code: def setUpClass(cls):
      master_seed(seed=1234, set_tensorflow=True)
      super().setUpClass()
  
      import tensorflow as tf
      import keras
      from keras.models import Model
  
      tf.compat.v1.disable_eager_execution()
  
      def functional():
          in_layer = keras.layers.Input(shape=(10,))
          layer = keras.layers.Dense(100, activation=tf.nn.relu)(
              in_layer
          )
          layer = keras.layers.Dense(10, activation=tf.nn.relu)(layer)
          out_layer = keras.layers.Dense(1)(layer)
  
          model = Model(inputs=[in_layer], outputs=[out_layer])
  
          model.compile(
              loss=keras.losses.MeanAbsoluteError(),
              optimizer=keras.optimizers.Adam(learning_rate=0.01),
              metrics=["accuracy"],
          )
  
          return model
  
      cls.keras_model = functional()
      cls.keras_model.fit(cls.x_train_diabetes, cls.y_train_diabetes)
  
      cls.art_model = KerasRegressor(model=cls.keras_model)
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import unittest
  import numpy
  from art.estimators.regression.keras import KerasRegressor
  from tests.utils import TestBase, master_seed, get_tabular_regressor_kr
label_desc: Use image node_id 1 for calling the TestKerasRegressorFunctional obj's underlying member method code with example usage: obj.setUpClass(cls) and returns: model