code: def test_dynamic_batchsize(self):
      n_tests = 0
      failures = []
  
      override_args = {
          "micro_batch_size": 2,
          "num_layers": 16,
          "hidden_size": 256,
          "num_attention_heads": 8,
          "max_position_embeddings": 512,
          "seq_length": 512,
          "global_batch_size": 128,
          "use_cpu_initialization": True,
          "world_size": self.world_size,
          "rank": self.rank,
      }
  
      global_vars.set_global_variables(
          args_defaults={
              "global_batch_size": 512,
              "rampup_batch_size": [64, 64, 1000],
          },
          ignore_unknown_args=True,
          override_args=override_args,
      )
  
      args = global_vars.get_args()
  
      setup_microbatch_calculator(
          args.rank,
          args.rampup_batch_size,
          args.global_batch_size,
          args.micro_batch_size,
          1,  # args.data_parallel_size,
      )
      for BatchSamplerCls in (
          MegatronPretrainingSampler,
          MegatronPretrainingRandomSampler,
      ):
          for forward_only in (False, True):
              n_tests += 1
              pipeline_model_parallel_size = self.world_size
              try:
                  run_interleaved_with_dynamic_batch_size(
                      pipeline_model_parallel_size,
                      forward_only,
                      BatchSamplerCls,
                  )
              except Exception as e:
                  msg = (
                      f"\tforward_only: {forward_only}\n"
                      f"pipeline rank: {parallel_state.get_pipeline_model_parallel_rank()}, "
                      f"virtual pipeline rank: {parallel_state.get_virtual_pipeline_model_parallel_rank()}\n"
                      f"{str(e)}"
                  )
                  raise RuntimeError(msg)
              finally:
                  parallel_state.destroy_model_parallel()
      if failures:
          print_separator("TEST FAILED:")
          print("\n".join(failures))
          msg = f"{len(failures)} / {n_tests} cases failed"
          raise RuntimeError(msg)
      else:
          if torch.distributed.get_rank() == 0:
              print_separator("TEST RESULT: ### PASS!")
  
imports: from typing import Tuple, List
  import torch
  import unittest
  from apex.transformer import parallel_state
  from apex.transformer.pipeline_parallel.utils import get_num_microbatches
  from apex.transformer.pipeline_parallel.schedules.common import _get_params_for_weight_decay_optimization, build_model
  from apex.transformer.pipeline_parallel.schedules.fwd_bwd_pipelining_with_interleaving import _forward_backward_pipelining_with_interleaving
  from apex.transformer.pipeline_parallel.utils import setup_microbatch_calculator, _reconfigure_microbatch_calculator, update_num_microbatches
  from apex.transformer.testing import global_vars
  from apex.transformer.testing.commons import print_separator, fwd_step_func, model_provider_func
  from apex.transformer.log_util import get_transformer_logger
  from apex.transformer._data import MegatronPretrainingRandomSampler, MegatronPretrainingSampler
  from apex.transformer.testing.distributed_test_base import NcclDistributedTestBase
  from torch.testing._internal import common_utils
label_desc: Use image node_id 1 for calling the DynamicBatchsizeTestBase obj's underlying member method code with example usage: obj.test_dynamic_batchsize() without return types