class_docstr: Common class for different variations of implementation of the Projected Gradient Descent attack. The attack is an
  iterative method in which, after each iteration, the perturbation is projected on an lp-ball of specified radius (in
  addition to clipping the values of the adversarial sample so that it lies in the permitted data range). This is the
  attack proposed by Madry et al. for adversarial training.
  
  | Paper link: https://arxiv.org/abs/1706.06083
code: def _check_params(self) -> None:  # pragma: no cover
      if self.norm not in [1, 2, np.inf, "inf"]:
          raise ValueError(
              'Norm order must be either 1, 2, `np.inf` or "inf".'
          )
  
      if not (
          isinstance(self.eps, (int, float))
          and isinstance(self.eps_step, (int, float))
          or isinstance(self.eps, np.ndarray)
          and isinstance(self.eps_step, np.ndarray)
      ):
          raise TypeError(
              "The perturbation size `eps` and the perturbation step-size `eps_step` must have the same type of `int`"
              ", `float`, or `np.ndarray`."
          )
  
      if isinstance(self.eps, (int, float)):
          if self.eps < 0:
              raise ValueError(
                  "The perturbation size `eps` has to be non-negative."
              )
      else:
          if (self.eps < 0).any():
              raise ValueError(
                  "The perturbation size `eps` has to be non-negative."
              )
  
      if isinstance(self.eps_step, (int, float)):
          if self.eps_step <= 0:
              raise ValueError(
                  "The perturbation step-size `eps_step` has to be positive."
              )
      else:
          if (self.eps_step <= 0).any():
              raise ValueError(
                  "The perturbation step-size `eps_step` has to be positive."
              )
  
      if isinstance(self.eps, np.ndarray) and isinstance(
          self.eps_step, np.ndarray
      ):
          if self.eps.shape != self.eps_step.shape:
              raise ValueError(
                  "The perturbation size `eps` and the perturbation step-size `eps_step` must have the same shape."
              )
  
      if not isinstance(self.targeted, bool):
          raise ValueError(
              "The flag `targeted` has to be of type bool."
          )
  
      if not isinstance(self.num_random_init, int):
          raise TypeError(
              "The number of random initialisations has to be of type integer."
          )
  
      if self.num_random_init < 0:
          raise ValueError(
              "The number of random initialisations `random_init` has to be greater than or equal to 0."
          )
  
      if self.batch_size <= 0:
          raise ValueError(
              "The batch size `batch_size` has to be positive."
          )
  
      if self.max_iter < 0:
          raise ValueError(
              "The number of iterations `max_iter` has to be a non-negative integer."
          )
  
      if self.decay is not None and self.decay < 0.0:
          raise ValueError(
              "The decay factor `decay` has to be a nonnegative float."
          )
  
      if not isinstance(self.verbose, bool):
          raise ValueError("The verbose has to be a Boolean.")
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import logging
  from typing import Optional, Union, TYPE_CHECKING
  import numpy
  from scipy.stats import truncnorm
  from tqdm.auto import trange
  from art.attacks.evasion.fast_gradient import FastGradientMethod
  from art.config import ART_NUMPY_DTYPE
  from art.estimators.classification.classifier import ClassifierMixin
  from art.estimators.estimator import BaseEstimator, LossGradientsMixin
  from art.utils import compute_success, get_labels_np_array, check_and_transform_label_format, compute_success_array
  from art.summary_writer import SummaryWriter
label_desc: Use image node_id 4 for calling the ProjectedGradientDescentCommon obj's underlying member method code with example usage: obj._check_params() without return types