code: def test_call_many(hidden_dim=16):
      k_min = 1
      timeout_after_k_min = None
      backward_k_min = 1
      forward_timeout = None
      backward_timeout = None
      detect_anomalies = False
      allow_zero_outputs = False
      atol = 1e-5
  
      with background_server(
          num_experts=5,
          device="cpu",
          expert_cls="ffn",
          num_handlers=1,
          hidden_dim=hidden_dim,
          optim_cls=None,
      ) as server_peer_info:
          inputs = torch.randn(4, hidden_dim, requires_grad=True)
          inputs_clone = inputs.clone().detach().requires_grad_(True)
  
          dht = DHT(initial_peers=server_peer_info.addrs, start=True)
          e0, e1, e2, e3, e4 = create_remote_experts(
              [
                  ExpertInfo(
                      uid=f"expert.{i}",
                      peer_id=server_peer_info.peer_id,
                  )
                  for i in range(5)
              ],
              dht,
          )
          e5 = RemoteExpert(
              ExpertInfo(f"thisshouldnotexist", server_peer_info), None
          )
  
          mask, expert_outputs = _RemoteCallMany.apply(
              DUMMY,
              [[e0, e1, e2], [e2, e4], [e1, e5, e3], []],
              k_min,
              backward_k_min,
              timeout_after_k_min,
              forward_timeout,
              backward_timeout,
              detect_anomalies,
              allow_zero_outputs,
              e1.info,
              inputs,
          )
          assert mask.shape == (4, 3)
          assert expert_outputs.shape == (4, 3, hidden_dim)
  
          assert np.all(
              mask.data.numpy()
              == np.array(
                  [
                      [True, True, True],
                      [True, True, False],
                      [True, False, True],
                      [False, False, False],
                  ]
              )
          ), f"Incorrect mask, {mask}"
  
          reference_outputs = torch.zeros_like(expert_outputs)
          reference_outputs[0, 0] = e0(inputs_clone[0:1])
          reference_outputs[0, 1] = e1(inputs_clone[0:1])
          reference_outputs[0, 2] = e2(inputs_clone[0:1])
          reference_outputs[1, 0] = e2(inputs_clone[1:2])
          reference_outputs[1, 1] = e4(inputs_clone[1:2])
          reference_outputs[2, 0] = e1(inputs_clone[2:3])
          reference_outputs[2, 2] = e3(inputs_clone[2:3])
  
          assert torch.allclose(
              expert_outputs, reference_outputs, atol=atol, rtol=0
          )
          proj = torch.randn(4, hidden_dim)
          loss = (
              expert_outputs[(0, 1, 1, 2), (0, 2, 1, 0)] * proj
          ).sum()
          loss.backward()
          our_grad = inputs.grad.data.cpu().clone()
  
          reference_loss = (
              reference_outputs[(0, 1, 1, 2), (0, 2, 1, 0)] * proj
          ).sum()
          reference_loss.backward()
          reference_grad = inputs_clone.grad.data.cpu().clone()
          assert torch.allclose(
              our_grad, reference_grad, atol=atol, rtol=0
          )
  
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 3 for calling a global function with example usage: test_call_many(hidden_dim) without return types