class_docstr: Featurizer that takes utterances and extracts ordered word cooccurrence
  features matrix from them
class_docstr_tok: ["Featurizer","that","takes","utterances","and","extracts","ordered","word","cooccurrence","features","matrix","from","them"]
code: def _enrich_utterance(self, x, builtin_ents, custom_ents):
      utterance = get_text_from_chunks(x[DATA])
      all_entities = builtin_ents + custom_ents
      placeholder_fn = self._placeholder_fn
      # Replace entities with placeholders
      enriched_utterance = replace_entities_with_placeholders(
          utterance, all_entities, placeholder_fn
      )[1]
      # Tokenize
      enriched_utterance = tokenize_light(
          enriched_utterance, self.language
      )
      # Remove the unknownword strings if needed
      if self.config.unknown_words_replacement_string:
          enriched_utterance = [
              t
              for t in enriched_utterance
              if t != self.config.unknown_words_replacement_string
          ]
      return enriched_utterance
  
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 CooccurrenceVectorizer obj's underlying member method code with example usage: obj._enrich_utterance(x, builtin_ents, custom_ents) and returns: enriched_utterance