code: def configure_optimizers(self):
      model_params, quant_params = self.model.collect_parameters()
  
      optimizer = hydra.utils.instantiate(
          self.optimizer_config.model_opt, params=model_params
      )
      discriminator_optimizer = hydra.utils.instantiate(
          self.optimizer_config.discriminator_opt,
          params=self.discriminator.parameters(),
      )
      if len(quant_params) > 0:
          aux_optimizer = hydra.utils.instantiate(
              self.optimizer_config.aux_opt, params=quant_params
          )
      else:
          aux_optimizer = None
  
      lr_sched = hydra.utils.instantiate(
          self.optimizer_config.model_opt_schedule, optimizer=optimizer
      )
      discriminator_sched = hydra.utils.instantiate(
          self.optimizer_config.discriminator_opt_schedule,
          optimizer=discriminator_optimizer,
      )
      if aux_optimizer is not None:
          aux_lr_sched = hydra.utils.instantiate(
              self.optimizer_config.aux_opt_schedule,
              optimizer=aux_optimizer,
          )
  
          return (
              [optimizer, discriminator_optimizer, aux_optimizer],
              [lr_sched, discriminator_sched, aux_lr_sched],
          )
      else:
          return (
              [optimizer, discriminator_optimizer],
              [lr_sched, discriminator_sched],
          )
  
imports: from typing import Optional, Tuple
  import hydra
  import torch
  import torch.nn
  from omegaconf import DictConfig
  from target_compression_module import TargetRateCompressionModule
  from torch import Tensor
  from neuralcompression import HyperpriorOutput
  from neuralcompression.loss_fn import DiscriminatorLoss, DistortionLoss, GeneratorLoss, TargetRateConfig
  from neuralcompression.models import HyperpriorAutoencoderBase
label_desc: Use image node_id 10 for calling the GANCompressionModule obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list, list,  list