docstr: we don't change the edge_index, just update the edge_weight;
  some edge_weight are regarded as removed if it equals to zero
docstr_tok: ["we","do","n't","change","the","edge_index",",","just","update","the","edge_weight",";","some","edge_weight","are","regarded","as","removed","if","it","equals","to","zero"]
code: def forward(self, x, adj):
      """we don't change the edge_index, just update the edge_weight;
      some edge_weight are regarded as removed if it equals to zero"""
      x = x.to_dense()
  
      """GCN and GAT"""
      if self.attention:
          adj = self.att_coef(x, adj, i=0)
      # Add this line
      edge_index = adj._indices().to(self.device)
  
      x = self.gc1(x, edge_index, edge_weight=adj._values())
      x = F.relu(x)
      # x = self.bn1(x)
      if self.attention:  # if attention=True, use attention mechanism
          adj_2 = self.att_coef(x, adj, i=1)
          adj_memory = adj_2.to_dense()  # without memory
          # adj_memory = self.gate * adj.to_dense() + (1 - self.gate) * adj_2.to_dense()
          row, col = (
              adj_memory.nonzero()[:, 0],
              adj_memory.nonzero()[:, 1],
          )
          edge_index = torch.stack((row, col), dim=0)
          adj_values = adj_memory[row, col]
      else:
          edge_index = adj._indices()
          adj_values = adj._values()
      # Add this line
      edge_index = edge_index.to(self.device)
      adj_values = adj_values.to(self.device)
  
      x = F.dropout(x, self.dropout, training=self.training)
      x = self.gc2(x, edge_index, edge_weight=adj_values)
  
      return F.log_softmax(x, dim=1)
  
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 2 for calling the GCN4GNNGuard obj's underlying member method code with example usage: obj.forward(x, adj) and returns: F