code: def test_class_weight(queue):
      X = np.array(
          [[-2, -1], [-1, -1], [-1, -2], [1, 1], [1, 2], [2, 1]]
      )
      y = np.array([1, 1, 1, 2, 2, 2])
  
      clf = SVC(class_weight={1: 0.1})
      clf.fit(X, y, queue=queue)
      assert_array_almost_equal(clf.predict(X, queue=queue), [2] * 6)
  
imports: import numpy
  import pytest
  import sklearn.utils.estimator_checks
  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.utils.estimator_checks import check_estimator
  from onedal.svm import SVC
  from onedal.tests.utils._device_selection import get_queues, pass_if_not_implemented_for_gpu
label_desc: Use image node_id 6 for calling a global function with example usage: test_class_weight(queue) without return types