code: def norm_trans_adj(self, adj):  # maybe can use softmax
      adj = F.relu(adj)
      r = F.softmax(adj, dim=-1)
      # print(adj.size())
      # row_sum = adj.sum(-1).unsqueeze(-1)
      # d_mat = row_sum.expand(adj.size())
      # r = torch.div(row_sum,d_mat)
      # r[torch.isnan(r)] = 0
  
      return r
  
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 6 for calling the Graph_trans obj's underlying member method code with example usage: obj.norm_trans_adj(adj) and returns: r