fix: add tuple cast to avoid nd slicing deprecation warning (#3319)

Signed-off-by: Kyle Mylonakis <kyle@protopia.ai>
Co-authored-by: @VijayVignesh1 <vijayvigneshp02@gmail.com>

Author: KyleMylonakisProtopia

src/torchmetrics/utilities/distributed.py

@@ -19,7 +19,7 @@
 from typing_extensions import Literal
 
 
-def reduce(x: Tensor, reduction: Literal["elementwise_mean", "sum", "none", None]) -> Tensor:
+def reduce(x: Tensor, reduction: Optional[Literal["elementwise_mean", "sum", "none"]]) -> Tensor:
     """Reduces a given tensor by a given reduction method.
 
     Args:
@@ -46,7 +46,7 @@ def class_reduce(
     num: Tensor,
     denom: Tensor,
     weights: Tensor,
-    class_reduction: Literal["micro", "macro", "weighted", "none", None] = "none",
+    class_reduction: Optional[Literal["micro", "macro", "weighted", "none"]] = "none",
 ) -> Tensor:
     """Reduce classification metrics of the form ``num / denom * weights``.
 
@@ -147,7 +147,7 @@ def gather_all_tensors(result: Tensor, group: Optional[Any] = None) -> List[Tens
         torch.distributed.all_gather(gathered_result, result_padded, group)
         for idx, item_size in enumerate(local_sizes):
             slice_param = [slice(dim_size) for dim_size in item_size]
-            gathered_result[idx] = gathered_result[idx][slice_param]
+            gathered_result[idx] = gathered_result[idx][tuple(slice_param)]
     # to propagate autograd graph from local rank
     gathered_result[torch.distributed.get_rank(group)] = result
     return gathered_result