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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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. """

  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