code: def fast_gradient_method(
      model_fn,
      x,
      eps,
      norm,
      clip_min=None,
      clip_max=None,
      y=None,
      targeted=False,
  ):
      """
      JAX implementation of the Fast Gradient Method.
      :param model_fn: a callable that takes an input tensor and returns the model logits.
      :param x: input tensor.
      :param eps: epsilon (input variation parameter); see https://arxiv.org/abs/1412.6572.
      :param norm: Order of the norm (mimics NumPy). Possible values: np.inf or 2.
      :param clip_min: (optional) float. Minimum float value for adversarial example components.
      :param clip_max: (optional) float. Maximum float value for adversarial example components.
      :param y: (optional) Tensor with one-hot true labels. If targeted is true, then provide the
                target one-hot label. Otherwise, only provide this parameter if you'd like to use true
                labels when crafting adversarial samples. Otherwise, model predictions are used
                as labels to avoid the "label leaking" effect (explained in this paper:
                https://arxiv.org/abs/1611.01236). Default is None. This argument does not have
                to be a binary one-hot label (e.g., [0, 1, 0, 0]), it can be floating points values
                that sum up to 1 (e.g., [0.05, 0.85, 0.05, 0.05]).
      :param targeted: (optional) bool. Is the attack targeted or untargeted?
                Untargeted, the default, will try to make the label incorrect.
                Targeted will instead try to move in the direction of being more like y.
      :return: a tensor for the adversarial example
      """
      if norm not in [np.inf, 2]:
          raise ValueError("Norm order must be either np.inf or 2.")
  
      if y is None:
          # Using model predictions as ground truth to avoid label leaking
          x_labels = np.argmax(model_fn(x), 1)
          y = one_hot(x_labels, 10)
  
      def loss_adv(image, label):
          pred = model_fn(image[None])
          loss = -np.sum(logsoftmax(pred) * label)
          if targeted:
              loss = -loss
          return loss
  
      grads_fn = vmap(grad(loss_adv), in_axes=(0, 0), out_axes=0)
      grads = grads_fn(x, y)
  
      axis = list(range(1, len(grads.shape)))
      avoid_zero_div = 1e-12
      if norm == np.inf:
          perturbation = eps * np.sign(grads)
      elif norm == 1:
          raise NotImplementedError(
              "L_1 norm has not been implemented yet."
          )
      elif norm == 2:
          square = np.maximum(
              avoid_zero_div,
              np.sum(np.square(grads), axis=axis, keepdims=True),
          )
          perturbation = grads / np.sqrt(square)
  
      adv_x = x + perturbation
  
      # If clipping is needed, reset all values outside of [clip_min, clip_max]
      if (clip_min is not None) or (clip_max is not None):
          # We don't currently support one-sided clipping
          assert clip_min is not None and clip_max is not None
          adv_x = np.clip(adv_x, a_min=clip_min, a_max=clip_max)
  
      return adv_x
  
imports: import jax.numpy
  from jax import grad, vmap
  from jax.nn import log_softmax
  from cleverhans.jax.utils import one_hot
label_desc: Use image node_id 1 for calling a global function with example usage: fast_gradient_method(model_fn, x, eps, norm, clip_min, clip_max, y, targeted) and returns: adv_x, loss