message: Update doc for tf.keras.layers.Masking.
diff: @@ -67,18 +67,30 @@ class Masking(Layer):
  Example:
  Consider a Numpy data array `x` of shape `(samples, timesteps, features)`,
  - to be fed to an LSTM layer.
  - You want to mask timestep #3 and #5 because you lack data for
  - these timesteps. You can:
  + to be fed to an LSTM layer. You want to mask timestep #3 and #5 because you
  + lack data for these timesteps. You can:
  - Set `x[:, 3, :] = 0.` and `x[:, 5, :] = 0.`
  - Insert a `Masking` layer with `mask_value=0.` before the LSTM layer:
  ```python
  - model = Sequential()
  - model.add(Masking(mask_value=0., input_shape=(timesteps, features)))
  - model.add(LSTM(32))
  + samples, timesteps, features = 32, 10, 8
  + inputs = np.random.random([samples, timesteps, features]).astype(np.float32)
  + inputs[:, 3, :] = 0.
  + inputs[:, 5, :] = 0.
  +
  + model = tf.keras.models.Sequential()
  + model.add(tf.keras.layers.Masking(mask_value=0.,
  + input_shape=(timesteps, features)))
  + model.add(tf.keras.layers.LSTM(32))
  +
  + output = model(inputs)
  + # The time step 3 and 5 will be skipped from LSTM calculation.
  ```
  +
  + See [the masking and padding
  + guide](https://www.tensorflow.org/beta/guide/keras/masking_and_padding)
  + for more details.
  """
  def __init__(self, mask_value=0., **kwargs):
  