How to use the torchdiffeq._impl.misc._compute_error_ratio function in torchdiffeq

def _adaptive_heun_step(self, rk_state):
        """Take an adaptive Runge-Kutta step to integrate the ODE."""
        y0, f0, _, t0, dt, interp_coeff = rk_state
        ########################################################
        #                      Assertions                      #
        ########################################################
        assert t0 + dt > t0, 'underflow in dt {}'.format(dt.item())
        for y0_ in y0:
            assert _is_finite(torch.abs(y0_)), 'non-finite values in state `y`: {}'.format(y0_)
        y1, f1, y1_error, k = _runge_kutta_step(self.func, y0, f0, t0, dt, tableau=_ADAPTIVE_HEUN_TABLEAU)

        ########################################################
        #                     Error Ratio                      #
        ########################################################
        mean_sq_error_ratio = _compute_error_ratio(y1_error, atol=self.atol, rtol=self.rtol, y0=y0, y1=y1)
        accept_step = (torch.tensor(mean_sq_error_ratio) <= 1).all()

        ########################################################
        #                   Update RK State                    #
        ########################################################
        y_next = y1 if accept_step else y0
        f_next = f1 if accept_step else f0
        t_next = t0 + dt if accept_step else t0
        interp_coeff = _interp_fit_adaptive_heun(y0, y1, k, dt) if accept_step else interp_coeff
        dt_next = _optimal_step_size(
            dt, mean_sq_error_ratio, safety=self.safety, ifactor=self.ifactor, dfactor=self.dfactor, order=5
        )
        rk_state = _RungeKuttaState(y_next, f_next, t0, t_next, dt_next, interp_coeff)
        return rk_state

########################################################
        assert t0 + dt > t0, 'underflow in dt {}'.format(dt.item())
        for y0_ in y0:
            #assert _is_finite(torch.abs(y0_)), 'non-finite values in state `y`: {}'.format(y0_)
            is_finite= _is_finite(torch.abs(y0_))
            if not is_finite:
                print(" non-finite elements exist, try to fix")
                y0_[y0_ != y0_] = 0.
                y0_[y0_ == float("Inf")] = 0.

        y1, f1, y1_error, k = _runge_kutta_step(self.func, y0, f0, t0, dt, tableau=_DORMAND_PRINCE_SHAMPINE_TABLEAU)

        ########################################################
        #                     Error Ratio                      #
        ########################################################
        mean_sq_error_ratio = _compute_error_ratio(y1_error, atol=self.atol, rtol=self.rtol, y0=y0, y1=y1)
        accept_step = (torch.tensor(mean_sq_error_ratio) <= 1).all()

        ########################################################
        #                   Update RK State                    #
        ########################################################
        dt_next = _optimal_step_size(
            dt, mean_sq_error_ratio, safety=self.safety, ifactor=self.ifactor, dfactor=self.dfactor, order=5)
        if not (dt_next<0.02): #not (dt_next<0.02 or dt_next>0.1):
            y_next = y1 if accept_step else y0
            f_next = f1 if accept_step else f0
            t_next = t0 + dt if accept_step else t0
            interp_coeff = _interp_fit_dopri5(y0, y1, k, dt) if accept_step else interp_coeff
        else:
            if dt_next<0.02:
                print("warning the step of dopri5 {} is too small, set to 0.01".format(dt_next))
                dt_next = _convert_to_tensor(0.01, dtype=torch.float64, device=y0[0].device)