diff --git a/src/TensorFlowNET.Core/Gradients/array_grad.cs b/src/TensorFlowNET.Core/Gradients/array_grad.cs
new file mode 100644
index 00000000..399578cf
--- /dev/null
+++ b/src/TensorFlowNET.Core/Gradients/array_grad.cs
@@ -0,0 +1,146 @@
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+using Tensorflow.Operations;
+using static Tensorflow.Python;
+
+namespace Tensorflow.Gradients
+{
+    /// <summary>
+    /// tensorflow\python\ops\array_grad.py
+    /// </summary>
+    public class array_grad
+    {
+        public static Tensor[] _ConcatGradV2(Operation op, Tensor[] grads)
+        {
+            var grad = grads[0];
+            return _ConcatGradHelper(op, grad, start_value_index: 0, end_value_index: -1, dim_index: -1);
+        }
+
+        /// <summary>
+        /// Gradient for concat op.
+        /// </summary>
+        /// <param name="op">An operation.</param>
+        /// <param name="grad">
+        /// `Tensor` or `IndexedSlices` representing the gradients with respect
+        /// to each output of the op.
+        /// </param>
+        /// <param name="start_value_index">An integer index of the first value in the op.inputs.</param>
+        /// <param name="end_value_index">An integer index of the last value in the op.inputs.</param>
+        /// <param name="dim_index">An interger index of concat_dim or axis parameter in op.inputs.</param>
+        /// <returns>
+        /// Tensors representing the partial gradients with respect to each input
+        /// of the op.
+        /// </returns>
+        private static Tensor[] _ConcatGradHelper(Operation op, Tensor grad, int start_value_index, int end_value_index, int dim_index)
+        {
+            // Degenerate concatenation, just return grad.
+            if (len(op.inputs) == 2)
+                return end_value_index <= dim_index ? new Tensor[] { grad, null } : new Tensor[] { null, grad };
+
+            var concat_dim = op.inputs[dim_index];
+            var input_values = op.inputs._inputs.Skip(start_value_index).Take(end_value_index - start_value_index).ToArray();
+
+            var out_grads = new List<Tensor>();
+            if (constant_op.is_constant(concat_dim))
+            {
+                /*If concat_dim is a constant defined in a different context,
+                then we duplicate it in the current context to avoid passing it
+                through an Enter node.
+                This is a small optimization in general, but it is required when
+                compiling with XLA, as XLA needs the concat input to be folded into a
+                constant.*/
+                var grad_context = control_flow_util.GetOutputContext(grad.op);
+                var dim_context = control_flow_util.GetOutputContext(concat_dim.op);
+                if (dim_context != grad_context)
+                {
+                    var value = tensor_util.constant_value(concat_dim);
+                    concat_dim = constant_op.constant(value: value, dtype: concat_dim.dtype);
+                }
+            }
+
+            // Using mod here for convenience since concat_dim is already verified
+            // in concat implementation to be within the allowed [-rank, rank) range.
+            var non_neg_concat_dim = concat_dim % array_ops.rank(input_values[0]);
+
+            // Get the inputs' tensor shapes
+            var sizes = _ExtractInputShapes(input_values);
+
+            /* The magic number of 16 was found through benchmarking a range of sizes
+             on CPUs and a Maxwell TitanX.  A speedup was seen in a large majority of
+             cases when switching implementations at N=16, but it is possible that
+             there will be a small number of performance regressions.*/
+            if (len(sizes) > 16)
+            {
+                // extract the size of each input along the concat dimension
+                var slice = array_ops.slice(array_ops.stack(sizes, axis: 1),
+                        new Tensor[] { non_neg_concat_dim, tf.constant(0) },
+                        new Tensor[] { tf.constant(1), tf.constant(-1) });
+                var squeeze_sizes = array_ops.squeeze(slice);
+                out_grads = gen_ops.split(grad, squeeze_sizes, non_neg_concat_dim).ToList();
+            }
+            else
+            {
+                var offset = gen_ops.concat_offset(non_neg_concat_dim, sizes);
+                foreach (var (begin, size) in zip(offset, sizes))
+                    out_grads.Add(gen_ops.slice(grad, begin, size));
+            }
+
+            return (end_value_index <= dim_index ? 
+                out_grads.ToArray().Concat(null) : 
+                new Tensor[] { null }.Concat(out_grads)).ToArray();
+        }
+
+        /// <summary>
+        /// Extract the shapes of a set of input tensors.
+        /// </summary>
+        /// <param name="inputs"></param>
+        /// <returns></returns>
+        private static Tensor[] _ExtractInputShapes(Tensor[] inputs)
+        {
+            var sizes = new Tensor[inputs.Length];
+            bool fully_known = true;
+            for(int i = 0; i < inputs.Length; i++)
+            {
+                var x = inputs[i];
+
+                var input_shape = array_ops.shape(x);
+                if (!(input_shape is Tensor) || input_shape.op.type != "Const")
+                {
+                    fully_known = false;
+                    break;
+                }
+
+              sizes[i] = input_shape;
+            }
+
+            if (fully_known)
+                return sizes;
+            else
+                return gen_ops.shape_n(inputs);
+        }
+
+
+        public static Tensor[] _ReshapeGrad(Operation op, Tensor[] grads)
+        {
+            return new Tensor[] { array_ops.reshape(grads[0], array_ops.shape(op.inputs[0])), null };
+        }
+
+        public static Tensor[] _SqueezeGrad(Operation op, Tensor[] grads)
+        {
+            return new Tensor[] { _ReshapeToInput(op, grads[0]) };
+        }
+
+        private static Tensor _ReshapeToInput(Operation op, Tensor grad)
+        {
+            return array_ops.reshape(grad, array_ops.shape(op.inputs[0]));
+        }
+
+        public static Tensor[] _TransposeGrad(Operation op, Tensor[] grads)
+        {
+            var p = op.inputs[1];
+            return new Tensor[] { array_ops.transpose(grads[0], array_ops.invert_permutation(p)), null };
+        }
+    }
+}
diff --git a/src/TensorFlowNET.Core/Gradients/array_grad.py.cs b/src/TensorFlowNET.Core/Gradients/array_grad.py.cs
deleted file mode 100644
index cdd319ea..00000000
--- a/src/TensorFlowNET.Core/Gradients/array_grad.py.cs
+++ /dev/null
@@ -1,30 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-
-namespace Tensorflow.Gradients
-{
-    public class array_grad
-    {
-        public static Tensor[] _ReshapeGrad(Operation op, Tensor[] grads)
-        {
-            return new Tensor[] { array_ops.reshape(grads[0], array_ops.shape(op.inputs[0])), null };
-        }
-
-        public static Tensor[] _SqueezeGrad(Operation op, Tensor[] grads)
-        {
-            return new Tensor[] { _ReshapeToInput(op, grads[0]) };
-        }
-
-        private static Tensor _ReshapeToInput(Operation op, Tensor grad)
-        {
-            return array_ops.reshape(grad, array_ops.shape(op.inputs[0]));
-        }
-
-        public static Tensor[] _TransposeGrad(Operation op, Tensor[] grads)
-        {
-            var p = op.inputs[1];
-            return new Tensor[] { array_ops.transpose(grads[0], array_ops.invert_permutation(p)), null };
-        }
-    }
-}
diff --git a/src/TensorFlowNET.Core/Gradients/nn_grad.py.cs b/src/TensorFlowNET.Core/Gradients/nn_grad.cs
similarity index 99%
rename from src/TensorFlowNET.Core/Gradients/nn_grad.py.cs
rename to src/TensorFlowNET.Core/Gradients/nn_grad.cs
index 0bd03046..9a70b90d 100644
--- a/src/TensorFlowNET.Core/Gradients/nn_grad.py.cs
+++ b/src/TensorFlowNET.Core/Gradients/nn_grad.cs
@@ -6,6 +6,9 @@ using Tensorflow.Operations;
 
 namespace Tensorflow.Gradients
 {
+    /// <summary>
+    /// 
+    /// </summary>
     public class nn_grad
     {
         /// <summary>
diff --git a/src/TensorFlowNET.Core/Gradients/ops.gradient_function_mapping.cs b/src/TensorFlowNET.Core/Gradients/ops.gradient_function_mapping.cs
index d01d47be..eab9948b 100644
--- a/src/TensorFlowNET.Core/Gradients/ops.gradient_function_mapping.cs
+++ b/src/TensorFlowNET.Core/Gradients/ops.gradient_function_mapping.cs
@@ -22,6 +22,8 @@ namespace Tensorflow
                         return math_grad._AddGrad(oper, out_grads);
                     case "BiasAdd":
                         return nn_grad._BiasAddGrad(oper, out_grads);
+                    case "ConcatV2":
+                        return array_grad._ConcatGradV2(oper, out_grads);
                     case "Exp":
                         return math_grad._ExpGrad(oper, out_grads);
                     case "Identity":
diff --git a/src/TensorFlowNET.Core/Tensors/constant_op.cs b/src/TensorFlowNET.Core/Tensors/constant_op.cs
index cabb4e37..0d4ef0b4 100644
--- a/src/TensorFlowNET.Core/Tensors/constant_op.cs
+++ b/src/TensorFlowNET.Core/Tensors/constant_op.cs
@@ -88,5 +88,15 @@ namespace Tensorflow
 
             return constant_op.constant(s_list, name: name);
         }
+
+        public static bool is_constant(ITensorOrOperation tensor_or_op)
+        {
+            if (tensor_or_op is Tensor tensor)
+                return tensor.op.type == "Const";
+            else if (tensor_or_op is Operation op)
+                return op.type == "Const";
+            else
+                throw new ValueError("is_constant");
+        }
     }
 }