code: def test_batch_sampler_with_varying_batch_size(self):
      batch_sampler = [
          [0, 1, 2],
          [3, 4],
          [5, 6, 7, 8],
          [9, 10, 11],
          [12, 13],
      ]
      batch_sampler_shards = [
          BatchSamplerShard(batch_sampler, 2, i, even_batches=False)
          for i in range(2)
      ]
  
      self.assertEqual(len(batch_sampler_shards[0]), 3)
      self.assertEqual(len(batch_sampler_shards[1]), 2)
  
      self.assertListEqual(
          list(batch_sampler_shards[0]),
          [[0, 1, 2], [5, 6, 7, 8], [12, 13]],
      )
      self.assertListEqual(
          list(batch_sampler_shards[1]), [[3, 4], [9, 10, 11]]
      )
  
imports: import random
  import unittest
  from torch.utils.data import BatchSampler, DataLoader, IterableDataset
  from accelerate import Accelerator
  from accelerate.data_loader import BatchSamplerShard, DataLoaderDispatcher, DataLoaderShard, IterableDatasetShard, SkipBatchSampler, SkipDataLoader, skip_first_batches
label_desc: Use image node_id 6 for calling the DataLoaderTester obj's underlying member method code with example usage: obj.test_batch_sampler_with_varying_batch_size() without return types