code: def test_to_iterable(self):
      sampler = TrainingSampler(100, seed=10)
      gt_output = list(itertools.islice(sampler, 100))
      self.assertEqual(set(gt_output), set(range(100)))
  
      dataset = DatasetFromList(list(range(100)))
      dataset = ToIterableDataset(dataset, sampler)
      data_loader = data.DataLoader(
          dataset, num_workers=0, collate_fn=operator.itemgetter(0)
      )
  
      output = list(itertools.islice(data_loader, 100))
      self.assertEqual(output, gt_output)
  
      data_loader = data.DataLoader(
          dataset,
          num_workers=2,
          collate_fn=operator.itemgetter(0),
          worker_init_fn=worker_init_reset_seed,
          # reset seed should not affect behavior of TrainingSampler
      )
      output = list(itertools.islice(data_loader, 100))
      # multiple workers should not lead to duplicate or different data
      self.assertEqual(output, gt_output)
  
imports: import itertools
  import math
  import operator
  import unittest
  import torch
  from torch.utils import data
  from torch.utils.data.sampler import SequentialSampler
  from detectron2.data.build import worker_init_reset_seed
  from detectron2.data.common import DatasetFromList, ToIterableDataset
  from detectron2.data.samplers import GroupedBatchSampler, InferenceSampler, RepeatFactorTrainingSampler, TrainingSampler
  from detectron2.utils.env import seed_all_rng
label_desc: Use image node_id 1 for calling the TestSamplerDeterministic obj's underlying member method code with example usage: obj.test_to_iterable() without return types