class_docstr: Mean Squared Discriminator Loss 
class_docstr_tok: ["Mean","Squared","Discriminator","Loss"]
code: def forward(self, score_fake, score_real):
      loss_real = self.loss_func(
          score_real, score_real.new_ones(score_real.shape)
      )
      loss_fake = self.loss_func(
          score_fake, score_fake.new_zeros(score_fake.shape)
      )
      loss_d = loss_real + loss_fake
      return loss_d, loss_real, loss_fake
  
imports: import torch
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 2 for calling the MSEDLoss obj's underlying member method code with example usage: obj.forward(score_fake, score_real) and returns: loss_d,  loss_real,  loss_fake