- Created `loss.py` with a `LossManager` class to handle loss calculation
- Integrated `LossManager` into the training pipeline in `train.py`
- Implemented a basic manifold loss computation using mean absolute value
- Added `data_loader.py` with `NHREP_Dataset` class for loading point cloud, feature mask, and CSG tree data
- Implemented `CustomDataLoader` for flexible data loading with configurable parameters
- Refactored `train.py` to create a structured training pipeline for NHRepNet
- Added support for feature sampling, device selection, and TensorBoard logging
- Introduced modular training methods with error handling and logging