class_docstr: Seq2Slate learned with simulation data, with the action
  generated randomly and the reward computed by a reward network
code: def training_step(
      self, batch: rlt.PreprocessedRankingInput, batch_idx: int
  ):
      assert type(batch) is rlt.PreprocessedRankingInput
      training_batch = self._simulated_training_input(batch)
      return super().training_step(training_batch, batch_idx)
  
imports: import logging
  from itertools import permutations
  from typing import List, Optional
  import numpy
  import reagent.core.types
  import torch
  import torch.nn
  from reagent.core.dataclasses import field
  from reagent.core.parameters import Seq2SlateParameters
  from reagent.core.torch_utils import gather
  from reagent.models.seq2slate import BaselineNet, Seq2SlateMode, Seq2SlateTransformerNet
  from reagent.optimizer.union import Optimizer__Union
  from reagent.training.ranking.seq2slate_trainer import Seq2SlateTrainer
label_desc: Use image node_id 3 for calling the Seq2SlateSimulationTrainer obj's underlying member method code with example usage: obj.training_step(batch, batch_idx) and returns: super