class_docstr: Heterogeneous autoencoder. The model follows the standard autoencoder architecture and includes and additional
  type check to ensure that the output of the model is a list of tensors. For more details, see
  :py:class:`alibi.models.pytorch.autoencoder.AE`.
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docstr: Forward pass.
  
  Parameters
  ----------
  x
      Input tensor.
  **kwargs
      Other arguments passed to the encoder/decoder.
  
  Returns
  --------
  List of reconstruction of the input tensor. First element corresponds to the reconstruction of all the         numerical features if they exist, and the rest of the elements correspond to each categorical feature.
docstr_tok: ["Forward","pass",".","Parameters","--","--","--","--","--","x","Input","tensor",".","*","*","kwargs","Other","arguments","passed","to","the","encoder\/decoder",".","Returns","--","--","--","--","List","of","reconstruction","of","the","input","tensor",".","First","element","corresponds","to","the","reconstruction","of","all","the","numerical","features","if","they","exist",",","and","the","rest","of","the","elements","correspond","to","each","categorical","feature","."]
code: def call(self, x: tf.Tensor, **kwargs) -> List[tf.Tensor]:
      """
          Forward pass.
  
          Parameters
          ----------
          x
              Input tensor.
          **kwargs
              Other arguments passed to the encoder/decoder.
  
          Returns
          --------
          List of reconstruction of the input tensor. First element corresponds to the reconstruction of all the \
          numerical features if they exist, and the rest of the elements correspond to each categorical feature.
          """
      return super().call(x, **kwargs)
  
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 3 for calling the HeAE obj's underlying member method code with example usage: obj.call(x) and returns: super