code: def __init__(self):
      super(Generator, self).__init__()
      self.main = nn.Sequential(
          # input is Z, going into a convolution
          nn.ConvTranspose2d(nz, ngf * 4, 4, 1, 0, bias=False),
          nn.BatchNorm2d(ngf * 4),
          nn.ReLU(True),
          nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
          nn.BatchNorm2d(ngf * 2),
          nn.ReLU(True),
          nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
          nn.BatchNorm2d(ngf),
          nn.ReLU(True),
          nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
          nn.Tanh(),
      )
  
imports: import ray
  import os
  import torch
  import torch.nn
  import torch.nn.parallel
  import torch.utils.data
  import torchvision.datasets
  import torchvision.transforms
  import torchvision.utils
  import numpy
  from torch.autograd import Variable
  from torch.nn import functional
  from scipy.stats import entropy
  import matplotlib.pyplot
  import matplotlib.animation
label_desc: Use image node_id 1 to create a new Generator object from inherited base classes: nn with example: obj = Generator()