class_docstr: Seq2Slate learned in a teach-forcing fashion (only used if the
  the ground-truth sequences are available)
class_docstr_tok: ["Seq2Slate","learned","in","a","teach-forcing","fashion","(","only","used","if","the","the","ground-truth","sequences","are","available",")"]
code: def configure_optimizers(self):
      optimizers = []
      optimizers.append(
          self.policy_optimizer.make_optimizer_scheduler(
              self.seq2slate_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 Seq2SlateTransformerNet
  from reagent.optimizer.union import Optimizer__Union
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
label_desc: Use image node_id 2 for calling the Seq2SlateTeacherForcingTrainer obj's underlying member method code with example usage: obj.configure_optimizers() and returns: optimizers