code: def _pyfunc_entrypoint(
      env_manager, model_uri, install_mlflow, enable_mlserver
  ):
      if model_uri:
          # The pyfunc image runs the same server as the Sagemaker image
          pyfunc_entrypoint = (
              'ENTRYPOINT ["python", "-c", "from mlflow.models import container as C;'
              f'C._serve({env_manager!r})"]'
          )
      else:
          entrypoint_code = "; ".join(
              [
                  "from mlflow.models import container as C",
                  "from mlflow.models.container import _install_pyfunc_deps",
                  (
                      "_install_pyfunc_deps("
                      + '"/opt/ml/model", '
                      + f"install_mlflow={install_mlflow}, "
                      + f"enable_mlserver={enable_mlserver}, "
                      + f'env_manager="{env_manager}"'
                      + ")"
                  ),
                  f'C._serve("{env_manager}")',
              ]
          )
          pyfunc_entrypoint = (
              'ENTRYPOINT ["python", "-c", "{entrypoint_code}"]'.format(
                  entrypoint_code=entrypoint_code.replace('"', '\\"')
              )
          )
  
      return pyfunc_entrypoint
  
imports: import ctypes
  import logging
  import os
  import pathlib
  import posixpath
  import shlex
  import signal
  import subprocess
  import sys
  import warnings
  from pathlib import Path
  from mlflow.environment_variables import MLFLOW_DISABLE_ENV_CREATION
  from mlflow.exceptions import MlflowException
  from mlflow.models import FlavorBackend
  from mlflow.models.container import ENABLE_MLSERVER
  from mlflow.models.docker_utils import SETUP_MINICONDA, SETUP_PYENV_AND_VIRTUALENV, _build_image, _generate_dockerfile_content, _get_mlflow_install_step
  from mlflow.pyfunc import ENV, _extract_conda_env, _mlflow_pyfunc_backend_predict, mlserver, scoring_server
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils import env_manager
  from mlflow.utils.conda import get_conda_bin_executable, get_or_create_conda_env
  from mlflow.utils.environment import Environment
  from mlflow.utils.file_utils import get_or_create_nfs_tmp_dir, get_or_create_tmp_dir, path_to_local_file_uri
  from mlflow.utils.nfs_on_spark import get_nfs_cache_root_dir
  from mlflow.utils.process import ShellCommandException, cache_return_value_per_process
  from mlflow.utils.virtualenv import _get_or_create_virtualenv, _get_pip_install_mlflow
  from mlflow.version import VERSION
label_desc: Use image node_id 1 for calling a global function with example usage: _pyfunc_entrypoint(env_manager, model_uri, install_mlflow, enable_mlserver) and returns: pyfunc_entrypoint