code: def configure_optimizers(self):
      optimizers = []
      optimizers.append(
          self.rl_opt.make_optimizer_scheduler(
              self.seq2slate_net.parameters()
          )
      )
      if self.baseline_net:
          optimizers.append(
              self.baseline_opt.make_optimizer_scheduler(
                  self.baseline_net.parameters()
              )
          )
      return optimizers
  
imports: import logging
  from typing import List, Optional, Tuple
  import reagent.core.types
  import torch
  import torch.nn
  import torch.nn.functional
  from reagent.core.dataclasses import field
  from reagent.core.parameters import Seq2SlateParameters
  from reagent.evaluation.evaluation_data_page import EvaluationDataPage
  from reagent.model_utils.seq2slate_utils import Seq2SlateMode
  from reagent.models.seq2slate import BaselineNet, Seq2SlateTransformerNet
  from reagent.optimizer.union import Optimizer__Union
  from reagent.training.ranking.helper import ips_clamp
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
label_desc: Use image node_id 2 for calling the Seq2SlateTrainer obj's underlying member method code with example usage: obj.configure_optimizers() and returns: optimizers