code: def test_sided_sample_weight(queue):
      clf = SVR(C=1e-2, kernel="linear")
  
      X = [[-2, 0], [-1, -1], [0, -2], [0, 2], [1, 1], [2, 0]]
      Y = [1, 1, 1, 2, 2, 2]
  
      sample_weight = [10.0, 0.1, 0.1, 0.1, 0.1, 10]
      clf.fit(X, Y, sample_weight=sample_weight, queue=queue)
      y_pred = clf.predict([[-1.0, 1.0]], queue=queue)
      assert y_pred < 1.5
  
      sample_weight = [1.0, 0.1, 10.0, 10.0, 0.1, 0.1]
      clf.fit(X, Y, sample_weight=sample_weight, queue=queue)
      y_pred = clf.predict([[-1.0, 1.0]], queue=queue)
      assert y_pred > 1.5
  
      sample_weight = [1] * 6
      clf.fit(X, Y, sample_weight=sample_weight, queue=queue)
      y_pred = clf.predict([[-1.0, 1.0]], queue=queue)
      assert y_pred == pytest.approx(1.5)
  
imports: import numpy
  import pytest
  import sklearn.utils.estimator_checks
  from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal
  from sklearn import datasets
  from sklearn.metrics.pairwise import rbf_kernel
  from sklearn.svm import SVR
  from sklearn.utils.estimator_checks import check_estimator
  from onedal.svm import SVR
  from onedal.tests.utils._device_selection import get_queues, pass_if_not_implemented_for_gpu
label_desc: Use image node_id 16 for calling a global function with example usage: test_sided_sample_weight(queue) without return types