How to use the nni.nas.pytorch.utils.AverageMeter function in nni

def train(config, train_loader, model, optimizer, criterion, epoch):
    top1 = AverageMeter("top1")
    top5 = AverageMeter("top5")
    losses = AverageMeter("losses")

    cur_step = epoch * len(train_loader)
    cur_lr = optimizer.param_groups[0]["lr"]
    logger.info("Epoch %d LR %.6f", epoch, cur_lr)
    writer.add_scalar("lr", cur_lr, global_step=cur_step)

    model.train()

    for step, (x, y) in enumerate(train_loader):
        x, y = x.to(device, non_blocking=True), y.to(device, non_blocking=True)
        bs = x.size(0)

        optimizer.zero_grad()
        logits, aux_logits = model(x)
        loss = criterion(logits, y)
        if config.aux_weight > 0.:

def validate(config, valid_loader, model, criterion, epoch, cur_step):
    top1 = AverageMeter("top1")
    top5 = AverageMeter("top5")
    losses = AverageMeter("losses")

    model.eval()

    with torch.no_grad():
        for step, (X, y) in enumerate(valid_loader):
            X, y = X.to(device, non_blocking=True), y.to(device, non_blocking=True)
            bs = X.size(0)

            logits = model(X)
            loss = criterion(logits, y)

            accuracy = utils.accuracy(logits, y, topk=(1, 5))
            losses.update(loss.item(), bs)
            top1.update(accuracy["acc1"], bs)

def train(config, train_loader, model, optimizer, criterion, epoch):
    top1 = AverageMeter("top1")
    top5 = AverageMeter("top5")
    losses = AverageMeter("losses")

    cur_step = epoch * len(train_loader)
    cur_lr = optimizer.param_groups[0]["lr"]
    logger.info("Epoch %d LR %.6f", epoch, cur_lr)
    writer.add_scalar("lr", cur_lr, global_step=cur_step)

    model.train()

    for step, (x, y) in enumerate(train_loader):
        x, y = x.to(device, non_blocking=True), y.to(device, non_blocking=True)
        bs = x.size(0)

        optimizer.zero_grad()
        logits, aux_logits = model(x)

def validate(config, valid_loader, model, criterion, epoch, cur_step):
    top1 = AverageMeter("top1")
    top5 = AverageMeter("top5")
    losses = AverageMeter("losses")

    model.eval()

    with torch.no_grad():
        for step, (X, y) in enumerate(valid_loader):
            X, y = X.to(device, non_blocking=True), y.to(device, non_blocking=True)
            bs = X.size(0)

            logits = model(X)
            loss = criterion(logits, y)

            accuracy = utils.accuracy(logits, y, topk=(1, 5))
            losses.update(loss.item(), bs)
            top1.update(accuracy["acc1"], bs)
            top5.update(accuracy["acc5"], bs)

def validate(config, valid_loader, model, criterion, epoch, cur_step):
    top1 = AverageMeter("top1")
    top5 = AverageMeter("top5")
    losses = AverageMeter("losses")

    model.eval()

    with torch.no_grad():
        for step, (X, y) in enumerate(valid_loader):
            X, y = X.to(device, non_blocking=True), y.to(device, non_blocking=True)
            bs = X.size(0)

            logits = model(X)
            loss = criterion(logits, y)

            accuracy = utils.accuracy(logits, y, topk=(1, 5))
            losses.update(loss.item(), bs)
            top1.update(accuracy["acc1"], bs)
            top5.update(accuracy["acc5"], bs)

def train(config, train_loader, model, optimizer, criterion, epoch):
    top1 = AverageMeter("top1")
    top5 = AverageMeter("top5")
    losses = AverageMeter("losses")

    cur_step = epoch * len(train_loader)
    cur_lr = optimizer.param_groups[0]["lr"]
    logger.info("Epoch %d LR %.6f", epoch, cur_lr)
    writer.add_scalar("lr", cur_lr, global_step=cur_step)

    model.train()

    for step, (x, y) in enumerate(train_loader):
        x, y = x.to(device, non_blocking=True), y.to(device, non_blocking=True)
        bs = x.size(0)

        optimizer.zero_grad()
        logits, aux_logits = model(x)
        loss = criterion(logits, y)