code: def _test_binary_dataset(queue, kernel):
      X, y = make_classification(
          n_samples=80, n_features=20, n_classes=2, random_state=0
      )
      sparse_X = sp.csr_matrix(X)
  
      dataset = sparse_X, y, sparse_X
      clf = SVC(kernel=kernel)
      check_svm_model_equal(queue, clf, *dataset)
  
imports: import numpy
  import pytest
  import sklearn.utils.estimator_checks
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  from scipy import sparse
  from sklearn import datasets, metrics
  from sklearn.base import clone
  from sklearn.datasets import make_blobs, make_classification
  from sklearn.metrics.pairwise import rbf_kernel
  from sklearn.model_selection import train_test_split
  from sklearn.utils.estimator_checks import check_estimator
  from onedal.svm import SVC, SVR
  from onedal.tests.utils._device_selection import get_queues, pass_if_not_implemented_for_gpu
label_desc: Use image node_id 5 for calling a global function with example usage: _test_binary_dataset(queue, kernel) without return types