Keyword Arguments:
      out (Tensor, optional) -- the output tensor.
Example::
      >>> a = torch.arange(-0.5, 1, 0.5)
      >>> a
      tensor([-0.5000,  0.0000,  0.5000])
      >>> torch.special.entr(a)
      tensor([  -inf, 0.0000, 0.3466])
torch.special.erf(input, *, out=None) -> Tensor
Computes the error function of "input". The error function is
   defined as follows:
  \mathrm{erf}(x) = \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt

Parameters:
      input (Tensor) -- the input tensor.
Keyword Arguments:
      out (Tensor, optional) -- the output tensor.
Example:
  >>> torch.special.erf(torch.tensor([0, -1., 10.]))
  tensor([ 0.0000, -0.8427,  1.0000])

torch.special.erfc(input, *, out=None) -> Tensor
Computes the complementary error function of "input". The
   complementary error function is defined as follows:
  \mathrm{erfc}(x) = 1 - \frac{2}{\sqrt{\pi}} \int_{0}^{x}
  e^{-t^2} dt

Parameters: