text: 
  import tensorflow as tf
  
  def get_regularizer(regularizer, scope, is_bias=False):
      """
      get tensorflow regularizer method
      :param regularizer: DLMDL weight/bias regularizer declaration. Dictionary
      :param scope: weight scope
      :param is_bias: whether bias or not
      :return: weight/bias regularizer method
      """
      def get_value(key, default=None):
          """
          get attributes in regularizer
          :param key: key value for parsing
          :param default: default value of attribute
          :return: value
          """
          value = regularizer.get(key)
          if value is None:
              return default
          return value
  
      # get regularizer type
      # default: L2 regularizer
      type = get_value('type', default='L2')
      decay = float(get_value('bias', default=0.0)) if is_bias else float(get_value('weight', default=0.0))
      if type == 'L2':
          reg = tf.contrib.layers.l2_regularizer(scale=decay, scope=scope)
      elif type == 'L1':
          reg = tf.contrib.layers.l1_regularizer(scale=decay, scope=scope)
      elif type == 'L1_L2':
          reg = tf.contrib.layers.l1_l2_regularizer(scale_l1=decay, scale_12=decay, scope=scope)
      else: # TODO: error control
          reg = None
      return reg