code: def remove_mask(
      nmask,
      X_train,
      y_train,
      X_test,
      y_test,
      attr_test,
      model_generator,
      metric,
      trained_model,
      random_state,
  ):
      """Each test sample is masked by setting the important features to a constant."""
  
      X_train, X_test = to_array(X_train, X_test)
  
      # how many features to mask
      assert X_train.shape[1] == X_test.shape[1]
  
      # mask nmask top features for each test explanation
      X_test_tmp = X_test.copy()
      tie_breaking_noise = (
          const_rand(X_train.shape[1], random_state) * 1e-6
      )
      mean_vals = X_train.mean(0)
      for i in range(len(y_test)):
          if nmask[i] > 0:
              ordering = np.argsort(
                  -attr_test[i, :] + tie_breaking_noise
              )
              X_test_tmp[i, ordering[: nmask[i]]] = mean_vals[
                  ordering[: nmask[i]]
              ]
  
      yp_masked_test = trained_model.predict(X_test_tmp)
  
      return metric(y_test, yp_masked_test)
  
file_path: shap/shap/benchmark/measures.py
imports: import warnings
  import numpy
  import pandas
  import sklearn.utils
  from tqdm.auto import tqdm
label_desc: Use image node_id 2 for calling a global function with example usage: remove_mask(nmask, X_train, y_train, X_test, y_test, attr_test, model_generator, metric, trained_model, random_state) and returns: metric