code: def ensemble_classifier_basic_test(model_name=None):
      np.random.seed(0)
  
      (
          df_titanic_train,
          df_titanic_test,
      ) = utils.get_titanic_binary_classification_dataset()
  
      column_descriptions = {
          "survived": "output",
          "sex": "categorical",
          "embarked": "categorical",
          "pclass": "categorical",
      }
  
      ensemble_config = [
          {"model_name": "LGBMClassifier"},
          {"model_name": "RandomForestClassifier"},
      ]
  
      ml_predictor = Predictor(
          type_of_estimator="classifier",
          column_descriptions=column_descriptions,
      )
  
      ml_predictor.train(
          df_titanic_train, ensemble_config=ensemble_config
      )
  
      test_score = ml_predictor.score(
          df_titanic_test, df_titanic_test.survived
      )
  
      print("test_score")
      print(test_score)
  
      assert -0.15 < test_score < -0.131
  
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 1 for calling a global function with example usage: ensemble_classifier_basic_test(model_name) without return types