code: def test__configure_cpe_optimizers(self):
      reward_network = self.reward_network
      q_network_cpe = self.q_network_cpe
      q_network_cpe_target = self.q_network_cpe_target
      trainer = self._construct_trainer()
      trainer._initialize_cpe(
          reward_network,
          q_network_cpe,
          q_network_cpe_target,
          self.params.optimizer,
      )
      _, _, optimizers = trainer._configure_cpe_optimizers()
      # expecting a [reward_network_optimizer, q_network_cpe_optimizer] list
      self.assertEqual(len(optimizers), 2)
  
imports: import unittest
  from typing import List, Optional, Tuple
  import torch
  from reagent.core.parameters import EvaluationParameters, RLParameters
  from reagent.core.torch_utils import masked_softmax
  from reagent.core.types import DiscreteDqnInput, ExtraData, FeatureData
  from reagent.evaluation.evaluation_data_page import EvaluationDataPage
  from reagent.evaluation.evaluator import get_metrics_to_score
  from reagent.models.dqn import FullyConnectedDQN
  from reagent.training.dqn_trainer_base import DQNTrainerBaseLightning
  from reagent.training.parameters import DQNTrainerParameters
  from reagent.workflow.types import RewardOptions
label_desc: Use image node_id 7 for calling the TestDQNTrainerBaseLightning obj's underlying member method code with example usage: obj.test__configure_cpe_optimizers() without return types