I’m fitting the Laplace approximation to a model and I would like to find the unconstrained MAP parameters given a dictionary containing the constrained MAP parameters. One way to do this is the following:

```
guide = AutoLaplaceApproximation(model, init_loc_fn=init_to_value(values=map_params_constrained))
svi = SVI(model, guide, numpyro.optim.Minimize(method='BFGS'), Trace_ELBO())
state = svi.init(
random.PRNGKey(0), **model_kwargs,
)
map_params_unconstrained = svi.get_params(state)
```

`svi.get_params(state)`

returns a dictionary with a key `auto_loc`

containing an array with the unconstrained parameters. The problem with this approach is that if I `jax.jit`

this function, the order of the parameters in `map_params_unconstrained['auto_loc']`

gets permuted for some reason!

Is there a way to get a dictionary of the unconstrained parameters instead of an array?