code: def test_kernel_housing_dataset(housing_dataset):
      X, y, _ = housing_dataset
  
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.25, random_state=42
      )
  
      # making all float32 to use gpu predict on random forest
      X_train = X_train.astype(np.float32)
      X_test = X_test.astype(np.float32)
      y_train = y_train.astype(np.float32)
      y_test = y_test.astype(np.float32)
  
      cumodel = cuml.RandomForestRegressor().fit(X_train, y_train)
  
      explainer = KernelExplainer(
          model=cumodel.predict, data=X_train[:100], output_type="numpy"
      )
  
      cu_shap_values = explainer.shap_values(X_test[:2])
  
      assert np.allclose(
          cu_shap_values,
          housing_regression_result,
          rtol=1e-01,
          atol=1e-01,
      )
  
imports: from sklearn.model_selection import train_test_split
  from cuml.testing.utils import create_synthetic_dataset, ClassEnumerator, get_shap_values
  from cuml.datasets import make_regression
  from cuml.internals.import_utils import has_shap
  from cuml.internals.import_utils import has_scipy
  from cuml import KernelExplainer
  from cuml import Lasso
  import sklearn.neighbors
  import pytest
  import math
  from cuml.internals.safe_imports import cpu_only_import
  import cuml
  from cuml.internals.safe_imports import gpu_only_import
label_desc: Use image node_id 6 for calling a global function with example usage: test_kernel_housing_dataset(housing_dataset) without return types