code: def evaluate(model, A_norm, H, label, val_mask, test_mask):
      model.eval()
      logits = model(A_norm, H)
      pred = logits.argmax(dim=1)
  
      # Compute accuracy on validation/test set.
      val_acc = (pred[val_mask] == label[val_mask]).float().mean()
      test_acc = (pred[test_mask] == label[test_mask]).float().mean()
      return val_acc, test_acc
  
file_path: dgl/examples/sparse/gcnii.py
imports: import math
  import dgl.sparse
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.data import CoraGraphDataset
  from torch.optim import Adam
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(model, A_norm, H, label, val_mask, test_mask) and returns: val_acc,  test_acc