code: def test_sample_weight(queue):
      X = np.array([[-2, 0], [-1, -1], [0, -2], [0, 2], [1, 1], [2, 2]])
      y = np.array([1, 1, 1, 2, 2, 2])
  
      clf = NuSVC(kernel="linear")
      clf.fit(X, y, sample_weight=[1] * 6, queue=queue)
      assert_array_almost_equal(clf.intercept_, [0.0])
  
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 4 for calling a global function with example usage: test_sample_weight(queue) without return types