def METHOD_NAME(self):
    N = 1000
    BIG_VAL = 100000
    _values = list(get_values(lo=-BIG_VAL, hi=BIG_VAL, n=N))
    values = torch.tensor(_values, dtype=torch.float64)
    stats_to_test = [
        Mean(),
        Var(),
        Var(order=1),
        StdDev(),
        StdDev(order=1),
        Min(),
        Max(),
        Sum(),
        MSE(),
    ]
    stat_names = [
        "mean",
        "variance",
        "sample_variance",
        "std_dev",
        "sample_std_dev",
        "min",
        "max",
        "sum",
        "mse",
    ]
    gt_fns = [
        torch.mean,
        lambda x: torch.var(x, unbiased=False),
        lambda x: torch.var(x, unbiased=True),
        lambda x: torch.std(x, unbiased=False),
        lambda x: torch.std(x, unbiased=True),
        torch.min,
        torch.max,
        torch.sum,
        lambda x: torch.sum((x - torch.mean(x)) ** 2),
    ]
    for stat, name, gt in zip(stats_to_test, stat_names, gt_fns):
        summ = Summarizer([stat])
        actual = gt(values)
        for x in values:
            summ.update(x)
        stat_val = summ.summary[name]
        # rounding errors is a serious issue (moreso for MSE)
        assertTensorAlmostEqual(self, stat_val, actual, delta=0.005)