def METHOD_NAME():
    true_mean = torch.randn([1, 2, 3])
    samples, weights = [], []
    for i in range(10):
        samples.append(true_mean)
        weights.append(torch.tensor(-1000.0))
    samples, weights = torch.stack(samples), torch.stack(weights)
    empirical_dist = Empirical(samples, weights)
    assert_equal(empirical_dist.mean, true_mean)
    assert_equal(empirical_dist.variance, torch.zeros_like(true_mean))