How to use the stable-baselines.stable_baselines.common.base_class._UnvecWrapper function in stable-baselines

assert self.action_space == env.action_space, \
            "Error: the environment passed must have at least the same action space as the model was trained on."
        if self._requires_vec_env:
            assert isinstance(env, VecEnv), \
                "Error: the environment passed is not a vectorized environment, however {} requires it".format(
                    self.__class__.__name__)
            assert not self.policy.recurrent or self.n_envs == env.num_envs, \
                "Error: the environment passed must have the same number of environments as the model was trained on." \
                "This is due to the Lstm policy not being capable of changing the number of environments."
            self.n_envs = env.num_envs
        else:
            # for models that dont want vectorized environment, check if they make sense and adapt them.
            # Otherwise tell the user about this issue
            if isinstance(env, VecEnv):
                if env.num_envs == 1:
                    env = _UnvecWrapper(env)
                    self._vectorize_action = True
                else:
                    raise ValueError("Error: the model requires a non vectorized environment or a single vectorized "
                                     "environment.")
            else:
                self._vectorize_action = False

            self.n_envs = 1

        self.env = env

        # Invalidated by environment change.
        self.episode_reward = None
        self.ep_info_buf = None

self.observation_space = env.observation_space
            self.action_space = env.action_space
            if requires_vec_env:
                if isinstance(env, VecEnv):
                    self.n_envs = env.num_envs
                else:
                    # The model requires a VecEnv
                    # wrap it in a DummyVecEnv to avoid error
                    self.env = DummyVecEnv([lambda: env])
                    if self.verbose >= 1:
                        print("Wrapping the env in a DummyVecEnv.")
                    self.n_envs = 1
            else:
                if isinstance(env, VecEnv):
                    if env.num_envs == 1:
                        self.env = _UnvecWrapper(env)
                        self._vectorize_action = True
                    else:
                        raise ValueError("Error: the model requires a non vectorized environment or a single vectorized"
                                         " environment.")
                self.n_envs = 1