diff --git a/src/TensorFlowNET.Core/APIs/tf.array.cs b/src/TensorFlowNET.Core/APIs/tf.array.cs
index 4d9c3da5..b529cd31 100644
--- a/src/TensorFlowNET.Core/APIs/tf.array.cs
+++ b/src/TensorFlowNET.Core/APIs/tf.array.cs
@@ -140,6 +140,16 @@ namespace Tensorflow
         public Tensor gather(Tensor @params, Tensor indices, string name = null, int axis = 0)
             => array_ops.gather(@params, indices, name: name, axis: ops.convert_to_tensor(axis));
 
+        /// <summary>
+        /// Gather slices from `params` into a Tensor with shape specified by `indices`.
+        /// </summary>
+        /// <param name="params"></param>
+        /// <param name="indices"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public Tensor gather_nd(Tensor @params, Tensor indices, string name = null)
+            => gen_array_ops.gather_nd(@params, indices, name: name);
+
         /// <summary>
         /// Return the elements, either from `x` or `y`, depending on the `condition`.
         /// </summary>
diff --git a/src/TensorFlowNET.Core/Gradients/array_grad.cs b/src/TensorFlowNET.Core/Gradients/array_grad.cs
index 016e4f02..a4da60ee 100644
--- a/src/TensorFlowNET.Core/Gradients/array_grad.cs
+++ b/src/TensorFlowNET.Core/Gradients/array_grad.cs
@@ -403,7 +403,26 @@ namespace Tensorflow.Gradients
                 input_grad.set_shape(op.inputs[0].GetShape());
             }
             return new Tensor[] { input_grad, null };
+        }
 
+        [RegisterGradient("GatherNd")]
+        public static Tensor[] _GatherNdGrad(Operation op, Tensor[] grads)
+        {
+            var @ref = op.inputs[0];
+            var indices = op.inputs[1];
+            var grad = grads[0];
+            var ref_shape = array_ops.shape(@ref, out_type: indices.dtype);
+            Tensor ref_grad = null;
+            if (indices.shape.ndim == 2 && indices.shape.dims[indices.shape.Length - 1] == 1)
+            {
+                ref_grad = (Tensor)new IndexedSlices(grad, array_ops.squeeze(indices, axis: -1), ref_shape);
+            }
+            else
+            {
+                ref_grad = gen_array_ops.scatter_nd(indices, grad, ref_shape);
+            }
+            return new Tensor[] { ref_grad, null };
         }
+
     }
 }
diff --git a/src/TensorFlowNET.Core/Operations/array_ops.cs b/src/TensorFlowNET.Core/Operations/array_ops.cs
index abf44c64..57af3b83 100644
--- a/src/TensorFlowNET.Core/Operations/array_ops.cs
+++ b/src/TensorFlowNET.Core/Operations/array_ops.cs
@@ -829,7 +829,7 @@ namespace Tensorflow
         /// <returns>A `Tensor`. Has the same type as `input`.
         /// Contains the same data as `input`, but has one or more dimensions of
         /// size 1 removed.</returns>
-        public static Tensor squeeze(Tensor input, int[] axis = null, string name = null)
+        public static Tensor squeeze(Tensor input, Axis axis = null, string name = null)
             => gen_array_ops.squeeze(input, axis, name);
 
         public static Tensor identity(Tensor input, string name = null)
diff --git a/test/TensorFlowNET.UnitTest/GradientTest/GradientEagerTest.cs b/test/TensorFlowNET.UnitTest/GradientTest/GradientEagerTest.cs
index ed759904..1cfceb3e 100644
--- a/test/TensorFlowNET.UnitTest/GradientTest/GradientEagerTest.cs
+++ b/test/TensorFlowNET.UnitTest/GradientTest/GradientEagerTest.cs
@@ -62,7 +62,7 @@ namespace TensorFlowNET.UnitTest.Gradient
             // Calcute the gradient of (x1-x2)^2 
             // by Automatic Differentiation in Eager mode
             // Expected is 2*(abs(x1-x2))
-            Tensor x1 = new NDArray( new float[] { 1, 3, 5, 21, 19, 17 });
+            Tensor x1 = new NDArray(new float[] { 1, 3, 5, 21, 19, 17 });
             Tensor x2 = new NDArray(new float[] { 29, 27, 23, 7, 11, 13 });
             float[] expected = new float[]
             {
@@ -187,5 +187,20 @@ namespace TensorFlowNET.UnitTest.Gradient
                 Assert.AreEqual((float)grad.numpy(), 2.0f);
             }
         }
+
+        [TestMethod]
+        public void GatherNdTest()
+        {
+            var x = tf.constant(new float[,] { { 1.0f, 2.0f, 3.0f }, { 1.0f, 2.0f, 3.0f }, { 1.0f, 2.0f, 3.0f } }, dtype: TF_DataType.TF_FLOAT);
+            var indices = tf.constant(new int[,] { { 0, 1 }, { 1, 1 }, { 2, 1 } }, dtype: TF_DataType.TF_INT32);
+            using (var tape = tf.GradientTape())
+            {
+                tape.watch(x);
+                var res = tf.gather_nd(x, indices);
+                var grad = tape.gradient(res, x);
+                var expected = np.array(new float[,] { { 0f, 1f, 0f }, { 0f, 1f, 0f }, { 0f, 1f, 0f } });
+                Assert.IsTrue(Enumerable.SequenceEqual(grad.ToArray<float>(), expected.ToArray<float>()));
+            }
+        }
     }
 }