code: def test_rf_regression_pickle(
      tmpdir, datatype, nrows, ncols, n_info, n_classes, key
  ):
      result = {}
      if datatype == np.float64:
          pytest.xfail(
              "Pickling is not supported for dataset with"
              " dtype float64"
          )
  
      def create_mod():
          if key == "RandomForestRegressor":
              X_train, y_train, X_test = make_dataset(
                  datatype, nrows, ncols, n_info
              )
          else:
              X_train, y_train, X_test = make_classification_dataset(
                  datatype, nrows, ncols, n_info, n_classes
              )
  
          model = rf_models[key]()
  
          model.fit(X_train, y_train)
          if datatype == np.float32:
              predict_model = "GPU"
          else:
              predict_model = "CPU"
          result["rf_res"] = model.predict(
              X_test, predict_model=predict_model
          )
          return model, X_test
  
      def assert_model(pickled_model, X_test):
          assert array_equal(
              result["rf_res"], pickled_model.predict(X_test)
          )
          # Confirm no crash from score
          pickled_model.score(
              X_test, np.zeros(X_test.shape[0]), predict_model="GPU"
          )
  
          pickle_save_load(tmpdir, create_mod, assert_model)
  
imports: from sklearn.model_selection import train_test_split
  from sklearn.manifold import trustworthiness
  from sklearn.datasets import load_iris, make_classification, make_regression
  from sklearn.base import clone
  from cuml.testing.utils import array_equal, unit_param, stress_param, ClassEnumerator, get_classes_from_package, compare_svm, compare_probabilistic_svm
  from cuml.tsa.arima import ARIMA
  import pytest
  import pickle
  import cuml
  from cuml.internals.safe_imports import cpu_only_import, cpu_only_import_from
label_desc: Use image node_id 4 for calling a global function with example usage: test_rf_regression_pickle(tmpdir, datatype, nrows, ncols, n_info, n_classes, key) and returns: model,  X_test