class_docstr: Seq2Slate without a decoder learned in a supervised learning fashion (
  https://arxiv.org/pdf/1904.06813.pdf )
class_docstr_tok: ["Seq2Slate","without","a","decoder","learned","in","a","supervised","learning","fashion","(","https",":","\/\/arxiv.org\/pdf\/1904.06813.pdf",")"]
code: def configure_optimizers(self):
      optimizers = []
      optimizers.append(
          self.policy_optimizer.make_optimizer_scheduler(
              self.seq2slate_net.parameters()
          )
      )
      return optimizers
  
file_path: ReAgent/reagent/training/ranking/seq2slate_attn_trainer.py
imports: import logging
  import numpy
  import reagent.core.types
  import torch
  import torch.nn
  from reagent.core.dataclasses import field
  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
  from sklearn.metrics import average_precision_score, dcg_score, ndcg_score, roc_auc_score
label_desc: Use image node_id 2 for calling the Seq2SlatePairwiseAttnTrainer obj's underlying member method code with example usage: obj.configure_optimizers() and returns: optimizers