[FEATURE] Add batch_fitness support to pg.unconstrain()ed problems

**Is your feature request related to a problem? Please describe.**
I am trialling pygmo2 with a constrained optimisation problem, where each function evaluation takes a few minutes but I can write a highly parallel batch_fitness function. I've implemented a batch_fitness method in my problem, and want to use PSO to optimise it. Since PSO doesn't support constraints I run problem = pg.unconstrain(problem). Running my code then throws:

`what: The batch_fitness() method has been invoked, but it is not implemented in a UDP of type ' [unconstrained]'`

This was already on the radar [here](https://github.com/esa/pagmo2/issues/283).

Start at the pg.unconstrain() implementation and trace how batch_fitness() is dispatched for the wrapped problem. Reproduce the reported case with a constrained problem using PSO and a batch_fitness method. Done means the unconstrained problem supports batch_fitness without the reported exception.

Written by the indexing model from the issue text.