class_docstr: Wrapper of the scikit-learn TfidfVectorizer
class_docstr_tok: ["Wrapper","of","the","scikit-learn","TfidfVectorizer"]
code: def _enrich_utterance(
      self, utterance, builtin_entities, custom_entities, word_clusters
  ):
      custom_entities_features = [
          _entity_name_to_feature(e[ENTITY_KIND], self.language)
          for e in custom_entities
      ]
  
      builtin_entities_features = [
          _builtin_entity_to_feature(ent[ENTITY_KIND], self.language)
          for ent in builtin_entities
      ]
  
      # We remove values of builtin slots from the utterance to avoid
      # learning specific samples such as '42' or 'tomorrow'
      filtered_tokens = [
          chunk[TEXT]
          for chunk in utterance[DATA]
          if ENTITY not in chunk or not is_builtin_entity(chunk[ENTITY])
      ]
  
      features = get_default_sep(self.language).join(filtered_tokens)
  
      if builtin_entities_features:
          features += " " + " ".join(sorted(builtin_entities_features))
      if custom_entities_features:
          features += " " + " ".join(sorted(custom_entities_features))
      if word_clusters:
          features += " " + " ".join(sorted(word_clusters))
  
      return features
  
imports: from __future__ import division, unicode_literals
  import json
  from builtins import str, zip
  from copy import deepcopy
  from pathlib import Path
  from future.utils import iteritems
  from snips_nlu.common.utils import json_string, fitted_required, replace_entities_with_placeholders, check_persisted_path
  from snips_nlu.constants import DATA, ENTITY, ENTITY_KIND, LANGUAGE, NGRAM, TEXT, ENTITIES
  from snips_nlu.dataset import get_text_from_chunks, validate_and_format_dataset
  from snips_nlu.entity_parser.builtin_entity_parser import is_builtin_entity
  from snips_nlu.exceptions import _EmptyDatasetUtterancesError, LoadingError
  from snips_nlu.languages import get_default_sep
  from snips_nlu.pipeline.configs import FeaturizerConfig
  from snips_nlu.pipeline.configs.intent_classifier import CooccurrenceVectorizerConfig, TfidfVectorizerConfig
  from snips_nlu.pipeline.processing_unit import ProcessingUnit
  from snips_nlu.preprocessing import stem, tokenize_light
  from snips_nlu.resources import get_stop_words, get_word_cluster
  from snips_nlu.slot_filler.features_utils import get_all_ngrams
label_desc: Use image node_id 7 for calling the TfidfVectorizer obj's underlying member method code with example usage: obj._enrich_utterance(utterance, builtin_entities, custom_entities, word_clusters) and returns: features