class_docstr: >>> GAN(img_shape=(1, 8, 8))  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
  GAN(
    (generator): Generator(
      (model): Sequential(...)
    )
    (discriminator): Discriminator(
      (model): Sequential(...)
    )
  )
class_docstr_tok: [">",">",">","GAN","(","img_shape=","(","1",",","8",",","8",")",")","#","doctest",":","+ELLIPSIS","+NORMALIZE_WHITESPACE","GAN","(","(","generator",")",":","Generator","(","(","model",")",":","Sequential","(","...",")",")","(","discriminator",")",":","Discriminator","(","(","model",")",":","Sequential","(","...",")",")",")"]
code: def configure_optimizers(self):
      lr = self.hparams.lr
      b1 = self.hparams.b1
      b2 = self.hparams.b2
  
      opt_g = torch.optim.Adam(
          self.generator.parameters(), lr=lr, betas=(b1, b2)
      )
      opt_d = torch.optim.Adam(
          self.discriminator.parameters(), lr=lr, betas=(b1, b2)
      )
      return opt_g, opt_d
  
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 5 for calling the GAN obj's underlying member method code with example usage: obj.configure_optimizers() and returns: opt_g,  opt_d