code: def __init__(self):
      super(Generator, self).__init__()
  
      # Map z & y (noise and label) into the hidden layer.
      # TO DO: How to run this with a function defined here?
      self.z_map = nn.Sequential(
          nn.Linear(opt.latent_dim, 200),
          nn.BatchNorm1d(200),
          nn.ReLU(inplace=True),
      )
      self.y_map = nn.Sequential(
          nn.Linear(opt.n_classes, 1000),
          nn.BatchNorm1d(1000),
          nn.ReLU(inplace=True),
      )
      self.zy_map = nn.Sequential(
          nn.Linear(1200, 1200),
          nn.BatchNorm1d(1200),
          nn.ReLU(inplace=True),
      )
  
      self.model = nn.Sequential(nn.Linear(1200, n_features), nn.Tanh())
  
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 Generator object from inherited base classes: nn with example: obj = Generator()