Add super resolution

…ion configurable

Changes:
1. Phase 2.2: Remove dummy validation dataset workaround (lit_data.py)
   - Replaced DummyDataset with proper warning when validation fails
   - More honest error handling instead of masking configuration issues

2. Phase 2.3: Make hardcoded deep supervision values configurable (hydra_config.py, lit_model.py)
   - Added ModelConfig fields:
     * deep_supervision_weights (default: [1.0, 0.5, 0.25, 0.125, 0.0625])
     * deep_supervision_clamp_min (default: -20.0)
     * deep_supervision_clamp_max (default: 20.0)
   - Updated lit_model.py to use configurable values in 3 locations:
     * Multi-task output clamping
     * Deep supervision output clamping
     * Deep supervision scale weights with validation

Benefits:
- Users can now customize deep supervision behavior without code changes
- Removes technical debt items #2 and #4 from REFACTORING_PLAN.md
- Better error handling for missing validation data

Split the monolithic lit_model.py (1846 lines) into focused, maintainable modules:
- lit_model.py (539 lines, 71% reduction): Core Lightning orchestration
- deep_supervision.py (404 lines): Deep supervision and multi-task learning
- inference.py (868 lines): Sliding window inference, TTA, postprocessing
- debugging.py (173 lines): NaN detection and debugging utilities

Key improvements:
- Eliminated code duplication through delegation pattern
- Clear separation of concerns with single responsibility per module
- Improved maintainability and testability
- No backward compatibility methods (clean design per user requirement)

All modules pass Python syntax compilation. Integration tests pending.

Closes #2.1 in REFACTORING_PLAN.md