code: def evaluate(model, g, labels, val_nid, test_nid, batch_size, 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.
      batch_size : Number of nodes to compute at the same time.
      device : The GPU device to evaluate on.
      """
      model.eval()
      with th.no_grad():
          inputs = g.ndata["feat"]
          model = model.cpu()
          pred = model.inference(g, inputs, batch_size, device)
      model.train()
      return (
          compute_acc(pred[val_nid], labels[val_nid]),
          compute_acc(pred[test_nid], labels[test_nid]),
          pred,
      )
  
imports: import argparse
  import time
  import traceback
  from functools import partial
  import dgl
  import dgl.function
  import dgl.nn.pytorch
  import numpy
  import torch
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import tqdm
  from dgl.data import RedditDataset
  from ogb.nodeproppred import DglNodePropPredDataset
  from sampler import ClusterIter, subgraph_collate_fn
  from torch.utils.data import DataLoader
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(model, g, labels, val_nid, test_nid, batch_size, device) and returns: compute_acc,  compute_acc,  pred