code: def evaluate(model, dataloader, device, num_classes):
      model.eval()
      y_hats = []
      ys = []
      for minibatch in dataloader:
          node_features = minibatch.node_features["feat"]
          labels = minibatch.labels
          out = model(minibatch.blocks, node_features, device)
          y_hats.append(out)
          ys.append(labels)
  
      return MF.accuracy(
          torch.cat(y_hats),
          torch.cat(ys),
          task="multiclass",
          num_classes=num_classes,
      )
  
file_path: dgl/examples/sampling/pyg/node_classification.py
imports: import argparse
  import dgl.graphbolt
  import torch
  import torch.nn.functional
  import torchmetrics.functional
  from torch_geometric.nn import SAGEConv
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate(model, dataloader, device, num_classes) and returns: MF