def save(self):
        tmp_path = self.path + '.tmp'
        with open(tmp_path, 'w') as f:
            f.write(json.dumps(self.body_labels.items()))
        os.rename(tmp_path, self.path)
def _cost_gp_withGradients(self,x):
        """
        Predicts the time cost and its gradient of evaluating the function at x.
        """
        m, _, dmdx, _= self.cost_model.predict_withGradients(x)
        return np.exp(m), np.exp(m)*dmdx