parameters: (
      encoder_inputs, decoder_inputs, cell, dtype=dtypes.float32, scope=None)
docstring: Basic RNN sequence-to-sequence model.
  
    This model first runs an RNN to encode encoder_inputs into a state vector,
    then runs decoder, initialized with the last encoder state, on decoder_inputs.
    Encoder and decoder use the same RNN cell type, but don't 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.
      dtype: The dtype of the initial state of the RNN cell (default: tf.float32).
      scope: VariableScope for the created subgraph; default: "basic_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 the final time-step.
          It is a 2D Tensor of shape [batch_size x cell.state_size].
docstring_summary: Basic RNN sequence-to-sequence model.
docstring_tokens: Basic RNN sequence - to - sequence model .
function: def basic_rnn_seq2seq(
      encoder_inputs, decoder_inputs, cell, dtype=dtypes.float32, scope=None):
    """Basic RNN sequence-to-sequence model.
  
    This model first runs an RNN to encode encoder_inputs into a state vector,
    then runs decoder, initialized with the last encoder state, on decoder_inputs.
    Encoder and decoder use the same RNN cell type, but don't 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.
      dtype: The dtype of the initial state of the RNN cell (default: tf.float32).
      scope: VariableScope for the created subgraph; default: "basic_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 the final time-step.
          It is a 2D Tensor of shape [batch_size x cell.state_size].
    """
    with variable_scope.variable_scope(scope or "basic_rnn_seq2seq"):
      _, enc_state = rnn.rnn(cell, encoder_inputs, dtype=dtype)
      return rnn_decoder(decoder_inputs, enc_state, cell)
function_tokens: def basic_rnn_seq2seq ( encoder_inputs , decoder_inputs , cell , dtype = dtypes . float32 , scope = None ) : with variable_scope . variable_scope ( scope or "basic_rnn_seq2seq" ) : _ , enc_state = rnn . rnn ( cell , encoder_inputs , dtype = dtype ) return rnn_decoder ( decoder_inputs , enc_state , cell )