Bayes101: Difference between prior predictive and forward sampling?

I’m finding myself unclear on the difference between priors and forward sampling. If someone was willing I could use some tutoring.

Here’s the implementation of what I think in code

Here’s the what I think it is in words
Prior Dist - A distribution what you think parameters might be before seeing anything
Prior Samples - Random draws from your prior distribution

Here’s where I get confused
Prior predictive - Random samples of data that could be observed.
Forward sampling??? - Not sure how this is different

They are the same in terms of algorithm and the way you sample from prior distribution and pass through deterministic function. The differences is that forward sampling returns samples of the latent variables and the (would be) observed y, and prior predictive only returns the observed y.