code: def forward(self, inputs, targets=None):
      fea = inputs[-1]
      batch_size = fea.shape[0]
      # reshape
      fea = paddle.reshape(fea, [fea.shape[0], fea.shape[1], -1])
      fea = fea.transpose([0, 2, 1])  # (NTC)(batch, width, channels)
  
      hidden = paddle.zeros((batch_size, self.hidden_size))
      structure_preds = paddle.zeros(
          (batch_size, self.max_text_length + 1, self.num_embeddings)
      )
      loc_preds = paddle.zeros(
          (batch_size, self.max_text_length + 1, self.loc_reg_num)
      )
      structure_preds.stop_gradient = True
      loc_preds.stop_gradient = True
      if self.training and targets is not None:
          structure = targets[0]
          for i in range(self.max_text_length + 1):
              hidden, structure_step, loc_step = self._decode(
                  structure[:, i], fea, hidden
              )
              structure_preds[:, i, :] = structure_step
              loc_preds[:, i, :] = loc_step
      else:
          pre_chars = paddle.zeros(shape=[batch_size], dtype="int32")
          max_text_length = paddle.to_tensor(self.max_text_length)
          # for export
          loc_step, structure_step = None, None
          for i in range(max_text_length + 1):
              hidden, structure_step, loc_step = self._decode(
                  pre_chars, fea, hidden
              )
              pre_chars = structure_step.argmax(axis=1, dtype="int32")
              structure_preds[:, i, :] = structure_step
              loc_preds[:, i, :] = loc_step
      if not self.training:
          structure_preds = F.softmax(structure_preds)
      return {
          "structure_probs": structure_preds,
          "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 2 for calling the SLAHead obj's underlying member method code with example usage: obj.forward(inputs, targets) and returns: dict