code: def evaluate(model, A, dataloader, ndata, num_classes):
      model.eval()
      ys = []
      y_hats = []
      fanouts = [10, 10, 10]
      for it, seeds in enumerate(dataloader):
          with torch.no_grad():
              sampled_matrices, x, y = multilayer_sample(
                  A, fanouts, seeds, ndata
              )
              ys.append(y)
              y_hats.append(model(sampled_matrices, x))
  
      return MF.accuracy(
          torch.cat(y_hats),
          torch.cat(ys),
          task="multiclass",
          num_classes=num_classes,
      )
  
imports: import argparse
  import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  from dgl.data import AsNodePredDataset
  from ogb.nodeproppred import DglNodePropPredDataset
label_desc: Use image node_id 2 for calling a global function with example usage: evaluate(model, A, dataloader, ndata, num_classes) and returns: MF