code: def test_l1_regularization(exact_shap_regression_dataset, l1_type):
      # currently this is a code test, not mathematical results test.
      # Hard to test without falling into testing the underlying algorithms
      # which are out of this unit test scope.
      X, w = cuml.explainer.kernel_shap._powerset(
          5, 2, 2**5 - 2, full_powerset=True
      )
  
      y = cp.random.rand(X.shape[0])
      nz = cuml.explainer.kernel_shap._l1_regularization(
          X=cp.asarray(X).astype(np.float32),
          y=cp.asarray(y).astype(np.float32),
          weights=cp.asarray(w),
          expected_value=0.0,
          fx=0.0,
          link_fn=cuml.explainer.common.identity,
          l1_reg=l1_type,
      )
      assert isinstance(nz, cp.ndarray)
  
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 13 for calling a global function with example usage: test_l1_regularization(exact_shap_regression_dataset, l1_type) without return types