docs: def log_estimator_evaluation_result(self, eval_results):
      """Log the evaluation result for a estimator.
  
      The evaluate result is a directory that contains metrics defined in
      model_fn. It also contains a entry for global_step which contains the value
      of the global step when evaluation was performed.
  
      Args:
        eval_results: dict, the result of evaluate() from a estimator.
      """
      if not isinstance(eval_results, dict):
        tf.logging.warning("eval_results should be directory for logging. Got %s",
                           type(eval_results))
        return
      global_step = eval_results[tf.GraphKeys.GLOBAL_STEP]
      for key in sorted(eval_results):
        if key != tf.GraphKeys.GLOBAL_STEP:
          self.log_metric(key, eval_results[key], global_step=global_step)