code: def ensemble_regressor_basic_test():
      np.random.seed(0)
  
      (
          df_boston_train,
          df_boston_test,
      ) = utils.get_boston_regression_dataset()
  
      column_descriptions = {"MEDV": "output", "CHAS": "categorical"}
  
      ensemble_config = [
          {"model_name": "LGBMRegressor"},
          {"model_name": "RandomForestRegressor"},
      ]
  
      ml_predictor = Predictor(
          type_of_estimator="regressor",
          column_descriptions=column_descriptions,
      )
  
      ml_predictor.train(df_boston_train, ensemble_config=None)
  
      test_score = ml_predictor.score(
          df_boston_test, df_boston_test.MEDV
      )
  
      print("test_score")
      print(test_score)
  
      assert -3.0 < test_score < -2.8
  
file_path: auto_ml/tests/core_tests/ensemble_tests.py
imports: import datetime
  import os
  import random
  import sys
  from auto_ml import Predictor
  from auto_ml.utils_models import load_ml_model
  from nose.tools import assert_equal, assert_not_equal, with_setup
  from sklearn.metrics import accuracy_score
  import dill
  import numpy
  import utils_testing
label_desc: Use image node_id 2 for calling a global function with example usage: ensemble_regressor_basic_test() without return types