code: def evaluate(g, features, labels, mask, model):
      model.eval()
      with torch.no_grad():
          logits = model(g, features)
          logits = logits[mask]
          labels = labels[mask]
          _, indices = torch.max(logits, dim=1)
          correct = torch.sum(indices == labels)
          return correct.item() * 1.0 / len(labels)
  
file_path: dgl/examples/core/gat/train.py
imports: import argparse
  import time
  import dgl.nn
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl import AddSelfLoop
  from dgl.data import CiteseerGraphDataset, CoraGraphDataset, PubmedGraphDataset
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(g, features, labels, mask, model) and returns: unknown