code: def evaluate(model, g, nfeat, labels, val_nid, test_nid, device):
      """
      Evaluate the model on the validation set specified by ``val_mask``.
      g : The entire graph.
      inputs : The features of all the nodes.
      labels : The labels of all the nodes.
      val_mask : A 0-1 mask indicating which nodes do we actually compute the accuracy for.
      device : The GPU device to evaluate on.
      """
      model.eval()
      with th.no_grad():
          pred = model.inference(g, nfeat, device)
      model.train()
      return (
          compute_acc(pred[val_nid], labels[val_nid]),
          compute_acc(pred[test_nid], labels[test_nid]),
          pred,
      )
  
file_path: dgl/examples/pytorch/ogb/ogbn-products/graphsage/main.py
imports: import argparse
  import time
  import dgl
  import dgl.nn.pytorch
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import tqdm
  from ogb.nodeproppred import DglNodePropPredDataset
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(model, g, nfeat, labels, val_nid, test_nid, device) and returns: compute_acc,  compute_acc,  pred