code: def eval_model(data, g_data, model, loss_func, device):
      model.eval()
      out = model(g_data)
  
      # train loss and auc
      tmp_loss, auc_train = get_loss(
          "train", data, out, loss_func, device
      )
      loss_train = tmp_loss.cpu().data.numpy()
  
      # val loss and auc
      _, auc_val = get_loss("val", data, out, loss_func, device)
  
      # test loss and auc
      _, auc_test = get_loss("test", data, out, loss_func, device)
  
      return loss_train, auc_train, auc_val, auc_test
  
file_path: dgl/examples/pytorch/P-GNN/main.py
imports: import os
  import warnings
  import dgl
  import numpy
  import torch
  import torch.nn
  from model import PGNN
  from sklearn.metrics import roc_auc_score
  from utils import get_dataset, preselect_anchor
label_desc: Use image node_id 3 for calling a global function with example usage: eval_model(data, g_data, model, loss_func, device) and returns: loss_train,  auc_train,  auc_val,  auc_test