code: def evaluate(model, features, labels, mask):
      model.eval()
      with torch.no_grad():
          logits = model(features)
          logits = logits[mask]
          labels = labels[mask]
          return accuracy(logits, labels)
  
file_path: dgl/examples/pytorch/hardgat/train.py
imports: import argparse
  import time
  import dgl
  import numpy
  import torch
  import torch.nn.functional
  from dgl.data import CiteseerGraphDataset, CoraGraphDataset, PubmedGraphDataset, register_data_args
  from hgao import HardGAT
  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