run ./runtests.sh --autofix to check formatting

Author: phisanti

monai/networks/blocks/cablock.py

@@ -13,12 +13,12 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+
+from monai.networks.blocks.convolutions import Convolution
 from monai.utils import optional_import
 
 rearrange, _ = optional_import("einops", name="rearrange")
 
-from monai.networks.blocks.convolutions import Convolution
-
 __all__ = ["FeedForward", "CABlock"]
 
 

monai/networks/nets/restormer.py

@@ -61,7 +61,7 @@ class OverlapPatchEmbed(nn.Module):
     """Initial feature extraction using overlapped convolutions.
     Unlike standard patch embeddings that use non-overlapping patches,
     this approach maintains spatial continuity through 3x3 convolutions.
-    
+
     Args:
         spatial_dims: Number of spatial dimensions (2D or 3D)
         in_channels: Number of input channels
@@ -300,7 +300,7 @@ def forward(self, x) -> torch.Tensor:
             Restored image tensor of shape (B, C, H, W, [D])
         """
         assert all(
-            x.shape[-i] > 2 ** self.num_steps for i in range(1, self.spatial_dims + 1)
+            x.shape[-i] > 2**self.num_steps for i in range(1, self.spatial_dims + 1)
         ), "All spatial dimensions should be larger than 2^number_of_step"
 
         # Patch embedding

monai/utils/jupyter_utils.py

@@ -234,7 +234,7 @@ def plot_engine_status(
 
 
 def _get_loss_from_output(
-    output: list[torch.Tensor | dict[str, torch.Tensor]] | dict[str, torch.Tensor] | torch.Tensor,
+    output: list[torch.Tensor | dict[str, torch.Tensor]] | dict[str, torch.Tensor] | torch.Tensor
 ) -> torch.Tensor:
     """Returns a single value from the network output, which is a dict or tensor."""