def METHOD_NAME():
    torch.manual_seed(0)
    act_size = 4
    dist_instance = GaussianDistInstance(
        torch.zeros(1, act_size), torch.ones(1, act_size)
    )
    action = dist_instance.sample()
    assert action.shape == (1, act_size)
    for log_prob in (
        dist_instance.log_prob(torch.zeros((1, act_size))).flatten().tolist()
    ):
        # Log prob of standard normal at 0
        assert log_prob == pytest.approx(-0.919, abs=0.01)
    for ent in dist_instance.entropy().flatten().tolist():
        # entropy of standard normal at 0, based on 1/2 + ln(sqrt(2pi)sigma)
        assert ent == pytest.approx(1.42, abs=0.01)