code: def __init__(
      self,
      graph,
      rep_size=128,
      batch_size=1000,
      negative_ratio=5,
      order=3,
  ):
      self.cur_epoch = 0
      self.order = order
      self.g = graph
      self.node_size = graph.G.number_of_nodes()
      self.rep_size = rep_size
      self.batch_size = batch_size
      self.negative_ratio = negative_ratio
  
      self.gen_sampling_table()
      self.sess = tf.Session()
      cur_seed = random.getrandbits(32)
      initializer = tf.contrib.layers.xavier_initializer(
          uniform=False, seed=cur_seed
      )
      with tf.variable_scope(
          "model", reuse=None, initializer=initializer
      ):
          self.build_graph()
      self.sess.run(tf.global_variables_initializer())
  
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 1 to create a new _LINE object from inherited base classes: object with example: obj = _LINE(graph, rep_size, batch_size, negative_ratio, order)