@parameterized.named_parameters(('1', np.int32([[1]]), np.int32([0])), ('2', np.int32([[10], [20]]), np.int32([15])))
def testWindowDatasetPaddedBatchDenseInvalid(self, shapes, padded_shape):
    'Tests invalid padded batching of dense tensor windows.\n\n    Args:\n      shapes: the input shapes\n      padded_shape: the shape to pad the output to\n    '
    dataset = dataset_ops.Dataset.from_tensor_slices(shapes).map((lambda shape: array_ops.zeros(shape, dtype=dtypes.int32))).apply(grouping.window_dataset(len(shapes))).apply(grouping._map_x_dataset((lambda x: batching.padded_batch_window(x, padded_shape))))
    get_next = dataset.make_one_shot_iterator().get_next()
    with self.cached_session() as sess:
        with self.assertRaises(errors.InvalidArgumentError):
            sess.run(get_next)
Tests invalid padded batching of dense tensor windows.

Args:
  shapes: the input shapes
  padded_shape: the shape to pad the output to
@parameterized.named_parameters(('1', np.int32([[1]]), np.int32([0])), ('2', np.int32([[10], [20]]), np.int32([15])))
def testWindowDatasetPaddedBatchDenseInvalid(self, shapes, padded_shape):
    'Tests invalid padded batching of dense tensor windows.\n\n    Args:\n      shapes: the input shapes\n      padded_shape: the shape to pad the output to\n    '
    dataset = dataset_ops.Dataset.from_tensor_slices(shapes).map((lambda shape: array_ops.zeros(shape, dtype=dtypes.int32))).apply(grouping.window_dataset(len(shapes))).apply(grouping._map_x_dataset((lambda x: batching.padded_batch_window(x, padded_shape))))
    get_next = dataset.make_one_shot_iterator().get_next()
    with self.cached_session() as sess:
        with self.assertRaises(errors.InvalidArgumentError):
            sess.run(get_next)