class_docstr: Represents a generic Python model that evaluates inputs and produces API-compatible outputs.
  By subclassing :class:`~PythonModel`, users can create customized MLflow models with the
  "python_function" ("pyfunc") flavor, leveraging custom inference logic and artifact
  dependencies.
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docstr: Loads artifacts from the specified :class:`~PythonModelContext` that can be used by
  :func:`~PythonModel.predict` when evaluating inputs. When loading an MLflow model with
  :func:`~load_model`, this method is called as soon as the :class:`~PythonModel` is
  constructed.
  
  The same :class:`~PythonModelContext` will also be available during calls to
  :func:`~PythonModel.predict`, but it may be more efficient to override this method
  and load artifacts from the context at model load time.
  
  :param context: A :class:`~PythonModelContext` instance containing artifacts that the model
                  can use to perform inference.
code: def load_context(self, context):
      """
      Loads artifacts from the specified :class:`~PythonModelContext` that can be used by
      :func:`~PythonModel.predict` when evaluating inputs. When loading an MLflow model with
      :func:`~load_model`, this method is called as soon as the :class:`~PythonModel` is
      constructed.
  
      The same :class:`~PythonModelContext` will also be available during calls to
      :func:`~PythonModel.predict`, but it may be more efficient to override this method
      and load artifacts from the context at model load time.
  
      :param context: A :class:`~PythonModelContext` instance containing artifacts that the model
                      can use to perform inference.
      """
  
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 1 for calling the PythonModel obj's underlying member method code with example usage: obj.load_context(context) without return types