code: def test_dbscan_pickle(tmpdir, datatype, keys, data_size):
      if data_size[0] == 500000 and pytest.max_gpu_memory < 32:
          if pytest.adapt_stress_test:
              data_size[0] = data_size[0] * pytest.max_gpu_memory // 32
          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
          X_train, _, _ = make_dataset(datatype, nrows, ncols, n_info)
          model = dbscan_model[keys]()
          result["dbscan"] = model.fit_predict(X_train)
          return model, X_train
  
      def assert_model(pickled_model, X_train):
          pickle_after_predict = pickled_model.fit_predict(X_train)
          assert array_equal(result["dbscan"], pickle_after_predict)
  
      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 16 for calling a global function with example usage: test_dbscan_pickle(tmpdir, datatype, keys, data_size) and returns: model,  X_train