"input".
  \text{out}_{i} = \exp(-|x|) * i1(x) =     \exp(-|x|) *
  \frac{(\text{input}_{i})}{2} * \sum_{k=0}^{\infty}
  \frac{(\text{input}_{i}^2/4)^k}{(k!) * (k+1)!}

Parameters:
      input (Tensor) -- the input tensor.
Keyword Arguments:
      out (Tensor, optional) -- the output tensor.
Example::
      >>> torch.special.i1e(torch.arange(5, dtype=torch.float32))
      tensor([0.0000, 0.2079, 0.2153, 0.1968, 0.1788])
torch.special.log1p(input, *, out=None) -> Tensor
Alias for "torch.log1p()".
torch.special.log_ndtr(input, *, out=None) -> Tensor
Computes the log of the area under the standard Gaussian
   probability density function, integrated from minus infinity to
   "input", elementwise.
  \text{log\_ndtr}(x) = \log\left(\frac{1}{\sqrt{2
  \pi}}\int_{-\infty}^{x} e^{-\frac{1}{2}t^2} dt \right)

Parameters:
      input (Tensor) -- the input tensor.
Keyword Arguments:
      out (Tensor, optional) -- the output tensor.