如何使用tensorflow函数的'权重'参数tf.contrib.legacy_seq2seq.sequence_loss_by_example？

The code:

import tensorflow as tf

A = tf.constant([[0.1,0.2,0.3,0.4],[0.2,0.1,0.4,0.3],[0.4,0.3,0.2,0.1],[0.3,0.2,0.1,0.4],[0.1,0.4,0.3,0.2]], dtype=tf.float32)
B = tf.constant([1, 2, 1, 3, 3], dtype=tf.int32)

w_1 = tf.constant(value=[1,1,1,1,1], dtype=tf.float32)
w_2 = tf.constant(value=[1,2,3,4,5], dtype=tf.float32)

D   = tf.contrib.legacy_seq2seq.sequence_loss_by_example([A], [B], [w_1])
D_1 = tf.contrib.legacy_seq2seq.sequence_loss_by_example([A], [B], [w_1], average_across_timesteps=False)
D_2 = tf.contrib.legacy_seq2seq.sequence_loss_by_example([A], [B], [w_2])
D_3 = tf.contrib.legacy_seq2seq.sequence_loss_by_example([A], [B], [w_2], average_across_timesteps=False)

with tf.Session() as sess:
  print(sess.run(D))
  print(sess.run(D_1))
  print(sess.run(D_2))
  print(sess.run(D_3))
import tensorflow as tf

A = tf.con