code: def fine_tune():
      with tf.compat.v1.Session() as sess:
          train_dataset = (
              tf.data.Dataset.from_tensor_slices(
                  (train_images, train_labels)
              )
              .repeat(EPOCHS)
              .shuffle(len(train_images) // 2)
              .batch(BATCH_SIZE)
          )
  
          train_iterator = tf.compat.v1.data.make_one_shot_iterator(
              train_dataset
          ).get_next()
          fetches = train_step(train_iterator)
  
          tf_saver = tf.compat.v1.train.Saver()
          tf_saver.restore(
              sess, tf.train.latest_checkpoint(checkpoint_dir)
          )
          for _ in range(
              min(10, len(train_images) // BATCH_SIZE * EPOCHS)
          ):
              loss, _ = sess.run(fetches)
              print(f"train_loss: {loss}")
  
file_path: autodist/tests/checkpoint/test_keras_saver.py
imports: import os
  import atexit
  import numpy
  import tensorflow
  from multiprocessing import Process
  from autodist import AutoDist
  from autodist.checkpoint.saver import Saver
label_desc: Use image node_id 4 for calling a global function with example usage: fine_tune() without return types