code: def forward_expected(self, inputs):
      h, ws, bs, xs = self.process_inputs(inputs)
  
      e_hy = h.copy()
      ys = []
      for ind in range(len(xs)):
          x = xs[ind]
          batch = x.shape[0]
          for layer in range(self.n_layers):
              w = ws[layer]
              b = bs[layer]
              h_prev = e_hy[layer, :batch]
              if self.activation == "tanh":
                  e_h = numpy.tanh(
                      x.dot(w[0].T) + h_prev.dot(w[1].T) + b[0] + b[1]
                  )
              elif self.activation == "relu":
                  e_h = _relu(
                      x.dot(w[0].T) + h_prev.dot(w[1].T) + b[0] + b[1]
                  )
  
              e_hy[layer, :batch] = e_h
  
              x = e_h
          ys.append(x)
      rets = []
      rets.append(e_hy)
      for i in range(len(ys)):
          rets.append(ys[i])
      return tuple(rets)
  
imports: import numpy
  import chainer.functions
  from chainer import testing
  from chainer.testing import backend
label_desc: Use image node_id 5 for calling the TestNStepRNN obj's underlying member method code with example usage: obj.forward_expected(inputs) and returns: tuple