code: def validation_step(
      self, batch: rlt.PreprocessedRankingInput, batch_idx: int
  ):
      reward = self._get_target_reward(batch)
      self.reporter.log(eval_rewards=reward.flatten().detach().cpu())
  
      pred_reward = self.reward_net(batch).predicted_reward
      self.reporter.log(
          eval_pred_rewards=pred_reward.flatten().detach().cpu()
      )
  
      weight = self._get_sample_weight(batch)
  
      loss = self.loss_fn(pred_reward, reward, weight, batch)
  
      detached_loss = loss.detach().cpu()
      self.reporter.log(eval_loss=detached_loss)
  
      if weight is not None:
          unweighted_loss = self._compute_unweighted_loss(
              pred_reward, reward, batch
          )
          self.reporter.log(eval_unweighted_loss=unweighted_loss)
  
      return detached_loss.item()
  
imports: import logging
  from enum import Enum
  from typing import Optional
  import numpy
  import reagent.core.types
  import torch
  from reagent.core.dataclasses import field
  from reagent.models.base import ModelBase
  from reagent.optimizer.union import Optimizer__Union
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
label_desc: Use image node_id 7 for calling the RewardNetTrainer obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx) and returns: detached_loss