the parameter being pruned. If unspecified or None, the
           module parameter will be used in its place.
apply_mask(module)
  Simply handles the multiplication between the parameter being
  pruned and the generated mask. Fetches the mask and the original
  tensor from the module and returns the pruned version of the
  tensor.

  Parameters:
     **module** (*nn.Module*) -- module containing the tensor to
     prune

  Returns:
     pruned version of the input tensor

  Return type:
     pruned_tensor (torch.Tensor)

compute_mask(t, default_mask)
  Computes and returns a mask for the input tensor "t". Starting
  from a base "default_mask" (which should be a mask of ones if
  the tensor has not been pruned yet), generate a mask to apply on
  top of the "default_mask" by zeroing out the channels along the
  specified dim with the lowest L"n"-norm.

  Parameters:
