code: def single_parse_fn(e):
      keys_to_features = {
          "image/encoded": tf.FixedLenFeature(
              (), tf.string, default_value=""
          ),
          "image/format": tf.FixedLenFeature(
              (), tf.string, default_value="raw"
          ),
          "image/class/label": tf.FixedLenFeature(
              [1], tf.int64, default_value=tf.zeros([1], dtype=tf.int64)
          ),
      }
      items_to_handlers = {
          "image": tf.contrib.slim.tfexample_decoder.Image(
              shape=[28, 28, 1], channels=1
          ),
          "label": tf.contrib.slim.tfexample_decoder.Tensor(
              "image/class/label", shape=[]
          ),
      }
      decoder = tf.contrib.slim.tfexample_decoder.TFExampleDecoder(
          keys_to_features, items_to_handlers
      )
      results = decoder.decode(e)
      if len(results[0].shape) > 0:
          feature = results[0]
          label = results[1]
      else:
          feature = results[1]
          label = results[0]
      return feature, label
  
imports: import pytest
  from pyspark.ml.linalg import DenseVector
  from zoo.feature.common import ChainedPreprocessing, FeatureSet
  from zoo.feature.image import *
  from test.zoo.pipeline.utils.test_utils import ZooTestCase
  from zoo.pipeline.api.keras.optimizers import Adam
  from zoo.tfpark import TFNet, TFOptimizer
  import tensorflow
  import numpy
  import os
  from zoo.tfpark import KerasModel, TFDataset
label_desc: Use image node_id 1 for calling a global function with example usage: single_parse_fn(e) and returns: feature,  label