parameters: (self, raw_documents, copy=True)
return_statement: return self._tfidf.transform(X, copy=False)
docstring: Transform documents to document-term matrix.
  
          Uses the vocabulary and document frequencies (df) learned by fit (or
          fit_transform).
  
          Parameters
          ----------
          raw_documents : iterable
              an iterable which yields either str, unicode or file objects
  
          copy : boolean, default True
              Whether to copy X and operate on the copy or perform in-place
              operations.
  
          Returns
          -------
          X : sparse matrix, [n_samples, n_features]
              Tf-idf-weighted document-term matrix.
docstring_summary: Transform documents to document-term matrix.
docstring_tokens: Transform documents to document - term matrix .
function: def transform(self, raw_documents, copy=True):
          """Transform documents to document-term matrix.
  
          Uses the vocabulary and document frequencies (df) learned by fit (or
          fit_transform).
  
          Parameters
          ----------
          raw_documents : iterable
              an iterable which yields either str, unicode or file objects
  
          copy : boolean, default True
              Whether to copy X and operate on the copy or perform in-place
              operations.
  
          Returns
          -------
          X : sparse matrix, [n_samples, n_features]
              Tf-idf-weighted document-term matrix.
          """
          check_is_fitted(self, '_tfidf', 'The tfidf vector is not fitted')
  
          X = super(TfidfVectorizer, self).transform(raw_documents)
          return self._tfidf.transform(X, copy=False)
function_tokens: def transform ( self , raw_documents , copy = True ) : check_is_fitted ( self , '_tfidf' , 'The tfidf vector is not fitted' ) X = super ( TfidfVectorizer , self ) . transform ( raw_documents ) return self . _tfidf . transform ( X , copy = False )