code: def _te_ss_t_build(self):
      from sklearn.datasets import fetch_20newsgroups
      from sklearn.feature_extraction.text import CountVectorizer
  
      newsgroups_train = fetch_20newsgroups(
          subset="train", remove=("headers", "footers", "quotes")
      )
      count_vectorizer = CountVectorizer()
      X_counts = count_vectorizer.fit_transform(newsgroups_train.data)
      corpus = CorpusFromScikit(
          X=X_counts,
          y=newsgroups_train.target,
          feature_vocabulary=count_vectorizer.vocabulary_,
          category_names=newsgroups_train.target_names,
          raw_texts=newsgroups_train.data,
      ).build()
      self.assertEqual(
          corpus.get_categories()[:2], ["alt.atheism", "comp.graphics"]
      )
      self.assertEqual(
          corpus.get_term_freq_df()
          .assign(score=corpus.get_scaled_f_scores("alt.atheism"))
          .sort_values(by="score", ascending=False)
          .index.tolist()[:5],
          ["atheism", "atheists", "islam", "atheist", "belief"],
      )
      self.assertGreater(len(corpus.get_texts()[0]), 5)
  
imports: from unittest import TestCase
  from scattertext.CorpusFromScikit import CorpusFromScikit
label_desc: Use image node_id 2 for calling the TestCorpusFromScikit obj's underlying member method code with example usage: obj._te_ss_t_build() without return types