def METHOD_NAME(self):
    for _ in range(4):
        probs,probs2 = np.random.uniform(0,1,(10)), np.random.uniform(0,1,(10))
        probs,probs2 = probs / probs.sum(),probs2 / probs2.sum()
        jc, jc2 = jd.Categorical(jt.array(probs)),jd.Categorical(jt.array(probs2))
        tc, tc2 = torch.distributions.Categorical(torch.tensor(probs)),torch.distributions.Categorical(torch.tensor(probs2))
        assert np.allclose(jc.entropy().data, tc.entropy().numpy()), (jc.entropy().data, tc.entropy().numpy())
        x = np.random.randint(0,10,(4))
        np.testing.assert_allclose(jc.log_prob(x), tc.log_prob(torch.tensor(x)), atol=1e-5)
        assert np.allclose(jd.kl_divergence(jc,jc2),torch.distributions.kl_divergence(tc,tc2))