code: def forward(self, x, y):
      # maxout for x
      x = x.view(-1, n_features)
      x = self.x_map(x)
      x, _ = x.view(-1, 240, 5).max(
          dim=2
      )  # pytorch outputs max values and indices
      # .. and y
      y = y.view(-1, opt.n_classes)
      y = self.y_map(y)
      y, _ = y.view(-1, 50, 5).max(dim=2)
      # joint maxout layer
      jmx = torch.cat((x, y), dim=1)
      jmx = self.j_map(jmx)
      jmx, _ = jmx.view(-1, 240, 4).max(dim=2)
  
      prob = self.model(jmx)
      return prob
  
file_path: gans-with-pytorch/cgan/cgan.py
imports: import argparse
  import torch
  from torch import nn, optim
  from torch.autograd.variable import Variable
  from torchvision import transforms, datasets
  from torch.utils.data import DataLoader
label_desc: Use image node_id 2 for calling the Discriminator obj's underlying member method code with example usage: obj.forward(x, y) and returns: prob