code: def metric_smaller(
      best_eval_result,
      current_eval_result,
      default_key=metric_keys.MetricKeys.AUC,
  ):
      """Compares two evaluation results and returns true if the 2nd one is smaller.
      Both evaluation results should have the values for MetricKeys.LOSS, which are
      used for comparison.
      Args:
        best_eval_result: best eval metrics.
        current_eval_result: current eval metrics.
        default_key: metric_keys.MericKeys
      Returns:
        True if the loss of current_eval_result is smaller; otherwise, False.
      Raises:
       ValueError: If input eval result is None or no loss is available.
      """
      if not best_eval_result or default_key not in best_eval_result:
          raise ValueError(
              "best_eval_result cannot be empty or no loss is found in it."
          )
  
      if (
          not current_eval_result
          or default_key not in current_eval_result
      ):
          raise ValueError(
              "current_eval_result cannot be empty or no loss is found in it."
          )
      return (
          best_eval_result[default_key]
          > current_eval_result[default_key]
      )
  
imports: import os
  from absl import logging
  import numpy
  import delta.compat
  from tensorflow.python.client import device_lib
  from tensorflow.python.estimator.canned import metric_keys
  from tensorflow.python.util import nest
  from delta.utils.register import registers
label_desc: Use image node_id 13 for calling a global function with example usage: metric_smaller(best_eval_result, current_eval_result, default_key) and returns: unknown