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 model_predict(
      self,
      model,
      input_buffer,
      copy_event,
      compute_event,
      copy_sem,
      compute_sem,
      response_list,
      request_time,
  ):
      # copy_sem makes sure copy_event has been recorded in the data copying thread
      copy_sem.acquire()
      self.stream_map[threading.get_native_id()].wait_event(copy_event)
      with torch.cuda.stream(
          self.stream_map[threading.get_native_id()]
      ):
          with torch.no_grad():
              response_list.append(model(input_buffer))
              compute_event.record()
              compute_sem.release()
      del input_buffer
  
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 3 for calling the BackendWorker obj's underlying member method code with example usage: obj.model_predict(model, input_buffer, copy_event, compute_event, copy_sem, compute_sem, response_list, request_time) without return types