code: def get_kth_microbatch(
      batch: Optional[List[torch.Tensor]], k: int
  ) -> List[torch.Tensor]:
      """Create a list of microbatches from a list of local minibatches.
  
      This function creates a list of `k`th microbatches from a list of local minibatches.
      `a local minibatch` consists of `global_batch_size / data_parallel_size` samples.
      """
      if batch is None or not isinstance(batch, (List, Tuple)):
          return batch
      micro_batch_size = get_micro_batch_size()
      start = k * micro_batch_size
      end = start + micro_batch_size
      microbatch = list()
      for x in batch:
          size = x.size(0)
          assert size > start and size >= end
          microbatch.append(x[start:end])
      assert len(microbatch) > 0
      return microbatch
  
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 11 for calling a global function with example usage: get_kth_microbatch(batch, k) and returns: microbatch, batch