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: Build method.
  
  Parameters
  ----------
  input_shape
      Tensor's input shape.
docstr_tok: ["Build","method",".","Parameters","--","--","--","--","--","input_shape","Tensor","'s","input","shape","."]
code: def build(self, input_shape: Tuple[int, ...]) -> None:
      """
      Build method.
  
      Parameters
      ----------
      input_shape
          Tensor's input shape.
      """
      super().build(input_shape)
  
      # Check if the output is a list
      input = tf.zeros(input_shape)
      output = self.call(input)
  
      if not isinstance(output, list):
          raise ValueError("The output of HeAE should be a list.")
  
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 HeAE obj's underlying member method code with example usage: obj.build(input_shape) without return types