from transformers import AutoConfig
    from .models.ModelContext import get_context

    config.model_config = AutoConfig.from_pretrained(config.model_path)
    config.context = get_context(config.model_config.model_type)
    ......

        with config.context.init_context:
            model = AutoModelForCausalLM.from_pretrained(config.model_path).to(config.device)
            tokenizer = AutoTokenizer.from_pretrained(config.model_path)
            if config.half:
                model = model.half()

    # ================== baseline ==================
    if "baseline" in config.pipeline:
        tprint("Baseline")
        from ..benchmarks import dataset2benchmark
        benchmark = dataset2benchmark[config.dataset]

        with config.context.baseline_context:
            score_baseline = benchmark(model, tokenizer, **(config.to_dict()))

class ContextList(list):

    # return value as the object initiator
    def __enter__(self):
        for item in self:
            item.__enter__()
        return self

    def __exit__(self, exc_type, exc_value, traceback):
        for item in self:
            item.__exit__(exc_type, exc_value, traceback)
        return False