Hello!

I am currently experimenting with different variants of a model and I would like to be able to set parameters to constant values for some variants of the model.

For example, if my model looks like:

```
with pm.Model():
mu = pm.Normal("mu", mu=0, sigma=1)
sigma = pm.LogNormal("sigma",mu=0, sigma=1)
x = pm.Normal("x", mu=mu, sigma=sigma)
```

I might want to set `mu`

to 10 and sample from that model. Then in a different run set `sigma`

to 7 (and use the prior defined above for `mu`

). I would like to do this programatically, without manually editing the model code for each version.

My current solution has been something like:

```
def my_model(priors: dict):
with pm.Model() as m:
mu = priors["mu"]
sigma = priors["sigma"]
x = pm.Normal("x", mu=mu, sigma=sigma)
return m
with my_model({
"mu": pm.ConstantData("mu", 10)
"sigma": pm.LogNormal("sigma",mu=0, sigma=1)
}):
...
```

But as the model gets more complex this style is hurting the readability of the model (e.g. difficult to know the `dims`

of `mu`

from `mu = priors["mu"]`

).

Is there a better way to do this, or is my current solution already about as good as I can get?

Thank you!