class_docstr: Trainer for fitting Seq2Reward planning outcomes to a neural network-based policy
class_docstr_tok: ["Trainer","for","fitting","Seq2Reward","planning","outcomes","to","a","neural","network-based","policy"]
code: def configure_optimizers(self):
      optimizers = []
      optimizers.append(
          {
              "optimizer": torch.optim.Adam(
                  self.compress_model_network.parameters(),
                  lr=self.params.compress_model_learning_rate,
              )
          }
      )
      return optimizers
  
imports: import logging
  import reagent.core.types
  import torch
  import torch.nn.functional
  from reagent.core.parameters import Seq2RewardTrainerParameters
  from reagent.core.types import FeatureData
  from reagent.models.fully_connected_network import FloatFeatureFullyConnected
  from reagent.models.seq2reward_model import Seq2RewardNetwork
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
  from reagent.training.utils import gen_permutations
  from reagent.training.world_model.seq2reward_trainer import get_Q
label_desc: Use image node_id 2 for calling the CompressModelTrainer obj's underlying member method code with example usage: obj.configure_optimizers() and returns: optimizers