indicate the importance of the corresponding elements in
           the parameter being pruned. If unspecified or None, the
           parameter will be used in its place.
     * **kwargs** -- keyword arguments passed on to a subclass of
       a "BasePruningMethod"

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)

abstract 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 random mask to
