code: def test_main(self):
      categories, documents = get_docs_categories()
      clean_function = lambda text: "" if text.startswith("[") else text
      entity_types = set(["GPE"])
      term_doc_mat = TermDocMatrixFactory(
          category_text_iter=zip(categories, documents),
          clean_function=clean_function,
          nlp=_testing_nlp,
          feats_from_spacy_doc=FeatsFromSpacyDoc(
              entity_types_to_censor=entity_types
          ),
      ).build()
      clf = PassiveAggressiveClassifier()
      fdc = FeatsFromDoc(
          term_doc_mat._term_idx_store,
          clean_function=clean_function,
          feats_from_spacy_doc=FeatsFromSpacyDoc(
              entity_types_to_censor=entity_types
          ),
      ).set_nlp(_testing_nlp)
      tfidf = TfidfTransformer(norm="l1")
      X = tfidf.fit_transform(term_doc_mat._X)
      clf.fit(X, term_doc_mat._y)
      X_to_predict = fdc.feats_from_doc(
          "Did sometimes march UNKNOWNWORD"
      )
      pred = clf.predict(tfidf.transform(X_to_predict))
      dec = clf.decision_function(X_to_predict)
  
imports: import re
  from unittest import TestCase
  import pandas
  from sklearn.feature_extraction.text import TfidfTransformer
  from sklearn.linear_model import PassiveAggressiveClassifier
  from scattertext import CorpusFromParsedDocuments
  from scattertext import FeatsFromSpacyDoc, FeatsFromSpacyDocAndEmpath
  from scattertext import TermDocMatrixFactory
  from scattertext.TermDocMatrixFactory import FeatsFromDoc
  from scattertext.WhitespaceNLP import whitespace_nlp, Doc, Tok
label_desc: Use image node_id 1 for calling the TestFeatsFromDoc obj's underlying member method code with example usage: obj.test_main() without return types