code: def test_causal_classification_scikitlearn_issue():
      # This test gets stuck on SciKit-Learn v1.1.0
      # See PR #1429
      (
          data_train,
          data_test,
          _,
          _,
          categorical_features,
          _,
          target_name,
          classes,
          _,
          _,
      ) = create_adult_income_dataset()
  
      rai_i = RAIInsights(
          model=None,
          train=data_train,
          test=data_test,
          task_type="classification",
          target_column=target_name,
          categorical_features=categorical_features,
          classes=classes,
      )
      assert rai_i is not None
  
      treatment_features = ["age", "gender"]
      cat_expansion = 49
      rai_i.causal.add(
          treatment_features=treatment_features,
          heterogeneity_features=["marital_status"],
          nuisance_model="automl",
          heterogeneity_model="forest",
          alpha=0.06,
          upper_bound_on_cat_expansion=cat_expansion,
          treatment_cost=[0.1, 0.2],
          min_tree_leaf_samples=2,
          skip_cat_limit_checks=False,
          categories="auto",
          n_jobs=1,
          verbose=1,
          random_state=100,
      )
  
      rai_i.compute()
  
      results = rai_i.causal.get()
      assert results is not None
      assert isinstance(results, list)
      assert len(results) == 1
      _check_causal_result(results[0])
  
label_desc: Use image node_id 1 for calling a global function with example usage: test_causal_classification_scikitlearn_issue() without return types