code: def __init__(self, nz=6, ngf=9, nc=3):
      super().__init__()
      self.main = torch.nn.Sequential(
          # input is Z, going into a convolution
          torch.nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
          torch.nn.BatchNorm2d(ngf * 8),
          torch.nn.ReLU(True),
          # state size. (ngf*8) x 4 x 4
          torch.nn.ConvTranspose2d(
              ngf * 8, ngf * 4, 4, 2, 1, bias=False
          ),
          torch.nn.BatchNorm2d(ngf * 4),
          torch.nn.ReLU(True),
          # state size. (ngf*4) x 8 x 8
          torch.nn.ConvTranspose2d(
              ngf * 4, ngf * 2, 4, 2, 1, bias=False
          ),
          torch.nn.BatchNorm2d(ngf * 2),
          torch.nn.ReLU(True),
          # state size. (ngf*2) x 16 x 16
          torch.nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
          torch.nn.BatchNorm2d(ngf),
          torch.nn.ReLU(True),
          # state size. (ngf) x 32 x 32
          torch.nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
          torch.nn.Tanh()
          # state size. (nc) x 64 x 64
      )
  
imports: import torch
label_desc: Use image node_id 1 to create a new TestNnModule object from inherited base classes: torch.nn with example: obj = TestNnModule(nz, ngf, nc)