def METHOD_NAME(bento_model: Model):
    """
    Private API: use :obj:`~bentoml.Model.to_runnable` instead.
    """
    class SklearnRunnable(bentoml.Runnable):
        SUPPORTED_RESOURCES = ("cpu",)
        SUPPORTS_CPU_MULTI_THREADING = True
        def __init__(self):
            super().__init__()
            self.model = load_model(bento_model)
    def add_runnable_method(method_name: str, options: ModelSignature):
        def _run(
            self: SklearnRunnable, input_data: ext.NpNDArray | ext.PdDataFrame
        ) -> ext.NpNDArray:
            # TODO: set inner_max_num_threads and n_jobs param here base on strategy env vars
            with parallel_backend(backend="loky"):
                return getattr(self.model, method_name)(input_data)
        SklearnRunnable.add_method(
            _run,
            name=method_name,
            batchable=options.batchable,
            batch_dim=options.batch_dim,
            input_spec=options.input_spec,
            output_spec=options.output_spec,
        )
    for method_name, options in bento_model.info.signatures.items():
        add_runnable_method(method_name, options)
    return SklearnRunnable