How to use the networkml.utils.model.Model function in networkml

def test_augment_data():
    model = Model(10, labels=['Unknown'])
    a = [[1, 2, 3], [4, 5, 6]]
    x = np.array(a)
    a = ['label1', 'label2', 'label3']
    y = np.array(a)
    model._augment_data(x, y)

def train(self, data_dir, save_path):
        m = RandomForestClassifier(
            n_estimators=100,
            min_samples_split=5,
            class_weight='balanced'
        )

        # Initialize the model
        model = Model(
            duration=self.duration,
            labels=self.conf_labels,
            model=m,
            model_type='randomforest'
        )
        # Train the model
        model.train(data_dir)
        # Save the model to the specified path
        model.save(save_path)

def train(self, data_dir, save_path, m, algorithm):
        # Initialize the model
        model = Model(
            duration=self.duration,
            hidden_size=self.state_size,
            labels=self.conf_labels,
            model=m,
            model_type=algorithm,
            threshold_time=self.threshold
        )
        # Train the model
        model.train(data_dir)
        # Save the model to the specified path
        model.save(save_path)

time_const,
    model_path='networkml/trained_models/onelayer/OneLayerModel.pkl',
    label=None,
    model_type='randomforest'
):
    logger = logging.getLogger(__name__)
    try:
        if 'LOG_LEVEL' in os.environ and os.environ['LOG_LEVEL'] != '':
            logger.setLevel(os.environ['LOG_LEVEL'])
    except Exception as e:  # pragma: no cover
        logger.error(
            'Unable to set logging level because: {0} defaulting to INFO.'.format(str(e)))

    # Load the model
    logger.debug('Loading model')
    model = Model(duration=None, hidden_size=None, model_type=model_type)
    model.load(model_path)

    # Get all the pcaps in the training directory
    logger.debug('Getting pcaps')
    pcaps = []
    try:
        ext = os.path.splitext(data_dir)[-1]
        if ext == '.pcap':
            pcaps.append(data_dir)
    except Exception as e:  # pragma: no cover
        logger.debug('Skipping {0} because: {1}'.format(data_dir, str(e)))

    for dirpath, _, filenames in os.walk(data_dir):
        for filename in filenames:
            ext = os.path.splitext(filename)[-1]
            if ext == '.pcap':

## Take arguments from command line
        self.args = None
        self.read_args()

        ## Take input from configuration file
        self.get_config()
        self.common = Common(config=self.config)

        ## Instantiate a logger to to leg messages to aid debugging
        self.logger = Common().setup_logger(self.logger)

        ## Add network traffic files for parsing
        self.get_files()
        self.model_hash = None
        self.model = Model(duration=self.duration, hidden_size=None,
                           model_type=self.args.algorithm)

        def create_base_alg():
            return BaseAlgorithm(
                files=self.files, config=self.config,
                model=self.model, model_hash=self.model_hash,
                model_path=self.args.trained_model)

        ## Check whether operation is evaluation, train, or test
        ## Evaluation returns predictions that are useful for the deployment
        ## of networkml in an operational environment.
        if self.args.operation == 'eval':
            self.load_model()

            if (self.args.algorithm == 'onelayer' or self.args.algorithm == 'randomforest'):
                base_alg = create_base_alg()