class_docstr: A simple and fast gradient-based adversarial attack.
  Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, Pascal Frossard,
  "DeepFool: a simple and accurate method to fool deep neural
  networks", https://arxiv.org/abs/1511.04599
  
  :param predict: forward pass function.
  :param num_classes: number of classes considered
  :param nb_iter: number of iterations.
  :eps=0.1
  :param clip_min: mininum value per input dimension.
  :param clip_max: maximum value per input dimension.
class_docstr_tok: ["A","simple","and","fast","gradient-based","adversarial","attack",".","Seyed-Mohsen","Moosavi-Dezfooli",",","Alhussein","Fawzi",",","Pascal","Frossard",",","''","DeepFool",":","a","simple","and","accurate","method","to","fool","deep","neural","networks","''",",","https",":","\/\/arxiv.org\/abs\/1511.04599",":","param","predict",":","forward","pass","function",".",":","param","num_classes",":","number","of","classes","considered",":","param","nb_iter",":","number","of","iterations",".",":","eps=0.1",":","param","clip_min",":","mininum","value","per","input","dimension",".",":","param","clip_max",":","maximum","value","per","input","dimension","."]
code: def _verify_and_process_inputs(self, x, y):
      if self.targeted:
          assert y is not None
  
      if not self.targeted:
          if y is None:
              y = self._get_predicted_label(x)
  
      x = replicate_input_withgrad(x)
      y = replicate_input(y)
      return x, y
  
file_path: advertorch/advertorch/attacks/deepfool.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  from advertorch.utils import batch_clamp, clamp
  from advertorch.utils import replicate_input, replicate_input_withgrad
  import torch
  from .base import Attack, LabelMixin
label_desc: Use image node_id 8 for calling the DeepfoolLinfAttack obj's underlying member method code with example usage: obj._verify_and_process_inputs(x, y) and returns: x,  y