What latent variables are packed in pyro.param() in the Bayesian Regression tutorial?

I am going through the tutorial Bayesian Regression

In cell [10]:

for name, value in pyro.get_param_store().items():
    print(name, pyro.param(name))

Output:
auto_loc tensor([-2.2026, 0.2936, -1.8873, -0.1607, 9.1753], requires_grad=True)
auto_scale tensor([0.2285, 0.0954, 0.1376, 0.0600, 0.1042], grad_fn=)

The tutorial goes on saying “Note that Autoguide packs the latent variables into a tensor, in this case, one entry per variable sampled in our model.”

Could someone explain which the 5 latent variables are? Thanks.

everything in a pyro.sample() statement, so in order: linear.weight (2), linear.bias (1), factor (1), and sigma (1). the reason why w_prior, b_prior, and f_prior don’t have pyro.sample() is because random_module does that under the hood.