code: def create_seq2slate_transformer(
      state_dim, candidate_num, candidate_dim, hidden_size, output_arch
  ):
      return Seq2SlateTransformerNet(
          state_dim=state_dim,
          candidate_dim=candidate_dim,
          num_stacked_layers=2,
          num_heads=2,
          dim_model=hidden_size,
          dim_feedforward=hidden_size,
          max_src_seq_len=candidate_num,
          max_tgt_seq_len=candidate_num,
          output_arch=output_arch,
          temperature=0.5,
      )
  
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 2 for calling a global function with example usage: create_seq2slate_transformer(state_dim, candidate_num, candidate_dim, hidden_size, output_arch) and returns: Seq2SlateTransformerNet