def METHOD_NAME(cls, mu: Tensor, *args, **kwargs):
    """
    Private factory method to create an instance with pretrained parameters
    Args:
        mu (Tensor): FloatTensor containing weights for the pretrained mu
        mask (Tensor, optional): If provided, this allows grouping multiple
            input tensor elements to share the same stochastic gate.
            This tensor should be broadcastable to match the input shape
            and contain integers in the range 0 to n_gates - 1.
            Indices grouped to the same stochastic gate should have the same value.
            If not provided, each element in the input tensor
            (on dimensions other than dim 0 - batch dim) is gated separately.
            Default: None
        reg_weight (float, optional): rescaling weight for L0 regularization term.
            Default: 1.0
        std (float, optional): standard deviation that will be fixed throughout.
            Default: 0.5
        reg_reduction (str, optional): the reduction to apply to the regularization:
            ``'none'`` | ``'mean'`` | ``'sum'``. ``'none'``: no reduction will be
            applied and it will be the same as the return of ``get_active_probs``,
            ``'mean'``: the sum of the gates non-zero probabilities will be divided
            by the number of gates, ``'sum'``: the gates non-zero probabilities will
            be summed.
            Default: ``'sum'``
    Returns:
        stg (GaussianStochasticGates): StochasticGates instance
    """
    n_gates = mu.numel()
    stg = cls(n_gates, *args, **kwargs)
    stg.load_state_dict({"mu": mu}, strict=False)
    return stg