code: def n_step_rnn_impl(
      f, n_layers, dropout_ratio, hx, cx, ws, bs, xs, use_bi_direction
  ):
      direction = 2 if use_bi_direction else 1
      hx = chainer.functions.separate(hx)
      use_cell = cx is not None
      if use_cell:
          cx = chainer.functions.separate(cx)
      else:
          cx = [None] * len(hx)
  
      xs_next = xs
      hy = []
      cy = []
      for layer in six.moves.range(n_layers):
          # Forward RNN
          if layer == 0:
              xs = xs_next
          else:
              xs = _dropout_sequence(xs_next, dropout_ratio)
          idx = direction * layer
          h, c, h_forward = _one_directional_loop(
              f, xs, hx[idx], cx[idx], ws[idx], bs[idx]
          )
          hy.append(h)
          cy.append(c)
  
          if use_bi_direction:
              # Backward RNN
              idx = direction * layer + 1
              if layer == 0:
                  xs = xs_next
              else:
                  xs = _dropout_sequence(xs_next, dropout_ratio)
              h, c, h_backward = _one_directional_loop(
                  f, reversed(xs), hx[idx], cx[idx], ws[idx], bs[idx]
              )
              h_backward.reverse()
              # Concat
              xs_next = [
                  concat.concat([hfi, hbi], axis=1)
                  for hfi, hbi in six.moves.zip(h_forward, h_backward)
              ]
              hy.append(h)
              cy.append(c)
          else:
              # Uni-directional RNN
              xs_next = h_forward
  
      ys = xs_next
      hy = stack.stack(hy)
      if use_cell:
          cy = stack.stack(cy)
      else:
          cy = None
      return hy, cy, tuple(ys)
  
imports: import itertools
  import numpy
  import six
  import chainer
  import chainerx
  from chainer import backend
  from chainer import variable
  from chainer.backends import cuda
  from chainer import configuration
  from chainer import function
  from chainer.functions.activation import relu
  from chainer.functions.activation import tanh
  from chainer.functions.array import concat
  from chainer.functions.array import split_axis
  from chainer.functions.array import stack
  from chainer.functions.connection import linear
  from chainer.functions.noise import dropout
  from chainer.utils import argument
  from chainer.utils import type_check
label_desc: Use image node_id 8 for calling a global function with example usage: n_step_rnn_impl(f, n_layers, dropout_ratio, hx, cx, ws, bs, xs, use_bi_direction) and returns: hy,  cy,  tuple