code: def all_reduce(tensors, average=True):
      """
      All reduce the provided tensors from all processes across machines.
      Args:
          tensors (list): tensors to perform all reduce across all processes in
          all machines.
          average (bool): scales the reduced tensor by the number of overall
          processes across all machines.
      """
  
      for tensor in tensors:
          dist.all_reduce(tensor, async_op=False)
      if average:
          world_size = dist.get_world_size()
          for tensor in tensors:
              tensor.mul_(1.0 / world_size)
      return tensors
  
imports: import functools
  import logging
  import pickle
  import torch
  import torch.distributed
  from pytorchvideo.layers.distributed import cat_all_gather, get_local_process_group, get_local_rank, get_local_size, get_world_size, init_distributed_training
label_desc: Use image node_id 3 for calling a global function with example usage: all_reduce(tensors, average) and returns: tensors