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 replace_class_cov_dict(
      self,
      class_cov_dict: Dict[int, Tensor],
  ):
      self.class_cov_dict = class_cov_dict
      self._vectorize_cov_dict()
  
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 13 for calling the FeCAMClassifier obj's underlying member method code with example usage: obj.replace_class_cov_dict(class_cov_dict) without return types