code: def test_sklearn_rf_regressor():
      n_samples = 100
      X, y = make_regression(
          n_samples=n_samples,
          n_features=8,
          n_informative=8,
          n_targets=1,
          random_state=2021,
      )
      X, y = X.astype(np.float32), y.astype(np.float32)
      skl_model = sklrfr(
          max_features=1.0,
          max_samples=0.1,
          min_samples_leaf=2,
          random_state=123,
          n_estimators=10,
          max_depth=16,
      )
      skl_model.fit(X, y)
  
      explainer = TreeExplainer(model=skl_model)
      out = explainer.shap_values(X)
  
      ref_explainer = shap.explainers.Tree(model=skl_model)
      correct_out = ref_explainer.shap_values(X)
      np.testing.assert_almost_equal(out, correct_out, decimal=5)
      np.testing.assert_almost_equal(
          explainer.expected_value,
          ref_explainer.expected_value,
          decimal=5,
      )
  
imports: from cuml.testing.utils import as_type
  import cuml
  from cuml.ensemble import RandomForestClassifier
  from cuml.ensemble import RandomForestRegressor
  from cuml.common.exceptions import NotFittedError
  from cuml.internals.import_utils import has_sklearn
  from cuml.internals.import_utils import has_lightgbm, has_shap
  from cuml.explainer.tree_shap import TreeExplainer
  from hypothesis import given, settings, assume, HealthCheck, strategies
  from cuml.internals.safe_imports import gpu_only_import
  import json
  import pytest
  import treelite
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 10 for calling a global function with example usage: test_sklearn_rf_regressor() without return types