message: Add swap_memory to tf.contrib.rnn.stack_bidirectional_dynamic_rnn, propagating to tf.nn.bidirectional_dynamic_rnn.
diff: @@ -131,7 +131,8 @@ def stack_bidirectional_dynamic_rnn(cells_fw,
  sequence_length=None,
  parallel_iterations=None,
  time_major=False,
  - scope=None):
  + scope=None,
  + swap_memory=False):
  """Creates a dynamic bidirectional recurrent neural network.
  Stacks several bidirectional rnn layers. The combined forward and backward
  @@ -171,6 +172,10 @@ def stack_bidirectional_dynamic_rnn(cells_fw,
  data is batch-major, so by default this function accepts input and emits
  output in batch-major form.
  scope: VariableScope for the created subgraph; defaults to None.
  + swap_memory: Transparently swap the tensors produced in forward inference
  + but needed for back prop from GPU to CPU. This allows training RNNs
  + which would typically not fit on a single GPU, with very minimal (or no)
  + performance penalty.
  Returns:
  A tuple (outputs, output_state_fw, output_state_bw) where:
  @@ -230,6 +235,7 @@ def stack_bidirectional_dynamic_rnn(cells_fw,
  sequence_length=sequence_length,
  parallel_iterations=parallel_iterations,
  dtype=dtype,
  + swap_memory=swap_memory,
  time_major=time_major)
  # Concat the outputs to create the new input.
  prev_layer = array_ops.concat(outputs, 2)
  