diff --git a/RELEASENOTES.md b/RELEASENOTES.md
index ac759131b..3e93c0382 100644
--- a/RELEASENOTES.md
+++ b/RELEASENOTES.md
@@ -8,6 +8,8 @@ __Breaking Changes__:
 
 __API Changes__:
 
+- #1291 `Tensor.grad()` and `Tensor.set_grad()` have been replaced by a new property `Tensor.grad`.
+
 __Bug Fixes__:
 
 
diff --git a/src/Examples/AdversarialExampleGeneration.cs b/src/Examples/AdversarialExampleGeneration.cs
index 1e558c791..7bfc174b2 100644
--- a/src/Examples/AdversarialExampleGeneration.cs
+++ b/src/Examples/AdversarialExampleGeneration.cs
@@ -133,7 +133,7 @@ private static double Test(
                         model.zero_grad();
                         loss.backward();
 
-                        var perturbed = Attack(data, ε, data.grad());
+                        var perturbed = Attack(data, ε, data.grad);
 
                         using (var final = model.call(perturbed)) {
 
diff --git a/src/FSharp.Examples/AdversarialExampleGeneration.fs b/src/FSharp.Examples/AdversarialExampleGeneration.fs
index 09dd6fdf3..46ff364f3 100644
--- a/src/FSharp.Examples/AdversarialExampleGeneration.fs
+++ b/src/FSharp.Examples/AdversarialExampleGeneration.fs
@@ -79,7 +79,7 @@ let test (model:MNIST.Model) (eps:float) (data:Dataset) size =
             model.zero_grad()
             loss.backward()
 
-            use perturbed = attack input (eps.ToScalar()) (input.grad())
+            use perturbed = attack input (eps.ToScalar()) (input.grad)
             use final = perturbed --> model
             correct <- correct + final.argmax(1L).eq(labels).sum().ToInt32()
         end
diff --git a/src/TorchSharp/NN/Module.cs b/src/TorchSharp/NN/Module.cs
index 6d6cca212..f5e49616b 100644
--- a/src/TorchSharp/NN/Module.cs
+++ b/src/TorchSharp/NN/Module.cs
@@ -240,7 +240,7 @@ private void _toEpilog(ScalarType? dtype, Device device)
 
                     foreach (var (name, param) in named_parameters(false).ToList()) {
                         if (!param.toWillCopy(dtype ?? param.dtype, device ?? param.device) &&
-                            (param.grad() is null || !param.grad().toWillCopy(dtype ?? param.dtype, device ?? param.device)))
+                            (param.grad is null || !param.grad.toWillCopy(dtype ?? param.dtype, device ?? param.device)))
                             continue;
 
                         Parameter p;
@@ -256,11 +256,10 @@ private void _toEpilog(ScalarType? dtype, Device device)
                                 .DetachFromDisposeScope() as Parameter;
 
                             // Copy the gradient over as well, if it exists
-                            var grad = param.grad();
+                            var grad = param.grad;
                             if (grad is not null) {
-                                p.set_grad(grad.to(paramType, device ?? param.device)
-                                                .with_requires_grad(grad.requires_grad)
-                                                .MoveToOtherDisposeScope(p));
+                                p.grad = grad.to(paramType, device ?? param.device)
+                                    .with_requires_grad(grad.requires_grad);
                             }
 
                             // Dispose the param and gradient
@@ -360,10 +359,10 @@ public virtual void zero_grad(bool set_to_none = true)
                     CheckForErrors();
 
                     foreach (var (_, p) in named_parameters()) {
-                        var grad = p.grad();
+                        var grad = p.grad;
                         if (grad is not null) {
                             if (set_to_none) {
-                                p.set_grad(null);
+                                p.grad = null;
                                 grad.DetachFromDisposeScope().Dispose();
                             } else {
                                 grad.zero_();
diff --git a/src/TorchSharp/Optimizers/ASGD.cs b/src/TorchSharp/Optimizers/ASGD.cs
index c4ed54ead..260810aa0 100644
--- a/src/TorchSharp/Optimizers/ASGD.cs
+++ b/src/TorchSharp/Optimizers/ASGD.cs
@@ -145,7 +145,7 @@ public override Tensor step(Func<Tensor> closure = null)
 
                     foreach (var param in group.Parameters) {
 
-                        var grad = param.grad();
+                        var grad = param.grad;
 
                         if (grad is null) continue;
 
diff --git a/src/TorchSharp/Optimizers/Adadelta.cs b/src/TorchSharp/Optimizers/Adadelta.cs
index e262ceda9..8e2027a1a 100644
--- a/src/TorchSharp/Optimizers/Adadelta.cs
+++ b/src/TorchSharp/Optimizers/Adadelta.cs
@@ -136,7 +136,7 @@ public override Tensor step(Func<Tensor> closure = null)
 
                     foreach (var param in group.Parameters) {
 
-                        var grad = (maximize) ? -param.grad() : param.grad();
+                        var grad = (maximize) ? -param.grad : param.grad;
 
                         if (grad is null) continue;
 
diff --git a/src/TorchSharp/Optimizers/Adagrad.cs b/src/TorchSharp/Optimizers/Adagrad.cs
index 7abdab0eb..a4d4b70fc 100644
--- a/src/TorchSharp/Optimizers/Adagrad.cs
+++ b/src/TorchSharp/Optimizers/Adagrad.cs
@@ -147,7 +147,7 @@ public override Tensor step(Func<Tensor> closure = null)
 
                         var state = (State)_state[param.handle];
 
-                        var grad = param.grad();
+                        var grad = param.grad;
 
                         if (grad is null) continue;
 
diff --git a/src/TorchSharp/Optimizers/Adam.cs b/src/TorchSharp/Optimizers/Adam.cs
index 8233e05b9..39156f6a6 100644
--- a/src/TorchSharp/Optimizers/Adam.cs
+++ b/src/TorchSharp/Optimizers/Adam.cs
@@ -164,7 +164,7 @@ public override Tensor step(Func<Tensor> closure = null)
 
                         var state = (State)_state[param.handle];
 
-                        var grad = (maximize) ? -param.grad() : param.grad();
+                        var grad = (maximize) ? -param.grad : param.grad;
 
                         if (grad is null) continue;
 
diff --git a/src/TorchSharp/Optimizers/AdamW.cs b/src/TorchSharp/Optimizers/AdamW.cs
index 225c6660e..23624369b 100644
--- a/src/TorchSharp/Optimizers/AdamW.cs
+++ b/src/TorchSharp/Optimizers/AdamW.cs
@@ -164,7 +164,7 @@ public override Tensor step(Func<Tensor> closure = null)
 
                         var state = (State)_state[param.handle];
 
-                        var grad = (maximize) ? -param.grad() : param.grad();
+                        var grad = (maximize) ? -param.grad : param.grad;
 
                         if (grad is null) continue;
 
diff --git a/src/TorchSharp/Optimizers/Adamax.cs b/src/TorchSharp/Optimizers/Adamax.cs
index 4e421a8ee..e09ef9170 100644
--- a/src/TorchSharp/Optimizers/Adamax.cs
+++ b/src/TorchSharp/Optimizers/Adamax.cs
@@ -148,7 +148,7 @@ public override Tensor step(Func<Tensor> closure = null)
 
                     foreach (var param in group.Parameters) {
 
-                        var grad = param.grad();
+                        var grad = param.grad;
 
                         if (grad is null) continue;
 
diff --git a/src/TorchSharp/Optimizers/NAdam.cs b/src/TorchSharp/Optimizers/NAdam.cs
index 251b22e7d..6118cc5d1 100644
--- a/src/TorchSharp/Optimizers/NAdam.cs
+++ b/src/TorchSharp/Optimizers/NAdam.cs
@@ -154,7 +154,7 @@ public override Tensor step(Func<Tensor> closure = null)
 
                     foreach (var param in group.Parameters) {
 
-                        var grad = param.grad();
+                        var grad = param.grad;
 
                         if (grad is null) continue;
 
diff --git a/src/TorchSharp/Optimizers/Optimizer.cs b/src/TorchSharp/Optimizers/Optimizer.cs
index 79b542de8..002aa7fea 100644
--- a/src/TorchSharp/Optimizers/Optimizer.cs
+++ b/src/TorchSharp/Optimizers/Optimizer.cs
@@ -399,7 +399,7 @@ public override void zero_grad()
 
                     foreach (var p in g.Parameters) {
 
-                        using var grad = p.grad();
+                        using var grad = p.grad;
 
                         if (grad is null) continue;
 
diff --git a/src/TorchSharp/Optimizers/RAdam.cs b/src/TorchSharp/Optimizers/RAdam.cs
index 4b8690839..d64416196 100644
--- a/src/TorchSharp/Optimizers/RAdam.cs
+++ b/src/TorchSharp/Optimizers/RAdam.cs
@@ -147,7 +147,7 @@ public override Tensor step(Func<Tensor> closure = null)
 
                     foreach (var param in group.Parameters) {
 
-                        var grad = param.grad();
+                        var grad = param.grad;
 
                         if (grad is null) continue;
 
diff --git a/src/TorchSharp/Optimizers/RMSprop.cs b/src/TorchSharp/Optimizers/RMSprop.cs
index 2ce65d874..9bc77f95f 100644
--- a/src/TorchSharp/Optimizers/RMSprop.cs
+++ b/src/TorchSharp/Optimizers/RMSprop.cs
@@ -162,7 +162,7 @@ public override Tensor step(Func<Tensor> closure = null)
 
                         var state = (State)_state[param.handle];
 
-                        var grad = param.grad();
+                        var grad = param.grad;
 
                         if (grad is null) continue;
 
diff --git a/src/TorchSharp/Optimizers/Rprop.cs b/src/TorchSharp/Optimizers/Rprop.cs
index 1bcf41f2b..47e01d982 100644
--- a/src/TorchSharp/Optimizers/Rprop.cs
+++ b/src/TorchSharp/Optimizers/Rprop.cs
@@ -144,7 +144,7 @@ public override Tensor step(Func<Tensor> closure = null)
 
                     foreach (var param in group.Parameters) {
 
-                        var grad = param.grad();
+                        var grad = param.grad;
 
                         if (grad is null) continue;
 
diff --git a/src/TorchSharp/Optimizers/SGD.cs b/src/TorchSharp/Optimizers/SGD.cs
index 87cacdadd..fed1f912b 100644
--- a/src/TorchSharp/Optimizers/SGD.cs
+++ b/src/TorchSharp/Optimizers/SGD.cs
@@ -150,7 +150,7 @@ public override Tensor step(Func<Tensor> closure = null)
 
                         var state = (State)_state[param.handle];
 
-                        var grad = param.grad();
+                        var grad = param.grad;
 
                         if (grad is null) continue;
 
diff --git a/src/TorchSharp/Tensor/Tensor.cs b/src/TorchSharp/Tensor/Tensor.cs
index 8fe5fe013..4c53ff6df 100644
--- a/src/TorchSharp/Tensor/Tensor.cs
+++ b/src/TorchSharp/Tensor/Tensor.cs
@@ -1340,25 +1340,21 @@ public Tensor pin_memory()
             /// This attribute is null by default and becomes a Tensor the first time a call to backward() computes gradients for the tensor.
             /// The attribute will then contain the gradients computed and future calls to backward() will accumulate (add) gradients into it.
             /// </summary>
-            public Tensor? grad()
-            {
-                var res = NativeMethods.THSTensor_grad(Handle);
-                CheckForErrors();
-
-                if (res == IntPtr.Zero)
-                    return null;
+            public Tensor? grad {
+                get {
+                    var res = NativeMethods.THSTensor_grad(Handle);
+                    CheckForErrors();
 
-                return new Tensor(res);
-            }
+                    if (res == IntPtr.Zero)
+                        return null;
 
-            /// <summary>
-            /// This function will set the `tensor.grad()` attribute to a custom tensor. 
-            /// </summary>
-            /// <param name="grad">The new gradient tensor</param>
-            public void set_grad(Tensor grad)
-            {
-                NativeMethods.THSTensor_set_grad(Handle, grad?.DetachFromDisposeScope().Handle ?? IntPtr.Zero);
-                CheckForErrors();
+                    return new Tensor(res);
+                }
+                set {
+                    value?.DetachFromDisposeScope();
+                    NativeMethods.THSTensor_set_grad(Handle, value?.Handle ?? IntPtr.Zero);
+                    CheckForErrors();
+                }
             }
 
             internal void EncodeIndices(TensorIndex[] indices,
diff --git a/test/TorchSharpTest/NN.cs b/test/TorchSharpTest/NN.cs
index cd59ad5b9..e448d2982 100644
--- a/test/TorchSharpTest/NN.cs
+++ b/test/TorchSharpTest/NN.cs
@@ -2152,14 +2152,14 @@ public void TestBackward()
             output.backward();
 
             foreach (var parm in seq.parameters()) {
-                var grad = parm.grad();
+                var grad = parm.grad;
                 Assert.NotNull(grad);
             }
 
             seq.zero_grad();
 
             foreach (var parm in seq.parameters()) {
-                var grad = parm.grad();
+                var grad = parm.grad;
                 Assert.True(grad is null || grad!.count_nonzero().item<long>() == 0);
             }
         }
@@ -2186,14 +2186,14 @@ public void TestGettingParameters()
             output.backward();
 
             foreach (var parm in seq.parameters()) {
-                var grad = parm.grad();
+                var grad = parm.grad;
                 Assert.NotNull(grad);
             }
 
             seq.zero_grad();
 
             foreach (var parm in seq.parameters()) {
-                var grad = parm.grad();
+                var grad = parm.grad;
                 Assert.True(grad is null || grad!.count_nonzero().item<long>() == 0);
             }
         }
@@ -2220,14 +2220,14 @@ public void TestGrad()
             output.backward();
 
             foreach (var parm in seq.parameters()) {
-                var grad = parm.grad();
+                var grad = parm.grad;
                 Assert.NotNull(grad);
             }
 
             seq.zero_grad();
 
             foreach (var parm in seq.parameters()) {
-                var grad = parm.grad();
+                var grad = parm.grad;
                 Assert.True(grad is null || grad!.count_nonzero().item<long>() == 0);
             }
         }
@@ -2254,9 +2254,9 @@ public void TestGrad2()
 
             output.backward();
 
-            var scalerGrad = scaler.grad();
-            var weightGrad = linear.weight.grad();
-            var biasGrad = linear.bias.grad();
+            var scalerGrad = scaler.grad;
+            var weightGrad = linear.weight.grad;
+            var biasGrad = linear.bias.grad;
             Assert.True(scalerGrad is not null && scalerGrad.shape.Length == 2);
             Assert.True(weightGrad is not null && weightGrad.shape.Length == 2);
             Assert.True(biasGrad is not null && biasGrad.shape.Length == 2);
@@ -2328,7 +2328,7 @@ public void TestGradConditional()
             var gradCounts = 0;
 
             foreach (var (name, parm) in modT.named_parameters()) {
-                var grad = parm.grad();
+                var grad = parm.grad;
                 gradCounts += grad is not null ? (grad.Handle == IntPtr.Zero ? 0 : 1) : 0;
             }
 
@@ -2346,7 +2346,7 @@ public void TestGradConditional()
             gradCounts = 0;
 
             foreach (var parm in modF.parameters()) {
-                var grad = parm.grad();
+                var grad = parm.grad;
                 gradCounts += grad is not null ? (grad.Handle == IntPtr.Zero ? 0 : 1) : 0;
             }
 
@@ -2839,14 +2839,14 @@ public void TestCustomModule1()
             output.backward();
 
             foreach (var (pName, parm) in module.named_parameters()) {
-                var grad = parm.grad();
+                var grad = parm.grad;
                 Assert.NotNull(grad);
             }
 
             module.zero_grad();
 
             foreach (var (pName, parm) in module.named_parameters()) {
-                var grad = parm.grad();
+                var grad = parm.grad;
                 Assert.True(grad is null || grad!.count_nonzero().item<long>() == 0);
             }
 
@@ -3016,7 +3016,7 @@ public void TestDerivedSequence1Grad()
             output.backward();
 
             foreach (var parm in seq.parameters()) {
-                var grad = parm.grad();
+                var grad = parm.grad;
             }
         }
 
@@ -3037,7 +3037,7 @@ public void TestDerivedSequence2Grad()
             output.backward();
 
             foreach (var parm in seq.parameters()) {
-                var grad = parm.grad();
+                var grad = parm.grad;
             }
         }
 
@@ -3121,7 +3121,7 @@ public void TestCustomModuleWithDeviceMove()
                 var y = torch.randn(2, device: torch.CUDA);
                 torch.nn.functional.mse_loss(module.call(x), y).backward();
                 foreach (var (pName, parm) in module.named_parameters()) {
-                    var grad = parm.grad();
+                    var grad = parm.grad;
                     Assert.NotNull(grad);
                 }
 
@@ -3134,7 +3134,7 @@ public void TestCustomModuleWithDeviceMove()
                 y = torch.randn(2);
                 torch.nn.functional.mse_loss(module.call(x), y).backward();
                 foreach (var (pName, parm) in module.named_parameters()) {
-                    var grad = parm.grad();
+                    var grad = parm.grad;
                     Assert.NotNull(grad);
                 }
             }
@@ -3151,7 +3151,7 @@ public void TestCustomModuleWithTypeMove()
             var y = torch.randn(2, float64);
             torch.nn.functional.mse_loss(module.call(x), y).backward();
             foreach (var (pName, parm) in module.named_parameters()) {
-                var grad = parm.grad();
+                var grad = parm.grad;
                 Assert.NotNull(grad);
             }
 
@@ -3164,7 +3164,7 @@ public void TestCustomModuleWithTypeMove()
             y = torch.randn(2);
             torch.nn.functional.mse_loss(module.call(x), y).backward();
             foreach (var (pName, parm) in module.named_parameters()) {
-                var grad = parm.grad();
+                var grad = parm.grad;
                 Assert.NotNull(grad);
             }
         }
@@ -3180,7 +3180,7 @@ public void TestCustomModuleWithDeviceAndTypeMove()
                 var y = torch.randn(2, float16, torch.CUDA);
                 torch.nn.functional.mse_loss(module.call(x), y).backward();
                 foreach (var (pName, parm) in module.named_parameters()) {
-                    var grad = parm.grad();
+                    var grad = parm.grad;
                     Assert.NotNull(grad);
                 }
 
@@ -3193,7 +3193,7 @@ public void TestCustomModuleWithDeviceAndTypeMove()
                 y = torch.randn(2);
                 torch.nn.functional.mse_loss(module.call(x), y).backward();
                 foreach (var (pName, parm) in module.named_parameters()) {
-                    var grad = parm.grad();
+                    var grad = parm.grad;
                     Assert.NotNull(grad);
                 }
             }
diff --git a/test/TorchSharpTest/TestAutogradFunction.cs b/test/TorchSharpTest/TestAutogradFunction.cs
index 2c137116f..f778d45d2 100644
--- a/test/TorchSharpTest/TestAutogradFunction.cs
+++ b/test/TorchSharpTest/TestAutogradFunction.cs
@@ -21,8 +21,8 @@ private void TestCustomLinearFunction(Device device, bool requires_grad)
 
             y.sum().backward();
 
-            Assert.NotNull(x.grad());
-            Assert.NotNull(weight.grad());
+            Assert.NotNull(x.grad);
+            Assert.NotNull(weight.grad);
         }
 
 
@@ -37,9 +37,9 @@ private void TestCustomTwoInputLinearFunction(Device device, bool requires_grad)
 
             (y[0].sum() + y[1].sum()).backward();
 
-            Assert.NotNull(x1.grad());
-            Assert.NotNull(x2.grad());
-            Assert.NotNull(weight.grad());
+            Assert.NotNull(x1.grad);
+            Assert.NotNull(x2.grad);
+            Assert.NotNull(weight.grad);
         }
 
         private void TestCustomTwoInputOneGradientLinearFunction(Device device, bool requires_grad)
@@ -53,9 +53,9 @@ private void TestCustomTwoInputOneGradientLinearFunction(Device device, bool req
 
             (y[0].sum() + y[1].sum()).backward();
 
-            Assert.NotNull(x1.grad());
-            Assert.NotNull(x2.grad());
-            Assert.Null(weight.grad());
+            Assert.NotNull(x1.grad);
+            Assert.NotNull(x2.grad);
+            Assert.Null(weight.grad);
         }
 
         private float TrainXOR(Device device)
@@ -203,8 +203,8 @@ private void TestCustomLinearFunctionWithGC()
 
             y.sum().backward();
 
-            Assert.NotNull(x.grad());
-            Assert.NotNull(weight.grad());
+            Assert.NotNull(x.grad);
+            Assert.NotNull(weight.grad);
         }
 
         [Fact]
@@ -214,7 +214,7 @@ private void TestBackwardWithPartialGradInput()
             var y = MulConstantFunction.apply(x, 2.0);
             y.sum().backward();
 
-            Assert.NotNull(x.grad());
+            Assert.NotNull(x.grad);
         }
 
         class MulConstantFunction : torch.autograd.SingleTensorFunction<MulConstantFunction>
diff --git a/test/TorchSharpTest/TestNNUtils.cs b/test/TorchSharpTest/TestNNUtils.cs
index ddf35bbfe..e01fe3bdf 100644
--- a/test/TorchSharpTest/TestNNUtils.cs
+++ b/test/TorchSharpTest/TestNNUtils.cs
@@ -80,10 +80,10 @@ public void TestAutoGradBackward1()
             var y = x1.pow(2) + 5 * x2;
 
             torch.autograd.backward(new[] { y }, new[] { torch.ones_like(y) });
-            Assert.Equal(x1.shape, x1.grad().shape);
-            Assert.Equal(x2.shape, x2.grad().shape);
-            Assert.Equal(2.0f*x1.item<float>(), x1.grad().item<float>());
-            Assert.Equal(5.0f, x2.grad().item<float>());
+            Assert.Equal(x1.shape, x1.grad.shape);
+            Assert.Equal(x2.shape, x2.grad.shape);
+            Assert.Equal(2.0f*x1.item<float>(), x1.grad.item<float>());
+            Assert.Equal(5.0f, x2.grad.item<float>());
         }
 
         [Fact]
@@ -96,10 +96,10 @@ public void TestAutoGradBackward2()
             var y = x1.pow(2) + 5 * x2;
 
             y.backward(new[] { torch.ones_like(y) });
-            Assert.Equal(x1.shape, x1.grad().shape);
-            Assert.Equal(x2.shape, x2.grad().shape);
-            Assert.Equal(2.0f * x1.item<float>(), x1.grad().item<float>());
-            Assert.Equal(5.0f, x2.grad().item<float>());
+            Assert.Equal(x1.shape, x1.grad.shape);
+            Assert.Equal(x2.shape, x2.grad.shape);
+            Assert.Equal(2.0f * x1.item<float>(), x1.grad.item<float>());
+            Assert.Equal(5.0f, x2.grad.item<float>());
         }
 
         [Fact]
diff --git a/test/TorchSharpTest/TestTorchTensor.cs b/test/TorchSharpTest/TestTorchTensor.cs
index 60f06d812..9bbbb2a34 100644
--- a/test/TorchSharpTest/TestTorchTensor.cs
+++ b/test/TorchSharpTest/TestTorchTensor.cs
@@ -4429,7 +4429,7 @@ public void AutoGradMode()
                     Assert.True(torch.is_grad_enabled());
                     var sum = x.sum();
                     sum.backward();
-                    var grad = x.grad();
+                    var grad = x.grad;
                     Assert.False(grad is null || grad.Handle == IntPtr.Zero);
                     var data = grad is not null ? grad.data<float>().ToArray() : new float[] { };
                     for (int i = 0; i < 2 * 3; i++) {
@@ -4448,7 +4448,7 @@ public void AutoGradMode()
                     Assert.True(torch.is_grad_enabled());
                     var sum = x.sum();
                     sum.backward();
-                    var grad = x.grad();
+                    var grad = x.grad;
                     Assert.False(grad is not null && grad.Handle == IntPtr.Zero);
                     var data = grad is not null ? grad.data<float>().ToArray() : new float[] { };
                     for (int i = 0; i < 2 * 3; i++) {
diff --git a/test/TorchSharpTest/TestTorchTensorBugs.cs b/test/TorchSharpTest/TestTorchTensorBugs.cs
index e2b65ac13..7c3a44fcc 100644
--- a/test/TorchSharpTest/TestTorchTensorBugs.cs
+++ b/test/TorchSharpTest/TestTorchTensorBugs.cs
@@ -571,10 +571,10 @@ public void ValidateIssue516()
                 loss.backward();
                 optimizer.step();
 
-                var grad1 = optimizer.parameters().ToArray()[0].grad();
+                var grad1 = optimizer.parameters().ToArray()[0].grad;
                 Assert.NotNull(grad1);
 
-                var grad2 = model.Weight.grad();
+                var grad2 = model.Weight.grad;
                 Assert.NotNull(grad2);
             }
         }
@@ -1198,7 +1198,7 @@ public void Validate1116_1()
 
                 example.backward();
 
-                var grads = x1.grad();
+                var grads = x1.grad;
                 Assert.True(x1.requires_grad);
                 Assert.NotNull(grads);
             }
@@ -1217,7 +1217,7 @@ public void Validate1116_2()
 
                 example.backward();
 
-                var grads = x1.grad();
+                var grads = x1.grad;
                 Assert.True(x1.requires_grad);
                 Assert.NotNull(grads);
             }
@@ -1235,7 +1235,7 @@ public void Validate1116_3()
 
             example.backward();
 
-            var grads = x1.grad();
+            var grads = x1.grad;
             Assert.True(x1.requires_grad);
             Assert.NotNull(grads);
         }
@@ -1253,7 +1253,7 @@ public void Validate1116_4()
 
                 example.backward();
 
-                var grads = x1.grad();
+                var grads = x1.grad;
                 Assert.True(x1.requires_grad);
                 Assert.NotNull(grads);
             }
@@ -1272,7 +1272,7 @@ public void Validate1116_5()
 
                 example.backward();
 
-                var grads = x1.grad();
+                var grads = x1.grad;
                 Assert.True(x1.requires_grad);
                 Assert.NotNull(grads);
             }
@@ -1291,7 +1291,7 @@ public void Validate1116_6()
 
                 example.backward();
 
-                var grads = x1.grad();
+                var grads = x1.grad;
                 Assert.True(x1.requires_grad);
                 Assert.NotNull(grads);
             }
@@ -1517,14 +1517,14 @@ public void Validate_1191_1()
                 // Build graph 1 on CUDA
                 torch.nn.functional.mse_loss(module.forward(torch.rand(10).cuda()), torch.rand(10).cuda()).backward();
 
-                Assert.Equal(DeviceType.CUDA, module.ln.weight!.grad()!.device_type);
-                Assert.Equal(DeviceType.CUDA, module.p.grad()!.device_type);
+                Assert.Equal(DeviceType.CUDA, module.ln.weight!.grad!.device_type);
+                Assert.Equal(DeviceType.CUDA, module.p.grad!.device_type);
 
                 // Move to CPU
                 module.to(torch.CPU);
 
-                Assert.Equal(DeviceType.CPU, module.ln.weight!.grad()!.device_type);
-                Assert.Equal(DeviceType.CPU, module.p.grad()!.device_type);
+                Assert.Equal(DeviceType.CPU, module.ln.weight!.grad!.device_type);
+                Assert.Equal(DeviceType.CPU, module.p.grad!.device_type);
 
                 // Build graph 2 on CPU.
                 // This should've crashed, saying something about the gradients being on the wrong device.
@@ -1545,7 +1545,7 @@ public void Validate_1191_2()
                     var resultBatch = rand(32, 1).to(aDevice);
                     aModule.to(aDevice);
                     foreach (var (name, p) in aModule.named_parameters()) {
-                        Console.WriteLine($"{name} {p.device} {p.grad()}");
+                        Console.WriteLine($"{name} {p.device} {p.grad}");
                     }
                     var aMseLoss = nn.MSELoss();
                     var optimizer = torch.optim.AdamW(aModule.parameters());
@@ -1570,7 +1570,7 @@ public void Validate_1191_2()
                     aModule.to(aDevice);
                     aModule.zero_grad();
                     foreach (var (name, p) in aModule.named_parameters()) {
-                        Console.WriteLine($"{name} {p.device} {p.grad()}");
+                        Console.WriteLine($"{name} {p.device} {p.grad}");
                     }
                     var aMseLoss = nn.MSELoss();
                     var optimizer = torch.optim.AdamW(aModule.parameters());
@@ -1600,18 +1600,18 @@ public void Validate_1191_3()
 
             module.zero_grad();
 
-            Assert.Null(module.p.grad());
-            Assert.Null(module.ln.weight!.grad());
-            Assert.Null(module.ln.bias!.grad());
+            Assert.Null(module.p.grad);
+            Assert.Null(module.ln.weight!.grad);
+            Assert.Null(module.ln.bias!.grad);
 
             // Build graph again, this time convert gradients to zero
             torch.nn.functional.mse_loss(module.forward(torch.rand(10)), torch.rand(10)).backward();
 
             module.zero_grad(false);
 
-            Assert.NotNull(module.p.grad());
-            Assert.NotNull(module.ln.weight!.grad());
-            Assert.NotNull(module.ln.bias!.grad());
+            Assert.NotNull(module.p.grad);
+            Assert.NotNull(module.ln.weight!.grad);
+            Assert.NotNull(module.ln.bias!.grad);
             
         }
 
diff --git a/test/TorchSharpTest/TestTraining.cs b/test/TorchSharpTest/TestTraining.cs
index 877a68ffd..1ff1336a2 100644
--- a/test/TorchSharpTest/TestTraining.cs
+++ b/test/TorchSharpTest/TestTraining.cs
@@ -55,7 +55,7 @@ public void Training1()
 
                 using (torch.no_grad()) {
                     foreach (var param in seq.parameters()) {
-                        var grad = param.grad();
+                        var grad = param.grad;
                         if (grad is not null) {
                             var update = grad.mul(learning_rate);
                             param.sub_(update);
@@ -99,7 +99,7 @@ public void TrainingWithDropout()
 
                 using (torch.no_grad()) {
                     foreach (var param in seq.parameters()) {
-                        var grad = param.grad();
+                        var grad = param.grad;
                         if (grad is not null) {
                             var update = grad.mul(learning_rate);
                             param.sub_(update);