code: def test_gen_permutations_seq_len_3_num_action_2(self):
      SEQ_LEN = 3
      NUM_ACTION = 2
      expected_outcome = torch.tensor(
          [
              [0, 0, 0],
              [0, 0, 1],
              [0, 1, 0],
              [0, 1, 1],
              [1, 0, 0],
              [1, 0, 1],
              [1, 1, 0],
              [1, 1, 1],
          ]
      )
      self._test_gen_permutations(SEQ_LEN, NUM_ACTION, expected_outcome)
  
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 6 for calling the TestSeq2Reward obj's underlying member method code with example usage: obj.test_gen_permutations_seq_len_3_num_action_2() without return types