text: import torch
  import torch.nn as nn
  
  from ..attack import Attack
  
  
  class FFGSM(Attack):
      r"""
      New FGSM proposed in 'Fast is better than free: Revisiting adversarial training'
      [https://arxiv.org/abs/2001.03994]
      
      Distance Measure : Linf
  
      Arguments:
          model (nn.Module): model to attack.
          eps (float): maximum perturbation. (DEFAULT: 8/255)
          alpha (float): step size. (DEFAULT: 10/255)
      
      Shape:
          - images: :math:`(N, C, H, W)` where `N = number of batches`, `C = number of channels`,        `H = height` and `W = width`. It must have a range [0, 1].
          - labels: :math:`(N)` where each value :math:`y_i` is :math:`0 \leq y_i \leq` `number of labels`.
          - output: :math:`(N, C, H, W)`.
            
      Examples::
          >>> attack = torchattacks.FFGSM(model, eps=8/255, alpha=10/255)
          >>> adv_images = attack(images, labels)
      """
      def __init__(self, model, eps=8/255, alpha=10/255):
          super(FFGSM, self).__init__("FFGSM", model)
          self.eps = eps
          self.alpha = alpha
  
      def forward(self, images, labels):
          r"""
          Overridden.
          """
          images = images.clone().detach().to(self.device)
          labels = labels.clone().detach().to(self.device)
          labels = self._transform_label(images, labels)
          
          loss = nn.CrossEntropyLoss()
  
          adv_images = images + torch.randn_like(images).uniform_(-self.eps, self.eps)
          adv_images = torch.clamp(adv_images, min=0, max=1).detach()
          adv_images.requires_grad = True
  
          outputs = self.model(adv_images)
          cost = self._targeted*loss(outputs, labels)
  
          grad = torch.autograd.grad(cost, adv_images,
                                     retain_graph=False, create_graph=False)[0]
  
          adv_images = adv_images - self.alpha*grad.sign()
          delta = torch.clamp(adv_images - images, min=-self.eps, max=self.eps)
          adv_images = torch.clamp(images + delta, min=0, max=1).detach()
  
          return adv_images
  