code: def forward_expected(self, inputs):
      h, c, ws, bs, xs = self.process_inputs(inputs)
      xs_next = xs
      e_hy = h.copy()
      e_cy = c.copy()
      for layer in range(self.n_layers):
          # forward
          di = 0
          xf = []
          layer_idx = layer * 2 + di
          w = ws[layer_idx]
          b = bs[layer_idx]
          for ind in range(len(xs)):
              x = xs_next[ind]
              batch = x.shape[0]
              h_prev = e_hy[layer_idx, :batch]
              c_prev = e_cy[layer_idx, :batch]
              i = sigmoid(
                  x.dot(w[0].T) + h_prev.dot(w[4].T) + b[0] + b[4]
              )
              f = sigmoid(
                  x.dot(w[1].T) + h_prev.dot(w[5].T) + b[1] + b[5]
              )
              c_bar = numpy.tanh(
                  x.dot(w[2].T) + h_prev.dot(w[6].T) + b[2] + b[6]
              )
              o = sigmoid(
                  x.dot(w[3].T) + h_prev.dot(w[7].T) + b[3] + b[7]
              )
              e_c = f * c_prev + i * c_bar
              e_h = o * numpy.tanh(e_c)
              e_hy[layer_idx, :batch] = e_h
              e_cy[layer_idx, :batch] = e_c
  
              xf.append(e_h)
  
          # backward
          di = 1
          xb = []
          layer_idx = layer * 2 + di
          w = ws[layer_idx]
          b = bs[layer_idx]
          for ind in reversed(range(len(xs))):
              x = xs_next[ind]
              batch = x.shape[0]
              h_prev = e_hy[layer_idx, :batch]
              c_prev = e_cy[layer_idx, :batch]
              i = sigmoid(
                  x.dot(w[0].T) + h_prev.dot(w[4].T) + b[0] + b[4]
              )
              f = sigmoid(
                  x.dot(w[1].T) + h_prev.dot(w[5].T) + b[1] + b[5]
              )
              c_bar = numpy.tanh(
                  x.dot(w[2].T) + h_prev.dot(w[6].T) + b[2] + b[6]
              )
              o = sigmoid(
                  x.dot(w[3].T) + h_prev.dot(w[7].T) + b[3] + b[7]
              )
              e_c = f * c_prev + i * c_bar
              e_h = o * numpy.tanh(e_c)
              e_hy[layer_idx, :batch] = e_h
              e_cy[layer_idx, :batch] = e_c
  
              xb.append(e_h)
  
          xb.reverse()
          xs_next = [
              numpy.concatenate([hfi, hbi], axis=1)
              for (hfi, hbi) in zip(xf, xb)
          ]
  
      rets = []
      rets.append(e_hy)
      rets.append(e_cy)
      for x in xs_next:
          rets.append(x)
      return tuple(rets)
  
imports: import unittest
  import numpy
  import chainer
  from chainer.backends import cuda
  import chainer.functions
  from chainer import testing
  from chainer.testing import attr
  from chainer.testing import backend
  from chainer.testing import condition
label_desc: Use image node_id 5 for calling the TestNStepBiLSTM obj's underlying member method code with example usage: obj.forward_expected(inputs) and returns: tuple