code: def nce_loss(config, predictions):
      """doc"""
      log.info("use nce loss")
      logits = predictions["logits"]
      tao = 5
      # 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)
  
      return loss
  
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 2 for calling a global function with example usage: nce_loss(config, predictions) and returns: loss