code: def training_step(
      self, batch: rlt.PreprocessedRankingInput, batch_idx: int
  ):
      assert type(batch) is rlt.PreprocessedRankingInput
  
      batch_size = batch.state.float_features.shape[0]
  
      reward = batch.slate_reward
      assert reward is not None
  
      optimizers = self.optimizers()
      if self.baseline_net:
          assert len(optimizers) == 2
          baseline_opt = optimizers[1]
      else:
          assert len(optimizers) == 1
      rl_opt = optimizers[0]
  
      if self.baseline_net:
          # Train baseline
          b = self.baseline_net(batch)
          baseline_loss = (
              1.0 / batch_size * torch.sum((b - reward) ** 2)
          )
          baseline_opt.zero_grad()
          self.manual_backward(baseline_loss)
          baseline_opt.step()
      else:
          b = torch.zeros_like(reward)
          baseline_loss = torch.zeros(1)
  
      # Train Seq2Slate using REINFORCE
      # log probs of tgt seqs
      model_propensities = torch.exp(
          self.seq2slate_net(
              batch, mode=Seq2SlateMode.PER_SEQ_LOG_PROB_MODE
          ).log_probs
      )
      b = b.detach()
      assert (
          b.shape == reward.shape == model_propensities.shape
      ), f"{b.shape} {reward.shape} {model_propensities.shape}"
  
      impt_smpl, clamped_impt_smpl = self._compute_impt_smpl(
          model_propensities, batch.tgt_out_probs
      )
      assert (
          impt_smpl.shape == clamped_impt_smpl.shape == reward.shape
      ), f"{impt_smpl.shape} {clamped_impt_smpl.shape} {reward.shape}"
      # gradient is only w.r.t model_propensities
      assert (
          not reward.requires_grad
          # pyre-fixme[16]: `Optional` has no attribute `requires_grad`.
          and not batch.tgt_out_probs.requires_grad
          and impt_smpl.requires_grad
          and clamped_impt_smpl.requires_grad
          and not b.requires_grad
      )
      # add negative sign because we take gradient descent but we want to
      # maximize rewards
      batch_obj_loss = -clamped_impt_smpl * (reward - b)
      obj_loss = torch.mean(batch_obj_loss)
  
      # condition to perform policy gradient update:
      # 1. no baseline
      # 2. or baseline is present and it passes the warm up stage
      # 3. the last policy gradient was performed policy_gradient_interval minibatches ago
      if (
          self.baseline_net is None
          or (self.all_batches_processed + 1)
          >= self.baseline_warmup_num_batches
      ):
          self.manual_backward(obj_loss)
          if (
              self.all_batches_processed + 1
          ) % self.policy_gradient_interval == 0:
              rl_opt.step()
              rl_opt.zero_grad()
      else:
          logger.info(
              "Not update RL model because now is baseline warmup phase"
          )
  
      ips_loss = torch.mean(-impt_smpl * reward).cpu().detach().numpy()
      clamped_ips_loss = (
          torch.mean(-clamped_impt_smpl * reward).cpu().detach().numpy()
      )
      baseline_loss = baseline_loss.detach().cpu().numpy().item()
      advantage = (reward - b).detach().cpu().numpy()
      logged_slate_rank_probs = (
          model_propensities.detach().cpu().numpy()
      )
  
      if (self.all_batches_processed + 1) % self.print_interval == 0:
          logger.info(
              "{} batch: ips_loss={}, clamped_ips_loss={}, baseline_loss={}, max_ips={}, mean_ips={}, grad_update={}".format(
                  self.all_batches_processed + 1,
                  ips_loss,
                  clamped_ips_loss,
                  baseline_loss,
                  torch.max(impt_smpl),
                  torch.mean(impt_smpl),
                  (self.all_batches_processed + 1)
                  % self.policy_gradient_interval
                  == 0,
              )
          )
      self.reporter.log(
          train_ips_score=torch.tensor(ips_loss).reshape(1),
          train_clamped_ips_score=torch.tensor(
              clamped_ips_loss
          ).reshape(1),
          train_baseline_loss=torch.tensor(baseline_loss).reshape(1),
          train_logged_slate_rank_probs=torch.FloatTensor(
              logged_slate_rank_probs
          ),
          train_ips_ratio=impt_smpl,
          train_clamped_ips_ratio=clamped_impt_smpl,
          train_advantages=advantage,
      )
  
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 4 for calling the Seq2SlateTrainer obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) without return types