code: def test_regressor_pickle(
      tmpdir, datatype, keys, data_size, fit_intercept
  ):
      if (
          data_size[0] == 500000
          and datatype == np.float64
          and ("LogisticRegression" in keys or "Ridge" in keys)
          and pytest.max_gpu_memory < 32
      ):
          if pytest.adapt_stress_test:
              data_size[0] = data_size[0] * pytest.max_gpu_memory // 640
              data_size[1] = data_size[1] * pytest.max_gpu_memory // 640
              data_size[2] = data_size[2] * pytest.max_gpu_memory // 640
          else:
              pytest.skip(
                  "Insufficient GPU memory for this test."
                  "Re-run with 'CUML_ADAPT_STRESS_TESTS=True'"
              )
      result = {}
  
      def create_mod():
          nrows, ncols, n_info = data_size
          if "LogisticRegression" in keys and nrows == 500000:
              nrows, ncols, n_info = (
                  nrows // 20,
                  ncols // 20,
                  n_info // 20,
              )
  
          X_train, y_train, X_test = make_dataset(
              datatype, nrows, ncols, n_info
          )
          if "MBSGD" in keys:
              model = regression_models[keys](
                  fit_intercept=fit_intercept, batch_size=nrows / 100
              )
          else:
              model = regression_models[keys](
                  fit_intercept=fit_intercept
              )
          model.fit(X_train, y_train)
          result["regressor"] = model.predict(X_test)
          return model, X_test
  
      def assert_model(pickled_model, X_test):
          assert array_equal(
              result["regressor"], pickled_model.predict(X_test)
          )
  
      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 5 for calling a global function with example usage: test_regressor_pickle(tmpdir, datatype, keys, data_size, fit_intercept) and returns: model,  X_test