code: def bestfit_func(self, bestfit_x):
          
          bestfit_x = np.array(bestfit_x)
          if not self.done_bestfit:
              raise KeyError("Do do_bestfit first")
          bestfit_y = 0
          for idx, val in enumerate(self.fit_args):
              bestfit_y += val * (bestfit_x **
                                  (self.args.get("degree", 1) - idx))
          return bestfit_y
docs: Returns bestfit_y value
  
          args:
              bestfit_x: scalar, array_like
                  x value
          return: scalar, array_like
              bestfit y value