code: def _validate_pyfunc_model_config(model_config):
      """
      Validates the values passes in the model_config section. There are no typing
      restrictions but we require them being JSON-serializable.
      """
  
      if not model_config:
          return
  
      if not isinstance(model_config, dict) or not all(
          isinstance(key, str) for key in model_config
      ):
          raise MlflowException(
              "An invalid ``model_config`` structure was passed. ``model_config`` must be of type "
              "``dict`` with string keys."
          )
  
      try:
          json.dumps(model_config)
      except (TypeError, OverflowError):
          raise MlflowException(
              "Values in the provided ``model_config`` are of an unsupported type. Only "
              "JSON-serializable data types can be provided as values."
          )
  
imports: import json
  import os
  import sys
  from typing import Any, Dict
  from mlflow.exceptions import MlflowException
  from mlflow.models import Model
  from mlflow.models.model import MLMODEL_FILE_NAME
  from mlflow.protos.databricks_pb2 import RESOURCE_ALREADY_EXISTS, RESOURCE_DOES_NOT_EXIST
  from mlflow.store.artifact.artifact_repository_registry import get_artifact_repository
  from mlflow.store.artifact.models_artifact_repo import ModelsArtifactRepository
  from mlflow.store.artifact.runs_artifact_repo import RunsArtifactRepository
  from mlflow.tracking.artifact_utils import _download_artifact_from_uri
  from mlflow.utils.file_utils import _copy_file_or_tree
  from mlflow.utils.uri import append_to_uri_path
label_desc: Use image node_id 11 for calling a global function with example usage: _validate_pyfunc_model_config(model_config) without return types