code: def testCanHandleUnknownDims(self):
      dataset = dataset_ops.Dataset.range(1000)
      dataset = dataset.batch(10, drop_remainder=False)
      dataset = dataset.batch(10, drop_remainder=False)
      self.assertEqual([[None, None]], _flat_shapes(dataset))
      rebatched_dataset = distribute._LegacyRebatchDataset(
          dataset, num_replicas=4
      )
      # Note that we are just testing the dataset shapes, not the actual output.
      self.assertEqual([[None, None]], _flat_shapes(rebatched_dataset))
  
imports: from absl.testing import parameterized
  import numpy
  from tensorflow.python.data.experimental.ops import distribute
  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.framework import constant_op
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import tensor_shape
  from tensorflow.python.framework import tensor_util
  from tensorflow.python.ops import image_ops
  from tensorflow.python.ops.ragged import ragged_tensor
  from tensorflow.python.platform import test
label_desc: Use image node_id 3 for calling the LegacyRebatchDatasetTest obj's underlying member method code with example usage: obj.testCanHandleUnknownDims() without return types