code: def create_trainer(
      seq2slate_net,
      learning_rate,
      seq2slate_params,
      policy_gradient_interval,
  ):
      return Seq2SlateTrainer(
          seq2slate_net=seq2slate_net,
          params=seq2slate_params,
          policy_optimizer=Optimizer__Union(
              SGD=classes["SGD"](lr=learning_rate)
          ),
          policy_gradient_interval=policy_gradient_interval,
          print_interval=1,
      )
  
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 1 for calling a global function with example usage: create_trainer(seq2slate_net, learning_rate, seq2slate_params, policy_gradient_interval) and returns: Seq2SlateTrainer