def METHOD_NAME(
    tasks,
    measure_option,
    tuner="xgb",
    n_trial=2048,
    early_stopping=None,
    log_filename="tuning.log",
    use_transfer_learning=True,
):
    """Tune tasks with different tuners"""
    tmp_log_file = log_filename + ".tmp"
    if os.path.exists(tmp_log_file):
        os.remove(tmp_log_file)
    for i, tsk in enumerate(reversed(tasks)):
        prefix = "[Task %2d/%2d] " % (i + 1, len(tasks))
        if tuner == "xgb":
            tuner_obj = XGBTuner(tsk, loss_type="reg")
        elif tuner == "xgb_knob":
            tuner_obj = XGBTuner(tsk, loss_type="reg", feature_type="knob")
        elif tuner == "xgb_itervar":
            tuner_obj = XGBTuner(tsk, loss_type="reg", feature_type="itervar")
        elif tuner == "xgb_curve":
            tuner_obj = XGBTuner(tsk, loss_type="reg", feature_type="curve")
        elif tuner == "xgb_rank":
            tuner_obj = XGBTuner(tsk, loss_type="rank")
        elif tuner == "xgb_rank_knob":
            tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="knob")
        elif tuner == "xgb_rank_itervar":
            tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="itervar")
        elif tuner == "xgb_rank_curve":
            tuner_obj = XGBTuner(tsk, loss_type="rank", feature_type="curve")
        elif tuner == "xgb_rank_binary":
            tuner_obj = XGBTuner(tsk, loss_type="rank-binary")
        elif tuner == "xgb_rank_binary_knob":
            tuner_obj = XGBTuner(tsk, loss_type="rank-binary", feature_type="knob")
        elif tuner == "xgb_rank_binary_itervar":
            tuner_obj = XGBTuner(tsk, loss_type="rank-binary", feature_type="itervar")
        elif tuner == "xgb_rank_binary_curve":
            tuner_obj = XGBTuner(tsk, loss_type="rank-binary", feature_type="curve")
        elif tuner == "ga":
            tuner_obj = GATuner(tsk, pop_size=50)
        elif tuner == "random":
            tuner_obj = RandomTuner(tsk)
        elif tuner == "gridsearch":
            tuner_obj = GridSearchTuner(tsk)
        else:
            raise ValueError("Invalid tuner: " + tuner)
        if use_transfer_learning:
            if os.path.isfile(tmp_log_file):
                tuner_obj.load_history(autotvm.record.load_from_file(tmp_log_file))
        tsk_trial = min(n_trial, len(tsk.config_space))
        tuner_obj.tune(
            n_trial=tsk_trial,
            early_stopping=early_stopping,
            measure_option=measure_option,
            callbacks=[
                autotvm.callback.progress_bar(tsk_trial, prefix=prefix),
                autotvm.callback.log_to_file(tmp_log_file),
            ],
        )
    autotvm.record.pick_best(tmp_log_file, log_filename)
    os.remove(tmp_log_file)