code: def _get_python_env_file(model_config):
      from mlflow.pyfunc import EnvType
  
      for flavor, config in model_config.flavors.items():
          if flavor == mlflow.pyfunc.FLAVOR_NAME:
              env = config.get(mlflow.pyfunc.ENV)
              if isinstance(env, dict):
                  # Models saved in MLflow >= 2.0 use a dictionary for the pyfunc flavor
                  # `env` config, where the keys are different environment managers (e.g.
                  # conda, virtualenv) and the values are corresponding environment paths
                  return env[EnvType.VIRTUALENV]
      return _PYTHON_ENV_FILE_NAME
  
imports: import logging
  import os
  import re
  import shutil
  import sys
  import tempfile
  import uuid
  from pathlib import Path
  from packaging.version import Version
  import mlflow
  from mlflow.environment_variables import MLFLOW_ENV_ROOT
  from mlflow.exceptions import MlflowException
  from mlflow.models.model import MLMODEL_FILE_NAME, Model
  from mlflow.utils.conda import _PIP_CACHE_DIR
  from mlflow.utils.databricks_utils import is_in_databricks_runtime
  from mlflow.utils.environment import _CONDA_ENV_FILE_NAME, _PYTHON_ENV_FILE_NAME, _REQUIREMENTS_FILE_NAME, _get_mlflow_env_name, _get_pip_install_mlflow, _PythonEnv
  from mlflow.utils.file_utils import remove_on_error
  from mlflow.utils.process import _IS_UNIX, _exec_cmd, _join_commands
  from mlflow.utils.requirements_utils import _parse_requirements
label_desc: Use image node_id 10 for calling a global function with example usage: _get_python_env_file(model_config) and returns: _PYTHON_ENV_FILE_NAME, env