class_docstr: CANLoss is consist of two part:
      word_average_loss: average accuracy of the symbol
      counting_loss: counting loss of every symbol
code: def forward(self, preds, batch):
      word_probs = preds[0]
      counting_preds = preds[1]
      counting_preds1 = preds[2]
      counting_preds2 = preds[3]
      labels = batch[2]
      labels_mask = batch[3]
      counting_labels = gen_counting_label(
          labels, self.out_channel, True
      )
      counting_loss = (
          self.counting_loss(counting_preds1, counting_labels)
          + self.counting_loss(counting_preds2, counting_labels)
          + self.counting_loss(counting_preds, counting_labels)
      )
  
      word_loss = self.cross(
          paddle.reshape(word_probs, [-1, word_probs.shape[-1]]),
          paddle.reshape(labels, [-1]),
      )
      word_average_loss = (
          paddle.sum(paddle.reshape(word_loss * labels_mask, [-1]))
          / (paddle.sum(labels_mask) + 1e-10)
          if self.use_label_mask
          else word_loss
      )
      loss = word_average_loss + counting_loss
      return {"loss": loss}
  
file_path: PaddleOCR/ppocr/losses/rec_can_loss.py
imports: import paddle
  import paddle.nn
  import numpy
label_desc: Use image node_id 2 for calling the CANLoss obj's underlying member method code with example usage: obj.forward(preds, batch) and returns: dict