Extract trained BayesianNN from model

I have a Custom_Model class which represents Pyro’s model() function and hence describes all parameters involved in the prior and likelihood. In addition, it contains a Bayesian Neural Network instance (from the tutorials).
After a successfull training, I wonder how I can extract this learned Bayesian network to make predictions on new data, i.e. how can I construct an instance of BayesianRegression containing the correct guide parameters?

nn_model = ?

My setup looks like the following:

class ModelWrapper(PyroModule):
    self.model = Custom_Model()
    self.guide = AutoNormal(self.model)

class Custom_Model(PyroModule):
    """Implements the Pyro model() functionality"""
    self.net = BayesianRegression(in_features=10, out_features=5)
    def forward(self):
        ... # some other sample statements
        yhat = F.log_softmax(self.net(x), dim=1)
        return pyro.sample("obs_labels", dist.Categorical(logits=yhat))

class BayesianRegression(PyroModule):
    def __init__(self, in_features, out_features):
        self.linear = PyroModule[nn.Linear](in_features, out_features)
        self.linear.weight = PyroSample(dist.Normal(0., 1.).expand([out_features, in_features]).to_event(2))
        self.linear.bias = PyroSample(dist.Normal(0., 10.).expand([out_features]).to_event(1))

    def forward(self, x, y=None):
        # I moved the sample statement from the tutorial
        # into the model's forward function 
        mean = self.linear(x).squeeze(-1)
        return mean

I guess one way is to manually extract all the posterior weights from the guide, e.g. wrapper.guide._get_loc_and_scale('model.net.linear.weight')[0]
and load them into an instance of the BayesianRegression class. However, this is very cumbersome and error prone.

Are there better ways to achieve this?