class_docstr: Represents the distribution of an affinely transformed random variable.
  
  This is the distribution of ``Y = scale * X + loc``, where ``X`` is a
  random variable distributed according to ``base_distribution``.
  
  Parameters
  ----------
  base_distribution
      Original distribution
  loc
      Translation parameter of the affine transformation.
  scale
      Scaling parameter of the affine transformation.
class_docstr_tok: ["Represents","the","distribution","of","an","affinely","transformed","random","variable",".","This","is","the","distribution","of","``","Y","=","scale","*","X","+","loc","``",",","where","``","X","``","is","a","random","variable","distributed","according","to","``","base_distribution","``",".","Parameters","--","--","--","--","--","base_distribution","Original","distribution","loc","Translation","parameter","of","the","affine","transformation",".","scale","Scaling","parameter","of","the","affine","transformation","."]
code: def __init__(
      self, base_distribution: Distribution, loc=None, scale=None
  ):
      self.scale = 1.0 if scale is None else scale
      self.loc = 0.0 if loc is None else loc
  
      super().__init__(
          base_distribution, [AffineTransform(self.loc, self.scale)]
      )
  
imports: from torch.distributions import AffineTransform, Distribution, TransformedDistribution
label_desc: Use image node_id 1 to create a new AffineTransformed object from inherited base classes: TransformedDistribution with example: obj = AffineTransformed(base_distribution, loc, scale)