code: def test_estimator():
      def dummy(*args, **kwargs):
          pass
  
      md = sklearn.utils.estimator_checks
      saved = _replace_and_save(
          md,
          [
              "check_sample_weights_invariance",  # Max absolute difference: 0.0002
              "check_estimators_fit_returns_self",  # ???
              "check_regressors_train",  # Cannot get data type from empty metadata
              "check_estimators_unfitted",  # expected NotFittedError from sklearn
          ],
          dummy,
      )
      check_estimator(SVR())
      _restore_from_saved(md, saved)
  
imports: import numpy
  import pytest
  import sklearn.utils.estimator_checks
  from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal
  from sklearn import datasets
  from sklearn.metrics.pairwise import rbf_kernel
  from sklearn.svm import SVR
  from sklearn.utils.estimator_checks import check_estimator
  from onedal.svm import SVR
  from onedal.tests.utils._device_selection import get_queues, pass_if_not_implemented_for_gpu
label_desc: Use image node_id 3 for calling a global function with example usage: test_estimator() without return types