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 decode(self, input, feature, src_mask, tgt_mask):
      # main process of transformer decoder.
      x = self.embedding(input)  # x: 1*x*512, feature: 1*3600,512
      x = self.positional_encoding(x)
  
      # origin transformer layers
      for i, layer in enumerate(self.layers):
          x = layer(x, feature, src_mask, tgt_mask)
  
      # cls head
      cls_x = x
      for layer in self.cls_layer:
          cls_x = layer(x, feature, src_mask, tgt_mask)
      cls_x = self.norm(cls_x)
  
      # bbox head
      bbox_x = x
      for layer in self.bbox_layer:
          bbox_x = layer(x, feature, src_mask, tgt_mask)
      bbox_x = self.norm(bbox_x)
      return self.cls_fc(cls_x), self.bbox_fc(bbox_x)
  
imports: import copy
  import math
  import paddle
  from paddle import nn
  from paddle.nn import functional
label_desc: Use image node_id 3 for calling the TableMasterHead obj's underlying member method code with example usage: obj.decode(input, feature, src_mask, tgt_mask) and returns: self,  self