code: def _construct_trainer(self, new_params=None, no_cpe=False):
      reward_network = self.reward_network
      q_network_cpe = self.q_network_cpe
      q_network_cpe_target = self.q_network_cpe_target
      evaluation = self.eval_parameters
      params = self.params
  
      if new_params is not None:
          params = new_params
      if no_cpe:
          reward_network = q_network_cpe = q_network_cpe_target = None
          evaluation = EvaluationParameters(calc_cpe_in_training=False)
  
      return QRDQNTrainer(
          q_network=self.q_network,
          q_network_target=self.q_network_target,
          reward_network=reward_network,
          q_network_cpe=q_network_cpe,
          q_network_cpe_target=q_network_cpe_target,
          metrics_to_score=self.metrics_to_score,
          evaluation=evaluation,
          # pyre-fixme[16]: `QRDQNTrainerParameters` has no attribute `asdict`.
          **params.asdict(),
      )
  
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 2 for calling the TestQRDQN obj's underlying member method code with example usage: obj._construct_trainer(new_params, no_cpe) and returns: QRDQNTrainer