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`.
class_docstr_tok: ["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","`","."]
docstr: Constructor. Combine encoder and decoder in HeAE.
  
  Parameters
  ----------
  encoder
      Encoder network.
  decoder
      Decoder network.
docstr_tok: ["Constructor",".","Combine","encoder","and","decoder","in","HeAE",".","Parameters","--","--","--","--","--","encoder","Encoder","network",".","decoder","Decoder","network","."]
code: def __init__(self, encoder: nn.Module, decoder: nn.Module, **kwargs):
      """
      Constructor. Combine encoder and decoder in HeAE.
  
      Parameters
      ----------
      encoder
          Encoder network.
      decoder
          Decoder network.
      """
      super().__init__(encoder=encoder, decoder=decoder, **kwargs)
  
      # send to device
      self.to(self.device)
  
file_path: alibi/alibi/models/pytorch/autoencoder.py
imports: import torch
  import torch.nn
  from alibi.models.pytorch.model import Model
  from typing import List, Union
label_desc: Use image node_id 1 to create a new HeAE object from inherited base classes: AE with example: obj = HeAE(encoder, decoder)