I’m debugging a miss-specified model and trying to figure out why the logp of my observations is `inf`

.

Unfortunately for me, one of the variables is transformed, I believe because it’s a `TruncatedNormal`

. Is there any way to recover the *un-transformed* value from a point?

Here’s me in the debugger:

```
ipdb> p point
{'βtemp1': array(1.11700581), 'βtemp2': array(0.93765761), 'βod1': array(0.03401939), 'βod2': array(0.63373602),
'AND Output_interval__': array([ 0.2386266 , 0.13873085, -0.53425323, -0.16257509]),
'medium influences': array([[-0.69232133, 0.76863831, 0.71339414, -0.31977171],
[ 0.55893651, -0.30711682, 0.10664718, 0.68078547],
[-0.29005906, 0.37676899, 0.62135856, -0.14252686]]),
'err_sd_log__': array([-0.07765381, -1.20334943, -0.99376342, ..., 0.09904621,
-0.8738467 , -0.15598646])}
```

Is there any way I can find the values of `"AND Output"`

from the values of `"AND Output__interval__"`

? If I can do that, I think I will be able to tell almost instantly why my model is breaking.

In general, if there was a way to interpret the un-transformed, named variables of a model from a point, that would be great.

There’s probably a function or method to do this, I just don’t know where to look for it.

Thanks!