code: def pgd_attack_transform(
      loss_fn,
      transform_fn,
      init_aug_params,
      input_image,
      label,
      epsilon,
      num_steps,
      rng,
      optimizer_def,
      projection=None,
  ):
      """PGD attack through a transform."""
      del rng
      local_batch_size = input_image.shape[0]
  
      wrapped_loss_fn = lambda x: loss_fn(
          transform_fn(input_image, x), x, label
      )
  
      train_params = init_aug_params
  
      tx = optimizer_def(params=train_params)
      opt_state = jax.jit(tx.init, backend="tpu")(train_params)
  
      augmentation_params_list = []
      logits_list = []
      loss_breakdown_list = []
      compute_grad_fn = jax.value_and_grad(
          wrapped_loss_fn, has_aux=True
      )
      for _ in range(num_steps):
          (_, (loss_breakdown, logits)), grad = compute_grad_fn(
              train_params
          )
          loss_breakdown_list.append(loss_breakdown)
          logits_list.append(logits)
  
          edit_grad = jax.tree_util.tree_map(jnp.sign, grad)
          updates, opt_state = tx.update(
              edit_grad, opt_state, train_params
          )
          new_train_params = optax.apply_updates(
              params=train_params, updates=updates
          )
  
          if projection is not None:
              image_bounds = (-1, 1)
              new_train_params = projection(
                  new_train_params, epsilon, input_image, image_bounds
              )
  
          augmentation_params_list.append(
              jax.lax.stop_gradient(new_train_params)
          )
          train_params = new_train_params
  
      (_, (loss_breakdown, logits)), grad = compute_grad_fn(
          train_params
      )
      loss_breakdown_list.append(loss_breakdown)
      logits_list.append(logits)
      augmentation_params_list.append(
          jax.lax.stop_gradient(train_params)
      )
  
      final_aug_params = train_params
      steps_per_example = (
          jnp.ones(shape=(local_batch_size,), dtype=jnp.int32)
          * num_steps
      )
  
      adversarial_image = transform_fn(input_image, final_aug_params)
  
      misc_artifacts = {
          "steps_per_example": steps_per_example,
          "augmentation_params_list": augmentation_params_list,
          "loss_breakdown_list": loss_breakdown_list,
          "logits_list": logits_list,
      }
      return (
          jax.lax.stop_gradient(adversarial_image),
          jax.lax.stop_gradient(adversarial_image - input_image),
          jax.lax.stop_gradient(final_aug_params),
          misc_artifacts,
      )
  
imports: import functools
  import jax
  import jax.numpy
  import optax
label_desc: Use image node_id 4 for calling a global function with example usage: pgd_attack_transform(loss_fn, transform_fn, init_aug_params, input_image, label, epsilon, num_steps, rng, optimizer_def, projection) and returns: jax,  jax,  jax,  misc_artifacts