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 expand(self, batch_shape, _instance=None):
      new = self._get_checked_instance(AffineBeta, _instance)
      return super(AffineBeta, self).expand(batch_shape, _instance=new)
  
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 3 for calling the AffineBeta obj's underlying member method code with example usage: obj.expand(batch_shape, _instance) and returns: super