code: def __init__(
      self,
      seq2slate_net: Seq2SlateTransformerNet,
      params: Seq2SlateParameters = field(  # noqa: B008
          default_factory=Seq2SlateParameters
      ),
      baseline_net: Optional[BaselineNet] = None,
      baseline_warmup_num_batches: int = 0,
      policy_optimizer: Optimizer__Union = field(  # noqa: B008
          default_factory=Optimizer__Union.default
      ),
      baseline_optimizer: Optimizer__Union = field(  # noqa: B008
          default_factory=Optimizer__Union.default
      ),
      policy_gradient_interval: int = 1,
      print_interval: int = 100,
      calc_cpe: bool = False,
      reward_network: Optional[nn.Module] = None,
  ) -> None:
      super().__init__()
      self.seq2slate_net = seq2slate_net
      self.params = params
      self.policy_gradient_interval = policy_gradient_interval
      self.print_interval = print_interval
  
      self.baseline_net = baseline_net
      self.baseline_warmup_num_batches = baseline_warmup_num_batches
  
      self.rl_opt = policy_optimizer
      if self.baseline_net:
          self.baseline_opt = baseline_optimizer
  
      # use manual optimization to get more flexibility
      self.automatic_optimization = False
  
      assert not calc_cpe or reward_network is not None
      self.calc_cpe = calc_cpe
      self.reward_network = reward_network
  
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 1 to create a new Seq2SlateTrainer object from inherited base classes: ReAgentLightningModule with example: obj = Seq2SlateTrainer(seq2slate_net, params, baseline_net, baseline_warmup_num_batches, policy_optimizer, baseline_optimizer, policy_gradient_interval, print_interval, calc_cpe, reward_network)