code: def optimize_parallel_cpu_private(args):
      trial_params, train_function = args[0], args[1]
  
      sleep(random.randint(0, 4))
  
      # run training fx on the specific gpus
      results = train_function(trial_params)
  
      # True = completed
      return [trial_params, results]
  
imports: import argparse
  import json
  import math
  import os
  import random
  import re
  import traceback
  from argparse import ArgumentParser
  from copy import deepcopy
  from gettext import gettext
  from multiprocessing import Pool, Queue
  from time import sleep
  import numpy
  from .hyper_opt_utils import strategies
label_desc: Use image node_id 2 for calling a global function with example usage: optimize_parallel_cpu_private(args) and returns: list