code: def convert_to_softmax_logits(logits):
      """Convert the logits returned by the base model to softmax logits.
  
      Args:
        logits: used to create softmax.
  
      Returns:
        Softmax with the first column of zeros is equivalent to sigmoid.
      """
      softmax_logits = tf.concat([logits * 0, logits], axis=1)
      return softmax_logits
  
file_path: autodist/examples/benchmark/utils/recommendation/ncf_common.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import json
  import os
  import numpy
  from absl import flags
  from absl import logging
  import tensorflow
  from utils.recommendation import constants
  from utils.recommendation import data_pipeline
  from utils.recommendation import data_preprocessing
  from utils.recommendation import movielens
  from utils.flags import core
  from utils.misc import keras_utils
label_desc: Use image node_id 4 for calling a global function with example usage: convert_to_softmax_logits(logits) and returns: softmax_logits