text: import torch
  import torch.nn as nn
  
  from ..attack import Attack
  
  
  class PGD(Attack):
      r"""
      PGD in the paper 'Towards Deep Learning Models Resistant to Adversarial Attacks'
      [https://arxiv.org/abs/1706.06083]
      
      Distance Measure : Linf
  
      Arguments:
          model (nn.Module): model to attack.
          eps (float): maximum perturbation. (DEFAULT: 0.3)
          alpha (float): step size. (DEFAULT: 2/255)
          steps (int): number of steps. (DEFAULT: 40)
          random_start (bool): using random initialization of delta. (DEFAULT: False)
          
      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.PGD(model, eps=8/255, alpha=1/255, steps=40, random_start=False)
          >>> adv_images = attack(images, labels)
          
      """
      def __init__(self, model, eps=0.3, alpha=2/255, steps=40, random_start=False):
          super(PGD, self).__init__("PGD", model)
          self.eps = eps
          self.alpha = alpha
          self.steps = steps
          self.random_start = random_start
  
      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.clone().detach()
  
          if self.random_start:
              # Starting at a uniformly random point
              adv_images = adv_images + torch.empty_like(adv_images).uniform_(-self.eps, self.eps)
              adv_images = torch.clamp(adv_images, min=0, max=1).detach()
  
          for i in range(self.steps):
              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.detach() - 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
  