code: def __init__(self, args):
      super().__init__()
      self.args = args
      if args.graph_enc == "gat":
          # we only support gtrans, don't use this one
          self.gat = nn.ModuleList(
              [
                  GAT(
                      args.nhid,
                      args.nhid // 4,
                      4,
                      attn_drop=args.attn_drop,
                      trans=False,
                  )
                  for _ in range(args.prop)
              ]
          )  # untested
      else:
          self.gat = nn.ModuleList(
              [
                  GAT(
                      args.nhid,
                      args.nhid // 4,
                      4,
                      attn_drop=args.attn_drop,
                      ffn_drop=args.drop,
                      trans=True,
                  )
                  for _ in range(args.prop)
              ]
          )
      self.prop = args.prop
  
imports: import math
  import torch
  import torch.nn.functional
  from torch import nn
  from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
  from utlis import *
  import dgl.function
  from dgl.nn.functional import edge_softmax
label_desc: Use image node_id 1 to create a new GraphTrans object from inherited base classes: nn with example: obj = GraphTrans(args)