code: def make_discriminator_model(capacity: int) -> tf.keras.Sequential():
      model = tf.keras.Sequential()
  
      model.add(
          tf.keras.layers.Conv2D(
              capacity,
              (5, 5),
              strides=(2, 2),
              padding="same",
              input_shape=[28, 28, 1],
          )
      )
      model.add(tf.keras.layers.LeakyReLU())
      model.add(tf.keras.layers.Dropout(0.3))
  
      model.add(
          tf.keras.layers.Conv2D(
              capacity * 2, (5, 5), strides=(2, 2), padding="same"
          )
      )
      model.add(tf.keras.layers.LeakyReLU())
      model.add(tf.keras.layers.Dropout(0.3))
  
      model.add(tf.keras.layers.Flatten())
      model.add(tf.keras.layers.Dense(1))
  
      return model
  
file_path: adversarial-robustness-toolbox/examples/backdoor_attack_dgm_trail.py
imports: import numpy
  import tensorflow
  from art.attacks.poisoning.backdoor_attack_dgm.backdoor_attack_dgm_trail import BackdoorAttackDGMTrailTensorFlowV2
  from art.estimators.gan.tensorflow import TensorFlowV2GAN
  from art.estimators.generation.tensorflow import TensorFlowV2Generator
  from art.estimators.classification.tensorflow import TensorFlowV2Classifier
label_desc: Use image node_id 2 for calling a global function with example usage: make_discriminator_model(capacity) and returns: model