I just tried using new numpyro release 0.14.0 and my posterior predictive modelling broke , because of the breaking change:

Breaking change: Predictive will try to avoid recomputing “deterministic” sites if it is provided in posterior_samples. Those deterministic sites are excluded in the previous releases.

I am currently using something like following:

```
def model(x,y):
.....
mu = numpyro.deterministic("mu", .... < calculate expected value>... )
numpyro.sample("obs", dist.Normal(mu, residual_sigma), obs=y)
```

Then, to analyze model prediction, I would use

```
mcmc = MCMC(NUTS(model),...)
mcmc.run(rng_key1,real_x,real_y)
simulated_x=jnp.linspace(-1.0, 1.0, 100)
mu = Predictive(mcmc.sampler.model, mcmc.get_samples(),return_sites=["mu"])(rng_key2, simulated_x,None)["mu"]
```

Where `real_x`

and `real_y`

- are my real observations and `simulated_x`

- just values to show mean expected values.

Prior to the release **0.14.0** this code worked as expected, but with the new breaking changes this code just returns `mu`

that was sampled during inference phase (i.e with too many samples for my purposes).

So, the question - how do I revert to the old behavior of `Predictive`

?