code: def train_auto_encoder(self, generative_model, a_logs_list):
          '''
          Train the generative model as the Auto-Encoder.
  
          Args:
              generative_model:   Generator which draws samples from the `fake` distribution.
              a_logs_list:        `list` of the reconstruction errors.
          
          Returns:
              The tuple data. The shape is...
              - Generator which draws samples from the `fake` distribution.
              - `list` of the reconstruction errors.
          '''
          error_arr = generative_model.update()
          if error_arr.ndim > 1:
              error_arr = error_arr.mean()
          a_logs_list.append(error_arr)
  
          self.__logger.debug("The reconstruction error (mean): " + str(error_arr))
  
          return generative_model, a_logs_lisf train_auto_encoder(self, generative_model, a_logs_list):
          '''
          Train the generative model as the Auto-Encoder.
  
          Args:
              generative_model:   Generator which draws samples from the `fake` distribution.
              a_logs_list:        `list` of the reconstruction errors.
          
          Returns:
              The tuple data. The shape is...
              - Generator which draws samples from the `fake` distribution.
              - `list` of the reconstruction errors.
          '''
          error_arr = generative_model.update()
          if error_arr.ndim > 1:
              error_arr = error_arr.mean()
          a_logs_list.append(error_arr)
  
          self.__logger.debug("The reconstruction error (mean): " + str(error_arr))
  
          return generative_model, a_logs_list
docs: Train the generative model as the Auto-Encoder.
  
          Args:
              generative_model:   Generator which draws samples from the `fake` distribution.
              a_logs_list:        `list` of the reconstruction errors.
          
          Returns:
              The tuple data. The shape is...
              - Generator which draws samples from the `fake` distribution.
              - `list` of the reconstruction errors.