message: Enhance the documentation regarding UPDATE_OPS.
diff: @@ -36,6 +36,18 @@ class BatchNormalization(keras_layers.BatchNormalization, base.Layer):
  Sergey Ioffe, Christian Szegedy
  + Keras APIs handle BatchNormalization updates to the moving_mean and
  + moving_variance as part of their `fit()` and `evaluate()` loops. However, if a
  + custom training loop is used with an instance of `Model`, these updates need
  + to be explicitly included. Here's a simple example of how it can be done:
  +
  + ```python
  + #`model` is an instance of `Model` with `tf.keras.layers.BatchNormalization`
  + update_ops = model.get_updates_for(None) + model.get_updates_for(features)
  + train_op = optimizer.minimize(loss)
  + train_op = tf.group([train_op, update_ops])
  + ```
  +
  Arguments:
  axis: An `int` or list of `int`, the axis or axes that should be
  normalized, typically the features axis/axes. For instance, after a
  @@ -156,7 +168,10 @@ class BatchNormalization(keras_layers.BatchNormalization, base.Layer):
  @deprecation.deprecated(
  - date=None, instructions='Use keras.layers.BatchNormalization instead.')
  + date=None, instructions='Use keras.layers.BatchNormalization instead. In '
  + 'particular, `tf.control_dependencies(tf.GraphKeys.UPDATE_OPS)` should not '
  + 'be used (consult the `tf.keras.layers.batch_normalization`).'
  + 'documentation.')
  @tf_export(v1=['layers.batch_normalization'])
  def batch_normalization(inputs,
  axis=-1,
  