Machine learning

Current implementation of Go binding can not specify options.

GPUOptions struct is in internal package. And go generate doesn't work for protobuf directory. So we can't specify GPUOptions for NewSession.

📚 The doc issue

The documentation of multilabel_soft_margin_loss misses reduction parameter.

Suggest a potential alternative/fix

The right signature should be

torch.nn.functional.multilabel_soft_margin_loss(input, target, weight=None, size_average=None, reduction='mean') → 

These examples take quite a long time to run, and they make our documentation CI fail quite frequently due to timeout. It'd be nice to speed the up a little bit.

To contributors: if you want to work on an example, first have a look at the example, and if you think you're comfortable working on it and have found a potential way to speed-up execution time while preserving the educational message

It might be useful to have rationalize(x) return x if x is already rational, to allow generic code that takes both reals and rationals as inputs.

Currently many more Python projects like dask and optuna are using Python type hints. With the Python package of xgboost gaining more and more features, we should also adopt mypy as a safe guard against some type errors and for better code documentation.