code: def test_validation_step(self):
      trainer = self._construct_trainer()
      edp = trainer.validation_step(self.inp, batch_idx=1)
      out = trainer.actor_network(self.inp.state)
      # Note: in current code EDP assumes policy induced by q-net instead of actor
      self.assertTrue(
          torch.all(torch.isclose(edp.optimal_q_values, out.action))
      )
  
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 9 for calling the TestCRR obj's underlying member method code with example usage: obj.test_validation_step() without return types