Example::
      >>> torch.special.log_ndtr(torch.tensor([-3., -2, -1, 0, 1, 2, 3]))
      tensor([-6.6077 -3.7832 -1.841  -0.6931 -0.1728 -0.023  -0.0014])
torch.special.log_softmax(input, dim, *, dtype=None) -> Tensor
Computes softmax followed by a logarithm.
While mathematically equivalent to log(softmax(x)), doing these two
   operations separately is slower and numerically unstable. This
   function is computed as:
  \text{log\_softmax}(x_{i}) = \log\left(\frac{\exp(x_i) }{ \sum_j
  \exp(x_j)} \right)

Parameters:
      * input (Tensor) -- input
  * **dim** (*int*) -- A dimension along which log_softmax will be
    computed.

  * **dtype** ("torch.dtype", optional) -- the desired data type
    of returned tensor. If specified, the input tensor is cast to
    "dtype" before the operation is performed. This is useful for
    preventing data type overflows. Default: None.

Example::
      >>> t = torch.ones(2, 2)