code: def forward(self, predicts, batch):
      targets = batch[1].astype("int64")
      label_lengths = batch[2].astype("int64")
      batch_size, num_steps, num_classes = (
          predicts.shape[0],
          predicts.shape[1],
          predicts.shape[2],
      )
      assert (
          len(targets.shape) == len(list(predicts.shape)) - 1
      ), "The target's shape and inputs's shape is [N, d] and [N, num_steps]"
  
      inputs = paddle.reshape(predicts, [-1, predicts.shape[-1]])
      targets = paddle.reshape(targets, [-1])
  
      return {"loss": paddle.sum(self.loss_func(inputs, targets))}
  
file_path: PaddleOCR/ppocr/losses/rec_att_loss.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from paddle import nn
label_desc: Use image node_id 2 for calling the AttentionLoss obj's underlying member method code with example usage: obj.forward(predicts, batch) and returns: dict