code: def _set_function_for_aggregated_feature_attribution(
      subset, X, model, evaluate_only_one_sample=True
  ):
      tmp = permute_features(
          X, np.arange(0, X.shape[1])[subset == 0], False
      )
      tmp[:, subset == 1] = X[0, subset == 1]
  
      if evaluate_only_one_sample:
          return means_difference(model(tmp), X[0])
      else:
          return np.array([means_difference(model(tmp), x) for x in X])
  
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 13 for calling a global function with example usage: _set_function_for_aggregated_feature_attribution(subset, X, model, evaluate_only_one_sample) and returns: means_difference, np