code: def test_seq2slate_trainer_off_policy_gpu(
      self, policy_gradient_interval, output_arch
  ):
      self._test_seq2slate_trainer_off_policy(
          policy_gradient_interval,
          output_arch,
          device=torch.device("cuda"),
      )
  
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 7 for calling the TestSeq2SlateTrainer obj's underlying member method code with example usage: obj.test_seq2slate_trainer_off_policy_gpu(policy_gradient_interval, output_arch) without return types