code: def generate_utterance(contexts_iterator, entities_iterators):
      context = deepcopy(next(contexts_iterator))
      context_data = []
      for chunk in context[DATA]:
          if ENTITY in chunk:
              chunk[TEXT] = deepcopy(
                  next(entities_iterators[chunk[ENTITY]])
              )
          chunk[TEXT] = chunk[TEXT].strip() + " "
          context_data.append(chunk)
      context[DATA] = context_data
      return context
  
file_path: snips-nlu/snips_nlu/data_augmentation.py
imports: from __future__ import unicode_literals
  from builtins import next
  from copy import deepcopy
  from itertools import cycle
  from future.utils import iteritems
  from snips_nlu.constants import CAPITALIZE, DATA, ENTITIES, ENTITY, INTENTS, TEXT, UTTERANCES
  from snips_nlu.entity_parser.builtin_entity_parser import is_builtin_entity
  from snips_nlu.languages import get_default_sep
  from snips_nlu.preprocessing import tokenize_light
  from snips_nlu.resources import get_stop_words
label_desc: Use image node_id 3 for calling a global function with example usage: generate_utterance(contexts_iterator, entities_iterators) and returns: context