code: def __init__(
      self,
      graph,
      encoder_layer_list,
      alpha=1e-6,
      beta=5.0,
      nu1=1e-5,
      nu2=1e-5,
      batch_size=100,
      max_iter=2000,
      learning_rate=None,
  ):
      self.g = graph
  
      self.node_size = self.g.G.number_of_nodes()
      self.rep_size = encoder_layer_list[-1]
  
      self.encoder_layer_list = [self.node_size] + encoder_layer_list
      self.encoder_layer_num = len(encoder_layer_list) + 1
  
      self.alpha = alpha
      self.beta = beta
      self.nu1 = nu1
      self.nu2 = nu2
      self.bs = batch_size
      self.max_iter = max_iter
      self.lr = learning_rate
      if self.lr is None:
          self.lr = tf.train.inverse_time_decay(
              0.1, self.max_iter, decay_steps=1, decay_rate=0.9999
          )
  
      self.sess = tf.Session()
      self.vectors = {}
  
      self.adj_mat = self.getAdj()
      self.deg_vec = np.sum(self.adj_mat, axis=1)
      self.embeddings = self.get_train()
  
      look_back = self.g.look_back_list
  
      for i, embedding in enumerate(self.embeddings):
          self.vectors[look_back[i]] = embedding
  
file_path: OpenNE/src/openne/sdne.py
imports: import tensorflow
  import numpy
label_desc: Use image node_id 1 to create a new SDNE2 object from inherited base classes: object with example: obj = SDNE2(graph, encoder_layer_list, alpha, beta, nu1, nu2, batch_size, max_iter, learning_rate)