diff --git a/ai/.gitignore b/ai/.gitignore
index 7cd2cc2..fc80064 100644
--- a/ai/.gitignore
+++ b/ai/.gitignore
@@ -1,4 +1,5 @@
 .env
 datasets
 envs
-__pycache__
\ No newline at end of file
+__pycache__
+results
\ No newline at end of file
diff --git a/ai/code/efficientad_fns copy.py b/ai/code/efficientad_fns copy.py
deleted file mode 100644
index 999aee8..0000000
--- a/ai/code/efficientad_fns copy.py	
+++ /dev/null
@@ -1,148 +0,0 @@
-# Some functions are copied and adjusted from this source https://github.com/nelson1425/EfficientAD/blob/main/efficientad.py
-from efficientad_helper import *
-
-class EfficientAD:
-    @torch.no_grad()
-    def predict(self, image):
-        teacher_output = self.teacher(image)
-        teacher_output = (teacher_output - self.teacher_mean) / self.teacher_std
-        student_output = self.student(image)
-        autoencoder_output = self.autoencoder(image)
-        
-        map_st = torch.mean(
-            (teacher_output - student_output[:, :self.cfg["out_channels"]])**2,
-            dim=1,
-            keepdim=True
-        )
-        
-        map_ae = torch.mean(
-            (autoencoder_output - student_output[:, self.cfg["out_channels"]:])**2,
-            dim=1, 
-            keepdim=True
-        )
-        
-        if self.q_st_start is not None:
-            map_st = 0.1 * (map_st - self.q_st_start) / (self.q_st_end - self.q_st_start)
-        
-        if self.q_ae_start is not None:
-            map_ae = 0.1 * (map_ae - self.q_ae_start) / (self.q_ae_end - self.q_ae_start)
-            
-        map_combined = 0.5 * map_st + 0.5 * map_ae
-        return map_combined, map_st, map_ae
-
-        
-    def define_best_threshold(self, y_true, y_score):
-        # Metrics calculation  
-        self.final_auc = roc_auc_score(y_true=y_true, y_score=y_score)  
-        print(f"\n     - AUC: {self.final_auc * 100:.2f}%")  
-        
-        fpr, tpr, thresholds = roc_curve(y_true, y_score)  
-        optimal_idx = np.argmax(tpr - fpr)  
-        optimal_threshold = thresholds[optimal_idx]  
-        print(f"    - Optimal Threshold: {optimal_threshold:.7f}")  
-        
-        y_pred = (y_score >= optimal_threshold).astype(int)  
-        conf_matrix = confusion_matrix(y_true, y_pred)  
-        
-        self.final_f1 = f1_score(y_true, y_pred)
-        self.optimal_threshold = optimal_threshold
-        print(f"    - F1 Score: {self.final_f1:.2f}")
-        print("    - CONFUSION MATRIX:\n", conf_matrix, "\n")  
-        
-        # Plotting  
-        plt.figure(figsize=(8, 6))  
-        plt.plot(fpr, tpr, label=f"ROC Curve (AUC = {self.final_auc:.2f})")  
-        plt.scatter(fpr[optimal_idx], tpr[optimal_idx], marker='o', color='red', label=f'Optimal threshold = {self.optimal_threshold:.7f}')  
-        plt.plot([0, 1], [0, 1], 'k--')  # dashed diagonal line  
-        plt.xlabel("False Positive Rate")  
-        plt.ylabel("True Positive Rate")  
-        plt.title("ROC Curve")  
-        plt.legend(loc="best")  
-        plt.grid(True)  
-        plt.show()
-        
-        path_threshold=f'{self.cfg["output_folder_path"]}/best_threshold.pkl'
-    
-        with open(path_threshold, 'wb') as f:  
-            pickle.dump(self.optimal_threshold, f)
-                   
-    def accuracy_threshold(self, y_true, y_score):  
-        y_pred = (np.array(y_score) >= self.optimal_threshold).astype(int)  
-        correct_predictions = (y_pred == y_true).sum()  
-        accuracy = (correct_predictions / len(y_true)) * 100  
-        return accuracy
-    
-    def evaluate_model(self):
-        start_time = time.time()
-        print(f"- Evaluating model")
-        
-        y_true_all, y_score_all = self.get_scores(self.datasets_path["all"], "     inference all")
-        self.define_best_threshold(y_true_all, y_score_all)
-        
-        y_true_a1, y_score_a1 = self.get_scores(self.datasets_path["anomaly_lvl_1_paths"], "    inference anomaly lvl 1")
-        y_true_a2, y_score_a2 = self.get_scores(self.datasets_path["anomaly_lvl_2_paths"], "    inference anomaly lvl 2")
-        y_true_a3, y_score_a3 = self.get_scores(self.datasets_path["anomaly_lvl_3_paths"], "    inference anomaly lvl 3")
-        y_true_a, y_score_a = self.get_scores(self.datasets_path["all_anomaly_paths"], "    inference all anomaly")
-        print("")
-        y_true_no_anomaly_train, y_score_no_anomaly_train = self.get_scores(self.datasets_path["no_anomaly_train_paths"] + self.datasets_path["no_anomaly_val_paths"], "    inference no anomaly train")
-        y_true_no_anomaly_test, y_score_no_anomaly_test = self.get_scores(self.datasets_path["no_anomaly_test_paths"] , "    inference no anomaly test")
-        y_true_no_anomaly_all, y_score_no_anomaly_all = self.get_scores(self.datasets_path["no_anomaly_train_paths"] + self.datasets_path["no_anomaly_val_paths"] + self.datasets_path["no_anomaly_test_paths"], "    inference all no anomaly")
-        print("")
-        y_true_test, y_score_test = self.get_scores(self.datasets_path["test_paths"], "    Test dataset")
-        
-        self.scores = {
-            "y_true_test": y_true_test,  
-            "y_score_test": y_score_test,
-            
-            "y_true_a1": y_true_a1,  
-            "y_score_a1": y_score_a1,
-            
-            "y_true_a2": y_true_a2,  
-            "y_score_a2": y_score_a2,  
-            
-            "y_true_a3": y_true_a3,  
-            "y_score_a3": y_score_a3,  
-            
-            "y_true_a": y_true_a,  
-            "y_score_a": y_score_a,  
-            
-            "y_true_no_anomaly_train": y_true_no_anomaly_train,  
-            "y_score_no_anomaly_train": y_score_no_anomaly_train,
-              
-            "y_true_no_anomaly_test": y_true_no_anomaly_test,  
-            "y_score_no_anomaly_test": y_score_no_anomaly_test, 
-             
-            "y_true_no_anomaly_all": y_true_no_anomaly_all,  
-            "y_score_no_anomaly_all": y_score_no_anomaly_all,  
-            
-            "y_true_all": y_true_all,  
-            "y_score_all": y_score_all,
-        }
-        # self.display_eval_result()
-        elapsed_time = (time.time() - start_time)
-        print(f"- OK - Evaluating model ({elapsed_time:.2f} s)\n")
-        
-    def display_eval_result(self):  
-        # Define the column widths  
-        width_label = 20  
-        width_value = 10  
-        
-        # Header  
-        print(f"{'Dataset':{width_label}}{'Accuracy':>{width_value}}")  
-        print("-" * (width_label + width_value))  
-        
-        # Data rows  
-        print(f"{'Anonaly lvl 1':{width_label}}{self.accuracy_threshold(self.scores['y_true_a1'], self.scores['y_score_a1']):>{width_value}.2f}")  
-        print(f"{'Anonaly lvl 2':{width_label}}{self.accuracy_threshold(self.scores['y_true_a2'], self.scores['y_score_a2']):>{width_value}.2f}")  
-        print(f"{'Anonaly lvl 3':{width_label}}{self.accuracy_threshold(self.scores['y_true_a3'], self.scores['y_score_a3']):>{width_value}.2f}")  
-        print(f"\n{'Anomaly all':{width_label}}{self.accuracy_threshold(self.scores['y_true_a'], self.scores['y_score_a']):>{width_value}.2f}")  
-    
-        print("")  
-        print(f"{'No Anomaly Train':{width_label}}{self.accuracy_threshold(self.scores['y_true_no_anomaly_train'], self.scores['y_score_no_anomaly_train']):>{width_value}.2f}")  
-        print(f"{'No Anomaly Test':{width_label}}{self.accuracy_threshold(self.scores['y_true_no_anomaly_test'],  self.scores['y_score_no_anomaly_test']):>{width_value}.2f}")  
-        print(f"{'No Anomaly All':{width_label}}{self.accuracy_threshold(self.scores['y_true_no_anomaly_all'],  self.scores['y_score_no_anomaly_all']):>{width_value}.2f}")  
-    
-        print("")  
-        print(f"{'All without train':{width_label}}{self.accuracy_threshold(self.scores['y_true_test'], self.scores['y_score_test']):>{width_value}.2f}")  
-        print(f"{'All with train':{width_label}}{self.accuracy_threshold(self.scores['y_true_all'], self.scores['y_score_all']):>{width_value}.2f}")  
-    
\ No newline at end of file
diff --git a/ai/code/efficientad_fns.py b/ai/code/efficientad_fns.py
index 9c4451a..6451a5d 100644
--- a/ai/code/efficientad_fns.py
+++ b/ai/code/efficientad_fns.py
@@ -34,8 +34,8 @@ def do_all(self):
         """Executes the full pipeline from data loading to model evaluation."""
         
         # Initialize datasets with corresponding transformations  
-        self.train_dataset = ImageDataset(self.datasets_path["no_anomaly_train_paths"], transform=transforms.Lambda(self.transforms_class.train_transform))  
-        self.validation_dataset = ImageDataset(self.datasets_path["no_anomaly_val_paths"], transform=transforms.Lambda(self.transforms_class.train_transform))
+        self.train_dataset = ImageDataset(self.datasets_path["train_paths"], transform=transforms.Lambda(self.transforms_class.train_transform))  
+        self.validation_dataset = ImageDataset(self.datasets_path["val_paths"], transform=transforms.Lambda(self.transforms_class.val_transform))
 
         # Prepare DataLoaders for training and validation  
         self.train_loader = DataLoader(self.train_dataset, batch_size=1, shuffle=True, num_workers=4, pin_memory=True)
@@ -73,17 +73,12 @@ def create_model(self):
                 mlflow.log_metric("f1", self.final_f1)
                 mlflow.log_metric("threshold", self.optimal_threshold)
                 
-                mlflow.log_metric("all pred", self.accuracy_threshold(self.scores['y_true_all'], self.scores['y_score_all']))
-                mlflow.log_metric("test pred", self.accuracy_threshold(self.scores['y_true_test'], self.scores['y_score_test']))
+                mlflow.log_metric("anomaly lvl 1 test pred", self.get_best_f1(self.scores['y_true_a1'], self.scores['y_score_a1']))
+                mlflow.log_metric("anomaly lvl 2 test pred", self.get_best_f1(self.scores['y_true_a2'], self.scores['y_score_a2']))
+                mlflow.log_metric("anomaly lvl 3 test pred", self.get_best_f1(self.scores['y_true_a3'], self.scores['y_score_a3']))
+                mlflow.log_metric("anomaly all test pred", self.get_best_f1(self.scores['y_true_a'], self.scores['y_score_a']))
                 
-                mlflow.log_metric("anomaly lvl 1 pred", self.accuracy_threshold(self.scores['y_true_a1'], self.scores['y_score_a1']))
-                mlflow.log_metric("anomaly lvl 2 pred", self.accuracy_threshold(self.scores['y_true_a2'], self.scores['y_score_a2']))
-                mlflow.log_metric("anomaly lvl 3 pred", self.accuracy_threshold(self.scores['y_true_a3'], self.scores['y_score_a3']))
-                mlflow.log_metric("anomaly all pred", self.accuracy_threshold(self.scores['y_true_a'], self.scores['y_score_a']))
-                
-                mlflow.log_metric("no anomaly train pred", self.accuracy_threshold(self.scores['y_true_no_anomaly_train'], self.scores['y_score_no_anomaly_train']))
-                mlflow.log_metric("no anomaly test pred",self.accuracy_threshold(self.scores['y_true_no_anomaly_test'],  self.scores['y_score_no_anomaly_test']))
-                mlflow.log_metric("no anomaly all pred", self.accuracy_threshold(self.scores['y_true_no_anomaly_all'],  self.scores['y_score_no_anomaly_all']))
+                mlflow.log_metric("no anomaly test pred",self.get_best_f1(self.scores['y_true_no_anomaly_test'],  self.scores['y_score_no_anomaly_test'], 0))
                 
                 # Log artifacts to reuse them for deployments
                 mlflow.log_artifact(os.path.join(self.cfg["output_folder_path"], 'all_models.pth')  )
@@ -145,27 +140,30 @@ def set_dataset_paths(self):
         # Get paths, and split into train, test, validation sets  
         no_anomaly_paths = glob.glob(f"{self.cfg['dataset_path']}/{self.cfg['subdataset']}/no_anomaly/*.jpg")  # adjust the pattern if needed
 
-        train_paths, test_paths = train_test_split(no_anomaly_paths, test_size=0.2, random_state=self.cfg["seed"])
-        train_paths, val_paths = train_test_split(train_paths, test_size=0.1, random_state=self.cfg["seed"])
+        no_anomaly_train_paths, no_anomaly_test_paths = train_test_split(no_anomaly_paths, test_size=0.2, random_state=self.cfg["seed"])
+        no_anomaly_train_paths,  no_anomaly_val_paths = train_test_split(no_anomaly_train_paths, test_size=0.1, random_state=self.cfg["seed"])
 
         # Path setting for different levels of anomalies
         anomaly_lvl_1_paths = glob.glob(f"{self.cfg['dataset_path']}/{self.cfg['subdataset']}/anomaly_lvl_1/*.jpg")
         anomaly_lvl_2_paths = glob.glob(f"{self.cfg['dataset_path']}/{self.cfg['subdataset']}/anomaly_lvl_2/*.jpg")
         anomaly_lvl_3_paths = glob.glob(f"{self.cfg['dataset_path']}/{self.cfg['subdataset']}/anomaly_lvl_3/*.jpg")
         
+        anomaly_lvl_1_test_paths, anomaly_lvl_1_val_paths = train_test_split(anomaly_lvl_1_paths, test_size=0.2, random_state=self.cfg["seed"])
+        anomaly_lvl_2_test_paths, anomaly_lvl_2_val_paths = train_test_split(anomaly_lvl_2_paths, test_size=0.2, random_state=self.cfg["seed"])
+        anomaly_lvl_3_test_paths, anomaly_lvl_3_val_paths = train_test_split(anomaly_lvl_3_paths, test_size=0.2, random_state=self.cfg["seed"])
+        
         # Structuring all paths into a dictionary for easy access 
         datasets_path = {
-            "no_anomaly_train_paths": train_paths,
-            "no_anomaly_test_paths": test_paths,
-            "no_anomaly_val_paths": val_paths,
+            "anomaly_lvl_1_test_paths": anomaly_lvl_1_test_paths,
+            "anomaly_lvl_2_test_paths": anomaly_lvl_2_test_paths,
+            "anomaly_lvl_3_test_paths": anomaly_lvl_3_test_paths,
+            "no_anomaly_test_paths": no_anomaly_test_paths,
             
-            "anomaly_lvl_1_paths": anomaly_lvl_1_paths,
-            "anomaly_lvl_2_paths": anomaly_lvl_2_paths,
-            "anomaly_lvl_3_paths": anomaly_lvl_3_paths,
-            "all_anomaly_paths": anomaly_lvl_1_paths + anomaly_lvl_2_paths + anomaly_lvl_3_paths,
+            "all_anomaly_test_paths": anomaly_lvl_1_paths + anomaly_lvl_2_paths + anomaly_lvl_3_paths,
             
-            "test_paths": test_paths + anomaly_lvl_1_paths + anomaly_lvl_2_paths + anomaly_lvl_3_paths,
-            "all": no_anomaly_paths + anomaly_lvl_1_paths + anomaly_lvl_2_paths + anomaly_lvl_3_paths
+            "train_paths": no_anomaly_train_paths,
+            "test_paths": no_anomaly_test_paths + anomaly_lvl_1_test_paths + anomaly_lvl_2_test_paths + anomaly_lvl_3_test_paths,
+            "val_paths": no_anomaly_val_paths + anomaly_lvl_1_val_paths + anomaly_lvl_2_val_paths + anomaly_lvl_3_val_paths
         }
         
         print("     Dataset paths:", datasets_path.keys())
@@ -174,8 +172,6 @@ def set_dataset_paths(self):
         print(f"- OK - Setting datasets path ({elapsed_time:.2f} ms)\n")
         self.datasets_path = datasets_path
         
-        
-        
     def prepare_teacher_student_autoencoder(self):
         """Initializes the teacher, student, and autoencoder models based on config settings."""
         start_time = time.time()  
@@ -219,7 +215,11 @@ def prepare_teacher_student_autoencoder(self):
         
         elapsed_time = (time.time() - start_time) * 1000
         print(f"- OK - Prepare teacher, student & autoencoder ({elapsed_time:.2f} ms)\n")
-        
+    
+    def compute_loss(self, outputs, targets):  
+        loss = torch.mean((outputs - targets) ** 2)  
+        return loss  
+    
     def train(self):
         """  
         Train the autoencoder and student models with the teacher model outputs as guidance.  
@@ -241,18 +241,25 @@ def train(self):
         # Schedule learning rate updates 
         scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=int(0.95 * self.cfg["train_steps"]), gamma=0.1)
         
+        # Initialize variables for tracking best performance and applying early stopping  
+        best_f1_score = 0.0 
+        patience_counter = 0
+        patience_limit = 15
+        
         # Progress bar for visual feedback  
-        tqdm_obj = tqdm(range(self.cfg["train_steps"]))
+        tqdm_obj = tqdm(range(self.cfg["train_steps"]) , desc="Training Iterations")
 
         # Training loop
-        for iteration, (image_st, image_ae) in zip(
-                tqdm_obj, self.train_loader_infinite):
+        for iteration in tqdm_obj:
+
+             # Fetch infinite loop of data batches  
+            image_st, image_ae = next(self.train_loader_infinite)      
             
             # Move data to GPU if enabled 
             if self.cfg["on_gpu"]:
                 image_st = image_st.cuda()
                 image_ae = image_ae.cuda()
-                
+            
             # Teacher model inference followed by normalization 
             with torch.no_grad():
                 teacher_output_st = self.teacher(image_st)
@@ -291,6 +298,20 @@ def train(self):
             if iteration % 10 == 0:
                 tqdm_obj.set_description(
                     "       Current loss: {:.4f}  ".format(loss_total.item()))
+                
+             # Validation checking  
+            if iteration % 100 == 0:
+                f1_score = self.validate_anomaly_detection()  
+                if f1_score > best_f1_score:  
+                    best_f1_score = f1_score  
+                    patience_counter = 0  
+                    # Save best model here if desired  
+                else:  
+                    patience_counter += 1  
+    
+                if patience_counter >= patience_limit:  
+                    print(f"Early stopping at iteration {iteration + 1} because validation F1 did not improve.")  
+                    break  
         
         # Switch models to evaluation mode after training
         self.teacher.eval()
@@ -299,7 +320,26 @@ def train(self):
         
         elapsed_time = (time.time() - start_time)
         print(f"- OK - Train ({elapsed_time:.2f} s)\n")
-
+        
+    @torch.no_grad()  
+    def validate_anomaly_detection(self):  
+        self.student.eval()  
+        self.autoencoder.eval()  
+        
+        y_true = []
+        y_score = []
+        
+        # Loop between the validation dataset 20 times
+        # It will do image augmentation every time
+        for image_path in self.datasets_path["val_paths"]:
+            y_true_image, y_score_image = self.get_score(image_path)
+            
+            y_true.append(y_true_image)
+            y_score.append(y_score_image)
+            
+        optimal_threshold, final_f1, final_auc, conf_matrix = self.get_best_threshold(y_true, y_score)
+        print("F1 Validation", final_f1)
+        return final_f1 
 
     def save_models(self):
         """  
@@ -419,38 +459,43 @@ def get_scores(self, dataset_path, desc):
         
         for path in tqdm(dataset_path, desc=desc):
             
-            image = Image.open(path)
-            orig_width, orig_height = image.size  
-            image = self.transforms_class.default_transform(image)
-            image = image[None]
-            
-            if self.cfg["on_gpu"]:
-                image = image.cuda()
-                
-            map_combined, map_st, map_ae = self.predict(image)
-            
-            map_combined = torch.nn.functional.pad(
-                map_combined, 
-                (4, 4, 4, 4)
-            )
-            
-            map_combined = torch.nn.functional.interpolate(
-                map_combined, 
-                (orig_height, orig_width), 
-                mode='bilinear'
-            )
-            
-            map_combined = map_combined[0, 0].cpu().numpy()
-
-            defect_class = os.path.basename(os.path.dirname(path))
-
-            y_true_image = 0 if defect_class == 'no_anomaly' else 1
-            y_score_image = np.max(map_combined)
+            y_true_image, y_score_image = self.get_score(path)
             
             y_true.append(y_true_image)
             y_score.append(y_score_image)
             
         return y_true, y_score
+    
+    def get_score(self, path):
+        image = Image.open(path)
+        orig_width, orig_height = image.size  
+        image = self.transforms_class.default_transform(image)
+        image = image[None]
+        
+        if self.cfg["on_gpu"]:
+            image = image.cuda()
+            
+        map_combined, map_st, map_ae = self.predict(image)
+        
+        map_combined = torch.nn.functional.pad(
+            map_combined, 
+            (4, 4, 4, 4)
+        )
+        
+        map_combined = torch.nn.functional.interpolate(
+            map_combined, 
+            (orig_height, orig_width), 
+            mode='bilinear'
+        )
+        
+        map_combined = map_combined[0, 0].cpu().numpy()
+
+        defect_class = os.path.basename(os.path.dirname(path))
+
+        y_true_image = 0 if defect_class == 'no_anomaly' else 1
+        y_score_image = np.max(map_combined)
+
+        return y_true_image, y_score_image
 
     @torch.no_grad()
     def predict(self, image):  
@@ -494,7 +539,14 @@ def predict(self, image):
         return map_combined, map_st, map_ae
 
         
-    def define_best_threshold(self, y_true, y_score):  
+    def get_best_f1(self, y_true, y_score, pos_label=1, display=False):  
+        y_pred = (y_score >= self.optimal_threshold).astype(int)
+        # print("y_true = ", y_true)
+        # print("y_score = ", y_score)
+        # print("optimal_threshold = ", self.optimal_threshold)
+        # print("Y PRED = ", y_pred)
+        return f1_score(y_true, y_pred, pos_label=pos_label)
+    def get_best_threshold(self, y_true, y_score, display=False):  
         """  
         Identifies the best threshold by the ROC curve and evaluates model by AUC, F1 score and confusion matrix.  
   
@@ -503,55 +555,33 @@ def define_best_threshold(self, y_true, y_score):
             y_score: list. Target scores, can either be probability estimates of the positive class.  
         """  
         # AUC calculation 
-        self.final_auc = roc_auc_score(y_true=y_true, y_score=y_score)  
-        print(f"\n     - AUC: {self.final_auc * 100:.2f}%")  
+        final_auc = roc_auc_score(y_true=y_true, y_score=y_score)  
         
         fpr, tpr, thresholds = roc_curve(y_true, y_score)  
         optimal_idx = np.argmax(tpr - fpr)  
         optimal_threshold = thresholds[optimal_idx]  
-        print(f"    - Optimal Threshold: {optimal_threshold:.7f}")  
         
-        y_pred = (y_score >= optimal_threshold).astype(int)  
+        y_pred = (y_score >= optimal_threshold).astype(int)
         conf_matrix = confusion_matrix(y_true, y_pred)  
         
         # Calculate F1 score and set the optimal threshold  
-        self.final_f1 = f1_score(y_true, y_pred)
-        self.optimal_threshold = optimal_threshold
-        print(f"    - F1 Score: {self.final_f1:.2f}")
-        print("    - CONFUSION MATRIX:\n", conf_matrix, "\n")  
-        
-        # ROC curve plotting
-        plt.figure(figsize=(8, 6))  
-        plt.plot(fpr, tpr, label=f"ROC Curve (AUC = {self.final_auc:.2f})")  
-        plt.scatter(fpr[optimal_idx], tpr[optimal_idx], marker='o', color='red', label=f'Optimal threshold = {self.optimal_threshold:.7f}')  
-        plt.plot([0, 1], [0, 1], 'k--')  # dashed diagonal line  
-        plt.xlabel("False Positive Rate")  
-        plt.ylabel("True Positive Rate")  
-        plt.title("ROC Curve")  
-        plt.legend(loc="best")  
-        plt.grid(True)  
-        plt.show()
-        
-        # Save the optimal threshold for later use 
-        path_threshold=f'{self.cfg["output_folder_path"]}/best_threshold.pkl'
-        with open(path_threshold, 'wb') as f:  
-            pickle.dump(self.optimal_threshold, f)
-                   
-    def accuracy_threshold(self, y_true, y_score): 
-        """  
-        Calculates accuracy based on a predefined threshold.  
-  
-        Args:  
-            y_true: list or numpy.array. True binary labels.  
-            y_score: list or numpy.array. Target scores.  
-  
-        Returns:  
-            float: Accuracy percentage.  
-        """   
-        y_pred = (np.array(y_score) >= self.optimal_threshold).astype(int)  
-        correct_predictions = (y_pred == y_true).sum()  
-        accuracy = (correct_predictions / len(y_true)) * 100  
-        return accuracy
+        final_f1 = f1_score(y_true, y_pred)
+        optimal_threshold
+        
+        if display:
+            # ROC curve plotting
+            plt.figure(figsize=(8, 6))  
+            plt.plot(fpr, tpr, label=f"ROC Curve (AUC = {final_auc:.2f})")  
+            plt.scatter(fpr[optimal_idx], tpr[optimal_idx], marker='o', color='red', label=f'Optimal threshold = {optimal_threshold:.7f}')  
+            plt.plot([0, 1], [0, 1], 'k--')  # dashed diagonal line  
+            plt.xlabel("False Positive Rate")  
+            plt.ylabel("True Positive Rate")  
+            plt.title("ROC Curve")  
+            plt.legend(loc="best")  
+            plt.grid(True)  
+            plt.show()
+        
+        return optimal_threshold, final_f1, final_auc, conf_matrix
     
     def evaluate_model(self):
         """   
@@ -565,25 +595,35 @@ def evaluate_model(self):
         print(f"- Evaluating model")
         
         # Get scores for combined datasets  
-        y_true_all, y_score_all = self.get_scores(self.datasets_path["all"], "     inference all")
-        self.define_best_threshold(y_true_all, y_score_all)
+        y_true_test, y_score_test = self.get_scores(self.datasets_path["test_paths"], "     inference test")
+        optimal_threshold, final_f1, final_auc, conf_matrix = self.get_best_threshold(y_true_test, y_score_test, True)
+        
+        self.final_auc = final_auc
+        self.final_f1 = final_f1
+        self.optimal_threshold = optimal_threshold
+        
+        
+        print(f"\n     - AUC: {final_auc * 100:.2f}%")  
+        print(f"    - Optimal Threshold: {optimal_threshold:.7f}")  
+        print(f"    - F1 Score: {final_f1:.2f}")
+        print("    - CONFUSION MATRIX:\n", conf_matrix, "\n")  
+        
+         # Save the optimal threshold for later use 
+        path_threshold=f'{self.cfg["output_folder_path"]}/best_threshold.pkl'
+        with open(path_threshold, 'wb') as f:  
+            pickle.dump(self.optimal_threshold, f)
         
         # Get scores for datasets with different anomaly levels  
-        y_true_a1, y_score_a1 = self.get_scores(self.datasets_path["anomaly_lvl_1_paths"], "    inference anomaly lvl 1")
-        y_true_a2, y_score_a2 = self.get_scores(self.datasets_path["anomaly_lvl_2_paths"], "    inference anomaly lvl 2")
-        y_true_a3, y_score_a3 = self.get_scores(self.datasets_path["anomaly_lvl_3_paths"], "    inference anomaly lvl 3")
-        y_true_a, y_score_a = self.get_scores(self.datasets_path["all_anomaly_paths"], "    inference all anomaly")
+        y_true_a1, y_score_a1 = self.get_scores(self.datasets_path["anomaly_lvl_1_test_paths"], "    inference anomaly lvl 1 test")
+        y_true_a2, y_score_a2 = self.get_scores(self.datasets_path["anomaly_lvl_2_test_paths"], "    inference anomaly lvl 2 test")
+        y_true_a3, y_score_a3 = self.get_scores(self.datasets_path["anomaly_lvl_3_test_paths"], "    inference anomaly lvl 3 test")
+        y_true_a, y_score_a = self.get_scores(self.datasets_path["all_anomaly_test_paths"], "    inference all anomaly test")
         print("")
         
         # Get scores for no anomaly across training, validation, and test sets  
-        y_true_no_anomaly_train, y_score_no_anomaly_train = self.get_scores(self.datasets_path["no_anomaly_train_paths"] + self.datasets_path["no_anomaly_val_paths"], "    inference no anomaly train")
         y_true_no_anomaly_test, y_score_no_anomaly_test = self.get_scores(self.datasets_path["no_anomaly_test_paths"] , "    inference no anomaly test")
-        y_true_no_anomaly_all, y_score_no_anomaly_all = self.get_scores(self.datasets_path["no_anomaly_train_paths"] + self.datasets_path["no_anomaly_val_paths"] + self.datasets_path["no_anomaly_test_paths"], "    inference all no anomaly")
         print("")
         
-        # Get scores for only the test dataset 
-        y_true_test, y_score_test = self.get_scores(self.datasets_path["test_paths"], "    Test dataset")
-        
         # Collect all test scores in a dictionary
         self.scores = {
             "y_true_test": y_true_test,  
@@ -601,17 +641,8 @@ def evaluate_model(self):
             "y_true_a": y_true_a,  
             "y_score_a": y_score_a,  
             
-            "y_true_no_anomaly_train": y_true_no_anomaly_train,  
-            "y_score_no_anomaly_train": y_score_no_anomaly_train,
-              
             "y_true_no_anomaly_test": y_true_no_anomaly_test,  
             "y_score_no_anomaly_test": y_score_no_anomaly_test, 
-             
-            "y_true_no_anomaly_all": y_true_no_anomaly_all,  
-            "y_score_no_anomaly_all": y_score_no_anomaly_all,  
-            
-            "y_true_all": y_true_all,  
-            "y_score_all": y_score_all,
         }
         # self.display_eval_result()
         elapsed_time = (time.time() - start_time)
@@ -626,21 +657,18 @@ def display_eval_result(self):
         width_value = 10  
         
         # Header  
-        print(f"{'Dataset':{width_label}}{'Accuracy':>{width_value}}")  
+        print(f"{'Dataset':{width_label}}{'F1 Score':>{width_value}}")  
         print("-" * (width_label + width_value))  
         
         # Data rows  
-        print(f"{'Anonaly lvl 1':{width_label}}{self.accuracy_threshold(self.scores['y_true_a1'], self.scores['y_score_a1']):>{width_value}.2f}")  
-        print(f"{'Anonaly lvl 2':{width_label}}{self.accuracy_threshold(self.scores['y_true_a2'], self.scores['y_score_a2']):>{width_value}.2f}")  
-        print(f"{'Anonaly lvl 3':{width_label}}{self.accuracy_threshold(self.scores['y_true_a3'], self.scores['y_score_a3']):>{width_value}.2f}")  
-        print(f"\n{'Anomaly all':{width_label}}{self.accuracy_threshold(self.scores['y_true_a'], self.scores['y_score_a']):>{width_value}.2f}")  
+        print(f"{'Anonaly lvl 1 test':{width_label}}{self.get_best_f1(self.scores['y_true_a1'], self.scores['y_score_a1']):>{width_value}.2f}")  
+        print(f"{'Anonaly lvl 2 test':{width_label}}{self.get_best_f1(self.scores['y_true_a2'], self.scores['y_score_a2']):>{width_value}.2f}")  
+        print(f"{'Anonaly lvl 3 test':{width_label}}{self.get_best_f1(self.scores['y_true_a3'], self.scores['y_score_a3']):>{width_value}.2f}")  
+        print(f"\n{'Anomaly all test':{width_label}}{self.get_best_f1(self.scores['y_true_a'], self.scores['y_score_a']):>{width_value}.2f}")  
     
         print("")  
-        print(f"{'No Anomaly Train':{width_label}}{self.accuracy_threshold(self.scores['y_true_no_anomaly_train'], self.scores['y_score_no_anomaly_train']):>{width_value}.2f}")  
-        print(f"{'No Anomaly Test':{width_label}}{self.accuracy_threshold(self.scores['y_true_no_anomaly_test'],  self.scores['y_score_no_anomaly_test']):>{width_value}.2f}")  
-        print(f"{'No Anomaly All':{width_label}}{self.accuracy_threshold(self.scores['y_true_no_anomaly_all'],  self.scores['y_score_no_anomaly_all']):>{width_value}.2f}")  
+        print(f"{'No Anomaly Test':{width_label}}{self.get_best_f1(self.scores['y_true_no_anomaly_test'],  self.scores['y_score_no_anomaly_test'], 0):>{width_value}.2f}")  
     
         print("")  
-        print(f"{'All without train':{width_label}}{self.accuracy_threshold(self.scores['y_true_test'], self.scores['y_score_test']):>{width_value}.2f}")  
-        print(f"{'All with train':{width_label}}{self.accuracy_threshold(self.scores['y_true_all'], self.scores['y_score_all']):>{width_value}.2f}")  
+        print(f"{'All test':{width_label}}{self.get_best_f1(self.scores['y_true_test'], self.scores['y_score_test']):>{width_value}.2f}")  
     
\ No newline at end of file
diff --git a/ai/code/efficientad_helper.py b/ai/code/efficientad_helper.py
index 3ba86b4..6e295ea 100644
--- a/ai/code/efficientad_helper.py
+++ b/ai/code/efficientad_helper.py
@@ -56,12 +56,18 @@ class ImageTransforms:
         image_size: int. The size to which the images will be resized.  
     """  
     def __init__(self, image_size):  
+        self.training_transform = transforms.Compose([  
+            transforms.Resize((image_size, image_size)),  
+            transforms.RandomRotation(20),
+            transforms.ToTensor(),  
+            transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  
+        ])
+        
         self.default_transform = transforms.Compose([  
             transforms.Resize((image_size, image_size)),  
             transforms.ToTensor(),  
             transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  
         ])
-  
 
         # Define a transformation that applies a random choice of color adjustments  for the auto encoder
         self.transform_ae = transforms.RandomChoice([  
@@ -70,6 +76,9 @@ def __init__(self, image_size):
             transforms.ColorJitter(saturation=0.2)  
         ])  
   
+    def val_transform(self, image):
+        return self.default_transform(image), self.default_transform(self.transform_ae(image))  
+    
     def train_transform(self, image):
         """   
         Apply designated image transformations useful for training.  
@@ -80,7 +89,7 @@ def train_transform(self, image):
         Returns:  
             Tuple containing the default transformed image and a variably transformed image for autoencoder tasks.  
         """
-        return self.default_transform(image), self.default_transform(self.transform_ae(image))  
+        return self.training_transform(image), self.training_transform(self.transform_ae(image))  
   
 class ImageDataset(Dataset):  
     """   
diff --git a/ai/code/evaluate.py b/ai/code/evaluate.py
index 39d32ff..d138dae 100644
--- a/ai/code/evaluate.py
+++ b/ai/code/evaluate.py
@@ -46,7 +46,7 @@ def inference_baseline(model, image_path, img_height=160, img_width=160):
     img_array /= 255.
       
     # Prediction  
-    predictions = model.predict(img_array)  
+    predictions = model.predict(img_array, verbose=0)
     predicted_class = np.argmax(predictions, axis=1)  
     predicted_proba = np.max(predictions)  
   
diff --git a/ai/notebooks/2_efficientad.ipynb b/ai/notebooks/2_efficientad.ipynb
index 6fb0896..5f857ff 100644
--- a/ai/notebooks/2_efficientad.ipynb
+++ b/ai/notebooks/2_efficientad.ipynb
@@ -119,7 +119,8 @@
     "    # Or pretrain it yourself here: https://github.com/nelson1425/EfficientAD/blob/main/pretraining.py\n",
     "    \"weight_path\": \"\",  # Path to pre-trained weights (empty means no pre-loading)  \n",
     "      \n",
-    "    \"mlflow_tracking_uri\": \"http://54.90.97.27:5000/\",  # URI for MLflow tracking server  \n",
+    "    # \"mlflow_tracking_uri\": \"\",  # URI for MLflow tracking server  \n",
+    "    \"mlflow_tracking_uri\": \"http://3.90.103.58:5000/\",  # URI for MLflow tracking server  \n",
     "      \n",
     "    \"train_steps\": 20,  # Number of training steps  \n",
     "    \"learning_rate\": 1e-4,  # Learning rate for model training  \n",
@@ -174,20 +175,20 @@
      "output_type": "stream",
      "text": [
       "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (1.13 ms)\n",
+      "- OK - Setting seed to 42 (1.09 ms)\n",
       "\n",
       "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (1.94 ms)\n",
+      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
+      "- OK - Setting datasets path (3.26 ms)\n",
       "\n",
       "- Setting config\n",
       "     Output folder path: ../output/cookies_3_steps_20_small\n",
-      "- OK - Setting config (4.10 ms)\n",
+      "- OK - Setting config (4.36 ms)\n",
       "\n",
       "- Prepare teacher, student & autoencoder\n",
       "     No weight to load\n",
       "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (274.59 ms)\n",
+      "- OK - Prepare teacher, student & autoencoder (254.78 ms)\n",
       "\n",
       "- Normalizing teacher\n"
      ]
@@ -196,15 +197,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 46.79it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 55.08it/s]\n"
+      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 46.89it/s]\n",
+      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.77it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (2.89 s)\n",
+      "- OK - Normalizing teacher (2.97 s)\n",
       "\n",
       "- Train\n"
      ]
@@ -213,61 +214,61 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 28.2339  : 100%|██████████████████████████████████████████████████████████| 20/20 [00:03<00:00,  6.21it/s]\n"
+      "       Current loss: 23.5000  :   5%|████▎                                                                                 | 1/20 [00:02<00:45,  2.37s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (3.22 s)\n",
-      "\n",
-      "- Saving models to ../output/cookies_3_steps_20_small/all_models.pth\n",
-      "- OK - Saving models (67.94 ms)\n",
-      "\n",
-      "- Saving map normalization to ../output/cookies_3_steps_20_small/map_normalization.pth\n"
+      "F1 Validation 0.6451612903225806\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 29.28it/s]\n"
+      "       Current loss: 100.5513  : 100%|████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:04<00:00,  4.31it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (305.59 ms)\n",
+      "- OK - Train (4.64 s)\n",
       "\n",
-      "- Evaluating model\n"
+      "- Saving models to ../output/cookies_3_steps_20_small/all_models.pth\n",
+      "- OK - Saving models (69.14 ms)\n",
+      "\n",
+      "- Saving map normalization to ../output/cookies_3_steps_20_small/map_normalization.pth\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:08<00:00, 22.88it/s]\n"
+      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:00<00:00, 28.75it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "- OK - Saving map normalization (1008.84 ms)\n",
       "\n",
-      "     - AUC: 52.10%\n",
-      "    - Optimal Threshold: 0.0818198\n",
-      "    - F1 Score: 0.60\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[42 58]\n",
-      " [33 67]] \n",
-      "\n"
+      "- Evaluating model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.03it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIhCAYAAABdSTJTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACPs0lEQVR4nOzdd1xT1/sH8E+AhKUgDmSoiHVrXeDWr9Uq7lHroODAVRG3Feuqiq1aZ9VacYtaB85at9S9FZRq1VarCA5QURRkhuT8/vBHKhKQYEIS+Lxfr7w0J+fe+4RDwpOT554rEUIIEBEREREZIRN9B0BERERElFdMZomIiIjIaDGZJSIiIiKjxWSWiIiIiIwWk1kiIiIiMlpMZomIiIjIaDGZJSIiIiKjxWSWiIiIiIwWk1kiIiIiMlpMZomI1AgKCoJEIlHdzMzM4OjoCE9PT9y9e1ftNnK5HIGBgWjcuDFsbW1haWmJatWqYeLEiXjx4oXabZRKJTZt2oTWrVujZMmSkEqlsLe3R6dOnbBv3z4olcoPxpqamoply5ahWbNmsLOzg0wmg7OzM3r16oVTp0591M+BiMjQMZklIsrB+vXrceHCBfzxxx8YMWIEfv/9dzRr1gxxcXGZ+iUlJaFNmzYYOXIk6tati61bt+LgwYPo27cvVq1ahbp16+Kff/7JtE1KSgo6dOiA/v37w97eHoGBgTh+/DhWrFgBJycn9OzZE/v27csxvtjYWDRt2hTjxo1DzZo1ERQUhGPHjmHhwoUwNTXF559/jj///FPrPxciIoMhiIgoi/Xr1wsA4sqVK5naAwICBACxbt26TO1ff/21ACC2bduWZV///POPsLW1FTVq1BDp6emq9mHDhgkAYsOGDWpjuHPnjvjzzz9zjLN9+/bCzMxMHDt2TO3jly9fFpGRkTnuI7eSkpK0sh8iIm3izCwRkQbc3d0BAE+fPlW1xcTEYN26dWjbti169+6dZZvKlSvj22+/xc2bN/Hbb7+ptlmzZg3atm2Lfv36qT1WpUqVUKtWrWxjCQsLw6FDhzBo0CC0atVKbZ/69eujXLlyAIAZM2ZAIpFk6ZNRUvHgwQNVW/ny5dGpUyfs3r0bdevWhYWFBQICAlC3bl00b948yz4UCgWcnZ3RvXt3VVtaWhp++OEHVK1aFebm5ihVqhQGDBiA58+fZ/uciIg0xWSWiEgDERERAN4mqBlOnDiB9PR0dOvWLdvtMh4LCQlRbSOXy3Pc5kOOHj2aad/advXqVfj7+2PUqFE4fPgwvvzySwwYMABnz57NUjd89OhRPHnyBAMGDADwtha4a9eu+PHHH+Hl5YUDBw7gxx9/REhICD777DMkJyfrJGYiKnzM9B0AEZEhUygUSE9PR0pKCs6dO4cffvgB//vf/9ClSxdVn6ioKACAq6trtvvJeCyjb262+RBt7CMnz549w61btzIl7hUqVIC/vz+CgoIwa9YsVXtQUBBKly6N9u3bAwC2b9+Ow4cPY9euXZlma2vXro369esjKCgIw4YN00ncRFS4cGaWiCgHjRo1glQqRdGiRdGuXTvY2dlh7969MDPL21yAuq/5DVWtWrUyJbIAUKJECXTu3BkbNmxQrbQQFxeHvXv3ol+/fqqfy/79+1GsWDF07twZ6enpqludOnXg4OCAkydP5vfTIaICisksEVEONm7ciCtXruD48eMYOnQobt++ja+++ipTn4ya1IwSBHUyHitbtmyut/kQbewjJ46OjmrbBw4ciMePH6tKJrZu3YrU1FT4+Pio+jx9+hSvXr2CTCaDVCrNdIuJiUFsbKxOYiaiwofJLBFRDqpVqwZ3d3e0bNkSK1aswODBg3H48GHs3LlT1adly5YwMzNTndylTsZjbdq0UW0jlUpz3OZD2rZtm2nfH2JhYQHg7bq078ouscxuFrlt27ZwcnLC+vXrAbxdvqxhw4aoXr26qk/JkiVRokQJXLlyRe1t+fLluYqZiOhDmMwSEWlg3rx5sLOzw7Rp01Rfszs4OGDgwIE4cuQIgoODs2xz584dzJ07FzVq1FCdrOXg4IDBgwfjyJEj2Lhxo9pj3bt3D9evX882lnr16qF9+/ZYu3Ytjh8/rrZPaGioqra2fPnyAJBlnx9ay/Z9pqam6Nu3L3777TecOXMGoaGhGDhwYKY+nTp1wosXL6BQKODu7p7lVqVKFY2OSUSUHYkQQug7CCIiQxMUFIQBAwbgypUrquW4MsyfPx8TJkzApk2b0KdPHwBAYmIiOnbsiHPnzuHrr79G586dYW5ujosXL2LBggWwsrLCH3/8kSmJS0lJQbdu3XD06FF89dVX+OKLL1C6dGnExsYiJCQE69evx7Zt29C1a9ds44yNjUW7du1w48YNDBw4EO3bt4ednR2io6Oxb98+bN26FWFhYahduzbi4+Ph6uoKZ2dnzJw5E2ZmZggKCsLVq1cRERGBiIgIVcJbvnx51KxZE/v371d73Dt37qBKlSooU6YMXrx4gejoaNja2qoeVygU6Ny5My5duoTRo0ejQYMGkEqlePToEU6cOIGuXbviiy++yOvwEBH9R98L3RIRGaLsLpoghBDJycmiXLlyolKlSpkugpCWliZ++eUX0bBhQ1GkSBFhbm4uqlSpIiZMmCBiY2PVHic9PV1s2LBBtGrVShQvXlyYmZmJUqVKifbt24stW7YIhULxwViTk5PF0qVLRePGjYWNjY0wMzMTTk5Oonv37uLAgQOZ+l6+fFk0adJEWFtbC2dnZzF9+nSxZs0aAUBERESo+rm4uIiOHTvmeNwmTZoIAMLb21vt43K5XCxYsEDUrl1bWFhYiCJFioiqVauKoUOHirt3737weRER5QZnZomIiIjIaLFmloiIiIiMFpNZIiIiIjJaTGaJiIiIyGgxmSUiIiIio8VkloiIiIiMFpNZIiIiIjJaZvoOIL8plUo8efIERYsWzfZSjURERESkP0IIJCQkwMnJCSYmOc+9Frpk9smTJyhbtqy+wyAiIiKiD3j48CHKlCmTY59Cl8wWLVoUwNsfjo2NTb4cUy6X4+jRo/Dw8IBUKs2XY5L2cPyMH8fQ+HEMjRvHz/jl9xjGx8ejbNmyqrwtJ4Uumc0oLbCxscnXZNbKygo2NjZ8ERshjp/x4xgaP46hceP4GT99jWFuSkJ5AhgRERERGS0ms0RERERktJjMEhEREZHRKnQ1s7khhEB6ejoUCoVW9ieXy2FmZoaUlBSt7ZPyD8fP+OlyDE1NTWFmZsal/oiI9ITJ7HvS0tIQHR2NpKQkre1TCAEHBwc8fPiQf/CMEMfP+Ol6DK2srODo6AiZTKb1fRMRUc6YzL5DqVQiIiICpqamcHJygkwm08ofPqVSiTdv3qBIkSIfXPiXDA/Hz/jpagyFEEhLS8Pz588RERGBSpUq8XeEiCifMZl9R1paGpRKJcqWLQsrKyut7VepVCItLQ0WFhb8Q2eEOH7GT5djaGlpCalUisjISNUxiIgo//AvsxpMWIhIE3zPICLSH74DExEREZHRYjJLREREREaLySzR/0tLS0PFihVx7tw5fYdSYDx79gylSpXC48eP9R0KEREVUExmCwgfHx9IJBJIJBKYmZmhXLlyGDZsGOLi4rL0PX/+PDp06AA7OztYWFjg008/xcKFC9Wuv3nixAl06NABJUqUgJWVFapXr45vvvnmg8nJtWvX0LNnT5QuXRoWFhaoXLkyhgwZgjt37mjtOWvbqlWr4OLigqZNm2Z5bMyYMZBKpdi2bVuWx3x8fNCtW7cs7eHh4ZBIJHjw4IGqTQiBVatWoWHDhihSpAiKFSsGd3d3LF68WKvLwb0vLi4Offv2ha2tLWxtbdG3b1+8evUqx23e/Z3KuDVq1Ej1+MuXLzFy5EhUqVIFVlZWKFeuHEaNGoXXr1+r+tjb26Nv376YPn26rp4aEREVckxmC5B27dohOjoaDx48wJo1a7Bv3z74+fll6rNnzx60aNECZcqUwYkTJ/D3339j9OjRmDVrFjw9PSGEUPVduXIlWrduDQcHB+zatQu3bt3CihUr8Pr1ayxcuDDbOPbv349GjRohNTUVmzdvxu3bt7Fp0ybY2triu+++y/Pzk8vled42N37++WcMHjw4S3tSUhJ2796N8ePHY+3atR91jL59+2LMmDHo2rUrTpw4gfDwcHz33XfYu3cvjh49+lH7zomXlxfCw8Nx+PBhHD58GOHh4ejbt+8Ht8v4ncq4HTx4UPXYkydP8OTJEyxYsAA3btxAUFAQDh8+jEGDBmXax4ABA7B582a1H6yIiIg+mihkXr9+LQCI169fZ3ksOTlZ3Lp1SyQnJ6valEqlSEyVf9QtITlVPHkaKxKSUzXaTqlU5vp59e/fX3Tt2jVT27hx40Tx4sVV99+8eSNKlCghunfvnmX733//XQAQ27ZtE0II8fDhQyGTycSYMWPUHi8uLk5te2JioihZsqTo1q1bjtutX79e2NraZnpsz5494t1fyenTp4vatWuLtWvXCldXVyGRSMSKFSuEk5OTUCgUmbbt3Lmz6NevX6bnU69ePWFubi5cXV3FjBkzhFwuVxuTEEKEhYUJExMTtb8X69atE/Xr1xcvX74UlpaWIiIiItPj6n72Qghx7do1AUDVPzg4WAAQv/32W5a+SqVSvHr1Ktv4PsatW7cEAHHx4kVV24ULFwQA8ffff2e7XXbPKyfbt28XMpksy8+6fPnyYu3atRrtS5sUCoWIi4vL8nujLereO0i70tLSxG+//SbS0tL0HQrlAcfP+OX3GOaUr71Pr+vMnj59GvPnz0dYWBiio6OxZ88etV/XvuvUqVMYN24cbt68CScnJ0yYMAG+vr46izFZrkD1aUd0tv+c3JrZFlayvA3R/fv3cfjwYUilUlXb0aNH8eLFC4wfPz5L/86dO6Ny5crYunUrevfujR07diAtLQ0TJkxQu/9ixYqpbT9y5AhiY2M13i47//77L7Zv345du3bB1NQUzs7OGDVqFE6cOIHPP/8cwNuv0I8cOYJ9+/apYujTpw+WLl2K5s2b4969e/j6668BINuvu0+fPo3KlSvDxsYmy2Pr169Hz549YWtriw4dOmD9+vUICAjQ6HkAwObNm1GlShV07do1y2MSiQS2trbZblukSJEc9928eXMcOnRI7WMXLlyAra0tGjZsqGpr1KgRbG1tcf78eVSpUiXb/Z48eRL29vYoVqwYWrRogVmzZsHe3j7b/q9fv4aNjQ3MzDL/3jZo0ABnzpzBwIEDc3weREREmtJrMpuYmIjatWtjwIAB+PLLLz/YPyIiAh06dMCQIUPw66+/4ty5c/Dz80OpUqVytX1Bt3//fhQpUgQKhQIpKSkAgEWLFqkez6hXrVatmtrtq1atqupz9+5d2NjYwNHRUaMY7t69q9qXNqSlpWHTpk0oVaqUqq1du3bYsmWLKpndsWMHihcvrro/a9YsTJw4Ef379wcAVKhQAd9//z0mTJiQbTL74MEDODk5qX0+Fy9exPr16wEAffr0wahRozB9+nSN1xa9e/dujoljTsLDw3N83NLSMtvHYmJi1Cag9vb2iImJyXa79u3bo2fPnnBxcUFERAS+++47tGrVCmFhYTA3N8/S/8WLF/j+++8xdOjQLI85Ozvj2rVrOT4HIiKivNBrMtu+fXu0b98+1/1XrFiBcuXKYfHixQDeJmWhoaFYsGCBzpJZS6kpbs1s+1H7UCqVSIhPQFGboholQJZSU42O07JlSwQGBiIpKQlr1qzBnTt3MHLkyCz9xDt1se+3Z1y+993/ayK7feeVi4tLpkQWALy9vfH1119j+fLlMDc3x+bNm+Hp6QlT07c/r7CwMFy5cgWzZs1SbZOR4CclJam9ultycrLaKzetXbsWHh4eKFGiBACgQ4cOGDRoEP744w94eHho9Fzy+jMFgIoVK+ZpuwzqjvuheHr37q36f82aNeHu7g4XFxccOHAA3bt3z9Q3Pj4eHTt2RPXq1dV+YLC0tNTpCW5ERJQ9IQSS5VlP8tZEcnIKUhXa/zuvDUZ1OdsLFy5kSSDatm2LtWvXQi6XZ/pKPUNqaipSU1NV9+Pj4wG8PZno/ROK5HI5hBBQKpVQKpWqdguzjztPTggJ0mWmsJSaapTMCCFy/UsjhICVlRUqVKgAAFi8eDE+//xzzJgxAzNnzgTwX0J08+ZNNGnSJMs+/v77b1SrVg1KpRKVKlXC69ev8fjxY41mZzOOcevWLTRu3PiDMb/7c84Yp4w2IQSsra0z9QGAjh07QqlUYt++fahfvz7OnDmDBQsWqPoplUrMmDEDX3zxRZZjymSyLPsDgBIlSuDGjRuZHlMoFNi4cSNiYmJQsmTJTO1r1qxB69atAQBFixZFZGRklv2+fPlS9XjGz/T27dtqj/8h6sof3tWsWbNMJ2e9y97eHk+fPs1y3OfPn6NUqVK5jqd06dJwcXHBnTt3Mm2TkJCA9u3bo0iRIqpykPf3+eLFC5QsWTJPz10bMl5H7//OaYtSqYQQAnK5XPWhirQr4/1a1yeCkm5w/PRHCAHPNVdwNepVnveRdPcS4o6vQWnPH9CqVSps8zgxowlNfleMKpmNiYlB6dKlM7WVLl0a6enpiI2NVZt0zZkzR21949GjR7PM0JmZmcHBwQFv3rxBWlqadoPH2z/6uiKXy5Genq5K1gHgm2++Qc+ePeHt7Q1HR0c0atQIdnZ2mDdvHjZu3Jhp+4MHD+Lu3buYOHEi4uPj4eHhAZlMhlmzZmH27NlZjvf69Wu1NZ6NGjVCiRIlMGfOHPz666/ZbmdtbY2EhARER0fD2toaAHD58mUA/33gSE1NhUKhyPScMnTq1AkbN27EzZs3UbFiRVSqVEnVr1atWvjrr7/Uft395s0btT+/KlWqIDAwEK9fv1Z94Dh06BASEhJw6tSpTAnK3bt38fXXX+PBgwcoXrw4XFxcsHXrVjx79izT7O7Zs2dRsmRJmJqaIj4+Ht26dcOgQYOwbds2dOjQIdPxhRCIj4/Ptm729OnTatszWFhYqP05AcCnn36K169f48SJE3BzcwMAhIaG4vXr16hVq1a2273v5cuXePjwIYoVK6baJj4+Hj169IBMJsPGjRuRlpam9rXz559/olmzZrk+lq7o6jWYlpaG5ORknD59Gunp6To5Br0VEhKi7xDoI3D88l+qArgalbd0TyjkiDsZhITQvQCA1xd24PjxEjDPh8/smnybJxEGMl8skUg+eAJY5cqVMWDAAEyaNEnVdu7cOTRr1gzR0dFwcHDIso26mdmyZcsiNjY2y2xXSkoKHj58iPLly6v9yjmvhBBISEhA0aJF8/w184cMGDAAr169wp49ezK1N2jQAA0bNsTPP/8MANi5cye8vLwwYMAADB8+HDY2Njh27Bi+/fZbtGrVCsHBwaoYAwMDMXLkSPj4+KBv374oX748Hj16hE2bNqFIkSJYsGCB2lj27t2L3r17o23bthg5ciQqVqyI2NhY7NixA1FRUdi6dStevnyJ8uXLY+DAgRgxYgQuX76Mb7/9Fk+ePFGtdxsQEIC9e/fi6tWrWY4REhKCrl27onz58vD29saUKVNUjx05cgRdunTB5MmT0aNHD5iYmOD69ev466+/8P3336uN+cWLFyhTpgyuXLmCmjVrAgC++OILWFhYYMuWLZnGTwgBFxcXjB8/XrWuavXq1dG0aVNMmDABdnZ2uHDhAsaMGYOJEyeqTrgTQsDLywv79u3D1KlT0bp1a5QqVQo3btzAkiVLMHz48A+eAJlXHTp0QHR0NAIDAwEAvr6+KFeuHH7//XdVn+rVq2PWrFn44osv8ObNGwQEBKB79+5wdHTEgwcPMHXqVERFReHmzZsoWrQoEhIS0LZtWyQlJWHXrl2qDyUAUKpUKdUHgKSkJNjb2+PQoUNo3ry5Tp7fh+j6NZiSkoIHDx6gbNmyWn3voP/I5XKEhISgTZs2ar+FI8PG8dOfpLR01P7+OADg4rctYCnLXSb64MEDDPLph7DQUADA0GF++F+LlujaqT1kMpnO4s0QHx+PkiVLqk4szpH2FlH4OADEnj17cuzTvHlzMWrUqExtu3fvFmZmZrleKkLTpbm0QdfLAgmR/TJKmzdvFjKZTERFRanaTp8+Ldq1aydsbW2FTCYT1atXFwsWLBDp6elZtg8JCRFt27YVdnZ2wsLCQlStWlWMHz9ePHnyJMd4rly5Irp37y5KlSolzM3NRcWKFcXXX38t7t69q+qzZ88eUbFiRWFhYSE6deokVq1apXZpLnXS09OFo6OjACDu3buX5fHDhw+LJk2aCEtLS2FjYyMaNGggVq1alWPMnp6eYuLEiUIIIWJiYoSZmZnYvn272vEbOXKk+PTTT1X37969K7788kvh7OwsrK2txaeffiqWLVuWZcwVCoUIDAwU9evXF1ZWVsLGxka4ubmJJUuWiKSkpBzj+xgvXrwQ3t7eomjRoqJo0aLC29s7y/JqAMT69euFEEIkJSUJDw8PUapUKSGVSkW5cuVE//79M/0enThxQgBQe3t3+bItW7aIKlWq6Oy55QaX5jJ+XNrJuHH88u5jlwh9npAiXL7dL1y+3S8SU7NfovJdu3btEra2tgKAsLOzE3v37jXopbmMamb222+/xb59+3Dr1i1V27BhwxAeHo4LFy7k6jgZX+Wqy/RTUlIQEREBV1dXrc6uKJVKxMfHw8bGRuMz4Cn/3LhxA61bt8a///6LokWLqto5fh+nQYMGGDNmDLy8vPQWg67HUFfvHfQfuVyOgwcPokOHDpzZM0Icv7wRQqDHigsIi9TORWdys+RnXFwcKlSogFevXqFx48bYunUrXFxc8n0Mc8rX3qfXv8xv3rxBeHi4atmhiIgIhIeHIyoqCgAwadIk9OvXT9Xf19cXkZGRGDduHG7fvo1169Zh7dq1atdNJdLUp59+innz5mW6/Cx9nGfPnqFHjx746quv9B0KEZHRSZYrtJbIurvY5WqVJDs7O6xfvx4TJkzAqVOn4OLiopXj65JeTwALDQ1Fy5YtVffHjRsHAOjfvz+CgoIQHR2tSmwBwNXVFQcPHsTYsWPxyy+/wMnJCUuXLuUas6Q1GWvTknbY29tnewENIiLKvdCprWGVy3pXdXJaUWn79u2wsbFBu3btAADdunXT2TkcuqDXZPazzz7LcempoKCgLG0tWrRQe0IQERERUUFlJTPN81VBs5OcnIxx48ZhxYoVKFGiBK5fv672AkKGzqiW5iIiIiKij/fPP/+gV69euH79OiQSCXx9fXO8XLkhYzJLREREVIhs3rwZQ4cORWJiIuzt7fHrr7+iTZs2+g4rz5jMEhERERUCCoUCQ4cOxdq1awEALVu2xObNmzW60qch4jpDRERERIVAxsVsJBIJZsyYgZCQEKNPZAHOzBIREREVaCkpKao1sJcuXQofHx80a9ZMz1FpD2dmiYiIiAqgN2/eoH///ujatSuUSiUAwMrKqkAlsgCTWdLAjBkzUKdOHYM9zmeffYYxY8ZoPZ4PKV++PBYvXvxR+/Dx8fngmn76en5ERGR8bty4gfr162Pjxo34448/cPHiRX2HpDNMZguQhw8fYtCgQXBycoJMJoOLiwtGjx6NFy9eaLwviUSC3377LVPb+PHjcezYMS1Fm3cnT56ERCLBq1ev9B1KgXPjxg20aNEClpaWcHZ2xsyZM3NcCxp4e+nDvn37wtbWFra2tujbt2+Wsbly5Qo+//xzFCtWDHZ2dvDw8FBd+Q94+xWYj48PPv30U5iZmWWb2G/evBm1a9eGlZUVHB0dMWDAgEy/3zdv3sSXX36J8uXLQyKRqP2QkZCQgDFjxsDFxQWWlpZo0qQJrly5kqnPmzdvMGLECJQpUwaWlpaoVq0aAgMDc/w5EBEZAiEEVq9ejQYNGuDvv/+Gs7MzTp48iSZNmug7NJ1hMqsrCgVw8iSwdevbfxUKnR7u/v37cHd3x507d7B161b8+++/WLFiBY4dO4bGjRvj5cuXH32MIkWKoESJElqI1nDI5XJ9h2Aw4uPj0aZNGzg5OeHKlSv4+eefsWDBAixatCjH7by8vBAeHo7Dhw/j8OHDCA8PR9++fVWPJyQkoG3btihXrhwuXbqEs2fPwsbGBm3btlX9/BUKBSwtLTFq1Ci0bt1a7XHOnj2Lfv36YdCgQbh58yZ27NiBK1euYPDgwao+SUlJqFChAn788Uc4ODio3c/gwYMREhKCTZs24caNG/Dw8EDr1q3x+PFjVZ+xY8fi8OHD+PXXX3H79m2MHTsWI0eOxN69e3P98yQi0pQQAklp6e/cNMsd4uPj4eXlha+//hopKSlo3749wsPD0bx5cx1FbCBEIfP69WsBQLx+/TrLY8nJyeLWrVsiOTn54w6ya5cQZcoIAahuCicnodix4+P2m4N27dqJMmXKiKSkpEzt0dHRwsrKSvj6+qraXFxcxMyZM8VXX30lrK2thaOjo1i6dGmmxwGobi4uLkIIIaZPny5q166t6te/f3/RtWtXMWvWLGFvby9sbW3FjBkzhFwuF+PHjxd2dnbC2dlZrF27NlNMEyZMEJUqVRKWlpbC1dVVTJ06VaSlpakef/8474qIiMgUGwDRv39/IYQQLVq0ECNHjhT+/v7Czs5OlC5dWkyfPj3T9gBEYGCg6NKli7CyshLTpk0TQgjx+++/i3r16glzc3Ph6uqqeh5CCKFQKMS3334rypYtK2QymXB0dBQjR47M9POaNWuWGDBggChSpIgoW7asWLlyZabjXr9+XbRs2VJYWFiI4sWLiyFDhoiEhIQsP8sMb968EX379hXW1tbCwcFBLFiwQLRo0UKMHj1a7c9FG5YvXy5sbW1FSkqKqm3OnDnCyclJKJVKtdvcunVLABAXL15UtV24cEEAEH///bcQQogrV64IACIqKkrV5/r16wKA+Pfff7Ps8/2fRYb58+eLChUqZGpbunSpKFOmjNrYXFxcxE8//SSEeDuGcXFx4s2bN8LU1FTs378/U9/atWuLKVOmqO7XqFFDzJw5M1OfevXqialTp6o9ltbeOyhbaWlp4rfffsv0XkHGg+P3YUqlUnRffk64fLtf7S0xVf7BfbRv314AEKampmLevHlCoVBoLb78HsOc8rX3cWZW23bvBnr0AB49ytQsiY6GpFevt49r2cuXL3HkyBH4+fnB0tIy02MODg7w9vZGcHBwpq+L58+fj1q1auHq1auYNGkSxo4di5CQEABQfeW6fv16REdHZ/kK9l3Hjx/HkydPcPr0aSxatAgzZsxAp06dYGdnh0uXLsHX1xe+vr54+PChapuiRYsiKCgIt27dwpIlS7B69Wr89NNPuXquZcuWxa5duwC8vXpJdHQ0lixZonp8w4YNsLa2xqVLlzBv3jzMnDlT9bwyTJ8+HV27dsWNGzcwcOBAHDlyBH369MGoUaNw69YtrFy5EkFBQZg1axYAYOfOnVi+fDkCAwNx9+5d/Pbbb/j0008z7XPhwoVwd3fHtWvX4Ofnh2HDhuHvv/8G8Ha2sF27drCzs8OVK1ewY8cO/PHHHxgxYkS2z9Pf3x8nTpzAnj17cPToUZw8eRJhYWE5/mzOnDmDIkWK5HibPXt2tttfuHABLVq0gLm5uaqtbdu2ePLkCR48eJDtNra2tmjYsKGqrVGjRrC1tcX58+cBAFWqVEHJkiWxdu1apKWlITk5GWvXrkWNGjXg4uKS43N6V5MmTfDo0SMcPHgQQgg8ffoUO3fuRMeOHXO9j/T0dCgUCtVZvRksLS1x9uxZ1f1mzZrh999/x+PHjyGEwIkTJ3Dnzh20bds218ciItJEslyBsMg4tY+5u9jBUmr6wX3MmjULlSpVwpkzZ+Dv7w8Tk0KS5uk6szY0Op2ZTU/PMiP77k0pkQhRtuzbflp08eJFAUDs2bNH7eOLFi0SAMTTp0+FEG9nrNq1a5epT+/evUX79u1V99XtT93MrIuLS6ZPflWqVBHNmzdX3U9PTxfW1tZi69at2cY/b9484ebmlu1x3nfixAkBQMTFxWVqb9GihWjWrFmmtvr164tvv/020/MaM2ZMpj7NmzcXs2fPztS2adMm4ejoKIQQYsGCBaJixYqZZizf5eLiIvr06aO6r1Qqhb29vQgMDBRCCLFq1SphZ2cn3rx5o+pz4MABYWJiImJiYoQQmWcjExIShEwmE9u2bVP1f/HihbC0tMxxZjYpKUncvXs3x9uLFy+y3b5NmzZiyJAhmdoeP34sAIjz58+r3WbWrFmiUqVKWdorVaqU6Wf6119/iU8++USYmJgIExMTUbVqVREZGal2n9nNzAohxI4dO0SRIkWEmZmZACC6dOmS7SyBuplZhUIhGjduLFq0aCEeP34s0tPTxaZNm4REIhGVK1dWbZuamir69esnAAgzMzMhk8nExo0b1R5HCM7M5gfO7Bk3jt+HJabKVbOwzxNSRGKqXHXL7tuxV69eiYMHD2Zqy/hWUds4M1tYnDmTZUb2XRIhgIcP3/bLR+L/Z2QlEomqrXHjxpn6NG7cGLdv39Z43zVq1Mj0ya906dKZZi1NTU1RokQJPHv2TNW2c+dONGvWDA4ODihSpAi+++47REVFaXxsdWrVqpXpvqOjY6ZjA4C7u3um+2FhYZg5c2amGcwhQ4YgOjoaSUlJ6NGjB5KTk1GxYkUMGTIEe/bsQXp6erbHlUgkcHBwUB339u3bqF27NqytrVV9mjZtCqVSiX/++SfLc7h37x7S0tIyjVHx4sVRpUqVHJ+7paUlKlasmOOtePHiOe7j3d8RQP3vzoe2ydguoz05ORkDBw5E06ZNcfHiRZw7dw41atRAhw4dkJycnGM877p16xZGjRqFadOmISwsDIcPH0ZERAR8fX1zvQ8A2LRpE4QQcHZ2hrm5OZYuXQovLy/VYuLA23UYL168iN9//x1hYWFYuHAh/Pz88Mcff2h0LCIqPESWeldNb//Vx1rJTGElM1Pd1L3PhoaGol69eujatStCQ0NV7WZmhe8SAoXvGetSdLR2++VSxYoVIZFIcOvWLbVngf/999+ws7NDyZIlc9xPTglLdqRSaZZ9qGvLWN/u4sWL8PT0REBAANq2bQtbW1ts27YNCxcu1PjYuY0n49gZ3k0qAUCpVCIgIADdu3fPsj8LCwuULVsWV65cwaVLl3D8+HH4+flh/vz5OHXqlOp4OR333cTufdklgnlx5swZtG/fPsc+kydPxuTJk9U+5uDggJiYmExtGQl56dKls93m6dOnWdqfP3+u2mbLli148OABLly4oPrgs2XLFtjZ2WHv3r3w9PTM+Yn9vzlz5qBp06bw9/cH8PYDhLW1NZo3b44ffvgh11ex+eSTT3Dq1CkkJiYiPj4ejo6O6N27N1xdXQG8Tb4nT56MPXv2qEoYatWqhfDwcCxYsCDbE9SIqPASQqDHigvZlglo+1hLly6Fv78/5HI5ypcvr/NjGjoms9qU20vCafnScSVKlECbNm2wfPlyjB07NlPdbExMDDZv3ox+/fplSpzeX2/u4sWLqFq1quq+VCqFQgcrMJw7dw4uLi6YMmWKqi0yMlKjfchkMgDQWnz16tXDP//8g4oVK6p9XKlUwtLSEl26dEG3bt0wfPhwVK1aFTdu3EC9evU+uP/q1atjw4YNSExMVCXS586dg4mJCSpXrpylf8WKFSGVSnHx4kWUK1cOwNvlr+7cuYMWLVpkexx3d/dMy12pk9PMbOPGjTF58mSkpaWpfsZHjx6Fk5NTtm+WjRs3xuvXr3H58mU0aNAAAHDp0iW8fv1atQxMUlISTExMMv3+Zdx//4NGTpKSkrLMOGTMpublA4C1tTWsra0RFxeHI0eOYN68eQDernAhl8uz1JqZmppqFC8RFR451btqKqf62Li4OAwcOFC1dGb37t2xdu1aFCtWTCvHNlZMZrWpeXOgTBng8eO3VbLvERIJJGXKvO2nZcuWLUOTJk3Qtm1b/PDDD3B1dcXNmzfh7+8PZ2dn1clMGc6dO4d58+ahW7duCAkJwY4dO3DgwAHV4+XLl8exY8fQtGlTmJubw87OTitxVqxYEVFRUdi2bRvq16+PAwcOYM+ePRrtw8XFBRKJBPv370eHDh1gaWmJIkWK5DmmadOmoVOnTihbtix69uwJExMTXL9+HTdu3MAPP/yAoKAgJCYmokWLFihSpAg2bdoES0vLXJ+85O3tjenTp6N///6YMWMGnj9/jpEjR6Jv375qZzyLFCmCQYMGwd/fHyVKlEDp0qUxZcqUDxbyZ5QZ5JWXlxcCAgLg4+ODyZMn4+7du5g9ezamTZumSkQvX76Mfv364dixY3B2dka1atXQrl07DBkyBCtXrgQAfP311+jUqZOqLKJNmzbw9/fH8OHDMXLkSCiVSvz4448wMzNDy5YtVce/desW0tLS8PLlSyQkJKgS84wLaHTu3BlDhgxBYGAg2rZti+joaIwZMwYNGjSAk5MTACAtLQ23bt1S/f/x48cIDw+HlZUV7O3tAQBHjhyBEAJVqlTBv//+C39/f1SpUgUDBgwAANjY2KBFixbw9/dXjfOpU6ewcePGDy5TRkQUOrU1rGQfPlkrO5ZSU7Xf2l26dAm9e/dGZGQkZDIZFi5ciOHDh+fpW9UCR1eFu4ZK50tz7dolhETy9vbeyV9KieTt4zry4MED4ePjIxwcHIRUKhVly5YVI0eOFLGxsZn6ubi4iICAANGrVy9hZWUlSpcuLRYvXpypz++//y4qVqwozMzMPrg017vULR/17ok4Qgjh7+8vSpQoIYoUKSJ69+4tfvrpJ2Fra6t6/EMngAkhxMyZM4WDg4OQSCSZluZ6/9hdu3ZVPS6E+hPbhBDi8OHDokmTJsLS0lLY2NiIBg0aiFWrVgkhhNi1a5dwd3cXNjY2wtraWjRq1Ej88ccf2T4/Id4u9fTusmCaLs2VkJAg+vTpoxqfefPm6Xxprow4mzdvLszNzYWDg4OYMWNGphMPMk6+i4iIULW9ePFCeHt7i6JFi4qiRYsKb2/vLCfnHT16VDRt2lTY2toKOzs70apVK3HhwoVMfd5fEi7j9q6lS5eK6tWrC0tLS+Ho6Ci8vb3Fo0ePVI+rW7oNgGjRooXqBLDg4GBRoUIFIZPJhIODgxg+fLh49epVpuNER0cLHx8f4eTkJCwsLESVKlXEwoULsz0JgyeA6R5PIDJuhj5+SqUy0wlXmt6eJ6RotIRWXsydO1cAEJ988okICwvTyTFyYsgngEmEyGOBnpGKj4+Hra0tXr9+DRsbm0yPpaSkICIiAq6urlmW7tHI7t3A6NGZTgZTOjsDixfDpEePvO9XS8qXL48xY8bw0qi5pFQqER8fDxsbm8KzzEkBo+sx1Np7B2VLLpfj4MGD6NChQ5YadTJ8hjx+Qsv1rrdmtoWVTPtffCuVSixcuBBDhw7Nkr/kh/wew5zytffxL7MudO8OPHgAnDgBbNkC5bFjiP/zz7ftREREZDDyq95VU2fPnkXbtm2RmJgI4O25Bv7+/npJZA0da2Z1xdQU+Oyzt/9XKoH4eL2GQ0RERDnTVb2rJpRKJebOnYvvvvsOCoUCs2bNyvGCN8RktlDK7mpOREREhVnG+q768uzZM/Tt2xdHjx4FAPTp0yfb5RTpP0xmiYiIiPTs5MmT8PLyQnR0NCwtLbFs2TIMGDCAqxXkApNZNQrZOXFE9JH4nkFEH+PXX39F//79oVQqUb16dWzfvh01atTQd1hGgyeAvSPj7LykpCQ9R0JExiTjPcPQztImIuPQqlUrlChRAgMGDMDly5eZyGqIM7PvMDU1RbFixVSX8LSystLK9L5SqURaWhpSUlK4tJMR4vgZP12NoRACSUlJePbsGYoVK6a6IhkR5R8hBJLleb8iZFKa9q92mRt37txRXQXSyckJf/75Z64vy02ZMZl9j4ODA4D/rkmvDUIIJCcnw9LSkrUvRojjZ/x0PYbFihVTvXcQUf7R9hqx+SE9PR0zZ87ErFmzsH37dnz55ZcAwET2IzCZfY9EIoGjoyPs7e0hl8u1sk+5XI7Tp0/jf//7H7+GNEIcP+OnyzGUSqWckSXSE0NdIzY7jx8/hpeXF06fPg0AuHjxoiqZpbxjMpsNU1NTrf2BMjU1RXp6OiwsLJgMGSGOn/HjGBIVfIawRmxODh8+jL59+yI2NhZFihTB6tWr4enpqbPjFSZMZomIiMjo6XuN2OzI5XJ89913mDt3LgCgbt26CA4ORqVKlfQcWcHBs1mIiIjI4AkhkJSW/t5NPydvaeL06dOqRHb48OE4f/48E1ktM7yPMERERETvMMYTvTJ8/vnnmDx5MurWrYsePXroO5wCiTOzREREZNA+dKJXfpy8lVtpaWn47rvv8PjxY1XbrFmzmMjqEGdmiYiIyGioO9FL1ydv5daDBw/Qu3dvXL58GWfOnMGJEycMIq6CjsksERERGZT3L4Twbm2soZ7otWfPHgwcOBCvXr1CsWLFMHbsWCay+cTwfhuIiIio0DK2+tjU1FT4+/vj559/BgA0atQI27Ztg4uLi54jKzyYzBIREZHByKk+1pBqY4G3F0Ho2rUrwsLCAAD+/v6YNWsW17POZ0xmiYiIyCC9Xx9rKLWxGYoVK4bk5GSUKFECGzZsQMeOHfUdUqHEZJaIiIgMkiHWx6akpEAmk8HExATW1tbYs2cPrKysUKZMGX2HVmhxaS4iIiKiXPjnn3/QsGFDzJs3T9VWuXJlJrJ6xmSWiIiI6AM2b94MNzc3XL9+HUuXLkViYqK+Q6L/x2SWiIiIKBtJSUkYPHgw+vTpg8TERHz22WcIDQ2FtbW1vkOj/2dYhShERERU4AghkKoAktLSIRU5n8D17pqy+nb79m306tULf/31FyQSCaZNm4bvvvsOpqaGs6ICMZklIiIiHRJCwHPNFVyNMsOEy8f1HU6uxcfHo2nTpoiLi4ODgwM2b96MVq1a6TssUoNlBkRERKQzyXIFrka90ng7fa8pa2Njg5kzZ6J169YIDw9nImvAODNLRERE+eLity1gY22Rq776WFP2xo0bUCgUqFOnDgBg+PDh8PPzg4kJ5/4MGZNZIiIi0pgQAsnyD9e3vlsDa2mA68YCb5/LmjVrMGrUKDg7O+Pq1auwsbGBRCIxqIs0kHqG9xtFREREBk0IgR4rLmR72VljkpCQgKFDh2Lr1q0AgEqVKkEul+s5KtIE582JiIhII8lyhcaJrGtRodcaWHXCw8Ph5uaGrVu3wtTUFD/++CMOHDiAEiVK6Ds00gBnZomIiCjPQqe2hpUs5yRVLpfjRMhRg/nKXgiBFStWYOzYsUhNTUXZsmWxbds2NGnSRN+hUR4wmSUiIqI8s8pFHaxcImAgeSyAt8ns77//jtTUVHTu3Bnr16/nbKwRYzJLREREhYqJiQk2btyIHTt2YNiwYQYzY0x5w5pZIiIiKtCEEFiyZAmGDRumaitVqhT8/PyYyBYAnJklIiKiAisuLg4DBw7Eb7/9BgDo2bMnL4BQwDCZJSIiogLp0qVL6N27NyIjIyGTybBw4UK0bNlS32GRlrHMgIiIiAoUIQQWLlyIZs2aITIyEp988gnOnz+PESNGsKygAOLMLBERERUoAwcORFBQEACgV69eWLVqFWxtbfUbFOkMZ2aJiIioQOnduzcsLS0RGBiIbdu2MZEt4DgzS0REREZNqVTizp07qFq1KgCgXbt2iIiIQOnSpfUcGeUHzswSERGR0Xr27Bk6dOiARo0aISIiQtXORLbwYDJLRERERunUqVOoU6cOjhw5grS0NNy4cUPfIZEeMJklIiIio6JQKDBz5ky0atUK0dHRqFatGi5fvowuXbroOzTSA9bMEhERkdGIiYlBnz59cOzYMQCAj48Pli1bBmtraz1HRvrCZJaIiIgAvF2fNVmu+GC/pLQP99GVJUuW4NixY7CyskJgYCD69eunt1jIMDCZJSIiIggh0GPFBYRFxuk7lBxNnz4djx49wpQpU1SrF1DhxppZIiIiQrJcoXEi6+5iB0upqY4ieuvx48cYP3480tPTAQAWFhbYtGkTE1lS4cwsERERZRI6tTWsZB9OUi2lpjq9POzhw4fRt29fxMbGwsbGBtOmTdPZsch4MZklIiKiTKxkprCS6S9FkMvlmDZtGn788UcAQJ06deDp6am3eMiwMZklIiIig/Hw4UN4enri/PnzAAA/Pz8sXLgQFhYWeo6MDBWTWSIiIjIIx44dQ69evfDy5UvY2NhgzZo16Nmzp77DIgPHZJaIiIgMgoODA5KTk+Hm5obg4GB88skn+g6JjACTWSIiItKbxMRE1QUPatSogWPHjqFevXowNzfXc2RkLLg0FxEREenFb7/9hvLly6vqYwGgcePGTGRJI0xmiYiIKF+lpqZi9OjR+OKLLxAbG4uffvpJ3yGREdN7Mrt8+XK4urrCwsICbm5uOHPmTI79N2/ejNq1a8PKygqOjo4YMGAAXrx4kU/REhER0ce4d+8emjZtiqVLlwIAxo8fjy1btug5KjJmek1mg4ODMWbMGEyZMgXXrl1D8+bN0b59e0RFRantf/bsWfTr1w+DBg3CzZs3sWPHDly5cgWDBw/O58iJiIhIUzt27EDdunURFhaG4sWLY//+/Zg/fz6kUqm+QyMjptdkdtGiRRg0aBAGDx6MatWqYfHixShbtiwCAwPV9r948SLKly+PUaNGwdXVFc2aNcPQoUMRGhqaz5ETEREZByEEktLSc3FT6DSOGzduwNvbGwkJCWjatCnCw8PRsWNHnR6TCge9rWaQlpaGsLAwTJw4MVO7h4dHpkLwdzVp0gRTpkzBwYMH0b59ezx79gw7d+7M8cWQmpqK1NRU1f34+HgAb68uIpfLtfBMPizjOPl1PNIujp/x4xgaP45h3ggh4LnmCq5GvdJoO7lcDrlEaC0OuVyOmjVrolu3bqhcuTJmzJgBMzMzjqcRye/XoCbHkQghtPfbqoEnT57A2dkZ586dQ5MmTVTts2fPxoYNG/DPP/+o3W7nzp0YMGAAUlJSkJ6eji5dumDnzp3ZfkUxY8YMBAQEZGnfsmULrKystPNkiIiIDFCqAphwWbN5K9eiAqNrKCCRfPzxz58/jzp16qj+3iqVSpiY6P10HTICSUlJ8PLywuvXr2FjY5NjX72vMyt579UihMjSluHWrVsYNWoUpk2bhrZt2yI6Ohr+/v7w9fXF2rVr1W4zadIkjBs3TnU/Pj4eZcuWhYeHxwd/ONoil8sREhKCNm3asC7ICHH8jB/H0PhxDPMmKS0dEy4fBwBc/LYFLGWmH9zGUmqa7d/hXB83KQnjxo3DunXr0LNnT6xfvx5//PEH2rZty/EzUvn9Gsz4Jj039JbMlixZEqampoiJicnU/uzZM5QuXVrtNnPmzEHTpk3h7+8PAKhVqxasra3RvHlz/PDDD3B0dMyyjbm5udr16qRSab6/oPRxTNIejp/x4xgaP46hZqTiv6TUxtoCVjLd/9m/ffs2evXqhb/++gsSiQTVqlWDmdnb43L8jF9+jaEmx9DbXL9MJoObmxtCQkIytYeEhGQqO3hXUlJSlq8nTE3ffsrUU7UEERER/b8NGzbA3d0df/31F0qXLo2QkBAEBASwtIB0Sq+/XePGjcOaNWuwbt063L59G2PHjkVUVBR8fX0BvC0R6Nevn6p/586dsXv3bgQGBuL+/fs4d+4cRo0ahQYNGsDJyUlfT4OIiKhQS0xMhI+PD3x8fJCUlITPP/8c4eHh+Pzzz/UdGhUCeq2Z7d27N168eIGZM2ciOjoaNWvWxMGDB+Hi4gIAiI6OzrTmrI+PDxISErBs2TJ88803KFasGFq1aoW5c+fq6ykQEREVeklJSTh69ChMTEwQEBCASZMmqb45JdI1vZ8A5ufnBz8/P7WPBQUFZWkbOXIkRo4cqeOoiIiIDJMQAsny3K0Jq+u1YzOUKlUKwcHBUCqVaNGiRb4ckyiD3pNZIiIiyh0hBHqsuICwyDi9xpGQkABfX1906NAB3t7eAIDmzZvrNSYqvFiRTUREZCSS5Yo8JbLuLnawlGrna//w8HC4ublhy5YtGDFihEZLKBHpAmdmiYiIjFDo1NawysW6sYB21o4VQmDFihUYO3YsUlNTUaZMGWzbti3f1mwnyg6TWSIiIgP1fn3suzWwVjLTfFk3FgBev36NIUOGYMeOHQCATp06ISgoCCVKlMiX4xPlhMksERGRATKU+tjExES4ubnh3r17MDMzw9y5czF27NiPnukl0hbWzBIRERmgnOpjtVkD+yHW1tb48ssv4eLigrNnz2LcuHFMZMmgcGaWiIjIwL1fH6uNGticxMXFITExEWXKlAEA/PDDD5g4cSLs7Ox0dkyivOLMLBERkYHLqI/NuOkykb106RLq1q2LHj16QC6XAwCkUikTWTJYnJklIiL6CJpcxEAT+XXBgwxCCCxatAgTJ05Eeno6TE1N8fjxY5QvXz5f4yDSFJNZIiKiPDKUk7Q+1osXL+Dj44P9+/cDAHr27InVq1fD1tZWz5ERfRjLDIiIiPIorxcx0ISuT/Y6d+4c6tSpg/3798Pc3ByBgYEIDg5mIktGgzOzREREWqDJRQw0ocuTvYQQGDt2LB49eoRKlSph+/btqFOnjk6ORaQrTGaJiIi0ID8vYqAtEokEmzdvxty5c/HTTz+haNGi+g6JSGMsMyAiIipETp06hSVLlqjuV6pUCWvWrGEiS0bLuD5CEhERUZ4oFArMnj0bM2bMgBAC9erVQ/PmzfUdFtFHYzJLRERUwMXExKBPnz44duwYAKB///6oV6+enqMi0g4ms0RERO/J7dqx+b0WbF4cO3YM3t7eePr0KaysrLB8+XL0799f32ERaQ2TWSIioncUlLVjAWDOnDmYMmUKhBCoWbMmgoODUb16dX2HRaRVPAGMiIjoHXlZO1bXa8Hmlb29PYQQGDx4MC5dusRElgokzswSERFlI7drx+pyLVhNvXnzBkWKFAEADBw4EFWqVEGzZs30HBWR7nBmloiIKBsZa8d+6GYIiWx6ejomTZqEmjVr4uXLlwDeriPLRJYKOiazRERERu7hw4f47LPP8OOPPyIyMhK7du3Sd0hE+YbJLBERkRE7cOAA6tSpg3PnzsHGxgbBwcEYMmSIvsMiyjdMZomIiIxQWloaxo8fj06dOuHly5dwc3PD1atX0atXL32HRpSvmMwSEVGhJYRAUlr6ezfDXzsWAGbMmIGFCxcCAEaNGoVz587hk08+0XNURPmPqxkQEVGhZOzryY4fPx4HDx7E9OnT8cUXX+g7HCK94cwsEREVSh9aT9bQ1o5NTU3Fxo0bIYQAABQvXhxXr15lIkuFHmdmiYio0FO3nqwhrR17//599OrVC2FhYUhNTVWd4GViwjkpIiazRERU6GWsJ2uIdu7ciUGDBiE+Ph7FixeHo6OjvkMiMij8SEdERGSAUlJSMHz4cPTs2RPx8fFo0qQJwsPD0alTJ32HRmRQmMwSEREZmLt376Jx48ZYvnw5AGDixIk4efIkypYtq+fIiAyPYX6nQkREVIg9evQIf/75J0qWLIlNmzahXbt2+g6JyGAxmSUiIjIAQgjVCWctW7ZEUFAQPv/8czg7O+s5MiLDxjIDIiIiPbt9+zaaNWuGO3fuqNr69evHRJYoF5jMEhER6dGGDRvg7u6O8+fPY9SoUfoOh8joMJklIiLSg8TERPj4+MDHxwdJSUlo1aoVgoKC9B0WkdFhMktERJTP/vrrL9SvXx8bNmyAiYkJZs6ciaNHj8LBwUHfoREZHZ4ARkRElI8uXbqEli1bIjk5GY6OjtiyZQs+++wzfYdFZLSYzBIREeWjevXqoXbt2rCxscGmTZtgb2+v75CIjBqTWSIiIh27desWKlWqBKlUCqlUigMHDqBYsWIwMWG1H9HH4quIiIhIR4QQWLFiBerVq4cpU6ao2osXL85ElkhLODNLREQGTwiBVAWQlJYOqZBoZZ9JaQqt7Cc78fHxGDJkCLZv3w7g7VqyCoUCpqamOj0uUWHDZJaIiPKXQgGcOQNERwOOjkDz5kAOCZ4QAp5rruBqlBkmXD6ej4HmXVhYGHr37o179+7BzMwMc+bMwbhx4zgbS6QDTGaJiCj/7N4NjB4NPHr0X1uZMsCSJUD37mo3SZYrcDXqlc5Ccnexg6VUO7OlQggsW7YM48ePR1paGlxcXLBt2zY0atRIK/snoqyYzBIRUf7YvRvo0QMQInP748dv23fuzDahzXDx2xawsbbQaliWUlNIJNopXXj8+DEmT56MtLQ0dOvWDevWrYOdnZ1W9k1E6jGZJSIi3VMo3s7I/n8iKwAkS83/e1wiAb6ZALTvmKXk4N3aVkuZKaxkhvunq0yZMli9ejWePXuGkSNHai1JJqLsGe47AhERFRxnzqhKCwSAHt7zEFametZ+AX/kb1wfSQiBn376CXXr1kXLli0BAJ6ennqOiqhwYTJLRES6Fx2t+m+y1Fx9IvsBrkWF1mpbteHly5fw8fHBvn374ODggFu3brGkgEgPmMwSEZHuOTqqbQ792RtW8pT/Gg4eAv7XPEs/uVyOEyFHDeZr+/Pnz8PT0xMPHz6Eubk5pk2bhmLFiuk7LKJCicksERHpXvPmb1ctePw4U7OVPAVW8tS3NbNlygAt/6d2mS65RMAQ8lilUon58+djypQpUCgUqFSpErZv3446deroOzSiQosL3hERke6Zmr5dfgtAlqw04/7ixTmuN6tvycnJ6NSpEyZOnAiFQoGvvvoKYWFhTGSJ9IzJLBER5Y/u3d8uv+XolLm9TJlcLculbxYWFihWrBgsLCywatUqbN68GUWLFtV3WESFHpNZIiLKP927A7dv/Xf/4CEgIsJgE1mFQoHExEQAgEQiwcqVK3HlyhUMGTLEYOp3iQo7JrNERJS/3i0l+F/Ol7LVp6dPn6Jdu3bo06cPxP+vj1u0aFHUrFlTz5ER0bt4AhgREemUEALJ8v8ufPDuRRAM1fHjx+Ht7Y2YmBhYWVnh77//RrVq1fQdFhGpwWSWiIh0RgiBHisuICwyTt+h5IpCocDMmTPx/fffQwiBGjVqYPv27UxkiQwYk1kiItKZZLki20TW3cXOoC6C8OTJE3h7e+PkyZMAgEGDBmHp0qWwsrLSb2BElCMms0RElC9Cp7aGley/5NVSamowJ1EJIdC1a1eEhobC2toaK1euhLe3t77DIqJcyNMJYOnp6fjjjz+wcuVKJCQkAHj7ifbNmzdaDY6IiAoOK5kprGRmqpuhJLLA25UKli5dCjc3N1y9epWJLJER0XhmNjIyEu3atUNUVBRSU1PRpk0bFC1aFPPmzUNKSgpWrFihiziJiIi06tGjRwgPD0enTp0AAI0bN8aVK1cMKskmog/TeGZ29OjRcHd3R1xcHCwtLVXtX3zxBY4dO6bV4IiIiHTh4MGDqFOnDnr16oW//vpL1c5Elsj4aDwze/bsWZw7dw4ymSxTu4uLCx6/d81tIiIiQyKXyzFlyhTMnz8fAFCvXr1MEzNEZHw0TmaVSiUUiqxrBD569IiX9SMiKmDeXyNWU4a0pmxkZCQ8PT1x8eJFAMDIkSMxf/58mJub6zkyIvoYGiezbdq0weLFi7Fq1SoAb7+SefPmDaZPn44OHTpoPUAiItIPY1sjNid79+7FgAEDEBcXB1tbW6xbtw7dDfQSukSkGY2T2Z9++gktW7ZE9erVkZKSAi8vL9y9exclS5bE1q1bdREjERHpQU5rxGpK32vKXr16FXFxcWjQoAG2bdsGV1dXvcVCRNqlcTLr5OSE8PBwbNu2DWFhYVAqlRg0aBC8vb1Zd0REVEC9v0aspvSxpqwQQnXMadOmwd7eHkOGDMlyzgcRGTeNk9nTp0+jSZMmGDBgAAYMGKBqT09Px+nTp/G///1PqwESEZH+ZawRayx27dqFZcuW4dChQ7CwsICpqSmGDx+u77CISAc0XpqrZcuWePnyZZb2169fo2XLlloJioiIKC9SUlIwYsQI9OjRAydPnsQvv/yi75CISMc0/pj97tc273rx4gWsra21EhQREZGm7t69i969e+PatWsAgG+//RajRo3Sc1REpGu5TmYzzvqUSCTw8fHJtJSJQqHA9evX0aRJE+1HSERE9AHbtm3DkCFD8ObNG5QsWRIbN25E+/bt9R0WEeWDXCeztra2AN7OzBYtWjTTyV4ymQyNGjXCkCFDtB8hERFRDhYuXIjx48cDAJo3b46tW7fC2dlZz1ERUX7JdTK7fv16AED58uUxfvx4lhQQERUg6i6OYEgXPMjJl19+idmzZ8PPzw/Tp0+HmZnxnKhGRB9P41f89OnTdREHERHpiTFeHOHatWuoW7cugLeTLHfv3kXx4sX1HBUR6YPGqxkAwM6dO9GrVy80atQI9erVy3TT1PLly+Hq6goLCwu4ubnhzJkzOfZPTU3FlClT4OLiAnNzc3zyySdYt25dXp4GERHhwxdH0PcFD96VmJiIgQMHol69ejh48KCqnYksUeGl8czs0qVLMWXKFPTv3191ecB79+7hypUrGq/hFxwcjDFjxmD58uVo2rQpVq5cifbt2+PWrVsoV66c2m169eqFp0+fYu3atahYsSKePXuG9PR0TZ8GERGpoe7iCPq44IE6UVFRaNKkCW7fvg0TExP8888/vIw6EWmezC5fvhyrVq3CV199hQ0bNmDChAmoUKECpk2bpnb92ZwsWrQIgwYNwuDBgwEAixcvxpEjRxAYGIg5c+Zk6X/48GGcOnUK9+/fV30KL1++vKZPgYioUHu/Pvbd2lhDvDiCEAJBQUEYP3480tLS4ODggK1bt+Kzzz7Td2hEZAA0fsfK+GQMAJaWlkhISAAA9O3bF40aNcKyZctytZ+0tDSEhYVh4sSJmdo9PDxw/vx5tdv8/vvvcHd3x7x587Bp0yZYW1ujS5cu+P7777O9lG5qaipSU1NV9+Pj4wEAcrkccrk8V7F+rIzj5NfxSLs4fsaPY/gfIQQ811zB1ahXah+Xy+WQS0T+BpWDN2/eYMSIEdiyZQsA4PPPP8eGDRtgb2/P8TQifA0av/weQ02Oo3Ey6+DggBcvXsDFxQUuLi64ePEiateujYiICAiR+zfA2NhYKBQKlC5dOlN76dKlERMTo3ab+/fv4+zZs7CwsMCePXsQGxsLPz8/vHz5Mtu62Tlz5iAgICBL+9GjR2FlZZXreLUhJCQkX49H2sXxM34cQyBVAVyNUv/W71pU4ETIURhARYHKhQsXsGXLFpiYmMDLywvdu3dHaGiovsOiPOJr0Pjl1xgmJSXluq/GyWyrVq2wb98+1KtXD4MGDcLYsWOxc+dOhIaGqi6soIn367Cyu8IYACiVSkgkEmzevFm17u2iRYvQo0cP/PLLL2pnZydNmoRx48ap7sfHx6Ns2bLw8PCAjY2NxvHmhVwuR0hICNq0aQOpVJovxyTt4fgZP47hf5LS0jHh8nEAwMVvW8DynfpYQ6mNfVeHDh0ghECbNm2QmJjIMTRSfA0av/wew4xv0nND42R21apVUCqVAABfX18UL14cZ8+eRefOneHr65vr/ZQsWRKmpqZZZmGfPXuWZbY2g6OjI5ydnVWJLABUq1YNQgg8evQIlSpVyrKNubl5pquVZZBKpfn+gtLHMUl7OH7Gj2MISMV/yaqNtYXB1cfGx8dj0qRJmD59Ouzt7QEAc+fOhVwux8GDBzmGRo7jZ/zyaww1OYbGS3OZmJhkWpC6V69eWLp0KUaNGoXnz5/nej8ymQxubm5ZpqtDQkKyvSxu06ZN8eTJE7x580bVdufOHZiYmKBMmTIaPhMiIjIkV69eRb169bB8+XIMGjRI3+EQkZHI0zqz74uJicHIkSNRsWJFjbYbN24c1qxZg3Xr1uH27dsYO3YsoqKiVDO8kyZNQr9+/VT9vby8UKJECQwYMAC3bt3C6dOn4e/vj4EDB2Z7AhgRERk2IQSWLVuGxo0b4969eyhXrhwmT56s77CIyEjkOpl99eoVvL29UapUKTg5OWHp0qVQKpWYNm0aKlSogIsXL2p88YLevXtj8eLFmDlzJurUqYPTp0/j4MGDcHFxAQBER0cjKipK1b9IkSIICQnBq1ev4O7uDm9vb3Tu3BlLly7V6LhERGQYXr16hR49emDkyJFIS0tDly5dcO3aNTRu3FjfoRGRkch1sdTkyZNx+vRp9O/fH4cPH8bYsWNx+PBhpKSk4NChQ2jRokWeAvDz84Ofn5/ax4KCgrK0Va1alWdDEhHlwvvryQKZ15TVt7///hsdOnRAREQEpFIp5s+fj1GjRhncSWhEZNhyncweOHAA69evR+vWreHn54eKFSuicuXKWLx4sQ7DIyKivBBCoMeKCzleplbfnJycYGpqCldXVwQHB6N+/fr6DomIjFCuk9knT56gevXqAIAKFSrAwsJCdeUuIiIyLMlyRY6JrLuLHSylptk+rivx8fEoWrQoJBIJbGxssH//fpQuXRrFihXL91iIqGDIdTKrVCozLZNgamoKa2trnQRFRETaEzq1NaxkmRNXfawpe+HCBfTu3Rv+/v4YOXIkAKBKlSr5GgMRFTy5TmaFEPDx8VGt2ZqSkgJfX98sCe3u3bu1GyEREeXoQ7WxVjJTva4nq1QqsWDBAkyePBkKhQIrV66Er68v1xslIq3I9btb//79M93v06eP1oMhIiLNGHpt7PPnz9G/f38cOnQIAODp6YmVK1cykSUircl1Mrt+/XpdxkFERHlgqLWxAHD69Gl89dVXePLkCSwsLLB06VIMHjyYqxUQkVYZ1nUMiYgozwylNhZ4u064h4cHUlNTUaVKFWzfvh21atXK9ziIqOBjMktEVEDouzb2XY6OjggICMDNmzexfPlyFClSRN8hEVEBZRjvekREZPROnDgBe3t71KhRAwAwYcIEAGBZARHpVK4vZ0tERKSOQqHAjBkz8Pnnn6NXr15ITEwE8DaJZSJLRLrGmVkiIsqz6OhoeHt748SJEwCARo0aMYElonyVp5nZTZs2oWnTpnByckJkZCQAYPHixdi7d69WgyMiIsMVEhKCOnXq4MSJE7C2tsamTZuwdu1aWFlZ6Ts0IipENE5mAwMDMW7cOHTo0AGvXr2CQvF2Ye5ixYph8eLF2o6PiIgMTHp6OqZOnYq2bdvi2bNnqFWrFkJDQ7n+OBHphcbJ7M8//4zVq1djypQpMDX9bwkYd3d33LhxQ6vBERGR4ZFIJDh79iyEEBg6dCguXryIqlWr6jssIiqkNK6ZjYiIQN26dbO0m5ubq4r+iYio4BFCQCKRwNTUFFu2bMHZs2fRq1cvfYdFRIWcxjOzrq6uCA8Pz9J+6NAhVK9eXRsxERGRAZHL5ZgwYQLGjBmjanNycmIiS0QGQeOZWX9/fwwfPhwpKSkQQuDy5cvYunUr5syZgzVr1ugiRiIi0pOoqCh4enriwoULAICBAweidu3aeo6KiOg/GiezAwYMQHp6OiZMmICkpCR4eXnB2dkZS5Ysgaenpy5iJCIiPfj999/h4+ODuLg42NraYu3atUxkicjg5Gmd2SFDhmDIkCGIjY2FUqmEvb29tuMiIiI9SUtLw7fffqtaoaZ+/foIDg6Gq6urfgMjIlJD45rZgIAA3Lt3DwBQsmRJJrJERAWIEAKdO3dWJbJjx47F2bNnmcgSkcHSOJndtWsXKleujEaNGmHZsmV4/vy5LuIiIiI1hBBISkt/56bQ6v4lEgmGDh0KOzs77N27F4sWLYJMJtPqMYiItEnjMoPr16/j5s2b2Lx5MxYtWoRx48ahdevW6NOnD7p168YrvxAR6YgQAj1WXEBYZJxW95uSkoK7d+/i008/BQB0794drVq1QrFixbR6HCIiXcjT5Wxr1KiB2bNn4/79+zhx4gRcXV0xZswYODg4aDs+IiL6f8lyRbaJrLuLHSylpmofy8m///6LJk2aoFWrVnj8+LGqnYksERmLPJ0A9i5ra2tYWlpCJpMhISFBGzEREdEHhE5tDSvZf8mrpdQUEolEo30EBwdjyJAhSEhIQIkSJRAREQFnZ2dth0pEpFN5mpmNiIjArFmzUL16dbi7u+Pq1auYMWMGYmJitB0fERGpYSUzhZXMTHXTJJFNTk6Gr68vPD09kZCQgGbNmiE8PBzNmjXTYcRERLqh8cxs48aNcfnyZXz66acYMGCAap1ZIiIyfP/88w969eqF69evQyKRYPLkyZgxYwbMzD76izoiIr3Q+N2rZcuWWLNmDWrUqKGLeIiISIeWLFmC69evw97eHr/++ivatGmj75CIiD6Kxsns7NmzdREHERHlg/nz5yM9PR0BAQFwdHTUdzhERB8tV8nsuHHj8P3338Pa2hrjxo3Lse+iRYu0EhgREX28mzdvYuXKlVi8eDFMTExgbW2NVatW6TssIiKtyVUye+3aNcjlctX/iYjIsAkhEBQUhOHDhyM5ORkVKlTAmDFj9B0WEZHW5SqZPXHihNr/ExGR4Xnz5g38/PywadMmAICHhwe8vLz0HBURkW5ovDTXwIED1a4nm5iYiIEDB2olKCIiypvr16/D3d0dmzZtgomJCWbNmoVDhw7B3t5e36EREemExsnshg0bkJycnKU9OTkZGzdu1EpQRESkueDgYDRs2BD//PMPnJ2dcfLkSUyePBkmJnlaUpyIyCjkejWD+Ph4CCEghEBCQgIsLCxUjykUChw8eJCf/ImI9KhixYpQKpVo3749Nm7ciJIlS+o7JCIinct1MlusWDFIJBJIJBJUrlw5y+MSiQQBAQFaDY6IiHL26tUrFCtWDADg5uaGCxcuoE6dOpyNJaJCI9fJ7IkTJyCEQKtWrbBr1y4UL15c9ZhMJoOLiwucnJx0EiQREWUmhMAvv/yCyZMn48SJE6hXrx4AqP4lIioscp3MtmjRAgAQERGBcuXKaXQdcCIi0h5lyhv09foKe3bvAgAEBQUxiSWiQitXyez169dRs2ZNmJiY4PXr17hx40a2fWvVqqW14IiIKLPU6DuI3TsXe14/hVQqxbx58zB69Gh9h0VEpDe5Smbr1KmDmJgY2Nvbo06dOpBIJBBCZOknkUigUCi0HiQRUWEnhMCypUsQ8+tEQJmO8uVdsX17MOrXr6/v0IiI9CpXyWxERARKlSql+j8REeWvXbt24Vv/8QAAq8pNcO7MXjjZc7UCIqJcJbMuLi5q/09ERPmje/fu6NipMy6kOqNI3Y6qFQyIiAq7PF004cCBA6r7EyZMQLFixdCkSRNERkZqNTgiosJKqVRi9erVSEpKAgCYmJggeOcuFK3XiSfgEhG9Q+Nkdvbs2bC0tAQAXLhwAcuWLcO8efNQsmRJjB07VusBEhEVNrGxsejcuTO+/vprjBw5UtXOJJaIKKtcL82V4eHDh6hYsSIA4LfffkOPHj3w9ddfo2nTpvjss8+0HR8RUaFy5swZfPXVV3j8+DEsLCzQsGFDCCGYyBIRZUPjmdkiRYrgxYsXAICjR4+idevWAAALCwskJydrNzoiokJCqVRi9uzZaNmyJR4/fowqVarg0qVL+Prrr5nIEhHlQOOZ2TZt2mDw4MGoW7cu7ty5g44dOwIAbt68ifLly2s7PiKiAu/Zs2fo27cvjh49CgDo06cPAgMDUaRIET1HRkRk+DSemf3ll1/QuHFjPH/+HLt27UKJEiUAAGFhYfjqq6+0HiARUUEnl8tx9epVWFpaYu3atdi4cSMTWSKiXNJ4ZrZYsWJYtmxZlvaAgACtBEREVBi8Wwfr7OyMHTt2oFSpUqhRo4aeIyMiMi4aJ7MA8OrVK6xduxa3b9+GRCJBtWrVMGjQINja2mo7PiKiAicmJgbe3t4YMWIEvvjiCwDgCbRERHmkcZlBaGgoPvnkE/z00094+fIlYmNj8dNPP+GTTz7B1atXdREjEVGB8ccff6B27do4fvw4Ro0ahbS0NH2HRERk1DROZseOHYsuXbrgwYMH2L17N/bs2YOIiAh06tQJY8aM0UGIRETGLz09HVOnToWHhweePXuGWrVq4Y8//oBMJtN3aERERk3jMoPQ0FCsXr0aZmb/bWpmZoYJEybA3d1dq8ERERUEjx8/xldffYUzZ84AAIYOHYqffvpJdQEaIiLKO42TWRsbG0RFRaFq1aqZ2h8+fIiiRYtqLTAiooLg+fPnqFOnDmJjY1G0aFGsWrUKnp6e+g6LiKjA0LjMoHfv3hg0aBCCg4Px8OFDPHr0CNu2bcPgwYO5NBcR0XtKlSqF3r17o27duggLC2MiS0SkZRrPzC5YsAASiQT9+vVDeno6AEAqlWLYsGH48ccftR4gEZGxiYqKglQqhaOjIwBg4cKFEELAwsJCz5ERERU8Gs/MymQyLFmyBHFxcQgPD8e1a9fw8uVL/PTTTzA3N9dFjERERmPfvn2oU6cOvvrqK9UHfnNzcyayREQ6kutkNikpCcOHD4ezszPs7e0xePBgODo6olatWrCystJljEREBi8tLQ3ffPMNunTpgri4OCQlJSEuLk7fYRERFXi5TmanT5+OoKAgdOzYEZ6enggJCcGwYcN0GRsRkVGIiIhA8+bNsWjRIgBvlzA8e/YsSpUqpefIiIgKvlzXzO7evRtr165VnbzQp08fNG3aFAqFAqampjoLkIjIkO3evRsDBw7E69evYWdnh6CgIHTp0iXX2wshkCxX5KpvUlru+hERFSa5TmYfPnyI5s2bq+43aNAAZmZmePLkCcqWLauT4IiIDJlcLsd3332H169fo3Hjxti6dStcXFxyvb0QAj1WXEBYJMsRiIjyKtdlBgqFIsuVaszMzFQnOBARFTZSqRTBwcGYNGkSTp06pVEiCwDJckWeEll3FztYSvmNGBERoMHMrBACPj4+mVYsSElJga+vL6ytrVVtu3fv1m6EREQGZPv27Xj27BlGjBgBAKhZsyZmz5790fsNndoaVrLcJaiWUlNIJJKPPiYRUUGQ62S2f//+Wdr69Omj1WCIiAxVcnIyxo4di5UrV8LU1BRNmzZF3bp11fbNbR3suzWwVjJTWMk0XvqbiKjQy/U75/r163UZBxGRwfrnn3/Qq1cvXL9+HRKJBBMnTsSnn36qti/rYImI8henAYiIcvDrr7/C19cXiYmJsLe3x6+//oo2bdpk2z8vdbCsgSUiyjsms0RE2fDz80NgYCAAoGXLlti8ebPqErW5kds6WNbAEhHlHZNZIqJsVK1aFRKJBNOnT8fUqVM1XlObdbBERLrHd1kione8fPkSxYsXBwCMHDkSLVq0QO3atfUcFRERZSfX68wSERVkb968Qf/+/dGwYUPEx8cDACQSCRNZIiIDl6dkdtOmTWjatCmcnJwQGRkJAFi8eDH27t2r1eCIiPLDjRs3UL9+fWzcuBH379/HiRMn9B0SERHlksbJbGBgIMaNG4cOHTrg1atXUCjerpNYrFgxLF68WNvxERHpjBACq1evRoMGDfD333/D2dkZJ0+eRNeuXfUdGhER5ZLGyezPP/+M1atXY8qUKZlOhnB3d8eNGze0GhwRka4kJCTA29sbX3/9NVJSUtC+fXuEh4ejefPm+g6NiIg0oHEyGxERofaqN+bm5khMTNRKUEREuvbNN99g69atMDU1xbx587B//36ULFlS32EREZGGNE5mXV1dER4enqX90KFDqF69usYBLF++HK6urrCwsICbmxvOnDmTq+3OnTsHMzMz1KlTR+NjEhH98MMPaNSoEc6cOQN/f3+YmPB8WCIiY6Txu7e/vz+GDx+O4OBgCCFw+fJlzJo1C5MnT4a/v79G+woODsaYMWMwZcoUXLt2Dc2bN0f79u0RFRWV43avX79Gv3798Pnnn2saPhEVUomJiVi7dq3qvr29Pc6fP4/GjRvrMSoiIvpYGq8zO2DAAKSnp2PChAlISkqCl5cXnJ2dsWTJEnh6emq0r0WLFmHQoEEYPHgwgLcrIhw5cgSBgYGYM2dOttsNHToUXl5eMDU1xW+//abpUyCiQiYsLAzjxo3D06dPYWNjAy8vLwDQylW3hBBIlitU95PSFDn0JiIibcvTRROGDBmCIUOGIDY2FkqlEvb29hrvIy0tDWFhYZg4cWKmdg8PD5w/fz7b7davX4979+7h119/xQ8//PDB46SmpiI1NVV1P2P9SLlcDrlcrnHceZFxnPw6HmkXx894CSGwbNkyTJw4EXK5HC4uLihfvrzWxlIIAc81V3A16pXax+VyOeQSoZVjFXZ8HRo3jp/xy+8x1OQ4H3UFsI85WSI2NhYKhQKlS5fO1F66dGnExMSo3ebu3buYOHEizpw5AzOz3IU+Z84cBAQEZGk/evQorKysNA/8I4SEhOTr8Ui7OH7G5c2bN/j5559x6dIlAECjRo0wYsQIPH/+HAcPHtTKMVIVwNUo9e9FrkUFToQchRYmf+kdfB0aN46f8cuvMUxKSsp1X42TWVdX1xy/mrt//75G+3t/X0IItftXKBTw8vJCQEAAKleunOv9T5o0CePGjVPdj4+PR9myZeHh4QEbGxuNYs0ruVyOkJAQtGnTBlKpNF+OSdrD8TM+ly9fxujRoxEZGQmZTIY5c+agQoUK8PDw0OoYJqWlY8Ll4wCAi9+2gKXsv+UKLaWmWiljoLf4OjRuHD/jl99jmPFNem5onMyOGTMm0325XI5r167h8OHDGp0AVrJkSZiammaZhX327FmW2Vrg7ZqQoaGhuHbtGkaMGAEAUCqVEELAzMwMR48eRatWrbJsZ25uDnNz8yztUqk0319Q+jgmaQ/Hz3i8fv0akZGR+OSTT7B9+3Z8+umnOHjw4EeN4fu1sQAgF/+dQ2tjbQEr2Ud92UW5wNehceP4Gb/8GkNNjqHxO+/o0aPVtv/yyy8IDQ3N9X5kMhnc3NwQEhKCL774QtUeEhKi9uo7NjY2WS7KsHz5chw/fhw7d+6Eq6trro9NRAXPu9/qdOjQAVu2bEHHjh1hY2Pz0TVeQgj0WHEBYZFx2giViIi0SGsLK7Zv3x67du3SaJtx48ZhzZo1WLduHW7fvo2xY8ciKioKvr6+AN6WCPTr1+9toCYmqFmzZqabvb09LCwsULNmTVhbW2vrqRCRkTl79ixq166NyMhIVdtXX32ltVKiZLkix0TW3cUOllLTbB8nIiLd0dp3Yjt37kTx4sU12qZ379548eIFZs6ciejoaNSsWRMHDx6Ei4sLACA6OvqDa84SUeGlVCoxd+5cfPfdd1AoFJg6dSo2bdqk02OGTm0NK1nmxJX1sURE+qNxMlu3bt1Mb9pCCMTExOD58+dYvny5xgH4+fnBz89P7WNBQUE5bjtjxgzMmDFD42MSkfF79uwZ+vbti6NHjwIA+vTpg8DAQI32oa4OVp131461kpmyNpaIyIBo/I7crVu3TPdNTExQqlQpfPbZZ6hataq24iIiytbJkyfh5eWF6OhoWFpa4pdffoGPj49Gs6OsgyUiKhg0SmbT09NRvnx5tG3bFg4ODrqKiYgoW4cOHUKnTp2gVCpRvXp1bN++HTVq1NB4Px+qg1WHtbFERIZHo2TWzMwMw4YNw+3bt3UVDxFRjlq2bIlatWqhbt26+Pnnn7Vy8qe6Olh1WBtLRGR4NC4zaNiwIa5du6Y6SYuISNcuXboEd3d3mJqawsLCAqdPn0bRokW1tn/WwRIRGS+N3739/PzwzTff4NGjR3Bzc8syK1KrVi2tBUdEhVt6ejoCAgIwa9YsTJs2TXXCpzYTWSIiMm65TmYHDhyIxYsXo3fv3gCAUaNGqR6TSCSqBcsVig+fGUxE9CGPHz+Gl5cXTp8+DQB4+vRptpe7JiKiwivXyeyGDRvw448/IiIiQpfxEBHh8OHD6Nu3L2JjY1GkSBGsXr0anp6e+g6LiIgMUK6TWSEEALBWloh0Ri6XY9q0afjxxx8BvF3XOjg4GJUqVdJzZEREZKg0upwtv94jIl26f/8+Fi9eDAAYPnw4zp8/r5VEVgiBVAWQlJb+zo0lUUREBYFGJ4BVrlz5gwnty5cvPyogIiq8qlSpgpUrV8LKygo9evTQyj6FEPBccwVXo8ww4fJxreyTiIgMh0bJbEBAAGxtbXUVCxEVMmlpaZg6dSq++OILNG7cGADQr18/rR4jWa7A1ahX2T7OCyEQERk3jZJZT09P2Nvb6yoWIipEHjx4AE9PT1y6dAnbt2/H33//DQsLC50e8+K3LWBjnfkYvBACEZFxy3Uyyzd7ItKWPXv2YODAgXj16hWKFSuGJUuWaCWRFUIgWZ65Fvbd2lhLXhyBiKjA0Xg1AyKivEpNTYW/vz9+/vlnAECjRo2wbds2raySIoRAjxUXEBYZ99H7IiIi45HrZFapVOoyDiIq4OLi4tCmTRuEhYUBAPz9/TFr1ixIpVKt7D9ZrsgxkXUtKlgbS0RUAPH7NiLKF8WKFUOZMmXw4MEDbNiwAR07dtTZsUKntoaV7L/EVS6X40TIUZZLEREVQExmiUhnUlJSkJ6ejiJFikAikWDdunVISkpCmTJlPnrf79fHvlsba/VebaxcIsA8loioYGIyS0Q6cefOHfTq1QvVq1fH5s2bIZFIULx4cRQvXvyj9836WCIiyqDRFcCIiHJjy5YtcHNzw59//ok//vgDjx8/1ur+c6qP5bqxRESFC2dmiUhrkpKSMHr0aKxZswYA8Nlnn2Hz5s1wcnLS2THfr4/lurFERIULk1ki0orbt2+jV69e+OuvvyCRSDBt2jR89913MDXN3SypujVis5NTfSwRERUu/AtARB8tPT0dnTt3xr179+Dg4IDNmzejVatWud6eNbBERJRXrJkloo9mZmaGVatWoW3btggPD9cokQU+vEZsdlgfS0REnJklojy5ceMGIiMj0alTJwBAq1at0LJly4+uV32/BjYnrI8lIiIms0SkESEE1q5di5EjR8LMzAxXr15FpUqVACBLYpnbOljWwBIRUV7xLwYR5VpCQgJ8fX2xZcsWAEC7du1QrFgxtX1ZB0tERPmBNbNElCvh4eFwc3PDli1bYGpqirlz5+LAgQMoVaqU2v55qYNlDSwREWmKM7NE9EErVqzAmDFjkJqairJly2Lbtm1o0qRJrrfPbR0sa2CJiEhTTGaJ6IPu3buH1NRUdO7cGevXr0eJEiU02p51sEREpCv860JEaimVSpiYvK1Emj17NmrXrg1vb+9sZ07fP9nr3ZO6iIiIdIXJLBFlIoTA0qVLsWfPHoSEhEAqlUIqlaJPnz45bsOTvYiISB94AhgRqcTFxaF79+4YM2YMTp06ha1bt+Zqu5xO9uJJXUREpEucmSUiAMClS5fQu3dvREZGQiaTYeHChejbt6/G+3n/ZC+e1EVERLrEZJaokFMqlVi0aBEmTZqE9PR0VKjwCTZu3oy69dxydcEDgBc9ICIi/eFfHKJCzt/fH4sWLQIAWFVtDnm7kfD+LRb47YieIyMiIvow1swSFXJ9fQbCxNIGxdsOR8kuE2BibpXnfbE+loiI8htnZokKGaVSifPnz6NZs2YAgMpVqsDZdx1MZBa5vrhBdlgfS0RE+Y3JLFEB9P6arxmePXuGIQMH4PixP3Dg8FH8r0ULJKUpYCKzAMB6VyIiMj78q0VUwGS35mtK1A3E7psPxZuXkJiZo+/PR2B9LEVPURIREWkHk1miAub9NV+FUoHXF7bj9bmtgFBCWqIsSnb9FrJS5TNtx3pXIiIyRkxmiQqwA0NqYsSQgTh59jgAoE+/fli0eCmsra2z9GW9KxERGSMms0RG7v362HfXfD1zPAQnTxyHlZUVAgMD0a9fP32ESEREpDNMZomMWHb1sRn69uuPx1GR8PLyQrVq1fI5OiIiIt3jOrNERuz9+tj0hBeI3bcAipQ3cHexg5XMDN9//z0TWSIiKrCYzBIVEHMbKpG6/Rsk3jqJps/2YodvY9bAEhFRgccyAyIjoW7t2KQ0BYRSgVdnNsFz7k4AQJ06dfB9QAATWSIiKhSYzBIZgexqY9PjnyP29/lIfXwLAODn54eFCxfCwsJCH2ESERHlOyazREbg/dpYAEh9/Dee7QyAMiUBZhbW2LJxPXr27KmnCImIiPSDySyRkQmd2hpWMlO8fFkfjU8tgb19ZWwPDsYnn3yi79CIiIjyHZNZIiOiSHwFS6kJrGRmsHKwx/Fjx1CuXDmYm5vrOzQiIiK94GoGRAZGCIGktPT3bgok3TmPJ6uHYmPQelXfSpUqMZElIqJCjTOzRAZE3YleIl2OuJPrkBC2DwCwbdtW+H49hKsVEBERgckskUF5/0QveVw0Yn+fi7SYfwEAlVp/hZADQUxkiYiI/h/LDIgM1Hc1XuPNtm+QFvMvihcvjl179uKfo5shk8n0HRoREZHB4MwskQGSv3yMr32GQalUomnTpti6dSvKli2r77CIiIgMDpNZIgMkLe6MSVOmQpkux8yZM2FmxpcqERGROvwLSWQgtm7dihq16qjuT576HaxkfIkSERHlhDWzRHqWlJSEwYMHw8vLC/36eEGkp+k7JCIiIqPBZJZIj27fvo2GDRti7dq1kEgk6NCxE2Biqu+wiIiIjAaTWSI92bBhA9zd3fHXX3+hdOnSCAkJwdRp0yFhMktERJRrTGaJ8llSUhL69+8PHx8fJCUl4fPPP0d4eDg+//xzfYdGRERkdJjMEuUzMzMz/P333zAxMcH333+PI0eOwMHBQd9hERERGSWeKk2UD4QQEELAxMQEMpkMwcHBiIyMRIsWLfQdGhERkVHjzCyRjiUkJKBPnz6YNGmSqq18+fJMZImIiLSAM7NEOhQeHo5evXrh7t27MDMzw7Bhw1C+fHl9h0VERFRgcGaWSAeEEAgMDESjRo1w9+5dlClTBidPnmQiS0REpGWcmSXSstevX2PIkCHYsWMHAKBTp04ICgpCiRIlsvQVQiBZrlDdT0pTZOlDRERE2WMyS6RFSqUSLVq0wJ9//gkzMzPMnTsXY8eOhUQiydJXCIEeKy4gLDJOD5ESEREVDCwzINIiExMT+Pv7w8XFBWfPnsW4cePUJrIAkCxXZJvIurvYwVLKiycQERF9CGdmiT5SXFwcIiMjUadOHQCAt7c3vvjiC1hZWeV6H6FTW8NK9l/yaik1zTYJJiIiov9wZpboI1y6dAl169ZFhw4d8Pz5c1W7JoksAFjJTGElM1PdmMgSERHlDpNZojwQQmDhwoVo1qwZIiMjYWlpiWfPnuk7LCIiokKHZQZEGnrx4gV8fHywf/9+AEDPnj2xevVq2Nra6jkyIiKiwkfvM7PLly+Hq6srLCws4ObmhjNnzmTbd/fu3WjTpg1KlSoFGxsbNG7cGEeOHMnHaKmwO3fuHOrUqYP9+/fD3Nwcy5cvR3BwMBNZIiIiPdFrMhscHIwxY8ZgypQpuHbtGpo3b4727dsjKipKbf/Tp0+jTZs2OHjwIMLCwtCyZUt07twZ165dy+fIqbAKDAzEo0ePUKlSJVy8eBHDhg1jfSsREZEe6bXMYNGiRRg0aBAGDx4MAFi8eDGOHDmCwMBAzJkzJ0v/xYsXZ7o/e/Zs7N27F/v27UPdunXzI2Qq5JYvX47SpUtjxowZKFq0qL7DISIiKvT0lsympaUhLCwMEydOzNTu4eGB8+fP52ofSqUSCQkJKF68eLZ9UlNTkZqaqrofHx8PAJDL5ZDL5XmIXHMZx8mv45H2nD59GsHBwejQoQPkcjksLS3x448/Avj48ZTL09/5vxxyifio/VH2+Bo0fhxD48bxM375PYaaHEdvyWxsbCwUCgVKly6dqb106dKIiYnJ1T4WLlyIxMRE9OrVK9s+c+bMQUBAQJb2o0eParx80scKCQnJ1+NR3ikUCuzcuRPBwcFQKpWwsLDQejlBqgLIeAkeOXIU5rxGgs7xNWj8OIbGjeNn/PJrDJOSknLdV++rGbyfIAghcpU0bN26FTNmzMDevXthb2+fbb9JkyZh3Lhxqvvx8fEoW7YsPDw8YGNjk/fANSCXyxESEoI2bdpAKpXmyzEp72JiYuDj44Pjx48DeHsRhKZNm2p9/JLS0jHh8ttjtG3rASuZ3l+OBRZfg8aPY2jcOH7GL7/HMOOb9NzQ21/PkiVLwtTUNMss7LNnz7LM1r4vODgYgwYNwo4dO9C6desc+5qbm8Pc3DxLu1QqzfcXlD6OSZo5duwYvL298fTpU1hZWWH58uXw8vLCwYMHcz1+QggkyxUf7CcX/51/+XbfTGZ1ja9B48cxNG4cP+OXX2OoyTH09tdTJpPBzc0NISEh+OKLL1TtISEh6Nq1a7bbbd26FQMHDsTWrVvRsWPH/AiVCoklS5Zg7NixEEKgZs2a2L59O6pVq6ZR3Y4QAj1WXEBYZJwOIyUiIqIMep0KGjduHPr27Qt3d3c0btwYq1atQlRUFHx9fQG8LRF4/PgxNm7cCOBtItuvXz8sWbIEjRo1Us3qWlpacp1P+mj169eHiYkJBgwYgCVLluSppjpZrtA4kXV3sYOllAWzREREeaHXZLZ379548eIFZs6ciejoaNSsWRMHDx6Ei4sLACA6OjrTmrMrV65Eeno6hg8fjuHDh6va+/fvj6CgoPwOnwqAp0+fqspamjRpgr/++gtVq1bVyr5Dp7aGlezDSaql1JRr1RIREeWR3ov0/Pz84Ofnp/ax9xPUkydP6j4gKhTS09Px3Xff4eeff8alS5dQo0YNANBaIgsAVjJTntRFRESkY/xLS4XOw4cP8dVXX+HcuXMAgH379qmSWSIiIjIuTGapUDlw4AD69euHly9fwsbGBqtXr85xnWIiIiIybCYf7kJk/ORyOcaPH49OnTrh5cuXcHNzw9WrV5nIEhERGTnOzFKhsHbtWixcuBAAMGrUKMydOxdKEzMkpaV/YMu3l51NVby9yIFU5HyiVlLah9eXJSIiIu1hMkuFwuDBg3HkyBH069cP3bp1y8NasGaqq3URERGR4WCZARVIaWlpmD9/PlJTUwEAZmZm2LNnD7744os8rQWrKa4dS0RElD84M0sFzv3799G7d2+EhoYiKioKP//8c7Z9c7MWrFwux5EjR9G2rUeuL6/HtWOJiIjyB5NZKlB27tyJQYMGIT4+HsWLF0fbtm1z7J+btWDlEgFzU8BKZgaplC8ZIiIiQ8IyAyoQUlJS4Ofnh549eyI+Ph5NmzZFeHg4OnXqpO/QiIiISIeYzJLRu3fvHho3bozAwEAAwMSJE3HixAmULVtWz5ERERGRrvE7UzJ6JiYmiIiIQMmSJbFp0ya0a9dO3yERERFRPmEyS0ZJoVDA1PTtiVuurq7Ys2cPKleuDGdnZz1HRkRERPmJZQZkdG7fvo169erh8OHDqraWLVuqTWSFEEhKS3/vxgsbEBERFRScmSWjsnHjRgwbNgxJSUnw9/eHh4cHTEzUfyYTQuTh4ghERERkTDgzS0YhMTERAwYMQP/+/ZGUlIRWrVohJCQk20QWwAcvjsALGxARERk/zsySwfvrr7/Qq1cv3L59GyYmJpgxYwYmT56sqpnNDXUXR+CFDYiIiIwfk1kyaPfv30eDBg2QnJwMR0dHbNmyBZ999pnG+8nNxRGIiIjI+PCvOxm0ChUqwNPTE0+ePMHGjRthb2+v75CIiIjIgDCZJYPz559/wsnJCaVKlQIABAYGQiqV5lgfS0RERIUTswMyGEIIrFixAg0bNkS/fv2gVCoBAObm5kxkiYiISC1mCGQQXr9+DU9PTwwbNgypqakwNTVFUlKSvsMiIiIiA8dklvQuLCwMbm5u2L59O8zMzDB//nz8/vvvKFKkiL5DIyIiIgPHmlnSGyEEli1bhvHjxyMtLQ0uLi7Ytm0bGjVqpO/QiIiIyEhwZpb0JjExEUuWLEFaWhq6du2Ka9euMZElIiIijXBmlvSmSJEiCA4OxtmzZzFq1KiPvoCBEALJcoXqflKaIofeREREVBAwmaV8I4TA4sWLYWlpCV9fXwCAm5sb3NzctLLvHisu5Hj5WiIiIip4mMxSvnj58iV8fHywb98+yGQytGnTBp988onW9p8sV2SbyLq72MFSmvtL3xIREZHxYDJLOnf+/Hl4enri4cOHMDc3x08//YQKFSro7HihU1vDSvZf8mopNf3oEgYiIiIyTExmSWeUSiXmz5+PKVOmQKFQoFKlSti+fTvq1Kmj0+NayUxhJeOvNhERUWHAv/iUa++fYJUTpVKJ3j2+xMED+wEAPXt74udflqNo0aJISkvXemw82YuIiKhwYjJLuZKXE6xeJxSHxEwGu9ZDccnFAw3nn9dhhERERFQYMZmlXMnpBKsMQqmAMjkeptZ2AACbRj1gVbUZpHZO+REiAJ7sRUREVNgwmSWNvX+CFQA8ffoUg3z6IyYmBqfPnYeVlZVeYuPJXkRERIULk9lCLrd1sO/WpL5/gtXx48fh5eWFp0+fwsrKCn//dR3NmjXTSbxERERE72IyW4h97IUGFAoFZs6cie+//x5CCNSoUQPbt29H9erVtRwpERERkXpMZgux3NTBvi+jJvXJkyfw9vbGyZMnAQCDBg3C0qVL9VZeQERERIUTk1kCoL4OVp2MmtSRI0fi5MmTsLa2xsqVK+Ht7Z0PURIRERFlxmS2ANJGHeyHLF26FK9fv8Yvv/yCKlWq5ClOIiIioo/FZLaA+dg62Ow8evQIe/fuxfDhwwEAzs7O+OOPP7R6DCIiIiJNMZktYD6mDjY7Bw8eRL9+/fDixQs4OzujW7duHxklERERkXYwmS3ANK2DfZ9cLseUKVMwf/58AEC9evXw6aefaj1OIiIiorxiMmsEclsDC3xcHey7IiMj4enpiYsXLwIARo4cifnz58Pc3DxP+yMiIiLSBSazBk5XNbA52b9/P/r27YtXr17B1tYW69atQ/fu3fPt+ERERES5xWTWwOWlBhb4cB1sTlJTU/Hq1Ss0aNAA27Ztg6ura572Q0RERKRrTGaNSG5rYIHs62Czk56eDjOzt78OX375JXbt2oVOnTpBJpPlKVYiIiKi/GCi7wAo9zJqYHNz0ySR3blzJ6pXr44nT56o2rp3785EloiIiAwek9lCLCUlBcOHD0fPnj1x9+5d1aoFRERERMaCZQaF1N27d9G7d29cu3YNAPDtt9/i+++/13NURERERJphMlsIbdu2DUOGDMGbN29QsmRJbNy4Ee3bt9d3WEREREQaYzJbyGzcuBH9+/cHADRv3hxbt26Fs7OznqMiIiIiyhvWzBYyX375JWrUqIGpU6fi+PHjTGSJiIjIqHFmthAICQnB559/DhMTE1hbWyM0NBQWFhb6DouIiIjoo3FmtgBLTEzEgAED4OHhgYULF6ramcgSERFRQcGZ2QLq5s2b6NWrF27dugUTExPI5XJ9h0RERESkdUxmCxghBNavX48RI0YgOTkZDg4O2Lp1Kz777DN9h0ZERESkdUxmC5A3b97A19cXmzdvBgB4eHhg06ZNsLe313NkRERERLrBmtkC5M6dO9i+fTtMTU0xe/ZsHDp0iIksERERFWicmS1A6tWrh5UrV6JSpUpo1qyZvsMhIiIi0jnOzBqx+Ph49OvXT3VJWgAYMGAAE1kiIiIqNDgza6SuXr2KXr164d69ewgNDcWNGzdgamqq77CIiIiI8hVnZo2MEALLli1D48aNce/ePZQrVw5r165lIktERESFEmdmjcirV6/Qd9hQ7N69GwDQpUsXrF+/HsWLF9dzZERERET6wWTWSKTHx6JJg/qIjHwAqVSK+fPnY9SoUZBIJPoOjYiIiEhvmMwaCdOixfFJxYowMZEgODgY9evX13dIRERERHrHZNaAvXz5EkqTt0MkkZhg/cZNsLGyQLFixfQbGBEREZGB4AlgBur8+fOoU6cO/L8Zq2orWbIkE1kiIiKidzCZNTBKpRLz5s3D//73Pzx8+BBnTp2GMuWNvsMiIiIiMkhMZg3I8+fP0alTJ3z77bdQKBTw9PTE2YuXYGJRRN+hERERERkkJrMG4syZM6hTpw4OHToECwsLrFy5Elu2bIGNjY2+QyMiIiIyWDwBzAAkJSWhZ8+eePr0KapUqYLt27ejVq1a+g6LiIiIyOBxZtYAWFlZYd26dejbty9CQ0OZyBIRERHlEmdm9eTEiRNITk5Ghw4dAAAdOnRAhw4dIIRAUlq6ql9SmkJfIRIREREZPCaz+UyhUOD777/HzJkzYWtriz///BPlypUDAAgh0GPFBYRFxuk5SiIiIiLjoPcyg+XLl8PV1RUWFhZwc3PDmTNncux/6tQpuLm5wcLCAhUqVMCKFSvyKdKPFx0djTZt2iAgIABCCHTv3h0lS5ZUPZ4sV2SbyLq72MFSappfoRIREREZBb3OzAYHB2PMmDFYvnw5mjZtipUrV6J9+/a4deuWarbyXREREejQoQOGDBmCX3/9FefOnYOfnx9KlSqFL7/8Ug/PIPeuXbuGIUOG4Pnz57C2tsaKFSvQp0+fbPuHTm0NK9l/yaul1BQSiSQ/QiUiIiIyGnpNZhctWoRBgwZh8ODBAIDFixfjyJEjCAwMxJw5c7L0X7FiBcqVK4fFixcDAKpVq4bQ0FAsWLDAYJNZpVKJSZOnYNHCBRBCoOann2LT5q2oXKVKptpYIHN9rJXMFFYyVoEQERER5URv2VJaWhrCwsIwceLETO0eHh44f/682m0uXLgADw+PTG1t27bF2rVrIZfLIZVKs2yTmpqK1NRU1f34+HgAgFwuh1wu/9in8UFJaelYeewmhBAoUqcdXrcagm6bHwB4kON2crkcconQeXz0YRm/J/nx+0K6wTE0fhxD48bxM375PYaaHEdvyWxsbCwUCgVKly6dqb106dKIiYlRu01MTIza/unp6YiNjYWjo2OWbebMmYOAgIAs7UePHoWVldVHPIPcSVUAxVsNhmUFd1hVbJCrbVyLCpwIOQpWFRiWkJAQfYdAH4ljaPw4hsaN42f88msMk5KSct1X799jv18HKoTIsTZUXX917RkmTZqEcePGqe7Hx8ejbNmy8PDwyJerawkh0KpVKo4fN0GrVv+DVPrhHznrYw2LXC5HSEgI2rRpo3b2nwwfx9D4cQyNG8fP+OX3GGZ8k54bektmS5YsCVNT0yyzsM+ePcsy+5rBwcFBbX8zMzOUKFFC7Tbm5uYwNzfP0i6VSvPtBWUrkcDcFLC1tuCL2Ijl5+8M6QbH0PhxDI0bx8/45dcYanIMvS3NJZPJ4ObmlmW6OiQkBE2aNFG7TePGjbP0P3r0KNzd3fniICIiIiqE9LrO7Lhx47BmzRqsW7cOt2/fxtixYxEVFQVfX18Ab0sE+vXrp+rv6+uLyMhIjBs3Drdv38a6deuwdu1ajB8/Xl9PgYiIiIj0SK81s71798aLFy8wc+ZMREdHo2bNmjh48CBcXFwAvL3IQFRUlKq/q6srDh48iLFjx+KXX36Bk5MTli5darDLchERERGRbun9BDA/Pz/4+fmpfSwoKChLW4sWLXD16lUdR0VERERExkDvl7MlIiIiIsorJrNEREREZLSYzBIRERGR0WIyS0RERERGi8ksERERERktJrNEREREZLSYzBIRERGR0WIyS0RERERGi8ksERERERktJrNEREREZLSYzBIRERGR0WIyS0RERERGi8ksERERERktM30HkN+EEACA+Pj4fDumXC5HUlIS4uPjIZVK8+24pB0cP+PHMTR+HEPjxvEzfvk9hhl5WkbelpNCl8wmJCQAAMqWLavnSIiIiIgoJwkJCbC1tc2xj0TkJuUtQJRKJZ48eYKiRYtCIpHkyzHj4+NRtmxZPHz4EDY2NvlyTNIejp/x4xgaP46hceP4Gb/8HkMhBBISEuDk5AQTk5yrYgvdzKyJiQnKlCmjl2Pb2NjwRWzEOH7Gj2No/DiGxo3jZ/zycww/NCObgSeAEREREZHRYjJLREREREaLyWw+MDc3x/Tp02Fubq7vUCgPOH7Gj2No/DiGxo3jZ/wMeQwL3QlgRERERFRwcGaWiIiIiIwWk1kiIiIiMlpMZomIiIjIaDGZJSIiIiKjxWRWC5YvXw5XV1dYWFjAzc0NZ86cybH/qVOn4ObmBgsLC1SoUAErVqzIp0gpO5qM4e7du9GmTRuUKlUKNjY2aNy4MY4cOZKP0ZI6mr4OM5w7dw5mZmaoU6eObgOkD9J0DFNTUzFlyhS4uLjA3Nwcn3zyCdatW5dP0dL7NB2/zZs3o3bt2rCysoKjoyMGDBiAFy9e5FO09L7Tp0+jc+fOcHJygkQiwW+//fbBbQwmnxH0UbZt2yakUqlYvXq1uHXrlhg9erSwtrYWkZGRavvfv39fWFlZidGjR4tbt26J1atXC6lUKnbu3JnPkVMGTcdw9OjRYu7cueLy5cvizp07YtKkSUIqlYqrV6/mc+SUQdMxzPDq1StRoUIF4eHhIWrXrp0/wZJaeRnDLl26iIYNG4qQkBAREREhLl26JM6dO5ePUVMGTcfvzJkzwsTERCxZskTcv39fnDlzRtSoUUN069YtnyOnDAcPHhRTpkwRu3btEgDEnj17cuxvSPkMk9mP1KBBA+Hr65uprWrVqmLixIlq+0+YMEFUrVo1U9vQoUNFo0aNdBYj5UzTMVSnevXqIiAgQNuhUS7ldQx79+4tpk6dKqZPn85kVs80HcNDhw4JW1tb8eLFi/wIjz5A0/GbP3++qFChQqa2pUuXijJlyugsRsq93CSzhpTPsMzgI6SlpSEsLAweHh6Z2j08PHD+/Hm121y4cCFL/7Zt2yI0NBRyuVxnsZJ6eRnD9ymVSiQkJKB48eK6CJE+IK9juH79ety7dw/Tp0/XdYj0AXkZw99//x3u7u6YN28enJ2dUblyZYwfPx7Jycn5ETK9Iy/j16RJEzx69AgHDx6EEAJPnz7Fzp070bFjx/wImbTAkPIZs3w9WgETGxsLhUKB0qVLZ2ovXbo0YmJi1G4TExOjtn96ejpiY2Ph6Oios3gpq7yM4fsWLlyIxMRE9OrVSxch0gfkZQzv3r2LiRMn4syZMzAz49ugvuVlDO/fv4+zZ8/CwsICe/bsQWxsLPz8/PDy5UvWzeazvIxfkyZNsHnzZvTu3RspKSlIT09Hly5d8PPPP+dHyKQFhpTPcGZWCyQSSab7QogsbR/qr66d8o+mY5hh69atmDFjBoKDg2Fvb6+r8CgXcjuGCoUCXl5eCAgIQOXKlfMrPMoFTV6HSqUSEokEmzdvRoMGDdChQwcsWrQIQUFBnJ3VE03G79atWxg1ahSmTZuGsLAwHD58GBEREfD19c2PUElLDCWf4ZTERyhZsiRMTU2zfPJ89uxZlk8rGRwcHNT2NzMzQ4kSJXQWK6mXlzHMEBwcjEGDBmHHjh1o3bq1LsOkHGg6hgkJCQgNDcW1a9cwYsQIAG8TIyEEzMzMcPToUbRq1SpfYqe38vI6dHR0hLOzM2xtbVVt1apVgxACjx49QqVKlXQaM/0nL+M3Z84cNG3aFP7+/gCAWrVqwdraGs2bN8cPP/zAbymNgCHlM5yZ/QgymQxubm4ICQnJ1B4SEoImTZqo3aZx48ZZ+h89ehTu7u6QSqU6i5XUy8sYAm9nZH18fLBlyxbWeOmZpmNoY2ODGzduIDw8XHXz9fVFlSpVEB4ejoYNG+ZX6PT/8vI6bNq0KZ48eYI3b96o2u7cuQMTExOUKVNGp/FSZnkZv6SkJJiYZE5BTE1NAfw3u0eGzaDymXw/5ayAyViOZO3ateLWrVtizJgxwtraWjx48EAIIcTEiRNF3759Vf0zlrIYO3asuHXrlli7di2X5tIzTcdwy5YtwszMTPzyyy8iOjpadXv16pW+nkKhp+kYvo+rGeifpmOYkJAgypQpI3r06CFu3rwpTp06JSpVqiQGDx6sr6dQqP1fO/caElX6xwH8O6NOTaN2kSU1zSljukC7m91bSiwjMTQmsttQrnQv17B7bxphKYhosqKsFzFTYqiUE0FFlKllBU1qmRWhrchuGcvSZUtL03774o8Hx1tp/rWZvh84L57zPPOc3+ND+fV4jp3dP6vVKp6ennL06FF5+vSpFBYWyoQJE2TSpEm9tYTv3tu3b6WkpERKSkoEgFgsFikpKVH+vNq3nGcYZrvBkSNHJCQkRDQajYSFhUlBQYHSFx8fL+Hh4U7j8/PzZdy4caLRaESv10taWloPV0wtdWYPw8PDBUCrIz4+vucLJ0Vn/x02xzD7bejsHj5+/FgiIyNFq9VKUFCQbNq0SWpra3u4amrS2f07dOiQjBkzRrRarQQEBIjJZJK//vqrh6umJnl5eR1+b/uW84xKhPfziYiIiMg18ZlZIiIiInJZDLNERERE5LIYZomIiIjIZTHMEhEREZHLYpglIiIiIpfFMEtERERELothloiIiIhcFsMsEREREbkshlkiIgA2mw0DBgzo7TK6TK/XIzU1tcMxKSkp+Pnnn3ukHiKinsIwS0Ru49dff4VKpWp1VFRU9HZpsNlsTjUFBARg4cKFqKys7Jb5HQ4HVq9erbRVKhXOnTvnNGbLli3Izc3tluu1p+U6Bw8ejJiYGDx8+LDT87jyDxdE1HMYZonIrURFRaG6utrpGDZsWG+XBQDw9fVFdXU1nj9/jtOnT+PevXuIjY1FY2PjV8/9ww8/oF+/fh2O8fb2hp+f31df63Oar/PChQuoqanB3LlzUV9f/3+/NhF9fxhmicit9OnTB/7+/k6Hh4cHLBYLxo4dC51Oh+DgYKxfvx7v3r1rd5779+8jIiICPj4+8PX1xfjx43H37l2l/9atW5gxYwa0Wi2Cg4ORlJSEmpqaDmtTqVTw9/dHQEAAIiIiYDabUVZWptw5TktLQ2hoKDQaDUaOHIn09HSnz6ekpGDo0KHo06cPAgMDkZSUpPQ1f8xAr9cDAIxGI1QqldJu/pjB5cuX0bdvX7x+/drpGklJSQgPD++2dU6YMAHJycmoqqrCkydPlDEd7Ud+fj4SEhLw5s0b5Q5vSkoKAKC+vh7btm3DkCFDoNPpMHnyZOTn53dYDxG5N4ZZIvouqNVqHDp0CGVlZTh58iSuXbuGbdu2tTveZDIhKCgIDocDRUVF2LFjB7y8vAAADx48wJw5czB//nyUlpYiKysLhYWFSExM7FRNWq0WAPDx40fY7XZs3LgRmzdvRllZGdasWYOEhATk5eUBAM6cOYMDBw7g+PHjKC8vx7lz5zB27Ng253U4HAAAq9WK6upqpd1cZGQkBgwYgLNnzyrnGhsbkZ2dDZPJ1G3rfP36NU6fPg0AytcP6Hg/pk2bhtTUVOUOb3V1NbZs2QIASEhIwM2bN5GZmYnS0lLExcUhKioK5eXlX1wTEbkZISJyE/Hx8eLh4SE6nU45FixY0ObY7Oxs8fPzU9pWq1X69++vtH18fMRms7X52WXLlsnq1audzt24cUPUarW8f/++zc+0nP/PP/+UKVOmSFBQkNTV1cm0adNk1apVTp+Ji4uT6OhoERHZv3+/GAwGqa+vb3P+kJAQOXDggNIGIHa73WmM2WyWn376SWknJSXJzJkzlfbly5dFo9HIy5cvv2qdAESn00m/fv0EgACQ2NjYNsc3+dx+iIhUVFSISqWSZ8+eOZ2fNWuW7Ny5s8P5ich9efZulCYi6l4RERFIS0tT2jqdDgCQl5eHPXv24NGjR/j333/R0NCADx8+oKamRhnT3KZNm7By5Uqkp6cjMjIScXFxCA0NBQAUFRWhoqICGRkZyngRwadPn1BZWYnRo0e3WdubN2/g7e0NEUFtbS3CwsKQk5MDjUaDx48fO73ABQC//PILDh48CACIi4tDamoqhg8fjqioKERHRyMmJgaenl3/b9xkMmHq1Kl4/vw5AgMDkZGRgejoaAwcOPCr1unj44Pi4mI0NDSgoKAA+/btw7Fjx5zGdHY/AKC4uBgiAoPB4HS+rq6uR54FJqJvE8MsEbkVnU6HESNGOJ2rqqpCdHQ01q5di99//x2DBg1CYWEhVqxYgY8fP7Y5T0pKCpYuXYoLFy7g0qVLMJvNyMzMhNFoxKdPn7BmzRqnZ1abDB06tN3amkKeWq3G4MGDW4U2lUrl1BYR5VxwcDCePHmCK1eu4OrVq1i/fj327duHgoICp1/fd8akSZMQGhqKzMxMrFu3Dna7HVarVenv6jrVarWyB6NGjcKLFy+waNEiXL9+HUDX9qOpHg8PDxQVFcHDw8Opz9vbu1NrJyL3wTBLRG7v7t27aGhowP79+6FW/+9Vgezs7M9+zmAwwGAwIDk5GUuWLIHVaoXRaERYWBgePnzYKjR/TvOQ19Lo0aNRWFiI5cuXK+du3brldPdTq9UiNjYWsbGx2LBhA0aNGoUHDx4gLCys1XxeXl5f9FcSli5dioyMDAQFBUGtVmPu3LlKX1fX2VJycjIsFgvsdjuMRuMX7YdGo2lV/7hx49DY2Ii///4b06dP/6qaiMh98AUwInJ7oaGhaGhowOHDh/HHH38gPT291a+9m3v//j0SExORn5+Pqqoq3Lx5Ew6HQwmW27dvx+3bt7Fhwwbcu3cP5eXlOH/+PH777bcu17h161bYbDYcO3YM5eXlsFgsyMnJUV58stlsOHHiBMrKypQ1aLVahISEtDmfXq9Hbm4uXrx4gVevXrV7XZPJhOLiYuzevRsLFixA3759lb7uWqevry9WrlwJs9kMEfmi/dDr9Xj37h1yc3Pxzz//oLa2FgaDASaTCcuXL0dOTg4qKyvhcDiwd+9eXLx4sVM1EZEb6c0HdomIulN8fLzMmzevzT6LxSIBAQGi1Wplzpw5curUKQEgr169EhHnF47q6upk8eLFEhwcLBqNRgIDAyUxMdHppac7d+7I7NmzxdvbW3Q6nfz444+ye/fudmtr64Wmlo4ePSrDhw8XLy8vMRgMcurUKaXPbrfL5MmTxdfXV3Q6nUyZMkWuXr2q9Ld8Aez8+fMyYsQI8fT0lJCQEBFp/QJYk4kTJwoAuXbtWqu+7lpnVVWVeHp6SlZWloh8fj9ERNauXSt+fn4CQMxms4iI1NfXy65du0Sv14uXl5f4+/uL0WiU0tLSdmsiIvemEhHp3ThNRERERNQ1fMyAiIiIiFwWwywRERERuSyGWSIiIiJyWQyzREREROSyGGaJiIiIyGUxzBIRERGRy2KYJSIiIiKXxTBLRERERC6LYZaIiIiIXBbDLBERERG5LIZZIiIiInJZ/wHKuLrdT0Wi2gAAAABJRU5ErkJggg==",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -275,20 +276,17 @@
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 22.91it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 22.93it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 22.90it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 23.00it/s]\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\n",
+      "     - AUC: 46.56%\n",
+      "    - Optimal Threshold: 0.0438165\n",
+      "    - F1 Score: 0.78\n",
+      "    - CONFUSION MATRIX:\n",
+      " [[ 8 12]\n",
+      " [21 59]] \n",
       "\n"
      ]
     },
@@ -296,9 +294,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:03<00:00, 23.07it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 23.37it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 23.00it/s]\n"
+      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 22.32it/s]\n",
+      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.18it/s]\n",
+      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.17it/s]\n",
+      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.14it/s]\n"
      ]
     },
     {
@@ -312,29 +311,27 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:05<00:00, 22.93it/s]\n"
+      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.19it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (31.59 s)\n",
       "\n",
-      "Dataset               Accuracy\n",
+      "- OK - Evaluating model (13.78 s)\n",
+      "\n",
+      "Dataset               F1 Score\n",
       "------------------------------\n",
-      "Anonaly lvl 1            70.00\n",
-      "Anonaly lvl 2            76.00\n",
-      "Anonaly lvl 3            52.00\n",
+      "Anonaly lvl 1 test        0.93\n",
+      "Anonaly lvl 2 test        0.86\n",
+      "Anonaly lvl 3 test        0.62\n",
       "\n",
-      "Anomaly all              67.00\n",
+      "Anomaly all test          0.86\n",
       "\n",
-      "No Anomaly Train         43.75\n",
-      "No Anomaly Test          35.00\n",
-      "No Anomaly All           42.00\n",
+      "No Anomaly Test           0.57\n",
       "\n",
-      "All without train        61.67\n",
-      "All with train           54.50\n"
+      "All test                  0.78\n"
      ]
     }
    ],
@@ -355,20 +352,20 @@
      "output_type": "stream",
      "text": [
       "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (0.28 ms)\n",
+      "- OK - Setting seed to 42 (0.75 ms)\n",
       "\n",
       "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (3.60 ms)\n",
+      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
+      "- OK - Setting datasets path (4.08 ms)\n",
       "\n",
       "- Setting config\n",
       "     Output folder path: ../output/cookies_3_steps_500_small\n",
-      "- OK - Setting config (4.99 ms)\n",
+      "- OK - Setting config (3.91 ms)\n",
       "\n",
       "- Prepare teacher, student & autoencoder\n",
       "     No weight to load\n",
       "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (65.61 ms)\n",
+      "- OK - Prepare teacher, student & autoencoder (65.90 ms)\n",
       "\n",
       "- Normalizing teacher\n"
      ]
@@ -377,15 +374,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.40it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 56.18it/s]\n"
+      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.39it/s]\n",
+      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.61it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (2.69 s)\n",
+      "- OK - Normalizing teacher (2.80 s)\n",
       "\n",
       "- Train\n"
      ]
@@ -394,61 +391,117 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 6.1933  : 100%|█████████████████████████████████████████████████████████| 500/500 [00:59<00:00,  8.34it/s]\n"
+      "       Current loss: 23.5000  :   0%|▏                                                                                    | 1/500 [00:01<13:07,  1.58s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (59.94 s)\n",
-      "\n",
-      "- Saving models to ../output/cookies_3_steps_500_small/all_models.pth\n",
-      "- OK - Saving models (73.85 ms)\n",
-      "\n",
-      "- Saving map normalization to ../output/cookies_3_steps_500_small/map_normalization.pth\n"
+      "F1 Validation 0.6451612903225806\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 14.7164  :  20%|████████████████▊                                                                  | 101/500 [00:15<03:20,  1.99it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.7878787878787878\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 6.1046  :  40%|█████████████████████████████████▊                                                  | 201/500 [00:28<02:31,  1.98it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.85\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 6.5540  :  60%|██████████████████████████████████████████████████▌                                 | 301/500 [00:42<01:40,  1.98it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.85\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 4.9979  :  80%|███████████████████████████████████████████████████████████████████▎                | 401/500 [00:56<00:50,  1.97it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.7096774193548387\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 30.38it/s]\n"
+      "       Current loss: 4.3021  : 100%|████████████████████████████████████████████████████████████████████████████████████| 500/500 [01:08<00:00,  7.29it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (294.67 ms)\n",
+      "- OK - Train (68.59 s)\n",
       "\n",
-      "- Evaluating model\n"
+      "- Saving models to ../output/cookies_3_steps_500_small/all_models.pth\n",
+      "- OK - Saving models (79.69 ms)\n",
+      "\n",
+      "- Saving map normalization to ../output/cookies_3_steps_500_small/map_normalization.pth\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:08<00:00, 23.10it/s]\n"
+      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:00<00:00, 28.58it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "- OK - Saving map normalization (1013.53 ms)\n",
       "\n",
-      "     - AUC: 88.64%\n",
-      "    - Optimal Threshold: 0.1094586\n",
-      "    - F1 Score: 0.81\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[92  8]\n",
-      " [26 74]] \n",
-      "\n"
+      "- Evaluating model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 21.98it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIhCAYAAABdSTJTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACKEklEQVR4nOzdeVzN2f8H8NdtX1TWUjHJvg1Sdsa+r2MMkSU7ITQYxgwxgxmGwSCGCGPJPsPYQva11GAYDMlWiFRab/ee3x9+3a+06Obe++nW6/l49Ji5536W1+108+7c8zkfmRBCgIiIiIhIDxlIHYCIiIiIKL9YzBIRERGR3mIxS0RERER6i8UsEREREektFrNEREREpLdYzBIRERGR3mIxS0RERER6i8UsEREREektFrNEREREpLdYzBIRZSMgIAAymUz1ZWRkBHt7e7i7u+Pu3bvZ7iOXy+Hn54cmTZrAxsYG5ubmqFGjBqZPn46XL19mu49SqcTmzZvRrl07lC5dGsbGxrC1tUW3bt2wf/9+KJXKD2ZNTU3FihUr0Lx5c5QoUQImJiZwdHRE3759cerUqY/6PhARFXQsZomIcrFhwwZcuHABx44dw/jx4/Hnn3+iefPmiI2NzbRdUlIS2rdvjwkTJsDFxQXbtm3DwYMHMWjQIPz2229wcXHB7du3M+2TkpKCLl26YMiQIbC1tYWfnx9OnDiB1atXw8HBAV9++SX279+fa76YmBg0a9YMPj4+qF27NgICAnD8+HEsXrwYhoaGaNu2Lf7++2+Nf1+IiAoMQUREWWzYsEEAEFeuXMnUPmfOHAFArF+/PlP7qFGjBACxffv2LMe6ffu2sLGxEbVq1RLp6emq9rFjxwoAYuPGjdlmuHPnjvj7779zzdm5c2dhZGQkjh8/nu3zly9fFpGRkbkeI6+SkpI0chwiIk3iyCwRkRrc3NwAAM+ePVO1RUdHY/369ejYsSP69euXZZ+qVavi66+/xj///IN9+/ap9lm3bh06duyIwYMHZ3uuKlWqoE6dOjlmCQ0NxaFDhzB8+HC0adMm220aNGiATz75BADg6+sLmUyWZZuMKRUPHjxQtVWoUAHdunXDnj174OLiAjMzM8yZMwcuLi5o0aJFlmMoFAo4Ojqid+/eqra0tDT88MMPqF69OkxNTVGmTBkMHToUL168yPE1ERGpi8UsEZEaIiIiALwtUDMEBwcjPT0dvXr1ynG/jOeCgoJU+8jl8lz3+ZCjR49mOramXb16FVOnToW3tzcOHz6ML774AkOHDsXZs2ezzBs+evQonj59iqFDhwJ4Oxe4Z8+e+PHHHzFgwAD89ddf+PHHHxEUFIRWrVohOTlZK5mJqOgxkjoAEVFBplAokJ6ejpSUFJw7dw4//PADPvvsM/To0UO1zcOHDwEAzs7OOR4n47mMbfOyz4do4hi5ef78OW7evJmpcK9YsSKmTp2KgIAAzJs3T9UeEBAAOzs7dO7cGQCwY8cOHD58GLt37840Wlu3bl00aNAAAQEBGDt2rFZyE1HRwpFZIqJcNG7cGMbGxrCyskKnTp1QokQJ/PHHHzAyyt9YQHYf8xdUderUyVTIAkCpUqXQvXt3bNy4UbXSQmxsLP744w8MHjxY9X05cOAAihcvju7duyM9PV31Va9ePZQtWxYnT57U9cshokKKxSwRUS42bdqEK1eu4MSJExg9ejRu3bqF/v37Z9omY05qxhSE7GQ8V758+Tzv8yGaOEZu7O3ts20fNmwYnjx5opoysW3bNqSmpsLT01O1zbNnz/D69WuYmJjA2Ng401d0dDRiYmK0kpmIih4Ws0REuahRowbc3NzQunVrrF69GiNGjMDhw4exa9cu1TatW7eGkZGR6uKu7GQ81759e9U+xsbGue7zIR07dsx07A8xMzMD8HZd2nflVFjmNIrcsWNHODg4YMOGDQDeLl/WqFEj1KxZU7VN6dKlUapUKVy5ciXbr1WrVuUpMxHRh7CYJSJSw8KFC1GiRAnMmjVL9TF72bJlMWzYMBw5cgSBgYFZ9rlz5w5++ukn1KpVS3WxVtmyZTFixAgcOXIEmzZtyvZc9+7dw7Vr13LMUr9+fXTu3Bn+/v44ceJEttuEhISo5tZWqFABALIc80Nr2b7P0NAQgwYNwr59+3DmzBmEhIRg2LBhmbbp1q0bXr58CYVCATc3tyxf1apVU+ucREQ5kQkhhNQhiIgKmoCAAAwdOhRXrlxRLceVYdGiRZg2bRo2b96MgQMHAgASExPRtWtXnDt3DqNGjUL37t1hamqKixcv4ueff4aFhQWOHTuWqYhLSUlBr169cPToUfTv3x+ff/457OzsEBMTg6CgIGzYsAHbt29Hz549c8wZExODTp064fr16xg2bBg6d+6MEiVKICoqCvv378e2bdsQGhqKunXrIj4+Hs7OznB0dMTcuXNhZGSEgIAAXL16FREREYiIiFAVvBUqVEDt2rVx4MCBbM97584dVKtWDeXKlcPLly8RFRUFGxsb1fMKhQLdu3fHpUuXMHHiRDRs2BDGxsZ4/PgxgoOD0bNnT3z++ef57R4iov+ReqFbIqKCKKebJgghRHJysvjkk09ElSpVMt0EIS0tTaxcuVI0atRIFCtWTJiamopq1aqJadOmiZiYmGzPk56eLjZu3CjatGkjSpYsKYyMjESZMmVE586dxdatW4VCofhg1uTkZLF8+XLRpEkTYW1tLYyMjISDg4Po3bu3+OuvvzJte/nyZdG0aVNhaWkpHB0dxezZs8W6desEABEREaHazsnJSXTt2jXX8zZt2lQAEB4eHtk+L5fLxc8//yzq1q0rzMzMRLFixUT16tXF6NGjxd27dz/4uoiI8oIjs0RERESktzhnloiIiIj0FotZIiIiItJbLGaJiIiISG+xmCUiIiIivcViloiIiIj0FotZIiIiItJbRlIH0DWlUomnT5/Cysoqx1s1EhEREZF0hBBISEiAg4MDDAxyH3stcsXs06dPUb58ealjEBEREdEHPHr0COXKlct1myJXzFpZWQF4+82xtrbWyTnlcjmOHj2KDh06wNjYWCfnJM1h/+k/9qH+Yx/qN/af/tN1H8bHx6N8+fKqui03Ra6YzZhaYG1trdNi1sLCAtbW1nwT6yH2n/5jH+o/9qF+Y//pP6n6MC9TQnkBGBERERHpLRazRERERKS3WMwSERERkd4qcnNm80IIgfT0dCgUCo0cTy6Xw8jICCkpKRo7JukO+0//abMPDQ0NYWRkxKX+iIgkwmL2PWlpaYiKikJSUpLGjimEQNmyZfHo0SP+g6eH2H/6T9t9aGFhAXt7e5iYmGj82ERElDsWs+9QKpWIiIiAoaEhHBwcYGJiopF/+JRKJd68eYNixYp9cOFfKnjYf/pPW30ohEBaWhpevHiBiIgIVKlShT8jREQ6xmL2HWlpaVAqlShfvjwsLCw0dlylUom0tDSYmZnxHzo9xP7Tf9rsQ3NzcxgbGyMyMlJ1DiIi0h3+y5wNFixEpA7+ziAikg5/AxMRERGR3mIxS0RERER6i8Us0f9LS0tD5cqVce7cOamjFBrPnz9HmTJl8OTJE6mjEBFRIcVitpDw9PSETCaDTCaDkZERPvnkE4wdOxaxsbFZtj1//jy6dOmCEiVKwMzMDJ9++ikWL16c7fqbwcHB6NKlC0qVKgULCwvUrFkTX3311QeLk7CwMHz55Zews7ODmZkZqlatipEjR+LOnTsae82a9ttvv8HJyQnNmjXL8tykSZNgbGyM7du3Z3nO09MTvXr1ytIeHh4OmUyGBw8eqNqEEPjtt9/QqFEjFCtWDMWLF4ebmxuWLl2q0eXg3hcbG4tBgwbBxsYGNjY2GDRoEF6/fv3B/W7duoUePXrAxsYGVlZWaNy4MR4+fAgAePDggepn7v2vnTt3AgBsbW0xaNAgzJ49W2uvjYiIijYWs4VIp06dEBUVhQcPHmDdunXYv38/vLy8Mm2zd+9etGzZEuXKlUNwcDD+/fdfTJw4EfPmzYO7uzuEEKpt16xZg3bt2qFs2bLYvXs3bt68idWrVyMuLg6LFy/OMceBAwfQuHFjpKamYsuWLbh16xY2b94MGxsbfPfdd/l+fXK5PN/75sWvv/6KESNGZGlPSkrCnj17MGXKFPj7+3/UOQYNGoRJkyahZ8+eCA4ORnh4OL777jv88ccfOHr06EcdOzcDBgxAeHg4Dh8+jMOHDyM8PByDBg3KdZ979+6hefPmqF69Ok6ePIm///4b3333nepq/fLlyyMqKirT15w5c2BpaYnOnTurjjN06FBs2bIl2z+siIiIPpooYuLi4gQAERcXl+W55ORkcfPmTZGcnKxqUyqVIjFV/lFfCcmp4umzGJGQnKrWfkqlMs+va8iQIaJnz56Z2nx8fETJkiVVj9+8eSNKlSolevfunWX/P//8UwAQ27dvF0II8ejRI2FiYiImTZqU7fliY2OzbU9MTBSlS5cWvXr1ynW/DRs2CBsbm0zP7d27V7z7Izl79mxRt25d4e/vL5ydnYVMJhOrV68WDg4OQqFQZNq3e/fuYvDgwZleT/369YWpqalwdnYWvr6+Qi6XZ5tJCCFCQ0OFgYFBtj8X69evFw0aNBCvXr0S5ubmIiIiItPz2X3vhRAiLCxMAFBtHxgYKACIffv2ZdlWqVSK169f55jvY9y8eVMAEBcvXlS1XbhwQQAQ//77b4779evXTwwcOFCtc9WrV08MGzYsS3uFChWEv7+/WsfSJIVCIWJjY7P83GhKdr87SLPS0tLEvn37RFpamtRRKB/Yf/pP132YW732PknXmT19+jQWLVqE0NBQREVFYe/evdl+XPuuU6dOwcfHB//88w8cHBwwbdo0jBkzRmsZk+UK1Jx1RGvHz83NuR1hYZK/Lrp//z4OHz4MY2NjVdvRo0fx8uVLTJkyJcv23bt3R9WqVbFt2zb069cPO3fuRFpaGqZNm5bt8YsXL55t+5EjRxATE6P2fjn577//sGPHDuzevRuGhoZwdHSEt7c3goOD0bZtWwBvP0I/cuQI9u/fr8owcOBALF++HC1atMC9e/cwatQoAMjx4+7Tp0+jatWqsLa2zvLchg0b8OWXX8LGxgZdunTBhg0bMGfOHLVeBwBs2bIF1apVQ8+ePbM8J5PJYGNjk+O+xYoVy/XYLVq0wKFDh7J97sKFC7CxsUGjRo1UbY0bN4aNjQ3Onz+PatWqZdlHqVTir7/+wrRp09CxY0eEhYXB2dkZM2bMyPE9GhoaivDwcKxcuTLLcw0bNsSZM2cwbNiwXF8HERGRuiQtZhMTE1G3bl0MHToUX3zxxQe3j4iIQJcuXTBy5Ej8/vvvOHfuHLy8vFCmTJk87V/YHThwAMWKFYNCoUBKSgoAYMmSJarnM+ar1qhRI9v9q1evrtrm7t27sLa2hr29vVoZ7t69qzqWJqSlpWHz5s0oU6aMqq1Tp07YunWrqpjduXMnSpYsqXo8b948TJ8+HUOGDAEAVKxYEd9//z2mTZuWYzH74MEDODg4ZPt6Ll68iA0bNgAABg4cCG9vb8yePVvttUXv3r2bbeGYF+Hh4bk+b25unuNz0dHRsLW1zdJua2uL6OjobPd5/vw53rx5gx9//BE//PADfvrpJxw+fBi9e/dGcHAwWrZsmWUff39/1KhRA02bNs3ynKOjI8LCwnJ9DURERPkhaTHbuXPnTHPrPmT16tX45JNPsHTpUgBvi7KQkBD8/PPPWitmzY0NcXNux486hlKpREJ8AqysrdQqgMyNDdU6T+vWreHn54ekpCSsW7cOd+7cwYQJE7JsJ96ZF/t+e8bte9/9f3XkdOz8cnJyylTIAoCHhwdGjRqFVatWwdTUFFu2bIG7uzsMDd9+v0JDQ3HlyhXMmzdPtU9GgZ+UlJTt3d2Sk5OzvXOTv78/OnTogFKlSgEAunTpguHDh+PYsWPo0KGDWq8lv99TAKhcuXK+9suQ3Xlzy6NUKgEAPXv2xOTJkwEA9erVw/nz57F69eosxWxycjK2bt2a45xoc3NzrV7gRkT0PiEEkuVZL2ym/ElOTkGqQvP/zmuCXt3O9sKFC1kKiI4dO8Lf3x9yuTzTR+oZUlNTkZqaqnocHx8P4O3FRO9fUCSXyyGEgFKpVP1jDgBmRh93nZwQMqSbGMLc2FCtYkYIkecfGiEELCwsULFiRQDA0qVL0bZtW/j6+mLu3LkA/lcQ/fPPP9mOnv3777+oUaMGlEolqlSpgri4ODx58kSt0dmMc9y8eRNNmjT5YOZ3v88Z/ZTRJoSApaVlpm0AoGvXrlAqldi/fz8aNGiAM2fO4Oeff1Ztp1Qq4evri88//zzLOU1MTLIcDwBKlSqF69evZ3pOoVBg06ZNiI6ORunSpTO1r1u3Du3atQMAWFlZITIyMstxX716pXo+43t669atbM//IdlNf3hX8+bNcfDgwWyfs7W1xbNnz7Kc98WLFyhTpky2eUqWLAkjIyPVz0OG6tWr49y5c1n22bFjB5KSkjBw4MBsj/fy5UuULl06X69dEzLeR+//zGmKUqmEEAJyuVz1RxVpVsbva21fCEraoev+E0LAfd0VXH34WifnK+yS7l5C7Il1sHP/AW3apMImnwMz6lDnZ0Wvitno6GjY2dllarOzs0N6ejpiYmKyLboWLFiQ7fzGo0ePZhmhMzIyQtmyZfHmzRukpaVpNjyAhIQEjR8zg1wuR3p6uqpYB4CvvvoKX375JTw8PGBvb4/GjRujRIkSWLhwITZt2pRp/4MHD+Lu3buYPn064uPj0aFDB5iYmGDevHmYP39+lvPFxcVlO8ezcePGKFWqFBYsWIDff/89x/0sLS2RkJCAqKgoWFpaAgAuX74M4H9/cKSmpkKhUGR6TRm6deuGTZs24Z9//kHlypVRpUoV1XZ16tTBjRs3MHr06Cz7vXnzJtvvX7Vq1eDn54e4uDjVHxyHDh1CQkICTp06lalAuXv3LkaNGoUHDx6gZMmScHJywrZt2/D8+fNMo7tnz55F6dKlYWhoiPj4ePTq1QvDhw/H9u3b0aVLl0znF0IgPj4+x3mzp0+fzrY9g5mZWbbfJwD49NNPERcXh+DgYLi6ugIAQkJCEBcXhzp16uS4n4uLC27cuJHp+Zs3b8Le3j7LPmvXrkXnzp1hamqa7fH+/vtvNG/ePMdz6Yq23oNpaWlITk7G6dOnkZ6erpVz0FtBQUFSR6CPoKv+S1UAVx/qVYlTIAmFHLEnA5AQ8gcAIO7CTpw4UQqmOvibXZ1P8/Sup98f2cwYcclpxHPGjBnw8fFRPY6Pj0f58uXRoUOHLKNdKSkpePToEYoVK5btR875JYRAQkICrKys8v0x84cYGxvDyMgo02vq0qULatWqhRUrVuDXX3+FtbU1Vq9ejQEDBmDq1KkYN24crK2tcfz4cXz99df44osvMGTIEMhkMtSsWRNLlizBhAkTkJKSgkGDBqFChQp4/PgxNm/ejGLFiuHnn3/OksPa2hpr165Fv379MGjQIEyYMAGVK1dGTEwMdu7ciYcPH2Lbtm1o3bo1LCws8NNPP2H8+PG4fPmyag3XjNdgamoKQ0PDbEclhwwZgp49e+LOnTsYNGhQpm18fX3Ro0cPVKxYEX369IGBgQGuXbuGGzdu4Pvvv8/2+9elSxeMGjUKjx49Qu3atQFAVXQ2bdo0U/81atQIM2fOxJ9//glvb28MHz4cixcvxvjx4zFt2jSUKFECFy5cwNKlSzF9+nRVtiFDhuDIkSMYMWIEvv32W7Rr1w5lypTB9evXsWzZMowbNy7Hi6vq1av34R+CHDRo0AAdO3aEj48P/Pz8AAA+Pj7o2rWrqrgFgJo1a2LevHmqEe2vv/4a/fv3R5s2bdC6dWscOXIEhw8fxokTJzJ9v//77z+cP38eBw4cyLavkpKS8Pfff+PHH3/84Aiztmj7PZiSkgJzc3N89tlnGv3dQf8jl8sRFBSE9u3bZ/spHBVsuu6/pLR0TLt8AgBw8euWMDfhJybqevDgAYZ7DsbDkBAAwOixXvisZWt07dgOJiYmWj+/WoMfGl1H4SMAEHv37s11mxYtWghvb+9MbXv27BFGRkZ5XipC3aW5NEHbywIJkfPyUFu2bBEmJibi4cOHqrbTp0+LTp06CRsbG2FiYiJq1qwpfv75Z5Genp5l/6CgINGxY0dRokQJYWZmJqpXry6mTJkinj59mmueK1euiN69e4syZcoIU1NTUblyZTFq1Chx9+5d1TZ79+4VlStXFmZmZqJbt27it99+y3Zpruykp6cLe3t7AUDcu3cvy/OHDx8WTZs2Febm5sLa2lo0bNhQ/Pbbb7lmdnd3F9OnTxdCCBEdHS2MjIzEjh07su2/CRMmiE8//VT1+O7du+KLL74Qjo6OwtLSUnz66adixYoVWfpcoVAIPz8/0aBBA2FhYSGsra2Fq6urWLZsmUhKSso138d4+fKl8PDwEFZWVsLKykp4eHhkWV4NgNiwYUOmNn9/f1Uf1a1bN9tlxWbMmCHKlSuX48/31q1bRbVq1TT1UvKFS3PpPy7tpN903X+JqXLh9PUB4fT1AZGYmvOyjJS93bt3CxsbGwFAlChRQvzxxx8FemkumRAFYyavTCb74NJcX3/9Nfbv34+bN2+q2saOHYvw8HBcuHAhT+fJ+Cg3Li4u25HZiIgIODs7a3R0RalUIj4+HtbW1mpfAU+6c/36dbRr1w7//fcfrKysVO3sv4/TsGFDTJo0CQMGDJAsg7b7UFu/O+h/5HI5Dh48iC5dunBkVg/puv+S0tJVy2p+zDKXRVFsbCwqVqyI169fo0mTJti2bRucnJx03oe51Wvvk/Rf5jdv3iA8PFy17FBERATCw8NVt8ucMWMGBg8erNp+zJgxiIyMhI+PD27duoX169fD398/23VTidT16aefYuHChZluP0sf5/nz5+jTpw/69+8vdRQiIsqDEiVKYMOGDZg2bRpOnToFJycnqSN9kKR/qoSEhKB169aqxxlzW4cMGYKAgABERUWpClsAcHZ2xsGDBzF58mSsXLkSDg4OWL58OdeYJY3JWJuWNMPW1jbHG2gQEVHBsGPHDlhbW6NTp04AgF69en3wJlYFiaTFbKtWrXJdeiogICBLW8uWLXH16lUtpiIiIiIq/JKTk+Hj44PVq1ejVKlSuHbtWrY3ECroOImEiIiIqIi5ffs2+vbti2vXrkEmk2HMmDHZ3i1SH7CYJSIiIq0SQiBV8fbCLGOh/QX3k9J456/cbNmyBaNHj0ZiYiJsbW3x+++/o3379lLHyjcWs0RERKQ1QnU3LiPV2q8kDYVCgdGjR8Pf3x8A0Lp1a2zZskWtO30WRFxniIiIiLQmWa6Q7Laybk4lYG7MGyZkyLibpUwmg6+vL4KCgvS+kAU4MktEREQ6cvHrlrC21N1azObGhlq786Y+SUlJUa2BvXz5cnh6eqJ58+YSp9IcFrNERESkE+YmhryBgQ69efMG48aNQ3R0NA4dOgQDAwNYWFgUqkIW4DQDUoOvry/q1atXYM/TqlUrTJo0SeN5PqRChQpYunTpRx3D09Pzg2v6SfX6iEj7hBBISksvpF+8GEsK169fR4MGDbBp0yYcO3YMFy9elDqS1vDPo0Lk0aNH8PX1xaFDhxATEwN7e3v06tULs2bNQqlSpdQ6Vna3F54yZQomTJig4dTqO3nyJFq3bo3Y2FgUL15c6jiFyvXr1zF+/HhcvnwZJUuWxOjRo/Hdd9/l+jFdbGwsvL298eeffwIAevTogV9//TVT32S3v5+fH8aMGQPg7UdgY8aMQWhoKG7duoVu3bph3759WfZZuXIlVqxYgQcPHuCTTz7BzJkzM90lcM+ePZg/fz7+++8/yOVyVKlSBV999RU8PDxU2yxYsAB79uzBv//+C3NzczRt2hQ//fQTqlWrptrG09MTGzduzHTuRo0aFep/DEg6Qgj0WX0BoZGxUkehQkAIgXXr1sHb2xspKSlwdHTEtm3b0LRpU6mjaQ2LWW1RKIAzZ4CoKMDODqhbV6unu3//Ppo0aYKqVati27ZtcHZ2xj///IOpU6fi0KFDuHjxIkqWLPlR5yhWrBiKFSumocQFg1wu533e/198fDzat2+P1q1b48qVK7hz5w48PT1haWmJr776Ksf9BgwYgMePH+Pw4cMAgFGjRmHQoEHYv39/pu02bNigursMANjY2Kj+X6FQwNzcHN7e3ti9e3e25/Hz88OMGTOwdu1aNGjQAJcvX8bIkSNRokQJdO/eHQBQsmRJzJw5E9WrV4eJiQkOHDiAoUOHonTp0mjSpAkA4NSpUxg3bhwaNGiA9PR0zJw5Ex06dMDNmzdhaWmpOl+nTp2wYcMG1WMTE5O8fiuJ1JIsVxSJQtbZSvBiLC2Lj4/H6NGjsX37dgBA586dsWnTJpQuXVriZFomipi4uDgBQMTFxWV5Ljk5Wdy8eVMkJyd/3El27xaiXDkhANWXwsFBKHbu/Ljj5qJTp06iXLlyIikpKVN7VFSUsLCwEGPGjFG1OTk5iblz54r+/fsLS0tLYW9vL5YvX57peQCqLycnJyGEELNnzxZ169ZVbTdkyBDRs2dPMW/ePGFraytsbGyEr6+vkMvlYsqUKaJEiRLC0dFR+Pv7Z8o0bdo0UaVKFWFubi6cnZ3Ft99+K9LS0lTPv3+ed0VERGTKBkAMGTJECCFEy5YtxYQJE8TUqVNFiRIlhJ2dnZg9e3am/QEIPz8/0aNHD2FhYSFmzZolhBDizz//FPXr1xempqbC2dlZ9TqEEEKhUIivv/5alC9fXpiYmAh7e3sxYcKETN+vefPmiaFDh4pixYqJ8uXLizVr1mQ677Vr10Tr1q2FmZmZKFmypBg5cqRISEjI8r3M8ObNGzFo0CBhaWkpypYtK37++WfRsmVLMXHixGy/L5qwatUqYWNjI1JSUlRtCxYsEA4ODkKpVGa7z82bNwUAcfHiRVXbhQsXBADx77//qtoAiL179+Ypx/vfiwxNmjQRU6ZMydQ2ceJE0axZs1yP5+LiImbOnCliY2OFQqHI8vzz588FAHHq1KkPZsiJxn53UI7S0tLEvn37Mv2uKCwSU+XC6esDwunrA+JFQopITJUXuq/Xb5LE3r2Fs/8Kks6dOwsAwtDQUCxcuDDb33n5pev3YG712vs4Z1bT9uwB+vQBHj/O1CyLioKsb9+3z2vYq1evcOTIEXh5ecHc3DzTc2XLloWHhwcCAwMz3Tp40aJFqFOnDq5evYoZM2Zg8uTJCAoKAgBcuXIFwNuRtKioKNXj7Jw4cQJPnz7F6dOnsWTJEvj6+qJbt24oUaIELl26hDFjxmDMmDF49OiRah8rKysEBATg5s2bWLZsGdauXYtffvklT6+1fPnyqpG727dvIyoqCsuWLVM9v3HjRlhaWuLSpUtYuHAh5s6dq3pdGWbPno2ePXvi+vXrGDZsGI4cOYKBAwfC29sbN2/exJo1axAQEIB58+YBAHbt2oVVq1bBz88Pd+/exb59+/Dpp59mOubixYvh5uaGsLAweHl5YezYsfj3338BAElJSejUqRNKlCiBK1euYOfOnTh27BjGjx+f4+ucOnUqgoODsXfvXhw9ehQnT55EaGhort+bM2fOqEbPc/qaP39+jvtfuHABLVu2hKmpqaqtY8eOePr0KR48eJDjPjY2NmjUqJGqrXHjxrCxscH58+czbTt+/HiULl0aDRo0wOrVq6FUKnN9Pe9LTU1VXY2bwdzcHJcvX4ZcLs+yvRACx48fx+3bt9GiRYscjxsXFwcAWT65OHnyJGxtbVG1alWMHDkSz58/VysvUX5Y/P8FUoXxi4sKaN+8efNQpUoVnDlzBlOnToWBQREp87ReWhcwWh2ZTU/PMiL77pdSJhOifPm322nQxYsXcx35WrJkiQAgnj17JoR4O5LYqVOnTNv069dPdO7cWfU4u+NlNzLr5OSU6S+/atWqiRYtWqgep6enC0tLS7Ft27Yc8y9cuFC4urrmeJ73BQcHCwAiNjY2U3vLli1F8+bNM7U1aNBAfP3115le16RJkzJt06JFCzF//vxMbZs3bxb29vZCCCF+/vlnUbly5Uwjlu9ycnISAwcOVD1WKpXC1tZW+Pn5CSGE+O2330SJEiXEmzdvVNv89ddfwsDAQERHRwshMo8EJiQkCBMTE7F9+3bV9i9fvhTm5ua5jswmJSWJu3fv5vr18uXLHPdv3769GDlyZKa2J0+eCADi/Pnz2e4zb948UaVKlSztVapUyfQ9/f7778X58+dFWFiY+Pnnn4WFhYX4/vvvsz1mTqOiM2bMEGXLlhUhISFCqVSKK1euCFtbWwFAPH36VLXd69evhaWlpTAyMhKmpqbC399fKBSKbEdmlUql6N69e5afm+3bt4sDBw6I69eviz///FPUrVtX1KpVK8efAY7Mal9RGZlNTJVLHUcrCnP/Sen169fi4MGDmdoyPlXUtII8Mss5s5p05kyWEdl3yYQAHj16u12rVjqLJf5/RPbdi3Ay5g+++zg/V+TXqlUr019+dnZ2qF27tuqxoaEhSpUqlWlUa9euXVi6dCn+++8/vHnzBunp6bC2tlb73NmpU6dOpsf29vZZRtTc3NwyPQ4NDcWVK1dUI7HA2zmcKSkpSEpKQp8+ffDLL7+gcuXK6NSpE7p06YLu3bvDyOh/b593zyuTyVC2bFnVeW/duoW6detmmo/ZrFkzKJVK3L59G3Z2dpny3Lt3D2lpaZn6qGTJkpkuUMqOubk5KleunOs2H/L+hVrZ/ex8aJ+M/d5t//bbb1X/n7FSxdy5czO1f8h3332H6OhoNG7cGEII2NnZwdPTEwsXLlQtBA68HfkPDw/HmzdvcPz4cfj4+KBChQqoX79+lmOOHz8e165dw9mzZzO19+vXT/X/tWvXhpubG5ycnPDXX3+hd+/eec5MRKQtISEh6NevHx49eoTz58+r/m1799+moqKIjD/rSFSUZrfLo8qVK0Mmk+HmzZvZPv/vv/+iRIkSH5wAnp+Fpd+/eEomk2XblvGR8sWLF+Hu7o7OnTvjwIEDCAsLw8yZM5GWlqb2ufOa5/2Ps98tKgFAqVRizpw5CA8PV31dv34dd+/ehZmZGcqXL48rV67g119/hbm5Oby8vPDZZ59l+mg7t/O+X9i9v937xDvTQdTxsdMMypYti+jo6ExtGQX5+wX3u/s8e/YsS/uLFy9y3Ad4OxUhPj4+231zYm5ujvXr1yMpKQkPHjzAw4cPUaFCBVhZWWX62TYwMEDlypVRr149fPXVV+jTpw9+/PHHLMebMGEC/vzzTwQHB6NcuXK5ntve3h5OTk64e/dunvMSEWmDEALLli1D06ZNcf/+fTg6OkodSXJFr3zXprzeEk7Dt44rVaoU2rdvj1WrVmHy5MmZ5s1GR0djy5YtGDx4cKbC6f0lhi5evIjq1aurHhsbG0Oh0PzagOfOnYOTkxNmzpypaouMjFTrGBlXlWsqX/369XH79u0cRzWVSiXMzc3Ro0cP9OrVC+PGjUP16tVx/fr1bEf73lezZk1s3LgRiYmJqkL63LlzMDAwQNWqVbNsX7lyZRgbG+PixYv45JNPALxd/urOnTto2bJljudxc3NDeHh4rllyW9GiSZMm+Oabb5CWlqb6Hh89ehQODg6oUKFCjvvExcXh8uXLaNiwIQDg0qVLiIuLy3UZmLCwMJiZmeVraTVjY2NV8bl9+3Z069Yt13lhQohMfywJITBhwgTs3bsXJ0+ehLOz8wfP+fLlSzx69KhQ3PZRLykUQMbo+dmzwGefAYa8Kp6KntjYWAwbNky1dGHv3r3h7+9f5Jep5MisJrVoAZQrh5xmuQuZDChf/u12GrZixQqkpqaiY8eOOH36NB49eoTDhw+jffv2cHR0zPQROvC2mFq4cCHu3LmDlStXYufOnZg4caLq+QoVKuD48eOIjo5GbKzmloypXLkyHj58iO3bt+PevXtYvnw59u7dq9YxnJycIJPJcODAAbx48QJv3rz5qEyzZs3Cpk2b4Ovri3/++Qe3bt1CYGCg6iPwgIAAbN68GTdu3MD9+/exefNmmJubw8nJKU/H9/DwgJmZGYYMGYIbN24gODgYEyZMwKBBg7IdvSxWrBiGDx+OqVOn4vjx47hx4wY8PT0/OJE/Y5pBbl+5FbMDBgyAqakpPD09cePGDezduxfz58+Hj4+P6g+hy5cvo3r16njy5AkAoEaNGujUqRNGjhyJixcv4uLFixg5ciS6deummhaxf/9+rF27Fjdu3MC9e/ewbt06zJw5E6NGjcp0sdnNmzcRHh6OV69eIS4uTjVKnuHOnTv4/fffcffuXVy+fBnu7u64ceNGptHmBQsWICgoCPfv38e///6LJUuWYNOmTRgwYIBqm3HjxuH333/H1q1bYWVlhejoaERHRyM5ORnA2zvmTJkyBRcuXMCDBw9w8uRJdO/eHaVLl8bnn3+eax+QFuzZA1SoAHTt+vZx165vH2vhYlqiguzSpUtwcXHBvn37YGJigl9//RW7du0q8oUswGJWswwNgYwr69+fe5jxeOlSrYwoVKlSBSEhIahUqRL69euHSpUqYdSoUWjdujUuXLiQpYj56quvEBoaChcXF3z//fdYvHgxOnbsqHp+8eLFCAoKQvny5eHi4qKxnD179sTkyZMxfvx41KtXD+fPn8d3332n1jEcHR0xZ84cTJ8+HXZ2drmuCpAXHTt2xIEDBxAUFIQGDRqgcePGWLJkiapYLV68ODZt2oQWLVqgTp06OH78OPbv35/nG1FYWFjgyJEjePXqFRo0aIA+ffqgbdu2WLFiRY77LFq0CJ999hl69OiBdu3aoXnz5nB1df2o1/khNjY2CAoKwuPHj+Hm5gYvLy/4+PjAx8dHtU1SUhJu376daYrFli1b8Omnn6JDhw7o0KED6tSpg82bN6ueNzY2xqpVq9CkSRPUqVMHy5Ytw9y5c7F48eJM5+/SpQtcXFywf/9+nDx5Ei4uLpl+9hQKBRYvXoy6deuiffv2SElJwfnz5zONGicmJsLLywu1atVC06ZNsWvXLvz+++8YMWKEahs/Pz/ExcWhVatWsLe3V30FBgYCeDvP+/r16+jZsyeqVq2KIUOGoGrVqrhw4QKsrKw09v2mPMhhdRg8efK2nQUtFSGnTp1CZGQkKlWqhAsXLmD8+PH5mh5YGMlEfifo6an4+HjY2NggLi4uy0VHKSkpiIiIgLOzc5YlgNSyZw8wcWKmX8BKR0dg6VIY9OmT/+NqSIUKFTBp0iTeGjWPlEol4uPjYW1tXXSWOSlktN2HGvvdQf+jULwdgf3/36Np5ubY9/s2dPT0hHFK8tsBAwdH4OY/ej/lIClNAbcfjgEAbs7tCAuTwjcDUC6X4+DBg+jSpQtvVJNPSqUSixcvxujRozV20bQ6dN2HudVr7yt875iCoHdvoGdP1R3AlHZ2iK9bF9YlSkidjIhIP7yzOowA4N7ne1y9bIRpXr9n3m7OMd1nI9KBs2fP4vvvv8eePXtgaWkJAwMDTJ06VepYBRKHmbTF0PDt8lv9+7/9r56PHBAR6dQ7q74kG5viqkP1XDYuHNycSvB2rwSlUokFCxagVatWOHr0aJZrXigrjswWQTndzYmIqMDIYeWIi2uHwzrh9f8aDh4CPtP8RbVSMDc25BzIIu758+cYNGgQjh49CgAYOHAgvvnmG4lTFXwsZomIqODJWB3m/1fOyGAuT4WFPPXtnNly5YDWXKaLCoeTJ09iwIABiIqKgrm5OVasWIGhQ4fyD5w84DSDbBSxa+KI6CPxd4YW5LI6DLS8OgyRrv3+++9o27YtoqKiULNmTVy5cgXDhg1jIZtHLGbfkXF1XlJSksRJiEifZPzO4FXaGta7N7BrF2DvkLm9XLm37by1MBUSbdq0QalSpTB06FBcvnwZtWrVkjqSXuE0g3cYGhqiePHiqlt4WlhYaOSvIqVSibS0NKSkpHBpJz3E/tN/2upDIQSSkpLw/PlzFC9eHIYcJdS83r2Bzl3/t2rB7t1Am5YckSW9d+fOHdVdIB0cHPD333/zLoP5xGL2PWXLlgXwv3vSa4IQAsnJyTA3N+dHBnqI/af/tN2HxYsXV/3uIC14t3Bt2oSFLOm19PR0zJ07F/PmzcOOHTvwxRdfAAAL2Y/AYvY9MpkM9vb2sLW1zXSXo48hl8tx+vRpfPbZZ/wYUg+x//SfNvvQ2NiYI7JElCdPnjzBgAEDcPr0aQDAxYsXVcUs5R+L2RwYGhpq7B8oQ0NDpKenw8zMjMWQHmL/6T/2IRFJ7fDhwxg0aBBiYmJQrFgxrF27Fu7u7lLHKhQ4AZCIiIhIS+RyOaZPn47OnTsjJiYGLi4uuHr1KgtZDWIxS0RERKQlp0+fxk8//QQAGDduHM6fP48qVapInKpw4TQDIiIiIi1p27YtvvnmG7i4uKBPnz5SxymUODJLREREpCFpaWn47rvv8OSdu9fNmzePhawWcWSWiIiISAMePHiAfv364fLlyzhz5gyCg4O5pKMOcGSWiIiI6CPt3bsXLi4uuHz5MooXL47JkyezkNURFrNERERE+ZSamgpvb2/07t0br1+/RuPGjREeHo6ePXtKHa3I4DQDIiIionx48uQJevbsidDQUADA1KlTMW/ePK5nrWMsZomIiIjyoXjx4khOTkapUqWwceNGdO3aVepIRRKLWSIiIqI8SklJgYmJCQwMDGBpaYm9e/fCwsIC5cqVkzpakcU5s0RERER5cPv2bTRq1AgLFy5UtVWtWpWFrMRYzBIRERF9wJYtW+Dq6opr165h+fLlSExMlDoS/T8Ws0REREQ5SEpKwogRIzBw4EAkJiaiVatWCAkJgaWlpdTR6P+xmCUiIiLKxq1bt9CoUSP4+/tDJpNh9uzZOHbsGBwcHKSORu/gBWBERERE74mPj0ezZs0QGxuLsmXLYsuWLWjTpo3UsSgbHJklIiIieo+1tTXmzp2Ldu3aITw8nIVsAcZiloiIiAjA9evXER4erno8btw4HDlyBHZ2dtKFog9iMUtERERFmhACa9euRcOGDdGnTx/Ex8cDAGQyGQwMWCoVdJwzS0REREVWQkICRo8ejW3btgEAqlSpArlcLnEqUgf/3CAiIqIiKTw8HK6urti2bRsMDQ3x448/4q+//kKpUqWkjkZq4MgsERGpTQiBZLlCJ+dKStPNeajoEEJg9erVmDx5MlJTU1G+fHls374dTZs2lToa5QOLWSIiUosQAn1WX0BoZKzUUYjyRQiBP//8E6mpqejevTs2bNjA0Vg9xmKWiIjUkixXSFLIOlsJmBsb6vy8VPgYGBhg06ZN2LlzJ8aOHQuZTCZ1JPoILGaJiCjfQr5tBwsT7ReYcrkcwUFHWXRQvgghsHz5cvz777/w8/MDAJQpUwZeXl4SJyNNYDFLRET5ZmFiCAsT7f9TIpcJsI6l/IiNjcWwYcOwb98+AMCXX37JGyAUMixmiYiIqFC6dOkS+vXrh8jISJiYmGDx4sVo3bq11LFIw7g0FxERERUqQggsXrwYzZs3R2RkJCpVqoTz589j/PjxnKpSCHFkloiIiAqVYcOGISAgAADQt29f/Pbbb7CxsZE2FGkNR2aJiIioUOnXrx/Mzc3h5+eH7du3s5At5DgyS0RERHpNqVTizp07qF69OgCgU6dOiIiIgJ2dncTJSBc4MktERER66/nz5+jSpQsaN26MiIgIVTsL2aKDxSwRERHppVOnTqFevXo4cuQI0tLScP36dakjkQRYzBIREZFeUSgUmDt3Ltq0aYOoqCjUqFEDly9fRo8ePaSORhLgnFkiIiLSG9HR0Rg4cCCOHz8OAPD09MSKFStgaWkpcTKSCotZIiIi0hvLli3D8ePHYWFhAT8/PwwePFjqSCQxFrNERESkN2bPno3Hjx9j5syZqtULqGjjnFkiIiIqsJ48eYIpU6YgPT0dAGBmZobNmzezkCUVjswSERFRgXT48GEMGjQIMTExsLa2xqxZs6SORAUQR2aJiIioQJHL5ZgxYwY6d+6MmJgY1KtXD+7u7lLHogKKI7NERERUYDx69Aju7u44f/48AMDLywuLFy+GmZmZxMmooGIxS0RERAXC8ePH0bdvX7x69QrW1tZYt24dvvzyS6ljUQHHYpaIiIgKhLJlyyI5ORmurq4IDAxEpUqVpI5EeoDFLBEREUkmMTFRdcODWrVq4fjx46hfvz5MTU0lTkb6gheAERERkST27duHChUqqObHAkCTJk1YyJJaWMwSERGRTqWmpmLixIn4/PPPERMTg19++UXqSKTHJC9mV61aBWdnZ5iZmcHV1RVnzpzJdfstW7agbt26sLCwgL29PYYOHYqXL1/qKC0RERF9jHv37qFZs2ZYvnw5AGDKlCnYunWrxKlIn0lazAYGBmLSpEmYOXMmwsLC0KJFC3Tu3BkPHz7MdvuzZ89i8ODBGD58OP755x/s3LkTV65cwYgRI3ScnIiIiNS1c+dOuLi4IDQ0FCVLlsSBAwewaNEiGBsbSx2N9JikxeySJUswfPhwjBgxAjVq1MDSpUtRvnx5+Pn5Zbv9xYsXUaFCBXh7e8PZ2RnNmzfH6NGjERISouPkREREpI7r16/Dw8MDCQkJaNasGcLDw9G1a1epY1EhINlqBmlpaQgNDcX06dMztXfo0CHTRPB3NW3aFDNnzsTBgwfRuXNnPH/+HLt27cr1zZCamorU1FTV4/j4eABv7y4il8s18Eo+LOM8ujofaRb7T/+xDzVLLk9/5//lkMuEDs7JPtRncrkctWvXRq9evVC1alX4+vrCyMiI/alHdP0eVOc8MiGE9n8LZePp06dwdHTEuXPn0LRpU1X7/PnzsXHjRty+fTvb/Xbt2oWhQ4ciJSUF6enp6NGjB3bt2pXjRxS+vr6YM2dOlvatW7fCwsJCMy+GiKgISVUA0y6/HQtZ2DAdpoYSB6IC6/z586hXr57q31ulUgkDA8kv1yE9kJSUhAEDBiAuLg7W1ta5biv5OrMymSzTYyFElrYMN2/ehLe3N2bNmoWOHTsiKioKU6dOxZgxY+Dv75/tPjNmzICPj4/qcXx8PMqXL48OHTp88JujKXK5HEFBQWjfvj3nBekh9p/+Yx9qVlJaOqZdPgEA6NixAyxMtP9PCftQvyQlJcHHxwfr16/Hl19+iQ0bNuDYsWPo2LEj+09P6fo9mPFJel5IVsyWLl0ahoaGiI6OztT+/Plz2NnZZbvPggUL0KxZM0ydOhUAUKdOHVhaWqJFixb44YcfYG9vn2UfU1PTbNerMzY21vkbSopzkuaw//Qf+1AzjMX/Bhzefk91908J+7Dgu3XrFvr27YsbN25AJpOhRo0aMDJ6+zPC/tN/uupDdc4h2Vi/iYkJXF1dERQUlKk9KCgo07SDdyUlJWX5eMLQ8O3nWxLNliAiIqL/t3HjRri5ueHGjRuws7NDUFAQ5syZw6kFpFWS/nT5+Phg3bp1WL9+PW7duoXJkyfj4cOHGDNmDIC3UwQGDx6s2r579+7Ys2cP/Pz8cP/+fZw7dw7e3t5o2LAhHBwcpHoZRERERVpiYiI8PT3h6emJpKQktG3bFuHh4Wjbtq3U0agIkHTObL9+/fDy5UvMnTsXUVFRqF27Ng4ePAgnJycAQFRUVKY1Zz09PZGQkIAVK1bgq6++QvHixdGmTRv89NNPUr0EIiKiIi8pKQlHjx6FgYEB5syZgxkzZqg+OSXSNskvAPPy8oKXl1e2zwUEBGRpmzBhAiZMmKDlVERERJRXZcqUQWBgIJRKJVq2bCl1HCpiOImFiIiI1JKQkAAPDw9s2bJF1daiRQsWsiQJFrNERESUZ+Hh4XB1dcXWrVsxfvx4tZZQItIGFrNERET0QUII+Pn5oXHjxrh79y7KlSuHAwcO6GzNdqKcSD5nloiIiAq2uLg4jBw5Ejt37gQAdOvWDQEBAShVqpTEyYhYzBIREVEuEhMT4erqinv37sHIyAg//fQTJk+enOPdOol0jdMMiIiIKEeWlpb44osv4OTkhLNnz8LHx4eFLBUoLGaJiIgok9jYWDx+/Fj1+IcffkBYWBgaNWokYSqi7LGYJSIiIpVLly7BxcUFffr0gVwuBwAYGxujRIkSEicjyh6LWSIiIoIQAosXL0bz5s0RGRmJFy9e4MmTJ1LHIvogFrNERERF3MuXL9GjRw9MmTIF6enp+PLLL3H16lVUqFBB6mhEH8RiloiIqAg7d+4c6tWrhwMHDsDU1BR+fn4IDAyEjY2N1NGI8oRLcxERERVRQghMnjwZjx8/RpUqVbBjxw7Uq1dP6lhEauHILBERURElk8mwZcsWDB8+HKGhoSxkSS+xmCUiIipCTp06hWXLlqkeV6lSBevWrYOVlZWEqYjyj9MMiIiIigCFQoH58+fD19cXQgjUr18fLVq0kDoW0UdjMUtERFTIRUdHY+DAgTh+/DgAYMiQIahfv77EqYg0g8UsERFRIXb8+HF4eHjg2bNnsLCwwKpVqzBkyBCpYxFpDOfMEhERFVILFixA+/bt8ezZM9SuXRtXrlxhIUuFDotZIiKiQsrW1hZCCIwYMQKXLl1CzZo1pY5EpHGcZkBERFSIvHnzBsWKFQMADBs2DNWqVUPz5s0lTkWkPRyZJSIiKgTS09MxY8YM1K5dG69evQLwdh1ZFrJU2LGYJSIi0nOPHj1Cq1at8OOPPyIyMhK7d++WOhKRzrCYJSIi0mN//fUX6tWrh3PnzsHa2hqBgYEYOXKk1LGIdIbFLBERkR5KS0vDlClT0K1bN7x69Qqurq64evUq+vbtK3U0Ip1iMUtERKSHfH19sXjxYgCAt7c3zp07h0qVKkmcikj3WMwSERHpoSlTpqBu3brYs2cPli1bBlNTU6kjEUmCxSwREZEeSE1NxaZNmyCEAACULFkSV69exeeffy5xMiJpcZ1ZIiKiAu7+/fvo27cvQkNDkZqaqrrAy8CAY1JEfBcQEREVYLt27YKLiwtCQ0NRsmRJ2NvbSx2JqEBhMUtERFQApaSkYNy4cfjyyy8RHx+Ppk2bIjw8HN26dZM6GlGBwmKWiIiogLl79y6aNGmCVatWAQCmT5+OkydPonz58hInIyp4OGeWiIiogHn8+DH+/vtvlC5dGps3b0anTp2kjkRUYLGYJSIiKgCEEJDJZACA1q1bIyAgAG3btoWjo6PEyYgKNhazRER6TgiBZLlCZ+dLStPduYqKW7duYcSIEdiwYQOqVq0KABg8eLDEqYj0A4tZIiI9JoRAn9UXEBoZK3UUyqeNGzfCy8sLSUlJ8Pb2xuHDh6WORKRXeAEYEZEeS5YrJCtk3ZxKwNzYUJJzFwaJiYnw9PSEp6cnkpKS0KZNGwQEBEgdi0jvcGSWiKiQCPm2HSxMdFdcmhsbquZ4knpu3LiBvn374tatWzAwMICvry+++eYbGBryjwMidbGYJSIqJCxMDGFhwl/rBd2lS5fQunVrJCcnw97eHlu3bkWrVq2kjkWkt/hbj4iISIfq16+PunXrwtraGps3b4atra3UkYj0GotZIiIiLbt58yaqVKkCY2NjGBsb46+//kLx4sVhYMBLV4g+Ft9FREREWiKEwOrVq1G/fn3MnDlT1V6yZEkWskQawpFZIiIiLYiPj8fIkSOxY8cOAG/XklUoFLzIi0jD+GchERGRhoWGhqJ+/frYsWMHjIyMsGjRIvzxxx8sZIm0gCOzREREGiKEwIoVKzBlyhSkpaXByckJ27dvR+PGjaWORlRocWSWiIhIQ548eYJvvvkGaWlp6NWrF8LCwljIEmkZR2aJiIg0pFy5cli7di2eP3+OCRMm8KYSRDrAYpaIiCifhBD45Zdf4OLigtatWwMA3N3dJU5FVLSwmCUiIsqHV69ewdPTE/v370fZsmVx8+ZNlChRQupYREUOi1kiIg0TQiBZrtDJuZLSdHMeyuz8+fNwd3fHo0ePYGpqilmzZqF48eJSxyIqkljMEhFpkBACfVZfQGhkrNRRSAuUSiUWLVqEmTNnQqFQoEqVKtixYwfq1asndTSiIovFLBGRBiXLFZIUsm5OJWBuzDVMtSk5ORlffPEFDh06BADo378/1qxZAysrK4mTERVtLGaJiLQk5Nt2sDDRTYFpbmzIK+e1zMzMDMWLF4eZmRmWL1+OESNG8HtOVACwmCUi0hILE0NYmPDXrD5TKBRISUmBpaUlZDIZ1qxZg2+++Qa1a9eWOhoR/T/eNIGIiCgbz549Q6dOnTBw4EAIIQAAVlZWLGSJChgOGRAREb3nxIkT8PDwQHR0NCwsLPDvv/+iRo0aUsciomxwZJaIiOj/KRQKzJ49G+3atUN0dDRq1aqFK1eusJAlKsA4MktERATg6dOn8PDwwMmTJwEAw4cPx/Lly2FhYSFtMCLKFYtZIiIq8oQQ6NmzJ0JCQmBpaYk1a9bAw8ND6lhElAf5mmaQnp6OY8eOYc2aNUhISADw9i/aN2/eaDQcERGRLshkMixfvhyurq64evUqC1kiPaL2yGxkZCQ6deqEhw8fIjU1Fe3bt4eVlRUWLlyIlJQUrF69Whs5iYiINOrx48cIDw9Ht27dAABNmjTBlStXuHYskZ5Re2R24sSJcHNzQ2xsLMzNzVXtn3/+OY4fP67RcERERNpw8OBB1KtXD3379sWNGzdU7SxkifSP2iOzZ8+exblz52BiYpKp3cnJCU+ePNFYMCIiIk2Ty+WYOXMmFi1aBACoX79+poEZItI/ahezSqUSCoUiS/vjx495f2oiIiqwIiMj4e7ujosXLwIAJkyYgEWLFsHU1FTiZET0MdSeZtC+fXssXbpU9Vgmk+HNmzeYPXs2unTposlsREREGvHHH3/AxcUFFy9ehI2NDXbv3o3ly5ezkCUqBNQemf3ll1/QunVr1KxZEykpKRgwYADu3r2L0qVLY9u2bdrISET0UYQQSFUASWnpMBbanROZlJb1kyuS3tWrVxEbG4uGDRti+/btcHZ2ljoSEWmI2sWsg4MDwsPDsX37doSGhkKpVGL48OHw8PDgvCMiKnCEEHBfdwVXHxph2uUTUschHRJCqC7omjVrFmxtbTFy5Mgs13wQkX5Te5rB6dOnYWxsjKFDh2LFihVYtWoVRowYAWNjY5w+fVobGYmI8i1ZrsDVh691fl43pxIwNzbU+Xnprd27d6NNmzZISUkBABgaGmLcuHEsZIkKIbVHZlu3bo2oqCjY2tpmao+Li0Pr1q2zvTiMiKgguPh1S1hbmunkXObGhlzmSQIpKSmYMmUKVq5cCQBYuXIlvvrqK4lTEZE2qV3MvvuxzbtevnwJS0tLjYQiItIGcxNDWJjwLt6F1d27d9GvXz+EhYUBAL7++mt4e3tLnIqItC3Pv9V79+4N4O3qBZ6enpmuAFUoFLh27RqaNm2q+YREREQfsH37dowcORJv3rxB6dKlsWnTJnTu3FnqWESkA3kuZm1sbAC8HZm1srLKdLGXiYkJGjdujJEjR2o+IRERUS4WL16MKVOmAABatGiBbdu2wdHRUeJURKQreS5mN2zYAACoUKECpkyZwikFRERUIHzxxReYP38+vLy8MHv2bBgZcSoJUVGi9jt+9uzZ2shBRESUZ2FhYXBxcQHwdpDl7t27KFmypMSpiEgKai/NBQC7du1C37590bhxY9SvXz/Tl7pWrVoFZ2dnmJmZwdXVFWfOnMl1+9TUVMycORNOTk4wNTVFpUqVsH79+vy8DCIi0jOJiYkYNmwY6tevj4MHD6raWcgSFV1qF7PLly/H0KFDYWtri7CwMDRs2BClSpXC/fv31Z5sHxgYiEmTJmHmzJkICwtDixYt0LlzZzx8+DDHffr27Yvjx4/D398ft2/fxrZt21C9enV1XwYREemZhw8fomnTptiwYQMMDAxw+/ZtqSMRUQGg9jSDVatW4bfffkP//v2xceNGTJs2DRUrVsSsWbPw6tUrtY61ZMkSDB8+HCNGjAAALF26FEeOHIGfnx8WLFiQZfvDhw/j1KlTuH//vuqv8AoVKqj7EoiISI8IIRAQEIApU6YgLS0NZcuWxbZt29CqVSupoxFRAaB2MZvxlzEAmJubIyEhAQAwaNAgNG7cGCtWrMjTcdLS0hAaGorp06dnau/QoQPOnz+f7T5//vkn3NzcsHDhQmzevBmWlpbo0aMHvv/++xxvpZuamorU1FTV4/j4eACAXC6HXC7PU9aPlXEeXZ2PNIv9p9/k8vRM/89+1C9v3rzB+PHjsXXrVgBA27ZtsXHjRtja2rIv9Qh/j+o/XfehOudRu5gtW7YsXr58CScnJzg5OeHixYuoW7cuIiIiIITI83FiYmKgUChgZ2eXqd3Ozg7R0dHZ7nP//n2cPXsWZmZm2Lt3L2JiYuDl5YVXr17lOG92wYIFmDNnTpb2o0ePwsLCIs95NSEoKEin5yPNYv/pp1QFkPGr7sSJEzDlHWb1yoULF7B161YYGBhgwIAB6N27N0JCQqSORfnE36P6T1d9mJSUlOdt1S5m27Rpg/3796N+/foYPnw4Jk+ejF27diEkJER1YwV1vH83sZzuMAYASqUSMpkMW7ZsUa17u2TJEvTp0wcrV67MdnR2xowZ8PHxUT2Oj49H+fLl0aFDB1hbW6udNz/kcjmCgoLQvn17GBsb6+ScpDnsP/2WlJaOaZdPAHj7+8tGR7ezJc3o0qULhBBo3749EhMT+T7UU/w9qv903YcZn6TnhdrF7G+//QalUgkAGDNmDEqWLImzZ8+ie/fuGDNmTJ6PU7p0aRgaGmYZhX3+/HmW0doM9vb2cHR0VBWyAFCjRg0IIfD48WNUqVIlyz6mpqaZ7laWwdjYWOdvKCnOSZrD/tNPxuJ/fxwbGxuxDwu4+Ph4zJgxA7Nnz4atrS0A4KeffoJcLsfBgwf5PtRz7D/9p6s+VOccaq9mYGBgkGlB6r59+2L58uXw9vbGixcv8nwcExMTuLq6ZhmuDgoKyvG2uM2aNcPTp0/x5s0bVdudO3dgYGCAcuXKqflKiIioILl69Srq16+PVatWYfjw4VLHISI9ka91Zt8XHR2NCRMmoHLlymrt5+Pjg3Xr1mH9+vW4desWJk+ejIcPH6pGeGfMmIHBgwerth8wYABKlSqFoUOH4ubNmzh9+jSmTp2KYcOG5XgBGBERFWxCCKxYsQJNmjTBvXv38Mknn+Cbb76ROhYR6Yk8F7OvX7+Gh4cHypQpAwcHByxfvhxKpRKzZs1CxYoVcfHiRbVvXtCvXz8sXboUc+fORb169XD69GkcPHgQTk5OAICoqKhMa84WK1YMQUFBeP36Ndzc3ODh4YHu3btj+fLlap2XiIgKhtevX6NPnz6YMGEC0tLS0KNHD4SFhaFJkyZSRyMiPZHnObPffPMNTp8+jSFDhuDw4cOYPHkyDh8+jJSUFBw6dAgtW7bMVwAvLy94eXll+1xAQECWturVq/NqSCKiQuDff/9Fly5dEBERAWNjYyxatAje3t45XgRMRJSdPBezf/31FzZs2IB27drBy8sLlStXRtWqVbF06VItxiMiosLKwcEBhoaGcHZ2RmBgIBo0aCB1JCLSQ3kuZp8+fYqaNWsCACpWrAgzMzPVnbuIiIjyIj4+HlZWVpDJZLC2tsaBAwdgZ2eH4sWLSx2NiPRUnufMKpXKTMskGBoawtLSUiuhiIio8Llw4QJq166d6U6R1apVYyFLRB8lzyOzQgh4enqq1mxNSUnBmDFjshS0e/bs0WxCIiLSa0qlEj///DO++eYbKBQKrFmzBmPGjOF6o0SkEXkuZocMGZLp8cCBAzUehoiICpcXL15gyJAhOHToEADA3d0da9asYSFLRBqT52J2w4YN2sxBRESFzOnTp9G/f388ffoUZmZmWL58OUaMGMHVCohIo9S+nS0REdGHREVFoUOHDkhNTUW1atWwY8cO1KlTR+pYRFQIsZglIiKNs7e3x5w5c/DPP/9g1apVKFasmNSRiKiQYjFLREQaERwcDFtbW9SqVQsAMG3aNADgtAIi0qo8L81FRESUHYVCAV9fX7Rt2xZ9+/ZFYmIigLdFLAtZItI2jswSEVG+RUVFwcPDA8HBwQCAxo0bs4AlIp3K18js5s2b0axZMzg4OCAyMhIAsHTpUvzxxx8aDUdERAVXUFAQ6tWrh+DgYFhaWmLz5s3w9/eHhYWF1NGIqAhRe2TWz88Ps2bNwqRJkzBv3jwoFAoAQPHixbF06VL07NlT4yGJSLuEEEiWK6SOoRVJaYXzdUkpPT0dvr6+mD9/PoQQqFOnDgIDA1G9enWpoxFREaR2Mfvrr79i7dq16NWrF3788UdVu5ubG6ZMmaLRcESkfUII9Fl9AaGRsVJHIT0hk8lw9uxZCCEwevRo/PLLLzA3N5c6FhEVUWoXsxEREXBxccnSbmpqqpr0T0T6I1muKBKFrLOVgLmxodQx9JoQAjKZDIaGhti6dSvOnj2Lvn37Sh2LiIo4tYtZZ2dnhIeHw8nJKVP7oUOHULNmTY0FIyLdC/m2HSxMCl/BJ5fLERx0lBcm5ZNcLsfMmTORmpqKZcuWAQAcHBxYyBJRgaB2MTt16lSMGzcOKSkpEELg8uXL2LZtGxYsWIB169ZpIyMR6YiFiSEsTArfIidymQDr2Px5+PAh3N3dceHCBQDAsGHDULduXYlTERH9j9r/ag0dOhTp6emYNm0akpKSMGDAADg6OmLZsmVwd3fXRkYiIpLAn3/+CU9PT8TGxsLGxgb+/v4sZImowMnXEMzIkSMxcuRIxMTEQKlUwtbWVtO5iIhIImlpafj666+xdOlSAECDBg0QGBgIZ2dnaYMREWVD7XVm58yZg3v37gEASpcuzUKWiKgQEUKge/fuqkJ28uTJOHv2LAtZIiqw1C5md+/ejapVq6Jx48ZYsWIFXrx4oY1cREQkAZlMhtGjR6NEiRL4448/sGTJEpiYmEgdi4goR2oXs9euXcO1a9fQpk0bLFmyBI6OjujSpQu2bt2KpKQkbWQkIiItSklJwfXr11WPe/fujfv376NHjx4SpiIiypt83c62Vq1amD9/Pu7fv4/g4GA4Oztj0qRJKFu2rKbzERGRFv33339o2rQp2rRpgydPnqjaixcvLl0oIiI15KuYfZelpSXMzc1hYmICuVyuiUxERKQDgYGBqF+/PsLCwiCEQEREhNSRiIjUlq9iNiIiAvPmzUPNmjXh5uaGq1evwtfXF9HR0ZrOR0REGpacnIwxY8bA3d0dCQkJaN68OcLDw9G8eXOpoxERqU3tpbmaNGmCy5cv49NPP8XQoUNV68wSEVHBd/v2bfTt2xfXrl2DTCbDN998A19fXxgZFb6bZRBR0aD2b6/WrVtj3bp1qFWrljbyEBGRFi1btgzXrl2Dra0tfv/9d7Rv317qSEREH0XtYnb+/PnayEFERDqwaNEipKenY86cObC3t5c6DhHRR8tTMevj44Pvv/8elpaW8PHxyXXbJUuWaCQYERF9vH/++Qdr1qzB0qVLYWBgAEtLS/z2229SxyIi0pg8FbNhYWGqlQrCwsK0GoiIiD6eEAIBAQEYN24ckpOTUbFiRUyaNEnqWEREGpenYjY4ODjb/yciooLnzZs38PLywubNmwEAHTp0wIABAyRORUSkHWovzTVs2DAkJCRkaU9MTMSwYcM0EoqIiPLn2rVrcHNzw+bNm2FgYIB58+bh0KFDsLW1lToaEZFWqF3Mbty4EcnJyVnak5OTsWnTJo2EIiIi9QUGBqJRo0a4ffs2HB0dcfLkSXzzzTcwMPjo++MQERVYeV7NID4+HkIICCGQkJAAMzMz1XMKhQIHDx7kX/5ERBKqXLkylEolOnfujE2bNqF06dJSRyIi0ro8F7PFixeHTCaDTCZD1apVszwvk8kwZ84cjYYjIqLcvX79GsWLFwcAuLq64sKFC6hXrx5HY4moyMhzMRscHAwhBNq0aYPdu3ejZMmSqudMTEzg5OQEBwcHrYQkIqLMhBBYtWoVvvnmGwQHB6N+/foAoPovEVFRkeditmXLlgCAiIgIfPLJJ5DJZFoLRUREOXv9+jVGjhyJXbt2AQACAgJYxBJRkZWnYvbatWuoXbs2DAwMEBcXh+vXr+e4bZ06dTQWjoiIMrty5Qr69euHiIgIGBsbY+HChZg4caLUsYiIJJOnYrZevXqIjo6Gra0t6tWrB5lMBiFElu1kMhkUCoXGQxIRFXVCCCxbtgzTpk2DXC6Hs7MzAgMD0aBBA6mjERFJKk/FbEREBMqUKaP6fyIi0q3du3dj8uTJAIAvvvgC69atU134RURUlOWpmHVycsr2/4mISDd69+6NHj16oEOHDvDy8uJ1C0RE/y9fN03466+/VI+nTZuG4sWLo2nTpoiMjNRoOCKiokqpVGLt2rVISkoCABgYGGDfvn0YN24cC1kioneoXczOnz8f5ubmAIALFy5gxYoVWLhwIUqXLq36CIyIiPIvJiYG3bt3x6hRozBhwgRVO4tYIqKs8rw0V4ZHjx6hcuXKAIB9+/ahT58+GDVqFJo1a4ZWrVppOh8RUZFy5swZ9O/fH0+ePIGZmRkaNWoEIQQLWSKiHKg9MlusWDG8fPkSAHD06FG0a9cOAGBmZobk5GTNpiMiKiKUSiXmz5+P1q1b48mTJ6hWrRouXbqEUaNGsZAlIsqF2iOz7du3x4gRI+Di4oI7d+6ga9euAIB//vkHFSpU0HQ+IqJC7/nz5xg0aBCOHj0KABg4cCD8/PxQrFgxiZMRERV8ao/Mrly5Ek2aNMGLFy+we/dulCpVCgAQGhqK/v37azwgEVFhJ5fLcfXqVZibm8Pf3x+bNm1iIUtElEdqj8wWL14cK1asyNI+Z84cjQQiIioK3p0H6+joiJ07d6JMmTKoVauWxMmIiPSL2sUs8Pa+4P7+/rh16xZkMhlq1KiB4cOHw8bGRtP5iIgKnejoaHh4eGD8+PH4/PPPAYAX0BIR5ZPa0wxCQkJQqVIl/PLLL3j16hViYmLwyy+/oFKlSrh69ao2MhIRFRrHjh1D3bp1ceLECXh7eyMtLU3qSEREek3tYnby5Mno0aMHHjx4gD179mDv3r2IiIhAt27dMGnSJC1EJCLSf+np6fj222/RoUMHPH/+HHXq1MGxY8dgYmIidTQiIr2m9jSDkJAQrF27FkZG/9vVyMgI06ZNg5ubm0bDEREVBk+ePEH//v1x5swZAMDo0aPxyy+/qG5AQ0RE+ad2MWttbY2HDx+ievXqmdofPXoEKysrjQUjIioMXrx4gXr16iEmJgZWVlb47bff4O7uLnUsIqJCQ+1pBv369cPw4cMRGBiIR48e4fHjx9i+fTtGjBjBpbmIiN5TpkwZ9OvXDy4uLggNDWUhS0SkYWqPzP7888+QyWQYPHgw0tPTAQDGxsYYO3YsfvzxR40HJCLSNw8fPoSxsTHs7e0BAIsXL4YQAmZmZhInIyIqfNQemTUxMcGyZcsQGxuL8PBwhIWF4dWrV/jll19gamqqjYxERHpj//79qFevHvr376/6g9/U1JSFLBGRluS5mE1KSsK4cePg6OgIW1tbjBgxAvb29qhTpw4sLCy0mZGIqMBLS0vDV199hR49eiA2NhZJSUmIjY2VOhYRUaGX52J29uzZCAgIQNeuXeHu7o6goCCMHTtWm9mIiPRCREQEWrRogSVLlgB4u4Th2bNnUaZMGYmTEREVfnmeM7tnzx74+/urLl4YOHAgmjVrBoVCAUNDQ60FJCIqyPbs2YNhw4YhLi4OJUqUQEBAAHr06CF1LCKiIiPPI7OPHj1CixYtVI8bNmwIIyMjPH36VCvBiIgKOrlcju+++w5xcXFo0qQJwsLCWMgSEelYnotZhUKR5U41RkZGqgsciIiKGmNjYwQGBmLGjBk4deoUnJycpI5ERFTk5HmagRACnp6emVYsSElJwZgxY2Bpaalq27Nnj2YTEhEVIDt27MDz588xfvx4AEDt2rUxf/58iVMRERVdeS5mhwwZkqVt4MCBGg1DRFRQJScnY/LkyVizZg0MDQ3RrFkzuLi4SB2LiKjIy3Mxu2HDBm3mICIqsG7fvo2+ffvi2rVrkMlkmD59Oj799FOpYxEREfJxBzAioqLk999/x5gxY5CYmAhbW1v8/vvvaN++vdSxiIjo/6l9BzAioqLCy8sLgwYNQmJiIlq3bo3w8HAWskREBQyLWSKiHFSvXh0ymQy+vr4ICgqCvb291JGIiOg9nGZARPSOV69eoWTJkgCACRMmoGXLlqhbt67EqYiIKCccmSUiAvDmzRsMGTIEjRo1Qnx8PABAJpOxkCUiKuDyVcxu3rwZzZo1g4ODAyIjIwEAS5cuxR9//KHRcEREunD9+nU0aNAAmzZtwv379xEcHCx1JCIiyiO1i1k/Pz/4+PigS5cueP36NRQKBQCgePHiWLp0qabzERFpjRACa9euRcOGDfHvv//C0dERJ0+eRM+ePaWORkREeaR2Mfvrr79i7dq1mDlzJgwNDVXtbm5uuH79ukbDERFpS0JCAjw8PDBq1CikpKSgc+fOCA8PR4sWLaSORkREalC7mI2IiMj2rjempqZITEzUSCgiIm376quvsG3bNhgaGmLhwoU4cOAASpcuLXUsIiJSk9rFrLOzM8LDw7O0Hzp0CDVr1lQ7wKpVq+Ds7AwzMzO4urrizJkzedrv3LlzMDIyQr169dQ+JxHRDz/8gMaNG+PMmTOYOnUqDAx4PSwRkT5S+7f31KlTMW7cOAQGBkIIgcuXL2PevHn45ptvMHXqVLWOFRgYiEmTJmHmzJkICwtDixYt0LlzZzx8+DDX/eLi4jB48GC0bdtW3fhEVEQlJibC399f9djW1hbnz59HkyZNJExFREQfS+11ZocOHYr09HRMmzYNSUlJGDBgABwdHbFs2TK4u7urdawlS5Zg+PDhGDFiBIC3KyIcOXIEfn5+WLBgQY77jR49GgMGDIChoSH27dun7ksgUosQAqkKICktHcZCJnUcjUtKU0gdQetCQ0Ph4+ODZ8+ewdraGgMGDADwduktIiLSb/m6acLIkSMxcuRIxMTEQKlUwtbWVu1jpKWlITQ0FNOnT8/U3qFDB5w/fz7H/TZs2IB79+7h999/xw8//PDB86SmpiI1NVX1OGP9SLlcDrlcrnbu/Mg4j67OR5ojhEC/3y4h7LERpl0+IXUcrZPL5ZDLhNQxNEYIgRUrVmD69OmQy+VwcnJChQoV+F7UQ/w9qt/Yf/pP132oznk+6g5gH3OxRExMDBQKBezs7DK129nZITo6Ott97t69i+nTp+PMmTMwMspb9AULFmDOnDlZ2o8ePQoLCwv1g3+EoKAgnZ6PPl6qAgh7XDRulOdsJRAcdBSFZbDyzZs3+PXXX3Hp0iUAQOPGjTF+/Hi8ePECBw8elDgd5Rd/j+o39p/+01UfJiUl5Xlbtf+VdnZ2zvWjufv376t1vPePJYTI9vgKhQIDBgzAnDlzULVq1Twff8aMGfDx8VE9jo+PR/ny5dGhQwdYW1urlTW/5HI5goKC0L59exgbG+vknKQZSWnpqhHZM181g7WFqcSJtMfc2LDQfOx++fJlTJw4EZGRkTAxMcGCBQtQsWJFdOjQge9BPcXfo/qN/af/dN2HGZ+k54XaxeykSZMyPZbL5QgLC8Phw4fVugCsdOnSMDQ0zDIK+/z58yyjtcDbNSFDQkIQFhaG8ePHAwCUSiWEEDAyMsLRo0fRpk2bLPuZmprC1DRrAWJsbKzzN5QU56SP8+4cWWsLU9hYmkuYhvIqLi4OkZGRqFSpEnbs2IFPP/0UBw8e5HuwEGAf6jf2n/7TVR+qcw61i9mJEydm275y5UqEhITk+TgmJiZwdXVFUFAQPv/8c1V7UFBQtnffsba2znJThlWrVuHEiRPYtWsXnJ2d83xuIip83v1Up0uXLti6dSu6du0Ka2trztMjIirENLawYufOnbF792619vHx8cG6deuwfv163Lp1C5MnT8bDhw8xZswYAG+nCAwePPhtUAMD1K5dO9OXra0tzMzMULt2bVhaWmrqpRCRnjl79izq1q2LyMhIVVv//v11NpWIiIiko7ErW3bt2oWSJUuqtU+/fv3w8uVLzJ07F1FRUahduzYOHjwIJycnAEBUVNQH15wloqJLqVTip59+wnfffQeFQoFvv/0WmzdvljoWERHpkNrFrIuLS6aLRIQQiI6OxosXL7Bq1Sq1A3h5ecHLyyvb5wICAnLd19fXF76+vmqfk4j03/PnzzFo0CAcPXoUADBw4ED4+flJnIqIiHRN7WK2V69emR4bGBigTJkyaNWqFapXr66pXEREOTp58iQGDBiAqKgomJubY+XKlfD09Cw0qzEQEVHeqVXMpqeno0KFCujYsSPKli2rrUxERDk6dOgQunXrBqVSiZo1a2LHjh2oVauW1LGIiEgial0AZmRkhLFjx2a6oxYRkS61bt0aderUwdChQ3H58mUWskRERZza0wwaNWqEsLAw1UVaRETadunSJbi5ucHQ0BBmZmY4ffo0rKyspI5FREQFgNrFrJeXF7766is8fvwYrq6uWZbEqlOnjsbCEVHRlp6ejjlz5mDevHmYNWuW6oJPFrJERJQhz8XssGHDsHTpUvTr1w8A4O3trXpOJpOpFixXKBSaT0lERc6TJ08wYMAAnD59GgDw7NmzHG93TURERVeei9mNGzfixx9/REREhDbzEBHh8OHDGDRoEGJiYlCsWDGsXbsW7u7uUsciIqICKM/FrBACADhXloi0Ri6XY9asWfjxxx8BvF3XOjAwEFWqVJE4GRERFVRqrWbAj/eISJvu37+PpUuXAgDGjRuH8+fPs5AlIqJcqXUBWNWqVT9Y0L569eqjAhFR0VWtWjWsWbMGFhYW6NOnj9RxiIhID6hVzM6ZMwc2NjbaykJERUxaWhq+/fZbfP7552jSpAkAYPDgwRKnIiIifaJWMevu7g5bW1ttZSGiIuTBgwdwd3fHpUuXsGPHDvz7778wMzOTOhYREemZPM+Z5XxZItKUvXv3wsXFBZcuXULx4sWxbNkyFrJERJQveS5mM1YzICLKr9TUVHh7e6N37954/fo1GjdujPDwcPTs2VPqaEREpKfyPM1AqVRqMwcRFXKxsbFo3749QkNDAQBTp07FvHnzYGxsLHEyIiLSZ2rfzpaIKD+KFy+OcuXK4cGDB9i4cSO6du0qdSQiIioEWMwSkdakpKQgPT0dxYoVg0wmw/r165GUlIRy5cpJHY2IiAoJtW6aQESUV3fu3EHjxo0xatQo1Zz7kiVLspAlIiKNYjFLRBq3detWuLq64u+//8axY8fw5MkTqSMREVEhxWKWiDQmKSkJI0eOhIeHB968eYNWrVohPDyco7FERKQ1LGaJSCNu3bqFRo0aYd26dZDJZJg9ezaOHTsGBwcHqaMREVEhxgvAiOijpaeno3v37rh37x7Kli2LLVu2oE2bNlLHIiKiIoAjs0T00YyMjPDbb7+hY8eOCA8PZyFLREQ6w2KWiPLl+vXrOHDggOpxmzZtcOjQIdjZ2UmYioiIihoWs0SkFiEE1q1bh4YNG6J///64e/eu6jmZTCZhMiIiKopYzBJRniUkJGDgwIEYOXIkUlJS0Lx5cxQvXlzqWEREVISxmCWiPAkPD4erqyu2bt0KQ0ND/PTTT/jrr79QpkwZqaMREVERxtUMSO8IIZAsV+jkXElpujlPQbd69WpMmjQJqampKF++PLZv346mTZtKHYuIiIjFLOkXIQT6rL6A0MhYqaMUKffu3UNqaiq6d++ODRs2oFSpUlJHIiIiAsBilvRMslwhSSHrbCVgbmyo8/NKSalUwsDg7Uyk+fPno27duvDw8OBFXkREVKCwmCW9FfJtO1iYaL/AlMvlCA46WmSKOCEEli9fjr179yIoKAjGxsYwNjbGwIEDpY5GRESUBYtZ0lsWJoawMNH+j7BcJlBE6ljExsZi2LBh2LdvHwBg27ZtGDx4sLShiIiIcsFilogAAJcuXUK/fv0QGRkJExMTLF68GIMGDZI6FhERUa64NBdREadUKrF48WI0b94ckZGRqFSpEs6fP4/x48cXmakVRESkv1jMEhVx06ZNw5QpU5Ceno6+ffvi6tWrcHV1lToWERFRnrCYJSriRo4cidKlS2P16tXYvn07rK2tpY5ERESUZ5wzS1TEKJVKnD9/Hs2bNwcAVKtWDQ8ePIClpaXEyYiIiNTHkVmiIuT58+fo0qULWrZsiZMnT6raWcgSEZG+4sgsURFx6tQp9O/fH1FRUTA3N0dUVJTUkYiIiD4aR2aJCjmFQoHvv/8ebdq0QVRUFGrUqIHLly+jf//+UkcjIiL6aByZJSrEoqOjMXDgQBw/fhwA4OnpiRUrVnBaARERFRosZokKsUOHDuH48eOwsLCAn58f7+ZFRESFDotZokLM09MT9+/fx4ABA1CjRg2p4xAREWkc58wSFSJPnz7FwIEDERsbCwCQyWT4/vvvWcgSEVGhxZFZokLi8OHDGDRoEGJiYgAAv//+u8SJiIiItI8js0R6Lj09HTNmzEDnzp0RExODevXqYfbs2VLHIiIi0gmOzBLpsUePHqF///44d+4cAMDLywuLFy+GmZmZxMmIiIh0g8UskZ66ePEiunbtilevXsHa2hr+/v7o06eP1LGIiIh0isUskZ6qWrUqLC0tUbFiRQQGBqJixYpSRyIiItI5FrNEeuT58+coU6YMZDIZSpYsiePHj+OTTz6Bqamp1NGIiIgkwQvAiPTE3r17Ua1aNaxfv17VVqVKFRayRERUpLGYJSrgUlNT4e3tjd69e+P169fYsmULhBBSxyIiIioQWMwSFWD37t1Ds2bN8OuvvwIApkyZgiNHjkAmk0mcjIiIqGDgnFmiAmrnzp0YMWIE4uPjUbJkSWzatAldu3aVOhYREVGBwmKWqAC6c+cO3N3doVQq0axZM2zbtg3ly5eXOhYREVGBw2KWqACqWrUqZs2ahdTUVMydOxdGRnyrEhERZYf/QhIVENu2bYObmxuqVKkCALwlLRERUR7wAjAiiSUlJWHEiBEYMGAA+vXrh5SUFKkjERER6Q2OzBJJ6NatW+jbty9u3LgBmUyG7t27w9jYWOpYREREeoPFLH00IQSS5QqdnCspTTfn0YWNGzfCy8sLSUlJsLOzw5YtW9C2bVupYxEREekVFrP0UYQQ6LP6AkIjY6WOojeSkpIwduxYbNq0CQDQtm1b/P777yhbtqzEyYiIiPQP58zSR0mWKyQpZN2cSsDc2FDn59UEIyMj/PvvvzAwMMD333+PI0eOsJAlIiLKJ47MksaEfNsOFia6KTDNjQ316i5YQggIIWBgYAATExMEBgYiMjISLVu2lDoaERGRXmMxSxpjYWIICxP+SL0vISEBY8aMQbly5fDTTz8BACpUqIAKFSpIG4yIiKgQYOVBpEXh4eHo27cv7t69CyMjI4wdO5ZFLBERkQZxziyRFggh4Ofnh8aNG+Pu3bsoV64cTp48yUKWiIhIwzgyS6RhcXFxGDlyJHbu3AkA6NatGwICAlCqVCmJkxERERU+LGaJNEipVKJly5b4+++/YWRkhJ9++gmTJ0/Wq4vViIiI9AmnGRBpkIGBAaZOnQonJyecPXsWPj4+LGSJiIi0iMUs0UeKjY1FeHi46rGHhwdu3ryJRo0aSReKiIioiGAxS/QRLl26BBcXF3Tp0gUvXrxQtVtYWEiYioiIqOhgMUuUD0IILF68GM2bN0dkZCTMzc3x/PlzqWMREREVObwAjEhNL1++hKenJw4cOAAA+PLLL7F27VrY2NhInIyIiKjokXxkdtWqVXB2doaZmRlcXV1x5syZHLfds2cP2rdvjzJlysDa2hpNmjTBkSNHdJiWirpz586hXr16OHDgAExNTbFq1SoEBgaykCUiIpKIpMVsYGAgJk2ahJkzZyIsLAwtWrRA586d8fDhw2y3P336NNq3b4+DBw8iNDQUrVu3Rvfu3REWFqbj5FRU+fn54fHjx6hSpQouXryIsWPHcrUCIiIiCUk6zWDJkiUYPnw4RowYAQBYunQpjhw5Aj8/PyxYsCDL9kuXLs30eP78+fjjjz+wf/9+uLi46CKyXhBCIFmu0Mm5ktJ0c56CYtWqVbCzs4Ovry+srKykjkNERFTkSVbMpqWlITQ0FNOnT8/U3qFDB5w/fz5Px1AqlUhISEDJkiVz3CY1NRWpqamqx/Hx8QAAuVwOuVyej+TqyziPLs4nhID7uiu4+vC11s/1PrlcDrlM6Py82nT69GkEBgaiS5cukMvlMDc3x48//ghAN/1JmqHL9yBpB/tQv7H/9J+u+1Cd80hWzMbExEChUMDOzi5Tu52dHaKjo/N0jMWLFyMxMRF9+/bNcZsFCxZgzpw5WdqPHj2q8+WTgoKCtH6OVAVw9aHuu9XZSiA46CgKyyfuCoUCu3btQmBgIJRKJczMzDidoBDQxXuQtIt9qN/Yf/pPV32YlJSU520lX83g/QJBCJGnomHbtm3w9fXFH3/8AVtb2xy3mzFjBnx8fFSP4+PjUb58eXTo0AHW1tb5D64GuVyOoKAgtG/fHsbGxlo9V1JaOqZdPgEAuPh1S5ibGGr1fBnMjQ0LTbEXHR0NT09PnDjx9vvo4eGBZs2a6aT/SDt0+R4k7WAf6jf2n/7TdR9mfJKeF5IVs6VLl4ahoWGWUdjnz59nGa19X2BgIIYPH46dO3eiXbt2uW5ramoKU1PTLO3GxsY6f0Pp4pzG4n8FpbWlGSxMJP97Ra8cP34cHh4eePbsGSwsLLBq1SoMGDAABw8elORnhjSLfaj/2If6jf2n/3TVh+qcQ7LVDExMTODq6ppluDooKAhNmzbNcb9t27bB09MTW7duRdeuXbUdk4qQZcuWoX379nj27Blq166NkJAQDBkyROpYRERElAtJh+18fHwwaNAguLm5oUmTJvjtt9/w8OFDjBkzBsDbKQJPnjzBpk2bALwtZAcPHoxly5ahcePGqlFdc3NzrvNJH61BgwYwMDDA0KFDsWzZMt6SloiISA9IWsz269cPL1++xNy5cxEVFYXatWvj4MGDcHJyAgBERUVlWnN2zZo1SE9Px7hx4zBu3DhV+5AhQxAQEKDr+FQIPHv2TDWtpWnTprhx4waqV68ucSoiIiLKK8knVHp5ecHLyyvb594vUE+ePKn9QFQkpKen47vvvsOvv/6KS5cuoVatWgDAQpaIiEjPSF7MEunao0eP0L9/f5w7dw4AsH//flUxS0RERPqFxSwVKX/99RcGDx6MV69ewdraGmvXrs11nWIiIiIq2CRbzYBIl+RyOaZMmYJu3brh1atXcHV1xdWrV1nIEhER6TkWs1Qk+Pv7Y/HixQAAb29vnDt3DpUqVZI4FREREX0sTjOgImHEiBE4cuQIBg8ejM8//1zqOERERKQhHJmlQiktLQ2LFi1CamoqAMDIyAh79+5lIUtERFTIcGSWCp379++jX79+CAkJwcOHD/Hrr79KHYmIiIi0hCOzVKjs2rULLi4uCAkJQcmSJdGxY0epIxEREZEWsZilQiElJQVeXl748ssvER8fj2bNmiE8PBzdunWTOhoRERFpEYtZ0nv37t1DkyZN4OfnBwCYPn06goODUb58eYmTERERkbZxzizpPQMDA0RERKB06dLYvHkzOnXqJHUkIiIi0hEWs6SXFAoFDA0NAQDOzs7Yu3cvqlatCkdHR4mTERERkS5xmgHpnVu3bqF+/fo4fPiwqq1169YsZImIiIogFrOkVzZt2gQ3Nzdcu3YNU6dOhVKplDoSERERSYjFLOmFxMREDB06FEOGDEFSUhLatGmDoKAgGBjwR5iIiKgoYyVABd6NGzfQoEEDBAQEwMDAAHPnzsXRo0dRtmxZqaMRERGRxHgBGBVo9+/fR8OGDZGcnAx7e3ts3boVrVq1kjoWERERFRAsZqlAq1ixItzd3fH06VNs2rQJtra2UkciIiKiAoTFLBU4f//9NxwcHFCmTBkAgJ+fH4yNjTk/loiIiLJgdUAFhhACq1evRqNGjTB48GDVSgWmpqYsZImIiChbrBCoQIiLi4O7uzvGjh2L1NRUGBoaIikpSepYREREVMCxmCXJhYaGwtXVFTt27ICRkREWLVqEP//8E8WKFZM6GhERERVwnDNLkhFCYMWKFZgyZQrS0tLg5OSE7du3o3HjxlJHIyIiIj3BkVmSTGJiIpYtW4a0tDT07NkTYWFhLGSJiIhILRyZJckUK1YMgYGBOHv2LLy9vSGTyaSORERERHqGxSzpjBACS5cuhbm5OcaMGQMAcHV1haurq8TJiIiISF+xmCWdePXqFTw9PbF//36YmJigffv2qFSpktSxiIiISM+xmCWtO3/+PNzd3fHo0SOYmpril19+QcWKFaWORURERIUALwAjrVEqlfjpp5/w2Wef4dGjR6hSpQouXryIsWPHcn4sERERaQRHZkkrlEolevXqhf379wMA+vfvjzVr1sDKykriZERERFSYcGSWtMLAwABNmjSBmZkZ1q5diy1btrCQJSIiIo3jyCxpjEKhQExMDOzs7AAAX3/9Nb788ktUrlxZ4mRERERUWHFkljTi2bNn6NSpE9q2bYukpCQAb0dnWcgSERGRNrGYpY924sQJ1K1bF8eOHUNERASuXr0qdSQiIiIqIljMUr4pFArMnj0b7dq1w7Nnz1CrVi1cuXIFzZs3lzoaERERFRGcM0v58vTpU3h4eODkyZMAgOHDh2P58uWwsLCQNhgREREVKSxmKV8mTJiAkydPwtLSEmvWrIGHh4fUkYiIiKgIYjFL+bJ8+XLExcVh5cqVqFatmtRxiIiIqIjinFnKk8ePH2PlypWqx46Ojjh27BgLWSIiIpIUR2bpgw4ePIjBgwfj5cuXcHR0RK9evaSORERERASAI7OUC7lcjmnTpqFr1654+fIl6tevj08//VTqWEREREQqHJmlbEVGRsLd3R0XL14E8PaCr0WLFsHU1FTiZERERET/w2KWsjhw4AAGDRqE169fw8bGBuvXr0fv3r2ljkVERESUBYtZyiI1NRWvX79Gw4YNsX37djg7O0sdiYiIiChbLGYJAJCeng4jo7c/Dl988QV2796Nbt26wcTEROJkRERERDnjBWCEXbt2oWbNmnj69KmqrXfv3ixkiYiIqMBjMVuEpaSkYNy4cfjyyy9x9+5dLFq0SOpIRERERGrhNIMi6u7du+jXrx/CwsIAAF9//TW+//57iVMRERERqYfFbBG0fft2jBw5Em/evEHp0qWxadMmdO7cWepYRERERGpjMVvEbNq0CUOGDAEAtGjRAtu2bYOjo6PEqYiIiIjyh3Nmi5gvvvgCtWrVwrfffosTJ06wkCUiIiK9xpHZIiAoKAht27aFgYEBLC0tERISAjMzM6ljEREREX00jswWYomJiRg6dCg6dOiAxYsXq9pZyBIREVFhwZHZQirtRSRaNpuGW7duwsDAAHK5XOpIRERERBrHYlYHhBBIVQBJaekwFjKtnisxNR1vrh3Fq6A1iEpPRdmyZbFt2za0atVKq+clIiIikgKLWS0TQsB93RVcfWiEaZdPaPVcyrRkvDqyEok3TwIA2rZrj61bfoetra1Wz0tEREQkFRazWpYsV+Dqw9c6OZf81RMk/nsWkBmgVveROLJnJQwNDXVybiIiIiIpsJjVoYtft4S1pTYvvuqITc0sUblyZbRt1RIymXanNBARERFJjcWsDpmbGMLCRHPf8vj4eIwfPx6TJ0+Gi4sLAGDMyBEaOz4RERFRQcdiVk9dvXoVffv2xb179xASEoLr169zSgEREREVOVxnVs8IIbBixQo0adIE9+7dwyeffAJ/f38WskRERFQkcWRWj7x+/RrDhw/Hnj17AAA9evTAhg0bULJkSYmTEREREUmDxayeePz4MVq0aIEHDx7A2NgYixYtgre3Ny/yIiIioiKNxayecHBwQJUqVSCTyRAYGIgGDRpIHYmIiIhIcixmC7BXr17BzMwMFhYWMDAwwNatW2FkZITixYtLHY2IiIioQOAFYAXU+fPnUa9ePUycOFHVVrp0aRayRERERO9gMVvAKJVKLFy4EJ999hkePXqEkydP4vXr11LHIiIiIiqQWMwWIC9evEC3bt3w9ddfQ6FQwN3dHaGhoRyNJSIiIsoB58wWEGfOnIG7uzuePn0KMzMzLFu2DCNHjuRqBURERES5YDFbACQlJeHLL7/Es2fPUK1aNezYsQN16tSROhYRERFRgcdpBgWAhYUF1q9fj0GDBiEkJISFLBEREVEecWRWIsHBwUhOTkaXLl0AAF26dFH9PxERERHlDUdmdUyhUMDX1xdt27aFh4cHHj58KHUkIiIiIr0leTG7atUqODs7w8zMDK6urjhz5kyu2586dQqurq4wMzNDxYoVsXr1ah0l/XjR0VFo37495syZAyEEevfujdKlS0sdi4iIiEhvSVrMBgYGYtKkSZg5cybCwsLQokULdO7cOcfRyoiICHTp0gUtWrRAWFgYvvnmG3h7e2P37t06Tq6+5IiraN64MYKDg2FpaYnNmzfD398fFhYWUkcjIiIi0luSzpldsmQJhg8fjhEjRgAAli5diiNHjsDPzw8LFizIsv3q1avxySefYOnSpQCAGjVqICQkBD///DO++OILXUbPMyEEYk9vQvyFnQAE6tSpgx07dqBatWpSRyMiIiLSe5IVs2lpaQgNDcX06dMztXfo0AHnz5/Pdp8LFy6gQ4cOmdo6duwIf39/yOVyGBsbZ9knNTUVqampqsfx8fEAALlcDrlc/rEv44PS09OhTHkDQGCI5zAsX/YLzM3NdXJu0oyMvmKf6S/2of5jH+o39p/+03UfqnMeyYrZmJgYKBQK2NnZZWq3s7NDdHR0tvtER0dnu316ejpiYmJgb2+fZZ8FCxZgzpw5WdqPHj2qk4/4UxVAyTYjYF7RDV2610dwcLDWz0naERQUJHUE+kjsQ/3HPtRv7D/9p6s+TEpKyvO2ki/N9f4droQQud71Krvts2vPMGPGDPj4+Kgex8fHo3z58ujQoQOsra3zGzvPhBBo0yYVJ04YoGvHdjAxMdH6OUmz5HI5goKC0L59+2xH/6ngYx/qP/ahfmP/6T9d92HGJ+l5IVkxW7p0aRgaGmYZhX3+/HmW0dcMZcuWzXZ7IyMjlCpVKtt9TE1NYWpqmqXd2NhYZ28oG5kMpoaAiYkJ38R6TJc/M6Qd7EP9xz7Ub+w//aerPlTnHJKtZmBiYgJXV9csw9VBQUFo2rRptvs0adIky/ZHjx6Fm5sb3xxERERERZCkS3P5+Phg3bp1WL9+PW7duoXJkyfj4cOHGDNmDIC3UwQGDx6s2n7MmDGIjIyEj48Pbt26hfXr18Pf3x9TpkyR6iUQERERkYQknTPbr18/vHz5EnPnzkVUVBRq166NgwcPwsnJCQAQFRWVac1ZZ2dnHDx4EJMnT8bKlSvh4OCA5cuXF9hluYiIiIhIuyS/AMzLywteXl7ZPhcQEJClrWXLlrh69aqWUxERERGRPpD8drZERERERPnFYpaIiIiI9BaLWSIiIiLSWyxmiYiIiEhvsZglIiIiIr3FYpaIiIiI9BaLWSIiIiLSWyxmiYiIiEhvsZglIiIiIr3FYpaIiIiI9BaLWSIiIiLSWyxmiYiIiEhvsZglIiIiIr1lJHUAXRNCAADi4+N1dk65XI6kpCTEx8fD2NhYZ+clzWD/6T/2of5jH+o39p/+03UfZtRpGXVbbopcMZuQkAAAKF++vMRJiIiIiCg3CQkJsLGxyXUbmchLyVuIKJVKPH36FFZWVpDJZDo5Z3x8PMqXL49Hjx7B2tpaJ+ckzWH/6T/2of5jH+o39p/+03UfCiGQkJAABwcHGBjkPiu2yI3MGhgYoFy5cpKc29ramm9iPcb+03/sQ/3HPtRv7D/9p8s+/NCIbAZeAEZEREREeovFLBERERHpLRazOmBqaorZs2fD1NRU6iiUD+w//cc+1H/sQ/3G/tN/BbkPi9wFYERERERUeHBkloiIiIj0FotZIiIiItJbLGaJiIiISG+xmCUiIiIivcViVgNWrVoFZ2dnmJmZwdXVFWfOnMl1+1OnTsHV1RVmZmaoWLEiVq9eraOklBN1+nDPnj1o3749ypQpA2trazRp0gRHjhzRYVrKjrrvwwznzp2DkZER6tWrp92A9EHq9mFqaipmzpwJJycnmJqaolKlSli/fr2O0tL71O2/LVu2oG7durCwsIC9vT2GDh2Kly9f6igtve/06dPo3r07HBwcIJPJsG/fvg/uU2DqGUEfZfv27cLY2FisXbtW3Lx5U0ycOFFYWlqKyMjIbLe/f/++sLCwEBMnThQ3b94Ua9euFcbGxmLXrl06Tk4Z1O3DiRMnip9++klcvnxZ3LlzR8yYMUMYGxuLq1ev6jg5ZVC3DzO8fv1aVKxYUXTo0EHUrVtXN2EpW/npwx49eohGjRqJoKAgERERIS5duiTOnTunw9SUQd3+O3PmjDAwMBDLli0T9+/fF2fOnBG1atUSvXr10nFyynDw4EExc+ZMsXv3bgFA7N27N9ftC1I9w2L2IzVs2FCMGTMmU1v16tXF9OnTs91+2rRponr16pnaRo8eLRo3bqy1jJQ7dfswOzVr1hRz5szRdDTKo/z2Yb9+/cS3334rZs+ezWJWYur24aFDh4SNjY14+fKlLuLRB6jbf4sWLRIVK1bM1LZ8+XJRrlw5rWWkvMtLMVuQ6hlOM/gIaWlpCA0NRYcOHTK1d+jQAefPn892nwsXLmTZvmPHjggJCYFcLtdaVspefvrwfUqlEgkJCShZsqQ2ItIH5LcPN2zYgHv37mH27NnajkgfkJ8+/PPPP+Hm5oaFCxfC0dERVatWxZQpU5CcnKyLyPSO/PRf06ZN8fjxYxw8eBBCCDx79gy7du1C165ddRGZNKAg1TNGOj1bIRMTEwOFQgE7O7tM7XZ2doiOjs52n+jo6Gy3T09PR0xMDOzt7bWWl7LKTx++b/HixUhMTETfvn21EZE+ID99ePfuXUyfPh1nzpyBkRF/DUotP314//59nD17FmZmZti7dy9iYmLg5eWFV69ecd6sjuWn/5o2bYotW7agX79+SElJQXp6Onr06IFff/1VF5FJAwpSPcORWQ2QyWSZHgshsrR9aPvs2kl31O3DDNu2bYOvry8CAwNha2urrXiUB3ntQ4VCgQEDBmDOnDmoWrWqruJRHqjzPlQqlZDJZNiyZQsaNmyILl26YMmSJQgICODorETU6b+bN2/C29sbs2bNQmhoKA4fPoyIiAiMGTNGF1FJQwpKPcMhiY9QunRpGBoaZvnL8/nz51n+WslQtmzZbLc3MjJCqVKltJaVspefPswQGBiI4cOHY+fOnWjXrp02Y1Iu1O3DhIQEhISEICwsDOPHjwfwtjASQsDIyAhHjx5FmzZtdJKd3srP+9De3h6Ojo6wsbFRtdWoUQNCCDx+/BhVqlTRamb6n/z034IFC9CsWTNMnToVAFCnTh1YWlqiRYsW+OGHH/gppR4oSPUMR2Y/gomJCVxdXREUFJSpPSgoCE2bNs12nyZNmmTZ/ujRo3Bzc4OxsbHWslL28tOHwNsRWU9PT2zdupVzvCSmbh9aW1vj+vXrCA8PV32NGTMG1apVQ3h4OBo1aqSr6PT/8vM+bNasGZ4+fYo3b96o2u7cuQMDAwOUK1dOq3kps/z0X1JSEgwMMpcghoaGAP43ukcFW4GqZ3R+yVkhk7Ecib+/v7h586aYNGmSsLS0FA8ePBBCCDF9+nQxaNAg1fYZS1lMnjxZ3Lx5U/j7+3NpLomp24dbt24VRkZGYuXKlSIqKkr19fr1a6leQpGnbh++j6sZSE/dPkxISBDlypUTffr0Ef/88484deqUqFKlihgxYoRUL6FIU7f/NmzYIIyMjMSqVavEvXv3xNmzZ4Wbm5to2LChVC+hyEtISBBhYWEiLCxMABBLliwRYWFhquXVCnI9w2JWA1auXCmcnJyEiYmJqF+/vjh16pTquSFDhoiWLVtm2v7kyZPCxcVFmJiYiAoVKgg/Pz8dJ6b3qdOHLVu2FACyfA0ZMkT3wUlF3ffhu1jMFgzq9uGtW7dEu3bthLm5uShXrpzw8fERSUlJOk5NGdTtv+XLl4uaNWsKc3NzYW9vLzw8PMTjx491nJoyBAcH5/pvW0GuZ2RCcDyfiIiIiPQT58wSERERkd5iMUtEREREeovFLBERERHpLRazRERERKS3WMwSERERkd5iMUtEREREeovFLBERERHpLRazRERERKS3WMwSEQEICAhA8eLFpY6RbxUqVMDSpUtz3cbX1xf16tXTSR4iIl1hMUtEhYanpydkMlmWr//++0/qaAgICMiUyd7eHn379kVERIRGjn/lyhWMGjVK9Vgmk2Hfvn2ZtpkyZQqOHz+ukfPl5P3XaWdnh+7du+Off/5R+zj6/McFEekOi1kiKlQ6deqEqKioTF/Ozs5SxwIAWFtbIyoqCk+fPsXWrVsRHh6OHj16QKFQfPSxy5QpAwsLi1y3KVasGEqVKvXR5/qQd1/nX3/9hcTERHTt2hVpaWlaPzcRFT0sZomoUDE1NUXZsmUzfRkaGmLJkiX49NNPYWlpifLly8PLywtv3rzJ8Th///03WrduDSsrK1hbW8PV1RUhISGq58+fP4/PPvsM5ubmKF++PLy9vZGYmJhrNplMhrJly8Le3h6tW7fG7NmzcePGDdXIsZ+fHypVqgQTExNUq1YNmzdvzrS/r68vPvnkE5iamsLBwQHe3t6q596dZlChQgUAwOeffw6ZTKZ6/O40gyNHjsDMzAyvX7/OdA5vb2+0bNlSY6/Tzc0NkydPRmRkJG7fvq3aJrf+OHnyJIYOHYq4uDjVCK+vry8AIC0tDdOmTYOjoyMsLS3RqFEjnDx5Mtc8RFS4sZgloiLBwMAAy5cvx40bN7Bx40acOHEC06ZNy3F7Dw8PlCtXDleuXEFoaCimT58OY2NjAMD169fRsWNH9O7dG9euXUNgYCDOnj2L8ePHq5XJ3NwcACCXy7F3715MnDgRX331FW7cuIHRo0dj6NChCA4OBgDs2rULv/zyC9asWYO7d+9i3759+PT/2rm7kCbbOAzgl5uO1tQ+PKhE23Ay9OAtEiozOqiMZNFi4KgcKZGVpQVGEZ20IAxCNCMoT2JDWSyhDYSKSCtLC1pK2BcxSUaUEpEVpWnT/3vw4kPT9VYmb+/G9QMP7o/n9n97g1zM5/avvyKu6/f7AQBOpxN9fX1K+1v5+fmYPXs2Ll26pPSNjo6iqakJdrt92vb5/v17XLhwAQCUnx/w7+eRl5eHuro65RPevr4+HDx4EACwfft2dHR0wOPxoLu7GzabDQUFBQgEAj9dExHFGCEiihElJSWiVqtFp9MpX4WFhRHnNjU1SUpKitJ2Op0ya9YspZ2UlCQulyvis9u2bZNdu3aF9d25c0dUKpUMDQ1FfGbi+i9fvpTc3FxJS0uT4eFhycvLk507d4Y9Y7PZxGw2i4hITU2NmEwmGRkZibi+Xq+XU6dOKW0A4vP5wuY4HA5ZvHix0t6/f7+sWbNGaV+7dk00Go28e/fut/YJQHQ6ncycOVMACACxWCwR54/70XmIiPT09EhcXJy8evUqrH/t2rVy5MiRf12fiGJX/J+N0kRE02v16tU4d+6c0tbpdACAmzdv4sSJE3j69Ck+fvyIUCiEL1++4PPnz8qcbx04cAClpaVobGxEfn4+bDYbjEYjAKCzsxM9PT1wu93KfBHB2NgYent7kZ2dHbG2Dx8+IDExESKCwcFB5OTkwOv1QqPR4NmzZ2EXuABg5cqVOH36NADAZrOhrq4OGRkZKCgogNlsxsaNGxEfP/Vf43a7HStWrMDr16+RmpoKt9sNs9mMOXPm/NY+k5KS0NXVhVAohLa2NlRXV6O+vj5szq+eBwB0dXVBRGAymcL6h4eH/5N3gYno/4lhlohiik6nQ2ZmZlhfMBiE2WxGWVkZjh8/jrlz56K9vR07duzA169fI65z7NgxFBUV4fLly7h69SocDgc8Hg+sVivGxsawe/fusHdWxy1cuPC7tY2HPJVKhXnz5k0KbXFxcWFtEVH60tPT8fz5c1y/fh0tLS3Yu3cvqqur0dbWFvbn+1+xbNkyGI1GeDwe7NmzBz6fD06nUxmf6j5VKpVyBllZWejv78fmzZtx+/ZtAFM7j/F61Go1Ojs7oVarw8YSExN/ae9EFDsYZoko5j148AChUAg1NTVQqf65KtDU1PTD50wmE0wmEyorK7F161Y4nU5YrVbk5OTgyZMnk0Lzj3wb8ibKzs5Ge3s7iouLlb67d++Gffqp1WphsVhgsVhQXl6OrKwsPHr0CDk5OZPWS0hI+Kn/klBUVAS32420tDSoVCps2LBBGZvqPieqrKxEbW0tfD4frFbrT52HRqOZVP+SJUswOjqKN2/eYNWqVb9VExHFDl4AI6KYZzQaEQqFcObMGbx48QKNjY2T/uz9raGhIVRUVODWrVsIBoPo6OiA3+9XguXhw4dx7949lJeX4+HDhwgEAmhubsa+ffumXOOhQ4fgcrlQX1+PQCCA2tpaeL1e5eKTy+XC+fPn8fjxY2UPWq0Wer0+4noGgwGtra3o7+/HwMDAd7+v3W5HV1cXqqqqUFhYiBkzZihj07XP5ORklJaWwuFwQER+6jwMBgM+ffqE1tZWvH37FoODgzCZTLDb7SguLobX60Vvby/8fj9OnjyJK1eu/FJNRBRD/uQLu0RE06mkpEQ2bdoUcay2tlYWLFggWq1W1q9fLw0NDQJABgYGRCT8wtHw8LBs2bJF0tPTRaPRSGpqqlRUVIRderp//76sW7dOEhMTRafTyaJFi6Sqquq7tUW60DTR2bNnJSMjQxISEsRkMklDQ4My5vP5ZPny5ZKcnCw6nU5yc3OlpaVFGZ94Aay5uVkyMzMlPj5e9Hq9iEy+ADZu6dKlAkBu3LgxaWy69hkMBiU+Pl4uXrwoIj8+DxGRsrIySUlJEQDicDhERGRkZESOHj0qBoNBEhISZP78+WK1WqW7u/u7NRFRbIsTEfmzcZqIiIiIaGr4mgERERERRS2GWSIiIiKKWgyzRERERBS1GGaJiIiIKGoxzBIRERFR1GKYJSIiIqKoxTBLRERERFGLYZaIiIiIohbDLBERERFFLYZZIiIiIopaDLNEREREFLX+Bl/HcvFmEsTtAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -456,20 +509,17 @@
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 22.97it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 22.93it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 22.79it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.98it/s]\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\n",
+      "     - AUC: 66.62%\n",
+      "    - Optimal Threshold: 0.0519325\n",
+      "    - F1 Score: 0.90\n",
+      "    - CONFUSION MATRIX:\n",
+      " [[ 8 12]\n",
+      " [ 5 75]] \n",
       "\n"
      ]
     },
@@ -477,9 +527,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:03<00:00, 22.94it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.92it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.92it/s]\n"
+      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 22.02it/s]\n",
+      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 21.99it/s]\n",
+      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 21.96it/s]\n",
+      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.06it/s]\n"
      ]
     },
     {
@@ -493,29 +544,27 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:05<00:00, 22.80it/s]\n"
+      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.02it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (31.58 s)\n",
       "\n",
-      "Dataset               Accuracy\n",
+      "- OK - Evaluating model (13.85 s)\n",
+      "\n",
+      "Dataset               F1 Score\n",
       "------------------------------\n",
-      "Anonaly lvl 1           100.00\n",
-      "Anonaly lvl 2            48.00\n",
-      "Anonaly lvl 3            48.00\n",
+      "Anonaly lvl 1 test        0.93\n",
+      "Anonaly lvl 2 test        1.00\n",
+      "Anonaly lvl 3 test        1.00\n",
       "\n",
-      "Anomaly all              74.00\n",
+      "Anomaly all test          0.97\n",
       "\n",
-      "No Anomaly Train         93.75\n",
-      "No Anomaly Test          85.00\n",
-      "No Anomaly All           92.00\n",
+      "No Anomaly Test           0.57\n",
       "\n",
-      "All without train        75.83\n",
-      "All with train           83.00\n"
+      "All test                  0.90\n"
      ]
     }
    ],
@@ -536,20 +585,20 @@
      "output_type": "stream",
      "text": [
       "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (0.55 ms)\n",
+      "- OK - Setting seed to 42 (0.82 ms)\n",
       "\n",
       "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (2.83 ms)\n",
+      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
+      "- OK - Setting datasets path (4.21 ms)\n",
       "\n",
       "- Setting config\n",
       "     Output folder path: ../output/cookies_3_steps_5000_small\n",
-      "- OK - Setting config (6.16 ms)\n",
+      "- OK - Setting config (4.03 ms)\n",
       "\n",
       "- Prepare teacher, student & autoencoder\n",
       "     No weight to load\n",
       "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (61.26 ms)\n",
+      "- OK - Prepare teacher, student & autoencoder (61.64 ms)\n",
       "\n",
       "- Normalizing teacher\n"
      ]
@@ -558,15 +607,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 53.63it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 53.53it/s]\n"
+      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.22it/s]\n",
+      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 50.12it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (2.69 s)\n",
+      "- OK - Normalizing teacher (2.85 s)\n",
       "\n",
       "- Train\n"
      ]
@@ -575,513 +624,490 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 0.8730  : 100%|███████████████████████████████████████████████████████| 5000/5000 [10:04<00:00,  8.28it/s]\n"
+      "       Current loss: 23.5000  :   0%|                                                                                  | 1/5000 [00:01<2:12:18,  1.59s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (604.10 s)\n",
-      "\n",
-      "- Saving models to ../output/cookies_3_steps_5000_small/all_models.pth\n",
-      "- OK - Saving models (76.92 ms)\n",
-      "\n",
-      "- Saving map normalization to ../output/cookies_3_steps_5000_small/map_normalization.pth\n"
+      "F1 Validation 0.6451612903225806\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 29.84it/s]\n"
+      "       Current loss: 14.7530  :   2%|█▋                                                                                | 101/5000 [00:15<41:08,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (299.94 ms)\n",
-      "\n",
-      "- Evaluating model\n"
+      "F1 Validation 0.75\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:08<00:00, 22.84it/s]\n"
+      "       Current loss: 6.0656  :   4%|███▎                                                                               | 201/5000 [00:28<40:06,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "     - AUC: 99.21%\n",
-      "    - Optimal Threshold: 0.1119701\n",
-      "    - F1 Score: 0.95\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[97  3]\n",
-      " [ 7 93]] \n",
-      "\n"
+      "F1 Validation 0.8205128205128205\n"
      ]
     },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIhCAYAAABdSTJTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACHbUlEQVR4nOzdd1iT198G8DtAmAoutoq4Vx1AVRy1orhXrQPFgXvPqnW0zqqt1j1woahFxd26oYp7I1Sts4obVBQFmSE57x++5GdkSBB4CNyf6+Jqc/KMOxyCX07Ocx6ZEEKAiIiIiEgH6UkdgIiIiIgoq1jMEhEREZHOYjFLRERERDqLxSwRERER6SwWs0RERESks1jMEhEREZHOYjFLRERERDqLxSwRERER6SwWs0RERESks1jMEhGlwdfXFzKZTP1lYGAAW1tbeHh44N69e2nuo1Ao4O3tDVdXV1hYWMDExARVqlTBpEmT8Pr16zT3UalU2LJlC5o1a4YSJUpALpfDysoKbdu2xf79+6FSqT6bNTExEStWrEDDhg1RtGhRGBoawt7eHl27dsXJkye/6PtARJTXsZglIsrAxo0bcf78efz9998YMWIE/vrrLzRs2BBRUVEa28XFxcHd3R0jR45E7dq1sW3bNhw6dAi9evXC2rVrUbt2bdy5c0djn4SEBLRu3Rp9+vSBlZUVvL29cfz4caxevRp2dnbo0qUL9u/fn2G+yMhINGjQAOPGjUP16tXh6+uLY8eOYeHChdDX10fTpk3xzz//ZPv3hYgozxBERJTKxo0bBQBx+fJljfaZM2cKAGLDhg0a7YMGDRIAxPbt21Md686dO8LCwkJUq1ZNJCcnq9uHDh0qAIhNmzalmeHu3bvin3/+yTBnq1athIGBgTh27Fiaz1+6dEk8evQow2NkVlxcXLYch4goO3FklohICy4uLgCAFy9eqNsiIiKwYcMGtGjRAt26dUu1T8WKFfHjjz/i33//xb59+9T7rF+/Hi1atEDv3r3TPFeFChVQo0aNdLMEBwfj8OHD6N+/P9zc3NLc5uuvv0bp0qUBADNmzIBMJku1TcqUiocPH6rbypQpg7Zt22LPnj2oXbs2jI2NMXPmTNSuXRuNGjVKdQylUgl7e3t06tRJ3ZaUlIRffvkFlStXhpGRESwtLdG3b1+8evUq3ddERKQtFrNERFoICwsD8KFATREUFITk5GR07Ngx3f1SngsMDFTvo1AoMtzncwICAjSOnd2uXr2KCRMmYNSoUThy5Ai+//579O3bF2fOnEk1bzggIADPnz9H3759AXyYC9yhQwf8+uuv6NGjBw4ePIhff/0VgYGB+PbbbxEfH58jmYmo4DGQOgARUV6mVCqRnJyMhIQEnD17Fr/88gu++eYbtG/fXr3N48ePAQCOjo7pHifluZRtM7PP52THMTLy8uVL3Lx5U6NwL1u2LCZMmABfX1/MmTNH3e7r6wtra2u0atUKALBjxw4cOXIEu3fv1hitrVmzJr7++mv4+vpi6NChOZKbiAoWjswSEWWgXr16kMvlKFy4MFq2bImiRYvizz//hIFB1sYC0vqYP6+qUaOGRiELAMWLF0e7du2wadMm9UoLUVFR+PPPP9G7d2/19+XAgQMoUqQI2rVrh+TkZPVXrVq1YGNjgxMnTuT2yyGifIrFLBFRBjZv3ozLly/j+PHjGDx4MG7duoXu3btrbJMyJzVlCkJaUp4rVapUpvf5nOw4RkZsbW3TbO/Xrx+ePXumnjKxbds2JCYmwsvLS73Nixcv8PbtWxgaGkIul2t8RUREIDIyMkcyE1HBw2KWiCgDVapUgYuLC5o0aYLVq1djwIABOHLkCHbt2qXepkmTJjAwMFBf3JWWlOfc3d3V+8jl8gz3+ZwWLVpoHPtzjI2NAXxYl/Zj6RWW6Y0it2jRAnZ2dti4cSOAD8uX1a1bF1WrVlVvU6JECRQvXhyXL19O82vVqlWZykxE9DksZomItDB//nwULVoU06ZNU3/MbmNjg379+uHo0aPw9/dPtc/du3fx22+/oVq1auqLtWxsbDBgwAAcPXoUmzdvTvNc9+/fx7Vr19LN4uTkhFatWsHHxwfHjx9Pc5srV66o59aWKVMGAFId83Nr2X5KX18fvXr1wr59+3D69GlcuXIF/fr109imbdu2eP36NZRKJVxcXFJ9VapUSatzEhGlRyaEEFKHICLKa3x9fdG3b19cvnxZvRxXigULFmDixInYsmULevbsCQCIjY1FmzZtcPbsWQwaNAjt2rWDkZERLly4gN9//x2mpqb4+++/NYq4hIQEdOzYEQEBAejevTu+++47WFtbIzIyEoGBgdi4cSO2b9+ODh06pJszMjISLVu2xPXr19GvXz+0atUKRYsWRXh4OPbv349t27YhODgYNWvWRHR0NBwdHWFvb49Zs2bBwMAAvr6+uHr1KsLCwhAWFqYueMuUKYPq1avjwIEDaZ737t27qFSpEkqWLInXr18jPDwcFhYW6ueVSiXatWuHixcvYvTo0ahTpw7kcjmePn2KoKAgdOjQAd99911Wu4eI6H+kXuiWiCgvSu+mCUIIER8fL0qXLi0qVKigcROEpKQksXLlSlG3bl1RqFAhYWRkJCpVqiQmTpwoIiMj0zxPcnKy2LRpk3BzcxPFihUTBgYGwtLSUrRq1Ups3bpVKJXKz2aNj48Xy5YtE66ursLc3FwYGBgIOzs70alTJ3Hw4EGNbS9duiTq168vzMzMhL29vZg+fbpYv369ACDCwsLU2zk4OIg2bdpkeN769esLAMLT0zPN5xUKhfj9999FzZo1hbGxsShUqJCoXLmyGDx4sLh3795nXxcRUWZwZJaIiIiIdBbnzBIRERGRzmIxS0REREQ6i8UsEREREeksFrNEREREpLNYzBIRERGRzmIxS0REREQ6y0DqALlNpVLh+fPnKFy4cLq3aiQiIiIi6QghEBMTAzs7O+jpZTz2WuCK2efPn6NUqVJSxyAiIiKiz3jy5AlKliyZ4TYFrpgtXLgwgA/fHHNz81w5p0KhQEBAAJo3bw65XJ4r56Tsw/7TfexD3cc+1G3sP92X230YHR2NUqVKqeu2jBS4YjZlaoG5uXmuFrOmpqYwNzfnm1gHsf90H/tQ97EPdRv7T/dJ1YeZmRLKC8CIiIiISGexmCUiIiIincViloiIiIh0FotZIiIiItJZLGaJiIiISGexmCUiIiIincViloiIiIh0FotZIiIiItJZLGaJiIiISGexmCUiIiIincViloiIiIh0FotZIiIiItJZLGaJiIiISGexmCUiIiIinSVpMXvq1Cm0a9cOdnZ2kMlk2Ldv32f3OXnyJJydnWFsbIyyZcti9erVOR+UiIiIiPIkSYvZ2NhY1KxZEytWrMjU9mFhYWjdujUaNWqEkJAQTJkyBaNGjcLu3btzOCkRERER5UUGUp68VatWaNWqVaa3X716NUqXLo0lS5YAAKpUqYIrV67g999/x/fff59DKUkqQgjEK5RSx4BCkYxEJRCXlAy5kEkdh7KAfaj72Ie6jf2n++LjE5Co/PBvc14jaTGrrfPnz6N58+YabS1atICPjw8UCgXkcnmqfRITE5GYmKh+HB0dDQBQKBRQKBQ5G/j/pZwnt86XHwgh4LH+Mq4+fit1lP9ngImXjksdgr4I+1D3sQ91G/tPV8Xdu4io4+th7fEL3NwSYSHL+T9ItKmZdKqYjYiIgLW1tUabtbU1kpOTERkZCVtb21T7zJs3DzNnzkzVHhAQAFNT0xzLmpbAwMBcPZ8uS1QCVx/r1I8nERFRviKUCkSd8EXMlT8BAO/O78Tx48VhpJ/z546Li8v0tjpXLcg++WsgZbj70/YUkydPxrhx49SPo6OjUapUKTRv3hzm5uY5F/QjCoUCgYGBcHd3T3P0mFKLS0pW/wV/4cfGMDHMhXdOOhSKZBw/fhxubm6Qy3XuLUNgH+YH7EPdxv7TPQ8fPkR/r954fOUKAGDw0GH4pnETtGnRDIaGhjl+/pRP0jNDp36ibGxsEBERodH28uVLGBgYoHjx4mnuY2RkBCMjo1Ttcrk81wtLKc6pqz6eU2VuZgxTQ+l+VBUKBYz0AQszY/afjmIf6j72oW5j/+mWPXv2oF+/fnj37h2KFi0KX19ftGrVCocOHYKhoWGu9KE259CpdWZdXV1TfVQfEBAAFxcXvjmIiIiIvlBUVBT69++Pd+/ewdXVFSEhIWjfvr3UsTIkaTH7/v17hIaGIjQ0FMCHpbdCQ0Px+PFjAB+mCPTu3Vu9/ZAhQ/Do0SOMGzcOt27dwoYNG+Dj44Px48dLEZ+IiIgoXylatCg2btyIiRMn4uTJk3BwcJA60mdJOs3gypUraNKkifpxytzWPn36wNfXF+Hh4erCFgAcHR1x6NAhjB07FitXroSdnR2WLVvGZbmIiIiIsmjHjh0wNzdHy5YtAQAdO3ZEx44dpQ2lBUmL2W+//TbD9cp8fX1TtTVu3BhXr17NwVSUntxc9zUuSfr1ZYmIiPKz+Ph4jBs3DqtXr0bx4sVx7do12NnZSR1Lazp1ARhJRwiBzqvPI/hRlNRRiIiI6AvduXMHXbt2xbVr1yCTyTBkyBBYWVlJHStLWMxSpsQrlJIUsi4ORWEil25ZLiIiovzGz88PgwcPRmxsLKysrPDHH3/A3d1d6lhZxmKWtHblp2YwzaV1X03k+umuIUxERESZp1QqMXjwYPj4+AAAmjRpAj8/vzRvOqVLWMxSmj6dH/vxHFZTQ31J130lIiIi7enrfxiIkslkmD59On766Sd1my5jRUKpcH4sERFR/pGQkABjY2MAwLJly+Dl5YWGDRtKnCr76NRNEyh3ZDQ/lnNYiYiIdMP79+/Rp08fdOjQASqVCgBgamqarwpZgCOz9Bmfzo/lHFYiIqK87/r16+jatStu374NPT09XLhwAfXr15c6Vo7gyCxlKGV+bMoXC1kiIqK8SwiBdevWoU6dOrh9+zbs7e1x4sSJfFvIAhyZLfDSuhECb1hARESke6KjozF48GBs374dANCqVSts3rwZJUqUkDhZzmIxW4DxQi8iIqL8w8PDA4cPH4a+vj7mzZuHH374AXp6+f9DeBazBdjnboTAi72IiIh0x5w5c/Dff/9h06ZNcHV1lTpOrmExSwDSvhECL/YiIiLKu969e4dz586hVatWAIDatWvj5s2bMDAoWOVd/h97pkz59EIvXuxFRESUd125cgVOTk7o0KEDrly5om4vaIUswGKWiIiISGcIIbB06VLUr18fDx48gL29vdSRJFfwynciIiIiHRQVFYV+/fph3759AIBOnTrBx8cHRYoUkTSX1DgyS0RERJTHXbx4EbVr18a+fftgaGiI5cuXY9euXQW+kAU4MpsvpbV2bFq4niwREZFuOHnyJB49eoRy5cphx44dcHJykjpSnsFiNq9TKoHTp4HwcMDWFmjUCNBPf7ksrh1LRESU/4wfPx4ymQyDBw+Gubm51HHyFE4zyMv27AHKlAGaNAF69Pjw3zJlPrSn43Nrx6aF68kSERHlLWfOnEGLFi0QGxsLANDT08OECRNYyKaBI7N51Z49QOfOgBCa7c+efWjftQvo1CnDQ6S1dmxauJ4sERFR3qBSqfDbb7/h559/hlKpxJw5czB37lypY+VpLGbzIqUSGD1aXcgKAPFyo/89L5MBP0wEWrVJNeXg43mwKWvHEhERUd738uVL9OrVCwEBAQCAnj17YsqUKRKnyvtY6eRFp08DT58C+FDIdvacj+CSVVNvN/Pv3M1FREREOeLEiRPo0aMHwsPDYWJighUrVqBv37785DQTWMzmReHh6v+NlxulXch+BufBEhER6YY//vgDffr0gUqlQtWqVbFjxw5Uq1ZN6lg6g8VsXmRrm2bzleWeMFUk/K/h0GHgm0Zpbst5sERERLrBzc0NxYsXR9u2bbF8+XKYmZlJHUmnsJjNixo1AkqW/HCx10dMFQkwVSR+mDNbsiTQ5JsMl+kiIiKivOnu3buoWLEiAMDOzg7//PMPbNMZzKKMcWmuvEhfH1i69MP/fzq6mvJ4yRIWskRERDomOTkZ06ZNQ5UqVbB79251OwvZrGMxm1d16vRh+S1bO832kiUztSwXERER5S3Pnj1D06ZNMXv2bKhUKly4cEHqSPkCi9m8rFMn4NbN/z0+dBgIC2MhS0REpGOOHDmCWrVq4dSpUyhUqBC2bduGBQsWSB0rX2Axm9d9PJXgm4xvZUtERER5i0KhwKRJk9CqVStERkaidu3auHr1Kjw8PKSOlm+wmCUiIiLKIadOncJvv/0GABg+fDjOnTuHChUqSJwqf+FqBkREREQ5pGnTppgyZQpq166Nzp07Sx0nX+LILBEREVE2SUpKws8//4xnHy2vOWfOHBayOYgjs0RERETZ4OHDh+jWrRsuXbqE06dPIygoiDcwygUsZiUihEC8QvnZ7eKSPr8NERERSWvv3r3o168f3r59iyJFimDs2LEsZHMJi1kJCCHQefV5BD+KkjoKERERfYHExERMmDABy5cvBwDUq1cP27dvh4ODg8TJCg4WsxKIVyi1LmRdHIrCRM5luYiIiPKKZ8+eoUOHDggODgYATJgwAXPmzIFcLpc4WcHCYlZiV35qBlPDzxepJnJ9flxBRESUhxQpUgTx8fEoXrw4Nm3ahDZt2kgdqUBiMSsxU0N9mBqyG4iIiHRBQkICDA0NoaenBzMzM+zduxempqYoWbKk1NEKLC7NRURERJQJd+7cQd26dTF//nx1W8WKFVnISozFLBEREdFn+Pn5wdnZGdeuXcOyZcsQGxsrdST6fyxmiYiIiNIRFxeHAQMGoGfPnoiNjcW3336LK1euwMzMTOpo9P9YzBIRERGl4datW6hbty58fHwgk8kwffp0/P3337Czs5M6Gn2EVx4RERERfSI6OhoNGjRAVFQUbGxs4OfnBzc3N6ljURo4MktERET0CXNzc8yaNQvNmjVDaGgoC9k8jMUsEREREYDr168jNDRU/Xj48OE4evQorK2tpQtFn8ViloiIiAo0IQTWrVuHOnXqoHPnzoiOjgYAyGQy6OmxVMrrOGeWiIiICqyYmBgMHjwY27ZtAwBUqFABCoVC4lSkDf65QURERAVSaGgonJ2dsW3bNujr6+PXX3/FwYMHUbx4camjkRY4MktEREQFihACq1evxtixY5GYmIhSpUph+/btqF+/vtTRKAs4MktEREQFihACf/31FxITE9GuXTuEhISwkNVhHJklIiKiAkVPTw+bN2/Gzp07MXToUMhkMqkj0RfgyCwRERHla0IILF26FEOHDlW3WVpaYtiwYSxk8wGOzBIREVG+FRUVhX79+mHfvn0AgC5duvAGCPkMi1kiIiLKly5evIhu3brh0aNHMDQ0xMKFC9GkSROpY1E24zQDIiIiyleEEFi4cCEaNmyIR48eoVy5cjh37hxGjBjBaQX5EEdmiYiIKF/p168ffH19AQBdu3bF2rVrYWFhIW0oyjEcmSUiIqJ8pVu3bjAxMYG3tze2b9/OQjaf48gsERER6TSVSoW7d++icuXKAICWLVsiLCwM1tbWEiej3MCR2VwghECiEohLSv7/L6XUkYiIiPKFly9fonXr1qhXrx7CwsLU7SxkCw6OzOYwIQQ81l/G1ccGmHjpuNRxiIiI8o2TJ0+ie/fuCA8Ph4mJCa5fvw5HR0epY1Eu48hsDotXKHH18ds0n3NxKAoTuX7uBiIiItJxSqUSs2bNgpubG8LDw1GlShVcunQJ7du3lzoaSYAjs7nowo+NYW5mrH5sItfnEiFERERaiIiIQM+ePXHs2DEAgJeXF1asWAEzMzOJk5FUWMzmIhNDfZga8ltORESUVUuXLsWxY8dgamoKb29v9O7dW+pIJDFWVkRERKQzpk+fjqdPn2Lq1Knq1QuoYOOcWSIiIsqznj17hvHjxyM5ORkAYGxsjC1btrCQJTWOzBIREVGedOTIEfTq1QuRkZEwNzfHtGnTpI5EeRBHZomIiChPUSgUmDx5Mlq1aoXIyEjUqlULHh4eUseiPIojs0RERJRnPHnyBB4eHjh37hwAYNiwYVi4cCGMjY0/sycVVCxmiYiIKE84duwYunbtijdv3sDc3Bzr169Hly5dpI5FeRyLWSIiIsoTbGxsEB8fD2dnZ/j7+6NcuXJSRyIdwGKWiIiIJBMbG6u+4UG1atVw7NgxODk5wcjISOJkpCt4ARgRERFJYt++fShTpox6fiwAuLq6spAlrbCYJSIiolyVmJiI0aNH47vvvkNkZCQWL14sdSTSYZIXs6tWrYKjoyOMjY3h7OyM06dPZ7i9n58fatasCVNTU9ja2qJv3754/fp1LqUlIiKiL3H//n00aNAAy5YtAwCMHz8eW7dulTgV6TJJi1l/f3+MGTMGU6dORUhICBo1aoRWrVrh8ePHaW5/5swZ9O7dG/3798e///6LnTt34vLlyxgwYEAuJyciIiJt7dy5E7Vr10ZwcDCKFSuGAwcOYMGCBZDL5VJHIx0maTG7aNEi9O/fHwMGDECVKlWwZMkSlCpVCt7e3mluf+HCBZQpUwajRo2Co6MjGjZsiMGDB+PKlSu5nJyIiIi0cf36dXh6eiImJgYNGjRAaGgo2rRpI3UsygckW80gKSkJwcHBmDRpkkZ78+bNNSaCf6x+/fqYOnUqDh06hFatWuHly5fYtWtXhm+GxMREJCYmqh9HR0cD+HB3EYVCkQ2vJGMKRbLG/+fGOSl7pfQZ+053sQ91H/tQtykUClSvXh0dO3ZExYoVMWPGDBgYGLA/dUhuvwe1OY9kxWxkZCSUSiWsra012q2trREREZHmPvXr14efnx+6deuGhIQEJCcno3379li+fHm655k3bx5mzpyZqj0gIACmpqZf9iIyIVEJpHybjx8/DiP9HD8l5ZDAwECpI9AXYh/qPvahbjl37hxq1aoFU1NTyGQy9O7dG3p6eggICJA6GmVRbr0H4+LiMr2t5OvMymQyjcdCiFRtKW7evIlRo0Zh2rRpaNGiBcLDwzFhwgQMGTIEPj4+ae4zefJkjBs3Tv04OjoapUqVQvPmzWFubp59LyQdcUnJmHjpOADAzc0NFma8HZ+uUSgUCAwMhLu7O+d16Sj2oe5jH+qWuLg4jBs3Dhs2bECXLl2wceNG/P3332jRogX7T0fl9nsw5ZP0zJCsmC1RogT09fVTjcK+fPky1Whtinnz5qFBgwaYMGECAKBGjRowMzNDo0aN8Msvv8DW1jbVPkZGRmmuVyeXy3OlM+Tif4W5XG7AN7EOy62fGco57EPdxz7M+27duoWuXbvixo0bkMlkqFKlCgwMPpQb7D/dl2v1kxbnkOwCMENDQzg7O6carg4MDET9+vXT3CcuLg56epqR9fU/fG4vhMiZoERERJQpmzZtgouLC27cuAFra2sEBgZi5syZqf7tJspOkv50jRs3DuvXr8eGDRtw69YtjB07Fo8fP8aQIUMAfJgi0Lt3b/X27dq1w549e+Dt7Y0HDx7g7NmzGDVqFOrUqQM7OzupXgYREVGBFhsbCy8vL3h5eSEuLg5NmzZFaGgomjZtKnU0KgAknTPbrVs3vH79GrNmzUJ4eDiqV6+OQ4cOwcHBAQAQHh6useasl5cXYmJisGLFCvzwww8oUqQI3Nzc8Ntvv0n1EoiIiAq8uLg4BAQEQE9PDzNnzsTkyZPVn5wS5TTJLwAbNmwYhg0bluZzvr6+qdpGjhyJkSNH5nAqIiIiyixLS0v4+/tDpVKhcePGUsehAoaTWIiIiEgrMTEx8PT0hJ+fn7qtUaNGLGRJEixmiYiIKNNCQ0Ph7OyMrVu3YsSIEVotoUSUE1jMEhER0WcJIeDt7Y169erh3r17KFmyJA4cOJAra7YTZUTyObNERESUt7179w4DBw7Ezp07AQBt27aFr68vihcvLnEyIhazRERElIHY2Fg4Ozvj/v37MDAwwG+//YaxY8eme7dOotzGaQZERESULjMzM3z//fdwcHDAmTNnMG7cOBaylKewmCUiIiINUVFRePr0qfrxL7/8gpCQENStW1fCVERpYzFLREREahcvXkTt2rXRuXNnKBQKAIBcLkfRokUlTkaUNhazREREBCEEFi5ciIYNG+LRo0d49eoVnj17JnUsos9iMUtERFTAvX79Gu3bt8f48eORnJyMLl264OrVqyhTpozU0Yg+i8UsERFRAXb27FnUqlULBw4cgJGREby9veHv7w8LCwupoxFlCpfmIiIiKqCEEBg7diyePn2KChUqYMeOHahVq5bUsYi0wpFZIiKiAkomk8HPzw/9+/dHcHAwC1nSSSxmiYiICpCTJ09i6dKl6scVKlTA+vXrUbhwYQlTEWUdpxkQEREVAEqlEnPnzsWMGTMghICTkxMaNWokdSyiL8ZiloiIKJ+LiIhAz549cezYMQBAnz594OTkJHEqouzBYpaIiCgfO3bsGDw9PfHixQuYmppi1apV6NOnj9SxiLIN58wSERHlU/PmzYO7uztevHiB6tWr4/LlyyxkKd9hMUtERJRPWVlZQQiBAQMG4OLFi6hatarUkYiyHacZEBER5SPv379HoUKFAAD9+vVDpUqV0LBhQ4lTEeUcjswSERHlA8nJyZg8eTKqV6+ON2/eAPiwjiwLWcrvWMwSERHpuCdPnuDbb7/Fr7/+ikePHmH37t1SRyLKNSxmiYiIdNjBgwdRq1YtnD17Fubm5vD398fAgQOljkWUa1jMEhER6aCkpCSMHz8ebdu2xZs3b+Ds7IyrV6+ia9euUkcjylUsZomIiHTQjBkzsHDhQgDAqFGjcPbsWZQrV07iVES5j8UsERGRDho/fjxq1qyJPXv2YOnSpTAyMpI6EpEkWMwSERHpgMTERGzevBlCCABAsWLFcPXqVXz33XcSJyOSFteZJSIiyuMePHiArl27Ijg4GImJieoLvPT0OCZFxHcBERFRHrZr1y7Url0bwcHBKFasGGxtbaWORJSnsJglIiLKgxISEjB8+HB06dIF0dHRqF+/PkJDQ9G2bVupoxHlKSxmiYiI8ph79+7B1dUVq1atAgBMmjQJJ06cQKlSpSRORpT3cM4sERFRHvP06VP8888/KFGiBLZs2YKWLVtKHYkoz2IxS0RElAcIISCTyQAATZo0ga+vL5o2bQp7e3uJkxHlbZxmQEREJLFbt26hYcOGuHv3rrqtd+/eLGSJMoHFLBERkYQ2bdoEFxcXnDt3DqNGjZI6DpHOYTFLREQkgdjYWHh5ecHLywtxcXFwc3ODr6+v1LGIdA6LWSIiolx248YNfP3119i0aRP09PQwa9YsBAQEwMbGRupoRDqHF4ARERHloosXL6JJkyaIj4+Hra0ttm7dim+//VbqWEQ6i8UsERFRLnJyckLNmjVhbm6OLVu2wMrKSupIRDqNxSwREVEOu3nzJipUqAC5XA65XI6DBw+iSJEi0NPjbD+iL8V3ERERUQ4RQmD16tVwcnLC1KlT1e3FihVjIUuUTTgyS0RElAOio6MxcOBA7NixA8CHtWSVSiX09fUlTkaUv/DPQiIiomwWHBwMJycn7NixAwYGBliwYAH+/PNPFrJEOYAjs0RERNlECIEVK1Zg/PjxSEpKgoODA7Zv34569epJHY0o3+LILBERUTZ59uwZpkyZgqSkJHTs2BEhISEsZIlyGEdmiYiIsknJkiWxbt06vHz5EiNHjoRMJpM6ElG+x2KWiIgoi4QQWLx4MWrXro0mTZoAADw8PCRORVSwsJglIiLKgjdv3sDLywv79++HjY0Nbt68iaJFi0odi6jAYTFLRESkpXPnzsHDwwNPnjyBkZERpk2bhiJFikgdi6hA4gVgREREmaRSqfDbb7/hm2++wZMnT1ChQgVcuHABQ4cO5fxYIolwZJaIiCgT4uPj8f333+Pw4cMAgO7du2PNmjUoXLiwxMmICjaOzBIREWWCsbExihQpAmNjY6xduxZ+fn4sZInyABazRERE6VAqlYiNjQUAyGQyrFmzBpcvX8bAgQM5rYAoj2AxS0RElIYXL16gZcuW6NmzJ4QQAIDChQujevXqEicjoo9xziwREdEnjh8/Dk9PT0RERMDU1BS3b99GlSpVpI5FRGngyCwREdH/UyqVmD59Opo1a4aIiAhUq1YNly9fZiFLlIdxZJaIiAjA8+fP4enpiRMnTgAA+vfvj2XLlsHU1FTaYESUIRazRERU4Akh0KFDB1y5cgVmZmZYs2YNPD09pY5FRJmQpWkGycnJ+Pvvv7FmzRrExMQA+PAX7fv377M1HBERUW6QyWRYtmwZnJ2dcfXqVRayRDpE65HZR48eoWXLlnj8+DESExPh7u6OwoULY/78+UhISMDq1atzIicREVG2evr0KUJDQ9G2bVsAgKurKy5fvswlt4h0jNYjs6NHj4aLiwuioqJgYmKibv/uu+9w7NixbA1HRESUEw4dOoRatWqha9euuHHjhrqdhSyR7tF6ZPbMmTM4e/YsDA0NNdodHBzw7NmzbAtGRESU3RQKBaZOnYoFCxYAAJycnDQGZohI92hdzKpUKiiVylTtT58+5W39iIgoz3r06BE8PDxw4cIFAMDIkSOxYMECGBkZSZyMiL6E1tMM3N3dsWTJEvVjmUyG9+/fY/r06WjdunV2ZiMiIsoWf/75J2rXro0LFy7AwsICu3fvxrJly1jIEuUDWo/MLl68GE2aNEHVqlWRkJCAHj164N69eyhRogS2bduWExmJiIi+yNWrVxEVFYU6depg+/btcHR0lDoSEWUTrYtZOzs7hIaGYvv27QgODoZKpUL//v3h6enJeUdERJRnCCHUF3RNmzYNVlZWGDhwYKprPohIt2k9zeDUqVOQy+Xo27cvVqxYgVWrVmHAgAGQy+U4depUTmQkIiLSyu7du+Hm5oaEhAQAgL6+PoYPH85Cligf0rqYbdKkCd68eZOq/d27d2jSpEm2hCIiIsqKhIQEjBgxAp07d8aJEyewcuVKqSMRUQ7TeprBxx/bfOz169cwMzPLllBERETaunfvHrp164aQkBAAwI8//ohRo0ZJnIqIclqmi9lOnToB+LB6gZeXl8YVoEqlEteuXUP9+vWzPyEREdFnbN++HQMHDsT79+9RokQJbN68Ga1atZI6FhHlgkwXsxYWFgA+jMwWLlxY42IvQ0ND1KtXDwMHDsz+hERERBlYuHAhxo8fDwBo1KgRtm3bBnt7e4lTEVFuyXQxu3HjRgBAmTJlMH78eE4pICKiPOH777/H3LlzMWzYMEyfPh0GBlrPoCMiHab1O3769Ok5kYOIiCjTQkJCULt2bQAfBlnu3buHYsWKSZyKiKSg9WoGALBr1y507doV9erVg5OTk8aXtlatWgVHR0cYGxvD2dkZp0+fznD7xMRETJ06FQ4ODjAyMkK5cuWwYcOGrLwMIiLSMbGxsejXrx+cnJxw6NAhdTsLWaKCS+tidtmyZejbty+srKwQEhKCOnXqoHjx4njw4IHWk+39/f0xZswYTJ06FSEhIWjUqBFatWqFx48fp7tP165dcezYMfj4+ODOnTvYtm0bKleurO3LICIiHfP48WPUr18fGzduhJ6eHu7cuSN1JCLKA7SeZrBq1SqsXbsW3bt3x6ZNmzBx4kSULVsW06ZNS3P92YwsWrQI/fv3x4ABAwAAS5YswdGjR+Ht7Y158+al2v7IkSM4efIkHjx4oP4rvEyZMtq+BCIi0iFCCPj6+mL8+PFISkqCjY0Ntm3bhm+//VbqaESUB2hdzKb8ZQwAJiYmiImJAQD06tUL9erVw4oVKzJ1nKSkJAQHB2PSpEka7c2bN8e5c+fS3Oevv/6Ci4sL5s+fjy1btsDMzAzt27fH7Nmz072VbmJiIhITE9WPo6OjAQAKhQIKhSJTWb+EQpGs8f+5cU7KXil9xr7TXexD3fX+/XuMGDECW7duBQA0bdoUmzZtgpWVFftTh/A9qPtyuw+1OY/WxayNjQ1ev34NBwcHODg44MKFC6hZsybCwsIghMj0cSIjI6FUKmFtba3Rbm1tjYiIiDT3efDgAc6cOQNjY2Ps3bsXkZGRGDZsGN68eZPuvNl58+Zh5syZqdoDAgJgamqa6bxZlagEUr7Nx48fh5F+jp+SckhgYKDUEegLsQ91z/nz57F161bo6emhR48e6NSpE65cuSJ1LMoivgd1X271YVxcXKa31bqYdXNzw/79++Hk5IT+/ftj7Nix2LVrF65cuaK+sYI2Pr2bWHp3GAMAlUoFmUwGPz8/9bq3ixYtQufOnbFy5co0R2cnT56McePGqR9HR0ejVKlSaN68OczNzbXOq624pGRMvHQcwIfvnYWZcY6fk7KXQqFAYGAg3N3dIZfLpY5DWcA+1F2tW7eGEALu7u6IjY1lH+oovgd1X273Ycon6ZmhdTG7du1aqFQqAMCQIUNQrFgxnDlzBu3atcOQIUMyfZwSJUpAX18/1Sjsy5cvU43WprC1tYW9vb26kAWAKlWqQAiBp0+fokKFCqn2MTIy0rhbWQq5XJ4rnSEX/yvM5XIDvol1WG79zFDOYR/mfdHR0Zg8eTKmT58OKysrAMBvv/0GhUKBQ4cOsQ91HPtP9+Va/aTFObRezUBPT09jQequXbti2bJlGDVqFF69epXp4xgaGsLZ2TnVcHVgYGC6t8Vt0KABnj9/jvfv36vb7t69Cz09PZQsWVLLV0JERHnJ1atX4eTkhFWrVqF///5SxyEiHZGldWY/FRERgZEjR6J8+fJa7Tdu3DisX78eGzZswK1btzB27Fg8fvxYPcI7efJk9O7dW719jx49ULx4cfTt2xc3b97EqVOnMGHCBPTr1y/dC8CIiChvE0JgxYoVcHV1xf3791G6dGlMmTJF6lhEpCMyXcy+ffsWnp6esLS0hJ2dHZYtWwaVSoVp06ahbNmyuHDhgtY3L+jWrRuWLFmCWbNmoVatWjh16hQOHToEBwcHAEB4eLjGmrOFChVCYGAg3r59CxcXF3h6eqJdu3ZYtmyZVuclIqK84e3bt+jcuTNGjhyJpKQktG/fHiEhIXB1dZU6GhHpiEzPmZ0yZQpOnTqFPn364MiRIxg7diyOHDmChIQEHD58GI0bN85SgGHDhmHYsGFpPufr65uqrXLlyrwakogoH7h9+zZat26NsLAwyOVyLFiwAKNGjUr3ImAiorRkupg9ePAgNm7ciGbNmmHYsGEoX748KlasiCVLluRgPCIiyq/s7Oygr68PR0dH+Pv74+uvv5Y6EhHpoEwXs8+fP0fVqlUBAGXLloWxsbH6zl1ERESZER0djcKFC0Mmk8Hc3BwHDhyAtbU1ihQpInU0ItJRmZ4zq1KpNJZJ0NfXh5mZWY6EIiKi/Of8+fOoXr26xp0iK1WqxEKWiL5IpkdmhRDw8vJSr9makJCAIUOGpCpo9+zZk70JiYhIp6lUKvz++++YMmUKlEol1qxZgyFDhnC9USLKFpkuZvv06aPxuGfPntkehoiI8pdXr16hT58+OHz4MADAw8MDa9asYSFLRNkm08Xsxo0bczIHERHlM6dOnUL37t3x/PlzGBsbY9myZRgwYABXKyCibKX17WyJiIg+Jzw8HM2bN0diYiIqVaqEHTt2oEaNGlLHIqJ8iMUsERFlO1tbW8ycORP//vsvVq1ahUKFCkkdiYjyKRazRESULYKCgmBlZYVq1aoBACZOnAgAnFZARDkq00tzERERpUWpVGLGjBlo2rQpunbtitjYWAAfilgWskSU0zgyS0REWRYeHg5PT08EBQUBAOrVq8cClohyVZZGZrds2YIGDRrAzs4Ojx49AgAsWbIEf/75Z7aGIyKivCswMBC1atVCUFAQzMzMsGXLFvj4+MDU1FTqaERUgGhdzHp7e2PcuHFo3bo13r59C6VSCQAoUqQIlixZkt35iIgoj0lOTsZPP/2EFi1a4OXLl6hRowauXLnC9ceJSBJaF7PLly/HunXrMHXqVOjr66vbXVxccP369WwNR0REeY9MJsOZM2cghMDgwYNx4cIFVK5cWepYRFRAaT1nNiwsDLVr107VbmRkpJ70T0RE+Y8QAjKZDPr6+ti6dSvOnDmDrl27Sh2LiAo4rUdmHR0dERoamqr98OHDqFq1anZkIiKiPEShUGDixIkYM2aMus3Ozo6FLBHlCVqPzE6YMAHDhw9HQkIChBC4dOkStm3bhnnz5mH9+vU5kZGIiCTy+PFjeHh44Pz58wCAfv36oWbNmhKnIiL6H62L2b59+yI5ORkTJ05EXFwcevToAXt7eyxduhQeHh45kZGIiCTw119/wcvLC1FRUbCwsICPjw8LWSLKc7K0zuzAgQMxcOBAREZGQqVSwcrKKrtzERGRRJKSkvDjjz+qV6j5+uuv4e/vD0dHR2mDERGlQes5szNnzsT9+/cBACVKlGAhS0SUjwgh0K5dO3UhO3bsWJw5c4aFLBHlWVoXs7t370bFihVRr149rFixAq9evcqJXEREJAGZTIbBgwejaNGi+PPPP7Fo0SIYGhpKHYuIKF1aF7PXrl3DtWvX4ObmhkWLFsHe3h6tW7fG1q1bERcXlxMZiYgoByUkJGisE96pUyc8ePAA7du3lzAVEVHmZOl2ttWqVcPcuXPx4MEDBAUFwdHREWPGjIGNjU125yMiohz033//oX79+nBzc8OzZ8/U7UWKFJEuFBGRFrJUzH7MzMwMJiYmMDQ0hEKhyI5MRESUC/z9/eHk5ISQkBAIIRAWFiZ1JCIirWWpmA0LC8OcOXNQtWpVuLi44OrVq5gxYwYiIiKyOx8REWWz+Ph4DBkyBB4eHoiJiUHDhg0RGhqKhg0bSh2NiEhrWi/N5erqikuXLuGrr75C37591evMEhFR3nfnzh107doV165dg0wmw5QpUzBjxgwYGGRppUYiIslp/durSZMmWL9+PapVq5YTeYiIKActXboU165dg5WVFf744w+4u7tLHYmI6ItoXczOnTs3J3IQEVEuWLBgAZKTkzFz5kzY2tpKHYeI6ItlqpgdN24cZs+eDTMzM4wbNy7DbRctWpQtwYiI6Mv9+++/WLNmDZYsWQI9PT2YmZlh7dq1UsciIso2mSpmQ0JC1CsVhISE5GggIiL6ckII+Pr6Yvjw4YiPj0fZsmUxZswYqWMREWW7TBWzQUFBaf4/ERHlPe/fv8ewYcOwZcsWAEDz5s3Ro0cPiVMREeUMrZfm6tevH2JiYlK1x8bGol+/ftkSioiIsubatWtwcXHBli1boKenhzlz5uDw4cOwsrKSOhoRUY7QupjdtGkT4uPjU7XHx8dj8+bN2RKKiIi05+/vj7p16+LOnTuwt7fHiRMnMGXKFOjpffH9cYiI8qxMr2YQHR0NIQSEEIiJiYGxsbH6OaVSiUOHDvEvfyIiCZUvXx4qlQqtWrXC5s2bUaJECakjERHluEwXs0WKFIFMJoNMJkPFihVTPS+TyTBz5sxsDUdERBl7+/YtihQpAgBwdnbG+fPnUatWLY7GElGBkeliNigoCEIIuLm5Yffu3ShWrJj6OUNDQzg4OMDOzi5HQhIRkSYhBFatWoUpU6YgKCgITk5OAKD+LxFRQZHpYrZx48YAgLCwMJQuXRoymSzHQhERUfrevn2LgQMHYteuXQAAX19fFrFEVGBlqpi9du0aqlevDj09Pbx79w7Xr19Pd9saNWpkWzgiItJ0+fJldOvWDWFhYZDL5Zg/fz5Gjx4tdSwiIslkqpitVasWIiIiYGVlhVq1akEmk0EIkWo7mUwGpVKZ7SGJiAo6IQSWLl2KiRMnQqFQwNHREf7+/vj666+ljkZEJKlMFbNhYWGwtLRU/z8REeWu3bt3Y+zYsQCA77//HuvXr1df+EVEVJBlqph1cHBI8/+JiCh3dOrUCe3bt0fz5s0xbNgwXrdARPT/snTThIMHD6ofT5w4EUWKFEH9+vXx6NGjbA1HRFRQqVQqrFu3DnFxcQAAPT097Nu3D8OHD2chS0T0Ea2L2blz58LExAQAcP78eaxYsQLz589HiRIl1B+BERFR1kVGRqJdu3YYNGgQRo4cqW5nEUtElFqml+ZK8eTJE5QvXx4AsG/fPnTu3BmDBg1CgwYN8O2332Z3PiKiAuX06dPo3r07nj17BmNjY9StWxdCCBayRETp0HpktlChQnj9+jUAICAgAM2aNQMAGBsbIz4+PnvTEREVECqVCnPnzkWTJk3w7NkzVKpUCRcvXsSgQYNYyBIRZUDrkVl3d3cMGDAAtWvXxt27d9GmTRsAwL///osyZcpkdz4ionzv5cuX6NWrFwICAgAAPXv2hLe3NwoVKiRxMiKivE/rkdmVK1fC1dUVr169wu7du1G8eHEAQHBwMLp3757tAYmI8juFQoGrV6/CxMQEPj4+2Lx5MwtZIqJM0npktkiRIlixYkWq9pkzZ2ZLICKiguDjebD29vbYuXMnLC0tUa1aNYmTERHpFq2LWeDDfcF9fHxw69YtyGQyVKlSBf3794eFhUV25yMiynciIiLg6emJESNG4LvvvgMAXkBLRJRFWk8zuHLlCsqVK4fFixfjzZs3iIyMxOLFi1GuXDlcvXo1JzISEeUbf//9N2rWrInjx49j1KhRSEpKkjoSEZFO07qYHTt2LNq3b4+HDx9iz5492Lt3L8LCwtC2bVuMGTMmByISEem+5ORk/PTTT2jevDlevnyJGjVq4O+//4ahoaHU0YiIdJrW0wyuXLmCdevWwcDgf7saGBhg4sSJcHFxydZwRET5wbNnz9C9e3ecPn0aADB48GAsXrxYfQMaIiLKOq2LWXNzczx+/BiVK1fWaH/y5AkKFy6cbcGIiPKDV69eoVatWoiMjEThwoWxdu1aeHh4SB2LiCjf0HqaQbdu3dC/f3/4+/vjyZMnePr0KbZv344BAwZwaS4iok9YWlqiW7duqF27NoKDg1nIEhFlM61HZn///XfIZDL07t0bycnJAAC5XI6hQ4fi119/zfaARES65vHjx5DL5bC1tQUALFy4EEIIGBsbS5yMiCj/0Xpk1tDQEEuXLkVUVBRCQ0MREhKCN2/eYPHixTAyMsqJjEREOmP//v2oVasWunfvrv6D38jIiIUsEVEOyXQxGxcXh+HDh8Pe3h5WVlYYMGAAbG1tUaNGDZiamuZkRiKiPC8pKQk//PAD2rdvj6ioKMTFxSEqKkrqWERE+V6mi9np06fD19cXbdq0gYeHBwIDAzF06NCczEZEpBPCwsLQqFEjLFq0CMCHJQzPnDkDS0tLiZMREeV/mZ4zu2fPHvj4+KgvXujZsycaNGgApVIJfX39HAtIRJSX7dmzB/369cO7d+9QtGhR+Pr6on379lLHIiIqMDI9MvvkyRM0atRI/bhOnTowMDDA8+fPcyQYEVFep1Ao8PPPP+Pdu3dwdXVFSEgIC1kiolyW6WJWqVSmulONgYGB+gIHIqKCRi6Xw9/fH5MnT8bJkyfh4OAgdSQiogIn09MMhBDw8vLSWLEgISEBQ4YMgZmZmbptz5492ZuQiCgP2bFjB16+fIkRI0YAAKpXr465c+dKnIqIqODKdDHbp0+fVG09e/bM1jBERHlVfHw8xo4dizVr1kBfXx8NGjRA7dq1pY5FRFTgZbqY3bhxY07mICLKs+7cuYOuXbvi2rVrkMlkmDRpEr766iupYxEREbJwBzAiooLkjz/+wJAhQxAbGwsrKyv88ccfcHd3lzoWERH9P63vAEZEVFAMGzYMvXr1QmxsLJo0aYLQ0FAWskREeQyLWSKidFSuXBkymQwzZsxAYGAgbG1tpY5ERESf4DQDIqKPvHnzBsWKFQMAjBw5Eo0bN0bNmjUlTkVEROnhyCwREYD379+jT58+qFu3LqKjowEAMpmMhSwRUR6XpWJ2y5YtaNCgAezs7PDo0SMAwJIlS/Dnn39mazgiotxw/fp1fP3119i8eTMePHiAoKAgqSMREVEmaV3Ment7Y9y4cWjdujXevn0LpVIJAChSpAiWLFmS3fmIiHKMEALr1q1DnTp1cPv2bdjb2+PEiRPo0KGD1NGIiCiTtC5mly9fjnXr1mHq1KnQ19dXt7u4uOD69evZGo6IKKfExMTA09MTgwYNQkJCAlq1aoXQ0FA0atRI6mhERKQFrYvZsLCwNO96Y2RkhNjY2GwJRUSU03744Qds27YN+vr6mD9/Pg4cOIASJUpIHYuIiLSkdTHr6OiI0NDQVO2HDx9G1apVtQ6watUqODo6wtjYGM7Ozjh9+nSm9jt79iwMDAxQq1Ytrc9JRPTLL7+gXr16OH36NCZMmAA9PV4PS0Ski7T+7T1hwgQMHz4c/v7+EELg0qVLmDNnDqZMmYIJEyZodSx/f3+MGTMGU6dORUhICBo1aoRWrVrh8ePHGe737t079O7dG02bNtU2PhEVULGxsfDx8VE/trKywrlz5+Dq6iphKiIi+lJarzPbt29fJCcnY+LEiYiLi0OPHj1gb2+PpUuXwsPDQ6tjLVq0CP3798eAAQMAfFgR4ejRo/D29sa8efPS3W/w4MHo0aMH9PX1sW/fPm1fAhEVMMHBwRg3bhxevHgBc3Nz9OjRA8CHpbeIiEi3ZemmCQMHDsTAgQMRGRkJlUoFKysrrY+RlJSE4OBgTJo0SaO9efPmOHfuXLr7bdy4Effv38cff/yBX3755bPnSUxMRGJiovpxyvqRCoUCCoVC69zaUiiSNf4/N85J2Sulz9h3ukcIgRUrVmDSpElQKBRwcHBAmTJl2Jc6iO9D3cb+03253YfanOeL7gD2JRdLREZGQqlUwtraWqPd2toaERERae5z7949TJo0CadPn4aBQeaiz5s3DzNnzkzVHhAQAFNTU+2DaylRCaR8m48fPw4j/Qw3pzwsMDBQ6gikhffv32P58uW4ePEiAKBevXoYMWIEXr16hUOHDkmcjrKK70Pdxv7TfbnVh3FxcZneVuti1tHRMcOP5h48eKDV8T49lhAizeMrlUr06NEDM2fORMWKFTN9/MmTJ2PcuHHqx9HR0ShVqhSaN28Oc3NzrbJmRVxSMiZeOg4AcHNzg4WZcY6fk7KXQqFAYGAg3N3dIZfLpY5DmXDp0iWMHj0ajx49gqGhIebNm4eyZcuiefPm7EMdxfehbmP/6b7c7sOUT9IzQ+tidsyYMRqPFQoFQkJCcOTIEa0uACtRogT09fVTjcK+fPky1Wgt8GFNyCtXriAkJAQjRowAAKhUKgghYGBggICAALi5uaXaz8jICEZGRqna5XJ5rnSGXPyvMJfLDfgm1mG59TNDX+7du3d49OgRypUrhx07duCrr77CoUOH2If5APtQt7H/dF+u1U9anEPrYnb06NFptq9cuRJXrlzJ9HEMDQ3h7OyMwMBAfPfdd+r2wMDANO++Y25unuqmDKtWrcLx48exa9cuODo6ZvrcRJT/fPypTuvWrbF161a0adMG5ubmnKdHRJSPZdvCiq1atcLu3bu12mfcuHFYv349NmzYgFu3bmHs2LF4/PgxhgwZAuDDFIHevXt/CKqnh+rVq2t8WVlZwdjYGNWrV4eZmVl2vRQi0jFnzpxBzZo18ejRI3Vb9+7dc2UqERERSeuLLgD72K5du1CsWDGt9unWrRtev36NWbNmITw8HNWrV8ehQ4fg4OAAAAgPD//smrNEVHCpVCr89ttv+Pnnn6FUKvHTTz9hy5YtUsciIqJcpHUxW7t2bY0LtIQQiIiIwKtXr7Bq1SqtAwwbNgzDhg1L8zlfX98M950xYwZmzJih9TmJSPe9fPkSvXr1QkBAAACgZ8+e8Pb2ljgVERHlNq2L2Y4dO2o81tPTg6WlJb799ltUrlw5u3IREaXrxIkT6NGjB8LDw2FiYoKVK1fCy8uLN0EgIiqAtCpmk5OTUaZMGbRo0QI2NjY5lYmIKF2HDx9G27ZtoVKpULVqVezYsQPVqlWTOhYREUlEqwvADAwMMHToUI07ahER5aYmTZqgRo0a6Nu3Ly5dusRCloiogNN6mkHdunUREhKivkiLiCinXbx4ES4uLtDX14exsTFOnTqFwoULSx2LiIjyAK2L2WHDhuGHH37A06dP4ezsnGpJrBo1amRbOCIq2JKTkzFz5kzMmTMH06ZNU1/wyUKWiIhSZLqY7devH5YsWYJu3boBAEaNGqV+TiaTqRcsVyqV2Z+SiAqcZ8+eoUePHjh16hQA4MWLF+ne7pqIiAquTBezmzZtwq+//oqwsLCczENEhCNHjqBXr16IjIxEoUKFsG7dOnh4eEgdi4iI8qBMF7NCCADgXFkiyjEKhQLTpk3Dr7/+CuDDutb+/v6oUKGCxMmIiCiv0mo1A368R0Q56cGDB1iyZAkAYPjw4Th37hwLWSIiypBWF4BVrFjxswXtmzdvvigQERVclSpVwpo1a2BqaorOnTtLHYeIiHSAVsXszJkzYWFhkVNZiKiASUpKwk8//YTvvvsOrq6uAIDevXtLnIqIiHSJVsWsh4cHrKyscioLERUgDx8+hIeHBy5evIgdO3bg9u3bMDY2ljoWERHpmEzPmeV8WSLKLnv37kXt2rVx8eJFFClSBEuXLmUhS0REWZLpYjZlNQMioqxKTEzEqFGj0KlTJ7x9+xb16tVDaGgoOnToIHU0IiLSUZmeZqBSqXIyBxHlc1FRUXB3d0dwcDAAYMKECZgzZw7kcrnEyYiISJdpfTtbIqKsKFKkCEqWLImHDx9i06ZNaNOmjdSRiIgoH2AxS0Q5JiEhAcnJyShUqBBkMhk2bNiAuLg4lCxZUupoRESUT2h10wQiosy6e/cu6tWrh0GDBqnn3BcrVoyFLBERZSsWs0SU7bZu3QpnZ2f8888/+Pvvv/Hs2TOpIxERUT7FYpaIsk1cXBwGDhwIT09PvH//Ht9++y1CQ0M5GktERDmGxSwRZYtbt26hbt26WL9+PWQyGaZPn46///4bdnZ2UkcjIqJ8jBeAEdEXS05ORrt27XD//n3Y2NjAz88Pbm5uUsciIqICgCOzRPTFDAwMsHbtWrRo0QKhoaEsZImIKNewmCWiLLl+/ToOHDigfuzm5obDhw/D2tpawlRERFTQsJglIq0IIbB+/XrUqVMH3bt3x71799TPyWQyCZMREVFBxGKWiDItJiYGPXv2xMCBA5GQkICGDRuiSJEiUsciIqICjMUsEWVKaGgonJ2dsXXrVujr6+O3337DwYMHYWlpKXU0IiIqwLiaARF91urVqzFmzBgkJiaiVKlS2L59O+rXry91LCIiIo7MEtHn3b9/H4mJiWjXrh1CQkJYyBIRUZ7BkVkiSpNKpYKe3oe/d+fOnYuaNWvC09OTF3kREVGewpFZItIghMDSpUvh5uYGhUIBAJDL5ejZsycLWSIiynNYzBKRWlRUFDp16oQxY8bg5MmT2LZtm9SRiIiIMsRpBkQEALh48SK6deuGR48ewdDQEAsXLkSvXr2kjkVERJQhjswSFXAqlQoLFy5Ew4YN8ejRI5QrVw7nzp3DiBEjOK2AiIjyPBazRAXcxIkTMX78eCQnJ6Nr1664evUqnJ2dpY5FRESUKSxmiQq4gQMHokSJEli9ejW2b98Oc3NzqSMRERFlGufMEhUwKpUK586dQ8OGDQEAlSpVwsOHD2FmZiZxMiIiIu1xZJaoAHn58iVat26Nxo0b48SJE+p2FrJERKSrODJLVECcPHkS3bt3R3h4OExMTBAeHi51JCIioi/GkVmifE6pVGL27Nlwc3NDeHg4qlSpgkuXLqF79+5SRyMiIvpiHJklysciIiLQs2dPHDt2DADg5eWFFStWcFoBERHlGyxmifKxw4cP49ixYzA1NYW3tzd69+4tdSQiIqJsxWKWKB/z8vLCgwcP0KNHD1SpUkXqOERERNmOc2aJ8pHnz5+jZ8+eiIqKAgDIZDLMnj2bhSwREeVbHJklyieOHDmCXr16ITIyEgDwxx9/SJyIiIgo53FklkjHJScnY/LkyWjVqhUiIyNRq1YtTJ8+XepYREREuYIjs0Q67MmTJ+jevTvOnj0LABg2bBgWLlwIY2NjiZMRERHlDhazRDrqwoULaNOmDd68eQNzc3P4+Pigc+fOUsciIiLKVSxmiXRUxYoVYWZmhrJly8Lf3x9ly5aVOhIREVGuYzFLpENevnwJS0tLyGQyFCtWDMeOHUPp0qVhZGQkdTQiIiJJ8AIwIh2xd+9eVKpUCRs2bFC3VahQgYUsEREVaCxmifK4xMREjBo1Cp06dcLbt2/h5+cHIYTUsYiIiPIEFrNEedj9+/fRoEEDLF++HAAwfvx4HD16FDKZTOJkREREeQPnzBLlUTt37sSAAQMQHR2NYsWKYfPmzWjTpo3UsYiIiPIUFrNEedDdu3fh4eEBlUqFBg0aYNu2bShVqpTUsYiIiPIcFrNEeVDFihUxbdo0JCYmYtasWTAw4FuViIgoLfwXkiiP2LZtG1xcXFChQgUA4C1piYiIMoEXgBFJLC4uDgMGDECPHj3QrVs3JCQkSB2JiIhIZ3BklkhCt27dQteuXXHjxg3IZDK0a9cOcrlc6lhEREQ6g8UskUQ2bdqEYcOGIS4uDtbW1vDz80PTpk2ljkVERKRTWMwS5bK4uDgMHToUmzdvBgA0bdoUf/zxB2xsbCRORkREpHs4Z5YolxkYGOD27dvQ09PD7NmzcfToURayREREWcSRWaJcIISAEAJ6enowNDSEv78/Hj16hMaNG0sdjYiISKdxZJYoh8XExKBnz56YPHmyuq1MmTIsZImIiLIBR2aJclBoaCi6du2Ke/fuwcDAAEOHDkWZMmWkjkVERJRvcGSWKAcIIeDt7Y169erh3r17KFmyJE6cOMFCloiIKJtxZJYom7179w4DBw7Ezp07AQBt27aFr68vihcvLnEyIiKi/IfFLFE2UqlUaNy4Mf755x8YGBjgt99+w9ixYyGTyaSORkRElC9xmgFRNtLT08OECRPg4OCAM2fOYNy4cSxkiYiIchCLWaIvFBUVhdDQUPVjT09P3Lx5E3Xr1pUuFBERUQHBYpboC1y8eBG1a9dG69at8erVK3W7qamphKmIiIgKDhazRFkghMDChQvRsGFDPHr0CCYmJnj58qXUsYiIiAocXgBGpKXXr1/Dy8sLBw4cAAB06dIF69atg4WFhcTJiIiICh7JR2ZXrVoFR0dHGBsbw9nZGadPn0532z179sDd3R2WlpYwNzeHq6srjh49motpqaA7e/YsatWqhQMHDsDIyAirVq2Cv78/C1kiIiKJSFrM+vv7Y8yYMZg6dSpCQkLQqFEjtGrVCo8fP05z+1OnTsHd3R2HDh1CcHAwmjRpgnbt2iEkJCSXk1NB5e3tjadPn6JChQq4cOEChg4dytUKiIiIJCTpNINFixahf//+GDBgAABgyZIlOHr0KLy9vTFv3rxU2y9ZskTj8dy5c/Hnn39i//79qF27dm5EpgJu1apVsLa2xowZM1C4cGGp4xARERV4khWzSUlJCA4OxqRJkzTamzdvjnPnzmXqGCqVCjExMShWrFi62yQmJiIxMVH9ODo6GgCgUCigUCiykFw7CkWyxv/nxjkp+5w6dQr+/v5o3bo1FAoFTExM8OuvvwIA+1KHpPQV+0x3sQ91G/tP9+V2H2pzHsmK2cjISCiVSlhbW2u0W1tbIyIiIlPHWLhwIWJjY9G1a9d0t5k3bx5mzpyZqj0gICBXlk9KVAIp3+bjx4/DSD/HT0nZQKlUYteuXfD394dKpYKxsTGnE+QDgYGBUkegL8Q+1G3sP92XW30YFxeX6W0lX83g0wJBCJGpomHbtm2YMWMG/vzzT1hZWaW73eTJkzFu3Dj14+joaJQqVQrNmzeHubl51oNnUlxSMiZeOg4AcHNzg4WZcY6fk75MREQEvLy8cPz4h37z9PREgwYN4O7uDrlcLnE6ygqFQoHAwED2oQ5jH+o29p/uy+0+TPkkPTMkK2ZLlCgBfX39VKOwL1++TDVa+yl/f3/0798fO3fuRLNmzTLc1sjICEZGRqna5XJ5rnSGXPyvMJfLDfgmzuOOHTsGT09PvHjxAqampli1ahV69OiBQ4cO5drPDOUc9qHuYx/qNvaf7su1+kmLc0i2moGhoSGcnZ1TDVcHBgaifv366e63bds2eHl5YevWrWjTpk1Ox6QCZOnSpXB3d8eLFy9QvXp1XLlyBX369JE6FhEREWVA0mkG48aNQ69eveDi4gJXV1esXbsWjx8/xpAhQwB8mCLw7NkzbN68GcCHQrZ3795YunQp6tWrpx7VNTEx4Tqf9MW+/vpr6OnpoW/fvli6dClvSUtERKQDJC1mu3XrhtevX2PWrFkIDw9H9erVcejQITg4OAAAwsPDNdacXbNmDZKTkzF8+HAMHz5c3d6nTx/4+vrmdnzKB168eKGe1lK/fn3cuHEDlStXljgVERERZZbkF4ANGzYMw4YNS/O5TwvUEydO5HwgKhCSk5Px888/Y/ny5bh48SKqVasGACxkiYiIdIzkxSxRbnvy5Am6d++Os2fPAgD279+vLmaJiIhIt7CYpQLl4MGD6N27N968eQNzc3OsW7cuw3WKiYiIKG+TbDUDotykUCgwfvx4tG3bFm/evIGzszOuXr3KQpaIiEjHsZilAsHHxwcLFy4EAIwaNQpnz55FuXLlJE5FREREX4rTDKhAGDBgAI4ePYrevXvju+++kzoOERERZROOzFK+lJSUhAULFiAxMREAYGBggL1797KQJSIiymc4Mkv5zoMHD9CtWzdcuXIFjx8/xvLly6WORERERDmEI7OUr+zatQu1a9fGlStXUKxYMbRo0ULqSERERJSDWMxSvpCQkIBhw4ahS5cuiI6ORoMGDRAaGoq2bdtKHY2IiIhyEItZ0nn379+Hq6srvL29AQCTJk1CUFAQSpUqJXEyIiIiymmcM0s6T09PD2FhYShRogS2bNmCli1bSh2JiIiIcgmLWdJJSqUS+vr6AABHR0fs3bsXFStWhL29vcTJiIiIKDdxmgHpnFu3bsHJyQlHjhxRtzVp0oSFLBERUQHEYpZ0yubNm+Hi4oJr165hwoQJUKlUUkciIiIiCbGYJZ0QGxuLvn37ok+fPoiLi4ObmxsCAwOhp8cfYSIiooKMlQDleTdu3MDXX38NX19f6OnpYdasWQgICICNjY3U0YiIiEhivACM8rQHDx6gTp06iI+Ph62tLbZu3Ypvv/1W6lhERESUR7CYpTytbNmy8PDwwPPnz7F582ZYWVlJHYmIiIjyEBazlOf8888/sLOzg6WlJQDA29sbcrmc82OJiIgoFVYHlGcIIbB69WrUrVsXvXv3Vq9UYGRkxEKWiIiI0sQKgfKEd+/ewcPDA0OHDkViYiL09fURFxcndSwiIiLK41jMkuSCg4Ph7OyMHTt2wMDAAAsWLMBff/2FQoUKSR2NiIiI8jjOmSXJCCGwYsUKjB8/HklJSXBwcMD27dtRr149qaMRERGRjuDILEkmNjYWS5cuRVJSEjp06ICQkBAWskRERKQVjsySZAoVKgR/f3+cOXMGo0aNgkwmkzoSERER6RgWs5RrhBBYsmQJTExMMGTIEACAs7MznJ2dJU5GREREuorFLOWKN2/ewMvLC/v374ehoSHc3d1Rrlw5qWMRERGRjmMxSznu3Llz8PDwwJMnT2BkZITFixejbNmyUsciIiKifIAXgFGOUalU+O233/DNN9/gyZMnqFChAi5cuIChQ4dyfiwRERFlC47MUo5QqVTo2LEj9u/fDwDo3r071qxZg8KFC0ucjIiIiPITjsxSjtDT04OrqyuMjY2xbt06+Pn5sZAlIiKibMeRWco2SqUSkZGRsLa2BgD8+OOP6NKlC8qXLy9xMiIiIsqvODJL2eLFixdo2bIlmjZtiri4OAAfRmdZyBIREVFOYjFLX+z48eOoWbMm/v77b4SFheHq1atSRyIiIqICgsUsZZlSqcT06dPRrFkzvHjxAtWqVcPly5fRsGFDqaMRERFRAcE5s5Qlz58/h6enJ06cOAEA6N+/P5YtWwZTU1NpgxEREVGBwmKWsmTkyJE4ceIEzMzMsGbNGnh6ekodiYiIiAogFrOUJcuWLcO7d++wcuVKVKpUSeo4REREVEBxzixlytOnT7Fy5Ur1Y3t7e/z9998sZImIiEhSHJmlzzp06BB69+6N169fw97eHh07dpQ6EhEREREAjsxSBhQKBSZOnIg2bdrg9evXcHJywldffSV1LCIiIiI1jsxSmh49egQPDw9cuHABwIcLvhYsWAAjIyOJkxERERH9D4tZSuXAgQPo1asX3r59CwsLC2zYsAGdOnWSOhYRERFRKixmKZXExES8ffsWderUwfbt2+Ho6Ch1JCIiIqI0sZglAEBycjIMDD78OHz//ffYvXs32rZtC0NDQ4mTEZEuUalUSEpKyvbjKhQKGBgYICEhAUqlMtuPTzmL/af7cqIPDQ0Noaf35ZdvsZgl7Nq1C1OmTMGJEydgZ2cHAJxWQERaS0pKQlhYGFQqVbYfWwgBGxsbPHnyBDKZLNuPTzmL/af7cqIP9fT04Ojo+MUDZyxmC7CEhAT88MMPWLVqFQBgwYIFWLx4scSpiEgXCSEQHh4OfX19lCpVKltGWz6mUqnw/v17FCpUKNuPTTmP/af7srsPVSoVnj9/jvDwcJQuXfqLCmQWswXUvXv30K1bN4SEhAAAfvzxR8yePVviVESkq5KTkxEXFwc7OzuYmppm+/FTpi8YGxuzGNJB7D/dlxN9aGlpiefPnyM5ORlyuTzLx2ExWwBt374dAwcOxPv371GiRAls3rwZrVq1kjoWEemwlDl0nGdPRJmV8vtCqVSymKXM27x5M/r06QMAaNSoEbZt2wZ7e3uJUxFRfsH5kESUWdk29zZbjkI64/vvv0e1atXw008/4fjx4yxkiYiISKexmC0AAgMD1VcXm5mZ4cqVK5g9e7Z6KS4iIqKsSkpKQvny5XH27Fmpo+QbL1++hKWlJZ49eyZ1FJ3AYjYfi42NRd++fdG8eXMsXLhQ3W5sbCxhKiKivMPLywsymQwymQwGBgYoXbo0hg4diqioqFTbnjt3Dq1bt0bRokVhbGyMr776CgsXLkxzzc2goCC0bt0axYsXh6mpKapWrYoffvjhs8VJSEgIunTpAmtraxgbG6NixYoYOHAg7t69m22vObutXbsWDg4OaNCgQarnBg0aBH19fWzfvj3Vc15eXujYsWOq9tDQUMhkMjx8+FDdJoTA2rVrUbduXRQqVAhFihSBi4sLlixZgri4uOx8ORqioqLQq1cvWFhYwMLCQn13zIy8ePECXl5e6oshW7ZsiXv37mlsc//+fXz33XewtLSEubk5unbtihcvXqift7KyQq9evTB9+vSceFn5DovZfOrff/9FnTp14OvrCz09PSgUCqkjERHlSS1btkR4eDgePnyI9evXY//+/Rg2bJjGNnv37kXjxo1RsmRJBAUF4fbt2xg9ejTmzJkDDw8PCCHU265ZswbNmjWDjY0Ndu/ejZs3b2L16tV49+6dxsDCpw4cOIB69eohMTERfn5+uHXrFrZs2QILCwv8/PPPWX59Of37f/ny5RgwYECq9ri4OPj7+2PChAnYsGHDF52jV69eGDNmDDp06ICgoCCEhobi559/xp9//omAgIAvOnZGevTogdDQUBw5cgRHjhxBaGgoevXqle72Qgh07NgRDx48wJ9//omQkBA4ODigWbNmiI2NBfBhoKl58+aQyWQ4fvw4zp49i6SkJLRr105jjea+ffvCz88vzT+s6BOigHn37p0AIN69e5cr54tNVAiHHw8Ihx8PiLfv43L8fCqVSvj4+AgTExMBQNjY2IigoKAcP29+lpSUJPbt2yeSkpKkjkJZxD7MefHx8eLmzZsiPj5eCPHhd1FsoiLbvmLiE8XzF5EiJj7xs9uqVKpM5+7Tp4/o0KGDRtu4ceNEsWLF1I/fv38vihcvLjp16pRq/7/++ksAENu3bxdCCPHkyRNhaGgoxowZk+b5oqKi0myPjY0VJUqUEB07dsxwv40bNwoLCwuN5/bu3Ss+/ud8+vTpombNmsLHx0c4OjoKmUwmVq9eLezs7IRSqdTYt127dqJ3794ar8fJyUkYGRkJR0dHMWPGDKFQKNLMJIQQwcHBQk9PL81/U319fUW9evXE27dvhYmJifjnn380zp/W914IIUJCQgQAERYWJoQQwt/fXwAQ+/btS7WtSqUSb9++TTffl7h586YAIC5cuKBuO3/+vAAgbt++neY+d+7cEQDEjRs31G3JycmiWLFiYt26dUIIIY4ePZrqe/bmzRsBQAQGBmocr0yZMsLHxyc7X1aWKZVKERUVlepn6Et8+nvjY9rUa5w0mY+8f/8eQ4YMgZ+fHwCgefPm2LJlC6ysrCRORkQFTbxCiarTjkpy7puzWsDUMGv/vD148ABHjhzRWCYoICAAr1+/xvjx41Nt365dO1SsWBHbtm1Dt27dsHPnTiQlJWHixIlpHr9IkSJpth89ehSRkZFa75ee//77Dzt27MDu3buhr68Pe3t7jBo1CkFBQWjatCmADx+hHz16FPv371dn6NmzJ5YtW4ZGjRrh/v37GDRoEACk+3H3qVOnULFiRZibm6d6zsfHBz179oSFhQVatWoFPz8/zJs3T6vXAQB+fn6oVKkSOnTokOo5mUwGCwuLdPctVKhQhsdu1KgRDh8+nOZz58+fh4WFBerWratuq1evHiwsLHDu3DlUqlQp1T6JiYkANKfz6evrw9DQEGfOnMGAAQOQmJgImUwGIyMj9TYpa7eeOXMGzZo1U7fXqVMHp0+fRr9+/TJ8HQUdi9l85O7du9ixYwf09fUxe/Zs/Pjjj1ycmojoMw4cOIBChQpBqVQiISEBALBo0SL18ynzVatUqZLm/pUrV1Zvc+/ePZibm8PW1larDClzKitXrqx1/rQkJSVhy5YtsLS0VLe1bNkSW7duVRezO3fuRLFixdSP58yZg0mTJqmXbyxbtixmz56NiRMnplvMPnz4UH0b9E9fz4ULF7Bnzx4AgKenJ0aNGoU5c+Zo/e/SvXv30iwcMyM0NDTD501MTNJ9LiIiIs3BICsrK0RERKS5T+XKleHg4IDJkydjzZo1MDMzw6JFixAREYHw8HAAHwpiMzMz/Pjjj5g7dy6EEPjxxx+hUqnU26Swt7dX39yI0sdiNh9xcnLCmjVrUKFCBTRs2FDqOERUgJnI9XFzVotsO55KpUJMdAwKmxf+bDFkItfX6thNmjSBt7c34uLisH79ety9excjR45MtZ34aF7sp+0p62V+/P/aSO/YWeXg4KBRyAIfCspBgwZh1apVMDIygp+fHzw8PKCv/+H7FRwcjMuXL2POnDnqfVIK/Li4uDTv7BYfH5/mRcU+Pj5o0aIFSpQoAQBo3bo1BgwYgL///hstW7bU6rVk9XsKAOXLl8/SfinSOm9GeeRyOXbv3o3+/fujWLFi0NfXR7NmzTRuTGRpaYmdO3di6NChWLZsGfT09NC9e3c4OTmp+yKFiYlJjl7gll+wmNVh0dHRGDFiBMaOHYvatWsD+DBhnIhIajKZLMsf9adFpVIh2VAfpoYG2f6Jk5mZmbroWbZsGZo0aYKZM2eqb/FdsWJFAMCtW7dQv379VPvfvn0bVatWVW/77t07hIeHazU6m3KO27dvw9XVNd3t9PT0UhW+aV3gZWZmlqot5QKjgwcP4uuvv8bp06c1RqBVKhVmzpyJTp06pdo3vVVwSpQogevXr2u0KZVKbN68GRERERpLQCqVSmzYsEFdzJqbm+PRo0epjpmyWkDK9IGKFSvi1q1baZ7/c75kmoGNjY3GCgMpXr16BWtr63SP6ezsjNDQULx79w5JSUmwtLRE3bp14eLiot6mefPmuH//PiIjI2FgYIAiRYrAxsYGjo6OGsd68+ZNqj9KKDV+Bq2jrl69CicnJ2zZsgWenp5pLg1DRETamz59On7//Xc8f/4cwIfCo1ixYmmuRPDXX3/h3r176N69OwCgc+fOMDQ0xPz589M8dnrLOjVv3hwlSpT47H6WlpaIiYlRXxkPfP6j9BQmJibo1KkT/Pz8sG3bNlSsWBHOzs7q552cnHDnzh2UL18+1Vd6f0DUrl0bt2/f1iiwDx06hJiYGISEhCA0NBShoaG4evUqfH198eeff+L169cAPnwkf+PGDfXUjhSXL1+GpaUlihYtCuDDigJ3797Fn3/+mer8Qgi8e/cu3deccv70vtavX5/uvq6urnj37h0uXbqkbrt48SLevXuX5h81n7KwsIClpSXu3buHK1eupDnnt0SJEihSpAiOHz+Oly9fon379hrP37hxQz1YRRnItkvSdISur2agUqnE8uXLhaGhoQAgSpcuLc6dO5cNSSk9vBJe97EPc15GVyVnh5y4klqI9K+od3Z2FsOHD1c/3rlzp9DX1xcDBw4U//zzjwgLCxPr168XRYsWFZ07d9ZYQWHlypVCJpOJfv36iRMnToiHDx+KM2fOiEGDBolx48alm2Xfvn1CLpeLdu3aicDAQBEWFiYuX74sJkyYILp16yaEEOL169fCzMxMjBo1Sty7d0/4+fkJOzu7NFczSEtAQIAwMjISlSpVErNnz9Z47siRI8LAwEBMnz5d3LhxQ9y8eVNs375dTJ06Nd3MkZGRwtDQUFy/fl3d1qFDB3XeFEqlUrx580bY29uLJUuWCCGEePv2rbCxsRGdO3cWly9fFv/995/YsmWLKFq0qJg/f756X5VKJbp16yZMTEzE3LlzxeXLl8XDhw/F/v37hZubm9i7d2+6+b5Uy5YtRY0aNcT58+fF+fPnxVdffSXatm2rsU2lSpXEnj171I937NghgoKCxP3798W+ffuEg4NDqpUwNmzYIM6fP69+zcWKFUv1sxEbGytMTEzEqVOncuz1aSMvr2bAYjaHZWcxGxUVJTp16iQACACiffv24vXr19mUlNLDQkj3sQ9zXn4rZv38/IShoaF4/Pixuu3UqVOiZcuWwsLCQhgaGoqqVauK33//XSQnJ6faPzAwULRo0UIULVpUGBsbi8qVK4vx48eL58+fZ5jn8uXLolOnTsLS0lIYGRmJ8uXLi0GDBol79+6pt9m7d68oX768MDY2Fm3bthVr167NdDGbnJwsbG1tBQBx//79VM8fOXJE1K9fX5iYmAhzc3NRp04dsXbt2gwze3h4iEmTJgkhhIiIiBAGBgZix44dGtuk9N+IESPEV199pW6/d++e+P7774W9vb0wMzMTX331lVixYkWqflYqlcLb21t8/fXXwtTUVJibmwtnZ2exdOlSEReXc8tevn79Wnh6eorChQuLwoULC09Pz1TLqwEQGzduVD9eunSpKFmypJDL5aJ06dLip59+EomJiRr7/Pjjj8La2lrI5XJRoUIFsXDhwlRLym3dulVUqlQpp16a1vJyMSsTIptnnedx0dHRsLCwwLt379JcSiS7xSUlq5en+ednN1iYpX/lZEaePn2KRo0a4eHDh5DL5ViwYAFGjRqV5UnxlHkKhQKHDh1C69atNZbrId3BPsx5CQkJCAsLg6OjY47cZVClUiE6Ohrm5uZcpSWPuX79Opo1a4b//vsPhQsXTnMb9p/26tSpgzFjxqBHjx5SRwGQM32Y0e8Nbeo1XgCmI+zs7FChQgXIZDL4+/vj66+/ljoSERERvvrqK8yfPx8PHz7EV199JXWcfOHly5fo3Lmzei42ZYzFbB725s0bGBsbw9TUFHp6eti6dav6qkciIqK8ImVtWsoeVlZW6d5Ag1LjWH8ede7cOdSqVQujR49Wt6Vc9UhEREREH7CYzWNUKhXmz5+Pb775Bk+ePMGJEyfSXcqFiIiIqKBjMZuHvHr1Cm3btsWPP/4IpVIJDw8PBAcHczSWiIiIKB2cM5tHnD59Gh4eHnj+/DmMjY2xdOlSDBw4kKsVEBEREWWAxWweEBcXhy5duuDFixeoVKkSduzYgRo1akgdi4iIiCjP4zSDPMDU1BQbNmxAr169cOXKFRayRERERJnEkVmJBAUFIT4+Hq1btwYAtG7dWv3/RERERJQ5HJnNZUqlEjNmzEDTpk3h6emJx48fSx2JiIhy0IwZM1CrVq08e55vv/0WY8aMyfY8n1OmTBksWbLki47h5eWFjh07ZriNVK+Pco/kxeyqVavUtzFzdnbG6dOnM9z+5MmTcHZ2hrGxMcqWLYvVq1fnUtIvFxERDnd3d8ycORNCCHTq1AklSpSQOhYRUYH25MkT9O/fH3Z2djA0NISDgwNGjx6N169fa30smUyGffv2abSNHz8ex44dy6a0WXfixAnIZDIu95gDrl+/jsaNG8PExAT29vaYNWsWhBAZ7jNnzhzUr18fpqam6a5aNHr0aDg7O8PIyCjNP1QSEhLg5eWFr776CgYGBukW9itXrkSVKlVgYmKCSpUqYfPmzRrPf/vtt5DJZKm+2rRpo7Hd+vXrUa5cuXRrtj179qBFixYoUaIEZDIZQkNDM/weZBdJi1l/f3+MGTMGU6dORUhICBo1aoRWrVqlO1oZFhaG1q1bo1GjRggJCcGUKVMwatQo7N69O5eTay8+7Coa1quHoKAgmJmZYcuWLfDx8YGpqanU0YiI8g6lEjhxAti27cN/lcocPd2DBw/g4uKCu3fvYtu2bfjvv/+wevVqHDt2DK6urnjz5s0Xn6NQoUIoXrx4NqTNOxQKhdQR8ozo6Gi4u7vDzs4Oly9fxvLly/H7779j0aJFGe6XlJSELl26YOjQoeluI4RAv3790K1btzSfVyqVMDExwahRo9CsWbM0t/H29sbkyZMxY8YM/Pvvv5g5cyaGDx+O/fv3q7fZs2cPwsPD1V83btyAvr4+unTpot7G398fU6ZMweTJk9Ot2WJjY9GgQQP8+uuvGb72bCckVKdOHTFkyBCNtsqVK4tJkyaluf3EiRNF5cqVNdoGDx4s6tWrl+lzvnv3TgAQ79690z5wFrxPSBLmrl0FIBMARI0aNcTt27dz5dyUPZKSksS+fftEUlKS1FEoi9iHOS8+Pl7cvHlTxMfHZ/0gu3cLUbKkEMD/vkqWFGL3bqFUKkVUVJRQKpXZF1oI0bJlS1GyZEkRFxen0R4eHi5MTU01/o1ycHAQs2bNEt27dxdmZmbC1tZWLFu2TON5AOovBwcHIYQQ06dPFzVr1lRv16dPH9GhQwcxZ84cYWVlJSwsLMSMGTOEQqEQ48ePF0WLFhX29vbCx8dHI9PEiRNFhQoVhImJiXB0dBQ//fSTxs/0p+f5WFhYmEY2AKJPnz5CCCEaN24sRo4cKSZMmCCKFi0qrK2txfTp0zX2ByC8vb1F+/bthampqZg2bZoQQoi//vpLODk5CSMjI+Ho6Kh+HR9nKlWqlDA0NBQ2NjZixIgRGt+vOXPmiL59+4pChQqJUqVKiTVr1mic99q1a6JJkybC2NhYFCtWTAwcOFDExMSk+l6meP/+vejVq5cwMzMTNjY24vfffxeNGzcWo0ePTvP7kh1WrVolLCwsREJCgrpt3rx5ws7OTqhUqs/uv3HjRmFhYZHhNhn1bYpPvxcpXF1dxfjx4zXaRo8eLRo0aJDusRYvXiwKFy4s3r9/r26rU6eO6Nu3r8Z7ML2aLeXnLSQkJMPMGf3e0KZek+wCsKSkJAQHB2PSpEka7c2bN8e5c+fS3Of8+fNo3ry5RluLFi3g4+MDhUIBuVyeap/ExEQkJiaqH0dHRwP48FdlbvxlmZycDFXCewACfbz6YdnSxTAxMeFftTokpa/YZ7qLfZjzFAoFhBBQqVRQqVTaH2DPHsi6dgWEwMera4tnz4DOnSF27ACaNVOfIzu8efMGR48exS+//AIjIyON41pZWaFHjx7w9/fHihUr1Gt+L1iwAJMnT8a0adMQEBCAsWPHomLFinB3d8fFixdhY2MDHx8ftGzZEvr6+lCpVOqPm1OOL4TA8ePHYW9vjxMnTuDs2bMYOHAgzp07h2+++Qbnz5/Hjh07MGTIEDRt2hSlSpUC8GGEd8OGDbCzs8P169cxePBgFCpUCBMmTFAf9+PzfMze3h47d+5Ely5dcOvWLZibm8PExES97aZNmzB27FicP38e58+fR79+/eDq6gp3d3f1MaZPn445c+Zg4cKF0NfXx+HDh9GzZ08sWbIEjRo1wv379zFkyBAIITBt2jTs2rULixcvxtatW1G1alU8ePAA//33n0a+hQsXYtasWZg0aRJ2796NoUOHomHDhqhcuTLi4uLQsmVL1K1bFxcvXsTLly8xaNAgDB8+HBs3blS/5o9/JsaPH4+goCDs3r0bNjY2mDp1KoKDg1GzZs10f25Onz6d6uP0T02ePBmTJ09O87mUfpPL5epzuLu7Y/LkyXjw4AEcHR0zPHbKPhn9XGfUtx9vk9b7IzExMdXPt7GxMS5duoTExMQ0aycfHx9069ZN/TOSUrONHDlS4xzu7u44d+5cqnN+/Joyypzy/lAoFNDX19d4Tpvf15IVs5GRkVAqlbC2ttZot7a2RkRERJr7REREpLl9cnIyIiMjYWtrm2qfefPmYebMmanaAwICcuUj/kQlUMxtAEzKuqB1OycEBQXl+DkpZwQGBkodgb4Q+zDnGBgYwMbGBu/fv0dSUpJ2OyuVMB89OlUhCwAyISBkMsjGjAH++QcxMTHZFRmhoaEQQsDBwUE90PExR0dHREVF4cGDB7C0tIRKpUKdOnXUHwv37t0bJ06cwO+//466devCyMgIAGBkZKT+9yU6OhqJiYlQKpUagylFihTB7Nmzoaenh86dO2P+/PmIiYnB8OHDAQDDhg3Db7/9hr///hvff/89AGDkyJHqbI0bN8awYcOwfft2DB48GABSnedTxsbGAAATExONfMnJyahatar6IqmOHTti+fLlOHz4MOrWrave//vvv0fnzp3Vj2fPno3Ro0fju+++AwCUKFECkyZNwowZMzBmzBjcu3cPVlZWqFOnDuRyOZydneHs7KzOp1Kp0KxZM3h6egIAhgwZgsWLF+PIkSOws7PDpk2bEBcXh+XLl8PMzAylS5fGr7/+iu7du2Pq1KmwsrKCQqFAcnIyoqOj8f79e2zYsAHe3t7q3MuXL0e1atWQlJSU7velYsWKOHXqVJrPpShatGi6+z979gylS5fWeD7l+3v//v3PTjFJSEiAECLd4wOf71sAGt+LjzVu3Bjr169Hs2bNULNmTYSGhmLDhg1QKBQICwuDjY2NxvbBwcG4ceMGlixZoj5WeHg4lEolLC0tNd6DFhYWeP78eapzvn//HsCHaQcZZU5KSkJ8fDxOnTqF5ORkjefi4uLS3e9Tki/N9ekdroQQGd71Kq3t02pPMXnyZIwbN079ODo6GqVKlULz5s1hbm6e1diZJoSAm1sijh/XQ5sWzWBoaJjj56TspVAoEBgYCHd39zT/gqW8j32Y8xISEvDkyRMUKlRIXTRl2okT0Hv+PN2nZUJA9uwZDM6fh0mrVtl2Z0QzMzMAH4q7tP49SClOzc3NYW5uDj09PTRq1Ehj22+++QZLly7VaPv0eEZGRtDX11e3yeVyVK9eXeOiH1tbW1SrVk1jv+LFi+P9+/fqtl27dmHZsmX477//8P79eyQnJ6uzpXWeT6UUWIULF9bYxsDAADVq1NBos7e3x7t37zTaXF1dNR7/888/CAkJ0ZgbqlQqkZCQAAMDA/Ts2RNr1qyBk5MTWrRogW+//RZdunRRvwf19PTg7OyscUxbW1vExMTA3NwcDx8+RK1atTQGqtzd3aFSqfD8+XOUL18ecrkcBgYGMDc3R1hYGJKSkuDm5qY+prm5OSpVqgRDQ8N0vy/m5uapBsq0oa+vn+r4KQVfoUKFPltrGBsbQyaTZbjd5/oWgMb34mOzZ89GVFQU3N3dIYSAtbU1vLy8sGDBAhQpUiTV9v7+/qhevTqaNGmibkspTmUyGQoXLqx+D6aXq1ChQgA+vMcyypyQkAATExN88803qX5vZFQEf0qyYrZEiRLQ19dPNQr78uXLdH+obGxs0tzewMAg3b98jIyM1L+QPiaXy3PtHzULmQxG+oChoSH/IdVhufkzQzmDfZhzlEolZDIZ9PT0oKen5bXFL15kajNZRIT6HNmhYsWKkMlkuH37dprHvHPnDooWLQorKyv1P96fnj/lqu+P2z79HqTsm9Imk8lgaGiYapu02oQQ0NPTw4ULF9CjRw/MnDkTLVq0gIWFBbZv346FCxdqHPfj83wqpT2tPvr03Hp6eupzpyhcuLDGY5VKhZkzZ6JTp06pzmVqaopChQrhzp07CAwMRGBgIMaPH49Vq1bh5MmT6vdhRq855fGnuYAPBaSenp7G9//j1//p68vo5+b06dNo1apVms+lmDJlCqZMmZLmc7a2tnjx4oXG8SMjI9XPfe7n9eN+Sc/n+jZlm7Rep5mZGTZu3Ii1a9fixYsXsLW1xdq1a1G4cGFYWVlpbB8XFwd/f3/MmjVLo93Kygr6+vp4+fKlxjlevXoFa2vrVOfM6Gft0+1kMlmav5u1+V0tWTFraGgIZ2dnBAYGqj+iAD58DNihQ4c093F1ddW4+g74MF3AxcWF/0AREemqNKaIpUV88nHolypevDjc3d2xatUqjB07FiYmJurnIiIi4Ofnh969e2uMBF+4cEHjGBcuXEDlypXVj+VyOZQ5sALD2bNn4eDggKlTp6rbHj16pNUxUj4ZzK58Tk5OuHPnDsqXL5/uNiYmJmjfvj3atm2L3r17o06dOrh+/TqcnJw+e/yqVati06ZNiI2NVY+inz17Fnp6eqhYsWKq7VNGai9cuIDSpUsDAKKionD37l00btw43fO4uLh8dgmpYsWKpfucq6srpkyZgqSkJPX3OCAgAHZ2dihTpsxnXmXukcvlKFmyJABg+/btaNu2bapCc8eOHUhMTETPnj012lNqtqCgIPTo0UPdnlHNlpsknWYwbtw49OrVCy4uLnB1dcXatWvx+PFjDBkyBMCHKQLPnj1Tr4c2ZMgQrFixAuPGjcPAgQNx/vx5+Pj4YNu2bVK+DCIi+hKNGgElSwLPnn1Yw+BTMhlEyZJIdnXN9lOvWLEC9evXR4sWLfDLL7/A0dER//77LyZMmAB7e3vMmTNHY/uzZ89i/vz56NixIwIDA7Fz504cPHhQ/XyZMmVw7NgxNGjQAEZGRihatGi25CxfvjweP36M7du34+uvv8bBgwexd+9erY7h4OAAmUyGAwcOoHXr1jAxMVF/HJwV06ZNQ9u2bVGqVCl06dIFenp6uHbtGq5fv45ffvkFvr6+UCqVqFu3LoyNjeHv7w8TExM4ODhk6vienp6YPn06+vTpgxkzZuDVq1cYOXIkevXqleYnuIUKFUL//v0xYcIEFC9eHNbW1pg6depnR0ZNTEwyLMg/J2XE3MvLC1OmTMG9e/cwd+5cTJs2Tf2H0KVLl9C7d28cO3YM9vb2AIDHjx/jzZs3ePz4MZRKpbqgLl++vLpfUqaUREREID4+Xr1N1apV1YXzzZs3kZSUhDdv3iAmJka9Tcq6tHfv3sWlS5dQt25dREVFYdGiRbhx4wY2bdqU6rX4+PigY8eOaX7aPWbMGPTp0weurq5o0KBBqpoNgPr1PP//aUN37twB8OGT9U/n5marz653kMNWrlwpHBwchKGhoXBychInT55UP9enTx/RuHFjje1PnDghateuLQwNDUWZMmWEt7e3VufL7aW5hOCyQLqO/af72Ic574uX5tq9WwiZ7MPXx0tz/X+bcufOHFmaSwghHj58KLy8vISNjY2Qy+WiVKlSYuTIkSIyMlJjOwcHBzFz5kzRtWtXYWpqKqytrcWSJUs0tvnrr79E+fLlhYGBwWeX5vpYWstHOTg4iMWLF6sfT5gwQRQvXlwUKlRIdOvWTSxevFhjSafMLN80a9YsYWNjI2QymcbSXJ+eu0OHDurnhfiwNNfevXtTHe/IkSOifv36wsTERJibm4s6deqItWvXCiGE2Lt3r6hbt64wNzcXZmZm4uuvvxYBAQHpvj4hhKhZs6bGsmDaLs0VExMjevbsqe6f+fPn5/jSXCk5GzVqJIyMjISNjY2YMWOGxrJcQUFBAoAICwvTyI5PlksDIIKCgtTbNG7cOM1tPj7Op0vCpXyluHnzpqhVq5a6jzp06JDmEqF37twRADT66GNKpVIsWLAg3ZpNiA/LjKWV5dOl3lJk19JcMiE+c4uKfCY6OhoWFhapJrbnJIVCgUOHDqF169acDqGD2H+6j32Y8xISEhAWFqa+o2OW7NkDjB4NPH36v7ZSpYAlS6Dq2BHR0dHqC7GkUKZMGYwZM4a3Rs0ClUolef/Rl8mJPszo94Y29ZrkqxkQEREBADp1Ajp0AE6fBsLDP8ylbdQI0NcHsmltWSLKf1jMEhFR3qGvD3z7rdQpiEiHsJglIiLKhIcPH0odgYjSwIkrRERERKSzWMwSEVG2KWDXFBPRF8iu3xcsZomI6Ivp6+sD+HCvdSKizEj5fZHy+yOrOGeWiIi+mIGBAUxNTfHq1SvI5fJsX35JpVIhKSkJCQkJXNpJB7H/dF9296FKpcKrV69gamoKA4MvK0dZzBIR0ReTyWSwtbVFWFiY1rdZzQwhBOLj42FiYqJxe1nSDew/3ZcTfainp4fSpUt/8fFYzBIRUbYwNDREhQoVcmSqgUKhwKlTp/DNN9/wxhc6iP2n+3KiDw0NDbNllJfFLBERZRs9Pb2s3wEsA/r6+khOToaxsTGLIR3E/tN9ebkPOXGFiIiIiHQWi1kiIiIi0lksZomIiIhIZxW4ObMpC/RGR0fn2jkVCgXi4uIQHR2d5+aZ0Oex/3Qf+1D3sQ91G/tP9+V2H6bUaZm5sUKBK2ZjYmIAAKVKlZI4CRERERFlJCYmBhYWFhluIxMF7N6DKpUKz58/R+HChXNtrbvo6GiUKlUKT548gbm5ea6ck7IP+0/3sQ91H/tQt7H/dF9u96EQAjExMbCzs/vs8l0FbmRWT08PJUuWlOTc5ubmfBPrMPaf7mMf6j72oW5j/+m+3OzDz43IpuAFYERERESks1jMEhEREZHOYjGbC4yMjDB9+nQYGRlJHYWygP2n+9iHuo99qNvYf7ovL/dhgbsAjIiIiIjyD47MEhEREZHOYjFLRERERDqLxSwRERER6SwWs0RERESks1jMZoNVq1bB0dERxsbGcHZ2xunTpzPc/uTJk3B2doaxsTHKli2L1atX51JSSo82fbhnzx64u7vD0tIS5ubmcHV1xdGjR3MxLaVF2/dhirNnz8LAwAC1atXK2YD0Wdr2YWJiIqZOnQoHBwcYGRmhXLly2LBhQy6lpU9p239+fn6oWbMmTE1NYWtri759++L169e5lJY+derUKbRr1w52dnaQyWTYt2/fZ/fJM/WMoC+yfft2IZfLxbp168TNmzfF6NGjhZmZmXj06FGa2z948ECYmpqK0aNHi5s3b4p169YJuVwudu3alcvJKYW2fTh69Gjx22+/iUuXLom7d++KyZMnC7lcLq5evZrLySmFtn2Y4u3bt6Js2bKiefPmombNmrkTltKUlT5s3769qFu3rggMDBRhYWHi4sWL4uzZs7mYmlJo23+nT58Wenp6YunSpeLBgwfi9OnTolq1aqJjx465nJxSHDp0SEydOlXs3r1bABB79+7NcPu8VM+wmP1CderUEUOGDNFoq1y5spg0aVKa20+cOFFUrlxZo23w4MGiXr16OZaRMqZtH6alatWqYubMmdkdjTIpq33YrVs38dNPP4np06ezmJWYtn14+PBhYWFhIV6/fp0b8egztO2/BQsWiLJly2q0LVu2TJQsWTLHMlLmZaaYzUv1DKcZfIGkpCQEBwejefPmGu3NmzfHuXPn0tzn/PnzqbZv0aIFrly5AoVCkWNZKW1Z6cNPqVQqxMTEoFixYjkRkT4jq324ceNG3L9/H9OnT8/piPQZWenDv/76Cy4uLpg/fz7s7e1RsWJFjB8/HvHx8bkRmT6Slf6rX78+nj59ikOHDkEIgRcvXmDXrl1o06ZNbkSmbJCX6hmDXD1bPhMZGQmlUglra2uNdmtra0RERKS5T0RERJrbJycnIzIyEra2tjmWl1LLSh9+auHChYiNjUXXrl1zIiJ9Rlb68N69e5g0aRJOnz4NAwP+GpRaVvrwwYMHOHPmDIyNjbF3715ERkZi2LBhePPmDefN5rKs9F/9+vXh5+eHbt26ISEhAcnJyWjfvj2WL1+eG5EpG+SleoYjs9lAJpNpPBZCpGr73PZptVPu0bYPU2zbtg0zZsyAv78/rKyscioeZUJm+1CpVKJHjx6YOXMmKlasmFvxKBO0eR+qVCrIZDL4+fmhTp06aN26NRYtWgRfX1+OzkpEm/67efMmRo0ahWnTpiE4OBhHjhxBWFgYhgwZkhtRKZvklXqGQxJfoESJEtDX10/1l+fLly9T/bWSwsbGJs3tDQwMULx48RzLSmnLSh+m8Pf3R//+/bFz5040a9YsJ2NSBrTtw5iYGFy5cgUhISEYMWIEgA+FkRACBgYGCAgIgJubW65kpw+y8j60tbWFvb09LCws1G1VqlSBEAJPnz5FhQoVcjQz/U9W+m/evHlo0KABJkyYAACoUaMGzMzM0KhRI/zyyy/8lFIH5KV6hiOzX8DQ0BDOzs4IDAzUaA8MDET9+vXT3MfV1TXV9gEBAXBxcYFcLs+xrJS2rPQh8GFE1svLC1u3buUcL4lp24fm5ua4fv06QkND1V9DhgxBpUqVEBoairp16+ZWdPp/WXkfNmjQAM+fP8f79+/VbXfv3oWenh5KliyZo3lJU1b6Ly4uDnp6miWIvr4+gP+N7lHelqfqmVy/5CyfSVmOxMfHR9y8eVOMGTNGmJmZiYcPHwohhJg0aZLo1auXevuUpSzGjh0rbt68KXx8fLg0l8S07cOtW7cKAwMDsXLlShEeHq7+evv2rVQvocDTtg8/xdUMpKdtH8bExIiSJUuKzp07i3///VecPHlSVKhQQQwYMECql1Cgadt/GzduFAYGBmLVqlXi/v374syZM8LFxUXUqVNHqpdQ4MXExIiQkBAREhIiAIhFixaJkJAQ9fJqebmeYTGbDVauXCkcHByEoaGhcHJyEidPnlQ/16dPH9G4cWON7U+cOCFq164tDA0NRZkyZYS3t3cuJ6ZPadOHjRs3FgBSffXp0yf3g5Oatu/Dj7GYzRu07cNbt26JZs2aCRMTE1GyZEkxbtw4ERcXl8upKYW2/bds2TJRtWpVYWJiImxtbYWnp6d4+vRpLqemFEFBQRn+25aX6xmZEBzPJyIiIiLdxDmzRERERKSzWMwSERERkc5iMUtEREREOovFLBERERHpLBazRERERKSzWMwSERERkc5iMUtEREREOovFLBERERHpLBazREQAfH19UaRIEaljZFmZMmWwZMmSDLeZMWMGatWqlSt5iIhyC4tZIso3vLy8IJPJUn39999/UkeDr6+vRiZbW1t07doVYWFh2XL8y5cvY9CgQerHMpkM+/bt09hm/PjxOHbsWLacLz2fvk5ra2u0a9cO//77r9bH0eU/Logo97CYJaJ8pWXLlggPD9f4cnR0lDoWAMDc3Bzh4eF4/vw5tm7ditDQULRv3x5KpfKLj21paQlTU9MMtylUqBCKFy/+xef6nI9f58GDBxEbG4s2bdogKSkpx89NRAUPi1kiyleMjIxgY2Oj8aWvr49Fixbhq6++gpmZGUqVKoVhw4bh/fv36R7nn3/+QZMmTVC4cGGYm5vD2dkZV65cUT9/7tw5fPPNNzAxMUGpUqUwatQoxMbGZphNJpPBxsYGtra2aNKkCaZPn44bN26oR469vb1Rrlw5GBoaolKlStiyZYvG/jNmzEDp0qVhZGQEOzs7jBo1Sv3cx9MMypQpAwD47rvvIJPJ1I8/nmZw9OhRGBsb4+3btxrnGDVqFBo3bpxtr9PFxQVjx47Fo0ePcOfOHfU2GfXHiRMn0LdvX7x79049wjtjxgwAQFJSEiZOnAh7e3uYmZmhbt26OHHiRIZ5iCh/YzFLRAWCnp4eli1bhhs3bmDTpk04fvw4Jk6cmO72np6eKFmyJC5fvozg4GBMmjQJcrkcAHD9+nW0aNECnTp1wrVr1+Dv748zZ85gxIgRWmUyMTEBACgUCuzduxejR4/GDz/8gBs3bmDw4MHo27cvgoKCAAC7du3C4sWLsWbNGty7dw/79u3DV199leZxL1++DADYuHEjwsPD1Y8/1qxZMxQpUgS7d+9WtymVSuzYsQOenp7Z9jrfvn2LrVu3AoD6+wdk3B/169fHkiVL1CO84eHhGD9+PACgb9++OHv2LLZv345r166hS5cuaNmyJe7du5fpTESUzwgionyiT58+Ql9fX5iZmam/OnfunOa2O3bsEMWLF1c/3rhxo7CwsFA/Lly4sPD19U1z3169eolBgwZptJ0+fVro6emJ+Pj4NPf59PhPnjwR9erVEyVLlhSJiYmifv36YuDAgRr7dOnSRbRu3VoIIcTChQtFxYoVRVJSUprHd3BwEIsXL1Y/BiD27t2rsc306dNFzZo11Y9HjRol3Nzc1I+PHj0qDA0NxZs3b77odQIQZmZmwtTUVAAQAET79u3T3D7F5/pDCCH+++8/IZPJxLNnzzTamzZtKiZPnpzh8Yko/zKQtpQmIspeTZo0gbe3t/qxmZkZACAoKAhz587FzZs3ER0djeTkZCQkJCA2Nla9zcfGjRuHAQMGYMuWLWjWrBm6dOmCcuXKAQCCg4Px33//wc/PT729EAIqlQphYWGoUqVKmtnevXuHQoUKQQiBuLg4ODk5Yc+ePTA0NMStW7c0LuACgAYNGmDp0qUAgC5dumDJkiUoW7YsWrZsidatW6Ndu3YwMMj6r3FPT0+4urri+fPnsLOzg5+fH1q3bo2iRYt+0essXLgwrl69iuTkZJw8eRILFizA6tWrNbbRtj8A4OrVqxBCoGLFihrtiYmJuTIXmIjyJhazRJSvmJmZoXz58hptjx49QuvWrTFkyBDMnj0bxYoVw5kzZ9C/f38oFIo0jzNjxgz06NEDBw8exOHDhzF9+nRs374d3333HVQqFQYPHqwxZzVF6dKl082WUuTp/V879w6S6h/GAfybqSSKS0MXCiXjxZYgocvSGA2BIQRdBFsaKm1wiLacHCJyCQ4tkRhBNSg0tKRR0QWyIroMIRRuEoFLFIXx/Ic/R45ZdDnBQfl+Nn3w9Xn4LV9830eVChUVFXmhraSkJOe1iGTfq62txdXVFTY2NhCNRjE6Oorp6Wlsb2/n3L7/ipaWFlgsFiwvL2NkZASRSAQLCwvZ+nfnVKlU2TOwWq1IpVLo7e3Fzs4OgO+dx+9+SktLcXx8jNLS0pyawWD40uxEVDwYZomo6B0dHSGTyWBmZgYq1f+rAqurqx9+TlEUKIoCr9eL/v5+LCwswOFwwGaz4fLyMi80f+TPkPdaQ0MDdnd34XK5su/t7+/n/Pqp0+lgt9tht9vhdrthtVpxfn4Om82Wdz2NRvOpf0kYGBjA0tISampqoFKp0NXVla19d87XvF4vAoEAIpEIHA7Hp85Dq9Xm9d/U1ISXlxfc3t6ivb39r3oiouLBBTAiKnoWiwWZTAazs7O4vr7G4uJi3m3vPz0+PsLj8WBrawvJZBJ7e3uIx+PZYDkxMYGDgwO43W6cnp4ikUhgbW0NY2Nj3+5xfHwcwWAQc3NzSCQSCAQCCIfD2cWnYDCI+fl5XFxcZGfQ6XQwmUxvXs9sNiMWiyGVSiGdTr/7vU6nEycnJ/D7/ejp6UFZWVm29lNzGo1GDA0NwefzQUQ+dR5msxn39/eIxWK4u7vDw8MDFEWB0+mEy+VCOBzGzc0N4vE4pqamsL6+/qWeiKiI/MsHdomIftLg4KB0d3e/WQsEAlJVVSU6nU46OzslFAoJAEmn0yKSu3D09PQkfX19UltbK1qtVqqrq8Xj8eQsPR0eHkpHR4cYDAbR6/XS2Ngofr//3d7eWmh67devX1JXVycajUYURZFQKJStRSIRaW1tFaPRKHq9Xtra2iQajWbrrxfA1tbWpL6+XtRqtZhMJhHJXwD7rbm5WQDI5uZmXu2n5kwmk6JWq2VlZUVEPj4PEZHh4WEpLy8XAOLz+URE5Pn5WSYnJ8VsNotGo5HKykpxOBxydnb2bk9EVNxKRET+bZwmIiIiIvoePmZARERERAWLYZaIiIiIChbDLBEREREVLIZZIiIiIipYDLNEREREVLAYZomIiIioYDHMEhEREVHBYpglIiIiooLFMEtEREREBYthloiIiIgKFsMsERERERWs/wBulJN8jqWNCAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 23.13it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 23.09it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 23.14it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 23.12it/s]\n"
+      "       Current loss: 6.4918  :   6%|████▉                                                                              | 301/5000 [00:42<39:44,  1.97it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.6666666666666666\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:03<00:00, 23.19it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 23.13it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 23.17it/s]\n"
+      "       Current loss: 4.9741  :   8%|██████▋                                                                            | 401/5000 [00:56<38:45,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.7096774193548387\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:05<00:00, 23.18it/s]\n"
+      "       Current loss: 5.8714  :  10%|████████▎                                                                          | 501/5000 [01:10<38:00,  1.97it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (31.43 s)\n",
-      "\n",
-      "Dataset               Accuracy\n",
-      "------------------------------\n",
-      "Anonaly lvl 1           100.00\n",
-      "Anonaly lvl 2            88.00\n",
-      "Anonaly lvl 3            84.00\n",
-      "\n",
-      "Anomaly all              93.00\n",
-      "\n",
-      "No Anomaly Train         98.75\n",
-      "No Anomaly Test          90.00\n",
-      "No Anomaly All           97.00\n",
-      "\n",
-      "All without train        92.50\n",
-      "All with train           95.00\n"
+      "F1 Validation 0.8717948717948718\n"
      ]
-    }
-   ],
-   "source": [
-    "# STEPS = 5000, MODEL TYPE = SMALL, WEIGHT = none\n",
-    "model3 = EfficientAD({**config, \"train_steps\": 5000, \"model_type\": \"small\", \"weight_path\":\"\"})\n",
-    "model3.create_model()\n",
-    "model3.display_eval_result()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 5.3687  :  12%|█████████▉                                                                         | 601/5000 [01:24<37:03,  1.98it/s]"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (0.49 ms)\n",
-      "\n",
-      "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (2.37 ms)\n",
-      "\n",
-      "- Setting config\n",
-      "     Output folder path: ../output/cookies_3_steps_10000_small\n",
-      "- OK - Setting config (3.79 ms)\n",
-      "\n",
-      "- Prepare teacher, student & autoencoder\n",
-      "     No weight to load\n",
-      "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (63.69 ms)\n",
-      "\n",
-      "- Normalizing teacher\n"
+      "F1 Validation 0.8\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 53.19it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 54.49it/s]\n"
+      "       Current loss: 3.4545  :  14%|███████████▋                                                                       | 701/5000 [01:37<36:05,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (2.68 s)\n",
-      "\n",
-      "- Train\n"
+      "F1 Validation 0.5714285714285714\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 0.4457  : 100%|█████████████████████████████████████████████████████| 10000/10000 [20:10<00:00,  8.26it/s]\n"
+      "       Current loss: 5.2899  :  16%|█████████████▎                                                                     | 801/5000 [01:51<35:46,  1.96it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (1210.36 s)\n",
-      "\n",
-      "- Saving models to ../output/cookies_3_steps_10000_small/all_models.pth\n",
-      "- OK - Saving models (76.54 ms)\n",
-      "\n",
-      "- Saving map normalization to ../output/cookies_3_steps_10000_small/map_normalization.pth\n"
+      "F1 Validation 0.625\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 29.21it/s]\n"
+      "       Current loss: 2.3064  :  18%|██████████████▉                                                                    | 901/5000 [02:05<34:36,  1.97it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (306.73 ms)\n",
-      "\n",
-      "- Evaluating model\n"
+      "F1 Validation 0.6875\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:08<00:00, 22.71it/s]\n"
+      "       Current loss: 3.9667  :  20%|████████████████▍                                                                 | 1001/5000 [02:19<33:40,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "     - AUC: 99.68%\n",
-      "    - Optimal Threshold: 0.1156689\n",
-      "    - F1 Score: 0.98\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[97  3]\n",
-      " [ 2 98]] \n",
-      "\n"
+      "F1 Validation 0.7777777777777778\n"
      ]
     },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 22.87it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 23.02it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 23.06it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.97it/s]\n"
+      "       Current loss: 4.5612  :  22%|██████████████████                                                                | 1101/5000 [02:32<32:50,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.7777777777777778\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:03<00:00, 23.13it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 23.16it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 23.15it/s]\n"
+      "       Current loss: 3.1548  :  24%|███████████████████▋                                                              | 1201/5000 [02:46<31:57,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.7777777777777778\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:05<00:00, 23.08it/s]\n"
+      "       Current loss: 2.6697  :  26%|█████████████████████▎                                                            | 1301/5000 [03:00<32:03,  1.92it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (31.59 s)\n",
-      "\n",
-      "Dataset               Accuracy\n",
-      "------------------------------\n",
-      "Anonaly lvl 1           100.00\n",
-      "Anonaly lvl 2            92.00\n",
-      "Anonaly lvl 3           100.00\n",
-      "\n",
-      "Anomaly all              98.00\n",
-      "\n",
-      "No Anomaly Train         98.75\n",
-      "No Anomaly Test          90.00\n",
-      "No Anomaly All           97.00\n",
-      "\n",
-      "All without train        96.67\n",
-      "All with train           97.50\n"
+      "F1 Validation 0.5517241379310345\n"
      ]
-    }
-   ],
-   "source": [
-    "# STEPS = 10000, MODEL TYPE = SMALL, WEIGHT = none\n",
-    "model4 = EfficientAD({**config, \"train_steps\": 10000, \"model_type\": \"small\", \"weight_path\":\"\"})\n",
-    "model4.create_model()\n",
-    "model4.display_eval_result()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# To save:  \n",
-    "torch.save({  \n",
-    "    'q_st_start': model4.q_st_start,  \n",
-    "    'q_st_end': model4.q_st_end,  \n",
-    "    'q_ae_start': model4.q_ae_start,  \n",
-    "    'q_ae_end': model4.q_ae_end  \n",
-    "}, \"test.pth\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 1.9708  :  28%|██████████████████████▉                                                           | 1401/5000 [03:14<30:18,  1.98it/s]"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (0.27 ms)\n",
-      "\n",
-      "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (2.93 ms)\n",
-      "\n",
-      "- Setting config\n",
-      "     Output folder path: ../output/cookies_3_steps_70000_small\n",
-      "- OK - Setting config (0.58 ms)\n",
-      "\n",
-      "- Prepare teacher, student & autoencoder\n",
-      "     No weight to load\n",
-      "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (61.52 ms)\n",
-      "\n",
-      "- Normalizing teacher\n"
+      "F1 Validation 0.8\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 53.46it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 54.23it/s]\n"
+      "       Current loss: 2.1729  :  30%|████████████████████████▌                                                         | 1501/5000 [03:27<29:24,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (2.68 s)\n",
-      "\n",
-      "- Train\n"
+      "F1 Validation 0.7058823529411765\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 0.1277  : 100%|███████████████████████████████████████████████████| 70000/70000 [2:20:48<00:00,  8.29it/s]\n"
+      "       Current loss: 1.4776  :  32%|██████████████████████████▎                                                       | 1601/5000 [03:41<29:06,  1.95it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (8448.14 s)\n",
-      "\n",
-      "- Saving models to ../output/cookies_3_steps_70000_small/all_models.pth\n",
-      "- OK - Saving models (78.08 ms)\n",
-      "\n",
-      "- Saving map normalization to ../output/cookies_3_steps_70000_small/map_normalization.pth\n"
+      "F1 Validation 0.8571428571428571\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 29.40it/s]\n"
+      "       Current loss: 2.6586  :  34%|███████████████████████████▉                                                      | 1701/5000 [03:55<27:34,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (303.73 ms)\n",
-      "\n",
-      "- Evaluating model\n"
+      "F1 Validation 0.7096774193548387\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:08<00:00, 23.20it/s]\n"
+      "       Current loss: 3.3335  :  36%|█████████████████████████████▌                                                    | 1801/5000 [04:09<29:54,  1.78it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "     - AUC: 99.69%\n",
-      "    - Optimal Threshold: 0.1502251\n",
-      "    - F1 Score: 0.97\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[99  1]\n",
-      " [ 5 95]] \n",
-      "\n"
+      "F1 Validation 0.8333333333333334\n"
      ]
     },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIhCAYAAABdSTJTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACGJ0lEQVR4nOzdd1iT198G8DuBsAUnU0Xcqw6wKo6qKO5V60Bx4N6zah2titbaap21olUUtai4W60D3HshVK1WreIGFRfIDMl5//AlPyNDgoGHwP25Lq42J8+4wyH45eQ855EJIQSIiIiIiAyQXOoARERERETZxWKWiIiIiAwWi1kiIiIiMlgsZomIiIjIYLGYJSIiIiKDxWKWiIiIiAwWi1kiIiIiMlgsZomIiIjIYLGYJSIiIiKDxWKWiCgdAQEBkMlkmi9jY2M4ODjAy8sLt2/fTncfpVIJPz8/uLu7w8bGBubm5qhSpQqmTJmCFy9epLuPWq3Gxo0b0aJFCxQvXhwKhQK2trZo37499uzZA7Va/dGsSUlJWL58ORo1aoQiRYrAxMQETk5O6N69O44fP/5J3wcioryOxSwRUSbWrVuHs2fP4tChQxg1ahT+/PNPNGrUCK9evdLaLj4+Hp6enhg9ejRq166NzZs3Y9++fejTpw9+++031K5dGzdv3tTaJzExEW3btkW/fv1ga2sLPz8/HDlyBCtXroSjoyO6deuGPXv2ZJovOjoaDRs2xIQJE1C9enUEBATg8OHDWLhwIYyMjNC8eXP8/fffev++EBHlGYKIiNJYt26dACAuXryo1e7r6ysAiLVr12q1DxkyRAAQW7ZsSXOsmzdvChsbG1GtWjWRkpKiaR8+fLgAINavX59uhlu3bom///4705xt2rQRxsbG4vDhw+k+f+HCBXH//v1Mj5FV8fHxejkOEZE+cWSWiEgHderUAQA8ffpU0xYVFYW1a9eiVatW6NGjR5p9KlasiG+++Qb//PMPdu/erdlnzZo1aNWqFfr27ZvuuSpUqIAaNWpkmCU0NBT79+/HwIED4eHhke42n3/+OUqXLg0AmDVrFmQyWZptUqdU3Lt3T9NWpkwZtG/fHjt37kTt2rVhZmYGX19f1K5dG40bN05zDJVKBScnJ3Tp0kXTlpycjO+//x6VK1eGqakpSpQogf79++P58+cZviYiIl2xmCUi0kFERASAdwVqqqNHjyIlJQWdO3fOcL/U50JCQjT7KJXKTPf5mODgYK1j69vly5cxadIkjBkzBgcOHMBXX32F/v3749SpU2nmDQcHB+PJkyfo378/gHdzgTt16oQff/wRvXr1wl9//YUff/wRISEhaNq0KRISEnIkMxEVPMZSByAiystUKhVSUlKQmJiI06dP4/vvv8cXX3yBjh07arZ58OABAMDFxSXD46Q+l7ptVvb5GH0cIzPPnj3D9evXtQr3smXLYtKkSQgICMDcuXM17QEBAbCzs0ObNm0AAFu3bsWBAwewY8cOrdHamjVr4vPPP0dAQACGDx+eI7mJqGDhyCwRUSbq168PhUKBQoUKoXXr1ihSpAj++OMPGBtnbywgvY/586oaNWpoFbIAUKxYMXTo0AHr16/XrLTw6tUr/PHHH+jbt6/m+7J3714ULlwYHTp0QEpKiuarVq1asLe3x7Fjx3L75RBRPsVilogoExs2bMDFixdx5MgRDB06FDdu3EDPnj21tkmdk5o6BSE9qc+VKlUqy/t8jD6OkRkHB4d02wcMGIDHjx9rpkxs3rwZSUlJ8PHx0Wzz9OlTvH79GiYmJlAoFFpfUVFRiI6OzpHMRFTwsJglIspElSpVUKdOHTRr1gwrV67EoEGDcODAAWzfvl2zTbNmzWBsbKy5uCs9qc95enpq9lEoFJnu8zGtWrXSOvbHmJmZAXi3Lu37MiosMxpFbtWqFRwdHbFu3ToA75Yvq1evHqpWrarZpnjx4ihWrBguXryY7teKFSuylJmI6GNYzBIR6WD+/PkoUqQIZsyYofmY3d7eHgMGDMDBgwcRFBSUZp9bt27hp59+QrVq1TQXa9nb22PQoEE4ePAgNmzYkO657ty5gytXrmSYxdXVFW3atIG/vz+OHDmS7jaXLl3SzK0tU6YMAKQ55sfWsv2QkZER+vTpg927d+PkyZO4dOkSBgwYoLVN+/bt8eLFC6hUKtSpUyfNV6VKlXQ6JxFRRmRCCCF1CCKivCYgIAD9+/fHxYsXNctxpVqwYAEmT56MjRs3onfv3gCAuLg4tGvXDqdPn8aQIUPQoUMHmJqa4ty5c/j5559hYWGBQ4cOaRVxiYmJ6Ny5M4KDg9GzZ098+eWXsLOzQ3R0NEJCQrBu3Tps2bIFnTp1yjBndHQ0WrdujatXr2LAgAFo06YNihQpgsjISOzZswebN29GaGgoatasiZiYGLi4uMDJyQmzZ8+GsbExAgICcPnyZURERCAiIkJT8JYpUwbVq1fH3r170z3vrVu3UKlSJZQsWRIvXrxAZGQkbGxsNM+rVCp06NAB58+fx9ixY1G3bl0oFAo8evQIR48eRadOnfDll19mt3uIiP5H6oVuiYjyooxumiCEEAkJCaJ06dKiQoUKWjdBSE5OFr/++quoV6+esLKyEqampqJSpUpi8uTJIjo6Ot3zpKSkiPXr1wsPDw9RtGhRYWxsLEqUKCHatGkjNm3aJFQq1UezJiQkiGXLlgl3d3dhbW0tjI2NhaOjo+jSpYv466+/tLa9cOGCaNCggbC0tBROTk5i5syZYs2aNQKAiIiI0Gzn7Ows2rVrl+l5GzRoIAAIb2/vdJ9XKpXi559/FjVr1hRmZmbCyspKVK5cWQwdOlTcvn37o6+LiCgrODJLRERERAaLc2aJiIiIyGCxmCUiIiIig8ViloiIiIgMFotZIiIiIjJYLGaJiIiIyGCxmCUiIiIig2UsdYDcplar8eTJExQqVCjDWzUSERERkXSEEIiNjYWjoyPk8szHXgtcMfvkyROUKlVK6hhERERE9BEPHz5EyZIlM92mwBWzhQoVAvDum2NtbZ0r51QqlQgODkbLli2hUChy5ZykP+w/w8c+NHzsQ8PG/jN8ud2HMTExKFWqlKZuy0yBK2ZTpxZYW1vnajFrYWEBa2trvokNEPvP8LEPDR/70LCx/wyfVH2YlSmhvACMiIiIiAwWi1kiIiIiMlgsZomIiIjIYLGYJSIiIiKDxWKWiIiIiAwWi1kiIiIiMlgsZomIiIjIYLGYJSIiIiKDxWKWiIiIiAwWi1kiIiIiMlgsZomIiIjIYLGYJSIiIiKDxWKWiIiIiAwWi1kiIiIiMliSFrMnTpxAhw4d4OjoCJlMht27d390n+PHj8PNzQ1mZmYoW7YsVq5cmfNBiYiIiChPkrSYjYuLQ82aNbF8+fIsbR8REYG2bduicePGCAsLw7Rp0zBmzBjs2LEjh5MSERERUV5kLOXJ27RpgzZt2mR5+5UrV6J06dJYsmQJAKBKlSq4dOkSfv75Z3z11Vc5lDJ/EEIgQamSOoZBUipTkKQC4pNToBAyqeNQNrAPDR/70LCx/wxfQkIiklTv6om8RtJiVldnz55Fy5YttdpatWoFf39/KJVKKBSKNPskJSUhKSlJ8zgmJgYAoFQqoVQqczbw/0s9T26d70NCCHituYjLD15Lcv78wRiTLxyROgR9Evah4WMfGjb2n6GKv30er46sgZ3X9/DwSIKNLOf/INGlZjKoYjYqKgp2dnZabXZ2dkhJSUF0dDQcHBzS7DNv3jz4+vqmaQ8ODoaFhUWOZU1PSEhIrp4vVZIKuPzAoLqaiIiIJCZUSrw6FoDYS38AAN6c3YYjR4rB1Cjnzx0fH5/lbQ2uwpF98NdA6nD3h+2ppk6digkTJmgex8TEoFSpUmjZsiWsra1zLuh7lEolQkJC4Onpme7ocU6LT07R/DV87psmMDfJhZ/CfESpTMGRI0fg4eEBhcLg3jIE9mF+wD40bOw/w3Pv3j0M9OmLB5cuAQCGDh+BL5o0Q7tWLWBiYpLj50/9JD0rDOonyt7eHlFRUVptz549g7GxMYoVK5buPqampjA1NU3TrlAocr2wlOKcALTmJ1lbmsHCxKC6XXJKpRKmRoCNpZkk/Uefjn1o+NiHho39Z1h27tyJAQMG4M2bNyhSpAgCAgLQpk0b7Nu3DyYmJrnSh7qcw6DWmXV3d0/zUX1wcDDq1KnDNwcRERHRJ3r16hUGDhyIN2/ewN3dHWFhYejYsaPUsTIlaTH79u1bhIeHIzw8HMC7pbfCw8Px4MEDAO+mCPTt21ez/bBhw3D//n1MmDABN27cwNq1a+Hv74+JEydKEZ+IiIgoXylSpAjWrVuHyZMn4/jx43B2dpY60kdJ+nnzpUuX0KxZM83j1Lmt/fr1Q0BAACIjIzWFLQC4uLhg3759GD9+PH799Vc4Ojpi2bJlXJaLiIiIKJu2bt0Ka2trtG7dGgDQuXNndO7cWdpQOpC0mG3atGmm65UFBASkaWvSpAkuX76cg6mIiIiI8r+EhARMmDABK1euRLFixXDlyhU4OjpKHUtnvBKIiIiIqIC5efMmunfvjitXrkAmk2HYsGGwtbWVOla2sJglIiIiKkACAwMxdOhQxMXFwdbWFr///js8PT2ljpVtLGaJiIiICgCVSoWhQ4fC398fANCsWTMEBgame9MpQ8Ji1gAIIZCgVGV7//jk7O9LRERE+YOR0bubJslkMsycORPffvutps2QsZjN44QQ6LryLELvv5I6ChERERmgxMREmJmZAQCWLVsGHx8fNGrUSOJU+mNQN00oiBKUKr0VsnWci8BcYfh/gREREdHHvX37Fv369UOnTp2gVqsBABYWFvmqkAU4MmtQLn3bAhYm2S9GzRVGkMlkH9+QiIiIDNrVq1fRvXt3/Pvvv5DL5Th37hwaNGggdawcwWLWgFiYGMHChF1GRERE6RNCYM2aNRgzZgwSExPh5OSEzZs359tCFmAxS0RERJQvxMTEYOjQodiyZQsAoE2bNtiwYQOKFy8ucbKcxWJWCioVcPIkEBkJODgAjRsD+eBqQiIiIpKOl5cX9u/fDyMjI8ybNw9ff/015PL8f3kUi9nctnMnMHYs8OjR/9pKlgSWLgW6dJEuFxERERm0uXPn4r///sP69evh7u4udZxck//L9bxkzx6ga1ftQhYAHj9+175zpzS5iIiIyOC8efMG+/fv1zyuXbs2rl+/XqAKWYDFbO765htACACAABCvMH33ZWzy7r9fT0Z8QhLik1Pe++IND4iIiEjbpUuX4Orqik6dOuHSpUuadmPjgvehe8F7xVJ6/BjAu0K2q/d8hJasmnYb30O5m4mIiIgMhhACy5Ytw6RJk6BUKlGmTBmpI0mOxawEEhSm6ReymeAND4iIiAq2V69eYcCAAdi9ezcAoEuXLvD390fhwoUlzSU1FrMSu/SLNyyUif9r2Lcf+KJxmu14wwMiIqKC6/z58+jRowfu378PExMTLFy4ECNHjmRtABazucvJCbhzR6vJQpkIC2USIJO9W9Wg2RdcpouIiIi0HD9+HPfv30e5cuWwdetWuLq6Sh0pz2Axm5t++undqgUf/hWV+njJEhayRERElMbEiRMhk8kwdOhQWFtbSx0nT+FqBrmpQwdg+3bAwVG7vWTJd+1cZ5aIiIgAnDp1Cq1atUJcXBwAQC6XY9KkSSxk08FiNrd16QLcuP6/x/v2AxERLGSJiIgIarUa8+bNQ9OmTREcHIy5c+dKHSnP4zQDKbw/leAL3sqWiIiIgGfPnqFPnz4IDg4GAPTu3RvTpk2TOFXex2KWiIiISGLHjh1Dr169EBkZCXNzcyxfvhz9+/fnagVZwGKWiIiISEK///47+vXrB7VajapVq2Lr1q2oVq2a1LEMBufMEhEREUnIw8MDxYoVQ//+/XHhwgUWsjriyCwRERFRLrt16xYqVqwIAHB0dMTff/8NBwcHiVMZJo7MEhEREeWSlJQUzJgxA1WqVMGOHTs07Sxks4/FLBEREVEuePz4MZo3b445c+ZArVbj3LlzUkfKFzjNgIiIiCiHHThwAH369EF0dDSsrKywevVqeHl5SR0rX+DILBEREVEOUSqVmDJlCtq0aYPo6GjUrl0bly9fZiGrRyxmiYiIiHLIiRMn8NNPPwEARo4ciTNnzqBChQoSp8pfOM2AiIiIKIc0b94c06ZNQ+3atdG1a1ep4+RLHJklIiIi0pPk5GR89913ePz4saZt7ty5LGRzEEdmiYiIiPTg3r176NGjBy5cuICTJ0/i6NGjvB1tLuDILBEREdEn2rVrF2rXro0LFy6gcOHCGD9+PAvZXMJiloiIiCibkpKSMGbMGHTp0gWvX79G/fr1ER4ejk6dOkkdrcDgNAMiIiKibHj8+DE6deqE0NBQAMCkSZMwd+5cKBQKiZMVLCxmiYiIiLKhcOHCSEhIQLFixbB+/Xq0a9dO6kgFEotZIiIioixKTEyEiYkJ5HI5LC0tsWvXLlhYWKBkyZJSRyuwOGeWiIiIKAtu3ryJevXqYf78+Zq2ihUrspCVGItZIiIioo8IDAyEm5sbrly5gmXLliEuLk7qSPT/WMwSERERZSA+Ph6DBg1C7969ERcXh6ZNm+LSpUuwtLSUOhr9PxazuUAIgSQVEJ+c8v9fKqkjERER0UfcuHED9erVg7+/P2QyGWbOnIlDhw7B0dFR6mj0Hl4AlsOEEPBacxGXHxhj8oUjUschIiKiLIiJiUHDhg3x6tUr2NvbIzAwEB4eHlLHonRwZDaHJShVuPzgdbrP1XEuAnOFUe4GIiIioo+ytrbG7Nmz0aJFC4SHh7OQzcM4MpuLzn3TBNaWZprH5goj3uqOiIgoj7h69SpUKhVq1aoFABg5ciRGjBgBuZxjf3kZeycXmZsYwcLEWPPFQpaIiEh6QgisXr0adevWRdeuXRETEwMAkMlkLGQNAEdmiYiIqMCKjY3F0KFDsXnzZgBAhQoVoFQqJU5FuuCfG0RERFQghYeHw83NDZs3b4aRkRF+/PFH/PXXXyhWrJjU0UgHHJklIiKiAkUIgZUrV2L8+PFISkpCqVKlsGXLFjRo0EDqaJQNHJklIiKiAkUIgT///BNJSUno0KEDwsLCWMgaMI7MEhERUYEil8uxYcMGbNu2DcOHD+cF2QaOI7NERESUrwkhsHTpUgwfPlzTVqJECYwYMYKFbD7AkVkiIiLKt169eoUBAwZg9+7dAIBu3brxBgj5DItZIiIiypfOnz+PHj164P79+zAxMcHChQvRrFkzqWORnnGaAREREeUrQggsXLgQjRo1wv3791GuXDmcOXMGo0aN4rSCfIgjs0RERJSvDBgwAAEBAQCA7t2747fffoONjY20oSjHcGSWiIiI8pUePXrA3Nwcfn5+2LJlCwvZfI4js0RERGTQ1Go1bt26hcqVKwMAWrdujYiICNjZ2UmcjHIDR2aJiIjIYD179gxt27ZF/fr1ERERoWlnIVtwsJglIiIig3T8+HHUqlULBw8eRHJyMq5evSp1JJIAi1kiIiIyKCqVCrNnz4aHhwciIyNRpUoVXLhwAR07dpQ6GkmAc2aJiIjIYERFRaF37944fPgwAMDHxwfLly+HpaWlxMlIKixmiYiIyGAsXboUhw8fhoWFBfz8/NC3b1+pI5HEWMwSERGRwZg5cyYePXqE6dOna1YvoIKNc2aJiIgoz3r8+DEmTpyIlJQUAICZmRk2btzIQpY0ODJLREREedKBAwfQp08fREdHw9raGjNmzJA6EuVBHJklIiKiPEWpVGLq1Klo06YNoqOjUatWLXh5eUkdi/IojswSERFRnvHw4UN4eXnhzJkzAIARI0Zg4cKFMDMzkzgZ5VUsZomIiChPOHz4MLp3746XL1/C2toaa9asQbdu3aSORXkci1kiIiLKE+zt7ZGQkAA3NzcEBQWhXLlyUkciA8BiloiIiCQTFxenueFBtWrVcPjwYbi6usLU1FTiZGQoeAEYERERSWL37t0oU6aMZn4sALi7u7OQJZ2wmCUiIqJclZSUhLFjx+LLL79EdHQ0Fi9eLHUkMmCSF7MrVqyAi4sLzMzM4ObmhpMnT2a6fWBgIGrWrAkLCws4ODigf//+ePHiRS6lJSIiok9x584dNGzYEMuWLQMATJw4EZs2bZI4FRkySYvZoKAgjBs3DtOnT0dYWBgaN26MNm3a4MGDB+luf+rUKfTt2xcDBw7EP//8g23btuHixYsYNGhQLicnIiIiXW3btg21a9dGaGgoihYtir1792LBggVQKBRSRyMDJmkxu2jRIgwcOBCDBg1ClSpVsGTJEpQqVQp+fn7pbn/u3DmUKVMGY8aMgYuLCxo1aoShQ4fi0qVLuZyciIiIdHH16lV4e3sjNjYWDRs2RHh4ONq1ayd1LMoHJFvNIDk5GaGhoZgyZYpWe8uWLbUmgr+vQYMGmD59Ovbt24c2bdrg2bNn2L59e6ZvhqSkJCQlJWkex8TEAHh3dxGlUqmHV5I5pTJF6/9z45ykX6l9xr4zXOxDw8c+NGxKpRLVq1dH586dUbFiRcyaNQvGxsbsTwOS2+9BXc4jWTEbHR0NlUoFOzs7rXY7OztERUWlu0+DBg0QGBiIHj16IDExESkpKejYsSN++eWXDM8zb948+Pr6pmkPDg6GhYXFp72ILEhSAanf5iNHjsDUKMdPSTkkJCRE6gj0idiHho99aFjOnDmDWrVqwcLCAjKZDH379oVcLkdwcLDU0Sibcus9GB8fn+VtJV9nViaTaT0WQqRpS3X9+nWMGTMGM2bMQKtWrRAZGYlJkyZh2LBh8Pf3T3efqVOnYsKECZrHMTExKFWqFFq2bAlra2v9vZAMxCenYPKFIwAADw8P2FjydnyGRqlUIiQkBJ6enpzXZaDYh4aPfWhY4uPjMWHCBKxduxbdunXDunXrcOjQIbRq1Yr9Z6By+z2Y+kl6VkhWzBYvXhxGRkZpRmGfPXuWZrQ21bx589CwYUNMmjQJAFCjRg1YWlqicePG+P777+Hg4JBmH1NT03TXq1MoFLnSGQrxv8JcoTDmm9iA5dbPDOUc9qHhYx/mfTdu3ED37t1x7do1yGQyVKlSBcbG78oN9p/hy7X6SYdzSHYBmImJCdzc3NIMV4eEhKBBgwbp7hMfHw+5XDuykdG7z+2FEDkTlIiIiLJk/fr1qFOnDq5duwY7OzuEhITA19c3zb/dRPok6U/XhAkTsGbNGqxduxY3btzA+PHj8eDBAwwbNgzAuykCffv21WzfoUMH7Ny5E35+frh79y5Onz6NMWPGoG7dunB0dJTqZRARERVocXFx8PHxgY+PD+Lj49G8eXOEh4ejefPmUkejAkDSObM9evTAixcvMHv2bERGRqJ69erYt28fnJ2dAQCRkZFaa876+PggNjYWy5cvx9dff43ChQvDw8MDP/30k1QvgYiIqMCLj49HcHAw5HI5fH19MXXqVM0np0Q5TfILwEaMGIERI0ak+1xAQECattGjR2P06NE5nIqIiIiyqkSJEggKCoJarUaTJk2kjkMFDCexEBERkU5iY2Ph7e2NwMBATVvjxo1ZyJIkWMwSERFRloWHh8PNzQ2bNm3CqFGjdFpCiSgnsJglIiKijxJCwM/PD/Xr18ft27dRsmRJ7N27N1fWbCfKjORzZomIiChve/PmDQYPHoxt27YBANq3b4+AgAAUK1ZM4mRELGaJiIgoE3FxcXBzc8OdO3dgbGyMn376CePHj8/wbp1EuY3TDIiIiChDlpaW+Oqrr+Ds7IxTp05hwoQJLGQpT2ExS0RERFpevXqFR48eaR5///33CAsLQ7169SRMRZQ+FrNERESkcf78edSuXRtdu3aFUqkEACgUChQpUkTiZETpYzFLREREEEJg4cKFaNSoEe7fv4/nz5/j8ePHUsci+igWs0RERAXcixcv0LFjR0ycOBEpKSno1q0bLl++jDJlykgdjeijWMwSEREVYKdPn0atWrWwd+9emJqaws/PD0FBQbCxsZE6GlGWcGkuIiKiAkoIgfHjx+PRo0eoUKECtm7dilq1akkdi0gnHJklIiIqoGQyGQIDAzFw4ECEhoaykCWDxGKWiIioADl+/DiWLl2qeVyhQgWsWbMGhQoVkjAVUfZxmgEREVEBoFKp8MMPP2DWrFkQQsDV1RWNGzeWOhbRJ2MxS0RElM9FRUWhd+/eOHz4MACgX79+cHV1lTgVkX6wmCUiIsrHDh8+DG9vbzx9+hQWFhZYsWIF+vXrJ3UsIr3hnFkiIqJ8at68efD09MTTp09RvXp1XLx4kYUs5TssZomIiPIpW1tbCCEwaNAgnD9/HlWrVpU6EpHecZoBERFRPvL27VtYWVkBAAYMGIBKlSqhUaNGEqciyjkcmSUiIsoHUlJSMHXqVFSvXh0vX74E8G4dWRaylN+xmCUiIjJwDx8+RNOmTfHjjz/i/v372LFjh9SRiHINi1kiIiID9tdff6FWrVo4ffo0rK2tERQUhMGDB0sdiyjXsJglIiIyQMnJyZg4cSLat2+Ply9fws3NDZcvX0b37t2ljkaUq1jMEhERGaBZs2Zh4cKFAIAxY8bg9OnTKFeunMSpiHIfi1kiIiIDNHHiRNSsWRM7d+7E0qVLYWpqKnUkIkmwmCUiIjIASUlJ2LBhA4QQAICiRYvi8uXL+PLLLyVORiQtrjNLRESUx929exfdu3dHaGgokpKSNBd4yeUckyLiu4CIiCgP2759O2rXro3Q0FAULVoUDg4OUkciylNYzBIREeVBiYmJGDlyJLp164aYmBg0aNAA4eHhaN++vdTRiPIUFrNERER5zO3bt+Hu7o4VK1YAAKZMmYJjx46hVKlSEicjyns4Z5aIiCiPefToEf7++28UL14cGzduROvWraWORJRnsZglIiLKA4QQkMlkAIBmzZohICAAzZs3h5OTk8TJiPI2TjMgIiKS2I0bN9CoUSPcunVL09a3b18WskRZwGKWiIhIQuvXr0edOnVw5swZjBkzRuo4RAaHxSwREZEE4uLi4OPjAx8fH8THx8PDwwMBAQFSxyIyOCxmiYiIctm1a9fw+eefY/369ZDL5Zg9ezaCg4Nhb28vdTQig8MLwIiIiHLR+fPn0axZMyQkJMDBwQGbNm1C06ZNpY5FZLBYzBIREeUiV1dX1KxZE9bW1ti4cSNsbW2ljkRk0FjMEhER5bDr16+jQoUKUCgUUCgU+Ouvv1C4cGHI5ZztR/Sp+C4iIiLKIUIIrFy5Eq6urpg+fbqmvWjRoixkifSEI7NEREQ5ICYmBoMHD8bWrVsBvFtLVqVSwcjISOJkRPkL/ywkIiLSs9DQULi6umLr1q0wNjbGggUL8Mcff7CQJcoBHJklIiLSEyEEli9fjokTJyI5ORnOzs7YsmUL6tevL3U0onyLI7NERER68vjxY0ybNg3Jycno3LkzwsLCWMgS5TCOzBIREelJyZIlsXr1ajx79gyjR4+GTCaTOhJRvsdiloiIKJuEEFi8eDFq166NZs2aAQC8vLwkTkVUsLCYJSIiyoaXL1/Cx8cHe/bsgb29Pa5fv44iRYpIHYuowGExS0REpKMzZ87Ay8sLDx8+hKmpKWbMmIHChQtLHYuoQOIFYERERFmkVqvx008/4YsvvsDDhw9RoUIFnDt3DsOHD+f8WCKJcGSWiIgoCxISEvDVV19h//79AICePXti1apVKFSokMTJiAo2jswSERFlgZmZGQoXLgwzMzP89ttvCAwMZCFLlAewmCUiIsqASqVCXFwcAEAmk2HVqlW4ePEiBg8ezGkFRHkEi1kiIqJ0PH36FK1bt0bv3r0hhAAAFCpUCNWrV5c4GRG9j3NmiYiIPnDkyBF4e3sjKioKFhYW+Pfff1GlShWpYxFROjgyS0RE9P9UKhVmzpyJFi1aICoqCtWqVcPFixdZyBLlYRyZJSIiAvDkyRN4e3vj2LFjAICBAwdi2bJlsLCwkDYYEWWKxSwRERV4Qgh06tQJly5dgqWlJVatWgVvb2+pYxFRFmRrmkFKSgoOHTqEVatWITY2FsC7v2jfvn2r13BERES5QSaTYdmyZXBzc8Ply5dZyBIZEJ1HZu/fv4/WrVvjwYMHSEpKgqenJwoVKoT58+cjMTERK1euzImcREREevXo0SOEh4ejffv2AAB3d3dcvHiRS24RGRidR2bHjh2LOnXq4NWrVzA3N9e0f/nllzh8+LBewxEREeWEffv2oVatWujevTuuXbumaWchS2R4dB6ZPXXqFE6fPg0TExOtdmdnZzx+/FhvwYiIiPRNqVRi+vTpWLBgAQDA1dVVa2CGiAyPzsWsWq2GSqVK0/7o0SPe1o+IiPKs+/fvw8vLC+fOnQMAjB49GgsWLICpqanEyYjoU+g8zcDT0xNLlizRPJbJZHj79i1mzpyJtm3b6jMbERGRXvzxxx+oXbs2zp07BxsbG+zYsQPLli1jIUuUD+g8Mrt48WI0a9YMVatWRWJiInr16oXbt2+jePHi2Lx5c05kJCIi+iSXL1/Gq1evULduXWzZsgUuLi5SRyIiPdG5mHV0dER4eDi2bNmC0NBQqNVqDBw4EN7e3px3REREeYYQQnNB14wZM2Bra4vBgwenueaDiAybztMMTpw4AYVCgf79+2P58uVYsWIFBg0aBIVCgRMnTuRERiIiIp3s2LEDHh4eSExMBAAYGRlh5MiRLGSJ8iGdi9lmzZrh5cuXadrfvHmDZs2a6SUUERFRdiQmJmLUqFHo2rUrjh07hl9//VXqSESUw3SeZvD+xzbve/HiBSwtLfUSioiISFe3b99Gjx49EBYWBgD45ptvMGbMGIlTEVFOy3Ix26VLFwDvVi/w8fHRugJUpVLhypUraNCggf4TEhERfcSWLVswePBgvH37FsWLF8eGDRvQpk0bqWMRUS7IcjFrY2MD4N3IbKFChbQu9jIxMUH9+vUxePBg/SckIiLKxMKFCzFx4kQAQOPGjbF582Y4OTlJnIqIckuWi9l169YBAMqUKYOJEydySgEREeUJX331FX744QeMGDECM2fOhLGxzjPoiMiA6fyOnzlzZk7kICIiyrKwsDDUrl0bwLtBltu3b6No0aISpyIiKei8mgEAbN++Hd27d0f9+vXh6uqq9aWrFStWwMXFBWZmZnBzc8PJkycz3T4pKQnTp0+Hs7MzTE1NUa5cOaxduzY7L4OIiAxMXFwcBgwYAFdXV+zbt0/TzkKWqODSuZhdtmwZ+vfvD1tbW4SFhaFu3booVqwY7t69q/Nk+6CgIIwbNw7Tp09HWFgYGjdujDZt2uDBgwcZ7tO9e3ccPnwY/v7+uHnzJjZv3ozKlSvr+jKIiMjAPHjwAA0aNMC6desgl8tx8+ZNqSMRUR6g8zSDFStW4LfffkPPnj2xfv16TJ48GWXLlsWMGTPSXX82M4sWLcLAgQMxaNAgAMCSJUtw8OBB+Pn5Yd68eWm2P3DgAI4fP467d+9q/govU6aMri+BiIgMiBACAQEBmDhxIpKTk2Fvb4/NmzejadOmUkcjojxA52I29S9jADA3N0dsbCwAoE+fPqhfvz6WL1+epeMkJycjNDQUU6ZM0Wpv2bIlzpw5k+4+f/75J+rUqYP58+dj48aNsLS0RMeOHTFnzpwMb6WblJSEpKQkzeOYmBgAgFKphFKpzFLWT6FUpmj9f26ck/Qrtc/Yd4aLfWi43r59i1GjRmHTpk0AgObNm2P9+vWwtbVlfxoQvgcNX273oS7n0bmYtbe3x4sXL+Ds7AxnZ2ecO3cONWvWREREBIQQWT5OdHQ0VCoV7OzstNrt7OwQFRWV7j53797FqVOnYGZmhl27diE6OhojRozAy5cvM5w3O2/ePPj6+qZpDw4OhoWFRZbzZleSCkj9Nh85cgSmRjl+SsohISEhUkegT8Q+NDxnz57Fpk2bIJfL0atXL3Tp0gWXLl2SOhZlE9+Dhi+3+jA+Pj7L2+pczHp4eGDPnj1wdXXFwIEDMX78eGzfvh2XLl3S3FhBFx/eTSyjO4wBgFqthkwmQ2BgoGbd20WLFqFr16749ddf0x2dnTp1KiZMmKB5HBMTg1KlSqFly5awtrbWOa+u4pNTMPnCEQDvvnc2lmY5fk7SL6VSiZCQEHh6ekKhUEgdh7KBfWi42rZtCyEEPD09ERcXxz40UHwPGr7c7sPUT9KzQudi9rfffoNarQYADBs2DEWLFsWpU6fQoUMHDBs2LMvHKV68OIyMjNKMwj579izNaG0qBwcHODk5aQpZAKhSpQqEEHj06BEqVKiQZh9TU1Otu5WlUigUudIZCvG/wlyhMOab2IDl1s8M5Rz2Yd4XExODqVOnYubMmbC1tQUA/PTTT1Aqldi3bx/70MCx/wxfrtVPOpxD59UM5HK51oLU3bt3x7JlyzBmzBg8f/48y8cxMTGBm5tbmuHqkJCQDG+L27BhQzx58gRv377VtN26dQtyuRwlS5bU8ZUQEVFecvnyZbi6umLFihUYOHCg1HGIyEBka53ZD0VFRWH06NEoX768TvtNmDABa9aswdq1a3Hjxg2MHz8eDx480IzwTp06FX379tVs36tXLxQrVgz9+/fH9evXceLECUyaNAkDBgzI8AIwIiLK24QQWL58Odzd3XHnzh2ULl0a06ZNkzoWERmILBezr1+/hre3N0qUKAFHR0csW7YMarUaM2bMQNmyZXHu3Dmdb17Qo0cPLFmyBLNnz0atWrVw4sQJ7Nu3D87OzgCAyMhIrTVnraysEBISgtevX6NOnTrw9vZGhw4dsGzZMp3OS0REecPr16/RtWtXjB49GsnJyejYsSPCwsLg7u4udTQiMhBZnjM7bdo0nDhxAv369cOBAwcwfvx4HDhwAImJidi/fz+aNGmSrQAjRozAiBEj0n0uICAgTVvlypV5NSQRUT7w77//om3btoiIiIBCocCCBQswZsyYDC8CJiJKT5aL2b/++gvr1q1DixYtMGLECJQvXx4VK1bEkiVLcjAeERHlV46OjjAyMoKLiwuCgoLw+eefSx2JiAxQlovZJ0+eoGrVqgCAsmXLwszMTHPnLiIioqyIiYlBoUKFIJPJYG1tjb1798LOzg6FCxeWOhoRGagsz5lVq9VayyQYGRnB0tIyR0IREVH+c/bsWVSvXl3rTpGVKlViIUtEnyTLI7NCCPj4+GjWbE1MTMSwYcPSFLQ7d+7Ub0IiIjJoarUaP//8M6ZNmwaVSoVVq1Zh2LBhXG+UiPQiy8Vsv379tB737t1b72GIiCh/ef78Ofr164f9+/cDALy8vLBq1SoWskSkN1kuZtetW5eTOYiIKJ85ceIEevbsiSdPnsDMzAzLli3DoEGDuFoBEemVzrezJSIi+pjIyEi0bNkSSUlJqFSpErZu3YoaNWpIHYuI8iEWs0REpHcODg7w9fXFP//8gxUrVsDKykrqSESUT7GYJSIivTh69ChsbW1RrVo1AMDkyZMBgNMKiChHZXlpLiIiovSoVCrMmjULzZs3R/fu3REXFwfgXRHLQpaIchpHZomIKNsiIyPh7e2No0ePAgDq16/PApaIclW2RmY3btyIhg0bwtHREffv3wcALFmyBH/88YdewxERUd4VEhKCWrVq4ejRo7C0tMTGjRvh7+8PCwsLqaMRUQGiczHr5+eHCRMmoG3btnj9+jVUKhUAoHDhwliyZIm+8xERUR6TkpKCb7/9Fq1atcKzZ89Qo0YNXLp0ieuPE5EkdC5mf/nlF6xevRrTp0+HkZGRpr1OnTq4evWqXsMREVHeI5PJcOrUKQghMHToUJw7dw6VK1eWOhYRFVA6z5mNiIhA7dq107SbmppqJv0TEVH+I4SATCaDkZERNm3ahFOnTqF79+5SxyKiAk7nkVkXFxeEh4enad+/fz+qVq2qj0xERJSHKJVKTJ48GePGjdO0OTo6spAlojxB55HZSZMmYeTIkUhMTIQQAhcuXMDmzZsxb948rFmzJicyEhGRRB48eAAvLy+cPXsWADBgwADUrFlT4lRERP+jczHbv39/pKSkYPLkyYiPj0evXr3g5OSEpUuXwsvLKycyEhGRBP7880/4+Pjg1atXsLGxgb+/PwtZIspzsrXO7ODBgzF48GBER0dDrVbD1tZW37mIiEgiycnJ+OabbzQr1Hz++ecICgqCi4uLtMGIiNKh85xZX19f3LlzBwBQvHhxFrJERPmIEAIdOnTQFLLjx4/HqVOnWMgSUZ6lczG7Y8cOVKxYEfXr18fy5cvx/PnznMhFREQSkMlkGDp0KIoUKYI//vgDixYtgomJidSxiIgypHMxe+XKFVy5cgUeHh5YtGgRnJyc0LZtW2zatAnx8fE5kZGIiHJQYmKi1jrhXbp0wd27d9GxY0cJUxERZU22bmdbrVo1/PDDD7h79y6OHj0KFxcXjBs3Dvb29vrOR0REOei///5DgwYN4OHhgcePH2vaCxcuLF0oIiIdZKuYfZ+lpSXMzc1hYmICpVKpj0xERJQLgoKC4OrqirCwMAghEBERIXUkIiKdZauYjYiIwNy5c1G1alXUqVMHly9fxqxZsxAVFaXvfEREpGcJCQkYNmwYvLy8EBsbi0aNGiE8PByNGjWSOhoRkc50XprL3d0dFy5cwGeffYb+/ftr1pklIqK87+bNm+jevTuuXLkCmUyGadOmYdasWTA2ztZKjUREktP5t1ezZs2wZs0aVKtWLSfyEBFRDlq6dCmuXLkCW1tb/P777/D09JQ6EhHRJ9G5mP3hhx9yIgcREeWCBQsWICUlBb6+vnBwcJA6DhHRJ8tSMTthwgTMmTMHlpaWmDBhQqbbLlq0SC/BiIjo0/3zzz9YtWoVlixZArlcDktLS/z2229SxyIi0pssFbNhYWGalQrCwsJyNBAREX06IQQCAgIwcuRIJCQkoGzZshg3bpzUsYiI9C5LxezRo0fT/X8iIsp73r59ixEjRmDjxo0AgJYtW6JXr14SpyIiyhk6L801YMAAxMbGpmmPi4vDgAED9BKKiIiy58qVK6hTpw42btwIuVyOuXPnYv/+/bC1tZU6GhFRjtC5mF2/fj0SEhLStCckJGDDhg16CUVERLoLCgpCvXr1cPPmTTg5OeHYsWOYNm0a5PJPvj8OEVGeleXVDGJiYiCEgBACsbGxMDMz0zynUqmwb98+/uVPRCSh8uXLQ61Wo02bNtiwYQOKFy8udSQiohyX5WK2cOHCkMlkkMlkqFixYprnZTIZfH199RqOiIgy9/r1axQuXBgA4ObmhrNnz6JWrVocjSWiAiPLxezRo0chhICHhwd27NiBokWLap4zMTGBs7MzHB0dcyQkERFpE0JgxYoVmDZtGo4ePQpXV1cA0PyXiKigyHIx26RJEwBAREQESpcuDZlMlmOhiIgoY69fv8bgwYOxfft2AEBAQACLWCIqsLJUzF65cgXVq1eHXC7HmzdvcPXq1Qy3rVGjht7CERGRtosXL6JHjx6IiIiAQqHA/PnzMXbsWKljERFJJkvFbK1atRAVFQVbW1vUqlULMpkMQog028lkMqhUKr2HJCIq6IQQWLp0KSZPngylUgkXFxcEBQXh888/lzoaEZGkslTMRkREoESJEpr/JyKi3LVjxw6MHz8eAPDVV19hzZo1mgu/iIgKsiwVs87Ozun+PxER5Y4uXbqgY8eOaNmyJUaMGMHrFoiI/l+2bprw119/aR5PnjwZhQsXRoMGDXD//n29hiMiKqjUajVWr16N+Ph4AIBcLsfu3bsxcuRIFrJERO/RuZj94YcfYG5uDgA4e/Ysli9fjvnz56N48eKaj8CIiCj7oqOj0aFDBwwZMgSjR4/WtLOIJSJKK8tLc6V6+PAhypcvDwDYvXs3unbtiiFDhqBhw4Zo2rSpvvMRERUoJ0+eRM+ePfH48WOYmZmhXr16EEKwkCUiyoDOI7NWVlZ48eIFACA4OBgtWrQAAJiZmSEhIUG/6YiICgi1Wo0ffvgBzZo1w+PHj1GpUiWcP38eQ4YMYSFLRJQJnUdmPT09MWjQINSuXRu3bt1Cu3btAAD//PMPypQpo+98RET53rNnz9CnTx8EBwcDAHr37g0/Pz9YWVlJnIyIKO/TeWT2119/hbu7O54/f44dO3agWLFiAIDQ0FD07NlT7wGJiPI7pVKJy5cvw9zcHP7+/tiwYQMLWSKiLNJ5ZLZw4cJYvnx5mnZfX1+9BCIiKgjenwfr5OSEbdu2oUSJEqhWrZrEyYiIDIvOxSzw7r7g/v7+uHHjBmQyGapUqYKBAwfCxsZG3/mIiPKdqKgoeHt7Y9SoUfjyyy8BgBfQEhFlk87TDC5duoRy5cph8eLFePnyJaKjo7F48WKUK1cOly9fzomMRET5xqFDh1CzZk0cOXIEY8aMQXJystSRiIgMms7F7Pjx49GxY0fcu3cPO3fuxK5duxAREYH27dtj3LhxORCRiMjwpaSk4Ntvv0XLli3x7Nkz1KhRA4cOHYKJiYnU0YiIDJrO0wwuXbqE1atXw9j4f7saGxtj8uTJqFOnjl7DERHlB48fP0bPnj1x8uRJAMDQoUOxePFizQ1oiIgo+3QuZq2trfHgwQNUrlxZq/3hw4coVKiQ3oIREeUHz58/R61atRAdHY1ChQrht99+g5eXl9SxiIjyDZ2nGfTo0QMDBw5EUFAQHj58iEePHmHLli0YNGgQl+YiIvpAiRIl0KNHD9SuXRuhoaEsZImI9Eznkdmff/4ZMpkMffv2RUpKCgBAoVBg+PDh+PHHH/UekIjI0Dx48AAKhQIODg4AgIULF0IIATMzM4mTERHlPzqPzJqYmGDp0qV49eoVwsPDERYWhpcvX2Lx4sUwNTXNiYxERAZjz549qFWrFnr27Kn5g9/U1JSFLBFRDslyMRsfH4+RI0fCyckJtra2GDRoEBwcHFCjRg1YWFjkZEYiojwvOTkZX3/9NTp27IhXr14hPj4er169kjoWEVG+l+VidubMmQgICEC7du3g5eWFkJAQDB8+PCezEREZhIiICDRu3BiLFi0C8G4Jw1OnTqFEiRISJyMiyv+yPGd2586d8Pf311y80Lt3bzRs2BAqlQpGRkY5FpCIKC/buXMnBgwYgDdv3qBIkSIICAhAx44dpY5FRFRgZHlk9uHDh2jcuLHmcd26dWFsbIwnT57kSDAiorxOqVTiu+++w5s3b+Du7o6wsDAWskREuSzLxaxKpUpzpxpjY2PNBQ5ERAWNQqFAUFAQpk6diuPHj8PZ2VnqSEREBU6WpxkIIeDj46O1YkFiYiKGDRsGS0tLTdvOnTv1m5CIKA/ZunUrnj17hlGjRgEAqlevjh9++EHiVEREBVeWi9l+/fqlaevdu7dewxAR5VUJCQkYP348Vq1aBSMjIzRs2BC1a9eWOhYRUYGX5WJ23bp1OZmDiCjPunnzJrp3744rV65AJpNhypQp+Oyzz6SORUREyMYdwIiICpLff/8dw4YNQ1xcHGxtbfH777/D09NT6lhERPT/dL4DGBFRQTFixAj06dMHcXFxaNasGcLDw1nIEhHlMSxmiYgyULlyZchkMsyaNQshISFwcHCQOhIREX2A0wyIiN7z8uVLFC1aFAAwevRoNGnSBDVr1pQ4FRERZYQjs0REAN6+fYt+/fqhXr16iImJAQDIZDIWskREeVy2itmNGzeiYcOGcHR0xP379wEAS5YswR9//KHXcEREueHq1av4/PPPsWHDBty9exdHjx6VOhIREWWRzsWsn58fJkyYgLZt2+L169dQqVQAgMKFC2PJkiX6zkdElGOEEFi9ejXq1q2Lf//9F05OTjh27Bg6deokdTQiIsoinYvZX375BatXr8b06dNhZGSkaa9Tpw6uXr2q13BERDklNjYW3t7eGDJkCBITE9GmTRuEh4ejcePGUkcjIiId6FzMRkREpHvXG1NTU8TFxeklFBFRTvv666+xefNmGBkZYf78+di7dy+KFy8udSwiItKRzsWsi4sLwsPD07Tv378fVatW1TnAihUr4OLiAjMzM7i5ueHkyZNZ2u/06dMwNjZGrVq1dD4nEdH333+P+vXr4+TJk5g0aRLkcl4PS0RkiHT+7T1p0iSMHDkSQUFBEELgwoULmDt3LqZNm4ZJkybpdKygoCCMGzcO06dPR1hYGBo3bow2bdrgwYMHme735s0b9O3bF82bN9c1PhEVUHFxcfD399c8trW1xZkzZ+Du7i5hKiIi+lQ6rzPbv39/pKSkYPLkyYiPj0evXr3g5OSEpUuXwsvLS6djLVq0CAMHDsSgQYMAvFsR4eDBg/Dz88O8efMy3G/o0KHo1asXjIyMsHv3bl1fAhEVMKGhoZgwYQKePn0Ka2tr9OrVC8C7pbeIiMiwZeumCYMHD8bgwYMRHR0NtVoNW1tbnY+RnJyM0NBQTJkyRau9ZcuWOHPmTIb7rVu3Dnfu3MHvv/+O77///qPnSUpKQlJSkuZx6vqRSqUSSqVS59y6UipTtP4/N85J+pXaZ+w7wyOEwPLlyzFlyhQolUo4OzujTJky7EsDxPehYWP/Gb7c7kNdzvNJdwD7lIsloqOjoVKpYGdnp9VuZ2eHqKiodPe5ffs2pkyZgpMnT8LYOGvR582bB19f3zTtwcHBsLCw0D24jpJUQOq3+ciRIzA1ynRzysNCQkKkjkA6ePv2LX755RecP38eAFC/fn2MGjUKz58/x759+yROR9nF96FhY/8Zvtzqw/j4+Cxvq3Mx6+LikulHc3fv3tXpeB8eSwiR7vFVKhV69eoFX19fVKxYMcvHnzp1KiZMmKB5HBMTg1KlSqFly5awtrbWKWt2xCenYPKFIwAADw8P2Fia5fg5Sb+USiVCQkLg6ekJhUIhdRzKggsXLmDs2LG4f/8+TExMMG/ePJQtWxYtW7ZkHxoovg8NG/vP8OV2H6Z+kp4VOhez48aN03qsVCoRFhaGAwcO6HQBWPHixWFkZJRmFPbZs2dpRmuBd2tCXrp0CWFhYRg1ahQAQK1WQwgBY2NjBAcHw8PDI81+pqamMDU1TdOuUChypTMU4n+FuUJhzDexAcutnxn6dG/evMH9+/dRrlw5bN26FZ999hn27dvHPswH2IeGjf1n+HKtftLhHDoXs2PHjk23/ddff8WlS5eyfBwTExO4ubkhJCQEX375paY9JCQk3bvvWFtbp7kpw4oVK3DkyBFs374dLi4uWT43EeU/73+q07ZtW2zatAnt2rWDtbU15+kREeVjeltYsU2bNtixY4dO+0yYMAFr1qzB2rVrcePGDYwfPx4PHjzAsGHDALybItC3b993QeVyVK9eXevL1tYWZmZmqF69OiwtLfX1UojIwJw6dQo1a9bE/fv3NW09e/bMlalEREQkrU+6AOx927dvR9GiRXXap0ePHnjx4gVmz56NyMhIVK9eHfv27YOzszMAIDIy8qNrzhJRwaVWq/HTTz/hu+++g0qlwrfffouNGzdKHYuIiHKRzsVs7dq1tS7QEkIgKioKz58/x4oVK3QOMGLECIwYMSLd5wICAjLdd9asWZg1a5bO5yQiw/fs2TP06dMHwcHBAIDevXvDz89P4lRERJTbdC5mO3furPVYLpejRIkSaNq0KSpXrqyvXEREGTp27Bh69eqFyMhImJub49dff4WPjw9vgkBEVADpVMympKSgTJkyaNWqFezt7XMqExFRhvbv34/27dtDrVajatWq2Lp1K6pVqyZ1LCIikohOF4AZGxtj+PDhWnfUIiLKTc2aNUONGjXQv39/XLhwgYUsEVEBp/M0g3r16iEsLExzkRYRUU47f/486tSpAyMjI5iZmeHEiRMoVKiQ1LGIiCgP0LmYHTFiBL7++ms8evQIbm5uaZbEqlGjht7CEVHBlpKSAl9fX8ydOxczZszQXPDJQpaIiFJluZgdMGAAlixZgh49egAAxowZo3lOJpNpFixXqVT6T0lEBc7jx4/Rq1cvnDhxAgDw9OnTDG93TUREBVeWi9n169fjxx9/RERERE7mISLCgQMH0KdPH0RHR8PKygqrV6+Gl5eX1LGIiCgPynIxK4QAAM6VJaIco1QqMWPGDPz4448A3q1rHRQUhAoVKkicjIiI8iqdVjPgx3tElJPu3r2LJUuWAABGjhyJM2fOsJAlIqJM6XQBWMWKFT9a0L58+fKTAhFRwVWpUiWsWrUKFhYW6Nq1q9RxiIjIAOhUzPr6+sLGxianshBRAZOcnIxvv/0WX375Jdzd3QEAffv2lTgVEREZEp2KWS8vL9ja2uZUFiIqQO7duwcvLy+cP38eW7duxb///gszMzOpYxERkYHJ8pxZzpclIn3ZtWsXateujfPnz6Nw4cJYunQpC1kiIsqWLBezqasZEBFlV1JSEsaMGYMuXbrg9evXqF+/PsLDw9GpUyepoxERkYHK8jQDtVqdkzmIKJ979eoVPD09ERoaCgCYNGkS5s6dC4VCIXEyIiIyZDrfzpaIKDsKFy6MkiVL4t69e1i/fj3atWsndSQiIsoHWMwSUY5JTExESkoKrKysIJPJsHbtWsTHx6NkyZJSRyMionxCp5smEBFl1a1bt1C/fn0MGTJEM+e+aNGiLGSJiEivWMwSkd5t2rQJbm5u+Pvvv3Ho0CE8fvxY6khERJRPsZglIr2Jj4/H4MGD4e3tjbdv36Jp06YIDw/naCwREeUYFrNEpBc3btxAvXr1sGbNGshkMsycOROHDh2Co6Oj1NGIiCgf4wVgRPTJUlJS0KFDB9y5cwf29vYIDAyEh4eH1LGIiKgA4MgsEX0yY2Nj/Pbbb2jVqhXCw8NZyBIRUa5hMUtE2XL16lXs3btX89jDwwP79++HnZ2dhKmIiKigYTFLRDoRQmDNmjWoW7cuevbsidu3b2uek8lkEiYjIqKCiMUsEWVZbGwsevfujcGDByMxMRGNGjVC4cKFpY5FREQFGItZIsqS8PBwuLm5YdOmTTAyMsJPP/2Ev/76CyVKlJA6GhERFWBczYCIPmrlypUYN24ckpKSUKpUKWzZsgUNGjSQOhYRERFHZono4+7cuYOkpCR06NABYWFhLGSJiCjP4MgsEaVLrVZDLn/39+4PP/yAmjVrwtvbmxd5ERFRnsKRWSLSIoTA0qVL4eHhAaVSCQBQKBTo3bs3C1kiIspzWMwSkcarV6/QpUsXjBs3DsePH8fmzZuljkRERJQpTjMgIgDA+fPn0aNHD9y/fx8mJiZYuHAh+vTpI3UsIiKiTHFklqiAU6vVWLhwIRo1aoT79++jXLlyOHPmDEaNGsVpBURElOexmCUq4CZPnoyJEyciJSUF3bt3x+XLl+Hm5iZ1LCIioixhMUtUwA0ePBjFixfHypUrsWXLFlhbW0sdiYiIKMs4Z5aogFGr1Thz5gwaNWoEAKhUqRLu3bsHS0tLiZMRERHpjiOzRAXIs2fP0LZtWzRp0gTHjh3TtLOQJSIiQ8WRWaIC4vjx4+jZsyciIyNhbm6OyMhIqSMRERF9Mo7MEuVzKpUKc+bMgYeHByIjI1GlShVcuHABPXv2lDoaERHRJ+PILFE+FhUVhd69e+Pw4cMAAB8fHyxfvpzTCoiIKN9gMUuUj+3fvx+HDx+GhYUF/Pz80LdvX6kjERER6RWLWaJ8zMfHB3fv3kWvXr1QpUoVqeMQERHpHefMEuUjT548Qe/evfHq1SsAgEwmw5w5c1jIEhFRvsWRWaJ84sCBA+jTpw+io6MBAL///rvEiYiIiHIeR2aJDFxKSgqmTp2KNm3aIDo6GrVq1cLMmTOljkVERJQrODJLZMAePnyInj174vTp0wCAESNGYOHChTAzM5M4GRERUe5gMUtkoM6dO4d27drh5cuXsLa2hr+/P7p27Sp1LCIiolzFYpbIQFWsWBGWlpYoW7YsgoKCULZsWakjERER5ToWs0QG5NmzZyhRogRkMhmKFi2Kw4cPo3Tp0jA1NZU6GhERkSR4ARiRgdi1axcqVaqEtWvXatoqVKjAQpaIiAo0FrNEeVxSUhLGjBmDLl264PXr1wgMDIQQQupYREREeQKLWaI87M6dO2jYsCF++eUXAMDEiRNx8OBByGQyiZMRERHlDZwzS5RHbdu2DYMGDUJMTAyKFi2KDRs2oF27dlLHIiIiylNYzBLlQbdu3YKXlxfUajUaNmyIzZs3o1SpUlLHIiIiynNYzBLlQRUrVsSMGTOQlJSE2bNnw9iYb1UiIqL08F9Iojxi8+bNqFOnDipUqAAAvCUtERFRFvACMCKJxcfHY9CgQejVqxd69OiBxMREqSMREREZDI7MEknoxo0b6N69O65duwaZTIYOHTpAoVBIHYuIiMhgsJglksj69esxYsQIxMfHw87ODoGBgWjevLnUsYiIiAwKi1miXBYfH4/hw4djw4YNAIDmzZvj999/h729vcTJiIiIDA/nzBLlMmNjY/z777+Qy+WYM2cODh48yEKWiIgomzgyS5QLhBAQQkAul8PExARBQUG4f/8+mjRpInU0IiIig8aRWaIcFhsbi969e2Pq1KmatjJlyrCQJSIi0gOOzBLloPDwcHTv3h23b9+GsbExhg8fjjJlykgdi4iIKN/gyCxRDhBCwM/PD/Xr18ft27dRsmRJHDt2jIUsERGRnnFklkjP3rx5g8GDB2Pbtm0AgPbt2yMgIADFihWTOBkREVH+w2KWSI/UajWaNGmCv//+G8bGxvjpp58wfvx4yGQyqaMRERHlS5xmQKRHcrkckyZNgrOzM06dOoUJEyawkCUiIspBLGaJPtGrV68QHh6ueezt7Y3r16+jXr160oUiIiIqIFjMEn2C8+fPo3bt2mjbti2eP3+uabewsJAwFRERUcHBYpYoG4QQWLhwIRo1aoT79+/D3Nwcz549kzoWERFRgcMLwIh09OLFC/j4+GDv3r0AgG7dumH16tWwsbGROBkREVHBI/nI7IoVK+Di4gIzMzO4ubnh5MmTGW67c+dOeHp6okSJErC2toa7uzsOHjyYi2mpoDt9+jRq1aqFvXv3wtTUFCtWrEBQUBALWSIiIolIWswGBQVh3LhxmD59OsLCwtC4cWO0adMGDx48SHf7EydOwNPTE/v27UNoaCiaNWuGDh06ICwsLJeTU0Hl5+eHR48eoUKFCjh37hyGDx/O1QqIiIgkJOk0g0WLFmHgwIEYNGgQAGDJkiU4ePAg/Pz8MG/evDTbL1myROvxDz/8gD/++AN79uxB7dq1cyMyFXArVqyAnZ0dZs2ahUKFCkkdh4iIqMCTrJhNTk5GaGgopkyZotXesmVLnDlzJkvHUKvViI2NRdGiRTPcJikpCUlJSZrHMTExAAClUgmlUpmN5LpRKlO0/j83zkn6c+LECQQFBaFt27ZQKpUwNzfHjz/+CADsSwOS2lfsM8PFPjRs7D/Dl9t9qMt5JCtmo6OjoVKpYGdnp9VuZ2eHqKioLB1j4cKFiIuLQ/fu3TPcZt68efD19U3THhwcnCvLJyWpgNRv85EjR2BqlOOnJD1QqVTYvn07goKCoFarYWZmxukE+UBISIjUEegTsQ8NG/vP8OVWH8bHx2d5W8lXM/iwQBBCZKlo2Lx5M2bNmoU//vgDtra2GW43depUTJgwQfM4JiYGpUqVQsuWLWFtbZ394FkUn5yCyReOAAA8PDxgY2mW4+ekTxMVFQUfHx8cOfKu37y9vdGwYUN4enpCoVBInI6yQ6lUIiQkhH1owNiHho39Z/hyuw9TP0nPCsmK2eLFi8PIyCjNKOyzZ8/SjNZ+KCgoCAMHDsS2bdvQokWLTLc1NTWFqalpmnaFQpErnaEQ/yvMFQpjvonzuMOHD8Pb2xtPnz6FhYUFVqxYgV69emHfvn259jNDOYd9aPjYh4aN/Wf4cq1+0uEckq1mYGJiAjc3tzTD1SEhIWjQoEGG+23evBk+Pj7YtGkT2rVrl9MxqQBZunQpPD098fTpU1SvXh2XLl1Cv379pI5FREREmZB0msGECRPQp08f1KlTB+7u7vjtt9/w4MEDDBs2DMC7KQKPHz/Ghg0bALwrZPv27YulS5eifv36mlFdc3NzrvNJn+zzzz+HXC5H//79sXTpUt6SloiIyABIWsz26NEDL168wOzZsxEZGYnq1atj3759cHZ2BgBERkZqrTm7atUqpKSkYOTIkRg5cqSmvV+/fggICMjt+JQPPH36VDOtpUGDBrh27RoqV64scSoiIiLKKskvABsxYgRGjBiR7nMfFqjHjh3L+UBUIKSkpOC7777DL7/8gvPnz6NatWoAwEKWiIjIwEhezBLltocPH6Jnz544ffo0AGDPnj2aYpaIiIgMC4tZKlD++usv9O3bFy9fvoS1tTVWr16d6TrFRERElLdJtpoBUW5SKpWYOHEi2rdvj5cvX8LNzQ2XL19mIUtERGTgWMxSgeDv74+FCxcCAMaMGYPTp0+jXLlyEqciIiKiT8VpBlQgDBo0CAcPHkTfvn3x5ZdfSh2HiIiI9IQjs5QvJScnY8GCBUhKSgIAGBsbY9euXSxkiYiI8hmOzFK+c/fuXfTo0QOXLl3CgwcP8Msvv0gdiYiIiHIIR2YpX9m+fTtq166NS5cuoWjRomjVqpXUkYiIiCgHsZilfCExMREjRoxAt27dEBMTg4YNGyI8PBzt27eXOhoRERHlIBazZPDu3LkDd3d3+Pn5AQCmTJmCo0ePolSpUhInIyIiopzGObNk8ORyOSIiIlC8eHFs3LgRrVu3ljoSERER5RIWs2SQVCoVjIyMAAAuLi7YtWsXKlasCCcnJ4mTERERUW7iNAMyODdu3ICrqysOHDigaWvWrBkLWSIiogKIxSwZlA0bNqBOnTq4cuUKJk2aBLVaLXUkIiIikhCLWTIIcXFx6N+/P/r164f4+Hh4eHggJCQEcjl/hImIiAoyVgKU5127dg2ff/45AgICIJfLMXv2bAQHB8Pe3l7qaERERCQxXgBGedrdu3dRt25dJCQkwMHBAZs2bULTpk2ljkVERER5BItZytPKli0LLy8vPHnyBBs2bICtra3UkYiIiCgPYTFLec7ff/8NR0dHlChRAgDg5+cHhULB+bFERESUBqsDyjOEEFi5ciXq1auHvn37alYqMDU1ZSFLRERE6WKFQHnCmzdv4OXlheHDhyMpKQlGRkaIj4+XOhYRERHlcSxmSXKhoaFwc3PD1q1bYWxsjAULFuDPP/+ElZWV1NGIiIgoj+OcWZKMEALLly/HxIkTkZycDGdnZ2zZsgX169eXOhoREREZCI7MkmTi4uKwdOlSJCcno1OnTggLC2MhS0RERDrhyCxJxsrKCkFBQTh16hTGjBkDmUwmdSQiIiIyMCxmKdcIIbBkyRKYm5tj2LBhAAA3Nze4ublJnIyIiIgMFYtZyhUvX76Ej48P9uzZAxMTE3h6eqJcuXJSxyIiIiIDx2KWctyZM2fg5eWFhw8fwtTUFIsXL0bZsmWljkVERET5AC8AoxyjVqvx008/4YsvvsDDhw9RoUIFnDt3DsOHD+f8WCIiItILjsxSjlCr1ejcuTP27NkDAOjZsydWrVqFQoUKSZyMiIiI8hOOzFKOkMvlcHd3h5mZGVavXo3AwEAWskRERKR3HJklvVGpVIiOjoadnR0A4JtvvkG3bt1Qvnx5iZMRERFRfsWRWdKLp0+fonXr1mjevDni4+MBvBudZSFLREREOYnFLH2yI0eOoGbNmjh06BAiIiJw+fJlqSMRERFRAcFilrJNpVJh5syZaNGiBZ4+fYpq1arh4sWLaNSokdTRiIiIqIDgnFnKlidPnsDb2xvHjh0DAAwcOBDLli2DhYWFtMGIiIioQGExS9kyevRoHDt2DJaWlli1ahW8vb2ljkREREQFEItZypZly5bhzZs3+PXXX1GpUiWp4xAREVEBxTmzlCWPHj3Cr7/+qnns5OSEQ4cOsZAlIiIiSXFklj5q37596Nu3L168eAEnJyd07txZ6khEREREADgyS5lQKpWYPHky2rVrhxcvXsDV1RWfffaZ1LGIiIiINDgyS+m6f/8+vLy8cO7cOQDvLvhasGABTE1NJU5GRERE9D8sZimNvXv3ok+fPnj9+jVsbGywdu1adOnSRepYRERERGmwmKU0kpKS8Pr1a9StWxdbtmyBi4uL1JGIiIiI0sVilgAAKSkpMDZ+9+Pw1VdfYceOHWjfvj1MTEwkTkZEhkStViM5OVnvx1UqlTA2NkZiYiJUKpXej085i/1n+HKiD01MTCCXf/rlWyxmCdu3b8e0adNw7NgxODo6AgCnFRCRzpKTkxEREQG1Wq33YwshYG9vj4cPH0Imk+n9+JSz2H+GLyf6UC6Xw8XF5ZMHzljMFmCJiYn4+uuvsWLFCgDAggULsHjxYolTEZEhEkIgMjISRkZGKFWqlF5GW96nVqvx9u1bWFlZ6f3YlPPYf4ZP332oVqvx5MkTREZGonTp0p9UILOYLaBu376NHj16ICwsDADwzTffYM6cORKnIiJDlZKSgvj4eDg6OsLCwkLvx0+dvmBmZsZiyACx/wxfTvRhiRIl8OTJE6SkpEChUGT7OCxmC6AtW7Zg8ODBePv2LYoXL44NGzagTZs2UsciIgOWOoeO8+yJKKtSf1+oVCoWs5R1GzZsQL9+/QAAjRs3xubNm+Hk5CRxKiLKLzgfkoiySm9zb/VyFDIYX331FapVq4Zvv/0WR44cYSFLREREBo3FbAEQEhKiubrY0tISly5dwpw5czRLcREREWVXcnIyypcvj9OnT0sdJd949uwZSpQogcePH0sdxSCwmM3H4uLi0L9/f7Rs2RILFy7UtJuZmUmYiogo7/Dx8YFMJoNMJoOxsTFKly6N4cOH49WrV2m2PXPmDNq2bYsiRYrAzMwMn332GRYuXJjumptHjx5F27ZtUaxYMVhYWKBq1ar4+uuvP1qchIWFoVu3brCzs4OZmRkqVqyIwYMH49atW3p7zfr222+/wdnZGQ0bNkzz3JAhQ2BkZIQtW7akec7HxwedO3dO0x4eHg6ZTIZ79+5p2oQQ+O2331CvXj1YWVmhcOHCqFOnDpYsWYL4+Hh9vhwtc+fORYMGDWBhYYHChQtnaR8hBGbNmgVHR0eYm5ujadOm+Oeff7S2SUpKwujRo1G8eHFYWlqiY8eOePTokeZ5W1tb9OnTBzNnztTny8m3WMzmU//88w/q1q2LgIAAyOVyKJVKqSMREeVJrVu3RmRkJO7du4c1a9Zgz549GDFihNY2u3btQpMmTVCyZEkcPXoU//77L8aOHYu5c+fCy8sLQgjNtqtWrUKLFi1gb2+PHTt24Pr161i5ciXevHmjNbDwob1796J+/fpISkpCYGAgbty4gY0bN8LGxgbfffddtl9fTv/+/+WXXzBo0KA07fHx8QgKCsKkSZOwdu3aTzpHnz59MG7cOHTq1AlHjx5FeHg4vvvuO/zxxx8IDg7+pGNnJjk5Gd26dcPw4cOzvM/8+fOxaNEiLF++HBcvXoS9vT08PT0RGxur2WbcuHHYtWsXtmzZglOnTuHt27do37691h9G/fv3R2BgYLp/WNEHRAHz5s0bAUC8efMmV84Xl6QUzt/sFc7f7BWv38bn+PnUarXw9/cX5ubmAoCwt7cXR48ezfHz5mfJycli9+7dIjk5WeoolE3sw5yXkJAgrl+/LhISEoQQ734XxSUp9fYVm5AknjyNFrEJSR/dVq1WZzl3v379RKdOnbTaJkyYIIoWLap5/PbtW1GsWDHRpUuXNPv/+eefAoDYsmWLEEKIhw8fChMTEzFu3Lh0z/fq1at02+Pi4kTx4sVF586dM91v3bp1wsbGRuu5Xbt2iff/OZ85c6aoWbOm8Pf3Fy4uLkImk4mVK1cKR0dHoVKptPbt0KGD6Nu3r9brcXV1FaampsLFxUXMmjVLKJXKdDMJIURoaKiQy+Xp/psaEBAg6tevL16/fi3Mzc3F33//rXX+9L73QggRFhYmAIiIiAghhBBBQUECgNi9e3eabdVqtXj9+nWG+fQlve97etRqtbC3txc//vijpi0xMVHY2NiIlStXCiGEeP36tVAoFJqfGSGEePz4sZDL5eLAgQNaxytTpozw9/fXz4v4RCqVSrx69SrNz9Cn+PD3xvt0qdc4aTIfefv2LYYNG4bAwEAAQMuWLbFx40bY2tpKnIyICpoEpQpVZxyU5NzXZ7eChUn2/nm7e/cuDhw4oLVMUHBwMF68eIGJEyem2b5Dhw6oWLEiNm/ejB49emDbtm1ITk7G5MmT0z1+Rh9VHzx4ENHR0Trvl5H//vsPW7duxY4dO2BkZAQnJyeMGTMGR48eRfPmzQEAr169wsGDB7Fnzx5Nht69e2PZsmVo3Lgx7ty5gyFDhgBAhh93nzhxAhUrVoS1tXWa5/z9/dG7d2/Y2NigTZs2CAwMxLx583R6HQAQGBiISpUqoVOnTmmek8lksLGxyXBfKyurTI/duHFj7N+/X+dMGYmIiEBUVBRatmypaTM1NUWTJk1w5swZDB06FKGhoVAqlVrbODo6onr16jhz5gxatWqlaa9bty5OnjyJAQMG6C1jfsRiNh+5desWtm7dCiMjI8yZMwfffPMNF6cmIvqIvXv3wsrKCiqVComJiQCARYsWaZ5Pna9apUqVdPevXLmyZpvbt2/D2toaDg4OOmW4ffu25lj6kJycjI0bN6JEiRKattatW2PTpk2aYnbbtm0oWrSo5vHcuXMxZcoUzfKNZcuWxZw5czB58uQMi9l79+5pboP+4es5d+4cdu7cCQDw9vbGmDFjMHfuXJ3/Xbp9+zYqVaqk0z6pwsPDM33e3Nw8W8fNSFRUFADAzs5Oq93Ozg7379/XbGNiYoIiRYqk2SZ1/1ROTk6amxtRxljM5iOurq5YtWoVKlSogEaNGkkdh4gKMHOFEa7PbvXxDbNIrVYjNiYWhawLfbQYMlcY6XTsZs2awc/PD/Hx8VizZg1u3bqF0aNHp9lOvDcv9sP21PUy3/9/XWR07OxydnbWKmSBdwXlkCFDsGLFCpiamiIwMBBeXl4wMnr3/QoNDcXFixcxd+5czT6pBX58fHy6d3ZLSEhI96Jif39/tGrVCsWLFwcAtG3bFoMGDcKhQ4fQunVrnV5Ldr+nAFC+fPls7fepPsybldeQ3jbm5uY5eoFbfsFhOwMWExODvn37av3V1r9/fxayRCQ5mUwGCxNjvX6ZmxhlaTtdCx9LS0uUL18eNWrUwLJly5CUlARfX1/N8xUrVgQA3LhxI939//33X1SoUEGz7Zs3bxAZGalThtRz/Pvvv5luJ5fL0xS+6V3gZWlpmaatQ4cOUKvV+Ouvv/Dw4UOcPHkSvXv31jyvVqvh6+uL8PBwzdfVq1dx+/btDFfBKV68eJoLlFQqFTZs2IC//voLxsbGMDY2hpWVFV69eqV1IZi1tTXevHmT5pivX78GAM30gYoVK2b4vf8YKyurTL/0ffdLe3t7AEgzwvrs2TPNaK29vT2Sk5PTfN/e3ybVy5cv0/xRQmmxmDVQly9fhqurKzZu3Ahvb+90l4YhIiLdzZw5Ez///DOePHkC4N31B0WLFk13JYI///wTt2/fRs+ePQEAXbt2hYmJCebPn5/usVMLtQ+1bNkSxYsX/+h+JUqUQGxsLOLi4jTPfeyj9FTm5ubo0qULAgMDsXnzZlSsWBFubm6a511dXXHz5k2UL18+zVdGo+G1a9fGv//+q1Vg79u3D7GxsQgLC9MUxZcvX0ZAQAD++OMPvHjxAsC7KRXXrl3TTO1IdfHiRZQoUULzMXyvXr1w69Yt/PHHH2nOL4RItyB+/3uT2deaNWuy9L3LKhcXF9jb2yMkJETTlpycjOPHj6NBgwYAADc3NygUCq1tIiMjce3aNc02qa5du4batWvrNWO+pLdL0gyEoa9moFarxS+//CJMTEwEAFG6dGlx5swZPSSljPBKeMPHPsx5mV2VrA85cSW1EBlfUe/m5iZGjhypebxt2zZhZGQkBg8eLP7++28REREh1qxZI4oUKSK6du2qtYLCr7/+KmQymRgwYIA4duyYuHfvnjh16pQYMmSImDBhQoZZdu/eLRQKhejQoYMICQkRERER4uLFi2LSpEmiR48eQgghXrx4ISwtLcWYMWPE7du3RWBgoHB0dEx3NYP0BAcHC1NTU1GpUiUxZ84crecOHDggjI2NxcyZM8W1a9fE9evXxZYtW8T06dMzzBwdHS1MTEzE1atXNW2dOnXS5E2lUqnEy5cvhZOTk1iyZIkQ4t1V/fb29qJr167i4sWL4r///hMbN24URYoUEfPnz9fsq1arRY8ePYS5ubn44YcfxMWLF8W9e/fEnj17hIeHh9i1a1eG+T7V/fv3RVhYmPD19RVWVlYiLCxMhIWFidjYWM02lSpVEjt37tQ8/vHHH4WNjY3YuXOnuHr1qujZs6dwcHAQMTExmm2GDRsmSpYsKQ4dOiQuX74sPDw8RM2aNUVKSopmm7i4OGFubi5OnDiRY69PF3l5NQMWszlMn8Xsq1evRJcuXQQAAUB07NhRvHjxQk9JKSMshAwf+zDn5bdiNjAwUJiYmIgHDx5o2k6cOCFat24tbGxshImJiahatar4+eeftQqQVCEhIaJVq1aiSJEiwszMTFSuXFlMnDhRPHnyJNM8Fy9eFF26dBElSpQQpqamonz58mLIkCHi9u3bmm127dolypcvL8zMzET79u3Fb7/9luViNiUlRTg4OAgA4s6dO2meP3DggGjQoIEwNzcX1tbWom7duuK3337LNLOXl5eYMmWKEEKIqKgoYWxsLLZu3aq1TWr/jRo1Snz22Wea9tu3b4uvvvpKODk5CUtLS/HZZ5+J5cuXp+lnlUol/Pz8xOeffy4sLCyEtbW1cHNzE0uXLhXx8Tm37GW/fv00/+a+//X+kpcAxLp16zSP1Wq1mDlzprC3txempqbiiy++0Cr2hXj3fhk1apQoWrSoMDc3F+3bt9f6WRNCiE2bNolKlSrl2GvTVV4uZmVC6HnWeR4XExMDGxsbvHnzJt2lRPQtPjlFszzN3995wMYye1dOPnr0CI0bN8a9e/egUCiwYMECjBkzJtuT4inrlEol9u3bh7Zt22ot10OGg32Y8xITExEREQEXF5ccucugWq1GTEwMrK2tuUpLHnP16lW0aNEC//33HwoVKpTuNuw/3dWtWxfjxo1Dr169pI4CIGf6MLPfG7rUa1zNwEA4OjqiQoUKkMlkCAoKwueffy51JCIiInz22WeYP38+7t27h88++0zqOPnCs2fP0LVrV81cbMoci9k87OXLlzAzM4OFhQXkcjk2bdoEY2NjnRfPJiIiykmpa9OSftja2mZ4Aw1Ki2P9edSZM2dQq1YtjB07VtNWvHhxFrJERERE72Exm8eo1WrMnz8fX3zxBR4+fIhjx45luJQLERERUUHHYjYPef78Odq3b49vvvkGKpUKXl5eCA0N5WgsERERUQY4ZzaPOHnyJLy8vPDkyROYmZlh6dKlGDx4MFcrICIiIsoEi9k8ID4+Ht26dcPTp09RqVIlbN26FTVq1JA6FhEREVGex2kGeYCFhQXWrl2LPn364NKlSyxkiYiIiLKII7MSOXr0KBISEtC2bVsAQNu2bTX/T0RERERZw5HZXKZSqTBr1iw0b94c3t7eePDggdSRiIgoB82aNQu1atXKs+dp2rQpxo0bp/c8H1OmTBksWbLkk47h4+ODzp07Z7qNVK+Pco/kxeyKFSs0tzFzc3PDyZMnM93++PHjcHNzg5mZGcqWLYuVK1fmUtJPFxUVCU9PT/j6+kIIgS5duqB48eJSxyIiKtAePnyIgQMHwtHRESYmJnB2dsbYsWPx4sULnY8lk8mwe/durbaJEyfi8OHDekqbfceOHYNMJuNyjzng6tWraNKkCczNzeHk5ITZs2dDCJHpPnPnzkWDBg1gYWGR4apFMpkszdeHdc/Hzr1z5054enqiRIkSsLa2hru7Ow4ePKh1jNWrV6Nx48YoUqQIihQpghYtWuDChQta2/j6+qJIkSIwMjLSZLG3t9faZufOnWjVqhWKFy8OmUyG8PDwj3zn9EPSYjYoKAjjxo3D9OnTERYWhsaNG6NNmzYZjlZGRESgbdu2aNy4McLCwjBt2jSMGTMGO3bsyOXkukuIuIxG9evj6NGjsLS0xMaNG+Hv7w8LCwupoxER5R0qFXDsGLB587v/qlQ5erq7d++iTp06uHXrFjZv3oz//vsPK1euxOHDh+Hu7o6XL19+8jmsrKxQrFgxPaTNO5RKpdQR8oyYmBh4enrC0dERFy9exC+//IKff/4ZixYtynS/5ORkdOvWDcOHD890u3Xr1iEyMlLz9f7d1rJy7hMnTsDT0xP79u1DaGgomjVrhg4dOiAsLEyzzbFjx9CzZ08cPXoUZ8+eRenSpdGyZUs8fvxYK0vlypXx+PFjTZarV69qPR8XF4eGDRvixx9//Oj3Ta+EhOrWrSuGDRum1Va5cmUxZcqUdLefPHmyqFy5slbb0KFDRf369bN8zjdv3ggA4s2bN7oHzoa3icnC2r27AGQCgKhRo4b4999/c+XcpB/Jycli9+7dIjk5WeoolE3sw5yXkJAgrl+/LhISErJ/kB07hChZUgjgf18lSwqxY4dQqVTi1atXQqVS6S+0EKJ169aiZMmSIj4+Xqs9MjJSWFhYaP0b5ezsLGbPni169uwpLC0thYODg1i2bJnW8wA0X87OzkIIIWbOnClq1qyp2a5fv36iU6dOYu7cucLW1lbY2NiIWbNmCaVSKSZOnCiKFCkinJychL+/v1amyZMniwoVKghzc3Ph4uIivv32W62f6Q/P876IiAitbABEv379hBBCNGnSRIwePVpMmjRJFClSRNjZ2YmZM2dq7Q9A+Pn5iY4dOwoLCwsxY8YMIYQQf/75p3B1dRWmpqbCxcVF8zrez1SqVClhYmIi7O3txahRo7S+X3PnzhX9+/cXVlZWolSpUmLVqlVa571y5Ypo1qyZMDMzE0WLFhWDBw8WsbGxab6Xqd6+fSv69OkjLC0thb29vfj5559FkyZNxNixY9P9vujDihUrhI2NjUhMTNS0zZs3Tzg6Ogq1Wv3R/detWydsbGzSfQ6A2LVrl97PXbVqVeHr65vh8ykpKaJQoUJi/fr1mrYZM2aI6tWrZ+k9mPrzFhYWlul2mf3e0KVek+wCsOTkZISGhmLKlCla7S1btsSZM2fS3efs2bNo2bKlVlurVq3g7+8PpVIJhUKRZp+kpCQkJSVpHsfExAB491dlbvxlmZKSAnXiWwAC/XwGYNnSxTA3N+dftQYkta/YZ4aLfZjzlEolhBBQq9VQq9W6H2DnTsi6dweEwPura4vHj4GuXSG2bgVatNCcQx9evnyJgwcP4vvvv4epqanWcW1tbdGrVy8EBQVh+fLlmjW/FyxYgKlTp2LGjBkIDg7G+PHjUbFiRXh6euL8+fOwt7eHv78/WrduDSMjI6jVas1HvqnHF0LgyJEjcHJywrFjx3D69GkMHjwYZ86cwRdffIGzZ89i69atGDZsGJo3b45SpUoBeDfCu3btWjg6OuLq1asYOnQorKysMGnSJM1x3z/P+5ycnLBt2zZ069YNN27cgLW1NczNzTXbrl+/HuPHj8fZs2dx9uxZDBgwAO7u7vD09NQcY+bMmZg7dy4WLlwIIyMj7N+/H71798aSJUvQuHFj3LlzB8OGDYMQAjNmzMD27duxePFibNq0CVWrVsXdu3fx33//aeVbuHAhZs+ejSlTpmDHjh0YPnw4GjVqhMqVKyM+Ph6tW7dGvXr1cP78eTx79gxDhgzByJEjsW7dOs1rfv9nYuLEiTh69Ch27NgBe3t7TJ8+HaGhoahZs2aGPzcnT55Eu3btMv1ZmTp1KqZOnZruc6n9plAoNOfw9PTE1KlTcffuXbi4uGR67NR9Mso3atQoDBo0CC4uLujfvz+GDBkCuVye7XOr1WrExsaiSJEiGZ7z7du3UCqVKFy4sNbP7d27d1GyZEmYmpqibt26mDt3LsqWLZvpa8rs/Zr6/lAqlTAyMtJ6Tpff15IVs9HR0VCpVLCzs9Nqt7OzQ1RUVLr7REVFpbt9SkoKoqOj4eDgkGafefPmwdfXN017cHBwrnzEn6QCinoMgnnZOmjbwRVHjx7N8XNSzggJCZE6An0i9mHOMTY2hr29Pd6+fYvk5GTddlapYD12bJpCFgBkQkDIZJCNGwf8/TdiY2P1FRnh4eEQQsDZ2Vkz0PE+FxcXvHr1Cnfv3kWJEiWgVqtRt25dzcfCffv2xbFjx/Dzzz+jXr16MDU1BQCYmppq/n2JiYlBUlISVCqV1mBK4cKFMWfOHMjlcnTt2hXz589HbGwsRo4cCQAYMWIEfvrpJxw6dAhfffUVAGD06NGabE2aNMGIESOwZcsWDB06FADSnOdDZmZmAABzc3OtfCkpKahatarmIqnOnTvjl19+wf79+1GvXj3N/l999RW6du2qeTxnzhyMHTsWX375JQCgePHimDJlCmbNmoVx48bh9u3bsLW1Rd26daFQKODm5gY3NzdNPrVajRYtWsDb2xsAMGzYMCxevBgHDhyAo6Mj1q9fj/j4ePzyyy+wtLRE6dKl8eOPP6Jnz56YPn06bG1toVQqkZKSgpiYGLx9+xZr166Fn5+fJvcvv/yCatWqITk5OcPvS8WKFXHixIl0n0tVpEiRDPd//PgxSpcurfV86vf3zp07H51ikpiYCCFEusefPn06vvjiC5ibm+P48eOYNGkSnjx5gokTJ2b73MuWLcPbt2/RunXrDF/TxIkT4eDggLp162q2+eyzz+Dn54dy5crh+fPn+Pnnn9GwYUOcPXsWRYsW1dr/7du3AN5NO8joHMC7gc2EhAScOHECKSkpWs/Fx8dnuN+HJF+a68M7XAkhMr3rVXrbp9eeaurUqZgwYYLmcUxMDEqVKoWWLVvC2to6u7GzTAgBD48kHDkiR7tWLWBiYpLj5yT9UiqVCAkJgaenZ7qj/5T3sQ9zXmJiIh4+fAgrKytN0ZRlx45B/uRJhk/LhIDs8WMYnz0L8zZt9HZnREtLSwDvirv0/j1ILU6tra1hbW0NuVyOxo0ba237xRdfYOnSpVptHx7P1NQURkZGmjaFQoHq1atrXfTj4OCAatWqae1XrFgxvH37VtO2fft2LFu2DP/99x/evn2LlJQUTbb0zvOh1CKnUKFCWtsYGxujRo0aWm1OTk548+aNVpu7u7vW47///hthYWFa8zNVKhUSExNhbGyM3r17Y9WqVXB1dUWrVq3QtGlTdOvWTfMelMvlcHNz0zqmg4MDYmNjYW1tjXv37qFWrVpaA1Wenp5Qq9V48uQJypcvD4VCAWNjY1hbWyMiIgLJycnw8PDQHNPa2hqVKlWCiYlJht8Xa2vrNANlujAyMkpz/NQ/uqysrD5aa5iZmUEmk6W73ezZszX/37BhQ5iZmWHOnDmadl3PvXnzZvz000/YtWsXypUrl26eBQsWYOfOnThy5AhsbW017V26dEFsbCwKFSoEmUyG5s2bo0KFCti1axfGjx+vdQwrKysA795jmb3+xMREmJub44svvkjzeyOzIvhDkhWzxYsXh5GRUZpR2GfPnmX4Q2Vvb5/u9sbGxhn+5WNqaqr5hfQ+hUKRa/+o2chkMDUCTExM+A+pAcvNnxnKGezDnKNSqSCTySCXyzUfgWbZ06dZ2kwWFaU5hz5UrFgRMpkM//77b7rHvHnzJooUKQJbW1tNAf3h+VOv6n6/7cPvQeq+qW0ymQwmJiZptkmvTQgBuVyOc+fOoVevXvD19UWrVq1gY2ODLVu2YOHChVrHff88H0ptT6+PPjy3XC7XnDtVoUKFtB6r1Wr4+vqiS5cuac5lYWEBKysr3Lx5EyEhIQgJCcHEiROxYsUKHD9+XPM+zOw1pz7+MBfwroiTy+Va3//3X/+Hry+zn5uTJ0+iTZs26T6Xatq0aZg2bVq6zzk4OODp06dax4+OjtY897Gf1/f75WPc3d0RExOD58+fw87OTqdzBwUFYfDgwdi2bVuaKZupfv75Z8ybNw+HDh1Ks8xb6nSB1O9loUKF8Nlnn+G///5Lkz2zn7UPt5PJZOn+btbld7VkxayJiQnc3NwQEhKi+YgCePcxYKdOndLdx93dHXv27NFqCw4ORp06dfgPFBGRoUpnilh6xAfLAH2qYsWKwdPTEytWrMD48eNhbm6ueS4qKgqBgYHo27ev1kjwuXPntI5x7tw5VK5cWfNYoVBAlQMrMJw+fRrOzs6YPn26pu3+/fs6HSP1k0F95XN1dcXNmzdRvnz5DLcxNzdHx44d0b59e/Tt2xd169bF1atX4erq+tHjV61aFevXr0dcXJxmFP306dOQy+WoWLFimu1TR2rPnTuH0qVLAwBevXqFW7duoUmTJhmep06dOh9dQurDj9Hf5+7ujmnTpiE5OVnzPQ4ODoajoyPKlCnzkVepm7CwMJiZmWlG9bN67s2bN2PAgAHYvHlzhvODFyxYgO+//x4HDx5EnTp1PpolKSkJN27cQOPGjT/5dX0qSZfmmjBhAtasWYO1a9fixo0bGD9+PB48eIBhw4YBeDdFoG/fvprthw0bhvv372PChAm4ceMG1q5dC39/f83cESIiMkCNGwMlSwIZTR+QySBKlUKKu7veT718+XIkJSWhVatWOHHiBB4+fIgDBw7A09MTTk5OmDt3rtb2p0+fxvz583Hr1i38+uuv2LZtG8aOHat5vkyZMjh8+DCioqLw6tUrveUsX748Hjx4gC1btuDOnTtYtmwZdu3apdMxnJ2dIZPJsHfvXjx//lwzrzG7ZsyYgQ0bNmDWrFn4559/cOPGDQQFBeHbb78FAAQEBMDf3x/Xrl3D3bt3ERQUBHNzczg7O2fp+N7e3jAzM0O/fv1w7do1HD16FKNHj0afPn3S/QTXysoKAwcOxKRJk3D48GFcu3YNPj4+Hx3xNDc3R/ny5TP9yqyY7dWrF0xNTeHj44Nr165h165d+OGHHzBhwgTNH0IXLlzQLGuV6sGDBwgPD8eDBw+gUqkQHh6O8PBwTb/s2bMHq1evxrVr13Dnzh2sWbMG06dPx5AhQzSfOGfl3Js3b0bfvn2xcOFC1K9fH1FRUYiKisKbN280WebPn49vv/0Wa9euRZkyZTTbvP8zMmnSJJw+fRoRERE4f/48unbtipiYGK2lwl6+fInw8HBcv34dwLtPN8LDwzO8FkpvPrreQQ779ddfhbOzszAxMRGurq7i+PHjmuf69esnmjRporX9sWPHRO3atYWJiYkoU6aM8PPz0+l8ub00lxBcFsjQsf8MH/sw533y0lw7dgghk737en9prv9vU23bliNLcwkhxL1794SPj4+wt7cXCoVClCpVSowePVpER0drbefs7Cx8fX1F9+7dhYWFhbCzsxNLlizR2ubPP/8U5cuXF8bGxh9dmut96S0f5ezsLBYvXqx5PGnSJFGsWDFhZWUlevToIRYvXqy1pFNmS3Olmj17trC3txcymUxraa4Pz92pUyfN80JkvETUgQMHRIMGDYS5ubmwtrYWdevWFb/99psQQohdu3aJevXqCWtra2FpaSk+//xzERwcnOHrE0KImjVrai0LpuvSXLGxsaJ3796a/pk/f36OL82VmrNx48bC1NRU2Nvbi1mzZmktjXX06FEBQERERGhlxwfLpQEQR48eFUIIsX//flGrVi1hZWUlLCwsRPXq1cWSJUu0lj7LyrmbNGmS7nne798Pl5VL/Xq/L7p37655jzg6OoouXbqIf/75RyvLunXrPnqc9+lraS6ZEB+5RUU+ExMTAxsbmzQT23OSUqnEvn370LZtW06HMEDsP8PHPsx5iYmJiIiI0NzRMVt27gTGjgUePfpfW6lSwJIlUHfujJiYGM2FWFIoU6YMxo0bx1ujZoNarZa8/+jT5EQfZvZ7Q5d6TfLVDIiIiAAAXboAnToBJ08CkZHv5tI2bgwYGQF6WluWiPIfFrNERJR3GBkBTZtKnYKIDAiLWSIioiy4d++e1BGIKB2cuEJEREREBovFLBER6U0Bu6aYiD6Bvn5fsJglIqJPZmRkBODdvdaJiLIi9fdF6u+P7OKcWSIi+mTGxsawsLDA8+fPoVAo9L78klqtRnJyMhITE7m0kwFi/xk+ffehWq3G8+fPYWFhAWPjTytHWcwSEdEnk8lkcHBwQEREhM63Wc0KIQQSEhJgbm6udXtZMgzsP8OXE30ol8tRunTpTz4ei1kiItILExMTVKhQIUemGiiVSpw4cQJffPEFb3xhgNh/hi8n+tDExEQvo7wsZomISG/kcnn27wCWCSMjI6SkpMDMzIzFkAFi/xm+vNyHnLhCRERERAaLxSwRERERGSwWs0RERERksArcnNnUBXpjYmJy7ZxKpRLx8fGIiYnJc/NM6OPYf4aPfWj42IeGjf1n+HK7D1PrtKzcWKHAFbOxsbEAgFKlSkmchIiIiIgyExsbCxsbm0y3kYkCdu9BtVqNJ0+eoFChQrm21l1MTAxKlSqFhw8fwtraOlfOSfrD/jN87EPDxz40bOw/w5fbfSiEQGxsLBwdHT+6fFeBG5mVy+UoWbKkJOe2trbmm9iAsf8MH/vQ8LEPDRv7z/DlZh9+bEQ2FS8AIyIiIiKDxWKWiIiIiAwWi9lcYGpqipkzZ8LU1FTqKJQN7D/Dxz40fOxDw8b+M3x5uQ8L3AVgRERERJR/cGSWiIiIiAwWi1kiIiIiMlgsZomIiIjIYLGYJSIiIiKDxWJWD1asWAEXFxeYmZnBzc0NJ0+ezHT748ePw83NDWZmZihbtixWrlyZS0kpI7r04c6dO+Hp6YkSJUrA2toa7u7uOHjwYC6mpfTo+j5Mdfr0aRgbG6NWrVo5G5A+Stc+TEpKwvTp0+Hs7AxTU1OUK1cOa9euzaW09CFd+y8wMBA1a9aEhYUFHBwc0L9/f7x48SKX0tKHTpw4gQ4dOsDR0REymQy7d+/+6D55pp4R9Em2bNkiFAqFWL16tbh+/boYO3assLS0FPfv3093+7t37woLCwsxduxYcf36dbF69WqhUCjE9u3bczk5pdK1D8eOHSt++uknceHCBXHr1i0xdepUoVAoxOXLl3M5OaXStQ9TvX79WpQtW1a0bNlS1KxZM3fCUrqy04cdO3YU9erVEyEhISIiIkKcP39enD59OhdTUypd++/kyZNCLpeLpUuXirt374qTJ0+KatWqic6dO+dyckq1b98+MX36dLFjxw4BQOzatSvT7fNSPcNi9hPVrVtXDBs2TKutcuXKYsqUKeluP3nyZFG5cmWttqFDh4r69evnWEbKnK59mJ6qVasKX19ffUejLMpuH/bo0UN8++23YubMmSxmJaZrH+7fv1/Y2NiIFy9e5EY8+ghd+2/BggWibNmyWm3Lli0TJUuWzLGMlHVZKWbzUj3DaQafIDk5GaGhoWjZsqVWe8uWLXHmzJl09zl79mya7Vu1aoVLly5BqVTmWFZKX3b68ENqtRqxsbEoWrRoTkSkj8huH65btw537tzBzJkzczoifUR2+vDPP/9EnTp1MH/+fDg5OaFixYqYOHEiEhISciMyvSc7/degQQM8evQI+/btgxACT58+xfbt29GuXbvciEx6kJfqGeNcPVs+Ex0dDZVKBTs7O612Ozs7REVFpbtPVFRUutunpKQgOjoaDg4OOZaX0spOH35o4cKFiIuLQ/fu3XMiIn1Edvrw9u3bmDJlCk6ePAljY/4alFp2+vDu3bs4deoUzMzMsGvXLkRHR2PEiBF4+fIl583msuz0X4MGDRAYGIgePXogMTERKSkp6NixI3755ZfciEx6kJfqGY7M6oFMJtN6LIRI0/ax7dNrp9yjax+m2rx5M2bNmoWgoCDY2trmVDzKgqz2oUqlQq9eveDr64uKFSvmVjzKAl3eh2q1GjKZDIGBgahbty7atm2LRYsWISAggKOzEtGl/65fv44xY8ZgxowZCA0NxYEDBxAREYFhw4blRlTSk7xSz3BI4hMUL14cRkZGaf7yfPbsWZq/VlLZ29unu72xsTGKFSuWY1kpfdnpw1RBQUEYOHAgtm3bhhYtWuRkTMqErn0YGxuLS5cuISwsDKNGjQLwrjASQsDY2BjBwcHw8PDIlez0Tnbehw4ODnBycoKNjY2mrUqVKhBC4NGjR6hQoUKOZqb/yU7/zZs3Dw0bNsSkSZMAADVq1IClpSUaN26M77//np9SGoC8VM9wZPYTmJiYwM3NDSEhIVrtISEhaNCgQbr7uLu7p9k+ODgYderUgUKhyLGslL7s9CHwbkTWx8cHmzZt4hwvienah9bW1rh69SrCw8M1X8OGDUOlSpUQHh6OevXq5VZ0+n/ZeR82bNgQT548wdu3bzVtt27dglwuR8mSJXM0L2nLTv/Fx8dDLtcuQYyMjAD8b3SP8rY8Vc/k+iVn+UzqciT+/v7i+vXrYty4ccLS0lLcu3dPCCHElClTRJ8+fTTbpy5lMX78eHH9+nXh7+/Ppbkkpmsfbtq0SRgbG4tff/1VREZGar5ev34t1Uso8HTtww9xNQPp6dqHsbGxomTJkqJr167in3/+EcePHxcVKlQQgwYNkuolFGi69t+6deuEsbGxWLFihbhz5444deqUqFOnjqhbt65UL6HAi42NFWFhYSIsLEwAEIsWLRJhYWGa5dXycj3DYlYPfv31V+Hs7CxMTEyEq6urOH78uOa5fv36iSZNmmhtf+zYMVG7dm1hYmIiypQpI/z8/HI5MX1Ilz5s0qSJAJDmq1+/frkfnDR0fR++j8Vs3qBrH964cUO0aNFCmJubi5IlS4oJEyaI+Pj4XE5NqXTtv2XLlomqVasKc3Nz4eDgILy9vcWjR49yOTWlOnr0aKb/tuXlekYmBMfziYiIiMgwcc4sERERERksFrNEREREZLBYzBIRERGRwWIxS0REREQGi8UsERERERksFrNEREREZLBYzBIRERGRwWIxS0REREQGi8UsERGAgIAAFC5cWOoY2VamTBksWbIk021mzZqFWrVq5UoeIqLcwmKWiPINHx8fyGSyNF///fef1NEQEBCglcnBwQHdu3dHRESEXo5/8eJFDBkyRPNYJpNh9+7dWttMnDgRhw8f1sv5MvLh67Szs0OHDh3wzz//6HwcQ/7jgohyD4tZIspXWrdujcjISK0vFxcXqWMBAKytrREZGYknT55g06ZNCA8PR8eOHaFSqT752CVKlICFhUWm21hZWaFYsWKffK6Pef91/vXXX4iLi0O7du2QnJyc4+cmooKHxSwR5Sumpqawt7fX+jIyMsKiRYvw2WefwdLSEqVKlcKIESPw9u3bDI/z999/o1mzZihUqBCsra3h5uaGS5cuaZ4/c+YMvvjiC5ibm6NUqVIYM2YM4uLiMs0mk8lgb28PBwcHNGvWDDNnzsS1a9c0I8d+fn4oV64cTExMUKlSJWzcuFFr/1mzZqF06dIwNTWFo6MjxowZo3nu/WkGZcqUAQB8+eWXkMlkmsfvTzM4ePAgzMzM8Pr1a61zjBkzBk2aNNHb66xTpw7Gjx+P+/fv4+bNm5ptMuuPY8eOoX///njz5o1mhHfWrFkAgOTkZEyePBlOTk6wtLREvXr1cOzYsUzzEFH+xmKWiAoEuVyOZcuW4dq1a1i/fj2OHDmCyZMnZ7i9t7c3SpYsiYsXLyI0NBRTpkyBQqEAAFy9ehWtWrVCly5dcOXKFQQFBeHUqVMYNWqUTpnMzc0BAEqlErt27cLYsWPx9ddf49q1axg6dCj69++Po0ePAgC2b9+OxYsXY9WqVbh9+zZ2796Nzz77LN3jXrx4EQCwbt06REZGah6/r0WLFihcuDB27NihaVOpVNi6dSu8vb319jpfv36NTZs2AYDm+wdk3h8NGjTAkiVLNCO8kZGRmDhxIgCgf//+OH36NLZs2YIrV66gW7duaN26NW7fvp3lTESUzwgionyiX79+wsjISFhaWmq+unbtmu62W7duFcWKFdM8XrdunbCxsdE8LlSokAgICEh33z59+oghQ4ZotZ08eVLI5XKRkJCQ7j4fHv/hw4eifv36omTJkiIpKUk0aNBADB48WGufbt26ibZt2wohhFi4cKGoWLGiSE5OTvf4zs7OYvHixZrHAMSuXbu0tpk5c6aoWbOm5vGYMWOEh4eH5vHBgweFiYmJePny5Se9TgDC0tJSWFhYCAACgOjYsWO626f6WH8IIcR///0nZDKZePz4sVZ78+bNxdSpUzM9PhHlX8bSltJERPrVrFkz+Pn5aR5bWloCAI4ePYoffvgB169fR0xMDFJSUpCYmIi4uDjNNu+bMGECBg0ahI0bN6JFixbo1q0bypUrBwAIDQ3Ff//9h8DAQM32Qgio1WpERESgSpUq6WZ78+YNrKysIIRAfHw8XF1dsXPnTpiYmODGjRtaF3ABQMOGDbF06VIAQLdu3bBkyRKULVsWrVu3Rtu2bdGhQwcYG2f/17i3tzfc3d3x5MkTODo6IjAwEG3btkWRIkU+6XUWKlQIly9fRkpKCo4fP44FCxZg5cqVWtvo2h8AcPnyZQghULFiRa32pKSkXJkLTER5E4tZIspXLC0tUb58ea22+/fvo23bthg2bBjmzJmDokWL4tSpUxg4cCCUSmW6x5k1axZ69eqFv/76C/v378fMmTOxZcsWfPnll1Cr1Rg6dKjWnNVUpUuXzjBbapEnl8thZ2eXpmiTyWRaj4UQmrZSpUrh5s2bCAkJwaFDhzBixAgsWLAAx48f1/r4Xhd169ZFuXLlsGXLFgwfPhy7du3CunXrNM9n93XK5XJNH1SuXBlRUVHo0aMHTpw4ASB7/ZGax8jICKGhoTAyMtJ6zsrKSqfXTkT5B4tZIsr3Ll26hJSUFCxcuBBy+btLBbZu3frR/SpWrIiKFSti/Pjx6NmzJ9atW4cvv/wSrq6u+Oeff9IUzR/zfpH3oSpVquDUqVPo27evpu3MmTNao5/m5ubo2PH/2rlDkNb+MIzjz5Wd4ZisWFSUDZVhEhREi1EMgjIwiAMtBmEzGMTm0oLlFEEswpgYLBssmJxYRHAgoraBsiYirIiiKO9Nd/yvU/5er3A58v3UF855X37lgfN7z7jGx8eVSCTU09Oj8/Nz9ff31z3PcZwP/SVhenpa29vbam9vV0NDg8bGxmq1z8752uLiolzXVT6fVywW+9B5+P3+uv77+vr08vKim5sbDQ8P/1VPAL4PFsAAfHtdXV16fn7W2tqaLi8vtbW1VffZ+78eHh6UTCZ1cHCgSqWiw8NDlUqlWrBcXl7W0dGREomETk9PVS6XVSgUtLCw8Okel5aWlMlktLGxoXK5LNd1lcvlaotPmUxGm5uburi4qM0QCAQUDofffF4kElGxWNT19bWq1eq7743H4zo5OVE6ndbk5KQaGxtrta+aMxQKaW5uTqlUSmb2ofOIRCK6u7tTsVjU7e2t7u/vFY1GFY/HNTMzo1wup6urK5VKJa2urmp3d/ePegLwjfzLC7sA8JVmZ2dtYmLizZrrutba2mqBQMBGR0ctm82aJKtWq2b2+8LR4+OjTU1NWUdHh/n9fmtra7NkMvnb0tPx8bGNjIxYU1OTBYNB6+3ttXQ6/W5vby00vba+vm6dnZ3mOI5Fo1HLZrO1Wj6ft8HBQQuFQhYMBm1oaMj29vZq9dcLYIVCwbq7u83n81k4HDaz+gWwXwYGBkyS7e/v19W+as5KpWI+n892dnbM7P/Pw8xsfn7empubTZKlUikzM3t6erKVlRWLRCLmOI61tLRYLBazs7Ozd3sC8L39MDP7t3EaAAAA+ByuGQAAAMCzCLMAAADwLMIsAAAAPIswCwAAAM8izAIAAMCzCLMAAADwLMIsAAAAPIswCwAAAM8izAIAAMCzCLMAAADwLMIsAAAAPOsnh1ZHNLqD/sYAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 23.16it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 23.31it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 23.18it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 23.22it/s]\n"
+      "       Current loss: 1.5362  :  38%|███████████████████████████████▏                                                  | 1901/5000 [04:23<26:03,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.75\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:03<00:00, 23.18it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 23.23it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 23.21it/s]\n"
+      "       Current loss: 1.7777  :  40%|████████████████████████████████▊                                                 | 2000/5000 [04:36<06:55,  7.23it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.7777777777777778\n",
+      "Early stopping at iteration 2001 because validation F1 did not improve.\n",
+      "- OK - Train (276.81 s)\n",
+      "\n",
+      "- Saving models to ../output/cookies_3_steps_5000_small/all_models.pth\n",
+      "- OK - Saving models (59.25 ms)\n",
+      "\n",
+      "- Saving map normalization to ../output/cookies_3_steps_5000_small/map_normalization.pth\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:05<00:00, 23.19it/s]\n"
+      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:00<00:00, 28.73it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (31.26 s)\n",
-      "\n",
-      "Dataset               Accuracy\n",
-      "------------------------------\n",
-      "Anonaly lvl 1           100.00\n",
-      "Anonaly lvl 2            88.00\n",
-      "Anonaly lvl 3            92.00\n",
-      "\n",
-      "Anomaly all              95.00\n",
+      "- OK - Saving map normalization (1008.47 ms)\n",
       "\n",
-      "No Anomaly Train        100.00\n",
-      "No Anomaly Test          95.00\n",
-      "No Anomaly All           99.00\n",
-      "\n",
-      "All without train        95.00\n",
-      "All with train           97.00\n"
+      "- Evaluating model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.16it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "     - AUC: 88.62%\n",
+      "    - Optimal Threshold: 0.0289546\n",
+      "    - F1 Score: 0.86\n",
+      "    - CONFUSION MATRIX:\n",
+      " [[18  2]\n",
+      " [18 62]] \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 22.19it/s]\n",
+      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.18it/s]\n",
+      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.08it/s]\n",
+      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.14it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.16it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "- OK - Evaluating model (13.76 s)\n",
+      "\n",
+      "Dataset               F1 Score\n",
+      "------------------------------\n",
+      "Anonaly lvl 1 test        1.00\n",
+      "Anonaly lvl 2 test        0.75\n",
+      "Anonaly lvl 3 test        0.67\n",
+      "\n",
+      "Anomaly all test          0.86\n",
+      "\n",
+      "No Anomaly Test           0.95\n",
+      "\n",
+      "All test                  0.86\n"
+     ]
+    }
+   ],
+   "source": [
+    "# STEPS = 5000, MODEL TYPE = SMALL, WEIGHT = none\n",
+    "model3 = EfficientAD({**config, \"train_steps\": 5000, \"model_type\": \"small\", \"weight_path\":\"\"})\n",
+    "model3.create_model()\n",
+    "model3.display_eval_result()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- Setting seed to 42\n",
+      "- OK - Setting seed to 42 (0.22 ms)\n",
+      "\n",
+      "- Setting datasets path\n",
+      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
+      "- OK - Setting datasets path (4.31 ms)\n",
+      "\n",
+      "- Setting config\n",
+      "     Output folder path: ../output/cookies_3_steps_10000_small\n",
+      "- OK - Setting config (3.99 ms)\n",
+      "\n",
+      "- Prepare teacher, student & autoencoder\n",
+      "     No weight to load\n",
+      "     Training\n",
+      "- OK - Prepare teacher, student & autoencoder (61.42 ms)\n",
+      "\n",
+      "- Normalizing teacher\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.34it/s]\n",
+      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 50.81it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- OK - Normalizing teacher (2.83 s)\n",
+      "\n",
+      "- Train\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 23.5000  :   0%|                                                                                 | 1/10000 [00:01<4:30:22,  1.62s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.6451612903225806\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 31.6716  :   0%|▍                                                                                 | 49/10000 [00:07<25:23,  6.53it/s]\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[6], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# STEPS = 10000, MODEL TYPE = SMALL, WEIGHT = none\u001b[39;00m\n\u001b[1;32m      2\u001b[0m model4 \u001b[38;5;241m=\u001b[39m EfficientAD({\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain_steps\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m10000\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_type\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msmall\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mweight_path\u001b[39m\u001b[38;5;124m\"\u001b[39m:\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m})\n\u001b[0;32m----> 3\u001b[0m \u001b[43mmodel4\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m model4\u001b[38;5;241m.\u001b[39mdisplay_eval_result()\n",
+      "File \u001b[0;32m~/workspace/ai/notebooks/../code/efficientad_fns.py:69\u001b[0m, in \u001b[0;36mEfficientAD.create_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     64\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mlog_params({\n\u001b[1;32m     65\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcfg,\n\u001b[1;32m     66\u001b[0m }) \n\u001b[1;32m     68\u001b[0m \u001b[38;5;66;03m# Execute all train-validation-testing steps\u001b[39;00m\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;66;03m# Log performance metrics\u001b[39;00m\n\u001b[1;32m     72\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mlog_metric(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfinal_auc)\n",
+      "File \u001b[0;32m~/workspace/ai/notebooks/../code/efficientad_fns.py:49\u001b[0m, in \u001b[0;36mEfficientAD.do_all\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprepare_teacher_student_autoencoder()\n\u001b[1;32m     47\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mteacher_normalization()\n\u001b[0;32m---> 49\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_models()\n\u001b[1;32m     51\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_normalization()\n",
+      "File \u001b[0;32m~/workspace/ai/notebooks/../code/efficientad_fns.py:260\u001b[0m, in \u001b[0;36mEfficientAD.train\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    258\u001b[0m \u001b[38;5;66;03m# Move data to GPU if enabled \u001b[39;00m\n\u001b[1;32m    259\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcfg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mon_gpu\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[0;32m--> 260\u001b[0m     image_st \u001b[38;5;241m=\u001b[39m \u001b[43mimage_st\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcuda\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    261\u001b[0m     image_ae \u001b[38;5;241m=\u001b[39m image_ae\u001b[38;5;241m.\u001b[39mcuda()\n\u001b[1;32m    263\u001b[0m \u001b[38;5;66;03m# Teacher model inference followed by normalization \u001b[39;00m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
    "source": [
-    "# STEPS = 70000, MODEL TYPE = SMALL, WEIGHT = none\n",
-    "model5 = EfficientAD({**config, \"train_steps\": 70000, \"model_type\": \"small\", \"weight_path\":\"\"})\n",
-    "model5.create_model()\n",
-    "model5.display_eval_result()"
+    "# STEPS = 10000, MODEL TYPE = SMALL, WEIGHT = none\n",
+    "model4 = EfficientAD({**config, \"train_steps\": 10000, \"model_type\": \"small\", \"weight_path\":\"\"})\n",
+    "model4.create_model()\n",
+    "model4.display_eval_result()"
    ]
   },
   {
@@ -1093,7 +1119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -1101,20 +1127,20 @@
      "output_type": "stream",
      "text": [
       "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (2.33 ms)\n",
+      "- OK - Setting seed to 42 (0.50 ms)\n",
       "\n",
       "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (1.95 ms)\n",
+      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
+      "- OK - Setting datasets path (3.57 ms)\n",
       "\n",
       "- Setting config\n",
       "     Output folder path: ../output/cookies_3_steps_20_medium\n",
-      "- OK - Setting config (0.10 ms)\n",
+      "- OK - Setting config (11.83 ms)\n",
       "\n",
       "- Prepare teacher, student & autoencoder\n",
       "     No weight to load\n",
       "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (172.64 ms)\n",
+      "- OK - Prepare teacher, student & autoencoder (180.60 ms)\n",
       "\n",
       "- Normalizing teacher\n"
      ]
@@ -1123,15 +1149,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 22.93it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 22.89it/s]\n"
+      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.89it/s]\n",
+      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.85it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (6.29 s)\n",
+      "- OK - Normalizing teacher (6.59 s)\n",
       "\n",
       "- Train\n"
      ]
@@ -1140,61 +1166,61 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 32.1385  : 100%|██████████████████████████████████████████████████████████| 20/20 [00:06<00:00,  3.10it/s]\n"
+      "       Current loss: 91.8934  :   5%|████▎                                                                                 | 1/20 [00:03<01:07,  3.53s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (6.46 s)\n",
-      "\n",
-      "- Saving models to ../output/cookies_3_steps_20_medium/all_models.pth\n",
-      "- OK - Saving models (190.81 ms)\n",
-      "\n",
-      "- Saving map normalization to ../output/cookies_3_steps_20_medium/map_normalization.pth\n"
+      "F1 Validation 0.5517241379310345\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 11.39it/s]\n"
+      "       Current loss: 59.0334  : 100%|█████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:09<00:00,  2.06it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (797.40 ms)\n",
+      "- OK - Train (9.72 s)\n",
       "\n",
-      "- Evaluating model\n"
+      "- Saving models to ../output/cookies_3_steps_20_medium/all_models.pth\n",
+      "- OK - Saving models (197.52 ms)\n",
+      "\n",
+      "- Saving map normalization to ../output/cookies_3_steps_20_medium/map_normalization.pth\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:21<00:00,  9.47it/s]\n"
+      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 10.38it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "- OK - Saving map normalization (2794.26 ms)\n",
       "\n",
-      "     - AUC: 50.78%\n",
-      "    - Optimal Threshold: 0.0653680\n",
-      "    - F1 Score: 0.66\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[27 73]\n",
-      " [15 85]] \n",
-      "\n"
+      "- Evaluating model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.19it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -1202,20 +1228,17 @@
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:05<00:00,  9.46it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:02<00:00,  9.45it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:02<00:00,  9.44it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.44it/s]\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\n",
+      "     - AUC: 45.31%\n",
+      "    - Optimal Threshold: 0.0491857\n",
+      "    - F1 Score: 0.90\n",
+      "    - CONFUSION MATRIX:\n",
+      " [[ 2 18]\n",
+      " [ 0 80]] \n",
       "\n"
      ]
     },
@@ -1223,9 +1246,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:08<00:00,  9.44it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.42it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.42it/s]\n"
+      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:04<00:00,  9.16it/s]\n",
+      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.18it/s]\n",
+      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.19it/s]\n",
+      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.17it/s]\n"
      ]
     },
     {
@@ -1239,29 +1263,27 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:12<00:00,  9.42it/s]\n"
+      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.17it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (76.48 s)\n",
       "\n",
-      "Dataset               Accuracy\n",
+      "- OK - Evaluating model (32.92 s)\n",
+      "\n",
+      "Dataset               F1 Score\n",
       "------------------------------\n",
-      "Anonaly lvl 1            94.00\n",
-      "Anonaly lvl 2            92.00\n",
-      "Anonaly lvl 3            60.00\n",
+      "Anonaly lvl 1 test        1.00\n",
+      "Anonaly lvl 2 test        1.00\n",
+      "Anonaly lvl 3 test        1.00\n",
       "\n",
-      "Anomaly all              85.00\n",
+      "Anomaly all test          0.99\n",
       "\n",
-      "No Anomaly Train         27.50\n",
-      "No Anomaly Test          25.00\n",
-      "No Anomaly All           27.00\n",
+      "No Anomaly Test           0.18\n",
       "\n",
-      "All without train        75.00\n",
-      "All with train           56.00\n"
+      "All test                  0.90\n"
      ]
     }
    ],
@@ -1274,7 +1296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -1282,20 +1304,20 @@
      "output_type": "stream",
      "text": [
       "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (0.28 ms)\n",
+      "- OK - Setting seed to 42 (0.26 ms)\n",
       "\n",
       "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (3.72 ms)\n",
+      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
+      "- OK - Setting datasets path (4.93 ms)\n",
       "\n",
       "- Setting config\n",
       "     Output folder path: ../output/cookies_3_steps_500_medium\n",
-      "- OK - Setting config (0.12 ms)\n",
+      "- OK - Setting config (10.06 ms)\n",
       "\n",
       "- Prepare teacher, student & autoencoder\n",
       "     No weight to load\n",
       "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (162.90 ms)\n",
+      "- OK - Prepare teacher, student & autoencoder (164.97 ms)\n",
       "\n",
       "- Normalizing teacher\n"
      ]
@@ -1304,15 +1326,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 22.71it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 22.84it/s]\n"
+      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.62it/s]\n",
+      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.71it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (6.33 s)\n",
+      "- OK - Normalizing teacher (6.65 s)\n",
       "\n",
       "- Train\n"
      ]
@@ -1321,61 +1343,117 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 2.5265  : 100%|█████████████████████████████████████████████████████████| 500/500 [02:39<00:00,  3.13it/s]\n"
+      "       Current loss: 91.8934  :   0%|▏                                                                                    | 1/500 [00:03<29:39,  3.57s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (159.76 s)\n",
-      "\n",
-      "- Saving models to ../output/cookies_3_steps_500_medium/all_models.pth\n",
-      "- OK - Saving models (200.47 ms)\n",
-      "\n",
-      "- Saving map normalization to ../output/cookies_3_steps_500_medium/map_normalization.pth\n"
+      "F1 Validation 0.5517241379310345\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 10.9337  :  20%|████████████████▊                                                                  | 101/500 [00:39<08:22,  1.26s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8108108108108109\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 7.4086  :  40%|█████████████████████████████████▊                                                  | 201/500 [01:15<06:17,  1.26s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.75\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 7.4997  :  60%|██████████████████████████████████████████████████▌                                 | 301/500 [01:52<04:11,  1.26s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.7272727272727273\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 11.0552  :  80%|██████████████████████████████████████████████████████████████████▌                | 401/500 [02:28<02:05,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8333333333333334\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 11.32it/s]\n"
+      "       Current loss: 3.2578  : 100%|████████████████████████████████████████████████████████████████████████████████████| 500/500 [03:01<00:00,  2.76it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (801.11 ms)\n",
+      "- OK - Train (181.31 s)\n",
       "\n",
-      "- Evaluating model\n"
+      "- Saving models to ../output/cookies_3_steps_500_medium/all_models.pth\n",
+      "- OK - Saving models (206.78 ms)\n",
+      "\n",
+      "- Saving map normalization to ../output/cookies_3_steps_500_medium/map_normalization.pth\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:21<00:00,  9.45it/s]\n"
+      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 10.35it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "- OK - Saving map normalization (2804.30 ms)\n",
       "\n",
-      "     - AUC: 91.19%\n",
-      "    - Optimal Threshold: 0.0865702\n",
-      "    - F1 Score: 0.85\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[96  4]\n",
-      " [23 77]] \n",
-      "\n"
+      "- Evaluating model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.11it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIhCAYAAABdSTJTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACLEElEQVR4nOzdd1iT198G8DtAmApOEFAR96oLHDjqRsVZ60AR98RN1TpaV6u2rqK14kJR68Bt3VL3VhCq1vlTxAUqDkBmSM77hy+pyJBA4CFwf66Lq83JM+5wDHw5Oc95ZEIIASIiIiIiHaQndQAiIiIioqxiMUtEREREOovFLBERERHpLBazRERERKSzWMwSERERkc5iMUtEREREOovFLBERERHpLBazRERERKSzWMwSERERkc5iMUtElAZfX1/IZDL1l4GBAaytreHq6ooHDx6kuY9CoYC3tzecnJxgYWEBExMTVKtWDVOnTsWbN2/S3EelUmHz5s1o06YNSpQoAblcDktLS3Tq1AkHDhyASqX6YtaEhASsWLECTZs2RdGiRWFoaAhbW1v06tULZ86cydb3gYgor2MxS0SUgQ0bNuDSpUv4+++/MWbMGPz1119o2rQp3r17l2K72NhYtG3bFmPHjkXdunWxbds2HD58GO7u7lizZg3q1q2Le/fupdgnPj4eLi4uGDBgACwtLeHt7Y2TJ09i1apVsLGxQc+ePXHgwIEM80VERKBJkybw9PREzZo14evrixMnTmDJkiXQ19dH69at8c8//2j9+0JElGcIIiJKZcOGDQKAuHbtWor2OXPmCABi/fr1KdqHDx8uAIjt27enOta9e/eEhYWFqFGjhkhKSlK3jxo1SgAQGzduTDPD/fv3xT///JNhzg4dOggDAwNx4sSJNJ+/evWqCA0NzfAYmRUbG6uV4xARaRNHZomINODo6AgAePnypbotPDwc69evR7t27dC7d+9U+1SuXBnff/89/v33X+zbt0+9z7p169CuXTv0798/zXNVqlQJtWrVSjdLYGAgjhw5giFDhqBVq1ZpblO/fn2ULVsWADB79mzIZLJU2yRPqXj8+LG6rVy5cujUqRP27NmDunXrwtjYGHPmzEHdunXRrFmzVMdQKpWwtbVF9+7d1W2JiYn4+eefUbVqVRgZGaFkyZIYNGgQXr9+ne5rIiLSFItZIiINhISEAPhYoCY7deoUkpKS0K1bt3T3S37O399fvY9Cochwny85fvx4imNr2/Xr1zF58mSMGzcOR48exbfffotBgwbh/PnzqeYNHz9+HC9evMCgQYMAfJwL3LVrV/zyyy/o27cvDh06hF9++QX+/v5o0aIF4uLiciQzERU8BlIHICLKy5RKJZKSkhAfH48LFy7g559/xtdff40uXbqot3ny5AkAwN7ePt3jJD+XvG1m9vkSbRwjI69evcLt27dTFO7ly5fH5MmT4evri3nz5qnbfX19YWVlhQ4dOgAAduzYgaNHj2L37t0pRmtr166N+vXrw9fXF6NGjcqR3ERUsHBklogoA40aNYJcLkfhwoXRvn17FC1aFPv374eBQdbGAtL6mD+vqlWrVopCFgCKFy+Ozp07Y+PGjeqVFt69e4f9+/ejf//+6u/LwYMHUaRIEXTu3BlJSUnqrzp16qBUqVI4ffp0br8cIsqnWMwSEWVg06ZNuHbtGk6ePIkRI0bgzp076NOnT4ptkuekJk9BSEvyc2XKlMn0Pl+ijWNkxNraOs32wYMH4/nz5+opE9u2bUNCQgIGDhyo3ubly5d4//49DA0NIZfLU3yFh4cjIiIiRzITUcHDYpaIKAPVqlWDo6MjWrZsiVWrVmHo0KE4evQodu3apd6mZcuWMDAwUF/clZbk59q2baveRy6XZ7jPl7Rr1y7Fsb/E2NgYwMd1aT+VXmGZ3ihyu3btYGNjgw0bNgD4uHxZw4YNUb16dfU2JUqUQPHixXHt2rU0v1auXJmpzEREX8JilohIAwsXLkTRokUxc+ZM9cfspUqVwuDBg3Hs2DH4+fml2uf+/fv49ddfUaNGDfXFWqVKlcLQoUNx7NgxbNq0Kc1zPXz4EDdu3Eg3S7169dChQwf4+Pjg5MmTaW4TEBCgnltbrlw5AEh1zC+tZfs5fX19uLu7Y9++fTh37hwCAgIwePDgFNt06tQJb968gVKphKOjY6qvKlWqaHROIqL0yIQQQuoQRER5ja+vLwYNGoRr166pl+NKtmjRIkyZMgWbN29Gv379AAAxMTHo2LEjLly4gOHDh6Nz584wMjLC5cuXsXjxYpiamuLvv/9OUcTFx8ejW7duOH78OPr06YNvvvkGVlZWiIiIgL+/PzZs2IDt27eja9eu6eaMiIhA+/btcfPmTQwePBgdOnRA0aJFERYWhgMHDmDbtm0IDAxE7dq1ERUVBXt7e9ja2mLu3LkwMDCAr68vrl+/jpCQEISEhKgL3nLlyqFmzZo4ePBgmue9f/8+qlSpgtKlS+PNmzcICwuDhYWF+nmlUonOnTvjypUrGD9+PBo0aAC5XI5nz57h1KlT6Nq1K7755pusdg8R0X+kXuiWiCgvSu+mCUIIERcXJ8qWLSsqVaqU4iYIiYmJ4o8//hANGzYUhQoVEkZGRqJKlSpiypQpIiIiIs3zJCUliY0bN4pWrVqJYsWKCQMDA1GyZEnRoUMHsXXrVqFUKr+YNS4uTixfvlw4OTkJc3NzYWBgIGxsbET37t3FoUOHUmx79epV0bhxY2FmZiZsbW3FrFmzxLp16wQAERISot7Ozs5OdOzYMcPzNm7cWAAQbm5uaT6vUCjE4sWLRe3atYWxsbEoVKiQqFq1qhgxYoR48ODBF18XEVFmcGSWiIiIiHQW58wSERERkc5iMUtEREREOovFLBERERHpLBazRERERKSzWMwSERERkc5iMUtEREREOstA6gC5TaVS4cWLFyhcuHC6t2okIiIiIukIIRAdHQ0bGxvo6WU89lrgitkXL16gTJkyUscgIiIioi94+vQpSpcuneE2Ba6YLVy4MICP3xxzc/NcOadCocDx48fh7OwMuVyeK+ck7WH/6T72oe5jH+o29p/uy+0+jIqKQpkyZdR1W0YKXDGbPLXA3Nw8V4tZU1NTmJub802sg9h/uo99qPvYh7qN/af7pOrDzEwJ5QVgRERERKSzWMwSERERkc5iMUtEREREOqvAzZnNDCEEkpKSoFQqtXI8hUIBAwMDxMfHa+2YlHvYf7ovJ/tQX18fBgYGXOqPiEgiLGY/k5iYiLCwMMTGxmrtmEIIlCpVCk+fPuUvPB3E/tN9Od2HpqamsLa2hqGhodaPTUREGWMx+wmVSoWQkBDo6+vDxsYGhoaGWvnFp1Kp8OHDBxQqVOiLC/9S3sP+03051YdCCCQmJuL169cICQlBpUqV+G+EiCiXsZj9RGJiIlQqFcqUKQNTU1OtHVelUiExMRHGxsb8RaeD2H+6Lyf70MTEBHK5HKGhoepzEBFR7uFv5jSwYCEiTfBnBhGRdPgTmIiIiIh0FotZIiIiItJZLGaJ/l9iYiIqVqyICxcuSB0l33j16hVKliyJ58+fSx2FiIjyKRaz+cTAgQMhk8kgk8lgYGCAsmXLYtSoUXj37l2qbS9evAgXFxcULVoUxsbG+Oqrr7BkyZI01988deoUXFxcULx4cZiamqJ69er47rvvvlicBAUFoWfPnrCysoKxsTEqV66MYcOG4f79+1p7zdq2Zs0a2NnZoUmTJqmemzBhAuRyObZv357quYEDB6Jbt26p2oODgyGTyfD48WN1mxACa9asQcOGDVGoUCEUKVIEjo6O8PLy0upycJ979+4d3N3dYWFhAQsLC7i7u+P9+/cZ7vPy5UsMHDgQNjY2MDU1Rfv27fHgwYMU26xZswYtWrSAubk5ZDJZqmNaWlrC3d0ds2bN0vIrIiIi+ojFbD7Svn17hIWF4fHjx1i3bh0OHDgADw+PFNvs3bsXzZs3R+nSpXHq1CncvXsX48ePx7x58+Dq6gohhHrb1atXo02bNihVqhR2796N27dvY9WqVYiMjMSSJUvSzXHw4EE0atQICQkJ2LJlC+7cuYPNmzfDwsICP/74Y5Zfn0KhyPK+mfH7779j6NChqdpjY2OxZ88eTJo0CT4+Ptk6h7u7OyZMmICuXbvi1KlTCA4Oxo8//oj9+/fj+PHj2Tp2Rvr27Yvg4GAcPXoUR48eRXBwMNzd3dPdXgiBbt264dGjR9i/fz+CgoJgZ2eHNm3aICYmRr1dbGws2rdvj+nTp6d7rEGDBmHLli1p/mFFRESUbaKAiYyMFABEZGRkqufi4uLE7du3RVxcnLpNpVKJmARFtr6i4xLEi5cRIjouQaP9VCpVpl/XgAEDRNeuXVO0eXp6imLFiqkff/jwQRQvXlx079491f5//fWXACC2b98uhBDi6dOnwtDQUEyYMCHN87179y7N9piYGFGiRAnRrVu3DPfbsGGDsLCwSPHc3r17xaf/JGfNmiVq164tfHx8hL29vZDJZGLVqlXCxsZGKJXKFPt27txZ9O/fP8XrqVevnjAyMhL29vZi9uzZQqFQpJlJCCECAwOFnp5emv8u1q9fL+rXry/evn0rTExMREhISIrn0/reCyFEUFCQAKDe3s/PTwAQ+/btS7WtSqUS79+/Tzdfdty+fVsAEJcvX1a3Xbp0SQAQd+/eTXOfe/fuCQDi1q1b6rakpCRRrFgxsXbt2lTbnzp1SgBI999FuXLlhI+PT/ZeSDYolUrx7t27VP9utCWtnx2kXYmJiWLfvn0iMTFR6iiUBew/3ZfbfZhRvfY5SdeZPXv2LBYtWoTAwECEhYVh7969aX5c+6kzZ87A09MT//77L2xsbDBlyhSMHDkyxzLGKZSoPvNYjh0/I7fntoOpYda66NGjRzh69Cjkcrm67fjx43jz5g0mTZqUavvOnTujcuXK2LZtG3r37o2dO3ciMTERU6ZMSfP4RYoUSbP92LFjiIiI0Hi/9Pzvf//Djh07sHv3bujr68PW1hbjxo3DqVOn0Lp1awAfP0I/duwYDhw4oM7Qr18/LF++HM2aNcPDhw8xfPhwAEj34+6zZ8+icuXKMDc3T/Xchg0b0LNnT1hYWMDFxQUbNmzAnDlzNHodALBlyxZUqVIFXbt2TfWcTCaDhYVFuvsWKlQow2M3a9YMR44cSfO5S5cuwcLCAg0bNlS3NWrUCBYWFrh48SKqVKmSap+EhAQASLFmqr6+PgwNDXH+/Pk0R7Az0qBBA5w7dw6DBw/WaD8iIqIvkbSYjYmJQe3atTFo0CB8++23X9w+JCQELi4uGDZsGP78809cuHABHh4eKFmyZKb2z+8OHjyIQoUKQalUIj4+HgCwdOlS9fPJ81WrVauW5v5Vq1ZVb/PgwQOYm5vD2tpaowzJcyqrVq2qcf60JCYmYvPmzShZsqS6rX379ti6dau6mN25cyeKFSumfjxv3jxMnToVAwYMAACUL18eP/30E6ZMmZJuMfv48WPY2Nik+XouX76MDRs2AAD69euHcePGYdasWRqvLfrgwYM0C8fMCA4OzvB5ExOTdJ8LDw+HpaVlqnZLS0uEh4enuU/VqlVhZ2eHadOmYfXq1TAzM8PSpUsRHh6OsLAwjbIDgK2tLYKCgjTej4iI6EskLWY7dOiADh06ZHr7VatWoWzZsvDy8gLwsSgLCAjA4sWLc6yYNZHr4/bcdtk6hkqlQnRUNAqbF9aoADKR62t0npYtW8Lb2xuxsbFYt24d7t+/j7Fjx6baTnwyL/bz9uTb9376/5pI79hZZWdnl6KQBQA3NzcMHz4cK1euhJGREbZs2QJXV1fo63/8fgUGBuLatWuYN2+eep/kAj82NjbNu7vFxcWleecmHx8fODs7o3jx4gAAFxcXDBkyBH///TecnZ01ei1Z/Z4CQMWKFbO0X7K0zptRHrlcjt27d2PIkCEoVqwY9PX10aZNG43er58yMTHJ0QvciEg7hBCIU6S+GDi7FIokJCiB2MQkyEX2bxNPuS8uLh4JSu3/ntcGnbqd7aVLl1IVEO3atYOPjw8UCkWKj9STJSQkqD8yBYCoqCgAHy8m+vyCIoVCASEEVCoVVCqVut3YIHvXyQkhQ5KhPkzk+hoVM0KITP+jEULA1NQU5cuXBwB4eXmhdevWmD17NubOnQvgv4Lo33//RePGjVMd4+7du6hWrRpUKhUqVaqEyMhIPH/+XKPR2eRz3L59G05OTl/M/On3ObmfktuEEDAzM0uxDQB07NgRKpUKBw4cQP369XHu3DksXrxYvZ1KpcLs2bPxzTffpDqnoaFhquMBQPHixXHz5s0UzymVSmzatAnh4eEoUaJEivZ169ahTZs2AIDChQsjNDQ01XHfvn2rfj75e3rnzp00z/8laU1/+FTTpk1x+PDhNJ+ztLTEy5cvU5339evXKFmyZLp56tati+vXryMyMhKJiYkoWbIknJyc4ODgkGqfT7/3aR3vzZs3KFGiRJZeuzYkv48+/zenLSqVCkIIKBQK9R9VpF3JP69z+kLQgkwIAdd113D9yfscOoMBplw9mUPHppwU++AK3p1cByvXn9GqVQIssjgwowlN3us6VcyGh4fDysoqRZuVlRWSkpIQERGRZtG1YMGCNOc3Hj9+PNUInYGBAUqVKoUPHz4gMTFRu+EBREdHa/2YyRQKBZKSktTFOgB899136NmzJ9zc3GBtbY1GjRqhaNGiWLhwITZt2pRi/8OHD+PBgweYOnUqoqKi4OzsDENDQ8ybNw/z589Pdb7IyMg053g2atQIxYsXx4IFC/Dnn3+mu5+ZmRmio6MRFhYGMzMzAMDVq1cB/PcHR0JCApRKZYrXlKxTp07YtGkT/v33X1SsWBGVKlVSb1erVi3cunULI0aMSLXfhw8f0vz+ValSBd7e3oiMjFT/wXHkyBFER0fjzJkzKQqUBw8eYPjw4Xj8+DGKFSsGOzs7bNu2Da9evUoxunv+/HmUKFEC+vr6iIqKQrdu3TBkyBBs374dLi4uKc4vhEBUVFS682bPnj2bZnsyY2PjNL9PAPDVV18hMjISp06dgoODAwAgICAAkZGRqFWrVrr7JZPJZDAyMkJQUBACAgLw/fffp9onedQ1Ojo6zU8f/vnnHzRt2vSL58ppOfUeTExMRFxcHM6ePYukpKQcOQd95O/vL3WEfCtBCVx/olNlAeUwoVTg3WlfRAfsBwBEXtqJkyeLwygX/mbX5NM8nftX+/nIZvKIS3ojntOmTYOnp6f6cVRUFMqUKQNnZ+dUo13x8fF4+vQpChUqlOZHzlklhEB0dDQKFy6c5Y+Zv0Qul8PAwCDFa3JxcUGNGjWwYsUK/P777zA3N8eqVavQt29fTJ48GaNHj4a5uTlOnDiB77//Ht9++y0GDBgAmUyG6tWrY+nSpRg7dizi4+Ph7u6OcuXK4dmzZ9i8eTMKFSqExYsXp8phbm6OtWvXonfv3nB3d8fYsWNRsWJFREREYOfOnXjy5Am2bduGli1bwtTUFL/++ivGjBmDq1evqtdwTX4NRkZG0NfXT3NUcsCAAejatSvu378Pd3f3FNvMnj0bXbp0Qfny5dGjRw/o6enhxo0buHXrFn766ac0v38uLi4YPnw4nj59ipo1awKAuuhs3Lhxiv5r2LAhZsyYgb/++gvjxo3DkCFDsGTJEowZMwZTpkxB0aJFcenSJXh5eWHq1KnqbAMGDMCxY8cwdOhQ/PDDD2jTpg1KliyJmzdvYtmyZRg9enS6F0DWqVPny/8I0lG/fn20a9cOnp6e8Pb2BgB4enqiY8eO6uIWAKpXr4558+apR7R37tyJkiVLomzZsrh58yYmTpyIrl27psgYHh6O8PBwvHjxAsDHuceFCxdG2bJlUaxYMQAffyD9888/+OWXX744wpxTcvo9GB8fDxMTE3z99dda/dlB/1EoFPD390fbtm3T/BSOsi82MUk9cnr5++YwMdRexaJQJOHkyZNo1aoV5HKdKz0KpMePH2PIwP54EhAAABgxygNfN2+Jju3awNDQMMfPr9Hgh1bXUcgGAGLv3r0ZbtOsWTMxbty4FG179uwRBgYGmV4qQtOlubQhp5cFEiL95aG2bNkiDA0NxZMnT9RtZ8+eFe3btxcWFhbC0NBQVK9eXSxevFgkJSWl2t/f31+0a9dOFC1aVBgbG4uqVauKSZMmiRcvXmSY59q1a6J79+6iZMmSwsjISFSsWFEMHz5cPHjwQL3N3r17RcWKFYWxsbHo1KmTWLNmTZpLc6UlKSlJWFtbCwDi4cOHqZ4/evSoaNy4sTAxMRHm5uaiQYMGYs2aNRlmdnV1FVOnThVCCBEeHi4MDAzEjh070uy/sWPHiq+++kr9+MGDB+Lbb78Vtra2wszMTHz11VdixYoVqfpcqVQKb29vUb9+fWFqairMzc2Fg4ODWLZsmYiNjc0wX3a8efNGuLm5icKFC4vChQsLNze3VMtoARAbNmxQP162bJkoXbq0kMvlomzZsuKHH34QCQkJKfaZNWuWAJDq69PjbN26VVSpUiXHXltmcGku3celnb4su0tJvo6OF3bfHxR23x8UMQnpL2WYFew/3bJ7925hYWEhAIiiRYuK/fv35+mluWRC5I2ZvDKZ7ItLc33//fc4cOAAbt++rW4bNWoUgoODcenSpUydJ/mj3MjIyDRHZkNCQmBvb6/V0RWVSoWoqCiYm5trfAU85Z6bN2+iTZs2+N///ofChQur29l/2dOgQQNMmDABffv2lSxDTvdhTv3soP8oFAocPnwYLi4uHJlNgxACPVZdQmCodm5Okp2lIdPC/tMd7969Q/ny5fH+/Xs4OTlh27ZtsLOzy/U+zKhe+5ykv5k/fPiA4OBg9bJDISEhCA4OxpMnTwB8nCLQv39/9fYjR45EaGgoPD09cefOHaxfvx4+Pj5prptKpKmvvvoKCxcuTHH7WcqeV69eoUePHujTp4/UUYjytTiFUmuFrKNdUY1X06H8o2jRotiwYQOmTJmCM2fOwM7OTupIXyTpxJWAgAC0bNlS/Th5buuAAQPg6+uLsLAwdWELAPb29jh8+DAmTpyIP/74AzY2Nli+fDnXmCWtSV6blrTD0tIy3RtoEFHOCPihDUyzMd9V05V3SPft2LED5ubmaN++PQCgW7duX7yJVV4iaTHbokWLDJee8vX1TdXWvHlzXL9+PQdTERGRLhA5tCaqLopN/O/7YGqor9UpApR/xcXFwdPTE6tWrULx4sVx48aNNG8glNfxXzsREekcbc8RJSpo7t27h169euHGjRuQyWQYOXJkmneL1AUsZomISOdoc45ofsL5rpQZW7ZswYgRIxATEwNLS0v8+eefaNu2rdSxsozFLBER6bTszhHNTzjflTKiVCoxYsQI+Pj4AABatmyJLVu2aHSnz7yIxSwREWkst+erKhRJSFB+XNhfLmScI0qUBcl3s5TJZJg1axZ++OGHfHELbr77iYhII9LNVzVQ36GKiDIvPj5evQb28uXLMXDgQDRt2lTiVNrDFeCJiEgjeWm+KueIEqXvw4cP6tu/q1QqAICpqWm+KmQBjsySBmbPno19+/apb3KR187TokUL1KlTB15eXjmSKz3lypXDhAkTMGHChCwfY+DAgXj//j327duX7jZSvT6ijOTWfFWFQoFjx46jXTvnFHcf4hxRorTdvHkTvXr1wt27d6Gnp4fLly+jcePGUsfKERyZzUeePn2KIUOGwMbGBoaGhrCzs8P48ePx5s0bjY8lk8lSFVaTJk3CiRMntJQ2606fPg2ZTIb3799LHSXfuXnzJpo3bw4TExPY2tpi7ty5Ga4FDXy89aG7uzssLCxgYWEBd3f3VH1z7do1tG7dGkWKFEHRokXh7Oyc6o8VIQQWL16MypUrw8jICGXKlMH8+fPVzyf3++dfd+/eVW/TokWLNLfp1KlTinOtXLlSfetZBwcHnDt3Tv2cQqHA999/j6+++gpmZmawsbFB//798eLFCw2/mwVD8nzV3Pgy0keqNhayRCkJIbB27Vo0aNAAd+/eha2tLU6fPp1vC1mAxWzOUSqB06eBbds+/leZsxdKPHr0CI6Ojrh//z62bduG//3vf1i1ahVOnDgBJycnvH37NtvnKFSoEIoXL66FtHmHQqGQOkKeERUVhbZt28LGxgbXrl3D77//jsWLF2Pp0qUZ7te3b18EBwfj6NGjOHr0KIKDg+Hu7q5+Pjo6Gu3atUPZsmVx5coVnD9/Hubm5mjXrl2K7//48eOxbt06LF68GHfv3sWBAwfQoEGDVOe7d+8ewsLC1F+VKlVSP7dnz54Uz926dQv6+vro0aOHehs/Pz9MmDABM2bMQFBQEJo1a4YOHTqo7zYYGxuL69ev48cff8T169exZ88e3L9/H126dMny91YXCCEQm5iUyS/eqIAoL4qKikLfvn0xfPhwxMfHo0OHDggODkazZs2kjpazRAETGRkpAIjIyMhUz8XFxYnbt2+LuLi47J1k924hSpcWAlB/KW1shHLnzuwdNwPt27cXpUuXFrGxsSnaw8LChKmpqRg5cqS6zc7OTsydO1f06dNHmJmZCWtra7F8+fIUzwNQf9nZ2QkhhJg1a5aoXbu2ersBAwaIrl27innz5glLS0thYWEhZs+eLRQKhZg0aZIoWrSosLW1FT4+PikyTZkyRVSqVEmYmJgIe3t78cMPP4jExET185+f51MhISEpsgEQAwYMEEII0bx5czF27FgxefJkUbRoUWFlZSVmzZqVYn8AwtvbW3Tp0kWYmpqKmTNnCiGE+Ouvv0S9evWEkZGRsLe3V78OIYRQKpXi+++/F2XKlBGGhobC2tpajB07NsX3a968eWLQoEGiUKFCokyZMmL16tUpznvjxg3RsmVLYWxsLIoVKyaGDRsmoqOjU30vk3348EG4u7sLMzMzUapUKbF48WLRvHlzMX78+DS/L9qwcuVKYWFhIeLj49VtCxYsEDY2NkKlUqW5z+3btwUAcfnyZXXbpUuXBABx9+5dIYQQ165dEwDEkydP1NvcuHFDABD/+9//1McxMDBQ75OWU6dOCQDi3bt3mX5Nv/32myhcuLCIiooS7969E0qlUjRo0CDF+0EIIapWrSqmTp2a7nGuXr0qAIjQ0NA0n9fazw6JqFQq0X3lBWH3/UGNv2ISFLmSMTExUezbty/FzwrSHey/3NGhQwcBQOjr64uFCxcKpVKptWPndh9mVK99jiOz2rZnD9CjB/DsWYpmWVgYZL16fXxey96+fYtjx47Bw8MDJiYmKZ4rVaoU3Nzc4Ofnl+Lj4kWLFqFWrVq4fv06pk2bhokTJ8Lf3x/Ax4+EAWDDhg0ICwtTP07LyZMn8eLFC5w9exZLly7F7Nmz0alTJxQtWhRXrlzByJEjMXLkSDx9+lS9T+HCheHr64vbt29j2bJlWLt2LX777bdMvdYyZcpg9+7dAP4boVu2bJn6+Y0bN8LMzAxXrlzBwoULMXfuXPXrSjZr1ix07doVN2/exODBg3Hs2DH069cP48aNw+3bt7F69Wr4+vpi3rx5AIBdu3Zh5cqV8Pb2xoMHD7Bv3z589dVXKY65ZMkSODo6IigoCB4eHhg1apT64+/Y2Fi0b98eRYsWxbVr17Bz5078/fffGDNmTLqvc/LkyTh16hT27t2L48eP4/Tp0wgMDMzwe3Pu3DkUKlQow69PP7b/3KVLl9C8eXMYGRmp29q1a4cXL17g8ePH6e5jYWGBhg0bqtsaNWoECwsLXLx4EQBQpUoVlChRAj4+PkhMTERcXBx8fHxQo0YN2NnZAQAOHDiA8uXL4+DBg7C3t0e5cuUwdOjQND9RqFu3LqytrdG6dWucOnUqw++Jj48PXF1dYWZmBgBITExEYGAgnJ2dU2zn7OyszpuWyMhIyGQyFClSJMPz6aqsXtDFi6+I8pZ58+ahUqVKOHfuHCZPngw9vQJS5uV8bZ235OjIbFJSqhHZT79UMpkQZcp83E6LLl++LACIvXv3pvn80qVLBQDx8uVLIcTHkcT27dun2KZ3796iQ4cO6sdpHS+tkVk7O7sUf/lVqVJFNGvWTP04KSlJmJmZiW3btqWbf+HChcLBwSHd83wuvRG65s2bi6ZNm6Zoq1+/vvj+++9TvK4JEyak2KZZs2Zi/vz5Kdo2b94srK2thRBCLF68WFSsWDHFiOWn7OzsRL9+/dSPVSqVsLS0FN7e3kIIIdasWSOKFi0qPnz4oN7m0KFDQk9PT4SHhwshUo7MRkdHC0NDQ7F9+3b19m/evBEmJiYZjszGxsaKBw8eZPj15s2bdPdv27atGDZsWIq258+fCwDi4sWLae4zb948UalSpVTtlSpVSvE9vXXrlqhQoYLQ09MTenp6omrVqilGOUeMGCGMjIxEw4YNxdmzZ8WpU6dEnTp1RMuWLdXb3L17V6xZs0YEBgaKixcvilGjRgmZTCbOnDmTZrYrV64IAOLKlStCqVSKd+/eiadPnwoA4sKFC6leR+XKldM8TlxcnHBwcBBubm5pPp+8jS6PzMYkKNQjra+j40VMgiJTX+mN2OcEjuzpNvZfznj//r04fPhwirbkTxW1LS+PzHI1A206dy7ViOynZEIAT59+3K5Fi1yLJf5/RPbTCyWcnJxSbOPk5JSlq+Rr1KiR4i8/Kysr1KxZU/1YX18fxYsXx6tXr9Rtu3btgpeXF/73v//hw4cPSEpKgrm5ucbnTkutWrVSPLa2tk5xbgBwdHRM8TgwMBDXrl1Tj8QCH++SEh8fj9jYWPTo0QO//fYbKlasiPbt28PFxQWdO3eGgcF/b59PzyuTyVCqVCn1ee/cuYPatWurRwcBoEmTJlCpVLh37x6srKxS5Hn48CESExNT9FGxYsVQpUqVDF+7iYkJKlasmOE2X/L5xTRp/dv50j7J+yW3x8XFYfDgwWjSpAm2bdsGpVKJxYsXw8XFBdeuXYOJiQlUKhUSEhKwadMmVK5cGcDHUVUHBwfcu3cPVapUUX8lc3JywtOnT7F48WJ8/fXXqTL4+PigZs2aaNCggXpJmoxeZ1qvQ6FQwNXVFSqVCitXrkz3eyA1kc0bGPAGBES6JyAgAL1798bTp09x8eJF9e+2T383FRQF7xXnpLAw7W6XSRUrVoRMJsPt27fRrVu3VM/fvXsXRYsWRYkSJTI8TlauCv50iZzkY6TVllxMXL58Ga6urpgzZw7atWsHCwsLbN++HUuWLNH43JnN83kh82lRCQAqlQpz5sxB9+7dUx3P2NgYZcqUwbVr13DlyhWcPHkSHh4eWLRoEc6cOaM+X0bnTa9QSt7uc+ILqwek59y5c+jQoUOG20yfPh3Tp09P87lSpUohPDw8RVtyQf55wf3pPi9fvkzV/vr1a/U+W7duxePHj3Hp0iX1Hz5bt25F0aJFsX//fri6usLa2hoGBgbqQhYAqlWrBgB48uRJuoV8o0aN8Oeff6Zqj42Nxfbt2zF37twU7SVKlIC+vn6ar/Pz16hQKNCrVy+EhITg5MmTWvuDS9uEZDcwICIpCCGwfPlyTJ48GQqFAuXKlZM6kuRYzGpTZu9trOV7IBcvXhxt27bFypUrMXHixBTzZsPDw7Flyxb0798/ReF0+fLlFMe4fPkyqlatqn4sl8uhzIEVGC5cuAA7OzvMmDFD3RYaGqrRMQwNDQFAa/nq1auHe/fupTuqqVKpYGJigi5duqBbt24YPXo0qlatips3b6JevXpfPH716tWxceNGxMTEqAvpCxcuQE9PL0XxlqxixYqQy+W4fPkyypYtC+Dj8lf3799H8+bN0z2Po6PjF9fmLVasWLrPOTk5Yfr06UhMTFR/j48fPw4bG5t0f1g6OTkhMjISV69eVa88cOXKFURGRqqXgYmNjYWenl6Kf3/Jj5ML/iZNmiApKQkPHz5EhQoVAAD3798HAPW82rQEBQWleU/xHTt2ICEhAf369UvRbmhoCAcHB/j7++Obb75Rt/v7+6Nr167qx8mF7IMHD3Dq1Kk8vYqHNm9gwDmwRHnbu3fvMHjwYPXSmd27d4ePj0++nc+fWSxmtalZM6B0aeD584+zZD8jZDLISpf+uJ2WrVixAo0bN0a7du3w888/w97eHv/++y8mT54MW1vbFB+hAx+LqYULF6Jbt27w9/fHzp07cejQIfXz5cqVw4kTJ9CkSRMYGRmhaNGiWslZsWJFPHnyBNu3b0f9+vVx6NAh7N27V6Nj2NnZQSaT4eDBg3BxcYGJiQkKFSqU5UwzZ85Ep06dUKZMGfTs2RN6enq4ceMGbt68iZ9//hm+vr6IiYlB8+bNUahQIWzevBkmJiYZFlmfcnNzw6xZszBgwADMnj0br1+/xtixY+Hu7p7miGehQoUwZMgQTJ48GcWLF4eVlRVmzJjxxYn82Z1m0LdvX8yZMwcDBw7E9OnT8eDBA8yfPx8zZ85UF6JXr15F//79ceLECdja2qJatWpo3749hg0bhtWrVwMAhg8fjk6dOqlHU9u2bYvJkydj9OjRGDt2LFQqFX755RcYGBigZcuWAIA2bdqgXr16GDx4MLy8vKBSqTB69Gi0bdtWXfB7eXmhXLlyqFGjBhITE/Hnn39i9+7d6gsCP+Xj44Nu3bqlWYR6enrC3d0djo6OcHJywpo1a/DkyROMHDkSAJCUlIQePXrg+vXrOHjwIJRKpXokt1ixYupCPy/K7g0MeAMCorzrypUr6N27N0JDQ2FoaIglS5Zg9OjRfM+C68xql74+kHxl/edz8pIfe3l93E7LKlWqhICAAFSoUAG9e/dGhQoVMHz4cLRs2RKXLl1KNSL33XffITAwEHXr1sVPP/2EJUuWoF27durnlyxZAn9/f5QpUwZ169bVWs6uXbti4sSJGDNmDOrUqYOLFy/ixx9/1OgYtra2mDNnDqZOnQorK6sMVwXIjHbt2uHgwYPw9/dH/fr10ahRIyxdulRdrBYpUgSbNm1Cs2bNUKtWLZw4cQIHDhzI9Gidqakpjh07hrdv36J+/fro0aMHWrdujRUrVqS7z6JFi/D111+jS5cuaNOmDZo2bQoHB4dsvc4vsbCwgL+/P549ewZHR0d4eHjA09MTnp6e6m1iY2Nx7969FOvDbtmyBV999RWcnZ3h7OyMWrVqYfPmzernq1atigMHDuDGjRtwcnJCs2bN8OLFCxw9elQ9qqqnp4cDBw6gRIkS+Prrr9GxY0dUq1YN27dvVx8nMTERkyZNQq1atdCsWTOcP38ehw4dSjU95P79+zh//jyGDBmS5uvs3bs3vLy8MHfuXNSpUwdnz57F4cOH1f397Nkz/PXXX3j27Bnq1KkDa2tr9VdGKx7kBdm9gQF/KRLlXWfOnEFoaCgqVKiAS5cuYcyYMXzP/j+ZyOoEPR0VFRUFCwsLREZGppoDFx8fj5CQEPWdgbJszx5g/PgUF4OpbG0BLy/ofbJ4u1S0cfvVgkSlUiEqKgrm5uYFZ5mTfCan+1BrPzuyIDYxCdVnHgMA3J7bLt9evKVQKHD48GG4uLikmqNOeR/7L/tUKhWWLFmCESNGSDKHP7f7MKN67XP8zZwTuncHHj8GTp0Ctm6F6sQJRP3zz8d2IiIioi84f/482rVrh5iYGAAfP8GaPHlynr0YVUosZnOKvv7H5bf69Pn43xyYWkBERET5i0qlwoIFC9CiRQscP3481TUvlFr+/DyKMpTe3ZyIKO/I7Nqxn64RS0S67dWrV3B3d8fx48cBAP369Ut3OUX6D4tZIqI8hmvHEhU8p0+fRt++fREWFgYTExOsWLECgwYN4kVemcBiNg0F7Jo4Isombf/MyMrasVwjlkh3/fnnnxgwYABUKhWqV6+OHTt2oEaNGlLH0hksZj+RfHVebGxsihsPEBFlJDY2FkDqO8FpQ2bXjuUasUS6q1WrVihevDg6deqE33//PdWdKiljLGY/oa+vjyJFiqhv4WlqaqqVXw4qlQqJiYmIj4/n0k46iP2n+3KqD4UQiI2NxatXr1CkSBHo58CFnslrxxJR/nL//n31TWFsbGzwzz//pHlHQ/oy/oT8TKlSpQD8d096bRBCIC4uDiYmJhw50UHsP92X031YpEgR9c8OIqKMJCUlYe7cuZg3bx527NiBb7/9FgBYyGYDi9nPyGQyWFtbw9LSMsVdjrJDoVDg7Nmz+Prrr7lYtA5i/+m+nOxDuVyeIyOyRJT/PH/+HH379sXZs2cBAJcvX1YXs5R1LGbToa+vr7VfUPr6+khKSoKxsTGLIR3E/tN97EMiktrRo0fh7u6OiIgIFCpUCGvXroWrq6vUsfIFTgAkIiIiyiEKhQJTp05Fhw4dEBERgbp16+L69essZLWII7NElGMyu/B/TlMokpCgBGITkyAXeX/eM2+EQJR/nD17Fr/++isAYPTo0Vi8eDGMjY0lTpW/sJglohyR9xb+N8CUqyelDkFEBUzr1q0xffp01K1bFz169JA6Tr7EaQZElCOysvA/pcQbIRDpnsTERPz44494/vy5um3evHksZHMQR2aJKMdlduH/nKJQKHDs2HG0a+esUxeA8UYIRLrl8ePH6N27N65evYpz587h1KlTfA/nAhazRJSm7M53/XTep9QL/ytkAkb6gKmhAeRy/tgjIu3bu3cvBg8ejPfv36NIkSKYOHEiC9lcwp/qRJRK3pvvSkSUNyUkJGDy5Mn4/fffAQCNGjXC9u3bYWdnJ3GygoPFLBGlos35rpz3SUT51fPnz9G1a1cEBgYCACZPnox58+bp1HSm/IDFLBFlKLvzXTnvk4jyqyJFiiAuLg7FixfHxo0b0bFjR6kjFUgsZol0XE6s5ZqX5rsSEeUl8fHxMDQ0hJ6eHszMzLB3716YmpqidOnSUkcrsPgbikiHcW4rEVHuuXfvHnr16oU+ffpg6tSpAIDKlStLnIq4ziyRDsvptVw535WI6KMtW7bAwcEBN27cwPLlyxETEyN1JPp/HJklyidyYi1XznclooIuNjYW48aNg4+PDwCgRYsW2LJlC8zMzCRORslYzBLlE5zbSkSkXXfu3EGvXr1w69YtyGQyzJw5Ez/++CP09fmJVV7C33xEREREn4mKikKTJk3w7t07lCpVClu2bEGrVq2kjkVp4JxZIiIios+Ym5tj7ty5aNOmDYKDg1nI5mEsZomIiIgA3Lx5E8HBwerHo0ePxrFjx2BlZSVdKPoiTjMgymM0WTf20/VgiYgoa4QQWLduHcaNGwdbW1tcv34d5ubmkMlkvAhWB7CYJcpDuG4sEVHuio6OxogRI7Bt2zYAQKVKlaBQKCRORZrgNAOiPCSr68ZyPVgiIs0FBwfDwcEB27Ztg76+Pn755RccOnQIxYsXlzoaaYAjs0R5lCbrxnI9WCKizBNCYNWqVZg4cSISEhJQpkwZbN++HY0bN5Y6GmUBi1miPIrrxhIR5QwhBP766y8kJCSgc+fO2LBhA0djdRh/UxJJ6POLvXhBFxFRztPT08OmTZuwc+dOjBo1ip9s6TgWs0QS4cVeRES5QwiB5cuX4+7du/D29gYAlCxZEh4eHhInI21gMUskkYwu9uIFXURE2vHu3TsMHjwY+/btAwD07NmTN0DIZ1jMEuUBn1/sxQu6iIiy78qVK+jduzdCQ0NhaGiIJUuWoGXLllLHIi1jMUuUB/BiLyIi7RFCYOnSpZg6dSqSkpJQoUIF+Pn5wcHBQepolAP425OIiIjylcGDB8PX1xcA0KtXL6xZswYWFhbShqIcw5smEBERUb7Su3dvmJiYwNvbG9u3b2chm89xZJaIiIh0mkqlwv3791G1alUAQPv27RESEgIrKyuJk1Fu4MgsERER6axXr17BxcUFjRo1QkhIiLqdhWzBwWKWiIiIdNKZM2dQp04dHDt2DImJibh586bUkUgCLGaJiIhIpyiVSsydOxetWrVCWFgYqlWrhqtXr6JLly5SRyMJcM4sERER6Yzw8HD069cPJ06cAAAMHDgQK1asgJmZmcTJSCosZomySAiBOIUyy/vHJmZ9XyKigmrZsmU4ceIETE1N4e3tjf79+0sdiSTGYpYoC4QQ6LHqUrq3oyUiopwxa9YsPHv2DDNmzFCvXkAFG+fMEmVBnEKptULW0a4oTOT6X96QiKgAev78OSZNmoSkpCQAgLGxMTZv3sxCltQ4MkuUTQE/tIGpYdaLURO5PmQymRYTERHlD0ePHoW7uzsiIiJgbm6OmTNnSh2J8iAWs5SvZXdeKwAoFElIUAKxiUmQi49F56fzXU0N9WFqyLcSEZG2KBQKzJw5E7/88gsAoE6dOnB1dZU4FeVV/A1M+ZZ257UaYMrVk1o4DhERZeTp06dwdXXFxYsXAQAeHh5YsmQJjI2NJU5GeRWLWcq3tDmvNT2c70pEpD0nTpxAr1698PbtW5ibm2PdunXo2bOn1LEoj2MxSwVCdua1KhQKHDt2HO3aOUMul6d4jvNdiYi0p1SpUoiLi4ODgwP8/PxQoUIFqSORDmAxSwVCdua1KmQCRvqAqaEB5HK+ZYiItCkmJkZ9w4MaNWrgxIkTqFevHoyMjCRORrqCS3MRERGRJPbt24dy5cqp58cCgJOTEwtZ0giLWSIiIspVCQkJGD9+PL755htERETgt99+kzoS6TDJi9mVK1fC3t4exsbGcHBwwLlz5zLcfsuWLahduzZMTU1hbW2NQYMG4c2bN7mUloiIiLLj4cOHaNKkCZYvXw4AmDRpErZu3SpxKtJlkhazfn5+mDBhAmbMmIGgoCA0a9YMHTp0wJMnT9Lc/vz58+jfvz+GDBmCf//9Fzt37sS1a9cwdOjQXE5OREREmtq5cyfq1q2LwMBAFCtWDAcPHsSiRYtSXVxLpAlJi9mlS5diyJAhGDp0KKpVqwYvLy+UKVMG3t7eaW5/+fJllCtXDuPGjYO9vT2aNm2KESNGICAgIJeTk5SEEIhNTMrEV/ZulkBERNpz8+ZNuLm5ITo6Gk2aNEFwcDA6duwodSzKByS7NDsxMRGBgYGYOnVqinZnZ+cUE8E/1bhxY8yYMQOHDx9Ghw4d8OrVK+zatSvDN0NCQgISEhLUj6OiogB8XG5JoVBo4ZV8WfJ5cut8+ZkQAq7rruH6k/ca7adQKKCQiSydk/2n+9iHuo99qNsUCgVq1qyJbt26oXLlypg9ezYMDAzYnzokt9+DmpxHJoTI2m/4bHrx4gVsbW1x4cIFNG7cWN0+f/58bNy4Effu3Utzv127dmHQoEGIj49HUlISunTpgl27dqX7EcXs2bMxZ86cVO1bt26Fqampdl4M5ZoEJTDlqmZ/g9kXFhhfQwkuB0tElLsuXryIOnXqqH/fqlQq6OlJfrkO6YDY2Fj07dsXkZGRMDc3z3BbyRfN/HzBeSFEuovQ3759G+PGjcPMmTPRrl07hIWFYfLkyRg5ciR8fHzS3GfatGnw9PRUP46KikKZMmXg7Oz8xW+OtigUCvj7+6Nt27acF5RNsYlJ6tvKXv6+OUwycSOE7N7YgP2n+9iHuo99qFtiY2Ph6emJ9evXo2fPntiwYQP+/vtvtGvXjv2no3L7PZj8SXpmSFbMlihRAvr6+ggPD0/R/urVK1hZWaW5z4IFC9CkSRNMnjwZAFCrVi2YmZmhWbNm+Pnnn2FtbZ1qHyMjozTXq5PL5bn+hpLinNokhECcQtp5qArx31/05mbGWb4RQlboev8R+zA/YB/mfXfu3EGvXr1w69YtyGQyVKtWDQYGH39Ws/90X271oSbnkKyYNTQ0hIODA/z9/fHNN9+o2/39/dG1a9c094mNjVW/IZLp638cmZNotkSBIYRAj1WXEBj6TuooRESUR23cuBEeHh6IjY2FlZUVtmzZgtatW3NuLOUoSSeueHp6Yt26dVi/fj3u3LmDiRMn4smTJxg5ciSAj1ME+vfvr96+c+fO2LNnD7y9vfHo0SNcuHAB48aNQ4MGDWBjYyPVyygQ4hTKPFXIOtoVhYn8y1MMiIgo58XExGDgwIEYOHAgYmNj0bp1awQHB6N169ZSR6MCQNI5s71798abN28wd+5chIWFoWbNmjh8+DDs7OwAAGFhYSnWnB04cCCio6OxYsUKfPfddyhSpAhatWqFX3/9VaqXUCAF/NAGppmYq5qTsjsPloiItCc2NhbHjx+Hnp4e5syZg2nTpqk/OSXKaZJfAObh4QEPD480n/P19U3VNnbsWIwdOzaHUxUcmZ0H++maraaG+rk6V5WIiPK2kiVLws/PDyqVCs2bN5c6DhUwrEh0lVIJnDsHhIUB1tZAs2aAhn8Fcx4sERFlRXR0NEaOHAkXFxe4ubkBAJo1ayZxKiqouNibLtqzByhXDmjZEujb9+N/y5X72K6BrMyD5VxVIqKCLTg4GA4ODti6dSvGjBmj0RJKRDmBI7O6Zs8eoEcP4PPVG54//9i+axfQvbvGh83sPFjOVSUiKpiEEFi1ahUmTpyIhIQElC5dGtu3b8+1NduJ0sNiVpcolcD48epCVgCIk3+yhq5MBnw3BejQMVNTDjgPloiIMiMyMhLDhg3Dzp07AQCdOnWCr68vihcvLnEyIhazuuXcOeDZMwAfC9kebgsRWLp66u3m/J27uYiIKN+KiYmBg4MDHj58CAMDA/z666+YOHEiP6WjPINzZnVJWJj6f+PkRmkXslnAebBERJQeMzMzfPvtt7Czs8P58+fh6enJQpbyFI7M6pI0btcLAAG/u8FUEf9fw+EjwNeZv6qU82CJiOhT7969Q0xMDEqXLg0A+PnnnzF16lQULVpU4mREqbGY1SXNmgGlS3+82OsTpop4mCoSPs6ZLV0aaPm1xst0ERERAcCVK1fQu3dvlCpVCufOnYNcLodcLmchS3kWpxnoEn19YNmyj///+Uhq8mMvLxayRESkMSEElixZgqZNmyI0NBSvX7/G888GT4jyIhazuqZ794/Lb1nbpGwvXTrLy3IREVHB9ubNG3Tp0gWTJk1CUlISevbsievXr6NcuXJSRyP6Ihazuqh7d+DO7f8eHz4ChISwkCUiIo1duHABderUwcGDB2FkZARvb2/4+fnBwsJC6mhEmcI5s7rq06kEX2t+K1siIiIhBCZOnIhnz56hUqVK2LFjB+rUqSN1LCKNcGSWiIiogJLJZNiyZQuGDBmCwMBAFrKkk1jMEhERFSBnzpzBsuSLiQFUqlQJ69atQ+HChSVMRZR1nGZARERUACiVSsyfPx+zZ8+GEAL16tVDs2aZX5OcKK9iMUtERJTPhYeHo1+/fjhx4gQAYMCAAahXr57EqYi0g8UsERFRPnbixAm4ubnh5cuXMDU1xcqVKzFgwACpYxFpDefMEhER5VMLFixA27Zt8fLlS9SsWRPXrl1jIUv5DotZIiKifMrS0hJCCAwdOhRXrlxB9erVpY5EpHWcZkBERJSPfPjwAYUKFQIADB48GFWqVEHTpk0lTkWUczgyS0RElA8kJSVh2rRpqFmzJt6+fQvg4zqyLGQpv2MxS0REpOOePn2KFi1a4JdffkFoaCh2794tdSSiXMNiloiISIcdOnQIderUwYULF2Bubg4/Pz8MGzZM6lhEuYbFLBERkQ5KTEzEpEmT0KlTJ7x9+xYODg64fv06evXqJXU0olzFYlYHCCEQm5j02ZdS6lhERCSh2bNnY8mSJQCAcePG4cKFC6hQoYLEqYhyH1czyOOEEOix6hICQ99JHYWIiPKQSZMm4fDhw5g1axa++eYbqeMQSYYjs3lcnEKZYSHraFcUJnL9XExERERSSEhIwKZNmyCEAAAUK1YM169fZyFLBR5HZnVIwA9tYGqYsnA1ketDJpNJlIiIiHLDo0eP0KtXLwQGBiIhIUF9gZeeHsekiFjM6hBTQ32YGrLLiIgKkl27dmHIkCGIiopCsWLFYG1tLXUkojyFf9IRERHlQfHx8Rg9ejR69uyJqKgoNG7cGMHBwejUqZPU0YjyFBazREREecyDBw/g5OSElStXAgCmTp2K06dPo0yZMhInI8p7+Jk1ERFRHvPs2TP8888/KFGiBDZv3oz27dtLHYkoz2IxS0RElAcIIdQX9LZs2RK+vr5o3bo1bG1tJU5GlLdxmgEREZHE7ty5g6ZNm+L+/fvqtv79+7OQJcoEFrNEREQS2rhxIxwdHXHx4kWMGzdO6jhEOofFLBERkQRiYmIwcOBADBw4ELGxsWjVqhV8fX2ljkWkc1jMEhER5bJbt26hfv362LhxI/T09DB37lwcP34cpUqVkjoakc7hBWBERES56MqVK2jZsiXi4uJgbW2NrVu3okWLFlLHItJZLGaJiIhyUb169VC7dm2Ym5tj8+bNsLS0lDoSkU5jMUtERJTDbt++jUqVKkEul0Mul+PQoUMoUqQI9PQ4248ou/guIiIiyiFCCKxatQr16tXDjBkz1O3FihVjIUukJRyZJSIiygFRUVEYNmwYduzYAeDjWrJKpRL6+voSJyPKX/hnIRERkZYFBgaiXr162LFjBwwMDLBo0SLs37+fhSxRDuDILBERkZYIIbBixQpMmjQJiYmJsLOzw/bt29GoUSOpoxHlWxyZJSIi0pLnz59j+vTpSExMRLdu3RAUFMRCliiHcWSWiIhIS0qXLo21a9fi1atXGDt2LGQymdSRiPI9FrNERERZJITAb7/9hrp166Jly5YAAFdXV4lTERUsLGaJiIiy4O3btxg4cCAOHDiAUqVK4fbt2yhatKjUsYgKHBazREREGrp48SJcXV3x9OlTGBkZYebMmShSpIjUsYgKJF4ARkRElEkqlQq//vorvv76azx9+hSVKlXC5cuXMWrUKM6PJZIIR2aJiIgyIS4uDt9++y2OHDkCAOjTpw9Wr16NwoULS5yMqGDjyCwREVEmGBsbo0iRIjA2NsaaNWuwZcsWFrJEeQCLWSIionQolUrExMQAAGQyGVavXo1r165h2LBhnFZAlEewmCUiIkrDy5cv0b59e/Tr1w9CCABA4cKFUbNmTYmTEdGnOGeWiIjoMydPnoSbmxvCw8NhamqKu3fvolq1alLHIqI0cGSWiIjo/ymVSsyaNQtt2rRBeHg4atSogWvXrrGQJcrDODJLREQE4MWLF3Bzc8Pp06cBAEOGDMHy5cthamoqbTAiyhCLWSIiKvCEEOjatSsCAgJgZmaG1atXw83NTepYRJQJWZpmkJSUhL///hurV69GdHQ0gI9/0X748EGr4QoiIQRiE5M++VJKHYmIKN+TyWRYvnw5HBwccP36dRayRDpE45HZ0NBQtG/fHk+ePEFCQgLatm2LwoULY+HChYiPj8eqVatyImeBIIRAj1WXEBj6TuooRET53rNnzxAcHIxOnToBAJycnHDt2jUuuUWkYzQemR0/fjwcHR3x7t07mJiYqNu/+eYbnDhxQqvhCpo4hTLdQtbRrihM5Pq5nIiIKH86fPgw6tSpg169euHWrVvqdhayRLpH45HZ8+fP48KFCzA0NEzRbmdnh+fPn2stWEEX8EMbmBr+V7yayPX5Q5aIKJsUCgVmzJiBRYsWAQDq1auXYmCGiHSPxsWsSqWCUpl6HuezZ894Wz8tMjXUh6khr88jItKW0NBQuLq64vLlywCAsWPHYtGiRTAyMpI4GRFlh8bTDNq2bQsvLy/1Y5lMhg8fPmDWrFlwcXHRZjYiIiKt2L9/P+rWrYvLly/DwsICu3fvxvLly1nIEuUDGg/9/fbbb2jZsiWqV6+O+Ph49O3bFw8ePECJEiWwbdu2nMhIRESULdevX8e7d+/QoEEDbN++Hfb29lJHIiIt0biYtbGxQXBwMLZv347AwECoVCoMGTIEbm5unHdERER5hhBCfa3BzJkzYWlpiWHDhqW65oOIdJvG0wzOnj0LuVyOQYMGYcWKFVi5ciWGDh0KuVyOs2fP5kRGIiIijezevRutWrVCfHw8AEBfXx+jR49mIUuUD2lczLZs2RJv375N1R4ZGYmWLVtqJRQREVFWxMfHY8yYMejRowdOnz6NP/74Q+pIRJTDNJ5m8OnHNp968+YNzMzMtBKKiIhIUw8ePEDv3r0RFBQEAPj+++8xbtw4iVMRUU7LdDHbvXt3AB9XLxg4cGCKK0CVSiVu3LiBxo0baz8hERHRF2zfvh3Dhg3Dhw8fUKJECWzatAkdOnSQOhYR5YJMF7MWFhYAPo7MFi5cOMXFXoaGhmjUqBGGDRum/YREREQZWLJkCSZNmgQAaNasGbZt2wZbW1uJUxFRbsl0MbthwwYAQLly5TBp0iROKcgmIQTiFClvPhGbmPpmFERElLFvv/0W8+fPh4eHB2bNmgUDA95whqgg0fgdP2vWrJzIUaAIIdBj1SUEhr6TOgoRkU4KCgpC3bp1AXwcZHnw4AGKFSsmcSoikoLGqxkAwK5du9CrVy80atQI9erVS/GlqZUrV8Le3h7GxsZwcHDAuXPnMtw+ISEBM2bMgJ2dHYyMjFChQgWsX78+Ky9DMnEKZYaFrKNdUZjI9XMxERGRboiJicHgwYNRr149HD58WN3OQpao4NJ4ZHb58uWYMWMGBgwYgP3792PQoEF4+PAhrl27htGjR2t0LD8/P0yYMAErV65EkyZNsHr1anTo0AG3b99G2bJl09ynV69eePnyJXx8fFCxYkW8evUKSUlJmr6MPCPghzYwNUxZuJrI9dNcMYKIqCB78uQJGjdujDt37kBPTw/37t3jbdSJSPNiduXKlVizZg369OmDjRs3YsqUKShfvjxmzpyZ5vqzGVm6dCmGDBmCoUOHAgC8vLxw7NgxeHt7Y8GCBam2P3r0KM6cOYNHjx6p/wovV66cpi8hTzE11IepIed3ERGlRwgBX19fTJo0CYmJiShVqhS2bduGFi1aSB2NiPIAjauo5L+MAcDExATR0dEAAHd3dzRq1AgrVqzI1HESExMRGBiIqVOnpmh3dnbGxYsX09znr7/+gqOjIxYuXIjNmzfDzMwMXbp0wU8//ZTurXQTEhKQkJCgfhwVFQUAUCgUUCgUmcqaXcnn+e+/SSmeU8hEruSgrPm8/0j3sA9114cPHzBmzBhs3boVANC6dWts3LgRlpaW7E8dwveg7svtPtTkPBoXs6VKlcKbN29gZ2cHOzs7XL58GbVr10ZISAiEyHxRFhERAaVSCSsrqxTtVlZWCA8PT3OfR48e4fz58zA2NsbevXsREREBDw8PvH37Nt15swsWLMCcOXNStR8/fhympqaZzqsN/v7+AIAEJZD8rT927DiMOD1WJyT3H+ku9qHuuXTpErZu3Qo9PT307dsX3bt3R0BAgNSxKIv4HtR9udWHsbGxmd5W42K2VatWOHDgAOrVq4chQ4Zg4sSJ2LVrFwICAtQ3VtDE53ND07vDGACoVCrIZDJs2bJFve7t0qVL0aNHD/zxxx9pjs5OmzYNnp6e6sdRUVEoU6YMnJ2dYW5urnHerFAoFPD390fbtm0hl8sRm5iEKVdPAgDatXPmNIM87vP+I93DPtRdLi4uEEKgbdu2iImJYR/qKL4HdV9u92HyJ+mZoXEVtWbNGqhUKgDAyJEjUaxYMZw/fx6dO3fGyJEjM32cEiVKQF9fP9Uo7KtXr1KN1iaztraGra2tupAFgGrVqkEIgWfPnqFSpUqp9jEyMkpxt7Jkcrk8199QyeeUC9lnbSxmdYEU/2ZIu9iHeV9UVBSmTZuGWbNmwdLSEgDw66+/QqFQ4PDhw+xDHcf+03251YeanEPjpbn09PRSLEjdq1cvLF++HOPGjcPr168zfRxDQ0M4ODikGq729/dP97a4TZo0wYsXL/Dhwwd12/3796Gnp4fSpUtr+EqIiCgvuX79OurVq4eVK1diyJAhUschIh2RpXVmPxceHo6xY8eiYsWKGu3n6emJdevWYf369bhz5w4mTpyIJ0+eqEd4p02bhv79+6u379u3L4oXL45Bgwbh9u3bOHv2LCZPnozBgwenewEYERHlbUIIrFixAk5OTnj48CHKli2L6dOnSx2LiHREpovZ9+/fw83NDSVLloSNjQ2WL18OlUqFmTNnonz58rh8+bLGNy/o3bs3vLy8MHfuXNSpUwdnz57F4cOHYWdnBwAICwvDkydP1NsXKlQI/v7+eP/+PRwdHeHm5obOnTtj+fLlGp2XiIjyhvfv36NHjx4YO3YsEhMT0aVLFwQFBcHJyUnqaESkIzI9WXP69Ok4e/YsBgwYgKNHj2LixIk4evQo4uPjceTIETRv3jxLATw8PODh4ZHmc76+vqnaqlatyqshiYjygbt378LFxQUhISGQy+VYtGgRxo0bx5vGEJFGMl3MHjp0CBs2bECbNm3g4eGBihUronLlyvDy8srBeERElF/Z2NhAX18f9vb28PPzQ/369aWOREQ6KNPF7IsXL1C9enUAQPny5WFsbKy+cxcREVFmREVFoXDhwpDJZDA3N8fBgwdhZWWFIkWKSB2NiHRUpufMqlSqFMsk6Ovrw8zMLEdCERFR/nPp0iXUrFkzxZ0iq1SpwkKWiLIl0yOzQggMHDhQvWZrfHw8Ro4cmaqg3bNnj3YTEhGRTlOpVFi8eDGmT58OpVKJ1atXY+TIkVxvlIi0ItPF7IABA1I87tevn9bDEBFR/vL69WsMGDAAR44cAQC4urpi9erVLGSJSGsyXcxu2LAhJ3MQEVE+c/bsWfTp0wcvXryAsbExli9fjqFDh3K1AiLSKt5HlYiItC4sLAzOzs5ISEhAlSpVsGPHDtSqVUvqWESUD7GYJSIirbO2tsacOXPw77//YuXKlShUqJDUkYgon2IxS0REWnHq1ClYWlqiRo0aAIApU6YAAKcVEFGOyvTSXERERGlRKpWYPXs2WrdujV69eiEmJgbAxyKWhSwR5TSOzBIRUZaFhYXBzc0Np06dAgA0atSIBSwR5aosjcxu3rwZTZo0gY2NDUJDQwEAXl5e2L9/v1bDERFR3uXv7486derg1KlTMDMzw+bNm+Hj4wNTU1OpoxFRAaJxMevt7Q1PT0+4uLjg/fv3UCqVAIAiRYrAy8tL2/mIiCiPSUpKwg8//IB27drh1atXqFWrFgICArj+OBFJQuNi9vfff8fatWsxY8YM6Ovrq9sdHR1x8+ZNrYYjIqK8RyaT4fz58xBCYMSIEbh8+TKqVq0qdSwiKqA0njMbEhKCunXrpmo3MjJST/onIqL8RwgBmUwGfX19bN26FefPn0evXr2kjkVEBZzGI7P29vYIDg5O1X7kyBFUr15dG5mIiCgPUSgUmDJlCiZMmKBus7GxYSFLRHmCxiOzkydPxujRoxEfHw8hBK5evYpt27ZhwYIFWLduXU5kJCIiiTx58gSurq64dOkSAGDw4MGoXbu2xKmIiP6jcTE7aNAgJCUlYcqUKYiNjUXfvn1ha2uLZcuWwdXVNScyEhGRBP766y8MHDgQ7969g4WFBXx8fFjIElGek6V1ZocNG4Zhw4YhIiICKpUKlpaW2s5FREQSSUxMxPfff69eoaZ+/frw8/ODvb29tMGIiNKg8ZzZOXPm4OHDhwCAEiVKsJAlIspHhBDo3LmzupCdOHEizp8/z0KWiPIsjYvZ3bt3o3LlymjUqBFWrFiB169f50QuIiKSgEwmw4gRI1C0aFHs378fS5cuhaGhodSxiIjSpXExe+PGDdy4cQOtWrXC0qVLYWtrCxcXF2zduhWxsbE5kZGIiHJQfHx8inXCu3fvjkePHqFLly4SpiIiypws3c62Ro0amD9/Ph49eoRTp07B3t4eEyZMQKlSpbSdj4iIctD//vc/NG7cGK1atcLz58/V7UWKFJEuFBGRBrJUzH7KzMwMJiYmMDQ0hEKh0EYmIiLKBX5+fqhXrx6CgoIghEBISIjUkYiINJalYjYkJATz5s1D9erV4ejoiOvXr2P27NkIDw/Xdj4iItKyuLg4jBw5Eq6uroiOjkbTpk0RHByMpk2bSh2NiEhjGi/N5eTkhKtXr+Krr77CoEGD1OvMEhFR3nfv3j306tULN27cgEwmw/Tp0zF79mwYGGRppUYiIslp/NOrZcuWWLduHWrUqJETeYiIKActW7YMN27cgKWlJf7880+0bdtW6khERNmicTE7f/78nMhBRES5YNGiRUhKSsKcOXNgbW0tdRwiomzLVDHr6emJn376CWZmZvD09Mxw26VLl2olGBERZd+///6L1atXw8vLC3p6ejAzM8OaNWukjkVEpDWZKmaDgoLUKxUEBQXlaCAiIso+IQR8fX0xevRoxMXFoXz58pgwYYLUsYiItC5TxeypU6fS/H8iIsp7Pnz4AA8PD2zevBkA4OzsjL59+0qciogoZ2i8NNfgwYMRHR2dqj0mJgaDBw/WSigiIsqaGzduwNHREZs3b4aenh7mzZuHI0eOwNLSUupoREQ5QuNiduPGjYiLi0vVHhcXh02bNmklFBERac7Pzw8NGzbEvXv3YGtri9OnT2P69OnQ08v2/XGIiPKsTK9mEBUVBSEEhBCIjo6GsbGx+jmlUonDhw/zL38iIglVrFgRKpUKHTp0wKZNm1CiRAmpIxER5bhMF7NFihSBTCaDTCZD5cqVUz0vk8kwZ84crYYjIqKMvX//HkWKFAEAODg44NKlS6hTpw5HY4mowMh0MXvq1CkIIdCqVSvs3r0bxYoVUz9naGgIOzs72NjY5EhIIiJKSQiBlStXYvr06Th16hTq1asHAOr/EhEVFJkuZps3bw4ACAkJQdmyZSGTyXIsFBERpe/9+/cYNmwYdu3aBQDw9fVlEUtEBVamitkbN26gZs2a0NPTQ2RkJG7evJnutrVq1dJaOCIiSunatWvo3bs3QkJCIJfLsXDhQowfP17qWEREkslUMVunTh2Eh4fD0tISderUgUwmgxAi1XYymQxKpVLrIYmICjohBJYtW4YpU6ZAoVDA3t4efn5+qF+/vtTRiIgklaliNiQkBCVLllT/PxER5a7du3dj4sSJAIBvv/0W69atU1/4RURUkGWqmLWzs0vz/4mIKHd0794dXbp0gbOzMzw8PHjdAhHR/8vSTRMOHTqkfjxlyhQUKVIEjRs3RmhoqFbDEREVVCqVCmvXrkVsbCwAQE9PD/v27cPo0aNZyBIRfULjYnb+/PkwMTEBAFy6dAkrVqzAwoULUaJECfVHYERElHURERHo3Lkzhg8fjrFjx6rbWcQSEaWW6aW5kj19+hQVK1YEAOzbtw89evTA8OHD0aRJE7Ro0ULb+YiICpRz586hT58+eP78OYyNjdGwYUMIIVjIEhGlQ+OR2UKFCuHNmzcAgOPHj6NNmzYAAGNjY8TFxWk3HRFRAaFSqTB//ny0bNkSz58/R5UqVXDlyhUMHz6chSwRUQY0Hplt27Ythg4dirp16+L+/fvo2LEjAODff/9FuXLltJ2PiCjfe/XqFdzd3XH8+HEAQL9+/eDt7Y1ChQpJnIyIKO/TeGT2jz/+gJOTE16/fo3du3ejePHiAIDAwED06dNH6wGJiPI7hUKB69evw8TEBD4+Pti0aRMLWSKiTNJ4ZLZIkSJYsWJFqvY5c+ZoJRARUUHw6TxYW1tb7Ny5EyVLlkSNGjUkTkZEpFs0LmaBj/cF9/HxwZ07dyCTyVCtWjUMGTIEFhYW2s5HRJTvhIeHw83NDWPGjME333wDALyAlogoizSeZhAQEIAKFSrgt99+w9u3bxEREYHffvsNFSpUwPXr13MiIxFRvvH333+jdu3aOHnyJMaNG4fExESpIxER6TSNi9mJEyeiS5cuePz4Mfbs2YO9e/ciJCQEnTp1woQJE3Igou4TQiBBCcQmJv3/l1LqSESUy5KSkvDDDz/A2dkZr169Qq1atfD333/D0NBQ6mhERDpN42kGAQEBWLt2LQwM/tvVwMAAU6ZMgaOjo1bD5QdCCLiuu4brTwww5epJqeMQkQSeP3+OPn364Ny5cwCAESNG4LffflPfgIaIiLJO42LW3NwcT548QdWqVVO0P336FIULF9ZasPwiTqHE9Sfv03zO0a4oTOT6uRuIiHLV69evUadOHURERKBw4cJYs2YNXF1dpY5FRJRvaFzM9u7dG0OGDMHixYvRuHFjyGQynD9/HpMnT+bSXF9w+fvmMDczVj82ketzMXSifK5kyZLo3bs3Ll68CD8/P1SqVEnqSERE+YrGxezixYshk8nQv39/JCUlAQDkcjlGjRqFX375ResB8xMTQ32YGmZpAQki0iFPnjyBXC6HtbU1AGDJkiUQQsDY2PgLexIRkaY0vgDM0NAQy5Ytw7t37xAcHIygoCC8ffsWv/32G4yMjHIiIxGRzjhw4ADq1KmDPn36qP/gNzIyYiFLRJRDMl3MxsbGYvTo0bC1tYWlpSWGDh0Ka2tr1KpVC6ampjmZkYgoz0tMTMR3332HLl264N27d4iNjcW7d++kjkVElO9lupidNWsWfH190bFjR7i6usLf3x+jRo3KyWxERDohJCQEzZo1w9KlSwF8XMLw/PnzKFmypMTJiIjyv0xP4NyzZw98fHzUV+H269cPTZo0gVKphL4+r8gnooJpz549GDx4MCIjI1G0aFH4+vqiS5cuUsciIiowMj0y+/TpUzRr1kz9uEGDBjAwMMCLFy9yJBgRUV6nUCjw448/IjIyEk5OTggKCmIhS0SUyzJdzCqVylR3qjEwMFBf4EBEVNDI5XL4+flh2rRpOHPmDOzs7KSORERU4GR6moEQAgMHDkyxYkF8fDxGjhwJMzMzdduePXu0m5CIKA/ZsWMHXr16hTFjxgAAatasifnz50ucioio4Mp0MTtgwIBUbf369dNqGCKivCouLg4TJ07E6tWroa+vjyZNmqBu3bpSxyIiKvAyXcxu2LAhJ3MQEeVZ9+7dQ69evXDjxg3IZDJMnToVX331ldSxiIgIWbgDGBFRQfLnn39i5MiRiImJgaWlJf7880+0bdtW6lhERPT/NL4DGBFRQeHh4QF3d3fExMSgZcuWCA4OZiFLRJTHsJglIkpH1apVIZPJMHv2bPj7+8Pa2lrqSERE9BlOMyAi+sTbt29RrFgxAMDYsWPRvHlz1K5dW+JURESUHo7MEhEB+PDhAwYMGICGDRsiKioKACCTyVjIEhHlcVkqZjdv3owmTZrAxsYGoaGhAAAvLy/s379fq+GIiHLDzZs3Ub9+fWzatAmPHj3CqVOnpI5ERESZpHEx6+3tDU9PT7i4uOD9+/dQKpUAgCJFisDLy0vb+YiIcowQAmvXrkWDBg1w9+5d2Nra4vTp0+jatavU0YiIKJM0LmZ///13rF27FjNmzIC+vr663dHRETdv3tRqOCKinBIdHQ03NzcMHz4c8fHx6NChA4KDg9GsWTOpoxERkQY0LmZDQkLSvOuNkZERYmJitBKKiCinfffdd9i2bRv09fWxcOFCHDx4ECVKlJA6FhERaUjjYtbe3h7BwcGp2o8cOYLq1atrHGDlypWwt7eHsbExHBwccO7cuUztd+HCBRgYGKBOnToan5OI6Oeff0ajRo1w7tw5TJ48GXp6vB6WiEgXafzTe/LkyRg9ejT8/PwghMDVq1cxb948TJ8+HZMnT9boWH5+fpgwYQJmzJiBoKAgNGvWDB06dMCTJ08y3C8yMhL9+/dH69atNY1PRAVUTEwMfHx81I8tLS1x8eJFODk5SZiKiIiyS+N1ZgcNGoSkpCRMmTIFsbGx6Nu3L2xtbbFs2TK4urpqdKylS5diyJAhGDp0KICPKyIcO3YM3t7eWLBgQbr7jRgxAn379oW+vj727dun6UsgogImMDAQnp6eePnyJczNzdG3b18AH5feIiIi3ZalmyYMGzYMw4YNQ0REBFQqFSwtLTU+RmJiIgIDAzF16tQU7c7Ozrh48WK6+23YsAEPHz7En3/+iZ9//vmL50lISEBCQoL6cfL6kQqFAgqFQuPcmlIoklL8f26ck7Qruc/Yd7pHCIEVK1Zg6tSpUCgUsLOzQ7ly5diXOojvQ93G/tN9ud2HmpwnW3cAy87FEhEREVAqlbCyskrRbmVlhfDw8DT3efDgAaZOnYpz587BwCBz0RcsWIA5c+akaj9+/DhMTU01D66hBCWQ/G0+efIkjPQz3JzyMH9/f6kjkAY+fPiA33//HVeuXAEANGrUCGPGjMHr169x+PBhidNRVvF9qNvYf7ovt/owNjY209tqXMza29tn+NHco0ePNDre58cSQqR5fKVSib59+2LOnDmoXLlypo8/bdo0eHp6qh9HRUWhTJkycHZ2hrm5uUZZsyI2MQlTrp4EALRq1QoWZsY5fk7SLoVCAX9/f7Rt2xZyuVzqOJQJV69exfjx4xEaGgpDQ0MsWLAA5cuXh7OzM/tQR/F9qNvYf7ovt/sw+ZP0zNC4mJ0wYUKKxwqFAkFBQTh69KhGF4CVKFEC+vr6qUZhX716lWq0Fvi4JmRAQACCgoIwZswYAIBKpYIQAgYGBjh+/DhatWqVaj8jIyMYGRmlapfL5bnSGXLxX2EulxvwTazDcuvfDGVfZGQkQkNDUaFCBezYsQNfffUVDh8+zD7MB9iHuo39p/tyrX7S4BwaF7Pjx49Ps/2PP/5AQEBApo9jaGgIBwcH+Pv745tvvlG3+/v7p3n3HXNz81Q3ZVi5ciVOnjyJXbt2wd7ePtPnJqL859NPdVxcXLB161Z07NgR5ubmnKdHRJSPaW1hxQ4dOmD37t0a7ePp6Yl169Zh/fr1uHPnDiZOnIgnT55g5MiRAD5OEejfv//HoHp6qFmzZoovS0tLGBsbo2bNmjAzM9PWSyEiHXP+/HnUrl0boaGh6rY+ffrkylQiIiKSVrYuAPvUrl27UKxYMY326d27N968eYO5c+ciLCwMNWvWxOHDh2FnZwcACAsL++Kas0RUcKlUKvz666/48ccfoVQq8cMPP2Dz5s1SxyIiolykcTFbt27dFBdoCSEQHh6O169fY+XKlRoH8PDwgIeHR5rP+fr6Zrjv7NmzMXv2bI3PSUS679WrV3B3d8fx48cBAP369YO3t7fEqYiIKLdpXMx269YtxWM9PT2ULFkSLVq0QNWqVbWVi4goXadPn0bfvn0RFhYGExMT/PHHHxg4cCBvgkBEVABpVMwmJSWhXLlyaNeuHUqVKpVTmYiI0nXkyBF06tQJKpUK1atXx44dO1CjRg2pYxERkUQ0ugDMwMAAo0aNSnFHLSKi3NSyZUvUqlULgwYNwtWrV1nIEhEVcBpPM2jYsCGCgoLUF2kREeW0K1euwNHREfr6+jA2NsbZs2dRuHBhqWMREVEeoHEx6+Hhge+++w7Pnj2Dg4NDqiWxatWqpbVwRFSwJSUlYc6cOZg3bx5mzpypvuCThSwRESXLdDE7ePBgeHl5oXfv3gCAcePGqZ+TyWTqBcuVSqX2UxJRgfP8+XP07dsXZ8+eBQC8fPky3dtdExFRwZXpYnbjxo345ZdfEBISkpN5iIhw9OhRuLu7IyIiAoUKFcLatWvh6uoqdSwiIsqDMl3MCiEAgHNliSjHKBQKzJw5E7/88guAj+ta+/n5oVKlShInIyKivEqj1Qz48R4R5aRHjx7By8sLADB69GhcvHiRhSwREWVIowvAKleu/MWC9u3bt9kKREQFV5UqVbB69WqYmpqiR48eUschIiIdoFExO2fOHFhYWORUFiIqYBITE/HDDz/gm2++gZOTEwCgf//+EqciIiJdolEx6+rqCktLy5zKQkQFyOPHj+Hq6oorV65gx44duHv3LoyNjaWORUREOibTc2Y5X5aItGXv3r2oW7curly5giJFimDZsmUsZImIKEsyXcwmr2ZARJRVCQkJGDduHLp3747379+jUaNGCA4ORteuXaWORkREOirT0wxUKlVO5iCifO7du3do27YtAgMDAQCTJ0/GvHnzIJfLJU5GRES6TOPb2RIRZUWRIkVQunRpPH78GBs3bkTHjh2ljkRERPkAi1kiyjHx8fFISkpCoUKFIJPJsH79esTGxqJ06dJSRyMionxCo5smEBFl1v3799GoUSMMHz5cPee+WLFiLGSJiEirWMwSkdZt3boVDg4O+Oeff/D333/j+fPnUkciIqJ8isUsEWlNbGwshg0bBjc3N3z48AEtWrRAcHAwR2OJiCjHsJglIq24c+cOGjZsiHXr1kEmk2HWrFn4+++/YWNjI3U0IiLKx3gBGBFlW1JSEjp37oyHDx+iVKlS2LJlC1q1aiV1LCIiKgA4MktE2WZgYIA1a9agXbt2CA4OZiFLRES5hsUsEWXJzZs3cfDgQfXjVq1a4ciRI7CyspIwFRERFTQsZolII0IIrFu3Dg0aNECfPn3w4MED9XMymUzCZEREVBCxmCWiTIuOjka/fv0wbNgwxMfHo2nTpihSpIjUsYiIqABjMUtEmRIcHAwHBwds3boV+vr6+PXXX3Ho0CGULFlS6mhERFSAcTUDIvqiVatWYcKECUhISECZMmWwfft2NG7cWOpYREREHJkloi97+PAhEhIS0LlzZwQFBbGQJSKiPIMjs0SUJpVKBT29j3/vzp8/H7Vr14abmxsv8iIiojyFI7NElIIQAsuWLUOrVq2gUCgAAHK5HP369WMhS0REeQ6LWSJSe/fuHbp3744JEybgzJkz2LZtm9SRiIiIMsRpBkQEALhy5Qp69+6N0NBQGBoaYsmSJXB3d5c6FhERUYY4MktUwKlUKixZsgRNmzZFaGgoKlSogIsXL2LMmDGcVkBERHkei1miAm7KlCmYNGkSkpKS0KtXL1y/fh0ODg5SxyIiIsoUFrNEBdywYcNQokQJrFq1Ctu3b4e5ubnUkYiIiDKNc2aJChiVSoWLFy+iadOmAIAqVarg8ePHMDMzkzgZERGR5jgyS1SAvHr1Ci4uLmjevDlOnz6tbmchS0REuoojs0QFxJkzZ9CnTx+EhYXBxMQEYWFhUkciIiLKNo7MEuVzSqUSP/30E1q1aoWwsDBUq1YNV69eRZ8+faSORkRElG0cmSXKx8LDw9GvXz+cOHECADBw4ECsWLGC0wqIiCjfYDFLlI8dOXIEJ06cgKmpKby9vdG/f3+pIxEREWkVi1mifGzgwIF49OgR+vbti2rVqkkdh4iISOs4Z5YoH3nx4gX69euHd+/eAQBkMhl++uknFrJERJRvcWSWKJ84evQo3N3dERERAQD4888/JU5ERESU8zgyS6TjkpKSMG3aNHTo0AERERGoU6cOZs2aJXUsIiKiXMGRWSId9vTpU/Tp0wcXLlwAAHh4eGDJkiUwNjaWOBkREVHuYDFLpKMuX76Mjh074u3btzA3N4ePjw969OghdSwiIqJcxWKWSEdVrlwZZmZmKF++PPz8/FC+fHmpIxEREeU6FrNEOuTVq1coWbIkZDIZihUrhhMnTqBs2bIwMjKSOhoREZEkeAEYkY7Yu3cvqlSpgvXr16vbKlWqxEKWiIgKNBazRHlcQkICxo0bh+7du+P9+/fYsmULhBBSxyIiIsoTWMwS5WEPHz5EkyZN8PvvvwMAJk2ahGPHjkEmk0mcjIiIKG/gnFmiPGrnzp0YOnQooqKiUKxYMWzatAkdO3aUOhYREVGewmKWKA+6f/8+XF1doVKp0KRJE2zbtg1lypSROhYREVGew2KWKA+qXLkyZs6ciYSEBMydOxcGBnyrEhERpYW/IYnyiG3btsHR0RGVKlUCAN6SloiIKBN4ARiRxGJjYzF06FD07dsXvXv3Rnx8vNSRiIiIdAZHZokkdOfOHfTq1Qu3bt2CTCZD586dIZfLpY5FRESkM1jMEklk48aN8PDwQGxsLKysrLBlyxa0bt1a6lhEREQ6hcUsUS6LjY3FqFGjsGnTJgBA69at8eeff6JUqVISJyMiItI9nDNLlMsMDAxw9+5d6Onp4aeffsKxY8dYyBIREWURR2aJcoEQAkII6OnpwdDQEH5+fggNDUXz5s2ljkZERKTTODJLlMOio6PRr18/TJs2Td1Wrlw5FrJERERawJFZohwUHByMXr164cGDBzAwMMCoUaNQrlw5qWMRERHlGxyZJcoBQgh4e3ujUaNGePDgAUqXLo3Tp0+zkCUiItIyjswSaVlkZCSGDRuGnTt3AgA6deoEX19fFC9eXOJkRERE+Q+LWSItUqlUaN68Of755x8YGBjg119/xcSJEyGTyaSORkRElC9xmgGRFunp6WHy5Mmws7PD+fPn4enpyUKWiIgoB7GYJcqmd+/eITg4WP3Yzc0Nt2/fRsOGDaULRUREVECwmCXKhitXrqBu3bpwcXHB69ev1e2mpqYSpiIiIio4WMwSZYEQAkuWLEHTpk0RGhoKExMTvHr1SupYREREBQ4vACPS0Js3bzBw4EAcPHgQANCzZ0+sXbsWFhYWEicjIiIqeCQfmV25ciXs7e1hbGwMBwcHnDt3Lt1t9+zZg7Zt26JkyZIwNzeHk5MTjh07lotpqaC7cOEC6tSpg4MHD8LIyAgrV66En58fC1kiIiKJSFrM+vn5YcKECZgxYwaCgoLQrFkzdOjQAU+ePElz+7Nnz6Jt27Y4fPgwAgMD0bJlS3Tu3BlBQUG5nJwKKm9vbzx79gyVKlXC5cuXMWrUKK5WQEREJCFJpxksXboUQ4YMwdChQwEAXl5eOHbsGLy9vbFgwYJU23t5eaV4PH/+fOzfvx8HDhxA3bp1cyMyFXArV66ElZUVZs+ejcKFC0sdh4iIqMCTrJhNTExEYGAgpk6dmqLd2dkZFy9ezNQxVCoVoqOjUaxYsXS3SUhIQEJCgvpxVFQUAEChUEChUGQhuWYUiqQU/58b5yTtOXv2LPz8/ODi4gKFQgETExP88ssvAMC+1CHJfcU+013sQ93G/tN9ud2HmpxHsmI2IiICSqUSVlZWKdqtrKwQHh6eqWMsWbIEMTEx6NWrV7rbLFiwAHPmzEnVfvz48VxZPilBCSR/m0+ePAkj/Rw/JWmBUqnErl274OfnB5VKBWNjY04nyAf8/f2ljkDZxD7Ubew/3ZdbfRgbG5vpbSVfzeDzAkEIkamiYdu2bZg9ezb2798PS0vLdLebNm0aPD091Y+joqJQpkwZODs7w9zcPOvBMyk2MQlTrp4EALRq1QoWZsY5fk7KnvDwcAwcOBAnT37sNzc3NzRp0gRt27aFXC6XOB1lhUKhgL+/P/tQh7EPdRv7T/fldh8mf5KeGZIVsyVKlIC+vn6qUdhXr16lGq39nJ+fH4YMGYKdO3eiTZs2GW5rZGQEIyOjVO1yuTxXOkMu/ivM5XIDvonzuBMnTsDNzQ0vX76EqakpVq5cib59++Lw4cO59m+Gcg77UPexD3Ub+0/35Vr9pME5JFvNwNDQEA4ODqmGq/39/dG4ceN099u2bRsGDhyIrVu3omPHjjkdkwqQZcuWoW3btnj58iVq1qyJgIAADBgwQOpYRERElAFJpxl4enrC3d0djo6OcHJywpo1a/DkyROMHDkSwMcpAs+fP8emTZsAfCxk+/fvj2XLlqFRo0bqUV0TExOu80nZVr9+fejp6WHQoEFYtmwZb0lLRESkAyQtZnv37o03b95g7ty5CAsLQ82aNXH48GHY2dkBAMLCwlKsObt69WokJSVh9OjRGD16tLp9wIAB8PX1ze34lA+8fPlSPa2lcePGuHXrFqpWrSpxKiIiIsosyS8A8/DwgIeHR5rPfV6gnj59OucDUYGQlJSEH3/8Eb///juuXLmCGjVqAAALWSIiIh0jeTFLlNuePn2KPn364MKFCwCAAwcOqItZIiIi0i0sZqlAOXToEPr374+3b9/C3Nwca9euzXCdYiIiIsrbJFvNgCg3KRQKTJo0CZ06dcLbt2/h4OCA69evs5AlIiLScSxmqUDw8fHBkiVLAADjxo3DhQsXUKFCBYlTERERUXZxmgEVCEOHDsWxY8fQv39/fPPNN1LHISIiIi3hyCzlS4mJiVi0aBESEhIAAAYGBti7dy8LWSIionyGI7OU7zx69Ai9e/dGQEAAnjx5gt9//13qSERERJRDODJL+cquXbtQt25dBAQEoFixYmjXrp3UkYiIiCgHsZilfCE+Ph4eHh7o2bMnoqKi0KRJEwQHB6NTp05SRyMiIqIcxGKWdN7Dhw/h5OQEb29vAMDUqVNx6tQplClTRuJkRERElNM4Z5Z0np6eHkJCQlCiRAls3rwZ7du3lzoSERER5RIWs6STlEol9PX1AQD29vbYu3cvKleuDFtbW4mTERERUW7iNAPSOXfu3EG9evVw9OhRdVvLli1ZyBIRERVALGZJp2zatAmOjo64ceMGJk+eDJVKJXUkIiIikhCLWdIJMTExGDRoEAYMGIDY2Fi0atUK/v7+0NPjP2EiIqKCjJUA5Xm3bt1C/fr14evrCz09PcydOxfHjx9HqVKlpI5GREREEuMFYJSnPXr0CA0aNEBcXBysra2xdetWtGjRQupYRERElEewmKU8rXz58nB1dcWLFy+wadMmWFpaSh2JiIiI8hAWs5Tn/PPPP7CxsUHJkiUBAN7e3pDL5ZwfS0RERKmwOqA8QwiBVatWoWHDhujfv796pQIjIyMWskRERJQmVgiUJ0RGRsLV1RWjRo1CQkIC9PX1ERsbK3UsIiIiyuNYzJLkAgMD4eDggB07dsDAwACLFi3CX3/9hUKFCkkdjYiIiPI4zpklyQghsGLFCkyaNAmJiYmws7PD9u3b0ahRI6mjERERkY7gyCxJJiYmBsuWLUNiYiK6du2KoKAgFrJERESkEY7MkmQKFSoEPz8/nD9/HuPGjYNMJpM6EhEREekYFrOUa4QQ8PLygomJCUaOHAkAcHBwgIODg8TJiIiISFexmKVc8fbtWwwcOBAHDhyAoaEh2rZtiwoVKkgdi4iIiHQci1nKcRcvXoSrqyuePn0KIyMj/PbbbyhfvrzUsYiIiCgf4AVglGNUKhV+/fVXfP3113j69CkqVaqEy5cvY9SoUZwfS0RERFrBkVnKESqVCt26dcOBAwcAAH369MHq1atRuHBhiZMRERFRfsKRWcoRenp6cHJygrGxMdauXYstW7awkCUiIiKt48gsaY1SqURERASsrKwAAN9//z169uyJihUrSpyMiIiI8iuOzJJWvHz5Eu3bt0fr1q0RGxsL4OPoLAtZIiIiykksZinbTp48idq1a+Pvv/9GSEgIrl+/LnUkIiIiKiBYzFKWKZVKzJo1C23atMHLly9Ro0YNXLt2DU2bNpU6GhERERUQnDNLWfLixQu4ubnh9OnTAIAhQ4Zg+fLlMDU1lTYYERERFSgsZilLxo4di9OnT8PMzAyrV6+Gm5ub1JGIiIioAGIxS1myfPlyREZG4o8//kCVKlWkjkNEREQFFOfMUqY8e/YMf/zxh/qxra0t/v77bxayREREJCmOzNIXHT58GP3798ebN29ga2uLbt26SR2JiIiICABHZikDCoUCU6ZMQceOHfHmzRvUq1cPX331ldSxiIiIiNQ4MktpCg0NhaurKy5fvgzg4wVfixYtgpGRkcTJiIiIiP7DYpZSOXjwINzd3fH+/XtYWFhg/fr16N69u9SxiIiIiFJhMUupJCQk4P3792jQoAG2b98Oe3t7qSMRERERpYnFLAEAkpKSYGDw8Z/Dt99+i927d6NTp04wNDSUOBkRERFR+ngBGGHXrl2oXr06Xrx4oW7r3r07C1kiIiLK81jMFmDx8fEYPXo0evbsiQcPHmDRokVSRyIiIiLSCKcZFFAPHjxA7969ERQUBAD4/vvv8dNPP0mcioiIiEgzLGYLoO3bt2PYsGH48OEDSpQogU2bNqFDhw5SxyIiIiLSGIvZAmbTpk0YMGAAAKBZs2bYtm0bbG1tJU5FRERElDWcM1vAfPvtt6hRowZ++OEHnDx5koUsERER6TSOzBYA/v7+aN26NfT09GBmZoaAgAAYGxtLHYuIiIgo2zgym4/FxMRg0KBBcHZ2xpIlS9TtLGSJiIgov+DIbD7177//olevXrh9+zb09PSgUCikjkRERESkdSxm8xkhBDZs2IAxY8YgLi4OpUqVwrZt29CiRQupoxERERFpHYvZfOTDhw8YOXIktmzZAgBwdnbG5s2bYWlpKXEyIiIiopzBObP5yP3797Fjxw7o6+tj/vz5OHLkCAtZIiIiytc4MpuP1KtXD6tXr0alSpXQtGlTqeMQERER5TiOzOqwqKgo9O/fX31LWgAYNGgQC1kiIiIqMDgyq6OuX7+OXr164eHDhwgICMDNmzehr68vdSwiIiKiXMWRWR0jhMCKFSvg5OSEhw8fomzZsvDx8WEhS0RERAUSR2Z1yPv37zFkyBDs2bMHANClSxds2LABxYoVkzgZERERkTRYzOqIZ8+eoVmzZnj8+DHkcjkWLVqEcePGQSaTSR2NiIiISDIsZnWEjY0NKlWqBJlMBj8/P9SvX1/qSERERESSYzGbh719+xbGxsYwNTWFnp4etm7dCgMDAxQpUkTqaERERER5Ai8Ay6MuXryIOnXqYPz48eq2EiVKsJAlIiIi+gSL2TxGpVJh4cKF+Prrr/H06VOcPn0a79+/lzoWERERUZ7EYjYPef36NTp16oTvv/8eSqUSrq6uCAwM5GgsERERUTo4ZzaPOHfuHFxdXfHixQsYGxtj2bJlGDZsGFcrICIiIsoAi9k8IDY2Fj179sTLly9RpUoV7NixA7Vq1ZI6FhEREVGex2kGeYCpqSnWr18Pd3d3BAQEsJAlIiIiyiSOzErk1KlTiIuLg4uLCwDAxcVF/f9ERERElDkcmc1lSqUSs2fPRuvWreHm5oYnT55IHYmIiIhIZ0lezK5cuRL29vYwNjaGg4MDzp07l+H2Z86cgYODA4yNjVG+fHmsWrUql5JmX3h4GNq2bYs5c+ZACIHu3bujRIkSUsciIiIi0lmSFrN+fn6YMGECZsyYgaCgIDRr1gwdOnRId7QyJCQELi4uaNasGYKCgjB9+nSMGzcOu3fvzuXkmosLuY6mjRrh1KlTMDMzw+bNm+Hj4wNTU1OpoxERERHpLEnnzC5duhRDhgzB0KFDAQBeXl44duwYvL29sWDBglTbr1q1CmXLloWXlxcAoFq1aggICMDixYvx7bff5mb0TBNC4N3ZTYi6tBOAQK1atbBjxw5UqVJF6mhEREREOk+yYjYxMRGBgYGYOnVqinZnZ2dcvHgxzX0uXboEZ2fnFG3t2rWDj48PFAoF5HJ5qn0SEhKQkJCgfhwVFQUAUCgUUCgU2X0ZX5SUlARV/AcAAgMGDsbyZb/BxMQkV85N2pHcV+wz3cU+1H3sQ93G/tN9ud2HmpxHsmI2IiICSqUSVlZWKdqtrKwQHh6e5j7h4eFpbp+UlISIiAhYW1un2mfBggWYM2dOqvbjx4/nykf8CUqgWKuhMCnvCJfO9XDq1KkcPyflDH9/f6kjUDaxD3Uf+1C3sf90X271YWxsbKa3lXxprs/vcCWEyPCuV2ltn1Z7smnTpsHT01P9OCoqCmXKlIGzszPMzc2zGjvThBBo1SoBJ0/qoWO7NjA0NMzxc5J2KRQK+Pv7o23btmmO/lPexz7UfexD3cb+03253YfJn6RnhmTFbIkSJaCvr59qFPbVq1epRl+TlSpVKs3tDQwMULx48TT3MTIygpGRUap2uVyea28oC5kMRvqAoaEh38Q6LDf/zVDOYB/qPvahbmP/6b7c6kNNziHZagaGhoZwcHBINVzt7++Pxo0bp7mPk5NTqu2PHz8OR0dHvjmIiIiICiBJl+by9PTEunXrsH79ety5cwcTJ07EkydPMHLkSAAfpwj0799fvf3IkSMRGhoKT09P3LlzB+vXr4ePjw8mTZok1UsgIiIiIglJOme2d+/eePPmDebOnYuwsDDUrFkThw8fhp2dHQAgLCwsxZqz9vb2OHz4MCZOnIg//vgDNjY2WL58eZ5dlouIiIiIcpbkF4B5eHjAw8Mjzed8fX1TtTVv3hzXr1/P4VREREREpAskv50tEREREVFWsZglIiIiIp3FYpaIiIiIdBaLWSIiIiLSWSxmiYiIiEhnsZglIiIiIp3FYpaIiIiIdBaLWSIiIiLSWSxmiYiIiEhnsZglIiIiIp3FYpaIiIiIdBaLWSIiIiLSWSxmiYiIiEhnGUgdILcJIQAAUVFRuXZOhUKB2NhYREVFQS6X59p5STvYf7qPfaj72Ie6jf2n+3K7D5PrtOS6LSMFrpiNjo4GAJQpU0biJERERESUkejoaFhYWGS4jUxkpuTNR1QqFV68eIHChQtDJpPlyjmjoqJQpkwZPH36FObm5rlyTtIe9p/uYx/qPvahbmP/6b7c7kMhBKKjo2FjYwM9vYxnxRa4kVk9PT2ULl1aknObm5vzTazD2H+6j32o+9iHuo39p/tysw+/NCKbjBeAEREREZHOYjFLRERERDqLxWwuMDIywqxZs2BkZCR1FMoC9p/uYx/qPvahbmP/6b683IcF7gIwIiIiIso/ODJLRERERDqLxSwRERER6SwWs0RERESks1jMEhEREZHOYjGrBStXroS9vT2MjY3h4OCAc+fOZbj9mTNn4ODgAGNjY5QvXx6rVq3KpaSUHk36cM+ePWjbti1KliwJc3NzODk54dixY7mYltKi6fsw2YULF2BgYIA6derkbED6Ik37MCEhATNmzICdnR2MjIxQoUIFrF+/PpfS0uc07b8tW7agdu3aMDU1hbW1NQYNGoQ3b97kUlr63NmzZ9G5c2fY2NhAJpNh3759X9wnz9QzgrJl+/btQi6Xi7Vr14rbt2+L8ePHCzMzMxEaGprm9o8ePRKmpqZi/Pjx4vbt22Lt2rVCLpeLXbt25XJySqZpH44fP178+uuv4urVq+L+/fti2rRpQi6Xi+vXr+dyckqmaR8me//+vShfvrxwdnYWtWvXzp2wlKas9GGXLl1Ew4YNhb+/vwgJCRFXrlwRFy5cyMXUlEzT/jt37pzQ09MTy5YtE48ePRLnzp0TNWrUEN26dcvl5JTs8OHDYsaMGWL37t0CgNi7d2+G2+eleobFbDY1aNBAjBw5MkVb1apVxdSpU9PcfsqUKaJq1aop2kaMGCEaNWqUYxkpY5r2YVqqV68u5syZo+1olElZ7cPevXuLH374QcyaNYvFrMQ07cMjR44ICwsL8ebNm9yIR1+gaf8tWrRIlC9fPkXb8uXLRenSpXMsI2VeZorZvFTPcJpBNiQmJiIwMBDOzs4p2p2dnXHx4sU097l06VKq7du1a4eAgAAoFIocy0ppy0offk6lUiE6OhrFihXLiYj0BVntww0bNuDhw4eYNWtWTkekL8hKH/71119wdHTEwoULYWtri8qVK2PSpEmIi4vLjcj0iaz0X+PGjfHs2TMcPnwYQgi8fPkSu3btQseOHXMjMmlBXqpnDHL1bPlMREQElEolrKysUrRbWVkhPDw8zX3Cw8PT3D4pKQkRERGwtrbOsbyUWlb68HNLlixBTEwMevXqlRMR6Quy0ocPHjzA1KlTce7cORgY8Meg1LLSh48ePcL58+dhbGyMvXv3IiIiAh4eHnj79i3nzeayrPRf48aNsWXLFvTu3Rvx8fFISkpCly5d8Pvvv+dGZNKCvFTPcGRWC2QyWYrHQohUbV/aPq12yj2a9mGybdu2Yfbs2fDz84OlpWVOxaNMyGwfKpVK9O3bF3PmzEHlypVzKx5lgibvQ5VKBZlMhi1btqBBgwZwcXHB0qVL4evry9FZiWjSf7dv38a4ceMwc+ZMBAYG4ujRowgJCcHIkSNzIyppSV6pZzgkkQ0lSpSAvr5+qr88X716leqvlWSlSpVKc3sDAwMUL148x7JS2rLSh8n8/PwwZMgQ7Ny5E23atMnJmJQBTfswOjoaAQEBCAoKwpgxYwB8LIyEEDAwMMDx48fRqlWrXMlOH2XlfWhtbQ1bW1tYWFio26pVqwYhBJ49e4ZKlSrlaGb6T1b6b8GCBWjSpAkmT54MAKhVqxbMzMzQrFkz/Pzzz/yUUgfkpXqGI7PZYGhoCAcHB/j7+6do9/f3R+PGjdPcx8nJKdX2x48fh6OjI+RyeY5lpbRlpQ+BjyOyAwcOxNatWznHS2Ka9qG5uTlu3ryJ4OBg9dfIkSNRpUoVBAcHo2HDhrkVnf5fVt6HTZo0wYsXL/Dhwwd12/3796Gnp4fSpUvnaF5KKSv9FxsbCz29lCWIvr4+gP9G9yhvy1P1TK5fcpbPJC9H4uPjI27fvi0mTJggzMzMxOPHj4UQQkydOlW4u7urt09eymLixIni9u3bwsfHh0tzSUzTPty6daswMDAQf/zxhwgLC1N/vX//XqqXUOBp2oef42oG0tO0D6Ojo0Xp0qVFjx49xL///ivOnDkjKlWqJIYOHSrVSyjQNO2/DRs2CAMDA7Fy5Urx8OFDcf78eeHo6CgaNGgg1Uso8KKjo0VQUJAICgoSAMTSpUtFUFCQenm1vFzPsJjVgj/++EPY2dkJQ0NDUa9ePXHmzBn1cwMGDBDNmzdPsf3p06dF3bp1haGhoShXrpzw9vbO5cT0OU36sHnz5gJAqq8BAwbkfnBS0/R9+CkWs3mDpn14584d0aZNG2FiYiJKly4tPD09RWxsbC6npmSa9t/y5ctF9erVhYmJibC2thZubm7i2bNnuZyakp06dSrD3215uZ6RCcHxfCIiIiLSTZwzS0REREQ6i8UsEREREeksFrNEREREpLNYzBIRERGRzmIxS0REREQ6i8UsEREREeksFrNEREREpLNYzBIRERGRzmIxS0QEwNfXF0WKFJE6RpaVK1cOXl5eGW4ze/Zs1KlTJ1fyEBHlFhazRJRvDBw4EDKZLNXX//73P6mjwdfXN0Uma2tr9OrVCyEhIVo5/rVr1zB8+HD1Y5lMhn379qXYZtKkSThx4oRWzpeez1+nlZUVOnfujH///Vfj4+jyHxdElHtYzBJRvtK+fXuEhYWl+LK3t5c6FgDA3NwcYWFhePHiBbZu3Yrg4GB06dIFSqUy28cuWbIkTE1NM9ymUKFCKF68eLbP9SWfvs5Dhw4hJiYGHTt2RGJiYo6fm4gKHhazRJSvGBkZoVSpUim+9PX1sXTpUnz11VcwMzNDmTJl4OHhgQ8fPqR7nH/++QctW7ZE4cKFYW5uDgcHBwQEBKifv3jxIr7++muYmJigTJkyGDduHGJiYjLMJpPJUKpUKVhbW6Nly5aYNWsWbt26pR459vb2RoUKFWBoaIgqVapg8+bNKfafPXs2ypYtCyMjI9jY2GDcuHHq5z6dZlCuXDkAwDfffAOZTKZ+/Ok0g2PHjsHY2Bjv379PcY5x48ahefPmWnudjo6OmDhxIkJDQ3Hv3j31Nhn1x+nTpzFo0CBERkaqR3hnz54NAEhMTMSUKVNga2sLMzMzNGzYEKdPn84wDxHlbyxmiahA0NPTw/Lly3Hr1i1s3LgRJ0+exJQpU9Ld3s3NDaVLl8a1a9cQGBiIqVOnQi6XAwBu3ryJdv/Xzt2FNNnGYQC/nHO0pvbhQSWaojL0oKJBZUYHlaEsMhaOypEiWVquwj6ITloQBiF+RFB2EBPFUCkXQkWkmaUFmRJmRUwSiVIisqI0dfp/D158aLo+NHl7N64feHB/PLf/2xvkYj63SUnYunUrOjo6UF1djebmZlit1inVpNVqAQAjIyNwOBw4ePAgDh8+jM7OTmRnZyMzMxONjY0AgCtXrqC4uBgXL16E0+nEtWvXsGTJEo/rtra2AgDsdjt6e3uV9vcSExMxd+5cXL16VekbHR1FTU0NLBbLjO3z48ePuHz5MgAoPz/g5+eRkJCAkpIS5RPe3t5eHDlyBACQmZmJlpYWVFVVoaOjA2azGcnJyXA6nb9dExH5GCEi8hEZGRni7+8vOp1O+UpNTfU4t6amRkJCQpS23W6XOXPmKO2goCApKyvz+OzOnTtlz549bn33798XlUolg4ODHp+ZuP7r168lPj5ewsLCZGhoSBISEmT37t1uz5jNZjEajSIiUlhYKHq9XoaHhz2uHxERIcXFxUobgDgcDrc5NptNli1bprQPHDgg69evV9q3bt0SjUYjHz58+KN9AhCdTiezZ88WAAJAUlJSPM4f96vzEBHp6uoSPz8/efPmjVv/hg0b5Pjx4z9dn4h8l/rvRmkiopm1bt06XLhwQWnrdDoAQGNjI06fPo3nz5/j8+fPcLlc+PbtG75+/arM+d6hQ4eQlZWFiooKJCYmwmw2Izo6GgDQ1taGrq4uVFZWKvNFBGNjY+ju7kZcXJzH2j59+oTAwECICAYGBmAwGFBbWwuNRoMXL164XeACgDVr1uDs2bMAALPZjJKSEkRFRSE5ORlGoxGbN2+GWj39X+MWiwWrV6/G27dvERoaisrKShiNRsybN++P9hkUFIT29na4XC40NTWhoKAApaWlbnOmeh4A0N7eDhGBXq936x8aGvpP3gUmov8nhlki8ik6nQ4xMTFufT09PTAajcjJycGpU6cwf/58NDc3Y9euXRgZGfG4zsmTJ5GWlobr16/j5s2bsNlsqKqqgslkwtjYGLKzs93eWR23ePHiH9Y2HvJUKhUWLFgwKbT5+fm5tUVE6QsPD8fLly9x+/Zt1NfXY9++fSgoKEBTU5Pbn++nYuXKlYiOjkZVVRX27t0Lh8MBu92ujE93nyqVSjmD2NhY9PX1Ydu2bbh37x6A6Z3HeD3+/v5oa2uDv7+/21hgYOCU9k5EvoNhloh83uPHj+FyuVBYWAiV6t+rAjU1Nb98Tq/XQ6/XIy8vDzt27IDdbofJZILBYMCzZ88mheZf+T7kTRQXF4fm5makp6crfQ8ePHD79FOr1SIlJQUpKSnIzc1FbGwsnj59CoPBMGm9gICA3/ovCWlpaaisrERYWBhUKhU2bdqkjE13nxPl5eWhqKgIDocDJpPpt85Do9FMqn/58uUYHR3Fu3fvsHbt2j+qiYh8By+AEZHPi46Ohsvlwrlz5/Dq1StUVFRM+rP39wYHB2G1WnH37l309PSgpaUFra2tSrA8duwYHj58iNzcXDx58gROpxN1dXXYv3//tGs8evQoysrKUFpaCqfTiaKiItTW1ioXn8rKynDp0iV0dnYqe9BqtYiIiPC4XmRkJBoaGtDX14f+/v4ffl+LxYL29nbk5+cjNTUVs2bNUsZmap/BwcHIysqCzWaDiPzWeURGRuLLly9oaGjA+/fvMTAwAL1eD4vFgvT0dNTW1qK7uxutra04c+YMbty4MaWaiMiH/M0XdomIZlJGRoZs2bLF41hRUZEsWrRItFqtJCUlSXl5uQCQ/v5+EXG/cDQ0NCTbt2+X8PBw0Wg0EhoaKlar1e3S06NHj2Tjxo0SGBgoOp1Oli5dKvn5+T+szdOFponOnz8vUVFREhAQIHq9XsrLy5Uxh8Mhq1atkuDgYNHpdBIfHy/19fXK+MQLYHV1dRITEyNqtVoiIiJEZPIFsHErVqwQAHLnzp1JYzO1z56eHlGr1VJdXS0ivz4PEZGcnBwJCQkRAGKz2UREZHh4WE6cOCGRkZESEBAgCxcuFJPJJB0dHT+siYh8m5+IyN+N00RERERE08PXDIiIiIjIazHMEhEREZHXYpglIiIiIq/FMEtEREREXothloiIiIi8FsMsEREREXkthlkiIiIi8loMs0RERETktRhmiYiIiMhrMcwSERERkddimCUiIiIir/UPfZOp6vklW90AAAAASUVORK5CYII=",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -1383,20 +1461,17 @@
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:05<00:00,  9.45it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:02<00:00,  9.45it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:02<00:00,  9.39it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.47it/s]\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\n",
+      "     - AUC: 89.75%\n",
+      "    - Optimal Threshold: 0.0412511\n",
+      "    - F1 Score: 0.87\n",
+      "    - CONFUSION MATRIX:\n",
+      " [[18  2]\n",
+      " [17 63]] \n",
       "\n"
      ]
     },
@@ -1404,9 +1479,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:08<00:00,  9.49it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.50it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.49it/s]\n"
+      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:04<00:00,  9.14it/s]\n",
+      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.16it/s]\n",
+      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.15it/s]\n",
+      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.16it/s]\n"
      ]
     },
     {
@@ -1420,29 +1496,27 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:12<00:00,  9.48it/s]\n"
+      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.17it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (76.28 s)\n",
       "\n",
-      "Dataset               Accuracy\n",
+      "- OK - Evaluating model (33.06 s)\n",
+      "\n",
+      "Dataset               F1 Score\n",
       "------------------------------\n",
-      "Anonaly lvl 1           100.00\n",
-      "Anonaly lvl 2            52.00\n",
-      "Anonaly lvl 3            56.00\n",
+      "Anonaly lvl 1 test        0.99\n",
+      "Anonaly lvl 2 test        0.82\n",
+      "Anonaly lvl 3 test        0.67\n",
       "\n",
-      "Anomaly all              77.00\n",
+      "Anomaly all test          0.88\n",
       "\n",
-      "No Anomaly Train         98.75\n",
-      "No Anomaly Test          85.00\n",
-      "No Anomaly All           96.00\n",
+      "No Anomaly Test           0.95\n",
       "\n",
-      "All without train        78.33\n",
-      "All with train           86.50\n"
+      "All test                  0.87\n"
      ]
     }
    ],
@@ -1455,7 +1529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -1463,20 +1537,20 @@
      "output_type": "stream",
      "text": [
       "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (0.67 ms)\n",
+      "- OK - Setting seed to 42 (0.27 ms)\n",
       "\n",
       "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (2.89 ms)\n",
+      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
+      "- OK - Setting datasets path (4.74 ms)\n",
       "\n",
       "- Setting config\n",
       "     Output folder path: ../output/cookies_3_steps_5000_medium\n",
-      "- OK - Setting config (0.10 ms)\n",
+      "- OK - Setting config (0.12 ms)\n",
       "\n",
       "- Prepare teacher, student & autoencoder\n",
       "     No weight to load\n",
       "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (153.69 ms)\n",
+      "- OK - Prepare teacher, student & autoencoder (160.22 ms)\n",
       "\n",
       "- Normalizing teacher\n"
      ]
@@ -1485,15 +1559,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 22.62it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 22.93it/s]\n"
+      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.55it/s]\n",
+      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.52it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (6.33 s)\n",
+      "- OK - Normalizing teacher (6.69 s)\n",
       "\n",
       "- Train\n"
      ]
@@ -1502,430 +1576,2648 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 0.7681  : 100%|███████████████████████████████████████████████████████| 5000/5000 [26:42<00:00,  3.12it/s]\n"
+      "       Current loss: 91.8934  :   0%|                                                                                  | 1/5000 [00:03<4:58:50,  3.59s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (1602.36 s)\n",
-      "\n",
-      "- Saving models to ../output/cookies_3_steps_5000_medium/all_models.pth\n",
-      "- OK - Saving models (199.26 ms)\n",
-      "\n",
-      "- Saving map normalization to ../output/cookies_3_steps_5000_medium/map_normalization.pth\n"
+      "F1 Validation 0.5517241379310345\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 11.35it/s]\n"
+      "       Current loss: 10.9421  :   2%|█▌                                                                              | 101/5000 [00:39<1:43:11,  1.26s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (798.86 ms)\n",
-      "\n",
-      "- Evaluating model\n"
+      "F1 Validation 0.8108108108108109\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:21<00:00,  9.41it/s]\n"
+      "       Current loss: 7.5011  :   4%|███▎                                                                             | 201/5000 [01:16<1:41:18,  1.27s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "     - AUC: 99.14%\n",
-      "    - Optimal Threshold: 0.0991140\n",
-      "    - F1 Score: 0.96\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[97  3]\n",
-      " [ 5 95]] \n",
-      "\n"
+      "F1 Validation 0.75\n"
      ]
     },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:05<00:00,  9.42it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:02<00:00,  9.41it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:02<00:00,  9.41it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.42it/s]\n"
+      "       Current loss: 7.5772  :   6%|████▉                                                                            | 301/5000 [01:52<1:39:31,  1.27s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.7647058823529411\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:08<00:00,  9.43it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.38it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.39it/s]\n"
+      "       Current loss: 11.0889  :   8%|██████▍                                                                         | 401/5000 [02:28<1:37:15,  1.27s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.8333333333333334\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:12<00:00,  9.41it/s]\n"
+      "       Current loss: 6.0276  :  10%|████████                                                                         | 501/5000 [03:05<1:34:52,  1.27s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (76.74 s)\n",
-      "\n",
-      "Dataset               Accuracy\n",
-      "------------------------------\n",
-      "Anonaly lvl 1           100.00\n",
-      "Anonaly lvl 2            84.00\n",
-      "Anonaly lvl 3            96.00\n",
+      "F1 Validation 0.9\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 10.1115  :  12%|█████████▌                                                                      | 601/5000 [03:41<1:33:16,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.7647058823529411\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 3.4369  :  14%|███████████▎                                                                     | 701/5000 [04:18<1:31:02,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 5.4180  :  16%|████████████▉                                                                    | 801/5000 [04:54<1:28:50,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8333333333333334\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 4.4896  :  18%|██████████████▌                                                                  | 901/5000 [05:31<1:26:41,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8717948717948718\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 2.0717  :  20%|████████████████                                                                | 1001/5000 [06:07<1:24:38,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.7647058823529411\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 2.3179  :  22%|█████████████████▌                                                              | 1101/5000 [06:43<1:22:30,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.7096774193548387\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 2.6633  :  24%|███████████████████▏                                                            | 1201/5000 [07:20<1:20:23,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 2.8343  :  26%|████████████████████▊                                                           | 1301/5000 [07:56<1:19:08,  1.28s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.7272727272727273\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 3.3417  :  28%|██████████████████████▍                                                         | 1401/5000 [08:33<1:16:12,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9047619047619048\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 2.0842  :  30%|████████████████████████                                                        | 1501/5000 [09:09<1:14:08,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.7096774193548387\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 3.1276  :  32%|█████████████████████████▌                                                      | 1601/5000 [09:46<1:11:43,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 2.1818  :  34%|███████████████████████████▏                                                    | 1701/5000 [10:22<1:09:46,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8780487804878049\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 2.2747  :  36%|████████████████████████████▊                                                   | 1801/5000 [10:59<1:10:19,  1.32s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8717948717948718\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 3.8479  :  38%|██████████████████████████████▍                                                 | 1901/5000 [11:35<1:05:27,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8333333333333334\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 7.6651  :  40%|████████████████████████████████                                                | 2001/5000 [12:12<1:03:31,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9047619047619048\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 2.1156  :  42%|█████████████████████████████████▌                                              | 2101/5000 [12:48<1:01:26,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 1.9430  :  44%|████████████████████████████████████                                              | 2201/5000 [13:25<59:14,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8717948717948718\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 1.5055  :  46%|█████████████████████████████████████▋                                            | 2301/5000 [14:01<57:02,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.926829268292683\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 2.7284  :  48%|███████████████████████████████████████▍                                          | 2401/5000 [14:38<55:06,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9047619047619048\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 3.6469  :  50%|█████████████████████████████████████████                                         | 2501/5000 [15:14<53:01,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.926829268292683\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 3.2708  :  52%|██████████████████████████████████████████▋                                       | 2601/5000 [15:51<51:03,  1.28s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 3.8423  :  54%|████████████████████████████████████████████▎                                     | 2701/5000 [16:27<48:37,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.7272727272727273\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 1.4583  :  56%|█████████████████████████████████████████████▉                                    | 2801/5000 [17:04<46:40,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.7777777777777778\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 2.9438  :  58%|███████████████████████████████████████████████▌                                  | 2901/5000 [17:41<44:36,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.7777777777777778\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 1.4852  :  60%|█████████████████████████████████████████████████▏                                | 3001/5000 [18:17<42:19,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8333333333333334\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 1.7922  :  62%|██████████████████████████████████████████████████▊                               | 3101/5000 [18:54<40:32,  1.28s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 1.9035  :  64%|████████████████████████████████████████████████████▍                             | 3201/5000 [19:30<38:03,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 0.9275  :  66%|██████████████████████████████████████████████████████▏                           | 3301/5000 [20:06<35:54,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.6666666666666666\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 1.6014  :  68%|███████████████████████████████████████████████████████▊                          | 3401/5000 [20:43<33:54,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.7272727272727273\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 1.8778  :  70%|█████████████████████████████████████████████████████████▍                        | 3501/5000 [21:20<31:48,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.7272727272727273\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 4.0031  :  72%|███████████████████████████████████████████████████████████                       | 3601/5000 [21:56<30:53,  1.33s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8333333333333334\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 1.2849  :  74%|████████████████████████████████████████████████████████████▋                     | 3701/5000 [22:33<27:28,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 1.8299  :  76%|██████████████████████████████████████████████████████████████▎                   | 3800/5000 [23:09<07:18,  2.73it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8\n",
+      "Early stopping at iteration 3801 because validation F1 did not improve.\n",
+      "- OK - Train (1389.47 s)\n",
+      "\n",
+      "- Saving models to ../output/cookies_3_steps_5000_medium/all_models.pth\n",
+      "- OK - Saving models (148.95 ms)\n",
+      "\n",
+      "- Saving map normalization to ../output/cookies_3_steps_5000_medium/map_normalization.pth\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 10.29it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- OK - Saving map normalization (2820.04 ms)\n",
+      "\n",
+      "- Evaluating model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.13it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIhCAYAAABdSTJTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACJ80lEQVR4nOzdd1iT198G8DuBsAUHMlVEK86qCA4ctQ5wj1oHigP3nlXraFW01rZW694DtYq4rdaFq+6FULXOKm5QcYHMkJz3D3/kLTIkGPIQuD/XxdXm5Bl3OAS/nJznPDIhhAARERERkQGSSx2AiIiIiCinWMwSERERkcFiMUtEREREBovFLBEREREZLBazRERERGSwWMwSERERkcFiMUtEREREBovFLBEREREZLBazRERERGSwWMwSEWUgMDAQMplM82VsbAxHR0f4+vrizp07Ge6jVCqxdOlSeHl5wcbGBubm5qhYsSImTJiAly9fZriPWq3Ghg0b0LRpU9ja2kKhUMDOzg6tW7fGnj17oFarP5o1KSkJixYtQv369VGkSBGYmJjA2dkZnTt3xl9//fVJ3wcioryOxSwRURbWrl2Ls2fP4vDhwxg2bBj++OMP1K9fH69fv06zXXx8PLy9vTF8+HC4u7sjKCgI+/btQ48ePbBixQq4u7vj1q1bafZJTExEy5Yt0atXL9jZ2WHp0qU4evQoli1bBicnJ3Tq1Al79uzJMl90dDTq1auHMWPGoEqVKggMDMSRI0cwZ84cGBkZoUmTJvj77791/n0hIsozBBERpbN27VoBQFy8eDFNe0BAgAAg1qxZk6Z9wIABAoDYvHlzumPdunVL2NjYiMqVK4uUlBRN++DBgwUAsW7dugwz3L59W/z9999Z5mzRooUwNjYWR44cyfD5CxcuiAcPHmR5jOyKj4/XyXGIiHSJI7NERFrw9PQEADx79kzTFhUVhTVr1qBZs2bo0qVLun3c3Nzw7bff4p9//sGuXbs0+6xatQrNmjVDz549MzxXuXLlULVq1UyzhIaGYv/+/ejbty8aN26c4TY1a9ZEqVKlAADTpk2DTCZLt03qlIr79+9r2kqXLo3WrVtjx44dcHd3h5mZGQICAuDu7o4GDRqkO4ZKpYKzszM6dOigaUtOTsYPP/yAChUqwNTUFMWLF0fv3r3x4sWLTF8TEZG2WMwSEWkhIiICwPsCNdWxY8eQkpKC9u3bZ7pf6nMhISGafZRKZZb7fMyhQ4fSHFvXLl++jHHjxmHEiBE4cOAAvv76a/Tu3RunTp1KN2/40KFDePr0KXr37g3g/Vzgdu3a4aeffkK3bt3w559/4qeffkJISAi+/PJLJCQk5EpmIip4jKUOQESUl6lUKqSkpCAxMRGnT5/GDz/8gC+++AJt27bVbPPw4UMAgKura6bHSX0uddvs7PMxujhGVp4/f47r16+nKdzLlCmDcePGITAwEDNnztS0BwYGwt7eHi1atAAAbNmyBQcOHMD27dvTjNZWq1YNNWvWRGBgIAYPHpwruYmoYOHILBFRFurUqQOFQoFChQqhefPmKFKkCHbv3g1j45yNBWT0MX9eVbVq1TSFLAAUK1YMbdq0wbp16zQrLbx+/Rq7d+9Gz549Nd+XvXv3onDhwmjTpg1SUlI0X9WrV4eDgwOOHz+u75dDRPkUi1kioiysX78eFy9exNGjRzFw4EDcuHEDXbt2TbNN6pzU1CkIGUl9rmTJktne52N0cYysODo6Ztjep08fPHnyRDNlIigoCElJSfD399ds8+zZM7x58wYmJiZQKBRpvqKiohAdHZ0rmYmo4GExS0SUhYoVK8LT0xONGjXCsmXL0K9fPxw4cADbtm3TbNOoUSMYGxtrLu7KSOpz3t7emn0UCkWW+3xMs2bN0hz7Y8zMzAC8X5f2vzIrLDMbRW7WrBmcnJywdu1aAO+XL6tduzYqVaqk2cbW1hbFihXDxYsXM/xasmRJtjITEX0Mi1kiIi388ssvKFKkCKZMmaL5mN3BwQF9+vTBwYMHERwcnG6f27dv4+eff0blypU1F2s5ODigX79+OHjwINavX5/hue7evYsrV65kmqVGjRpo0aIFVq9ejaNHj2a4zaVLlzRza0uXLg0A6Y75sbVsP2RkZIQePXpg165dOHnyJC5duoQ+ffqk2aZ169Z4+fIlVCoVPD09032VL19eq3MSEWVGJoQQUocgIsprAgMD0bt3b1y8eFGzHFeq2bNnY/z48diwYQO6d+8OAIiLi0OrVq1w+vRpDBgwAG3atIGpqSnOnTuHX3/9FRYWFjh8+HCaIi4xMRHt27fHoUOH0LVrV3z11Vewt7dHdHQ0QkJCsHbtWmzevBnt2rXLNGd0dDSaN2+Oq1evok+fPmjRogWKFCmCyMhI7NmzB0FBQQgNDUW1atUQExMDV1dXODs7Y/r06TA2NkZgYCAuX76MiIgIREREaAre0qVLo0qVKti7d2+G5719+zbKly+PEiVK4OXLl4iMjISNjY3meZVKhTZt2uD8+fMYOXIkatWqBYVCgcePH+PYsWNo164dvvrqq5x2DxHR/5N6oVsiorwos5smCCFEQkKCKFWqlChXrlyamyAkJyeLxYsXi9q1awsrKythamoqypcvL8aPHy+io6MzPE9KSopYt26daNy4sShatKgwNjYWxYsXFy1atBCbNm0SKpXqo1kTEhLEggULhJeXl7C2thbGxsbCyclJdOjQQfz5559ptr1w4YKoW7eusLS0FM7OzmLq1Kli1apVAoCIiIjQbOfi4iJatWqV5Xnr1q0rAAg/P78Mn1cqleLXX38V1apVE2ZmZsLKykpUqFBBDBw4UNy5c+ejr4uIKDs4MktEREREBotzZomIiIjIYLGYJSIiIiKDxWKWiIiIiAwWi1kiIiIiMlgsZomIiIjIYLGYJSIiIiKDZSx1AH1Tq9V4+vQpChUqlOmtGomIiIhIOkIIxMbGwsnJCXJ51mOvBa6Yffr0KUqWLCl1DCIiIiL6iEePHqFEiRJZblPgitlChQoBeP/Nsba21ss5lUolDh06BB8fHygUCr2ck3SH/Wf42IeGj31o2Nh/hk/ffRgTE4OSJUtq6rasFLhiNnVqgbW1tV6LWQsLC1hbW/NNbIDYf4aPfWj42IeGjf1n+KTqw+xMCeUFYERERERksFjMEhEREZHBYjFLRERERAaLxSwRERERGSwWs0RERERksFjMEhEREZHBYjFLRERERAaLxSwRERERGSwWs0RERERksFjMEhEREZHBYjFLRERERAaLxSwRERERGSwWs0RERERksFjMEhEREZHBkrSYPXHiBNq0aQMnJyfIZDLs2rXro/v89ddf8PDwgJmZGcqUKYNly5blflAiIiIiypMkLWbj4uJQrVo1LFq0KFvbR0REoGXLlmjQoAHCwsIwadIkjBgxAtu3b8/lpERERESUFxlLefIWLVqgRYsW2d5+2bJlKFWqFObNmwcAqFixIi5duoRff/0VX3/9dS6lJCIiok8hhECSCohPToFCyKSOQzmQkJCIJNX7vsxrJC1mtXX27Fn4+PikaWvWrBlWr14NpVIJhUKRbp+kpCQkJSVpHsfExAAAlEollEpl7gb+n9Tz6Ot8pFvsP8PHPjR87EPDJYRAlxXnEfbYGOMvHJU6DuVA/J3zeH10Fex9f0DjxkmwkeX+HyTavNcNqpiNioqCvb19mjZ7e3ukpKQgOjoajo6O6faZNWsWAgIC0rUfOnQIFhYWuZY1IyEhIXo9H+kW+8/wsQ8NH/vQ8CSpgLDHBlVu0P8IlRKvjwci9tJuAMDbs1tx9GgxmBrl/rnj4+Ozva3B/XTJPvhrIHW4+8P2VBMnTsSYMWM0j2NiYlCyZEn4+PjA2to694L+h1KpREhICLy9vTMcPaa8jf1n+NiHho99aLjik1M0I7Inv6kHawtTiRNRdty/fx99/Xvi4aVLAICBg4fgi4aN0KpZU5iYmOT6+VM/Sc8OgypmHRwcEBUVlabt+fPnMDY2RrFixTLcx9TUFKam6d84CoVC778QpTgn6Q77z/CxDw0f+9Dw/HeOrLWFKWwszSVMQ9mxY8cO9OnTB2/fvkWRIkUQGBiIFi1aYN++fTAxMdHLe1CbcxhUMevl5YU9e/akaTt06BA8PT35y42IiHRGCIEEpUrqGPlCfDK/j4bk9evX6Nu3L96+fQsvLy8EBQXBxcUlT89Xl7SYfffuHf7991/N44iICISHh6No0aIoVaoUJk6ciCdPnmD9+vUAgEGDBmHRokUYM2YM+vfvj7Nnz2L16tUICgqS6iUQEVE+I4RAx2VnEfrgtdRRiPSuSJEiWLt2Lc6ePYsffvjBIAYLJS1mL126hEaNGmkep85t7dWrFwIDAxEZGYmHDx9qnnd1dcW+ffswevRoLF68GE5OTliwYAGX5SIiIp1JUKpYyOYC10IC5go9XDlEWtuyZQusra3RvHlzAED79u3Rvn17aUNpQdJi9ssvv8xyvbLAwMB0bQ0bNsTly5dzMRUREdF7l75rCgsTFmCfSqlU4ljIoUwv1iZpJCQkYMyYMVi2bBmKFSuGK1euwMnJSepYWjOoObNERET6ZGFiBAsT/lP5qZQyAdaxecutW7fQuXNnXLlyBTKZDIMGDYKdnZ3UsXKE71AiItKavi+QUipT9HYHKV6wRPndxo0bMXDgQMTFxcHOzg6///47vL29pY6VYyxmiYhIK9JdIMU7SBF9CpVKhYEDB2L16tUAgEaNGmHjxo0Z3nTKkMilDkBERIaloFwg5elShBcsUb5iZPT+51kmk2HatGkICQkx+EIW4MgsERF9An1dIKVUKnHw4CE0a+ajt6WCzBVGvGCJ8oXExESYmZkBABYsWAB/f3/Ur19f4lS6w2KWiIhyTF8XSCllAqZGgIWJMRQK/tNFlB3v3r3D0KFDERUVhf3790Mul8PCwiJfFbIAi1kiIp3L73eP4gVSRHnf1atX0blzZ9y8eRNyuRznzp1D3bp1pY6VK1jMEhHpEO8eRURSEkJg1apVGDFiBBITE+Hs7IygoKB8W8gCLGaJiHSqoFwcBfACKaK8JiYmBgMHDsTmzZsBAC1atMD69etha2srcbLcxWKWiCiX5Pe7R/ECKaK8xdfXF/v374eRkRFmzZqFb775BnJ5/l+4isUsEVEu4d2jiEifZs6ciX///Rfr1q2Dl5eX1HH0Jv+X60RERET50Nu3b7F//37NY3d3d1y/fr1AFbIAi1kiIiIig3Pp0iXUqFED7dq1w6VLlzTtxsYF79MgFrNEREREBkIIgfnz56Nu3bq4d+8enJ2dpY4kuYJXvhMREREZoNevX6NPnz7YtWsXAKBDhw5YvXo1ChcuLGkuqXFkloiIiCiPO3/+PNzd3bFr1y6YmJhg4cKF2LZtW4EvZAGOzBIRERHleX/99RcePHiAsmXLYsuWLahRo4bUkfIMFrNEpHf6vt2rUpmCJBUQn5wChcjddVF5q1ciyg1jx46FTCbDwIEDYW1tLXWcPIXFLBHplXS3ezXG+AtH9XxOIqKcOXXqFGbMmIEdO3bA0tIScrkc48aNkzpWnsQ5s0SkVwXldq+81SsR5YRarcasWbPw5Zdf4tChQ5g5c6bUkfI8jswSkWT0dbtXpVKJgwcPoVkzHygUilw/H8BbvRKR9p4/f44ePXrg0KFDAIDu3btj0qRJEqfK+1jMEpFk9HW7V6VMwNQIsDAxhkLBX3tElPccP34c3bp1Q2RkJMzNzbFo0SL07t2bfxRnA3+rE5FeL8jiBVJERGn9/vvv6NWrF9RqNSpVqoQtW7agcuXKUscyGCxmiQo46S7IIiIiAGjcuDGKFSuG1q1bY+HChbC0tJQ6kkFhMUtUwEl1QRYvkCKiguz27dtwc3MDADg5OeHvv/+Go6OjxKkME4tZItLQ1wVZAC+QIqKCKSUlBdOnT8fMmTOxZcsWfP311wDAQvYTsJglIg19XZBFRFQQPXnyBN26dcOJEycAAOfOndMUs5Rz/FeLiIiIKJcdOHAAPXr0QHR0NKysrLBy5Ur4+vpKHStf4E0TiIiIiHKJUqnEhAkT0KJFC0RHR8Pd3R2XL19mIatDLGaJiIiIcsmJEyfw888/AwCGDh2KM2fOoFy5chKnyl84zYCIiIgolzRp0gSTJk2Cu7s7OnbsKHWcfIkjs0REREQ6kpycjO+//x5PnjzRtM2cOZOFbC7iyCwRERGRDty/fx9dunTBhQsXcPLkSRw7doxLEOoBR2aJiIiIPtHOnTvh7u6OCxcuoHDhwhg9ejQLWT1hMUtERESUQ0lJSRgxYgQ6dOiAN2/eoE6dOggPD0e7du2kjlZgcJoBERERUQ48efIE7dq1Q2hoKABg3LhxmDlzJhQKhcTJChYWs0REREQ5ULhwYSQkJKBYsWJYt24dWrVqJXWkAonFLBEREVE2JSYmwsTEBHK5HJaWlti5cycsLCxQokQJqaMVWJwzS0RERJQNt27dQu3atfHLL79o2tzc3FjISozFLBEREdFHbNy4ER4eHrhy5QoWLFiAuLg4qSPR/7CYJSIiIspEfHw8+vXrh+7duyMuLg5ffvklLl26BEtLS6mj0f+wmCUiIiLKwI0bN1C7dm2sXr0aMpkMU6dOxeHDh+Hk5CR1NPoPXgBGRERE9IGYmBjUq1cPr1+/hoODAzZu3IjGjRtLHYsywJFZIiIiog9YW1tj+vTpaNq0KcLDw1nI5mEsZomIiIgAXL16FeHh4ZrHQ4cOxcGDB2Fvby9dKPooFrNERERUoAkhsHLlStSqVQsdO3ZETEwMAEAmk0EuZ6mU13HOLBERERVYsbGxGDhwIIKCggAA5cqVg1KplDgVaYN/bhAREVGBFB4eDg8PDwQFBcHIyAg//fQT/vzzTxQrVkzqaKQFjswS5TFCCCQoVXo7X3yy/s5FRJQXCCGwbNkyjB49GklJSShZsiQ2b96MunXrSh2NcoDFLFEeIoRAx2VnEfrgtdRRiIjyLSEE/vjjDyQlJaFNmzZYu3YtR2MNGItZojwkQamSrJD1dCkCc4WRJOcmItInuVyO9evXY+vWrRg8eDBkMpnUkegTsJglyqMufdcUFib6Ky7NFUb8hU5E+ZIQAgsWLMDNmzexdOlSAEDx4sUxZMgQiZORLrCYJcqjLEyMYGHCtygR0ad4/fo1+vTpg127dgEAOnXqxBsg5DP8l5KIiIjypfPnz6NLly548OABTExMMGfOHDRq1EjqWKRjLGaJPkIIgSQVEJ+cAoXI3Y/hubIAEdGnE0Jg7ty5mDBhAlJSUlC2bFkEBwfDw8ND6miUC1jMEmVBCAHfVRdx+aExxl84KnUcIiLKhj59+iAwMBAA0LlzZ6xYsQI2NjbShqJcw5smEGUhQanC5Ydv9H5erixARJRzXbp0gbm5OZYuXYrNmzezkM3nODJLlE3nvm0Ia0szvZyLKwsQEWWfWq3G7du3UaFCBQBA8+bNERERAXt7e4mTkT6wmCXKJnOuLkBElOc8f/4cPXv2xLlz5xAWFgZXV1cAYCFbgHCaARERERmkv/76C9WrV8fBgweRnJyMq1evSh2JJMBiloiIiAyKSqXC9OnT0bhxY0RGRqJixYq4cOEC2rZtK3U0kgA/MyUiIiKDERUVhe7du+PIkSMAAH9/fyxatAiWlpYSJyOpsJglIiIigzF//nwcOXIEFhYWWLp0KXr27Cl1JJIYi1kiIiIyGFOnTsXjx48xefJkzeoFVLBxziwRERHlWU+ePMHYsWORkpICADAzM8OGDRtYyJIGR2aJiIgoTzpw4AB69OiB6OhoWFtbY8qUKVJHojyII7NERESUpyiVSkycOBEtWrRAdHQ0qlevDl9fX6ljUR7FkVkiIiLKMx49egRfX1+cOXMGADBkyBDMmTMHZmb6uQMjGR4Ws0RERJQnHDlyBJ07d8arV69gbW2NVatWoVOnTlLHojyOxSwRERHlCQ4ODkhISICHhweCg4NRtmxZqSORAWAxS0RERJKJi4vT3PCgcuXKOHLkCGrUqAFTU1OJk5Gh4AVgREREJIldu3ahdOnSmvmxAODl5cVClrTCYpYMi0oFHD8OBAW9/69KJXUiIiLSUlJSEkaOHImvvvoK0dHR+O2336SORAZM8mJ2yZIlcHV1hZmZGTw8PHDy5Mkst9+4cSOqVasGCwsLODo6onfv3nj58qWe0pKkduwASpcGGjUCunV7/9/Spd+3ExGRQbh79y7q1auHBQsWAADGjh2LTZs2SZyKDJmkxWxwcDBGjRqFyZMnIywsDA0aNECLFi3w8OHDDLc/deoUevbsib59++Kff/7B1q1bcfHiRfTr10/PyUnvduwAOnYEHj9O2/7kyft2FrRERHne1q1b4e7ujtDQUBQtWhR79+7F7NmzoVAopI5GBkzSYnbu3Lno27cv+vXrh4oVK2LevHkoWbIkli5dmuH2586dQ+nSpTFixAi4urqifv36GDhwIC5duqTn5KRXKhUwciQgBABAAIhXmL7/MjZ5/99vxiM+IQnxySk6/uI0BiIiXbh69Sr8/PwQGxuLevXqITw8HK1atZI6FuUDkq1mkJycjNDQUEyYMCFNu4+PT5qJ4P9Vt25dTJ48Gfv27UOLFi3w/PlzbNu2Lcs3Q1JSEpKSkjSPY2JiALy/u4hSqdTBK/m41PPo63z5zqlTwMuXgLk5BADfjjNw2SmDe3IHHM7VGEplCvvQQPE9aPjYh4ZNqVSiSpUqaN++Pdzc3DBt2jQYGxuzPw2Ivt+D2pxHJsT/hrv07OnTp3B2dsbp06dRt25dTfuPP/6IdevW4datWxnut23bNvTu3RuJiYlISUlB27ZtsW3btkw/opg2bRoCAgLStW/atAkWFha6eTGkN0kqYPwF/f8N5lpIYGRlFWQyvZ+aiMhgnTlzBtWrV9f8e6tWqyGXS365DhmA+Ph4dOvWDW/fvoW1tXWW20q+zqzsg+pACJGuLdX169cxYsQITJkyBc2aNUNkZCTGjRuHQYMGYfXq1RnuM3HiRIwZM0bzOCYmBiVLloSPj89Hvzm6olQqERISAm9vb84LyolTp4D/jb7HG5ti/JDfAQDnVvaFufL/R92xfTtQ10vnp1cqU3Dq+FH4+LD/DBXfg4aPfWhY4uPjMWbMGKxZswadOnXC2rVrcfjwYTRr1oz9Z6D0/R5M/SQ9OyQrZm1tbWFkZISoqKg07c+fP4e9vX2G+8yaNQv16tXDuHHjAABVq1aFpaUlGjRogB9++AGOjo7p9jE1Nc1wvTqFQqH3N5QU58wXvvgCKFYMePIECmO1ptk69g0slEmATAaUKAE0bggYGen89EqlEjIZ+y8/YB8aPvZh3nfjxg107twZ165dg0wmQ8WKFWFs/L7cYP8ZPn31oTbnkGys38TEBB4eHggJCUnTHhISkmbawX/Fx8en+3jC6H/Fi0SzJUgfjIyA+fPf//+Ho/apj+fNy5VCloiIsm/dunXw9PTEtWvXYG9vj5CQEAQEBHBqAeUqSX+6xowZg1WrVmHNmjW4ceMGRo8ejYcPH2LQoEEA3k8R6Nmzp2b7Nm3aYMeOHVi6dCnu3buH06dPY8SIEahVqxacnJykehmkDx06ANu2AY4f9HOJEu/bO3SQJhcRESEuLg7+/v7w9/dHfHw8mjRpgvDwcDRp0kTqaFQASDpntkuXLnj58iWmT5+OyMhIVKlSBfv27YOLiwsAIDIyMs2as/7+/oiNjcWiRYvwzTffoHDhwmjcuDF+/vlnqV4C6VOHDkCLVv+/asG+/UCjLzgiS0Qksfj4eBw6dAhyuRwBAQGYOHGi5pNTotwm+QVgQ4YMwZAhQzJ8LjAwMF3b8OHDMXz48FxORXnWf385ftGAhSwRUR5QvHhxBAcHQ61Wo2HDhlLHoQKGk1iIiIhIK7GxsfDz88PGjRs1bQ0aNGAhS5JgMUtERETZFh4eDg8PD2zatAnDhg3TagklotzAYpaIiIg+SgiBpUuXok6dOrhz5w5KlCiBvXv36m3NdqLMSD5nloiIiPK2t2/fon///ti6dSsAoHXr1ggMDESxYsUkTkbEYpaIiIiyEBcXBw8PD9y9exfGxsb4+eefMXr06Ezv1kmkb5xmQERERJmytLTE119/DRcXF5w6dQpjxoxhIUt5CotZIiIiSuP169d4/Pix5vEPP/yAsLAw1K5dW8JURBljMUtEREQa58+fh7u7Ozp27AilUgkAUCgUKFKkiMTJiDLGYpaIiIgghMCcOXNQv359PHjwAC9evMCTJ0+kjkX0USxmiYiICriXL1+ibdu2GDt2LFJSUtCpUydcvnwZpUuXljoa0UexmCUiIirATp8+jerVq2Pv3r0wNTXF0qVLERwcDBsbG6mjEWULl+YiIiIqoIQQGD16NB4/foxy5cphy5YtqF69utSxiLTCkVkiIqICSiaTYePGjejbty9CQ0NZyJJBYjFLRERUgPz111+YP3++5nG5cuWwatUqFCpUSMJURDnHaQZEREQFgEqlwo8//ohp06ZBCIEaNWqgQYMGUsci+mQsZomIiPK5qKgodO/eHUeOHAEA9OrVCzVq1JA4FZFusJglIiLKx44cOQI/Pz88e/YMFhYWWLJkCXr16iV1LCKd4ZxZIiKifGrWrFnw9vbGs2fPUKVKFVy8eJGFLOU7LGaJiIjyKTs7Owgh0K9fP5w/fx6VKlWSOhKRznGaARERUT7y7t07WFlZAQD69OmD8uXLo379+hKnIso9HJklIiLKB1JSUjBx4kRUqVIFr169AvB+HVkWspTfsZglIiIycI8ePcKXX36Jn376CQ8ePMD27duljkSkNyxmiYiIDNiff/6J6tWr4/Tp07C2tkZwcDD69+8vdSwivWExS0REZICSk5MxduxYtG7dGq9evYKHhwcuX76Mzp07Sx2NSK9YzBIRERmgadOmYc6cOQCAESNG4PTp0yhbtqzEqYj0j8UsERGRARo7diyqVauGHTt2YP78+TA1NZU6EpEkWMwSEREZgKSkJKxfvx5CCABA0aJFcfnyZXz11VcSJyOSFteZJSIiyuPu3buHzp07IzQ0FElJSZoLvORyjkkR8V1ARESUh23btg3u7u4IDQ1F0aJF4ejoKHUkojyFxSwREVEelJiYiKFDh6JTp06IiYlB3bp1ER4ejtatW0sdjShPYTFLRESUx9y5cwdeXl5YsmQJAGDChAk4fvw4SpYsKXEyoryHc2aJiIjymMePH+Pvv/+Gra0tNmzYgObNm0sdiSjPYjFLRESUBwghIJPJAACNGjVCYGAgmjRpAmdnZ4mTEeVtnGZAREQksRs3bqB+/fq4ffu2pq1nz54sZImygcUsERGRhNatWwdPT0+cOXMGI0aMkDoOkcFhMUtERCSBuLg4+Pv7w9/fH/Hx8WjcuDECAwOljkVkcFjMEhER6dm1a9dQs2ZNrFu3DnK5HNOnT8ehQ4fg4OAgdTQig8MLwIiIiPTo/PnzaNSoERISEuDo6IhNmzbhyy+/lDoWkcFiMUtERKRHNWrUQLVq1WBtbY0NGzbAzs5O6khEBo3FLBERUS67fv06ypUrB4VCAYVCgT///BOFCxeGXM7ZfkSfiu8iIiKiXCKEwLJly1CjRg1MnjxZ0160aFEWskQ6wpFZIiKiXBATE4P+/ftjy5YtAN6vJatSqWBkZCRxMqL8hX8WEhER6VhoaChq1KiBLVu2wNjYGLNnz8bu3btZyBLlAo7MEhER6YgQAosWLcLYsWORnJwMFxcXbN68GXXq1JE6GlG+xZFZIiIiHXny5AkmTZqE5ORktG/fHmFhYSxkiXIZR2aJiIh0pESJEli5ciWeP3+O4cOHQyaTSR2JKN9jMUtERJRDQgj89ttvcHd3R6NGjQAAvr6+EqciKlhYzBIREeXAq1ev4O/vjz179sDBwQHXr19HkSJFpI5FVOCwmCUiItLSmTNn4Ovri0ePHsHU1BRTpkxB4cKFpY5FVCDxAjAiIqJsUqvV+Pnnn/HFF1/g0aNHKFeuHM6dO4fBgwdzfiyRRDgyS0RElA0JCQn4+uuvsX//fgBA165dsXz5chQqVEjiZEQFG0dmiYiIssHMzAyFCxeGmZkZVqxYgY0bN7KQJcoDWMwSERFlQqVSIS4uDgAgk8mwfPlyXLx4Ef379+e0AqI8gsUsERFRBp49e4bmzZuje/fuEEIAAAoVKoQqVapInIyI/otzZumTCSGQoFTp5Vzxyfo5DxEVbEePHoWfnx+ioqJgYWGBmzdvomLFilLHIqIMsJilTyKEQMdlZxH64LXUUYiIPplKpcL06dMxY8YMCCFQuXJlbNmyhYUsUR7GYpY+SYJSJUkh6+lSBOYKI72fl4jyr6dPn8LPzw/Hjx8HAPTt2xcLFiyAhYWFtMGIKEssZklnLn3XFBYm+ikwzRVGvPiCiHRGCIF27drh0qVLsLS0xPLly+Hn5yd1LCLKhhwVsykpKTh+/Dju3r2Lbt26oVChQnj69Cmsra1hZWWl64xkICxMjGBhwr+PiMjwyGQyLFiwAMOHD8emTZvg5uYmdSQiyiatK48HDx6gefPmePjwIZKSkuDt7Y1ChQrhl19+QWJiIpYtW5YbOYmIiHTq8ePHCA8PR+vWrQEAXl5euHjxIj/1ITIwWi/NNXLkSHh6euL169cwNzfXtH/11Vc4cuSITsMRERHlhn379qF69ero3Lkzrl27pmlnIUtkeLQemT116hROnz4NExOTNO0uLi548uSJzoIRERHpmlKpxOTJkzF79mwAQI0aNdIMzBCR4dG6mFWr1VCp0q/1+fjxY97Wj4iI8qwHDx7A19cX586dAwAMHz4cs2fPhqmpqcTJiOhTaD3NwNvbG/PmzdM8lslkePfuHaZOnYqWLVvqMhsREZFO7N69G+7u7jh37hxsbGywfft2LFiwgIUsUT6g9cjsb7/9hkaNGqFSpUpITExEt27dcOfOHdja2iIoKCg3MpKWeEcuIqK0Ll++jNevX6NWrVrYvHkzXF1dpY5ERDqidTHr5OSE8PBwbN68GaGhoVCr1ejbty/8/Pw47ygP4B25iIjeE0JoLuiaMmUK7Ozs0L9//3TXfBCRYdN6msGJEyegUCjQu3dvLFq0CEuWLEG/fv2gUChw4sSJ3MhIWuAduYiIgO3bt6Nx48ZITEwEABgZGWHo0KEsZInyIa1HZhs1aoTIyEjY2dmlaX/79i0aNWqU4cVhJA3ekYuICprExESMHTsWixcvBgAsXrwY33zzjcSpiCg3aV3M/vdjm/96+fIlLC0tdRKKdIN35CKiguTOnTvo0qULwsLCAADffvstRowYIXEqIspt2a50OnToAOD96gX+/v5prgBVqVS4cuUK6tatq/uE+YAQAkkqID45BQqRu6OXvCCLiAqizZs3o3///nj37h1sbW2xfv16tGjRQupYRKQH2S5mbWxsALwvzAoVKpTmYi8TExPUqVMH/fv3131CAyeEgO+qi7j80BjjLxyVOg4RUb4zZ84cjB07FgDQoEEDBAUFwdnZWeJURKQv2S5m165dCwAoXbo0xo4dyykF2ZSgVOHywzd6Py8vyCKiguLrr7/Gjz/+iCFDhmDq1KkwNub0KqKCROt3/NSpU3MjR4Fw7tuGsLY008u5eEEWEeVnYWFhcHd3B/B+kOXOnTsoWrSoxKmISApaL80FANu2bUPnzp1Rp04d1KhRI82XtpYsWQJXV1eYmZnBw8MDJ0+ezHL7pKQkTJ48GS4uLjA1NUXZsmWxZs2anLwMvTP/3wVZ+vhiIUtE+VFcXBz69OmDGjVqYN++fZp2FrJEBZfWxeyCBQvQu3dv2NnZISwsDLVq1UKxYsVw7949rSfbBwcHY9SoUZg8eTLCwsLQoEEDtGjRAg8fPsx0n86dO+PIkSNYvXo1bt26haCgIFSoUEHbl0FERAbm4cOHqFu3LtauXQu5XI5bt25JHYmI8gCtpxksWbIEK1asQNeuXbFu3TqMHz8eZcqUwZQpU/Dq1SutjjV37lz07dsX/fr1AwDMmzcPBw8exNKlSzFr1qx02x84cAB//fUX7t27p/krvHTp0tq+BCIiMiBCCAQGBmLs2LFITk6Gg4MDgoKC8OWXX0odjYjyAK2L2dS/jAHA3NwcsbGxAIAePXqgTp06WLRoUbaOk5ycjNDQUEyYMCFNu4+PD86cOZPhPn/88Qc8PT3xyy+/YMOGDbC0tETbtm0xY8aMTG+lm5SUhKSkJM3jmJgYAIBSqYRSqcxW1k+hVKak+X99nJN0K7XP2HeGi31ouN69e4dhw4Zh06ZNAIAmTZpg3bp1sLOzY38aEL4HDZ+++1Cb82hdzDo4OODly5dwcXGBi4sLzp07h2rVqiEiIgJCiGwfJzo6GiqVCvb29mna7e3tERUVleE+9+7dw6lTp2BmZoadO3ciOjoaQ4YMwatXrzKdNztr1iwEBASkaz906BAsLCyynTenklRA6rf56NGjMOUCAwYrJCRE6gj0idiHhufs2bPYtGkT5HI5unXrhg4dOuDSpUtSx6Ic4nvQ8OmrD+Pj47O9rdbFbOPGjbFnzx7UqFEDffv2xejRo7Ft2zZcunRJc2MFbXx4oVJmdxgDALVaDZlMho0bN2rWvZ07dy46duyIxYsXZzg6O3HiRIwZM0bzOCYmBiVLloSPjw+sra21zqut+OQUzfqyjRs3ho2eVjMg3VEqlQgJCYG3tzcUCoXUcSgH2IeGq2XLlhBCwNvbG3FxcexDA8X3oOHTdx+mfpKeHVoXsytWrIBarQYADBo0CEWLFsWpU6fQpk0bDBo0KNvHsbW1hZGRUbpR2OfPn6cbrU3l6OgIZ2dnTSELABUrVoQQAo8fP0a5cuXS7WNqaprmbmWpFAqFXjrjv3f8UiiM+SY2YPr6maHcwz7M+2JiYjBx4kRMnToVdnZ2AICff/4ZSqUS+/btYx8aOPaf4dNb/aTFObRezUAul6dZkLpz585YsGABRowYgRcvXmT7OCYmJvDw8Eg3XB0SEpLpbXHr1auHp0+f4t27d5q227dvQy6Xo0SJElq+EiIiyksuX76MGjVqYMmSJejbt6/UcYjIQORondkPRUVFYfjw4fjss8+02m/MmDFYtWoV1qxZgxs3bmD06NF4+PChZoR34sSJ6Nmzp2b7bt26oVixYujduzeuX7+OEydOYNy4cejTp0+mF4AREVHeJoTAokWL4OXlhbt376JUqVKYNGmS1LGIyEBku5h98+YN/Pz8ULx4cTg5OWHBggVQq9WYMmUKypQpg3Pnzml984IuXbpg3rx5mD59OqpXr44TJ05g3759cHFxAQBERkamWXPWysoKISEhePPmDTw9PeHn54c2bdpgwYIFWp2XiIjyhjdv3qBjx44YPnw4kpOT0bZtW4SFhcHLy0vqaERkILI9Z3bSpEk4ceIEevXqhQMHDmD06NE4cOAAEhMTsX//fjRs2DBHAYYMGYIhQ4Zk+FxgYGC6tgoVKvBqSCKifODmzZto2bIlIiIioFAoMHv2bIwYMYJ3MCQirWS7mP3zzz+xdu1aNG3aFEOGDMFnn30GNzc3zJs3LxfjERFRfuXk5AQjIyO4uroiODgYNWvWlDoSERmgbBezT58+RaVKlQAAZcqUgZmZmebOXURERNkRExODQoUKQSaTwdraGnv37oW9vT0KFy4sdTQiMlDZnjOrVqvTLJNgZGQES0vLXAlFRET5z9mzZ1GlSpU0d4osX748C1ki+iTZHpkVQsDf31+zZmtiYiIGDRqUrqDdsWOHbhMSEZFBU6vV+PXXXzFp0iSoVCosX74cgwYN4nqjRKQT2S5me/XqleZx9+7ddR6GiIjylxcvXqBXr17Yv38/AMDX1xfLly9nIUtEOpPtYnbt2rW5mYOIiPKZEydOoGvXrnj69CnMzMywYMEC9OvXj6sVEJFOaX07WyIioo+JjIyEj48PkpKSUL58eWzZsgVVq1aVOhYR5UMsZomISOccHR0REBCAf/75B0uWLIGVlZXUkYgon2IxS0REOnHs2DHY2dmhcuXKAIDx48cDAKcVEFGuyvbSXERERBlRqVSYNm0amjRpgs6dOyMuLg7A+yKWhSwR5TaOzBIRUY5FRkbCz88Px44dAwDUqVOHBSwR6VWORmY3bNiAevXqwcnJCQ8ePAAAzJs3D7t379ZpOCIiyrtCQkJQvXp1HDt2DJaWltiwYQNWr14NCwsLqaMRUQGidTG7dOlSjBkzBi1btsSbN2+gUqkAAIULF8a8efN0nY+IiPKYlJQUfPfdd2jWrBmeP3+OqlWr4tKlS1x/nIgkoXUxu3DhQqxcuRKTJ0+GkZGRpt3T0xNXr17VaTgiIsp7ZDIZTp06BSEEBg4ciHPnzqFChQpSxyKiAkrrObMRERFwd3dP125qaqqZ9E9ERPmPEAIymQxGRkbYtGkTTp06hc6dO0sdi4gKOK1HZl1dXREeHp6uff/+/ahUqZIuMhERUR6iVCoxfvx4jBo1StPm5OTEQpaI8gStR2bHjRuHoUOHIjExEUIIXLhwAUFBQZg1axZWrVqVGxmJiEgiDx8+hK+vL86ePQsA6NOnD6pVqyZxKiKi/6d1Mdu7d2+kpKRg/PjxiI+PR7du3eDs7Iz58+fD19c3NzISEZEE/vjjD/j7++P169ewsbHB6tWrWcgSUZ6To3Vm+/fvj/79+yM6OhpqtRp2dna6zkVERBJJTk7Gt99+q1mhpmbNmggODoarq6u0wYiIMqD1nNmAgADcvXsXAGBra8tClogoHxFCoE2bNppCdvTo0Th16hQLWSLKs7QuZrdv3w43NzfUqVMHixYtwosXL3IjFxERSUAmk2HgwIEoUqQIdu/ejblz58LExETqWEREmdK6mL1y5QquXLmCxo0bY+7cuXB2dkbLli2xadMmxMfH50ZGIiLKRYmJiWnWCe/QoQPu3buHtm3bSpiKiCh7cnQ728qVK+PHH3/EvXv3cOzYMbi6umLUqFFwcHDQdT4iIspF//77L+rWrYvGjRvjyZMnmvbChQtLF4qISAs5Kmb/y9LSEubm5jAxMYFSqdRFJiIi0oPg4GDUqFEDYWFhEEIgIiJC6khERFrLUTEbERGBmTNnolKlSvD09MTly5cxbdo0REVF6TofERHpWEJCAgYNGgRfX1/Exsaifv36CA8PR/369aWORkSkNa2X5vLy8sKFCxfw+eefo3fv3pp1ZomIKO+7desWOnfujCtXrkAmk2HSpEmYNm0ajI1ztFIjEZHktP7t1ahRI6xatQqVK1fOjTxERJSL5s+fjytXrsDOzg6///47vL29pY5ERPRJtC5mf/zxx9zIQUREejB79mykpKQgICAAjo6OUschIvpk2Spmx4wZgxkzZsDS0hJjxozJctu5c+fqJBgREX26f/75B8uXL8e8efMgl8thaWmJFStWSB2LiEhnslXMhoWFaVYqCAsLy9VARET06YQQCAwMxNChQ5GQkIAyZcpg1KhRUsciItK5bBWzx44dy/D/iYgo73n37h2GDBmCDRs2AAB8fHzQrVs3iVMREeUOrZfm6tOnD2JjY9O1x8XFoU+fPjoJRUREOXPlyhV4enpiw4YNkMvlmDlzJvbv3w87OzupoxER5Qqti9l169YhISEhXXtCQgLWr1+vk1BERKS94OBg1K5dG7du3YKzszOOHz+OSZMmQS7/5PvjEBHlWdlezSAmJgZCCAghEBsbCzMzM81zKpUK+/bt41/+REQS+uyzz6BWq9GiRQusX78etra2UkciIsp12S5mCxcuDJlMBplMBjc3t3TPy2QyBAQE6DQcERFl7c2bNyhcuDAAwMPDA2fPnkX16tU5GktEBUa2i9ljx45BCIHGjRtj+/btKFq0qOY5ExMTuLi4wMnJKVdCEhFRWkIILFmyBJMmTcKxY8dQo0YNAND8l4iooMh2MduwYUMAQEREBEqVKgWZTJZroYiIKHNv3rxB//79sW3bNgBAYGAgi1giKrCyVcxeuXIFVapUgVwux9u3b3H16tVMt61atarOwhERUVoXL15Ely5dEBERAYVCgV9++QUjR46UOhYRkWSyVcxWr14dUVFRsLOzQ/Xq1SGTySCESLedTCaDSqXSeUgiooJOCIH58+dj/PjxUCqVcHV1RXBwMGrWrCl1NCIiSWWrmI2IiEDx4sU1/09ERPq1fft2jB49GgDw9ddfY9WqVZoLv4iICrJsFbMuLi4Z/j8REelHhw4d0LZtW/j4+GDIkCG8boGI6H9ydNOEP//8U/N4/PjxKFy4MOrWrYsHDx7oNBwRUUGlVquxcuVKxMfHAwDkcjl27dqFoUOHspAlIvoPrYvZH3/8Eebm5gCAs2fPYtGiRfjll19ga2ur+QiMiIhyLjo6Gm3atMGAAQMwfPhwTTuLWCKi9LK9NFeqR48e4bPPPgMA7Nq1Cx07dsSAAQNQr149fPnll7rOR0RUoJw8eRJdu3bFkydPYGZmhtq1a0MIwUKWiCgTWo/MWllZ4eXLlwCAQ4cOoWnTpgAAMzMzJCQk6DYdEVEBoVar8eOPP6JRo0Z48uQJypcvj/Pnz2PAgAEsZImIsqD1yKy3tzf69esHd3d33L59G61atQIA/PPPPyhdurSu8xER5XvPnz9Hjx49cOjQIQBA9+7dsXTpUlhZWUmcjIgo79N6ZHbx4sXw8vLCixcvsH37dhQrVgwAEBoaiq5du+o8IBFRfqdUKnH58mWYm5tj9erVWL9+PQtZIqJs0npktnDhwli0aFG69oCAAJ0EIiIqCP47D9bZ2Rlbt25F8eLFUblyZYmTEREZFq2LWeD9fcFXr16NGzduQCaToWLFiujbty9sbGx0nY+IKN+JioqCn58fhg0bhq+++goAeAEtEVEOaT3N4NKlSyhbtix+++03vHr1CtHR0fjtt99QtmxZXL58OTcyEhHlG4cPH0a1atVw9OhRjBgxAsnJyVJHIiIyaFoXs6NHj0bbtm1x//597NixAzt37kRERARat26NUaNG5UJEIiLDl5KSgu+++w4+Pj54/vw5qlatisOHD8PExETqaEREBk3raQaXLl3CypUrYWz8/7saGxtj/Pjx8PT01Gk4IqL84MmTJ+jatStOnjwJABg4cCB+++03zQ1oiIgo57QuZq2trfHw4UNUqFAhTfujR49QqFAhnQUjIsoPXrx4gerVqyM6OhqFChXCihUr4OvrK3UsIqJ8Q+tpBl26dEHfvn0RHByMR48e4fHjx9i8eTP69evHpbmIiD5QvHhxdOnSBe7u7ggNDWUhS0SkY1qPzP7666+QyWTo2bMnUlJSAAAKhQKDBw/GTz/9pPOARESG5uHDh1AoFHB0dAQAzJkzB0IImJmZSZyMiCj/0Xpk1sTEBPPnz8fr168RHh6OsLAwvHr1Cr/99htMTU1zIyMRkcHYs2cPqlevjq5du2r+4Dc1NWUhS0SUS7JdzMbHx2Po0KFwdnaGnZ0d+vXrB0dHR1StWhUWFha5mZGIKM9LTk7GN998g7Zt2+L169eIj4/H69evpY5FRJTvZbuYnTp1KgIDA9GqVSv4+voiJCQEgwcPzs1sREQGISIiAg0aNMDcuXMBvF/C8NSpUyhevLjEyYiI8r9sz5ndsWMHVq9erbl4oXv37qhXrx5UKhWMjIxyLSARUV62Y8cO9OnTB2/fvkWRIkUQGBiItm3bSh2LiKjAyPbI7KNHj9CgQQPN41q1asHY2BhPnz7NlWBERHmdUqnE999/j7dv38LLywthYWEsZImI9CzbxaxKpUp3pxpjY2PNBQ5ERAWNQqFAcHAwJk6ciL/++gsuLi5SRyIiKnCyPc1ACAF/f/80KxYkJiZi0KBBsLS01LTt2LFDtwmJiPKQLVu24Pnz5xg2bBgAoEqVKvjxxx8lTkVEVHBlu5jt1atXurbu3bvrNAwRUV6VkJCA0aNHY/ny5TAyMkK9evXg7u4udSwiogIv28Xs2rVrczMHEVGedevWLXTu3BlXrlyBTCbDhAkT8Pnnn0sdi4iIkIM7gBERFSS///47Bg0ahLi4ONjZ2eH333+Ht7e31LGIiOh/tL4DGBFRQTFkyBD06NEDcXFxaNSoEcLDw1nIEhHlMSxmiYgyUaFCBchkMkybNg0hISFwdHSUOhIREX2A0wyIiP7j1atXKFq0KABg+PDhaNiwIapVqyZxKiIiygxHZomIALx79w69evVC7dq1ERMTAwCQyWQsZImI8rgcFbMbNmxAvXr14OTkhAcPHgAA5s2bh927d+s0HBGRPly9ehU1a9bE+vXrce/ePRw7dkzqSERElE1aF7NLly7FmDFj0LJlS7x58wYqlQoAULhwYcybN0/X+YiIco0QAitXrkStWrVw8+ZNODs74/jx42jXrp3U0YiIKJu0LmYXLlyIlStXYvLkyTAyMtK0e3p64urVqzoNR0SUW2JjY+Hn54cBAwYgMTERLVq0QHh4OBo0aCB1NCIi0oLWxWxERESGd70xNTVFXFycTkIREeW2b775BkFBQTAyMsIvv/yCvXv3wtbWVupYRESkJa2LWVdXV4SHh6dr379/PypVqqR1gCVLlsDV1RVmZmbw8PDAyZMns7Xf6dOnYWxsjOrVq2t9TiKiH374AXXq1MHJkycxbtw4yOW8HpaIyBBp/dt73LhxGDp0KIKDgyGEwIULFzBz5kxMmjQJ48aN0+pYwcHBGDVqFCZPnoywsDA0aNAALVq0wMOHD7Pc7+3bt+jZsyeaNGmibXwiKqDi4uKwevVqzWM7OzucOXMGXl5eEqYiIqJPpfU6s71790ZKSgrGjx+P+Ph4dOvWDc7Ozpg/fz58fX21OtbcuXPRt29f9OvXD8D7FREOHjyIpUuXYtasWZnuN3DgQHTr1g1GRkbYtWuXti+BiAqY0NBQjBkzBs+ePYO1tTW6desG4P3SW0REZNhydNOE/v37o3///oiOjoZarYadnZ3Wx0hOTkZoaCgmTJiQpt3HxwdnzpzJdL+1a9fi7t27+P333/HDDz989DxJSUlISkrSPE5dP1KpVEKpVGqdW1tKZUqa/9fHOUm3UvuMfWd4hBBYtGgRJkyYAKVSCRcXF5QuXZp9aYD4PjRs7D/Dp+8+1OY8n3QHsE+5WCI6OhoqlQr29vZp2u3t7REVFZXhPnfu3MGECRNw8uRJGBtnL/qsWbMQEBCQrv3QoUOwsLDQPriWklRA6rf56NGjMDXKcnPKw0JCQqSOQFp49+4dFi5ciPPnzwMA6tSpg2HDhuHFixfYt2+fxOkop/g+NGzsP8Onrz6Mj4/P9rZaF7Ourq5ZfjR37949rY734bGEEBkeX6VSoVu3bggICICbm1u2jz9x4kSMGTNG8zgmJgYlS5aEj48PrK2ttcqaE/HJKRh/4SgAoHHjxrCxNMv1c5JuKZVKhISEwNvbGwqFQuo4lA0XLlzAyJEj8eDBA5iYmGDWrFkoU6YMfHx82IcGiu9Dw8b+M3z67sPUT9KzQ+tidtSoUWkeK5VKhIWF4cCBA1pdAGZrawsjI6N0o7DPnz9PN1oLvF8T8tKlSwgLC8OwYcMAAGq1GkIIGBsb49ChQ2jcuHG6/UxNTWFqapquXaFQ6KUzFOL/C3OFwphvYgOmr58Z+nRv377FgwcPULZsWWzZsgWff/459u3bxz7MB9iHho39Z/j0Vj9pcQ6ti9mRI0dm2L548WJcunQp28cxMTGBh4cHQkJC8NVXX2naQ0JCMrz7jrW1dbqbMixZsgRHjx7Ftm3b4Orqmu1zE1H+899PdVq2bIlNmzahVatWsLa25jw9IqJ8TGcLK7Zo0QLbt2/Xap8xY8Zg1apVWLNmDW7cuIHRo0fj4cOHGDRoEID3UwR69uz5PqhcjipVqqT5srOzg5mZGapUqQJLS0tdvRQiMjCnTp1CtWrV8ODBA01b165d9TKViIiIpPVJF4D917Zt21C0aFGt9unSpQtevnyJ6dOnIzIyElWqVMG+ffvg4uICAIiMjPzomrNEVHCp1Wr8/PPP+P7776FSqfDdd99hw4YNUsciIiI90rqYdXd3T3OBlhACUVFRePHiBZYsWaJ1gCFDhmDIkCEZPhcYGJjlvtOmTcO0adO0PicRGb7nz5+jR48eOHToEACge/fuWLp0qcSpiIhI37QuZtu3b5/msVwuR/HixfHll1+iQoUKuspFRJSp48ePo1u3boiMjIS5uTkWL14Mf39/3gSBiKgA0qqYTUlJQenSpdGsWTM4ODjkViYiokzt378frVu3hlqtRqVKlbBlyxZUrlxZ6lhERCQRrS4AMzY2xuDBg9PcUYuISJ8aNWqEqlWronfv3rhw4QILWSKiAk7raQa1a9dGWFiY5iItIqLcdv78eXh6esLIyAhmZmY4ceIEChUqJHUsIiLKA7QuZocMGYJvvvkGjx8/hoeHR7olsapWraqzcERUsKWkpCAgIAAzZ87ElClTNBd8spAlIqJU2S5m+/Tpg3nz5qFLly4AgBEjRmiek8lkmgXLVSqV7lMSUYHz5MkTdOvWDSdOnAAAPHv2LNPbXRMRUcGV7WJ23bp1+OmnnxAREZGbeYiIcODAAfTo0QPR0dGwsrLCypUr4evrK3UsIiLKg7JdzAohAIBzZYko1yiVSkyZMgU//fQTgPfrWgcHB6NcuXISJyMiorxKq9UM+PEeEeWme/fuYd68eQCAoUOH4syZMyxkiYgoS1pdAObm5vbRgvbVq1efFIiICq7y5ctj+fLlsLCwQMeOHaWOQ0REBkCrYjYgIAA2Nja5lYWICpjk5GR89913+Oqrr+Dl5QUA6Nmzp8SpiIjIkGhVzPr6+sLOzi63shBRAXL//n34+vri/Pnz2LJlC27evAkzMzOpYxERkYHJ9pxZzpclIl3ZuXMn3N3dcf78eRQuXBjz589nIUtERDmS7WI2dTUDIqKcSkpKwogRI9ChQwe8efMGderUQXh4ONq1ayd1NCIiMlDZnmagVqtzMwcR5XOvX7+Gt7c3QkNDAQDjxo3DzJkzoVAoJE5GRESGTOvb2RIR5UThwoVRokQJ3L9/H+vWrUOrVq2kjkRERPkAi1kiyjWJiYlISUmBlZUVZDIZ1qxZg/j4eJQoUULqaERElE9oddMEIqLsun37NurUqYMBAwZo5twXLVqUhSwREekUi1ki0rlNmzbBw8MDf//9Nw4fPownT55IHYmIiPIpFrNEpDPx8fHo378//Pz88O7dO3z55ZcIDw/naCwREeUaFrNEpBM3btxA7dq1sWrVKshkMkydOhWHDx+Gk5OT1NGIiCgf4wVgRPTJUlJS0KZNG9y9excODg7YuHEjGjduLHUsIiIqADgyS0SfzNjYGCtWrECzZs0QHh7OQpaIiPSGxSwR5cjVq1exd+9ezePGjRtj//79sLe3lzAVEREVNCxmiUgrQgisWrUKtWrVQteuXXHnzh3NczKZTMJkRERUELGYJaJsi42NRffu3dG/f38kJiaifv36KFy4sNSxiIioAGMxS0TZEh4eDg8PD2zatAlGRkb4+eef8eeff6J48eJSRyMiogKMqxkQ0UctW7YMo0aNQlJSEkqWLInNmzejbt26UsciIiLiyCwRfdzdu3eRlJSENm3aICwsjIUsERHlGRyZJaIMqdVqyOXv/9798ccfUa1aNfj5+fEiLyIiylM4MktEaQghMH/+fDRu3BhKpRIAoFAo0L17dxayRESU57CYJSKN169fo0OHDhg1ahT++usvBAUFSR2JiIgoS5xmQEQAgPPnz6NLly548OABTExMMGfOHPTo0UPqWERERFniyCxRAadWqzFnzhzUr18fDx48QNmyZXHmzBkMGzaM0wqIiCjPYzFLVMCNHz8eY8eORUpKCjp37ozLly/Dw8ND6lhERETZwmKWqIDr378/bG1tsWzZMmzevBnW1tZSRyIiIso2zpklKmDUajXOnDmD+vXrAwDKly+P+/fvw9LSUuJkRERE2uPILFEB8vz5c7Rs2RINGzbE8ePHNe0sZImIyFBxZJaogPjrr7/QtWtXREZGwtzcHJGRkVJHIiIi+mQcmSXK51QqFWbMmIHGjRsjMjISFStWxIULF9C1a1epoxEREX0yjswS5WNRUVHo3r07jhw5AgDw9/fHokWLOK2AiIjyDRazRPnY/v37ceTIEVhYWGDp0qXo2bOn1JGIiIh0isUsUT7m7++Pe/fuoVu3bqhYsaLUcYiIiHSOc2aJ8pGnT5+ie/fueP36NQBAJpNhxowZLGSJiCjf4sgsUT5x4MAB9OjRA9HR0QCA33//XeJEREREuY8js0QGLiUlBRMnTkSLFi0QHR2N6tWrY+rUqVLHIiIi0guOzBIZsEePHqFr1644ffo0AGDIkCGYM2cOzMzMJE5GRESkHyxmiQzUuXPn0KpVK7x69QrW1tZYvXo1OnbsKHUsIiIivWIxS2Sg3NzcYGlpiTJlyiA4OBhlypSROhIREZHesZglMiDPnz9H8eLFIZPJULRoURw5cgSlSpWCqamp1NGIiIgkwQvAiAzEzp07Ub58eaxZs0bTVq5cORayRERUoLGYJcrjkpKSMGLECHTo0AFv3rzBxo0bIYSQOhYREVGewGKWKA+7e/cu6tWrh4ULFwIAxo4di4MHD0Imk0mcjIiIKG/gnFmiPGrr1q3o168fYmJiULRoUaxfvx6tWrWSOhYREVGewmKWKA+6ffs2fH19oVarUa9ePQQFBaFkyZJSxyIiIspzWMwS5UFubm6YMmUKkpKSMH36dBgb861KRESUEf4LSZRHBAUFwdPTE+XKlQMA3pKWiIgoG3gBGJHE4uPj0a9fP3Tr1g1dunRBYmKi1JGIiIgMBkdmiSR048YNdO7cGdeuXYNMJkObNm2gUCikjkVERGQwWMwSSWTdunUYMmQI4uPjYW9vj40bN6JJkyZSxyIiIjIoLGaJ9Cw+Ph6DBw/G+vXrAQBNmjTB77//DgcHB4mTERERGR7OmSXSM2NjY9y8eRNyuRwzZszAwYMHWcgSERHlEEdmifRACAEhBORyOUxMTBAcHIwHDx6gYcOGUkcjIiIyaByZJcplsbGx6N69OyZOnKhpK126NAtZIiIiHeDILFEuCg8PR+fOnXHnzh0YGxtj8ODBKF26tNSxiIiI8g2OzBLlAiEEli5dijp16uDOnTsoUaIEjh8/zkKWiIhIxzgyS6Rjb9++Rf/+/bF161YAQOvWrREYGIhixYpJnIyIiCj/YTFLpENqtRoNGzbE33//DWNjY/z8888YPXo0ZDKZ1NGIiIjyJU4zINIhuVyOcePGwcXFBadOncKYMWNYyBIREeUiFrNEn+j169cIDw/XPPbz88P169dRu3Zt6UIREREVECxmiT7B+fPn4e7ujpYtW+LFixeadgsLCwlTERERFRwsZolyQAiBOXPmoH79+njw4AHMzc3x/PlzqWMREREVOLwAjEhLL1++hL+/P/bu3QsA6NSpE1auXAkbGxuJkxERERU8ko/MLlmyBK6urjAzM4OHhwdOnjyZ6bY7duyAt7c3ihcvDmtra3h5eeHgwYN6TEsF3enTp1G9enXs3bsXpqamWLJkCYKDg1nIEhERSUTSYjY4OBijRo3C5MmTERYWhgYNGqBFixZ4+PBhhtufOHEC3t7e2LdvH0JDQ9GoUSO0adMGYWFhek5OBdXSpUvx+PFjlCtXDufOncPgwYO5WgEREZGEJJ1mMHfuXPTt2xf9+vUDAMybNw8HDx7E0qVLMWvWrHTbz5s3L83jH3/8Ebt378aePXvg7u6uj8hUwC1ZsgT29vaYNm0aChUqJHUcIiKiAk+yYjY5ORmhoaGYMGFCmnYfHx+cOXMmW8dQq9WIjY1F0aJFM90mKSkJSUlJmscxMTEAAKVSCaVSmYPk2lEqU9L8vz7OSbpz4sQJBAcHo2XLllAqlTA3N8dPP/0EAOxLA5LaV+wzw8U+NGzsP8On7z7U5jySFbPR0dFQqVSwt7dP025vb4+oqKhsHWPOnDmIi4tD586dM91m1qxZCAgISNd+6NAhvSyflKQCUr/NR48ehalRrp+SdEClUmHbtm0IDg6GWq2GmZkZpxPkAyEhIVJHoE/EPjRs7D/Dp68+jI+Pz/a2kq9m8GGBIITIVtEQFBSEadOmYffu3bCzs8t0u4kTJ2LMmDGaxzExMShZsiR8fHxgbW2d8+DZFJ+cgvEXjgIAGjduDBtLs1w/J32aqKgo+Pv74+jR9/3m5+eHevXqwdvbGwqFQuJ0lBNKpRIhISHsQwPGPjRs7D/Dp+8+TP0kPTskK2ZtbW1hZGSUbhT2+fPn6UZrPxQcHIy+ffti69ataNq0aZbbmpqawtTUNF27QqHQS2coxP8X5gqFMd/EedyRI0fg5+eHZ8+ewcLCAkuWLEG3bt2wb98+vf3MUO5hHxo+9qFhY/8ZPr3VT1qcQ7LVDExMTODh4ZFuuDokJAR169bNdL+goCD4+/tj06ZNaNWqVW7HpAJk/vz58Pb2xrNnz1ClShVcunQJvXr1kjoWERERZUHSaQZjxoxBjx494OnpCS8vL6xYsQIPHz7EoEGDALyfIvDkyROsX78ewPtCtmfPnpg/fz7q1KmjGdU1NzfnOp/0yWrWrAm5XI7evXtj/vz5vCUtERGRAZC0mO3SpQtevnyJ6dOnIzIyElWqVMG+ffvg4uICAIiMjEyz5uzy5cuRkpKCoUOHYujQoZr2Xr16ITAwUN/xKR949uyZZlpL3bp1ce3aNVSoUEHiVERERJRdkl8ANmTIEAwZMiTD5z4sUI8fP577gahASElJwffff4+FCxfi/PnzqFy5MgCwkCUiIjIwkhezRPr26NEjdO3aFadPnwYA7NmzR1PMEhERkWFhMUsFyp9//omePXvi1atXsLa2xsqVK7Ncp5iIiIjyNslWMyDSJ6VSibFjx6J169Z49eoVPDw8cPnyZRayREREBo7FLBUIq1evxpw5cwAAI0aMwOnTp1G2bFmJUxEREdGn4jQDKhD69euHgwcPomfPnvjqq6+kjkNEREQ6wpFZypeSk5Mxe/ZsJCUlAQCMjY2xc+dOFrJERET5DEdmKd+5d+8eunTpgkuXLuHhw4dYuHCh1JGIiIgol3BklvKVbdu2wd3dHZcuXULRokXRrFkzqSMRERFRLmIxS/lCYmIihgwZgk6dOiEmJgb16tVDeHg4WrduLXU0IiIiykUsZsng3b17F15eXli6dCkAYMKECTh27BhKliwpcTIiIiLKbZwzSwZPLpcjIiICtra22LBhA5o3by51JCIiItITFrNkkFQqFYyMjAAArq6u2LlzJ9zc3ODs7CxxMiIiItInTjMgg3Pjxg3UqFEDBw4c0LQ1atSIhSwREVEBxGKWDMr69evh6emJK1euYNy4cVCr1VJHIiIiIgmxmCWDEBcXh969e6NXr16Ij49H48aNERISArmcP8JEREQFGSsByvOuXbuGmjVrIjAwEHK5HNOnT8ehQ4fg4OAgdTQiIiKSGC8Aozzt3r17qFWrFhISEuDo6IhNmzbhyy+/lDoWERER5REsZilPK1OmDHx9ffH06VOsX78ednZ2UkciIiKiPITFLOU5f//9N5ycnFC8eHEAwNKlS6FQKDg/loiIiNJhdUB5hhACy5YtQ+3atdGzZ0/NSgWmpqYsZImIiChDrBAoT3j79i18fX0xePBgJCUlwcjICPHx8VLHIiIiojyOxSxJLjQ0FB4eHtiyZQuMjY0xe/Zs/PHHH7CyspI6GhEREeVxnDNLkhFCYNGiRRg7diySk5Ph4uKCzZs3o06dOlJHIyIiIgPBkVmSTFxcHObPn4/k5GS0a9cOYWFhLGSJiIhIKxyZJclYWVkhODgYp06dwogRIyCTyaSORERERAaGxSzpjRAC8+bNg7m5OQYNGgQA8PDwgIeHh8TJiIiIyFCxmCW9ePXqFfz9/bFnzx6YmJjA29sbZcuWlToWERERGTgWs5Trzpw5A19fXzx69Aimpqb47bffUKZMGaljERERUT7AC8Ao16jVavz888/44osv8OjRI5QrVw7nzp3D4MGDOT+WiIiIdIIjs5Qr1Go12rdvjz179gAAunbtiuXLl6NQoUISJyMiIqL8hCOzlCvkcjm8vLxgZmaGlStXYuPGjSxkiYiISOc4Mks6o1KpEB0dDXt7ewDAt99+i06dOuGzzz6TOBkRERHlVxyZJZ149uwZmjdvjiZNmiA+Ph7A+9FZFrJERESUm1jM0ic7evQoqlWrhsOHDyMiIgKXL1+WOhIREREVECxmKcdUKhWmTp2Kpk2b4tmzZ6hcuTIuXryI+vXrSx2NiIiICgjOmaUcefr0Kfz8/HD8+HEAQN++fbFgwQJYWFhIG4yIiIgKFBazlCPDhw/H8ePHYWlpieXLl8PPz0/qSERERFQAsZilHFmwYAHevn2LxYsXo3z58lLHISIiogKKc2YpWx4/fozFixdrHjs7O+Pw4cMsZImIiEhSHJmlj9q3bx969uyJly9fwtnZGe3bt5c6EhEREREAjsxSFpRKJcaPH49WrVrh5cuXqFGjBj7//HOpYxERERFpcGSWMvTgwQP4+vri3LlzAN5f8DV79myYmppKnIyIiIjo/7GYpXT27t2LHj164M2bN7CxscGaNWvQoUMHqWMRERERpcNiltJJSkrCmzdvUKtWLWzevBmurq5SRyIiIiLKEItZAgCkpKTA2Pj9j8PXX3+N7du3o3Xr1jAxMZE4GREZErVajeTkZJ0fV6lUwtjYGImJiVCpVDo/PuUu9p/hy40+NDExgVz+6ZdvsZglbNu2DZMmTcLx48fh5OQEAJxWQERaS05ORkREBNRqtc6PLYSAg4MDHj16BJlMpvPjU+5i/xm+3OhDuVwOV1fXTx44YzFbgCUmJuKbb77BkiVLAACzZ8/Gb7/9JnEqIjJEQghERkbCyMgIJUuW1Mloy3+p1Wq8e/cOVlZWOj825T72n+HTdR+q1Wo8ffoUkZGRKFWq1CcVyCxmC6g7d+6gS5cuCAsLAwB8++23mDFjhsSpiMhQpaSkID4+Hk5OTrCwsND58VOnL5iZmbEYMkDsP8OXG31YvHhxPH36FCkpKVAoFDk+DovZAmjz5s3o378/3r17B1tbW6xfvx4tWrSQOhYRGbDUOXScZ09E2ZX6+0KlUrGYpexbv349evXqBQBo0KABgoKC4OzsLHEqIsovOB+SiLJLZ3NvdXIUMhhff/01KleujO+++w5Hjx5lIUtEREQGjcVsARASEqK5utjS0hKXLl3CjBkzNEtxERER5VRycjI+++wznD59Wuoo+cbz589RvHhxPHnyROooBoHFbD4WFxeH3r17w8fHB3PmzNG0m5mZSZiKiCjv8Pf3h0wmg0wmg7GxMUqVKoXBgwfj9evX6bY9c+YMWrZsiSJFisDMzAyff/455syZk+Gam8eOHUPLli1RrFgxWFhYoFKlSvjmm28+WpyEhYWhU6dOsLe3h5mZGdzc3NC/f3/cvn1bZ69Z11asWAEXFxfUq1cv3XMDBgyAkZERNm/enO45f39/tG/fPl17eHg4ZDIZ7t+/r2kTQmDFihWoXbs2rKysULhwYXh6emLevHmIj4/X5ctJ4/Xr1+jRowdsbGxgY2OjuTtmVp49ewZ/f3/NxZDNmzfHnTt30myTlJSE4cOHw9bWFpaWlmjbti0eP36sed7Ozg49evTA1KlTc+Nl5TssZvOpf/75B7Vq1UJgYCDkcjmUSqXUkYiI8qTmzZsjMjIS9+/fx6pVq7Bnzx4MGTIkzTY7d+5Ew4YNUaJECRw7dgw3b97EyJEjMXPmTPj6+kIIodl2+fLlaNq0KRwcHLB9+3Zcv34dy5Ytw9u3b9MMLHxo7969qFOnDpKSkrBx40bcuHEDGzZsgI2NDb7//vscv77c/v2/cOFC9OvXL117fHw8goODMW7cOKxZs+aTztGjRw+MGjUK7dq1w7FjxxAeHo7vv/8eu3fvxqFDhz7p2Fnp1q0bwsPDceDAARw4cADh4eHo0aNHptsLIdC+fXvcu3cPu3fvRlhYGFxcXNC0aVPExcVpths1ahR27tyJzZs349SpU3j37h1at26d5g+j3r17Y+PGjRn+YUUfEAXM27dvBQDx9u1bvZwvLkkpXL7dK1y+3SvevIvP9fOp1WqxevVqYW5uLgAIBwcHcezYsVw/b36WnJwsdu3aJZKTk6WOQjnEPsx9CQkJ4vr16yIhIUEI8f53UVySUmdfsQlJ4umzaBGbkPTRbdVqdbZz9+rVS7Rr1y5N25gxY0TRokU1j9+9eyeKFSsmOnTokG7/P/74QwAQmzdvFkII8ejRI2FiYiJGjRqV4flev36dYXtcXJywtbUV7du3z3K/tWvXChsbmzTP7dy5U/z3n/OpU6eKatWqidWrVwtXV1chk8nEsmXLhJOTk1CpVGn2bdOmjejZs2ea11OjRg1hamoqXF1dxbRp04RSqcwwkxBChIaGCrlcnuG/qYGBgaJOnTrizZs3wtzcXPz9999pzp/R914IIcLCwgQAERERIYQQIjg4WAAQu3btSretWq0Wb968yTTfp7h+/boAIM6dO6dpO3v2rAAgbt68meE+t27dEgDEtWvXNG0pKSmiaNGiYuXKlUIIId68eSMUCoXmZ0YIIZ48eSLkcrk4cOBAmuOVLl1arF69WpcvK8dUKpV4/fp1up+hT/Hh743/0qZe46TJfOTdu3cYNGgQNm7cCADw8fHBhg0bYGdnJ3EyIipoEpQqVJpyUJJzX5/eDBYmOfvn7d69ezhw4ECaZYIOHTqEly9fYuzYsem2b9OmDdzc3BAUFIQuXbpg69atSE5Oxvjx4zM8fuHChTNsP3jwIKKjo7XeLzP//vsvtmzZgu3bt8PIyAjOzs4YMWIEjh07hiZNmgB4/xH6wYMHsWfPHk2G7t27Y8GCBWjQoAHu3r2LAQMGAECmH3efOHECbm5usLa2Tvfc6tWr0b17d9jY2KBFixbYuHEjZs2apdXrAICNGzeifPnyaNeuXbrnZDIZbGxsMt3Xysoqy2M3aNAA+/fvz/C5s2fPwsbGBrVr19a01alTBzY2Njhz5gzKly+fbp+kpCQAaafzGRkZwcTEBKdOnUK/fv0QGhoKpVIJHx8fzTZOTk6oUqUKzpw5g2bNmmnaa9WqhZMnT6JPnz5Zvo6CjsVsPnL79m1s2bIFRkZGmDFjBr799lsuTk1E9BF79+6FlZUVVCoVEhMTAQBz587VPJ86X7VixYoZ7l+hQgXNNnfu3IG1tTUcHR21ypA6p7JChQpa589IcnIyNmzYgOLFi2vamjdvjk2bNmmK2a1bt6Jo0aKaxzNnzsSECRM0yzeWKVMGM2bMwPjx4zMtZu/fv6+5DfqHr+fcuXPYsWMHAMDPzw8jRozAzJkztf536c6dOxkWjtkRHh6e5fPm5uaZPhcVFZXhYJCdnR2ioqIy3KdChQpwcXHBxIkTsXz5clhaWmLu3LmIiopCZGSk5rgmJiYoUqRImn3t7e3THdfZ2VlzcyPKHIvZfKRGjRpYvnw5ypUrh/r160sdh4gKMHOFEa5Pb/bxDbNJrVYjNiYWhawLfbQYMlcYaXXsRo0aYenSpYiPj8eqVatw+/ZtDB8+PN124j/zYj9sT10v87//r43Mjp1TLi4uaQpZ4H1BOWDAACxZsgSmpqbYuHEjfH19YWT0/vsVGhqKixcvYubMmZp9Ugv8+Pj4DO/slpCQkOFFxatXr0azZs1ga2sLAGjZsiX69euHw4cPo3nz5lq9lpx+TwHgs88+y9F+qTI6b1Z5FAoFtm/fjr59+6Jo0aIwMjJC06ZNs3VjooyOa25unqsXuOUXHLYzYDExMejZs2eav9p69+7NQpaIJCeTyWBhYqzTL3MTo2xtp23hY2lpic8++wxVq1bFggULkJSUhICAAM3zbm5uAIAbN25kuP/NmzdRrlw5zbZv377VjMJlV+o5bt68meV2crk8XeGb0QVelpaW6dratGkDtVqNP//8E48ePcLJkyfRvXt3zfNqtRoBAQEIDw/XfF29ehV37tzJdBUcW1vbdBcoqVQqrF+/Hn/++SeMjY1hbGwMKysrvH79Os2FYNbW1nj79m26Y6auFpA6fcDNzS3T7/3HWFlZZfmVVZHp4OCAZ8+epWt/8eIF7O3tM93Pw8MD4eHhePPmDSIjI3HgwAG8fPkSrq6umuMmJyen+749f/483XFfvXqV7o8SSo/FrIG6fPkyatSogQ0bNsDPzy/DpWGIiEh7U6dOxa+//oqnT58CeH/9QdGiRTNcieCPP/7AnTt30LVrVwBAx44dYWJigl9++SXDY2e2rJOPjw9sbW0/ul/x4sURGxub5sr4j32Unsrc3BwdOnTAxo0bERQUBDc3N3h4eGier1GjBm7duoXPPvss3Vdmo+Hu7u64efNmmgJ73759iI2NRVhYmKYovnz5MgIDA7F79268fPkSwPuP5K9du6aZ2pHq4sWLKF68uOZj+G7duuH27dvYvXt3uvMLITIsiP/7vcnqa9WqVZnu6+Xlhbdv3+LChQuatvPnz+Pt27eoW7dupvulsrGxQfHixXHnzh1cunRJM+fXw8MDCoUCISEhmm0jIyNx7dq1dMe9du0a3N3dP3quAk9nl6QZCENfzUCtVouFCxcKExMTAUCUKlVKnDlzRgdJKTO8Et7wsQ9zX1ZXJetCblxJLUTmV9R7eHiIoUOHah5v3bpVGBkZif79+4u///5bREREiFWrVokiRYqIjh07pllBYfHixUImk4k+ffqI48ePi/v374tTp06JAQMGiDFjxmSaZdeuXUKhUIg2bdqIkJAQERERIS5evCjGjRsnunTpIoQQ4uXLl8LS0lKMGDFC3LlzR2zcuFE4OTlluJpBRg4dOiRMTU1F+fLlxYwZM9I8d+DAAWFsbCymTp0qrl27Jq5fvy42b94sJk+enGnm6OhoYWJiIq5evappa9eunSZvKpVKJV69eiWcnZ3FvHnzhBDvr+p3cHAQHTt2FBcvXhT//vuv2LBhgyhSpIj45ZdfNPuq1WrRpUsXYW5uLn788Udx8eJFcf/+fbFnzx7RuHFjsXPnzkzzfarmzZuLqlWrirNnz4qzZ8+Kzz//XLRu3TrNNuXLlxc7duzQPN6yZYs4duyYuHv3rti1a5dwcXFJtxLGoEGDRIkSJcThw4fF5cuXRePGjUW1atVESkqKZpu4uDhhbm4uTpw4kWuvTxt5eTUDFrO5TJfF7OvXr0WHDh0EAAFAtG3bVrx8+VJHSSkzLIQMH/sw9+W3Ynbjxo3CxMREPHz4UNN24sQJ0bx5c2FjYyNMTExEpUqVxK+//pqmAEkVEhIimjVrJooUKSLMzMxEhQoVxNixY8XTp0+zzHPx4kXRoUMHUbx4cWFqaio+++wzMWDAAHHnzh3NNjt37hSfffaZMDMzE61btxYrVqzIdjGbkpIiHB0dBQBx9+7ddM8fOHBA1K1bV5ibmwtra2tRq1YtsWLFiiwz+/r6igkTJgghhIiKihLGxsZiy5YtabZJ7b9hw4aJzz//XNN+584d8fXXXwtnZ2dhaWkpPv/8c7Fo0aJ0/axSqcTSpUtFzZo1hYWFhbC2thYeHh5i/vz5Ij4+95a9fPnypfDz8xOFChUShQoVEn5+fumWVwMg1q5dq3k8f/58UaJECaFQKESpUqXEd999J5KSktLsk5CQIIYNGyaKFi0qzM3NRevWrdP8rAkhxKZNm0T58uVz66VpLS8XszIhdDzrPI+LiYmBjY0N3r59m+FSIroWn5yiWZ7m7+8bw8Yy8ysns/L48WM0aNAA9+/fh0KhwOzZszFixIgcT4qn7FMqldi3bx9atmyZZrkeMhzsw9yXmJiIiIgIuLq65spdBtVqNWJiYmBtbc1VWvKYq1evomnTpvj3339RqFChDLdh/2mvVq1aGDVqFLp16yZ1FAC504dZ/d7Qpl7jagYGwsnJCeXKlYNMJkNwcDBq1qwpdSQiIiJ8/vnn+OWXX3D//n18/vnnUsfJF54/f46OHTtq5mJT1ljM5mGvXr2CmZkZLCwsIJfLsWnTJhgbG2u9eDYREVFuSl2blnTDzs4u0xtoUHoc68+jzpw5g+rVq2PkyJGaNltbWxayRERERP/BYjaPUavV+OWXX/DFF1/g0aNHOH78eKZLuRAREREVdCxm85AXL16gdevW+Pbbb6FSqeDr64vQ0FCOxhIRERFlgnNm84iTJ0/C19cXT58+hZmZGebPn4/+/ftztQIiIiKiLLCYzQPi4+PRqVMnPHv2DOXLl8eWLVtQtWpVqWMRERER5XmcZpAHWFhYYM2aNejRowcuXbrEQpaIiIgomzgyK5Fjx44hISEBLVu2BAC0bNlS8/9ERERElD0cmdUzlUqFadOmoUmTJvDz88PDhw+ljkRERLlo2rRpqF69ep49z5dffolRo0bpPM/HlC5dGvPmzfukY/j7+6N9+/ZZbiPV6yP9kbyYXbJkieY2Zh4eHjh58mSW2//111/w8PCAmZkZypQpg2XLlukp6aeLioqEt7c3AgICIIRAhw4dYGtrK3UsIqIC7dGjR+jbty+cnJxgYmICFxcXjBw5Ei9fvtT6WDKZDLt27UrTNnbsWBw5ckRHaXPu+PHjkMlkXO4xF1y9ehUNGzaEubk5nJ2dMX36dAghstzn9evX6NGjB2xsbGBjY4MePXqk6ZuXL1+iefPmcHJygqmpKUqWLIlhw4YhJiZGs820adMgk8nSfVlaWmq22bFjB7y9vVG8eHFYW1vDy8sLBw8eTJMlMDAww+MkJiZmmH3WrFmQyWTp/kiYNm0aKlSoAEtLSxQpUgRNmzbF+fPns/ldzDlJi9ng4GCMGjUKkydPRlhYGBo0aIAWLVpkOloZERGBli1bokGDBggLC8OkSZMwYsQIbN++Xc/JtZcQcRn169TBsWPHYGlpiQ0bNmD16tWwsLCQOhoRUd6hUgHHjwNBQe//q1Ll6unu3bsHT09P3L59G0FBQfj333+xbNkyHDlyBF5eXnj16tUnn8PKygrFihXTQdq8Q6lUSh0hz4iJiYG3tzecnJxw8eJFLFy4EL/++ivmzp2b5X7dunVDeHg4Dhw4gAMHDiA8PBw9evTQPC+Xy9GuXTv88ccfuH37NgIDA3H48GEMGjRIs83YsWMRGRmZ5qtSpUro1KmTZpsTJ07A29sb+/btQ2hoKBo1aoQ2bdogLCwsTR5ra+t0xzIzM0uX++LFi1ixYkWG1/e4ublh0aJFuHr1Kk6dOoXSpUvDx8cHL168yPb3M0eEhGrVqiUGDRqUpq1ChQpiwoQJGW4/fvx4UaFChTRtAwcOFHXq1Mn2Od++fSsAiLdv32ofOAfeJSYLa6/OApAJAKJq1ari5s2bejk36UZycrLYtWuXSE5OljoK5RD7MPclJCSI69evi4SEhJwfZPt2IUqUEAL4/68SJYTYvl2oVCrx+vVroVKpdBdaCNG8eXNRokQJER8fn6Y9MjJSWFhYpPk3ysXFRUyfPl107dpVWFpaCkdHR7FgwYI0zwPQfLm4uAghhJg6daqoVq2aZrtevXqJdu3aiZkzZwo7OzthY2Mjpk2bJpRKpRg7dqwoUqSIcHZ2FqtXr06Tafz48aJcuXLC3NxcuLq6iu+++y7Nz/SH5/mviIiINNkAiF69egkhhGjYsKEYPny4GDdunChSpIiwt7cXU6dOTbM/ALF06VLRtm1bYWFhIaZMmSKEEOKPP/4QNWrUEKampsLV1VXzOv6bqWTJksLExEQ4ODiIYcOGpfl+zZw5U/Tu3VtYWVmJkiVLiuXLl6c575UrV0SjRo2EmZmZKFq0qOjfv7+IjY1N971M9e7dO9GjRw9haWkpHBwcxK+//ioaNmwoRo4cmeH3RReWLFkibGxsRGJioqZt1qxZwsnJSajV6gz3uX79ugAgzp07p2k7e/asAJBljTB//nxRokSJTJ8PDw8XAMSJEyeyzFypUiUREBCgebx27VphY2OT5T4qlUo8evRIlCtXToSEhGTr+5pacx0+fDjD57P6vaFNvSbZBWDJyckIDQ3FhAkT0rT7+PjgzJkzGe5z9uxZ+Pj4pGlr1qwZVq9eDaVSCYVCkW6fpKQkJCUlaR6nDs8rlUq9/GWZkpICdeI7AAK9/PtgwfzfYG5uzr9qDUhqX7HPDBf7MPcplUoIIaBWq6FWq7U/wI4dkHXuDAiB/66uLZ48ATp2hNiyBWjaVHMOXXj16hUOHjyIH374AaampmmOa2dnh27duiE4OBiLFi3SrPk9e/ZsTJw4EVOmTMGhQ4cwevRouLm5wdvbG+fPn4eDgwNWr16N5s2bw8jICGq1WvNxc+rxhRA4evQonJ2dcfz4cZw+fRr9+/fHmTNn8MUXX+Ds2bPYsmULBg0ahCZNmqBkyZIA3o/wrlmzBk5OTrh69SoGDhwIKysrjBs3TnPc/57nv5ydnbF161Z06tQJN27cgLW1NczNzTXbrlu3DqNHj8bZs2dx9uxZ9OnTB15eXvD29tYcY+rUqZg5cybmzJkDIyMj7N+/H927d8e8efPQoEED3L17F4MGDYIQAlOmTMG2bdvw22+/YdOmTahUqRLu3buHf//9N02+OXPmYPr06ZgwYQK2b9+OwYMHo379+qhQoQLi4+PRvHlz1K5dG+fPn8fz588xYMAADB06FGvXrtW85v/+TIwdOxbHjh3D9u3b4eDggMmTJyM0NBTVqlXL9Ofm5MmTaNWqVZY/KxMnTsTEiRMzfC613xQKheYc3t7emDhxIu7duwdXV9d0+5w+fRo2NjaoWbOmZp9atWrBxsYGp06dQrly5dLt8/TpU+zYsQNffPFFpq9l5cqVcHNzQ7169TLdRq1WIzY2FkWKFNFso1ar8e7dO7i4uEClUqFatWqYPn063N3dNfsJITBu3Di0aNECjRs3xg8//JDl+zE5ORnLly+HjY0NPv/88wy3S31/KJVKGBkZpXlOm9/XkhWz0dHRUKlUsLe3T9Nub2+PqKioDPeJiorKcPuUlBRER0fD0dEx3T6zZs1CQEBAuvZDhw7p5SP+JBVQtHE/mJfxRMs2NXDs2LFcPyfljpCQEKkj0CdiH+YeY2NjODg44N27d0hOTtZuZ5UK1iNHpitkAUAmBIRMBtmoUcDffyM2NlZXkREeHg4hBFxcXNLMQ0zl6uqK169f4969eyhevDjUajVq1aqFwYMHAwB69uyJ48eP49dff0Xt2rVhamoKADA1NdX8+xITE4OkpCSoVKo0gymFCxfGjBkzIJfL0bFjR/zyyy+IjY3F0KFDAQBDhgzBzz//jMOHD+Prr78GAAwfPlyTrWHDhhgyZAg2b96MgQMHAkC683wo9SNjc3PzNPlSUlJQqVIlzfzH9u3bY+HChdi/fz9q166t2f/rr79Gx44dNY9nzJiBkSNH4quvvgIA2NraYsKECZg2bRpGjRqFO3fuwM7ODrVq1YJCoYCHhwc8PDw0+dRqNZo2bQo/Pz8AwKBBg/Dbb7/hwIEDcHJywrp16xAfH4+FCxfC0tISpUqVwk8//YSuXbti8uTJsLOzg1KpREpKCmJiYvDu3TusWbMGS5cu1eReuHAhKleujOTk5Ey/L25ubjhx4kSGz6UqUqRIpvs/efIEpUqVSvN86vf37t27GU4xefDgAWxtbdMd09bWFg8ePEjT3rdvX+zfvx8JCQlo3rw55syZk2GWpKQkbNy4EaNGjco0KwAsWLAA7969Q/PmzTXblSxZEosXL0alSpUQGxuL5cuXo0GDBjh58iTKli0LANi+fTv+/vtvHD16VPNzk9H39cCBA+jXrx/i4+Ph4OCAHTt2wMTEJMNMycnJSEhIwIkTJ5CSkpLmufj4+Exfw4ckX5rrwztcCSGyvOtVRttn1J5q4sSJGDNmjOZxTEwMSpYsCR8fH1hbW+c0drYJIdC4cRKOHpWjVbOmMDExyfVzkm4plUqEhITA29s7w9F/yvvYh7kvMTERjx49gpWVVYbz7LJ0/DjkT59m+rRMCMiePIHx2bMwb9FCZ3dGTL1IxtzcPMN/D1KLU2tra1hbW0Mul6NBgwZptv3iiy8wf/78NG0fHs/U1BRGRkaaNoVCgSpVqqS5VbmjoyMqV66cZr9ixYrh3bt3mrZt27ZhwYIF+Pfff/Hu3TukpKRosmV0ng+lFliFChVKs42xsTGqVq2aps3Z2Rlv375N0+bl5ZXm8d9//42wsLA0c0NVKhUSExNhbGyM7t27Y/ny5ahRowaaNWuGL7/8Ep06ddK8B+VyOTw8PNIc09HREbGxsbC2tsb9+/dRvXr1NANV3t7eUKvVePr0KT777DMoFAoYGxvD2toaERERSE5ORuPGjTXHtLa2Rvny5WFiYpLp98Xa2jrdQJk2jIyM0h0/9Y8uKyurDM9rZmaWYV/JZLJ0Pz8LFy7EmzdvcOvWLXz33XcICAjA4sWL0x0zKCgI7969Q//+/TN9rUFBQfj555+xc+dOTZEKAE2aNEGTJk00j318fODp6YnAwEDMnz8fjx49wqRJk7Bt2zYUL14cMpkMxsbGGX5fW7VqhcuXLyM6OhqrVq1C3759cfbsWdjZ2aXLk5iYCHNzc3zxxRfpfm9kVZB/SLJi1tbWFkZGRulGYZ8/f57pD5WDg0OG2xsbG2c6ud7U1FTzC+m/FAqF3v5Rs5HJYGoEmJiY8B9SA6bPnxnKHezD3KNSqSCTySCXyyGXa3lt8bNn2dpMFhWlOYcuuLm5QSaT4ebNmxke89atWyhSpAjs7Ow0BfSH50+96vu/bR9+D1L3TW2TyWQwMTFJt01GbUIIyOVynDt3Dt26dUNAQACaNWsGGxsbbN68GXPmzElz3P+e50Op7Rn10YfnlsvlmnOnKlSoUJrHarUaAQEB6NChQ7pzWVhYwMrKCrdu3UJISAhCQkIwduxYLFmyBH/99ZfmfZjVa059/GEu4H0BKZfL03z///v6P3x9Wf3cnDx5Ei1atMjwuVSTJk3CpEmTMnzO0dERz549S3P86OhozXMZnTejfQDgxYsXcHBwSNPu5OQEJycnVKpUCcWLF0eDBg0wZcqUdJ9Gr1mzBq1bt4aTk1OGOYODg9G/f39s3bo13ZTND8nlctSsWRP//vsv5HI5wsLC8Pz5czRq1EizjUqlwokTJ7B48WIkJSVppgkUKlQIhQoVgpubG+rWrYty5cph7dq1GU7TSO23jH43a/O7WrJi1sTEBB4eHggJCdF8RAG8/xiwXbt2Ge7j5eWFPXv2pGk7dOgQPD09+Q8UEZGhymCKWEaEg4NOT1usWDF4e3tjyZIlGD16NMzNzTXPRUVFYePGjejZs2eakeBz586lOca5c+dQoUIFzWOFQgFVLqzAcPr0abi4uGDy5MmatgcPHmh1jNRPBnWVr0aNGrh16xY+++yzTLcxNzdH27Zt0bp1a/Ts2RO1atXC1atXUaNGjY8ev1KlSli3bh3i4uI0o+inT5+GXC6Hm5tbuu1TR2rPnTuHUqVKAXi//NXt27fRsGHDTM/j6emJ8PDwLLMULVo00+e8vLwwadIkJCcna77Hhw4dgpOTE0qXLp3pPm/fvsWFCxdQq1YtAMD58+fx9u1b1K1bN9NzpX4a/d9rgYD3qz0dO3YMf/zxR4b7BQUFoU+fPggKCvro/ODU84SHh+Pzzz8H8H7k9u+//8a7d+9gZWUFuVyO3r17o0KFCvj222/TzXf98Fgf5tU1SacZjBkzBj169ICnpye8vLywYsUKPHz4ULPsxMSJE/HkyROsX78ewPv5NIsWLcKYMWPQv39/nD17FqtXr0ZQUJCUL4OIiD5FgwZAiRLAkyfv1zD4kEwGUaIEUry8dH7qRYsWoW7dumjWrBl++OEHuLq64p9//sG4cePg7OyMmTNnptn+9OnT+OWXX9C+fXuEhIRg69at+PPPPzXPly5dGkeOHEG9evVgamqKIkWK6CTnZ599hocPH2Lz5s2oWbMm/vzzT+zcuVOrY7i4uEAmk2Hv3r1o2bIlzM3NYWVlleNMU6ZMQevWrVGyZEl06tQJcrkcV65cwdWrV/HDDz8gMDAQKpUKtWvXhpmZGYKDg2Fubg4XF5dsHd/Pzw9Tp05Fr169MG3aNLx48QLDhw9Hjx49MvwE18rKCn379sW4ceNQrFgx2NvbY/LkyR8dyTc3N8+yIP+Y1BFzf39/TJo0CXfu3MGPP/6IKVOmaP4QunDhAnr27IkjR47A2dkZFStWRPPmzdG/f38sX74cADBgwAC0bt0a5cuXBwDs27cPz549Q82aNWFlZYXr169j/PjxqFevXroiec2aNXB0dMxwhDkoKAg9e/bE/PnzUadOHc0n3Obm5rCxsQEABAQEoE6dOihXrhxiYmKwYMEChIeHa6YzFCpUCFWqVEFMTIxmyo2lpSWKFSuGKlWqAADi4uIwc+ZMtG3bFo6Ojnj58iWWLFmCx48fp1kqLDdIus5sly5dMG/ePEyfPh3Vq1fHiRMnsG/fPs0PemRkZJo1Z11dXbFv3z4cP34c1atXx4wZM7BgwQLN5HgiIjJARkbA/Pnv///D+bD/eyzmzn2/nY6VK1cOly5dQtmyZdGlSxeULVsWAwYMQKNGjXD27Nl0I3LffPMNQkND4e7ujhkzZmDOnDlo1qyZ5vk5c+YgJCQEJUuWTHMl+Kdq164dRo8ejWHDhqF69eo4c+YMvv/+e62O4ezsjICAAEyYMAH29vYYNmzYJ2Vq1qwZ9u7di5CQENSsWRN16tTB3LlzNf+GFy5cGCtXrkS9evU0/8bv3r0722vuWlhY4ODBg3j16hVq1qyJjh07okmTJli0aFGm+8yePRtffPEF2rZti6ZNm6J+/frw8PD4pNf5MTY2NggJCcHjx4/h6emJIUOGYMyYMWmu14mPj8etW7fSXKG/ceNGfP755/Dx8YGPjw+qVq2KDRs2aJ43NzfHypUrUb9+fVSsWBGjRo1C69atsXfv3jTnV6vVCAwMhL+/f4YjpMuXL0dKSgqGDh0KR0dHzdfIkSM127x58wYDBgxAxYoV4ePjgydPnuDEiROaUePsMDIyws2bN/H111/Dzc0NrVu3xosXL3Dy5ElUrlw528fJCZkQGf0ZnH/FxMTAxsYm3cT23KRUKrFv3z60bNmS0yEMEPvP8LEPc19iYiIiIiI0d3TMkR07gJEjgceP/7+tZElg3jyo27dPMyokhdKlS2PUqFG8NWoOqNVqyfuPPk1u9GFWvze0qdckX82AiIgIANChA9CuHXDyJBAZ+X4ubYMG70dkdbS2LBHlPyxmiYgo7zAyAr78UuoURGRAWMwSERFlw/3796WOQEQZ4MQVIiIiIjJYLGaJiEhnCtg1xUT0CXT1+4LFLBERfbLUJYGSk5MlTkJEhiL190VWN13IDs6ZJSKiT2ZsbAwLCwu8ePECCoVC58svqdVqJCcnIzExkUs7GSD2n+HTdR+q1Wq8ePECFhYWMDb+tHKUxSwREX0ymUwGR0dHREREaH2b1ewQQiAhIQHm5uZpbi9LhoH9Z/hyow/lcjlKlSr1ycdjMUtERDphYmKCcuXK5cpUA6VSiRMnTuCLL77gjS8MEPvP8OVGH5qYmOhklJfFLBER6YxcLs/5HcCyYGRkhJSUFJiZmbEYMkDsP8OXl/uQE1eIiIiIyGCxmCUiIiIig8ViloiIiIgMVoGbM5u6QG9MTIzezqlUKhEfH4+YmJg8N8+EPo79Z/jYh4aPfWjY2H+GT999mFqnZefGCgWumI2NjQUAlCxZUuIkRERERJSV2NhY2NjYZLmNTBSwew+q1Wo8ffoUhQoV0ttadzExMShZsiQePXoEa2trvZyTdIf9Z/jYh4aPfWjY2H+GT999KIRAbGwsnJycPrp8V4EbmZXL5ShRooQk57a2tuab2ICx/wwf+9DwsQ8NG/vP8OmzDz82IpuKF4ARERERkcFiMUtEREREBovFrB6Ymppi6tSpMDU1lToK5QD7z/CxDw0f+9Cwsf8MX17uwwJ3ARgRERER5R8cmSUiIiIig8ViloiIiIgMFotZIiIiIjJYLGaJiIiIyGCxmNWBJUuWwNXVFWZmZvDw8MDJkyez3P6vv/6Ch4cHzMzMUKZMGSxbtkxPSSkz2vThjh074O3tjeLFi8Pa2hpeXl44ePCgHtNSRrR9H6Y6ffo0jI2NUb169dwNSB+lbR8mJSVh8uTJcHFxgampKcqWLYs1a9boKS19SNv+27hxI6pVqwYLCws4Ojqid+/eePnypZ7S0odOnDiBNm3awMnJCTKZDLt27froPnmmnhH0STZv3iwUCoVYuXKluH79uhg5cqSwtLQUDx48yHD7e/fuCQsLCzFy5Ehx/fp1sXLlSqFQKMS2bdv0nJxSaduHI0eOFD///LO4cOGCuH37tpg4caJQKBTi8uXLek5OqbTtw1Rv3rwRZcqUET4+PqJatWr6CUsZykkftm3bVtSuXVuEhISIiIgIcf78eXH69Gk9pqZU2vbfyZMnhVwuF/Pnzxf37t0TJ0+eFJUrVxbt27fXc3JKtW/fPjF58mSxfft2AUDs3Lkzy+3zUj3DYvYT1apVSwwaNChNW4UKFcSECRMy3H78+PGiQoUKadoGDhwo6tSpk2sZKWva9mFGKlWqJAICAnQdjbIpp33YpUsX8d1334mpU6eymJWYtn24f/9+YWNjI16+fKmPePQR2vbf7NmzRZkyZdK0LViwQJQoUSLXMlL2ZaeYzUv1DKcZfILk5GSEhobCx8cnTbuPjw/OnDmT4T5nz55Nt32zZs1w6dIlKJXKXMtKGctJH35IrVYjNjYWRYsWzY2I9BE57cO1a9fi7t27mDp1am5HpI/ISR/+8ccf8PT0xC+//AJnZ2e4ublh7NixSEhI0Edk+o+c9F/dunXx+PFj7Nu3D0IIPHv2DNu2bUOrVq30EZl0IC/VM8Z6PVs+Ex0dDZVKBXt7+zTt9vb2iIqKynCfqKioDLdPSUlBdHQ0HB0dcy0vpZeTPvzQnDlzEBcXh86dO+dGRPqInPThnTt3MGHCBJw8eRLGxvw1KLWc9OG9e/dw6tQpmJmZYefOnYiOjsaQIUPw6tUrzpvVs5z0X926dbFx40Z06dIFiYmJSElJQdu2bbFw4UJ9RCYdyEv1DEdmdUAmk6V5LIRI1/ax7TNqJ/3Rtg9TBQUFYdq0aQgODoadnV1uxaNsyG4fqlQqdOvWDQEBAXBzc9NXPMoGbd6HarUaMpkMGzduRK1atdCyZUvMnTsXgYGBHJ2ViDb9d/36dYwYMQJTpkxBaGgoDhw4gIiICAwaNEgfUUlH8ko9wyGJT2BrawsjI6N0f3k+f/483V8rqRwcHDLc3tjYGMWKFcu1rJSxnPRhquDgYPTt2xdbt25F06ZNczMmZUHbPoyNjcWlS5cQFhaGYcOGAXhfGAkhYGxsjEOHDqFx48Z6yU7v5eR96OjoCGdnZ9jY2GjaKlasCCEEHj9+jHLlyuVqZvp/Oem/WbNmoV69ehg3bhwAoGrVqrC0tESDBg3www8/8FNKA5CX6hmOzH4CExMTeHh4ICQkJE17SEgI6tatm+E+Xl5e6bY/dOgQPD09oVAoci0rZSwnfQi8H5H19/fHpk2bOMdLYtr2obW1Na5evYrw8HDN16BBg1C+fHmEh4ejdu3a+opO/5OT92G9evXw9OlTvHv3TtN2+/ZtyOVylChRIlfzUlo56b/4+HjI5WlLECMjIwD/P7pHeVueqmf0fslZPpO6HMnq1avF9evXxahRo4SlpaW4f/++EEKICRMmiB49emi2T13KYvTo0eL69eti9erVXJpLYtr24aZNm4SxsbFYvHixiIyM1Hy9efNGqpdQ4Gnbhx/iagbS07YPY2NjRYkSJUTHjh3FP//8I/766y9Rrlw50a9fP6leQoGmbf+tXbtWGBsbiyVLloi7d++KU6dOCU9PT1GrVi2pXkKBFxsbK8LCwkRYWJgAIObOnSvCwsI0y6vl5XqGxawOLF68WLi4uAgTExNRo0YN8ddff2me69Wrl2jYsGGa7Y8fPy7c3d2FiYmJKF26tFi6dKmeE9OHtOnDhg0bCgDpvnr16qX/4KSh7fvwv1jM5g3a9uGNGzdE06ZNhbm5uShRooQYM2aMiI+P13NqSqVt/y1YsEBUqlRJmJubC0dHR+Hn5yceP36s59SU6tixY1n+25aX6xmZEBzPJyIiIiLDxDmzRERERGSwWMwSERERkcFiMUtEREREBovFLBEREREZLBazRERERGSwWMwSERERkcFiMUtEREREBovFLBEREREZLBazREQAAgMDUbhwYalj5Fjp0qUxb968LLeZNm0aqlevrpc8RET6wmKWiPINf39/yGSydF///vuv1NEQGBiYJpOjoyM6d+6MiIgInRz/4sWLGDBggOaxTCbDrl270mwzduxYHDlyRCfny8yHr9Pe3h5t2rTBP//8o/VxDPmPCyLSHxazRJSvNG/eHJGRkWm+XF1dpY4FALC2tkZkZCSePn2KTZs2ITw8HG3btoVKpfrkYxcvXhwWFhZZbmNlZYVixYp98rk+5r+v888//0RcXBxatWqF5OTkXD83ERU8LGaJKF8xNTWFg4NDmi8jIyPMnTsXn3/+OSwtLVGyZEkMGTIE7969y/Q4f//9Nxo1aoRChQrB2toaHh4euHTpkub5M2fO4IsvvoC5uTlKliyJESNGIC4uLstsMpkMDg4OcHR0RKNGjTB16lRcu3ZNM3K8dOlSlC1bFiYmJihfvjw2bNiQZv9p06ahVKlSMDU1hZOTE0aMGKF57r/TDEqXLg0A+OqrryCTyTSP/zvN4ODBgzAzM8ObN2/SnGPEiBFo2LChzl6np6cnRo8ejQcPHuDWrVuabbLqj+PHj6N37954+/atZoR32rRpAIDk5GSMHz8ezs7OsLS0RO3atXH8+PEs8xBR/sZilogKBLlcjgULFuDatWtYt24djh49ivHjx2e6vZ+fH0qUKIGLFy8iNDQUEyZMgEKhAABcvXoVzZo1Q4cOHXDlyhUEBwfj1KlTGDZsmFaZzM3NAQBKpRI7d+7EyJEj8c033+DatWsY+H/t3F1Ik20cBvDLfZFN7cODSrQNlaEHFQmVGR1URrJoMXBUjhTJ0tICo4hOWhAGIU4jKDuIiWKYlAuhItLM0oJMCfsiJsmIUiKyojRt+n8PXnxour5M3t6N6wce3B/P7f/2BrmYz21+PnJzc9HS0gIAuHjxIsrLy3H27Fl4PB5cvnwZixYtCrhuR0cHAMDlcqGvr09pfys9PR2zZ8/GpUuXlL7R0VHU19fDbrdP2z7fv3+P8+fPA4Dy8wN+fB5paWmoqKhQPuHt6+vDgQMHAAC5ublob29HXV0duru7YbPZkJGRAY/H88s1EVGIESKiEJGTkyNqtVr0er3ylZmZGXBufX29REdHK22XyyWzZs1S2pGRkVJVVRXw2e3bt8uuXbv8+u7cuSMqlUqGhoYCPjNx/ZcvX0pqaqrExsbK8PCwpKWlyc6dO/2esdlsYjabRUSkrKxMTCaTjIyMBFzfYDBIeXm50gYgbrfbb47D4ZAlS5Yo7X379snatWuV9vXr10Wn08m7d+/+aJ8ARK/Xy8yZMwWAABCLxRJw/rifnYeISE9Pj4SFhcmrV6/8+tetWyeHDx/+4fpEFLo0fzdKExFNrzVr1uDMmTNKW6/XAwBaWlpw/PhxPH36FB8/foTP58OXL1/w+fNnZc639u/fj7y8PNTU1CA9PR02mw0JCQkAgM7OTvT09KC2tlaZLyIYGxtDb28vkpOTA9b24cMHREREQEQwODiIlJQUNDQ0QKfT4dmzZ34XuABg1apVOHnyJADAZrOhoqIC8fHxyMjIgNlsxqZNm6DRTP3XuN1ux8qVK/H69WvExMSgtrYWZrMZc+bM+aN9RkZGoqurCz6fD62trSgtLUVlZaXfnN89DwDo6uqCiMBkMvn1Dw8P/yfvAhPR/xPDLBGFFL1ej8TERL8+r9cLs9mMgoICHDt2DHPnzkVbWxt27NiBr1+/Blzn6NGjyMrKwpUrV3Dt2jU4HA7U1dXBarVibGwM+fn5fu+sjlu4cOF3axsPeSqVCvPmzZsU2sLCwvzaIqL0xcXF4fnz57hx4waampqwZ88elJaWorW11e/P979j+fLlSEhIQF1dHXbv3g232w2Xy6WMT3WfKpVKOYOkpCT09/djy5YtuH37NoCpncd4PWq1Gp2dnVCr1X5jERERv7V3IgodDLNEFPIePHgAn8+HsrIyqFT/XhWor6//6XMmkwkmkwnFxcXYtm0bXC4XrFYrUlJS8OTJk0mh+We+DXkTJScno62tDdnZ2Urf3bt3/T79DA8Ph8VigcViQWFhIZKSkvDo0SOkpKRMWk+r1f7Sf0nIyspCbW0tYmNjoVKpsHHjRmVsqvucqLi4GE6nE263G1ar9ZfOQ6fTTap/6dKlGB0dxZs3b7B69eo/qomIQgcvgBFRyEtISIDP58OpU6fw4sUL1NTUTPqz97eGhoZQVFSEW7duwev1or29HR0dHUqwPHToEO7du4fCwkI8fPgQHo8HjY2N2Lt375RrPHjwIKqqqlBZWQmPxwOn04mGhgbl4lNVVRXOnTuHx48fK3sIDw+HwWAIuJ7RaERzczP6+/sxMDDw3e9rt9vR1dWFkpISZGZmYsaMGcrYdO0zKioKeXl5cDgcEJFfOg+j0YhPnz6hubkZb9++xeDgIEwmE+x2O7Kzs9HQ0IDe3l50dHTgxIkTuHr16m/VREQh5G++sEtENJ1ycnJk8+bNAcecTqcsWLBAwsPDZcOGDVJdXS0AZGBgQET8LxwNDw/L1q1bJS4uTnQ6ncTExEhRUZHfpaf79+/L+vXrJSIiQvR6vSxevFhKSkq+W1ugC00TnT59WuLj40Wr1YrJZJLq6mplzO12y4oVKyQqKkr0er2kpqZKU1OTMj7xAlhjY6MkJiaKRqMRg8EgIpMvgI1btmyZAJCbN29OGpuufXq9XtFoNHLhwgUR+fl5iIgUFBRIdHS0ABCHwyEiIiMjI3LkyBExGo2i1Wpl/vz5YrVapbu7+7s1EVFoCxMR+btxmoiIiIhoaviaAREREREFLYZZIiIiIgpaDLNEREREFLQYZomIiIgoaDHMEhEREVHQYpglIiIioqDFMEtEREREQYthloiIiIiCFsMsEREREQUthlkiIiIiCloMs0REREQUtP4B3/7Pq/L40AUAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "     - AUC: 90.19%\n",
+      "    - Optimal Threshold: 0.0372543\n",
+      "    - F1 Score: 0.87\n",
+      "    - CONFUSION MATRIX:\n",
+      " [[17  3]\n",
+      " [16 64]] \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:04<00:00,  9.10it/s]\n",
+      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.10it/s]\n",
+      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.12it/s]\n",
+      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.11it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.11it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "- OK - Evaluating model (33.13 s)\n",
+      "\n",
+      "Dataset               F1 Score\n",
+      "------------------------------\n",
+      "Anonaly lvl 1 test        1.00\n",
+      "Anonaly lvl 2 test        0.79\n",
+      "Anonaly lvl 3 test        0.71\n",
+      "\n",
+      "Anomaly all test          0.88\n",
+      "\n",
+      "No Anomaly Test           0.92\n",
+      "\n",
+      "All test                  0.87\n"
+     ]
+    }
+   ],
+   "source": [
+    "# STEPS = 5000, MODEL TYPE = MEDIUM, WEIGHT = none\n",
+    "model8 = EfficientAD({**config, \"train_steps\": 5000, \"model_type\": \"medium\", \"weight_path\":\"\"})\n",
+    "model8.create_model()\n",
+    "model8.display_eval_result()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- Setting seed to 42\n",
+      "- OK - Setting seed to 42 (0.50 ms)\n",
+      "\n",
+      "- Setting datasets path\n",
+      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
+      "- OK - Setting datasets path (4.48 ms)\n",
+      "\n",
+      "- Setting config\n",
+      "     Output folder path: ../output/cookies_3_steps_10000_medium\n",
+      "- OK - Setting config (0.11 ms)\n",
+      "\n",
+      "- Prepare teacher, student & autoencoder\n",
+      "     No weight to load\n",
+      "     Training\n",
+      "- OK - Prepare teacher, student & autoencoder (160.58 ms)\n",
+      "\n",
+      "- Normalizing teacher\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.63it/s]\n",
+      "    Computing std of features:   0%|                                                                                               | 0/72 [00:00<?, ?it/s]\n",
+      "    Computing std of features:   0%|                                                                                               | 0/72 [00:00<?, ?it/s]\n"
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[10], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# STEPS = 10000, MODEL TYPE = MEDIUM, WEIGHT = none\u001b[39;00m\n\u001b[1;32m      2\u001b[0m model9 \u001b[38;5;241m=\u001b[39m EfficientAD({\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain_steps\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m10000\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_type\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmedium\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mweight_path\u001b[39m\u001b[38;5;124m\"\u001b[39m:\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m})\n\u001b[0;32m----> 3\u001b[0m \u001b[43mmodel9\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m model9\u001b[38;5;241m.\u001b[39mdisplay_eval_result()\n",
+      "File \u001b[0;32m~/workspace/ai/notebooks/../code/efficientad_fns.py:69\u001b[0m, in \u001b[0;36mEfficientAD.create_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     64\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mlog_params({\n\u001b[1;32m     65\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcfg,\n\u001b[1;32m     66\u001b[0m }) \n\u001b[1;32m     68\u001b[0m \u001b[38;5;66;03m# Execute all train-validation-testing steps\u001b[39;00m\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;66;03m# Log performance metrics\u001b[39;00m\n\u001b[1;32m     72\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mlog_metric(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfinal_auc)\n",
+      "File \u001b[0;32m~/workspace/ai/notebooks/../code/efficientad_fns.py:47\u001b[0m, in \u001b[0;36mEfficientAD.do_all\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;66;03m# Prepare models and perform training  \u001b[39;00m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprepare_teacher_student_autoencoder()\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mteacher_normalization\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain()\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_models()\n",
+      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    114\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/workspace/ai/notebooks/../code/efficientad_fns.py:439\u001b[0m, in \u001b[0;36mEfficientAD.teacher_normalization\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    436\u001b[0m channel_mean \u001b[38;5;241m=\u001b[39m channel_mean[\u001b[38;5;28;01mNone\u001b[39;00m, :, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m]\n\u001b[1;32m    438\u001b[0m mean_distances \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m--> 439\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m train_image, _ \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_loader, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m    Computing std of features\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m    440\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcfg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mon_gpu\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m    441\u001b[0m         train_image \u001b[38;5;241m=\u001b[39m train_image\u001b[38;5;241m.\u001b[39mcuda()\n",
+      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/tqdm/std.py:1181\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1178\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m   1180\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1181\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m   1182\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m   1183\u001b[0m         \u001b[38;5;66;03m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m   1184\u001b[0m         \u001b[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/utils/data/dataloader.py:631\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    628\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    629\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m    630\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 631\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    632\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    633\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m    634\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m    635\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n",
+      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1329\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1326\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_data(data)\n\u001b[1;32m   1328\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shutdown \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tasks_outstanding \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m-> 1329\u001b[0m idx, data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1330\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tasks_outstanding \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   1331\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable:\n\u001b[1;32m   1332\u001b[0m     \u001b[38;5;66;03m# Check for _IterableDatasetStopIteration\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1285\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._get_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1283\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory:\n\u001b[1;32m   1284\u001b[0m     \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_pin_memory_thread\u001b[38;5;241m.\u001b[39mis_alive():\n\u001b[0;32m-> 1285\u001b[0m         success, data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_try_get_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1286\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m success:\n\u001b[1;32m   1287\u001b[0m             \u001b[38;5;28;01mreturn\u001b[39;00m data\n",
+      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1133\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._try_get_data\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1120\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_try_get_data\u001b[39m(\u001b[38;5;28mself\u001b[39m, timeout\u001b[38;5;241m=\u001b[39m_utils\u001b[38;5;241m.\u001b[39mMP_STATUS_CHECK_INTERVAL):\n\u001b[1;32m   1121\u001b[0m     \u001b[38;5;66;03m# Tries to fetch data from `self._data_queue` once for a given timeout.\u001b[39;00m\n\u001b[1;32m   1122\u001b[0m     \u001b[38;5;66;03m# This can also be used as inner loop of fetching without timeout, with\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1130\u001b[0m     \u001b[38;5;66;03m# Returns a 2-tuple:\u001b[39;00m\n\u001b[1;32m   1131\u001b[0m     \u001b[38;5;66;03m#   (bool: whether successfully get data, any: data if successful else None)\u001b[39;00m\n\u001b[1;32m   1132\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1133\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_queue\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1134\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m (\u001b[38;5;28;01mTrue\u001b[39;00m, data)\n\u001b[1;32m   1135\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m   1136\u001b[0m         \u001b[38;5;66;03m# At timeout and error, we manually check whether any worker has\u001b[39;00m\n\u001b[1;32m   1137\u001b[0m         \u001b[38;5;66;03m# failed. Note that this is the only mechanism for Windows to detect\u001b[39;00m\n\u001b[1;32m   1138\u001b[0m         \u001b[38;5;66;03m# worker failures.\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/queue.py:180\u001b[0m, in \u001b[0;36mQueue.get\u001b[0;34m(self, block, timeout)\u001b[0m\n\u001b[1;32m    178\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m remaining \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.0\u001b[39m:\n\u001b[1;32m    179\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m Empty\n\u001b[0;32m--> 180\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnot_empty\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mwait\u001b[49m\u001b[43m(\u001b[49m\u001b[43mremaining\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    181\u001b[0m item \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get()\n\u001b[1;32m    182\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnot_full\u001b[38;5;241m.\u001b[39mnotify()\n",
+      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/threading.py:324\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    322\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    323\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 324\u001b[0m         gotit \u001b[38;5;241m=\u001b[39m \u001b[43mwaiter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    325\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    326\u001b[0m         gotit \u001b[38;5;241m=\u001b[39m waiter\u001b[38;5;241m.\u001b[39macquire(\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    },
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[10], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# STEPS = 10000, MODEL TYPE = MEDIUM, WEIGHT = none\u001b[39;00m\n\u001b[1;32m      2\u001b[0m model9 \u001b[38;5;241m=\u001b[39m EfficientAD({\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mconfig, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain_steps\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;241m10000\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmodel_type\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmedium\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mweight_path\u001b[39m\u001b[38;5;124m\"\u001b[39m:\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m})\n\u001b[0;32m----> 3\u001b[0m \u001b[43mmodel9\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      4\u001b[0m model9\u001b[38;5;241m.\u001b[39mdisplay_eval_result()\n",
+      "File \u001b[0;32m~/workspace/ai/notebooks/../code/efficientad_fns.py:69\u001b[0m, in \u001b[0;36mEfficientAD.create_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     64\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mlog_params({\n\u001b[1;32m     65\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcfg,\n\u001b[1;32m     66\u001b[0m }) \n\u001b[1;32m     68\u001b[0m \u001b[38;5;66;03m# Execute all train-validation-testing steps\u001b[39;00m\n\u001b[0;32m---> 69\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdo_all\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;66;03m# Log performance metrics\u001b[39;00m\n\u001b[1;32m     72\u001b[0m mlflow\u001b[38;5;241m.\u001b[39mlog_metric(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauc\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfinal_auc)\n",
+      "File \u001b[0;32m~/workspace/ai/notebooks/../code/efficientad_fns.py:47\u001b[0m, in \u001b[0;36mEfficientAD.do_all\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;66;03m# Prepare models and perform training  \u001b[39;00m\n\u001b[1;32m     46\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprepare_teacher_student_autoencoder()\n\u001b[0;32m---> 47\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mteacher_normalization\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     49\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain()\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msave_models()\n",
+      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    112\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m    113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    114\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/workspace/ai/notebooks/../code/efficientad_fns.py:439\u001b[0m, in \u001b[0;36mEfficientAD.teacher_normalization\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    436\u001b[0m channel_mean \u001b[38;5;241m=\u001b[39m channel_mean[\u001b[38;5;28;01mNone\u001b[39;00m, :, \u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;28;01mNone\u001b[39;00m]\n\u001b[1;32m    438\u001b[0m mean_distances \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m--> 439\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m train_image, _ \u001b[38;5;129;01min\u001b[39;00m tqdm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtrain_loader, desc\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m    Computing std of features\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m    440\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcfg[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mon_gpu\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m    441\u001b[0m         train_image \u001b[38;5;241m=\u001b[39m train_image\u001b[38;5;241m.\u001b[39mcuda()\n",
+      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/tqdm/std.py:1181\u001b[0m, in \u001b[0;36mtqdm.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1178\u001b[0m time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_time\n\u001b[1;32m   1180\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1181\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m obj \u001b[38;5;129;01min\u001b[39;00m iterable:\n\u001b[1;32m   1182\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m obj\n\u001b[1;32m   1183\u001b[0m         \u001b[38;5;66;03m# Update and possibly print the progressbar.\u001b[39;00m\n\u001b[1;32m   1184\u001b[0m         \u001b[38;5;66;03m# Note: does not call self.update(1) for speed optimisation.\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/utils/data/dataloader.py:439\u001b[0m, in \u001b[0;36mDataLoader.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    437\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_iterator\n\u001b[1;32m    438\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 439\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_iterator\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/utils/data/dataloader.py:387\u001b[0m, in \u001b[0;36mDataLoader._get_iterator\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    385\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    386\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcheck_worker_number_rationality()\n\u001b[0;32m--> 387\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_MultiProcessingDataLoaderIter\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1040\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter.__init__\u001b[0;34m(self, loader)\u001b[0m\n\u001b[1;32m   1033\u001b[0m w\u001b[38;5;241m.\u001b[39mdaemon \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m   1034\u001b[0m \u001b[38;5;66;03m# NB: Process.start() actually take some time as it needs to\u001b[39;00m\n\u001b[1;32m   1035\u001b[0m \u001b[38;5;66;03m#     start a process and pass the arguments over via a pipe.\u001b[39;00m\n\u001b[1;32m   1036\u001b[0m \u001b[38;5;66;03m#     Therefore, we only add a worker to self._workers list after\u001b[39;00m\n\u001b[1;32m   1037\u001b[0m \u001b[38;5;66;03m#     it started, so that we do not call .join() if program dies\u001b[39;00m\n\u001b[1;32m   1038\u001b[0m \u001b[38;5;66;03m#     before it starts, and __del__ tries to join but will get:\u001b[39;00m\n\u001b[1;32m   1039\u001b[0m \u001b[38;5;66;03m#     AssertionError: can only join a started process.\u001b[39;00m\n\u001b[0;32m-> 1040\u001b[0m \u001b[43mw\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstart\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1041\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_index_queues\u001b[38;5;241m.\u001b[39mappend(index_queue)\n\u001b[1;32m   1042\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_workers\u001b[38;5;241m.\u001b[39mappend(w)\n",
+      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/multiprocessing/process.py:121\u001b[0m, in \u001b[0;36mBaseProcess.start\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m _current_process\u001b[38;5;241m.\u001b[39m_config\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdaemon\u001b[39m\u001b[38;5;124m'\u001b[39m), \\\n\u001b[1;32m    119\u001b[0m        \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdaemonic processes are not allowed to have children\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    120\u001b[0m _cleanup()\n\u001b[0;32m--> 121\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_popen \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_Popen\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m    122\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sentinel \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_popen\u001b[38;5;241m.\u001b[39msentinel\n\u001b[1;32m    123\u001b[0m \u001b[38;5;66;03m# Avoid a refcycle if the target function holds an indirect\u001b[39;00m\n\u001b[1;32m    124\u001b[0m \u001b[38;5;66;03m# reference to the process object (see bpo-30775)\u001b[39;00m\n",
+      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/multiprocessing/context.py:224\u001b[0m, in \u001b[0;36mProcess._Popen\u001b[0;34m(process_obj)\u001b[0m\n\u001b[1;32m    222\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m    223\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_Popen\u001b[39m(process_obj):\n\u001b[0;32m--> 224\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_default_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_context\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mProcess\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_Popen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/multiprocessing/context.py:281\u001b[0m, in \u001b[0;36mForkProcess._Popen\u001b[0;34m(process_obj)\u001b[0m\n\u001b[1;32m    278\u001b[0m \u001b[38;5;129m@staticmethod\u001b[39m\n\u001b[1;32m    279\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_Popen\u001b[39m(process_obj):\n\u001b[1;32m    280\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpopen_fork\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Popen\n\u001b[0;32m--> 281\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mPopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/multiprocessing/popen_fork.py:19\u001b[0m, in \u001b[0;36mPopen.__init__\u001b[0;34m(self, process_obj)\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturncode \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfinalizer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_launch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_obj\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/opt/conda/envs/pytorch/lib/python3.10/multiprocessing/popen_fork.py:66\u001b[0m, in \u001b[0;36mPopen._launch\u001b[0;34m(self, process_obj)\u001b[0m\n\u001b[1;32m     64\u001b[0m parent_r, child_w \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpipe()\n\u001b[1;32m     65\u001b[0m child_r, parent_w \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpipe()\n\u001b[0;32m---> 66\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpid \u001b[38;5;241m=\u001b[39m \u001b[43mos\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfork\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     67\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpid \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "# STEPS = 10000, MODEL TYPE = MEDIUM, WEIGHT = none\n",
+    "model9 = EfficientAD({**config, \"train_steps\": 10000, \"model_type\": \"medium\", \"weight_path\":\"\"})\n",
+    "model9.create_model()\n",
+    "model9.display_eval_result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- Setting seed to 42\n",
+      "- OK - Setting seed to 42 (0.42 ms)\n",
+      "\n",
+      "- Setting datasets path\n",
+      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
+      "- OK - Setting datasets path (4.70 ms)\n",
+      "\n",
+      "- Setting config\n",
+      "     Output folder path: ../output/cookies_3_steps_20_small_weighted\n",
+      "- OK - Setting config (0.21 ms)\n",
+      "\n",
+      "- Prepare teacher, student & autoencoder\n",
+      "     Loading weight ../weights/teacher_small.pth\n",
+      "     Training\n",
+      "- OK - Prepare teacher, student & autoencoder (80.52 ms)\n",
+      "\n",
+      "- Normalizing teacher\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 50.12it/s]\n",
+      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 49.47it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- OK - Normalizing teacher (2.90 s)\n",
+      "\n",
+      "- Train\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 39.9363  :   5%|████▎                                                                                 | 1/20 [00:01<00:30,  1.61s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9230769230769231\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 60.6087  : 100%|█████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:03<00:00,  5.16it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- OK - Train (3.88 s)\n",
+      "\n",
+      "- Saving models to ../output/cookies_3_steps_20_small_weighted/all_models.pth\n",
+      "- OK - Saving models (81.50 ms)\n",
+      "\n",
+      "- Saving map normalization to ../output/cookies_3_steps_20_small_weighted/map_normalization.pth\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:00<00:00, 28.43it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- OK - Saving map normalization (1018.72 ms)\n",
+      "\n",
+      "- Evaluating model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.01it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIhCAYAAABdSTJTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACKMklEQVR4nOzdd1iT198G8DtAmAqoyFQR917g1rpxa7VWERy4FTdV62gr2qqtVqvWilvUOnC3WheuugcIrVarVnGDigOQGZLz/uFLfiJDgkkeAvfnurjanDzjDofgl5PznEcmhBAgIiIiIjJARlIHICIiIiLKKxazRERERGSwWMwSERERkcFiMUtEREREBovFLBEREREZLBazRERERGSwWMwSERERkcFiMUtEREREBovFLBEREREZLBazRERZCAoKgkwmU3+ZmJjAyckJXl5euH37dpb7KBQKBAYGonHjxrCxsYGFhQWqVq2KqVOn4sWLF1nuo1KpsGnTJrRt2xZ2dnaQy+Wwt7dHly5dsG/fPqhUqg9mTUlJwbJly9CsWTMUK1YMpqamcHFxQe/evfHnn39+1PeBiCi/YzFLRJSD9evX4/z58zh69CjGjBmD33//Hc2aNcOrV68ybJeYmIh27dph7NixqFu3LrZu3YoDBw6gf//+WLVqFerWrYubN29m2Cc5ORmdOnXCwIEDYW9vj8DAQBw/fhwrVqyAs7MzPv/8c+zbty/HfDExMWjatCn8/f1Ro0YNBAUF4dixY1i4cCGMjY3Rpk0b/PXXX1r/vhAR5RuCiIgyWb9+vQAgLl++nKF91qxZAoBYt25dhvbhw4cLAGLbtm2ZjnXz5k1hY2MjqlevLtLS0tTto0aNEgDEhg0bssxw69Yt8ddff+WYs2PHjsLExEQcO3Ysy+cvXbok7t+/n+MxcisxMVErxyEi0iaOzBIRacDDwwMA8PTpU3VbdHQ01q1bh/bt26NPnz6Z9qlUqRK+/PJL/PPPP9i7d696nzVr1qB9+/YYMGBAlueqWLEiatWqlW2WsLAwHDx4EEOGDEHr1q2z3KZ+/fooU6YMACAgIAAymSzTNulTKu7du6duK1u2LLp06YLdu3ejbt26MDc3x6xZs1C3bl00b9480zGUSiVcXFzQs2dPdVtqaiq+++47VKlSBWZmZihZsiQGDRqE58+fZ/uaiIg0xWKWiEgDkZGRAN4WqOlOnDiBtLQ0fPrpp9nul/5cSEiIeh+FQpHjPh9y5MiRDMfWtitXrmDy5MkYN24cDh06hM8++wyDBg3CmTNnMs0bPnLkCJ48eYJBgwYBeDsXuHv37vj+++/h7e2NP/74A99//z1CQkLQsmVLJCUl6SQzERU+JlIHICLKz5RKJdLS0pCcnIyzZ8/iu+++wyeffIJu3bqpt3nw4AEAwM3NLdvjpD+Xvm1u9vkQbRwjJ8+ePcP169czFO7lypXD5MmTERQUhDlz5qjbg4KC4ODggI4dOwIAtm/fjkOHDmHXrl0ZRmtr166N+vXrIygoCKNGjdJJbiIqXDgyS0SUg0aNGkEul6No0aLo0KEDihUrht9++w0mJnkbC8jqY/78qlatWhkKWQAoUaIEunbtig0bNqhXWnj16hV+++03DBgwQP192b9/P2xtbdG1a1ekpaWpv+rUqQNHR0ecPHlS3y+HiAooFrNERDnYuHEjLl++jOPHj2PEiBG4ceMG+vbtm2Gb9Dmp6VMQspL+XOnSpXO9z4do4xg5cXJyyrJ98ODBePz4sXrKxNatW5GSkgJfX1/1Nk+fPsXr169hamoKuVye4Ss6OhoxMTE6yUxEhQ+LWSKiHFStWhUeHh5o1aoVVqxYgaFDh+LQoUPYuXOneptWrVrBxMREfXFXVtKfa9eunXofuVye4z4f0r59+wzH/hBzc3MAb9elfVd2hWV2o8jt27eHs7Mz1q9fD+Dt8mUNGzZEtWrV1NvY2dmhRIkSuHz5cpZfy5cvz1VmIqIPYTFLRKSB+fPno1ixYvjmm2/UH7M7Ojpi8ODBOHz4MIKDgzPtc+vWLfzwww+oXr26+mItR0dHDB06FIcPH8bGjRuzPNedO3fw999/Z5ulXr166NixI9auXYvjx49nuU1oaKh6bm3ZsmUBINMxP7SW7fuMjY3Rv39/7N27F6dPn0ZoaCgGDx6cYZsuXbrgxYsXUCqV8PDwyPRVuXJljc5JRJQdmRBCSB2CiCi/CQoKwqBBg3D58mX1clzpFixYgClTpmDTpk3o168fACAhIQGdO3fG2bNnMXz4cHTt2hVmZma4cOECfvzxR1haWuLo0aMZirjk5GR8+umnOHLkCPr27YsePXrAwcEBMTExCAkJwfr167Ft2zZ0794925wxMTHo0KEDrl69isGDB6Njx44oVqwYoqKisG/fPmzduhVhYWGoXbs24uLi4ObmBhcXF8yePRsmJiYICgrClStXEBkZicjISHXBW7ZsWdSoUQP79+/P8ry3bt1C5cqVUapUKbx48QJRUVGwsbFRP69UKtG1a1dcvHgR48ePR4MGDSCXy/Ho0SOcOHEC3bt3R48ePfLaPURE/yP1QrdERPlRdjdNEEKIpKQkUaZMGVGxYsUMN0FITU0Vv/zyi2jYsKEoUqSIMDMzE5UrVxZTpkwRMTExWZ4nLS1NbNiwQbRu3VoUL15cmJiYiJIlS4qOHTuKLVu2CKVS+cGsSUlJYunSpaJx48bC2tpamJiYCGdnZ9GzZ0/xxx9/ZNj20qVLokmTJsLKykq4uLiImTNnijVr1ggAIjIyUr2dq6ur6Ny5c47nbdKkiQAgfHx8snxeoVCIH3/8UdSuXVuYm5uLIkWKiCpVqogRI0aI27dvf/B1ERHlBkdmiYiIiMhgcc4sERERERksFrNEREREZLBYzBIRERGRwWIxS0REREQGi8UsERERERksFrNEREREZLBMpA6gbyqVCk+ePEHRokWzvVUjEREREUlHCIH4+Hg4OzvDyCjnsddCV8w+efIEpUuXljoGEREREX3Aw4cPUapUqRy3KXTFbNGiRQG8/eZYW1vr5ZwKhQJHjhyBp6cn5HK5Xs5J2sP+M3zsQ8PHPjRs7D/Dp+8+jIuLQ+nSpdV1W04KXTGbPrXA2tpar8WspaUlrK2t+SY2QOw/w8c+NHzsQ8PG/jN8UvVhbqaE8gIwIiIiIjJYLGaJiIiIyGCxmCUiIiIig1Xo5szmhhACaWlpUCqVWjmeQqGAiYkJkpOTtXZM0h/2n+HTZR8aGxvDxMSES/0REUmExex7UlNTERUVhcTERK0dUwgBR0dHPHz4kP/gGSD2n+HTdR9aWlrCyckJpqamWj82ERHljMXsO1QqFSIjI2FsbAxnZ2eYmppq5R8+lUqFN2/eoEiRIh9c+JfyH/af4dNVHwohkJqaiufPnyMyMhIVK1bkzwgRkZ6xmH1HamoqVCoVSpcuDUtLS60dV6VSITU1Febm5vyHzgCx/wyfLvvQwsICcrkc9+/fV5+DiIj0h/8yZ4EFCxFpgr8ziIikw9/ARERERGSwWMwSERERkcFiMUv0/1JTU1GhQgWcPXtW6igFxrNnz1CyZEk8fvxY6ihERFRAsZgtIHx9fSGTySCTyWBiYoIyZcpg1KhRePXqVaZtz507h06dOqFYsWIwNzdHzZo1sXDhwizX3zxx4gQ6deqEEiVKwNLSEtWqVcMXX3zxweIkPDwcn3/+ORwcHGBubo5KlSph2LBhuHXrltZes7atWrUKrq6uaNq0aabnJkyYALlcjm3btmV6ztfXF59++mmm9oiICMhkMty7d0/dJoTAqlWr0LBhQxQpUgS2trbw8PDA4sWLtboc3PtevXqF/v37w8bGBjY2Nujfvz9ev36d4z7pP0/vfy1YsEC9TUpKCsaOHQs7OztYWVmhW7duePTokfp5e3t79O/fHzNnztTVSyMiokKOxWwB0qFDB0RFReHevXtYs2YN9u3bBz8/vwzb7NmzBy1atECpUqVw4sQJ/Pvvvxg/fjzmzJkDLy8vCCHU265cuRJt27aFo6Mjdu3ahevXr2PFihWIjY3FwoULs82xf/9+NGrUCCkpKdi8eTNu3LiBTZs2wcbGBl9//XWeX59Cocjzvrnx888/Y+jQoZnaExMTsXv3bkyaNAlr1679qHP0798fEyZMQPfu3XHixAlERETg66+/xm+//YYjR4581LFz4u3tjYiICBw6dAiHDh1CREQE+vfvn+M+UVFRGb7WrVsHmUyGzz77TL3NhAkTsGfPHmzbtg1nzpzBmzdv0KVLlwx/GA0aNAibN2/O8g8rIiKijyYKmdjYWAFAxMbGZnouKSlJXL9+XSQlJanbVCqVSEhRfNRXfFKKePI0RsQnpWi0n0qlyvXrGjhwoOjevXuGNn9/f1G8eHH14zdv3ogSJUqInj17Ztr/999/FwDEtm3bhBBCPHz4UJiamooJEyZkeb5Xr15l2Z6QkCDs7OzEp59+muN+69evFzY2Nhme27Nnj3j3R3LmzJmidu3aYu3atcLNzU3IZDKxYsUK4ezsLJRKZYZ9u3btKgYMGJDh9dSrV0+YmZkJNzc3ERAQIBQKRZaZhBAiLCxMGBkZZflzsW7dOlG/fn3x8uVLYWFhISIjIzM8n9X3XgghwsPDBQD19sHBwQKA2Lt3b6ZtVSqVeP36dbb5Psb169cFAHHhwgV12/nz5wUA8e+//+b6ON27dxetW7dWP379+rWQy+XqnxkhhHj8+LEwMjIShw4dyrBv2bJlxdq1az/iVXwcpVIpXr16lennRluy+t1B2pWamir27t0rUlNTpY5CecD+M3z67sOc6rX3SbrO7KlTp7BgwQKEhYUhKioKe/bsyfLj2nf9+eef8Pf3xz///ANnZ2dMmTIFI0eO1FnGJIUS1b45rLPj5+T67PawNM1bF929exeHDh2CXC5Xtx05cgQvXrzApEmTMm3ftWtXVKpUCVu3bkWfPn2wY8cOpKamYsqUKVke39bWNsv2w4cPIyYmRuP9svPff/9h+/bt2LVrF4yNjeHi4oJx48bhxIkTaNOmDYC3H6EfPnwY+/btU2fo168fli5diubNm+POnTsYPnw4AGT7cfepU6dQqVIlWFtbZ3pu/fr1+Pzzz2FjY4NOnTph/fr1mDVrlkavAwA2b96MypUro3v37pmek8lksLGxyXbfIkWK5Hjs5s2b4+DBg1k+d/78edjY2KBhw4bqtkaNGsHGxgbnzp1D5cqVP5j96dOn+OOPP7BhwwZ1W1hYGBQKBTw9PdVtzs7OqFGjBs6dO4f27dur2xs0aIDTp09j8ODBHzwXERGRJiQtZhMSElC7dm0MGjQow0eX2YmMjESnTp0wbNgw/Prrrzh79iz8/PxQsmTJXO1f0O3fvx9FihSBUqlEcnIyAGDRokXq59Pnq1atWjXL/atUqaLe5vbt27C2toaTk5NGGW7fvq0+ljakpqZi06ZNKFmypLqtQ4cO2LJli7qY3bFjB4oXL65+PGfOHEydOhUDBw4EAJQrVw7ffvstpkyZkm0xe+/ePTg7O2f5ei5cuID169cDAPr164dx48Zh5syZGq8tevv27VwVjlmJiIjI8XkLC4tsn4uOjoa9vX2mdnt7e0RHR+fq/Bs2bEDRokXRs2fPDMc1NTVFsWLFMmzr4OCQ6bguLi4IDw/P1bmIiIg0IWkx27FjR3Ts2DHX269YsQJlypTB4sWLAbwtykJDQ/Hjjz/qrJi1kBvj+uz2H94wByqVCvFx8ShqXVSjAshCbqzReVq1aoXAwEAkJiZizZo1uHXrFsaOHZtpO/HOvNj329Nv3/vu/2siu2Pnlaura4ZCFgB8fHwwfPhwLF++HGZmZti8eTO8vLxgbPz2+xUWFobLly9jzpw56n3SC/zExMQs7+6WlJSU5Z2b1q5dC09PT5QoUQIA0KlTJwwZMgRHjx7NMCKZG3n9ngJAhQoV8rRfuqzOq0medevWwcfHJ1d3t8rquBYWFjq9wI2IiHRHCIG4xGSkKLX/77w2GNTtbM+fP5+pgGjfvj3Wrl0LhUKR4SP1dCkpKUhJSVE/jouLA/D2YqL3LyhSKBQQQkClUkGlUqnbzU0+7jo5IWRIMzWGhdxYo2JGCJHrHxohBCwtLVGuXDkAwOLFi9GmTRsEBARg9uzZAP5XEP3zzz9o0qRJpmP8+++/qFq1KlQqFSpWrIjY2Fg8fvxYo9HZ9HNcv34djRs3/mDmd7/P6f2U3iaEgJWVVYZtAKBz585QqVTYt28f6tevj9OnT+PHH39Ub6dSqRAQEIAePXpkOqepqWmm4wFAiRIlcPXq1QzPKZVKbNy4EdHR0bCzs8vQvmbNGrRt2xYAULRoUdy/fz/TcV++fKl+Pv17euPGjSzP/yFZTX94V7NmzXDgwIEsn7O3t8fTp08znff58+coWbLkB/OcPn0aN2/exNatWzNsa29vj9TUVLx48SLD6OyzZ8/QuHHjDNu+ePECdnZ2eXrt2pD+Pnr/Z05bVCoVhBBQKBTqP6pIu9J/X+v6QlDSDfafYdu5Zy8GjBgPB6/v0Lp1CmzyODCjCU1+VgyqmI2OjoaDg0OGNgcHB6SlpSEmJibLomvevHlZzm88cuRIphE6ExMTODo64s2bN0hNTdVueADx8fFaP2Y6hUKBtLQ0dbEOAF988QU+//xz+Pj4wMnJCY0aNUKxYsUwf/58bNy4McP+Bw4cwO3btzF16lTExcXB09MTpqammDNnDubOnZvpfLGxsVnO8WzUqBFKlCiBefPm4ddff812PysrK8THxyMqKgpWVlYAgEuXLgH43x8cKSkpUCqVGV5Tui5dumDjxo34559/UKFCBVSsWFG9Xa1atXDt2jWMGDEi035v3rzJ8vtXuXJlBAYGIjY2Vv0Hx8GDBxEfH48///wzQ4Fy+/ZtDB8+HPfu3UPx4sXh6uqKrVu34tmzZxlGLs+cOQM7OzsYGxsjLi4On376KYYMGYJt27ahU6dOGc4vhEBcXFy282ZPnTqVZXs6c3PzLL9PAFCzZk3ExsbixIkTcHd3BwCEhoYiNjYWtWrVyna/dCtXrkSdOnXg5uaWYduKFStCLpfj999/V//hEB0djWvXruGbb77JsO1ff/2FZs2affBcuqar92BqaiqSkpJw6tQppKWl6eQc9FZISIjUEegjsP8Mi0KhwKZNm/D7778DAGLP78Dx4yVgpoe/2TX5NE8m8sl4sUwm++AFYJUqVcKgQYMwbdo0ddvZs2fRrFkzREVFwdHRMdM+WY3Mli5dGjExMZlGu5KTk/Hw4UOULVs2Vx+n5pYQAvHx8ShatGieP2b+kEGDBuH169fYs2dPhvYGDRqgYcOG+PnnnwEAO3fuhLe3NwYNGoTRo0fD2toax44dw5dffonWrVsjODhYnTEwMBBjx46Fr68v+vfvj7Jly+LRo0fYtGkTihQpgh9//DHLLL/99hv69OmD9u3bY+zYsahQoQJiYmKwY8cOPHjwAFu3bsXLly9RtmxZDB48GGPGjMGlS5fw5Zdf4smTJ+plnWbNmoXffvsNV65cyXSOkJAQdO/eHWXLloWPjw9mzJihfu7w4cPo1q0bpk+fjl69esHIyAh///03rl27hm+//TbLzC9evECpUqVw+fJl1KhRAwDQo0cPmJubY8uWLRn6TwgBV1dXTJo0CePGjUNsbCyqVauGpk2bYsqUKShWrBjOnz+PCRMmYOrUqeoL7oQQ8Pb2xr59+/DVV1+hbdu2KFmyJK5evYolS5Zg9OjRH7wAMq86deqEqKgoBAYGAgBGjhyJMmXKqH9BAUC1atUwZ86cDCPacXFxcHFxwYIFC7K80NLPzw9//PEH1q1bh+LFi2PKlCl48eIFLl++rP4DIDExEfb29jh48CCaN2+uk9f3Ibp+DyYnJ+PevXsoXbq0Vn930P8oFAqEhISgXbt2WX4KR/kb+8/wREZGol+/frh8+TIAoKhHdxRr6YuwGW1gY6X733NxcXGws7NDbGzsBz+dzDdLcwEQe/bsyXGb5s2bi3HjxmVo2717tzAxMcn1UhGaLs2lDbpeFkiI7JeH2rx5szA1NRUPHjxQt506dUp06NBB2NjYCFNTU1GtWjXx448/irS0tEz7h4SEiPbt24tixYoJc3NzUaVKFTFp0iTx5MmTHPNcvnxZ9OzZU5QsWVKYmZmJChUqiOHDh4vbt2+rt9mzZ4+oUKGCMDc3F126dBGrVq3KcmmurKSlpQknJycBQNy5cyfT84cOHRJNmjQRFhYWwtraWjRo0ECsWrUqx8xeXl5i6tSpQgghoqOjhYmJidi+fXuW/Td27FhRs2ZN9ePbt2+Lzz77TLi4uAgrKytRs2ZNsWzZskx9rlQqRWBgoKhfv76wtLQU1tbWwt3dXSxZskQkJibmmO9jvHjxQvj4+IiiRYuKokWLCh8fn0zLqwEQ69evz9C2cuVKYWFhke2yYUlJSWLMmDGiePHiwsLCQnTp0iXDz5oQQmzZskVUrlxZmy9HY1yay/BxaSfDxv4zLLt27RI2NjYCgChWrJjYvnO3cP1yv3D9cr94/UZ3/1a9S5OluQxqZPbLL7/Evn37cP36dXXbqFGjEBERgfPnz+fqPOkf5WZV6ScnJyMyMhJubm5aHV1RqVSIi4uDtbW1xlfAk/5cvXoVbdu2xX///YeiRYuq29l/H6dBgwaYMGECvL29Jcug6z7U1e8O+h+FQoEDBw6gU6dOHNnTAiEEkhSZ7/qoKwqFAocPH0H79p7sv3zu1atXqFGlEl6/fo2GjRohaOOvsHMqBY/vjgIA/vq6NWyssl9BR1tyqtfeJ+mc2Tdv3uC///5TP46MjERERASKFy+OMmXKYNq0aXj8+LF6fufIkSOxbNky+Pv7Y9iwYTh//jzWrl2LrVu3SvUSqACpWbMm5s+fj3v37qFmzZpSxykQnj17hl69eqFv375SRyGi/yeEQK8V5xF2X9935TPBlEvH9XxOygt5q9GwfvIvopr1R4e1/wL4V+pIOZK0mA0NDUWrVq3Uj/39/QEAAwcORFBQEKKiovDgwQP1825ubjhw4AAmTpyIX375Bc7Ozli6dCnXmCWtSV+blrTD3t4+2xtoEJE0khRKCQpZys8SbpyGkZklLMq9vUjYslJjWFbKvCKRW1Gh8bKh+iBpMduyZcscl54KCgrK1NaiRYssLwgiIiIizYR+1RaWprovTjjNIH9KSkrC1MmTsOb3VShRogSOzb8CpyxuIAS87cMTIUd0diH7xzCopbmIiIhIeyxNjfN823RNKGQCZsaApakJ5HKWHvnBzZs30bt3b/z999+QyWQYOXIkXEs5w8Qk6/5RyATyYR0LgMUsERFRJvq+QEqfElML5uui3Nu8eTNGjBiBhIQE2Nvb49dff0W7du2kjpVnLGaJiIjeId0FUkS6pVQqMWLECKxduxYA0KpVK2zevFmjO33mR1xniIiI6B2F5QIpD9di+fJiHtKd9JvZyGQyBAQEICQkxOALWYAjs0RERNnS1wVSUrCQG+fLi3lI+5KTk9VrYC9duhS+vr5o1qyZxKm0h8UsERFRNvR1gRSRLrx58wajR49GdHQ0Dh48CCMjI1haWhaoQhbgNAPSQEBAAOrUqZNvz9OyZUtMmDBB63k+pGzZsli8ePFHHcPX1zfHu98B0r0+IiIyPFevXkX9+vWxceNGHD16FBcuXJA6ks6wmC1AHj58iCFDhsDZ2RmmpqZwdXXF+PHj8eLFC42PJZPJsHfv3gxtkyZNwrFjx7SUNu9OnjwJmUyG169fSx2lwLl69SpatGgBCwsLuLi4YPbs2TmuBQ28vfVh//79YWNjAxsbG/Tv3z/LvgkKCkKtWrVgbm4OR0dHjBkzRv1cQEAAZDJZpi8rK6ssz3n27FmYmJhk+qMnKCgoy+MkJydn2G758uXqW8+6u7vj9OnTGZ739fXNdIxGjRrl+H0gIsoPhBBYvXo1GjRogH///RcuLi44efIkmjRpInU0neFnJ7qiVAKnTwNRUYCDA1C7tk5Pd/fuXTRu3BiVKlXC1q1b4ebmhn/++QeTJ0/GwYMHceHCBRQvXvyjzlGkSBEUKVJES4nzB4VCwQW8/19cXBzatWuHVq1a4fLly7h16xZ8fX1hZWWFL774Itv9vL298ejRIxw6dAgAMHz4cPTv3x/79u1Tb7No0SIsXLgQCxYsQMOGDZGcnIy7d++qn580aRJGjhyZ4bht2rRB/fr1M50vNjYWAwYMQJs2bfD06dNMz1tbW+PmzZsZ2szNzZGamgoACA4OxoQJE7B8+XI0bdoUK1euRMeOHXH9+nWUKVNGvU+HDh2wfv169WNTU9NsvwdERPlBXFwcRowYgW3btgEAOnbsiI0bN8LOzk7iZLrFkVld2L0bKFsWaNUK8PaGUZs2sK5V6227jowePRqmpqY4cuQIWrRogTJlyqBjx444evQoHj9+jBkzZqi3LVu2LL799lt4e3ujSJEicHZ2xs8//5zheQDo0aMHZDKZ+vH7H/+nfzQ+d+5cODg4wNbWFrNmzUJaWhomT56M4sWLo1SpUli3bl2GrF9++SUqVaoES0tLlCtXDl9//TUUCkWuXue9e/fUt0AuVqwYZDIZfH191c+rVCpMmTIFxYsXh6OjIwICAjLsL5PJsGLFCnTv3h1WVlb47rvvAAD79u2Du7s7zM3NUa5cOfXrSPf999+jbNmyMDMzg7OzM8aNG5fhuImJiRg8eDCKFi2KMmXKYNWqVRmev3r1Klq3bg0LCwuUKFECw4cPx5s3b7J9nQkJCRgwYACKFCkCJycnLFy4MFffn4+xefNmJCcnIygoCDVq1EDPnj0xffp0LFq0KNvR2Rs3buDQoUNYs2YNGjdujMaNG2P16tXYv3+/uqB89eoVvvrqK2zcuBHe3t4oX748qlevjq5du6qPU6RIETg6Oqq/nj59iuvXr2PIkCGZzjlixAh4e3ujcePMt1oE3vbxu8dydHTM8PyiRYswZMgQDB06FFWrVsXixYtRunRpBAYGZtjOzMwswzE+9o9BIiJd8/LywrZt22BsbIz58+dj//79Bb6QBVjMat/u3UCvXsCjRxmaZVFRkPXurZOC9uXLlzh8+DD8/PxgYWGR4TlHR0f4+PggODg4Q0GyYMEC1KpVC1euXMG0adMwceJEhISEAAAuX74MAFi/fj2ioqLUj7Ny/PhxPHnyBKdOncKiRYsQEBCALl26oFixYrh48SJGjhyJkSNH4uHDh+p9ihYtiqCgIFy/fh1LlizB6tWr8dNPP+XqtZYuXRq7du0C8PbuJVFRUViyZIn6+Q0bNsDKygoXL17E/PnzMXv2bPXrSjdz5kx0794dV69exeDBg3H48GH069cP48aNw/Xr17Fy5UoEBQVhzpw5AICdO3di+fLlCAwMxO3bt7F3717UrFkzwzEXLlwIDw8PhIeHw8/PD6NGjcK///4L4G2h26FDBxQrVgyXL1/Gjh07cPTo0Qwfs79v8uTJOHHiBPbs2YMjR47g5MmTCAsLy/F7c/r0afXoeXZfc+fOzXb/8+fPo0WLFjAzM1O3tW/fHk+ePMG9e/ey3cfGxgYNGzZUtzVq1Ag2NjY4d+4cACAkJAQqlQqPHz9G1apVUapUKfTu3TvDz8T71qxZg0qVKqF58+YZ2tevX487d+5g5syZ2e775s0buLq6olSpUujSpQvCw8PVz6WmpiIsLAyenp4Z9vH09FTnTXfy5EnY29ujUqVKGDZsGJ49e5btOYmI8oM5c+agYsWKOH36NCZPngwjo0JS5olCJjY2VgAQsbGxmZ5LSkoS169fF0lJSXk7eFqaEKVKCQFk+aWSyYQoXfrtdlp04cIFAUDs2bMny+cXLVokAIinT58KIYRwdXUVHTp0yLBNnz59RMeOHdWPszrezJkzRe3atdWPBw4cKFxdXYVSqVS3Va5cWTRv3lz9OC0tTVhZWYmtW7dmm3/+/PnC3d092/O878SJEwKAePXqVYb2Fi1aiGbNmmVoq1+/vvjyyy8zvK4JEyZk2KZ58+Zi7ty5Gdo2bdoknJychBBC/Pjjj6JChQoiOTk5yzyurq6iX79+6scqlUrY29uLwMBAIYQQq1atEsWKFRNv3rxRb/PHH38IIyMjER0dLYR4+73s3r27EEKI+Ph4YWpqKrZt26be/sWLF8LCwkKMHz8+u2+LSExMFLdv387x68WLF9nu365dOzFs2LAMbY8fPxYAxLlz57LcZ86cOaJixYqZ2itWrKj+ns6bN0/I5XJRuXJlcejQIXH+/HnRpk0bUblyZZGSkpJp3+TkZFGsWDHxww8/ZGi/deuWsLe3Fzdv3hRCZP1zcv78ebFp0yYREREhTp06JT777DNhYWEh/v33X/Hq1Svx8OFDAUCcPXs20+uoVKmS+vG2bdvE/v37xdWrV8Xvv/8uateuLapXr57tz8BH/+6gD0pNTRV79+4VqampOj9XQopCuH65X7h+uV8kpCh0fr7CQJ/9V5i8fv1aHDhwIEObQqGbn1l992FO9dr7OGdWm06fzjQi+y6ZEMDDh2+3a9lSb7HE/4/Ivrue4Psf0TZu3DhPV+RXr149w19+Dg4OqFGjhvqxsbExSpQokWFUa+fOnVi8eDH+++8/vHnzBmlpabC2ttb43FmpVatWhsdOTk6ZRtQ8PDwyPA4LC8Ply5fVI7HA27ukJCcnIzExEb169cJPP/2EChUqoEOHDujUqRO6du2a4f7V7543/WPu9PPeuHEDtWvXznAxU9OmTaFSqXDz5k04ODhkyHPnzh2kpqZm6KPixYujcuXKOb52CwsLVKhQIcdtPuT9NSez+tn50D7p+6W3q1QqKBQKLF26VD0iunXrVjg6OuLEiRNo3759hn13796N+Ph4DBgwQN2mVCrh7e2NWbNmoVKlStlmadSoUYYLtZo2bYp69eph2bJl+Pbbb3N8ne+29enTR/3/NWrUgIeHB1xdXfHHH3+gZ8+e2Z6fiEhfQkND0adPHzx8+BDnzp1T/9v27r9NhUUhGX/Wk6go7W6XSxUqVIBMJsP169ezfP7ff/9FsWLFPjhvJi+LZ79/8ZRMJsuyTaVSAQAuXLgALy8vdOzYEfv370d4eDhmzJihvjjnY+V07nTvXyGvUqkwa9YsREREqL+uXr2K27dvw9zcHKVLl8bly5fx888/w8LCAn5+fvjkk08yzPPN6bzvF0rvb/c+8YHVA7LzsdMMHB0dER0dnaEtvSB/v+B+d5+sLsJ6/vy5ep/0u8tUq1ZN/XzJkiVhZ2eHBw8eZNp3zZo16NKlS4a5rvHx8QgNDcWYMWNgYmICExMTzJ49G3/99RdMTExw/PjxLPMZGRmhfv36uH37NgDAzs4OxsbGWb7O7F5j+mtwdXVVH4eISCpCCCxZsgRNmjTB3bt34eLiInUkyRW+8l2XcntLOC3fOq5EiRJo164dli9fjokTJ2aYNxsdHY3NmzdjwIABGQqn99ebu3DhAqpUqaJ+LJfLoVQqtZoTeLukkqura4YL0u7fv6/RMdKvKtdWvnr16uHmzZvZjmqqVCpYWFigW7du+PTTTzF69GhUqVIFV69eRb169T54/GrVqmHDhg1ISEhQF9Jnz56FkZFRlqOMFSpUgFwux4ULF9RX17969Qq3bt1CixYtsj2Ph4cHIiIicsyS00VMjRs3xvTp05Gamqr+Hh85cgTOzs7qiwCz2ic2NhaXLl1CgwYNAAAXL15EbGysehmYpk2bAng7x7lUqVIA3s7zjomJgaura4bjRUZG4sSJE/j9998ztFtbW+Pq1asZ2pYvX47jx49j586dcHNzyzKfEAIRERHqTwtMTU3h7u6OkJAQ9OjRQ71dSEgIunfvnu335sWLF3j48GGBuO0jERmuV69eYfDgweqlM3v27Im1a9fC1tZW0lxS48isNjVvDpQqBWQzCidkMqB06bfbadmyZcuQkpKC9u3b49SpU3j48CEOHTqEdu3awcXFJcNH6MDbYmr+/Pm4desWfvnlF+zYsQPjx49XP1+2bFkcO3YM0dHRePVKe/cor1ChAh48eIBt27bhzp07WLp0Kfbs2aPRMVxdXSGTybB//348f/48x1UBcuObb77Bxo0bERAQgH/++Qc3btxAcHAwvvrqKwBv1y7dtGkTrl27hrt372LTpk2wsLDIVIhlx8fHB+bm5hg4cCCuXbuGEydOYOzYsejfv3+Wo4FFihTBkCFDMHnyZBw7dgzXrl2Dr6/vByfyp08zyOkrp2LW29sbZmZm8PX1xbVr17Bnzx7MnTsX/v7+6j+ELl26hCpVquDx48cAgKpVq6JDhw4YNmwYLly4gAsXLmDYsGHo0qWLelpEpUqV0L17d4wfPx7nzp3DtWvXMHDgQFSpUkW9MkW6devWwcnJCR07dszQbmRkhBo1amT4sre3h7m5OWrUqKH+I2HWrFk4fPgw7t69i4iICAwZMgQREREYMWKE+lj+/v5Ys2YN1q1bhxs3bmDixIl48OCBemmwN2/eYNKkSTh//jzu3buHkydPomvXrrCzs8tQABMR6dPFixdRt25d7N27F6ampvj555+xc+fOQl/IAixmtcvYGEi/sv79OXnpjxcvfrudllWsWBGhoaEoX748+vTpg/Lly2P48OFo1aoVzp8/n6mI+eKLLxAWFoa6devi22+/xcKFCzPMXVy4cCFCQkJQunRp1K1bV2s5u3fvjokTJ2LMmDGoU6cOzp07h6+//lqjY7i4uGDWrFmYOnUqHBwcclwVIDfat2+P/fv3IyQkBPXr10ejRo2waNEidbFqa2uLjRs3onnz5qhVqxaOHTuGffv2oUSJErk6vqWlJQ4fPoyXL1+ifv366NWrF9q0aYNly5Zlu8+CBQvwySefoFu3bmjbti2aNWsGd3f3j3qdH2JjY4OQkBA8evQIHh4e8PPzg7+/P/z9/dXbJCYm4ubNmxmmWGzevBk1a9aEp6cnPD09UatWLWzatCnDsTdu3IiGDRuic+fOaNGiBeRyOQ4dOpRheoZKpUJQUBB8fX1hnMf3yOvXrzF8+HBUrVoVnp6eePz4MU6dOqUeNQbezoddvHgxZs+ejTp16uDUqVM4cOCAur+NjY1x9epVdO/eHZUqVcLAgQNRqVIlnD9/HkWLFs1TLiKij/Xnn3/i/v37KF++PM6fP48xY8bkaXpgQSQTeZ2gZ6Di4uJgY2OD2NjYTBcdJScnIzIyUn1noDzbvRsYPz7DxWAqFxdg8WIY9eqV9+NqSdmyZTFhwgTeGjWXVCoV4uLiYG1tXXiWOSlgdN2HWvvdQdlKTU3F3v0H0b69p85vdJKYqoTHd0cBANdnt4elKWfkfSyFQoEDBw6gU6dOvFFNHqlUKixcuBAjRozQ2kXTmtB3H+ZUr72P71Bd6NkT6N5dfQcwlYMD4mrXhnWxYlInIyIyOEIIeK25jCsPTDDlUtYX+xEVNGfOnMG3336L3bt3w8rKCkZGRpg8ebLUsfIlDjPpirHx2+W3+vZ9+18dTC0gIioMkhRKXHnwWu/n9XAtBgs5f3eTfqlUKsybNw8tW7bEkSNHMl3zQplxZLYQyu5uTkRE+d2FL1vA2ko/Uzks5Mack0h69ezZM/Tv3x9HjhwBAPTr1w/Tp0+XOFX+x2KWiIgMhoWpMeewUoF08uRJeHt7IyoqChYWFli2bBkGDRrEP6hygb8RslDIrokjoo/E3xlE9DF+/fVXDBw4ECqVCtWqVcP27dtRvXp1qWMZDM6ZfUf61XmJiYkSJyEiQ5L+O4NXaRNRXrRu3RolSpTAoEGDcOnSJRayGuLI7DuMjY1ha2urvoWnpaWlVob3VSoVUlNTkZyczKWdDBD7z/Dpqg+FEEhMTMSzZ89ga2ub5/VxiajwuXXrlvoukM7Ozvjrr794l8E8YjH7nvT7wacXtNoghEBSUhIsLCw498UAsf8Mn6770NbWVv27g4goJ2lpaZg9ezbmzJmD7du347PPPgMAFrIfgcXse2QyGZycnGBvb5/hLkcfQ6FQ4NSpU/jkk0/4MaQBYv8ZPl32oVwu54gsEeXK48eP4e3tjVOnTgEALly4oC5mKe9YzGbD2NhYa/9AGRsbIy0tDebm5iyGDBD7z/CxD7VPCIEkhVIv50pM1c95iHTp0KFD6N+/P2JiYlCkSBGsXr0aXl5eUscqEFjMEhGRRoQQ6LXiPMLuv5I6ClG+p1Ao8PXXX+OHH34AANStWxfBwcGoWLGixMkKDl7NQkREGklSKCUpZN2KCt6RiwzOqVOn1IXs6NGjce7cORayWsaRWSIiyrPQr9rC0lT3BaZCocCJkCO8CJMMTps2bTB9+nTUrVsXvXr1kjpOgcRiloiI8sxST3fkUsgEWMeSIUhNTcW3336LkSNHwsXFBQAwZ84ciVMVbCxmiYiIiLTg3r176NOnDy5duoTTp0/jxIkT/DRBDzhnloiIiOgj7dmzB3Xr1sWlS5dga2uLiRMnspDVExazRERERHmUkpKCcePGoWfPnnj9+jUaNWqEiIgIdO/eXepohQanGRARERHlwePHj9G9e3eEhYUBACZPnow5c+ZwPWs9YzFLRERElAe2trZISkpCiRIlsGHDBnTu3FnqSIUSi1kiIiKiXEpOToapqSmMjIxgZWWFPXv2wNLSEqVKlZI6WqHFObNEREREuXDz5k00bNgQ8+fPV7dVqlSJhazEWMwSERERfcDmzZvh7u6Ov//+G0uXLkVCQoLUkej/sZglIiIiykZiYiKGDh2Kfv36ISEhAS1btkRoaCisrKykjkb/j8UsERERURZu3LiBhg0bYu3atZDJZJg5cyaOHj0KZ2dnqaPRO3gBGBEREdF74uLi0LRpU7x69QqOjo7YvHkzWrduLXUsygKLWSIiAyeEQJJCqbfzJabq71xEUrG2tsbs2bPx22+/4ddff4WDg4PUkSgbLGaJiAyYEAK9VpxH2P1XUkchMnhXr16FUqlEnTp1AACjR4+Gn58fjIw4KzM/Y+8QERmwJIVSskLWw7UYLOTGkpybSJuEEFi9ejUaNGiAXr16IS4uDgAgk8lYyBoAjswSERUQoV+1haWp/opLC7kxZDKZ3s5HpAvx8fEYMWIEtm7dCgCoWLEiFAqFxKlIEyxmiYgKCEtTY1ia8tc6UW5FRESgd+/euH37NoyNjTFnzhxMnjyZo7EGhr/1iIiIqFARQmDFihWYOHEiUlJSULp0aWzbtg1NmjSROhrlAf/0ICIiokJFCIHff/8dKSkp6Nq1K8LDw1nIGjCOzBIREVGhYmRkhI0bN2LHjh0YNWoU534bOI7MEhERUYEmhMCSJUswatQodVvJkiXh5+fHQrYA4MgsERERFVivXr3C4MGDsXfvXgDA559/zjt5FTAsZomIiKhAunjxIvr06YP79+/D1NQUCxcuRKtWraSORVrGaQZERERUoAghsHDhQjRr1gz3799H+fLlce7cOYwZM4bTCgogjswSERFRgTJ48GAEBQUBAHr37o1Vq1bBxsZG2lCkMxyZJSIiogKlT58+sLCwQGBgILZt28ZCtoDjyCwREREZNJVKhVu3bqFKlSoAgA4dOiAyMhIODg4SJyN94MgsERERGaxnz56hU6dOaNSoESIjI9XtLGQLDxazREREZJD+/PNP1KlTB4cPH0ZqaiquXr0qdSSSAItZIiIiMihKpRKzZ89G69atERUVhapVq+LSpUvo1q2b1NFIApwzS0RERAYjOjoa/fr1w7FjxwAAvr6+WLZsGaysrCRORlJhMUtEREQGY8mSJTh27BgsLS0RGBiIAQMGSB2JJMZiloiIiAzGzJkz8ejRI8yYMUO9egEVbpwzS0RERPnW48ePMWnSJKSlpQEAzM3NsWnTJhaypMaRWSIiIsqXDh06hP79+yMmJgbW1tb45ptvpI5E+RBHZomIiChfUSgUmDZtGjp27IiYmBjUqVMHXl5eUseifIojs0RERJRvPHz4EF5eXjh37hwAwM/PDwsXLoS5ubnEySi/YjFLRERE+cKxY8fQu3dvvHz5EtbW1lizZg0+//xzqWNRPsdiloiIiPIFR0dHJCUlwd3dHcHBwShfvrzUkcgAsJglIiIiySQkJKhveFC9enUcO3YM9erVg5mZmcTJyFDwAjAiIiKSxN69e1G2bFn1/FgAaNy4MQtZ0giLWSIiItKrlJQUjB8/Hj169EBMTAx++uknqSORAZO8mF2+fDnc3Nxgbm4Od3d3nD59OsftN2/ejNq1a8PS0hJOTk4YNGgQXrx4oae0RERE9DHu3LmDpk2bYunSpQCASZMmYcuWLRKnIkMmaTEbHByMCRMmYMaMGQgPD0fz5s3RsWNHPHjwIMvtz5w5gwEDBmDIkCH4559/sGPHDly+fBlDhw7Vc3IiIiLS1I4dO1C3bl2EhYWhePHi2L9/PxYsWAC5XC51NDJgkhazixYtwpAhQzB06FBUrVoVixcvRunSpREYGJjl9hcuXEDZsmUxbtw4uLm5oVmzZhgxYgRCQ0P1nJyIiIg0cfXqVfj4+CA+Ph5NmzZFREQEOnfuLHUsKgAkW80gNTUVYWFhmDp1aoZ2T0/PDBPB39WkSRPMmDEDBw4cQMeOHfHs2TPs3LkzxzdDSkoKUlJS1I/j4uIAvL27iEKh0MIr+bD08+jrfKRd7D/DV5D7UKFIe+f/FVDIhIRpdKcg92FhoFAoUKNGDXz66aeoVKkSAgICYGJiwv40IPp+D2pyHpkQQpLffE+ePIGLiwvOnj2LJk2aqNvnzp2LDRs24ObNm1nut3PnTgwaNAjJyclIS0tDt27dsHPnzmw/oggICMCsWbMytW/ZsgWWlpbaeTFERBJJUQJTLr0dl5jfIA1mxhIHInrHuXPnUKdOHfW/tyqVCkZGkl+uQwYgMTER3t7eiI2NhbW1dY7bSr7OrEwmy/BYCJGpLd3169cxbtw4fPPNN2jfvj2ioqIwefJkjBw5EmvXrs1yn2nTpsHf31/9OC4uDqVLl4anp+cHvznaolAoEBISgnbt2nFekAFi/xm+gtyHialpmHLpOACgfXtPWJpK/mtdJwpyHxZEiYmJ8Pf3x7p16/D5559j/fr1OHr0KNq3b8/+M1D6fg+mf5KeG5L91rOzs4OxsTGio6MztD979gwODg5Z7jNv3jw0bdoUkydPBgDUqlULVlZWaN68Ob777js4OTll2sfMzCzL9erkcrne31BSnJO0h/1n+ApiH8rF//74f/v6CmYxm64g9mFBc+PGDfTu3RvXrl2DTCZD1apVYWLy9ueS/Wf49NWHmpxDst96pqamcHd3R0hICHr06KFuDwkJQffu3bPcJzExUf2GSGds/PYzNYlmSxCRARBCIEX5dhTz3eKvIEhMVUodgUhtw4YN8PPzQ2JiIhwcHLB582a0adOGc2NJpyT9E97f3x/9+/eHh4cHGjdujFWrVuHBgwcYOXIkgLdTBB4/foyNGzcCALp27Yphw4YhMDBQPc1gwoQJaNCgAZydnaV8KUSUTwkh4LXmMq48MFF/HE9E2pWQkIDRo0djw4YNAIA2bdrg119/haOjo8TJqDCQtJjt06cPXrx4gdmzZyMqKgo1atTAgQMH4OrqCgCIiorKsOasr68v4uPjsWzZMnzxxRewtbVF69at8cMPP0j1EohIU0olcPo0EBUFODkBzZsDxrq7ailJocSVB691dvz8wsO1GCzkvPqLpJGYmIgjR47AyMgIs2bNwrRp09SfnBLpmuSTq/z8/ODn55flc0FBQZnaxo4di7Fjx+o4FRHpxO7dwPjxwKNH/2srVQpYsgTo2VPnp7/wZQtYW5nr/DxSsJAbZ3vxLJGulSxZEsHBwVCpVGjRooXUcaiQkbyYJaJCYvduoFcv4P357Y8fv23fuVPnBa2FqXGBvdqfSJ/i4+MxcuRIdOrUCT4+PgCA5s2bS5yKCiv+Vici3VMq347I/n8hKwAkyd9ZZUQmA76YAnTsrPUpB7xAiki7IiIi0Lt3b9y+fRsHDhxA165d9bbUJVFWWMwSke6dPq2eWiAA9PKZj7BS1TJvN+uofnMRUa4JIbBixQpMnDgRKSkpKFWqFLZt28ZCliTHYpaIdC8qSv2/SXKzrAtZHXMrKniBFFEexcbGYtiwYdixYwcAoEuXLggKCkKJEiUkTkbEYpaI9CGLG5oAQOjPPrBUJP+v4cBB4BPtz7tTKBQ4EXKEF0gR5UFCQgLc3d1x584dmJiY4IcffsDEiRP5fqJ8g8UsEele8+ZvVy14/DhDs6UiGZaKlLdzZkuVAlp9opNluhQyAf67S5Q3VlZW+OyzzxAcHIzg4GA0bNhQ6khEGRhJHYCICgFj47fLbwHIVFWmP168WKfrzRJR7r169QqP3llC77vvvkN4eDgLWcqXWMwSkX707Pl2+S2n9+7WV6qUXpblIqLcuXjxIurWrYtevXqpb0Mrl8tRrFgxiZMRZY3FLBHpT8+ewI3r/3t84CAQGclCligfEEJg4cKFaNasGe7fv4/nz5/j8XtTg4jyIxazRKRf704l+ES3t7Ilotx58eIFunXrhkmTJiEtLQ2ff/45rly5grJly0odjeiDWMwSEREVYmfPnkWdOnWwf/9+mJmZITAwEMHBwbCxsZE6GlGucDUDIiKiQkoIgYkTJ+LRo0eoWLEitm/fjjp16kgdi0gjHJklIiIqpGQyGTZv3owhQ4YgLCyMhSwZJBazREREhciff/6JJelL5QGoWLEi1qxZg6JFi0qYiijvOM2AiIioEFAqlZg7dy4CAgIghEC9evXQvLn277hHpG8sZomIiAq46Oho9OvXD8eOHQMADBw4EPXq1ZM4FZF2sJglIiIqwI4dOwYfHx88ffoUlpaWWL58OQYOHCh1LCKt4ZxZIiKiAmrevHlo164dnj59iho1auDy5cssZKnAYTFLRERUQNnb20MIgaFDh+LixYuoVq2a1JGItI7TDIiIiAqQN2/eoEiRIgCAwYMHo3LlymjWrJnEqYh0hyOzREREBUBaWhqmTZuGGjVq4OXLlwDeriPLQpYKOhazREREBu7hw4do2bIlvv/+e9y/fx+7du2SOhKR3rCYJSIiMmB//PEH6tSpg7Nnz8La2hrBwcEYNmyY1LGI9IbFLBERkQFKTU3FpEmT0KVLF7x8+RLu7u64cuUKevfuLXU0Ir1iMUtERGSAAgICsHDhQgDAuHHjcPbsWZQvX17iVET6x2KWiIjIAE2aNAm1a9fG7t27sWTJEpiZmUkdiUgSLGaJiIgMQEpKCjZu3AghBACgePHiuHLlCnr06CFxMiJpcZ1ZIiKifO7u3bvo3bs3wsLCkJKSor7Ay8iIY1JEfBcQERHlYzt37kTdunURFhaG4sWLw8nJSepIRPkKi1kiIqJ8KDk5GaNHj8bnn3+OuLg4NGnSBBEREejSpYvU0YjyFRazRERE+czt27fRuHFjLF++HAAwdepUnDx5EqVLl5Y4GVH+wzmzRERE+cyjR4/w119/wc7ODps2bUKHDh2kjkSUb7GYJSIiygeEEJDJZACAVq1aISgoCG3atIGLi4vEyYjyN04zICIiktiNGzfQrFkz3Lp1S902YMAAFrJEucBiloiISEIbNmyAh4cHzp07h3Hjxkkdh8jgsJglIiKSQEJCAnx9feHr64vExES0bt0aQUFBUsciMjgsZomIiPTs2rVrqF+/PjZs2AAjIyPMnj0bR44cgaOjo9TRiAwOLwAjIiLSo4sXL6JVq1ZISkqCk5MTtmzZgpYtW0odi8hgsZglIiLSo3r16qF27dqwtrbGpk2bYG9vL3UkIoPGYpaIiEjHrl+/jooVK0Iul0Mul+OPP/6Ara0tjIw424/oY/FdREREpCNCCKxYsQL16tXDjBkz1O3FixdnIUukJRyZJSIi0oG4uDgMGzYM27dvB/B2LVmlUgljY2OJkxEVLPyzkIiISMvCwsJQr149bN++HSYmJliwYAF+++03FrJEOsCRWSKCEAJJCqVezpWYqp/zEElBCIFly5Zh0qRJSE1NhaurK7Zt24ZGjRpJHY2owGIxS1TICSHQa8V5hN1/JXUUIoP3+PFjTJ8+Hampqfj000+xbt06FCtWTOpYRAUai1miQi5JoZSkkPVwLQYLOT9ypYKlVKlSWL16NZ49e4axY8dCJpNJHYmowGMxS0RqoV+1haWpfgpMC7kx/6EngyeEwE8//YS6deuiVatWAAAvLy+JUxEVLixmiUjN0tQYlqb8tUCUGy9fvoSvry/27dsHR0dHXL9+nVMKiCTAf7WIiIg0dO7cOXh5eeHhw4cwMzPDN998A1tbW6ljERVKXJqLiIgol1QqFX744Qd88sknePjwISpWrIgLFy5g1KhRnDZDJBGOzBIREeVCUlISPvvsMxw8eBAA0LdvX6xcuRJFixaVOBlR4caRWSIiolwwNzeHra0tzM3NsWrVKmzevJmFLFE+wGKWiIgoG0qlEgkJCQAAmUyGlStX4vLlyxg2bBinFRDlEyxmiYiIsvD06VN06NAB/fr1gxACAFC0aFHUqFFD4mRE9C7OmSUiInrP8ePH4ePjg+joaFhaWuLff/9F1apVpY5FRFngyCwREdH/UyqVmDlzJtq2bYvo6GhUr14dly9fZiFLlI9xZJaIiAjAkydP4OPjg5MnTwIAhgwZgqVLl8LS0lLaYESUIxazRERU6Akh0L17d4SGhsLKygorV66Ej4+P1LGIKBfyNM0gLS0NR48excqVKxEfHw/g7V+0b9680Wo4IiIifZDJZFi6dCnc3d1x5coVFrJEBkTjkdn79++jQ4cOePDgAVJSUtCuXTsULVoU8+fPR3JyMlasWKGLnERERFr16NEjREREoEuXLgCAxo0b4/Lly1xyi8jAaDwyO378eHh4eODVq1ewsLBQt/fo0QPHjh3TajgiIiJdOHDgAOrUqYPevXvj2rVr6nYWskSGR+OR2TNnzuDs2bMwNTXN0O7q6orHjx9rLRgREZG2KRQKzJgxAwsWLAAA1KtXL8PADBEZHo2LWZVKBaVSman90aNHvK0fERHlW/fv34eXlxcuXLgAABg7diwWLFgAMzMziZMR0cfQeJpBu3btsHjxYvVjmUyGN2/eYObMmejUqZM2sxEREWnFb7/9hrp16+LChQuwsbHBrl27sHTpUhayRAWAxiOzP/30E1q1aoVq1aohOTkZ3t7euH37Nuzs7LB161ZdZCQiIvooV65cwatXr9CgQQNs27YNbm5uUkciIi3RuJh1dnZGREQEtm3bhrCwMKhUKgwZMgQ+Pj6cd0RERPmGEEJ9Qdc333wDe3t7DBs2LNM1H0Rk2DSeZnDq1CnI5XIMGjQIy5Ytw/LlyzF06FDI5XKcOnVKFxmJiIg0smvXLrRu3RrJyckAAGNjY4wePZqFLFEBpHEx26pVK7x8+TJTe2xsLFq1aqWVUERERHmRnJyMMWPGoFevXjh58iR++eUXqSMRkY5pPM3g3Y9t3vXixQtYWVlpJRQREZGmbt++jT59+iA8PBwA8OWXX2LcuHESpyIiXct1MduzZ08Ab1cv8PX1zXAFqFKpxN9//40mTZpoPyEREdEHbNu2DcOGDcObN29gZ2eHjRs3omPHjlLHIiI9yHUxa2NjA+DtyGzRokUzXOxlamqKRo0aYdiwYdpPSERElIOFCxdi0qRJAIDmzZtj69atcHFxkTgVEelLrovZ9evXAwDKli2LSZMmcUoBERHlC5999hnmzp0LPz8/zJw5EyYmGs+gIyIDpvE7fubMmbrIQURElGvh4eGoW7cugLeDLLdv30bx4sUlTkVEUtB4NQMA2LlzJ3r37o1GjRqhXr16Gb40tXz5cri5ucHc3Bzu7u44ffp0jtunpKRgxowZcHV1hZmZGcqXL49169bl5WUQEZGBSUhIwODBg1GvXj0cOHBA3c5Clqjw0riYXbp0KQYNGgR7e3uEh4ejQYMGKFGiBO7evavxZPvg4GBMmDABM2bMQHh4OJo3b46OHTviwYMH2e7Tu3dvHDt2DGvXrsXNmzexdetWVKlSRdOXQUREBubBgwdo0qQJ1q9fDyMjI9y8eVPqSESUD2g8zWD58uVYtWoV+vbtiw0bNmDKlCkoV64cvvnmmyzXn83JokWLMGTIEAwdOhQAsHjxYhw+fBiBgYGYN29epu0PHTqEP//8E3fv3lX/FV62bFlNXwIRERkQIQSCgoIwadIkpKamwtHREVu3bkXLli2ljkZE+YDGxWz6X8YAYGFhgfj4eABA//790ahRIyxbtixXx0lNTUVYWBimTp2aod3T0xPnzp3Lcp/ff/8dHh4emD9/PjZt2gQrKyt069YN3377bba30k1JSUFKSor6cVxcHABAoVBAoVDkKuvHSj+Pvs5H2lXQ+0+hSHvn/xVQyISEaXSjoPdhQfbmzRuMGTMGW7ZsAQC0adMGGzZsgL29PfvTgPA9aPj03YeanEfjYtbR0REvXryAq6srXF1dceHCBdSuXRuRkZEQIvf/CMbExECpVMLBwSFDu4ODA6Kjo7Pc5+7duzhz5gzMzc2xZ88exMTEwM/PDy9fvsx23uy8efMwa9asTO1HjhyBpaVlrvNqQ0hIiF7PR9pVUPsvRQmk/yo4fPgIzIwljaNTBbUPC7Lz589jy5YtMDIygre3N3r27InQ0FCpY1Ee8T1o+PTVh4mJibneVuNitnXr1ti3bx/q1auHIUOGYOLEidi5cydCQ0PVN1bQxPt3E8vuDmMAoFKpIJPJsHnzZvW6t4sWLUKvXr3wyy+/ZDk6O23aNPj7+6sfx8XFoXTp0vD09IS1tbXGefNCoVAgJCQE7dq1g1wu18s5SXsKev8lpqZhyqXjAID27T1haVrwljUq6H1YkHXq1AlCCLRr1w4JCQnsQwPF96Dh03cfpn+Snhsa/6u1atUqqFQqAMDIkSNRvHhxnDlzBl27dsXIkSNzfRw7OzsYGxtnGoV99uxZptHadE5OTnBxcVEXsgBQtWpVCCHw6NEjVKxYMdM+ZmZmGe5Wlk4ul+v9DSXFOUl7Cmr/ycX//nh8+xoLXjGbrqD2YUESFxeHadOmYebMmbC3twcA/PDDD1AoFDhw4AD70MCx/wyfvvpQk3NovJqBkZFRhgWpe/fujaVLl2LcuHF4/vx5ro9jamoKd3f3TMPVISEh2d4Wt2nTpnjy5AnevHmjbrt16xaMjIxQqlQpDV8JERHlJ1euXEG9evWwfPlyDBkyROo4RGQg8rTO7Puio6MxduxYVKhQQaP9/P39sWbNGqxbtw43btzAxIkT8eDBA/UI77Rp0zBgwAD19t7e3ihRogQGDRqE69ev49SpU5g8eTIGDx6c7QVgRESUvwkhsGzZMjRu3Bh37txBmTJlMH36dKljEZGByHUx+/r1a/j4+KBkyZJwdnbG0qVLoVKp8M0336BcuXK4cOGCxjcv6NOnDxYvXozZs2ejTp06OHXqFA4cOABXV1cAQFRUVIY1Z4sUKYKQkBC8fv0aHh4e8PHxQdeuXbF06VKNzktERPnD69ev0atXL4wdOxapqano1q0bwsPD0bhxY6mjEZGByPXkuOnTp+PUqVMYOHAgDh06hIkTJ+LQoUNITk7GwYMH0aJFizwF8PPzg5+fX5bPBQUFZWqrUqUKr4YkIioA/v33X3Tq1AmRkZGQy+VYsGABxo0bl+1FwEREWcl1MfvHH39g/fr1aNu2Lfz8/FChQgVUqlQJixcv1mE8IiIqqJydnWFsbAw3NzcEBwejfv36UkciIgOU62L2yZMnqFatGgCgXLlyMDc3V9+5i4iIKDfi4uJQtGhRyGQyWFtbY//+/XBwcICtra3U0YjIQOV6zqxKpcqwTIKxsTGsrKx0EoqIiAqe8+fPo0aNGhnuFFm5cmUWskT0UXI9MiuEgK+vr3rN1uTkZIwcOTJTQbt7927tJiQiIoOmUqnw448/Yvr06VAqlVi5ciVGjhzJ9UaJSCtyXcwOHDgww+N+/fppPQwRERUsz58/x8CBA3Hw4EEAgJeXF1auXMlCloi0JtfF7Pr163WZg4iICphTp06hb9++ePLkCczNzbF06VIMHTqUqxUQkVYV3PtWEhGRZKKiouDp6YmUlBRUrlwZ27dvR61ataSORUQFEItZIiLSOicnJ8yaNQv//PMPli9fjiJFikgdiYgKKBazRESkFSdOnIC9vT2qV68OAJgyZQoAcFoBEelUrpfmIiIiyopSqURAQADatGmD3r17IyEhAcDbIpaFLBHpGkdmiYgoz6KiouDj44MTJ04AABo1asQCloj0Kk8js5s2bULTpk3h7OyM+/fvAwAWL16M3377TavhiIgo/woJCUGdOnVw4sQJWFlZYdOmTVi7di0sLS2ljkZEhYjGxWxgYCD8/f3RqVMnvH79GkqlEgBga2uLxYsXazsfERHlM2lpafjqq6/Qvn17PHv2DLVq1UJoaCjXHyciSWhczP78889YvXo1ZsyYAWNjY3W7h4cHrl69qtVwRESU/8hkMpw5cwZCCIwYMQIXLlxAlSpVpI5FRIWUxnNmIyMjUbdu3UztZmZm6kn/RERU8AghIJPJYGxsjC1btuDMmTPo3bu31LGIqJDTeGTWzc0NERERmdoPHjyIatWqaSMTERHlIwqFAlOmTMGECRPUbc7OzixkiShf0HhkdvLkyRg9ejSSk5MhhMClS5ewdetWzJs3D2vWrNFFRiIiksiDBw/g5eWF8+fPAwAGDx6M2rVrS5yKiOh/NC5mBw0ahLS0NEyZMgWJiYnw9vaGi4sLlixZAi8vL11kJCIiCfz+++/w9fXFq1evYGNjg7Vr17KQJaJ8J0/rzA4bNgzDhg1DTEwMVCoV7O3ttZ2LiIgkkpqaii+//FK9Qk39+vURHBwMNzc3aYMREWVB4zmzs2bNwp07dwAAdnZ2LGSJiAoQIQS6du2qLmQnTpyIM2fOsJAlonxL42J2165dqFSpEho1aoRly5bh+fPnushFREQSkMlkGDFiBIoVK4bffvsNixYtgqmpqdSxiIiypXEx+/fff+Pvv/9G69atsWjRIri4uKBTp07YsmULEhMTdZGRiIh0KDk5OcM64T179sTdu3fRrVs3CVMREeVOnm5nW716dcydOxd3797FiRMn4ObmhgkTJsDR0VHb+YiISIf+++8/NGnSBK1bt8bjx4/V7ba2ttKFIiLSQJ6K2XdZWVnBwsICpqamUCgU2shERER6EBwcjHr16iE8PBxCCERGRkodiYhIY3kqZiMjIzFnzhxUq1YNHh4euHLlCgICAhAdHa3tfEREpGVJSUkYOXIkvLy8EB8fj2bNmiEiIgLNmjWTOhoRkcY0XpqrcePGuHTpEmrWrIlBgwap15klIqL87+bNm+jduzf+/vtvyGQyTJ8+HQEBATAxydNKjUREktP4t1erVq2wZs0aVK9eXRd5iIhIh5YsWYK///4b9vb2+PXXX9GuXTupIxERfRSNi9m5c+fqIgcR/T8hBJIUSr2dLzFVf+ci6S1YsABpaWmYNWsWnJycpI5DRPTRclXM+vv749tvv4WVlRX8/f1z3HbRokVaCUZUGAkh0GvFeYTdfyV1FCog/vnnH6xcuRKLFy+GkZERrKyssGrVKqljERFpTa6K2fDwcPVKBeHh4ToNRFSYJSmUkhWyHq7FYCE3luTcpH1CCAQFBWH06NFISkpCuXLlMGHCBKljERFpXa6K2RMnTmT5/0SkO6FftYWlqf6KSwu5MWQymd7OR7rz5s0b+Pn5YdOmTQAAT09PeHt7S5yKiEg3NF6aa/DgwYiPj8/UnpCQgMGDB2slFBEBlqbGsDQ10dsXC9mC4e+//4aHhwc2bdoEIyMjzJkzBwcPHoS9vb3U0YiIdELjYnbDhg1ISkrK1J6UlISNGzdqJRQREWkuODgYDRs2xM2bN+Hi4oKTJ09i+vTpMDL66PvjEBHlW7lezSAuLg5CCAghEB8fD3Nzc/VzSqUSBw4c4F/+REQSqlChAlQqFTp27IiNGzfCzs5O6khERDqX62LW1tYWMpkMMpkMlSpVyvS8TCbDrFmztBqOiIhy9vr1a9ja2gIA3N3dcf78edSpU4ejsURUaOS6mD1x4gSEEGjdujV27dqF4sWLq58zNTWFq6srnJ2ddRKSiIgyEkJg+fLlmD59Ok6cOIF69eoBgPq/RESFRa6L2RYtWgAAIiMjUaZMGV4sQkQkkdevX2PYsGHYuXMnACAoKIhFLBEVWrkqZv/++2/UqFEDRkZGiI2NxdWrV7PdtlatWloLR0REGV2+fBl9+vRBZGQk5HI55s+fj/Hjx0sdi4hIMrkqZuvUqYPo6GjY29ujTp06kMlkEEJk2k4mk0Gp5K0xiYi0TQiBJUuWYMqUKVAoFHBzc0NwcDDq168vdTQiIknlqpiNjIxEyZIl1f9PRET6tWvXLkycOBEA8Nlnn2HNmjXqC7+IiAqzXBWzrq6uWf4/ERHpR8+ePdGtWzd4enrCz8+P1y0QEf2/PN004Y8//lA/njJlCmxtbdGkSRPcv39fq+GIiAorlUqF1atXIzExEQBgZGSEvXv3YvTo0SxkiYjeoXExO3fuXFhYWAAAzp8/j2XLlmH+/Pmws7NTfwRGRER5FxMTg65du2L48OEYO3asup1FLBFRZrlemivdw4cPUaFCBQDA3r170atXLwwfPhxNmzZFy5YttZ2PiKhQOX36NPr27YvHjx/D3NwcDRs2hBCChSwRUTY0HpktUqQIXrx4AQA4cuQI2rZtCwAwNzdHUlKSdtMRERUSKpUKc+fORatWrfD48WNUrlwZFy9exPDhw1nIEhHlQOOR2Xbt2mHo0KGoW7cubt26hc6dOwMA/vnnH5QtW1bb+YiICrxnz56hf//+OHLkCACgX79+CAwMRJEiRSRORkSU/2k8MvvLL7+gcePGeP78OXbt2oUSJUoAAMLCwtC3b1+tByQiKugUCgWuXLkCCwsLrF27Fhs3bmQhS0SUSxqPzNra2mLZsmWZ2mfNmqWVQEREhcG782BdXFywY8cOlCxZEtWrV5c4GRGRYdG4mAXe3hd87dq1uHHjBmQyGapWrYohQ4bAxsZG2/mIiAqc6Oho+Pj4YMyYMejRowcA8AJaIqI80niaQWhoKMqXL4+ffvoJL1++RExMDH766SeUL18eV65c0UVGIqIC4+jRo6hduzaOHz+OcePGITU1VepIREQGTeNiduLEiejWrRvu3buH3bt3Y8+ePYiMjESXLl0wYcIEHUQkIjJ8aWlp+Oqrr+Dp6Ylnz56hVq1aOHr0KExNTaWORkRk0DSeZhAaGorVq1fDxOR/u5qYmGDKlCnw8PDQajgiooLg8ePH6Nu3L06fPg0AGDFiBH766Sf1DWiIiCjvNC5mra2t8eDBA1SpUiVD+8OHD1G0aFGtBSMiKgieP3+OOnXqICYmBkWLFsWqVavg5eUldSwiogJD42kGffr0wZAhQxAcHIyHDx/i0aNH2LZtG4YOHcqluYiI3lOyZEn06dMHdevWRVhYGAtZIiIt03hk9scff4RMJsOAAQOQlpYGAJDL5Rg1ahS+//57rQckIjI0Dx48gFwuh5OTEwBg4cKFEELA3Nxc4mRERAWPxiOzpqamWLJkCV69eoWIiAiEh4fj5cuX+Omnn2BmZqaLjEREBmPfvn2oU6cO+vbtq/6D38zMjIUsEZGO5LqYTUxMxOjRo+Hi4gJ7e3sMHToUTk5OqFWrFiwtLXWZkYgo30tNTcUXX3yBbt264dWrV0hMTMSrV6+kjkVEVODlupidOXMmgoKC0LlzZ3h5eSEkJASjRo3SZTYiIoMQGRmJ5s2bY9GiRQDeLmF45swZlCxZUuJkREQFX67nzO7evRtr165VX7zQr18/NG3aFEqlEsbGxjoLSESUn+3evRuDBw9GbGwsihUrhqCgIHTr1k3qWEREhUauR2YfPnyI5s2bqx83aNAAJiYmePLkiU6CERHldwqFAl9//TViY2PRuHFjhIeHs5AlItKzXBezSqUy051qTExM1Bc4EBEVNnK5HMHBwZg2bRr+/PNPuLq6Sh2JiKjQyfU0AyEEfH19M6xYkJycjJEjR8LKykrdtnv3bu0mJCLKR7Zv345nz55hzJgxAIAaNWpg7ty5EqciIiq8cl3MDhw4MFNbv379tBqGiCi/SkpKwsSJE7Fy5UoYGxujadOmqFu3rtSxiIgKvVwXs+vXr9dlDiKifOvmzZvo3bs3/v77b8hkMkydOhU1a9aUOhYRESEPdwAjIipMfv31V4wcORIJCQmwt7fHr7/+inbt2kkdi4iI/p/GdwAjIios/Pz80L9/fyQkJKBVq1aIiIhgIUtElM+wmCUiykaVKlUgk8kQEBCAkJAQODk5SR2JiIjew2kGRETvePnyJYoXLw4AGDt2LFq0aIHatWtLnIqIiLLDkVkiIgBv3rzBwIED0bBhQ8TFxQEAZDIZC1kionwuT8Xspk2b0LRpUzg7O+P+/fsAgMWLF+O3337TajgiIn24evUq6tevj40bN+Lu3bs4ceKE1JGIiCiXNC5mAwMD4e/vj06dOuH169dQKpUAAFtbWyxevFjb+YiIdEYIgdWrV6NBgwb4999/4eLigpMnT6J79+5SRyMiolzSuJj9+eefsXr1asyYMQPGxsbqdg8PD1y9elWr4YiIdCU+Ph4+Pj4YPnw4kpOT0bFjR0RERKB58+ZSRyMiIg1oXMxGRkZmedcbMzMzJCQkaCUUEZGuffHFF9i6dSuMjY0xf/587N+/H3Z2dlLHIiIiDWlczLq5uSEiIiJT+8GDB1GtWjWNAyxfvhxubm4wNzeHu7s7Tp8+nav9zp49CxMTE9SpU0fjcxIRfffdd2jUqBFOnz6NyZMnw8iI18MSERkijX97T548GaNHj0ZwcDCEELh06RLmzJmD6dOnY/LkyRodKzg4GBMmTMCMGTMQHh6O5s2bo2PHjnjw4EGO+8XGxmLAgAFo06aNpvGJqJBKSEjA2rVr1Y/t7e1x7tw5NG7cWMJURET0sTReZ3bQoEFIS0vDlClTkJiYCG9vb7i4uGDJkiXw8vLS6FiLFi3CkCFDMHToUABvV0Q4fPgwAgMDMW/evGz3GzFiBLy9vWFsbIy9e/dq+hKIqJAJCwuDv78/nj59Cmtra3h7ewN4u/QWEREZtjzdNGHYsGEYNmwYYmJioFKpYG9vr/ExUlNTERYWhqlTp2Zo9/T0xLlz57Ldb/369bhz5w5+/fVXfPfddx88T0pKClJSUtSP09ePVCgUUCgUGufOi/Tz6Ot8pF367D+FIi3DeRUyofNzFmRCCCxbtgxTp06FQqGAq6srypYty/eiAeLvUcPG/jN8+u5DTc7zUXcA+5iLJWJiYqBUKuHg4JCh3cHBAdHR0Vnuc/v2bUydOhWnT5+GiUnuos+bNw+zZs3K1H7kyBFYWlpqHvwjhISE6PV8pF366L8UJZD+tjx8+AjMjHPcnHLw5s0b/Pzzz7h48SIAoFGjRhgzZgyeP3+OAwcOSJyO8oq/Rw0b+8/w6asPExMTc72txsWsm5tbjh/N3b17V6PjvX8sIUSWx1cqlfD29sasWbNQqVKlXB9/2rRp8Pf3Vz+Oi4tD6dKl4enpCWtra42y5pVCoUBISAjatWsHuVyul3OS9uiz/xJT0zDl0nEAQPv2nrA05R2n8+LSpUsYP3487t+/D1NTU8ybNw/lypWDp6cn34MGir9HDRv7z/Dpuw/TP0nPDY3/pZwwYUKGxwqFAuHh4Th06JBGF4DZ2dnB2Ng40yjss2fPMo3WAm/XhAwNDUV4eDjGjBkDAFCpVBBCwMTEBEeOHEHr1q0z7WdmZgYzM7NM7XK5XO9vKCnOSdqjj/6Ti//9Iff2fCxm8yI2Nhb3799H+fLlsX37dtSsWRMHDhzge7AAYB8aNvaf4dNXH2pyDo3/pRw/fnyW7b/88gtCQ0NzfRxTU1O4u7sjJCQEPXr0ULeHhIRkefcda2vrTDdlWL58OY4fP46dO3fCzc0t1+cmooLn3U91OnXqhC1btqBz586wtrbmPD0iogJMawsrduzYEbt27dJoH39/f6xZswbr1q3DjRs3MHHiRDx48AAjR44E8HaKwIABA94GNTJCjRo1MnzZ29vD3NwcNWrUgJWVlbZeChEZmDNnzqB27dq4f/++uq1v3756m0pERETS0dpnmDt37kTx4sU12qdPnz548eIFZs+ejaioKNSoUQMHDhyAq6srACAqKuqDa84SUeGlUqnwww8/4Ouvv4ZSqcRXX32FTZs2SR2LiIj0SONitm7duhku0BJCIDo6Gs+fP8fy5cs1DuDn5wc/P78snwsKCspx34CAAAQEBGh8TiIyfM+ePUP//v1x5MgRAEC/fv0QGBgocSoiItI3jYvZTz/9NMNjIyMjlCxZEi1btkSVKlW0lYuIKFsnT56Et7c3oqKiYGFhgV9++QW+vr68CQIRUSGkUTGblpaGsmXLon379nB0dNRVJiKibB08eBBdunSBSqVCtWrVsH37dlSvXl3qWEREJBGNLgAzMTHBqFGjMtxRi4hIn1q1aoVatWph0KBBuHTpEgtZIqJCTuNpBg0bNkR4eLj6Ii0iIl27ePEiPDw8YGxsDHNzc5w6dQpFixaVOhYREeUDGhezfn5++OKLL/Do0SO4u7tnWhKrVq1aWgtHRIVbWloaZs2ahTlz5uCbb75RX/DJQpaIiNLlupgdPHgwFi9ejD59+gAAxo0bp35OJpOpFyxXKpXaT0lEhc7jx4/h7e2NU6dOAQCePn2a7e2uiYio8Mp1MbthwwZ8//33iIyM1GUeIiIcOnQI/fv3R0xMDIoUKYLVq1fDy8tL6lhERJQP5bqYFUIAAOfKEpHOKBQKfPPNN/j+++8BvF3XOjg4GBUrVpQ4GRER5VcarWbAj/eISJfu3r2LxYsXAwBGjx6Nc+fOsZAlIqIcaXQBWKVKlT5Y0L58+fKjAhFR4VW5cmWsXLkSlpaW6NWrl9RxiIjIAGhUzM6aNQs2Nja6ykJEhUxqaiq++uor9OjRA40bNwYADBgwQOJURERkSDQqZr28vGBvb6+rLERUiNy7dw9eXl64ePEitm/fjn///Rfm5uZSxyIiIgOT6zmznC9LRNqyZ88e1K1bFxcvXoStrS2WLFnCQpaIiPIk18Vs+moGRER5lZKSgnHjxqFnz554/fo1GjVqhIiICHTv3l3qaEREZKByPc1ApVLpMgcRFXCvXr1Cu3btEBYWBgCYPHky5syZA7lcLnEyIiIyZBrfzpaIKC9sbW1RqlQp3Lt3Dxs2bEDnzp2ljkRERAUAi1ki0pnk5GSkpaWhSJEikMlkWLduHRITE1GqVCmpoxERUQGh0U0TiIhy69atW2jUqBGGDx+unnNfvHhxFrJERKRVLGaJSOu2bNkCd3d3/PXXXzh69CgeP34sdSQiIiqgWMwSkdYkJiZi2LBh8PHxwZs3b9CyZUtERERwNJaIiHSGxSwRacWNGzfQsGFDrFmzBjKZDDNnzsTRo0fh7OwsdTQiIirAeAEYEX20tLQ0dO3aFXfu3IGjoyM2b96M1q1bSx2LiIgKAY7MEtFHMzExwapVq9C+fXtERESwkCUiIr1hMUtEeXL16lXs379f/bh169Y4ePAgHBwcJExFRESFDYtZItKIEAJr1qxBgwYN0LdvX9y+fVv9nEwmkzAZEREVRixmiSjX4uPj0a9fPwwbNgzJyclo1qwZbG1tpY5FRESFGItZIsqViIgIuLu7Y8uWLTA2NsYPP/yAP/74AyVLlpQ6GhERFWJczYCIPmjFihWYMGECUlJSULp0aWzbtg1NmjSROhYRERFHZonow+7cuYOUlBR07doV4eHhLGSJiCjf4MgsEWVJpVLByOjt37tz585F7dq14ePjw4u8iIgoX+HILBFlIITAkiVL0Lp1aygUCgCAXC5Hv379WMgSEVG+w2KWiNRevXqFnj17YsKECfjzzz+xdetWqSMRERHliNMMiAgAcPHiRfTp0wf379+HqakpFi5ciP79+0sdi4iIKEccmSUq5FQqFRYuXIhmzZrh/v37KF++PM6dO4cxY8ZwWgEREeV7LGaJCrkpU6Zg0qRJSEtLQ+/evXHlyhW4u7tLHYuIiChXWMwSFXLDhg2DnZ0dVqxYgW3btsHa2lrqSERERLnGObNEhYxKpcK5c+fQrFkzAEDlypVx7949WFlZSZyMiIhIcxyZJSpEnj17hk6dOqFFixY4efKkup2FLBERGSqOzBIVEn/++Sf69u2LqKgoWFhYICoqSupIREREH40js0QFnFKpxLfffovWrVsjKioKVatWxaVLl9C3b1+poxEREX00jswSFWDR0dHo168fjh07BgDw9fXFsmXLOK2AiIgKDBazRAXYwYMHcezYMVhaWiIwMBADBgyQOhIREZFWsZglKsB8fX1x9+5deHt7o2rVqlLHISIi0jrOmSUqQJ48eYJ+/frh1atXAACZTIZvv/2WhSwRERVYHJklKiAOHTqE/v37IyYmBgDw66+/SpyIiIhI9zgyS2Tg0tLSMG3aNHTs2BExMTGoU6cOZs6cKXUsIiIiveDILJEBe/jwIfr27YuzZ88CAPz8/LBw4UKYm5tLnIyIiEg/WMwSGagLFy6gc+fOePnyJaytrbF27Vr06tVL6lhERER6xWKW6AOEEEhRAompaZALmU7PlZiqzPW2lSpVgpWVFcqVK4fg4GCUK1dOh8mIiIjyJxazRDkQQsBrzWVceWCCKZeOSx0Hz549Q8mSJSGTyVC8eHEcO3YMZcqUgZmZmdTRiIiIJMELwIhykKRQ4sqD13o/r4drMVjIjTO07dmzB5UrV8a6devUbRUrVmQhS0REhRpHZoly6cKXLWBtpZ8LqyzkxpDJ3k5pSElJweTJk/Hzzz8DADZv3ozBgwernyciIirMWMwS5ZKFqTEsTfX7lrlz5w769OmDsLAwAMCkSZMwd+5cFrJERET/j8UsUT61Y8cODB06FHFxcShevDg2btyIzp07Sx2LiIgoX2ExS5QP3bp1C15eXlCpVGjatCm2bt2K0qVLSx2LiIgo32ExS5QPVapUCd988w1SUlIwe/ZsmJjwrUpERJQV/gtJlE9s3boVHh4eqFixIgDwlrRERES5wKW5iCSWmJiIoUOHwtvbG3369EFycrLUkYiIiAwGR2aJJHTjxg307t0b165dg0wmQ9euXSGXy6WORUREZDBYzBJJZMOGDfDz80NiYiIcHBywefNmtGnTRupYREREBoXFLJGeJSYmYtSoUdi4cSMAoE2bNvj111/h6OgocTIiIiLDwzmzRHpmYmKCf//9F0ZGRvj2229x+PBhFrJERER5xJFZIj0QQkAIASMjI5iamiI4OBj3799HixYtpI5GRERk0DgyS6Rj8fHx6NevH6ZNm6ZuK1u2LAtZIiIiLeDILJEORUREoHfv3rh9+zZMTEwwatQolC1bVupYREREBQZHZol0QAiBwMBANGrUCLdv30apUqVw8uRJFrJERERaxpFZIi2LjY3FsGHDsGPHDgBAly5dEBQUhBIlSkicjIiIqOBhMUukRSqVCi1atMBff/0FExMT/PDDD5g4cSJkMpnU0YiIiAokTjMg0iIjIyNMnjwZrq6uOHPmDPz9/VnIEhER6RCLWaKP9OrVK0RERKgf+/j44Pr162jYsKF0oYiIiAoJFrNEH+HixYuoW7cuOnXqhOfPn6vbLS0tJUxFRERUeLCYJcoDIQQWLlyIZs2a4f79+7CwsMCzZ8+kjkVERFTo8AIwIg29ePECvr6+2L9/PwDg888/x+rVq2FjYyNxMiIiosJH8pHZ5cuXw83NDebm5nB3d8fp06ez3Xb37t1o164dSpYsCWtrazRu3BiHDx/WY1oq7M6ePYs6depg//79MDMzw/LlyxEcHMxCloiISCKSFrPBwcGYMGECZsyYgfDwcDRv3hwdO3bEgwcPstz+1KlTaNeuHQ4cOICwsDC0atUKXbt2RXh4uJ6TU2EVGBiIR48eoWLFirhw4QJGjRrF1QqIiIgkJOk0g0WLFmHIkCEYOnQoAGDx4sU4fPgwAgMDMW/evEzbL168OMPjuXPn4rfffsO+fftQt25dfUSmQm758uVwcHBAQEAAihYtKnUcIiKiQk+yYjY1NRVhYWGYOnVqhnZPT0+cO3cuV8dQqVSIj49H8eLFs90mJSUFKSkp6sdxcXEAAIVCAYVCkYfkmks/j77OR9pz4vgJvDiyHMXbjYJCkQYbKwt8//33ANifhoTvQcPHPjRs7D/Dp+8+1OQ8khWzMTExUCqVcHBwyNDu4OCA6OjoXB1j4cKFSEhIQO/evbPdZt68eZg1a1am9iNHjuh9+aSQkBC9no/yTqlUYufOndgWHAyhUsHMqRKOH1fCzFjqZPQx+B40fOxDw8b+M3z66sPExMRcbyv5agbvzzcUQuRqDuLWrVsREBCA3377Dfb29tluN23aNPj7+6sfx8XFoXTp0vD09IS1tXXeg2tAoVAgJCQE7dq1g1wu18s5Ke+io6Ph6+uL48ePAwCsarSBZeVmaN26NWyszCVOR3nB96DhYx8aNvaf4dN3H6Z/kp4bkhWzdnZ2MDY2zjQK++zZs0yjte8LDg7GkCFDsGPHDrRt2zbHbc3MzGBmZpapXS6X6/0NJcU5STPHjh2Dj48Pnj59CktLS/y09GfMvf3251EuN2H/GTi+Bw0f+9Cwsf8Mn776UJNzSLaagampKdzd3TMNV4eEhKBJkybZ7rd161b4+vpiy5Yt6Ny5s65jUiGyZMkStGvXDk+fPkWNGjUQGhqKfv0HSB2LiIiIciDpNAN/f3/0798fHh4eaNy4MVatWoUHDx5g5MiRAN5OEXj8+DE2btwI4G0hO2DAACxZsgSNGjVSj+paWFhwnU/6aPXr14eRkREGDRqEJUuWwNLSEompaVLHIiIiohxIWsz26dMHL168wOzZsxEVFYUaNWrgwIEDcHV1BQBERUVlWHN25cqVSEtLw+jRozF69Gh1+8CBAxEUFKTv+FQAPH36VD2tpUmTJrh27RqqVKkicSoiIiLKLckvAPPz84Ofn1+Wz71foJ48eVL3gahQSEtLw9dff42ff/4ZFy9eRPXq1QGAhSwREZGBkbyYJdK3hw8fom/fvjh79iwAYN++fepiloiIiAwLi1kqVP744w8MGDAAL1++hLW1NVavXp3jOsVERESUv0m2mgGRPikUCkyaNAldunTBy5cv4e7ujitXrrCQJSIiMnAsZqlQWLt2LRYuXAgAGDduHM6ePYvy5ctLnIqIiIg+FqcZUKEwdOhQHD58GAMGDECPHj2kjkNERERawpFZKpBSU1OxYMECpKSkAABMTEywZ88eFrJEREQFDEdm6aMJIZCkUEodQy3y7l0M6OeNK2FhuBt5DwsXL8nzsRJT88/rIiIiosxYzNJHEUKg14rzCLv/SuooAICEf8/gxcGlEKmJMDIvil3P7HDwm8NSxyIiIiIdYTFLHyVJocwXhaxIS8XL42vwJvwAAMDMpRrsuk2GiXVJrRzfraiAhdxYK8ciIiIi7WExS1oT+lVbWJrqv+C7e+cOfPr2wYO//gIAfDF5Cr6eGQC5XK6V4ysUCpwIOQKZTKaV4xEREZH2sJglrbE0NYalqf5/pKzMTXH/3j3Y2dlh06ZN6NChg1aPr5AJsI4lIiLKn1jMkkFSKpUwNn47Cuzm5oY9e/agUqVKcHFxkTgZERER6ROL2QJIn6sLSHG1/40bN+Dl5YUffvhBPQrbqlUrvecgIiIi6bGYLWDy2+oC2rZx40aMGjUKiYmJmDx5Mjw9PWFkxOWSiYiICitWAQWMVKsLeLgW0+nV/gkJCRg0aBAGDhyIxMREtG7dGiEhISxkiYiICjmOzBZg+lxdwEJurLOr/a9du4bevXvjxo0bMDIyQkBAAKZPn66eM0tERESFF4vZAkyq1QW06e7du2jQoAGSkpLg5OSELVu2oGXLllLHIiIionzCsCsdAyGEQIoSSExNg1zodo2ngnb71XLlysHLywtPnjzBxo0bYW9vL3UkIiIiykdYzOqYEAJeay7jygMTTLl0XOo4BuGvv/6Cs7MzSpZ8e/euwMBAyOVyzo8lIiKiTFgd6FiSQokrD17r/by6viBLF4QQWLFiBRo2bIgBAwZApVIBAMzMzFjIEhERUZY4MqtHF75sAWsrc72cS5cXZOlCbGwshg8fju3btwMAjI2NkZiYiCJFikicjIiIiPIzFrN6ZFEALsjShbCwMPTp0wd37tyBiYkJ5s2bB39/f47GEhER0QexsiLJCCGwbNkyTJo0CampqXB1dcW2bdvQqFEjqaMRERGRgeDQF0kmISEBS5YsQWpqKrp3747w8HAWskRERKQRjsySZIoUKYLg4GCcOXMG48aNM6g5vkRERJQ/sJglvRFCYPHixbCwsMDIkSMBAO7u7nB3d5c4GRERERkqFrOkFy9fvoSvry/27dsHU1NTtGvXDuXLl5c6FhERERk4FrOkc+fOnYOXlxcePnwIMzMz/PTTTyhXrpzUsYiIiKgA4AVgpDMqlQo//PADPvnkEzx8+BAVK1bEhQsXMGrUKM6PJSIiIq3gyCzphEqlwqeffop9+/YBAPr27YuVK1eiaNGiEicjIiKigoQjs6QTRkZGaNy4MczNzbF69Wps3ryZhSwRERFpHUdmSWuUSiViYmLg4OAAAPjyyy/x+eefo0KFChInIyIiooKKI7OkFU+fPkWHDh3Qpk0bJCYmAng7OstCloiIiHSJxSx9tOPHj6N27do4evQoIiMjceXKFakjERERUSHBYpbyTKlUYubMmWjbti2ePn2K6tWr4/Lly2jWrJnU0YiIiKiQ4JxZypMnT57Ax8cHJ0+eBAAMGTIES5cuhaWlpbTBiIiIqFBhMUt5MnbsWJw8eRJWVlZYuXIlfHx8pI5EREREhRCLWcqTpUuXIjY2Fr/88gsqV64sdRwiIiIqpDhnlnLl0aNH+OWXX9SPXVxccPToURayREREJCmOzNIHHThwAAMGDMCLFy/g4uKCTz/9VOpIRERERAA4Mks5UCgUmDJlCjp37owXL16gXr16qFmzptSxiIiIiNQ4MktZun//Pry8vHDhwgUAby/4WrBgAczMzCRORkRERPQ/LGYpk/3796N///54/fo1bGxssG7dOvTs2VPqWERERESZsJilTFJSUvD69Ws0aNAA27Ztg5ubm9SRiIiIiLLEYpYAAGlpaTAxefvj8Nlnn2HXrl3o0qULTE1NJU5GRERElD1eAEbYuXMnqlWrhidPnqjbevbsyUKWiIiI8j0Ws4VYcnIyRo8ejc8//xy3b9/GggULpI5EREREpBFOMyikbt++jT59+iA8PBwA8OWXX+Lbb7+VOBURERGRZljMFkLbtm3DsGHD8ObNG9jZ2WHjxo3o2LGj1LGIiIiINMZitpDZuHEjBg4cCABo3rw5tm7dChcXF4lTEREREeUN58wWMp999hmqV6+Or776CsePH2chS0RERAaNI7OFQEhICNq0aQMjIyNYWVkhNDQU5ubmUsciIiIi+mgcmS3AEhISMGjQIHh6emLhwoXqdhayREREVFBwZLaA+ueff9C7d29cv34dRkZGUCgUUkciIiIi0joWswWMEALr16/HmDFjkJSUBEdHR2zduhUtW7aUOhoRERGR1rGYLUDevHmDkSNHYvPmzQAAT09PbNq0Cfb29hInIyIiItINzpktQG7duoXt27fD2NgYc+fOxcGDB1nIEhERUYHGkdkCpF69eli5ciUqVqyIZs2aSR2HiIiISOc4MmvA4uLiMGDAAPUtaQFg0KBBLGSJiIio0ODIrIG6cuUKevfujTt37iA0NBRXr16FsbGx1LGIiIiI9IojswZGCIFly5ahcePGuHPnDsqUKYO1a9eykCUiIqJCiSOzBuT169cYMmQIdu/eDQDo1q0b1q9fj+LFi0ucjIiIiEgaLGYNxKNHj9C8eXPcu3cPcrkcCxYswLhx4yCTyaSORkRERCQZFrMGwtnZGRUrVoRMJkNwcDDq168vdSQiIiIiybGYzcdevnwJc3NzWFpawsjICFu2bIGJiQlsbW2ljkZERESUL/ACsHzq3LlzqFOnDsaPH69us7OzYyFLRERE9A4Ws/mMSqXC/Pnz8cknn+Dhw4c4efIkXr9+LXUsIiIionyJxWw+8vz5c3Tp0gVffvkllEolvLy8EBYWxtFYIiIiomxwzmw+cfr0aXh5eeHJkycwNzfHkiVLMGzYMK5WQERERJQDFrP5QGJiIj7//HM8ffoUlStXxvbt21GrVi2pYxERERHle5xmkA9YWlpi3bp16N+/P0JDQ1nIEhEREeUSR2YlcuLECSQlJaFTp04AgE6dOqn/n4iIiIhyhyOzeqZUKhEQEIA2bdrAx8cHDx48kDoSERERkcGSvJhdvnw53NzcYG5uDnd3d5w+fTrH7f/880+4u7vD3Nwc5cqVw4oVK/SU9ONFR0ehXbt2mDVrFoQQ6NmzJ+zs7KSORURERGSwJC1mg4ODMWHCBMyYMQPh4eFo3rw5OnbsmO1oZWRkJDp16oTmzZsjPDwc06dPx7hx47Br1y49J9dcUuQVNGvUCCdOnICVlRU2bdqEtWvXwtLSUupoRERERAZL0jmzixYtwpAhQzB06FAAwOLFi3H48GEEBgZi3rx5mbZfsWIFypQpg8WLFwMAqlatitDQUPz444/47LPP9Bk914QQeHVqI+LO7wAgUKtWLWzfvh2VK1eWOhoRERGRwZOsmE1NTUVYWBimTp2aod3T0xPnzp3Lcp/z58/D09MzQ1v79u2xdu1aKBQKyOXyTPukpKQgJSVF/TguLg4AoFAooFAoPvZlfFBaWhpUyW8ACAz0HYylS36ChYWFXs5N2pHeV+wzw8U+NHzsQ8PG/jN8+u5DTc4jWTEbExMDpVIJBweHDO0ODg6Ijo7Ocp/o6Ogst09LS0NMTAycnJwy7TNv3jzMmjUrU/uRI0f08hF/ihIo3nooLMp5oFPXejhx4oTOz0m6ERISInUE+kjsQ8PHPjRs7D/Dp68+TExMzPW2ki/N9f4droQQOd71Kqvts2pPN23aNPj7+6sfx8XFoXTp0vD09IS1tXVeY+eaEAKtW6fg+HEjdG7fFqampjo/J2mXQqFASEgI2rVrl+XoP+V/7EPDxz40bOw/w6fvPkz/JD03JCtm7ezsYGxsnGkU9tmzZ5lGX9M5Ojpmub2JiQlKlCiR5T5mZmYwMzPL1C6Xy/X2hrKRyWBmDJiamvJNbMD0+TNDusE+NHzsQ8PG/jN8+upDTc4h2WoGpqamcHd3zzRcHRISgiZNmmS5T+PGjTNtf+TIEXh4ePDNQURERFQISbo0l7+/P9asWYN169bhxo0bmDhxIh48eICRI0cCeDtFYMCAAertR44cifv378Pf3x83btzAunXrsHbtWkyaNEmql0BEREREEpJ0zmyfPn3w4sULzJ49G1FRUahRowYOHDgAV1dXAEBUVFSGNWfd3Nxw4MABTJw4Eb/88gucnZ2xdOnSfLssFxERERHpluQXgPn5+cHPzy/L54KCgjK1tWjRAleuXNFxKiIiIiIyBJLfzpaIiIiIKK9YzBIRERGRwWIxS0REREQGi8UsERERERksFrNEREREZLBYzBIRERGRwWIxS0REREQGi8UsERERERksFrNEREREZLBYzBIRERGRwWIxS0REREQGi8UsERERERksFrNEREREZLBMpA6gb0IIAEBcXJzezqlQKJCYmIi4uDjI5XK9nZe0g/1n+NiHho99aNjYf4ZP332YXqel1205KXTFbHx8PACgdOnSEichIiIiopzEx8fDxsYmx21kIjclbwGiUqnw5MkTFC1aFDKZTC/njIuLQ+nSpfHw4UNYW1vr5ZykPew/w8c+NHzsQ8PG/jN8+u5DIQTi4+Ph7OwMI6OcZ8UWupFZIyMjlCpVSpJzW1tb801swNh/ho99aPjYh4aN/Wf49NmHHxqRTccLwIiIiIjIYLGYJSIiIiKDxWJWD8zMzDBz5kyYmZlJHYXygP1n+NiHho99aNjYf4YvP/dhobsAjIiIiIgKDo7MEhEREZHBYjFLRERERAaLxSwRERERGSwWs0RERERksFjMasHy5cvh5uYGc3NzuLu74/Tp0zlu/+eff8Ld3R3m5uYoV64cVqxYoaeklB1N+nD37t1o164dSpYsCWtrazRu3BiHDx/WY1rKiqbvw3Rnz56FiYkJ6tSpo9uA9EGa9mFKSgpmzJgBV1dXmJmZoXz58li3bp2e0tL7NO2/zZs3o3bt2rC0tISTkxMGDRqEFy9e6Cktve/UqVPo2rUrnJ2dIZPJsHfv3g/uk2/qGUEfZdu2bUIul4vVq1eL69evi/HjxwsrKytx//79LLe/e/eusLS0FOPHjxfXr18Xq1evFnK5XOzcuVPPySmdpn04fvx48cMPP4hLly6JW7duiWnTpgm5XC6uXLmi5+SUTtM+TPf69WtRrlw54enpKWrXrq2fsJSlvPRht27dRMOGDUVISIiIjIwUFy9eFGfPntVjakqnaf+dPn1aGBkZiSVLloi7d++K06dPi+rVq4tPP/1Uz8kp3YEDB8SMGTPErl27BACxZ8+eHLfPT/UMi9mP1KBBAzFy5MgMbVWqVBFTp07NcvspU6aIKlWqZGgbMWKEaNSokc4yUs407cOsVKtWTcyaNUvb0SiX8tqHffr0EV999ZWYOXMmi1mJadqHBw8eFDY2NuLFixf6iEcfoGn/LViwQJQrVy5D29KlS0WpUqV0lpFyLzfFbH6qZzjN4COkpqYiLCwMnp6eGdo9PT1x7ty5LPc5f/58pu3bt2+P0NBQKBQKnWWlrOWlD9+nUqkQHx+P4sWL6yIifUBe+3D9+vW4c+cOZs6cqeuI9AF56cPff/8dHh4emD9/PlxcXFCpUiVMmjQJSUlJ+ohM78hL/zVp0gSPHj3CgQMHIITA06dPsXPnTnTu3FkfkUkL8lM9Y6LXsxUwMTExUCqVcHBwyNDu4OCA6OjoLPeJjo7Ocvu0tDTExMTAyclJZ3kps7z04fsWLlyIhIQE9O7dWxcR6QPy0oe3b9/G1KlTcfr0aZiY8Neg1PLSh3fv3sWZM2dgbm6OPXv2ICYmBn5+fnj58iXnzepZXvqvSZMm2Lx5M/r06YPk5GSkpaWhW7du+Pnnn/URmbQgP9UzHJnVAplMluGxECJT24e2z6qd9EfTPky3detWBAQEIDg4GPb29rqKR7mQ2z5UKpXw9vbGrFmzUKlSJX3Fo1zQ5H2oUqkgk8mwefNmNGjQAJ06dcKiRYsQFBTE0VmJaNJ/169fx7hx4/DNN98gLCwMhw4dQmRkJEaOHKmPqKQl+aWe4ZDER7Czs4OxsXGmvzyfPXuW6a+VdI6Ojllub2JighIlSugsK2UtL32YLjg4GEOGDMGOHTvQtm1bXcakHGjah/Hx8QgNDUV4eDjGjBkD4G1hJISAiYkJjhw5gtatW+slO72Vl/ehk5MTXFxcYGNjo26rWrUqhBB49OgRKlasqNPM9D956b958+ahadOmmDx5MgCgVq1asLKyQvPmzfHdd9/xU0oDkJ/qGY7MfgRTU1O4u7sjJCQkQ3tISAiaNGmS5T6NGzfOtP2RI0fg4eEBuVyus6yUtbz0IfB2RNbX1xdbtmzhHC+JadqH1tbWuHr1KiIiItRfI0eOROXKlREREYGGDRvqKzr9v7y8D5s2bYonT57gzZs36rZbt27ByMgIpUqV0mleyigv/ZeYmAgjo4wliLGxMYD/je5R/pav6hm9X3JWwKQvR7J27Vpx/fp1MWHCBGFlZSXu3bsnhBBi6tSpon///urt05eymDhxorh+/bpYu3Ytl+aSmKZ9uGXLFmFiYiJ++eUXERUVpf56/fq1VC+h0NO0D9/H1Qykp2kfxsfHi1KlSolevXqJf/75R/z555+iYsWKYujQoVK9hEJN0/5bv369MDExEcuXLxd37twRZ86cER4eHqJBgwZSvYRCLz4+XoSHh4vw8HABQCxatEiEh4erl1fLz/UMi1kt+OWXX4Srq6swNTUV9erVE3/++af6uYEDB4oWLVpk2P7kyZOibt26wtTUVJQtW1YEBgbqOTG9T5M+bNGihQCQ6WvgwIH6D05qmr4P38ViNn/QtA9v3Lgh2rZtKywsLESpUqWEv7+/SExM1HNqSqdp/y1dulRUq1ZNWFhYCCcnJ+Hj4yMePXqk59SU7sSJEzn+25af6xmZEBzPJyIiIiLDxDmzRERERGSwWMwSERERkcFiMUtEREREBovFLBEREREZLBazRERERGSwWMwSERERkcFiMUtEREREBovFLBEREREZLBazREQAgoKCYGtrK3WMPCtbtiwWL16c4zYBAQGoU6eOXvIQEekLi1kiKjB8fX0hk8kyff33339SR0NQUFCGTE5OTujduzciIyO1cvzLly9j+PDh6scymQx79+7NsM2kSZNw7NgxrZwvO++/TgcHB3Tt2hX//POPxscx5D8uiEh/WMwSUYHSoUMHREVFZfhyc3OTOhYAwNraGlFRUXjy5Am2bNmCiIgIdOvWDUql8qOPXbJkSVhaWua4TZEiRVCiRImPPteHvPs6//jjDyQkJKBz585ITU3V+bmJqPBhMUtEBYqZmRkcHR0zfBkbG2PRokWoWbMmrKysULp0afj5+eHNmzfZHuevv/5Cq1atULRoUVhbW8Pd3R2hoaHq58+dO4dPPvkEFhYWKF26NMaNG4eEhIQcs8lkMjg6OsLJyQmtWrXCzJkzce3aNfXIcWBgIMqXLw9TU1NUrlwZmzZtyrB/QEAAypQpAzMzMzg7O2PcuHHq596dZlC2bFkAQI8ePSCTydSP351mcPjwYZibm+P169cZzjFu3Di0aNFCa6/Tw8MDEydOxP3793Hz5k31Njn1x8mTJzFo0CDExsaqR3gDAgIAAKmpqZgyZQpcXFxgZWWFhg0b4uTJkznmIaKCjcUsERUKRkZGWLp0Ka5du4YNGzbg+PHjmDJlSrbb+/j4oFSpUrh8+TLCwsIwdepUyOVyAMDVq1fRvn179OzZE3///TeCg4Nx5swZjBkzRqNMFhYWAACFQoE9e/Zg/Pjx+OKLL3Dt2jWMGDECgwYNwokTJwAAO3fuxE8//YSVK1fi9u3b2Lt3L2rWrJnlcS9fvvx/7dxdSJNtHAbwy22O1qP24UEl2sTJ0JOiQWVGB5WRLFoMHJUjJbKytMAoopMWhEGIywjKk9hQDBNyIFREWllakClhX8QkGVFKRFaUpk3/78GLD03Xl8nbu3H9wIP747n9394gF/O5BQB4PB709fWp7W/l5ORg9uzZuHTpkto3OjqKhoYGOJ3Oadvn+/fvceHCBQBQf37Aj88jOzsbVVVV6ie8fX19OHjwIABg+/btaG9vR319Pbq7u+FwOJCbmwu/3//LNRFRlBEioihRWFgoWq1WFEVRv/Ly8sLObWhokMTERLXt8Xhk1qxZajs+Pl68Xm/YZ7dt2ya7du0K6btz545oNBoZGhoK+8zE9V++fClZWVmSnJwsw8PDkp2dLTt37gx5xuFwiNVqFRGRyspKMZvNMjIyEnZ9o9Eop06dUtsAxOfzhcxxuVyyePFitb1//35Zs2aN2r527Zro9Xp59+7dH+0TgCiKIjNnzhQAAkBsNlvY+eN+dh4iIj09PRITEyOvXr0K6V+7dq0cOXLkh+sTUfTS/d0oTUQ0vVavXo1z586pbUVRAAA3b97EiRMn8PTpU3z8+BHBYBBfvnzB58+f1TnfOnDgAIqKilBbW4ucnBw4HA6YTCYAQGdnJ3p6elBXV6fOFxGMjY2ht7cXmZmZYWv78OED4uLiICIYHByExWJBY2Mj9Ho9nj17FnKBCwBWrlyJ06dPAwAcDgeqqqqQlpaG3NxcWK1WbNy4ETrd1H+NO51OrFixAq9fv0ZSUhLq6upgtVoxZ86cP9pnfHw8urq6EAwG0draioqKClRXV4fM+d3zAICuri6ICMxmc0j/8PDwf/IuMBH9PzHMElFUURQF6enpIX2BQABWqxXFxcU4fvw45s6di7a2NuzYsQNfv34Nu86xY8eQn5+Py5cv4+rVq3C5XKivr4fdbsfY2Bh2794d8s7quIULF363tvGQp9FoMG/evEmhLSYmJqQtImpfSkoKnj9/juvXr6O5uRl79+5FRUUFWltbQ/58/zuWLVsGk8mE+vp67NmzBz6fDx6PRx2f6j41Go16BhkZGejv78fmzZtx+/ZtAFM7j/F6tFotOjs7odVqQ8bi4uJ+a+9EFD0YZoko6j148ADBYBCVlZXQaP69KtDQ0PDT58xmM8xmM8rKyrB161Z4PB7Y7XZYLBY8efJkUmj+mW9D3kSZmZloa2tDQUGB2nf37t2QTz8NBgNsNhtsNhtKSkqQkZGBR48ewWKxTFovNjb2l/5LQn5+Purq6pCcnAyNRoMNGzaoY1Pd50RlZWVwu93w+Xyw2+2/dB56vX5S/UuWLMHo6CjevHmDVatW/VFNRBQ9eAGMiKKeyWRCMBjEmTNn8OLFC9TW1k76s/e3hoaGUFpailu3biEQCKC9vR0dHR1qsDx8+DDu3buHkpISPHz4EH6/H01NTdi3b9+Uazx06BC8Xi+qq6vh9/vhdrvR2NioXnzyer04f/48Hj9+rO7BYDDAaDSGXS81NRUtLS3o7+/HwMDAd7+v0+lEV1cXysvLkZeXhxkzZqhj07XPhIQEFBUVweVyQUR+6TxSU1Px6dMntLS04O3btxgcHITZbIbT6URBQQEaGxvR29uLjo4OnDx5EleuXPmtmogoivzNF3aJiKZTYWGhbNq0KeyY2+2WBQsWiMFgkPXr10tNTY0AkIGBAREJvXA0PDwsW7ZskZSUFNHr9ZKUlCSlpaUhl57u378v69atk7i4OFEURRYtWiTl5eXfrS3chaaJzp49K2lpaRIbGytms1lqamrUMZ/PJ8uXL5eEhARRFEWysrKkublZHZ94AaypqUnS09NFp9OJ0WgUkckXwMYtXbpUAMiNGzcmjU3XPgOBgOh0Orl48aKI/Pw8RESKi4slMTFRAIjL5RIRkZGRETl69KikpqZKbGyszJ8/X+x2u3R3d3+3JiKKbjEiIn83ThMRERERTQ1fMyAiIiKiiMUwS0REREQRi2GWiIiIiCIWwywRERERRSyGWSIiIiKKWAyzRERERBSxGGaJiIiIKGIxzBIRERFRxGKYJSIiIqKIxTBLRERERBGLYZaIiIiIItY/F9qR65YWjjsAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "     - AUC: 70.12%\n",
+      "    - Optimal Threshold: 0.0674505\n",
+      "    - F1 Score: 0.83\n",
+      "    - CONFUSION MATRIX:\n",
+      " [[13  7]\n",
+      " [18 62]] \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 22.27it/s]\n",
+      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.20it/s]\n",
+      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.28it/s]\n",
+      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.13it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.14it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "- OK - Evaluating model (13.78 s)\n",
+      "\n",
+      "Dataset               F1 Score\n",
+      "------------------------------\n",
+      "Anonaly lvl 1 test        0.92\n",
+      "Anonaly lvl 2 test        0.86\n",
+      "Anonaly lvl 3 test        0.79\n",
+      "\n",
+      "Anomaly all test          0.88\n",
+      "\n",
+      "No Anomaly Test           0.79\n",
+      "\n",
+      "All test                  0.83\n"
+     ]
+    }
+   ],
+   "source": [
+    "# STEPS = 20, MODEL TYPE = SMALL, WEIGHT = on\n",
+    "model11 = EfficientAD({**config, \"train_steps\": 20, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\"})\n",
+    "model11.create_model()\n",
+    "model11.display_eval_result()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- Setting seed to 42\n",
+      "- OK - Setting seed to 42 (0.78 ms)\n",
+      "\n",
+      "- Setting datasets path\n",
+      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
+      "- OK - Setting datasets path (3.94 ms)\n",
+      "\n",
+      "- Setting config\n",
+      "     Output folder path: ../output/cookies_3_steps_500_small_weighted\n",
+      "- OK - Setting config (0.11 ms)\n",
+      "\n",
+      "- Prepare teacher, student & autoencoder\n",
+      "     Loading weight ../weights/teacher_small.pth\n",
+      "     Training\n",
+      "- OK - Prepare teacher, student & autoencoder (74.82 ms)\n",
+      "\n",
+      "- Normalizing teacher\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 49.95it/s]\n",
+      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 49.40it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- OK - Normalizing teacher (2.90 s)\n",
+      "\n",
+      "- Train\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 39.9363  :   0%|▏                                                                                    | 1/500 [00:01<13:26,  1.62s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9230769230769231\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 20.2424  :  20%|████████████████▊                                                                  | 101/500 [00:15<03:21,  1.98it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.918918918918919\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 12.9697  :  40%|█████████████████████████████████▎                                                 | 201/500 [00:29<02:30,  1.98it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.918918918918919\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 11.6319  :  60%|█████████████████████████████████████████████████▉                                 | 301/500 [00:43<01:40,  1.97it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.975609756097561\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 9.2919  :  80%|███████████████████████████████████████████████████████████████████▎                | 401/500 [00:56<00:50,  1.96it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.918918918918919\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 8.1361  : 100%|████████████████████████████████████████████████████████████████████████████████████| 500/500 [01:09<00:00,  7.22it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- OK - Train (69.28 s)\n",
+      "\n",
+      "- Saving models to ../output/cookies_3_steps_500_small_weighted/all_models.pth\n",
+      "- OK - Saving models (79.04 ms)\n",
+      "\n",
+      "- Saving map normalization to ../output/cookies_3_steps_500_small_weighted/map_normalization.pth\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:00<00:00, 28.24it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- OK - Saving map normalization (1025.01 ms)\n",
+      "\n",
+      "- Evaluating model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 21.85it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "     - AUC: 97.94%\n",
+      "    - Optimal Threshold: 0.0322094\n",
+      "    - F1 Score: 0.96\n",
+      "    - CONFUSION MATRIX:\n",
+      " [[18  2]\n",
+      " [ 4 76]] \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 22.12it/s]\n",
+      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.10it/s]\n",
+      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.01it/s]\n",
+      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.06it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.12it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "- OK - Evaluating model (13.87 s)\n",
+      "\n",
+      "Dataset               F1 Score\n",
+      "------------------------------\n",
+      "Anonaly lvl 1 test        1.00\n",
+      "Anonaly lvl 2 test        1.00\n",
+      "Anonaly lvl 3 test        0.89\n",
+      "\n",
+      "Anomaly all test          0.98\n",
+      "\n",
+      "No Anomaly Test           0.95\n",
+      "\n",
+      "All test                  0.96\n"
+     ]
+    }
+   ],
+   "source": [
+    "# STEPS = 500, MODEL TYPE = SMALL, WEIGHT = on\n",
+    "model12 = EfficientAD({**config, \"train_steps\": 500, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\"})\n",
+    "model12.create_model()\n",
+    "model12.display_eval_result()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- Setting seed to 42\n",
+      "- OK - Setting seed to 42 (0.27 ms)\n",
+      "\n",
+      "- Setting datasets path\n",
+      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
+      "- OK - Setting datasets path (4.67 ms)\n",
+      "\n",
+      "- Setting config\n",
+      "     Output folder path: ../output/cookies_3_steps_5000_small_weighted\n",
+      "- OK - Setting config (0.14 ms)\n",
+      "\n",
+      "- Prepare teacher, student & autoencoder\n",
+      "     Loading weight ../weights/teacher_small.pth\n",
+      "     Training\n",
+      "- OK - Prepare teacher, student & autoencoder (74.20 ms)\n",
+      "\n",
+      "- Normalizing teacher\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.03it/s]\n",
+      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 49.76it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- OK - Normalizing teacher (2.86 s)\n",
+      "\n",
+      "- Train\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 39.9363  :   0%|                                                                                  | 1/5000 [00:01<2:13:48,  1.61s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9230769230769231\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 20.2371  :   2%|█▋                                                                                | 101/5000 [00:15<41:17,  1.98it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.918918918918919\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 12.9873  :   4%|███▎                                                                              | 201/5000 [00:29<40:32,  1.97it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.918918918918919\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 11.6855  :   6%|████▉                                                                             | 301/5000 [00:43<39:49,  1.97it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.975609756097561\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 9.3164  :   8%|██████▋                                                                            | 401/5000 [00:57<38:47,  1.98it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.918918918918919\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 10.1041  :  10%|████████▏                                                                         | 501/5000 [01:11<38:14,  1.96it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9743589743589743\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 8.1916  :  12%|█████████▉                                                                         | 601/5000 [01:25<37:40,  1.95it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9743589743589743\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 7.4264  :  14%|███████████▋                                                                       | 701/5000 [01:39<36:17,  1.97it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9473684210526315\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 8.9889  :  16%|█████████████▎                                                                     | 801/5000 [01:53<36:03,  1.94it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9473684210526315\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 7.6123  :  18%|██████████████▉                                                                    | 901/5000 [02:07<34:44,  1.97it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9473684210526315\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 6.0918  :  20%|████████████████▍                                                                 | 1001/5000 [02:21<33:56,  1.96it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9743589743589743\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 7.6462  :  22%|██████████████████                                                                | 1101/5000 [02:35<32:54,  1.97it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.975609756097561\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 7.4376  :  24%|███████████████████▋                                                              | 1201/5000 [02:49<32:13,  1.96it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.975609756097561\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 6.3026  :  26%|█████████████████████▎                                                            | 1301/5000 [03:03<32:17,  1.91it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.975609756097561\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 6.3493  :  28%|██████████████████████▉                                                           | 1401/5000 [03:17<30:47,  1.95it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9743589743589743\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 6.6122  :  30%|████████████████████████▌                                                         | 1501/5000 [03:31<29:39,  1.97it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.975609756097561\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 13.8073  :  32%|█████████████████████████▉                                                       | 1601/5000 [03:45<29:10,  1.94it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9743589743589743\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 6.6579  :  34%|███████████████████████████▉                                                      | 1701/5000 [03:59<28:15,  1.95it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 7.0115  :  36%|█████████████████████████████▌                                                    | 1801/5000 [04:13<31:47,  1.68it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 5.4137  :  38%|███████████████████████████████▏                                                  | 1901/5000 [04:27<26:20,  1.96it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.975609756097561\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 5.8775  :  40%|████████████████████████████████▊                                                 | 2001/5000 [04:41<25:23,  1.97it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 5.6189  :  42%|██████████████████████████████████▍                                               | 2101/5000 [04:55<24:29,  1.97it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.975609756097561\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 6.2148  :  44%|████████████████████████████████████                                              | 2201/5000 [05:09<24:07,  1.93it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 6.0581  :  46%|█████████████████████████████████████▋                                            | 2301/5000 [05:23<22:56,  1.96it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 6.0633  :  48%|███████████████████████████████████████▍                                          | 2401/5000 [05:37<22:26,  1.93it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9473684210526315\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 5.7604  :  50%|█████████████████████████████████████████                                         | 2501/5000 [05:51<21:04,  1.98it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 6.0447  :  52%|██████████████████████████████████████████▋                                       | 2601/5000 [06:05<20:35,  1.94it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 5.9753  :  54%|████████████████████████████████████████████▎                                     | 2701/5000 [06:19<19:29,  1.97it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 4.8369  :  56%|█████████████████████████████████████████████▉                                    | 2801/5000 [06:33<18:54,  1.94it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 5.9145  :  58%|███████████████████████████████████████████████▌                                  | 2901/5000 [06:47<17:48,  1.96it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 5.3674  :  60%|█████████████████████████████████████████████████▏                                | 3001/5000 [07:01<17:11,  1.94it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 5.0132  :  62%|██████████████████████████████████████████████████▊                               | 3101/5000 [07:15<16:40,  1.90it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 4.9510  :  64%|████████████████████████████████████████████████████▍                             | 3200/5000 [07:29<04:12,  7.12it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 1.0\n",
+      "Early stopping at iteration 3201 because validation F1 did not improve.\n",
+      "- OK - Train (449.25 s)\n",
+      "\n",
+      "- Saving models to ../output/cookies_3_steps_5000_small_weighted/all_models.pth\n",
+      "- OK - Saving models (61.16 ms)\n",
+      "\n",
+      "- Saving map normalization to ../output/cookies_3_steps_5000_small_weighted/map_normalization.pth\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:00<00:00, 28.64it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- OK - Saving map normalization (1011.50 ms)\n",
+      "\n",
+      "- Evaluating model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.17it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "     - AUC: 99.94%\n",
+      "    - Optimal Threshold: 0.0333683\n",
+      "    - F1 Score: 0.99\n",
+      "    - CONFUSION MATRIX:\n",
+      " [[20  0]\n",
+      " [ 1 79]] \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 21.95it/s]\n",
+      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 21.95it/s]\n",
+      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 21.97it/s]\n",
+      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 21.94it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 21.96it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "- OK - Evaluating model (13.85 s)\n",
+      "\n",
+      "Dataset               F1 Score\n",
+      "------------------------------\n",
+      "Anonaly lvl 1 test        1.00\n",
+      "Anonaly lvl 2 test        1.00\n",
+      "Anonaly lvl 3 test        0.97\n",
+      "\n",
+      "Anomaly all test          0.99\n",
+      "\n",
+      "No Anomaly Test           1.00\n",
+      "\n",
+      "All test                  0.99\n"
+     ]
+    }
+   ],
+   "source": [
+    "# STEPS = 5000, MODEL TYPE = SMALL, WEIGHT = on\n",
+    "model13 = EfficientAD({**config, \"train_steps\": 5000, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\"})\n",
+    "model13.create_model()\n",
+    "model13.display_eval_result()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# STEPS = 10000, MODEL TYPE = SMALL, WEIGHT = on\n",
+    "model14 = EfficientAD({**config, \"train_steps\": 10000, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\"})\n",
+    "model14.create_model()\n",
+    "model14.display_eval_result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- Setting seed to 42\n",
+      "- OK - Setting seed to 42 (0.28 ms)\n",
+      "\n",
+      "- Setting datasets path\n",
+      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
+      "- OK - Setting datasets path (4.62 ms)\n",
+      "\n",
+      "- Setting config\n",
+      "     Output folder path: ../output/cookies_3_steps_20_medium_weighted\n",
+      "- OK - Setting config (0.11 ms)\n",
+      "\n",
+      "- Prepare teacher, student & autoencoder\n",
+      "     Loading weight ../weights/teacher_medium.pth\n",
+      "     Training\n",
+      "- OK - Prepare teacher, student & autoencoder (211.90 ms)\n",
+      "\n",
+      "- Normalizing teacher\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.57it/s]\n",
+      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.53it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- OK - Normalizing teacher (6.69 s)\n",
+      "\n",
+      "- Train\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 68.6309  :   5%|████▎                                                                                 | 1/20 [00:03<01:07,  3.55s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.95\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 42.8470  : 100%|█████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:09<00:00,  2.05it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- OK - Train (9.74 s)\n",
+      "\n",
+      "- Saving models to ../output/cookies_3_steps_20_medium_weighted/all_models.pth\n",
+      "- OK - Saving models (204.05 ms)\n",
+      "\n",
+      "- Saving map normalization to ../output/cookies_3_steps_20_medium_weighted/map_normalization.pth\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 10.37it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- OK - Saving map normalization (2796.52 ms)\n",
+      "\n",
+      "- Evaluating model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.14it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIhCAYAAABdSTJTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACK2klEQVR4nOzdd1iT198G8DuEDYKTqSLuVRXBgaOOCu5RaxUHiltxW7WO1llr1bqte6AWFbd1Q91bQai2WrWKG1QUAZkhOe8fvuQnAkow5CFwf66Lq83JM+5wCH45Oc95ZEIIASIiIiIiPWQgdQAiIiIiopxiMUtEREREeovFLBERERHpLRazRERERKS3WMwSERERkd5iMUtEREREeovFLBERERHpLRazRERERKS3WMwSERERkd5iMUtElAk/Pz/IZDL1l6GhIezt7eHl5YW7d+9muo9CocDKlSvh7u4Oa2trmJmZoUqVKpg4cSJevXqV6T4qlQpbtmxBixYtULx4cRgZGcHGxgbt2rXDgQMHoFKpPpk1OTkZy5cvR6NGjVCkSBEYGxvD0dERXbt2xenTpz/r+0BElNexmCUi+oiNGzfi4sWL+PPPPzF8+HD88ccfaNSoEaKjo9Ntl5CQAA8PD4wYMQIuLi7Ytm0bDh8+DG9vb6xZswYuLi64fft2un2SkpLQpk0b9OnTBzY2Nli5ciVOnDiBVatWwcHBAd9++y0OHDjw0XxRUVFo2LAhxo4di+rVq8PPzw/Hjx/HggULIJfL8dVXX+Gvv/7S+veFiCjPEERElMHGjRsFAHH16tV07TNmzBAAxIYNG9K1Dxo0SAAQ27dvz3Cs27dvC2tra1GtWjWRmpqqbh86dKgAIDZt2pRphjt37oi//vrrozlbt24tDA0NxfHjxzN9/sqVK+Lhw4cfPUZ2JSQkaOU4RETaxJFZIiINuLm5AQCeP3+ubouMjMSGDRvQsmVLdOvWLcM+FStWxPfff49//vkH+/btU++zbt06tGzZEr179870XBUqVECNGjWyzBISEoIjR46gf//+aN68eabb1KlTB6VLlwYATJ8+HTKZLMM2aVMqHjx4oG4rU6YM2rVrhz179sDFxQWmpqaYMWMGXFxc0Lhx4wzHUCqVcHR0ROfOndVtKSkp+Omnn1C5cmWYmJigRIkS6Nu3L16+fJnlayIi0hSLWSIiDYSHhwN4V6CmOXnyJFJTU9GpU6cs90t7LigoSL2PQqH46D6fEhgYmO7Y2nbt2jWMHz8eI0eOxNGjR/HNN9+gb9++OHfuXIZ5w4GBgXj27Bn69u0L4N1c4I4dO+KXX35Bjx49cOjQIfzyyy8ICgpC06ZNkZiYmCuZiajgMZQ6ABFRXqZUKpGamoqkpCScP38eP/30E7788kt06NBBvc2jR48AAM7OzlkeJ+25tG2zs8+naOMYH/PixQvcvHkzXeFetmxZjB8/Hn5+fpg9e7a63c/PD7a2tmjdujUAYMeOHTh69Ch2796dbrS2Zs2aqFOnDvz8/DB06NBcyU1EBQtHZomIPqJ+/fowMjJCoUKF0KpVKxQpUgT79++HoWHOxgIy+5g/r6pRo0a6QhYAihUrhvbt22PTpk3qlRaio6Oxf/9+9O7dW/19OXjwIAoXLoz27dsjNTVV/VWrVi3Y2dnh1KlTun45RJRPsZglIvqIzZs34+rVqzhx4gQGDx6MW7duoXv37um2SZuTmjYFITNpz5UqVSrb+3yKNo7xMfb29pm29+vXD0+fPlVPmdi2bRuSk5Ph4+Oj3ub58+d48+YNjI2NYWRklO4rMjISUVFRuZKZiAoeFrNERB9RpUoVuLm5oVmzZli1ahUGDBiAo0ePYteuXeptmjVrBkNDQ/XFXZlJe87Dw0O9j5GR0Uf3+ZSWLVumO/anmJqaAni3Lu37siossxpFbtmyJRwcHLBx40YA75Yvq1evHqpWrarepnjx4ihWrBiuXr2a6deKFSuylZmI6FNYzBIRaWDevHkoUqQIpk6dqv6Y3c7ODv369cOxY8cQEBCQYZ87d+5g7ty5qFatmvpiLTs7OwwYMADHjh3D5s2bMz3XvXv3cP369Syz1K5dG61bt8b69etx4sSJTLcJDg5Wz60tU6YMAGQ45qfWsv2QXC6Ht7c39u3bh7NnzyI4OBj9+vVLt027du3w6tUrKJVKuLm5ZfiqVKmSRuckIsqKTAghpA5BRJTX+Pn5oW/fvrh69ap6Oa408+fPx4QJE7Blyxb06tULABAfH4+2bdvi/PnzGDRoENq3bw8TExNcunQJv/76K8zNzfHnn3+mK+KSkpLQqVMnBAYGonv37vj6669ha2uLqKgoBAUFYePGjdi+fTs6duyYZc6oqCi0atUKN27cQL9+/dC6dWsUKVIEEREROHDgALZt24aQkBDUrFkTsbGxcHZ2hqOjI2bOnAlDQ0P4+fnh2rVrCA8PR3h4uLrgLVOmDKpXr46DBw9met47d+6gUqVKKFmyJF69eoWIiAhYW1urn1cqlWjfvj0uX76MUaNGoW7dujAyMsKTJ09w8uRJdOzYEV9//XVOu4eI6H+kXuiWiCgvyuqmCUIIkZiYKEqXLi0qVKiQ7iYIKSkp4rfffhP16tUTlpaWwsTERFSqVElMmDBBREVFZXqe1NRUsWnTJtG8eXNRtGhRYWhoKEqUKCFat24ttm7dKpRK5SezJiYmiqVLlwp3d3dhZWUlDA0NhYODg+jcubM4dOhQum2vXLkiGjRoICwsLISjo6OYNm2aWLdunQAgwsPD1ds5OTmJtm3bfvS8DRo0EABEz549M31eoVCIX3/9VdSsWVOYmpoKS0tLUblyZTF48GBx9+7dT74uIqLs4MgsEREREektzpklIiIiIr3FYpaIiIiI9BaLWSIiIiLSWyxmiYiIiEhvsZglIiIiIr3FYpaIiIiI9Jah1AF0TaVS4dmzZyhUqFCWt2okIiIiIukIIRAXFwcHBwcYGHx87LXAFbPPnj1DqVKlpI5BRERERJ/w+PFjlCxZ8qPbFLhitlChQgDefXOsrKx0ck6FQoHAwEB4enrCyMhIJ+ck7WH/6T/2of5jH+o39p/+03UfxsbGolSpUuq67WMKXDGbNrXAyspKp8Wsubk5rKys+CbWQ+w//cc+1H/sQ/3G/tN/UvVhdqaE8gIwIiIiItJbLGaJiIiISG+xmCUiIiIivcViloiIiIj0FotZIiIiItJbLGaJiIiISG+xmCUiIiIivcViloiIiIj0FotZIiIiItJbLGaJiIiISG+xmCUiIiIivcViloiIiIj0FotZIiIiItJbLGaJiIiISG9JWsyeOXMG7du3h4ODA2QyGfbt2/fJfU6fPg1XV1eYmpqibNmyWLVqVe4HJSIiIqI8SdJiNj4+HjVr1sTy5cuztX14eDjatGmDxo0bIzQ0FJMnT8bIkSOxe/fuXE5KRERERHmRoZQnb926NVq3bp3t7VetWoXSpUtj8eLFAIAqVaogODgYv/76K7755ptcSklERET6RgiBRIVS6hj5RmJiEpKV776veY2kxaymLl68CE9Pz3RtLVu2xPr166FQKGBkZJRhn+TkZCQnJ6sfx8bGAgAUCgUUCkXuBv5/aefR1flIu9h/+o99qP/Yh/pN1/0nhIDXuqu49uiNTs6X3yXcvYzoE+tg6/UTmjdPhrVMluvn1ORnRa+K2cjISNja2qZrs7W1RWpqKqKiomBvb59hnzlz5mDGjBkZ2gMDA2Fubp5rWTMTFBSk0/ORdrH/9B/7UP+xD/WbrvovWQlce6RXJU6eJJQKRJ/yQ1zwfgBAzMWdOHGiGEzkuX/uhISEbG+rdz0t++CvgbTh7g/b00yaNAljx45VP46NjUWpUqXg6ekJKyur3Av6HoVCgaCgIHh4eGQ6ekx5G/tP/7EP9R/7UL/puv8SUlIx4coJAMCl75vAzFgH1Vc+8+DBA/T36Y1HwcEAgMFDffFlk2Zo27IFjI2Nc/38aZ+kZ4deFbN2dnaIjIxM1/bixQsYGhqiWLFime5jYmICExOTDO1GRkY6/4UoxTlJe9h/+o99qP/Yh/pNV/1nJP43wGVlYQpzY70qdyS3Z88e9OvXDzExMShSpAj8/PzQunVrHD58GMbGxrrpQw3OoVe96+7ujgMHDqRrCwwMhJubG3+5ERERZZOuL45SKFKRrHw3Yvp+oZlbElJ44VdORUdHo3///oiJiYG7uzu2bdsGJyenPD1fXdJi9u3bt/jvv//Uj8PDwxEWFoaiRYuidOnSmDRpEp4+fYrNmzcDAIYMGYLly5dj7NixGDhwIC5evIj169dj27ZtUr0EIiIivSKEQJdVFxHyMFrHZzZUf/RPeVeRIkWwceNGXLx4ET/99JNeDBZKWswGBwejWbNm6sdpc1v79OkDPz8/RERE4NGjR+rnnZ2dcfjwYYwZMwa//fYbHBwcsHTpUi7LRURElE2JCqUEhaw03JyKwMyI82U/ZceOHbCyskKrVq0AAJ06dUKnTp2kDaUBSYvZpk2bfnS9Mj8/vwxtTZo0wbVr13IxFRERUcEQ/EMLmOvg4iiFQoFjxwLRsqWnTkf6zIzkWV4gTkBiYiLGjh2LVatWoVixYrh+/TocHBykjqUxvZozS0RERNpjbizXycVRCpmAiRwwNzaEkRFLj7zg9u3b6Nq1K65fvw6ZTIYhQ4bAxsZG6lg5wp8oIiLK84QQOr2AKD/jxVHk7++PwYMHIz4+HjY2Nvj999/h4eEhdawcYzFLRER52v/u5sQLiIg+h1KpxODBg7F+/XoAQLNmzeDv75/pTaf0iYHUAYiIiD4mUaHkbUlzAS+OKnjk8nf9LZPJMH36dAQFBel9IQtwZJaIiPTIpe+bwMrCVOoY+QIvjio4kpKSYGr67n2zdOlS+Pj4oFGjRhKn0h4Ws0REpDfMdHTBElF+8PbtWwwbNgyRkZE4cuQIDAwMYG5unq8KWYDFLBEREVG+c+PGDXTt2hX//vsvDAwMcOnSJTRo0EDqWLmCc2aJiIiI8gkhBNauXYu6devi33//haOjI06dOpVvC1mAI7NERERE+UJsbCwGDx6M7du3AwBat26NzZs3o3jx4hIny10cmSUiIiLKB7y8vLB9+3bI5XLMmzcPBw8ezPeFLMCRWSIiIqJ8Yfbs2fjvv/+wadMmuLu7Sx1HZzgyS0RERKSHYmJicOTIEfVjFxcX3Lx5s0AVsgBHZomItE4IgUQFbxmqLbz9KlFGwcHB6NatGx4/fowLFy7Azc0NAGBoWPBKu4L3iomIcpEQAl1WXUTIw2ipoxBRPiSEwNKlSzF+/HgoFAqUKVNG6kiSYzFLRKRFiQolC9lc4lxI8ParVKBFR0ejX79+2LdvHwCgc+fOWL9+PQoXLixpLqmxmCUiyiXBP7SAuTGLL21QKBQ4GRTI269SgXX58mV069YNDx8+hLGxMRYsWIBhw4bxPQEWs0REucact17VGoVMgP9mU0F2+vRpPHz4EOXKlcOOHTtQu3ZtqSPlGfwtS0RERJTHjRs3DjKZDIMHD4aVlZXUcfIULs1FRERElMecO3cOLVu2RHx8PADAwMAA48ePZyGbCRazRERERHmESqXCnDlz0LRpUwQGBmL27NlSR8rzOM2AiIiIKA948eIFvL29ERgYCADo1asXJk+eLHGqvI/FLBEREZHETp06hR49eiAiIgJmZmZYvnw5+vbty9UKsoHFLBHpnK7vkKVQpCJZCSSkpMJI5O4/DLxbFRFp6vfff0efPn2gUqlQtWpV7NixA9WqVZM6lt5gMUtEOiXdHbIMMeHKCR2fk4jo05o3b45ixYqhXbt2WLZsGSwsLKSOpFdYzBKRThWUO2S5ORXh3aqIKEt37txBxYoVAQAODg7466+/YG9vL3Eq/cRilogko6s7ZCkUChw7FoiWLT1hZGSU6+cDADMjOee6EVEGqampmDlzJmbPno0dO3bgm2++AQAWsp+BxSwRSUZXd8hSyARM5IC5sSGMjPhrj4ik8fTpU/To0QNnzpwBAFy6dEldzFLO8bc6Een0gixeIEVEBdHRo0fh7e2NqKgoWFpaYu3atfDy8pI6Vr7AYpaogJPugiwiovxPoVDgxx9/xNy5cwEALi4uCAgIQIUKFSROln/wDmBEBZxUF2TxAikiKgjOnDmjLmSHDRuGCxcusJDVMo7MEpGari7IAniBFBEVDF999RUmT54MFxcXdOnSReo4+RKLWSJS09UFWURE+VVKSgpmzZqFIUOGwNHREQAwe/ZsiVPlb/xXi4iIiEgLHjx4gG7duuHKlSs4e/YsTp48yU+gdIBzZomIiIg+0969e+Hi4oIrV66gcOHCGDNmDAtZHWExS0RERJRDycnJGDlyJDp37ow3b96gfv36CAsLQ8eOHaWOVmBwmgERERFRDjx9+hQdO3ZESEgIAGD8+PGYPXu2zu40SO+wmCUiIiLKgcKFCyMxMRHFihXDpk2b0LZtW6kjFUgsZomIiIiyKSkpCcbGxjAwMICFhQX27t0Lc3NzlCxZUupoBRbnzBIRERFlw+3bt1GvXj3MmzdP3VaxYkUWshJjMUtERET0Cf7+/nB1dcX169exdOlSxMfHSx2J/h+LWSIiIqIsJCQkYMCAAejVqxfi4+PRtGlTBAcHw8LCQupo9P9YzBIRERFl4tatW6hXrx7Wr18PmUyGadOm4c8//4SDg4PU0eg9vACMiIiI6AOxsbFo2LAhoqOjYWdnB39/fzRv3lzqWJQJjswSERERfcDKygozZ85EixYtEBYWxkI2D2MxS0RERATgxo0bCAsLUz8eNmwYjh07BltbW+lC0SexmCUiIqICTQiBtWvXom7duujSpQtiY2MBADKZDAYGLJXyOs6ZJSIiogIrLi4OgwcPxrZt2wAAFSpUgEKhkDgVaYJ/bhAREVGBFBYWBldXV2zbtg1yuRy//PILDh06hGLFikkdjTTAkVkiIiIqUIQQWLVqFcaMGYPk5GSUKlUK27dvR4MGDaSORjnAkVkiIiIqUIQQ+OOPP5CcnIz27dsjNDSUhawe48gsERERFSgGBgbYvHkzdu7ciaFDh0Imk0kdiT4DR2aJPkapBM6de/f/5869e0xERHpFCIElS5Zg6NCh6rYSJUrA19eXhWw+wGKWKCt79gBlygBt27573Lbtu8d79kiZioiINBAdHY3OnTtj9OjRWLVqFU6cOCF1JNIyFrNEmdmzB+jSBXjyJH3706fv2lnQEhHleZcvX4aLiwv27dsHY2NjLFu2DM2aNZM6FmkZ58wSfUipBEaNAoQAAAgAyUogwdAERoYqQCYDvpsAtG4LyOXSZtWChBROnSCi/EUIgYULF2LixIlITU1FuXLlEBAQAFdXV6mjUS5gMUv0obNn1SOyAoBXl1m4dsUQE3x/T7/djD91n42IiD6pX79+8PPzAwB07doVa9asgbW1tbShKNdwmgHRhyIi1P+baGSCaw6VJQyjO25ORWBmpP8jzURE3bp1g5mZGVauXInt27ezkM3nODJL9CF7+0ybL63tD6u4N/9rOHwE+LKxbjLpgJmRnFf1EpFeUqlUuHPnDipXfjf40KpVK4SHh8PW1lbiZKQLLGaJPtS4MVCy5LuLvd5jpkiGuSL53ZzZkiWBZl/mizmzRET67MWLF+jduzcuXbqE0NBQODs7AwAL2QKE0wyIPiSXA0uWvPv/D0cq0x4vXsxClohIYqdPn0atWrVw7NgxpKSk4MaNG1JHIgmwmCXKTOfOwK5dgL1D+vaSJd+1d+4sTS4iIoJSqcTMmTPRvHlzREREoEqVKrhy5Qo6dOggdTSSAItZoqx07gzcuvm/x7t3A+HhLGSJiCQUGRmJli1bYtq0aVCpVPDx8cHVq1dRvXp1qaORRDhnluhj3p9K0MCdUwuIiCS2ZMkSHD9+HObm5li5ciV69+4tdSSSGItZIiIi0hvTpk3DkydPMGXKFPXqBVSwcZoBERER5VlPnz7FuHHjkJqaCgAwNTXFli1bWMiSGkdmiYiIKE86evQovL29ERUVBSsrK0ydOlXqSJQHcWSWiIiI8hSFQoFJkyahdevWiIqKQq1ateDl5SV1LMqjODJLREREecbjx4/h5eWFCxcuAAB8fX2xYMECmJqaSpyM8ioWs0RERJQnHD9+HF27dsXr169hZWWFdevW4dtvv5U6FuVxLGaJiIgoT7Czs0NiYiJcXV0REBCAcuXKSR2J9ACLWSIiIpJMfHw8LCwsAADVqlXD8ePHUbt2bZiYmEicjPQFLwAjIiIiSezbtw9lypRRz48FAHd3dxaypBEWs0RERKRTycnJGDVqFL7++mtERUVh0aJFUkciPSZ5MbtixQo4OzvD1NQUrq6uOHv27Ee39/f3R82aNWFubg57e3v07dsXr1690lFaIiIi+hz37t1Dw4YNsXTpUgDAuHHjsHXrVolTkT6TtJgNCAjA6NGjMWXKFISGhqJx48Zo3bo1Hj16lOn2586dQ+/evdG/f3/8888/2LlzJ65evYoBAwboODkRERFpaufOnXBxcUFISAiKFi2KgwcPYv78+TAyMpI6GukxSYvZhQsXon///hgwYACqVKmCxYsXo1SpUli5cmWm21+6dAllypTByJEj4ezsjEaNGmHw4MEIDg7WcXIiIiLSxI0bN9CzZ0/ExcWhYcOGCAsLQ9u2baWORfmAZKsZpKSkICQkBBMnTkzX7unpmW4i+PsaNGiAKVOm4PDhw2jdujVevHiBXbt2ffTNkJycjOTkZPXj2NhYAO/uLqJQKLTwSj4t7Ty6Oh9pj0KRmu7/2Yf6ie9B/cc+1G8KhQLVq1dHp06dULFiRUyfPh2GhobsTz2i6/egJueRrJiNioqCUqmEra1tunZbW1tERkZmuk+DBg3g7++Pbt26ISkpCampqejQoQOWLVuW5XnmzJmDGTNmZGgPDAyEubn5570IDQUFBen0fPT5kpVA2tvkxIkTMJFLGoc+E9+D+o99qF8uXLiAWrVqwdzcHDKZDL1794aBgQECAwOljkY5pKv3YEJCQra3lXydWZlMlu6xECJDW5qbN29i5MiRmDp1Klq2bImIiAiMHz8eQ4YMwfr16zPdZ9KkSRg7dqz6cWxsLEqVKgVPT09YWVlp74V8hEKhQFBQEDw8PDgvSM8kpKRiwpUTAIDmzZvD2oK3U9RHfA/qP/ahfklISMDYsWOxYcMGfPvtt9i4cSP+/PNPtGzZkv2np3T9Hkz7JD07JCtmixcvDrlcnmEU9sWLFxlGa9PMmTMHDRs2xPjx4wEANWrUgIWFBRo3boyffvoJ9vb2GfYxMTHJdL06IyMjnb+hpDgnfR4j8b8/rIyMDNl/eo7vQf3HPsz7bt26ha5du+Lvv/+GTCZDlSpVYGj4rtxg/+k/XfWhJueQ7AIwY2NjuLq6ZhiuDgoKQoMGDTLdJyEhAQYG6SPL5e8+9xVC5E5QIiIiypZNmzbBzc0Nf//9N2xtbREUFIQZM2Zk+LebSJsk/ekaO3Ys1q1bhw0bNuDWrVsYM2YMHj16hCFDhgB4N0Wgd+/e6u3bt2+PPXv2YOXKlbh//z7Onz+PkSNHom7dunBwcJDqZRARERVo8fHx8PHxgY+PDxISEvDVV18hLCwMX331ldTRqACQdM5st27d8OrVK8ycORMRERGoXr06Dh8+DCcnJwBAREREujVnfXx8EBcXh+XLl+O7775D4cKF0bx5c8ydO1eql0BERFTgJSQkIDAwEAYGBpgxYwYmTZqk/uSUKLdJfgGYr68vfH19M33Oz88vQ9uIESMwYsSIXE5FRERE2VWiRAkEBARApVKhSZMmUsehAoaTWIiIiEgjcXFx6NmzJ/z9/dVtjRs3ZiFLkmAxS0RERNkWFhYGV1dXbN26FcOHD9doCSWi3MBiloiIiD5JCIGVK1eifv36uHv3LkqWLImDBw/qbM12oqxIPmeWiIiI8raYmBgMHDgQO3fuBAC0a9cOfn5+KFasmMTJiFjMEhER0UfEx8fD1dUV9+7dg6GhIebOnYsxY8ZkebdOIl3jNAMiIiLKkoWFBb755hs4OTnh3LlzGDt2LAtZylNYzBIREVE60dHRePLkifrxTz/9hNDQUNSrV0/CVESZYzFLREREapcvX4aLiwu6dOkChUIBADAyMkKRIkUkTkaUORazREREBCEEFixYgEaNGuHhw4d4+fIlnj59KnUsok9iMUtERFTAvXr1Ch06dMC4ceOQmpqKb7/9FteuXUOZMmWkjkb0SSxmiYiICrDz58+jVq1aOHjwIExMTLBy5UoEBATA2tpa6mhE2cKluYiIiAooIQTGjBmDJ0+eoEKFCtixYwdq1aoldSwijXBkloiIqICSyWTw9/dH//79ERISwkKW9BKLWSIiogLk9OnTWLJkifpxhQoVsG7dOhQqVEjCVEQ5x2kGREREBYBSqcTPP/+M6dOnQwiB2rVro3HjxlLHIvpsLGaJiIjyucjISPTq1QvHjx8HAPTp0we1a9eWOBWRdrCYJSIiyseOHz+Onj174vnz5zA3N8eKFSvQp08fqWMRaQ3nzBIREeVTc+bMgYeHB54/f47q1avj6tWrLGQp32ExS0RElE/Z2NhACIEBAwbg8uXLqFq1qtSRiLSO0wyIiIjykbdv38LS0hIA0K9fP1SqVAmNGjWSOBVR7uHILBERUT6QmpqKSZMmoXr16nj9+jWAd+vIspCl/I7FLBERkZ57/PgxmjZtil9++QUPHz7E7t27pY5EpDMsZomIiPTYoUOHUKtWLZw/fx5WVlYICAjAwIEDpY5FpDMsZomIiPRQSkoKxo0bh3bt2uH169dwdXXFtWvX0LVrV6mjEekUi1kiIiI9NH36dCxYsAAAMHLkSJw/fx7lypWTOBWR7rGYJSIi0kPjxo1DzZo1sWfPHixZsgQmJiZSRyKSBItZIiIiPZCcnIzNmzdDCAEAKFq0KK5du4avv/5a4mRE0uI6s0RERHnc/fv30bVrV4SEhCA5OVl9gZeBAcekiPguICIiysN27doFFxcXhISEoGjRorC3t5c6ElGewmKWiIgoD0pKSsKwYcPw7bffIjY2Fg0aNEBYWBjatWsndTSiPIXFLBERUR5z9+5duLu7Y8WKFQCAiRMn4tSpUyhVqpTEyYjyHs6ZJSIiymOePHmCv/76C8WLF8eWLVvQqlUrqSMR5VksZomIiPIAIQRkMhkAoFmzZvDz88NXX30FR0dHiZMR5W2cZkBERCSxW7duoVGjRrhz5466rXfv3ixkibKBxSwREZGENm3aBDc3N1y4cAEjR46UOg6R3mExS0REJIH4+Hj4+PjAx8cHCQkJaN68Ofz8/KSORaR3WMwSERHp2N9//406depg06ZNMDAwwMyZMxEYGAg7OzupoxHpHV4ARkREpEOXL19Gs2bNkJiYCHt7e2zduhVNmzaVOhaR3mIxS0REpEO1a9dGzZo1YWVlhS1btsDGxkbqSER6jcUsERFRLrt58yYqVKgAIyMjGBkZ4dChQyhcuDAMDDjbj+hz8V1ERESUS4QQWLVqFWrXro0pU6ao24sWLcpClkhLODJLRESUC2JjYzFw4EDs2LEDwLu1ZJVKJeRyucTJiPIX/llIRESkZSEhIahduzZ27NgBQ0NDzJ8/H/v372chS5QLODJLRESkJUIILF++HOPGjUNKSgqcnJywfft21K9fX+poRPkWR2aJiIi05OnTp5g8eTJSUlLQqVMnhIaGspAlymUcmSUiItKSkiVLYu3atXjx4gVGjBgBmUwmdSSifI/FLBERUQ4JIbBo0SK4uLigWbNmAAAvLy+JUxEVLCxmiYiIcuD169fw8fHBgQMHYGdnh5s3b6JIkSJSxyIqcFjMEhERaejChQvw8vLC48ePYWJigqlTp6Jw4cJSxyIqkHgBGBERUTapVCrMnTsXX375JR4/fowKFSrg0qVLGDp0KOfHEkmEI7NERETZkJiYiG+++QZHjhwBAHTv3h2rV69GoUKFJE5GVLBxZJaIiCgbTE1NUbhwYZiammLNmjXw9/dnIUuUB7CYJSIiyoJSqUR8fDwAQCaTYfXq1bh69SoGDhzIaQVEeQSLWSIiokw8f/4crVq1Qq9evSCEAAAUKlQI1atXlzgZEb2Pc2aJiIg+cOLECfTs2RORkZEwNzfHv//+iypVqkgdi4gywZFZIiKi/6dUKjFt2jS0aNECkZGRqFatGq5evcpCligP48gsERERgGfPnqFnz544deoUAKB///5YunQpzM3NpQ1GRB/FYpaIiAo8IQQ6duyI4OBgWFhYYPXq1ejZs6fUsYgoG3I0zSA1NRV//vknVq9ejbi4OADv/qJ9+/atVsMRERHpgkwmw9KlS+Hq6opr166xkCXSIxqPzD58+BCtWrXCo0ePkJycDA8PDxQqVAjz5s1DUlISVq1alRs5iYiItOrJkycICwtDu3btAADu7u64evUql9wi0jMaj8yOGjUKbm5uiI6OhpmZmbr966+/xvHjx7UajoiIKDccPnwYtWrVQteuXfH333+r21nIEukfjUdmz507h/Pnz8PY2Dhdu5OTE54+faq1YERERNqmUCgwZcoUzJ8/HwBQu3btdAMzRKR/NC5mVSoVlEplhvYnT57wtn5ERJRnPXz4EF5eXrh06RIAYMSIEZg/fz5MTEwkTkZEn0PjaQYeHh5YvHix+rFMJsPbt28xbdo0tGnTRpvZiIiItGL//v1wcXHBpUuXYG1tjd27d2Pp0qUsZInyAY1HZhctWoRmzZqhatWqSEpKQo8ePXD37l0UL14c27Zty42MREREn+XatWuIjo5G3bp1sX37djg7O0sdiYi0RONi1sHBAWFhYdi+fTtCQkKgUqnQv39/9OzZk/OOiIgozxBCqC/omjp1KmxsbDBw4MAM13wQkX7TeJrBmTNnYGRkhL59+2L58uVYsWIFBgwYACMjI5w5cyY3MhIREWlk9+7daN68OZKSkgAAcrkcw4YNYyFLlA9pXMw2a9YMr1+/ztAeExODZs2aaSUUERFRTiQlJWH48OHo0qULTp06hd9++03qSESUyzSeZvD+xzbve/XqFSwsLLQSioiISFN3795Ft27dEBoaCgD4/vvvMXLkSIlTEVFuy3Yx27lzZwDvVi/w8fFJdwWoUqnE9evX0aBBA+0nJPqAEAKJiozLw+WGhBTdnIeIPs/27dsxcOBAvH37FsWLF8fmzZvRunVrqWMRkQ5ku5i1trYG8K6QKFSoULqLvYyNjVG/fn0MHDhQ+wmJ3iOEQJdVFxHyMFrqKESURyxYsADjxo0DADRu3Bjbtm2Do6OjxKmISFeyXcxu3LgRAFCmTBmMGzeOUwpIEokKpSSFrHMhATMjuc7PS0Sf9s033+Dnn3+Gr68vpk2bBkNDjWfQEZEe0/gdP23atNzIQaSx4B9awNw49wtMhUKBk0GBvGc7UR4SGhoKFxcXAO8GWe7evYuiRYtKnIqIpKDxagYAsGvXLnTt2hX169dH7dq1031pasWKFXB2doapqSlcXV1x9uzZj26fnJyMKVOmwMnJCSYmJihXrhw2bNiQk5dBes7cWA5zY0OdfLGOJcob4uPj0a9fP9SuXRuHDx9Wt7OQJSq4NC5mly5dir59+8LGxgahoaGoW7cuihUrhvv372s82T4gIACjR4/GlClTEBoaisaNG6N169Z49OhRlvt07doVx48fx/r163H79m1s27YNlStX1vRlEBGRnnn06BEaNGiAjRs3wsDAALdv35Y6EhHlARpPM1ixYgXWrFmD7t27Y9OmTZgwYQLKli2LqVOnZrr+7McsXLgQ/fv3x4ABAwAAixcvxrFjx7By5UrMmTMnw/ZHjx7F6dOncf/+ffVf4WXKlNH0JRARkR4RQsDPzw/jxo1DSkoK7OzssG3bNjRt2lTqaESUB2hczKb9ZQwAZmZmiIuLAwB4e3ujfv36WL58ebaOk5KSgpCQEEycODFdu6enJy5cuJDpPn/88Qfc3Nwwb948bNmyBRYWFujQoQNmzZqV5a10k5OTkZycrH4cGxsL4N08SIVCka2snyvtPLo6X36mUKS+9/8KKGRCB+dk/+k79qH+evv2LYYPH46tW7cCAL766its2rQJNjY27E89wveg/tN1H2pyHo2LWTs7O7x69QpOTk5wcnLCpUuXULNmTYSHh0OI7BcWUVFRUCqVsLW1Tddua2uLyMjITPe5f/8+zp07B1NTU+zduxdRUVHw9fXF69evs5w3O2fOHMyYMSNDe2BgIMzNzbOdVxuCgoJ0er78KFkJpP3YHjsWCBMdLjDA/tN/7EP9c/HiRWzduhUGBgbo0aMHOnfujODgYKljUQ7xPaj/dNWHCQkJ2d5W42K2efPmOHDgAGrXro3+/ftjzJgx2LVrF4KDg9U3VtDEh1eIZ3WHMQBQqVSQyWTw9/dXr3u7cOFCdOnSBb/99lumo7OTJk3C2LFj1Y9jY2NRqlQpeHp6wsrKSuO8OaFQKBAUFAQPDw8YGRnp5Jz5VUJKKiZcOQEAaNnSE+bGub8ED/tP/7EP9VebNm0ghICHhwfi4+PZh3qK70H9p+s+TPskPTs0rgTWrFkDlUoFABgyZAiKFi2Kc+fOoX379hgyZEi2j1O8eHHI5fIMo7AvXrzIMFqbxt7eHo6OjupCFgCqVKkCIQSePHmCChUqZNjHxMQk3d3K0hgZGen8DSXFOfMbI/G/P3TefT91t54k+0//sQ/zvtjYWEyaNAnTpk2DjY0NAGDu3LlQKBQ4fPgw+1DPsf/0n676UJNzaLyagYGBQboFqbt27YqlS5di5MiRePnyZbaPY2xsDFdX1wzD1UFBQVneFrdhw4Z49uwZ3r59q267c+cODAwMULJkSQ1fCRER5SXXrl1D7dq1sWLFCvTv31/qOESkJ3K0zuyHIiMjMWLECJQvX16j/caOHYt169Zhw4YNuHXrFsaMGYNHjx6pR3gnTZqE3r17q7fv0aMHihUrhr59++LmzZs4c+YMxo8fj379+mV5ARgREeVtQggsX74c7u7uuHfvHkqXLo3JkydLHYuI9ES2i9k3b96gZ8+eKFGiBBwcHLB06VKoVCpMnToVZcuWxaVLlzS+eUG3bt2wePFizJw5E7Vq1cKZM2dw+PBhODk5AQAiIiLSrTlraWmJoKAgvHnzBm5ubujZsyfat2+PpUuXanReIiLKG968eYMuXbpgxIgRSElJQYcOHRAaGgp3d3epoxGRnsj2hMPJkyfjzJkz6NOnD44ePYoxY8bg6NGjSEpKwpEjR9CkSZMcBfD19YWvr2+mz/n5+WVoq1y5Mq+GJCLKB/7991+0adMG4eHhMDIywvz58zFy5EjeOpqINJLtYvbQoUPYuHEjWrRoAV9fX5QvXx4VK1bE4sWLczEeERHlVw4ODpDL5XB2dkZAQADq1KkjdSQi0kPZLmafPXuGqlWrAgDKli0LU1NT9Z27iIiIsiM2NhaFChWCTCaDlZUVDh48CFtbWxQuXFjqaESkp7I9Z1alUqVbJkEul8PCwiJXQhERUf5z8eJFVK9ePd2dIitVqsRClog+S7ZHZoUQ8PHxUa/ZmpSUhCFDhmQoaPfs2aPdhEREpNdUKhV+/fVXTJ48GUqlEqtXr8aQIUO43igRaUW2i9k+ffqke9yrVy+thyEiovzl5cuX6NOnD44cOQIA8PLywurVq1nIEpHWZLuY3bhxY27mICKifObMmTPo3r07nj17BlNTUyxduhQDBgzgagVEpFW6uxco5VtCCCQqlDo5V0KKbs5DRJ8nIiICnp6eSE5ORqVKlbBjxw7UqFFD6lhElA+xmKXPIoRAl1UXEfIwWuooRJSH2NvbY8aMGfjnn3+wYsUKWFpaSh2JiPIpFrP0WRIVSkkKWTenIjAzkuv8vESUtZMnT8LGxgbVqlUDAEyYMAEAOK2AiHIVi1nSmuAfWsDcWDcFppmRnP9AEuURSqUSs2bNwsyZM1GlShVcuXIFFhYWfI8SkU6wmCWtMTeWw9yYP1JEBUlERAR69uyJkydPAgDq16/PIpaIdCrbN01435YtW9CwYUM4ODjg4cOHAIDFixdj//79Wg1HRER5V1BQEGrVqoWTJ0/CwsICW7Zswfr162Fubi51NCIqQDQuZleuXImxY8eiTZs2ePPmDZTKd1eXFy5cGIsXL9Z2PiIiymNSU1Pxww8/oGXLlnjx4gVq1KiB4OBgrj9ORJLQuJhdtmwZ1q5diylTpkAu/9/8SDc3N9y4cUOr4YiIKO+RyWQ4d+4chBAYPHgwLl26hMqVK0sdi4gKKI0nOIaHh8PFxSVDu4mJCeLj47USioiI8h4hBGQyGeRyObZu3Ypz586ha9euUsciogJO45FZZ2dnhIWFZWg/cuQIqlatqo1MRESUhygUCkyYMAGjR49Wtzk4OLCQJaI8QeOR2fHjx2PYsGFISkqCEAJXrlzBtm3bMGfOHKxbty43MhIRkUQePXoELy8vXLx4EQDQr18/1KxZU+JURET/o3Ex27dvX6SmpmLChAlISEhAjx494OjoiCVLlsDLyys3MhIRkQT++OMP+Pj4IDo6GtbW1li/fj0LWSLKc3K0KOjAgQMxcOBAREVFQaVSwcbGRtu5iIhIIikpKfj+++/VK9TUqVMHAQEBcHZ2ljYYEVEmNJ4zO2PGDNy7dw8AULx4cRayRET5iBAC7du3VxeyY8aMwblz51jIElGepXExu3v3blSsWBH169fH8uXL8fLly9zIRUREEpDJZBg8eDCKFCmC/fv3Y+HChTA2NpY6FhFRljQuZq9fv47r16+jefPmWLhwIRwdHdGmTRts3boVCQkJuZGRiIhyUVJSUrp1wjt37oz79++jQ4cOEqYiIsqeHN3Otlq1avj5559x//59nDx5Es7Ozhg9ejTs7Oy0nY+IiHLRf//9hwYNGqB58+Z4+vSpur1w4cLShSIi0kCOitn3WVhYwMzMDMbGxlAoFNrIREREOhAQEIDatWsjNDQUQgiEh4dLHYmISGM5KmbDw8Mxe/ZsVK1aFW5ubrh27RqmT5+OyMhIbecjIiItS0xMxJAhQ+Dl5YW4uDg0atQIYWFhaNSokdTRiIg0pvHSXO7u7rhy5Qq++OIL9O3bV73OLBER5X23b99G165dcf36dchkMkyePBnTp0+HoWGOVmokIpKcxr+9mjVrhnXr1qFatWq5kYeIiHLRkiVLcP36ddjY2OD333+Hh4eH1JGIiD6LxsXszz//nBs5iIhIB+bPn4/U1FTMmDED9vb2UschIvps2Spmx44di1mzZsHCwgJjx4796LYLFy7USjAiIvp8//zzD1avXo3FixfDwMAAFhYWWLNmjdSxiIi0JlvFbGhoqHqlgtDQ0FwNREREn08IAT8/PwwbNgyJiYkoW7YsRo8eLXUsIiKty1Yxe/LkyUz/n4iI8p63b9/C19cXW7ZsAQB4enqiR48eEqciIsodGi/N1a9fP8TFxWVoj4+PR79+/bQSioiIcub69etwc3PDli1bYGBggNmzZ+PIkSOwsbGROhoRUa7QuJjdtGkTEhMTM7QnJiZi8+bNWglFRESaCwgIQL169XD79m04Ojri1KlTmDx5MgwMPvv+OEREeVa2VzOIjY2FEAJCCMTFxcHU1FT9nFKpxOHDh/mXPxGRhMqXLw+VSoXWrVtj8+bNKF68uNSRiIhyXbaL2cKFC0Mmk0Emk6FixYoZnpfJZJgxY4ZWwxER0ce9efMGhQsXBgC4urri4sWLqFWrFkdjiajAyHYxe/LkSQgh0Lx5c+zevRtFixZVP2dsbAwnJyc4ODjkSkgiIkpPCIEVK1Zg8uTJOHnyJGrXrg0A6v8SERUU2S5mmzRpAgAIDw9H6dKlIZPJci0UERFl7c2bNxg4cCB27doFAPDz82MRS0QFVraK2evXr6N69eowMDBATEwMbty4keW2NWrU0Fo4IiJK7+rVq+jWrRvCw8NhZGSEefPmYdSoUVLHIiKSTLaK2Vq1aiEyMhI2NjaoVasWZDIZhBAZtpPJZFAqlVoPSURU0AkhsGTJEkyYMAEKhQLOzs4ICAhAnTp1pI5GRCSpbBWz4eHhKFGihPr/iYhIt3bv3o0xY8YAAL755husW7dOfeEXEVFBlq1i1snJKdP/JyIi3ejcuTM6dOgAT09P+Pr68roFIqL/l6ObJhw6dEj9eMKECShcuDAaNGiAhw8fajUcEVFBpVKpsHbtWiQkJAAADAwMsG/fPgwbNoyFLBHRezQuZn/++WeYmZkBAC5evIjly5dj3rx5KF68uPojMCIiyrmoqCi0b98egwYNwogRI9TtLGKJiDLK9tJcaR4/fozy5csDAPbt24cuXbpg0KBBaNiwIZo2bartfEREBcrZs2fRvXt3PH36FKampqhXrx6EECxkiYiyoPHIrKWlJV69egUACAwMRIsWLQAApqamSExM1G46IqICQqVS4eeff0azZs3w9OlTVKpUCZcvX8agQYNYyBIRfYTGI7MeHh4YMGAAXFxccOfOHbRt2xYA8M8//6BMmTLazkdElO+9ePEC3t7eCAwMBAD06tULK1euhKWlpcTJiIjyPo1HZn/77Te4u7vj5cuX2L17N4oVKwYACAkJQffu3bUekIgov1MoFLh27RrMzMywfv16bN68mYUsEVE2aTwyW7hwYSxfvjxD+4wZM7QSiIioIHh/HqyjoyN27tyJEiVKoFq1ahInIyLSLxoXs8C7+4KvX78et27dgkwmQ5UqVdC/f39YW1trOx8RUb4TGRmJnj17Yvjw4fj6668BgBfQEhHlkMbTDIKDg1GuXDksWrQIr1+/RlRUFBYtWoRy5crh2rVruZGRiCjf+PPPP1GzZk2cOHECI0eOREpKitSRiIj0msbF7JgxY9ChQwc8ePAAe/bswd69exEeHo527dph9OjRuRCRiEj/paam4ocffoCnpydevHiBGjVq4M8//4SxsbHU0YiI9JrG0wyCg4Oxdu1aGBr+b1dDQ0NMmDABbm5uWg1HRJQfPH36FN27d8fZs2cBAIMHD8aiRYvUN6AhIqKc07iYtbKywqNHj1C5cuV07Y8fP0ahQoW0FoyIKD94+fIlatWqhaioKBQqVAhr1qyBl5eX1LGIiPINjacZdOvWDf3790dAQAAeP36MJ0+eYPv27RgwYACX5iIi+kCJEiXQrVs3uLi4ICQkhIUsEZGWaTwy++uvv0Imk6F3795ITU0FABgZGWHo0KH45ZdftB6QiEjfPHr0CEZGRrC3twcALFiwAEIImJqaSpyMiCj/0Xhk1tjYGEuWLEF0dDTCwsIQGhqK169fY9GiRTAxMcmNjEREeuPAgQOoVasWunfvrv6D38TEhIUsEVEuyXYxm5CQgGHDhsHR0RE2NjYYMGAA7O3tUaNGDZibm+dmRiKiPC8lJQXfffcdOnTogOjoaCQkJCA6OlrqWERE+V62i9lp06bBz88Pbdu2hZeXF4KCgjB06NDczEZEpBfCw8PRuHFjLFy4EMC7JQzPnTuHEiVKSJyMiCj/y/ac2T179mD9+vXqixd69eqFhg0bQqlUQi6X51pAIqK8bM+ePejXrx9iYmJQpEgR+Pn5oUOHDlLHIiIqMLI9Mvv48WM0btxY/bhu3bowNDTEs2fPciUYEVFep1Ao8OOPPyImJgbu7u4IDQ1lIUtEpGPZLmaVSmWGO9UYGhqqL3AgIipojIyMEBAQgEmTJuH06dNwcnKSOhIRUYGT7WkGQgj4+PikW7EgKSkJQ4YMgYWFhbptz5492k1IRJSH7NixAy9evMDw4cMBANWrV8fPP/8scSoiooIr28Vsnz59MrT16tVLq2GIiPKqxMREjBkzBqtXr4ZcLkfDhg3h4uIidSwiogIv28Xsxo0bczMHEVGedfv2bXTt2hXXr1+HTCbDxIkT8cUXX0gdi4iIkIM7gBERFSS///47hgwZgvj4eNjY2OD333+Hh4eH1LGIiOj/aXwHMCKigsLX1xfe3t6Ij49Hs2bNEBYWxkKWiCiPYTFLRJSFypUrQyaTYfr06QgKCoK9vb3UkYiI6AOcZkBE9J7Xr1+jaNGiAIARI0agSZMmqFmzpsSpiIgoKxyZJSIC8PbtW/Tp0wf16tVDbGwsAEAmk7GQJSLK43JUzG7ZsgUNGzaEg4MDHj58CABYvHgx9u/fr9VwRES6cOPGDdSpUwebN2/G/fv3cfLkSakjERFRNmlczK5cuRJjx45FmzZt8ObNGyiVSgBA4cKFsXjxYm3nIyLKNUIIrF27FnXr1sW///4LR0dHnDp1Ch07dpQ6GhERZZPGxeyyZcuwdu1aTJkyBXK5XN3u5uaGGzduaDUcEVFuiYuLQ8+ePTFo0CAkJSWhdevWCAsLQ+PGjaWORkREGtC4mA0PD8/0rjcmJiaIj4/XSigiotz23XffYdu2bZDL5Zg3bx4OHjyI4sWLSx2LiIg0pHEx6+zsjLCwsAztR44cQdWqVTUOsGLFCjg7O8PU1BSurq44e/ZstvY7f/48DA0NUatWLY3PSUT0008/oX79+jh79izGjx8PAwNeD0tEpI80/u09fvx4DBs2DAEBARBC4MqVK5g9ezYmT56M8ePHa3SsgIAAjB49GlOmTEFoaCgaN26M1q1b49GjRx/dLyYmBr1798ZXX32laXwiKqDi4+Oxfv169WMbGxtcuHAB7u7uEqYiIqLPpfE6s3379kVqaiomTJiAhIQE9OjRA46OjliyZAm8vLw0OtbChQvRv39/DBgwAMC7FRGOHTuGlStXYs6cOVnuN3jwYPTo0QNyuRz79u3T9CUQUQETEhKCsWPH4vnz57CyskKPHj0AvFt6i4iI9FuObpowcOBADBw4EFFRUVCpVLCxsdH4GCkpKQgJCcHEiRPTtXt6euLChQtZ7rdx40bcu3cPv//+O3766adPnic5ORnJycnqx2nrRyoUCigUCo1z50TaeXR1Pl1SKFLf+38FFDIhYZrckZ/7L78TQmD58uWYOHEiFAoFnJycUKZMGfalHuL7UL+x//SfrvtQk/N81h3APudiiaioKCiVStja2qZrt7W1RWRkZKb73L17FxMnTsTZs2dhaJi96HPmzMGMGTMytAcGBsLc3Fzz4J8hKChIp+fThWQlkPZjdOxYIEzkH91cr+XH/svP3r59i2XLluHy5csAgPr162P48OF4+fIlDh8+LHE6yim+D/Ub+0//6aoPExISsr2txsWss7PzRz+au3//vkbH+/BYQohMj69UKtGjRw/MmDEDFStWzPbxJ02ahLFjx6ofx8bGolSpUvD09ISVlZVGWXNKoVAgKCgIHh4eMDIy0sk5dSUhJRUTrpwAALRs6Qlz4/x3h+T83H/51ZUrVzBq1Cg8fPgQxsbGmDNnDsqWLQtPT0/2oZ7i+1C/sf/0n677MO2T9OzQuPIYPXp0uscKhQKhoaE4evSoRheAFS9eHHK5PMMo7IsXLzKM1gLv1oQMDg5GaGgohg8fDgBQqVQQQsDQ0BCBgYFo3rx5hv1MTExgYmKSod3IyEjnbygpzpnbjMT//vB49/ryXzGbJj/2X34VExODhw8foly5ctixYwe++OILHD58mH2YD7AP9Rv7T//pqg81OYfGlceoUaMybf/tt98QHByc7eMYGxvD1dUVQUFB+Prrr9XtQUFBmd59x8rKKsNNGVasWIETJ05g165dcHZ2zva5iSj/ef9TnTZt2mDr1q1o27YtrKysOE+PiCgf09rCiq1bt8bu3bs12mfs2LFYt24dNmzYgFu3bmHMmDF49OgRhgwZAuDdFIHevXu/C2pggOrVq6f7srGxgampKapXrw4LCwttvRQi0jPnzp1DzZo18fDhQ3Vb9+7ddTaViIiIpKO1z4R37dqFokWLarRPt27d8OrVK8ycORMRERGoXr06Dh8+DCcnJwBARETEJ9ecJaKCS6VSYe7cufjxxx+hVCrxww8/YMuWLVLHIiIiHdK4mHVxcUl3gZYQApGRkXj58iVWrFihcQBfX1/4+vpm+pyfn99H950+fTqmT5+u8TmJSP+9ePEC3t7eCAwMBAD06tULK1eulDgVERHpmsbFbKdOndI9NjAwQIkSJdC0aVNUrlxZW7mIiLJ06tQp9OjRAxERETAzM8Nvv/0GHx8f3gSBiKgA0qiYTU1NRZkyZdCyZUvY2dnlViYioiwdOXIE7dq1g0qlQtWqVbFjxw5Uq1ZN6lhERCQRjS4AMzQ0xNChQ9PdUYuISJeaNWuGGjVqoG/fvrhy5QoLWSKiAk7jaQb16tVDaGio+iItIqLcdvnyZbi5uUEul8PU1BRnzpxBoUKFpI5FRER5gMbFrK+vL7777js8efIErq6uGZbEqlGjhtbCEVHBlpqaihkzZmD27NmYOnWq+oJPFrJERJQm28Vsv379sHjxYnTr1g0AMHLkSPVzMplMvWC5UqnUfkoiKnCePn2KHj164MyZMwCA58+fZ3m7ayIiKriyXcxu2rQJv/zyC8LDw3MzDxERjh49Cm9vb0RFRcHS0hJr166Fl5eX1LGIiCgPynYxK4QAAM6VJaJco1AoMHXqVPzyyy8A3q1rHRAQgAoVKkicjIiI8iqNVjPgx3tElJvu37+PxYsXAwCGDRuGCxcusJAlIqKP0ugCsIoVK36yoH39+vVnBSKigqtSpUpYvXo1zM3N0aVLF6njEBGRHtComJ0xYwasra1zKwsRFTApKSn44Ycf8PXXX8Pd3R0A0Lt3b4lTERGRPtGomPXy8oKNjU1uZSGiAuTBgwfw8vLC5cuXsWPHDvz7778wNTWVOhYREemZbM+Z5XxZItKWvXv3wsXFBZcvX0bhwoWxZMkSFrJERJQj2S5m01YzICLKqeTkZIwcORKdO3fGmzdvUL9+fYSFhaFjx45SRyMiIj2V7WkGKpUqN3MQUT4XHR0NDw8PhISEAADGjx+P2bNnw8jISOJkRESkzzS+nS0RUU4ULlwYJUuWxIMHD7Bp0ya0bdtW6khERJQPsJglolyTlJSE1NRUWFpaQiaTYcOGDUhISEDJkiWljkZERPmERjdNICLKrjt37qB+/foYNGiQes590aJFWcgSEZFWsZglIq3bunUrXF1d8ddff+HPP//E06dPpY5ERET5FItZItKahIQEDBw4ED179sTbt2/RtGlThIWFcTSWiIhyDYtZItKKW7duoV69eli3bh1kMhmmTZuGP//8Ew4ODlJHIyKifIwXgBHRZ0tNTUX79u1x79492NnZwd/fH82bN5c6FhERFQAcmSWiz2ZoaIg1a9agZcuWCAsLYyFLREQ6w2KWiHLkxo0bOHjwoPpx8+bNceTIEdja2kqYioiIChoWs0SkESEE1q1bh7p166J79+64e/eu+jmZTCZhMiIiKohYzBJRtsXFxaFXr14YOHAgkpKS0KhRIxQuXFjqWEREVICxmCWibAkLC4Orqyu2bt0KuVyOuXPn4tChQyhRooTU0YiIqADjagZE9EmrVq3C6NGjkZycjFKlSmH79u1o0KCB1LGIiIg4MktEn3bv3j0kJyejffv2CA0NZSFLRER5BkdmiShTKpUKBgbv/t79+eefUbNmTfTs2ZMXeRERUZ7CkVkiSkcIgSVLlqB58+ZQKBQAACMjI/Tq1YuFLBER5TksZolILTo6Gp07d8bo0aNx+vRpbNu2TepIREREH8VpBkQEALh8+TK6deuGhw8fwtjYGAsWLIC3t7fUsYiIiD6KI7NEBZxKpcKCBQvQqFEjPHz4EOXKlcOFCxcwfPhwTisgIqI8j8UsUQE3YcIEjBs3DqmpqejatSuuXbsGV1dXqWMRERFlC4tZogJu4MCBKF68OFatWoXt27fDyspK6khERETZxjmzRAWMSqXChQsX0KhRIwBApUqV8ODBA1hYWEicjIiISHMcmSUqQF68eIE2bdqgSZMmOHXqlLqdhSwREekrjswSFRCnT59G9+7dERERATMzM0REREgdiYiI6LNxZJYon1MqlZg1axaaN2+OiIgIVKlSBVeuXEH37t2ljkZERPTZODJLlI9FRkaiV69eOH78OADAx8cHy5cv57QCIiLKN1jMEuVjR44cwfHjx2Fubo6VK1eid+/eUkciIiLSKhazRPmYj48P7t+/jx49eqBKlSpSxyEiItI6zpklykeePXuGXr16ITo6GgAgk8kwa9YsFrJERJRvcWSWKJ84evQovL29ERUVBQD4/fffJU5ERESU+zgyS6TnUlNTMWnSJLRu3RpRUVGoVasWpk2bJnUsIiIineDILJEee/z4Mbp3747z588DAHx9fbFgwQKYmppKnIyIiEg3WMwS6alLly6hbdu2eP36NaysrLB+/Xp06dJF6lhEREQ6xWKWSE9VrFgRFhYWKFu2LAICAlC2bFmpIxEREekci1kiPfLixQuUKFECMpkMRYsWxfHjx1G6dGmYmJhIHY2IiEgSvACMSE/s3bsXlSpVwoYNG9RtFSpUYCFLREQFGotZojwuOTkZI0eOROfOnfHmzRv4+/tDCCF1LCIiojyBxSxRHnbv3j00bNgQy5YtAwCMGzcOx44dg0wmkzgZERFR3sA5s0R51M6dOzFgwADExsaiaNGi2Lx5M9q2bSt1LCIiojyFxSxRHnTnzh14eXlBpVKhYcOG2LZtG0qVKiV1LCIiojyHxSxRHlSxYkVMnToVycnJmDlzJgwN+VYlIiLKDP+FJMojtm3bBjc3N1SoUAEAeEtaIiKibOAFYEQSS0hIwIABA9CjRw9069YNSUlJUkciIiLSGxyZJZLQrVu30LVrV/z999+QyWRo3749jIyMpI5FRESkN1jMEklk06ZN8PX1RUJCAmxtbeHv74+vvvpK6lhERER6hcUskY4lJCRg6NCh2Lx5MwDgq6++wu+//w47OzuJkxEREekfzpkl0jFDQ0P8+++/MDAwwKxZs3Ds2DEWskRERDnEkVkiHRBCQAgBAwMDGBsbIyAgAA8fPkSTJk2kjkZERKTXODJLlMvi4uLQq1cvTJo0Sd1WpkwZFrJERERawJHZfEgIgUSFUifnSkjRzXn0VVhYGLp27Yq7d+/C0NAQQ4cORZkyZaSORURElG+wmM1nhBDosuoiQh5GSx2lQBNCYNWqVRgzZgySk5NRsmRJbN++nYUsERGRlrGYzWcSFUpJClk3pyIwM5Lr/Lx5UUxMDAYOHIidO3cCANq1awc/Pz8UK1ZM4mRERET5D4vZfCz4hxYwN9ZNgWlmJIdMJtPJufIylUqFJk2a4K+//oKhoSHmzp2LMWPG8HtDRESUS1jM5mPmxnKYG7OLdcnAwADjx4/HlClTEBAQgHr16kkdiYiIKF/jagZEnyk6OhphYWHqxz179sTNmzdZyBIREekAi1miz3D58mW4uLigTZs2ePnypbrd3NxcwlREREQFB4tZohwQQmDBggVo1KgRHj58CDMzM7x48ULqWERERAUOJ1QSaejVq1fw8fHBwYMHAQDffvst1q5dC2tra4mTERERFTySj8yuWLECzs7OMDU1haurK86ePZvltnv27IGHhwdKlCgBKysruLu749ixYzpMSwXd+fPnUatWLRw8eBAmJiZYsWIFAgICWMgSERFJRNJiNiAgAKNHj8aUKVMQGhqKxo0bo3Xr1nj06FGm2585cwYeHh44fPgwQkJC0KxZM7Rv3x6hoaE6Tk4F1cqVK/HkyRNUqFABly5dwtChQ7nsFhERkYQknWawcOFC9O/fHwMGDAAALF68GMeOHcPKlSsxZ86cDNsvXrw43eOff/4Z+/fvx4EDB+Di4qKLyFTArVixAra2tpg+fToKFSokdRwiIqICT7JiNiUlBSEhIZg4cWK6dk9PT1y4cCFbx1CpVIiLi0PRokWz3CY5ORnJycnqx7GxsQAAhUIBhUKRg+SaSzuPLs6nUKSmO69CJnL9nPnZmTNnEBAQgDZt2kChUMDMzAy//PILAN30J2mHLt+DlDvYh/qN/af/dN2HmpxHsmI2KioKSqUStra26dptbW0RGRmZrWMsWLAA8fHx6Nq1a5bbzJkzBzNmzMjQHhgYqPPlk4KCgnL9HMlKIK1bjx0LhAnvMJsjSqUSu3btQkBAAFQqFUxNTTmdIB/QxXuQchf7UL+x//SfrvowISEh29tKvprBhwWCECJbRcO2bdswffp07N+/HzY2NlluN2nSJIwdO1b9ODY2FqVKlYKnpyesrKxyHlwDCoUCQUFB8PDwgJGRUa6eKyElFROunAAAtGzpyTuA5UBkZCR8fHxw4sS772PPnj3RsGFDnfQf5Q5dvgcpd7AP9Rv7T//pug/TPknPDskqneLFi0Mul2cYhX3x4kWG0doPBQQEoH///ti5cydatGjx0W1NTExgYmKSod3IyEjnbyhdnNNI/O8PgXfnYzGriePHj6Nnz554/vw5zM3NsWLFCvTo0QOHDx+W5GeGtIt9qP/Yh/qN/af/dNWHmpxDstUMjI2N4erqmmG4OigoCA0aNMhyv23btsHHxwdbt25F27ZtczsmFSBLliyBh4cHnj9/jurVqyM4OBh9+vSROhYRERF9hKTDdmPHjoW3tzfc3Nzg7u6ONWvW4NGjRxgyZAiAd1MEnj59is2bNwN4V8j27t0bS5YsQf369dWjumZmZlznkz5bnTp1YGBggL59+2LJkiW8JS0REZEekLSY7datG169eoWZM2ciIiIC1atXx+HDh+Hk5AQAiIiISLfm7OrVq5Gamophw4Zh2LBh6vY+ffrAz89P1/EpH3j+/Ll6WkuDBg3w999/o3LlyhKnIiIiouySfEKlr68vfH19M33uwwL11KlTuR+ICoTU1FT8+OOPWLZsGS5fvoxq1aoBAAtZIiIiPSN5MUuka48fP0b37t1x/vx5AMCBAwfUxSwRERHpFxazVKAcOnQIvXv3xuvXr2FlZYW1a9d+dJ1iIiIiytskW82ASJcUCgXGjRuHdu3a4fXr13B1dcW1a9dYyBIREek5FrNUIKxfvx4LFiwAAIwcORLnz59HuXLlJE5FREREn4vTDKhAGDBgAI4dO4bevXvj66+/ljoOERERaQlHZilfSklJwfz585GcnAwAMDQ0xN69e1nIEhER5TMcmaV85/79++jWrRuCg4Px6NEjLFu2TOpIRERElEs4Mkv5yq5du+Di4oLg4GAULVoULVu2lDoSERER5SIWs5QvJCUlwdfXF99++y1iY2PRsGFDhIWFoV27dlJHIyIiolzEYpb03r179+Du7o6VK1cCACZOnIiTJ0+iVKlSEicjIiKi3MY5s6T3DAwMEB4ejuLFi2PLli1o1aqV1JGIiIhIR1jMkl5SKpWQy+UAAGdnZ+zduxcVK1aEo6OjxMmIiIhIlzjNgPTOrVu3ULt2bRw9elTd1qxZMxayREREBRCLWdIrmzdvhpubG65fv47x48dDpVJJHYmIiIgkxGKW9EJ8fDz69u2LPn36ICEhAc2bN0dQUBAMDPgjTEREVJCxEqA87++//0adOnXg5+cHAwMDzJw5E4GBgbCzs5M6GhEREUmMF4BRnnb//n3UrVsXiYmJsLe3x9atW9G0aVOpYxEREVEewWKW8rSyZcvCy8sLz549w+bNm2FjYyN1JCIiIspDWMxSnvPXX3/BwcEBJUqUAACsXLkSRkZGnB9LREREGbA6oDxDCIFVq1ahXr166N27t3qlAhMTExayRERElClWCJQnxMTEwMvLC0OHDkVycjLkcjkSEhKkjkVERER5HItZklxISAhcXV2xY8cOGBoaYv78+fjjjz9gaWkpdTQiIiLK4zhnVgeEEEhWAgkpqTASslw9V0KKMlePr01CCCxfvhzjxo1DSkoKnJycsH37dtSvX1/qaERERKQnWMzmMiEEvNZdxbVHhphw5YTUcfKU+Ph4LFmyBCkpKejYsSM2btyIIkWKSB2LiIiI9AiL2VyWqFDi2qM3Oj+vm1MRmBnJdX5eTVhaWiIgIADnzp3DyJEjIZPl7qg1ERER5T8sZnXo0vdNYGVhqpNzmRnJ81xxKITA4sWLYWZmhiFDhgAAXF1d4erqKnEyIiIi0lcsZnXIzFgOc+OC+S1//fo1fHx8cODAARgbG8PDwwPlypWTOhYRERHpuYJZWZFOXbhwAV5eXnj8+DFMTEywaNEilC1bVupYRERElA9waS7KNSqVCnPnzsWXX36Jx48fo0KFCrh06RKGDh2a56ZAEBERkX7iyCzlCpVKhU6dOuHAgQMAgO7du2P16tUoVKiQxMmIiIgoP+HILOUKAwMDuLu7w9TUFGvXroW/vz8LWSIiItI6jsyS1iiVSkRFRcHW1hYA8P333+Pbb79F+fLlJU5GRERE+RVHZkkrnj9/jlatWuGrr75CQkICgHejsyxkiYiIKDexmKXPduLECdSsWRN//vknwsPDce3aNakjERERUQHBYpZyTKlUYtq0aWjRogWeP3+OatWq4erVq2jUqJHU0YiIiKiA4JxZypFnz56hZ8+eOHXqFACgf//+WLp0KczNzaUNRkRERAUKi1nKkREjRuDUqVOwsLDA6tWr0bNnT6kjERERUQHEYpZyZOnSpYiJicFvv/2GSpUqSR2HiIiICijOmaVsefLkCX777Tf1Y0dHR/z5558sZImIiEhSHJmlTzp8+DB69+6NV69ewdHREZ06dZI6EhEREREAjszSRygUCkyYMAFt27bFq1evULt2bXzxxRdSxyIiIiJS48gsZerhw4fw8vLCpUuXALy74Gv+/PkwMTGROBkRERHR/7CYpQwOHjwIb29vvHnzBtbW1tiwYQM6d+4sdSwiIiKiDFjMUgbJycl48+YN6tati+3bt8PZ2VnqSERERESZYjFLAIDU1FQYGr77cfjmm2+we/dutGvXDsbGxhInIyJ9olKpkJKSovXjKhQKGBoaIikpCUqlUuvHp9zF/tN/udGHxsbGMDD4/Mu3WMwSdu3ahcmTJ+PUqVNwcHAAAE4rICKNpaSkIDw8HCqVSuvHFkLAzs4Ojx8/hkwm0/rxKXex//RfbvShgYEBnJ2dP3vgjMVsAZaUlITvvvsOK1asAADMnz8fixYtkjgVEekjIQQiIiIgl8tRqlQprYy2vE+lUuHt27ewtLTU+rEp97H/9J+2+1ClUuHZs2eIiIhA6dKlP6tAZjFbQN29exfdunVDaGgoAOD777/HrFmzJE5FRPoqNTUVCQkJcHBwgLm5udaPnzZ9wdTUlMWQHmL/6b/c6MMSJUrg2bNnSE1NhZGRUY6Pw2K2ANq+fTsGDhyIt2/fonjx4ti8eTNat24tdSwi0mNpc+g4z56Isivt94VSqWQxS9m3efNm9OnTBwDQuHFjbNu2DY6OjhKnIqL8gvMhiSi7tDb3VitHIb3xzTffoFq1avjhhx9w4sQJFrJERESk11jMFgBBQUHqq4stLCwQHByMWbNmqZfiIiIiyqmUlBSUL18e58+flzpKvvHixQuUKFECT58+lTqKXmAxm4/Fx8ejb9++8PT0xIIFC9TtpqamEqYiIso7fHx8IJPJIJPJYGhoiNKlS2Po0KGIjo7OsO2FCxfQpk0bFClSBKampvjiiy+wYMGCTNfcPHnyJNq0aYNixYrB3NwcVatWxXfffffJ4iQ0NBTffvstbG1tYWpqiooVK2LgwIG4c+eO1l6ztq1ZswZOTk5o2LBhhucGDRoEuVyO7du3Z3jOx8cHnTp1ytAeFhYGmUyGBw8eqNuEEFizZg3q1asHS0tLFC5cGG5ubli8eDESEhK0+XLSiY6Ohre3N6ytrWFtba2+O+bHvH37FsOHD0fJkiVhZmaGKlWqYOXKlZluK4RA69atIZPJsG/fPnW7jY0NvL29MW3aNC2+mvyLxWw+9c8//6Bu3brw8/ODgYEBFAqF1JGIiPKkVq1aISIiAg8ePMC6detw4MAB+Pr6pttm7969aNKkCUqWLImTJ0/i33//xahRozB79mx4eXlBCKHedvXq1WjRogXs7Oywe/du3Lx5E6tWrUJMTEy6gYUPHTx4EPXr10dycjL8/f1x69YtbNmyBdbW1vjxxx9z/Ppy+/f/smXLMGDAgAztCQkJCAgIwPjx47Fhw4bPOoe3tzdGjx6Njh074uTJkwgLC8OPP/6I/fv3IzAw8LOO/TE9evRAWFgYjh49iqNHjyIsLAze3t4f3WfMmDE4evQofv/9d9y6dQtjxozBiBEjsH///gzbLl68OMt5o3379oW/v3+mf1jRB0QBExMTIwCImJgYnZwvPlkhnL4/KJy+PyjevE3I9fOpVCqxfv16YWZmJgAIOzs7cfLkyVw/b36WkpIi9u3bJ1JSUqSOQjnEPsx9iYmJ4ubNmyIxMVEI8e53UXyyQmtfcYnJ4tnzKBGXmPzJbVUqVbZz9+nTR3Ts2DFd29ixY0XRokXVj9++fSuKFSsmOnfunGH/P/74QwAQ27dvF0II8fjxY2FsbCxGjx6d6fmio6MzbY+PjxfFixcXnTp1+uh+GzduFNbW1ume27t3r3j/n/Np06aJmjVrivXr1wtnZ2chk8nEqlWrhIODg1Aqlen2bd++vejdu3e611O7dm1hYmIinJ2dxfTp04VCocg0kxBChISECAMDg0z/TfXz8xP169cXb968EWZmZuKvv/5Kd/7MvvdCCBEaGioAiPDwcCGEEAEBAQKA2LdvX4ZtVSqVePPmTZb5PsfNmzcFAHHp0iV128WLFwUA8e+//2a5X7Vq1cTMmTPTtdWuXVv88MMP6drCwsJEyZIlRUREhAAg9u7dm+FYZcqUEevXr/+8F6IlSqVSREdHZ/gZ+hwf/t54nyb1GidN5iNv377FkCFD4O/vDwDw9PTEli1bYGNjI3EyIipoEhVKVJ16TJJz35zZEubGOfvn7f79+zh69Gi6ZYICAwPx6tUrjBs3LsP27du3R8WKFbFt2zZ069YNO3fuREpKCiZMmJDp8QsXLpxp+7FjxxAVFaXxfln577//sGPHDuzevRtyuRyOjo4YOXIkTp48ia+++grAu4/Qjx07hgMHDqgz9OrVC0uXLkXjxo1x7949DBo0CACy/Lj7zJkzqFixIqysrDI8t379evTq1QvW1tZo3bo1/P39MWfOHI1eBwD4+/ujUqVK6NixY4bnZDIZrK2ts9zX0tLyo8du3Lgxjhw5kulzFy9ehLW1NerVq6duq1+/PqytrXHhwgVUqlQp0/0aNWqEP/74A/369YODgwNOnTqFO3fuYMmSJeptEhIS0L17dyxfvhx2dnZZ5qtbty7Onj2Lfv36ffR1FHQsZvORO3fuYMeOHZDL5Zg1axa+//57Lk5NRPQJBw8ehKWlJZRKJZKSkgAACxcuVD+fNl+1SpUqme5fuXJl9TZ3796FlZUV7O3tNcpw9+5d9bG0ISUlBVu2bEGJEiXUba1atcLWrVvVxezOnTtRtGhR9ePZs2dj4sSJ6uUby5Yti1mzZmHChAlZFrMPHjxQ3wb9w9dz6dIl7NmzBwDQs2dPjBw5ErNnz9b436W7d+9mWTh+SlhY2EefNzMzy/K5yMjITAeDbGxsEBkZmeV+S5cuxcCBA1GyZEkYGhrCwMAA69atQ6NGjdTbjBkzBg0aNMi0QH+fo6Oj+uZGlDUWs/lI7dq1sXr1alSoUCHdm4aISNfMjOS4ObOl1o6nUqkQFxuHQlaFPlkMmRnJNTp2s2bNsHLlSiQkJGDdunW4c+cORowYkWE78d682A/b0+Y9vv//msjq2Dnl5OSUrpAF3hWUgwYNwooVK2BiYgJ/f394eXlBLn/3/QoJCcHVq1cxe/Zs9T5pBX5CQkKmd3ZLTEzM9KLi9evXo2XLlihevDgAoE2bNhgwYAD+/PNPtGrVSqPXktPvKQCUL18+R/ulyey8n8qzdOlSXLp0CX/88QecnJxw5swZ+Pr6wt7eHi1atMAff/yBEydOZKtINTMzy9UL3PILDtvpsdjYWPTu3TvdG6Jv374sZIlIcjKZDObGhlr9MjOWZ2s7TQsfCwsLlC9fHjVq1MDSpUuRnJyMGTNmqJ+vWLEiAODWrVuZ7v/vv/+iQoUK6m1jYmIQERGhUYa0c/z7778f3c7AwCBD4ZvZBV4WFhYZ2tq3bw+VSoVDhw7h8ePHOHv2LHr16qV+XqVSYcaMGQgLC1N/3bhxA3fv3s1yFZzixYtnuEBJqVRi8+bNOHToEAwNDWFoaAhLS0tER0enuxDMysoKMTExGY6ZtlpA2vSBihUrZvm9/xRLS8uPfn3s7pd2dnZ4/vx5hvaXL1/C1tY2030SExMxefJkLFy4EO3bt0eNGjUwfPhwdOvWDb/++isA4MSJE7h37x4KFy6s/v4A79aBb9q0abrjvX79OsMfJZQRR2b11LVr19C1a1fcu3cPwcHBuHHjhvqvayIiyrlp06ahdevWGDp0KBwcHODp6YmiRYtiwYIFaNCgQbpt//jjD9y9exezZs0CAHTp0gUTJ07EvHnzsGjRogzHfvPmTabzXz09PVG8eHHMmzcPe/fuzXK/EiVKIC4uDvHx8eqC9VMfpacxMzND586d4e/vj//++w8VK1aEq6ur+vnatWvj9u3bGo1muri4YOXKlelGKw8fPoy4uDiEhoaq/11SqVQIDQ3FoEGD8OrVKxQrVgyVK1fGtm3bkJSUlK5Yvnr1KkqUKIEiRYoAeLeigJeXF/bv35/hY3khBGJjY7OcN/s50wzc3d0RExODK1euoG7dugCAy5cvIyYmJsPPQRqFQgGFQpHh0wO5XK5e733ixIkZVn/44osvsGjRIrRv3z5d+99//52hwKVMaO2SND2h76sZqFQqsWzZMmFsbCwAiNKlS4sLFy5oISllhVfC6z/2Ye772FXJ2pAbV1ILkfUV9a6urmLYsGHqxzt37hRyuVwMHDhQ/PXXXyI8PFysW7dOFClSRHTp0iXdCgq//fabkMlkol+/fuLUqVPiwYMH4ty5c2LQoEFi7NixWWbZt2+fMDIyEu3btxdBQUEiPDxcXL16VYwfP15069ZNCCHEq1evhIWFhRg5cqS4e/eu8Pf3Fw4ODpmuZpCZwMBAYWJiIipVqiRmzZqV7rmjR48KQ0NDMW3aNPH333+Lmzdviu3bt4spU6ZkmTkqKkoYGxuLGzduqNs6duyozptGqVSK169fC0dHR7F48WIhhBBv3rwRdnZ2okuXLuLq1aviv//+E1u2bBFFihQR8+bNU++rUqlEt27dhJmZmfj555/F1atXxYMHD8SBAwdE8+bNM10FQFtatWolatSoIS5evCguXrwovvjiC9GuXbt021SqVEns2bNH/bhJkyaiWrVq4uTJk+L+/fti48aNwtTUVKxYsSLL8yCT1Qzi4+OFmZmZOHPmjFZfU07l5dUMWMzmMm0Ws9HR0aJz584CgAAgOnToIF69eqWlpJQVFkL6j32Y+/JbMevv7y+MjY3Fo0eP1G1nzpwRrVq1EtbW1sLY2FhUrVpV/PrrryI1NTXD/kFBQaJly5aiSJEiwtTUVFSuXFmMGzdOPHv27KN5rl69Kjp37ixKlCghTExMRPny5cWgQYPE3bt31dvs3btXlC9fXpiamop27dqJNWvWZLuYTU1NFfb29gKAuHfvXobnjx49Kho0aCDMzMyElZWVqFu3rlizZs1HM3t5eYmJEycKIYSIjIwUhoaGYseOHem2Seu/4cOHiy+++ELdfvfuXfHNN98IR0dHYWFhIb744guxfPnyDP2sVCrFypUrRZ06dYS5ubmwsrISrq6uYsmSJSIhIfeWvXz16pXo2bOnKFSokChUqJDo2bNnhuXVAIiNGzeqH0dERAgfHx/h4OAgTE1NRaVKlcSCBQs+umRcZsXs1q1bRaVKlbT4aj5PXi5mZUJoedZ5Hpf2cURMTEymS4loW0JKqnp5mr9+bA5ri6w/0viYJ0+eoHHjxnjw4AGMjIwwf/58jBw5MseT4in7FAoFDh8+jDZt2qRbrof0B/sw9yUlJSE8PBzOzs65cpdBlUqF2NhYWFlZcZWWPObGjRto0aIF/vvvPxQqVCjTbdh/mqtbty5Gjx6NHj16SB0FQO704cd+b2hSr3HOrJ5wcHBAhQoVIJPJEBAQgDp16kgdiYiICF988QXmzZuHBw8e4IsvvpA6Tr7w4sULdOnSBd27d5c6il5gMZuHvX79GqampjA3N4eBgQG2bt0KQ0NDjRfPJiIiyk1pa9OSdtjY2GR5Aw3KiGP9edSFCxdQq1YtjBo1St1WvHhxFrJERERE72Exm8eoVCrMmzcPX375JR4/foxTp06p19wjIiIiovRYzOYhL1++RLt27fD9999DqVTCy8sLISEhHI0lIiIiygLnzOYRZ8+ehZeXF549ewZTU1MsWbIEAwcO5GoFRERERB/BYjYPSEhIwLfffovnz5+jUqVK2LFjB2rUqCF1LCIiIqI8j9MM8gBzc3Ns2LAB3t7eCA4OZiFLRERElE0cmZXIyZMnkZiYiDZt2gAA2rRpo/5/IiIiIsoejszqmFKpxPTp0/HVV1+hZ8+eePTokdSRiIgoF02fPh21atXKs+dp2rQpRo8erfU8n1KmTBksXrz4s47h4+ODTp06fXQbqV4f6Y7kxeyKFSvUtzFzdXXF2bNnP7r96dOn4erqClNTU5QtWxarVq3SUdLPFxkZAQ8PD8yYMQNCCHTu3BnFixeXOhYRUYH2+PFj9O/fHw4ODjA2NoaTkxNGjRqFV69eaXwsmUyGffv2pWsbN24cjh8/rqW0OXfq1CnIZDIu95gLbty4gSZNmsDMzAyOjo6YOXMmhBAf3Sc6Ohre3t6wtraGtbU1vL290/XNq1ev0KpVKzg4OMDExASlSpXC8OHDERsbq97m1KlT6NixI+zt7WFhYYFatWrB398/w7mSk5MxZcoUODk5wcTEBOXKlcOGDRvUz+/Zswdubm4oXLiw+jhbtmxJd4y4uDhMmjQJzs7OMDMzQ4MGDXD16tUsX9/gwYMhk8k++w+W7JB0mkFAQABGjx6NFStWoGHDhli9ejVat26NmzdvonTp0hm2Dw8PR5s2bTBw4ED8/vvvOH/+PHx9fVGiRAl88803EryC7EsMv4ZG9fsiKuolLCwssGrVKvTq1UvqWEREeYtSCZw9C0REAPb2QOPGgFyea6e7f/8+3N3dUbFiRWzbtg3Ozs74559/MH78eBw5cgSXLl1C0aJFP+sclpaWsLS01FLivEGhUMDIyEjqGHlCbGwsPDw80KxZM1y9ehV37tyBj48PLCws8N1332W5X48ePfDkyRMcPXoUADBo0CB4e3vjwIEDAAADAwN07NgRP/30E0qUKIH//vsPw4YNw+vXr7F161YA726wVKNGDXz//fewtbXFoUOH0Lt3b1hZWaF9+/bqc3Xt2hXPnz/H+vXrUb58ebx48QKpqanq54sWLYopU6agcuXKMDY2xsGDB9G3b1/Y2NigZcuWAICBAwfixo0b2LRpE0qWLInff/8dLVq0wM2bN+Ho6Jjute3btw+XL1+Gg4ODdr7JnyIkVLduXTFkyJB0bZUrVxYTJ07MdPsJEyaIypUrp2sbPHiwqF+/frbPGRMTIwCImJgYzQPnwNukFGHl3lUAMgFA1KhRQ/z77786OTdpR0pKiti3b59ISUmROgrlEPsw9yUmJoqbN2+KxMTEnB9k924hSpYUAvjfV8mSQuzeLZRKpYiOjhZKpVJ7oYUQrVq1EiVLlhQJCQnp2iMiIoS5uXm6f6OcnJzEzJkzRffu3YWFhYWwt7cXS5cuTfc8APWXk5OTEEKIadOmiZo1a6q369Onj+jYsaOYPXu2sLGxEdbW1mL69OlCoVCIcePGiSJFighHR0exfv36dJkmTJggKlSoIMzMzISzs7P44Ycf0v1Mf3ie94WHh6fLBkD06dNHCCFEkyZNxIgRI8T48eNFkSJFhK2trZg2bVq6/QGIlStXig4dOghzc3MxdepUIYQQf/zxh6hdu7YwMTERzs7O6tfxfqZSpUoJY2NjYWdnJ4YPH57u+zV79mzRt29fYWlpKUqVKiVWr16d7rzXr18XzZo1E6ampqJo0aJi4MCBIi4uLsP3Ms3bt2+Ft7e3sLCwEHZ2duLXX38VTZo0EaNGjcr0+6INK1asENbW1iIpKUndNmfOHOHg4CBUKlWm+9y8eVMAEJcuXVK3Xbx4UQD4aI2wZMkSUbJkyY/madOmjejbt6/68ZEjR4S1tbV49epVdl+SEEIIFxcX8cMPPwghhEhISBByuVxs37493XuwZs2aYsqUKen2e/LkiXB0dBR///23cHJyEosWLcryHB/7vaFJvSbZyGxKSgpCQkIwceLEdO2enp64cOFCpvtcvHgRnp6e6dpatmyJ9evXZ/lXYnJyMpKTk9WP04bnFQoFFArF576MT0pNTYUq6S0AgT4+/bB0ySKYmZnp5NykHWl9xT7TX+zD3KdQKCCEgEqlgkql0vwAe/ZA1rUrIATeX11bPH0KdOkCsWMH0KKF+hza8Pr1axw7dgw//fQTTExM0h3XxsYGPXr0QEBAAJYvX65e83v+/PmYNGkSpk6disDAQIwZMwYVK1aEh4cHLl++DDs7O6xfvx6tWrWCXC6HSqVSf9ycdnwhBE6cOAFHR0ecOnUK58+fx8CBA3HhwgV8+eWXuHjxInbs2IEhQ4bgq6++QqlSpQC8G+HdsGEDHBwccOPGDQwePBiWlpYYP368+rjvn+d9jo6O2LlzJ7799lvcunULVlZWMDMzU2+7adMmjBkzBhcvXsTFixfRr18/uLu7w8PDQ32MadOmYfbs2ViwYAHkcjmOHDmCXr16YfHixWjcuDHu3buHIUOGQAiBqVOnYteuXVi0aBG2bt2KqlWr4v79+/jvv//S5VuwYAFmzpyJiRMnYvfu3Rg6dCgaNWqEypUrIyEhAa1atUK9evVw+fJlvHjxAoMGDcKwYcOwceNG9Wt+/2di3LhxOHnyJHbv3g07OztMmTIFISEhqFmzZpY/N2fPnkXbtm0/+rMyadIkTJo0KdPn0vrNyMhIfQ4PDw9MmjQJ9+/fh7Ozc4Z9zp8/D2tra9SpU0e9T926dWFtbY1z586hQoUKGfZ59uwZ9uzZgy+//PKj74GYmBhUrlxZvc3+/fvh5uaGuXPn4vfff4eFhQXat2+PmTNnwszMLMP+aT+ft2/fxpw5c6BSqZCSkgKlUglTU9N0328zMzOcO3dO/VilUsHb2xvjxo1DlSpV1MfLKm/a+0OhUED+wScwmvy+lqyYjYqKglKphK2tbbp2W1tbREZGZrpPZGRkptunpqYiKioK9vb2GfaZM2cOZsyYkaE9MDAQ5ubmn/EKsidZCRRtPgBmZd3Qpn1tnDx5MtfPSbkjKChI6gj0mdiHucfQ0BB2dnZ4+/YtUlJSNNtZqYTVqFEZClkAkAkBIZNBNno08NdfiIuL01ZkhIWFQQgBJyendPMQ0zg7OyM6Ohr3799HiRIloFKpULduXQwdOhQA0Lt3b5w6dQq//vor6tWrBxMTEwCAiYmJ+t+X2NhYJCcnQ6lUphtMKVy4MGbNmgUDAwN06dIF8+bNQ1xcHIYNGwYA8PX1xdy5c/Hnn3+qp9GNGDFCna1Jkybw9fXF9u3bMXjwYADIcJ4PmZqaAnhXgLyfLzU1FVWrVlVfJNWpUycsW7YMR44cQb169dT7f/PNN+jSpYv68axZszBq1Ch8/fXXAIDixYtj4sSJmD59OkaPHo27d+/CxsYGdevWhZGREVxdXeHq6qrOp1Kp0KJFC/Ts2RMAMGTIECxatAhHjx6Fg4MDNm3ahISEBCxbtgwWFhYoXbo0fvnlF3Tv3h1TpkyBjY0NFAoFUlNTERsbi7dv32LDhg1YuXKlOveyZctQrVo1pKSkZPl9qVixIs6cOZPpc2mKFCmS5f5Pnz5F6dKl0z2f9v29d+8eihUrlmGfhw8fonjx4hmOWbx4cTx8+DBde//+/XHkyBEkJiaiVatWWLBgQZZZ9u/fj6tXr2L+/Pnqbe7evYtz585BLpdj8+bNePXqFcaNG4fnz59j+fLl6n1jYmJQrVo1JCcnQy6Xq3+u045Tp04dzJ8/HxUrVoSNjQ127dqFy5cvo1y5cuptFi5cCADo06cPYmNjoVKpkJSUlGXelJQUJCYm4syZM+mmPQDv1uDPLsmX5vrwDldCiI/e9Sqz7TNrTzNp0iSMHTtW/Tg2NhalSpWCp6cnrKyscho724QQaN48GSdOGKBtyxYwNjbO9XOSdikUCgQFBcHDw4NzxPQU+zD3JSUl4fHjx7C0tFQXTdl26hQMnj3L8mmZEJA9fQrDixdh1rq11u6MaGFhAeBdcZfZvwdpxamVlRWsrKxgYGCAxo0bp9v2yy+/xJIlS9K1fXg8ExMTyOVydZuRkRGqV6+e7lbl9vb2qFatWrr9ihUrhrdv36rbdu3ahaVLl+K///7D27dvkZqaqs6W2Xk+lFZgFSpUKN02hoaGqFGjRro2R0dHxMTEpGtzd3dP9/ivv/5CaGiouoAB3q3Yk5SUBENDQ/Tq1QurV69G7dq10bJlSzRt2hTffvut+j1oYGAAV1fXdMe0t7dHXFwcrKys8ODBA9SqVSvdQJWHhwdUKhWePXuG8uXLw8jICIaGhrCyskJ4eDhSUlLQvHlz9TGtrKxQqVIlGBsbZ/l9sbKyyjBQpgm5XJ7h+Gl/dFlaWmZ6XlNT00z7SiaTZfj5WbZsGd68eYPbt2/jhx9+wIwZM/Dbb79lOOapU6cwbNgwrF69Ot0fITKZDDKZDNu3b4e1tTWAd9/7rl27YvXq1erRWUtLS1y7dg1v377FiRMn8MMPP6Bq1apo2rQpAOD3339H3759UbVqVcjlctSuXRvdu3dHaGgorKysEBISgjVr1iA4ODjdeUxNTbP83iclJcHMzAxffvllht8bWRXAmZGsmC1evDjkcnmGUdgXL15k+UNlZ2eX6faGhoaZ/uUDvHtzp/1Cep+RkZHO/lGzlslgIgeMjY35D6ke0+XPDOUO9mHuUSqVkMlkMDAwgIGBhgvlPH+erc1kkZHqc2hDxYoVIZPJ8O+//2Z6zNu3b6NIkSKwsbFRF9Afnj+tUHi/7cPvQdq+aW0ymQzGxsYZtsmsTQgBAwMDXLp0CT169MCMGTPQsmVLWFtbY/v27ViwYEG6475/ng+ltWfWRx+e28DAQH3uNIUKFUr3WKVSYcaMGejcuXOGc5mbm8PS0hK3b99GUFAQgoKCMG7cOKxYsQKnT59Wvw8/9prTHn+YC3hXQBoYGKT7/r//+j98fR/7uTl79ixat26d6XNpJk+ejMmTJ2f6nL29PZ4/f57u+FFRUernMjtvZvsAwMuXL2FnZ5eu3cHBAQ4ODqhatSpKlCiBxo0bY+rUqemK/NOnT6Njx45YuHAhfHx80h3TwcEBjo6OKFKkiLqtWrVqEELg2bNn6ikNBgYGqFixIgCgdu3a+PfffzF37lw0b94cAFC+fHkcOnQIcrkcb9++hb29Pbp16wZnZ2cYGBjg/PnzePHiBcqUKaM+j1KpxLhx47BkyRI8ePAgw/chrd8y+92sye9qyYpZY2NjuLq6IigoSP0RBfDuY8COHTtmuo+7u7v6Kr80gYGBcHNz4z9QRET6KpMpYpkRdnZaPW2xYsXg4eGBFStWYMyYMenmD0ZGRsLf3x+9e/dONxJ86dKldMe4dOkSKleurH5sZGQEpVKp1ZzAuzmWTk5OmDJlirrt4cOHGh0j7ZNBbeWrXbs2bt++jfLly2e5jZmZGTp06IB27dqhd+/eqFu3Lm7cuIHatWt/8vhVq1bFpk2bEB8frx5FP3/+fLqi631pI7WXLl1Sr4gUHR2NO3fuoEmTJlmex83NDWFhYR/N8rEVLdzd3TF58mSkpKSov8eBgYFwcHBIV9h9uE9MTAyuXLmCunXrAgAuX76MmJgYNGjQIMtzpX0a/f61QKdOnUK7du0wd+5cDBo0KMM+DRs2xM6dO/H27Vv1qhp37tyBgYEBSpYs+dFzvX+eNBYWFihUqBCio6Nx7NgxzJs3DwDg7e2NFi1apNu2ZcuW8Pb2Rt++fbM8jzZIOs1g7Nix8Pb2hpubG9zd3bFmzRo8evQIQ4YMAfBuisDTp0+xefNmAO/m0yxfvhxjx47FwIEDcfHiRaxfvx7btm2T8mUQEdHnaNwYKFkSePr03RoGH5LJIEqWRKq7u9ZPvXz5cjRo0AAtW7bETz/9lG5pLkdHR8yePTvd9ufPn8e8efPQqVMnBAUFYefOnTh06JD6+TJlyuD48eNo2LAhTExM0o2GfY7y5cvj0aNH2L59O+rUqYNDhw5h7969Gh3DyckJMpkMBw8eRJs2bWBmZvZZS4ZNnToV7dq1Q6lSpfDtt9/CwMAA169fx40bN/DTTz/Bz88PSqUS9erVg6mpKQICAmBmZgYnJ6dsHb9nz56YNm0a+vTpg+nTp+Ply5cYMWIEvL29M/0E19LSEv3798f48eNRrFgx2NraYsqUKZ8cyTczM/toQf4paSPmPj4+mDx5Mu7evYuff/4ZU6dOVf8hdOXKFfTu3RvHjx+Ho6MjqlSpglatWmHgwIFYvXo1gHdLc7Vr1w6VKlUCABw+fBjPnz9HnTp1YGlpiZs3b2LChAlo2LChukg+deoU2rZti1GjRuGbb75Rf3ptbGysLsB79OiBWbNmoW/fvpgxYwaioqIwfvx49OvXT/0H3Jw5c+Dm5oZy5cohJSUFhw8fxubNm7Fy5Ur16zx27Bji4+Ph4uKC+/fvY/z48ahUqZK6UC1WrFiGT8mNjIxgZ2enfk25RdKbJnTr1g2LFy/GzJkzUatWLZw5cwaHDx9W/6BHRESku0OWs7MzDh8+jFOnTqFWrVqYNWsWli5dmufXmCUioo+Qy4ElS979/4fzYf//sVi4MFfWm61QoQKCg4NRrlw5dOvWDeXKlcOgQYPQrFkzXLx4McOI3HfffYeQkBC4uLhg1qxZWLBggXodTuDd1flBQUEoVaoUXFxctJazY8eOGDNmDIYPH45atWrhwoUL+PHHHzU6hqOjI2bMmIGJEyfC1tYWw4cP/6xMLVu2xMGDBxEUFIQ6deqgfv36WLhwofrf8MKFC2Pt2rVo2LCh+t/4/fv3Zzkt8EPm5uY4duwYXr9+jTp16qBLly746quv0l209KH58+fjyy+/RIcOHdCiRQs0atQIrq6un/U6P8Xa2hpBQUF48uQJ3Nzc4Ovri7Fjx6a7XichIQG3b99Od4W+v78/vvjiC3h6esLT0xM1atRId6MCMzMzrF27Fo0aNUKVKlUwevRotGvXDgcPHlRv4+fnh4SEBMyZMwf29vbqr/enflhaWiIoKAhv3ryBm5sbevbsifbt22Pp0qXqbeLj4+Hr64tq1aqhQYMG2LVrF37//XcMGDBAvU1MTAzGjx+PqlWronfv3mjUqBECAwPzxCfjMiEy+zM4/4qNjYW1tXWGie25SaFQ4PDhw2jTpk2e6HTSDPtP/7EPc19SUhLCw8PVd3TMkT17gFGjgCdP/tdWqhSweDFUnTohNjZWfSGWFMqUKYPRo0fz1qg5oFKpJO8/+jy50Ycf+72hSb0m+WoGREREAIDOnYGOHTO/A5iW1pYlovyHxSwREeUdcjnw/0sBERFlB4tZIiKibMhsaSEikh4nrhARERGR3mIxS0REWlPArikmos+grd8XLGaJiOizyf9/2ayUlBSJkxCRvkj7fSH/zGX3OGeWiIg+m6GhIczNzfHy5UsYGRlpffkllUqFlJQUJCUlcWknPcT+03/a7kOVSoWXL1/C3NwchoafV46ymCUios8mk8lgb2+P8PBwjW+zmh1CCCQmJsLMzCzd7WVJP7D/9F9u9KGBgQFKly792cdjMUtERFphbGyMChUq5MpUA4VCgTNnzuDLL7/kjS/0EPtP/+VGHxobG2tllJfFLBERaY2BgUHO7wD2EXK5HKmpqTA1NWUxpIfYf/ovL/chJ64QERERkd5iMUtEREREeovFLBERERHprQI3ZzZtgd7Y2FidnVOhUCAhIQGxsbF5bp4JfRr7T/+xD/Uf+1C/sf/0n677MK1Oy86NFQpcMRsXFwcAKFWqlMRJiIiIiOhj4uLiYG1t/dFtZKKA3XtQpVLh2bNnKFSokM7WuouNjUWpUqXw+PFjWFlZ6eScpD3sP/3HPtR/7EP9xv7Tf7ruQyEE4uLi4ODg8MnluwrcyKyBgQFKliwpybmtrKz4JtZj7D/9xz7Uf+xD/cb+03+67MNPjcim4QVgRERERKS3WMwSERERkd5iMasDJiYmmDZtGkxMTKSOQjnA/tN/7EP9xz7Ub+w//ZeX+7DAXQBGRERERPkHR2aJiIiISG+xmCUiIiIivcViloiIiIj0FotZIiIiItJbLGa1YMWKFXB2doapqSlcXV1x9uzZj25/+vRpuLq6wtTUFGXLlsWqVat0lJSyokkf7tmzBx4eHihRogSsrKzg7u6OY8eO6TAtZUbT92Ga8+fPw9DQELVq1crdgPRJmvZhcnIypkyZAicnJ5iYmKBcuXLYsGGDjtLShzTtP39/f9SsWRPm5uawt7dH37598erVKx2lpQ+dOXMG7du3h4ODA2QyGfbt2/fJffJMPSPos2zfvl0YGRmJtWvXips3b4pRo0YJCwsL8fDhw0y3v3//vjA3NxejRo0SN2/eFGvXrhVGRkZi165dOk5OaTTtw1GjRom5c+eKK1euiDt37ohJkyYJIyMjce3aNR0npzSa9mGaN2/eiLJlywpPT09Rs2ZN3YSlTOWkDzt06CDq1asngoKCRHh4uLh8+bI4f/68DlNTGk377+zZs8LAwEAsWbJE3L9/X5w9e1ZUq1ZNdOrUScfJKc3hw4fFlClTxO7duwUAsXfv3o9un5fqGRazn6lu3bpiyJAh6doqV64sJk6cmOn2EyZMEJUrV07XNnjwYFG/fv1cy0gfp2kfZqZq1apixowZ2o5G2ZTTPuzWrZv44YcfxLRp01jMSkzTPjxy5IiwtrYWr1690kU8+gRN+2/+/PmibNmy6dqWLl0qSpYsmWsZKfuyU8zmpXqG0ww+Q0pKCkJCQuDp6Zmu3dPTExcuXMh0n4sXL2bYvmXLlggODoZCoci1rJS5nPThh1QqFeLi4lC0aNHciEifkNM+3LhxI+7du4dp06bldkT6hJz04R9//AE3NzfMmzcPjo6OqFixIsaNG4fExERdRKb35KT/GjRogCdPnuDw4cMQQuD58+fYtWsX2rZtq4vIpAV5qZ4x1OnZ8pmoqCgolUrY2tqma7e1tUVkZGSm+0RGRma6fWpqKqKiomBvb59reSmjnPThhxYsWID4+Hh07do1NyLSJ+SkD+/evYuJEyfi7NmzMDTkr0Gp5aQP79+/j3PnzsHU1BR79+5FVFQUfH198fr1a86b1bGc9F+DBg3g7++Pbt26ISkpCampqejQoQOWLVumi8ikBXmpnuHIrBbIZLJ0j4UQGdo+tX1m7aQ7mvZhmm3btmH69OkICAiAjY1NbsWjbMhuHyqVSvTo0QMzZsxAxYoVdRWPskGT96FKpYJMJoO/vz/q1q2LNm3aYOHChfDz8+PorEQ06b+bN29i5MiRmDp1KkJCQnD06FGEh4djyJAhuohKWpJX6hkOSXyG4sWLQy6XZ/jL88WLFxn+WkljZ2eX6faGhoYoVqxYrmWlzOWkD9MEBASgf//+2LlzJ1q0aJGbMekjNO3DuLg4BAcHIzQ0FMOHDwfwrjASQsDQ0BCBgYFo3ry5TrLTOzl5H9rb28PR0RHW1tbqtipVqkAIgSdPnqBChQq5mpn+Jyf9N2fOHDRs2BDjx48HANSoUQMWFhZo3LgxfvrpJ35KqQfyUj3DkdnPYGxsDFdXVwQFBaVrDwoKQoMGDTLdx93dPcP2gYGBcHNzg5GRUa5lpczlpA+BdyOyPj4+2Lp1K+d4SUzTPrSyssKNGzcQFham/hoyZAgqVaqEsLAw1KtXT1fR6f/l5H3YsGFDPHv2DG/fvlW33blzBwYGBihZsmSu5qX0ctJ/CQkJMDBIX4LI5XIA/xvdo7wtT9UzOr/kLJ9JW45k/fr14ubNm2L06NHCwsJCPHjwQAghxMSJE4W3t7d6+7SlLMaMGSNu3rwp1q9fz6W5JKZpH27dulUYGhqK3377TURERKi/3rx5I9VLKPA07cMPcTUD6Wnah3FxcaJkyZKiS5cu4p9//hGnT58WFSpUEAMGDJDqJRRomvbfxo0bhaGhoVixYoW4d++eOHfunHBzcxN169aV6iUUeHFxcSI0NFSEhoYKAGLhwoUiNDRUvbxaXq5nWMxqwW+//SacnJyEsbGxqF27tjh9+rT6uT59+ogmTZqk2/7UqVPCxcVFGBsbizJlyoiVK1fqODF9SJM+bNKkiQCQ4atPnz66D05qmr4P38diNm/QtA9v3bolWrRoIczMzETJkiXF2LFjRUJCgo5TUxpN+2/p0qWiatWqwszMTNjb24uePXuKJ0+e6Dg1pTl58uRH/23Ly/WMTAiO5xMRERGRfuKcWSIiIiLSWyxmiYiIiEhvsZglIiIiIr3FYpaIiIiI9BaLWSIiIiLSWyxmiYiIiEhvsZglIiIiIr3FYpaIiIiI9BaLWSIiAH5+fihcuLDUMXKsTJkyWLx48Ue3mT59OmrVqqWTPEREusJilojyDR8fH8hksgxf//33n9TR4Ofnly6Tvb09unbtivDwcK0c/+rVqxg0aJD6sUwmw759+9JtM27cOBw/flwr58vKh6/T1tYW7du3xz///KPxcfT5jwsi0h0Ws0SUr7Rq1QoRERHpvpydnaWOBQCwsrJCREQEnj17hq1btyIsLAwdOnSAUqn87GOXKFEC5ubmH93G0tISxYoV++xzfcr7r/PQoUOIj49H27ZtkZKSkuvnJqKCh8UsEeUrJiYmsLOzS/cll8uxcOFCfPHFF7CwsECpUqXg6+uLt2/fZnmcv/76C82aNUOhQoVgZWUFV1dXBAcHq5+/cOECvvzyS5iZmaFUqVIYOXIk4uPjP5pNJpPBzs4O9vb2aNasGaZNm4a///5bPXK8cuVKlCtXDsbGxqhUqRK2bNmSbv/p06ejdOnSMDExgYODA0aOHKl+7v1pBmXKlAEAfP3115DJZOrH708zOHbsGExNTfHmzZt05xg5ciSaNPm/du42pOl3jQP41+lGa+q/8kVlmqIy9EVFUmlGHCpDWWQslMqRIllaWmEPVm+aEAYhPlRQ9iJmiqFSLoSKyMfSgpYSaoVMEolSIjKjNHV6nReHfrS0J5PT0fP9wF7cD79717UbxsVvv3v/mrI8V6xYgYyMDHR3d6Ojo0OZ86P9qK+vR1JSEvr7+5U7vFlZWQCA4eFhZGZmYtGiRdDpdAgLC0N9ff0P4yGimY3FLBH9X1CpVDh37hza29tx5coV1NbWIjMz87vzTSYTfHx8YLPZ0NzcjOPHj0OtVgMA2traEBUVha1bt6K1tRXl5eVobGxEenr6b8Wk1WoBACMjI7BarTh48CAOHz6M9vZ2pKSkICkpCXV1dQCAa9euIT8/H5cuXYLdbseNGzewZMmSCde12WwAAIvFgp6eHqX9tcjISMyZMwfXr19X+kZHR1FRUQGTyTRleb5//x5Xr14FAOXzA368HxERESgoKFDu8Pb09ODIkSMAgKSkJDQ1NaGsrAytra2Ii4tDdHQ07Hb7L8dERDOMEBHNEImJieLq6io6nU55xcbGTji3oqJCvLy8lLbFYpF//vlHaXt4eEhRUdGE1+7cuVP27Nnj1Hf//n1RqVQyODg44TXfrv/y5UsJDw8XHx8fGRoakoiICNm9e7fTNXFxcWIwGEREJDc3V/R6vQwPD0+4vp+fn+Tn5yttAGK1Wp3mmM1mWbZsmdI+cOCArF+/XmnfuXNHNBqNvHv37o/yBCA6nU5mz54tAASAxMTETDj/i5/th4hIZ2enuLi4yKtXr5z6N2zYICdOnPjh+kQ0c7n93VKaiGhqrVu3DhcvXlTaOp0OAFBXV4fTp0/j2bNn+PDhAxwOBz5//oxPnz4pc7526NAhJCcno6SkBJGRkYiLi0NgYCAAoLm5GZ2dnSgtLVXmiwjGxsbQ1dWFkJCQCWPr7++Hu7s7RAQDAwMIDQ1FZWUlNBoNnj9/7nSACwDWrFmDs2fPAgDi4uJQUFCAgIAAREdHw2AwYPPmzXBzm/zXuMlkwurVq/H69Wt4e3ujtLQUBoMBc+fO/aM8PTw80NLSAofDgYaGBuTk5KCwsNBpzu/uBwC0tLRARKDX6536h4aG/ivPAhPR/yYWs0Q0o+h0OgQFBTn1dXd3w2AwIDU1FadOncK8efPQ2NiIXbt2YWRkZMJ1srKyEB8fj5s3b+L27dswm80oKyuD0WjE2NgYUlJSnJ5Z/WLx4sXfje1LkadSqTB//vxxRZuLi4tTW0SUPl9fX3R0dODu3buorq7Gvn37kJOTg4aGBqef73/HqlWrEBgYiLKyMuzduxdWqxUWi0UZn2yeKpVK2YPg4GD09vZi27ZtuHfvHoDJ7ceXeFxdXdHc3AxXV1enMXd399/KnYhmDhazRDTjPX78GA6HA7m5uVCp/nNUoKKi4qfX6fV66PV6ZGRkYMeOHbBYLDAajQgNDcXTp0/HFc0/83WR962QkBA0NjYiISFB6Xvw4IHT3U+tVouYmBjExMQgLS0NwcHBaGtrQ2ho6Lj11Gr1L/1LQnx8PEpLS+Hj4wOVSoVNmzYpY5PN81sZGRnIy8uD1WqF0Wj8pf3QaDTj4l++fDlGR0fx5s0brF279o9iIqKZgwfAiGjGCwwMhMPhwPnz5/HixQuUlJSM+9n7a4ODg0hPT0d9fT26u7vR1NQEm82mFJbHjh3Dw4cPkZaWhidPnsBut6Oqqgr79++fdIxHjx5FUVERCgsLYbfbkZeXh8rKSuXgU1FRES5fvoz29nYlB61WCz8/vwnX8/f3R01NDXp7e9HX1/fd9zWZTGhpaUF2djZiY2Mxa9YsZWyq8vT09ERycjLMZjNE5Jf2w9/fHx8/fkRNTQ3evn2LgYEB6PV6mEwmJCQkoLKyEl1dXbDZbDhz5gxu3br1WzER0QzyNx/YJSKaSomJibJly5YJx/Ly8mThwoWi1WolKipKiouLBYD09fWJiPOBo6GhIdm+fbv4+vqKRqMRb29vSU9Pdzr09OjRI9m4caO4u7uLTqeTpUuXSnZ29ndjm+hA07cuXLggAQEBolarRa/XS3FxsTJmtVolLCxMPD09RafTSXh4uFRXVyvj3x4Aq6qqkqCgIHFzcxM/Pz8RGX8A7IuVK1cKAKmtrR03NlV5dnd3i5ubm5SXl4vIz/dDRCQ1NVW8vLwEgJjNZhERGR4elpMnT4q/v7+o1WpZsGCBGI1GaW1t/W5MRDSzuYiI/N1ymoiIiIhocviYARERERFNWyxmiYiIiGjaYjFLRERERNMWi1kiIiIimrZYzBIRERHRtMViloiIiIimLRazRERERDRtsZglIiIiommLxSwRERERTVssZomIiIho2mIxS0RERETT1r8B1nKZchNHS9wAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "     - AUC: 83.69%\n",
+      "    - Optimal Threshold: 0.0326394\n",
+      "    - F1 Score: 0.89\n",
+      "    - CONFUSION MATRIX:\n",
+      " [[16  4]\n",
+      " [12 68]] \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:04<00:00,  9.16it/s]\n",
+      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.15it/s]\n",
+      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.14it/s]\n",
+      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.14it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.12it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "- OK - Evaluating model (33.04 s)\n",
+      "\n",
+      "Dataset               F1 Score\n",
+      "------------------------------\n",
+      "Anonaly lvl 1 test        0.96\n",
+      "Anonaly lvl 2 test        1.00\n",
+      "Anonaly lvl 3 test        0.71\n",
+      "\n",
+      "Anomaly all test          0.92\n",
+      "\n",
+      "No Anomaly Test           0.89\n",
+      "\n",
+      "All test                  0.89\n"
+     ]
+    }
+   ],
+   "source": [
+    "# STEPS = 20, MODEL TYPE = MEDIUM, WEIGHT = on\n",
+    "model16 = EfficientAD({**config, \"train_steps\": 20, \"model_type\": \"medium\", \"weight_path\":\"../weights/teacher_medium.pth\"})\n",
+    "model16.create_model()\n",
+    "model16.display_eval_result()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- Setting seed to 42\n",
+      "- OK - Setting seed to 42 (0.45 ms)\n",
+      "\n",
+      "- Setting datasets path\n",
+      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
+      "- OK - Setting datasets path (4.02 ms)\n",
+      "\n",
+      "- Setting config\n",
+      "     Output folder path: ../output/cookies_3_steps_500_medium_weighted\n",
+      "- OK - Setting config (10.11 ms)\n",
+      "\n",
+      "- Prepare teacher, student & autoencoder\n",
+      "     Loading weight ../weights/teacher_medium.pth\n",
+      "     Training\n",
+      "- OK - Prepare teacher, student & autoencoder (176.04 ms)\n",
+      "\n",
+      "- Normalizing teacher\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 22.03it/s]\n",
+      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.70it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- OK - Normalizing teacher (6.59 s)\n",
+      "\n",
+      "- Train\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 68.6309  :   0%|▏                                                                                    | 1/500 [00:03<29:31,  3.55s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.95\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 20.9917  :  20%|████████████████▊                                                                  | 101/500 [00:39<08:24,  1.26s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.926829268292683\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 14.8370  :  40%|█████████████████████████████████▎                                                 | 201/500 [01:15<06:18,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8571428571428571\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 15.0840  :  60%|█████████████████████████████████████████████████▉                                 | 301/500 [01:52<04:12,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.918918918918919\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 12.1250  :  80%|██████████████████████████████████████████████████████████████████▌                | 401/500 [02:29<02:06,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.918918918918919\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 9.2769  : 100%|████████████████████████████████████████████████████████████████████████████████████| 500/500 [03:02<00:00,  2.74it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- OK - Train (182.58 s)\n",
+      "\n",
+      "- Saving models to ../output/cookies_3_steps_500_medium_weighted/all_models.pth\n",
+      "- OK - Saving models (210.89 ms)\n",
+      "\n",
+      "- Saving map normalization to ../output/cookies_3_steps_500_medium_weighted/map_normalization.pth\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 10.20it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- OK - Saving map normalization (2843.68 ms)\n",
+      "\n",
+      "- Evaluating model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:11<00:00,  9.09it/s]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIhCAYAAABdSTJTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACIVklEQVR4nOzdd1iT198G8DuBMBUcyFQR96oKuHDUiXvVOlBEcc86qFpH66y11bqto4qiFhXramsd4N4LoWq1ahU3qCgCMkNy3j98ya8IKMGEh8D9uS6uNifPuMMh+OXkPOeRCSEEiIiIiIgMkFzqAEREREREucViloiIiIgMFotZIiIiIjJYLGaJiIiIyGCxmCUiIiIig8ViloiIiIgMFotZIiIiIjJYLGaJiIiIyGCxmCUiIiIig8VilogoCwEBAZDJZJovY2NjODg4wMvLC3fu3MlyH6VSidWrV8PDwwPW1tYwNzdHtWrVMGXKFLx8+TLLfdRqNbZs2YLWrVvDxsYGCoUCtra26NSpE/744w+o1eoPZk1JScHKlSvRpEkTFC9eHCYmJnByckKvXr1w4sSJj/o+EBHldyxmiYjeY+PGjTh37hwOHz6MMWPG4Pfff0eTJk0QExOTYbvExER4enriiy++gKurK7Zt24b9+/fDx8cHP//8M1xdXXHr1q0M+yQnJ6NDhw4YMGAAbG1tsXr1ahw9ehRr1qyBo6MjevbsiT/++OO9+aKjo9G4cWP4+fmhZs2aCAgIwJEjR7Bo0SIYGRmhVatW+Ouvv3T+fSEiyjcEERFlsnHjRgFAXLp0KUP77NmzBQCxYcOGDO3Dhg0TAMT27dszHevWrVvC2tpa1KhRQ6SlpWnaR44cKQCITZs2ZZnh9u3b4q+//npvzvbt2wtjY2Nx5MiRLJ+/ePGiePDgwXuPkVOJiYk6OQ4RkS5xZJaISAt169YFADx79kzTFhUVhQ0bNqBt27bo3bt3pn0qV66Mr776Cn///Tf27t2r2Wf9+vVo27Yt+vfvn+W5KlWqhFq1amWbJTQ0FAcOHMDgwYPRsmXLLLepV68eypYtCwCYNWsWZDJZpm3Sp1Tcv39f01auXDl06tQJu3fvhqurK8zMzDB79my4urqiadOmmY6hUqng5OSE7t27a9pSU1Px7bffomrVqjA1NUWpUqUwcOBAvHjxItvXRESkLRazRERaiIiIAPC2QE137NgxpKWloVu3btnul/5cSEiIZh+lUvnefT4kODg4w7F17cqVK5g0aRLGjh2LgwcP4vPPP8fAgQNx+vTpTPOGg4OD8fTpUwwcOBDA27nAXbt2xffff4++ffvizz//xPfff4+QkBA0b94cSUlJeslMRIWPsdQBiIjyM5VKhbS0NCQnJ+PMmTP49ttv8emnn6JLly6abR4+fAgAcHFxyfY46c+lb5uTfT5EF8d4n+fPn+PGjRsZCvfy5ctj0qRJCAgIwLx58zTtAQEBsLOzQ/v27QEAO3bswMGDB7Fr164Mo7W1a9dGvXr1EBAQgJEjR+olNxEVLhyZJSJ6j4YNG0KhUKBo0aJo164dihcvjt9++w3GxrkbC8jqY/78qlatWhkKWQAoWbIkOnfujE2bNmlWWoiJicFvv/2G/v37a74v+/btQ7FixdC5c2ekpaVpvurUqQN7e3scP348r18OERVQLGaJiN5j8+bNuHTpEo4ePYrhw4fj5s2b6NOnT4Zt0uekpk9ByEr6c2XKlMnxPh+ii2O8j4ODQ5btgwYNwpMnTzRTJrZt24aUlBT4+vpqtnn27Blev34NExMTKBSKDF9RUVGIjo7WS2YiKnxYzBIRvUe1atVQt25dtGjRAmvWrMGQIUNw8OBB7Ny5U7NNixYtYGxsrLm4Kyvpz3l6emr2USgU793nQ9q2bZvh2B9iZmYG4O26tP+VXWGZ3Shy27Zt4ejoiI0bNwJ4u3xZgwYNUL16dc02NjY2KFmyJC5dupTl16pVq3KUmYjoQ1jMEhFpYcGCBShevDhmzJih+Zjd3t4egwYNwqFDhxAUFJRpn9u3b+OHH35AjRo1NBdr2dvbY8iQITh06BA2b96c5bnu3r2Lq1evZpvFzc0N7du3h7+/P44ePZrlNpcvX9bMrS1XrhwAZDrmh9ayfZeRkRF8fHywd+9enDp1CpcvX8agQYMybNOpUye8fPkSKpUKdevWzfRVpUoVrc5JRJQdmRBCSB2CiCi/CQgIwMCBA3Hp0iXNclzpFi5ciMmTJ2PLli3o168fACAhIQEdO3bEmTNnMGzYMHTu3BmmpqY4f/48fvzxR1hYWODw4cMZirjk5GR069YNwcHB6NOnDz777DPY2dkhOjoaISEh2LhxI7Zv346uXbtmmzM6Ohrt2rXDtWvXMGjQILRv3x7FixdHZGQk/vjjD2zbtg2hoaGoXbs24uLi4OLiAicnJ8yZMwfGxsYICAjAlStXEBERgYiICE3BW65cOdSsWRP79u3L8ry3b99GlSpVULp0abx8+RKRkZGwtrbWPK9SqdC5c2dcuHAB48aNQ/369aFQKPD48WMcO3YMXbt2xWeffZbb7iEi+h+pF7olIsqPsrtpghBCJCUlibJly4pKlSpluAlCamqq+Omnn0SDBg1EkSJFhKmpqahSpYqYPHmyiI6OzvI8aWlpYtOmTaJly5aiRIkSwtjYWJQqVUq0b99ebN26VahUqg9mTUpKEsuXLxceHh7CyspKGBsbC0dHR9G9e3fx559/Ztj24sWLolGjRsLS0lI4OTmJmTNnivXr1wsAIiIiQrOds7Oz6Nix43vP26hRIwFAeHt7Z/m8UqkUP/74o6hdu7YwMzMTRYoUEVWrVhXDhw8Xd+7c+eDrIiLKCY7MEhEREZHB4pxZIiIiIjJYLGaJiIiIyGCxmCUiIiIig8ViloiIiIgMFotZIiIiIjJYLGaJiIiIyGAZSx0gr6nVajx9+hRFixbN9laNRERERCQdIQTi4+Ph6OgIufz9Y6+Frph9+vQpypQpI3UMIiIiIvqAR48eoXTp0u/dptAVs0WLFgXw9ptjZWWVJ+dUKpUIDg5GmzZtoFAo8uScpDvsP8PHPjR87EPDxv4zfHndh3FxcShTpoymbnufQlfMpk8tsLKyytNi1sLCAlZWVnwTGyD2n+FjHxo+9qFhY/8ZPqn6MCdTQnkBGBEREREZLBazRERERGSwWMwSERERkcFiMUtEREREBovFLBEREREZLBazRERERGSwWMwSERERkcFiMUtEREREBovFLBEREREZLBazRERERGSwWMwSERERkcFiMUtEREREBovFLBEREREZLBazRERERGSwJC1mT548ic6dO8PR0REymQx79+794D4nTpyAu7s7zMzMUL58eaxZs0b/QYmIiIgoX5K0mE1ISEDt2rWxcuXKHG0fERGBDh06oGnTpggLC8O0adMwduxY7Nq1S89JiYiIiCg/Mpby5O3bt0f79u1zvP2aNWtQtmxZLF26FABQrVo1XL58GT/++CM+//xzPaWk/EYIgSSlKs/Op1SmIUUFJKamQSFkeXZe0h32oeFjHxo29p/hS0pKRorq7b/B+Y2kxay2zp07hzZt2mRoa9u2Lfz9/aFUKqFQKDLtk5KSgpSUFM3juLg4AIBSqYRSqdRv4P+Xfp68Ol9BJoSA1/pLuPLwdR6f2RiTLx7N43OSbrEPDR/70LCx/wxV4p0LiDm6HnZe36JlyxRYy/T/B4k2NZNBFbNRUVGws7PL0GZnZ4e0tDRER0fDwcEh0z7z58/H7NmzM7UHBwfDwsJCb1mzEhISkqfnK4hSVMCVhwb1Y0tERGSQhEqJmOMBiL/8GwAg9tyvOHq0JEyN9H/uxMTEHG9rcFWB7J2/BtKHu99tTzd16lT4+flpHsfFxaFMmTJo06YNrKys9Bf0P5RKJUJCQuDp6Znl6DHlXGJqmuYv+/NfNYO5if7fUUplGo4ePYqWLVtCoTC4twyBfVgQsA8NG/vP8Ny/fx+Dffvj4eXLAIDhI0fh02Yt0LFta5iYmOj9/OmfpOeEQf1E2dvbIyoqKkPb8+fPYWxsjJIlS2a5j6mpKUxNTTO1KxSKPC8spThnQfPfuVZWlmawMNH/j7BSqYSpEWBtacb+M1DsQ8PHPjRs7D/Dsnv3bgwaNAixsbEoXrw4AgIC0L59e+zfvx8mJiZ50ofanMOg1pn18PDI9FF9cHAw6tatyzcHERER0UeKiYnB4MGDERsbCw8PD4SFhaFLly5Sx3ovSYvZN2/eIDw8HOHh4QDeLr0VHh6Ohw8fAng7RaB///6a7UeMGIEHDx7Az88PN2/exIYNG+Dv74+JEydKEZ+IiIioQClevDg2btyIyZMn48SJE3B2dpY60gdJOs3g8uXLaNGiheZx+tzWAQMGICAgAJGRkZrCFgBcXFywf/9+TJgwAT/99BMcHR2xfPlyLstFRERElEs7duyAlZUV2rVrBwDo1q0bunXrJm0oLUhazDZv3vy965UFBARkamvWrBmuXLmix1REREREBV9SUhL8/PywZs0alCxZElevXoWjo6PUsbRmUBeAEREREdHHu3XrFnr16oWrV69CJpNhxIgRsLW1lTpWrrCYJSIiIipEAgMDMXz4cCQkJMDW1ha//PILPD09pY6VayxmiYiIiAoBlUqF4cOHw9/fHwDQokULBAYGZnnTKUNiUEtzEREREVHuGBm9vdGQTCbDrFmzEBISYvCFLMCRWSIiIqICLTk5GWZmZgCA5cuXw9fXF02aNJE4le5wZJaIiIioAHrz5g0GDBiArl27Qq1WAwAsLCwKVCELcGSWiIiIqMC5du0aevXqhX/++QdyuRznz59Ho0aNpI6lFxyZJSIiIioghBBYt24d6tevj3/++QdOTk44fvx4gS1kAY7MEhERERUIcXFxGD58OLZv3w4AaN++PTZv3gwbGxuJk+kXR2aJiIiICgAvLy9s374dRkZGWLBgAfbt21fgC1mAI7NEREREBcK8efPw77//YtOmTfDw8JA6Tp7hyCwRERGRAYqNjcWBAwc0j11dXXHjxo1CVcgCLGaJiIiIDM7ly5fh5uaGrl274vLly5p2Y+PC96E7i1kiIiIiAyGEwLJly9CoUSPcu3cPTk5OUkeSXOEr34mIiIgMUExMDAYNGoS9e/cCALp37w5/f38UK1ZM0lxS48gsERERUT534cIFuLq6Yu/evTAxMcGKFSuwc+fOQl/IAhyZJSIiIsr3Tpw4gQcPHqBChQrYsWMH3NzcpI6Ub7CYpY8mhECSUpUn50pMzZvzEBER5ScTJ06ETCbD8OHDYWVlJXWcfIXFLH0UIQR6rDmH0AcxUkchIiIqME6fPo25c+di9+7dsLS0hFwux6RJk6SOlS9xzix9lCSlSpJCtq5zcZgrjPL8vERERPqkVqsxf/58NG/eHMHBwZg3b57UkfI9jsySzlz+ujUsTPKmwDRXGEEmk+XJuYiIiPLC8+fP4ePjg+DgYABAv379MG3aNIlT5X8sZklnLEyMYGHCHykiIiJtHT9+HH379kVkZCTMzc2xcuVKDBw4kAM3OcDKg4iIiEhCv/zyCwYMGAC1Wo3q1atjx44dqFGjhtSxDAbnzBIRERFJqGXLlihZsiQGDhyIixcvspDVEkdmiYiIiPLY7du3UblyZQCAo6Mj/vrrLzg4OEicyjBxZJaIiIgoj6SlpWHGjBmoVq0adu3apWlnIZt7LGaJiIiI8sCTJ0/QqlUrzJ07F2q1GufPn5c6UoHAaQZEREREenbw4EH4+PggOjoaRYoUwbp16+Dl5SV1rAKBI7NEREREeqJUKjFlyhS0b98e0dHRcHV1xZUrV1jI6hCLWSIiIiI9OXnyJH744QcAwOjRo3H27FlUqlRJ4lQFC6cZEBEREelJq1atMG3aNLi6uqJHjx5SxymQODJLREREpCOpqan45ptv8OTJE03bvHnzWMjqEUdmiYiIiHTg/v376N27Ny5evIhTp07h2LFjvB1tHmAxWwAJIZCkVOXJuRJT8+Y8RERE+dmePXswaNAgvH79GsWKFcOECRNYyOYRFrMFjBACPdacQ+iDGKmjEBERFXgpKSmYNGkSVqxYAQBo2LAhtm/fDmdnZ4mTFR4sZguYJKVKkkK2rnNxmCuM8vy8REREUnny5Am6du2K0NBQAMCkSZMwb948KBQKiZMVLixmC7DLX7eGhUneFJjmCiN+nEJERIVKsWLFkJSUhJIlS2LTpk3o2LGj1JEKJRazBZiFiREsTNjFREREupKcnAwTExPI5XJYWlpiz549sLCwQOnSpaWOVmhxaS4iIiKiHLh16xYaNGiABQsWaNoqV67MQlZiLGaJiIiIPiAwMBDu7u64evUqli9fjoSEBKkj0f9jMUtERESUjcTERAwZMgT9+vVDQkICmjdvjsuXL8PS0lLqaPT/WMwSERERZeHmzZto0KAB/P39IZPJMHPmTBw+fBiOjo5SR6P/4NVBRERERO+Ii4tD48aNERMTA3t7ewQGBqJly5ZSx6IscGSWiIiI6B1WVlaYM2cOWrdujfDwcBay+RiLWSIiIiIA165dQ3h4uObx6NGjcejQIdjZ2UkXij6IxSwREREVakIIrFu3DvXr10ePHj0QFxcHAJDJZJDLWSrld5wzS0RERIVWfHw8hg8fjm3btgEAKlWqBKVSKXEq0gb/3CAiIqJCKTw8HO7u7ti2bRuMjIzw/fff488//0TJkiWljkZa4MgsERERFSpCCKxZswYTJkxASkoKypQpg+3bt6NRo0ZSR6Nc4MgsERERFSpCCPz+++9ISUlB586dERYWxkLWgHFkloiIiAoVuVyOzZs349dff8XIkSMhk8mkjkQfgSOz+qZSAadPv/3/06ffPiYiIqI8I4TAsmXLMHLkSE1bqVKlMGrUKBayBQCLWX3avRsoVw7o2PHt444d3z7evVvKVERERIVGTEwMunfvjvHjx2PNmjU4evSo1JFIx1jM6svu3UCPHsDjxxnbnzx5286CloiISK8uXLgAV1dX7N27FyYmJlixYgVatGghdSzSMc6Z1QeVChg3DhACACAApKiARGNTKIzVgEwGfDkZaN8RMDLS6akTUzmNgYiICjchBBYvXowpU6YgLS0NFSpUQFBQENzd3aWORnrAYlYfTp3SjMgKAF495uLKRWNMHvVLxu1mH877bERERAXcoEGDEBAQAADo1asXfv75Z1hbW0sbivSG0wz0ITJS879JClNccaya5xHqOheHuUK3o75ERESGoHfv3jA3N8fq1auxfft2FrIFHEdm9cHBIcvm8+sGwyr+9f8a9h8APm2qlwjmCiNeoUlERIWCWq3G7du3UbXq28Gjdu3aISIiAnZ2dhIno7zAYlYfmjYFSpd+e7HXf5grU2ChTHk7Z7Z0aaDFpzqfM0tERFSYPH/+HP3798f58+cRFhYGFxcXAGAhW4hwmoE+GBkBy5a9/f93R0fTHy9dykKWiIjoI5w4cQJ16tTBoUOHkJqaimvXrkkdiSTAYlZfuncHdu4EHBwztpcu/ba9e3dpchERERk4lUqFOXPmoGXLloiMjES1atVw8eJFdOnSRepoJAEWs/rUvTtw88b/Hu/aBUREsJAlIiLKpaioKLRt2xYzZ86EWq2Gr68vLl26hJo1a0odjSTCObP69t+pBI08OLWAiIjoIyxbtgxHjhyBhYUFVq9ejf79+0sdiSTGYpaIiIgMxsyZM/H48WNMnz5ds3oBFW6cZkBERET51pMnTzBx4kSkpaUBAMzMzLBlyxYWsqTBkVkiIiLKlw4ePAgfHx9ER0fDysoKM2bMkDoS5UMcmSUiIqJ8RalUYurUqWjfvj2io6NRp04deHl5SR2L8imOzBIREVG+8ejRI3h5eeHs2bMAgFGjRmHRokUwMzOTOBnlVyxmiYiIKF84cuQIevXqhVevXsHKygrr169Hz549pY5F+RyLWSIiIsoX7O3tkZSUBHd3dwQFBaFChQpSRyIDwGKWiIiIJJOQkABLS0sAQI0aNXDkyBG4ubnB1NRU4mRkKHgBGBEREUli7969KFeunGZ+LAB4eHiwkCWtsJglIiKiPJWSkoJx48bhs88+Q3R0NJYsWSJ1JDJgkhezq1atgouLC8zMzODu7o5Tp069d/vAwEDUrl0bFhYWcHBwwMCBA/Hy5cs8SktEREQf4+7du2jcuDGWL18OAJg4cSK2bt0qcSoyZJIWs0FBQRg/fjymT5+OsLAwNG3aFO3bt8fDhw+z3P706dPo378/Bg8ejL///hu//vorLl26hCFDhuRxciIiItLWr7/+CldXV4SGhqJEiRLYt28fFi5cCIVCIXU0MmCSFrOLFy/G4MGDMWTIEFSrVg1Lly5FmTJlsHr16iy3P3/+PMqVK4exY8fCxcUFTZo0wfDhw3H58uU8Tk5ERETauHbtGry9vREfH4/GjRsjPDwcHTt2lDoWFQCSrWaQmpqK0NBQTJkyJUN7mzZtMkwE/69GjRph+vTp2L9/P9q3b4/nz59j586d730zpKSkICUlRfM4Li4OwNu7iyiVSh28kvdTKtMy/H9enJN0K73P2HeGi31o+NiHhk2pVKJmzZro1q0bKleujFmzZsHY2Jj9aUDy+j2ozXkkK2ajo6OhUqlgZ2eXod3Ozg5RUVFZ7tOoUSMEBgaid+/eSE5ORlpaGrp06YIVK1Zke5758+dj9uzZmdqDg4NhYWHxcS8iB1JUQPq3+ejRozA10vspSU9CQkKkjkAfiX1o+NiHhuXs2bOoU6cOLCwsIJPJ0L9/f8jlcgQHB0sdjXIpr96DiYmJOd5W8nVmZTJZhsdCiExt6W7cuIGxY8dixowZaNu2LSIjIzFp0iSMGDEC/v7+We4zdepU+Pn5aR7HxcWhTJkyaNOmDaysrHT3QrKRmJqGyRePAgBatmwJa0vejs/QKJVKhISEwNPTk/O6DBT70PCxDw1LYmIi/Pz8sGHDBvTs2RMbN27E4cOH0bZtW/afgcrr92D6J+k5IVkxa2NjAyMjo0yjsM+fP880Wptu/vz5aNy4MSZNmgQAqFWrFiwtLdG0aVN8++23cHBwyLSPqalpluvVKRSKPOkMhfhfYa5QGPNNbMDy6meG9Id9aPjYh/nfzZs30atXL1y/fh0ymQzVqlWDsfHbcoP9Z/jyrH7S4hySXQBmYmICd3f3TMPVISEhaNSoUZb7JCYmQi7PGNnI6O3n9kII/QQlIiKiHNm0aRPq1q2L69evw87ODiEhIZg9e3amf7uJdEnSny4/Pz+sX78eGzZswM2bNzFhwgQ8fPgQI0aMAPB2ikD//v0123fu3Bm7d+/G6tWrce/ePZw5cwZjx45F/fr14ejoKNXLICIiKtQSEhLg6+sLX19fJCYmolWrVggPD0erVq2kjkaFgKRzZnv37o2XL19izpw5iIyMRM2aNbF//344OzsDACIjIzOsOevr64v4+HisXLkSX375JYoVK4aWLVvihx9+kOolEBERFXqJiYkIDg6GXC7H7NmzMXXqVM0np0T6JvkFYKNGjcKoUaOyfC4gICBT2xdffIEvvvhCz6mIiIgop0qVKoWgoCCo1Wo0a9ZM6jhUyHASCxEREWklPj4e3t7eCAwM1LQ1bdqUhSxJgsUsERER5Vh4eDjc3d2xdetWjBkzRqsllIj0gcUsERERfZAQAqtXr0bDhg1x584dlC5dGvv27cuTNduJ3kfyObNERESUv8XGxmLo0KH49ddfAQCdOnVCQEAASpYsKXEyIhazRERE9B4JCQlwd3fH3bt3YWxsjB9++AETJkzI9m6dRHmN0wyIiIgoW5aWlvj888/h7OyM06dPw8/Pj4Us5SssZomIiCiDmJgYPH78WPP422+/RVhYGBo0aCBhKqKssZglIiIijQsXLsDV1RU9evSAUqkEACgUChQvXlziZERZYzFLREREEEJg0aJFaNKkCR48eIAXL17gyZMnUsci+iAWs0RERIXcy5cv0aVLF0ycOBFpaWno2bMnrly5gnLlykkdjeiDWMwSEREVYmfOnEGdOnWwb98+mJqaYvXq1QgKCoK1tbXU0YhyhEtzERERFVJCCEyYMAGPHz9GpUqVsGPHDtSpU0fqWERa4cgsERFRISWTyRAYGIjBgwcjNDSUhSwZJBazREREhciJEyewbNkyzeNKlSph/fr1KFq0qISpiHKP0wyIiIgKAZVKhe+++w6zZs2CEAJubm5o2rSp1LGIPhqLWSIiogIuKioK/fr1w5EjRwAAAwYMgJubm8SpiHSDxSwREVEBduTIEXh7e+PZs2ewsLDAqlWrMGDAAKljEekM58wSEREVUPPnz4enpyeePXuGmjVr4tKlSyxkqcBhMUtERFRA2draQgiBIUOG4MKFC6hevbrUkYh0jtMMiIiICpA3b96gSJEiAIBBgwahSpUqaNKkicSpiPSHI7NEREQFQFpaGqZOnYqaNWvi1atXAN6uI8tClgo6FrNEREQG7tGjR2jevDm+//57PHjwALt27ZI6ElGeYTFLRERkwP7880/UqVMHZ86cgZWVFYKCgjB06FCpYxHlGRazREREBig1NRUTJ05Ep06d8OrVK7i7u+PKlSvo1auX1NGI8hSLWSIiIgM0a9YsLFq0CAAwduxYnDlzBhUqVJA4FVHeYzFLRERkgCZOnIjatWtj9+7dWLZsGUxNTaWORCQJFrNEREQGICUlBZs3b4YQAgBQokQJXLlyBZ999pnEyYikxXVmiYiI8rl79+6hV69eCA0NRUpKiuYCL7mcY1JEfBcQERHlYzt37oSrqytCQ0NRokQJODg4SB2JKF9hMUtERJQPJScnY/To0ejZsyfi4uLQqFEjhIeHo1OnTlJHI8pXWMwSERHlM3fu3IGHhwdWrVoFAJgyZQqOHz+OMmXKSJyMKP/hnFkiIqJ85vHjx/jrr79gY2ODLVu2oF27dlJHIsq3WMwSERHlA0IIyGQyAECLFi0QEBCAVq1awcnJSeJkRPkbpxkQERFJ7ObNm2jSpAlu376taevfvz8LWaIcYDFLREQkoU2bNqFu3bo4e/Ysxo4dK3UcIoPDYpaIiEgCCQkJ8PX1ha+vLxITE9GyZUsEBARIHYvI4LCYJSIiymPXr19HvXr1sGnTJsjlcsyZMwfBwcGwt7eXOhqRweEFYERERHnowoULaNGiBZKSkuDg4ICtW7eiefPmUsciMlgsZomIiPKQm5sbateuDSsrK2zZsgW2trZSRyIyaCxmiYiI9OzGjRuoVKkSFAoFFAoF/vzzTxQrVgxyOWf7EX0svouIiIj0RAiBNWvWwM3NDdOnT9e0lyhRgoUskY5wZJaIiEgP4uLiMHToUOzYsQPA27VkVSoVjIyMJE5GVLDwz0IiIiIdCw0NhZubG3bs2AFjY2MsXLgQv/32GwtZIj3gyCwREZGOCCGwcuVKTJw4EampqXB2dsb27dvRsGFDqaMRFVgcmSUiItKRJ0+eYNq0aUhNTUW3bt0QFhbGQpZIzzgyS0REpCOlS5fGunXr8Pz5c3zxxReQyWRSRyIq8FjMEhER5ZIQAkuWLIGrqytatGgBAPDy8pI4FVHhwmKWiIgoF169egVfX1/88ccfsLe3x40bN1C8eHGpYxEVOixmiYiItHT27Fl4eXnh0aNHMDU1xYwZM1CsWDGpYxEVSrwAjIiIKIfUajV++OEHfPrpp3j06BEqVaqE8+fPY+TIkZwfSyQRjswSERHlQFJSEj7//HMcOHAAANCnTx+sXbsWRYsWlTgZUeHGkVkiIqIcMDMzQ7FixWBmZoaff/4ZgYGBLGSJ8gEWs0RERNlQqVRISEgAAMhkMqxduxaXLl3C0KFDOa2AKJ9gMUtERJSFZ8+eoV27dujXrx+EEACAokWLombNmhInI6L/4pxZIiKidxw9ehTe3t6IioqChYUF/vnnH1SrVk3qWESUBY7MEhER/T+VSoWZM2eidevWiIqKQo0aNXDp0iUWskT5GEdmiYiIADx9+hTe3t44fvw4AGDw4MFYvnw5LCwspA1GRO/FYpaIiAo9IQS6du2Ky5cvw9LSEmvXroW3t7fUsYgoB3I1zSAtLQ2HDx/G2rVrER8fD+DtX7Rv3rzRaTgiIqK8IJPJsHz5cri7u+PKlSssZIkMiNYjsw8ePEC7du3w8OFDpKSkwNPTE0WLFsWCBQuQnJyMNWvW6CMnERGRTj1+/Bjh4eHo1KkTAMDDwwOXLl3ikltEBkbrkdlx48ahbt26iImJgbm5uab9s88+w5EjR3QajoiISB/279+POnXqoFevXrh+/bqmnYUskeHRemT29OnTOHPmDExMTDK0Ozs748mTJzoLRkREpGtKpRLTp0/HwoULAQBubm4ZBmaIyPBoXcyq1WqoVKpM7Y8fP+Zt/YiIKN968OABvLy8cP78eQDAF198gYULF8LU1FTiZET0MbSeZuDp6YmlS5dqHstkMrx58wYzZ85Ehw4ddJmNiIhIJ3777Te4urri/PnzsLa2xq5du7B8+XIWskQFgNYjs0uWLEGLFi1QvXp1JCcno2/fvrhz5w5sbGywbds2fWQkIiL6KFeuXEFMTAzq16+P7du3w8XFRepIRKQjWhezjo6OCA8Px/bt2xEaGgq1Wo3BgwfD29ub846IiCjfEEJoLuiaMWMGbG1tMXTo0EzXfBCRYdN6msHJkyehUCgwcOBArFy5EqtWrcKQIUOgUChw8uRJfWQkIiLSyq5du9CyZUskJycDAIyMjDB69GgWskQFkNbFbIsWLfDq1atM7bGxsWjRooVOQhEREeVGcnIyxowZgx49euD48eP46aefpI5ERHqm9TSD/35s818vX76EpaWlTkIRERFp686dO+jduzfCwsIAAF999RXGjh0rcSoi0rccF7Pdu3cH8Hb1Al9f3wxXgKpUKly9ehWNGjXSfUIiIqIP2L59O4YOHYo3b97AxsYGmzdvRvv27aWORUR5IMfFrLW1NYC3I7NFixbNcLGXiYkJGjZsiKFDh+o+IRER0XssWrQIEydOBAA0bdoU27Ztg5OTk8SpiCiv5LiY3bhxIwCgXLlymDhxIqcUEBFRvvD555/ju+++w6hRozBz5kwYG2s9g46IDJjW7/iZM2fqIwcREVGOhYWFwdXVFcDbQZY7d+6gRIkSEqciIilovZoBAOzcuRO9evVCw4YN4ebmluFLW6tWrYKLiwvMzMzg7u6OU6dOvXf7lJQUTJ8+Hc7OzjA1NUWFChWwYcOG3LwMIiIyMAkJCRg0aBDc3Nywf/9+TTsLWaLCS+tidvny5Rg4cCBsbW0RFhaG+vXro2TJkrh3757Wk+2DgoIwfvx4TJ8+HWFhYWjatCnat2+Phw8fZrtPr169cOTIEfj7++PWrVvYtm0bqlatqu3LICIiA/Pw4UM0atQIGzduhFwux61bt6SORET5gNbTDFatWoWff/4Zffr0waZNmzB58mSUL18eM2bMyHL92fdZvHgxBg8ejCFDhgAAli5dikOHDmH16tWYP39+pu0PHjyIEydO4N69e5q/wsuVK6ftSyAiIgMihEBAQAAmTpyI1NRU2NvbY9u2bWjevLnU0YgoH9C6mE3/yxgAzM3NER8fDwDw8fFBw4YNsXLlyhwdJzU1FaGhoZgyZUqG9jZt2uDs2bNZ7vP777+jbt26WLBgAbZs2QJLS0t06dIFc+fOzfZWuikpKUhJSdE8jouLAwAolUoolcocZf0YSmVahv/Pi3OSbqX3GfvOcLEPDdebN28wZswYbN26FQDQqlUrbNq0Cba2tuxPA8L3oOHL6z7U5jxaF7P29vZ4+fIlnJ2d4ezsjPPnz6N27dqIiIiAECLHx4mOjoZKpYKdnV2Gdjs7O0RFRWW5z71793D69GmYmZlhz549iI6OxqhRo/Dq1ats583Onz8fs2fPztQeHBwMCwuLHOfNrRQVkP5tPnr0KEyN9H5K0pOQkBCpI9BHYh8annPnzmHr1q2Qy+Xo27cvunfvjsuXL0sdi3KJ70HDl1d9mJiYmONttS5mW7ZsiT/++ANubm4YPHgwJkyYgJ07d+Ly5cuaGyto4927iWV3hzEAUKvVkMlkCAwM1Kx7u3jxYvTo0QM//fRTlqOzU6dOhZ+fn+ZxXFwcypQpgzZt2sDKykrrvNpKTE3D5ItHAbz93llbmun9nKRbSqUSISEh8PT0hEKhkDoO5QL70HB16NABQgh4enoiISGBfWig+B40fHndh+mfpOeE1sXszz//DLVaDQAYMWIESpQogdOnT6Nz584YMWJEjo9jY2MDIyOjTKOwz58/zzRam87BwQFOTk6aQhYAqlWrBiEEHj9+jEqVKmXax9TUNMPdytIpFIo86QyF+F9hrlAY801swPLqZ4b0h32Y/8XFxWHq1KmYOXMmbG1tAQA//PADlEol9u/fzz40cOw/w5dn9ZMW59B6NQO5XJ5hQepevXph+fLlGDt2LF68eJHj45iYmMDd3T3TcHVISEi2t8Vt3Lgxnj59ijdv3mjabt++DblcjtKlS2v5SoiIKD+5cuUK3NzcsGrVKgwePFjqOERkIHK1zuy7oqKi8MUXX6BixYpa7efn54f169djw4YNuHnzJiZMmICHDx9qRninTp2K/v37a7bv27cvSpYsiYEDB+LGjRs4efIkJk2ahEGDBmV7ARgREeVvQgisXLkSHh4euHv3LsqWLYtp06ZJHYuIDESOi9nXr1/D29sbpUqVgqOjI5YvXw61Wo0ZM2agfPnyOH/+vNY3L+jduzeWLl2KOXPmoE6dOjh58iT2798PZ2dnAEBkZGSGNWeLFCmCkJAQvH79GnXr1oW3tzc6d+6M5cuXa3VeIiLKH16/fo0ePXrgiy++QGpqKrp06YKwsDB4eHhIHY2IDESO58xOmzYNJ0+exIABA3Dw4EFMmDABBw8eRHJyMg4cOIBmzZrlKsCoUaMwatSoLJ8LCAjI1Fa1alVeDUlEVAD8888/6NChAyIiIqBQKLBw4UKMHTs224uAiYiykuNi9s8//8TGjRvRunVrjBo1ChUrVkTlypWxdOlSPcYjIqKCytHREUZGRnBxcUFQUBDq1asndSQiMkA5LmafPn2K6tWrAwDKly8PMzMzzZ27iIiIciIuLg5FixaFTCaDlZUV9u3bBzs7OxQrVkzqaERkoHI8Z1atVmdYJsHIyAiWlpZ6CUVERAXPuXPnULNmzQx3iqxSpQoLWSL6KDkemRVCwNfXV7Nma3JyMkaMGJGpoN29e7duExIRkUFTq9X48ccfMW3aNKhUKqxduxYjRozgeqNEpBM5LmYHDBiQ4XG/fv10HoaIiAqWFy9eYMCAAThw4AAAwMvLC2vXrmUhS0Q6k+NiduPGjfrMQUREBczJkyfRp08fPH36FGZmZli+fDmGDBnC1QqISKe0vp0tERHRh0RGRqJNmzZISUlBlSpVsGPHDtSqVUvqWERUALGYJSIinXNwcMDs2bPx999/Y9WqVShSpIjUkYiogGIxS0REOnHs2DHY2tqiRo0aAIDJkycDAKcVEJFe5XhpLiIioqyoVCrMmjULrVq1Qq9evZCQkADgbRHLQpaI9I0js0RElGuRkZHw9vbGsWPHAAANGzZkAUtEeSpXI7NbtmxB48aN4ejoiAcPHgAAli5dit9++02n4YiIKP8KCQlBnTp1cOzYMVhaWmLLli3w9/eHhYWF1NGIqBDRuphdvXo1/Pz80KFDB7x+/RoqlQoAUKxYMSxdulTX+YiIKJ9JS0vD119/jbZt2+L58+eoVasWLl++zPXHiUgSWhezK1aswLp16zB9+nQYGRlp2uvWrYtr167pNBwREeU/MpkMp0+fhhACw4cPx/nz51G1alWpYxFRIaX1nNmIiAi4urpmajc1NdVM+iciooJHCAGZTAYjIyNs3boVp0+fRq9evaSORUSFnNYjsy4uLggPD8/UfuDAAVSvXl0XmYiIKB9RKpWYPHkyxo8fr2lzdHRkIUtE+YLWI7OTJk3C6NGjkZycDCEELl68iG3btmH+/PlYv369PjISEZFEHj58CC8vL5w7dw4AMGjQINSuXVviVERE/6N1MTtw4ECkpaVh8uTJSExMRN++feHk5IRly5bBy8tLHxmJiEgCv//+O3x9fRETEwNra2v4+/uzkCWifCdX68wOHToUQ4cORXR0NNRqNWxtbXWdi4iIJJKamoqvvvpKs0JNvXr1EBQUBBcXF2mDERFlQes5s7Nnz8bdu3cBADY2NixkiYgKECEEOnfurClkJ0yYgNOnT7OQJaJ8S+tidteuXahcuTIaNmyIlStX4sWLF/rIRUREEpDJZBg+fDiKFy+O3377DYsXL4aJiYnUsYiIsqV1MXv16lVcvXoVLVu2xOLFi+Hk5IQOHTpg69atSExM1EdGIiLSo+Tk5AzrhHfv3h337t1Dly5dJExFRJQzubqdbY0aNfDdd9/h3r17OHbsGFxcXDB+/HjY29vrOh8REenRv//+i0aNGqFly5Z48uSJpr1YsWLShSIi0kKuitn/srS0hLm5OUxMTKBUKnWRiYiI8kBQUBDc3NwQFhYGIQQiIiKkjkREpLVcFbMRERGYN28eqlevjrp16+LKlSuYNWsWoqKidJ2PiIh0LCkpCSNGjICXlxfi4+PRpEkThIeHo0mTJlJHIyLSmtZLc3l4eODixYv45JNPMHDgQM06s0RElP/dunULvXr1wtWrVyGTyTBt2jTMmjULxsa5WqmRiEhyWv/2atGiBdavX48aNWroIw8REenRsmXLcPXqVdja2uKXX36Bp6en1JGIiD6K1sXsd999p48cRESUBxYuXIi0tDTMnj0bDg4OUschIvpoOSpm/fz8MHfuXFhaWsLPz++92y5evFgnwYiI6OP9/fffWLt2LZYuXQq5XA5LS0v8/PPPUsciItKZHBWzYWFhmpUKwsLC9BqIiIg+nhACAQEBGD16NJKSklC+fHmMHz9e6lhERDqXo2L22LFjWf4/ERHlP2/evMGoUaOwZcsWAECbNm3Qt29fiVMREemH1ktzDRo0CPHx8ZnaExISMGjQIJ2EIiKi3Ll69Srq1q2LLVu2QC6XY968eThw4ABsbW2ljkZEpBdaF7ObNm1CUlJSpvakpCRs3rxZJ6GIiEh7QUFBaNCgAW7dugUnJyccP34c06ZNg1z+0ffHISLKt3K8mkFcXByEEBBCID4+HmZmZprnVCoV9u/fz7/8iYgkVLFiRajVarRv3x6bN2+GjY2N1JGIiPQux8VssWLFIJPJIJPJULly5UzPy2QyzJ49W6fhiIjo/V6/fo1ixYoBANzd3XHu3DnUqVOHo7FEVGjkuJg9duwYhBBo2bIldu3ahRIlSmieMzExgbOzMxwdHfUSkoiIMhJCYNWqVZg2bRqOHTsGNzc3AND8l4iosMhxMdusWTMAQEREBMqWLQuZTKa3UERElL3Xr19j6NCh2LlzJwAgICCARSwRFVo5KmavXr2KmjVrQi6XIzY2FteuXct221q1auksHBERZXTp0iX07t0bERERUCgUWLBgAcaNGyd1LCIiyeSomK1Tpw6ioqJga2uLOnXqQCaTQQiRaTuZTAaVSqXzkEREhZ0QAsuWLcPkyZOhVCrh4uKCoKAg1KtXT+poRESSylExGxERgVKlSmn+n4iI8tauXbswYcIEAMDnn3+O9evXay78IiIqzHJUzDo7O2f5/0RElDe6d++OLl26oE2bNhg1ahSvWyAi+n+5umnCn3/+qXk8efJkFCtWDI0aNcKDBw90Go6IqLBSq9VYt24dEhMTAQByuRx79+7F6NGjWcgSEf2H1sXsd999B3NzcwDAuXPnsHLlSixYsAA2Njaaj8CIiCj3oqOj0blzZwwbNgxffPGFpp1FLBFRZjlemivdo0ePULFiRQDA3r170aNHDwwbNgyNGzdG8+bNdZ2PiKhQOXXqFPr06YMnT57AzMwMDRo0gBCChSwRUTa0HpktUqQIXr58CQAIDg5G69atAQBmZmZISkrSbToiokJCrVbju+++Q4sWLfDkyRNUqVIFFy5cwLBhw1jIEhG9h9Yjs56enhgyZAhcXV1x+/ZtdOzYEQDw999/o1y5crrOR0RU4D1//hw+Pj4IDg4GAPTr1w+rV69GkSJFJE5GRJT/aT0y+9NPP8HDwwMvXrzArl27ULJkSQBAaGgo+vTpo/OAREQFnVKpxJUrV2Bubg5/f39s3ryZhSwRUQ5pPTJbrFgxrFy5MlP77NmzdRKIiKgw+O88WCcnJ/z6668oVaoUatSoIXEyIiLDonUxC7y9L7i/vz9u3rwJmUyGatWqYfDgwbC2ttZ1PiKiAicqKgre3t4YM2YMPvvsMwDgBbRERLmk9TSDy5cvo0KFCliyZAlevXqF6OhoLFmyBBUqVMCVK1f0kZGIqMA4fPgwateujaNHj2Ls2LFITU2VOhIRkUHTupidMGECunTpgvv372P37t3Ys2cPIiIi0KlTJ4wfP14PEYmIDF9aWhq+/vprtGnTBs+fP0etWrVw+PBhmJiYSB2NiMigaT3N4PLly1i3bh2Mjf+3q7GxMSZPnoy6devqNBwRUUHw5MkT9OnTB6dOnQIADB8+HEuWLNHcgIaIiHJP62LWysoKDx8+RNWqVTO0P3r0CEWLFtVZMCKiguDFixeoU6cOoqOjUbRoUfz888/w8vKSOhYRUYGh9TSD3r17Y/DgwQgKCsKjR4/w+PFjbN++HUOGDOHSXERE7yhVqhR69+4NV1dXhIaGspAlItIxrUdmf/zxR8hkMvTv3x9paWkAAIVCgZEjR+L777/XeUAiIkPz8OFDKBQKODg4AAAWLVoEIQTMzMwkTkZEVPBoPTJrYmKCZcuWISYmBuHh4QgLC8OrV6+wZMkSmJqa6iMjEZHB+OOPP1CnTh306dNH8we/qakpC1kiIj3JcTGbmJiI0aNHw8nJCba2thgyZAgcHBxQq1YtWFhY6DMjEVG+l5qaii+//BJdunRBTEwMEhMTERMTI3UsIqICL8fF7MyZMxEQEICOHTvCy8sLISEhGDlypD6zEREZhIiICDRt2hSLFy8G8HYJw9OnT6NUqVISJyMiKvhyPGd29+7d8Pf311y80K9fPzRu3BgqlQpGRkZ6C0hElJ/t3r0bgwYNQmxsLIoXL46AgAB06dJF6lhERIVGjkdmHz16hKZNm2oe169fH8bGxnj69KleghER5XdKpRLffPMNYmNj4eHhgbCwMBayRER5LMfFrEqlynSnGmNjY80FDkREhY1CoUBQUBCmTp2KEydOwNnZWepIRESFTo6nGQgh4Ovrm2HFguTkZIwYMQKWlpaatt27d+s2IRFRPrJjxw48f/4cY8aMAQDUrFkT3333ncSpiIgKrxwXswMGDMjU1q9fP52GISLKr5KSkjBhwgSsXbsWRkZGaNy4MVxdXaWORURU6OW4mN24caM+cxAR5Vu3bt1Cr169cPXqVchkMkyZMgWffPKJ1LGIiAi5uAMYEVFh8ssvv2DEiBFISEiAra0tfvnlF3h6ekodi4iI/p/WdwAjIiosRo0aBR8fHyQkJKBFixYIDw9nIUtElM+wmCUiykbVqlUhk8kwa9YshISEwMHBQepIRET0Dk4zICL6j1evXqFEiRIAgC+++ALNmjVD7dq1JU5FRETZ4cgsERGAN2/eYMCAAWjQoAHi4uIAADKZjIUsEVE+l6tidsuWLWjcuDEcHR3x4MEDAMDSpUvx22+/6TQcEVFeuHbtGurVq4fNmzfj3r17OHbsmNSRiIgoh7QuZlevXg0/Pz906NABr1+/hkqlAgAUK1YMS5cu1XU+IiK9EUJg3bp1qF+/Pv755x84OTnh+PHj6Nq1q9TRiIgoh7QuZlesWIF169Zh+vTpMDIy0rTXrVsX165d02k4IiJ9iY+Ph7e3N4YNG4bk5GS0b98e4eHhaNq0qdTRiIhIC1oXsxEREVne9cbU1BQJCQk6CUVEpG9ffvkltm3bBiMjIyxYsAD79u2DjY2N1LGIiEhLWhezLi4uCA8Pz9R+4MABVK9eXesAq1atgouLC8zMzODu7o5Tp07laL8zZ87A2NgYderU0fqcRETffvstGjZsiFOnTmHSpEmQy3k9LBGRIdL6t/ekSZMwevRoBAUFQQiBixcvYt68eZg2bRomTZqk1bGCgoIwfvx4TJ8+HWFhYWjatCnat2+Phw8fvne/2NhY9O/fH61atdI2PhEVUgkJCfD399c8trW1xdmzZ+Hh4SFhKiIi+lharzM7cOBApKWlYfLkyUhMTETfvn3h5OSEZcuWwcvLS6tjLV68GIMHD8aQIUMAvF0R4dChQ1i9ejXmz5+f7X7Dhw9H3759YWRkhL1792r7EoiokAkNDYWfnx+ePXsGKysr9O3bF8DbpbeIiMiw5eqmCUOHDsXQoUMRHR0NtVoNW1tbrY+RmpqK0NBQTJkyJUN7mzZtcPbs2Wz327hxI+7evYtffvkF33777QfPk5KSgpSUFM3j9PUjlUollEql1rm1pVSmZfj/vDgn6VZ6n7HvDI8QAitXrsSUKVOgVCrh7OyMcuXKsS8NEN+Hho39Z/jyug+1Oc9H3QHsYy6WiI6Ohkqlgp2dXYZ2Ozs7REVFZbnPnTt3MGXKFJw6dQrGxjmLPn/+fMyePTtTe3BwMCwsLLQPrqUUFZD+bT569ChMjd67OeVjISEhUkcgLbx58wYrVqzAhQsXAAANGzbEmDFj8OLFC+zfv1/idJRbfB8aNvaf4curPkxMTMzxtloXsy4uLu/9aO7evXtaHe/dYwkhsjy+SqVC3759MXv2bFSuXDnHx586dSr8/Pw0j+Pi4lCmTBm0adMGVlZWWmXNjcTUNEy+eBQA0LJlS1hbmun9nKRbSqUSISEh8PT0hEKhkDoO5cDFixcxbtw4PHjwACYmJpg/fz7Kly+PNm3asA8NFN+Hho39Z/jyug/TP0nPCa2L2fHjx2d4rFQqERYWhoMHD2p1AZiNjQ2MjIwyjcI+f/4802gt8HZNyMuXLyMsLAxjxowBAKjVagghYGxsjODgYLRs2TLTfqampjA1Nc3UrlAo8qQzFOJ/hblCYcw3sQHLq58Z+nixsbF48OABKlSogB07duCTTz7B/v372YcFAPvQsLH/DF+e1U9anEPrYnbcuHFZtv/000+4fPlyjo9jYmICd3d3hISE4LPPPtO0h4SEZHn3HSsrq0w3ZVi1ahWOHj2KnTt3wsXFJcfnJqKC57+f6nTo0AFbt25Fx44dYWVlxXl6REQFmM4WVmzfvj127dql1T5+fn5Yv349NmzYgJs3b2LChAl4+PAhRowYAeDtFIH+/fu/DSqXo2bNmhm+bG1tYWZmhpo1a8LS0lJXL4WIDMzp06dRu3ZtPHjwQNPWp0+fPJlKRERE0vqoC8D+a+fOnShRooRW+/Tu3RsvX77EnDlzEBkZiZo1a2L//v1wdnYGAERGRn5wzVkiKrzUajV++OEHfPPNN1CpVPj666+xZcsWqWMREVEe0rqYdXV1zXCBlhACUVFRePHiBVatWqV1gFGjRmHUqFFZPhcQEPDefWfNmoVZs2ZpfU4iMnzPnz+Hj48PgoODAQD9+vXD6tWrJU5FRER5Tetitlu3bhkey+VylCpVCs2bN0fVqlV1lYuIKFvHjx9H3759ERkZCXNzc/z000/w9fXlTRCIiAohrYrZtLQ0lCtXDm3btoW9vb2+MhERZevAgQPo1KkT1Go1qlevjh07dqBGjRpSxyIiIolodQGYsbExRo4cmeGOWkREealFixaoVasWBg4ciIsXL7KQJSIq5LSeZtCgQQOEhYVpLtIiItK3CxcuoG7dujAyMoKZmRlOnjyJokWLSh2LiIjyAa2L2VGjRuHLL7/E48eP4e7unmlJrFq1auksHBEVbmlpaZg9ezbmzZuHGTNmaC74ZCFLRETpclzMDho0CEuXLkXv3r0BAGPHjtU8J5PJNAuWq1Qq3ackokLnyZMn6Nu3L06ePAkAePbsWba3uyYiosIrx8Xspk2b8P333yMiIkKfeYiIcPDgQfj4+CA6OhpFihTBunXr4OXlJXUsIiLKh3JczAohAIBzZYlIb5RKJWbMmIHvv/8ewNt1rYOCglCpUiWJkxERUX6l1WoG/HiPiPTp3r17WLp0KQBg9OjROHv2LAtZIiJ6L60uAKtcufIHC9pXr159VCAiKryqVKmCtWvXwsLCAj169JA6DhERGQCtitnZs2fD2tpaX1mIqJBJTU3F119/jc8++wweHh4AgP79+0ucioiIDIlWxayXlxdsbW31lYWICpH79+/Dy8sLFy5cwI4dO/DPP//AzMxM6lhERGRgcjxnlvNliUhX9uzZA1dXV1y4cAHFihXDsmXLWMgSEVGu5LiYTV/NgIgot1JSUjB27Fh0794dr1+/RsOGDREeHo6uXbtKHY2IiAxUjqcZqNVqfeYgogIuJiYGnp6eCA0NBQBMmjQJ8+bNg0KhkDgZEREZMq1vZ0tElBvFihVD6dKlcf/+fWzatAkdO3aUOhIRERUALGaJSG+Sk5ORlpaGIkWKQCaTYcOGDUhMTETp0qWljkZERAWEVjdNICLKqdu3b6Nhw4YYNmyYZs59iRIlWMgSEZFOsZglIp3bunUr3N3d8ddff+Hw4cN48uSJ1JGIiKiAYjFLRDqTmJiIoUOHwtvbG2/evEHz5s0RHh7O0VgiItIbFrNEpBM3b95EgwYNsH79eshkMsycOROHDx+Go6Oj1NGIiKgA4wVgRPTR0tLS0LlzZ9y9exf29vYIDAxEy5YtpY5FRESFAEdmieijGRsb4+eff0bbtm0RHh7OQpaIiPIMi1kiypVr165h3759msctW7bEgQMHYGdnJ2EqIiIqbFjMEpFWhBBYv3496tevjz59+uDOnTua52QymYTJiIioMGIxS0Q5Fh8fj379+mHo0KFITk5GkyZNUKxYMaljERFRIcZilohyJDw8HO7u7ti6dSuMjIzwww8/4M8//0SpUqWkjkZERIUYVzMgog9as2YNxo8fj5SUFJQpUwbbt29Ho0aNpI5FRETEkVki+rC7d+8iJSUFnTt3RlhYGAtZIiLKNzgyS0RZUqvVkMvf/r373XffoXbt2vD29uZFXkRElK9wZJaIMhBCYNmyZWjZsiWUSiUAQKFQoF+/fixkiYgo32ExS0QaMTEx6N69O8aPH48TJ05g27ZtUkciIiJ6L04zICIAwIULF9C7d288ePAAJiYmWLRoEXx8fKSORURE9F4cmSUq5NRqNRYtWoQmTZrgwYMHqFChAs6ePYsxY8ZwWgEREeV7LGaJCrnJkydj4sSJSEtLQ69evXDlyhW4u7tLHYuIiChHWMwSFXJDhw6FjY0N1qxZg+3bt8PKykrqSERERDnGObNEhYxarcbZs2fRpEkTAECVKlVw//59WFpaSpyMiIhIexyZJSpEnj9/jg4dOqBZs2Y4fvy4pp2FLBERGSqOzBIVEidOnECfPn0QGRkJc3NzREZGSh2JiIjoo3FklqiAU6lUmDt3Llq2bInIyEhUq1YNFy9eRJ8+faSORkRE9NE4MktUgEVFRaFfv344cuQIAMDX1xcrV67ktAIiIiowWMwSFWAHDhzAkSNHYGFhgdWrV6N///5SRyIiItIpFrNEBZivry/u3buHvn37olq1alLHISIi0jnOmSUqQJ4+fYp+/fohJiYGACCTyTB37lwWskREVGBxZJaogDh48CB8fHwQHR0NAPjll18kTkRERKR/HJklMnBpaWmYOnUq2rdvj+joaNSpUwczZ86UOhYREVGe4MgskQF79OgR+vTpgzNnzgAARo0ahUWLFsHMzEziZERERHmDxSyRgTp//jw6duyIV69ewcrKCv7+/ujRo4fUsYiIiPIUi1kiA1W5cmVYWlqifPnyCAoKQvny5aWORERElOdYzBIZkOfPn6NUqVKQyWQoUaIEjhw5grJly8LU1FTqaERERJLgBWBEBmLPnj2oUqUKNmzYoGmrVKkSC1kiIirUWMwS5XMpKSkYO3YsunfvjtevXyMwMBBCCKljERER5QssZonysbt376Jx48ZYsWIFAGDixIk4dOgQZDKZxMmIiIjyB86ZJcqnfv31VwwZMgRxcXEoUaIENm/ejI4dO0odi4iIKF9hMUuUD92+fRteXl5Qq9Vo3Lgxtm3bhjJlykgdi4iIKN9hMUuUD1WuXBkzZsxASkoK5syZA2NjvlWJiIiywn8hifKJbdu2oW7duqhUqRIA8Ja0REREOcALwIgklpiYiCFDhqBv377o3bs3kpOTpY5ERERkMDgySyShmzdvolevXrh+/TpkMhk6d+4MhUIhdSwiIiKDwWKWSCKbNm3CqFGjkJiYCDs7OwQGBqJVq1ZSxyIiIjIoLGaJ8lhiYiJGjhyJzZs3AwBatWqFX375Bfb29hInIyIiMjycM0uUx4yNjfHPP/9ALpdj7ty5OHToEAtZIiKiXOLILFEeEEJACAG5XA4TExMEBQXhwYMHaNasmdTRiIiIDBpHZon0LD4+Hv369cPUqVM1beXKlWMhS0REpAMcmSXSo/DwcPTq1Qt37tyBsbExRo4ciXLlykkdi4iIqMDgyCyRHgghsHr1ajRs2BB37txB6dKlcfz4cRayREREOsaRWSIdi42NxdChQ/Hrr78CADp16oSAgACULFlS4mREREQFD4tZIh1Sq9Vo1qwZ/vrrLxgbG+OHH37AhAkTIJPJpI5GRERUIHGaAZEOyeVyTJo0Cc7Ozjh9+jT8/PxYyBIREekRi1mijxQTE4Pw8HDNY29vb9y4cQMNGjSQLhQREVEhwWKW6CNcuHABrq6u6NChA168eKFpt7CwkDAVERFR4cFiligXhBBYtGgRmjRpggcPHsDc3BzPnz+XOhYREVGhwwvAiLT08uVL+Pr6Yt++fQCAnj17Yt26dbC2tpY4GRERUeEj+cjsqlWr4OLiAjMzM7i7u+PUqVPZbrt79254enqiVKlSsLKygoeHBw4dOpSHaamwO3PmDOrUqYN9+/bB1NQUq1atQlBQEAtZIiIiiUhazAYFBWH8+PGYPn06wsLC0LRpU7Rv3x4PHz7McvuTJ0/C09MT+/fvR2hoKFq0aIHOnTsjLCwsj5NTYbV69Wo8fvwYlSpVwvnz5zFy5EiuVkBERCQhSacZLF68GIMHD8aQIUMAAEuXLsWhQ4ewevVqzJ8/P9P2S5cuzfD4u+++w2+//YY//vgDrq6ueRGZCrlVq1bBzs4Os2bNQtGiRaWOQ0REVOhJVsympqYiNDQUU6ZMydDepk0bnD17NkfHUKvViI+PR4kSJbLdJiUlBSkpKZrHcXFxAAClUgmlUpmL5NpRKtMy/H9enJN05+TJkwgKCkKHDh2gVCphbm6O77//HgDYlwYkva/YZ4aLfWjY2H+GL6/7UJvzSFbMRkdHQ6VSwc7OLkO7nZ0doqKicnSMRYsWISEhAb169cp2m/nz52P27NmZ2oODg/Nk+aQUFZD+bT569ChMjfR+StIBlUqFnTt3IigoCGq1GmZmZpxOUACEhIRIHYE+EvvQsLH/DF9e9WFiYmKOt5V8NYN3CwQhRI6Khm3btmHWrFn47bffYGtrm+12U6dOhZ+fn+ZxXFwcypQpgzZt2sDKyir3wXMoMTUNky8eBQC0bNkS1pZmej8nfZyoqCj4+vri6NG3/ebt7Y3GjRvD09MTCoVC4nSUG0qlEiEhIexDA8Y+NGzsP8OX132Y/kl6TkhWzNrY2MDIyCjTKOzz588zjda+KygoCIMHD8avv/6K1q1bv3dbU1NTmJqaZmpXKBR50hkK8b/CXKEw5ps4nzty5Ai8vb3x7NkzWFhYYNWqVejbty/279+fZz8zpD/sQ8PHPjRs7D/Dl2f1kxbnkGw1AxMTE7i7u2carg4JCUGjRo2y3W/btm3w9fXF1q1b0bFjR33HpEJk2bJl8PT0xLNnz1CzZk1cvnwZAwYMkDoWERERvYek0wz8/Pzg4+ODunXrwsPDAz///DMePnyIESNGAHg7ReDJkyfYvHkzgLeFbP/+/bFs2TI0bNhQM6prbm7OdT7po9WrVw9yuRwDBw7EsmXLeEtaIiIiAyBpMdu7d2+8fPkSc+bMQWRkJGrWrIn9+/fD2dkZABAZGZlhzdm1a9ciLS0No0ePxujRozXtAwYMQEBAQF7HpwLg2bNnmmktjRo1wvXr11G1alWJUxEREVFOSX4B2KhRozBq1Kgsn3u3QD1+/Lj+A1GhkJaWhm+++QYrVqzAhQsXUKNGDQBgIUtERGRgJC9mifLao0eP0KdPH5w5cwYA8Mcff2iKWSIiIjIsLGapUPnzzz/Rv39/vHr1ClZWVli3bt171ykmIiKi/E2y1QyI8pJSqcTEiRPRqVMnvHr1Cu7u7rhy5QoLWSIiIgPHYpYKBX9/fyxatAgAMHbsWJw5cwYVKlSQOBURERF9LE4zoEJhyJAhOHToEPr374/PPvtM6jhERESkIxyZpQIpNTUVCxcuREpKCgDA2NgYe/bsYSFLRERUwHBklgqce/fuoXfv3rh8+TIePnyIFStWSB2JiIiI9IQjs1Sg7Ny5E66urrh8+TJKlCiBtm3bSh2JiIiI9IjFLBUIycnJGDVqFHr27Im4uDg0btwY4eHh6NSpk9TRiIiISI9YzJLBu3v3Ljw8PLB69WoAwJQpU3Ds2DGUKVNG4mRERESkb5wzSwZPLpcjIiICNjY22LJlC9q1ayd1JCIiIsojLGbJIKlUKhgZGQEAXFxcsGfPHlSuXBlOTk4SJyMiIqK8xGkGZHBu3rwJNzc3HDx4UNPWokULFrJERESFEItZMiibN29G3bp1cfXqVUyaNAlqtVrqSERERCQhFrNkEBISEjBw4EAMGDAAiYmJaNmyJUJCQiCX80eYiIioMGMlQPne9evXUa9ePQQEBEAul2POnDkIDg6Gvb291NGIiIhIYrwAjPK1e/fuoX79+khKSoKDgwO2bt2K5s2bSx2LiIiI8gkWs5SvlS9fHl5eXnj69Ck2b94MW1tbqSMRERFRPsJilvKdv/76C46OjihVqhQAYPXq1VAoFJwfS0RERJmwOqB8QwiBNWvWoEGDBujfv79mpQJTU1MWskRERJQlVgiUL8TGxsLLywsjR45ESkoKjIyMkJiYKHUsIiIiyudYzJLkQkND4e7ujh07dsDY2BgLFy7E77//jiJFikgdjYiIiPI5zpklyQghsHLlSkycOBGpqalwdnbG9u3b0bBhQ6mjERERkYHgyCxJJiEhAcuWLUNqaiq6du2KsLAwFrJERESkFY7MkmSKFCmCoKAgnD59GmPHjoVMJpM6EhERERkYFrOUZ4QQWLp0KczNzTFixAgAgLu7O9zd3SVORkRERIaKxSzliVevXsHX1xd//PEHTExM4OnpiQoVKkgdi4iIiAwci1nSu7Nnz8LLywuPHj2CqakplixZgvLly0sdi4iIiAoAXgBGeqNWq/HDDz/g008/xaNHj1CpUiWcP38eI0eO5PxYIiIi0gmOzJJeqNVqdOvWDX/88QcAoE+fPli7di2KFi0qcTIiIiIqSDgyS3ohl8vh4eEBMzMzrFu3DoGBgSxkiYiISOc4Mks6o1KpEB0dDTs7OwDAV199hZ49e6JixYoSJyMiIqKCiiOzpBPPnj1Du3bt0KpVKyQmJgJ4OzrLQpaIiIj0icUsfbSjR4+idu3aOHz4MCIiInDlyhWpIxEREVEhwWKWck2lUmHmzJlo3bo1nj17hho1auDSpUto0qSJ1NGIiIiokOCcWcqVp0+fwtvbG8ePHwcADB48GMuXL4eFhYW0wYiIiKhQYTFLufLFF1/g+PHjsLS0xNq1a+Ht7S11JCIiIiqEWMxSrixfvhyxsbH46aefUKVKFanjEBERUSHFObOUI48fP8ZPP/2keezk5ITDhw+zkCUiIiJJcWSWPmj//v3o378/Xr58CScnJ3Tr1k3qSEREREQAODJL76FUKjF58mR07NgRL1++hJubGz755BOpYxERERFpcGSWsvTgwQN4eXnh/PnzAN5e8LVw4UKYmppKnIyIiIjof1jMUib79u2Dj48PXr9+DWtra2zYsAHdu3eXOhYRERFRJixmKZOUlBS8fv0a9evXx/bt2+Hi4iJ1JCIiIqIssZglAEBaWhqMjd/+OHz++efYtWsXOnXqBBMTE4mTEZEhUavVSE1N1flxlUoljI2NkZycDJVKpfPjk36x/wyfPvrQxMQEcvnHX77FYpawc+dOTJs2DcePH4ejoyMAcFoBEWktNTUVERERUKvVOj+2EAL29vZ49OgRZDKZzo9P+sX+M3z66EO5XA4XF5ePHjhjMVuIJScn48svv8SqVasAAAsXLsSSJUskTkVEhkgIgcjISBgZGaFMmTI6GW35L7VajTdv3qBIkSI6PzbpH/vP8Om6D9VqNZ4+fYrIyEiULVv2owpkFrOF1J07d9C7d2+EhYUBAL766ivMnTtX4lREZKjS0tKQmJgIR0dHWFhY6Pz46dMXzMzMWAwZIPaf4dNHH5YqVQpPnz5FWloaFApFro/DYrYQ2r59O4YOHYo3b97AxsYGmzdvRvv27aWORUQGLH0OHefZE1FOpf++UKlULGYp5zZv3owBAwYAAJo2bYpt27bByclJ4lREVFBwPiQR5ZTO5t7q5ChkMD7//HPUqFEDX3/9NY4ePcpCloiIiAwai9lCICQkRHN1saWlJS5fvoy5c+dqluIiIiLKrdTUVFSsWBFnzpyROkqB8fz5c5QqVQpPnjyROopBYDFbgCUkJGDgwIFo06YNFi1apGk3MzOTMBURUf7h6+sLmUwGmUwGY2NjlC1bFiNHjkRMTEymbc+ePYsOHTqgePHiMDMzwyeffIJFixZluebmsWPH0KFDB5QsWRIWFhaoXr06vvzyyw8WJ2FhYejZsyfs7OxgZmaGypUrY+jQobh9+7bOXrOu/fzzz3B2dkbjxo0zPTds2DAYGRlh+/btmZ7z9fVFt27dMrWHh4dDJpPh/v37mjYhBH7++Wc0aNAARYoUQbFixVC3bl0sXboUiYmJunw5GcTExMDHxwfW1tawtrbW3B3zfZ49ewZfX1/NxZDt2rXDnTt3MmwTFRUFHx8f2Nvbw9LSEm5ubti5c6fmeVtbW/j4+GDmzJn6eFkFDovZAurvv/9G/fr1ERAQALlcDqVSKXUkIqJ8qV27doiMjMT9+/exfv16/PHHHxg1alSGbfbs2YNmzZqhdOnSOHbsGP755x+MGzcO8+bNg5eXF4QQmm3Xrl2L1q1bw97eHrt27cKNGzewZs0axMbGZhhYeNe+ffvQsGFDpKSkIDAwEDdv3sSWLVtgbW2Nb775JtevT9+//1esWIEhQ4Zkak9MTERQUBAmTZqEDRs2fNQ5fHx8MH78eHTt2hXHjh1DeHg4vvnmG/z2228IDg7+qGO/T9++fREeHo6DBw/i4MGDCA8Ph4+PT7bbCyHQrVs33Lt3D7/99hvCwsLg7OyM1q1bIyEhIcPruXXrFn7//Xdcu3YN3bt3z7DCEAAMHDgQgYGBWf5hRe8QhUxsbKwAIGJjY/PkfAkpSuH81T7h/NU+8fpNot7Pp1arhb+/vzA3NxcAhL29vTh27Jjez1uQpaamir1794rU1FSpo1AusQ/1LykpSdy4cUMkJSUJId7+LkpIUersKz4pRTx9Fi3ik1I+uK1arc5x7gEDBoiuXbtmaPPz8xMlSpTQPH7z5o0oWbKk6N69e6b9f//9dwFAbN++XQghxKNHj4SJiYkYP358lueLiYnJsj0hIUHY2NiIbt26vXe/jRs3Cmtr6wzP7dmzR/z3n/OZM2eK2rVrC39/f+Hi4iJkMplYs2aNcHR0FCqVKsO+nTt3Fv3798/wetzc3ISpqalwcXERs2bNEkqlMstMQggRGhoq5HJ5lv+mBgQEiIYNG4rXr18Lc3Nz8ddff2U4f1bfeyGECAsLEwBERESEEEKIoKAgAUDs3bs307ZqtVq8fv0623wf48aNGwKAOH/+vKbt3LlzAoD4559/stzn1q1bAoC4fv26pi0tLU2UKFFCrFu3TtNmaWkpNm/enGHfEiVKiPXr12doK1eunPD399fFy/loKpVKxMTEZPoZ+hjv/t74L23qNU6aLEDevHmDESNGIDAwEADQpk0bbNmyBba2thInI6LCJkmpQvUZhyQ59405bWFhkrt/3u7du4eDBw9mWCYoODgYL1++xMSJEzNt37lzZ1SuXBnbtm1D79698euvvyI1NRWTJ0/O8vjFihXLsv3QoUOIjo7Wer/s/Pvvv9ixYwd27doFIyMjODk5YezYsTh27BhatWoF4O1H6IcOHcIff/yhydCvXz8sX74cTZs2xd27dzFs2DAAyPbj7pMnT6Jy5cqwsrLK9Jy/vz/69esHa2trtG/fHoGBgZg/f75WrwMAAgMDUaVKFXTt2jXTczKZDNbW1tnuW6RIkfceu2nTpjhw4ECWz507dw7W1tZo0KCBpq1hw4awtrbG2bNnUaVKlUz7pKSkAMg4nc/IyAgmJiY4ffq0ZgS7SZMmCAoKQseOHVGsWDHs2LEDKSkpaN68eYbj1a9fH6dOncKgQYPe+zoKOxazBcjt27exY8cOGBkZYe7cufjqq6+4ODUR0Qfs27cPRYoUgUqlQnJyMgBg8eLFmufT56tWq1Yty/2rVq2q2ebOnTuwsrKCg4ODVhnS51RWrVpV6/xZSU1NxZYtW1CqVClNW7t27bB161ZNMfvrr7+iRIkSmsfz5s3DlClTNMs3li9fHnPnzsXkyZOzLWbv37+vuQ36u6/n/Pnz2L17NwDA29sbY8eOxbx587T+d+nOnTtZFo45ER4e/t7nzc3Ns30uKioqy8EgW1tbREVFZblP1apV4ezsjKlTp2Lt2rWwtLTE4sWLERUVhcjISM12QUFB6N27N0qWLAljY2NYWFhgz549qFChQobjOTk5ZZh6QFljMVuAuLm5Ye3atahUqRKaNGkidRwiKsTMFUa4Maetzo6nVqsRHxePolZFP1gMmSuMtDp2ixYtsHr1aiQmJmL9+vW4ffs2vvjii0zbif/Mi323PX29zP/+vzayO3ZuOTs7ZyhkgbcF5bBhw7Bq1SqYmpoiMDAQXl5eMDJ6+/0KDQ3FpUuXMG/ePM0+6QV+YmJilnd2S0pKyvKiYn9/f7Rt2xY2NjYAgA4dOmDIkCE4fPgw2rVrp9Vrye33FAAqVqyYq/3SZXXe9+VRKBTYtWsXBg8ejBIlSsDIyAitW7fOdGOir7/+GjExMTh8+DBsbGywd+9e9OzZE6dOncInn3yi2c7c3FyvF7gVFCxmDVhcXBzGjBmDCRMmwNXVFcDbCeNERFKTyWS5/qg/K2q1GmkmRrAwMdb5J06Wlpaaomf58uVo0aIFZs+erbnFd+XKlQEAN2/eRKNGjTLt/88//6B69eqabWNjYxEZGanV6Gz6Of755x94eHhku51cLs9U+GZ1gZelpWWmts6dO0OtVuPPP/9EvXr1cOrUqQwj0Gq1GrNnz0b37t0z7ZvdKjg2Nja4du1ahjaVSoXNmzcjKioqwxKQKpUKGzZs0BSzVlZWePDgQaZjpq8WkD59oHLlyrh582aW5/+Qj5lmYG9vj2fPnmVqf/HiBezs7LI9pru7O8LDwxEbG4vU1FSUKlUKDRo0QN26dQEAd+/excqVK3H9+nXUqFEDAFC7dm2cOnUKP/30E9asWaM51qtXrzL9UUKZ8TNoA3XlyhW4ublhy5Yt8Pb2znJpGCIi0t7MmTPx448/4unTpwDeXn9QokSJLFci+P3333Hnzh306dMHANCjRw+YmJhgwYIFWR47u2Wd2rRpAxsbmw/uV6pUKcTHx2e4Mv5DH6WnMzc3R/fu3REYGIht27ahcuXKcHd31zzv5uaGW7duoWLFipm+svsDwtXVFf/880+GAnv//v2Ij49HWFgYwsPDER4ejitXriAgIAC//fYbXr58CeDtR/LXr1/XTO1Id+nSJZQqVQrFixcH8HZFgdu3b+O3337LdH4hBGJjY7N9zennz+5r/fr12e7r4eGB2NhYXLx4UdN24cIFxMbGZvlHzbusra1RqlQp3LlzB5cvX9bM+U0faX33e2pkZKRZEz7d9evXNYNV9B46uyTNQBj6agZqtVqsWLFCmJiYCACibNmy4uzZszpIStnhlfCGj32of++7KlkX9HEltRDZX1Hv7u4uRo8erXn866+/CiMjIzF06FDx119/iYiICLF+/XpRvHhx0aNHjwwrKPz0009CJpOJQYMGiePHj4v79++L06dPi2HDhgk/P79ss+zdu1coFArRuXNnERISIiIiIsSlS5fEpEmTRO/evYUQQrx8+VJYWlqKsWPHijt37ojAwEDh6OiY5WoGWQkODhampqaiSpUqYu7cuRmeO3jwoDA2NhYzZ84U169fFzdu3BDbt28X06dPzzZzdHS0MDExEdeuXdO0de3aVZM3nUqlEq9evRJOTk5i6dKlQgghXr9+Lezt7UWPHj3EpUuXxL///iu2bNkiihcvLhYsWKDZV61Wi969ewtzc3Px3XffiUuXLon79++LP/74Q7Rs2VLs2bMn23wfq127dqJWrVri3Llz4ty5c+KTTz4RnTp1yrBNlSpVxO7duzWPd+zYIY4dOybu3r0r9u7dK5ydnTOshJGamioqVqwomjZtKi5cuCD+/fdf8eOPPwqZTCb+/PNPzXYJCQnC3NxcnDx5Um+vTxv5eTUDFrN6pstiNiYmRnTv3l0AEABEly5dxMuXL3WUlLLDQsjwsQ/1r6AVs4GBgcLExEQ8fPhQ03by5EnRrl07YW1tLUxMTET16tXFjz/+KNLS0jLtHxISItq2bSuKFy8uzMzMRNWqVcXEiRPF06dP35vn0qVLonv37qJUqVLC1NRUVKxYUQwbNkzcuXNHs82ePXtExYoVhZmZmejUqZP4+eefc1zMpqWlCQcHBwFA3L17N9PzBw8eFI0aNRLm5ubCyspK1K9fX/z888/vzezl5SWmTJkihBAiKipKGBsbix07dmTYJr3/xowZIz755BNN+507d8Tnn38unJychKWlpfjkk0/EypUrM/WzSqUSq1evFvXq1RMWFhbCyspKuLu7i2XLlonERP0te/ny5Uvh7e0tihYtKooWLSq8vb0zLa8GQGzcuFHzeNmyZaJ06dJCoVCIsmXLiq+//lqkpKRk2Of27duie/fuwtbWVlhYWIhatWplWqpr69atokqVKvp6aVrLz8WsTAgdzzrP5+Li4mBtbY3Y2NgslxLRtcTUNM3yNH990xLWltlfOfk+jx8/RtOmTXH//n0oFAosXLgQY8eOzfWkeMo5pVKJ/fv3o0OHDhmW6yHDwT7Uv+TkZERERMDFxUUvdxlUq9WIi4uDlZUVV2nJZ65du4bWrVvj33//RdGiRbPchv2nvfr162P8+PHo27ev1FEA6KcP3/d7Q5t6jReAGQhHR0dUqlQJMpkMQUFBqFevntSRiIiI8Mknn2DBggW4f/9+hivxKfeeP3+OHj16aOZi0/uxmM3HXr16BTMzM1hYWEAul2Pr1q0wNjbWevFsIiIifUpfm5Z0w9bWNtsbaFBmHOvPp86ePYs6depg3LhxmjYbGxsWskRERET/wWI2n1Gr1ViwYAE+/fRTPHr0CMePH892KRciIiKiwo7FbD7y4sULdOrUCV999RVUKhW8vLwQGhrK0VgiIiKibHDObD5x6tQpeHl54enTpzAzM8OyZcswdOhQrlZARERE9B4sZvOBxMRE9OzZE8+ePUOVKlWwY8cO1KpVS+pYRERERPkepxnkAxYWFtiwYQN8fHxw+fJlFrJEREREOcSRWYkcO3YMSUlJ6NChAwCgQ4cOmv8nIiIiopzhyGweU6lUmDVrFlq1agVvb288fPhQ6khERKRHs2bNQp06dfLteZo3b47x48frPM+HlCtXDkuXLv2oY/j6+qJbt27v3Uaq10d5R/JidtWqVZrbmLm7u+PUqVPv3f7EiRNwd3eHmZkZypcvjzVr1uRR0o8XFRUJT09PzJ49G0IIdO/eHTY2NlLHIiIq1B49eoTBgwfD0dERJiYmcHZ2xrhx4/Dy5UutjyWTybB3794MbRMnTsSRI0d0lDb3jh8/DplMxuUe9eDatWto1qwZzM3N4eTkhDlz5kAI8d59YmJi4OPjA2tra1hbW8PHxyfLvgkICECtWrVgZmYGe3t7jBkzRutz56R2Wrp0KapUqQJzc3OUKVMGEyZMQHJysub5+Ph4TJ06FS4uLjA3N0ejRo1w6dKlbF/f8OHDIZPJPvoPlpyQdJpBUFAQxo8fj1WrVqFx48ZYu3Yt2rdvjxs3bqBs2bKZto+IiECHDh0wdOhQ/PLLLzhz5gxGjRqFUqVK4fPPP5fgFeRcUsQVNGk4ENHRL2BpaYk1a9agX79+UsciIspfVCrg1CkgMhJwcACaNgWMjPR2unv37sHDwwOVK1fGtm3b4OLigr///huTJk3CgQMHcP78eZQoUeKjzlGkSBEUKVJER4nzB6VSCYVCIXWMfCEuLg6enp5o0aIFLl26hNu3b8PX1xeWlpb48ssvs92vb9++ePz4MQ4ePAgAGDZsGHx8fPDHH39otlm8eDEWLVqEhQsXokGDBkhOTsa9e/e0OndOaqfAwEBMmTIFGzZsQKNGjTTHAYAlS5YAAIYOHYpr165h06ZNKF26NH755Re0bt0aN27cgJOTU4bXtnfvXly4cAGOjo4f/w3OCSGh+vXrixEjRmRoq1q1qpgyZUqW20+ePFlUrVo1Q9vw4cNFw4YNc3zO2NhYAUDExsZqHzgX3iSnCiuPXgKQCQCiVq1a4p9//smTc5NupKamir1794rU1FSpo1AusQ/1LykpSdy4cUMkJSXl/iC7dglRurQQwP++SpcWYtcuoVKpRExMjFCpVLoLLYRo166dKF26tEhMTMzQHhkZKSwsLDL8G+Xs7CzmzJkj+vTpIywtLYWDg4NYvnx5hucBaL6cnZ2FEELMnDlT1K5dW7PdgAEDRNeuXcW8efOEra2tsLa2FrNmzRJKpVJMnDhRFC9eXDg5OQl/f/8MmSZPniwqVaokzM3NhYuLi/j6668z/Ey/e57/ioiIyJANgBgwYIAQQohmzZqJL774QkyaNEkUL15c2NnZiZkzZ2bYH4BYvXq16NKli7CwsBAzZswQQgjx+++/Czc3N2FqaipcXFw0r+O/mcqUKSNMTEyEvb29GDNmTIbv17x588TAgQNFkSJFRJkyZcTatWsznPfq1auiRYsWwszMTJQoUUIMHTpUxMfHZ/pepnvz5o3w8fERlpaWwt7eXvz444+iWbNmYty4cVl+X3Rh1apVwtraWiQnJ2va5s+fLxwdHYVarc5ynxs3bggA4vz585q2c+fOCQCaGuHVq1fC3NxcHD58+KPOnZPaafTo0aJly5YZtvHz8xNNmjQRQgiRmJgojIyMxPbt2zO8B2vXri2mT5+eYb/Hjx8LJycncf36deHs7CyWLFmSbf73/d7Qpl6TbGQ2NTUVoaGhmDJlSob2Nm3a4OzZs1nuc+7cObRp0yZDW9u2beHv75/tX4kpKSlISUnRPI6LiwPw9q9KpVL5sS/jg9LS0qBOfgNAYIDvICxftgTm5uZ5cm7SjfS+Yp8ZLvah/imVSgghoFaroVartT/A7t2Q9eoFCIH/rq4tnjwBevSA2LEDaN1acw5dePXqFQ4dOoRvv/0WpqamGY5ra2uLvn37IigoCCtXrtSs+b1w4UJMnToVM2bMQHBwMCZMmIDKlSvD09MTFy5cgL29Pfz9/dGuXTsYGRlBrVZrPvJNP74QAkePHoWTkxOOHz+OM2fOYOjQoTh79iw+/fRTnDt3Djt27MCIESPQqlUrlClTBsDbEd4NGzbA0dER165dw/Dhw1GkSBFMmjRJc9z/nue/nJyc8Ouvv6Jnz564efMmrKysYG5urtl206ZNmDBhAs6dO4dz585h0KBB8PDwgKenp+YYM2fOxLx587Bo0SIYGRnhwIED6NevH5YuXYqmTZvi7t27GDFiBIQQmDFjBnbu3IklS5Zg69atqF69Ou7du4d///03Q75FixZhzpw5mDJlCnbt2oWRI0eiSZMmqFq1KhITE9GuXTs0aNAAFy5cwPPnzzFs2DCMHj0aGzdu1Lzm//5MTJw4EceOHcOuXbtgb2+P6dOnIzQ0FLVr18725+bUqVPo2LHje39Wpk6diqlTp2b5XHq/KRQKzTk8PT0xdepU3Lt3Dy4uLpn2OXPmDKytrVGvXj3NPvXr14e1tTVOnz6NSpUq4dChQ1Cr1Xj06BGqVauG+Ph4eHh44Mcff9T8TOTk3OfOnYOnp2eG1+/p6Ql/f3+kpKRAoVCgUaNG+OWXX3D+/HnUr18f9+7dw/79+9G/f3+o1WqkpqZCpVLBzMwsw/fb3Nwcp0+f1jxWq9Xw8fHBxIkTUa1aNU0fZfe9T39/KJVKGL3zCYw2v68lK2ajo6OhUqlgZ2eXod3Ozg5RUVFZ7hMVFZXl9mlpaYiOjoaDg0OmfebPn4/Zs2dnag8ODoaFhcVHvIKcSVEBJVoOgXn5uujQ2Q3Hjh3T+zlJP0JCQqSOQB+Jfag/xsbGsLe3x5s3b5CamqrdzioVrMaNy1TIAoBMCAiZDLLx44G//kJ8fLyuIiM8PBxCCDg7O2sGOv7LxcUFMTExuHfvHkqVKgW1Wo369etj5MiRAID+/fvj+PHj+PHHH9GgQQOYmpoCAExNTTX/vsTFxSElJQUqlSrDYEqxYsUwd+5cyOVy9OjRAwsWLEB8fDxGjx4NABg1ahR++OEHHD58WPNR8BdffKHJ1qxZM4waNQrbt2/H8OHDASDTed5lZmYG4G0B8t98aWlpqF69uuYiqW7dumHFihU4cOAAGjRooNn/888/R48ePTSP586di3HjxuGzzz4DANjY2GDKlCmYNWsWxo8fjzt37sDW1hb169eHQqGAu7s73N3dNfnUajVat24Nb29vAMCIESOwZMkSHDx4EI6Ojti0aRMSExOxYsUKWFpaomzZsvj+++/Rp08fTJ8+Hba2tlAqlUhLS0NcXBzevHmDDRs2YPXq1ZrcK1asQI0aNZCamprt96Vy5co4efJkls+lK168eLb7P3nyBGXLls3wfPr39+7duyhZsmSmfR48eAAbG5tMx7SxscGDBw8QFxeHmzdvQq1W47vvvsP8+fNhZWWFefPmwdPTE6dPn4aJiUmOzv306VM0a9YswzZFihRBWloaIiIiYG9vjw4dOmDatGn49NNPIYRAWloaBg0ahJEjR2r2q1evHhYuXIjKlSvD1tYWO3fuxIULF1ChQgXNNosXLwYADBgwAHFxcVCr1UhOTs72e5eamoqkpCScPHkSaWlpGZ5LTEzMcp+sSL4017t3uBJCvPeuV1ltn1V7uqlTp8LPz0/zOC4uDmXKlEGbNm1gZWWV29g5JoRAy5YpOHpUjo5tW8PExETv5yTdUiqVCAkJgaenJ+eIGSj2of4lJyfj0aNHKFKkiKZoyrHjxyF/+jTbp2VCQPbkCYzPnYN5+/Y6uzOipaUlgLfFXVb/HqQXp1ZWVrCysoJcLkfTpk0zbPvpp59i2bJlGdrePZ6pqSmMjIw0bQqFAjVr1sxwq3IHBwfUqFEjw34lS5bEmzdvNG07d+7E8uXL8e+//+LNmzdIS0vTZMvqPO9KL3KKFi2aYRtjY2PUqlUrQ5uTkxNiY2MztHl4eGR4/NdffyEsLExTwABvV+xJTk6GsbEx+vXrh7Vr18LNzQ1t27ZF8+bN0bNnT817UC6Xw93dPcMxHRwcEB8fDysrK9y/fx916tTJMFCVPsL49OlTVKxYEQqFAsbGxrCyskJERARSU1PRsmVLzTGtrKxQpUoVmJiYZPt9sbKyyjRQpg0jI6NMx0//o6tIkSJZntfMzCzLvpLJZJqfHxMTEyiVSixfvlzzqXStWrXg6OiI0NBQtG3bNkfnlsvlmX4m038W0n9+jh8/jsWLF2PlypVo0KAB/v33X0yYMAHLly/H119/DQD45ZdfMHDgQFSvXh1GRkZwc3NDnz59EBYWBisrK4SGhuLnn3/G5cuXYW1tDeBtH5uZmWX7vU9OToa5uTk+/fTTTL83siuAsyJZMWtjYwMjI6NMo7DPnz/P9ofK3t4+y+2NjY2z/MsHePvmTv+F9F8KhSLP/lGzlslgagSYmJjwH1IDlpc/M6Qf7EP9UalUkMlkkMvlkMu1XCjn2bMcbSaLitKcQxcqV64MmUyGf/75J8tj3rp1C8WLF4etra2mgH73/DKZLFPbu9+D9H3T22QyGUxMTDJtk1WbEAJyuRznz59H3759MXv2bLRt2xbW1tbYvn07Fi1alOG4/z3Pu9Lbs+qjd88tl8s1505XtGjRDI/VajVmz56N7t27ZzqXhYUFihQpglu3biEkJAQhISGYOHEiVq1ahRMnTmjeh+97zemP380FvC0g5XJ5hu//f1//u6/vfT83p06dQvv27bN8Lt20adMwbdq0LJ9zcHDAs2fPMhw/Ojpa81xW581qHwB48eIF7O3tIZfLNRdP1axZU7OdnZ0dbGxs8PjxY8jl8hyd297ePsttjI2NUapUKcjlcsycORM+Pj4YNmwYAKB27dpISkrCsGHD8PXXX0Mul6NixYr4888/YWRkhDdv3sDBwQG9e/eGi4sL5HI5zpw5g+fPn6NcuXKa86hUKkycOBHLli3D/fv3M30f0vstq9/N2vyulqyYNTExgbu7O0JCQjQfUQBvPwbs2rVrlvt4eHhkuMoPeDtdoG7duvwHiojIUGUxRSwrwt5ep6ctWbIkPD09sWrVKkyYMAHm5uaa56KiohAYGIj+/ftnGAk+f/58hmOcP38eVatW1TxWKBRQqVQ6zQm8nWPp7OyM6dOna9oePHig1THSPxnUVT43NzfcunULFStWzHYbc3NzdOnSBZ06dUL//v1Rv359XLt2DW5ubh88fvXq1bFp0yYkJCRoRtHPnDkDuVyOypUrZ9o+faT2/PnzmhWRYmJicPv2bTRr1izb89StWxfh4eHvzfK+FS08PDwwbdo0pKamar7HwcHBcHR0zFDYvbtPbGwsLl68iPr16wMALly4gNjYWDRq1AgA0LhxYwBv/6gqXbo0gLfzvKOjo+Hs7Jzjc+ekdkpMTMxUWBsZGWnmJP+XpaUlihYtipiYGBw6dAgLFiwAAPj4+KB169YZtm3bti18fHwwcODAbL9/uiDpOrN+fn5Yv349NmzYgJs3b2LChAl4+PAhRowYAeDtFIH+/ftrth8xYgQePHgAPz8/3Lx5Exs2bIC/vz8mTpwo1UsgIqKP1bQpULo0kN30AZkMokwZpHl46PzUK1euREpKCtq2bYuTJ0/i0aNHOHjwIDw9PeHk5IR58+Zl2P7MmTNYsGABbt++jZ9++gm//vorxo0bp3m+XLlyOHLkCKKiohATE6OznBUrVsTDhw+xfft23L17F8uXL8eePXu0OoazszNkMhn27duHFy9e4M2bNx+VacaMGdi8eTNmzZqFv//+Gzdv3kRQUJDmY+mAgAD4+/vj+vXruHfvHoKCgmBubq4pxD7E29sbZmZmGDBgAK5fv45jx47hiy++gI+PT5af4BYpUgSDBw/GpEmTcOTIEVy/fh2+vr4fHMk3NzdHxYoV3/v1vmK2b9++MDU1ha+vL65fv449e/bgu+++g5+fn+YPoYsXL6Jq1ap48uQJAKBatWpo164dhg4divPnz+P8+fMYOnQoOnXqhCpVqgB4+8lB165dMW7cOJw9exbXr1/HgAEDULVqVbRo0SLH585J7dS5c2esXr0a27dvR0REBEJCQvDNN9+gS5cumguzDh06hMOHD2ueb9GiBapUqaIpVEuWLImaNWtm+FIoFLC3t9e8Jn2RtJjt3bs3li5dijlz5qBOnTo4efIk9u/fr/lBj4yMzHCHLBcXF+zfvx/Hjx9HnTp1MHfuXCxfvjzfrzFLRETvYWQELFv29v/fLWj//7FYvFgv681WqlQJly9fRoUKFdC7d29UqFABw4YNQ4sWLXDu3LlMRcyXX36J0NBQuLq6Yu7cuVi0aBHatm2reX7RokUICQlBmTJl4OrqqrOcXbt2xYQJEzBmzBjUqVMHZ8+exTfffKPVMZycnDB79mxMmTIFdnZ2mRbf11bbtm2xb98+hISEoF69emjYsCEWL16s+Te8WLFiWLduHRo3bqz5N/63337LdlrguywsLHDo0CG8evUK9erVQ48ePdCqVSusXLky230WLlyITz/9FF26dEHr1q3RpEkTuLu7f9Tr/BBra2uEhITg8ePHqFu3LkaNGgU/P78M1+skJibi1q1bGa7QDwwMxCeffII2bdqgTZs2qFWrFrZs2ZLh2Js3b0aDBg3QsWNHNGvWDAqFAgcPHtSMqObk3Dmpnb7++mt8+eWX+Prrr1G9enUMHjwYbdu2xdq1azXbxMbGYtKkSahevTr69++PJk2aIDg4OF98Mi4T744fF3BxcXGwtrbONLFdn5RKJfbv348OHTrki04n7bD/DB/7UP+Sk5MRERGhuaNjruzeDYwbBzx+/L+2MmWApUuh7tYNcXFxmgtapFCuXDmMHz+et0bNBbVaLXn/0cfRRx++7/eGNvWa5KsZEBERAQC6dwe6ds36DmA6WluWiAoeFrNERJR/GBkBzZtLnYKIDAiLWSIiohzIamkhIpIeJ64QERERkcFiMUtERDpTyK4pJqKPoKvfFyxmiYjoo6WvRZmamipxEiIyFOm/L4w+ctk9zpklIqKPZmxsDAsLC7x48QIKhULnyy+p1WqkpqYiOTmZSzsZIPaf4dN1H6rVarx48QIWFhYwNv64cpTFLBERfTSZTAYHBwdERERofZvVnBBCICkpCebm5hluL0uGgf1n+PTRh3K5HGXLlv3o47GYJSIinTAxMUGlSpX0MtVAqVTi5MmT+PTTT3njCwPE/jN8+uhDExMTnYzyspglIiKdkcvlub8D2HsYGRkhLS0NZmZmLIYMEPvP8OXnPuTEFSIiIiIyWCxmiYiIiMhgsZglIiIiIoNV6ObMpi/QGxcXl2fnVCqVSExMRFxcXL6bZ0Ifxv4zfOxDw8c+NGzsP8OX132YXqfl5MYKha6YjY+PBwCUKVNG4iRERERE9D7x8fGwtrZ+7zYyUcjuPahWq/H06VMULVo0z9a6i4uLQ5kyZfDo0SNYWVnlyTlJd9h/ho99aPjYh4aN/Wf48roPhRCIj4+Ho6PjB5fvKnQjs3K5HKVLl5bk3FZWVnwTGzD2n+FjHxo+9qFhY/8Zvrzsww+NyKbjBWBEREREZLBYzBIRERGRwWIxmwdMTU0xc+ZMmJqaSh2FcoH9Z/jYh4aPfWjY2H+GLz/3YaG7AIyIiIiICg6OzBIRERGRwWIxS0REREQGi8UsERERERksFrNEREREZLBYzOrAqlWr4OLiAjMzM7i7u+PUqVPv3f7EiRNwd3eHmZkZypcvjzVr1uRRUsqONn24e/dueHp6olSpUrCysoKHhwcOHTqUh2kpK9q+D9OdOXMGxsbGqFOnjn4D0gdp24cpKSmYPn06nJ2dYWpqigoVKmDDhg15lJbepW3/BQYGonbt2rCwsICDgwMGDhyIly9f5lFaetfJkyfRuXNnODo6QiaTYe/evR/cJ9/UM4I+yvbt24VCoRDr1q0TN27cEOPGjROWlpbiwYMHWW5/7949YWFhIcaNGydu3Lgh1q1bJxQKhdi5c2ceJ6d02vbhuHHjxA8//CAuXrwobt++LaZOnSoUCoW4cuVKHiendNr2YbrXr1+L8uXLizZt2ojatWvnTVjKUm76sEuXLqJBgwYiJCREREREiAsXLogzZ87kYWpKp23/nTp1SsjlcrFs2TJx7949cerUKVGjRg3RrVu3PE5O6fbv3y+mT58udu3aJQCIPXv2vHf7/FTPsJj9SPXr1xcjRozI0Fa1alUxZcqULLefPHmyqFq1aoa24cOHi4YNG+otI72ftn2YlerVq4vZs2frOhrlUG77sHfv3uLrr78WM2fOZDErMW378MCBA8La2lq8fPkyL+LRB2jbfwsXLhTly5fP0LZ8+XJRunRpvWWknMtJMZuf6hlOM/gIqampCA0NRZs2bTK0t2nTBmfPns1yn3PnzmXavm3btrh8+TKUSqXeslLWctOH71Kr1YiPj0eJEiX0EZE+ILd9uHHjRty9exczZ87Ud0T6gNz04e+//466detiwYIFcHJyQuXKlTFx4kQkJSXlRWT6j9z0X6NGjfD48WPs378fQgg8e/YMO3fuRMeOHfMiMulAfqpnjPP0bAVMdHQ0VCoV7OzsMrTb2dkhKioqy32ioqKy3D4tLQ3R0dFwcHDQW17KLDd9+K5FixYhISEBvXr10kdE+oDc9OGdO3cwZcoUnDp1CsbG/DUotdz04b1793D69GmYmZlhz549iI6OxqhRo/Dq1SvOm81juem/Ro0aITAwEL1790ZycjLS0tLQpUsXrFixIi8ikw7kp3qGI7M6IJPJMjwWQmRq+9D2WbVT3tG2D9Nt27YNs2bNQlBQEGxtbfUVj3Igp32oUqnQt29fzJ49G5UrV86reJQD2rwP1Wo1ZDIZAgMDUb9+fXTo0AGLFy9GQEAAR2clok3/3bhxA2PHjsWMGTMQGhqKgwcPIiIiAiNGjMiLqKQj+aWe4ZDER7CxsYGRkVGmvzyfP3+e6a+VdPb29llub2xsjJIlS+otK2UtN32YLigoCIMHD8avv/6K1q1b6zMmvYe2fRgfH4/Lly8jLCwMY8aMAfC2MBJCwNjYGMHBwWjZsmWeZKe3cvM+dHBwgJOTE6ytrTVt1apVgxACjx8/RqVKlfSamf4nN/03f/58NG7cGJMmTQIA1KpVC5aWlmjatCm+/fZbfkppAPJTPcOR2Y9gYmICd3d3hISEZGgPCQlBo0aNstzHw8Mj0/bBwcGoW7cuFAqF3rJS1nLTh8DbEVlfX19s3bqVc7wkpm0fWllZ4dq1awgPD9d8jRgxAlWqVEF4eDgaNGiQV9Hp/+Xmfdi4cWM8ffoUb9680bTdvn0bcrkcpUuX1mteyig3/ZeYmAi5PGMJYmRkBOB/o3uUv+WreibPLzkrYNKXI/H39xc3btwQ48ePF5aWluL+/ftCCCGmTJkifHx8NNunL2UxYcIEcePGDeHv78+luSSmbR9u3bpVGBsbi59++klERkZqvl6/fi3VSyj0tO3Dd3E1A+lp24fx8fGidOnSokePHuLvv/8WJ06cEJUqVRJDhgyR6iUUatr238aNG4WxsbFYtWqVuHv3rjh9+rSoW7euqF+/vlQvodCLj48XYWFhIiwsTAAQixcvFmFhYZrl1fJzPcNiVgd++ukn4ezsLExMTISbm5s4ceKE5rkBAwaIZs2aZdj++PHjwtXVVZiYmIhy5cqJ1atX53Fiepc2fdisWTMBINPXgAED8j44aWj7PvwvFrP5g7Z9ePPmTdG6dWthbm4uSpcuLfz8/ERiYmIep6Z02vbf8uXLRfXq1YW5ublwcHAQ3t7e4vHjx3mcmtIdO3bsvf+25ed6RiYEx/OJiIiIyDBxziwRERERGSwWs0RERERksFjMEhEREZHBYjFLRERERAaLxSwRERERGSwWs0RERERksFjMEhEREZHBYjFLRERERAaLxSwREYCAgAAUK1ZM6hi5Vq5cOSxduvS928yaNQt16tTJkzxERHmFxSwRFRi+vr6QyWSZvv7991+poyEgICBDJgcHB/Tq1QsRERE6Of6lS5cwbNgwzWOZTIa9e/dm2GbixIk4cuSITs6XnXdfp52dHTp37oy///5b6+MY8h8XRJR3WMwSUYHSrl07REZGZvhycXGROhYAwMrKCpGRkXj69Cm2bt2K8PBwdOnSBSqV6qOPXapUKVhYWLx3myJFiqBkyZIffa4P+e/r/PPPP5GQkICOHTsiNTVV7+cmosKHxSwRFSimpqawt7fP8GVkZITFixfjk08+gaWlJcqUKYNRo0bhzZs32R7nr7/+QosWLVC0aFFYWVnB3d0dly9f1jx/9uxZfPrppzA3N0eZMmUwduxYJCQkvDebTCaDvb09HBwc0KJFC8ycORPXr1/XjByvXr0aFSpUgImJCapUqYItW7Zk2H/WrFkoW7YsTE1N4ejoiLFjx2qe++80g3LlygEAPvvsM8hkMs3j/04zOHToEMzMzPD69esM5xg7diyaNWums9dZt25dTJgwAQ8ePMCtW7c027yvP44fP46BAwciNjZWM8I7a9YsAEBqaiomT54MJycnWFpaokGDBjh+/Ph78xBRwcZilogKBblcjuXLl+P69evYtGkTjh49ismTJ2e7vbe3N0qXLo1Lly4hNDQUU6ZMgUKhAABcu3YNbdu2Rffu3XH16lUEBQXh9OnTGDNmjFaZzM3NAQBKpRJ79uzBuHHj8OWXX+L69esYPnw4Bg4ciGPHjgEAdu7ciSVLlmDt2rW4c+cO9u7di08++STL4166dAkAsHHjRkRGRmoe/1fr1q1RrFgx7Nq1S9OmUqmwY8cOeHt76+x1vn79Glu3bgUAzfcPeH9/NGrUCEuXLtWM8EZGRmLixIkAgIEDB+LMmTPYvn07rl69ip49e6Jdu3a4c+dOjjMRUQEjiIgKiAEDBggjIyNhaWmp+erRo0eW2+7YsUOULFlS83jjxo3C2tpa87ho0aIiICAgy319fHzEsGHDMrSdOnVKyOVykZSUlOU+7x7/0aNHomHDhqJ06dIiJSVFNGrUSAwdOjTDPj179hQdOnQQQgixaNEiUblyZZGamprl8Z2dncWSJUs0j4H/a+eOQppqwziA/ze30TpLqC5KyRxODnpTNMiK8CIrikWLQVI5sIuENC1QiOimBWEg0kgC9SYUZWBdbCBURFoZVpA5olIRRdlFISJKkaa1fLr48NBx+pkmX9/G/3d33vfd2fPywviznWeQUCikW+Pz+WT79u3a9YULFyQvL0+7fvjwoVgsFhkfH/+jfQIQRVFk7dq1AkAAiNvtXnD9nKXOQ0RkcHBQDAaDfPjwQTe+f/9+uXz58r/en4gSl+nvRmkiotW1b98+1NXVadeKogAAnjx5guvXr6O3txefP39GNBrF9PQ0JicntTW/qqioQFFREZqbm3HgwAHk5+fD4XAAALq7uzE4OIhAIKCtFxHMzs5ieHgY2dnZC9b26dMn2Gw2iAimpqbgdDoRDAZhsVjQ19ena+ACgL1796KmpgYAkJ+fj5s3byIjIwOHDx+Gy+XC0aNHYTKt/GPc6/Viz549+PjxI1JTUxEIBOByubB+/fo/2ue6desQDocRjUbR0dGB6upq1NfX69Ys9zwAIBwOQ0SgqqpufGZm5j95FpiI/p8YZokooSiKgszMTN1YJBKBy+VCcXExrl27hg0bNqCzsxNnzpzB9+/fF7zP1atXUVBQgHv37uHBgwfw+XxoaWmBx+PB7Owszp49q3tmdc7WrVsXrW0u5BmNRmzatCkmtBkMBt21iGhjaWlp6O/vx6NHj9DW1oZz586huroaHR0dup/vlyMnJwcOhwMtLS0oKSlBKBRCQ0ODNr/SfRqNRu0MsrKyMDIyghMnTuDZs2cAVnYec/UkJSWhu7sbSUlJujmbzbasvRNR4mCYJaKE9/r1a0SjUdy4cQNG4z+tAnfv3l3ydaqqQlVVlJeX49SpU2hoaIDH44HT6URPT09MaF7KryFvvuzsbHR2dqKwsFAbe/Hihe7bT6vVCrfbDbfbjdLSUmRlZeHdu3dwOp0x9zObzb/1LwkFBQUIBALYsmULjEYjjhw5os2tdJ/zlZeXw+/3IxQKwePx/NZ5WCyWmPp37NiBHz9+YHR0FLm5uX9UExElDjaAEVHCczgciEajuHXrFoaGhtDc3Bzzs/evvn79irKyMjx9+hSRSATPnz9HV1eXFiwvXbqEly9forS0FG/evMHAwABaW1tx/vz5Fdd48eJFNDY2or6+HgMDA/D7/QgGg1rjU2NjI27fvo33799re7BarUhPT1/wfna7He3t7RgZGcHExMSi7+v1ehEOh1FZWYnjx49jzZo12txq7TM5ORlFRUXw+XwQkd86D7vdji9fvqC9vR1jY2OYmpqCqqrwer0oLCxEMBjE8PAwurq6UFVVhfv37y+rJiJKIH/zgV0iotV0+vRpOXbs2IJzfr9fUlJSxGq1yqFDh6SpqUkAyMTEhIjoG45mZmbk5MmTkpaWJhaLRVJTU6WsrEzX9PTq1Ss5ePCg2Gw2URRFtm3bJpWVlYvWtlBD03y1tbWSkZEhZrNZVFWVpqYmbS4UCsmuXbskOTlZFEWR3bt3S1tbmzY/vwGstbVVMjMzxWQySXp6uojENoDN2blzpwCQx48fx8yt1j4jkYiYTCa5c+eOiCx9HiIixcXFsnHjRgEgPp9PRES+ffsmV65cEbvdLmazWTZv3iwej0fevn27aE1ElNgMIiJ/N04TEREREa0MHzMgIiIiorjFMEtEREREcYthloiIiIjiFsMsEREREcUthlkiIiIiilsMs0REREQUtxhmiYiIiChuMcwSERERUdximCUiIiKiuMUwS0RERERxi2GWiIiIiOLWT0RX4vrN89rdAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "     - AUC: 98.44%\n",
+      "    - Optimal Threshold: 0.0600894\n",
+      "    - F1 Score: 0.92\n",
+      "    - CONFUSION MATRIX:\n",
+      " [[20  0]\n",
+      " [12 68]] \n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:04<00:00,  9.10it/s]\n",
+      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.11it/s]\n",
+      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.11it/s]\n",
+      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:11<00:00,  9.09it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.10it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "- OK - Evaluating model (33.23 s)\n",
+      "\n",
+      "Dataset               F1 Score\n",
+      "------------------------------\n",
+      "Anonaly lvl 1 test        1.00\n",
+      "Anonaly lvl 2 test        0.95\n",
+      "Anonaly lvl 3 test        0.67\n",
       "\n",
-      "Anomaly all              95.00\n",
+      "Anomaly all test          0.91\n",
       "\n",
-      "No Anomaly Train         98.75\n",
-      "No Anomaly Test          90.00\n",
-      "No Anomaly All           97.00\n",
+      "No Anomaly Test           1.00\n",
       "\n",
-      "All without train        94.17\n",
-      "All with train           96.00\n"
+      "All test                  0.92\n"
      ]
     }
    ],
    "source": [
-    "# STEPS = 5000, MODEL TYPE = MEDIUM, WEIGHT = none\n",
-    "model8 = EfficientAD({**config, \"train_steps\": 5000, \"model_type\": \"medium\", \"weight_path\":\"\"})\n",
-    "model8.create_model()\n",
-    "model8.display_eval_result()"
+    "# STEPS = 500, MODEL TYPE = MEDIUM, WEIGHT = on\n",
+    "model17 = EfficientAD({**config, \"train_steps\": 500, \"model_type\": \"medium\", \"weight_path\":\"../weights/teacher_medium.pth\"})\n",
+    "model17.create_model()\n",
+    "model17.display_eval_result()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (0.63 ms)\n",
-      "\n",
-      "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (3.28 ms)\n",
-      "\n",
-      "- Setting config\n",
-      "     Output folder path: ../output/cookies_3_steps_10000_medium\n",
-      "- OK - Setting config (0.11 ms)\n",
-      "\n",
-      "- Prepare teacher, student & autoencoder\n",
-      "     No weight to load\n",
-      "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (159.12 ms)\n",
-      "\n",
-      "- Normalizing teacher\n"
+      "- Setting seed to 42\n",
+      "- OK - Setting seed to 42 (0.55 ms)\n",
+      "\n",
+      "- Setting datasets path\n",
+      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
+      "- OK - Setting datasets path (4.36 ms)\n",
+      "\n",
+      "- Setting config\n",
+      "     Output folder path: ../output/cookies_3_steps_5000_medium_weighted\n",
+      "- OK - Setting config (0.12 ms)\n",
+      "\n",
+      "- Prepare teacher, student & autoencoder\n",
+      "     Loading weight ../weights/teacher_medium.pth\n",
+      "     Training\n",
+      "- OK - Prepare teacher, student & autoencoder (190.37 ms)\n",
+      "\n",
+      "- Normalizing teacher\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.36it/s]\n",
+      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 21.49it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- OK - Normalizing teacher (6.73 s)\n",
+      "\n",
+      "- Train\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 68.6309  :   0%|                                                                                  | 1/5000 [00:03<4:59:09,  3.59s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.95\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 20.9237  :   2%|█▌                                                                              | 101/5000 [00:40<1:43:55,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.926829268292683\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 14.7919  :   4%|███▏                                                                            | 201/5000 [01:16<1:41:43,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9230769230769231\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 15.1827  :   6%|████▊                                                                           | 301/5000 [01:53<1:39:48,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.918918918918919\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 12.6867  :   8%|██████▍                                                                         | 401/5000 [02:30<1:37:45,  1.28s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9473684210526315\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 11.1644  :  10%|████████                                                                        | 501/5000 [03:06<1:35:23,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9743589743589743\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 11.8949  :  12%|█████████▌                                                                      | 601/5000 [03:43<1:33:37,  1.28s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.975609756097561\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 9.6371  :  14%|███████████▎                                                                     | 701/5000 [04:20<1:31:20,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.975609756097561\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 11.0092  :  16%|████████████▊                                                                   | 801/5000 [04:57<1:29:30,  1.28s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.975609756097561\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 8.0689  :  18%|██████████████▌                                                                  | 901/5000 [05:34<1:27:04,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9473684210526315\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 7.4727  :  20%|████████████████                                                                | 1001/5000 [06:11<1:25:12,  1.28s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 7.5814  :  22%|█████████████████▌                                                              | 1101/5000 [06:48<1:22:49,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 6.9276  :  24%|███████████████████▏                                                            | 1201/5000 [07:25<1:20:37,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.975609756097561\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 7.7713  :  26%|████████████████████▊                                                           | 1301/5000 [08:02<1:19:24,  1.29s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.9743589743589743\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 8.5872  :  28%|██████████████████████▍                                                         | 1401/5000 [08:39<1:16:35,  1.28s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.975609756097561\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 22.50it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 22.91it/s]\n"
+      "       Current loss: 6.7772  :  30%|████████████████████████                                                        | 1501/5000 [09:16<1:14:17,  1.27s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (6.35 s)\n",
-      "\n",
-      "- Train\n"
+      "F1 Validation 0.9743589743589743\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 0.4128  : 100%|█████████████████████████████████████████████████████| 10000/10000 [53:23<00:00,  3.12it/s]\n"
+      "       Current loss: 7.7838  :  32%|█████████████████████████▌                                                      | 1601/5000 [09:53<1:12:15,  1.28s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (3203.09 s)\n",
-      "\n",
-      "- Saving models to ../output/cookies_3_steps_10000_medium/all_models.pth\n",
-      "- OK - Saving models (200.33 ms)\n",
-      "\n",
-      "- Saving map normalization to ../output/cookies_3_steps_10000_medium/map_normalization.pth\n"
+      "F1 Validation 0.9473684210526315\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 11.38it/s]\n"
+      "       Current loss: 6.4460  :  34%|███████████████████████████▏                                                    | 1701/5000 [10:29<1:10:02,  1.27s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (797.16 ms)\n",
-      "\n",
-      "- Evaluating model\n"
+      "F1 Validation 0.9743589743589743\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:21<00:00,  9.45it/s]\n"
+      "       Current loss: 7.3359  :  36%|████████████████████████████▊                                                   | 1801/5000 [11:06<1:11:02,  1.33s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "     - AUC: 99.46%\n",
-      "    - Optimal Threshold: 0.1133724\n",
-      "    - F1 Score: 0.96\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[97  3]\n",
-      " [ 4 96]] \n",
-      "\n"
+      "F1 Validation 1.0\n"
      ]
     },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:05<00:00,  9.42it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:02<00:00,  9.42it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:02<00:00,  9.41it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.42it/s]\n"
+      "       Current loss: 10.7029  :  38%|██████████████████████████████                                                 | 1901/5000 [11:43<1:06:01,  1.28s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 1.0\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:08<00:00,  9.47it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.50it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.47it/s]\n"
+      "       Current loss: 10.7263  :  40%|███████████████████████████████▌                                               | 2001/5000 [12:20<1:03:56,  1.28s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.9743589743589743\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:12<00:00,  9.47it/s]\n"
+      "       Current loss: 7.4899  :  42%|█████████████████████████████████▌                                              | 2101/5000 [12:57<1:01:49,  1.28s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (76.41 s)\n",
-      "\n",
-      "Dataset               Accuracy\n",
-      "------------------------------\n",
-      "Anonaly lvl 1           100.00\n",
-      "Anonaly lvl 2            84.00\n",
-      "Anonaly lvl 3           100.00\n",
-      "\n",
-      "Anomaly all              96.00\n",
-      "\n",
-      "No Anomaly Train         98.75\n",
-      "No Anomaly Test          90.00\n",
-      "No Anomaly All           97.00\n",
-      "\n",
-      "All without train        95.00\n",
-      "All with train           96.50\n"
+      "F1 Validation 1.0\n"
      ]
-    }
-   ],
-   "source": [
-    "# STEPS = 10000, MODEL TYPE = MEDIUM, WEIGHT = none\n",
-    "model9 = EfficientAD({**config, \"train_steps\": 10000, \"model_type\": \"medium\", \"weight_path\":\"\"})\n",
-    "model9.create_model()\n",
-    "model9.display_eval_result()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 7.1119  :  44%|████████████████████████████████████                                              | 2201/5000 [13:34<59:39,  1.28s/it]"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (0.24 ms)\n",
-      "\n",
-      "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (3.19 ms)\n",
-      "\n",
-      "- Setting config\n",
-      "     Output folder path: ../output/cookies_3_steps_20_small_weighted\n",
-      "- OK - Setting config (0.10 ms)\n",
-      "\n",
-      "- Prepare teacher, student & autoencoder\n",
-      "     Loading weight ../weights/teacher_small.pth\n",
-      "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (76.65 ms)\n",
-      "\n",
-      "- Normalizing teacher\n"
+      "F1 Validation 0.9473684210526315\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 53.39it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 54.27it/s]\n"
+      "       Current loss: 6.1965  :  46%|█████████████████████████████████████▋                                            | 2301/5000 [14:11<57:25,  1.28s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (2.68 s)\n",
-      "\n",
-      "- Train\n"
+      "F1 Validation 1.0\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 40.6282  : 100%|██████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  8.00it/s]\n"
+      "       Current loss: 6.8960  :  48%|███████████████████████████████████████▍                                          | 2401/5000 [14:48<55:23,  1.28s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (2.50 s)\n",
-      "\n",
-      "- Saving models to ../output/cookies_3_steps_20_small_weighted/all_models.pth\n",
-      "- OK - Saving models (79.82 ms)\n",
-      "\n",
-      "- Saving map normalization to ../output/cookies_3_steps_20_small_weighted/map_normalization.pth\n"
+      "F1 Validation 0.975609756097561\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 29.45it/s]\n"
+      "       Current loss: 8.0248  :  50%|█████████████████████████████████████████                                         | 2500/5000 [15:25<15:25,  2.70it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (304.86 ms)\n",
+      "F1 Validation 1.0\n",
+      "Early stopping at iteration 2501 because validation F1 did not improve.\n",
+      "- OK - Train (925.18 s)\n",
       "\n",
-      "- Evaluating model\n"
+      "- Saving models to ../output/cookies_3_steps_5000_medium_weighted/all_models.pth\n",
+      "- OK - Saving models (153.25 ms)\n",
+      "\n",
+      "- Saving map normalization to ../output/cookies_3_steps_5000_medium_weighted/map_normalization.pth\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:08<00:00, 22.98it/s]\n"
+      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:02<00:00, 10.25it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "- OK - Saving map normalization (2833.80 ms)\n",
       "\n",
-      "     - AUC: 77.20%\n",
-      "    - Optimal Threshold: 0.1018831\n",
-      "    - F1 Score: 0.75\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[62 38]\n",
-      " [17 83]] \n",
-      "\n"
+      "- Evaluating model\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:11<00:00,  9.06it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -1933,20 +4225,17 @@
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 23.18it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 23.13it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 23.36it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 23.23it/s]\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\n",
+      "     - AUC: 98.81%\n",
+      "    - Optimal Threshold: 0.0241348\n",
+      "    - F1 Score: 0.99\n",
+      "    - CONFUSION MATRIX:\n",
+      " [[18  2]\n",
+      " [ 0 80]] \n",
       "\n"
      ]
     },
@@ -1954,9 +4243,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:03<00:00, 23.24it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 23.19it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 23.16it/s]\n"
+      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:04<00:00,  9.09it/s]\n",
+      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.11it/s]\n",
+      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.05it/s]\n",
+      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:11<00:00,  9.09it/s]\n"
      ]
     },
     {
@@ -1970,42 +4260,78 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:05<00:00, 23.18it/s]\n"
+      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.08it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (31.34 s)\n",
       "\n",
-      "Dataset               Accuracy\n",
+      "- OK - Evaluating model (33.28 s)\n",
+      "\n",
+      "Dataset               F1 Score\n",
       "------------------------------\n",
-      "Anonaly lvl 1            96.00\n",
-      "Anonaly lvl 2            88.00\n",
-      "Anonaly lvl 3            52.00\n",
+      "Anonaly lvl 1 test        1.00\n",
+      "Anonaly lvl 2 test        1.00\n",
+      "Anonaly lvl 3 test        1.00\n",
       "\n",
-      "Anomaly all              83.00\n",
+      "Anomaly all test          1.00\n",
       "\n",
-      "No Anomaly Train         60.00\n",
-      "No Anomaly Test          70.00\n",
-      "No Anomaly All           62.00\n",
+      "No Anomaly Test           0.95\n",
       "\n",
-      "All without train        80.83\n",
-      "All with train           72.50\n"
+      "All test                  0.99\n"
      ]
     }
    ],
    "source": [
-    "# STEPS = 20, MODEL TYPE = SMALL, WEIGHT = on\n",
-    "model11 = EfficientAD({**config, \"train_steps\": 20, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\"})\n",
-    "model11.create_model()\n",
-    "model11.display_eval_result()"
+    "# STEPS = 5000, MODEL TYPE = MEDIUM, WEIGHT = on\n",
+    "model18 = EfficientAD({**config, \"train_steps\": 5000, \"model_type\": \"medium\", \"weight_path\":\"../weights/teacher_medium.pth\"})\n",
+    "model18.create_model()\n",
+    "model18.display_eval_result()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# STEPS = 10000, MODEL TYPE = MEDIUM, WEIGHT = on\n",
+    "model19 = EfficientAD({**config, \"train_steps\": 10000, \"model_type\": \"medium\", \"weight_path\":\"../weights/teacher_medium.pth\"})\n",
+    "model19.create_model()\n",
+    "model19.display_eval_result()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# STEPS = 5000, MODEL TYPE = SMALL, WEIGHT = on Cookies 1\n",
+    "model20 = EfficientAD({**config, \"train_steps\": 5000, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\", \"subdataset\": \"cookies_1\"})\n",
+    "model20.create_model()\n",
+    "model20.display_eval_result()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Create model for cookies 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -2013,20 +4339,20 @@
      "output_type": "stream",
      "text": [
       "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (0.70 ms)\n",
+      "- OK - Setting seed to 42 (0.25 ms)\n",
       "\n",
       "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (2.55 ms)\n",
+      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
+      "- OK - Setting datasets path (4.36 ms)\n",
       "\n",
       "- Setting config\n",
-      "     Output folder path: ../output/cookies_3_steps_500_small_weighted\n",
-      "- OK - Setting config (0.10 ms)\n",
+      "     Output folder path: ../output/cookies_2_steps_5000_small_weighted\n",
+      "- OK - Setting config (0.14 ms)\n",
       "\n",
       "- Prepare teacher, student & autoencoder\n",
       "     Loading weight ../weights/teacher_small.pth\n",
       "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (76.63 ms)\n",
+      "- OK - Prepare teacher, student & autoencoder (77.57 ms)\n",
       "\n",
       "- Normalizing teacher\n"
      ]
@@ -2035,15 +4361,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 54.45it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 53.43it/s]\n"
+      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.45it/s]\n",
+      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 50.70it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (2.67 s)\n",
+      "- OK - Normalizing teacher (2.83 s)\n",
       "\n",
       "- Train\n"
      ]
@@ -2052,395 +4378,287 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 8.0053  : 100%|█████████████████████████████████████████████████████████| 500/500 [01:00<00:00,  8.27it/s]\n"
+      "       Current loss: 45.0005  :   0%|                                                                                  | 1/5000 [00:01<2:16:40,  1.64s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (60.47 s)\n",
-      "\n",
-      "- Saving models to ../output/cookies_3_steps_500_small_weighted/all_models.pth\n",
-      "- OK - Saving models (79.77 ms)\n",
-      "\n",
-      "- Saving map normalization to ../output/cookies_3_steps_500_small_weighted/map_normalization.pth\n"
+      "F1 Validation 0.9743589743589743\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 30.26it/s]\n"
+      "       Current loss: 21.3039  :   2%|█▋                                                                                | 101/5000 [00:15<41:08,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (296.61 ms)\n",
-      "\n",
-      "- Evaluating model\n"
+      "F1 Validation 0.95\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:08<00:00, 23.14it/s]\n"
+      "       Current loss: 15.4218  :   4%|███▎                                                                              | 201/5000 [00:29<40:05,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "     - AUC: 99.82%\n",
-      "    - Optimal Threshold: 0.3038742\n",
-      "    - F1 Score: 0.97\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[100   0]\n",
-      " [  5  95]] \n",
-      "\n"
+      "F1 Validation 0.9473684210526315\n"
      ]
     },
     {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 12.9569  :   6%|████▉                                                                             | 301/5000 [00:43<39:49,  1.97it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 1.0\n"
+     ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 22.79it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 22.69it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 22.89it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.91it/s]\n"
+      "       Current loss: 12.2017  :   8%|██████▌                                                                           | 401/5000 [00:57<38:36,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 1.0\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:03<00:00, 22.96it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.79it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.95it/s]\n"
+      "       Current loss: 12.1251  :  10%|████████▏                                                                         | 501/5000 [01:11<37:37,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 1.0\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:05<00:00, 22.92it/s]\n"
+      "       Current loss: 9.4938  :  12%|█████████▉                                                                         | 601/5000 [01:25<37:39,  1.95it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (31.57 s)\n",
-      "\n",
-      "Dataset               Accuracy\n",
-      "------------------------------\n",
-      "Anonaly lvl 1           100.00\n",
-      "Anonaly lvl 2           100.00\n",
-      "Anonaly lvl 3            80.00\n",
-      "\n",
-      "Anomaly all              95.00\n",
-      "\n",
-      "No Anomaly Train        100.00\n",
-      "No Anomaly Test         100.00\n",
-      "No Anomaly All          100.00\n",
-      "\n",
-      "All without train        95.83\n",
-      "All with train           97.50\n"
+      "F1 Validation 1.0\n"
      ]
-    }
-   ],
-   "source": [
-    "# STEPS = 500, MODEL TYPE = SMALL, WEIGHT = on\n",
-    "model12 = EfficientAD({**config, \"train_steps\": 500, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\"})\n",
-    "model12.create_model()\n",
-    "model12.display_eval_result()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 9.2203  :  14%|███████████▋                                                                       | 701/5000 [01:39<36:02,  1.99it/s]"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (0.26 ms)\n",
-      "\n",
-      "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (3.11 ms)\n",
-      "\n",
-      "- Setting config\n",
-      "     Output folder path: ../output/cookies_3_steps_5000_small_weighted\n",
-      "- OK - Setting config (0.11 ms)\n",
-      "\n",
-      "- Prepare teacher, student & autoencoder\n",
-      "     Loading weight ../weights/teacher_small.pth\n",
-      "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (74.94 ms)\n",
-      "\n",
-      "- Normalizing teacher\n"
+      "F1 Validation 1.0\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 52.86it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 53.52it/s]\n"
+      "       Current loss: 7.6707  :  16%|█████████████▎                                                                     | 801/5000 [01:53<35:40,  1.96it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (2.71 s)\n",
-      "\n",
-      "- Train\n"
+      "F1 Validation 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 8.4446  :  18%|██████████████▉                                                                    | 901/5000 [02:07<34:21,  1.99it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 1.0\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 7.4782  :  20%|████████████████▍                                                                 | 1001/5000 [02:20<33:44,  1.98it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 1.0\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 5.6433  : 100%|███████████████████████████████████████████████████████| 5000/5000 [10:08<00:00,  8.22it/s]\n"
+      "       Current loss: 6.9326  :  22%|██████████████████                                                                | 1101/5000 [02:35<32:53,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (608.47 s)\n",
-      "\n",
-      "- Saving models to ../output/cookies_3_steps_5000_small_weighted/all_models.pth\n",
-      "- OK - Saving models (82.01 ms)\n",
-      "\n",
-      "- Saving map normalization to ../output/cookies_3_steps_5000_small_weighted/map_normalization.pth\n"
+      "F1 Validation 1.0\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 29.54it/s]\n"
+      "       Current loss: 7.2532  :  24%|███████████████████▋                                                              | 1201/5000 [02:49<32:35,  1.94it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (304.11 ms)\n",
-      "\n",
-      "- Evaluating model\n"
+      "F1 Validation 1.0\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:08<00:00, 22.93it/s]\n"
+      "       Current loss: 6.8009  :  26%|█████████████████████▎                                                            | 1301/5000 [03:03<32:28,  1.90it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "     - AUC: 100.00%\n",
-      "    - Optimal Threshold: 0.3997492\n",
-      "    - F1 Score: 1.00\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[100   0]\n",
-      " [  0 100]] \n",
-      "\n"
+      "F1 Validation 1.0\n"
      ]
     },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 23.18it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 23.15it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 23.08it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 23.11it/s]\n"
+      "       Current loss: 6.8656  :  28%|██████████████████████▉                                                           | 1401/5000 [03:17<30:51,  1.94it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 1.0\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:03<00:00, 22.81it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 23.10it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 23.03it/s]\n"
+      "       Current loss: 8.0031  :  30%|████████████████████████▌                                                         | 1501/5000 [03:31<29:32,  1.97it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 1.0\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:05<00:00, 23.06it/s]\n"
+      "       Current loss: 6.6937  :  32%|██████████████████████████▎                                                       | 1601/5000 [03:45<29:02,  1.95it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (31.52 s)\n",
-      "\n",
-      "Dataset               Accuracy\n",
-      "------------------------------\n",
-      "Anonaly lvl 1           100.00\n",
-      "Anonaly lvl 2           100.00\n",
-      "Anonaly lvl 3           100.00\n",
-      "\n",
-      "Anomaly all             100.00\n",
-      "\n",
-      "No Anomaly Train        100.00\n",
-      "No Anomaly Test         100.00\n",
-      "No Anomaly All          100.00\n",
-      "\n",
-      "All without train       100.00\n",
-      "All with train          100.00\n"
-     ]
-    }
-   ],
-   "source": [
-    "# STEPS = 5000, MODEL TYPE = SMALL, WEIGHT = on\n",
-    "model13 = EfficientAD({**config, \"train_steps\": 5000, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\"})\n",
-    "model13.create_model()\n",
-    "model13.display_eval_result()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (0.26 ms)\n",
-      "\n",
-      "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (3.31 ms)\n",
-      "\n",
-      "- Setting config\n",
-      "     Output folder path: ../output/cookies_3_steps_10000_small_weighted\n",
-      "- OK - Setting config (0.11 ms)\n",
-      "\n",
-      "- Prepare teacher, student & autoencoder\n",
-      "     Loading weight ../weights/teacher_small.pth\n",
-      "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (73.51 ms)\n",
-      "\n",
-      "- Normalizing teacher\n"
+      "F1 Validation 1.0\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 52.21it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 54.24it/s]\n"
+      "       Current loss: 6.6296  :  34%|███████████████████████████▉                                                      | 1701/5000 [03:59<27:46,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (2.71 s)\n",
-      "\n",
-      "- Train\n"
+      "F1 Validation 1.0\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 3.6031  : 100%|█████████████████████████████████████████████████████| 10000/10000 [20:16<00:00,  8.22it/s]\n"
+      "       Current loss: 7.7260  :  36%|█████████████████████████████▌                                                    | 1800/5000 [04:13<07:30,  7.11it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (1216.70 s)\n",
+      "F1 Validation 1.0\n",
+      "Early stopping at iteration 1801 because validation F1 did not improve.\n",
+      "- OK - Train (253.17 s)\n",
       "\n",
-      "- Saving models to ../output/cookies_3_steps_10000_small_weighted/all_models.pth\n",
-      "- OK - Saving models (79.84 ms)\n",
+      "- Saving models to ../output/cookies_2_steps_5000_small_weighted/all_models.pth\n",
+      "- OK - Saving models (66.52 ms)\n",
       "\n",
-      "- Saving map normalization to ../output/cookies_3_steps_10000_small_weighted/map_normalization.pth\n"
+      "- Saving map normalization to ../output/cookies_2_steps_5000_small_weighted/map_normalization.pth\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 29.48it/s]\n"
+      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:00<00:00, 28.77it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (304.57 ms)\n",
+      "- OK - Saving map normalization (1008.57 ms)\n",
       "\n",
       "- Evaluating model\n"
      ]
@@ -2449,26 +4667,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:08<00:00, 22.82it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "     - AUC: 100.00%\n",
-      "    - Optimal Threshold: 0.4698580\n",
-      "    - F1 Score: 1.00\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[100   0]\n",
-      " [  0 100]] \n",
-      "\n"
+      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.33it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -2476,20 +4680,17 @@
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 23.11it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 23.09it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 23.06it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.96it/s]\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\n",
+      "     - AUC: 96.88%\n",
+      "    - Optimal Threshold: 0.0606422\n",
+      "    - F1 Score: 0.90\n",
+      "    - CONFUSION MATRIX:\n",
+      " [[20  0]\n",
+      " [15 65]] \n",
       "\n"
      ]
     },
@@ -2497,9 +4698,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:03<00:00, 23.00it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 23.01it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.90it/s]\n"
+      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 22.29it/s]\n",
+      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.24it/s]\n",
+      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.25it/s]\n",
+      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.32it/s]\n"
      ]
     },
     {
@@ -2513,49 +4715,47 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:05<00:00, 22.86it/s]\n"
+      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.32it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (31.85 s)\n",
       "\n",
-      "Dataset               Accuracy\n",
+      "- OK - Evaluating model (13.67 s)\n",
+      "\n",
+      "Dataset               F1 Score\n",
       "------------------------------\n",
-      "Anonaly lvl 1           100.00\n",
-      "Anonaly lvl 2           100.00\n",
-      "Anonaly lvl 3           100.00\n",
+      "Anonaly lvl 1 test        1.00\n",
+      "Anonaly lvl 2 test        0.97\n",
+      "Anonaly lvl 3 test        0.46\n",
       "\n",
-      "Anomaly all             100.00\n",
+      "Anomaly all test          0.90\n",
       "\n",
-      "No Anomaly Train        100.00\n",
-      "No Anomaly Test         100.00\n",
-      "No Anomaly All          100.00\n",
+      "No Anomaly Test           1.00\n",
       "\n",
-      "All without train       100.00\n",
-      "All with train          100.00\n"
+      "All test                  0.90\n"
      ]
     }
    ],
    "source": [
-    "# STEPS = 10000, MODEL TYPE = SMALL, WEIGHT = on\n",
-    "model14 = EfficientAD({**config, \"train_steps\": 10000, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\"})\n",
-    "model14.create_model()\n",
-    "model14.display_eval_result()"
+    "# STEPS = 5000, MODEL TYPE = SMALL, WEIGHT = on Cookies 2\n",
+    "model21 = EfficientAD({**config, \"train_steps\": 5000, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\", \"subdataset\": \"cookies_2\"})\n",
+    "model21.create_model()\n",
+    "model21.display_eval_result()"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "---"
+    "### Create model for cookies 1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -2563,20 +4763,20 @@
      "output_type": "stream",
      "text": [
       "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (0.20 ms)\n",
+      "- OK - Setting seed to 42 (0.60 ms)\n",
       "\n",
       "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (2.65 ms)\n",
+      "     Dataset paths: dict_keys(['anomaly_lvl_1_test_paths', 'anomaly_lvl_2_test_paths', 'anomaly_lvl_3_test_paths', 'no_anomaly_test_paths', 'all_anomaly_test_paths', 'train_paths', 'test_paths', 'val_paths'])\n",
+      "- OK - Setting datasets path (5.18 ms)\n",
       "\n",
       "- Setting config\n",
-      "     Output folder path: ../output/cookies_3_steps_20_medium_weighted\n",
-      "- OK - Setting config (0.10 ms)\n",
+      "     Output folder path: ../output/cookies_1_steps_10000_small_weighted\n",
+      "- OK - Setting config (0.12 ms)\n",
       "\n",
       "- Prepare teacher, student & autoencoder\n",
-      "     Loading weight ../weights/teacher_medium.pth\n",
+      "     Loading weight ../weights/teacher_small.pth\n",
       "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (208.75 ms)\n",
+      "- OK - Prepare teacher, student & autoencoder (74.60 ms)\n",
       "\n",
       "- Normalizing teacher\n"
      ]
@@ -2585,15 +4785,15 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 22.47it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 22.59it/s]\n"
+      "    Computing mean of features: 100%|█████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.11it/s]\n",
+      "    Computing std of features: 100%|██████████████████████████████████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 51.27it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (6.40 s)\n",
+      "- OK - Normalizing teacher (2.82 s)\n",
       "\n",
       "- Train\n"
      ]
@@ -2602,1138 +4802,733 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 37.6393  : 100%|██████████████████████████████████████████████████████████| 20/20 [00:06<00:00,  3.07it/s]\n"
+      "       Current loss: 29.0107  :   0%|                                                                                 | 1/10000 [00:01<4:27:46,  1.61s/it]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (6.52 s)\n",
-      "\n",
-      "- Saving models to ../output/cookies_3_steps_20_medium_weighted/all_models.pth\n",
-      "- OK - Saving models (202.60 ms)\n",
-      "\n",
-      "- Saving map normalization to ../output/cookies_3_steps_20_medium_weighted/map_normalization.pth\n"
+      "F1 Validation 0.8571428571428571\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 11.31it/s]\n"
+      "       Current loss: 16.2858  :   1%|▊                                                                              | 101/10000 [00:15<1:23:02,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (801.08 ms)\n",
-      "\n",
-      "- Evaluating model\n"
+      "F1 Validation 0.7878787878787878\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:21<00:00,  9.37it/s]\n"
+      "       Current loss: 11.0156  :   2%|█▌                                                                             | 201/10000 [00:29<1:22:09,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "     - AUC: 64.58%\n",
-      "    - Optimal Threshold: 0.0098568\n",
-      "    - F1 Score: 0.71\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[36 64]\n",
-      " [ 9 91]] \n",
-      "\n"
+      "F1 Validation 0.8571428571428571\n"
      ]
     },
     {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 10.6710  :   3%|██▍                                                                            | 301/10000 [00:43<1:21:19,  1.99it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8571428571428571\n"
+     ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:05<00:00,  9.37it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:02<00:00,  9.39it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:02<00:00,  9.41it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.39it/s]\n"
+      "       Current loss: 10.1724  :   4%|███▏                                                                           | 401/10000 [00:57<1:20:11,  2.00it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.8571428571428571\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:08<00:00,  9.39it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.39it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.36it/s]\n"
+      "       Current loss: 9.2264  :   5%|████                                                                            | 501/10000 [01:11<1:20:38,  1.96it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.8571428571428571\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:12<00:00,  9.38it/s]\n"
+      "       Current loss: 8.6771  :   6%|████▊                                                                           | 601/10000 [01:25<1:18:50,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (77.00 s)\n",
-      "\n",
-      "Dataset               Accuracy\n",
-      "------------------------------\n",
-      "Anonaly lvl 1            82.00\n",
-      "Anonaly lvl 2           100.00\n",
-      "Anonaly lvl 3           100.00\n",
-      "\n",
-      "Anomaly all              91.00\n",
-      "\n",
-      "No Anomaly Train         36.25\n",
-      "No Anomaly Test          35.00\n",
-      "No Anomaly All           36.00\n",
-      "\n",
-      "All without train        81.67\n",
-      "All with train           63.50\n"
+      "F1 Validation 0.8571428571428571\n"
      ]
-    }
-   ],
-   "source": [
-    "# STEPS = 20, MODEL TYPE = MEDIUM, WEIGHT = on\n",
-    "model16 = EfficientAD({**config, \"train_steps\": 20, \"model_type\": \"medium\", \"weight_path\":\"../weights/teacher_medium.pth\"})\n",
-    "model16.create_model()\n",
-    "model16.display_eval_result()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 9.7556  :   7%|█████▌                                                                          | 701/10000 [01:38<1:18:12,  1.98it/s]"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (0.27 ms)\n",
-      "\n",
-      "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (3.29 ms)\n",
-      "\n",
-      "- Setting config\n",
-      "     Output folder path: ../output/cookies_3_steps_500_medium_weighted\n",
-      "- OK - Setting config (0.10 ms)\n",
-      "\n",
-      "- Prepare teacher, student & autoencoder\n",
-      "     Loading weight ../weights/teacher_medium.pth\n",
-      "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (188.02 ms)\n",
-      "\n",
-      "- Normalizing teacher\n"
+      "F1 Validation 0.8571428571428571\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 22.26it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 22.49it/s]\n"
+      "       Current loss: 8.3746  :   8%|██████▍                                                                         | 801/10000 [01:53<1:17:51,  1.97it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (6.44 s)\n",
-      "\n",
-      "- Train\n"
+      "F1 Validation 0.8571428571428571\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 9.1853  : 100%|█████████████████████████████████████████████████████████| 500/500 [02:41<00:00,  3.09it/s]\n"
+      "       Current loss: 9.5946  :   9%|███████▏                                                                        | 901/10000 [02:06<1:15:53,  2.00it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (161.58 s)\n",
-      "\n",
-      "- Saving models to ../output/cookies_3_steps_500_medium_weighted/all_models.pth\n",
-      "- OK - Saving models (202.63 ms)\n",
-      "\n",
-      "- Saving map normalization to ../output/cookies_3_steps_500_medium_weighted/map_normalization.pth\n"
+      "F1 Validation 0.8571428571428571\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 11.27it/s]\n"
+      "       Current loss: 8.4501  :  10%|███████▉                                                                       | 1001/10000 [02:20<1:15:38,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (804.28 ms)\n",
-      "\n",
-      "- Evaluating model\n"
+      "F1 Validation 0.8571428571428571\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:21<00:00,  9.40it/s]\n"
+      "       Current loss: 8.2370  :  11%|████████▋                                                                      | 1101/10000 [02:34<1:14:31,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "     - AUC: 99.43%\n",
-      "    - Optimal Threshold: 0.2382182\n",
-      "    - F1 Score: 0.96\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[ 92   8]\n",
-      " [  0 100]] \n",
-      "\n"
+      "F1 Validation 0.8571428571428571\n"
      ]
     },
     {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIhCAYAAABdSTJTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACIyklEQVR4nOzdd1iT198G8DuBMBVcbBVxV60KWBVHrSjuVetAceDei7ptVbTWqnXvgaLWgbutdYB7L4SqdVZxg4qiIDMk5/3Dl/waGRIEHgL357q42pw84w6H4JeT85xHJoQQICIiIiLSQ3KpAxARERERZRWLWSIiIiLSWyxmiYiIiEhvsZglIiIiIr3FYpaIiIiI9BaLWSIiIiLSWyxmiYiIiEhvsZglIiIiIr3FYpaIiIiI9BaLWSKiNPj7+0Mmk2m+DA0NYWdnB09PT9y7dy/NfZRKJVauXAk3NzdYWlrC1NQUX3zxBSZOnIjXr1+nuY9arcbmzZvRtGlTlChRAgqFAtbW1mjTpg3+/PNPqNXqT2ZNTEzEsmXL0KBBAxQtWhRGRkZwcHBAly5dcPLkyc/6PhAR5XUsZomIMrBhwwacP38eR44cwfDhw/HHH3+gQYMGiIqK0touLi4OHh4eGDFiBJydnbFt2zYcOHAAPXv2xJo1a+Ds7Iw7d+5o7ZOQkIBWrVqhd+/esLa2xsqVK3Hs2DGsWrUK9vb26Ny5M/78888M80VGRqJ+/frw8fFBtWrV4O/vj6NHj2L+/PkwMDBAkyZN8Pfff2f794WIKM8QRESUyoYNGwQAcfnyZa12X19fAUCsX79eq33gwIECgNi+fXuqY925c0dYWlqKqlWriuTkZE37kCFDBACxcePGNDPcvXtX/P333xnmbNmypTA0NBRHjx5N8/lLly6JR48eZXiMzIqLi8uW4xARZSeOzBIR6aBWrVoAgBcvXmjaIiIisH79ejRv3hxdu3ZNtU/FihUxYcIE/PPPP9i3b59mn3Xr1qF58+bo1atXmueqUKECqlevnm6W4OBgHDx4EP369YO7u3ua23z11VcoXbo0AGD69OmQyWSptkmZUvHw4UNNW5kyZdCmTRvs2bMHzs7OMDExga+vL5ydndGwYcNUx1CpVHBwcEDHjh01bUlJSfjpp59QuXJlGBsbw8rKCn369MGrV6/SfU1ERLpiMUtEpIOwsDAAHwrUFMePH0dycjI6dOiQ7n4pzwUFBWn2USqVGe7zKYGBgVrHzm5Xr17FuHHjMHLkSBw6dAjfffcd+vTpgzNnzqSaNxwYGIjnz5+jT58+AD7MBW7fvj1++eUXdO/eHX/99Rd++eUXBAUF4ZtvvkF8fHyOZCaigsdQ6gBERHmZSqVCcnIyEhIScPbsWfz000/4+uuv0a5dO802jx8/BgA4OTmle5yU51K2zcw+n5Idx8jIy5cvcfPmTa3CvWzZshg3bhz8/f0xa9YsTbu/vz9sbGzQsmVLAMCOHTtw6NAh7N69W2u0tkaNGvjqq6/g7++PIUOG5EhuIipYODJLRJSBunXrQqFQoHDhwmjRogWKFi2K33//HYaGWRsLSOtj/ryqevXqWoUsABQvXhxt27bFxo0bNSstREVF4ffff0evXr0035f9+/ejSJEiaNu2LZKTkzVfNWvWhK2tLU6cOJHbL4eI8ikWs0REGdi0aRMuX76MY8eOYdCgQbh16xa6deumtU3KnNSUKQhpSXmuVKlSmd7nU7LjGBmxs7NLs71v37549uyZZsrEtm3bkJiYCG9vb802L168wNu3b2FkZASFQqH1FRERgcjIyBzJTEQFD4tZIqIMfPHFF6hVqxYaN26MVatWoX///jh06BB27dql2aZx48YwNDTUXNyVlpTnPDw8NPsoFIoM9/mU5s2bax37U0xMTAB8WJf2v9IrLNMbRW7evDns7e2xYcMGAB+WL6tTpw6qVKmi2aZEiRIoXrw4Ll++nObXihUrMpWZiOhTWMwSEelg7ty5KFq0KKZOnar5mN3W1hZ9+/bF4cOHERAQkGqfu3fvYs6cOahatarmYi1bW1v0798fhw8fxqZNm9I81/3793Ht2rV0s7i4uKBly5bw8/PDsWPH0tzmypUrmrm1ZcqUAYBUx/zUWrYfMzAwQM+ePbFv3z6cPn0aV65cQd++fbW2adOmDV6/fg2VSoVatWql+qpUqZJO5yQiSo9MCCGkDkFElNf4+/ujT58+uHz5smY5rhTz5s3D+PHjsXnzZvTo0QMAEBsbi9atW+Ps2bMYOHAg2rZtC2NjY1y4cAG//vorzMzMcOTIEa0iLiEhAR06dEBgYCC6deuGb7/9FjY2NoiMjERQUBA2bNiA7du3o3379unmjIyMRIsWLXD9+nX07dsXLVu2RNGiRREeHo4///wT27ZtQ3BwMGrUqIHo6Gg4OTnBwcEBM2bMgKGhIfz9/XH16lWEhYUhLCxMU/CWKVMG1apVw/79+9M87927d1GpUiWULFkSr1+/Rnh4OCwtLTXPq1QqtG3bFhcvXsSoUaNQu3ZtKBQKPH36FMePH0f79u3x7bffZrV7iIj+R+qFbomI8qL0bpoghBDx8fGidOnSokKFClo3QUhKShLLly8XderUEYUKFRLGxsaiUqVKYvz48SIyMjLN8yQnJ4uNGzcKd3d3UaxYMWFoaCisrKxEy5YtxdatW4VKpfpk1vj4eLFkyRLh5uYmLCwshKGhobC3txcdO3YUf/31l9a2ly5dEvXq1RPm5ubCwcFBTJs2Taxbt04AEGFhYZrtHB0dRevWrTM8b7169QQA4eXllebzSqVS/Prrr6JGjRrCxMREFCpUSFSuXFkMGjRI3Lt375Ovi4goMzgyS0RERER6i3NmiYiIiEhvsZglIiIiIr3FYpaIiIiI9BaLWSIiIiLSWyxmiYiIiEhvsZglIiIiIr1lKHWA3KZWq/H8+XMULlw43Vs1EhEREZF0hBCIiYmBvb095PKMx14LXDH7/PlzlCpVSuoYRERERPQJT548QcmSJTPcpsAVs4ULFwbw4ZtjYWGRK+dUKpUIDAxEs2bNoFAocuWclH3Yf/qPfaj/2If6jf2n/3K7D6Ojo1GqVClN3ZaRAlfMpkwtsLCwyNVi1szMDBYWFnwT6yH2n/5jH+o/9qF+Y//pP6n6MDNTQnkBGBERERHpLRazRERERKS3WMwSERERkd5iMUtEREREeovFLBERERHpLRazRERERKS3WMwSERERkd5iMUtEREREeovFLBERERHpLRazRERERKS3WMwSERERkd5iMUtEREREeovFLBERERHpLUOpA1AepFIBp08D4eGAnR3QsCFgYCB1KiIiIqJUJB2ZPXXqFNq2bQt7e3vIZDLs27fvk/ucPHkSrq6uMDExQdmyZbFq1aqcD1qQ7NkDlCkDNG4MdO/+4b9lynxoJyIiIspjJC1mY2NjUaNGDSxbtixT24eFhaFVq1Zo2LAhQkJCMHnyZIwcORK7d+/O4aQFxJ49QKdOwNOn2u3Pnn1oZ0FLREREeYyk0wxatmyJli1bZnr7VatWoXTp0li0aBEA4IsvvsCVK1fw66+/4rvvvsuhlAWESgWMGgUIAQAQAOIVxv97XiYDvh8PtGxd4KYcKJXJSFQBcUnJUAiZ1HEoC9iH+o99qN/Yf/ovPj4BiSpA/H+dkJfo1ZzZ8+fPo1mzZlptzZs3h5+fH5RKJRQKRap9EhMTkZiYqHkcHR0NAFAqlVAqlTkb+P+lnCe3zpclZ84Ar18DpqYQADw7zcRV+8qpt/M9kuvR8gZDjL90TOoQ9FnYh/qPfajf2H/6Ku7eRUQdWwcbz5/g7p4IS1nO/0GiS82kV8VsREQEbGxstNpsbGyQnJyMyMhI2NnZpdpn9uzZ8PX1TdUeGBgIMzOzHMualqCgoFw9n862bQMAJKqAq5f06keDiIiIsplQKRF1wh8xV34HALw7vxPHjhWHcS58QBsXF5fpbfWuYpF99NdAynD3x+0pJk2aBB8fH83j6OholCpVCs2aNYOFhUXOBf0PpVKJoKAgeHh4pDl6nCecOQO0bg0AiDM0xvihvwEALqztB1Pl/0a2sXs3UM9NioSSUSqTcezYMbi7u0Oh0Lu3DIF9mB+wD/Ub+0//PHz4EP28e+HxlSsAgEFDhuLrRo3RunlTGBkZ5fj5Uz5Jzwy9+omytbVFRESEVtvLly9haGiI4sWLp7mPsbExjI2NU7UrFIpcLyylOGemff01ULw48OwZFIZqTbNFzFuYKRM/zJktWRJwb1QA58wqYWwAWJqb5N3+owyxD/Uf+1C/sf/0y549e9C3b1+8e/cORYsWhb+/P1q2bIkDBw7AyMgoV/pQl3Po1U0T3NzcUn1UHxgYiFq1avHN8bkMDIDFiz/8/8ej3CmPFy0qcIUsERFRQRIVFYV+/frh3bt3cHNzQ0hICNq1ayd1rAxJWsy+f/8eoaGhCA0NBfBh6a3Q0FA8fvwYwIcpAr169dJsP3jwYDx69Ag+Pj64desW1q9fDz8/P4wdO1aK+PlPx47Arl2Anb12e8mSH9o7dpQmFxEREeWKokWLYsOGDRg/fjxOnjwJR0dHqSN9kqTF7JUrV+Ds7AxnZ2cAgI+PD5ydnTF16lQAQHh4uKawBQAnJyccOHAAJ06cQM2aNTFz5kwsWbKEy3Jlp44dgVs3//f4wEEgLIyFLBERUT61Y8cOHDp0SPO4Q4cOmDNnjt586i3pnNlvvvkmw/XK/P39U7U1atQIV69ezcFUpDWV4GveypaIiCg/io+Ph4+PD1atWoXixYvj2rVrsLe3//SOeYxeXQBGRERERJ/vzp076NKlC65duwaZTIbBgwfD2tpa6lhZwmKWiIiIqADZsmULBg0ahNjYWFhbW+O3336Dh4eH1LGyjMUsERERUQGgUqkwaNAg+Pn5AQAaN26MLVu2pHnTKX2iV0tzUfYTQiAuKfmjL5XUsYiIiCibGfz/NTAymQzTp09HUFCQ3heyAEdmCzQhBDqtOo/gR1FSRyEiIqIckpCQABMTEwDAkiVL4O3tjQYNGkicKvtwZLYAi1eqMixkazkWhamCKxkQERHpo/fv36N3795o37491OoPd/c0MzPLV4UswJFZ+n9XfmgKMyPtwtVUYQDZx3cDIyIiojzv+vXr6NKlC27fvg25XI4LFy6gXr16UsfKERyZJQCAmZEBzIwMtb5YyBIREekXIQTWrl2L2rVr4/bt23BwcMCJEyfybSELcGS2wBBCIF6pfWEXL/QiIiLKP6KjozFo0CBs374dANCyZUts2rQJJUqUkDhZzmIxWwDwQi8iIqL8z9PTEwcPHoSBgQFmz56N77//HnJ5/v8QnsVsAcALvYiIiPK/WbNm4d9//8XGjRvh5uYmdZxcw2K2gOGFXkRERPnDu3fvcO7cObRs2RIA4OzsjJs3b8LQsGCVd/l/7LkASn0jhP/NjeWFXkRERPrvypUrcHFxQfv27XHlyhVNe0ErZAGOzOY7nB9LRESUfwkhsGTJEowbNw5KpRJlypSROpLkWMzmMxnNj+XcWCIiIv0VFRWFvn37Yt++fQCAjh07ws/PD0WKFJE0l9RYzOZjH8+P5dxYIiIi/XTx4kV07doVjx49gpGREebPn49hw4bx33WwmNVrn1o7NmV+LBEREem3kydP4tGjRyhXrhx27NgBFxcXqSPlGax09BTnxhIRERUcY8eOhUwmw6BBg2BhYSF1nDyFqxnoKa4dS0RElH+dOXMGzZs3R2xsLABALpdj3LhxLGTTwJHZfIBrxxIREeUParUac+bMwY8//giVSoVZs2bh559/ljpWnsZiNh/g3FgiIiL99/LlS/Ts2ROBgYEAgB49emDy5MkSp8r7WAERERERSezEiRPo3r07wsPDYWpqimXLlqFPnz78lDUTWMwSERERSei3335D7969oVarUaVKFezYsQNVq1aVOpbe4AVgRERERBJyd3dH8eLF0adPH1y6dImFrI44MktERESUy+7evYuKFSsCAOzt7fH333/Dzs5O4lT6iSOzRERERLkkOTkZU6dOxRdffIHdu3dr2lnIZh2LWSIiIqJc8OzZMzRp0gQzZ86EWq3GhQsXpI6UL3CaAREREVEOO3ToEHr27InIyEgUKlQIa9euhaenp9Sx8gWOzBIRERHlEKVSiYkTJ6Jly5aIjIyEs7Mzrl69ykI2G7GY1QNCCMQlJX/0pZI6FhEREX3CqVOnMGfOHADAsGHDcO7cOVSoUEHiVPkLpxnkcUIIdFp1HsGPoqSOQkRERDpq0qQJJk+eDGdnZ3Tq1EnqOPkSR2bzuHilKsNCtpZjUZgqDHIxEREREaUnKSkJP/74I549e6ZpmzVrFgvZHMSRWT1y5YemMDPSLlxNFQa81R0REVEe8PDhQ3Tt2hWXLl3C6dOncfz4cf4bnQtYzOYCIQQSVUBcUjIUQrcf6v/OjTUzMoCZEbuMiIgor9m7dy/69u2Lt2/fokiRIhgzZgwL2VzCyiiHCSHgue4yrj42xPhLx6SOQ0RERNkoMTER48aNw9KlSwEAdevWxfbt2+Ho6ChxsoKDxWwOi1eqcPXx288+DufGEhER5S3Pnj1D+/btERwcDAAYN24cZs2aBYVCIXGygoXFbC66MKERLMxNsrQv58YSERHlLUWKFEF8fDyKFy+OjRs3onXr1lJHKpBYzOYiU855JSIi0msJCQkwMjKCXC6Hubk59u7dCzMzM5QsWVLqaAUWl+YiIiIiyoQ7d+6gTp06mDt3rqatYsWKLGQlxmKWiIiI6BO2bNkCV1dXXLt2DUuWLEFsbKzUkej/sZglIiIiSkdcXBz69++PHj16IDY2Ft988w2uXLkCc3NzqaPR/2MxS0RERJSGW7duoU6dOvDz84NMJsO0adNw5MgR2NvbSx2N/oNXIxERERF9JDo6GvXr10dUVBRsbW2xZcsWuLu7Sx2L0sCRWSIiIqKPWFhYYMaMGWjatClCQ0NZyOZhLGaJiIiIAFy/fh2hoaGax8OGDcPhw4dhY2MjXSj6JBazREREVKAJIbB27VrUrl0bnTp1QnR0NABAJpNBLmeplNdxziwREREVWDExMRg0aBC2bdsGAKhQoQKUSqXEqUgX/HODiIiICqTQ0FC4urpi27ZtMDAwwC+//IK//voLxYsXlzoa6YAjs0RERFSgCCGwatUqjBkzBomJiShVqhS2b9+OevXqSR2NsoAjs0RERFSgCCHwxx9/IDExEW3btkVISAgLWT3GkVkiIiIqUORyOTZt2oSdO3diyJAhkMlkUkeiz8CRWSIiIsrXhBBYvHgxhgwZommzsrLC0KFDWcjmAxyZJSIionwrKioKffv2xb59+wAAnTt35g0Q8hkWs0RERJQvXbx4EV27dsWjR49gZGSE+fPno3HjxlLHomzGaQZERESUrwghMH/+fDRo0ACPHj1CuXLlcO7cOQwfPpzTCvIhjswSERFRvtK3b1/4+/sDALp06YI1a9bA0tJS2lCUYzgyS0RERPlK165dYWpqipUrV2L79u0sZPM5jswSERGRXlOr1bh79y4qV64MAGjRogXCwsJgY2MjcTLKDRyZJSIiIr318uVLtGrVCnXr1kVYWJimnYVswcFiloiIiPTSyZMnUbNmTRw+fBhJSUm4fv261JFIAixmiYiISK+oVCrMmDED7u7uCA8PxxdffIFLly6hXbt2UkcjCXDOLBEREemNiIgI9OjRA0ePHgUAeHt7Y9myZTA3N5c4GUmFxSwRERHpjcWLF+Po0aMwMzPDypUr0atXL6kjkcRYzBIREZHemDZtGp4+fYopU6ZoVi+ggo1zZomIiCjPevbsGcaOHYvk5GQAgImJCTZv3sxCljQ4MktERER50qFDh9CzZ09ERkbCwsICU6dOlToS5UEcmSUiIqI8RalUYtKkSWjZsiUiIyNRs2ZNeHp6Sh2L8iiOzBIREVGe8eTJE3h6euLcuXMAgKFDh2L+/PkwMTGROBnlVSxmiYiIKE84evQounTpgjdv3sDCwgLr1q1D586dpY5FeRyLWSIiIsoTbG1tER8fD1dXVwQEBKBcuXJSRyI9wGKWiIiIJBMbG6u54UHVqlVx9OhRuLi4wNjYWOJkpC94ARgRERFJYt++fShTpoxmfiwAuLm5sZAlnbCYJSIiolyVmJiIUaNG4dtvv0VkZCQWLlwodSTSY5IXsytWrICTkxNMTEzg6uqK06dPZ7j9li1bUKNGDZiZmcHOzg59+vTB69evcyktERERfY779++jfv36WLJkCQBg7Nix2Lp1q8SpSJ9JWswGBARg9OjRmDJlCkJCQtCwYUO0bNkSjx8/TnP7M2fOoFevXujXrx/++ecf7Ny5E5cvX0b//v1zOTkRERHpaufOnXB2dkZwcDCKFSuG/fv3Y968eVAoFFJHIz0maTG7YMEC9OvXD/3798cXX3yBRYsWoVSpUli5cmWa21+4cAFlypTByJEj4eTkhAYNGmDQoEG4cuVKLicnIiIiXVy/fh1eXl6IiYlB/fr1ERoaitatW0sdi/IByVYzSEpKQnBwMCZOnKjV3qxZM62J4P9Vr149TJkyBQcOHEDLli3x8uVL7Nq1K8M3Q2JiIhITEzWPo6OjAXy4u4hSqcyGV5IxpTJZ6/9z45yUvVL6jH2nv9iH+o99qN+USiWqVauGDh06oGLFipg+fToMDQ3Zn3okt9+DupxHsmI2MjISKpUKNjY2Wu02NjaIiIhIc5969ephy5Yt6Nq1KxISEpCcnIx27dph6dKl6Z5n9uzZ8PX1TdUeGBgIMzOzz3sRmZCoAlK+zceOHYOxQY6fknJIUFCQ1BHoM7EP9R/7UL+cO3cONWvWhJmZGWQyGXr16gW5XI7AwECpo1EW5dZ7MC4uLtPbSr7OrEwm03oshEjVluLmzZsYOXIkpk6diubNmyM8PBzjxo3D4MGD4efnl+Y+kyZNgo+Pj+ZxdHQ0SpUqhWbNmsHCwiL7Xkg64pKSMf7SMQCAu7s7LM15Oz59o1QqERQUBA8PD87r0lPsQ/3HPtQvcXFx8PHxwfr169G5c2ds2LABR44cQfPmzdl/eiq334Mpn6RnhmTFbIkSJWBgYJBqFPbly5epRmtTzJ49G/Xr18e4ceMAANWrV4e5uTkaNmyIn376CXZ2dqn2MTY2TnO9OoVCkSudoRD/K8wVCkO+ifVYbv3MUM5hH+o/9mHed+vWLXTp0gU3btyATCbDF198AUPDD+UG+0//5Vr9pMM5JLsAzMjICK6urqmGq4OCglCvXr0094mLi4Ncrh3ZwODD5/ZCiJwJSkRERJmyceNG1KpVCzdu3ICNjQ2CgoLg6+ub6t9uouwk6U+Xj48P1q1bh/Xr1+PWrVsYM2YMHj9+jMGDBwP4MEWgV69emu3btm2LPXv2YOXKlXjw4AHOnj2LkSNHonbt2rC3t5fqZRARERVosbGx8Pb2hre3N+Li4tCkSROEhoaiSZMmUkejAkDSObNdu3bF69evMWPGDISHh6NatWo4cOAAHB0dAQDh4eFaa856e3sjJiYGy5Ytw/fff48iRYrA3d0dc+bMkeolEBERFXhxcXEIDAyEXC6Hr68vJk2apPnklCinSX4B2NChQzF06NA0n/P390/VNmLECIwYMSKHUxEREVFmWVlZISAgAGq1Go0aNZI6DhUwnMRCREREOomJiYGXlxe2bNmiaWvYsCELWZIEi1kiIiLKtNDQULi6umLr1q0YPny4TksoEeUEFrNERET0SUIIrFy5EnXr1sW9e/dQsmRJ7N+/P1fWbCfKiORzZomIiChve/fuHQYMGICdO3cCANq0aQN/f38UL15c4mRELGaJiIgoA7GxsXB1dcX9+/dhaGiIOXPmYMyYMenerZMot3GaAREREaXL3Nwc3333HRwdHXHmzBn4+PiwkKU8hcUsERERaYmKisLTp081j3/66SeEhISgTp06EqYiShuLWSIiItK4ePEinJ2d0alTJyiVSgCAQqFA0aJFJU5GlDYWs0RERAQhBObPn48GDRrg0aNHePXqFZ49eyZ1LKJPYjFLRERUwL1+/Rrt2rXD2LFjkZycjM6dO+Pq1asoU6aM1NGIPonFLBERUQF29uxZ1KxZE/v374exsTFWrlyJgIAAWFpaSh2NKFO4NBcREVEBJYTAmDFj8PTpU1SoUAE7duxAzZo1pY5FpBOOzBIRERVQMpkMW7ZsQb9+/RAcHMxClvQSi1kiIqIC5OTJk1i8eLHmcYUKFbBu3ToULlxYwlREWcdpBkRERAWASqXCzz//jOnTp0MIARcXFzRs2FDqWESfjcUsERFRPhcREYEePXrg6NGjAIDevXvDxcVF4lRE2YPFLBERUT529OhReHl54cWLFzAzM8OKFSvQu3dvqWMRZRvOmSUiIsqnZs+eDQ8PD7x48QLVqlXD5cuXWchSvsNiloiIKJ+ytraGEAL9+/fHxYsXUaVKFakjEWU7TjMgIiLKR96/f49ChQoBAPr27YtKlSqhQYMGEqciyjkcmSUiIsoHkpOTMWnSJFSrVg1v3rwB8GEdWRaylN+xmCUiItJzT548wTfffINffvkFjx49wu7du6WORJRrWMwSERHpsb/++gs1a9bE2bNnYWFhgYCAAAwYMEDqWES5hsUsERGRHkpKSsLYsWPRpk0bvHnzBq6urrh69Sq6dOkidTSiXMViloiISA9Nnz4d8+fPBwCMHDkSZ8+eRbly5SRORZT7WMwSERHpobFjx6JGjRrYs2cPFi9eDGNjY6kjEUmCxSwREZEeSExMxKZNmyCEAAAUK1YMV69exbfffitxMiJpcZ1ZIiKiPO7Bgwfo0qULgoODkZiYqLnASy7nmBQR3wVERER52K5du+Ds7Izg4GAUK1YMdnZ2UkciylNYzBIREeVBCQkJGDZsGDp37ozo6GjUq1cPoaGhaNOmjdTRiPIUFrNERER5zL179+Dm5oYVK1YAACZOnIgTJ06gVKlSEicjyns4Z5aIiCiPefr0Kf7++2+UKFECmzdvRosWLaSORJRnsZglIiLKA4QQkMlkAIDGjRvD398fTZo0gYODg8TJiPI2TjMgIiKS2K1bt9CgQQPcvXtX09arVy8WskSZwGKWiIhIQhs3bkStWrVw7tw5jBw5Uuo4RHqHxSwREZEEYmNj4e3tDW9vb8TFxcHd3R3+/v5SxyLSOyxmiYiIctmNGzfw1VdfYePGjZDL5ZgxYwYCAwNha2srdTQivcMLwIiIiHLRxYsX0bhxY8THx8POzg5bt27FN998I3UsIr3FYpaIiCgXubi4oEaNGrCwsMDmzZthbW0tdSQivcZiloiIKIfdvHkTFSpUgEKhgEKhwF9//YUiRYpALudsP6LPxXcRERFRDhFCYNWqVXBxccGUKVM07cWKFWMhS5RNODJLRESUA6KjozFgwADs2LEDwIe1ZFUqFQwMDCRORpS/8M9CIiKibBYcHAwXFxfs2LEDhoaGmDdvHn7//XcWskQ5gCOzRERE2UQIgWXLlmHs2LFISkqCo6Mjtm/fjrp160odjSjf4sgsERFRNnn27BkmT56MpKQkdOjQASEhISxkiXIYR2aJiIiyScmSJbF27Vq8fPkSI0aMgEwmkzoSUb7HYpaIiCiLhBBYuHAhnJ2d0bhxYwCAp6enxKmIChYWs0RERFnw5s0beHt7488//4StrS1u3ryJokWLSh2LqMBhMUtERKSjc+fOwdPTE0+ePIGxsTGmTp2KIkWKSB2LqEDiBWBERESZpFarMWfOHHz99dd48uQJKlSogAsXLmDIkCGcH0skEY7MEhERZUJ8fDy+++47HDx4EADQrVs3rF69GoULF5Y4GVHBxpFZIiKiTDAxMUGRIkVgYmKCNWvWYMuWLSxkifIAFrNERETpUKlUiI2NBQDIZDKsXr0aly9fxoABAzitgCiPYDFLRESUhhcvXqBFixbo0aMHhBAAgMKFC6NatWoSJyOi/+KcWSIioo8cO3YMXl5eiIiIgJmZGW7fvo0vvvhC6lhElAaOzBIREf0/lUqFadOmoWnTpoiIiEDVqlVx+fJlFrJEeRhHZomIiAA8f/4cXl5eOHHiBACgX79+WLJkCczMzKQNRkQZYjFLREQFnhAC7du3x5UrV2Bubo7Vq1fDy8tL6lhElAlZmmaQnJyMI0eOYPXq1YiJiQHw4S/a9+/fZ2s4IiKi3CCTybBkyRK4urri6tWrLGSJ9IjOI7OPHj1CixYt8PjxYyQmJsLDwwOFCxfG3LlzkZCQgFWrVuVETiIiomz19OlThIaGok2bNgAANzc3XL58mUtuEekZnUdmR40ahVq1aiEqKgqmpqaa9m+//RZHjx7N1nBEREQ54cCBA6hZsya6dOmCGzduaNpZyBLpH51HZs+cOYOzZ8/CyMhIq93R0RHPnj3LtmBERETZTalUYsqUKZg3bx4AwMXFRWtghoj0j87FrFqthkqlStX+9OlT3taPiIjyrEePHsHT0xMXLlwAAIwYMQLz5s2DsbGxxMmI6HPoPM3Aw8MDixYt0jyWyWR4//49pk2bhlatWmVnNiIiomzx+++/w9nZGRcuXIClpSV2796NJUuWsJAlygd0HplduHAhGjdujCpVqiAhIQHdu3fHvXv3UKJECWzbti0nMhIREX2Wq1evIioqCrVr18b27dvh5OQkdSQiyiY6F7P29vYIDQ3F9u3bERwcDLVajX79+sHLy4vzjoiIKM8QQmgu6Jo6dSqsra0xYMCAVNd8EJF+03mawalTp6BQKNCnTx8sW7YMK1asQP/+/aFQKHDq1KmcyEhERKST3bt3w93dHQkJCQAAAwMDDBs2jIUsUT6kczHbuHFjvHnzJlX7u3fv0Lhx42wJRURElBUJCQkYPnw4OnXqhBMnTmD58uVSRyKiHKbzNIP/fmzzX69fv4a5uXm2hCIiItLVvXv30LVrV4SEhAAAJkyYgJEjR0qciohyWqaL2Y4dOwL4sHqBt7e31hWgKpUK165dQ7169bI/IRER0Sds374dAwYMwPv371GiRAls2rQJLVu2lDoWEeWCTBezlpaWAD6MzBYuXFjrYi8jIyPUrVsXAwYMyP6EREREGZg/fz7Gjh0LAGjYsCG2bdsGBwcHiVMRUW7JdDG7YcMGAECZMmUwduxYTikgIqI84bvvvsPPP/+MoUOHYtq0aTA01HkGHRHpMZ3f8dOmTcuJHERERJkWEhICZ2dnAB8GWe7du4dixYpJnIqIpKDzagYAsGvXLnTp0gV169aFi4uL1peuVqxYAScnJ5iYmMDV1RWnT5/OcPvExERMmTIFjo6OMDY2Rrly5bB+/fqsvAwiItIzsbGx6Nu3L1xcXHDgwAFNOwtZooJL52J2yZIl6NOnD6ytrRESEoLatWujePHiePDggc6T7QMCAjB69GhMmTIFISEhaNiwIVq2bInHjx+nu0+XLl1w9OhR+Pn54c6dO9i2bRsqV66s68sgIiI98/jxY9SrVw8bNmyAXC7HnTt3pI5ERHmAztMMVqxYgTVr1qBbt27YuHEjxo8fj7Jly2Lq1Klprj+bkQULFqBfv37o378/AGDRokU4fPgwVq5cidmzZ6fa/tChQzh58iQePHig+Su8TJkyur4EIiLSI0II+Pv7Y+zYsUhKSoKtrS22bduGb775RupoRJQH6FzMpvxlDACmpqaIiYkBAPTs2RN169bFsmXLMnWcpKQkBAcHY+LEiVrtzZo1w7lz59Lc548//kCtWrUwd+5cbN68Gebm5mjXrh1mzpyZ7q10ExMTkZiYqHkcHR0NAFAqlVAqlZnK+jmUymSt/8+Nc1L2Sukz9p3+Yh/qr/fv32P48OHYunUrAKBJkybYuHEjrK2t2Z96hO9B/ZfbfajLeXQuZm1tbfH69Ws4OjrC0dERFy5cQI0aNRAWFgYhRKaPExkZCZVKBRsbG612GxsbREREpLnPgwcPcObMGZiYmGDv3r2IjIzE0KFD8ebNm3Tnzc6ePRu+vr6p2gMDA2FmZpbpvFmVqAJSvs3Hjh2DsUGOn5JySFBQkNQR6DOxD/XP+fPnsXXrVsjlcnTv3h0dO3bElStXpI5FWcT3oP7LrT6Mi4vL9LY6F7Pu7u74888/4eLign79+mHMmDHYtWsXrly5ormxgi4+vptYencYAwC1Wg2ZTIYtW7Zo1r1dsGABOnXqhOXLl6c5Ojtp0iT4+PhoHkdHR6NUqVJo1qwZLCwsdM6rq7ikZIy/dAzAh++dpblJjp+TspdSqURQUBA8PDygUCikjkNZwD7UX61atYIQAh4eHoiNjWUf6im+B/VfbvdhyifpmaFzMbtmzRqo1WoAwODBg1GsWDGcOXMGbdu2xeDBgzN9nBIlSsDAwCDVKOzLly9TjdamsLOzg4ODg6aQBYAvvvgCQgg8ffoUFSpUSLWPsbGx1t3KUigUilzpDIX4X2GuUBjyTazHcutnhnIO+zDvi46OxqRJkzBt2jRYW1sDAObMmQOlUokDBw6wD/Uc+0//5Vr9pMM5dF7NQC6Xay1I3aVLFyxZsgQjR47Eq1evMn0cIyMjuLq6phquDgoKSve2uPXr18fz58/x/v17Tdvdu3chl8tRsmRJHV8JERHlJVevXoWLiwtWrFiBfv36SR2HiPREltaZ/VhERARGjBiB8uXL67Sfj48P1q1bh/Xr1+PWrVsYM2YMHj9+rBnhnTRpEnr16qXZvnv37ihevDj69OmDmzdv4tSpUxg3bhz69u2b7gVgRESUtwkhsGzZMri5ueH+/fsoXbo0Jk+eLHUsItITmS5m3759Cy8vL1hZWcHe3h5LliyBWq3G1KlTUbZsWVy4cEHnmxd07doVixYtwowZM1CzZk2cOnUKBw4cgKOjIwAgPDxca83ZQoUKISgoCG/fvkWtWrXg5eWFtm3bYsmSJTqdl4iI8oa3b9+iU6dOGDFiBJKSktCuXTuEhITAzc1N6mhEpCcyPWd28uTJOHXqFHr37o1Dhw5hzJgxOHToEBISEnDw4EE0atQoSwGGDh2KoUOHpvmcv79/qrbKlSvzakgionzg9u3baNWqFcLCwqBQKDBv3jyMHDky3YuAiYjSkuli9q+//sKGDRvQtGlTDB06FOXLl0fFihWxaNGiHIxHRET5lb29PQwMDODk5ISAgAB89dVXUkciIj2U6WL2+fPnqFKlCgCgbNmyMDEx0dy5i4iIKDOio6NRuHBhyGQyWFhYYP/+/bCxsUGRIkWkjkZEeirTc2bVarXWMgkGBgYwNzfPkVBERJT/nD9/HtWqVdO6U2SlSpVYyBLRZ8n0yKwQAt7e3po1WxMSEjB48OBUBe2ePXuyNyEREek1tVqNX3/9FZMnT4ZKpcLq1asxePBgrjdKRNki08Vs7969tR736NEj28MQEVH+8urVK/Tu3RsHDx4EAHh6emL16tUsZIko22S6mN2wYUNO5iAionzm1KlT6NatG54/fw4TExMsWbIE/fv352oFRJStdL6dLRER0aeEh4ejWbNmSExMRKVKlbBjxw5Ur15d6lhElA+xmCUiomxnZ2cHX19f/PPPP1ixYgUKFSokdSQiyqdYzBIRUbY4fvw4rK2tUbVqVQDA+PHjAYDTCogoR2V6aS4iIqK0qFQqTJ8+HU2aNEGXLl0QGxsL4EMRy0KWiHIaR2aJiCjLwsPD4eXlhePHjwMA6tatywKWiHJVlkZmN2/ejPr168Pe3h6PHj0CACxatAi///57toYjIqK8KygoCDVr1sTx48dhbm6OzZs3w8/PD2ZmZlJHI6ICROdiduXKlfDx8UGrVq3w9u1bqFQqAECRIkWwaNGi7M5HRER5THJyMn744Qc0b94cL1++RPXq1XHlyhWuP05EktC5mF26dCnWrl2LKVOmwMDAQNNeq1YtXL9+PVvDERFR3iOTyXDmzBkIITBo0CBcuHABlStXljoWERVQOs+ZDQsLg7Ozc6p2Y2NjzaR/IiLKf4QQkMlkMDAwwNatW3HmzBl06dJF6lhEVMDpPDLr5OSE0NDQVO0HDx5ElSpVsiMTERHlIUqlEuPHj8fo0aM1bfb29ixkiShP0Hlkdty4cRg2bBgSEhIghMClS5ewbds2zJ49G+vWrcuJjEREJJHHjx/D09MT58+fBwD07dsXNWrUkDgVEdH/6FzM9unTB8nJyRg/fjzi4uLQvXt3ODg4YPHixfD09MyJjEREJIE//vgD3t7eiIqKgqWlJfz8/FjIElGek6V1ZgcMGIABAwYgMjISarUa1tbW2Z2LiIgkkpSUhAkTJmhWqPnqq68QEBAAJycnaYMREaVB5zmzvr6+uH//PgCgRIkSLGSJiPIRIQTatm2rKWTHjBmDM2fOsJAlojxL52J29+7dqFixIurWrYtly5bh1atXOZGLiIgkIJPJMGjQIBQtWhS///47FixYACMjI6ljERGlS+di9tq1a7h27Rrc3d2xYMECODg4oFWrVti6dSvi4uJyIiMREeWghIQErXXCO3bsiAcPHqBdu3YSpiIiypws3c62atWq+Pnnn/HgwQMcP34cTk5OGD16NGxtbbM7HxER5aB///0X9erVg7u7O549e6ZpL1KkiHShiIh0kKVi9r/Mzc1hamoKIyMjKJXK7MhERES5ICAgAC4uLggJCYEQAmFhYVJHIiLSWZaK2bCwMMyaNQtVqlRBrVq1cPXqVUyfPh0RERHZnY+IiLJZfHw8Bg8eDE9PT8TExKBBgwYIDQ1FgwYNpI5GRKQznZfmcnNzw6VLl/Dll1+iT58+mnVmiYgo77tz5w66dOmCa9euQSaTYfLkyZg+fToMDbO0UiMRkeR0/u3VuHFjrFu3DlWrVs2JPERElIMWL16Ma9euwdraGr/99hs8PDykjkRE9Fl0LmZ//vnnnMhBRES5YN68eUhOToavry/s7OykjkNE9NkyVcz6+Phg5syZMDc3h4+PT4bbLliwIFuCERHR5/vnn3+wevVqLFq0CHK5HObm5lizZo3UsYiIsk2mitmQkBDNSgUhISE5GoiIiD6fEAL+/v4YNmwY4uPjUbZsWYwePVrqWERE2S5Txezx48fT/H8iIsp73r9/j6FDh2Lz5s0AgGbNmqF79+4SpyIiyhk6L83Vt29fxMTEpGqPjY1F3759syUUERFlzbVr11CrVi1s3rwZcrkcs2bNwsGDB2FtbS11NCKiHKFzMbtx40bEx8enao+Pj8emTZuyJRQREekuICAAderUwZ07d+Dg4IATJ05g8uTJkMs/+/44RER5VqZXM4iOjoYQAkIIxMTEwMTERPOcSqXCgQMH+Jc/EZGEypcvD7VajZYtW2LTpk0oUaKE1JGIiHJcpovZIkWKQCaTQSaToWLFiqmel8lk8PX1zdZwRESUsbdv36JIkSIAAFdXV5w/fx41a9bkaCwRFRiZLmaPHz8OIQTc3d2xe/duFCtWTPOckZERHB0dYW9vnyMhiYhImxACK1aswOTJk3H8+HG4uLgAgOa/REQFRaaL2UaNGgEAwsLCULp0achkshwLRURE6Xv79i0GDBiAXbt2AQD8/f1ZxBJRgZWpYvbatWuoVq0a5HI53r17h+vXr6e7bfXq1bMtHBERabt8+TK6du2KsLAwKBQKzJ07F6NGjZI6FhGRZDJVzNasWRMRERGwtrZGzZo1IZPJIIRItZ1MJoNKpcr2kEREBZ0QAosXL8b48eOhVCrh5OSEgIAAfPXVV1JHIyKSVKaK2bCwMFhZWWn+n4iIctfu3bsxZswYAMB3332HdevWaS78IiIqyDJVzDo6Oqb5/0RElDs6duyIdu3aoVmzZhg6dCivWyAi+n9ZumnCX3/9pXk8fvx4FClSBPXq1cOjR4+yNRwRUUGlVquxdu1axMXFAQDkcjn27duHYcOGsZAlIvoPnYvZn3/+GaampgCA8+fPY9myZZg7dy5KlCih+QiMiIiyLjIyEm3btsXAgQMxYsQITTuLWCKi1DK9NFeKJ0+eoHz58gCAffv2oVOnThg4cCDq16+Pb775JrvzEREVKKdPn0a3bt3w7NkzmJiYoE6dOhBCsJAlIkqHziOzhQoVwuvXrwEAgYGBaNq0KQDAxMQE8fHx2ZuOiKiAUKvV+Pnnn9G4cWM8e/YMlSpVwsWLFzFw4EAWskREGdB5ZNbDwwP9+/eHs7Mz7t69i9atWwMA/vnnH5QpUya78xER5XsvX75Ez549ERgYCADo0aMHVq5ciUKFCkmcjIgo79N5ZHb58uVwc3PDq1evsHv3bhQvXhwAEBwcjG7dumV7QCKi/E6pVOLq1aswNTWFn58fNm3axEKWiCiTdB6ZLVKkCJYtW5aq3dfXN1sCEREVBP+dB+vg4ICdO3fCysoKVatWlTgZEZF+0bmYBT7cF9zPzw+3bt2CTCbDF198gX79+sHS0jK78xER5TsRERHw8vLC8OHD8e233wIAL6AlIsoinacZXLlyBeXKlcPChQvx5s0bREZGYuHChShXrhyuXr2aExmJiPKNI0eOoEaNGjh27BhGjhyJpKQkqSMREek1nYvZMWPGoF27dnj48CH27NmDvXv3IiwsDG3atMHo0aNzICIRkf5LTk7GDz/8gGbNmuHly5eoXr06jhw5AiMjI6mjERHpNZ2nGVy5cgVr166FoeH/djU0NMT48eNRq1atbA1HRJQfPHv2DN26dcPp06cBAIMGDcLChQs1N6AhIqKs07mYtbCwwOPHj1G5cmWt9idPnqBw4cLZFoyIKD949eoVatasicjISBQuXBhr1qyBp6en1LGIiPINnacZdO3aFf369UNAQACePHmCp0+fYvv27ejfvz+X5iIi+oiVlRW6du0KZ2dnBAcHs5AlIspmOo/M/vrrr5DJZOjVqxeSk5MBAAqFAkOGDMEvv/yS7QGJiPTN48ePoVAoYGdnBwCYP38+hBAwMTGROBkRUf6j88iskZERFi9ejKioKISGhiIkJARv3rzBwoULYWxsnBMZiYj0xp9//omaNWuiW7dumj/4jY2NWcgSEeWQTBezcXFxGDZsGBwcHGBtbY3+/fvDzs4O1atXh5mZWU5mJCLK85KSkvD999+jXbt2iIqKQlxcHKKioqSORUSU72W6mJ02bRr8/f3RunVreHp6IigoCEOGDMnJbEREeiEsLAwNGzbEggULAHxYwvDMmTOwsrKSOBkRUf6X6Tmze/bsgZ+fn+bihR49eqB+/fpQqVQwMDDIsYBERHnZnj170LdvX7x79w5FixaFv78/2rVrJ3UsIqICI9Mjs0+ePEHDhg01j2vXrg1DQ0M8f/48R4IREeV1SqUSP/74I969ewc3NzeEhISwkCUiymWZLmZVKlWqO9UYGhpqLnAgIipoFAoFAgICMGnSJJw8eRKOjo5SRyIiKnAyPc1ACAFvb2+tFQsSEhIwePBgmJuba9r27NmTvQmJiPKQHTt24OXLlxg+fDgAoFq1avj5558lTkVEVHBlupjt3bt3qrYePXpkaxgiorwqPj4eY8aMwerVq2FgYID69evD2dlZ6lhERAVepovZDRs25GQOIqI8686dO+jSpQuuXbsGmUyGiRMn4ssvv5Q6FhERIQt3ACMiKkh+++03DB48GLGxsbC2tsZvv/0GDw8PqWMREdH/0/kOYEREBcXQoUPRs2dPxMbGonHjxggNDWUhS0SUx7CYJSJKR+XKlSGTyTB9+nQEBQXBzs5O6khERPQRTjMgIvqPN2/eoFixYgCAESNGoFGjRqhRo4bEqYiIKD0cmSUiAvD+/Xv07t0bderUQXR0NABAJpOxkCUiyuOyVMxu3rwZ9evXh729PR49egQAWLRoEX7//fdsDUdElBuuX7+Or776Cps2bcKDBw9w/PhxqSMREVEm6VzMrly5Ej4+PmjVqhXevn0LlUoFAChSpAgWLVqU3fmIiHKMEAJr165F7dq1cfv2bTg4OODEiRNo37691NGIiCiTdC5mly5dirVr12LKlCkwMDDQtNeqVQvXr1/P1nBERDklJiYGXl5eGDhwIBISEtCyZUuEhoaiYcOGUkcjIiId6FzMhoWFpXnXG2NjY8TGxmZLKCKinPb9999j27ZtMDAwwNy5c7F//36UKFFC6lhERKQjnYtZJycnhIaGpmo/ePAgqlSponOAFStWwMnJCSYmJnB1dcXp06cztd/Zs2dhaGiImjVr6nxOIqKffvoJdevWxenTpzFu3DjI5bwelohIH+n823vcuHEYNmwYAgICIITApUuXMGvWLEyePBnjxo3T6VgBAQEYPXo0pkyZgpCQEDRs2BAtW7bE48ePM9zv3bt36NWrF5o0aaJrfCIqoGJjY+Hn56d5bG1tjXPnzsHNzU3CVERE9Ll0Xme2T58+SE5Oxvjx4xEXF4fu3bvDwcEBixcvhqenp07HWrBgAfr164f+/fsD+LAiwuHDh7Fy5UrMnj073f0GDRqE7t27w8DAAPv27dP1JRBRARMcHAwfHx+8ePECFhYW6N69O4APS28REZF+y9JNEwYMGIABAwYgMjISarUa1tbWOh8jKSkJwcHBmDhxolZ7s2bNcO7cuXT327BhA+7fv4/ffvsNP/300yfPk5iYiMTERM3jlPUjlUollEqlzrl1pVQma/1/bpyTsldKn7Hv9I8QAsuWLcPEiROhVCrh6OiIMmXKsC/1EN+H+o39p/9yuw91Oc9n3QHscy6WiIyMhEqlgo2NjVa7jY0NIiIi0tzn3r17mDhxIk6fPg1Dw8xFnz17Nnx9fVO1BwYGwszMTPfgOkpUASnf5mPHjsHYIMPNKQ8LCgqSOgLp4P3791i6dCkuXrwIAKhbty6GDx+OV69e4cCBAxKno6zi+1C/sf/0X271YVxcXKa31bmYdXJyyvCjuQcPHuh0vI+PJYRI8/gqlQrdu3eHr68vKlasmOnjT5o0CT4+PprH0dHRKFWqFJo1awYLCwudsmZFXFIyxl86BgBwd3eHpblJjp+TspdSqURQUBA8PDygUCikjkOZcOnSJYwaNQqPHj2CkZERZs+ejbJly6JZs2bsQz3F96F+Y//pv9zuw5RP0jND52J29OjRWo+VSiVCQkJw6NAhnS4AK1GiBAwMDFKNwr58+TLVaC3wYU3IK1euICQkBMOHDwcAqNVqCCFgaGiIwMBAuLu7p9rP2NgYxsbGqdoVCkWudIZC/K8wVygM+SbWY7n1M0Of7927d3j06BHKlSuHHTt24Msvv8SBAwfYh/kA+1C/sf/0X67VTzqcQ+didtSoUWm2L1++HFeuXMn0cYyMjODq6oqgoCB8++23mvagoKA0775jYWGR6qYMK1aswLFjx7Br1y44OTll+txElP/891OdVq1aYevWrWjdujUsLCw4T4+IKB/LtoUVW7Zsid27d+u0j4+PD9atW4f169fj1q1bGDNmDB4/fozBgwcD+DBFoFevXh+CyuWoVq2a1pe1tTVMTExQrVo1mJubZ9dLISI9c+bMGdSoUQOPHj3StHXr1i1XphIREZG0PusCsP/atWsXihUrptM+Xbt2xevXrzFjxgyEh4ejWrVqOHDgABwdHQEA4eHhn1xzlogKLrVajTlz5uDHH3+ESqXCDz/8gM2bN0sdi4iIcpHOxayzs7PWBVpCCERERODVq1dYsWKFzgGGDh2KoUOHpvmcv79/hvtOnz4d06dP1/mcRKT/Xr58iZ49eyIwMBAA0KNHD6xcuVLiVERElNt0LmY7dOig9Vgul8PKygrffPMNKleunF25iIjSdeLECXTv3h3h4eEwNTXF8uXL4e3tzZsgEBEVQDoVs8nJyShTpgyaN28OW1vbnMpERJSugwcPok2bNlCr1ahSpQp27NiBqlWrSh2LiIgkotMFYIaGhhgyZIjWHbWIiHJT48aNUb16dfTp0weXLl1iIUtEVMDpPM2gTp06CAkJ0VykRUSU0y5evIhatWrBwMAAJiYmOHXqFAoXLix1LCIiygN0LmaHDh2K77//Hk+fPoWrq2uqJbGqV6+ebeGIqGBLTk6Gr68vZs2ahalTp2ou+GQhS0REKTJdzPbt2xeLFi1C165dAQAjR47UPCeTyTQLlqtUquxPSUQFzrNnz9C9e3ecOnUKAPDixYt0b3dNREQFV6aL2Y0bN+KXX35BWFhYTuYhIsKhQ4fQs2dPREZGolChQli7di08PT2ljkVERHlQpotZIQQAcK4sEeUYpVKJqVOn4pdffgHwYV3rgIAAVKhQQeJkRESUV+m0mgE/3iOinPTgwQMsWrQIADBs2DCcO3eOhSwREWVIpwvAKlas+MmC9s2bN58ViIgKrkqVKmH16tUwMzNDp06dpI5DRER6QKdi1tfXF5aWljmVhYgKmKSkJPzwww/49ttv4ebmBgDo1auXxKmIiEif6FTMenp6wtraOqeyEFEB8vDhQ3h6euLixYvYsWMHbt++DRMTE6ljERGRnsn0nFnOlyWi7LJ37144Ozvj4sWLKFKkCBYvXsxCloiIsiTTxWzKagZERFmVmJiIkSNHomPHjnj79i3q1q2L0NBQtG/fXupoRESkpzI9zUCtVudkDiLK56KiouDh4YHg4GAAwLhx4zBr1iwoFAqJkxERkT7T+Xa2RERZUaRIEZQsWRIPHz7Exo0b0bp1a6kjERFRPsBilohyTEJCApKTk1GoUCHIZDKsX78ecXFxKFmypNTRiIgon9DppglERJl19+5d1K1bFwMHDtTMuS9WrBgLWSIiylYsZoko223duhWurq74+++/ceTIETx79kzqSERElE+xmCWibBMXF4cBAwbAy8sL79+/xzfffIPQ0FCOxhIRUY5hMUtE2eLWrVuoU6cO1q1bB5lMhmnTpuHIkSOwt7eXOhoREeVjvACMiD5bcnIy2rZti/v378PW1hZbtmyBu7u71LGIiKgA4MgsEX02Q0NDrFmzBs2bN0doaCgLWSIiyjUsZokoS65fv479+/drHru7u+PgwYOwsbGRMBURERU0LGaJSCdCCKxbtw61a9dGt27dcO/ePc1zMplMwmRERFQQsZglokyLiYlBjx49MGDAACQkJKBBgwYoUqSI1LGIiKgAYzFLRJkSGhoKV1dXbN26FQYGBpgzZw7++usvWFlZSR2NiIgKMK5mQESftGrVKowePRqJiYkoVaoUtm/fjnr16kkdi4iIiCOzRPRp9+/fR2JiItq2bYuQkBAWskRElGdwZJaI0qRWqyGXf/h79+eff0aNGjXg5eXFi7yIiChP4cgsEWkRQmDx4sVwd3eHUqkEACgUCvTo0YOFLBER5TksZolIIyoqCh07dsTo0aNx8uRJbNu2TepIREREGeI0AyICAFy8eBFdu3bFo0ePYGRkhPnz56Nnz55SxyIiIsoQR2aJCji1Wo358+ejQYMGePToEcqVK4dz585h+PDhnFZARER5HotZogJu/PjxGDt2LJKTk9GlSxdcvXoVrq6uUsciIiLKFBazRAXcgAEDUKJECaxatQrbt2+HhYWF1JGIiIgyjXNmiQoYtVqNc+fOoUGDBgCASpUq4eHDhzA3N5c4GRERke44MktUgLx8+RKtWrVCo0aNcOLECU07C1kiItJXHJklKiBOnjyJbt26ITw8HKampggPD5c6EhER0WfjyCxRPqdSqTBz5ky4u7sjPDwcX3zxBS5duoRu3bpJHY2IiOizcWSWKB+LiIhAjx49cPToUQCAt7c3li1bxmkFRESUb7CYJcrHDh48iKNHj8LMzAwrV65Er169pI5ERESUrVjMEuVj3t7eePDgAbp3744vvvhC6jhERETZjnNmifKR58+fo0ePHoiKigIAyGQyzJw5k4UsERHlWxyZJconDh06hJ49eyIyMhIA8Ntvv0mciIiIKOdxZJZIzyUnJ2PSpElo2bIlIiMjUbNmTUybNk3qWERERLmCI7NEeuzJkyfo1q0bzp49CwAYOnQo5s+fDxMTE4mTERER5Q4Ws0R66sKFC2jdujXevHkDCwsL+Pn5oVOnTlLHIiIiylUsZon0VMWKFWFubo6yZcsiICAAZcuWlToSERFRrmMxS6RHXr58CSsrK8hkMhQrVgxHjx5F6dKlYWxsLHU0IiIiSfACMCI9sXfvXlSqVAnr16/XtFWoUIGFLBERFWgsZonyuMTERIwcORIdO3bE27dvsWXLFgghpI5FRESUJ7CYJcrD7t+/j/r162Pp0qUAgLFjx+Lw4cOQyWQSJyMiIsobOGeWKI/auXMn+vfvj+joaBQrVgybNm1C69atpY5FRESUp7CYJcqD7t69C09PT6jVatSvXx/btm1DqVKlpI5FRESU57CYJcqDKlasiKlTpyIxMREzZsyAoSHfqkRERGnhv5BEecS2bdtQq1YtVKhQAQB4S1oiIqJM4AVgRBKLi4tD//790b17d3Tt2hUJCQlSRyIiItIbHJklktCtW7fQpUsX3LhxAzKZDG3btoVCoZA6FhERkd5gMUskkY0bN2Lo0KGIi4uDjY0NtmzZgiZNmkgdi4iISK+wmCXKZXFxcRgyZAg2bdoEAGjSpAl+++032NraSpyMiIhI/3DOLFEuMzQ0xO3btyGXyzFz5kwcPnyYhSwREVEWcWSWKBcIISCEgFwuh5GREQICAvDo0SM0atRI6mhERER6jSOzRDksJiYGPXr0wKRJkzRtZcqUYSFLRESUDTgyS5SDQkND0aVLF9y7dw+GhoYYMmQIypQpI3UsIiKifIMjs0Q5QAiBlStXom7durh37x5KliyJEydOsJAlIiLKZhyZJcpm7969w4ABA7Bz504AQJs2beDv74/ixYtLnIyIiCj/YTFLlI3UajUaNWqEv//+G4aGhpgzZw7GjBkDmUwmdTQiIqJ8idMMiLKRXC7HuHHj4OjoiDNnzsDHx4eFLBERUQ5iMUv0maKiohAaGqp57OXlhZs3b6JOnTrShSIiIiogWMwSfYaLFy/C2dkZrVq1wqtXrzTtZmZmEqYiIiIqOFjMEmWBEALz589HgwYN8OjRI5iamuLly5dSxyIiIipweAEYkY5ev34Nb29v7N+/HwDQuXNnrF27FpaWlhInIyIiKngkH5ldsWIFnJycYGJiAldXV5w+fTrdbffs2QMPDw9YWVnBwsICbm5uOHz4cC6mpYLu7NmzqFmzJvbv3w9jY2OsWLECAQEBLGSJiIgkImkxGxAQgNGjR2PKlCkICQlBw4YN0bJlSzx+/DjN7U+dOgUPDw8cOHAAwcHBaNy4Mdq2bYuQkJBcTk4F1cqVK/H06VNUqFABFy5cwJAhQ7haARERkYQknWawYMEC9OvXD/379wcALFq0CIcPH8bKlSsxe/bsVNsvWrRI6/HPP/+M33//HX/++SecnZ1zIzIVcCtWrICNjQ2mT5+OwoULSx2HiIiowJOsmE1KSkJwcDAmTpyo1d6sWTOcO3cuU8dQq9WIiYlBsWLF0t0mMTERiYmJmsfR0dEAAKVSCaVSmYXkulEqk7X+PzfOSdnn1KlTCAgIQKtWraBUKmFqaopffvkFANiXeiSlr9hn+ot9qN/Yf/ovt/tQl/NIVsxGRkZCpVLBxsZGq93GxgYRERGZOsb8+fMRGxuLLl26pLvN7Nmz4evrm6o9MDAwV5ZPSlQBKd/mY8eOwdggx09J2UClUmHXrl0ICAiAWq2GiYkJpxPkA0FBQVJHoM/EPtRv7D/9l1t9GBcXl+ltJV/N4OMCQQiRqaJh27ZtmD59On7//XdYW1unu92kSZPg4+OjeRwdHY1SpUqhWbNmsLCwyHrwTIpLSsb4S8cAAO7u7rA0N8nxc9LniYiIgLe3N44d+9BvXl5eqF+/Pjw8PKBQKCROR1mhVCoRFBTEPtRj7EP9xv7Tf7ndhymfpGeGZMVsiRIlYGBgkGoU9uXLl6lGaz8WEBCAfv36YefOnWjatGmG2xobG8PY2DhVu0KhyJXOUIj/FeYKhSHfxHnc0aNH4eXlhRcvXsDMzAwrVqxA9+7dceDAgVz7maGcwz7Uf+xD/cb+03+5Vj/pcA7JVjMwMjKCq6trquHqoKAg1KtXL939tm3bBm9vb2zduhWtW7fO6ZhUgCxevBgeHh548eIFqlWrhitXrqB3795SxyIiIqIMSDrNwMfHBz179kStWrXg5uaGNWvW4PHjxxg8eDCAD1MEnj17hk2bNgH4UMj26tULixcvRt26dTWjuqamplznkz7bV199Bblcjj59+mDx4sW8JS0REZEekLSY7dq1K16/fo0ZM2YgPDwc1apVw4EDB+Do6AgACA8P11pzdvXq1UhOTsawYcMwbNgwTXvv3r3h7++f2/EpH3jx4oVmWku9evVw48YNVK5cWeJURERElFmSXwA2dOhQDB06NM3nPi5QT5w4kfOBqEBITk7Gjz/+iKVLl+LixYuoWrUqALCQJSIi0jOSF7NEue3Jkyfo1q0bzp49CwD4888/NcUsERER6RcWs1Sg/PXXX+jVqxfevHkDCwsLrF27NsN1iomIiChvk2w1A6LcpFQqMXbsWLRp0wZv3ryBq6srrl69ykKWiIhIz7GYpQLBz88P8+fPBwCMHDkSZ8+eRbly5SRORURERJ+L0wyoQOjfvz8OHz6MXr164dtvv5U6DhEREWUTjsxSvpSUlIR58+YhMTERAGBoaIi9e/eykCUiIspnODJL+c6DBw/QtWtXXLlyBY8fP8bSpUuljkREREQ5hCOzlK/s2rULzs7OuHLlCooVK4bmzZtLHYmIiIhyEItZyhcSEhIwdOhQdO7cGdHR0ahfvz5CQ0PRpk0bqaMRERFRDmIxS3rv/v37cHNzw8qVKwEAEydOxPHjx1GqVCmJkxEREVFO45xZ0ntyuRxhYWEoUaIENm/ejBYtWkgdiYiIiHIJi1nSSyqVCgYGBgAAJycn7N27FxUrVoSDg4PEyYiIiCg3cZoB6Z1bt27BxcUFhw4d0rQ1btyYhSwREVEBxGKW9MqmTZtQq1YtXLt2DePGjYNarZY6EhEREUmIxSzphdjYWPTp0we9e/dGXFwc3N3dERQUBLmcP8JEREQFGSsByvNu3LiBr776Cv7+/pDL5ZgxYwYCAwNha2srdTQiIiKSGC8AozztwYMHqF27NuLj42FnZ4etW7fim2++kToWERER5REsZilPK1u2LDw9PfH8+XNs2rQJ1tbWUkciIiKiPITFLOU5f//9N+zt7WFlZQUAWLlyJRQKBefHEhERUSqsDijPEEJg1apVqFOnDnr16qVZqcDY2JiFLBEREaWJFQLlCe/evYOnpyeGDBmCxMREGBgYIC4uTupYRERElMexmCXJBQcHw9XVFTt27IChoSHmzZuHP/74A4UKFZI6GhEREeVxnDNLkhFCYNmyZRg7diySkpLg6OiI7du3o27dulJHIyIiIj3BkVmSTGxsLBYvXoykpCS0b98eISEhLGSJiIhIJxyZJckUKlQIAQEBOHPmDEaOHAmZTCZ1JCIiItIzLGYp1wghsGjRIpiammLw4MEAAFdXV7i6ukqcjIiIiPQVi1nKFW/evIG3tzf+/PNPGBkZwcPDA+XKlZM6FhEREek5FrOU486dOwdPT088efIExsbGWLhwIcqWLSt1LCIiIsoHeAEY5Ri1Wo05c+bg66+/xpMnT1ChQgVcuHABQ4YM4fxYIiIiyhYcmaUcoVar0aFDB/z5558AgG7dumH16tUoXLiwxMmIiIgoP+HILOUIuVwONzc3mJiYYO3atdiyZQsLWSIiIsp2HJmlbKNSqRAZGQkbGxsAwIQJE9C5c2eUL19e4mRERESUX3FklrLFixcv0KJFCzRp0gRxcXEAPozOspAlIiKinMRilj7bsWPHUKNGDRw5cgRhYWG4evWq1JGIiIiogGAxS1mmUqkwbdo0NG3aFC9evEDVqlVx+fJlNGjQQOpoREREVEBwzixlyfPnz+Hl5YUTJ04AAPr164clS5bAzMxM2mBERERUoLCYpSwZMWIETpw4AXNzc6xevRpeXl5SRyIiIqICiMUsZcmSJUvw7t07LF++HJUqVZI6DhERERVQnDNLmfL06VMsX75c89jBwQFHjhxhIUtERESS4sgsfdKBAwfQq1cvvH79Gg4ODujQoYPUkYiIiIgAcGSWMqBUKjF+/Hi0bt0ar1+/houLC7788kupYxERERFpcGSW0vTo0SN4enriwoULAD5c8DVv3jwYGxtLnIyIiIjof1jMUir79+9Hz5498fbtW1haWmL9+vXo2LGj1LGIiIiIUmExS6kkJibi7du3qF27NrZv3w4nJyepIxERERGlicUsAQCSk5NhaPjhx+G7777D7t270aZNGxgZGUmcjIj0iVqtRlJSUrYfV6lUwtDQEAkJCVCpVNl+fMpZ7D/9lxN9aGRkBLn88y/fYjFL2LVrFyZPnowTJ07A3t4eADitgIh0lpSUhLCwMKjV6mw/thACtra2ePLkCWQyWbYfn3IW+0//5UQfyuVyODk5ffbAGYvZAiwhIQHff/89VqxYAQCYN28eFi5cKHEqItJHQgiEh4fDwMAApUqVypbRlv9Sq9V4//49ChUqlO3HppzH/tN/2d2HarUaz58/R3h4OEqXLv1ZBTKL2QLq3r176Nq1K0JCQgAAEyZMwMyZMyVORUT6Kjk5GXFxcbC3t4eZmVm2Hz9l+oKJiQmLIT3E/tN/OdGHVlZWeP78OZKTk6FQKLJ8HBazBdD27dsxYMAAvH//HiVKlMCmTZvQsmVLqWMRkR5LmUPHefZElFkpvy9UKhWLWcq8TZs2oXfv3gCAhg0bYtu2bXBwcJA4FRHlF5wPSUSZlW1zb7PlKKQ3vvvuO1StWhU//PADjh07xkKWiIiI9BqL2QIgKChIc3Wxubk5rly5gpkzZ2qW4iIiIsqqpKQklC9fHmfPnpU6Sr7x8uVLWFlZ4dmzZ1JH0QssZvOx2NhY9OnTB82aNcP8+fM17SYmJhKmIiLKO7y9vSGTySCTyWBoaIjSpUtjyJAhiIqKSrXtuXPn0KpVKxQtWhQmJib48ssvMX/+/DTX3Dx+/DhatWqF4sWLw8zMDFWqVMH333//yeIkJCQEnTt3ho2NDUxMTFCxYkUMGDAAd+/ezbbXnN3WrFkDR0dH1K9fP9VzAwcOhIGBAbZv357qOW9vb3To0CFVe2hoKGQyGR4+fKhpE0JgzZo1qFOnDgoVKoQiRYqgVq1aWLRoEeLi4rLz5WiJiopCz549YWlpCUtLS83dMTPy4sULeHt7ay6GbNGiBe7du6e1zf379/Htt9/CysoKFhYW6NKlC168eKF53traGj179sS0adNy4mXlOyxm86l//vkHtWvXhr+/P+RyOZRKpdSRiIjypBYtWiA8PBwPHz7EunXr8Oeff2Lo0KFa2+zduxeNGjVCyZIlcfz4cdy+fRujRo3CrFmz4OnpCSGEZtvVq1ejadOmsLW1xe7du3Hz5k2sWrUK79690xpY+Nj+/ftRt25dJCYmYsuWLbh16xY2b94MS0tL/Pjjj1l+fTn9+3/p0qXo379/qva4uDgEBARg3LhxWL9+/Wedo2fPnhg9ejTat2+P48ePIzQ0FD/++CN+//13BAYGftaxM9K9e3eEhobi0KFDOHToEEJDQ9GzZ890txdCoEOHDnjw4AF+//13hISEwNHREU2bNkVsbCyADwNNzZo1g0wmw7Fjx3D27FkkJSWhbdu2Wms09+nTB1u2bEnzDyv6iChg3r17JwCId+/e5cr5YhOVwnHCfuE4Yb94+z4ux8+nVquFn5+fMDU1FQCEra2tOH78eI6fNz9LSkoS+/btE0lJSVJHoSxiH+a8+Ph4cfPmTREfHy+E+PC7KDZRmW1fMfGJ4vmLSBETn/jJbdVqdaZz9+7dW7Rv316rzcfHRxQrVkzz+P3796J48eKiY8eOqfb/448/BACxfft2IYQQT548EUZGRmL06NFpni8qKirN9tjYWFGiRAnRoUOHDPfbsGGDsLS01Hpu79694r//nE+bNk3UqFFD+Pn5CScnJyGTycSqVauEvb29UKlUWvu2bdtW9OrVS+v1uLi4CGNjY+Hk5CSmT58ulEplmpmEECI4OFjI5fI0/0319/cXdevWFW/fvhWmpqbi77//1jp/Wt97IYQICQkRAERYWJgQQoiAgAABQOzbty/Vtmq1Wrx9+zbdfJ/j5s2bAoC4cOGCpu38+fMCgLh9+3aa+9y5c0cAEDdu3NC0JScni2LFiom1a9cKIYQ4fPhwqu/ZmzdvBAARFBSkdbwyZcoIPz+/7HxZWaZSqURUVFSqn6HP8fHvjf/SpV7jpMl85P379xg8eDC2bNkCAGjWrBk2b94Ma2triZMRUUETr1ShytTDkpz75ozmMDPK2j9vDx48wKFDh7SWCQoMDMTr168xduzYVNu3bdsWFStWxLZt29C1a1fs3LkTSUlJGD9+fJrHL1KkSJrthw8fRmRkpM77pefff//Fjh07sHv3bhgYGMDBwQEjR47E8ePH0aRJEwAfPkI/fPgw/vzzT02GHj16YMmSJWjYsCHu37+PgQMHAkC6H3efOnUKFStWhIWFRarn/Pz80KNHD1haWqJly5bYsmULZs+erdPrAIAtW7agUqVKaN++farnZDIZLC0t0923UKFCGR67YcOGOHjwYJrPnT9/HpaWlqhTp46mrW7durC0tMS5c+dQqVKlVPskJiYC0J7OZ2BgACMjI5w5cwb9+/dHYmIiZDIZjI2NNdukrN165swZNG3aVNNeu3ZtnD59Gn379s3wdRR0LGbzkbt372LHjh0wMDDAzJkzMWHCBC5OTUT0Cfv370ehQoWgUqmQkJAAAFiwYIHm+ZT5ql988UWa+1euXFmzzb1792BhYQE7OzudMqTMqaxcubLO+dOSlJSEzZs3w8rKStPWokULbN26VVPM7ty5E8WKFdM8njVrFiZOnKhZvrFs2bKYOXMmxo8fn24x+/DhQ81t0D9+PRcuXMCePXsAAF5eXhg5ciRmzZql879L9+7dS7NwzIzQ0NAMnzc1NU33uYiIiDQHg6ytrREREZHmPpUrV4ajoyMmTZqE1atXw9zcHAsWLEBERATCw8MBfCiIzc3NMWHCBPz8888QQmDChAlQq9WabVI4ODhobm5E6WMxm4+4uLhg9erVqFChAho0aCB1HCIqwEwVBrg5o3m2HU+tViMmOgaFLQp/shgyVRjodOzGjRtj5cqViIuLw7p163D37l2MGDEi1XbiP/NiP25PWS/zv/+vi/SOnVWOjo5ahSzwoaAcOHAgVqxYAWNjY2zZsgWenp4wMPjw/QoODsbly5cxa9YszT4pBX5cXFyad3aLj49P86JiPz8/NG/eHCVKlAAAtGrVCv3798eRI0fQokULnV5LVr+nAFC+fPks7ZcirfNmlEehUGD37t3o168fihUrBgMDAzRt2lTrxkRWVlbYuXMnhgwZgiVLlkAul6Nbt25wcXHR9EUKU1PTHL3ALb9gMavHoqOjMXz4cIwZMwbOzs4APkwYJyKSmkwmy/JH/WlRq9VINjKAmZFhtn/iZG5uril6lixZgsaNG8PX11dzi++KFSsCAG7duoV69eql2v/27duoUqWKZtt3794hPDxcp9HZlHPcvn0bbm5u6W4nl8tTFb5pXeBlbm6eqi3lAqO//voLX331FU6fPq01Aq1Wq+Hr64uOHTum2je9VXBKlCiB69eva7WpVCps2rQJERERWktAqlQqrF+/XlPMWlhY4NGjR6mOmbJaQMr0gYoVK+LWrVtpnv9TPmeaga2trdYKAylevXoFGxubdI/p6uqK0NBQvHv3DklJSbCyskKdOnVQq1YtzTbNmjXD/fv3ERkZCUNDQxQpUgS2trZwcnLSOtabN29S/VFCqfEzaD119epVuLi4YPPmzfDy8kpzaRgiItLdtGnT8Ouvv+L58+cAPhQexYoVS3Mlgj/++AP37t1Dt27dAACdOnWCkZER5s6dm+ax01vWqVmzZihRosQn97OyskJMTIzmynjg0x+lpzA1NUXHjh2xZcsWbNu2DRUrVoSrq6vmeRcXF9y5cwfly5dP9ZXeHxDOzs64ffu2VoF94MABxMTEICQkBKGhoQgNDcXVq1fh7++P33//Ha9fvwbw4SP5GzduaKZ2pLh8+TKsrKxQtGhRAB9WFLh79y5+//33VOcXQuDdu3fpvuaU86f3tW7dunT3dXNzw7t373Dp0iVN28WLF/Hu3bs0/6j5mKWlJaysrHDv3j1cuXIlzTm/JUqUQJEiRXDs2DG8fPkS7dq103r+xo0bmsEqykC2XZKmJ/R9NQO1Wi2WLl0qjIyMBABRunRpce7cuWxISunhlfD6j32Y8zK6Kjk75MSV1EKkf0W9q6urGDZsmObxzp07hYGBgRgwYID4+++/RVhYmFi3bp0oWrSo6NSpk9YKCsuXLxcymUz07dtXnDhxQjx8+FCcOXNGDBw4UPj4+KSbZd++fUKhUIi2bduKoKAgERYWJi5fvizGjRsnunbtKoQQ4vXr18Lc3FyMHDlS3Lt3T2zZskXY29unuZpBWgIDA4WxsbGoVKmSmDlzptZzhw4dEoaGhmLatGnixo0b4ubNm2L79u1iypQp6WaOjIwURkZG4vr165q29u3ba/KmUKlU4s2bN8LBwUEsWrRICCHE27dvha2trejUqZO4fPmy+Pfff8XmzZtF0aJFxdy5czX7qtVq0bVrV2Fqaip+/vlncfnyZfHw4UPx559/Cnd3d7F37950832uFi1aiOrVq4vz58+L8+fPiy+//FK0adNGa5tKlSqJPXv2aB7v2LFDHD9+XNy/f1/s27dPODo6ploJY/369eL8+fOa11ysWLFUPxuxsbHC1NRUnDp1Ksdeny7y8moGLGZzWHYWs1FRUaJjx44CgAAg2rVrJ16/fp1NSSk9LIT0H/sw5+W3YnbLli3CyMhIPH78WNN26tQp0aJFC2FpaSmMjIxElSpVxK+//iqSk5NT7R8UFCSaN28uihYtKkxMTETlypXF2LFjxfPnzzPMc/nyZdGxY0dhZWUljI2NRfny5cXAgQPFvXv3NNvs3btXlC9fXpiYmIg2bdqINWvWZLqYTU5OFnZ2dgKAuH//fqrnDx06JOrVqydMTU2FhYWFqF27tlizZk2GmT09PcXEiROFEEJEREQIQ0NDsWPHDq1tUvpv+PDh4ssvv9S037t3T3z33XfCwcFBmJubiy+//FIsW7YsVT+rVCqxcuVK8dVXXwkzMzNhYWEhXF1dxeLFi0VcXM4te/n69Wvh5eUlChcuLAoXLiy8vLxSLa8GQGzYsEHzePHixaJkyZJCoVCI0qVLix9++EEkJiZq7TNhwgRhY2MjFAqFqFChgpg/f36qJeW2bt0qKlWqlFMvTWd5uZiVCZHNs87zuOjoaFhaWuLdu3dpLiWS3eKSkjXL0/z9ozsszdO/cjIjT58+RcOGDfHw4UMoFArMmzcPI0eOzPKkeMo8pVKJAwcOoFWrVlrL9ZD+YB/mvISEBISFhcHJySlH7jKoVqsRHR0NCwsLrtKSx1y/fh1NmzbFv//+i8KFC6e5DftPd7Vr18bo0aPRvXt3qaMAyJk+zOj3hi71Gi8A0xP29vaoUKECZDIZAgIC8NVXX0kdiYiICF9++SXmzp2Lhw8f4ssvv5Q6Tr7w8uVLdOrUSTMXmzLGYjYPe/PmDUxMTGBmZga5XI6tW7dqrnokIiLKK1LWpqXsYW1tne4NNCg1jvXnUefOnUPNmjUxatQoTVvKVY9ERERE9AGL2TxGrVZj7ty5+Prrr/HkyROcOHEi3aVciIiIiAo6FrN5yKtXr9CmTRtMmDABKpUKnp6eCA4O5mgsERERUTo4ZzaPOH36NDw9PfH8+XOYmJhg8eLFGDBgAFcrICIiIsoAi9k8IC4uDp07d8aLFy9QqVIl7NixA9WrV5c6FhEREVGex2kGeYCZmRnWr1+Pnj174sqVKyxkiYiIiDKJI7MSOX78OOLj49GqVSsAQKtWrTT/T0RERESZw5HZXKZSqTB9+nQ0adIEXl5eePz4sdSRiIgoB02fPh01a9bMs+f55ptvMHr06GzP8yllypTBokWLPusY3t7e6NChQ4bbSPX6KPdIXsyuWLFCcxszV1dXnD59OsPtT548CVdXV5iYmKBs2bJYtWpVLiX9fBER4fDw8ICvry+EEOjYsSNKlCghdSwiogLtyZMn6NevH+zt7WFkZARHR0eMGjUKr1+/1vlYMpkM+/bt02obO3Ysjh49mk1ps+7EiROQyWRc7jEHXL9+HY0aNYKpqSkcHBwwY8YMCCHS3f7hw4fo168fnJycYGpqinLlymHatGlISkrSbPP69Wu0aNEC9vb2MDY2RqlSpTB8+HBER0drHevw4cOoW7cuChcuDCsrK3z33XcICwvTPL9nzx54eHjAysoKFhYWcHNzw+HDh7WO8c8//+C7775DmTJlIJPJ0vwjIzk5GT/99BPKlSsHU1NTlC1bFjNmzIBarQbw4bbhEyZMwJdffglzc3PY29ujV69eeP78eVa+pTqRtJgNCAjA6NGjMWXKFISEhKBhw4Zo2bJluqOVYWFhaNWqFRo2bIiQkBBMnjwZI0eOxO7du3M5ue7iw66iQd26OH78OMzNzbF582b4+fnBzMxM6mhERHmHSgWcOAFs2/bhvypVjp7uwYMHqFWrFu7evYtt27bh33//xapVq3D06FG4ubnhzZs3n32OQoUKoXjx4tmQNu9QKpVSR8gzoqOj4eHhAXt7e1y+fBlLly7Fr7/+igULFqS7z+3bt6FWq7F69Wr8888/WLhwIVatWoXJkydrtpHL5Wjfvj3++OMP3L17F/7+/jhy5AgGDx6s2ebBgwdo37493N3dERoaisOHDyMyMhIdO3bUbHPq1Cl4eHjgwIEDCA4ORuPGjdG2bVuEhIRotomLi0PZsmXxyy+/wNbWNs3Mc+fOxYYNG7BkyRLcunULc+fOxbx587B06VLNMa5evYoff/wRV69exZ49e3D37l20a9cuy9/bTBMSql27thg8eLBWW+XKlcXEiRPT3H78+PGicuXKWm2DBg0SdevWzfQ53717JwCId+/e6R44C94nJAkLty4CkAkAonr16uL27du5cm7KHklJSWLfvn0iKSlJ6iiURezDnBcfHy9u3rwp4uPjs36Q3buFKFlSCOB/XyVLCrF7t1CpVCIqKkqoVKrsCy2EaNGihShZsqSIi4vTag8PDxdmZmZa/0Y5OjqKGTNmiG7duglzc3NhZ2cnlixZovU8AM2Xo6OjEEKIadOmiRo1ami26927t2jfvr2YNWuWsLa2FpaWlmL69OlCqVSKsWPHiqJFiwoHBwfh5+enlWn8+PGiQoUKwtTUVDg5OYkffvhB62f64/P8V1hYmFY2AKJ3795CCCEaNWokRowYIcaNGyeKFi0qbGxsxLRp07T2ByBWrlwp2rVrJ8zMzMTUqVOFEEL88ccfwsXFRRgbGwsnJyfN6/hvplKlSgkjIyNha2srhg8frvX9mjVrlujTp48oVKiQKFWqlFi9erXWea9duyYaN24sTExMRLFixcSAAQNETExMqu9livfv34uePXsKc3NzYWtrK3799VfRqFEjMWrUqDS/L9lhxYoVwtLSUiQkJGjaZs+eLezt7YVarc70cebOnSucnJwy3Gbx4sWiZMmSmsc7d+4UhoaGWu+LP/74Q8hksgx/31WpUkX4+vqm+Zyjo6NYuHBhqvZWrVqJHj16aJ2rY8eOokePHume59KlSwKAePToUZrPZ/R7Q5d6TbILwJKSkhAcHIyJEydqtTdr1gznzp1Lc5/z58+jWbNmWm3NmzeHn58flEolFApFqn0SExORmJioeZwyPK9UKnPlL8vk5GSoE94DEOjt3RdLFi+Eqakp/6rVIyl9xT7TX+zDnKdUKiGEgFqt1nzsqJM9eyDr0gUQAv9dXVs8ewZ06gSxYwfQtKnmHNnhzZs3OHz4MH766ScYGxtrHdfa2hrdu3dHQEAAli1bplnze968eZg0aRKmTp2KwMBAjBkzBhUrVoSHhwcuXrwIW1tb+Pn5oUWLFjAwMIBardZ83JxyfCEEjh07BgcHB5w4cQJnz57FgAEDcO7cOXz99dc4f/48duzYgcGDB6NJkyYoVaoUgA8jvOvXr4e9vT2uX7+OQYMGoVChQhg3bpzmuP89z385ODhg586d6Ny5M27dugULCwuYmppqtt24cSPGjBmD8+fP4/z58+jbty/c3Nzg4eGhOca0adMwa9YszJ8/HwYGBjh48CB69OiBRYsWoWHDhrh//z4GDx4MIQSmTp2KXbt2YeHChdi6dSuqVKmCBw8e4N9//9XKN3/+fMyYMQMTJ07E7t27MWTIEDRo0ACVK1dGXFwcWrRogTp16uDixYt4+fIlBg4ciGHDhmHDhg2a1/zfn4mxY8fi+PHj2L17N2xtbTFlyhQEBwejRo0a6f7cnD59Gq1bt87wZ2XSpEmYNGlSms+l9JtCodCcw8PDA5MmTcKDBw/g5OSU4bFTvH37FsWKFUs35/Pnz7Fnzx58/fXXmm1cXFxgYGAAPz8/eHt74/3799i0aRM8PDw0P38fU6vViImJQdGiRdM9V1rvs/r162PVqlW4c+cOKlWqhL///htnzpzBggUL0j1OVFQUZDIZLCws0s0ihIBSqYSBgYHWc7r8vpasmI2MjIRKpYKNjY1Wu42NDSIiItLcJyIiIs3tk5OTERkZCTs7u1T7zJ49G76+vqnaAwMDc+Uj/kQVUMy9P0zL1kKrti44fvx4jp+TckZQUJDUEegzsQ9zjqGhIWxtbfH+/XuteX+ZolLBYtSoVIUsAMiEgJDJIBs9Gvj7b8TExGRXZISGhkIIAUdHx1TzEAHAyckJUVFRePDgAaysrKBWq1G7dm0MGTIEANCrVy+cOHECv/76K+rUqQNjY2MAgLGxsebfl+joaCQmJkKlUmkNphQpUgQzZ86EXC5Hp06dMHfuXMTExGDYsGEAgKFDh2LOnDk4cuQIvvvuOwDAiBEjNNkaNWqEoUOHYvv27Rg0aBAApDrPx0xMTAAApqamWvmSk5NRpUoVzUVSHTp0wNKlS3Hw4EHUqVNHs/93332HTp06aR7PnDkTo0aNwrfffgsAKFGiBCZOnIjp06dj9OjRuHfvHqytrVG7dm0oFAq4urrC1dVVk0+tVqNp06bw8vICAAwePBgLFy7EoUOHYG9vj40bNyIuLg5Lly6Fubk5SpcujV9++QXdunXDlClTYG1tDaVSieTkZERHR+P9+/dYv349Vq5cqcm9dOlSVK1aFUlJSel+XypWrIhTp06l+VyKokWLprv/s2fPULp0aa3nU76/9+/fz9QUk7CwMCxduhQ//fRTqvP069cPBw8eRHx8PFq0aIH58+drtilWrBh2796NPn36YMiQIVCpVPjqq6+wc+fOdPMuWbIE79+/R4sWLdLcRq1WIyEhIdVzQ4YMwatXr1C1alUYGBhApVLhhx9+QOvWrdM8TkJCAiZMmKD5mUlrm6SkJMTHx+PUqVNITk7Wei4uLi6D75g2yZfm+vgOV0KIDO96ldb2abWnmDRpEnx8fDSPo6OjUapUKTRr1gwWFhZZjZ1pQgi4uyfi2DE5WjdvCiMjoxw/J2UvpVKJoKAgeHh4pDn6T3kf+zDnJSQk4MmTJyhUqJCmaMq0Eycgz+AiEZkQkD17BsPz52HasmW23RnR3NwcwIfiLq1/D1KKUwsLC1hYWEAul6Nhw4Za23799ddYvHixVtvHxzM2NoaBgYGmTaFQoFq1alq3Krezs0PVqlW19itevDjev3+vadu1axeWLFmCf//9F+/fv0dycrImW1rn+VhKgVW4cGGtbQwNDVG9enWtNgcHB7x7906rzc3NTevx33//jZCQEK25oSqVCgkJCTA0NESPHj2wevVquLi4oHnz5vjmm2/QuXNnzXtQLpfD1dVV65h2dnaIiYmBhYUFHj58iJo1a2oNVHl4eECtVuP58+coX748FAoFDA0NYWFhgbCwMCQlJcHd3V1zTAsLC1SqVAlGRkbpfl8sLCxSDZTpwsDAINXxU/7oKlSo0CdrjefPn6NLly7o3Lkzhg8fnur5pUuX4u3bt7hz5w5++OEH+Pr6Yvny5QA+DPKNGTMGvXv3hqenJ2JiYjB9+nT069cPhw8fTvVe2bZtG+bMmYO9e/eiXLlyaeaRy+UwMTFJlXv79u3YsWMHfvvtN1StWhWhoaHw8fGBk5MTevfurbWtUqlE7969IZPJsGbNmnS/BwkJCTA1NcXXX3+d6vdGesV4WiQrZkuUKAEDA4NUo7AvX75M94fK1tY2ze0NDQ3T/cvH2NhY8wvpvxQKRa79o2Ypk8HYADAyMuI/pHosN39mKGewD3OOSqWCTCaDXC6HXK7jtcUvXmRqM1lEhOYc2aFixYqQyWS4fft2mse8c+cOihYtCmtra01R8PH5ZTJZqraPvwcp+6a0yWQyGBkZpdomrTYhBORyOS5cuIDu3bvD19cXzZs3h6WlJbZv34758+drHfe/5/lYSntaffTxueVyuebcKQoXLqz1WK1Ww9fXV+tioxRmZmYoVKgQ7ty5g6CgIAQFBWHs2LFYsWIFTp48qXkfZvSaUx5/nAv4UEDK5XKt7/9/X//Hry+jn5vTp0+jZcuWaT6XYvLkyVoXZ/2XnZ0dXrx4oXX8yMhIzXMZ/bw+f/4cTZo0gZubG9auXZvmtvb29rC3t0eVKlVgZWWFhg0bYurUqbCzs8PKlSthYWGBefPmabb/7bffUKpUKVy+fBl169bVtAcEBGDAgAHYuXNnqimbH0vr+zVhwgSMHj0anp6ekMvlqFGjBp48eYI5c+agT58+mu2USiU8PT3x8OFDHDt2TOuPto+l9Ftav5t1+V0tWTFrZGQEV1dXBAUFaT6iAD58DNi+ffs093Fzc8Off/6p1RYYGIhatWrxHygiIn2VxhSxtIh0rrLOquLFi8PDwwMrVqzAmDFjYGpqqnkuIiICW7ZsQa9evbRGty5cuKB1jAsXLqBy5cqaxwqFAqocWIHh7NmzcHR0xJQpUzRtjx490ukYKZ8MZlc+FxcX3LlzB+XLl093G1NTU7Rr1w5t2rRBr169ULt2bVy/fh0uLi6fPH6VKlWwceNGxMbGakbRz549C7lcjooVK6baPmWk9sKFCyhdujSAD3M27969i0aNGqV7nlq1aiE0NDTDLMWKFUv3OTc3N0yePBlJSUma73FgYCDs7e1RpkyZdPd79uwZGjduDFdXV2zYsCFTf6SlfBqdci1QXFxcqrmmKY//O0d127Zt6Nu3L7Zt2/bJ+cHpiYuLS5Xx43m5SqUSXbp0wb1793D8+PFcW8VD0qW5fHx8sG7dOqxfvx63bt3CmDFj8PjxY82yE5MmTUKvXr002w8ePBiPHj2Cj48Pbt26hfXr18PPzw9jx46V6iUQEdHnatgQKFkSSG/6gEwGUaoUkt3csv3Uy5YtQ2JiIpo3b45Tp07hyZMnOHToEDw8PODg4IBZs2ZpbX/27FnMnTsXd+/exfLly7Fz506MGjVK83yZMmVw9OhRREREICoqKttyli9fHo8fP8b27dtx//59LFmyBHv37tXpGI6OjpDJZNi/fz9evXqF9+/ff1amqVOnYtOmTZg+fTr++ecf3Lp1CwEBAfjhhx8AAP7+/vDz88ONGzfw4MEDBAQEwNTUFI6Ojpk6vpeXF0xMTNC7d2/cuHEDx48fx4gRI9CzZ880P8EtVKgQ+vXrh3HjxuHo0aO4ceMGvL29P1kkmpqaonz58hl+ZVTMdu/eHcbGxvD29saNGzewd+9e/Pzzz/Dx8dH8IXTp0iVUrlwZz549A/BhRPabb75BqVKl8Ouvv+LVq1eIiIjQ+vT5wIED2LBhA27cuIGHDx/iwIEDGDJkCOrXr68pklu3bo3Lly9jxowZuHfvHq5evYo+ffrA0dERzs7OAD4Usr169cL8+fNRt25dzXnevXunOVdSUhJCQ0MRGhqKpKQkPHv2DKGhofj3338127Rp0wYLFizAX3/9hYcPH2Lv3r1YsGCBZkAyOTkZnTp1wpUrV7BlyxaoVCrNuXSeR6+rT653kMOWL18uHB0dhZGRkXBxcREnT57UPNe7d2/RqFEjre1PnDghnJ2dhZGRkShTpoxYuXKlTufL7aW5hOCyQPqO/af/2Ic577OX5tq9WwiZ7MPXf5fm+v821c6dObI0lxBCPHz4UHh7ewtbW1uhUChEqVKlxIgRI0RkZKTWdo6OjsLX11d06dJFmJmZCRsbG7Fo0SKtbf744w9Rvnx5YWho+Mmluf4rreWjPl4iady4caJ48eKiUKFComvXrmLhwoXC0tJS83xGS3OlmDFjhrC1tRUymUxraa6Pz92+fXvN80J8WJpr7969qY536NAhUa9ePWFqaiosLCxE7dq1xZo1a4QQQuzdu1fUqVNHWFhYCHNzc/HVV1+JwMDAdF+fEELUqFFDa1kwXZfmiomJET169ND0z9y5c3N8aa6UnA0bNhTGxsbC1tZWTJ8+XWtZruPHjwsAIiwsTAghxIYNG1ItlZbyleLYsWPCzc1NWFpaChMTE1GhQgUxYcIEERUVpXXubdu2CWdnZ2Fubi6srKxEu3btxK1btzTPN2rUKM3z/Ld/01q6DYBWDfb27VsxePBgUbp0aWFiYiLKli0rpkyZIhITEzM8BgBx/PjxNL9v2bU0l0yIDG5RkQ9FR0fD0tIy1cT2nKRUKnHgwAG0atWK0yH0EPtP/7EPc15CQgLCwsI0d3TMkj17gFGjgKdP/9dWqhSwaBHUHTogOjpacyGWFMqUKYPRo0fz1qhZoFarJe8/+jw50YcZ/d7QpV6TfDUDIiIiAEDHjkD79sDp00B4+Ie5tA0bAgYGQDatLUtE+Q+LWSIiyjsMDIBvvpE6BRHpERazREREmfDw4UOpIxBRGjhxhYiIiIj0FotZIiLKNgXsmmIi+gzZ9fuCxSwREX22lIXac3w9SSLKN1J+X3x84wddcc4sERF9NkNDQ5iZmeHVq1dQKBTZvvySWq1GUlISEhISuLSTHmL/6b/s7kO1Wo1Xr17BzMwMhoafV46ymCUios8mk8lgZ2eHsLAwnW+zmhlCCMTHx8PU1FTr9rKkH9h/+i8n+lAul6N06dKffTwWs0RElC2MjIxQoUKFHJlqoFQqcerUKXz99de88YUeYv/pv5zoQyMjo2wZ5WUxS0RE2UYul2f9DmAZMDAwQHJyMkxMTFgM6SH2n/7Ly33IiStEREREpLdYzBIRERGR3mIxS0RERER6q8DNmU1ZoDc6OjrXzqlUKhEXF4fo6Og8N8+EPo39p//Yh/qPfajf2H/6L7f7MKVOy8yNFQpcMRsTEwMAKFWqlMRJiIiIiCgjMTExsLS0zHAbmShg9x5Uq9V4/vw5ChcunGtr3UVHR6NUqVJ48uQJLCwscuWclH3Yf/qPfaj/2If6jf2n/3K7D4UQiImJgb29/SeX7ypwI7NyuRwlS5aU5NwWFhZ8E+sx9p/+Yx/qP/ahfmP/6b/c7MNPjcim4AVgRERERKS3WMwSERERkd5iMZsLjI2NMW3aNBgbG0sdhbKA/af/2If6j32o39h/+i8v92GBuwCMiIiIiPIPjswSERERkd5iMUtEREREeovFLBERERHpLRazRERERKS3WMxmgxUrVsDJyQkmJiZwdXXF6dOnM9z+5MmTcHV1hYmJCcqWLYtVq1blUlJKjy59uGfPHnh4eMDKygoWFhZwc3PD4cOHczEtpUXX92GKs2fPwtDQEDVr1szZgPRJuvZhYmIipkyZAkdHRxgbG6NcuXJYv359LqWlj+naf1u2bEGNGjVgZmYGOzs79OnTB69fv86ltPSxU6dOoW3btrC3t4dMJsO+ffs+uU+eqWcEfZbt27cLhUIh1q5dK27evClGjRolzM3NxaNHj9Lc/sGDB8LMzEyMGjVK3Lx5U6xdu1YoFAqxa9euXE5OKXTtw1GjRok5c+aIS5cuibt374pJkyYJhUIhrl69msvJKYWufZji7du3omzZsqJZs2aiRo0auROW0pSVPmzXrp2oU6eOCAoKEmFhYeLixYvi7NmzuZiaUujaf6dPnxZyuVwsXrxYPHjwQJw+fVpUrVpVdOjQIZeTU4oDBw6IKVOmiN27dwsAYu/evRlun5fqGRazn6l27dpi8ODBWm2VK1cWEydOTHP78ePHi8qVK2u1DRo0SNStWzfHMlLGdO3DtFSpUkX4+vpmdzTKpKz2YdeuXcUPP/wgpk2bxmJWYrr24cGDB4WlpaV4/fp1bsSjT9C1/+bNmyfKli2r1bZkyRJRsmTJHMtImZeZYjYv1TOcZvAZkpKSEBwcjGbNmmm1N2vWDOfOnUtzn/Pnz6favnnz5rhy5QqUSmWOZaW0ZaUPP6ZWqxETE4NixYrlRET6hKz24YYNG3D//n1MmzYtpyPSJ2SlD//44w/UqlULc+fOhYODAypWrIixY8ciPj4+NyLTf2Sl/+rVq4enT5/iwIEDEELgxYsX2LVrF1q3bp0bkSkb5KV6xjBXz5bPREZGQqVSwcbGRqvdxsYGERERae4TERGR5vbJycmIjIyEnZ1djuWl1LLShx+bP38+YmNj0aVLl5yISJ+QlT68d+8eJk6ciNOnT8PQkL8GpZaVPnzw4AHOnDkDExMT7N27F5GRkRg6dCjevHnDebO5LCv9V69ePWzZsgVdu3ZFQkICkpOT0a5dOyxdujQ3IlM2yEv1DEdms4FMJtN6LIRI1fap7dNqp9yjax+m2LZtG6ZPn46AgABYW1vnVDzKhMz2oUqlQvfu3eHr64uKFSvmVjzKBF3eh2q1GjKZDFu2bEHt2rXRqlUrLFiwAP7+/hydlYgu/Xfz5k2MHDkSU6dORXBwMA4dOoSwsDAMHjw4N6JSNskr9QyHJD5DiRIlYGBgkOovz5cvX6b6ayWFra1tmtsbGhqiePHiOZaV0paVPkwREBCAfv36YefOnWjatGlOxqQM6NqHMTExuHLlCkJCQjB8+HAAHwojIQQMDQ0RGBgId3f3XMlOH2TlfWhnZwcHBwdYWlpq2r744gsIIfD06VNUqFAhRzPT/2Sl/2bPno369etj3LhxAIDq1avD3NwcDRs2xE8//cRPKfVAXqpnODL7GYyMjODq6oqgoCCt9qCgINSrVy/Nfdzc3FJtHxgYiFq1akGhUORYVkpbVvoQ+DAi6+3tja1bt3KOl8R07UMLCwtcv34doaGhmq/BgwejUqVKCA0NRZ06dXIrOv2/rLwP69evj+fPn+P9+/eatrt370Iul6NkyZI5mpe0ZaX/4uLiIJdrlyAGBgYA/je6R3lbnqpncv2Ss3wmZTkSPz8/cfPmTTF69Ghhbm4uHj58KIQQYuLEiaJnz56a7VOWshgzZoy4efOm8PPz49JcEtO1D7du3SoMDQ3F8uXLRXh4uObr7du3Ur2EAk/XPvwYVzOQnq59GBMTI0qWLCk6deok/vnnH3Hy5ElRoUIF0b9/f6leQoGma/9t2LBBGBoaihUrVoj79++LM2fOiFq1aonatWtL9RIKvJiYGBESEiJCQkIEALFgwQIREhKiWV4tL9czLGazwfLly4Wjo6MwMjISLi4u4uTJk5rnevfuLRo1aqS1/YkTJ4Szs7MwMjISZcqUEStXrszlxPQxXfqwUaNGAkCqr969e+d+cNLQ9X34Xyxm8wZd+/DWrVuiadOmwtTUVJQsWVL4+PiIuLi4XE5NKXTtvyVLlogqVaoIU1NTYWdnJ7y8vMTTp09zOTWlOH78eIb/tuXlekYmBMfziYiIiEg/cc4sEREREektFrNEREREpLdYzBIRERGR3mIxS0RERER6i8UsEREREektFrNEREREpLdYzBIRERGR3mIxS0RERER6i8UsEREAf39/FClSROoYWVamTBksWrQow22mT5+OmjVr5koeIqLcwmKWiPINb29vyGSyVF///vuv1NHg7++vlcnOzg5dunRBWFhYthz/8uXLGDhwoOaxTCbDvn37tLYZO3Ysjh49mi3nS8/Hr9PGxgZt27bFP//8o/Nx9PmPCyLKPSxmiShfadGiBcLDw7W+nJycpI4FALCwsEB4eDieP3+OrVu3IjQ0FO3atYNKpfrsY1tZWcHMzCzDbQoVKoTixYt/9rk+5b+v86+//kJsbCxat26NpKSkHD83ERU8LGaJKF8xNjaGra2t1peBgQEWLFiAL7/8Eubm5ihVqhSGDh2K9+/fp3ucv//+G40bN0bhwoVhYWEBV1dXXLlyRfP8uXPn8PXXX8PU1BSlSpXCyJEjERsbm2E2mUwGW1tb2NnZoXHjxpg2bRpu3LihGTleuXIlypUrByMjI1SqVAmbN2/W2n/69OkoXbo0jI2NYW9vj5EjR2qe++80gzJlygAAvv32W8hkMs3j/04zOHz4MExMTPD27Vutc4wcORKNGjXKttdZq1YtjBkzBo8ePcKdO3c022TUHydOnECfPn3w7t07zQjv9OnTAQBJSUkYP348HBwcYG5ujjp16uDEiRMZ5iGi/I3FLBEVCHK5HEuWLMGNGzewceNGHDt2DOPHj093ey8vL5QsWRKXL19GcHAwJk6cCIVCAQC4fv06mjdvjo4dO+LatWsICAjAmTNnMHz4cJ0ymZqaAgCUSiX27t2LUaNG4fvvv8eNGzcwaNAg9OnTB8ePHwcA7Nq1CwsXLsTq1atx79497Nu3D19++WWax718+TIAYMOGDQgPD9c8/q+mTZuiSJEi2L17t6ZNpVJhx44d8PLyyrbX+fbtW2zduhUANN8/IOP+qFevHhYtWqQZ4Q0PD8fYsWMBAH369MHZs2exfft2XLt2DZ07d0aLFi1w7969TGcionxGEBHlE7179xYGBgbC3Nxc89WpU6c0t92xY4coXry45vGGDRuEpaWl5nHhwoWFv79/mvv27NlTDBw4UKvt9OnTQi6Xi/j4+DT3+fj4T548EXXr1hUlS5YUiYmJol69ev/Xzv2FNNmGYQC/NrfRfJeQHVSSbTh50ZOiQVZEB/2jWLQYNCoHdpCRpQUGEZ00IQxCXEVQnoSiDKyDDYSKSCvDCjKHWBahJDsoRCQp0tKWdwcfvvS6+ZkmX9/G9Tt7/uzZ/fDAuNjeZ3LkyBHda3w+n7jdbhERqa2tFVVVZWJiIun6drtdLl26pLUBSCQS0c0JBAKyZs0arX3y5EnZunWr1r53755YLBb5+PHjH+0TgCiKIpmZmQJAAIjH40k6f8ps5yEi0t/fLwaDQd6/f6/r37Ztm5w9e/Zf1yei9GX6u1GaiGhhbdmyBdevX9faiqIAAB4+fIgLFy7g9evX+Pz5M+LxOL59+4bR0VFtzq9OnTqF0tJSNDU1Yfv27fD5fHA6nQCArq4u9Pf3IxQKafNFBJOTkxgYGEBhYWHS2j59+gSbzQYRwdjYGFwuF8LhMCwWC968eaO7wAUAmzZtwpUrVwAAPp8Ply9fRl5eHnbt2gW32409e/bAZJr/x7jf78fGjRvx4cMH5OTkIBQKwe12Y8mSJX+0z8WLFyMajSIej6O9vR01NTWoq6vTzZnreQBANBqFiEBVVV3/+Pj4f/IsMBH9PzHMElFaURQF+fn5ur5YLAa3242ysjKcP38e2dnZ6OjowOHDh/H9+/ek61RVVaG4uBi3b9/G3bt3EQgE0NzcDK/Xi8nJSRw9elT3zOqUVatWzVjbVMgzGo1YtmxZQmgzGAy6tohofbm5uXj79i3u37+P1tZWHD9+HDU1NWhvb9f9fD8XRUVFcDqdaG5uxrFjxxCJRFBfX6+Nz3efRqNRO4OCggIMDg5i//79ePz4MYD5ncdUPRkZGejq6kJGRoZuzGazzWnvRJQ+GGaJKO29ePEC8XgctbW1MBr/uSpw69atWV+nqipUVUVlZSUOHjyI+vp6eL1euFwu9Pb2JoTm2fwa8qYrLCxER0cHSkpKtL6nT5/qvv20Wq3weDzweDwoLy9HQUEBXr58CZfLlbCe2Wz+rX9JKC4uRigUwsqVK2E0GrF7925tbL77nK6yshLBYBCRSARer/e3zsNisSTUv3btWvz48QNDQ0PYvHnzH9VEROmDF8CIKO05nU7E43FcvXoV7969Q1NTU8LP3r/6+vUrKioq8OjRI8RiMTx58gSdnZ1asDxz5gyePXuG8vJydHd3o6+vDy0tLThx4sS8azx9+jQaGhpQV1eHvr4+BINBhMNh7eJTQ0MDbty4gVevXml7sFqtsNvtSddzOBxoa2vD4OAgRkZGZnxfv9+PaDSK6upq7Nu3D4sWLdLGFmqfWVlZKC0tRSAQgIj81nk4HA58+fIFbW1tGB4extjYGFRVhd/vR0lJCcLhMAYGBtDZ2YmLFy/izp07c6qJiNLI33xgl4hoIR06dEj27t2bdCwYDMqKFSvEarXKzp07pbGxUQDIyMiIiOgvHI2Pj8uBAwckNzdXLBaL5OTkSEVFhe7S0/Pnz2XHjh1is9lEURRZvXq1VFdXz1hbsgtN0127dk3y8vLEbDaLqqrS2NiojUUiEVm/fr1kZWWJoiiyYcMGaW1t1canXwBraWmR/Px8MZlMYrfbRSTxAtiUdevWCQB58OBBwthC7TMWi4nJZJKbN2+KyOznISJSVlYmS5cuFQASCARERGRiYkLOnTsnDodDzGazLF++XLxer/T09MxYExGlN4OIyN+N00RERERE88PHDIiIiIgoZTHMEhEREVHKYpglIiIiopTFMEtEREREKYthloiIiIhSFsMsEREREaUshlkiIiIiSlkMs0RERESUshhmiYiIiChlMcwSERERUcpimCUiIiKilPUTyu432LhT69gAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 9.9462  :  12%|█████████▍                                                                     | 1201/10000 [02:48<1:13:50,  1.99it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.8888888888888888\n"
+     ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:05<00:00,  9.41it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:02<00:00,  9.41it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:02<00:00,  9.38it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.42it/s]\n"
+      "       Current loss: 8.4517  :  13%|██████████▎                                                                    | 1301/10000 [03:02<1:15:27,  1.92it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.918918918918919\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:08<00:00,  9.39it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.37it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.40it/s]\n"
+      "       Current loss: 9.5347  :  14%|███████████                                                                    | 1401/10000 [03:16<1:13:10,  1.96it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.8888888888888888\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:12<00:00,  9.42it/s]\n"
+      "       Current loss: 9.7705  :  15%|███████████▊                                                                   | 1501/10000 [03:30<1:10:52,  2.00it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (76.80 s)\n",
-      "\n",
-      "Dataset               Accuracy\n",
-      "------------------------------\n",
-      "Anonaly lvl 1           100.00\n",
-      "Anonaly lvl 2           100.00\n",
-      "Anonaly lvl 3           100.00\n",
-      "\n",
-      "Anomaly all             100.00\n",
-      "\n",
-      "No Anomaly Train         91.25\n",
-      "No Anomaly Test          95.00\n",
-      "No Anomaly All           92.00\n",
-      "\n",
-      "All without train        99.17\n",
-      "All with train           96.00\n"
+      "F1 Validation 0.8888888888888888\n"
      ]
-    }
-   ],
-   "source": [
-    "# STEPS = 500, MODEL TYPE = MEDIUM, WEIGHT = on\n",
-    "model17 = EfficientAD({**config, \"train_steps\": 500, \"model_type\": \"medium\", \"weight_path\":\"../weights/teacher_medium.pth\"})\n",
-    "model17.create_model()\n",
-    "model17.display_eval_result()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 7.7871  :  16%|████████████▋                                                                  | 1601/10000 [03:44<1:11:44,  1.95it/s]"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (0.26 ms)\n",
-      "\n",
-      "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (3.26 ms)\n",
-      "\n",
-      "- Setting config\n",
-      "     Output folder path: ../output/cookies_3_steps_5000_medium_weighted\n",
-      "- OK - Setting config (0.10 ms)\n",
-      "\n",
-      "- Prepare teacher, student & autoencoder\n",
-      "     Loading weight ../weights/teacher_medium.pth\n",
-      "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (187.94 ms)\n",
-      "\n",
-      "- Normalizing teacher\n"
+      "F1 Validation 0.918918918918919\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 22.17it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 22.56it/s]\n"
+      "       Current loss: 7.9733  :  17%|█████████████▍                                                                 | 1701/10000 [03:58<1:09:43,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (6.44 s)\n",
-      "\n",
-      "- Train\n"
+      "F1 Validation 0.8571428571428571\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 4.8386  : 100%|███████████████████████████████████████████████████████| 5000/5000 [27:05<00:00,  3.08it/s]\n"
+      "       Current loss: 7.5294  :  18%|██████████████▏                                                                | 1801/10000 [04:12<1:18:35,  1.74it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (1625.47 s)\n",
-      "\n",
-      "- Saving models to ../output/cookies_3_steps_5000_medium_weighted/all_models.pth\n",
-      "- OK - Saving models (203.16 ms)\n",
-      "\n",
-      "- Saving map normalization to ../output/cookies_3_steps_5000_medium_weighted/map_normalization.pth\n"
+      "F1 Validation 0.8571428571428571\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 11.27it/s]\n"
+      "       Current loss: 7.6900  :  19%|███████████████                                                                | 1901/10000 [04:26<1:07:50,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (806.40 ms)\n",
-      "\n",
-      "- Evaluating model\n"
+      "F1 Validation 0.8571428571428571\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:21<00:00,  9.41it/s]\n"
+      "       Current loss: 9.6443  :  20%|███████████████▊                                                               | 2001/10000 [04:40<1:06:58,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "     - AUC: 100.00%\n",
-      "    - Optimal Threshold: 0.6405318\n",
-      "    - F1 Score: 1.00\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[100   0]\n",
-      " [  0 100]] \n",
-      "\n"
+      "F1 Validation 0.918918918918919\n"
      ]
     },
     {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIhCAYAAABdSTJTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACGLElEQVR4nOzdd1iTV/8G8DthD8GBTBVxr6qAVXFURXGvWgeKA7fiturr6OustWqdteIWtai4Wze4V10IVatVq7hBiwtkhuT8/vBHXiOgBBMeAvfnurjanDzjTg7BL4fznEcmhBAgIiIiIjJAcqkDEBERERHlFItZIiIiIjJYLGaJiIiIyGCxmCUiIiIig8ViloiIiIgMFotZIiIiIjJYLGaJiIiIyGCxmCUiIiIig8ViloiIiIgMFotZIqJMBAUFQSaTqb+MjY3h5OQEX19f3LlzJ9N9FAoFAgMD4eXlBVtbW1hYWKBy5cqYOHEiXrx4kek+KpUKmzZtQrNmzWBnZwcTExPY29ujbdu22Lt3L1Qq1SezpqSkYNmyZWjQoAGKFCkCU1NTuLi4oGvXrjh58uRnvQ9ERHkdi1kioo9Yv349/vjjDxw5cgTDhw/H77//jgYNGuDVq1ca2yUmJsLHxwcjRoyAu7s7tmzZggMHDqBXr15YtWoV3N3dcevWLY19kpOT0bp1a/Tp0wf29vYIDAzEsWPHsGLFCjg7O6NLly7Yu3fvR/PFxsaifv36GDt2LKpVq4agoCAcPXoUCxYsgJGREZo2bYo///xT5+8LEVGeIYiIKIP169cLAOLSpUsa7TNmzBAAxLp16zTaBw0aJACIrVu3ZjjWrVu3hK2trahatapIS0tTtw8dOlQAEBs2bMg0w+3bt8Wff/750ZytWrUSxsbG4ujRo5k+f/HiRfHgwYOPHiO7EhMTdXIcIiJd4sgsEZEWatWqBQB49uyZui0mJgbr1q1DixYt0K1btwz7VKhQAf/5z3/w119/Yc+ePep91qxZgxYtWqB3796Znqt8+fKoXr16llnCw8Nx8OBB9O/fH97e3plu8+WXX6JUqVIAgOnTp0Mmk2XYJn1Kxf3799VtpUuXRtu2bbFr1y64u7vD3NwcM2bMgLu7Oxo2bJjhGEqlEi4uLujUqZO6LTU1Fd9//z0qVaoEMzMzFC9eHH379sW///6b5WsiItIWi1kiIi1ERUUBeFegpjt+/DjS0tLQsWPHLPdLfy4sLEy9j0Kh+Og+nxIaGqpxbF27cuUKxo8fj5EjR+LQoUP45ptv0LdvX5w5cybDvOHQ0FA8ffoUffv2BfBuLnCHDh3w448/okePHti/fz9+/PFHhIWFoXHjxkhKStJLZiIqeIylDkBElJcplUqkpaUhOTkZZ8+exffff4+vvvoK7du3V2/z8OFDAICbm1uWx0l/Ln3b7OzzKbo4xsc8f/4cN27c0Cjcy5Qpg/HjxyMoKAizZ89WtwcFBcHBwQGtWrUCAGzbtg2HDh3Czp07NUZra9SogS+//BJBQUEYOnSoXnITUcHCkVkioo+oW7cuTExMUKhQIbRs2RJFihTBb7/9BmPjnI0FZPZn/ryqevXqGoUsABQrVgzt2rXDhg0b1CstvHr1Cr/99ht69+6tfl/27duHwoULo127dkhLS1N/1axZE46Ojjhx4kRuvxwiyqdYzBIRfcTGjRtx6dIlHDt2DIMHD8bNmzfRvXt3jW3S56SmT0HITPpzJUuWzPY+n6KLY3yMk5NTpu39+vXDkydP1FMmtmzZgpSUFPj7+6u3efbsGV6/fg1TU1OYmJhofMXExCA2NlYvmYmo4GExS0T0EZUrV0atWrXQpEkTrFixAgMGDMChQ4ewY8cO9TZNmjSBsbGx+uKuzKQ/5+Pjo97HxMTko/t8SosWLTSO/Snm5uYA3q1L+76sCsusRpFbtGgBZ2dnrF+/HsC75cvq1KmDKlWqqLexs7NDsWLFcOnSpUy/li9fnq3MRESfwmKWiEgL8+bNQ5EiRTB16lT1n9kdHR3Rr18/HD58GCEhIRn2uX37NubOnYuqVauqL9ZydHTEgAEDcPjwYWzcuDHTc929exdXr17NMouHhwdatWqFtWvX4tixY5luc/nyZfXc2tKlSwNAhmN+ai3bDxkZGaFXr17Ys2cPTp8+jcuXL6Nfv34a27Rt2xYvXryAUqlErVq1MnxVrFhRq3MSEWVFJoQQUocgIsprgoKC0LdvX1y6dEm9HFe6+fPnY8KECdi0aRN69uwJAEhISECbNm1w9uxZDBo0CO3atYOZmRnOnz+Pn376CZaWljhy5IhGEZecnIyOHTsiNDQU3bt3x9dffw0HBwfExsYiLCwM69evx9atW9GhQ4csc8bGxqJly5a4du0a+vXrh1atWqFIkSKIjo7G3r17sWXLFoSHh6NGjRqIi4uDm5sbXFxcMHPmTBgbGyMoKAhXrlxBVFQUoqKi1AVv6dKlUa1aNezbty/T896+fRsVK1ZEiRIl8OLFC0RHR8PW1lb9vFKpRLt27XDhwgWMGjUKtWvXhomJCR4/fozjx4+jQ4cO+Prrr3PaPURE/yP1QrdERHlRVjdNEEKIpKQkUapUKVG+fHmNmyCkpqaKX375RdSpU0dYW1sLMzMzUbFiRTFhwgQRGxub6XnS0tLEhg0bhLe3tyhatKgwNjYWxYsXF61atRKbN28WSqXyk1mTkpLE0qVLhZeXl7CxsRHGxsbC2dlZdOrUSezfv19j24sXL4p69eoJKysr4eLiIqZNmybWrFkjAIioqCj1dq6urqJNmzYfPW+9evUEAOHn55fp8wqFQvz000+iRo0awtzcXFhbW4tKlSqJwYMHizt37nzydRERZQdHZomIiIjIYHHOLBEREREZLBazRERERGSwWMwSERERkcFiMUtEREREBovFLBEREREZLBazRERERGSwjKUOkNtUKhWePn2KQoUKZXmrRiIiIiKSjhAC8fHxcHZ2hlz+8bHXAlfMPn36FCVLlpQ6BhERERF9wqNHj1CiRImPblPgitlChQoBePfm2NjY5Mo5FQoFQkND0bx5c5iYmOTKOUl32H+Gj31o+NiHho39Z/hyuw/j4uJQsmRJdd32MQWumE2fWmBjY5OrxaylpSVsbGz4ITZA7D/Dxz40fOxDw8b+M3xS9WF2poTyAjAiIiIiMlgsZomIiIjIYLGYJSIiIiKDxWKWiIiIiAwWi1kiIiIiMlgsZomIiIjIYLGYJSIiIiKDxWKWiIiIiAwWi1kiIiIiMlgsZomIiIjIYLGYJSIiIiKDxWKWiIiIiAwWi1kiIiIiMlgsZvVNqQTOnHn3/2fOvHtMRERERDohaTF76tQptGvXDs7OzpDJZNizZ88n9zl58iQ8PT1hbm6OMmXKYMWKFfoPmlO7dgGlSwNt2rx73KbNu8e7dkmZioiIiCjfkLSYTUhIQI0aNbBs2bJsbR8VFYXWrVujYcOGiIiIwOTJkzFy5Ejs3LlTz0lzYNcuoHNn4PFjzfYnT961s6AlIiIi+mzGUp68VatWaNWqVba3X7FiBUqVKoXFixcDACpXrozLly/jp59+wjfffKOnlDmgVAKjRgFCAAAEgBQlkGhsBhNjFSCTAd9OAFq1AYyMpM1Kn6RQpL3rv9Q0mAiZ1HEoB9iHho99aNjYf4YvKSkZKUpA/H9tk5fIRB5JJZPJsHv3bnTs2DHLbb766iu4u7tjyZIl6rbdu3eja9euSExMhImJSYZ9UlJSkJKSon4cFxeHkiVLIjY2FjY2Njp9DWpnzqinFggA3brORoRjBf2ci4iIiEiPEu9cwKtja+Dg+z0i53SBrZW53s8ZFxcHOzs7vHnz5pP1mqQjs9qKiYmBg4ODRpuDgwPS0tIQGxsLJyenDPvMmTMHM2bMyNAeGhoKS0tLvWXFli0A3o3IRlw0qLeZiIiICEKpwKsTQYi//BsA4M0f23HsWDGY5cIflRMTE7O9rcFVWTKZ5p8n0geWP2xPN2nSJIwdO1b9OH1ktnnz5rkyMptobIYJAb8CAE5vGgab+Df/227nTqCel34ykM4oFGk4duwYvL29YWJicB8ZAvswP2AfGjb2n+G5f/8++vv3xsPLlwEAg4cG4KtGTdCmRTOYmprq/fxxcXHZ3tagvqMcHR0RExOj0fb8+XMYGxujWLFime5jZmYGMzOzDO0mJiaZTkvQia++AooVA548eTdH9v/ZxL+Bbfzrd3NmS5QAvBtxzqwBUCgUMDMCbK3M9fc9Q3rFPjR87EPDxv4zLLt27UK/fv3w5s0bFClSBEFBQWjVqhUOHDgAU1PTXOlDbc5hUOvMenl5ISwsTKMtNDQUtWrVylsfDiMjIH1e74cjxumPFy9mIUtERER5yqtXr9C/f3+8efMGXl5eiIiIQPv27aWO9VGSFrNv375FZGQkIiMjAbxbeisyMhIPHz4E8G6KQO/evdXbDxkyBA8ePMDYsWNx8+ZNrFu3DmvXrsW4ceOkiP9xnToBO3YATs6a7SVKvGvv1EmaXERERERZKFKkCNavX48JEybg5MmTcHV1lTrSJ0lazF6+fBnu7u5wd3cHAIwdOxbu7u6YOnUqACA6Olpd2AKAm5sbDhw4gBMnTqBmzZqYNWsWli5dmreW5Xpfp07AzRv/e7xzJxAVxUKWiIiI8oxt27bh0KFD6scdO3bE3Llz89ZfvT9C0jmzjRs3/uh6ZUFBQRnaGjVqhCtXrugxlY69P5WgnhenFhAREVGekJSUhLFjx2LFihUoVqwYrl69Cmdn50/vmMcY1AVgRERERPT5bt26ha5du+Lq1auQyWQYMmQI7O3tpY6VIyxmiYiIiAqQ4OBgDB48GAkJCbC3t8evv/4KHx8fqWPlGItZIiIiogJAqVRi8ODBWLt2LQCgSZMmCA4OzvSmU4bEoJbmIiIiIqKcMfr/63ZkMhmmT5+OsLAwgy9kAY7MEhEREeVrycnJMDc3BwAsXboU/v7+aNCggcSpdIcjs0RERET50Nu3b9GnTx906NABKtW7O5JaWlrmq0IW4MgsERERUb5z7do1dO3aFX///TfkcjnOnz+PevXqSR1LLzgyS0RERJRPCCGwevVq1K5dG3///TdcXFxw4sSJfFvIAhyZJSIiIsoX4uLiMHjwYGzduhUA0KpVK2zcuBF2dnYSJ9MvjswSERER5QO+vr7YunUrjIyMMG/ePOzbty/fF7IAR2aJiIiI8oXZs2fjn3/+wYYNG+Dl5SV1nFzDkVkiIiIiA/TmzRscPHhQ/djd3R03btwoUIUswGKWiIiIyOBcvnwZHh4e6NChAy5fvqxuNzYueH90ZzFLREREZCCEEFiyZAnq1auHe/fuwcXFRepIkit45TsRERGRAXr16hX69euHPXv2AAA6deqEtWvXonDhwpLmkhpHZomIiIjyuAsXLsDd3R179uyBqakpfv75Z+zYsaPAF7IAR2aJiIiI8ryTJ0/iwYMHKFu2LLZt2wYPDw+pI+UZLGaJiIiI8rhx48ZBJpNh8ODBsLGxkTpOnsJpBkRERER5zJkzZ9CiRQskJCQAAORyOcaPH89CNhMsZomIiIjyCJVKhTlz5qBx48YIDQ3F7NmzpY6U53GaAREREVEe8Pz5c/Tq1QuhoaEAgJ49e2Ly5MkSp8r7WMwSERERSezEiRPo0aMHoqOjYWFhgWXLlqFv376QyWRSR8vzWMwSERERSejXX39Fnz59oFKpUKVKFWzbtg1Vq1aVOpbB4JxZIiIiIgl5e3ujWLFi6Nu3Ly5evMhCVkscmSUiIiLKZbdv30aFChUAAM7Ozvjzzz/h5OQkcSrDxJFZIiIiolySlpaGqVOnonLlyti5c6e6nYVszrGYJSIiIsoFT548QdOmTTFr1iyoVCqcP39e6kj5AqcZEBEREenZoUOH0KtXL8TGxsLa2hqrV6+Gr6+v1LHyBY7MEhEREemJQqHAxIkT0apVK8TGxsLd3R1XrlxhIatDLGaJiIiI9OTUqVOYO3cuAGDYsGE4d+4cypcvL3Gq/IXTDIiIiIj0pGnTppg8eTLc3d3RuXNnqePkSxyZJSIiItKR1NRU/Pe//8WTJ0/UbbNnz2Yhq0ccmSUiIiLSgfv376Nbt264ePEiTp8+jePHj/N2tLmAI7NEREREn2n37t1wd3fHxYsXUbhwYYwZM4aFbC5hMUtERESUQykpKRg5ciQ6deqE169fo27duoiMjESHDh2kjlZgcJoBERERUQ48efIEHTp0QHh4OABg/PjxmD17NkxMTCROVrCwmCUiIiLKgcKFCyMpKQnFihXDhg0b0KZNG6kjFUgsZomIiIiyKTk5GaamppDL5bCyssLu3bthaWmJEiVKSB2twOKcWSIiIqJsuHXrFurUqYN58+ap2ypUqMBCVmIsZomIiIg+ITg4GJ6enrh69SqWLl2KhIQEqSPR/2MxS0RERJSFxMREDBgwAD179kRCQgIaN26My5cvw8rKSupo9P9YzBIRERFl4ubNm6hTpw7Wrl0LmUyGadOm4ciRI3B2dpY6Gr2HF4ARERERfSAuLg7169fHq1ev4OjoiODgYHh7e0sdizLBkVkiIiKiD9jY2GDmzJlo1qwZIiMjWcjmYSxmiYiIiABcu3YNkZGR6sfDhg3D4cOH4eDgIF0o+iQWs0RERFSgCSGwevVq1K5dG507d0ZcXBwAQCaTQS5nqZTXcc4sERERFVjx8fEYPHgwtmzZAgAoX748FAqFxKlIG/x1g4iIiAqkyMhIeHp6YsuWLTAyMsKPP/6I/fv3o1ixYlJHIy1wZJaIiIgKFCEEVqxYgTFjxiAlJQUlS5bE1q1bUa9ePamjUQ5wZJaIiIgKFCEEfv/9d6SkpKBdu3aIiIhgIWvAODJLREREBYpcLsfGjRuxfft2DB06FDKZTOpI9Bk4MktERET5mhACS5YswdChQ9VtxYsXR0BAAAvZfIAjs0RERJRvvXr1Cv369cOePXsAAF26dOENEPIZFrNERESUL124cAHdunXDgwcPYGpqigULFqBJkyZSxyId4zQDIiIiyleEEFiwYAEaNGiABw8eoGzZsjh37hyGDx/OaQX5EEdmiYiIKF/p168fgoKCAABdu3bFqlWrYGtrK20o0huOzBIREVG+0q1bN1hYWCAwMBBbt25lIZvPcWSWiIiIDJpKpcLt27dRqVIlAEDLli0RFRUFBwcHiZNRbuDILBERERms58+fo3Xr1qhbty6ioqLU7SxkCw4Ws0RERGSQTp48iZo1a+Lw4cNITU3FtWvXpI5EEmAxS0RERAZFqVRi5syZ8Pb2RnR0NCpXroyLFy+iffv2UkcjCXDOLBERERmMmJgY9OzZE0ePHgUA+Pv7Y9myZbCyspI4GUmFxSwREREZjCVLluDo0aOwtLREYGAgevfuLXUkkhiLWSIiIjIY06ZNw+PHjzFlyhT16gVUsHHOLBEREeVZT548wbhx45CWlgYAMDc3x6ZNm1jIkhpHZomIiChPOnToEHr16oXY2FjY2Nhg6tSpUkeiPIgjs0RERJSnKBQKTJo0Ca1atUJsbCxq1qwJX19fqWNRHsWRWSIiIsozHj16BF9fX5w7dw4AEBAQgAULFsDc3FziZJRXsZglIiKiPOHo0aPo2rUrXr58CRsbG6xZswZdunSROhblcSxmiYiIKE9wdHREUlISPD09ERISgrJly0odiQwAi1kiIiKSTEJCgvqGB1WrVsXRo0fh4eEBMzMziZORoeAFYERERCSJPXv2oHTp0ur5sQDg5eXFQpa0wmKWiIiIclVKSgpGjRqFr7/+GrGxsVi0aJHUkciASV7MLl++HG5ubjA3N4enpydOnz790e2Dg4NRo0YNWFpawsnJCX379sWLFy9yKS0RERF9jrt376J+/fpYunQpAGDcuHHYvHmzxKnIkElazIaEhGD06NGYMmUKIiIi0LBhQ7Rq1QoPHz7MdPszZ86gd+/e6N+/P/766y9s374dly5dwoABA3I5OREREWlr+/btcHd3R3h4OIoWLYp9+/Zh/vz5MDExkToaGTBJi9mFCxeif//+GDBgACpXrozFixejZMmSCAwMzHT78+fPo3Tp0hg5ciTc3NzQoEEDDB48GJcvX87l5ERERKSNa9euwc/PD/Hx8ahfvz4iIyPRpk0bqWNRPiDZagapqakIDw/HxIkTNdqbN2+uMRH8ffXq1cOUKVNw4MABtGrVCs+fP8eOHTs++mFISUlBSkqK+nFcXByAd3cXUSgUOnglH6dQpGn8f26ck3Qrvc/Yd4aLfWj42IeGTaFQoFq1aujYsSMqVKiA6dOnw9jYmP1pQHL7M6jNeSQrZmNjY6FUKuHg4KDR7uDggJiYmEz3qVevHoKDg9GtWzckJycjLS0N7du3x88//5zleebMmYMZM2ZkaA8NDYWlpeXnvYhsSFEC6W/zsWPHYGak91OSnoSFhUkdgT4T+9DwsQ8Ny7lz51CzZk1YWlpCJpOhd+/ekMvlCA0NlToa5VBufQYTExOzva3k68zKZDKNx0KIDG3pbty4gZEjR2Lq1Klo0aIFoqOjMX78eAwZMgRr167NdJ9JkyZh7Nix6sdxcXEoWbIkmjdvDhsbG929kCwkpqZhwsVjAABvb2/YWvF2fIZGoVAgLCwMPj4+nNdloNiHho99aFgSExMxduxYrFu3Dl26dMH69etx5MgRtGjRgv1noHL7M5j+l/TskKyYtbOzg5GRUYZR2OfPn2cYrU03Z84c1K9fH+PHjwcAVK9eHVZWVmjYsCG+//57ODk5ZdjHzMws0/XqTExMcqUzTMT/CnMTE2N+iA1Ybn3PkP6wDw0f+zDvu3nzJrp27Yrr169DJpOhcuXKMDZ+V26w/wxfrtVPWpxDsgvATE1N4enpmWG4OiwsDPXq1ct0n8TERMjlmpGNjN793V4IoZ+gRERElC0bNmxArVq1cP36dTg4OCAsLAwzZszI8G83kS5J+t01duxYrFmzBuvWrcPNmzcxZswYPHz4EEOGDAHwbopA79691du3a9cOu3btQmBgIO7du4ezZ89i5MiRqF27NpydnaV6GURERAVaQkIC/P394e/vj8TERDRt2hSRkZFo2rSp1NGoAJB0zmy3bt3w4sULzJw5E9HR0ahWrRoOHDgAV1dXAEB0dLTGmrP+/v6Ij4/HsmXL8O2336Jw4cLw9vbG3LlzpXoJREREBV5iYiJCQ0Mhl8sxY8YMTJo0Sf2XUyJ9k/wCsICAAAQEBGT6XFBQUIa2ESNGYMSIEXpORURERNlVvHhxhISEQKVSoVGjRlLHoQKGk1iIiIhIK/Hx8fDz80NwcLC6rWHDhixkSRIsZomIiCjbIiMj4enpic2bN2P48OFaLaFEpA8sZomIiOiThBAIDAxE3bp1cefOHZQoUQL79u3LlTXbiT5G8jmzRERElLe9efMGAwcOxPbt2wEAbdu2RVBQEIoVKyZxMiIWs0RERPQRCQkJ8PT0xN27d2FsbIy5c+dizJgxWd6tkyi3cZoBERERZcnKygrffPMNXF1dcebMGYwdO5aFLOUpLGaJiIhIw6tXr/D48WP14++//x4RERGoU6eOhKmIMsdiloiIiNQuXLgAd3d3dO7cGQqFAgBgYmKCIkWKSJyMKHMsZomIiAhCCCxYsAANGjTAgwcP8O+//+LJkydSxyL6JBazREREBdyLFy/Qvn17jBs3DmlpaejSpQuuXLmC0qVLSx2N6JNYzBIRERVgZ8+eRc2aNbFv3z6YmZkhMDAQISEhsLW1lToaUbZwaS4iIqICSgiBMWPG4PHjxyhfvjy2bduGmjVrSh2LSCscmSUiIiqgZDIZgoOD0b9/f4SHh7OQJYPEYpaIiKgAOXnyJJYsWaJ+XL58eaxZswaFChWSMBVRznGaARERUQGgVCrxww8/YPr06RBCwMPDAw0bNpQ6FtFnYzFLRESUz8XExKBnz544evQoAKBPnz7w8PCQOBWRbrCYJSIiyseOHj0KPz8/PHv2DJaWlli+fDn69OkjdSwineGcWSIionxqzpw58PHxwbNnz1CtWjVcunSJhSzlOyxmiYiI8il7e3sIITBgwABcuHABVapUkToSkc5xmgEREVE+8vbtW1hbWwMA+vXrh4oVK6JBgwYSpyLSH47MEhER5QNpaWmYNGkSqlWrhpcvXwJ4t44sC1nK71jMEhERGbhHjx6hcePG+PHHH/HgwQPs3LlT6khEuYbFLBERkQHbv38/atasibNnz8LGxgYhISEYOHCg1LGIcg2LWSIiIgOUmpqKcePGoW3btnj58iU8PT1x5coVdO3aVepoRLmKxSwREZEBmj59OhYsWAAAGDlyJM6ePYuyZctKnIoo97GYJSIiMkDjxo1DjRo1sGvXLixZsgRmZmZSRyKSBItZIiIiA5CSkoKNGzdCCAEAKFq0KK5cuYKvv/5a4mRE0uI6s0RERHncvXv30LVrV4SHhyMlJUV9gZdczjEpIn4KiIiI8rAdO3bA3d0d4eHhKFq0KJycnKSORJSnsJglIiLKg5KTkzFs2DB06dIFcXFxqFevHiIjI9G2bVupoxHlKSxmiYiI8pg7d+7Ay8sLy5cvBwBMnDgRJ06cQMmSJSVORpT3cM4sERFRHvP48WP8+eefsLOzw6ZNm9CyZUupIxHlWSxmiYiI8gAhBGQyGQCgSZMmCAoKQtOmTeHi4iJxMqK8jdMMiIiIJHbz5k00aNAAt2/fVrf17t2bhSxRNrCYJSIiktCGDRtQq1YtnDt3DiNHjpQ6DpHBYTFLREQkgYSEBPj7+8Pf3x+JiYnw9vZGUFCQ1LGIDA6LWSIiolx2/fp1fPnll9iwYQPkcjlmzpyJ0NBQODo6Sh2NyODwAjAiIqJcdOHCBTRp0gRJSUlwcnLC5s2b0bhxY6ljERksFrNERES5yMPDAzVq1ICNjQ02bdoEe3t7qSMRGTQWs0RERHp248YNlC9fHiYmJjAxMcH+/ftRuHBhyOWc7Uf0ufgpIiIi0hMhBFasWAEPDw9MmTJF3V60aFEWskQ6wpFZIiIiPYiLi8PAgQOxbds2AO/WklUqlTAyMpI4GVH+wl8LiYiIdCw8PBweHh7Ytm0bjI2NMX/+fPz2228sZIn0gCOzREREOiKEwLJlyzBu3DikpqbC1dUVW7duRd26daWORpRvcWSWiIhIR548eYLJkycjNTUVHTt2REREBAtZIj3jyCwREZGOlChRAqtXr8bz588xYsQIyGQyqSMR5XssZomIiHJICIFFixbB3d0dTZo0AQD4+vpKnIqoYGExS0RElAMvX76Ev78/9u7dC0dHR9y4cQNFihSROhZRgcNiloiISEvnzp2Dr68vHj16BDMzM0ydOhWFCxeWOhZRgcQLwIiIiLJJpVJh7ty5+Oqrr/Do0SOUL18e58+fx9ChQzk/lkgiHJklIiLKhqSkJHzzzTc4ePAgAKB79+5YuXIlChUqJHEyooKNI7NERETZYG5ujsKFC8Pc3ByrVq1CcHAwC1miPIDFLBERURaUSiUSEhIAADKZDCtXrsSlS5cwcOBATisgyiNYzBIREWXi2bNnaNmyJXr27AkhBACgUKFCqFatmsTJiOh9nDNLRET0gWPHjsHPzw8xMTGwtLTE33//jcqVK0sdi4gywZFZIiKi/6dUKjFt2jQ0a9YMMTExqFq1Ki5dusRCligP48gsERERgKdPn8LPzw8nTpwAAPTv3x9Lly6FpaWltMGI6KNYzBIRUYEnhECHDh1w+fJlWFlZYeXKlfDz85M6FhFlQ46mGaSlpeHIkSNYuXIl4uPjAbz7jfbt27c6DUdERJQbZDIZli5dCk9PT1y5coWFLJEB0Xpk9sGDB2jZsiUePnyIlJQU+Pj4oFChQpg3bx6Sk5OxYsUKfeQkIiLSqcePHyMyMhJt27YFAHh5eeHSpUtccovIwGg9Mjtq1CjUqlULr169goWFhbr966+/xtGjR3UajoiISB8OHDiAmjVromvXrrh+/bq6nYUskeHRemT2zJkzOHv2LExNTTXaXV1d8eTJE50FIyIi0jWFQoEpU6Zg/vz5AAAPDw+NgRkiMjxaF7MqlQpKpTJD++PHj3lbPyIiyrMePHgAX19fnD9/HgAwYsQIzJ8/H2ZmZhInI6LPofU0Ax8fHyxevFj9WCaT4e3bt5g2bRpat26ty2xEREQ68dtvv8Hd3R3nz5+Hra0tdu7ciaVLl7KQJcoHtB6ZXbRoEZo0aYIqVaogOTkZPXr0wJ07d2BnZ4ctW7boIyMREdFnuXLlCl69eoXatWtj69atcHNzkzoSEemI1sWss7MzIiMjsXXrVoSHh0OlUqF///7w8/PjvCMiIsozhBDqC7qmTp0Ke3t7DBw4MMM1H0Rk2LSeZnDq1CmYmJigb9++WLZsGZYvX44BAwbAxMQEp06d0kdGIiIirezcuRPe3t5ITk4GABgZGWHYsGEsZInyIa2L2SZNmuDly5cZ2t+8eYMmTZroJBQREVFOJCcnY/jw4ejcuTNOnDiBX375RepIRKRnWk8zeP/PNu978eIFrKysdBKKiIhIW3fu3EG3bt0QEREBAPjPf/6DkSNHSpyKiPQt28Vsp06dALxbvcDf31/jClClUomrV6+iXr16uk9IRET0CVu3bsXAgQPx9u1b2NnZYePGjWjVqpXUsYgoF2S7mLW1tQXwbmS2UKFCGhd7mZqaom7duhg4cKDuExIREX3EggULMG7cOABAw4YNsWXLFri4uEiciohyS7aL2fXr1wMASpcujXHjxnFKARER5QnffPMNfvjhBwQEBGDatGkwNtZ6Bh0RGTCtP/HTpk3TRw4iIqJsi4iIgLu7O4B3gyx37txB0aJFJU5FRFLQejUDANixYwe6du2KunXrwsPDQ+NLW8uXL4ebmxvMzc3h6emJ06dPf3T7lJQUTJkyBa6urjAzM0PZsmWxbt26nLwMIiIyMAkJCejXrx88PDxw4MABdTsLWaKCS+tidunSpejbty/s7e0RERGB2rVro1ixYrh3757Wk+1DQkIwevRoTJkyBREREWjYsCFatWqFhw8fZrlP165dcfToUaxduxa3bt3Cli1bUKlSJW1fBhERGZiHDx+iXr16WL9+PeRyOW7duiV1JCLKA7SeZrB8+XKsWrUK3bt3x4YNGzBhwgSUKVMGU6dOzXT92Y9ZuHAh+vfvjwEDBgAAFi9ejMOHDyMwMBBz5szJsP2hQ4dw8uRJ3Lt3T/1beOnSpbV9CUREZECEEAgKCsK4ceOQmpoKR0dHbNmyBY0bN5Y6GhHlAVoXs+m/GQOAhYUF4uPjAQC9evVC3bp1sWzZsmwdJzU1FeHh4Zg4caJGe/PmzXHu3LlM9/n9999Rq1YtzJs3D5s2bYKVlRXat2+PWbNmZXkr3ZSUFKSkpKgfx8XFAQAUCgUUCkW2sn4OhSJN4/9z45ykW+l9xr4zXOxDw/X27VsMHz4cmzdvBgA0bdoUGzZsgL29PfvTgPAzaPhyuw+1OY/WxayjoyNevHgBV1dXuLq64vz586hRowaioqIghMj2cWJjY6FUKuHg4KDR7uDggJiYmEz3uXfvHs6cOQNzc3Ps3r0bsbGxCAgIwMuXL7OcNztnzhzMmDEjQ3toaCgsLS2znTenUpRA+tt87NgxmBnp/ZSkJ2FhYVJHoM/EPjQ8f/zxBzZv3gy5XI4ePXqgU6dOuHz5stSxKIf4GTR8udWHiYmJ2d5W62LW29sbe/fuhYeHB/r3748xY8Zgx44duHz5svrGCtr48G5iWd1hDABUKhVkMhmCg4PV694uXLgQnTt3xi+//JLp6OykSZMwduxY9eO4uDiULFkSzZs3h42NjdZ5tZWYmoYJF48BePfe2VqZ6/2cpFsKhQJhYWHw8fGBiYmJ1HEoB9iHhqt169YQQsDHxwcJCQnsQwPFz6Dhy+0+TP9LenZoXcyuWrUKKpUKADBkyBAULVoUZ86cQbt27TBkyJBsH8fOzg5GRkYZRmGfP3+eYbQ2nZOTE1xcXNSFLABUrlwZQgg8fvwY5cuXz7CPmZmZxt3K0pmYmORKZ5iI/xXmJibG/BAbsNz6niH9YR/mfXFxcZg0aRKmTZsGe3t7AMDcuXOhUChw4MAB9qGBY/8Zvlyrn7Q4h9arGcjlco0Fqbt27YqlS5di5MiR+Pfff7N9HFNTU3h6emYYrg4LC8vytrj169fH06dP8fbtW3Xb7du3IZfLUaJECS1fCRER5SVXrlyBh4cHli9fjv79+0sdh4gMRI7Wmf1QTEwMRowYgXLlymm139ixY7FmzRqsW7cON2/exJgxY/Dw4UP1CO+kSZPQu3dv9fY9evRAsWLF0LdvX9y4cQOnTp3C+PHj0a9fvywvACMiorxNCIFly5bBy8sLd+/eRalSpTB58mSpYxGRgch2Mfv69Wv4+fmhePHicHZ2xtKlS6FSqTB16lSUKVMG58+f1/rmBd26dcPixYsxc+ZM1KxZE6dOncKBAwfg6uoKAIiOjtZYc9ba2hphYWF4/fo1atWqBT8/P7Rr1w5Lly7V6rxERJQ3vH79Gp07d8aIESOQmpqK9u3bIyIiAl5eXlJHIyIDke05s5MnT8apU6fQp08fHDp0CGPGjMGhQ4eQnJyMgwcPolGjRjkKEBAQgICAgEyfCwoKytBWqVIlXg1JRJQP/P3332jdujWioqJgYmKC+fPnY+TIkVleBExElJlsF7P79+/H+vXr0axZMwQEBKBcuXKoUKECFi9erMd4RESUXzk7O8PIyAhubm4ICQnBl19+KXUkIjJA2S5mnz59iipVqgAAypQpA3Nzc/Wdu4iIiLIjLi4OhQoVgkwmg42NDfbt2wcHBwcULlxY6mhEZKCyPWdWpVJpLJNgZGQEKysrvYQiIqL8548//kC1atU07hRZsWJFFrJE9FmyPTIrhIC/v796zdbk5GQMGTIkQ0G7a9cu3SYkIiKDplKp8NNPP2Hy5MlQKpVYuXIlhgwZwvVGiUgnsl3M9unTR+Nxz549dR6GiIjyl3///Rd9+vTBwYMHAQC+vr5YuXIlC1ki0plsF7Pr16/XZw4iIspnTp06he7du+Pp06cwNzfH0qVLMWDAAK5WQEQ6pfXtbImIiD4lOjoazZs3R0pKCipWrIht27ahevXqUscionyIxSwREemck5MTZsyYgb/++gvLly+HtbW11JGIKJ9iMUtERDpx/Phx2Nvbo2rVqgCACRMmAACnFRCRXmV7aS4iIqLMKJVKTJ8+HU2bNkXXrl2RkJAA4F0Ry0KWiPSNI7NERJRj0dHR8PPzw/HjxwEAdevWZQFLRLkqRyOzmzZtQv369eHs7IwHDx4AABYvXozffvtNp+GIiCjvCgsLQ82aNXH8+HFYWVlh06ZNWLt2LSwtLaWORkQFiNbFbGBgIMaOHYvWrVvj9evXUCqVAIDChQtj8eLFus5HRER5TFpaGr777ju0aNECz58/R/Xq1XH58mWuP05EktC6mP3555+xevVqTJkyBUZGRur2WrVq4dq1azoNR0REeY9MJsOZM2cghMDgwYNx/vx5VKpUSepYRFRAaT1nNioqCu7u7hnazczM1JP+iYgo/xFCQCaTwcjICJs3b8aZM2fQtWtXqWMRUQGn9cism5sbIiMjM7QfPHgQVapU0UUmIiLKQxQKBSZMmIDRo0er25ydnVnIElGeoPXI7Pjx4zFs2DAkJydDCIGLFy9iy5YtmDNnDtasWaOPjEREJJGHDx/C19cXf/zxBwCgX79+qFGjhsSpiIj+R+titm/fvkhLS8OECROQmJiIHj16wMXFBUuWLIGvr68+MhIRkQR+//13+Pv749WrV7C1tcXatWtZyBJRnpOjdWYHDhyIgQMHIjY2FiqVCvb29rrORUREEklNTcV//vMf9Qo1X375JUJCQuDm5iZtMCKiTGg9Z3bGjBm4e/cuAMDOzo6FLBFRPiKEQLt27dSF7JgxY3DmzBkWskSUZ2ldzO7cuRMVKlRA3bp1sWzZMvz777/6yEVERBKQyWQYPHgwihQpgt9++w0LFy6Eqamp1LGIiLKkdTF79epVXL16Fd7e3li4cCFcXFzQunVrbN68GYmJifrISEREepScnKyxTninTp1w7949tG/fXsJURETZk6Pb2VatWhU//PAD7t27h+PHj8PNzQ2jR4+Go6OjrvMREZEe/fPPP6hXrx68vb3x5MkTdXvhwoWlC0VEpIUcFbPvs7KygoWFBUxNTaFQKHSRiYiIckFISAg8PDwQEREBIQSioqKkjkREpLUcFbNRUVGYPXs2qlSpglq1auHKlSuYPn06YmJidJ2PiIh0LCkpCUOGDIGvry/i4+PRoEEDREZGokGDBlJHIyLSmtZLc3l5eeHixYv44osv0LdvX/U6s0RElPfdunULXbt2xdWrVyGTyTB58mRMnz4dxsY5WqmRiEhyWv/0atKkCdasWYOqVavqIw8REenRkiVLcPXqVdjb2+PXX3+Fj4+P1JGIiD6L1sXsDz/8oI8cRESUC+bPn4+0tDTMmDEDTk5OUschIvps2Spmx44di1mzZsHKygpjx4796LYLFy7USTAiIvp8f/31F1auXInFixdDLpfDysoKq1atkjoWEZHOZKuYjYiIUK9UEBERoddARET0+YQQCAoKwrBhw5CUlIQyZcpg9OjRUsciItK5bBWzx48fz/T/iYgo73n79i0CAgKwadMmAEDz5s3Ro0cPiVMREemH1ktz9evXD/Hx8RnaExIS0K9fP52EIiKinLl69Spq1aqFTZs2QS6XY/bs2Th48CDs7e2ljkZEpBdaF7MbNmxAUlJShvakpCRs3LhRJ6GIiEh7ISEhqFOnDm7dugUXFxecOHECkydPhlz+2ffHISLKs7K9mkFcXByEEBBCID4+Hubm5urnlEolDhw4wN/8iYgkVK5cOahUKrRq1QobN26EnZ2d1JGIiPQu28Vs4cKFIZPJIJPJUKFChQzPy2QyzJgxQ6fhiIjo416/fo3ChQsDADw9PfHHH3+gZs2aHI0logIj28Xs8ePHIYSAt7c3du7ciaJFi6qfMzU1haurK5ydnfUSkoiINAkhsHz5ckyePBnHjx+Hh4cHAKj/S0RUUGS7mG3UqBEAICoqCqVKlYJMJtNbKCIiytrr168xcOBA7NixAwAQFBTEIpaICqxsFbNXr15FtWrVIJfL8ebNG1y7di3LbatXr66zcEREpOnSpUvo1q0boqKiYGJignnz5mHUqFFSxyIikky2itmaNWsiJiYG9vb2qFmzJmQyGYQQGbaTyWRQKpU6D0lEVNAJIbBkyRJMmDABCoUCbm5uCAkJwZdffil1NCIiSWWrmI2KikLx4sXV/09ERLlr586dGDNmDADgm2++wZo1a9QXfhERFWTZKmZdXV0z/X8iIsodnTp1Qvv27dG8eXMEBATwugUiov+Xo5sm7N+/X/14woQJKFy4MOrVq4cHDx7oNBwRUUGlUqmwevVqJCYmAgDkcjn27NmDYcOGsZAlInqP1sXsDz/8AAsLCwDAH3/8gWXLlmHevHmws7NT/wmMiIhyLjY2Fu3atcOgQYMwYsQIdTuLWCKijLK9NFe6R48eoVy5cgCAPXv2oHPnzhg0aBDq16+Pxo0b6zofEVGBcvr0aXTv3h1PnjyBubk56tSpAyEEC1kioixoPTJrbW2NFy9eAABCQ0PRrFkzAIC5uTmSkpJ0m46IqIBQqVT44Ycf0KRJEzx58gQVK1bEhQsXMGjQIBayREQfofXIrI+PDwYMGAB3d3fcvn0bbdq0AQD89ddfKF26tK7zERHle8+fP0evXr0QGhoKAOjZsycCAwNhbW0tcTIiorxP65HZX375BV5eXvj333+xc+dOFCtWDAAQHh6O7t276zwgEVF+p1AocOXKFVhYWGDt2rXYuHEjC1kiomzSemS2cOHCWLZsWYb2GTNm6CQQEVFB8P48WBcXF2zfvh3FixdH1apVJU5GRGRYtC5mgXf3BV+7di1u3rwJmUyGypUro3///rC1tdV1PiKifCcmJgZ+fn4YPnw4vv76awDgBbRERDmk9TSDy5cvo2zZsli0aBFevnyJ2NhYLFq0CGXLlsWVK1f0kZGIKN84cuQIatSogWPHjmHkyJFITU2VOhIRkUHTupgdM2YM2rdvj/v372PXrl3YvXs3oqKi0LZtW4wePVoPEYmIDF9aWhq+++47NG/eHM+fP0f16tVx5MgRmJqaSh2NiMigaT3N4PLly1i9ejWMjf+3q7GxMSZMmIBatWrpNBwRUX7w5MkTdO/eHadPnwYADB48GIsWLVLfgIaIiHJO62LWxsYGDx8+RKVKlTTaHz16hEKFCuksGBFRfvDvv/+iZs2aiI2NRaFChbBq1Sr4+vpKHYuIKN/QeppBt27d0L9/f4SEhODRo0d4/Pgxtm7digEDBnBpLiKiDxQvXhzdunWDu7s7wsPDWcgSEemY1iOzP/30E2QyGXr37o20tDQAgImJCYYOHYoff/xR5wGJiAzNw4cPYWJiAicnJwDAggULIISAubm5xMmIiPIfrUdmTU1NsWTJErx69QqRkZGIiIjAy5cvsWjRIpiZmekjIxGRwdi7dy9q1qyJ7t27q3/hNzMzYyFLRKQn2S5mExMTMWzYMLi4uMDe3h4DBgyAk5MTqlevDktLS31mJCLK81JTU/Htt9+iffv2ePXqFRITE/Hq1SupYxER5XvZLmanTZuGoKAgtGnTBr6+vggLC8PQoUP1mY2IyCBERUWhYcOGWLhwIYB3SxieOXMGxYsXlzgZEVH+l+05s7t27cLatWvVFy/07NkT9evXh1KphJGRkd4CEhHlZbt27UK/fv3w5s0bFClSBEFBQWjfvr3UsYiICoxsj8w+evQIDRs2VD+uXbs2jI2N8fTpU70EIyLK6xQKBf773//izZs38PLyQkREBAtZIqJclu1iVqlUZrhTjbGxsfoCByKigsbExAQhISGYNGkSTp48CVdXV6kjEREVONmeZiCEgL+/v8aKBcnJyRgyZAisrKzUbbt27dJtQiKiPGTbtm14/vw5hg8fDgCoVq0afvjhB4lTEREVXNkuZvv06ZOhrWfPnjoNQ0SUVyUlJWHMmDFYuXIljIyMUL9+fbi7u0sdi4iowMt2Mbt+/Xp95iAiyrNu3bqFrl274urVq5DJZJg4cSK++OILqWMRERFycAcwIqKC5Ndff8WQIUOQkJAAe3t7/Prrr/Dx8ZE6FhER/T+t7wBGRFRQBAQEoFevXkhISECTJk0QGRnJQpaIKI9hMUtElIVKlSpBJpNh+vTpCAsLg5OTk9SRiIjoA5xmQET0npcvX6Jo0aIAgBEjRqBRo0aoUaOGxKmIiCgrHJklIgLw9u1b9OnTB3Xq1EFcXBwAQCaTsZAlIsrjclTMbtq0CfXr14ezszMePHgAAFi8eDF+++03nYYjIsoN165dw5dffomNGzfi3r17OH78uNSRiIgom7QuZgMDAzF27Fi0bt0ar1+/hlKpBAAULlwYixcv1nU+IiK9EUJg9erVqF27Nv7++2+4uLjgxIkT6NChg9TRiIgom7QuZn/++WesXr0aU6ZMgZGRkbq9Vq1auHbtmk7DERHpS3x8PPz8/DBo0CAkJyejVatWiIyMRMOGDaWORkREWtC6mI2Kisr0rjdmZmZISEjQSSgiIn379ttvsWXLFhgZGWHevHnYt28f7OzspI5FRERa0rqYdXNzQ2RkZIb2gwcPokqVKloHWL58Odzc3GBubg5PT0+cPn06W/udPXsWxsbGqFmzptbnJCL6/vvvUbduXZw+fRrjx4+HXM7rYYmIDJHWP73Hjx+PYcOGISQkBEIIXLx4EbNnz8bkyZMxfvx4rY4VEhKC0aNHY8qUKYiIiEDDhg3RqlUrPHz48KP7vXnzBr1790bTpk21jU9EBVRCQgLWrl2rfmxvb49z587By8tLwlRERPS5tF5ntm/fvkhLS8OECROQmJiIHj16wMXFBUuWLIGvr69Wx1q4cCH69++PAQMGAHi3IsLhw4cRGBiIOXPmZLnf4MGD0aNHDxgZGWHPnj3avgQiKmDCw8MxduxYPHv2DDY2NujRoweAd0tvERGRYcvRTRMGDhyIgQMHIjY2FiqVCvb29lofIzU1FeHh4Zg4caJGe/PmzXHu3Lks91u/fj3u3r2LX3/9Fd9///0nz5OSkoKUlBT14/T1IxUKBRQKhda5taVQpGn8f26ck3Qrvc/Yd4ZHCIFly5Zh4sSJUCgUcHV1RenSpdmXBoifQ8PG/jN8ud2H2pzns+4A9jkXS8TGxkKpVMLBwUGj3cHBATExMZnuc+fOHUycOBGnT5+GsXH2os+ZMwczZszI0B4aGgpLS0vtg2spRQmkv83Hjh2DmdFHN6c8LCwsTOoIpIW3b9/i559/xoULFwAAdevWxfDhw/Hvv//iwIEDEqejnOLn0LCx/wxfbvVhYmJitrfVuph1c3P76J/m7t27p9XxPjyWECLT4yuVSvTo0QMzZsxAhQoVsn38SZMmYezYserHcXFxKFmyJJo3bw4bGxutsuZEYmoaJlw8BgDw9vaGrZW53s9JuqVQKBAWFgYfHx+YmJhIHYey4eLFixg1ahQePHgAU1NTzJkzB2XKlEHz5s3ZhwaKn0PDxv4zfLndh+l/Sc8OrYvZ0aNHazxWKBSIiIjAoUOHtLoAzM7ODkZGRhlGYZ8/f55htBZ4tybk5cuXERERgeHDhwMAVCoVhBAwNjZGaGgovL29M+xnZmYGMzOzDO0mJia50hkm4n+FuYmJMT/EBiy3vmfo87158wYPHjxA2bJlsW3bNnzxxRc4cOAA+zAfYB8aNvaf4cu1+kmLc2hdzI4aNSrT9l9++QWXL1/O9nFMTU3h6emJsLAwfP311+r2sLCwTO++Y2Njk+GmDMuXL8exY8ewY8cOuLm5ZfvcRJT/vP9XndatW2Pz5s1o06YNbGxsOE+PiCgf09nCiq1atcLOnTu12mfs2LFYs2YN1q1bh5s3b2LMmDF4+PAhhgwZAuDdFIHevXu/CyqXo1q1ahpf9vb2MDc3R7Vq1WBlZaWrl0JEBubMmTOoUaMGHjx4oG7r3r17rkwlIiIiaX3WBWDv27FjB4oWLarVPt26dcOLFy8wc+ZMREdHo1q1ajhw4ABcXV0BANHR0Z9cc5aICi6VSoW5c+fiv//9L5RKJb777jts2rRJ6lhERJSLtC5m3d3dNS7QEkIgJiYG//77L5YvX651gICAAAQEBGT6XFBQ0Ef3nT59OqZPn671OYnI8D1//hy9evVCaGgoAKBnz54IDAyUOBUREeU2rYvZjh07ajyWy+UoXrw4GjdujEqVKukqFxFRlk6cOIEePXogOjoaFhYW+OWXX+Dv78+bIBARFUBaFbNpaWkoXbo0WrRoAUdHR31lIiLK0sGDB9G2bVuoVCpUqVIF27ZtQ9WqVaWORUREEtHqAjBjY2MMHTpU445aRES5qUmTJqhevTr69u2LixcvspAlIirgtJ5mUKdOHURERKgv0iIi0rcLFy6gVq1aMDIygrm5OU6dOoVChQpJHYuIiPIArYvZgIAAfPvtt3j8+DE8PT0zLIlVvXp1nYUjooItLS0NM2bMwOzZszF16lT1BZ8sZImIKF22i9l+/fph8eLF6NatGwBg5MiR6udkMpl6wXKlUqn7lERU4Dx58gQ9evTAqVOnAADPnj3L8nbXRERUcGW7mN2wYQN+/PFHREVF6TMPEREOHTqEXr16ITY2FtbW1li9ejV8fX2ljkVERHlQtotZIQQAcK4sEemNQqHA1KlT8eOPPwJ4t651SEgIypcvL3EyIiLKq7RazYB/3iMifbp37x4WL14MABg2bBjOnTvHQpaIiD5KqwvAKlSo8MmC9uXLl58ViIgKrooVK2LlypWwtLRE586dpY5DREQGQKtidsaMGbC1tdVXFiIqYFJTU/Hdd9/h66+/hpeXFwCgd+/eEqciIiJDolUx6+vrC3t7e31lIaIC5P79+/D19cWFCxewbds2/P333zA3N5c6FhERGZhsz5nlfFki0pXdu3fD3d0dFy5cQOHChbFkyRIWskRElCPZLmbTVzMgIsqplJQUjBw5Ep06dcLr169Rt25dREZGokOHDlJHIyIiA5XtaQYqlUqfOYgon3v16hV8fHwQHh4OABg/fjxmz54NExMTiZMREZEh0/p2tkREOVG4cGGUKFEC9+/fx4YNG9CmTRupIxERUT7AYpaI9CY5ORlpaWmwtraGTCbDunXrkJiYiBIlSkgdjYiI8gmtbppARJRdt2/fRt26dTFo0CD1nPuiRYuykCUiIp1iMUtEOrd582Z4enrizz//xJEjR/DkyROpIxERUT7FYpaIdCYxMREDBw6En58f3r59i8aNGyMyMpKjsUREpDcsZolIJ27evIk6depgzZo1kMlkmDZtGo4cOQJnZ2epoxERUT7GC8CI6LOlpaWhXbt2uHv3LhwdHREcHAxvb2+pYxERUQHAkVki+mzGxsZYtWoVWrRogcjISBayRESUa1jMElGOXLt2Dfv27VM/9vb2xsGDB+Hg4CBhKiIiKmhYzBKRVoQQWLNmDWrXro3u3bvjzp076udkMpmEyYiIqCBiMUtE2RYfH4+ePXti4MCBSE5ORoMGDVC4cGGpYxERUQHGYpaIsiUyMhKenp7YvHkzjIyMMHfuXOzfvx/FixeXOhoRERVgXM2AiD5pxYoVGD16NFJSUlCyZEls3boV9erVkzoWERERR2aJ6NPu3r2LlJQUtGvXDhERESxkiYgoz+DILBFlSqVSQS5/9/vuDz/8gBo1asDPz48XeRERUZ7CkVki0iCEwJIlS+Dt7Q2FQgEAMDExQc+ePVnIEhFRnsNilojUXr16hU6dOmH06NE4efIktmzZInUkIiKij+I0AyICAFy4cAHdunXDgwcPYGpqigULFqBXr15SxyIiIvoojswSFXAqlQoLFixAgwYN8ODBA5QtWxbnzp3D8OHDOa2AiIjyPBazRAXchAkTMG7cOKSlpaFr1664cuUKPD09pY5FRESULSxmiQq4gQMHws7ODitWrMDWrVthY2MjdSQiIqJs45xZogJGpVLh3LlzaNCgAQCgYsWKuH//PqysrCRORkREpD2OzBIVIM+fP0fr1q3RqFEjnDhxQt3OQpaIiAwVR2aJCoiTJ0+ie/fuiI6OhoWFBaKjo6WORERE9Nk4MkuUzymVSsyaNQve3t6Ijo5G5cqVcfHiRXTv3l3qaERERJ+NI7NE+VhMTAx69uyJo0ePAgD8/f2xbNkyTisgIqJ8g8UsUT528OBBHD16FJaWlggMDETv3r2ljkRERKRTLGaJ8jF/f3/cu3cPPXr0QOXKlaWOQ0REpHOcM0uUjzx9+hQ9e/bEq1evAAAymQyzZs1iIUtERPkWR2aJ8olDhw6hV69eiI2NBQD8+uuvEiciIiLSP47MEhm4tLQ0TJo0Ca1atUJsbCxq1qyJadOmSR2LiIgoV3BklsiAPXr0CN27d8fZs2cBAAEBAViwYAHMzc0lTkZERJQ7WMwSGajz58+jTZs2ePnyJWxsbLB27Vp07txZ6lhERES5isUskYGqUKECrKysUKZMGYSEhKBMmTJSRyIiIsp1LGaJDMjz589RvHhxyGQyFC1aFEePHkWpUqVgZmYmdTQiIiJJ8AIwIgOxe/duVKxYEevWrVO3lS9fnoUsEREVaCxmifK4lJQUjBw5Ep06dcLr168RHBwMIYTUsYiIiPIEFrNEedjdu3dRv359/PzzzwCAcePG4fDhw5DJZBInIyIiyhs4Z5Yoj9q+fTsGDBiAuLg4FC1aFBs3bkSbNm2kjkVERJSnsJglyoNu374NX19fqFQq1K9fH1u2bEHJkiWljkVERJTnsJglyoMqVKiAqVOnIiUlBTNnzoSxMT+qREREmeG/kER5xJYtW1CrVi2UL18eAHhLWiIiomzgBWBEEktMTMSAAQPQo0cPdOvWDcnJyVJHIiIiMhgcmSWS0M2bN9G1a1dcv34dMpkM7dq1g4mJidSxiIiIDAaLWSKJbNiwAQEBAUhMTISDgwOCg4PRtGlTqWMREREZFBazRLksMTERQ4cOxcaNGwEATZs2xa+//gpHR0eJkxERERkezpklymXGxsb4+++/IZfLMWvWLBw+fJiFLBERUQ5xZJYoFwghIISAXC6HqakpQkJC8ODBAzRq1EjqaERERAaNI7NEehYfH4+ePXti0qRJ6rbSpUuzkCUiItIBjswS6VFkZCS6du2KO3fuwNjYGEOHDkXp0qWljkVERJRvcGSWSA+EEAgMDETdunVx584dlChRAidOnGAhS0REpGMcmSXSsTdv3mDgwIHYvn07AKBt27YICgpCsWLFJE5GRESU/7CYJdIhlUqFRo0a4c8//4SxsTHmzp2LMWPGQCaTSR2NiIgoX+I0AyIdksvlGD9+PFxdXXHmzBmMHTuWhSwREZEesZgl+kyvXr1CZGSk+rGfnx9u3LiBOnXqSBeKiIiogGAxS/QZLly4AHd3d7Ru3Rr//vuvut3S0lLCVERERAUHi1miHBBCYMGCBWjQoAEePHgACwsLPH/+XOpYREREBQ4vACPS0osXL+Dv7499+/YBALp06YLVq1fD1tZW4mREREQFj+Qjs8uXL4ebmxvMzc3h6emJ06dPZ7ntrl274OPjg+LFi8PGxgZeXl44fPhwLqalgu7s2bOoWbMm9u3bBzMzMyxfvhwhISEsZImIiCQiaTEbEhKC0aNHY8qUKYiIiEDDhg3RqlUrPHz4MNPtT506BR8fHxw4cADh4eFo0qQJ2rVrh4iIiFxOTgVVYGAgHj9+jPLly+P8+fMYOnQoVysgIiKSkKTTDBYuXIj+/ftjwIABAIDFixfj8OHDCAwMxJw5czJsv3jxYo3HP/zwA3777Tfs3bsX7u7uuRGZCrjly5fDwcEB06dPR6FChaSOQ0REVOBJVsympqYiPDwcEydO1Ghv3rw5zp07l61jqFQqxMfHo2jRolluk5KSgpSUFPXjuLg4AIBCoYBCochBcu0oFGka/58b5yTdOXXqFEJCQtC6dWsoFApYWFjgxx9/BAD2pQFJ7yv2meFiHxo29p/hy+0+1OY8khWzsbGxUCqVcHBw0Gh3cHBATExMto6xYMECJCQkoGvXrlluM2fOHMyYMSNDe2hoaK4sn5SiBNLf5mPHjsHMSO+nJB1QKpXYsWMHQkJCoFKpYG5uzukE+UBYWJjUEegzsQ8NG/vP8OVWHyYmJmZ7W8lXM/iwQBBCZKto2LJlC6ZPn47ffvsN9vb2WW43adIkjB07Vv04Li4OJUuWRPPmzWFjY5Pz4NmUmJqGCRePAQC8vb1ha2Wu93PS54mJiYG/vz+OHXvXb35+fqhfvz58fHxgYmIicTrKCYVCgbCwMPahAWMfGjb2n+HL7T5M/0t6dkhWzNrZ2cHIyCjDKOzz588zjNZ+KCQkBP3798f27dvRrFmzj25rZmYGMzOzDO0mJia50hkm4n+FuYmJMT/EedzRo0fh5+eHZ8+ewdLSEsuXL0ePHj1w4MCBXPueIf1hHxo+9qFhY/8Zvlyrn7Q4h2SrGZiamsLT0zPDcHVYWBjq1auX5X5btmyBv78/Nm/ejDZt2ug7JhUgS5YsgY+PD549e4Zq1arh8uXL6NOnj9SxiIiI6CMknWYwduxY9OrVC7Vq1YKXlxdWrVqFhw8fYsiQIQDeTRF48uQJNm7cCOBdIdu7d28sWbIEdevWVY/qWlhYcJ1P+mxffvkl5HI5+vbtiyVLlvCWtERERAZA0mK2W7duePHiBWbOnIno6GhUq1YNBw4cgKurKwAgOjpaY83ZlStXIi0tDcOGDcOwYcPU7X369EFQUFBux6d84NmzZ+ppLfXq1cP169dRqVIliVMRERFRdkl+AVhAQAACAgIyfe7DAvXEiRP6D0QFQlpaGv773//i559/xoULF1C1alUAYCFLRERkYCQvZoly26NHj9C9e3ecPXsWALB37151MUtERESGhcUsFSj79+9H79698fLlS9jY2GD16tUfXaeYiIiI8jbJVjMgyk0KhQLjxo1D27Zt8fLlS3h6euLKlSssZImIiAwci1kqENauXYsFCxYAAEaOHImzZ8+ibNmyEqciIiKiz8VpBlQgDBgwAIcPH0bv3r3x9ddfSx2HiIiIdIQjs5QvpaamYv78+UhJSQEAGBsbY/fu3SxkiYiI8hmOzFK+c+/ePXTr1g2XL1/Gw4cP8fPPP0sdiYiIiPSEI7OUr+zYsQPu7u64fPkyihYtihYtWkgdiYiIiPSIxSzlC8nJyQgICECXLl0QFxeH+vXrIzIyEm3btpU6GhEREekRi1kyeHfv3oWXlxcCAwMBABMnTsTx48dRsmRJiZMRERGRvnHOLBk8uVyOqKgo2NnZYdOmTWjZsqXUkYiIiCiXsJglg6RUKmFkZAQAcHNzw+7du1GhQgW4uLhInIyIiIhyE6cZkMG5efMmPDw8cOjQIXVbkyZNWMgSEREVQCxmyaBs3LgRtWrVwtWrVzF+/HioVCqpIxEREZGEWMySQUhISEDfvn3Rp08fJCYmwtvbG2FhYZDL+S1MRERUkLESoDzv+vXr+PLLLxEUFAS5XI6ZM2ciNDQUjo6OUkcjIiIiifECMMrT7t27h9q1ayMpKQlOTk7YvHkzGjduLHUsIiIiyiNYzFKeVqZMGfj6+uLp06fYuHEj7O3tpY5EREREeQiLWcpz/vzzTzg7O6N48eIAgMDAQJiYmHB+LBEREWXA6oDyDCEEVqxYgTp16qB3797qlQrMzMxYyBIREVGmWCFQnvDmzRv4+vpi6NChSElJgZGRERITE6WORURERHkci1mSXHh4ODw9PbFt2zYYGxtj/vz5+P3332FtbS11NCIiIsrjOGeWJCOEwLJlyzBu3DikpqbC1dUVW7duRd26daWORkRERAaCI7MkmYSEBCxZsgSpqano0KEDIiIiWMgSERGRVjgyS5KxtrZGSEgIzpw5g5EjR0Imk0kdiYiIiAwMi1nKNUIILF68GBYWFhgyZAgAwNPTE56enhInIyIiIkPFYpZyxcuXL+Hv74+9e/fC1NQUPj4+KFu2rNSxiIiIyMCxmCW9O3fuHHx9ffHo0SOYmZlh0aJFKFOmjNSxiIiIKB/gBWCkNyqVCnPnzsVXX32FR48eoXz58jh//jyGDh3K+bFERESkExyZJb1QqVTo2LEj9u7dCwDo3r07Vq5ciUKFCkmcjIiIiPITjsySXsjlcnh5ecHc3ByrV69GcHAwC1kiIiLSOY7Mks4olUrExsbCwcEBAPCf//wHXbp0Qbly5SRORkRERPkVR2ZJJ549e4aWLVuiadOmSExMBPBudJaFLBEREekTi1n6bMeOHUONGjVw5MgRREVF4cqVK1JHIiIiogKCxSzlmFKpxLRp09CsWTM8e/YMVatWxaVLl9CgQQOpoxEREVEBwTmzlCNPnz6Fn58fTpw4AQDo378/li5dCktLS2mDERERUYHCYpZyZMSIEThx4gSsrKywcuVK+Pn5SR2JiIiICiAWs5QjS5cuxZs3b/DLL7+gYsWKUschIiKiAopzZilbHj9+jF9++UX92MXFBUeOHGEhS0RERJLiyCx90oEDB9C7d2+8ePECLi4u6Nixo9SRiIiIiABwZJY+QqFQYMKECWjTpg1evHgBDw8PfPHFF1LHIiIiIlLjyCxl6sGDB/D19cX58+cBvLvga/78+TAzM5M4GREREdH/sJilDPbt24devXrh9evXsLW1xbp169CpUyepYxERERFlwGKWMkhJScHr169Ru3ZtbN26FW5ublJHIiIiIsoUi1kCAKSlpcHY+N23wzfffIOdO3eibdu2MDU1lTgZERkSlUqF1NRUnR9XoVDA2NgYycnJUCqVOj8+6Rf7z/Dpow9NTU0hl3/+5VssZgk7duzA5MmTceLECTg7OwMApxUQkdZSU1MRFRUFlUql82MLIeDo6IhHjx5BJpPp/PikX+w/w6ePPpTL5XBzc/vsgTMWswVYcnIyvv32WyxfvhwAMH/+fCxatEjiVERkiIQQiI6OhpGREUqWLKmT0Zb3qVQqvH37FtbW1jo/Nukf+8/w6boPVSoVnj59iujoaJQqVeqzCmQWswXUnTt30K1bN0RERAAA/vOf/2DWrFkSpyIiQ5WWlobExEQ4OzvD0tJS58dPn75gbm7OYsgAsf8Mnz76sHjx4nj69CnS0tJgYmKS4+OwmC2Atm7dioEDB+Lt27ews7PDxo0b0apVK6ljEZEBS59Dx3n2RJRd6T8vlEoli1nKvo0bN6JPnz4AgIYNG2LLli1wcXGROBUR5RecD0lE2aWzubc6OQoZjG+++QZVq1bFd999h2PHjrGQJSIiIoPGYrYACAsLU19dbGVlhcuXL2PWrFnqpbiIiIhyKjU1FeXKlcPZs2eljpJvPH/+HMWLF8eTJ0+kjmIQWMzmYwkJCejbty+aN2+OBQsWqNvNzc0lTEVElHf4+/tDJpNBJpPB2NgYpUqVwtChQ/Hq1asM2547dw6tW7dGkSJFYG5uji+++AILFizIdM3N48ePo3Xr1ihWrBgsLS1RpUoVfPvtt58sTiIiItClSxc4ODjA3NwcFSpUwMCBA3H79m2dvWZdW7VqFVxdXVG/fv0Mzw0aNAhGRkbYunVrhuf8/f3RsWPHDO2RkZGQyWS4f/++uk0IgVWrVqFOnTqwtrZG4cKFUatWLSxevBiJiYm6fDkaZs+ejXr16sHS0hKFCxfO1j5CCEyfPh3Ozs6wsLBA48aN8ddff2lsk5KSghEjRsDOzg5WVlZo3749Hj9+rH7e3t4evXr1wrRp03T5cvItFrP51F9//YXatWsjKCgIcrkcCoVC6khERHlSy5YtER0djfv372PNmjXYu3cvAgICNLbZvXs3GjVqhBIlSuD48eP4+++/MWrUKMyePRu+vr4QQqi3XblyJZo1awZHR0fs3LkTN27cwIoVK/DmzRuNgYUP7du3D3Xr1kVKSgqCg4Nx8+ZNbNq0Cba2tvjvf/+b49en75//P//8MwYMGJChPTExESEhIRg/fjzWrVv3Wefo1asXRo8ejQ4dOuD48eOIjIzEf//7X/z2228IDQ39rGN/TGpqKrp06YKhQ4dme5958+Zh4cKFWLZsGS5dugRHR0f4+PggPj5evc3o0aOxe/dubN26FWfOnMHbt2/Rtm1bjV+M+vbti+Dg4Ex/saIPiALmzZs3AoB48+ZNrpwvIUUhXP+zT7j+Z594/TZR7+dTqVRi7dq1wsLCQgAQjo6O4vjx43o/b36Wmpoq9uzZI1JTU6WOQjnEPtS/pKQkcePGDZGUlCSEePezKCFFobOv+KQU8fRZrIhPSvnktiqVKtu5+/TpIzp06KDRNnbsWFG0aFH147dv34pixYqJTp06Zdj/999/FwDE1q1bhRBCPHr0SJiamorRo0dner5Xr15l2p6QkCDs7OxEx44dP7rf+vXrha2trcZzu3fvFu//cz5t2jRRo0YNsXbtWuHm5iZkMplYsWKFcHZ2FkqlUmPfdu3aid69e2u8Hg8PD2FmZibc3NzE9OnThUKhyDSTEEKEh4cLuVye6b+pQUFBom7duuL169fCwsJC/Pnnnxrnz+y9F0KIiIgIAUBERUUJIYQICQkRAMSePXsybKtSqcTr16+zzKcrmb3vmVGpVMLR0VH8+OOP6rbk5GRha2srVqxYIYQQ4vXr18LExET9PSOEEE+ePBFyuVwcOnRI43ilS5cWa9eu1c2L+ExKpVK8evUqw/fQ5/jw58b7tKnXOGkyH3n79i2GDBmC4OBgAEDz5s2xadMm2NvbS5yMiAqaJIUSVaYeluTcN2a2gKVpzv55u3fvHg4dOqSxTFBoaChevHiBcePGZdi+Xbt2qFChArZs2YJu3bph+/btSE1NxYQJEzI9flZ/qj58+DBiY2O13i8r//zzD7Zt24adO3fCyMgILi4uGDlyJI4fP46mTZsCAF69eoXDhw9j79696gw9e/bE0qVL0bBhQ9y9exeDBg0CgCz/3H3q1ClUqFABNjY2GZ5bu3YtevbsCVtbW7Rq1QrBwcGYM2eOVq8DAIKDg1GxYkV06NAhw3MymQy2trZZ7mttbf3RYzds2BAHDx7UOlNWoqKiEBMTg+bNm6vbzMzM0KhRI5w7dw6DBw9GeHg4FAqFxjbOzs6oVq0azp07hxYtWqjba9eujdOnT6Nfv346y5gfsZjNR27fvo1t27bByMgIs2bNwn/+8x8uTk1E9An79u2DtbU1lEolkpOTAQALFy5UP58+X7Vy5cqZ7l+pUiX1Nnfu3IGNjQ2cnJy0ynDnzh31sXQhNTUVmzZtQvHixdVtLVu2xObNm9XF7Pbt21G0aFH149mzZ2PixInq5RvLlCmDWbNmYcKECVkWs/fv31ffBv3D13P+/Hns2rULAODn54eRI0di9uzZWv+7dOfOHVSsWFGrfdJFRkZ+9HkLC4scHTcrMTExAAAHBweNdgcHBzx48EC9jampKYoUKZJhm/T907m4uKhvbkRZYzGbj3h4eGDlypUoX748GjRoIHUcIirALEyMcGNmi09vmE0qlQrxcfEoZFPok8WQhYmRVsdu0qQJAgMDkZiYiDVr1uD27dsYMWJEhu3Ee/NiP2xPXy/z/f/XRlbHzilXV1eNQhZ4V1AOGjQIy5cvh5mZGYKDg+Hr6wsjo3fvV3h4OC5duoTZs2er90kv8BMTEzO9s1tSUlKmFxWvXbsWLVq0gJ2dHQCgdevWGDBgAI4cOYKWLVtq9Vpy+p4CQLly5XK03+f6MG92XkNm21hYWOj1Arf8gsN2BiwuLg69e/fW+K2tb9++LGSJSHIymQyWpsY6/bIwNcrWdtoWPlZWVihXrhyqV6+OpUuXIiUlBTNmzFA/X6FCBQDAzZs3M93/77//Rvny5dXbvnnzBtHR0VplSD/H33///dHt5HJ5hsI3swu8rKysMrS1a9cOKpUK+/fvx6NHj3D69Gn07NlT/bxKpcKMGTMQGRmp/rp27Rru3LmT5So4dnZ2GS5QUiqV2LhxI/bv3w9jY2MYGxvD2toar1690rgQzMbGBm/evMlwzNevXwOAevpAhQoVsnzvP8Xa2vqjX7q++6WjoyMAZBhhff78uXq01tHREampqRnet/e3Sffy5csMv5RQRixmDdSVK1fg4eGBTZs2wc/PL9OlYYiISHvTpk3DTz/9hKdPnwJ4d/1B0aJFM12J4Pfff8edO3fQvXt3AEDnzp1hamqKefPmZXrs9ELtQ82bN4ednd0n9ytevDji4+ORkJCgfu5Tf0pPZ2FhgU6dOiE4OBhbtmxBhQoV4OnpqX7ew8MDt27dQrly5TJ8ZTUa7u7ujr///lujwD5w4ADi4+MRERGhLoqvXLmCoKAg/Pbbb3jx4gWAd1Mqrl+/rp7ake7SpUsoXry4+s/wPXr0wO3bt/Hbb79lOL8QItOC+P335mNfa9asydZ7l11ubm5wdHREWFiYui01NRUnT55EvXr1AACenp4wMTHR2CY6OhrXr19Xb5Pu+vXrcHd312nGfElnl6QZCENfzUClUomff/5ZmJqaCgCiVKlS4ty5czpISlnhlfCGj32ofx+7KlkX9HEltRBZX1Hv6ekphg0bpn68fft2YWRkJAYOHCj+/PNPERUVJdasWSOKFCkiOnfurLGCwi+//CJkMpno16+fOHHihLh//744c+aMGDRokBg7dmyWWfbs2SNMTExEu3btRFhYmIiKihKXLl0S48ePF926dRNCCPHixQthZWUlRo4cKe7cuSOCg4OFs7NzpqsZZCY0NFSYmZmJihUrilmzZmk8d+jQIWFsbCymTZsmrl+/Lm7cuCG2bt0qpkyZkmXm2NhYYWpqKq5du6Zu69ChgzpvOqVSKV6+fClcXFzE4sWLhRDvrup3dHQUnTt3FpcuXRL//POP2LRpkyhSpIiYN2+eel+VSiW6desmLCwsxA8//CAuXbok7t+/L/bu3Su8vb3F7t27s8z3uR48eCAiIiLEjBkzhLW1tYiIiBAREREiPj5evU3FihXFrl271I9//PFHYWtrK3bt2iWuXbsmunfvLpycnERcXJx6myFDhogSJUqII0eOiCtXrghvb29Ro0YNkZaWpt4mISFBWFhYiFOnTunt9WkjL69mwGJWz3RZzL569Up06tRJABAARPv27cWLFy90lJSywkLI8LEP9S+/FbPBwcHC1NRUPHz4UN126tQp0bJlS2FraytMTU1FlSpVxE8//aRRgKQLCwsTLVq0EEWKFBHm5uaiUqVKYty4ceLp06cfzXPp0iXRqVMnUbx4cWFmZibKlSsnBg0aJO7cuaPeZvfu3aJcuXLC3NxctG3bVqxatSrbxWxaWppwcnISAMTdu3czPH/o0CFRr149YWFhIWxsbETt2rXFqlWrPprZ19dXTJw4UQghRExMjDA2Nhbbtm3T2Ca9/4YPHy6++OILdfudO3fEN998I1xcXISVlZX44osvxLJlyzL0s1KpFIGBgeLLL78UlpaWwsbGRnh6eoolS5aIxET9LXvZp08f9b+573+9v+QlALF+/Xr1Y5VKJaZNmyYcHR2FmZmZ+OqrrzSKfSHefV6GDx8uihYtKiwsLETbtm01vteEEGLz5s2iYsWKentt2srLxaxMCB3POs/j4uLiYGtrizdv3mS6lIiuJaamqZen+fO/3rC1ytmVk48fP0bDhg1x//59mJiYYP78+Rg5cmSOJ8VT9ikUChw4cACtW7fWWK6HDAf7UP+Sk5MRFRUFNzc3vdxlUKVSIS4uDjY2NlylJY+5du0amjVrhn/++QeFChXKdBv2n/Zq166N0aNHo0ePHlJHAaCfPvzYzw1t6jWuZmAgnJ2dUb58echkMoSEhODLL7+UOhIRERG++OILzJs3D/fv38cXX3whdZx84fnz5+jcubN6LjZ9HIvZPOzly5cwNzeHpaUl5HI5Nm/eDGNjY60XzyYiItKn9LVpSTfs7e2zvIEGZcSx/jzq3LlzqFmzJkaNGqVus7OzYyFLRERE9B4Ws3mMSqXCvHnz8NVXX+HRo0c4ceJElku5EBERERV0LGbzkH///Rdt27bFf/7zHyiVSvj6+iI8PJyjsURERERZ4JzZPOL06dPw9fXF06dPYW5ujiVLlmDgwIFcrYCIiIjoI1jM5gGJiYno0qULnj17hooVK2Lbtm2oXr261LGIiIiI8jxOM8gDLC0tsW7dOvTq1QuXL19mIUtERESUTRyZlcjx48eRlJSE1q1bAwBat26t/n8iIiIiyh6OzOYypVKJ6dOno2nTpvDz88PDhw+ljkRERHo0ffp01KxZM8+ep3Hjxhg9erTO83xK6dKlsXjx4s86hr+/Pzp27PjRbaR6fZR7JC9mly9frr6NmaenJ06fPv3R7U+ePAlPT0+Ym5ujTJkyWLFiRS4l/XwxMdHw8fHBjBkzIIRAp06dYGdnJ3UsIqIC7dGjR+jfvz+cnZ1hamoKV1dXjBo1Ci9evND6WDKZDHv27NFoGzduHI4ePaqjtDl34sQJyGQyLveoB9euXUOjRo1gYWEBFxcXzJw5E0KIT+63f/9+1KlTBxYWFrCzs0OnTp0y3e7FixcoUaJEpv33qXOn9/uHX3///bd6m127dqFWrVooXLgwrKysULNmTWzatEnjPKdOnYKvr686x4ff5wDw9u1bDB8+HCVKlICFhQUqV66MwMDAT74Pn0vSaQYhISEYPXo0li9fjvr162PlypVo1aoVbty4gVKlSmXYPioqCq1bt8bAgQPx66+/4uzZswgICEDx4sXxzTffSPAKsi8p6goa1O2L2Nh/YWVlhRUrVqBnz55SxyIiyluUSuD0aSA6GnByAho2BIyM9Ha6e/fuwcvLCxUqVMCWLVvg5uaGv/76C+PHj8fBgwdx/vx5FC1a9LPOYW1tDWtrax0lzhsUCgVMTEykjpEnxMXFwcfHB02aNMGlS5dw+/Zt+Pv7w8rKCt9++22W++3cuRMDBw7EDz/8AG9vbwghcO3atUy37d+/P6pXr44nT57k+Ny3bt2CjY2N+nHx4sXV/1+0aFFMmTIFlSpVgqmpKfbt24e+ffvC3t4eLVq0AAAkJCSgWrVqGDBgALp06ZJpzjFjxuD48eP49ddfUbp0aYSGhiIgIADOzs7o0KHDx9/IzyEkVLt2bTFkyBCNtkqVKomJEydmuv2ECRNEpUqVNNoGDx4s6tatm+1zvnnzRgAQb9680T5wDrxNThU2Xl0FIBMARPXq1cXff/+dK+cm3UhNTRV79uwRqampUkehHGIf6l9SUpK4ceOGSEpKyvlBdu4UokQJIYD/fZUoIcTOnUKpVIpXr14JpVKpu9BCiJYtW4oSJUqIxMREjfbo6GhhaWmp8W+Uq6urmDlzpujevbuwsrISTk5OYunSpRrPA1B/ubq6CiGEmDZtmqhRo4Z6uz59+ogOHTqI2bNnC3t7e2FrayumT58uFAqFGDdunChSpIhwcXERa9eu1cg0YcIEUb58eWFhYSHc3NzEd999p/E9/eF53hcVFaWRDYDo06ePEEKIRo0aiREjRojx48eLIkWKCAcHBzFt2jSN/QGIwMBA0b59e2FpaSmmTp0qhBDi999/Fx4eHsLMzEy4ubmpX8f7mUqWLClMTU2Fo6OjGD58uMb7NXv2bNG3b19hbW0tSpYsKVauXKlx3qtXr4omTZoIc3NzUbRoUTFw4EARHx+f4b1M9/btW9GrVy9hZWUlHB0dxU8//SQaNWokRo0alen7ogvLly8Xtra2Ijk5Wd02Z84c4ezsLFQqVab7KBQK4eLiItasWZOt4zdq1EgcPXpUABCvXr3S6tzHjx/PsF92uLu7i++++079+P3PIACxe/fuDPtUrVpVzJw5U6PNw8ND4zjv+9jPDW3qNclGZlNTUxEeHo6JEydqtDdv3hznzp3LdJ8//vgDzZs312hr0aIF1q5dm+VviSkpKUhJSVE/jouLA/Dut0qFQvG5L+OT0tLSoEp+C0Cgj38/LF2yCBYWFrlybtKN9L5inxku9qH+KRQKCCGgUqmgUqm0P8CuXZB17QoIgfdX1xZPngCdO0Ns2wY0a6Y+hy68fPkShw8fxvfffw8zMzON49rb26NHjx4ICQnBsmXL1Gt+z58/H5MmTcLUqVMRGhqKMWPGoEKFCvDx8cGFCxfg6OiItWvXomXLljAyMoJKpVL/yTf9+EIIHDt2DC4uLjhx4gTOnj2LgQMH4ty5c/jqq6/wxx9/YNu2bRgyZAiaNm2KkiVLAng3wrtu3To4Ozvj2rVrGDx4MKytrTF+/Hj1cd8/z/tcXFywfft2dOnSBTdv3oSNjQ0sLCzU227YsAFjxozBH3/8gT/++AP9+vWDl5cXfHx81MeYNm0aZs+ejQULFsDIyAgHDx5Ez549sXjxYjRs2BB3797FkCFDIITA1KlTsWPHDixatAibN29GlSpVcO/ePfzzzz8a+RYsWICZM2di4sSJ2LlzJ4YOHYoGDRqgUqVKSExMRMuWLVGnTh1cuHABz58/x6BBgzBs2DCsX79e/Zrf/54YN24cjh8/jp07d8LR0RFTpkxBeHg4atSokeX3zenTp9GmTZuPfq9MmjQJkyZNyvS59H4zMTFRn8PHxweTJk3CvXv34ObmlmGfy5cvq0dZ3d3dERMTgxo1amD+/PmoWrWqersbN25g5syZ+OOPP3Dv3j0A0PiMZefc6e3u7u5ITk5GlSpVMHnyZDRp0iTT15P+/Xnr1i3MmTNH4/v2/f9m9lmvX78+fv/9d/j7+8PZ2RknTpzA7du3sWjRokzf//TPh0KhgNEHf4HR5ue1ZMVsbGwslEolHBwcNNodHBwQExOT6T4xMTGZbp+WlobY2Fg4OTll2GfOnDmYMWNGhvbQ0FBYWlp+xivInhQlUNR7ACzK1ELrdh44fvy43s9J+hEWFiZ1BPpM7EP9MTY2hqOjI96+fYvU1FTtdlYqYTNqVIZCFgBkQkDIZJCNHg38+Sfi4+N1FRmRkZEQQsDV1VU90PE+Nzc3vHr1Cvfu3UPx4sWhUqlQu3ZtDB06FADQu3dvnDhxAj/99BPq1KkDMzMzAICZmZn635e4uDikpKRAqVRqDKYULlwYs2bNglwuR+fOnTFv3jzEx8dj2LBhAICAgADMnTsXR44cUU+jGzFihDpbo0aNEBAQgK1bt2Lw4MEAkOE8HzI3NwcAWFhYaORLS0tDlSpV1BdJdezYET///DMOHjyIOnXqqPf/5ptv0LlzZ/XjWbNmYdSoUfj6668BAHZ2dpg4cSKmT5+O0aNH486dO7C3t0ft2rVhYmICT09PeHp6qvOpVCo0a9YMfn5+AIAhQ4Zg0aJFOHToEJydnbFhwwYkJibi559/hpWVFUqVKoUff/wR3bt3x5QpU2Bvbw+FQoG0tDTExcXh7du3WLduHQIDA9W5f/75Z1StWhWpqalZvi8VKlTAqVOnMn0uXZEiRbLc/8mTJyhVqpTG8+nv7927d1GsWLEM+/z1118A3l20N3v2bJQqVQrLli1D48aNcfnyZRQpUgQpKSnw9fXF9OnTUbhwYSQmJgIA4uPjIZfLs33uQoUKYfHixahZsyZSUlIQEhICHx8f7N27F/Xr11fv9+bNG1StWhUpKSkwMjJSf19/+LrTP4NJSUkZnkv/nihVqhSMjY0hl8uxZMkSVK9ePdP3LzU1FUlJSTh16hTS0tI0nkt/vdkh+dJcH97hSgjx0bteZbZ9Zu3pJk2ahLFjx6ofx8XFoWTJkmjevLnG3BF9EULA2zsFx47J0aZFM5iamur9nKRbCoUCYWFh8PHx4RwxA8U+1L/k5GQ8evQI1tbW6qIp206cgPzp0yyflgkB2ZMnMP7jD1i0aqWzOyNaWVkBeFfcZfbvQXpxamNjAxsbG8jlcjRs2FBj26+++gpLlizRaPvweGZmZjAyMlK3mZiYoFq1ahq3KndyckLVqlU19itWrBjevn2rbtuxYweWLl2Kf/75B2/fvkVaWpo6W2bn+VB6kVOoUCGNbYyNjVG9enWNNhcXF7x580ajzcvLS+Pxn3/+iYiICCxcuFDdplQqkZycDGNjY/Ts2RMrV66Eh4cHWrRogcaNG6NLly7qz6BcLoenp6fGMZ2cnBAfHw8bGxvcv38fNWvW1Bio8vHxgUqlwtOnT1GuXDmYmJjA2NgYNjY2iIqKQmpqKry9vdXHtLGxQcWKFWFqaprl+2JjY5NhoEwbRkZGGY6fXvBZW1t/9HtrypQp6utn6tSpg1KlSuHQoUMYPHgwvv32W1StWhUDBw4EkHn/Zefc6b9EpGvWrBmePXuGFStWoFWrVup2a2trXLlyBW/fvsWxY8fw3XffoUqVKmjcuDGAd/VMfHw8ChUqBCDzz82CBQtw5coV7NmzB66urjh9+jTGjx+PMmXKoFmzZhneh+TkZFhYWOCrr77K8HMjq18eMiNZMWtnZwcjI6MMo7DPnz/P8pvK0dEx0+2NjY0z/c0HePcNk/5N8z4TE5Nc+0fNViaDmRFgamrKf0gNWG5+z5B+sA/1R6lUQiaTQS6Xq0eNsu3Zs2xtJouJUZ9DFypUqKC+qjuzY966dQtFihSBvb29uoD+8PzpV4a/3/bhe5C+b3qbTCaDqalphm0yaxNCQC6X4/z58+jRowdmzJiBFi1awNbWFlu3bsWCBQs0jvv+eT6U3p5ZH314brlcrj53ukKFCmk8VqlUmDFjRqZX4FtaWsLa2hq3bt1CWFgYwsLCMG7cOCxfvhwnT55Ufw4/9prTH3+YC3hXxMnlco33//3X/+Hr+9j3zenTpzWKusxMnjwZkydPzvQ5JycnPHv2TOP4sbGx6ucyO6+LiwsAoFq1aurnLSwsUKZMGTx+/BhyuRzHjx/HtWvXsHPnTgD/G7yzt7fHlClTMGPGjBydG3j3i8mvv/6a4b2tUKECAMDDwwN///035s6dC29vbwD/m76S1fuclJSEKVOmYPfu3eppGzVr1sSff/6JhQsXZpgmmn4MmUyW6c9mbX5WS1bMmpqawtPTE2FhYeo/UQDv/gyY1RVvXl5e2Lt3r0ZbaGgoatWqxX+giIgMVSZTxDIjHB11etpixYrBx8cHy5cvx5gxY2BhYaF+LiYmBsHBwejdu7fGSPD58+c1jnH+/HlUqlRJ/djExARKpVKnOQHg7NmzcHV1xZQpU9RtDx480OoY6X8Z1FU+Dw8P3Lp1C+XKlctyGwsLC7Rv3x5t27ZF7969Ubt2bVy7dg0eHh6fPH6VKlWwYcMGJCQkqEfRz549q1F0vS99pPb8+fPqFZFevXqF27dvo1GjRlmep1atWoiMjPxolo+taOHl5YXJkycjNTVV/R6HhobC2dkZpUuXznQfT09PmJmZ4datW2jQoAGAd39Bun//PlxdXQG8W+0gKSlJvc+lS5fQr18/nD59GmXLls3xuQEgIiIi06mZ7xNCaFxz9Cnp1yJ9WECnzx3XJ0mnGYwdOxa9evVCrVq14OXlhVWrVuHhw4cYMmQIgHdTBJ48eYKNGzcCeDefZtmyZRg7diwGDhyIP/74A2vXrsWWLVukfBlERPQ5GjYESpQAnjx5t4bBh2QyiBIlkOblpfNTL1u2DPXq1UOLFi3w/fffayzN5eLigtmzZ2tsf/bsWcybNw8dO3ZEWFgYtm/fjv3796ufL126NI4ePYr69evDzMwMRYoU0UnOcuXK4eHDh9i6dSu+/PJL7N+/H7t379bqGK6urpDJZNi3bx9at24NCwuLz1oybOrUqWjbti1KliyJLl26QC6X4+rVq7h27Rq+//57BAUFQalUok6dOjA3N0dISAgsLCzUxdqn+Pn5Ydq0aejTpw+mT5+Of//9FyNGjECvXr0y/QuutbU1+vfvj/Hjx6NYsWJwcHDAlClTPjmSb2Fh8dGC/FPSR8z9/f0xefJk3LlzBz/88AOmTp2q/kXo4sWL6N27N44ePQoXFxfY2NhgyJAhmDZtGkqWLAlXV1fMnz8fANTLXqUXrOnSR1wrV66snqKSnXMvXrwYpUuXVs8d/vXXX7Fz5071iC/w7vqiWrVqoWzZskhNTcWBAwewceNGjTVi3759i2vXrql/sYiKikJkZCSKFi2KUqVKwcbGBo0aNcL48ePV/Xzy5Els3LhRYyqKXnxyvQM9++WXX4Srq6swNTUVHh4e4uTJk+rn+vTpIxo1aqSx/YkTJ4S7u7swNTUVpUuXFoGBgVqdL7eX5hKCywIZOvaf4WMf6t9nL821c6cQMtm7r/eX5vr/NuX27XpZmksIIe7fvy/8/f2Fo6OjMDExESVLlhQjRowQsbGxGtu5urqKGTNmiK5duwpLS0vh4OAgFi9erLHN77//LsqVKyeMjY0/uTTX+zJbPsrV1VUsWrRI/Xj8+PGiWLFiwtraWnTr1k0sWrRI2Nraqp//2NJc6WbOnCkcHR2FTCbTWJrrw3N36NBB/bwQIsulmA4dOiTq1asnLCwshI2Njahdu7ZYtWqVEEKI3bt3izp16ggbGxthZWUlvvzySxEaGprl6xNCiBo1amgsC6bt0lzx8fGiZ8+e6v6ZN2+e3pfmSs/ZsGFDYWZmJhwdHcX06dM1luVKXx4rKipK3Zaamiq+/fZbYW9vLwoVKiSaNWsmrl+/nuU5slpi61Pnnjt3rihbtqwwNzcXRYoUEQ0aNBD79+/XOMaUKVNEuXLl1Nt4eXmJrVu3amyTvjTYh1/vf59ER0cLf39/4ezsLMzNzUXFihXFggULslyiTFdLc8mEyMYtKvKRuLg42NraZpjYrk8KhQIHDhxA69atOR3CALH/DB/7UP+Sk5MRFRWlvqNjjuzaBYwaBTx+/L+2kiWBxYuh6tgRcXFx6guxpFC6dGmMHj2at0bNAZVKJXn/0efRRx9+7OeGNvWa5KsZEBERAQA6dQI6dMj8DmB6nnNHRIaLxSwREeUdRkbA/y8FRESUHSxmiYiIsuH+/ftSRyCiTHDiChEREREZLBazRESkMwXsmmIi+gy6+nnBYpaIiD6bkZERgHf3Wiciyo70nxfpPz9yinNmiYjosxkbG8PS0hL//vsvTExMdL78kkqlQmpqKpKTk7m0kwFi/xk+XfehSqXCv//+C0tLSxgbf145ymKWiIg+m0wmg5OTE6KiorS+zWp2CCGQlJQECwsLjdvLkmFg/xk+ffShXC5HqVKlPvt4LGaJiEgnTE1NUb58eb1MNVAoFDh16hS++uor3vjCALH/DJ8++tDU1FQno7wsZomISGfkcnnO7wD2EUZGRkhLS4O5uTmLIQPE/jN8ebkPOXGFiIiIiAwWi1kiIiIiMlgsZomIiIjIYBW4ObPpC/TGxcXl2jkVCgUSExMRFxeX5+aZ0Kex/wwf+9DwsQ8NG/vP8OV2H6bXadm5sUKBK2bj4+MBACVLlpQ4CRERERF9THx8PGxtbT+6jUwUsHsPqlQqPH36FIUKFcq1te7i4uJQsmRJPHr0CDY2NrlyTtId9p/hYx8aPvahYWP/Gb7c7kMhBOLj4+Hs7PzJ5bsK3MisXC5HiRIlJDm3jY0NP8QGjP1n+NiHho99aNjYf4YvN/vwUyOy6XgBGBEREREZLBazRERERGSwWMzmAjMzM0ybNg1mZmZSR6EcYP8ZPvah4WMfGjb2n+HLy31Y4C4AIyIiIqL8gyOzRERERGSwWMwSERERkcFiMUtEREREBovFLBEREREZLBazOrB8+XK4ubnB3Nwcnp6eOH369Ee3P3nyJDw9PWFubo4yZcpgxYoVuZSUsqJNH+7atQs+Pj4oXrw4bGxs4OXlhcOHD+diWsqMtp/DdGfPnoWxsTFq1qyp34D0Sdr2YUpKCqZMmQJXV1eYmZmhbNmyWLduXS6lpQ9p23/BwcGoUaMGLC0t4eTkhL59++LFixe5lJY+dOrUKbRr1w7Ozs6QyWTYs2fPJ/fJM/WMoM+ydetWYWJiIlavXi1u3LghRo0aJaysrMSDBw8y3f7evXvC0tJSjBo1Sty4cUOsXr1amJiYiB07duRyckqnbR+OGjVKzJ07V1y8eFHcvn1bTJo0SZiYmIgrV67kcnJKp20fpnv9+rUoU6aMaN68uahRo0buhKVM5aQP27dvL+rUqSPCwsJEVFSUuHDhgjh79mwupqZ02vbf6dOnhVwuF0uWLBH37t0Tp0+fFlWrVhUdO3bM5eSU7sCBA2LKlCli586dAoDYvXv3R7fPS/UMi9nPVLt2bTFkyBCNtkqVKomJEydmuv2ECRNEpUqVNNoGDx4s6tatq7eM9HHa9mFmqlSpImbMmKHraJRNOe3Dbt26ie+++05MmzaNxazEtO3DgwcPCltbW/HixYvciEefoG3/zZ8/X5QpU0ajbenSpaJEiRJ6y0jZl51iNi/VM5xm8BlSU1MRHh6O5s2ba7Q3b94c586dy3SfP/74I8P2LVq0wOXLl6FQKPSWlTKXkz78kEqlQnx8PIoWLaqPiPQJOe3D9evX4+7du5g2bZq+I9In5KQPf//9d9SqVQvz5s2Di4sLKlSogHHjxiEpKSk3ItN7ctJ/9erVw+PHj3HgwAEIIfDs2TPs2LEDbdq0yY3IpAN5qZ4xztWz5TOxsbFQKpVwcHDQaHdwcEBMTEym+8TExGS6fVpaGmJjY+Hk5KS3vJRRTvrwQwsWLEBCQgK6du2qj4j0CTnpwzt37mDixIk4ffo0jI35Y1BqOenDe/fu4cyZMzA3N8fu3bsRGxuLgIAAvHz5kvNmc1lO+q9evXoIDg5Gt27dkJycjLS0NLRv3x4///xzbkQmHchL9QxHZnVAJpNpPBZCZGj71PaZtVPu0bYP023ZsgXTp09HSEgI7O3t9RWPsiG7fahUKtGjRw/MmDEDFSpUyK14lA3afA5VKhVkMhmCg4NRu3ZttG7dGgsXLkRQUBBHZyWiTf/duHEDI0eOxNSpUxEeHo5Dhw4hKioKQ4YMyY2opCN5pZ7hkMRnsLOzg5GRUYbfPJ8/f57ht5V0jo6OmW5vbGyMYsWK6S0rZS4nfZguJCQE/fv3x/bt29GsWTN9xqSP0LYP4+PjcfnyZURERGD48OEA3hVGQggYGxsjNDQU3t7euZKd3snJ59DJyQkuLi6wtbVVt1WuXBlCCDx+/Bjly5fXa2b6n5z035w5c1C/fn2MHz8eAFC9enVYWVmhYcOG+P777/lXSgOQl+oZjsx+BlNTU3h6eiIsLEyjPSwsDPXq1ct0Hy8vrwzbh4aGolatWjAxMdFbVspcTvoQeDci6+/vj82bN3OOl8S07UMbGxtcu3YNkZGR6q8hQ4agYsWKiIyMRJ06dXIrOv2/nHwO69evj6dPn+Lt27fqttu3b0Mul6NEiRJ6zUuactJ/iYmJkMs1SxAjIyMA/xvdo7wtT9UzuX7JWT6TvhzJ2rVrxY0bN8To0aOFlZWVuH//vhBCiIkTJ4pevXqpt09fymLMmDHixo0bYu3atVyaS2La9uHmzZuFsbGx+OWXX0R0dLT66/Xr11K9hAJP2z78EFczkJ62fRgfHy9KlCghOnfuLP766y9x8uRJUb58eTFgwACpXkKBpm3/rV+/XhgbG4vly5eLu3fvijNnzohatWqJ2rVrS/USCrz4+HgREREhIiIiBACxcOFCERERoV5eLS/XMyxmdeCXX34Rrq6uwtTUVHh4eIiTJ0+qn+vTp49o1KiRxvYnTpwQ7u7uwtTUVJQuXVoEBgbmcmL6kDZ92KhRIwEgw1efPn1yPzipafs5fB+L2bxB2z68efOmaNasmbCwsBAlSpQQY8eOFYmJibmcmtJp239Lly4VVapUERYWFsLJyUn4+fmJx48f53JqSnf8+PGP/tuWl+sZmRAczyciIiIiw8Q5s0RERERksFjMEhEREZHBYjFLRERERAaLxSwRERERGSwWs0RERERksFjMEhEREZHBYjFLRERERAaLxSwRERERGSwWs0REAIKCglC4cGGpY+RY6dKlsXjx4o9uM336dNSsWTNX8hAR5RYWs0SUb/j7+0Mmk2X4+ueff6SOhqCgII1MTk5O6Nq1K6KionRy/EuXLmHQoEHqxzKZDHv27NHYZty4cTh69KhOzpeVD1+ng4MD2rVrh7/++kvr4xjyLxdElHtYzBJRvtKyZUtER0drfLm5uUkdCwBgY2OD6OhoPH36FJs3b0ZkZCTat28PpVL52ccuXrw4LC0tP7qNtbU1ihUr9tnn+pT3X+f+/fuRkJCANm3aIDU1Ve/nJqKCh8UsEeUrZmZmcHR01PgyMjLCwoUL8cUXX8DKygolS5ZEQEAA3r59m+Vx/vzzTzRp0gSFChWCjY0NPD09cfnyZfXz586dw1dffQULCwuULFkSI0eOREJCwkezyWQyODo6wsnJCU2aNMG0adNw/fp19chxYGAgypYtC1NTU1SsWBGbNm3S2H/69OkoVaoUzMzM4OzsjJEjR6qfe3+aQenSpQEAX3/9NWQymfrx+9MMDh8+DHNzc7x+/VrjHCNHjkSjRo109jpr1aqFMWPG4MGDB7h165Z6m4/1x4kTJ9C3b1+8efNGPcI7ffp0AEBqaiomTJgAFxcXWFlZoU6dOjhx4sRH8xBR/sZilogKBLlcjqVLl+L69evYsGEDjh07hgkTJmS5vZ+fH0qUKIFLly4hPDwcEydOhImJCQDg2rVraNGiBTp16oSrV68iJCQEZ86cwfDhw7XKZGFhAQBQKBTYvXs3Ro0ahW+//RbXr1/H4MGD0bdvXxw/fhwAsGPHDixatAgrV67EnTt3sGfPHnzxxReZHvfSpUsAgPXr1yM6Olr9+H3NmjVD4cKFsXPnTnWbUqnEtm3b4Ofnp7PX+fr1a2zevBkA1O8f8PH+qFevHhYvXqwe4Y2Ojsa4ceMAAH379sXZs2exdetWXL16FV26dEHLli1x586dbGcionxGEBHlE3369BFGRkbCyspK/dW5c+dMt922bZsoVqyY+vH69euFra2t+nGhQoVEUFBQpvv26tVLDBo0SKPt9OnTQi6Xi6SkpEz3+fD4jx49EnXr1hUlSpQQKSkpol69emLgwIEa+3Tp0kW0bt1aCCHEggULRIUKFURqamqmx3d1dRWLFi1SPwYgdu/erbHNtGnTRI0aNdSPR44cKby9vdWPDx8+LExNTcXLly8/63UCEFZWVsLS0lIAEABE+/btM90+3af6Qwgh/vnnHyGTycSTJ0802ps2bSomTZr00eMTUf5lLG0pTUSkW02aNEFgYKD6sZWVFQDg+PHj+OGHH3Djxg3ExcUhLS0NycnJSEhIUG/zvrFjx2LAgAHYtGkTmjVrhi5duqBs2bIAgPDwcPzzzz8IDg5Wby+EgEqlQlRUFCpXrpxptjdv3sDa2hpCCCQmJsLDwwO7du2Cqakpbt68qXEBFwDUr18fS5YsAQB06dIFixcvRpkyZdCyZUu0bt0a7dq1g7Fxzn+M+/n5wcvLC0+fPoWzszOCg4PRunVrFClS5LNeZ6FChXDlyhWkpaXh5MmTmD9/PlasWKGxjbb9AQBXrlyBEAIVKlTQaE9JScmVucBElDexmCWifMXKygrlypXTaHvw4AFat26NIUOGYNasWShatCjOnDmD/v37Q6FQZHqc6dOno0ePHti/fz8OHjyIadOmYevWrfj666+hUqkwePBgjTmr6UqVKpVltvQiTy6Xw8HBIUPRJpPJNB4LIdRtJUuWxK1btxAWFoYjR44gICAA8+fPx8mTJzX+fK+N2rVro2zZsti6dSuGDh2K3bt3Y/369ernc/o65XK5ug8qVaqEmJgYdOvWDadOnQKQs/5Iz2NkZITw8HAYGRlpPGdtba3Vayei/IPFLBHle5cvX0ZaWhoWLFgAufzdpQLbtv1fO3cP0jgchgH8OWmKpdLFRUVpUQm6CBZEF0dxECoBB7VQFwehdeggbnbq4JJFEBexVBy8oYUOTlYc/AALIn5MBaWbiNBFFEV5brpyWkXPE47I8xvzkuT9818ekrz5+e55pmnCNE3E43GMjY1hZWUFlmUhGAzi7OysKjS/58+Q91JnZyd2dnYQiUQqx/b29p49/fR4PAiFQgiFQohGo+jo6MDJyQmCwWDV9QzD+NBfEsbHx7G2tobm5mbU1NRgaGioUvvsOl+Kx+OwbRvZbBaWZX1oP9xud1X/3d3deHp6wtXVFfr7+/+pJxH5PjQAJiLfXltbGx4fH7GwsIDz83Osrq5Wvfb+093dHWKxGLa3t1EqlbC7u4tCoVAJlrOzs9jf30c0GsXR0RGKxSJyuRymp6c/3ePMzAxSqRSWlpZQLBZh2zYymUxl8CmVSmF5eRmnp6eVNXg8Hvj9/levFwgEkM/ncXl5iXK5/OZ9w+EwDg8PkUwmMTIygtra2krtq9bp8/kwOTmJRCIBkh/aj0AggJubG+TzeVxfX+P29hamaSIcDiMSiSCTyeDi4gKFQgHz8/PY2Nj4q55E5Bv5nx/sioh8pYmJCQ4PD79as22bjY2N9Hg8HBwcZDqdJgCWy2WSzweO7u/vOTo6ypaWFrrdbjY1NTEWiz0bejo4OODAwADr6uro9XrZ1dXFZDL5Zm+vDTS9tLi4yNbWVhqGQdM0mU6nK7VsNsve3l76fD56vV729fVxc3OzUn85AJbL5dje3k6Xy0W/30+yegDst56eHgLg1tZWVe2r1lkqlehyubi+vk7y/f0gyampKdbX1xMAE4kESfLh4YFzc3MMBAI0DIMNDQ20LIvHx8dv9iQi39sPkvy/cVpERERE5HP0mYGIiIiIOJbCrIiIiIg4lsKsiIiIiDiWwqyIiIiIOJbCrIiIiIg4lsKsiIiIiDiWwqyIiIiIOJbCrIiIiIg4lsKsiIiIiDiWwqyIiIiIOJbCrIiIiIg41i9CkxCxQhJFMgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 8.9753  :  21%|████████████████▌                                                              | 2101/10000 [04:54<1:06:31,  1.98it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1 Validation 0.918918918918919\n"
+     ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:05<00:00,  9.42it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:02<00:00,  9.35it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:02<00:00,  9.42it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.40it/s]\n"
+      "       Current loss: 7.7862  :  22%|█████████████████▍                                                             | 2201/10000 [05:07<1:05:50,  1.97it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.8888888888888888\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:08<00:00,  9.39it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.37it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.37it/s]\n"
+      "       Current loss: 7.0736  :  23%|██████████████████▏                                                            | 2301/10000 [05:21<1:04:17,  2.00it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.9230769230769231\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:12<00:00,  9.40it/s]\n"
+      "       Current loss: 6.8569  :  24%|██████████████████▉                                                            | 2401/10000 [05:35<1:04:27,  1.96it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (76.85 s)\n",
-      "\n",
-      "Dataset               Accuracy\n",
-      "------------------------------\n",
-      "Anonaly lvl 1           100.00\n",
-      "Anonaly lvl 2           100.00\n",
-      "Anonaly lvl 3           100.00\n",
-      "\n",
-      "Anomaly all             100.00\n",
-      "\n",
-      "No Anomaly Train        100.00\n",
-      "No Anomaly Test         100.00\n",
-      "No Anomaly All          100.00\n",
-      "\n",
-      "All without train       100.00\n",
-      "All with train          100.00\n"
+      "F1 Validation 0.8888888888888888\n"
      ]
-    }
-   ],
-   "source": [
-    "# STEPS = 5000, MODEL TYPE = MEDIUM, WEIGHT = on\n",
-    "model18 = EfficientAD({**config, \"train_steps\": 5000, \"model_type\": \"medium\", \"weight_path\":\"../weights/teacher_medium.pth\"})\n",
-    "model18.create_model()\n",
-    "model18.display_eval_result()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 6.9773  :  25%|███████████████████▊                                                           | 2501/10000 [05:49<1:04:05,  1.95it/s]"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (0.26 ms)\n",
-      "\n",
-      "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (3.70 ms)\n",
-      "\n",
-      "- Setting config\n",
-      "     Output folder path: ../output/cookies_3_steps_10000_medium_weighted\n",
-      "- OK - Setting config (0.12 ms)\n",
-      "\n",
-      "- Prepare teacher, student & autoencoder\n",
-      "     Loading weight ../weights/teacher_medium.pth\n",
-      "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (185.38 ms)\n",
-      "\n",
-      "- Normalizing teacher\n"
+      "F1 Validation 0.9230769230769231\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 22.24it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:03<00:00, 22.40it/s]\n"
+      "       Current loss: 7.5698  :  26%|████████████████████▌                                                          | 2601/10000 [06:03<1:02:38,  1.97it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (6.46 s)\n",
-      "\n",
-      "- Train\n"
+      "F1 Validation 0.9230769230769231\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 3.0638  : 100%|█████████████████████████████████████████████████████| 10000/10000 [54:11<00:00,  3.08it/s]\n"
+      "       Current loss: 7.0992  :  27%|█████████████████████▎                                                         | 2701/10000 [06:17<1:00:54,  2.00it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (3251.29 s)\n",
-      "\n",
-      "- Saving models to ../output/cookies_3_steps_10000_medium_weighted/all_models.pth\n",
-      "- OK - Saving models (205.26 ms)\n",
-      "\n",
-      "- Saving map normalization to ../output/cookies_3_steps_10000_medium_weighted/map_normalization.pth\n"
+      "F1 Validation 0.918918918918919\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 11.28it/s]\n"
+      "       Current loss: 6.8597  :  28%|██████████████████████▏                                                        | 2801/10000 [06:31<1:00:27,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (803.15 ms)\n",
-      "\n",
-      "- Evaluating model\n"
+      "F1 Validation 0.8888888888888888\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:21<00:00,  9.37it/s]\n"
+      "       Current loss: 6.6128  :  29%|███████████████████████▍                                                         | 2901/10000 [06:45<59:38,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "     - AUC: 100.00%\n",
-      "    - Optimal Threshold: 0.5380728\n",
-      "    - F1 Score: 1.00\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[100   0]\n",
-      " [  0 100]] \n",
-      "\n"
+      "F1 Validation 0.8888888888888888\n"
      ]
     },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIhCAYAAABdSTJTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAACGtklEQVR4nOzdd1iTV/8G8DvsJTiZKmLdWhWwKo6qKO5V60Bx4N6LV62jr7PWqnVb0SqKWlTcrdYBDpx1IVStVq3iBi2KsiEk5/eHP/IaASWY8BC4P9fF1ebkGXdyAL+cnOc8MiGEABERERGRHjKQOgARERERUV6xmCUiIiIivcViloiIiIj0FotZIiIiItJbLGaJiIiISG+xmCUiIiIivcViloiIiIj0FotZIiIiItJbLGaJiIiISG+xmCUiykZgYCBkMpnqy8jICA4ODvD29sbdu3ez3Ucul8Pf3x8eHh6wsbGBubk5qlevjqlTp+Lly5fZ7qNUKrF161a0atUKpUuXhrGxMWxtbdGxY0ccOHAASqXyo1nT0tKwevVqNGnSBCVKlICJiQmcnJzQs2dPnDp16pPeByKigo7FLBHRB2zatAl//PEHjh07hjFjxuC3335DkyZNEBcXp7ZdcnIyvLy8MHbsWLi6umL79u04dOgQ+vXrh59//hmurq64ffu22j6pqalo3749BgwYAFtbW/j7++PEiRNYu3YtHB0d0aNHDxw4cOCD+WJjY9G4cWP4+fmhVq1aCAwMxPHjx7FkyRIYGhqiZcuW+PPPP7X+vhARFRiCiIiy2LRpkwAgLl++rNY+Z84cAUBs3LhRrX3YsGECgNixY0eWY92+fVvY2NiImjVrioyMDFX7yJEjBQCxefPmbDPcuXNH/Pnnnx/M2a5dO2FkZCSOHz+e7fOXLl0SDx8+/OAxcis5OVkrxyEi0iaOzBIRaaBevXoAgOfPn6vaYmJisHHjRrRp0wa9evXKsk+VKlXwzTff4K+//sL+/ftV+2zYsAFt2rRB//79sz1X5cqVUbt27RyzhIeH4/Dhwxg8eDA8PT2z3eaLL75A+fLlAQCzZ8+GTCbLsk3mlIoHDx6o2ipUqICOHTti7969cHV1hZmZGebMmQNXV1c0bdo0yzEUCgWcnJzQrVs3VVt6ejq+++47VKtWDaampihTpgwGDhyIf//9N8fXRESkKRazREQaiIqKAvC2QM108uRJZGRkoGvXrjnul/lcaGioah+5XP7BfT4mJCRE7djadvXqVUyePBnjxo3DkSNH8PXXX2PgwIE4e/ZslnnDISEhePbsGQYOHAjg7VzgLl264IcffkCfPn3w+++/44cffkBoaCiaN2+OlJQUnWQmoqLHSOoAREQFmUKhQEZGBlJTU3Hu3Dl89913+PLLL9G5c2fVNo8ePQIAuLi45HiczOcyt83NPh+jjWN8yIsXL3Dz5k21wr1ixYqYPHkyAgMDMX/+fFV7YGAg7Ozs0K5dOwDAzp07ceTIEezZs0dttLZOnTr44osvEBgYiJEjR+okNxEVLRyZJSL6gIYNG8LY2BjFihVD27ZtUaJECfz6668wMsrbWEB2H/MXVLVr11YrZAGgVKlS6NSpEzZv3qxaaSEuLg6//vor+vfvr3pfDh48iOLFi6NTp07IyMhQfdWtWxf29vYICwvL75dDRIUUi1kiog/YsmULLl++jBMnTmD48OG4desWevfurbZN5pzUzCkI2cl8rly5crne52O0cYwPcXBwyLZ90KBBePr0qWrKxPbt25GWlgZfX1/VNs+fP8fr169hYmICY2Njta+YmBjExsbqJDMRFT0sZomIPqB69eqoV68eWrRogbVr12LIkCE4cuQIdu/erdqmRYsWMDIyUl3clZ3M57y8vFT7GBsbf3Cfj2nTpo3asT/GzMwMwNt1ad+VU2GZ0yhymzZt4OjoiE2bNgF4u3xZgwYNUKNGDdU2pUuXRqlSpXD58uVsv9asWZOrzEREH8NilohIA4sWLUKJEiUwc+ZM1cfs9vb2GDRoEI4ePYrg4OAs+9y5cwcLFy5EzZo1VRdr2dvbY8iQITh69Ci2bNmS7bnu3buHa9eu5ZjFzc0N7dq1Q0BAAE6cOJHtNleuXFHNra1QoQIAZDnmx9ayfZ+hoSH69euH/fv348yZM7hy5QoGDRqktk3Hjh3x8uVLKBQK1KtXL8tX1apVNTonEVFOZEIIIXUIIqKCJjAwEAMHDsTly5dVy3FlWrx4MaZMmYKtW7eib9++AICkpCR06NAB586dw7Bhw9CpUyeYmpriwoUL+PHHH2FhYYFjx46pFXGpqano2rUrQkJC0Lt3b3z11Vews7NDbGwsQkNDsWnTJuzYsQNdunTJMWdsbCzatm2L69evY9CgQWjXrh1KlCiB6OhoHDhwANu3b0d4eDjq1KmD+Ph4uLi4wMnJCXPnzoWRkRECAwNx9epVREVFISoqSlXwVqhQAbVq1cLBgwezPe+dO3dQtWpVlC1bFi9fvkR0dDRsbGxUzysUCnTq1AkXL17E+PHjUb9+fRgbG+PJkyc4efIkunTpgq+++iqv3UNE9D9SL3RLRFQQ5XTTBCGESElJEeXLlxeVK1dWuwlCenq6+Omnn0SDBg2ElZWVMDU1FVWrVhVTpkwRsbGx2Z4nIyNDbN68WXh6eoqSJUsKIyMjUaZMGdGuXTuxbds2oVAoPpo1JSVFrFy5Unh4eAhra2thZGQkHB0dRbdu3cTvv/+utu2lS5dEo0aNhKWlpXBychKzZs0SGzZsEABEVFSUajtnZ2fRoUOHD563UaNGAoDw8fHJ9nm5XC5+/PFHUadOHWFmZiasrKxEtWrVxPDhw8Xdu3c/+rqIiHKDI7NEREREpLc4Z5aIiIiI9BaLWSIiIiLSWyxmiYiIiEhvsZglIiIiIr3FYpaIiIiI9BaLWSIiIiLSW0ZSB8hvSqUSz549Q7FixXK8VSMRERERSUcIgYSEBDg6OsLA4MNjr0WumH327BnKlSsndQwiIiIi+ojHjx+jbNmyH9ymyBWzxYoVA/D2zbG2ts6Xc8rlcoSEhKB169YwNjbOl3OS9rD/9B/7UP+xD/Ub+0//5XcfxsfHo1y5cqq67UOKXDGbObXA2to6X4tZCwsLWFtb84dYD7H/9B/7UP+xD/Ub+0//SdWHuZkSygvAiIiIiEhvsZglIiIiIr3FYpaIiIiI9BaLWSIiIiLSWyxmiYiIiEhvsZglIiIiIr3FYpaIiIiI9BaLWSIiIiLSWyxmiYiIiEhvsZglIiIiIr3FYpaIiIiI9BaLWSIiIiLSWyxmiYiIiEhvsZjVNYUCOHv27f+fPfv2MRERERFphaTF7OnTp9GpUyc4OjpCJpNh//79H93n1KlTcHd3h5mZGSpWrIi1a9fqPmhe7d0LVKgAdOjw9nGHDm8f790rZSoiIiKiQkPSYjYpKQl16tTB6tWrc7V9VFQU2rdvj6ZNmyIiIgLTp0/HuHHjsGfPHh0nzYO9e4Hu3YEnT9Tbnz59286CloiIiOiTGUl58nbt2qFdu3a53n7t2rUoX748li9fDgCoXr06rly5gh9//BFff/21jlLmgUIBjB8PCAEAEADSFECykSmMjZSATAb8ZwrQrgNgaChtVvoouTzjbf+lZ8BYyKSOQ3nAPtR/7EP9xv7TfykpqUhTAOL/a5uCRCYKSCqZTIZ9+/aha9euOW7z5ZdfwtXVFStWrFC17du3Dz179kRycjKMjY2z7JOWloa0tDTV4/j4eJQrVw6xsbGwtrbW6mtQOXtWNbVAAOjVcz4i7Kvo5lxEREREOpR89yLiTmyAnfd3iFzQAzaWZjo/Z3x8PEqXLo03b958tF6TdGRWUzExMbCzs1Nrs7OzQ0ZGBmJjY+Hg4JBlnwULFmDOnDlZ2kNCQmBhYaGzrNi+HcDbEdmIS3r1NhMRERFBKOSICwtEwpVfAQBv/tiFEydKwTQfPlROTk7O9bZ6V2XJZOofT2QOLL/fnmnatGnw8/NTPc4cmW3dunW+jMwmG5liyqhfAABnto6GdcKb/223Zw/QyEM3GUhr5PIMnDhxAp6enjA21rsfGQL7sDBgH+o39p/+efDgAQb79sejK1cAAMNHjsKXzVqgQ5tWMDEx0fn54+Pjc72tXn1H2dvbIyYmRq3txYsXMDIyQqlSpbLdx9TUFKamplnajY2Ns52WoBVffgmUKgU8ffp2juz/s054A5uE12/nzJYtC3g245xZPSCXy2FqCNhYmunue4Z0in2o/9iH+o39p1/27t2LQYMG4c2bNyhRogQCAwPRrl07HDp0CCYmJvnSh5qcQ6/WmfXw8EBoaKhaW0hICOrVq1ewfjgMDYHMeb3vjxhnPl6+nIUsERERFShxcXEYPHgw3rx5Aw8PD0RERKBz585Sx/ogSYvZxMREREZGIjIyEsDbpbciIyPx6NEjAG+nCPTv31+1/YgRI/Dw4UP4+fnh1q1b2LhxIwICAjBp0iQp4n9Yt27A7t2Ag6N6e9myb9u7dZMmFxEREVEOSpQogU2bNmHKlCk4deoUnJ2dpY70UZIWs1euXIGrqytcXV0BAH5+fnB1dcXMmTMBANHR0arCFgBcXFxw6NAhhIWFoW7dupg3bx5WrlxZsJblele3bsCtm/97vGcPEBXFQpaIiIgKjJ07d+LIkSOqx127dsXChQsL1qfeHyDpnNnmzZt/cL2ywMDALG3NmjXD1atXdZhKy96dStDIg1MLiIiIqEBISUmBn58f1q5di1KlSuHatWtwdHT8+I4FjF5dAEZEREREn+727dvo2bMnrl27BplMhhEjRsDW1lbqWHnCYpaIiIioCAkKCsLw4cORlJQEW1tb/PLLL/Dy8pI6Vp6xmCUiIiIqAhQKBYYPH46AgAAAQIsWLRAUFJTtTaf0iV4tzUVEREREeWP4/9ftyGQyzJ49G6GhoXpfyAIcmSUiIiIq1FJTU2FmZgYAWLlyJXx9fdGkSROJU2kPR2aJiIiICqHExEQMGDAAXbp0gVL59o6kFhYWhaqQBTgyS0RERFToXL9+HT179sTff/8NAwMDXLhwAY0aNZI6lk5wZJaIiIiokBBCYP369ahfvz7+/vtvODk5ISwsrNAWsgBHZomIiIgKhfj4eAwfPhw7duwAALRr1w5btmxB6dKlJU6mWxyZJSIiIioEvL29sWPHDhgaGmLRokU4ePBgoS9kAY7MEhERERUK8+fPxz///IPNmzfDw8ND6jj5hiOzRERERHrozZs3OHz4sOqxq6srbt68WaQKWYDFLBEREZHeuXLlCtzc3NClSxdcuXJF1W5kVPQ+dGcxS0RERKQnhBBYsWIFGjVqhPv378PJyUnqSJIreuU7ERERkR6Ki4vDoEGDsH//fgBAt27dEBAQgOLFi0uaS2ocmSUiIiIq4C5evAhXV1fs378fJiYmWLVqFXbv3l3kC1mAI7NEREREBd6pU6fw8OFDfPbZZ9i5cyfc3NykjlRgsJglIiIiKuAmTZoEmUyG4cOHw9raWuo4BQqnGRAREREVMGfPnkWbNm2QlJQEADAwMMDkyZNZyGaDxSwRERFRAaFUKrFgwQI0b94cISEhmD9/vtSRCjxOMyAiIiIqAF68eIF+/fohJCQEANC3b19Mnz5d4lQFH4tZIiIiIomFhYWhT58+iI6Ohrm5OVavXo2BAwdCJpNJHa3AYzFLREREJKFffvkFAwYMgFKpRI0aNbBz507UrFlT6lh6g3NmiYiIiCTk6emJUqVKYeDAgbh06RILWQ1xZJaIiIgon925cwdVqlQBADg6OuLPP/+Eg4ODxKn0E0dmiYiIiPJJRkYGZs6cierVq2PPnj2qdhayecdiloiIiCgfPH36FC1btsS8efOgVCpx4cIFqSMVCpxmQERERKRjR44cQb9+/RAbGwsrKyusX78e3t7eUscqFDgyS0RERKQjcrkcU6dORbt27RAbGwtXV1dcvXqVhawWsZglIiIi0pHTp09j4cKFAIDRo0fj/PnzqFy5ssSpChdOMyAiIiLSkZYtW2L69OlwdXVF9+7dpY5TKHFkloiIiEhL0tPT8d///hdPnz5Vtc2fP5+FrA5xZJaIiIhICx48eIBevXrh0qVLOHPmDE6ePMnb0eYDjswSERERfaJ9+/bB1dUVly5dQvHixTFx4kQWsvmExSwRERFRHqWlpWHcuHHo1q0bXr9+jYYNGyIyMhJdunSROlqRwWkGRERERHnw9OlTdOnSBeHh4QCAyZMnY/78+TA2NpY4WdHCYpaIiIgoD4oXL46UlBSUKlUKmzdvRocOHaSOVCSxmCUiIiLKpdTUVJiYmMDAwACWlpbYt28fLCwsULZsWamjFVmcM0tERESUC7dv30aDBg2waNEiVVuVKlVYyEqMxSwRERHRRwQFBcHd3R3Xrl3DypUrkZSUJHUk+n8sZomIiIhykJycjCFDhqBv375ISkpC8+bNceXKFVhaWkodjf4fi1kiIiKibNy6dQsNGjRAQEAAZDIZZs2ahWPHjsHR0VHqaPQOXgBGRERE9J74+Hg0btwYcXFxsLe3R1BQEDw9PaWORdngyCwRERHRe6ytrTF37ly0atUKkZGRLGQLMBazRERERACuX7+OyMhI1ePRo0fj6NGjsLOzky4UfRSLWSIiIirShBBYv3496tevj+7duyM+Ph4AIJPJYGDAUqmg45xZIiIiKrISEhIwfPhwbN++HQBQuXJlyOVyiVORJvjnBhERERVJkZGRcHd3x/bt22FoaIgffvgBv//+O0qVKiV1NNIAR2aJiIioSBFCYO3atZg4cSLS0tJQrlw57NixA40aNZI6GuUBR2aJiIioSBFC4LfffkNaWho6deqEiIgIFrJ6jCOzREREVKQYGBhgy5Yt2LVrF0aOHAmZTCZ1JPoEHJklIiKiQk0IgRUrVmDkyJGqtjJlymDUqFEsZAsBjswSERFRoRUXF4dBgwZh//79AIAePXrwBgiFDItZIiIiKpQuXryIXr164eHDhzAxMcGSJUvQokULqWORlnGaARERERUqQggsWbIETZo0wcOHD/HZZ5/h/PnzGDNmDKcVFEIcmSUiIqJCZdCgQQgMDAQA9OzZEz///DNsbGykDUU6w5FZIiIiKlR69eoFc3Nz+Pv7Y8eOHSxkCzmOzBIREZFeUyqVuHPnDqpVqwYAaNu2LaKiomBnZydxMsoPHJklIiIivfXixQu0b98eDRs2RFRUlKqdhWzRwWKWiIiI9NKpU6dQt25dHD16FOnp6bh+/brUkUgCLGaJiIhIrygUCsydOxeenp6Ijo5G9erVcenSJXTu3FnqaCQBzpklIiIivRETE4O+ffvi+PHjAABfX1+sXr0alpaWEicjqbCYJSIiIr2xYsUKHD9+HBYWFvD390f//v2ljkQSYzFLREREemPWrFl48uQJZsyYoVq9gIo2zpklIiKiAuvp06eYNGkSMjIyAABmZmbYunUrC1lS4cgsERERFUhHjhxBv379EBsbC2tra8ycOVPqSFQAcWSWiIiIChS5XI5p06ahXbt2iI2NRd26deHt7S11LCqgODJLREREBcbjx4/h7e2N8+fPAwBGjRqFJUuWwMzMTOJkVFCxmCUiIqIC4fjx4+jZsydevXoFa2trbNiwAT169JA6FhVwLGaJiIioQLC3t0dKSgrc3d0RHByMzz77TOpIpAdYzBIREZFkkpKSVDc8qFmzJo4fPw43NzeYmppKnIz0BS8AIyIiIkns378fFSpUUM2PBQAPDw8WsqQRFrNERESUr9LS0jB+/Hh89dVXiI2NxbJly6SORHpM8mJ2zZo1cHFxgZmZGdzd3XHmzJkPbh8UFIQ6derAwsICDg4OGDhwIF6+fJlPaYmIiOhT3Lt3D40bN8bKlSsBAJMmTcK2bdskTkX6TNJiNjg4GBMmTMCMGTMQERGBpk2bol27dnj06FG22589exb9+/fH4MGD8ddff2HXrl24fPkyhgwZks/JiYiISFO7du2Cq6srwsPDUbJkSRw8eBCLFy+GsbGx1NFIj0lazC5duhSDBw/GkCFDUL16dSxfvhzlypWDv79/tttfuHABFSpUwLhx4+Di4oImTZpg+PDhuHLlSj4nJyIiIk1cv34dPj4+SEhIQOPGjREZGYkOHTpIHYsKAclWM0hPT0d4eDimTp2q1t66dWu1ieDvatSoEWbMmIFDhw6hXbt2ePHiBXbv3v3BH4a0tDSkpaWpHsfHxwN4e3cRuVyuhVfyYXJ5htr/58c5Sbsy+4x9p7/Yh/qPfajf5HI5atWqha5du6JKlSqYPXs2jIyM2J96JL9/BjU5j2TFbGxsLBQKBezs7NTa7ezsEBMTk+0+jRo1QlBQEHr16oXU1FRkZGSgc+fOWLVqVY7nWbBgAebMmZOlPSQkBBYWFp/2InIhTQFkvs0nTpyAqaHOT0k6EhoaKnUE+kTsQ/3HPtQv58+fR926dWFhYQGZTIb+/fvDwMAAISEhUkejPMqvn8Hk5ORcbyv5OrMymUztsRAiS1ummzdvYty4cZg5cybatGmD6OhoTJ48GSNGjEBAQEC2+0ybNg1+fn6qx/Hx8ShXrhxat24Na2tr7b2QHCSnZ2DKpRMAAE9PT9hY8nZ8+kYulyM0NBReXl6c16Wn2If6j32oX5KTk+Hn54eNGzeiR48e2LRpE44dO4Y2bdqw//RUfv8MZn6SnhuSFbOlS5eGoaFhllHYFy9eZBmtzbRgwQI0btwYkydPBgDUrl0blpaWaNq0Kb777js4ODhk2cfU1DTb9eqMjY3zpTOMxf8Kc2NjI/4Q67H8+p4h3WEf6j/2YcF369Yt9OzZEzdu3IBMJkP16tVhZPS23GD/6b98q580OIdkF4CZmJjA3d09y3B1aGgoGjVqlO0+ycnJMDBQj2xo+PZzeyGEboISERFRrmzevBn16tXDjRs3YGdnh9DQUMyZMyfLv91E2iTpd5efnx82bNiAjRs34tatW5g4cSIePXqEESNGAHg7RaB///6q7Tt16oS9e/fC398f9+/fx7lz5zBu3DjUr18fjo6OUr0MIiKiIi0pKQm+vr7w9fVFcnIyWrZsicjISLRs2VLqaFQESDpntlevXnj58iXmzp2L6Oho1KpVC4cOHYKzszMAIDo6Wm3NWV9fXyQkJGD16tX4z3/+g+LFi8PT0xMLFy6U6iUQEREVecnJyQgJCYGBgQHmzJmDadOmqT45JdI1yS8AGzVqFEaNGpXtc4GBgVnaxo4di7Fjx+o4FREREeVWmTJlEBwcDKVSiWbNmkkdh4oYTmIhIiIijSQkJMDHxwdBQUGqtqZNm7KQJUmwmCUiIqJci4yMhLu7O7Zt24YxY8ZotIQSkS6wmCUiIqKPEkLA398fDRs2xN27d1G2bFkcPHgwX9ZsJ/oQyefMEhERUcH25s0bDB06FLt27QIAdOzYEYGBgShVqpTEyYhYzBIREdEHJCUlwd3dHffu3YORkREWLlyIiRMn5ni3TqL8xmkGRERElCNLS0t8/fXXcHZ2xtmzZ+Hn58dClgoUFrNERESkJi4uDk+ePFE9/u677xAREYEGDRpImIooeyxmiYiISOXixYtwdXVF9+7dIZfLAQDGxsYoUaKExMmIssdiloiIiCCEwJIlS9CkSRM8fPgQ//77L54+fSp1LKKPYjFLRERUxL18+RKdO3fGpEmTkJGRgR49euDq1auoUKGC1NGIPorFLBERURF27tw51K1bFwcPHoSpqSn8/f0RHBwMGxsbqaMR5QqX5iIiIiqihBCYOHEinjx5gsqVK2Pnzp2oW7eu1LGINMKRWSIioiJKJpMhKCgIgwcPRnh4OAtZ0kssZomIiIqQU6dOYcWKFarHlStXxoYNG1CsWDEJUxHlHacZEBERFQEKhQLff/89Zs+eDSEE3Nzc0LRpU6ljEX0yFrNERESFXExMDPr27Yvjx48DAAYMGAA3NzeJUxFpB4tZIiKiQuz48ePw8fHB8+fPYWFhgTVr1mDAgAFSxyLSGs6ZJSIiKqQWLFgALy8vPH/+HLVq1cLly5dZyFKhw2KWiIiokLK1tYUQAkOGDMHFixdRo0YNqSMRaR2nGRARERUiiYmJsLKyAgAMGjQIVatWRZMmTSRORaQ7HJklIiIqBDIyMjBt2jTUqlULr169AvB2HVkWslTYsZglIiLSc48fP0bz5s3xww8/4OHDh9izZ4/UkYjyDYtZIiIiPfb777+jbt26OHfuHKytrREcHIyhQ4dKHYso37CYJSIi0kPp6emYNGkSOnbsiFevXsHd3R1Xr15Fz549pY5GlK9YzBIREemh2bNnY8mSJQCAcePG4dy5c/jss88kTkWU/1jMEhER6aFJkyahTp062Lt3L1asWAFTU1OpIxFJgsUsERGRHkhLS8OWLVsghAAAlCxZElevXsVXX30lcTIiaXGdWSIiogLu/v376NmzJ8LDw5GWlqa6wMvAgGNSRPwpICIiKsB2794NV1dXhIeHo2TJknBwcJA6ElGBwmKWiIioAEpNTcXo0aPRo0cPxMfHo1GjRoiMjETHjh2ljkZUoLCYJSIiKmDu3r0LDw8PrFmzBgAwdepUhIWFoVy5chInIyp4OGeWiIiogHny5An+/PNPlC5dGlu3bkXbtm2ljkRUYLGYJSIiKgCEEJDJZACAFi1aIDAwEC1btoSTk5PEyYgKNk4zICIiktitW7fQpEkT3LlzR9XWv39/FrJEucBiloiISEKbN29GvXr1cP78eYwbN07qOER6h8UsERGRBJKSkuDr6wtfX18kJyfD09MTgYGBUsci0jssZomIiPLZjRs38MUXX2Dz5s0wMDDA3LlzERISAnt7e6mjEekdXgBGRESUjy5evIgWLVogJSUFDg4O2LZtG5o3by51LCK9xWKWiIgoH7m5uaFOnTqwtrbG1q1bYWtrK3UkIr3GYpaIiEjHbt68icqVK8PY2BjGxsb4/fffUbx4cRgYcLYf0afiTxEREZGOCCGwdu1auLm5YcaMGar2kiVLspAl0hKOzBIREelAfHw8hg4dip07dwJ4u5asQqGAoaGhxMmIChf+WUhERKRl4eHhcHNzw86dO2FkZITFixfj119/ZSFLpAMcmSUiItISIQRWr16NSZMmIT09Hc7OztixYwcaNmwodTSiQosjs0RERFry9OlTTJ8+Henp6ejatSsiIiJYyBLpGEdmiYiItKRs2bJYv349Xrx4gbFjx0Imk0kdiajQYzFLRESUR0IILFu2DK6urmjRogUAwNvbW+JUREULi1kiIqI8ePXqFXx9fXHgwAHY29vj5s2bKFGihNSxiIocFrNEREQaOn/+PLy9vfH48WOYmppi5syZKF68uNSxiIokXgBGRESUS0qlEgsXLsSXX36Jx48fo3Llyrhw4QJGjhzJ+bFEEuHILBERUS6kpKTg66+/xuHDhwEAvXv3xrp161CsWDGJkxEVbRyZJSIiygUzMzMUL14cZmZm+PnnnxEUFMRClqgAYDFLRESUA4VCgaSkJACATCbDunXrcPnyZQwdOpTTCogKCBazRERE2Xj+/Dnatm2Lvn37QggBAChWrBhq1aolcTIiehfnzBIREb3nxIkT8PHxQUxMDCwsLPD333+jevXqUsciomxwZJaIiOj/KRQKzJo1C61atUJMTAxq1qyJy5cvs5AlKsA4MktERATg2bNn8PHxQVhYGABg8ODBWLlyJSwsLKQNRkQfxGKWiIiKPCEEunTpgitXrsDS0hLr1q2Dj4+P1LGIKBfyNM0gIyMDx44dw7p165CQkADg7V+0iYmJWg1HRESUH2QyGVauXAl3d3dcvXqVhSyRHtF4ZPbhw4do27YtHj16hLS0NHh5eaFYsWJYtGgRUlNTsXbtWl3kJCIi0qonT54gMjISHTt2BAB4eHjg8uXLXHKLSM9oPDI7fvx41KtXD3FxcTA3N1e1f/XVVzh+/LhWwxEREenCoUOHULduXfTs2RM3btxQtbOQJdI/Go/Mnj17FufOnYOJiYlau7OzM54+faq1YERERNoml8sxY8YMLF68GADg5uamNjBDRPpH42JWqVRCoVBkaX/y5Alv60dERAXWw4cP4e3tjQsXLgAAxo4di8WLF8PU1FTiZET0KTSeZuDl5YXly5erHstkMiQmJmLWrFlo3769NrMRERFpxa+//gpXV1dcuHABNjY22LNnD1auXMlClqgQ0HhkdtmyZWjRogVq1KiB1NRU9OnTB3fv3kXp0qWxfft2XWQkIiL6JFevXkVcXBzq16+PHTt2wMXFRepIRKQlGhezjo6OiIyMxI4dOxAeHg6lUonBgwfDx8eH846IiKjAEEKoLuiaOXMmbG1tMXTo0CzXfBCRftN4msHp06dhbGyMgQMHYvXq1VizZg2GDBkCY2NjnD59WhcZiYiINLJnzx54enoiNTUVAGBoaIjRo0ezkCUqhDQuZlu0aIFXr15laX/z5g1atGihlVBERER5kZqaijFjxqB79+4ICwvDTz/9JHUkItIxjacZvPuxzbtevnwJS0tLrYQiIiLS1N27d9GrVy9EREQAAL755huMGzdO4lREpGu5Lma7desG4O3qBb6+vmpXgCoUCly7dg2NGjXSfkIiIqKP2LFjB4YOHYrExESULl0aW7ZsQbt27aSORUT5INfFrI2NDYC3I7PFihVTu9jLxMQEDRs2xNChQ7WfkIiI6AOWLFmCSZMmAQCaNm2K7du3w8nJSeJURJRfcl3Mbtq0CQBQoUIFTJo0iVMKiIioQPj666/x/fffY9SoUZg1axaMjDSeQUdEekzjn/hZs2bpIgcREVGuRUREwNXVFcDbQZa7d++iZMmSEqciIilovJoBAOzevRs9e/ZEw4YN4ebmpvalqTVr1sDFxQVmZmZwd3fHmTNnPrh9WloaZsyYAWdnZ5iamuKzzz7Dxo0b8/IyiIhIzyQlJWHQoEFwc3PDoUOHVO0sZImKLo2L2ZUrV2LgwIGwtbVFREQE6tevj1KlSuH+/fsaT7YPDg7GhAkTMGPGDERERKBp06Zo164dHj16lOM+PXv2xPHjxxEQEIDbt29j+/btqFatmqYvg4iI9MyjR4/QqFEjbNq0CQYGBrh9+7bUkYioANB4msGaNWvw888/o3fv3ti8eTOmTJmCihUrYubMmdmuP/shS5cuxeDBgzFkyBAAwPLly3H06FH4+/tjwYIFWbY/cuQITp06hfv376v+Cq9QoYKmL4GIiPSIEAKBgYGYNGkS0tPTYW9vj+3bt6N58+ZSRyOiAkDjYjbzL2MAMDc3R0JCAgCgX79+aNiwIVavXp2r46SnpyM8PBxTp05Va2/dujXOnz+f7T6//fYb6tWrh0WLFmHr1q2wtLRE586dMW/evBxvpZuWloa0tDTV4/j4eACAXC6HXC7PVdZPIZdnqP1/fpyTtCuzz9h3+ot9qL8SExMxZswYbNu2DQDQsmVLbN68Gba2tuxPPcKfQf2X332oyXk0Lmbt7e3x8uVLODs7w9nZGRcuXECdOnUQFRUFIUSujxMbGwuFQgE7Ozu1djs7O8TExGS7z/3793H27FmYmZlh3759iI2NxahRo/Dq1asc580uWLAAc+bMydIeEhICCwuLXOfNqzQFkPk2nzhxAqaGOj8l6UhoaKjUEegTsQ/1zx9//IFt27bBwMAAffr0Qbdu3XDlyhWpY1Ee8WdQ/+VXHyYnJ+d6W42LWU9PTxw4cABubm4YPHgwJk6ciN27d+PKlSuqGyto4v27ieV0hzEAUCqVkMlkCAoKUq17u3TpUnTv3h0//fRTtqOz06ZNg5+fn+pxfHw8ypUrh9atW8Pa2lrjvJpKTs/AlEsnALx972wszXR+TtIuuVyO0NBQeHl5wdjYWOo4lAfsQ/3Vvn17CCHg5eWFpKQk9qGe4s+g/svvPsz8JD03NC5mf/75ZyiVSgDAiBEjULJkSZw9exadOnXCiBEjcn2c0qVLw9DQMMso7IsXL7KM1mZycHCAk5OTqpAFgOrVq0MIgSdPnqBy5cpZ9jE1NVW7W1kmY2PjfOkMY/G/wtzY2Ig/xHosv75nSHfYhwVffHw8pk2bhlmzZsHW1hYAsHDhQsjlchw6dIh9qOfYf/ov3+onDc6h8WoGBgYGagtS9+zZEytXrsS4cePw77//5vo4JiYmcHd3zzJcHRoamuNtcRs3boxnz54hMTFR1Xbnzh0YGBigbNmyGr4SIiIqSK5evQo3NzesWbMGgwcPljoOEemJPK0z+76YmBiMHTsWlSpV0mg/Pz8/bNiwARs3bsStW7cwceJEPHr0SDXCO23aNPTv31+1fZ8+fVCqVCkMHDgQN2/exOnTpzF58mQMGjQoxwvAiIioYBNCYPXq1fDw8MC9e/dQvnx5TJ8+XepYRKQncl3Mvn79Gj4+PihTpgwcHR2xcuVKKJVKzJw5ExUrVsSFCxc0vnlBr169sHz5csydOxd169bF6dOncejQITg7OwMAoqOj1dactbKyQmhoKF6/fo169erBx8cHnTp1wsqVKzU6LxERFQyvX79G9+7dMXbsWKSnp6Nz586IiIiAh4eH1NGISE/kes7s9OnTcfr0aQwYMABHjhzBxIkTceTIEaSmpuLw4cNo1qxZngKMGjUKo0aNyva5wMDALG3VqlXj1ZBERIXA33//jfbt2yMqKgrGxsZYvHgxxo0bl+NFwERE2cl1Mfv7779j06ZNaNWqFUaNGoVKlSqhSpUqWL58uQ7jERFRYeXo6AhDQ0O4uLggODgYX3zxhdSRiEgP5bqYffbsGWrUqAEAqFixIszMzFR37iIiIsqN+Ph4FCtWDDKZDNbW1jh48CDs7OxQvHhxqaMRkZ7K9ZxZpVKptkyCoaEhLC0tdRKKiIgKnz/++AO1atVSu1Nk1apVWcgS0SfJ9cisEAK+vr6qNVtTU1MxYsSILAXt3r17tZuQiIj0mlKpxI8//ojp06dDoVBg3bp1GDFiBNcbJSKtyHUxO2DAALXHffv21XoYIiIqXP79918MGDAAhw8fBgB4e3tj3bp1LGSJSGtyXcxu2rRJlzmIiKiQOX36NHr37o1nz57BzMwMK1euxJAhQ7haARFplca3syUiIvqY6OhotG7dGmlpaahatSp27tyJ2rVrSx2LiAohFrNERKR1Dg4OmDNnDv766y+sWbMGVlZWUkciokKKxSwREWnFyZMnYWtri5o1awIApkyZAgCcVkBEOpXrpbmIiIiyo1AoMHv2bLRs2RI9e/ZEUlISgLdFLAtZItI1jswSEVGeRUdHw8fHBydPngQANGzYkAUsEeWrPI3Mbt26FY0bN4ajoyMePnwIAFi+fDl+/fVXrYYjIqKCKzQ0FHXr1sXJkydhaWmJrVu3IiAgABYWFlJHI6IiRONi1t/fH35+fmjfvj1ev34NhUIBAChevDiWL1+u7XxERFTAZGRk4Ntvv0WbNm3w4sUL1K5dG1euXOH640QkCY2L2VWrVmH9+vWYMWMGDA0NVe316tXD9evXtRqOiIgKHplMhrNnz0IIgeHDh+PChQuoVq2a1LGIqIjSeM5sVFQUXF1ds7SbmpqqJv0TEVHhI4SATCaDoaEhtm3bhrNnz6Jnz55SxyKiIk7jkVkXFxdERkZmaT98+DBq1KihjUxERFSAyOVyTJkyBRMmTFC1OTo6spAlogJB45HZyZMnY/To0UhNTYUQApcuXcL27duxYMECbNiwQRcZiYhIIo8ePYK3tzf++OMPAMCgQYNQp04diVMREf2PxsXswIEDkZGRgSlTpiA5ORl9+vSBk5MTVqxYAW9vb11kJCIiCfz222/w9fVFXFwcbGxsEBAQwEKWiAqcPK0zO3ToUAwdOhSxsbFQKpWwtbXVdi4iIpJIeno6vvnmG9UKNV988QWCg4Ph4uIibTAiomxoPGd2zpw5uHfvHgCgdOnSLGSJiAoRIQQ6deqkKmQnTpyIs2fPspAlogJL42J2z549qFKlCho2bIjVq1fj33//1UUuIiKSgEwmw/Dhw1GiRAn8+uuvWLp0KUxMTKSORUSUI42L2WvXruHatWvw9PTE0qVL4eTkhPbt22Pbtm1ITk7WRUYiItKh1NRUtXXCu3Xrhvv376Nz584SpiIiyp083c62Zs2a+P7773H//n2cPHkSLi4umDBhAuzt7bWdj4iIdOiff/5Bo0aN4OnpiadPn6raixcvLl0oIiIN5KmYfZelpSXMzc1hYmICuVyujUxERJQPgoOD4ebmhoiICAghEBUVJXUkIiKN5amYjYqKwvz581GjRg3Uq1cPV69exezZsxETE6PtfEREpGUpKSkYMWIEvL29kZCQgCZNmiAyMhJNmjSROhoRkcY0XprLw8MDly5dwueff46BAweq1pklIqKC7/bt2+jZsyeuXbsGmUyG6dOnY/bs2TAyytNKjUREktP4t1eLFi2wYcMG1KxZUxd5iIhIh1asWIFr167B1tYWv/zyC7y8vKSORET0STQuZr///ntd5CAionywePFiZGRkYM6cOXBwcJA6DhHRJ8tVMevn54d58+bB0tISfn5+H9x26dKlWglGRESf7q+//sK6deuwfPlyGBgYwNLSEj///LPUsYiItCZXxWxERIRqpYKIiAidBiIiok8nhEBgYCBGjx6NlJQUVKxYERMmTJA6FhGR1uWqmD158mS2/09ERAVPYmIiRo0aha1btwIAWrdujT59+kiciohINzRemmvQoEFISEjI0p6UlIRBgwZpJRQREeXNtWvXUK9ePWzduhUGBgaYP38+Dh8+DFtbW6mjERHphMbF7ObNm5GSkpKlPSUlBVu2bNFKKCIi0lxwcDAaNGiA27dvw8nJCWFhYZg+fToMDD75/jhERAVWrlcziI+PhxACQggkJCTAzMxM9ZxCocChQ4f4lz8RkYQqVaoEpVKJdu3aYcuWLShdurTUkYiIdC7XxWzx4sUhk8kgk8lQpUqVLM/LZDLMmTNHq+GIiOjDXr9+jeLFiwMA3N3d8ccff6Bu3bocjSWiIiPXxezJkychhICnpyf27NmDkiVLqp4zMTGBs7MzHB0ddRKSiIjUCSGwZs0aTJ8+HSdPnoSbmxsAqP5LRFRU5LqYbdasGQAgKioK5cuXh0wm01koIiLK2evXrzF06FDs3r0bABAYGMgiloiKrFwVs9euXUOtWrVgYGCAN2/e4Pr16zluW7t2ba2FIyIidZcvX0avXr0QFRUFY2NjLFq0COPHj5c6FhGRZHJVzNatWxcxMTGwtbVF3bp1IZPJIITIsp1MJoNCodB6SCKiok4IgRUrVmDKlCmQy+VwcXFBcHAwvvjiC6mjERFJKlfFbFRUFMqUKaP6fyIiyl979uzBxIkTAQBff/01NmzYoLrwi4ioKMtVMevs7Jzt/xMRUf7o1q0bOnfujNatW2PUqFG8boGI6P/l6aYJv//+u+rxlClTULx4cTRq1AgPHz7UajgioqJKqVRi/fr1SE5OBgAYGBhg//79GD16NAtZIqJ3aFzMfv/99zA3NwcA/PHHH1i9ejUWLVqE0qVLqz4CIyKivIuNjUWnTp0wbNgwjB07VtXOIpaIKKtcL82V6fHjx6hUqRIAYP/+/ejevTuGDRuGxo0bo3nz5trOR0RUpJw5cwa9e/fG06dPYWZmhgYNGkAIwUKWiCgHGo/MWllZ4eXLlwCAkJAQtGrVCgBgZmaGlJQU7aYjIioilEolvv/+e7Ro0QJPnz5F1apVcfHiRQwbNoyFLBHRB2g8Muvl5YUhQ4bA1dUVd+7cQYcOHQAAf/31FypUqKDtfEREhd6LFy/Qr18/hISEAAD69u0Lf39/WFlZSZyMiKjg03hk9qeffoKHhwf+/fdf7NmzB6VKlQIAhIeHo3fv3loPSERU2Mnlcly9ehXm5uYICAjAli1bWMgSEeWSxiOzxYsXx+rVq7O0z5kzRyuBiIiKgnfnwTo5OWHXrl0oU6YMatasKXEyIiL9onExC7y9L3hAQABu3boFmUyG6tWrY/DgwbCxsdF2PiKiQicmJgY+Pj4YM2YMvvrqKwDgBbRERHmk8TSDK1eu4LPPPsOyZcvw6tUrxMbGYtmyZfjss89w9epVXWQkIio0jh07hjp16uDEiRMYN24c0tPTpY5ERKTXNC5mJ06ciM6dO+PBgwfYu3cv9u3bh6ioKHTs2BETJkzQQUQiIv2XkZGBb7/9Fq1bt8aLFy9Qu3ZtHDt2DCYmJlJHIyLSaxpPM7hy5QrWr18PI6P/7WpkZIQpU6agXr16Wg1HRFQYPH36FL1798aZM2cAAMOHD8eyZctUN6AhIqK807iYtba2xqNHj1CtWjW19sePH6NYsWJaC0ZEVBj8+++/qFu3LmJjY1GsWDH8/PPP8Pb2ljoWEVGhofE0g169emHw4MEIDg7G48eP8eTJE+zYsQNDhgzh0lxERO8pU6YMevXqBVdXV4SHh7OQJSLSMo1HZn/88UfIZDL0798fGRkZAABjY2OMHDkSP/zwg9YDEhHpm0ePHsHY2BgODg4AgCVLlkAIATMzM4mTEREVPhqPzJqYmGDFihWIi4tDZGQkIiIi8OrVKyxbtgympqa6yEhEpDcOHDiAunXronfv3qo/+E1NTVnIEhHpSK6L2eTkZIwePRpOTk6wtbXFkCFD4ODggNq1a8PCwkKXGYmICrz09HT85z//QefOnREXF4fk5GTExcVJHYuIqNDLdTE7a9YsBAYGokOHDvD29kZoaChGjhypy2xERHohKioKTZs2xdKlSwG8XcLw7NmzKFOmjMTJiIgKv1zPmd27dy8CAgJUFy/07dsXjRs3hkKhgKGhoc4CEhEVZHv37sWgQYPw5s0blChRAoGBgejcubPUsYiIioxcj8w+fvwYTZs2VT2uX78+jIyM8OzZM50EIyIq6ORyOf773//izZs38PDwQEREBAtZIqJ8lutiVqFQZLlTjZGRkeoCByKiosbY2BjBwcGYNm0aTp06BWdnZ6kjEREVObmeZiCEgK+vr9qKBampqRgxYgQsLS1VbXv37tVuQiKiAmTnzp148eIFxowZAwCoVasWvv/+e4lTEREVXbkuZgcMGJClrW/fvloNQ0RUUKWkpGDixIlYt24dDA0N0bhxY7i6ukodi4ioyMt1Mbtp0yZd5iAiKrBu376Nnj174tq1a5DJZJg6dSo+//xzqWMRERHycAcwIqKi5JdffsGIESOQlJQEW1tb/PLLL/Dy8pI6FhER/T+N7wBGRFRUjBo1Cv369UNSUhJatGiByMhIFrJERAUMi1kiohxUq1YNMpkMs2fPRmhoKBwcHKSORERE7+E0AyKid7x69QolS5YEAIwdOxbNmjVDnTp1JE5FREQ54cgsERGAxMREDBgwAA0aNEB8fDwAQCaTsZAlIirg8lTMbt26FY0bN4ajoyMePnwIAFi+fDl+/fVXrYYjIsoP169fxxdffIEtW7bg/v37OHnypNSRiIgolzQuZv39/eHn54f27dvj9evXUCgUAIDixYtj+fLl2s5HRKQzQgisX78e9evXx99//w0nJyeEhYWhS5cuUkcjIqJc0riYXbVqFdavX48ZM2bA0NBQ1V6vXj1cv35dq+GIiHQlISEBPj4+GDZsGFJTU9GuXTtERkaiadOmUkcjIiINaFzMRkVFZXvXG1NTUyQlJWklFBGRrv3nP//B9u3bYWhoiEWLFuHgwYMoXbq01LGIiEhDGhezLi4uiIyMzNJ++PBh1KhRQ+MAa9asgYuLC8zMzODu7o4zZ87kar9z587ByMgIdevW1ficRETfffcdGjZsiDNnzmDy5MkwMOD1sERE+kjj396TJ0/G6NGjERwcDCEELl26hPnz52P69OmYPHmyRscKDg7GhAkTMGPGDERERKBp06Zo164dHj169MH93rx5g/79+6Nly5aaxieiIiopKQkBAQGqx7a2tjh//jw8PDwkTEVERJ9K43VmBw4ciIyMDEyZMgXJycno06cPnJycsGLFCnh7e2t0rKVLl2Lw4MEYMmQIgLcrIhw9ehT+/v5YsGBBjvsNHz4cffr0gaGhIfbv36/pSyCiIiY8PBx+fn54/vw5rK2t0adPHwBvl94iIiL9lqebJgwdOhRDhw5FbGwslEolbG1tNT5Geno6wsPDMXXqVLX21q1b4/z58znut2nTJty7dw+//PILvvvuu4+eJy0tDWlpaarHmetHyuVyyOVyjXNrSi7PUPv//DgnaVdmn7Hv9I8QAqtXr8bUqVMhl8vh7OyMChUqsC/1EH8O9Rv7T//ldx9qcp5PugPYp1wsERsbC4VCATs7O7V2Ozs7xMTEZLvP3bt3MXXqVJw5cwZGRrmLvmDBAsyZMydLe0hICCwsLDQPrqE0BZD5Np84cQKmhh/cnAqw0NBQqSOQBhITE7Fq1SpcvHgRANCwYUOMGTMG//77Lw4dOiRxOsor/hzqN/af/suvPkxOTs71thoXsy4uLh/8aO7+/fsaHe/9Ywkhsj2+QqFAnz59MGfOHFSpUiXXx582bRr8/PxUj+Pj41GuXDm0bt0a1tbWGmXNi+T0DEy5dAIA4OnpCRtLM52fk7RLLpcjNDQUXl5eMDY2ljoO5cKlS5cwfvx4PHz4ECYmJliwYAEqVqyI1q1bsw/1FH8O9Rv7T//ldx9mfpKeGxoXsxMmTFB7LJfLERERgSNHjmh0AVjp0qVhaGiYZRT2xYsXWUZrgbdrQl65cgUREREYM2YMAECpVEIIASMjI4SEhMDT0zPLfqampjA1Nc3SbmxsnC+dYSz+V5gbGxvxh1iP5df3DH26N2/e4OHDh/jss8+wc+dOfP755zh06BD7sBBgH+o39p/+y7f6SYNzaFzMjh8/Ptv2n376CVeuXMn1cUxMTODu7o7Q0FB89dVXqvbQ0NBs775jbW2d5aYMa9aswYkTJ7B79264uLjk+txEVPi8+6lO+/btsW3bNnTo0AHW1tacp0dEVIhpbWHFdu3aYc+ePRrt4+fnhw0bNmDjxo24desWJk6ciEePHmHEiBEA3k4R6N+//9ugBgaoVauW2petrS3MzMxQq1YtWFpaauulEJGeOXv2LOrUqYOHDx+q2nr37p0vU4mIiEhan3QB2Lt2796NkiVLarRPr1698PLlS8ydOxfR0dGoVasWDh06BGdnZwBAdHT0R9ecJaKiS6lUYuHChfjvf/8LhUKBb7/9Flu3bpU6FhER5SONi1lXV1e1C7SEEIiJicG///6LNWvWaBxg1KhRGDVqVLbPBQYGfnDf2bNnY/bs2Rqfk4j034sXL9CvXz+EhIQAAPr27Qt/f3+JUxERUX7TuJjt2rWr2mMDAwOUKVMGzZs3R7Vq1bSVi4goR2FhYejTpw+io6Nhbm6On376Cb6+vrwJAhFREaRRMZuRkYEKFSqgTZs2sLe311UmIqIcHT58GB07doRSqUSNGjWwc+dO1KxZU+pYREQkEY0uADMyMsLIkSPV7qhFRJSfWrRogdq1a2PgwIG4dOkSC1kioiJO42kGDRo0QEREhOoiLSIiXbt48SLq1asHQ0NDmJmZ4fTp0yhWrJjUsYiIqADQuJgdNWoU/vOf/+DJkydwd3fPsiRW7dq1tRaOiIq2jIwMzJkzB/Pnz8fMmTNVF3yykCUioky5LmYHDRqE5cuXo1evXgCAcePGqZ6TyWSqBcsVCoX2UxJRkfP06VP06dMHp0+fBgA8f/48x9tdExFR0ZXrYnbz5s344YcfEBUVpcs8REQ4cuQI+vXrh9jYWFhZWWH9+vXw9vaWOhYRERVAuS5mhRAAwLmyRKQzcrkcM2fOxA8//ADg7brWwcHBqFy5ssTJiIiooNJoNQN+vEdEunT//n0sX74cADB69GicP3+ehSwREX2QRheAValS5aMF7atXrz4pEBEVXVWrVsW6detgYWGB7t27Sx2HiIj0gEbF7Jw5c2BjY6OrLERUxKSnp+Pbb7/FV199BQ8PDwBA//79JU5FRET6RKNi1tvbG7a2trrKQkRFyIMHD+Dt7Y2LFy9i586d+Pvvv2FmZiZ1LCIi0jO5njPL+bJEpC379u2Dq6srLl68iOLFi2PFihUsZImIKE9yXcxmrmZARJRXaWlpGDduHLp164bXr1+jYcOGiIyMRJcuXaSORkREeirX0wyUSqUucxBRIRcXFwcvLy+Eh4cDACZPnoz58+fD2NhY4mRERKTPNL6dLRFRXhQvXhxly5bFgwcPsHnzZnTo0EHqSEREVAiwmCUinUlNTUVGRgasrKwgk8mwceNGJCcno2zZslJHIyKiQkKjmyYQEeXWnTt30LBhQwwbNkw1575kyZIsZImISKtYzBKR1m3btg3u7u74888/cezYMTx9+lTqSEREVEixmCUirUlOTsbQoUPh4+ODxMRENG/eHJGRkRyNJSIinWExS0RacevWLTRo0AAbNmyATCbDrFmzcOzYMTg6OkodjYiICjFeAEZEnywjIwOdOnXCvXv3YG9vj6CgIHh6ekodi4iIigCOzBLRJzMyMsLPP/+MNm3aIDIykoUsERHlGxazRJQn169fx8GDB1WPPT09cfjwYdjZ2UmYioiIihoWs0SkESEENmzYgPr166N37964e/eu6jmZTCZhMiIiKopYzBJRriUkJKBv374YOnQoUlNT0aRJExQvXlzqWEREVISxmCWiXImMjIS7uzu2bdsGQ0NDLFy4EL///jvKlCkjdTQiIirCuJoBEX3U2rVrMWHCBKSlpaFcuXLYsWMHGjVqJHUsIiIijswS0cfdu3cPaWlp6NSpEyIiIljIEhFRgcGRWSLKllKphIHB2793v//+e9SpUwc+Pj68yIuIiAoUjswSkRohBFasWAFPT0/I5XIAgLGxMfr27ctCloiIChwWs0SkEhcXh27dumHChAk4deoUtm/fLnUkIiKiD+I0AyICAFy8eBG9evXCw4cPYWJigiVLlqBfv35SxyIiIvogjswSFXFKpRJLlixBkyZN8PDhQ3z22Wc4f/48xowZw2kFRERU4LGYJSripkyZgkmTJiEjIwM9e/bE1atX4e7uLnUsIiKiXGExS1TEDR06FKVLl8batWuxY8cOWFtbSx2JiIgo1zhnlqiIUSqVOH/+PJo0aQIAqFq1Kh48eABLS0uJkxEREWmOI7NERciLFy/Qvn17NGvWDGFhYap2FrJERKSvODJLVEScOnUKvXv3RnR0NMzNzREdHS11JCIiok/GkVmiQk6hUGDevHnw9PREdHQ0qlevjkuXLqF3795SRyMiIvpkHJklKsRiYmLQt29fHD9+HADg6+uL1atXc1oBEREVGixmiQqxw4cP4/jx47CwsIC/vz/69+8vdSQiIiKtYjFLVIj5+vri/v376NOnD6pXry51HCIiIq3jnFmiQuTZs2fo27cv4uLiAAAymQzz5s1jIUtERIUWR2aJCokjR46gX79+iI2NBQD88ssvEiciIiLSPY7MEum5jIwMTJs2De3atUNsbCzq1q2LWbNmSR2LiIgoX3BklkiPPX78GL1798a5c+cAAKNGjcKSJUtgZmYmcTIiIqL8wWKWSE9duHABHTp0wKtXr2BtbY2AgAB0795d6lhERET5isUskZ6qUqUKLC0tUbFiRQQHB6NixYpSRyIiIsp3LGaJ9MiLFy9QpkwZyGQylCxZEsePH0f58uVhamoqdTQiIiJJ8AIwIj2xb98+VK1aFRs3blS1Va5cmYUsEREVaSxmiQq4tLQ0jBs3Dt26dcPr168RFBQEIYTUsYiIiAoEFrNEBdi9e/fQuHFjrFq1CgAwadIkHD16FDKZTOJkREREBQPnzBIVULt27cKQIUMQHx+PkiVLYsuWLejQoYPUsYiIiAoUFrNEBdCdO3fg7e0NpVKJxo0bY/v27ShXrpzUsYiIiAocFrNEBVCVKlUwc+ZMpKWlYe7cuTAy4o8qERFRdvgvJFEBsX37dtSrVw+VK1cGAN6SloiIKBd4ARiRxJKTkzFkyBD06dMHvXr1QmpqqtSRiIiI9AZHZokkdOvWLfTs2RM3btyATCZDp06dYGxsLHUsIiIivcFilkgimzdvxqhRo5CcnAw7OzsEBQWhZcuWUsciIiLSKyxmifJZcnIyRo4ciS1btgAAWrZsiV9++QX29vYSJyMiItI/nDNLlM+MjIzw999/w8DAAPPmzcPRo0dZyBIREeURR2aJ8oEQAkIIGBgYwMTEBMHBwXj48CGaNWsmdTQiIiK9xpFZIh1LSEhA3759MW3aNFVbhQoVWMgSERFpAUdmiXQoMjISPXv2xN27d2FkZISRI0eiQoUKUsciIiIqNDgyS6QDQgj4+/ujYcOGuHv3LsqWLYuwsDAWskRERFrGkVkiLXvz5g2GDh2KXbt2AQA6duyIwMBAlCpVSuJkREREhQ+LWSItUiqVaNasGf78808YGRlh4cKFmDhxImQymdTRiIiICiVOMyDSIgMDA0yePBnOzs44e/Ys/Pz8WMgSERHpEItZok8UFxeHyMhI1WMfHx/cvHkTDRo0kC4UERFREcFilugTXLx4Ea6urmjfvj3+/fdfVbuFhYWEqYiIiIoOFrNEeSCEwJIlS9CkSRM8fPgQ5ubmePHihdSxiIiIihxeAEakoZcvX8LX1xcHDx4EAPTo0QPr16+HjY2NxMmIiIiKHslHZtesWQMXFxeYmZnB3d0dZ86cyXHbvXv3wsvLC2XKlIG1tTU8PDxw9OjRfExLRd25c+dQt25dHDx4EKamplizZg2Cg4NZyBIREUlE0mI2ODgYEyZMwIwZMxAREYGmTZuiXbt2ePToUbbbnz59Gl5eXjh06BDCw8PRokULdOrUCREREfmcnIoqf39/PHnyBJUrV8aFCxcwcuRIrlZAREQkIUmnGSxduhSDBw/GkCFDAADLly/H0aNH4e/vjwULFmTZfvny5WqPv//+e/z66684cOAAXF1d8yMyFXFr1qyBnZ0dZs+ejWLFikkdh4iIqMiTrJhNT09HeHg4pk6dqtbeunVrnD9/PlfHUCqVSEhIQMmSJXPcJi0tDWlpaarH8fHxAAC5XA65XJ6H5JqRyzPU/j8/zknac/r0aQQHB6N9+/aQy+UwNzfHDz/8AADsSz2S2VfsM/3FPtRv7D/9l999qMl5JCtmY2NjoVAoYGdnp9ZuZ2eHmJiYXB1jyZIlSEpKQs+ePXPcZsGCBZgzZ06W9pCQkHxZPilNAWS+zSdOnICpoc5PSVqgUCiwe/duBAcHQ6lUwszMjNMJCoHQ0FCpI9AnYh/qN/af/suvPkxOTs71tpKvZvB+gSCEyFXRsH37dsyePRu//vorbG1tc9xu2rRp8PPzUz2Oj49HuXLl0Lp1a1hbW+c9eC4lp2dgyqUTAABPT0/YWJrp/Jz0aWJiYuDr64sTJ972m4+PDxo3bgwvLy8YGxtLnI7yQi6XIzQ0lH2ox9iH+o39p//yuw8zP0nPDcmK2dKlS8PQ0DDLKOyLFy+yjNa+Lzg4GIMHD8auXbvQqlWrD25ramoKU1PTLO3Gxsb50hnG4n+FubGxEX+IC7jjx4/Dx8cHz58/h4WFBdasWYM+ffrg0KFD+fY9Q7rDPtR/7EP9xv7Tf/lWP2lwDslWMzAxMYG7u3uW4erQ0FA0atQox/22b98OX19fbNu2DR06dNB1TCpCVqxYAS8vLzx//hy1atXClStXMGDAAKljERER0QdIOs3Az88P/fr1Q7169eDh4YGff/4Zjx49wogRIwC8nSLw9OlTbNmyBcDbQrZ///5YsWIFGjZsqBrVNTc35zqf9Mm++OILGBgYYODAgVixYgVvSUtERKQHJC1me/XqhZcvX2Lu3LmIjo5GrVq1cOjQITg7OwMAoqOj1dacXbduHTIyMjB69GiMHj1a1T5gwAAEBgbmd3wqBJ4/f66a1tKoUSPcuHED1apVkzgVERER5ZbkF4CNGjUKo0aNyva59wvUsLAw3QeiIiEjIwP//e9/sWrVKly8eBE1a9YEABayREREekbyYpYovz1+/Bi9e/fGuXPnAAAHDhxQFbNERESkX1jMUpHy+++/o3///nj16hWsra2xfv36D65TTERERAWbZKsZEOUnuVyOSZMmoWPHjnj16hXc3d1x9epVFrJERER6jsUsFQkBAQFYsmQJAGDcuHE4d+4cPvvsM4lTERER0afiNAMqEoYMGYKjR4+if//++Oqrr6SOQ0RERFrCkVkqlNLT07F48WKkpaUBAIyMjLBv3z4WskRERIUMR2ap0Ll//z569eqFK1eu4NGjR1i1apXUkYiIiEhHODJLhcru3bvh6uqKK1euoGTJkmjTpo3UkYiIiEiHWMxSoZCamopRo0ahR48eiI+PR+PGjREZGYmOHTtKHY2IiIh0iMUs6b179+7Bw8MD/v7+AICpU6fi5MmTKFeunMTJiIiISNc4Z5b0noGBAaKiolC6dGls3boVbdu2lToSERER5RMWs6SXFAoFDA0NAQAuLi7Yt28fqlSpAicnJ4mTERERUX7iNAPSO7du3YKbmxuOHDmiamvRogULWSIioiKIxSzplS1btqBevXq4du0aJk+eDKVSKXUkIiIikhCLWdILSUlJGDhwIAYMGIDk5GR4enoiNDQUBgb8FiYiIirKWAlQgXfjxg188cUXCAwMhIGBAebOnYuQkBDY29tLHY2IiIgkxgvAqEC7f/8+6tevj5SUFDg4OGDbtm1o3ry51LGIiIiogGAxSwVaxYoV4e3tjWfPnmHLli2wtbWVOhIREREVICxmqcD5888/4ejoiDJlygAA/P39YWxszPmxRERElAWrAyowhBBYu3YtGjRogP79+6tWKjA1NWUhS0RERNlihUAFwps3b+Dt7Y2RI0ciLS0NhoaGSE5OljoWERERFXAsZkly4eHhcHd3x86dO2FkZITFixfjt99+g5WVldTRiIiIqIDjnFmSjBACq1evxqRJk5Ceng5nZ2fs2LEDDRs2lDoaERER6QmOzJJkkpKSsGLFCqSnp6NLly6IiIhgIUtEREQa4cgsScbKygrBwcE4e/Ysxo0bB5lMJnUkIiIi0jMsZinfCCGwfPlymJubY8SIEQAAd3d3uLu7S5yMiIiI9BWLWcoXr169gq+vLw4cOAATExN4eXnhs88+kzoWERER6TkWs6Rz58+fh7e3Nx4/fgxTU1MsW7YMFStWlDoWERERFQK8AIx0RqlUYuHChfjyyy/x+PFjVK5cGRcuXMDIkSM5P5aIiIi0giOzpBNKpRJdu3bFgQMHAAC9e/fGunXrUKxYMYmTERERUWHCkVnSCQMDA3h4eMDMzAzr169HUFAQC1kiIiLSOo7MktYoFArExsbCzs4OAPDNN9+gR48eqFSpksTJiIiIqLDiyCxpxfPnz9G2bVu0bNkSycnJAN6OzrKQJSIiIl1iMUuf7MSJE6hTpw6OHTuGqKgoXL16VepIREREVESwmKU8UygUmDVrFlq1aoXnz5+jZs2auHz5Mpo0aSJ1NCIiIioiOGeW8uTZs2fw8fFBWFgYAGDw4MFYuXIlLCwspA1GRERERQqLWcqTsWPHIiwsDJaWlli3bh18fHykjkRERERFEItZypOVK1fizZs3+Omnn1C1alWp4xAREVERxTmzlCtPnjzBTz/9pHrs5OSEY8eOsZAlIiIiSXFklj7q0KFD6N+/P16+fAknJyd07dpV6khEREREADgySx8gl8sxZcoUdOjQAS9fvoSbmxs+//xzqWMRERERqXBklrL18OFDeHt748KFCwDeXvC1ePFimJqaSpyMiIiI6H9YzFIWBw8eRL9+/fD69WvY2Nhg48aN6Natm9SxiIiIiLJgMUtZpKWl4fXr16hfvz527NgBFxcXqSMRERERZYvFLAEAMjIyYGT09tvh66+/xp49e9CxY0eYmJhInIyI9IlSqUR6errWjyuXy2FkZITU1FQoFAqtH590i/2n/3TRhyYmJjAw+PTLt1jMEnbv3o3p06cjLCwMjo6OAMBpBUSksfT0dERFRUGpVGr92EII2Nvb4/Hjx5DJZFo/PukW+0//6aIPDQwM4OLi8skDZyxmi7DU1FT85z//wZo1awAAixcvxrJlyyRORUT6SAiB6OhoGBoaoly5cloZbXmXUqlEYmIirKystH5s0j32n/7Tdh8qlUo8e/YM0dHRKF++/CcVyCxmi6i7d++iV69eiIiIAAB88803mDdvnsSpiEhfZWRkIDk5GY6OjrCwsND68TOnL5iZmbEY0kPsP/2niz4sU6YMnj17hoyMDBgbG+f5OCxmi6AdO3Zg6NChSExMROnSpbFlyxa0a9dO6lhEpMcy59Bxnj0R5Vbm7wuFQsFilnJvy5YtGDBgAACgadOm2L59O5ycnCRORUSFBedDElFuaW3urVaOQnrj66+/Rs2aNfHtt9/ixIkTLGSJiIhIr7GYLQJCQ0NVVxdbWlriypUrmDdvnmopLiIiorxKT09HpUqVcO7cOamjFBovXrxAmTJl8PTpU6mj6AUWs4VYUlISBg4ciNatW2PJkiWqdjMzMwlTEREVHL6+vpDJZJDJZDAyMkL58uUxcuRIxMXFZdn2/PnzaN++PUqUKAEzMzN8/vnnWLJkSbZrbp48eRLt27dHqVKlYGFhgRo1auA///nPR4uTiIgI9OjRA3Z2djAzM0OVKlUwdOhQ3LlzR2uvWdt+/vlnODs7o3HjxlmeGzZsGAwNDbFjx44sz/n6+qJr165Z2iMjIyGTyfDgwQNVmxACP//8Mxo0aAArKysUL14c9erVw/Lly5GcnKzNl6Nm/vz5aNSoESwsLFC8ePFc7SOEwOzZs+Ho6Ahzc3M0b94cf/31l9o2aWlpGDt2LEqXLg1LS0t07twZT548UT1va2uLfv36YdasWdp8OYUWi9lC6q+//kL9+vURGBgIAwMDyOVyqSMRERVIbdu2RXR0NB48eIANGzbgwIEDGDVqlNo2+/btQ7NmzVC2bFmcPHkSf//9N8aPH4/58+fD29sbQgjVtuvWrUOrVq1gb2+PPXv24ObNm1i7di3evHmjNrDwvoMHD6Jhw4ZIS0tDUFAQbt26ha1bt8LGxgb//e9/8/z6dP37f9WqVRgyZEiW9uTkZAQHB2Py5MnYuHHjJ52jX79+mDBhArp06YKTJ08iMjIS//3vf/Hrr78iJCTkk479Ienp6ejRowdGjhyZ630WLVqEpUuXYvXq1bh8+TLs7e3h5eWFhIQE1TYTJkzAvn37sGPHDpw9exaJiYno2LGj2h9GAwcORFBQULZ/WNF7RBHz5s0bAUC8efMmX86XlCYXzt8cFM7fHBSvE5N1fj6lUikCAgKEubm5ACDs7e3FyZMndX7ewiw9PV3s379fpKenSx2F8oh9qHspKSni5s2bIiUlRQjx9ndRUppca18JKWni2fNYkZCS9tFtlUplrnMPGDBAdOnSRa3Nz89PlCxZUvU4MTFRlCpVSnTr1i3L/r/99psAIHbs2CGEEOLx48fCxMRETJgwIdvzxcXFZduelJQkSpcuLbp27frB/TZt2iRsbGzUntu3b59495/zWbNmiTp16oiAgADh4uIiZDKZWLt2rXB0dBQKhUJt306dOon+/furvR43NzdhamoqXFxcxOzZs4VcLs82kxBChIeHCwMDg2z/TQ0MDBQNGzYUr1+/Fubm5uLPP/9UO392770QQkRERAgAIioqSgghRHBwsAAg9u/fn2VbpVIpXr9+nWM+bcnufc+OUqkU9vb24ocfflC1paamChsbG7F27VohhBCvX78WxsbGqu8ZIYR4+vSpMDAwEEeOHFE7XoUKFURAQIB2XsQnUigUIi4uLsv30Kd4//fGuzSp1zhpshBJTEzEiBEjEBQUBABo3bo1tm7dCltbW4mTEVFRkyJXoMbMo5Kc++bcNrAwyds/b/fv38eRI0fUlgkKCQnBy5cvMWnSpCzbd+rUCVWqVMH27dvRq1cv7Nq1C+np6ZgyZUq2x8/po+qjR48iNjZW4/1y8s8//2Dnzp3Ys2cPDA0N4eTkhHHjxuHkyZNo2bIlACAuLg5Hjx7FgQMHVBn69u2LlStXomnTprh37x6GDRsGADl+3H369GlUqVIF1tbWWZ4LCAhA3759YWNjg3bt2iEoKAgLFizQ6HUAQFBQEKpWrYouXbpkeU4mk8HGxibHfa2srD547KZNm+Lw4cMaZ8pJVFQUYmJi0Lp1a1WbqakpmjVrhvPnz2P48OEIDw+HXC5X28bR0RG1atXC+fPn0aZNG1V7/fr1cebMGQwaNEhrGQsjFrOFyJ07d7Bz504YGhpi3rx5+Oabb7g4NRHRRxw8eBBWVlZQKBRITU0FACxdulT1fOZ81erVq2e7f7Vq1VTb3L17F9bW1nBwcNAow927d1XH0ob09HRs3boVZcqUUbW1bdsW27ZtUxWzu3btQsmSJVWP58+fj6lTp6qWb6xYsSLmzZuHKVOm5FjMPnjwQHUb9Pdfz4ULF7B3714AgI+PD8aNG4f58+dr/O/S3bt3UbVqVY32yRQZGfnB583NzfN03JzExMQAAOzs7NTa7ezs8PDhQ9U2JiYmKFGiRJZtMvfP5OTkpLq5EeWMxWwh4ubmhnXr1qFy5cpo0qSJ1HGIqAgzNzbEzbltPr5hLimVSiTEJ6CYdbGPFkPmxoYaHbtFixbw9/dHcnIyNmzYgDt37mDs2LFZthPvzIt9vz1zvcx3/18TOR07r5ydndUKWeBtQTls2DCsWbMGpqamCAoKgre3NwwN375f4eHhuHz5MubPn6/aJ7PAT05OzvbObikpKdleVBwQEIA2bdqgdOnSAID27dtjyJAhOHbsGNq2bavRa8nrewoAlSpVytN+n+r9vLl5DdltY25urtML3AoLDtvpsfj4ePTv31/tr7aBAweykCUiyclkMliYGGn1y9zEMFfbaVr4WFpaolKlSqhduzZWrlyJtLQ0zJkzR/V8lSpVAAC3bt3Kdv+///4blStXVm375s0bREdHa5Qh8xx///33B7czMDDIUvhmd4GXpaVllrZOnTpBqVTi999/x+PHj3HmzBn07dtX9bxSqcScOXMQGRmp+rp+/Tru3r2b4yo4pUuXznKBkkKhwJYtW/D777/DyMgIRkZGsLKyQlxcnNqFYNbW1njz5k2WY75+/RoAVNMHqlSpkuN7/zFWVlYf/NL23S/t7e0BIMsI64sXL1Sjtfb29khPT8/yvr27TaZXr15l+aOEsmIxq6euXr0KNzc3bN26FT4+PtkuDUNERJqbNWsWfvzxRzx79gzA2+sPSpYsme1KBL/99hvu3r2L3r17AwC6d+8OExMTLFq0KNtjZxZq72vdujVKly790f3KlCmDhIQEJCUlqZ772EfpmczNzdGtWzcEBQVh+/btqFKlCtzd3VXPu7m54fbt26hUqVKWr5xGw11dXfH333+rFdiHDh1CQkICIiIiVEXx1atXERgYiF9//RUvX74E8HZKxY0bN1RTOzJdvnwZZcqUUX0M36dPH9y5cwe//vprlvMLIbItiN99bz70tWHDhly9d7nl4uICe3t7hIaGqtrS09Nx6tQpNGrUCADg7u4OY2NjtW2io6Nx48YN1TaZbty4AVdXV61mLJS0dkmantD31QyUSqVYtWqVMDExEQBE+fLlxfnz57WQlHLCK+H1H/tQ9z50VbI26OJKaiFyvqLe3d1djB49WvV4165dwtDQUAwdOlT8+eefIioqSmzYsEGUKFFCdO/eXW0FhZ9++knIZDIxaNAgERYWJh48eCDOnj0rhg0bJvz8/HLMsn//fmFsbCw6deokQkNDRVRUlLh8+bKYPHmy6NWrlxBCiJcvXwpLS0sxbtw4cffuXREUFCQcHR2zXc0gOyEhIcLU1FRUrVpVzJs3T+25I0eOCCMjIzFr1ixx48YNcfPmTbFjxw4xY8aMHDPHxsYKExMTcf36dVVbly5dVHkzKRQK8erVK+Hk5CSWL18uhHh7Vb+9vb3o3r27uHz5svjnn3/E1q1bRYkSJcSiRYtU+yqVStGrVy9hbm4uvv/+e3H58mXx4MEDceDAAeHp6Sn27duXY75P9fDhQxERESHmzJkjrKysREREhIiIiBAJCQmqbapWrSr27t2revzDDz8IGxsbsXfvXnH9+nXRu3dv4eDgIOLj41XbjBgxQpQtW1YcO3ZMXL16VXh6eoo6deqIjIwM1TZJSUnC3NxcnD59WmevTxMFeTUDFrM6ps1iNi4uTnTr1k0AEABE586dxcuXL7WUlHLCQkj/sQ91r7AVs0FBQcLExEQ8evRI1Xb69GnRtm1bYWNjI0xMTESNGjXEjz/+qFaAZAoNDRVt2rQRJUqUEGZmZqJatWpi0qRJ4tmzZx/Mc/nyZdGtWzdRpkwZYWpqKipVqiSGDRsm7t69q9pm3759olKlSsLMzEx07NhR/Pzzz7kuZjMyMoSDg4MAIO7du5fl+SNHjohGjRoJc3NzYW1tLerXry9+/vnnD2b29vYWU6dOFUIIERMTI4yMjMTOnTvVtsnsvzFjxojPP/9c1X737l3x9ddfCycnJ2FpaSk+//xzsXr16iz9rFAohL+/v/jiiy+EhYWFsLa2Fu7u7mLFihUiOVl3y14OGDBA9W/uu1/vLnkJQGzatEn1WKlUilmzZgl7e3thamoqvvzyS7ViX4i3Py9jxowRJUuWFObm5qJjx45q32tCCLFt2zZRtWpVnb02TRXkYlYmhJZnnRdw8fHxsLGxwZs3b7JdSkTbktMzVMvT/PlfT9hY5u3KySdPnqBp06Z48OABjI2NsXjxYowbNy7Pk+Ip9+RyOQ4dOoT27durLddD+oN9qHupqamIioqCi4uLTu4yqFQqER8fD2tra67SUsBcv34drVq1wj///INixYpluw37T3P169fHhAkT0KdPH6mjANBNH37o94Ym9RpXM9ATjo6OqFy5MmQyGYKDg/HFF19IHYmIiAiff/45Fi1ahAcPHuDzzz+XOk6h8OLFC3Tv3l01F5s+jMVsAfbq1SuYmZnBwsICBgYG2LZtG4yMjDRePJuIiEiXMtemJe2wtbXN8QYalBXH+guo8+fPo27duhg/fryqrXTp0ixkiYiIiN7BYraAUSqVWLRoEb788ks8fvwYYWFhOS7lQkRERFTUsZgtQP7991907NgR33zzDRQKBby9vREeHs7RWCIiIqIccM5sAXHmzBl4e3vj2bNnMDMzw4oVKzB06FCuVkBERET0ASxmC4Dk5GT06NEDz58/R9WqVbFz507Url1b6lhEREREBR6nGRQAFhYW2LhxI/r164crV66wkCUiIiLKJY7MSuTkyZNISUlB+/btAQDt27dX/T8RERER5Q5HZvOZQqHA7Nmz0bJlS/j4+ODRo0dSRyIiIh2aPXs26tatW2DP07x5c0yYMEHreT6mQoUKWL58+Scdw9fXF127dv3gNlK9Pso/kheza9asUd3GzN3dHWfOnPng9qdOnYK7uzvMzMxQsWJFrF27Np+SfrqYmGh4eXlhzpw5EEKgW7duKF26tNSxiIiKtMePH2Pw4MFwdHSEiYkJnJ2dMX78eLx8+VLjY8lkMuzfv1+tbdKkSTh+/LiW0uZdWFgYZDIZl3vUgevXr6NZs2YwNzeHk5MT5s6dCyHEB/epUKECZDKZ2tfUqVNVz798+RJt27aFo6MjTE1NUa5cOYwZMwbx8fFqxzl69CgaNmyIYsWKoUyZMvj6668RFRWlts3HaqfmzZtnySKTydChQwfVNj/88AM8PT1hY2MDW1tbdO3aFbdv31Y7TmJiIsaMGYOyZcvC3Nwc1atXh7+/v0bvZV5IWswGBwdjwoQJmDFjBiIiItC0aVO0a9cux9HKqKgotG/fHk2bNkVERASmT5+OcePGYc+ePfmcXHMpUVfRpGFDnDx5EpaWlti6dSsCAgJgYWEhdTQiooJDoQDCwoDt29/+V6HQ6enu37+PevXq4c6dO9i+fTv++ecfrF27FsePH4eHhwdevXr1yeewsrJCqVKltJC24JDL5VJHKDDi4+Ph5eUFR0dHXL58GatWrcKPP/6IpUuXfnTfuXPnIjo6WvX17bffqp4zMDBAly5d8Ntvv+HOnTsIDAzEsWPHMGLECNU29+/fR5cuXeDp6YnIyEgcPXoUsbGx6Natm2qb3NROe/fuVctx48YNGBoaokePHqptTp06hSFDhuD8+fMIDQ1FRkYGWrdujaSkJNU2EydOxJEjR/DLL7/g1q1bmDhxIsaOHYtff/01z+9vrggJ1a9fX4wYMUKtrVq1amLq1KnZbj9lyhRRrVo1tbbhw4eLhg0b5vqcb968EQDEmzdvNA+cB4mp6cLao6cAZAKAqF27tvj777/z5dykHenp6WL//v0iPT1d6iiUR+xD3UtJSRE3b94UKSkpeT/Inj1ClC0rBPC/r7JlhdizRygUChEXFycUCoX2Qgsh2rZtK8qWLSuSk5PV2qOjo4WFhYXav1HOzs5i7ty5onfv3sLS0lI4ODiIlStXqj0PQPXl7OwshBBi1qxZok6dOqrtBgwYILp06SLmz58vbG1thY2NjZg9e7aQy+Vi0qRJokSJEsLJyUkEBASoZZoyZYqoXLmyMDc3Fy4uLuLbb79V+55+/zzvioqKUssGQAwYMEAIIUSzZs3E2LFjxeTJk0WJEiWEnZ2dmDVrltr+AIS/v7/o3LmzsLCwEDNnzhRCCPHbb78JNzc3YWpqKlxcXFSv491M5cqVEyYmJsLe3l6MGTNG7f2aP3++GDhwoLCyshLlypUT69atUzvvtWvXRIsWLYSZmZkoWbKkGDp0qEhISMjyXmZKTEwU/fr1E5aWlsLe3l78+OOPolmzZmL8+PHZvi/asGbNGmFjYyNSU1NVbQsWLBCOjo5CqVTmuJ+zs7NYtmyZRudasWKFKFu2rOrxrl27hJGRkdrPxW+//SZkMpnqeyMvtdOyZctEsWLFRGJioqrt/Z/BFy9eCADi1KlTqm1q1qwp5s6dq3YsNzc38e2332Z7ng/93tCkXpPsArD09HSEh4erDakDQOvWrXH+/Pls9/njjz/QunVrtbY2bdogICAAcrkcxsbGWfZJS0tDWlqa6nHm8LxcLs+XvywzMjKgTE0EIDDAdxBWrlgGc3Nz/lWrRzL7in2mv9iHuieXyyGEgFKphFKp1PwAe/dC1rMnIATeXV1bPH0KdO8OsXMn0KqV6hza8OrVKxw9ehTfffcdTE1N1Y5ra2uLPn36IDg4GKtXr1at+b148WJMmzYNM2fOREhICCZOnIgqVarAy8sLFy9ehL29PQICAtC2bVsYGhpCqVSqPm7OPL4QAidOnICTkxPCwsJw7tw5DB06FOfPn8eXX36JP/74Azt37sSIESPQsmVLlCtXDsDbEd6NGzfC0dER169fx/Dhw2FlZYXJkyerjvvued7l5OSEXbt2oUePHrh16xasra1hbm6u2nbz5s2YOHEi/vjjD/zxxx8YNGgQPDw84OXlpTrGrFmzMH/+fCxZsgSGhoY4fPgw+vbti+XLl6Np06a4d+8eRowYASEEZs6cid27d2PZsmXYtm0batSogfv37+Off/5Ry7dkyRLMnTsXU6dOxZ49ezBy5Eg0adIE1apVQ3JyMtq2bYsGDRrg4sWLePHiBYYNG4bRo0dj06ZNqtf87vfEpEmTcPLkSezZswf29vaYMWMGwsPDUadOnRy/b86cOaP2cXp2pk2bhmnTpmX7XGa/GRsbq87h5eWFadOm4f79+3BxccnxuAsXLsS8efNQrlw5dO/eHZMmTYKJiUm22z579gx79+7Fl19+qTqPm5sbDA0NERAQAF9fXyQmJmLLli3w8vJSff/98ccf8PLyUnv9Xl5eCAgIQFpaWra1U0BAAHr16qX2PZL5/ZX5fsfFxQEAihcvrtqmcePG+O233+Dr6wtHR0eEhYXhzp07WLZsWbbvf+bPh1wuh6Ghodpzmvy+lqyYjY2NhUKhgJ2dnVq7nZ0dYmJist0nJiYm2+0zMjIQGxsLBweHLPssWLAAc+bMydIeEhKSLx/xpymAkp5DYF6xHtp3csPJkyd1fk7SjdDQUKkj0CdiH+qOkZER7O3tkZiYiPT0dM12VihgPX58lkIWAGRCQMhkkE2YAPz5JxISErQVGZGRkRBCwNnZOcs8RABwcXFBXFwc7t+/jzJlykCpVKJ+/foYOXIkAKB///4ICwvDjz/+iAYNGsDU1BQAYGpqqvr3JT4+HmlpaVAoFGqDKcWLF8e8efNgYGCA7t27Y9GiRUhISMDo0aMBAKNGjcLChQtx7NgxfP311wCAsWPHqrI1a9YMo0aNwo4dOzB8+HAAyHKe95mZmQEAzM3N1fJlZGSgRo0aqoukunbtilWrVuHw4cNo0KCBav+vv/4a3bt3Vz2eN28exo8fj6+++goAULp0aUydOhWzZ8/GhAkTcPfuXdja2qJ+/fowNjaGu7s73N3dVfmUSiVatWoFHx8fAMCIESOwbNkyHDlyBI6Ojti8eTOSk5OxatUqWFpaonz58vjhhx/Qu3dvzJgxA7a2tpDL5cjIyEB8fDwSExOxceNG+Pv7q3KvWrUKNWvWRHp6eo7vS5UqVXD69Olsn8tUokSJHPd/+vQpypcvr/Z85vt77969HKeYDBs2DHXq1IGNjQ2uXr2KuXPn4s6dO1i5cqXadoMHD8bhw4eRkpKCtm3bYsmSJapzlSxZEnv27MHAgQMxcuRIKBQKfPHFF9i1a5dqm2fPnqFZs2Zq+aysrJCRkYGoqCjY29urnS88PBw3btzA8uXLs33NCQkJEEJg/PjxaNiwodprz/yeKF++PIyMjGBgYIAVK1agdu3a2R4rPT0dKSkpOH36NDIyMtSeS05OzvZ9y47kS3O9f4crIcQH73qV3fbZtWeaNm0a/Pz8VI/j4+NRrlw5tG7dGtbW1nmNnWtCCHh6puHECQN0aNMqx7+4qOCSy+UIDQ2Fl5dXtn/BUsHHPtS91NRUPH78GFZWVqqiKdfCwmDw7FmOT8uEgOzpUxj98QfM27XT2p0RLS0tAbwt7rL79yCzOLW2toa1tTUMDAzQtGlTtW2//PJLrFixQq3t/eOZmprC0NBQ1WZsbIxatWqp3arcwcEBNWvWVNuvVKlSSExMVLXt3r0bK1euxD///IPExERkZGSosmV3nvdlFljFihVT28bIyAi1a9dWa3NycsKbN2/U2jw8PNQe//nnn4iIiFCbG6pQKJCamgojIyP07dsX69atg5ubG9q0aYPmzZujR48eqp9BAwMDuLu7qx3TwcEBCQkJsLa2xoMHD1C3bl21garMEcZnz56hUqVKMDY2hpGREaytrREVFYX09HR4enqqjmltbY2qVavCxMQkx/fF2to6y0CZJgwNDbMcP/OPLisrqxzP++4n040aNYKDgwN69uyJJUuWqBXAq1atwuvXr3H79m18++23mDNnDn766ScAbwf5Jk6ciAEDBsDb2xsJCQmYPXs2Bg8ejKNHj0Imk8HAwCDL92Tm98K73z+ZgoODUatWLbRo0UKtXQiBhIQEFCtWDGPHjsWtW7dw+vRptf2XLFmCq1evYv/+/XB2dsaZM2cwefJkVKxYEa1atcryHqSmpsLc3Bxffvlllt8bOf3xkB3JitnSpUvD0NAwyyjsixcvcvymsre3z3Z7IyOjHP/yMTU1Vf1CepexsXG+/aNmI5PB1BAwMTHhP6R6LD+/Z0g32Ie6o1AoVP9wGhhoeG3x8+e52kwWE6M6hzZUqVIFMpkMf//9d7bHvH37NkqUKAFbW1tVAf3++TOv+n637f33IHPfzDaZTAYTE5Ms22TXJoSAgYEBLly4gD59+mDOnDlo06YNbGxssGPHDixZskTtuO+e532Z7dn10fvnNjAwUJ07U7FixdQeK5VKzJkzR+1io0wWFhawsrLC7du3ERoaitDQUEyaNAlr1qzBqVOnVD+HH3rNmY/fzwW8LSANDAzU3v93X//7r+9D3zdnzpxBu3btsn0u0/Tp0zF9+vRsn3NwcMDz58/Vjh8bG6t6Lrffr40aNQIA1ScBmRwdHeHo6IgaNWqgTJkyaNq0KWbOnAkHBwf4+/vD2toaixcvVm3/yy+/oFy5crh8+TIaNmwIe3v7bPMZGRmhTJkyau3JyckIDg7G3Llzs+TOnCYwfvx4HDhwAKdPn0b58uVVz6ekpGDGjBnYt2+fatpG3bp18eeff2Lp0qVZpokCUPVbdr+bNfldLVkxa2JiAnd3d4SGhqo+ogDefgzYpUuXbPfx8PDAgQMH1NpCQkJQr149/gNFRKSvspkilh3x3sehn6pUqVLw8vLCmjVrMHHiRJibm6uei4mJQVBQEPr37682EnzhwgW1Y1y4cAHVqlVTPTY2NoZCByswnDt3Ds7OzpgxY4aq7eHDhxodI/OTQW3lc3Nzw+3bt1GpUqUctzE3N0fnzp3RsWNH9O/fH/Xr18f169fh5ub20ePXqFEDmzdvRlJSkmoU/dy5czAwMECVKlWybJ85UnvhwgVVkRUXF4c7d+6gWbNmOZ6nXr16iIyM/GCWkiVL5vich4cHpk+fjvT0dNV7HBISAkdHR1SoUOEjr/J/IiIiACDbKZOZMj+NzrwWKDk5Octc08zHmcWnJrXTzp07kZaWhr59+2Z77smTJ+PQoUMICwvLMhc481qk94vgzLm7uiTp0lx+fn7YsGEDNm7cqFrC4dGjR6plJ6ZNm4b+/furth8xYgQePnwIPz8/3Lp1Cxs3bkRAQAAmTZok1UsgIqJP1bQpULYskNP0AZkMolw5ZHh4aP3Uq1evRlpaGtq0aYPTp0/j8ePHOHLkCLy8vODk5IT58+erbX/u3DksWrQId+7cwU8//YRdu3Zh/PjxqucrVKiA48ePIyYmRnWBjDZUqlQJjx49wo4dO3Dv3j2sXLkS+/bt0+gYzs7OkMlkOHjwIP79918kJiZ+UqaZM2diy5YtmD17Nv766y/cunULwcHBquWlAgMDERAQgBs3buD+/fsIDg6Gubk5nJ2dc3V8Hx8fmJmZYcCAAbhx4wZOnjyJsWPHol+/ftl+gmtlZYXBgwdj8uTJOH78OG7cuAFfX9+Pjoyam5ujUqVKH/z6UDHbp08fmJqawtfXFzdu3MC+ffvw/fffw8/PT/WH0KVLl1CtWjU8ffoUwNsL2pctW4bIyEhERUVh586dGD58ODp37qwqxA8dOoRNmzbhxo0bePDgAQ4dOoSRI0eicePGqiK5Q4cOuHz5MubOnYu7d+/i6tWrGDhwIJydneHq6gpAs9opICAAXbt2zfbT7jFjxmDnzp345ZdfUKxYMcTExCAmJgYpKSkA3k5ZaNasGSZPnoywsDBERUUhMDAQW7ZsURu01ImPrnegYz/99JNwdnYWJiYmws3NTW2JhwEDBohmzZqpbR8WFiZcXV2FiYmJqFChgvD399fofPm9NJcQXBZI37H/9B/7UPc+eWmuPXuEkMnefr27NNf/tyl27dLJ0lxCCPHgwQPh6+sr7O3thbGxsShXrpwYO3asiI2NVdvO2dlZzJkzR/Ts2VNYWFgIOzs7sXz5crVtfvvtN1GpUiVhZGT00aW53pXd8lHvL900efJkUapUKWFlZSV69eolli1bJmxsbFTPf2hprkxz584V9vb2QiaTqS3N9f65u3TponpeiLdLc+3bty/L8Y4cOSIaNWokzM3NhbW1tahfv774+eefhRBC7Nu3TzRo0EBYW1sLS0tL8cUXX4iQkJAcX58QQtSpU0dtWTBNl+ZKSEgQffv2VfXPokWLdL40V2bOpk2bClNTU2Fvby9mz56ttizXyZMnBQARFRUlhBAiPDxcNGjQQNjY2AgzMzNRtWpVMWvWLJGUlKTa58SJE8LDw0O1TeXKlcU333wj4uLi1M69fft24erqKiwtLUWZMmVE586dxa1bt9S2yU3tdPv2bQFArY/ehfeWdsv82rRpk2qb6Oho4evrKxwdHVWva8mSJTkuUaatpblk/x+wyIiPj4eNjU2Wie26JJfLcejQIbRv357TIfQQ+0//sQ91LzU1FVFRUao7OubJ3r3A+PHAkyf/aytXDli+HMquXREfH6+6EEsKFSpUwIQJE3hr1DxQKpWS9x99Gl304Yd+b2hSr0m+mgEREREAoFs3oEsX4MwZIDr67Vzapk0BQ0NAx3PuiEh/sZglIqKCw9AQaN5c6hREpEdYzBIREeXCgwcPpI5ARNngxBUiIiIi0lssZomISGuK2DXFRPQJtPX7gsUsERF9ssyF2tPT0yVOQkT6IvP3xfs3ftAU58wSEdEnMzIygoWFBf79918YGxtrffklpVKJ9PR0pKamcmknPcT+03/a7kOlUol///0XFhYWMDL6tHKUxSwREX0ymUwGBwcHREVFaXyb1dwQQiAlJQXm5uZqt5cl/cD+03+66EMDAwOUL1/+k4/HYpaIiLTCxMQElStX1slUA7lcjtOnT+PLL7/kjS/0EPtP/+miD01MTLQyystiloiItMbAwCDvdwD7AENDQ2RkZMDMzIzFkB5i/+m/gtyHnLhCRERERHqLxSwRERER6S0Ws0RERESkt4rcnNnMBXrj4+Pz7ZxyuRzJycmIj48vcPNM6OPYf/qPfaj/2If6jf2n//K7DzPrtNzcWKHIFbMJCQkAgHLlykmchIiIiIg+JCEhATY2Nh/cRiaK2L0HlUolnj17hmLFiuXbWnfx8fEoV64cHj9+DGtr63w5J2kP+0//sQ/1H/tQv7H/9F9+96EQAgkJCXB0dPzo8l1FbmTWwMAAZcuWleTc1tbW/CHWY+w//cc+1H/sQ/3G/tN/+dmHHxuRzcQLwIiIiIhIb7GYJSIiIiK9xWI2H5iammLWrFkwNTWVOgrlAftP/7EP9R/7UL+x//RfQe7DIncBGBEREREVHhyZJSIiIiK9xWKWiIiIiPQWi1kiIiIi0lssZomIiIhIb7GY1YI1a9bAxcUFZmZmcHd3x5kzZz64/alTp+Du7g4zMzNUrFgRa9euzaeklBNN+nDv3r3w8vJCmTJlYG1tDQ8PDxw9ejQf01J2NP05zHTu3DkYGRmhbt26ug1IH6VpH6alpWHGjBlwdnaGqakpPvvsM2zcuDGf0tL7NO2/oKAg1KlTBxYWFnBwcMDAgQPx8uXLfEpL7zt9+jQ6deoER0dHyGQy7N+//6P7FJh6RtAn2bFjhzA2Nhbr168XN2/eFOPHjxeWlpbi4cOH2W5///59YWFhIcaPHy9u3rwp1q9fL4yNjcXu3bvzOTll0rQPx48fLxYuXCguXbok7ty5I6ZNmyaMjY3F1atX8zk5ZdK0DzO9fv1aVKxYUbRu3VrUqVMnf8JStvLSh507dxYNGjQQoaGhIioqSly8eFGcO3cuH1NTJk3778yZM8LAwECsWLFC3L9/X5w5c0bUrFlTdO3aNZ+TU6ZDhw6JGTNmiD179ggAYt++fR/cviDVMyxmP1H9+vXFiBEj1NqqVasmpk6dmu32U6ZMEdWqVVNrGz58uGjYsKHOMtKHadqH2alRo4aYM2eOtqNRLuW1D3v16iW+/fZbMWvWLBazEtO0Dw8fPixsbGzEy5cv8yMefYSm/bd48WJRsWJFtbaVK1eKsmXL6iwj5V5uitmCVM9wmsEnSE9PR3h4OFq3bq3W3rp1a5w/fz7bff74448s27dp0wZXrlyBXC7XWVbKXl768H1KpRIJCQkoWbKkLiLSR+S1Dzdt2oR79+5h1qxZuo5IH5GXPvztt99Qr149LFq0CE5OTqhSpQomTZqElJSU/IhM78hL/zVq1AhPnjzBoUOHIITA8+fPsXv3bnTo0CE/IpMWFKR6xihfz1bIxMbGQqFQwM7OTq3dzs4OMTEx2e4TExOT7fYZGRmIjY2Fg4ODzvJSVnnpw/ctWbIESUlJ6Nmzpy4i0kfkpQ/v3r2LqVOn4syZMzAy4q9BqeWlD+/fv4+zZ8/CzMwM+/btQ2xsLEaNGoVXr15x3mw+y0v/NWrUCEFBQejVqxdSU1ORkZGBzp07Y9WqVfkRmbSgINUzHJnVAplMpvZYCJGl7WPbZ9dO+UfTPsy0fft2zJ49G8HBwbC1tdVVPMqF3PahQqFAnz59MGfOHFSpUiW/4lEuaPJzqFQqIZPJEBQUhPr166N9+/ZYunQpAgMDOTorEU367+bNmxg3bhxmzpyJ8PBwHDlyBFFRURgxYkR+RCUtKSj1DIckPkHp0qVhaGiY5S/PFy9eZPlrJZO9vX222xsZGaFUqVI6y0rZy0sfZgoODsbgwYOxa9cutGrVSpcx6QM07cOEhARcuXIFERERGDNmDIC3hZEQAkZGRggJCYGnp2e+ZKe38vJz6ODgACcnJ9jY2KjaqlevDiEEnjx5gsqVK+s0M/1PXvpvwYIFaNy4MSZPngwAqF27NiwtLdG0aVN89913/JRSDxSkeoYjs5/AxMQE7u7uCA0NVWsPDQ1Fo0aNst3Hw8Mjy/YhISGoV68ejI2NdZaVspeXPgTejsj6+vpi27ZtnOMlMU370NraGtevX0dkZKTqa8SIEahatSoiIyPRoEGD/IpO/y8vP4eNGzfGs2fPkJiYqGq7c+cODAwMULZsWZ3mJXV56b/k5GQYGKiXIIaGhgD+N7pHBVuBqmfy/ZKzQiZzOZKAgABx8+ZNMWHCBGFpaSkePHgghBBi6tSpol+/fqrtM5eymDhxorh586YICAjg0lwS07QPt23bJoyMjMRPP/0koqOjVV+vX7+W6iUUeZr24fu4moH0NO3DhIQEUbZsWdG9e3fx119/iVOnTonKlSuLIUOGSPUSijRN+2/Tpk3CyMhIrFmzRty7d0+cPXtW1KtXT9SvX1+ql1DkJSQkiIiICBERESEAiKVLl4qIiAjV8moFuZ5hMasFP/30k3B2dhYmJibCzc1NnDp1SvXcgAEDRLNmzdS2DwsLE66ursLExERUqFBB+Pv753Niep8mfdisWTMBIMvXgAED8j84qWj6c/guFrMFg6Z9eOvWLdGqVSthbm4uypYtK/z8/ERycnI+p6ZMmvbfypUrRY0aNYS5ublwcHAQPj4+4smTJ/mcmjKdPHnyg/+2FeR6RiYEx/OJiIiISD9xziwRERER6S0Ws0RERESkt1jMEhEREZHeYjFLRERERHqLxSwRERER6S0Ws0RERESkt1jMEhEREZHeYjFLRERERHqLxSwREYDAwEAUL15c6hh5VqFCBSxfvvyD28yePRt169bNlzxERPmFxSwRFRq+vr6QyWRZvv755x+poyEwMFAtk4ODA3r27ImoqCitHP/y5csYNmyY6rFMJsP+/fvVtpk0aRKOHz+ulfPl5P3XaWdnh06dOuGvv/7S+Dj6/McFEeUfFrNEVKi0bdsW0dHRal8uLi5SxwIAWFtbIzo6Gs+ePcO2bdsQGRmJzp07Q6FQfPKxy5QpAwsLiw9uY2VlhVKlSn3yuT7m3df5+++/IykpCR06dEB6errOz01ERQ+LWSIqVExNTWFvb6/2ZWhoiKVLl+Lzzz+HpaUlypUrh1GjRiExMTHH4/z5559o0aIFihUrBmtra7i7u+PKlSuq58+fP48vv/wS5ubmKFeuHMaNG4ekpKQPZpPJZLC3t4eDgwNatGiBWbNm4caNG6qRY39/f3z22WcwMTFB1apVsXXrVrX9Z8+ejfLly8PU1BSOjo4YN26c6rl3pxlUqFABAPDVV19BJpOpHr87zeDo0aMwMzPD69ev1c4xbtw4NGvWTGuvs169epg4cSIePnyI27dvq7b5UH+EhYVh4MCBePPmjWqEd/bs2QCA9PR0TJkyBU5OTrC0tESDBg0QFhb2wTxEVLixmCWiIsHAwAArV67EjRs3sHnzZpw4cQJTpkzJcXsfHx+ULVsWly9fRnh4OKZOnQpjY2MAwPXr19GmTRt069YN165dQ3BwMM6ePYsxY8ZolMnc3BwAIJfLsW/fPowfPx7/+c9/cOPGDQwfPhwDBw7EyZMnAQC7d+/GsmXLsG7dOty9exf79+/H559/nu1xL1++DADYtGkToqOjVY/f1apVKxQvXhx79uxRtSkUCuzcuRM+Pj5ae52vX7/Gtm3bAED1/gEf7o9GjRph+fLlqhHe6OhoTJo0CQAwcOBAnDt3Djt27MC1a9fQo0cPtG3bFnfv3s11JiIqZAQRUSExYMAAYWhoKCwtLVVf3bt3z3bbnTt3ilKlSqkeb9q0SdjY2KgeFytWTAQGBma7b79+/cSwYcPU2s6cOSMMDAxESkpKtvu8f/zHjx+Lhg0birJly4q0tDTRqFEjMXToULV9evToIdq3by+EEGLJkiWiSpUqIj09PdvjOzs7i2XLlqkeAxD79u1T22bWrFmiTp06qsfjxo0Tnp6eqsdHjx4VJiYm4tWrV5/0OgEIS0tLYWFhIQAIAKJz587Zbp/pY/0hhBD//POPkMlk4unTp2rtLVu2FNOmTfvg8Ymo8DKStpQmItKuFi1awN/fX/XY0tISAHDy5El8//33uHnzJuLj45GRkYHU1FQkJSWptnmXn58fhgwZgq1bt6JVq1bo0aMHPvvsMwBAeHg4/vnnHwQFBam2F0JAqVQiKioK1atXzzbbmzdvYGVlBSEEkpOT4ebmhr1798LExAS3bt1Su4ALABo3bowVK1YAAHr06IHly5ejYsWKaNu2Ldq3b49OnTrByCjvv8Z9fHzg4eGBZ8+ewdHREUFBQWjfvj1KlCjxSa+zWLFiuHr1KjIyMnDq1CksXrwYa9euVdtG0/4AgKtXr0IIgSpVqqi1p6Wl5ctcYCIqmFjMElGhYmlpiUqVKqm1PXz4EO3bt8eIESMwb948lCxZEmfPnsXgwYMhl8uzPc7s2bPRp08f/P777zh8+DBmzZqFHTt24KuvvoJSqcTw4cPV5qxmKl++fI7ZMos8g/9r595BUv3DOIB/M5VEcWnoQqFkvNgSJHRZGqMhMISgi2BLQ6UNDtGWk0NELsGhJRIjqAaFhpY0KrpAVkSXIYTCTSJwiaIwnv/w58gxiy4nOCjfz6YPvj4Pv+WL7/uoUqGioiIvtJWUlOS8FpHse7W1tbi6usLGxgai0ShGR0cxPT2N7e3tnNv3X9HS0gKLxYLl5WWMjIwgEolgYWEhW//unCqVKnsGVqsVqVQKvb292NnZAfC98/jdT2lpKY6Pj1FaWppTMxgMX5qdiIoHwywRFb2joyNkMhnMzMxApfp/VWB1dfXDzymKAkVR4PV60d/fj4WFBTgcDthsNlxeXuaF5o/8GfJea2howO7uLlwuV/a9/f39nF8/dTod7HY77HY73G43rFYrzs/PYbPZ8q6n0Wg+9S8JAwMDWFpaQk1NDVQqFbq6urK17875mtfrRSAQQCQSgcPh+NR5aLXavP6bmprw8vKC29tbtLe3/1VPRFQ8uABGREXPYrEgk8lgdnYW19fXWFxczLvt/afHx0d4PB5sbW0hmUxib28P8Xg8GywnJiZwcHAAt9uN09NTJBIJrK2tYWxs7Ns9jo+PIxgMYm5uDolEAoFAAOFwOLv4FAwGMT8/j4uLi+wMOp0OJpPpzeuZzWbEYjGkUimk0+l3v9fpdOLk5AR+vx89PT0oKyvL1n5qTqPRiKGhIfh8PojIp87DbDbj/v4esVgMd3d3eHh4gKIocDqdcLlcCIfDuLm5QTwex9TUFNbX17/UExEVkX/5wC4R0U8aHByU7u7uN2uBQECqqqpEp9NJZ2enhEIhASDpdFpEcheOnp6epK+vT2pra0Wr1Up1dbV4PJ6cpafDw0Pp6OgQg8Eger1eGhsbxe/3v9vbWwtNr/369Uvq6upEo9GIoigSCoWytUgkIq2trWI0GkWv10tbW5tEo9Fs/fUC2NramtTX14tarRaTySQi+QtgvzU3NwsA2dzczKv91JzJZFLUarWsrKyIyMfnISIyPDws5eXlAkB8Pp+IiDw/P8vk5KSYzWbRaDRSWVkpDodDzs7O3u2JiIpbiYjIv43TRERERETfw8cMiIiIiKhgMcwSERERUcFimCUiIiKigsUwS0REREQFi2GWiIiIiAoWwywRERERFSyGWSIiIiIqWAyzRERERFSwGGaJiIiIqGAxzBIRERFRwWKYJSIiIqKC9R9f1VvyEd6Y2wAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:05<00:00,  9.37it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:02<00:00,  9.38it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:02<00:00,  9.40it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.37it/s]\n"
+      "       Current loss: 7.6677  :  30%|████████████████████████▎                                                        | 3001/10000 [06:59<58:52,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.8888888888888888\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:08<00:00,  9.36it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:02<00:00,  9.37it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:10<00:00,  9.38it/s]\n"
+      "       Current loss: 5.8616  :  31%|████████████████████████▍                                                      | 3101/10000 [07:13<1:00:03,  1.91it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.8888888888888888\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:12<00:00,  9.37it/s]\n"
+      "       Current loss: 7.1846  :  32%|█████████████████████████▉                                                       | 3201/10000 [07:27<57:01,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (77.06 s)\n",
-      "\n",
-      "Dataset               Accuracy\n",
-      "------------------------------\n",
-      "Anonaly lvl 1           100.00\n",
-      "Anonaly lvl 2           100.00\n",
-      "Anonaly lvl 3           100.00\n",
-      "\n",
-      "Anomaly all             100.00\n",
-      "\n",
-      "No Anomaly Train        100.00\n",
-      "No Anomaly Test         100.00\n",
-      "No Anomaly All          100.00\n",
-      "\n",
-      "All without train       100.00\n",
-      "All with train          100.00\n"
+      "F1 Validation 0.918918918918919\n"
      ]
-    }
-   ],
-   "source": [
-    "# STEPS = 10000, MODEL TYPE = MEDIUM, WEIGHT = on\n",
-    "model19 = EfficientAD({**config, \"train_steps\": 10000, \"model_type\": \"medium\", \"weight_path\":\"../weights/teacher_medium.pth\"})\n",
-    "model19.create_model()\n",
-    "model19.display_eval_result()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024/04/21 14:39:53 INFO mlflow.tracking.fluent: Experiment with name 'cookies_1' does not exist. Creating a new experiment.\n"
+      "       Current loss: 6.9880  :  33%|██████████████████████████▋                                                      | 3301/10000 [07:41<56:16,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (0.26 ms)\n",
-      "\n",
-      "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (3.15 ms)\n",
-      "\n",
-      "- Setting config\n",
-      "     Output folder path: ../output/cookies_1_steps_5000_small_weighted\n",
-      "- OK - Setting config (0.90 ms)\n",
-      "\n",
-      "- Prepare teacher, student & autoencoder\n",
-      "     Loading weight ../weights/teacher_small.pth\n",
-      "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (78.87 ms)\n",
-      "\n",
-      "- Normalizing teacher\n"
+      "F1 Validation 0.8888888888888888\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 52.39it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 53.84it/s]\n"
+      "       Current loss: 5.6562  :  34%|███████████████████████████▌                                                     | 3401/10000 [07:55<55:38,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (2.72 s)\n",
-      "\n",
-      "- Train\n"
+      "F1 Validation 0.918918918918919\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 5.3975  : 100%|███████████████████████████████████████████████████████| 5000/5000 [10:07<00:00,  8.23it/s]\n"
+      "       Current loss: 6.5604  :  35%|████████████████████████████▎                                                    | 3501/10000 [08:08<54:08,  2.00it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (607.47 s)\n",
-      "\n",
-      "- Saving models to ../output/cookies_1_steps_5000_small_weighted/all_models.pth\n",
-      "- OK - Saving models (80.25 ms)\n",
-      "\n",
-      "- Saving map normalization to ../output/cookies_1_steps_5000_small_weighted/map_normalization.pth\n"
+      "F1 Validation 0.918918918918919\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 29.89it/s]\n"
+      "       Current loss: 6.4236  :  36%|████████████████████████████▍                                                  | 3601/10000 [08:23<1:01:09,  1.74it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (300.51 ms)\n",
-      "\n",
-      "- Evaluating model\n"
+      "F1 Validation 0.95\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:08<00:00, 23.29it/s]\n"
+      "       Current loss: 5.4188  :  37%|█████████████████████████████▉                                                   | 3701/10000 [08:36<52:52,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "     - AUC: 97.82%\n",
-      "    - Optimal Threshold: 0.1295042\n",
-      "    - F1 Score: 0.92\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[88 12]\n",
-      " [ 5 95]] \n",
-      "\n"
+      "F1 Validation 0.918918918918919\n"
      ]
     },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 23.39it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 23.07it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 23.33it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 23.25it/s]\n"
+      "       Current loss: 5.6183  :  38%|██████████████████████████████▊                                                  | 3801/10000 [08:50<52:06,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.8888888888888888\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:03<00:00, 23.21it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 23.19it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 23.24it/s]\n"
+      "       Current loss: 5.4557  :  39%|███████████████████████████████▌                                                 | 3901/10000 [09:04<51:20,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.8888888888888888\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:05<00:00, 23.25it/s]\n"
+      "       Current loss: 5.8281  :  40%|████████████████████████████████▍                                                | 4001/10000 [09:18<50:06,  2.00it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (31.18 s)\n",
-      "\n",
-      "Dataset               Accuracy\n",
-      "------------------------------\n",
-      "Anonaly lvl 1           100.00\n",
-      "Anonaly lvl 2           100.00\n",
-      "Anonaly lvl 3            80.00\n",
-      "\n",
-      "Anomaly all              95.00\n",
-      "\n",
-      "No Anomaly Train         90.00\n",
-      "No Anomaly Test          80.00\n",
-      "No Anomaly All           88.00\n",
-      "\n",
-      "All without train        92.50\n",
-      "All with train           91.50\n"
+      "F1 Validation 0.918918918918919\n"
      ]
-    }
-   ],
-   "source": [
-    "# STEPS = 5000, MODEL TYPE = SMALL, WEIGHT = on Cookies 1\n",
-    "model20 = EfficientAD({**config, \"train_steps\": 5000, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\", \"subdataset\": \"cookies_1\"})\n",
-    "model20.create_model()\n",
-    "model20.display_eval_result()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create model for cookies 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
+    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "2024/04/21 14:50:36 INFO mlflow.tracking.fluent: Experiment with name 'cookies_2' does not exist. Creating a new experiment.\n"
+      "       Current loss: 6.3562  :  41%|█████████████████████████████████▏                                               | 4101/10000 [09:32<49:33,  1.98it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (0.56 ms)\n",
-      "\n",
-      "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (3.68 ms)\n",
-      "\n",
-      "- Setting config\n",
-      "     Output folder path: ../output/cookies_2_steps_5000_small_weighted\n",
-      "- OK - Setting config (0.22 ms)\n",
-      "\n",
-      "- Prepare teacher, student & autoencoder\n",
-      "     Loading weight ../weights/teacher_small.pth\n",
-      "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (73.29 ms)\n",
-      "\n",
-      "- Normalizing teacher\n"
+      "F1 Validation 0.8888888888888888\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 52.08it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 54.27it/s]\n"
+      "       Current loss: 4.9683  :  42%|██████████████████████████████████                                               | 4201/10000 [09:46<49:08,  1.97it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (2.71 s)\n",
-      "\n",
-      "- Train\n"
+      "F1 Validation 0.8888888888888888\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 5.6100  : 100%|███████████████████████████████████████████████████████| 5000/5000 [10:09<00:00,  8.20it/s]\n"
+      "       Current loss: 6.6889  :  43%|██████████████████████████████████▊                                              | 4301/10000 [10:00<47:50,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (609.97 s)\n",
-      "\n",
-      "- Saving models to ../output/cookies_2_steps_5000_small_weighted/all_models.pth\n",
-      "- OK - Saving models (81.75 ms)\n",
-      "\n",
-      "- Saving map normalization to ../output/cookies_2_steps_5000_small_weighted/map_normalization.pth\n"
+      "F1 Validation 0.918918918918919\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 29.77it/s]\n"
+      "       Current loss: 6.5786  :  44%|███████████████████████████████████▋                                             | 4401/10000 [10:14<47:31,  1.96it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (301.98 ms)\n",
-      "\n",
-      "- Evaluating model\n"
+      "F1 Validation 0.8888888888888888\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:08<00:00, 23.12it/s]\n"
+      "       Current loss: 6.2688  :  45%|████████████████████████████████████▍                                            | 4501/10000 [10:28<45:47,  2.00it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n",
-      "     - AUC: 99.98%\n",
-      "    - Optimal Threshold: 0.3812251\n",
-      "    - F1 Score: 1.00\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[ 99   1]\n",
-      " [  0 100]] \n",
-      "\n"
+      "F1 Validation 0.8888888888888888\n"
      ]
     },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 800x600 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 23.00it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 22.92it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 23.08it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 23.08it/s]\n"
+      "       Current loss: 5.7612  :  46%|█████████████████████████████████████▎                                           | 4601/10000 [10:41<45:10,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.8888888888888888\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:03<00:00, 23.06it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 23.04it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 23.05it/s]\n"
+      "       Current loss: 5.4185  :  47%|██████████████████████████████████████                                           | 4701/10000 [10:55<44:18,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "\n"
+      "F1 Validation 0.918918918918919\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:05<00:00, 23.08it/s]\n"
+      "       Current loss: 4.9782  :  48%|██████████████████████████████████████▉                                          | 4801/10000 [11:09<43:35,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (31.43 s)\n",
-      "\n",
-      "Dataset               Accuracy\n",
-      "------------------------------\n",
-      "Anonaly lvl 1           100.00\n",
-      "Anonaly lvl 2           100.00\n",
-      "Anonaly lvl 3           100.00\n",
-      "\n",
-      "Anomaly all             100.00\n",
-      "\n",
-      "No Anomaly Train        100.00\n",
-      "No Anomaly Test          95.00\n",
-      "No Anomaly All           99.00\n",
-      "\n",
-      "All without train        99.17\n",
-      "All with train           99.50\n"
+      "F1 Validation 0.8888888888888888\n"
      ]
-    }
-   ],
-   "source": [
-    "# STEPS = 5000, MODEL TYPE = SMALL, WEIGHT = on Cookies 2\n",
-    "model21 = EfficientAD({**config, \"train_steps\": 5000, \"model_type\": \"small\", \"weight_path\":\"../weights/teacher_small.pth\", \"subdataset\": \"cookies_2\"})\n",
-    "model21.create_model()\n",
-    "model21.display_eval_result()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Create model for cookies 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "       Current loss: 5.3264  :  49%|███████████████████████████████████████▋                                         | 4901/10000 [11:23<44:11,  1.92it/s]"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (0.64 ms)\n",
-      "\n",
-      "- Setting datasets path\n",
-      "     Dataset paths: dict_keys(['no_anomaly_train_paths', 'no_anomaly_test_paths', 'no_anomaly_val_paths', 'anomaly_lvl_1_paths', 'anomaly_lvl_2_paths', 'anomaly_lvl_3_paths', 'all_anomaly_paths', 'test_paths', 'all'])\n",
-      "- OK - Setting datasets path (2.61 ms)\n",
-      "\n",
-      "- Setting config\n",
-      "     Output folder path: ../output/cookies_1_steps_10000_small_weighted\n",
-      "- OK - Setting config (0.39 ms)\n",
-      "\n",
-      "- Prepare teacher, student & autoencoder\n",
-      "     Loading weight ../weights/teacher_small.pth\n",
-      "     Training\n",
-      "- OK - Prepare teacher, student & autoencoder (74.02 ms)\n",
-      "\n",
-      "- Normalizing teacher\n"
+      "F1 Validation 0.8888888888888888\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Computing mean of features: 100%|██████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 52.23it/s]\n",
-      "    Computing std of features: 100%|███████████████████████████████████████████████████████████| 72/72 [00:01<00:00, 54.76it/s]\n"
+      "       Current loss: 4.9857  :  50%|████████████████████████████████████████▌                                        | 5001/10000 [11:37<41:55,  1.99it/s]"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Normalizing teacher (2.70 s)\n",
-      "\n",
-      "- Train\n"
+      "F1 Validation 0.918918918918919\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "       Current loss: 3.2799  : 100%|█████████████████████████████████████████████████████| 10000/10000 [20:13<00:00,  8.24it/s]\n"
+      "       Current loss: 5.9820  :  51%|█████████████████████████████████████████▎                                       | 5100/10000 [11:51<11:23,  7.17it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Train (1213.83 s)\n",
+      "F1 Validation 0.8888888888888888\n",
+      "Early stopping at iteration 5101 because validation F1 did not improve.\n",
+      "- OK - Train (711.43 s)\n",
       "\n",
       "- Saving models to ../output/cookies_1_steps_10000_small_weighted/all_models.pth\n",
-      "- OK - Saving models (82.12 ms)\n",
+      "- OK - Saving models (65.02 ms)\n",
       "\n",
       "- Saving map normalization to ../output/cookies_1_steps_10000_small_weighted/map_normalization.pth\n"
      ]
@@ -3742,14 +5537,14 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     Map normalisation: 100%|████████████████████████████████████████████████████████████████████| 8/8 [00:00<00:00, 29.33it/s]\n"
+      "     Map normalisation: 100%|█████████████████████████████████████████████████████████████████████████████████████████████| 28/28 [00:00<00:00, 28.63it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Saving map normalization (306.06 ms)\n",
+      "- OK - Saving map normalization (1013.09 ms)\n",
       "\n",
       "- Evaluating model\n"
      ]
@@ -3758,26 +5553,12 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "     inference all: 100%|████████████████████████████████████████████████████████████████████| 200/200 [00:08<00:00, 22.98it/s]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "     - AUC: 98.22%\n",
-      "    - Optimal Threshold: 0.1656640\n",
-      "    - F1 Score: 0.94\n",
-      "    - CONFUSION MATRIX:\n",
-      " [[91  9]\n",
-      " [ 4 96]] \n",
-      "\n"
+      "     inference test: 100%|██████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.14it/s]\n"
      ]
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 800x600 with 1 Axes>"
       ]
@@ -3785,20 +5566,17 @@
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "    inference anomaly lvl 1: 100%|█████████████████████████████████████████████████████████████| 50/50 [00:02<00:00, 23.20it/s]\n",
-      "    inference anomaly lvl 2: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 23.15it/s]\n",
-      "    inference anomaly lvl 3: 100%|█████████████████████████████████████████████████████████████| 25/25 [00:01<00:00, 23.32it/s]\n",
-      "    inference all anomaly: 100%|█████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 23.20it/s]\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
+      "\n",
+      "     - AUC: 96.81%\n",
+      "    - Optimal Threshold: 0.0233271\n",
+      "    - F1 Score: 0.90\n",
+      "    - CONFUSION MATRIX:\n",
+      " [[20  0]\n",
+      " [14 66]] \n",
       "\n"
      ]
     },
@@ -3806,9 +5584,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    inference no anomaly train: 100%|██████████████████████████████████████████████████████████| 80/80 [00:03<00:00, 23.26it/s]\n",
-      "    inference no anomaly test: 100%|███████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 23.11it/s]\n",
-      "    inference all no anomaly: 100%|██████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 23.07it/s]\n"
+      "    inference anomaly lvl 1 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 40/40 [00:01<00:00, 22.32it/s]\n",
+      "    inference anomaly lvl 2 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.35it/s]\n",
+      "    inference anomaly lvl 3 test: 100%|███████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.50it/s]\n",
+      "    inference all anomaly test: 100%|███████████████████████████████████████████████████████████████████████████████████| 100/100 [00:04<00:00, 22.39it/s]\n"
      ]
     },
     {
@@ -3822,29 +5601,27 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "    Test dataset: 100%|██████████████████████████████████████████████████████████████████████| 120/120 [00:05<00:00, 23.00it/s]\n"
+      "    inference no anomaly test: 100%|██████████████████████████████████████████████████████████████████████████████████████| 20/20 [00:00<00:00, 22.53it/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "- OK - Evaluating model (31.40 s)\n",
       "\n",
-      "Dataset               Accuracy\n",
+      "- OK - Evaluating model (13.67 s)\n",
+      "\n",
+      "Dataset               F1 Score\n",
       "------------------------------\n",
-      "Anonaly lvl 1           100.00\n",
-      "Anonaly lvl 2           100.00\n",
-      "Anonaly lvl 3            84.00\n",
+      "Anonaly lvl 1 test        1.00\n",
+      "Anonaly lvl 2 test        1.00\n",
+      "Anonaly lvl 3 test        0.46\n",
       "\n",
-      "Anomaly all              96.00\n",
+      "Anomaly all test          0.90\n",
       "\n",
-      "No Anomaly Train         93.75\n",
-      "No Anomaly Test          80.00\n",
-      "No Anomaly All           91.00\n",
+      "No Anomaly Test           1.00\n",
       "\n",
-      "All without train        93.33\n",
-      "All with train           93.50\n"
+      "All test                  0.90\n"
      ]
     }
    ],
@@ -3854,6 +5631,13 @@
     "model22.create_model()\n",
     "model22.display_eval_result()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -3872,7 +5656,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.10.14"
   }
  },
  "nbformat": 4,
diff --git a/ai/notebooks/model_comparaison.ipynb b/ai/notebooks/model_comparaison.ipynb
index 444f296..a5c8d28 100644
--- a/ai/notebooks/model_comparaison.ipynb
+++ b/ai/notebooks/model_comparaison.ipynb
@@ -80,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,6 +98,7 @@
    "source": [
     "\n",
     "config = {\n",
+    "    \"seed\": 42,\n",
     "    \"dataset_path\": \"datasets\",\n",
     "    \"baseline\" : {\n",
     "        \"cookies_1\": \"../output/baseline/cookies_1\",\n",
@@ -134,8631 +135,244 @@
    "outputs": [],
    "source": [
     "def load_dataset(subdataset):\n",
-    "    no_anomaly_paths = glob.glob(f\"{config['dataset_path']}/{subdataset}/no_anomaly/*.jpg\")  # adjust the pattern if needed\n",
+    "     torch.manual_seed(config[\"seed\"])\n",
+    "     np.random.seed(config[\"seed\"])\n",
+    "     random.seed(config[\"seed\"])\n",
     "\n",
-    "    anomaly_lvl_1_paths = glob.glob(f\"{config['dataset_path']}/{subdataset}/anomaly_lvl_1/*.jpg\")\n",
-    "    anomaly_lvl_2_paths = glob.glob(f\"{config['dataset_path']}/{subdataset}/anomaly_lvl_2/*.jpg\")\n",
-    "    anomaly_lvl_3_paths = glob.glob(f\"{config['dataset_path']}/{subdataset}/anomaly_lvl_3/*.jpg\")\n",
+    "     no_anomaly_paths = glob.glob(f\"{config['dataset_path']}/{subdataset}/no_anomaly/*.jpg\")  # adjust the pattern if needed\n",
     "\n",
-    "    no_anomaly_labels = [\"no_anomaly\"] * len(no_anomaly_paths)\n",
-    "    no_anomaly_difficulty = [\"na\"] * len(no_anomaly_paths)\n",
+    "     no_anomaly_train_paths, no_anomaly_test_paths = train_test_split(no_anomaly_paths, test_size=0.2, random_state=config[\"seed\"])\n",
+    "     no_anomaly_train_paths,  no_anomaly_val_paths = train_test_split(no_anomaly_train_paths, test_size=0.1, random_state=config[\"seed\"])\n",
     "\n",
-    "    anomaly_1_labels = [\"anomaly\"] * len(anomaly_lvl_1_paths)\n",
-    "    anomaly_1_difficulty = [\"easy\"] * len(anomaly_lvl_1_paths)\n",
-    "    \n",
-    "    anomaly_2_labels = [\"anomaly\"] * len(anomaly_lvl_2_paths)\n",
-    "    anomaly_2_difficulty = [\"medium\"] * len(anomaly_lvl_2_paths)\n",
-    "    \n",
-    "    anomaly_3_labels = [\"anomaly\"] * len(anomaly_lvl_3_paths)\n",
-    "    anomaly_3_difficulty = [\"hard\"] * len(anomaly_lvl_3_paths)\n",
-    "\n",
-    "    no_anomaly_df = pd.DataFrame({'filename': no_anomaly_paths, 'class': no_anomaly_labels, 'difficulty': no_anomaly_difficulty})  \n",
-    "\n",
-    "    anomaly_1_df = pd.DataFrame({'filename': anomaly_lvl_1_paths, 'class': anomaly_1_labels, 'difficulty': anomaly_1_difficulty})  \n",
-    "    anomaly_2_df = pd.DataFrame({'filename': anomaly_lvl_2_paths, 'class': anomaly_2_labels, 'difficulty': anomaly_2_difficulty})  \n",
-    "    anomaly_3_df = pd.DataFrame({'filename': anomaly_lvl_3_paths, 'class': anomaly_3_labels, 'difficulty': anomaly_3_difficulty})\n",
-    "    \n",
-    "    combined_df = pd.concat([no_anomaly_df, anomaly_1_df, anomaly_2_df, anomaly_3_df])\n",
-    "    return combined_df.reset_index(drop=True)  "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def load_baseline(dataset_name):\n",
-    "    baseline_model = tf.keras.models.load_model(f'{config[\"baseline\"][dataset_name]}/local.keras')\n",
-    "    edge_impulse_model_converted = ImageImpulseRunner(f'{config[\"baseline\"][dataset_name]}/ei.eim')\n",
-    "    edge_impulse_model_converted.init()\n",
-    "        \n",
-    "    return baseline_model, edge_impulse_model_converted\n",
+    "     anomaly_lvl_1_paths = glob.glob(f\"{config['dataset_path']}/{subdataset}/anomaly_lvl_1/*.jpg\")\n",
+    "     anomaly_lvl_2_paths = glob.glob(f\"{config['dataset_path']}/{subdataset}/anomaly_lvl_2/*.jpg\")\n",
+    "     anomaly_lvl_3_paths = glob.glob(f\"{config['dataset_path']}/{subdataset}/anomaly_lvl_3/*.jpg\")\n",
     "\n",
-    "def evaluate_baseline(baseline_model, edge_impulse_model_converted, item):\n",
-    "    cl, time, memory, score = inference_baseline(baseline_model, item[\"filename\"])\n",
-    "    cl_ei, time_ei, memory_ei, score_ei = inference_baseline_ei(edge_impulse_model_converted, item[\"filename\"])\n",
-    "    return cl, time, memory, score, cl_ei, time_ei, memory_ei, score_ei"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def load_efficientad(dataset_name):\n",
-    "    config_efficient_ad = {\n",
-    "        \"seed\": 42,\n",
-    "        \"on_gpu\": torch.cuda.is_available(),\n",
-    "        \n",
-    "        \"out_channels\": 384,\n",
-    "        \"image_size\": 256,\n",
-    "    }\n",
-    "    \n",
-    "    efficientad_models = torch.load(f'{config[\"efficientad\"][dataset_name]}/all_models.pth', map_location=torch.device('cpu'))\n",
-    "    map_normalization = torch.load(f'{config[\"efficientad\"][dataset_name]}/map_normalization.pth', map_location=torch.device('cpu')) \n",
-    "    \n",
-    "    with open(f'{config[\"efficientad\"][dataset_name]}/best_threshold.pkl', 'rb') as file:  \n",
-    "        best_threshold = pickle.load(file) \n",
+    "     anomaly_lvl_1_test_paths, anomaly_lvl_1_val_paths = train_test_split(anomaly_lvl_1_paths, test_size=0.2, random_state=config[\"seed\"])\n",
+    "     anomaly_lvl_2_test_paths, anomaly_lvl_2_val_paths = train_test_split(anomaly_lvl_2_paths, test_size=0.2, random_state=config[\"seed\"])\n",
+    "     anomaly_lvl_3_test_paths, anomaly_lvl_3_val_paths = train_test_split(anomaly_lvl_3_paths, test_size=0.2, random_state=config[\"seed\"])\n",
     "     \n",
-    "    efficientad = EfficientADInference(\n",
-    "        config=config_efficient_ad,\n",
-    "        models=efficientad_models,\n",
-    "        map_normalization=map_normalization,\n",
-    "        threshold=best_threshold,\n",
-    "    )\n",
-    "    \n",
-    "    return efficientad\n",
-    "\n",
-    "def evaluate_efficientad(efficientad, item):\n",
-    "    cl, time, memory, score = inference_efficientad(efficientad, item[\"filename\"])\n",
-    "    return cl, time, memory, score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def load_fomoad(dataset_name):\n",
-    "    edge_impulse_model_fomoad = ImageImpulseRunner(f'{config[\"fomoad\"][dataset_name]}/fomoad_runner-mac-x86_64.eim')\n",
-    "    edge_impulse_model_fomoad.init()\n",
     "     \n",
-    "    return edge_impulse_model_fomoad\n",
+    "     no_anomaly_labels = [\"no_anomaly\"] * len(no_anomaly_test_paths)\n",
+    "     no_anomaly_difficulty = [\"na\"] * len(no_anomaly_test_paths)\n",
     "\n",
-    "def evaluate_fomoad(edge_impulse_model_fomoad, item, threshold):\n",
-    "    cl, time, memory, score = inference_fomoad_ei(edge_impulse_model_fomoad, item[\"filename\"], threshold)\n",
-    "    return cl, time, memory, score\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def evaluate_one_dataset(dataset_name):\n",
-    "    print(f\"Evaluating {dataset_name}\")\n",
-    "    \n",
-    "    # Loading all model\n",
-    "    baseline_model, ei_model_converted = load_baseline(dataset_name)\n",
-    "    efficientad = load_efficientad(dataset_name)\n",
-    "    ei_model_fomoad = load_fomoad(dataset_name)\n",
-    "    \n",
-    "    # Loading dataset\n",
-    "    dataset = load_dataset(dataset_name)\n",
-    "    \n",
-    "    # Looping through the full dataset\n",
-    "    for index, row in tqdm(dataset.iterrows(), total=len(dataset)):\n",
-    "        # Evaluate each model\n",
-    "        cl_baseline, time_baseline, memory_baseline, score_baseline, cl_ei_baseline, time_ei_baseline, memory_ei_baseline, score_ei_baseline = evaluate_baseline(baseline_model, ei_model_converted, row)\n",
-    "        \n",
-    "        cl_efficientad, time_efficientad, memory_efficientad, score_efficientad = evaluate_efficientad(efficientad, row)\n",
-    "        \n",
-    "        cl_fomodad, time_fomoad, memory_fomoad, score_fomoad = evaluate_fomoad(ei_model_fomoad, row, config[\"fomoad\"][f\"{dataset_name}_threshold\"])\n",
-    "        \n",
-    "        # Update the dataframe to add the different scroes\n",
-    "        dataset.at[index, 'cl_baseline'] = cl_baseline\n",
-    "        dataset.at[index, 'time_baseline'] = time_baseline\n",
-    "        dataset.at[index, 'memory_baseline'] = memory_baseline\n",
-    "        dataset.at[index, 'score_baseline'] = score_baseline\n",
-    "        \n",
-    "        dataset.at[index, 'cl_ei_baseline'] = cl_ei_baseline\n",
-    "        dataset.at[index, 'time_ei_baseline'] = time_ei_baseline\n",
-    "        dataset.at[index, 'memory_ei_baseline'] = memory_ei_baseline\n",
-    "        dataset.at[index, 'score_ei_baseline'] = score_ei_baseline\n",
-    "        \n",
-    "        dataset.at[index, 'cl_efficientad'] = cl_efficientad\n",
-    "        dataset.at[index, 'time_efficientad'] = time_efficientad\n",
-    "        dataset.at[index, 'memory_efficientad'] = memory_efficientad\n",
-    "        dataset.at[index, 'score_efficientad'] = score_efficientad\n",
-    "        \n",
-    "        dataset.at[index, 'cl_fomodad'] = cl_fomodad\n",
-    "        dataset.at[index, 'time_fomoad'] = time_fomoad\n",
-    "        dataset.at[index, 'memory_fomoad'] = memory_fomoad\n",
-    "        dataset.at[index, 'score_fomoad'] = score_fomoad\n",
-    "        \n",
-    "        save_path = row[\"filename\"].replace('.jpg', '_result.jpg')  \n",
-    "        dataset.at[index, 'result_img_path'] = save_path\n",
-    "    return dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Evaluating cookies_1\n",
-      "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (3.41 ms)\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 0/200 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 213ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 1/200 [00:00<02:42,  1.23it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 30ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  1%|          | 2/200 [00:01<01:55,  1.72it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  2%|▏         | 3/200 [00:01<01:41,  1.94it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  2%|▏         | 4/200 [00:02<01:32,  2.11it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  2%|▎         | 5/200 [00:02<01:26,  2.25it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  3%|▎         | 6/200 [00:02<01:24,  2.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  4%|▎         | 7/200 [00:03<01:24,  2.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  4%|▍         | 8/200 [00:03<01:21,  2.35it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  4%|▍         | 9/200 [00:04<01:19,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  5%|▌         | 10/200 [00:04<01:19,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  6%|▌         | 11/200 [00:04<01:17,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 65ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  6%|▌         | 12/200 [00:05<01:19,  2.35it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  6%|▋         | 13/200 [00:05<01:18,  2.39it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 25ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  7%|▋         | 14/200 [00:06<01:17,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  8%|▊         | 15/200 [00:06<01:15,  2.45it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  8%|▊         | 16/200 [00:07<01:16,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  8%|▊         | 17/200 [00:07<01:15,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  9%|▉         | 18/200 [00:07<01:16,  2.38it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 32ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 10%|▉         | 19/200 [00:08<01:15,  2.39it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 61ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 10%|█         | 20/200 [00:08<01:24,  2.12it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 17ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 10%|█         | 21/200 [00:09<01:19,  2.25it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 11%|█         | 22/200 [00:09<01:17,  2.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 56ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 12%|█▏        | 23/200 [00:10<01:16,  2.31it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 12%|█▏        | 24/200 [00:10<01:15,  2.33it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 12%|█▎        | 25/200 [00:10<01:12,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 13%|█▎        | 26/200 [00:11<01:14,  2.34it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 14%|█▎        | 27/200 [00:11<01:12,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 14%|█▍        | 28/200 [00:12<01:10,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 14%|█▍        | 29/200 [00:12<01:09,  2.47it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 15%|█▌        | 30/200 [00:12<01:10,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 16%|█▌        | 31/200 [00:13<01:08,  2.46it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 16%|█▌        | 32/200 [00:13<01:07,  2.49it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 16%|█▋        | 33/200 [00:14<01:06,  2.52it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 17%|█▋        | 34/200 [00:14<01:09,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 18%|█▊        | 35/200 [00:14<01:06,  2.47it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 18%|█▊        | 36/200 [00:15<01:05,  2.50it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 18%|█▊        | 37/200 [00:15<01:05,  2.49it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 19%|█▉        | 38/200 [00:16<01:07,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 20%|█▉        | 39/200 [00:16<01:05,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 17ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 20%|██        | 40/200 [00:17<01:05,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 20%|██        | 41/200 [00:17<01:04,  2.47it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 21%|██        | 42/200 [00:17<01:05,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 22%|██▏       | 43/200 [00:18<01:04,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 22%|██▏       | 44/200 [00:18<01:04,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 22%|██▎       | 45/200 [00:19<01:03,  2.45it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 23%|██▎       | 46/200 [00:19<01:03,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 24%|██▎       | 47/200 [00:19<01:01,  2.50it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 24%|██▍       | 48/200 [00:20<01:00,  2.52it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 24%|██▍       | 49/200 [00:20<00:59,  2.54it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 25%|██▌       | 50/200 [00:21<01:00,  2.49it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 26%|██▌       | 51/200 [00:21<00:59,  2.51it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 26%|██▌       | 52/200 [00:21<00:59,  2.51it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 26%|██▋       | 53/200 [00:22<00:57,  2.54it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 27%|██▋       | 54/200 [00:22<00:57,  2.54it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 44ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 28%|██▊       | 55/200 [00:23<00:58,  2.49it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 28%|██▊       | 56/200 [00:23<00:59,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 28%|██▊       | 57/200 [00:23<00:57,  2.51it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 29%|██▉       | 58/200 [00:24<00:56,  2.53it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 30%|██▉       | 59/200 [00:24<00:56,  2.49it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 29ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 30%|███       | 60/200 [00:25<00:56,  2.49it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 30%|███       | 61/200 [00:25<00:55,  2.51it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 31%|███       | 62/200 [00:25<00:55,  2.47it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 32%|███▏      | 63/200 [00:26<00:57,  2.38it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 24ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 32%|███▏      | 64/200 [00:26<00:57,  2.38it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 32%|███▎      | 65/200 [00:27<00:56,  2.39it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 33%|███▎      | 66/200 [00:27<00:55,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 34%|███▎      | 67/200 [00:28<00:55,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 34%|███▍      | 68/200 [00:28<00:53,  2.45it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 31ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 34%|███▍      | 69/200 [00:28<00:53,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 35%|███▌      | 70/200 [00:29<00:52,  2.47it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 36%|███▌      | 71/200 [00:29<00:51,  2.49it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 72ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 36%|███▌      | 72/200 [00:30<00:52,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 36%|███▋      | 73/200 [00:30<00:51,  2.46it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 37%|███▋      | 74/200 [00:30<00:50,  2.49it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 38%|███▊      | 75/200 [00:31<00:49,  2.53it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 38%|███▊      | 76/200 [00:31<00:50,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 38%|███▊      | 77/200 [00:32<00:49,  2.48it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 39%|███▉      | 78/200 [00:32<00:49,  2.47it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 40%|███▉      | 79/200 [00:32<00:48,  2.49it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 40%|████      | 80/200 [00:33<00:49,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 40%|████      | 81/200 [00:33<00:47,  2.50it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 41%|████      | 82/200 [00:34<00:47,  2.50it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 42%|████▏     | 83/200 [00:34<00:46,  2.53it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 42%|████▏     | 84/200 [00:34<00:46,  2.48it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 25ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 42%|████▎     | 85/200 [00:35<00:46,  2.49it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 43%|████▎     | 86/200 [00:35<00:45,  2.48it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 44%|████▎     | 87/200 [00:36<00:45,  2.50it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 28ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 44%|████▍     | 88/200 [00:36<00:44,  2.49it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 44%|████▍     | 89/200 [00:36<00:46,  2.39it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 45%|████▌     | 90/200 [00:37<00:45,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 46%|████▌     | 91/200 [00:37<00:44,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 46%|████▌     | 92/200 [00:38<00:44,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 63ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 46%|████▋     | 93/200 [00:38<00:45,  2.37it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 47%|████▋     | 94/200 [00:38<00:43,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 48%|████▊     | 95/200 [00:39<00:42,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 48%|████▊     | 96/200 [00:39<00:42,  2.45it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 48%|████▊     | 97/200 [00:40<00:42,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 49%|████▉     | 98/200 [00:40<00:41,  2.45it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|████▉     | 99/200 [00:40<00:40,  2.47it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|█████     | 100/200 [00:41<00:41,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|█████     | 101/200 [00:41<00:40,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 51%|█████     | 102/200 [00:42<00:39,  2.50it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 52%|█████▏    | 103/200 [00:42<00:38,  2.54it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 52%|█████▏    | 104/200 [00:43<00:43,  2.22it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 52%|█████▎    | 105/200 [00:43<00:41,  2.31it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 53%|█████▎    | 106/200 [00:43<00:39,  2.36it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 54%|█████▎    | 107/200 [00:44<00:38,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 64ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 54%|█████▍    | 108/200 [00:44<00:38,  2.38it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 55%|█████▍    | 109/200 [00:45<00:37,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 55%|█████▌    | 110/200 [00:45<00:37,  2.39it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 56%|█████▌    | 111/200 [00:46<00:36,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 72ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 56%|█████▌    | 112/200 [00:46<00:37,  2.37it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 56%|█████▋    | 113/200 [00:46<00:35,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 24ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 57%|█████▋    | 114/200 [00:47<00:34,  2.46it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 57%|█████▊    | 115/200 [00:47<00:35,  2.39it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 58%|█████▊    | 116/200 [00:48<00:35,  2.38it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 58%|█████▊    | 117/200 [00:48<00:34,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 29ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 59%|█████▉    | 118/200 [00:49<00:35,  2.28it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 60%|█████▉    | 119/200 [00:49<00:34,  2.35it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 60%|██████    | 120/200 [00:49<00:33,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 60%|██████    | 121/200 [00:50<00:33,  2.37it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 61%|██████    | 122/200 [00:50<00:32,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 62%|██████▏   | 123/200 [00:51<00:31,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 62%|██████▏   | 124/200 [00:51<00:30,  2.48it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 62%|██████▎   | 125/200 [00:51<00:31,  2.38it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 17ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 63%|██████▎   | 126/200 [00:52<00:30,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 64%|██████▎   | 127/200 [00:52<00:29,  2.45it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 64%|██████▍   | 128/200 [00:53<00:29,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 64%|██████▍   | 129/200 [00:53<00:28,  2.45it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 28ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 65%|██████▌   | 130/200 [00:53<00:28,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 66%|██████▌   | 131/200 [00:54<00:28,  2.45it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 66%|██████▌   | 132/200 [00:54<00:27,  2.48it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 66%|██████▋   | 133/200 [00:55<00:26,  2.51it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 67%|██████▋   | 134/200 [00:55<00:26,  2.53it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 68%|██████▊   | 135/200 [00:55<00:26,  2.48it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 68%|██████▊   | 136/200 [00:56<00:25,  2.50it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 68%|██████▊   | 137/200 [00:56<00:25,  2.51it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 69%|██████▉   | 138/200 [00:57<00:25,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 70%|██████▉   | 139/200 [00:57<00:24,  2.48it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 70%|███████   | 140/200 [00:57<00:24,  2.50it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 70%|███████   | 141/200 [00:58<00:23,  2.53it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 71%|███████   | 142/200 [00:58<00:24,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 72%|███████▏  | 143/200 [00:59<00:23,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 24ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 72%|███████▏  | 144/200 [00:59<00:23,  2.39it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 72%|███████▎  | 145/200 [01:00<00:23,  2.32it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 73%|███████▎  | 146/200 [01:00<00:22,  2.39it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 74%|███████▎  | 147/200 [01:00<00:21,  2.45it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 35ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 74%|███████▍  | 148/200 [01:01<00:22,  2.36it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 74%|███████▍  | 149/200 [01:01<00:21,  2.36it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 75%|███████▌  | 150/200 [01:02<00:20,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 76%|███████▌  | 151/200 [01:02<00:20,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 76%|███████▌  | 152/200 [01:02<00:19,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 76%|███████▋  | 153/200 [01:03<00:18,  2.49it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 77%|███████▋  | 154/200 [01:03<00:19,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 78%|███████▊  | 155/200 [01:04<00:18,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 78%|███████▊  | 156/200 [01:04<00:17,  2.46it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 78%|███████▊  | 157/200 [01:04<00:17,  2.49it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 79%|███████▉  | 158/200 [01:05<00:17,  2.45it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 80%|███████▉  | 159/200 [01:05<00:16,  2.47it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 80%|████████  | 160/200 [01:06<00:16,  2.39it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 80%|████████  | 161/200 [01:06<00:16,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 81%|████████  | 162/200 [01:07<00:15,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 82%|████████▏ | 163/200 [01:07<00:14,  2.48it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 67ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 82%|████████▏ | 164/200 [01:07<00:14,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 82%|████████▎ | 165/200 [01:08<00:14,  2.45it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 83%|████████▎ | 166/200 [01:08<00:14,  2.39it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 84%|████████▎ | 167/200 [01:09<00:13,  2.36it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 84%|████████▍ | 168/200 [01:09<00:13,  2.36it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 84%|████████▍ | 169/200 [01:09<00:13,  2.32it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 85%|████████▌ | 170/200 [01:10<00:12,  2.33it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 71ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 86%|████████▌ | 171/200 [01:10<00:12,  2.32it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 86%|████████▌ | 172/200 [01:11<00:11,  2.36it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 86%|████████▋ | 173/200 [01:11<00:11,  2.37it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 87%|████████▋ | 174/200 [01:12<00:11,  2.33it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 88%|████████▊ | 175/200 [01:12<00:10,  2.36it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 88%|████████▊ | 176/200 [01:12<00:10,  2.38it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 88%|████████▊ | 177/200 [01:13<00:09,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 89%|████████▉ | 178/200 [01:13<00:09,  2.35it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 90%|████████▉ | 179/200 [01:14<00:08,  2.38it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 90%|█████████ | 180/200 [01:14<00:08,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 90%|█████████ | 181/200 [01:15<00:08,  2.37it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 91%|█████████ | 182/200 [01:15<00:07,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 92%|█████████▏| 183/200 [01:15<00:06,  2.46it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 27ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 92%|█████████▏| 184/200 [01:16<00:06,  2.37it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 92%|█████████▎| 185/200 [01:16<00:06,  2.38it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 93%|█████████▎| 186/200 [01:17<00:05,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 94%|█████████▎| 187/200 [01:17<00:05,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 48ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 94%|█████████▍| 188/200 [01:17<00:05,  2.37it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 94%|█████████▍| 189/200 [01:18<00:04,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 95%|█████████▌| 190/200 [01:18<00:04,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 96%|█████████▌| 191/200 [01:19<00:03,  2.46it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 61ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 96%|█████████▌| 192/200 [01:19<00:03,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 96%|█████████▋| 193/200 [01:19<00:02,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 97%|█████████▋| 194/200 [01:20<00:02,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 98%|█████████▊| 195/200 [01:20<00:02,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 76ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 98%|█████████▊| 196/200 [01:21<00:01,  2.27it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 98%|█████████▊| 197/200 [01:21<00:01,  2.33it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 99%|█████████▉| 198/200 [01:22<00:00,  2.38it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████▉| 199/200 [01:22<00:00,  2.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 200/200 [01:22<00:00,  2.41it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "dataset_1 = evaluate_one_dataset(\"cookies_1\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Evaluating cookies_2\n",
-      "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (1.05 ms)\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 0/200 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 163ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 1/200 [00:00<02:07,  1.57it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 32ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  1%|          | 2/200 [00:01<01:43,  1.92it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 49ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  2%|▏         | 3/200 [00:01<01:35,  2.05it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  2%|▏         | 4/200 [00:01<01:29,  2.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  2%|▎         | 5/200 [00:02<01:25,  2.28it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  3%|▎         | 6/200 [00:02<01:24,  2.29it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  4%|▎         | 7/200 [00:03<01:22,  2.34it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 40ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  4%|▍         | 8/200 [00:03<01:23,  2.31it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 28ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  4%|▍         | 9/200 [00:04<01:27,  2.17it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 17ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  5%|▌         | 10/200 [00:04<01:24,  2.24it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  6%|▌         | 11/200 [00:04<01:22,  2.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  6%|▌         | 12/200 [00:05<01:19,  2.35it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  6%|▋         | 13/200 [00:05<01:21,  2.29it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  7%|▋         | 14/200 [00:06<01:19,  2.34it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  8%|▊         | 15/200 [00:06<01:18,  2.35it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  8%|▊         | 16/200 [00:07<01:18,  2.35it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  8%|▊         | 17/200 [00:07<01:20,  2.27it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  9%|▉         | 18/200 [00:07<01:19,  2.29it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 10%|▉         | 19/200 [00:08<01:17,  2.34it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 10%|█         | 20/200 [00:08<01:15,  2.37it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 10%|█         | 21/200 [00:09<01:14,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 73ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 11%|█         | 22/200 [00:09<01:15,  2.35it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 12%|█▏        | 23/200 [00:10<01:13,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 12%|█▏        | 24/200 [00:10<01:12,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 12%|█▎        | 25/200 [00:10<01:11,  2.46it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 13%|█▎        | 26/200 [00:11<01:10,  2.47it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 14%|█▎        | 27/200 [00:11<01:12,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 14%|█▍        | 28/200 [00:12<01:11,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 14%|█▍        | 29/200 [00:12<01:10,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 15%|█▌        | 30/200 [00:12<01:09,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 16%|█▌        | 31/200 [00:13<01:08,  2.45it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 16%|█▌        | 32/200 [00:13<01:10,  2.39it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 16%|█▋        | 33/200 [00:14<01:09,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 17%|█▋        | 34/200 [00:14<01:08,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 18%|█▊        | 35/200 [00:14<01:07,  2.45it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 18%|█▊        | 36/200 [00:15<01:06,  2.46it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 18%|█▊        | 37/200 [00:15<01:07,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 19%|█▉        | 38/200 [00:16<01:06,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 20%|█▉        | 39/200 [00:16<01:06,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 20%|██        | 40/200 [00:17<01:05,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 20%|██        | 41/200 [00:17<01:05,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 21%|██        | 42/200 [00:17<01:08,  2.32it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 22%|██▏       | 43/200 [00:18<01:06,  2.35it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 24ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 22%|██▏       | 44/200 [00:18<01:05,  2.37it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 22%|██▎       | 45/200 [00:19<01:04,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 23%|██▎       | 46/200 [00:19<01:03,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 24%|██▎       | 47/200 [00:20<01:05,  2.33it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 24%|██▍       | 48/200 [00:20<01:03,  2.39it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 24%|██▍       | 49/200 [00:20<01:02,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 25%|██▌       | 50/200 [00:21<01:01,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 26%|██▌       | 51/200 [00:21<01:01,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 26%|██▌       | 52/200 [00:22<01:02,  2.38it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 26%|██▋       | 53/200 [00:22<01:00,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 35ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 27%|██▋       | 54/200 [00:22<01:00,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 28%|██▊       | 55/200 [00:23<00:59,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 28%|██▊       | 56/200 [00:23<00:58,  2.45it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 28%|██▊       | 57/200 [00:24<00:59,  2.39it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 35ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 29%|██▉       | 58/200 [00:24<01:00,  2.37it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 30%|██▉       | 59/200 [00:24<00:58,  2.39it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 30%|███       | 60/200 [00:25<00:58,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 30%|███       | 61/200 [00:25<00:57,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 31%|███       | 62/200 [00:26<00:58,  2.37it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 32%|███▏      | 63/200 [00:26<00:57,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 32%|███▏      | 64/200 [00:27<00:56,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 32%|███▎      | 65/200 [00:27<00:55,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 33%|███▎      | 66/200 [00:27<00:57,  2.34it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 32ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 34%|███▎      | 67/200 [00:28<00:59,  2.25it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 34%|███▍      | 68/200 [00:28<01:00,  2.17it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 40ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 34%|███▍      | 69/200 [00:29<01:02,  2.11it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 35%|███▌      | 70/200 [00:29<01:00,  2.15it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 36%|███▌      | 71/200 [00:30<01:00,  2.13it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 29ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 36%|███▌      | 72/200 [00:31<01:08,  1.87it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 36%|███▋      | 73/200 [00:31<01:05,  1.93it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 34ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 37%|███▋      | 74/200 [00:31<01:04,  1.95it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 69ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 38%|███▊      | 75/200 [00:32<01:05,  1.91it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 38%|███▊      | 76/200 [00:33<01:06,  1.85it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 33ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 38%|███▊      | 77/200 [00:33<01:04,  1.90it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 39%|███▉      | 78/200 [00:34<01:05,  1.87it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 29ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 40%|███▉      | 79/200 [00:34<01:04,  1.88it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 27ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 40%|████      | 80/200 [00:35<01:01,  1.95it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 40%|████      | 81/200 [00:35<00:59,  2.02it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 41%|████      | 82/200 [00:36<01:04,  1.84it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 29ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 42%|████▏     | 83/200 [00:36<01:02,  1.88it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 42%|████▏     | 84/200 [00:37<00:58,  1.98it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 24ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 42%|████▎     | 85/200 [00:37<00:55,  2.06it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 43%|████▎     | 86/200 [00:38<00:53,  2.12it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 30ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 44%|████▎     | 87/200 [00:38<00:55,  2.05it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 44%|████▍     | 88/200 [00:39<00:53,  2.09it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 44%|████▍     | 89/200 [00:39<00:52,  2.13it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 45%|████▌     | 90/200 [00:39<00:51,  2.15it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 46%|████▌     | 91/200 [00:40<00:50,  2.15it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 46%|████▌     | 92/200 [00:41<00:57,  1.87it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 46%|████▋     | 93/200 [00:41<00:57,  1.86it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 47%|████▋     | 94/200 [00:42<00:53,  1.97it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 25ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 48%|████▊     | 95/200 [00:42<00:50,  2.07it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 48%|████▊     | 96/200 [00:42<00:47,  2.18it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 48%|████▊     | 97/200 [00:43<00:47,  2.17it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 49%|████▉     | 98/200 [00:43<00:47,  2.17it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 24ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|████▉     | 99/200 [00:44<00:47,  2.13it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|█████     | 100/200 [00:44<00:45,  2.21it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|█████     | 101/200 [00:45<00:43,  2.28it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 51%|█████     | 102/200 [00:45<00:44,  2.21it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 52%|█████▏    | 103/200 [00:46<00:44,  2.18it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 52%|█████▏    | 104/200 [00:46<00:43,  2.20it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 52%|█████▎    | 105/200 [00:47<00:42,  2.22it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 53%|█████▎    | 106/200 [00:47<00:42,  2.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 54%|█████▎    | 107/200 [00:47<00:42,  2.17it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 54%|█████▍    | 108/200 [00:48<00:41,  2.20it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 55%|█████▍    | 109/200 [00:48<00:41,  2.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 55%|█████▌    | 110/200 [00:49<00:40,  2.20it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 56%|█████▌    | 111/200 [00:49<00:40,  2.22it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 56%|█████▌    | 112/200 [00:50<00:40,  2.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 56%|█████▋    | 113/200 [00:50<00:38,  2.25it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 57%|█████▋    | 114/200 [00:51<00:37,  2.31it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 57%|█████▊    | 115/200 [00:51<00:36,  2.35it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 58%|█████▊    | 116/200 [00:51<00:35,  2.33it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 58%|█████▊    | 117/200 [00:52<00:36,  2.24it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 59%|█████▉    | 118/200 [00:52<00:38,  2.15it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 60%|█████▉    | 119/200 [00:53<00:37,  2.14it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 60%|██████    | 120/200 [00:53<00:37,  2.16it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 41ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 60%|██████    | 121/200 [00:54<00:37,  2.11it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 61%|██████    | 122/200 [00:54<00:37,  2.08it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 62%|██████▏   | 123/200 [00:55<00:36,  2.09it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 62%|██████▏   | 124/200 [00:55<00:34,  2.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 62%|██████▎   | 125/200 [00:56<00:33,  2.26it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 48ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 63%|██████▎   | 126/200 [00:56<00:34,  2.15it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 64%|██████▎   | 127/200 [00:57<00:34,  2.13it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 55ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 64%|██████▍   | 128/200 [00:57<00:34,  2.11it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 64%|██████▍   | 129/200 [00:58<00:33,  2.12it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 65%|██████▌   | 130/200 [00:58<00:31,  2.22it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 66%|██████▌   | 131/200 [00:58<00:30,  2.25it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 66%|██████▌   | 132/200 [00:59<00:30,  2.22it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 66%|██████▋   | 133/200 [00:59<00:30,  2.20it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 27ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 67%|██████▋   | 134/200 [01:00<00:30,  2.14it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 62ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 68%|██████▊   | 135/200 [01:00<00:31,  2.09it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 25ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 68%|██████▊   | 136/200 [01:01<00:30,  2.10it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 68%|██████▊   | 137/200 [01:01<00:30,  2.06it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 17ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 69%|██████▉   | 138/200 [01:02<00:29,  2.13it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 70%|██████▉   | 139/200 [01:02<00:28,  2.16it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 70%|███████   | 140/200 [01:03<00:27,  2.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 70%|███████   | 141/200 [01:03<00:26,  2.23it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 24ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 71%|███████   | 142/200 [01:04<00:26,  2.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 72%|███████▏  | 143/200 [01:04<00:25,  2.20it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 38ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 72%|███████▏  | 144/200 [01:04<00:25,  2.22it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 29ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 72%|███████▎  | 145/200 [01:05<00:24,  2.25it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 73%|███████▎  | 146/200 [01:05<00:23,  2.29it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 24ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 74%|███████▎  | 147/200 [01:06<00:23,  2.25it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 74%|███████▍  | 148/200 [01:06<00:22,  2.27it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 74%|███████▍  | 149/200 [01:07<00:22,  2.29it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 75%|███████▌  | 150/200 [01:07<00:21,  2.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 46ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 76%|███████▌  | 151/200 [01:07<00:21,  2.28it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 76%|███████▌  | 152/200 [01:08<00:21,  2.22it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 76%|███████▋  | 153/200 [01:08<00:21,  2.23it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 77%|███████▋  | 154/200 [01:09<00:20,  2.26it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 78%|███████▊  | 155/200 [01:09<00:19,  2.27it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 24ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 78%|███████▊  | 156/200 [01:10<00:19,  2.27it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 78%|███████▊  | 157/200 [01:10<00:19,  2.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 79%|███████▉  | 158/200 [01:11<00:19,  2.18it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 80%|███████▉  | 159/200 [01:11<00:18,  2.22it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 80%|████████  | 160/200 [01:12<00:17,  2.24it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 80%|████████  | 161/200 [01:12<00:17,  2.25it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 81%|████████  | 162/200 [01:12<00:17,  2.18it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 25ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 82%|████████▏ | 163/200 [01:13<00:17,  2.16it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 82%|████████▏ | 164/200 [01:13<00:16,  2.21it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 24ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 82%|████████▎ | 165/200 [01:14<00:15,  2.21it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 83%|████████▎ | 166/200 [01:14<00:15,  2.25it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 24ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 84%|████████▎ | 167/200 [01:15<00:15,  2.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 24ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 84%|████████▍ | 168/200 [01:15<00:14,  2.21it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 84%|████████▍ | 169/200 [01:16<00:13,  2.24it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 85%|████████▌ | 170/200 [01:16<00:13,  2.26it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 86%|████████▌ | 171/200 [01:16<00:12,  2.28it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 37ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 86%|████████▌ | 172/200 [01:17<00:12,  2.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 86%|████████▋ | 173/200 [01:17<00:12,  2.24it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 87%|████████▋ | 174/200 [01:18<00:13,  1.91it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 88%|████████▊ | 175/200 [01:19<00:13,  1.91it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 88%|████████▊ | 176/200 [01:19<00:11,  2.02it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 71ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 88%|████████▊ | 177/200 [01:20<00:11,  2.04it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 89%|████████▉ | 178/200 [01:20<00:10,  2.12it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 26ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 90%|████████▉ | 179/200 [01:20<00:09,  2.16it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 90%|█████████ | 180/200 [01:21<00:09,  2.22it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 25ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 90%|█████████ | 181/200 [01:21<00:08,  2.23it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 77ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 91%|█████████ | 182/200 [01:22<00:08,  2.18it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 92%|█████████▏| 183/200 [01:22<00:07,  2.21it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 92%|█████████▏| 184/200 [01:23<00:07,  2.28it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 92%|█████████▎| 185/200 [01:23<00:06,  2.32it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 93%|█████████▎| 186/200 [01:23<00:05,  2.35it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 44ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 94%|█████████▎| 187/200 [01:24<00:05,  2.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 94%|█████████▍| 188/200 [01:24<00:05,  2.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 94%|█████████▍| 189/200 [01:25<00:04,  2.28it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 27ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 95%|█████████▌| 190/200 [01:25<00:04,  2.26it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 96%|█████████▌| 191/200 [01:26<00:04,  2.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 96%|█████████▌| 192/200 [01:26<00:03,  2.24it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 96%|█████████▋| 193/200 [01:27<00:03,  2.27it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 97%|█████████▋| 194/200 [01:27<00:02,  2.29it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 25ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 98%|█████████▊| 195/200 [01:27<00:02,  2.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 79ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 98%|█████████▊| 196/200 [01:28<00:01,  2.24it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 98%|█████████▊| 197/200 [01:28<00:01,  2.28it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 99%|█████████▉| 198/200 [01:29<00:00,  2.31it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|█████████▉| 199/200 [01:29<00:00,  2.24it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100%|██████████| 200/200 [01:30<00:00,  2.22it/s]\n"
-     ]
-    }
-   ],
-   "source": [
-    "dataset_2 = evaluate_one_dataset(\"cookies_2\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Evaluating cookies_3\n",
-      "- Setting seed to 42\n",
-      "- OK - Setting seed to 42 (1.42 ms)\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 0/200 [00:00<?, ?it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 175ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  0%|          | 1/200 [00:00<02:19,  1.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 42ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  1%|          | 2/200 [00:01<01:51,  1.77it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  2%|▏         | 3/200 [00:01<01:39,  1.99it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  2%|▏         | 4/200 [00:02<01:33,  2.10it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  2%|▎         | 5/200 [00:02<01:29,  2.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  3%|▎         | 6/200 [00:02<01:27,  2.21it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  4%|▎         | 7/200 [00:03<01:28,  2.17it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  4%|▍         | 8/200 [00:03<01:29,  2.14it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  4%|▍         | 9/200 [00:04<01:26,  2.20it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  5%|▌         | 10/200 [00:04<01:24,  2.26it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  6%|▌         | 11/200 [00:05<01:22,  2.28it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  6%|▌         | 12/200 [00:05<01:21,  2.32it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  6%|▋         | 13/200 [00:05<01:19,  2.34it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  7%|▋         | 14/200 [00:06<01:19,  2.35it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  8%|▊         | 15/200 [00:06<01:18,  2.36it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  8%|▊         | 16/200 [00:07<01:20,  2.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  8%|▊         | 17/200 [00:07<01:18,  2.32it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "  9%|▉         | 18/200 [00:08<01:17,  2.34it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 10%|▉         | 19/200 [00:08<01:16,  2.36it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 10%|█         | 20/200 [00:08<01:18,  2.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 10%|█         | 21/200 [00:09<01:17,  2.32it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 11%|█         | 22/200 [00:09<01:16,  2.34it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 12%|█▏        | 23/200 [00:10<01:15,  2.35it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 61ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 12%|█▏        | 24/200 [00:10<01:16,  2.29it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 12%|█▎        | 25/200 [00:11<01:15,  2.31it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 24ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 13%|█▎        | 26/200 [00:11<01:16,  2.28it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 24ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 14%|█▎        | 27/200 [00:12<01:18,  2.21it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 14%|█▍        | 28/200 [00:12<01:17,  2.22it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 14%|█▍        | 29/200 [00:12<01:16,  2.25it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 15%|█▌        | 30/200 [00:13<01:17,  2.20it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 16%|█▌        | 31/200 [00:13<01:19,  2.12it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 16%|█▌        | 32/200 [00:14<01:17,  2.17it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 16%|█▋        | 33/200 [00:14<01:15,  2.21it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 17%|█▋        | 34/200 [00:15<01:13,  2.24it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 18%|█▊        | 35/200 [00:15<01:12,  2.28it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 18%|█▊        | 36/200 [00:16<01:12,  2.28it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 18%|█▊        | 37/200 [00:16<01:10,  2.32it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 19%|█▉        | 38/200 [00:16<01:09,  2.32it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 20%|█▉        | 39/200 [00:17<01:11,  2.24it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 45ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 20%|██        | 40/200 [00:17<01:11,  2.23it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 20%|██        | 41/200 [00:18<01:10,  2.27it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 21%|██        | 42/200 [00:18<01:08,  2.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 22%|██▏       | 43/200 [00:19<01:07,  2.33it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 22%|██▏       | 44/200 [00:19<01:06,  2.35it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 22%|██▎       | 45/200 [00:19<01:05,  2.36it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 23%|██▎       | 46/200 [00:20<01:05,  2.36it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 24%|██▎       | 47/200 [00:20<01:09,  2.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 16ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 24%|██▍       | 48/200 [00:21<01:12,  2.11it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 24%|██▍       | 49/200 [00:21<01:09,  2.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 25%|██▌       | 50/200 [00:22<01:08,  2.18it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 26%|██▌       | 51/200 [00:22<01:08,  2.17it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 26%|██▌       | 52/200 [00:23<01:14,  1.99it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 26%|██▋       | 53/200 [00:23<01:11,  2.07it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 27%|██▋       | 54/200 [00:24<01:07,  2.15it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 17ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 28%|██▊       | 55/200 [00:24<01:06,  2.17it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 37ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 28%|██▊       | 56/200 [00:25<01:05,  2.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 28%|██▊       | 57/200 [00:25<01:02,  2.29it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 29%|██▉       | 58/200 [00:25<01:00,  2.36it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 30%|██▉       | 59/200 [00:26<00:58,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 30%|███       | 60/200 [00:26<00:58,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 30%|███       | 61/200 [00:27<00:57,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 17ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 31%|███       | 62/200 [00:27<00:56,  2.43it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 32%|███▏      | 63/200 [00:27<00:57,  2.38it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 32%|███▏      | 64/200 [00:28<00:56,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 32%|███▎      | 65/200 [00:28<00:55,  2.42it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 17ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 33%|███▎      | 66/200 [00:29<00:54,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 34%|███▎      | 67/200 [00:29<00:54,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 17ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 34%|███▍      | 68/200 [00:30<00:53,  2.45it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 34%|███▍      | 69/200 [00:30<00:53,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 35%|███▌      | 70/200 [00:30<00:54,  2.38it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 36%|███▌      | 71/200 [00:31<00:53,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 36%|███▌      | 72/200 [00:31<00:54,  2.33it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 36%|███▋      | 73/200 [00:32<00:55,  2.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 37%|███▋      | 74/200 [00:32<00:54,  2.31it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 38%|███▊      | 75/200 [00:33<00:53,  2.33it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 38%|███▊      | 76/200 [00:33<00:53,  2.33it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 67ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 38%|███▊      | 77/200 [00:33<00:53,  2.28it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 39%|███▉      | 78/200 [00:34<00:52,  2.34it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 17ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 40%|███▉      | 79/200 [00:34<00:53,  2.28it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 40%|████      | 80/200 [00:35<00:51,  2.35it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 40%|████      | 81/200 [00:35<00:49,  2.39it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 41%|████      | 82/200 [00:36<00:48,  2.41it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 42%|████▏     | 83/200 [00:36<00:48,  2.44it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 42%|████▏     | 84/200 [00:37<01:02,  1.85it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 16ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 42%|████▎     | 85/200 [00:37<00:57,  1.99it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 43%|████▎     | 86/200 [00:38<00:53,  2.12it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 44%|████▎     | 87/200 [00:38<00:51,  2.20it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 44%|████▍     | 88/200 [00:38<00:49,  2.27it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 44%|████▍     | 89/200 [00:39<00:47,  2.34it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 45%|████▌     | 90/200 [00:39<00:46,  2.37it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 46%|████▌     | 91/200 [00:40<00:46,  2.36it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 46%|████▌     | 92/200 [00:40<00:44,  2.40it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 46%|████▋     | 93/200 [00:40<00:46,  2.32it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 47%|████▋     | 94/200 [00:41<00:44,  2.37it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 48%|████▊     | 95/200 [00:41<00:45,  2.33it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 48%|████▊     | 96/200 [00:42<00:45,  2.29it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 48%|████▊     | 97/200 [00:42<00:44,  2.32it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 49%|████▉     | 98/200 [00:43<00:45,  2.26it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|████▉     | 99/200 [00:43<00:43,  2.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|█████     | 100/200 [00:44<00:43,  2.32it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 50%|█████     | 101/200 [00:44<00:43,  2.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 51%|█████     | 102/200 [00:44<00:43,  2.23it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 52%|█████▏    | 103/200 [00:45<00:45,  2.13it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 52%|█████▏    | 104/200 [00:45<00:46,  2.05it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 17ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 52%|█████▎    | 105/200 [00:46<00:45,  2.07it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 16ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 53%|█████▎    | 106/200 [00:46<00:45,  2.07it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 17ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 54%|█████▎    | 107/200 [00:47<00:43,  2.13it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 54%|█████▍    | 108/200 [00:47<00:41,  2.23it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 55%|█████▍    | 109/200 [00:48<00:40,  2.26it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 55%|█████▌    | 110/200 [00:48<00:38,  2.32it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 56%|█████▌    | 111/200 [00:49<00:40,  2.20it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 56%|█████▌    | 112/200 [00:49<00:40,  2.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 17ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 56%|█████▋    | 113/200 [00:50<00:39,  2.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 57%|█████▋    | 114/200 [00:50<00:43,  1.98it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 29ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 57%|█████▊    | 115/200 [00:51<00:42,  2.00it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 58%|█████▊    | 116/200 [00:51<00:40,  2.09it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 58%|█████▊    | 117/200 [00:52<00:38,  2.15it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 59%|█████▉    | 118/200 [00:52<00:37,  2.20it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 60%|█████▉    | 119/200 [00:52<00:37,  2.15it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 60%|██████    | 120/200 [00:53<00:36,  2.19it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 60%|██████    | 121/200 [00:53<00:35,  2.26it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 61%|██████    | 122/200 [00:54<00:36,  2.11it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 62%|██████▏   | 123/200 [00:54<00:35,  2.14it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 62%|██████▏   | 124/200 [00:55<00:34,  2.18it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 62%|██████▎   | 125/200 [00:55<00:33,  2.24it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 63%|██████▎   | 126/200 [00:56<00:33,  2.23it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 64%|██████▎   | 127/200 [00:56<00:34,  2.09it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 64%|██████▍   | 128/200 [00:57<00:36,  1.96it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 64%|██████▍   | 129/200 [00:57<00:35,  2.01it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 17ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 65%|██████▌   | 130/200 [00:58<00:33,  2.09it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 17ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 66%|██████▌   | 131/200 [00:58<00:31,  2.18it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 66%|██████▌   | 132/200 [00:58<00:30,  2.23it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 18ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 66%|██████▋   | 133/200 [00:59<00:29,  2.28it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 24ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 67%|██████▋   | 134/200 [00:59<00:29,  2.23it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 68%|██████▊   | 135/200 [01:00<00:28,  2.29it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 68%|██████▊   | 136/200 [01:00<00:27,  2.32it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 68%|██████▊   | 137/200 [01:01<00:27,  2.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 76ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 69%|██████▉   | 138/200 [01:01<00:27,  2.24it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 70%|██████▉   | 139/200 [01:02<00:26,  2.27it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 70%|███████   | 140/200 [01:02<00:25,  2.31it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 26ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 70%|███████   | 141/200 [01:02<00:25,  2.33it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 71%|███████   | 142/200 [01:03<00:25,  2.24it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 42ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 72%|███████▏  | 143/200 [01:03<00:25,  2.26it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 72%|███████▏  | 144/200 [01:04<00:24,  2.28it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 25ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 72%|███████▎  | 145/200 [01:04<00:24,  2.23it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 25ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 73%|███████▎  | 146/200 [01:05<00:24,  2.20it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 97ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 74%|███████▎  | 147/200 [01:05<00:25,  2.06it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 26ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 74%|███████▍  | 148/200 [01:06<00:24,  2.11it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 26ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 74%|███████▍  | 149/200 [01:06<00:24,  2.12it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 29ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 75%|███████▌  | 150/200 [01:07<00:23,  2.12it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 76%|███████▌  | 151/200 [01:07<00:23,  2.11it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 76%|███████▌  | 152/200 [01:08<00:23,  2.02it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 42ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 76%|███████▋  | 153/200 [01:08<00:22,  2.07it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 77%|███████▋  | 154/200 [01:08<00:21,  2.15it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 78%|███████▊  | 155/200 [01:09<00:20,  2.20it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 78%|███████▊  | 156/200 [01:09<00:19,  2.25it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 78%|███████▊  | 157/200 [01:10<00:19,  2.20it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 79%|███████▉  | 158/200 [01:10<00:18,  2.24it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 80%|███████▉  | 159/200 [01:11<00:18,  2.27it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 80%|████████  | 160/200 [01:11<00:17,  2.31it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 80%|████████  | 161/200 [01:11<00:16,  2.33it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 81%|████████  | 162/200 [01:12<00:17,  2.15it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 27ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 82%|████████▏ | 163/200 [01:13<00:17,  2.12it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 30ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 82%|████████▏ | 164/200 [01:13<00:17,  2.08it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 25ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 82%|████████▎ | 165/200 [01:13<00:16,  2.14it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 72ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 83%|████████▎ | 166/200 [01:14<00:16,  2.12it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 84%|████████▎ | 167/200 [01:14<00:15,  2.14it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 84%|████████▍ | 168/200 [01:15<00:14,  2.17it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 84%|████████▍ | 169/200 [01:15<00:13,  2.22it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 75ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 85%|████████▌ | 170/200 [01:16<00:13,  2.21it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 29ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 86%|████████▌ | 171/200 [01:16<00:13,  2.23it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 36ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 86%|████████▌ | 172/200 [01:17<00:12,  2.25it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 86%|████████▋ | 173/200 [01:17<00:11,  2.29it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 24ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 87%|████████▋ | 174/200 [01:17<00:11,  2.25it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 88%|████████▊ | 175/200 [01:18<00:11,  2.24it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 88%|████████▊ | 176/200 [01:18<00:10,  2.30it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 24ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 88%|████████▊ | 177/200 [01:19<00:09,  2.31it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 89%|████████▉ | 178/200 [01:19<00:09,  2.33it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 90%|████████▉ | 179/200 [01:20<00:08,  2.35it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 90%|█████████ | 180/200 [01:20<00:08,  2.34it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 69ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 90%|█████████ | 181/200 [01:21<00:08,  2.29it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 91%|█████████ | 182/200 [01:21<00:07,  2.32it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 92%|█████████▏| 183/200 [01:21<00:07,  2.34it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 23ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 92%|█████████▏| 184/200 [01:22<00:06,  2.35it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 71ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 92%|█████████▎| 185/200 [01:22<00:07,  2.01it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 19ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 93%|█████████▎| 186/200 [01:23<00:06,  2.11it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 94%|█████████▎| 187/200 [01:23<00:06,  2.14it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 94%|█████████▍| 188/200 [01:24<00:05,  2.18it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 28ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 94%|█████████▍| 189/200 [01:24<00:05,  2.17it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 95%|█████████▌| 190/200 [01:25<00:04,  2.23it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 96%|█████████▌| 191/200 [01:25<00:03,  2.26it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 96%|█████████▌| 192/200 [01:25<00:03,  2.26it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 96%|█████████▋| 193/200 [01:26<00:03,  2.20it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 97%|█████████▋| 194/200 [01:26<00:02,  2.24it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 24ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 98%|█████████▊| 195/200 [01:27<00:02,  2.24it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 98%|█████████▊| 196/200 [01:27<00:01,  2.28it/s]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1/1 [==============================] - 0s 74ms/step\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      " 98%|█████████▊| 197/200 [01:28<00:01,  2.20it/s]"
-     ]
-    },
+    "     anomaly_1_labels = [\"anomaly\"] * len(anomaly_lvl_1_test_paths)\n",
+    "     anomaly_1_difficulty = [\"easy\"] * len(anomaly_lvl_1_test_paths)\n",
+    "\n",
+    "     anomaly_2_labels = [\"anomaly\"] * len(anomaly_lvl_2_test_paths)\n",
+    "     anomaly_2_difficulty = [\"medium\"] * len(anomaly_lvl_2_test_paths)\n",
+    "\n",
+    "     anomaly_3_labels = [\"anomaly\"] * len(anomaly_lvl_3_test_paths)\n",
+    "     anomaly_3_difficulty = [\"hard\"] * len(anomaly_lvl_3_test_paths)\n",
+    "\n",
+    "     no_anomaly_df = pd.DataFrame({'filename': no_anomaly_test_paths, 'class': no_anomaly_labels, 'difficulty': no_anomaly_difficulty})  \n",
+    "\n",
+    "     \n",
+    "     anomaly_1_df = pd.DataFrame({'filename': anomaly_lvl_1_test_paths, 'class': anomaly_1_labels, 'difficulty': anomaly_1_difficulty})  \n",
+    "     anomaly_2_df = pd.DataFrame({'filename': anomaly_lvl_2_test_paths, 'class': anomaly_2_labels, 'difficulty': anomaly_2_difficulty})  \n",
+    "     anomaly_3_df = pd.DataFrame({'filename': anomaly_lvl_3_test_paths, 'class': anomaly_3_labels, 'difficulty': anomaly_3_difficulty})\n",
+    "\n",
+    "     combined_df = pd.concat([no_anomaly_df, anomaly_1_df, anomaly_2_df, anomaly_3_df])\n",
+    "     return combined_df.reset_index(drop=True)  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_baseline(dataset_name):\n",
+    "    baseline_model = tf.keras.models.load_model(f'{config[\"baseline\"][dataset_name]}/local.keras')\n",
+    "    edge_impulse_model_converted = ImageImpulseRunner(f'{config[\"baseline\"][dataset_name]}/ei.eim')\n",
+    "    edge_impulse_model_converted.init()\n",
+    "        \n",
+    "    return baseline_model, edge_impulse_model_converted\n",
+    "\n",
+    "def evaluate_baseline(baseline_model, edge_impulse_model_converted, item):\n",
+    "    cl, time, memory, score = inference_baseline(baseline_model, item[\"filename\"])\n",
+    "    cl_ei, time_ei, memory_ei, score_ei = inference_baseline_ei(edge_impulse_model_converted, item[\"filename\"])\n",
+    "    return cl, time, memory, score, cl_ei, time_ei, memory_ei, score_ei"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_efficientad(dataset_name):\n",
+    "    config_efficient_ad = {\n",
+    "        \"seed\": 42,\n",
+    "        \"on_gpu\": torch.cuda.is_available(),\n",
+    "        \n",
+    "        \"out_channels\": 384,\n",
+    "        \"image_size\": 256,\n",
+    "    }\n",
+    "    \n",
+    "    efficientad_models = torch.load(f'{config[\"efficientad\"][dataset_name]}/all_models.pth', map_location=torch.device('cpu'))\n",
+    "    map_normalization = torch.load(f'{config[\"efficientad\"][dataset_name]}/map_normalization.pth', map_location=torch.device('cpu')) \n",
+    "    \n",
+    "    with open(f'{config[\"efficientad\"][dataset_name]}/best_threshold.pkl', 'rb') as file:  \n",
+    "        best_threshold = pickle.load(file) \n",
+    "     \n",
+    "    efficientad = EfficientADInference(\n",
+    "        config=config_efficient_ad,\n",
+    "        models=efficientad_models,\n",
+    "        map_normalization=map_normalization,\n",
+    "        threshold=best_threshold,\n",
+    "    )\n",
+    "    \n",
+    "    return efficientad\n",
+    "\n",
+    "def evaluate_efficientad(efficientad, item):\n",
+    "    cl, time, memory, score = inference_efficientad(efficientad, item[\"filename\"])\n",
+    "    return cl, time, memory, score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_fomoad(dataset_name):\n",
+    "    edge_impulse_model_fomoad = ImageImpulseRunner(f'{config[\"fomoad\"][dataset_name]}/fomoad_runner-mac-x86_64.eim')\n",
+    "    edge_impulse_model_fomoad.init()\n",
+    "     \n",
+    "    return edge_impulse_model_fomoad\n",
+    "\n",
+    "def evaluate_fomoad(edge_impulse_model_fomoad, item, threshold):\n",
+    "    cl, time, memory, score = inference_fomoad_ei(edge_impulse_model_fomoad, item[\"filename\"], threshold)\n",
+    "    return cl, time, memory, score\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def evaluate_one_dataset(dataset_name):\n",
+    "    print(f\"Evaluating {dataset_name}\")\n",
+    "    \n",
+    "    # Loading all model\n",
+    "    baseline_model, ei_model_converted = load_baseline(dataset_name)\n",
+    "    efficientad = load_efficientad(dataset_name)\n",
+    "    ei_model_fomoad = load_fomoad(dataset_name)\n",
+    "    \n",
+    "    # Loading dataset\n",
+    "    dataset = load_dataset(dataset_name)\n",
+    "    \n",
+    "    # Looping through the full dataset\n",
+    "    for index, row in tqdm(dataset.iterrows(), total=len(dataset)):\n",
+    "        # Evaluate each model\n",
+    "        cl_baseline, time_baseline, memory_baseline, score_baseline, cl_ei_baseline, time_ei_baseline, memory_ei_baseline, score_ei_baseline = evaluate_baseline(baseline_model, ei_model_converted, row)\n",
+    "        \n",
+    "        cl_efficientad, time_efficientad, memory_efficientad, score_efficientad = evaluate_efficientad(efficientad, row)\n",
+    "        \n",
+    "        cl_fomodad, time_fomoad, memory_fomoad, score_fomoad = evaluate_fomoad(ei_model_fomoad, row, config[\"fomoad\"][f\"{dataset_name}_threshold\"])\n",
+    "        \n",
+    "        # Update the dataframe to add the different scroes\n",
+    "        dataset.at[index, 'cl_baseline'] = cl_baseline\n",
+    "        dataset.at[index, 'time_baseline'] = time_baseline\n",
+    "        dataset.at[index, 'memory_baseline'] = memory_baseline\n",
+    "        dataset.at[index, 'score_baseline'] = score_baseline\n",
+    "        \n",
+    "        dataset.at[index, 'cl_ei_baseline'] = cl_ei_baseline\n",
+    "        dataset.at[index, 'time_ei_baseline'] = time_ei_baseline\n",
+    "        dataset.at[index, 'memory_ei_baseline'] = memory_ei_baseline\n",
+    "        dataset.at[index, 'score_ei_baseline'] = score_ei_baseline\n",
+    "        \n",
+    "        dataset.at[index, 'cl_efficientad'] = cl_efficientad\n",
+    "        dataset.at[index, 'time_efficientad'] = time_efficientad\n",
+    "        dataset.at[index, 'memory_efficientad'] = memory_efficientad\n",
+    "        dataset.at[index, 'score_efficientad'] = score_efficientad\n",
+    "        \n",
+    "        dataset.at[index, 'cl_fomodad'] = cl_fomodad\n",
+    "        dataset.at[index, 'time_fomoad'] = time_fomoad\n",
+    "        dataset.at[index, 'memory_fomoad'] = memory_fomoad\n",
+    "        dataset.at[index, 'score_fomoad'] = score_fomoad\n",
+    "        \n",
+    "        save_path = row[\"filename\"].replace('.jpg', '_result.jpg')  \n",
+    "        dataset.at[index, 'result_img_path'] = save_path\n",
+    "    return dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1/1 [==============================] - 0s 20ms/step\n"
+      "Evaluating cookies_1\n",
+      "- Setting seed to 42\n",
+      "- OK - Setting seed to 42 (2.80 ms)\n",
+      "\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      " 99%|█████████▉| 198/200 [01:28<00:00,  2.27it/s]"
+      "100%|██████████| 100/100 [00:40<00:00,  2.45it/s]\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "dataset_1 = evaluate_one_dataset(\"cookies_1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1/1 [==============================] - 0s 21ms/step\n"
+      "Evaluating cookies_2\n",
+      "- Setting seed to 42\n",
+      "- OK - Setting seed to 42 (1.18 ms)\n",
+      "\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|█████████▉| 199/200 [01:29<00:00,  2.32it/s]"
+      "100%|██████████| 100/100 [00:43<00:00,  2.29it/s]\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "dataset_2 = evaluate_one_dataset(\"cookies_2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1/1 [==============================] - 0s 22ms/step\n"
+      "Evaluating cookies_3\n",
+      "- Setting seed to 42\n",
+      "- OK - Setting seed to 42 (1.07 ms)\n",
+      "\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████| 200/200 [01:29<00:00,  2.23it/s]\n"
+      "100%|██████████| 100/100 [00:42<00:00,  2.37it/s]\n"
      ]
     }
    ],
@@ -8817,118 +431,118 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>datasets/cookies_3/no_anomaly/20240417_134027.jpg</td>\n",
+       "      <td>datasets/cookies_3/no_anomaly/20240417_134414.jpg</td>\n",
        "      <td>no_anomaly</td>\n",
        "      <td>na</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>215.291262</td>\n",
-       "      <td>11.968750</td>\n",
-       "      <td>0.872619</td>\n",
+       "      <td>205.285788</td>\n",
+       "      <td>3760.0</td>\n",
+       "      <td>0.629698</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>74.587107</td>\n",
-       "      <td>1.765625</td>\n",
-       "      <td>0.790563</td>\n",
+       "      <td>75.807095</td>\n",
+       "      <td>-368.0</td>\n",
+       "      <td>0.571942</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>330.019951</td>\n",
-       "      <td>-2176.0</td>\n",
-       "      <td>0.196108</td>\n",
+       "      <td>287.076235</td>\n",
+       "      <td>160.0</td>\n",
+       "      <td>0.028224</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>73.952198</td>\n",
-       "      <td>-23.515625</td>\n",
-       "      <td>3.384082</td>\n",
-       "      <td>datasets/cookies_3/no_anomaly/20240417_134027_...</td>\n",
+       "      <td>116.169930</td>\n",
+       "      <td>3072.0</td>\n",
+       "      <td>3.977825</td>\n",
+       "      <td>datasets/cookies_3/no_anomaly/20240417_134414_...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>datasets/cookies_3/no_anomaly/20240417_134225.jpg</td>\n",
+       "      <td>datasets/cookies_3/no_anomaly/20240417_134257.jpg</td>\n",
        "      <td>no_anomaly</td>\n",
        "      <td>na</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>79.087973</td>\n",
-       "      <td>0.156250</td>\n",
-       "      <td>0.815615</td>\n",
+       "      <td>60.723066</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>0.747607</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>44.698000</td>\n",
-       "      <td>0.281250</td>\n",
-       "      <td>0.836219</td>\n",
+       "      <td>37.753105</td>\n",
+       "      <td>240.0</td>\n",
+       "      <td>0.736483</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>306.039095</td>\n",
-       "      <td>7424.0</td>\n",
-       "      <td>0.062137</td>\n",
+       "      <td>291.661978</td>\n",
+       "      <td>48.0</td>\n",
+       "      <td>0.010483</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>38.233995</td>\n",
-       "      <td>4.078125</td>\n",
-       "      <td>2.895372</td>\n",
-       "      <td>datasets/cookies_3/no_anomaly/20240417_134225_...</td>\n",
+       "      <td>35.536766</td>\n",
+       "      <td>3200.0</td>\n",
+       "      <td>2.709480</td>\n",
+       "      <td>datasets/cookies_3/no_anomaly/20240417_134257_...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>datasets/cookies_3/no_anomaly/20240417_134351.jpg</td>\n",
+       "      <td>datasets/cookies_3/no_anomaly/20240417_134101.jpg</td>\n",
        "      <td>no_anomaly</td>\n",
        "      <td>na</td>\n",
+       "      <td>anomaly</td>\n",
+       "      <td>37.206888</td>\n",
+       "      <td>208.0</td>\n",
+       "      <td>0.677503</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>42.485952</td>\n",
-       "      <td>0.046875</td>\n",
-       "      <td>0.831380</td>\n",
-       "      <td>no_anomaly</td>\n",
-       "      <td>33.839941</td>\n",
-       "      <td>1.203125</td>\n",
-       "      <td>0.623720</td>\n",
+       "      <td>36.581278</td>\n",
+       "      <td>1280.0</td>\n",
+       "      <td>0.567319</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>313.975096</td>\n",
-       "      <td>13488.0</td>\n",
-       "      <td>0.063899</td>\n",
+       "      <td>321.985006</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.025547</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>36.097050</td>\n",
-       "      <td>3.046875</td>\n",
-       "      <td>3.439135</td>\n",
-       "      <td>datasets/cookies_3/no_anomaly/20240417_134351_...</td>\n",
+       "      <td>37.643194</td>\n",
+       "      <td>3168.0</td>\n",
+       "      <td>4.017349</td>\n",
+       "      <td>datasets/cookies_3/no_anomaly/20240417_134101_...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>datasets/cookies_3/no_anomaly/20240417_134353.jpg</td>\n",
+       "      <td>datasets/cookies_3/no_anomaly/20240417_134452.jpg</td>\n",
        "      <td>no_anomaly</td>\n",
        "      <td>na</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>41.754246</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.828037</td>\n",
+       "      <td>41.023016</td>\n",
+       "      <td>112.0</td>\n",
+       "      <td>0.697850</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>33.387184</td>\n",
-       "      <td>1.203125</td>\n",
-       "      <td>0.633182</td>\n",
+       "      <td>30.738115</td>\n",
+       "      <td>208.0</td>\n",
+       "      <td>0.583880</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>320.642233</td>\n",
-       "      <td>-31632.0</td>\n",
-       "      <td>0.047855</td>\n",
+       "      <td>290.608883</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.013774</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>36.247015</td>\n",
-       "      <td>3.109375</td>\n",
-       "      <td>3.617379</td>\n",
-       "      <td>datasets/cookies_3/no_anomaly/20240417_134353_...</td>\n",
+       "      <td>35.781145</td>\n",
+       "      <td>4016.0</td>\n",
+       "      <td>4.191556</td>\n",
+       "      <td>datasets/cookies_3/no_anomaly/20240417_134452_...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>datasets/cookies_3/no_anomaly/20240417_134227.jpg</td>\n",
+       "      <td>datasets/cookies_3/no_anomaly/20240417_134334.jpg</td>\n",
        "      <td>no_anomaly</td>\n",
        "      <td>na</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>39.963245</td>\n",
-       "      <td>0.171875</td>\n",
-       "      <td>0.682166</td>\n",
-       "      <td>no_anomaly</td>\n",
-       "      <td>32.882929</td>\n",
-       "      <td>1.250000</td>\n",
-       "      <td>0.871255</td>\n",
+       "      <td>39.387941</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.642211</td>\n",
+       "      <td>anomaly</td>\n",
+       "      <td>31.686783</td>\n",
+       "      <td>192.0</td>\n",
+       "      <td>0.502757</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>311.959028</td>\n",
-       "      <td>16352.0</td>\n",
-       "      <td>0.059206</td>\n",
+       "      <td>293.304920</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.008547</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>38.475990</td>\n",
-       "      <td>2.156250</td>\n",
-       "      <td>3.650349</td>\n",
-       "      <td>datasets/cookies_3/no_anomaly/20240417_134227_...</td>\n",
+       "      <td>35.309076</td>\n",
+       "      <td>4192.0</td>\n",
+       "      <td>3.373817</td>\n",
+       "      <td>datasets/cookies_3/no_anomaly/20240417_134334_...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -8954,205 +568,205 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>195</th>\n",
-       "      <td>datasets/cookies_3/anomaly_lvl_3/20240417_1405...</td>\n",
+       "      <th>95</th>\n",
+       "      <td>datasets/cookies_3/anomaly_lvl_3/20240417_1406...</td>\n",
        "      <td>anomaly</td>\n",
        "      <td>hard</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>45.591116</td>\n",
-       "      <td>0.187500</td>\n",
-       "      <td>0.593224</td>\n",
-       "      <td>anomaly</td>\n",
-       "      <td>34.983873</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.677545</td>\n",
+       "      <td>40.112257</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.667141</td>\n",
+       "      <td>no_anomaly</td>\n",
+       "      <td>31.785965</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.570048</td>\n",
        "      <td>anomaly</td>\n",
-       "      <td>299.077988</td>\n",
-       "      <td>1936.0</td>\n",
-       "      <td>0.953776</td>\n",
+       "      <td>306.662083</td>\n",
+       "      <td>3152.0</td>\n",
+       "      <td>0.075944</td>\n",
        "      <td>anomaly</td>\n",
-       "      <td>37.696123</td>\n",
-       "      <td>0.093750</td>\n",
-       "      <td>13.224107</td>\n",
-       "      <td>datasets/cookies_3/anomaly_lvl_3/20240417_1405...</td>\n",
+       "      <td>36.483049</td>\n",
+       "      <td>-63008.0</td>\n",
+       "      <td>5.378742</td>\n",
+       "      <td>datasets/cookies_3/anomaly_lvl_3/20240417_1406...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>196</th>\n",
-       "      <td>datasets/cookies_3/anomaly_lvl_3/20240417_1406...</td>\n",
+       "      <th>96</th>\n",
+       "      <td>datasets/cookies_3/anomaly_lvl_3/20240417_1401...</td>\n",
        "      <td>anomaly</td>\n",
        "      <td>hard</td>\n",
-       "      <td>no_anomaly</td>\n",
-       "      <td>100.214720</td>\n",
-       "      <td>0.109375</td>\n",
-       "      <td>0.648835</td>\n",
-       "      <td>no_anomaly</td>\n",
-       "      <td>40.426970</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.539894</td>\n",
        "      <td>anomaly</td>\n",
-       "      <td>312.687159</td>\n",
-       "      <td>2736.0</td>\n",
-       "      <td>0.426243</td>\n",
+       "      <td>42.176008</td>\n",
+       "      <td>-30576.0</td>\n",
+       "      <td>0.566778</td>\n",
        "      <td>anomaly</td>\n",
-       "      <td>36.779881</td>\n",
-       "      <td>0.203125</td>\n",
-       "      <td>5.771534</td>\n",
-       "      <td>datasets/cookies_3/anomaly_lvl_3/20240417_1406...</td>\n",
+       "      <td>32.583952</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.825505</td>\n",
+       "      <td>anomaly</td>\n",
+       "      <td>295.277834</td>\n",
+       "      <td>-37664.0</td>\n",
+       "      <td>0.061785</td>\n",
+       "      <td>anomaly</td>\n",
+       "      <td>38.079023</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>8.081696</td>\n",
+       "      <td>datasets/cookies_3/anomaly_lvl_3/20240417_1401...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>197</th>\n",
+       "      <th>97</th>\n",
        "      <td>datasets/cookies_3/anomaly_lvl_3/20240417_1405...</td>\n",
        "      <td>anomaly</td>\n",
        "      <td>hard</td>\n",
-       "      <td>no_anomaly</td>\n",
-       "      <td>43.220043</td>\n",
-       "      <td>0.328125</td>\n",
-       "      <td>0.816310</td>\n",
-       "      <td>no_anomaly</td>\n",
-       "      <td>33.733130</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.571221</td>\n",
        "      <td>anomaly</td>\n",
-       "      <td>290.580273</td>\n",
-       "      <td>9408.0</td>\n",
-       "      <td>0.455816</td>\n",
+       "      <td>41.497946</td>\n",
+       "      <td>1072.0</td>\n",
+       "      <td>0.652064</td>\n",
+       "      <td>anomaly</td>\n",
+       "      <td>37.628889</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.857419</td>\n",
+       "      <td>anomaly</td>\n",
+       "      <td>292.452812</td>\n",
+       "      <td>-21984.0</td>\n",
+       "      <td>0.062294</td>\n",
        "      <td>anomaly</td>\n",
-       "      <td>36.035061</td>\n",
-       "      <td>0.234375</td>\n",
-       "      <td>5.345970</td>\n",
+       "      <td>35.465240</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.917777</td>\n",
        "      <td>datasets/cookies_3/anomaly_lvl_3/20240417_1405...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>198</th>\n",
+       "      <th>98</th>\n",
        "      <td>datasets/cookies_3/anomaly_lvl_3/20240417_1405...</td>\n",
        "      <td>anomaly</td>\n",
        "      <td>hard</td>\n",
-       "      <td>no_anomaly</td>\n",
-       "      <td>43.244123</td>\n",
-       "      <td>0.046875</td>\n",
-       "      <td>0.522906</td>\n",
        "      <td>anomaly</td>\n",
-       "      <td>36.174059</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.653283</td>\n",
+       "      <td>37.263870</td>\n",
+       "      <td>288.0</td>\n",
+       "      <td>0.627439</td>\n",
        "      <td>anomaly</td>\n",
-       "      <td>294.780016</td>\n",
+       "      <td>32.335281</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.501725</td>\n",
+       "      <td>0.783686</td>\n",
+       "      <td>anomaly</td>\n",
+       "      <td>301.133871</td>\n",
+       "      <td>-27680.0</td>\n",
+       "      <td>0.033368</td>\n",
        "      <td>anomaly</td>\n",
-       "      <td>36.408663</td>\n",
-       "      <td>0.109375</td>\n",
-       "      <td>8.528876</td>\n",
+       "      <td>35.921335</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.952106</td>\n",
        "      <td>datasets/cookies_3/anomaly_lvl_3/20240417_1405...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>199</th>\n",
+       "      <th>99</th>\n",
        "      <td>datasets/cookies_3/anomaly_lvl_3/20240417_1406...</td>\n",
        "      <td>anomaly</td>\n",
        "      <td>hard</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>43.908119</td>\n",
-       "      <td>0.109375</td>\n",
-       "      <td>0.623962</td>\n",
+       "      <td>99.685907</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.624606</td>\n",
        "      <td>no_anomaly</td>\n",
-       "      <td>34.702063</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.615970</td>\n",
-       "      <td>anomaly</td>\n",
-       "      <td>302.365780</td>\n",
-       "      <td>4672.0</td>\n",
-       "      <td>0.440680</td>\n",
+       "      <td>33.328056</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.819309</td>\n",
        "      <td>anomaly</td>\n",
-       "      <td>38.964033</td>\n",
-       "      <td>0.203125</td>\n",
-       "      <td>5.112953</td>\n",
+       "      <td>295.104027</td>\n",
+       "      <td>-2400.0</td>\n",
+       "      <td>0.126895</td>\n",
+       "      <td>no_anomaly</td>\n",
+       "      <td>36.050081</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>4.260826</td>\n",
        "      <td>datasets/cookies_3/anomaly_lvl_3/20240417_1406...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>200 rows × 20 columns</p>\n",
+       "<p>100 rows × 20 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "                                              filename       class difficulty  \\\n",
-       "0    datasets/cookies_3/no_anomaly/20240417_134027.jpg  no_anomaly         na   \n",
-       "1    datasets/cookies_3/no_anomaly/20240417_134225.jpg  no_anomaly         na   \n",
-       "2    datasets/cookies_3/no_anomaly/20240417_134351.jpg  no_anomaly         na   \n",
-       "3    datasets/cookies_3/no_anomaly/20240417_134353.jpg  no_anomaly         na   \n",
-       "4    datasets/cookies_3/no_anomaly/20240417_134227.jpg  no_anomaly         na   \n",
-       "..                                                 ...         ...        ...   \n",
-       "195  datasets/cookies_3/anomaly_lvl_3/20240417_1405...     anomaly       hard   \n",
-       "196  datasets/cookies_3/anomaly_lvl_3/20240417_1406...     anomaly       hard   \n",
-       "197  datasets/cookies_3/anomaly_lvl_3/20240417_1405...     anomaly       hard   \n",
-       "198  datasets/cookies_3/anomaly_lvl_3/20240417_1405...     anomaly       hard   \n",
-       "199  datasets/cookies_3/anomaly_lvl_3/20240417_1406...     anomaly       hard   \n",
+       "                                             filename       class difficulty  \\\n",
+       "0   datasets/cookies_3/no_anomaly/20240417_134414.jpg  no_anomaly         na   \n",
+       "1   datasets/cookies_3/no_anomaly/20240417_134257.jpg  no_anomaly         na   \n",
+       "2   datasets/cookies_3/no_anomaly/20240417_134101.jpg  no_anomaly         na   \n",
+       "3   datasets/cookies_3/no_anomaly/20240417_134452.jpg  no_anomaly         na   \n",
+       "4   datasets/cookies_3/no_anomaly/20240417_134334.jpg  no_anomaly         na   \n",
+       "..                                                ...         ...        ...   \n",
+       "95  datasets/cookies_3/anomaly_lvl_3/20240417_1406...     anomaly       hard   \n",
+       "96  datasets/cookies_3/anomaly_lvl_3/20240417_1401...     anomaly       hard   \n",
+       "97  datasets/cookies_3/anomaly_lvl_3/20240417_1405...     anomaly       hard   \n",
+       "98  datasets/cookies_3/anomaly_lvl_3/20240417_1405...     anomaly       hard   \n",
+       "99  datasets/cookies_3/anomaly_lvl_3/20240417_1406...     anomaly       hard   \n",
        "\n",
-       "    cl_baseline  time_baseline  memory_baseline  score_baseline  \\\n",
-       "0    no_anomaly     215.291262        11.968750        0.872619   \n",
-       "1    no_anomaly      79.087973         0.156250        0.815615   \n",
-       "2    no_anomaly      42.485952         0.046875        0.831380   \n",
-       "3    no_anomaly      41.754246         0.000000        0.828037   \n",
-       "4    no_anomaly      39.963245         0.171875        0.682166   \n",
-       "..          ...            ...              ...             ...   \n",
-       "195  no_anomaly      45.591116         0.187500        0.593224   \n",
-       "196  no_anomaly     100.214720         0.109375        0.648835   \n",
-       "197  no_anomaly      43.220043         0.328125        0.816310   \n",
-       "198  no_anomaly      43.244123         0.046875        0.522906   \n",
-       "199  no_anomaly      43.908119         0.109375        0.623962   \n",
+       "   cl_baseline  time_baseline  memory_baseline  score_baseline cl_ei_baseline  \\\n",
+       "0   no_anomaly     205.285788           3760.0        0.629698     no_anomaly   \n",
+       "1   no_anomaly      60.723066             48.0        0.747607     no_anomaly   \n",
+       "2      anomaly      37.206888            208.0        0.677503     no_anomaly   \n",
+       "3   no_anomaly      41.023016            112.0        0.697850     no_anomaly   \n",
+       "4   no_anomaly      39.387941              0.0        0.642211        anomaly   \n",
+       "..         ...            ...              ...             ...            ...   \n",
+       "95  no_anomaly      40.112257              0.0        0.667141     no_anomaly   \n",
+       "96     anomaly      42.176008         -30576.0        0.566778        anomaly   \n",
+       "97     anomaly      41.497946           1072.0        0.652064        anomaly   \n",
+       "98     anomaly      37.263870            288.0        0.627439        anomaly   \n",
+       "99  no_anomaly      99.685907              0.0        0.624606     no_anomaly   \n",
        "\n",
-       "    cl_ei_baseline  time_ei_baseline  memory_ei_baseline  score_ei_baseline  \\\n",
-       "0       no_anomaly         74.587107            1.765625           0.790563   \n",
-       "1       no_anomaly         44.698000            0.281250           0.836219   \n",
-       "2       no_anomaly         33.839941            1.203125           0.623720   \n",
-       "3       no_anomaly         33.387184            1.203125           0.633182   \n",
-       "4       no_anomaly         32.882929            1.250000           0.871255   \n",
-       "..             ...               ...                 ...                ...   \n",
-       "195        anomaly         34.983873            0.000000           0.677545   \n",
-       "196     no_anomaly         40.426970            0.000000           0.539894   \n",
-       "197     no_anomaly         33.733130            0.000000           0.571221   \n",
-       "198        anomaly         36.174059            0.000000           0.653283   \n",
-       "199     no_anomaly         34.702063            0.000000           0.615970   \n",
+       "    time_ei_baseline  memory_ei_baseline  score_ei_baseline cl_efficientad  \\\n",
+       "0          75.807095              -368.0           0.571942     no_anomaly   \n",
+       "1          37.753105               240.0           0.736483     no_anomaly   \n",
+       "2          36.581278              1280.0           0.567319     no_anomaly   \n",
+       "3          30.738115               208.0           0.583880     no_anomaly   \n",
+       "4          31.686783               192.0           0.502757     no_anomaly   \n",
+       "..               ...                 ...                ...            ...   \n",
+       "95         31.785965                 0.0           0.570048        anomaly   \n",
+       "96         32.583952                 0.0           0.825505        anomaly   \n",
+       "97         37.628889                 0.0           0.857419        anomaly   \n",
+       "98         32.335281                 0.0           0.783686        anomaly   \n",
+       "99         33.328056                 0.0           0.819309        anomaly   \n",
        "\n",
-       "    cl_efficientad  time_efficientad  memory_efficientad  score_efficientad  \\\n",
-       "0       no_anomaly        330.019951             -2176.0           0.196108   \n",
-       "1       no_anomaly        306.039095              7424.0           0.062137   \n",
-       "2       no_anomaly        313.975096             13488.0           0.063899   \n",
-       "3       no_anomaly        320.642233            -31632.0           0.047855   \n",
-       "4       no_anomaly        311.959028             16352.0           0.059206   \n",
-       "..             ...               ...                 ...                ...   \n",
-       "195        anomaly        299.077988              1936.0           0.953776   \n",
-       "196        anomaly        312.687159              2736.0           0.426243   \n",
-       "197        anomaly        290.580273              9408.0           0.455816   \n",
-       "198        anomaly        294.780016                 0.0           0.501725   \n",
-       "199        anomaly        302.365780              4672.0           0.440680   \n",
+       "    time_efficientad  memory_efficientad  score_efficientad  cl_fomodad  \\\n",
+       "0         287.076235               160.0           0.028224  no_anomaly   \n",
+       "1         291.661978                48.0           0.010483  no_anomaly   \n",
+       "2         321.985006                 0.0           0.025547  no_anomaly   \n",
+       "3         290.608883                 0.0           0.013774  no_anomaly   \n",
+       "4         293.304920                 0.0           0.008547  no_anomaly   \n",
+       "..               ...                 ...                ...         ...   \n",
+       "95        306.662083              3152.0           0.075944     anomaly   \n",
+       "96        295.277834            -37664.0           0.061785     anomaly   \n",
+       "97        292.452812            -21984.0           0.062294     anomaly   \n",
+       "98        301.133871            -27680.0           0.033368     anomaly   \n",
+       "99        295.104027             -2400.0           0.126895  no_anomaly   \n",
        "\n",
-       "     cl_fomodad  time_fomoad  memory_fomoad  score_fomoad  \\\n",
-       "0    no_anomaly    73.952198     -23.515625      3.384082   \n",
-       "1    no_anomaly    38.233995       4.078125      2.895372   \n",
-       "2    no_anomaly    36.097050       3.046875      3.439135   \n",
-       "3    no_anomaly    36.247015       3.109375      3.617379   \n",
-       "4    no_anomaly    38.475990       2.156250      3.650349   \n",
-       "..          ...          ...            ...           ...   \n",
-       "195     anomaly    37.696123       0.093750     13.224107   \n",
-       "196     anomaly    36.779881       0.203125      5.771534   \n",
-       "197     anomaly    36.035061       0.234375      5.345970   \n",
-       "198     anomaly    36.408663       0.109375      8.528876   \n",
-       "199     anomaly    38.964033       0.203125      5.112953   \n",
+       "    time_fomoad  memory_fomoad  score_fomoad  \\\n",
+       "0    116.169930         3072.0      3.977825   \n",
+       "1     35.536766         3200.0      2.709480   \n",
+       "2     37.643194         3168.0      4.017349   \n",
+       "3     35.781145         4016.0      4.191556   \n",
+       "4     35.309076         4192.0      3.373817   \n",
+       "..          ...            ...           ...   \n",
+       "95    36.483049       -63008.0      5.378742   \n",
+       "96    38.079023           16.0      8.081696   \n",
+       "97    35.465240            0.0      6.917777   \n",
+       "98    35.921335            0.0     12.952106   \n",
+       "99    36.050081            0.0      4.260826   \n",
        "\n",
-       "                                       result_img_path  \n",
-       "0    datasets/cookies_3/no_anomaly/20240417_134027_...  \n",
-       "1    datasets/cookies_3/no_anomaly/20240417_134225_...  \n",
-       "2    datasets/cookies_3/no_anomaly/20240417_134351_...  \n",
-       "3    datasets/cookies_3/no_anomaly/20240417_134353_...  \n",
-       "4    datasets/cookies_3/no_anomaly/20240417_134227_...  \n",
-       "..                                                 ...  \n",
-       "195  datasets/cookies_3/anomaly_lvl_3/20240417_1405...  \n",
-       "196  datasets/cookies_3/anomaly_lvl_3/20240417_1406...  \n",
-       "197  datasets/cookies_3/anomaly_lvl_3/20240417_1405...  \n",
-       "198  datasets/cookies_3/anomaly_lvl_3/20240417_1405...  \n",
-       "199  datasets/cookies_3/anomaly_lvl_3/20240417_1406...  \n",
+       "                                      result_img_path  \n",
+       "0   datasets/cookies_3/no_anomaly/20240417_134414_...  \n",
+       "1   datasets/cookies_3/no_anomaly/20240417_134257_...  \n",
+       "2   datasets/cookies_3/no_anomaly/20240417_134101_...  \n",
+       "3   datasets/cookies_3/no_anomaly/20240417_134452_...  \n",
+       "4   datasets/cookies_3/no_anomaly/20240417_134334_...  \n",
+       "..                                                ...  \n",
+       "95  datasets/cookies_3/anomaly_lvl_3/20240417_1406...  \n",
+       "96  datasets/cookies_3/anomaly_lvl_3/20240417_1401...  \n",
+       "97  datasets/cookies_3/anomaly_lvl_3/20240417_1405...  \n",
+       "98  datasets/cookies_3/anomaly_lvl_3/20240417_1405...  \n",
+       "99  datasets/cookies_3/anomaly_lvl_3/20240417_1406...  \n",
        "\n",
-       "[200 rows x 20 columns]"
+       "[100 rows x 20 columns]"
       ]
      },
      "execution_count": 11,
@@ -9168,35 +782,12 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "cl_efficientad\n",
-       "anomaly       101\n",
-       "no_anomaly     99\n",
-       "Name: count, dtype: int64"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dataset_2['cl_efficientad'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/x_/0z24g8110_n09vlvhxmrh0_w0000gp/T/ipykernel_67880/2594109346.py:61: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
+      "/var/folders/x_/0z24g8110_n09vlvhxmrh0_w0000gp/T/ipykernel_94509/1648641828.py:61: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
       "  benchmarks = pd.concat([benchmarks, results_dataset1, results_dataset2, results_dataset3], ignore_index=True)\n"
      ]
     }
@@ -9204,16 +795,16 @@
    "source": [
     "def get_score(dataset, name):  \n",
     "    row = [name]  \n",
-    "      \n",
+    "    \n",
     "    # For Baseline  \n",
     "    f1_all_baseline = f1_score(dataset['class'], dataset['cl_baseline'], pos_label='anomaly')  \n",
+    "    \n",
     "    f1_all_baseline_na = f1_score(dataset[dataset[\"difficulty\"] == \"na\"]['class'], dataset[dataset[\"difficulty\"] == \"na\"]['cl_baseline'], pos_label='no_anomaly')  \n",
     "    f1_all_baseline_lvl1 = f1_score(dataset[dataset[\"difficulty\"] == \"easy\"]['class'], dataset[dataset[\"difficulty\"] == \"easy\"]['cl_baseline'], pos_label='anomaly')  \n",
     "    f1_all_baseline_lvl2 = f1_score(dataset[dataset[\"difficulty\"] == \"medium\"]['class'], dataset[dataset[\"difficulty\"] == \"medium\"]['cl_baseline'], pos_label='anomaly')  \n",
     "    f1_all_baseline_lvl3 = f1_score(dataset[dataset[\"difficulty\"] == \"hard\"]['class'], dataset[dataset[\"difficulty\"] == \"hard\"]['cl_baseline'], pos_label='anomaly')  \n",
     "    row.extend([name, f1_all_baseline, f1_all_baseline_na, f1_all_baseline_lvl1, f1_all_baseline_lvl2, f1_all_baseline_lvl3,  \n",
     "                dataset['time_baseline'].mean(), dataset['memory_baseline'].mean()])  \n",
-    "  \n",
     "    # For Baseline EI  \n",
     "    f1_all_baseline_ei = f1_score(dataset['class'], dataset['cl_ei_baseline'], pos_label='anomaly')  \n",
     "    f1_all_baseline_ei_na = f1_score(dataset[dataset[\"difficulty\"] == \"na\"]['class'], dataset[dataset[\"difficulty\"] == \"na\"]['cl_ei_baseline'], pos_label='no_anomaly')  \n",
@@ -9243,10 +834,10 @@
     "  \n",
     "    return row\n",
     "\n",
-    "benchmarks = pd.DataFrame(columns=[\"Dataset\", 'Baseline Dataset', 'Baseline F1 ALL', 'Baseline F1 NO ANOMALY', 'Baseline F1 EASY', 'Baseline F1 MEDIUM', 'Baseline F1 HARD', 'Baseline Time (ms)', 'Baseline RAM (KB)',  \n",
-    "                                   'Baseline EI Dataset', 'Baseline EI F1 ALL', 'Baseline EI F1 NO ANOMALY', 'Baseline EI F1 EASY', 'Baseline EI F1 MEDIUM', 'Baseline EI F1 HARD', 'Baseline EI Time (ms)', 'Baseline EI RAM (KB)',  \n",
-    "                                   'EfficientAD Dataset', 'EfficientAD F1 ALL', 'EfficientAD F1 NO ANOMALY', 'EfficientAD F1 EASY', 'EfficientAD F1 MEDIUM', 'EfficientAD F1 HARD', 'EfficientAD Time (ms)', 'EfficientAD RAM (KB)',  \n",
-    "                                   'FOMOAD Dataset', 'FOMOAD F1 ALL', 'FOMOAD F1 NO ANOMALY', 'FOMOAD F1 EASY', 'FOMOAD F1 MEDIUM', 'FOMOAD F1 HARD', 'FOMOAD Time (ms)', 'FOMOAD RAM (KB)'])  \n",
+    "benchmarks = pd.DataFrame(columns=[\"Dataset\", 'Baseline Dataset', 'Baseline F1 TEST', 'Baseline F1 NO ANOMALY TEST', 'Baseline F1 EASY TEST', 'Baseline F1 MEDIUM TEST', 'Baseline F1 HARD TEST', 'Baseline Time (ms)', 'Baseline RAM (KB)',  \n",
+    "                                   'Baseline EI Dataset', 'Baseline EI F1 TEST', 'Baseline EI F1 NO ANOMALY TEST', 'Baseline EI F1 EASY TEST', 'Baseline EI F1 MEDIUM TEST', 'Baseline EI F1 HARD TEST', 'Baseline EI Time (ms)', 'Baseline EI RAM (KB)',  \n",
+    "                                   'EfficientAD Dataset', 'EfficientAD F1 TEST', 'EfficientAD F1 NO ANOMALY TEST', 'EfficientAD F1 EASY TEST', 'EfficientAD F1 MEDIUM TEST', 'EfficientAD F1 HARD TEST', 'EfficientAD Time (ms)', 'EfficientAD RAM (KB)',  \n",
+    "                                   'FOMOAD Dataset', 'FOMOAD F1 TEST', 'FOMOAD F1 NO ANOMALY TEST', 'FOMOAD F1 EASY TEST', 'FOMOAD F1 MEDIUM TEST', 'FOMOAD F1 HARD TEST', 'FOMOAD Time (ms)', 'FOMOAD RAM (KB)'])  \n",
     "  \n",
     "# Assuming dataset1, dataset2, dataset3 are your actual datasets  \n",
     "results_dataset1 = pd.DataFrame([get_score(dataset_1, \"cookies_1\")], columns=benchmarks.columns)  \n",
@@ -9270,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -9296,11 +887,11 @@
        "      <th></th>\n",
        "      <th>Dataset</th>\n",
        "      <th>Baseline Dataset</th>\n",
-       "      <th>Baseline F1 ALL</th>\n",
-       "      <th>Baseline F1 NO ANOMALY</th>\n",
-       "      <th>Baseline F1 EASY</th>\n",
-       "      <th>Baseline F1 MEDIUM</th>\n",
-       "      <th>Baseline F1 HARD</th>\n",
+       "      <th>Baseline F1 TEST</th>\n",
+       "      <th>Baseline F1 NO ANOMALY TEST</th>\n",
+       "      <th>Baseline F1 EASY TEST</th>\n",
+       "      <th>Baseline F1 MEDIUM TEST</th>\n",
+       "      <th>Baseline F1 HARD TEST</th>\n",
        "      <th>Baseline Time (ms)</th>\n",
        "      <th>Baseline RAM (KB)</th>\n",
        "      <th>Baseline EI Dataset</th>\n",
@@ -9308,11 +899,11 @@
        "      <th>EfficientAD Time (ms)</th>\n",
        "      <th>EfficientAD RAM (KB)</th>\n",
        "      <th>FOMOAD Dataset</th>\n",
-       "      <th>FOMOAD F1 ALL</th>\n",
-       "      <th>FOMOAD F1 NO ANOMALY</th>\n",
-       "      <th>FOMOAD F1 EASY</th>\n",
-       "      <th>FOMOAD F1 MEDIUM</th>\n",
-       "      <th>FOMOAD F1 HARD</th>\n",
+       "      <th>FOMOAD F1 TEST</th>\n",
+       "      <th>FOMOAD F1 NO ANOMAL TEST</th>\n",
+       "      <th>FOMOAD F1 EASY TEST</th>\n",
+       "      <th>FOMOAD F1 MEDIUM TEST</th>\n",
+       "      <th>FOMOAD F1 HARD TEST</th>\n",
        "      <th>FOMOAD Time (ms)</th>\n",
        "      <th>FOMOAD RAM (KB)</th>\n",
        "    </tr>\n",
@@ -9322,25 +913,25 @@
        "      <th>0</th>\n",
        "      <td>cookies_1</td>\n",
        "      <td>cookies_1</td>\n",
-       "      <td>0.882682</td>\n",
+       "      <td>0.865248</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.275862</td>\n",
-       "      <td>43.899704</td>\n",
-       "      <td>0.132109</td>\n",
+       "      <td>0.095238</td>\n",
+       "      <td>39.690981</td>\n",
+       "      <td>436.64</td>\n",
        "      <td>cookies_1</td>\n",
        "      <td>...</td>\n",
-       "      <td>299.686815</td>\n",
-       "      <td>8231.44</td>\n",
+       "      <td>299.973309</td>\n",
+       "      <td>9296.64</td>\n",
        "      <td>cookies_1</td>\n",
-       "      <td>0.943005</td>\n",
-       "      <td>0.989899</td>\n",
+       "      <td>0.954248</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.780488</td>\n",
-       "      <td>34.033175</td>\n",
-       "      <td>2.381328</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.787879</td>\n",
+       "      <td>35.132482</td>\n",
+       "      <td>2337.6</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -9348,28 +939,31 @@
        "</div>"
       ],
       "text/plain": [
-       "     Dataset Baseline Dataset  Baseline F1 ALL  Baseline F1 NO ANOMALY  \\\n",
-       "0  cookies_1        cookies_1         0.882682                     1.0   \n",
+       "     Dataset Baseline Dataset  Baseline F1 TEST  Baseline F1 NO ANOMALY TEST  \\\n",
+       "0  cookies_1        cookies_1          0.865248                          1.0   \n",
+       "\n",
+       "   Baseline F1 EASY TEST  Baseline F1 MEDIUM TEST  Baseline F1 HARD TEST  \\\n",
+       "0                    1.0                      1.0               0.095238   \n",
        "\n",
-       "   Baseline F1 EASY  Baseline F1 MEDIUM  Baseline F1 HARD  Baseline Time (ms)  \\\n",
-       "0               1.0                 1.0          0.275862           43.899704   \n",
+       "   Baseline Time (ms)  Baseline RAM (KB) Baseline EI Dataset  ...  \\\n",
+       "0           39.690981             436.64           cookies_1  ...   \n",
        "\n",
-       "   Baseline RAM (KB) Baseline EI Dataset  ...  EfficientAD Time (ms)  \\\n",
-       "0           0.132109           cookies_1  ...             299.686815   \n",
+       "   EfficientAD Time (ms)  EfficientAD RAM (KB)  FOMOAD Dataset  \\\n",
+       "0             299.973309               9296.64       cookies_1   \n",
        "\n",
-       "   EfficientAD RAM (KB)  FOMOAD Dataset  FOMOAD F1 ALL  FOMOAD F1 NO ANOMALY  \\\n",
-       "0               8231.44       cookies_1       0.943005              0.989899   \n",
+       "   FOMOAD F1 TEST  FOMOAD F1 NO ANOMAL TEST  FOMOAD F1 EASY TEST  \\\n",
+       "0        0.954248                       1.0                  1.0   \n",
        "\n",
-       "   FOMOAD F1 EASY  FOMOAD F1 MEDIUM FOMOAD F1 HARD  FOMOAD Time (ms)  \\\n",
-       "0             1.0               1.0       0.780488         34.033175   \n",
+       "   FOMOAD F1 MEDIUM TEST FOMOAD F1 HARD TEST  FOMOAD Time (ms)  \\\n",
+       "0                    1.0            0.787879         35.132482   \n",
        "\n",
        "   FOMOAD RAM (KB)  \n",
-       "0         2.381328  \n",
+       "0           2337.6  \n",
        "\n",
        "[1 rows x 33 columns]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9380,7 +974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -9412,7 +1006,7 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Dataset</th>\n",
-       "      <th>F1 ALL</th>\n",
+       "      <th>F1 TEST</th>\n",
        "      <th>F1 NO ANOMALY</th>\n",
        "      <th>F1 EASY</th>\n",
        "      <th>F1 MEDIUM</th>\n",
@@ -9425,50 +1019,50 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>cookies_1</td>\n",
-       "      <td>0.882682</td>\n",
+       "      <td>0.865248</td>\n",
        "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.275862</td>\n",
-       "      <td>43.899704</td>\n",
-       "      <td>0.132109</td>\n",
+       "      <td>0.095238</td>\n",
+       "      <td>39.690981</td>\n",
+       "      <td>436.64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>cookies_2</td>\n",
-       "      <td>0.880435</td>\n",
-       "      <td>0.984772</td>\n",
+       "      <td>0.888889</td>\n",
+       "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.387097</td>\n",
-       "      <td>48.814734</td>\n",
-       "      <td>-0.298672</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>43.644302</td>\n",
+       "      <td>125.12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>cookies_3</td>\n",
-       "      <td>0.888889</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>0.917197</td>\n",
+       "      <td>0.857143</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.333333</td>\n",
-       "      <td>47.018294</td>\n",
-       "      <td>-0.634219</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>42.710402</td>\n",
+       "      <td>-192.32</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "     Dataset    F1 ALL  F1 NO ANOMALY  F1 EASY  F1 MEDIUM   F1 HARD  \\\n",
-       "0  cookies_1  0.882682       1.000000      1.0        1.0  0.275862   \n",
-       "1  cookies_2  0.880435       0.984772      1.0        1.0  0.387097   \n",
-       "2  cookies_3  0.888889       1.000000      1.0        1.0  0.333333   \n",
+       "     Dataset   F1 TEST  F1 NO ANOMALY  F1 EASY  F1 MEDIUM   F1 HARD  \\\n",
+       "0  cookies_1  0.865248       1.000000      1.0        1.0  0.095238   \n",
+       "1  cookies_2  0.888889       1.000000      1.0        1.0  0.333333   \n",
+       "2  cookies_3  0.917197       0.857143      1.0        1.0  0.750000   \n",
        "\n",
        "   Time (ms)  RAM (KB)  \n",
-       "0  43.899704  0.132109  \n",
-       "1  48.814734 -0.298672  \n",
-       "2  47.018294 -0.634219  "
+       "0  39.690981    436.64  \n",
+       "1  43.644302    125.12  \n",
+       "2  42.710402   -192.32  "
       ]
      },
      "metadata": {},
@@ -9503,7 +1097,7 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Dataset</th>\n",
-       "      <th>F1 ALL</th>\n",
+       "      <th>F1 TEST</th>\n",
        "      <th>F1 NO ANOMALY</th>\n",
        "      <th>F1 EASY</th>\n",
        "      <th>F1 MEDIUM</th>\n",
@@ -9516,50 +1110,50 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>cookies_1</td>\n",
-       "      <td>0.877778</td>\n",
-       "      <td>0.994975</td>\n",
+       "      <td>0.875000</td>\n",
+       "      <td>0.974359</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.275862</td>\n",
-       "      <td>35.320630</td>\n",
-       "      <td>0.440625</td>\n",
+       "      <td>0.260870</td>\n",
+       "      <td>31.956229</td>\n",
+       "      <td>476.80</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>cookies_2</td>\n",
-       "      <td>0.878505</td>\n",
-       "      <td>0.888889</td>\n",
+       "      <td>0.954248</td>\n",
+       "      <td>1.000000</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.863636</td>\n",
-       "      <td>40.969303</td>\n",
-       "      <td>-0.857500</td>\n",
+       "      <td>0.787879</td>\n",
+       "      <td>33.593299</td>\n",
+       "      <td>17.92</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>cookies_3</td>\n",
-       "      <td>0.930481</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>0.941935</td>\n",
+       "      <td>0.947368</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.648649</td>\n",
-       "      <td>38.269688</td>\n",
-       "      <td>-0.221016</td>\n",
+       "      <td>0.787879</td>\n",
+       "      <td>33.523099</td>\n",
+       "      <td>244.48</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "     Dataset    F1 ALL  F1 NO ANOMALY  F1 EASY  F1 MEDIUM   F1 HARD  \\\n",
-       "0  cookies_1  0.877778       0.994975      1.0        1.0  0.275862   \n",
-       "1  cookies_2  0.878505       0.888889      1.0        1.0  0.863636   \n",
-       "2  cookies_3  0.930481       1.000000      1.0        1.0  0.648649   \n",
+       "     Dataset   F1 TEST  F1 NO ANOMALY  F1 EASY  F1 MEDIUM   F1 HARD  \\\n",
+       "0  cookies_1  0.875000       0.974359      1.0        1.0  0.260870   \n",
+       "1  cookies_2  0.954248       1.000000      1.0        1.0  0.787879   \n",
+       "2  cookies_3  0.941935       0.947368      1.0        1.0  0.787879   \n",
        "\n",
        "   Time (ms)  RAM (KB)  \n",
-       "0  35.320630  0.440625  \n",
-       "1  40.969303 -0.857500  \n",
-       "2  38.269688 -0.221016  "
+       "0  31.956229    476.80  \n",
+       "1  33.593299     17.92  \n",
+       "2  33.523099    244.48  "
       ]
      },
      "metadata": {},
@@ -9594,7 +1188,7 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Dataset</th>\n",
-       "      <th>F1 ALL</th>\n",
+       "      <th>F1 TEST</th>\n",
        "      <th>F1 NO ANOMALY</th>\n",
        "      <th>F1 EASY</th>\n",
        "      <th>F1 MEDIUM</th>\n",
@@ -9607,50 +1201,50 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>cookies_1</td>\n",
-       "      <td>0.931373</td>\n",
-       "      <td>0.952880</td>\n",
+       "      <td>0.904110</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.888889</td>\n",
-       "      <td>299.686815</td>\n",
-       "      <td>8231.44</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.461538</td>\n",
+       "      <td>299.973309</td>\n",
+       "      <td>9296.64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>cookies_2</td>\n",
-       "      <td>0.995025</td>\n",
-       "      <td>0.994975</td>\n",
+       "      <td>0.888889</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>319.951710</td>\n",
-       "      <td>284.24</td>\n",
+       "      <td>0.918919</td>\n",
+       "      <td>0.518519</td>\n",
+       "      <td>318.657351</td>\n",
+       "      <td>2638.08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>cookies_3</td>\n",
-       "      <td>0.994975</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>0.993711</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.979592</td>\n",
-       "      <td>319.854028</td>\n",
-       "      <td>3025.52</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.974359</td>\n",
+       "      <td>305.910184</td>\n",
+       "      <td>2326.72</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "     Dataset    F1 ALL  F1 NO ANOMALY  F1 EASY  F1 MEDIUM   F1 HARD  \\\n",
-       "0  cookies_1  0.931373       0.952880      1.0        1.0  0.888889   \n",
-       "1  cookies_2  0.995025       0.994975      1.0        1.0  1.000000   \n",
-       "2  cookies_3  0.994975       1.000000      1.0        1.0  0.979592   \n",
+       "     Dataset   F1 TEST  F1 NO ANOMALY  F1 EASY  F1 MEDIUM   F1 HARD  \\\n",
+       "0  cookies_1  0.904110            1.0      1.0   1.000000  0.461538   \n",
+       "1  cookies_2  0.888889            1.0      1.0   0.918919  0.518519   \n",
+       "2  cookies_3  0.993711            1.0      1.0   1.000000  0.974359   \n",
        "\n",
        "    Time (ms)  RAM (KB)  \n",
-       "0  299.686815   8231.44  \n",
-       "1  319.951710    284.24  \n",
-       "2  319.854028   3025.52  "
+       "0  299.973309   9296.64  \n",
+       "1  318.657351   2638.08  \n",
+       "2  305.910184   2326.72  "
       ]
      },
      "metadata": {},
@@ -9685,7 +1279,7 @@
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Dataset</th>\n",
-       "      <th>F1 ALL</th>\n",
+       "      <th>F1 TEST</th>\n",
        "      <th>F1 NO ANOMALY</th>\n",
        "      <th>F1 EASY</th>\n",
        "      <th>F1 MEDIUM</th>\n",
@@ -9698,50 +1292,50 @@
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>cookies_1</td>\n",
-       "      <td>0.943005</td>\n",
-       "      <td>0.989899</td>\n",
+       "      <td>0.954248</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.780488</td>\n",
-       "      <td>34.033175</td>\n",
-       "      <td>2.381328</td>\n",
+       "      <td>0.787879</td>\n",
+       "      <td>35.132482</td>\n",
+       "      <td>2337.60</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>cookies_2</td>\n",
-       "      <td>0.980198</td>\n",
-       "      <td>0.984772</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.979592</td>\n",
-       "      <td>38.634827</td>\n",
-       "      <td>-0.465625</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>38.245327</td>\n",
+       "      <td>129.12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>cookies_3</td>\n",
-       "      <td>0.989899</td>\n",
-       "      <td>1.000000</td>\n",
+       "      <td>0.993711</td>\n",
        "      <td>1.0</td>\n",
        "      <td>1.0</td>\n",
-       "      <td>0.958333</td>\n",
-       "      <td>40.206773</td>\n",
-       "      <td>0.079219</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.974359</td>\n",
+       "      <td>38.416507</td>\n",
+       "      <td>956.32</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "     Dataset    F1 ALL  F1 NO ANOMALY  F1 EASY  F1 MEDIUM   F1 HARD  \\\n",
-       "0  cookies_1  0.943005       0.989899      1.0        1.0  0.780488   \n",
-       "1  cookies_2  0.980198       0.984772      1.0        1.0  0.979592   \n",
-       "2  cookies_3  0.989899       1.000000      1.0        1.0  0.958333   \n",
+       "     Dataset   F1 TEST  F1 NO ANOMALY  F1 EASY  F1 MEDIUM   F1 HARD  \\\n",
+       "0  cookies_1  0.954248            1.0      1.0        1.0  0.787879   \n",
+       "1  cookies_2  1.000000            1.0      1.0        1.0  1.000000   \n",
+       "2  cookies_3  0.993711            1.0      1.0        1.0  0.974359   \n",
        "\n",
        "   Time (ms)  RAM (KB)  \n",
-       "0  34.033175  2.381328  \n",
-       "1  38.634827 -0.465625  \n",
-       "2  40.206773  0.079219  "
+       "0  35.132482   2337.60  \n",
+       "1  38.245327    129.12  \n",
+       "2  38.416507    956.32  "
       ]
      },
      "metadata": {},
@@ -9752,13 +1346,13 @@
     "new_columns = pd.MultiIndex.from_tuples([  \n",
     "    ('', 'Dataset'),                                # Common dataset column  \n",
     "    ('Baseline', 'Dataset'),  \n",
-    "    ('Baseline', 'F1 ALL'), ('Baseline', 'F1 NO ANOMALY'), ('Baseline', 'F1 EASY'), ('Baseline', 'F1 MEDIUM'), ('Baseline', 'F1 HARD'), ('Baseline', 'Time (ms)'), ('Baseline', 'RAM (KB)'),  \n",
+    "    ('Baseline', 'F1 TEST'), ('Baseline', 'F1 NO ANOMALY'), ('Baseline', 'F1 EASY'), ('Baseline', 'F1 MEDIUM'), ('Baseline', 'F1 HARD'), ('Baseline', 'Time (ms)'), ('Baseline', 'RAM (KB)'),  \n",
     "    ('Baseline EI', 'Dataset'),  \n",
-    "    ('Baseline EI', 'F1 ALL'), ('Baseline EI', 'F1 NO ANOMALY'), ('Baseline EI', 'F1 EASY'), ('Baseline EI', 'F1 MEDIUM'), ('Baseline EI', 'F1 HARD'), ('Baseline EI', 'Time (ms)'), ('Baseline EI', 'RAM (KB)'),  \n",
+    "    ('Baseline EI', 'F1 TEST'), ('Baseline EI', 'F1 NO ANOMALY'), ('Baseline EI', 'F1 EASY'), ('Baseline EI', 'F1 MEDIUM'), ('Baseline EI', 'F1 HARD'), ('Baseline EI', 'Time (ms)'), ('Baseline EI', 'RAM (KB)'),  \n",
     "    ('EfficientAD', 'Dataset'),  \n",
-    "    ('EfficientAD', 'F1 ALL'), ('EfficientAD', 'F1 NO ANOMALY'), ('EfficientAD', 'F1 EASY'), ('EfficientAD', 'F1 MEDIUM'), ('EfficientAD', 'F1 HARD'), ('EfficientAD', 'Time (ms)'), ('EfficientAD', 'RAM (KB)'),  \n",
+    "    ('EfficientAD', 'F1 TEST'), ('EfficientAD', 'F1 NO ANOMALY'), ('EfficientAD', 'F1 EASY'), ('EfficientAD', 'F1 MEDIUM'), ('EfficientAD', 'F1 HARD'), ('EfficientAD', 'Time (ms)'), ('EfficientAD', 'RAM (KB)'),  \n",
     "    ('FOMOAD', 'Dataset'),  \n",
-    "    ('FOMOAD', 'F1 ALL'), ('FOMOAD', 'F1 NO ANOMALY'), ('FOMOAD', 'F1 EASY'), ('FOMOAD', 'F1 MEDIUM'), ('FOMOAD', 'F1 HARD'), ('FOMOAD', 'Time (ms)'), ('FOMOAD', 'RAM (KB)')  \n",
+    "    ('FOMOAD', 'F1 TEST'), ('FOMOAD', 'F1 NO ANOMALY'), ('FOMOAD', 'F1 EASY'), ('FOMOAD', 'F1 MEDIUM'), ('FOMOAD', 'F1 HARD'), ('FOMOAD', 'Time (ms)'), ('FOMOAD', 'RAM (KB)')  \n",
     "])  \n",
     "benchmarks.columns = new_columns  \n",
     "\n",
@@ -9775,16 +1369,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
-    "benchmarks.to_csv('../output/benchmarks.csv', index=False)  \n"
+    "benchmarks.to_csv('../output/benchmarks.csv', index=False)  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
diff --git a/website/src/components/react/DatasetReactDetail.tsx b/website/src/components/react/DatasetReactDetail.tsx
index 7545460..7f14580 100644
--- a/website/src/components/react/DatasetReactDetail.tsx
+++ b/website/src/components/react/DatasetReactDetail.tsx
@@ -48,6 +48,8 @@ export default function DatasetReactDetail() {
       <div className="text-center md:gap-16 pt-12 md:py-10">
         <h3 className="text-2xl font-bold tracking-tight dark:text-white sm:text-3xl mb-2">
           <span className="text-[#2a9d8f]">Dataset</span> Explorer
+          <br />
+          <span className="text-[0.6em]">Explore Test data (val and train not included)</span>
         </h3>
       </div>
       <div className="md:gap-16 max-w-7xl mx-auto md:py-10 md:flex">
@@ -123,7 +125,7 @@ export default function DatasetReactDetail() {
             </div>
           </div>
         </div>
-      </div> 
+      </div>
 
       <div className="mx-auto md:gap-16 py-12 p-4 md:py-10 max-w-7xl">
         <h4 className="text-center font-bold mb-5">Table representing the full dataset and model selected</h4>
diff --git a/website/src/utils/stats/benchmarks.json b/website/src/utils/stats/benchmarks.json
index 4f181d4..fd61934 100644
--- a/website/src/utils/stats/benchmarks.json
+++ b/website/src/utils/stats/benchmarks.json
@@ -1 +1 @@
-{"cookies_1": {"Dataset": "cookies_1", "Baseline Dataset": "cookies_1", "Baseline F1 ALL": 0.88268156424581, "Baseline F1 NO ANOMALY": 1.0, "Baseline F1 EASY": 1.0, "Baseline F1 MEDIUM": 1.0, "Baseline F1 HARD": 0.27586206896551724, "Baseline Time (ms)": 43.89970421791077, "Baseline RAM (KB)": 0.132109375, "Baseline EI Dataset": "cookies_1", "Baseline EI F1 ALL": 0.8777777777777778, "Baseline EI F1 NO ANOMALY": 0.9949748743718593, "Baseline EI F1 EASY": 1.0, "Baseline EI F1 MEDIUM": 1.0, "Baseline EI F1 HARD": 0.27586206896551724, "Baseline EI Time (ms)": 35.32063007354736, "Baseline EI RAM (KB)": 0.440625, "EfficientAD Dataset": "cookies_1", "EfficientAD F1 ALL": 0.9313725490196079, "EfficientAD F1 NO ANOMALY": 0.9528795811518325, "EfficientAD F1 EASY": 1.0, "EfficientAD F1 MEDIUM": 1.0, "EfficientAD F1 HARD": 0.8888888888888888, "EfficientAD Time (ms)": 299.6868145465851, "EfficientAD RAM (KB)": 8231.44, "FOMOAD Dataset": "cookies_1", "FOMOAD F1 ALL": 0.9430051813471503, "FOMOAD F1 NO ANOMALY": 0.98989898989899, "FOMOAD F1 EASY": 1.0, "FOMOAD F1 MEDIUM": 1.0, "FOMOAD F1 HARD": 0.7804878048780488, "FOMOAD Time (ms)": 34.03317451477051, "FOMOAD RAM (KB)": 2.381328125}, "cookies_2": {"Dataset": "cookies_2", "Baseline Dataset": "cookies_2", "Baseline F1 ALL": 0.8804347826086957, "Baseline F1 NO ANOMALY": 0.9847715736040609, "Baseline F1 EASY": 1.0, "Baseline F1 MEDIUM": 1.0, "Baseline F1 HARD": 0.3870967741935484, "Baseline Time (ms)": 48.81473422050476, "Baseline RAM (KB)": -0.298671875, "Baseline EI Dataset": "cookies_2", "Baseline EI F1 ALL": 0.8785046728971962, "Baseline EI F1 NO ANOMALY": 0.8888888888888888, "Baseline EI F1 EASY": 1.0, "Baseline EI F1 MEDIUM": 1.0, "Baseline EI F1 HARD": 0.8636363636363636, "Baseline EI Time (ms)": 40.96930265426636, "Baseline EI RAM (KB)": -0.8575, "EfficientAD Dataset": "cookies_2", "EfficientAD F1 ALL": 0.9950248756218906, "EfficientAD F1 NO ANOMALY": 0.9949748743718593, "EfficientAD F1 EASY": 1.0, "EfficientAD F1 MEDIUM": 1.0, "EfficientAD F1 HARD": 1.0, "EfficientAD Time (ms)": 319.9517095088959, "EfficientAD RAM (KB)": 284.24, "FOMOAD Dataset": "cookies_2", "FOMOAD F1 ALL": 0.9801980198019802, "FOMOAD F1 NO ANOMALY": 0.9847715736040609, "FOMOAD F1 EASY": 1.0, "FOMOAD F1 MEDIUM": 1.0, "FOMOAD F1 HARD": 0.9795918367346939, "FOMOAD Time (ms)": 38.63482713699341, "FOMOAD RAM (KB)": -0.465625}, "cookies_3": {"Dataset": "cookies_3", "Baseline Dataset": "cookies_3", "Baseline F1 ALL": 0.8888888888888888, "Baseline F1 NO ANOMALY": 1.0, "Baseline F1 EASY": 1.0, "Baseline F1 MEDIUM": 1.0, "Baseline F1 HARD": 0.3333333333333333, "Baseline Time (ms)": 47.01829433441162, "Baseline RAM (KB)": -0.63421875, "Baseline EI Dataset": "cookies_3", "Baseline EI F1 ALL": 0.93048128342246, "Baseline EI F1 NO ANOMALY": 1.0, "Baseline EI F1 EASY": 1.0, "Baseline EI F1 MEDIUM": 1.0, "Baseline EI F1 HARD": 0.6486486486486487, "Baseline EI Time (ms)": 38.26968789100647, "Baseline EI RAM (KB)": -0.221015625, "EfficientAD Dataset": "cookies_3", "EfficientAD F1 ALL": 0.9949748743718593, "EfficientAD F1 NO ANOMALY": 1.0, "EfficientAD F1 EASY": 1.0, "EfficientAD F1 MEDIUM": 1.0, "EfficientAD F1 HARD": 0.9795918367346939, "EfficientAD Time (ms)": 319.85402822494507, "EfficientAD RAM (KB)": 3025.52, "FOMOAD Dataset": "cookies_3", "FOMOAD F1 ALL": 0.98989898989899, "FOMOAD F1 NO ANOMALY": 1.0, "FOMOAD F1 EASY": 1.0, "FOMOAD F1 MEDIUM": 1.0, "FOMOAD F1 HARD": 0.9583333333333334, "FOMOAD Time (ms)": 40.20677328109741, "FOMOAD RAM (KB)": 0.07921875}}
\ No newline at end of file
+{"cookies_1": {"Dataset": "cookies_1", "Baseline Dataset": "cookies_1", "Baseline F1 TEST": 0.8652482269503546, "Baseline F1 NO ANOMALY TEST": 1.0, "Baseline F1 EASY TEST": 1.0, "Baseline F1 MEDIUM TEST": 1.0, "Baseline F1 HARD TEST": 0.09523809523809523, "Baseline Time (ms)": 39.69098091125488, "Baseline RAM (KB)": 436.64, "Baseline EI Dataset": "cookies_1", "Baseline EI F1 TEST": 0.875, "Baseline EI F1 NO ANOMALY TEST": 0.9743589743589743, "Baseline EI F1 EASY TEST": 1.0, "Baseline EI F1 MEDIUM TEST": 1.0, "Baseline EI F1 HARD TEST": 0.2608695652173913, "Baseline EI Time (ms)": 31.956229209899902, "Baseline EI RAM (KB)": 476.8, "EfficientAD Dataset": "cookies_1", "EfficientAD F1 TEST": 0.9041095890410958, "EfficientAD F1 NO ANOMALY TEST": 1.0, "EfficientAD F1 EASY TEST": 1.0, "EfficientAD F1 MEDIUM TEST": 1.0, "EfficientAD F1 HARD TEST": 0.46153846153846156, "EfficientAD Time (ms)": 299.9733090400696, "EfficientAD RAM (KB)": 9296.64, "FOMOAD Dataset": "cookies_1", "FOMOAD F1 TEST": 0.954248366013072, "FOMOAD F1 NO ANOMALY TEST": 1.0, "FOMOAD F1 EASY TEST": 1.0, "FOMOAD F1 MEDIUM TEST": 1.0, "FOMOAD F1 HARD TEST": 0.7878787878787878, "FOMOAD Time (ms)": 35.132482051849365, "FOMOAD RAM (KB)": 2337.6}, "cookies_2": {"Dataset": "cookies_2", "Baseline Dataset": "cookies_2", "Baseline F1 TEST": 0.8888888888888888, "Baseline F1 NO ANOMALY TEST": 1.0, "Baseline F1 EASY TEST": 1.0, "Baseline F1 MEDIUM TEST": 1.0, "Baseline F1 HARD TEST": 0.3333333333333333, "Baseline Time (ms)": 43.644301891326904, "Baseline RAM (KB)": 125.12, "Baseline EI Dataset": "cookies_2", "Baseline EI F1 TEST": 0.954248366013072, "Baseline EI F1 NO ANOMALY TEST": 1.0, "Baseline EI F1 EASY TEST": 1.0, "Baseline EI F1 MEDIUM TEST": 1.0, "Baseline EI F1 HARD TEST": 0.7878787878787878, "Baseline EI Time (ms)": 33.5932993888855, "Baseline EI RAM (KB)": 17.92, "EfficientAD Dataset": "cookies_2", "EfficientAD F1 TEST": 0.8888888888888888, "EfficientAD F1 NO ANOMALY TEST": 1.0, "EfficientAD F1 EASY TEST": 1.0, "EfficientAD F1 MEDIUM TEST": 0.918918918918919, "EfficientAD F1 HARD TEST": 0.5185185185185185, "EfficientAD Time (ms)": 318.65735054016113, "EfficientAD RAM (KB)": 2638.08, "FOMOAD Dataset": "cookies_2", "FOMOAD F1 TEST": 1.0, "FOMOAD F1 NO ANOMALY TEST": 1.0, "FOMOAD F1 EASY TEST": 1.0, "FOMOAD F1 MEDIUM TEST": 1.0, "FOMOAD F1 HARD TEST": 1.0, "FOMOAD Time (ms)": 38.24532747268677, "FOMOAD RAM (KB)": 129.12}, "cookies_3": {"Dataset": "cookies_3", "Baseline Dataset": "cookies_3", "Baseline F1 TEST": 0.9171974522292994, "Baseline F1 NO ANOMALY TEST": 0.8571428571428571, "Baseline F1 EASY TEST": 1.0, "Baseline F1 MEDIUM TEST": 1.0, "Baseline F1 HARD TEST": 0.75, "Baseline Time (ms)": 42.71040201187134, "Baseline RAM (KB)": -192.32, "Baseline EI Dataset": "cookies_3", "Baseline EI F1 TEST": 0.9419354838709677, "Baseline EI F1 NO ANOMALY TEST": 0.9473684210526315, "Baseline EI F1 EASY TEST": 1.0, "Baseline EI F1 MEDIUM TEST": 1.0, "Baseline EI F1 HARD TEST": 0.7878787878787878, "Baseline EI Time (ms)": 33.523099422454834, "Baseline EI RAM (KB)": 244.48, "EfficientAD Dataset": "cookies_3", "EfficientAD F1 TEST": 0.9937106918238994, "EfficientAD F1 NO ANOMALY TEST": 1.0, "EfficientAD F1 EASY TEST": 1.0, "EfficientAD F1 MEDIUM TEST": 1.0, "EfficientAD F1 HARD TEST": 0.9743589743589743, "EfficientAD Time (ms)": 305.91018438339233, "EfficientAD RAM (KB)": 2326.72, "FOMOAD Dataset": "cookies_3", "FOMOAD F1 TEST": 0.9937106918238994, "FOMOAD F1 NO ANOMALY TEST": 1.0, "FOMOAD F1 EASY TEST": 1.0, "FOMOAD F1 MEDIUM TEST": 1.0, "FOMOAD F1 HARD TEST": 0.9743589743589743, "FOMOAD Time (ms)": 38.41650724411011, "FOMOAD RAM (KB)": 956.32}}
\ No newline at end of file
diff --git a/website/src/utils/stats/stats.ts b/website/src/utils/stats/stats.ts
index 19ff93e..7f35ea8 100644
--- a/website/src/utils/stats/stats.ts
+++ b/website/src/utils/stats/stats.ts
@@ -12,16 +12,16 @@ export const getDatasetSizeChart = () => {
 export const getBenchmarkF1Score = (dataset) => {
     return [{
         name: "Baseline",
-        "F1 score": benchmark[dataset]["Baseline F1 ALL"].toFixed(2)
+        "F1 score": benchmark[dataset]["Baseline F1 TEST"].toFixed(2)
     }, {
         name: "Baseline EI",
-        "F1 score": benchmark[dataset]["Baseline EI F1 ALL"].toFixed(2)
+        "F1 score": benchmark[dataset]["Baseline EI F1 TEST"].toFixed(2)
     }, {
         name: "Efficient AD",
-        "F1 score": benchmark[dataset]["EfficientAD F1 ALL"].toFixed(2)
+        "F1 score": benchmark[dataset]["EfficientAD F1 TEST"].toFixed(2)
     }, {
         name: "FOMO AD",
-        "F1 score": benchmark[dataset]["FOMOAD F1 ALL"].toFixed(2)
+        "F1 score": benchmark[dataset]["FOMOAD F1 TEST"].toFixed(2)
     }]
 }
 export const getBenchmarkF1ScorePerDifficulty = (dataset, model) => {
@@ -37,19 +37,19 @@ export const getBenchmarkF1ScorePerDifficulty = (dataset, model) => {
 
     return [{
         name: "No anomaly",
-        "F1 score": parseFloat(benchmark[dataset][`${m} F1 NO ANOMALY`].toFixed(2)),
+        "F1 score": parseFloat(benchmark[dataset][`${m} F1 NO ANOMALY TEST`].toFixed(2)),
         fill: COLORS[3]
     }, {
         name: "Easy",
-        "F1 score": parseFloat(benchmark[dataset][`${m} F1 EASY`].toFixed(2)),
+        "F1 score": parseFloat(benchmark[dataset][`${m} F1 EASY TEST`].toFixed(2)),
         fill: COLORS[0]
     }, {
         name: "Medium",
-        "F1 score": parseFloat(benchmark[dataset][`${m} F1 MEDIUM`].toFixed(2)),
+        "F1 score": parseFloat(benchmark[dataset][`${m} F1 MEDIUM TEST`].toFixed(2)),
         fill: COLORS[1]
     }, {
         name: "Hard",
-        "F1 score": parseFloat(benchmark[dataset][`${m} F1 HARD`].toFixed(2)),
+        "F1 score": parseFloat(benchmark[dataset][`${m} F1 HARD TEST`].toFixed(2)),
         fill: COLORS[2]
     }]
 }
diff --git a/website/src/utils/stats/stats_for_website.json b/website/src/utils/stats/stats_for_website.json
index ebc6dea..52fa19c 100644
--- a/website/src/utils/stats/stats_for_website.json
+++ b/website/src/utils/stats/stats_for_website.json
@@ -1,19832 +1 @@
-{
-  "cookies_1": {
-    "baseline": [
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112745.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6885677576065063,
-        "t": 261.66605949401855
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112023.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7316128015518188,
-        "t": 52.2160530090332
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111927.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7790935635566711,
-        "t": 37.7202033996582
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113117.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7643111348152161,
-        "t": 39.3369197845459
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111853.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8303715586662292,
-        "t": 42.63925552368164
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112816.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8406801819801331,
-        "t": 43.11394691467285
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112828.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7468494176864624,
-        "t": 41.793107986450195
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112223.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7683916091918945,
-        "t": 39.079904556274414
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112237.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8036873936653137,
-        "t": 38.040876388549805
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112009.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7877269983291626,
-        "t": 38.561105728149414
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112752.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.751685619354248,
-        "t": 40.65418243408203
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113114.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6270976066589355,
-        "t": 88.6068344116211
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112801.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8358895182609558,
-        "t": 41.02587699890137
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112154.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8563663959503174,
-        "t": 43.88689994812012
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112150.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6644109487533569,
-        "t": 39.23296928405762
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112226.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7246798872947693,
-        "t": 38.81025314331055
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112232.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7693866491317749,
-        "t": 39.669036865234375
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112958.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7388356328010559,
-        "t": 41.84913635253906
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111921.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7720507979393005,
-        "t": 72.1440315246582
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111935.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8021633625030518,
-        "t": 91.16816520690918
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112409.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8598558306694031,
-        "t": 34.33990478515625
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112421.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7714780569076538,
-        "t": 39.212942123413086
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112810.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5793555378913879,
-        "t": 76.1709213256836
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112838.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8175971508026123,
-        "t": 38.38706016540527
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112219.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7743093967437744,
-        "t": 37.82200813293457
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112027.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7377780079841614,
-        "t": 40.03310203552246
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112741.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6552329063415527,
-        "t": 37.08195686340332
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111705.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6592632532119751,
-        "t": 45.45092582702637
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112813.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7542796730995178,
-        "t": 37.73093223571777
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112121.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.767880380153656,
-        "t": 41.93878173828125
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112525.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7963200211524963,
-        "t": 38.18917274475098
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112727.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6739753484725952,
-        "t": 37.99867630004883
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112914.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8344675898551941,
-        "t": 38.31982612609863
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112901.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7133530378341675,
-        "t": 37.68205642700195
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112256.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7006509900093079,
-        "t": 36.61704063415527
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112518.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6722748875617981,
-        "t": 36.68975830078125
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113000.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6754862666130066,
-        "t": 42.27471351623535
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111826.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6669784188270569,
-        "t": 42.16480255126953
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112903.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8619998693466187,
-        "t": 43.78008842468262
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112719.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6915009021759033,
-        "t": 35.943031311035156
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113003.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7196997404098511,
-        "t": 36.47303581237793
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112651.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7593010067939758,
-        "t": 36.68618202209473
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112912.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8047858476638794,
-        "t": 39.016008377075195
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112126.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7484652400016785,
-        "t": 36.88502311706543
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112654.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7105329036712646,
-        "t": 40.850162506103516
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112508.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7441871762275696,
-        "t": 35.81428527832031
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112938.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7618421316146851,
-        "t": 37.30297088623047
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112723.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8337277770042419,
-        "t": 37.0938777923584
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112253.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6728473901748657,
-        "t": 37.635087966918945
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113005.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7813147902488708,
-        "t": 36.55099868774414
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112858.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.784222424030304,
-        "t": 40.00997543334961
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112657.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7232108116149902,
-        "t": 41.65220260620117
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112841.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7972258925437927,
-        "t": 35.30383110046387
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112855.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.770932137966156,
-        "t": 36.0560417175293
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112302.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7944220900535583,
-        "t": 64.13102149963379
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113008.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7990281581878662,
-        "t": 35.63714027404785
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112909.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8585007190704346,
-        "t": 37.245988845825195
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112713.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7207356095314026,
-        "t": 40.40789604187012
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112511.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6364820599555969,
-        "t": 37.668704986572266
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111839.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6021881699562073,
-        "t": 47.79386520385742
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113035.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.705615222454071,
-        "t": 36.23080253601074
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113037.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7969406247138977,
-        "t": 39.49308395385742
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112705.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7324842214584351,
-        "t": 44.387102127075195
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112116.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6918696165084839,
-        "t": 43.396711349487305
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112112.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6805323362350464,
-        "t": 38.96784782409668
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112853.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7344591617584229,
-        "t": 38.50102424621582
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112927.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8541102409362793,
-        "t": 36.40604019165039
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112701.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7685565948486328,
-        "t": 39.289236068725586
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112259.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5052832961082458,
-        "t": 54.302215576171875
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112305.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8255069851875305,
-        "t": 40.79294204711914
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112105.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6711005568504333,
-        "t": 37.14632987976074
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112515.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7408745288848877,
-        "t": 91.54510498046875
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112924.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.762854814529419,
-        "t": 39.25490379333496
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112716.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6788265705108643,
-        "t": 37.22500801086426
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112822.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7274863123893738,
-        "t": 38.150787353515625
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113043.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8015134930610657,
-        "t": 39.086103439331055
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112413.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7050288319587708,
-        "t": 35.95924377441406
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113123.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8316828608512878,
-        "t": 37.26792335510254
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112201.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7591837048530579,
-        "t": 40.60506820678711
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112016.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7828199863433838,
-        "t": 36.850929260253906
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112406.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7194902896881104,
-        "t": 36.98897361755371
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113040.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7908033728599548,
-        "t": 38.87486457824707
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113120.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6869837045669556,
-        "t": 39.473772048950195
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111938.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8020341396331787,
-        "t": 38.29693794250488
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112941.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8249087929725647,
-        "t": 43.734073638916016
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112954.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7711849808692932,
-        "t": 35.047292709350586
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112944.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6164059638977051,
-        "t": 36.90195083618164
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111848.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7155172824859619,
-        "t": 46.642303466796875
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112831.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7231912016868591,
-        "t": 37.544965744018555
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112819.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8589324355125427,
-        "t": 42.36912727355957
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112158.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6848112940788269,
-        "t": 41.22209548950195
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112833.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6049192547798157,
-        "t": 41.964054107666016
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112402.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8504793047904968,
-        "t": 88.52314949035645
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113046.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.623792827129364,
-        "t": 36.87310218811035
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112204.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8454285264015198,
-        "t": 40.91310501098633
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113126.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8556872010231018,
-        "t": 43.19119453430176
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111916.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7639741897583008,
-        "t": 38.34104537963867
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112947.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6642532348632812,
-        "t": 36.6971492767334
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112749.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7742463946342468,
-        "t": 36.740779876708984
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112013.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6985099911689758,
-        "t": 37.78815269470215
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114123.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9840025305747986,
-        "t": 37.946224212646484
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113920.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9935095906257629,
-        "t": 37.76717185974121
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113706.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.988100528717041,
-        "t": 36.803245544433594
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113514.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9950453042984009,
-        "t": 41.55325889587402
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114118.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.989985466003418,
-        "t": 39.402008056640625
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113729.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9240877628326416,
-        "t": 38.763999938964844
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113701.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9703605771064758,
-        "t": 44.074058532714844
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113932.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9869792461395264,
-        "t": 88.49978446960449
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114126.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9944333434104919,
-        "t": 38.17009925842285
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113940.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9677634239196777,
-        "t": 48.496246337890625
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114154.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9476577043533325,
-        "t": 39.60299491882324
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113438.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9884589314460754,
-        "t": 91.05992317199707
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114021.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9948423504829407,
-        "t": 40.10510444641113
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113202.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9860209822654724,
-        "t": 41.407108306884766
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113214.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9314103722572327,
-        "t": 37.79196739196777
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114157.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9029171466827393,
-        "t": 40.98105430603027
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113638.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9581395983695984,
-        "t": 41.970014572143555
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114143.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9766975045204163,
-        "t": 48.438072204589844
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113349.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9817102551460266,
-        "t": 42.465925216674805
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113359.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9783176183700562,
-        "t": 38.057804107666016
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113403.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9704654216766357,
-        "t": 38.89012336730957
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114031.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9952638149261475,
-        "t": 42.3579216003418
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114151.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9759737849235535,
-        "t": 39.23797607421875
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113207.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9662362337112427,
-        "t": 39.21818733215332
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113430.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9898026585578918,
-        "t": 41.62788391113281
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113800.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9283844232559204,
-        "t": 34.446001052856445
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114202.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9892395734786987,
-        "t": 36.52596473693848
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113223.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9706888794898987,
-        "t": 39.40987586975098
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113156.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9431055784225464,
-        "t": 37.46795654296875
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114017.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9917952418327332,
-        "t": 52.681922912597656
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114003.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9902141690254211,
-        "t": 41.1219596862793
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113807.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9775828123092651,
-        "t": 37.70804405212402
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113813.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9587603807449341,
-        "t": 40.10891914367676
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113344.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9488731622695923,
-        "t": 36.312103271484375
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113353.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9834213852882385,
-        "t": 41.02897644042969
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113421.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9970609545707703,
-        "t": 38.76829147338867
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113434.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9843015074729919,
-        "t": 42.491912841796875
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114011.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9958032965660095,
-        "t": 38.50507736206055
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113724.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8753779530525208,
-        "t": 36.164045333862305
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113526.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.996311366558075,
-        "t": 37.014007568359375
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113915.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9912818670272827,
-        "t": 38.965702056884766
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113646.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9441637396812439,
-        "t": 41.26477241516113
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113928.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9828742742538452,
-        "t": 40.3599739074707
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113519.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9948732256889343,
-        "t": 44.74782943725586
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113531.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9881644248962402,
-        "t": 39.737701416015625
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113657.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9644671082496643,
-        "t": 38.788795471191406
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114059.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9952539205551147,
-        "t": 38.437843322753906
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113522.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9886343479156494,
-        "t": 61.82217597961426
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113442.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9604032039642334,
-        "t": 38.65790367126465
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114106.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9850265383720398,
-        "t": 37.581682205200195
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114533.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9699264764785767,
-        "t": 38.79809379577637
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114725.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.981616735458374,
-        "t": 39.17098045349121
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114651.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9889132380485535,
-        "t": 37.091970443725586
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114644.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9333534836769104,
-        "t": 37.07695007324219
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114732.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9437193274497986,
-        "t": 38.56515884399414
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114444.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9427350163459778,
-        "t": 36.92007064819336
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114647.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9641220569610596,
-        "t": 38.46597671508789
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114736.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9776043891906738,
-        "t": 38.86914253234863
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114520.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.95941162109375,
-        "t": 37.146806716918945
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114640.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9821464419364929,
-        "t": 38.333892822265625
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114537.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9595292806625366,
-        "t": 38.40517997741699
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114815.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9498929381370544,
-        "t": 38.49291801452637
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114829.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9808045029640198,
-        "t": 37.106990814208984
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114418.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.973922610282898,
-        "t": 86.4870548248291
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114545.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9885269403457642,
-        "t": 40.56906700134277
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114810.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9756784439086914,
-        "t": 38.665056228637695
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114805.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9750091433525085,
-        "t": 36.89002990722656
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114636.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9863254427909851,
-        "t": 39.53814506530762
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114742.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8412531614303589,
-        "t": 38.683176040649414
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114410.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.973957896232605,
-        "t": 37.83106803894043
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114438.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9875890612602234,
-        "t": 89.47920799255371
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114825.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9610499143600464,
-        "t": 39.399147033691406
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114429.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9825863838195801,
-        "t": 39.733171463012695
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114549.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9649295210838318,
-        "t": 39.57104682922363
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114729.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9722015261650085,
-        "t": 36.138057708740234
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114916.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.8637984991073608,
-        "t": 41.31793975830078
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115017.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.69268798828125,
-        "t": 47.1041202545166
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115015.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.7254744172096252,
-        "t": 36.53311729431152
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115149.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.664541482925415,
-        "t": 38.535118103027344
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114910.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6331126689910889,
-        "t": 35.96806526184082
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114913.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6067406535148621,
-        "t": 38.691043853759766
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114906.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6304408311843872,
-        "t": 37.33110427856445
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115117.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6419996619224548,
-        "t": 36.581993103027344
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115249.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6382073760032654,
-        "t": 46.83518409729004
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115106.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5767656564712524,
-        "t": 39.36910629272461
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115258.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.7150980234146118,
-        "t": 36.006927490234375
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115121.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5532740354537964,
-        "t": 37.532806396484375
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115109.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5482063293457031,
-        "t": 98.02818298339844
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114941.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6913067698478699,
-        "t": 39.76106643676758
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115255.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.7168619632720947,
-        "t": 39.569854736328125
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114947.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6779420971870422,
-        "t": 38.29789161682129
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115246.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.7106007933616638,
-        "t": 82.2451114654541
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115252.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.7419332265853882,
-        "t": 37.40096092224121
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114944.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6649131774902344,
-        "t": 37.82987594604492
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114950.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6623270511627197,
-        "t": 40.58098793029785
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115141.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5357560515403748,
-        "t": 95.24989128112793
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115021.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6123650074005127,
-        "t": 38.72513771057129
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115144.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6293430328369141,
-        "t": 38.10906410217285
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115153.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.7085463404655457,
-        "t": 39.843082427978516
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115146.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5272461175918579,
-        "t": 38.01393508911133
-      }
-    ],
-    "baseline-ei": [
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112745.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.754544734954834,
-        "t": 61.605215072631836
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112023.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7614856362342834,
-        "t": 43.68472099304199
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111927.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7513126134872437,
-        "t": 31.964778900146484
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113117.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7233745455741882,
-        "t": 33.90002250671387
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111853.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6307761073112488,
-        "t": 31.957149505615234
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112816.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8890193700790405,
-        "t": 29.954195022583008
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112828.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7057376503944397,
-        "t": 37.465810775756836
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112223.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6639108657836914,
-        "t": 29.783010482788086
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112237.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8336992859840393,
-        "t": 30.012130737304688
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112009.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6803333163261414,
-        "t": 34.3317985534668
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112752.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7902358770370483,
-        "t": 29.399633407592773
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113114.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8139030933380127,
-        "t": 34.422874450683594
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112801.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7971761226654053,
-        "t": 31.126022338867188
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112154.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7487548589706421,
-        "t": 33.127784729003906
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112150.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6570953726768494,
-        "t": 31.003952026367188
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112226.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7775947451591492,
-        "t": 66.78104400634766
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112232.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7471581101417542,
-        "t": 29.843807220458984
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112958.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7015341520309448,
-        "t": 35.00795364379883
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111921.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.666137158870697,
-        "t": 38.52391242980957
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111935.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7839574217796326,
-        "t": 37.80508041381836
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112409.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7090408802032471,
-        "t": 29.490947723388672
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112421.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7917223572731018,
-        "t": 30.658960342407227
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112810.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7488535046577454,
-        "t": 34.66176986694336
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112838.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7460721135139465,
-        "t": 31.54778480529785
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112219.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6724474430084229,
-        "t": 30.11775016784668
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112027.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5973905920982361,
-        "t": 42.19317436218262
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112741.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6113925576210022,
-        "t": 29.898881912231445
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111705.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6644830107688904,
-        "t": 29.206037521362305
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112813.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7827091217041016,
-        "t": 29.195070266723633
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112121.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6505733728408813,
-        "t": 64.06402587890625
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112525.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.601153552532196,
-        "t": 32.43613243103027
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112727.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.5163295269012451,
-        "t": 29.851198196411133
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112914.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7880367040634155,
-        "t": 29.87384796142578
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112901.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.658697247505188,
-        "t": 34.66606140136719
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112256.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7938125729560852,
-        "t": 30.546903610229492
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112518.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6397702693939209,
-        "t": 29.616117477416992
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113000.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7609825134277344,
-        "t": 31.889915466308594
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111826.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.741855263710022,
-        "t": 33.84280204772949
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112903.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7487578988075256,
-        "t": 29.987096786499023
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112719.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7804400324821472,
-        "t": 33.01811218261719
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113003.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7340741753578186,
-        "t": 30.870914459228516
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112651.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7521920800209045,
-        "t": 69.0608024597168
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112912.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.76155686378479,
-        "t": 32.53769874572754
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112126.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7743462920188904,
-        "t": 31.138896942138672
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112654.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6945974230766296,
-        "t": 33.289194107055664
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112508.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6528485417366028,
-        "t": 51.819801330566406
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112938.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7786000370979309,
-        "t": 30.054092407226562
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112723.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.79303377866745,
-        "t": 30.2121639251709
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112253.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5618036389350891,
-        "t": 32.19795227050781
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113005.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6953296661376953,
-        "t": 29.83689308166504
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112858.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7791431546211243,
-        "t": 33.722877502441406
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112657.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8614075183868408,
-        "t": 34.95192527770996
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112841.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.694697916507721,
-        "t": 30.141830444335938
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112855.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6484765410423279,
-        "t": 29.4797420501709
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112302.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8117436766624451,
-        "t": 29.839277267456055
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113008.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7302595376968384,
-        "t": 31.8601131439209
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112909.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7971200346946716,
-        "t": 29.732227325439453
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112713.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5765509605407715,
-        "t": 29.267072677612305
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112511.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5658231377601624,
-        "t": 62.37602233886719
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111839.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.586295485496521,
-        "t": 31.628847122192383
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113035.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7860759496688843,
-        "t": 32.379150390625
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113037.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8119194507598877,
-        "t": 32.988786697387695
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112705.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7992920279502869,
-        "t": 74.57900047302246
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112116.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.613322377204895,
-        "t": 29.963016510009766
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112112.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6900195479393005,
-        "t": 37.333011627197266
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112853.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.741862416267395,
-        "t": 31.409025192260742
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112927.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8378848433494568,
-        "t": 30.01880645751953
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112701.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7627514600753784,
-        "t": 30.72810173034668
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112259.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8099237084388733,
-        "t": 36.24892234802246
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112305.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7123590707778931,
-        "t": 29.369831085205078
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112105.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5377256274223328,
-        "t": 35.284996032714844
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112515.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7378491759300232,
-        "t": 30.081987380981445
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112924.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7899656295776367,
-        "t": 37.75501251220703
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112716.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6761803030967712,
-        "t": 33.89620780944824
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112822.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7848756909370422,
-        "t": 30.2579402923584
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113043.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.9115252494812012,
-        "t": 61.39874458312988
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112413.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7576714754104614,
-        "t": 33.05387496948242
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113123.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8412087559700012,
-        "t": 34.29388999938965
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112201.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7908193469047546,
-        "t": 33.634185791015625
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112016.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7149035334587097,
-        "t": 31.631946563720703
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112406.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7823108434677124,
-        "t": 29.927730560302734
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113040.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7726505398750305,
-        "t": 31.833171844482422
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113120.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8671700954437256,
-        "t": 33.28371047973633
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111938.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7720561623573303,
-        "t": 33.9360237121582
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112941.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.695692241191864,
-        "t": 32.23586082458496
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112954.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7964102029800415,
-        "t": 30.035018920898438
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112944.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7634173035621643,
-        "t": 30.481815338134766
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111848.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6275884509086609,
-        "t": 32.20105171203613
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112831.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.708415150642395,
-        "t": 79.5891284942627
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112819.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8093357682228088,
-        "t": 32.35816955566406
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112158.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7055188417434692,
-        "t": 35.80331802368164
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112833.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.802584707736969,
-        "t": 30.864953994750977
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112402.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7961775064468384,
-        "t": 31.78119659423828
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113046.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8192360997200012,
-        "t": 32.48000144958496
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112204.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7357233762741089,
-        "t": 30.683279037475586
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113126.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8937074542045593,
-        "t": 40.24195671081543
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111916.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5165098309516907,
-        "t": 81.49409294128418
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112947.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7888754606246948,
-        "t": 32.523155212402344
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112749.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7499343156814575,
-        "t": 32.801151275634766
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112013.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6520932912826538,
-        "t": 35.040855407714844
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114123.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9932438731193542,
-        "t": 37.41002082824707
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113920.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9932112693786621,
-        "t": 31.078100204467773
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113706.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9151985049247742,
-        "t": 33.19096565246582
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113514.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9930108189582825,
-        "t": 46.17023468017578
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114118.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9921700358390808,
-        "t": 31.068086624145508
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113729.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9766161441802979,
-        "t": 31.61311149597168
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113701.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9535718560218811,
-        "t": 30.696868896484375
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113932.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.978507399559021,
-        "t": 32.3028564453125
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114126.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9952974915504456,
-        "t": 35.74395179748535
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113940.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9939044117927551,
-        "t": 31.41498565673828
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114154.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9647612571716309,
-        "t": 31.775712966918945
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113438.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9847537279129028,
-        "t": 30.802249908447266
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114021.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9794616103172302,
-        "t": 31.958818435668945
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113202.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8896743655204773,
-        "t": 31.14008903503418
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113214.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9799438118934631,
-        "t": 79.38599586486816
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114157.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9392431378364563,
-        "t": 35.63714027404785
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113638.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9084133505821228,
-        "t": 29.98805046081543
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114143.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9904477596282959,
-        "t": 41.28265380859375
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113349.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9778881072998047,
-        "t": 31.40115737915039
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113359.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9058483839035034,
-        "t": 32.98377990722656
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113403.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9265739917755127,
-        "t": 68.07684898376465
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114031.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9717161655426025,
-        "t": 29.701948165893555
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114151.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9749556183815002,
-        "t": 29.69980239868164
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113207.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9319629669189453,
-        "t": 31.324148178100586
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113430.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9927277565002441,
-        "t": 35.09211540222168
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113800.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8744453191757202,
-        "t": 30.405044555664062
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114202.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9820756316184998,
-        "t": 30.46107292175293
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113223.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9369470477104187,
-        "t": 39.38937187194824
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113156.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9609290361404419,
-        "t": 30.807018280029297
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114017.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9907329082489014,
-        "t": 34.41214561462402
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114003.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.991858184337616,
-        "t": 30.58314323425293
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113807.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9474481344223022,
-        "t": 32.6840877532959
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113813.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9417674541473389,
-        "t": 32.84883499145508
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113344.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.975975513458252,
-        "t": 32.4099063873291
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113353.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9818939566612244,
-        "t": 41.33892059326172
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113421.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9801827669143677,
-        "t": 33.48207473754883
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113434.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9759929776191711,
-        "t": 31.186342239379883
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114011.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9797903299331665,
-        "t": 31.914949417114258
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113724.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.944337785243988,
-        "t": 30.98297119140625
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113526.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9793499112129211,
-        "t": 31.839847564697266
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113915.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.969562828540802,
-        "t": 31.187772750854492
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113646.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9803395867347717,
-        "t": 36.72599792480469
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113928.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9801273345947266,
-        "t": 31.23617172241211
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113519.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9930385947227478,
-        "t": 42.68598556518555
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113531.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9088396430015564,
-        "t": 33.82396697998047
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113657.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.978996753692627,
-        "t": 31.56900405883789
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114059.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9983630776405334,
-        "t": 31.604290008544922
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113522.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9779115915298462,
-        "t": 34.111976623535156
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113442.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9868564009666443,
-        "t": 40.51995277404785
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114106.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9938948750495911,
-        "t": 31.14604949951172
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114533.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9626549482345581,
-        "t": 31.625032424926758
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114725.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8550400137901306,
-        "t": 35.40611267089844
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114651.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9677366018295288,
-        "t": 31.5091609954834
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114644.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9662502408027649,
-        "t": 33.212900161743164
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114732.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9608831405639648,
-        "t": 32.65714645385742
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114444.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9310063123703003,
-        "t": 32.54103660583496
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114647.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9437726140022278,
-        "t": 31.163930892944336
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114736.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.918245792388916,
-        "t": 31.51392936706543
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114520.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8960081934928894,
-        "t": 34.39784049987793
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114640.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9529655575752258,
-        "t": 35.74490547180176
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114537.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9614825248718262,
-        "t": 35.83502769470215
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114815.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9913634061813354,
-        "t": 32.510995864868164
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114829.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9172426462173462,
-        "t": 33.53714942932129
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114418.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9840896129608154,
-        "t": 32.6991081237793
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114545.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9893139004707336,
-        "t": 32.09400177001953
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114810.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9811805486679077,
-        "t": 36.255836486816406
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114805.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9343779683113098,
-        "t": 57.72209167480469
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114636.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9819610118865967,
-        "t": 31.013965606689453
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114742.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.924883246421814,
-        "t": 33.52212905883789
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114410.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9684889912605286,
-        "t": 34.62791442871094
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114438.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9833514094352722,
-        "t": 33.892154693603516
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114825.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9087287783622742,
-        "t": 35.07709503173828
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114429.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9490428566932678,
-        "t": 30.681133270263672
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114549.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9701907634735107,
-        "t": 33.21695327758789
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114729.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9637668132781982,
-        "t": 31.53204917907715
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114916.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.7170832753181458,
-        "t": 33.634185791015625
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115017.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6732720732688904,
-        "t": 31.023025512695312
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115015.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6557801365852356,
-        "t": 73.19974899291992
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115149.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5903311371803284,
-        "t": 33.97679328918457
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114910.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5152060389518738,
-        "t": 33.51020812988281
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114913.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5986131429672241,
-        "t": 35.0039005279541
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114906.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.7923974394798279,
-        "t": 32.12308883666992
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115117.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6410588026046753,
-        "t": 31.73995018005371
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115249.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.7133982181549072,
-        "t": 32.23085403442383
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115106.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6195663809776306,
-        "t": 31.083106994628906
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115258.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5370099544525146,
-        "t": 33.01572799682617
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115121.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.7195541858673096,
-        "t": 33.49614143371582
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115109.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6239798665046692,
-        "t": 30.43365478515625
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114941.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5657088756561279,
-        "t": 33.385276794433594
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115255.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6970793604850769,
-        "t": 33.435821533203125
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114947.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.7401916980743408,
-        "t": 33.10823440551758
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115246.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6172546148300171,
-        "t": 35.36510467529297
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115252.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.7298187017440796,
-        "t": 31.095027923583984
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114944.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6092252731323242,
-        "t": 35.10093688964844
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114950.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5941953659057617,
-        "t": 31.2349796295166
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115141.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5810264945030212,
-        "t": 32.92393684387207
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115021.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6310500502586365,
-        "t": 33.86998176574707
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115144.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6001012325286865,
-        "t": 31.94904327392578
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115153.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6187416911125183,
-        "t": 41.73421859741211
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115146.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6511600613594055,
-        "t": 33.782958984375
-      }
-    ],
-    "efficientad": [
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112745.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05279792845249176,
-        "t": 402.6608467102051,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112745_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112023.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.02908455766737461,
-        "t": 284.69204902648926,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112023_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111927.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.2526693344116211,
-        "t": 329.26487922668457,
-        "r": "datasets/cookies_1/no_anomaly/20240417_111927_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113117.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.14047634601593018,
-        "t": 298.43878746032715,
-        "r": "datasets/cookies_1/no_anomaly/20240417_113117_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111853.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0330771841108799,
-        "t": 283.6170196533203,
-        "r": "datasets/cookies_1/no_anomaly/20240417_111853_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112816.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.10380303859710693,
-        "t": 306.7469596862793,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112816_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112828.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07066550850868225,
-        "t": 321.5460777282715,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112828_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112223.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0279559213668108,
-        "t": 297.67322540283203,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112223_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112237.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.08052832633256912,
-        "t": 293.2252883911133,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112237_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112009.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.030446933582425117,
-        "t": 307.02805519104004,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112009_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112752.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07086455076932907,
-        "t": 295.1071262359619,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112752_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113114.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.11463827639818192,
-        "t": 298.6140251159668,
-        "r": "datasets/cookies_1/no_anomaly/20240417_113114_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112801.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.14105097949504852,
-        "t": 298.2211112976074,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112801_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112154.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07978420704603195,
-        "t": 295.4378128051758,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112154_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112150.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.10792328417301178,
-        "t": 285.8467102050781,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112150_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112226.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.04875445365905762,
-        "t": 297.35589027404785,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112226_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112232.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06464021652936935,
-        "t": 293.658971786499,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112232_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112958.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06722196191549301,
-        "t": 302.30236053466797,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112958_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111921.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.043468013405799866,
-        "t": 270.1570987701416,
-        "r": "datasets/cookies_1/no_anomaly/20240417_111921_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111935.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.20400530099868774,
-        "t": 431.75506591796875,
-        "r": "datasets/cookies_1/no_anomaly/20240417_111935_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112409.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0813259407877922,
-        "t": 283.8759422302246,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112409_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112421.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0762815922498703,
-        "t": 311.25402450561523,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112421_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112810.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.1505049765110016,
-        "t": 279.90198135375977,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112810_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112838.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05035068839788437,
-        "t": 312.41798400878906,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112838_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112219.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.035195328295230865,
-        "t": 278.1519889831543,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112219_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112027.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05108204483985901,
-        "t": 344.88987922668457,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112027_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112741.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.16326802968978882,
-        "t": 282.1488380432129,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112741_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111705.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.08425930142402649,
-        "t": 289.82090950012207,
-        "r": "datasets/cookies_1/no_anomaly/20240417_111705_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112813.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05664924904704094,
-        "t": 285.5710983276367,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112813_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112121.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.04740418866276741,
-        "t": 289.5829677581787,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112121_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112525.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.054173488169908524,
-        "t": 287.92381286621094,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112525_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112727.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.13819921016693115,
-        "t": 287.75811195373535,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112727_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112914.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.4016227126121521,
-        "t": 284.18684005737305,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112914_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112901.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.36473947763442993,
-        "t": 352.2000312805176,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112901_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112256.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.12671709060668945,
-        "t": 274.16491508483887,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112256_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112518.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.04480387642979622,
-        "t": 289.71171379089355,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112518_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113000.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.1330317258834839,
-        "t": 295.6411838531494,
-        "r": "datasets/cookies_1/no_anomaly/20240417_113000_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111826.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09527398645877838,
-        "t": 334.52701568603516,
-        "r": "datasets/cookies_1/no_anomaly/20240417_111826_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112903.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.38007935881614685,
-        "t": 290.6968593597412,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112903_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112719.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09983029961585999,
-        "t": 307.4967861175537,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112719_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113003.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0849963128566742,
-        "t": 290.7071113586426,
-        "r": "datasets/cookies_1/no_anomaly/20240417_113003_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112651.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06435303390026093,
-        "t": 305.6187629699707,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112651_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112912.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.36925747990608215,
-        "t": 299.0231513977051,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112912_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112126.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09043225646018982,
-        "t": 302.4868965148926,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112126_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112654.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0360928475856781,
-        "t": 289.9940013885498,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112654_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112508.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07380658388137817,
-        "t": 298.56085777282715,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112508_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112938.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05833258852362633,
-        "t": 272.6747989654541,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112938_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112723.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.10946591943502426,
-        "t": 287.05382347106934,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112723_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112253.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.15588156878948212,
-        "t": 281.7409038543701,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112253_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113005.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.15816430747509003,
-        "t": 317.8420066833496,
-        "r": "datasets/cookies_1/no_anomaly/20240417_113005_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112858.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.02249506488442421,
-        "t": 285.25805473327637,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112858_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112657.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06883450597524643,
-        "t": 288.89012336730957,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112657_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112841.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.057560183107852936,
-        "t": 283.47110748291016,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112841_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112855.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0289794709533453,
-        "t": 293.12992095947266,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112855_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112302.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09242984652519226,
-        "t": 293.6520576477051,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112302_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113008.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.04372844099998474,
-        "t": 327.06522941589355,
-        "r": "datasets/cookies_1/no_anomaly/20240417_113008_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112909.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.41468116641044617,
-        "t": 271.7759609222412,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112909_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112713.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0365135483443737,
-        "t": 281.02803230285645,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112713_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112511.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.04883148521184921,
-        "t": 283.43987464904785,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112511_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111839.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09864231944084167,
-        "t": 289.05797004699707,
-        "r": "datasets/cookies_1/no_anomaly/20240417_111839_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113035.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06786537170410156,
-        "t": 285.43806076049805,
-        "r": "datasets/cookies_1/no_anomaly/20240417_113035_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113037.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06019384786486626,
-        "t": 312.79516220092773,
-        "r": "datasets/cookies_1/no_anomaly/20240417_113037_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112705.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07611159235239029,
-        "t": 303.9250373840332,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112705_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112116.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05157911032438278,
-        "t": 310.11199951171875,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112116_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112112.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.041830845177173615,
-        "t": 302.4468421936035,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112112_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112853.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05041002482175827,
-        "t": 310.1010322570801,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112853_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112927.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.04498457908630371,
-        "t": 288.3760929107666,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112927_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112701.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.026371963322162628,
-        "t": 284.49010848999023,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112701_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112259.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.14553461968898773,
-        "t": 288.36894035339355,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112259_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112305.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07796084880828857,
-        "t": 288.91801834106445,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112305_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112105.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.03699217364192009,
-        "t": 287.46700286865234,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112105_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112515.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0813799723982811,
-        "t": 281.69989585876465,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112515_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112924.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.04918167367577553,
-        "t": 279.42395210266113,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112924_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112716.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06529838591814041,
-        "t": 283.7560176849365,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112716_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112822.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.10564166307449341,
-        "t": 277.83799171447754,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112822_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113043.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.058861102908849716,
-        "t": 311.59186363220215,
-        "r": "datasets/cookies_1/no_anomaly/20240417_113043_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112413.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.08572164177894592,
-        "t": 282.4549674987793,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112413_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113123.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0980525016784668,
-        "t": 303.1899929046631,
-        "r": "datasets/cookies_1/no_anomaly/20240417_113123_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112201.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.038039956241846085,
-        "t": 285.2940559387207,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112201_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112016.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06204311549663544,
-        "t": 327.27813720703125,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112016_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112406.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.08036951720714569,
-        "t": 275.5310535430908,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112406_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113040.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.03229444473981857,
-        "t": 293.90406608581543,
-        "r": "datasets/cookies_1/no_anomaly/20240417_113040_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113120.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06938032805919647,
-        "t": 274.17802810668945,
-        "r": "datasets/cookies_1/no_anomaly/20240417_113120_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111938.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.22303742170333862,
-        "t": 308.2559108734131,
-        "r": "datasets/cookies_1/no_anomaly/20240417_111938_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112941.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07884136587381363,
-        "t": 286.6179943084717,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112941_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112954.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.13292254507541656,
-        "t": 307.50417709350586,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112954_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112944.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.12292464822530746,
-        "t": 289.5231246948242,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112944_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111848.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.023074397817254066,
-        "t": 289.2189025878906,
-        "r": "datasets/cookies_1/no_anomaly/20240417_111848_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112831.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.039898887276649475,
-        "t": 305.94801902770996,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112831_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112819.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.047380734235048294,
-        "t": 293.2710647583008,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112819_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112158.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07132921367883682,
-        "t": 294.86608505249023,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112158_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112833.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06766786426305771,
-        "t": 308.79688262939453,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112833_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112402.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.04721927270293236,
-        "t": 289.0172004699707,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112402_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113046.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09640887379646301,
-        "t": 293.43104362487793,
-        "r": "datasets/cookies_1/no_anomaly/20240417_113046_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112204.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05468180775642395,
-        "t": 290.4520034790039,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112204_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113126.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.10457951575517654,
-        "t": 285.2129936218262,
-        "r": "datasets/cookies_1/no_anomaly/20240417_113126_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111916.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.21327050030231476,
-        "t": 279.10304069519043,
-        "r": "datasets/cookies_1/no_anomaly/20240417_111916_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112947.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.10871665924787521,
-        "t": 286.7603302001953,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112947_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112749.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.10506178438663483,
-        "t": 289.3798351287842,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112749_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112013.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.03140319511294365,
-        "t": 339.1540050506592,
-        "r": "datasets/cookies_1/no_anomaly/20240417_112013_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114123.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.5726003646850586,
-        "t": 281.0633182525635,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114123_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113920.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 4.791325092315674,
-        "t": 275.46119689941406,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113920_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113706.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.583435535430908,
-        "t": 273.3192443847656,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113706_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113514.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.7052180767059326,
-        "t": 463.87720108032227,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113514_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114118.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.441357135772705,
-        "t": 287.280797958374,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114118_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113729.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.2646539211273193,
-        "t": 294.3589687347412,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113729_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113701.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.7317187786102295,
-        "t": 277.38213539123535,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113701_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113932.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.820199966430664,
-        "t": 284.3058109283447,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113932_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114126.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 4.427257537841797,
-        "t": 278.34105491638184,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114126_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113940.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 4.153791427612305,
-        "t": 320.07384300231934,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113940_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114154.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.315380096435547,
-        "t": 292.8581237792969,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114154_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113438.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.5521178245544434,
-        "t": 290.70281982421875,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113438_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114021.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 4.260752201080322,
-        "t": 282.31000900268555,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114021_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113202.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.5056636333465576,
-        "t": 283.9522361755371,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113202_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113214.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 4.0108208656311035,
-        "t": 296.3728904724121,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113214_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114157.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 4.243374347686768,
-        "t": 306.72478675842285,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114157_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113638.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.9098374843597412,
-        "t": 300.08482933044434,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113638_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114143.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 4.182662010192871,
-        "t": 363.2340431213379,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114143_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113349.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.3879873752593994,
-        "t": 284.37113761901855,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113349_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113359.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.286593437194824,
-        "t": 284.210205078125,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113359_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113403.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 4.385079383850098,
-        "t": 296.9939708709717,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113403_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114031.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 4.510674476623535,
-        "t": 295.745849609375,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114031_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114151.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.7810580730438232,
-        "t": 287.1060371398926,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114151_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113207.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.096155881881714,
-        "t": 285.76087951660156,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113207_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113430.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.528394937515259,
-        "t": 344.07496452331543,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113430_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113800.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.081923723220825,
-        "t": 291.00513458251953,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113800_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114202.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.8647539615631104,
-        "t": 300.26698112487793,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114202_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113223.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.407217025756836,
-        "t": 316.0121440887451,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113223_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113156.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.91839861869812,
-        "t": 289.351224899292,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113156_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114017.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.2485361099243164,
-        "t": 294.31605339050293,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114017_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114003.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.0281341075897217,
-        "t": 295.2091693878174,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114003_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113807.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.4883573055267334,
-        "t": 287.5089645385742,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113807_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113813.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.4302682876586914,
-        "t": 278.69296073913574,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113813_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113344.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.014643669128418,
-        "t": 282.8350067138672,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113344_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113353.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.3931972980499268,
-        "t": 302.94322967529297,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113353_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113421.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.633657217025757,
-        "t": 285.31789779663086,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113421_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113434.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.8442723751068115,
-        "t": 283.0021381378174,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113434_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114011.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.597362518310547,
-        "t": 334.3780040740967,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114011_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113724.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.8042712211608887,
-        "t": 284.6651077270508,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113724_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113526.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.04880952835083,
-        "t": 287.68086433410645,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113526_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113915.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.5836098194122314,
-        "t": 278.2888412475586,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113915_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113646.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.402395009994507,
-        "t": 352.30517387390137,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113646_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113928.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.3318047523498535,
-        "t": 297.4538803100586,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113928_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113519.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.9224960803985596,
-        "t": 305.2949905395508,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113519_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113531.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.4383459091186523,
-        "t": 347.17416763305664,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113531_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113657.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.834777593612671,
-        "t": 286.5598201751709,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113657_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114059.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.649247169494629,
-        "t": 275.14028549194336,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114059_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113522.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.1857757568359375,
-        "t": 329.3590545654297,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113522_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113442.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.4696462154388428,
-        "t": 304.7149181365967,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113442_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114106.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 5.041826248168945,
-        "t": 286.94772720336914,
-        "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114106_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114533.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.7992727756500244,
-        "t": 299.2889881134033,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114533_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114725.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.7505968809127808,
-        "t": 305.0379753112793,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114725_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114651.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 2.1382172107696533,
-        "t": 274.34396743774414,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114651_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114644.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.6800984740257263,
-        "t": 346.23003005981445,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114644_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114732.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 2.3130381107330322,
-        "t": 285.6910228729248,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114732_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114444.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.8484989404678345,
-        "t": 295.0258255004883,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114444_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114647.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.3703839778900146,
-        "t": 282.3920249938965,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114647_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114736.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.8339619636535645,
-        "t": 319.6742534637451,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114736_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114520.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.4293923377990723,
-        "t": 285.4740619659424,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114520_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114640.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.1311805248260498,
-        "t": 340.008020401001,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114640_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114537.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.748539686203003,
-        "t": 299.0131378173828,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114537_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114815.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 2.1209282875061035,
-        "t": 297.28007316589355,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114815_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114829.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 2.1503663063049316,
-        "t": 275.9091854095459,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114829_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114418.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 3.785572052001953,
-        "t": 279.92916107177734,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114418_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114545.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 2.082768440246582,
-        "t": 286.7929935455322,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114545_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114810.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 4.9459919929504395,
-        "t": 330.9178352355957,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114810_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114805.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 2.1644175052642822,
-        "t": 304.0499687194824,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114805_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114636.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.6072242259979248,
-        "t": 317.52610206604004,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114636_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114742.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 2.183677911758423,
-        "t": 335.68620681762695,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114742_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114410.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 3.6779019832611084,
-        "t": 314.65816497802734,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114410_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114438.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 3.236937999725342,
-        "t": 278.1639099121094,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114438_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114825.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.2174694538116455,
-        "t": 297.9879379272461,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114825_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114429.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 4.136043548583984,
-        "t": 304.1098117828369,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114429_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114549.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9747417569160461,
-        "t": 335.0379467010498,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114549_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114729.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.6842350959777832,
-        "t": 308.8951110839844,
-        "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114729_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114916.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 1.322859287261963,
-        "t": 302.2751808166504,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_114916_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115017.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.2998558580875397,
-        "t": 285.5639457702637,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115017_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115015.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.41661372780799866,
-        "t": 305.91773986816406,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115015_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115149.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.22363710403442383,
-        "t": 304.1210174560547,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115149_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114910.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.7448408007621765,
-        "t": 290.49086570739746,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_114910_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114913.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6330876350402832,
-        "t": 330.5540084838867,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_114913_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114906.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.957312822341919,
-        "t": 291.31388664245605,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_114906_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115117.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.20069417357444763,
-        "t": 285.34913063049316,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115117_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115249.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.26030233502388,
-        "t": 340.96312522888184,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115249_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115106.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.2818661630153656,
-        "t": 312.3009204864502,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115106_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115258.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.11828314512968063,
-        "t": 296.7648506164551,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115258_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115121.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.14199677109718323,
-        "t": 294.7988510131836,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115121_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115109.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.17836281657218933,
-        "t": 278.68103981018066,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115109_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114941.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.1656639724969864,
-        "t": 294.0528392791748,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_114941_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115255.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.23219875991344452,
-        "t": 301.5861511230469,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115255_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114947.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.1697627156972885,
-        "t": 280.336856842041,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_114947_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115246.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.25334492325782776,
-        "t": 282.912015914917,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115246_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115252.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.2176104635000229,
-        "t": 294.766902923584,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115252_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114944.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.1466170698404312,
-        "t": 312.8519058227539,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_114944_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114950.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.10586525499820709,
-        "t": 306.49399757385254,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_114950_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115141.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.31677430868148804,
-        "t": 335.83617210388184,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115141_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115021.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.35897886753082275,
-        "t": 294.10409927368164,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115021_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115144.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.20789943635463715,
-        "t": 296.5822219848633,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115144_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115153.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.27713802456855774,
-        "t": 345.80469131469727,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115153_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115146.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.21846866607666016,
-        "t": 294.59285736083984,
-        "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115146_result.jpg"
-      }
-    ],
-    "fomoad": [
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112745.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.5669713020324707,
-        "t": 79.39720153808594
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112023.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.6027333736419678,
-        "t": 35.65692901611328
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111927.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.4297893047332764,
-        "t": 33.979177474975586
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113117.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.6575679779052734,
-        "t": 35.173892974853516
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111853.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.4836137294769287,
-        "t": 33.96296501159668
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112816.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.629633665084839,
-        "t": 35.27092933654785
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112828.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.6196417808532715,
-        "t": 32.86099433898926
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112223.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.5015206336975098,
-        "t": 32.219886779785156
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112237.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.5082952976226807,
-        "t": 33.56814384460449
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112009.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.6224472522735596,
-        "t": 33.32400321960449
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112752.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.8137624263763428,
-        "t": 33.33091735839844
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113114.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.5471088886260986,
-        "t": 33.03790092468262
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112801.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.7213077545166016,
-        "t": 32.861948013305664
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112154.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.329754590988159,
-        "t": 32.20009803771973
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112150.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.002690076828003,
-        "t": 32.60517120361328
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112226.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.5272676944732666,
-        "t": 33.1571102142334
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112232.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.3880321979522705,
-        "t": 33.661842346191406
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112958.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.3655917644500732,
-        "t": 56.35213851928711
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111921.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.071432113647461,
-        "t": 32.66572952270508
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111935.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.7130985260009766,
-        "t": 31.612873077392578
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112409.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.3955185413360596,
-        "t": 32.50527381896973
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112421.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.4769434928894043,
-        "t": 32.073974609375
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112810.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.5585246086120605,
-        "t": 32.38987922668457
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112838.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.332935094833374,
-        "t": 34.436941146850586
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112219.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.70290207862854,
-        "t": 32.11331367492676
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112027.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.696206569671631,
-        "t": 32.20486640930176
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112741.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.8564515113830566,
-        "t": 41.45693778991699
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111705.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.8488409519195557,
-        "t": 32.44304656982422
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112813.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.6815757751464844,
-        "t": 34.191131591796875
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112121.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.575251340866089,
-        "t": 32.19199180603027
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112525.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.595059633255005,
-        "t": 32.36198425292969
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112727.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.7602756023406982,
-        "t": 31.584978103637695
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112914.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.595003604888916,
-        "t": 32.24492073059082
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112901.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.72645902633667,
-        "t": 35.90083122253418
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112256.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.8767271041870117,
-        "t": 32.730817794799805
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112518.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.802074670791626,
-        "t": 33.62107276916504
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113000.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.8477180004119873,
-        "t": 33.756256103515625
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111826.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 4.035316467285156,
-        "t": 32.05990791320801
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112903.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.386702537536621,
-        "t": 31.89682960510254
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112719.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.7573113441467285,
-        "t": 32.01413154602051
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113003.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.814953327178955,
-        "t": 32.65380859375
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112651.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.6833465099334717,
-        "t": 33.370018005371094
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112912.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.5453004837036133,
-        "t": 31.342744827270508
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112126.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.536449909210205,
-        "t": 40.358781814575195
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112654.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.465677261352539,
-        "t": 31.68201446533203
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112508.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.3019022941589355,
-        "t": 32.34100341796875
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112938.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.9016480445861816,
-        "t": 32.346248626708984
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112723.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.5687506198883057,
-        "t": 32.128095626831055
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112253.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.079240083694458,
-        "t": 33.93888473510742
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113005.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.8788325786590576,
-        "t": 32.21607208251953
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112858.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.5468711853027344,
-        "t": 32.364845275878906
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112657.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.6843929290771484,
-        "t": 32.3789119720459
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112841.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.8053441047668457,
-        "t": 31.471967697143555
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112855.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.3752832412719727,
-        "t": 32.38701820373535
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112302.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.82745099067688,
-        "t": 32.35602378845215
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113008.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.378845453262329,
-        "t": 32.61899948120117
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112909.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.6556549072265625,
-        "t": 32.02080726623535
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112713.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.706681728363037,
-        "t": 31.98695182800293
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112511.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.5348589420318604,
-        "t": 32.39607810974121
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111839.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.7431323528289795,
-        "t": 32.08279609680176
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113035.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.7154102325439453,
-        "t": 32.95493125915527
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113037.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.457549810409546,
-        "t": 33.579111099243164
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112705.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.4321632385253906,
-        "t": 33.32090377807617
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112116.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.984253168106079,
-        "t": 32.119035720825195
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112112.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.5091969966888428,
-        "t": 32.18793869018555
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112853.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.4491994380950928,
-        "t": 32.16886520385742
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112927.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.3948593139648438,
-        "t": 56.04410171508789
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112701.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.585118055343628,
-        "t": 32.32097625732422
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112259.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.6175904273986816,
-        "t": 31.59809112548828
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112305.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.8041164875030518,
-        "t": 33.254146575927734
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112105.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.6059515476226807,
-        "t": 33.56599807739258
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112515.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.4332897663116455,
-        "t": 33.68711471557617
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112924.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.647962808609009,
-        "t": 33.720970153808594
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112716.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.767383098602295,
-        "t": 31.68797492980957
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112822.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.4876646995544434,
-        "t": 31.705856323242188
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113043.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.6302690505981445,
-        "t": 31.907081604003906
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112413.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.9850430488586426,
-        "t": 32.87076950073242
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113123.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.308338165283203,
-        "t": 32.29403495788574
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112201.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.3183164596557617,
-        "t": 31.901121139526367
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112016.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.7563953399658203,
-        "t": 32.30476379394531
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112406.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.0918474197387695,
-        "t": 32.727718353271484
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113040.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.3309428691864014,
-        "t": 31.85582160949707
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113120.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.5188698768615723,
-        "t": 36.934852600097656
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111938.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.9636785984039307,
-        "t": 38.137197494506836
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112941.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.985468626022339,
-        "t": 31.946897506713867
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112954.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.1930649280548096,
-        "t": 34.774065017700195
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112944.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.505126953125,
-        "t": 34.19089317321777
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111848.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.441115379333496,
-        "t": 32.20081329345703
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112831.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.7234954833984375,
-        "t": 32.25994110107422
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112819.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.5894839763641357,
-        "t": 33.538103103637695
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112158.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.557013988494873,
-        "t": 32.88102149963379
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112833.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.288264751434326,
-        "t": 32.32598304748535
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112402.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.613337516784668,
-        "t": 33.4019660949707
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113046.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.384923219680786,
-        "t": 32.472848892211914
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112204.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.2991268634796143,
-        "t": 32.713890075683594
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_113126.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.511009693145752,
-        "t": 34.36923027038574
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_111916.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.5537145137786865,
-        "t": 32.27400779724121
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112947.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 4.118626117706299,
-        "t": 32.948970794677734
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112749.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.663466453552246,
-        "t": 33.94794464111328
-      },
-      {
-        "i": "datasets/cookies_1/no_anomaly/20240417_112013.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.4168810844421387,
-        "t": 32.39774703979492
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114123.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 13.325844764709473,
-        "t": 34.14130210876465
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113920.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 16.817148208618164,
-        "t": 33.084869384765625
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113706.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 13.817278861999512,
-        "t": 32.67312049865723
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113514.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 11.040229797363281,
-        "t": 32.66477584838867
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114118.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 14.018916130065918,
-        "t": 32.63092041015625
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113729.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.223296165466309,
-        "t": 33.42604637145996
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113701.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 15.382932662963867,
-        "t": 32.22513198852539
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113932.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 14.991363525390625,
-        "t": 32.907962799072266
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114126.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 13.474974632263184,
-        "t": 32.62066841125488
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113940.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 14.20977783203125,
-        "t": 33.81204605102539
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114154.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 14.72214412689209,
-        "t": 32.03320503234863
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113438.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.374995231628418,
-        "t": 32.81092643737793
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114021.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 9.991497039794922,
-        "t": 32.27806091308594
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113202.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 11.409708023071289,
-        "t": 31.972885131835938
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113214.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 15.936113357543945,
-        "t": 34.23714637756348
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114157.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 13.957559585571289,
-        "t": 40.621042251586914
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113638.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.404473304748535,
-        "t": 32.53912925720215
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114143.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 9.521726608276367,
-        "t": 35.26902198791504
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113349.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 8.810449600219727,
-        "t": 32.4859619140625
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113359.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 14.452795028686523,
-        "t": 32.89079666137695
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113403.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 20.376800537109375,
-        "t": 33.8900089263916
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114031.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 14.301910400390625,
-        "t": 35.41088104248047
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114151.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 17.576900482177734,
-        "t": 34.69586372375488
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113207.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 8.7174072265625,
-        "t": 33.02502632141113
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113430.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 12.684404373168945,
-        "t": 34.77001190185547
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113800.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 12.355374336242676,
-        "t": 33.921003341674805
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114202.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 12.94670581817627,
-        "t": 32.54818916320801
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113223.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.041842460632324,
-        "t": 33.42795372009277
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113156.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.143872261047363,
-        "t": 33.05411338806152
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114017.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.842761039733887,
-        "t": 34.097909927368164
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114003.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.105137825012207,
-        "t": 32.958269119262695
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113807.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 13.413707733154297,
-        "t": 33.71310234069824
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113813.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 16.813297271728516,
-        "t": 33.59794616699219
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113344.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 11.245850563049316,
-        "t": 33.68496894836426
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113353.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 12.257253646850586,
-        "t": 33.035993576049805
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113421.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.837519645690918,
-        "t": 35.893917083740234
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113434.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.058905601501465,
-        "t": 33.59699249267578
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114011.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 9.927388191223145,
-        "t": 33.032894134521484
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113724.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 9.447102546691895,
-        "t": 33.57410430908203
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113526.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 8.944421768188477,
-        "t": 34.2099666595459
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113915.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 15.618802070617676,
-        "t": 33.659934997558594
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113646.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 13.590190887451172,
-        "t": 33.66494178771973
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113928.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 19.003541946411133,
-        "t": 33.32018852233887
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113519.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 12.582403182983398,
-        "t": 37.58096694946289
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113531.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.421842575073242,
-        "t": 33.54191780090332
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113657.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 9.290889739990234,
-        "t": 34.95621681213379
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114059.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 15.213801383972168,
-        "t": 35.05587577819824
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113522.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 15.124086380004883,
-        "t": 32.29212760925293
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_113442.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.344398498535156,
-        "t": 34.204959869384766
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_1/20240417_114106.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 16.766849517822266,
-        "t": 37.78791427612305
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114533.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 12.282515525817871,
-        "t": 33.86688232421875
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114725.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 7.878947734832764,
-        "t": 33.09297561645508
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114651.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 11.052139282226562,
-        "t": 33.66684913635254
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114644.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 8.135551452636719,
-        "t": 33.782958984375
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114732.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 15.281309127807617,
-        "t": 34.44719314575195
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114444.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 9.476000785827637,
-        "t": 33.71286392211914
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114647.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 7.626359462738037,
-        "t": 35.37487983703613
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114736.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 12.932724952697754,
-        "t": 33.84089469909668
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114520.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 14.461578369140625,
-        "t": 33.97679328918457
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114640.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 8.185355186462402,
-        "t": 34.2860221862793
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114537.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 8.845043182373047,
-        "t": 34.3780517578125
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114815.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 9.321946144104004,
-        "t": 33.5690975189209
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114829.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 7.642153739929199,
-        "t": 33.785104751586914
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114418.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 16.0140323638916,
-        "t": 34.44504737854004
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114545.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 15.361237525939941,
-        "t": 34.27600860595703
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114810.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 16.58502769470215,
-        "t": 35.212039947509766
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114805.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 7.945466995239258,
-        "t": 34.070730209350586
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114636.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 10.8995361328125,
-        "t": 34.46030616760254
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114742.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 19.5487060546875,
-        "t": 36.68498992919922
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114410.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 10.261463165283203,
-        "t": 34.61623191833496
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114438.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 6.9735894203186035,
-        "t": 33.75577926635742
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114825.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 8.234457015991211,
-        "t": 34.02972221374512
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114429.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 10.364762306213379,
-        "t": 37.748098373413086
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114549.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 10.188965797424316,
-        "t": 35.97688674926758
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_2/20240417_114729.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 11.519067764282227,
-        "t": 33.19811820983887
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114916.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 9.022894859313965,
-        "t": 33.143043518066406
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115017.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 3.7956368923187256,
-        "t": 33.70404243469238
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115015.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 5.667520046234131,
-        "t": 33.59699249267578
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115149.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 3.647526979446411,
-        "t": 33.570051193237305
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114910.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 3.906104803085327,
-        "t": 34.976959228515625
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114913.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 5.348978042602539,
-        "t": 33.9808464050293
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114906.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 3.7223403453826904,
-        "t": 34.64913368225098
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115117.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 3.871356248855591,
-        "t": 34.48772430419922
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115249.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 3.1529524326324463,
-        "t": 33.562660217285156
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115106.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 3.3653550148010254,
-        "t": 32.88006782531738
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115258.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 3.6323275566101074,
-        "t": 34.895896911621094
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115121.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 3.796689510345459,
-        "t": 35.8431339263916
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115109.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 3.962489128112793,
-        "t": 35.88509559631348
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114941.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 3.998396635055542,
-        "t": 34.0421199798584
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115255.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 3.4497060775756836,
-        "t": 34.57808494567871
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114947.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 4.458125114440918,
-        "t": 34.90424156188965
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115246.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 3.081604242324829,
-        "t": 33.53404998779297
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115252.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 3.5117602348327637,
-        "t": 34.193992614746094
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114944.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 3.7276594638824463,
-        "t": 34.194231033325195
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_114950.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 4.784890651702881,
-        "t": 34.23786163330078
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115141.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 4.611706256866455,
-        "t": 33.76197814941406
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115021.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 3.9316298961639404,
-        "t": 34.04808044433594
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115144.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 3.2504162788391113,
-        "t": 34.36017036437988
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115153.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 4.066354274749756,
-        "t": 34.905195236206055
-      },
-      {
-        "i": "datasets/cookies_1/anomaly_lvl_3/20240417_115146.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 3.6700284481048584,
-        "t": 35.90083122253418
-      }
-    ]
-  },
-  "cookies_2": {
-    "baseline": [
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133615.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7223763465881348,
-        "t": 186.3088607788086
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133403.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7373055815696716,
-        "t": 56.84614181518555
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133238.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7921434044837952,
-        "t": 69.91791725158691
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133210.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7034498453140259,
-        "t": 42.719125747680664
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133600.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8213160037994385,
-        "t": 39.85786437988281
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133602.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.835301399230957,
-        "t": 41.8550968170166
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133414.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.708065390586853,
-        "t": 38.33270072937012
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133428.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7882866859436035,
-        "t": 74.33414459228516
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133212.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5867552757263184,
-        "t": 51.14603042602539
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133159.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7415677905082703,
-        "t": 39.34502601623535
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133617.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6444535851478577,
-        "t": 40.209054946899414
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133613.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5952883362770081,
-        "t": 38.40780258178711
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133405.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.784189760684967,
-        "t": 37.5058650970459
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133558.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8384528160095215,
-        "t": 43.38192939758301
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133410.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7158473134040833,
-        "t": 40.88783264160156
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133638.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.801189661026001,
-        "t": 42.511701583862305
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133412.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.648637592792511,
-        "t": 43.03693771362305
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133228.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.769294798374176,
-        "t": 45.36890983581543
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133215.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7294291853904724,
-        "t": 39.37888145446777
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133605.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8057631850242615,
-        "t": 44.72017288208008
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133138.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8023738265037537,
-        "t": 40.843963623046875
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133528.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8029568195343018,
-        "t": 91.41302108764648
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133058.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6823179721832275,
-        "t": 38.91873359680176
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133515.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7967426180839539,
-        "t": 43.853044509887695
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133449.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7456387877464294,
-        "t": 42.39511489868164
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133307.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5978406071662903,
-        "t": 42.60993003845215
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133105.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6792672872543335,
-        "t": 40.82298278808594
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133649.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6133307218551636,
-        "t": 45.404911041259766
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133311.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.5210781693458557,
-        "t": 41.322946548461914
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133339.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6155087947845459,
-        "t": 40.77291488647461
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133258.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.719700038433075,
-        "t": 41.93687438964844
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133102.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7038630843162537,
-        "t": 40.506839752197266
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133328.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6647783517837524,
-        "t": 39.62087631225586
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133300.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7568951845169067,
-        "t": 42.31691360473633
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133314.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.5041428208351135,
-        "t": 41.631221771240234
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133512.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7937150001525879,
-        "t": 42.08993911743164
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133249.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8531125783920288,
-        "t": 40.274858474731445
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133510.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7821593880653381,
-        "t": 37.457942962646484
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133316.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.5472259521484375,
-        "t": 41.93997383117676
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133643.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7950817942619324,
-        "t": 41.47791862487793
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133508.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8003061413764954,
-        "t": 42.8311824798584
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133534.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8347647786140442,
-        "t": 40.190935134887695
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133252.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7611756920814514,
-        "t": 43.16377639770508
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133520.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7812867164611816,
-        "t": 45.55797576904297
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133332.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.648008406162262,
-        "t": 40.62914848327637
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133642.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8175464868545532,
-        "t": 42.35529899597168
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133130.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7462684512138367,
-        "t": 39.53099250793457
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133132.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8295376300811768,
-        "t": 40.00401496887207
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133456.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6155417561531067,
-        "t": 39.0169620513916
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133330.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6097508072853088,
-        "t": 37.7960205078125
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133536.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8059282898902893,
-        "t": 42.012929916381836
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133522.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7755705714225769,
-        "t": 42.50597953796387
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133052.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6658222675323486,
-        "t": 40.609121322631836
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133645.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7258557677268982,
-        "t": 56.817054748535156
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133309.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6724172234535217,
-        "t": 39.63303565979004
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133447.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7628334164619446,
-        "t": 41.330814361572266
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133321.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5191327929496765,
-        "t": 40.746212005615234
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133335.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6707050800323486,
-        "t": 56.00595474243164
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133453.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.673337459564209,
-        "t": 44.647216796875
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133241.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8512131571769714,
-        "t": 39.76583480834961
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133532.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8018566966056824,
-        "t": 41.687965393066406
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133254.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8391548991203308,
-        "t": 42.77610778808594
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133452.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7761150002479553,
-        "t": 40.48728942871094
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133136.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7800912857055664,
-        "t": 40.52400588989258
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133108.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8320531249046326,
-        "t": 43.576717376708984
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133134.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7031485438346863,
-        "t": 39.49117660522461
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133256.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7925034165382385,
-        "t": 67.79980659484863
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133055.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6498820781707764,
-        "t": 42.40727424621582
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133445.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7473102807998657,
-        "t": 89.27607536315918
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133337.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6292096376419067,
-        "t": 38.89894485473633
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133647.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6337842345237732,
-        "t": 49.64113235473633
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133152.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7999889850616455,
-        "t": 81.04085922241211
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133422.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8167157173156738,
-        "t": 44.12698745727539
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133556.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8022412061691284,
-        "t": 82.9610824584961
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133218.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7475943565368652,
-        "t": 92.91529655456543
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133032.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7683651447296143,
-        "t": 47.093868255615234
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133027.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7302900552749634,
-        "t": 60.16898155212402
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133225.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6297093033790588,
-        "t": 48.51031303405762
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133621.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7523502707481384,
-        "t": 72.01671600341797
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133147.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8133907914161682,
-        "t": 67.3520565032959
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133025.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7561717629432678,
-        "t": 40.892839431762695
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133030.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7530563473701477,
-        "t": 45.28999328613281
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133554.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7634739279747009,
-        "t": 58.85004997253418
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133408.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7165898680686951,
-        "t": 46.295166015625
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133434.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8478705286979675,
-        "t": 44.56186294555664
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133150.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7823503613471985,
-        "t": 39.99018669128418
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133140.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7842929363250732,
-        "t": 52.87289619445801
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133154.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7810501456260681,
-        "t": 46.92387580871582
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133430.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6235930323600769,
-        "t": 47.57499694824219
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133236.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8170521855354309,
-        "t": 44.44313049316406
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133020.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7728762030601501,
-        "t": 49.043893814086914
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133619.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6636338233947754,
-        "t": 43.238162994384766
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133157.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8228639364242554,
-        "t": 43.58720779418945
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133221.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7626473307609558,
-        "t": 42.37699508666992
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133023.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7710792422294617,
-        "t": 46.640872955322266
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133234.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7291291952133179,
-        "t": 39.24727439880371
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133208.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7466256022453308,
-        "t": 38.78974914550781
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133426.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7794255018234253,
-        "t": 45.49813270568848
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133432.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6488977670669556,
-        "t": 45.372962951660156
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133624.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7576903700828552,
-        "t": 43.93887519836426
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141240.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7301948666572571,
-        "t": 39.69979286193848
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141334.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8314298987388611,
-        "t": 40.96674919128418
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141320.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9718706011772156,
-        "t": 41.62096977233887
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141731.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7881443500518799,
-        "t": 41.69106483459473
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141527.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8990302681922913,
-        "t": 49.169301986694336
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141531.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8023399710655212,
-        "t": 44.7390079498291
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140820.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9245113134384155,
-        "t": 46.20504379272461
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141323.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8400096893310547,
-        "t": 44.571876525878906
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140940.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8275198340415955,
-        "t": 42.72294044494629
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141134.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8908815383911133,
-        "t": 50.23598670959473
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141120.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8457472324371338,
-        "t": 45.00317573547363
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141124.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8402755856513977,
-        "t": 44.67010498046875
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141326.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8235734701156616,
-        "t": 41.246891021728516
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140945.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.927323579788208,
-        "t": 39.283037185668945
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141521.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9445898532867432,
-        "t": 40.534019470214844
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141245.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.809008002281189,
-        "t": 42.00387001037598
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141318.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9418272972106934,
-        "t": 42.8469181060791
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141536.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8514074683189392,
-        "t": 49.681901931762695
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141223.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8103330135345459,
-        "t": 45.558929443359375
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140935.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8288280963897705,
-        "t": 48.708200454711914
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141424.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8032780885696411,
-        "t": 77.55708694458008
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141626.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9326897859573364,
-        "t": 47.524213790893555
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141550.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9156665205955505,
-        "t": 44.18206214904785
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141234.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8889418244361877,
-        "t": 44.72780227661133
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141546.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9345855116844177,
-        "t": 41.09597206115723
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141631.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.895543098449707,
-        "t": 68.85504722595215
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141231.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9644358158111572,
-        "t": 42.9842472076416
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141621.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9219919443130493,
-        "t": 74.44405555725098
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140853.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9401186108589172,
-        "t": 38.6660099029541
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141540.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9195764660835266,
-        "t": 40.374040603637695
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140845.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7946394085884094,
-        "t": 42.558908462524414
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141636.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8472752571105957,
-        "t": 45.94612121582031
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141421.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7928593158721924,
-        "t": 49.45111274719238
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141227.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.79807049036026,
-        "t": 52.60300636291504
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140849.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8647034764289856,
-        "t": 95.72315216064453
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141411.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.6192272901535034,
-        "t": 53.36499214172363
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141639.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9016616940498352,
-        "t": 41.5189266204834
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141406.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9284757971763611,
-        "t": 39.13092613220215
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140858.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8912498950958252,
-        "t": 42.8929328918457
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141400.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.738412082195282,
-        "t": 46.66709899902344
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141414.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7671757340431213,
-        "t": 42.5410270690918
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141315.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9279612302780151,
-        "t": 47.84893989562988
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141116.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8824111223220825,
-        "t": 46.536922454833984
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140948.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8642981052398682,
-        "t": 61.73276901245117
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141129.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.6822037696838379,
-        "t": 51.41282081604004
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141713.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8162092566490173,
-        "t": 46.890974044799805
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141717.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8765867948532104,
-        "t": 45.67408561706543
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140959.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9207374453544617,
-        "t": 44.22116279602051
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141728.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7993386387825012,
-        "t": 42.7250862121582
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141112.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7184731960296631,
-        "t": 46.69618606567383
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142006.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.736106276512146,
-        "t": 67.80600547790527
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142010.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.6958173513412476,
-        "t": 44.510841369628906
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141849.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8721898794174194,
-        "t": 44.62003707885742
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141915.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.814158022403717,
-        "t": 48.05874824523926
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142029.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9233385324478149,
-        "t": 45.207977294921875
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142000.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.7382473945617676,
-        "t": 44.12078857421875
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142003.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.7019561529159546,
-        "t": 47.348976135253906
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142049.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9217521548271179,
-        "t": 45.96090316772461
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142046.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8044903874397278,
-        "t": 48.573970794677734
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142052.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8905997276306152,
-        "t": 45.91488838195801
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142054.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9471796751022339,
-        "t": 47.61505126953125
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142055.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8982629179954529,
-        "t": 41.7940616607666
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141922.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8234538435935974,
-        "t": 48.98524284362793
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141923.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8668152689933777,
-        "t": 41.24879837036133
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142027.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8394821882247925,
-        "t": 48.766136169433594
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142025.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8940384387969971,
-        "t": 46.24295234680176
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141855.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8447078466415405,
-        "t": 47.725677490234375
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141840.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8374553918838501,
-        "t": 46.170949935913086
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142020.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.7547014951705933,
-        "t": 44.77095603942871
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142008.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8182744979858398,
-        "t": 46.87190055847168
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141918.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.7750509977340698,
-        "t": 42.64998435974121
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141851.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.822920560836792,
-        "t": 82.19289779663086
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141853.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9143557548522949,
-        "t": 44.05498504638672
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141926.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8601046204566956,
-        "t": 45.787811279296875
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142022.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.7402634620666504,
-        "t": 44.068098068237305
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142416.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5684163570404053,
-        "t": 45.48907279968262
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142238.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5642862915992737,
-        "t": 98.58393669128418
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142414.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.7043935060501099,
-        "t": 44.67415809631348
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142410.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5182380080223083,
-        "t": 48.06804656982422
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142202.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5163269639015198,
-        "t": 41.383981704711914
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142200.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5411097407341003,
-        "t": 46.09417915344238
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142412.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6846405267715454,
-        "t": 103.63602638244629
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142111.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5530638694763184,
-        "t": 42.69695281982422
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142113.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5215775966644287,
-        "t": 41.471004486083984
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142329.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5646148324012756,
-        "t": 42.88291931152344
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142115.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5305525660514832,
-        "t": 44.567108154296875
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142118.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5811034440994263,
-        "t": 91.56203269958496
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142326.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.506730854511261,
-        "t": 42.311906814575195
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142324.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.555452287197113,
-        "t": 48.41303825378418
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142320.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5421212315559387,
-        "t": 47.75810241699219
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142240.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.523438036441803,
-        "t": 49.39103126525879
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142322.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.531369686126709,
-        "t": 43.801069259643555
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142108.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6461730003356934,
-        "t": 44.088125228881836
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142243.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5169731974601746,
-        "t": 45.7761287689209
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142151.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6309089660644531,
-        "t": 53.78103256225586
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142232.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5475961565971375,
-        "t": 101.45378112792969
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142418.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6865099668502808,
-        "t": 45.762062072753906
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142154.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5688737630844116,
-        "t": 43.82491111755371
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142235.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.525343656539917,
-        "t": 43.00999641418457
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142157.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6259387731552124,
-        "t": 46.67496681213379
-      }
-    ],
-    "baseline-ei": [
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133615.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5160900950431824,
-        "t": 52.449941635131836
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133403.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5012149214744568,
-        "t": 44.873952865600586
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133238.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6787699460983276,
-        "t": 37.326812744140625
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133210.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.5588451623916626,
-        "t": 31.21781349182129
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133600.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7091507315635681,
-        "t": 30.954837799072266
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133602.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7137372493743896,
-        "t": 32.20176696777344
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133414.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6256474852561951,
-        "t": 30.45511245727539
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133428.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6172502636909485,
-        "t": 31.18109703063965
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133212.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.623191237449646,
-        "t": 36.59319877624512
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133159.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7142592072486877,
-        "t": 31.146764755249023
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133617.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.6150215268135071,
-        "t": 31.42714500427246
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133613.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.59198397397995,
-        "t": 31.803131103515625
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133405.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6027496457099915,
-        "t": 31.29100799560547
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133558.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7458369135856628,
-        "t": 33.24317932128906
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133410.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5517845153808594,
-        "t": 33.69784355163574
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133638.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5877675414085388,
-        "t": 34.288883209228516
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133412.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6666545271873474,
-        "t": 34.16705131530762
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133228.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6322255730628967,
-        "t": 31.9671630859375
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133215.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6867414712905884,
-        "t": 31.696796417236328
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133605.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6748155951499939,
-        "t": 31.383037567138672
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133138.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6458162665367126,
-        "t": 32.12404251098633
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133528.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6363773941993713,
-        "t": 34.516096115112305
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133058.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.5262245535850525,
-        "t": 31.653881072998047
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133515.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6992911100387573,
-        "t": 33.56003761291504
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133449.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.5141580700874329,
-        "t": 32.41586685180664
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133307.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.7142995595932007,
-        "t": 31.912803649902344
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133105.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5941734910011292,
-        "t": 69.62084770202637
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133649.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7482057213783264,
-        "t": 31.93187713623047
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133311.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.8120073676109314,
-        "t": 33.4620475769043
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133339.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5070522427558899,
-        "t": 33.05506706237793
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133258.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6808122396469116,
-        "t": 33.67209434509277
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133102.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.5048099756240845,
-        "t": 71.06518745422363
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133328.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.5996730327606201,
-        "t": 34.7752571105957
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133300.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7466172575950623,
-        "t": 33.705949783325195
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133314.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.717269778251648,
-        "t": 40.46297073364258
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133512.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7321485280990601,
-        "t": 31.458139419555664
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133249.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6958086490631104,
-        "t": 67.14701652526855
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133510.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7343425154685974,
-        "t": 33.035993576049805
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133316.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.7443307638168335,
-        "t": 32.62519836425781
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133643.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.523960292339325,
-        "t": 32.366037368774414
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133508.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7190782427787781,
-        "t": 32.72819519042969
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133534.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6957080364227295,
-        "t": 73.29773902893066
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133252.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6107556819915771,
-        "t": 34.21616554260254
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133520.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7379520535469055,
-        "t": 34.28816795349121
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133332.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6561693549156189,
-        "t": 33.209800720214844
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133642.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5137585997581482,
-        "t": 32.987356185913086
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133130.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7135671377182007,
-        "t": 70.81413269042969
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133132.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.593589723110199,
-        "t": 32.60207176208496
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133456.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5738644599914551,
-        "t": 32.720088958740234
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133330.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5442389845848083,
-        "t": 32.34601020812988
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133536.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6454246044158936,
-        "t": 32.45282173156738
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133522.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7438384294509888,
-        "t": 65.93990325927734
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133052.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5142165422439575,
-        "t": 32.03892707824707
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133645.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7148577570915222,
-        "t": 32.914161682128906
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133309.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.7229865789413452,
-        "t": 32.65023231506348
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133447.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5345724821090698,
-        "t": 32.90414810180664
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133321.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.8142209053039551,
-        "t": 36.87405586242676
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133335.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6110126376152039,
-        "t": 38.57302665710449
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133453.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.695955753326416,
-        "t": 33.040761947631836
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133241.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5618305206298828,
-        "t": 34.84702110290527
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133532.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7076138854026794,
-        "t": 34.503936767578125
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133254.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6339234709739685,
-        "t": 44.158220291137695
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133452.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7277681827545166,
-        "t": 33.01692008972168
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133136.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6598841547966003,
-        "t": 33.15591812133789
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133108.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.5637508630752563,
-        "t": 33.98609161376953
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133134.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.767652690410614,
-        "t": 36.4840030670166
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133256.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6834434866905212,
-        "t": 76.89094543457031
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133055.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.5122503042221069,
-        "t": 33.490896224975586
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133445.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5638052225112915,
-        "t": 51.94497108459473
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133337.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.5626513361930847,
-        "t": 39.22390937805176
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133647.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6730502843856812,
-        "t": 31.417131423950195
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133152.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6052242517471313,
-        "t": 86.66110038757324
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133422.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6078127026557922,
-        "t": 33.35309028625488
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133556.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7648349404335022,
-        "t": 37.02497482299805
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133218.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5694292783737183,
-        "t": 37.62102127075195
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133032.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5544875860214233,
-        "t": 35.64858436584473
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133027.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6111966371536255,
-        "t": 81.5439224243164
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133225.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5653116703033447,
-        "t": 34.48009490966797
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133621.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.5660425424575806,
-        "t": 39.501190185546875
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133147.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.754849374294281,
-        "t": 52.92487144470215
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133025.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5712294578552246,
-        "t": 31.557083129882812
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133030.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6305410861968994,
-        "t": 127.28023529052734
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133554.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6483911275863647,
-        "t": 46.13208770751953
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133408.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.5714389681816101,
-        "t": 33.869028091430664
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133434.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6859297156333923,
-        "t": 32.03988075256348
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133150.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7000795006752014,
-        "t": 33.252716064453125
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133140.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6981026530265808,
-        "t": 42.25015640258789
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133154.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6281241178512573,
-        "t": 32.672882080078125
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133430.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7243353128433228,
-        "t": 34.79290008544922
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133236.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6325961351394653,
-        "t": 32.150983810424805
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133020.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5970026850700378,
-        "t": 35.11214256286621
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133619.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.5267543196678162,
-        "t": 70.08576393127441
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133157.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6786511540412903,
-        "t": 36.8199348449707
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133221.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.501807451248169,
-        "t": 34.42502021789551
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133023.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5953516364097595,
-        "t": 34.32512283325195
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133234.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5905604958534241,
-        "t": 32.47523307800293
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133208.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.5101449489593506,
-        "t": 70.6322193145752
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133426.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5620237588882446,
-        "t": 41.21685028076172
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133432.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6200845241546631,
-        "t": 77.16178894042969
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133624.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.5395022034645081,
-        "t": 35.16268730163574
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141240.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8067865967750549,
-        "t": 34.551143646240234
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141334.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.939331591129303,
-        "t": 37.590980529785156
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141320.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9566511511802673,
-        "t": 33.74314308166504
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141731.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8305788636207581,
-        "t": 32.475948333740234
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141527.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9074695110321045,
-        "t": 33.93101692199707
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141531.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9762430787086487,
-        "t": 35.55583953857422
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140820.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7239791750907898,
-        "t": 82.69119262695312
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141323.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9826921224594116,
-        "t": 36.91911697387695
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140940.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9392725229263306,
-        "t": 33.51306915283203
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141134.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.873248815536499,
-        "t": 55.11188507080078
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141120.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9108697772026062,
-        "t": 33.847808837890625
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141124.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8217811584472656,
-        "t": 77.13007926940918
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141326.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7489046454429626,
-        "t": 34.35015678405762
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140945.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9415338635444641,
-        "t": 32.08613395690918
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141521.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7190586924552917,
-        "t": 36.232948303222656
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141245.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9245351552963257,
-        "t": 47.04594612121582
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141318.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9642161130905151,
-        "t": 38.09499740600586
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141536.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7724869251251221,
-        "t": 32.73200988769531
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141223.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.836726725101471,
-        "t": 43.82014274597168
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140935.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8298658132553101,
-        "t": 36.03315353393555
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141424.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.6829559803009033,
-        "t": 42.368173599243164
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141626.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9623093008995056,
-        "t": 47.791242599487305
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141550.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7792901992797852,
-        "t": 34.40594673156738
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141234.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.759310245513916,
-        "t": 32.01603889465332
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141546.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.968623161315918,
-        "t": 36.511898040771484
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141631.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9423547387123108,
-        "t": 66.27798080444336
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141231.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8958366513252258,
-        "t": 102.49996185302734
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141621.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.809942364692688,
-        "t": 46.92983627319336
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140853.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9118592143058777,
-        "t": 34.12318229675293
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141540.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9563001990318298,
-        "t": 35.1102352142334
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140845.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9013075232505798,
-        "t": 34.49821472167969
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141636.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9547209739685059,
-        "t": 63.8580322265625
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141421.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.949369490146637,
-        "t": 44.65484619140625
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141227.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7060568928718567,
-        "t": 36.57221794128418
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140849.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9052174687385559,
-        "t": 34.39807891845703
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141411.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7706855535507202,
-        "t": 35.93111038208008
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141639.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8379738330841064,
-        "t": 47.06001281738281
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141406.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9423807859420776,
-        "t": 34.301042556762695
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140858.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.959990918636322,
-        "t": 38.28310966491699
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141400.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8036482930183411,
-        "t": 36.05079650878906
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141414.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9213213920593262,
-        "t": 35.83168983459473
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141315.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7337365746498108,
-        "t": 82.66496658325195
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141116.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.934200644493103,
-        "t": 34.60288047790527
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140948.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.6733303070068359,
-        "t": 35.32910346984863
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141129.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9159274101257324,
-        "t": 35.57419776916504
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141713.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9450041651725769,
-        "t": 35.53581237792969
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141717.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9478166699409485,
-        "t": 75.55913925170898
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140959.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8429234623908997,
-        "t": 36.715030670166016
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141728.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9326026439666748,
-        "t": 37.39285469055176
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141112.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9208284616470337,
-        "t": 35.01105308532715
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142006.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8548647165298462,
-        "t": 35.40468215942383
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142010.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8018601536750793,
-        "t": 84.31482315063477
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141849.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9212568402290344,
-        "t": 35.42208671569824
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141915.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.7625051140785217,
-        "t": 36.87596321105957
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142029.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9739797711372375,
-        "t": 33.93101692199707
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142000.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8785056471824646,
-        "t": 35.886287689208984
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142003.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8295310735702515,
-        "t": 93.98293495178223
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142049.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9059033393859863,
-        "t": 39.28184509277344
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142046.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8971518874168396,
-        "t": 34.70420837402344
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142052.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8837954998016357,
-        "t": 35.17484664916992
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142054.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.961409330368042,
-        "t": 37.603139877319336
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142055.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9416783452033997,
-        "t": 90.82889556884766
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141922.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.911831259727478,
-        "t": 39.14999961853027
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141923.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8524993658065796,
-        "t": 34.44981575012207
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142027.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9801784157752991,
-        "t": 36.36884689331055
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142025.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9047930836677551,
-        "t": 35.48622131347656
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141855.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9019267559051514,
-        "t": 40.72690010070801
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141840.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8458648920059204,
-        "t": 34.4541072845459
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142020.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8816420435905457,
-        "t": 36.40007972717285
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142008.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9331113696098328,
-        "t": 38.31291198730469
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141918.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9222348928451538,
-        "t": 36.16595268249512
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141851.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9300496578216553,
-        "t": 61.87272071838379
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141853.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9025083780288696,
-        "t": 34.48081016540527
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141926.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8425257802009583,
-        "t": 35.47978401184082
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142022.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9011247158050537,
-        "t": 35.29214859008789
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142416.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5539907217025757,
-        "t": 34.974098205566406
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142238.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.717125654220581,
-        "t": 37.512779235839844
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142414.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5414297580718994,
-        "t": 35.604000091552734
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142410.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6289452910423279,
-        "t": 35.48479080200195
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142202.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6630042791366577,
-        "t": 33.239126205444336
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142200.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5415846109390259,
-        "t": 35.13193130493164
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142412.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.8591774106025696,
-        "t": 36.04698181152344
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142111.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.7172160148620605,
-        "t": 33.66684913635254
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142113.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.858160138130188,
-        "t": 33.64729881286621
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142329.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6319668292999268,
-        "t": 33.66971015930176
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142115.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.7868637442588806,
-        "t": 34.07692909240723
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142118.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.8156174421310425,
-        "t": 32.357215881347656
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142326.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.655935525894165,
-        "t": 33.544063568115234
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142324.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5722770690917969,
-        "t": 35.241127014160156
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142320.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6419046521186829,
-        "t": 58.69913101196289
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142240.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6872844696044922,
-        "t": 36.2699031829834
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142322.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5210086703300476,
-        "t": 34.89518165588379
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142108.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.7315535545349121,
-        "t": 36.48805618286133
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142243.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6687958836555481,
-        "t": 36.58485412597656
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142151.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5172134637832642,
-        "t": 35.2330207824707
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142232.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6788119077682495,
-        "t": 36.90600395202637
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142418.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5931801795959473,
-        "t": 36.72385215759277
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142154.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5244742631912231,
-        "t": 35.971879959106445
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142235.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.7080988883972168,
-        "t": 35.514116287231445
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142157.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.565158486366272,
-        "t": 39.186954498291016
-      }
-    ],
-    "efficientad": [
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133615.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.13287605345249176,
-        "t": 308.94994735717773,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133615_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133403.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07975493371486664,
-        "t": 295.87483406066895,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133403_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133238.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06929836422204971,
-        "t": 302.1278381347656,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133238_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133210.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.062137871980667114,
-        "t": 301.53512954711914,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133210_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133600.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.04713619500398636,
-        "t": 298.0167865753174,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133600_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133602.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07538576424121857,
-        "t": 317.8071975708008,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133602_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133414.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.11071030050516129,
-        "t": 298.22707176208496,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133414_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133428.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07803301513195038,
-        "t": 299.03197288513184,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133428_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133212.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.1006876677274704,
-        "t": 341.60304069519043,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133212_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133159.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.08807793259620667,
-        "t": 305.54914474487305,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133159_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133617.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.11426303535699844,
-        "t": 299.5619773864746,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133617_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133613.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.1771896928548813,
-        "t": 298.7949848175049,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133613_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133405.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06054283678531647,
-        "t": 356.22406005859375,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133405_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133558.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.038184553384780884,
-        "t": 296.4310646057129,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133558_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133410.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09339088201522827,
-        "t": 308.5329532623291,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133410_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133638.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.17345254123210907,
-        "t": 309.51809883117676,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133638_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133412.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09074568003416061,
-        "t": 360.5661392211914,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133412_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133228.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07664267718791962,
-        "t": 313.84897232055664,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133228_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133215.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.04414336010813713,
-        "t": 299.10826683044434,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133215_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133605.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0736202821135521,
-        "t": 293.82801055908203,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133605_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133138.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09746880829334259,
-        "t": 295.2301502227783,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133138_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133528.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.04818817600607872,
-        "t": 285.11881828308105,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133528_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133058.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.052832189947366714,
-        "t": 283.6639881134033,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133058_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133515.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06468405574560165,
-        "t": 288.9101505279541,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133515_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133449.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.14238980412483215,
-        "t": 284.743070602417,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133449_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133307.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.1925414353609085,
-        "t": 289.5941734313965,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133307_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133105.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05143542215228081,
-        "t": 297.43003845214844,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133105_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133649.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.2545987665653229,
-        "t": 293.0028438568115,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133649_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133311.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.17578396201133728,
-        "t": 293.24865341186523,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133311_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133339.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05440402403473854,
-        "t": 291.08285903930664,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133339_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133258.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09498859196901321,
-        "t": 292.15407371520996,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133258_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133102.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07554974406957626,
-        "t": 296.8258857727051,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133102_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133328.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05219234526157379,
-        "t": 286.9071960449219,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133328_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133300.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.040095437318086624,
-        "t": 291.8097972869873,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133300_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133314.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.21953481435775757,
-        "t": 285.78996658325195,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133314_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133512.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06815212965011597,
-        "t": 294.57926750183105,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133512_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133249.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.032271940261125565,
-        "t": 294.0690517425537,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133249_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133510.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.08476559817790985,
-        "t": 295.5470085144043,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133510_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133316.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.18011076748371124,
-        "t": 299.5562553405762,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133316_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133643.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.08951570838689804,
-        "t": 297.35517501831055,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133643_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133508.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.10903359204530716,
-        "t": 301.6009330749512,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133508_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133534.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05495252460241318,
-        "t": 322.0200538635254,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133534_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133252.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.030976787209510803,
-        "t": 295.8519458770752,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133252_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133520.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05028874799609184,
-        "t": 295.7141399383545,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133520_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133332.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.051953546702861786,
-        "t": 292.8318977355957,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133332_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133642.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.17035706341266632,
-        "t": 294.22998428344727,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133642_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133130.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.10371886938810349,
-        "t": 316.8628215789795,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133130_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133132.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.04099087789654732,
-        "t": 285.37607192993164,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133132_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133456.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.11978902667760849,
-        "t": 290.4167175292969,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133456_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133330.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06351739913225174,
-        "t": 297.7609634399414,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133330_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133536.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07330650836229324,
-        "t": 299.52502250671387,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133536_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133522.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05644793063402176,
-        "t": 297.8711128234863,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133522_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133052.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.08843155950307846,
-        "t": 286.8959903717041,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133052_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133645.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.10933881998062134,
-        "t": 286.65900230407715,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133645_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133309.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.14593559503555298,
-        "t": 296.2346076965332,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133309_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133447.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07631659507751465,
-        "t": 295.1030731201172,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133447_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133321.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.18059976398944855,
-        "t": 319.08416748046875,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133321_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133335.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.14473876357078552,
-        "t": 301.9380569458008,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133335_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133453.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 0.4139099419116974,
-        "t": 292.2959327697754,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133453_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133241.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07815629243850708,
-        "t": 300.4438877105713,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133241_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133532.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.056021664291620255,
-        "t": 291.26501083374023,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133532_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133254.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.030561918392777443,
-        "t": 322.5548267364502,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133254_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133452.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.16573460400104523,
-        "t": 291.6111946105957,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133452_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133136.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07776226848363876,
-        "t": 306.351900100708,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133136_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133108.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06600160896778107,
-        "t": 294.8620319366455,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133108_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133134.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0762375146150589,
-        "t": 291.19300842285156,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133134_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133256.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.04149635508656502,
-        "t": 302.325963973999,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133256_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133055.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.08513053506612778,
-        "t": 302.6890754699707,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133055_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133445.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07531656324863434,
-        "t": 326.9529342651367,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133445_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133337.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07598753273487091,
-        "t": 320.76525688171387,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133337_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133647.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.17688286304473877,
-        "t": 354.1731834411621,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133647_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133152.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.15749742090702057,
-        "t": 477.68497467041016,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133152_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133422.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.10191483050584793,
-        "t": 359.3120574951172,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133422_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133556.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06001882255077362,
-        "t": 332.36122131347656,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133556_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133218.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.046985458582639694,
-        "t": 371.43397331237793,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133218_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133032.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.03149789199233055,
-        "t": 460.6938362121582,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133032_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133027.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.056334421038627625,
-        "t": 314.42713737487793,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133027_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133225.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0772857815027237,
-        "t": 403.3942222595215,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133225_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133621.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.1090206429362297,
-        "t": 349.7748374938965,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133621_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133147.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.10283081233501434,
-        "t": 311.36322021484375,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133147_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133025.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06849294900894165,
-        "t": 296.81921005249023,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133025_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133030.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06589711457490921,
-        "t": 428.9391040802002,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133030_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133554.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07931075245141983,
-        "t": 362.6842498779297,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133554_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133408.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07698283344507217,
-        "t": 321.4890956878662,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133408_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133434.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.10547024011611938,
-        "t": 325.58703422546387,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133434_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133150.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.14238165318965912,
-        "t": 328.2580375671387,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133150_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133140.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.12710832059383392,
-        "t": 390.88940620422363,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133140_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133154.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09575966000556946,
-        "t": 329.97703552246094,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133154_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133430.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07925780862569809,
-        "t": 328.51314544677734,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133430_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133236.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09178458154201508,
-        "t": 331.1629295349121,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133236_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133020.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09755408018827438,
-        "t": 343.555212020874,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133020_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133619.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.12342601269483566,
-        "t": 544.3100929260254,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133619_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133157.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.1049790233373642,
-        "t": 432.4207305908203,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133157_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133221.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07846582680940628,
-        "t": 324.66602325439453,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133221_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133023.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.12446945905685425,
-        "t": 306.92362785339355,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133023_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133234.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.1316184252500534,
-        "t": 292.7532196044922,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133234_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133208.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.08985542505979538,
-        "t": 313.58981132507324,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133208_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133426.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07188931852579117,
-        "t": 335.1900577545166,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133426_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133432.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.12176011502742767,
-        "t": 330.2597999572754,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133432_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133624.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.11441869288682938,
-        "t": 297.2450256347656,
-        "r": "datasets/cookies_2/no_anomaly/20240417_133624_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141240.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.745119094848633,
-        "t": 297.12796211242676,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141240_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141334.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.367042303085327,
-        "t": 368.4689998626709,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141334_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141320.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.9813873767852783,
-        "t": 360.5387210845947,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141320_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141731.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.6086065769195557,
-        "t": 332.7620029449463,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141731_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141527.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.958138942718506,
-        "t": 317.61622428894043,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141527_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141531.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.552922487258911,
-        "t": 348.6177921295166,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141531_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140820.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 4.935487270355225,
-        "t": 304.671049118042,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_140820_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141323.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.4271349906921387,
-        "t": 312.762975692749,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141323_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140940.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.034430503845215,
-        "t": 343.8141345977783,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_140940_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141134.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.6493008136749268,
-        "t": 307.24573135375977,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141134_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141120.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.1201400756835938,
-        "t": 319.256067276001,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141120_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141124.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.792515754699707,
-        "t": 312.6981258392334,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141124_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141326.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 4.0709614753723145,
-        "t": 300.83417892456055,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141326_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140945.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.968827486038208,
-        "t": 296.71287536621094,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_140945_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141521.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.221076726913452,
-        "t": 295.5200672149658,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141521_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141245.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.9105286598205566,
-        "t": 306.73980712890625,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141245_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141318.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.8158068656921387,
-        "t": 364.7758960723877,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141318_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141536.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.846953868865967,
-        "t": 346.47202491760254,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141536_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141223.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.5271549224853516,
-        "t": 331.70199394226074,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141223_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140935.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.877593755722046,
-        "t": 322.09324836730957,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_140935_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141424.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.856137990951538,
-        "t": 337.40687370300293,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141424_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141626.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.0675318241119385,
-        "t": 368.58391761779785,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141626_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141550.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.719208240509033,
-        "t": 353.02186012268066,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141550_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141234.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.16772723197937,
-        "t": 293.16115379333496,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141234_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141546.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.057767391204834,
-        "t": 293.5621738433838,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141546_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141631.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.8838536739349365,
-        "t": 345.062255859375,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141631_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141231.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.506479501724243,
-        "t": 294.6896553039551,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141231_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141621.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.3366057872772217,
-        "t": 323.1339454650879,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141621_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140853.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 4.243487358093262,
-        "t": 353.97887229919434,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_140853_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141540.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.4948947429656982,
-        "t": 292.09375381469727,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141540_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140845.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 4.31516695022583,
-        "t": 304.10289764404297,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_140845_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141636.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.2016854286193848,
-        "t": 309.7527027130127,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141636_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141421.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.3863651752471924,
-        "t": 329.88691329956055,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141421_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141227.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 4.153046607971191,
-        "t": 353.21879386901855,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141227_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140849.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 4.744839668273926,
-        "t": 330.4178714752197,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_140849_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141411.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 4.613285064697266,
-        "t": 341.6109085083008,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141411_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141639.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.4021308422088623,
-        "t": 373.5308647155762,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141639_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141406.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.993306279182434,
-        "t": 318.91775131225586,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141406_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140858.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.535719394683838,
-        "t": 329.8609256744385,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_140858_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141400.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.4255881309509277,
-        "t": 317.7828788757324,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141400_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141414.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.6611076593399048,
-        "t": 311.15078926086426,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141414_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141315.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 3.0101468563079834,
-        "t": 310.2278709411621,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141315_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141116.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.859757661819458,
-        "t": 327.78072357177734,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141116_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140948.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 5.472847938537598,
-        "t": 302.67906188964844,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_140948_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141129.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.436461091041565,
-        "t": 305.55081367492676,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141129_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141713.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.8320623636245728,
-        "t": 298.4199523925781,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141713_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141717.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.1424906253814697,
-        "t": 305.1338195800781,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141717_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140959.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 4.017390251159668,
-        "t": 308.4089756011963,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_140959_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141728.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.2991039752960205,
-        "t": 311.6569519042969,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141728_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141112.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 2.867258310317993,
-        "t": 307.4779510498047,
-        "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141112_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142006.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.2014203071594238,
-        "t": 308.3322048187256,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142006_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142010.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.0737624168395996,
-        "t": 305.9701919555664,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142010_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141849.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9932183027267456,
-        "t": 324.4519233703613,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_141849_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141915.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8743047714233398,
-        "t": 309.3676567077637,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_141915_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142029.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.6135246753692627,
-        "t": 317.98362731933594,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142029_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142000.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9625742435455322,
-        "t": 315.9210681915283,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142000_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142003.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.074314832687378,
-        "t": 313.26889991760254,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142003_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142049.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9267026782035828,
-        "t": 333.9550495147705,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142049_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142046.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.0044469833374023,
-        "t": 301.23305320739746,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142046_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142052.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.884239137172699,
-        "t": 313.83705139160156,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142052_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142054.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9982011318206787,
-        "t": 316.6646957397461,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142054_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142055.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.1185537576675415,
-        "t": 319.0269470214844,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142055_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141922.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9354775547981262,
-        "t": 347.9597568511963,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_141922_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141923.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.1125562191009521,
-        "t": 308.9017868041992,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_141923_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142027.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.8080154657363892,
-        "t": 327.6369571685791,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142027_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142025.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.861001968383789,
-        "t": 308.57181549072266,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142025_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141855.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.123816967010498,
-        "t": 353.27911376953125,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_141855_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141840.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.911929726600647,
-        "t": 320.3260898590088,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_141840_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142020.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.6534943580627441,
-        "t": 313.5101795196533,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142020_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142008.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.0356767177581787,
-        "t": 306.9889545440674,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142008_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141918.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.1539702415466309,
-        "t": 311.2471103668213,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_141918_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141851.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.1214417219161987,
-        "t": 316.37096405029297,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_141851_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141853.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.0088820457458496,
-        "t": 305.0580024719238,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_141853_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141926.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.1299062967300415,
-        "t": 585.5300426483154,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_141926_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142022.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.680979609489441,
-        "t": 404.4761657714844,
-        "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142022_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142416.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6128200888633728,
-        "t": 308.351993560791,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142416_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142238.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.4123280644416809,
-        "t": 304.6128749847412,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142238_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142414.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.42414137721061707,
-        "t": 308.513879776001,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142414_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142410.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6200454235076904,
-        "t": 320.32179832458496,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142410_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142202.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.521645188331604,
-        "t": 309.37981605529785,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142202_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142200.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.4439812898635864,
-        "t": 324.09119606018066,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142200_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142412.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5540238618850708,
-        "t": 301.60021781921387,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142412_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142111.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 1.0547114610671997,
-        "t": 323.4992027282715,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142111_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142113.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 1.0828081369400024,
-        "t": 292.61207580566406,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142113_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142329.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 1.24735426902771,
-        "t": 299.3752956390381,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142329_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142115.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 1.4443954229354858,
-        "t": 293.8847541809082,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142115_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142118.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 2.3280601501464844,
-        "t": 300.18115043640137,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142118_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142326.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 1.1043317317962646,
-        "t": 317.9628849029541,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142326_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142324.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 1.0250437259674072,
-        "t": 321.6817378997803,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142324_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142320.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 1.07322096824646,
-        "t": 303.88712882995605,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142320_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142240.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.3812253177165985,
-        "t": 366.67585372924805,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142240_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142322.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 1.1435534954071045,
-        "t": 302.8130531311035,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142322_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142108.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 1.3363815546035767,
-        "t": 312.6649856567383,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142108_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142243.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5978065133094788,
-        "t": 306.52308464050293,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142243_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142151.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.4253476858139038,
-        "t": 299.52287673950195,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142151_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142232.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6608911156654358,
-        "t": 301.11002922058105,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142232_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142418.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.7009356617927551,
-        "t": 298.2940673828125,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142418_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142154.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.44911473989486694,
-        "t": 300.4469871520996,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142154_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142235.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6709843277931213,
-        "t": 350.0549793243408,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142235_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142157.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6288148164749146,
-        "t": 328.82189750671387,
-        "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142157_result.jpg"
-      }
-    ],
-    "fomoad": [
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133615.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.5060455799102783,
-        "t": 82.91792869567871
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133403.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.066981315612793,
-        "t": 36.065101623535156
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133238.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.4332468509674072,
-        "t": 34.97004508972168
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133210.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.3107829093933105,
-        "t": 35.118818283081055
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133600.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.114921808242798,
-        "t": 34.06095504760742
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133602.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.019571542739868,
-        "t": 40.71497917175293
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133414.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.955576181411743,
-        "t": 37.22691535949707
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133428.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.971430540084839,
-        "t": 37.671804428100586
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133212.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.297926664352417,
-        "t": 86.88688278198242
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133159.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.027308940887451,
-        "t": 34.69395637512207
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133617.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.3560214042663574,
-        "t": 34.12008285522461
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133613.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.024972677230835,
-        "t": 35.188913345336914
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133405.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.214942216873169,
-        "t": 33.4630012512207
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133558.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.017094612121582,
-        "t": 34.935951232910156
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133410.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.000944137573242,
-        "t": 34.974098205566406
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133638.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.0034961700439453,
-        "t": 35.19320487976074
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133412.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.083559513092041,
-        "t": 37.481069564819336
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133228.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 5.042861461639404,
-        "t": 36.23509407043457
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133215.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.1246588230133057,
-        "t": 33.796072006225586
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133605.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.2912607192993164,
-        "t": 34.45124626159668
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133138.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.2248802185058594,
-        "t": 34.55710411071777
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133528.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.179311513900757,
-        "t": 33.00189971923828
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133058.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.5084166526794434,
-        "t": 34.111976623535156
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133515.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.3109290599823,
-        "t": 35.58707237243652
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133449.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.9396259784698486,
-        "t": 34.78097915649414
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133307.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.749024391174316,
-        "t": 34.27410125732422
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133105.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.637773275375366,
-        "t": 35.70270538330078
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133649.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.6180269718170166,
-        "t": 34.82508659362793
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133311.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 6.486281394958496,
-        "t": 35.16507148742676
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133339.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.3156514167785645,
-        "t": 37.33396530151367
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133258.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.248297691345215,
-        "t": 35.59088706970215
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133102.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.8945462703704834,
-        "t": 34.81292724609375
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133328.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.1618049144744873,
-        "t": 36.38291358947754
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133300.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.819526195526123,
-        "t": 34.998178482055664
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133314.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.637389183044434,
-        "t": 34.33108329772949
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133512.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.0170819759368896,
-        "t": 33.57100486755371
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133249.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.4289615154266357,
-        "t": 34.12604331970215
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133510.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.3074114322662354,
-        "t": 34.802913665771484
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133316.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.811711311340332,
-        "t": 34.50894355773926
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133643.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.247082471847534,
-        "t": 34.73305702209473
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133508.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.024240493774414,
-        "t": 38.61117362976074
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133534.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.082925319671631,
-        "t": 36.19790077209473
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133252.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.054872512817383,
-        "t": 35.88390350341797
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133520.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.1601202487945557,
-        "t": 34.863948822021484
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133332.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.282330513000488,
-        "t": 34.42692756652832
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133642.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.208893299102783,
-        "t": 34.976959228515625
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133130.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.375908613204956,
-        "t": 36.32521629333496
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133132.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.317793369293213,
-        "t": 35.15791893005371
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133456.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.64445161819458,
-        "t": 33.802032470703125
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133330.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.8645083904266357,
-        "t": 35.95614433288574
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133536.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.95991849899292,
-        "t": 34.316062927246094
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133522.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.0857326984405518,
-        "t": 34.25788879394531
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133052.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.9461374282836914,
-        "t": 33.914804458618164
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133645.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.198446750640869,
-        "t": 34.075021743774414
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133309.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 5.5393385887146,
-        "t": 34.757137298583984
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133447.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.051800727844238,
-        "t": 33.80584716796875
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133321.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "anomaly",
-        "s": 5.475006580352783,
-        "t": 38.285017013549805
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133335.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.061313629150391,
-        "t": 35.822153091430664
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133453.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.663031101226807,
-        "t": 34.65580940246582
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133241.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.960418939590454,
-        "t": 34.66606140136719
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133532.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.112732410430908,
-        "t": 33.934831619262695
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133254.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.1425957679748535,
-        "t": 34.33108329772949
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133452.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.665304660797119,
-        "t": 35.79902648925781
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133136.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.858278512954712,
-        "t": 34.5609188079834
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133108.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.824921131134033,
-        "t": 33.94484519958496
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133134.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.19962477684021,
-        "t": 90.72709083557129
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133256.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.84447979927063,
-        "t": 34.32440757751465
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133055.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.5153908729553223,
-        "t": 115.64803123474121
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133445.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.148278713226318,
-        "t": 35.87508201599121
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133337.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.2342915534973145,
-        "t": 38.67816925048828
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133647.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.690516710281372,
-        "t": 41.08881950378418
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133152.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.6630265712738037,
-        "t": 40.071964263916016
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133422.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.6526520252227783,
-        "t": 42.54889488220215
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133556.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.991842269897461,
-        "t": 44.21710968017578
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133218.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.343750476837158,
-        "t": 41.21279716491699
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133032.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.3880109786987305,
-        "t": 35.23898124694824
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133027.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.2934679985046387,
-        "t": 36.27967834472656
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133225.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.336224317550659,
-        "t": 63.88211250305176
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133621.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.5505223274230957,
-        "t": 43.99275779724121
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133147.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.7615809440612793,
-        "t": 35.03680229187012
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133025.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.7099123001098633,
-        "t": 85.57295799255371
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133030.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.5742902755737305,
-        "t": 48.7217903137207
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133554.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.4098782539367676,
-        "t": 37.425994873046875
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133408.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.4974992275238037,
-        "t": 35.722970962524414
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133434.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.690532922744751,
-        "t": 34.52801704406738
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133150.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.363110303878784,
-        "t": 39.3831729888916
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133140.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.9915711879730225,
-        "t": 37.7650260925293
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133154.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.1814064979553223,
-        "t": 43.770790100097656
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133430.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.8871026039123535,
-        "t": 35.89916229248047
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133236.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.4279415607452393,
-        "t": 43.048858642578125
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133020.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 5.040414810180664,
-        "t": 35.1560115814209
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133619.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.1948888301849365,
-        "t": 34.555912017822266
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133157.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.012237548828125,
-        "t": 35.25400161743164
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133221.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.0019047260284424,
-        "t": 34.5149040222168
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133023.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.154984951019287,
-        "t": 35.26806831359863
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133234.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.9313619136810303,
-        "t": 34.304141998291016
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133208.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.856881856918335,
-        "t": 40.66896438598633
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133426.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.387061595916748,
-        "t": 38.89107704162598
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133432.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.9989919662475586,
-        "t": 35.600900650024414
-      },
-      {
-        "i": "datasets/cookies_2/no_anomaly/20240417_133624.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.4588916301727295,
-        "t": 34.47914123535156
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141240.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 45.47643280029297,
-        "t": 35.03894805908203
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141334.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 48.757930755615234,
-        "t": 36.67092323303223
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141320.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 50.98466491699219,
-        "t": 34.4700813293457
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141731.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 34.357269287109375,
-        "t": 36.437034606933594
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141527.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 34.33831024169922,
-        "t": 37.65082359313965
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141531.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 34.334693908691406,
-        "t": 39.52527046203613
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140820.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 46.25642395019531,
-        "t": 36.03196144104004
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141323.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 51.358821868896484,
-        "t": 41.43810272216797
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140940.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 36.17974090576172,
-        "t": 38.93899917602539
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141134.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 38.367149353027344,
-        "t": 35.93015670776367
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141120.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 30.121288299560547,
-        "t": 38.98215293884277
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141124.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 29.997486114501953,
-        "t": 35.70914268493652
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141326.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 48.800289154052734,
-        "t": 36.09490394592285
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140945.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 33.44463348388672,
-        "t": 35.53271293640137
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141521.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 43.91867446899414,
-        "t": 35.40492057800293
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141245.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 39.86558532714844,
-        "t": 37.22119331359863
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141318.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 55.3377685546875,
-        "t": 37.86802291870117
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141536.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 48.8928337097168,
-        "t": 77.03590393066406
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141223.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 35.407379150390625,
-        "t": 52.39391326904297
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140935.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 52.88850402832031,
-        "t": 41.4431095123291
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141424.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 42.0013313293457,
-        "t": 36.95106506347656
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141626.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 58.28141403198242,
-        "t": 36.003828048706055
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141550.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 54.23351287841797,
-        "t": 36.0109806060791
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141234.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 34.00712966918945,
-        "t": 35.92705726623535
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141546.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 34.72270202636719,
-        "t": 35.22324562072754
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141631.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 33.97644805908203,
-        "t": 35.14981269836426
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141231.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 27.172456741333008,
-        "t": 35.610198974609375
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141621.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 53.94574737548828,
-        "t": 35.7818603515625
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140853.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 43.29283905029297,
-        "t": 37.6279354095459
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141540.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 74.32418823242188,
-        "t": 35.99095344543457
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140845.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 45.824363708496094,
-        "t": 43.20216178894043
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141636.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 48.56343460083008,
-        "t": 41.17107391357422
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141421.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 42.51222229003906,
-        "t": 40.79008102416992
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141227.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 37.24085235595703,
-        "t": 50.43625831604004
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140849.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 61.175254821777344,
-        "t": 39.586782455444336
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141411.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 33.05671310424805,
-        "t": 36.459922790527344
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141639.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 54.7225227355957,
-        "t": 41.06426239013672
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141406.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 41.8612060546875,
-        "t": 37.004947662353516
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140858.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 45.38267517089844,
-        "t": 38.62571716308594
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141400.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 38.820133209228516,
-        "t": 37.33682632446289
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141414.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 33.30618667602539,
-        "t": 36.19718551635742
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141315.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 40.59771728515625,
-        "t": 36.74578666687012
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141116.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 40.783878326416016,
-        "t": 37.01305389404297
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140948.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 34.28593063354492,
-        "t": 36.61918640136719
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141129.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 33.064186096191406,
-        "t": 36.53693199157715
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141713.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 45.908973693847656,
-        "t": 37.26696968078613
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141717.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 36.05101013183594,
-        "t": 36.599159240722656
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_140959.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 40.89436721801758,
-        "t": 36.331892013549805
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141728.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 42.6511344909668,
-        "t": 36.10587120056152
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_1/20240417_141112.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 43.088897705078125,
-        "t": 36.70907020568848
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142006.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 33.31501770019531,
-        "t": 36.898136138916016
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142010.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 35.545162200927734,
-        "t": 37.30201721191406
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141849.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 44.37229537963867,
-        "t": 36.05389595031738
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141915.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 26.3785400390625,
-        "t": 35.746097564697266
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142029.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 39.916568756103516,
-        "t": 36.76295280456543
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142000.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 26.236583709716797,
-        "t": 38.140058517456055
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142003.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 26.381267547607422,
-        "t": 42.00005531311035
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142049.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 31.626197814941406,
-        "t": 40.17519950866699
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142046.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 19.370769500732422,
-        "t": 45.23515701293945
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142052.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 23.27415657043457,
-        "t": 37.4140739440918
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142054.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 21.61459732055664,
-        "t": 37.94097900390625
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142055.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 26.236751556396484,
-        "t": 36.741018295288086
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141922.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 27.7585506439209,
-        "t": 38.67006301879883
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141923.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 38.2921142578125,
-        "t": 36.160945892333984
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142027.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 32.368858337402344,
-        "t": 37.921905517578125
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142025.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 49.82709503173828,
-        "t": 37.18709945678711
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141855.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 29.767038345336914,
-        "t": 37.992000579833984
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141840.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 27.613189697265625,
-        "t": 37.77718544006348
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142020.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 46.56580352783203,
-        "t": 38.205862045288086
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142008.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 27.236595153808594,
-        "t": 36.83805465698242
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141918.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 38.29072189331055,
-        "t": 36.74626350402832
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141851.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 23.393625259399414,
-        "t": 36.726951599121094
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141853.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 17.14127540588379,
-        "t": 35.68410873413086
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_141926.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 36.262821197509766,
-        "t": 33.354997634887695
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_2/20240417_142022.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 29.772964477539062,
-        "t": 35.86411476135254
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142416.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 6.011517524719238,
-        "t": 36.44227981567383
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142238.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 12.315911293029785,
-        "t": 35.707950592041016
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142414.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 6.030016899108887,
-        "t": 40.48919677734375
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142410.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 6.226258277893066,
-        "t": 35.14814376831055
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142202.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 9.71426010131836,
-        "t": 36.14091873168945
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142200.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 14.605753898620605,
-        "t": 36.61680221557617
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142412.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 5.91170597076416,
-        "t": 37.3539924621582
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142111.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 8.322336196899414,
-        "t": 35.83979606628418
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142113.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 12.38150691986084,
-        "t": 36.45181655883789
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142329.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 24.618350982666016,
-        "t": 35.034894943237305
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142115.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 12.65952205657959,
-        "t": 36.03482246398926
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142118.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 16.78221893310547,
-        "t": 35.237789154052734
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142326.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 16.74004554748535,
-        "t": 36.78488731384277
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142324.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 20.22923469543457,
-        "t": 40.318965911865234
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142320.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 12.116340637207031,
-        "t": 38.2077693939209
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142240.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 6.950046539306641,
-        "t": 35.8579158782959
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142322.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 10.724370002746582,
-        "t": 36.1027717590332
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142108.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 8.95866870880127,
-        "t": 36.332130432128906
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142243.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 5.447047233581543,
-        "t": 36.35525703430176
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142151.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 8.129228591918945,
-        "t": 36.561012268066406
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142232.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 6.717199325561523,
-        "t": 36.302804946899414
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142418.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 5.807551383972168,
-        "t": 36.99374198913574
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142154.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 14.440510749816895,
-        "t": 36.006927490234375
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142235.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 4.890600681304932,
-        "t": 44.173240661621094
-      },
-      {
-        "i": "datasets/cookies_2/anomaly_lvl_3/20240417_142157.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 12.169938087463379,
-        "t": 44.98481750488281
-      }
-    ]
-  },
-  "cookies_3": {
-    "baseline": [
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134027.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8726192712783813,
-        "t": 215.29126167297363
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134225.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.815615177154541,
-        "t": 79.08797264099121
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134351.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8313804268836975,
-        "t": 42.485952377319336
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134353.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8280369639396667,
-        "t": 41.75424575805664
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134227.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6821655035018921,
-        "t": 39.9632453918457
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133945.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8834826946258545,
-        "t": 43.227195739746094
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134025.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8096165657043457,
-        "t": 43.69997978210449
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133950.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7495928406715393,
-        "t": 41.877031326293945
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134356.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7778421640396118,
-        "t": 43.810129165649414
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134424.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7585095763206482,
-        "t": 39.37411308288574
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134222.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7713082432746887,
-        "t": 41.832923889160156
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134236.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.764842689037323,
-        "t": 37.96792030334473
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134034.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7871346473693848,
-        "t": 41.95523262023926
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134009.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.760904848575592,
-        "t": 39.17217254638672
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134431.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7919517755508423,
-        "t": 40.7412052154541
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134427.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8786821365356445,
-        "t": 42.05799102783203
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133943.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8821916580200195,
-        "t": 42.33598709106445
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134023.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.761056125164032,
-        "t": 40.96412658691406
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134426.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7915106415748596,
-        "t": 39.92295265197754
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133845.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8386188745498657,
-        "t": 43.15590858459473
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134509.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8125112056732178,
-        "t": 39.971113204956055
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134247.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7904746532440186,
-        "t": 39.373159408569336
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134051.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7740345001220703,
-        "t": 43.15590858459473
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133930.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8451185822486877,
-        "t": 87.99982070922852
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133918.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.873521625995636,
-        "t": 38.78378868103027
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134534.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8094042539596558,
-        "t": 48.333168029785156
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134332.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6495880484580994,
-        "t": 48.35915565490723
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134440.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8301094770431519,
-        "t": 44.137001037597656
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134124.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.9069262742996216,
-        "t": 45.0139045715332
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134126.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.870505154132843,
-        "t": 46.13304138183594
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134442.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7997210025787354,
-        "t": 48.55990409851074
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134536.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7886624336242676,
-        "t": 41.48292541503906
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133932.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8624308705329895,
-        "t": 45.05586624145508
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133926.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8556853532791138,
-        "t": 49.99804496765137
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134052.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.78544020652771,
-        "t": 40.8177375793457
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134047.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.698083221912384,
-        "t": 41.42498970031738
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134331.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8748666644096375,
-        "t": 39.15905952453613
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133853.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8298398852348328,
-        "t": 40.46988487243652
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133843.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8210392594337463,
-        "t": 42.37079620361328
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133857.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.818794310092926,
-        "t": 74.12219047546387
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134447.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8512449860572815,
-        "t": 42.78707504272461
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134532.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.790480375289917,
-        "t": 42.56105422973633
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134254.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7747462391853333,
-        "t": 40.34996032714844
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134240.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7671393752098083,
-        "t": 40.637969970703125
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134334.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7831103801727295,
-        "t": 41.290998458862305
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134452.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.793221652507782,
-        "t": 42.80877113342285
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134122.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7523096799850464,
-        "t": 38.84696960449219
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133856.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8420141339302063,
-        "t": 71.28286361694336
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134108.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7697421312332153,
-        "t": 43.05887222290039
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134336.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7913997173309326,
-        "t": 43.200016021728516
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134530.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.782397985458374,
-        "t": 43.97296905517578
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133934.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.867405354976654,
-        "t": 42.24109649658203
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133909.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8481327295303345,
-        "t": 38.85293006896973
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134257.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8845602869987488,
-        "t": 38.465023040771484
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134451.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8412730693817139,
-        "t": 36.61394119262695
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134445.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8588381409645081,
-        "t": 74.71466064453125
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133841.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.9097548127174377,
-        "t": 38.355112075805664
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134528.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8349632024765015,
-        "t": 37.82200813293457
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134105.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.788278341293335,
-        "t": 40.04812240600586
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133859.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7231941223144531,
-        "t": 38.535118103027344
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134311.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.9138587117195129,
-        "t": 40.18592834472656
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134502.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8284827470779419,
-        "t": 38.014888763427734
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134258.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8120598793029785,
-        "t": 37.52589225769043
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134338.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.730412483215332,
-        "t": 37.15205192565918
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134106.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8152181506156921,
-        "t": 37.95504570007324
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134314.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8782431483268738,
-        "t": 37.23907470703125
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134300.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8124605417251587,
-        "t": 38.027048110961914
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133916.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.881318211555481,
-        "t": 36.546945571899414
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133903.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8205223679542542,
-        "t": 37.8570556640625
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134249.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.839344322681427,
-        "t": 38.81096839904785
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134101.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6400958895683289,
-        "t": 38.48600387573242
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134511.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7812631726264954,
-        "t": 38.80715370178223
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134505.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.835951030254364,
-        "t": 41.082143783569336
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133928.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7769075036048889,
-        "t": 40.84587097167969
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133914.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8565638065338135,
-        "t": 39.62588310241699
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134504.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.730230987071991,
-        "t": 47.26767539978027
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134316.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8667310476303101,
-        "t": 89.75696563720703
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134128.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8823431730270386,
-        "t": 39.34192657470703
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134205.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8163698315620422,
-        "t": 38.285255432128906
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134007.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8465781807899475,
-        "t": 38.780927658081055
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134238.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7209396958351135,
-        "t": 38.568973541259766
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133838.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.84125155210495,
-        "t": 37.99009323120117
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134158.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7706038951873779,
-        "t": 38.21277618408203
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134414.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8921108841896057,
-        "t": 38.823843002319336
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134212.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7797589898109436,
-        "t": 33.99991989135742
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134004.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8577351570129395,
-        "t": 37.89114952087402
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134429.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.85953289270401,
-        "t": 42.607784271240234
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134159.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8229279518127441,
-        "t": 38.598060607910156
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134203.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8336682915687561,
-        "t": 38.42020034790039
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134217.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7759813070297241,
-        "t": 38.55419158935547
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133948.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8147037029266357,
-        "t": 38.75303268432617
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134216.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.830263614654541,
-        "t": 39.68620300292969
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134404.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7958581447601318,
-        "t": 39.682865142822266
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134412.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8075500130653381,
-        "t": 40.12894630432129
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134214.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7822123765945435,
-        "t": 39.78610038757324
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134228.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8746832609176636,
-        "t": 52.58989334106445
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134003.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8424662351608276,
-        "t": 36.77701950073242
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134201.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7444565892219543,
-        "t": 40.82512855529785
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134349.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8007592558860779,
-        "t": 39.24298286437988
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134407.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8470891118049622,
-        "t": 38.63811492919922
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134810.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9466290473937988,
-        "t": 39.930105209350586
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134804.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.880445122718811,
-        "t": 45.475006103515625
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134838.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.6930278539657593,
-        "t": 41.68224334716797
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135313.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8331581950187683,
-        "t": 40.48275947570801
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134757.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9247138500213623,
-        "t": 36.90218925476074
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135500.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9226543307304382,
-        "t": 39.855003356933594
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135504.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9303311705589294,
-        "t": 46.521663665771484
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135128.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8536800742149353,
-        "t": 40.60697555541992
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135316.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8950821757316589,
-        "t": 38.909912109375
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134753.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8206927180290222,
-        "t": 38.977861404418945
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134816.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8663932085037231,
-        "t": 37.99295425415039
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134631.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9610676765441895,
-        "t": 44.298648834228516
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135249.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9399789571762085,
-        "t": 38.28310966491699
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134624.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.910715639591217,
-        "t": 93.23906898498535
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135428.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8015520572662354,
-        "t": 71.09880447387695
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135158.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9419333934783936,
-        "t": 42.244672775268555
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135205.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9526622295379639,
-        "t": 46.83709144592285
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135201.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9167859554290771,
-        "t": 41.77379608154297
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134725.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9136383533477783,
-        "t": 45.651912689208984
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134732.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8355100750923157,
-        "t": 45.72606086730957
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134900.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8163626790046692,
-        "t": 38.2080078125
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135434.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9180662035942078,
-        "t": 37.41717338562012
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134852.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.6644144654273987,
-        "t": 37.168264389038086
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135231.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7382903099060059,
-        "t": 39.700984954833984
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134714.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8933170437812805,
-        "t": 39.5200252532959
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134728.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.804443359375,
-        "t": 38.17105293273926
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135032.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7503533363342285,
-        "t": 38.4519100189209
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134847.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7627800703048706,
-        "t": 47.9278564453125
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134857.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7285720705986023,
-        "t": 38.60664367675781
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135208.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9169256687164307,
-        "t": 37.60886192321777
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134710.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8180896639823914,
-        "t": 37.467002868652344
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135237.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8803391456604004,
-        "t": 41.94211959838867
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135021.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.7970997095108032,
-        "t": 41.298866271972656
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135431.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8718611001968384,
-        "t": 50.88996887207031
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135424.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8862651586532593,
-        "t": 42.54627227783203
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135418.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9564642906188965,
-        "t": 40.26293754577637
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135154.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8573280572891235,
-        "t": 42.32215881347656
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135245.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8553896546363831,
-        "t": 101.31120681762695
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134615.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8880119323730469,
-        "t": 41.1679744720459
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134946.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8720930218696594,
-        "t": 41.80908203125
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135457.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9465207457542419,
-        "t": 50.30107498168945
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135324.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9257374405860901,
-        "t": 40.71784019470215
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134628.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8926847577095032,
-        "t": 65.1099681854248
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135454.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9050717949867249,
-        "t": 43.08295249938965
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135327.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9443281888961792,
-        "t": 48.79927635192871
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135253.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9032681584358215,
-        "t": 61.39397621154785
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135451.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9095480442047119,
-        "t": 132.4138641357422
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134612.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.930891752243042,
-        "t": 51.172733306884766
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135320.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8839284181594849,
-        "t": 67.17801094055176
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134942.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8462942838668823,
-        "t": 58.1202507019043
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_140003.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.5958507061004639,
-        "t": 58.645009994506836
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135715.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.7878910303115845,
-        "t": 48.2022762298584
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135701.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.5520183444023132,
-        "t": 71.70510292053223
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135924.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.89032381772995,
-        "t": 40.05026817321777
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_140028.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.6981852650642395,
-        "t": 44.14796829223633
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135936.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8811179995536804,
-        "t": 41.932106018066406
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_140012.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8404096961021423,
-        "t": 40.544986724853516
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135822.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.925323486328125,
-        "t": 46.057939529418945
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135955.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.6600536704063416,
-        "t": 43.0910587310791
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135612.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.906220018863678,
-        "t": 42.39296913146973
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135607.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9488791823387146,
-        "t": 40.997982025146484
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135811.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8237810730934143,
-        "t": 40.03405570983887
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135806.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8586963415145874,
-        "t": 57.348012924194336
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135621.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.576616644859314,
-        "t": 55.69887161254883
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135817.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9169279336929321,
-        "t": 48.355817794799805
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135624.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9435421824455261,
-        "t": 92.91481971740723
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135814.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8519084453582764,
-        "t": 38.38086128234863
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135626.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8416171669960022,
-        "t": 43.14303398132324
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135912.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9049033522605896,
-        "t": 43.67995262145996
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135654.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.7181671857833862,
-        "t": 97.19610214233398
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135720.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9081683158874512,
-        "t": 49.88813400268555
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_140021.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.715359091758728,
-        "t": 56.84208869934082
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135928.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8166013360023499,
-        "t": 45.52507400512695
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135725.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.679695188999176,
-        "t": 45.50909996032715
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135916.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.7406543493270874,
-        "t": 44.97790336608887
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140605.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6563747525215149,
-        "t": 46.9059944152832
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140613.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.7482886910438538,
-        "t": 44.6779727935791
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140607.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5035969018936157,
-        "t": 41.94498062133789
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140602.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6066633462905884,
-        "t": 45.72796821594238
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140548.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.512409508228302,
-        "t": 42.50073432922363
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140603.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5441449880599976,
-        "t": 89.72501754760742
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140617.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.7707290649414062,
-        "t": 40.87209701538086
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140615.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.7486072182655334,
-        "t": 40.305137634277344
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140600.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5651815533638,
-        "t": 43.52402687072754
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140116.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6749680638313293,
-        "t": 93.35708618164062
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140113.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5640653371810913,
-        "t": 39.372920989990234
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140517.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.564248263835907,
-        "t": 41.304826736450195
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140106.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.7398868203163147,
-        "t": 42.45591163635254
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140110.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6749525666236877,
-        "t": 47.94597625732422
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140514.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5300725102424622,
-        "t": 39.778947830200195
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140059.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6778920888900757,
-        "t": 42.378902435302734
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140525.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5886169672012329,
-        "t": 42.82498359680176
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140532.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6625184416770935,
-        "t": 41.36013984680176
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140523.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5841175317764282,
-        "t": 45.29523849487305
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140535.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5276758074760437,
-        "t": 46.16570472717285
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140520.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.593223512172699,
-        "t": 45.591115951538086
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140618.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6488353610038757,
-        "t": 100.21471977233887
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140550.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.8163102269172668,
-        "t": 43.22004318237305
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140551.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5229061841964722,
-        "t": 43.244123458862305
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140620.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6239616870880127,
-        "t": 43.908119201660156
-      }
-    ],
-    "baseline-ei": [
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134027.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7905631065368652,
-        "t": 74.58710670471191
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134225.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8362189531326294,
-        "t": 44.69799995422363
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134351.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6237199306488037,
-        "t": 33.83994102478027
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134353.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6331819295883179,
-        "t": 33.387184143066406
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134227.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.871255099773407,
-        "t": 32.8829288482666
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133945.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.88399738073349,
-        "t": 33.00285339355469
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134025.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8837043046951294,
-        "t": 35.04014015197754
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133950.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8669834136962891,
-        "t": 77.667236328125
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134356.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6683863401412964,
-        "t": 32.10091590881348
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134424.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8129484057426453,
-        "t": 32.56702423095703
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134222.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7228723168373108,
-        "t": 34.70897674560547
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134236.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8001672625541687,
-        "t": 32.73510932922363
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134034.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.9053288698196411,
-        "t": 32.47785568237305
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134009.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8337813019752502,
-        "t": 33.496856689453125
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134431.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8248672485351562,
-        "t": 32.88698196411133
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134427.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8177942633628845,
-        "t": 73.82392883300781
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133943.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8763395547866821,
-        "t": 34.049034118652344
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134023.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7817835807800293,
-        "t": 32.7448844909668
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134426.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7616440057754517,
-        "t": 33.31875801086426
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133845.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8969177603721619,
-        "t": 33.57815742492676
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134509.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6927257180213928,
-        "t": 34.281015396118164
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134247.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8526067137718201,
-        "t": 33.38027000427246
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134051.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8812115788459778,
-        "t": 34.54995155334473
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133930.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8022136688232422,
-        "t": 33.879995346069336
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133918.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8576027750968933,
-        "t": 33.12420845031738
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134534.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7413581013679504,
-        "t": 35.32290458679199
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134332.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7618603110313416,
-        "t": 33.87713432312012
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134440.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7503236532211304,
-        "t": 34.10983085632324
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134124.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.869319498538971,
-        "t": 34.44814682006836
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134126.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8910221457481384,
-        "t": 47.33705520629883
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134442.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7550113797187805,
-        "t": 73.49395751953125
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134536.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8505440354347229,
-        "t": 33.79988670349121
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133932.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8495771288871765,
-        "t": 33.84804725646973
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133926.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.743913471698761,
-        "t": 34.3630313873291
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134052.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8763256669044495,
-        "t": 34.1796875
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134047.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8276395201683044,
-        "t": 33.442020416259766
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134331.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8341497182846069,
-        "t": 34.29007530212402
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133853.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8040607571601868,
-        "t": 33.838748931884766
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133843.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7929970026016235,
-        "t": 33.3709716796875
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133857.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8364728093147278,
-        "t": 37.927865982055664
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134447.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.810223400592804,
-        "t": 33.434152603149414
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134532.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6568316221237183,
-        "t": 33.44011306762695
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134254.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7051023244857788,
-        "t": 32.920122146606445
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134240.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8349921107292175,
-        "t": 33.421993255615234
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134334.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7951305508613586,
-        "t": 34.55924987792969
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134452.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6098920106887817,
-        "t": 33.08391571044922
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134122.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8365842700004578,
-        "t": 33.87117385864258
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133856.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8920812010765076,
-        "t": 32.68098831176758
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134108.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8938296437263489,
-        "t": 34.36112403869629
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134336.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8602514863014221,
-        "t": 34.55519676208496
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134530.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7553884983062744,
-        "t": 33.42604637145996
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133934.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7395821213722229,
-        "t": 36.78584098815918
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133909.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7931657433509827,
-        "t": 33.7831974029541
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134257.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8110876679420471,
-        "t": 33.24413299560547
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134451.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7339089512825012,
-        "t": 34.10792350769043
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134445.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7841974496841431,
-        "t": 33.88524055480957
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133841.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8670069575309753,
-        "t": 33.15091133117676
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134528.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8232019543647766,
-        "t": 35.33291816711426
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134105.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7696852087974548,
-        "t": 31.993865966796875
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133859.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7968729138374329,
-        "t": 32.5770378112793
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134311.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8481498956680298,
-        "t": 32.55105018615723
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134502.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6735756993293762,
-        "t": 32.80806541442871
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134258.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8586775064468384,
-        "t": 32.75322914123535
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134338.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7600119709968567,
-        "t": 33.14089775085449
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134106.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.9107964038848877,
-        "t": 33.94293785095215
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134314.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7097043395042419,
-        "t": 33.19406509399414
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134300.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8026455640792847,
-        "t": 34.175872802734375
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133916.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8030350208282471,
-        "t": 32.79519081115723
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133903.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8016977310180664,
-        "t": 33.00976753234863
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134249.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.826654851436615,
-        "t": 59.03124809265137
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134101.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.755354106426239,
-        "t": 33.57100486755371
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134511.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6402665376663208,
-        "t": 33.39099884033203
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134505.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6024779081344604,
-        "t": 32.37104415893555
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133928.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7489036321640015,
-        "t": 32.32002258300781
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133914.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7796900272369385,
-        "t": 33.66518020629883
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134504.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6444759964942932,
-        "t": 32.76991844177246
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134316.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8135627508163452,
-        "t": 39.76583480834961
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134128.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8667625784873962,
-        "t": 32.768964767456055
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134205.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8495418429374695,
-        "t": 34.85107421875
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134007.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8446894884109497,
-        "t": 33.21695327758789
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134238.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7653264999389648,
-        "t": 32.240867614746094
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133838.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7621811628341675,
-        "t": 34.16085243225098
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134158.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8209596276283264,
-        "t": 32.582998275756836
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134414.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8262933492660522,
-        "t": 33.1110954284668
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134212.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7755010724067688,
-        "t": 30.75718879699707
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134004.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7804483771324158,
-        "t": 31.70490264892578
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134429.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8015995621681213,
-        "t": 34.70492362976074
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134159.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8412923216819763,
-        "t": 33.35285186767578
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134203.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8543086647987366,
-        "t": 33.434152603149414
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134217.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8760321736335754,
-        "t": 33.03861618041992
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133948.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7676844000816345,
-        "t": 52.15787887573242
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134216.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8783773183822632,
-        "t": 33.96892547607422
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134404.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7114033102989197,
-        "t": 32.774925231933594
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134412.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7074394226074219,
-        "t": 32.74083137512207
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134214.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7909525632858276,
-        "t": 33.2639217376709
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134228.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.8437972068786621,
-        "t": 55.11307716369629
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134003.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6553165912628174,
-        "t": 34.00111198425293
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134201.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7626541256904602,
-        "t": 72.6158618927002
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134349.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.6820130944252014,
-        "t": 35.7823371887207
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134407.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.7469780445098877,
-        "t": 34.26504135131836
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134810.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9206749200820923,
-        "t": 34.755706787109375
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134804.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8808459043502808,
-        "t": 34.69395637512207
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134838.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8874089121818542,
-        "t": 33.16903114318848
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135313.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.884791374206543,
-        "t": 33.60795974731445
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134757.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9250947833061218,
-        "t": 33.5392951965332
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135500.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9620681405067444,
-        "t": 74.70512390136719
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135504.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9509914517402649,
-        "t": 47.29604721069336
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135128.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8969405889511108,
-        "t": 32.82523155212402
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135316.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9365519881248474,
-        "t": 34.47723388671875
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134753.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9451282620429993,
-        "t": 33.72907638549805
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134816.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9465665221214294,
-        "t": 33.3399772644043
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134631.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.95250004529953,
-        "t": 33.967018127441406
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135249.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9628172516822815,
-        "t": 72.31736183166504
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134624.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9465226531028748,
-        "t": 34.4691276550293
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135428.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9731119275093079,
-        "t": 35.70413589477539
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135158.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9735643267631531,
-        "t": 34.10792350769043
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135205.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9170750975608826,
-        "t": 33.225297927856445
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135201.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9751188158988953,
-        "t": 33.83517265319824
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134725.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9572290778160095,
-        "t": 60.971975326538086
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134732.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9379556179046631,
-        "t": 33.65588188171387
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134900.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.912426233291626,
-        "t": 35.243988037109375
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135434.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9451984763145447,
-        "t": 33.55121612548828
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134852.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8808512687683105,
-        "t": 33.26892852783203
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135231.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8649298548698425,
-        "t": 34.93309020996094
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134714.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9740126729011536,
-        "t": 34.483909606933594
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134728.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9729218482971191,
-        "t": 61.97404861450195
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135032.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9600645303726196,
-        "t": 33.56575965881348
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134847.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8734626770019531,
-        "t": 40.51518440246582
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134857.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8078833222389221,
-        "t": 33.43510627746582
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135208.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9264088273048401,
-        "t": 32.39583969116211
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134710.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9417620301246643,
-        "t": 32.891035079956055
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135237.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9566701054573059,
-        "t": 33.57815742492676
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135021.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9569609761238098,
-        "t": 33.42890739440918
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135431.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9267640113830566,
-        "t": 71.76804542541504
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135424.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9472572207450867,
-        "t": 33.288002014160156
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135418.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9406988024711609,
-        "t": 33.419132232666016
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135154.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8715304732322693,
-        "t": 34.0878963470459
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135245.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9706498980522156,
-        "t": 34.66510772705078
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134615.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9244219660758972,
-        "t": 38.949012756347656
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134946.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9390779733657837,
-        "t": 34.3780517578125
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135457.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9559177160263062,
-        "t": 35.1099967956543
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135324.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.961341142654419,
-        "t": 94.81477737426758
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134628.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8727182745933533,
-        "t": 33.61773490905762
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135454.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9455189108848572,
-        "t": 37.15085983276367
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135327.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9386898875236511,
-        "t": 38.18202018737793
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135253.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8674346208572388,
-        "t": 37.11104393005371
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135451.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9183104634284973,
-        "t": 41.046142578125
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134612.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.966924786567688,
-        "t": 35.24041175842285
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135320.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9553282260894775,
-        "t": 42.24514961242676
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134942.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.8967490792274475,
-        "t": 37.66274452209473
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_140003.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8756323456764221,
-        "t": 38.63692283630371
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135715.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8956145644187927,
-        "t": 99.51090812683105
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135701.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8640040755271912,
-        "t": 33.76483917236328
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135924.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9115993976593018,
-        "t": 36.01527214050293
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_140028.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8542695045471191,
-        "t": 35.18795967102051
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135936.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9506756663322449,
-        "t": 35.021066665649414
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_140012.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8530533313751221,
-        "t": 89.05816078186035
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135822.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9463725090026855,
-        "t": 35.28404235839844
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135955.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8488245010375977,
-        "t": 35.09974479675293
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135612.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8680424690246582,
-        "t": 34.13224220275879
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135607.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.934203565120697,
-        "t": 34.293174743652344
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135811.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8943992257118225,
-        "t": 113.31987380981445
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135806.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.669307291507721,
-        "t": 45.23587226867676
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135621.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9247487783432007,
-        "t": 36.3309383392334
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135817.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.89071124792099,
-        "t": 39.320945739746094
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135624.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.891025722026825,
-        "t": 34.26098823547363
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135814.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8748388886451721,
-        "t": 34.84702110290527
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135626.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9758169054985046,
-        "t": 35.22777557373047
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135912.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9155890941619873,
-        "t": 36.5900993347168
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135654.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8870514035224915,
-        "t": 33.41388702392578
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135720.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9591735601425171,
-        "t": 35.66288948059082
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_140021.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.821799635887146,
-        "t": 34.92283821105957
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135928.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8432040810585022,
-        "t": 35.20393371582031
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135725.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8484444618225098,
-        "t": 41.68701171875
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135916.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8970271944999695,
-        "t": 33.51569175720215
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140605.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5189223885536194,
-        "t": 36.25082969665527
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140613.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.6612028479576111,
-        "t": 35.597801208496094
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140607.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5640031099319458,
-        "t": 35.201072692871094
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140602.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.50492262840271,
-        "t": 34.52801704406738
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140548.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5197851061820984,
-        "t": 47.34373092651367
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140603.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5425063371658325,
-        "t": 39.808034896850586
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140617.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.7751298546791077,
-        "t": 37.928104400634766
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140615.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.8154672384262085,
-        "t": 34.62791442871094
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140600.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6096083521842957,
-        "t": 34.53707695007324
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140116.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.674041748046875,
-        "t": 40.85206985473633
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140113.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6545602083206177,
-        "t": 34.08193588256836
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140517.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.8602285981178284,
-        "t": 37.07098960876465
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140106.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5304396152496338,
-        "t": 34.07716751098633
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140110.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.7158907055854797,
-        "t": 33.692121505737305
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140514.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.8524132966995239,
-        "t": 33.94126892089844
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140059.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5265427231788635,
-        "t": 35.26782989501953
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140525.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.645136296749115,
-        "t": 38.935184478759766
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140532.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5773601531982422,
-        "t": 40.90476036071777
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140523.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.8630183935165405,
-        "t": 37.796974182128906
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140535.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.681283712387085,
-        "t": 34.597158432006836
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140520.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6775447726249695,
-        "t": 34.98387336730957
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140618.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5398935079574585,
-        "t": 40.42696952819824
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140550.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.5712210536003113,
-        "t": 33.73312950134277
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140551.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.653282642364502,
-        "t": 36.17405891418457
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140620.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.615969717502594,
-        "t": 34.70206260681152
-      }
-    ],
-    "efficientad": [
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134027.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.19610755145549774,
-        "t": 330.0199508666992,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134027_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134225.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.062136828899383545,
-        "t": 306.03909492492676,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134225_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134351.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0638992190361023,
-        "t": 313.97509574890137,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134351_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134353.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.04785534366965294,
-        "t": 320.64223289489746,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134353_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134227.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05920551344752312,
-        "t": 311.95902824401855,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134227_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133945.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06849484145641327,
-        "t": 304.7637939453125,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133945_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134025.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.1563054621219635,
-        "t": 350.97765922546387,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134025_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133950.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.16193746030330658,
-        "t": 319.2918300628662,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133950_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134356.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05963866412639618,
-        "t": 311.0020160675049,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134356_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134424.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.12924478948116302,
-        "t": 304.00896072387695,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134424_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134222.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05547665059566498,
-        "t": 312.2880458831787,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134222_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134236.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.036059536039829254,
-        "t": 306.82897567749023,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134236_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134034.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.04784372076392174,
-        "t": 302.23703384399414,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134034_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134009.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.10211723297834396,
-        "t": 310.2531433105469,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134009_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134431.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.16073375940322876,
-        "t": 306.3030242919922,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134431_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134427.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.119661845266819,
-        "t": 307.4951171875,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134427_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133943.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07022874802350998,
-        "t": 307.34801292419434,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133943_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134023.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.12286899238824844,
-        "t": 303.4100532531738,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134023_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134426.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.10472676903009415,
-        "t": 305.82189559936523,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134426_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133845.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.11402930319309235,
-        "t": 342.71788597106934,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133845_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134509.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.04692540690302849,
-        "t": 307.66987800598145,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134509_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134247.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.17593547701835632,
-        "t": 308.6240291595459,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134247_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134051.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09627275168895721,
-        "t": 301.8503189086914,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134051_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133930.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.040500834584236145,
-        "t": 302.1247386932373,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133930_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133918.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09930089861154556,
-        "t": 312.55412101745605,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133918_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134534.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.08766862004995346,
-        "t": 328.3579349517822,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134534_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134332.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05397506058216095,
-        "t": 360.1200580596924,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134332_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134440.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0846254751086235,
-        "t": 327.1338939666748,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134440_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134124.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0886751264333725,
-        "t": 312.62803077697754,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134124_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134126.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.10648998618125916,
-        "t": 345.40796279907227,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134126_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134442.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09920968860387802,
-        "t": 344.6629047393799,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134442_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134536.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05429606884717941,
-        "t": 315.889835357666,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134536_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133932.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05229352414608002,
-        "t": 314.41783905029297,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133932_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133926.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06631188839673996,
-        "t": 308.13002586364746,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133926_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134052.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0581306628882885,
-        "t": 308.87293815612793,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134052_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134047.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.040041789412498474,
-        "t": 303.4822940826416,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134047_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134331.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0790204107761383,
-        "t": 298.27404022216797,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134331_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133853.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.054751139134168625,
-        "t": 315.98377227783203,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133853_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133843.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0864967480301857,
-        "t": 366.5170669555664,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133843_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133857.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.16653309762477875,
-        "t": 297.0757484436035,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133857_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134447.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09838110953569412,
-        "t": 306.7362308502197,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134447_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134532.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06667430698871613,
-        "t": 305.9346675872803,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134532_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134254.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.08237229287624359,
-        "t": 307.6159954071045,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134254_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134240.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07642677426338196,
-        "t": 301.85604095458984,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134240_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134334.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07923590391874313,
-        "t": 304.42094802856445,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134334_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134452.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.03553621098399162,
-        "t": 309.68189239501953,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134452_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134122.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.050749510526657104,
-        "t": 329.20122146606445,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134122_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133856.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.053795117884874344,
-        "t": 355.49116134643555,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133856_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134108.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.1988985240459442,
-        "t": 301.20277404785156,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134108_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134336.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0531340092420578,
-        "t": 332.63421058654785,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134336_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134530.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07449081540107727,
-        "t": 352.66995429992676,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134530_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133934.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06366331875324249,
-        "t": 446.57373428344727,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133934_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133909.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.27473998069763184,
-        "t": 325.0002861022949,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133909_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134257.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.12669149041175842,
-        "t": 311.5720748901367,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134257_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134451.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.060783423483371735,
-        "t": 338.651180267334,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134451_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134445.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07809998095035553,
-        "t": 298.0532646179199,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134445_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133841.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.12687933444976807,
-        "t": 282.62782096862793,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133841_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134528.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05539601296186447,
-        "t": 285.1681709289551,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134528_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134105.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.1689896136522293,
-        "t": 289.5040512084961,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134105_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133859.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.22205482423305511,
-        "t": 300.915002822876,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133859_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134311.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07181034237146378,
-        "t": 296.42510414123535,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134311_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134502.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.1036970391869545,
-        "t": 296.65374755859375,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134502_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134258.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09274143725633621,
-        "t": 335.07490158081055,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134258_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134338.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07048659771680832,
-        "t": 301.4941215515137,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134338_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134106.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.14830030500888824,
-        "t": 294.90017890930176,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134106_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134314.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05483211576938629,
-        "t": 293.0331230163574,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134314_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134300.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.08474159985780716,
-        "t": 297.2588539123535,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134300_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133916.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.1042027696967125,
-        "t": 295.86315155029297,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133916_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133903.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07027190923690796,
-        "t": 304.3069839477539,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133903_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134249.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06677141040563583,
-        "t": 305.5267333984375,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134249_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134101.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.37525373697280884,
-        "t": 295.8991527557373,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134101_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134511.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.062418654561042786,
-        "t": 337.94617652893066,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134511_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134505.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.20461814105510712,
-        "t": 331.84218406677246,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134505_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133928.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.08169642090797424,
-        "t": 316.9240951538086,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133928_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133914.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.09956885874271393,
-        "t": 303.009033203125,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133914_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134504.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06607771664857864,
-        "t": 307.5251579284668,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134504_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134316.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.02711407095193863,
-        "t": 290.1749610900879,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134316_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134128.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07717632502317429,
-        "t": 292.3169136047363,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134128_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134205.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.10477982461452484,
-        "t": 353.6229133605957,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134205_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134007.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.14931949973106384,
-        "t": 284.43002700805664,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134007_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134238.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.08024720102548599,
-        "t": 292.7727699279785,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134238_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133838.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.12103656679391861,
-        "t": 295.7110404968262,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133838_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134158.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.053268417716026306,
-        "t": 291.93115234375,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134158_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134414.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.12058798223733902,
-        "t": 734.5678806304932,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134414_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134212.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.04825269430875778,
-        "t": 315.1061534881592,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134212_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134004.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.08502642810344696,
-        "t": 290.18712043762207,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134004_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134429.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.11735205352306366,
-        "t": 300.74405670166016,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134429_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134159.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.035636208951473236,
-        "t": 301.1751174926758,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134159_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134203.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.040130410343408585,
-        "t": 286.4100933074951,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134203_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134217.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.03786339610815048,
-        "t": 295.99905014038086,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134217_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133948.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.10369688272476196,
-        "t": 301.4059066772461,
-        "r": "datasets/cookies_3/no_anomaly/20240417_133948_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134216.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.05036454647779465,
-        "t": 287.0011329650879,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134216_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134404.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.12065329402685165,
-        "t": 341.88103675842285,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134404_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134412.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0869952067732811,
-        "t": 291.1207675933838,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134412_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134214.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06152201071381569,
-        "t": 330.88159561157227,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134214_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134228.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06611377745866776,
-        "t": 306.98704719543457,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134228_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134003.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.08442873507738113,
-        "t": 306.87904357910156,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134003_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134201.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.06119248643517494,
-        "t": 317.9173469543457,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134201_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134349.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.07056072354316711,
-        "t": 300.2800941467285,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134349_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134407.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 0.0981682762503624,
-        "t": 309.47399139404297,
-        "r": "datasets/cookies_3/no_anomaly/20240417_134407_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134810.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.234063982963562,
-        "t": 330.82103729248047,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134810_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134804.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.439520001411438,
-        "t": 318.7718391418457,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134804_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134838.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9384483098983765,
-        "t": 400.45905113220215,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134838_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135313.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.1639745235443115,
-        "t": 383.09597969055176,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135313_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134757.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.3962836265563965,
-        "t": 356.2958240509033,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134757_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135500.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.2857989072799683,
-        "t": 303.73501777648926,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135500_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135504.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.4288744926452637,
-        "t": 303.4250736236572,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135504_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135128.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.2339707612991333,
-        "t": 290.4529571533203,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135128_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135316.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.4221620559692383,
-        "t": 312.5612735748291,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135316_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134753.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.3852908611297607,
-        "t": 292.76299476623535,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134753_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134816.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.1202291250228882,
-        "t": 388.65208625793457,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134816_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134631.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.0669853687286377,
-        "t": 344.0718650817871,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134631_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135249.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.4832566976547241,
-        "t": 301.88965797424316,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135249_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134624.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.0923576354980469,
-        "t": 429.66675758361816,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134624_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135428.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.5586638450622559,
-        "t": 340.2230739593506,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135428_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135158.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.6206400394439697,
-        "t": 308.8397979736328,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135158_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135205.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.118816614151001,
-        "t": 316.4658546447754,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135205_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135201.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.4459627866744995,
-        "t": 313.8570785522461,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135201_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134725.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.51283597946167,
-        "t": 337.6798629760742,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134725_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134732.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.3429571390151978,
-        "t": 319.2451000213623,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134732_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134900.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.3633009195327759,
-        "t": 298.18105697631836,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134900_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135434.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.3970372676849365,
-        "t": 435.80102920532227,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135434_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134852.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.1915030479431152,
-        "t": 336.1830711364746,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134852_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135231.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.1705241203308105,
-        "t": 325.927734375,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135231_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134714.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.3968688249588013,
-        "t": 304.4569492340088,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134714_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134728.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.2157360315322876,
-        "t": 312.3958110809326,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134728_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135032.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.316336750984192,
-        "t": 427.0153045654297,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135032_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134847.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.2115252017974854,
-        "t": 446.5489387512207,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134847_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134857.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.328348994255066,
-        "t": 301.03302001953125,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134857_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135208.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.631272792816162,
-        "t": 325.2427577972412,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135208_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134710.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.3771990537643433,
-        "t": 298.95925521850586,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134710_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135237.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.2050312757492065,
-        "t": 307.36804008483887,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135237_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135021.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.3041099309921265,
-        "t": 302.11710929870605,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135021_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135431.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.421085238456726,
-        "t": 309.4637393951416,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135431_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135424.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.487107515335083,
-        "t": 297.6219654083252,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135424_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135418.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.4703952074050903,
-        "t": 302.0188808441162,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135418_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135154.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 0.9115586876869202,
-        "t": 324.8419761657715,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135154_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135245.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.159118413925171,
-        "t": 298.2618808746338,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135245_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134615.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.3558733463287354,
-        "t": 305.117130279541,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134615_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134946.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.4221434593200684,
-        "t": 299.8478412628174,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134946_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135457.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.842010736465454,
-        "t": 297.2400188446045,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135457_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135324.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.3810969591140747,
-        "t": 307.35015869140625,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135324_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134628.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.1068562269210815,
-        "t": 296.13304138183594,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134628_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135454.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.5074845552444458,
-        "t": 308.2621097564697,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135454_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135327.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.2681792974472046,
-        "t": 333.63795280456543,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135327_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135253.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.4971332550048828,
-        "t": 322.1602439880371,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135253_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135451.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.0939078330993652,
-        "t": 345.656156539917,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135451_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134612.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.0599093437194824,
-        "t": 319.31018829345703,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134612_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135320.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.3784873485565186,
-        "t": 316.23005867004395,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135320_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134942.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 1.0504807233810425,
-        "t": 333.40001106262207,
-        "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134942_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_140003.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.977466344833374,
-        "t": 336.45009994506836,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_140003_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135715.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.6418675184249878,
-        "t": 356.85205459594727,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135715_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135701.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9027572870254517,
-        "t": 315.63806533813477,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135701_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135924.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.141499400138855,
-        "t": 302.016019821167,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135924_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_140028.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.1443144083023071,
-        "t": 311.1600875854492,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_140028_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135936.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.0411772727966309,
-        "t": 301.4960289001465,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135936_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_140012.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.078170895576477,
-        "t": 306.72168731689453,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_140012_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135822.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.0854170322418213,
-        "t": 306.689977645874,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135822_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135955.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9223095774650574,
-        "t": 308.6259365081787,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135955_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135612.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9303677082061768,
-        "t": 302.584171295166,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135612_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135607.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.416784405708313,
-        "t": 302.9658794403076,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135607_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135811.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.0125737190246582,
-        "t": 317.3990249633789,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135811_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135806.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.799491822719574,
-        "t": 340.9268856048584,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135806_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135621.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9247022271156311,
-        "t": 368.20316314697266,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135621_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135817.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.9595586657524109,
-        "t": 306.9491386413574,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135817_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135624.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.7612487077713013,
-        "t": 311.7339611053467,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135624_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135814.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.003527283668518,
-        "t": 341.0968780517578,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135814_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135626.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.0485988855361938,
-        "t": 322.12305068969727,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135626_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135912.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.7756297588348389,
-        "t": 312.014102935791,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135912_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135654.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.0127311944961548,
-        "t": 287.6400947570801,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135654_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135720.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.0495713949203491,
-        "t": 312.08324432373047,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135720_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_140021.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.8695951104164124,
-        "t": 302.905797958374,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_140021_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135928.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.4970086812973022,
-        "t": 297.9612350463867,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135928_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135725.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 1.0589545965194702,
-        "t": 339.0841484069824,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135725_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135916.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 0.7755832672119141,
-        "t": 327.5127410888672,
-        "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135916_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140605.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.9239838123321533,
-        "t": 290.3790473937988,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140605_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140613.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5605015158653259,
-        "t": 305.3867816925049,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140613_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140607.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.8210369348526001,
-        "t": 306.87475204467773,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140607_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140602.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.9539052844047546,
-        "t": 298.9792823791504,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140602_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140548.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.4964374899864197,
-        "t": 298.48694801330566,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140548_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140603.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 1.0479995012283325,
-        "t": 292.0839786529541,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140603_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140617.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5025002956390381,
-        "t": 299.4732856750488,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140617_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140615.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.4978812038898468,
-        "t": 304.6860694885254,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140615_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140600.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.8411810398101807,
-        "t": 301.3749122619629,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140600_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140116.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.49893811345100403,
-        "t": 493.1678771972656,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140116_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140113.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6633710861206055,
-        "t": 305.16624450683594,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140113_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140517.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.9229753613471985,
-        "t": 340.9099578857422,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140517_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140106.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 0.39974918961524963,
-        "t": 322.45898246765137,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140106_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140110.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5669101476669312,
-        "t": 342.7600860595703,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140110_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140514.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.9330470561981201,
-        "t": 302.9520511627197,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140514_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140059.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6116341352462769,
-        "t": 313.96007537841797,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140059_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140525.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.8402252197265625,
-        "t": 319.46396827697754,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140525_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140532.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.6414665579795837,
-        "t": 359.2948913574219,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140532_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140523.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.9381839036941528,
-        "t": 302.990198135376,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140523_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140535.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5761953592300415,
-        "t": 325.545072555542,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140535_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140520.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.9537755846977234,
-        "t": 299.07798767089844,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140520_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140618.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.4262426495552063,
-        "t": 312.6871585845947,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140618_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140550.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.45581600069999695,
-        "t": 290.58027267456055,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140550_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140551.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.5017250776290894,
-        "t": 294.78001594543457,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140551_result.jpg"
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140620.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 0.44068044424057007,
-        "t": 302.365779876709,
-        "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140620_result.jpg"
-      }
-    ],
-    "fomoad": [
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134027.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.384082317352295,
-        "t": 73.95219802856445
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134225.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.895371913909912,
-        "t": 38.23399543762207
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134351.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.4391348361968994,
-        "t": 36.097049713134766
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134353.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.6173791885375977,
-        "t": 36.24701499938965
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134227.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.6503489017486572,
-        "t": 38.475990295410156
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133945.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.04183292388916,
-        "t": 59.46707725524902
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134025.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.894997596740723,
-        "t": 44.26217079162598
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133950.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.465174198150635,
-        "t": 38.266897201538086
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134356.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.0059642791748047,
-        "t": 40.22026062011719
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134424.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.154247283935547,
-        "t": 36.132097244262695
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134222.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.837677001953125,
-        "t": 36.80992126464844
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134236.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.628969430923462,
-        "t": 36.299943923950195
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134034.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.3861865997314453,
-        "t": 37.33110427856445
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134009.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.8941285610198975,
-        "t": 36.62610054016113
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134431.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.5702264308929443,
-        "t": 36.01408004760742
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134427.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.3519368171691895,
-        "t": 37.15920448303223
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133943.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.9100754261016846,
-        "t": 37.10794448852539
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134023.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.707996368408203,
-        "t": 36.00716590881348
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134426.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.4512078762054443,
-        "t": 37.50205039978027
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133845.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.04488468170166,
-        "t": 40.64774513244629
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134509.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.434913396835327,
-        "t": 36.01193428039551
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134247.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.549715518951416,
-        "t": 36.17429733276367
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134051.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.123044490814209,
-        "t": 38.703203201293945
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133930.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.766103744506836,
-        "t": 37.00113296508789
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133918.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.03190541267395,
-        "t": 36.25297546386719
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134534.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.3850290775299072,
-        "t": 38.86675834655762
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134332.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.7040164470672607,
-        "t": 40.872812271118164
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134440.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.18685245513916,
-        "t": 39.711952209472656
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134124.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.552433729171753,
-        "t": 36.206960678100586
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134126.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.9633374214172363,
-        "t": 38.38300704956055
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134442.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.243478536605835,
-        "t": 42.500972747802734
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134536.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.294576406478882,
-        "t": 39.129018783569336
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133932.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.613187074661255,
-        "t": 36.66520118713379
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133926.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.11915922164917,
-        "t": 37.6741886138916
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134052.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.0003762245178223,
-        "t": 36.479949951171875
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134047.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.125859498977661,
-        "t": 60.59694290161133
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134331.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.749436140060425,
-        "t": 36.78321838378906
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133853.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.754873037338257,
-        "t": 36.23199462890625
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133843.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.3301639556884766,
-        "t": 38.292884826660156
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133857.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.9474501609802246,
-        "t": 42.428016662597656
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134447.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.9989655017852783,
-        "t": 37.25290298461914
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134532.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.037404775619507,
-        "t": 36.8351936340332
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134254.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.627908706665039,
-        "t": 35.74776649475098
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134240.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.838757276535034,
-        "t": 37.16087341308594
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134334.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.373816967010498,
-        "t": 35.788774490356445
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134452.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.191556453704834,
-        "t": 37.40692138671875
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134122.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.9648611545562744,
-        "t": 127.70891189575195
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133856.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.012298583984375,
-        "t": 45.23205757141113
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134108.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.077030897140503,
-        "t": 36.2551212310791
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134336.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.168799638748169,
-        "t": 46.97608947753906
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134530.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.9592792987823486,
-        "t": 36.910057067871094
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133934.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.061436414718628,
-        "t": 71.9752311706543
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133909.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.8100388050079346,
-        "t": 39.106130599975586
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134257.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.709480047225952,
-        "t": 37.05477714538574
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134451.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.718829154968262,
-        "t": 37.4140739440918
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134445.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.098456382751465,
-        "t": 36.56411170959473
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133841.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.581751823425293,
-        "t": 35.24208068847656
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134528.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.129509210586548,
-        "t": 35.87603569030762
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134105.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.070388078689575,
-        "t": 35.78805923461914
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133859.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.8825161457061768,
-        "t": 37.18280792236328
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134311.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.295933723449707,
-        "t": 36.48090362548828
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134502.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.285107135772705,
-        "t": 36.817073822021484
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134258.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.4605636596679688,
-        "t": 36.06224060058594
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134338.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.1805248260498047,
-        "t": 36.2091064453125
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134106.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.340229034423828,
-        "t": 36.12208366394043
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134314.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.23085880279541,
-        "t": 37.17613220214844
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134300.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.7315115928649902,
-        "t": 37.8720760345459
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133916.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.381448268890381,
-        "t": 36.58103942871094
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133903.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.145279884338379,
-        "t": 37.2622013092041
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134249.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.7142677307128906,
-        "t": 36.76414489746094
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134101.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.0173492431640625,
-        "t": 38.651227951049805
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134511.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.780324697494507,
-        "t": 44.779062271118164
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134505.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.526883602142334,
-        "t": 41.54396057128906
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133928.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.118988990783691,
-        "t": 35.820960998535156
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133914.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.227236270904541,
-        "t": 45.21489143371582
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134504.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.315681457519531,
-        "t": 36.730051040649414
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134316.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.7279534339904785,
-        "t": 36.949872970581055
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134128.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.5381906032562256,
-        "t": 36.170005798339844
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134205.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.893174171447754,
-        "t": 37.91522979736328
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134007.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.867459535598755,
-        "t": 37.01472282409668
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134238.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.9453725814819336,
-        "t": 35.797119140625
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133838.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.2956759929656982,
-        "t": 36.70191764831543
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134158.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.8892953395843506,
-        "t": 36.473989486694336
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134414.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.977825403213501,
-        "t": 32.43684768676758
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134212.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.438807964324951,
-        "t": 34.2411994934082
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134004.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.7723798751831055,
-        "t": 35.54892539978027
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134429.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.5595285892486572,
-        "t": 35.63284873962402
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134159.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.8895246982574463,
-        "t": 36.3161563873291
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134203.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.102370500564575,
-        "t": 36.546945571899414
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134217.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.9221174716949463,
-        "t": 35.817861557006836
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_133948.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.163142442703247,
-        "t": 36.28969192504883
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134216.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.1798532009124756,
-        "t": 35.96091270446777
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134404.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.517521858215332,
-        "t": 50.46987533569336
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134412.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.4695887565612793,
-        "t": 36.15689277648926
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134214.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.264676809310913,
-        "t": 36.87715530395508
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134228.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 2.8778388500213623,
-        "t": 36.702871322631836
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134003.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 4.033176422119141,
-        "t": 36.63516044616699
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134201.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.9781758785247803,
-        "t": 37.30201721191406
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134349.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.175339698791504,
-        "t": 38.69128227233887
-      },
-      {
-        "i": "datasets/cookies_3/no_anomaly/20240417_134407.jpg",
-        "c": "no_anomaly",
-        "d": "na",
-        "cl": "no_anomaly",
-        "s": 3.1016409397125244,
-        "t": 37.59407997131348
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134810.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 15.764870643615723,
-        "t": 38.20300102233887
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134804.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 7.369954586029053,
-        "t": 77.28409767150879
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134838.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.028023719787598,
-        "t": 43.47109794616699
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135313.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 17.540021896362305,
-        "t": 66.98775291442871
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134757.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 9.09726619720459,
-        "t": 42.387962341308594
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135500.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 15.157452583312988,
-        "t": 63.790082931518555
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135504.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 15.761528015136719,
-        "t": 37.03498840332031
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135128.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 13.448103904724121,
-        "t": 37.40382194519043
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135316.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 15.92177677154541,
-        "t": 37.65392303466797
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134753.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 18.043285369873047,
-        "t": 37.18400001525879
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134816.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 12.436928749084473,
-        "t": 50.98104476928711
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134631.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 9.543059349060059,
-        "t": 37.27102279663086
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135249.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 14.495829582214355,
-        "t": 36.839962005615234
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134624.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.37634563446045,
-        "t": 59.11612510681152
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135428.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 11.712638854980469,
-        "t": 39.359331130981445
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135158.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 15.70052433013916,
-        "t": 38.2230281829834
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135205.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 16.15715217590332,
-        "t": 36.71097755432129
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135201.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 14.50143814086914,
-        "t": 36.84878349304199
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134725.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 16.749799728393555,
-        "t": 42.8009033203125
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134732.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.712708473205566,
-        "t": 36.55505180358887
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134900.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.073373794555664,
-        "t": 38.4218692779541
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135434.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.368729591369629,
-        "t": 38.17486763000488
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134852.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 11.690964698791504,
-        "t": 38.983821868896484
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135231.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 8.089816093444824,
-        "t": 37.59479522705078
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134714.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 12.720117568969727,
-        "t": 38.12885284423828
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134728.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 16.228660583496094,
-        "t": 36.44084930419922
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135032.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.631302833557129,
-        "t": 45.46189308166504
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134847.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 6.745754241943359,
-        "t": 49.67927932739258
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134857.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 8.891317367553711,
-        "t": 95.20602226257324
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135208.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 15.021328926086426,
-        "t": 37.333011627197266
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134710.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.956341743469238,
-        "t": 38.684844970703125
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135237.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 11.125260353088379,
-        "t": 37.29081153869629
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135021.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 11.527172088623047,
-        "t": 38.9409065246582
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135431.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 14.25806713104248,
-        "t": 37.61410713195801
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135424.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 11.942025184631348,
-        "t": 36.6668701171875
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135418.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 11.129603385925293,
-        "t": 40.548086166381836
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135154.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 19.391660690307617,
-        "t": 36.61489486694336
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135245.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 9.39006233215332,
-        "t": 36.98897361755371
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134615.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 11.528029441833496,
-        "t": 39.26587104797363
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134946.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 9.718783378601074,
-        "t": 37.0786190032959
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135457.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 11.79050350189209,
-        "t": 37.045955657958984
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135324.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 11.294169425964355,
-        "t": 38.27309608459473
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134628.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 15.89712905883789,
-        "t": 36.750078201293945
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135454.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 17.286802291870117,
-        "t": 37.8110408782959
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135327.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 17.416915893554688,
-        "t": 51.00107192993164
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135253.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 12.55109691619873,
-        "t": 42.26088523864746
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135451.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 20.559627532958984,
-        "t": 40.46893119812012
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134612.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 13.045555114746094,
-        "t": 38.733720779418945
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_135320.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 10.095617294311523,
-        "t": 38.83695602416992
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_1/20240417_134942.jpg",
-        "c": "anomaly",
-        "d": "easy",
-        "cl": "anomaly",
-        "s": 12.749030113220215,
-        "t": 41.455745697021484
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_140003.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 8.741913795471191,
-        "t": 40.206193923950195
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135715.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 13.48764705657959,
-        "t": 37.41884231567383
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135701.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 15.632122039794922,
-        "t": 36.50188446044922
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135924.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 14.106823921203613,
-        "t": 38.12599182128906
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_140028.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 10.759296417236328,
-        "t": 41.04900360107422
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135936.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 12.868910789489746,
-        "t": 37.199974060058594
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_140012.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 10.601659774780273,
-        "t": 37.261247634887695
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135822.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 10.476201057434082,
-        "t": 37.77813911437988
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135955.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 9.981470108032227,
-        "t": 37.26506233215332
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135612.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 18.801511764526367,
-        "t": 38.10691833496094
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135607.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 20.81032371520996,
-        "t": 37.8718376159668
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135811.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 12.352483749389648,
-        "t": 77.61311531066895
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135806.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 14.378853797912598,
-        "t": 42.67525672912598
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135621.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 14.006473541259766,
-        "t": 39.29710388183594
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135817.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 14.931626319885254,
-        "t": 38.54799270629883
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135624.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 19.78631019592285,
-        "t": 38.71273994445801
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135814.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 10.480988502502441,
-        "t": 41.69416427612305
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135626.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 12.15313720703125,
-        "t": 40.52400588989258
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135912.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 15.01714038848877,
-        "t": 37.17494010925293
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135654.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 10.553946495056152,
-        "t": 36.86690330505371
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135720.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 9.887994766235352,
-        "t": 37.511348724365234
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_140021.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 11.283671379089355,
-        "t": 38.79809379577637
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135928.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 15.896834373474121,
-        "t": 36.34023666381836
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135725.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 11.830191612243652,
-        "t": 36.741018295288086
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_2/20240417_135916.jpg",
-        "c": "anomaly",
-        "d": "medium",
-        "cl": "anomaly",
-        "s": 12.092806816101074,
-        "t": 40.283918380737305
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140605.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 4.891644477844238,
-        "t": 36.57817840576172
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140613.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 5.810284614562988,
-        "t": 36.364078521728516
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140607.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 6.203559875488281,
-        "t": 37.87684440612793
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140602.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 6.370449066162109,
-        "t": 37.58120536804199
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140548.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 9.302361488342285,
-        "t": 37.9939079284668
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140603.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 6.761882781982422,
-        "t": 37.51683235168457
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140617.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "no_anomaly",
-        "s": 4.260826110839844,
-        "t": 38.448333740234375
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140615.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 5.378742218017578,
-        "t": 37.307024002075195
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140600.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 6.507815361022949,
-        "t": 37.47296333312988
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140116.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 6.197762966156006,
-        "t": 34.75785255432129
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140113.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 8.081695556640625,
-        "t": 35.8738899230957
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140517.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 9.648399353027344,
-        "t": 36.44609451293945
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140106.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 5.294248580932617,
-        "t": 36.427974700927734
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140110.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 8.735136032104492,
-        "t": 36.35907173156738
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140514.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 6.917776584625244,
-        "t": 38.74015808105469
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140059.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 5.414760112762451,
-        "t": 37.200927734375
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140525.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 9.296920776367188,
-        "t": 38.899898529052734
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140532.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 7.868703365325928,
-        "t": 39.2911434173584
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140523.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 13.1094970703125,
-        "t": 36.911725997924805
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140535.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 12.952106475830078,
-        "t": 38.35701942443848
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140520.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 13.224106788635254,
-        "t": 37.696123123168945
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140618.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 5.771534442901611,
-        "t": 36.77988052368164
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140550.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 5.345970153808594,
-        "t": 36.03506088256836
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140551.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 8.528876304626465,
-        "t": 36.40866279602051
-      },
-      {
-        "i": "datasets/cookies_3/anomaly_lvl_3/20240417_140620.jpg",
-        "c": "anomaly",
-        "d": "hard",
-        "cl": "anomaly",
-        "s": 5.112953186035156,
-        "t": 38.964033126831055
-      }
-    ]
-  }
-}
+{"cookies_1": {"baseline": [{"i": "datasets/cookies_1/no_anomaly/20240417_111938.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7350378036499023, "t": 222.76997566223145}, {"i": "datasets/cookies_1/no_anomaly/20240417_112855.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.8343085050582886, "t": 51.116943359375}, {"i": "datasets/cookies_1/no_anomaly/20240417_112105.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.5159133672714233, "t": 40.3289794921875}, {"i": "datasets/cookies_1/no_anomaly/20240417_112508.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6698240637779236, "t": 34.78717803955078}, {"i": "datasets/cookies_1/no_anomaly/20240417_112654.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7697063684463501, "t": 38.053035736083984}, {"i": "datasets/cookies_1/no_anomaly/20240417_112719.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6854037046432495, "t": 34.414052963256836}, {"i": "datasets/cookies_1/no_anomaly/20240417_112810.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7337035536766052, "t": 34.1029167175293}, {"i": "datasets/cookies_1/no_anomaly/20240417_112406.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7556677460670471, "t": 33.704280853271484}, {"i": "datasets/cookies_1/no_anomaly/20240417_112752.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.695551335811615, "t": 33.403873443603516}, {"i": "datasets/cookies_1/no_anomaly/20240417_112745.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.8126462697982788, "t": 34.219980239868164}, {"i": "datasets/cookies_1/no_anomaly/20240417_111921.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7801173329353333, "t": 33.40625762939453}, {"i": "datasets/cookies_1/no_anomaly/20240417_112525.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6796907782554626, "t": 34.12365913391113}, {"i": "datasets/cookies_1/no_anomaly/20240417_112716.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7205228805541992, "t": 33.20503234863281}, {"i": "datasets/cookies_1/no_anomaly/20240417_112901.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7844415903091431, "t": 33.33020210266113}, {"i": "datasets/cookies_1/no_anomaly/20240417_112158.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.8018472790718079, "t": 31.745195388793945}, {"i": "datasets/cookies_1/no_anomaly/20240417_111853.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7714378237724304, "t": 33.96320343017578}, {"i": "datasets/cookies_1/no_anomaly/20240417_112413.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7798773050308228, "t": 34.44099426269531}, {"i": "datasets/cookies_1/no_anomaly/20240417_113123.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.8407951593399048, "t": 32.65571594238281}, {"i": "datasets/cookies_1/no_anomaly/20240417_112801.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7507284283638, "t": 33.12993049621582}, {"i": "datasets/cookies_1/no_anomaly/20240417_112727.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7735750675201416, "t": 35.57229042053223}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114021.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.893032431602478, "t": 34.80386734008789}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114118.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8612581491470337, "t": 39.324045181274414}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114011.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8416495323181152, "t": 35.06922721862793}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114126.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9437416195869446, "t": 35.333871841430664}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113514.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9271723031997681, "t": 32.57608413696289}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113701.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8934139013290405, "t": 31.712055206298828}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113646.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8925787806510925, "t": 37.18090057373047}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114059.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9545832872390747, "t": 34.76595878601074}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113522.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9404080510139465, "t": 36.75508499145508}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114157.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8551173210144043, "t": 36.022186279296875}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113940.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9194042086601257, "t": 33.43701362609863}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113638.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9272367358207703, "t": 34.3320369720459}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113430.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9045412540435791, "t": 33.6458683013916}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113353.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9627719521522522, "t": 35.47477722167969}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113807.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9110901355743408, "t": 34.83390808105469}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114123.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.909381628036499, "t": 33.87641906738281}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113531.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8933104872703552, "t": 33.9968204498291}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113223.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.7776983380317688, "t": 35.47215461730957}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113344.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8815689086914062, "t": 35.089731216430664}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113729.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9048978686332703, "t": 34.50489044189453}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114017.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9383046627044678, "t": 33.40005874633789}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113438.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.880818784236908, "t": 32.43589401245117}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113434.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8847605586051941, "t": 34.0878963470459}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113920.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9225398898124695, "t": 33.4930419921875}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114031.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8378984928131104, "t": 34.252166748046875}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113706.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9285485744476318, "t": 32.582759857177734}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113519.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8670478463172913, "t": 35.44878959655762}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113421.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9691883325576782, "t": 35.91179847717285}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113207.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8600938320159912, "t": 35.49623489379883}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113915.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9430015683174133, "t": 33.659934997558594}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114154.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.7765025496482849, "t": 35.30287742614746}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114151.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8912833333015442, "t": 62.56103515625}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113349.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8434243202209473, "t": 33.4780216217041}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114106.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9451454281806946, "t": 33.85806083679199}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113403.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9392315745353699, "t": 32.37009048461914}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113932.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9099200963973999, "t": 39.489030838012695}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113928.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9585156440734863, "t": 37.73021697998047}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113214.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8388734459877014, "t": 86.68994903564453}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113156.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.7758703231811523, "t": 47.73688316345215}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113724.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8455557227134705, "t": 59.93819236755371}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114640.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8769235014915466, "t": 32.292842864990234}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114418.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9284310936927795, "t": 46.34284973144531}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114725.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8227718472480774, "t": 34.43193435668945}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114429.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9563220143318176, "t": 47.673702239990234}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114444.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9155468344688416, "t": 33.90002250671387}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114651.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8245460391044617, "t": 36.1940860748291}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114829.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8797990679740906, "t": 32.66787528991699}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114810.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9318199753761292, "t": 33.15114974975586}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114644.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.7536240816116333, "t": 33.36310386657715}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114732.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.7178027629852295, "t": 33.653974533081055}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114438.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9180921316146851, "t": 33.25176239013672}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114636.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9424940347671509, "t": 38.88988494873047}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114825.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.877559244632721, "t": 38.86675834655762}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114742.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.7992117404937744, "t": 35.26496887207031}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114729.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9026843905448914, "t": 34.90400314331055}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114736.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8915510177612305, "t": 33.2331657409668}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114537.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9184305667877197, "t": 38.07711601257324}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114545.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9472939372062683, "t": 35.59589385986328}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114410.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9320238828659058, "t": 55.51314353942871}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114647.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9434162378311157, "t": 48.632144927978516}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115106.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5829430222511292, "t": 35.199880599975586}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114941.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.637367308139801, "t": 33.48588943481445}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115017.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.7134944796562195, "t": 40.699005126953125}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115144.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6404460072517395, "t": 62.80016899108887}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114913.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.686103880405426, "t": 96.17400169372559}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115015.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.7685564756393433, "t": 36.604881286621094}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115109.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.543251633644104, "t": 46.82588577270508}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114947.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.701184093952179, "t": 39.54482078552246}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115149.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.7156947255134583, "t": 38.90514373779297}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114910.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.610124945640564, "t": 33.30087661743164}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115141.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.5320350527763367, "t": 43.16234588623047}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115252.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.7905014157295227, "t": 33.42103958129883}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115021.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6420228481292725, "t": 33.220767974853516}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114944.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.7042553424835205, "t": 32.60326385498047}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115146.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.686366617679596, "t": 33.44273567199707}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115117.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6452158689498901, "t": 33.023834228515625}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115258.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.71962970495224, "t": 32.93585777282715}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115255.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.8062075972557068, "t": 35.330772399902344}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114950.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6747211813926697, "t": 38.58613967895508}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114906.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.657557487487793, "t": 33.32686424255371}], "baseline-ei": [{"i": "datasets/cookies_1/no_anomaly/20240417_111938.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7120270133018494, "t": 75.91986656188965}, {"i": "datasets/cookies_1/no_anomaly/20240417_112855.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.5913758277893066, "t": 40.51685333251953}, {"i": "datasets/cookies_1/no_anomaly/20240417_112105.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6516129374504089, "t": 32.42206573486328}, {"i": "datasets/cookies_1/no_anomaly/20240417_112508.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.5940641760826111, "t": 29.48784828186035}, {"i": "datasets/cookies_1/no_anomaly/20240417_112654.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6290813088417053, "t": 29.867887496948242}, {"i": "datasets/cookies_1/no_anomaly/20240417_112719.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6793059706687927, "t": 29.510974884033203}, {"i": "datasets/cookies_1/no_anomaly/20240417_112810.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6657845377922058, "t": 29.329776763916016}, {"i": "datasets/cookies_1/no_anomaly/20240417_112406.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6597681045532227, "t": 29.102087020874023}, {"i": "datasets/cookies_1/no_anomaly/20240417_112752.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6365600228309631, "t": 31.460285186767578}, {"i": "datasets/cookies_1/no_anomaly/20240417_112745.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6881528496742249, "t": 29.248952865600586}, {"i": "datasets/cookies_1/no_anomaly/20240417_111921.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.5336619019508362, "t": 29.167890548706055}, {"i": "datasets/cookies_1/no_anomaly/20240417_112525.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.5817994475364685, "t": 32.49192237854004}, {"i": "datasets/cookies_1/no_anomaly/20240417_112716.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.5497035980224609, "t": 29.745817184448242}, {"i": "datasets/cookies_1/no_anomaly/20240417_112901.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7413327097892761, "t": 29.491186141967773}, {"i": "datasets/cookies_1/no_anomaly/20240417_112158.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6259781718254089, "t": 29.214859008789062}, {"i": "datasets/cookies_1/no_anomaly/20240417_111853.jpg", "c": "no_anomaly", "d": "na", "cl": "anomaly", "s": 0.5186603665351868, "t": 28.682947158813477}, {"i": "datasets/cookies_1/no_anomaly/20240417_112413.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6058717370033264, "t": 29.11996841430664}, {"i": "datasets/cookies_1/no_anomaly/20240417_113123.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6426830291748047, "t": 29.296159744262695}, {"i": "datasets/cookies_1/no_anomaly/20240417_112801.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.5397472381591797, "t": 29.178857803344727}, {"i": "datasets/cookies_1/no_anomaly/20240417_112727.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.61995530128479, "t": 31.02278709411621}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114021.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9233096837997437, "t": 34.00897979736328}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114118.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9015141129493713, "t": 30.462026596069336}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114011.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9080805778503418, "t": 29.712915420532227}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114126.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9340479373931885, "t": 28.894901275634766}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113514.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9445652961730957, "t": 29.568195343017578}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113701.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8117350339889526, "t": 28.491973876953125}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113646.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9316017627716064, "t": 31.82816505432129}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114059.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9622302651405334, "t": 29.079914093017578}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113522.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.938150942325592, "t": 28.465986251831055}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114157.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9677896499633789, "t": 32.38701820373535}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113940.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9314365386962891, "t": 28.738975524902344}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113638.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9101703763008118, "t": 31.863689422607422}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113430.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.929349422454834, "t": 29.019832611083984}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113353.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9752054214477539, "t": 29.043912887573242}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113807.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8365324139595032, "t": 31.23784065246582}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114123.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9137210249900818, "t": 29.104948043823242}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113531.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8302304744720459, "t": 31.361103057861328}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113223.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8982495665550232, "t": 29.01291847229004}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113344.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9301292896270752, "t": 29.819250106811523}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113729.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9439070820808411, "t": 28.973102569580078}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114017.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9051020741462708, "t": 28.519868850708008}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113438.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9383897185325623, "t": 28.831958770751953}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113434.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.95432049036026, "t": 33.390045166015625}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113920.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.954168438911438, "t": 32.517194747924805}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114031.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9481521248817444, "t": 33.30588340759277}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113706.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.7780031561851501, "t": 29.21319007873535}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113519.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9352437257766724, "t": 30.00783920288086}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113421.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9471229314804077, "t": 29.133081436157227}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113207.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8106737732887268, "t": 33.576250076293945}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113915.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9557984471321106, "t": 29.924631118774414}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114154.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9007911086082458, "t": 28.789043426513672}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114151.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8880688548088074, "t": 39.57009315490723}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113349.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9615924954414368, "t": 29.223203659057617}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114106.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9364272356033325, "t": 28.725147247314453}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113403.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9178189039230347, "t": 28.586149215698242}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113932.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8664274215698242, "t": 28.60093116760254}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113928.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.912016749382019, "t": 32.457828521728516}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113214.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8984788060188293, "t": 46.76079750061035}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113156.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.926263689994812, "t": 41.037797927856445}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113724.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9208726286888123, "t": 34.45720672607422}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114640.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8630053400993347, "t": 29.86288070678711}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114418.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9191147685050964, "t": 75.09183883666992}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114725.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.7572797536849976, "t": 29.260635375976562}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114429.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.922978401184082, "t": 29.810190200805664}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114444.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9090206623077393, "t": 29.259204864501953}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114651.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.931570291519165, "t": 29.363155364990234}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114829.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8450482487678528, "t": 30.542850494384766}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114810.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9334213137626648, "t": 29.015064239501953}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114644.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8643856644630432, "t": 29.027700424194336}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114732.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8239955306053162, "t": 29.163122177124023}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114438.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9434731006622314, "t": 31.445980072021484}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114636.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.7839519381523132, "t": 29.620885848999023}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114825.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.7279837727546692, "t": 32.089948654174805}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114742.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.917843222618103, "t": 30.06124496459961}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114729.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8353708982467651, "t": 29.414892196655273}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114736.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9080738425254822, "t": 29.433012008666992}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114537.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8801224231719971, "t": 33.01095962524414}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114545.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9251123666763306, "t": 29.67095375061035}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114410.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9260213375091553, "t": 32.4711799621582}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114647.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.87104332447052, "t": 30.269861221313477}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115106.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5478744506835938, "t": 29.67691421508789}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114941.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6177481412887573, "t": 29.367923736572266}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115017.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6277860403060913, "t": 39.03794288635254}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115144.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.7413002252578735, "t": 31.27598762512207}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114913.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.6065933704376221, "t": 54.8858642578125}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115015.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6573994159698486, "t": 29.529809951782227}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115109.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6469244360923767, "t": 29.74987030029297}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114947.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6656567454338074, "t": 33.911943435668945}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115149.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6047102212905884, "t": 30.991077423095703}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114910.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.5092912912368774, "t": 30.18498420715332}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115141.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.652152419090271, "t": 37.86921501159668}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115252.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.613935112953186, "t": 29.41107749938965}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115021.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5855720043182373, "t": 29.42514419555664}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114944.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.70087069272995, "t": 29.52289581298828}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115146.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.7186696529388428, "t": 30.79986572265625}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115117.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6815587878227234, "t": 29.505014419555664}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115258.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5762017965316772, "t": 30.5941104888916}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115255.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.7494263052940369, "t": 29.590845108032227}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114950.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5525051355361938, "t": 30.50518035888672}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114906.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.636498749256134, "t": 29.918909072875977}], "efficientad": [{"i": "datasets/cookies_1/no_anomaly/20240417_111938.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.01319180242717266, "t": 303.07602882385254, "r": "datasets/cookies_1/no_anomaly/20240417_111938_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_112855.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.0037540204357355833, "t": 289.95776176452637, "r": "datasets/cookies_1/no_anomaly/20240417_112855_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_112105.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.0024981095921248198, "t": 303.041934967041, "r": "datasets/cookies_1/no_anomaly/20240417_112105_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_112508.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.00889018177986145, "t": 306.45203590393066, "r": "datasets/cookies_1/no_anomaly/20240417_112508_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_112654.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.0017625741893425584, "t": 287.13393211364746, "r": "datasets/cookies_1/no_anomaly/20240417_112654_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_112719.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.003832348855212331, "t": 293.08485984802246, "r": "datasets/cookies_1/no_anomaly/20240417_112719_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_112810.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.009713159874081612, "t": 287.08505630493164, "r": "datasets/cookies_1/no_anomaly/20240417_112810_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_112406.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.005625682882964611, "t": 294.8172092437744, "r": "datasets/cookies_1/no_anomaly/20240417_112406_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_112752.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.003107338910922408, "t": 354.6741008758545, "r": "datasets/cookies_1/no_anomaly/20240417_112752_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_112745.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.002082049148157239, "t": 285.42518615722656, "r": "datasets/cookies_1/no_anomaly/20240417_112745_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_111921.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.002347181551158428, "t": 291.50915145874023, "r": "datasets/cookies_1/no_anomaly/20240417_111921_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_112525.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.005549972411245108, "t": 287.3661518096924, "r": "datasets/cookies_1/no_anomaly/20240417_112525_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_112716.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.0015785486903041601, "t": 274.49917793273926, "r": "datasets/cookies_1/no_anomaly/20240417_112716_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_112901.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.016808509826660156, "t": 277.6062488555908, "r": "datasets/cookies_1/no_anomaly/20240417_112901_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_112158.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.0023051477037370205, "t": 274.64914321899414, "r": "datasets/cookies_1/no_anomaly/20240417_112158_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_111853.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.0021897940896451473, "t": 285.10427474975586, "r": "datasets/cookies_1/no_anomaly/20240417_111853_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_112413.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.004413678776472807, "t": 282.18793869018555, "r": "datasets/cookies_1/no_anomaly/20240417_112413_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_113123.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.0016947230324149132, "t": 284.3658924102783, "r": "datasets/cookies_1/no_anomaly/20240417_113123_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_112801.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.000459307455457747, "t": 284.0390205383301, "r": "datasets/cookies_1/no_anomaly/20240417_112801_result.jpg"}, {"i": "datasets/cookies_1/no_anomaly/20240417_112727.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.011652624234557152, "t": 292.78016090393066, "r": "datasets/cookies_1/no_anomaly/20240417_112727_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114021.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.14957918226718903, "t": 292.7680015563965, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114021_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114118.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.09713346511125565, "t": 285.1681709289551, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114118_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114011.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.15156923234462738, "t": 287.5652313232422, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114011_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114126.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1634267121553421, "t": 268.68510246276855, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114126_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113514.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.10858243703842163, "t": 394.0880298614502, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113514_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113701.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11328290402889252, "t": 278.6576747894287, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113701_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113646.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11730803549289703, "t": 288.2649898529053, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113646_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114059.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11184744536876678, "t": 290.63916206359863, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114059_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113522.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.15041954815387726, "t": 271.8069553375244, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113522_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114157.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1970454901456833, "t": 273.33712577819824, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114157_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113940.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.12066654115915298, "t": 263.5769844055176, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113940_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113638.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.09277904033660889, "t": 277.72998809814453, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113638_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113430.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.13618509471416473, "t": 282.3219299316406, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113430_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113353.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11524202674627304, "t": 274.7178077697754, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113353_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113807.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.12889643013477325, "t": 290.71712493896484, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113807_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114123.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.07410458475351334, "t": 275.45809745788574, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114123_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113531.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.14317861199378967, "t": 282.8400135040283, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113531_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113223.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.10728275775909424, "t": 279.3099880218506, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113223_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113344.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1075458899140358, "t": 312.3350143432617, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113344_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113729.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11976774036884308, "t": 273.51903915405273, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113729_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114017.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11795113235712051, "t": 281.45885467529297, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114017_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113438.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1295144408941269, "t": 287.69612312316895, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113438_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113434.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11482489109039307, "t": 297.81293869018555, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113434_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113920.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11994267255067825, "t": 291.69297218322754, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113920_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114031.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.12494665384292603, "t": 273.24700355529785, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114031_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113706.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11294175684452057, "t": 265.789270401001, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113706_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113519.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11387816816568375, "t": 279.1471481323242, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113519_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113421.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.13033686578273773, "t": 313.6320114135742, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113421_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113207.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11495944112539291, "t": 278.2590389251709, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113207_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113915.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.08235287666320801, "t": 294.8880195617676, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113915_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114154.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.13003233075141907, "t": 313.79103660583496, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114154_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114151.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.09939699620008469, "t": 308.6998462677002, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114151_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113349.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.13359422981739044, "t": 278.14698219299316, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113349_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114106.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1558590531349182, "t": 268.68200302124023, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_114106_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113403.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11243390291929245, "t": 459.18893814086914, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113403_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113932.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.13902199268341064, "t": 273.90003204345703, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113932_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113928.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1167125329375267, "t": 300.3418445587158, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113928_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113214.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.13124006986618042, "t": 357.4719429016113, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113214_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113156.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.12809473276138306, "t": 438.6458396911621, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113156_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113724.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.10763994604349136, "t": 302.83498764038086, "r": "datasets/cookies_1/anomaly_lvl_1/20240417_113724_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114640.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.07728660851716995, "t": 328.08780670166016, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114640_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114418.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.19259552657604218, "t": 429.00896072387695, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114418_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114725.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.09574035555124283, "t": 292.93107986450195, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114725_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114429.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.09708011895418167, "t": 290.9829616546631, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114429_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114444.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.09133690595626831, "t": 324.89609718322754, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114444_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114651.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.11105652153491974, "t": 289.61730003356934, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114651_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114829.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.11587805300951004, "t": 287.4901294708252, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114829_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114810.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.17233212292194366, "t": 276.28517150878906, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114810_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114644.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.05940885469317436, "t": 284.2578887939453, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114644_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114732.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.12060268968343735, "t": 268.81885528564453, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114732_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114438.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.13197889924049377, "t": 273.64301681518555, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114438_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114636.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.0784732922911644, "t": 304.29530143737793, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114636_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114825.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.07317757606506348, "t": 293.1387424468994, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114825_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114742.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.11061038076877594, "t": 286.0710620880127, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114742_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114729.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.09547772258520126, "t": 274.74284172058105, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114729_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114736.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.10933836549520493, "t": 313.95888328552246, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114736_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114537.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.12521697580814362, "t": 358.6091995239258, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114537_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114545.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.12160568684339523, "t": 292.71888732910156, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114545_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114410.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.13337232172489166, "t": 338.29689025878906, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114410_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114647.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.08113730698823929, "t": 279.0651321411133, "r": "datasets/cookies_1/anomaly_lvl_2/20240417_114647_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115106.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.011435868218541145, "t": 274.2729187011719, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115106_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114941.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.00960354320704937, "t": 358.7319850921631, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_114941_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115017.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.024672873318195343, "t": 329.26487922668457, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115017_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115144.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.00783553533256054, "t": 301.0141849517822, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115144_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114913.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.037010498344898224, "t": 354.2799949645996, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_114913_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115015.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.02612464129924774, "t": 341.08805656433105, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115015_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115109.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.015383615158498287, "t": 331.0861587524414, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115109_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114947.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.0098007433116436, "t": 410.40778160095215, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_114947_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115149.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.011869876645505428, "t": 300.54402351379395, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115149_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114910.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.04201556742191315, "t": 294.9562072753906, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_114910_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115141.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.010179624892771244, "t": 275.3760814666748, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115141_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115252.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.011040279641747475, "t": 282.96589851379395, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115252_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115021.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.023327093571424484, "t": 278.0780792236328, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115021_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114944.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.0039312769658863544, "t": 283.4920883178711, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_114944_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115146.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.01634083315730095, "t": 280.3328037261963, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115146_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115117.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.011512755416333675, "t": 287.2650623321533, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115117_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115258.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.006025257054716349, "t": 282.84335136413574, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115258_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115255.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.009919787757098675, "t": 293.1828498840332, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_115255_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114950.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.008510833606123924, "t": 292.28782653808594, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_114950_result.jpg"}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114906.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.07200103253126144, "t": 311.25593185424805, "r": "datasets/cookies_1/anomaly_lvl_3/20240417_114906_result.jpg"}], "fomoad": [{"i": "datasets/cookies_1/no_anomaly/20240417_111938.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.9636785984039307, "t": 91.18008613586426}, {"i": "datasets/cookies_1/no_anomaly/20240417_112855.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.3752832412719727, "t": 33.41984748840332}, {"i": "datasets/cookies_1/no_anomaly/20240417_112105.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.6059515476226807, "t": 33.5543155670166}, {"i": "datasets/cookies_1/no_anomaly/20240417_112508.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.3019022941589355, "t": 43.695688247680664}, {"i": "datasets/cookies_1/no_anomaly/20240417_112654.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.465677261352539, "t": 32.80377388000488}, {"i": "datasets/cookies_1/no_anomaly/20240417_112719.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.7573113441467285, "t": 33.1120491027832}, {"i": "datasets/cookies_1/no_anomaly/20240417_112810.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.5585246086120605, "t": 35.54391860961914}, {"i": "datasets/cookies_1/no_anomaly/20240417_112406.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.0918474197387695, "t": 32.636165618896484}, {"i": "datasets/cookies_1/no_anomaly/20240417_112752.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.8137624263763428, "t": 36.934852600097656}, {"i": "datasets/cookies_1/no_anomaly/20240417_112745.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.5669713020324707, "t": 32.17577934265137}, {"i": "datasets/cookies_1/no_anomaly/20240417_111921.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.071432113647461, "t": 33.23698043823242}, {"i": "datasets/cookies_1/no_anomaly/20240417_112525.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.595059633255005, "t": 32.80997276306152}, {"i": "datasets/cookies_1/no_anomaly/20240417_112716.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.767383098602295, "t": 32.17196464538574}, {"i": "datasets/cookies_1/no_anomaly/20240417_112901.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.72645902633667, "t": 31.853914260864258}, {"i": "datasets/cookies_1/no_anomaly/20240417_112158.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.557013988494873, "t": 32.05108642578125}, {"i": "datasets/cookies_1/no_anomaly/20240417_111853.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.4836137294769287, "t": 32.51814842224121}, {"i": "datasets/cookies_1/no_anomaly/20240417_112413.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.9850430488586426, "t": 32.02700614929199}, {"i": "datasets/cookies_1/no_anomaly/20240417_113123.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.308338165283203, "t": 31.98385238647461}, {"i": "datasets/cookies_1/no_anomaly/20240417_112801.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.7213077545166016, "t": 32.36103057861328}, {"i": "datasets/cookies_1/no_anomaly/20240417_112727.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.7602756023406982, "t": 32.54199028015137}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114021.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 9.991497039794922, "t": 32.01913833618164}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114118.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 14.018916130065918, "t": 32.15289115905762}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114011.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 9.927388191223145, "t": 34.31582450866699}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114126.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 13.474974632263184, "t": 31.918764114379883}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113514.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 11.040229797363281, "t": 32.073020935058594}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113701.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 15.382932662963867, "t": 32.84907341003418}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113646.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 13.590190887451172, "t": 32.38391876220703}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114059.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 15.213801383972168, "t": 36.06700897216797}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113522.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 15.124086380004883, "t": 31.36420249938965}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114157.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 13.957559585571289, "t": 31.028032302856445}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113940.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 14.20977783203125, "t": 31.47578239440918}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113638.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 10.404473304748535, "t": 31.903982162475586}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113430.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 12.684404373168945, "t": 33.987998962402344}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113353.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 12.257253646850586, "t": 32.4249267578125}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113807.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 13.413707733154297, "t": 31.68797492980957}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114123.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 13.325844764709473, "t": 31.579017639160156}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113531.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 10.421842575073242, "t": 31.54301643371582}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113223.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 10.041842460632324, "t": 32.151222229003906}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113344.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 11.245850563049316, "t": 31.748056411743164}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113729.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 10.223296165466309, "t": 32.01794624328613}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114017.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 10.842761039733887, "t": 30.98011016845703}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113438.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 10.374995231628418, "t": 31.61907196044922}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113434.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 10.058905601501465, "t": 32.057762145996094}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113920.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 16.817148208618164, "t": 31.349897384643555}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114031.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 14.301910400390625, "t": 32.240867614746094}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113706.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 13.817278861999512, "t": 32.160043716430664}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113519.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 12.582403182983398, "t": 38.713932037353516}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113421.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 10.837519645690918, "t": 33.53309631347656}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113207.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 8.7174072265625, "t": 31.421899795532227}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113915.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 15.618802070617676, "t": 32.134056091308594}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114154.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 14.72214412689209, "t": 46.820878982543945}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114151.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 17.576900482177734, "t": 32.22298622131348}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113349.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 8.810449600219727, "t": 32.74393081665039}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_114106.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 16.766849517822266, "t": 32.12904930114746}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113403.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 20.376800537109375, "t": 33.30183029174805}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113932.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 14.991363525390625, "t": 31.011104583740234}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113928.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 19.003541946411133, "t": 39.24918174743652}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113214.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 15.936113357543945, "t": 39.62302207946777}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113156.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 10.143872261047363, "t": 44.29316520690918}, {"i": "datasets/cookies_1/anomaly_lvl_1/20240417_113724.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 9.447102546691895, "t": 32.23013877868652}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114640.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 8.185355186462402, "t": 35.38990020751953}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114418.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 16.0140323638916, "t": 35.22205352783203}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114725.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 7.878947734832764, "t": 40.86875915527344}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114429.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 10.364762306213379, "t": 33.06698799133301}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114444.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 9.476000785827637, "t": 34.79194641113281}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114651.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 11.052139282226562, "t": 33.833980560302734}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114829.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 7.642153739929199, "t": 32.04822540283203}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114810.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 16.58502769470215, "t": 32.50479698181152}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114644.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 8.135551452636719, "t": 32.190799713134766}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114732.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 15.281309127807617, "t": 32.259225845336914}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114438.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 6.9735894203186035, "t": 33.14995765686035}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114636.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 10.8995361328125, "t": 35.253286361694336}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114825.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 8.234457015991211, "t": 32.53507614135742}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114742.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 19.5487060546875, "t": 32.63282775878906}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114729.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 11.519067764282227, "t": 32.27400779724121}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114736.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 12.932724952697754, "t": 37.001848220825195}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114537.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 8.845043182373047, "t": 39.910078048706055}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114545.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 15.361237525939941, "t": 41.294097900390625}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114410.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 10.261463165283203, "t": 37.867069244384766}, {"i": "datasets/cookies_1/anomaly_lvl_2/20240417_114647.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 7.626359462738037, "t": 32.640933990478516}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115106.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 3.3653550148010254, "t": 32.3481559753418}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114941.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 3.998396635055542, "t": 32.97114372253418}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115017.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 3.7956368923187256, "t": 90.27290344238281}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115144.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 3.2504162788391113, "t": 39.1840934753418}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114913.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 5.348978042602539, "t": 41.465044021606445}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115015.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 5.667520046234131, "t": 35.32910346984863}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115109.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 3.962489128112793, "t": 35.10880470275879}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114947.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 4.458125114440918, "t": 41.131019592285156}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115149.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 3.647526979446411, "t": 34.063100814819336}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114910.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 3.906104803085327, "t": 38.00010681152344}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115141.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 4.611706256866455, "t": 33.212900161743164}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115252.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 3.5117602348327637, "t": 32.71484375}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115021.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 3.9316298961639404, "t": 32.800912857055664}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114944.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 3.7276594638824463, "t": 32.20319747924805}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115146.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 3.6700284481048584, "t": 32.456159591674805}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115117.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 3.871356248855591, "t": 32.78684616088867}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115258.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 3.6323275566101074, "t": 32.73582458496094}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_115255.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 3.4497060775756836, "t": 33.780813217163086}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114950.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 4.784890651702881, "t": 33.84900093078613}, {"i": "datasets/cookies_1/anomaly_lvl_3/20240417_114906.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 3.7223403453826904, "t": 34.9581241607666}]}, "cookies_2": {"baseline": [{"i": "datasets/cookies_2/no_anomaly/20240417_133408.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7253808975219727, "t": 253.2632350921631}, {"i": "datasets/cookies_2/no_anomaly/20240417_133645.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7857491374015808, "t": 43.771982192993164}, {"i": "datasets/cookies_2/no_anomaly/20240417_133647.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6128745079040527, "t": 34.98101234436035}, {"i": "datasets/cookies_2/no_anomaly/20240417_133642.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7663455009460449, "t": 38.500070571899414}, {"i": "datasets/cookies_2/no_anomaly/20240417_133332.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6303662657737732, "t": 36.19813919067383}, {"i": "datasets/cookies_2/no_anomaly/20240417_133643.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7675604224205017, "t": 36.581993103027344}, {"i": "datasets/cookies_2/no_anomaly/20240417_133058.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7535675168037415, "t": 47.88994789123535}, {"i": "datasets/cookies_2/no_anomaly/20240417_133025.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.8214728236198425, "t": 37.917137145996094}, {"i": "datasets/cookies_2/no_anomaly/20240417_133617.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7094327211380005, "t": 42.05179214477539}, {"i": "datasets/cookies_2/no_anomaly/20240417_133615.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7542770504951477, "t": 39.98994827270508}, {"i": "datasets/cookies_2/no_anomaly/20240417_133215.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6988669633865356, "t": 46.128034591674805}, {"i": "datasets/cookies_2/no_anomaly/20240417_133258.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6892141103744507, "t": 36.32521629333496}, {"i": "datasets/cookies_2/no_anomaly/20240417_133556.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7947263121604919, "t": 36.56315803527832}, {"i": "datasets/cookies_2/no_anomaly/20240417_133300.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.779316782951355, "t": 37.57905960083008}, {"i": "datasets/cookies_2/no_anomaly/20240417_133020.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7642516493797302, "t": 35.30311584472656}, {"i": "datasets/cookies_2/no_anomaly/20240417_133600.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7826337218284607, "t": 36.71669960021973}, {"i": "datasets/cookies_2/no_anomaly/20240417_133027.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.700048565864563, "t": 33.55097770690918}, {"i": "datasets/cookies_2/no_anomaly/20240417_133225.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6878859996795654, "t": 37.22882270812988}, {"i": "datasets/cookies_2/no_anomaly/20240417_133405.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7353904843330383, "t": 35.06207466125488}, {"i": "datasets/cookies_2/no_anomaly/20240417_133102.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6622740626335144, "t": 35.22491455078125}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141326.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9054424166679382, "t": 37.60981559753418}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141527.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9109010696411133, "t": 57.760000228881836}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141406.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9261568188667297, "t": 104.6898365020752}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140940.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.822718620300293, "t": 51.76186561584473}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141731.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8830235600471497, "t": 99.46703910827637}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140820.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9138970375061035, "t": 36.28802299499512}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141315.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8726511001586914, "t": 51.94592475891113}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141717.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.921701192855835, "t": 41.0008430480957}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140959.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8074327111244202, "t": 36.77487373352051}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141245.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.797987699508667, "t": 48.34914207458496}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141134.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8624612092971802, "t": 36.55719757080078}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141318.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8220621347427368, "t": 36.35001182556152}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141546.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8847013115882874, "t": 35.414934158325195}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140849.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8124538064002991, "t": 36.50784492492676}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141636.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8330958485603333, "t": 40.65299034118652}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141240.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8298667073249817, "t": 40.7869815826416}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141129.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.7105996608734131, "t": 35.74109077453613}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141621.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8922433853149414, "t": 40.62366485595703}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141227.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.773074209690094, "t": 35.858869552612305}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141531.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.7504420876502991, "t": 40.38810729980469}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141540.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.947404682636261, "t": 46.34809494018555}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141124.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9330083131790161, "t": 40.56811332702637}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141639.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8757110834121704, "t": 35.49075126647949}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141334.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8250584602355957, "t": 35.30693054199219}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141626.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.881194531917572, "t": 38.02609443664551}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141320.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8837952613830566, "t": 50.128936767578125}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140948.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8871751427650452, "t": 42.37985610961914}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141411.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.7899899482727051, "t": 36.544084548950195}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141234.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9203808903694153, "t": 47.5611686706543}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141414.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.888160765171051, "t": 38.69009017944336}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141120.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8961667418479919, "t": 45.37701606750488}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141550.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8959398865699768, "t": 34.6372127532959}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141223.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8320736289024353, "t": 56.05292320251465}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141112.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8732976317405701, "t": 41.25499725341797}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141424.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8259593844413757, "t": 41.55993461608887}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141323.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8233000040054321, "t": 39.54577445983887}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141116.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8591758608818054, "t": 37.59574890136719}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141521.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9455212950706482, "t": 35.291194915771484}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140853.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.7928224205970764, "t": 37.416934967041016}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140858.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8705273866653442, "t": 37.322998046875}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142052.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8656099438667297, "t": 34.54780578613281}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141923.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.7083626985549927, "t": 36.10396385192871}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142010.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.654965877532959, "t": 40.139198303222656}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141853.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.544312596321106, "t": 40.25769233703613}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142000.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.5889235138893127, "t": 37.0938777923584}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141849.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.7418338060379028, "t": 43.68019104003906}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141922.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.5002378225326538, "t": 43.06602478027344}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142025.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8315010070800781, "t": 36.073923110961914}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141915.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.6217925548553467, "t": 37.66608238220215}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142029.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.751022219657898, "t": 36.234140396118164}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141918.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.6238449811935425, "t": 38.027048110961914}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141840.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8739141225814819, "t": 42.08993911743164}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141851.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.6436504125595093, "t": 38.70201110839844}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142020.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8471013903617859, "t": 37.54281997680664}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142022.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8377110958099365, "t": 51.989078521728516}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142049.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8991969227790833, "t": 37.828922271728516}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142054.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.7902440428733826, "t": 42.33717918395996}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142027.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.6892759203910828, "t": 49.22890663146973}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142008.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.5841768383979797, "t": 36.92483901977539}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142003.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.5648655891418457, "t": 34.44194793701172}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142329.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5279859900474548, "t": 35.492897033691406}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142324.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6013481616973877, "t": 37.071943283081055}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142238.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5565568208694458, "t": 39.70789909362793}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142154.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6688388586044312, "t": 50.110816955566406}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142200.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6909257769584656, "t": 39.827823638916016}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142414.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.7145299911499023, "t": 50.16183853149414}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142326.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6315498948097229, "t": 36.985158920288086}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142240.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5442366003990173, "t": 36.26704216003418}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142410.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.523375391960144, "t": 37.01591491699219}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142202.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6244741678237915, "t": 47.02401161193848}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142232.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6191349625587463, "t": 35.11381149291992}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142108.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6650552749633789, "t": 35.620927810668945}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142418.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6017581224441528, "t": 44.60716247558594}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142243.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.5482221245765686, "t": 39.7639274597168}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142157.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5451927185058594, "t": 40.592193603515625}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142111.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5746297240257263, "t": 37.29987144470215}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142115.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.7850285172462463, "t": 43.998003005981445}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142320.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.5879907608032227, "t": 60.461997985839844}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142151.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.59596186876297, "t": 42.3741340637207}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142412.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5211564302444458, "t": 38.50197792053223}], "baseline-ei": [{"i": "datasets/cookies_2/no_anomaly/20240417_133408.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7348803281784058, "t": 100.70204734802246}, {"i": "datasets/cookies_2/no_anomaly/20240417_133645.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.8634157180786133, "t": 49.65400695800781}, {"i": "datasets/cookies_2/no_anomaly/20240417_133647.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.8017579317092896, "t": 32.729148864746094}, {"i": "datasets/cookies_2/no_anomaly/20240417_133642.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.716247022151947, "t": 32.00721740722656}, {"i": "datasets/cookies_2/no_anomaly/20240417_133332.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6462416052818298, "t": 31.88300132751465}, {"i": "datasets/cookies_2/no_anomaly/20240417_133643.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7526552677154541, "t": 29.95586395263672}, {"i": "datasets/cookies_2/no_anomaly/20240417_133058.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6275838017463684, "t": 33.67304801940918}, {"i": "datasets/cookies_2/no_anomaly/20240417_133025.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.8408161401748657, "t": 32.217979431152344}, {"i": "datasets/cookies_2/no_anomaly/20240417_133617.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.5833712816238403, "t": 33.184051513671875}, {"i": "datasets/cookies_2/no_anomaly/20240417_133615.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6525365710258484, "t": 39.24202919006348}, {"i": "datasets/cookies_2/no_anomaly/20240417_133215.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6468552947044373, "t": 31.535863876342773}, {"i": "datasets/cookies_2/no_anomaly/20240417_133258.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.8712916970252991, "t": 29.967069625854492}, {"i": "datasets/cookies_2/no_anomaly/20240417_133556.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7558121085166931, "t": 30.04002571105957}, {"i": "datasets/cookies_2/no_anomaly/20240417_133300.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.8490268588066101, "t": 30.50994873046875}, {"i": "datasets/cookies_2/no_anomaly/20240417_133020.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.8005852699279785, "t": 30.17711639404297}, {"i": "datasets/cookies_2/no_anomaly/20240417_133600.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7908315658569336, "t": 31.445026397705078}, {"i": "datasets/cookies_2/no_anomaly/20240417_133027.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.768047571182251, "t": 30.726194381713867}, {"i": "datasets/cookies_2/no_anomaly/20240417_133225.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.8447405099868774, "t": 29.88409996032715}, {"i": "datasets/cookies_2/no_anomaly/20240417_133405.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7986161112785339, "t": 30.244112014770508}, {"i": "datasets/cookies_2/no_anomaly/20240417_133102.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6652194857597351, "t": 30.043840408325195}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141326.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8798298835754395, "t": 30.65800666809082}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141527.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9296305775642395, "t": 31.167984008789062}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141406.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8714978694915771, "t": 34.61194038391113}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140940.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9087541699409485, "t": 32.150983810424805}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141731.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.7076009511947632, "t": 31.20899200439453}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140820.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.7712832689285278, "t": 30.462026596069336}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141315.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.812782347202301, "t": 32.060861587524414}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141717.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8902034759521484, "t": 32.05680847167969}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140959.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8382035493850708, "t": 29.627084732055664}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141245.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8147542476654053, "t": 82.31711387634277}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141134.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8376588225364685, "t": 29.681921005249023}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141318.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9072701334953308, "t": 30.564069747924805}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141546.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9106296300888062, "t": 30.647754669189453}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140849.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8354198932647705, "t": 33.01095962524414}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141636.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8977054953575134, "t": 31.27598762512207}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141240.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.7176845073699951, "t": 33.9818000793457}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141129.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9006776213645935, "t": 30.714035034179688}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141621.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8449434638023376, "t": 32.460927963256836}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141227.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.739201009273529, "t": 30.472755432128906}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141531.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9138220548629761, "t": 29.7698974609375}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141540.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9115170836448669, "t": 31.479835510253906}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141124.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.7869049906730652, "t": 30.949831008911133}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141639.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.815143883228302, "t": 30.588150024414062}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141334.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8985634446144104, "t": 31.544923782348633}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141626.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.903709352016449, "t": 30.484914779663086}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141320.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8685858249664307, "t": 33.34665298461914}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140948.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.781579315662384, "t": 33.2028865814209}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141411.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.7919803857803345, "t": 38.86008262634277}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141234.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8296383619308472, "t": 34.432172775268555}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141414.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.7089987993240356, "t": 34.24477577209473}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141120.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8867917060852051, "t": 35.73727607727051}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141550.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9393139481544495, "t": 31.213045120239258}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141223.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8096670508384705, "t": 37.26696968078613}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141112.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.798774778842926, "t": 30.607938766479492}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141424.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9133316874504089, "t": 32.27090835571289}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141323.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8812733292579651, "t": 37.548065185546875}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141116.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8322160243988037, "t": 30.456066131591797}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141521.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9033553004264832, "t": 30.749797821044922}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140853.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8831954598426819, "t": 34.583091735839844}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140858.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.7027775645256042, "t": 31.569719314575195}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142052.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8586244583129883, "t": 32.956838607788086}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141923.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.701728105545044, "t": 31.229019165039062}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142010.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.5839152932167053, "t": 33.11920166015625}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141853.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.6540434956550598, "t": 30.086994171142578}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142000.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.7459555864334106, "t": 31.406879425048828}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141849.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8275124430656433, "t": 33.455848693847656}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141922.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.725857675075531, "t": 38.34080696105957}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142025.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8456791639328003, "t": 30.905961990356445}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141915.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8306789398193359, "t": 30.933856964111328}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142029.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.7737921476364136, "t": 32.87029266357422}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141918.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.6762201189994812, "t": 31.278133392333984}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141840.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.7073187232017517, "t": 33.799171447753906}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141851.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8131773471832275, "t": 31.110048294067383}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142020.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8496356010437012, "t": 30.47013282775879}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142022.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.7469648122787476, "t": 40.780067443847656}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142049.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9037668108940125, "t": 31.293869018554688}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142054.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9231712818145752, "t": 30.430078506469727}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142027.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8086045384407043, "t": 36.93199157714844}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142008.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.7772734761238098, "t": 32.10711479187012}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142003.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.7449008226394653, "t": 30.7161808013916}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142329.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.6328575611114502, "t": 30.45487403869629}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142324.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.5015762448310852, "t": 33.36286544799805}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142238.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.5043080449104309, "t": 31.22711181640625}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142154.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5094984769821167, "t": 35.60805320739746}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142200.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6429325342178345, "t": 31.644821166992188}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142414.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.7073937654495239, "t": 32.33695030212402}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142326.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.5337611436843872, "t": 30.5330753326416}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142240.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.6958650350570679, "t": 31.288862228393555}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142410.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.5514615178108215, "t": 30.95388412475586}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142202.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6509075164794922, "t": 31.679868698120117}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142232.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.5352159738540649, "t": 30.67779541015625}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142108.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5764723420143127, "t": 30.90214729309082}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142418.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.645526111125946, "t": 33.90002250671387}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142243.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.5136743783950806, "t": 31.660079956054688}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142157.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6958475112915039, "t": 31.885147094726562}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142111.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.5525065660476685, "t": 30.824899673461914}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142115.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.6695737838745117, "t": 34.49606895446777}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142320.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.6329163908958435, "t": 35.57610511779785}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142151.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.5528302192687988, "t": 33.965110778808594}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142412.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.6612294316291809, "t": 32.26900100708008}], "efficientad": [{"i": "datasets/cookies_2/no_anomaly/20240417_133408.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.010480072349309921, "t": 343.2600498199463, "r": "datasets/cookies_2/no_anomaly/20240417_133408_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133645.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.02640705555677414, "t": 275.8059501647949, "r": "datasets/cookies_2/no_anomaly/20240417_133645_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133647.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.05019203573465347, "t": 280.825138092041, "r": "datasets/cookies_2/no_anomaly/20240417_133647_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133642.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.010566155426204205, "t": 279.8941135406494, "r": "datasets/cookies_2/no_anomaly/20240417_133642_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133332.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.005533927585929632, "t": 502.9580593109131, "r": "datasets/cookies_2/no_anomaly/20240417_133332_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133643.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.006661382969468832, "t": 335.0710868835449, "r": "datasets/cookies_2/no_anomaly/20240417_133643_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133058.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.02193724550306797, "t": 395.56884765625, "r": "datasets/cookies_2/no_anomaly/20240417_133058_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133025.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.021729065105319023, "t": 320.01495361328125, "r": "datasets/cookies_2/no_anomaly/20240417_133025_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133617.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.013029190711677074, "t": 283.29014778137207, "r": "datasets/cookies_2/no_anomaly/20240417_133617_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133615.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.011701659299433231, "t": 330.7347297668457, "r": "datasets/cookies_2/no_anomaly/20240417_133615_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133215.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.004985379986464977, "t": 298.1407642364502, "r": "datasets/cookies_2/no_anomaly/20240417_133215_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133258.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.031900499016046524, "t": 298.6290454864502, "r": "datasets/cookies_2/no_anomaly/20240417_133258_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133556.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.00036854619975201786, "t": 282.17482566833496, "r": "datasets/cookies_2/no_anomaly/20240417_133556_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133300.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.003385686781257391, "t": 287.3508930206299, "r": "datasets/cookies_2/no_anomaly/20240417_133300_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133020.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.03121213987469673, "t": 356.5061092376709, "r": "datasets/cookies_2/no_anomaly/20240417_133020_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133600.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.001046287245117128, "t": 296.62418365478516, "r": "datasets/cookies_2/no_anomaly/20240417_133600_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133027.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.0068531278520822525, "t": 286.0410213470459, "r": "datasets/cookies_2/no_anomaly/20240417_133027_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133225.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.045673977583646774, "t": 285.7389450073242, "r": "datasets/cookies_2/no_anomaly/20240417_133225_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133405.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.009697233326733112, "t": 287.647008895874, "r": "datasets/cookies_2/no_anomaly/20240417_133405_result.jpg"}, {"i": "datasets/cookies_2/no_anomaly/20240417_133102.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.055432844907045364, "t": 303.8370609283447, "r": "datasets/cookies_2/no_anomaly/20240417_133102_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141326.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.13148565590381622, "t": 321.98619842529297, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141326_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141527.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1430836319923401, "t": 363.3232116699219, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141527_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141406.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.14377951622009277, "t": 351.3510227203369, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141406_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140940.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1295776218175888, "t": 362.67590522766113, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_140940_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141731.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.09847865253686905, "t": 359.24220085144043, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141731_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140820.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11939319223165512, "t": 397.9480266571045, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_140820_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141315.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.19314590096473694, "t": 295.99618911743164, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141315_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141717.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1742541640996933, "t": 274.4708061218262, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141717_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140959.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.13179337978363037, "t": 291.72396659851074, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_140959_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141245.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.16629497706890106, "t": 310.72998046875, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141245_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141134.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.13985805213451385, "t": 278.1960964202881, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141134_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141318.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.20153701305389404, "t": 281.8000316619873, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141318_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141546.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.12891623377799988, "t": 285.8612537384033, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141546_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140849.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.13000349700450897, "t": 346.2331295013428, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_140849_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141636.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.16279785335063934, "t": 319.63086128234863, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141636_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141240.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.13007692992687225, "t": 282.9089164733887, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141240_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141129.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.09269002825021744, "t": 286.3879203796387, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141129_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141621.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.122137151658535, "t": 285.04204750061035, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141621_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141227.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1269792914390564, "t": 369.2049980163574, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141227_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141531.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1451147198677063, "t": 328.7382125854492, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141531_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141540.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.14745254814624786, "t": 311.7489814758301, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141540_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141124.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.12141825258731842, "t": 286.15403175354004, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141124_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141639.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.09997565299272537, "t": 286.0851287841797, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141639_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141334.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.14343076944351196, "t": 300.76098442077637, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141334_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141626.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1304844617843628, "t": 321.8648433685303, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141626_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141320.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1922941505908966, "t": 329.39696311950684, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141320_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140948.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.147069051861763, "t": 288.36798667907715, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_140948_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141411.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.15095798671245575, "t": 370.4822063446045, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141411_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141234.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.18226845562458038, "t": 292.22726821899414, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141234_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141414.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11019931733608246, "t": 311.4440441131592, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141414_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141120.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11903142929077148, "t": 372.97797203063965, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141120_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141550.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11363869905471802, "t": 297.01685905456543, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141550_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141223.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1369544118642807, "t": 352.4019718170166, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141223_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141112.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.13954223692417145, "t": 283.28561782836914, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141112_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141424.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.12980765104293823, "t": 294.0208911895752, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141424_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141323.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.13567233085632324, "t": 297.9891300201416, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141323_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141116.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11599664390087128, "t": 286.1299514770508, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141116_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141521.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1295141577720642, "t": 295.43185234069824, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_141521_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140853.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1460576057434082, "t": 325.5159854888916, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_140853_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140858.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.16202272474765778, "t": 289.2911434173584, "r": "datasets/cookies_2/anomaly_lvl_1/20240417_140858_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142052.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.06851384043693542, "t": 332.3650360107422, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142052_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141923.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.10255870223045349, "t": 295.3770160675049, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_141923_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142010.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.06906720250844955, "t": 294.29101943969727, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142010_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141853.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.07586271315813065, "t": 298.6159324645996, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_141853_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142000.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.06497033685445786, "t": 294.29006576538086, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142000_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141849.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.0686550885438919, "t": 288.2680892944336, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_141849_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141922.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.09466400742530823, "t": 310.09936332702637, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_141922_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142025.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.09680534899234772, "t": 292.77586936950684, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142025_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141915.jpg", "c": "anomaly", "d": "medium", "cl": "no_anomaly", "s": 0.05366120859980583, "t": 293.27392578125, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_141915_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142029.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.15000402927398682, "t": 382.0919990539551, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142029_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141918.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.08746035397052765, "t": 295.95303535461426, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_141918_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141840.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.07481776922941208, "t": 401.83377265930176, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_141840_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141851.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.08300046622753143, "t": 299.33691024780273, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_141851_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142020.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.111331507563591, "t": 324.97096061706543, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142020_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142022.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.10335037857294083, "t": 302.567720413208, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142022_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142049.jpg", "c": "anomaly", "d": "medium", "cl": "no_anomaly", "s": 0.059870749711990356, "t": 307.6009750366211, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142049_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142054.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.07047779858112335, "t": 295.48025131225586, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142054_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142027.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.11682076007127762, "t": 298.4161376953125, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142027_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142008.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.0757613405585289, "t": 300.0030517578125, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142008_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142003.jpg", "c": "anomaly", "d": "medium", "cl": "no_anomaly", "s": 0.06064215674996376, "t": 294.1608428955078, "r": "datasets/cookies_2/anomaly_lvl_2/20240417_142003_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142329.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.08482853323221207, "t": 300.19521713256836, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142329_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142324.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.08056410402059555, "t": 295.7580089569092, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142324_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142238.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.05170254036784172, "t": 287.1072292327881, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142238_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142154.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.02416887879371643, "t": 347.6700782775879, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142154_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142200.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.046454839408397675, "t": 326.25508308410645, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142200_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142414.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.04685103893280029, "t": 465.8071994781494, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142414_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142326.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.07614675909280777, "t": 297.2910404205322, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142326_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142240.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.03302312269806862, "t": 294.95906829833984, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142240_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142410.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.05833539739251137, "t": 327.3329734802246, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142410_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142202.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.05300965532660484, "t": 300.6291389465332, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142202_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142232.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.0780390128493309, "t": 368.43085289001465, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142232_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142108.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.047874804586172104, "t": 294.6193218231201, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142108_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142418.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.049646127969026566, "t": 305.8960437774658, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142418_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142243.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.06228902190923691, "t": 302.02198028564453, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142243_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142157.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.03667832165956497, "t": 300.3361225128174, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142157_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142111.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.026993677020072937, "t": 369.87996101379395, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142111_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142115.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.09089507162570953, "t": 462.2042179107666, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142115_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142320.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.08411672711372375, "t": 418.21908950805664, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142320_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142151.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.02514224499464035, "t": 371.08469009399414, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142151_result.jpg"}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142412.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.04512764513492584, "t": 312.10803985595703, "r": "datasets/cookies_2/anomaly_lvl_3/20240417_142412_result.jpg"}], "fomoad": [{"i": "datasets/cookies_2/no_anomaly/20240417_133408.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.4974992275238037, "t": 87.46099472045898}, {"i": "datasets/cookies_2/no_anomaly/20240417_133645.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.198446750640869, "t": 34.114837646484375}, {"i": "datasets/cookies_2/no_anomaly/20240417_133647.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.690516710281372, "t": 33.30111503601074}, {"i": "datasets/cookies_2/no_anomaly/20240417_133642.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.208893299102783, "t": 32.715797424316406}, {"i": "datasets/cookies_2/no_anomaly/20240417_133332.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 4.282330513000488, "t": 32.63592720031738}, {"i": "datasets/cookies_2/no_anomaly/20240417_133643.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.247082471847534, "t": 39.893150329589844}, {"i": "datasets/cookies_2/no_anomaly/20240417_133058.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.5084166526794434, "t": 36.50474548339844}, {"i": "datasets/cookies_2/no_anomaly/20240417_133025.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.7099123001098633, "t": 35.739898681640625}, {"i": "datasets/cookies_2/no_anomaly/20240417_133617.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.3560214042663574, "t": 33.22482109069824}, {"i": "datasets/cookies_2/no_anomaly/20240417_133615.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.5060455799102783, "t": 51.90300941467285}, {"i": "datasets/cookies_2/no_anomaly/20240417_133215.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.1246588230133057, "t": 32.91440010070801}, {"i": "datasets/cookies_2/no_anomaly/20240417_133258.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.248297691345215, "t": 33.17618370056152}, {"i": "datasets/cookies_2/no_anomaly/20240417_133556.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.991842269897461, "t": 35.36796569824219}, {"i": "datasets/cookies_2/no_anomaly/20240417_133300.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.819526195526123, "t": 33.982276916503906}, {"i": "datasets/cookies_2/no_anomaly/20240417_133020.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 5.040414810180664, "t": 36.98396682739258}, {"i": "datasets/cookies_2/no_anomaly/20240417_133600.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.114921808242798, "t": 32.83095359802246}, {"i": "datasets/cookies_2/no_anomaly/20240417_133027.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.2934679985046387, "t": 33.42175483703613}, {"i": "datasets/cookies_2/no_anomaly/20240417_133225.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.336224317550659, "t": 33.67280960083008}, {"i": "datasets/cookies_2/no_anomaly/20240417_133405.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.214942216873169, "t": 34.18588638305664}, {"i": "datasets/cookies_2/no_anomaly/20240417_133102.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.8945462703704834, "t": 35.447120666503906}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141326.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 48.800289154052734, "t": 48.35629463195801}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141527.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 34.33831024169922, "t": 39.9322509765625}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141406.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 41.8612060546875, "t": 40.18998146057129}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140940.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 36.17974090576172, "t": 44.55089569091797}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141731.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 34.357269287109375, "t": 40.293216705322266}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140820.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 46.25642395019531, "t": 41.68581962585449}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141315.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 40.59771728515625, "t": 34.192800521850586}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141717.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 36.05101013183594, "t": 33.262014389038086}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140959.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 40.89436721801758, "t": 39.363861083984375}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141245.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 39.86558532714844, "t": 34.11412239074707}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141134.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 38.367149353027344, "t": 33.056020736694336}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141318.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 55.3377685546875, "t": 33.998727798461914}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141546.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 34.72270202636719, "t": 33.92481803894043}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140849.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 61.175254821777344, "t": 42.038917541503906}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141636.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 48.56343460083008, "t": 34.98077392578125}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141240.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 45.47643280029297, "t": 34.23786163330078}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141129.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 33.064186096191406, "t": 37.09816932678223}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141621.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 53.94574737548828, "t": 34.09314155578613}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141227.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 37.24085235595703, "t": 40.14992713928223}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141531.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 34.334693908691406, "t": 44.454097747802734}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141540.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 74.32418823242188, "t": 34.616947174072266}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141124.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 29.997486114501953, "t": 34.18087959289551}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141639.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 54.7225227355957, "t": 34.60574150085449}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141334.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 48.757930755615234, "t": 36.459922790527344}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141626.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 58.28141403198242, "t": 38.01393508911133}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141320.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 50.98466491699219, "t": 36.61680221557617}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140948.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 34.28593063354492, "t": 34.67917442321777}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141411.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 33.05671310424805, "t": 38.99693489074707}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141234.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 34.00712966918945, "t": 35.27712821960449}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141414.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 33.30618667602539, "t": 36.13710403442383}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141120.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 30.121288299560547, "t": 38.125038146972656}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141550.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 54.23351287841797, "t": 39.04533386230469}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141223.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 35.407379150390625, "t": 38.2387638092041}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141112.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 43.088897705078125, "t": 35.537004470825195}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141424.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 42.0013313293457, "t": 36.286354064941406}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141323.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 51.358821868896484, "t": 34.776926040649414}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141116.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 40.783878326416016, "t": 33.911943435668945}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_141521.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 43.91867446899414, "t": 35.60996055603027}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140853.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 43.29283905029297, "t": 37.06979751586914}, {"i": "datasets/cookies_2/anomaly_lvl_1/20240417_140858.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 45.38267517089844, "t": 34.29007530212402}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142052.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 23.27415657043457, "t": 33.766746520996094}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141923.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 38.2921142578125, "t": 34.240007400512695}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142010.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 35.545162200927734, "t": 35.288095474243164}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141853.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 17.14127540588379, "t": 34.654855728149414}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142000.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 26.236583709716797, "t": 36.77082061767578}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141849.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 44.37229537963867, "t": 37.74094581604004}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141922.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 27.7585506439209, "t": 34.99794006347656}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142025.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 49.82709503173828, "t": 35.11333465576172}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141915.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 26.3785400390625, "t": 34.20209884643555}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142029.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 39.916568756103516, "t": 34.92927551269531}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141918.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 38.29072189331055, "t": 34.39807891845703}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141840.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 27.613189697265625, "t": 42.34814643859863}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_141851.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 23.393625259399414, "t": 37.02402114868164}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142020.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 46.56580352783203, "t": 37.4758243560791}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142022.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 29.772964477539062, "t": 38.897037506103516}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142049.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 31.626197814941406, "t": 34.2409610748291}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142054.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 21.61459732055664, "t": 47.393083572387695}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142027.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 32.368858337402344, "t": 35.90893745422363}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142008.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 27.236595153808594, "t": 34.3470573425293}, {"i": "datasets/cookies_2/anomaly_lvl_2/20240417_142003.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 26.381267547607422, "t": 37.286996841430664}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142329.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 24.618350982666016, "t": 35.17317771911621}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142324.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 20.22923469543457, "t": 33.86187553405762}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142238.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 12.315911293029785, "t": 39.855241775512695}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142154.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 14.440510749816895, "t": 37.15682029724121}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142200.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 14.605753898620605, "t": 49.6058464050293}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142414.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 6.030016899108887, "t": 33.6461067199707}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142326.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 16.74004554748535, "t": 33.84518623352051}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142240.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 6.950046539306641, "t": 34.12771224975586}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142410.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 6.226258277893066, "t": 36.54003143310547}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142202.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 9.71426010131836, "t": 34.11102294921875}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142232.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 6.717199325561523, "t": 43.00117492675781}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142108.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 8.95866870880127, "t": 36.95988655090332}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142418.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 5.807551383972168, "t": 41.053056716918945}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142243.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 5.447047233581543, "t": 35.72821617126465}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142157.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 12.169938087463379, "t": 34.787893295288086}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142111.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 8.322336196899414, "t": 114.67123031616211}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142115.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 12.65952205657959, "t": 63.18807601928711}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142320.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 12.116340637207031, "t": 36.264896392822266}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142151.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 8.129228591918945, "t": 37.10794448852539}, {"i": "datasets/cookies_2/anomaly_lvl_3/20240417_142412.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 5.91170597076416, "t": 36.8959903717041}]}, "cookies_3": {"baseline": [{"i": "datasets/cookies_3/no_anomaly/20240417_134414.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6296981573104858, "t": 205.28578758239746}, {"i": "datasets/cookies_3/no_anomaly/20240417_134257.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7476074695587158, "t": 60.723066329956055}, {"i": "datasets/cookies_3/no_anomaly/20240417_134101.jpg", "c": "no_anomaly", "d": "na", "cl": "anomaly", "s": 0.6775026321411133, "t": 37.20688819885254}, {"i": "datasets/cookies_3/no_anomaly/20240417_134452.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6978501081466675, "t": 41.02301597595215}, {"i": "datasets/cookies_3/no_anomaly/20240417_134334.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6422110199928284, "t": 39.38794136047363}, {"i": "datasets/cookies_3/no_anomaly/20240417_133857.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6930940747261047, "t": 34.277915954589844}, {"i": "datasets/cookies_3/no_anomaly/20240417_134051.jpg", "c": "no_anomaly", "d": "na", "cl": "anomaly", "s": 0.506409227848053, "t": 38.15007209777832}, {"i": "datasets/cookies_3/no_anomaly/20240417_134238.jpg", "c": "no_anomaly", "d": "na", "cl": "anomaly", "s": 0.5973427891731262, "t": 35.264015197753906}, {"i": "datasets/cookies_3/no_anomaly/20240417_134222.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.5961651802062988, "t": 40.51923751831055}, {"i": "datasets/cookies_3/no_anomaly/20240417_134027.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.755547285079956, "t": 35.11500358581543}, {"i": "datasets/cookies_3/no_anomaly/20240417_134426.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6759738922119141, "t": 36.14997863769531}, {"i": "datasets/cookies_3/no_anomaly/20240417_134442.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6079365611076355, "t": 37.590980529785156}, {"i": "datasets/cookies_3/no_anomaly/20240417_133928.jpg", "c": "no_anomaly", "d": "na", "cl": "anomaly", "s": 0.5021955370903015, "t": 114.60089683532715}, {"i": "datasets/cookies_3/no_anomaly/20240417_133926.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.5530720949172974, "t": 51.92208290100098}, {"i": "datasets/cookies_3/no_anomaly/20240417_133948.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6204327344894409, "t": 47.89924621582031}, {"i": "datasets/cookies_3/no_anomaly/20240417_134227.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6692628860473633, "t": 38.439273834228516}, {"i": "datasets/cookies_3/no_anomaly/20240417_134316.jpg", "c": "no_anomaly", "d": "na", "cl": "anomaly", "s": 0.5045731067657471, "t": 41.59212112426758}, {"i": "datasets/cookies_3/no_anomaly/20240417_134128.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.5488722920417786, "t": 43.44010353088379}, {"i": "datasets/cookies_3/no_anomaly/20240417_134034.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.5684294104576111, "t": 37.89806365966797}, {"i": "datasets/cookies_3/no_anomaly/20240417_134536.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6454157829284668, "t": 38.95711898803711}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135249.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9698006510734558, "t": 38.253068923950195}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134757.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.963039755821228, "t": 35.29620170593262}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135245.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9561824798583984, "t": 84.98787879943848}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135316.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9619738459587097, "t": 44.42167282104492}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135313.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9160199165344238, "t": 51.132917404174805}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135504.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9669238924980164, "t": 43.59602928161621}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135324.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9722097516059875, "t": 38.620948791503906}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135451.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9431157112121582, "t": 39.15810585021973}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134612.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9108403921127319, "t": 39.4902229309082}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135158.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9571095705032349, "t": 41.272878646850586}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134753.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8681249618530273, "t": 37.93191909790039}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135205.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9546228647232056, "t": 39.12496566772461}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134714.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9390471577644348, "t": 43.3049201965332}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135424.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9371227025985718, "t": 42.52290725708008}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135237.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9911903142929077, "t": 36.9420051574707}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134810.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9445094466209412, "t": 47.58810997009277}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135327.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9464353919029236, "t": 36.821603775024414}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134847.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9313974380493164, "t": 38.68603706359863}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135431.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9559544324874878, "t": 36.41104698181152}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135500.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9482297301292419, "t": 35.81881523132324}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135208.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9929511547088623, "t": 37.220001220703125}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134631.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9704176187515259, "t": 40.69089889526367}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135154.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8693444728851318, "t": 38.0551815032959}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134804.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.962615430355072, "t": 39.36910629272461}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135434.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9683171510696411, "t": 35.643815994262695}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134838.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.838257908821106, "t": 35.719871520996094}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135454.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9747145771980286, "t": 47.84989356994629}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135418.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9794549942016602, "t": 37.49418258666992}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135231.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8914509415626526, "t": 38.559913635253906}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135457.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9940536618232727, "t": 41.07785224914551}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134816.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9243367910385132, "t": 36.68928146362305}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134852.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8764005303382874, "t": 37.4760627746582}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134725.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9751715660095215, "t": 35.39085388183594}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134942.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9274460673332214, "t": 50.84633827209473}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134900.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9248321056365967, "t": 35.76803207397461}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135128.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9644420742988586, "t": 35.38203239440918}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134628.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9839644432067871, "t": 35.726070404052734}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135428.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9242523908615112, "t": 35.98284721374512}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134857.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.7736692428588867, "t": 37.61577606201172}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134615.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9798862338066101, "t": 38.68699073791504}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135612.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9243934750556946, "t": 35.83407402038574}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135621.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.6047413945198059, "t": 35.82501411437988}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135715.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.882926344871521, "t": 36.48185729980469}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135928.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8616629838943481, "t": 36.32307052612305}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135936.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9311096668243408, "t": 34.481048583984375}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135701.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.7128572463989258, "t": 37.039756774902344}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135806.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8483877778053284, "t": 36.24892234802246}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135624.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9802098870277405, "t": 37.85967826843262}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135924.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9180012345314026, "t": 37.18304634094238}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_140028.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9584760069847107, "t": 38.69891166687012}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135720.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9061204791069031, "t": 38.65218162536621}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135626.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9274560213088989, "t": 36.52215003967285}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_140021.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9362961649894714, "t": 35.71605682373047}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135912.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9164877533912659, "t": 39.46328163146973}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135916.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.7170374989509583, "t": 34.78503227233887}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135822.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9336448907852173, "t": 36.61203384399414}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135607.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9551288485527039, "t": 36.956071853637695}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135817.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9218471646308899, "t": 36.96322441101074}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135654.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8640611171722412, "t": 36.8809700012207}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_140012.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9226430654525757, "t": 37.71400451660156}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140116.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.5832906365394592, "t": 36.54885292053223}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140110.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.688422441482544, "t": 39.48521614074707}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140613.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.7765113711357117, "t": 41.4888858795166}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140550.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.6340427994728088, "t": 39.86501693725586}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140603.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.535165011882782, "t": 43.28298568725586}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140607.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6000674366950989, "t": 37.20498085021973}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140106.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5442472100257874, "t": 38.91873359680176}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140059.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5190651416778564, "t": 36.78703308105469}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140602.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6380591988563538, "t": 38.213253021240234}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140548.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.6431636214256287, "t": 40.636301040649414}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140520.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.7549601197242737, "t": 39.78300094604492}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140532.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.7805773019790649, "t": 40.99464416503906}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140618.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.8020007610321045, "t": 39.528846740722656}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140523.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.8078572750091553, "t": 42.44089126586914}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140620.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.5842496156692505, "t": 40.815114974975586}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140615.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6671411395072937, "t": 40.11225700378418}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140113.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.5667778253555298, "t": 42.176008224487305}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140514.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.6520641446113586, "t": 41.49794578552246}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140535.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.6274389624595642, "t": 37.26387023925781}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140617.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6246063113212585, "t": 99.6859073638916}], "baseline-ei": [{"i": "datasets/cookies_3/no_anomaly/20240417_134414.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.571942150592804, "t": 75.80709457397461}, {"i": "datasets/cookies_3/no_anomaly/20240417_134257.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7364833950996399, "t": 37.75310516357422}, {"i": "datasets/cookies_3/no_anomaly/20240417_134101.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.5673186779022217, "t": 36.58127784729004}, {"i": "datasets/cookies_3/no_anomaly/20240417_134452.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.5838797688484192, "t": 30.738115310668945}, {"i": "datasets/cookies_3/no_anomaly/20240417_134334.jpg", "c": "no_anomaly", "d": "na", "cl": "anomaly", "s": 0.5027574300765991, "t": 31.686782836914062}, {"i": "datasets/cookies_3/no_anomaly/20240417_133857.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7303986549377441, "t": 31.149864196777344}, {"i": "datasets/cookies_3/no_anomaly/20240417_134051.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6640363335609436, "t": 31.688928604125977}, {"i": "datasets/cookies_3/no_anomaly/20240417_134238.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.5313403010368347, "t": 32.99307823181152}, {"i": "datasets/cookies_3/no_anomaly/20240417_134222.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.5501621961593628, "t": 31.723976135253906}, {"i": "datasets/cookies_3/no_anomaly/20240417_134027.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.589806318283081, "t": 32.124996185302734}, {"i": "datasets/cookies_3/no_anomaly/20240417_134426.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7355192303657532, "t": 32.01889991760254}, {"i": "datasets/cookies_3/no_anomaly/20240417_134442.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6340337991714478, "t": 31.673908233642578}, {"i": "datasets/cookies_3/no_anomaly/20240417_133928.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6449626684188843, "t": 32.05084800720215}, {"i": "datasets/cookies_3/no_anomaly/20240417_133926.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6602771878242493, "t": 34.950971603393555}, {"i": "datasets/cookies_3/no_anomaly/20240417_133948.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.676025927066803, "t": 35.819053649902344}, {"i": "datasets/cookies_3/no_anomaly/20240417_134227.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7551377415657043, "t": 31.956911087036133}, {"i": "datasets/cookies_3/no_anomaly/20240417_134316.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.6829252243041992, "t": 32.013654708862305}, {"i": "datasets/cookies_3/no_anomaly/20240417_134128.jpg", "c": "no_anomaly", "d": "na", "cl": "anomaly", "s": 0.5675291419029236, "t": 31.41188621520996}, {"i": "datasets/cookies_3/no_anomaly/20240417_134034.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.8098694682121277, "t": 31.50796890258789}, {"i": "datasets/cookies_3/no_anomaly/20240417_134536.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.7112342715263367, "t": 31.80694580078125}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135249.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9769938588142395, "t": 31.602859497070312}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134757.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9270321130752563, "t": 31.210899353027344}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135245.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9689713716506958, "t": 31.795978546142578}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135316.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9334633946418762, "t": 32.33981132507324}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135313.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8820251822471619, "t": 38.040876388549805}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135504.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9507578611373901, "t": 32.881975173950195}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135324.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9846436977386475, "t": 32.48786926269531}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135451.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9098698496818542, "t": 33.14995765686035}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134612.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9755319356918335, "t": 31.792879104614258}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135158.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9177006483078003, "t": 32.48906135559082}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134753.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9075337648391724, "t": 31.773090362548828}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135205.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9058845639228821, "t": 33.952951431274414}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134714.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9196234345436096, "t": 32.64284133911133}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135424.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9651976227760315, "t": 32.24802017211914}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135237.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9381471872329712, "t": 31.777143478393555}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134810.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9123766422271729, "t": 31.757831573486328}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135327.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9579103589057922, "t": 32.63688087463379}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134847.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8520011901855469, "t": 31.898975372314453}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135431.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8960827589035034, "t": 31.919240951538086}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135500.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9640535712242126, "t": 32.24778175354004}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135208.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9447511434555054, "t": 31.485795974731445}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134631.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9271175861358643, "t": 31.98409080505371}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135154.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.6690331697463989, "t": 69.20599937438965}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134804.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9032878279685974, "t": 32.119035720825195}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135434.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8758015632629395, "t": 31.773805618286133}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134838.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8361387252807617, "t": 32.00387954711914}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135454.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.912212610244751, "t": 33.45918655395508}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135418.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9579256772994995, "t": 32.04512596130371}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135231.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.6205525398254395, "t": 32.920122146606445}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135457.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9774464964866638, "t": 30.990123748779297}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134816.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9186938405036926, "t": 32.33003616333008}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134852.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.772332489490509, "t": 31.560182571411133}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134725.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9728982448577881, "t": 31.58116340637207}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134942.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9018280506134033, "t": 32.86600112915039}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134900.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.7183358669281006, "t": 44.26884651184082}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135128.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9299030303955078, "t": 32.00078010559082}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134628.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9449462294578552, "t": 32.29117393493652}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135428.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.8932759761810303, "t": 31.75210952758789}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134857.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.7456804513931274, "t": 32.35888481140137}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134615.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.9407469630241394, "t": 32.215118408203125}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135612.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9167344570159912, "t": 32.06300735473633}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135621.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.7916002869606018, "t": 31.83722496032715}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135715.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8642048239707947, "t": 31.908273696899414}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135928.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8457755446434021, "t": 31.998872756958008}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135936.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8285729289054871, "t": 31.58283233642578}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135701.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8164640665054321, "t": 32.40799903869629}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135806.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.6163040399551392, "t": 32.09233283996582}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135624.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8335019946098328, "t": 31.185150146484375}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135924.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9296335577964783, "t": 32.51910209655762}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_140028.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9047104120254517, "t": 45.70794105529785}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135720.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8858898282051086, "t": 32.12904930114746}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135626.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.939687192440033, "t": 32.121896743774414}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_140021.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9220149517059326, "t": 31.93378448486328}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135912.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8301647305488586, "t": 32.41896629333496}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135916.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.88393235206604, "t": 30.618906021118164}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135822.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8962686657905579, "t": 32.67502784729004}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135607.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9163339734077454, "t": 35.635948181152344}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135817.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9180397987365723, "t": 32.009124755859375}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135654.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.8829472661018372, "t": 32.95183181762695}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_140012.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.9257593154907227, "t": 32.006025314331055}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140116.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.8535552620887756, "t": 32.174110412597656}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140110.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5654951930046082, "t": 31.984806060791016}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140613.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.5257236957550049, "t": 33.92910957336426}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140550.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.7870315909385681, "t": 33.20121765136719}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140603.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5399034023284912, "t": 32.83405303955078}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140607.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.5150652527809143, "t": 33.01501274108887}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140106.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.6935380101203918, "t": 33.16092491149902}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140059.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.617440938949585, "t": 31.77022933959961}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140602.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5430906414985657, "t": 33.00285339355469}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140548.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.6595554947853088, "t": 31.903982162475586}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140520.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.760198175907135, "t": 31.508922576904297}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140532.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.7602968215942383, "t": 31.595945358276367}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140618.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6075969934463501, "t": 31.91399574279785}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140523.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.8786618709564209, "t": 32.57584571838379}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140620.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.6707810759544373, "t": 32.956838607788086}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140615.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.5700482726097107, "t": 31.785964965820312}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140113.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.8255047798156738, "t": 32.58395195007324}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140514.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.8574185371398926, "t": 37.628889083862305}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140535.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.7836863994598389, "t": 32.33528137207031}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140617.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.8193090558052063, "t": 33.32805633544922}], "efficientad": [{"i": "datasets/cookies_3/no_anomaly/20240417_134414.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.02822374738752842, "t": 287.0762348175049, "r": "datasets/cookies_3/no_anomaly/20240417_134414_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_134257.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.01048250962048769, "t": 291.66197776794434, "r": "datasets/cookies_3/no_anomaly/20240417_134257_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_134101.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.025547316297888756, "t": 321.98500633239746, "r": "datasets/cookies_3/no_anomaly/20240417_134101_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_134452.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.013773787766695023, "t": 290.60888290405273, "r": "datasets/cookies_3/no_anomaly/20240417_134452_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_134334.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.008546669036149979, "t": 293.3049201965332, "r": "datasets/cookies_3/no_anomaly/20240417_134334_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_133857.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.008125646971166134, "t": 294.02875900268555, "r": "datasets/cookies_3/no_anomaly/20240417_133857_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_134051.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.006216969806700945, "t": 296.41270637512207, "r": "datasets/cookies_3/no_anomaly/20240417_134051_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_134238.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.005281155928969383, "t": 295.604944229126, "r": "datasets/cookies_3/no_anomaly/20240417_134238_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_134222.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.008646819740533829, "t": 285.10522842407227, "r": "datasets/cookies_3/no_anomaly/20240417_134222_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_134027.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.009536173194646835, "t": 283.7967872619629, "r": "datasets/cookies_3/no_anomaly/20240417_134027_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_134426.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.010746106505393982, "t": 292.7060127258301, "r": "datasets/cookies_3/no_anomaly/20240417_134426_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_134442.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.011090182699263096, "t": 359.98010635375977, "r": "datasets/cookies_3/no_anomaly/20240417_134442_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_133928.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.006526576820760965, "t": 318.59517097473145, "r": "datasets/cookies_3/no_anomaly/20240417_133928_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_133926.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.011808662675321102, "t": 435.8508586883545, "r": "datasets/cookies_3/no_anomaly/20240417_133926_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_133948.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.0066328635439276695, "t": 309.5269203186035, "r": "datasets/cookies_3/no_anomaly/20240417_133948_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_134227.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.02517668716609478, "t": 400.7391929626465, "r": "datasets/cookies_3/no_anomaly/20240417_134227_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_134316.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.00480315275490284, "t": 344.7680473327637, "r": "datasets/cookies_3/no_anomaly/20240417_134316_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_134128.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.01967935636639595, "t": 321.75397872924805, "r": "datasets/cookies_3/no_anomaly/20240417_134128_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_134034.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.00855488982051611, "t": 307.21473693847656, "r": "datasets/cookies_3/no_anomaly/20240417_134034_result.jpg"}, {"i": "datasets/cookies_3/no_anomaly/20240417_134536.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 0.003972603939473629, "t": 306.81324005126953, "r": "datasets/cookies_3/no_anomaly/20240417_134536_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135249.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1481965035200119, "t": 292.9091453552246, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135249_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134757.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.2004047930240631, "t": 327.1663188934326, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134757_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135245.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1061793863773346, "t": 295.60327529907227, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135245_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135316.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.12448742985725403, "t": 347.3062515258789, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135316_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135313.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1175486147403717, "t": 305.0670623779297, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135313_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135504.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.12383916229009628, "t": 299.7250556945801, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135504_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135324.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1410604566335678, "t": 302.288293838501, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135324_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135451.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.08527615666389465, "t": 303.3268451690674, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135451_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134612.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11345718055963516, "t": 310.40000915527344, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134612_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135158.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.14724330604076385, "t": 301.6357421875, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135158_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134753.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.16173595190048218, "t": 303.4639358520508, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134753_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135205.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11500789225101471, "t": 299.95107650756836, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135205_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134714.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.10337681323289871, "t": 304.394006729126, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134714_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135424.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.10803379118442535, "t": 288.82479667663574, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135424_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135237.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11454616487026215, "t": 294.8489189147949, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135237_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134810.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1215171068906784, "t": 328.2601833343506, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134810_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135327.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.12419632822275162, "t": 283.6291790008545, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135327_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134847.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.117610864341259, "t": 286.76295280456543, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134847_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135431.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.12774479389190674, "t": 290.5690670013428, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135431_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135500.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.14725543558597565, "t": 306.92291259765625, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135500_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135208.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.12004438042640686, "t": 319.0569877624512, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135208_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134631.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.09850431233644485, "t": 300.78887939453125, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134631_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135154.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.08873289823532104, "t": 300.0068664550781, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135154_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134804.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.15507708489894867, "t": 289.5939350128174, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134804_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135434.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.19288228452205658, "t": 287.5399589538574, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135434_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134838.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.092588409781456, "t": 292.1609878540039, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134838_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135454.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.12363473325967789, "t": 282.9408645629883, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135454_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135418.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.14756552875041962, "t": 282.2279930114746, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135418_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135231.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.14734023809432983, "t": 273.0588912963867, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135231_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135457.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.14568442106246948, "t": 288.8798713684082, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135457_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134816.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.10574228316545486, "t": 333.58097076416016, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134816_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134852.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.15151198208332062, "t": 294.36802864074707, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134852_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134725.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1545133739709854, "t": 284.404993057251, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134725_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134942.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.0913783460855484, "t": 305.4189682006836, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134942_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134900.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.1881323754787445, "t": 297.47891426086426, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134900_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135128.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.10654903948307037, "t": 284.9709987640381, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135128_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134628.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.11513505131006241, "t": 288.2869243621826, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134628_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135428.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.2376423478126526, "t": 289.5691394805908, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_135428_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134857.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.22105886042118073, "t": 292.1710014343262, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134857_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134615.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 0.15790554881095886, "t": 291.0492420196533, "r": "datasets/cookies_3/anomaly_lvl_1/20240417_134615_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135612.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.0874999389052391, "t": 296.71716690063477, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135612_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135621.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.05283408239483833, "t": 287.96982765197754, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135621_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135715.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.043982237577438354, "t": 329.6811580657959, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135715_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135928.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.13989494740962982, "t": 285.2919101715088, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135928_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135936.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.06800147891044617, "t": 284.23213958740234, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135936_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135701.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.06003059446811676, "t": 293.5149669647217, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135701_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135806.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.12805593013763428, "t": 319.8270797729492, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135806_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135624.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.05008538067340851, "t": 303.94721031188965, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135624_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135924.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.08440708369016647, "t": 304.68201637268066, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135924_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_140028.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.07744865119457245, "t": 316.44701957702637, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_140028_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135720.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.07195519655942917, "t": 288.36822509765625, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135720_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135626.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.06365485489368439, "t": 292.53602027893066, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135626_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_140021.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.07365089654922485, "t": 300.80318450927734, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_140021_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135912.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.05548826605081558, "t": 522.7570533752441, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135912_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135916.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.04864198714494705, "t": 303.36904525756836, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135916_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135822.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.09129971265792847, "t": 294.91519927978516, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135822_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135607.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.09724744409322739, "t": 299.2360591888428, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135607_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135817.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.11477351188659668, "t": 288.6488437652588, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135817_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135654.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.06662946939468384, "t": 285.2911949157715, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_135654_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_140012.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 0.08391112834215164, "t": 341.13001823425293, "r": "datasets/cookies_3/anomaly_lvl_2/20240417_140012_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140116.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.05824127420783043, "t": 293.1327819824219, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140116_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140110.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.04325661435723305, "t": 308.58421325683594, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140110_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140613.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.08002676069736481, "t": 295.30906677246094, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140613_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140550.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 0.02775067836046219, "t": 303.7850856781006, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140550_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140603.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.10068640112876892, "t": 297.7101802825928, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140603_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140607.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.07463119179010391, "t": 297.821044921875, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140607_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140106.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.034061554819345474, "t": 317.4328804016113, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140106_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140059.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.042621780186891556, "t": 308.3970546722412, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140059_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140602.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.08582320809364319, "t": 341.5670394897461, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140602_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140548.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.05025699734687805, "t": 288.12098503112793, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140548_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140520.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.0669245645403862, "t": 295.4690456390381, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140520_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140532.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.04399169981479645, "t": 314.39208984375, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140532_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140618.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.1341952085494995, "t": 307.92784690856934, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140618_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140523.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.06845739483833313, "t": 299.12304878234863, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140523_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140620.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.11664098501205444, "t": 304.1069507598877, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140620_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140615.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.0759437307715416, "t": 306.66208267211914, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140615_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140113.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.06178450584411621, "t": 295.27783393859863, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140113_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140514.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.062293510884046555, "t": 292.4528121948242, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140514_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140535.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.033368319272994995, "t": 301.1338710784912, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140535_result.jpg"}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140617.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 0.12689520418643951, "t": 295.1040267944336, "r": "datasets/cookies_3/anomaly_lvl_3/20240417_140617_result.jpg"}], "fomoad": [{"i": "datasets/cookies_3/no_anomaly/20240417_134414.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.977825403213501, "t": 116.16992950439453}, {"i": "datasets/cookies_3/no_anomaly/20240417_134257.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.709480047225952, "t": 35.536766052246094}, {"i": "datasets/cookies_3/no_anomaly/20240417_134101.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 4.0173492431640625, "t": 37.6431941986084}, {"i": "datasets/cookies_3/no_anomaly/20240417_134452.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 4.191556453704834, "t": 35.781145095825195}, {"i": "datasets/cookies_3/no_anomaly/20240417_134334.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.373816967010498, "t": 35.3090763092041}, {"i": "datasets/cookies_3/no_anomaly/20240417_133857.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.9474501609802246, "t": 34.17205810546875}, {"i": "datasets/cookies_3/no_anomaly/20240417_134051.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.123044490814209, "t": 35.2320671081543}, {"i": "datasets/cookies_3/no_anomaly/20240417_134238.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.9453725814819336, "t": 35.84408760070801}, {"i": "datasets/cookies_3/no_anomaly/20240417_134222.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.837677001953125, "t": 35.00986099243164}, {"i": "datasets/cookies_3/no_anomaly/20240417_134027.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.384082317352295, "t": 34.90495681762695}, {"i": "datasets/cookies_3/no_anomaly/20240417_134426.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.4512078762054443, "t": 34.603118896484375}, {"i": "datasets/cookies_3/no_anomaly/20240417_134442.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.243478536605835, "t": 52.64687538146973}, {"i": "datasets/cookies_3/no_anomaly/20240417_133928.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 4.118988990783691, "t": 63.759803771972656}, {"i": "datasets/cookies_3/no_anomaly/20240417_133926.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.11915922164917, "t": 55.60898780822754}, {"i": "datasets/cookies_3/no_anomaly/20240417_133948.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.163142442703247, "t": 34.73305702209473}, {"i": "datasets/cookies_3/no_anomaly/20240417_134227.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.6503489017486572, "t": 34.98983383178711}, {"i": "datasets/cookies_3/no_anomaly/20240417_134316.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 2.7279534339904785, "t": 38.30289840698242}, {"i": "datasets/cookies_3/no_anomaly/20240417_134128.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.5381906032562256, "t": 38.542985916137695}, {"i": "datasets/cookies_3/no_anomaly/20240417_134034.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.3861865997314453, "t": 35.832881927490234}, {"i": "datasets/cookies_3/no_anomaly/20240417_134536.jpg", "c": "no_anomaly", "d": "na", "cl": "no_anomaly", "s": 3.294576406478882, "t": 35.063743591308594}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135249.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 14.495829582214355, "t": 35.56227684020996}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134757.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 9.09726619720459, "t": 35.08400917053223}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135245.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 9.39006233215332, "t": 34.55209732055664}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135316.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 15.92177677154541, "t": 48.52914810180664}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135313.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 17.540021896362305, "t": 36.31401062011719}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135504.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 15.761528015136719, "t": 34.44314002990723}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135324.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 11.294169425964355, "t": 35.909175872802734}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135451.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 20.559627532958984, "t": 35.0189208984375}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134612.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 13.045555114746094, "t": 41.72801971435547}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135158.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 15.70052433013916, "t": 35.71295738220215}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134753.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 18.043285369873047, "t": 37.014007568359375}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135205.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 16.15715217590332, "t": 36.18288040161133}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134714.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 12.720117568969727, "t": 37.4758243560791}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135424.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 11.942025184631348, "t": 35.279035568237305}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135237.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 11.125260353088379, "t": 38.86294364929199}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134810.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 15.764870643615723, "t": 49.578189849853516}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135327.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 17.416915893554688, "t": 36.16905212402344}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134847.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 6.745754241943359, "t": 34.867048263549805}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135431.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 14.25806713104248, "t": 35.41898727416992}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135500.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 15.157452583312988, "t": 35.449981689453125}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135208.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 15.021328926086426, "t": 35.15887260437012}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134631.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 9.543059349060059, "t": 35.75611114501953}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135154.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 19.391660690307617, "t": 35.833120346069336}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134804.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 7.369954586029053, "t": 35.82000732421875}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135434.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 10.368729591369629, "t": 35.26878356933594}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134838.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 10.028023719787598, "t": 41.04185104370117}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135454.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 17.286802291870117, "t": 35.77899932861328}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135418.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 11.129603385925293, "t": 34.0878963470459}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135231.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 8.089816093444824, "t": 35.12096405029297}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135457.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 11.79050350189209, "t": 34.87682342529297}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134816.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 12.436928749084473, "t": 34.85417366027832}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134852.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 11.690964698791504, "t": 35.066843032836914}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134725.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 16.749799728393555, "t": 51.61690711975098}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134942.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 12.749030113220215, "t": 34.79194641113281}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134900.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 10.073373794555664, "t": 36.00478172302246}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135128.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 13.448103904724121, "t": 35.47334671020508}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134628.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 15.89712905883789, "t": 36.00883483886719}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_135428.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 11.712638854980469, "t": 35.35103797912598}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134857.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 8.891317367553711, "t": 35.65692901611328}, {"i": "datasets/cookies_3/anomaly_lvl_1/20240417_134615.jpg", "c": "anomaly", "d": "easy", "cl": "anomaly", "s": 11.528029441833496, "t": 35.65526008605957}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135612.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 18.801511764526367, "t": 35.26878356933594}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135621.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 14.006473541259766, "t": 35.27498245239258}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135715.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 13.48764705657959, "t": 35.074710845947266}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135928.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 15.896834373474121, "t": 73.86279106140137}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135936.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 12.868910789489746, "t": 36.29708290100098}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135701.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 15.632122039794922, "t": 35.67790985107422}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135806.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 14.378853797912598, "t": 35.948753356933594}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135624.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 19.78631019592285, "t": 35.94970703125}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135924.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 14.106823921203613, "t": 35.95590591430664}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_140028.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 10.759296417236328, "t": 35.94517707824707}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135720.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 9.887994766235352, "t": 36.45801544189453}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135626.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 12.15313720703125, "t": 35.45880317687988}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_140021.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 11.283671379089355, "t": 35.3999137878418}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135912.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 15.01714038848877, "t": 33.447980880737305}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135916.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 12.092806816101074, "t": 34.219980239868164}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135822.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 10.476201057434082, "t": 34.696102142333984}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135607.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 20.81032371520996, "t": 35.199880599975586}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135817.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 14.931626319885254, "t": 34.98482704162598}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_135654.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 10.553946495056152, "t": 34.590959548950195}, {"i": "datasets/cookies_3/anomaly_lvl_2/20240417_140012.jpg", "c": "anomaly", "d": "medium", "cl": "anomaly", "s": 10.601659774780273, "t": 35.27212142944336}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140116.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 6.197762966156006, "t": 36.04722023010254}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140110.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 8.735136032104492, "t": 34.26027297973633}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140613.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 5.810284614562988, "t": 35.114288330078125}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140550.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 5.345970153808594, "t": 35.27498245239258}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140603.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 6.761882781982422, "t": 35.01296043395996}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140607.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 6.203559875488281, "t": 35.04204750061035}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140106.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 5.294248580932617, "t": 74.20206069946289}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140059.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 5.414760112762451, "t": 34.903764724731445}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140602.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 6.370449066162109, "t": 42.32192039489746}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140548.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 9.302361488342285, "t": 36.09108924865723}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140520.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 13.224106788635254, "t": 35.08424758911133}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140532.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 7.868703365325928, "t": 34.097909927368164}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140618.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 5.771534442901611, "t": 35.34102439880371}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140523.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 13.1094970703125, "t": 34.87205505371094}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140620.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 5.112953186035156, "t": 38.93923759460449}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140615.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 5.378742218017578, "t": 36.483049392700195}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140113.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 8.081695556640625, "t": 38.079023361206055}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140514.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 6.917776584625244, "t": 35.465240478515625}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140535.jpg", "c": "anomaly", "d": "hard", "cl": "anomaly", "s": 12.952106475830078, "t": 35.921335220336914}, {"i": "datasets/cookies_3/anomaly_lvl_3/20240417_140617.jpg", "c": "anomaly", "d": "hard", "cl": "no_anomaly", "s": 4.260826110839844, "t": 36.05008125305176}]}}
\ No newline at end of file