L ~ LKJCholesky(dim, concentration)
      X = L @ L' ~ LKJCorr(dim, concentration)
Note that this distribution samples the Cholesky factor of
   correlation matrices and not the correlation matrices themselves
   and thereby differs slightly from the derivations in [1] for the
   LKJCorr distribution. For sampling, this uses the Onion method
   from [1] Section 3.
Example:
  >>> l = LKJCholesky(3, 0.5)
  >>> l.sample()  # l @ l.T is a sample of a correlation 3x3 matrix
  tensor([[ 1.0000,  0.0000,  0.0000],
          [ 0.3516,  0.9361,  0.0000],
          [-0.1899,  0.4748,  0.8593]])

Parameters:
      * dimension (dim) -- dimension of the matrices
  * **concentration** (*float** or **Tensor*) --
    concentration/shape parameter of the distribution (often
    referred to as eta)

References
[1] Generating random correlation matrices based on vines and
   extended onion method (2009), Daniel Lewandowski, Dorota