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

def get_dataloader(net, val_dataset, val_transform, batch_size, num_workers):
    """Get dataloader."""
    val_bfn = batchify.Tuple(*[batchify.Append() for _ in range(4)])
    short = net.short[-1] if isinstance(net.short, (tuple, list)) else net.short
    val_loader = mx.gluon.data.DataLoader(
        val_dataset.transform(val_transform(short, net.max_size, net.base_stride)),
        batch_size, False, batchify_fn=val_bfn, last_batch='keep', num_workers=num_workers)
    return val_loader

def get_dataloader(net, val_dataset, batch_size, num_workers):
    """Get dataloader."""
    val_bfn = batchify.Tuple(*[batchify.Append() for _ in range(2)])
    val_loader = mx.gluon.data.DataLoader(val_dataset, batch_size, False,
            batchify_fn=val_bfn, last_batch='keep', num_workers=num_workers)
    return val_loader

def get_dataloader(net, train_dataset, val_dataset, train_transform, val_transform, batch_size,
                   num_shards_per_process, args):
    """Get dataloader."""
    train_bfn = batchify.MaskRCNNTrainBatchify(net, num_shards_per_process)
    train_sampler = \
        gcv.nn.sampler.SplitSortedBucketSampler(train_dataset.get_im_aspect_ratio(),
                                                batch_size,
                                                num_parts=hvd.size() if args.horovod else 1,
                                                part_index=hvd.rank() if args.horovod else 0,
                                                shuffle=True)
    train_loader = mx.gluon.data.DataLoader(train_dataset.transform(
        train_transform(net.short, net.max_size, net, ashape=net.ashape, multi_stage=args.use_fpn)),
        batch_sampler=train_sampler, batchify_fn=train_bfn, num_workers=args.num_workers)
    val_bfn = batchify.Tuple(*[batchify.Append() for _ in range(2)])
    short = net.short[-1] if isinstance(net.short, (tuple, list)) else net.short
    # validation use 1 sample per device
    val_loader = mx.gluon.data.DataLoader(
        val_dataset.transform(val_transform(short, net.max_size)), num_shards_per_process, False,
        batchify_fn=val_bfn, last_batch='keep', num_workers=args.num_workers)
    return train_loader, val_loader

def get_dataloader(net, train_dataset, val_dataset, train_transform, val_transform, batch_size,
                   num_workers, multi_stage):
    """Get dataloader."""
    train_bfn = batchify.Tuple(*[batchify.Append() for _ in range(6)])
    train_loader = mx.gluon.data.DataLoader(
        train_dataset.transform(train_transform(net.short, net.max_size, net, ashape=net.ashape,
                                                multi_stage=multi_stage)),
        batch_size, True, batchify_fn=train_bfn, last_batch='rollover', num_workers=num_workers)
    val_bfn = batchify.Tuple(*[batchify.Append() for _ in range(2)])
    val_loader = mx.gluon.data.DataLoader(
        val_dataset.transform(val_transform(net.short, net.max_size)),
        batch_size, False, batchify_fn=val_bfn, last_batch='keep', num_workers=num_workers)
    return train_loader, val_loader

def get_dataloader(net, train_dataset, val_dataset, batch_size, num_workers):
    """Get dataloader."""
    train_bfn = batchify.Tuple(*[batchify.Append() for _ in range(5)])
    train_loader = mx.gluon.data.DataLoader(
        train_dataset.transform(FasterRCNNDefaultTrainTransform(net.short, net.max_size, net)),
        batch_size, True, batchify_fn=train_bfn, last_batch='rollover', num_workers=num_workers)
    val_bfn = batchify.Tuple(*[batchify.Append() for _ in range(3)])
    val_loader = mx.gluon.data.DataLoader(
        val_dataset.transform(FasterRCNNDefaultValTransform(net.short, net.max_size)),
        batch_size, False, batchify_fn=val_bfn, last_batch='keep', num_workers=num_workers)
    return train_loader, val_loader

def get_dataloader(net, val_dataset, batch_size, num_workers):
    """Get dataloader."""
    val_bfn = batchify.Tuple(*[batchify.Append() for _ in range(3)])
    val_loader = mx.gluon.data.DataLoader(
        val_dataset.transform(FasterRCNNDefaultValTransform(net.short, net.max_size)),
        batch_size, False, batchify_fn=val_bfn, last_batch='keep', num_workers=num_workers)
    return val_loader

def get_dataloader(net, val_dataset, batch_size, num_workers):
    """Get dataloader."""
    val_bfn = batchify.Tuple(*[batchify.Append() for _ in range(3)])
    val_loader = mx.gluon.data.DataLoader(
        val_dataset.transform(FasterRCNNDefaultValTransform(net.short, net.max_size)),
        batch_size, False, batchify_fn=val_bfn, last_batch='keep', num_workers=num_workers)
    return val_loader

def get_dataloader(net, train_dataset, val_dataset, batch_size, num_workers):
    """Get dataloader."""
    train_bfn = batchify.Tuple(*[batchify.Append() for _ in range(5)])
    train_loader = mx.gluon.data.DataLoader(
        train_dataset.transform(FasterRCNNDefaultTrainTransform(net.short, net.max_size, net)),
        batch_size, True, batchify_fn=train_bfn, last_batch='rollover', num_workers=num_workers)
    val_bfn = batchify.Tuple(*[batchify.Append() for _ in range(3)])
    val_loader = mx.gluon.data.DataLoader(
        val_dataset.transform(FasterRCNNDefaultValTransform(net.short, net.max_size)),
        batch_size, False, batchify_fn=val_bfn, last_batch='keep', num_workers=num_workers)
    return train_loader, val_loader

def get_dataloader(net, train_dataset, val_dataset, train_transform, val_transform, batch_size,
                   num_workers):
    """Get dataloader."""
    train_bfn = batchify.Tuple(*[batchify.Append() for _ in range(5)])
    train_loader = mx.gluon.data.DataLoader(
        train_dataset.transform(train_transform(
            net.short, net.max_size, net.base_stride, net.valid_range)),
            batch_size, True, batchify_fn=train_bfn, last_batch='rollover',
            num_workers=num_workers)
    val_bfn = batchify.Tuple(*[batchify.Append() for _ in range(4)])
    short = net.short[-1] if isinstance(net.short, (tuple, list)) else net.short
    val_loader = mx.gluon.data.DataLoader(
        val_dataset.transform(val_transform(short, net.max_size, net.base_stride)),
        batch_size, False, batchify_fn=val_bfn, last_batch='keep', num_workers=num_workers)
    return train_loader, val_loader