code: def create_trainer(
      seq2slate_net,
      learning_method,
      batch_size,
      learning_rate,
      policy_gradient_interval,
      device,
  ):
      if learning_method == ON_POLICY:
          seq2slate_params = Seq2SlateParameters(
              on_policy=True,
              learning_method=LearningMethod.REINFORCEMENT_LEARNING,
          )
          trainer_cls = Seq2SlateOnPolicyTrainer
      elif learning_method == OFF_POLICY:
          seq2slate_params = Seq2SlateParameters(
              on_policy=False,
              learning_method=LearningMethod.REINFORCEMENT_LEARNING,
          )
          trainer_cls = Seq2SlateTrainer
      elif learning_method == SIMULATION:
          temp_reward_model_path = tempfile.mkstemp(suffix=".pt")[1]
          reward_model = torch.jit.script(TSPRewardModel())
          torch.jit.save(reward_model, temp_reward_model_path)
          seq2slate_params = Seq2SlateParameters(
              on_policy=True,
              learning_method=LearningMethod.SIMULATION,
              simulation=SimulationParameters(
                  reward_name_weight={"tour_length": 1.0},
                  reward_name_power={"tour_length": 1.0},
                  reward_name_path={
                      "tour_length": temp_reward_model_path
                  },
              ),
          )
          trainer_cls = Seq2SlateSimulationTrainer
  
      param_dict = {
          "seq2slate_net": seq2slate_net,
          "params": seq2slate_params,
          "policy_optimizer": Optimizer__Union.default(
              lr=learning_rate
          ),
          "print_interval": 1,
          "policy_gradient_interval": policy_gradient_interval,
      }
      return trainer_cls(**param_dict)
  
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 2 for calling a global function with example usage: create_trainer(seq2slate_net, learning_method, batch_size, learning_rate, policy_gradient_interval, device) and returns: trainer_cls