code: def test_default_configuration(self):
      for i in range(2):
          X_train, Y_train, X_test, Y_test = get_dataset(
              dataset="diabetes"
          )
          auto = SimpleRegressionPipeline(random_state=1)
          auto = auto.fit(X_train, Y_train)
          predictions = auto.predict(copy.deepcopy(X_test))
          # The lower the worse
          r2_score = sklearn.metrics.r2_score(Y_test, predictions)
          self.assertAlmostEqual(0.3458397471855429, r2_score, places=2)
          model_score = auto.score(copy.deepcopy(X_test), Y_test)
          self.assertAlmostEqual(model_score, r2_score, places=5)
  
imports: import copy
  import itertools
  import resource
  import tempfile
  import numpy
  import sklearn.datasets
  import sklearn.decomposition
  import sklearn.ensemble
  import sklearn.svm
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter
  from joblib import Memory
  from sklearn.base import clone
  from sklearn.utils.validation import check_is_fitted
  import autosklearn.pipeline.components.feature_preprocessing
  import autosklearn.pipeline.components.regression
  from autosklearn.pipeline.components.base import AutoSklearnChoice, AutoSklearnComponent, AutoSklearnPreprocessingAlgorithm, AutoSklearnRegressionAlgorithm
  from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SIGNED_DATA, SPARSE, UNSIGNED_DATA
  from autosklearn.pipeline.regression import SimpleRegressionPipeline
  from autosklearn.pipeline.util import get_dataset
  import unittest
  import unittest.mock
  from test.test_pipeline.ignored_warnings import ignore_warnings, regressor_warnings
label_desc: Use image node_id 9 for calling the SimpleRegressionPipelineTest obj's underlying member method code with example usage: obj.test_default_configuration() without return types