code: def test_seq2slate_trainer_off_policy_with_clamp(
      self, clamp_method, output_arch
  ):
      batch_size = 32
      state_dim = 2
      candidate_num = 15
      candidate_dim = 4
      hidden_size = 16
      learning_rate = 1.0
      device = torch.device("cpu")
      policy_gradient_interval = 1
      seq2slate_params = Seq2SlateParameters(
          on_policy=False,
          ips_clamp=IPSClamp(clamp_method=clamp_method, clamp_max=0.3),
      )
  
      seq2slate_net = create_seq2slate_transformer(
          state_dim,
          candidate_num,
          candidate_dim,
          hidden_size,
          output_arch,
      )
      seq2slate_net_copy = copy.deepcopy(seq2slate_net)
      trainer = create_trainer(
          seq2slate_net,
          learning_rate,
          seq2slate_params,
          policy_gradient_interval,
      )
      batch = create_off_policy_batch(
          seq2slate_net,
          batch_size,
          state_dim,
          candidate_num,
          candidate_dim,
          device,
      )
  
      training_data = DataLoader([batch], collate_fn=lambda x: x[0])
      pl_trainer = pl.Trainer(
          max_epochs=policy_gradient_interval, logger=False
      )
      pl_trainer.fit(trainer, training_data)
  
      # manual compute gradient
      ranked_per_seq_probs = torch.exp(
          seq2slate_net_copy(
              batch, mode=Seq2SlateMode.PER_SEQ_LOG_PROB_MODE
          ).log_probs
      )
      logger.info(
          f"ips ratio={ranked_per_seq_probs / batch.tgt_out_probs}"
      )
      loss = -(
          torch.mean(
              ips_clamp(
                  ranked_per_seq_probs / batch.tgt_out_probs,
                  seq2slate_params.ips_clamp,
              )
              * batch.slate_reward
          )
      )
      loss.backward()
      self.assert_correct_gradient(
          seq2slate_net_copy,
          seq2slate_net,
          policy_gradient_interval,
          learning_rate,
      )
  
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 10 for calling the TestSeq2SlateTrainer obj's underlying member method code with example usage: obj.test_seq2slate_trainer_off_policy_with_clamp(clamp_method, output_arch) without return types