code: def forward(self, predicts, batch):
      if isinstance(predicts, (list, tuple)):
          predicts = predicts[-1]
      predicts = predicts.transpose((1, 0, 2))
      N, B, _ = predicts.shape
      preds_lengths = paddle.to_tensor(
          [N] * B, dtype="int64", place=paddle.CPUPlace()
      )
      labels = batch[1].astype("int32")
      label_lengths = batch[2].astype("int64")
      loss = self.loss_func(
          predicts, labels, preds_lengths, label_lengths
      )
      if self.use_focal_loss:
          weight = paddle.exp(-loss)
          weight = paddle.subtract(paddle.to_tensor([1.0]), weight)
          weight = paddle.square(weight)
          loss = paddle.multiply(loss, weight)
      loss = loss.mean()
      return {"loss": loss}
  
file_path: PaddleOCR/ppocr/losses/rec_ctc_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 CTCLoss obj's underlying member method code with example usage: obj.forward(predicts, batch) and returns: dict