How to use the stable-baselines.stable_baselines.acer.acer_simple._Runner function in stable-baselines

def __init__(self, env, model, n_steps):
        """
        A runner to learn the policy of an environment for a model

        :param env: (Gym environment) The environment to learn from
        :param model: (Model) The model to learn
        :param n_steps: (int) The number of steps to run for each environment
        """

        super(_Runner, self).__init__(env=env, model=model, n_steps=n_steps)
        self.env = env
        self.model = model
        self.n_env = n_env = env.num_envs
        if isinstance(env.action_space, Discrete):
            self.n_act = env.action_space.n
        else:
            self.n_act = env.action_space.shape[-1]
        self.n_batch = n_env * n_steps

        if len(env.observation_space.shape) > 1:
            self.raw_pixels = True
            obs_height, obs_width, obs_num_channels = env.observation_space.shape
            self.batch_ob_shape = (n_env * (n_steps + 1), obs_height, obs_width, obs_num_channels)
            self.obs_dtype = np.uint8
            self.obs = np.zeros((n_env, obs_height, obs_width, obs_num_channels), dtype=self.obs_dtype)
            self.num_channels = obs_num_channels

def _make_runner(self) -> AbstractEnvRunner:
        return _Runner(env=self.env, model=self, n_steps=self.n_steps)