class_docstr: Seq2Slate learned with simulation data, with the action
  generated randomly and the reward computed by a reward network
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__(
          seq2slate_net,
          params=params,
          baseline_net=baseline_net,
          baseline_warmup_num_batches=baseline_warmup_num_batches,
          policy_optimizer=policy_optimizer,
          baseline_optimizer=baseline_optimizer,
          policy_gradient_interval=policy_gradient_interval,
          print_interval=print_interval,
          calc_cpe=calc_cpe,
          reward_network=reward_network,
      )
      self.sim_param = params.simulation
      assert self.sim_param is not None
      # loaded when used
      self.reward_name_and_net = nn.ModuleDict({})
      self.MAX_DISTANCE = (
          seq2slate_net.max_src_seq_len
          * (seq2slate_net.max_src_seq_len - 1)
          / 2
      )
  
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 1 to create a new Seq2SlateSimulationTrainer object from inherited base classes: Seq2SlateTrainer with example: obj = Seq2SlateSimulationTrainer(seq2slate_net, params, baseline_net, baseline_warmup_num_batches, policy_optimizer, baseline_optimizer, policy_gradient_interval, print_interval, calc_cpe, reward_network)