docstr: Encoder of VAE.
  
  Parameters
  ----------
  encoder_net
      Layers for the encoder wrapped in a tf.keras.Sequential class.
  latent_dim
      Dimensionality of the latent space.
  name
      Name of encoder.
docstr_tok: ["Encoder","of","VAE",".","Parameters","--","--","--","--","--","encoder_net","Layers","for","the","encoder","wrapped","in","a","tf.keras.Sequential","class",".","latent_dim","Dimensionality","of","the","latent","space",".","name","Name","of","encoder","."]
code: def __init__(
      self,
      encoder_net: tf.keras.Model,
      latent_dim: int,
      name: str = "encoder_vae",
  ) -> None:
      """
      Encoder of VAE.
  
      Parameters
      ----------
      encoder_net
          Layers for the encoder wrapped in a tf.keras.Sequential class.
      latent_dim
          Dimensionality of the latent space.
      name
          Name of encoder.
      """
      super(EncoderVAE, self).__init__(name=name)
      self.encoder_net = encoder_net
      self.fc_mean = Dense(latent_dim, activation=None)
      self.fc_log_var = Dense(latent_dim, activation=None)
      self.sampling = Sampling()
  
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 EncoderVAE object from inherited base classes: Layer with example: obj = EncoderVAE(encoder_net, latent_dim, name)