code: def main():
      args = parse_args()
      model_path = args.model_path
      flavor = args.flavor
      output_file = args.output_file
      # Mirror `sys.path` of the parent process
      sys.path = json.loads(args.sys_path)
  
      if flavor == mlflow.spark.FLAVOR_NAME:
          # Create a local spark environment within the subprocess
          from mlflow.utils._spark_utils import (
              _create_local_spark_session_for_loading_spark_model,
          )
  
          _prepare_subprocess_environ_for_creating_local_spark_session()
          _create_local_spark_session_for_loading_spark_model()
  
      cap_cm = _CaptureImportedModules()
      store_imported_modules(cap_cm, model_path, flavor, output_file)
  
      # Clean up a spark session created by `mlflow.spark._load_pyfunc`
      if flavor == mlflow.spark.FLAVOR_NAME:
          from mlflow.utils._spark_utils import (
              _get_active_spark_session,
          )
  
          spark = _get_active_spark_session()
          if spark:
              try:
                  spark.stop()
              except Exception:
                  # Swallow unexpected exceptions
                  pass
  
imports: import argparse
  import builtins
  import functools
  import importlib
  import json
  import os
  import sys
  import mlflow
  from mlflow.models.model import MLMODEL_FILE_NAME, Model
  from mlflow.pyfunc import MAIN
  from mlflow.utils._spark_utils import _prepare_subprocess_environ_for_creating_local_spark_session
  from mlflow.utils.file_utils import write_to
  from mlflow.utils.requirements_utils import DATABRICKS_MODULES_TO_PACKAGES, MLFLOW_MODULES_TO_PACKAGES
label_desc: Use image node_id 5 for calling a global function with example usage: main() without return types