code: def test_default_boston(self):
      if self.__class__ == BaseRegressionComponentTest:
          return
  
      for _ in range(2):
          with ignore_warnings(regressor_warnings):
              predictions, targets, n_calls = _test_regressor(
                  dataset="boston", Regressor=self.module
              )
  
          score = sklearn.metrics.r2_score(
              y_true=targets, y_pred=predictions
          )
  
          # Special treatment for Gaussian Process Regression
          if "default_boston_le_ge" in self.res:
              upper, lower = self.res["default_boston_le_ge"]
              assert lower <= score <= upper
  
          else:
              fixture = self.res["default_boston"]
              places = self.res.get("default_boston_places", 7)
  
              if score < -1e10:
                  score = np.log(-score)
                  fixture = np.log(-fixture)
  
              self.assertAlmostEqual(fixture, score, places)
  
          if "boston_n_calls" in self.res:
              expected = self.res["boston_n_calls"]
              if isinstance(expected, Container):
                  assert n_calls in expected
              else:
                  assert n_calls == expected
  
imports: from typing import Container, Type
  import numpy
  import sklearn.metrics
  from autosklearn.pipeline.components.regression import RegressorChoice, _regressors
  from autosklearn.pipeline.components.regression.libsvm_svr import LibSVM_SVR
  from autosklearn.pipeline.constants import SPARSE
  from autosklearn.pipeline.util import _test_regressor, _test_regressor_iterative_fit
  import pytest
  import unittest
  from test.test_pipeline.ignored_warnings import ignore_warnings, regressor_warnings
label_desc: Use image node_id 1 for calling the BaseRegressionComponentTest obj's underlying member method code with example usage: obj.test_default_boston() without return types