code: def Dataset(
      num_samples: int,
  ) -> List[Tuple[torch.Tensor, torch.Tensor]]:
      return [
          (
              torch.randn(HIDDEN_SIZE, HIDDEN_SIZE),
              torch.randn(HIDDEN_SIZE // 2, HIDDEN_SIZE // 2),
          )
          for _ in range(num_samples)
      ]
  
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 a global function with example usage: Dataset(num_samples) and returns: unknown