class_docstr: This wrapper augments a keras MHA layer so that the weights can be clustered.
code: def set_weight_to_layer(self, weight_name, new_weight):
      pre, _, post = weight_name.rpartition(".")
      layer = attrgetter(pre)(self.layer)
      setattr(layer, post, new_weight)
  
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 2 for calling the ClusterWeightsMHA obj's underlying member method code with example usage: obj.set_weight_to_layer(weight_name, new_weight) without return types