code: def testBatchSizeEqualToOriginal(self, batch_size, drop_remainder):
      # `drop_remainder` needs to be `False` in `rebatch` call
      # so that the remainder batch is preserved.
      #
      # For example:
      # d = range(3).batch(2, drop_remainder=True)
      # d2 = d.rebatch(2, drop_remainder=True)
      # d becomes [[0, 1], [2]] and d2 becomes [[0, 1]],
      # which is a mismatch we do not want.
  
      dataset = dataset_ops.Dataset.range(11).batch(
          batch_size, drop_remainder=drop_remainder
      )
      expected_output = self.getDatasetOutput(dataset)
      rebatched_dataset = dataset.rebatch(
          batch_size=batch_size, drop_remainder=False
      )
  
      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 11 for calling the RebatchTest obj's underlying member method code with example usage: obj.testBatchSizeEqualToOriginal(batch_size, drop_remainder) without return types