class_docstr: A structured pruning algorithm that prunes the filters with the
  smallest mean value of output activations.
  Pavlo Molchanov, Stephen Tyree, Tero Karras, Timo Aila and Jan Kautz,
  "Pruning Convolutional Neural Networks for Resource Efficient Inference", ICLR 2017.
  https://arxiv.org/abs/1611.06440
class_docstr_tok: ["A","structured","pruning","algorithm","that","prunes","the","filters","with","the","smallest","mean","value","of","output","activations",".","Pavlo","Molchanov",",","Stephen","Tyree",",","Tero","Karras",",","Timo","Aila","and","Jan","Kautz",",","''","Pruning","Convolutional","Neural","Networks","for","Resource","Efficient","Inference","''",",","ICLR","2017.","https",":","\/\/arxiv.org\/abs\/1611.06440"]
docstr: Calculate mean value of activations.
  
  Parameters
  ----------
  activations : list
      Layer's output activations
  
  Returns
  -------
  torch.Tensor
      Filter's mean value of the output activations
docstr_tok: ["Calculate","mean","value","of","activations",".","Parameters","--","--","--","--","--","activations",":","list","Layer","'s","output","activations","Returns","--","--","--","-","torch.Tensor","Filter","'s","mean","value","of","the","output","activations"]
code: def _cal_mean_activation(self, activations):
      """
      Calculate mean value of activations.
  
      Parameters
      ----------
      activations : list
          Layer's output activations
  
      Returns
      -------
      torch.Tensor
          Filter's mean value of the output activations
      """
      activations = torch.cat(activations, 0)
      mean_activation = torch.mean(activations, dim=(0, 2, 3))
      return mean_activation
  
imports: import logging
  import math
  import numpy
  import torch
  from .weight_masker import WeightMasker
label_desc: Use image node_id 2 for calling the ActivationMeanRankFilterPrunerMasker obj's underlying member method code with example usage: obj._cal_mean_activation(activations) and returns: mean_activation