Add with_rolled_lon to downscaling models

[frodre]

PR 4 of 5 in the prime-meridian longitude stack. Lets a model re-express its grid in a seam-crossing coarse domain's longitude convention while sharing the trained network weights, so a single checkpoint can generate over a domain expressed west of 0 or east of 360. Depends on the roll primitives (PR 2) and the constructor-time expert validation (PR 1).

Changes:

• fme.downscaling.models.DiffusionModel.with_rolled_lon: rebuild the model through its constructor with full_fine_coords and static_inputs rolled to match the coarse grid, anchored on the western coarse-cell edge so the fine grid stays aligned to whole coarse cells; returns self when no roll is needed.
• fme.downscaling.predictors.serial_denoising.DenoisingMoEPredictor.with_rolled_lon: roll every expert (preserving the shared-grid invariant) and rebuild so the sigma dispatcher is reconstructed from the rolled experts.
• fme.downscaling.test_models: tests for no-roll passthrough, coord shifting with shared weights, idempotency, coarse-cell alignment, and rolling all MoE experts.
• Tests added
• If dependencies changed, "deps only" image rebuilt and "latest_deps_only_image.txt" file updated

Base: feature/lon-roll-data-layer (PR 3)

Stack

PR	Head → Base	Title
#1234	refactor/moe-validate-experts-init → main	Validate expert grid compatibility in DenoisingMoEPredictor.__init__
#1235	feature/lon-roll-primitives → PR1	Add longitude roll primitives
#1236	feature/lon-roll-data-layer → PR2	Roll seam-crossing longitudes in the data layer
#1237	feature/lon-roll-model → PR3	Add with_rolled_lon to models
#1238	feature/lon-roll-integration → PR4	Roll the model in inference/predict/evaluator