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