Instantiates an Archive from an archived `tar.gz` file.

    Parameters
    ----------
    archive_file: ``str``
        The archive file to load the model from.
    weights_file: ``str``, optional (default = None)
        The weights file to use.  If unspecified, weights.th in the archive_file will be used.
    cuda_device: ``int``, optional (default = -1)
        If `cuda_device` is >= 0, the model will be loaded onto the
        corresponding GPU. Otherwise it will be loaded onto the CPU.
    overrides: ``str``, optional (default = "")
        JSON overrides to apply to the unarchived ``Params`` object.

def load_archive(archive_file: str,
                 cuda_device: int = -1,
                 overrides: str = "",
                 weights_file: str = None) -> Archive:
    """
    Instantiates an Archive from an archived `tar.gz` file.

    Parameters
    ----------
    archive_file: ``str``
        The archive file to load the model from.
    weights_file: ``str``, optional (default = None)
        The weights file to use.  If unspecified, weights.th in the archive_file will be used.
    cuda_device: ``int``, optional (default = -1)
        If `cuda_device` is >= 0, the model will be loaded onto the
        corresponding GPU. Otherwise it will be loaded onto the CPU.
    overrides: ``str``, optional (default = "")
        JSON overrides to apply to the unarchived ``Params`` object.
    """
    # redirect to the cache, if necessary
    resolved_archive_file = cached_path(archive_file)

    if resolved_archive_file == archive_file:
        logger.info(f"loading archive file {archive_file}")
    else:
        logger.info(f"loading archive file {archive_file} from cache at {resolved_archive_file}")

    if os.path.isdir(resolved_archive_file):
        serialization_dir = resolved_archive_file
    else:
        # Extract archive to temp dir
        tempdir = tempfile.mkdtemp()
        logger.info(f"extracting archive file {resolved_archive_file} to temp dir {tempdir}")
        with tarfile.open(resolved_archive_file, 'r:gz') as archive:
            archive.extractall(tempdir)
        # Postpone cleanup until exit in case the unarchived contents are needed outside
        # this function.
        atexit.register(_cleanup_archive_dir, tempdir)

        serialization_dir = tempdir

    # Check for supplemental files in archive
    fta_filename = os.path.join(serialization_dir, _FTA_NAME)
    if os.path.exists(fta_filename):
        with open(fta_filename, 'r') as fta_file:
            files_to_archive = json.loads(fta_file.read())

        # Add these replacements to overrides
        replacements_dict: Dict[str, Any] = {}
        for key, original_filename in files_to_archive.items():
            replacement_filename = os.path.join(serialization_dir, f"fta/{key}")
            if os.path.exists(replacement_filename):
                replacements_dict[key] = replacement_filename
            else:
                logger.warning(f"Archived file {replacement_filename} not found! At train time "
                               f"this file was located at {original_filename}. This may be "
                               "because you are loading a serialization directory. Attempting to "
                               "load the file from its train-time location.")

        overrides_dict = parse_overrides(overrides)
        combined_dict = with_fallback(preferred=overrides_dict, fallback=unflatten(replacements_dict))
        overrides = json.dumps(combined_dict)

    # Load config
    config = Params.from_file(os.path.join(serialization_dir, CONFIG_NAME), overrides)
    config.loading_from_archive = True

    if weights_file:
        weights_path = weights_file
    else:
        weights_path = os.path.join(serialization_dir, _WEIGHTS_NAME)
        # Fallback for serialization directories.
        if not os.path.exists(weights_path):
            weights_path = os.path.join(serialization_dir, _DEFAULT_WEIGHTS)


    # Instantiate model. Use a duplicate of the config, as it will get consumed.
    model = Model.load(config.duplicate(),
                       weights_file=weights_path,
                       serialization_dir=serialization_dir,
                       cuda_device=cuda_device)

    return Archive(model=model, config=config)