code: def train_one_epoch(self):
      sum_loss = 0.0
      batches = self.batch_iter()
      batch_id = 0
      for batch in batches:
          h, t, sign = batch
          feed_dict = {
              self.h: h,
              self.t: t,
              self.sign: sign,
          }
          _, cur_loss = self.sess.run(
              [self.train_op, self.loss], feed_dict
          )
          sum_loss += cur_loss
          batch_id += 1
      print(
          "epoch:{} sum of loss:{!s}".format(self.cur_epoch, sum_loss)
      )
      self.cur_epoch += 1
  
imports: from __future__ import print_function
  import random
  import math
  import numpy
  from sklearn.linear_model import LogisticRegression
  import tensorflow
  from .classify import Classifier, read_node_label
label_desc: Use image node_id 3 for calling the _LINE obj's underlying member method code with example usage: obj.train_one_epoch() without return types