code: def configure_optimizers(self):
      optimizers = []
  
      optimizers.append(
          self.q_network_optimizer.make_optimizer_scheduler(
              self.q_network.parameters()
          )
      )
  
      target_params = list(self.q_network_target.parameters())
      source_params = list(self.q_network.parameters())
      optimizers.append(
          SoftUpdate.make_optimizer_scheduler(
              target_params, source_params, tau=self.tau
          )
      )
  
      return optimizers
  
imports: import enum
  import logging
  from typing import Optional
  import reagent.core.parameters
  import reagent.core.types
  import torch
  import torch.nn.functional
  from reagent.core.dataclasses import field
  from reagent.optimizer import Optimizer__Union, SoftUpdate
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
  from reagent.training.rl_trainer_pytorch import RLTrainerMixin
label_desc: Use image node_id 2 for calling the SlateQTrainer obj's underlying member method code with example usage: obj.configure_optimizers() and returns: optimizers