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

# 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]

    # init models
    names = [args.name + "-a", "battle"]
    batch_size = 512
    unroll_step = 16
    train_freq = 5

    models = []

    # load opponent
    if args.opponent >= 0:
        from magent.builtin.tf_model import DeepQNetwork
        models.append(magent.ProcessingModel(env, handles[1], names[1], 20000, 0, DeepQNetwork))
        models[0].load("data/battle_model", args.opponent)

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)

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

    if args.alg == 'dqn':
        from magent.builtin.tf_model import DeepQNetwork
        RLModel = DeepQNetwork
        base_args = {'batch_size': batch_size,
                     'memory_size': 2 ** 20,
                     'target_update': target_update, 'train_freq': train_freq}
    elif args.alg == 'drqn':
        from magent.builtin.tf_model import DeepRecurrentQNetwork
        RLModel = DeepRecurrentQNetwork

log.getLogger('').addHandler(console)

    # 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
    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)[0]

    # init models
    batch_size = 512
    unroll_step = 8
    target_update = 1200
    train_freq = 5

    models = []
    if args.alg == 'dqn':
        from magent.builtin.tf_model import DeepQNetwork
        models.append(DeepQNetwork(env, handles[0], args.name,
                                   batch_size=batch_size,
                                   learning_rate=3e-4,
                                   memory_size=2 ** 21, target_update=target_update,
                                   train_freq=train_freq, eval_obs=eval_obs))
    elif args.alg == 'drqn':

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

    handles = env.get_handles()
    food_handle = handles[0]
    player_handles = handles[1:]

    # sample eval observation set
    eval_obs = None
    if args.eval:
        print("sample eval set...")
        env.reset()
        generate_map(env, args.map_size, food_handle, player_handles)
        eval_obs = magent.utility.sample_observation(env, player_handles, 0, 2048, 500)

    # load models
    models = [
        RLModel(env, player_handles[0], args.name,
                batch_size=512, memory_size=2 ** 20, target_update=1000,
                train_freq=4, eval_obs=eval_obs)
    ]

    # load saved model
    save_dir = "save_model"
    if args.load_from is not None:
        start_from = args.load_from
        print("load models...")
        for model in models:
            model.load(save_dir, start_from)
    else:

# 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)

    # load models
    batch_size = 256
    unroll_step = 8
    target_update = 1200
    train_freq = 5

    if args.alg == 'dqn':
        RLModel = DeepQNetwork
        base_args = {'batch_size': batch_size,
                     'memory_size': 2 ** 21, 'learning_rate': 1e-4,
                     'target_update': target_update, 'train_freq': train_freq}
    elif args.alg == 'drqn':
        RLModel = DeepRecurrentQNetwork
        base_args = {'batch_size': batch_size / unroll_step, 'unroll_step': unroll_step,
                     'memory_size': 8 * 625, 'learning_rate': 1e-4,

# 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)

    # load models
    batch_size = 256
    unroll_step = 8
    target_update = 1200
    train_freq = 5

    if args.alg == 'dqn':
        from magent.builtin.tf_model import DeepQNetwork
        RLModel = DeepQNetwork
        base_args = {'batch_size': batch_size,
                     'memory_size': 2 ** 20, 'learning_rate': 1e-4,
                     'target_update': target_update, 'train_freq': train_freq}
    elif args.alg == 'drqn':
        from magent.builtin.tf_model import DeepRecurrentQNetwork
        RLModel = DeepRecurrentQNetwork

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

    handles = env.get_handles()
    food_handle = handles[0]
    player_handles = handles[1:]

    # sample eval observation set
    eval_obs = None
    if args.eval:
        print("sample eval set...")
        env.reset()
        generate_map(env, args.map_size, food_handle, player_handles)
        eval_obs = magent.utility.sample_observation(env, player_handles, 0, 2048, 500)

    # load models
    models = [
        RLModel(env, player_handles[0], args.name,
                batch_size=512, memory_size=2 ** 20, target_update=1000,
                train_freq=4, eval_obs=eval_obs)
    ]

    # load saved model
    save_dir = "save_model"
    if args.load_from is not None:
        start_from = args.load_from
        print("load models...")
        for model in models:
            model.load(save_dir, start_from)
    else: