Add training scripts for all-future and next-step supervision with DeepHealth

- Implement `train_all_future.py` for training with query-conditioned all-future supervision.
- Implement `train_next_step.py` for training with next-token/next-time-point supervision.
- Introduce `train_util.py` for shared utility functions including logging, dataset splitting, and model checkpointing.
- Enhance argument parsing for both training scripts to accommodate new parameters.
- Update loss functions and model configurations to support the new training paradigms.

evaluate_landmark_auc.py

@@ -0,0 +1,7 @@
+from __future__ import annotations
+
+from evaluate_auc_v2 import main
+
+
+if __name__ == "__main__":
+    main()