code: def evaluate(args, model, graph, features, itemset, num_classes):
      model.eval()
      y = []
      y_hats = []
      dataloader = create_dataloader(
          graph=graph,
          features=features,
          itemset=itemset,
          batch_size=args.batch_size,
          fanout=args.fanout,
          device=args.device,
          num_workers=args.num_workers,
          job="evaluate",
      )
  
      for step, data in tqdm(enumerate(dataloader), "Evaluating"):
          x = data.node_features["feat"]
          y.append(data.labels)
          y_hats.append(model(data.blocks, x))
  
      return MF.accuracy(
          torch.cat(y_hats),
          torch.cat(y),
          task="multiclass",
          num_classes=num_classes,
      )
  
imports: import argparse
  import time
  import dgl.graphbolt
  import dgl.nn
  import torch
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate(args, model, graph, features, itemset, num_classes) and returns: MF