code: def test_configure_optimizers(self):
      trainer = self._construct_trainer()
      optimizers = trainer.configure_optimizers()
      self.assertEqual(len(optimizers), 4)
      train_step_yield_order = [
          trainer.q_network,
          trainer.reward_network,
          trainer.q_network_cpe,
          trainer.q_network,
      ]
      for i in range(len(train_step_yield_order)):
          opt_param = optimizers[i]["optimizer"].param_groups[0][
              "params"
          ][0]
          loss_param = list(train_step_yield_order[i].parameters())[0]
          self.assertTrue(
              torch.all(torch.isclose(opt_param, loss_param))
          )
  
      trainer = self._construct_trainer(no_cpe=True)
      optimizers = trainer.configure_optimizers()
      self.assertEqual(len(optimizers), 2)
  
file_path: ReAgent/reagent/test/training/test_qrdqn.py
imports: import unittest
  import torch
  from reagent.core.parameters import EvaluationParameters, RLParameters
  from reagent.core.types import DiscreteDqnInput, ExtraData, FeatureData
  from reagent.evaluation.evaluator import get_metrics_to_score
  from reagent.models.dqn import FullyConnectedDQN
  from reagent.training.parameters import QRDQNTrainerParameters
  from reagent.training.qrdqn_trainer import QRDQNTrainer
  from reagent.workflow.types import RewardOptions
label_desc: Use image node_id 5 for calling the TestQRDQN obj's underlying member method code with example usage: obj.test_configure_optimizers() without return types