diff --git a/src/TensorFlowNET.Core/Framework/op_def_registry.py.cs b/src/TensorFlowNET.Core/Framework/op_def_registry.py.cs
index a10a099a..11a89af1 100644
--- a/src/TensorFlowNET.Core/Framework/op_def_registry.py.cs
+++ b/src/TensorFlowNET.Core/Framework/op_def_registry.py.cs
@@ -1,6 +1,7 @@
 using System;
 using System.Collections.Generic;
 using System.Text;
+using static Tensorflow.OpDef.Types;
 
 namespace Tensorflow
 {
@@ -19,9 +20,32 @@ namespace Tensorflow
 
                 foreach (var op_def in op_list.Op)
                     _registered_ops[op_def.Name] = op_def;
+
+                if (!_registered_ops.ContainsKey("NearestNeighbors"))
+                    _registered_ops["NearestNeighbors"] = op_NearestNeighbors();
             }
 
             return _registered_ops;
         }
+
+        /// <summary>
+        /// Doesn't work because the op can't be found on binary
+        /// </summary>
+        /// <returns></returns>
+        private static OpDef op_NearestNeighbors()
+        {
+            var def = new OpDef
+            {
+                Name = "NearestNeighbors"
+            };
+
+            def.InputArg.Add(new ArgDef { Name = "points", Type = DataType.DtFloat });
+            def.InputArg.Add(new ArgDef { Name = "centers", Type = DataType.DtFloat });
+            def.InputArg.Add(new ArgDef { Name = "k", Type = DataType.DtInt64 });
+            def.OutputArg.Add(new ArgDef { Name = "nearest_center_indices", Type = DataType.DtInt64 });
+            def.OutputArg.Add(new ArgDef { Name = "nearest_center_distances", Type = DataType.DtFloat });
+
+            return def;
+        }
     }
 }
diff --git a/test/TensorFlowNET.Examples/KMeansClustering.cs b/test/TensorFlowNET.Examples/KMeansClustering.cs
index b09e38fc..d18a1153 100644
--- a/test/TensorFlowNET.Examples/KMeansClustering.cs
+++ b/test/TensorFlowNET.Examples/KMeansClustering.cs
@@ -33,6 +33,8 @@ namespace TensorFlowNET.Examples
 
         public bool Run()
         {
+            tf.train.import_meta_graph("kmeans.meta");
+
             // Input images
             var X = tf.placeholder(tf.float32, shape: new TensorShape(-1, num_features));
             //  Labels (for assigning a label to a centroid and testing)