class_docstr: A collection of artifacts that a :class:`~PythonModel` can use when performing inference.
  :class:`~PythonModelContext` objects are created *implicitly* by the
  :func:`save_model() <mlflow.pyfunc.save_model>` and
  :func:`log_model() <mlflow.pyfunc.log_model>` persistence methods, using the contents specified
  by the ``artifacts`` parameter of these methods.
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docstr: A dictionary containing ``<config, value>`` entries, where ``config`` is the name
  of the model configuration keys and ``value`` is the value of the given configuration.
code: def model_config(self):
      """
      A dictionary containing ``<config, value>`` entries, where ``config`` is the name
      of the model configuration keys and ``value`` is the value of the given configuration.
      """
  
      return self._model_config
  
imports: import inspect
  import logging
  import os
  import shutil
  from abc import ABCMeta, abstractmethod
  from pathlib import Path
  from typing import Any, Dict, List, Optional
  import cloudpickle
  import yaml
  import mlflow.pyfunc
  import mlflow.utils
  from mlflow.exceptions import MlflowException
  from mlflow.models import Model
  from mlflow.models.model import MLMODEL_FILE_NAME
  from mlflow.models.signature import _extract_type_hints
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.annotations import experimental
  from mlflow.utils.environment import _CONDA_ENV_FILE_NAME, _CONSTRAINTS_FILE_NAME, _PYTHON_ENV_FILE_NAME, _REQUIREMENTS_FILE_NAME, _mlflow_conda_env, _process_conda_env, _process_pip_requirements, _PythonEnv
  from mlflow.utils.file_utils import TempDir, _copy_file_or_tree, get_total_file_size, write_to
  from mlflow.utils.model_utils import _get_flavor_configuration
  from mlflow.utils.requirements_utils import _get_pinned_requirement
label_desc: Use image node_id 3 for calling the PythonModelContext obj's underlying member method code with example usage: obj.model_config() and returns: self