code: def __init__(self):
      super(Generator, self).__init__()
  
      def ganlayer(n_input, n_output, dropout=True):
          pipeline = [nn.Linear(n_input, n_output)]
          pipeline.append(nn.LeakyReLU(0.2, inplace=True))
          if dropout:
              pipeline.append(nn.Dropout(0.25))
          return pipeline
  
      self.model = nn.Sequential(
          *ganlayer(opt.latent_dim, 128, dropout=False),
          *ganlayer(128, 256),
          *ganlayer(256, 512),
          *ganlayer(512, 1024),
          nn.Linear(1024, n_features),
          nn.Tanh(),
      )
  
file_path: gans-with-pytorch/gan/gan.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()