code: def test_batch_sampler_shards_with_splits_no_even(self):
      # Check the shards when the dataset is a round multiple of batch size.
      batch_sampler = BatchSampler(
          range(24), batch_size=4, drop_last=False
      )
      expected = [
          [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
          [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19], [22, 23]],
      ]
      self.check_batch_sampler_shards(
          batch_sampler,
          expected,
          split_batches=True,
          even_batches=False,
      )
  
      batch_sampler = BatchSampler(
          range(24), batch_size=4, drop_last=True
      )
      # Expected shouldn't change
      self.check_batch_sampler_shards(
          batch_sampler,
          expected,
          split_batches=True,
          even_batches=False,
      )
  
      # Check the shards when the dataset is not a round multiple of batch size.
      batch_sampler = BatchSampler(
          range(22), batch_size=4, drop_last=False
      )
      expected = [
          [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20, 21]],
          [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
      ]
      self.check_batch_sampler_shards(
          batch_sampler,
          expected,
          split_batches=True,
          even_batches=False,
      )
  
      batch_sampler = BatchSampler(
          range(22), batch_size=4, drop_last=True
      )
      expected = [
          [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
          [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
      ]
      self.check_batch_sampler_shards(
          batch_sampler,
          expected,
          split_batches=True,
          even_batches=False,
      )
  
      # Check the shards when the dataset is not a round multiple of batch size or num_processes.
      batch_sampler = BatchSampler(
          range(21), batch_size=4, drop_last=False
      )
      expected = [
          [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17], [20]],
          [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
      ]
      self.check_batch_sampler_shards(
          batch_sampler,
          expected,
          split_batches=True,
          even_batches=False,
      )
  
      batch_sampler = BatchSampler(
          range(21), batch_size=4, drop_last=True
      )
      expected = [
          [[0, 1], [4, 5], [8, 9], [12, 13], [16, 17]],
          [[2, 3], [6, 7], [10, 11], [14, 15], [18, 19]],
      ]
      self.check_batch_sampler_shards(
          batch_sampler,
          expected,
          split_batches=True,
          even_batches=False,
      )
  
      # Check the shards when the dataset is very small.
      batch_sampler = BatchSampler(
          range(2), batch_size=4, drop_last=False
      )
      expected = [[[0, 1]], []]
      self.check_batch_sampler_shards(
          batch_sampler,
          expected,
          split_batches=True,
          even_batches=False,
      )
  
      batch_sampler = BatchSampler(
          range(2), batch_size=4, drop_last=True
      )
      expected = [[], []]
      self.check_batch_sampler_shards(
          batch_sampler,
          expected,
          split_batches=True,
          even_batches=False,
      )
  
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 5 for calling the DataLoaderTester obj's underlying member method code with example usage: obj.test_batch_sampler_shards_with_splits_no_even() without return types