code: def test_l1_attack(self, attack_name, random_start):
      num_iter, step_size, epsilon, percentile = 4, 1.0, 2.5, 99
      gin.parse_config(
          [
              f"attacks.l1_config.num_iter = {num_iter}",
              f"attacks.l1_config.step_size = {step_size}",
              f"attacks.l1_config.epsilon = {epsilon}",
              f"attacks.l1_config.percentile = {percentile}",
              "attacks.union_config.restart = 5",
          ]
      )
  
      x = tf.random.uniform(shape=self.batched_input_shape)
      y = tf.random.categorical(
          tf.zeros([self.batch_size, self.num_classes]), 1
      )
      attack = attacks.construct_attack(attack_name)
      adv_x = attack.attack(
          tf.constant(x),
          tf.constant(y),
          self.model,
          self.loss_fn,
          random_start=random_start,
      )
  
      diff = tf.reshape(adv_x - x, (self.batch_size, -1)).numpy()
      l1_norm = np.linalg.norm(diff, ord=1, axis=-1)
      self.assertAllLessEqual(l1_norm, epsilon + 1e-5)
      touched = np.count_nonzero(diff, axis=-1)
      self.assertAllLessEqual(
          touched, diff.shape[1] * num_iter * percentile / 100
      )
  
file_path: neural-structured-learning/research/multi_representation_adversary/multi_representation_adversary/attacks_test.py
imports: from absl.testing import parameterized
  import gin
  from multi_representation_adversary import attacks
  import numpy
  import tensorflow.compat.v2
label_desc: Use image node_id 4 for calling the AttacksTest obj's underlying member method code with example usage: obj.test_l1_attack(attack_name, random_start) without return types