`Module.to(ScalarType)` has restrictions in `PyTorch` which aren't restricted in `TorchSharp`

[shaltielshmid]

When calling Module.to(ScalarType) on a module, PyTorch has a restriction that the target dtype must be a floating point or a complex number. See here.

Also, when it gets a floating point type, PyTorch only moves parameters that are already a floating_point type to the new type. See for example here.

This is relatively trivial to add, would TorchSharp want to add the same restrictions?

[NiklasGustafsson]

Under the assumption that modules always participate in backprop, this constraint makes sense.