Added check before clone_model when we copy layers: if layer is SubClass model, we throw an exception.

This PR addresses reviewer's comment.

Change-Id: I0bd72324fe60da7eda3d3c440c68d1797beecd6c

Author: wwwind

tensorflow_model_optimization/python/core/clustering/keras/cluster.py

@@ -126,7 +126,15 @@ def cluster_weights(to_cluster,
         format(cluster_centroids_init))
 
   def _add_clustering_wrapper(layer):
+
     if (isinstance(layer, keras.Model)):
+      # Check whether the model is a subclass.
+      # NB: This check is copied from keras.py file in tensorflow.
+      # There is no available public API to do this check.
+      if (not layer._is_graph_network and
+          not isinstance(layer, keras.models.Sequential)):
+        raise ValueError("SubClass models are not supported currently.")
+
       return keras.models.clone_model(layer,
                                     input_tensors=None,
                                     clone_function=_add_clustering_wrapper)

tensorflow_model_optimization/python/core/clustering/keras/cluster_test.py

@@ -392,6 +392,20 @@ def testClusterSubclassModel(self):
     with self.assertRaises(ValueError):
       _ = cluster.cluster_weights(model, **self.params)
 
+  @keras_parameterized.run_all_keras_modes
+  def testClusterSubclassModelAsSubmodel(self):
+    """
+    Verifies that attempting to cluster a model with submodel
+    that is a subclass throws an exception.
+    """
+    model_subclass = TestModel()
+    model = keras.Sequential([
+        layers.Dense(10),
+        model_subclass
+    ])
+    with self.assertRaisesRegexp(ValueError, "SubClass models.*"):
+      _ = cluster.cluster_weights(model, **self.params)
+
   @keras_parameterized.run_all_keras_modes
   def testStripClusteringSequentialModel(self):
     """