code: def test(self, data, batch_size):
      num_examples = data.num_examples
      total_loss = []
      total_accuracy = []
      for i in range(num_examples // batch_size):
          batch = data.next_batch(batch_size)
          feed_dict = {
              self.images: batch[0],
              self.labels: batch[1],
              self.is_training: False,
          }
          fetches = [self.cross_entropy, self.accuracy]
          loss, accuracy = self.sess.run(fetches, feed_dict=feed_dict)
          total_loss.append(loss)
          total_accuracy.append(accuracy)
      mean_loss = np.mean(total_loss)
      mean_accuracy = np.mean(total_accuracy)
      remain_num = num_examples % batch_size
      if remain_num != 0:
          batch = data.next_batch(remain_num)
          feed_dict = {
              self.images: batch[0],
              self.labels: batch[1],
              self.is_training: False,
          }
          fetches = [self.cross_entropy, self.accuracy]
          loss, accuracy = self.sess.run(fetches, feed_dict=feed_dict)
  
          mean_loss = (
              mean_loss * (num_examples - remain_num)
              + loss * remain_num
          ) / num_examples
          mean_accuracy = (
              mean_accuracy * (num_examples - remain_num)
              + accuracy * remain_num
          ) / num_examples
      return mean_loss, mean_accuracy
  
imports: import os
  import shutil
  import tensorflow
  import numpy
  import time
  from datetime import timedelta
  import json
  import pickle
label_desc: Use image node_id 9 for calling the BasicModel obj's underlying member method code with example usage: obj.test(data, batch_size) and returns: mean_loss,  mean_accuracy