How to use the reader.fusionnet.utils.AverageMeter function in reader

def eval_accuracies(pred_s, target_s, pred_e, target_e):
    """An unofficial evalutation helper.
    Compute exact start/end/complete match accuracies for a batch.
    """
    # Convert 1D tensors to lists of lists (compatibility)
    if torch.is_tensor(target_s):
        target_s = [[e.item()] for e in target_s]
        target_e = [[e.item()] for e in target_e]

    # Compute accuracies from targets
    batch_size = len(pred_s)
    start = utils.AverageMeter()
    end = utils.AverageMeter()
    em = utils.AverageMeter()
    for i in range(batch_size):
        # Start matches
        if pred_s[i] in target_s[i]:
            start.update(1)
        else:
            start.update(0)

        # End matches
        if pred_e[i] in target_e[i]:
            end.update(1)
        else:
            end.update(0)

        # Both start and end match

def validate_unofficial(args, data_loader, model, global_stats, mode):
    """Run one full unofficial validation.
    Unofficial = doesn't use SQuAD script.
    """
    eval_time = utils.Timer()
    start_acc = utils.AverageMeter()
    end_acc = utils.AverageMeter()
    exact_match = utils.AverageMeter()
    loss = utils.AverageMeter()

    # Make predictions
    examples = 0
    for ex in data_loader:
        batch_size = ex[0].size(0)
        (pred_s, pred_e, _), batch_loss = model.predict(ex)
        target_s, target_e = ex[-3:-1]

        # We get metrics for independent start/end and joint start/end
        accuracies = eval_accuracies(pred_s, target_s, pred_e, target_e)
        start_acc.update(accuracies[0], batch_size)
        end_acc.update(accuracies[1], batch_size)
        exact_match.update(accuracies[2], batch_size)
        loss.update(batch_loss, batch_size)

def train(args, data_loader, model, global_stats):
    """Run through one epoch of model training with the provided data loader."""
    # Initialize meters + timers
    train_loss = utils.AverageMeter()
    epoch_time = utils.Timer()

    # Run one epoch
    for idx, ex in enumerate(data_loader):
        train_loss.update(*model.update(ex))
        if idx % args.display_iter == 0:
            logger.info('train: Epoch = %d | iter = %d/%d | ' %
                        (global_stats['epoch'], idx, len(data_loader)) +
                        'loss = %.2f | elapsed time = %.2f (s)' %
                        (train_loss.avg, global_stats['timer'].time()))
            train_loss.reset()

    logger.info('train: Epoch %d done. Time for epoch = %.2f (s)' %
                (global_stats['epoch'], epoch_time.time()))

    # Checkpoint

def validate_unofficial(args, data_loader, model, global_stats, mode):
    """Run one full unofficial validation.
    Unofficial = doesn't use SQuAD script.
    """
    eval_time = utils.Timer()
    start_acc = utils.AverageMeter()
    end_acc = utils.AverageMeter()
    exact_match = utils.AverageMeter()
    loss = utils.AverageMeter()

    # Make predictions
    examples = 0
    for ex in data_loader:
        batch_size = ex[0].size(0)
        (pred_s, pred_e, _), batch_loss = model.predict(ex)
        target_s, target_e = ex[-3:-1]

        # We get metrics for independent start/end and joint start/end
        accuracies = eval_accuracies(pred_s, target_s, pred_e, target_e)
        start_acc.update(accuracies[0], batch_size)
        end_acc.update(accuracies[1], batch_size)
        exact_match.update(accuracies[2], batch_size)
        loss.update(batch_loss, batch_size)
        # If getting train accuracies, sample max 10k
        examples += batch_size

def validate_unofficial(args, data_loader, model, global_stats, mode):
    """Run one full unofficial validation.
    Unofficial = doesn't use SQuAD script.
    """
    eval_time = utils.Timer()
    start_acc = utils.AverageMeter()
    end_acc = utils.AverageMeter()
    exact_match = utils.AverageMeter()
    loss = utils.AverageMeter()

    # Make predictions
    examples = 0
    for ex in data_loader:
        batch_size = ex[0].size(0)
        (pred_s, pred_e, _), batch_loss = model.predict(ex)
        target_s, target_e = ex[-3:-1]

        # We get metrics for independent start/end and joint start/end
        accuracies = eval_accuracies(pred_s, target_s, pred_e, target_e)
        start_acc.update(accuracies[0], batch_size)
        end_acc.update(accuracies[1], batch_size)
        exact_match.update(accuracies[2], batch_size)

def validate_official(args, data_loader, model, global_stats,
                      offsets, texts, answers):
    """Run one full official validation. Uses exact spans and same
    exact match/F1 score computation as in the SQuAD script.

    Extra arguments:
        offsets: The character start/end indices for the tokens in each context.
        texts: Map of qid --> raw text of examples context (matches offsets).
        answers: Map of qid --> list of accepted answers.
    """
    eval_time = utils.Timer()
    f1 = utils.AverageMeter()
    exact_match = utils.AverageMeter()
    # Run through examples
    examples = 0
    for ex in data_loader:
        ex_id, batch_size = ex[-1], ex[0].size(0)
        (pred_s, pred_e, _ ), _ = model.predict(ex)
        for i in range(batch_size):
            s_offset = offsets[ex_id[i]][pred_s[i][0]][0]
            e_offset = offsets[ex_id[i]][pred_e[i][0]][1]
            prediction = texts[ex_id[i]][s_offset:e_offset]

            # Compute metrics
            ground_truths = answers[ex_id[i]]
            exact_match.update(utils.metric_max_over_ground_truths(
                utils.exact_match_score, prediction, ground_truths))
            f1.update(utils.metric_max_over_ground_truths(

def validate_unofficial(args, data_loader, model, global_stats, mode):
    """Run one full unofficial validation.
    Unofficial = doesn't use SQuAD script.
    """
    eval_time = utils.Timer()
    start_acc = utils.AverageMeter()
    end_acc = utils.AverageMeter()
    exact_match = utils.AverageMeter()
    loss = utils.AverageMeter()

    # Make predictions
    examples = 0
    for ex in data_loader:
        batch_size = ex[0].size(0)
        (pred_s, pred_e, _), batch_loss = model.predict(ex)
        target_s, target_e = ex[-3:-1]

        # We get metrics for independent start/end and joint start/end
        accuracies = eval_accuracies(pred_s, target_s, pred_e, target_e)
        start_acc.update(accuracies[0], batch_size)
        end_acc.update(accuracies[1], batch_size)
        exact_match.update(accuracies[2], batch_size)
        loss.update(batch_loss, batch_size)
        # If getting train accuracies, sample max 10k

def eval_accuracies(pred_s, target_s, pred_e, target_e):
    """An unofficial evalutation helper.
    Compute exact start/end/complete match accuracies for a batch.
    """
    # Convert 1D tensors to lists of lists (compatibility)
    if torch.is_tensor(target_s):
        target_s = [[e.item()] for e in target_s]
        target_e = [[e.item()] for e in target_e]

    # Compute accuracies from targets
    batch_size = len(pred_s)
    start = utils.AverageMeter()
    end = utils.AverageMeter()
    em = utils.AverageMeter()
    for i in range(batch_size):
        # Start matches
        if pred_s[i] in target_s[i]:
            start.update(1)
        else:
            start.update(0)

        # End matches
        if pred_e[i] in target_e[i]:
            end.update(1)
        else:
            end.update(0)

        # Both start and end match
        if any([1 for _s, _e in zip(target_s[i], target_e[i])
                if _s == pred_s[i] and _e == pred_e[i]]):