class_docstr: `LowProFool` attack.
  
  | Paper link: https://arxiv.org/abs/1911.03274
class_docstr_tok: ["`","LowProFool","`","attack",".","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/1911.03274"]
docstr: Lp-norm of perturbation vectors weighted by feature importance.
  
  :param perturbations: Perturbations of samples towards being adversarial.
  :return: Array with weighted Lp-norm of perturbations.
docstr_tok: ["Lp-norm","of","perturbation","vectors","weighted","by","feature","importance",".",":","param","perturbations",":","Perturbations","of","samples","towards","being","adversarial",".",":","return",":","Array","with","weighted","Lp-norm","of","perturbations","."]
code: def __weighted_lp_norm(self, perturbations: np.ndarray) -> np.ndarray:
      """
      Lp-norm of perturbation vectors weighted by feature importance.
  
      :param perturbations: Perturbations of samples towards being adversarial.
      :return: Array with weighted Lp-norm of perturbations.
      """
      order: Union[int, float] = (
          np.inf if self.norm == "inf" else float(self.norm)
      )
      return self.lambd * np.linalg.norm(
          self.importance_vec * perturbations, axis=1, ord=order
      ).reshape(-1, 1)
  
imports: import logging
  from typing import Callable, Optional, Union, TYPE_CHECKING
  import numpy
  from scipy.stats import pearsonr
  from tqdm.auto import trange
  from art.attacks.attack import EvasionAttack
  from art.estimators.estimator import LossGradientsMixin
  from art.estimators.estimator import BaseEstimator
  from art.estimators.classification.classifier import ClassifierMixin
label_desc: Use image node_id 2 for calling the LowProFool obj's underlying member method code with example usage: obj.__weighted_lp_norm(perturbations) and returns: unknown