code: def _parse_example_proto(example_serialized):
      # Dense features in Example proto.
      feature_map = {
          "image/encoded": tf.io.FixedLenFeature(
              [], dtype=tf.string, default_value=""
          ),
          "image/class/label": tf.io.FixedLenFeature(
              [], dtype=tf.int64, default_value=-1
          ),
          "image/class/text": tf.io.FixedLenFeature(
              [], dtype=tf.string, default_value=""
          ),
      }
  
      # Sparse features in Example proto.
  
      features = tf.io.parse_single_example(
          serialized=example_serialized, features=feature_map
      )
      label = tf.cast(features["image/class/label"], dtype=tf.int32)
  
      return features["image/encoded"], label
  
imports: import os
  import argparse
  from optparse import OptionParser
  from bigdl.dllib.nncontext import *
  from bigdl.dllib.feature.image import *
  from bigdl.dllib.keras.metrics import *
  from bigdl.dllib.nnframes import *
  from bigdl.dllib.utils.log4Error import invalidInputError
  from bigdl.orca import init_orca_context, stop_orca_context
  from bigdl.orca.learn.tf.estimator import Estimator
  from bigdl.orca.learn.trigger import EveryEpoch, SeveralIteration
  from bigdl.orca.data.image import write_tfrecord, read_tfrecord
  from inception_preprocessing import preprocess_for_train, preprocess_for_eval
  from tensorflow.contrib.slim.python.slim.nets import inception_v1
  import tensorflow
  from math import ceil
label_desc: Use image node_id 2 for calling a global function with example usage: _parse_example_proto(example_serialized) and returns: features,  label