code: def forward_expected(self, inputs):
      h, ws, bs, xs = self.process_inputs(inputs)
      xs_next = xs
      e_hy = h.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]
              # GRU
              z = sigmoid(
                  x.dot(w[1].T) + h_prev.dot(w[4].T) + b[1] + b[4]
              )
              r = sigmoid(
                  x.dot(w[0].T) + h_prev.dot(w[3].T) + b[0] + b[3]
              )
              h_bar = numpy.tanh(
                  x.dot(w[2].T)
                  + r * ((h_prev).dot(w[5].T) + b[5])
                  + b[2]
              )
              e_h = (1 - z) * h_bar + z * h_prev
              e_hy[layer_idx, :batch] = e_h
              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]
              # GRU
              z = sigmoid(
                  x.dot(w[1].T) + h_prev.dot(w[4].T) + b[1] + b[4]
              )
              r = sigmoid(
                  x.dot(w[0].T) + h_prev.dot(w[3].T) + b[0] + b[3]
              )
              h_bar = numpy.tanh(
                  x.dot(w[2].T)
                  + r * ((h_prev).dot(w[5].T) + b[5])
                  + b[2]
              )
              e_h = (1 - z) * h_bar + z * h_prev
              e_hy[layer_idx, :batch] = e_h
              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)
      for x in xs_next:
          rets.append(x)
      return tuple(rets)
  
imports: import numpy
  import chainer
  import chainer.functions
  from chainer import testing
  from chainer.testing import backend
label_desc: Use image node_id 5 for calling the TestNStepBiGRU obj's underlying member method code with example usage: obj.forward_expected(inputs) and returns: tuple