class_docstr: A bijection of the form,
  
      :math:`\mathbf{y} = \mathbf{L} \mathbf{x} + \mathbf{r}`
  
  where `\mathbf{L}` is a lower triangular matrix and `\mathbf{r}` is a vector.
  
  :param loc: the fixed D-dimensional vector to shift the input by.
  :type loc: torch.tensor
  :param scale_tril: the D x D lower triangular matrix used in the transformation.
  :type scale_tril: torch.tensor
class_docstr_tok: ["A","bijection","of","the","form",",",":","math",":","`","\\mathbf","{","y","}","=","\\mathbf","{","L","}","\\mathbf","{","x","}","+","\\mathbf","{","r","}","`","where","`","\\mathbf","{","L","}","`","is","a","lower","triangular","matrix","and","`","\\mathbf","{","r","}","`","is","a","vector",".",":","param","loc",":","the","fixed","D-dimensional","vector","to","shift","the","input","by",".",":","type","loc",":","torch.tensor",":","param","scale_tril",":","the","D","x","D","lower","triangular","matrix","used","in","the","transformation",".",":","type","scale_tril",":","torch.tensor"]
code: def __init__(self, loc, scale_tril, cache_size=0):
      super().__init__(cache_size=cache_size)
      self.loc = loc
      self.scale_tril = scale_tril
      assert (
          loc.size(-1) == scale_tril.size(-1) == scale_tril.size(-2)
      ), "loc and scale_tril must be of size D and D x D, respectively (instead: {}, {})".format(
          loc.shape, scale_tril.shape
      )
  
file_path: pyro/pyro/distributions/transforms/lower_cholesky_affine.py
imports: import torch
  from torch.distributions.transforms import Transform
  from ..None import constraints
  from ..util import copy_docs_from
label_desc: Use image node_id 1 to create a new LowerCholeskyAffine object from inherited base classes: Transform with example: obj = LowerCholeskyAffine(loc, scale_tril, cache_size)