code: def test_svr_skl_cmp_weighted():
      """Compare to Sklearn SVR, use sample weights"""
      X, y = make_regression(
          n_samples=100,
          n_features=5,
          n_informative=2,
          n_targets=1,
          random_state=137,
          noise=10,
      )
      sample_weights = (
          10 * np.sin(np.linspace(0, 2 * np.pi, len(y))) + 10.1
      )
  
      params = {"kernel": "linear", "C": 10, "gamma": 1}
      cuSVR = cu_svm.SVR(**params)
      cuSVR.fit(X, y, sample_weights)
  
      sklSVR = svm.SVR(**params)
      sklSVR.fit(X, y, sample_weights)
  
      compare_svr(cuSVR, sklSVR, X, y)
  
imports: import platform
  from sklearn.preprocessing import StandardScaler
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import mean_squared_error
  from sklearn.datasets import make_classification, make_gaussian_quantiles
  from sklearn.datasets import make_regression, make_friedman1
  from sklearn.datasets import load_iris, make_blobs
  from sklearn import svm
  from cuml.testing.utils import unit_param, quality_param, stress_param, compare_svm, compare_probabilistic_svm, svm_array_equal
  from cuml.common import input_to_cuml_array
  import cuml.svm
  import cuml
  from cuml.internals.safe_imports import gpu_only_import_from
  from cuml.internals.safe_imports import cpu_only_import
  import pytest
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 18 for calling a global function with example usage: test_svr_skl_cmp_weighted() without return types