def _test(self, args, expected_out=None, expected_err_re=None):
    if expected_err_re is None:
      decode = parsing_ops.decode_csv(**args)
      out = self.evaluate(decode)

      for i, field in enumerate(out):
        if field.dtype == np.float32 or field.dtype == np.float64:
          self.assertAllClose(field, expected_out[i])
        else:
          self.assertAllEqual(field, expected_out[i])
    else:
      with self.assertRaisesOpError(expected_err_re):
        decode = parsing_ops.decode_csv(**args)
        self.evaluate(decode)

 def _apply_transform(self, input_tensors):
   default_consts = [constant_op.constant(d, shape=[1])
                     for d in self._default_values]
   parsed_values = parsing_ops.decode_csv(input_tensors[0],
                                          record_defaults=default_consts)
   # pylint: disable=not-callable
   return self.return_type(*parsed_values)

 def _make_test_datasets(self, inputs, **kwargs):
   # Test by comparing its output to what we could get with map->decode_csv
   filenames = self._setup_files(inputs)
   dataset_expected = core_readers.TextLineDataset(filenames)
   dataset_expected = dataset_expected.map(
       lambda l: parsing_ops.decode_csv(l, **kwargs))
   dataset_actual = readers.CsvDataset(filenames, **kwargs)
   return (dataset_actual, dataset_expected)

 def benchmarkMapWithStrings(self):
   self._setUp(self.STR_VAL)
   for i in range(len(self._filenames)):
     num_cols = self._num_cols[i]
     kwargs = {'record_defaults': [['']] * num_cols}
     dataset = core_readers.TextLineDataset(self._filenames[i]).repeat()
     dataset = dataset.map(lambda l: parsing_ops.decode_csv(l, **kwargs))  # pylint: disable=cell-var-from-loop
     self._runBenchmark(dataset, num_cols, 'csv_strings_map_decode_csv')
   self._tearDown()

 def decode_csv_fn(x):
   return parsing_ops.decode_csv(
       x,
       record_defaults=[
           constant_op.constant([], dtypes.float32),
           constant_op.constant([], dtypes.int32),
           constant_op.constant([], dtypes.string)
       ],
       field_delim=":")