diff --git a/README.md b/README.md
index fd00bcaa6bd..c77320656fd 100644
--- a/README.md
+++ b/README.md
@@ -39,13 +39,15 @@ ______________________________________________________________________
 </div>
 
 # Looking for GPUs?
-Over 340,000 developers use [Lightning Cloud](https://lightning.ai/?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme) - purpose-built for PyTorch and PyTorch Lightning. 
-- [GPUs](https://lightning.ai/pricing?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme) from $0.19.   
-- [Clusters](https://lightning.ai/clusters?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): frontier-grade training/inference clusters.   
+
+Over 340,000 developers use [Lightning Cloud](https://lightning.ai/?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme) - purpose-built for PyTorch and PyTorch Lightning.
+
+- [GPUs](https://lightning.ai/pricing?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme) from $0.19.
+- [Clusters](https://lightning.ai/clusters?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): frontier-grade training/inference clusters.
 - [AI Studio (vibe train)](https://lightning.ai/studios?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): workspaces where AI helps you debug, tune and vibe train.
-- [AI Studio (vibe deploy)](https://lightning.ai/studios?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): workspaces where AI helps you optimize, and deploy models.     
+- [AI Studio (vibe deploy)](https://lightning.ai/studios?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): workspaces where AI helps you optimize, and deploy models.
 - [Notebooks](https://lightning.ai/notebooks?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): Persistent GPU workspaces where AI helps you code and analyze.
-- [Inference](https://lightning.ai/deploy?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): Deploy models as inference APIs.   
+- [Inference](https://lightning.ai/deploy?utm_source=tm_readme&utm_medium=referral&utm_campaign=tm_readme): Deploy models as inference APIs.
 
 # Installation
 
diff --git a/src/torchmetrics/utilities/distributed.py b/src/torchmetrics/utilities/distributed.py
index a68cffec3f1..0f3f2d62412 100644
--- a/src/torchmetrics/utilities/distributed.py
+++ b/src/torchmetrics/utilities/distributed.py
@@ -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