class_docstr: Deep Streaming Linear Discriminant Analysis.
  
  This strategy does not use backpropagation.
  Minibatches are first passed to the pretrained feature extractor.
  The result is processed one element at a time to fit the LDA.
  Original paper:
  "Hayes et. al., Lifelong Machine Learning with Deep Streaming Linear
  Discriminant Analysis, CVPR Workshop, 2020"
  https://openaccess.thecvf.com/content_CVPRW_2020/papers/w15/Hayes_Lifelong_Machine_Learning_With_Deep_Streaming_Linear_Discriminant_Analysis_CVPRW_2020_paper.pdf
class_docstr_tok: ["Deep","Streaming","Linear","Discriminant","Analysis",".","This","strategy","does","not","use","backpropagation",".","Minibatches","are","first","passed","to","the","pretrained","feature","extractor",".","The","result","is","processed","one","element","at","a","time","to","fit","the","LDA",".","Original","paper",":","''","Hayes","et",".","al.",",","Lifelong","Machine","Learning","with","Deep","Streaming","Linear","Discriminant","Analysis",",","CVPR","Workshop",",","2020","''","https",":","\/\/openaccess.thecvf.com\/content_CVPRW_2020\/papers\/w15\/Hayes_Lifelong_Machine_Learning_With_Deep_Streaming_Linear_Discriminant_Analysis_CVPRW_2020_paper.pdf"]
docstr: Load the model parameters into StreamingLDA object.
  :param save_path: the path where the model is saved
  :param save_name: the name of the saved file
  :return:
docstr_tok: ["Load","the","model","parameters","into","StreamingLDA","object",".",":","param","save_path",":","the","path","where","the","model","is","saved",":","param","save_name",":","the","name","of","the","saved","file",":","return",":"]
code: def load_model(self, save_path, save_name):
      """
      Load the model parameters into StreamingLDA object.
      :param save_path: the path where the model is saved
      :param save_name: the name of the saved file
      :return:
      """
      # load parameters
      d = torch.load(os.path.join(save_path, save_name + ".pth"))
      self.muK = d["muK"].to(self.device)
      self.cK = d["cK"].to(self.device)
      self.Sigma = d["Sigma"].to(self.device)
      self.num_updates = d["num_updates"]
  
imports: import warnings
  from typing import Callable, Optional, Sequence, Union
  import os
  import torch
  from avalanche.training.plugins import SupervisedPlugin
  from avalanche.training.templates import SupervisedTemplate
  from avalanche.training.plugins.evaluation import EvaluationPlugin, default_evaluator
  from avalanche.models.dynamic_modules import MultiTaskModule
  from avalanche.models import FeatureExtractorBackbone
label_desc: Use image node_id 9 for calling the StreamingLDA obj's underlying member method code with example usage: obj.load_model(save_path, save_name) without return types