code: def test_given_many_features_when_estimate_shapley_values_with_subset_sampling_then_returns_correct_linear_shapley_values():
      X, coefficients = _generate_data(15)
  
      def model(x):
          return np.sum(coefficients * x, axis=1)
  
      shapley_values = estimate_shapley_values(
          lambda subset: _set_function_for_aggregated_feature_attribution(
              subset, X, model
          ),
          X.shape[1],
          ShapleyConfig(
              approximation_method=ShapleyApproximationMethods.SUBSET_SAMPLING
          ),
      )
  
      assert coefficients * (X[0, :] - np.mean(X, axis=0)) == approx(
          shapley_values, abs=0.001
      )
  
imports: import random
  import numpy
  import pytest
  from flaky import flaky
  from pytest import approx
  from dowhy.gcm.shapley import ShapleyApproximationMethods, ShapleyConfig, estimate_shapley_values
  from dowhy.gcm.stats import permute_features
  from dowhy.gcm.util.general import means_difference
label_desc: Use image node_id 6 for calling a global function with example usage: test_given_many_features_when_estimate_shapley_values_with_subset_sampling_then_returns_correct_linear_shapley_values() and returns: np