def dist(a, b):
    return sum((i-j)**2 for i, j in zip(a, b))
def _cost_gp(self,x):
        """
        Predicts the time cost of evaluating the function at x.
        """
        m, _, _, _ = self.cost_model.predict_withGradients(x)
        return np.exp(m)