class_docstr: One step fast gradient sign method (Goodfellow et al, 2014).
  Paper: https://arxiv.org/abs/1412.6572
  
  :param predict: forward pass function.
  :param loss_fn: loss function.
  :param eps: attack step size.
  :param clip_min: mininum value per input dimension.
  :param clip_max: maximum value per input dimension.
  :param targeted: indicate if this is a targeted attack.
class_docstr_tok: ["One","step","fast","gradient","sign","method","(","Goodfellow","et","al",",","2014",")",".","Paper",":","https",":","\/\/arxiv.org\/abs\/1412.6572",":","param","predict",":","forward","pass","function",".",":","param","loss_fn",":","loss","function",".",":","param","eps",":","attack","step","size",".",":","param","clip_min",":","mininum","value","per","input","dimension",".",":","param","clip_max",":","maximum","value","per","input","dimension",".",":","param","targeted",":","indicate","if","this","is","a","targeted","attack","."]
docstr: Create an instance of the GradientSignAttack.
docstr_tok: ["Create","an","instance","of","the","GradientSignAttack","."]
code: def __init__(
      self,
      predict,
      loss_fn=None,
      eps=0.3,
      clip_min=0.0,
      clip_max=1.0,
      targeted=False,
  ):
      """
      Create an instance of the GradientSignAttack.
      """
      super(GradientSignAttack, self).__init__(
          predict, loss_fn, clip_min, clip_max
      )
  
      self.eps = eps
      self.targeted = targeted
      if self.loss_fn is None:
          self.loss_fn = nn.CrossEntropyLoss(reduction="sum")
  
file_path: advertorch/advertorch/attacks/one_step_gradient.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import torch.nn
  from advertorch.utils import clamp
  from advertorch.utils import normalize_by_pnorm
  from advertorch.utils import batch_multiply
  from .base import Attack
  from .base import LabelMixin
label_desc: Use image node_id 1 to create a new GradientSignAttack object from inherited base classes: Attack, LabelMixin with example: obj = GradientSignAttack(predict, loss_fn, eps, clip_min, clip_max, targeted)