code: def _test_cancer_rbf_compare_with_sklearn(queue, nu, gamma):
      cancer = datasets.load_breast_cancer()
  
      clf = NuSVC(kernel="rbf", gamma=gamma, nu=nu)
      clf.fit(cancer.data, cancer.target, queue=queue)
      result = clf.score(cancer.data, cancer.target, queue=queue)
  
      clf = SklearnNuSVC(kernel="rbf", gamma=gamma, nu=nu)
      clf.fit(cancer.data, cancer.target)
      expected = clf.score(cancer.data, cancer.target)
  
      assert result > 0.4
      assert abs(result - expected) < 1e-4
  
imports: import numpy
  import pytest
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  from sklearn import datasets
  from sklearn.datasets import make_blobs
  from sklearn.metrics.pairwise import rbf_kernel
  from sklearn.model_selection import train_test_split
  from sklearn.svm import NuSVC
  from onedal.svm import NuSVC
  from onedal.tests.utils._device_selection import get_queues, pass_if_not_implemented_for_gpu
label_desc: Use image node_id 9 for calling a global function with example usage: _test_cancer_rbf_compare_with_sklearn(queue, nu, gamma) without return types