diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/AdaDelta.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/AdaDelta.java
index 19f7584b152..15c5ff4d0cb 100644
--- a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/AdaDelta.java
+++ b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/AdaDelta.java
@@ -20,6 +20,7 @@
 import org.tensorflow.Operand;
 import org.tensorflow.Output;
 import org.tensorflow.op.Op;
+import org.tensorflow.op.Ops;
 import org.tensorflow.op.core.Variable;
 import org.tensorflow.op.train.ApplyAdadelta;
 import org.tensorflow.types.family.TType;
@@ -150,16 +151,16 @@ private <T extends TType> void createAdaDeltaSlot(Output<T> v) {
 
   /** {@inheritDoc} */
   @Override
-  protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable) {
+  protected <T extends TType> Op applyDense(Ops deps, Output<T> gradient, Output<T> variable) {
     Variable<T> accumSlot = getSlot(variable, ACCUMULATOR).get();
     Variable<T> accumUpdateSlot = getSlot(variable, ACCUMULATOR_UPDATE).get();
-    return tf.train.applyAdadelta(
+    return deps.train.applyAdadelta(
         variable,
         accumSlot,
         accumUpdateSlot,
-        tf.dtypes.cast(tf.constant(learningRate), gradient.type()),
-        tf.dtypes.cast(tf.constant(rho), gradient.type()),
-        tf.dtypes.cast(tf.constant(epsilon), gradient.type()),
+        deps.dtypes.cast(deps.constant(learningRate), gradient.type()),
+        deps.dtypes.cast(deps.constant(rho), gradient.type()),
+        deps.dtypes.cast(deps.constant(epsilon), gradient.type()),
         gradient,
         ApplyAdadelta.useLocking(true));
   }
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/AdaGrad.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/AdaGrad.java
index 5c51bbc1e4b..fc11f60e1f4 100644
--- a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/AdaGrad.java
+++ b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/AdaGrad.java
@@ -20,6 +20,7 @@
 import org.tensorflow.Operand;
 import org.tensorflow.Output;
 import org.tensorflow.op.Op;
+import org.tensorflow.op.Ops;
 import org.tensorflow.op.core.Variable;
 import org.tensorflow.op.train.ApplyAdagrad;
 import org.tensorflow.types.family.TType;
@@ -140,10 +141,10 @@ private <T extends TType> void createAdaGradSlot(Output<T> v) {
 
   /** {@inheritDoc} */
   @Override
-  protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable) {
+  protected <T extends TType> Op applyDense(Ops deps, Output<T> gradient, Output<T> variable) {
     Variable<T> slot = getSlot(variable, ACCUMULATOR).get();
-    return tf.train.applyAdagrad(
-        variable, slot, tf.dtypes.cast(tf.constant(learningRate), gradient.type()), gradient, opts);
+    return deps.train.applyAdagrad(
+        variable, slot, deps.dtypes.cast(deps.constant(learningRate), gradient.type()), gradient, opts);
   }
 
   /** {@inheritDoc} */
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/AdaGradDA.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/AdaGradDA.java
index 62ab8d309c9..8dba8105d3e 100644
--- a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/AdaGradDA.java
+++ b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/AdaGradDA.java
@@ -22,6 +22,7 @@
 import org.tensorflow.Output;
 import org.tensorflow.ndarray.Shape;
 import org.tensorflow.op.Op;
+import org.tensorflow.op.Ops;
 import org.tensorflow.op.core.Variable;
 import org.tensorflow.op.train.ApplyAdagradDa;
 import org.tensorflow.types.TInt64;
@@ -209,17 +210,17 @@ private <T extends TType> void createAdaGradDASlot(Output<T> v) {
 
   /** {@inheritDoc} */
   @Override
-  protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable) {
+  protected <T extends TType> Op applyDense(Ops deps, Output<T> gradient, Output<T> variable) {
     Variable<T> gradSlot = getSlot(variable, ACCUMULATOR).get();
     Variable<T> gradSquaredSlot = getSlot(variable, SQUARED_ACCUMULATOR).get();
-    return tf.train.applyAdagradDa(
+    return deps.train.applyAdagradDa(
         variable,
         gradSlot,
         gradSquaredSlot,
         gradient,
-        tf.dtypes.cast(tf.constant(learningRate), gradient.type()),
-        tf.dtypes.cast(tf.constant(l1Strength), gradient.type()),
-        tf.dtypes.cast(tf.constant(l2Strength), gradient.type()),
+        deps.dtypes.cast(deps.constant(learningRate), gradient.type()),
+        deps.dtypes.cast(deps.constant(l1Strength), gradient.type()),
+        deps.dtypes.cast(deps.constant(l2Strength), gradient.type()),
         globalStep,
         ApplyAdagradDa.useLocking(true));
   }
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Adam.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Adam.java
index 6cf1dbcc7c5..c9a2a483c73 100644
--- a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Adam.java
+++ b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Adam.java
@@ -22,6 +22,7 @@
 import org.tensorflow.Output;
 import org.tensorflow.ndarray.Shape;
 import org.tensorflow.op.Op;
+import org.tensorflow.op.Ops;
 import org.tensorflow.op.Scope;
 import org.tensorflow.op.annotation.Endpoint;
 import org.tensorflow.op.annotation.Operator;
@@ -223,19 +224,19 @@ private <T extends TType> void createAdamSlot(Output<T> v) {
 
   /** {@inheritDoc} */
   @Override
-  protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable) {
+  protected <T extends TType> Op applyDense(Ops deps, Output<T> gradient, Output<T> variable) {
     Variable<T> firstMomentSlot = getSlot(variable, FIRST_MOMENT).get();
     Variable<T> secondMomentSlot = getSlot(variable, SECOND_MOMENT).get();
-    return tf.train.applyAdam(
+    return deps.train.applyAdam(
         variable,
         firstMomentSlot,
         secondMomentSlot,
-        tf.dtypes.cast(betaOnePower, gradient.type()),
-        tf.dtypes.cast(betaTwoPower, gradient.type()),
-        tf.dtypes.cast(learningRateConst, gradient.type()),
-        tf.dtypes.cast(betaOneConst, gradient.type()),
-        tf.dtypes.cast(betaTwoConst, gradient.type()),
-        tf.dtypes.cast(epsilonConst, gradient.type()),
+        deps.dtypes.cast(betaOnePower, gradient.type()),
+        deps.dtypes.cast(betaTwoPower, gradient.type()),
+        deps.dtypes.cast(learningRateConst, gradient.type()),
+        deps.dtypes.cast(betaOneConst, gradient.type()),
+        deps.dtypes.cast(betaTwoConst, gradient.type()),
+        deps.dtypes.cast(epsilonConst, gradient.type()),
         gradient,
         ApplyAdam.useLocking(true));
   }
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Adamax.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Adamax.java
index 635c2ecb862..0e6abfa0032 100644
--- a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Adamax.java
+++ b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Adamax.java
@@ -7,6 +7,7 @@
 import org.tensorflow.Output;
 import org.tensorflow.ndarray.Shape;
 import org.tensorflow.op.Op;
+import org.tensorflow.op.Ops;
 import org.tensorflow.op.core.Constant;
 import org.tensorflow.op.core.Variable;
 import org.tensorflow.op.train.ApplyAdaMax;
@@ -155,19 +156,19 @@ private <T extends TType> void createAdamaxSlot(Output<T> v) {
 
   /** {@inheritDoc} */
   @Override
-  protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable) {
+  protected <T extends TType> Op applyDense(Ops deps, Output<T> gradient, Output<T> variable) {
     Variable<T> firstMomentSlot = getSlot(variable, FIRST_MOMENT).get();
     Variable<T> secondMomentSlot = getSlot(variable, SECOND_MOMENT).get();
     return ApplyAdaMax.create(
-        this.tf.scope(),
+        deps.scope(),
         variable,
         firstMomentSlot,
         secondMomentSlot,
-        tf.dtypes.cast(betaOnePower, gradient.type()),
-        tf.dtypes.cast(learningRateConst, gradient.type()),
-        tf.dtypes.cast(betaOneConst, gradient.type()),
-        tf.dtypes.cast(betaTwoConst, gradient.type()),
-        tf.dtypes.cast(epsilonConst, gradient.type()),
+        deps.dtypes.cast(betaOnePower, gradient.type()),
+        deps.dtypes.cast(learningRateConst, gradient.type()),
+        deps.dtypes.cast(betaOneConst, gradient.type()),
+        deps.dtypes.cast(betaTwoConst, gradient.type()),
+        deps.dtypes.cast(epsilonConst, gradient.type()),
         gradient,
         ApplyAdaMax.useLocking(true));
   }
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Ftrl.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Ftrl.java
index 962b64bab8e..ad97573f586 100644
--- a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Ftrl.java
+++ b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Ftrl.java
@@ -5,6 +5,7 @@
 import org.tensorflow.Operand;
 import org.tensorflow.Output;
 import org.tensorflow.op.Op;
+import org.tensorflow.op.Ops;
 import org.tensorflow.op.core.Variable;
 import org.tensorflow.op.train.ApplyFtrl;
 import org.tensorflow.types.family.TType;
@@ -238,21 +239,21 @@ private <T extends TType> void createFtrlSlot(Output<T> v) {
 
   /** {@inheritDoc} */
   @Override
-  protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable) {
+  protected <T extends TType> Op applyDense(Ops deps, Output<T> gradient, Output<T> variable) {
     Variable<T> accumSlot = getSlot(variable, ACCUMULATOR).get();
     Variable<T> linearSlot = getSlot(variable, LINEAR_ACCUMULATOR).get();
     ApplyFtrl.Options options = ApplyFtrl.useLocking(true);
-    return this.tf.train.applyFtrl(
+    return deps.train.applyFtrl(
         variable,
         accumSlot, // accum
         linearSlot, // linear
         gradient, // gradient
-        tf.dtypes.cast(tf.constant(learningRate), gradient.type()), // lr
-        tf.dtypes.cast(tf.constant(l1RegularizationStrength), gradient.type()), // l1
-        tf.dtypes.cast(tf.constant(l2RegularizationStrength), gradient.type()), // l2
-        tf.dtypes.cast(
-            tf.constant(l2ShrinkageRegularizationStrength), gradient.type()), // l2Shrinkage
-        tf.dtypes.cast(tf.constant(learningRatePower), gradient.type()), // lrPower
+        deps.dtypes.cast(deps.constant(learningRate), gradient.type()), // lr
+        deps.dtypes.cast(deps.constant(l1RegularizationStrength), gradient.type()), // l1
+        deps.dtypes.cast(deps.constant(l2RegularizationStrength), gradient.type()), // l2
+        deps.dtypes.cast(
+            deps.constant(l2ShrinkageRegularizationStrength), gradient.type()), // l2Shrinkage
+        deps.dtypes.cast(deps.constant(learningRatePower), gradient.type()), // lrPower
         options);
   }
 
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/GradientDescent.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/GradientDescent.java
index 7e2ec9593ed..682f128f680 100644
--- a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/GradientDescent.java
+++ b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/GradientDescent.java
@@ -18,6 +18,7 @@
 import org.tensorflow.Graph;
 import org.tensorflow.Output;
 import org.tensorflow.op.Op;
+import org.tensorflow.op.Ops;
 import org.tensorflow.op.train.ApplyGradientDescent;
 import org.tensorflow.types.family.TType;
 
@@ -65,10 +66,10 @@ public GradientDescent(Graph graph, String name, float learningRate) {
 
   /** {@inheritDoc} */
   @Override
-  protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable) {
-    return tf.train.applyGradientDescent(
+  protected <T extends TType> Op applyDense(Ops deps, Output<T> gradient, Output<T> variable) {
+    return deps.train.applyGradientDescent(
         variable,
-        tf.dtypes.cast(tf.constant(learningRate), gradient.type()),
+        deps.dtypes.cast(deps.constant(learningRate), gradient.type()),
         gradient,
         ApplyGradientDescent.useLocking(true));
   }
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Momentum.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Momentum.java
index b1f6ac8f4e5..4de600808cf 100644
--- a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Momentum.java
+++ b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Momentum.java
@@ -20,6 +20,7 @@
 import org.tensorflow.Operand;
 import org.tensorflow.Output;
 import org.tensorflow.op.Op;
+import org.tensorflow.op.Ops;
 import org.tensorflow.op.core.Variable;
 import org.tensorflow.op.train.ApplyMomentum;
 import org.tensorflow.types.family.TType;
@@ -130,14 +131,14 @@ private <T extends TType> void createMomentumSlot(Output<T> v) {
 
   /** {@inheritDoc} */
   @Override
-  protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable) {
+  protected <T extends TType> Op applyDense(Ops deps, Output<T> gradient, Output<T> variable) {
     Variable<T> slot = getSlot(variable, MOMENTUM).get();
-    return tf.train.applyMomentum(
+    return deps.train.applyMomentum(
         variable,
         slot,
-        tf.dtypes.cast(tf.constant(learningRate), gradient.type()),
+        deps.dtypes.cast(deps.constant(learningRate), gradient.type()),
         gradient,
-        tf.dtypes.cast(tf.constant(momentum), gradient.type()),
+        deps.dtypes.cast(deps.constant(momentum), gradient.type()),
         ApplyMomentum.useNesterov(useNesterov),
         ApplyMomentum.useLocking(true));
   }
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Nadam.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Nadam.java
index f55fb8cdc59..9de52eb371b 100644
--- a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Nadam.java
+++ b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Nadam.java
@@ -7,6 +7,7 @@
 import org.tensorflow.Output;
 import org.tensorflow.ndarray.Shape;
 import org.tensorflow.op.Op;
+import org.tensorflow.op.Ops;
 import org.tensorflow.op.core.Assign;
 import org.tensorflow.op.core.Constant;
 import org.tensorflow.op.core.Variable;
@@ -224,53 +225,53 @@ protected Optional<Op> prepare(String scopeName) {
 
   /** {@inheritDoc} */
   @Override
-  protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable) {
+  protected <T extends TType> Op applyDense(Ops deps, Output<T> gradient, Output<T> variable) {
     Class<T> type = gradient.type();
     Variable<T> m = getSlot(variable, FIRST_MOMENT).get(); // first Moment
     Variable<T> v = getSlot(variable, SECOND_MOMENT).get(); // Second Moment
 
     //  gPrime = grad / coefficients['oneMinusMScheduleNew']
-    Operand<T> gPrime = tf.math.div(gradient, tf.dtypes.cast(oneMinusMScheduleNew, type));
+    Operand<T> gPrime = deps.math.div(gradient, deps.dtypes.cast(oneMinusMScheduleNew, type));
     // mT = (coefficients['beta_1_t'] * m + coefficients['one_minus_beta_1_t'] * grad)
     Operand<T> mT =
-        tf.math.add(
-            tf.math.mul(tf.dtypes.cast(betaOneConst, type), m),
-            tf.math.mul(tf.dtypes.cast(oneMinusBeta1, type), gradient));
+        deps.math.add(
+            deps.math.mul(deps.dtypes.cast(betaOneConst, type), m),
+            deps.math.mul(deps.dtypes.cast(oneMinusBeta1, type), gradient));
     // mT = state_ops.assign(m, mT, use_locking=self._use_locking)
     // update m
-    mT = tf.assign(m, mT, Assign.useLocking(true));
+    mT = deps.assign(m, mT, Assign.useLocking(true));
 
     // mTPrime = mT / coefficients['oneMinusMScheduleNext']
-    Operand<T> mTPrime = tf.math.div(mT, tf.dtypes.cast(oneMinusMScheduleNext, type));
+    Operand<T> mTPrime = deps.math.div(mT, deps.dtypes.cast(oneMinusMScheduleNext, type));
 
     // vT = (coefficients['beta_2_t'] * v + coefficients['one_minus_beta_2_t'] *
     // math_ops.square(grad))
     Operand<T> vT =
-        tf.math.add(
-            tf.math.mul(tf.dtypes.cast(betaTwoConst, type), v),
-            tf.math.mul(tf.dtypes.cast(oneMinusBeta2, type), tf.math.square(gradient)));
+        deps.math.add(
+            deps.math.mul(deps.dtypes.cast(betaTwoConst, type), v),
+            deps.math.mul(deps.dtypes.cast(oneMinusBeta2, type), deps.math.square(gradient)));
     // vT = state_ops.assign(v, vT, use_locking=self._use_locking)
     // update v
-    vT = tf.assign(v, vT, Assign.useLocking(true));
+    vT = deps.assign(v, vT, Assign.useLocking(true));
 
     // vTPrime = vT / coefficients['vTPrimeDenominator']
-    Operand<T> vTPrime = tf.math.div(vT, tf.dtypes.cast(vTPrimeDenominator, type));
+    Operand<T> vTPrime = deps.math.div(vT, deps.dtypes.cast(vTPrimeDenominator, type));
 
     // m_t_bar = (coefficients['oneMinusMT'] * gPrime + coefficients['mT1'] * mTPrime)
     Operand<T> m_t_bar =
-        tf.math.add(
-            tf.math.mul(tf.dtypes.cast(oneMinusMT, type), gPrime),
-            tf.math.mul(tf.dtypes.cast(mT1, type), mTPrime));
+        deps.math.add(
+            deps.math.mul(deps.dtypes.cast(oneMinusMT, type), gPrime),
+            deps.math.mul(deps.dtypes.cast(mT1, type), mTPrime));
     // varT = var - coefficients['lr_t'] * m_t_bar / (math_ops.sqrt(vTPrime) +
     // coefficients['epsilon'])
     Operand<T> varT =
-        tf.math.sub(
+        deps.math.sub(
             variable,
-            tf.math.div(
-                tf.math.mul(tf.dtypes.cast(learningRateConst, type), m_t_bar),
-                tf.math.add(tf.math.sqrt(vTPrime), tf.dtypes.cast(epsilonConst, type))));
+            deps.math.div(
+                deps.math.mul(deps.dtypes.cast(learningRateConst, type), m_t_bar),
+                deps.math.add(deps.math.sqrt(vTPrime), deps.dtypes.cast(epsilonConst, type))));
 
-    return tf.assign(variable, varT, Assign.useLocking(true));
+    return deps.assign(variable, varT, Assign.useLocking(true));
   }
 
   /**
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Optimizer.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Optimizer.java
index dc7047337e9..59129e8c103 100644
--- a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Optimizer.java
+++ b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/Optimizer.java
@@ -168,6 +168,8 @@ public Op applyGradients(List<GradAndVar<? extends TType>> gradsAndVars, String
         gradsAndVars.stream().map(GradAndVar::getVariable).collect(Collectors.toList());
 
     createSlots(variables);
+    List<Op> gradients = gradsAndVars.stream().map(GradAndVar::getGradient).filter(g -> !g.isClosed()).collect(Collectors.toList());
+    Ops tfOpsGrads = tf.withControlDependencies(gradients);
 
     Optional<Op> prepOp = prepare(name + "/prepare");
 
@@ -175,7 +177,7 @@ public Op applyGradients(List<GradAndVar<? extends TType>> gradsAndVars, String
     prepOp.ifPresent(updateOps::add);
     for (GradAndVar<? extends TType> pair : gradsAndVars) {
       if (!pair.gradient.isClosed()) {
-        updateOps.add(applyDense(pair));
+        updateOps.add(applyDense(tfOpsGrads, pair));
       }
     }
 
@@ -261,8 +263,8 @@ protected void createSlots(List<Output<? extends TType>> variables) {}
    * @param <T> the datatype of the gradients and variables.
    * @return An operand which applies the desired optimizer update to the variable.
    */
-  private <T extends TType> Op applyDense(GradAndVar<T> gradVarPair) {
-    return applyDense(gradVarPair.getGradient(), gradVarPair.getVariable());
+  private <T extends TType> Op applyDense(Ops opDependencies, GradAndVar<T> gradVarPair) {
+    return applyDense(opDependencies, gradVarPair.getGradient(), gradVarPair.getVariable());
   }
 
   /**
@@ -273,7 +275,7 @@ private <T extends TType> Op applyDense(GradAndVar<T> gradVarPair) {
    * @param <T> The type of the variable.
    * @return An operand which applies the desired optimizer update to the variable.
    */
-  protected abstract <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable);
+  protected abstract <T extends TType> Op applyDense(Ops opDependencies, Output<T> gradient, Output<T> variable);
 
   /**
    * Gathers up the update operations into a single op that can be used as a run target.
diff --git a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/RMSProp.java b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/RMSProp.java
index 0d4daf748d4..433f969a0db 100644
--- a/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/RMSProp.java
+++ b/tensorflow-framework/src/main/java/org/tensorflow/framework/optimizers/RMSProp.java
@@ -20,6 +20,7 @@
 import org.tensorflow.Operand;
 import org.tensorflow.Output;
 import org.tensorflow.op.Op;
+import org.tensorflow.op.Ops;
 import org.tensorflow.op.core.Variable;
 import org.tensorflow.op.train.ApplyCenteredRmsProp;
 import org.tensorflow.op.train.ApplyRmsProp;
@@ -189,31 +190,31 @@ private <T extends TType> void createRMSPropSlot(Output<T> v) {
 
   /** {@inheritDoc} */
   @Override
-  protected <T extends TType> Op applyDense(Output<T> gradient, Output<T> variable) {
+  protected <T extends TType> Op applyDense(Ops deps, Output<T> gradient, Output<T> variable) {
     Variable<T> rmsSlot = getSlot(variable, RMS).get();
     Variable<T> momentumSlot = getSlot(variable, MOMENTUM).get();
     if (centered) {
       Variable<T> mgSlot = getSlot(variable, MG).get();
-      return tf.train.applyCenteredRmsProp(
+      return deps.train.applyCenteredRmsProp(
           variable,
           mgSlot,
           rmsSlot,
           momentumSlot,
-          tf.dtypes.cast(tf.constant(learningRate), gradient.type()),
-          tf.dtypes.cast(tf.constant(decay), gradient.type()),
-          tf.dtypes.cast(tf.constant(momentum), gradient.type()),
-          tf.dtypes.cast(tf.constant(epsilon), gradient.type()),
+          deps.dtypes.cast(deps.constant(learningRate), gradient.type()),
+          deps.dtypes.cast(deps.constant(decay), gradient.type()),
+          deps.dtypes.cast(deps.constant(momentum), gradient.type()),
+          deps.dtypes.cast(deps.constant(epsilon), gradient.type()),
           gradient,
           ApplyCenteredRmsProp.useLocking(true));
     }
-    return tf.train.applyRmsProp(
+    return deps.train.applyRmsProp(
         variable,
         rmsSlot,
         momentumSlot,
-        tf.dtypes.cast(tf.constant(learningRate), gradient.type()),
-        tf.dtypes.cast(tf.constant(decay), gradient.type()),
-        tf.dtypes.cast(tf.constant(momentum), gradient.type()),
-        tf.dtypes.cast(tf.constant(epsilon), gradient.type()),
+        deps.dtypes.cast(deps.constant(learningRate), gradient.type()),
+        deps.dtypes.cast(deps.constant(decay), gradient.type()),
+        deps.dtypes.cast(deps.constant(momentum), gradient.type()),
+        deps.dtypes.cast(deps.constant(epsilon), gradient.type()),
         gradient,
         ApplyRmsProp.useLocking(true));
   }
diff --git a/tensorflow-framework/src/test/java/org/tensorflow/framework/optimizers/GradientDescentTest.java b/tensorflow-framework/src/test/java/org/tensorflow/framework/optimizers/GradientDescentTest.java
index 10ee78b70c5..d4bc0a7346f 100644
--- a/tensorflow-framework/src/test/java/org/tensorflow/framework/optimizers/GradientDescentTest.java
+++ b/tensorflow-framework/src/test/java/org/tensorflow/framework/optimizers/GradientDescentTest.java
@@ -110,10 +110,6 @@ public void testBasic() {
     }
   }
 
-  // This test fails due to incorrect gradients being generated some of the time, when
-  // using an identical graph on identical data. It should not, but it seems to be a
-  // problem in TF-core.
-  @Disabled
   @Test
   public void testDeterminism() {
     ConfigProto config =