code: def test_batch_sampler_behavior(self):
      dataset = MyIterableDataset(0, 100)
  
      for num_workers in (1, 2, 4):
          torch.manual_seed(42)
          loader = DataLoader(
              dataset,
              batch_sampler=MegatronPretrainingRandomSampler(
                  100, 0, 4, 0, 1
              ),
              num_workers=num_workers,
          )
          samples = []
          for i, batch in enumerate(loader):
              samples.append(batch)
              if i == 2 - 1:
                  break
  
          torch.manual_seed(42)
          loader = DataLoader(
              dataset,
              batch_sampler=MegatronPretrainingRandomSampler(
                  100, 0, 2, 0, 1
              ),
              num_workers=num_workers,
          )
          samples2 = []
          for i, batch in enumerate(loader):
              samples2.append(batch)
              if i == 4 - 1:
                  break
          self.assertEqual(
              torch.cat(samples),
              torch.cat(samples2),
              msg=f"num_workers={num_workers}",
          )
  
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 2 for calling the TestBatchSamplerBehavior obj's underlying member method code with example usage: obj.test_batch_sampler_behavior() without return types