code: def _init(cmd, env_manager):
      """
      Initialize the container and execute command.
  
      :param cmd: Command param passed by Sagemaker. Can be  "serve" or "train" (unimplemented).
      """
      if cmd == "serve":
          _serve(env_manager)
      elif cmd == "train":
          _train()
      else:
          raise Exception(
              f"Unrecognized command {cmd}, full args = {sys.argv}"
          )
  
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 1 for calling a global function with example usage: _init(cmd, env_manager) without return types