code: def test_train_model(self):
      params = lambda: Params(
          {
              "model": {"type": "constant"},
              "dataset_reader": {
                  "type": "sequence_tagging",
                  "max_instances": 4,
              },
              "train_data_path": SEQUENCE_TAGGING_DATA_PATH,
              "validation_data_path": SEQUENCE_TAGGING_DATA_PATH,
              "data_loader": {"batch_size": 2},
              "trainer": {"type": "no_op"},
          }
      )
  
      serialization_dir = self.TEST_DIR / "serialization_directory"
      train_model(params(), serialization_dir=serialization_dir)
      archive = load_archive(serialization_dir / "model.tar.gz")
      model = archive.model
      assert model.forward(torch.tensor([1, 2, 3]))[
          "class"
      ] == torch.tensor(98)
      assert model.vocab.get_vocab_size() == 9
  
file_path: allennlp/tests/commands/no_op_train_test.py
imports: from typing import Dict
  import torch
  from allennlp.commands.train import train_model
  from allennlp.common import Params
  from allennlp.common.testing import AllenNlpTestCase
  from allennlp.models import load_archive, Model
label_desc: Use image node_id 1 for calling the TestTrain obj's underlying member method code with example usage: obj.test_train_model() without return types