def METHOD_NAME(config, hcl_logits_list):
    """ hierarchical contrastive learning loss"""
    log.info("use hcl loss")
    tao = 5
    METHOD_NAME = 0.0
    for logits in hcl_logits_list:
        # equal to L.elementwise_div(logits, 1/tao) and it will boardcast automatically
        logits = logits * tao
        labels = paddle.zeros([paddle.shape(logits)[0], 1], dtype="int64")
        loss = paddle.nn.functional.softmax_with_cross_entropy(logits, labels)
        loss = paddle.sum(loss)
        METHOD_NAME += loss
    return METHOD_NAME / len(hcl_logits_list)