diff --git a/tensorflow_model_optimization/python/core/clustering/keras/cluster_test.py b/tensorflow_model_optimization/python/core/clustering/keras/cluster_test.py
index 6e3319211..0c71f16f8 100644
--- a/tensorflow_model_optimization/python/core/clustering/keras/cluster_test.py
+++ b/tensorflow_model_optimization/python/core/clustering/keras/cluster_test.py
@@ -124,6 +124,7 @@ def setUp(self):
     self.custom_clusterable_layer = CustomClusterableLayer(10)
     self.custom_non_clusterable_layer = CustomNonClusterableLayer(10)
     self.keras_depthwiseconv2d_layer = layers.DepthwiseConv2D((3, 3), (1, 1))
+    self.clusterable_layer = MyClusterableLayer(10)
     self.keras_custom_layer = KerasCustomLayer()
     self.clusterable_layer = MyClusterableLayer(10)
 
@@ -242,6 +243,34 @@ def testClusterCustomNonClusterableLayer(self):
       cluster_wrapper.ClusterWeights(custom_non_clusterable_layer,
                                      **self.params)
 
+  def testClusterMyClusterableLayer(self):
+    # we have weights to cluster.
+    clusterable_layer = self.clusterable_layer
+    clusterable_layer.build(input_shape=(10, 10))
+
+    wrapped_layer = cluster_wrapper.ClusterWeights(clusterable_layer,
+                                     **self.params)
+
+    self.assertIsInstance(wrapped_layer, cluster_wrapper.ClusterWeights)
+
+  def testKerasCustomLayerClusterable(self):
+    """
+    Verifies that we can wrap keras custom layer that is customerable.
+    """
+    clusterable_layer = KerasCustomLayerClusterable()
+    wrapped_layer = cluster_wrapper.ClusterWeights(clusterable_layer,
+                                     **self.params)
+
+    self.assertIsInstance(wrapped_layer, cluster_wrapper.ClusterWeights)
+
+  def testClusterMyClusterableLayerInvalid(self):
+    """
+    Verifies that assertion is thrown when function
+    get_clusterable_weights is not provided.
+    """
+    with self.assertRaises(TypeError):
+      MyClusterableLayerInvalid(10) # pylint: disable=abstract-class-instantiated
+
   def testClusterKerasCustomLayer(self):
     """Verifies that attempting to cluster a keras custom layer raises an exception."""
     # If layer is not built, it has not weights, so
@@ -275,8 +304,7 @@ def testClusterMyClusterableLayerInvalid(self):
   @keras_parameterized.run_all_keras_modes
   def testClusterSequentialModelSelectively(self):
     clustered_model = keras.Sequential()
-    clustered_model.add(
-        cluster.cluster_weights(self.keras_clusterable_layer, **self.params))
+    clustered_model.add(cluster.cluster_weights(self.keras_clusterable_layer, **self.params))
     clustered_model.add(self.keras_clusterable_layer)
     clustered_model.build(input_shape=(1, 10))
 
diff --git a/tensorflow_model_optimization/python/core/clustering/keras/clustering_algorithm.py b/tensorflow_model_optimization/python/core/clustering/keras/clustering_algorithm.py
index 1be5da6c5..955044dbf 100644
--- a/tensorflow_model_optimization/python/core/clustering/keras/clustering_algorithm.py
+++ b/tensorflow_model_optimization/python/core/clustering/keras/clustering_algorithm.py
@@ -21,7 +21,7 @@
 
 @six.add_metaclass(abc.ABCMeta)
 class AbstractClusteringAlgorithm(object):
-  """Abstrac class to implement highly efficient vectorised look-ups.
+  """Abstract class to implement highly efficient vectorised look-ups.
 
     We do not utilise looping for that purpose, instead we `smartly` reshape and
     tile arrays. The trade-off is that we are potentially using way more memory