code: def forward(self, inputs, targets=None):
      # if and else branch are both needed when you want to assign a variable
      # if you modify the var in just one branch, then the modification will not work.
      fea = inputs[-1]
      last_shape = int(np.prod(fea.shape[2:]))  # gry added
      fea = paddle.reshape(
          fea, [fea.shape[0], fea.shape[1], last_shape]
      )
      fea = fea.transpose([0, 2, 1])  # (NTC)(batch, width, channels)
      batch_size = fea.shape[0]
  
      hidden = paddle.zeros((batch_size, self.hidden_size))
      output_hiddens = paddle.zeros(
          (batch_size, self.max_text_length + 1, self.hidden_size)
      )
      if self.training and targets is not None:
          structure = targets[0]
          for i in range(self.max_text_length + 1):
              elem_onehots = self._char_to_onehot(
                  structure[:, i], onehot_dim=self.out_channels
              )
              (outputs, hidden), alpha = self.structure_attention_cell(
                  hidden, fea, elem_onehots
              )
              output_hiddens[:, i, :] = outputs
          structure_probs = self.structure_generator(output_hiddens)
          loc_fea = fea.transpose([0, 2, 1])
          loc_fea = self.loc_fea_trans(loc_fea)
          loc_fea = loc_fea.transpose([0, 2, 1])
          loc_concat = paddle.concat([output_hiddens, loc_fea], axis=2)
          loc_preds = self.loc_generator(loc_concat)
          loc_preds = F.sigmoid(loc_preds)
      else:
          temp_elem = paddle.zeros(shape=[batch_size], dtype="int32")
          structure_probs = None
          loc_preds = None
          elem_onehots = None
          outputs = None
          alpha = None
          max_text_length = paddle.to_tensor(self.max_text_length)
          for i in range(max_text_length + 1):
              elem_onehots = self._char_to_onehot(
                  temp_elem, onehot_dim=self.out_channels
              )
              (outputs, hidden), alpha = self.structure_attention_cell(
                  hidden, fea, elem_onehots
              )
              output_hiddens[:, i, :] = outputs
              structure_probs_step = self.structure_generator(outputs)
              temp_elem = structure_probs_step.argmax(
                  axis=1, dtype="int32"
              )
  
          structure_probs = self.structure_generator(output_hiddens)
          structure_probs = F.softmax(structure_probs)
          loc_fea = fea.transpose([0, 2, 1])
          loc_fea = self.loc_fea_trans(loc_fea)
          loc_fea = loc_fea.transpose([0, 2, 1])
          loc_concat = paddle.concat([output_hiddens, loc_fea], axis=2)
          loc_preds = self.loc_generator(loc_concat)
          loc_preds = F.sigmoid(loc_preds)
      return {
          "structure_probs": structure_probs,
          "loc_preds": loc_preds,
      }
  
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 TableAttentionHead obj's underlying member method code with example usage: obj.forward(inputs, targets) and returns: dict