code: def draw(self):
          '''
          Draws samples from the `fake` distribution.
  
          Returns:
              `np.ndarray` of samples.
          '''
          observed_arr = self.noise_sampler.generate()
          _ = self.inference(observed_arr)
          feature_arr = self.__convolutional_auto_encoder.extract_feature_points_arr()
          for i in range(len(self.__deconvolution_layer_list)):
              try:
                  feature_arr = self.__deconvolution_layer_list[i].forward_propagate(feature_arr)
              except:
                  self.__logger.debug("Error raised in Deconvolution layer " + str(i + 1))
                  raise
  
          return feature_arf draw(self):
          '''
          Draws samples from the `fake` distribution.
  
          Returns:
              `np.ndarray` of samples.
          '''
          observed_arr = self.noise_sampler.generate()
          _ = self.inference(observed_arr)
          feature_arr = self.__convolutional_auto_encoder.extract_feature_points_arr()
          for i in range(len(self.__deconvolution_layer_list)):
              try:
                  feature_arr = self.__deconvolution_layer_list[i].forward_propagate(feature_arr)
              except:
                  self.__logger.debug("Error raised in Deconvolution layer " + str(i + 1))
                  raise
  
          return feature_arr
docs: Draws samples from the `fake` distribution.
  
          Returns:
              `np.ndarray` of samples.