code: def forward(self, ent, ent_mask, ent_len, rel, rel_mask, graphs):
      device = ent.device
      graphs = graphs.to(device)
      ent_mask = ent_mask == 0  # reverse mask
      rel_mask = rel_mask == 0
      init_h = []
      for i in range(graphs.batch_size):
          init_h.append(ent[i][ent_mask[i]])
          init_h.append(rel[i][rel_mask[i]])
      init_h = torch.cat(init_h, 0)
      feats = init_h
      for i in range(self.prop):
          feats = self.gat[i](graphs, feats)
      g_root = feats.index_select(
          0,
          graphs.filter_nodes(
              lambda x: x.data["type"] == NODE_TYPE["root"]
          ).to(device),
      )
      g_ent = pad(
          feats.index_select(
              0,
              graphs.filter_nodes(
                  lambda x: x.data["type"] == NODE_TYPE["entity"]
              ).to(device),
          ).split(ent_len),
          out_type="tensor",
      )
      return g_ent, g_root
  
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 2 for calling the GraphTrans obj's underlying member method code with example usage: obj.forward(ent, ent_mask, ent_len, rel, rel_mask, graphs) and returns: g_ent,  g_root