code: def train(self):
      self.adj = self.getAdjMat()
      self.node_size = self.adj.shape[0]
      self.Ak = np.matrix(np.identity(self.node_size))
      self.RepMat = np.zeros(
          (self.node_size, int(self.dim * self.Kstep))
      )
      for i in range(self.Kstep):
          print("Kstep =", i)
          self.Ak = np.dot(self.Ak, self.adj)
          probTranMat = self.GetProbTranMat(self.Ak)
          Rk = self.GetRepUseSVD(probTranMat, 0.5)
          Rk = normalize(Rk, axis=1, norm="l2")
          self.RepMat[:, self.dim * i : self.dim * (i + 1)] = Rk[:, :]
      # get embeddings
      self.vectors = {}
      look_back = self.g.look_back_list
      for i, embedding in enumerate(self.RepMat):
          self.vectors[look_back[i]] = embedding
  
file_path: OpenNE/src/openne/grarep.py
imports: import math
  import numpy
  from numpy import linalg
  from sklearn.preprocessing import normalize
label_desc: Use image node_id 6 for calling the GraRep obj's underlying member method code with example usage: obj.train() without return types