How to use the lagom.envs.wrappers.VecMonitor function in lagom

def make_env(config, seed, mode):
    assert mode in ['train', 'eval']
    def _make_env():
        env = gym.make(config['env.id'])
        env = ClipAction(env)
        return env
    env = make_vec_env(_make_env, 1, seed)  # single environment
    env = VecMonitor(env)
    if mode == 'train':
        env = VecStepInfo(env)
    return env

def run(config, seed, device, logdir):
    set_global_seeds(seed)
    
    env = make_env(config, seed)
    env = VecMonitor(env)
    env = VecStepInfo(env)
    
    eval_env = make_env(config, seed)
    eval_env = VecMonitor(eval_env)
    
    agent = Agent(config, env, device)
    replay = ReplayBuffer(env, config['replay.capacity'], device)
    engine = Engine(config, agent=agent, env=env, eval_env=eval_env, replay=replay, logdir=logdir)
    
    train_logs, eval_logs = engine.train()
    pickle_dump(obj=train_logs, f=logdir/'train_logs', ext='.pkl')
    pickle_dump(obj=eval_logs, f=logdir/'eval_logs', ext='.pkl')
    return None

def run(config, seed, device, logdir):
    set_global_seeds(seed)
    
    env = make_env(config, seed)
    env = VecMonitor(env)
    env = VecStepInfo(env)
    
    eval_env = make_env(config, seed)
    eval_env = VecMonitor(eval_env)
    
    agent = Agent(config, env, device)
    replay = ReplayBuffer(env, config['replay.capacity'], device)
    engine = Engine(config, agent=agent, env=env, eval_env=eval_env, replay=replay, logdir=logdir)
    
    train_logs, eval_logs = engine.train()
    pickle_dump(obj=train_logs, f=logdir/'train_logs', ext='.pkl')
    pickle_dump(obj=eval_logs, f=logdir/'eval_logs', ext='.pkl')
    return None

def run(config, seed, device, logdir):
    set_global_seeds(seed)
    
    env = make_env(config, seed)
    env = VecMonitor(env)
    if config['env.standardize_obs']:
        env = VecStandardizeObservation(env, clip=5.)
    if config['env.standardize_reward']:
        env = VecStandardizeReward(env, clip=10., gamma=config['agent.gamma'])
    env = VecStepInfo(env)
    
    agent = Agent(config, env, device)
    runner = EpisodeRunner(reset_on_call=False)
    engine = Engine(config, agent=agent, env=env, runner=runner)
    train_logs = []
    checkpoint_count = 0
    for i in count():
        if agent.total_timestep >= config['train.timestep']:
            break
        train_logger = engine.train(i)
        train_logs.append(train_logger.logs)

def run(config, seed, device):
    set_global_seeds(seed)
    logdir = Path(config['log.dir']) / str(config['ID']) / str(seed)
    
    env = make_env(config, seed)
    env = VecMonitor(env)
    if config['env.standardize_obs']:
        env = VecStandardizeObservation(env, clip=5.)
    if config['env.standardize_reward']:
        env = VecStandardizeReward(env, clip=10., gamma=config['agent.gamma'])
    
    agent = Agent(config, env, device)
    runner = EpisodeRunner(reset_on_call=False)
    engine = Engine(config, agent=agent, env=env, runner=runner)
    train_logs = []
    for i in count():
        if agent.total_timestep >= config['train.timestep']:
            break
        train_logger = engine.train(i)
        train_logs.append(train_logger.logs)
        if i == 0 or (i+1) % config['log.freq'] == 0:
            train_logger.dump(keys=None, index=0, indent=0, border='-'*50)

def run(config, seed, device, logdir):
    set_global_seeds(seed)
    
    env = make_env(config, seed)
    env = VecMonitor(env)
    env = VecStepInfo(env)
    
    eval_env = make_env(config, seed)
    eval_env = VecMonitor(eval_env)
    
    agent = Agent(config, env, device)
    replay = ReplayBuffer(env, config['replay.capacity'], device)
    engine = Engine(config, agent=agent, env=env, eval_env=eval_env, replay=replay, logdir=logdir)
    
    train_logs, eval_logs = engine.train()
    pickle_dump(obj=train_logs, f=logdir/'train_logs', ext='.pkl')
    pickle_dump(obj=eval_logs, f=logdir/'eval_logs', ext='.pkl')
    return None

def run(config, seed, device):
    set_global_seeds(seed)
    logdir = Path(config['log.dir']) / str(config['ID']) / str(seed)
    
    env = make_env(config, seed)
    env = VecMonitor(env)
    
    eval_env = make_env(config, seed)
    eval_env = VecMonitor(eval_env)
    
    agent = Agent(config, env, device)
    replay = ReplayBuffer(config['replay.capacity'], device)
    engine = Engine(config, agent=agent, env=env, eval_env=eval_env, replay=replay, logdir=logdir)
    
    train_logs, eval_logs = engine.train()
    pickle_dump(obj=train_logs, f=logdir/'train_logs', ext='.pkl')
    pickle_dump(obj=eval_logs, f=logdir/'eval_logs', ext='.pkl')
    return None

def initializer(config, seed, device):
    global env
    env = make_env(config, seed)
    env = VecMonitor(env)
    if config['env.standardize_obs']:
        env = VecStandardizeObservation(env, clip=10.)
    global agent
    agent = Agent(config, env, device)

def run(config, seed, device):
    set_global_seeds(seed)
    logdir = Path(config['log.dir']) / str(config['ID']) / str(seed)
    
    env = make_env(config, seed)
    env = VecMonitor(env)
    
    eval_env = make_env(config, seed)
    eval_env = VecMonitor(eval_env)
    
    agent = Agent(config, env, device)
    replay = ReplayBuffer(env, config['replay.capacity'], device)
    engine = Engine(config, agent=agent, env=env, eval_env=eval_env, replay=replay, logdir=logdir)
    
    train_logs, eval_logs = engine.train()
    pickle_dump(obj=train_logs, f=logdir/'train_logs', ext='.pkl')
    pickle_dump(obj=eval_logs, f=logdir/'eval_logs', ext='.pkl')
    return None

def make_env(config, seed, mode):
    assert mode in ['train', 'eval']
    def _make_env():
        env = gym.make(config['env.id'])
        env = ClipAction(env)
        return env
    env = make_vec_env(_make_env, 1, seed)  # single environment
    env = VecMonitor(env)
    if mode == 'train':
        env = VecStepInfo(env)
    return env