How to use the hanlp.utils.util.merge_locals_kwargs function in hanlp

optimizer='adamw',
            learning_rate=5e-5,
            weight_decay_rate=0,
            epsilon=1e-8,
            clipnorm=1.0,
            warmup_steps_ratio=0,
            use_amp=False,
            max_seq_length=128,
            batch_size=32,
            epochs=3,
            metrics='accuracy',
            run_eagerly=False,
            logger=None,
            verbose=True,
            **kwargs):
        return super().fit(**merge_locals_kwargs(locals(), kwargs))

def fit(self, trn_data: Any, dev_data: Any, save_dir: str, word_embed: Union[str, int, dict] = 200,
            ngram_embed: Union[str, int, dict] = 50, embedding_trainable=True, window_size=4, kernel_size=3,
            filters=(200, 200, 200, 200, 200), dropout_embed=0.2, dropout_hidden=0.2, weight_norm=True,
            loss: Union[tf.keras.losses.Loss, str] = None,
            optimizer: Union[str, tf.keras.optimizers.Optimizer] = 'adam', metrics='f1', batch_size=100,
            epochs=100, logger=None, verbose=True, **kwargs):
        return super().fit(**merge_locals_kwargs(locals(), kwargs))

def fit(self, trn_data: Any, dev_data: Any, save_dir: str, transformer: str, max_length: int = 128,
            optimizer='adamw', warmup_steps_ratio=0.1, use_amp=False, batch_size=32,
            epochs=3, logger=None, verbose=1, **kwargs):
        return super().fit(**merge_locals_kwargs(locals(), kwargs))

def fit(self, trn_data: str, dev_data: str = None, save_dir: str = None, embeddings=100, embedding_trainable=False,
            rnn_input_dropout=0.2, rnn_units=100, rnn_output_dropout=0.2, epochs=20, logger=None,
            loss: Union[tf.keras.losses.Loss, str] = None,
            optimizer: Union[str, tf.keras.optimizers.Optimizer] = 'adam', metrics='f1', batch_size=32,
            dev_batch_size=32, lr_decay_per_epoch=None,
            run_eagerly=False,
            verbose=True, **kwargs):
        # assert kwargs.get('run_eagerly', True), 'This component can only run eagerly'
        # kwargs['run_eagerly'] = True
        return super().fit(**merge_locals_kwargs(locals(), kwargs))

decay=.75,
            decay_steps=5000,
            patience=100,
            arc_loss='sparse_categorical_crossentropy',
            rel_loss='sparse_categorical_crossentropy',
            metrics=('UAS', 'LAS'),
            n_buckets=32,
            batch_size=5000,
            epochs=50000,
            early_stopping_patience=100,
            tree=False,
            punct=False,
            min_freq=2,
            run_eagerly=False, logger=None, verbose=True,
            **kwargs):
        return super().fit(**merge_locals_kwargs(locals(), kwargs))

def __init__(self, config: SerializableDict = None, map_x=True, map_y=True, use_char=False, **kwargs) -> None:
        super().__init__(**merge_locals_kwargs(locals(), kwargs))
        self.word_vocab: Optional[Vocab] = None
        self.tag_vocab: Optional[Vocab] = None
        self.char_vocab: Optional[Vocab] = None

def __init__(self, config: SerializableDict = None, map_x=True, map_y=True, lower=True, n_buckets=32,
                 n_tokens_per_batch=5000, min_freq=2,
                 **kwargs) -> None:
        super().__init__(**merge_locals_kwargs(locals(), kwargs))
        self.form_vocab: Vocab = None
        self.cpos_vocab: Vocab = None
        self.rel_vocab: Vocab = None
        self.puncts: tf.Tensor = None