code: def _client_thread_loop(  # noqa
      process_name: str,
      client_rank: Rank,
      all_server_ranks: List[Rank],
      q: mp.Queue,
      errq: mp.Queue,
      init_method: Optional[str],
      world_size: int,
      groups: List[List[Rank]],
      subprocess_init: Optional[Callable[[], None]] = None,
      max_bandwidth: float = 1e8,
      min_sleep_time: float = 0.01,
  ) -> None:
      try:
          tag_logs_with_process_name(process_name)
          if subprocess_init is not None:
              subprocess_init()
          init_process_group(
              rank=client_rank,
              init_method=init_method,
              world_size=world_size,
              groups=groups,
          )
  
          params = {}
          clients = [
              GradientParameterClient(server_rank)
              for server_rank in all_server_ranks
          ]
          log_time, log_rounds, log_bytes = time.perf_counter(), 0, 0
  
          # thread loop:
          # 1. check for a command from the main process
          # 2. update (push and pull) each parameter in my list of parameters
          # 3. if we're going to fast, sleep for a while
          while True:
              tic = time.perf_counter()
              bytes_transferred = 0
              try:
                  data = q.get(timeout=0.01)
                  cmd, args = data
                  if cmd == "params":
                      params[args[0]] = args[1]
                      log_time, log_rounds, log_bytes = (
                          time.perf_counter(),
                          0,
                          0,
                      )
                  elif cmd == "join":
                      for client in clients:
                          client.join()
                      break
              except queue.Empty:
                  pass
  
              for k, v in params.items():
                  param_size = v.numel() * v.element_size()
                  bytes_transferred += param_size
                  if param_size > MIN_BYTES_TO_SHARD:
                      chunks = v.chunk(len(clients), dim=0)
                      for client, chunk in zip(clients, chunks):
                          client.update(k, chunk)
                  else:
                      client_idx = hash(k) % len(clients)
                      clients[client_idx].update(k, v)
  
              log_bytes += bytes_transferred
              log_rounds += 1
              log_delta = time.perf_counter() - log_time
              if params and log_delta > 60:
                  logger.info(
                      f"Parameter client synced {log_rounds} rounds {log_bytes / 1e9:g} "
                      f"GB in {log_delta:g} s ({log_delta / log_rounds:g} s/round, "
                      f"{log_bytes / log_delta / 1e9:g} GB/s)"
                  )
                  log_time, log_rounds, log_bytes = (
                      time.perf_counter(),
                      0,
                      0,
                  )
  
              comm_time = time.perf_counter() - tic
              sleep_time = max(
                  bytes_transferred / max_bandwidth - comm_time,
                  min_sleep_time,
              )
              time.sleep(sleep_time)
  
      except BaseException as e:
          traceback.print_exc()
          errq.put(e)
          raise
  
imports: import logging
  import multiprocessing
  import queue
  import time
  import traceback
  from functools import lru_cache
  from typing import Callable, Dict, List, Optional, Sequence
  import torch
  import torch.distributed
  import torch.multiprocessing
  from torchbiggraph.distributed import init_process_group, Startable
  from torchbiggraph.types import CharTensorType, Rank
  from torchbiggraph.util import allocate_shared_tensor, split_almost_equally, tag_logs_with_process_name
label_desc: Use image node_id 5 for calling a global function with example usage: _client_thread_loop(process_name, client_rank, all_server_ranks, q, errq, init_method, world_size, groups, subprocess_init, max_bandwidth, min_sleep_time) without return types