code: def test_entity_censor(self):
      doc = whitespace_nlp("A a bb cc.", {"bb": "BAD"})
      term_freq = FeatsFromSpacyDoc(
          entity_types_to_censor=set(["BAD"])
      ).get_feats(doc)
      self.assertEqual(
          Counter(
              {
                  "a": 2,
                  "a _BAD": 1,
                  "_BAD cc": 1,
                  "cc": 1,
                  "a a": 1,
                  "_BAD": 1,
              }
          ),
          term_freq,
      )
  
imports: import re
  from collections import Counter
  from unittest import TestCase
  from scattertext import whitespace_nlp
  from scattertext.WhitespaceNLP import Tok, Doc, _regex_parse_sentence, whitespace_nlp_with_sentences
  from scattertext.features.FeatsFromSpacyDoc import FeatsFromSpacyDoc
  from scattertext.features.FeatsFromSpacyDocOnlyNounChunks import FeatsFromSpacyDocOnlyNounChunks
label_desc: Use image node_id 7 for calling the TestFeatsFromSpacyDoc obj's underlying member method code with example usage: obj.test_entity_censor() without return types