def METHOD_NAME(batch):
    """Custom collate fn for dealing with batches of images that have a different
    number of associated object annotations (bounding boxes).
    Arguments:
        batch: (tuple) A tuple of tensor images and lists of annotations
    Return:
        A tuple containing:
            1) (tensor) batch of images stacked on their 0 dim
            2) (list of tensors) annotations for a given image are stacked on 0 dim
    """
    targets = []
    imgs = []
    others = tuple([] for _ in batch[0][2:])
    for sample in batch:
        imgs.append(sample[0])
        targets.append(torch.FloatTensor(sample[1]))
        for o, b in zip(others, sample[2:]):
            o.append(b)
    return (torch.stack(imgs, 0), targets) + others