class_docstr: Implements a basic GAN for the purpose of illustrating multiple optimizers.
code: def configure_optimizers(self):
      lr = self.learning_rate
      b1 = self.b1
      b2 = self.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: 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 5 for calling the BasicGAN obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list