dev(narugo): add device id

                         f'but unsupported provider {provider!r} found.')

def _open_onnx_model(ckpt: str, provider: str, use_cpu: bool = True) -> InferenceSession:

def _open_onnx_model(ckpt: str, provider: str, use_cpu: bool = True,

                     cuda_device_id: Optional[int] = None) -> InferenceSession:

    options = SessionOptions()

    options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL

    if provider == "CPUExecutionProvider":

        options.intra_op_num_threads = os.cpu_count()

    if provider == 'CUDAExecutionProvider' and cuda_device_id is not None:

        providers = [

            ('CUDAExecutionProvider', {'device_id': cuda_device_id}),

        ]

    else:

        providers = [provider]

    if use_cpu and "CPUExecutionProvider" not in providers:

        providers.append("CPUExecutionProvider")

    return InferenceSession(ckpt, options, providers=providers)

def open_onnx_model(ckpt: str, mode: str = None) -> InferenceSession:

def open_onnx_model(ckpt: str, mode: str = None, cuda_device_id: Optional[int] = None) -> InferenceSession:

    """

    Overview:

        Open an ONNX model and load its ONNX runtime.

        on Linux, executing ``export ONNX_MODE=cpu`` will ignore any existing CUDA and force the model inference

        to run on CPU.

    """

    return _open_onnx_model(ckpt, get_onnx_provider(mode or os.environ.get('ONNX_MODE', None)))

    return _open_onnx_model(

        ckpt=ckpt,

        provider=get_onnx_provider(mode or os.environ.get('ONNX_MODE', None)),

        use_cpu=True,

        cuda_device_id=cuda_device_id,

    )