parameters: (encoder_inputs, decoder_inputs, cell,
                       loop_function=None, dtype=dtypes.float32, scope=None)
docstring: RNN sequence-to-sequence model with tied encoder and decoder parameters.
  
    This model first runs an RNN to encode encoder_inputs into a state vector, and
    then runs decoder, initialized with the last encoder state, on decoder_inputs.
    Encoder and decoder use the same RNN cell and share parameters.
  
    Args:
      encoder_inputs: A list of 2D Tensors [batch_size x input_size].
      decoder_inputs: A list of 2D Tensors [batch_size x input_size].
      cell: rnn_cell.RNNCell defining the cell function and size.
      loop_function: If not None, this function will be applied to i-th output
        in order to generate i+1-th input, and decoder_inputs will be ignored,
        except for the first element ("GO" symbol), see rnn_decoder for details.
      dtype: The dtype of the initial state of the rnn cell (default: tf.float32).
      scope: VariableScope for the created subgraph; default: "tied_rnn_seq2seq".
  
    Returns:
      A tuple of the form (outputs, state), where:
        outputs: A list of the same length as decoder_inputs of 2D Tensors with
          shape [batch_size x output_size] containing the generated outputs.
        state: The state of each decoder cell in each time-step. This is a list
          with length len(decoder_inputs) -- one item for each time-step.
          It is a 2D Tensor of shape [batch_size x cell.state_size].
docstring_summary: RNN sequence-to-sequence model with tied encoder and decoder parameters.
docstring_tokens: RNN sequence - to - sequence model with tied encoder and decoder parameters .
function: def tied_rnn_seq2seq(encoder_inputs, decoder_inputs, cell,
                       loop_function=None, dtype=dtypes.float32, scope=None):
    """RNN sequence-to-sequence model with tied encoder and decoder parameters.
  
    This model first runs an RNN to encode encoder_inputs into a state vector, and
    then runs decoder, initialized with the last encoder state, on decoder_inputs.
    Encoder and decoder use the same RNN cell and share parameters.
  
    Args:
      encoder_inputs: A list of 2D Tensors [batch_size x input_size].
      decoder_inputs: A list of 2D Tensors [batch_size x input_size].
      cell: rnn_cell.RNNCell defining the cell function and size.
      loop_function: If not None, this function will be applied to i-th output
        in order to generate i+1-th input, and decoder_inputs will be ignored,
        except for the first element ("GO" symbol), see rnn_decoder for details.
      dtype: The dtype of the initial state of the rnn cell (default: tf.float32).
      scope: VariableScope for the created subgraph; default: "tied_rnn_seq2seq".
  
    Returns:
      A tuple of the form (outputs, state), where:
        outputs: A list of the same length as decoder_inputs of 2D Tensors with
          shape [batch_size x output_size] containing the generated outputs.
        state: The state of each decoder cell in each time-step. This is a list
          with length len(decoder_inputs) -- one item for each time-step.
          It is a 2D Tensor of shape [batch_size x cell.state_size].
    """
    with variable_scope.variable_scope("combined_tied_rnn_seq2seq"):
      scope = scope or "tied_rnn_seq2seq"
      _, enc_state = rnn.rnn(
          cell, encoder_inputs, dtype=dtype, scope=scope)
      variable_scope.get_variable_scope().reuse_variables()
      return rnn_decoder(decoder_inputs, enc_state, cell,
                         loop_function=loop_function, scope=scope)
function_tokens: def tied_rnn_seq2seq ( encoder_inputs , decoder_inputs , cell , loop_function = None , dtype = dtypes . float32 , scope = None ) : with variable_scope . variable_scope ( "combined_tied_rnn_seq2seq" ) : scope = scope or "tied_rnn_seq2seq" _ , enc_state = rnn . rnn ( cell , encoder_inputs , dtype = dtype , scope = scope ) variable_scope . get_variable_scope ( ) . reuse_variables ( ) return rnn_decoder ( decoder_inputs , enc_state , cell , loop_function = loop_function , scope = scope )