code: def evaluate(model, H, X, Y, val_mask, test_mask, num_classes):
      model.eval()
      Y_hat = model(H, X)
      val_acc = accuracy(
          Y_hat[val_mask],
          Y[val_mask],
          task="multiclass",
          num_classes=num_classes,
      )
      test_acc = accuracy(
          Y_hat[test_mask],
          Y[test_mask],
          task="multiclass",
          num_classes=num_classes,
      )
      return val_acc, test_acc
  
imports: import argparse
  import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  import tqdm
  from dgl.data import CoraGraphDataset
  from torchmetrics.functional import accuracy
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate(model, H, X, Y, val_mask, test_mask, num_classes) and returns: val_acc,  test_acc