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 from_path(cls, path, **shared):
      path = Path(path)
      model_path = path / "vectorizer.json"
      if not model_path.exists():
          raise LoadingError(
              "Missing vectorizer model file: %s" % model_path.name
          )
  
      with model_path.open(encoding="utf8") as f:
          vectorizer_dict = json.load(f)
      config = vectorizer_dict.pop("config")
  
      self = cls(config, **shared)
      self._language = vectorizer_dict["language_code"]
      self._word_pairs = None
  
      builtin_entity_scope = vectorizer_dict["builtin_entity_scope"]
      if builtin_entity_scope is not None:
          builtin_entity_scope = set(builtin_entity_scope)
      self.builtin_entity_scope = builtin_entity_scope
  
      if vectorizer_dict["word_pairs"]:
          self._word_pairs = {
              tuple(p): int(i)
              for i, p in iteritems(vectorizer_dict["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 14 for calling the CooccurrenceVectorizer obj's underlying member method code with example usage: obj.from_path(cls, path) and returns: self