code: def hcl_loss(config, hcl_logits_list):
      """hierarchical contrastive learning loss"""
      log.info("use hcl loss")
      tao = 5
      hcl_loss = 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)
          hcl_loss += loss
  
      return hcl_loss / len(hcl_logits_list)
  
file_path: PGL/apps/PGLBox/src/models/loss.py
imports: import os
  import sys
  import math
  import time
  import numpy
  import pickle
  import paddle
  import paddle.static
  import paddle.fluid
  import pgl
  from pgl.utils.logger import log
label_desc: Use image node_id 3 for calling a global function with example usage: hcl_loss(config, hcl_logits_list) and returns: unknown