code: def test_ips_ratio_mean(self, output_arch, shape):
      output_arch = Seq2SlateOutputArch.FRECHET_SORT
      shape = 0.1
      logger.info(f"output arch: {output_arch}")
      logger.info(f"frechet shape: {shape}")
  
      candidate_num = 5
      candidate_dim = 2
      state_dim = 1
      hidden_size = 8
      device = torch.device("cpu")
      batch_size = 1024
      num_batches = 400
      learning_rate = 0.001
      policy_gradient_interval = 1
  
      state = torch.zeros(batch_size, state_dim)
      # all data have same candidates
      candidates = torch.randint(
          5, (batch_size, candidate_num, candidate_dim)
      ).float()
      candidates[1:] = candidates[0]
      candidate_scores = torch.sum(candidates, dim=-1)
  
      seq2slate_params = Seq2SlateParameters(
          on_policy=False,
      )
      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,
      )
  
      sampler = FrechetSort(shape=shape, topk=candidate_num)
      sum_of_ips_ratio = 0
  
      for i in range(num_batches):
          sample_outputs = [
              sampler.sample_action(candidate_scores[j : j + 1])
              for j in range(batch_size)
          ]
          action = torch.stack(
              list(map(lambda x: x.action.squeeze(0), sample_outputs))
          )
          logged_propensity = torch.stack(
              list(map(lambda x: torch.exp(x.log_prob), sample_outputs))
          )
          batch = rlt.PreprocessedRankingInput.from_input(
              state=state,
              candidates=candidates,
              device=device,
              action=action,
              logged_propensities=logged_propensity,
          )
          model_propensities = torch.exp(
              seq2slate_net(
                  batch, mode=Seq2SlateMode.PER_SEQ_LOG_PROB_MODE
              ).log_probs
          )
          impt_smpl, _ = trainer._compute_impt_smpl(
              model_propensities, logged_propensity
          )
          sum_of_ips_ratio += torch.mean(impt_smpl).detach().numpy()
          mean_of_ips_ratio = sum_of_ips_ratio / (i + 1)
          logger.info(
              f"{i}-th batch, mean ips ratio={mean_of_ips_ratio}"
          )
  
          if i > 100 and np.allclose(mean_of_ips_ratio, 1, atol=0.03):
              return
  
      raise Exception(
          f"Mean ips ratio {mean_of_ips_ratio} is not close to 1"
      )
  
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 12 for calling the TestSeq2SlateTrainer obj's underlying member method code with example usage: obj.test_ips_ratio_mean(output_arch, shape) without return types