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 __init__(
      self,
      concentration1,
      concentration0,
      loc,
      scale,
      validate_args=None,
  ):
      base_dist = Beta(
          concentration1, concentration0, validate_args=validate_args
      )
      super(AffineBeta, self).__init__(
          base_dist,
          AffineTransform(loc=loc, scale=scale),
          validate_args=validate_args,
      )
  
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 1 to create a new AffineBeta object from inherited base classes: TransformedDistribution with example: obj = AffineBeta(concentration1, concentration0, loc, scale, validate_args)