lint

src/mrpro/utils/reshape.py

@@ -119,7 +119,7 @@ def _reshape_idx(old_shape: tuple[int, ...], new_shape: tuple[int, ...], old_str
         If all axes that are grouped together in an "old" group are stride 0 (=broadcasted)
         we can collapse them to singleton dimensions.
     This function returns the indexer that either collapses dimensions to singleton or keeps all
-    elements, i.e. the slices in the returned list are all either slice(1) or slice(None).  
+    elements, i.e. the slices in the returned list are all either slice(1) or slice(None).
     """
     idx = []
     pointer_old, pointer_new = len(old_shape) - 1, len(new_shape) - 1  # start from the right
@@ -146,7 +146,7 @@ def _reshape_idx(old_shape: tuple[int, ...], new_shape: tuple[int, ...], old_str
             # preserve dimension
             idx.extend([slice(None)] * len(group))
     idx = idx[::-1] # we worked right to left, but our index should be left to right
-    return idx]
+    return idx
 
 
 def reshape_broadcasted(tensor: torch.Tensor, *shape: int) -> torch.Tensor:
@@ -172,8 +172,8 @@ def reshape_broadcasted(tensor: torch.Tensor, *shape: int) -> torch.Tensor:
             raise ValueError('Cannot reshape tensor to target shape, number of elements must match') from None
         # most of the broadcasted dimensions can be preserved: only dimensions that are joined with non
         # broadcasted dimensions can not be preserved and must be made contiguous.
-        # all dimensions that can be preserved as broadcasted are first collapsed to singleton, such that contiguous does not create 
-        # copies along these axes.
+        # all dimensions that can be preserved as broadcasted are first collapsed to singleton, 
+        # such that contiguous does not create copies along these axes.
         idx = _reshape_idx(tensor.shape, shape, tensor.stride())
         # make contiguous only in dimensions in which broadcasting cannot be preserved
         semicontiguous = tensor[idx].contiguous()