code: def n_step_gru_base(
      n_layers,
      dropout_ratio,
      hx,
      ws,
      bs,
      xs,
      use_bi_direction,
      **kwargs,
  ):
      """n_step_gru_base(n_layers, dropout_ratio, hx, ws, bs, xs, \
  use_bi_direction)
  
      Base function for Stack GRU/BiGRU functions.
  
      This function is used at  :func:`chainer.functions.n_step_bigru` and
      :func:`chainer.functions.n_step_gru`.
      This function's behavior depends on argument ``use_bi_direction``.
  
      Args:
          n_layers(int): Number of layers.
          dropout_ratio(float): Dropout ratio.
          hx (:class:`~chainer.Variable`):
              Variable holding stacked hidden states.
              Its shape is ``(S, B, N)`` where ``S`` is number of layers and is
              equal to ``n_layers``, ``B`` is mini-batch size, and ``N`` is
              dimension of hidden units. Because of bi-direction, the
              first dimension length is ``2S``.
          ws (list of list of :class:`~chainer.Variable`): Weight matrices.
              ``ws[i]`` represents weights for i-th layer.
              Each ``ws[i]`` is a list containing six matrices.
              ``ws[i][j]`` is corresponding with ``W_j`` in the equation.
              Only ``ws[0][j]`` where ``0 <= j < 3`` is ``(N, I)`` shape as they
              are multiplied with input variables. All other matrices has
              ``(N, N)`` shape.
          bs (list of list of :class:`~chainer.Variable`): Bias vectors.
              ``bs[i]`` represnents biases for i-th layer.
              Each ``bs[i]`` is a list containing six vectors.
              ``bs[i][j]`` is corresponding with ``b_j`` in the equation.
              Shape of each matrix is ``(N,)`` where ``N`` is dimension of
              hidden units.
          xs (list of :class:`~chainer.Variable`):
              A list of :class:`~chainer.Variable` holding input values.
              Each element ``xs[t]`` holds input value
              for time ``t``. Its shape is ``(B_t, I)``, where ``B_t`` is
              mini-batch size for time ``t``, and ``I`` is size of input units.
              Note that this function supports variable length sequences.
              When sequneces has different lengths, sort sequences in descending
              order by length, and transpose the sorted sequence.
              :func:`~chainer.functions.transpose_sequence` transpose a list
              of :func:`~chainer.Variable` holding sequence.
              So ``xs`` needs to satisfy
              ``xs[t].shape[0] >= xs[t + 1].shape[0]``.
          activation (str): Activation function name.
              Please select ``tanh`` or ``relu``.
          use_bi_direction (bool): If ``True``, this function uses
              Bi-direction GRU.
  
      .. seealso::
         :func:`chainer.functions.n_step_rnn`
         :func:`chainer.functions.n_step_birnn`
  
      """
      if kwargs:
          argument.check_unexpected_kwargs(
              kwargs,
              train="train argument is not supported anymore. "
              "Use chainer.using_config",
              use_cudnn="use_cudnn argument is not supported anymore. "
              "Use chainer.using_config",
          )
          argument.assert_kwargs_empty(kwargs)
  
      xp = backend.get_array_module(hx, hx.data)
  
      directions = 1
      if use_bi_direction:
          directions = 2
  
      combined = _combine_inputs(hx, ws, bs, xs, n_layers, directions)
      has_chainerx_array, combined = _extract_apply_in_data(combined)
      hx_chx, ws_chx, bs_chx, xs_chx = _seperate_inputs(
          combined, n_layers, len(xs), directions
      )
  
      if has_chainerx_array and xp is chainerx and dropout_ratio == 0:
          if use_bi_direction:
              hy, ys = chainerx.n_step_bigru(
                  n_layers, hx_chx, ws_chx, bs_chx, xs_chx
              )
          else:
              hy, ys = chainerx.n_step_gru(
                  n_layers, hx_chx, ws_chx, bs_chx, xs_chx
              )
          hy = variable.Variable._init_unchecked(
              hy,
              requires_grad=hy.is_backprop_required(),
              is_chainerx_array=True,
          )
          ys = [
              variable.Variable._init_unchecked(
                  y,
                  requires_grad=y.is_backprop_required(),
                  is_chainerx_array=True,
              )
              for y in ys
          ]
          return hy, ys
  
      if xp is cuda.cupy and chainer.should_use_cudnn(">=auto", 5000):
          lengths = [len(x) for x in xs]
          xs = chainer.functions.concat(xs, axis=0)
          with chainer.using_device(xs.device):
              states = cuda.get_cudnn_dropout_states()
              states.set_dropout_ratio(dropout_ratio)
  
          w = n_step_rnn.cudnn_rnn_weight_concat(
              n_layers, states, use_bi_direction, "gru", ws, bs
          )
  
          if use_bi_direction:
              rnn = NStepBiGRU
          else:
              rnn = NStepGRU
  
          hy, ys = rnn(n_layers, states, lengths)(hx, w, xs)
          sections = numpy.cumsum(lengths[:-1])
          ys = chainer.functions.split_axis(ys, sections, 0)
          return hy, ys
  
      else:
          hy, _, ys = n_step_rnn.n_step_rnn_impl(
              _gru,
              n_layers,
              dropout_ratio,
              hx,
              None,
              ws,
              bs,
              xs,
              use_bi_direction,
          )
          return hy, ys
  
imports: import numpy
  import chainer
  from chainer import backend
  from chainer.backends import cuda
  from chainer.functions.activation import sigmoid
  from chainer.functions.activation import tanh
  from chainer.functions.array import concat
  from chainer.functions.array import split_axis
  from chainer.functions.connection import linear
  from chainer.functions.rnn import n_step_rnn
  from chainer.utils import argument
  from chainer import variable
  import chainerx
label_desc: Use image node_id 6 for calling a global function with example usage: n_step_gru_base(n_layers, dropout_ratio, hx, ws, bs, xs, use_bi_direction) and returns: hy,  ys, hy,  ys, hy,  ys