I have a tensor of shape `[x, y]` and I want to subtract the mean and divide by the standard deviation row-wise (i.e. I want to do it for each row). What is the most efficient way to do this in TensorFlow?

Of course I can loop through rows as follows: 

```
new_tensor = [i - tf.reduce_mean(i) for i in old_tensor]

```

...to subtract the mean and then do something similar to find the standard deviation and divide by it, but is this the best way to do it in TensorFlow?