How to use the magent.utility.init_logger function in magent

parser = argparse.ArgumentParser()
    parser.add_argument("--save_every", type=int, default=5)
    parser.add_argument("--render_every", type=int, default=10)
    parser.add_argument("--n_round", type=int, default=2000)
    parser.add_argument("--render", action="store_true")
    parser.add_argument("--load_from", type=int)
    parser.add_argument("--train", action="store_true")
    parser.add_argument("--map_size", type=int, default=125)
    parser.add_argument("--greedy", action="store_true")
    parser.add_argument("--name", type=str, default="battle")
    parser.add_argument("--eval", action="store_true")
    parser.add_argument('--alg', default='dqn', choices=['dqn', 'drqn', 'a2c'])
    args = parser.parse_args()

    # set logger
    magent.utility.init_logger(args.name)

    # init the game
    env = magent.GridWorld(load_config(args.map_size))
    env.set_render_dir("build/render")

    # two groups of agents
    handles = env.get_handles()

    # sample eval observation set
    eval_obs = [None for _ in range(len(handles))]
    if args.eval:
        print("sample eval set...")
        env.reset()
        generate_map(env, args.map_size, handles)
        eval_obs = magent.utility.sample_observation(env, handles, 2048, 500)

parser = argparse.ArgumentParser()
    parser.add_argument("--save_every", type=int, default=5)
    parser.add_argument("--render_every", type=int, default=10)
    parser.add_argument("--n_round", type=int, default=2000)
    parser.add_argument("--render", action="store_true")
    parser.add_argument("--load_from", type=int)
    parser.add_argument("--train", action="store_true")
    parser.add_argument("--map_size", type=int, default=100)
    parser.add_argument("--greedy", action="store_true")
    parser.add_argument("--name", type=str, default="battle")
    parser.add_argument("--eval", action="store_true")
    parser.add_argument('--alg', default='dqn', choices=['dqn', 'drqn', 'a2c'])
    args = parser.parse_args()

    # set logger
    magent.utility.init_logger(args.name)

    # init the game
    env = magent.TransCity(get_config(args.map_size))
    env.set_render_dir("build/render")

    # init models
    batch_size = 256
    unroll_step = 8
    target_update = 1000
    train_freq = 5

    handles = [0]

    models = []
    # models.append(DeepQNetwork(env, handles[0], "cars",
    #                            batch_size=batch_size,

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--save_every", type=int, default=2)
    parser.add_argument("--render_every", type=int, default=10)
    parser.add_argument("--n_round", type=int, default=500)
    parser.add_argument("--render", action="store_true")
    parser.add_argument("--load_from", type=int)
    parser.add_argument("--train", action="store_true")
    parser.add_argument("--map_size", type=int, default=1000)
    parser.add_argument("--greedy", action="store_true")
    parser.add_argument("--eval", action="store_true")
    parser.add_argument("--name", type=str, default="pursuit")
    args = parser.parse_args()

    # set logger
    magent.utility.init_logger(args.name)

    # init the game
    env = magent.GridWorld("pursuit", map_size=args.map_size)
    env.set_render_dir("build/render")

    # two groups of agents
    handles = env.get_handles()

    # load models
    names = ["predator", "prey"]
    models = []

    for i in range(len(names)):
        models.append(magent.ProcessingModel(
            env, handles[i], names[i], 20000+i, 4000, DeepQNetwork,
            batch_size=512, memory_size=2 ** 22,

parser = argparse.ArgumentParser()
    parser.add_argument("--save_every", type=int, default=5)
    parser.add_argument("--render_every", type=int, default=10)
    parser.add_argument("--n_round", type=int, default=1500)
    parser.add_argument("--render", action="store_true")
    parser.add_argument("--load_from", type=int)
    parser.add_argument("--train", action="store_true")
    parser.add_argument("--map_size", type=int, default=125)
    parser.add_argument("--greedy", action="store_true")
    parser.add_argument("--name", type=str, default="battle")
    parser.add_argument("--eval", action="store_true")
    parser.add_argument('--alg', default='dqn', choices=['dqn', 'drqn', 'a2c'])
    args = parser.parse_args()

    # set logger
    magent.utility.init_logger(args.name)

    # init the game
    env = magent.GridWorld("battle", map_size=args.map_size)
    env.set_render_dir("build/render")

    # two groups of agents
    handles = env.get_handles()

    # sample eval observation set
    eval_obs = [None, None]
    if args.eval:
        print("sample eval set...")
        env.reset()
        generate_map(env, args.map_size, handles)
        for i in range(len(handles)):
            eval_obs[i] = magent.utility.sample_observation(env, handles, 2048, 500)

parser = argparse.ArgumentParser()
    parser.add_argument("--save_every", type=int, default=5)
    parser.add_argument("--render_every", type=int, default=10)
    parser.add_argument("--n_round", type=int, default=2000)
    parser.add_argument("--render", action="store_true")
    parser.add_argument("--load_from", type=int)
    parser.add_argument("--train", action="store_true")
    parser.add_argument("--map_size", type=int, default=125)
    parser.add_argument("--greedy", action="store_true")
    parser.add_argument("--name", type=str, default="battle")
    parser.add_argument("--eval", action="store_true")
    parser.add_argument('--alg', default='dqn', choices=['dqn', 'drqn', 'a2c'])
    args = parser.parse_args()

    # set logger
    magent.utility.init_logger(args.name)

    # init the game
    env = magent.GridWorld("battle", map_size=args.map_size)
    env.set_render_dir("build/render")

    # two groups of agents
    handles = env.get_handles()

    # sample eval observation set
    eval_obs = [None, None]
    if args.eval:
        print("sample eval set...")
        env.reset()
        generate_map(env, args.map_size, handles)
        for i in range(len(handles)):
            eval_obs[i] = magent.utility.sample_observation(env, handles, 2048, 500)

learning_rate=1e-2))
    else:
        raise NotImplementedError

    # load if
    savedir = 'save_model'
    if args.load_from is not None:
        start_from = args.load_from
        print("load ... %d" % start_from)
        for model in models:
            model.load(savedir, start_from)
    else:
        start_from = 0

    # init logger
    magent.utility.init_logger(args.name)

    # print debug info
    print(args)
    print("view_size", env.get_view_space(tiger_handle))

    # play
    train_id = 1 if args.train else -1
    start = time.time()
    for k in range(start_from, start_from + args.n_round):
        tic = time.time()
        eps = magent.utility.linear_decay(k, 10, 0.1) if not args.greedy else 0
        loss, reward, value = play_a_round(env, args.map_size, [deer_handle, tiger_handle], models,
                                           step_batch_size=step_batch_size, train_id=train_id,
                                           print_every=40, render=args.render,
                                           eps=eps)

parser.add_argument("--render", action="store_true")
    parser.add_argument("--load_from", type=int)
    parser.add_argument("--train", action="store_true")
    parser.add_argument("--map_size", type=int, default=125)
    parser.add_argument("--greedy", action="store_true")
    parser.add_argument("--name", type=str, default="against")
    parser.add_argument("--eval", action="store_true")
    parser.add_argument("--opponent", type=int, default=0)
    parser.add_argument('--alg', default='dqn', choices=['dqn', 'drqn', 'a2c'])
    args = parser.parse_args()

    # download opponent model
    magent.utility.check_model('against')

    # set logger
    magent.utility.init_logger(args.name)

    # init the game
    env = magent.GridWorld("battle", map_size=args.map_size)
    env.set_render_dir("build/render")

    # two groups of agents
    handles = env.get_handles()

    # sample eval observation set
    if args.eval:
        print("sample eval set...")
        env.reset()
        generate_map(env, args.map_size, handles)
        eval_obs = magent.utility.sample_observation(env, handles, n_obs=2048, step=500)
    else:
        eval_obs = [None, None]