code: def forward(self, graph):
      g = graph.g
      nids, eids = graph.nids, graph.eids
  
      # embed
      src_embed, src_pos = self.src_embed(graph.src[0]), self.pos_enc(
          graph.src[1]
      )
      tgt_embed, tgt_pos = self.tgt_embed(graph.tgt[0]), self.pos_enc(
          graph.tgt[1]
      )
      g.nodes[nids["enc"]].data["x"] = self.pos_enc.dropout(
          src_embed + src_pos
      )
      g.nodes[nids["dec"]].data["x"] = self.pos_enc.dropout(
          tgt_embed + tgt_pos
      )
  
      for i in range(self.encoder.N):
          pre_func = self.encoder.pre_func(i, "qkv")
          post_func = self.encoder.post_func(i)
          nodes, edges = nids["enc"], eids["ee"]
          self.update_graph(
              g, edges, [(pre_func, nodes)], [(post_func, nodes)]
          )
  
      for i in range(self.decoder.N):
          pre_func = self.decoder.pre_func(i, "qkv")
          post_func = self.decoder.post_func(i)
          nodes, edges = nids["dec"], eids["dd"]
          self.update_graph(
              g, edges, [(pre_func, nodes)], [(post_func, nodes)]
          )
          pre_q = self.decoder.pre_func(i, "q", 1)
          pre_kv = self.decoder.pre_func(i, "kv", 1)
          post_func = self.decoder.post_func(i, 1)
          nodes_e, edges = nids["enc"], eids["ed"]
          self.update_graph(
              g,
              edges,
              [(pre_q, nodes), (pre_kv, nodes_e)],
              [(post_func, nodes)],
          )
  
      # visualize attention
      """
              if self.att_weight_map is None:
                  self._register_att_map(g, graph.nid_arr['enc'][VIZ_IDX], graph.nid_arr['dec'][VIZ_IDX])
          """
  
      return self.generator(g.ndata["x"][nids["dec"]])
  
imports: from .config import *
  from .act import *
  from .attention import *
  from .viz import *
  from .layers import *
  from .functions import *
  from .embedding import *
  import threading
  import dgl.function
  import torch
  import torch.nn.init
label_desc: Use image node_id 4 for calling the Transformer obj's underlying member method code with example usage: obj.forward(graph) and returns: self