class_docstr: __init__(self, n_layers, in_size, out_size, dropout)
  
  Stacked Bi-directional GRU for sequences.
  
  This link is stacked version of Bi-directional GRU 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_bigru`, 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_bigru`
code: def n_cells(self):
      return 1
  
file_path: chainer/chainer/links/rnn/n_step_gru.py
imports: from chainer.functions.rnn import n_step_gru
  from chainer.links.rnn import n_step_rnn
label_desc: Use image node_id 2 for calling the NStepBiGRU obj's underlying member method code with example usage: obj.n_cells() and returns: int