Forward mode "adjoint"

[johannahaffner]

Here you go! This is the pragmatic solution, without support or test coverage for integer inputs and only a small comment explicating that forward mode is not really an adjoint, even though its diffrax interface is that of an adjoint.

Changes with respect to the last PR:

• renamed to ForwardMode everywhere
• add a sentence to the docstring that explains that this is not really an adjoint, but keep inheriting from AbstractAdjoint
• remove stub for "forward gradient with int" from test_adjoint.py and explain that since JAX does not offer this option, we're not writing our own workaround to test it either

On the last point: if I understood this correctly, then supporting this would entail writing a gradient-computation directly from a JVP with custom "unit pytrees". This is somewhat annoying for mixed array and non-array types.
I'm happy to try again if computing gradients with respect to integer elements of a PyTree is an expected use case (maybe arising from composed/layered transformations of a solve) that requires test coverage.

Earlier comments here.

(This is now rebased on main.)