Keyword Arguments:
      out (Tensor, optional) -- the output tensor.
Example:
  >>> a = torch.rand(5)
  >>> a
  tensor([0.2796, 0.9331, 0.6486, 0.1523, 0.6516])
  >>> torch.special.logit(a, eps=1e-6)
  tensor([-0.9466,  2.6352,  0.6131, -1.7169,  0.6261])

torch.special.logsumexp(input, dim, keepdim=False, *, out=None)
Alias for "torch.logsumexp()".
torch.special.multigammaln(input, p, *, out=None) -> Tensor
Computes the multivariate log-gamma function with dimension p
   element-wise, given by
  \log(\Gamma_{p}(a)) = C + \displaystyle \sum_{i=1}^{p}
  \log\left(\Gamma\left(a - \frac{i - 1}{2}\right)\right)

where C = \log(\pi) \cdot \frac{p (p - 1)}{4} and \Gamma(-) is the
   Gamma function.
All elements must be greater than \frac{p - 1}{2}, otherwise the
   behavior is undefiend.
Parameters:
      * input (Tensor) -- the tensor to compute the multivariate
        log-gamma function
  * **p** (*int*) -- the number of dimensions
