class_docstr: Hinge Discriminator Loss 
class_docstr_tok: ["Hinge","Discriminator","Loss"]
code: def forward(self, score_real):
      # TODO: this might be wrong
      loss_fake = torch.mean(F.relu(1.0 - score_real))
      return loss_fake
  
imports: import torch
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 1 for calling the HingeGLoss obj's underlying member method code with example usage: obj.forward(score_real) and returns: loss_fake