def export(
    preprocessor: Union["PreTrainedTokenizer", "FeatureExtractionMixin", "ProcessorMixin"],
    model: Union["PreTrainedModel", "TFPreTrainedModel"],
    config: OnnxConfig,
    opset: int,
    output: Path,
    tokenizer: "PreTrainedTokenizer" = None,
    device: str = "cpu",
) -> Tuple[List[str], List[str]]:
    """
    Export a Pytorch or TensorFlow model to an ONNX Intermediate Representation (IR)

    Args:
        preprocessor: ([`PreTrainedTokenizer`], [`FeatureExtractionMixin`] or [`ProcessorMixin`]):
            The preprocessor used for encoding the data.
        model ([`PreTrainedModel`] or [`TFPreTrainedModel`]):
            The model to export.
        config ([`~onnx.config.OnnxConfig`]):
            The ONNX configuration associated with the exported model.
        opset (`int`):
            The version of the ONNX operator set to use.
        output (`Path`):
            Directory to store the exported ONNX model.
        device (`str`, *optional*, defaults to `cpu`):
            The device on which the ONNX model will be exported. Either `cpu` or `cuda`. Only PyTorch is supported for
            export on CUDA devices.

    Returns:
        `Tuple[List[str], List[str]]`: A tuple with an ordered list of the model's inputs, and the named inputs from
        the ONNX configuration.
    """
    if not (is_torch_available() or is_tf_available()):
        raise ImportError(
            "Cannot convert because neither PyTorch nor TensorFlow are not installed. "
            "Please install torch or tensorflow first."
        )

    if is_tf_available() and isinstance(model, TFPreTrainedModel) and device == "cuda":
        raise RuntimeError("`tf2onnx` does not support export on CUDA device.")

    if isinstance(preprocessor, PreTrainedTokenizerBase) and tokenizer is not None:
        raise ValueError("You cannot provide both a tokenizer and a preprocessor to export the model.")
    if tokenizer is not None:
        warnings.warn(
            "The `tokenizer` argument is deprecated and will be removed in version 5 of Transformers. Use"
            " `preprocessor` instead.",
            FutureWarning,
        )
        logger.info("Overwriting the `preprocessor` argument with `tokenizer` to generate dummmy inputs.")
        preprocessor = tokenizer

    if is_torch_available():
        from ..utils import get_torch_version

        if not config.is_torch_support_available:
            logger.warning(
                f"Unsupported PyTorch version for this model. Minimum required is {config.torch_onnx_minimum_version},"
                f" got: {get_torch_version()}"
            )

    if is_torch_available() and issubclass(type(model), PreTrainedModel):
        return export_pytorch(preprocessor, model, config, opset, output, tokenizer=tokenizer, device=device)
    elif is_tf_available() and issubclass(type(model), TFPreTrainedModel):
        return export_tensorflow(preprocessor, model, config, opset, output, tokenizer=tokenizer)

    Export a Pytorch or TensorFlow model to an ONNX Intermediate Representation (IR)

    Args:
        preprocessor: ([`PreTrainedTokenizer`], [`FeatureExtractionMixin`] or [`ProcessorMixin`]):
            The preprocessor used for encoding the data.
        model ([`PreTrainedModel`] or [`TFPreTrainedModel`]):
            The model to export.
        config ([`~onnx.config.OnnxConfig`]):
            The ONNX configuration associated with the exported model.
        opset (`int`):
            The version of the ONNX operator set to use.
        output (`Path`):
            Directory to store the exported ONNX model.
        device (`str`, *optional*, defaults to `cpu`):
            The device on which the ONNX model will be exported. Either `cpu` or `cuda`. Only PyTorch is supported for
            export on CUDA devices.

    Returns:
        `Tuple[List[str], List[str]]`: A tuple with an ordered list of the model's inputs, and the named inputs from
        the ONNX configuration.
    
dpkd/transformers/src/transformers/onnx/convert.py