def METHOD_NAME(args: argparse.Namespace) -> None:
    filename = f"{args.dataset}-{args.model}-partial-report.json"
    df = pd.read_json(os.path.join("partial", filename))
    df["best_value"] = df.groupby(["opt", "uuid"]).generalization.cummin()
    summaries = (
        df.groupby(["opt", "iter"])
        .best_value.agg(["mean", "std"])
        .rename(columns={"mean": "best_mean", "std": "best_std"})
        .reset_index()
    )
    fig, ax = plt.subplots()
    fig.set_size_inches(12, 8)
    warmup = json.loads(args.plot_warmup)
    metric = df.metric[0]
    color_lookup = build_color_dict(sorted(df["opt"].unique()))
    for optimizer, summary in summaries.groupby("opt"):
        color = color_lookup[optimizer]
        make_plot(summary, ax, optimizer, metric, warmup, color)
    handles, labels = ax.get_legend_handles_labels()
    fig.legend(handles, labels)
    fig.suptitle(f"Bayesmark-{args.dataset.capitalize()}-{args.model}")
    fig.savefig(os.path.join("plots", f"optuna-{args.dataset}-{args.model}-sumamry.png"))