fix ruff lint in main branch (#3067)

Summary:

#3057 was landed with failing lint,
this PR fixes it.

Test Plan:

```
ruff format
```

Reviewers:

Subscribers:

Tasks:

Tags:

Author: vkuzo

torchao/dtypes/floatx/cutlass_semi_sparse_layout.py

@@ -191,46 +191,57 @@ def _linear_fp8_act_fp8_weight_sparse_cutlass_check(input_tensor, weight_tensor,
     from torchao.dtypes.floatx import Float8Layout
 
     base_check = (
-        isinstance(input_tensor, AffineQuantizedTensor) and 
-        isinstance(input_tensor._layout, Float8Layout) and
-        input_tensor.dtype in (torch.float16, torch.bfloat16) and
-        len(input_tensor.shape) >= 2 and
-        input_tensor.tensor_impl.scale.dtype == torch.float32 and
-        isinstance(weight_tensor, AffineQuantizedTensor) and
-        isinstance(weight_tensor._layout, CutlassSemiSparseLayout) and
-        weight_tensor.dtype == input_tensor.dtype and
-        len(weight_tensor.shape) == 2 and
-        weight_tensor.tensor_impl.scale.dtype == torch.float32 and
-        (bias is None or bias.dtype == input_tensor.dtype) and
-        (bias is None or len(bias.shape) == 1)
+        isinstance(input_tensor, AffineQuantizedTensor)
+        and isinstance(input_tensor._layout, Float8Layout)
+        and input_tensor.dtype in (torch.float16, torch.bfloat16)
+        and len(input_tensor.shape) >= 2
+        and input_tensor.tensor_impl.scale.dtype == torch.float32
+        and isinstance(weight_tensor, AffineQuantizedTensor)
+        and isinstance(weight_tensor._layout, CutlassSemiSparseLayout)
+        and weight_tensor.dtype == input_tensor.dtype
+        and len(weight_tensor.shape) == 2
+        and weight_tensor.tensor_impl.scale.dtype == torch.float32
+        and (bias is None or bias.dtype == input_tensor.dtype)
+        and (bias is None or len(bias.shape) == 1)
     )
 
     if base_check:
-        
         # do extra check and reshape if needed
         input_tensor_squeezed = False
-        if len(input_tensor.tensor_impl.scale.shape) == len(input_tensor.shape) and \
-            len(input_tensor.tensor_impl.scale.shape) > 1 and \
-            input_tensor.tensor_impl.scale.shape[-1] == 1:
-            input_tensor.tensor_impl.scale = torch.squeeze(input_tensor.tensor_impl.scale, dim=-1)
+        if (
+            len(input_tensor.tensor_impl.scale.shape) == len(input_tensor.shape)
+            and len(input_tensor.tensor_impl.scale.shape) > 1
+            and input_tensor.tensor_impl.scale.shape[-1] == 1
+        ):
+            input_tensor.tensor_impl.scale = torch.squeeze(
+                input_tensor.tensor_impl.scale, dim=-1
+            )
             input_tensor_squeezed = True
-        
+
         weight_tensor_squeezed = False
-        if len(weight_tensor.tensor_impl.scale.shape) == 2 and \
-            weight_tensor.tensor_impl.scale.shape[-1] == 1:
-            weight_tensor.tensor_impl.scale = torch.squeeze(weight_tensor.tensor_impl.scale, dim=-1)
+        if (
+            len(weight_tensor.tensor_impl.scale.shape) == 2
+            and weight_tensor.tensor_impl.scale.shape[-1] == 1
+        ):
+            weight_tensor.tensor_impl.scale = torch.squeeze(
+                weight_tensor.tensor_impl.scale, dim=-1
+            )
             weight_tensor_squeezed = True
 
         extra_check = (
             len(input_tensor.tensor_impl.scale.shape) == len(input_tensor.shape) - 1
             and len(weight_tensor.tensor_impl.scale.shape) == 1
         )
 
-        if not extra_check: # revert if extra check failed
+        if not extra_check:  # revert if extra check failed
             if input_tensor_squeezed:
-                input_tensor.tensor_impl.scale = torch.unsqueeze(input_tensor.tensor_impl.scale, dim=-1)
+                input_tensor.tensor_impl.scale = torch.unsqueeze(
+                    input_tensor.tensor_impl.scale, dim=-1
+                )
             if weight_tensor_squeezed:
-                weight_tensor.tensor_impl.scale = torch.unsqueeze(weight_tensor.tensor_impl.scale, dim=-1)
+                weight_tensor.tensor_impl.scale = torch.unsqueeze(
+                    weight_tensor.tensor_impl.scale, dim=-1
+                )
 
         return extra_check