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 _extract_word_pairs(self, utterance):
      if self.config.filter_stop_words:
          stop_words = get_stop_words(self.resources)
          utterance = [t for t in utterance if t not in stop_words]
      pairs = set()
      for j, w1 in enumerate(utterance):
          max_index = None
          if self.config.window_size is not None:
              max_index = j + self.config.window_size + 1
          for w2 in utterance[j + 1 : max_index]:
              key = (w1, w2)
              if not self.config.keep_order:
                  key = tuple(sorted(key))
              pairs.add(key)
      return pairs
  
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 10 for calling the CooccurrenceVectorizer obj's underlying member method code with example usage: obj._extract_word_pairs(utterance) and returns: pairs