We avoid the bug by not applying the transformation and casting the
result of jacobianInverseTransposed to a FieldMatrix. This way we do
not exploit the diagonal structure of the Transformation. Should get
fixed in the near future.

In the end I had to combine the last two versions. For jacobians:
- We want to split according to the FunctionView (e.g. Stokes).
- We don't want to split according to gradients (e.g. sumfactorization).

Example: Before this commit the expression

\sum_i a_i (\nabla v)_i + \sum_j b_j (\nabla v)_j

was split into two accumulation terms:

1) a_k with corresponding test function (\nabla v)_k
2) b_l with corresponding test function (\nabla v)_l

Now we split into:

a_k + b_k with corresponding test function (\nabla v)_k

This is possible since we have linearity in the test function.

TODO: Jacobians are not yet working

But only when they are actually used!

It should be possible to have multiple of these with different increment