Are there any neural network architectures that perform multiple forward passes? Across a many types of networks (simple feedforward, ResNets, RNNs, transformers, etc.) the model's output or prediction is produced by performing a single forward pass per input. Are there examples of network architectures that permit multiple forward passes per input?

You could repeatedly sample a VAE for a single input: for instance, obtain $\mu(x), \sigma^2(x)$ for a single input $x$ and then compute $y_i(x) = \mu(x) + \sigma(x) \epsilon_i$ for as many $\epsilon_i \sim \mathcal N(0,1^2)$ as you wish.
