code: def mmdet2torchserve(
      config_file: str,
      checkpoint_file: str,
      output_folder: str,
      model_name: str,
      model_version: str = "1.0",
      force: bool = False,
  ):
      """Converts MMDetection model (config + checkpoint) to TorchServe `.mar`.
  
      Args:
          config_file:
              In MMDetection config format.
              The contents vary for each task repository.
          checkpoint_file:
              In MMDetection checkpoint format.
              The contents vary for each task repository.
          output_folder:
              Folder where `{model_name}.mar` will be created.
              The file created will be in TorchServe archive format.
          model_name:
              If not None, used for naming the `{model_name}.mar` file
              that will be created under `output_folder`.
              If None, `{Path(checkpoint_file).stem}` will be used.
          model_version:
              Model's version.
          force:
              If True, if there is an existing `{model_name}.mar`
              file under `output_folder` it will be overwritten.
      """
      mkdir_or_exist(output_folder)
  
      config = Config.fromfile(config_file)
  
      with TemporaryDirectory() as tmpdir:
          config.dump(f"{tmpdir}/config.py")
  
          args = Namespace(
              **{
                  "model_file": f"{tmpdir}/config.py",
                  "config_file": f"{tmpdir}/config.py",
                  "serialized_file": checkpoint_file,
                  "handler": f"{Path(__file__).parent}/mmdet_handler.py",
                  "model_name": model_name
                  or Path(checkpoint_file).stem,
                  "version": model_version,
                  "export_path": output_folder,
                  "force": force,
                  "requirements_file": None,
                  "extra_files": None,
                  "runtime": "python",
                  "archive_format": "default",
              }
          )
          manifest = ModelExportUtils.generate_manifest_json(args)
          package_model(args, manifest)
  
imports: from argparse import ArgumentParser, Namespace
  from pathlib import Path
  from tempfile import TemporaryDirectory
  from mmengine.config import Config
  from mmengine.utils import mkdir_or_exist
label_desc: Use image node_id 1 for calling a global function with example usage: mmdet2torchserve(config_file, checkpoint_file, output_folder, model_name, model_version, force) without return types