code: def evaluate(features, support, labels, mask, placeholders):
      t_test = time.time()
      feed_dict_val = construct_feed_dict(
          features, support, labels, mask, placeholders
      )
      outs_val = sess.run(
          [model.loss, model.accuracy], feed_dict=feed_dict_val
      )
      return outs_val[0], outs_val[1], (time.time() - t_test)
  
imports: from __future__ import division
  from __future__ import print_function
  import time
  import tensorflow
  from gcn.utils import *
  from gcn.models import GCN, MLP
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(features, support, labels, mask, placeholders) and returns: outs_val,  outs_val,  unknown