class_docstr: Integration tests for clustering MHA layer.
code: def testMHA(self):
      model = self._get_model()
  
      clustered_model = cluster.cluster_weights(
          model, **self.params_clustering
      )
  
      clustered_model.compile(
          optimizer=tf.keras.optimizers.Adam(learning_rate=1e-4),
          loss=tf.keras.losses.SparseCategoricalCrossentropy(
              from_logits=True
          ),
          metrics=[
              tf.keras.metrics.SparseCategoricalAccuracy(
                  name="accuracy"
              )
          ],
      )
      clustered_model.fit(
          self.x_train,
          self.y_train,
          epochs=1,
          batch_size=100,
          verbose=1,
      )
  
      stripped_model = cluster.strip_clustering(clustered_model)
  
      layer_mha = stripped_model.layers[1]
      for weight in layer_mha.weights:
          if "kernel" in weight.name:
              nr_unique_weights = len(np.unique(weight.numpy()))
              assert nr_unique_weights == self.nr_of_clusters
  
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 3 for calling the ClusterMHAIntegrationTest obj's underlying member method code with example usage: obj.testMHA() without return types