code: def evaluate(sess, model, minibatch_iter, size=None):
      t_test = time.time()
      feed_dict_val = minibatch_iter.val_feed_dict(size)
      outs_val = sess.run(
          [model.loss, model.ranks, model.mrr], feed_dict=feed_dict_val
      )
      return (
          outs_val[0],
          outs_val[1],
          outs_val[2],
          (time.time() - t_test),
      )
  
file_path: GraphSAGE/graphsage/unsupervised_train.py
imports: from __future__ import division
  from __future__ import print_function
  import os
  import time
  import tensorflow
  import numpy
  from graphsage.models import SampleAndAggregate, SAGEInfo, Node2VecModel
  from graphsage.minibatch import EdgeMinibatchIterator
  from graphsage.neigh_samplers import UniformNeighborSampler
  from graphsage.utils import load_data
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(sess, model, minibatch_iter, size) and returns: outs_val,  outs_val,  outs_val,  unknown