class_docstr: DetailAggregateLoss's implementation based on PaddlePaddle.
  
  The original article refers to Meituan
  Fan, Mingyuan, et al. "Rethinking BiSeNet For Real-time Semantic Segmentation."
  (https://arxiv.org/abs/2104.13188)
  
  Args:
      ignore_index (int64, optional): Specifies a target value that is ignored
          and does not contribute to the input gradient. Default ``255``.
docstr: simplified diceloss for DetailAggregateLoss.
docstr_tok: ["simplified","diceloss","for","DetailAggregateLoss","."]
code: def fixed_dice_loss_func(self, input, target):
      """
      simplified diceloss for DetailAggregateLoss.
      """
      smooth = 1.0
      n = input.shape[0]
      iflat = paddle.reshape(input, [n, -1])
      tflat = paddle.reshape(target, [n, -1])
      intersection = paddle.sum((iflat * tflat), axis=1)
      loss = 1 - (
          (2.0 * intersection + smooth)
          / (
              paddle.sum(iflat, axis=1)
              + paddle.sum(tflat, axis=1)
              + smooth
          )
      )
      return paddle.mean(loss)
  
file_path: PaddleSeg/paddleseg/models/losses/detail_aggregate_loss.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
label_desc: Use image node_id 3 for calling the DetailAggregateLoss obj's underlying member method code with example usage: obj.fixed_dice_loss_func(input, target) and returns: paddle