code: def test_autogl(adj, features, device, attention):
      """"""
      """test on GCN """
      """model_name could be 'GCN', 'GAT', 'GIN','JK'  """
      accs = []
      for seed in tqdm(range(5)):
          # defense model
          gcn = (
              AutoGNNGuard(
                  num_features=pyg_data.num_node_features,
                  num_classes=pyg_data.num_classes,
                  device=args.device,
                  init=False,
              )
              .from_hyper_parameter(model_hp)
              .model
          )
          gcn = gcn.to(device)
          gcn.fit(
              features,
              adj,
              labels,
              idx_train,
              idx_val=idx_val,
              idx_test=idx_test,
              attention=attention,
              verbose=True,
              train_iters=81,
          )
          gcn.eval()
          acc_test, output = gcn.test(idx_test=idx_test)
          accs.append(acc_test.item())
      mean = np.mean(accs)
      std = np.std(accs)
      return {"mean": mean, "std": std}
  
imports: import os
  import torch
  from deeprobust.graph.targeted_attack import Nettack
  from deeprobust.graph.utils import *
  from deeprobust.graph.data import Dataset
  from deeprobust.graph.global_attack import Metattack
  import argparse
  from deeprobust.graph.defense import *
  from deeprobust.graph.data import Dataset, PrePtbDataset, Dpr2Pyg, AmazonPyg
  from scipy.sparse import csr_matrix
  from tqdm import tqdm
  import scipy
  import scipy.sparse
  import numpy
  from sklearn.preprocessing import normalize
  import pickle
  from autogl.module.model.pyg import AutoGNNGuard, AutoGNNGuard_attack
  from autogl.solver.utils import set_seed
label_desc: Use image node_id 5 for calling a global function with example usage: test_autogl(adj, features, device, attention) and returns: dict