Code Reading — 400-line PyTorch Refactor

A 400+ line PyTorch file is provided; complete 3 parts + 1 bonus. Lots of time/space complexity questions on the existing code. The codebase being modified is the noisy-annotator classifier seen elsewhere in the ML coding rounds. Overview A 400+ line PyTorch file; complete 3 parts + 1 bonus. The codebase being modified is the noisy-annotator classifier seen in the ML coding rounds — but the format here is code reading + refactoring, not from-scratch implementation. Examples "What's the time/space complexity of matrix A @ B?" — interviewers drill complexity questions on the existing code, not just the new code you add. A recurring stumble: candidates who've never thought past "matmul is O(n³)" get caught out — be ready for follow-ups on attention / conv / batched matmul as well. Notes Different from refactoring-style rounds: this round emphasizes understanding existing code + extending it, not bug fixing. The 400-line file is messy by design; you're judged on how quickly you orient and where you ask questions. Preparation Practice quickly reading other people's PyTorch — pick a handful of small-but-real codebases (a single-file GPT, a distributed-training scaffold, a trainer-loop framework) and force yourself to map the call graph in under 20 minutes Brush up on common op complexities (matmul / conv / attention) — both time and memory Drill: open an unfamiliar PyTorch repo, give yourself 10 min to explain the data flow