def METHOD_NAME():
    # import validation data and create dataframe here as well
    # random data subset will change every time, but should be fine
    csv_data = pd.read_csv(os.path.join(this_file_dir(), "reformer-data.csv"))
    data = csv_data.sample(n=100)  # randomly sample points for validation
    input_data = data.iloc[:, :2]
    input_labels = input_data.columns
    n_data = data[input_labels[0]].size  # size = 100
    data_training, METHOD_NAME = split_training_validation(data, 0.8, seed=n_data)
    return METHOD_NAME