class_docstr: Trainer for fitting Seq2Reward planning outcomes to a neural network-based policy
class_docstr_tok: ["Trainer","for","fitting","Seq2Reward","planning","outcomes","to","a","neural","network-based","policy"]
code: def validation_step(
      self, batch: rlt.MemoryNetworkInput, batch_idx: int
  ):
      mse, acc = self.get_loss(batch)
      detached_loss = mse.cpu().detach().item()
      acc = acc.item()
  
      state_first_step = CompressModelTrainer.extract_state_first_step(
          batch
      )
      # shape: batch_size, action_dim
      q_values_all_action_all_data = (
          self.compress_model_network(state_first_step).cpu().detach()
      )
      q_values = q_values_all_action_all_data.mean(0).tolist()
  
      action_distribution = torch.bincount(
          torch.argmax(q_values_all_action_all_data, dim=1),
          minlength=len(self.params.action_names),
      )
      # normalize
      action_distribution = (
          action_distribution.float() / torch.sum(action_distribution)
      ).tolist()
  
      self.reporter.log(
          eval_mse_loss=detached_loss,
          eval_accuracy=acc,
          eval_q_values=[q_values],
          eval_action_distribution=[action_distribution],
      )
  
      return (detached_loss, q_values, action_distribution, acc)
  
imports: import logging
  import reagent.core.types
  import torch
  import torch.nn.functional
  from reagent.core.parameters import Seq2RewardTrainerParameters
  from reagent.core.types import FeatureData
  from reagent.models.fully_connected_network import FloatFeatureFullyConnected
  from reagent.models.seq2reward_model import Seq2RewardNetwork
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
  from reagent.training.utils import gen_permutations
  from reagent.training.world_model.seq2reward_trainer import get_Q
label_desc: Use image node_id 5 for calling the CompressModelTrainer obj's underlying member method code with example usage: obj.validation_step(batch, batch_idx) and returns: detached_loss,  q_values,  action_distribution,  acc