def METHOD_NAME(
    cls, name: str, force_refresh: bool = False, **kwargs: Any
) -> ModelTracker:
    """
    Loads the data associated with a set of training jobs run on AWS
    Sagemaker. The tracker is cached such that it does not need to re-
    download data in case no new evaluations are available.
    Args:
        name: The name of the experiment.
        force_refresh: Whether to download experiment data even if a tracker is available
            locally. This ensures that new data is fetched.
        kwargs: Additional properties that may be passed to the initializer of the tracker.
    Returns:
        The tracker with all the available data.
    """
    # Generate the filename including all kwargs
    kwargs_suffix = "-".join(
        f"{k}_{v}" for k, v in sorted(kwargs.items(), key=lambda i: i[0])
    )
    if len(kwargs_suffix) > 0:
        kwargs_suffix = f"+{kwargs_suffix}"
    # If available in cache, return
    cache = (
        Path.home()
        / ".cache"
        / "tsbench"
        / f"experiment-{name}{kwargs_suffix}.pickle"
    )
    if cache.exists() and not force_refresh:
        with cache.open("rb") as f:
            return pickle.load(f)
    # Initialize connection to AWS
    analysis = aws.Analysis(name)
    assert all(
        job.status == "Completed" for job in analysis
    ), "Not all jobs have completed."
    # Initialize tracker
    jobs = load_jobs_from_analysis(analysis)
    tracker = ModelTracker(jobs, **kwargs)
    # Cache tracker and return
    cache.parent.mkdir(parents=True, exist_ok=True)
    with cache.open("wb+") as f:
        pickle.dump(tracker, f)
    return tracker