    absolute number of parameters to prune.
        n (int, float, inf, -inf, 'fro', 'nuc'): See documentation of valid
            entries for argument ``p`` in :func:`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:
        module (nn.Module): modified (i.e. pruned) version of the input module

    Examples:
        >>> from torch.nn.utils import prune
        >>> m = prune.ln_structured(
        ...     nn.Conv2d(5, 3, 2), 'weight', amount=0.3, dim=1, n=float('-inf')
        ... )
    r