code: def _meter_worker(
      qin, qout, meter, is_train, world_size, rank, filename
  ):
      backend = "gloo"
      torch.distributed.init_process_group(
          backend=backend,
          init_method="file://{filename}".format(filename=filename),
          world_size=world_size,
          rank=rank,
      )
      # Listen for commands on queues
      while True:
          try:
              signal, val = qin.get()
          except queue.Empty:
              continue
  
          if signal == UPDATE_SIGNAL:
              meter.update(val[0], val[1], is_train=is_train)
  
          elif signal == VALUE_SIGNAL:
              meter.sync_state()
              qout.put(meter.value)
  
          elif signal == SHUTDOWN_SIGNAL:
              break
  
          else:
              raise NotImplementedError("Bad signal value")
  
      return
  
imports: import multiprocessing
  import queue
  import tempfile
  import unittest
  import torch
label_desc: Use image node_id 3 for calling a global function with example usage: _meter_worker(qin, qout, meter, is_train, world_size, rank, filename) without return types