code: def evaluate(
      rank, model, graph, features, itemset, num_classes, device
  ):
      model.eval()
      y = []
      y_hats = []
      dataloader = create_dataloader(
          graph,
          features,
          itemset,
          drop_last=False,
          shuffle=False,
          drop_uneven_inputs=False,
          device=device,
      )
  
      for step, data in (
          tqdm.tqdm(enumerate(dataloader))
          if rank == 0
          else enumerate(dataloader)
      ):
          blocks = data.blocks
          x = data.node_features["feat"]
          y.append(data.labels)
          y_hats.append(model.module(blocks, x))
  
      res = MF.accuracy(
          torch.cat(y_hats),
          torch.cat(y),
          task="multiclass",
          num_classes=num_classes,
      )
  
      return res.to(device)
  
imports: import os
  import time
  import dgl.graphbolt
  import dgl.nn
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  import tqdm
  from torch.distributed.algorithms.join import Join
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(rank, model, graph, features, itemset, num_classes, device) and returns: res