code: def forward(self, input, relu=False, adj_return=False, adj=None):
      support = torch.matmul(input, self.weight)
      # print(support.size(),self.adj.size())
      if adj is None:
          adj = self.adj
      adj1 = self.norm_trans_adj(adj)
      output = torch.matmul(adj1, support)
      if adj_return:
          output1 = F.normalize(output, p=2, dim=-1)
          self.adj_mat = torch.matmul(
              output1, output1.transpose(-2, -1)
          )
      if self.bias is not None:
          return output + self.bias
      else:
          if relu:
              return F.relu(output)
          else:
              return output
  
imports: import torch
  from .None import graph
  import torch.nn
  import torch.nn.functional
  from torch.nn.parameter import Parameter
label_desc: Use image node_id 3 for calling the Graph_trans obj's underlying member method code with example usage: obj.forward(input, relu, adj_return, adj) and returns: unknown, F, output