How to use the forestci._bias_correction function in forestci

of Machine Learning Research vol. 15, pp. 1625-1651, 2014.
    """
    if inbag is None:
        inbag = calc_inbag_modified(X_train.shape[0], forest, is_ensemble)

    if not is_ensemble:
        pred = np.array([tree.predict(X_test) for tree in forest]).T
    else:
        pred = np.array([tree.predict(X_test) for tree in forest.model]).T
        pred = pred[0]
    pred_mean = np.mean(pred, 0)
    pred_centered = pred - pred_mean
    n_trees = forest.n_estimators
    V_IJ = fci._core_computation(X_train, X_test, inbag, pred_centered, n_trees,
                             memory_constrained, memory_limit)
    V_IJ_unbiased = fci._bias_correction(V_IJ, inbag, pred_centered, n_trees)

    # Correct for cases where resampling is done without replacement:
    if np.max(inbag) == 1:
        variance_inflation = 1 / (1 - np.mean(inbag)) ** 2
        V_IJ_unbiased *= variance_inflation

    if basic_IJ:
        return V_IJ

    if not calibrate:
        return V_IJ_unbiased

    if V_IJ_unbiased.shape[0] <= 20:
        print("No calibration with n_samples <= 20")
        return V_IJ_unbiased
    if calibrate: