Make evaluation of Binning corrections JAX-traceable

[eguiraud]

The evaluation of a scipy.interpolate.CubicSpline is not traceable by JAX (because of an explicit cast to np.array somewhere in there).

This in turn makes it impossible to pass CorrectionWithGrad.evaluate to jax.jit or jax.vmap if a Binning correction is involved.

• for simple Binning (1D histos with scalar bin contents) see below.
• for compound Binning (1D histos with Formulas or FormulaRefs as bin contents) there is the additional problem that JAX cannot trace through the bin look-up, and I'm not sure how to fix this
• for MultiBinning (ND histos) I don't know of a jax-friendly implementation of a bin look-up differentiable relaxation, we might have to come up with one. MultiBinning is not supported at the moment anyways, tracked in Add support for MultiBinning #15

[eguiraud]

Some discussion at https://iris-hep.slack.com/archives/C0155BGPGE4/p1699120426133849

[eguiraud]

options I see:

• re-implement scipy.interpolate.CubicSpline using XLA primitives or jax.numpy methods
• switch to sigmoids as per Lukas' suggestion
• switch to a sum of gaussians