code: def _get_batch(self, int_tensors: List[torch.Tensor]):
      data = int_tensors[0]
      # Unpack.
      tokens_ = data.long()
      labels = tokens_[:, 1:].contiguous()
      tokens = tokens_[:, :-1].contiguous()
      # Get the masks and position ids.
      (
          attention_mask,
          loss_mask,
          position_ids,
      ) = get_ltor_masks_and_position_ids(
          tokens,
          self.N_VOCAB,  # tokenizer.eod,
          False,  # args.reset_position_ids,
          False,  # args.reset_attention_mask,
          False,  # args.eod_mask_loss,
      )
      return tokens, labels, loss_mask, attention_mask, position_ids
  
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 3 for calling the GptTestBase obj's underlying member method code with example usage: obj._get_batch(int_tensors) and returns: tokens,  labels,  loss_mask,  attention_mask,  position_ids