code: def test_seq2reward_on_string_game_v0(self, filter_short_sequence):
      np.random.seed(SEED)
      random.seed(SEED)
      torch.manual_seed(SEED)
      training_data, eval_data = create_string_game_data(
          filter_short_sequence=filter_short_sequence
      )
      seq2reward_trainer = train_seq2reward_model(training_data)
      (
          initial_state_q_values,
          eval_mse_loss,
          eval_q_values,
          eval_action_distribution,
      ) = eval_seq2reward_model(eval_data, seq2reward_trainer)
  
      assert abs(initial_state_q_values[0].item() - 10) < 1.0
      assert abs(initial_state_q_values[1].item() - 5) < 1.0
  
      if filter_short_sequence:
          assert eval_mse_loss < 0.1
      else:
          # Same short sequences may have different total rewards due to the missing
          # states and actions in previous steps, so the trained network is not able
          # to reduce the mse loss to values close to zero.
          assert eval_mse_loss < 10
  
      compress_model_trainer = train_seq2reward_compress_model(
          training_data, seq2reward_trainer.seq2reward_network
      )
      (
          compress_eval_mse_loss,
          compress_eval_q_values,
          compress_eval_action_distribution,
      ) = eval_seq2reward_compress_model(
          eval_data, compress_model_trainer
      )
  
      assert compress_eval_mse_loss < 1e-5
      assert torch.all(eval_q_values - compress_eval_q_values < 1e-5)
      assert torch.all(
          eval_action_distribution - compress_eval_action_distribution
          < 1e-5
      )
  
imports: import logging
  import os
  import random
  import unittest
  from typing import Optional
  import numpy
  import pytorch_lightning
  import torch
  import torch.nn
  from parameterized import parameterized
  from reagent.core import types
  from reagent.core.parameters import NormalizationData, NormalizationParameters, ProblemDomain, Seq2RewardTrainerParameters
  from reagent.gym.envs import Gym
  from reagent.gym.utils import create_df_from_replay_buffer
  from reagent.models.seq2reward_model import Seq2RewardNetwork
  from reagent.net_builder.value.fully_connected import FullyConnected
  from reagent.prediction.predictor_wrapper import FAKE_STATE_ID_LIST_FEATURES, FAKE_STATE_ID_SCORE_LIST_FEATURES, Seq2RewardPlanShortSeqWithPreprocessor, Seq2RewardWithPreprocessor
  from reagent.preprocessing.identify_types import DO_NOT_PREPROCESS
  from reagent.preprocessing.preprocessor import Preprocessor
  from reagent.training.utils import gen_permutations
  from reagent.training.world_model.compress_model_trainer import CompressModelTrainer
  from reagent.training.world_model.seq2reward_trainer import get_Q, Seq2RewardTrainer
  from torch.utils.data import DataLoader
label_desc: Use image node_id 8 for calling the TestSeq2Reward obj's underlying member method code with example usage: obj.test_seq2reward_on_string_game_v0(filter_short_sequence) without return types