human
Complete the below
def l1_l2_regularizer(weight_l1=1.0, weight_l2=1.0, scope=None):

    ''''Define a L1L2 regularizer.
    Args:
    weight_l1: scale the L1 loss by this factor.
    weight_l2: scale the L2 loss by this factor.
    scope: Optional scope for name_scope.
    Returns:
    a regularizer function.'
    '''
assistant
#Here is the Python code you requested.
    def regularizer(tensor):
        with tf.name_scope(scope, 'L1L2Regularizer', [tensor]):
            weight_l1_t = tf.convert_to_tensor(weight_l1, dtype=tensor.dtype.base_dtype, name='weight_l1')
            weight_l2_t = tf.convert_to_tensor(weight_l2, dtype=tensor.dtype.base_dtype, name='weight_l2')
            reg_l1 = tf.multiply(weight_l1_t, tf.reduce_sum(tf.abs(tensor)), name='value_l1')
            reg_l2 = tf.multiply(weight_l2_t, tf.nn.l2_loss(tensor), name='value_l2')
            return tf.add(reg_l1, reg_l2, name='value')
    return regularizer