class_docstr: Unit tests for the ClusteringRegistry class.
class_docstr_tok: ["Unit","tests","for","the","ClusteringRegistry","class","."]
docstr: make_clusterable() makes a built-in RNN layer clusterable.
docstr_tok: ["make_clusterable","(",")","makes","a","built-in","RNN","layer","clusterable","."]
code: def testMakeClusterableWorksOnKerasRNNLayer(self):
      """make_clusterable() makes a built-in RNN layer clusterable."""
      layer = layers.LSTM(10)
      with self.assertRaises(AttributeError):
          layer.get_clusterable_weights()
  
      ClusterRegistry.make_clusterable(layer)
      keras.Sequential([layer]).build(input_shape=(2, 3, 4))
  
      expected_weights = [
          ("kernel/0", layer.cell.kernel),
          ("recurrent_kernel/0", layer.cell.recurrent_kernel),
      ]
      self.assertEqual(
          expected_weights, layer.get_clusterable_weights()
      )
  
imports: from absl.testing import parameterized
  import tensorflow
  from tensorflow_model_optimization.python.core.clustering.keras import clusterable_layer
  from tensorflow_model_optimization.python.core.clustering.keras import clustering_registry
  from tensorflow_model_optimization.python.core.clustering.keras.cluster_config import GradientAggregation
label_desc: Use image node_id 16 for calling the ClusterRegistryTest obj's underlying member method code with example usage: obj.testMakeClusterableWorksOnKerasRNNLayer() without return types