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 greedy_forward(self, SOS, feature):
      input = SOS
      output = paddle.zeros(
          [input.shape[0], self.max_text_length + 1, self.out_channels]
      )
      bbox_output = paddle.zeros(
          [input.shape[0], self.max_text_length + 1, self.loc_reg_num]
      )
      max_text_length = paddle.to_tensor(self.max_text_length)
      for i in range(max_text_length + 1):
          target_mask = self.make_mask(input)
          out_step, bbox_output_step = self.decode(
              input, feature, None, target_mask
          )
          prob = F.softmax(out_step, axis=-1)
          next_word = prob.argmax(axis=2, dtype="int64")
          input = paddle.concat(
              [input, next_word[:, -1].unsqueeze(-1)], axis=1
          )
          if i == self.max_text_length:
              output = out_step
              bbox_output = bbox_output_step
      return output, bbox_output
  
imports: import copy
  import math
  import paddle
  from paddle import nn
  from paddle.nn import functional
label_desc: Use image node_id 4 for calling the TableMasterHead obj's underlying member method code with example usage: obj.greedy_forward(SOS, feature) and returns: output,  bbox_output