apply on top of the "default_mask" according to the specific
      pruning method recipe.
  Parameters:
     * **t** (*torch.Tensor*) -- tensor representing the
       importance scores of the

     * **prune.** (*parameter to*) --

     * **default_mask** (*torch.Tensor*) -- Base mask from
       previous pruning

     * **iterations** --

     * **is** (*that need to be respected after the new mask*) --

     * **t.** (*applied. Same dims as*) --

  Returns:
     mask to apply to "t", of same dims as "t"

  Return type:
     mask (torch.Tensor)

prune(t, default_mask=None, importance_scores=None)
  Computes and returns a pruned version of input tensor "t"
  according to the pruning rule specified in "compute_mask()".

  Parameters:
     * **t** (*torch.Tensor*) -- tensor to prune (of same
       dimensions as "default_mask").

     * **importance_scores** (*torch.Tensor*) -- tensor of
