Worked on enhancing distributed input processing within the pytorch/torchrec repository by implementing explicit feature names in the ShardedQuantEbcInputDist class. This addition improved the tracking and management of feature order across distributed shards, supporting greater observability and correctness in distributed machine learning workflows. The approach focused on maintainability and reproducibility, enabling more reliable experimentation and faster debugging for distributed training scenarios. Leveraging Python and PyTorch, the work emphasized clear feature provenance throughout the input pipeline. No major bugs were reported during this period, as efforts centered on quality improvements and supporting robust distributed systems within the TorchRec stack.