class_docstr: Discriminator Loss module
class_docstr_tok: ["Discriminator","Loss","module"]
code: def forward(self, disc_real_outputs, disc_generated_outputs):
      r_losses = []
      g_losses = []
      loss = 0
      for i, (dr, dg) in enumerate(
          zip(disc_real_outputs, disc_generated_outputs)
      ):
          dr = dr.float()
          dg = dg.float()
          r_loss = torch.mean((1 - dr) ** 2)
          g_loss = torch.mean(dg**2)
          loss += r_loss + g_loss
          r_losses.append(r_loss.item())
          g_losses.append(g_loss.item())
  
      return loss, r_losses, g_losses
  
imports: import torch
  from nemo.core.classes import Loss, typecheck
  from nemo.core.neural_types.elements import LossType, VoidType
  from nemo.core.neural_types.neural_type import NeuralType
label_desc: Use image node_id 3 for calling the DiscriminatorLoss obj's underlying member method code with example usage: obj.forward(disc_real_outputs, disc_generated_outputs) and returns: loss,  r_losses,  g_losses