How to use the mlxtend._base._Classifier.__init__ function in mlxtend

def __init__(self, eta=0.5, epochs=50,
                 hidden_layers=[50],
                 n_classes=None,
                 momentum=0.0, l1=0.0, l2=0.0,
                 dropout=1.0,
                 decrease_const=0.0,
                 minibatches=1, random_seed=None,
                 print_progress=0):

        _BaseModel.__init__(self)
        _Classifier.__init__(self)
        _IterativeModel.__init__(self)
        _MultiClass.__init__(self)
        _MultiLayer.__init__(self)

        if len(hidden_layers) > 1:
            raise AttributeError('Currently, only 1 hidden layer is supported')
        self.hidden_layers = hidden_layers
        self.eta = eta
        self.n_classes = n_classes
        self.l1 = l1
        self.l2 = l2
        self.decrease_const = decrease_const
        self.momentum = momentum
        self.epochs = epochs
        self.minibatches = minibatches
        self.random_seed = random_seed

def __init__(self, eta=0.01, epochs=50,
                 l2=0.0,
                 minibatches=1,
                 n_classes=None,
                 random_seed=None,
                 print_progress=0):

        _BaseModel.__init__(self)
        _IterativeModel.__init__(self)
        _Classifier.__init__(self)
        _MultiClass.__init__(self)

        self.eta = eta
        self.epochs = epochs
        self.l2 = l2
        self.minibatches = minibatches
        self.n_classes = n_classes
        self.random_seed = random_seed
        self.print_progress = print_progress
        self._is_fitted = False

def __init__(self, eta=0.01, epochs=50,
                 l2_lambda=0.0, minibatches=1,
                 random_seed=None,
                 print_progress=0):

        _BaseModel.__init__(self)
        _IterativeModel.__init__(self)
        _Classifier.__init__(self)

        self.eta = eta
        self.epochs = epochs
        self.l2_lambda = l2_lambda
        self.minibatches = minibatches
        self.random_seed = random_seed
        self.print_progress = print_progress
        self._is_fitted = False

def __init__(self, eta=0.1, epochs=50, random_seed=None,
                 print_progress=0):

        _BaseModel.__init__(self)
        _IterativeModel.__init__(self)
        _Classifier.__init__(self)

        self.eta = eta
        self.epochs = epochs
        self.random_seed = random_seed
        self.print_progress = print_progress
        self._is_fitted = False

def __init__(self, eta=0.01, epochs=50,
                 minibatches=None, random_seed=None,
                 print_progress=0):

        _BaseModel.__init__(self)
        _IterativeModel.__init__(self)
        _Classifier.__init__(self)

        self.eta = eta
        self.minibatches = minibatches
        self.epochs = epochs
        self.random_seed = random_seed
        self.print_progress = print_progress
        self._is_fitted = False