code: def test_ips_clamp(self):
      importance_sampling = torch.tensor([0.5, 0.3, 3.0, 10.0, 40.0])
      assert torch.all(
          ips_clamp(importance_sampling, None) == importance_sampling
      )
      assert torch.all(
          ips_clamp(
              importance_sampling,
              IPSClamp(IPSClampMethod.AGGRESSIVE, 3.0),
          )
          == torch.tensor([0.5, 0.3, 3.0, 0.0, 0.0])
      )
      assert torch.all(
          ips_clamp(
              importance_sampling,
              IPSClamp(IPSClampMethod.UNIVERSAL, 3.0),
          )
          == torch.tensor([0.5, 0.3, 3.0, 3.0, 3.0])
      )
  
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 3 for calling the TestSeq2SlateTrainer obj's underlying member method code with example usage: obj.test_ips_clamp() without return types