Replace the `with profiler or nullcontext():` wrap around the entire
training loop with explicit `profiler.start()` / `profiler.finalize()`
calls, and tighten `_section(...)` regions in `update_policy` to only
wrap the hot calls (forward / backward / optimizer.step).
This avoids ~120 lines of pure re-indentation noise while keeping the
exact same artifacts on disk and the same public behavior.
lerobot_train.py diff vs main: 267 -> 29 changed lines.
Made-with: Cursor
1ac8e96575