class_docstr: Wrapper of the scikit-learn TfidfVectorizer
class_docstr_tok: ["Wrapper","of","the","scikit-learn","TfidfVectorizer"]
docstr: Featurizes the given utterances after enriching them with builtin
  entities matches, custom entities matches and the potential word
  clusters matches
  
  Args:
      x (list of dict): list of utterances
  
  Returns:
      :class:`.scipy.sparse.csr_matrix`: A sparse matrix X of shape
      (len(x), len(self.vocabulary)) where X[i, j] contains tfdif of
      the ngram of index j of the vocabulary in the utterance i
  
  Raises:
      NotTrained: when the vectorizer is not fitted:
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code: def transform(self, x):
      """Featurizes the given utterances after enriching them with builtin
      entities matches, custom entities matches and the potential word
      clusters matches
  
      Args:
          x (list of dict): list of utterances
  
      Returns:
          :class:`.scipy.sparse.csr_matrix`: A sparse matrix X of shape
          (len(x), len(self.vocabulary)) where X[i, j] contains tfdif of
          the ngram of index j of the vocabulary in the utterance i
  
      Raises:
          NotTrained: when the vectorizer is not fitted:
      """
      utterances = [
          self._enrich_utterance(*data)
          for data in zip(*self._preprocess(x))
      ]
      return self._tfidf_vectorizer.transform(utterances)
  
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 5 for calling the TfidfVectorizer obj's underlying member method code with example usage: obj.transform(x) and returns: self