code: 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
  
file_path: transformers/src/transformers/onnx/convert.py
imports: import warnings
  from inspect import signature
  from itertools import chain
  from pathlib import Path
  from typing import TYPE_CHECKING, Iterable, List, Tuple, Union
  import numpy
  from packaging.version import Version, parse
  from ..tokenization_utils_base import PreTrainedTokenizerBase
  from ..utils import TensorType, is_tf_available, is_torch_available, logging
  from .config import OnnxConfig
label_desc: Use image node_id 3 for calling a global function with example usage: export_tensorflow(preprocessor, model, config, opset, output, tokenizer) and returns: matched_inputs,  onnx_outputs