code: def _cost_gp_withGradients(self,x):
          
          m, _, dmdx, _= self.cost_model.predict_withGradients(x)
          return np.exp(m), np.exp(m)*dmdx
docs: Predicts the time cost and its gradient of evaluating the function at x.