au9LYQCIiIlIt5s+fT3R0NB4eHvTs2ZMff/zxpG379u2LyWQqtw0ePLgWE4uIiIiUpaJUA3D9FVfT3yMKgIUHNrF953aDE4mIiMjZeP/995k4cSLTpk3j559/pmvXrvTv358jR45U2H758uUkJyc7tp07d2KxWLjxxhtrObmIiIjI/1NRqoF4aPh42ll9KDHDtI3v8uV3XxkdSURERKpo7ty5jBs3jjFjxtChQwcWLlyIl5cXixcvrrB9UFAQ4eHhji0+Ph4vLy8VpURERMRQKko1EC4uLsy+6T6Ciy1ku9p4fNdq7lw4nZ17dhodTURERM5AUVER27ZtIyYmxrHPbDYTExPDli1bKnWON954g5tuuglvb++TtiksLCQ7O7vMJiIiIlKdVJRqQAIDgnj1pofo69oYsx1+t+Ry7zdxPLb4WVKOJBsdT0RERCohLS0Nq9VKWFhYmf1hYWGkpKSc9vgff/yRnTt3Mnbs2FO2mz17Nv7+/o4tMjLyrHKLiIiI/JuKUg1MSFAI00dP5JXLxtHZ5ofdBJutqYxcPocX3n+N/Px8oyOKiIhIDXrjjTfo3Lkz559//inbTZ48maysLMeWlJRUSwlFRESkoVBRqoFq27ItL90Ry6yu19Ck2I0iC6zI3stNcdN5/4uPsVpLjI4oIiIiFQgJCcFisZCamlpmf2pqKuHh4ac8Ni8vj/fee4///Oc/p/0cd3d3/Pz8ymwiIiIi1UlFqQbu4vMvYskdM7kr8kL8is1ku9p45e8t3PpqLBt/+NboeCIiIvIvbm5u9OjRg/Xr1zv22Ww21q9fT69evU557IcffkhhYSG33HJLTccUEREROS0VpQSLxYWhA67jvdHTud6vLW5WOOxaROwvn3DPqzPZ89ceoyOKiIjIP0ycOJFFixbx1ltvsXv3bsaPH09eXh5jxowBYOTIkUyePLnccW+88QbXXHMNwcHBtR1ZREREpBwXowNI3eHl5cW9w8Yx9EgKL3/+DpuKU9hpzmb8+kVc/G1j7rnyFkJDGhkdU0REpMEbNmwYR48eJTY2lpSUFLp168aaNWsck58nJiZiNpe997hnzx42bdrEunXrjIgsIiIiUo7JbrfbjQ5Rm7Kzs/H39ycrK0tzI5zGrj27ePnrD9ltyQXA3QpXB3dgzJAReHp4GJxORESkdqjvUErXQURERCqrsv0GPb4nJ9WxbUdeuXM6sZ2uJLzYlUILfJD5G8MXx7I8/hNsNpvREUVERERERETESakoJad1aa++vHP7LG5vfD6+JSYyXW28eOBbRi2cypafvzc6noiIiIiIiIg4IRWlpFJcXFwYMXgoy26dxlU+rXC1QpJrIZO3fcT9rz3On/v/NDqiiIiIiIiIiDgRFaXkjPj6+DBx+J3EXf0AF5hDwQ47TJncvm4hM996nrT0NKMjioiIiIiIiIgTUFFKqqRJRBOe+s8jvHDRSNpYvbGZ4auiQ9zywVMs+GgxxwsKjI4oIiIiIiIiInWYilJyVrp26MJrd85gSodBhBW7UmCBDzJKJ0P/aO1KTYYuIiIiIiIiIhVSUUqqRUzvS1l6+yzGNT7PMRn6y4mbGLlwCpt+2mx0PBERERERERGpY1SUkmrj4uLCzYOHsezWaVz9v8nQ/3YtYkrCCu55dSZ7/tpjdEQRERERERERqSNUlJJq5+vjwwPD72TJNQ/S2xKGyQ47zdmMX7+I2DfnkHo01eiIIiIiIiIiImIwFaWkxkSER/DEbQ/xcp8xtLf6YjPBxpJkbv34WV58fxH5+flGRxQRERERERERg6goJTWuY9uOvHLnNKZ1HkJEsStFFlievYeb4qbz3uqPsVpLjI4oIiIiIiIiIrVMRSmpNf0u6MM7d8xifNNe+BWbyXa1sfDQFm55dSpff/+N0fFEREREREREpBbViaLU/PnziY6OxsPDg549e/Ljjz+esn1mZiZ33303ERERuLu706ZNG1avXl1LaeVsWCwuDBt4Pe+Nns71/u1wt0KyazEzfv2U8Quns2vPLqMjioiIiIiIiEgtMLwo9f777zNx4kSmTZvGzz//TNeuXenfvz9HjhypsH1RURGXX345Bw4c4KOPPmLPnj0sWrSIJk2a1HJyORteXl7cO3QsS65/iD6uEZjtsNuSyz3fvMlji58h5Uiy0RFFREREREREpAaZ7Ha73cgAPXv25LzzzuPll18GwGazERkZyb333sujjz5arv3ChQt59tln+f3333F1dT3jz8vOzsbf35+srCz8/PzOOr9Ujz1/7eHlL9/nV3M2Zju8ctk42rZsa3QsERER9R3+R9dBREREKquy/QZDR0oVFRWxbds2YmJiHPvMZjMxMTFs2bKlwmNWrVpFr169uPvuuwkLC6NTp048+eSTWK3WCtsXFhaSnZ1dZpO6p23Ltrx0RyxPdLuW4SFdVJASERERERERqecMLUqlpaVhtVoJCwsrsz8sLIyUlJQKj9m3bx8fffQRVquV1atXM3XqVObMmcPjjz9eYfvZs2fj7+/v2CIjI6v9e0j16X1eb8ZdN9LoGCIiIiIiIiJSwwyfU+pM2Ww2GjVqxGuvvUaPHj0YNmwYjz32GAsXLqyw/eTJk8nKynJsSUlJtZxYRERERERERET+zcXIDw8JCcFisZCamlpmf2pqKuHh4RUeExERgaurKxaLxbGvffv2pKSkUFRUhJubW5n27u7uuLu7V394ERERERERERGpMkNHSrm5udGjRw/Wr1/v2Gez2Vi/fj29evWq8JjevXvz559/YrPZHPv27t1LREREuYKUiIiIiIiIiIjUTYY/vjdx4kQWLVrEW2+9xe7duxk/fjx5eXmMGTMGgJEjRzJ58mRH+/Hjx5Oens7999/P3r17+fzzz3nyySe5++67jfoKIiIiIiIiIiJyhgx9fA9g2LBhHD16lNjYWFJSUujWrRtr1qxxTH6emJiI2fz/tbPIyEjWrl3LAw88QJcuXWjSpAn3338/jzzyiFFfQUREREREREREzpDJbrfbjQ5Rm7Kzs/H39ycrKws/Pz+j44iIiEgdp75DKV0HERERqazK9hsMf3xPREREREREREQaHhWlRERERERERESk1qkoJSIiIiIiIiIitU5FKRERERERERERqXUqSomIiIiIiIiISK1TUUpERERERERERGqdilIiIiIiIiIiIlLrVJQSEREREREREZFap6KUiIiIiIiIiIjUOhWlRERERERERESk1qkoJSIiIiIiIiIitU5FKRERERERERERqXUqSomIiIiIiIiISK1TUUpERERERERERGqdilIiIiIiIiIiIlLrVJQSEREREREREZFap6KUiIiIiIiIiIjUOhWlRERERERERESk1qkoJSIiIiIiIiIitU5FKRERERExnN1ux2qzGR1DREREapGKUiIiIiJiqMNZuYx6azXPffmT0VFERESkFqkoJSIiIiKG+jkxlfjfD/Lyhu38lpxmdBwRERGpJSpKiYiIiIihBndqwcCOzSmx2Zj48deUWPUYn4iISEOgopSIiIiIGMpkMvHk1Zfg5+HGjr+P8vrmX4yOJCIiIrVARSkRERERMVy4nzfTBl8IwNPxP7I/LcvgRCIiIlLTVJQSERERcULz588nOjoaDw8Pevb8v/buPEqK8t4b+Leq9+6Z7tmYDYZ9VQQUhYvELaCAuUaUXNFwE+T1xVccCcoxCzGC5Bg5N1HjTeKRI1e8yUncMFGJ6zWoJBqMCkHwCsMiOCCzMGtP9/Rez/tHVdd0MwMMMNNVPf39nFOnn3qqqvvpKtv58aunnmc6Pvroo1Pu39bWhurqalRUVMDhcGDs2LF4/fXXM9Ta3rnl4gn42qjBCMfi+P6f3oMQwugmERERUT9iUoqIiIgoyzz//PNYuXIl1qxZgx07dmDy5MmYM2cOGhsbe9w/Go3i6quvxuHDh/Hiiy+ipqYGGzZswODBgzPc8lOTJAkP33glnDYrPvjiKzzz8R6jm0RERET9iEkpIiIioizz6KOPYunSpViyZAnOO+88rF+/Hm63Gxs3buxx/40bN6KlpQUvv/wyZs6cieHDh+OKK67A5MmTM9zy0xtW7MMPr5kGAFj72t9R1x4wuEVERETUX5iUIiIiIsoi0WgU27dvx+zZs/U6WZYxe/ZsbNu2rcdjNm/ejBkzZqC6uhplZWWYOHEiHnroISQSiUw1+4wsnTkJF1aVoiMSxapX/sbH+IiIiAYoJqWIiIiIskhTUxMSiQTKysrS6svKylBfX9/jMV988QVefPFFJBIJvP7667j//vvxyCOP4MEHHzzp50QiEfj9/rQlUyyyjEcWXAWrLOOtzw/h1d0HM/bZRERElDlMShERERENcIqioLS0FE8++SSmTp2KhQsX4r777sP69etPesy6devg8/n0paqqKoMtBiaUF+N7V10EAPjx5r+hJRjO6OcTERFR/2NSioiIiCiLlJSUwGKxoKGhIa2+oaEB5eXlPR5TUVGBsWPHwmKx6HUTJkxAfX09otFoj8esWrUK7e3t+nLkyJG++xK99L2rpmJMaSGaAiGsfe2DjH8+ERER9S8mpfqYEAI7ahtOvyMRERHRWbDb7Zg6dSq2bNmi1ymKgi1btmDGjBk9HjNz5kwcOHAAiqLodfv27UNFRQXsdnuPxzgcDni93rQl0xxWCx5dcBUkCXhhRw3e3Veb8TYQERFR/2FSqg9F4wnc+rs38K9P/BF//+Iro5tDREREA9TKlSuxYcMG/Pa3v8WePXuwbNkyBINBLFmyBADw3e9+F6tWrdL3X7ZsGVpaWrBixQrs27cPr732Gh566CFUV1cb9RV67eJh5bjt0kkAgO//6T0EIzGDW0RERER9hUmpPmS3WlDscUIIYPnzW9DWybEPiIiIqO8tXLgQDz/8MFavXo0pU6Zg586dePPNN/XBz2tra1FXV6fvX1VVhbfeegsff/wxJk2ahO9973tYsWIFfvSjHxn1Fc7Ij66ZjiEF+fiqLYB1b31odHOIiIioj0gix+bY9fv98Pl8aG9v75du6MFIDFf/6gUcam7HNyeNwvpbroEkSX3+OURERJQZ/R07ZAujz8N7+2pxy8ZXIUnA5jtuxMXDeh4/i4iIiIzX27iBPaX6mMdhw+M3z4ZFlrB510Fs2lFjdJOIiIiIst6VY4fipovGQQhg5R/fRSSeMLpJREREdI6YlOoHF1aV4d7ZlwAAfvzK33C4ud3gFhERERFlvwf+dSZK8lzY39iK/3xnu9HNISIionNkiqTU448/juHDh8PpdGL69On46KOPenXcc889B0mSMH/+/P5t4FlYfuVFmDa8AsFoDHc9/xfEE8rpDyIiIiKikyp0O7Hu+ssBAL9+bwc+r2syuEVERER0LgxPSj3//PNYuXIl1qxZgx07dmDy5MmYM2cOGhsbT3nc4cOHce+99+Kyyy7LUEvPjEWW8ZuFs5HvsGN7bQMee+cTo5tERERElPW+MXEk5p43AnFFwco/vssbf0RERFnM8KTUo48+iqVLl2LJkiU477zzsH79erjdbmzcuPGkxyQSCSxatAhr167FyJEjM9jaM1NVmI//uOEKAMAv39mOj7+sO80RRERERHQqkiRh3fzL4XXa8enR4/ivD3YZ3SQiIiI6S4YmpaLRKLZv347Zs2frdbIsY/bs2di2bdtJj/vpT3+K0tJS3HbbbZlo5jm5YcoYLLhwLBQhcNfzW9ARjhrdJCIiIqKsVu71YPW1lwIA/uPtjzh+JxERUZYyNCnV1NSERCKBsrKytPqysjLU19f3eMz777+Pp556Chs2bOjVZ0QiEfj9/rQl0x66/jJUFeajtsWP+zb/LeOfT0RERDTQfPuSCZg5cjDCsTju/eN7EEIY3SQiIiI6Q4Y/vncmOjo68J3vfAcbNmxASUlJr45Zt24dfD6fvlRVVfVzK7vzOh34zcLZkCUJm3bU4OVP92e8DUREREQDiSRJeHjBlXDarPjgi6/wzMd7jG4SERERnSFDk1IlJSWwWCxoaGhIq29oaEB5eXm3/Q8ePIjDhw/juuuug9VqhdVqxe9+9zts3rwZVqsVBw8e7HbMqlWr0N7eri9Hjhzpt+9zKtOGV+Dur08FAPzwpa042tZhSDuIiIiIBorhxT788OppAICfvv531PuDBreIiIiIzoShSSm73Y6pU6diy5Ytep2iKNiyZQtmzJjRbf/x48dj9+7d2Llzp75885vfxFVXXYWdO3f22AvK4XDA6/WmLUa55+sXY+rQMvjDUSx/fgsSCmeLISIiIjoX/3fmJEwZUgp/OIpVL/+Vj/ERERFlEcMf31u5ciU2bNiA3/72t9izZw+WLVuGYDCIJUuWAAC++93vYtWqVQAAp9OJiRMnpi0FBQXIz8/HxIkTYbfbjfwqp2W1yPjNwtnw2G348NAxPL71n0Y3iYiIiCirWS0yHllwJayyjDc/P4TXPvuizz/jq7YOvPX5IXxw8CvUtvgRSyT6/DOIiIhykdXoBixcuBDHjx/H6tWrUV9fjylTpuDNN9/UBz+vra2FLBueO+szw4t9eOj6y7Bi0zv4xdsf4/LRQzClquz0BxIRERFRj86rKMHyKy/CL9/5BKte+StmjhqMQrfzrN4rEk/gs2PH8cmXDdheW49PvqxH3QmPBcqShAqfB0MK8lFVmI+qQi+GFOahqtCLqsJ8VPryYLda+uKrERERDWiSyLE+zn6/Hz6fD+3t7YY9yieEwP975n/w590HMbLEh/9ZfhM8DpshbSEiIqJTM0PsYAZmPw+ReAJX/+oF7G9sxU0XjcN/3jSrV8fV+4P45Mt6bK+tx/baBuz66jgi8fSeUBZZwrjSIkTiCRxt6+i2/USSBJTnezCkMB9DClMSVwVq4mpwQR6ctnO7NyyEgKIv6rrDaoEkSef0vkRERH2ht3GD4T2lcpEkSfiPG67A9tp6fNHUjtWvvo9HFlxldLOIiIiIspbDasGjC67CN9f/CS/sqMENU8bgyrFD0/aJJRL432NN+KQ22QuqocfJZwrdTlwyrBxTh5bh4qHlmFJVCrddvYGoKALHA5042taBI63JxY+jrQHttQPheAJ1/iDq/EF8/GV9j+0tyXPBbpGhCKQkl4SabFJ6qNOST8m6ntgsMnwuB3wuBwq0RS070+r1srurzmWzMqFFREQZx55SBvrg4Ff4t/96BUIAT/37XFw7caSh7SEiIqLuzBQ7GClbzsNPNv8NT/19N4YU5OPFpd/E5/XN2F7bgE++rMenRxsRPqGXkyxJmFBehKlDtSTUsHKMKPaddYJGCIHmYEhPWB1NTVy1qYmrzmi8L75qn7KfkNDyuhzIc9jgsdvg1haPwwa3zQpPSr3HboNbX7fCo+1ns/DxRSKiXNbbuIFJKYP97I1t+M3Wf6LQ7cA7d9+Mcq/H6CYRERFRCrPFDkbJlvMQjMRw5S+f67EHFAAUuByYOrQMU4eW4+Jh5biwqhR5jsxNliOEQEtnGMfaAlCEgCxJ6iJLXWV9UZNmUrdtas/71DoBgY5wFO2hCNpCEbR1RtAeiujrqeW2UDitPqH0/T8HbBY5JaGlJrLy7HY10eWwIc+RLNv15FeeVp+6PbmPk48mEhFlFT6+lyW+f/U0bD1wFLu/Oo4VL2zBs//nOsgy/+ASERERnQ2Pw4aHF1yJRU+/CkUIjCstwtRh5bhYS0SNKikwNNaSJAnFHheKPa4+f2+v04HBBflndIwQAsFoTE1QaYms1lAY/lAUwWgMndEYgtEYgpEYQtG4Wj6hvjMa19djCQUAEEsoWgIs0iffzSJLyLMnE1Y2FLidGF1SgDFlhRhbWoSxZYUY7Mtj4oqIKMuwp5QJHDjeiqt/tQnhWBxrvnEp7rhsitFNIiIiIo0ZYwcjZNt5qPcH4bZb4XU6jG5KTonGE10Jq6iWsIrEEIio64FIFMGI+hqIxBCIxhBMlrX6zmhqufePOnrsNowpLcTY0kLtVU1WVRXmwzKAZvMmIsoG7CmVRUYPKsRP/3UmfvDSVqx780N8bdQQTKwsMbpZRERERFmLQyIYw261wG61oMDt7JP3SygKOqNxPYmVTGA1BUPY39iKfY2t2NfQgi+a2hGMxrDzaCN2Hm1Mew+n1YLRgwq1XlVdyarhRT5YLUxWEREZiUkpk/j3aedhS00t3vr8EO587m28ede39FleiIiIiIhykUWWke+0I9956nG/YokEDjW3q4mqhlYtYdWCA8fbEI4n8FldEz6ra0o7xmaRMbKkAGNL1d5UkiTpsx0KbaZDAejrAl2vil4HCEXo25LHOKwWVHg9qPDlocKnvlb68k77PYiIcg2TUiYhSRIeWXAl/nmkAfsbW/HgG9vw0PWXG90sIiIiIiLTs1ksag+o0iJ8Y2JXfUJRUNvSgX2NLdjX2JWs2t/Yis5oHDUNLahpaMlYO/McNjVR1S1h5UGFNw/lPg+K3E6OjUVEOYNjSpnMe/tqccvGVwEAv1t8La6eMNzYBhEREeU4s8cOmcLzQAOJogh81R5QE1QNrajzB/VZDSWosx5CAiSosxtKerlrH0hSt2OS5VAsjjp/EHXtAW0J9nrQd6fVgnItSVXh86DSl4dhxT5MHjwI48uLYLNY+u/EEBH1kd7GDUxKmdCaVz/Ak+9/imKPC+/evRCD8t198r6KItAUDCEYicHrssPndPA5eiIiotPIhtghE3geiM5NZzSGunY1UXWsPYA6fxD1yXUtgdUUCJ3yPZxWCy4YPAhThpRiSlUpLhxShuHFXvasIiLT4UDnWWzVnOl4/8BRfF7fjLtffAe/v/Ubp/1DE0sk0NDRqd+JSf7Bq/Or68faA2jwdyKuKGnH5Tls8LkcKHA54HM54HV2lX0uBwrcWtmZUqe92q1nf5dGCIGEIpAQAglFgSLUZ/MTioAsSXDarLBbZP6BJSIiIqIBwW23YdSgAowaVHDSfSLxBBr8auyemsDa39iKnUcb4Q9H8fGX9fj4y3r9mAKXA1OqSjFliJqkmlJVitI+uqlNRNTf2FPKpGoaWjD315sQjifwwDdm4poJw7UkU1BPPB3Tkk717UE0BjrRmyspSYDLZj2j6XVPxmWzwudywGWzdiWX9ESTgCIUKAqQEIq2nkxEKb1uq8NqhctmgcNqhdNmgdNqhSPl1WW1wmmzwmG1wGk7oay92q0W2C0ybBYLbBYZdosFNqsMmyzDdsI2m1ZO1tmt2jZZhiwzQUZElIuyJXbobzwPRMZSFIEvmtvUGQaPNOKfRxrx2bHjiCaUbvtW+vJwYVUpLqwqw4VDSjFpyCDkOc5skHUhBILRGFo7I2jtDKO1M4y2zjBaOyNo0cthtIUicFgtGF7sU5ciL4aX+FDpzWP8TJTD+PjeSWRTQLXx77tx3+a/9Xp/m0VGeXLQRK8nbaaPCp8H5V4PSvPdsFksiCUSaA9F4Q9H0B6KoC2kvibL/p7qwlG0d0bgj0R6lVQaaCyyBJvFAofFApe9K0mWTIYl1102ddHrkuWUxFnqPh6HDfkOO7xOO/KcdjitFvYQIyIykWyKHfoTzwOR+UTjCeypb8bOo2qS6p9HGrD/eGu3WF2SgDGDCnFhVRmmDFF7UiUTTWqySU00JZNMyQRUTwmv3nJYLagqzMeIYh+GFfu0Vy9GFPtQVZjPsbFyhBDq+G019S3YU9+MuvYgxpQWYsqQQZhQUQLHOTx9Q+bGpNRJZFNAJYTAbb9/E2/87yG4bFY9uVThy0O515O2XuH1oNjjysjdiISioCMcVRNW4SjCsTgssgSLJEGWZVgkCRZZgixLsEiyWpaS6xIssgxZ28ei7dO1TUJCEQjH44jEEgjH4ggly/E4wlpdJJ5AKBbXy+GUcigWRzieQER7jcYTiCYSiCcURBMKYokEYnFFrVMURONqXTShIK5tjyQShiXebBZt6mOHXX/1alMhJxev04E8hw1epwP5ThvynQ41qeWw6489dg28qZZl7TpIUAfrTA7GKSfLWj0TYkRE6bIpduhPPA9E2SEQiWLX0eP459FGLVnVgK/aAmf9fnaLjEK3U18K3A4Uup0oSlkPRmI43OzH4ZZ2HG5uR21LR7dhQ1LJkoQhhfkYXuzF8KL0hNXQIi/cdttZt5eM0xQIoaZBTT7tbWhBjfYaiMR63N9mkXFeRbE6Rpq2jCkthEXmuMcDAZNSJ5FtAVWy26zHbmOyIMMSioJYQl2iiYRajicQTibBUpJk4VhcT4aFosltyaVr/1DK/uF4HKFoHIFIDIFIFB2RqCl6oEkpSSpZAjwOe9eYYy61nBx7zJsyzpjXaU+r40D6RDRQZFvs0F94HoiyV2NHJ3Ye0ZJURxvQEY6mJZoKtUSTnnhyOVDoURNPLpv1jP8dEk8oONYewKFmNUmlLn71tcWPcOzUQ4kkb7aqN2ht8KSU81Ju3OYl61Ju6OY5uvYbqLFocgKrYymzOybHH2sKhOC221DkcaLY40KRdh2L81wodjtR5HGh2OOC237m1zUpEImipqEFe7XeTzUNLdjb0HLSgfqtsozRgwowvrwIFb487K1vwc6jjWjtDHfb12234oLKrsH8Jw8u5WD+WYpJqZNgQEVmpShqArIjEkVHWH200h+OIhCOwh9W67q2aevhKPyR1H0iiGvjd5mBx27TBsi3w+dywue0q8GORwt2tMCnSKtLrrMbLxGZCWMHFc8DEfUFIQQaOjrTklVftrSrCawmP9rDkT77LJfNijyHDU6bVR871ibLsOpjyZ44tqwMq6yOQWu1dL3q27UxaV02K9x2K9x2Gzx2m/Zqhctug8dhg9tmg9uujnV7Ngm9xkBn+jjCaeUg6v3BU/ZE6w2H1aImq5KJK48TRW4Xij1q4iqZ1HLbrfiiqR17G5q1R/BacLSto8f3lCRgWJEX48qKMaG8COPLijC+vBgjin3dJskSQuBIa4c6Rpo2Ttqur44jGO3eq6rA5cCkIYPSelRV+PLO6ftT/2NS6iQYUFGuEEJACEARAgICSrJ8Yr2ibhNI36YIddD6YCTWbZwxf8o4Y93rIiftottbbrsVBa7UZJUj5c6dul7kccLnckCChIRIfo+u2RzT60TaDI/J/YRIH4gfAPKd9rRZJpM9w85ltkkiym6MHVQ8D0SUCa2dYbQEw+jQbrwGIupN20Ck6yZtIBzTt3dEouiIxPRyIBxFOJ4w+msAUMekddu0RJWWxFIXq57MctosaAmG9d5ODR2dvbrBLEsSyvLd6lAu+ljCHpTkudEZjaElGEZzMIQW7Xy2dIbRHAihJRjqk/NT7vVgXJmaeJpQXoxx5UUYW1p4To9eJhQFB4634dNkoupoI/73WFOPY5uV5bsxZUgpJlQUwyrLEKLr3zvJf/Mo+oK0utR/73TVQz/vycmxkhNm2a1q4tJhtWiTYXXV2y0WfSIte8pEWQ7tGK/TgUF5rpzs6cWk1EkwoCLqf/GEog6MH4qgPRRGu1ZuC0X0mVpSZ3JJrreFwkgo5vxfkstm1RNVqUkrb0rZd0I532nXZ25MjqemjqXWNfZaLv6BOhlFG0+uMxpHKBqDIgQc2iQBydf+Ol+KIuAPd/032paShE0OANseiqA1OflDZxgWWdK6wGt3GZNd41PKyfE2BurjA7mCsYOK54GIskU0nugaIiMcRSSeQExRh+KIKYo21mzXmLOp67GUITxiybrkoiQQjSsIx+LojMYQjMbQGY1rr2q5MxpD5ByTPlY5OYFV+jjCqeXSPPdZxxed0Riag2G0aEmr5mBITVwFU8pafSAcxdAiL8aXF2F8WTHGlxdhXFkRCt3Oc/qOvZU6mH8yWVXT0GqaJ0N6o8DlUBN42jlMljN1Do3CpNRJMKAiMi9FEeiIRNMSVemJq/T69lBEHwNL1gfRByySDCll8Pyu7V2D7stSV6JI0soCAh0pvb/atd5g/UmS1MDDIqVPDqAnsJL1sgyrlsSypHwfKeV7WaTkunYeZBmSdj661lPeUzvWKqtd1S0WtZz8rNSyRevqrpe19qUeKwQQ0oK0UFQdZ01fj8X1On09pS4UjfXqrp3Dmpz10tItYeU8Yf3E+rgi0BZSE0xq0qkr2dQe7t9ZRQvdDhS5k93jUxJZKa8um1X/bzX1v2sJ0CYlOGFygpT91DHg0icxSObvkscn03lSynt2bU+W1HJy4oNkrazd6c3V5BpjBxXPAxFR78QTCjpjXUmqYCSGzlhcfT0hgRWKxVHodqYlnkoyNIFVtuqMxrD7WBN2HmnEF01teuxyYgzVNdFTVywlnySWSi4CUCfB0ibDisQT+hjDUa2s1qWWFX1yrdTt0biCjkj0pAm0snw3xiWTfVqiamxpETyOgTHQP5NSJ8GAiojOREJRe32lPr7YfkK5PdxDXSiCjnDsnJ/3z1XJXlGReCKjd8KSj44WuBwocDvSy271kdFCrSdcQgg0B0NoDoTR0hnS7zg2p9xlbAuFTTGBQV/Jd3Q93lrg1noHagPiJnsJFmrnqUDfz4k8R3ZP1sHYQcXzQEREdGbCsTgOHm/D3oYWfVyuvQ0tONLa87hcANSeaWVqj7QJ5WrvtFElBScdTkQIgYjWOzAYUR95DUTVnoJBrceg2nMwZbu+LYYN/z6nX3pt9TZusPb5JxMRDSAWuWsa5GFn+R6KIvSxqxInlhUFieRYV4qCuLZdUYRaTo6RpQjEtbIQSBs7SwgBRVHHx1JSx9JKPivfw9haihCIJ7o+O64oSCTUz48rChLJtigK4slyQtG2Ca0uvQwAbpsNLrtVGwDUBpdNLbvsPayn7mtTBwd1261wWq363UEh1PcPxxKIaDNYRrSZL5OvydkuI9rMmMnX5LaI9mqRZC3R1JU8SSZWCl1qIqWvxw6LJxS0hcJpiarmYFcCqyUYRnNnCC2BMMLxOIT2nU8c70CIlHqcpP6EcRQA6O+nvkKr047TXs9EhzZT6MkGOD0ZiyzB63Sg0N31eKs1Zbrn1PtjJzYptY3ihK2p2y4fPQR3XnHhGbWLiIiIqD85bVacX1mC8ytL0uoDkSj2NbRiT30z9ja0aLMZNuN4IITaFj9qW/z4nz2H9f2tsoyRJT6Uez0IRmPdEkvnciO8PRQx9FFCJqWIiPqZLEuQYYGNY6WfMUmStFlxLMiH3ejmnDGrRUZJnhsleW6jm3JayYkOUpNVybIiBAKRmNYjMKz3CtQfhdQep00diys5NlcknkBCEfrjt/2lNN/855iIiIgIAPIcdlw0tAwXDS1Lq28KhLCvoUXvWbW3Xk1Y+cNR7Gtsxb7G1lO+r1ObcTLPYUOe3Q5PsuxIlu36do9WLva4+vOrnhaTUkRERJQyDlXPj9k5bVaU5J150BKKxfVHWpNjwbWHIt0ey5RSPvdUT/qd+BhgcnVYER8nIyIiouxWkudCSd5gXDpqsF4nhECdP4g99c1oDoRSEksnJJ3s2Tn+J5NSRERE1G+Sj2yWez1GN4WIiIgo60iShEptEPyBKPvSaERERERERERElPWYlCIiIiIiIiIiooxjUoqIiIiIiIiIiDKOSSkiIiIiIiIiIso4JqWIiIiIiIiIiCjjmJQiIiIiIiIiIqKMY1KKiIiIiIiIiIgyjkkpIiIiIiIiIiLKOCaliIiIiIiIiIgo45iUIiIiIiIiIiKijGNSioiIiIiIiIiIMo5JKSIiIiIiIiIiyjgmpYiIiIiIiIiIKOOYlCIiIiIiIiIiooxjUoqIiIiIiIiIiDLOanQDMk0IAQDw+/0Gt4SIiIiyQTJmSMYQuYoxFBEREfVWb+OnnEtKdXR0AACqqqoMbgkRERFlk46ODvh8PqObYRjGUERERHSmThc/SSLHbvspioJjx44hPz8fkiTB7/ejqqoKR44cgdfrNbp5OY/Xw1x4PcyD18JceD3Mpb+vhxACHR0dqKyshCzn7sgHjKHMjdfDPHgtzIXXwzx4LczFLPFTzvWUkmUZQ4YM6Vbv9Xr5wzARXg9z4fUwD14Lc+H1MJf+vB653EMqiTFUduD1MA9eC3Ph9TAPXgtzMTp+yt3bfUREREREREREZBgmpYiIiIiIiIiIKONyPinlcDiwZs0aOBwOo5tC4PUwG14P8+C1MBdeD3Ph9TAGz7u58HqYB6+FufB6mAevhbmY5Xrk3EDnRERERERERERkvJzvKUVERERERERERJnHpBQREREREREREWUck1JERERERERERJRxOZ+UevzxxzF8+HA4nU5Mnz4dH330kdFNykkPPPAAJElKW8aPH290s3LCX//6V1x33XWorKyEJEl4+eWX07YLIbB69WpUVFTA5XJh9uzZ2L9/vzGNzQGnux633nprt9/K3LlzjWnsALdu3TpccsklyM/PR2lpKebPn4+ampq0fcLhMKqrq1FcXIy8vDwsWLAADQ0NBrV4YOvN9bjyyiu7/T7uuOMOg1o8sDF+MgfGT8ZiDGUujKHMgzGUeWRD/JTTSannn38eK1euxJo1a7Bjxw5MnjwZc+bMQWNjo9FNy0nnn38+6urq9OX99983ukk5IRgMYvLkyXj88cd73P7zn/8cv/rVr7B+/Xr84x//gMfjwZw5cxAOhzPc0txwuusBAHPnzk37rTz77LMZbGHu2Lp1K6qrq/Hhhx/i7bffRiwWwzXXXINgMKjvc8899+DPf/4zNm3ahK1bt+LYsWO48cYbDWz1wNWb6wEAS5cuTft9/PznPzeoxQMX4ydzYfxkHMZQ5sIYyjwYQ5lHVsRPIodNmzZNVFdX6+uJREJUVlaKdevWGdiq3LRmzRoxefJko5uR8wCIl156SV9XFEWUl5eLX/ziF3pdW1ubcDgc4tlnnzWghbnlxOshhBCLFy8W119/vSHtyXWNjY0CgNi6dasQQv0t2Gw2sWnTJn2fPXv2CABi27ZtRjUzZ5x4PYQQ4oorrhArVqwwrlE5gvGTeTB+Mg/GUObCGMpcGEOZhxnjp5ztKRWNRrF9+3bMnj1br5NlGbNnz8a2bdsMbFnu2r9/PyorKzFy5EgsWrQItbW1Rjcp5x06dAj19fVpvxOfz4fp06fzd2Kg9957D6WlpRg3bhyWLVuG5uZmo5uUE9rb2wEARUVFAIDt27cjFoul/T7Gjx+PoUOH8veRASdej6Q//OEPKCkpwcSJE7Fq1Sp0dnYa0bwBi/GT+TB+MifGUObEGMoYjKHMw4zxkzVjn2QyTU1NSCQSKCsrS6svKyvD3r17DWpV7po+fTr++7//G+PGjUNdXR3Wrl2Lyy67DJ999hny8/ONbl7Oqq+vB4AefyfJbZRZc+fOxY033ogRI0bg4MGD+PGPf4x58+Zh27ZtsFgsRjdvwFIUBXfffTdmzpyJiRMnAlB/H3a7HQUFBWn78vfR/3q6HgDw7W9/G8OGDUNlZSV27dqFH/7wh6ipqcGf/vQnA1s7sDB+MhfGT+bFGMp8GEMZgzGUeZg1fsrZpBSZy7x58/TypEmTMH36dAwbNgwvvPACbrvtNgNbRmQuN998s16+4IILMGnSJIwaNQrvvfceZs2aZWDLBrbq6mp89tlnHKvFJE52PW6//Xa9fMEFF6CiogKzZs3CwYMHMWrUqEw3k6jfMX4i6j3GUMZgDGUeZo2fcvbxvZKSElgslm4j/Dc0NKC8vNygVlFSQUEBxo4diwMHDhjdlJyW/C3wd2JeI0eORElJCX8r/eiuu+7Cq6++infffRdDhgzR68vLyxGNRtHW1pa2P38f/etk16Mn06dPBwD+PvoQ4ydzY/xkHoyhzI8xVP9jDGUeZo6fcjYpZbfbMXXqVGzZskWvUxQFW7ZswYwZMwxsGQFAIBDAwYMHUVFRYXRTctqIESNQXl6e9jvx+/34xz/+wd+JSRw9ehTNzc38rfQDIQTuuusuvPTSS3jnnXcwYsSItO1Tp06FzWZL+33U1NSgtraWv49+cLrr0ZOdO3cCAH8ffYjxk7kxfjIPxlDmxxiq/zCGMo9siJ9y+vG9lStXYvHixbj44osxbdo0PPbYYwgGg1iyZInRTcs59957L6677joMGzYMx44dw5o1a2CxWHDLLbcY3bQBLxAIpGXBDx06hJ07d6KoqAhDhw7F3XffjQcffBBjxozBiBEjcP/996OyshLz5883rtED2KmuR1FREdauXYsFCxagvLwcBw8exA9+8AOMHj0ac+bMMbDVA1N1dTWeeeYZvPLKK8jPz9fHOPD5fHC5XPD5fLjtttuwcuVKFBUVwev1Yvny5ZgxYwb+5V/+xeDWDzynux4HDx7EM888g2uvvRbFxcXYtWsX7rnnHlx++eWYNGmSwa0fWBg/mQfjJ2MxhjIXxlDmwRjKPLIifjJs3j+T+PWvfy2GDh0q7Ha7mDZtmvjwww+NblJOWrhwoaioqBB2u10MHjxYLFy4UBw4cMDoZuWEd999VwDotixevFgIoU5pfP/994uysjLhcDjErFmzRE1NjbGNHsBOdT06OzvFNddcIwYNGiRsNpsYNmyYWLp0qaivrze62QNST9cBgHj66af1fUKhkLjzzjtFYWGhcLvd4oYbbhB1dXXGNXoAO931qK2tFZdffrkoKioSDodDjB49Wnz/+98X7e3txjZ8gGL8ZA6Mn4zFGMpcGEOZB2Mo88iG+EnSGkpERERERERERJQxOTumFBERERERERERGYdJKSIiIiIiIiIiyjgmpYiIiIiIiIiIKOOYlCIiIiIiIiIiooxjUoqIiIiIiIiIiDKOSSkiIiIiIiIiIso4JqWIiIiIiIiIiCjjmJQiIiIiIiIiIqKMY1KKiOgMSZKEl19+2ehmEBEREWUVxlBEdCImpYgoq9x6662QJKnbMnfuXKObRkRERGRajKGIyIysRjeAiOhMzZ07F08//XRancPhMKg1RERERNmBMRQRmQ17ShFR1nE4HCgvL09bCgsLAajdwp944gnMmzcPLpcLI0eOxIsvvph2/O7du/H1r38dLpcLxcXFuP322xEIBNL22bhxI84//3w4HA5UVFTgrrvuStve1NSEG264AW63G2PGjMHmzZv1ba2trVi0aBEGDRoEl8uFMWPGdAsAiYiIiDKNMRQRmQ2TUkQ04Nx///1YsGABPv30UyxatAg333wz9uzZAwAIBoOYM2cOCgsL8fHHH2PTpk34y1/+khYwPfHEE6iursbtt9+O3bt3Y/PmzRg9enTaZ6xduxY33XQTdu3ahWuvvRaLFi1CS0uL/vmff/453njjDezZswdPPPEESkpKMncCiIiIiM4CYygiyjhBRJRFFi9eLCwWi/B4PGnLz372MyGEEADEHXfckXbM9OnTxbJly4QQQjz55JOisLBQBAIBfftrr70mZFkW9fX1QgghKisrxX333XfSNgAQP/nJT/T1QCAgAIg33nhDCCHEddddJ5YsWdI3X5iIiIioDzCGIiIz4phSRJR1rrrqKjzxxBNpdUVFRXp5xowZadtmzJiBnTt3AgD27NmDyZMnw+Px6NtnzpwJRVFQU1MDSZJw7NgxzJo165RtmDRpkl72eDzwer1obGwEACxbtgwLFizAjh07cM0112D+/Pm49NJLz+q7EhEREfUVxlBEZDZMShFR1vF4PN26gvcVl8vVq/1sNlvauiRJUBQFADBv3jx8+eWXeP311/H2229j1qxZqK6uxsMPP9zn7SUiIiLqLcZQRGQ2HFOKiAacDz/8sNv6hAkTAAATJkzAp59+imAwqG//4IMPIMsyxo0bh/z8fAwfPhxbtmw5pzYMGjQIixcvxu9//3s89thjePLJJ8/p/YiIiIj6G2MoIso09pQioqwTiURQX1+fVme1WvWBMDdt2oSLL74YX/va1/CHP/wBH330EZ566ikAwKJFi7BmzRosXrwYDzzwAI4fP47ly5fjO9/5DsrKygAADzzwAO644w6UlpZi3rx56OjowAcffIDly5f3qn2rV6/G1KlTcf755yMSieDVV1/VAzoiIiIiozCGIiKzYVKKiLLOm2++iYqKirS6cePGYe/evQDUWV2ee+453HnnnaioqMCzzz6L8847DwDgdrvx1ltvYcWKFbjkkkvgdruxYMECPProo/p7LV68GOFwGL/85S9x7733oqSkBN/61rd63T673Y5Vq1bh8OHDcLlcuOyyy/Dcc8/1wTcnIiIiOnuMoYjIbCQhhDC6EUREfUWSJLz00kuYP3++0U0hIiIiyhqMoYjICBxTioiIiIiIiIiIMo5JKSIiIiIiIiIiyjg+vkdERERERERERBnHnlJERERERERERJRxTEoREREREREREVHGMSlFREREREREREQZx6QUERERERERERFlHJNSRERERERERESUcUxKERERERERERFRxjEpRUREREREREREGcekFBERERERERERZRyTUkRERERERERElHH/H73h5VURuMM8AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 1200x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_training_histories(hist_datas, names, colors, title='Training History', include_val=True, save=True):\n",
+    "    fig, axs = plt.subplots(1, 2, figsize=(12, 5))\n",
+    "    color_keys = list(colors.keys())\n",
+    "    color_counter = 0\n",
+    "\n",
+    "    for hist_data, name in zip(hist_datas, names):\n",
+    "        epochs = range(1, len(hist_data['train_loss']) + 1)\n",
+    "\n",
+    "        color = colors[color_keys[color_counter]]\n",
+    "        color_counter = (color_counter + 1) % len(color_keys)\n",
+    "\n",
+    "        # Plot RMSE\n",
+    "        axs[1].plot(epochs, hist_data['train_rmse'], label=f'{name} Train RMSE', color=color)\n",
+    "        if include_val:\n",
+    "            axs[1].plot(epochs, hist_data['val_rmse'], label=f'{name} Validation RMSE', color=color, linestyle='dashed')\n",
+    "        axs[1].set_title('RMSE')\n",
+    "        axs[1].set_xlabel('Epochs')\n",
+    "        axs[1].set_ylabel('RMSE')\n",
+    "        axs[1].legend()\n",
+    "\n",
+    "        # Plot Loss\n",
+    "        axs[0].plot(epochs, hist_data['train_loss'], label=f'{name} Train Loss', color=color)\n",
+    "        if include_val:\n",
+    "            axs[0].plot(epochs, hist_data['val_loss'], label=f'{name} Validation Loss', color=color, linestyle='dashed')\n",
+    "        axs[0].set_title('Loss')\n",
+    "        axs[0].set_xlabel('Epochs')\n",
+    "        axs[0].set_ylabel('Loss')\n",
+    "        axs[0].legend()\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    plt.suptitle(title)\n",
+    "\n",
+    "    # Save plot\n",
+    "    if save:\n",
+    "        save_plot(plt, title)\n",
+    "    \n",
+    "    return plt\n",
+    "\n",
+    "plot_training_histories([hist_cnn, hist_trans, hist_cnn],  ['transformer', 'cnn', 'test'], colors=COLORS, include_val=False).show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_distributions(true_values, predicted_values_list, names, colors, title='Distribution of Predicted and True Values', save=True):\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    color_keys = list(colors.keys())\n",
+    "    color_counter = 0\n",
+    "\n",
+    "    # Plot true values\n",
+    "    plt.hist(true_values, bins=20, color=colors['green'], edgecolor='black', alpha=0.7, label='True Values')\n",
+    "\n",
+    "    # Plot predicted values for each model\n",
+    "    for predicted_values, name in zip(predicted_values_list, names):\n",
+    "        color = colors[color_keys[color_counter]]\n",
+    "        color_counter = (color_counter + 1) % len(color_keys)\n",
+    "        plt.hist(predicted_values, bins=20, color=color, edgecolor='black', alpha=0.7, label=f'{name} Predicted Values')\n",
+    "\n",
+    "    plt.title(title)\n",
+    "    plt.xlabel('Score')\n",
+    "    plt.ylabel('Frequency')\n",
+    "    plt.legend()\n",
+    "    plt.grid(axis='y', linestyle='--', alpha=0.7)\n",
+    "    # save plot\n",
+    "    if save:\n",
+    "        save_plot(plt, title)\n",
+    "    return plt\n",
+    "\n",
+    "plot_distributions(labels_cnn, [preds_cnn, preds_trans], names=['cnn', 'transformer'], colors=COLORS).show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.gridspec as gridspec\n",
+    "\n",
+    "from sklearn.linear_model import LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "def plot_multiple_residuals(labels, preds_list, names, colors, title='Residuals Plot', save=True):\n",
+    "    fig = plt.figure(figsize=(14, 6))\n",
+    "    gs = gridspec.GridSpec(1, 2, width_ratios=[4, 1])\n",
+    "    color_keys = list(colors.keys())\n",
+    "    color_counter = 0\n",
+    "\n",
+    "    # Main plot\n",
+    "    ax0 = plt.subplot(gs[0])\n",
+    "\n",
+    "    for preds, name in zip(preds_list, names):\n",
+    "        residuals = np.array(preds) - np.array(labels)\n",
+    "        color = colors[color_keys[color_counter]]\n",
+    "        color_counter = (color_counter + 1) % len(color_keys)\n",
+    "\n",
+    "        ax0.scatter(labels, residuals, label=f'{name} Residuals', color=color, alpha=0.5)\n",
+    "\n",
+    "        # Fit linear regression model to residuals\n",
+    "        labels_reshaped = np.array(labels).reshape(-1, 1)\n",
+    "        model = LinearRegression()\n",
+    "        model.fit(labels_reshaped, residuals)\n",
+    "        trend_line = model.predict(labels_reshaped)\n",
+    "\n",
+    "        # Plot trend line\n",
+    "        ax0.plot(labels, trend_line, color=color, label=f'{name} Trend Line', linewidth=2)\n",
+    "\n",
+    "    ax0.set_xlabel('True Values')\n",
+    "    ax0.set_ylabel('Residuals')\n",
+    "    ax0.axhline(y=0, color='k', linestyle='--')\n",
+    "    ax0.set_title(title)\n",
+    "    ax0.legend()\n",
+    "\n",
+    "    color_counter = 0\n",
+    "    # Side plot for distribution of residuals\n",
+    "    ax1 = plt.subplot(gs[1], sharey=ax0)\n",
+    "    for preds, name in zip(preds_list, names):\n",
+    "        residuals = np.array(preds) - np.array(labels)\n",
+    "        color = colors[color_keys[color_counter]]\n",
+    "        color_counter = (color_counter + 1) % len(color_keys)\n",
+    "        ax1.hist(residuals, bins=30, alpha=0.5, color=color, orientation='horizontal', label=f'{name} Residuals')\n",
+    "\n",
+    "    ax1.set_xlabel('Frequency')\n",
+    "    ax1.set_title('Distribution of Residuals')\n",
+    "    ax1.yaxis.tick_right()\n",
+    "    ax1.yaxis.set_label_position(\"right\")\n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "    # Save plot\n",
+    "    if save:\n",
+    "        save_plot(plt, title)\n",
+    "    \n",
+    "    return plt\n",
+    "\n",
+    "plot_multiple_residuals(labels_cnn, [preds_cnn, preds_trans], ['CNN', 'Transformer'], colors=COLORS).show()"
    ]
   }
  ],
  "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,
diff --git a/model_evaluation.ipynb b/model_evaluation.ipynb
index b707c5c..87d71e1 100644
--- a/model_evaluation.ipynb
+++ b/model_evaluation.ipynb
@@ -16,25 +16,40 @@
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading histories/CNN_history_20250215_170051.json\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "# load latest data if keyword is in the file name\n",
-    "hist_file_name = ml_helper.get_newest_file('histories/', name='CNN', extension=\".json\")\n",
-    "print(f\"Loading {hist_file_name}\")"
+    "COLORS = {\n",
+    "    'red': '#B56576',\n",
+    "    'blue': '#187795',\n",
+    "    'green': '#3F9A73' , #'#47B887', #'#38686A',\n",
+    "    'brown': '#DBCFB0',\n",
+    "    'yellow': '#D5C67A'\n",
+    "}"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loading histories/CNN_history_2025-02-16_03-37-28.json\n"
+     ]
+    }
+   ],
+   "source": [
+    "# load latest data if keyword is in the file name\n",
+    "hist_file_name = ml_helper.get_newest_file('histories/', name='CNN', extension=\".json\", ensemble=False)\n",
+    "print(f\"Loading {hist_file_name}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
@@ -54,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,12 +79,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x500 with 2 Axes>"
       ]
@@ -80,27 +95,7 @@
    ],
    "source": [
     "# plot the training history\n",
-    "ml_plots.plot_training_history(hist).show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ml_plots.plot_predictions(labels, preds).show()"
+    "ml_plots.plot_training_history(hist, colors=COLORS).show()"
    ]
   },
   {
@@ -110,7 +105,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -120,16 +115,102 @@
     }
    ],
    "source": [
-    "ml_plots.plot_distribution(labels, preds).show()\n"
+    "ml_plots.plot_predictions(labels, preds, colors=COLORS).show()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ml_plots.plot_distribution(labels, preds, colors=COLORS).show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot residuals with trend line\n",
+    "ml_plots.plot_residuals(labels, preds, colors=COLORS).show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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toOArIiIiIrWCgq+IiIiI1AoKviIiIiJSKyj4ioiIiEitoOArIiIiIrWCgq+IiIiI1AoKviIiIiJSKyj4ioiIiEitoOArIiIiIrWCgq+IiIiI1AoKviIiIiJSKyj4ioiIiEit4OfrAkRERESk5nAZBt/tTeboqSxiQoPp1iwWm7VqjLUq+IqIiIiIRyzfmsTDyzaSnO4sbIsNd/D48F4Ma9fCh5WdVjXit4iIiIhUa8u3JjH5nRVFQi/AkXQnk99ZwfKtST6q7H+qVfBdv349w4cPp0GDBlgsFj7++ONSj1+3bh0Wi+W8ryNHjlROwSIiIiK1gMsweHjZRsxinjvTNmPZ17gMozLLOk+1Cr5Op5OOHTvy8ssvu3Xerl27SE5OLvyqV6+elyoUERERqX2+25t83kjv2UzgcHom3+1NrryiilGt5vgOGTKEIUOGuH1evXr1qFOnjucLEhEREakGvH3D2dFTWR49zluqVfCtqPj4eHJzc2nXrh2PPvooPXv2LPHY3NxccnNzCx9nZGRURokiIiIiXlEZN5zFhAZ79DhvqVZTHdwVGxvLrFmzWLRoEYsWLSIuLo4+ffrw008/lXjOU089RXh4eOFXXFxcJVYsIiIi4jmVdcNZt2axxIY7sJTwvAVoEB5Ct2axHrleRVlM0yxuHnKVZ7FYWLJkCSNHjnTrvCuvvJLGjRvz9ttvF/t8cSO+cXFxpKenExYWdiEli4iIiFQal2Fw6dNvlzj31gLEhofw/V/HeWTaw5mQjWnSPc/JHj87x2z+hWF47rhBXlvSLCMjg/Dw8DLzWo0e8S3OZZddxp49e0p83m63ExYWVuRLREREpLqp7BvOhrVrwdwb+3JnXjoTnSeYmpmCn2kSGx7i1dDrjloxx/dsCQkJxMb6dphdRERExNsq+4az9N8P4Vi8jHhnOlgsNOrSnoVXdufyFg21c1tFZGZmFhmt3bt3LwkJCdStW5fGjRvz4IMPcujQIebPnw/A888/T7NmzWjbti05OTm89tprrF27lpUrV/rqJYiIiIhUisq64cw0TX5fu4mdi5ZjFLgIjAgnftJN1G3V7IL69YZqFXw3b95M3759Cx9Pnz4dgPHjxzNv3jySk5PZv39/4fN5eXnce++9HDp0iODgYDp06MDq1auL9CEiIiJSE5254exIurPYjSXOzPG9kBvO8jKdJL71Ecd+3g5ATMc2tJ8wigCHb1dvKEm1vbmtspR3srSIiIhIVVN4wxkUCb+euOEsdfdeEl57j5yT6Vj9bFxywzCa9O2BxVLS2g7eU968Vq1GfEVERERqq4psQjGsXQvmjhtUzDq+Icwc3rNCodc0DJI+/5JfP1kFpklwvSg6TRlLeOOGbvdV2RR8RURERKq4C9mEYli7Fgxu08wjO7flpGXw8xsLObHz9Pq/DS/vTNuxI/ELtLvdly9oqkMZNNVBREREfOnMdIVzA1tlrI97tuNbd/Hzm++Td8qJLcCftmOvpVGPLl6/bnloqoOIiIhINecyDB5etrHYm9NMToffGcu+ZnCbZl5bMswoKODXj1fy28qvAAhtFEunKTcTUj/aK9fzJgVfERERkSrKnU0oerTw/BzbrJRUtsxZQPq+AwA06dOdS0YNw+bv7/FrVQYFXxEREREfKu2mtcrehOJsyZsT+WX+RxTk5OIXHESHW26gfud2Hr9OZVLwFREREfGRsm5aq6xNKM7mystn+wfLOLD+OwDqNG9Mp8ljCYqM8Ng1fEXBV0RERMQHSrpp7Ui6k8nvrGDuuEEMbtPM65tQnO3U4aNsmbOAzMNHwGKhxeA+tBo+AKufzSP9+1rV2DhZREREpBYp66Y1OH3TGsDjw3sB/1vF4Ywzj2cO73nBN7aZpsmBjd/z9ZMvknn4CAFhIVx6921cfO3gGhN6QcFXREREpNK5c9PamU0o6oc7ihwTGx7ikaXM8rNzSHh9Ib/MX4SRn09U61b0fvgeottcdEH9VkWa6iAiIiJSydy9ac2Tm1CcLW3fQRLmLiDr+AksVisXjRhI80FXYvHS0mi+puArIiIiUskqctOazWr12JJlpmmyb/VGdi7+HNPlIrBuHTpNHktEiyYe6b+qUvAVERERqWTdmsVW6k1rZ8s75eTneR9w/JedAMR0akuHW27A3+G5lSGqqpo5ji0iIiJShdms1kq5ae1cJ3b9xobHn+f4Lzux+vnRduxIOk/7Q60IvaARXxERERGfOHPT2vnr+IYwc3jPC75p7WymYbBn+Rp2f7oGTBNHTDSdpowlLK6Bx65RHSj4ioiIiPiIt25aO1vOyXQSXl9I6q+/AdCwRxfa3jQSP3uAx65RXSj4ioiIiPiQJ29aO9exxB0kzvuQvEwnNnsA7W6+loaXd/bKtaoDBV8RERERL3EZhldHc0tiFBSwa8kX7F21AYCwuAbETxlLSEy0169dlSn4ioiIiHjB8q1JxczfdfD48F4enb97LuexEyTMXUD67wcBaHpVTy6+fig2f8U+vQMiIiIiHrZ8axKT31lx3lJlR9KdTH5nhUd2XCvO4e8T2PrOYgpycvEPDqLDhBuJiW/j8etUVwq+IiIiIh7kMgweXrax2PV5TU4vVzZj2dcMbtPMY9MeXLl5bH//Ew5s/AGAiJZNiZ90E0F163ik/5pCwVdERETEg77bm1xkesO5TOBweibf7U32yE1tpw4dYcucd8lMPgYWCy2H9qXl1f2x2mwX3HdNo+ArIiIi4kFHT2V59LiSmKbJgQ3fs/39TzDyC7CHhdJx0hiiLml5Qf3WZAq+IiIiIh4UE1q+XdDKe1xx8rOy2frOYpI3JwIQ1fYiOt46GntYSIX7rA0UfEVEREQ8qFuzWGLDHRxJdxY7z9fC6d3ZujWLrVD/aXsPsGXuArJTUrFYrVx87WCaDeiNpRKWSavuFHxFREREPMhmtfL48F5MfmcFFigSfi3//e/M4T3dvrHNNAz2rt7ArsVfYBoGQZERdJo8ljrNG3uq9BpPPxqIiIiIeNiwdi2YO24Q9cMdRdpjw0MqtJRZ7qlMNr80j50ffYZpGNTv0p5eD9+t0OsmjfiKiIiIeMGwdi0Y3KbZBe/cdmJnEgmvv0du+ims/n60uXE4cVd0w2KxlH2yFKHgKyIiIuIlNqu1wkuWGS4Xez5dw57P1oJpEhJbj/jJYwlrVLG5waLgKyIiIlLlZKemkfD6Qk7u3gtAo16X0mb0NfjZA3xcWfWm4CsiIiJShRxN2E7iWx+S78zCL9BOu3HX0eCyeF+XVSMo+IqIiIhUAa78AnYt/ox9a74GIKxJQzpNHoujXpSPK6s5FHxFREREfMx5NIUtcxeQsf8QAE379+KS64Zg9VNU8yS9myIiIiI+dOi7LWx9ZzGu3Dz8HcF0uPVGYjq09nVZNZKCr4iIiIgPFOTmsf29pRzctBmAuq2a0XHSGIIi6vi2sBpMwVdERETkArgMw+21ejMOJrNlzrs4jxwHi4WWw/rR6up+2nbYyxR8RURERCpo+dYkHl62keR0Z2FbbLiDx4f3KnZ3NtM02f/Vt+z44FOMggLsdcKInziGyIvd28lNKkbBV0RERKQClm9NYvI7KzDPaT+S7mTyOyvO25o4PyubX+Z/xJGftgIQ3e4SOtw6CntoSCVWXbsp+IqIiIi4yWUYPLxs43mhF8AELMCMZV8zuE0zbFYrJ3/7nYS575F94iQWm42LrxtMs369NLWhkin4ioiIiLjpu73JRaY3nMsEDqdn8u1vh6m/Zw+/frwC0zAIjqpL/JSx1GkaV3nFSiEFXxERERE3HT2VVeYxYYaLk+8tJv3A6bV5Y7t2oN246/APDvJ2eVICBV8RERGRcxS3UgNQ2Ha8jODbOj+bSZknsKa5sPr703bMNTTqdSkWi6UyypcSKPiKiIiInKW4lRrqBNuxACezcgvbrBYLhll0lq/VNBmRncbQnAysQEiDGDpNGUtog/qVVL2URsFXRERE5L9KWqkh7azAe8a5obeuq4ApzhRaFZw+1mx3CT2n3ozNHuCtcsVNCr4iIiJS67kMg01Jh7lv0bpiV2oojdVioWOuk1udJ3CYBjkWK/ZBfRl23UCv1CoVp+ArIiIitcq583dPOLN5ZPnXpa7SUBI/0+RGZyr9ck8BYIuNYcAfbyE0JsrTZYsHKPiKiIhIjXcm7H6xfS+LE37lhDPngvuMceUzLfM4jV35ADQbeAUXjxyE1U/xqqrS/xkRERGp0Yq7We1Cdc/NZJwzlUBMTlms1L/+aloP7Omx/sU7FHxFRESkRjh7CkOUIwgsJqt2/M7crxM9dg27aTDOmUqPvNMheoefnWWxTVjTv7vHriHeo+ArIiIi1ZY3pjCUJK4gj2mZx6lvFGAAnwSFszwwnDnX9sGmrYerBQVfERERqVYqM+wCYJpcbeYwPOM4fpikWm3McUThjIpizvCeDGvXwrvXF49R8BUREZFqwxvzdUsTa/fjHyEmBbuPAeDXshmOK3ryj+i6dGsWq5HeakbBV0RERKo8l2HwwtofeWb1D5VyPQvQMj+Hv+Y7KUjOxGKzcckNQ2l6VU9tO1yNVasfU9avX8/w4cNp0KABFouFjz/+uMxz1q1bR+fOnbHb7bRs2ZJ58+Z5vU4RERHxnOVbk+j6j/mVF3pNk9FmNn/NPIblVCbB9SLp8cAfadavl0JvNVetRnydTicdO3bktttu47rrrivz+L179zJs2DCmTZvGu+++y5o1a5g0aRKxsbEMGjSoEioWERGRC1HSFsKe1CA8hEeH9SDSEcTRoyk4vtxAwe+npzY0uCyeduOuwy/Q7sUKpLJUq+A7ZMgQhgwZUu7jZ82aRbNmzXj22WcBaN26NRs3buS5555T8BUREaniXIbBw8s2eiX0RjoCuS7+Iga3aVY4V/f4tl1kLfqEvFOZ2AL8aXPTCBr16KpR3hqkWgVfd33zzTf079+/SNugQYO45557SjwnNzeX3NzcwscZGRneKk9ERERK8d3eZI/fxDa5Z4ciYRfAKHCx8+PP+e2LdQCENqxPpyljCYmN8ei1xfdqdPA9cuQIMTFF/9LGxMSQkZFBdnY2QUFB553z1FNP8dhjj1VWiSIiIlKCo6eyPNZXg/AQZhaz9FhWSioJr71H2m/7AWh85eW0HnU1tgB/j11bqo4aHXwr4sEHH2T69OmFjzMyMoiLi/NhRSIiIrVTTGiw2+ecmcIw4JKmYDFJycwhJjS42KXHkn/8hV/mf0RBdg5+QYG0v+UGYru091D1UhXV6OBbv359jh49WqTt6NGjhIWFFTvaC2C327HbNYFdRETE17o1iyU23MGRdGep83yLm69bGldePjs+/JT9X30LQJ3mjYmfdBPBUXU9WL1URTU6+Hbv3p3PPvusSNuqVavo3l37aYuIiFR1NquVx4f3YvI7K7BAseH3vv6Xcs9VXcq9kURm8lG2zH2PUweTAWg+uA8XXTMQq5/Nc4VLlVWtgm9mZiZ79uwpfLx3714SEhKoW7cujRs35sEHH+TQoUPMnz8fgGnTpvHSSy9x//33c9ttt7F27Vo++OADli9f7quXICIiImU4syVxcrqTE85sJvZsz5KE3UW2Ji5pzm5JTNPk4KbNbH9vKa68fAJCQ+h422ii217krZchVVC1Cr6bN2+mb9++hY/PzMUdP3488+bNIzk5mf379xc+36xZM5YvX86f//xnXnjhBRo1asRrr72mpcxERESqmDNh94vte1mc8GuRkHtGXUcg17sxpeGMgpxctr6zmMPfJwAQeUlL4ieOwR4e6smXINWAxTRNb64JXe1lZGQQHh5Oeno6YWFhvi5HRESkxlm+NYmHl20s19JlFmDuuEHlHulN//0QW+a+S9axE1isVlpdM4AWg/tgKWdoluqhvHmtWo34ioiISM2yLHEPUxasdOucGcu+ZnCbZqWO+Jqmyb61X7Pzo88wXS4CI8KJnzyWui2bXmDFUp0p+IqIiIhPLEvcw7T3Vrl1jgkcTs/ku73J9GjRsNhj8jKdJM77kGOJOwCIiW9L+/E3EOBwf3k0qVkUfEVERKTSLd+a5PZI79lK2twidfdeEl57j5yT6Vj9bFxyw9U06dtd2w4LoOArIiIilcxlGDy8bOMF9XHu5hamYbDns7XsXrYaTBNHTBSdptxMWFyDC7qO1CwKviIiIlKpzixVVhEWIDY8hG7NYgvbctIy+Pn1hZzYlQRAw+6daXvTSPwCtSGVFKXgKyIiIpWqpGkK5TVzeM/CG9uO/bKTxDc/IC/Tic0eQNuxI2nUvYsnypQaSMFXREREKtVvKWkVOu/sTSuMggJ2fbyCvSvXAxDaKJZOU24mpH60ByuVmkbBV0RERCrN8q1J/Gv1D2UeZwFu7d6OJnXDiQwJJDYspHDTiqzjJ9gy9z3S9x0AoEnfHlxyw1Bs/v5erl6qOwVfERERqRQuw+DhT8p3U9vssQMZ3qHlee3Jm3/ml/mLKMjJxS84iA7jb6B+p3aeLlVqKAVfERER8ZozWxEnpztZtGUXyRll39T2l/6Xnhd6Xbl5bP9gGQc2fA9ARIsmxE+6iaDICK/ULTWTgq+IiIh4hTtbEZ+tWVSdIo9PHT7CljkLyDx8FCwWWgzuQ6trBmC12TxYrdQGCr4iIiLiccu3JjH5nRWYFTj3zBq9pmlycOMPbFv4CUZ+PgFhIcTfNoaoNq08W6zUGgq+IiIi4lFnNqioSOht8N81evOzc9j69iKSNycCENWmFR1vG409LNSzxUqtouArIiIiHnUhG1TMHN6TU/sPkzDnXbJSUrFYrVw0chDNB16B5b9r94pUlIKviIiIeNQX2/dW6Ly/9OtK6+RDfLP4C0yXi6DICOIn3UREiyYerlBqKwVfERER8ZhliXuY+3Wi2+e1cNjptWMrO7fuAqB+53a0v+UG/IODPF2i1GIKviIiInJBXIbBpqTDvPXtL3y2zf3R3kvyc7gv9TgpB7Kw+vnR+saraXzl5VgsFi9UK7WZgq+IiIhUiMsweGHtj7y8fgtZeQVun28xTW4scDIgMxWLaeKoH02nKTcT1ijWC9WKKPiKiIiIG86M7s7/biurd/5OToGrQv00CbByr+sUjpMnAGjUoyttbhqBnz3Ak+WKFKHgKyIiIuWyfGsS9y1eR1pW7gX183THpsR+/wP5zixs9gDajbuOht06eahKkZIp+IqIiEiZliXuYcqClRfUh800Ge9yEvXlV+QDYY0b0mnyWBwxUZ4pUqQMCr4iIiJSqqUJu7l94aoL6qOeK5+pmSk0deUB0LRfTy6+big2f0URqTz62yYiIiLFchkGdyxcxdLEpAvq57JcJ7dknSDINPF3BNNh/Chi4tt4qEqR8lPwFRERkfMs35rEvYu+JD07r8J9BJgGY52p9M47vYtbRMumxE+6iaC6dTxUpYh7FHxFRESkiOVbk5j0zooL6qNhQR7TMlNoYORjAq2G9aPl1f2w2myeKVKkAhR8RUREpJDLMHh42caKd2CaXJmbyZiskwRgYg8PJX7iTURe0sJzRYpUkIKviIiIFPpubzLJ6c4KnRthhXvIplFWKgDRbS+iw62jsYeFeLJEkQpT8BUREZFCR09luX2OI8CPP7dtTJuEBLJPnMRitXLxdYNp1r83FqvVC1WKVIyCr4iIiBT6LSXNreP/3Kcz15HD7o9XkG0YBEXVpdPksdRpFuedAkUugIKviIiI4DIMNu4+xItf/ljuc/7UrTV9d23j122/AlC/S3va/+F6/IODvFWmyAVR8BUREanlliXuYfqiL8nMzS/X8UH+frzQ/WKC127geMYprP5+tBl9DXG9L8NisXi5WpGKU/AVERGppSqyQYXVNHmuYSi2pZ+Ta5qExNaj05SbCW1Y34uViniGgq+IiEgtVJENKiJcBUxxpuD/034A4npdSpvR12CzB3irTBGPUvAVERGpRVyGwXNrNvPsms1unRefl8VtzhM4TANboJ32466jwWXx3ilSxEsUfEVERGoBl2Hwwtofeemrn8jOd5X7PD/TZFTWSfrnngIgOyKCwfdOxlEv0luliniNgq+IiEgN5+7Na2fUc+UzLTOFJq7T0yG+Congicen4x+gqQ1SPSn4ioiI1GCPLf+aWRt+dvu8y3Mz+YMzlUBMTlmsvOGI5K7brlXolWpNwVdERKSGenT518x2M/TaTYObnan0zDu9bfFOPzsfRDfk4VH9GdauhTfKFKk0Cr4iIiI10NKE3W6H3kYFeUzLPE6sUYABfOaIoMWQq/i6f1ds2npYagAFXxERkRpmWeIepi1cVf4TTJO+uZmMzkrFH0iz2ki7oicvjhmiwCs1ioKviIhIDbJ8axJTFqws9/HBhosJzlS65GcBsM0ezO2P30NInXBvlSjiMwq+IiIiNYTLMLhv0ZflPr5Ffi5TnceJNFwUAB8FRzB64g0KvVJjKfiKiIjUEM+v/ZG0cuzEZjFNBudkcG12GjbgqNWP96IacveYQbqBTWo0BV8REZEawGUYvPZ1YpnHhRkuJmWm0LYgB4CTjRrR4vqhfN66mebzSo2n4CsiIlIDPLdmM2nZuaUe0yY/m0mZKYSbBgVWK53GXUujnpdisVgqqUoR31LwFRERqaZchsGmpMP8Y+W3/HTgWInH2UyTEdlpDMnJwAok+wVww0N/JLxRbOUVK1IFKPiKiIhUMy7D4Lk1m3n5qy3kFLhKPTbSVcBkZwqtCk6PBq+zh3DlxBsVeqVWUvAVERGpRpZvTeKuD9aQlVdQ5rGd87KY4DyBwzTIslh4yxHJlAnXMqxDy0qoVKTqUfAVERGpJpZvTWLSOyvKPM7PNLkx6yT9ck8B8JstgNkhUfz9D8MYrtArtZiCr4iISDVQ3jV6Y1z5TMs8TmNXPgCfB4axJKgO0wdcptArtZ6Cr4iISDXwx4Wrylyjt0duJjc7UwnEJMNi5XVHFFsDgqgbHMjdV3WppEpFqi4FXxERkSpuWeIePklMKvH5QNPgZmcqPfKcAOzws/NaSBRp1tPf5v8x8gqt0SuCgq+IiEiV5jIMppcyxaFxQR5TM49T3yjAAJYGhbM8MBzzv2vzTuvdUVMcRP6r2v349/LLL9O0aVMCAwPp1q0b33//fYnHzps3D4vFUuQrMDCwEqsVERG5MM+t2Uxmbv75T5gmV+Vk8FBGMvWNAlKtNv4ZGsOnQXUKQ+/U3h15ZFjPSq5YpOqqViO+77//PtOnT2fWrFl069aN559/nkGDBrFr1y7q1atX7DlhYWHs2rWr8LF2pxERkepiacJunl2z+bx2h+HiVucJOuVnA7DFP4g3HZE4rTYAQuz+/Pv6vhrpFTlHtQq+//73v5k8eTK33norALNmzWL58uW88cYbPPDAA8WeY7FYqF+/frmvkZubS27u/7Z8zMjIuLCiRUREKuCx5V8za8PP57W3zM9hijOFSMNFPvBhcARr7KFgsTCkTTNu7dGOHs0bak6vSDGqzb+KvLw8fvzxR/r371/YZrVa6d+/P998802J52VmZtKkSRPi4uIYMWIE27ZtK/U6Tz31FOHh4YVfcXFxHnsNIiIi5fHIpxvPC70W02RYdjr3nzpKpOHiqNWPv4fVZ01gGFgszBozgDduGULvlnEKvSIlqDb/MlJSUnC5XMTExBRpj4mJ4ciRI8Wec/HFF/PGG2+wdOlS3nnnHQzDoEePHhw8eLDE6zz44IOkp6cXfh04cMCjr0NERKQ0j3y6kTkbE4u0hRsFTD91jOuy07AB3wQ4mBkey34/OwAjOrRgRHwrH1QrUr1Uq6kO7urevTvdu3cvfNyjRw9at27N7Nmzefzxx4s9x263Y7fbK6tEERER4PTqDbcvWMmyrb8VaW+Xl81EZwphpkEuFt5x1GVTgAP+e89KsL+Nl8cM8EXJItVOtQm+UVFR2Gw2jh49WqT96NGj5Z7D6+/vT6dOndizZ483ShQREamQZYl7+NP7q8l1GYVtNtPk2uw0huScvtfkgM2fWSHRHLH5Fzn3jis7a2qDSDlVm38pAQEBdOnShTVr1hS2GYbBmjVriozqlsblcvHLL78QGxvrrTJFRETcMvOzTUxZsLJI6I1y5fPXjCOFoXetPYQnw+qfF3ojgu3akU3EDdVmxBdg+vTpjB8/nq5du3LZZZfx/PPP43Q6C1d5uOWWW2jYsCFPPfUUADNnzuTyyy+nZcuWpKWl8cwzz/D7778zadIkX74MERER4PRI76vrE4q0dclzMsF5gmDTxGmxMs8RyU8BwcWe/8x1fTTaK+KGahV8R48ezfHjx5kxYwZHjhwhPj6eL774ovCGt/3792M96wPg5MmTTJ48mSNHjhAREUGXLl3YtGkTbdq08dVLEBGRWs5lGGxKOsz6PQd4Zf2WwnZ/02B01kn65mYCkGQLYHZINCds53+rtgCzxw5kWLsWlVW2SI1gMU3T9HURVVlGRgbh4eGkp6cTFhbm63JERKQaW741ifsWryMtK7dIe6wrn6mZx4lznd6hbXlgGEuD6uAqYdOlOWMHanMKkbOUN69VqxFfERGR6mr51iQmvbOiaKNp0ivPyVhnKnZM0i1WXg+JYpt/ULF9BAf48Z8b+2mkV6SCFHxFRES8zGUY3PX+miJtgabBH5wnuDwvC4BtfoG8FhJFxn+3HT7X1e2aM2vsQM3pFbkACr4iIiJecmY+710friYrv6CwvUlBLlMzU4gxCnABHwfV4fPAMMwSpjZM7d2RR4f1rKSqRWouBV8REREvWJa4h+mLviQzN/9/jaZJ/9xTjMo6iR9wwmpjtiOKJP/AYvvws1p5ZUx/zecV8RAFXxEREQ97bPnXzNrwc5G2EMPFrc4TxOdnA/CjfxBvOSJxljC1AeDXR28jKCDAq7WK1CYKviIiIh706PKvmX1O6L0oP4fJmSnUNV3kA+8H1+VLe0jhtsPFmda7o0KviIcp+IqIiHiAyzB4dvUPRUKvxTS5Oieda7LTsQJHrH7MConmgF/pgXZQm6Y8ojm9Ih6n4CsiInKBlv68m3s+XEtOgauwrY5RwOTMFC4pOL1m79cBDt511CXXUvKqDIH+Np6//ipGxLfyes0itZGCr4iIyAUY/9ZyVu74vUhb+7xsJjpTCDUNcrDwjqMu39hDSuyjU6N6PDi4Gz2aN9RyZSJepOArIiJSAS7DYMQri/nx4LHCNptpcn12GoNyMgDYb/NnVkg0R23+JfYzsHUT3ho/zOv1ioiCr4iIiFvyCgr4y+Kv+OCnXUXao1z5TM1MobkrD4DV9lA+DI6goJQb2Cb36sDMq3t5tV4R+R8FXxERkXIqbpkygEtzndySdYJg08RpsfKGI5KEgOAS+wn0t/GfUf20Pq9IJVPwFRERKYcJ8z9jxfZ9RdoCTIMxWSe5MjcTgN1+duY4oki1Ff/t1QJM79eVP/frqrm8Ij6g4CsiIlKGpQm7zwu9DQrymOZMoaErHwP4LDCMpUF1MEqZ2pA0c5LW5hXxIQVfERGRUmTn5XH3h2v+12CaXJGbyZisk9gxSbPYeC0kkh3+QaX2c/sV8Qq9Ij6m4CsiIlIMl2Hwx/dW8ckvSYVtQYbBLVknuCwvC4Bf/AN5wxFFRinbDlssMK13PDOG9vB6zSJSOgVfERGRcyxL3MMdC1eTbxiFbU0LcpmamUI9o4ACYElQHVYEhmGWMLUhyhHEHVfGc1uP9gT46dutSFWgf4kiIiJnmfnZJl5dn1D42GKaDMg5xfXZJ/EDjlttzAmJ5jc/e4l9DLikMfMnXO39YkXELQq+IiIi/7UscU+R0BtiuJjoPEGH/GwANvsHM88RSXYpKzJM7d2RR4f19HapIlIBCr4iIiKcntP714+/Knx8cX4OUzJTqGO6yMPCwuAIvrKHnJ60W4wbO1/EM9f10bQGkSpM/zpFRESA7/YmczIrF6tpMjw7natz0rECh61+zAqJ5pBfySsyPDK0B9OuiK+0WkWkYtwOvgcOHMBisdCoUSMAvv/+exYsWECbNm2YMmWKxwsUERGpDK9uSCDCKGByZgoXF+QCsCHAwQJHXfIsJU9tsFos3NajXWWVKSIXwO1tY8aOHcuXX34JwJEjRxgwYADff/89//d//8fMmTM9XqCIiIi3LUvcw/HEHTyanszFBbnkYGGOI4p5IVGlhl44PadX0xtEqge3g+/WrVu57LLLAPjggw9o164dmzZt4t1332XevHmerk9ERMSrcrJzWPfa+9yVeZwQ02CfLYDHwmP5zu4o9TyL5fSmFFqfV6T6cPtH1Pz8fOz200u4rF69mmuuuQaASy65hOTkZM9WJyIi4kXL1v/Iwfc+pp8rD4CV9lAWBUdQUMq2w03qhjHh8rZan1ekGnL7X2zbtm2ZNWsWw4YNY9WqVTz++OMAHD58mMjISI8XKCIi4g0vzf6ABj/+RBNMMi1W3nBE8nNAcKnnDGvbjNf+MKSSKhQRT3M7+D799NNce+21PPPMM4wfP56OHTsC8MknnxROgRAREamqCnLz+OLlt2m+81cAfvWzMyckipPWsr8ljr+8vbfLExEvcjv49unTh5SUFDIyMoiIiChsnzJlCsHBpf+kLCIi4ksZB5NZ/+I8rCfTMIBPA8NZFhSOUcrUhjMigu30aNHA+0WKiNdUaHKSaZr8+OOPJCUlMXbsWEJDQwkICFDwFRGRKsk0TX7/6lsS31uKn2mSZrExJySKXf6B5e7jmev6YCtlxzYRqfrcDr6///47gwcPZv/+/eTm5jJgwABCQ0N5+umnyc3NZdasWd6oU0REpELys7JZ8eJbWJP24gck+gfyuiOKTKut3H3MumkAw9q18F6RIlIp3P7R9e6776Zr166cPHmSoKCgwvZrr72WNWvWeLQ4ERGRC3F89298dN+TWJP2UgC8HxTBf0LquRV6J/dsz4iOrbxXpIhUGrdHfDds2MCmTZsICCi6dWPTpk05dOiQxwoTERGpqNy8PJ56ai6dD+0nHDhu9WNWSBT7/Oxu9RPfKJqZw3t7p0gRqXRuB1/DMHC5XOe1Hzx4kNDQUI8UJSIiUhEuw+Cet5bTaPNmLsvPAeD7gGDmB0eS7eb83Ek92/O4Qq9IjeL2VIeBAwfy/PPPFz62WCxkZmbyyCOPMHToUE/WJiIiUm5Lf97N4Pufp/u339A+P4dcLMwLrstsR5TbofeVG/sr9IrUQBbTNE13Tjh48CCDBg3CNE12795N165d2b17N1FRUaxfv5569ep5q1afyMjIIDw8nPT0dMLCwnxdjoiIFGPCvE8J/ulnhuZkYAUO2fyZ5YjisF9Ameeea1rvjjwyrKfnixQRrylvXnM7+AIUFBSwcOFCEhMTyczMpHPnztx8881FbnarKRR8RUSqrryCAq771zsM2P8brQpyAfjKHsLC4AjyLO4vPTa1d0ceVegVqXbKm9cqtI6vn58f48aNq3BxIiIiF+rRTzfy/dpvudV5AodpkGWxMD84kh/sDrf78rNaeGXMAIZ3aOmFSkWkqnA7+M6fP7/U52+55ZYKFyMiIlIal2Gwcfch7nzvcwacOMaduacA+M0WwOyQKFJs/m73Oaxdc2aPHajNKURqAbenOpy9TTFAfn4+WVlZhTu3paamerRAX9NUBxGRqmFZ4h7u+mAN4bk5TMs8TmNXPgArAsNYFFQHVzm2HT6bv9XCyxrlFakRvDbV4eTJk+e17d69m9tvv52//OUv7nYnIiJSojMjvH/7dAN7jqfRPTeTcc5UAjE5ZbHyuiOKXwLcu7/EAtzTtwv3DrhUo7witUyF5vieq1WrVvzjH/9g3Lhx7Ny50xNdiohILXdmhDenwIXdNLjNmUrPPCcAO/3szA2JIs3q3rcxTWsQqd08Enzh9A1vhw8f9lR3IiJSiz22/GtmbfgZgLiCPKZlHqe+UYABfBIUzqeB4ZhuTG24vGks708aToCfx77tiUg15PYnwCeffFLksWmaJCcn89JLL9Gzp5aAERGRCzNj2Qbmfv0LmCZ9czMZnZWKP5BqsTE3JIpf/QPd6m/WmAGMiG/lnWJFpFpxO/iOHDmyyGOLxUJ0dDRXXXUVzz77rKfqEhGRWuiWeZ+yaud+gg0XE5wn6JKfDUCCfxBvOiLJtNrc6m/O2IG6eU1ECrkdfA3D8EYdIiJSy41/azmrdu6nRX4OU50pRBouCoAPgyNYbQ8FN6Y21Amy86/r+zCsXQvvFSwi1Y4mO4mIiE/lFRTw5w/Xsmr7PobmZDAyOw0bcNTqx+yQKH73s5e7r3ohQbw0pj89mjfUDWwicp5yBd/p06eXu8N///vfFS5GRERqD5dhcPuCVSzbmkSY4eLPmSm0LcgB4NuAYN52RJLjxrbDk3t1YObVvbxVrojUAOUKvlu2bClXZxY3Fw8XEZHaJ6+ggHsXreOjLb8C0DY/m4mZKYSbBrlYWOCoy8YAR7mnNtwQfxHP3tBHKzaISJnK9Snx5ZdfersOERGpBWZ+tolX1ycAYDNNRmSnMSwnA4CDNn9mhUSTXM5th4e3b8GrNw3QlAYRKTf9eCwiIpXi7NAb6SpgijOFlgW5AHxpD+H94AjyyzG1oVvT+nww6RqN8IqI2yr0qbF582Y++OAD9u/fT15eXpHnFi9e7JHCRESk5sgrKCgMvZ3zspjgPIHDNMiyWHjLEcnmAEe5+pnauyOPDtOa8SJSMW7/fmjhwoX06NGDHTt2sGTJEvLz89m2bRtr164lPDzcGzWKiEg11/2f7+JnmtzsPMEdmcdxmAZJtgAeC4std+idNWaAQq+IXBC3R3z//ve/89xzz3HHHXcQGhrKCy+8QLNmzZg6dSqxsbHeqFFERKopl2FwzcuLME6m8bfM48S58gH4PDCMJUF1cJXzBjbtviYinuD2iG9SUhLDhg0DICAgAKfTicVi4c9//jNz5szxeIHnevnll2natCmBgYF069aN77//vtTjP/zwQy655BICAwNp3749n332mddrFBERWPrzbho/+CpBv+1lRnoyca58MixWngupx0fBEeUOvbdfEa/QKyIe4XbwjYiI4NSpUwA0bNiQrVu3ApCWlkZWVpZnqzvH+++/z/Tp03nkkUf46aef6NixI4MGDeLYsWPFHr9p0yZuuukmJk6cyJYtWxg5ciQjR44srFlERLxjwvzPuGfBCm5znuA25wnsmOzwC+TR8Fi2BgSVq49Afxtzxg5kxtAeXq5WRGoLi2mapjsnjB07lq5duzJ9+nQef/xxXnzxRUaMGMGqVavo3LmzV29u69atG5deeikvvfQScHr75Li4OP70pz/xwAMPnHf86NGjcTqdfPrpp4Vtl19+OfHx8cyaNavYa+Tm5pKbm1v4OCMjg7i4ONLT0wkLC/PwKxIRqVlchsG0d1eS+PMOpmWmEGMU4AKWBtXhs8AwzHKM8tYPDeb5UVfRq2UjLVUmIuWSkZFBeHh4mXmt3HN8t27dSrt27XjppZfIyTm9s87//d//4e/vz6ZNm7j++uv529/+duGVlyAvL48ff/yRBx98sLDNarXSv39/vvnmm2LP+eabb87bdW7QoEF8/PHHJV7nqaee4rHHHvNIzSIitcnSn3dz53sruTLnFA9lncQfOGG1MccRxR7/wDLP97daePHG/prWICJeU+7g26FDBy699FImTZrEmDFjgNPBs7iRVm9ISUnB5XIRExNTpD0mJoadO3cWe86RI0eKPf7IkSMlXufBBx8sEpbPjPiKiEjxXIbByFeXsOP3w0xznqBTfjYAW/yDeNMRidNqK7OPoW2bMufmwRrhFRGvKvcnzFdffUXbtm259957iY2NZfz48WzYsMGbtfmE3W4nLCysyJeIiJzPZRg8s+J7Gj00i/TffufRjGQ65WeTD7wbHMFLIdHlCr0tosJ5/Q9DFXpFxOvK/SnTu3dv3njjDZKTk3nxxRfZt28fV155JRdddBFPP/10qaOonhAVFYXNZuPo0aNF2o8ePUr9+vWLPad+/fpuHS8iIuWz9OfdNP3bbJ5b+wNXZ6dx/6mj1DVcHLH68fewWNYGhkE55vP6WeGr6TdVQsUiIhVY1cHhcHDrrbfy1Vdf8euvvzJq1ChefvllGjduzDXXXOONGoHTS6d16dKFNWvWFLYZhsGaNWvo3r17sed07969yPEAq1atKvF4EREpXV5BAVf+ewHT3luFoyCfe08d49rsdKzANwEOHg+PZb9fQLn7mzV2kEZ6RaTSXNBG5y1btuShhx6iSZMmPPjggyxfvtxTdRVr+vTpjB8/nq5du3LZZZfx/PPP43Q6ufXWWwG45ZZbaNiwIU899RQAd999N1deeSXPPvssw4YNY+HChWzevLlS1hsWEalpZn62qXDb4XZ52Ux0phBmGuRi4R1HXTbZQ8rdl91m5eWbBjCsXQsvVSsicr4KB9/169fzxhtvsGjRIqxWKzfeeCMTJ070ZG3nGT16NMePH2fGjBkcOXKE+Ph4vvjii8Ib2Pbv34/1rJGDHj16sGDBAv72t7/x0EMP0apVKz7++GPatWvn1TpFRGoSl2Fw+4JVLNuahM00uTY7jSE5GQDst/kzKySaozb/cvd315WduX/QZRrpFZFK59Y6vocPH2bevHnMmzePPXv20KNHDyZOnMiNN96Iw1G+vdarm/KuCyciUhMtS9zDnQtXkWeYRLnymZqZQnNXHgBr7KF8EBxBQTl3YAOY2rsjjw7r6a1yRaSW8vg6vkOGDGH16tVERUVxyy23cNttt3HxxRd7pFgREal6Hv10I7M3JgLQNc/JeOcJgk0Tp8XKPEckPwUEu9Xf7VfEaxc2EfGpcgdff39/PvroI66++mpstrKXpxERkerrD29+yupd+/E3DcZknaRPbiYAe/zszHFEccJW/plyN8RfxLM39CHA74JuKxERuWDl/hT65JNPvFmHiIhUEQP/8wG/HE4h1pXHtMwUGrnyMYDPA8NYGlQHVzmnNnSJq8fS26/TXF4RqTL047eIiBQa+ML7/HI4hV55mYx1nsSOSbrFymshUWz3DypXHzYLvDx6gLYeFpEqR8FXREQAGPfGMnYfPs5k5wkuz8sCYJtfIK+FRJFRjh3YAP7ctwv3DrhUo7wiUiUp+IqICOPeWMae7XuYkZlCjFGAC1gSVIcvAsMwyzG1oXOjaD754/UKvCJSpSn4iojUcgNfeJ/6e/fyUNZJ/IATVhuzHdEk+dvLdf6ASxozf8LV3i1SRMQDyhV83bmxzZvbFouIiOfkFRTQ58k3uPrYYeLzswH40T+IeY5Isso5tUHr8opIdVKu4Dty5Mgijy0WC2fve2E569dgLpfLM5WJiIjXzPxsE2vWfMsdmSlEmC7ygfeD6/KlPQTKMbXh8qaxvD9puJYoE5FqpVyTsQzDKPxauXIl8fHxfP7556SlpZGWlsZnn31G586d+eKLL7xdr4iIXKDHPt3IoRXr+Mupo0SYLpKtfjwRFsuXgaFlhl6rBeaMHciSadcq9IpIteP2p9Y999zDrFmz6NWrV2HboEGDCA4OZsqUKezYscOjBYqIiOcs3ZhA6GcrGVGQC8DGAAfvOuqSZyl7HKR+aBCbHxyvG9hEpNpyO/gmJSVRp06d89rDw8PZt2+fB0oSERFvWL7sS3I+XcklpkEOFt521OVbe0i5zvWzotArItWe259gl156KdOnT+fo0aOFbUePHuUvf/kLl112mUeLExGRC5eTk8OCf7+OZdkXhJoGv9sCmBkeW+7QCzBr7CCFXhGp9twe8X3jjTe49tprady4MXFxcQAcOHCAVq1a8fHHH3u6PhERqSCXYXDv60tp9dNPNHPlAbDaHsqHwREUlHPbYT+rhVljBzKsXQtvlioiUincDr4tW7YkMTGRVatWsXPnTgBat25N//79i6zuICIivrP059288dbH3JJ5giBMnBYrbzgiSQgILncfTeuGsfG+sRrpFZEaw2KevS6Zm3JycrDb7TU68GZkZBAeHk56ejphYWG+LkdEpFQuw+CGlz/i4l93cUVuJgC/+tmZ44jipK38Yx3tYyNZefdob5UpIuJR5c1rbv8YbxgGjz/+OA0bNiQkJIS9e/cC8PDDD/P6669XvGIREbkgy7cm0f2v/2Ho9kSuyM3EAJYFhvNMaIxbobffRXEKvSJSI7kdfJ944gnmzZvHP//5TwICAgrb27Vrx2uvvebR4kREpHyW/7KH+a99xEPpR2joyifNYuPZ0Hp8HFwHw43fyvW/OI53bhvuxUpFRHzH7eA7f/585syZw80334zN9r8tLTt27Fg451dERCpPjjOLhLnvMT4rlQBMfvEP5NHwWHb6B7nVT/+LG/P2rQq9IlJzuX1z26FDh2jZsuV57YZhkJ+f75GiRESkfNL2HuCbV+bTKSeTAmBxUB1WBoZhunnvRb+L4nj71qu9U6SISBXhdvBt06YNGzZsoEmTJkXaP/roIzp16uSxwkREpGSmYbB39QZ2LP4ci2Fy3Gpjdkg0e/3sbvfVsWGUpjeISK3gdvCdMWMG48eP59ChQxiGweLFi9m1axfz58/n008/9UaNIiJyluz0DNa/8g6uvb9jATb7BzPPEUl2BZYdm9yrAzOv7lX2gSIiNUCFljPbsGEDM2fO5OeffyYzM5POnTszY8YMBg4c6I0afUrLmYlIVZBXUMBrX//CxnXfcfXh/dQxXeRhYWFwBF/ZQ8CNqQ3hgQHc1bczk3p2IMDP7fEPEZEqp7x5za1PvIKCAv7+979z2223sWrVqgsuUkRESpdXUMCY15fx3W+HGZ6dzticdKzAYas/s0KiOOQXUGYfZ5s1ZgAj4lt5p1gRkSrO7RHfkJAQtm7dStOmTb1UUtWiEV8R8QWXYXD7glUs25pEhFHAlMwULirIBWC9PYT3giPIs5R/aoMF2PfEFI3wikiN5JURX4B+/frx1Vdf1ZrgKyJSmVyGwb9Xbea5LzdjAh3zsrjNeYIQ0yAbC/MdkXxvd7jd76s3DVDoFZFaz+1PwSFDhvDAAw/wyy+/0KVLFxyOoh/A11xzjceKExGpLc4E3ufXbcYwwc80uSHrJANyTwGwzxbA7JAojtn83e57wCVNGNFR0xtERNye6mAt5a5hi8WCy+W64KKqEk11EBFvW5a4hzsWribfMACo58pnamYKTV15AKy0h/JRcAQuN9fmBejQIIoVd93o0XpFRKoar011MP77wSwiIhdu5mebeHV9QuHjbrmZ3OJMJRCTUxYrbzgiSQwIrlDf/S+O005sIiJnuaAJXzk5OQQGBnqqFhGRWmVpwu7C0BtgGtzsTKVXnhOAXX525oZEcdJasY9prc8rInI+t1c7d7lcPP744zRs2JCQkBB+++03AB5++GFef/11jxcoIlLT5BUU8Kf3VzNt4ellIRsV5PFwejK98pwYwNLAcJ4JjalQ6A30szJn7ECFXhGRYrj9qfrkk0/y1ltv8c9//pPJkycXtrdr147nn3+eiRMnerRAEZGa4syavN/sTT7dYJr0yc1kTFYq/sBJi425IVHs8nf/N2mXNanP9H5d6dWyEbYK7OAmIlIbuB1858+fz5w5c+jXrx/Tpk0rbO/YsSM7d+70aHEiIjXFo59uZPbGxMLHQYbBBOcJuuZnAZDoH8TrjkgyrbZy9RcRZKfPRXGM7nKJwq6ISDm5HXwPHTpEy5Ytz2s3DIP8/HyPFCUiUpMMfvFDfj50vPBx84JcpmYeJ8pwUQAsCopgVWAoZjlWbbgoug6r7r5Ra/KKiFSA25+cbdq0YcOGDTRp0qRI+0cffUSnTp08VpiISE1wy7xPC0OvxTQZnJPBtdlp2IBjVj9mh0Sxz89eZj82C7w8WtsNi4hcCLeD74wZMxg/fjyHDh3CMAwWL17Mrl27mD9/Pp9++qk3ahQRqZaWJuxm1c79AIQZLiZmptCuIAeA7wOCmR8cSXY5pij8uW8X7h1wqaYziIhcILeD74gRI1i2bBkzZ87E4XAwY8YMOnfuzLJlyxgwYIA3ahQRqXZchsE9i9YC0Do/m0mZJ6hjusjFwnvBEWywh0AZUxs6N4rmkz9er8ArIuIhFZok1rt3b1atWuXpWkREaoxNvx0iL6+Aa7PTGJqTgRU4ZPNnliOKw34BZZ6vzSdERDxPd0eIiHjBwrWbuf/UUVoV5AKwzh7C+8ER5FnKHr0d2LoJb40f5u0SRURqnXIF34iICCzl3CM+NTX1ggoSEanuHnz2Lfr8uhOHaZBlsTA/OJIf7I4yzwv0s/H8DVfpBjYRES8pV/B9/vnnC/984sQJnnjiCQYNGkT37t0B+Oabb1ixYgUPP/ywV4oUEakOXPn5PP3wC/ROPb2Kw2+2AGaHRJFi8y/1vPhG9XhocDd6NG+o+bwiIl5kMU3TdOeE66+/nr59+3LnnXcWaX/ppZdYvXo1H3/8sSfr87mMjAzCw8NJT08nLCzM1+WISBV18lAyi/7+Kg3yT09t+CIwjMVBdXCV8duy32ZOIiig7Dm/IiJSsvLmNbeHFlasWMHgwYPPax88eDCrV692tzsRkWrNZRg8/uw8vnzsBRrk53LKYuW5kHp8GBxRZujte1GcQq+ISCVy++a2yMhIli5dyr333lukfenSpURGRnqsMBGRqiyvoIAHPlhD0Kbv6JnnBGCHn53XQqJIs5bvo7VPq8beLFFERM7hdvB97LHHmDRpEuvWraNbt24AfPfdd3zxxRfMnTvX4wWKiFQleQUFjHl9GQd3/860zOPUNwowgKVB4SwPDC/XtsMAVgtM6N7Wu8WKiEgRbgffCRMm0Lp1a/7zn/+wePFiAFq3bs3GjRsLg7CISE3jMgxuX7CKZb/s4arcU/xf1kn8gVSrjTmOKHb7B7rV35ReHQnw04qSIiKVya1P3fz8fKZOncrDDz/Mu+++662aRESqlGWJe7j9vVUEugq4w3mCzvnZACT4B/GGIxKn1eZWf4PaNOWRYT29UaqIiJTCrZvb/P39WbRokbdqERGpUlyGwZR3VjBlwUqa5WXzSEYynfOzKQDeC47gxZBot0KvBZg1ZgDzbhnqtZpFRKRkbv+ebeTIkXz88cf8+c9/9kY9IiJVwrLEPfzxvVW4DIOhORmMzE7DBhy1+jE7JIrf/exu9de0bhgb7xurdXpFRHzI7eDbqlUrZs6cyddff02XLl1wOIruRnTXXXd5rDgREV94bPnXzNrwM2GGi8mZKbQpyAHg24Bg3nZEklOObYfP1qFBJCvuGu2NUkVExA1ub2DRrFmzkjuzWPjtt98uuKiqRBtYiNQep29gW8myrb/RNj+biZkphJsGuVh411GXrwMcUM5VG86Y3KsDM6/u5aWKRUQEyp/X3B7x3bt37wUVJiJSFS1L3MOf3l9NQYGL67PTGJqTAcABmz+zQ6JJLmPb4XNdFF2HVXffqJUbRESqkAp/IqekpAAQFRXlsWJERCqTyzDYuPsQD3+6gd3H04h0FTA18zgtXHkAfGkP4f3gCPLdmNoQ6Gfj+RuuYkR8K2+VLSIiFeTWRLW0tDTuuOMOoqKiiImJISYmhqioKO68807S0tK8VOJpqamp3HzzzYSFhVGnTh0mTpxIZmZmqef06dMHi8VS5GvatGlerVNEqoflW5O46NHXGPPmMnYfT6NLnpNHMw7TwpVHlsXCKyFRvOOILHfojW9Ujw8mDWfPzMkKvSIiVVS5R3xTU1Pp3r07hw4d4uabb6Z169YAbN++nXnz5rFmzRo2bdpERESEVwq9+eabSU5OZtWqVeTn53PrrbcyZcoUFixYUOp5kydPZubMmYWPg4ODvVKfiFQfyxL3MGXBSgD8TYPRWSfpm3v6B+kkWwCzQ6I5YSvfx6OmNIiIVB/l/qSeOXMmAQEBJCUlERMTc95zAwcOZObMmTz33HMeL3LHjh188cUX/PDDD3Tt2hWAF198kaFDh/Kvf/2LBg0alHhucHAw9evXL/e1cnNzyc3NLXyckZFR8cJFpErJKyhg+kdfsihhNwCxrnymZh4nzpUPwGeBYXwcVAdXOW5gs1rgldEDNLorIlKNlHuqw8cff8y//vWv80IvQP369fnnP//JkiVLPFrcGd988w116tQpDL0A/fv3x2q18t1335V67rvvvktUVBTt2rXjwQcfJCsrq9Tjn3rqKcLDwwu/4uLiPPIaRMQ3XIbBV7sOcMWzC2jytzmnQ69p0jM3k4fTk4lz5ZNhsfLvkHosCo4oV+jtEleP/U9OU+gVEalmyj3im5ycTNu2bUt8vl27dhw5csQjRZ3ryJEj1KtXr0ibn58fdevWLfWaY8eOpUmTJjRo0IDExET++te/smvXLhYvXlziOQ8++CDTp08vfJyRkaHwK1JNLUvcw10frCGnwFXYFmga/MF5gsvzTv8QvN0vkNdCIkm3lu/jcFLP9jw+vLdX6hUREe8qd/CNiopi3759NGrUqNjn9+7dS926dd26+AMPPMDTTz9d6jE7duxwq8+zTZkypfDP7du3JzY2ln79+pGUlESLFi2KPcdut2O3u7cjk4hUPTM/28Sr6xOKtDUuyGVaZgoxRgEu4OOgOnweGIZZzrV5p/buyKPDenq+WBERqRTlDr6DBg3i//7v/1i1ahUBAQFFnsvNzeXhhx9m8ODBbl383nvvZcKECaUe07x5c+rXr8+xY8eKtBcUFJCamurW/N1u3boBsGfPnhKDr4hUf8sS9xQNvaZJ/9xTjMo6iR9wwmpjjiOKPf6B5erP7mflxRv7M7xDS6/UKyIilcOtm9u6du1Kq1atuOOOO7jkkkswTZMdO3bwyiuvkJuby9tvv+3WxaOjo4mOji7zuO7du5OWlsaPP/5Ily5dAFi7di2GYRSG2fJISEgAIDY21q06RaT6yCso4K4P1xQ+dhgubnOeID4/G4Cf/IOY54jEabWVq7/h7Vvw6k0DsFnd26ZYRESqHre2LN67dy9//OMfWblyJWdOs1gsDBgwgJdeeomWLb03GjJkyBCOHj3KrFmzCpcz69q1a+FyZocOHaJfv37Mnz+fyy67jKSkJBYsWMDQoUOJjIwkMTGRP//5zzRq1Iivvvqq3NfVlsUiVVteQQFvbNrKN78dZOeRVA6kZXLmQ61Vfg5TnCnUNVzkAx8ER7DWHlqubYcdAf48d0NfjfKKiFQDXtmyuFmzZnz++eecPHmS3btPLwfUsmVLt+f2VsS7777LnXfeSb9+/bBarVx//fX85z//KXw+Pz+fXbt2Fa7aEBAQwOrVq3n++edxOp3ExcVx/fXX87e//c3rtYpI5Zj52SZmrU/g3J/eLabJsJx0RmSnYwWOWP2YFRLNAb+A4ropolndMJ6+7kp6NG+oUV4RkRrGrRHf2kgjviJVU3E3rwHUMQqYlJlC64LT63FvCnDwjqMuueXYgU03r4mIVE9eGfEVEakK8goKmFVM6G2Xl80kZwqhpkEOFt5x1OUbe0iZ/XVv1oCFE6/W7msiIjWcPuVFpNqZ9822ItMbbKbJddlpDM45vdPifps/s0KiOWrzL7EPC3B9/EU8e0MfBV4RkVpCn/YiUu2s272/8M9RrnymZqbQ3JUHwBp7KB8ER1BQyg1sIQF+bJtxmwKviEgto099EalWXIbBN3sPA9A118n4rBMEmyZOi5U3HZFsCQgus49/33CVQq+ISC2kT34RqTZchsGfP1iLkZfPH7JO0ic3E4DdfnbmOqI4YSv7I+32K+K1RJmISC2l4Csi1cLyrUn86f3VRGRn8zdnCg1d+RjAZ4FhLA2qg1HG2ryBflb+o93XRERqNQVfEanylm9NYtLbX9A7N5Obsk5ixyTdYmVuSBQ7/INKPddus3Fnn078uV9XrcsrIlLLKfiKSJXmMgwe+mA1U50pXJZ3eoOarX6BvB4SRUYJ2w5bgT9eGc8VreK0EYWIiBRS8BWRKsdlGGxKOszGpINs+jaBPx87QD2jABewJKgOXwSGYZYytWFou+b835AelVewiIhUCwq+IlJluAyD59Zs5uWvtpCbX8CAnFNMzj6JH5BitTHHEU2Sv73Mfm7p1s77xYqISLWj4CsiVcLSn3dz1wdryHMZhBgupjpP0CE/G4Af/YOZ56hLVglTG84WYg+gR4sG3i5XRESqIQVfEfG5CfM/Y8X2fQBclJ/DlMwUIkwX+cDC4Lqss4dAGas2nPHv6/toTq+IiBRLwVdEfMZlGNy+YCUrtu/DYpoMz05neE46VuCw1Y/ZIdEc9Asod39ao1dEREqj4CsiPrF8axLTP1xLRm4+EUYBkzNTuLggF4CNAQ7eddQlz1K+kVt/m5WXR2uNXhERKZ2Cr4hUumWJe5iyYCUAHfKyuM15glDTIAcL8x11+c4eUu6+HP42tj8yUVsQi4hImfSdQkQqjcsweHb1Dzy39kdspskNWScZmHsKgH22AGaHRHHM5u9Wny+M7q/QKyIi5aLvFiJSKZZvTeKuD9aQlVdAPVc+UzNTaOrKA2CVPZSPgiMoKOcNbAB2m5WXbxrAsHYtvFWyiIjUMAq+IuJ1y7cmMemdFQBcluvkFucJgjDJtFh5wxHJzwHBbvU3vH0LXr1pgFZvEBERtyj4iohXuQyDv32ygQDTYKwzld55TgB+9bMzxxHFSVvZH0M2i4X2DaMY0aElt/Vor6kNIiJSIfruISJe4zIM5m5MxJaaxsOZKTQw8jGATwPDWRYUjlHG1AY/i4V3bh1Gr5aNNLorIiIXTMFXRDzKZRhsSjrMm9/8wqod++iZncHfsk4SgEmaxcackCh2+QeWq69ZNw/kyosae7liERGpLRR8ReSCnQm78779hdU7fyfPZRBkGEzKOsGleVkA/OIfyOuOKE6VY9thh92fF0ZdpRvXRETEoxR8RaRCigu7ZzQryGVq5nGiDRcFwOKgOqwMDMMsx6oNunFNRES8RcFXRNziMgyeW7OZl7/aQk6Bq8hzFtNkYE4G12Wn4Qcct/oxOySKvX72MvsNsFl5SbuviYiIFyn4iki5nb0W77lCDRcTnSm0z88B4IeAYN4KjiS7HCO3dpuVXx+bpNUaRETEq/RdRkTK5ey1eM91SX42kzNPUMd0kYeF94IjWG8PgXJuSHFX3y4KvSIi4nX6TiMiZXIZBvct+vK8dqtpck12OsNy0rECh2z+zHZEccgvoNx9RwTbufuqLh6sVkREpHgKviJSxJmb1jYmHeRQWiaxdRys3fk7adl5RY6LcBUwxZnCRQW5AKy3h/BecAR5FvduSnvmuj66kU1ERCqFgq+IAKXftHaujnlZTHSewGEaZGNhviOS7+0Ot64XERzIM9ddqSXLRESk0ij4igjLEvdw1wdrygy8fqbJqKyT9M89BcBeWwCzQ6I4bvMv13VaRddhSLvm9GrRkB7NG2qkV0REKpWCr0gtN/OzTby6PqHM4+q58pmWmUIT1+kpDysCQ1kUFIGrlBvYbBYL7RtGMaJDS27r0V43sImIiE/pu5BILbYscU+5Qu/luZn8wZlKICanLFbecESSGBBc7LH+VisDWjdhQvd2GtUVEZEqRcFXpJbKKyjgvsXnr9RwNrtpcLMzlZ55TgB2+tmZGxJFmvX8jw67zcpb44fSq2UjhV0REamSFHxFaqFliXu468M15OSXPKe3UUEe0zKPE2sUYADLgsJZFhhe4rbDL980gCsvauylikVERC6cgq9ILVPmnF7TpG9uJqOzUvEHTlpszAmJ4lf/wGIPd9j9eWHUVVqdQUREqjwFX5FawmUYPLv6h1JDb7DhYoIzlS75WQD87B/EG45IMq22Yo8f3r4Fr940QFMbRESkWlDwFakFliXu4c8frcWZV1DiMS3yc5nqPE6k4aIA+Cg4glX20GK3HQ6x+/Pv6/syvENLL1YtIiLiWQq+IjVcWVMbLKbJ4JwMrs1OwwYctfoxOySK3/3sWIFBbZrStUl90nPysGKhR4sGWq1BRESqJQVfkRqsrOXKwgwXkzJTaFuQA8B3AcHMd0SS899thxdOvIberRpVRqkiIiJep+ArUkPlFRRw14drSny+TX42kzJTCDcNcrGwwBHBxoCQwqkNDcId9GjRoLLKFRER8ToFX5EaqLTlymymyYjsNIbkZGAFDtr8mRUSRbItoMhxM4f30nQGERGpURR8RWoIl2Hw3d5kXlmfwJpdvxd7TKSrgMnOFFoV5AKwzh7CwuAI8i3/C7gRwYE8c92VWp5MRERqHAVfkRpgWeIeHly6nhPOnBKP6ZyXxQTnCRymQZbFwluOSDYHOAqfH9KmGbf20DbDIiJScyn4ilRjLsPgjoWrWJqYVOIxfqbJjVkn6Zd7CoDfbAHMDokixeZfeMycsQO1NJmIiNR4Cr4i1VR51uaNceUzLfM4jV35AHweGMaSoDq4/nsDW5C/jRdH99e0BhERqRUUfEWqgTPzd5PTnRzPzGLZL3v46cCxUs/pkZvJzc5UAjHJsFh53RHF1oCgwucdAf5sn3ErAX76GBARkdpB3/FEqqgzYffzbXv54KedZOTkleu8QNPgZmcqPfKcAOzws/NaSBRp1qL/3F+48SqFXhERqVX0XU+kCnEZBpuSDjP/u62s232AzNx8t85vXJDH1Mzj1DcKMIClQeEsDwzHPGvb4YhgO89c10fTG0REpNZR8BWpAlyGwQtrf+Tl9VvIKmXObolMk6tyT3Fj1kn8gVSrjTmOKHb7BxY5bHj7Frx60wCt2iAiIrWSgq+ID11w4AUchotbnSfolJ8NwBb/IN50ROK02oocN7V3Rx4d1vOCaxYREamuFHxFfMATgRegZX4OU5wpRBou8oEPgyNYYw8t3HYYINIRyFMjrtByZSIiUusp+IpUgrNXZdiw5yDLtu65oMBrMU2G5mQwIjsNG3DU6seskCj2+9kBCPS3Me7SNgxp25xuzWI1tUFERAQFXxGvW741iYeXbSQ53emR/sKNAiZlnqBNweld2r4JcPCOoy45/912+JoOLXhljObxioiInKvafGd88skn6dGjB8HBwdSpU6dc55imyYwZM4iNjSUoKIj+/fuze/du7xYqtd7plRkOsSRhN/9a9T2T3lnhsdDbLi+bR9OTaVOQQy4W3nBE8pojkhyLlRC7P3PGDmT22EEKvSIiIsWoNiO+eXl5jBo1iu7du/P666+X65x//vOf/Oc//+Gtt96iWbNmPPzwwwwaNIjt27cTGBhYdgcibvL06O4ZNtPk2uw0huRkAHDA5s+skGiO2PxxBPhz+xXx3HNVFwVeERGRUlhM0zR9XYQ75s2bxz333ENaWlqpx5mmSYMGDbj33nu57777AEhPTycmJoZ58+YxZsyYYs/Lzc0lNze38HFGRgZxcXGkp6cTFhbmsdchNc+yxD1MWbDS4/1GufKZkplCC9fpDSw2BIeREt+RlrHR9GjRgB7NGyrwiohIrZaRkUF4eHiZea3ajPi6a+/evRw5coT+/fsXtoWHh9OtWze++eabEoPvU089xWOPPVZZZUoNsSxxD9PeW+XxfrvkOZngTCXYNDADAnAM7c8Tg3sr6IqIiFRAjQ2+R44cASAmJqZIe0xMTOFzxXnwwQeZPn164eMzI74iJVm+NcnjI72hNgt3+eXRPDUFgDrNGxM/6SaCo+p69DoiIiK1iU+HjR544AEsFkupXzt37qzUmux2O2FhYUW+REriMgweXrbRY/05Avx56LKLeS0gh+bJhwFoPrgPl983TaFXRETkAvl0xPfee+9lwoQJpR7TvHnzCvVdv359AI4ePUpsbGxh+9GjR4mPj69Qn1K7nVmL9+ipLKIcQWAx2bjnkEduZHME+HN7747cEAg73/+EzLx8AkJD6HjbaKLbXuSB6kVERMSnwTc6Opro6Giv9N2sWTPq16/PmjVrCoNuRkYG3333HbfffrtXrik1l7dWa6gTZGdSzw7c2b0tO95byrbvEwCIbN2S+NvGYA8P9ej1REREarNqM8d3//79pKamsn//flwuFwkJCQC0bNmSkJAQAC655BKeeuoprr32WiwWC/fccw9PPPEErVq1KlzOrEGDBowcOdJ3L0SqneVbk5j8zgo8ufzJpB7tC3dVyzxwmG/+/hJZx09gsVppNWIgLQZdiUU3sImIiHhUtQm+M2bM4K233ip83KlTJwC+/PJL+vTpA8CuXbtIT08vPOb+++/H6XQyZcoU0tLS6NWrF1988YXW8JVyyyso4K9LvvJY6G0QHsLM4T0Z1q4Fpmmyb81Gdi76HNPlIrBuHTpNvomIFk09dDURERE5W7Vbx7eylXddOKl5lm9N4v4lX5HqzLmgfu7t15UW0RHEhAbTrVksNquVvFNOEt/6kGOJOwCI6dSW9rfcQIAj2BOli4iI1Cq1fh1fkYpyGQYvrP2RZ1b/cEH9RATbeea6Pgxr16JIe+qvv5Hw2nvkpGVg9bNxyairadKnOxaL5YKuJyIiIqVT8BU5y/KtSfztkw0cyci64L5m3zSI3q0aFT42DYM9n61l97LVYJo4YqLoNOVmwuIaXPC1REREpGwKviL/5amb2CxAbHgIPVr8L9DmpGWQ8Np7pP76GwANu3eh7U0j8Au0X+DVREREpLwUfEX430YUnprwPnN4z8JthY/9spPENz8gL9OJzR5Au7HX0rB7Zw9dSURERMpLwVcE2PSbZzaiOHvVBqOggF1LvmDvqg0AhMU1IH7KWEJivLN2tYiIiJROwVdqpbN3YdubksbcrxPdOr9usJ1XxgwgJTOHE85sIkMCiQ0LKVy1Iev4CbbMXUD6voMANLmqB5dcPxSbv783Xo6IiIiUg4Kv1ArnBt13vt9OckbFRngtwD+v68OVFzUu9vnDP/zM1rcXUZCTi39wEB0mjCImvu0FVC8iIiKeoOArNZIng+7ZGoQ7mDm813lLlAG4cvPY/sEyDmz4HoCIFk2JnzSGoMiIC76uiIiIXDgFX6lxlm9N4uFlGz0yZ/ds9/W/lHuu6lJ409rZTh0+wpY5C8g8fBQsFloM6Uur4f2x2mwerUFEREQqTsFXahRPLUl2tpI2ogAwTZMDG79n+8JlGPn52MNC6ThxDFGtW3qwAhEREfEEBV+pMTy9JNkZ525EcUZ+VjZb31lM8ubTN8ZFtb2IjrfeiD0s1MMViIiIiCco+EqN8d3eZI9ObyhuI4oz0vYdIGHOArJSUrFYrVw0chDNB16BpZhpECIiIlI1KPhKjeAyDDbsOeix/iz//e/ZG1HA6W2H967eyK7Fn2MaBkGREcRPvomI5k08dm0RERHxDgVfqfa8cTNb7FkbUZyReyqTxDc/5PjWnQDU79yO9rfcgH9wkMeuKyIiIt6j4CvVmqduZosNc3DzZW1oHlWHmNDgwo0ozjixM4mE198jN/0UVj8/Wo8eTuMrumGxWErpVURERKoSBV+pti7kZraygu4ZhsvFnuVr2LN8LZgmjvrRdJpyM2GNYi/8BYiIiEilUvCVasudm9nKG3TPln0yjZ9fW0jq7r0ANOrZlTZjRuBnD7jg2kVERKTyKfhKtXX0VFa5jrunbxfuG3BpmUG3SN8/bydx3ofkO7Ow2QNoN+46GnbrVNFSRUREpApQ8JVqKyY0uFzH9W7ZqNyh15VfwK7Fn7NvzUYAwho3pNOUsTjqRVW4ThEREakaFHyl2urWLJbYcAdH0p3FzvM9sw5vt2blm4/rPJbClrkLyPj9EABN+/Xi4uuGYPPXPxMREZGaQN/RpdqyWa08PrwXk99ZgQWKhN+S1uEtyaHvtrD1ncW4cvPwdwTTYcIoYjq28UbZIiIi4iMKvlKtuAyD7/Ymc/RUFjGhwQxu04y54wadt45vcevwFqcgN4/tC5dy8OvNAES0akb8pDEERdTx5ssQERERH1DwlSrp3IDbrVksX2zfW0zAdfD48F788Nc/nHd8WSO9GQeTSZi7gMzkY2Cx0HLYVbQc1g+rzebtlyciIiI+oOArVU5xO7HVCbaTlpV73rFH0p1MfmcFc8cNKnN09wzTNNm//jt2fLAMI78Ae3go8RNvIvKS8p0vIiIi1ZOCr1QpJe3EVlzohdPzei3AjGVfM7hNszJHefOzsvll/iKO/PQLANHtLqbDrTdiDw258OJFRESkSlPwlSqjojuxmcDh9Ey+25tMjxYNSzwu7bf9bJm7gOwTJ7FYrVx83RCa9e+FxY31fUVERKT6UvCVKsOdndiKU9KGFqZh8NvK9fz68QpMwyAoqi6dJo+lTrO4Cl9LREREqh8FX6kyyrsTW0mK29AiNyOTn998n5RtvwIQ27UD7cZdh39w0AVdS0RERKofBV+pMsq7E9u5StqoImXHHn5+fSG5Gaew+vvTdsw1NOp1KRaLpfiOREREpEZT8JUqo6yd2IpT3EYVhsvF7mWrSfr8SzBNQmLr0WnqzYQ2qO+VukVERKR6UPAVnzp3vd7HhvVk6oKVxe7EZgIRwXZOnrXCw7kbVWSfOEnCaws5mbQPgLjel9HmxuHY7AGV9ZJERESkilLwFa8pbhOKs5cbK2693thwB9OuiOfjn3cXuxPb4DbNSuzzyJZt/PLWh+RnZeMXaKfdH66nwaUdK+8Fi4iISJVmMU3T3dWjapWMjAzCw8NJT08nLCzM1+VUGyWF2seH92JYuxYlrtd7ZurC7LEDiXQElWsnNld+PjsXfcbvazcBEN6kEZ2mjCU4OtILr0xERESqmvLmNQXfMij4uq88ofaR5V+XuHTZmZvVvv/ruDI3pMg8epyEOQvIOHAYgGYDenPxtYOx+umXGSIiIrVFefOa0oF4VGmbUJzZZe3Bpes54cwpsY/ybkhx6Juf2LpgCa7cPAJCHHS49Ubqtb/kQl+CiIiI1FAKvuJRZW1CYUKpofdsJa3rW5CTy7b3lnLomx8BqHtRc+InjiEwItztekVERKT2UPAVj7rQTSjOVty6vhkHDrNlzgKcR4+DxUKr4f1pOfQqbTssIiIiZVLwFbeVtlpDeTehiHQEkurMKXZKRHEbUpimye/rvmHnh8sxCgoIrBNGx4k3EXlxcw+8IhEREakNFHzFLWWt1lDWJhRnQu2jw3qUuF4vFN2QIt+ZReL8jzi6ZRsA9Tq0psP4UQSEOrzxEkVERKSG0u+HpdzOrNZw7hzeI+lOJr+zguVbk7BZrTw+vBfwvxB7xtmhdniHlswdN4j64UXDa2x4CHPHDSrckOJk0j42PP4CR7dsw2Kz0Xr0cLrcMV6hV0RERNym5czKoOXMTnMZBpc+/Xa5lyArbmS4wTm7rJ3pt7hpE6ZhkLTiK3YvXYlpGARHR9JpyljCmzTy9ksVERGRakbLmYlHlWe1hrOXIBvWrkWpu6ydYbNaz1uyLDf9FAlvLOTEjj0AxF4WT7ubr8U/KNDjr0tERERqDwVfKZfyrtZw9nHFhdqyHN/2Kz+/8T55pzKx+vvT9qYRNOrZFYvl3IkTIiIiIu5R8K2lSluZoTjlXa2hvMedyyhw8esnK/nti3UAhDSoT+epYwmJjalQfyIiIiLnUvCthcpamaE45V2t4ewlyMor+8RJtsxdQNpv+wFofOXltB51NbYAf7f7EhERESmJVnWoQlyGwaakQyxJ2M2mpEO4DMPj1yjPygzFKe9qDaWNGhfnyE9b2fD4C6T9th+/oEA6TbmZdjdfq9ArIiIiHqcR3yqiIqOw7nIZBg8v21jsiK3J6QA7Y9nXDG7TrNgAO6xdC+aOG1RMneev1lBmLfn57PhwOfvXfQNAnWZxxE8eS3BUXTdflYiIiEj5KPhWAWdGYc8NpGdGYc9e1/ZCuLsyQ3HKu1pDaTKTj7Fl7gJOHUwGoPmgK7loxCCsfja3Xo+IiIiIOxR8fexCR2HdUZGVGYpTkdUa4PS2w4e++ZFtCz7GlZdPQKiDjreOJrrdxW73JSIiIuIuBV8f88QobHl5e2WG0hTk5LL13SUc/m4LAJGXtKDjbWMIrFN7NwURERGRyqXg62OeGoUtD2+uzFCa9N8PsWXuu2QdOwEWCxddM5AWQ/pgucARbBERERF3KHn4WGWOwnprZYaSmKbJvjVf883TL5N17ASBEeFcft9UWg67SqFXREREKp3Sh4+dGYUtaV8yC9DAg6OwZ1ZmqB/uKNIeGx7isZvoAPIynfz4yny2v/8JRoGLmI5t6DXjHuq2auaR/kVERETcVW2mOjz55JMsX76chIQEAgICSEtLK/OcCRMm8NZbbxVpGzRoEF988YWXqnTfmVHYye+swAJFpiB4YxQWPLMyQ2lSd+8l4bX3yDmZjtXPxiU3DKNJ3x7adlhERER8qtoE37y8PEaNGkX37t15/fXXy33e4MGDefPNNwsf2+12b5R3QTy5Pm55VXRlhtKYhkHS51/y6yerwDQJrhdFpyljCW/s2euIiIiIVES1Cb6PPfYYAPPmzXPrPLvdTv369ct9fG5uLrm5uYWPMzIy3LpeRXl7FNbbctIy+PmNhZzYeXrntwbdOtHu5mvxC6x6P2iIiIhI7VRtgm9FrVu3jnr16hEREcFVV13FE088QWRkZInHP/XUU4Uhu7J5YxS2Mhzfuouf33yfvFNObAH+tB17LY16dPF1WSIiIiJFWEzTLG5lqypr3rx53HPPPeWa47tw4UKCg4Np1qwZSUlJPPTQQ4SEhPDNN99gsxW/S1hxI75xcXGkp6cTFqY1Z89mFBTw68cr+W3lVwCENoql05SbCakf7ePKREREpDbJyMggPDy8zLzm0xHfBx54gKeffrrUY3bs2MEll1xSof7HjBlT+Of27dvToUMHWrRowbp16+jXr1+x59jt9io5D7iqyUpJZcucBaTvOwBAkz7duWTUMGz+/j6uTERERKR4Pg2+9957LxMmTCj1mObNm3vses2bNycqKoo9e/aUGHylbMmbE/ll/kcU5OTiFxxEh1tuoH7ndr4uS0RERKRUPg2+0dHRREdX3q/FDx48yIkTJ4iN9ezOZLWFKy+f7R8s48D67wCo07wxnSaPJSgywseViYiIiJSteiwZAOzfv5+EhAT279+Py+UiISGBhIQEMjMzC4+55JJLWLJkCQCZmZn85S9/4dtvv2Xfvn2sWbOGESNG0LJlSwYNGuSrl1FtnTp8lK///tLp0Gux0GJIXy6/b5pCr4iIiFQb1WZVhxkzZhTZjKJTp04AfPnll/Tp0weAXbt2kZ6eDoDNZiMxMZG33nqLtLQ0GjRowMCBA3n88cc1h9cNpmly8OvNbHtvKUZ+PgGhIXScOJroNhf5ujQRERERt1S7VR0qW3nvEqyJ8rNz2PruEpK/TwAgqnUrOt42Gnt4qG8LExERETlLtVjVQaqutH0HSZi7gKzjJ7BYrVw0YiDNB12JpZpsqCEiIiJyLgVfKcI0Tfat3sjOxZ9julwE1q1Dp8ljiWjRxNeliYiIiFwQBV8plHfKSeJbH3IscQcAMZ3a0uGWG/B3BPu4MhEREZELp+ArAJzY9RsJr79HbloGVj8/Wt94NY2vvByLxeLr0kREREQ8QsG3ljMNgz3L17D70zVgmjhiouk0ZSxhcQ18XZqIiIiIRyn41mI5J9NJeH0hqb/+BkDDHl1oO2YEfoFa7k1ERERqHgXfWupY4g4S531IXqYTmz2AdjdfS8PLO/u6LBERERGvUfCtZYyCAnYt+YK9qzYAEBbXgPgpYwmJqbyto0VERER8QcG3FnEeO0HC3AWk/34QgKZX9eTi64di89dfAxEREan5lHhqicM//MzWtxdRkJOLf3AQHSaMIia+ra/LEhEREak0Cr41nCs3j+3vf8KBjT8AENGyKfGTbiKobh3fFiYiIiJSyRR8a7BTh46wZc67ZCYfA4uFlkP70vLq/lhtNl+XJiIiIlLpFHxrINM0ObDhe7a//wlGfgH2sFA6ThxDVOuWvi5NRERExGcUfGuY/Kxstr6zmOTNiQBEtb2IjreOxh4W4uPKRERERHxLwbcGSdt7gC1zF5CdkorFauXiawfTbEBvLFarr0sTERER8TkF3xrANAz2rt7ArsVfYBoGQZERdJo8ljrNG/u6NBEREZEqQ8G3mss9lUnimx9wfOsuAOp3bk/7W67HPzjIx5WJiIiIVC0KvtXYiZ1JJLz+Hrnpp7D6+9HmxuHEXdENi8Xi69JEREREqhwF32rIcLnY8+ka9ny2FkyTkNh6xE8eS1ijWF+XJiIiIlJlKfhWM9kn00h4bSEnd+8FoFGvS2kz+hr87AE+rkxERESkalPwrUaO/rydxHkfku/Mwi/QTrtx19HgsnhflyUiIiJSLSj4VgOu/AJ2Lf6MfWu+BiCsSUM6TR6Lo16UjysTERERqT4UfKs459EUtsxdQMb+QwA07d+LS64bgtVP/+tERERE3KH0VIUd+m4LW99ZjCs3D39HMB1uvZGYDq19XZaIiIhItaTgWwUV5Oax/b2lHNy0GYC6rZrRcdIYgiLq+LYwERERkWpMwbeKyTiYzJY57+I8chwsFloO60erq/tp22ERERGRC6TgW4VkHj3Opr+/hFFQgL1OGPETxxB5cQtflyUiIiJSIyj4ViGOelHU79KefGc2HW4dhT00xNcliYiIiNQYCr5ViMViof0t12O12TS1QURERMTDFHyrGJu/v69LEBEREamRNKwoIiIiIrWCgq+IiIiI1AoKviIiIiJSKyj4ioiIiEitoOArIiIiIrWCgq+IiIiI1AoKviIiIiJSKyj4ioiIiEitoOArIiIiIrWCgq+IiIiI1AoKviIiIiJSKyj4ioiIiEitoOArIiIiIrWCgq+IiIiI1AoKviIiIiJSKyj4ioiIiEitoOArIiIiIrWCn68LqOpM0wQgIyPDx5WIiIiISHHO5LQzua0kCr5lOHXqFABxcXE+rkRERERESnPq1CnCw8NLfN5ilhWNaznDMDh8+DChoaFYLBavXisjI4O4uDgOHDhAWFiYV68l/6P33Xf03vuG3nff0PvuO3rvfaMy33fTNDl16hQNGjTAai15Jq9GfMtgtVpp1KhRpV4zLCxM/zB9QO+77+i99w29776h99139N77RmW976WN9J6hm9tEREREpFZQ8BURERGRWkHBtwqx2+088sgj2O12X5dSq+h99x29976h99039L77jt5736iK77tubhMRERGRWkEjviIiIiJSKyj4ioiIiEitoOArIiIiIrWCgq+IiIiI1AoKvlXUNddcQ+PGjQkMDCQ2NpY//OEPHD582Ndl1Xj79u1j4sSJNGvWjKCgIFq0aMEjjzxCXl6er0ur8Z588kl69OhBcHAwderU8XU5NdbLL79M06ZNCQwMpFu3bnz//fe+LqnGW79+PcOHD6dBgwZYLBY+/vhjX5dUKzz11FNceumlhIaGUq9ePUaOHMmuXbt8XVat8Oqrr9KhQ4fCjSu6d+/O559/7uuyAAXfKqtv37588MEH7Nq1i0WLFpGUlMQNN9zg67JqvJ07d2IYBrNnz2bbtm0899xzzJo1i4ceesjXpdV4eXl5jBo1ittvv93XpdRY77//PtOnT+eRRx7hp59+omPHjgwaNIhjx475urQazel00rFjR15++WVfl1KrfPXVV9xxxx18++23rFq1ivz8fAYOHIjT6fR1aTVeo0aN+Mc//sGPP/7I5s2bueqqqxgxYgTbtm3zdWlazqy6+OSTTxg5ciS5ubn4+/v7upxa5ZlnnuHVV1/lt99+83UptcK8efO45557SEtL83UpNU63bt249NJLeemllwAwDIO4uDj+9Kc/8cADD/i4utrBYrGwZMkSRo4c6etSap3jx49Tr149vvrqK6644gpfl1Pr1K1bl2eeeYaJEyf6tA6N+FYDqampvPvuu/To0UOh1wfS09OpW7eur8sQuSB5eXn8+OOP9O/fv7DNarXSv39/vvnmGx9WJlI50tPTAfR5XslcLhcLFy7E6XTSvXt3X5ej4FuV/fWvf8XhcBAZGcn+/ftZunSpr0uqdfbs2cOLL77I1KlTfV2KyAVJSUnB5XIRExNTpD0mJoYjR474qCqRymEYBvfccw89e/akXbt2vi6nVvjll18ICQnBbrczbdo0lixZQps2bXxdloJvZXrggQewWCylfu3cubPw+L/85S9s2bKFlStXYrPZuOWWW9DMlIpx970HOHToEIMHD2bUqFFMnjzZR5VXbxV530VEPO2OO+5g69atLFy40Nel1BoXX3wxCQkJfPfdd9x+++2MHz+e7du3+7oszfGtTMePH+fEiROlHtO8eXMCAgLOaz948CBxcXFs2rSpSvyqoLpx970/fPgwffr04fLLL2fevHlYrfoZsSIq8ndec3y9Iy8vj+DgYD766KMi80vHjx9PWlqafqNUSTTHt/LdeeedLF26lPXr19OsWTNfl1Nr9e/fnxYtWjB79myf1uHn06vXMtHR0URHR1foXMMwAMjNzfVkSbWGO+/9oUOH6Nu3L126dOHNN99U6L0AF/J3XjwrICCALl26sGbNmsLQZRgGa9as4c477/RtcSJeYJomf/rTn1iyZAnr1q1T6PUxwzCqRIZR8K2CvvvuO3744Qd69epFREQESUlJPPzww7Ro0UKjvV526NAh+vTpQ5MmTfjXv/7F8ePHC5+rX7++Dyur+fbv309qair79+/H5XKRkJAAQMuWLQkJCfFtcTXE9OnTGT9+PF27duWyyy7j+eefx+l0cuutt/q6tBotMzOTPXv2FD7eu3cvCQkJ1K1bl8aNG/uwsprtjjvuYMGCBSxdupTQ0NDCuezh4eEEBQX5uLqa7cEHH2TIkCE0btyYU6dOsWDBAtatW8eKFSt8XRqYUuUkJiaaffv2NevWrWva7XazadOm5rRp08yDBw/6urQa78033zSBYr/Eu8aPH1/s+/7ll1/6urQa5cUXXzQbN25sBgQEmJdddpn57bff+rqkGu/LL78s9u/2+PHjfV1ajVbSZ/mbb77p69JqvNtuu81s0qSJGRAQYEZHR5v9+vUzV65c6euyTNM0Tc3xFREREZFaQZMXRURERKRWUPAVERERkVpBwVdEREREagUFXxERERGpFRR8RURERKRWUPAVERERkVpBwVdEREREagUFXxERERGpFRR8RaRWWrduHRaLhbS0NF+X4haLxcLHH3/ssf6aNm3K888/77H+Ktu+ffuwWCyFW1xX1/+vIlI5FHxFpMaxWCylfj366KO+LrFMjz76KPHx8ee1JycnM2TIkMovqAqYMGECI0eOLNIWFxdHcnIy7dq1801RIlKt+Pm6ABERT0tOTi788/vvv8+MGTPYtWtXYVtISAibN2/2RWnk5eUREBBQ4fPr16/vwWqqP5vNpvdERMpNI74iUuPUr1+/8Cs8PByLxVKkLSQkpPDYH3/8ka5duxIcHEyPHj2KBGSApUuX0rlzZwIDA2nevDmPPfYYBQUFhc/v37+fESNGEBISQlhYGDfeeCNHjx4tfP7MyO1rr71Gs2bNCAwMBCAtLY1JkyYRHR1NWFgYV111FT///DMA8+bN47HHHuPnn38uHKWeN28ecP5Uh4MHD3LTTTdRt25dHA4HXbt25bvvvgMgKSmJESNGEBMTQ0hICJdeeimrV6926710uVxMnz6dOnXqEBkZyf3338/48eOLjLwWN10iPj6+yMj6v//9b9q3b4/D4SAuLo4//vGPZGZmFj4/b9486tSpw4oVK2jdujUhISEMHjy48IeYRx99lLfeeoulS5cWvifr1q07b6pDcTZu3Ejv3r0JCgoiLi6Ou+66C6fTWfj8K6+8QqtWrQgMDCQmJoYbbrjBrfdIRKoPBV8RqdX+7//+j2effZbNmzfj5+fHbbfdVvjchg0buOWWW7j77rvZvn07s2fPZt68eTz55JMAGIbBiBEjSE1N5auvvmLVqlX89ttvjB49usg19uzZw6JFi1i8eHFhQBs1ahTHjh3j888/58cff6Rz587069eP1NRURo8ezb333kvbtm1JTk4mOTn5vD4BMjMzufLKKzl06BCffPIJP//8M/fffz+GYRQ+P3ToUNasWcOWLVsYPHgww4cPZ//+/eV+f5599lnmzZvHG2+8wcaNG0lNTWXJkiXuvs1YrVb+85//sG3bNt566y3Wrl3L/fffX+SYrKws/vWvf/H222+zfv169u/fz3333QfAfffdx4033lgYhpOTk+nRo0eZ101KSmLw4MFcf/31JCYm8v7777Nx40buvPNOADZv3sxdd93FzJkz2bVrF1988QVXXHGF269PRKoJU0SkBnvzzTfN8PDw89q//PJLEzBXr15d2LZ8+XITMLOzs03TNM1+/fqZf//734uc9/bbb5uxsbGmaZrmypUrTZvNZu7fv7/w+W3btpmA+f3335umaZqPPPKI6e/vbx47dqzwmA0bNphhYWFmTk5Okb5btGhhzp49u/C8jh07nlc3YC5ZssQ0TdOcPXu2GRoaap44caKc74Zptm3b1nzxxRcLHzdp0sR87rnnSjw+NjbW/Oc//1n4OD8/32zUqJE5YsSIUvvo2LGj+cgjj5TY74cffmhGRkYWPn7zzTdNwNyzZ09h28svv2zGxMQUPh4/fnyR65qmae7du9cEzC1btpim+b//rydPnjRN0zQnTpxoTpkypcg5GzZsMK1Wq5mdnW0uWrTIDAsLMzMyMkqsVURqDs3xFZFarUOHDoV/jo2NBeDYsWP/3979hET17nEcf2s2oBGNxlQGOmJToqWhjJRBSjLhUAZjtegPpRERRdSiIKKYdBEalEFtXLRSxAqKBGsxRRg1RkUyY+RokaO4MELCZMhSG+9CPPzMP3fkXi6/353Pa3XmPOc858t5Nt/5nuc5h9TUVPx+P16v16jwwuSj/58/f/Ljxw8CgQApKSmkpKQY7VlZWZjNZgKBAPn5+QBYrVYsFotxjN/vJxQKsXz58mmxjIyM8Pnz54hj9/l85ObmkpSUNGt7KBSisrKSR48eMTAwwPj4OCMjIxFXfL9//87AwACbNm0y9sXFxWG325mYmIg4ToCnT59SXV1NV1cXw8PDjI+PG/cxISEBgISEBNasWWOck5yczNevXxd0nT/5/X46OjpobGw09k1MTBAOhwkGg2zfvh2r1Up6ejpOpxOn00lZWZkRk4j8f1HiKyJRbfHixcZ2TEwMwLSpAlVVVezevXvGeVNzdSOxZMmSab9DoRDJycm0trbOONZsNkfcb3x8/Lzt586d48mTJ1y7dg2bzUZ8fDx79+5ldHQ04mtEIjY2dkYiPDY2Zmz39vZSWlrKiRMnuHLlCklJSbx8+ZKjR48yOjpqJJl/HQuYHI+FJth/CoVCHD9+nNOnT89oS01NxWQy0d7eTmtrKx6PB7fbTWVlJW/fvl3QWIjIP4MSXxGROeTl5dHd3Y3NZpu1PTMzk/7+fvr7+42qb2dnJ0NDQ2RlZc3b75cvX4iLiyMtLW3WY0wmE79//543vpycHG7fvs23b99mrfp6vV4qKiooKysDJpPA3t7eefv8q2XLlpGcnMzr16+Nea/j4+PGnOQpFotl2ps0hoeHCQaDxu93794RDoe5fv06sbGTS0vu3bsXcRxTIrknf8rLy6Ozs3POMYTJKrbD4cDhcHD58mXMZjPPnj2b9Q+PiPyzaXGbiMgc3G439fX1VFVV8eHDBwKBAHfu3OHSpUsAOBwOsrOzOXjwIO3t7bx584bDhw9TVFSE3W6fs1+Hw0FBQQEulwuPx0Nvby9tbW1cvHjReM1aWloawWAQn8/H4OAgv379mtHP/v37WbVqFS6XC6/XS09PD/fv3+fVq1cArF271lhQ5/f7OXDggFHNjtSZM2eoqanh4cOHdHV1cfLkyRkfhyguLqahoYEXL17w/v17ysvLWbRokdFus9kYGxvj1q1b9PT00NDQQF1d3YLimLonHR0ddHd3Mzg4OK2qPJfz58/T1tbGqVOn8Pl8fPr0iebmZmNxW0tLCzdv3sTn89HX10d9fT3hcJiMjIwFxycif39KfEVE5lBSUkJLSwsej4f8/Hw2b97MjRs3sFqtwOSj+ObmZhITEyksLMThcJCens7du3fn7TcmJobHjx9TWFjIkSNHWLduHfv27aOvr4+VK1cCsGfPHpxOJ9u2bcNisdDU1DSjH5PJhMfjYcWKFezYsYPs7GxqamqMpLO2tpbExES2bNnCrl27KCkpmVapjcTZs2c5dOgQ5eXlFBQUsHTpUqOCPOXChQsUFRVRWlrKzp07cblc0+bqbty4kdraWq5evcqGDRtobGykurp6QXEAHDt2jIyMDOx2OxaLBa/X+2/PycnJ4fnz53z8+JGtW7eSm5uL2+1m9erVwOTUkgcPHlBcXExmZiZ1dXU0NTWxfv36BccnIn9/MRP/6QQqERGJKhUVFQwNDf1XP50sIvK/oIqviIiIiEQFJb4iIiIiEhU01UFEREREooIqviIiIiISFZT4ioiIiEhUUOIrIiIiIlFBia+IiIiIRAUlviIiIiISFZT4ioiIiEhUUOIrIiIiIlFBia+IiIiIRIV/ARf2Hyj+cZ7sAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Quantile-Quantile Plot\n",
+    "ml_plots.plot_qq(labels, preds, COLORS).show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "#TODO more plots"
+    "epochs = 10\n",
+    "# Reaorder hist['val_preds'] from [{epoch:int: [val_preds]}, ...] to {epoch:int: [val_preds], ...}\n",
+    "val_preds = {}\n",
+    "for i, val_pred in enumerate(hist['val_preds'][:epochs]):\n",
+    "    val_preds[i] = hist['val_preds'][i].values()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ml_plots.plot_val_preds(val_preds, hist['val_labels'], COLORS).show()"
    ]
   }
  ],
diff --git a/transformer_bootstrap_agg.py b/transformer_bootstrap_agg.py
index 5cfb764..3cca389 100644
--- a/transformer_bootstrap_agg.py
+++ b/transformer_bootstrap_agg.py
@@ -1,50 +1,33 @@
-import time
-import json
-import math
-
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-
-from nltk.tokenize import word_tokenize
-
+import random
 import torch
 import torch.nn as nn
 import torch.optim as optim
+import matplotlib.pyplot as plt
 from torch.utils.data import DataLoader, Subset
-from torch.optim.lr_scheduler import ReduceLROnPlateau
+from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
+import numpy as np
 
-from sklearn.metrics import accuracy_score, precision_recall_curve, f1_score, confusion_matrix, r2_score
-from sklearn.model_selection import KFold
-# local imports
-import ml_evaluation as ml_eval
+import Datasets
+import dataset_helper
+import EarlyStopping
 import ml_helper
 import ml_history
-import dataset_generator as data_gen
-# class imports
-import HumorDataset as humor_ds
-import EarlyStopping
-import BalancedCELoss
+import ml_train
 
+SEED = 501
+random.seed(SEED)
+np.random.seed(SEED)
+torch.manual_seed(SEED)
+torch.cuda.manual_seed_all(SEED)
+torch.backends.cudnn.deterministic = True
 
-torch.manual_seed(0)
-np.random.seed(0)
-
-
-best_model_filename = 'best_transformer_reg_model.pt'
-
-device = ml_helper.get_device(verbose=True)
-
-embedding_matrix, word_index, vocab_size, d_model = data_gen.create_embedding_matrix()
-
-vocab_size = len(embedding_matrix)
-d_model = len(embedding_matrix[0])
-vocab_size, d_model = embedding_matrix.size()
-print(f"vocab_size: {vocab_size}, d_model: {d_model}")
 
 
 class PositionalEncoding(nn.Module):
+    """
+    https://pytorch.org/tutorials/beginner/transformer_tutorial.html
+    """
+
     def __init__(self, d_model, vocab_size=5000, dropout=0.1):
         super().__init__()
         self.dropout = nn.Dropout(p=dropout)
@@ -66,6 +49,10 @@ class PositionalEncoding(nn.Module):
 
 
 class TransformerBinaryClassifier(nn.Module):
+    """
+    Text classifier based on a pytorch TransformerEncoder.
+    """
+
     def __init__(
         self,
         embeddings,
@@ -74,8 +61,8 @@ class TransformerBinaryClassifier(nn.Module):
         num_layers=6,
         positional_dropout=0.1,
         classifier_dropout=0.1,
-        activation="relu",
     ):
+
         super().__init__()
 
         vocab_size, d_model = embeddings.size()
@@ -99,6 +86,7 @@ class TransformerBinaryClassifier(nn.Module):
             encoder_layer,
             num_layers=num_layers,
         )
+        # normalize to stabilize and stop overfitting
         self.batch_norm = nn.BatchNorm1d(d_model)
         self.classifier = nn.Linear(d_model, 1)
         self.d_model = d_model
@@ -108,114 +96,71 @@ class TransformerBinaryClassifier(nn.Module):
         x = self.pos_encoder(x)
         x = self.transformer_encoder(x)
         x = x.mean(dim=1)
+        # normalize to stabilize and stop overfitting
+        #x = self.batch_norm(x)
+
+        #NOTE: no activation function for regression
         x = self.classifier(x)
+        x = x.squeeze(1)
         return x
 
-
-def load_preprocess_data(path_data='data/hack.csv'):
-    df = pd.read_csv(path_data)
-    df = df.dropna(subset=['humor_rating'])
-
-    df['y'] = df['humor_rating']
-    X = df['text']
-    y = df['y']
-    return X, y
-
-
-X, y = load_preprocess_data()
-
-ret_dict = data_gen.split_data(X, y)
-
-params = {
-    'equalize_classes_loss_factor': 0.15,
-    'batch_size': 32,
-    'epochs': 2,
-    'lr': 1e-4,
-    'clipping_max_norm': 0,
-    'early_stopping_patience': 5,
-    'lr_scheduler_factor': 0.5,
-    'lr_scheduler_patience': 3,
-    'nhead': 2,
-    'num_layers': 3,
-    'hidden_dim': 10,
-    'positional_dropout': 0.5,
-    'classifier_dropout': 0.5,
-    'weight_decay': 1e-2
-}
-
-max_len = 280
-
-train_dataset = humor_ds.TextDataset(ret_dict['train']['X'], ret_dict['train']['y'], word_index, max_len=max_len)
-val_dataset = humor_ds.TextDataset(ret_dict['val']['X'], ret_dict['val']['y'], word_index, max_len=max_len)
-test_dataset = humor_ds.TextDataset(ret_dict['test']['X'], ret_dict['test']['y'], word_index, max_len=max_len)
-
-train_loader = DataLoader(train_dataset, batch_size=params['batch_size'], shuffle=True)
-val_loader = DataLoader(val_dataset, batch_size=params['batch_size'], shuffle=False)
-test_loader = DataLoader(test_dataset, batch_size=params['batch_size'], shuffle=False)
-
-early_stopping = EarlyStopping.EarlyStopping(patience=params['early_stopping_patience'], verbose=False)
-
-
-def train_model(model, train_dataset, criterion, optimizer, epochs, batch_size):
-    dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
-    model.to(device)
-
-    # Store for plotting
-    train_losses, val_losses = [], []
-    train_r2_scores, val_r2_scores = [], []
-
+def train_model(model, train_dataset, test_dataset, criterion, optimizer, epochs, batch_size):
+    train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+    test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
+    
+    test_losses, train_losses = [], []
+    train_r2_scores, test_r2_scores = [], []
+    
     for epoch in range(epochs):
         model.train()
-        total_loss = 0
-        all_preds, all_targets = [], []
-
-        for inputs, targets in dataloader:
-            inputs, targets = inputs.to(device), targets.to(device)
+        running_loss = 0.0
+        running_r2 = 0.0
+        
+        # Training
+        for inputs, labels in train_loader:
+            inputs = inputs.to(device)
+            labels = labels.to(device)
+            
             optimizer.zero_grad()
-            outputs = model(inputs).squeeze()
-            loss = criterion(outputs, targets.float())
+            outputs = model(inputs)
+            loss = criterion(outputs, labels)
             loss.backward()
             optimizer.step()
-            total_loss += loss.item()
+            
+            running_loss += loss.item()
+            running_r2 += r2_score(labels.cpu().numpy(), outputs.cpu().detach().numpy())
+        
+        train_losses.append(running_loss / len(train_loader))
+        train_r2_scores.append(running_r2 / len(train_loader))
+        
+        # Test
+        model.eval()  # Set model to evaluation mode
+        test_loss = 0.0
+        test_r2 = 0.0
+        with torch.no_grad():  # No gradient calculation for testing
+            for inputs, labels in test_loader:
+                inputs = inputs.to(device)
+                labels = labels.to(device)
+                
+                outputs = model(inputs)
+                loss = criterion(outputs, labels)
+                
+                test_loss += loss.item()
+                test_r2 += r2_score(labels.cpu().numpy(), outputs.cpu().detach().numpy())
+        
+        test_losses.append(test_loss / len(test_loader))
+        test_r2_scores.append(test_r2 / len(test_loader))
+        
+        print(f'Epoch {epoch + 1}/{epochs}, Train Loss: {train_losses[-1]:.4f}, Train R²: {train_r2_scores[-1]:.4f}, Test Loss: {test_losses[-1]:.4f}, Test R²: {test_r2_scores[-1]:.4f}')
+    
+    return train_losses, test_losses, train_r2_scores, test_r2_scores
 
-            all_preds.extend(outputs.detach().cpu().numpy())
-            all_targets.extend(targets.detach().cpu().numpy())
-
-        # Calculate R2
-        r2 = r2_score(all_targets, all_preds)
-        train_losses.append(total_loss / len(dataloader))
-        train_r2_scores.append(r2)
-
-        # Validation phase
-        model.eval()
-        val_loss = 0
-        val_preds, val_targets = [], []
-
-        with torch.no_grad():
-            for inputs, targets in val_loader:
-                inputs, targets = inputs.to(device), targets.to(device)
-                outputs = model(inputs).squeeze()
-                loss = criterion(outputs, targets.float())
-                val_loss += loss.item()
-
-                val_preds.extend(outputs.cpu().numpy())
-                val_targets.extend(targets.cpu().numpy())
-
-        # Calculate Validation R2
-        val_r2 = r2_score(val_targets, val_preds)
-        val_losses.append(val_loss / len(val_loader))
-        val_r2_scores.append(val_r2)
-
-        print(f"Epoch {epoch + 1}/{epochs}, Loss: {total_loss / len(dataloader):.4f}, R^2 (Train): {r2:.4f}, Val R^2: {val_r2:.4f}")
-
-    return train_losses, val_losses, train_r2_scores, val_r2_scores
-
-
-def bootstrap_aggregation(ModelClass, train_dataset, num_models=5, epochs=10, batch_size=32, learning_rate=0.001):
+# Bootstrap Aggregation (Bagging) Update
+def bootstrap_aggregation(ModelClass, train_dataset, test_dataset, num_models=5, epochs=10, batch_size=32, learning_rate=0.001):
     models = []
-    all_train_losses, all_val_losses = [], []
-    all_train_r2_scores, all_val_r2_scores = [], []
-
+    all_train_losses, all_test_losses = [], []
+    all_train_r2_scores, all_test_r2_scores = [], []
+ 
     subset_size = len(train_dataset) // num_models
 
     for i in range(num_models):
@@ -225,20 +170,41 @@ def bootstrap_aggregation(ModelClass, train_dataset, num_models=5, epochs=10, ba
         subset_indices = list(range(0, start_idx)) + list(range(end_idx, len(train_dataset)))
         subset = Subset(train_dataset, subset_indices)
 
-        model = ModelClass()
+        model = ModelClass(vocab_size, EMBEDDING_DIM, params["filter_sizes"], params["num_filters"], embedding_matrix, params["dropout"])
+        model.to(device)
         criterion = nn.MSELoss()
         optimizer = optim.Adam(model.parameters(), lr=learning_rate)
 
-        train_losses, val_losses, train_r2_scores, val_r2_scores = train_model(model, subset, criterion, optimizer, epochs, batch_size)
+        train_losses, test_losses, train_r2_scores, test_r2_scores = train_model(model, subset, test_dataset, criterion, optimizer, epochs, batch_size)
         
         models.append(model)
         all_train_losses.append(train_losses)
-        all_val_losses.append(val_losses)
+        all_test_losses.append(test_losses)
         all_train_r2_scores.append(train_r2_scores)
-        all_val_r2_scores.append(val_r2_scores)
+        all_test_r2_scores.append(test_r2_scores)
 
-    return models, all_train_losses, all_val_losses, all_train_r2_scores, all_val_r2_scores
+    # Plot für alle Modelle
+    plt.figure(figsize=(12, 6))
+    for i in range(num_models):
+        plt.plot(all_train_losses[i], label=f'Model {i + 1} Train Loss')
+        plt.plot(all_test_losses[i], label=f'Model {i + 1} Test Loss', linestyle = 'dashed')
+    plt.title("Training and Test Loss for all Models")
+    plt.xlabel('Epochs')
+    plt.ylabel('Loss')
+    plt.legend()
+    plt.show()
 
+    plt.figure(figsize=(12, 6))
+    for i in range(num_models):
+        plt.plot(all_train_r2_scores[i], label=f'Model {i + 1} Train R²')
+        plt.plot(all_test_r2_scores[i], label=f'Model {i + 1} Test R²',  linestyle = 'dashed')
+    plt.title("Training and Test R² for all Models")
+    plt.xlabel('Epochs')
+    plt.ylabel('R²')
+    plt.legend()
+    plt.show()
+
+    return models, all_train_losses, all_test_losses, all_train_r2_scores, all_test_r2_scores
 
 # Ensemble Prediction
 def ensemble_predict(models, test_dataset):
@@ -254,57 +220,61 @@ def ensemble_predict(models, test_dataset):
 
     return np.array(all_predictions)
 
+if __name__ == '__main__':
+    # Hyperparameter und Konfigurationen
+    params = {
+        # Config
+        "max_len": 280,
+        # Training
+        "epochs": 25,
+        "patience": 7,
+        "batch_size": 32,
+        "learning_rate": 1e-4, # 1e-4
+        "weight_decay": 5e-4 ,
+        # Model
+        'nhead': 2, # 5
+        "dropout": 0.2,
+        'hiden_dim': 2048,
+        'num_layers': 6
+    }
+    # TODO set seeds
 
-# Bootstrap Aggregating
-num_models = 2
-ensemble_models, all_train_losses, all_val_losses, all_train_r2_scores, all_val_r2_scores = bootstrap_aggregation(
-    lambda: TransformerBinaryClassifier(
-        embeddings=embedding_matrix,
-        nhead=params['nhead'],
-        num_layers=params['num_layers'],
-        dim_feedforward=params['hidden_dim'],
-        positional_dropout=params['positional_dropout'],
-        classifier_dropout=params['classifier_dropout']
-    ).to(device),
-    train_dataset,
-    num_models=num_models,
-    epochs=params['epochs'],
-    batch_size=params['batch_size'],
-    learning_rate=params['lr']
-)
+    # Configs
+    MODEL_NAME = 'transfomrer.pt'
+    HIST_NAME = 'transformer_history'
+    GLOVE_PATH = 'data/glove.6B.100d.txt'
+    DATA_PATH = 'data/hack.csv'
+    EMBEDDING_DIM = 100
+    TEST_SIZE = 0.1
+    VAL_SIZE = 0.1
 
-# Ensemble Prediction on Testset
-ensemble_predictions = ensemble_predict(ensemble_models, test_dataset)
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 
+    # Daten laden und vorbereiten
+    embedding_matrix, word_index, vocab_size, d_model = dataset_helper.get_embedding_matrix(
+        gloVe_path=GLOVE_PATH, emb_len=EMBEDDING_DIM)
 
-# Plotting
-fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))
+    X, y = dataset_helper.load_preprocess_data(path_data=DATA_PATH, verbose=True)
 
-# Plot Train and Validation Losses
-for i in range(num_models):
-    ax1.plot(range(1, params['epochs'] + 1), all_train_losses[i], label=f"Train Model {i+1}")
-    ax1.plot(range(1, params['epochs'] + 1), all_val_losses[i], label=f"Val Model {i+1}", linestyle='dashed')
+    # Aufteilen der Daten
+    data_split = dataset_helper.split_data(X, y, test_size=TEST_SIZE, val_size=VAL_SIZE)
 
-ax1.set_title('Train and Validation Loss')
-ax1.set_xlabel('Epochs')
-ax1.set_ylabel('Loss')
-ax1.legend()
+    # Dataset und DataLoader
+    train_dataset = Datasets.GloveDataset(data_split['train']['X'], data_split['train']['y'], word_index, max_len=params["max_len"])
+    val_dataset = Datasets.GloveDataset(data_split['val']['X'], data_split['val']['y'], word_index, max_len=params["max_len"])
+    test_dataset = Datasets.GloveDataset(data_split['test']['X'], data_split['test']['y'], word_index, max_len=params["max_len"])
 
-# Plot Train and Validation R²
-for i in range(num_models):
-    ax2.plot(range(1, params['epochs'] + 1), all_train_r2_scores[i], label=f"Train Model {i+1}")
-    ax2.plot(range(1, params['epochs'] + 1), all_val_r2_scores[i], label=f"Val Model {i+1}", linestyle='dashed')
+    # Bootstrap Aggregation (Bagging) Training
+    models, all_train_losses, all_test_losses, all_train_r2_scores, all_test_r2_scores = bootstrap_aggregation(
+        TransformerBinaryClassifier, train_dataset, test_dataset, num_models=2, epochs=params["epochs"], batch_size=params["batch_size"], learning_rate=params["learning_rate"])
 
-ax2.set_title('Train and Validation R²')
-ax2.set_xlabel('Epochs')
-ax2.set_ylabel('R²')
-ax2.legend()
+    # Ensemble Prediction
+    test_predictions = ensemble_predict(models, test_dataset)
 
-plt.tight_layout()
-plt.show()
-
-# Evaluation
-mse = mean_squared_error(test_dataset.labels.to_numpy(), ensemble_predictions)
-mae = mean_absolute_error(test_dataset.labels.to_numpy(), ensemble_predictions)
-r2 = r2_score(test_dataset.labels.to_numpy(), ensemble_predictions)
-
-print(f"Ensemble MSE: {mse:.4f}, MAE: {mae:.4f}, R²: {r2:.4f}")
+    # Test Evaluation
+   # test_labels = np.array([y for _, y in test_dataset])
+    
+    test_mse = mean_squared_error(test_dataset.labels.to_numpy(), test_predictions)
+    test_mae = mean_absolute_error(test_dataset.labels.to_numpy(), test_predictions)
+    test_r2 = r2_score(test_dataset.labels.to_numpy(), test_predictions)
+    
+    print(f"Test RMSE: {test_mse:.4f}, Test MAE: {test_mae:.4f}, Test R²: {test_r2:.4f}")