code: def get_stop_words_whitelist(dataset, stop_words):
      """Extracts stop words whitelists per intent consisting of entity values
      that appear in the stop_words list"""
      entity_values_per_intent = extract_entity_values(
          dataset, apply_normalization=True
      )
      stop_words_whitelist = dict()
      for intent, entity_values in iteritems(entity_values_per_intent):
          whitelist = stop_words.intersection(entity_values)
          if whitelist:
              stop_words_whitelist[intent] = whitelist
      return stop_words_whitelist
  
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 6 for calling a global function with example usage: get_stop_words_whitelist(dataset, stop_words) and returns: stop_words_whitelist