code: def forward(self, x, adj):
      L = adj.sum(2).unsqueeze(2) + 1
      outputs = self.fc1(x)
      cache_list = [outputs]
      output_list = []
      for l in range(self.num_layers):
          Ax = adj.bmm(outputs)
          AxW = self.weight_list[l](Ax)
          AxW = AxW + self.weight_list[l](outputs)
          AxW = AxW / L
          gAxW = F.relu(AxW)
          cache_list.append(gAxW)
          outputs = torch.cat(cache_list, dim=2)
          output_list.append(self.dropout(gAxW))
      # gcn_outputs = torch.cat(output_list, dim=2)
      gcn_outputs = output_list[self.num_layers - 1]
      gcn_outputs = gcn_outputs + self.fc1(x)
  
      out = self.fc(gcn_outputs)
      return out
  
file_path: DeepKE/src/deepke/relation_extraction/standard/module/GCN.py
imports: import logging
  import torch
  import numpy
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the GCN obj's underlying member method code with example usage: obj.forward(x, adj) and returns: out