code: def stop_if_no_metric_improvement_fn(self):
      if self.scalar_vals:
          train_frac = self.scalar_vals[
              "OptimizeLoss/training_fraction:0"
          ]
          LOGGER.debug("Training Fraction = {}".format(train_frac))
          if train_frac == 1.0:
              LOGGER.info("Training is Complete")
              return True
      if not self.keep_best_model:
          return False
      eval_results = read_eval_metrics(self.estimator.eval_dir())
      if len(eval_results) == 0:
          return False
      most_recent_eval = max(
          eval_results.items(), key=lambda x: x[0]
      )  # last steps.
      best_eval = min(
          eval_results.items(), key=lambda x: x[1]["loss"]
      )  # lowest_loss
      if most_recent_eval == best_eval:
          self.get_current_weights = True
      steps_diff = most_recent_eval[0] - best_eval[0]
      tf.compat.v1.logging.info(
          "No improvement in {} steps".format(steps_diff)
      )
  
      if (
          steps_diff > self.early_stopping_steps
          and most_recent_eval[0] > self.steps_per_epoch
      ):
          LOGGER.info("Early stopping triggered.".format(steps_diff))
          return True
      return False
  
imports: import os
  from concurrent.futures import ThreadPoolExecutor
  import logging
  import sys
  import re
  import joblib
  import numpy
  import tensorflow
  from tensorflow.compat.v1.train import SessionRunHook
  from tensorflow_estimator.python.estimator.early_stopping import _StopOnPredicateHook
  from finetune.errors import FinetuneError
  from finetune.config import get_config
  from finetune.util.metrics import read_eval_metrics
label_desc: Use image node_id 2 for calling the SaverHook obj's underlying member method code with example usage: obj.stop_if_no_metric_improvement_fn() and returns: False, False, False, True, True