code: def model_fn(model_dir):
      """Placeholder docstring"""
      shared_libs_path = Path(model_dir + "/shared_libs")
  
      if shared_libs_path.exists():
          # before importing, place dynamic linked libraries in shared lib path
          shutil.copytree(shared_libs_path, "/lib", dirs_exist_ok=True)
  
      serve_path = Path(__file__).parent.joinpath("serve.pkl")
      with open(str(serve_path), mode="rb") as file:
          global inference_spec, native_model, schema_builder
          obj = cloudpickle.load(file)
          if isinstance(obj[0], InferenceSpec):
              inference_spec, schema_builder = obj
          elif isinstance(obj[0], str) and obj[0] == "xgboost":
              model_class_name = os.getenv("MODEL_CLASS_NAME")
              model_save_path = Path(__file__).parent.joinpath(
                  "model.json"
              )
              native_model = load_xgboost_from_json(
                  model_save_path=str(model_save_path),
                  class_name=model_class_name,
              )
              schema_builder = obj[1]
          else:
              native_model, schema_builder = obj
      if native_model:
          framework, _ = _detect_framework_and_version(
              model_base=str(_get_model_base(model=native_model))
          )
          if framework == "pytorch":
              native_model.eval()
          return (
              native_model
              if callable(native_model)
              else native_model.predict
          )
      elif inference_spec:
          return partial(
              inference_spec.invoke,
              model=inference_spec.load(model_dir),
          )
  
file_path: sagemaker-python-sdk/src/sagemaker/serve/model_server/torchserve/inference.py
imports: from __future__ import absolute_import
  import os
  import io
  import cloudpickle
  import shutil
  import platform
  from pathlib import Path
  from functools import partial
  from sagemaker.serve.validations.check_integrity import perform_integrity_check
  from sagemaker.serve.spec.inference_spec import InferenceSpec
  from sagemaker.serve.detector.image_detector import _detect_framework_and_version, _get_model_base
  from sagemaker.serve.detector.pickler import load_xgboost_from_json
  import logging
label_desc: Use image node_id 1 for calling a global function with example usage: model_fn(model_dir) and returns: unknown, partial