code: def __init__(
      self,
      nfeat,
      nclass,
      nhid,
      activation,
      dropout=0.5,
      lr=0.01,
      drop=False,
      weight_decay=5e-4,
      with_relu=True,
      with_bias=True,
      add_self_loops=True,
      normalize=True,
  ):
      super(GCN4GNNGuard_attack, self).__init__(
          nfeat,
          nclass,
          nhid,
          activation,
          dropout=dropout,
          add_self_loops=add_self_loops,
          normalize=normalize,
      )
  
      self.lr = lr
      self.weight_decay = weight_decay
      self.dropout = dropout
      self.nclass = nclass
      self.nfeat = nfeat
      self.hidden_sizes = nhid
      self.drop = drop
      if not with_relu:
          self.weight_decay = 0
      else:
          self.weight_decay = weight_decay
      self.with_relu = with_relu
      self.with_bias = with_bias
  
      self.gc1 = GCNConv(
          nfeat,
          nhid[0],
          bias=True,
      )
      self.gc2 = GCNConv(
          nhid[0],
          nclass,
          bias=True,
      )
  
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 1 to create a new GCN4GNNGuard_attack object from inherited base classes: GCN with example: obj = GCN4GNNGuard_attack(nfeat, nclass, nhid, activation, dropout, lr, drop, weight_decay, with_relu, with_bias, add__loops, normalize)