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

    Args:
        preprocessor: ([`PreTrainedTokenizer`] or [`FeatureExtractionMixin`]):
            The preprocessor used for encoding the data.
        model ([`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.

    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.
    """
    import onnx
    import tensorflow as tf
    import tf2onnx

    if isinstance(preprocessor, PreTrainedTokenizerBase) and tokenizer is not None:
        raise ValueError("You cannot provide both a tokenizer and 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

    model.config.return_dict = True

    # Check if we need to override certain configuration item
    if config.values_override is not None:
        logger.info(f"Overriding {len(config.values_override)} configuration item(s)")
        for override_config_key, override_config_value in config.values_override.items():
            logger.info(f"\t- {override_config_key} -> {override_config_value}")
            setattr(model.config, override_config_key, override_config_value)

    # Ensure inputs match
    model_inputs = config.generate_dummy_inputs(preprocessor, framework=TensorType.TENSORFLOW)
    inputs_match, matched_inputs = ensure_model_and_config_inputs_match(model, model_inputs.keys())
    onnx_outputs = list(config.outputs.keys())

    input_signature = [
        tf.TensorSpec([None] * tensor.ndim, dtype=tensor.dtype, name=key) for key, tensor in model_inputs.items()
    ]
    onnx_model, _ = tf2onnx.convert.from_keras(model, input_signature, opset=opset)
    onnx.save(onnx_model, output.as_posix())
    config.restore_ops()

    return matched_inputs, onnx_outputs

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

    Args:
        preprocessor: ([`PreTrainedTokenizer`] or [`FeatureExtractionMixin`]):
            The preprocessor used for encoding the data.
        model ([`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.

    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