Deep cloning of Learners

[sebffischer]

It would be nice to be able to create a proper deep clone of a trained torch learner.

• Support to clone nn_modules() has been already added in torch here
• We still need a function to clone the optimizer. But presumably we can simply iterate over the optimizer's state dict and clone the tensors if we encounter them (https://torch.mlverse.org/docs/articles/serialization.html?q=serialization#loading-models-saved-in-python) I created an issue here. Note that we also have to update the parameters that are registered in the optimizer to the new parameters of the cloned network. (addressed here: https://github.com/mlverse/torch/pull/1041/files)

[sebffischer]

Note that we need to distinguish between bundled and unbundled models.

[sebffischer]

related PRs:

• perf: skip unnecessary clone of learner's state in resample() mlr3#1003
• fix: deep-cloning for LearnerTorch and friends #198
• fix(cloning): metadata, finalizer, and repeated cloning mlverse/torch#1134