code: def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
      if bucket_size_mb > 0:
          bucket_size_bytes = bucket_size_mb * 1024 * 1024
          buckets = _take_tensors(tensors, bucket_size_bytes)
      else:
          buckets = OrderedDict()
          for tensor in tensors:
              tp = tensor.type()
              if tp not in buckets:
                  buckets[tp] = []
              buckets[tp].append(tensor)
          buckets = buckets.values()
  
      for bucket in buckets:
          flat_tensors = _flatten_dense_tensors(bucket)
          dist.all_reduce(flat_tensors)
          flat_tensors.div_(world_size)
          for tensor, synced in zip(
              bucket, _unflatten_dense_tensors(flat_tensors, bucket)
          ):
              tensor.copy_(synced)
  
file_path: lavis/lavis/common/annotator/uniformer/mmcv/runner/dist_utils.py
imports: import functools
  import os
  import subprocess
  from collections import OrderedDict
  import torch
  import torch.multiprocessing
  from torch import distributed
  from torch._utils import _flatten_dense_tensors, _take_tensors, _unflatten_dense_tensors
label_desc: Use image node_id 9 for calling a global function with example usage: _allreduce_coalesced(tensors, world_size, bucket_size_mb) without return types