code: def assert_dataset_module(dataset_module):
      train, test = dataset_module.load_data()
      trained_model = dataset_module.load_fitted_model(pretrained=False)
      assert_model_predict_on_data(trained_model, train, test)
  
      # The models were trained on python 3.8, therefore tests for equality of pretrained only on this version
      python_minor_version = sys.version_info[1]
      if python_minor_version == 8:
          if hasattr(dataset_module, "_MODEL_VERSION"):
              if (
                  sklearn.__version__ != dataset_module._MODEL_VERSION
              ):  # pylint: disable=protected-access
                  raise Exception(
                      f"Can't test pretrained model for non matching sklearn version {sklearn.__version__}"
                  )
          pretrained_model = dataset_module.load_fitted_model(
              pretrained=True
          )
          if isinstance(pretrained_model, BaseEstimator):
              assert_sklearn_model_params_equals(
                  pretrained_model, trained_model
              )
          assert_model_predict_on_data(pretrained_model, train, test)
  
file_path: deepchecks/tests/tabular/test_datasets.py
imports: import sys
  import numpy
  import sklearn
  from deepdiff import DeepDiff
  from hamcrest import assert_that, instance_of
  from sklearn.base import BaseEstimator
  from deepchecks.tabular.datasets.classification import adult, breast_cancer, iris, lending_club, phishing
  from deepchecks.tabular.datasets.regression import airbnb, avocado, wine_quality
label_desc: Use image node_id 3 for calling a global function with example usage: assert_dataset_module(dataset_module) without return types