docstr: Entity types to exclude from feature construction. Terms matching
  specificed entities, instead of labeled by their lower case orthographic
  form or lemma, will be labeled by their entity type.
  
  Parameters
  ----------
  entity_types : set of entity types outputted by spaCy
  
  
  
  Returns
  ---------
  self
docstr_tok: ["Entity","types","to","exclude","from","feature","construction",".","Terms","matching","specificed","entities",",","instead","of","labeled","by","their","lower","case","orthographic","form","or","lemma",",","will","be","labeled","by","their","entity","type",".","Parameters","--","--","--","--","--","entity_types",":","set","of","entity","types","outputted","by","spaCy","Returns","--","--","--","--","-","self"]
code: def censor_entity_types(self, entity_types):
      # type: (set) -> TermDocMatrixFactory
      """
      Entity types to exclude from feature construction. Terms matching
      specificed entities, instead of labeled by their lower case orthographic
      form or lemma, will be labeled by their entity type.
  
      Parameters
      ----------
      entity_types : set of entity types outputted by spaCy
  
  
  
      Returns
      ---------
      self
      """
      assert type(entity_types) == set
      self._entity_types_to_censor = entity_types
      self._feats_from_spacy_doc = FeatsFromSpacyDoc(
          use_lemmas=self._use_lemmas,
          entity_types_to_censor=self._entity_types_to_censor,
      )
      return self
  
imports: import string
  from collections import Counter
  import numpy
  import pandas
  from scattertext.CSRMatrixTools import CSRMatrixFactory
  from scattertext.TermDocMatrix import TermDocMatrix
  from scattertext.features.FeatsFromSpacyDoc import FeatsFromSpacyDoc
  from scattertext.indexstore.IndexStore import IndexStore
label_desc: Use image node_id 6 for calling the TermDocMatrixFactory obj's underlying member method code with example usage: obj.censor_entity_types(entity_types) and returns: self