code: def get_image_classifier_kr_tf_functional(
      input_layer=1, output_layer=1
  ):
      """
      Standard Keras_tf classifier for unit testing built with a functional model
  
      :return: KerasClassifier
      """
      import tensorflow as tf
  
      if tf.__version__[0] == "2":
          tf.compat.v1.disable_eager_execution()
      from tensorflow.keras.layers import (
          Conv2D,
          Dense,
          Dropout,
          Flatten,
          Input,
          MaxPooling2D,
      )
      from tensorflow.keras.models import Model
  
      from art.estimators.classification.keras import KerasClassifier
  
      def functional_model():
          in_layer = Input(shape=(28, 28, 1), name="input0")
          layer = Conv2D(32, kernel_size=(3, 3), activation="relu")(
              in_layer
          )
          layer = Conv2D(64, (3, 3), activation="relu")(layer)
          layer = MaxPooling2D(pool_size=(2, 2))(layer)
          layer = Dropout(0.25)(layer)
          layer = Flatten()(layer)
          layer = Dense(128, activation="relu")(layer)
          layer = Dropout(0.5)(layer)
          out_layer = Dense(10, activation="softmax", name="output0")(
              layer
          )
  
          in_layer_2 = Input(shape=(28, 28, 1), name="input1")
          layer = Conv2D(32, kernel_size=(3, 3), activation="relu")(
              in_layer_2
          )
          layer = Conv2D(64, (3, 3), activation="relu")(layer)
          layer = MaxPooling2D(pool_size=(2, 2))(layer)
          layer = Dropout(0.25)(layer)
          layer = Flatten()(layer)
          layer = Dense(128, activation="relu")(layer)
          layer = Dropout(0.5)(layer)
          out_layer_2 = Dense(10, activation="softmax", name="output1")(
              layer
          )
  
          model = Model(
              inputs=[in_layer, in_layer_2],
              outputs=[out_layer, out_layer_2],
          )
  
          model.compile(
              loss=tf.keras.losses.categorical_crossentropy,
              optimizer=tf.keras.optimizers.Adadelta(),
              metrics=["accuracy"],
              loss_weights=[1.0, 1.0],
          )
  
          return model
  
      return KerasClassifier(
          functional_model(),
          clip_values=(0, 1),
          input_layer=input_layer,
          output_layer=output_layer,
      )
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import json
  import logging
  import os
  import pickle
  import time
  import unittest
  import warnings
  import sklearn
  import numpy
  from art.estimators.classification.tensorflow import TensorFlowV2Classifier
  from art.estimators.encoding.tensorflow import TensorFlowEncoder
  from art.estimators.generation.tensorflow import TensorFlowGenerator, TensorFlowV2Generator
  from art.estimators.gan.tensorflow import TensorFlowV2GAN
  from art.utils import load_dataset
label_desc: Use image node_id 14 for calling a global function with example usage: get_image_classifier_kr_tf_functional(input_layer, output_layer) and returns: KerasClassifier, model