code: def _construct_trainer(
      self, new_params=None, no_cpe=False, no_q2=False
  ):
      trainer = DiscreteCRRTrainer(
          actor_network=self.actor_network,
          actor_network_target=self.actor_network_target,
          q1_network=self.q1_network,
          q1_network_target=self.q1_network_target,
          q2_network=(None if no_q2 else self.q2_network),
          q2_network_target=(None if no_q2 else self.q2_network_target),
          reward_network=(None if no_cpe else self.reward_network),
          q_network_cpe=(None if no_cpe else self.q_network_cpe),
          q_network_cpe_target=(
              None if no_cpe else self.q_network_cpe_target
          ),
          metrics_to_score=self.metrics_to_score,
          evaluation=EvaluationParameters(
              calc_cpe_in_training=(False if no_cpe else True)
          ),
          # pyre-fixme[16]: `QRDQNTrainerParameters` has no attribute `asdict`.
          **(
              new_params if new_params is not None else self.params
          ).asdict(),
      )
      trainer.log = self.dummy_log
      return trainer
  
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.actor import FullyConnectedActor
  from reagent.models.dqn import FullyConnectedDQN
  from reagent.training.discrete_crr_trainer import DiscreteCRRTrainer
  from reagent.training.parameters import CRRTrainerParameters
  from reagent.workflow.types import RewardOptions
label_desc: Use image node_id 3 for calling the TestCRR obj's underlying member method code with example usage: obj._construct_trainer(new_params, no_cpe, no_q2) and returns: trainer