How to use the lab.logger.add_indicator function in lab

# Creation of the trainer
    with logger.section("Create trainer"):
        optimizer = tf.train.AdamOptimizer(learning_rate=args.lr)
        train_iterator = train_dataset.make_initializable_iterator()
        data, target = train_iterator.get_next()
        train_loss = loss(model, data, target)
        train_op = optimizer.minimize(train_loss)

        test_iterator = test_dataset.make_initializable_iterator()
        data, target = test_iterator.get_next()
        test_loss = loss(model, data, target)
        test_accuracy = accuracy(model, data, target)

    logger.add_indicator("train_loss", queue_limit=10, is_print=True)
    logger.add_indicator("test_loss", is_histogram=False, is_print=True)
    logger.add_indicator("accuracy", is_histogram=False, is_print=True)

    #
    batches = len(x_train) // args.batch_size

    with tf.Session() as session:
        EXPERIMENT.start_train(session)

        # Loop through the monitored iterator
        for epoch in logger.loop(range(0, args.epochs)):
            # Delayed keyboard interrupt handling to use
            # keyboard interrupts to end the loop.
            # This will capture interrupts and finish
            # the loop at the end of processing the iteration;
            # i.e. the loop won't stop in the middle of an epoch.
            try:
                with logger.delayed_keyboard_interrupt():

def __call__(self):
        # Training and testing
        logger_util.add_model_indicators(self.model)

        logger.add_indicator("train_loss", IndicatorType.queue,
                             IndicatorOptions(queue_size=20, is_print=True))
        logger.add_indicator("test_loss", IndicatorType.histogram,
                             IndicatorOptions(is_print=True))
        logger.add_indicator("accuracy", IndicatorType.histogram,
                             IndicatorOptions(is_print=True))

        for _ in self.loop:
            self._train()
            self._test()
            self.__log_model_params()

def __call__(self):
        # Training and testing
        logger_util.add_model_indicators(self.model)

        logger.add_indicator("train_loss", IndicatorType.queue,
                             IndicatorOptions(queue_size=20, is_print=True))
        logger.add_indicator("test_loss", IndicatorType.histogram,
                             IndicatorOptions(is_print=True))
        logger.add_indicator("accuracy", IndicatorType.histogram,
                             IndicatorOptions(is_print=True))

        for _ in self.loop:
            self._train()
            self._test()
            self.__log_model_params()

def startup(self):
        logger_util.add_model_indicators(self.model)

        logger.add_indicator("train_loss", IndicatorType.queue,
                             IndicatorOptions(queue_size=20, is_print=True))
        logger.add_indicator("test_loss", IndicatorType.histogram,
                             IndicatorOptions(is_print=True))
        logger.add_indicator("accuracy", IndicatorType.histogram,
                             IndicatorOptions(is_print=True))

def loop():
    logger.info(a=2, b=1)

    logger.add_indicator('loss_ma', IndicatorType.queue, IndicatorOptions(queue_size=10))
    for i in range(10):
        logger.add_global_step(1)
        logger.store(loss=100 / (i + 1), loss_ma=100 / (i + 1))
        logger.write()
        if (i + 1) % 2 == 0:
            logger.new_line()

        time.sleep(2)

two=2,
            string="string")

# ### Set logger indicators

# Reward is queued; this is useful when you want to track the moving
# average of something.
logger.add_indicator("reward", queue_limit=10)

# By default everything is a set of values and will create a TensorBoard histogram
# We specify that `fps` is a scalar.
# If you store multiple values for this it will output the mean.
logger.add_indicator("fps", is_histogram=False, is_print=False)

# This will produce a histogram
logger.add_indicator("loss", is_print=False)

# A heat map
logger.add_indicator("advantage_reward", is_histogram=False, is_print=False, is_pair=True)

# Create a TensorFlow session
with tf.Session() as session:
    # Start the experiment from scratch, without loading from a
    # saved checkpoint ('is_init=True')
    # This will clear all the old checkpoints and summaries for this
    # experiment.
    # If you start with 'is_init=False',
    # the experiment will load from the last saved checkpoint.
    EXPERIMENT.start_train(session, is_init=True)

    # This is the main training loop of this project.
    for global_step in logger.loop(range(50)):

# ### Set logger indicators

# Reward is queued; this is useful when you want to track the moving
# average of something.
logger.add_indicator("reward", queue_limit=10)

# By default everything is a set of values and will create a TensorBoard histogram
# We specify that `fps` is a scalar.
# If you store multiple values for this it will output the mean.
logger.add_indicator("fps", is_histogram=False, is_print=False)

# This will produce a histogram
logger.add_indicator("loss", is_print=False)

# A heat map
logger.add_indicator("advantage_reward", is_histogram=False, is_print=False, is_pair=True)

# Create a TensorFlow session
with tf.Session() as session:
    # Start the experiment from scratch, without loading from a
    # saved checkpoint ('is_init=True')
    # This will clear all the old checkpoints and summaries for this
    # experiment.
    # If you start with 'is_init=False',
    # the experiment will load from the last saved checkpoint.
    EXPERIMENT.start_train(session, is_init=True)

    # This is the main training loop of this project.
    for global_step in logger.loop(range(50)):
        # You can set the global step explicitly with
        # 'logger.set_global_step(global_step)'

transforms.Normalize((0.1307,), (0.3081,))
            ])),
            batch_size=args.test_batch_size, shuffle=True, **kwargs)

    # Model creation
    with logger.section("Create model"):
        model = Net().to(device)
        optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)

    # Specify indicators
    logger.add_indicator("train_loss", queue_limit=10, is_print=True)
    logger.add_indicator("test_loss", is_histogram=False, is_print=True)
    logger.add_indicator("accuracy", is_histogram=False, is_print=True)
    for name, param in model.named_parameters():
        if param.requires_grad:
            logger.add_indicator(name, is_histogram=True, is_print=False)
            logger.add_indicator(f"{name}_grad", is_histogram=True, is_print=False)

    # Start the experiment
    EXPERIMENT.start_train()

    # Loop through the monitored iterator
    for epoch in logger.loop(range(0, args.epochs)):
        # Delayed keyboard interrupt handling to use
        # keyboard interrupts to end the loop.
        # This will capture interrupts and finish
        # the loop at the end of processing the iteration;
        # i.e. the loop won't stop in the middle of an epoch.
        try:
            with logger.delayed_keyboard_interrupt():

                # Training and testing

])),
            batch_size=args.batch_size, shuffle=True, **kwargs)
        test_loader = torch.utils.data.DataLoader(
            datasets.MNIST('./data', train=False, transform=transforms.Compose([
                transforms.ToTensor(),
                transforms.Normalize((0.1307,), (0.3081,))
            ])),
            batch_size=args.test_batch_size, shuffle=True, **kwargs)

    # Model creation
    with logger.section("Create model"):
        model = Net().to(device)
        optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)

    # Specify indicators
    logger.add_indicator("train_loss", queue_limit=10, is_print=True)
    logger.add_indicator("test_loss", is_histogram=False, is_print=True)
    logger.add_indicator("accuracy", is_histogram=False, is_print=True)
    for name, param in model.named_parameters():
        if param.requires_grad:
            logger.add_indicator(name, is_histogram=True, is_print=False)
            logger.add_indicator(f"{name}_grad", is_histogram=True, is_print=False)

    # Start the experiment
    EXPERIMENT.start_train()

    # Loop through the monitored iterator
    for epoch in logger.loop(range(0, args.epochs)):
        # Delayed keyboard interrupt handling to use
        # keyboard interrupts to end the loop.
        # This will capture interrupts and finish
        # the loop at the end of processing the iteration;