# Buggy code
def __init__(
    self,
    num_mixtures=None,
    ard_num_dims=1,
    batch_size=1,
    active_dims=None,
    eps=1e-6,
    mixture_scales_prior=None,
    mixture_means_prior=None,
    mixture_weights_prior=None,
    param_transform=softplus,
    inv_param_transform=None,
    **kwargs,
):
    mixture_scales_prior = _deprecate_kwarg(
        kwargs, "log_mixture_scales_prior", "mixture_scales_prior", mixture_scales_prior
    )
    mixture_means_prior = _deprecate_kwarg(
        kwargs, "log_mixture_means_prior", "mixture_means_prior", mixture_means_prior
    )
    mixture_weights_prior = _deprecate_kwarg(
        kwargs,
        "log_mixture_weights_prior",
        "mixture_weights_prior",
        mixture_weights_prior,
    )

    if num_mixtures is None:
        raise RuntimeError("num_mixtures is a required argument")
    if (
        mixture_means_prior is not None
        or mixture_scales_prior is not None
        or mixture_weights_prior is not None
    ):
        logger.warning("Priors not implemented for SpectralMixtureKernel")

    # This kernel does not use the default lengthscale
    super(SpectralMixtureKernel, self).__init__(
        active_dims=active_dims,
        param_transform=param_transform,
        inv_param_transform=inv_param_transform,
    )
    self.num_mixtures = num_mixtures
    self.batch_size = batch_size
    self.ard_num_dims = ard_num_dims
    self.eps = eps

    self.register_parameter(
        name="raw_mixture_weights",
        parameter=torch.nn.Parameter(torch.zeros(self.batch_size, self.num_mixtures)),
    )
    ms_shape = torch.Size([self.batch_size, self.num_mixtures, 1, self.ard_num_dims])
    self.register_parameter(
        name="raw_mixture_means", parameter=torch.nn.Parameter(torch.zeros(ms_shape))
    )
    self.register_parameter(
        name="raw_mixture_scales", parameter=torch.nn.Parameter(torch.zeros(ms_shape))
    )


# Traceback (TypeError)
import gpytorch
gl = gpytorch.likelihoods.GaussianLikelihood()
gl.initialize(noise=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File ".../gpytorch/gpytorch/module.py", line 89, in initialize
setattr(self, name, val)
File "../lib/python3.6/site-packages/torch/nn/modules/module.py", line 579, in __setattr__
object.__setattr__(self, name, value)
File ".../gpytorch/gpytorch/likelihoods/gaussian_likelihood.py", line 63, in noise
self.noise_covar.initialize(value)
TypeError: initialize() takes 1 positional argument but 2 were given

# Fixed code
def __init__(
    self,
    num_mixtures=None,
    ard_num_dims=1,
    batch_size=1,
    active_dims=None,
    eps=1e-6,
    mixture_scales_prior=None,
    mixture_means_prior=None,
    mixture_weights_prior=None,
    param_transform=softplus,
    inv_param_transform=None,
    **kwargs,
):
    if num_mixtures is None:
        raise RuntimeError("num_mixtures is a required argument")
    if (
        mixture_means_prior is not None
        or mixture_scales_prior is not None
        or mixture_weights_prior is not None
    ):
        logger.warning("Priors not implemented for SpectralMixtureKernel")

    # This kernel does not use the default lengthscale
    super(SpectralMixtureKernel, self).__init__(
        active_dims=active_dims,
        param_transform=param_transform,
        inv_param_transform=inv_param_transform,
    )
    self.num_mixtures = num_mixtures
    self.batch_size = batch_size
    self.ard_num_dims = ard_num_dims
    self.eps = eps

    self.register_parameter(
        name="raw_mixture_weights",
        parameter=torch.nn.Parameter(torch.zeros(self.batch_size, self.num_mixtures)),
    )
    ms_shape = torch.Size([self.batch_size, self.num_mixtures, 1, self.ard_num_dims])
    self.register_parameter(
        name="raw_mixture_means", parameter=torch.nn.Parameter(torch.zeros(ms_shape))
    )
    self.register_parameter(
        name="raw_mixture_scales", parameter=torch.nn.Parameter(torch.zeros(ms_shape))
    )