code: def evaluate(model, features, graph, labels, mask):
      model.eval()
      with torch.no_grad():
          logits = model(graph, features)[
              mask
          ]  # only compute the evaluation set
          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/pytorch/sgc/sgc_reddit.py
imports: import argparse
  import math
  import time
  import dgl.function
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl import DGLGraph
  from dgl.data import load_data, register_data_args
  from dgl.nn.pytorch.conv import SGConv
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(model, features, graph, labels, mask) and returns: unknown