As the model refers to a lot of stuff, I printed the final one here, using print(netG)
G_NET(
  (ca_net): CA_NET(
    (fc): Linear(in_features=256, out_features=400, bias=True)
    (relu): GLU()
  )
  (h_net1): INIT_STAGE_G(
    (fc): Sequential(
      (0): Linear(in_features=200, out_features=16384, bias=False)
      (1): BatchNorm1d(16384, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): GLU()
    )
    (upsample1): Sequential(
      (0): Upsample(scale_factor=2, mode=nearest)
      (1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (3): GLU()
    )
    (upsample2): Sequential(
      (0): Upsample(scale_factor=2, mode=nearest)
      (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (3): GLU()
    )
    (upsample3): Sequential(
      (0): Upsample(scale_factor=2, mode=nearest)
      (1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (3): GLU()
    )
    (upsample4): Sequential(
      (0): Upsample(scale_factor=2, mode=nearest)
      (1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (3): GLU()
    )
  )
  (img_net1): GET_IMAGE_G(
    (img): Sequential(
      (0): Conv2d(32, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): Tanh()
    )
  )
  (h_net2): NEXT_STAGE_G(
    (att): GlobalAttentionGeneral(
      (conv_context): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (sm): Softmax()
    )
    (residual): Sequential(
      (0): ResBlock(
        (block): Sequential(
          (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): GLU()
          (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): ResBlock(
        (block): Sequential(
          (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): GLU()
          (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
    )
    (upsample): Sequential(
      (0): Upsample(scale_factor=2, mode=nearest)
      (1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (3): GLU()
    )
  )
  (img_net2): GET_IMAGE_G(
    (img): Sequential(
      (0): Conv2d(32, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): Tanh()
    )
  )
  (h_net3): NEXT_STAGE_G(
    (att): GlobalAttentionGeneral(
      (conv_context): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (sm): Softmax()
    )
    (residual): Sequential(
      (0): ResBlock(
        (block): Sequential(
          (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): GLU()
          (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
      (1): ResBlock(
        (block): Sequential(
          (0): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): GLU()
          (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
          (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
        )
      )
    )
    (upsample): Sequential(
      (0): Upsample(scale_factor=2, mode=nearest)
      (1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (3): GLU()
    )
  )
  (img_net3): GET_IMAGE_G(
    (img): Sequential(
      (0): Conv2d(32, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (1): Tanh()
    )
  )
)