Suppose I have a training set of set of sequences of variable size, i.e.
D = { (x^i, y^i) | i \in (1...N)}
where
x^i = { x^{i,j} | j \in (1...N_i)} and y^i \in \R^N_i
where
x^{i,j} \in \R^N_{i,j}
with some sizes N_i, N_{i,j}.
What is the best way to save them in memory? Does torch::Tensor allow to save block of heterogeneous data via pointers to (dynamically allocated) other torch::Tensors, which would hold the actual data? (And do so possibly recursively?) Most importantly, does it allow all the nice PyTorch things like tracking gradients?
While I could manage this by storing tensors into std::vector<torch::Tensor> (and do so recursively), I lose the ability to use the torch interface when interacting with the data.
What is the recommended way to do this? I imagine similar problems arise often for graph neural networks.