code: def test_can_load_from_archive_model(self):
      serialization_dir = (
          self.FIXTURES_ROOT
          / "basic_classifier"
          / "from_archive_serialization"
      )
      archive_path = serialization_dir / "model.tar.gz"
      model = load_archive(archive_path).model
  
      # We want to be sure that we don't just not crash, but also be sure that we loaded the right
      # weights for the model.  We'll do that by making sure that we didn't just load the model
      # that's in the `archive_path` of the config file, which is this one.
      base_model_path = (
          self.FIXTURES_ROOT
          / "basic_classifier"
          / "serialization"
          / "model.tar.gz"
      )
      base_model = load_archive(base_model_path).model
      base_model_params = dict(base_model.named_parameters())
      for name, parameters in model.named_parameters():
          if parameters.size() == base_model_params[name].size():
              assert not (parameters == base_model_params[name]).all()
          else:
              # In this case, the parameters are definitely different, no need for the above
              # check.
              pass
  
imports: import copy
  import os
  import tempfile
  import tarfile
  import pytest
  import torch
  from allennlp.version import _MAJOR, _MINOR
  from allennlp.commands.train import train_model
  from allennlp.common import Params
  from allennlp.common.meta import Meta
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.testing import AllenNlpTestCase
  from allennlp.data.dataset_readers import DatasetReader
  from allennlp.models.archival import archive_model, load_archive, CONFIG_NAME, _check_version_compatibility
label_desc: Use image node_id 5 for calling the ArchivalTest obj's underlying member method code with example usage: obj.test_can_load_from_archive_model() without return types