def METHOD_NAME(mvn_dist):
    loc = torch.tensor([2.0, 1.0, 1.0, 2.0, 2.0])
    D = torch.tensor([1.0, 2.0, 3.0, 1.0, 3.0])
    W = torch.tensor([[1.0, -1.0, 2.0, 2.0, 4.0], [2.0, 1.0, 1.0, 2.0, 6.0]])
    x = torch.tensor([2.0, 3.0, 4.0, 1.0, 7.0])
    L = D.diag() + torch.tril(W.t().matmul(W))
    cov = torch.mm(L, L.t())
    mvn = MultivariateNormal(loc, cov)
    if mvn_dist == OMTMultivariateNormal:
        mvn_prime = OMTMultivariateNormal(loc, L)
    elif mvn_dist == AVFMultivariateNormal:
        CV = 0.2 * torch.rand(2, 2, 5)
        mvn_prime = AVFMultivariateNormal(loc, L, CV)
    assert_equal(mvn.log_prob(x), mvn_prime.log_prob(x))