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_mask(
      self,
      base_mask,
      weight,
      num_prune,
      wrapper,
      wrapper_idx,
      channel_masks=None,
  ):
      min_gm_idx = self._get_min_gm_kernel_idx(
          num_prune, wrapper, wrapper_idx, channel_masks
      )
      for idx in min_gm_idx:
          base_mask["weight_mask"][idx] = 0.0
          if base_mask["bias_mask"] is not None:
              base_mask["bias_mask"][idx] = 0.0
      return base_mask
  
imports: import logging
  import math
  import numpy
  import torch
  from .weight_masker import WeightMasker
label_desc: Use image node_id 1 for calling the FPGMPrunerMasker obj's underlying member method code with example usage: obj.get_mask(base_mask, weight, num_prune, wrapper, wrapper_idx, channel_masks) and returns: base_mask