code: def test_generate_counterfactuals_user_config_validations(
      self,
      method,
      sample_custom_query_2,
      custom_public_data_interface,
      sklearn_binary_classification_model_interface,
      explainer_function,
  ):
      exp = dice_ml.Dice(
          custom_public_data_interface,
          sklearn_binary_classification_model_interface,
          method=method,
      )
  
      explainer_function = getattr(exp, explainer_function)
  
      regex_pattern = re.escape(
          "The query instance(s) should not have any missing values. "
          "Please impute the missing values and try again."
      )
  
      query_instances_missing_values_numerical = pd.DataFrame(
          {"Categorical": ["a"], "Numerical": [np.nan]}
      )
      with pytest.raises(
          UserConfigValidationException, match=regex_pattern
      ):
          explainer_function(
              query_instances=query_instances_missing_values_numerical,
              desired_class="opposite",
              total_CFs=10,
          )
  
      query_instances_missing_values_categorical = pd.DataFrame(
          {"Categorical": [np.nan], "Numerical": [1]}
      )
      with pytest.raises(
          UserConfigValidationException, match=regex_pattern
      ):
          explainer_function(
              query_instances=query_instances_missing_values_categorical,
              desired_class="opposite",
              total_CFs=10,
          )
  
      with pytest.raises(
          UserConfigValidationException,
          match=r"The number of counterfactuals generated per query instance \(total_CFs\) "
          "should be a positive integer.",
      ):
          explainer_function(
              query_instances=sample_custom_query_2,
              total_CFs=-10,
              desired_class="opposite",
          )
  
      with pytest.raises(
          UserConfigValidationException,
          match=r"The number of counterfactuals generated per query instance \(total_CFs\) "
          "should be a positive integer.",
      ):
          explainer_function(
              query_instances=sample_custom_query_2,
              total_CFs=0,
              desired_class="opposite",
          )
  
      with pytest.raises(
          UserConfigValidationException,
          match=r"The posthoc_sparsity_algorithm should be linear or binary and not random",
      ):
          explainer_function(
              query_instances=sample_custom_query_2,
              total_CFs=10,
              posthoc_sparsity_algorithm="random",
          )
  
      with pytest.raises(
          UserConfigValidationException,
          match=r"The posthoc_sparsity_algorithm should be linear or binary and not random",
      ):
          explainer_function(
              query_instances=sample_custom_query_2,
              total_CFs=10,
              posthoc_sparsity_algorithm="random",
          )
  
      with pytest.raises(
          UserConfigValidationException,
          match=r"The stopping_threshold should lie between 0.0 and 1.0",
      ):
          explainer_function(
              query_instances=sample_custom_query_2,
              total_CFs=10,
              stopping_threshold=-10.0,
          )
  
      with pytest.raises(
          UserConfigValidationException,
          match=r"The posthoc_sparsity_param should lie between 0.0 and 1.0",
      ):
          explainer_function(
              query_instances=sample_custom_query_2,
              total_CFs=10,
              posthoc_sparsity_param=-10.0,
          )
  
      with pytest.raises(
          UserConfigValidationException,
          match=r"The desired_range parameter should not be set for classification task",
      ):
          explainer_function(
              query_instances=sample_custom_query_2,
              total_CFs=10,
              desired_range=[0, 10],
          )
  
      with pytest.raises(
          UserConfigValidationException,
          match=r"Some features need to be varied for generating counterfactuals.",
      ):
          explainer_function(
              query_instances=sample_custom_query_2,
              total_CFs=10,
              features_to_vary=[],
          )
  
imports: import re
  import numpy
  import pandas
  import pytest
  from rai_test_utils.datasets.tabular import create_housing_data
  from raiutils.exceptions import UserConfigValidationException
  from sklearn.ensemble import RandomForestRegressor
  import dice_ml
  from dice_ml.diverse_counterfactuals import CounterfactualExamples
  from dice_ml.explainer_interfaces.explainer_base import ExplainerBase
  from dice_ml.utils import helpers
  from ..conftest import _load_custom_testing_binary_model
label_desc: Use image node_id 1 for calling the TestExplainerBaseUserConfigValidations obj's underlying member method code with example usage: obj.test_generate_counterfactuals_user_config_validations(method, sample_custom_query_2, custom_public_data_interface, sklearn_binary_classification_model_interface, explainer_function) without return types