class_docstr: Prune (currently unpruned) units in a tensor by zeroing out the ones with the lowest L1-norm.
  
  Args:
      amount (int or float): quantity of parameters to 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.
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docstr: Add pruning on the fly and reparametrization of a tensor.
  
  Adds the forward pre-hook that enables pruning on the fly and
  the reparametrization of a tensor in terms of the original tensor
  and the pruning mask.
  
  Args:
      module (nn.Module): module containing the tensor to prune
      name (str): parameter name within ``module`` on which pruning
          will act.
      amount (int or float): quantity of parameters to 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.
      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.
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code: def apply(cls, module, name, amount, importance_scores=None):
      r"""Add pruning on the fly and reparametrization of a tensor.
  
      Adds the forward pre-hook that enables pruning on the fly and
      the reparametrization of a tensor in terms of the original tensor
      and the pruning mask.
  
      Args:
          module (nn.Module): module containing the tensor to prune
          name (str): parameter name within ``module`` on which pruning
              will act.
          amount (int or float): quantity of parameters to 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.
          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.
      """
      return super().apply(
          module,
          name,
          amount=amount,
          importance_scores=importance_scores,
      )
  
imports: import numbers
  from abc import ABC, abstractmethod
  from collections.abc import Iterable
  from typing import Tuple
  import torch
label_desc: Use image node_id 3 for calling the L1Unstructured obj's underlying member method code with example usage: obj.apply(cls, module, name, amount, importance_scores) and returns: super