Is it possible to get `log_prob`

estimates of samples from a trained autoregressive flow, e.g. AutoBNAFNormal? I understand that in the literature the inverse Jacobian of the BNAF was not available. From 1904.0467:

…note that, while B-NAFs and NAFs are invertible in principle, their inverses are not available in closed form.

Further, when attempting to do this in numpyro, I get the error: `NotImplementedError: Block neural autoregressive transform does not have an analytic inverse implemented.`

One of the benefits of (general) NFs are tractable densities – I am wondering since it seems this is not true for autoregressive flows, if it is otherwise possible to get exact densities of samples from the posterior (without the inverse Jacobian), or if people have strong preferences towards other methods (KDE, MCMC, etc…). Thanks!