code: def prepare_for_torchserve(
      model_path: str,
      shared_libs: List[str],
      dependencies: dict,
      session: Session,
      image_uri: str,
      inference_spec: InferenceSpec = None,
  ) -> str:
      """This is a one-line summary of the function.
  
      Args:to
          model_path (str) : Argument
          shared_libs (List[]) : Argument
          dependencies (dict) : Argument
          session (Session) : Argument
          inference_spec (InferenceSpec, optional) : Argument
              (default is None)
  
      Returns:
          ( str ) :
  
      """
      model_path = Path(model_path)
      if not model_path.exists():
          model_path.mkdir()
      elif not model_path.is_dir():
          raise Exception("model_dir is not a valid directory")
  
      if inference_spec:
          inference_spec.prepare(str(model_path))
  
      code_dir = model_path.joinpath("code")
      code_dir.mkdir(exist_ok=True)
      # https://github.com/aws/sagemaker-python-sdk/issues/4288
      if (
          is_1p_image_uri(image_uri=image_uri)
          and "xgboost" in image_uri
      ):
          shutil.copy2(
              Path(__file__).parent.joinpath("xgboost_inference.py"),
              code_dir,
          )
          os.rename(
              str(code_dir.joinpath("xgboost_inference.py")),
              str(code_dir.joinpath("inference.py")),
          )
      else:
          shutil.copy2(
              Path(__file__).parent.joinpath("inference.py"), code_dir
          )
  
      shared_libs_dir = model_path.joinpath("shared_libs")
      shared_libs_dir.mkdir(exist_ok=True)
      for shared_lib in shared_libs:
          shutil.copy2(Path(shared_lib), shared_libs_dir)
  
      capture_dependencies(dependencies=dependencies, work_dir=code_dir)
  
      secret_key = generate_secret_key()
      with open(str(code_dir.joinpath("serve.pkl")), "rb") as f:
          buffer = f.read()
      hash_value = compute_hash(buffer=buffer, secret_key=secret_key)
      with open(
          str(code_dir.joinpath("metadata.json")), "wb"
      ) as metadata:
          metadata.write(_MetaData(hash_value).to_json())
  
      return secret_key
  
file_path: sagemaker-python-sdk/src/sagemaker/serve/model_server/torchserve/prepare.py
imports: from __future__ import absolute_import
  import os
  from pathlib import Path
  import shutil
  from typing import List
  from sagemaker.session import Session
  from sagemaker.serve.spec.inference_spec import InferenceSpec
  from sagemaker.serve.detector.dependency_manager import capture_dependencies
  from sagemaker.serve.validations.check_integrity import generate_secret_key, compute_hash
  from sagemaker.serve.validations.check_image_uri import is_1p_image_uri
  from sagemaker.remote_function.core.serialization import _MetaData
label_desc: Use image node_id 1 for calling a global function with example usage: prepare_for_torchserve(model_path, shared_libs, dependencies, session, image_uri, inference_spec) and returns: secret_key