How to use the torchdiffeq._impl.interp._interp_evaluate function in torchdiffeq

def advance(self, next_t):
        """Interpolate through the next time point, integrating as necessary."""
        n_steps = 0
        while next_t > self.rk_state.t1:
            assert n_steps < self.max_num_steps, 'max_num_steps exceeded ({}>={})'.format(n_steps, self.max_num_steps)
            self.rk_state = self._adaptive_heun_step(self.rk_state)
            n_steps += 1
        return _interp_evaluate(self.rk_state.interp_coeff, self.rk_state.t0, self.rk_state.t1, next_t)

def advance(self, next_t):
        """Interpolate through the next time point, integrating as necessary."""
        n_steps = 0
        while next_t > self.rk_state.t1:
            assert n_steps < self.max_num_steps, 'max_num_steps exceeded ({}>={})'.format(n_steps, self.max_num_steps)
            self.rk_state = self._adaptive_dopri5_step(self.rk_state)
            n_steps += 1
        # if len(self.n_step_record)==100:
        #     print("this dopri5 step info will print every 100 calls, the current average step is {}".format(sum(self.n_step_record)/100))
        #     self.n_step_record=[]
        # else:
        #     self.n_step_record.append(n_steps)

        return _interp_evaluate(self.rk_state.interp_coeff, self.rk_state.t0, self.rk_state.t1, next_t)