code: def scatter_cache_ids(
      cache_ids_list: Optional[List[torch.Tensor]],
      concat_numel_list: List[int],
      group,
  ):
      world_size = dist.get_world_size()
      rank = dist.get_rank()
  
      max_numel = max(concat_numel_list)
  
      concat_cache_ids = torch.empty(max_numel, dtype=torch.int64)
      if rank == 0:
          concat_cache_ids_list = [concat_cache_ids] + [
              cache_ids.resize_(max_numel)
              for cache_ids in cache_ids_list[-world_size + 1 :]
          ]
          assert len(concat_cache_ids_list) == world_size
          dist.scatter(
              concat_cache_ids, concat_cache_ids_list, group=group
          )
      else:
          dist.scatter(concat_cache_ids, None, group=group)
          offset = 0
          for cache_ids in cache_ids_list:
              cache_ids[:] = concat_cache_ids[
                  offset : offset + cache_ids.numel()
              ]
              offset += cache_ids.numel()
  
imports: from typing import List, Optional
  import torch
  import torch.distributed
label_desc: Use image node_id 2 for calling a global function with example usage: scatter_cache_ids(cache_ids_list, concat_numel_list, group) without return types