code: def fit_transform(self, raw_documents, y=None):
          
          documents = super(TfidfVectorizer, self).fit_transform(
              raw_documents=raw_documents, y=y)
          count = CountVectorizer(encoding=self.encoding,
                                  decode_error=self.decode_error,
                                  strip_accents=self.strip_accents,
                                  lowercase=self.lowercase,
                                  preprocessor=self.preprocessor,
                                  tokenizer=self.tokenizer,
                                  stop_words=self.stop_words,
                                  token_pattern=self.token_pattern,
                                  ngram_range=self.ngram_range,
                                  analyzer=self.analyzer,
                                  max_df=self.max_df,
                                  min_df=self.min_df,
                                  max_features=self.max_features,
                                  vocabulary=self.vocabulary_,
                                  binary=self.binary,
                                  dtype=self.dtype)
          count.fit_transform(raw_documents=raw_documents, y=y)
          self.period_ = count.period_
          self.df_ = count.df_
          self.n = count.n
          return documents
docs: Learn vocabulary and idf, return term-document matrix.
          This is equivalent to fit followed by transform, but more efficiently
          implemented.
          Parameters
          ----------
          raw_documents : iterable
              an iterable which yields either str, unicode or file objects
          Returns
          -------
          X : sparse matrix, [n_samples, n_features]
              Tf-idf-weighted document-term matrix.