message: Update the docstring for Bidirectional wrapper wrt to the layer used.
  For any custom layer to work with Bidirectional wrapper, the layer need to support "go_backwards" as its init parameter and property.
diff: @@ -353,21 +353,30 @@ class Bidirectional(Wrapper):
  """Bidirectional wrapper for RNNs.
  Arguments:
  - layer: `Recurrent` instance.
  - merge_mode: Mode by which outputs of the
  - forward and backward RNNs will be combined.
  - One of {'sum', 'mul', 'concat', 'ave', None}.
  - If None, the outputs will not be combined,
  - they will be returned as a list.
  - backward_layer: Optional `Recurrent` instance to be used to handle
  - backwards input processing. If `backward_layer` is not provided,
  - the layer instance passed as the `layer` argument will be used to
  - generate the backward layer automatically.
  + layer: `keras.layers.RNN` instance, such as `keras.layers.LSTM` or
  + `keras.layers.GRU`. It could also be a `keras.layers.Layer` instance
  + that meets the following criteria:
  + 1. Be a sequence-processing layer (accepts 3D+ inputs).
  + 2. Have a `go_backwards`, `return_sequences` and `return_state`
  + attribute (with the same semantics as for the `RNN` class).
  + 3. Have an `input_spec` attribute.
  + 4. Implement serialization via `get_config()` and `from_config()`.
  + Note that the recommended way to create new RNN layers is to write a
  + custom RNN cell and use it with `keras.layers.RNN`, instead of
  + subclassing `keras.layers.Layer` directly.
  + merge_mode: Mode by which outputs of the forward and backward RNNs will be
  + combined. One of {'sum', 'mul', 'concat', 'ave', None}. If None, the
  + outputs will not be combined, they will be returned as a list. Default
  + value is 'concat'.
  + backward_layer: Optional `keras.layers.RNN`, or keras.layers.Layer` instance
  + to be used to handle backwards input processing. If `backward_layer` is
  + not provided, the layer instance passed as the `layer` argument will be
  + used to generate the backward layer automatically.
  Note that the provided `backward_layer` layer should have properties
  matching those of the `layer` argument, in particular it should have the
  same values for `stateful`, `return_states`, `return_sequence`, etc.
  - In addition, `backward_layer` and `layer` should have
  - different `go_backwards` argument values.
  + In addition, `backward_layer` and `layer` should have different
  + `go_backwards` argument values.
  A `ValueError` will be raised if these requirements are not met.
  Call arguments:
  