code: def broadcast(tensor, devices=None, *, out=None):
      r"""Broadcasts a tensor to specified GPU devices.
  
      Args:
          tensor (Tensor): tensor to broadcast. Can be on CPU or GPU.
          devices (Iterable[torch.device, str or int], optional): an iterable of
            GPU devices, among which to broadcast.
          out (Sequence[Tensor], optional, keyword-only): the GPU tensors to
            store output results.
  
      .. note::
          Exactly one of :attr:`devices` and :attr:`out` must be specified.
  
      Returns:
          - If :attr:`devices` is specified,
              a tuple containing copies of :attr:`tensor`, placed on
              :attr:`devices`.
          - If :attr:`out` is specified,
              a tuple containing :attr:`out` tensors, each containing a copy of
              :attr:`tensor`.
      """
      tensor = _handle_complex(tensor)
      if not ((devices is None) ^ (out is None)):
          raise RuntimeError(
              f"Exactly one of 'devices' and 'out' must be specified, but got devices={devices} and out={out}"
          )
      if devices is not None:
          devices = [_get_device_index(d) for d in devices]
          return torch._C._broadcast(tensor, devices)
      else:
          return torch._C._broadcast_out(tensor, out)
  
file_path: pytorch/torch/nn/parallel/comm.py
imports: import warnings
  import torch
  from torch.cuda import nccl
  from torch._utils import _take_tensors, _flatten_dense_tensors, _unflatten_dense_tensors, _reorder_tensors_as, _get_device_index, _handle_complex
  from typing import List
label_desc: Use image node_id 1 for calling a global function with example usage: broadcast(tensor, devices) and returns: torch, torch