Support dynamic shape and newton step

[unalmis]

• resolves Patch for differentiable code with dynamic shapes #1303 .
• Activates newton step to find bounce points.
  • It can be shown there exists O(sqrt(epsilon)) error for bounce integrals with 1/v_ll where epsilon is error of bounce point. For v_|| integrals error is conveniently O(epsilon times sqrt(epsilon)). Hence, the spline method would require thousands of knots per transit for just a couple digits of accuracy, and it would stop convergence at epsilon<=1e-5 (so sqrt(epsilon) <=3 digits) error due to condition number. Of course, the spline=False method has always computed the points with spectral accuracy and has very fast convergence after New inverse stream map to accelerate convergence #1919 ; that method converges to epsilon = machine precision without the newton step. With the newton step, fast convergence is achieved with the spline method as well.
  • I suspect fast ion confinement optimization will be easier now.

I did this on a couple lunch breaks, so it would be very weird if clicking the approve button to merge into master took a year.

Benchmarks

Here is a timing benchmark on my CPU with nufft_eps=1e-6. Prior to this PR, every adjoint call to nufft1 took >= 1 second and the full computation was 34 seconds. Now every adjoint call to nufft1 is 250 milliseconds, and the full computation is 14 seconds. These improvements become larger as the sparsity grows and error tolerance parameter for the nuffts epsilon tends to 0. Likewise, the improvement grows linearly with the problem size. As this is called within an optimization loop where time and memory are tight, the improvement is significant.

Before

After

[unalmis]

Equation 19 in the supplementary information in publications/unalmis2025.