code: def build_model(input_shape=(32, 32, 3), nb_classes=10):
      img_input = Input(shape=input_shape)
      conv2d_1 = Conv2D(
          6,
          (5, 5),
          padding="valid",
          kernel_regularizer=l2(0.0001),
          activation="relu",
          kernel_initializer="he_normal",
          input_shape=input_shape,
      )(img_input)
      conv2d_1_bn = BatchNormalization()(conv2d_1)
      conv2d_1_pool = MaxPooling2D((2, 2), strides=(2, 2))(conv2d_1_bn)
      conv2d_2 = Conv2D(
          16,
          (5, 5),
          padding="valid",
          activation="relu",
          kernel_initializer="he_normal",
      )(conv2d_1_pool)
      conv2d_2_pool = MaxPooling2D((2, 2), strides=(2, 2))(conv2d_2)
      flatten_1 = Flatten()(conv2d_2_pool)
      dense_1 = Dense(
          120, activation="relu", kernel_initializer="he_normal"
      )(flatten_1)
      dense_2 = Dense(
          84, activation="relu", kernel_initializer="he_normal"
      )(dense_1)
      img_output = Dense(
          nb_classes,
          activation="softmax",
          kernel_initializer="he_normal",
      )(dense_2)
      model = Model(img_input, img_output)
  
      model.compile(
          loss=keras.losses.categorical_crossentropy,
          optimizer=keras.optimizers.Adam(lr=0.01),
          metrics=["accuracy"],
      )
  
      return model
  
file_path: adversarial-robustness-toolbox/examples/adversarial_training_data_augmentation.py
imports: import tensorflow
  import keras
  import numpy
  from keras.layers import Conv2D, Dense, Flatten, MaxPooling2D, Input, BatchNormalization
  from keras.models import Model
  from keras.preprocessing.image import ImageDataGenerator
  from keras.regularizers import l2
  from art.attacks.evasion import ProjectedGradientDescent
  from art.estimators.classification import KerasClassifier
  from art.data_generators import KerasDataGenerator
  from art.defences.trainer import AdversarialTrainer
  from art.utils import load_cifar10
label_desc: Use image node_id 1 for calling a global function with example usage: build_model(input_shape, nb_classes) and returns: model