class_docstr: Combines the encoder and decoder into an end-to-end model for training.
class_docstr_tok: ["Combines","the","encoder","and","decoder","into","an","end-to-end","model","for","training","."]
code: def __init__(
      self,
      original_dim,
      intermediate_dim=64,
      latent_dim=32,
      name="autoencoder",
      **kwargs,
  ):
      super().__init__(name=name, **kwargs)
      self.original_dim = original_dim
      self.encoder = Encoder(
          latent_dim=latent_dim, intermediate_dim=intermediate_dim
      )
      self.decoder = Decoder(
          original_dim, intermediate_dim=intermediate_dim
      )
  
imports: import numpy
  import keras
  from keras import ops
  from keras import layers
label_desc: Use image node_id 1 to create a new VariationalAutoEncoder object from inherited base classes: keras with example: obj = VariationalAutoEncoder(original_dim, intermediate_dim, latent_dim, name)