code: def configure_optimizers(self):
      lr_d = self.learning_rate
      lr_g = self.lr_g_factor * self.learning_rate
      print("lr_d", lr_d)
      print("lr_g", lr_g)
      opt_ae = torch.optim.Adam(
          list(self.encoder.parameters())
          + list(self.decoder.parameters())
          + list(self.quantize.parameters())
          + list(self.quant_conv.parameters())
          + list(self.post_quant_conv.parameters()),
          lr=lr_g,
          betas=(0.5, 0.9),
      )
      opt_disc = torch.optim.Adam(
          self.loss.discriminator.parameters(),
          lr=lr_d,
          betas=(0.5, 0.9),
      )
  
      if self.scheduler_config is not None:
          scheduler = instantiate_from_config(self.scheduler_config)
  
          print("Setting up LambdaLR scheduler...")
          scheduler = [
              {
                  "scheduler": LambdaLR(
                      opt_ae, lr_lambda=scheduler.schedule
                  ),
                  "interval": "step",
                  "frequency": 1,
              },
              {
                  "scheduler": LambdaLR(
                      opt_disc, lr_lambda=scheduler.schedule
                  ),
                  "interval": "step",
                  "frequency": 1,
              },
          ]
          return [opt_ae, opt_disc], scheduler
      return [opt_ae, opt_disc], []
  
imports: import numpy
  import torch
  import pytorch_lightning
  import torch.nn.functional
  from contextlib import contextmanager
  from torch.optim.lr_scheduler import LambdaLR
  from ldm.modules.ema import LitEma
  from vqvae_quantize import VectorQuantizer2
  from ldm.modules.diffusionmodules.model import Encoder, Decoder
  from ldm.util import instantiate_from_config
  import ldm.models.autoencoder
  from packaging import version
label_desc: Use image node_id 14 for calling the VQModel obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list, list,  scheduler