Hello,

I have converted a part of my frequentist neural network model (`my_frequentist_model`

) into a Pyro model.

`my_frequentist_model`

consists of main body and an output head, and I only applied Pyro priors to the weights of the output head, since I do not want the weights of the main body to be changed (the main body weights are pre-trained). I used the following code to achieve this:

```
# convert our `model_gpt2` into a pyro module.
module.to_pyro_module_(my_frequentist_model)
# convert the output head of `my_frequentist_model` into a
# Bayesian layer.
for m in my_frequentist_model.output_head.modules():
for name, value in list(m.named_parameters(recurse=False)):
setattr(m, name, module.PyroSample(prior=dist.Normal(0, 1)
.expand(value.shape)
.to_event(value.dim())))
```

Now, after I execute the code above, if I do `my_frequentist_model(my_input)`

, would Pyro give the output of the `my_frequentist_model`

based on the new output head weights which are drawn from their prior distribution (`dist.Normal(0,1)`

)? or would the command `my_frequentist_model(my_input)`

generate the output based on the old frequentist output head weights?

Thank you,