class_docstr: Implements the Bernoulli likelihood used for GP classification, using
  Probit regression (i.e., the latent function is warped to be in [0,1]
  using the standard Normal CDF :math:`\Phi(x)`). Given the identity
  :math:`\Phi(-x) = 1-\Phi(x)`, we can write the likelihood compactly as:
  
  .. math::
      \begin{equation*}
          p(Y=y|f)=\Phi((2y - 1)f)
      \end{equation*}
  
  .. note::
      BernoulliLikelihood has an analytic marginal distribution.
  
  .. note::
      The labels should take values in {0, 1}.
code: def __init__(self) -> None:
      return super().__init__()
  
file_path: gpytorch/gpytorch/likelihoods/bernoulli_likelihood.py
imports: import warnings
  from typing import Any
  import torch
  from torch import Tensor
  from torch.distributions import Bernoulli
  from ..distributions import base_distributions, MultivariateNormal
  from ..functions import log_normal_cdf
  from .likelihood import _OneDimensionalLikelihood
label_desc: Use image node_id 1 to create a new BernoulliLikelihood object from inherited base classes: _OneDimensionalLikelihood with example: obj = BernoulliLikelihood()