code: def forward(self, input, target, mask, reduce=True):
      batch_size = input.shape[0]
      input = F.sigmoid(input)  # scale to 0-1
  
      input = input.reshape((batch_size, -1))
      target = paddle.cast(target.reshape((batch_size, -1)), "float32")
      mask = paddle.cast(mask.reshape((batch_size, -1)), "float32")
  
      input = input * mask
      target = target * mask
  
      a = paddle.sum(input * target, axis=1)
      b = paddle.sum(input * input, axis=1) + 0.001
      c = paddle.sum(target * target, axis=1) + 0.001
      d = (2 * a) / (b + c)
      loss = 1 - d
  
      loss = self.loss_weight * loss
  
      if reduce:
          loss = paddle.mean(loss)
  
      return loss
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  from paddle import nn
  import paddle.nn.functional
  import numpy
label_desc: Use image node_id 2 for calling the DiceLoss obj's underlying member method code with example usage: obj.forward(input, target, mask, reduce) and returns: loss