code: def test_distributed_sampler():
      sampler = torch.utils.data.SequentialSampler(range(19))
  
      num_replicas = 4
  
      expected_samples = []
      expected_samples.append([0, 4, 8, 12, 16])
      expected_samples.append([1, 5, 9, 13, 17])
      expected_samples.append([2, 6, 10, 14, 18])
      expected_samples.append([3, 7, 11, 15])
  
      for rank in range(num_replicas):
          dist_sampler = samplers.DistributedSampler(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 7 for calling a global function with example usage: test_distributed_sampler() without return types