class_docstr: >>> Discriminator(img_shape=(1, 28, 28))  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
  Discriminator(
    (model): Sequential(...)
  )
class_docstr_tok: [">",">",">","Discriminator","(","img_shape=","(","1",",","28",",","28",")",")","#","doctest",":","+ELLIPSIS","+NORMALIZE_WHITESPACE","Discriminator","(","(","model",")",":","Sequential","(","...",")",")"]
code: def __init__(self, img_shape):
      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),
      )
  
imports: from argparse import ArgumentParser, Namespace
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from lightning.pytorch import cli_lightning_logo
  from lightning.pytorch.core import LightningModule
  from lightning.pytorch.demos.mnist_datamodule import MNISTDataModule
  from lightning.pytorch.trainer import Trainer
  from lightning.pytorch.utilities.imports import _TORCHVISION_AVAILABLE
label_desc: Use image node_id 1 to create a new Discriminator object from inherited base classes: nn with example: obj = Discriminator(img_shape)