message: Clarified docs of Lambda function on usecase
  Felt a bit unclear about the usecase between `Lambda` and subclassing. Tried to clear that up.
diff: @@ -651,13 +651,16 @@ class Lambda(Layer):
  The `Lambda` layer exists so that arbitrary TensorFlow functions
  can be used when constructing `Sequential` and Functional API
  models. `Lambda` layers are best suited for simple operations or
  - quick experimentation. For more advanced use cases, subclassing
  - `keras.layers.Layer` is preferred. One reason for this is that
  - when saving a Model, `Lambda` layers are saved by serializing the
  - Python bytecode, whereas subclassed Layers are saved via overriding
  - their `get_config` method and are thus more portable. Models that rely
  - on subclassed Layers are also often easier to visualize and reason
  - about.
  + quick experimentation. For more advanced usecases, follow
  + [this guide](https://www.tensorflow.org/alpha/guide/keras/custom_layers_and_models)
  + for subclassing `tf.keras.layers.Layer`.
  +
  + The main reason to subclass `tf.keras.layers.Layer` instead using a
  + `Lambda` layer is for saving and inspecting a Model. `Lambda` layers
  + are saved by serializing the Python bytecode, whereas subclassed
  + Layers are saved via overriding their `get_config` method. Overriding
  + `get_config` improves portability of Models and are easier to inspect,
  + visualize and reason about.
  Examples:
  