code: def transform(self, y):
          
          check_is_fitted(self, 'classes_')
          y = column_or_1d(y, warn=True)
  
          classes = np.unique(y)
          _check_numpy_unicode_bug(classes)
          if len(np.intersect1d(classes, self.classes_)) < len(classes):
              diff = np.setdiff1d(classes, self.classes_)
              raise ValueError("y contains new labels: %s" % str(diff))
          return np.searchsorted(self.classes_, y)
docs: Transform labels to normalized encoding.
  
          Parameters
          ----------
          y : array-like of shape [n_samples]
              Target values.
  
          Returns
          -------
          y : array-like of shape [n_samples]