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 __init__(self, config=None, **shared):
      super(CooccurrenceVectorizer, self).__init__(config, **shared)
      self._word_pairs = None
      self._language = None
      self.builtin_entity_scope = None
  
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 1 to create a new CooccurrenceVectorizer object from inherited base classes: ProcessingUnit with example: obj = CooccurrenceVectorizer(config)