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`
code: def copy(self, mode="share"):
      ret = super(NStepRNNBase, self).copy(mode)
      ret.ws = [
          [
              getattr(layer, "w%d" % i)
              for i in six.moves.range(ret.n_weights)
          ]
          for layer in ret
      ]
      ret.bs = [
          [
              getattr(layer, "b%d" % i)
              for i in six.moves.range(ret.n_weights)
          ]
          for layer in ret
      ]
      return ret
  
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 2 for calling the NStepRNNBase obj's underlying member method code with example usage: obj.copy(mode) and returns: ret