code: def prepare_for_djl_serving(
      model_path: str,
      model: DJLModel,
      shared_libs: List[str] = None,
      dependencies: str = None,
      overwrite_props_from_file: bool = True,
      manual_set_props: dict = None,
  ):
      """Prepare serving when a HF model id is given
  
      Args:to
          model_path (str) : Argument
          model (DJLModel) : Argument
          shared_libs (List[]) : Argument
          dependencies (str) : Argument
  
      Returns:
          ( str ) :
  
      """
      model_path, code_dir = _create_dir_structure(model_path)
  
      if shared_libs:
          _store_share_libs(model_path, shared_libs)
  
      _copy_inference_script(code_dir)
  
      _generate_properties_file(
          model, code_dir, overwrite_props_from_file, manual_set_props
      )
  
imports: from __future__ import absolute_import
  import shutil
  import json
  import tarfile
  import logging
  from typing import List
  from pathlib import Path
  from sagemaker.utils import _tmpdir
  from sagemaker.s3 import S3Downloader
  from sagemaker.djl_inference import DJLModel
  from sagemaker.djl_inference.model import _read_existing_serving_properties
  from sagemaker.serve.utils.local_hardware import _check_disk_space, _check_docker_disk_usage
label_desc: Use image node_id 9 for calling a global function with example usage: prepare_for_djl_serving(model_path, model, shared_libs, dependencies, overwrite_props_from_file, manual_set_props) without return types