code: def test_compute_impt_smpl(
      self, output_arch, clamp_method, clamp_max, shape
  ):
      logger.info(f"output arch: {output_arch}")
      logger.info(f"clamp method: {clamp_method}")
      logger.info(f"clamp max: {clamp_max}")
      logger.info(f"frechet shape: {shape}")
  
      candidate_num = 5
      candidate_dim = 2
      state_dim = 1
      hidden_size = 32
      device = torch.device("cpu")
      learning_rate = 0.001
      policy_gradient_interval = 1
  
      candidates = torch.randint(
          5, (candidate_num, candidate_dim)
      ).float()
      candidate_scores = torch.sum(candidates, dim=1)
  
      seq2slate_params = Seq2SlateParameters(
          on_policy=False,
          ips_clamp=IPSClamp(
              clamp_method=clamp_method, clamp_max=clamp_max
          ),
      )
      seq2slate_net = create_seq2slate_transformer(
          state_dim,
          candidate_num,
          candidate_dim,
          hidden_size,
          output_arch,
      )
      trainer = create_trainer(
          seq2slate_net,
          learning_rate,
          seq2slate_params,
          policy_gradient_interval,
      )
  
      all_permt = torch.tensor(
          list(permutations(range(candidate_num), candidate_num))
      )
      sampler = FrechetSort(shape=shape, topk=candidate_num)
      sum_of_logged_propensity = 0
      sum_of_model_propensity = 0
      sum_of_ips_ratio = 0
  
      for i in range(len(all_permt)):
          sample_action = all_permt[i]
          logged_propensity = torch.exp(
              sampler.log_prob(candidate_scores, sample_action)
          )
          batch = rlt.PreprocessedRankingInput.from_input(
              state=torch.zeros(1, state_dim),
              candidates=candidates.unsqueeze(0),
              device=device,
              action=sample_action.unsqueeze(0),
              logged_propensities=logged_propensity.reshape(1, 1),
          )
          model_propensities = torch.exp(
              seq2slate_net(
                  batch, mode=Seq2SlateMode.PER_SEQ_LOG_PROB_MODE
              ).log_probs
          )
          impt_smpl, clamped_impt_smpl = trainer._compute_impt_smpl(
              model_propensities, logged_propensity
          )
          if impt_smpl > clamp_max:
              if clamp_method == IPSClampMethod.AGGRESSIVE:
                  npt.asset_allclose(
                      clamped_impt_smpl.detach().numpy(), 0, rtol=1e-5
                  )
              else:
                  npt.assert_allclose(
                      clamped_impt_smpl.detach().numpy(),
                      clamp_max,
                      rtol=1e-5,
                  )
  
          sum_of_model_propensity += model_propensities
          sum_of_logged_propensity += logged_propensity
          sum_of_ips_ratio += model_propensities / logged_propensity
          logger.info(
              f"shape={shape}, sample_action={sample_action}, logged_propensity={logged_propensity},"
              f" model_propensity={model_propensities}"
          )
  
      logger.info(
          f"shape {shape}, sum_of_logged_propensity={sum_of_logged_propensity}, "
          f"sum_of_model_propensity={sum_of_model_propensity}, "
          f"mean sum_of_ips_ratio={sum_of_ips_ratio / len(all_permt)}"
      )
      npt.assert_allclose(
          sum_of_logged_propensity.detach().numpy(), 1, rtol=1e-5
      )
      npt.assert_allclose(
          sum_of_model_propensity.detach().numpy(), 1, rtol=1e-5
      )
  
imports: import copy
  import itertools
  import logging
  import random
  import unittest
  from itertools import permutations
  import numpy
  import numpy.testing
  import pytorch_lightning
  import reagent.core.types
  import torch
  from parameterized import parameterized
  from reagent.core.parameters import Seq2SlateParameters
  from reagent.core.parameters_seq2slate import IPSClamp, IPSClampMethod
  from reagent.model_utils.seq2slate_utils import Seq2SlateOutputArch
  from reagent.models.seq2slate import Seq2SlateMode, Seq2SlateTransformerNet
  from reagent.optimizer.union import classes, Optimizer__Union
  from reagent.samplers.frechet import FrechetSort
  from reagent.training.ranking.helper import ips_clamp
  from reagent.training.ranking.seq2slate_trainer import Seq2SlateTrainer
  from torch.utils.data import DataLoader
label_desc: Use image node_id 11 for calling the TestSeq2SlateTrainer obj's underlying member method code with example usage: obj.test_compute_impt_smpl(output_arch, clamp_method, clamp_max, shape) without return types