code: def get_adversarial_image_and_perturbation(
      batch,
      clean_logits,
      config,
      training_loss_fn_single,
      train_state,
      dropout_rng,
      lr_rng,
  ):
      """Get adversarial image and perturbation."""
      # initialize misc_artifacts which is piped through loss and attack
      misc_artifacts = {}
  
      # get adversarial modes
      if not config.get("adversarial_augmentation_mode"):
          raise NotImplementedError("Adversarial should receive a mode")
      adversarial_augmentation_mode = config.get(
          "adversarial_augmentation_mode"
      )
      if "," in adversarial_augmentation_mode:
          adversarial_augmentation_modes = list(
              config.get("adversarial_augmentation_mode").split(",")
          )
      elif isinstance(adversarial_augmentation_mode, str):
          adversarial_augmentation_modes = [
              config.get("adversarial_augmentation_mode")
          ]
      else:
          raise NotImplementedError(
              "adversarial_augmentation_mode is not valid: %s"
              % str(adversarial_augmentation_mode)
          )
      adversarial_fns = [
          get_adversarial_fn(adversarial_name)
          for adversarial_name in adversarial_augmentation_modes
      ]
  
      # get loss function
      attack_in_train_mode = config.advprop.get(
          "attack_in_train_mode", default=True
      )
      attack_fn_str = config.advprop.get(
          "attack_fn_str", default="random_target"
      )
      attack_fn, misc_artifacts = attack_losses.get_attack_fn(
          attack_fn_str,
          training_loss_fn_single,
          batch,
          train_state,
          dropout_rng,
          misc_artifacts,
          attack_in_train_mode,
          config,
      )
  
      # get optimizer def
      learning_rate_fn = lr_schedules.get_learning_rate_fn(
          config.advprop
      )
      optimizer_str = config.advprop.get(
          "optimizer", default="GradientDescent"
      )
      optimizer_def = get_optimizer_def(optimizer_str, learning_rate_fn)
  
      # initialize augmentation params
      images_and_perturbations = []
  
      # run all augmentations
      for adv_fn in adversarial_fns:
          ims_and_pert, misc_artifacts = adv_fn(
              batch,
              clean_logits,
              attack_fn,
              optimizer_def,
              config,
              dropout_rng,
              lr_rng,
              misc_artifacts,
          )
          images_and_perturbations.append(ims_and_pert)
  
      # sanity check
      assert len(images_and_perturbations) == len(
          adversarial_augmentation_modes
      )
  
      # randomly run one of them
      random_index = jax.random.randint(
          key=dropout_rng,
          shape=(),
          minval=0,
          maxval=len(adversarial_augmentation_modes),
      )
      adversarial_image, adversarial_perturbation = jax.lax.switch(
          random_index, images_and_perturbations, None
      )
  
      return adversarial_image, adversarial_perturbation, misc_artifacts
  
file_path: scenic/scenic/projects/adversarialtraining/attacks/attack_compute.py
imports: import functools
  import jax
  import jax.numpy
  import ml_collections
  from scenic.projects.adversarialtraining.attacks import attack_losses
  from scenic.projects.adversarialtraining.attacks import attack_methods
  from scenic.projects.adversarialtraining.attacks import attack_transforms
  from scenic.train_lib import lr_schedules
  from scenic.train_lib import optimizers
label_desc: Use image node_id 5 for calling a global function with example usage: get_adversarial_image_and_perturbation(batch, clean_logits, config, training_loss_fn_single, train_state, dropout_rng, lr_rng) and returns: adversarial_image,  adversarial_perturbation,  misc_artifacts