如何在TensorFlow中定义加权损失函数？

import tensorflow as tf

x = tf.placeholder(tf.float32, shape=[None, input_size], name='x')
y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')
weights = tf.placeholder(tf.float32, shape=[None, num_classes], name='weights')

weights = tf.Variable(tf.zeros([input_size, num_classes]), name='weights')
biases = tf.Variable(tf.zeros([num_classes]), name='biases')

logits = tf.matmul(x, weights) + biases
y_pred = tf.nn.softmax(logits)

cross_entropy = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_true)
weighted_cross_entropy = tf.multiply(cross_entropy, weights)
loss = tf.reduce_mean(weighted_cross_entropy)

optimizer = tf.train.GradientDescentOptimizer(learning_rate)
train_op = optimizer.minimize(loss)