diff --git a/freqtrade/freqai/prediction_models/PyTorchClassifierMultiTarget.py b/freqtrade/freqai/prediction_models/PyTorchClassifierMultiTarget.py
index a98643b3f..a5b8b1591 100644
--- a/freqtrade/freqai/prediction_models/PyTorchClassifierMultiTarget.py
+++ b/freqtrade/freqai/prediction_models/PyTorchClassifierMultiTarget.py
@@ -32,6 +32,7 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
         dict: A dictionary mapping class names to their corresponding indices.
         dict: A dictionary mapping indices to their corresponding class names.
         """
+
         super().__init__(**kwargs)
         model_training_parameters = self.freqai_info["model_training_parameters"]
         self.n_hidden = model_training_parameters.get("n_hidden", 1024)
@@ -50,6 +51,7 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
         :raises ValueError: If self.class_names is not defined in the parent class.
 
         """
+
         if not hasattr(self, "class_names"):
             raise ValueError(
                 "Missing attribute: self.class_names "
@@ -93,7 +95,9 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
         :pred_df: dataframe containing the predictions
         :do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
         data (NaNs) or felt uncertain about data (PCA and DI index)
+        :raises ValueError: if 'class_name' doesn't exist in model meta_data.
         """
+
         class_names = self.model.model_meta_data.get("class_names", None)
         if not class_names:
             raise ValueError(
@@ -128,6 +132,7 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
         encode class name str -> int
         assuming first column of *_labels data frame to contain class names
         """
+
         target_column_name = dk.label_list[0]
         for split in ["train", "test"]:
             label_df = data_dictionary[f"{split}_labels"]
@@ -148,6 +153,7 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
         """
         decode class name int -> str
         """
+
         return list(map(lambda x: self.index_to_class_name[x.item()], classes))
 
     def init_class_names_to_index_mapping(self, class_names):