class_docstr: LKJ prior over n x n (positive definite) correlation matrices
  
  .. math:
  
      \begin{equation*}
          pdf(\Sigma) \sim |\Sigma| ^ (\eta  - 1)
      \end{equation*}
  
  where :math:`\eta > 0` is a shape parameter.
  
  Reference: Bayesian Data Analysis, 3rd ed., Gelman et al., p. 576
code: def sample(self, sample_shape=torch.Size()):
      R = super().sample(sample_shape=sample_shape)
      return R.matmul(R.transpose(-1, -2))
  
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 2 for calling the LKJPrior obj's underlying member method code with example usage: obj.sample(sample_shape) and returns: R