code: def __init__(self):
      super(Discriminator, self).__init__()
  
      self.model = nn.Sequential(nn.Linear(240, 1), nn.Sigmoid())
  
      # Imitating a 3d array by combining second and third dimensions via multiplication for maxout.
      self.x_map = nn.Sequential(nn.Linear(n_features, 240 * 5))
      self.y_map = nn.Sequential(nn.Linear(opt.n_classes, 50 * 5))
      self.j_map = nn.Sequential(nn.Linear(240 + 50, 240 * 4))
  
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 1 to create a new Discriminator object from inherited base classes: nn with example: obj = Discriminator()