- What tutorial are you running?

I am working on the VAE example. - What version of Pyro are you using?

I use Pyro 1.8.0 - Please link or paste relevant code, and steps to reproduce.

I am trying to visualize the prior p(z) in model. I added

```
plt.figure()
plt.hist(z.detach().cpu().numpy().flatten(),bins=100)
plt.show()
```

right after `z = pyro.sample("latent", dist.Normal(z_loc, z_scale).to_event(1))`

. It looks like a standard gaussian distribution, which makes sense to me.

I then updated the guide by multiple z_loc and z_scale with 100 as follows:

```
def guide(self, x):
# register PyTorch module `encoder` with Pyro
pyro.module("encoder", self.encoder)
with pyro.plate("data", x.shape[0]):
# use the encoder to get the parameters used to define q(z|x)
z_loc, z_scale = self.encoder.forward(x)
z_loc =z_loc*100
z_scale =z_scale*100
# sample the latent code z
pyro.sample("latent", dist.Normal(z_loc, z_scale).to_event(1))
```

After this modification, the distribution of p(z) is no longer a standard normal distribution.

I think p(z) in the model as a prior will always be a standard normal distribution. Can anyone explain why the update in the guide alters the prior p(z)?

Thank you!