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: Restrict the vectorizer word pairs to the given word pairs
  
  Args:
      word_pairs (iterable of 2-tuples (str, str)): word_pairs to keep
  
  Returns:
      :class:`.CooccurrenceVectorizer`: The vectorizer with limited
      word pairs
docstr_tok: ["Restrict","the","vectorizer","word","pairs","to","the","given","word","pairs","Args",":","word_pairs","(","iterable","of","2-tuples","(","str",",","str",")",")",":","word_pairs","to","keep","Returns",":",":","class",":","`",".CooccurrenceVectorizer","`",":","The","vectorizer","with","limited","word","pairs"]
code: def limit_word_pairs(self, word_pairs):
      """Restrict the vectorizer word pairs to the given word pairs
  
      Args:
          word_pairs (iterable of 2-tuples (str, str)): word_pairs to keep
  
      Returns:
          :class:`.CooccurrenceVectorizer`: The vectorizer with limited
          word pairs
      """
      word_pairs = set(word_pairs)
      existing_pairs = set(self.word_pairs)
      extra_values = word_pairs - existing_pairs
  
      if extra_values:
          raise ValueError(
              "Invalid word pairs %s, expected values in word_pairs"
              % sorted(extra_values)
          )
  
      self._word_pairs = {
          ng: new_i for new_i, ng in enumerate(sorted(word_pairs))
      }
      return self
  
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 11 for calling the CooccurrenceVectorizer obj's underlying member method code with example usage: obj.limit_word_pairs(word_pairs) and returns: self