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_min_gm_kernel_idx(
      self, num_prune, wrapper, wrapper_idx, channel_masks
  ):
      channel_dist = self.get_channel_sum(wrapper, wrapper_idx)
      if channel_masks is not None:
          channel_dist = channel_dist * channel_masks
      dist_list = [
          (channel_dist[i], i) for i in range(channel_dist.size(0))
      ]
      min_gm_kernels = sorted(dist_list, key=lambda x: x[0])[:num_prune]
      return [x[1] for x in min_gm_kernels]
  
imports: import logging
  import math
  import numpy
  import torch
  from .weight_masker import WeightMasker
label_desc: Use image node_id 2 for calling the FPGMPrunerMasker obj's underlying member method code with example usage: obj._get_min_gm_kernel_idx(num_prune, wrapper, wrapper_idx, channel_masks) and returns: unknown