class_docstr: Tie-in to incorporate sckit-learn's various vectorizers into Scattertext
  >>> from sklearn.datasets import fetch_20newsgroups
  >>> from sklearn.feature_extraction.text import CountVectorizer
  >>> from scattertext.CorpusFromScikit import CorpusFromScikit
  >>> 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()
docstr: Returns
  -------
  Corpus
docstr_tok: ["Returns","--","--","--","-","Corpus"]
code: def build(self):
      """
      Returns
      -------
      Corpus
      """
      constructor_kwargs = self._get_build_kwargs()
      if type(self.raw_texts) == list:
          constructor_kwargs["raw_texts"] = np.array(self.raw_texts)
      else:
          constructor_kwargs["raw_texts"] = self.raw_texts
      return Corpus(**constructor_kwargs)
  
imports: import numpy
  from scattertext.Corpus import Corpus
  from scattertext.TermDocMatrixFromScikit import TermDocMatrixFromScikit
label_desc: Use image node_id 2 for calling the CorpusFromScikit obj's underlying member method code with example usage: obj.build() and returns: Corpus