code: def _test_iris(queue, kernel):
      iris = datasets.load_iris()
      rng = np.random.RandomState(0)
      perm = rng.permutation(iris.target.size)
      iris.data = iris.data[perm]
      iris.target = iris.target[perm]
      sparse_iris_data = sp.csr_matrix(iris.data)
  
      dataset = sparse_iris_data, iris.target, sparse_iris_data
  
      clf = SVC(kernel=kernel)
      check_svm_model_equal(queue, clf, *dataset, decimal=2)
  
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 7 for calling a global function with example usage: _test_iris(queue, kernel) without return types