diff --git a/src/TensorFlowNET.Core/Gradients/math_grad.cs b/src/TensorFlowNET.Core/Gradients/math_grad.cs
index ee92b4ea..d9bc9b22 100644
--- a/src/TensorFlowNET.Core/Gradients/math_grad.cs
+++ b/src/TensorFlowNET.Core/Gradients/math_grad.cs
@@ -529,7 +529,12 @@ namespace Tensorflow.Gradients
                     }
                     else if (!input_0_shape.Contains(-1) && !tf.Context.executing_eagerly())
                     {
-                        throw new NotImplementedException("");
+                        axes = axes.reshape(new Shape(-1));
+                        var shape_tensor = tf.constant(op.inputs[0].shape.as_int_list());
+                        var output_shape_kept_dims = math_ops.reduced_shape(shape_tensor, axes);
+                        var tile_scaling = _safe_shape_div(shape_tensor, output_shape_kept_dims);
+                        grad = array_ops.reshape(grad, output_shape_kept_dims);
+                        return new Tensor[] { array_ops.tile(grad, tile_scaling), null };
                     }
                 }
             }
diff --git a/src/TensorFlowNET.Core/Operations/array_ops.cs b/src/TensorFlowNET.Core/Operations/array_ops.cs
index 049d874e..263509f6 100644
--- a/src/TensorFlowNET.Core/Operations/array_ops.cs
+++ b/src/TensorFlowNET.Core/Operations/array_ops.cs
@@ -585,9 +585,14 @@ namespace Tensorflow
         }
 
         public static Tensor tile(Tensor input, Tensor multiples, string name = null)
-        {
-            throw new NotImplementedException("tile");
-        }
+            => tf.Context.ExecuteOp("Tile", name, new ExecuteOpArgs(input, multiples)
+            {
+                GetGradientAttrs = (op) => new
+                {
+                    T = op.get_attr<TF_DataType>("T"),
+                    Tmultiples = op.get_attr<TF_DataType>("Tmultiples")
+                }
+            });
 
         public static Tensor zeros_like(Tensor tensor, TF_DataType dtype = TF_DataType.DtInvalid, string name = null, bool optimize = true)
         {
diff --git a/test/TensorFlowNET.Graph.UnitTest/GradientTest/GradientTest.cs b/test/TensorFlowNET.Graph.UnitTest/GradientTest/GradientTest.cs
index 12ad58e1..851a3bd7 100644
--- a/test/TensorFlowNET.Graph.UnitTest/GradientTest/GradientTest.cs
+++ b/test/TensorFlowNET.Graph.UnitTest/GradientTest/GradientTest.cs
@@ -178,6 +178,19 @@ namespace TensorFlowNET.UnitTest.Gradient
         [TestMethod]
         public void testReduceSumGradients()
         {
+            /* python code
+            import tensorflow.compat.v1 as tf
+            tf.disable_v2_behavior() 
+
+            x = tf.placeholder(tf.float64, shape = (1, 1))
+            m = tf.broadcast_to(x, (2, 3))
+            g0 = tf.gradients(tf.reduce_sum(m), x)[0]
+            g1 = tf.gradients(tf.reduce_sum(m, axis = 0), x)[0]
+            g2 = tf.gradients(tf.reduce_sum(m, axis = 1), x)[0]
+            with tf.compat.v1.Session() as sess:
+                (r0, r1, r2) = sess.run((g0, g1, g2), {x: [[1.0]]})
+            */
+
             var x = tf.placeholder(tf.float64, shape: new Shape(1, 1));
             var m = tf.broadcast_to(x, new Shape(2, 3));
             var g0 = tf.gradients(tf.reduce_sum(m), x)[0];
@@ -186,10 +199,10 @@ namespace TensorFlowNET.UnitTest.Gradient
 
             using (var session = tf.Session())
             {
-                var (r0, r1, r2) = session.run((g0, g1, g2), new FeedItem(x, 1.0));
+                var (r0, r1, r2) = session.run((g0, g1, g2), new FeedItem(x, new[,] { { 1.0 } }));
                 self.assertFloat64Equal(6.0, r0[0], $"tf.reduce_sum(...)");
-                self.assertFloat64Equal(2.0, r1[0], $"tf.reduce_sum(..., axis = 0)");
-                self.assertFloat64Equal(3.0, r2[0], $"tf.reduce_sum(..., axis = 1)");
+                self.assertFloat64Equal(6.0, r1[0], $"tf.reduce_sum(..., axis = 0)");
+                self.assertFloat64Equal(6.0, r2[0], $"tf.reduce_sum(..., axis = 1)");
             }
         }