code: def test_copy_gen_trainable_params_update(self):
      for params, init_case in itertools.product(
          self.PARAMS, self.INIT_CASES
      ):
          cgen = CopyGenerator(**init_case)
          trainable_params = {
              n: p
              for n, p in cgen.named_parameters()
              if p.requires_grad
          }
          assert len(trainable_params) > 0  # sanity check
          old_weights = deepcopy(trainable_params)
          dummy_in = self.dummy_inputs(params, init_case)
          res = cgen(*dummy_in)
          pretend_loss = res.sum()
          pretend_loss.backward()
          dummy_optim = torch.optim.SGD(trainable_params.values(), 1)
          dummy_optim.step()
          for param_name in old_weights.keys():
              self.assertTrue(
                  trainable_params[param_name]
                  .ne(old_weights[param_name])
                  .any(),
                  param_name + " " + init_case.__str__(),
              )
  
imports: import unittest
  from onmt.modules.copy_generator import CopyGenerator, CopyGeneratorLoss
  import itertools
  from copy import deepcopy
  import torch
  from torch.nn.functional import softmax
  from onmt.tests.utils_for_tests import product_dict
label_desc: Use image node_id 5 for calling the TestCopyGenerator obj's underlying member method code with example usage: obj.test_copy_gen_trainable_params_update() without return types