cluster, which is given as \left\lfloor\frac{\texttt{in_feature
     s}}{\texttt{div_value}^{idx}}\right\rfloor, where idx is the
     cluster index (with clusters for less frequent words having
     larger indices, and indices starting from 1).

"head_bias" if set to True, adds a bias term to the 'head' of the
     adaptive softmax. See paper for details. Set to False in the
     official implementation.

Warning:
 Labels passed as inputs to this module should be sorted according
 to their frequency. This means that the most frequent label
 should be represented by the index *0*, and the least frequent
 label should be represented by the index *n_classes - 1*.

Note:
 This module returns a "NamedTuple" with "output" and "loss"
 fields. See further documentation for details.

Note:
 To compute log-probabilities for all classes, the "log_prob"
 method can be used.

Parameters:
      * in_features (int) -- Number of features in the input