concentration parameter of the distribution (often referred to
        as beta)
arg_constraints: Dict[str, constraints.Constraint] = {'concentration0': GreaterThan(lower_bound=0.0), 'concentration1': GreaterThan(lower_bound=0.0)}
entropy()
expand(batch_shape, _instance=None)
has_rsample = True
property mean
property mode
support = Interval(lower_bound=0.0, upper_bound=1.0)
property variance
LKJCholesky
class torch.distributions.lkj_cholesky.LKJCholesky(dim, concentration=1.0, validate_args=None)
Bases: "Distribution"
LKJ distribution for lower Cholesky factor of correlation matrices.
   The distribution is controlled by "concentration" parameter \eta to
   make the probability of the correlation matrix M generated from a
   Cholesky factor proportional to \det(M)^{\eta - 1}. Because of
   that, when "concentration == 1", we have a uniform distribution
   over Cholesky factors of correlation matrices:
  L ~ LKJCholesky(dim, concentration)
