code: def assert_correct_gradient(
      self,
      net_with_gradient,
      net_after_gradient,
      policy_gradient_interval,
      learning_rate,
  ):
      for (n_c, w_c), (n, w) in zip(
          net_with_gradient.named_parameters(),
          net_after_gradient.named_parameters(),
      ):
          assert n_c == n
          if w_c.grad is not None:
              assert torch.allclose(
                  w_c
                  - policy_gradient_interval * learning_rate * w_c.grad,
                  w,
                  rtol=1e-4,
                  atol=2e-6,
              )
  
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 the TestSeq2SlateTrainer obj's underlying member method code with example usage: obj.assert_correct_gradient(net_with_gradient, net_after_gradient, policy_gradient_interval, learning_rate) without return types