code: def test_given_baseline_values_when_estimating_feature_relevance_sample_with_mean_diff_then_returns_expected_result():
      num_vars = 5
      X = np.random.normal(0, 1, (1000, num_vars))
      coefficients = np.random.choice(20, num_vars) - 10
  
      def model(x) -> np.ndarray:
          return np.sum(coefficients * x, axis=1)
  
      shapley_values_1 = feature_relevance_sample(
          model,
          feature_samples=X,
          baseline_samples=X[:20],
          subset_scoring_func=lambda x, y: np.mean(x) * y,
          baseline_target_values=np.zeros(20),
          shapley_config=ShapleyConfig(
              approximation_method=ShapleyApproximationMethods.EXACT
          ),
      )
  
      shapley_values_2 = feature_relevance_sample(
          model,
          feature_samples=X,
          baseline_samples=X[:20],
          subset_scoring_func=lambda x, y: np.mean(x) * y,
          baseline_target_values=np.zeros(20) + 1,
          shapley_config=ShapleyConfig(
              approximation_method=ShapleyApproximationMethods.EXACT
          ),
      )
  
      shapley_values_3 = feature_relevance_sample(
          model,
          feature_samples=X,
          baseline_samples=X[:20],
          subset_scoring_func=lambda x, y: np.mean(x) * y,
          baseline_target_values=np.zeros(20) + 2,
          shapley_config=ShapleyConfig(
              approximation_method=ShapleyApproximationMethods.EXACT
          ),
      )
  
      for i in range(20):
          assert shapley_values_1[i] == approx(np.zeros(5))
          assert shapley_values_2[i] * 2 == approx(shapley_values_3[i])
  
file_path: dowhy/tests/gcm/test_feature_relevance.py
imports: import networkx
  import numpy
  import pandas
  from flaky import flaky
  from pytest import approx
  from scipy import stats
  from dowhy.gcm import AdditiveNoiseModel, ClassifierFCM, ScipyDistribution, StructuralCausalModel, auto, confidence_intervals, fit
  from dowhy.gcm.feature_relevance import feature_relevance_distribution, feature_relevance_sample, parent_relevance
  from dowhy.gcm.ml import create_linear_regressor, create_logistic_regression_classifier
  from dowhy.gcm.shapley import ShapleyApproximationMethods, ShapleyConfig
  from dowhy.gcm.uncertainty import estimate_entropy_of_probabilities
  from dowhy.gcm.util.general import means_difference
label_desc: Use image node_id 4 for calling a global function with example usage: test_given_baseline_values_when_estimating_feature_relevance_sample_with_mean_diff_then_returns_expected_result() and returns: np