code: def _test_diabetes_compare_with_sklearn(queue, kernel):
      diabetes = datasets.load_diabetes()
      clf_onedal = NuSVR(kernel=kernel, nu=0.25, C=10.0)
      clf_onedal.fit(diabetes.data, diabetes.target, queue=queue)
      result = clf_onedal.score(
          diabetes.data, diabetes.target, queue=queue
      )
  
      clf_sklearn = SklearnNuSVR(kernel=kernel, nu=0.25, C=10.0)
      clf_sklearn.fit(diabetes.data, diabetes.target)
      expected = clf_sklearn.score(diabetes.data, diabetes.target)
  
      assert result > expected - 1e-5
      assert_allclose(
          clf_sklearn.intercept_, clf_onedal.intercept_, atol=1e-3
      )
      assert_allclose(
          clf_sklearn.support_vectors_.shape,
          clf_sklearn.support_vectors_.shape,
      )
      assert_allclose(
          clf_sklearn.dual_coef_, clf_onedal.dual_coef_, atol=1e-2
      )
  
file_path: scikit-learn-intelex/onedal/svm/tests/test_nusvr.py
imports: import numpy
  import pytest
  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 NuSVR
  from onedal.svm import NuSVR
  from onedal.tests.utils._device_selection import get_queues, pass_if_not_implemented_for_gpu
label_desc: Use image node_id 4 for calling a global function with example usage: _test_diabetes_compare_with_sklearn(queue, kernel) without return types