A data scientist logged a scikit-learn model to an MLflow run using autologging. A downstream engineer now needs to load that logged model and generate predictions on a new pandas DataFrame in a separate notebook, without needing to know which underlying ML framework was used. Which approach should the engineer use to load the model in the most framework-agnostic way?