code: def learn(self, grad_arr, fix_opt_flag=False):
          '''
          Update this Discriminator by ascending its stochastic gradient.
  
          Args:
              grad_arr:       `np.ndarray` of gradients.
              fix_opt_flag:   If `False`, no optimization in this model will be done.
          
          Returns:
              `np.ndarray` of delta or gradients.
          '''
          delta_arr = super().learn(grad_arr, fix_opt_flag)
          if self.__add_channel_flag is True:
              return delta_arr[:, 0]
          else:
              return delta_arf learn(self, grad_arr, fix_opt_flag=False):
          '''
          Update this Discriminator by ascending its stochastic gradient.
  
          Args:
              grad_arr:       `np.ndarray` of gradients.
              fix_opt_flag:   If `False`, no optimization in this model will be done.
          
          Returns:
              `np.ndarray` of delta or gradients.
          '''
          delta_arr = super().learn(grad_arr, fix_opt_flag)
          if self.__add_channel_flag is True:
              return delta_arr[:, 0]
          else:
              return delta_arr
docs: Update this Discriminator by ascending its stochastic gradient.
  
          Args:
              grad_arr:       `np.ndarray` of gradients.
              fix_opt_flag:   If `False`, no optimization in this model will be done.
          
          Returns:
              `np.ndarray` of delta or gradients.