class_docstr: Fast attack against a target internal representation of a model using
  gradient descent (Sabour et al. 2016).
  Paper: https://arxiv.org/abs/1511.05122
  
  :param predict: forward pass function.
  :param loss_fn: loss function.
  :param eps: maximum distortion.
  :param eps_iter: attack step size.
  :param nb_iter: number of iterations
  :param clip_min: mininum value per input dimension.
  :param clip_max: maximum value per input dimension.
class_docstr_tok: ["Fast","attack","against","a","target","internal","representation","of","a","model","using","gradient","descent","(","Sabour","et","al",".","2016",")",".","Paper",":","https",":","\/\/arxiv.org\/abs\/1511.05122",":","param","predict",":","forward","pass","function",".",":","param","loss_fn",":","loss","function",".",":","param","eps",":","maximum","distortion",".",":","param","eps_iter",":","attack","step","size",".",":","param","nb_iter",":","number","of","iterations",":","param","clip_min",":","mininum","value","per","input","dimension",".",":","param","clip_max",":","maximum","value","per","input","dimension","."]
docstr: Create an instance of the FastFeatureAttack.
docstr_tok: ["Create","an","instance","of","the","FastFeatureAttack","."]
code: def __init__(
      self,
      predict,
      loss_fn=None,
      eps=0.3,
      eps_iter=0.05,
      nb_iter=10,
      rand_init=True,
      clip_min=0.0,
      clip_max=1.0,
  ):
      """Create an instance of the FastFeatureAttack."""
      super(FastFeatureAttack, self).__init__(
          predict, loss_fn, clip_min, clip_max
      )
      self.eps = eps
      self.eps_iter = eps_iter
      self.nb_iter = nb_iter
      self.rand_init = rand_init
      self.clip_min = clip_min
      self.clip_max = clip_max
      if self.loss_fn is None:
          self.loss_fn = nn.MSELoss(reduction="sum")
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from __future__ import unicode_literals
  import numpy
  import torch
  import torch.nn
  from advertorch.utils import clamp
  from advertorch.utils import normalize_by_pnorm
  from advertorch.utils import clamp_by_pnorm
  from advertorch.utils import is_float_or_torch_tensor
  from advertorch.utils import batch_multiply
  from advertorch.utils import batch_clamp
  from advertorch.utils import replicate_input
  from advertorch.utils import batch_l1_proj
  from .base import Attack
  from .base import LabelMixin
  from .utils import rand_init_delta
label_desc: Use image node_id 1 to create a new FastFeatureAttack object from inherited base classes: Attack with example: obj = FastFeatureAttack(predict, loss_fn, eps, eps_iter, nb_iter, rand_init, clip_min, clip_max)