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 forward(self, img):
      img_flat = img.view(img.size(0), -1)
      return self.model(img_flat)
  
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 2 for calling the Discriminator obj's underlying member method code with example usage: obj.forward(img) and returns: self