class_docstr: This wrapper augments a keras MHA layer so that the weights can be clustered.
code: def get_weight_from_layer(self, weight_name):
      pre, _, post = weight_name.rpartition(".")
      return getattr(getattr(self.layer, pre), post)
  
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 1 for calling the ClusterWeightsMHA obj's underlying member method code with example usage: obj.get_weight_from_layer(weight_name) and returns: getattr