layer normalization in layer?

https://github.com/NLPLearn/QANet/blob/8107d223897775d0c3838cb97f93b089908781d4/layers.py#L52

execuse me, in the paper "Layer Normalization,Lei Jimmy Ba, Ryan Kiros, and Geoffrey E. Hinton", it said that the mean and variance is computed over all the hidden units in the same layer, and different training cases have different normalization terms. So I think the mean should be computed like this:
```python
axes = list(range(1, x.shape.ndims))
mean = tf.reduce_mean(x, axes)
```
So the shape of mean is [batch,]. also the variance is [batch,]
and then feed them to compute the normlized x.

In the tensorflow api of layer normalization, the source code is below, and I think it is the same with mine.
` norm_axes = list(range(begin_norm_axis, inputs_rank))`
https://github.com/tensorflow/tensorflow/blob/c19e29306ce1777456b2dbb3a14f511edf7883a8/tensorflow/contrib/layers/python/layers/layers.py#L2311

Start by reading layers.py at line 52 and compare its normalization axes with the Layer Normalization paper and the linked TensorFlow layer-normalization implementation. Confirm that the mean and variance are computed per training case across the hidden units, then verify the resulting behavior with the repository's available checks.

Written by the indexing model from the issue text.