class_docstr: Autoencoder. Standard autoencoder architecture. The model is composed from two submodules, the encoder and
  the decoder. The forward pass consists of passing the input to the encoder, obtain the input embedding and
  pass the embedding through the decoder. The abstraction can be used for multiple data modalities.
class_docstr_tok: ["Autoencoder",".","Standard","autoencoder","architecture",".","The","model","is","composed","from","two","submodules",",","the","encoder","and","the","decoder",".","The","forward","pass","consists","of","passing","the","input","to","the","encoder",",","obtain","the","input","embedding","and","pass","the","embedding","through","the","decoder",".","The","abstraction","can","be","used","for","multiple","data","modalities","."]
docstr: Constructor. Combine encoder and decoder in AE
  
  Parameters
  ----------
  encoder
      Encoder network.
  decoder
      Decoder network.
docstr_tok: ["Constructor",".","Combine","encoder","and","decoder","in","AE","Parameters","--","--","--","--","--","encoder","Encoder","network",".","decoder","Decoder","network","."]
code: def __init__(
      self, encoder: keras.Model, decoder: keras.Model, **kwargs
  ) -> None:
      """
      Constructor. Combine encoder and decoder in AE
  
      Parameters
      ----------
      encoder
          Encoder network.
      decoder
          Decoder network.
      """
      super().__init__(**kwargs)
      self.encoder = encoder
      self.decoder = decoder
  
file_path: alibi/alibi/models/tensorflow/autoencoder.py
imports: import tensorflow
  import tensorflow.keras
  from typing import List, Tuple, Union
label_desc: Use image node_id 1 to create a new AE object from inherited base classes: keras with example: obj = AE(encoder, decoder)