code: def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
      new_batch = post_preprocess_batch(
          self.seq2slate_net,
          self.seq2slate_net.max_src_seq_len,
          batch,
          batch.state.float_features.device,
          self.current_epoch,
      )
      for attr in dir(new_batch):
          if not callable(
              getattr(new_batch, attr)
          ) and not attr.startswith("__"):
              setattr(batch, attr, getattr(new_batch, attr))
      super().on_train_batch_start(batch, batch_idx, dataloader_idx)
  
imports: import logging
  import math
  import tempfile
  from itertools import permutations
  import pytorch_lightning
  import reagent.core.types
  import torch
  import torch.nn
  from reagent.core.parameters import Seq2SlateParameters
  from reagent.core.parameters_seq2slate import LearningMethod, SimulationParameters
  from reagent.core.torch_utils import gather
  from reagent.model_utils.seq2slate_utils import Seq2SlateOutputArch
  from reagent.models.seq2slate import Seq2SlateMode, Seq2SlateTransformerNet
  from reagent.optimizer.union import Optimizer__Union
  from reagent.training.ranking.seq2slate_sim_trainer import Seq2SlateSimulationTrainer
  from reagent.training.ranking.seq2slate_trainer import Seq2SlateTrainer
  from torch.utils.data import DataLoader
label_desc: Use image node_id 1 for calling the Seq2SlateOnPolicyTrainer obj's underlying member method code with example usage: obj.on_train_batch_start(batch, batch_idx, dataloader_idx) without return types