How to use the tf2rl.misc.discount_cumsum.discount_cumsum function in tf2rl

def test_discount_cumsum(self):
        x = np.array([1., 1., 1.])
        discount = 0.99
        expected = [
            1. + 1.*discount**1 + 1.*discount**2,
            1. + 1.*discount**1,
            1.]
        results = discount_cumsum(x, discount)
        np.testing.assert_array_equal(results, expected)

the targets for the value function.
        The "last_val" argument should be 0 if the trajectory ended
        because the agent reached a terminal state (died), and otherwise
        should be V(s_T), the value function estimated for the last state.
        This allows us to bootstrap the reward-to-go calculation to account
        for timesteps beyond the arbitrary episode horizon (or epoch cutoff).
        """
        samples = self.local_buffer._encode_sample(
            np.arange(self.local_buffer.get_stored_size()))
        rews = np.append(samples["rew"], last_val)
        vals = np.append(samples["val"], last_val)

        # GAE-Lambda advantage calculation
        deltas = rews[:-1] + self._policy.discount * vals[1:] - vals[:-1]
        if self._policy.enable_gae:
            advs = discount_cumsum(
                deltas, self._policy.discount * self._policy.lam)
        else:
            advs = deltas

        # Rewards-to-go, to be targets for the value function
        rets = discount_cumsum(rews, self._policy.discount)[:-1]
        self.replay_buffer.add(
            obs=samples["obs"], act=samples["act"], done=samples["done"],
            ret=rets, adv=advs, logp=np.squeeze(samples["logp"]))
        self.local_buffer.clear()

"""
        samples = self.local_buffer._encode_sample(
            np.arange(self.local_buffer.get_stored_size()))
        rews = np.append(samples["rew"], last_val)
        vals = np.append(samples["val"], last_val)

        # GAE-Lambda advantage calculation
        deltas = rews[:-1] + self._policy.discount * vals[1:] - vals[:-1]
        if self._policy.enable_gae:
            advs = discount_cumsum(
                deltas, self._policy.discount * self._policy.lam)
        else:
            advs = deltas

        # Rewards-to-go, to be targets for the value function
        rets = discount_cumsum(rews, self._policy.discount)[:-1]
        self.replay_buffer.add(
            obs=samples["obs"], act=samples["act"], done=samples["done"],
            ret=rets, adv=advs, logp=np.squeeze(samples["logp"]))
        self.local_buffer.clear()