class_docstr: __init__(self, n_layers, in_size, out_size, dropout)
  
  Stacked Bi-directional LSTM for sequences.
  
  This link is stacked version of Bi-directional LSTM for sequences.
  It calculates hidden and cell states of all layer at end-of-string,
  and all hidden states of the last layer for each time.
  
  Unlike :func:`chainer.functions.n_step_bilstm`, this function automatically
  sort inputs in descending order by length, and transpose the sequence.
  Users just need to call the link with a list of :class:`chainer.Variable`
  holding sequences.
  
  Args:
      n_layers (int): Number of layers.
      in_size (int): Dimensionality of input vectors.
      out_size (int): Dimensionality of hidden states and output vectors.
      dropout (float): Dropout ratio.
  
  .. seealso::
      :func:`chainer.functions.n_step_bilstm`
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code: def rnn(self, *args):
      return rnn.n_step_bilstm(*args)
  
file_path: chainer/chainer/links/rnn/n_step_lstm.py
imports: from chainer.functions.rnn import n_step_lstm
  from chainer.links.rnn import n_step_rnn
label_desc: Use image node_id 1 for calling the NStepBiLSTM obj's underlying member method code with example usage: obj.rnn() and returns: rnn