code: def convert_tf_saved_model_to_onnx(
      model: tf.Module, output_file_path: Union[str, Path]
  ):
      """Convert TF models into ONNX.
      Args:
          model (tf.Module): TF model.
          output_file_path (Path): Path where storing the output file.
      """
      with TemporaryDirectory() as temp_dir:
          tf.saved_model.save(model, export_dir=temp_dir)
  
          try:
              subprocess.check_output(["python3", "--version"])
              python_cmd = "python3"
          except subprocess.CalledProcessError:
              python_cmd = "python"
  
          onnx_cmd = [
              python_cmd,
              "-m",
              "tf2onnx.convert",
              "--saved-model",
              f"{temp_dir}",
              "--output",
              f"{output_file_path}",
              "--opset",
              f"{ONNX_OPSET_VERSION}",
          ]
          subprocess.run(onnx_cmd)
          onnx.load(output_file_path)
  
      return output_file_path
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/conversions/tensorflow.py
imports: import subprocess
  from pathlib import Path
  from tempfile import TemporaryDirectory
  from typing import Union
  from loguru import logger
  from nebullvm.config import ONNX_OPSET_VERSION
  from nebullvm.core.models import ModelParams
  from nebullvm.optional_modules.tensorflow import tensorflow, tf2onnx
  from nebullvm.optional_modules.onnx import onnx
  from nebullvm.tools.huggingface import TensorFlowTransformerWrapper
label_desc: Use image node_id 2 for calling a global function with example usage: convert_tf_saved_model_to_onnx(model, output_file_path) and returns: output_file_path