code: def test_remote_expert_worker_runs_coros_concurrently(
      n_processes=4, n_coros=10
  ):
      processes = []
      counter = mp.Value(ctypes.c_int64)
      for i in range(n_processes):
          elapsed_fut = MPFuture()
          factory = (
              threading.Thread if i % 2 == 0 else mp.Process
          )  # Test both threads and processes
  
          proc = factory(
              target=_measure_coro_running_time,
              args=(n_coros, elapsed_fut, counter),
          )
          proc.start()
          processes.append((proc, elapsed_fut))
  
      for proc, elapsed_fut in processes:
          # Ensure that the coroutines were run concurrently, not sequentially
          assert elapsed_fut.result() < 0.2
          proc.join()
  
      assert counter.value == n_processes * n_coros
  
imports: import asyncio
  import ctypes
  import multiprocessing
  import threading
  import time
  import numpy
  import pytest
  import torch
  from hivemind.dht import DHT
  from hivemind.moe.client.expert import RemoteExpert, create_remote_experts
  from hivemind.moe.client.moe import DUMMY, RemoteMixtureOfExperts, _RemoteCallMany
  from hivemind.moe.client.remote_expert_worker import RemoteExpertWorker
  from hivemind.moe.client.switch_moe import RemoteSwitchMixtureOfExperts
  from hivemind.moe.expert_uid import ExpertInfo
  from hivemind.moe.server import ModuleBackend, Server, background_server, declare_experts
  from hivemind.moe.server.layers import name_to_block
  from hivemind.p2p.p2p_daemon_bindings.control import P2PHandlerError
  from hivemind.utils import BatchTensorDescriptor, MPFuture, get_dht_time
label_desc: Use image node_id 10 for calling a global function with example usage: test_remote_expert_worker_runs_coros_concurrently(n_processes, n_coros) without return types