From a technical point of view, you can forwar propagate anything you like, at each time step. backpropagation will work fine (ish: within the constraints that you use functions that have backprop implemented).
From a conceptual point of view, what you will propagate is I guess out of scope of pytorch itself, and more of a research issue?
Note that conv nets can handle inputs of arbitrary size. You can look at neuralstyle for an example that forwards images of arbitrary size through a pretrained convnet. rnns can also handle inputs of arbitrary size.