class_docstr: Cosine layer defined in
  "Learning a Unified Classifier Incrementally via Rebalancing"
  by Saihui Hou et al.
  
  Implementation modified from https://github.com/G-U-N/PyCIL
  
  This layer is aimed at countering the task-recency bias by removing the bias
  in the classifier and normalizing the weight and the input feature before
  computing the weight-feature product
class_docstr_tok: ["Cosine","layer","defined","in","''","Learning","a","Unified","Classifier","Incrementally","via","Rebalancing","''","by","Saihui","Hou","et","al",".","Implementation","modified","from","https",":","\/\/github.com\/G-U-N\/PyCIL","This","layer","is","aimed","at","countering","the","task-recency","bias","by","removing","the","bias","in","the","classifier","and","normalizing","the","weight","and","the","input","feature","before","computing","the","weight-feature","product"]
code: def reset_parameters(self):
      stdv = 1.0 / math.sqrt(self.weight.size(1))
      self.weight.data.uniform_(-stdv, stdv)
      if self.sigma is not None:
          self.sigma.data.fill_(1)
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  import numpy
  from avalanche.models import DynamicModule
label_desc: Use image node_id 2 for calling the CosineLinear obj's underlying member method code with example usage: obj.reset_parameters() without return types