class_docstr: This module contains an implementation of the Over-the-Air Adversarial Flickering attack on video recognition
  networks.
  
  | Paper link: https://arxiv.org/abs/2002.05123
class_docstr_tok: ["This","module","contains","an","implementation","of","the","Over-the-Air","Adversarial","Flickering","attack","on","video","recognition","networks",".","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/2002.05123"]
docstr: Compute adversarial examples for one iteration.
  
  :param x_adv: Current adversarial examples.
  :param x: An array with the original inputs.
  :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)`.
  :param eps_step: Attack step size (input variation) at each iteration.
  :return: Adversarial examples.
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code: def _compute_torch(
      self,
      x_adv: "torch.Tensor",
      x: "torch.Tensor",
      y: "torch.Tensor",
      eps_step: float,
  ) -> "torch.Tensor":
      """
      Compute adversarial examples for one iteration.
  
      :param x_adv: Current adversarial examples.
      :param x: An array with the original inputs.
      :param y: Target values (class labels) one-hot-encoded of shape `(nb_samples, nb_classes)`.
      :param eps_step: Attack step size (input variation) at each iteration.
      :return: Adversarial examples.
      """
      # Get perturbation
      perturbation = x_adv - x
      grad = self._compute_perturbation(x_adv, y, perturbation)
  
      # Apply perturbation and clip
      x_adv = self._apply_perturbation(x_adv, grad, eps_step)
  
      return x_adv
  
imports: import logging
  from typing import Optional, TYPE_CHECKING
  import numpy
  from tqdm.auto import tqdm
  from art.config import ART_NUMPY_DTYPE
  from art.estimators.estimator import BaseEstimator
  from art.estimators.classification.classifier import ClassifierMixin, LossGradientsMixin
  from art.attacks.attack import EvasionAttack
  from art.utils import check_and_transform_label_format, get_labels_np_array
label_desc: Use image node_id 4 for calling the OverTheAirFlickeringPyTorch obj's underlying member method code with example usage: obj._compute_torch(x_adv, x, y, eps_step) and returns: x_adv