message: Updated documentation for GRU & LSTM
  Using cuDNN in GRU & LSTM require eager execution.
diff: @@ -205,6 +205,7 @@ class GRU(recurrent.DropoutRNNCellMixin, recurrent.GRU):
  5. `use_bias` is `True`
  6. `reset_after` is `True`
  7. Inputs, if use masking, are strictly right-padded.
  + 8. Eager execution is enabled in the outermost context.
  There are two variants of the GRU implementation. The default one is based on
  [v3](https://arxiv.org/abs/1406.1078v3) and has reset gate applied to hidden
  @@ -928,6 +929,7 @@ class LSTM(recurrent.DropoutRNNCellMixin, recurrent.LSTM):
  4. `unroll` is `False`
  5. `use_bias` is `True`
  6. Inputs, if use masking, are strictly right-padded.
  + 7. Eager execution is enabled in the outermost context.
  For example:
  