code: def _reconfigure_microbatch_calculator(
      rank: int,
      rampup_batch_size: Optional[List[int]],
      global_batch_size: int,
      micro_batch_size: int,
      data_parallel_size: int,
  ) -> None:
      if torch.distributed.get_rank() == 0:
          import warnings
  
          warnings.warn("This function is only for unittest")
      global _GLOBAL_NUM_MICROBATCHES_CALCULATOR
  
      _GLOBAL_NUM_MICROBATCHES_CALCULATOR = (
          build_num_microbatches_calculator(
              rank,
              rampup_batch_size,
              global_batch_size,
              micro_batch_size,
              data_parallel_size,
          )
      )
  
imports: from typing import Optional, List, Union, Tuple
  import torch
  from torch.nn.parallel import DistributedDataParallel
  from apex.multi_tensor_apply import multi_tensor_applier
  from apex.transformer import parallel_state
  from apex.transformer.enums import ModelType
  from apex.transformer.microbatches import build_num_microbatches_calculator
  from apex.transformer.pipeline_parallel._timers import _Timers
label_desc: Use image node_id 5 for calling a global function with example usage: _reconfigure_microbatch_calculator(rank, rampup_batch_size, global_batch_size, micro_batch_size, data_parallel_size) without return types