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 apply_invert_transforms(self, features):
      if self.tukey:
          features = self._tukey_invert_transforms(features)
      return features
  
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 17 for calling the FeCAMClassifier obj's underlying member method code with example usage: obj.apply_invert_transforms(features) and returns: features