code: def broadcast_tensors(tensors: Iterable[torch.Tensor], src: int = 0):
      """
      Broadcast the tensors from the given parameters to all workers.
      This can be used to ensure that all workers have the same model to start with.
      """
      if not _is_distributed():
          return
  
      tensors = [
          tensor for tensor in tensors if _is_complex_or_float(tensor)
      ]
      handles = []
      for tensor in tensors:
          handle = torch.distributed.broadcast(
              tensor.data, src=src, async_op=True
          )
          handles.append(handle)
      for handle in handles:
          handle.wait()
  
imports: from typing import Iterable
  import torch
label_desc: Use image node_id 3 for calling a global function with example usage: broadcast_tensors(tensors, src) without return types