code: def broadcast_transform_result(
      success: bool, ids_to_fetch: Optional[torch.Tensor], group
  ):
      if dist.get_rank() == 0:
          success_and_numel = torch.tensor(
              [1 if success else 0, ids_to_fetch.numel()],
              dtype=torch.int64,
          )
          dist.broadcast(success_and_numel, src=0, group=group)
      else:
          success_and_numel = torch.tensor([0, 0], dtype=torch.int64)
          dist.broadcast(success_and_numel, src=0, group=group)
          success, numel = success_and_numel.tolist()
          success = success != 0
          ids_to_fetch = torch.empty((numel // 2, 2), dtype=torch.int64)
  
      if ids_to_fetch.numel() > 0:
          dist.broadcast(ids_to_fetch, src=0, group=group)
      return success, ids_to_fetch
  
imports: from typing import List, Optional
  import torch
  import torch.distributed
label_desc: Use image node_id 3 for calling a global function with example usage: broadcast_transform_result(success, ids_to_fetch, group) and returns: success,  ids_to_fetch