Mypy fixes

monai/metrics/regression.py

@@ -555,6 +555,11 @@ def compute_ms_ssim(
             f" spatial dimensions, got {dims}."
         )
 
+    if not isinstance(kernel_size, Sequence):
+        kernel_size = ensure_tuple_rep(kernel_size, spatial_dims)
+
+    if not isinstance(kernel_sigma, Sequence):
+        kernel_sigma = ensure_tuple_rep(kernel_sigma, spatial_dims)
     # check if image have enough size for the number of downsamplings and the size of the kernel
     weights_div = max(1, (len(weights) - 1)) ** 2
     y_pred_spatial_dims = y_pred.shape[2:]
@@ -566,12 +571,12 @@ def compute_ms_ssim(
                 f"{(kernel_size[i] - 1) * weights_div}."
             )
 
-    weights = torch.tensor(weights, device=y_pred.device, dtype=torch.float)
+    weights_tensor = torch.tensor(weights, device=y_pred.device, dtype=torch.float)
 
     avg_pool = getattr(F, f"avg_pool{spatial_dims}d")
 
     multiscale_list: list[torch.Tensor] = []
-    for _ in range(len(weights)):
+    for _ in range(len(weights_tensor)):
         ssim, cs = compute_ssim_and_cs(
             y_pred=y_pred,
             y=y,
@@ -592,9 +597,9 @@ def compute_ms_ssim(
 
     ssim = ssim.view(ssim.shape[0], -1).mean(1)
     multiscale_list[-1] = torch.relu(ssim)
-    multiscale_list = torch.stack(multiscale_list)
+    multiscale_list_tensor = torch.stack(multiscale_list)
 
-    ms_ssim_value_full_image = torch.prod(multiscale_list ** weights.view(-1, 1), dim=0)
+    ms_ssim_value_full_image = torch.prod(multiscale_list_tensor ** weights_tensor.view(-1, 1), dim=0)
 
     ms_ssim_per_batch: torch.Tensor = ms_ssim_value_full_image.view(ms_ssim_value_full_image.shape[0], -1).mean(
         1, keepdim=True