code: def extract_utterance_entities(dataset):
      entities_values = {
          ent_name: set() for ent_name in dataset[ENTITIES]
      }
  
      for intent in itervalues(dataset[INTENTS]):
          for utterance in intent[UTTERANCES]:
              for chunk in utterance[DATA]:
                  if ENTITY in chunk:
                      entities_values[chunk[ENTITY]].add(
                          chunk[TEXT].strip()
                      )
      return {k: list(v) for k, v in iteritems(entities_values)}
  
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 1 for calling a global function with example usage: extract_utterance_entities(dataset) and returns: unknown