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.
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docstr: Forward pass.
  
  Parameters
  ----------
  x
      Input tensor.
  **kwargs
      Other arguments passed to encoder/decoder `call` method.
  
  Returns
  -------
  x_hat
      Reconstruction of the input tensor.
docstr_tok: ["Forward","pass",".","Parameters","--","--","--","--","--","x","Input","tensor",".","*","*","kwargs","Other","arguments","passed","to","encoder\/decoder","`","call","`","method",".","Returns","--","--","--","-","x_hat","Reconstruction","of","the","input","tensor","."]
code: def call(
      self, x: tf.Tensor, **kwargs
  ) -> Union[tf.Tensor, List[tf.Tensor]]:
      """
      Forward pass.
  
      Parameters
      ----------
      x
          Input tensor.
      **kwargs
          Other arguments passed to encoder/decoder `call` method.
  
      Returns
      -------
      x_hat
          Reconstruction of the input tensor.
      """
  
      z = self.encoder(x, **kwargs)
      x_hat = self.decoder(z, **kwargs)
      return x_hat
  
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 2 for calling the AE obj's underlying member method code with example usage: obj.call(x) and returns: x_hat