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 _tukey_invert_transforms(self, x):
      x = torch.tensor(x)
      if self.tukey1 == 0:
          return torch.exp(x)
      else:
          return torch.pow(x, 1 / self.tukey1)
  
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 5 for calling the FeCAMClassifier obj's underlying member method code with example usage: obj._tukey_invert_transforms(x) and returns: torch, torch