How to use the neuraxle.base.BaseStep.__init__ function in neuraxle

def __init__(self):
        BaseStep.__init__(self,
                          hyperparams=HYPERPARAMETERS,
                          hyperparams_space=HYPERPARAMETERS_SPACE,
                          name="MockStep"
                          )

def __init__(self):
        BaseStep.__init__(self)
        self.fitted_data = []

def __init__(self, wrapped):
        """
        Wrap a scikit-learn MetaEstimatorMixin for usage in Neuraxle. 
        This class is similar to the SKLearnWrapper class of Neuraxle that can wrap a scikit-learn BaseEstimator. 
        
        :param wrapped: a scikit-learn object of type "MetaEstimatorMixin". 
        """
        MetaStepMixin.__init__(self)
        BaseStep.__init__(self)
        self.wrapped_sklearn_metaestimator = wrapped  # TODO: use self.set_step of the MetaStepMixin instead?
        # sklearn.model_selection.RandomizedSearchCV

def __init__(self, columns_selection, n_dimension=3):
        BaseStep.__init__(self)

        col_selector = ColumnSelector2D(columns_selection=columns_selection)
        for _ in range(min(0, n_dimension - 2)):
            col_selector = ForEachDataInput(col_selector)

        MetaStepMixin.__init__(self, col_selector)
        self.n_dimension = n_dimension

def __init__(self):
        BaseStep.__init__(self)
        NonFittableMixin.__init__(self)

def __init__(self, scoring_function=r2_score, joiner=NumpyConcatenateOuterBatch()):
        MetaStepMixin.__init__(self)
        BaseStep.__init__(self)
        self.scoring_function = scoring_function
        self.joiner = joiner

def __init__(
            self,
            wrapped: BaseStep,
            cache_folder: str = DEFAULT_CACHE_FOLDER,
            value_hasher: 'BaseValueHasher' = None,
    ):
        BaseStep.__init__(self)
        MetaStepMixin.__init__(self, wrapped)
        self.value_hasher = value_hasher

        if self.value_hasher is None:
            self.value_hasher = Md5Hasher()

        self.cache_folder = cache_folder

def __init__(self, wrapped):
        MetaStepMixin.__init__(self, wrapped)
        BaseStep.__init__(self)

def __init__(
            self,
            hyperparameter_optimizer: BaseHyperparameterOptimizer,
            validation_technique: BaseCrossValidationWrapper = None,
            higher_score_is_better=True
    ):
        MetaStepMixin.__init__(self, None)
        BaseStep.__init__(self)

        if validation_technique is None:
            validation_technique = KFoldCrossValidationWrapper()
        self.validation_technique = validation_technique
        self.higher_score_is_better = higher_score_is_better
        self.hyperparameter_optimizer = hyperparameter_optimizer