class_docstr: A structured pruning algorithm that prunes the filters of smallest magnitude
  weights sum in the convolution layers to achieve a preset level of network sparsity.
  Hao Li, Asim Kadav, Igor Durdanovic, Hanan Samet and Hans Peter Graf,
  "PRUNING FILTERS FOR EFFICIENT CONVNETS", 2017 ICLR
  https://arxiv.org/abs/1608.08710
class_docstr_tok: ["A","structured","pruning","algorithm","that","prunes","the","filters","of","smallest","magnitude","weights","sum","in","the","convolution","layers","to","achieve","a","preset","level","of","network","sparsity",".","Hao","Li",",","Asim","Kadav",",","Igor","Durdanovic",",","Hanan","Samet","and","Hans","Peter","Graf",",","''","PRUNING","FILTERS","FOR","EFFICIENT","CONVNETS","''",",","2017","ICLR","https",":","\/\/arxiv.org\/abs\/1608.08710"]
code: def get_channel_sum(self, wrapper, wrapper_idx):
      weight = wrapper.module.weight.data
      filters = weight.shape[0]
      w_abs = weight.abs()
      w_abs_structured = w_abs.view(filters, -1).sum(dim=1)
      return w_abs_structured
  
imports: import logging
  import math
  import numpy
  import torch
  from .weight_masker import WeightMasker
label_desc: Use image node_id 2 for calling the L1FilterPrunerMasker obj's underlying member method code with example usage: obj.get_channel_sum(wrapper, wrapper_idx) and returns: w_abs_structured