class_docstr: Split to two transformer header at the last layer.
  Cls_layer is used to structure token classification.
  Bbox_layer is used to regress bbox coord.
code: def forward_train(self, out_enc, targets):
      # x is token of label
      # feat is feature after backbone before pe.
      # out_enc is feature after pe.
      padded_targets = targets[0]
      src_mask = None
      tgt_mask = self.make_mask(padded_targets[:, :-1])
      output, bbox_output = self.decode(
          padded_targets[:, :-1], out_enc, src_mask, tgt_mask
      )
      return {"structure_probs": output, "loc_preds": bbox_output}
  
imports: import copy
  import math
  import paddle
  from paddle import nn
  from paddle.nn import functional
label_desc: Use image node_id 5 for calling the TableMasterHead obj's underlying member method code with example usage: obj.forward_train(out_enc, targets) and returns: dict