class_docstr: Integration tests for clustering RNN layers.
code: def testClusterSimpleRNN(self):
      model = keras.models.Sequential()
      model.add(
          keras.layers.Embedding(
              self.max_features, 16, input_length=self.maxlen
          )
      )
      model.add(keras.layers.SimpleRNN(16, return_sequences=True))
      model.add(keras.layers.SimpleRNN(16))
      model.add(keras.layers.Dense(1))
      model.add(keras.layers.Activation("sigmoid"))
  
      stripped_model = self._clusterTrainStrip(model)
  
      self._assertNbUniqueWeights(
          weight=stripped_model.layers[1].cell.kernel,
          expected_unique_weights=self.params_clustering[
              "number_of_clusters"
          ],
      )
      self._assertNbUniqueWeights(
          weight=stripped_model.layers[1].cell.recurrent_kernel,
          expected_unique_weights=self.params_clustering[
              "number_of_clusters"
          ],
      )
  
      self._train(stripped_model)
  
imports: import os
  import tempfile
  from absl.testing import parameterized
  import numpy
  import tensorflow
  from tensorflow_model_optimization.python.core.clustering.keras import cluster
  from tensorflow_model_optimization.python.core.clustering.keras import cluster_config
  from tensorflow_model_optimization.python.core.clustering.keras.experimental import cluster
label_desc: Use image node_id 5 for calling the ClusterRNNIntegrationTest obj's underlying member method code with example usage: obj.testClusterSimpleRNN() without return types