def __init__(self, params, lr: float=required, beta: float=0.8, step_size: int=None, linear: bool=True, gamma: float=1, momentum: float=0, dampening: float=0, weight_decay: float=0, nesterov: bool=False):
    if ((lr is not required) and (lr < 0.0)):
        raise ValueError('Invalid learning rate: {}'.format(lr))
    if (momentum < 0.0):
        raise ValueError('Invalid momentum value: {}'.format(momentum))
    if (weight_decay < 0.0):
        raise ValueError('Invalid weight_decay value: {}'.format(weight_decay))
    defaults = dict(lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay, nesterov=nesterov)
    if (nesterov and ((momentum <= 0) or (dampening != 0))):
        raise ValueError('Nesterov momentum requires a momentum and zero dampening')
    super(Adas, self).__init__(params[:2], defaults)
    if (np.less(beta, 0) or np.greater_equal(beta, 1)):
        raise ValueError(f'Invalid beta: {beta}')
    if np.less(gamma, 0):
        raise ValueError(f'Invalid gamma: {gamma}')
    if (step_size is not None):
        if np.less_equal(step_size, 0):
            raise ValueError(f'Invalid step_size: {step_size}')
    self.step_size = step_size
    self.gamma = gamma
    self.beta = beta
    self.metrics = metrics = Metrics(params=params[2]['all_params'], linear=linear)
    self.lr_vector = np.repeat(a=lr, repeats=len(metrics.params))
    self.velocity = np.zeros((len(self.metrics.params) - len(self.metrics.mask)))
    self.not_ready = list(range(len(self.velocity)))
    self.init_lr = lr
    self.zeta = 1.0
    self.KG = 0.0
def __init__(self, params, lr: float=required, beta: float=0.8, step_size: int=None, linear: bool=True, gamma: float=1, momentum: float=0, dampening: float=0, weight_decay: float=0, nesterov: bool=False):
    if ((lr is not required) and (lr < 0.0)):
        raise ValueError('Invalid learning rate: {}'.format(lr))
    if (momentum < 0.0):
        raise ValueError('Invalid momentum value: {}'.format(momentum))
    if (weight_decay < 0.0):
        raise ValueError('Invalid weight_decay value: {}'.format(weight_decay))
    defaults = dict(lr=lr, momentum=momentum, dampening=dampening, weight_decay=weight_decay, nesterov=nesterov)
    if (nesterov and ((momentum <= 0) or (dampening != 0))):
        raise ValueError('Nesterov momentum requires a momentum and zero dampening')
    super(Adas, self).__init__(params[:2], defaults)
    if (np.less(beta, 0) or np.greater_equal(beta, 1)):
        raise ValueError(f'Invalid beta: {beta}')
    if np.less(gamma, 0):
        raise ValueError(f'Invalid gamma: {gamma}')
    if (step_size is not None):
        if np.less_equal(step_size, 0):
            raise ValueError(f'Invalid step_size: {step_size}')
    self.step_size = step_size
    self.gamma = gamma
    self.beta = beta
    self.metrics = metrics = Metrics(params=params[2]['all_params'], linear=linear)
    self.lr_vector = np.repeat(a=lr, repeats=len(metrics.params))
    self.velocity = np.zeros((len(self.metrics.params) - len(self.metrics.mask)))
    self.not_ready = list(range(len(self.velocity)))
    self.init_lr = lr
    self.zeta = 1.0
    self.KG = 0.0