code: def fine_tune():
      print("=====> Validation")
      with tf.compat.v1.Session() as sess:
          assert tf.compat.v1.saved_model.loader.maybe_saved_model_directory(
              EXPORT_DIR
          )
          loaded = tf.compat.v1.saved_model.loader.load(
              sess, [TAG_NAME], EXPORT_DIR
          )
  
          train_op = tf.compat.v1.get_collection(TRAIN_OP_KEY)
          serving_signature = loaded.signature_def["serving_default"]
  
          input_op_names, input_tensor_names = _get_input_tensor_and_op(
              serving_signature.inputs
          )
          (
              output_op_names,
              output_tensor_names,
          ) = _get_output_tensor_and_op(serving_signature.outputs)
  
          input_table = dict(zip(input_op_names, input_tensor_names))
          output_table = dict(zip(output_op_names, output_tensor_names))
          print("Retrieve training operation ...")
          for _ in range(EPOCHS):
              l, _ = sess.run(
                  [output_table["loss"], train_op],
                  feed_dict={input_table["input_data"]: inputs},
              )
              print("loss: {}\n".format(l))
      print("=====> Validation Over")
  
imports: import os
  import atexit
  from multiprocessing import Process
  import numpy
  import shutil
  import tensorflow
  from tensorflow.compat.v1 import saved_model
  from tensorflow.python.framework import ops
  from autodist import AutoDist
  from autodist.strategy.all_reduce_strategy import AllReduce
  from autodist.checkpoint.saver import Saver
  from autodist.checkpoint.saved_model_builder import SavedModelBuilder
label_desc: Use image node_id 5 for calling a global function with example usage: fine_tune() without return types