code: def remove_builtin_slots(dataset):
      filtered_dataset = deepcopy(dataset)
      for intent_data in itervalues(filtered_dataset[INTENTS]):
          for utterance in intent_data[UTTERANCES]:
              utterance[DATA] = [
                  chunk
                  for chunk in utterance[DATA]
                  if ENTITY not in chunk
                  or not is_builtin_entity(chunk[ENTITY])
              ]
      return filtered_dataset
  
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 1 for calling a global function with example usage: remove_builtin_slots(dataset) and returns: filtered_dataset