code: def client_process(
      can_start,
      benchmarking_failed,
      server_maddrs,
      server_peer_id,
      num_experts,
      batch_size,
      hid_dim,
      num_batches,
      backprop=True,
  ) -> None:
      torch.set_num_threads(1)
      can_start.wait()
  
      p2p = RemoteExpertWorker.run_coroutine(
          P2P.create(initial_peers=server_maddrs)
      )
      experts = [
          RemoteExpert(
              expert_info=ExpertInfo(
                  uid=f"expert.{i}", peer_id=server_peer_id
              ),
              p2p=p2p,
          )
          for i in range(num_experts)
      ]
  
      try:
          dummy_batch = torch.randn(batch_size, hid_dim)
          for _ in range(num_batches):
              expert = random.choice(experts)
              out = expert(dummy_batch)
              if backprop:
                  out.sum().backward()
      except BaseException as e:
          benchmarking_failed.set()
          raise e
  
file_path: hivemind/benchmarks/benchmark_throughput.py
imports: import argparse
  import multiprocessing
  import random
  import sys
  import time
  import torch
  from hivemind.dht import DHT
  from hivemind.moe.client.expert import RemoteExpert
  from hivemind.moe.client.remote_expert_worker import RemoteExpertWorker
  from hivemind.moe.expert_uid import ExpertInfo
  from hivemind.moe.server import ModuleBackend, Server
  from hivemind.moe.server.layers import name_to_block
  from hivemind.p2p import P2P
  from hivemind.utils.limits import increase_file_limit
  from hivemind.utils.logging import get_logger, use_hivemind_log_handler
  from hivemind.utils.tensor_descr import BatchTensorDescriptor
label_desc: Use image node_id 2 for calling a global function with example usage: client_process(can_start, benchmarking_failed, server_maddrs, server_peer_id, num_experts, batch_size, hid_dim, num_batches, backprop) without return types