prune. If "float", should be between 0.0 and 1.0 and represent
        the fraction of parameters to prune. If "int", it represents
        the absolute number of parameters to prune.
  * **n** (*int**, **float**, **inf**, **-inf**, **'fro'**,
    **'nuc'*) -- See documentation of valid entries for argument
    "p" in "torch.norm()".

  * **dim** (*int*) -- index of the dim along which we define
    channels to prune.

  * **importance_scores** (*torch.Tensor*) -- tensor of importance
    scores (of same shape as module parameter) used to compute
    mask for pruning. The values in this tensor indicate the
    importance of the corresponding elements in the parameter
    being pruned. If unspecified or None, the module parameter
    will be used in its place.

Returns:
      modified (i.e. pruned) version of the input module
Return type:
      module (nn.Module)
-[ Examples ]-



from torch.nn.utils import prune


