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

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

        _BaseModel.__init__(self)
        _IterativeModel.__init__(self)
        _Regressor.__init__(self)
        self.eta = eta
        self.epochs = epochs
        self.minibatches = minibatches
        self.random_seed = random_seed
        self.print_progress = print_progress
        self._is_fitted = False
        self.method = method

        if method != 'sgd' and minibatches is not None:
            raise ValueError(('Minibatches should be set to `None` '
                              'if `method` != `sgd`. Got method=`%s`.')
                             % (method))

        supported_methods = ('sgd', 'direct', 'svd', 'qr')
        if method not in supported_methods:

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,
                 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=0.5, 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
        self.print_progress = print_progress

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, k, max_iter=10,
                 convergence_tolerance=1e-05,
                 random_seed=None, print_progress=0):

        _BaseModel.__init__(self)
        _Cluster.__init__(self)
        _IterativeModel.__init__(self)
        self.k = k
        self.max_iter = max_iter
        self.convergence_tolerance = convergence_tolerance
        self.random_seed = random_seed
        self.print_progress = print_progress
        self._is_fitted = False