echoclient.py fails on recent MacOS and Linux Python releases that default to the spawn start‑method

Personne n'a encore pris cette issue.

Recent Python releases on macOS and Linux use the `spawn` start‑method by default for new processes.

IIRC spawn starts a brand‑new interpreter and imports the target module it relies on pickling to transport the target function.

As only module‑level objects can be pickled and `run_test` is defined inside the if `__name__ == "__main__"` block, the child process tries to unpickle and look up as an attribute of the module `__mp_main__`. That attribute does not exist, and the unpickler raises:

```
Process ForkServerProcess-2:
File "/usr/lib/python3.14/multiprocessing/process.py", line 320, in _bootstrap
self.run()
~~~~~~~~^^
File "/usr/lib/python3.14/multiprocessing/process.py", line 108, in run
self._target(*self._args, **self._kwargs)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/lib/python3.14/concurrent/futures/process.py", line 242, in _process_worker
call_item = call_queue.get(block=True)
File "/usr/lib/python3.14/multiprocessing/queues.py", line 120, in get
return _ForkingPickler.loads(res)
~~~~~~~~~~~~~~~~~~~~~^^^^^
AttributeError: module '__mp_main__' has no attribute 'run_test'
```

I am not filing a pull request because there are at least three fixes, and which one is implemented depends on the project team's preferences.

The options I see that fit the file's stated _minimal changes_ from upstream constraint:

1. Move `run_test` to the module level.
2. As this is I/O bound, change to the `ThreadPoolExecutor`
3. Force the use of `fork()`

```python
import multiprocessing
multiprocessing.set_start_method('fork', force=True)
```

All three options seem to benchmark close to each other on Linux/MacOS. Personally I lean towards the `ThreadPoolExecutor` option as that is my personal default for I/O bound concurrent operations.

Commencez par echoclient.py, en particulier par la définition de run_test dans le bloc __main__ et sa configuration du pool de processus. Examinez les trois approches proposées au regard de la contrainte du projet consistant à limiter les changements au minimum, puis exécutez le client sur une version de Python avec spawn afin de confirmer que le problème est résolu sans modifier son comportement prévu.

Rédigé par le modèle d'indexation à partir du texte de l'issue.