code: def _serve(env_manager):
      """
      Serve the model.
  
      Read the MLmodel config, initialize the Conda environment if needed and start python server.
      """
      model_config_path = os.path.join(MODEL_PATH, MLMODEL_FILE_NAME)
      m = Model.load(model_config_path)
  
      # Older versions of mlflow may not specify a deployment configuration
      serving_flavor = (
          MLFLOW_DEPLOYMENT_FLAVOR_NAME.get() or pyfunc.FLAVOR_NAME
      )
  
      if serving_flavor == mleap.FLAVOR_NAME:
          _serve_mleap()
      elif pyfunc.FLAVOR_NAME in m.flavors:
          _serve_pyfunc(m, env_manager)
      else:
          raise Exception(
              "This container only supports models with the MLeap or PyFunc flavors."
          )
  
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 2 for calling a global function with example usage: _serve(env_manager) without return types