def METHOD_NAME(config: SampleConfig) -> str:
    dataset = config.get("dataset", "imagenet2012")
    retval = config["model"] + "_" + dataset
    compression_config = config.get("compression", [])
    if not isinstance(compression_config, list):
        compression_config = [
            compression_config,
        ]
    for algo_dict in compression_config:
        algo_name = algo_dict["algorithm"]
        if algo_name == "quantization":
            initializer = algo_dict.get("initializer", {})
            precision = initializer.get("precision", {})
            if precision:
                retval += "_mixed_int"
            else:
                activations = algo_dict.get("activations", {})
                a_bits = activations.get("bits", QUANTIZATION_BITS)
                weights = algo_dict.get("weights", {})
                w_bits = weights.get("bits", QUANTIZATION_BITS)
                if a_bits == w_bits:
                    retval += "_int{}".format(a_bits)
                else:
                    retval += "_a_int{}_w_int{}".format(a_bits, w_bits)
        else:
            retval += "_{}".format(algo_name)
    return retval