# Evidence: Compile a function with jit

Copy this template into your own portfolio and fill it after running your modified exercise.

## Evidence required

Keep the independent loss/gradient calculation, backend/shapes/dtype, first-call interval, warmup/repeat policy, median/range samples, and synchronization of every output leaf. State what the benchmark excludes.

- Command: `python3 phases/02-transforms/04-compile-a-function-with-jit/code/main.py`
- Working directory: repository root
- Python / JAX / NumPy versions: 
- Hardware and device: 
- My prediction before running: 
- My change to the exercise: 
- Exit code and meaningful output: 
- Why the result supports or contradicts my prediction: 
- Checkpoint result: 
- Artifact link or saved filename: 
- Portfolio status: saved in local workspace