code: def __init__(self, img_shape: tuple):
      super().__init__()
  
      self.model = nn.Sequential(
          nn.Linear(int(np.prod(img_shape)), 512),
          nn.LeakyReLU(0.2, inplace=True),
          nn.Linear(512, 256),
          nn.LeakyReLU(0.2, inplace=True),
          nn.Linear(256, 1),
          nn.Sigmoid(),
      )
  
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from lightning.pytorch.core.module import LightningModule
  from torch.utils.data import DataLoader
  from tests_pytorch import _PATH_DATASETS
  from tests_pytorch.helpers.datasets import MNIST, AverageDataset, TrialMNIST
label_desc: Use image node_id 1 to create a new Discriminator object from inherited base classes: nn with example: obj = Discriminator(img_shape)