code: def generate_smart_noise(
      noise, augmented_utterances, replacement_string, language
  ):
      text_utterances = [
          get_text_from_chunks(u[DATA]) for u in augmented_utterances
      ]
      vocab = [
          w
          for u in text_utterances
          for w in tokenize_light(u, language)
      ]
      vocab = set(vocab)
      return [w if w in vocab else replacement_string for w in noise]
  
imports: from __future__ import division, unicode_literals
  import itertools
  import re
  from builtins import next, range, str
  from copy import deepcopy
  from uuid import uuid4
  from future.utils import iteritems, itervalues
  from snips_nlu.constants import DATA, ENTITY, INTENTS, TEXT, UNKNOWNWORD, UTTERANCES
  from snips_nlu.data_augmentation import augment_utterances
  from snips_nlu.dataset import get_text_from_chunks
  from snips_nlu.entity_parser.builtin_entity_parser import is_builtin_entity
  from snips_nlu.preprocessing import tokenize_light
  from snips_nlu.resources import get_noise
label_desc: Use image node_id 4 for calling a global function with example usage: generate_smart_noise(noise, augmented_utterances, replacement_string, language) and returns: unknown