class_docstr: A Beta likelihood for regressing over percentages.
  
  The Beta distribution is parameterized by :math:`\alpha > 0` and :math:`\beta > 0` parameters
  which roughly correspond to the number of prior positive and negative observations.
  We instead parameterize it through a mixture :math:`m \in [0, 1]` and scale :math:`s > 0` parameter.
  
  .. math::
      \begin{equation*}
          \alpha = ms, \quad \beta = (1-m)s
      \end{equation*}
  
  The mixture parameter is the output of the GP passed through a logit function :math:`\sigma(\cdot)`.
  The scale parameter is learned.
  
  .. math::
      p(y \mid f) = \text{Beta} \left( \sigma(f) s , (1 - \sigma(f)) s\right)
  
  :param batch_shape: The batch shape of the learned noise parameter (default: []).
  :param scale_prior: Prior for scale parameter :math:`s`.
  :param scale_constraint: Constraint for scale parameter :math:`s`.
  
  :ivar torch.Tensor scale: :math:`s` parameter (scale)
code: def scale(self) -> Tensor:
      return self.raw_scale_constraint.transform(self.raw_scale)
  
file_path: gpytorch/gpytorch/likelihoods/beta_likelihood.py
imports: from typing import Any, Optional
  import torch
  from torch import Tensor
  from torch.distributions import Beta
  from ..constraints import Interval, Positive
  from ..distributions import base_distributions
  from ..priors import Prior
  from .likelihood import _OneDimensionalLikelihood
label_desc: Use image node_id 2 for calling the BetaLikelihood obj's underlying member method code with example usage: obj.scale() and returns: self