class_docstr: Seq2Slate learned in a teach-forcing fashion (only used if the
  the ground-truth sequences are available)
class_docstr_tok: ["Seq2Slate","learned","in","a","teach-forcing","fashion","(","only","used","if","the","the","ground-truth","sequences","are","available",")"]
code: def training_step(
      self, batch: rlt.PreprocessedRankingInput, batch_idx: int
  ):
      assert type(batch) is rlt.PreprocessedRankingInput
  
      log_probs = self.seq2slate_net(
          batch, mode=Seq2SlateMode.PER_SYMBOL_LOG_PROB_DIST_MODE
      ).log_probs
      assert log_probs.requires_grad
  
      assert batch.optim_tgt_out_idx is not None
      labels = self._transform_label(batch.optim_tgt_out_idx)
      assert not labels.requires_grad
      loss = self.kl_div_loss(log_probs, labels)
  
      self.manual_backward(loss)
      if (
          self.all_batches_processed + 1
      ) % self.policy_gradient_interval == 0:
          opt = self.optimizers()[0]
          opt.step()
          opt.zero_grad()
  
      loss = loss.detach().cpu().numpy()
      log_probs = log_probs.detach()
      if (self.all_batches_processed + 1) % self.print_interval == 0:
          logger.info(
              f"{self.all_batches_processed + 1} batch: loss={loss}"
          )
  
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 Seq2SlateTransformerNet
  from reagent.optimizer.union import Optimizer__Union
  from reagent.training.reagent_lightning_module import ReAgentLightningModule
label_desc: Use image node_id 3 for calling the Seq2SlateTeacherForcingTrainer obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) without return types