code: def test_can_optimise_model_with_dense_and_sparse_params(self):
      optimizer_params = Params({"type": "dense_sparse_adam"})
      parameters = [
          [n, p]
          for n, p in self.model.named_parameters()
          if p.requires_grad
      ]
      optimizer = Optimizer.from_params(
          model_parameters=parameters, params=optimizer_params
      )
      for instance in self.instances:
          instance.index_fields(self.vocab)
      GradientDescentTrainer(
          self.model, optimizer, SimpleDataLoader(self.instances, 2)
      ).train()
  
imports: import copy
  import re
  from allennlp.common.params import Params
  from allennlp.common.testing import AllenNlpTestCase
  from allennlp.data import Vocabulary
  from allennlp.data.dataset_readers import SequenceTaggingDatasetReader
  from allennlp.data.data_loaders import SimpleDataLoader
  from allennlp.models.simple_tagger import SimpleTagger
  from allennlp.training import GradientDescentTrainer
  from allennlp.training.optimizers import Optimizer
label_desc: Use image node_id 2 for calling the TestDenseSparseAdam obj's underlying member method code with example usage: obj.test_can_optimise_model_with_dense_and_sparse_params() without return types