class_docstr: The weights' gradient statistic tracker will estimate the absolute mean (i.e. the mean of the absolute values of
  the weights' gradients), running absolute mean variance (i.e. the variance of the absolute mean), min, absolute min,
   max and absolute max per layer. The tracker is using the `Welford's online algorithm
   <https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Welford's_online_algorithm>`_
  to estimate the running mean and running variance of the absolute weights' gradients.
  
  Args:
      number_layers (int): The number of layers to track stats on.
class_docstr_tok: ["The","weights","'","gradient","statistic","tracker","will","estimate","the","absolute","mean","(","i.e",".","the","mean","of","the","absolute","values","of","the","weights","'","gradients",")",",","running","absolute","mean","variance","(","i.e",".","the","variance","of","the","absolute","mean",")",",","min",",","absolute","min",",","max","and","absolute","max","per","layer",".","The","tracker","is","using","the","`","Welford","'s","online","algorithm","<","https",":","\/\/en.wikipedia.org\/wiki\/Algorithms_for_calculating_variance","#","Welford's_online_algorithm",">","`","_","to","estimate","the","running","mean","and","running","variance","of","the","absolute","weights","'","gradients",".","Args",":","number_layers","(","int",")",":","The","number","of","layers","to","track","stats","on","."]
code: def __init__(self, number_layers: int) -> None:
      self.number_layers = number_layers
  
      self.reset()
  
imports: from typing import Dict, Iterable, List, Tuple
  import numpy
  import torch
  from poutyne.framework.callbacks.callbacks import Callback
label_desc: Use image node_id 1 to create a new WeightsGradientsStatsTracker object  with example: obj = WeightsGradientsStatsTracker(number_layers)