code: def _serve_mleap():
      serve_cmd = [
          "java",
          "-cp",
          '"/opt/java/jars/*"',
          "org.mlflow.sagemaker.ScoringServer",
          MODEL_PATH,
          str(DEFAULT_SAGEMAKER_SERVER_PORT),
      ]
      # Invoke `Popen` with a single string command in the shell to support wildcard usage
      # with the mlflow jar version.
      serve_cmd = " ".join(serve_cmd)
      mleap = Popen(serve_cmd, shell=True)
      signal.signal(
          signal.SIGTERM,
          lambda a, b: _sigterm_handler(pids=[mleap.pid]),
      )
      awaited_pids = _await_subprocess_exit_any(procs=[mleap])
      _sigterm_handler(awaited_pids)
  
imports: import logging
  import multiprocessing
  import os
  import shutil
  import signal
  import sys
  from pathlib import Path
  from subprocess import Popen, check_call
  import mlflow
  import mlflow.version
  from mlflow import mleap, pyfunc
  from mlflow.environment_variables import MLFLOW_DEPLOYMENT_FLAVOR_NAME, MLFLOW_DISABLE_ENV_CREATION
  from mlflow.models import Model
  from mlflow.models.model import MLMODEL_FILE_NAME
  from mlflow.pyfunc import _extract_conda_env, mlserver, scoring_server
  from mlflow.store.artifact.models_artifact_repo import REGISTERED_MODEL_META_FILE_NAME
  from mlflow.utils import env_manager
  from mlflow.utils.file_utils import read_yaml
  from mlflow.utils.virtualenv import _get_or_create_virtualenv
  from mlflow.version import VERSION
label_desc: Use image node_id 6 for calling a global function with example usage: _serve_mleap() without return types