code: def test_clipping(iris_dataset):
      """
      Check weather adversaries are clipped properly.
      """
      (
          (x_train, y_train, x_valid, y_valid),
          _,
          clip_values,
      ) = iris_dataset
  
      # Setup classifier
      lr_clf = LogisticRegression(penalty="none")
      lr_clf.fit(x_train, y_train)
  
      # Dataset min-max clipping values
      bottom_min, top_max = clip_values
      clf_slr_min_max = ScikitlearnLogisticRegression(
          model=lr_clf, clip_values=(bottom_min, top_max)
      )
  
      # Clip values
      bottom_custom = -3
      top_custom = 3
      clf_slr_custom = ScikitlearnLogisticRegression(
          model=lr_clf, clip_values=(bottom_custom, top_custom)
      )
  
      # Setting up LowProFool classes with different hyper-parameters
      lpf_min_max_default = LowProFool(
          classifier=clf_slr_min_max, n_steps=45, eta=0.02, lambd=1.5
      )
      lpf_min_max_high_eta = LowProFool(
          classifier=clf_slr_min_max, n_steps=45, eta=100000, lambd=1.5
      )
      lpf_custom_default = LowProFool(
          classifier=clf_slr_custom, n_steps=45, eta=0.02, lambd=1.5
      )
      lpf_custom_high_eta = LowProFool(
          classifier=clf_slr_custom, n_steps=45, eta=100000, lambd=1.5
      )
  
      lpf_min_max_default.fit_importances(x_train, y_train)
      lpf_min_max_high_eta.fit_importances(x_train, y_train)
      lpf_custom_default.fit_importances(x_train, y_train)
      lpf_custom_high_eta.fit_importances(x_train, y_train)
  
      # Generating adversaries
      sample = np.array([[5.5, 2.4, 3.7, 1.0]])
      target = np.array([[0.0, 0.0, 1.0]])
  
      adversaries_min_max_default = lpf_min_max_default.generate(
          x=sample, y=target
      )
      adversaries_min_max_high_eta = lpf_min_max_high_eta.generate(
          x=sample, y=target
      )
      adversaries_custom_default = lpf_custom_default.generate(
          x=sample, y=target
      )
      adversaries_custom_high_eta = lpf_custom_high_eta.generate(
          x=sample, y=target
      )
  
      # Checking whether adversaries were clipped properly
      eps = 1e-6
      is_valid_1 = (
          (bottom_min - eps).to_numpy() <= adversaries_min_max_default
      ).all() and (
          (top_max + eps).to_numpy() >= adversaries_min_max_default
      ).all()
      is_valid_2 = (
          (bottom_min - eps).to_numpy() <= adversaries_min_max_high_eta
      ).all() and (
          (top_max + eps).to_numpy() >= adversaries_min_max_high_eta
      ).all()
      is_valid_3 = (
          (bottom_custom - eps) <= adversaries_custom_default
      ).all() and (
          (top_custom + eps) >= adversaries_custom_default
      ).all()
      is_valid_4 = (
          bottom_custom - eps <= adversaries_custom_high_eta
      ).all() and (
          top_custom + eps >= adversaries_custom_high_eta
      ).all()
  
      is_clipping_valid = (
          is_valid_1 and is_valid_2 and is_valid_3 and is_valid_4
      )
      if is_clipping_valid:
          logger.info(
              "[Iris flower, Scikit-learn Logistic Regression] Clipping is valid."
          )
      else:
          logger.info(
              "[Iris flower, Scikit-learn Logistic Regression] Clipping is invalid."
          )
  
      assert is_valid_1
      assert is_valid_2
      assert is_valid_3
      assert is_valid_4
  
imports: import logging
  import pytest
  import numpy
  import pandas
  import torch
  import torch.nn
  from torch import optim
  from torch.autograd import Variable
  from sklearn import datasets
  from sklearn.preprocessing import StandardScaler
  from sklearn.model_selection import StratifiedShuffleSplit
  from sklearn.linear_model import LogisticRegression
  from sklearn.svm import SVC
  from scipy.stats import pearsonr
  from art.attacks.evasion import LowProFool
  from art.estimators.classification.scikitlearn import ScikitlearnLogisticRegression
  from art.estimators.classification import PyTorchClassifier
  from art.estimators.classification.scikitlearn import ScikitlearnSVC
  from tests.utils import ARTTestException
label_desc: Use image node_id 11 for calling a global function with example usage: test_clipping(iris_dataset) without return types