code: def _test_seq2reward_with_preprocessor(self, plan_short_sequence):
      state_dim = 4
      action_dim = 2
      seq_len = 3
      model = FakeSeq2RewardNetwork()
      state_normalization_parameters = {
          i: NormalizationParameters(
              feature_type=DO_NOT_PREPROCESS, mean=0.0, stddev=1.0
          )
          for i in range(1, state_dim)
      }
      state_preprocessor = Preprocessor(
          state_normalization_parameters, False
      )
  
      if plan_short_sequence:
          step_prediction_model = FakeStepPredictionNetwork(seq_len)
          model_with_preprocessor = (
              Seq2RewardPlanShortSeqWithPreprocessor(
                  model,
                  step_prediction_model,
                  state_preprocessor,
                  seq_len,
                  action_dim,
              )
          )
      else:
          model_with_preprocessor = Seq2RewardWithPreprocessor(
              model,
              state_preprocessor,
              seq_len,
              action_dim,
          )
      input_prototype = rlt.ServingFeatureData(
          float_features_with_presence=state_preprocessor.input_prototype(),
          id_list_features=FAKE_STATE_ID_LIST_FEATURES,
          id_score_list_features=FAKE_STATE_ID_SCORE_LIST_FEATURES,
      )
      q_values = model_with_preprocessor(input_prototype)
      if plan_short_sequence:
          # When planning for 1, 2, and 3 steps ahead,
          # the expected q values are respectively:
          # [0, 1], [1, 11], [11, 111]
          # Weighting the expected q values by predicted step
          # probabilities [0.33, 0.33, 0.33], we have [4, 41]
          expected_q_values = torch.tensor([[4.0, 41.0]])
      else:
          expected_q_values = torch.tensor([[11.0, 111.0]])
      assert torch.all(expected_q_values == q_values)
  
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 3 for calling the TestSeq2Reward obj's underlying member method code with example usage: obj._test_seq2reward_with_preprocessor(plan_short_sequence) without return types