code: def _broadcast_bucket(
      bucket_index: int,
      zero: ZeroRedundancyOptimizer,
  ):
      r"""
      Broadcasts a bucket's parameters.
  
      Arguments:
          bucket_index (int): the index of the bucket corresponding to the
              parameters to broadcast.
          zero (ZeroRedundancyOptimizer): the calling process's
              :class:`ZeroRedundancyOptimizer` instance.
      """
      overlap_info = zero._overlap_info
      assert (
          len(overlap_info.assigned_ranks_per_bucket) > bucket_index
      ), "`assigned_ranks_per_bucket` is not fully constructed"
      # Sort to ensure the same ordering across ranks
      assigned_ranks = sorted(
          overlap_info.assigned_ranks_per_bucket[bucket_index]
      )
      assert len(assigned_ranks) > 0, (
          f"Bucket {bucket_index} should be "
          "assigned to at least one rank"
      )
      for assigned_rank in assigned_ranks:
          bucket_assignments = zero._bucket_assignments_per_rank[
              assigned_rank
          ]
          if bucket_index in bucket_assignments:
              overlap_info.broadcast_handles.append(
                  dist.broadcast(
                      bucket_assignments[bucket_index].tensor,
                      src=dist.get_global_rank(
                          zero.process_group, assigned_rank
                      ),
                      group=zero.process_group,
                      async_op=True,
                  )
              )
  
file_path: pytorch/torch/distributed/algorithms/ddp_comm_hooks/ddp_zero_hook.py
imports: import weakref
  from typing import Any, Callable, List, Optional
  import torch
  import torch.distributed
  from torch.distributed.optim import ZeroRedundancyOptimizer
  from torch.distributed.optim.zero_redundancy_optimizer import _OverlapStatus
  from torch.nn.parallel.distributed import DistributedDataParallel
label_desc: Use image node_id 2 for calling a global function with example usage: _broadcast_bucket(bucket_index, zero) without return types