docstr: Combine encoder and decoder in VAE.
  
  Parameters
  ----------
  encoder_net
      Layers for the encoder wrapped in a tf.keras.Sequential class.
  decoder_net
      Layers for the decoder wrapped in a tf.keras.Sequential class.
  latent_dim
      Dimensionality of the latent space.
  beta
      Beta parameter for KL-divergence loss term.
  name
      Name of VAE model.
docstr_tok: ["Combine","encoder","and","decoder","in","VAE",".","Parameters","--","--","--","--","--","encoder_net","Layers","for","the","encoder","wrapped","in","a","tf.keras.Sequential","class",".","decoder_net","Layers","for","the","decoder","wrapped","in","a","tf.keras.Sequential","class",".","latent_dim","Dimensionality","of","the","latent","space",".","beta","Beta","parameter","for","KL-divergence","loss","term",".","name","Name","of","VAE","model","."]
code: def __init__(
      self,
      encoder_net: tf.keras.Model,
      decoder_net: tf.keras.Model,
      latent_dim: int,
      beta: float = 1.0,
      name: str = "vae",
  ) -> None:
      """
      Combine encoder and decoder in VAE.
  
      Parameters
      ----------
      encoder_net
          Layers for the encoder wrapped in a tf.keras.Sequential class.
      decoder_net
          Layers for the decoder wrapped in a tf.keras.Sequential class.
      latent_dim
          Dimensionality of the latent space.
      beta
          Beta parameter for KL-divergence loss term.
      name
          Name of VAE model.
      """
      super(VAE, self).__init__(name=name)
      self.encoder = EncoderVAE(encoder_net, latent_dim)
      self.decoder = Decoder(decoder_net)
      self.beta = beta
      self.latent_dim = latent_dim
  
imports: import numpy
  import tensorflow
  from tensorflow.keras.layers import Bidirectional, Concatenate, Dense, Flatten, Layer, LSTM
  from typing import Callable, List, Tuple
  from alibi_detect.utils.tensorflow.distance import relative_euclidean_distance
label_desc: Use image node_id 1 to create a new VAE object from inherited base classes: tf.keras with example: obj = VAE(encoder_net, decoder_net, latent_dim, beta, name)