message: Update TimeDistributed documentation
  Based on notes.
diff: @@ -93,7 +93,7 @@ class TimeDistributed(Wrapper):
  with `channels_last` data format, across 10 timesteps.
  The batch input shape is `(32, 10, 128, 128, 3)`.
  - You can then use `TimeDistributed` to apply a `Conv2D` layer to each of the
  + You can then use `TimeDistributed` to apply the same `Conv2D` layer to each of the
  10 timesteps, independently:
  >>> inputs = tf.keras.Input(shape=(10, 128, 128, 3))
  @@ -103,7 +103,7 @@ class TimeDistributed(Wrapper):
  TensorShape([None, 10, 126, 126, 64])
  Because `TimeDistributed` applies an instance of `Conv2D` to each of the
  - timestamps, the weights used at each timestamp are shared.
  + timestamps, the same set of weights are used at each timestamp.
  Arguments:
  layer: a `tf.keras.layers.Layer` instance.
  