code: def model_creator_multi_inputs_outputs(config):
      from tensorflow.keras.layers import Input, Dense, Concatenate
      from tensorflow.keras.models import Model
  
      input_1 = Input(shape=(32,))
      input_2 = Input(shape=(64,))
  
      x = Dense(config["dense_1"], activation="relu")(input_1)
      x = Model(inputs=input_1, outputs=x)
  
      middle = Dense(32, activation="relu", name="middle")(input_2)
      y = Dense(config["dense_1"], activation="relu")(middle)
      y = Model(inputs=input_2, outputs=y)
  
      combined = Concatenate(axis=1)([x.output, y.output])
  
      z = Dense(config["dense_2"], activation="relu")(combined)
      z = Dense(1, activation="sigmoid", name="output")(z)
  
      model = Model(inputs=[x.input, y.input], outputs=[middle, z])
  
      model.compile(
          loss={"middle": "mse", "output": "binary_crossentropy"},
          optimizer=tf.keras.optimizers.Adam(config["lr"]),
          metrics={"output": "accuracy"},
      )
  
      return model
  
imports: import tensorflow
  import numpy
  from unittest import TestCase
  from bigdl.orca.automl.auto_estimator import AutoEstimator
  import pytest
label_desc: Use image node_id 2 for calling a global function with example usage: model_creator_multi_inputs_outputs(config) and returns: model