code: def test_validation_step(self):
      inp = DiscreteDqnInput(
          state=FeatureData(
              float_features=torch.rand(self.batch_size, self.state_dim)
          ),
          next_state=FeatureData(
              float_features=torch.rand(self.batch_size, self.state_dim)
          ),
          reward=torch.ones(self.batch_size, 1),
          time_diff=torch.ones(self.batch_size, 1) * 2,
          step=torch.ones(self.batch_size, 1) * 2,
          not_terminal=torch.ones(self.batch_size, 1),
          action=torch.tensor([[0, 1], [1, 0], [0, 1]]),
          next_action=torch.tensor([[1, 0], [0, 1], [1, 0]]),
          possible_actions_mask=torch.ones(
              self.batch_size, self.action_dim
          ),
          possible_next_actions_mask=torch.ones(
              self.batch_size, self.action_dim
          ),
          extras=ExtraData(),
      )
      trainer = self._construct_trainer()
      data_page = trainer.validation_step(batch=inp, batch_idx=1)
      self.assertTrue(isinstance(data_page, EvaluationDataPage))
  
imports: import unittest
  import torch
  from reagent.core.parameters import EvaluationParameters, RLParameters
  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 import DQNTrainer
  from reagent.training.parameters import DQNTrainerParameters
  from reagent.workflow.types import RewardOptions
label_desc: Use image node_id 9 for calling the TestDQN obj's underlying member method code with example usage: obj.test_validation_step() without return types