code: def VAE(**kwargs):
      """VAE class for Variational AutoEncoder thresholder.
  
      Use a VAE to evaluate a non-parametric means
      to threshold scores generated by the decision_scores where outliers
      are set to any value beyond the maximum minus the minimum of the
      reconstructed distribution probabilities after encoding.
  
      Parameters
      ----------
  
      verbose : bool, optional (default=False)
           display training progress
  
      device : str, optional (default='cpu')
           device for pytorch
  
      latent_dims : int, optional (default='auto')
           number of latent dimensions the encoder will map the scores to.
           Default 'auto' applies automatic dimensionality selection using
           a profile likelihood.
  
      random_state : int, optional (default=1234)
           random seed for the normal distribution. Can also be set to None
  
      epochs : int, optional (default=100)
           number of epochs to train the VAE
  
      batch_size : int, optional (default=64)
           batch size for the dataloader during training
  
      loss : str, optional (default='kl')
           Loss function during training
  
           - 'kl' : use the combined negative log likelihood and Kullback-Leibler divergence
           - 'mmd': use the combined negative log likelihood and maximum mean discrepancy
  
      Attributes
      ----------
  
      thresh_ : threshold value that separates inliers from outliers
      """
  
      from pythresh.thresholds.vae import VAE as VAE_thres
  
      return VAE_thres(**kwargs)
  
label_desc: Use image node_id 25 for calling a global function with example usage: VAE() and returns: VAE_thres