parameters: (encoder_inputs,
                  decoder_inputs,
                  encoder_cell,
                  decoder_cell=None,
                  dtype=dtypes.float32,
                  scope=None)
docstring: RNN Sequence to Sequence model.
  
    Args:
      encoder_inputs: List of tensors, inputs for encoder.
      decoder_inputs: List of tensors, inputs for decoder.
      encoder_cell: RNN cell to use for encoder.
      decoder_cell: RNN cell to use for decoder, if None encoder_cell is used.
      dtype: Type to initialize encoder state with.
      scope: Scope to use, if None new will be produced.
  
    Returns:
      List of tensors for outputs and states for trianing and sampling sub-graphs.
docstring_summary: RNN Sequence to Sequence model.
docstring_tokens: RNN Sequence to Sequence model .
function: def rnn_seq2seq(encoder_inputs,
                  decoder_inputs,
                  encoder_cell,
                  decoder_cell=None,
                  dtype=dtypes.float32,
                  scope=None):
    """RNN Sequence to Sequence model.
  
    Args:
      encoder_inputs: List of tensors, inputs for encoder.
      decoder_inputs: List of tensors, inputs for decoder.
      encoder_cell: RNN cell to use for encoder.
      decoder_cell: RNN cell to use for decoder, if None encoder_cell is used.
      dtype: Type to initialize encoder state with.
      scope: Scope to use, if None new will be produced.
  
    Returns:
      List of tensors for outputs and states for trianing and sampling sub-graphs.
    """
    with vs.variable_scope(scope or "rnn_seq2seq"):
      _, last_enc_state = nn.rnn(encoder_cell, encoder_inputs, dtype=dtype)
      return rnn_decoder(decoder_inputs, last_enc_state, decoder_cell or
                         encoder_cell)
function_tokens: def rnn_seq2seq ( encoder_inputs , decoder_inputs , encoder_cell , decoder_cell = None , dtype = dtypes . float32 , scope = None ) : with vs . variable_scope ( scope or "rnn_seq2seq" ) : _ , last_enc_state = nn . rnn ( encoder_cell , encoder_inputs , dtype = dtype ) return rnn_decoder ( decoder_inputs , last_enc_state , decoder_cell or encoder_cell )