code: def evaluate(model, g, features, labels, mask):
      pred = model(g, features).argmax(axis=1)
      accuracy = ((pred == labels) * mask).sum() / mask.sum().asscalar()
      return accuracy.asscalar()
  
file_path: dgl/examples/mxnet/sgc/sgc.py
imports: import argparse
  import math
  import time
  import dgl
  import mxnet
  import numpy
  from dgl.data import CiteseerGraphDataset, CoraGraphDataset, PubmedGraphDataset, register_data_args
  from dgl.nn.mxnet.conv import SGConv
  from mxnet import gluon, nd
  from mxnet.gluon import nn
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(model, g, features, labels, mask) and returns: accuracy