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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def batch_remove_retrain(nmask_train, nmask_test, X_train, y_train, X_test, y_test, attr_train, attr_test, model_generator, metric):
 """ An approximation of holdout that only retraines the model once. This is alse called ROAR (RemOve And Retrain) in work by Google. It is much more computationally efficient that the holdout method because it masks the most important features in every sample and then retrains the model once, instead of retraining the model for every test sample like the holdout metric. """


    warnings.warn("The retrain based measures can incorrectly evaluate models in some cases!")

    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 explanation
    X_train_tmp = X_train.copy()
    X_train_mean = X_train.mean(0)
    tie_breaking_noise = const_rand(X_train.shape[1]) * 1e-6
    for i in range(len(y_train)):
        if nmask_train[i] > 0:
            ordering = np.argsort(-attr_train[i, :] + tie_breaking_noise)
            X_train_tmp[i, ordering[:nmask_train[i]]] = X_train_mean[ordering[:nmask_train[i]]]
    X_test_tmp = X_test.copy()
    for i in range(len(y_test)):
        if nmask_test[i] > 0:
            ordering = np.argsort(-attr_test[i, :] + tie_breaking_noise)
            X_test_tmp[i, ordering[:nmask_test[i]]] = X_train_mean[ordering[:nmask_test[i]]]

    # train the model with all the given features masked
    model_masked = model_generator()
    model_masked.fit(X_train_tmp, y_train)
    yp_test_masked = model_masked.predict(X_test_tmp)

    return metric(y_test, yp_test_masked)