code: def train_one_epoch(self, data, batch_size, learning_rate):
      num_examples = data.num_examples
      total_loss = []
      total_accuracy = []
      for i in range(num_examples // batch_size):
          batch = data.next_batch(batch_size)
          images, labels = batch
          feed_dict = {
              self.images: images,
              self.labels: labels,
              self.learning_rate: learning_rate,
              self.is_training: True,
          }
          fetches = [self.train_step, self.cross_entropy, self.accuracy]
          result = self.sess.run(fetches, feed_dict=feed_dict)
          _, loss, accuracy = result
          total_loss.append(loss)
          total_accuracy.append(accuracy)
          # save logs about "loss" and "acc" if the option is true
          if self.run_config.should_save_logs:
              self.batches_step += 1
              self.log_loss_accuracy(
                  loss,
                  accuracy,
                  self.batches_step,
                  prefix="per_batch",
                  should_print=False,
              )
      mean_loss = np.mean(total_loss)
      mean_accuracy = np.mean(total_accuracy)
      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 8 for calling the BasicModel obj's underlying member method code with example usage: obj.train_one_epoch(data, batch_size, learning_rate) and returns: mean_loss,  mean_accuracy