code: def test_multiclass(iris_dataframe, return_train_score):
      # loading dataset
      assert isinstance(iris_dataframe, pd.DataFrame)
  
      # init setup
      pycaret.classification.setup(
          iris_dataframe,
          target="species",
          log_experiment=True,
          html=False,
          session_id=123,
          n_jobs=1,
      )
  
      # compare models
      top3 = pycaret.classification.compare_models(
          errors="raise", n_select=100
      )[:3]
      assert isinstance(top3, list)
  
      # tune model
      tuned_top3 = [
          pycaret.classification.tune_model(
              i, return_train_score=return_train_score
          )
          for i in top3
      ]
      assert isinstance(tuned_top3, list)
  
      # ensemble model
      bagged_top3 = [
          pycaret.classification.ensemble_model(
              i, return_train_score=return_train_score
          )
          for i in tuned_top3
      ]
      assert isinstance(bagged_top3, list)
  
      # blend models
      pycaret.classification.blend_models(
          top3, return_train_score=return_train_score
      )
  
      # stack models
      stacker = pycaret.classification.stack_models(
          estimator_list=top3, return_train_score=return_train_score
      )
      pycaret.classification.predict_model(stacker)
  
      # plot model
      lr = pycaret.classification.create_model(
          "lr", return_train_score=return_train_score
      )
      pycaret.classification.plot_model(lr, save=True, scale=5)
  
      # select best model
      pycaret.classification.automl(optimize="MCC", use_holdout=True)
      best = pycaret.classification.automl(optimize="MCC")
  
      # hold out predictions
      predict_holdout = pycaret.classification.predict_model(best)
      assert isinstance(predict_holdout, pd.DataFrame)
  
      # predictions on new dataset
      predict_holdout = pycaret.classification.predict_model(
          best, data=iris_dataframe.drop("species", axis=1)
      )
      assert isinstance(predict_holdout, pd.DataFrame)
  
      # calibrate model
      pycaret.classification.calibrate_model(
          best, return_train_score=return_train_score
      )
  
      # finalize model
      pycaret.classification.finalize_model(best)
  
      # save model
      pycaret.classification.save_model(best, "best_model_23122019")
  
      # load model
      pycaret.classification.load_model("best_model_23122019")
  
      # returns table of models
      all_models = pycaret.classification.models()
      assert isinstance(all_models, pd.DataFrame)
  
      # get config
      X_train = pycaret.classification.get_config("X_train")
      X_test = pycaret.classification.get_config("X_test")
      y_train = pycaret.classification.get_config("y_train")
      y_test = pycaret.classification.get_config("y_test")
      assert isinstance(X_train, pd.DataFrame)
      assert isinstance(X_test, pd.DataFrame)
      assert isinstance(y_train, pd.Series)
      assert isinstance(y_test, pd.Series)
  
      # set config
      pycaret.classification.set_config("seed", 124)
      seed = pycaret.classification.get_config("seed")
      assert seed == 124
  
      assert 1 == 1
  
imports: import pandas
  import pytest
  import pycaret.classification
  import pycaret.datasets
label_desc: Use image node_id 2 for calling a global function with example usage: test_multiclass(iris_dataframe, return_train_score) without return types