code: def test(self, idx_test):
      self.eval()
      output = (
          self.predict()
      )  # here use the self.features and self.adj_norm in training stage
      loss_test = F.nll_loss(output[idx_test], self.labels[idx_test])
      acc_test = utils.accuracy(output[idx_test], self.labels[idx_test])
      print(
          "Test set results:",
          "loss= {:.4f}".format(loss_test.item()),
          "accuracy= {:.4f}".format(acc_test.item()),
      )
      return acc_test, output
  
imports: import numpy
  from copy import deepcopy
  from numba import njit
  import torch.nn
  import torch.nn.functional
  import math
  import torch
  import torch.optim
  import typing
  from torch_geometric.data import Data
  from sklearn.metrics.pairwise import euclidean_distances, cosine_similarity
  from scipy.sparse import lil_matrix
  from sklearn.preprocessing import normalize
  import scipy.sparse
  import autogl.data
  from ..None import register_model
  from .None import utils
  from ..gcn import GCN
  from .nn import GCNConv
  from ..base import BaseAutoModel
  from .....utils import get_logger
  from .utils import accuracy
label_desc: Use image node_id 9 for calling the GCN4GNNGuard_attack obj's underlying member method code with example usage: obj.test(idx_test) and returns: acc_test,  output