class_docstr: Implementation of the attack from Hirano and Takemoto (2019). Computes a fixed perturbation to be applied to all
  future inputs. To this end, it can use any adversarial attack method.
  
  | Paper link: https://arxiv.org/abs/1911.06502
class_docstr_tok: ["Implementation","of","the","attack","from","Hirano","and","Takemoto","(","2019",")",".","Computes","a","fixed","perturbation","to","be","applied","to","all","future","inputs",".","To","this","end",",","it","can","use","any","adversarial","attack","method",".","|","Paper","link",":","https",":","\/\/arxiv.org\/abs\/1911.06502"]
code: def _check_params(self) -> None:
      if (
          not isinstance(self.delta, (float, int))
          or self.delta < 0
          or self.delta > 1
      ):
          raise ValueError(
              "The desired accuracy must be in the range [0, 1]."
          )
  
      if not isinstance(self.max_iter, int) or self.max_iter <= 0:
          raise ValueError(
              "The number of iterations must be a positive integer."
          )
  
      if not isinstance(self.eps, (float, int)) or self.eps <= 0:
          raise ValueError(
              "The eps coefficient must be a positive float."
          )
  
file_path: adversarial-robustness-toolbox/art/attacks/evasion/targeted_universal_perturbation.py
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  import random
  import types
  from typing import Any, Dict, Optional, Union, TYPE_CHECKING
  import numpy
  from art.attacks.attack import EvasionAttack
  from art.estimators.estimator import BaseEstimator
  from art.estimators.classification.classifier import ClassifierMixin
  from art.utils import projection
label_desc: Use image node_id 3 for calling the TargetedUniversalPerturbation obj's underlying member method code with example usage: obj._check_params() without return types