code: def get_embeddings(self):
      vectors = {}
      embeddings = self.embeddings.eval(session=self.sess)
      # embeddings = self.sess.run(tf.nn.l2_normalize(self.embeddings.eval(session=self.sess), 1))
      look_back = self.g.look_back_list
      for i, embedding in enumerate(embeddings):
          vectors[look_back[i]] = embedding
      return vectors
  
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 6 for calling the _LINE obj's underlying member method code with example usage: obj.get_embeddings() and returns: vectors