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

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



m = prune.random_structured(
   ...     nn.Linear(5, 3), 'weight', amount=3, dim=1
   ... )
columns_pruned = int(sum(torch.sum(m.weight, dim=0) == 0))
print(columns_pruned)
   3


