code: def convert_tensorflow(nlp: Pipeline, opset: int, output: Path):
      """
      Export a TensorFlow backed pipeline to ONNX Intermediate Representation (IR)
  
      Args:
          nlp: The pipeline to be exported
          opset: The actual version of the ONNX operator set to use
          output: Path where will be stored the generated ONNX model
  
      Notes: TensorFlow cannot export model bigger than 2GB due to internal constraint from TensorFlow
  
      """
      if not is_tf_available():
          raise Exception(
              "Cannot convert because TF is not installed. Please install tensorflow first."
          )
  
      print(
          "/!\\ Please note TensorFlow doesn't support exporting model > 2Gb /!\\"
      )
  
      try:
          import tensorflow as tf
  
          import tf2onnx
          from tf2onnx import __version__ as t2ov
  
          print(
              f"Using framework TensorFlow: {tf.version.VERSION}, tf2onnx: {t2ov}"
          )
  
          # Build
          (
              input_names,
              output_names,
              dynamic_axes,
              tokens,
          ) = infer_shapes(nlp, "tf")
  
          # Forward
          nlp.model.predict(tokens.data)
          input_signature = [
              tf.TensorSpec.from_tensor(tensor, name=key)
              for key, tensor in tokens.items()
          ]
          model_proto, _ = tf2onnx.convert.from_keras(
              nlp.model,
              input_signature,
              opset=opset,
              output_path=output.as_posix(),
          )
  
      except ImportError as e:
          raise Exception(
              f"Cannot import {e.name} required to convert TF model to ONNX. Please install {e.name} first. {e}"
          )
  
imports: import warnings
  from argparse import ArgumentParser
  from os import listdir, makedirs
  from pathlib import Path
  from typing import Dict, List, Optional, Tuple
  from packaging.version import Version, parse
  from transformers.pipelines import Pipeline, pipeline
  from transformers.tokenization_utils import BatchEncoding
  from transformers.utils import ModelOutput, is_tf_available, is_torch_available
label_desc: Use image node_id 7 for calling a global function with example usage: convert_tensorflow(nlp, opset, output) without return types