code: def test_perfect_colinear():
      LinearRegression = pytest.importorskip(
          "sklearn.linear_model"
      ).LinearRegression
  
      X, y = shap.datasets.california(n_points=100)
      X.iloc[:, 0] = X.iloc[:, 4]  # test duplicated features
      X.iloc[:, 5] = (
          X.iloc[:, 6] - X.iloc[:, 6]
      )  # test multiple colinear features
      X.iloc[:, 3] = 0  # test null features
      model = LinearRegression()
      model.fit(X, y)
      explainer = shap.LinearExplainer(
          model, X, feature_dependence="correlation"
      )
      shap_values = explainer.shap_values(X)
      assert (
          np.abs(
              shap_values.sum(1)
              - model.predict(X)
              + model.predict(X).mean()
          ).sum()
          < 1e-7
      )
  
file_path: shap/tests/explainers/test_linear.py
imports: import numpy
  import pytest
  import scipy.special
  import shap
label_desc: Use image node_id 10 for calling a global function with example usage: test_perfect_colinear() without return types