class_docstr: A structured pruning algorithm that prunes the filters with the smallest
  importance approximations based on the first order taylor expansion on the weight.
  Molchanov, Pavlo and Mallya, Arun and Tyree, Stephen and Frosio, Iuri and Kautz, Jan,
  "Importance Estimation for Neural Network Pruning", CVPR 2019.
  http://jankautz.com/publications/Importance4NNPruning_CVPR19.pdf
class_docstr_tok: ["A","structured","pruning","algorithm","that","prunes","the","filters","with","the","smallest","importance","approximations","based","on","the","first","order","taylor","expansion","on","the","weight",".","Molchanov",",","Pavlo","and","Mallya",",","Arun","and","Tyree",",","Stephen","and","Frosio",",","Iuri","and","Kautz",",","Jan",",","''","Importance","Estimation","for","Neural","Network","Pruning","''",",","CVPR","2019.","http",":","\/\/jankautz.com\/publications\/Importance4NNPruning_CVPR19.pdf"]
code: def __init__(self, model, pruner, statistics_batch_num=1):
      super().__init__(model, pruner)
      self.pruner.statistics_batch_num = statistics_batch_num
      self.pruner.set_wrappers_attribute("contribution", None)
      self.pruner.iterations = 0
      self.pruner.patch_optimizer(self.calc_contributions)
  
imports: import logging
  import math
  import numpy
  import torch
  from .weight_masker import WeightMasker
label_desc: Use image node_id 1 to create a new TaylorFOWeightFilterPrunerMasker object from inherited base classes: StructuredWeightMasker with example: obj = TaylorFOWeightFilterPrunerMasker(model, pruner, statistics_batch_num)