code: def test_run_to_run_fit(queue):
      diabetes = datasets.load_diabetes()
      clf_first = SVR(kernel="linear", C=10.0)
      clf_first.fit(diabetes.data, diabetes.target, queue=queue)
  
      for _ in range(10):
          clf = SVR(kernel="linear", C=10.0)
          clf.fit(diabetes.data, diabetes.target, queue=queue)
          assert_allclose(clf_first.intercept_, clf.intercept_)
          assert_allclose(
              clf_first.support_vectors_, clf.support_vectors_
          )
          assert_allclose(clf_first.dual_coef_, clf.dual_coef_)
  
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 4 for calling a global function with example usage: test_run_to_run_fit(queue) without return types