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_float32 = tf.io.VarLenFeature(dtype=tf.float32)
      # Sparse features in Example proto.
      feature_map.update(
          {
              k: sparse_float32
              for k in [
                  "image/object/bbox/xmin",
                  "image/object/bbox/ymin",
                  "image/object/bbox/xmax",
                  "image/object/bbox/ymax",
              ]
          }
      )
  
      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: from zoo.orca import init_orca_context, stop_orca_context
  import os
  import argparse
  import tensorflow
label_desc: Use image node_id 9 for calling a global function with example usage: _parse_example_proto(example_serialized) and returns: features,  label