code: def evaluate(model, loader, num, device):
      error = 0
      for graphs, targets in loader:
          graphs = graphs.to(device)
  
          nfeat, efeat = graphs.ndata["attr"], graphs.edata["edge_attr"]
          targets = targets.to(device)
          error += (
              (model(graphs, nfeat, efeat) - targets).abs().sum().item()
          )
  
      error = error / num
  
      return error
  
file_path: dgl/examples/pytorch/infograph/semisupervised.py
imports: import argparse
  import dgl
  import numpy
  import torch
  import torch.nn.functional
  from dgl.data import QM9EdgeDataset
  from dgl.data.utils import Subset
  from dgl.dataloading import GraphDataLoader
  from model import InfoGraphS
label_desc: Use image node_id 3 for calling a global function with example usage: evaluate(model, loader, num, device) and returns: error