def METHOD_NAME(run_id: int):
    run = runs.get_run(int(run_id), ignore_cache=True)
    df = pd.DataFrame(run.fold_evaluations.items(), columns=["evaluations", "results"])
    # Evaluations table
    result_list = []
    result_string = []
    for result in df["results"]:
        k_folds = list(result[0].values())
        mean = str(np.round(np.mean(np.array(k_folds)), 3))
        std = str(np.round(np.std(np.array(k_folds)), 3))
        result_list.append(k_folds)
        result_string.append(mean + " \u00B1 " + std)
    df.drop(["results"], axis=1, inplace=True)
    df["results"] = result_list
    df["values"] = result_string
    # Add some more rows indicating output prediction file name
    df2 = pd.DataFrame(run.output_files.items(), columns=["evaluations", "results"])
    df2["values"] = ""
    df3 = pd.DataFrame(
        {"task_type": run.task_type}.items(), columns=["evaluations", "results"]
    )
    df2["values"] = ""
    df = df.append(df2)
    df = df.append(df3)
    df.to_pickle("cache/run" + str(run_id) + ".pkl")
    return run, df