code: def configure_optimizers(self):
      lr = self.learning_rate
      opt_ae = torch.optim.Adam(
          list(self.encoder.parameters())
          + list(self.decoder.parameters())
          + list(self.quant_conv.parameters())
          + list(self.post_quant_conv.parameters()),
          lr=lr,
          betas=(0.5, 0.9),
      )
      opt_disc = torch.optim.Adam(
          self.loss.discriminator.parameters(), lr=lr, betas=(0.5, 0.9)
      )
      return [opt_ae, opt_disc], []
  
imports: from contextlib import contextmanager
  import pytorch_lightning
  import torch
  import torch.nn.functional
  from nemo.collections.multimodal.modules.stable_diffusion.diffusionmodules.model import Decoder, Encoder
  from nemo.collections.multimodal.modules.stable_diffusion.distributions.distributions import DiagonalGaussianDistribution
  from nemo.collections.multimodal.parts.stable_diffusion.utils import instantiate_from_config
label_desc: Use image node_id 12 for calling the AutoencoderKL obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list