class_docstr: LKJ prior over n x n (positive definite) Cholesky-decomposed
  correlation matrices
  
  .. math:
  
      \begin{equation*}
          pdf(\Sigma) \sim |\Sigma| ^ (\eta  - 1)
      \end{equation*}
  
  where :math:`\eta > 0` is a shape parameter and n is the dimension of the
  correlation matrix.
  
  LKJCholeskyFactorPrior is different from LKJPrior in that it accepts the
  Cholesky factor of the correlation matrix to compute probabilities.
  
  This distribution assumes that the cholesky factor is lower-triangular.
code: def __init__(self, n, eta, validate_args=False, transform=None):
      TModule.__init__(self)
      LKJCholesky.__init__(
          self, dim=n, concentration=eta, validate_args=validate_args
      )
      self.n = self.dim
      self.eta = self.concentration
      self._transform = transform
  
imports: import torch
  from linear_operator.utils.cholesky import psd_safe_cholesky
  from torch.distributions import constraints, LKJCholesky
  from torch.nn import Module
  from ..None import settings
  from .prior import Prior
label_desc: Use image node_id 1 to create a new LKJCholeskyFactorPrior object from inherited base classes: Prior, LKJCholesky with example: obj = LKJCholeskyFactorPrior(n, eta, validate_args, transform)