diff --git a/src/TensorFlowNET.Core/Gradients/math_grad.cs b/src/TensorFlowNET.Core/Gradients/math_grad.cs
index 9ea40816..2d0d7d28 100644
--- a/src/TensorFlowNET.Core/Gradients/math_grad.cs
+++ b/src/TensorFlowNET.Core/Gradients/math_grad.cs
@@ -138,6 +138,9 @@ namespace Tensorflow.Gradients
         [RegisterNoGradient("GreaterEqual")]
         public static Tensor[] _GreaterEqualGrad(Operation op, Tensor[] grads) => null;
 
+        [RegisterNoGradient("OnesLike")]
+        public static Tensor[] _OnesLike(Operation op, Tensor[] grads) => null;
+
         [RegisterNoGradient("ZerosLike")]
         public static Tensor[] _ZerosLike(Operation op, Tensor[] grads) => null;
 
diff --git a/src/TensorFlowNET.Core/Operations/array_ops.cs b/src/TensorFlowNET.Core/Operations/array_ops.cs
index 1801d69a..625d76a1 100644
--- a/src/TensorFlowNET.Core/Operations/array_ops.cs
+++ b/src/TensorFlowNET.Core/Operations/array_ops.cs
@@ -274,7 +274,7 @@ namespace Tensorflow
                     {
                         if (elem is EagerTensor eager_tensor)
                         {
-                            if(switch_to_graph)
+                            if (switch_to_graph)
                                 elems_as_tensors.Add(constant_op.constant(eager_tensor.numpy(), dtype: dtype, name: i.ToString()));
                             else
                                 elems_as_tensors.Add(eager_tensor);
@@ -366,8 +366,30 @@ namespace Tensorflow
         /// <param name="name"></param>
         /// <param name="optimize"></param>
         /// <returns></returns>
-        public static Tensor ones_like<T>(T tensor, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool optimize = true)
-            => ones_like_impl(tensor, dtype, name, optimize);
+        public static Tensor ones_like(Tensor tensor, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool optimize = true)
+        {
+            return tf_with(ops.name_scope(name, "ones_like", new Tensor[] { tensor }), scope =>
+            {
+                name = scope;
+                tensor = ops.convert_to_tensor(tensor, name: "tensor");
+
+                // is_fully_defined return unexpected value.
+                if (optimize && tensor_util.to_shape(tensor.shape).is_fully_defined() && dtype != TF_DataType.TF_VARIANT)
+                {
+
+                }
+
+                if (dtype != TF_DataType.DtInvalid && dtype != tensor.dtype && dtype != TF_DataType.TF_VARIANT)
+                {
+                    throw new NotImplementedException("ones_like");
+                    // return ones(shape_internal(tensor, optimize: optimize), dtype: dtype, name: name);
+                }
+                else
+                {
+                    return gen_array_ops.ones_like(tensor, name: name);
+                }
+            });
+        }
 
         public static Tensor reshape(Tensor tensor, Tensor shape, string name = null)
             => gen_array_ops.reshape(tensor, shape, name: name);
@@ -888,7 +910,7 @@ namespace Tensorflow
             return tf_with(ops.name_scope(name, "transpose", new { a }), scope =>
             {
                 var a_tensor = ops.convert_to_tensor(a);
-                if(perm == null)
+                if (perm == null)
                 {
                     var rank = a_tensor.rank;
                     perm = range(0, rank).OrderByDescending(x => x).ToArray();
@@ -950,7 +972,9 @@ namespace Tensorflow
             => tf.Context.RunInAutoMode2(
                 () => tf.OpDefLib._apply_op_helper("Slice", name, new
                 {
-                    input, begin, size
+                    input,
+                    begin,
+                    size
                 }).output,
                 () => tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName,
                     "Slice", name,
@@ -966,8 +990,8 @@ namespace Tensorflow
                     tf.Runner.RecordGradient("Slice", op.inputs, attrs, op.outputs);
                 },
                 new Tensors(input, begin, size));
-        
-    public static Tensor stack(object values, int axis = 0, string name = "stack")
+
+        public static Tensor stack(object values, int axis = 0, string name = "stack")
         {
             if (axis == 0)
                 // If the input is a constant list, it can be converted to a constant op
diff --git a/src/TensorFlowNET.Core/Operations/gen_array_ops.cs b/src/TensorFlowNET.Core/Operations/gen_array_ops.cs
index a2db25d9..e29227c4 100644
--- a/src/TensorFlowNET.Core/Operations/gen_array_ops.cs
+++ b/src/TensorFlowNET.Core/Operations/gen_array_ops.cs
@@ -591,6 +591,15 @@ namespace Tensorflow
             return _op.outputs[0];
         }
 
+        public static Tensor ones_like(Tensor x, string name = null)
+            => tf.Context.RunInAutoMode(()
+                => tf.OpDefLib._apply_op_helper("OnesLike", name, new { x }).output, ()
+                => tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName,
+                    "OnesLike", name,
+                    null,
+                    x).FirstOrDefault(),
+                x);
+
         public static Tensor zeros_like(Tensor x, string name = null)
             => tf.Context.RunInAutoMode(()
                 => tf.OpDefLib._apply_op_helper("ZerosLike", name, new { x }).output, ()