def METHOD_NAME(dataloader, ks=[1, 5, 10], number_of_samples=None):
    label_ids = torch.cat([label_ids for _, _, _, label_ids, _ in dataloader])
    label_ids = label_ids + 1
    p = {}
    for k in ks:
        p[k] = 0
    for label in label_ids:
        if label > 0:
            for k in ks:
                if label <= k:
                    p[k] += 1
    for k in ks:
        if number_of_samples is None:
            p[k] /= len(label_ids)
        else:
            p[k] /= number_of_samples
    return p