code: def run_seq2slate_tsp(
      model_str,
      batch_size,
      epochs,
      candidate_num,
      num_batches,
      hidden_size,
      diverse_input,
      learning_rate,
      expect_reward_threshold,
      learning_method,
      policy_gradient_interval,
      device,
  ):
      pl.seed_everything(0)
  
      candidate_dim = 2
      eval_sample_size = 1
  
      train_batches, test_batch = create_train_and_test_batches(
          batch_size,
          candidate_num,
          candidate_dim,
          device,
          num_batches,
          learning_method,
          diverse_input,
      )
      best_test_possible_reward = compute_best_reward(
          test_batch.src_seq.float_features
      )
  
      seq2slate_net = create_seq2slate_net(
          model_str,
          candidate_num,
          candidate_dim,
          hidden_size,
          Seq2SlateOutputArch.AUTOREGRESSIVE,
          1.0,
          device,
      )
  
      trainer = create_trainer(
          seq2slate_net,
          learning_method,
          batch_size,
          learning_rate,
          policy_gradient_interval,
          device,
      )
  
      def evaluate():
          best_test_reward = torch.full((batch_size,), 1e9).to(device)
          for _ in range(eval_sample_size):
              model_propensities, _, reward = rank_on_policy_and_eval(
                  seq2slate_net.to(device),
                  test_batch,
                  candidate_num,
                  greedy=True,
              )
              best_test_reward = torch.where(
                  reward < best_test_reward, reward, best_test_reward
              )
          logger.info(
              f"Test mean model_propensities {torch.mean(model_propensities)}, "
              f"Test mean reward: {torch.mean(best_test_reward)}, "
              f"best possible reward {best_test_possible_reward}"
          )
          if torch.any(torch.isnan(model_propensities)):
              raise Exception("Model propensities contain NaNs")
          ratio = (
              torch.mean(best_test_reward) / best_test_possible_reward
          )
          return ratio < expect_reward_threshold, ratio
  
      evaluate()
  
      training_data = DataLoader(
          train_batches, collate_fn=lambda x: x[0]
      )
      pl_trainer = pl.Trainer(
          max_epochs=epochs,
          gpus=None if device == torch.device("cpu") else 1,
          logger=False,
      )
      pl_trainer.fit(trainer, training_data)
  
      result, ratio = evaluate()
  
      assert result, (
          f"Test failed because it did not reach expected test reward, "
          f"{ratio} > {expect_reward_threshold}."
      )
  
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 10 for calling a global function with example usage: run_seq2slate_tsp(model_str, batch_size, epochs, candidate_num, num_batches, hidden_size, diverse_input, learning_rate, expect_reward_threshold, learning_method, policy_gradient_interval, device) and returns: unknown,  ratio