code: def _train(
      self, model, optim, pipeline_model_parallel_size, async_comm
  ):
      args = global_vars.get_args()
      fwd_bwd_func = forward_backward_pipelining_without_interleaving
  
      tensor_shape = (
          args.seq_length,
          args.micro_batch_size,
          args.hidden_size,
      )
      runtime = 0
      # training loop
      for i in range(3):
          since = time.time()
          if torch.distributed.get_rank() == 0:
              print("begin iter", i)
          batch = [
              self._generate_fancy_data_labels(
                  args.seq_length, args.global_batch_size
              )
              for _ in range(pipeline_model_parallel_size)
          ]
          if torch.distributed.get_rank() == 0:
              print("finished making batch...")
          optim.zero_grad()
          fwd_bwd_func(
              self._fwd_step_func,
              batch,
              model,
              forward_only=False,
              tensor_shape=tensor_shape,
              async_comm=async_comm,
              sequence_parallel_enabled=args.sequence_parallel,
          )
          if torch.distributed.get_rank() == 0:
              print("finished forward step")
          # All-reduce layernorm parameters across model parallel nodes
          # when sequence parallelism is used
          if (
              parallel_state.get_tensor_model_parallel_world_size() > 1
              and global_vars.get_args().sequence_parallel
          ):
              for model_module in model:
                  unwrapped_model = unwrap_model(model_module)
                  for param in unwrapped_model.parameters():
                      if getattr(
                          param, "sequence_parallel_enabled", False
                      ):
                          grad = param.grad
                          torch.distributed.all_reduce(
                              grad,
                              group=parallel_state.get_tensor_model_parallel_group(),
                          )
          optim.step()
          if torch.distributed.get_rank() == 0:
              print("finished iter", i)
          runtime += time.time() - since
      return runtime / 3.0
  
imports: from functools import partial
  from typing import List
  import time
  import torch
  import unittest
  from apex.transformer._ucc_util import HAS_UCC
  from apex.transformer import parallel_state
  from apex.transformer.enums import ModelType
  from apex.transformer.tensor_parallel import model_parallel_cuda_manual_seed
  from apex.transformer.pipeline_parallel.utils import average_losses_across_data_parallel_group, unwrap_model, setup_microbatch_calculator, get_ltor_masks_and_position_ids
  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_without_interleaving import forward_backward_pipelining_without_interleaving
  from apex.transformer.testing.standalone_gpt import gpt_model_provider
  from apex.transformer.testing import global_vars
  from apex.transformer.testing.distributed_test_base import UccDistributedTestBase, NcclDistributedTestBase
  from torch.testing._internal import common_utils
  from torch.testing._internal.common_device_type import instantiate_device_type_tests
label_desc: Use image node_id 6 for calling the GptTestBase obj's underlying member method code with example usage: obj._train(model, optim, pipeline_model_parallel_size, async_comm) and returns: unknown