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

PiperOrigin-RevId: 201652888

2 files changed, 17 insertions, 17 deletions

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):