    def __init__(
        self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False
    ):
        """
        Initialization.
        INPUT:
            - in_features: shape of the input
            - alpha - trainable parameter that controls frequency
            - beta - trainable parameter that controls magnitude
            alpha is initialized to 1 by default, higher values = higher-frequency.
            beta is initialized to 1 by default, higher values = higher-magnitude.
            alpha will be trained along with the rest of your model.
        """
        super(SnakeBeta, self).__init__()
        self.in_features = in_features

        # initialize alpha
        self.alpha_logscale = alpha_logscale
        if self.alpha_logscale:  # log scale alphas initialized to zeros
            self.alpha = nn.Parameter(torch.zeros(in_features) * alpha)
            self.beta = nn.Parameter(torch.zeros(in_features) * alpha)
        else:  # linear scale alphas initialized to ones
            self.alpha = nn.Parameter(torch.ones(in_features) * alpha)
            self.beta = nn.Parameter(torch.ones(in_features) * alpha)

        self.alpha.requires_grad = alpha_trainable
        self.beta.requires_grad = alpha_trainable

        self.no_div_by_zero = 0.000000001
Initialization.
INPUT:
- in_features: shape of the input
- alpha - trainable parameter that controls frequency
- beta - trainable parameter that controls magnitude
alpha is initialized to 1 by default, higher values = higher-frequency.
beta is initialized to 1 by default, higher values = higher-magnitude.
alpha will be trained along with the rest of your model.
models/codec/facodec/modules/quantize.py