message: Changing documentation regarding batch_normalization update operations.
diff: @@ -190,10 +190,10 @@ def batch_normalization(inputs,
  Note: when training, the moving_mean and moving_variance need to be updated.
  By default the update ops are placed in `tf.GraphKeys.UPDATE_OPS`, so they
  - need to be added as a dependency to the `train_op`. Also, be sure to add
  - any batch_normalization ops before getting the update_ops collection.
  - Otherwise, update_ops will be empty, and training/inference will not work
  - properly. For example:
  + need to be executed alongside the `train_op`. Also, be sure to add any
  + batch_normalization ops before getting the update_ops collection. Otherwise,
  + update_ops will be empty, and training/inference will not work properly. For
  + example:
  ```python
  x_norm = tf.layers.batch_normalization(x, training=training)
  @@ -201,8 +201,8 @@ def batch_normalization(inputs,
  # ...
  update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
  - with tf.control_dependencies(update_ops):
  train_op = optimizer.minimize(loss)
  + train_op = tf.group([train_op, update_ops])
  ```
  Arguments:
  