code: def check_batch_sampler_shards(
      self,
      batch_sampler,
      expected,
      split_batches=False,
      even_batches=True,
  ):
      batch_sampler_shards = [
          BatchSamplerShard(
              batch_sampler,
              2,
              i,
              split_batches=split_batches,
              even_batches=even_batches,
          )
          for i in range(2)
      ]
      batch_sampler_lists = [
          list(batch_sampler_shard)
          for batch_sampler_shard in batch_sampler_shards
      ]
      if not split_batches:
          self.assertListEqual(
              [len(shard) for shard in batch_sampler_shards],
              [len(e) for e in expected],
          )
      self.assertListEqual(batch_sampler_lists, expected)
  
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 1 for calling the DataLoaderTester obj's underlying member method code with example usage: obj.check_batch_sampler_shards(batch_sampler, expected, split_batches, even_batches) without return types