code: def __init__(
      self,
      saver,
      estimator,
      keep_best_model,
      early_stopping_steps,
      steps_per_epoch,
      eval_frequency,
      cache_weights_to_file=False,
  ):
      super().__init__(
          self.stop_if_no_metric_improvement_fn,
          run_every_secs=None,
          run_every_steps=eval_frequency,
      )
      self.get_current_weights = False
      self.included = None
      self.scalar_vals = None
  
      self.saver = saver
      self.keep_best_model = keep_best_model
      self.early_stopping_steps = early_stopping_steps or sys.maxsize
      self.steps_per_epoch = steps_per_epoch
      self.estimator = estimator
      self.cache_weights_to_file = cache_weights_to_file
  
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 1 to create a new SaverHook object from inherited base classes: _StopOnPredicateHook with example: obj = SaverHook(saver, estimator, keep_best_model, early_stopping_steps, steps_per_epoch, eval_frequency, cache_weights_to_file)