code: def evaluate(model, features, labels, mask):
      logits = model(features, training=False)
      logits = logits[mask]
      labels = labels[mask]
      return accuracy(logits, labels)
  
file_path: dgl/examples/tensorflow/gat/train.py
imports: import argparse
  import time
  import dgl
  import networkx
  import numpy
  import tensorflow
  from dgl.data import CiteseerGraphDataset, CoraGraphDataset, PubmedGraphDataset, register_data_args
  from gat import GAT
  from utils import EarlyStopping
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(model, features, labels, mask) and returns: accuracy