code: def multi_output_model(config):
      image_input_1 = tf.keras.Input(shape=(32, 32, 3), name="input_1")
      image_input_2 = tf.keras.Input(shape=(32, 32, 3), name="input_2")
  
      x1 = tf.keras.layers.Conv2D(3, 3)(image_input_1)
      x1 = tf.keras.layers.GlobalMaxPooling2D()(x1)
      x2 = tf.keras.layers.Conv2D(3, 3)(image_input_2)
      x2 = tf.keras.layers.GlobalMaxPooling2D()(x2)
      x = tf.keras.layers.concatenate([x1, x2])
  
      score_output = tf.keras.layers.Dense(5, name="score_output")(x)
      class_output = tf.keras.layers.Dense(5, name="class_output")(x)
  
      model = tf.keras.Model(
          inputs=[image_input_1, image_input_2],
          outputs=[score_output, class_output],
      )
      return model
  
imports: import math
  import os
  import shutil
  import tempfile
  import pytest
  from unittest import TestCase
  import tensorflow
  import numpy
  from bigdl.orca.data.pandas import read_csv
  from bigdl.orca.data import SparkXShards, XShards
  from bigdl.orca.data.tf.data import Dataset
  from bigdl.orca.learn.tf2 import Estimator
  from bigdl.orca.ray import OrcaRayContext
  from bigdl.orca import OrcaContext
label_desc: Use image node_id 12 for calling a global function with example usage: multi_output_model(config) and returns: model