code: def evaluate(model, dataloader, num_classes):
      model.eval()
      ys = []
      y_hats = []
      for it, data in enumerate(dataloader):
          with torch.no_grad():
              node_feature = data.node_features["feat"].float()
              blocks = data.sampled_subgraphs
              y = data.labels
              ys.append(y)
              y_hats.append(model(blocks, node_feature))
  
      return MF.accuracy(
          torch.cat(y_hats),
          torch.cat(ys),
          task="multiclass",
          num_classes=num_classes,
      )
  
imports: import argparse
  from functools import partial
  import dgl.graphbolt
  import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  from dgl.graphbolt.subgraph_sampler import SubgraphSampler
  from torch.utils.data import functional_datapipe
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(model, dataloader, num_classes) and returns: MF