class_docstr: This worker will take tensors from the request queue, do some computation,
  and then return the result back in the response queue.
class_docstr_tok: ["This","worker","will","take","tensors","from","the","request","queue",",","do","some","computation",",","and","then","return","the","result","back","in","the","response","queue","."]
code: def respond(
      self, compute_event, compute_sem, response_list, request_time
  ):
      # compute_sem makes sure compute_event has been recorded in the model_predict thread
      compute_sem.acquire()
      self.d2h_stream.wait_event(compute_event)
      with torch.cuda.stream(self.d2h_stream):
          self.response_queue.put(
              (response_list[0].cpu(), request_time)
          )
  
imports: import argparse
  import asyncio
  import os.path
  import subprocess
  import threading
  import time
  from concurrent.futures import ThreadPoolExecutor
  from queue import Empty
  import numpy
  import pandas
  import torch
  import torch.multiprocessing
label_desc: Use image node_id 5 for calling the BackendWorker obj's underlying member method code with example usage: obj.respond(compute_event, compute_sem, response_list, request_time) without return types