Fix ruff style and typing

segmentation_models_pytorch/encoders/timm_universal.py

@@ -1,7 +1,7 @@
 """
 TimmUniversalEncoder provides a unified feature extraction interface built on the
 `timm` library, supporting various backbone architectures, including traditional
-CNNs (e.g., ResNet) and models adopting a transformer-like feature hierarchy 
+CNNs (e.g., ResNet) and models adopting a transformer-like feature hierarchy
 (e.g., Swin Transformer, ConvNeXt).
 
 This encoder produces standardized multi-level feature maps, facilitating integration
@@ -22,16 +22,16 @@
 - Traditional CNNs (e.g., ResNet) typically provide features at 1/2, 1/4, 1/8, 1/16,
   and 1/32 scales.
 - Transformer-style or next-generation models (e.g., Swin Transformer, ConvNeXt) often
-  start from the 1/4 scale (then 1/8, 1/16, 1/32), omitting the initial 1/2 scale 
+  start from the 1/4 scale (then 1/8, 1/16, 1/32), omitting the initial 1/2 scale
   feature. TimmUniversalEncoder compensates for this omission to ensure a unified
   multi-stage output.
 
 Notes:
-- Not all models support modifying `output_stride` (especially transformer-based or 
+- Not all models support modifying `output_stride` (especially transformer-based or
   transformer-like models).
 - Certain models (e.g., TResNet, DLA) require special handling to ensure correct
   feature indexing.
-- Most `timm` models output features in (B, C, H, W) format. However, some 
+- Most `timm` models output features in (B, C, H, W) format. However, some
   (e.g., MambaOut and certain Swin/SwinV2 variants) use (B, H, W, C) format, which is
   currently unsupported.
 """
@@ -46,7 +46,7 @@
 class TimmUniversalEncoder(nn.Module):
     """
     A universal encoder built on the `timm` library, designed to adapt to a wide variety of
-    model architectures, including both traditional CNNs and those that follow a 
+    model architectures, including both traditional CNNs and those that follow a
     transformer-like hierarchy.
 
     Features:
@@ -94,10 +94,8 @@ def __init__(
         # Determine if this model uses a transformer-like hierarchy (i.e., starting at 1/4 scale)
         # rather than a traditional CNN hierarchy (starting at 1/2 scale).
         if len(self.model.feature_info.channels()) == 5:
-            # This indicates a traditional hierarchy: (1/2, 1/4, 1/8, 1/16, 1/32)
             self._is_transformer_style = False
         else:
-            # This indicates a transformer-like hierarchy: (1/4, 1/8, 1/16, 1/32)
             self._is_transformer_style = True
 
         if self._is_transformer_style:
@@ -138,7 +136,7 @@ def forward(self, x: torch.Tensor) -> list[torch.Tensor]:
             x (torch.Tensor): Input tensor of shape (B, C, H, W).
 
         Returns:
-            List[torch.Tensor]: A list of feature maps extracted at various scales.
+            list[torch.Tensor]: A list of feature maps extracted at various scales.
         """
         features = self.model(x)
 
@@ -158,7 +156,7 @@ def forward(self, x: torch.Tensor) -> list[torch.Tensor]:
     def out_channels(self) -> list[int]:
         """
         Returns:
-            List[int]: A list of output channels for each stage of the encoder,
+            list[int]: A list of output channels for each stage of the encoder,
             including the input channels at the first stage.
         """
         return self._out_channels