code: def evaluate(model, dataset, itemset, device):
      model.eval()
      y = []
      y_hats = []
      dataloader = create_dataloader(dataset, itemset, device)
  
      for step, data in enumerate(dataloader):
          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=dataset.tasks[0].metadata["num_classes"],
      )
  
file_path: dgl/examples/sampling/graphbolt/quickstart/node_classification.py
imports: import dgl.graphbolt
  import dgl.nn
  import torch
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(model, dataset, itemset, device) and returns: MF