class_docstr: __init__(self, n_layers, in_size, out_size, dropout, *, initialW=None, initial_bias=None)
  
  Base link class for Stacked RNN/BiRNN links.
  
  This link is base link class for :func:`chainer.links.NStepRNN` and
  :func:`chainer.links.NStepBiRNN`.
  
  This link's behavior depends on argument, ``use_bi_direction``.
  
  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.
      initialW (:ref:`initializer <initializer>`): Initializer to initialize
          the weight. When it is :class:`numpy.ndarray`,
          its ``ndim`` should be 2. If ``initialW`` is ``None``, then the
          weights are initialized with i.i.d. Gaussian samples, each of which
          has zero mean and deviation :math:`\sqrt{1/\text{in_size}}`.
      initial_bias (:ref:`initializer <initializer>`): Initializer to
          initialize the bias. If ``None``, the bias will be initialized to
          zero. When it is :class:`numpy.ndarray`, its ``ndim`` should be 1.
  
  .. seealso::
      :func:`chainer.links.NStepRNNReLU`
      :func:`chainer.links.NStepRNNTanh`
      :func:`chainer.links.NStepBiRNNReLU`
      :func:`chainer.links.NStepBiRNNTanh`
docstr: Calls RNN function.
  
  Args:
      hs (list of ~chainer.Variable or None): Lisit of hidden states.
          Its length depends on its implementation.
          If ``None`` is specified zero-vector is used.
      xs (list of ~chainer.Variable): List of input sequences.
          Each element ``xs[i]`` is a :class:`chainer.Variable` holding
          a sequence.
  
  Returns:
      tuple: hs
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code: def _call(self, hs, xs, **kwargs):
      """Calls RNN function.
  
      Args:
          hs (list of ~chainer.Variable or None): Lisit of hidden states.
              Its length depends on its implementation.
              If ``None`` is specified zero-vector is used.
          xs (list of ~chainer.Variable): List of input sequences.
              Each element ``xs[i]`` is a :class:`chainer.Variable` holding
              a sequence.
  
      Returns:
          tuple: hs
      """
      if kwargs:
          argument.check_unexpected_kwargs(
              kwargs,
              train="train argument is not supported anymore. "
              "Use chainer.using_config",
          )
          argument.assert_kwargs_empty(kwargs)
  
      assert isinstance(xs, (list, tuple))
      indices = argsort_list_descent(xs)
  
      xs = permutate_list(xs, indices, inv=False)
      hxs = []
      for hx in hs:
          if hx is None:
              hx = self.init_hx(xs)
          else:
              hx = permutate.permutate(hx, indices, axis=1, inv=False)
          hxs.append(hx)
  
      trans_x = transpose_sequence.transpose_sequence(xs)
  
      args = (
          [self.n_layers, self.dropout]
          + hxs
          + [self.ws, self.bs, trans_x]
      )
      result = self.rnn(*args)
  
      hys = [
          permutate.permutate(h, indices, axis=1, inv=True)
          for h in result[:-1]
      ]
      trans_y = result[-1]
      ys = transpose_sequence.transpose_sequence(trans_y)
      ys = permutate_list(ys, indices, inv=True)
  
      return hys, ys
  
imports: import numpy
  import six
  import chainer
  from chainer.functions.array import permutate
  from chainer.functions.array import transpose_sequence
  from chainer.functions.rnn import n_step_rnn
  from chainer import initializers
  from chainer import link
  from chainer.utils import argument
  from chainer import variable
label_desc: Use image node_id 7 for calling the NStepRNNBase obj's underlying member method code with example usage: obj._call(hs, xs) and returns: hys,  ys