
output2: "Scalar"

log_prob(input)
  Computes log probabilities for all \texttt{n\_classes}

  Parameters:
     **input** (*Tensor*) -- a minibatch of examples

  Returns:
     log-probabilities of for each class c in range 0 <= c <=
     \texttt{n\_classes}, where \texttt{n\_classes} is a parameter
     passed to "AdaptiveLogSoftmaxWithLoss" constructor.

  Return type:
     *Tensor*

  Shape:
     * Input: (N, \texttt{in\_features})

     * Output: (N, \texttt{n\_classes})

predict(input)
  This is equivalent to *self.log_prob(input).argmax(dim=1)*, but
  is more efficient in some cases.

  Parameters:
     **input** (*Tensor*) -- a minibatch of examples

  Returns:
     a class with the highest probability for each example

  Return type:
     output (Tensor)

  Shape:
     * Input: (N, \texttt{in\_features})

     * Output: (N)
