code: def all_reduce_and_rescale_tensors(
      tensors, rescale_denom, buffer_size=104857600
  ):
      """All-reduce and rescale tensors in chunks of the specified size.
  
      Args:
          tensors: list of Tensors to all-reduce
          rescale_denom: denominator for rescaling summed Tensors
          buffer_size: all-reduce chunk size in bytes
      """
      # buffer size in bytes, determine equiv. # of elements based on data type
      buffer_t = (
          tensors[0]
          .new(math.ceil(buffer_size / tensors[0].element_size()))
          .zero_()
      )
      buffer = []
  
      def all_reduce_buffer():
          # copy tensors into buffer_t
          offset = 0
          for t in buffer:
              numel = t.numel()
              buffer_t[offset : offset + numel].copy_(t.view(-1))
              offset += numel
  
          # all-reduce and rescale
          torch.distributed.all_reduce(
              buffer_t[:offset], async_op=False
          )
          buffer_t.div_(rescale_denom)
  
          # copy all-reduced buffer back into tensors
          offset = 0
          for t in buffer:
              numel = t.numel()
              t.view(-1).copy_(buffer_t[offset : offset + numel])
              offset += numel
  
      filled = 0
      for t in tensors:
          sz = t.numel() * t.element_size()
          # print(filled, sz)
          if sz > buffer_size:
              # tensor is bigger than buffer, all-reduce and rescale directly
              torch.distributed.all_reduce(t, async_op=False)
              t.div_(rescale_denom)
          elif filled + sz > buffer_size:
              # buffer is full, all-reduce and replace buffer with grad
              all_reduce_buffer()
              buffer = [t]
              filled = sz
          else:
              # add tensor to buffer
              buffer.append(t)
              filled += sz
  
      if len(buffer) > 0:
          all_reduce_buffer()
  
imports: import os
  import signal
  import math
  import pickle
  import torch.distributed
  from datetime import timedelta
  from onmt.translate.translator import build_translator
  from onmt.transforms import get_transforms_cls
  from onmt.constants import CorpusTask
  from onmt.utils.logging import init_logger, logger
  from onmt.inputters.dynamic_iterator import build_dynamic_dataset_iter
label_desc: Use image node_id 3 for calling a global function with example usage: all_reduce_and_rescale_tensors(tensors, rescale_denom, buffer_size) without return types