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"]
docstr: Fits the vectorizer and returns the feature matrix
  
  Args:
      x (iterable): iterable of 3-tuples of the form
          (tokenized_utterances, builtin_entities, custom_entities)
      dataset (dict): dataset from which x was extracted (needed to
          extract the language and the builtin entity scope)
  
  Returns:
      :class:`.scipy.sparse.csr_matrix`: A sparse matrix X of shape
      (len(x), len(self.word_pairs)) where
      X[i, j] = 1.0 if x[i][0] contains the words cooccurrence
      (w1, w2) and if self.word_pairs[(w1, w2)] = j
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code: def fit_transform(self, x, dataset):
      """Fits the vectorizer and returns the feature matrix
  
      Args:
          x (iterable): iterable of 3-tuples of the form
              (tokenized_utterances, builtin_entities, custom_entities)
          dataset (dict): dataset from which x was extracted (needed to
              extract the language and the builtin entity scope)
  
      Returns:
          :class:`.scipy.sparse.csr_matrix`: A sparse matrix X of shape
          (len(x), len(self.word_pairs)) where
          X[i, j] = 1.0 if x[i][0] contains the words cooccurrence
          (w1, w2) and if self.word_pairs[(w1, w2)] = j
      """
      return self.fit(x, dataset).transform(x)
  
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 6 for calling the CooccurrenceVectorizer obj's underlying member method code with example usage: obj.fit_transform(x, dataset) and returns: self