code: def _test_n_step_forward(self, gpu):
      in_size = 2
      out_size = 6
  
      rseq = StatelessRecurrentSequential(
          L.Linear(in_size, 3),
          F.elu,
          L.NStepLSTM(1, 3, 4, 0),
          L.Linear(4, 5),
          L.NStepRNNTanh(1, 5, out_size, 0),
          F.tanh,
      )
  
      if gpu >= 0:
          chainer.cuda.get_device_from_id(gpu).use()
          rseq.to_gpu()
      xp = rseq.xp
  
      linear1 = rseq._layers[0]
      lstm = rseq._layers[2]
      linear2 = rseq._layers[3]
      rnn = rseq._layers[4]
  
      seqs_x = [
          xp.random.uniform(-1, 1, size=(4, in_size)).astype(
              np.float32
          ),
          xp.random.uniform(-1, 1, size=(1, in_size)).astype(
              np.float32
          ),
          xp.random.uniform(-1, 1, size=(3, in_size)).astype(
              np.float32
          ),
      ]
  
      concat_out, concat_state = rseq.n_step_forward(
          seqs_x, None, output_mode="concat"
      )
      self.assertEqual(concat_out.shape, (8, out_size))
  
      split_out, split_state = rseq.n_step_forward(
          seqs_x, None, output_mode="split"
      )
      self.assertIsInstance(split_out, list)
      self.assertEqual(len(split_out), len(seqs_x))
      for seq_x, seq_out in zip(seqs_x, split_out):
          self.assertEqual(seq_out.shape, (len(seq_x), out_size))
  
      # Check if output_mode='concat' and output_mode='split' are consistent
      xp.testing.assert_allclose(
          F.concat(split_out, axis=0).array,
          concat_out.array,
      )
  
      (concat_lstm_h, concat_lstm_c), concat_rnn_h = concat_state
      (split_lstm_h, split_lstm_c), split_rnn_h = split_state
      xp.testing.assert_allclose(
          concat_lstm_h.array, split_lstm_h.array
      )
      xp.testing.assert_allclose(
          concat_lstm_c.array, split_lstm_c.array
      )
      xp.testing.assert_allclose(concat_rnn_h.array, split_rnn_h.array)
  
      # Check if the output matches that of step-by-step execution
      def manual_n_step_forward(seqs_x):
          sorted_seqs_x = sorted(seqs_x, key=len, reverse=True)
          transposed_x = F.transpose_sequence(sorted_seqs_x)
          lstm_h = None
          lstm_c = None
          rnn_h = None
          ys = []
          for batch in transposed_x:
              if lstm_h is not None:
                  lstm_h = lstm_h[: len(batch)]
                  lstm_c = lstm_c[: len(batch)]
                  rnn_h = rnn_h[: len(batch)]
              h = linear1(batch)
              h = F.elu(h)
              h, (lstm_h, lstm_c) = _step_lstm(
                  lstm, h, (lstm_h, lstm_c)
              )
              h = linear2(h)
              h, rnn_h = _step_rnn_tanh(rnn, h, rnn_h)
              y = F.tanh(h)
              ys.append(y)
          sorted_seqs_y = F.transpose_sequence(ys)
          # Undo sort
          seqs_y = [
              sorted_seqs_y[0],
              sorted_seqs_y[2],
              sorted_seqs_y[1],
          ]
          return seqs_y
  
      manual_split_out = manual_n_step_forward(seqs_x)
      for man_seq_out, seq_out in zip(manual_split_out, split_out):
          xp.testing.assert_allclose(
              man_seq_out.array, seq_out.array, rtol=1e-5
          )
  
      # Finally, check the gradient (wrt linear1.W)
      (concat_grad,) = chainer.grad([F.sum(concat_out)], [linear1.W])
      (split_grad,) = chainer.grad(
          [F.sum(F.concat(split_out, axis=0))], [linear1.W]
      )
      (manual_split_grad,) = chainer.grad(
          [F.sum(F.concat(manual_split_out, axis=0))], [linear1.W]
      )
      xp.testing.assert_allclose(
          concat_grad.array, split_grad.array, rtol=1e-5
      )
      xp.testing.assert_allclose(
          concat_grad.array, manual_split_grad.array, rtol=1e-5
      )
  
imports: import unittest
  import chainer
  from chainer import functions
  from chainer import links
  from chainer import testing
  import numpy
  from chainerrl.links import StatelessRecurrentSequential
label_desc: Use image node_id 1 for calling the TestStatelessRecurrentSequential obj's underlying member method code with example usage: obj._test_n_step_forward(gpu) and returns: seqs_y