Fix issue reported by pre-commit.

Signed-off-by: Wil Kong <[email protected]>

Author: alpha0422

src/lightning/pytorch/core/module.py

@@ -660,14 +660,11 @@ def __to_tensor(self, value: Union[Tensor, numbers.Number], name: str) -> Tensor
             # Keep tensor on its original device to avoid unnecessary transfers
             value = value.clone().detach()
         else:
-            if self.device.type == "cuda":
-                # Place scalar metrics on CPU to avoid CPU-GPU transfer and synchronization.
-                # `torch.tensor(value, device="cuda")` contains such synchronization, while the metric
-                # itself is only used on the CPU side. So placing metric on CPU for scalar inputs is more efficient.
-                device = "cpu"
-            else:
-                # For non-CUDA devices, maintain original behavior
-                device = self.device
+            # Place scalar metrics on CPU to avoid CPU-GPU transfer and synchronization.
+            # `torch.tensor(value, device="cuda")` contains such synchronization, while the metric
+            # itself is only used on the CPU side. So placing metric on CPU for scalar inputs is more efficient.
+            # For non-CUDA devices, maintain original behavior
+            device = "cpu" if self.device.type == "cuda" else self.device
             value = torch.tensor(value, device=device, dtype=_get_default_dtype())
 
         if not torch.numel(value) == 1: