class_docstr: Base class for Gaussian Likelihoods, supporting general heteroskedastic noise models.
class_docstr_tok: ["Base","class","for","Gaussian","Likelihoods",",","supporting","general","heteroskedastic","noise","models","."]
code: def __init__(
      self, noise_covar: Union[Noise, FixedGaussianNoise], **kwargs: Any
  ) -> None:
      super().__init__()
      param_transform = kwargs.get("param_transform")
      if param_transform is not None:
          warnings.warn(
              "The 'param_transform' argument is now deprecated. If you want to use a different "
              "transformaton, specify a different 'noise_constraint' instead.",
              DeprecationWarning,
          )
  
      self.noise_covar = noise_covar
  
imports: import math
  import warnings
  from copy import deepcopy
  from typing import Any, Optional, Tuple, Union
  import torch
  from linear_operator.operators import LinearOperator, MaskedLinearOperator, ZeroLinearOperator
  from torch import Tensor
  from torch.distributions import Distribution, Normal
  from ..None import settings
  from ..constraints import Interval
  from ..distributions import base_distributions, MultivariateNormal
  from ..priors import Prior
  from ..utils.warnings import GPInputWarning
  from .likelihood import Likelihood
  from .noise_models import FixedGaussianNoise, HomoskedasticNoise, Noise
label_desc: Use image node_id 1 to create a new _GaussianLikelihoodBase object from inherited base classes: Likelihood with example: obj = _GaussianLikelihoodBase(noise_covar)