I have a model with transformed parameters and I want to fix some of those parameters using the `substitute`

effect handler. It seems passing the transformed parameters to `substitute`

has no effect. I noticed that I can add the suffix `_base`

and substitute the base parameters easily enough, but I’m wondering if there’s a way to substitute the transformed parameters instead without me having to calculate what the underlying base parameters ought to be to get the desired transformed values.

Here’s a simplified example to demonstrate what I mean

```
def model():
mu_a = numpyro.sample("mu_a", dist.Normal(0., 5.))
with numpyro.handlers.reparam(config={"a": TransformReparam()}):
a = numpyro.sample(
"a",
dist.TransformedDistribution(
dist.Normal(0., 1.), AffineTransform(mu_a, 1.)
)
)
return mu_a, a
# what I would like to do
subbed_model = substitute(model, data={"a": 0.0})
# what works
subbed_model = substitute(model, data={"mu_a": 0.0, "a_base": 0.0})
```

In this example if I want to substitute `b`

I have to fix the location parameter `mu_b`

and then also calculate the corresponding `b_base`

. Ideally I would like to be able to just set the desired value of `b`

. Is there a neat way to do that?