code: def test_svr_skl_cmp(params, dataset, n_rows, n_cols):
      """Compare to Sklearn SVR"""
      if dataset == "Friedman" and n_cols < 5:
          # We need at least 5 feature columns for the Friedman dataset
          return
      X_train, X_test, y_train, y_test = make_regression_dataset(
          dataset, n_rows, n_cols
      )
      cuSVR = cu_svm.SVR(**params)
      cuSVR.fit(X_train, y_train)
  
      sklSVR = svm.SVR(**params)
      sklSVR.fit(X_train, y_train)
  
      compare_svr(cuSVR, sklSVR, X_test, y_test)
  
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 17 for calling a global function with example usage: test_svr_skl_cmp(params, dataset, n_rows, n_cols) without return types