code: def incremental_evaluate(sess, model, minibatch_iter, size):
      t_test = time.time()
      finished = False
      val_losses = []
      val_mrrs = []
      iter_num = 0
      while not finished:
          (
              feed_dict_val,
              finished,
              _,
          ) = minibatch_iter.incremental_val_feed_dict(size, iter_num)
          iter_num += 1
          outs_val = sess.run(
              [model.loss, model.ranks, model.mrr],
              feed_dict=feed_dict_val,
          )
          val_losses.append(outs_val[0])
          val_mrrs.append(outs_val[2])
      return (
          np.mean(val_losses),
          np.mean(val_mrrs),
          (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 3 for calling a global function with example usage: incremental_evaluate(sess, model, minibatch_iter, size) and returns: np,  np,  unknown