code: def test_tied_triple():
      beta = np.array([0, 1, 0, 0])
      mu = 1 * np.ones(4)
      Sigma = np.array(
          [
              [1, 0.999999, 0.999999, 0],
              [0.999999, 1, 0.999999, 0],
              [0.999999, 0.999999, 1, 0],
              [0, 0, 0, 1],
          ]
      )
      X = 2 * np.ones((1, 4))
      explainer = shap.LinearExplainer(
          (beta, 0), (mu, Sigma), feature_dependence="correlation"
      )
      assert explainer.expected_value == 1
      assert (
          np.abs(
              explainer.shap_values(X)
              - np.array([0.33333, 0.33333, 0.33333, 0])
          ).max()
          < 0.05
      )
  
file_path: shap/tests/explainers/test_linear.py
imports: import numpy
  import pytest
  import scipy.special
  import shap
label_desc: Use image node_id 5 for calling a global function with example usage: test_tied_triple() without return types