code: def broadcast_coalesced(tensors, devices, buffer_size=10485760):
      """Broadcast a sequence of tensors to the specified GPUs.
  
      Small tensors are first coalesced into a buffer to reduce the number of synchronizations.
  
      Args:
          tensors (sequence): tensors to broadcast. Must be on the same device,
            either CPU or GPU.
          devices (Iterable[torch.device, str or int]): an iterable of GPU
            devices, among which to broadcast.
          buffer_size (int): maximum size of the buffer used for coalescing
  
      Returns:
          A tuple containing copies of :attr:`tensor`, placed on :attr:`devices`.
      """
      devices = [_get_device_index(d) for d in devices]
      tensors = [_handle_complex(t) for t in tensors]
      return torch._C._broadcast_coalesced(
          tensors, devices, buffer_size
      )
  
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 2 for calling a global function with example usage: broadcast_coalesced(tensors, devices, buffer_size) and returns: torch