Replace unnecessary `()` in `run_in_graph_and_eager_modes()`.

PiperOrigin-RevId: 201652888

27 files changed, 110 insertions, 110 deletions

diff --git a/tensorflow/contrib/checkpoint/python/containers_test.py b/tensorflow/contrib/checkpoint/python/containers_test.py
index 3717d7f583..12b99d3e22 100644
--- a/tensorflow/contrib/checkpoint/python/containers_test.py
+++ b/tensorflow/contrib/checkpoint/python/containers_test.py
@@ -32,7 +32,7 @@ from tensorflow.python.training.checkpointable import util as checkpointable_uti
 
 class UniqueNameTrackerTests(test.TestCase):
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testNames(self):
     checkpoint_directory = self.get_temp_dir()
     checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
@@ -65,7 +65,7 @@ class UniqueNameTrackerTests(test.TestCase):
     self.assertEqual(4., self.evaluate(restore_slots.x_1_1))
     self.assertEqual(5., self.evaluate(restore_slots.y))
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testExample(self):
     class SlotManager(checkpointable.Checkpointable):
 
@@ -97,7 +97,7 @@ class UniqueNameTrackerTests(test.TestCase):
         dependency_names,
         ["x", "x_1", "y", "slot_manager", "slotdeps", "save_counter"])
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testLayers(self):
     tracker = containers.UniqueNameTracker()
     tracker.track(layers.Dense(3), "dense")
diff --git a/tensorflow/contrib/checkpoint/python/split_dependency_test.py b/tensorflow/contrib/checkpoint/python/split_dependency_test.py
index 69dc0b9be2..43c5d6515b 100644
--- a/tensorflow/contrib/checkpoint/python/split_dependency_test.py
+++ b/tensorflow/contrib/checkpoint/python/split_dependency_test.py
@@ -73,7 +73,7 @@ class OnlyOneDep(checkpointable.Checkpointable):
 
 class SplitTests(test.TestCase):
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testSaveRestoreSplitDep(self):
     save_checkpoint = checkpointable_utils.Checkpoint(
         dep=SaveTensorSlicesAsDeps())
diff --git a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py
index 8285ea0492..252ea1560d 100644
--- a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py
+++ b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py
@@ -768,7 +768,7 @@ class CudnnRNNTestSaveRestoreCheckpointable(test_util.TensorFlowTestCase):
 
   @unittest.skipUnless(test.is_built_with_cuda(),
                        "Test only applicable when running on GPUs")
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testLSTMCheckpointableSingleLayer(self):
     num_units = 2
     direction = CUDNN_RNN_UNIDIRECTION
@@ -781,7 +781,7 @@ class CudnnRNNTestSaveRestoreCheckpointable(test_util.TensorFlowTestCase):
 
   @unittest.skipUnless(test.is_built_with_cuda(),
                        "Test only applicable when running on GPUs")
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testGRUCheckpointableSingleLayer(self):
     num_units = 2
     direction = CUDNN_RNN_UNIDIRECTION
@@ -826,7 +826,7 @@ class CudnnRNNTestSaveRestoreCheckpointable(test_util.TensorFlowTestCase):
 
   @unittest.skipUnless(test.is_built_with_cuda(),
                        "Test only applicable when running on GPUs")
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testCudnnCompatibleLSTMCheckpointablMultiLayer(self):
     num_units = 2
     num_layers = 3
diff --git a/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py
index d02b3abb92..42cada0b97 100644
--- a/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py
+++ b/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py
@@ -63,7 +63,7 @@ class ScanDatasetTest(test.TestCase):
         with self.assertRaises(errors.OutOfRangeError):
           sess.run(next_element)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testFibonacci(self):
     iterator = dataset_ops.Dataset.from_tensors(1).repeat(None).apply(
         scan_ops.scan([0, 1], lambda a, _: ([a[1], a[0] + a[1]], a[1]))
diff --git a/tensorflow/contrib/distribute/python/cross_tower_utils_test.py b/tensorflow/contrib/distribute/python/cross_tower_utils_test.py
index 4ef8db6815..d25964fa41 100644
--- a/tensorflow/contrib/distribute/python/cross_tower_utils_test.py
+++ b/tensorflow/contrib/distribute/python/cross_tower_utils_test.py
@@ -38,7 +38,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase):
         self.evaluate(ops.convert_to_tensor(left)),
         self.evaluate(ops.convert_to_tensor(right)))
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testAggregateTensors(self):
     t0 = constant_op.constant([[1., 2.], [0, 0], [3., 4.]])
     t1 = constant_op.constant([[0., 0.], [5, 6], [7., 8.]])
@@ -46,7 +46,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase):
     result = cross_tower_utils.aggregate_tensors_or_indexed_slices([t0, t1])
     self._assert_values_equal(total, result)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testAggregateIndexedSlices(self):
     t0 = math_ops._as_indexed_slices(
         constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
@@ -57,7 +57,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase):
     self.assertIsInstance(result, ops.IndexedSlices)
     self._assert_values_equal(total, result)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testDivideTensor(self):
     t = constant_op.constant([[1., 2.], [0, 0], [3., 4.]])
     n = 2
@@ -65,7 +65,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase):
     result = cross_tower_utils.divide_by_n_tensors_or_indexed_slices(t, n)
     self._assert_values_equal(expected, result)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testDivideIndexedSlices(self):
     t = math_ops._as_indexed_slices(
         constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
@@ -75,13 +75,13 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase):
     self.assertIsInstance(result, ops.IndexedSlices)
     self._assert_values_equal(expected, result)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testIsIndexedSlices(self):
     t = math_ops._as_indexed_slices(
         constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
     self.assertTrue(cross_tower_utils.contains_indexed_slices(t))
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testContainsIndexedSlices_List(self):
     t0 = math_ops._as_indexed_slices(
         constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
@@ -89,7 +89,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase):
         constant_op.constant([[0., 0.], [5, 6], [7., 8.]]))
     self.assertTrue(cross_tower_utils.contains_indexed_slices([t0, t1]))
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testContainsIndexedSlices_Tuple(self):
     t0 = math_ops._as_indexed_slices(
         constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
@@ -97,7 +97,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase):
         constant_op.constant([[0., 0.], [5, 6], [7., 8.]]))
     self.assertTrue(cross_tower_utils.contains_indexed_slices((t0, t1)))
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testContainsIndexedSlices_PerDevice(self):
     t0 = math_ops._as_indexed_slices(
         constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
@@ -106,7 +106,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase):
     per_device = value_lib.PerDevice({"/gpu:0": t0, "/cpu:0": t1})
     self.assertTrue(cross_tower_utils.contains_indexed_slices(per_device))
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testContainsIndexedSlices_PerDeviceMapOutput(self):
     t0 = math_ops._as_indexed_slices(
         constant_op.constant([[1., 2.], [0, 0], [3., 4.]]))
diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py
index cb150692de..647cf953d7 100644
--- a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py
+++ b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py
@@ -83,13 +83,13 @@ class MirroredTwoDeviceDistributionTest(strategy_test_lib.DistributionTestBase):
       self.skipTest("Not GPU test")
     self.assertEqual(2, self._get_distribution_strategy().num_towers)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testCallAndMergeExceptions(self):
     if not GPU_TEST:
       self.skipTest("Not GPU test")
     self._test_call_and_merge_exceptions(self._get_distribution_strategy())
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testRunRegroupError(self):
 
     def run_fn(device_id):
@@ -101,7 +101,7 @@ class MirroredTwoDeviceDistributionTest(strategy_test_lib.DistributionTestBase):
     with dist.scope(), self.assertRaises(AssertionError):
       dist.call_for_each_tower(run_fn, dist.worker_device_index)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testReduceToCpu(self):
     if not GPU_TEST:
       self.skipTest("Not GPU test")
diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy_test.py b/tensorflow/contrib/distribute/python/mirrored_strategy_test.py
index 61cbe6df81..a066adf124 100644
--- a/tensorflow/contrib/distribute/python/mirrored_strategy_test.py
+++ b/tensorflow/contrib/distribute/python/mirrored_strategy_test.py
@@ -47,7 +47,7 @@ class MirroredOneCPUDistributionTest(strategy_test_lib.DistributionTestBase):
   def testTowerId(self):
     self._test_tower_id(self._get_distribution_strategy())
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testCallAndMergeExceptions(self):
     self._test_call_and_merge_exceptions(self._get_distribution_strategy())
 
diff --git a/tensorflow/contrib/distribute/python/one_device_strategy_test.py b/tensorflow/contrib/distribute/python/one_device_strategy_test.py
index 7aad8a953c..4fdc0f72e6 100644
--- a/tensorflow/contrib/distribute/python/one_device_strategy_test.py
+++ b/tensorflow/contrib/distribute/python/one_device_strategy_test.py
@@ -44,7 +44,7 @@ class OneDeviceStrategyTest(strategy_test_lib.DistributionTestBase):
   def testTowerId(self):
     self._test_tower_id(self._get_distribution_strategy())
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testCallAndMergeExceptions(self):
     self._test_call_and_merge_exceptions(self._get_distribution_strategy())
 
diff --git a/tensorflow/contrib/distribute/python/shared_variable_creator_test.py b/tensorflow/contrib/distribute/python/shared_variable_creator_test.py
index a0b452fc2d..2a9ab51fcf 100644
--- a/tensorflow/contrib/distribute/python/shared_variable_creator_test.py
+++ b/tensorflow/contrib/distribute/python/shared_variable_creator_test.py
@@ -46,7 +46,7 @@ class CanonicalizeVariableNameTest(test.TestCase):
 
 class SharedVariableCreatorTest(test.TestCase):
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testSharedVariable(self):
 
     shared_variable_store = {}
diff --git a/tensorflow/contrib/distribute/python/values_test.py b/tensorflow/contrib/distribute/python/values_test.py
index b0bd92c7b0..c5b246e804 100644
--- a/tensorflow/contrib/distribute/python/values_test.py
+++ b/tensorflow/contrib/distribute/python/values_test.py
@@ -82,7 +82,7 @@ class DistributedValuesTest(test.TestCase):
 
 class DistributedDelegateTest(test.TestCase):
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testGetAttr(self):
     with ops.device("/device:CPU:0"):
 
@@ -97,7 +97,7 @@ class DistributedDelegateTest(test.TestCase):
       with self.assertRaises(AttributeError):
         _ = v.y
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testOperatorOverride(self):
     with ops.device("/device:CPU:0"):
       v = values.DistributedDelegate({"/device:CPU:0": 7, "/device:GPU:0": 8})
@@ -363,7 +363,7 @@ class PerDeviceDatasetTest(test.TestCase):
     self._test_iterator_no_prefetch(devices, dataset, expected_values)
     self._test_iterator_with_prefetch(devices, dataset, expected_values)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testOneDevice(self):
     devices = ["/device:CPU:0"]
     dataset = dataset_ops.Dataset.range(10)
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/fill_triangular_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/fill_triangular_test.py
index caeaf2a0c6..3530e142e4 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/fill_triangular_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/fill_triangular_test.py
@@ -31,7 +31,7 @@ from tensorflow.python.platform import test
 class FillTriangularBijectorTest(test.TestCase):
   """Tests the correctness of the FillTriangular bijector."""
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testBijector(self):
     x = np.float32(np.array([1., 2., 3.]))
     y = np.float32(np.array([[3., 0.],
@@ -51,7 +51,7 @@ class FillTriangularBijectorTest(test.TestCase):
     ildj = self.evaluate(b.inverse_log_det_jacobian(y, event_ndims=2))
     self.assertAllClose(ildj, 0.)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testShape(self):
     x_shape = tensor_shape.TensorShape([5, 4, 6])
     y_shape = tensor_shape.TensorShape([5, 4, 3, 3])
@@ -76,7 +76,7 @@ class FillTriangularBijectorTest(test.TestCase):
         b.inverse_event_shape_tensor(y_shape.as_list()))
     self.assertAllEqual(x_shape_tensor, x_shape.as_list())
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testShapeError(self):
 
     b = bijectors.FillTriangular(validate_args=True)
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/matrix_inverse_tril_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/matrix_inverse_tril_test.py
index 1839703557..85d604e34a 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/matrix_inverse_tril_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/matrix_inverse_tril_test.py
@@ -29,7 +29,7 @@ from tensorflow.python.platform import test
 class MatrixInverseTriLBijectorTest(test.TestCase):
   """Tests the correctness of the Y = inv(tril) transformation."""
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testComputesCorrectValues(self):
     inv = bijectors.MatrixInverseTriL(validate_args=True)
     self.assertEqual("matrix_inverse_tril", inv.name)
@@ -51,7 +51,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase):
     self.assertNear(expected_fldj_, fldj_, err=1e-3)
     self.assertNear(-expected_fldj_, ildj_, err=1e-3)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testOneByOneMatrix(self):
     inv = bijectors.MatrixInverseTriL(validate_args=True)
     x_ = np.array([[5.]], dtype=np.float32)
@@ -70,7 +70,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase):
     self.assertNear(expected_fldj_, fldj_, err=1e-3)
     self.assertNear(-expected_fldj_, ildj_, err=1e-3)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testZeroByZeroMatrix(self):
     inv = bijectors.MatrixInverseTriL(validate_args=True)
     x_ = np.eye(0, dtype=np.float32)
@@ -89,7 +89,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase):
     self.assertNear(expected_fldj_, fldj_, err=1e-3)
     self.assertNear(-expected_fldj_, ildj_, err=1e-3)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testBatch(self):
     # Test batch computation with input shape (2, 1, 2, 2), i.e. batch shape
     # (2, 1).
@@ -114,7 +114,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase):
     self.assertAllClose(expected_fldj_, fldj_, atol=0., rtol=1e-3)
     self.assertAllClose(-expected_fldj_, ildj_, atol=0., rtol=1e-3)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testErrorOnInputRankTooLow(self):
     inv = bijectors.MatrixInverseTriL(validate_args=True)
     x_ = np.array([0.1], dtype=np.float32)
@@ -149,7 +149,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase):
   ##                                              square_error_msg):
   ##       inv.inverse_log_det_jacobian(x_, event_ndims=2).eval()
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testErrorOnInputNotLowerTriangular(self):
     inv = bijectors.MatrixInverseTriL(validate_args=True)
     x_ = np.array([[1., 2.],
@@ -169,7 +169,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase):
                                                triangular_error_msg):
         inv.inverse_log_det_jacobian(x_, event_ndims=2).eval()
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testErrorOnInputSingular(self):
     inv = bijectors.MatrixInverseTriL(validate_args=True)
     x_ = np.array([[1., 0.],
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py
index a5f5219588..cb42331a21 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py
@@ -36,7 +36,7 @@ class OrderedBijectorTest(test.TestCase):
   def setUp(self):
     self._rng = np.random.RandomState(42)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testBijectorVector(self):
     with self.test_session():
       ordered = Ordered()
@@ -82,7 +82,7 @@ class OrderedBijectorTest(test.TestCase):
           atol=0.,
           rtol=1e-7)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testShapeGetters(self):
     with self.test_session():
       x = tensor_shape.TensorShape([4])
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/scale_tril_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/scale_tril_test.py
index 566a7b3dff..d5b3367f9a 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/scale_tril_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/scale_tril_test.py
@@ -46,7 +46,7 @@ class ScaleTriLBijectorTest(test.TestCase):
     x_ = self.evaluate(b.inverse(y))
     self.assertAllClose(x, x_)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testInvertible(self):
 
     # Generate random inputs from an unconstrained space, with
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softsign_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softsign_test.py
index 2ac06fce55..d0098c3c10 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softsign_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softsign_test.py
@@ -40,7 +40,7 @@ class SoftsignBijectorTest(test.TestCase):
   def setUp(self):
     self._rng = np.random.RandomState(42)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testBijectorBounds(self):
     bijector = Softsign(validate_args=True)
     with self.test_session():
@@ -54,7 +54,7 @@ class SoftsignBijectorTest(test.TestCase):
       with self.assertRaisesOpError("less than 1"):
         bijector.inverse_log_det_jacobian(3., event_ndims=0).eval()
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testBijectorForwardInverse(self):
     bijector = Softsign(validate_args=True)
     self.assertEqual("softsign", bijector.name)
@@ -64,7 +64,7 @@ class SoftsignBijectorTest(test.TestCase):
     self.assertAllClose(y, self.evaluate(bijector.forward(x)))
     self.assertAllClose(x, self.evaluate(bijector.inverse(y)))
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testBijectorLogDetJacobianEventDimsZero(self):
     bijector = Softsign(validate_args=True)
     y = self._rng.rand(2, 10)
@@ -74,7 +74,7 @@ class SoftsignBijectorTest(test.TestCase):
     self.assertAllClose(ildj, self.evaluate(
         bijector.inverse_log_det_jacobian(y, event_ndims=0)))
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testBijectorForwardInverseEventDimsOne(self):
     bijector = Softsign(validate_args=True)
     self.assertEqual("softsign", bijector.name)
@@ -83,7 +83,7 @@ class SoftsignBijectorTest(test.TestCase):
     self.assertAllClose(y, self.evaluate(bijector.forward(x)))
     self.assertAllClose(x, self.evaluate(bijector.inverse(y)))
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testBijectorLogDetJacobianEventDimsOne(self):
     bijector = Softsign(validate_args=True)
     y = self._rng.rand(2, 10)
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/transform_diagonal_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/transform_diagonal_test.py
index 6428a68702..efc9f266d1 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/transform_diagonal_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/transform_diagonal_test.py
@@ -31,7 +31,7 @@ class TransformDiagonalBijectorTest(test.TestCase):
   def setUp(self):
     self._rng = np.random.RandomState(42)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testBijector(self):
     x = np.float32(np.random.randn(3, 4, 4))
 
diff --git a/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py b/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py
index bbbec2103a..181c46d2e5 100644
--- a/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py
+++ b/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py
@@ -544,7 +544,7 @@ class PadDynamicTest(_PadTest, test.TestCase):
 
 class TestMoveDimension(test.TestCase):
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_move_dimension_static_shape(self):
 
     x = random_ops.random_normal(shape=[200, 30, 4, 1, 6])
@@ -561,7 +561,7 @@ class TestMoveDimension(test.TestCase):
     x_perm = distribution_util.move_dimension(x, 4, 2)
     self.assertAllEqual(x_perm.shape.as_list(), [200, 30, 6, 4, 1])
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_move_dimension_dynamic_shape(self):
 
     x_ = random_ops.random_normal(shape=[200, 30, 4, 1, 6])
diff --git a/tensorflow/contrib/eager/python/metrics_test.py b/tensorflow/contrib/eager/python/metrics_test.py
index 644d78f61f..20d938d492 100644
--- a/tensorflow/contrib/eager/python/metrics_test.py
+++ b/tensorflow/contrib/eager/python/metrics_test.py
@@ -206,7 +206,7 @@ class MetricsTest(test.TestCase):
       sess.run(accumulate, feed_dict={p: 7})
       self.assertAllEqual(m.result().eval(), 7)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testGraphAndEagerTensor(self):
     m = metrics.Mean()
     inputs = ops.convert_to_tensor([1.0, 2.0])
@@ -254,7 +254,7 @@ class MetricsTest(test.TestCase):
       self.assertAllEqual(m2.result().eval(), 2.0)
       self.assertAllEqual(m1.result().eval(), 1.0)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testSaveRestore(self):
     checkpoint_directory = self.get_temp_dir()
     checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt")
diff --git a/tensorflow/contrib/eager/python/network_test.py b/tensorflow/contrib/eager/python/network_test.py
index c92bd15b25..240f213c60 100644
--- a/tensorflow/contrib/eager/python/network_test.py
+++ b/tensorflow/contrib/eager/python/network_test.py
@@ -126,7 +126,7 @@ class NetworkTest(test.TestCase):
     self.assertAllEqual([[17.0], [34.0]], self.evaluate(result))
 
   # TODO(allenl): This test creates garbage in some Python versions
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testNetworkSaveRestoreAlreadyBuilt(self):
     net = MyNetwork(name="abcd")
     with self.assertRaisesRegexp(
@@ -138,7 +138,7 @@ class NetworkTest(test.TestCase):
     self._save_modify_load_network_built(net, global_step=10)
 
   # TODO(allenl): This test creates garbage in some Python versions
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testSaveRestoreDefaultGlobalStep(self):
     net = MyNetwork(name="abcd")
     net(constant_op.constant([[2.0]]))
@@ -149,7 +149,7 @@ class NetworkTest(test.TestCase):
     self.assertIn("abcd-4242", save_path)
 
   # TODO(allenl): This test creates garbage in some Python versions
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testNetworkSaveAndRestoreIntoUnbuilt(self):
     save_dir = self.get_temp_dir()
     net1 = MyNetwork()
@@ -166,7 +166,7 @@ class NetworkTest(test.TestCase):
     self.assertAllEqual(self.evaluate(net1.variables[0]),
                         self.evaluate(net2.variables[0]))
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testNetworkMatchesLayerVariableNames(self):
     zero = constant_op.constant([[0.]])
     layer_one = core.Dense(1, use_bias=False)
@@ -193,7 +193,7 @@ class NetworkTest(test.TestCase):
     self.assertEqual("two_layer_net/" + layer_two.variables[0].name,
                      net.second.variables[0].name)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testLoadIntoUnbuiltSharedLayer(self):
 
     class Owner(network.Network):
@@ -272,7 +272,7 @@ class NetworkTest(test.TestCase):
       network.restore_network_checkpoint(
           load_into, save_path, map_func=_restore_map_func)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testRestoreIntoSubNetwork(self):
 
     class Parent(network.Network):
@@ -327,7 +327,7 @@ class NetworkTest(test.TestCase):
       # The checkpoint is incompatible.
       network.restore_network_checkpoint(save_into_parent, checkpoint)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testCustomMapCollisionErrors(self):
 
     class Parent(network.Network):
@@ -372,7 +372,7 @@ class NetworkTest(test.TestCase):
       network.restore_network_checkpoint(
           loader, checkpoint, map_func=lambda n: "foo")
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testDefaultMapCollisionErrors(self):
 
     one = constant_op.constant([[1.]])
@@ -571,7 +571,7 @@ class NetworkTest(test.TestCase):
         expected_start="my_network_1/dense/",
         actual=outside_net_after.trainable_weights[0].name)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testVariableScopeStripping(self):
     with variable_scope.variable_scope("scope1"):
       with variable_scope.variable_scope("scope2"):
@@ -596,7 +596,7 @@ class NetworkTest(test.TestCase):
     self.assertAllEqual([[42.]],
                         self.evaluate(restore_net.variables[0]))
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testLayerNamesRespected(self):
     class ParentNetwork(network.Network):
 
@@ -677,7 +677,7 @@ class NetworkTest(test.TestCase):
     self.assertStartsWith(expected_start="my_network_1/dense/",
                           actual=net2.trainable_weights[0].name)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testNestableAnonymous(self):
 
     # The case where no explicit names are specified. We make up unique names,
@@ -721,7 +721,7 @@ class NetworkTest(test.TestCase):
     self.assertEqual("my_network", net2.first.name)
     self.assertEqual("my_network_1", net2.second.name)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testNestableExplicit(self):
 
     # We have explicit network names and everything is globally unique.
@@ -750,7 +750,7 @@ class NetworkTest(test.TestCase):
     self.assertEqual("first_unique_child_name", net.first.name)
     self.assertEqual("second_unique_child_name", net.second.name)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testLayerNetworkNameInteractions(self):
 
     # Same base name as core.Dense; Networks and non-Network Layers with the
@@ -801,7 +801,7 @@ class NetworkTest(test.TestCase):
                           actual=net.trainable_weights[4].name)
     self.assertEqual("mixed_layer_network", net.name)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testNestableExplicitCollisions(self):
 
     # We have explicit network names and they are unique within the layer
@@ -831,7 +831,7 @@ class NetworkTest(test.TestCase):
     self.assertEqual("nonunique_name", net.first.name)
     self.assertEqual("second_unique_child_name", net.second.name)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testNestableExplicitWithAnonymousParent(self):
 
     # A parent network is instantiated multiple times with explicitly named
@@ -873,7 +873,7 @@ class NetworkTest(test.TestCase):
     self.assertEqual("first_unique_child_name", net2.first.name)
     self.assertEqual("second_unique_child_name", net2.second.name)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testNestableExplicitSameLayerCollisions(self):
 
     # We have explicit network names and they are _not_ unique within the layer
@@ -891,7 +891,7 @@ class NetworkTest(test.TestCase):
     with self.assertRaisesRegexp(ValueError, "nonunique_name"):
       ParentNetwork()
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testAnonymousVariableSharing(self):
 
     # Two "owned" Networks
@@ -989,7 +989,7 @@ class NetworkTest(test.TestCase):
     self.assertEqual("my_network", net4.first.name)
     self.assertEqual("my_network", net4.second.name)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testRecursiveLayerRenaming(self):
     core.Dense(1)  # Under default Layer naming, would change subsequent names.
 
@@ -1041,7 +1041,7 @@ class NetworkTest(test.TestCase):
     self.assertEqual("dense", net.second.first.name)
     self.assertEqual("dense_1", net.second.second.name)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testCallInDifferentOrderThanConstruct(self):
     shared_network = MyNetwork()
 
@@ -1091,7 +1091,7 @@ class NetworkTest(test.TestCase):
     self.assertTrue(net2.first is net1.first)
     self.assertEqual("my_network", net2.second.name)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testLayerCallInDifferentOrderThanConstruct(self):
     # Same idea as testCallInDifferentOrderThanConstruct, but this time with a
     # non-Network Layer shared between two Networks rather than a
@@ -1144,7 +1144,7 @@ class NetworkTest(test.TestCase):
     self.assertTrue(net2.first is net1.first)
     self.assertEqual("dense", net2.second.name)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testLayerAlreadyBuilt(self):
     one = constant_op.constant([[1.]])
     core.Dense(1, use_bias=False)  # pre-built layers use global naming
diff --git a/tensorflow/contrib/framework/python/ops/critical_section_test.py b/tensorflow/contrib/framework/python/ops/critical_section_test.py
index df7d7e9dae..34fd5018af 100644
--- a/tensorflow/contrib/framework/python/ops/critical_section_test.py
+++ b/tensorflow/contrib/framework/python/ops/critical_section_test.py
@@ -34,7 +34,7 @@ from tensorflow.python.platform import tf_logging as logging
 
 class CriticalSectionTest(test.TestCase):
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testCreateCriticalSection(self):
     cs = critical_section_ops.CriticalSection(shared_name="cs")
     v = resource_variable_ops.ResourceVariable(0.0, name="v")
@@ -53,7 +53,7 @@ class CriticalSectionTest(test.TestCase):
     self.assertAllClose([2.0 * i for i in range(num_concurrent)],
                         sorted(r_value))
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testCriticalSectionWithControlFlow(self):
     for outer_cond in [False, True]:
       for inner_cond in [False, True]:
@@ -109,7 +109,7 @@ class CriticalSectionTest(test.TestCase):
       with self.assertRaisesOpError("Error"):
         self.evaluate(r)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testCreateCriticalSectionFnReturnsOp(self):
     cs = critical_section_ops.CriticalSection(shared_name="cs")
     v = resource_variable_ops.ResourceVariable(0.0, name="v")
@@ -332,7 +332,7 @@ class CriticalSectionTest(test.TestCase):
     self.evaluate(v.initializer)
     self.assertEqual(10, self.evaluate(out))
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testInsideFunction(self):
     cs = critical_section_ops.CriticalSection()
     v = resource_variable_ops.ResourceVariable(1)
diff --git a/tensorflow/contrib/lookup/lookup_ops_test.py b/tensorflow/contrib/lookup/lookup_ops_test.py
index 5a080cceab..889accdd5a 100644
--- a/tensorflow/contrib/lookup/lookup_ops_test.py
+++ b/tensorflow/contrib/lookup/lookup_ops_test.py
@@ -1397,7 +1397,7 @@ class KeyValueTensorInitializerTest(test.TestCase):
 
 class IndexTableFromTensor(test.TestCase):
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_index_table_from_tensor_with_tensor_init(self):
     table = lookup.index_table_from_tensor(
         mapping=("brain", "salad", "surgery"), num_oov_buckets=1)
@@ -1670,7 +1670,7 @@ class InitializeTableFromFileOpTest(test.TestCase):
       f.write("\n".join(values) + "\n")
     return vocabulary_file
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testInitializeStringTable(self):
     vocabulary_file = self._createVocabFile("one_column_1.txt")
     default_value = -1
diff --git a/tensorflow/contrib/mixed_precision/python/loss_scale_manager_test.py b/tensorflow/contrib/mixed_precision/python/loss_scale_manager_test.py
index 480f5f6eaf..1b0383d24c 100644
--- a/tensorflow/contrib/mixed_precision/python/loss_scale_manager_test.py
+++ b/tensorflow/contrib/mixed_precision/python/loss_scale_manager_test.py
@@ -34,7 +34,7 @@ def _GetExampleIter(inputs):
 
 class FixedLossScaleManagerTest(test.TestCase):
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_basic(self):
     itr = _GetExampleIter([True] * 10 + [False] * 10)
 
@@ -84,13 +84,13 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase):
       actual_outputs.append(self.evaluate(lsm.get_loss_scale()))
     self.assertEqual(actual_outputs, expected_outputs)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_increase_every_n_steps(self):
     inputs = [True] * 6
     expected_outputs = [1, 2, 2, 4, 4, 8]
     self._test_helper(inputs, expected_outputs)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_keep_increasing_until_capped(self):
     init_loss_scale = np.finfo(np.float32).max / 4 + 10
     max_float = np.finfo(np.float32).max
@@ -104,7 +104,7 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase):
 
     self._test_helper(inputs, expected_outputs, init_loss_scale)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_decrease_every_n_steps(self):
     inputs = [False] * 6
     init_loss_scale = 1024
@@ -112,7 +112,7 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase):
 
     self._test_helper(inputs, expected_outputs, init_loss_scale)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_keep_decreasing_until_one(self):
     inputs = [False] * 10
     init_loss_scale = 16
@@ -120,19 +120,19 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase):
 
     self._test_helper(inputs, expected_outputs, init_loss_scale)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_incr_bad_step_clear_good_step(self):
     inputs = [True, True, True, False, True]
     expected_outputs = [1, 2, 2, 2, 2]
     self._test_helper(inputs, expected_outputs)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_incr_good_step_does_not_clear_bad_step(self):
     inputs = [True, True, True, False, True, False]
     expected_outputs = [1, 2, 2, 2, 2, 1]
     self._test_helper(inputs, expected_outputs)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_trigger_loss_scale_update_each_step(self):
     """Test when incr_every_n_step and decr_every_n_nan_or_inf is 1."""
     init_loss_scale = 1
@@ -145,7 +145,7 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase):
     self._test_helper(inputs, expected_outputs, init_loss_scale,
                       incr_every_n_step, decr_every_n_nan_or_inf)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_alternating_good_and_bad_gradients_trigger_each_step(self):
     init_loss_scale = 1
     incr_every_n_step = 1
@@ -156,7 +156,7 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase):
     self._test_helper(inputs, expected_outputs, init_loss_scale,
                       incr_every_n_step, decr_every_n_nan_or_inf)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_alternating_good_and_bad_gradients_trigger_incr_every_2steps(self):
     init_loss_scale = 32
     incr_every_n_step = 2
@@ -167,7 +167,7 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase):
     self._test_helper(inputs, expected_outputs, init_loss_scale,
                       incr_every_n_step, decr_every_n_nan_or_inf)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_random_mix_good_and_bad_gradients(self):
     init_loss_scale = 4
     inputs = [
diff --git a/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer_test.py b/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer_test.py
index dded61ccd5..9009df0eef 100644
--- a/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer_test.py
+++ b/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer_test.py
@@ -54,7 +54,7 @@ class LossScaleOptimizerTest(test.TestCase):
       opt = loss_scale_opt_fn(opt)
     return x, loss, opt
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_float16_underflow_without_loss_scale(self):
     lr = 1
     init_val = 1.
@@ -73,7 +73,7 @@ class LossScaleOptimizerTest(test.TestCase):
         rtol=0,
         atol=min(symbolic_update, 1e-6))
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_float16_with_loss_scale(self):
     lr = 1.
     init_val = 1.
@@ -95,7 +95,7 @@ class LossScaleOptimizerTest(test.TestCase):
         rtol=0,
         atol=min(expected_update, 1e-6))
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_compute_gradients_with_loss_scale(self):
     lr = 1
     init_val = 1.
@@ -115,7 +115,7 @@ class LossScaleOptimizerTest(test.TestCase):
     # Gradients aren't applied.
     self.assertAllClose(init_val, self.evaluate(x), rtol=0, atol=1e-6)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_compute_gradients_without_loss_scale(self):
     lr = 1
     init_val = 1.
@@ -127,7 +127,7 @@ class LossScaleOptimizerTest(test.TestCase):
     g_v = self.evaluate(grads_and_vars[0][0])
     self.assertAllClose(g_v, 0)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_apply_gradients(self):
 
     x = variable_scope.get_variable("x", initializer=1., dtype=dtypes.float32)
@@ -155,7 +155,7 @@ class LossScaleOptimizerTest(test.TestCase):
       actual_output.append(self.evaluate(x))
     self.assertAllClose(expected_output, actual_output)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_apply_gradients_loss_scale_is_updated(self):
 
     class SimpleLossScaleManager(lsm_lib.LossScaleManager):
diff --git a/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py b/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py
index 64b95786b5..b6972a7a45 100644
--- a/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py
+++ b/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py
@@ -226,7 +226,7 @@ class CheckpointingTests(test.TestCase):
             optimizer_node.slot_variables[0]
             .slot_variable_node_id].attributes[0].checkpoint_key)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testSaveRestore(self):
     model = MyModel()
     optimizer = adam.AdamOptimizer(0.001)
@@ -347,7 +347,7 @@ class CheckpointingTests(test.TestCase):
             self.assertEqual(training_continuation + 1,
                              session.run(root.save_counter))
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testAgnosticUsage(self):
     """Graph/eager agnostic usage."""
     # Does create garbage when executing eagerly due to ops.Graph() creation.
@@ -381,7 +381,7 @@ class CheckpointingTests(test.TestCase):
                          self.evaluate(root.save_counter))
 
   # pylint: disable=cell-var-from-loop
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testWithDefun(self):
     num_training_steps = 2
     checkpoint_directory = self.get_temp_dir()
@@ -453,7 +453,7 @@ class CheckpointingTests(test.TestCase):
         optimizer.apply_gradients(
             [(g, v) for g, v in zip(grad, model.vars)])
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testDeferredSlotRestoration(self):
     checkpoint_directory = self.get_temp_dir()
 
@@ -616,7 +616,7 @@ class CheckpointingTests(test.TestCase):
 
 class TemplateTests(test.TestCase):
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def test_checkpointable_save_restore(self):
 
     def _templated():
@@ -712,7 +712,7 @@ class CheckpointCompatibilityTests(test.TestCase):
             sess=session, save_path=checkpoint_prefix,
             global_step=root.optimizer_step)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testLoadFromNameBasedSaver(self):
     """Save a name-based checkpoint, load it using the object-based API."""
     with test_util.device(use_gpu=True):
diff --git a/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py b/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py
index 8599af32f6..ec033c4a01 100644
--- a/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py
+++ b/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py
@@ -35,7 +35,7 @@ from tensorflow.python.platform import test
 
 class OptimizerTest(test.TestCase):
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testBasic(self):
     for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]):
       # Note that we name the variables uniquely here since the variables don't
@@ -113,7 +113,7 @@ class OptimizerTest(test.TestCase):
         self.assertAllClose([3.0 - 3 * 3 * 42.0, 4.0 - 3 * 3 * (-42.0)],
                             var1.eval())
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testNoVariables(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
       # pylint: disable=cell-var-from-loop
@@ -128,7 +128,7 @@ class OptimizerTest(test.TestCase):
       with self.assertRaisesRegexp(ValueError, 'No.*variables'):
         sgd_op.minimize(loss)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testNoGradients(self):
     for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]):
       # Note that we name the variables uniquely here since the variables don't
@@ -146,7 +146,7 @@ class OptimizerTest(test.TestCase):
         # var1 has no gradient
         sgd_op.minimize(loss, var_list=[var1])
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testNoGradientsForAnyVariables_Minimize(self):
     for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]):
       # Note that we name the variables uniquely here since the variables don't
@@ -162,7 +162,7 @@ class OptimizerTest(test.TestCase):
                                    'No gradients provided for any variable'):
         sgd_op.minimize(loss, var_list=[var0, var1])
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testNoGradientsForAnyVariables_ApplyGradients(self):
     for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]):
       # Note that we name the variables uniquely here since the variables don't
@@ -176,7 +176,7 @@ class OptimizerTest(test.TestCase):
                                    'No gradients provided for any variable'):
         sgd_op.apply_gradients([(None, var0), (None, var1)])
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testGradientsAsVariables(self):
     for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]):
       # Note that we name the variables uniquely here since the variables don't
@@ -216,7 +216,7 @@ class OptimizerTest(test.TestCase):
       self.assertAllClose([-14., -13.], self.evaluate(var0))
       self.assertAllClose([-6., -5.], self.evaluate(var1))
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testComputeGradientsWithTensors(self):
     x = ops.convert_to_tensor(1.0)
     def f():
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
index b8840a8f24..86f1e27abd 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py
@@ -443,7 +443,7 @@ class RNNCellTest(test.TestCase):
           self.assertTrue(
               float(np.linalg.norm((res[1][0, :] - res[1][i, :]))) < 1e-6)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testWrapperCheckpointing(self):
     for wrapper_type in [
         rnn_cell_impl.DropoutWrapper,
diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
index be99a5d67a..1c20d88fe4 100644
--- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
+++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py
@@ -921,7 +921,7 @@ class LSTMTest(test.TestCase):
     # Smoke test, this should not raise an error
     rnn.dynamic_rnn(cell, inputs, dtype=dtypes.float32)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testDynamicRNNWithTupleStates(self):
     num_units = 3
     input_size = 5
@@ -997,7 +997,7 @@ class LSTMTest(test.TestCase):
         self.assertAllEqual(array_ops.stack(outputs_static), outputs_dynamic)
       self.assertAllEqual(np.hstack(state_static), np.hstack(state_dynamic))
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testDynamicRNNWithNestedTupleStates(self):
     num_units = 3
     input_size = 5
@@ -1285,7 +1285,7 @@ class LSTMTest(test.TestCase):
             "Comparing individual variable gradients iteration %d" % i)
         self.assertAllEqual(a, b)
 
-  @test_util.run_in_graph_and_eager_modes()
+  @test_util.run_in_graph_and_eager_modes
   def testDynamicEquivalentToStaticRNN(self):
     self._testDynamicEquivalentToStaticRNN(use_sequence_length=False)
     self._testDynamicEquivalentToStaticRNN(use_sequence_length=False)