code: def _test_n_step_forward_with_tuple_output(self, gpu):
      in_size = 5
      out_size = 6
  
      def split_output(x):
          return tuple(F.split_axis(x, [2, 3], axis=1))
  
      rseq = StatelessRecurrentSequential(
          L.NStepRNNTanh(1, in_size, out_size, 0),
          split_output,
      )
  
      if gpu >= 0:
          chainer.cuda.get_device_from_id(gpu).use()
          rseq.to_gpu()
      xp = rseq.xp
  
      # Input is a list of two variables.
      seqs_x = [
          xp.random.uniform(-1, 1, size=(3, in_size)).astype(
              np.float32
          ),
          xp.random.uniform(-1, 1, size=(2, in_size)).astype(
              np.float32
          ),
      ]
  
      # Concatenated output should be a tuple of three variables.
      concat_out, concat_state = rseq.n_step_forward(
          seqs_x, None, output_mode="concat"
      )
      self.assertIsInstance(concat_out, tuple)
      self.assertEqual(len(concat_out), 3)
      self.assertEqual(concat_out[0].shape, (5, 2))
      self.assertEqual(concat_out[1].shape, (5, 1))
      self.assertEqual(concat_out[2].shape, (5, 3))
  
      # Split output should be a list of two tuples, each of which is of
      # three variables.
      split_out, split_state = rseq.n_step_forward(
          seqs_x, None, output_mode="split"
      )
      self.assertIsInstance(split_out, list)
      self.assertEqual(len(split_out), 2)
      self.assertIsInstance(split_out[0], tuple)
      self.assertIsInstance(split_out[1], tuple)
      for seq_x, seq_out in zip(seqs_x, split_out):
          self.assertEqual(len(seq_out), 3)
          self.assertEqual(seq_out[0].shape, (len(seq_x), 2))
          self.assertEqual(seq_out[1].shape, (len(seq_x), 1))
          self.assertEqual(seq_out[2].shape, (len(seq_x), 3))
  
      # Check if output_mode='concat' and output_mode='split' are consistent
      xp.testing.assert_allclose(
          F.concat(
              [F.concat(seq_out, axis=1) for seq_out in split_out],
              axis=0,
          ).array,
          F.concat(concat_out, axis=1).array,
      )
  
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 7 for calling the TestStatelessRecurrentSequential obj's underlying member method code with example usage: obj._test_n_step_forward_with_tuple_output(gpu) and returns: tuple