code: def create_seq2slate_net(
      model_str,
      candidate_num,
      candidate_dim,
      hidden_size,
      output_arch,
      temperature,
      device,
  ):
      if model_str == MODEL_TRANSFORMER:
          return Seq2SlateTransformerNet(
              state_dim=1,
              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=temperature,
              state_embed_dim=1,
          ).to(device)
      else:
          raise NotImplementedError(f"unknown model type {model_str}")
  
imports: import logging
  import math
  import tempfile
  from itertools import permutations
  import pytorch_lightning
  import reagent.core.types
  import torch
  import torch.nn
  from reagent.core.parameters import Seq2SlateParameters
  from reagent.core.parameters_seq2slate import LearningMethod, SimulationParameters
  from reagent.core.torch_utils import gather
  from reagent.model_utils.seq2slate_utils import Seq2SlateOutputArch
  from reagent.models.seq2slate import Seq2SlateMode, Seq2SlateTransformerNet
  from reagent.optimizer.union import Optimizer__Union
  from reagent.training.ranking.seq2slate_sim_trainer import Seq2SlateSimulationTrainer
  from reagent.training.ranking.seq2slate_trainer import Seq2SlateTrainer
  from torch.utils.data import DataLoader
label_desc: Use image node_id 3 for calling a global function with example usage: create_seq2slate_net(model_str, candidate_num, candidate_dim, hidden_size, output_arch, temperature, device) and returns: Seq2SlateTransformerNet