Example:
  >>> t = torch.randn(4)
  >>> t
  tensor([ 0.9213,  1.0887, -0.8858, -1.7683])
  >>> torch.special.expit(t)
  tensor([ 0.7153,  0.7481,  0.2920,  0.1458])

torch.special.expm1(input, *, out=None) -> Tensor
Computes the exponential of the elements minus 1 of "input".
  y_{i} = e^{x_{i}} - 1

Note:
 This function provides greater precision than exp(x) - 1 for
 small values of x.

Parameters:
      input (Tensor) -- the input tensor.
Keyword Arguments:
      out (Tensor, optional) -- the output tensor.
Example:
  >>> torch.special.expm1(torch.tensor([0, math.log(2.)]))
  tensor([ 0.,  1.])

torch.special.gammainc(input, other, *, out=None) -> Tensor
Computes the regularized lower incomplete gamma function:
  \text{out}_{i} = \frac{1}{\Gamma(\text{input}_i)}
  \int_0^{\text{other}_i} t^{\text{input}_i-1} e^{-t} dt

where both \text{input}_i and \text{other}_i are weakly positive