What do we visualize in showing a VAE latent space?

Question

I am trying to wrap my head around VAE's and have trouble understanding what is being visualized when people make scatter plots of the latent space. I think I understand the bottleneck concept; we go from $N$ input dimensions to $H$ hidden dimensions to a $Z$ dimensional Gaussian with $Z$ mean values, and $Z$ variance values. For example here (which is based off the official PyTorch VAE example), $N=784, H=400$ and $Z=20$.

When people make 2D scatter plots what do they actually plot? In the above example the bottleneck layer is 20 dimensional, which means there are 40 features (counting both $\mu$ and $\sigma$). Do people do PCA or tSNE or something on this? Even if $Z=2$ there is still four features so I don't understand how the scatter plot showing clustering, say in MNIST, is being made.