class_docstr: A structured pruning algorithm that prunes the filters with the
  smallest L2 norm of the weights.
class_docstr_tok: ["A","structured","pruning","algorithm","that","prunes","the","filters","with","the","smallest","L2","norm","of","the","weights","."]
code: def get_channel_sum(self, wrapper, wrapper_idx):
      weight = wrapper.module.weight.data
      filters = weight.shape[0]
      w = weight.view(filters, -1)
      w_l2_norm = torch.sqrt((w**2).sum(dim=1))
      return w_l2_norm
  
imports: import logging
  import math
  import numpy
  import torch
  from .weight_masker import WeightMasker
label_desc: Use image node_id 2 for calling the L2FilterPrunerMasker obj's underlying member method code with example usage: obj.get_channel_sum(wrapper, wrapper_idx) and returns: w_l2_norm