message: Cleanup some duplicated methods for UnifiedLSTM.
  The methods in the parent class should work the same way.
diff: @@ -2530,6 +2530,7 @@ class LSTM(RNN):
  config['implementation'] = 1
  return cls(**config)
  +
  @tf_export('keras.layers.LSTM', v1=[])
  class UnifiedLSTM(LSTM):
  """Long Short-Term Memory layer - Hochreiter 1997.
  @@ -2655,8 +2656,6 @@ class UnifiedLSTM(LSTM):
  self.state_spec = [
  InputSpec(shape=(None, dim)) for dim in (self.units, self.units)
  ]
  - self._num_constants = None
  - self._num_inputs = None
  self._dropout_mask = None
  self.could_use_cudnn = (
  activation == 'tanh' and recurrent_activation == 'sigmoid' and
  @@ -2775,46 +2774,6 @@ class UnifiedLSTM(LSTM):
  else:
  return output
  - @property
  - def trainable_weights(self):
  - if self.trainable:
  - weights = []
  - weights += self.cell.trainable_weights
  - return weights
  - return []
  -
  - @property
  - def non_trainable_weights(self):
  - if not self.trainable:
  - weights = []
  - weights += self.cell.non_trainable_weights
  - return weights
  - return []
  -
  - @property
  - def losses(self):
  - losses = []
  - losses += self.cell.losses
  - return losses + self._losses
  -
  - @property
  - def updates(self):
  - updates = []
  - updates += self.cell.updates
  - return updates + self._updates
  -
  - def get_weights(self):
  - weights = []
  - weights += self.cell.weights
  - return K.batch_get_value(weights)
  -
  - def set_weights(self, weights):
  - tuples = []
  - cell_weights = weights[:len(self.cell.weights)]
  - if cell_weights:
  - tuples.append((self.cell.weights, cell_weights))
  - K.batch_set_value(tuples)
  -
  def _canonical_to_params(weights, biases, shape, transpose_weights=False):
  """Utility function convert variable to CuDNN compatible parameter.
  