run linter

supervision/classification/core.py

@@ -22,7 +22,7 @@ def _validate_confidence(confidence: Any, n: int) -> None:
     """
     Ensure that confidence is a 1d np.ndarray with (n, ) shape.
     """
-    if confidence is not None:        
+    if confidence is not None:
         is_valid = isinstance(confidence, np.ndarray) and confidence.shape == (n,)
         if not is_valid:
             raise ValueError("confidence must be 1d np.ndarray with (n, ) shape")

supervision/dataset/core.py

@@ -397,9 +397,7 @@ class ClassificationDataset(BaseDataset):
         )
         return train_dataset, test_dataset
 
-    def as_multiclass_folder_structure(
-        self, output_directory_path: str
-    ) -> None:
+    def as_multiclass_folder_structure(self, output_directory_path: str) -> None:
         """
         Saves the dataset as a multi-class folder structure.