code: def create_off_policy_batch(
      seq2slate,
      batch_size,
      state_dim,
      candidate_num,
      candidate_dim,
      device,
  ):
      state = torch.randn(batch_size, state_dim)
      candidates = torch.randn(batch_size, candidate_num, candidate_dim)
      reward = torch.rand(batch_size, 1)
      action = torch.stack(
          [torch.randperm(candidate_num) for _ in range(batch_size)]
      )
      logged_slate_prob = torch.rand(batch_size, 1) / 1e12
      off_policy_batch = rlt.PreprocessedRankingInput.from_input(
          state=state,
          candidates=candidates,
          device=device,
          action=action,
          logged_propensities=logged_slate_prob,
          slate_reward=reward,
      )
      return off_policy_batch
  
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 4 for calling a global function with example usage: create_off_policy_batch(seq2slate, batch_size, state_dim, candidate_num, candidate_dim, device) and returns: off_policy_batch