code: def test_distributed_batch_sampler():
      worker_batch_size = 2
      sampler = torch.utils.data.SequentialSampler(range(19))
      sampler = torch.utils.data.BatchSampler(
          sampler, batch_size=worker_batch_size, drop_last=False
      )
  
      num_replicas = 4
  
      expected_samples = []
      expected_samples.append([[0, 1], [8, 9], [16, 17]])
      expected_samples.append([[2, 3], [10, 11], [18]])
      expected_samples.append([[4, 5], [12, 13]])
      expected_samples.append([[6, 7], [14, 15]])
  
      for rank in range(num_replicas):
          dist_sampler = samplers.DistributedBatchSampler(
              sampler, 4, rank
          )
          samples = list(dist_sampler)
          assert len(dist_sampler) == len(samples)
          assert samples == expected_samples[rank]
  
imports: import logging
  import queue
  import typing
  from logging import handlers
  import numpy
  import pytest
  import torch
  import determined
  from determined.pytorch import data_length, samplers, to_device
label_desc: Use image node_id 8 for calling a global function with example usage: test_distributed_batch_sampler() without return types