code: def testRaggedDataset(self, drop_remainder):
      # Set up a dataset that produces ragged tensors with a static batch size.
      dataset = dataset_ops.Dataset.from_tensor_slices(
          ragged_tensor.RaggedTensor.from_row_lengths(
              list(range(10)), [1, 2, 3, 4]
          )
      )
      # The map changes the internal representation of the ragged tensor.
      # This test will fail if we don't normalize the tensor representation.
      dataset = dataset.batch(4, drop_remainder=True).map(lambda x: x)
  
      rebatched_dataset = dataset.rebatch(batch_size=[2, 2])
  
      expected_output = [
          ragged_tensor.RaggedTensor.from_row_lengths(
              list(range(3)), [1, 2]
          ),
          ragged_tensor.RaggedTensor.from_row_lengths(
              list(range(3, 10)), [3, 4]
          ),
      ]
      self.assertDatasetProduces(rebatched_dataset, expected_output)
  
imports: from absl.testing import parameterized
  from tensorflow.python.data.kernel_tests import checkpoint_test_base
  from tensorflow.python.data.kernel_tests import test_base
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.util import nest
  from tensorflow.python.framework import combinations
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.platform import test
label_desc: Use image node_id 18 for calling the RebatchTest obj's underlying member method code with example usage: obj.testRaggedDataset(drop_remainder) without return types