code: def evaluate(model, g, labels, mask, multilabel=False):
      model.eval()
      with torch.no_grad():
          logits = model(g)
          logits = logits[mask]
          labels = labels[mask]
          f1_mic, f1_mac = calc_f1(
              labels.cpu().numpy(), logits.cpu().numpy(), multilabel
          )
          return f1_mic, f1_mac
  
file_path: dgl/examples/pytorch/graphsaint/utils.py
imports: import json
  import os
  from functools import namedtuple
  import dgl
  import numpy
  import scipy.sparse
  import torch
  from sklearn.metrics import f1_score
  from sklearn.preprocessing import StandardScaler
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate(model, g, labels, mask, multilabel) and returns: f1_mic,  f1_mac