code: def get_tabular_classifier_scikit_list(
      clipped=False, model_list_names=None
  ):
      from art.estimators.classification.scikitlearn import (  # ScikitlearnExtraTreeClassifier,
          ScikitlearnAdaBoostClassifier,
          ScikitlearnBaggingClassifier,
          ScikitlearnDecisionTreeClassifier,
          ScikitlearnExtraTreesClassifier,
          ScikitlearnGradientBoostingClassifier,
          ScikitlearnLogisticRegression,
          ScikitlearnRandomForestClassifier,
          ScikitlearnSVC,
      )
  
      sklearn_version = list(map(int, sklearn.__version__.split(".")))
      sklearn_ge_1_3_0 = (
          sklearn_version[0] == 1 and sklearn_version[1] >= 3
      )
      if sklearn_ge_1_3_0:
          suffix = "-ge-1.3.0"
      else:
          suffix = ""
  
      available_models = {
          "decisionTreeClassifier": ScikitlearnDecisionTreeClassifier,
          # "extraTreeClassifier": ScikitlearnExtraTreeClassifier,
          "adaBoostClassifier": ScikitlearnAdaBoostClassifier,
          "baggingClassifier": ScikitlearnBaggingClassifier,
          "extraTreesClassifier": ScikitlearnExtraTreesClassifier,
          "gradientBoostingClassifier": ScikitlearnGradientBoostingClassifier,
          "randomForestClassifier": ScikitlearnRandomForestClassifier,
          "logisticRegression": ScikitlearnLogisticRegression,
          "svc": ScikitlearnSVC,
          "linearSVC": ScikitlearnSVC,
      }
  
      if model_list_names is None:
          model_dict_names = available_models
      else:
          model_dict_names = dict()
          for name in model_list_names:
              model_dict_names[name] = available_models[name]
  
      classifier_list = list()
  
      if clipped:
          for model_name, model_class in model_dict_names.items():
              model = pickle.load(
                  open(
                      os.path.join(
                          os.path.dirname(os.path.dirname(__file__)),
                          "utils/resources/models/scikit/",
                          "scikit-"
                          + model_name
                          + "-iris-clipped"
                          + suffix
                          + ".pickle",
                      ),
                      "rb",
                  )
              )
              classifier_list.append(
                  model_class(model=model, clip_values=(0, 1))
              )
      else:
          for model_name, model_class in model_dict_names.items():
              model = pickle.load(
                  open(
                      os.path.join(
                          os.path.dirname(os.path.dirname(__file__)),
                          "utils/resources/models/scikit/",
                          "scikit-"
                          + model_name
                          + "-iris-unclipped"
                          + suffix
                          + ".pickle",
                      ),
                      "rb",
                  )
              )
              classifier_list.append(
                  model_class(model=model, clip_values=None)
              )
  
      return classifier_list
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import json
  import logging
  import os
  import pickle
  import time
  import unittest
  import warnings
  import sklearn
  import numpy
  from art.estimators.classification.tensorflow import TensorFlowV2Classifier
  from art.estimators.encoding.tensorflow import TensorFlowEncoder
  from art.estimators.generation.tensorflow import TensorFlowGenerator, TensorFlowV2Generator
  from art.estimators.gan.tensorflow import TensorFlowV2GAN
  from art.utils import load_dataset
label_desc: Use image node_id 29 for calling a global function with example usage: get_tabular_classifier_scikit_list(clipped, model_list_names) and returns: classifier_list