code: def test_moe():
      all_expert_uids = [
          f"ffn.{np.random.randint(0, 3)}.{np.random.randint(0, 3)}.{np.random.randint(0, 3)}"
          for _ in range(10)
      ]
      with background_server(
          expert_uids=all_expert_uids,
          device="cpu",
          expert_cls="ffn",
          num_handlers=1,
          hidden_dim=16,
      ) as server_peer_info:
          dht = DHT(start=True, initial_peers=server_peer_info.addrs)
  
          dmoe = RemoteMixtureOfExperts(
              in_features=16,
              grid_size=(4, 4, 4),
              dht=dht,
              k_best=3,
              uid_prefix="ffn.",
          )
  
          for i in range(3):
              out = dmoe(torch.randn(10, 16))
              out.sum().backward()
  
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 1 for calling a global function with example usage: test_moe() without return types