diff --git a/tensorflow/python/training/gradient_descent_test.py b/tensorflow/python/training/gradient_descent_test.py
index 2c7756e403f..e1a99bbbcf9 100644
--- a/tensorflow/python/training/gradient_descent_test.py
+++ b/tensorflow/python/training/gradient_descent_test.py
@@ -24,7 +24,6 @@ from tensorflow.python.eager import function
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
-from tensorflow.python.framework import test_util
 from tensorflow.python.ops import embedding_ops
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import resource_variable_ops
@@ -36,10 +35,10 @@ from tensorflow.python.training import gradient_descent
 
 class GradientDescentOptimizerTest(test.TestCase):
 
-  @test_util.run_deprecated_v1
   def testBasic(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.cached_session():
+      # train.GradientDescentOptimizer is V1 only API.
+      with ops.Graph().as_default(), self.cached_session():
         var0 = variables.Variable([1.0, 2.0], dtype=dtype)
         var1 = variables.Variable([3.0, 4.0], dtype=dtype)
         grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
@@ -60,10 +59,10 @@ class GradientDescentOptimizerTest(test.TestCase):
                                            self.evaluate(var1))
         self.assertEqual(0, len(optimizer.variables()))
 
-  @test_util.run_deprecated_v1
   def testBasicResourceVariable(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.cached_session():
+      # train.GradientDescentOptimizer is V1 only API.
+      with ops.Graph().as_default(), self.cached_session():
         var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
         var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype)
         grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
@@ -86,10 +85,10 @@ class GradientDescentOptimizerTest(test.TestCase):
         self.assertAllCloseAccordingToType([3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01],
                                            self.evaluate(var1))
 
-  @test_util.run_deprecated_v1
   def testBasicCallableParams(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.cached_session():
+      # train.GradientDescentOptimizer is V1 only API.
+      with ops.Graph().as_default(), self.cached_session():
         var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
         var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype)
         grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
@@ -113,10 +112,10 @@ class GradientDescentOptimizerTest(test.TestCase):
         self.assertAllCloseAccordingToType([3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01],
                                            self.evaluate(var1))
 
-  @test_util.run_deprecated_v1
   def testMinimizeResourceVariable(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.cached_session():
+      # train.GradientDescentOptimizer is V1 only API.
+      with ops.Graph().as_default(), self.cached_session():
         var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype)
         var1 = resource_variable_ops.ResourceVariable([3.0], dtype=dtype)
         x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
@@ -140,10 +139,10 @@ class GradientDescentOptimizerTest(test.TestCase):
             [[1.0 - np_grad * 4.0, 2.0 - np_grad * 5.0]], self.evaluate(var0))
         self.assertAllCloseAccordingToType([3.0 - np_grad], self.evaluate(var1))
 
-  @test_util.run_deprecated_v1
   def testMinimizeSparseResourceVariable(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.cached_session():
+      # train.GradientDescentOptimizer is V1 only API.
+      with ops.Graph().as_default(), self.cached_session():
         var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype)
         var1 = resource_variable_ops.ResourceVariable([3.0], dtype=dtype)
         x = constant_op.constant([[4.0], [5.0]], dtype=dtype)
@@ -168,10 +167,10 @@ class GradientDescentOptimizerTest(test.TestCase):
             [[1.0 - np_grad * 4.0, 2.0 - np_grad * 5.0]], self.evaluate(var0))
         self.assertAllCloseAccordingToType([3.0 - np_grad], self.evaluate(var1))
 
-  @test_util.run_deprecated_v1
   def testTensorLearningRate(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.cached_session():
+      # train.GradientDescentOptimizer is V1 only API.
+      with ops.Graph().as_default(), self.cached_session():
         var0 = variables.Variable([1.0, 2.0], dtype=dtype)
         var1 = variables.Variable([3.0, 4.0], dtype=dtype)
         grads0 = constant_op.constant([0.1, 0.1], dtype=dtype)
@@ -191,10 +190,10 @@ class GradientDescentOptimizerTest(test.TestCase):
         self.assertAllCloseAccordingToType([3.0 - 3.0 * 0.01, 4.0 - 3.0 * 0.01],
                                            self.evaluate(var1))
 
-  @test_util.run_deprecated_v1
   def testGradWrtRef(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.cached_session():
+      # train.GradientDescentOptimizer is V1 only API.
+      with ops.Graph().as_default(), self.cached_session():
         opt = gradient_descent.GradientDescentOptimizer(3.0)
         values = [1.0, 3.0]
         vars_ = [variables.Variable([v], dtype=dtype) for v in values]
@@ -203,10 +202,10 @@ class GradientDescentOptimizerTest(test.TestCase):
         for grad, _ in grads_and_vars:
           self.assertAllCloseAccordingToType([1.0], self.evaluate(grad))
 
-  @test_util.run_deprecated_v1
   def testWithGlobalStep(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.cached_session():
+      # train.GradientDescentOptimizer is V1 only API.
+      with ops.Graph().as_default(), self.cached_session():
         global_step = variables.Variable(0, trainable=False)
         var0 = variables.Variable([1.0, 2.0], dtype=dtype)
         var1 = variables.Variable([3.0, 4.0], dtype=dtype)
@@ -227,10 +226,10 @@ class GradientDescentOptimizerTest(test.TestCase):
                                            self.evaluate(var1))
         self.assertAllCloseAccordingToType(1, self.evaluate(global_step))
 
-  @test_util.run_deprecated_v1
   def testSparseBasic(self):
     for dtype in [dtypes.half, dtypes.float32, dtypes.float64]:
-      with self.cached_session():
+      # train.GradientDescentOptimizer is V1 only API.
+      with ops.Graph().as_default(), self.cached_session():
         var0 = variables.Variable([[1.0], [2.0]], dtype=dtype)
         var1 = variables.Variable([[3.0], [4.0]], dtype=dtype)
         grads0 = ops.IndexedSlices(