How to use the gluoncv.data.batchify.Stack function in gluoncv

def test_load():
    from opts import opts
    opt = opts().init()

    batch_size = 16
    #batchify_fn = Tuple(Stack(), Stack(), Stack(), Stack())  # stack image, heatmaps, scale, offset
    batchify_fn = Tuple(Stack(), Stack(), Stack(), Stack(), Stack(), Stack())  # stack image, heatmaps, scale, offset, ind, mask
    num_workers = 2

    train_dataset = CenterCOCODataset(opt, split = 'train')
    train_loader = gluon.data.DataLoader( train_dataset,
        batch_size, True, batchify_fn=batchify_fn, last_batch='rollover', num_workers=num_workers)
    ctx = [mx.gpu(int(i)) for i in opt.gpus_str.split(',') if i.strip()]
    ctx = ctx if ctx else [mx.cpu()]

    for i, batch in enumerate(train_loader):
        print("{} Batch".format(i))
        print("image batch shape: ", batch[0].shape)
        print("heatmap batch shape", batch[1].shape)
        print("scale batch shape", batch[2].shape)
        print("offset batch shape", batch[3].shape)
        print("indices batch shape", batch[4].shape)
        print("mask batch shape", batch[5].shape)

def get_dataloader(train_dataset, data_shape, batch_size, num_workers, ctx):
    """Get dataloader."""
    width, height = data_shape, data_shape

    batchify_fn = Tuple(Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack())
    train_loader = gluon.data.DataLoader(train_dataset, batch_size, True, batchify_fn=batchify_fn, last_batch='rollover', num_workers=num_workers)
    return train_loader

def batchify_val_fn():
    return Tuple(Stack(), Pad(pad_val=-1))

def get_dataloader(net, train_dataset, val_dataset, data_shape, batch_size, num_workers, ctx):
    """Get dataloader."""
    width, height = data_shape, data_shape
    # use fake data to generate fixed anchors for target generation
    with autograd.train_mode():
        _, _, anchors = net(mx.nd.zeros((1, 3, height, width), ctx))
    anchors = anchors.as_in_context(mx.cpu())
    batchify_fn = Tuple(Stack(), Stack(), Stack())  # stack image, cls_targets, box_targets
    train_loader = gluon.data.DataLoader(
        train_dataset.transform(SSDDefaultTrainTransform(width, height, anchors)),
        batch_size, True, batchify_fn=batchify_fn, last_batch='rollover', num_workers=num_workers)
    val_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
    val_loader = gluon.data.DataLoader(
        val_dataset.transform(SSDDefaultValTransform(width, height)),
        batch_size, False, batchify_fn=val_batchify_fn, last_batch='keep', num_workers=num_workers)
    return train_loader, val_loader

x * 32,
                x * 32,
                net,
                mixup=args.mixup) for x in range(
                10,
                20)]
        train_loader = RandomTransformDataLoader(
            transform_fns,
            train_dataset,
            batch_size=batch_size,
            interval=10,
            last_batch='rollover',
            shuffle=True,
            batchify_fn=batchify_fn,
            num_workers=num_workers)
    val_batchify_fn = Tuple(Stack(), Pad(pad_val=-1))
    val_loader = gluon.data.DataLoader(
        val_dataset.transform(
            YOLO3DefaultValTransform(
                width,
                height)),
        batch_size,
        False,
        batchify_fn=val_batchify_fn,
        last_batch='keep',
        num_workers=num_workers)
    return train_loader, val_loader

def get_dataloader(train_dataset, data_shape, batch_size, num_workers, ctx):
    """Get dataloader."""
    width, height = data_shape, data_shape

    batchify_fn = Tuple(Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack(), Stack())
    train_loader = gluon.data.DataLoader(train_dataset, batch_size, True, batchify_fn=batchify_fn, last_batch='rollover', num_workers=num_workers)
    return train_loader

def get_dataloader(net, train_dataset, data_shape, batch_size, num_workers):
    from gluoncv.data.batchify import Tuple, Stack, Pad
    from gluoncv.data.transforms.presets.ssd import SSDDefaultTrainTransform
    width, height = data_shape, data_shape
    # use fake data to generate fixed anchors for target generation
    with autograd.train_mode():
        _, _, anchors = net(mx.nd.zeros((1, 3, height, width)))
    batchify_fn = Tuple(Stack(), Stack(), Stack())  # stack image, cls_targets, box_targets
    train_loader = gluon.data.DataLoader(
        train_dataset.transform(SSDDefaultTrainTransform(width, height, anchors)),
        batch_size, True, batchify_fn=batchify_fn, last_batch='rollover', num_workers=num_workers)
    return train_loader