class_docstr: A filter pruner via geometric median.
  "Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration",
  https://arxiv.org/pdf/1811.00250.pdf
class_docstr_tok: ["A","filter","pruner","via","geometric","median",".","``","Filter","Pruning","via","Geometric","Median","for","Deep","Convolutional","Neural","Networks","Acceleration","''",",","https",":","\/\/arxiv.org\/pdf\/1811.00250.pdf"]
code: def get_channel_sum(self, wrapper, wrapper_idx):
      weight = wrapper.module.weight.data
      assert len(weight.size()) in [3, 4]
      dist_list = []
      for out_i in range(weight.size(0)):
          dist_sum = self._get_distance_sum(weight, out_i)
          dist_list.append(dist_sum)
      return torch.Tensor(dist_list).to(weight.device)
  
imports: import logging
  import math
  import numpy
  import torch
  from .weight_masker import WeightMasker
label_desc: Use image node_id 4 for calling the FPGMPrunerMasker obj's underlying member method code with example usage: obj.get_channel_sum(wrapper, wrapper_idx) and returns: torch