code: def create_ts1_art_mnist_classifier(min_pixel_value, max_pixel_value):
      input_ph = tf.placeholder(tf.float32, shape=[None, 28, 28, 1])
      labels_ph = tf.placeholder(tf.int32, shape=[None, 10])
  
      x = tf.layers.conv2d(
          input_ph, filters=4, kernel_size=5, activation=tf.nn.relu
      )
      x = tf.layers.max_pooling2d(x, 2, 2)
      x = tf.layers.conv2d(
          x, filters=10, kernel_size=5, activation=tf.nn.relu
      )
      x = tf.layers.max_pooling2d(x, 2, 2)
      x = tf.contrib.layers.flatten(x)
      x = tf.layers.dense(x, 100, activation=tf.nn.relu)
      logits = tf.layers.dense(x, 10)
  
      loss = tf.reduce_mean(
          tf.losses.softmax_cross_entropy(
              logits=logits, onehot_labels=labels_ph
          )
      )
      optimizer = tf.train.AdamOptimizer(learning_rate=0.01)
      train = optimizer.minimize(loss)
  
      sess.run(tf.global_variables_initializer())
  
      classifier = TensorFlowClassifier(
          clip_values=(min_pixel_value, max_pixel_value),
          input_ph=input_ph,
          output=logits,
          labels_ph=labels_ph,
          train=train,
          loss=loss,
          learning=None,
          sess=sess,
          preprocessing_defences=[],
      )
  
      return classifier
  
file_path: adversarial-robustness-toolbox/examples/get_started_inverse_gan.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import numpy
  import tensorflow
  from art.estimators.classification import TensorFlowClassifier
  from art.defences.preprocessor.inverse_gan import InverseGAN
  from art.estimators.encoding.tensorflow import TensorFlowEncoder
  from art.estimators.generation.tensorflow import TensorFlowGenerator
  from art.utils import load_mnist
  from art.attacks.evasion import FastGradientMethod
  from examples.inverse_gan_author_utils import EncoderReconstructor, GeneratorReconstructor
label_desc: Use image node_id 1 for calling a global function with example usage: create_ts1_art_mnist_classifier(min_pixel_value, max_pixel_value) and returns: classifier