docstr: Predict table label and coordinates for each step
  @param pre_chars: Table label in previous step
  @param features:
  @param hidden: hidden status in previous step
  @return:
docstr_tok: ["Predict","table","label","and","coordinates","for","each","step","@","param","pre_chars",":","Table","label","in","previous","step","@","param","features",":","@","param","hidden",":","hidden","status","in","previous","step","@","return",":"]
code: def _decode(self, pre_chars, features, hidden):
      """
      Predict table label and coordinates for each step
      @param pre_chars: Table label in previous step
      @param features:
      @param hidden: hidden status in previous step
      @return:
      """
      emb_feature = self.emb(pre_chars)
      # output shape is b * self.hidden_size
      (output, hidden), alpha = self.structure_attention_cell(
          hidden, features, emb_feature
      )
  
      # structure
      structure_step = self.structure_generator(output)
      # loc
      loc_step = self.loc_generator(output)
      return hidden, structure_step, loc_step
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import paddle
  import paddle.nn
  from paddle import ParamAttr
  import paddle.nn.functional
  import numpy
  from .rec_att_head import AttentionGRUCell
label_desc: Use image node_id 3 for calling the SLAHead obj's underlying member method code with example usage: obj._decode(pre_chars, features, hidden) and returns: hidden,  structure_step,  loc_step