code: def evaluate(model, graph, feats, labels, idxs):
      model.eval()
      with torch.no_grad():
          logits = model(graph, feats)
          results = ()
          for idx in idxs:
              loss = F.cross_entropy(logits[idx], labels[idx])
              acc = torch.sum(
                  logits[idx].argmax(dim=1) == labels[idx]
              ).item() / len(idx)
              results += (loss, acc)
      return results
  
imports: import random
  import numpy
  import torch
  from torch.nn import functional
label_desc: Use image node_id 1 for calling a global function with example usage: evaluate(model, graph, feats, labels, idxs) and returns: results