Example::
      >>> t = torch.ones(2, 2)
      >>> torch.special.log_softmax(t, 0)
      tensor([[-0.6931, -0.6931],
              [-0.6931, -0.6931]])
torch.special.logit(input, eps=None, *, out=None) -> Tensor
Returns a new tensor with the logit of the elements of "input".
   "input" is clamped to [eps, 1 - eps] when eps is not None. When eps
   is None and "input" < 0 or "input" > 1, the function will yields
   NaN.
  \begin{align} y_{i} &= \ln(\frac{z_{i}}{1 - z_{i}}) \\ z_{i} &=
  \begin{cases}     x_{i} & \text{if eps is None} \\
  \text{eps} & \text{if } x_{i} < \text{eps} \\     x_{i} &
  \text{if } \text{eps} \leq x_{i} \leq 1 - \text{eps} \\     1 -
  \text{eps} & \text{if } x_{i} > 1 - \text{eps} \end{cases}
  \end{align}

Parameters:
      * input (Tensor) -- the input tensor.
  * **eps** (*float**, **optional*) -- the epsilon for input clamp
    bound. Default: "None"

Keyword Arguments: