code: def extract_entity_values(dataset, apply_normalization):
      from snips_nlu_utils import normalize
  
      entities_per_intent = {
          intent: set() for intent in dataset[INTENTS]
      }
      intent_entities = extract_intent_entities(dataset)
      for intent, entities in iteritems(intent_entities):
          for entity in entities:
              entity_values = set(dataset[ENTITIES][entity][UTTERANCES])
              if apply_normalization:
                  entity_values = {normalize(v) for v in entity_values}
              entities_per_intent[intent].update(entity_values)
      return entities_per_intent
  
file_path: snips-nlu/snips_nlu/dataset/utils.py
imports: from __future__ import unicode_literals
  from future.utils import iteritems, itervalues
  from snips_nlu.constants import DATA, ENTITIES, ENTITY, INTENTS, TEXT, UTTERANCES
  from snips_nlu.entity_parser.builtin_entity_parser import is_gazetteer_entity
label_desc: Use image node_id 3 for calling a global function with example usage: extract_entity_values(dataset, apply_normalization) and returns: entities_per_intent