code: def test_AutoGluonModelEvaluator_regression():
      df_train = pd.read_csv(
          os.path.join(RESOURCE_PATH, "houses", "train_data.csv")
      ).sample(100, random_state=0)
      df_val = pd.read_csv(
          os.path.join(RESOURCE_PATH, "houses", "test_data.csv")
      ).sample(50, random_state=0)
      target_col = "SalePrice"
  
      with tempfile.TemporaryDirectory() as path:
          predictor = fit_model(path, df_train, target_col)
  
          state = auto.analyze(
              model=predictor,
              train_data=df_train,
              val_data=df_val,
              return_state=True,
              anlz_facets=[
                  eda.model.AutoGluonModelEvaluator(normalize="true")
              ],
          )
  
      assert state.model_evaluation.problem_type == REGRESSION
      assert len(state.model_evaluation.y_true_val) == len(df_val)
      assert len(state.model_evaluation.y_pred_val) == len(df_val)
      expected = [
          c
          for c in df_train.columns
          if c not in ["Street", "Utilities", "SalePrice", "PoolQC"]
      ]
      assert sorted(
          state.model_evaluation.importance.index.to_list()
      ) == sorted(expected)
      _assert_importance_is_present(state)
      assert state.model_evaluation.confusion_matrix is None
      assert state.model_evaluation.confusion_matrix_normalized is None
      assert state.model_evaluation.y_true_test is None
      assert state.model_evaluation.y_pred_test is None
      assert state.model_evaluation.y_true_train is None
      assert state.model_evaluation.y_pred_train is None
  
file_path: autogluon/eda/tests/unittests/analysis/test_model.py
imports: import os.path
  import tempfile
  import pandas
  import pytest
  import autogluon.eda.analysis
  import autogluon.eda.auto
  from autogluon.core.constants import REGRESSION
  from autogluon.tabular import TabularPredictor
label_desc: Use image node_id 2 for calling a global function with example usage: test_AutoGluonModelEvaluator_regression() without return types