code: def communicate(
      tensors: List[torch.Tensor],
      communication_op: Any,
      logger: logging.Logger = None,
  ) -> None:
      """
      Communicate a list of tensors
      Args:
          tensors (Iterable[Tensor]): list of tensors
          communication_op: a method or partial object which takes a tensor as
              input and communicates it. It can be a partial object around
              something like torch.distributed.all_reduce
      """
      tensors_by_dtype = group_by_dtype(tensors)
      for tensors_with_same_dtype in tensors_by_dtype.values():
          flat_tensor = flatten_tensors(tensors_with_same_dtype)
          if logger is not None:
              logger.debug("Flatten completed")
          communication_op(tensor=flat_tensor)
          if logger is not None:
              logger.debug("Commmunication completed")
          with torch.no_grad():
              for f, t in zip(
                  unflatten_tensors(
                      flat_tensor, tensors_with_same_dtype
                  ),
                  tensors_with_same_dtype,
              ):
                  t.copy_(f)
          if logger is not None:
              logger.debug("Unflatten completed")
  
file_path: fairscale/fairscale/experimental/nn/data_parallel/gossip/utils/helpers.py
imports: import collections
  import logging
  import sys
  from typing import Any, Dict, List, MutableMapping, Set, Tuple
  import torch
  import torch.distributed
label_desc: Use image node_id 4 for calling a global function with example usage: communicate(tensors, communication_op, logger) without return types