docstr: Decoder of (V)AE.
  
  Parameters
  ----------
  decoder_net
      Layers for the decoder wrapped in a tf.keras.Sequential class.
  name
      Name of decoder.
docstr_tok: ["Decoder","of","(","V",")","AE",".","Parameters","--","--","--","--","--","decoder_net","Layers","for","the","decoder","wrapped","in","a","tf.keras.Sequential","class",".","name","Name","of","decoder","."]
code: def __init__(
      self, decoder_net: tf.keras.Model, name: str = "decoder"
  ) -> None:
      """
      Decoder of (V)AE.
  
      Parameters
      ----------
      decoder_net
          Layers for the decoder wrapped in a tf.keras.Sequential class.
      name
          Name of decoder.
      """
      super(Decoder, self).__init__(name=name)
      self.decoder_net = decoder_net
  
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 Decoder object from inherited base classes: Layer with example: obj = Decoder(decoder_net, name)