code: def _test_synth_rbf_compare_with_sklearn(queue, C, gamma):
      x, y = datasets.make_regression(**synth_params)
      clf = SVR(kernel="rbf", gamma=gamma, C=C)
      clf.fit(x, y, queue=queue)
      result = clf.score(x, y, queue=queue)
  
      clf = SklearnSVR(kernel="rbf", gamma=gamma, C=C)
      clf.fit(x, y)
      expected = clf.score(x, y)
  
      assert result > 0.4
      assert result > expected - 1e-5
  
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 10 for calling a global function with example usage: _test_synth_rbf_compare_with_sklearn(queue, C, gamma) without return types