code: def test_split_batch(self):
      class MyIterableDataset(Dataset):
          def __init__(self, start, end):
              super().__init__()
              assert (
                  end > start
              ), "this example code only works with end >= start"
              self.start = start
              self.end = end
              self.samples = list(range(self.start, self.end))
  
          def __len__(self):
              return self.end - self.start
  
          def __iter__(self):
              return iter(range(self.start, self.end))
  
          def __getitem__(self, index):
              return (
                  torch.tensor([index, index]),
                  torch.tensor([index // 2, index // 2]),
              )
  
      dataset = MyIterableDataset(0, 100)
      torch.manual_seed(42)
      global_batch_size = 16
      loader = DataLoader(
          dataset,
          batch_sampler=MegatronPretrainingRandomSampler(
              100, 0, global_batch_size, 0, 1
          ),
          num_workers=2,
      )
      batch = next(iter(loader))
  
      for _micro_batch_size in (1, 2, 4, 8):
          microbatches = list(
              split_batch_into_microbatch(
                  batch,
                  _micro_batch_size=_micro_batch_size,
                  _global_batch_size=global_batch_size,
              )
          )
          self.assertEqual(
              len(microbatches), global_batch_size // _micro_batch_size
          )
          self.assertEqual(len(microbatches[0][0]), _micro_batch_size)
  
file_path: apex/tests/L0/run_transformer/test_batch_sampler.py
imports: import torch
  from torch.testing._internal import common_utils
  from torch.utils.data import Dataset
  from torch.utils.data import DataLoader
  from apex.transformer.pipeline_parallel.utils import _split_batch_into_microbatch
label_desc: Use image node_id 3 for calling the TestBatchSamplerBehavior obj's underlying member method code with example usage: obj.test_split_batch() and returns: unknown, iter, torch,  torch