code: def run_triple_classification(self, threshlod=None):
      self.lib.initTest()
      self.data_loader.set_sampling_mode("classification")
      score = []
      ans = []
      training_range = tqdm(self.data_loader)
      for index, [pos_ins, neg_ins] in enumerate(training_range):
          res_pos = self.test_one_step(pos_ins)
          ans = ans + [1 for i in range(len(res_pos))]
          score.append(res_pos)
  
          res_neg = self.test_one_step(neg_ins)
          ans = ans + [0 for i in range(len(res_pos))]
          score.append(res_neg)
  
      score = np.concatenate(score, axis=-1)
      ans = np.array(ans)
  
      if threshlod == None:
          threshlod, _ = self.get_best_threshlod(score, ans)
  
      res = np.concatenate(
          [ans.reshape(-1, 1), score.reshape(-1, 1)], axis=-1
      )
      order = np.argsort(score)
      res = res[order]
  
      total_all = (float)(len(score))
      total_current = 0.0
      total_true = np.sum(ans)
      total_false = total_all - total_true
  
      for index, [ans, score] in enumerate(res):
          if score > threshlod:
              acc = (
                  2 * total_current + total_false - index
              ) / total_all
              break
          elif ans == 1:
              total_current += 1.0
  
      return acc, threshlod
  
imports: import torch
  import torch.nn
  from torch.autograd import Variable
  import torch.optim
  import os
  import time
  import sys
  import datetime
  import ctypes
  import json
  import numpy
  from sklearn.metrics import roc_auc_score
  import copy
  from tqdm import tqdm
label_desc: Use image node_id 9 for calling the Tester obj's underlying member method code with example usage: obj.run_triple_classification(threshlod) and returns: acc,  threshlod