tensor
  * **n_classes** (*int*) -- Number of classes in the dataset

  * **cutoffs** (*Sequence*) -- Cutoffs used to assign targets to
    their buckets

  * **div_value** (*float**, **optional*) -- value used as an
    exponent to compute sizes of the clusters. Default: 4.0

  * **head_bias** (*bool**, **optional*) -- If "True", adds a bias
    term to the 'head' of the adaptive softmax. Default: "False"

Returns:
      * output is a Tensor of size "N" containing computed target
        log probabilities for each example
  * **loss** is a Scalar representing the computed negative log
    likelihood loss

Return type:
      "NamedTuple" with "output" and "loss" fields
Shape:
      * input: (N, \texttt{in_features}) or (\texttt{in_features})
  * target: (N) or () where each value satisfies 0 <=
    \texttt{target[i]} <= \texttt{n\_classes}

  * output1: (N) or ()

  * output2: "Scalar"

log_prob(input)