feat(policies): add LingBot-VA autoregressive video-action world model

Port the LingBot-VA policy (Wan2.2 dual-stream video+action world model) into
LeRobot, following the EO-1 / VLA-JEPA conventions. Covers inference, checkpoint
conversion, and predicted-video saving (training is deferred to a follow-up PR).

- Vendored Wan transformer/attention/flex/VAE/scheduler modules (key names preserved
  for near-identity conversion); torch SDPA default, flashattn/flex lazy-guarded.
- LingBotVAConfig (registered "lingbot_va") + processor with fixed-quantile action
  unnormalization; full dual-stream sampling loop with CFG, two flow-matching
  schedulers and KV cache, mapped onto select_action with observed-keyframe feedback.
- convert_lingbot_va_checkpoints.py (libero/robotwin variants): bundles the ~5B
  transformer, lazy-pulls the frozen VAE+UMT5 from the source repo.
- Predicted-video plumbing in lerobot_eval (predicted_frames_callback; opt-in via
  --policy.save_predicted_video) and ConstantWithWarmupSchedulerConfig.
- pyproject: widen diffusers-dep to <0.37, add lingbot_va + imageio-dep extras,
  add lingbot_va and (missing) eo1 to `all`.
- Factory + policies/__init__ wiring, docs page + toctree, and tests.

Note: the LIBERO success-rate correctness gate must be validated on a CUDA GPU
with the converted checkpoint.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

docs/source/_toctree.yml

@@ -67,6 +67,8 @@
     title: VLA-JEPA
   - local: eo1
     title: EO-1
+  - local: lingbot_va
+    title: LingBot-VA
   - local: groot
     title: NVIDIA GR00T N1.5
   - local: xvla