class_docstr: FeCAMClassifier
  
  Similar to NCM but uses malahanobis distance instead of l2 distance
  
  This approach has been proposed for continual learning in
  "FeCAM: Exploiting the Heterogeneity of Class Distributions
  in Exemplar-Free Continual Learning" Goswami et. al.
  (Neurips 2023)
  
  This requires the storage of full per-class covariance matrices
code: def init_missing_classes(self, classes, class_size, device):
      for k in classes:
          if k not in self.class_means_dict:
              self.class_means_dict[k] = torch.zeros(class_size).to(
                  device
              )
              self.class_cov_dict[k] = torch.eye(class_size).to(device)
  
imports: import copy
  from typing import Dict
  import numpy
  import torch
  import torch.nn.functional
  import tqdm
  from torch import Tensor, nn
  from avalanche.models import DynamicModule
label_desc: Use image node_id 14 for calling the FeCAMClassifier obj's underlying member method code with example usage: obj.init_missing_classes(classes, class_size, device) without return types