class_docstr: This wrapper augments a keras MHA layer so that the weights can be clustered.
docstr: The restore from config is not working for MHA layer.
  
  Weights are not created when the build function is called. Therefore,
  original weights have been replaced in the layer.
  
  Returns:
    Updated layer.
code: def strip_clustering(self):
      """The restore from config is not working for MHA layer.
  
      Weights are not created when the build function is called. Therefore,
      original weights have been replaced in the layer.
  
      Returns:
        Updated layer.
      """
      for (
          weight_name,
          original_weight,
      ) in self.original_clusterable_weights.items():
          # Get the clustered weights
          clustered_weight = self.get_weight_from_layer(weight_name)
  
          # Re-assign these weights to the original
          original_weight.assign(clustered_weight)
          setattr(self.layer, weight_name, original_weight)
  
      return self.layer
  
imports: import operator
  import tensorflow
  from tensorflow_model_optimization.python.core.clustering.keras import cluster_config
  from tensorflow_model_optimization.python.core.clustering.keras import clusterable_layer
  from tensorflow_model_optimization.python.core.clustering.keras import clustering_centroids
  from tensorflow_model_optimization.python.core.clustering.keras import clustering_registry
label_desc: Use image node_id 3 for calling the ClusterWeightsMHA obj's underlying member method code with example usage: obj.strip_clustering() and returns: self