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","."]
docstr: Returns the variance of the distribution.
docstr_tok: ["Returns","the","variance","of","the","distribution","."]
code: def variance(self):
      """
      Returns the variance of the distribution.
      """
      return self.base_dist.variance * self.scale**2
  
imports: from torch.distributions import AffineTransform, Distribution, TransformedDistribution
label_desc: Use image node_id 3 for calling the AffineTransformed obj's underlying member method code with example usage: obj.variance() and returns: unknown