code: def forward(self, x, y, loss_mask=None):
      shp_x = x.shape
  
      if self.batch_dice:
          axes = [0] + list(range(2, len(shp_x)))
      else:
          axes = list(range(2, len(shp_x)))
  
      if self.apply_softmax:
          x = F.softmax(x, axis=1)
  
      tp, fp, fn, _ = get_tp_fp_fn_tn(x, y, axes, loss_mask, False)
  
      nominator = 2 * tp + self.smooth
      denominator = 2 * tp + fp + fn + self.smooth
  
      dc = nominator / (denominator + 1e-8)
  
      if not self.do_bg:
          if self.batch_dice:
              dc = dc[1:]
          else:
              dc = dc[:, 1:]
      # dc = dc.mean()
  
      # return -dc
      return dc
  
file_path: PaddleSeg/contrib/MedicalSeg/nnunet/losses/dice_and_ce_loss.py
imports: import numpy
  import paddle
  from paddle import nn
  import paddle.nn.functional
  from medicalseg.cvlibs import manager
label_desc: Use image node_id 2 for calling the SoftDiceLoss obj's underlying member method code with example usage: obj.forward(x, y, loss_mask) and returns: dc