code: def validation_step(
      self, batch: rlt.PreprocessedRankingInput, batch_idx: int
  ):
      seq2slate_net = self.seq2slate_net
  
      assert seq2slate_net.training is False
  
      logged_slate_rank_prob = torch.exp(
          seq2slate_net(batch, mode=Seq2SlateMode.PER_SEQ_LOG_PROB_MODE)
          .log_probs.detach()
          .flatten()
          .cpu()
      )
  
      eval_baseline_loss = torch.tensor([0.0]).reshape(1)
      if self.baseline_net:
          baseline_net = self.baseline_net
          b = baseline_net(batch).detach()
          # pyre-fixme[6]: Expected `Tensor` for 2nd param but got
          #  `Optional[torch.Tensor]`.
          eval_baseline_loss = (
              F.mse_loss(b, batch.slate_reward).cpu().reshape(1)
          )
      else:
          # pyre-fixme[6]: For 1st param expected `Tensor` but got `Optional[Tensor]`.
          b = torch.zeros_like(batch.slate_reward)
  
      eval_advantage = (
          # pyre-fixme[58]: `-` is not supported for operand types
          #  `Optional[torch.Tensor]` and `Any`.
          (batch.slate_reward - b)
          .flatten()
          .cpu()
      )
  
      ranked_slate_output = seq2slate_net(
          batch, Seq2SlateMode.RANK_MODE, greedy=True
      )
      ranked_slate_rank_prob = (
          ranked_slate_output.ranked_per_seq_probs.cpu()
      )
  
      self.reporter.log(
          eval_baseline_loss=eval_baseline_loss,
          eval_advantages=eval_advantage,
          logged_slate_rank_probs=logged_slate_rank_prob,
          ranked_slate_rank_probs=ranked_slate_rank_prob,
      )
  
      if not self.calc_cpe:
          return
  
      edp_g = EvaluationDataPage.create_from_tensors_seq2slate(
          seq2slate_net,
          # pyre-fixme[6]: Expected `Module` for 2nd param but got
          #  `Optional[nn.Module]`.
          self.reward_network,
          batch,
          eval_greedy=True,
      )
  
      edp_ng = EvaluationDataPage.create_from_tensors_seq2slate(
          seq2slate_net,
          # pyre-fixme[6]: Expected `Module` for 2nd param but got
          #  `Optional[nn.Module]`.
          self.reward_network,
          batch,
          eval_greedy=False,
      )
  
      return edp_g, edp_ng
  
imports: import logging
  from typing import List, Optional, Tuple
  import reagent.core.types
  import torch
  import torch.nn
  import torch.nn.functional
  from reagent.core.dataclasses import field
  from reagent.core.parameters import Seq2SlateParameters
  from reagent.evaluation.evaluation_data_page import EvaluationDataPage
  from reagent.model_utils.seq2slate_utils import Seq2SlateMode
  from reagent.models.seq2slate import BaselineNet, Seq2SlateTransformerNet
  from reagent.optimizer.union import Optimizer__Union
  from reagent.training.ranking.helper import ips_clamp
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
label_desc: Use image node_id 5 for calling the Seq2SlateTrainer obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx) and returns: edp_g,  edp_ng