code: def _compute_unweighted_loss(
      self,
      predicted_reward: torch.Tensor,
      target_reward: torch.Tensor,
      batch: rlt.PreprocessedRankingInput,
  ):
      return self.loss_fn(
          predicted_reward,
          target_reward,
          weight=torch.ones_like(predicted_reward),
          batch=batch,
      )
  
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 5 for calling the RewardNetTrainer obj's underlying member method code with example usage: obj._compute_unweighted_loss(predicted_reward, target_reward, batch) and returns: self