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.