diff --git a/freqtrade/freqai/base_models/BasePyTorchModel.py b/freqtrade/freqai/base_models/BasePyTorchModel.py
index 8e608ee1a..d6372fa36 100644
--- a/freqtrade/freqai/base_models/BasePyTorchModel.py
+++ b/freqtrade/freqai/base_models/BasePyTorchModel.py
@@ -19,9 +19,9 @@ class BasePyTorchModel(IFreqaiModel):
     """
 
     def __init__(self, **kwargs):
-        super().__init__(config=kwargs['config'])
-        self.dd.model_type = 'pytorch'
-        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        super().__init__(config=kwargs["config"])
+        self.dd.model_type = "pytorch"
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
 
     def train(
         self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
diff --git a/freqtrade/freqai/base_models/PyTorchModelTrainer.py b/freqtrade/freqai/base_models/PyTorchModelTrainer.py
index a934814ef..0ca28d2e9 100644
--- a/freqtrade/freqai/base_models/PyTorchModelTrainer.py
+++ b/freqtrade/freqai/base_models/PyTorchModelTrainer.py
@@ -61,7 +61,7 @@ class PyTorchModelTrainer:
         """
         data_loaders_dictionary = self.create_data_loaders_dictionary(data_dictionary)
         epochs = self.calc_n_epochs(
-            n_obs=len(data_dictionary['train_features']),
+            n_obs=len(data_dictionary["train_features"]),
             batch_size=self.batch_size,
             n_iters=self.max_iters
         )
@@ -73,7 +73,7 @@ class PyTorchModelTrainer:
                 f" train loss {losses['train']:.4f} ; test loss {losses['test']:.4f}"
             )
             # training
-            for batch_data in data_loaders_dictionary['train']:
+            for batch_data in data_loaders_dictionary["train"]:
                 xb, yb = batch_data
                 xb = xb.to(self.device)
                 yb = yb.to(self.device)
@@ -93,12 +93,12 @@ class PyTorchModelTrainer:
 
         self.model.eval()
         epochs = self.calc_n_epochs(
-            n_obs=len(data_dictionary['test_features']),
+            n_obs=len(data_dictionary["test_features"]),
             batch_size=self.batch_size,
             n_iters=self.eval_iters
         )
         loss_dictionary = {}
-        for split in ['train', 'test']:
+        for split in ["train", "test"]:
             losses = torch.zeros(epochs)
             for i, batch in enumerate(data_loader_dictionary[split]):
                 xb, yb = batch
@@ -121,12 +121,12 @@ class PyTorchModelTrainer:
         Converts the input data to PyTorch tensors using a data loader.
         """
         data_loader_dictionary = {}
-        for split in ['train', 'test']:
-            labels_shape = data_dictionary[f'{split}_labels'].shape
+        for split in ["train", "test"]:
+            labels_shape = data_dictionary[f"{split}_labels"].shape
             labels_view = labels_shape[0] if labels_shape[1] == 1 else labels_shape
             dataset = TensorDataset(
-                torch.from_numpy(data_dictionary[f'{split}_features'].values).float(),
-                torch.from_numpy(data_dictionary[f'{split}_labels'].astype(float).values)
+                torch.from_numpy(data_dictionary[f"{split}_features"].values).float(),
+                torch.from_numpy(data_dictionary[f"{split}_labels"].astype(float).values)
                 .long()
                 .view(labels_view)
             )
@@ -148,6 +148,7 @@ class PyTorchModelTrainer:
         Calculates the number of epochs required to reach the maximum number
         of iterations specified in the model training parameters.
         """
+
         n_batches = n_obs // batch_size
         epochs = n_iters // n_batches
         return epochs
@@ -160,9 +161,9 @@ class PyTorchModelTrainer:
         """
 
         torch.save({
-            'model_state_dict': self.model.state_dict(),
-            'optimizer_state_dict': self.optimizer.state_dict(),
-            'model_meta_data': self.model_meta_data,
+            "model_state_dict": self.model.state_dict(),
+            "optimizer_state_dict": self.optimizer.state_dict(),
+            "model_meta_data": self.model_meta_data,
         }, path)
 
     def load_from_file(self, path: Path):
diff --git a/freqtrade/freqai/prediction_models/PyTorchClassifierMultiTarget.py b/freqtrade/freqai/prediction_models/PyTorchClassifierMultiTarget.py
index e8326ffe9..a98643b3f 100644
--- a/freqtrade/freqai/prediction_models/PyTorchClassifierMultiTarget.py
+++ b/freqtrade/freqai/prediction_models/PyTorchClassifierMultiTarget.py
@@ -59,7 +59,7 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
 
         self.init_class_names_to_index_mapping(self.class_names)
         self.encode_classes_name(data_dictionary, dk)
-        n_features = data_dictionary['train_features'].shape[-1]
+        n_features = data_dictionary["train_features"].shape[-1]
         model = PyTorchMLPModel(
             input_dim=n_features,
             hidden_dim=self.n_hidden,