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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def load_model(model_file):
 """Load a model by its file. This includes the model itself, but also the preprocessing queue, the feature list and the output semantics. """

    # Extract tar
    with tarfile.open(model_file) as tar:
        tarfolder = tempfile.mkdtemp()
        tar.extractall(path=tarfolder)

    from . import features
    from . import preprocessing

    # Get the preprocessing
    with open(os.path.join(tarfolder, "preprocessing.yml"), 'r') as ymlfile:
        preprocessing_description = yaml.load(ymlfile)
    preprocessing_queue = preprocessing.get_preprocessing_queue(
        preprocessing_description['queue'])

    # Get the features
    with open(os.path.join(tarfolder, "features.yml"), 'r') as ymlfile:
        feature_description = yaml.load(ymlfile)
    feature_str_list = feature_description['features']
    feature_list = features.get_features(feature_str_list)

    # Get the model
    import nntoolkit.utils
    model = nntoolkit.utils.get_model(model_file)

    output_semantics_file = os.path.join(tarfolder, 'output_semantics.csv')
    output_semantics = nntoolkit.utils.get_outputs(output_semantics_file)

    # Cleanup
    shutil.rmtree(tarfolder)

    return (preprocessing_queue, feature_list, model, output_semantics)