code: def testReadWithEquivalentDataset(self):
      features = {
          "file": parsing_ops.FixedLenFeature([], dtypes.int64),
          "record": parsing_ops.FixedLenFeature([], dtypes.int64),
      }
      dataset = (
          core_readers.TFRecordDataset(self._filenames)
          .map(lambda x: parsing_ops.parse_single_example(x, features))
          .repeat(10)
          .batch(2)
      )
      next_element = self.getNext(dataset)
      for (
          file_batch,
          _,
          _,
          _,
          record_batch,
          _,
      ) in self._next_expected_batch(range(self._num_files), 2, 10):
          actual_batch = self.evaluate(next_element())
          self.assertAllEqual(file_batch, actual_batch["file"])
          self.assertAllEqual(record_batch, actual_batch["record"])
      with self.assertRaises(errors.OutOfRangeError):
          self.evaluate(next_element())
  
imports: from absl.testing import parameterized
  import numpy
  from tensorflow.python.data.experimental.ops import readers
  from tensorflow.python.data.kernel_tests import test_base
  from tensorflow.python.data.kernel_tests import tf_record_test_base
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import readers
  from tensorflow.python.data.util import nest
  from tensorflow.python.framework import combinations
  from tensorflow.python.framework import dtypes
  from tensorflow.python.framework import errors
  from tensorflow.python.framework import tensor
  from tensorflow.python.ops import io_ops
  from tensorflow.python.ops import parsing_ops
  from tensorflow.python.platform import test
label_desc: Use image node_id 2 for calling the MakeBatchedFeaturesDatasetTest obj's underlying member method code with example usage: obj.testReadWithEquivalentDataset() without return types