it seems that the best pytorchthoning solution comes from either knowing torch.cat or torch.stack. In my use case I generate tensors and conceptually need to nest them in lists and eventually convert that to a final tensor (e.g. of size [d1, d2, d3]). I think the easiest solution to my problem append things to a list and then give it to torch.stack to form the new tensor then append that to a new list and then convert that to a tensor by again using torch.stack recursively.