[pre-commit.ci] auto fixes from pre-commit.com hooks

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kornia/models/segmentor/segmentation_models.py

@@ -21,19 +21,20 @@ class SegmentationModels(Module):
         classes: Number of classes to predict.
         **kwargs: Additional arguments to pass to the model. Detailed arguments can be found at:
             https://github.com/qubvel-org/segmentation_models.pytorch/tree/main/segmentation_models_pytorch/decoders
-    
+
     Note:
         Only encoder weights are available.
         Pretrained weights for the whole model are not available.
     """
+
     def __init__(
         self,
         model_name: str = "Unet",
         encoder_name: str = "resnet34",
         encoder_weights: Optional[str] = "imagenet",
         in_channels: int = 3,
         classes: int = 1,
-        **kwargs
+        **kwargs,
     ) -> None:
         super().__init__()
         self.preproc_params = smp.encoders.get_preprocessing_params(encoder_name)  # type: ignore
@@ -42,7 +43,7 @@ def __init__(
             encoder_weights=encoder_weights,
             in_channels=in_channels,
             classes=classes,
-            **kwargs
+            **kwargs,
         )
 
     def preprocessing(self, input: Tensor) -> Tensor:
@@ -52,7 +53,7 @@ def preprocessing(self, input: Tensor) -> Tensor:
             input = kornia.color.rgb_to_bgr(input)
         else:
             raise ValueError(f"Unsupported input space: {self.preproc_params['input_space']}")
-        
+
         if self.preproc_params["input_range"] is not None:
             if input.max() > 1 and self.preproc_params["input_range"][1] == 1:
                 input = input / 255.0
@@ -61,7 +62,7 @@ def preprocessing(self, input: Tensor) -> Tensor:
             mean = tensor(self.preproc_params["mean"]).to(input.device)
         else:
             mean = tensor(self.preproc_params["mean"]).to(input.device)
-        
+
         if self.preproc_params["std"] is None:
             std = tensor(self.preproc_params["std"]).to(input.device)
         else: