[python][torch] Support distributed iterable dataset sharding

[XiaoHongbo-Hope]

What changed

• Shard streaming Torch datasets across DDP ranks and DataLoader workers when auto_detect_rank=True.
• Keep rank sharding disabled by default because to_torch accepts caller-planned splits.
• Resolve rank from torch.distributed, then torchrun environment variables, and preserve it in spawned workers.
• Reject binding limits with multiple ranks.
• Keep shuffled reads reproducible per epoch, rank, and worker.
• Document uneven inputs and DistributedDataParallel.join().

Reference

The API is inspired by LanceDataset. Unlike Lance, this low-level API accepts external splits, so automatic sharding is opt-in.

Validation

• 43 Torch tests passed.
• CPU torchrun test executes DDP forward/backward with 2 ranks and 2 spawned DataLoader workers.
• Split assignment is complete and non-overlapping.
• Pre-sharded splits remain unchanged by default.
• flake8 and git diff --check passed.