class_docstr: Trainer for Seq2Reward
class_docstr_tok: ["Trainer","for","Seq2Reward"]
code: def configure_optimizers(self):
      optimizers = []
      optimizers.append(
          {
              "optimizer": torch.optim.Adam(
                  self.seq2reward_network.parameters(),
                  lr=self.params.learning_rate,
              ),
          }
      )
      optimizers.append(
          {
              "optimizer": torch.optim.Adam(
                  self.step_predict_network.parameters(),
                  lr=self.params.learning_rate,
              )
          },
      )
      return optimizers
  
imports: import logging
  import reagent.core.types
  import torch
  import torch.nn
  import torch.nn.functional
  from reagent.core.parameters import Seq2RewardTrainerParameters
  from reagent.models.fully_connected_network import FullyConnectedNetwork
  from reagent.models.seq2reward_model import Seq2RewardNetwork
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
  from reagent.training.utils import gen_permutations
label_desc: Use image node_id 2 for calling the Seq2RewardTrainer obj's underlying member method code with example usage: obj.configure_optimizers() and returns: optimizers