class_docstr: Beta distribution scaled by :attr:`scale` and shifted by :attr:`loc`::
  
      X ~ Beta(concentration1, concentration0)
      f(X) = loc + scale * X
      Y = f(X) ~ AffineBeta(concentration1, concentration0, loc, scale)
  
  :param concentration1: 1st concentration parameter
      (alpha) for the Beta distribution.
  :type concentration1: float or torch.Tensor
  :param concentration0: 2nd concentration parameter
      (beta) for the Beta distribution.
  :type concentration0: float or torch.Tensor
  :param loc: location parameter.
  :type loc: float or torch.Tensor
  :param scale: scale parameter.
  :type scale: float or torch.Tensor
code: def mean(self):
      return self.loc + self.scale * self.base_dist.mean
  
imports: import torch
  from torch.distributions import constraints
  from torch.distributions.transforms import AffineTransform
  from .torch import Beta, TransformedDistribution
  from .util import broadcast_shape
label_desc: Use image node_id 14 for calling the AffineBeta obj's underlying member method code with example usage: obj.mean() and returns: unknown